SEPA
United States
Environmental Protectio
Agency
EPA/600/R-15/175 | August 2015 | epa.gov
PHYSIOLOGICAL PARAMETERS
AND PHYSICAL ACTIVITY DATA FOR
EVALUATING EXPOSURE MODELING
PERFORMANCE: A SYNTHESIS
Thomas McCurdy
Exposure Modeling Research Branch
Human Exposure and Modeling Division
National Human Exposure Research Laboratory
Research Triangle Park NC 27711
Office of Research and Development
National Exposure Research Laboratory
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&EFK
EPA/600/R-15/175 | August 2015 | epa.gov
United States
Environmental Protection
Agency
PHYSIOLOGICAL PARAMETERS
AND PHYSICAL ACTIVITY DATA FOR
EVALUATING EXPOSURE MODELING
PERFORMANCE: A SYNTHESIS
Thomas McCurdy
Exposure Modeling Research Branch
Human Exposure and Modeling Division
National Human Exposure Research Laboratory
Research Triangle Park NC 27711
Office of Research and Development
National Exposure Research Laboratory
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Table of Contents
Foreword and Acknowledgements ix
Limitations ix
Future Contact x
Disclaimer x
1.0 Introduction 1
Literature Search Procedures 1
Conventions Used in the Report Concerning Metrics 3
Resting Energy Expenditure (REE); Resting Metabolic Rate (RMR); Basal Metabolic Rate (BMR) 3
Intra- and inter-variability in physiological parameters 4
2.0 Absolute and Relative General Physiological Metrics 5
3.0 Maximal Oxygen Consumption (V02max)—the "Controlling Parameter" 7
Overview of Tables 1&2 7
Concepts 32
V02 max and Age 33
V02MAX and Fitness Level 34
Predicting V02 MAX Using Anthropomorphic Inputs 35
Alternative (Allometric) Scaling Approaches 35
Relative V02MAX Metrics: One and Two-Sided 36
4.0 Ventilation Rate (VE) Considerations 39
Breathing Rate 50
Activity-Specific Estimates of VE (VEACT) 52
Equations for Predicting VE A from V02 A Estimates 52
EVR: Equivalent Ventilation Rate 53
Nasal/Oral Patterns Associated with V Levels 53
E
5.0 VQ: the Ratio of VE to V02 55
6.0 METS Considerations 61
7.0 Metabolic Chronotropic Relationship 65
8.0 Daily Total Energy Expenditure (DTEE) 69
Components of DTEE 79
Estimating DTEE: DLW and Other Methods 82
Within-Subject Variation in DTEE 85
Daily Variation of DTEE within a Week 85
Seasonal Variations in DTEE 85
9.0 Physical Activity Index (PAI) & Physical Activity Level (PAL) 87
Reasonable Boundaries of PAI 87
PAI and Health Issues 91
Individual (Longitudinal) Variability in PAI 91
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PAI and Physical Activity at Various Levels 97
Physical Activity and Physical Activity Index
in Asthmatics 98
10.0 Time Spent Per Day in Moderate / Vigorous Physical Activity (MVPA) 101
Introduction 101
Alternative Recommendations for Moderate and Vigorous Physical Activity 102
Alternative Indicators of MVPA Seen in the Literature 105
Estimating Non-clinical MVPA 105
Estimating the Proportion of Population Subgroups that Undertake MVPA 106
Methods of Estimating Physical Activity in
Free-living Individuals 106
Direct Observation Studies 106
Subjective Surveys, Questionnaires, and Interviews (Telephone or Face-to-Face) 108
PA Diaries (Paper or Electronic) 109
Objective Monitoring Methods: Overview 110
Heart Rate Monitoring 110
Accelerometers Ill
Principles and Overview Ill
Systemic Issues with Accelerometry 122
Intra-Day Patterns of MVPA 129
Longitudinal (multi-day) and Day-of-the-Week Effects on MVPA Estimates 129
Seasonal and Weather (Temperature and Precipitation) Impacts on MVPA 131
Locational Aspects of MVPA 132
Participation in Sports and Recreational Activities 132
Health and Other Impacts on MVPA 133
Number of days needed to adequately characterize MVPA 134
Pedometers 134
Multiple Methods to Capture MVPA 137
11.0 Activity-Specific Energy Expenditure Estimates 139
12.0 Human Exposure Modeling Research Needs 143
APPENDIX A Physiological Testing Protocols with an Emphasis on V02 MAX A-1
Exercise Testing Fundamentals A-l
Anaerobic Threshold / Ventilatory Threshold A-l
v°2max testing A"2
APPENDIX B Examples of the Three Types of General Metrics with a Focus on Heart Rate B-l
APPENDIX C Background on Reserve Metrics C-l
APPENDIX D Background Material on Exposure Modeling D-l
APPENDIX E Supplemental Material E-l
E-l. Abbreviations & Symbols Used in this Synthesis E-l
E-2 Glossary of Terms Used in this Synthesis E-7
E-3. Table of Conversion Factors Used in this Synthesis E-32
References 151
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List of Tables
Table 1. Estimates of V02MAX in females seen in the literature 8
Table 2. Estimates of V02 MAX in males seen in the literature 19
Table 3. Percentile distribution of V02MAX by age grouping 32
Table 4. Means and selectied percentiles of V02 values from the 1990-2004 NHANES surveys (All Ethnic Groups) . . . . 33
Table 5. Age and gender specific "CUTPOINTS" of aerobic fitness levels 34
Table 6. Estimates of V02 Reserve or both V02 Rest and V02 Max seen in the same article 36
Table 7. Estimates of V„ „ seen in the literature 40
E.Max
Table 8. Estimates of V, D or both V, D „ and V,.. seen in the same article 50
b. Reserve b.Rest b.Max
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature 55
Table 10. Estimates of METS.„ seen in-or calculated from-the literature 63
Max
Table 11. "WORKLOAD INTENSITY" for differing METS.Maxfitness level 67
Table 12. Estimates of DTEE & PAEE seen in the literature 70
Table 13. Group mean estimates of DTEE, REE, & PAI from BLACK (2000) 78
Table 14 Group mean estimates of DTEE, REE, & PAI from BROOKS ET AL. (2004) 80
Table 15. Group mean estimates of DTEE, REE, & PAEE from BUTTE ET AL. (2000) 81
Table 16. DTEE, REE, & PAI during pregnancy from BUTTE ET AL. (2004) 83
Table 17. Estimates of DTEE, SMR, & PAI for infants from BUTTE ET AL. (1990) 88
Table 18. Estimates of DTEE, REE, PAEE, & PAI for lactating & non-lactating Mothers from BUTTE ET AL. (2001)... 89
Table 19. DTEE & PAI estimates from DLW studies reviewed in ROBERTS & DALLEL (2005) 90
Table 20. Estimates of DTEE, REE, PAEE. & PAI from SHETTY ET AL. (1996) 91
Table 21. Estimates of PAI seen in the literature 91
Table 22. Alternative quantative metrics of MVPA seen in the EXERCISE PHYSIOLOGY LITERATURE
(for adults unless otherwise noted) 102
Table 23. Number of publication for the ActiPAL ACCELEROMETER by year of publication Ill
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) 113
Table 25. Time spent in MVPA categories from ARROYO (2000) 130
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List of Figures
Figure 1. Plot of LN V02 MAX versus age for two groups 34
Figure D-l. The individual is the unit of analysis. APEX and SHEDS construct simulated populations based
on the above characteristics D-2
Figure D-2. A Venn diagram of exposure D-3
Figure D-3. Exposure metrics available from an exposure time-series D-3
Figure D-4. Human exposure model principles. This schematic diagram illustrates the relationship among
activity level, energy expenditure, and the intakes needed to maintain that activity level D-4
Figure D-5. Logic flowchart of the APEX model D-10
Figure D-6. Percent of people in three groups—(1) all children, (2) asthmatic children, and (3) all persons—estimated
to experience 1+ days with an 8-h daily maximum 03 exposure >0.07 ppm while at moderate exercise
when the current 8-h daily maximum NAAQS of 0.08 ppm is just met D-ll
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Foreword and Acknowledgements
I hope that this report provides the basis for evaluating
selected critical outputs of EPA's exposure/intake dose
models. It represents about two years of part-time work, the
last one working as an unpaid "guest researcher." Even so,
I do not view this as a finished job, as frankly I ran out of
steam. I also do not consider it to be a "labor of love;" the
tedious nature of the work precluded that, but I was almost
compelled to do what I could, as no one else here at EPA
would be able to undertake the task for a number of reasons.
I have long felt that EPA exposure modelers should undertake
more evaluation of the Agency's exposure/intake dose model
performance than is typically done. This report, I hope, will
help in that regard.
I acknowledge the contributions that EPA colleagues have
made to improving to the APEX and SHEDS models
over the years. They are Dr. Janet Burke, Dr. Stephen
Graham, Dr. Kristin Isaacs, John Langstaff, Ted Palma,
Harvey Richmond, and Dr. Jianping Xue. The long-term,
fundamental involvement with APEX and its predecessor
models, NEM and pNEM, is also acknowledged. The
people who were so involved the longest were Jim Capel,
Ted Johnson, Roy Paul, and Luke Wijnberg. Ted Johnson
should be singled out in this regard since he generally led the
contractual work that constituted early exposure/intake dose
model development and numerous applications. More recent
contractor involvement involved staff of Alion Technology
(and ManTech before that), they include: Dr. Glen Graham,
Dr. Kristin Isaacs, Yeshpal Lakadi, and Dr. Luther Smith, and
Casson Stalling. Drs. William Biller and Thomas Feagans are
recognized for their fundamental contributions to the logic of
exposure modeling as an integral part of a probabilistic, time-
series, risk assessment process suitable for EPA's NAAQS-
standard review/setting activities. I also thank John Langstaff
and Ted Palma of the Office of Air Quality Planning and
Standards (OAQPS) for reviewing this report and providing
comments that improved the final project.
Several student contractors assisted me with basic data-
gathering and review of published papers that contributed to
many of the detailed tables sprinkled throughout this report.
They were: Jennifer Hutchinson, A'ja Moore, and Melissa
Smart. It was fairly tedious work that they were given, and
they did it with aplomb. Ms. Kriti Shanna developed tables
of resting metabolic rate that were not used here; both she
and Ms. Hutchinson were good at developing the "spider
diagrams" mentioned in Section 1, the logic of which was
used on various sets of papers contained in this report.
Finally, I thank HEASD management for letting me be a
guest researcher so that I could get the report to its current
state, that—while not totally complete—can be used by
exposure/intake dose modelers as a starting point, at least, for
evaluating model performance. The managers most involved
were Drs. Timothy Buckley and Roy Fortmann. Dr. Fortmann
expedited report publication. I again thank Dr. Kristin
Isaacs for being my project leader as a guest researcher.
Her patience with bureaucratic procedures during my guest
worker tenue is commendable.
Limitations
Citations to journal article titles generally follow ISSN
(International Standard Serial Numbering) conventions,
but deviate in a few ways. I usually use at least a 4-letter
abbreviation for country names, rather than the 2-3 letters
often used. Thus, for instance, American is abbreviated Amer.
rather than Am. Also, single-word journal titles are always
spelled out rather than being abbreviated (e.g.. Ergonomics
rather than "Ergonom").
Listing of multiple-author articles in the References
seemingly follows a random pattern: sometimes only one
author's name is provided, other times all co-author names
are included. Probably the main reason for this is that many
people have worked on my bibliography over the 20 years
that it has existed, and different people put entries into the
list in their own way. (I did not stress uniformity, as I could
deal with the differences, and was glad for the "outside"
assistance.) I certainly did not want to take the time to redo
the bibliography by following a strict rule (such as including
all authors up to three, followed by et al., unless there were
four authors total and then all four would be included, etc.).
In addition, given that there were so many citations in my
bibliography, I tried not to make any single citation take up
more than two lines of 8-point type. All references used in
this report should be available in Room E253 of EPA's Office
of Research and Development, either as a paper copy (in
whole or in part) or as a PDF file, so they are easy to obtain
and standardize (if desired).
The intent of this report is to present data, and not to
formulate testable hypotheses, etc. Thus, there is little
speculation about the etiology and functioning of the
physiological parameter for which data are provided. Such
explanatory variables as body composition, race or ethnic
origination of subjects, lean body mass, and the like are not
presented or discussed in any detail. Race/ethnicity does
not seem to be very important causes of differential basal
metabolism and other physiological parameters. Being
overweight or obese, on the other hand, greatly affects
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those parameters—even on a per kilogram basis—and are
mentioned where appropriate. Being overweight, obese,
or having health issues are treated basically as "gating
variables" in some of the tables presenting cohort-specific
data (see Tables 1-2, 6-10, 12, and 25).
Even though there is a lot of information and data contained
in this report, I got "burned out" after about a year as a guest
researcher on this project. While I consider many parts of
the report to be synoptic and dispositive, I did cut corners
on some Sections. I did not do a synoptic review of the
following topics/Sections: METSmax in Section 6, physical
activity levels of asthmatics (Section 9), and—in particular—
activity-specific energy expenditure estimates (EEACT) in
Section 11. The latter are needed to improve upon the METS
distribution data in CHAD (and in a larger sense, data in the
exercise physiologist's Compendium of physical activities
that are the basis for CHAD'S EEACT estimates). I feel
badly about not doing more on intra-individual variability
inherent in every physiologic and metabolic parameter that
underlies our intake dose modeling procedures. I was going
to attempt to rectify that shortcoming by back-calculating
intra-individual variance from papers that provide both an
ICC and inter-individual variance statistics, but those data
are rare and difficult to obtain. I believe that this would be a
fruitful effort for someone to attempt, although much more
valuable would be to undertake de novo longitudinal studies
designed explicitly to address both intra- and inter-individual
variability in physiological and metabolic parameters.
I also regret the lack of statistical analyses and graphical
display of the data contained in the report. While I did
more analyses than I include here, it was taking me too
long to do what others here at EPA can do quickly and very
efficiently. If there is any interest in doing these tasks, the
major data tables are in Excel and can easily be migrated
into a standard statistical package and analyzed/displayed.
(It will take a bit more work to separate standard deviations
from their means in Tables 6, 8, and 10 so that statistical
analyses can be undertaken, but that really is a "mechanical"
task.) The content of this report really should be based upon
formal meta-analyses of the important physiologic and time
use variables used in our models, explicitly accounting for
possible causal attributes of the variables, and inversely
weighting the sample means by their sample size (among
other statistical techniques needed to address unequal sample
variances and non-random sampling). I have neither the time
nor expertise needed to do such a task. Hopefully someone
will want to undertake that job.
Future Contact
Since my plan is to finally really retire after this report is
released (and a short "fun" paper is written), I won't be
around to answer questions or address concerns. I probably -
-and eventually (by that I mean the response may be delayed
if I am out of town)~canbe reached at 919-383-3052 or
landtmccurdv@gmail.com. Feel free to contact me related
to anything in this report. As mentioned, all the studies
cited in the report presently are available in E253 as a
paper copy (in whole or in part) or as a PDF file. Since
there is a lot of useful information in those papers/files,
I hope that EPA makes some effort to save this material
(unlike what happened when I formally retired and all my
computer files were removed by "user support".) I still have
not intellectually recovered from that event; I lost a lot of
information because of the unnecessary and unexpected
dumping of those files.
Disclaimer
This report was independently conceived and authored by
a "guest researcher" to the U.S. Enviromnental Protection
Agency (EPA). It has been subjected to Agency review and
has been approved for publication. Nothing in it should
be construed to represent Agency policy. The mentioning
of commercial product names or services does not imply
endorsement by EPA.
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1.0
Introduction
The purpose of this report is to develop a database of
physiological parameters needed for understanding and
evaluating performance of the APEX and SHEDS exposure/
intake dose rate model used by the Enviromnental Protection
Agency (EPA) as part of its regulatory activities. The APEX
model is the Air Pollution Exposure Model and SHEDS is
the Stochastic Human Exposure and Dose Simulation model.
APEX is used by both EPA's Office of Air Quality Planning
and Standards (OAQPS) and the National Exposure Research
Laboratory (NERL) in EPA's Office of Research and
Development (ORD), while SHEDS mostly is used by ORD.
Both models have been used by non-EPA organizations.
In spirit this paper follows the intent of Data Sources
Available for Modeling Exposures in Older Adults (McCurdy,
2011) but is expanded to include all age groups. Because a
nationally-applicable repository of data for physiological
factors does not exist, we looked to the clinical nutrition
and exercise physiology literatures for relevant information
on age- and gender-specific variables used in our exposure
models. Much of these data come from "panel" or
convenience studies of specific population subgroups, many
of them focused on people with a health or weight issue.
Since these studies—especially newer ones-often include a
similar age/gender control group of approximately the same
sample size (;?), information for "normal" subjects also is
reported. Control groups generally are defined to be subjects
with no known health/weight issues relevant to the topic
being investigated, which of course is not the same as having
no physical problem(s) or being healthy, although study
authors often labeled them as such. In our data tables, we
generally used the original author(s) delineation of the tested
groups that was identified even though the designations often
were not precise.
Combining data from disparate studies having different
objectives and using a variety of protocols and subjects
results in considerable uncertainty regarding general
applicability of the information gathered. A formal meta-
analysis of the data often is attempted in that situation (Egger
& Smith 1997; Egger et al., 1997), but that is not possible
to undertake at the present time. Perhaps this compilation
can become the basis for such an effort, since it provides
for each study its group mean, standard deviation (where
possible), and its sample size. (Where only standard errors
of the estimate [SE] are provided in an article, they are
converted to SD by multiplying by square root of the sample
size: SD = SE * a/ n.) Additional information would have to
be obtained for each study, however, in order to undertake a
complete meta-analysis of data contained in this report.
Single-gender data are emphasized in this report. Rather
large differences in oxygen consumption and ventilation rate
measures by gender are seen in the literature for the same
age cohort as a perusal of Tables 1 & 2 indicates. Rowland
et al. (1997) discusses the reasons for these differences
at some length. The same disparity is seen in maximal
ventilation rates (Section 4), daily energy expenditure
(Section 8), and time spent in moderate/vigorous physical
activity (Section 10). Combining data from females and
males results in an average value being presented that does
not reflect characteristics of either group, even on a per body
mass basis. As a general statement, V02 MAX and VE MAX on
both an absolute and relative body mass basis is higher in
males than in females for the same age grouping and fitness
level, as might be expected due to larger lung and oxygen-
carrying capacities sizes in males. Since many studies in
exercise physiology and clinical nutrition combine data
from females and males, particularly for young children
and older adults, ignoring gender removes over 100 papers
from our database. In the future, probably mixed-gender
studies could be utilized for children <8 y old or so, but
physiological changes associated with older children and
going through puberty definitely affect a number of important
physiological parameters for boys and girls at different rates
(Rudroff et al., 2013).
Literature Search Procedures
Tabular data in this report are only from U.S. studies
unless otherwise noted. Non-U.S. papers are used mostly to
document statements concerning theory, relationships, and
concepts. The focus on U.S. studies is due to (1) important
cultural aspects of diet and physical activity patterns that
affect some of our variables of interest, and (2) who we
are: a U.S. governmental agency (EPA). While we believe
that human physiological relationships generally are
similar among all people regardless of culture or country
of origin, there is a cultural and geographical component
of diet and time use behavior that is societal-specific. Even
resting metabolism and body composition metrics show
cultural influences, although "developed" countries across
the globe in general are seeing similar rates of obesity
and inactivity (Andersen et al., 2003). Physical activity
patterns in a population are greatly affected by social
factors, including occupational type, educational level,
and income (Welk, 2002). Geographic and climatic factors
also affect physical activity patterns. Certainly the use of
time is culturally dependent (Robinson 1977). Because
we are a U.S. governmental organization working only on
analyses affecting our country's population, it is prudent for
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us to focus entirely on characteristics and lifestyles of U.S.
citizens. There are copious amounts of U.S. data for almost
every physiological component included in our exposure
model, so we don't have to "go abroad" to obtain sufficient
relevant data.
The information presented here is from a literature search
designed by staff of EPA's library in RTP, NC. The review
strategy was developed by Ms. Susan Forbes of the
University of North Carolina's School of Library Science.
The search was first undertaken in 2005 and focused on
1990-2005 papers, and has been repeated periodically ever
since. Non-English papers were included if they provided
unique information on a physiological relationship (as
opposed to measurements). Even then they had to be in
Spanish, Portugese, or French, which could be read locally.
Databases searched originally included Dissertation Abstracts
Online, EMBASE, ExtraMED, MEDLINE, PASCAL,
SciSearch and 19 others. Only MEDLINE has been used
since 2010. Abstracts produced by the search were reviewed
using a philosophy of rejecting only obviously inapplicable
articles (presenting non-U.S. data; data from a study
occurring in a confined living or experimental chamber;
or being documented only in an abstract or a conference
presentation). Articles that made it through this stage—about
73% of them, were reviewed and further culled if they were
simply a review of other papers-presenting secondary data-
-or were "redundant" (discussed below). Over 5,000 articles
or books were reviewed at this stage of the process.
After narrowing our search results as described, we excluded
studies providing "redundant" data on a particular topic.
Redundant data is the same numerical information for a
particular parameter that appears in more than one paper.
We have frequently identified up to four different papers
that have published essentially the same data, often with
different first authors and appearing in different journals
(e.g., the work of Pollock and colleagues). Institutional
affiliation of the first author sometimes changes from paper-
to-paper, as does the order of authors. Occasionally, there are
slight differences in the sample sizes used, as later articles
usually—but not always-include more people in a study.
Thus, it is difficult to separate out papers with unique data
from multiple papers describing the same measurement
study, particularly because study locations and date of the
measurements are often not provided. Including the same
data from multiple papers would distort "real" variability
inherent in a physiological parameter, making it seem less
variable than it is. Egger and Smith (1998) call this "multiple
publication bias." We tried to minimize multiple listings
of the same data by devising a "spider web" of all authors
involved in a paper (a diagram linking all authors of possibly
redundant data papers), checking details of the clinical testing
protocol used and its frequency of testing, variables obtained,
etc., and (infrequently) by contacting first authors to ascertain
if their data were published in other papers. That last effort
was not very fruitful due to the difficultly of obtaining current
contact information, lack of response to our emails, and—
frankly—lack of candor on the part of some authors. Over
50 papers were removed from our review due to perceived
redundancy. Even so, we think that some data presented in
more than one paper has crept into this report. Hopefully
these data will not significantly distort our assessment due to
the sheer number of the studies included in our data base.
Finally, 17 papers first-authored by Dr. Eric T. Poehlman
were removed from our review due to their being
retracted from the scientific literature as a result of a
fraud investigation undertaken by the U.S. Department of
Health and Human Services, who funded much of his work
(Wikipedia, 2007).
The 4,000 or so papers that remained after these efforts
were read and some of their cited references were obtained
for further review even if they were published before 1990.
Thus, inclusion of pre-1990 data in this report is rather
eclectic, depending more on availability and personal
interest than rigorous adherence to a search strategy. Of
the papers reviewed, 49.0% of them were rejected due to
(1) presenting only "absolute" data (i.e., non-VE data were
provided without being on a body mass-specific basis: see
below), (2) providing only "mixed gender" data (data were
not separated by sex); or (3) measuring data using a "non-
conventional" protocol (e.g., oxygen consumption data not
coming from either a treadmill or a cycle ergometer, but from
an arm cranking protocol). The use of alternative protocols
usually results in physiological parameter estimates being
significantly different than those obtained using generally-
accepted methods. As an example of the measurement
problems associated with different protocols, see Appendix
A for a discussion of the oxygen consumption testing
protocols used by different researchers. As can be seen there,
there are a number of ways in which data are obtained for
physiological parameters of interest to us.
When a paper provides data from a "before-and-after"
experiment or trial, only the baseline (pre-experiment) data
are presented here. No post-exercise improvement in oxygen
consumption or fitness, etc. or any other "after" data appears
are used in this report.
With respect to temporal changes that may occur in
physiological parameters of interest, such as changes in
time spent in physical work or exercise (or changes in
anthropometric factors such as body mass), we formally
evaluated change over time for those parameters where we
had enough data to do so. Undertaking these temporal change
analyses is described in context of the parameter being
discussed. We could not formally evaluate temporal change
in most of the parameters used in our exposure models due to
a lack of longitudinal studies on most topics.
Although we use data from both clinical nutrition
and exercise physiology studies, there is a dichotomy
between these disciplines on the emphasis placed on
V02 measurements in their work. Generally, exercise
physiologists (and cardiologists) measure V02 MAX in
their subjects, but not resting oxygen consumption
(V02REST). Nutritionists, on the other hand, usually measure
V02 REST, which is often reported as basal metabolism in
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energy expenditure units (EE in kcal kg"1 min1), but do not
measure V02MAX (Patterson et al., 2005). This dichotomy
has resulted in a lack of emphasis in the literature on reserve
metrics—the difference between minimum and maximum
physiological states~and a loss of a "bounded physiological
anchor" in much of exercise and nutritional work. This point
will become clearer later. Basically, use of reserve metrics
allows the analyst to construct activity-specific energy
expenditure (EEA) work rates relative to both of a person's
physiological limits (lower and upper), thus fostering more
relational stability among parameters. This is one of the
fundamental points of this report.
Conventions Used in the Report
Concerning Metrics
Units and metrics in exercise physiology are tedious to
express in Word. Most importantly, since Word does not
allow you to overstrike symbols except in "Equation Writer,"
a number of normal notational conventions are not used here.
In the biological sciences, "rate" metrics—those involving
time in the denominator—are presented with a dot over the
parameter: usually a "V dot" for ventilatory-oriented metrics.
Such a convention cannot be done "gracefully" in Word. Not
using the dot convention might confuse readers conversant
with human physiological studies since volume metrics often
use the same letter symbol but have no dot over them. Thus,
not using dots over rate variables means that we cannot
differentiate symbolically between rate metrics and volume
metrics. As a general statement, we only present rate metrics
in this report. If a volume metric is discussed, we make it
clear that volume, not rate, is the parameter of concern.
Along that line, we only utilize V02 and most other
physiological parameters on a per-body mass (BM) basis—
which rigorously should be depicted as V02/BM or VE/BM,
etc. We consistently shorten the metric to just V02 or VE
to reduce subscripts. Thus, V02 MAX in this report, unless
otherwise noted, really is maximal oxygen consumption
per body mass having units of mL kg"1 min1 (also cited
as mL/kg-min in the Tables since superscripts cannot
be depicted in Excel). We focus on body mass-adjusted
physiological metrics, as doing so reduces—but does not
eliminate-gender and age variability in most parameters,
and allows a more intuitive comparison of the parameters
for disparate population groups. However, it is well known
that BM-nonnalized metrics are not without problems
themselves (Vanderburgh & Katch, 1996). Rowland (1996)
succinctly enumerates the limitations associated with per-
body mass "ratio" metrics. He says that there is no "universal
standardizing factor" devised that allows an analyst to
definitively compare population subgroups with respect to
aerobic capacity and most other physiological measures,
especially for children as they develop over time (Rowland,
1996). That being said, however:
"... Body mass is the dimensional measure adopted
by comparative biologists as the usual standard for
physiologic comparisons—assumed in this discussion to be
equivalent to body weight when subjects are in the same
gra\'itational condition... " (Rowland, 1996; p. 22).
See also Rowland (1991) for an interesting discussion of
"normalizing" oxygen consumption for use in exercise
physiology research. We also never present physiological
data using body surface area (BSA) as a normalizing
metric because there is no biological reason why doing so
improves generality of the parameter, especially for children
(Livingston & Lee, 2001; Rowland, 1996). Most of the
body's energy expenditure is used to keep the brain and other
body organs functioning, which are not a function of BSA
(McCurdy, 2000). The "hidden" BM convention used in
this report for V02 estimates is not used for ventilation—or
breathing rate-metrics (VE). VE data usually are presented
in the literature only in absolute terms, with units of
L min1 (L/min).
Rarely do we discuss lean body mass (LBM)-adjusted
metrics because of the dearth of information available
to exposure modelers regarding population-level LBM
measurements. (LBM is often called "fat-free mass" [FFM]
in the literature, but there are subtle differences in meaning,
so that term is not used here.) On occasion, BM to the 0.67
or 0.75 exponent will be discussed, as that adjustment-also
called "allometric scaling"—often reduces inter-individual
variability in many physiological and pharmacokinetic
variables (Nevill, 1994, 1997). When so discussed, the
full metric and units will be used: e.g., V02/BM°67 and
mLkgBM 0 67 min1.
Resting Energy Expenditure (REE); Resting
Metabolic Rate (RMR); Basal Metabolic
Rate (BMR)
Resting energy expenditure (REE), variously called resting
metabolic rate (RMR) or basal metabolic rate (BMR), is an
important physiologic metric, as will become abundantly
clear in the discussion below on METS (metabolic
equivalents of work). When RMR is discussed as a rate, we
use units of kcal kg"1 d"1 in this report. Again, the per-BM
subscript is to be implied. When basal metabolism data are
presented or discussed in conjunction with daily total energy
expenditure (DTEE) its units are kcal d1, the same as used
for DTEE. In this case, we label basal metabolism as REE,
resting energy expenditure to hopefully minimize confusion.
Basically, REE = RMR * 1,440 minutes, which implies that
BMR does not vary within a day. While this is known to be
incorrect as there is a circadian pattern to RMR data (Reilly
et al., 1997, 2000), there is no practical way to measure BMR
over an entire day except for comatose, hospitalized patients
or inactive people confined to a direct calorimeter. REE
data for selected special cohorts appear in Tables 13-15, 16,
18, and 20.
Some authors distinguish between BMR and RMR based
upon different measuring protocols used to ascertain resting
energy expenditure, but we treat them as synonyms. It is
3
-------
impractical to not do so, since the terms are used rather
interchangeably in the nutrition literature, and analyses of
possible practical differences among them are rare. RMR
usually is measured by oxygen consumption techniques
(indirect calorimetry) and often is reported in units of mL
V02 kg1 min-1. Infrequently RMR is measured directly in a
calorimeter based on temperature change measurements and
is reported in units of kcal kg"1 miff1, but doing so is rare.
Even though REE/RMR/BMR is a fundamental physiological
metric, we do not present specific tabular data for this
parameter, however measured, in this report. The reader
interested in data on basal metabolism is directed toward
McCurdy & Graham (2006) or the vast literature that
exists on the topic. A recent one is McMurray et al. (2014),
which presents BMR data from 197 studies published
between 1980 and 2011. There literally are hundreds of
prediction equations for RMR available in the literature.
Many compare predictions from one equation (or sets of
equations) to others, usually developed anew in the paper
cited. Most papers find that existing equations do not
adequately work for certain age/gender subgroups or those
cohorts with a physical or mental handicap. Shortcomings
with the Schofield (1985) equations used in the APEX and
SHEDS models have been extensively noted. The Schofield
equations certainly are based upon subjects that are, for
the most part, more active than people are currently; and,
in addition, his equations are not based upon many North
Americans, so the ethnic composition of his subjects is quite
different than contemporary United States inhabitants. The
Schofield equations used in EPA models should be revised
to incorporate updated information concerning REE in
contemporary times and ethnic composition.
Intra- and inter-variability in physiological
parameters
It is important to address intra-individual variability
in exposure assessments to better address longitudinal
variability in physiologic and time use parameters. Only in
this manner can we address uncertainty due to individual
characteristics per se in our models (Chikaraishi et al., 2010;
Isaacs et al., 2013; Xue et al., 2004). The same is true for
physiological parameters. Failure to do so results in incorrect
understanding of important dosimetric, metabolic, and
pharmacokinetic processes in the human body (Jamei et al.,
2009). It also results in downward biased estimates of both
the product-moment (Pearson) and rank-order (Spearman
or Kendall) correlation coefficients among variables in
an association.
One way to account for intra-individual variability is to
base the intra-individual COV on the ICC metric obtained
from an estimate of longitudinal data for a set of individuals
using repeated-measures statistical techniques. The ICC
metric describes the ratio of between-group variance to total
variance (between-group + within-group) explained, and
knowing inter-individual variability, you can approximate
intra-individual variability in the sample. However, except for
studies focused on reliability of physiological measurement
protocols, there are very little longitudinal analyses of
physiological data that allow rigorous characterization of the
ICC or intra-individual variability. Even those studies that
do investigate temporal changes in physiological parameters
in a sample over time—such as the work of Pollock and
colleagues (Pollock, 1974; Pollack et al., 1987, 1997)—really
only provide "sequential cross-sectional" data rather than
individual-specific longitudinal data. The work of Asmussen
et al. (1975), Sidney and colleagues (1998) and Van Pelt et al.
(1994) also is of this type. While rate-of-change statistics are
sometimes supplied for the time periods analyzed, they are
on a group-mean basis (Pollock et al., 1997), so an estimate
of intra-individual variability is impossible to obtain. This
is a major shortcoming of the physiological databases used
as input to the APEX and SHEDS models. It also is a major
hindrance in evaluating distributional aspects of our model
outputs to determine if a "proper" amount of intra-individual
variability is adequately captured. Grouped variability in a
sample for a physiological parameter can be approximated
from cross-sectional data by investigating the sample's
coefficient of variation (COV), but individual variability—a
major source of exposure modeling uncertainty—cannot.
We try to characterize intra-individual variability in important
physiological parameters wherever possible. Unhappily
however, there is little out there, as will be seen by reviewing
the tables presented in this report.
-------
2.0
Absolute and Relative General
Physiological Metrics
There are three generalized physiological metric formulations
used in this report, whether or not they are normalized by
body mass.
1. Absolute metrics, not being relative to any other
physiological measure. They may, however, be
normalized to an anthropogenic measure, such as
height or weight. An example is activity-specific
oxygen consumption (V02 ACT) or activity-
specific energy expenditure (EEACT), with units of
mL kgBM_1 min1 and kcal kg"1 min1, respectively.
2. "One-sided" relative metrics, related to some defined
construct, such as maximal level achieved. Examples
are %V02 MAX or %V02 MAX/BM. One-sided relative
metrics may also be anchored to a minimal level, such
as basal or resting metabolic rate (RMR). METS are
an example of this type, where activity-specific energy
expenditures (kcal min1 or kcal kg1 min1) are divided
by a person's RMR to produce a unitless metric caused
by canceling of units. Daily Physical Activity Level
(PAI) is another example of a metric being anchored to
the basal metabolic rate, in this case the ratio of total
daily energy expenditure to RMR needed to support the
activities undertaken (McCurdy, 2000).
3. "Two-sided" relative metrics, bounded by limits
on both the low- and high-end. These generally are
called "resen'e metrics, " as briefly mentioned above
and more fully explored below. Reserve metrics
retain their original units. Relating exercise data to
both resting and maximal exercise limits explicitly
adjusts for differences in fitness and age in children,
in particular (Logan et al., 2000). Examples are
oxygen consumption reserve (V02 RES) which is
equal to V02MAX - V02REST, ventilation reserve
(V which = V - V ) heart rate
v E.RES" v E.MAX E.REST-'"
reserve (HRR, which = HR,l ;: - HRrest), and
^TSres = METSmax ~ !•
Appendix B contains an extended discussion of these three
types of general physiological metrics using heart rate as an
example. We do not highlight HR in this paper as it is not
used in either the APEX or SHEDS models. However, it is
the physiological parameter having the most data—probably
due to the fact that there is a lot of concern regarding people
with cardiovascular problems and how their condition can
best be evaluated and treated in a clinical research setting.
How the general HR metrics relate to those of more interest
to us is also discussed in Appendix B.
5
-------
-------
3.0
Maximal Oxygen Consumption (V02max)
the "Controlling Parameter"
Overview of Tables 1 & 2
One of the most important parameters of interest to us is
V02 MAX. V02 MAX is alternately known as "aerobic capacity,"
"maximal oxygen uptake," "maximal aerobic power," or
just "aerobic power" (Armstrong, 2013; McArdle et al.,
2001). Strickland et al. (2012) call it the "gold standard
measure of aerobic fitness." It usually is expressed in units
of mL kg1 min1 or mL min1. It is frequently defined to be
the point in an exercise test where oxygen consumption
plateaus—or increases only slightly—with increasing work
rate. As mentioned in Appendix A, however, many subjects
never attain such a plateau, especially children people with
cardiovascular problems, and the elderly (Armstrong, 2013;
White et al., 1998), so additional—and usually "relaxed"--
criteria of V02 MAX attainment are used. Some authors then
call this measure of aerobic capacity "V02 PEAK" instead of
V02 MAX but this terminology is not universally used.
In general and for most people, undertaking steady-state
exercise at V02 levels less than 55-60% of V02MAX causes
little lactate accumulation (McArdle et al., 2001). Thus,
energy can be expended at that rate for relatively long
periods of time (4+ h). Increasing V02MAX due to an exercise
program results in—for a period of time, anyway—the
ability to accomplish the same amount of work using
less oxygen consumption, and also increases endurance
(Astrand, 1992). These improvements are not solely due to
an improved oxygen transport system (increased density
and size of capillaries), but also due to an increased use of
free fatty acids to supplement glycogen usage, and increased
mitochondrial enzyme activity (Astrand, 1992). A review of
important factors that determine maximal oxygen uptake/
consumption in individuals is found in Lamb (1984).
V02 values, including V02 MAX, generally are obtained
using a progressive treadmill or cycle ergometer tests, with
good agreement between them on a group basis, but not so
good on an individual basis (Bassett & Boulay, 2003). We
generally only provide V02 MAX data for exercise tests using
one of these two methods. However, there are many different
protocols used for each one of these general approaches, as
highlighted in Appendix A.
V02 MAX data from U.S. studies are presented in Tables 1
(females) and 2 (males) for various author-defined fitness,
health, or weight categories. We do not provide any V02
MAX data for "mixed-gender" studies, although a number are
reported in the literature. There are statistically significantly
differences in V02 MAX, either on an absolute or relative
basis, between the sexes at all ages except the very young or
old (Armstrong, 2013; Graves et al., 2013; Rowland, 2013;
Weiss et al. 2006). Values of V02MAX inTablesl and 2 are
provided only as relative estimates in terms of mL kgBM 1
min1 metrics (mL/kg-min). A reader interested in absolute—
non-body mass nonnalized~V02 MAX estimates can find them
for particular gender/age cohorts in hundreds of published
articles in the exercise literature. Likewise, V02MAX estimates
on a lean body mass (fat-free mass) basis can also be found:
e.g.. Graves et al (2013).
A paper that presents a summary of V02MAX data similar
to that presented in the Tables is contained in Smith &
Gilligan (1989) for 133 studies. About 30 of the studies in
paper are included in Tables 1 & 2. The other studies were
excluded here mostly because they were from non-US
citizens. Another published source of V02 MAX data from
scores of studies on females is found in Wells (1991). We do
not include any V02MAX data from the Wells (1991) "meta-
analysis" article in Tables 1 & 2, but do include data from
some of the U.S. studies used in that article if we could
obtain the cited article ourselves. Another published source
of V02 MAX data from scores of studies on females is found
inPatil et al. (1993). Other meta-analyses articles of U.S.
citizen's V02 MAX data exist but are not cited here as we
only used data from "original sources." An early paper that
provides the mean of maximal oxygen consumption by age
data in trained males from the US, two named countries
and "all-other" countries is Shephard (1966). The US data
are similar to residents of all the countries except for the
Scandinavian data, which has considerably higher grouped
V02 MAX data than the rest-of-the-world, at least prior to
age 50. His values for U.S. residents are not significantly
different than those shown in Table 1 and 2.
7
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Table 1. Estimates of V02 MAX in females seen in the literature
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean SD Status Mean SD (%) Citation Comment
Females: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for age
7.8
0.3
N
46.4
2.9
6.3
Livingstone et al. 1992
n=5
7.9
2.7
NS
49.1
6.5
13.2
DM Rogers et al. 1995a
n=15
8.2
1.2
N
45.5
4.7
10.3
Cureton et al. 1997
n=20; mixed fitness groups
8.5
0.8
H
45.3
4.0
8.8
Treuth et al. 2003
n=6
8.7
0.7
N
39.7
8.6
21.7
McMurray et al. 2003
n=403; CA
8.8
0.7
NS
50.2
3.6
7.2
DM Rogers et al. 1995b
n=21
8.8
0.7
N
38.4
9.8
25.5
McMurray et al. 2003
n=103; AA
9.0
2.0
N
38.0
7.0
18.4
Cooper et al. 1984
n=24
9.1
1.5
NS
33.9
2.3
6.8
Gilliam et al. 1977
n=15
9.4
0.5
N
41.9
2.5
6.0
Livingstone et al. 1992
n=4
9.4
1.0
NS
45.4
5.8
12.8
Cureton et al. 1995
n=106; multicenter study
9.8
0.6
H
35.4
7.5
21.2
KE Swain et al. 2010
n=20
9.8
0.7
NS
46.8
7.1
15.2
Loftin et al. 1998
n=19
10.0
0.8
N
38.3
9.1
23.8
McMurray et al. 2003
n=381; CA
10.0
1.8
N
42.5
6.7
15.8
McMurray et al. 1998
n=18
10.1
1.2
N
49.1
3.1
6.3
Chausow et al. 1984
n=3
10.1
0.8
N
37.2
9.8
26.3
McMurray et al. 2003
n=98; AA
10.2
1.0
N
41.0
7.0
17.1
Janz et al. 1998
n=62
10.2
2.8
N
44.8
6.2
13.8
Skinner et al. 1971
n=20; treadmill protocol #3
10.3
0.3
H
42.3
5.1
12.1
N Hopkins et al. 2011
n=70 (Summer only)
10.4
2.5
N
45.7
5.1
11.2
Skinner et al. 1971
n=20; treadmill protocol #2
10.4
2.8
N
43.0
6.9
16.0
Skinner et al. 1971
n=21; treadmill protocol #1
10.6
0.9
N
43.2
8.6
19.9
lannotti et al. 2004
n=33
10.6
1.4
H
41.4
5.2
12.6
Roemmich et al. 1998
n=12
10.8
0.6
N
49.2
3.5
7.1
Mahon et al. 1997a
n=15; Tanner 1
11.2
1.0
N
40.0
6.0
15.0
Janz et al. 1998
n=61
11.6
1.2
N
54.3
4.7
8.7
Mahon et al. 1997a
n=11; Tanner2
11.6
2.8
NS
36.7
5.4
14.7
Golden et al. 1991
n=101
12.0
0.8
N
35.0
5.4
15.4
McMurray et al. 2003
n=403; CA
12.0
0.7
N
52.5
5.0
9.5
Peyer et al. 2011
n=55
12.1
0.8
N
33.6
5.8
17.3
McMurray et al. 2003
n=103; AA
12.1
1.0
N
38.0
7.0
18.4
Janz et al. 1998
n=62
12.1
3.5
H
34.9
6.5
18.6
JK Murphy et al. 1988
n=42; CA
12.2
2.7
NS
45.4
7.3
16.1
DM Rogers et al. 1995
n=15
12.5
0.4
N
42.3
3.5
8.3
Livingstone et al. 1992
n=5
12.5
0.7
N
48.1
2.6
5.4
Mahon et al. 1997a
n=8;Tanner 3
12.9
1.0
N
31.9
5.1
16.0
Peyer et al. 2011
n=105
13.0
0.8
N
35.0
6.3
18.0
McMurray et al. 2003
n=349; CA
13.0
1.0
N
45.8
5.9
12.9
Cureton et al. 1997
n=26; mixed fitness groups
13.1
0.8
N
33.5
6.1
18.2
McMurray et al. 2003
n=36; AA
13.1
0.8
N
41.1
6.9
16.8
Boiarskaia et al. 2011
n=74
13.1
1.8
N
40.5
7.6
18.8
Mahar et al. 2011
n=90; validation sample
13.2
0.1
N
36.0
6.8
18.9
Peyer et al. 2011
n=128
13.2
1.0
N
38.0
7.0
18.4
Janz et al. 1998
n=58
13.2
1.5
NS
38.6
7.5
19.4
Mahar et al. 2011
n=36; cross-validation sample
13.2
3.3
H
33.2
5.9
17.8
JK Murphy et al. 1988
n=47; AA
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Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
13.3
5.7
NS
45.0
5.7
12.7
Cureton et al. 1995
n=45; multicenter study
13.4
1.1
N
47.7
5.4
11.3
Peyer et al. 2011
n=13; Tanner 4
13.4
1.6
N
38.5
6.8
17.7
Pivarnik et al. 1995
n=53; CA
13.5
0.8
N
37.3
6.3
16.9
Peyer et al. 2011
n=63
13.7
0.6
H
39.8
4.0
10.1
Grossner et al. 2005
n=10
13.7
1.3
H
39.3
5.1
13.0
Roemmich et al. 1998
n=18
13.7
1.7
N
41.2
5.1
12.4
Peyer et al. 2011
n=32
14.0
0.7
N
33.3
6.4
19.2
McMurray et al. 2003
n=312; CA
14.1
1.0
N
34.0
5.0
14.7
Janz et al. 1998
n=57
14.1
2.2
NS
39.5
4.2
10.6
Drinkwater et al. 1975
n=10
14.2
0.8
H
45.3
4.0
8.8
Treuth et al. 2003
n=6
14.2
0.8
N
31.4
6.5
20.7
McMurray et al. 2003
n=74; AA
14.3
1.8
N
41.5
6.4
15.4
Mahon et al. 1997a
n=13; Tanner 4
14.6
0.5
N
31.4
4.8
15.3
Pivarnik et al. 1998
n=19; AA
14.6
0.7
N
34.6
9.4
27.2
Crowhurst et al. 1993
n=9
15.0
0.6
N
36.0
5.1
14.2
Rowland et al. 2011
n=9
15.0
0.8
N
32.4
6.7
20.7
McMurray et al. 2003
n=297; CA
15.0
2.0
N
34.0
4.0
11.8
Cooper et al. 1984
n=27
15.1
0.8
N
30.3
5.9
19.5
McMurray et al. 2003
n=75; AA
15.2
1.5
N
47.3
5.2
11.0
Peyer et al. 2011
n=57
15.3
1.1
NS
40.4
5.1
12.6
Murray et al. 1993
n=32
15.6
3.4
N
38.2
6.9
18.1
Moffatt et al. 1984
n=13; controls
15.6
0.4
N
38.7
2.9
7.5
Livingstone et al. 1992
n=3
16.2
1.1
NS
34.2
7.0
20.5
Gutin et al. 2005
n=104; white adolescents
16.3
1.2
NS
29.6
6.8
23.0
Gutin et al. 2005
n=121; black adolescents
16.7
1.1
NS
46.0
4.7
10.2
Dill et al. 1972
n=10
16.9
3.0
NS
46.6
6.0
12.9
Loftin et al. 1998
n=?
18.9
0.5
N
31.7
2.5
7.9
Burke 1977
n=8; experimental group
18.9
0.5
N
34.9
5.0
14.3
Burke 1977
n=7; control group
19.1
2.8
NS
37.5
5.9
15.7
Dolgeneret al. 1994
n=45; cross-validation group
19.2
6.2
NS
23.4
5.1
21.8
AM Miller et al. 2012
n=13; siblings of survivors
19.4
3.1
NS
36.6
4.7
12.8
Dolgeneret al. 1994
n=100; validation group
19.5
1.4
NS
36.6
5.2
14.2
Darby & Pohlman 1999
n=15
19.7
2.4
N
49.2
9.8
19.9
K Sell et al. 2008
n=12; game players
19.8
2.5
N
39.5
6.7
17.0
Kaminsky et al. 1993
n=28
19.9
1.8
NS
36.7
10.2
27.8
Lepp et al. 2013
n=27
20.1
1.6
N
34.0
6.0
17.6
Hu et al. 2007
n=14; group #1
20.3
0.9
H
38.9
4.4
11.3
Deschenes et al. 2009
n=10
20.5
1.6
N
44.2
3.2
7.2
Bransford & Howley 1977
n=10; untrained
20.6
2.0
N
35.8
5.1
14.2
Hu et al. 2007
n=14; group #2
20.8
1.8
H
38.7
4.2
10.9
McComb et al. 2006
n=13
20.8
2.0
NS
37.5
6.6
17.6
Darby & Pohlman 1999
n=63
20.8
3.0
NS
39.0
8.1
20.8
Mole & Hoffmann 1999
n=38
21.0
3.0
N
42.4
10.4
24.5
J Kang et al. 2007
n=11
21.1
3.3
N
44.9
6.9
15.4
Latin & Elias 1993
n=25
21.2
1.0
N
27.9
4.6
16.5
Chitwood et al. 1996
n=11 ;black subjects
21.5
2.2
NS
39.2
4.2
10.7
JD George et al. 1998
n=49; test of protocol
21.6
2.9
NS
41.6
5.2
12.5
JD George et al. 1996
n=50
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Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age/Range
Health
cov
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
21.7
1.6
NS
44.2
5.6
12.7
Cureton et al. 1995
n=23; multicenter study
21.8
1.3
N
39.3
3.4
8.7
Kaminsky & Whaley 1993
n=5
22.2
3.6
N
29.3
2.9
9.9
Chitwood et al. 1996
n=11; white subjects
22.2
1.8
N
37.5
6.1
16.3
J Kang et al. 1999
n=7
22.3
2.8
NS
32.7
2.9
8.9
Drinkwater et al. 1975
n=10
22.8
3.2
H
38.7
8.7
22.5
Grossner et al. 2005
n=10
23.0
4.0
N
44.2
4.9
11.1
DW Hill 1996
n=12
23.5
5.0
N
34.8
5.3
15.2
Gonzales & Scheur.2006
n-11
23.7
6.6
NS
44.8
7.8
17.4
DM Rogers et al. 1995
n=15
23.8
2.4
N
32.2
6.6
20.5
Ridout et al. 2005
n=12
23.9
3.4
H
49.5
6.7
13.5
Porcari et al. 1997
n=16; control group
23.9
5.6
NS
47.3
8.3
17.5
Mole & Hoffmann 1999
n=44
24.0
3.6
N
36.2
7.0
19.3
S.-C. Chung et al. 1999
n=11
24.3
1.6
NS
34.8
2.7
7.8
Frey et al. 1993
n=7; untrained students
24.5
3.9
N
35.6
4.7
13.2
Kohrt et al. 1998
n=18
24.8
4.8
N
40.1
6.8
17.0
Blessinger et al. 2009
n=26
25.0
2.0
H
45.1
10.5
23.3
Pettitt et al. 2008
n=7
25.0
3.0
NS
34.3
3.8
11.1
Horton et al. 1998
n=6; untrained
25.0
4.9
NS
38.2
8.3
21.7
Swain et al. 1998
n=24
25.0
9.0
NS
40.1
7.2
18.0
Swain et al. 1994
n=81
25.1
4.3
N
36.3
4.6
12.7
Steffan et al. 1999
n=15; normal weight
25.6
4.9
N
40.0
6.1
15.3
BJ Sawyer et al. 2010
n=29
26.3
1.8
H
36.1
7.1
19.7
KJ Melanson et al. 1997
n=8
25.6
1.6
N
56.8
10.7
18.8
K Sell et al. 2008
n=7; not game players
26.6
5.5
N
38.6
4.4
11.4
Browning et al. 2006
n=10
27.3
5.0
N
47.6
6.5
13.7
Bailor & Poehlman 1992
n=13; resistance trained
27.3
5.1
H
30.3
4.7
15.5
Lovelady et al. 1990
n=8; lactating; controls
27.4
5.1
H
42.2
3.1
7.3
Thomsen & Balor 1991
n=8; group 1
27.5
5.1
H
41.0
4.7
11.5
Thomsen & Balor 1991
n=10; group 2
27.8
3.5
H
35.6
7.8
21.9
Dionne et al. 2004
n=19
27.9
5.7
H
31.4
2.7
8.6
Thomsen & Balor 1991
n=9; group 3
27.9
6.8
N
31.0
8.5
27.4
McMurray et al. 1998
n=286
28.0
6.1
N
43.6
7.9
18.1
Sheaff et al. 2010
n=7; firefighters
28.1
4.3
H
39.3
10.4
26.5
Treuth et al. 1996
n=8
28.3
7.7
NS
43.6
1.8
4.1
Foster 1975
?
28.9
7.8
NS
44.6
3.7
8.3
L Kravitz et al. 1997
n=9
29.0
3.5
H
34.3
5.5
16.0
BE Hunt et al. 1997
n=12
29.0
5.0
N
39.9
5.8
14.5
Horton et al. 2002
n=10
29.4
4.4
N
28.8
19.3
67.0
Soultankis et al. 1996
n=10
30.0
3.5
H
46.4
2.4
5.2
Lovelady et al. 1990
n=8; lactating; exercise group
30.0
4.0
H
45.7
2.6
5.7
Kaminsky et al. 1990
n=6
30.0
7.2
N
39.0
4.2
10.8
Horvath & Drink.1982
n=4
30.4
8.2
H
47.5
5.2
10.9
Olson et al. 1991
n=9
30.5
5.0
H
33.0
4.9
14.8
Byrne et al. 1996
n=28
31.0
6.8
N
34.2
5.6
16.4
Horton et al. 1994
n=5; controls
31.8
11.1
NS
36.3
7.9
21.8
Flint et al. 1974
n=7; non-exercisers
32.1
11.7
N
38.8
7.5
19.3
Warr et al. 2013
n=12; pre-deployed NG
33.0
3.0
N
46.8
4.0
8.5
Beidleman et al. 1999
n=8; follicular stage (Note 1)
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
33.5
4.9
N
47.7
4.3
9.0
Schraff et al. 1992
n=11
33.6
5.5
N
29.5
4.8
16.3
GR Hunter et al. 2011
n=49; African-American
34.0
6.3
H
47.1
4.6
9.8
Horton et al. 2006
n=11
34.2
2.7
N
30.1
5.7
18.9
Ridout et al. 2005
n=9
34.2
6.3
N
33.7
5.5
16.3
GR Hunter et al. 2011
n=47; European American
34.8
3.4
NS
31.7
3.0
9.5
Drinkwater et al. 1975
n=14
35.1
10.5
H
41.0
8.2
20.0
Engels et al. 1998
n=101
37.5
12.0
N
34.4
5.4
15.7
Nieman et al. 2005
n=15; age range: 20-55
37.6
3.4
N
32.8
0.7
2.1
Shvartz 1996
n=5
40.0
14.0
H
27.6
7.9
28.6
NP Greene et al. 2011
n=25
41.8
5.9
N
33.5
6.2
18.5
Evans 1990
n=20
42.0
14.0
H
30.0
9.0
30.0
Ardestani et al. 2011
n=102
42.4
9.0
N
31.9
7.5
23.5
LT Weir et al. 2006
n=384
42.6
17.6
H
37.8
12.0
31.7
Gardner & Poehl. 1993
n=111; validation sample
43.4
2.9
NS
29.5
2.7
9.2
Drinkwater et al. 1975
n=13
43.4
4.9
NS
23.0
5.7
24.8
Lind et al. 2005
n=23
43.6
2.4
N
26.5
2.7
10.2
Ridout et al. 2005
n=9
43.8
16.0
H
36.4
9.5
26.1
Gardner & Poehl. 1993
n=56; cross-validation sample
45.7
7.9
N
25.0
5.0
20.0
Motl & Fernhall 2012
n=16; controls
48.3
11.6
NS
32.2
7.5
23.3
Kline et al. 1987
n=86; cross-validation group
48.5
11.4
NS
31.4
8.5
27.1
Kline et al. 1987
n=92; validation group
48.6
16.0
H
29.4
7.1
24.1
Fleg et al. 2005
n=375
49.0
4.0
N
27.0
6.8
25.2
NA Lynch et al. 2002
n=18; perimenopausal
49.4
16.3
N
28.6
7.1
24.8
Talbot et al. 2000
n=497; BLSA participants
50.4
6.0
H
29.1
5.4
18.6
Byrne et al. 1996
n=375
50.9
9.5
H
24.1
4.5
18.7
Tosti et al. 2011
n=7; control group
51.5
6.6
H
20.2
3.3
16.3
Duscha et al. 2001
n=11
52.0
2.0
H
22.3
3.2
14.3
NA Lynch et al. 2002
n=18; postmenopausal
52.5
3.4
NS
23.7
3.5
14.8
Drinkwater et al. 1975
n=6
53.0
2.8
N
24.4
6.6
27.0
Ridout et al. 2005
n=10
53.2
4.8
H
28.6
5.0
17.5
JS Green et al. 2001
n=12; exer. no est.replace.
54.3
3.2
N
24.6
4.1
16.7
Johannessen et al. 1986
n=10; exercisers
54.3
8.2
N
33.1
7.5
22.7
Guderian et al. 2010
n=10; in an exercise program
55.4
5.4
N
21.3
7.2
33.8
Johannessen et al. 1986
n=5; controls
55.5
5.1
H
28.0
5.6
20.0
JS Green et al. 2001
n=10; exercisers, est. replace.
56.9
5.1
NS
25.9
4.7
18.1
Stefanick et al. 1998
n=117
57.1
4.3
NS
23.3
4.2
18.0
DR Young et al. 1994
n=160
61.0
3.0
N
26.0
3.0
11.5
Hagberg et al. 2003
n=9
61.0
3.8
H
22.2
4.6
20.7
BE Hunt et al. 1997
n=15; post-menopausal
61.0
4.0
N
22.2
4.7
21.2
Hunt et al. 1997
n=15
62.0
3.0
N
33.4
7.6
22.8
Hagberg et al. 1998
n=22
62.0
6.0
N
23.2
3.3
14.2
Sheldahl et al. 1996
n=9
62.0
6.0
NS
22.8
4.2
18.4
Tanaka et al. 1998
n=9; lean, mostly sedentary
62.0
7.0
NS
21.7
3.3
15.2
Sheldahl et al. 1996
n=11
63.0
5.0
NS
30.1
8.5
28.2
Hagberg et al. 1998
n=22
63.3
2.9
N
21.8
2.6
11.9
Kohrt et al. 1991
n=16; control group
64.0
3.1
N
21.6
2.9
13.4
Kohrt et al. 1991
n=57; experimental group
64.0
4.0
NS
24.3
4.3
17.7
Proctor et al. 2003
n=13
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
64.9
2.5
N
19.2
2.2
11.5
Ridout et al. 2005
n=10
65.0
5.0
H
27.0
4.0
14.8
Gonzales et al. 2011
n=21
65.5
7.8
N
16.1
4.8
29.8
Carter et al. 1994
n=16; control group
66.6
4.9
H
22.2
3.6
16.2
Dionne et al. 2004
n=12
67.0
3.7
N
19.4
3.4
17.5
ND Parker et al. 1996
?
67.0
3.9
H
19.3
3.9
20.2
Treuth et al. 1995
n=15
68.0
7.0
n
23.2
5.3
22.8
Pescatello et al. 1994
n=11
68.6
5.7
N
21.9
4.2
19.2
Panton et al. 1996
n=36
70.0
6.1
N
21.5
4.3
20.0
Parise et al. 2004
n=117
70.3
7.3
H
21.3
5.0
23.5
Byrne et al. 1996
n=?
70.4
3.9
N
17.5
2.8
16.0
Sergi et al. 2009
n=81
70.4
6.1
NS
17.6
5.0
28.4
Ainsworth et al. 1993
n=18
70.9
8.1
N
20.3
4.1
20.2
Simonsick et al. 2006
n=46
71.0
3.0
H
22.9
3.7
16.2
Blackman et al. 2002
n=14; test group 1
71.0
4.0
H
23.1
5.9
25.5
Blackman et al. 2002
n=12; test group 2
71.0
5.0
H
21.7
3.2
14.7
Blackman et al. 2002
n=16; test group 3
71.0
6.0
N
24.8
3.6
14.5
Stachenfeld et al. 1998
n=9; exercise group
71.1
5.1
N
17.3
4.0
23.1
Peterson et al. 2003
n=114
71.2
3.5
H
22.6
3.2
14.2
Fehling et al. 1999
n=42
71.3
4.4
H
23.7
4.7
19.8
Audette et al. 2006
n=8; walking group
71.5
4.6
H
21.6
5.2
24.1
Audette et al. 2006
n=11; Tai Chi group
72.0
5.0
H
21.4
4.5
21.0
Blackman et al. 2002
n=14; control group
72.3
2.1
H
21.0
4.3
20.5
KJ Melanson et al. 1997
n=8
73.0
8.5
N
25.1
6.2
24.7
Stachenfeld et al. 1998
n=8; control group
73.0
9.0
N
25.2
6.2
24.6
Stachenfeld et al. 1999
n=8
73.3
2.7
N
16.7
3.3
19.8
Perini et al. 2000
n=11; V02 range: 12.0-21.7
73.5
5.7
H
26.8
8.3
31.0
Audette et al. 2006
n=8; sedentary controls
74.5
7.8
NS
17.3
3.4
19.7
Fiser et al. 2010
n=24
74.6
4.0
N
18.0
4.0
22.2
Ridout et al. 2005
n=8
75.5
3.8
H
19.6
3.8
19.4
Deschenes et al. 2009
n=10
b. Complete age statistics are not provided
6.0
N
36.5
2.9
7.9
DW Morgan et al. 1999
n=20
8.0
N
42.3
4.9
11.6
Treuth et al. 2004
n=91
9.0
N
42.3
5.4
12.8
Treuth et al. 2004
n=88
10.0
N
41.9
6.1
14.6
Treuth et al. 2004
n=84
12-13
NS
39.3
-
Pate et al. 2006 APAM
50th per. CI: 37.8-39.9
12.7
NS
42.7
6.1
14.3
Eisenman & Golding 1975
n=8; experimental group
12.7
NS
44.5
6.2
13.9
Eisenman & Golding 1975
n=8; control group
14-15
NS
38.0
-
Pate et al. 2006 APAM
50th per. CI: 37.2-38.4
15.0
N
46.2
8.3
18.0
Cureton et al. 1997
n=7; mixed fitness groups
16-17
NS
37.6
-
Pate et al. 2006 APAM
50th perc.CI: 36.5-38.8
17-28
NS
33.8
4.6
13.6
Fringer & Stull 1974
n=44
18-19
NS
36.7
-
Pate et al. 2006 APAM
50th perc. CI: 35.7-37.8
20's
NS
34.1
5.0
14.7
Fleg et al. 1995a
18-21
N
34.4
3.4
9.9
SB Parker et al. 1989
n=14; exercise group
18-21
N
37.5
5.7
15.2
SB Parker et al. 1989
n=10; control group
18-34
N
43.2
4.1
9.5
Beidleman et al. 1995
n=10; control group
19.5
H
38.5
3.6
9.4
Humphrey & Falls 1975
n=15
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
19.6
NS
38.1
3.8
10.0
Eisenman & Golding 1975
n=8; experimental group
19.6
NS
39.0
3.9
10.0
Eisenman & Golding 1975
n=8; control group
20.0
N
37.7
2.9
7.7
Blessing et al. 1987
n=13; group 1
20.0
N
36.5
3.1
8.5
Blessing et al. 1987
n=13; group 2
29.0
NS
40.5
8.7
21.5
Diaz et al. 1978
n=5; treadmill only
30'3
NS
31.5
4.9
15.6
Fleg et al. 1995a
n=17
40's
NS
29.4
3.4
11.6
Fleg et al. 1995a
n=12
50's
NS
27.1
5.4
19.9
Fleg et al. 1995a
n=13
55-59
N
24.5
5.5
22.4
Hollenberg et al. 1998
n=100
60's
NS
25.7
4.4
17.1
Fleg et al. 1995a
n=12
60-64
N
22.7
4.4
19.4
Hollenberg et al. 1998
n=96
60-69
H
25.7
4.4
17.1
Fleg et al. 1995b
n=12
65-69
N
21.6
3.8
17.6
Hollenberg et al. 1998
n=109
65.0
N
21.9
4.5
20.5
Hollenberg et al. 2006
n=339; exercise group #1
60-77
H
19.4
3.6
18.6
ND Parker et al. 1996
n=16; control group
68.0
N
19.7
4.1
20.8
Hollenberg et al. 2006
n=293; exercise group #2
70's
NS
18.0
2.4
13.3
Fleg et al. 1995a
n=7
70-74
N
20.3
3.3
16.3
Hollenberg et al. 1998
n=88
75-79
N
19.2
3.1
16.1
Hollenberg et al. 1998
n=36
80's
NS
21.2
1.3
6.1
Fleg et al. 1995a
n=2
80-84
N
17.8
3.2
18.0
Hollenberg et al. 1998
n=18
>85
N
18.1
6.0
33.1
Hollenberg et al. 1998
n=7
Females: Active, Fit, or Athlete
a. Mean & Statistics are provided for age
9.1
1.5
Act
33.9
2.3
6.8
Gilliam et al. 1974
n=15; exercisers
11.3
1.1
Act
48.5
8.0
16.5
Rowland & Green 1988
n=18
13.0
2.0
Fit
58.7
4.5
7.7
Drinkwater et al. 1975
n=11
14.6
0.7
Ath
43.5
3.4
7.8
Rowland et al. 2011
n=13
15.2
4.1
Ath
45.2
5.3
11.7
Moffatt et al. 1984
n=13; gymnasts
15.6
1.1
Ath
50.8
4.6
9.1
Butts 1982
n=127; cross-country runners
15.9
1.0
Ath
61.7
7.1
11.5
Cunningham 1990
n=24
cross-country runners
19.0
1.0
Ath
52.1
5.1
9.8
Hill & Rowell 1997
n=13
track team members
19.0
3.6
Ath
49.0
10.8
22.0
Wenner et al. 2006
n=13
amenorrheic
19.6
1.1
Ath
46.9
5.6
11.9
Dellavalle & Haas 2012
n=24
rowers with low iron levels
20.1
1.1
Ath
49.5
5.6
11.3
Dellavalle & Haas 2012
n=24
rowers
20.0
1.4
Ath
45.7
4.9
10.7
Enemark-Miller et al. 2009
n=24
Lacrosse players
20.0
3.0
Ath
51.8
4.5
8.7
AS Ryan et al. 1996
n=14
exercise group
20.1
1.5
Ath
44.2
3.3
7.5
MS Green et al. 2013
n=39
soccer players
20.1
1.7
Fit
46.2
2.9
6.3
Getchell et al. 1977
n=21
joggers
20.5
1.6
Fit
44.0
4.7
10.7
Pintar et al. 2006
n=15
normal weight
20.6
2.8
Ath
48.8
4.1
8.4
Branford & Howley 1977
n=10
distance runners
20.7
3.3
Act
46.9
5.2
11.1
Nindletal. 1998
n=20
Army personnel
20.7
3.2
Act
36.9
3.8
10.3
Sharp et al. 2002
n=122
20.7
3.6
Act
41.3
4.0
9.7
Rowland & Green 1988
n=18
21.0
3.0
Act
42.4
10.4
24.5
J Kang et al. 2007
n=11
21.0
3.6
Ath
51.0
7.2
14.1
Wenner et al. 2006
n=13; eumenorrheic
21.3
1.2
NS
44.8
5.5
12.3
Gist et al. 2014
n=3;
mod.-trained college
21.4
3.4
Act
39.2
5.1
13.0
Sharp et al. 2002
n=155
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
21.9
2.0
Fit
48.7
4.5
9.2
Jeans et al. 2011
n=8; range of V02: 45.0-52.5
21.9
2.4
Fit
51.1
6.5
12.7
Drinkwater et al. 1975
n=10
22.0
2.5
Ath
34.1
4.5
13.2
AS Ryan et al. 1996
n=8; control group
22.0
3.6
Act
35.3
2.5
7.1
Proctor et al. 2004
n=13
22.8
4.5
Ath
50.7
9.0
17.8
Faria & Faria 1998
n=12; college rowers
22.9
3.2
Act
40.7
5.5
13.5
Astorino et al. 2010
n=17; recreationally active
23.0
2.7
Act
39.1
2.1
5.4
Astorino et al. 2012
n=4
23.0
3.0
Ath
55.0
6.0
10.9
Wenner et al. 2006
n=9; eumenorrheic/oral contrac.
23.0
3.7
NS
49.6
3.5
7.1
Darby et al. 1995
n=16; exercise dancers
23.0
8.5
Act
39.9
7.0
17.5
Kist et al. 2013
n=11; aerobically trained
23.4
2.1
Ath
53.4
2.7
5.1
Drenowatz & Eisen. 2011
n=10; endurance runners
23.5
6.4
Fit
33.9
4.5
13.3
Meyers & Sterling 2000
n=24; equestrians
25.0
3.0
Act
42.9
5.0
11.7
CB Scott 1997
n=10
25.0
4.6
Act
51.9
5.1
9.8
Sparling & Cureton 1983
n=34; distance runners
25.2
3.1
Act
41.1
6.1
14.8
Astorino et al. 2012
n=9; recreationally active
25.7
7.2
Act
44.0
9.6
21.8
Beckham & Earnest 2000
n=18; 79% are active
26.0
3.0
Act
52.1
3.1
6.0
Ogawa et al. 1992
n=13
26.0
3.3
Ath
66.0
4.0
6.1
LO Schultz et al. 1992
n=9; endurance trained
26.0
3.7
Act
55.0
3.7
6.7
Tanaka et al. 1997
n=14; endurance-trained
26.3
4.2
Ath
53.8
2.8
5.2
Laughlin & Yen 1996
n=8; amenorrhic
26.3
5.9
Fit
44.9
4.2
9.4
Nicklas et al. 1989
n=6; eumenorrheic
26.7
5.5
Fit
54.5
4.1
7.5
Bailor & Poehlman 1992
n=21 aerobically trained
26.9
5.3
Ex
45.3
4.2
9.3
SD Fox et al. 1993
n=9; recreational aerobics
27.0
2.1
Ath
63.0
4.6
7.3
Gojanovic et al. 2012
n=5
27.0
2.8
Ath
51.8
4.0
7.7
Proctor et al. 1998
n=8
27.0
5.0
Fit
55.3
6.6
11.9
Horton et al. 1998
n=8; competitive cyclists
27.8
2.0
Ath
53.5
1.1
2.1
Frey et al. 1993
n=6; cycling team members
28.0
3.4
Fit
45.4
4.5
9.9
Sandoval & Matt 2002
n=14
28.0
5.0
Act
42.5
5.1
12.0
Dean et al. 2003
n=8; mid-luteal phase (Note 2)
29.5
5.1
Act
42.6
3.7
8.7
EL Melanson et al. 2002
n=8; lean exercisers
30.0
3.7
Fit
53.0
5.6
10.6
Seals et al. 1999
n=14; endurance trained
30.0
3.9
Fit
53.4
5.0
9.4
BE Hunt et al. 1997
n=15; runners
30.0
5.5
Ath
57.0
5.1
8.9
Schaal et al. 2011
n=5; eumenorrheic
30.2
5.0
Fit
47.6
9.1
19.1
Quinn et al. 1994
n=8
30.7
3.4
Ath
60.8
8.5
14.0
Laughlin & Yen 1996
n=8; regular cycles
31.0
5.0
Ath
60.3
4.8
8.0
Thompson & Man.1996
n=13; endurance runners
31.0
9.6
Ath
56.0
3.4
6.1
Schaal et al. 2011
n=5; amenorrheic
31.7
9.2
Fit
41.1
7.1
17.3
Dalleck & Kravitz 2006
n=12; moderate exercisers
32.9
5.0
Fit
46.7
6.7
14.3
Dalleck & Kravitz 2006
n=24; moderate exercisers
32.9
4.0
Pg
27.7
1.4
5.1
Szymanski & Satin 2012
n=15; highly active
33.0
5.4
Fit
53.6
5.2
9.7
Horton et al. 1994
n=5; cyclists
34.0
3.3
Ath
56.5
5.1
9.0
AS Ryan et al. 1996
n=9; exercise group
34.0
4.6
Act
55.2
4.6
8.3
Tanaka et al. 1997
n=21; endurance-trained
34.3
4.0
Pg
23.8
2.2
9.2
Szymanski & Satin 2012
n=15; active
35.2
3.2
Fit
52.4
5.4
10.3
Drinkwater et al. 1975
n=10
35.7
4.2
Fit
50.2
1.7
3.4
Shvartz 1996
n=6
38.7
1.4
Ath
54.1
7.2
13.3
Wells et al. 1992
n=11; runners
39.7
10.1
Fit
43.6
7.6
17.4
Malek et al. 2004
n=49; aerobically trained
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
cov
(%)
Citation
Comment
41.3
1.4
Ath
47.4
6.7
14.1
Wells etal. 1992
n=11; runners
42.8
2.0
Ath
48.7
7.8
16.0
SA Hawkins et al. 2001
n=24; visit 1
44.8
3.7
Fit
50.4
2.8
5.6
Drinkwater et al. 1975
n=7
45.0
3.5
Ath
49.6
4.7
9.5
AS Ryan et al. 1996
n=10; exercise group
45.0
3.6
Act
51.4
6.5
12.6
Tanaka et al. 1997
n=3; endurance-trained
46.0
4.4
Ath
26.9
4.9
18.2
AS Ryan et al. 1996
n=6; control group
47.1
1.3
Ath
43.6
5.1
11.7
Wells etal. 1992
n=11; runners
49.8
2.8
Ath
46.7
5.2
11.1
SA Hawkins et al. 2001
n=16; visit 1
51.2
2.4
Ath
45.2
5.9
13.1
SA Hawkins et al. 2001
n=24; visit 2
52.3
2.7
Fit
46.1
9.7
21.0
Drinkwater et al. 1975
n=6
52.6
1.5
Ath
41.2
5.9
14.3
Wells etal. 1992
n=10; runners
54.0
4.3
Act
42.7
7.2
16.9
Tanaka et al. 1997
n=23; endurance-trained
55.7
7.8
Act
30.6
6.7
21.9
Nikolai et al. 2009
n=7; in a water exercise class
57.0
3.0
Act
35.3
3.3
9.3
Ogawa et al. 1992
n=13
58.0
1.9
Ath
43.8
6.6
15.1
AS Ryan et al. 1996
n=10; exercise group
58.0
3.5
Fit
39.1
5.9
15.1
Seals et al. 1999
n=12; endurance trained
58.0
3.7
Fit
38.7
5.6
14.5
BE Hunt etal. 1997
n=14; post-menopausal runners
58.0
6.3
Fit
40.0
6.0
15.0
Tanaka et al. 1998
n=10; runners
58.3
3.2
Ath
40.8
7.2
17.6
SA Hawkins et al. 2001
n=16; visit 2
59.0
6.3
Fit
30.7
6.6
21.5
Tanaka et al. 1998
n=10; swimmers
60.0
7.0
O
15.0
2.8
18.7
Jordan et al. 2005
n=24
61.0
8.0
Fit
40.0
4.8
12.0
Proctor et al. 1997
n=8; endurance trained
61.7
4.7
Ath
39.5
3.9
9.9
Wells etal. 1992
n=6; runners
63.3
2.0
Ath
46.2
9.0
19.5
SA Hawkins et al. 2001
n=13; visit #1
64.0
3.5
Act
24.6
4.2
17.1
Proctor et al. 2004
n=12
64.6
3.9
Ath
39.4
4.8
12.2
SA Hawkins et al. 2001
n=9; visit 1
64.7
2.0
Fit
35.6
4.4
12.4
Drinkwater et al. 1975
n=6
66.0
3.6
Act
32.5
4.7
14.5
Tanaka et al. 1997
n=13; endurance-trained
66.8
15.9
Ath
29.4
14.5
49.3
Wlund et al. 2008
n=6; Master athlete
73.2
5.7
Ath
31.8
8.4
26.4
SA Hawkins et al. 2001
n=9; visit 2
b. Complete age statistics are not provided
9-10
Ath
56.3
6.6
11.7
Eisenmann et al. 2001
n=9; distance runners
11.0
Ath
57.9
5.2
9.0
Eisenmann et al. 2001
n=11; distance runners
12.0
Ath
57.1
5.3
9.3
Eisenmann et al. 2001
n=15; distance runners
13.0
Ath
54.8
6.3
11.5
Eisenmann et al. 2001
n=17; distance runners
14.0
Ath
56.9
8.4
14.8
Eisenmann et al. 2001
n=14; distance runners
15.0
Ath
56.2
7.0
12.5
Eisenmann et al. 2001
n=911; distance runners
14-15
Ath
48.5
4.6
9.5
Drinkwater & Horvath1971
n=11; track athletes
16.0
Ath
54.3
6.8
12.5
Eisenmann et al. 2001
n=12; distance runners
17-18
Ath
51.8
6.4
12.4
Eisenmann et al. 2001
n=16; distance runners
18-21
Act
44.2
4.8
10.9
WL Daniels et al. 1982
n=7; Army cadets
18-23
Act
44.1
1.5
3.4
Kamon & Pandolf 1972
n=6
18-34
Fit
59.7
5.3
8.9
Fay et al. 1989
n=13; distance runners
18-34
Fit
60.2
4.7
7.8
Beidleman et al. 1995
n=10; endurance runners
19-21
Fit
47.7
2.8
5.9
Kamon & Pandolf 1972
n=4
20.0
Act
38.9
5.9
15.2
Sonna et al. 2001
n=97; non-participants
21.0
Act
39.6
5.1
12.9
Sonna et al. 2001
n=71; participants group
24.0
Ath
64.7
-
Wlhite etal. 2013
n=1; elite distance runner
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age/Range
Mean SD
Health
Status
Mean
SD
COV
(%)
Citation
Comment
25-34
Act
31.7
4.6
14.5
Bruce 1984b
n=?
26.0
Act
51.5
3.2
6.2
Proctor et al. 1997
n=8; endurance trained
35-44
Act
29.9
5.3
17.7
Bruce 1984b
n=?
55-64
Act
29.7
4.7
15.8
Bruce 1984b
n=?
Females: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for age
9.1
1.4
O
25.8
4.9
19.0
Gutin et al. 1995
n=12; AA exercise group
9.4
1.6
O
25.3
4.7
18.6
Gutin et al. 1995
n=10; AA control group
11.2
1.8
OW
32.1
5.1
15.9
Byrd-Wlliams et al. 2008
n=76; Hispanic youth
15.2
1.2
O
21.5
4.4
20.5
Gutin et al. 2002
n=39; black adolescents
15.3
1.2
O
24.7
3.9
15.8
Gutin et al. 2002
n=15; white adolescents
19.4
1.5
OW
42.8
3.3
7.7
Pintar et al. 2006
n=15; high fit
21.0
0.8
O-Sed.
28.3
1.4
4.9
Szmedra et al. 1998
n=7; AA
21.0
3.6
Sed.
38.2
3.0
7.9
Loucks et al. 1998
n=9
21.0
4.0
OW
32.4
3.1
9.6
Potteiger et al. 2008
n=18; control group
21.1
3.0
OW
30.9
5.0
16.2
Pintar et al. 2006
n=15; low fit
21.9
2.0
Sed.
30.4
4.3
14.1
Pintar et al. 2006
n=15; normal weight
22.0
3.0
Sed.
28.0
4.8
17.1
Croley et al. 2005
n=11
22.8
2.7
OW
24.4
6.4
26.2
MK Thornton et al. 2011
n=10; AA
23.0
2.0
Sed.
37.0
4.3
11.6
Ogawa et al. 1992
n=14
23.3
4.6
O-Sed.
32.7
3.8
11.6
Washburn et al. 2003
n=29
23.4
3.6
O
26.8
2.4
9.0
Kaminsky & Whaley 1993
n=5; Hispanic
24.0
5.0
OW
32.8
4.2
12.8
Potteiger et al. 2008
n=25; exercise group
25.0
3.3
Sed.
34.9
4.6
13.2
Tanaka et al. 1997
n=11
25.0
4.0
Sed.
34.2
5.2
15.2
Schiller et al. 2001
n=14; Caucasian
25.0
3.0
Sed.
34.0
7.6
22.4
Schiller et al. 2001
n=14; Hispanic
25.1
3.1
Sed.
38.5
4.0
10.4
MJ Turner et al. 1999
n=10
25.3
7.3
O
25.9
3.3
12.7
Browning et al. 2006
n=9
27.5
5.1
Sed.
39.0
2.3
5.9
Laughlin & Yen 1996
n=8; regular cycles
28.0
3.0
O
22.1
2.1
9.5
Henson et al. 1987
n=7
28.6
12.4
Sed.
23.9
9.4
39.3
Rynders et al. 2011
n=74
28.7
6.9
Sed.
42.1
4.8
11.4
Bailor & Poehlman 1992
n=48
28.7
6.9
Sed.
32.6
3.8
11.7
Dowdy et al. 1985
n=10; control group
29.0
3.5
Sed.
34.3
5.5
16.0
Seals et al. 1999
n=12
29.8
5.8
O
27.6
5.4
19.6
Steffan et al. 1999
n=20
30.1
4.7
OW
30.9
1.9
6.1
Lennon et al. 1985
n=8; exercise group #2
31.5
4.4
Sed.
32.4
6.4
19.8
Westerlind & Will. 2007
n=24
31.5
5.6
Sed.
33.8
3.9
11.5
Dowdy et al. 1985
n=18; experimental group
32.1
10.7
Sed.
24.7
5.4
21.9
Skinner et al. 2001
n=120; black
32.8
5.9
OW
25.0
3.8
15.2
Nehlsen et al. 1991
n=18; exercise group
33.0
3.3
Sed.
33.7
6.3
18.7
Tanaka et al. 1997
n=11
33.0
4.0
Sed.
29.5
6.4
21.7
Branch et al. 2000
n=18
33.0
4.0
Sed.
33.4
5.6
16.8
Schiller et al. 2001
n=14; Caucasian
34.0
4.0
Sed.
30.3
5.8
19.1
Schiller et al. 2001
n=13; Hispanic
34.5
13.7
Sed.
29.8
6.8
22.8
Skinner et al. 2001
n=226; white
35.9
7.2
OW
30.9
1.9
6.1
Lennon et al. 1985
n=11; exercise group #1
36.0
6.8
OW
25.7
3.8
14.8
Nehlsen et al. 1991
n=18; control group
37.1
4.0
OW
24.7
2.7
10.9
Nieman et al. 1988
n=11; exercise group
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
37.4
8.3
OW
29.6
2.9
9.8
Lennon et al. 1985
n=11; control group
38.0
6.3
ow
25.6
3.2
12.5
Nieman et al. 1988
n=10; control group
38.0
6.3
NF
25.8
3.7
14.3
Shvartz 1996
n=6
39.4
9.5
O
22.4
4.5
20.1
Jakicic et al. 1995
n=121
41.7
7.3
OW-O
27.0
13.4
49.6
CW Hall et al. 2012
n=24
43.0
11.0
OW
24.0
4.6
19.2
KA Snyder et al. 1997
n=15
43.4
4.9
Sed.
23.0
5.7
24.8
E Lind et al. 2005
n=23
44.0
3.0
Sed.
26.8
2.3
8.6
Schiller et al. 2001
n=8; Hispanic
45.0
3.7
Sed.
27.0
4.5
16.7
Tanaka et al. 1997
n=14
45.0
5.0
Sed.
28.0
5.0
17.9
Schiller et al. 2001
n=21; Caucasian
51.7
2.6
OW-O
16.9
2.5
14.8
Earnest et al. 2010
n=82
51.9
4.3
Sed.
20.7
3.6
17.4
JS Green et al. 2001
n=14; estrogen replacement
52.0
6.0
O
22.0
5.5
25.0
Lost citation 2012
n=8
52.0
6.6
OW
23.5
3.4
14.5
JL Robbins et al. 2009
n=12
53.0
4.0
Sed.
25.5
5.8
22.7
Schiller et al. 2001
n=15; Hispanic
54.0
4.5
Sed.
26.2
3.6
13.7
Tanaka et al. 1997
n=20
54.0
5.0
Sed.
26.2
3.9
14.9
Schiller et al. 2001
n=26; Caucasian
54.0
12.5
OW-O
27.0
7.0
25.9
Dionne et al. 2001
n=15; carriers of 2 genotypes
55.0
1.9
Sed.
25.5
5.7
22.4
Zarins et al. 2009
n=10; postmenopausal
56.6
6.6
OW-O
16.0
2.9
18.1
Church et al. 2007
n=103;sedentary group 3
56.3
6.0
OW
16.1
3.0
18.6
Sisson et al. 2009
n=88; Sed. Group 3
56.7
6.4
OW
14.9
2.3
15.4
Sisson et al. 2009
n=84; Sed. Group 2
56.8
12.2
OW-O
24.7
6.8
27.5
Dionne et al. 2001
n=16; carriers of 1 genotype
56.9
2.7
Sed.
21.1
3.9
18.5
JS Green et al. 2001
n=9; no estrogen replacement
57.2
5.8
OW-O
15.6
2.9
18.6
Church et al. 2007
n=102; sedentary control group
57.3
6.6
OW-O
14.9
2.4
16.1
Church et al. 2007
n=104;sedentary group 2
57.5
1.5
OW-O
16.0
2.6
16.3
Earnest et al. 2010
n=76
57.7
6.6
OW-O
15.5
2.9
18.7
Church et al. 2007
n=155;sedentary group 1
57.8
6.4
O-Sed.
15.3
2.0
13.1
AN Jordan et al. 2005
n=27; Exercise group #2
58.0
4.9
OW
19.8
3.9
19.7
AS Ryan et al. 2000
n=24
58.0
6.5
OW
15.4
3.0
19.5
Sisson et al. 2009
n=138; Sed. Group 1
58.3
5.9
O-Sed.
15.6
2.3
14.7
AN Jordan et al. 2005
n=60; exercise group #1
59.0
4.0
Sed.
27.5
4.5
16.4
Fielding et al. 1999
n=17; protocol test 1
60.0
7.0
O-Sed.
15.0
2.8
18.7
AN Jordan et al. 2005
n=24; exercise group #3
60.0
8.0
O-Sed.
21.1
1.6
7.6
Tanaka et al. 1998
n=9
62.0
4.5
Sed.
22.6
4.0
17.7
Seals et al. 1999
n=20
64.0
4.0
Sed.
22.2
3.1
14.0
Ogawa et al. 1992
n=14
64.0
4.0
Sed.
22.4
4.8
21.4
Tanaka et al. 1997
n=16; carriers of 1 genotype
64.0
4.0
Sed.
21.5
4.7
21.9
Schiller et al. 2001
n=18; Caucasian
64.0
5.0
OW-O
36.3
8.2
22.6
Nicklas et al. 2003
n=29
64.4
3.2
Sed.
22.0
2.2
10.0
MJ Turner et al. 1999
n=10
64.9
4.2
OW-O
14.8
2.4
16.2
Earnest et al. 2010
n=93
65.0
4.0
Sed.
20.7
2.9
14.0
Schiller et al. 2001
n=5; Hispanic
66.0
6.0
OW
20.2
3.6
17.8
JL Thompson et al. 1997
n=40; postmenopausal
66.0
4.0
Sed.
19.9
3.1
15.6
Kohrt et al. 1998
n=112
69.2
11.0
Sed.
20.3
7.6
37.4
Wlund et al. 2008
n=6
67.0
4.9
Sed.
16.2
3.5
21.6
White et al. 1998
n=60; exercise group #1
70.0
8.0
Sed.
17.6
4.5
25.6
Croley et al. 2005
n=9
-------
Table 1. Estimates of V02 MAX in females seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age/Range
Health
COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
72.0
3.0
Inact.
21.4
3.9
18.2
DiPietro et al. 2006
n=9; exercise group 3
72.0
8.0
Sed.
19.1
3.6
18.8
EP Weiss et al. 2006
n=83
73.0
3.0
Inact.
21.2
3.4
16.0
DiPietro et al. 2006
n=9; exercise group 2
74.7
3.4
Sed.
12.1
3.7
30.6
Church et al. 2008
n=20
75.0
5.0
Inact.
18.3
4.2
23.0
DiPietro et al. 2006
n=7; exercise group 1
72.0
8.0
Sed.
19.1
3.6
18.8
EP Weiss et al. 2006
n=83; non-smokers
b. Complete age statistics are not provided
17-18
Sed.
26.2
-
-
MA Edwards 1974
n=6
17-18
Sed.
38.2
0.1
0.3
Kamon & Pandolf 1972
n=?
17-22
Sed.
38.4
4.7
12.2
Kearney et al. 1976
n=14; exercise group 1
17-22
Sed.
38.5
5.0
13.0
Kearney et al. 1976
n=13; exercise group 2
18-20
Sed.
27.3
-
-
MA Edwards 1974
n=6
18-23
OW
43.6
6.8
15.6
O'Leary & Stav. 2012
n=9
25-34
Sed.
26.1
6.4
24.5
Bruce 1984b
n=?
35-44
Sed.
34.1
3.2
9.4
Bruce 1984b
n=?
45-54
Sed.
23.1
4.0
17.3
Bruce 1984b
n=?
55-64
Sed.
20.2
4.3
21.3
Bruce 1984b
n=?
Females: Health & Other Issues
21.9
2.7
MR
30.8
7.7
25.0
Draheim et al. 1999
n=13
22.3
6.9
CC
19.8
5.1
25.8
AM Miller et al. 2012
n=34; 12 y mean post-treatment
29.3
6.3
CHD
22.4
5.3
23.7
GKLuiet al. 2011
n=40; pregnant Cl=0.61 (0.15)
29.3
6.3
CHD
26.1
5.2
19.9
GKLuiet al. 2011
n=38; pregnant Cl=0.89 (0.07)
30.4
6.7
MR
28.1
7.1
25.3
Fernhall et al. 1996
n=20; no DS
31.7
7.2
DS
22.2
4.3
19.4
Fernhall et al. 1996
32.9
5.8
Pg
21.3
2.5
11.7
Szymanski & Satin 2012
n=15; non-exercisers
33.3
7.1
Pg
26.9
5.2
19.3
Soultankis et al. 1966
n=20
41.8
9.7
MS
21.7
6.0
27.6
Petruzzello & Motl 2011
n=25
43.6
7.8
MS
22.1
5.8
26.2
Motl & Fernhall 2012
n=32; relapsing-remitting MS
49.9
11.6
Ml
19.9
4.8
24.1
Pinkstaff et al. 2011
n=146
50.6
8.7
BC
22.0
4.0
18.2
Tosti et al. 2011
n=7
52.5
11.5
CHF
21.9
2.7
12.3
Duscha et al. 2001
n=13
59.2
11.0
HP
28.2
7.7
27.3
Shultz et al. 2010
n=49
62.0
6.6
CAD
21.7
3.3
15.2
Sheldahl et al. 1996
n=11
62.0
11.0
HP
14.5
3.9
26.9
Ades et al. 2006
n=815; multicenter study
63.7
5.8
COPD
11.3
3.0
26.5
Carter et al. 1994
n=58; severe airflow limitation
64.8
6.4
COPD
17.0
5.6
32.9
Carter et al. 1994
n=23; mild airflow limitation
65.0
5.2
COPD
13.9
3.5
25.2
Carter et al. 1994
n=42; moderate airflow limitation
69.0
6.0
HP
17.0
5.0
29.4
Ades et al. 1993
n=15
72.9 6.1 Cardio 14.2 2.9 20.4 Ades et al. 2005 n=21
Abbreviations:
A Asthmatic
AA African-American
Act Active (but non-athletes)
Ath Athletes
BC Breast cancer patient
BLSA Baltimore Longitudinal Study of Aging
CA Caucasian
Abbreviations:
CC Cancer survivor
CF Cystic Fibrosis
CHD Congenital heart disease
CI Chronotropic Index
CHF Chronic heart failure
COPD Chronic obstructive pulmonary disease
DS Down syndrome
-------
Abbreviations:
EX Exercisers (regular)
Fit Very active healthy exercisers
Frail Mild-to-moderate frailty
H Healthy
HP heart patients
Inact. Inactive (not necessarily sedentary)
N Normal (mostly healthy)
NF Not fit; poor fitness
NG National Guard (all types)
Abbreviations:
NS Not specified
O Obese
OW Overweight
Pg Pregnant
Sed Sedentary
Notes:
1.0 The study investigated V02 at sea level
(shown) and at altitude for two menstrual
phases;
There was no statistical differences between
phases; luteal: 46.3 ± 5.6 mL/kg-min
2.0 V02maxwas measured in 3 phases of the
menstrual cycle. V02max in the other 2
phases was slightly higher (not statistically
significant).
Ml Myocardial iscemia
^ Mentally retarded (some with Down
syndrome)
MS Multiple sclerosis
Table 2. Estimates of V02 MAX in males seen in the literature
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean SD Status Mean SD (%) Citation Comment
Males: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for age
7.3
1.0
H
47.8
9.1
19.0
Treuth et al. 2003
n=6
7.5
0.3
N
52.4
19.3
36.8
Livingstone et al. 1992
n=6
8.3
1.1
N
52.7
4.2
8.0
Cureton et al. 1997
n=27; mixed fitness group
8.3
1.9
NS
50.7
9.6
18.9
DM Rogers et al. 1995a
n=15
8.8
0.7
N
45.9
9.8
21.4
McMurray et al. 2003a
n=403; CA
9.0
0.7
NS
55.0
6.3
11.5
DM Rogers et al. 1995b
n=21
9.0
0.9
N
44.5
8.1
18.2
McMurray et al. 2003a
n=87; AA
9.1
1.0
NS
49.6
6.4
12.9
Cureton et al. 1995
n=200; multicenter study
9.3
0.2
N
48.9
12.9
26.4
Livingstone et al. 1992
n=5
9.4
1.7
NS
42.7
5.5
12.9
Gilliam et al. 1977
n=32
9.5
0.7
N
39.0
6.4
16.4
Becker & Vaccaro 1983
n=13; experimental group
9.6
2.6
NS
52.0
9.3
17.9
Fahey et al. 1979
n=7;Tanner 1
9.8
0.6
H
35.4
7.5
21.2
KE Swain et al. 2010
n=20
9.9
1.0
NS
46.7
8.0
17.1
lannotti et al. 2004
n=10
10.0
0.6
N
41.7
5.7
13.7
Becker & Vaccaro 1983
n=13; control group
10.0
1.0
H
45.6
4.0
8.8
Rogowski et al. 2012
n=19
10.0
2.0
N
42.0
6.0
14.3
Cooper et al. 1984
n=37
10.1
0.8
N
44.4
10.1
22.7
McMurray et al. 2003a
n=381; CA
10.2
1.2
N
48.7
5.5
11.3
Kanaley & Boileau 1988
n=10
10.3
0.3
N
44.6
9.4
21.1
McMurray et al. 2003a
n=79; AA
10.3
2.5
N
53.2
15.8
29.7
Chausow et al. 1984
n=8
10.4
0.3
H
47.2
6.0
12.7
N Hopkins et al. 2011
n=46 (Summer only)
10.4
1.1
N
45.9
2.6
5.7
Mayers & Gutin 1979
n=8
10.5
0.7
N
47.4
5.4
11.4
Mahon et al. 1997b
n=9
10.5
1.2
N
48.1
6.0
12.5
McMurray et al. 1998b
n=15
10.6
1.0
N
50.0
9.0
18.0
Janz et al. 1998
n=61
10.6
1.3
H
47.5
6.4
13.5
Roemmich et al. 1998
n=18
10.6
2.3
N
53.0
4.3
8.1
Skinner et al. 1971
n=26; treadmill protocol #3
10.7
0.6
N
52.3
6.0
11.5
JD Brown et al. 2002
n=16
10.7
0.7
NS
39.4
7.1
18.0
Fahey et al. 1979
n=7;Tanner 2
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
10.7
2.6
N
51.6
7.0
13.6
Skinner et al. 1971
n=31; treadmill protocol #1
10.8
0.4
H
38.4
2.4
6.3
Haffor et al. 1990
n=5
10.9
0.3
N
56.8
6.8
12.0
Paterson et al. 1986
n=19
10.9
1.3
H
50.9
8.3
16.3
Rowland et al. 1997 MSSE
n=15
10.9
2.6
N
50.0
5.5
11.0
Skinner et al. 1971
n=26; treadmill protocol #2
11.0
0.8
N
52.1
8.5
16.3
Mahon et al. 1997a
n=15; Tanner 1
11.1
2.4
H
44.1
7.9
17.9
JK Murphy et al. 1988
n=60; CA
11.1
3.0
H
42.6
8.6
20.2
JK Murphy et al. 1988
n=64; AA
11.2
2.6
H
37.4
12.1
32.4
Boas et al. 1999
n=23; control group
11.4
1.2
N
51.8
5.4
10.4
Mahon et al. 1997a
n=11; Tanner 2
11.6
1.0
N
49.0
6.0
12.2
Janz et al. 1998
n=58
11.6
1.8
N
45.2
5.0
11.1
Rogowski et al. 2012
n=19
11.8
2.8
N
44.3
7.4
16.7
Golden et al. 1991
n=101
12.0
0.3
N
60.0
5.7
9.5
Paterson et al. 1986
n=19
12.1
0.8
N
40.5
7.0
17.3
McMurray et al. 2003a
n=403; CA
12.3
0.9
N
39.9
6.9
17.3
McMurray et al. 2003a
n=87; AA
12.6
1.0
N
46.0
8.0
17.4
Janz et al. 1998
n=61
12.7
0.3
N
51.8
5.1
9.8
Livingstone et al. 1992
n=5
12.7
1.0
N
53.8
5.7
10.6
Cureton et al. 1997
n=27; mixed fitness group
12.8
1.1
N
48.7
5.3
10.9
Boileau et al. 1977
n=21; treadmill value
12.8
1.8
NS
48.7
9.1
18.7
Mahar et al. 2011
n=48; validation sample
12.8
1.8
NS
49.5
8.2
16.6
Mahar et al. 2011
n=34; cross-validation sample
12.8
2.1
N
52.5
2.7
5.1
Kwee & Wilmore 1990
n=181; "above average fitness"
12.9
0.3
N
60.9
5.3
8.7
Paterson et al. 1986
n=19
12.9
1.2
NS
41.3
9.1
22.0
Fahey et al. 1979
n=6; Tanner 3
13.0
0.7
N
48.5
8.3
17.1
Boiarskaia et al. 2011
n=61
13.1
0.8
N
42.8
8.2
19.2
McMurray et al. 2003
n=349; CA
13.2
1.2
N
55.7
5.0
9.0
Mahon et al. 1997a
n=8; Tanner 3
13.3
0.8
N
40.8
7.3
17.9
McMurray et al. 2003
n=72; AA
13.4
1.9
NS
53.4
5.9
11.0
DM Rogers et al. 1995b
n=15
13.6
1.0
N
48.0
7.0
14.6
Janz et al. 1998
n=56
13.6
1.4
N
44.4
2.7
6.1
Kwee & Wilmore 1990
n=163; "below average fitness"
13.6
1.5
NS
50.9
6.6
13.0
Cureton et al. 1995
n=99; multicenter study
13.7
0.5
N
51.8
4.8
9.3
Kanaley & Boileau 1988
n=10
13.9
0.3
N
61.1
5.7
9.3
Paterson et al. 1986
n=19
14.1
0.7
N
42.4
9.0
21.2
McMurray et al. 2003
n=312; CA
14.3
0.8
N
41.0
8.3
20.2
McMurray et al. 2003
n=66; AA
14.3
1.0
H
52.8
9.2
17.4
Treuth et al. 2003
n=6
14.6
1.0
N
46.0
7.0
15.2
Janz et al. 1998
n=53; exercise group
14.6
1.3
H
51.4
5.6
10.9
Roemmich et al. 1998
n=11
14.7
1.0
N
54.4
3.4
6.3
Mahon et al. 1997a
n=9; tanner 4
14.9
0.3
N
63.5
6.4
10.1
Paterson et al. 1986
n=19
15.1
0.7
N
42.6
10.8
25.4
McMurray et al. 2003
n=297; CA
15.1
1.0
NS
46.4
7.7
16.6
Murray et al. 1993
n=43
15.1
2.6
NS
45.2
11.3
25.0
Fahey et al. 1979
n=3; Tanner 5
15.3
1.1
N
44.7
5.3
11.9
Rogowski et al. 2012
n=20
15.4
0.4
N
51.3
2.9
5.7
Livingstone et al. 1992
n=3
15.4
0.8
N
41.1
10.1
24.6
McMurray et al. 2003
n=62; AA
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
15.4
1.8
NS
48.4
6.9
14.3
Fahey et al. 1979
n=5;Tanner 4
15.8
0.8
N
52.8
6.4
12.1
Cureton et al. 1997
n=38; mixed fitness group
15.9
1.1
ND
43.7
10.6
24.3
Gutin et al. 2005
n=94; black adolescents
16.0
1.0
NS
50.0
8.0
16.0
Cooper et al. 1984
n=33
16.2
1.3
NS
47.6
8.8
18.5
Gutin et al. 2005
n=102; white adolescents
17.9
1.6
NS
52.9
5.0
9.5
Dill et al. 1972
n=11
18.7
0.6
N
52.4
4.6
8.8
Wolfe etal. 1976
n=9; lean group
19.0
1.9
NS
43.5
4.7
10.8
Fox et al. 1975
n=26; exercise group 1
19.2
0.4
N
41.4
4.0
9.7
Burke 1977
n=7; control group
19.2
0.4
N
41.8
4.9
11.7
Burke 1977
n=9; experimental group
19.2
2.2
NS
44.2
4.9
11.1
Fox et al. 1975
n=23; exercise group 2
19.3
0.5
N
44.1
5.7
12.9
Golden & Vaccaro 1984
n=9 group 3
19.3
2.3
NS
46.3
8.0
17.3
Dolgener et al. 1994
n=96; validation group
19.4
1.3
NS
44.3
5.0
11.3
Dolgener et al. 1994
n=33; cross-validation group
19.5
0.9
H
59.6
3.5
5.9
Kasch et al. 1986
n=11
19.6
2.7
NS
45.1
3.5
7.8
Fox et al. 1975
n=16; control group
19.7
2.1
H
49.2
9.8
19.9
K. Sell et al. 2008
n=12
19.7
2.7
N
42.9
8.1
18.9
Golden & Vaccaro 1984
n=9; group 2
19.8
1.1
N
51.3
5.2
10.1
Kaminski et al. 1993
n=15
19.8
3.9
NS
43.2
4.8
11.1
Fox et al. 1975
n=10; exercise group 3
19.9
0.9
NS
55.8
3.5
6.3
Harms et al. 1995
n=8; low fat content
20.6
1.3
N
48.6
4.4
9.1
Browning et al. 2006
n=10
20.8
1.7
H
45.1
6.7
14.9
McComb et al. 2006
n=19
20.8
2.2
N
52.6
6.3
12.0
Wiley & Shaver 1972
n=35
20.8
2.4
NS
45.6
9.0
19.7
Lepp et al. 2013
n=22
20.9
1.9
H
42.0
7.2
17.1
Deschenes et al. 2006
n=9
21.0
0.9
NS
48.2
4.7
9.8
Ziemann et al. 2011
n=11; control group
21.0
9.3
NS
30.9
4.9
15.9
AM Miller et al. 2013
n=19; cancer siblings
21.1
1.5
N
54.3
4.2
7.7
Kanaley & Boileau 1988
n=10
21.2
1.6
N
54.8
4.9
8.9
Fl Katch etal. 1974
n=50; treadmill data
21.4
1.4
NS
53.9
6.4
11.9
Cureton et al. 1995
n=22; multicenter study
21.4
2.4
H
46.2
6.2
13.4
V Katch & Henry 1972
n=35
21.6
1.1
NS
50.1
3.1
6.2
Ziemann et al. 2011
n=10; exercise group
21.8
3.4
H
46.2
4.7
10.2
Baldwin et al. 2000
n=6; control group
22.5
2.0
N
52.5
5.1
9.7
JD Brown et al. 2002
n=21
22.5
2.6
NS
52.9
4.7
8.9
JA Davis et al. 1976
n=39; treadmill; Note 2
22.6
2.5
N
44.7
9.9
22.1
AT Peterson et al. 1999
n=16
22.7
3.7
N
32.7
7.6
23.2
McDowell et al. 2003
n=16
22.9
2.5
NS
43.4
4.2
9.7
Rogowski et al. 2012
n=19
23.0
3.1
NS
46.5
7.0
15.1
JD George 1996
n=50
23.0
4.0
N
48.3
12.8
26.5
DW Hill 1996
n=12
23.0
4.0
N
50.1
8.4
16.8
McMiken 1976
n=30
23.0
5.0
N
44.0
8.3
18.9
J Kang et al. 2007
n=11
23.2
7.4
N
70.7
12.0
17.0
Trappe et al. 1996
n=15
23.3
2.8
H
58.9
6.7
11.4
Porcari et al. 1997
n=16; control group
23.6
4.8
N
43.7
10.0
22.9
Kendall et al. 2009
n=42
23.6
6.6
NS
55.0
5.3
9.6
DM Rogers et al. 1995a
n=15
24.0
2.6
N
45.7
7.9
17.3
CM Thomas et al. 1999
n=7; exercise group 3
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
24.0
3.0
N
47.7
10.5
22.0
Kaminsky et al. 1987
n=14;ex group #1
24.0
3.0
N
40.2
6.3
15.7
Proctor et al. 2005
n=11
24.0
4.9
H
45.0
9.8
21.8
Zappe et al. 1996
n=6
24.0
9.0
NS
49.9
8.1
16.2
Swain et al. 1994
n=81
24.1
5.0
N
41.2
8.3
20.1
Gonzales & Scheun. 2006
n=11
24.6
3.4
N
35.5
3.1
8.7
KR Segal et al. 1985
n=10
24.8
2.9
H
42.2
3.7
8.8
JF Nichols et al. 1990
n=9
25.0
1.0
H
49.3
3.9
7.9
Davey et al. 1995
n=6
25.0
2.0
N
47.6
4.8
10.1
Coggan1993
n=6
25.0
2.2
N
45.3
3.1
6.8
CM Thomas et al. 1999
n=5; exercise group 1
25.0
2.8
N
47.9
5.4
11.3
Kohrt et al. 1991
n=28
25.0
3.0
NS
44.1
5.7
12.9
Proctor et al. 1995
n=6
25.0
3.0
H
57.1
10.5
18.4
Pettitt et al. 2008
n=7
25.0
3.2
N
55.5
6.6
11.9
BE Hunt et al. 2001
n=10; untrained
25.3
2.0
N
50.2
6.2
12.4
Mahon et al. 1997b
n=9
25.6
1.6
H
56.8
10.7
18.8
K Sell et al. 2008
n=7
26.0
2.6
N
46.8
3.2
6.8
CM Thomas et al. 1999
n=7; control group
26.0
5.1
H
51.8
11.1
21.4
Swain et al. 1998
n=26
26.0
6.0
N
43.0
4.0
9.3
JO Hill et al. 1984
n=4; low-V02max group
26.3
3.8
N
51.4
4.1
8.0
Bransford & Howley 1977
n=10; untrained
26.4
6.4
N
45.0
6.2
13.8
BJ Sawyer et al. 2010
n=23
26.4
8.5
NS
46.0
11.3
24.6
J Kang et al. 1999
n=17
26.6
6.3
N
48.3
7.4
15.3
Warr et al. 2013
n=76; pre-deployed NG
26.6
7.4
N
34.8
9.2
26.4
McMurray et al. 1998a
n=1396
26.8
6.4
N
42.2
6.1
14.5
Blessinger et al. 2009
n=19
26.9
3.8
N
43.0
5.1
11.9
Bullough et al. 1995
n=10
27.0
3.0
NS
42.9
3.7
8.6
Horton et al. 1998
n=6; untrained
27.0
7.1
N
40.9
8.7
21.3
Sheaff et al. 2010
n=26
27.0
7.8
NS
48.0
4.6
9.6
JQ Zhang et al. 1998
n=21
27.3
5.8
N
56.1
6.9
12.3
Latin & Elias 1993
n=28
27.6
3.8
H
46.1
8.5
18.4
Maresh et al. 1992
n=8
27.6
5.6
N
42.7
5.9
13.8
Katch & Katch 1973
n=75
27.8
5.8
H
46.0
7.0
15.2
LO Schulz et al. 1991
n=43
28.0
2.0
N
50.6
6.5
12.8
Kaminsky et al. 1987
n=10; ex. Group #2
28.0
7.0
N
29.0
5.6
19.3
Beere et al. 1999
n=13
28.4
4.8
H
41.8
10.4
24.9
Byrne et al. 1996
n=15
29.0
1.7
N
39.6
4.3
10.9
CM Thomas et al. 1999
n=3; exercise group 2
29.0
2.4
N
44.0
7.1
16.1
Tankersley et al. 1991
n=7
29.0
4.9
H
45.0
10.0
22.2
Sheffield-Moore et al. 2004
n=6
29.2
7.9
NS
49.9
7.5
15.0
L Kravitz et al. 1997
n=9
30.7
5.1
H
47.4
7.5
15.8
Rowland et al. 1997 MSSE
n=16
31.0
6.6
N
38.3
6.3
16.4
KR Segal et al. 1990
n=11
31.4
3.9
NS
41.9
4.3
10.3
Foxet al. 1975
n=10; exercise group 4
33.0
4.0
H
40.0
7.0
17.5
Fleg et al. 1993
n=21
33.8
6.2
H
53.8
7.4
13.8
Horton et al. 2006
n=13
34.0
3.0
N
37.2
8.0
21.5
Proctor et al. 2005
n=11
36.0
2.6
N
38.7
6.3
16.3
Kastello et al. 1993
n=7
36.5
2.3
H
40.6
7.3
18.0
Nelson et al. 2010
n=141; 30-39 cohort
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
37.0
10.0
N
47.5
10.3
21.7
Convertino & Ludwig 2000
n=66
37.8
3.4
N
39.7
2.0
5.0
Shvartz 1996
n=6
38.0
12.8
H
44.0
9.1
20.7
Engels et al. 1988
n=104
39.0
6.0
N
41.7
3.7
8.9
Spielmann et al. 2011
n=102; multi-site study
39.0
9.0
H
47.0
6.0
12.8
Donovan et al. 2009
n=182
39.0
15.0
H
40.0
9.0
22.5
Ardestani et al. 2011
n=92
39.1
7.4
NS
34.8
9.0
25.9
JA Davis et al. 1999
n=7; control
42.0
14.0
H
33.0
8.6
26.1
NP Greene et al. 2011
n=24
43.0
7.2
NS
31.0
7.2
23.2
JA Davis et al. 1999
n=9; experimental
44.6
2.8
H
38.4
7.0
18.2
Nelson et al. 2010
n=398; 40-49 cohort
44.6
12.5
N
36.2
7.6
21.0
Talbot et al. 2002
n=522; BLSA
45.3
8.9
N
46.7
8.5
18.2
Trappe et al. 1996
n=15
46.0
4.0
N
34.6
6.6
19.1
Proctor et al. 2005
n=12; exercise group C
46.2
2.2
H
39.2
5.5
14.0
Dehn & Bruce 1972
n=26
46.4
11.9
NS
42.4
10.5
24.8
Kline et al. 1987
n=83; cross-validation group
46.5
10.7
NS
42.2
9.8
23.2
Kline et al. 1987
n=82; validation group
47.7
7.3
N
38.8
5.4
13.9
FWKasch 1984
n=83
47.8
8.5
NS
37.5
6.7
17.9
Stefanick et al. 1998
n=190
48.7
6.0
H
36.9
7.9
21.4
Byrne et al. 1996
n=25
50.0
10.7
N
36.5
8.1
22.2
LT Weir et al. 2006
n=2417
50.1
5.8
N
35.5
7.9
22.3
McDonough et al. 1970
n=16
51.9
16.0
H
35.3
8.4
23.8
Fleg et al. 2005
n=435
52.0
10.0
N
36.2
4.4
12.2
Heath et al. 1981
n=9; lean, untrained
52.1
16.7
N
34.1
8.4
24.6
Talbot et al. 2000
n=619; BLSA
53.5
2.8
H
35.2
6.5
18.5
Nelson et al. 2010
n=235; 50-59 cohort
54.0
3.0
N
32.5
5.7
17.5
Proctor et al. 2005
n=101
54.6
3.0
H
37.7
4.5
11.9
Dehn & Bruce 1972
n=21
56.2
4.1
NS
29.9
5.0
16.7
DR Young et al. 1994
n=197
58.0
3.0
N
32.3
4.1
12.7
Proctor et al.1995
n=6
60.0
4.7
N
22.5
5.2
23.1
Carter et al. 1994
n=13; control group
60.6
6.3
N
20.4
6.3
30.9
Duscha et al. 2001
n=10
61.0
2.8
N
42.0
4.8
11.4
Kastello et al. 1993
n=8
61.0
3.0
NS
33.4
7.6
22.8
Hagberg et al. 1998
n=22
61.6
2.7
H
31.6
4.2
13.3
Nelson et al. 2010
n=42; 60-69 cohort
61.8
8.2
N
33.5
6.8
20.3
Guderian et al. 2010
n=10
62.0
6.0
N
34.9
3.3
9.5
Sheldahl etal. 1996
n=9
62.8
1.0
H
27.4
3.7
13.5
Seals et al. 1984
n=8
63.0
3.0
N
29.2
5.7
19.5
Coggan1993
n=6
63.0
3.0
N
26.5
3.5
13.2
Proctor et al. 2005
n=10
63.0
6.0
H
30.5
5.0
16.4
Fleg et al. 1995b
n=23; control group
63.0
7.3
H
35.2
5.4
15.3
BE Hunt etal. 2001
n=6; untrained
63.7
3.1
N
27.5
4.2
15.3
Kohrt et al., 1998
n=53; experimental group
63.9
1.7
H
31.3
6.9
22.0
Dehn & Bruce 1972
n=10
64.0
4.0
N
17.7
6.5
36.7
Beere et al. 1999
n=10
64.8
3.6
N
28.3
4.3
15.2
Kohrt et al., 1998
n=19; control group
65.0
2.0
H
31.5
5.6
17.8
Davy et al. 1995
n=6
65.0
5.0
H
36.0
8.0
22.2
Gonzales et al. 2011
n=19
65.0
5.0
NS
27.1
5.8
21.4
Hagberg et al. 1998
n=21
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
66.0
3.0
N
32.9
5.6
17.0
Tankersley et al. 1991
n=6
67.0
1.2
N
27.4
4.0
14.6
CM Thomas et al. 1999
n=4; exercise group 2
67.0
1.7
N
23.9
1.0
4.2
CM Thomas et al. 1999
n=3; exercise group 3
67.0
2.4
H
38.0
7.3
19.2
Zappe et al. 1996
n=6
67.7
3.7
N
27.0
7.2
26.7
McDowell et al. 2003
n=18
68.0
2.6
N
29.9
7.4
24.7
CM Thomas et al. 1999
n=7; control group
68.7
5.1
N
27.7
3.7
13.4
Panton et al. 1996
n=19
69.0
2.4
H
39.0
7.3
18.7
Sheffield-Moore et al. 2004
n=6
69.0
2.6
N
24.8
3.4
13.7
CM Thomas et al. 1999
n=7; exercise group 1
69.5
6.3
NS
20.7
6.7
32.4
Ainsworth et al. 1997
n=10
70.1
3.8
H
28.9
4.9
17.0
Fehling et al. 1999
n=44
70.6
9.0
NS
22.7
5.5
24.2
Fiser et al. 2010
n=25
71.4
6.3
N
28.2
5.0
17.7
Parise et al. 2004
n=95
72.1
7.6
N
23.7
4.0
16.9
Simonsick et al. 2006
n=56
72.5
4.9
N
21.7
4.8
22.1
Peterson et al. 2003
n=59
72.9
5.1
H
24.2
5.4
22.3
Deschenes et al. 2006
n=9
72.9
7.0
H
27.1
5.5
20.3
Byrne et al. 1996
n=35
73.5
5.9
N
27.6
6.0
21.7
Talbot et al. 2002
n=167; BLSA
74.0
4.0
N
24.6
5.6
22.8
Proctor et al. 2005
n=141; 30-39 cohort
74.0
5.0
H
29.0
5.0
17.2
Fleg et al. 1993
n=16; control group
74.7
2.8
N
23.5
3.8
16.2
Perini et al. 2000
n=12; 18.6-31.4
75.5
2.8
N
27.0
2.5
9.3
Benestad 1965
n=13
b. Complete age statistics are not provided
6.0
-
N
39.1
2.8
7.2
DW Morgan et al. 1999
n=15
12-13
-
NS
43.0
-
Pate et al. 2006 APAM
50th percentile (CI: 42.1-44.5)
14-15
-
NS
45.8
-
Pate et al. 2006 APAM
50th percentile (CI: 44.2-48.1)
16-17
-
NS
46.2
-
Pate et al. 2006 APAM
50th percentile (CI: 45.1-47.3)
18-19
-
NS
46.3
-
Pate et al. 2006 APAM
50th percentile (CI: 45.2-47.6)
18-21
-
N
58.3
3.5
6.0
WL Daniels et al. 1982
n=11; Army cadets
19-47
-
H
47.9
6.0
12.5
Lukaski et al. 1989
n=16; treadmill value
20's
-
NS
43.8
9.4
21.5
Fleq et al. 1995a
n=13
20-29
-
N
44.7
3.9
8.7
Mitchell et al. 1958
n=36
28.6
-
NS
50.7
4.2
8.3
Diaz et al. 1978
n=7; treadmill only
20-39
-
N
45.8
4.8
10.5
Milesis et al. 1976
n=16; control group
20-39
-
N
45.0
7.4
16.4
Milesis et al. 1976
n=15; exercise group A
20-39
-
N
41.5
5.2
12.5
Milesis et al. 1976
n=17; exercise group B
20-39
-
N
45.4
6.5
14.3
Milesis et al. 1976
n=12; exercise group C
30's
-
NS
40.3
7.6
18.9
Fleq et al. 1995a
n=30
30-39
-
N
35.4
3.3
9.3
Mitchell et al. 1958
n=8
40's
-
NS
33.5
4.9
14.6
Fleq et al. 1995a
n=12
40-44
-
H
40.5
4.7
11.6
McDonough et al. 1970
n=10
40-49
-
N
35.4
3.3
9.3
Mitchell et al. 1958
n=8
44.4
-
H
34.5
5.2
15.1
Blumenthal et al. 1988
n=18
45-49
-
H
38.4
5.3
13.8
McDonough et al. 1970
n=24
45-59
-
H
31.0
5.0
16.1
DA Meyers et al. 1991
n=68
50's
-
NS
35.7
6.6
18.5
Fleq et al. 1995a
n=25
50-54
-
H
37.5
5.3
14.1
McDonough et al. 1970
n=20
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean SD Status Mean SD (%) Citation Comment
55- 59 - N 30.3 5.9 19.5 Hollenberg et al. 1998 n=79
55-59
-
H
36.2
5.7
15.7
McDonough et al. 1970
n=19
60's
-
NS
30.4
8.2
27.0
Fleq et al. 1995a
n=26
60-64
-
N
29.7
6.1
20.5
Hollenberg et al. 1998
n=66
60-64
-
H
32.6
4.7
14.4
McDonough et al. 1970
n=9
60-79
-
H
27.0
5.0
18.5
DA Meyers et al. 1991
n=64
65-69
-
N
26.9
5.8
21.6
Hollenberg et al. 1998
n=73
66.0
-
N
28.5
6.0
21.1
Hollenberg et al. 2006
n=253; exercise group #1
65-69
-
H
27.7
4.2
15.2
McDonough et al. 1970
n=3
70's
-
NS
30.2
5.6
18.5
Fleq et al. 1995a
n=14
70-74
-
N
26.5
4.3
16.2
Hollenberg et al. 1998
n=81
70.0
-
N
23.8
5.3
22.3
Hollenberg et al. 2006
n=189; exercise group #2
75-79
-
N
22.4
3.2
14.3
Hollenberg et al. 1998
n=42; 60-69 cohort
80's
-
NS
23.2
5.8
25.0
Fleq et al. 1995a
n=3
80-84
-
N
22.1
2.5
11.3
Hollenberg et al. 1998
n=18
>85
-
N
18.3
2.1
11.5
Hollenberg et al. 1998
n=4
Males: Active, Fit, or Athlete
a. Mean & SD statistics are provided for age
9.4
1.7
Act
42.7
5.5
12.9
Gilliam et al. 1977
n=32; exercisers
10.2
1.3
Act
49.0
5.7
11.6
Sady & Katch 1981
n=21
10.5
1.1
Ath
56.6
2.0
3.5
Mayers & Gutin 1979
n=8; cross-country runners
10.8
2.2
Fit
61.0
2.4
3.9
Kwee & Wilmore 1990
n=24
17.3
0.9
Ath
73.4
4.9
6.7
AS Cole et al. 2006
n=15; cross-country runners
19.5
0.8
Ath
66.6
3.7
5.6
McMiken & Daniels 1976
n=8
19.7
1.1
Fit
57.4
3.6
6.3
Ribisl & Kachadorian 1969
n=11
19.9
2.7
Act
50.7
4.8
9.5
Sharp et al. 2002
n=122
20.0
1.6
Ath
69.3
2.8
4.0
Bransford & Howley 1977
n=10; distance runners
20.0
4.0
Fit
62.0
6.0
9.7
JO Hill et al. 1984
n=4; high-V02 group
20.4
2.0
Ath
50.2
5.3
10.6
Vander et al. 1984
n=7; national-class fencers
20.1
1.6
Act
50.4
4.0
7.9
Dolezal & Potteiger 1998
n=10
20.1
1.6
Act
50.7
5.8
11.4
Dolezal & Potteiger 1998
n=10
20.1
1.6
Act
52.3
4.4
8.4
Dolezal & Potteiger 1998
n=10
20.5
1.8
Ath
59.2
3.9
6.6
Peyer et al. 2011
n=13; hockey forwards
20.5
1.9
Fit
56.0
6.8
12.1
Jeans et al. 2011
n=6; range of V02: 51.5-64.0
20.5
2.1
Ath
56.2
1.9
3.4
Peyer et al. 2011
n=11; hockey defensemen
20.7
2.8
Act
53.8
5.3
9.9
Nindl et al. 1998
n=20; Army personnel
21.0
2.0
Fit
61.5
7.7
12.5
Darling et al. 2005
n=10; range=43-71
21.0
3.3
Act
44.5
5.0
11.2
Proctor et al. 2003
n=11
21.1
1.7
Ath
63.0
7.0
11.1
SR Hopkins et al. 1998
n=6
21.8
3.4
Act
50.6
6.2
12.3
Sharp et al. 2002
n=171
22.0
2.0
Ath
69.0
2.3
3.3
Heath et al. 1981
n=16
22.1
2.4
Fit
56.5
3.2
5.7
Gist et al., 2014
n=8; moderately-trained
22.6
3.1
Act
45.6
7.2
15.8
Astorino et al. 2012
n=5; recreationally active
22.7
3.7
Ath
57.1
6.0
10.5
McDowell et al. 2003
n=21; track runners
23.0
4.0
Ath
54.8
4.1
7.5
Gale & Flynn 1974
n=8; competitive wrestlers
23.0
5.0
Act
44.0
8.3
18.9
J Kang et al. 2008
n=11
23.4
4.5
Fit
69.1
5.0
7.2
Pereira & Freedson 1997
n=7; highly trained
23.5
1.0
Ath
70.6
4.5
6.4
Wilhite et al. 2013
n=6; wlite distance runners
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
23.5
4.5
Act
43.8
6.1
13.9
Astorino et al. 2010
n=13; recreationally active
23.9
1.3
Fit
59.2
8.4
14.2
C Kaufman et al. 2006
n=?; high-fit (active)
23.9
1.3
Ath
63.4
6.6
10.4
Drenowatz & Eisenman 2011
n=10; endurance run.
24.0
2.0
Ath
67.0
3.0
4.5
Romijn et al. 1993
n=5; endurance cyclists
24.0
5.7
Ath
62.0
4.0
6.5
Proctor et al. 1998
n=8
24.0
7.8
Act
54.8
10.5
19.2
Kist et al. 2013
n=11; aerobically trained
24.1
2.5
Act
55.8
4.2
7.5
Duncan et al. 1997
n=10; continuous treadmill
24.2
2.7
NS
47.8
6.3
13.2
JD George et al. 1998
n=36; test of protocol
24.2
5.7
Act
53.4
9.7
18.2
Beckham & Earnest 2000
n=12; 75% active
24.2
6.0
Act
48.5
6.2
12.8
Kolkhorst et al. 1994
n=9
24.3
3.1
Fit
68.1
5.7
8.4
JF Nichols et al. 1990
n=8; runners
24.3
3.8
Fit
44.7
7.6
17.0
Bloomer 2005
n=10; resistance & aerobic fit
24.5
4.7
Ath
77.0
8.0
10.4
LO Schultz et al. 1991
n=20; endurance trained
25.0
2.0
Fit
66.3
2.5
3.8
Proctor et al. 1995
n=6
25.0
4.0
Fit
64.4
3.7
5.7
Horton et al. 1998
n=8; competitive cyclists
25.0
5.0
Act
64.9
5.3
8.2
Hagberg et al. 1988
n=11
25.0
4.2
Fit
61.0
4.9
8.0
Sparling & Cureton 1983
n=34; distance runners
25.0
7.0
Fit
64.0
2.8
4.4
JM Wilson et al. 2010
n=10; endurance runners
25.3
5.5
Act
45.6
4.0
8.8
Astorino et al. 2012
n=11; recreationally active
25.7
2.3
Ath
61.1
4.1
6.7
JL Thompson et al. 1995
n=6; group 1
25.7
3.5
Fit
68.8
5.3
7.7
Trappe et al. 1996
n=10
26.0
4.9
Fit
42.7
2.7
6.3
Kenny & Ho 1995
n=?; V02 range: 39.9-46.8
26.0
6.3
Ath
61.1
5.7
9.3
Sedlock et al. 1989
n=10; triathletes
26.1
5.7
Fit
62.9
4.7
7.5
Bullough et al. 1995
n=10
26.1
6.9
Ath
68.6
6.3
9.2
Powers et al. 1983
n=9; distance runners
26.5
2.2
Ath
75.6
3.2
4.2
DW Morgan et al 1995
n=22; elite runners
26.6
6.1
Fit
52.2
5.4
10.3
Crawford et al. 2011
n=44; Army personnel BF<18%
26.8
4.5
Ath
62.5
5.0
8.0
JL Thompson et al. 1995
n=4; group 2
27.0
4.0
Ath
66.3
4.4
6.6
Coggan1993
n=6; endurance runners
27.0
2.8
Fit
62.0
5.7
9.2
Monahan et al. 2001
n=8
27.0
6.2
Act
61.3
5.6
9.1
Van Pelt et al. 2001
n=39; exercisers
27.1
5.0
Act
56.7
5.8
10.2
Quindry et al. 2013
n=12; recreationally active
27.1
6.7
Act
64.1
11.0
17.2
Trappe et al. 1996
n=18
27.3
3.6
Fit
70.5
4.0
5.7
DW Morgan et al. 1989
n=13
27.6
4.6
Fit
48.2
6.1
12.7
Dalleck & Kravitz 2006
n=12; moderate exercisers
27.6
7.2
Fit
63.4
6.6
10.4
DW Morgan et al 1995
n=41
27.7
6.3
Ath
63.4
6.7
10.6
Gojanovic et al. 2012
n=9; distance runners
28.0
3.0
Act
63.5
4.4
6.9
Ogawa et al 1992
n=14
28.0
4.0
Ath
54.3
6.5
12.0
Gale & Flynn 1974
n=9; Olympic team wrestlers
28.0
4.0
Fit
58.4
8.7
14.9
DeSouza et al. 2000
n=12; endothelial dysfunction
29.0
4.0
Ath
61.9
4.9
7.9
J Thompson & Manore 1996
n=24; endurance trained
29.2
6.8
Fit
27.6
7.2
26.1
Dalleck & Kravitz 2006
n=24; moderate exercisers
28.3
4.5
Ath
65.8
6.3
9.6
Baldwin et al. 2000
n=7; endurance trained
29.0
4.5
Fit
62.0
4.5
7.3
Tanaka et al. 2002
n=20; endurance-trained
29.8
3.9
Fit
49.9
4.3
8.6
Sandoval & Matt 2002
n=15
29.9
9.1
Act
63.5
6.4
10.1
Weltman et al. 1990
n=31; runners
30.0
5.6
Act
54.7
6.6
12.1
Sady & Katch 1981
n=21
30.0
6.0
Act
53.6
10.0
18.7
Fedel et al. 1995
n=12; competitive skaters
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
30.4
7.6
ACT
45.0
3.7
8.2
EL Melanson et al. 2002
n=8; lean exercisers
30.6
7.2
Fit
44.1
6.8
15.4
Crawford et al. 2011
n=55; Army men BF >18%
31.0
5.4
Fit
58.3
5.4
9.3
Pereira & Freedson 1997
n=8; moderately trained
31.8
9.2
Fit
56.1
7.9
14.1
Dumke et al. 2006
n=10; trained cyclists
32.3
6.4
Act
54.8
6.3
11.5
TE Ward et al. 1998
n=17; Bruce treadmill test
32.8
6.7
Fit
66.0
7.0
10.6
McDaniel et al. 2002
n=9; trained cyclists
34.6
2.8
H
50.6
10.0
19.8
CB Scott & Bogdanffy 1998
n=12
35.0
6.2
Ath
66.6
8.8
13.2
Costill et al. 1973
n=12; distance runners
36.9
5.0
Fit
59.2
4.1
6.9
DW Morgan et al. 1995
n=16
37.3
3.9
Fit
63.3
2.2
3.5
Shvartz 1996
n=6
39.0
3.0
Ath
60.7
5.1
8.4
Peiffer et al. 2008
n=14; Cyclist (CI 52.3-69.8)
39.0
11.1
Fit
53.4
8.4
15.7
Malek et al. 2004
n=93; aerobically trained
39.9
6.2
Fit
48.6
5.8
11.9
Ribisl & Kachadorian 1969
n=24; moderately well-trained
44.5
2.8
Ath
58.7
9.5
16.2
SA Hawkins et al. 2001
n=31; visit 1
44.6
6.9
Act
44.6
6.7
15.0
FWKasch etal. 1985
n=15;longitudinal yr 1 study
46.5
6.1
Ath.
47.2
7.2
15.3
Barnard et al. 1979
n=13; sprinters
46.8
9.8
Ath
60.3
13.3
22.1
Trappe et al. 1996
n=10
47.2
3.8
Fit
59.2
4.9
8.3
Trappe et al. 1996
n=10
47.2
5.8
Act
37.0
5.3
14.3
Loftin et al. 1996
n=12
48.7
7.6
Act
48.9
5.9
12.1
Trappe et al. 1996
n=18
49.0
3.0
Ath
55.2
6.6
12.0
Peiffer et al. 2008
n=10; cyclist CI: 43.7-64.6
49.0
4.4
Fit
52.0
4.4
8.5
Tanaka et al. 2002
n=19; endurance-trained
50.8
4.1
Act
51.0
13.5
26.5
TE Ward etal. 1998
n=10; Bruce treadmill test
51.4
6.8
Fit
38.0
6.5
17.1
McDonough et al. 1970
n=60
53.2
7.8
H
37.7
7.6
20.2
McDonough et al. 1970
n=34; runners
53.5
3.3
Ath
50.4
8.4
16.7
SA Hawkins et al. 2001
n=31; visit 2
53.9
2.9
Ath
53.4
8.2
15.4
SA Hawkins et al. 2001
n=34; visit 1
56.2
6.9
Act
45.2
10.0
22.1
FWKasch etal. 1985
n=15; longitudinal study: yr 2
55.2
8.6
Fit
45.0
5.4
12.0
Coyle et al. 1983
n=6; exercisers
55.3
11.2
Ath
54.4
10.8
19.9
Barnard et al. 1979
n=13; endurance trained
57.0
4.0
Act
52.7
3.3
6.3
Proctor et al. 1995
n=6
59.0
6.0
Ath
58.7
4.3
7.3
Heath et al. 1981
n=16; Masters athlete
59.0
6.9
Act
54.8
8.0
14.6
BE Hunt etal. 2001
n=12
59.1
7.6
Act
31.4
10.2
32.5
Nikolai et al. 2009
n=7; in a water exercise class
59.6
8.5
Fit
49.9
5.4
10.8
Schulman et al. 1996
n=8; endurance trained
60.0
6.9
Act
40.2
9.3
23.1
FWKasch etal. 1985
n=15;longitudinal study: yr 3
60.0
8.6
Fit
53.3
5.4
10.1
Pollock et al. 1987
n=11; competitive athlete
62.0
2.0
Fit
52.0
2.7
5.2
Coggan et al. 1993
n=6; endurance runners
62.0
8.9
Ath
54.0
6.6
12.2
MA Rogers et al. 1990
n=15
62.2
3.5
Ath
46.2
8.2
17.7
SA Hawkins et al. 2001
n=34; visit 2
62.3
2.9
Ath
46.2
9.0
19.5
SA Hawkins et al. 2001
n=13; visit 1
63.0
5.7
Act
42.3
7.4
17.5
Van Pelt et al. 2001
n=32; exercisers
63.0
3.0
Act
47.6
4.3
9.0
Ogawa et al 1992
n=14
63.0
5.6
Fit
45.0
2.8
6.2
Monahan et al. 2001
n=8
63.0
9.0
Ath
42.6
8.9
20.9
DeSouza et al. 2000
n=20; endothelial dysfunction
63.1
6.9
Act
43.1
8.4
19.5
FWKasch etal. 1985
n=13;longitudinal study: yr4
63.4
6.5
Ath
49.6
5.8
11.7
Katzel et al. 2001
n=42
63.7
5.0
Act
31.9
3.6
11.3
Hageman et al. 2000
n=9; resistance group
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
64.0
4.9
Fit
41.8
2.9
WL Kenny & Ho 1995
n=6; V02 range: 39.1-45.6
64.0
5.7
Ath
45.9
4.5
9.8
Proctor et al. 1998
n=8
64.2
9.4
Fit
45.9
6.7
14.6
Pollock et al. 1987
n=13; post-competitive athlete
65.0
4.0
Ath
45.9
4.6
10.0
Peiffer et al. 2008
n=8;Cyclists: CI 38.2-51.7
65.0
4.0
Act
50.0
4.8
9.6
Hagberg et al. 1988
n=11
65.0
6.0
Fit
43.3
6.3
14.5
JF Nichols et al. 1990
n=9
65.0
8.0
Ath
47.2
5.9
12.5
Fleg et al. 1995b
n=16;Endurance runners
66.0
3.0
Ath
46.4
5.1
11.0
Tankersley et al. 1991
n=7; Master runners
66.0
5.6
Act
31.4
4.8
15.3
Proctor et al. 2003
n=11
66.0
8.5
Ath
48.0
4.2
8.8
Goldberg et al. 2000
n=18;Endurance runners
66.2
6.5
Act
33.1
6.2
18.7
Hageman et al. 2000
n=9; control group
66.3
11.6
Ath
36.5
17.2
47.1
Wilund et al. 2008
n=7; Master athlete
67.0
8.2
Fit
Tanaka et al. 2002
n=17; endurance-trained
67.7
3.7
Act
McDowell et al. 2003
n=18; endurance runners
68.0
3.7
Act
Shi et al. 2008
n=8
68.4
9.8
Ath
Trappe et al. 1996
n=10
69.0
3.5
Act
45.0
6.9
15.3
BD Johnson et al. 1991
n=12
70.4
8.8
Ath
40.5
8.9
22.0
Pollock et al. 1997
n=21; still competes
71.1
3.2
Ath
36.4
9.4
25.8
SA Hawkins et al. 2001
n=13; visit 2
76.0
4.8
Ath
41.5
8.8
21.2
SA Hawkins et al. 2001
n=8; visit 1
82.8
4.0
Ath
28.4
7.6
26.8
SA Hawkins et al. 2001
n=8; visit 2
b. Complete age statistics are not provided
6-10
-
Ath
62.7
6.1
9.7
Eisenmann et al. 2001
n=13; distance runners
10.0
-
Ath
61.1
4.9
8.0
Daniels et al. 1978
n=4; mid-dist runner
11.0
-
Ath
63.6
7.1
11.2
Eisenmann et al. 2001
n=12; distance runners
12.0
-
Ath
63.3
6.3
10.0
Eisenmann et al. 2001
n=14; distance runners
12.0
-
Ath
59.0
6.6
11.2
Daniels et al. 1978
n=4; mid-dist runner
12.0
-
Ath
62.7
5.2
8.3
Daniels et al. 1978
n=7; mid-dist runner
13.0
-
Ath
60.8
7.2
11.8
Eisenmann et al. 2001
n=16; distance runners
14.0
-
Ath
63.5
5.2
8.2
Eisenmann et al. 2001
n=20; distance runners
15.0
-
Ath
62.7
6.3
10.0
Eisenmann et al. 2001
n=16; distance runners
16.0
-
Ath
64.8
5.0
7.7
Eisenmann et al. 2001
n=14; distance runners
17.0
-
Ath
67.5
5.6
8.3
Eisenmann et al. 2001
n=20; distance runners
17.0
-
Ath
61.2
4.4
7.2
Daniels et al. 1978
n=7; mid-dist runner
18.0
-
Ath
67.3
8.0
11.9
Eisenmann et al. 2001
n=14; distance runners
20.0
-
Act
50.2
6.5
12.9
Sonna et al. 2001
n=116; non-participants
21.0
-
Act
51.1
6.5
12.7
Sonna et al. 2001
n=66; participants
20-33
-
Ath
63.3
8.0
12.6
Ferley et al. 2013
n=12; group 1
20-33
-
Ath
59.4
8.9
15.0
Ferley et al. 2013
n=12; group 2
20-33
-
Ath
59.9
8.6
14.4
Ferley et al. 2013
n=8; group 3
25-34
-
Act
42.5
5.1
12.0
Bruce 1984
n=?
35-44
-
Act
39.9
5.4
13.5
Bruce 1984
n=?
40-49
-
Ath
57.5
-
-
Pollock et al. 1974
n=11; V02 range 46-64
45-54
-
Act
37.0
5.3
14.3
Bruce 1984
n=?
50-59
-
Ath.
54.4
-
Pollock et al. 1974
n=5; V02 range 49-57
55-64
-
Act
33.3
4.4
13.2
Bruce 1984
n=?
60-69
-
Ath
51.4
-
Pollock et al. 1974
n=6; V02 range 40-61
70-79
-
Ath
40.0
-
Pollock et al. 1974
n=3; VQ2 range 38-41
-------
Table 2. Estimates of V02 MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age /Range Health COV
Mean SD Status Mean SD (%) Citation Comment
Males: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for age
11.2
1.6
OW
37.5
6.9
18.4
Byrd-Wlliams et al. 2008
n=84; Hispanic
13.3
1.5
Sed
35.2
2.7
7.7
Kwee & Wilmore 1990
n=31; "low fit"
14.1
1.3
O
25.6
3.0
11.7
Gutin et al. 2002
n=16; black adolescents
14.5
1.3
O
31.0
8.5
27.4
Gutin et al. 2002
n=10; white adolescents
19.1
1.6
O
34.4
3.8
11.0
Wolfe etal. 1976
n=12; "moderately obese"
19.8
3.2
Sed
31.6
5.6
17.7
Lawrenson et al. 2003
n=6
17.7
3.6
OW
47.1
3.6
7.6
Harms et al. 1995
n=8; high fat content
22.0
4.0
OW-Sed
39.2
5.2
13.3
Potteiger et al. 2008
n=16; exercise group
22.9
5.0
O
31.0
5.6
18.1
Kyriazis et al. 2007
n=7; control group
23.9
3.8
O-Sed
39.6
5.7
14.4
Washburn et al. 2003
n=17
24.0
4.0
OW-Sed
39.5
5.7
14.4
Potteiger et al. 2008
n=15; control group
24.1
1.6
Sed
40.1
3.0
7.5
C Kaufman et al. 2006
n=?; low-fit
24.5
3.3
Sed
46.7
2.1
4.5
MJ Turner et al. 1999
n=9
25.4
4.5
O
28.9
5.4
18.7
KR Segal et al. 1985
n=10
25.6
7.0
O
35.1
7.5
21.4
Browning et al. 2006
n=10
26.0
3.0
Sed
46.4
4.0
8.6
Hagberg et al. 1988
n=13
26.0
3.0
Sed
49.3
4.7
9.5
Hoetzer et al. 2007
n=10
26.0
5.7
Sed
44.2
6.8
15.4
Van Pelt et al. 2001
n=32
26.3
3.6
Sed
51.4
3.9
7.6
DW Morgan et al. 1995
n=10
26.4
5.4
O
33.6
5.4
16.1
Kyriazis et al. 2007
n=8; exercise group
27.0
2.8
Sed
45.0
5.7
12.7
Monahan et al. 2001
n=8
27.0
3.0
Sed
45.9
6.1
13.3
Ogawa et al 1992
n=14
27.0
4.0
Sed
41.8
6.9
16.5
DeSouza et al. 2000
n=12; endothelial dysfunction
28.0
5.4
Sed
41.0
10.7
26.1
Tanaka et al. 2002
n=29
30.7
8.6
OW
39.7
4.7
11.8
Lennon et al. 1985
n=11; control group
31.4
3.1
Sed
42.6
9.5
22.3
Lieberet al. 1989
n=12; exercise group #1
31.8
6.9
O
35.4
6.6
18.6
Sopko et al. 1984
n=21
31.9
3.0
Sed
40.0
4.9
12.3
Lieberet al. 1989
n=12; exercise group #2
32.0
6.6
O
29.2
4.0
13.7
KR Segal et al. 1990
n=11
32.2
1.8
NF
26.3
3.6
13.7
Shvartz 1996
n=5
32.2
7.2
OW
40.5
3.4
8.4
Lennon et al. 1985
n=12; exercise group #2
32.3
2.2
Sed
40.0
4.1
10.3
Lieberet al. 1989
n=10; control group
32.3
15.2
Sed
32.9
9.8
29.8
Rynders et al. 2011
n=74
32.9
2.5
Sed
38.5
5.2
13.5
Maliszewski et al. 1995
n=10; range=30.3-48.6
33.9
11.7
Sed
33.3
6.1
18.3
Skinner et al. 2001
n=78; black
36.4
15.0
Sed
37.3
9.0
24.1
Skinner et al. 2001
n=209; white
37.0
5.5
OW
37.6
2.3
6.1
Lennon et al. 1985
n=12; exercise group #1
41.0
6.0
O
28.3
5.5
19.4
Greene et al. 2012
n=10
43.3
6.6
OW-O
37.2
7.2
19.4
CW Hall etal. 2012
n=9
45.7
2.1
Sed
33.2
4.5
13.6
Dehn & Bruce 1972
n=15
47.0
4.0
Sed
37.7
7.0
18.6
Hoetzer et al. 2007
n=15
47.7
7.2
H
32.0
8.0
25.0
McDonough et al. 1970
n=16
50.0
5.3
Sed
34.0
5.3
15.6
Tanaka et al. 2002
n=28
50.0
6.0
OW
30.4
2.8
9.2
Heath et al. 1981
n=9; sedentary
52.1
9.1
Sed
24.2
6.9
28.5
Costill et al. 1974
n=24; control group
52.2
6.6
OW-Sed
32.1
3.9
12.1
JL Robbins et al. 2009
n=9
-------
Table 2. Estimates of V02MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mL/kg-min )
Age /Range Health COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
55.0
2.7
Sed
31.2
4.7
15.1
Dehn & Bruce 1972
n=10
58.0
10.0
Sed
31.8
5.4
17.0
DeSouza et al. 2000
n=24; endothelial dysfunction
60.0
5.1
Sed
32.1
4.4
13.7
Schulman et al. 1996
n=10
61.1
6.2
Sed
30.1
5.5
18.3
Katzel et al. 2001
n=47
61.4
5.2
Sed
33.9
6.4
18.9
MA Rogers et al. 1990
n=14
62.0
5.8
Sed
31.0
6.4
20.6
Van Pelt et al. 2001
n=34
63.0
3.0
Sed
27.2
5.1
18.8
Ogawa et al 1992
n=3
63.0
5.0
Sed
33.6
6.4
19.0
Hoetzer et al. 2007
n=21
63.0
6.9
Sed
34.0
Goldberg et al. 2000
n=12; lean body mass
63.0
5.1
O.Sed
26.0
5.1
19.6
Goldberg et al. 2000
n=26; obese
64.0
3.0
Sed
29.6
4.1
13.9
Ehsani et al. 2003
n=10
64.7
1.5
Sed
28.8
3.1
10.8
Dehn & Bruce 1972
n=3
65.0
2.8
Sed
29.0
2.8
9.7
Monahan et al. 2001
n=8
65.0
3.0
Sed
24.9
5.0
20.1
DeSouza et al. 1997
n=11; normotensive
65.0
4.9
Sed
29.0
4.9
16.9
Tanaka et al. 2002
n=24
66.0
5.0
Sed
27.0
2.2
8.1
Hagberg et al. 1988
n=10
66.4
5.6
Sed
28.0
3.6
12.9
MJ Turner et al. 1999
n=11
66.5
5.1
Sed
20.3
4.4
21.7
Lawrenson et al. 2003
n=6
66.7
14.9
Sed
25.4
13.7
53.9
Wilund et al. 2008
n=6
67.5
5.4
Sed
25.2
3.4
13.5
Shi et al. 2008
n=8
68.7
4.8
Sed
26.3
5.2
19.8
JF Nichols et al. 1990
n=19
69.0
4.0
Sed
23.6
3.5
14.8
DeSouza et al. 1997
n=12; hypertensive
76.0
9.0
Sed
21.4
6.3
29.4
EP Weiss et al. 2006
n=33
b. Complete age statistics are not provided
18-23
-
OW
54.4
6.9
12.7
O'Leary & Stavrianeas 2012
n=8
25-34
-
Sed
36.7
5.6
15.3
Bruce 1984
n=?
35-44
-
Sed
36.6
4.3
11.7
Bruce 1984
n=?
45-54
-
Sed
32.7
4.7
14.4
Bruce 1984
n=?
55-64
-
Sed
29.8
4.8
16.1
Bruce 1984
n=?
60-72
-
Sed
26.9
2.8
10.4
Frontera et al. 1990
n=12
Males:
Health & Other Issues
11.5
2.9
A
35.8
9.5
26.5
Boas et al. 1999
n=22
11.6
2.8
CF
41.4
7.5
18.1
Boas et al. 1999
n=25
18.8
-
SC
33.8
-
-
JR Robinson et al. 1976
n=16; A anemia only
20.0
-
SC
33.2
-
-
JR Robinson et al. 1976
n=16; Hbs trait & anemia
21.4
2.6
MR
41.2
11.2
27.2
Draheim et al. 1999
n=10
21.4
8.4
CC
28.5
5.8
20.4
AM Miller et al. 2012
n=38
26.7
5.9
DS
27.6
6.4
23.2
Fernhall et al. 1996
n=31; 12 y mean post-treatment
26.9
6.4
MR
32.7
7.2
22.0
Fernhall et al. 1996
n=35;no DS
41.0
9.0
MS
42.0
5.0
11.9
Donovan et al. 2009
n=32
43.9
9.4
HBP
31.0
6.7
21.6
Eicheret al. 2010
n=45
45.7
5.0
Blind
24.0
2.9
12.1
WSiegel et al. 1970
n=9; cycle ergometry
49.9
11.6
Ml
25.5
7.1
27.8
Pinkstaff et al. 2011
n=157
52.1
9.1
IHD
20.0
6.4
32.0
Costill et al. 1974
n=24; experimental group
52.1
9.1
IHD
18.7
4.9
26.2
Costill et al. 1974
n=20; no experiment group
55.6
6.6
IHD
37.0
4.7
12.7
Coyle et al. 1983
n=6; trained (exercise program)
56.0
10.0
HF
15.4
-
Keteyian et al. 2010
n=160; COV=5.9%
57.4
12.5
CHF
14.8
2.5
16.9
Duscha et al. 2001
n=25
-------
Table 2. Estimates of VO,
MAX in males seen in the literature (continued)
Body Mass (BM)-Adjusted Estimates: Mean ± SD
V02Max Estimate ( mUkg-min )
Age/Range
Health
COV
Mean
SD
Status
Mean
SD
(%)
Citation
Comment
61.0
11.0
IHD
19.3
6.1
31.6
Ades et al. 2006
n=2081; multicenter study
61.2
12.0
HP
31.0
9.3
30.0
Shultz et al. 2010
n=60
64.0
3.0
CAD
27.6
5.7
20.7
Sheldahl etal. 1996
n=9; exercise group #2
64.0
11.0
CHF
14.2
2.6
18.3
Bowen et al. 2012
n=24; med. ramp protocol
65.3
6.5
COPD
17.0
5.6
32.9
Carter et al. 1994
n=32; mild airflow limitation
65.9
6.0
COPD
9.9
2.7
27.3
Montes de Oca et al. 1996
n=25; severe obstruction
66.3
6.2
COPD
16.2
4.9
30.2
Carter et al. 1994
n=57; mod. airflow limitation
66.6
6.5
COPD
13.5
3.8
28.1
Carter et al. 1994
n=176; severe airflow limitation
68.0
5.0
HP
21.0
5.0
23.8
Ades et al. 1993
n=30
68.0
5.7
CAD
25.3
2.8
11.1
Sheldahl etal. 1996
n=8; exercise group #1
69.0
3.3
CAD
26.0
5.3
20.4
Sheldahl etal. 1996
n=11; exercise group #3
76.0
9.0
Ml
24.0
4.0
16.7
Fleg et al. 1993
n=8; "silent" Ml
Abbreviations
A Asthmatic
AA African American
Act Active (but non-athletes)
Ath Athletes
BF Body fat
BLSA Baltimore Longitudinal Study of Aging
CAD Coronary artery disease
CC Cancer survivor
CF Cystic Fibrosis
CHD Chronic heart disease
CHF Chronic heart failure
COPD Chronic obstructive pulmonary disease
COV Coefficient of variation
DS Down syndrome
Fit Very active healthy exercisers
Frail Mild-to-moderate frailty
H Healthy
HBP High blood pressure
HP heart patients
IHD Ischemic heart disease
Abbreviations
Ml Myocardial iscemia
MR Mentally retarded (some with Down syndrome)
MS Metabolic syndrome
N Normal (mostly healthy)
NF Not fit; poor fitness
NG National Guard (all types)
NS Not specified
O Obese
OW Overweight
Pg Pregnant
SC Sickle cell
Sed Sedentary
Overall, we reviewed 1,649 papers for compilation of extant
V02 MAX data. Our cut-off date for a paper to be included in
Tables 1 & 2 was January 1, 2015. Useable data—having
both age- and gender-specific attributes-were obtained
and entered into the Tables from 381 of the papers that we
reviewed (23.1%). These papers provided 1,025 "lines"
(entries) of data: 909 entries having sample mean age
information, and 116 having only sample age range data.
Of the 1,268 papers that were not used (76.9% of the total),
507 were from non-US studies and 761 had some type of
data "issue." Data issues included presenting only mixed-
gender results, using a protocol other than treadmill or
cycle ergometer to estimate V02 MAX, or providing V02 MAX/
VO, data only in absolute or in per-LBM units.
It should be noted that all of the data in Tables 1 and 2
are cross-sectional in nature, even though some are from
"longitudinal" studies—usually a single measurement for
multiple time periods, often separated by years. Basically
these are treated as separate cross-sectional studies.
The coefficient of variation (COV), which is the standard
deviation of the sample divided by its mean, is an indicator
of the relative amount of variation in a sample. It usually
is expressed as a percentage, and it is a useful metric for
describing innate "inter-individual" (across subjects)
variability in the sample. COV's are provided in Table 1
& 2 —where possible—for the V02 MAX metric. As can be
seen, the range of inter-individual variation in V02 MAX among
the various samples is large, generally between 10-30%.
Studies having a VO,
. mean with a COV <7% and >35%
31
-------
should be viewed skeptically; studies with a V02 MAXCOV
outside of the 7-35% range probably have a highly biased
sample.
The studies in Tables 1 & 2 rarely provide distributional
information regarding variability in V02 MAX measurements
seen in the tested samples, but Safrit & Wood (1995) and
Pate et al. (2006) do so. Their data appear here as Table 3.
The 5th-95th range for college-aged students is -55% of the
median male value and -36% of the median female value.
The equivalent COV's for these two samples is about 22%
and 18%, respectively, not very different than those seen in
Tables 1 & 2. The 10th-90th range for youth listed in Table
3 varies between 43-51% of the median estimate, with no
trend discernable by age grouping. Basically there is a lot of
age-/gender-specific inter-individual variability in the V02
MAX metric, and our exposure models should capture that.
With respect to distributional aspects of the Safrit & Wood
(1995) data, for females they follow a normal probability
distribution between the 10th and 95th percentiles, while they
are normally distributed for males between the 5th and 95th
percentile (data not shown). For adolescents, males have a
longer-tail V02MAX distribution than females; otherwise they
are parallel (data not shown).
Concepts
V02 MAX (or V02 PEAK) is considered by many to be the
"standard" measure of physical fitness and/or the functional
limit of a person's cardiopulmonary system (Balady et al.,
2010; Foucquirer et al., 2013; Jacobs et al., 1997; Kemper
& Verschuur, 1985; McArdle et al., 2001, Mitchell et al.,
1958; Sharkey 1984; Wagner, 1996). It is a reliable measure
of impairment of oxygen delivery to cardiovascular and
muscular systems (Wagner, 2010), and thus is an indicator
of heart problems irrespective of their cause (Weber et al..
1988). V02MAX is much lower in people with decreased
exercise capacity due to cardiovascular issues (Fonnan et al.,
2009), and it is this capacity that more accurately predicts
mortality in people with a variety of coronary risk factors
than alternative respiratory metrics that have been evaluated
(Franklin, 2000, 2007).
It is possible to work at rates greater than V02 MAX for short
periods of time, but this can only be accomplished via energy
transfer due to glycolysis, resulting in lactate accumulation
after about 4-5 minutes and an inability to continue further.
The important point to note is that it is possible to record
>100% V02MAX values for short periods of time (Ogita et al.,
1999; Rowland, 1993).
A good, succinct review of the limiting factors associated
with the V02 MAX metric appears in Bassett Jr. and Howley
(1997 & 2000) and Rowell (1974). Good reviews of V02
MAX studies from all over the world are available that
contain "secondary data" from previously published studies,
although none of their information appears in Tables 1 &
2. Older reviews of female maximum oxygen consumption
are contained in three Drinkwater papers (1973, 1984,
& 1989). More recent reviews of female data appear in
Arena et al. (2007), Eisemnann & Malina (2002), Kelley &
Kelley (2006), and Wolfe & Weissgerber (2003). Reviews
of male V02 MAX data are contained in Eisemnann & Malina
(2002), FitzGerald et al. (1997), and Hartung et al. (1992).
Eisemnann et al. (2011) provide percentile distributions—
difficult to come by-for both female and male adolescent
V02 MAX values (aged 12-18 y). A good review of V02PEAK
measurements in people who experienced one or more
strokes in the past (hemiparetic stroke patients) is found in
Ivey et al. (2006). Their review of seven studies found that
V02 |1|;ak in stroke patients was approximately one-half that of
age-matched non-stroke "controls."
Table 3. Percentile distribution of VO
2 max hy a"c grouping
V02.MAX Estimate ( mL/kg-min )
College Age
12-13 y
14-15 y
16-17 y
18-19 y
Percentile
Ranking
Fem.
Male
Fem.
Male
Fem.
Male
Fem.
Male
Fem.
Male
5
29.6
34.1
10
31.8
36.6
31.0
34.7
30.6
38.1
30.5
36.4
28.9
37.6
20
32.6
39.1
33.2
37.3
32.1
40.0
32.8
38.9
31.0
40.3
30
34.0
41.6
35.8
39.0
34.5
41.9
34.5
42.0
33.5
43.0
40
34.4
43.3
37.2
41.0
36.2
43.8
36.1
44.4
35.4
44.4
50
35.1
45.8
39.3
43.0
38.0
45.8
37.6
46.2
36.7
46.3
60
35.7
47.5
40.4
45.0
38.9
48.2
39.4
47.9
38.3
48.7
70
36.3
49.2
43.2
47.3
40.5
50.2
41.4
50.2
39.6
50.8
80
37.0
52.5
45.1
51.5
43.2
52.5
44.2
53.8
41.9
53.7
90
38.5
57.6
48.4
56.2
48.8
58.8
49.8
58.3
47.2
58.4
100
42.2
60.9
Sources: Safrit & Wood (1995). Introduction to Measurement in Physical Education
and Exercise Science (3rd ed).
Pate et al. (2006). "Cardiorespiratory fitness levels among U.S. youth 12 to 19 years of age." Arch.
Pediatr. Adoles. Med. 160: 1005-1012.
Note: Fern. = Female
32
-------
V02 MAXand A§e
Many articles state that V02 MAX on a body-mass basis
declines with age for adults (Bruce, 1984; Minson & Denney,
1997; White et al. 1998), but exactly what the rate of decline
is with age is difficult to quantify. Some estimates state that
the rate of decline is 3-10% per decade for non-athletes
before the age of 70, and accelerates to >20% per decade
afterward (Fiser et al., 2010; Renlund & Gerstenblith, 1987).
Other papers find that the decline of V02 MAX with age is
modest between 20 and 50 yrs (Ceaser et al., 2013; Wang
et al., 2010). A summary of the Wang et al. (2010) findings
appears here as Table 4. Note the quite high COV for all of
the decadal groupings for both genders.
A large cross-sectional study of Canadian residents aged
20-65 indicates that V02MAX declined about 55.5% (14.5%
per decade) in females and about 39.0% (8.6% per decade)
in males (Bailey, et al., 1974). One interesting finding in their
study is that the decline in females was concave for the 45-y
period, while it was convex for males; in other words, the
rate of change is different for the genders. V02MAX measured
cross-sectionally drops faster during the 20-49 age range for
males and then decreases more slowly after that; for females,
the opposite is true (Bailey et al., 1974).
Another estimate of the decline in V02 MAX is on the order of
8-34% over 20 years in middle-aged subjects, with a larger
decrease in less-active people (Pollock et al., 1997; Smith
& Gilligan 1989; Tanaka et al., 1997). At age 75 y, V02MAX
has been found to be 50% of individual peak values (Barnard
et al., 1979). A reduction rate for V02 MAX of 10% per
decade is often seen in the literature (Schiller et al., 2001). A
V02 MAX <18 mL kg1 min1 in the elderly is used by the Social
Security Administration as an indicator of severe disability
and the need for non-independent living arrangements
(AS Jackson et al., 2009).
Middle-aged and older people who participate in aerobic
fitness programs have V02 MAXvalues 14-43% higher than
inactive people at the same age (Barnard et al., 1979).
Athletes who maintain their physical activity levels over time
as they age have significantly higher V02 MAX than less active
contemporaries (Hagberg, 1987; Hawkins et al., 2001, 2003;
MA Rogers et al. 1990). Active individuals who maintain
their physical fitness have a slower rate of decline in V02 MAX
(Barnard et al., 1979), although other authors disagree: "the
present cross-sectional meta-analytic findings do not support
the hypothesis that the rate of decline in V02 MAX with age is
related to habitual aerobic exercise status in men" (Wilson &
Tanaka, 2000).
In general, V02 MAX on a body mass basis is stable during
childhood (Rowland, 1989) and peaks around puberty, and
like HRmax and muscle mass, declines after that (Astrand,
1992; Barnard et al., 1979; Buskirk & Hodgson, 1987;
Freedson et al., 2000; Goodman & Thomas, 2002; Pollock
et al., 1997; Posner et al., 1987; Schiller et al., 2001). In
females, peak 02MAX per body mass occurs around 10-12
y, while in boys, the peak occurs somewhat later, around
14 y (Armstrong, 2013; Rowland, 2013). These findings
are different from those presented in Janz et al. (1998),
a longitudinal study of adolescents in Muscatine, Iowa
that found decreases in V02 PEAK per body mass as early
as 11 y of age in both females and males that continued
over a five year period: from 47 ± 7 to 34 ± 5 mL kg"1
min1 in females and from 50 ± 9 to 46 ± 7 (same units) in
males (Janz & Mahoney, 1997; Janz et al., 1998, 2000).
McMurray et al. (2002) also show relatively monotonic
decreases in V02 MAX/BM in children and adolescents beginning
at age 7-9, in both African-American and Caucasian youth.
Table 4. Means and selectied percentiles of VO values from the 1990-2004 NHANES surveys (All Ethnic Groups)
Age Range Mean
SD
COV
Value
Females
20th
95th CI
Value
80th
95th CI
20-29
36.5
9.6
26.3
30.6
30.0-31.5
41.7
40.6-42.5
576
30-39
35.4
9.3
26.3
29.0
28.2-30.3
41.1
40.1-42.6
542
40-49
34.4
10.3
29.9
28.1
27.1-28.9
40.0
38.7-40.9
425
Males
20-29
44.5
10.4
23.4
37.9
37.3-38.6
50.2
48.9-51.4
675
30-39
42.8
12.0
28.0
36.4
35.5-37.3
48.0
47.1-49.3
574
40-49
42.2
12.8
30.3
35.5
34.7-37.0
47.2
46.0-49.1
458
Abbreviations:
Abbreviations:
CI:
Confidence Interval
SD:
Standard deviation
COV:
NHANES:
Coefficient of variation
National Health and Nutrition
Examination Survey
Source: C.-Y. Wang, et al. (2010). Amer. J. Epidem.
171: 426-435.
33
-------
4.6
4.4
4.2
4
3.8
3.6
3.4
3.2
3
2.8
—i 1—
*xX*
^ A
^ X *~ X
V
~ if1 ffb ~ n 1=1
tr n ~ ~ ~ ~~
xnn°
x "Normals"
~ Fit, Active
X
X
X * * * X
* X *
X" *x ŁXX x
~ ~ *x:< x
° D>5a n * D n ^ D
D ~ B ~~ D x
~ ~ DD * «
Dx ~ ,-Sox
n y n
g=" ~ cbn D cP ~
~ n cP°~
~
~
~
4.6
4.4
4.2
4
3.8
3.6
3.4
3.2
3
2.8
25
45
Age
65
85
Figure 1. Plot of LN V02 MAX versus age for two groups
In body mass-adjusted units the cross-sectional decline
for normal-weight adult males is about 0.45 mL kg"1 min1
per year regardless of baseline physical activity level,
and about 0.30 mL kg"1 min1 per year in adult females
(Hodgson & Buskirk, 1977). The rate of decline is less in
some longitudinal studies, with a regression-based slope
of 0.40 mL kg1 min1 per year for males obtained by Delin
& Bruce in 1972 (reviewed in Hodgson & Buskirk, 1977).
However, one study that provided both cross-sectional and
longitudinal rates of decline in a sample of males states
that the cross-sectional decline was 0.4 mL kg1 min1 per
year and the average longitudinal decline in individuals
was 0.9 mL kg"1 min1 per year (Larson & Bruce, 1987).
Another study showing a high longitudinal decline in
V02 MAx is McClaran et al. (1995), which found a decrease of As introduced above, besides age and gender, fitness level
year, with very little variability over the entire 18 y period
(Kasch et al., 1985). Kasch & Wallace (1976) present V02
MAX data for 13 exercising males followed over 11 years, their
starting ages were between 32-56 y. There was no discernible
trend seen in most of the men and an Intraclass Correlation
Coefficient (ICC) analysis of their data undertaken by
me indicated that most of the variance seen in the data
was between individuals and not within an individual; the
ICC was 0.87 (p~0.011), indicating that only ~5% of total
explained variance was due to intra-individual variability.
Figure 1 depicts the natural logarithm of V02 MAX data shown
in Table 2 for normal and fit males.
V02 MflX and Fitness Level
0.75 kg1 min1 per year for a mixed gender cohort aged 67 y
at the beginning.
Not all longitudinal studies show a monotonic decline in
V02 MAX over the years, and one actually showed a small
increase in the 18th year of the study compared to the 10th
also affects V02 MAX measures as evidenced by the data on
oxygen consumption "standards" provided by the American
College of Sports Medicine (Sanders & Duncan, 2006).
Their standards are reproduced here as Table 5. Added to
Table 5. Age and gender specific "CUTPOINTS" of aerobic fitness levels
Age Range/
Fitness Level
20-29
40-49
ACLS
VO2.MAX Outpoints (mL/kg-min)
Females
NHANES
Males
ACLS
NHANES
Low
< 30.63
< 30.63
<37.13
< 3.94
Mod.
30.64 - 36.64
30.64 - 37.49
37.14-44.22
37.95-45.71
High
> 36.64
> 37.49
> 44.22
>45.71
30-39
Low
<28.70
<29.08
< 35.35
< 36.88
Mod.
28.71 -34.59
29.09 - 36.45
35.36-42.41
36.89-44.90
High
> 34.59
> 36.45
> 42.41
> 44.90
34
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Table 5. Age and gender specific "CUTPOINTS" of aerobic fitness levels (continued)
VO2.MAX Outpoints (mL/kg-min)
Age Range/ Females Males
Fitness Level ACLS NHANES ACLS NHANES
Low
< 26.54
<28.95
< 33.04
< 37.00
Mod.
26.55- 32.30
28.96- 35.40
33.05 - 39.88
37.01 - 44.36
High
> 32.30
> 35.40
> 39.88
> 44.36
Source: Sanders & Duncan (2006). Med. Sci. Sports Exer. 38: 701-707.
Females
Males
Notes:
60-69
ACLS
Aerobics Center Longitudinal
Poor
< 12.9
< 15.9
(Blair et al. 1989).
Fair
13.0-20.9
16.0-22.9
MOD.
Moderate fitness level
Average
21.0-32.9
23.0-35.9
NHANES
National Health and Nutrition
Good
33.0-36.9
36.0-40.9
Examination Survey (Duncan
Excellent
>37.0
>37.0
2005)
Source: McArdle et al. (2001). Exercise Physiology
(5th ed.). Philadelphia: Lippincott.
it is information on similar age/gender groups from the
Aerobics Center Longitudinal Study (Blair et al.. 1989) and
data for older adults obtained from McArdle et al. (2001).
The Table clearly shows the effect that different fitness levels
have on V02 MAX by age and gender; the difference in V02
MAX between low- and high-fit categories are in the range
of 20-30% for the various age categories, and even more
for the elderly.
Predicting V02MflX Using Anthropomorphic Inputs
There are a number of V02 MAX prediction equations in
the literature using only age, gender, and/or body mass as
independent variables. They will not be reviewed in this
report. The reader is directed to Armstrong & Welsman
(1994, 1997), Armstrong et al. (1999), and Bonen et al.
(1979) for a discussion of V02MAX prediction equations in
children and adolescents. V02MAX prediction equations for
older groups appears in Bradsford and Howley (1977), Darby
& Pohlman (1999), Dolenger et al. (1994); Fleg (1994), Fleg
et al. (2005), Latin & Elias (1993), and Rosen et al. (1998).
In addition, V02 MAX often is predicted using non-maximal
testing, such as measuring HR at sub-maximal rates (George
et al., 1993, 1997; Kline et al., 1987). See these citations—
really only a small sample of the information available—
for additional information regarding V02 MAX prediction
equations seen in the literature. There also are scores of
activity-specific V02 ACT prediction equations. Numerous
independent variables are used in these equations, such as
age, gender, fitness level, health status, body composition
(lean body mass, total body fat, fat distribution, etc.) and
body mass index. Many of these variables are not available
to EPA's exposure modelers in the data bases available
to the Agency. None of the prediction equations will be
reviewed here.
Alternative (Allometric) Scaling Approaches
V02 MAX scales most accurately with body mass to the
0.872 power (BM0-872); see Weibel et al. (2004). Alternative
exponents have frequently been presented in the comparative
physiology literature; the most prevalent values that are
seen are BM0 67 (Kleiber, 1947) and BM0 75 (Kleiber, 1950).
Additional power values could be cited, but suffice to say that
scaling to body mass provides lower standard errors when
V02 MAX is regressed against other feasible anthropogenic
or physiological metrics, such as body surface area or basal
metabolic rate (Weibel et al., 2004). Variation of V02MAX in
the majority of mammals is tightly associated with aerobic
capacity, the volume of capillaries and the total volume of
mitochondria. Athletes and other highly fit individuals are
more proficient than "ordinary" individuals in all three of
these attributes, which is why they have a higher V02 MAX
for identical body weights (Weibul et al., 2004; Weibul &
Hoppeler, 2005).
The work by McCann and colleagues on scaling of V02
metrics to account for body size differences when comparing
children and adults and females and males also is informative
(McCann, 2004; McCann & Adams, 2002a & b, 2003).
Markovic et al. (2007) compared empirically-derived
exponents for BM with respect to V02/BM at different work
rates from resting to maximal oxygen consumption; they
found that the exponents behaved differently in athletes and
normal (control) males. They also found that the best-fit
exponents varied between 0.67-0.98, so there was no single
value that performed best in either group or at varying work
rates (Markovic et al., 2007).
35
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It should be noted that allometric scaling is theoretically
correct only if there is no correlation between the ratio metric
(say, V02/BM) and the anthropometric variable(s) of interest
(BM, HR, BSA, etc.). For a discussion of this issue, see
Davies et al. (1995) and Katch & Katch (1974). Kolokotrones
et al. (2010) state that the relationship between mass and
metabolic rate has a convex curvature on a logarithmic
scale, and therefore does not follow a pure power law. Thus,
the theory behind allometric scaling does not fit with V02/
BMor V02MAX/BM data (Kolotrones et al., 2010: alternative
hypotheses have to be evaluated. One alternative hypothesis
that has been evaluated is heat flow rather than a purely
allometric approach (Roberts et al., 2010). An even harsher
critic of allometric scaling is Dr. I. Mahood of the Food and
Drug Administration. He states that: "the notion of a fixed
exponent is theoretical and there is no evidence that the
exponent of a physiological or pharmacokinetic parameter
resolves around a fixed number" (Mahood, 2010: p.2927).
Allometric scaling is also known as "fractals" when applied
to resting metabolic rate that scales to the quarter-power of
body mass (Rowland, 2007). The interested reader is referred
to Rowland (2007), Suarez & Darveau (2005), or Weibel
et al. (2004) and the references cited in them for additional
information on the topic.
Relative V02 MflX Metrics: One and Two-Sided
V02 MAX is an absolute oxygen consumption metric, as we
have defined it in Section 2. Discussion of it as a one-sided
V02 metric is contained in Appendix B, where the focus is
on its relationship with various heart rate metrics. In general,
the most common form of a one-sided V02 metric seen in
the literature is %V02 MAX. Basically, %V02 MAX is not linear
with either %HRR or %HRMAX metrics (Brawner et al., 2002;
Bruce, 1984b; Davis & Covertino, 1975; Swain 2000; Swain
& Franklin, 2002a).
As mentioned above, the most common form of a two-sided
V02 metric seen in the literature is V02RES. It more closely
follows a linear relationship with various other reserve
metrics, especially HRR (Brawner et al., 2002; Carvalho
et al., 2008, 2009; Swain & Franklin, 2002b). Even so, the
linear relationship is not tight, having mean differences
between %HRR and % V02 MAX of 6-8% in a set of exercising
individuals (Cunlia et al., 2011a, b). Additional information
on the V02 RES metric appears below in the discussion of the
metabolic chronotropic relationship (Section 7). Data on V02
RESERVE" 0I" fol" b0tl1 V02 MAX V02 REST (S0 tll3t V02 RESERVE "
can be calculated), are depicted in Table 6. Units of both mL/
kg-min and mL/min are provided. As seen, there is not much
information in the literature on the V0o metric.
2. RESERVE
Table 6. Estimates of VO, „ or both VO, „ , and VO, „ seen in the same article
2.Reserve 2.Rest 2.Max
Oxygen Consumption (in mL/kg-min)
Age Range Health V02.Rest V02.Max Mean
(Mean±SD) Status (Mean±SD) (MeaniSD) Diff. Citation Comments
Females: Normal, Healthy, or Not Specified
6
N
28.9
DW Morgan et al. 1999
n=20; SD 0fVO2.Res=
2.5
13.1 ±2.0
N
3.2 ±0.7
41.6 ±3.6
38.4
Hui & Chan 2006
n=21; Chinese data
14 ± NS N 4.0 ±1.6 28.7 ±5.4 24.7 Wilson et al. 1985 n=34; controls
24.3 ±4.2 NS 2.98 ±0.40 29.0 ± 5.3 26.0 Frey et al. 1993 n=7; untrained
31.1 ±8.8 N 3.6 ±0.4 39.7 ± 5.5 36.1 Dalleck & Kravitz, 2006 n=24; cross trainer
55.7 ±7.8 N 3.4 ±0.3 30.6 ± 6.7 27.2 Nikolai et al. 2009 n=7
Females: Active, Fit, or Athlete
13-19 Ath 3.5 ±0.1 52.7 ± 4.7 49.2 Guidetti et al. 1999 n=9; competitive gymnast
27.8 ±2.6 Fit 3.45 ±0.64 45.1 ± 5.4 41.6 Frey et al. 1993 n=6; trained exercisers
Females: Sedentary, Overweight, Obese, or Health Issues
14 ± NS EBP 3.2 ±1.4 22.7 ± 5.9 19.5 Wilson et al. 1985 n=34; subjects
26.8 ±7.9 Para. 3.02 ±0.64 28.0 ±6. 24.8 M Lee et al. 2010a n=19
Males: Normal, Healthy, or Not Specified
6 N 31.1 DW Morgan et al. 1999 n=15; SD of V02.Res=2.8
13.9 ±1.9 N 3.9 ±0.7 48.4 ±6.1 44.5 Hui & Chan 2006 n=28; Chinese data
14 ± NS N 3.2 ±1.4 22.7 ±5.9 33.8 Wlson et al. 1985 n=56; controls
19.7 ±2.1 N 45.9 Sell et al. 2008 n=12; Calculated
25.6 ±1.6 H 53.3 Sell et al. 2008 n=7; Calculated
29.2 ±6.8 N 4.4 ± 1.8 38.2 ± 9.2 43.0 Dalleck & Kravitz, 2006 n=24; cross trainer
59.1 ±7.6 N 3.5 ±0.2 31.4 ± 10.0 27.9 Nickolai et al. 2009 n=7
36
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Table 6. Estimates of VO, „ or both VO, „ , and VO, „ seen in the same article (continued)
2.Reserve 2.Rest 2.Max v 7
Oxygen Consumption (in mL/kg-min)
Age Range Health V02.Rest V02.Max Mean
(Mean±SD) Status (Mean±SD) (MeaniSD) Diff. Citation
Males: Sedentary, Overweight, Obese, or Health Issue
14 ± NS EBP 4.7 ±1.8 36.2 ±9.1 31.5
22.5 ±4.4 Sed 3.02 ± 0.64 27.9 ± 6.29 24.8
Wilson et al. 1985
M Lee et al. 2010a
Both Genders: Sedentary, Overweight, Obese, or Not Healthy
24.07 ±6.28
27.8 ±5.6
58.0 ±7.0
62.9 ±10.1
Para
SCI
Diet
Diet
3.12 ±0.50
3.8 ±22.9
2.9 ±1.3
2.9 ±1.4
26.3 ±6.2
22.9 ± NS
19.0 ±6.6
15.1 ±4.7
23.2
19.1
16.1
12.2
Females: Normal, Healthy, or Not Specified
21.8 ±6.0 Active 0.2 ± 0.02 2.4 ± 0.2 2.2
Males: Active, Fit, or Athlete
23.2 ±6.3 Active 0.30 ± 0.05 3.2 ± 0.4 2.9
Males: Sedentary, Overweight, Obese, or Health Issue
28.1 ±5.8 Para. 0.24 ± 0.05 2.24 ± 0.54 2.0
27.4 ±8.1 Para. 0.21 ± 0.08 1.56 ±0.35 1.4
Both Genders: Normal, Healthy, or Not Specified
6-14 N 0.15 ± 0.08 1.23 ±0.17 1.1
6-14 N 0.25 ±0.09 1.64 ±0.54 1.4
6-14 N 0.36 ±0.01 2.90 ±0.29 2.5
M Lee et al. 2010b
PL Jacobs et al. 1997
Colberg et al. 2003
Colberg et al. 2003
Blanksby & Reidy 1988
Blanksby & Reidy, 1988
Davis & Shephard, 1988
Davis & Shephard, 1988
Cabrera et al., 2002
Cabrera et al., 2002
Cabrera et al., 2002
Comments
n=68; subjects
n=19
n=19
n=11; assisted-walk test
n=10; no DAN
n=13; DAN
n=10; competitive dancers
n=10; competitive dancers
n=15; active
n=15; inactive
n=14; BSA<1.1
n=12; BSA 1.1-1.4
n=12; BSA>1.4
Abbreviations Abbreviations
Ath
Athlete
n
Sample size
BSA
Body Surface Area (meters**2)
N
Normal
DAN
Diabetic Autonomic Neuropathy
NS
Not Specified
DCI
Spinal Cord Injury
Para.
Paraplegic
Diet
Diabetic
SCI
Spinal Cord Injury
EBP
Elevated Blood Pressure
SD
Standard Deviation
Fit
An individual who is "fit" (active and has
V02.Res
Oxygen consumption reserve
good V02)
37
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-------
4.0
Ventilation Rate (VE) Considerations
VE is the ventilation rate associated with a specific oxygen
consumption. It also is known as the minute ventilation rate,
which is equal to breathing rate (fR, in breaths per minute:
bpm) * Tidal Volume (VT, in liters). Note that VT is one of the
few times in this report that the "V" symbol is for a volume,
not a rate. (See the glossary.) VE also = alveolar ventilation
rate (VA) + dead space ventilation rate (VD). See McArdle
et al. (2001) for more information on these relationships.
VE is an indicator of the body's ability to provide oxygen to
exercising muscles, but is not a limiting factor in exercising
at sea level. That is due to the fact that normal healthy people
do not approach diffusion limitations even at maximal work
rates (Beidleman et al., 1999). The indicator role that VE
plays is not as clear-cut for people exercising at altitude.
The units of VE are usually inL/min; only infrequently are
they presented as L/kg-min (Wilmore & Sigerseth, 1967).
Since there are so few articles using body mass-normalized
VE, we do not present any data using that metric. This is not
to say that body mass is not important in characterizing VE~it
is, but >95% of the articles available to us report data VE in
L/min units only.
VE increases linearly with increasing V02 up to about
60-70% of V02 MAX in adults, approximately equal to a
person's ventilatory anaerobic threshold (VAT) (Burnley
et al., 2011; Hansen et al., 1984; Hebestreit et al., 2000;
Washington, 1989, 1993). That threshold is variously called
the aerobic or the gas exchange threshold by different authors
(Wassennan, 1984). Above that inflection point, VE increases
faster than V02 resulting in an upward-increasing slope
for the relationship (Bernard & Franklin, 1979). In adults,
VAT occurs around 58-65% of V02 MAX and this inflection
point can be increased due to exercise and increasing fitness
(Haffor et al., 1990). Part of the non-linear increase in VE
with workload is due to the "cost of breathing," where the
energy needed to meet additional V02 demands increases
non-linearly with workload (Lorenzo & Babb, 2012;
McArdle et al., 2001). The percentage of total oxygen
consumption needed for the cardiovascular, peripheral
circulation, and respiratory systems is around 3-5% at low
workloads, but can be 10-15% at high workloads. There
does not seem to be a gender difference in the changes in
relative workload regardless of the sex-related differences
in ventilatory capacity (Lorenzo & Babb, 2012). Vella et al.
(2006) present data that indicates that the average oxygen
cost of breathing is 8.8 ± 3.3% at V02MAX, and ranged from
5.0-17.6% in different individuals. The metric of oxygen
cost is AV02 / AVe with units of mL/L. Increased oxygen
cost is met by increasing breathing and ventilation rates,
which in turn increases VQ. See Section 5. The increased
cost of breathing is not primarily responsible for pulmonary
function decrements often seen in exercise studies, which are
different for females than males (Coast et al., 1999; Sheel &
Guenette, 2008).
VAT for children and adolescents occurs at a higher
percentage of V02MAX. In children 6-15, VAT appears
between 71 ± 10% to 75 ± 13% in males and between 68 ±
10% to 72 ± 13% in females (Washington et al., 1988). The
overall range of these percentages were 37-97% for males
and 42-95% for females, so there is a wide range within
youth where relative VAT occurs (Washington et al., 1988).
The highest percentage values are for fit individuals.
VE and VE MAX measurements, like oxygen consumption data,
are protocol-dependent, varying considerably depending
upon the method used to estimate them (Garner et al., 2011;
Katch et al., 1974; Magel & Faulkner, 1967; Mahon et al.,
1998; Phillips et al., 2008; Price & Campbell, 1997; Toner
et al., 1990). VE MAX obtained using a treadmill is higher than
that estimated from a cycle ergometer, and the difference
usually is statistically significant (Katch et al., 1974; Lukasi
et al., 1989; McArdle & Magel, 1976; Rivera-Brown &
Frontera., 1998). Some studies, on the other hand, show small
differences in estimated VE MAX between the two exercise
modes (McArdle et al., 1973). Where possible, VE MAX values
shown in this report are from treadmills using a continuous
protocol without a mouthpiece. If a "sports mouthpiece" is
used for protecting an athlete's teeth subsequent VE estimates
using it will be slightly lower than estimates obtained without
a mouthpiece. When subjects can breathe through their nose
only, VE estimates are significantly higher (Garner et al.,
2011). Continuous protocols generally produce higher VE
estimates than discontinuous exercise protocols using the
same piece of equipment and nose/mouth breathing method
(McArdle et al., 1973).
VE MAX estimates obtained from the same subjects at different
times using the same measuring method and protocol are
reasonably reproducible on a group-mean basis (Rivera-
Brown & Frontera, 1998). The within-subject COV for both
treadmill and ergometer testing procedures (COV's were not
presented separately for each method) was estimated to be
9% on average for patients with pre-existing heart failure
problems. Their VE MAX's were estimated for a symptom-
limited maximum workload, considered also to be the point
where V02 MAX occurred. Individual COV's for a test on
the same piece of equipment ranged from 1.3-16.5%, quite
a wide range (Keteyian et al., 2010). I could not uncover
39
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additional papers providing the same type of data for
"normals," even when doing a literature search specifically
on the topic. Quite surprising, actually.
In general, VE MAX is higher in males than in females of the
same age and fitness level (Beals et al., 1996; Kamon &
Pandolf, 1972; Krahenbuhl et al., 1977, 1978, 1979). VEMAX
declines with age in both genders, as do most physiological
metrics (Pollock, et al., 1997). VEis more highly correlated
with BM than age. Correlations of VE with age for
submaximal activities are 0.11-0.76 (median=0.40), and are
0.30-0.73 withBM (median=0.57) (Beals et al., 1996).
Overall, we reviewed 543 papers for compilation of VE MAX
data. Useable data—having both age- and gender-specific
VE MAX and/or VQ data that utilized U.S. citizens-were
obtained and entered into Table 7 from 135 of the papers that
we reviewed (24.9%). These papers provided estimates for
376 samples depicted on individual "lines" of VE MAX data:
252 entries having sample mean and SD age information, and
124 having only sample age range data. Of the 408 papers
that were not used, 165 were from non-US studies (30.4%),
and 119 did not provide VE MAX data for any metric (21.9%).
Other data issues included presenting only mixed-gender
results (42 papers: 7.7% of the total reviewed); using a
protocol other than a treadmill or cycle ergometer to estimate
VE max; or presenting VE MAX data only graphically (31 papers:
5.7%). Other unused papers presented only sub-maximal,
activity-specific ventilation rate (VE ACT) information (11
papers: 2.0%). Finally, there were 29 review or conceptual
papers (5.3%) that were not used for Tables 7-9, and 11
"redundant" papers (2%) that presented relevant information
but were previously included in our Tables.
There is a temporal and gender pattern to the articles cited in
Table 7. Articles on females are almost equally distributed
among three temporal categories: before 1990, in the 1990s,
and in the 2000-2010s. There are between 61-66 articles for
each time span. The majority of articles on males, however,
occurred before 1980 (104), with 63 published in the
Table 7. Estimates of V,, „ seen in the literature
E.Max
VEmax (L/min)
cov
SD Cond. Mean SD (%)
Females: Normal, Healthy, or Not-specified
a. Mean & SD statistics are provided for age
Age
Mean
Citation
Sample
Size (n) Comments
7.6
1.0
NS
40.5
10.0
24.7
Krahenbuhl et al. 1978
49
8.2
1.0
N
37.6
7.7
20.5
Treuth et al. 1998
12
8.5
0.8
N
52.7
12.7
24.1
Wilmore& Sigerseth 1967
20
8.7
1.1
H
54.6
12.6
23.1
Krahenbuhl et al. 1977
20
9.1
1.5
NS
45.2
7.2
15.9
Gilliam et al. 1977
15
9.8
0.7
N
53.2
8.8
16.5
Loftin et al. 1998
19
10.4
0.5
N
59.5
15.8
26.6
Wilmore& Sigerseth 1967
20
12.4
0.5
N
70.1
10.9
15.5
Wilmore& Sigerseth 1967
22
Pregnant
13.7
0.6
N
85.0
13.0
15.3
Grossner et al. 2005
10
15.6
3.4
N
82.2
12.4
15.1
Moffatt et al. 1984
13
Controls
16.9
3.0
N
88.6
16.7
18.8
Loftin et al. 1998
19
18.9
2.5
N
70.7
14.6
20.7
Burke 1977
8
Group 1
18.9
2.5
N
81.3
22.6
27.8
Burke 1977
7
Control
19.0
0.9
H
67.0
13.0
19.4
Mahler et al. 2001
14
19.5
1.6
N
86.5
17.4
20.1
A Perry et al. 1988
24
Group 2
19.6
1.6
N
99.1
16.7
16.9
A Perry et al. 1988
21
Group 1
19.6
2.0
N
85.9
14.8
17.2
A Perry et al. 1988
24
Control
19.7
1.0
N
88.5
10.6
12.0
Lesmes et al. 1978
8
Group 1
19.7
1.6
N
95.6
18.3
19.1
Lesmes et al. 1978
8
Group 2
19.7
1.9
N
88.6
15.7
17.7
Vogel et al. 1986
212
V02max=46-132
19.9
2.0
N
80.5
9.5
11.8
Lesmes et al. 1978
8
Group 3
20.5
1.6
N
68.3
14.0
20.5
Pintar et al. 2006
15
Normal weight
20.8
1.1
N
81.9
11.7
14.3
McArdle et al. 1972
35
22.4
3.5
N
79.7
26.2
32.9
Lesmes et al. 1978
8
Group 4
22.8
3.2
N
82.0
20.0
24.4
Grossner et al. 2005
10
23.8
3.7
N
95.7
13.3
13.9
Gonzales 2002
8
24.1
3.5
N
87.8
14.9
17.0
Beidleman et al. 1995
10
Control group
26.2
10.4
N
85.1
12.9
15.2
Stephenson et al. 1982
6
Mean of all cycle days
40
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Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age
Mean
SD
Cond.
Mean
SD
COV
(%)
Citation
Sample
Size (n) Comments
29.4
3.8
n
81.4
14.6
17.9
Jaque-Fort. et al. 1996
22
Pregnant
30.3
4.3
H
78.0
16.0
20.5
Treuth et al. 2005
17
Normal BMI (<19.8)
30.4
4.3
N
74.9
15.0
20.0
Khodiguian et al. 1996
13
30.4
4.3
N
67.0
14.0
20.9
Treuth et al. 2005
34
Normal BMI; 6 wk PP
30.8
3.9
N
75.0
16.0
21.3
Treuth et al. 2005
17
Low BMI (<19.8)
30.8
4.4
H
74.0
15.0
20.3
Treuth et al. 2005
34
Normal BMI; 27 wk PP
30.9
3.9
N
58.0
12.0
20.7
Treuth et al. 2005
17
Low BMI; 6-wk PP
31.0
3.8
N
78.6
10.3
13.1
Jaque-Fort. et al. 1996
7
Postpartum
31.4
4.0
N
69.0
18.0
26.1
Treuth et al. 2005
17
Low BMI; 27 wk PP
31.8
11.1
H
72.2
12.7
17.6
Flint et al. 1974
7
VE range=56.4-89.9
33.0
3.0
H
100.0
14.0
14.0
Beidleman et al. 1999
8
33.5
4.9
N
77.4
8.8
11.4
Scharff-Olsen et al. 1992
11
37.5
12.0
NS
90.7
19.0
20.9
Nieman et al. 2005
15
Walkers aged 20-55
59.0
4.1
NS
46.8
9.1
19.4
Fielding et al. 1997
17
Probably sedentary
62.0
7.0
N
58.3
10.3
17.7
Sheldahl etal. 1996
11
63.3
2.9
H
50.1
9.8
19.6
Kohrt et al. 1991
16
Experimental Group
64.0
3.1
H
48.8
9.4
19.3
Kohrt et al. 1991
57
Control group
65.5
7.8
N
42.6
16.5
38.7
Carteret al. 1994
16
68.6
5.7
H
56.5
15.4
27.3
Panton et al. 1996
36
b. Complete age statistics are not provided
8-11
N
64.1
11.9
18.6
Rowland & Cunningham
1997
9
Longitudinal Study
9-11
N
50.5
8.9
17.6
Vaccaro & Clarke 1978
15
3 males
9-12
N
71.0
14.1
19.9
Rowland & Cunningham
1997
9
Longitudinal Study
11.1
N
55.9
Girandola et al. 1981
15
Pre-pubertal
10-13
N
76.3
19.8
26.0
Rowland & Cunningham
1997
9
Longitudinal Study
12.7
N
56.5
11.1
19.6
Eisenman & Golding 1975
8
Group 1
12.7
N
57.6
11.1
19.3
Eisenman & Golding 1975
8
Controls
11-14
N
83.1
18.0
21.7
Rowland & Cunningham
1997
9
Longitudinal Study
12-15
N
95.3
22.7
23.8
Rowland & Cunningham
1997
9
Longitudinal Study
15.9
N
76.9
Girandola et al. 1981
15
Pubertal
14-17
N
69.5
4.1
5.9
Drinkwater & Horvath 1972
7
17-28
N
80.3
18.4
22.9
Fringer & Stull, 1974
44
<19
N
70.7
18.5
26.2
Drinkwater et al. 1975
10
VQ=32.0
19.5
H
91.0
13.6
14.9
Humphrey & Falls 1975
15
VE @ HR.Max
19.6
N
64.5
11.5
17.8
Eisenman & Golding 1975
8
Group 2
19.6
N
60.2
10.4
17.3
Eisenman & Golding 1975
8
Control
19-24
NS
58.3
13.3
22.8
Rockenfeller & Burke 1979
21
29.0
N
72.0
15.1
21.0
Drinkwater et al. 1975
10
VQ=34.6
29.0
N
96.8
23.7
24.5
Diaz et al. 1978
5
Protocol study
30-39
N
68.4
10.6
15.5
Drinkwater et al. 1975
14
VQ=34.9
40-49
N
65.2
6.3
9.7
Drinkwater et al. 1975
13
VQ=36.2
50- 59
N
56.6
10.0
17.7
Drinkwater et al. 1975
6
VQ=36.6
55-59
N
56.5
13.4
23.7
Hollenberg et al. 1998
100
>60
N
45.4
11.8
26.0
Drinkwater et al. 1975
6
VQ=29.1
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age COV Sample
Mean SD Cond. Mean SD (%) Citation Size (n) Comments
60-64
N
51.2
9.3
18.2
Hollenberg et al. 1998
96
60-67
NS
56.0
14.0
25.0
Blackie et al. 1991
20
60-69
N
49.0
12.4
25.3
Hollenberg et al. 2006
339
65-69
N
49.3
9.6
19.5
Hollenberg et al. 1998
109
67.0
N
47.0
12.2
26.0
Hollenberg & Tager 2000
579
68.0
N
38.8
11.4
29.4
Hollenberg et al. 2006
293
70-74
N
45.8
9.7
21.2
Hollenberg et al. 1998
88
70-79
NS
48.0
12.0
25.0
Blackie et al. 1991
20
75-79
N
43.5
10.6
24.4
Hollenberg et al. 1998
36
80-84
N
40.6
10.6
26.1
Hollenberg et al. 1998
18
>85
N
34.9
6.0
17.2
Hollenberg et al. 1998
7
Females: Active, Fit, Athlete
a. Mean & SD statistics are provided for age
12.0
0.0
Ath
77.2
12.9
16.7
GD Wells et al. 2006
7
Elite swimmers
13.0
0.0
Ath
78.7
13.3
16.9
GD Wells et al. 2006
30
Elite swimmers
14.0
0.0
Ath
83.2
13.1
15.7
GD Wells et al. 2006
18
Elite swimmers
15.0
0.0
Ath
81.2
17.8
21.9
GD Wells et al. 2006
30
Elite swimmers
15.2
4.1
Ath
92.5
14.6
15.8
Moffatt et al. 1984
13
Gymnasts
16.0
0.0
Ath
87.9
9.5
10.8
GD Wells et al. 2006
10
Elite swimmers
17.0
0.0
Ath
72.5
10.5
14.5
GD Wells et al. 2006
6
Elite swimmers
18.0
0.0
Ath
99.2
4.1
4.1
GD Wells et al. 2006
2
Elite swimmers
19.0
1.0
Ath
119.0
18.0
15.1
DW Hill & Rowell 1997
13
Track team members
19.7
1.4
Fit
88.9
9.4
10.6
Astorino et al. 2004
13
VQ=36.7; preseason
19.7
1.4
Ath
96.2
13.0
13.5
Astorino et al. 2004
9
VQ=36.7; post-season
20.4
1.6
Ath
109.6
12.4
11.3
Wilmore et al. 1990
8
Amenorrheic runners
20.7
3.2
Fit
84.9
15.6
18.4
MA Sharp et al. 2002
155
Army recruits
21.2
2.3
Ath
98.2
8.7
8.9
McArdle et al. 1972
6
Misc. sports events
21.4
3.4
Fit
99.6
15.0
15.1
MA Sharp et al. 2002
122
Army recruits
21.5
1.9
Act
107.5
13.9
12.9
Beidleman et al. 1995
10
Runners
23.0
3.0
Ath
128.1
16.7
13.0
Kozak-Collins et al. 1994
7
Competitive cyclists
23.3
3.7
Fit
94.0
11.3
12.0
Williford etal. 1989
10
Aerobic dancers
23.5
6.4
Act
90.3
16.0
17.7
Meyers & Sterling 2000
24
Equestrians
23.6
5.7
Ath
111.6
13.8
12.4
Wilmore et al. 1990
5
Eumenorrheic runners
25.0
4.6
Fit
93.4
5.9
6.3
Wilmore et al. 1990
8
Eumenorrheic controls
25.2
3.1
Act
97.6
12.5
12.8
Astorino et al. 2011
9
VQ=39.0
26.0
3.7
Act
118.6
16.8
14.2
Tanaka et al. 1997
14
Endurance trained
26.9
5.3
Act
111.4
17.9
16.1
SD Fox et al. 1993
9
32.4
4.5
Ath
108.9
8.6
7.9
Wilmore et al. 1974b
11
Enduranced runners
34.0
4.6
Act
117.7
17.4
14.8
Tanaka et al. 1997
21
Enduranced trained
42.8
2.0
Ath
88.9
12.7
14.3
Hawkins et al. 2001
24
Master's athlete
45.0
3.6
Act
109.7
14.8
13.5
Tanaka et al. 1997
13
Enduranced trained
49.8
2.8
Ath
83.9
10.0
11.9
Hawkins et al. 2001
16
Master's athlete
51.2
2.4
Ath
83.8
14.2
16.9
Hawkins et al. 2001
24
Master's athlete
54.0
4.8
Act
103.3
19.2
18.6
Tanaka et al. 1997
23
Enduranced trained
58.3
3.2
Ath
76.3
13.6
17.8
Hawkins et al. 2001
16
Master's athlete
63.0
3.0
Fit
78.4
15.8
20.2
Kohrt et al. 1991
19
64.6
3.9
Ath
80.3
9.0
11.2
Hawkins et al. 2001
9
Visit #1
66.0
3.6
Ath
86.7
20.2
23.3
Tanaka et al. 1997
13
73.2
5.7
Ath
61.2
13.5
22.1
Hawkins et al. 2001
9
Visit #2
-------
Table 7. Estimates of VEMax seen in the literature (continued)
VEmax (L/min)
Age COV Sample
Mean SD Cond. Mean SD (%) Citation Size (n) Comments
b. Complete age statistics are not provided
9-11
Fit
45.7
9.4
20.6
Vaccaro & Clarke 1978
15
3 males
12-13
Ath
75.2
8.4
11.2
Drinkwater& Horvath 1971
2
14-15
Ath
80.2
11.4
14.2
Drinkwater& Horvath 1971
11
14-17
Ath
77.5
10.3
13.3
Drinkwater& Horvath 1972
7
16-18
Ath
90.9
4.4
4.8
Drinkwater& Horvath 1971
2
< 19
Fit
71.4
14.2
19.9
Drinkwater et al. 1975
11
VQ=31.9
20.0
Act
73.9
13.2
17.9
Blesssing et al. 1987
13
Group 1
20.0
Act
75.1
13.2
17.6
Blesssing et al. 1987
13
Group 2
18-21
Fit
90.8
17.4
19.2
Daniels et al. 1982
7
Army cadets
18-23
Act
76.9
8.5
11.1
Kamon & Pandolf 1972
4
19-21
Fit
85.6
8.1
9.5
Kamon & Pandolf 1972
4
19-29
Fit
77.0
9.6
12.5
Drinkwater et al. 1975
16
VQ=34.7
30-39
Fit
83.1
16.8
20.2
Drinkwater et al. 1975
10
VQ=36.4
40-49
Fit
82.6
11.2
13.6
Drinkwater et al. 1975
7
VQ=35.0
50-59
Fit
60.7
6.4
10.5
Drinkwater et al. 1975
6
VQ=30.4
Females: Sedentary, Overweight, Obese, or Health Issues
a. Mean & SD statistics are provided for age
8.7 0.7 OW 50.2 8.9 17.7 Treuth et al. 1998 12
19.4
1.5
OW
76.8
7.4
9.6
Pintar et al. 2006
15
Fit
21.1
3.0
OW
58.6
3.0
5.1
Pintar et al. 2006
15
Low fit also
21.9
2.0
Sed
53.4
14.8
27.7
Pintar et al. 2006
15
Normal weight
25.0
3.3
Sed
88.1
15.6
17.7
Tanaka et al. 1997
11
25.0
4.0
Sed
63.5
12.0
18.9
Schiller et al. 2001
12
Caucasian
25.0
3.0
Sed
63.2
13.2
20.9
Schiller et al. 2001
12
Hispanic
31.2
4.5
OW
78.0
10.0
12.8
Treuth et al. 2005
12
High BMI (>28.6)
31.3
4.5
OW
65.0
11.0
16.9
Treuth et al. 2005
12
High BMI; 6 wk PP
31.7
4.6
OW
70.0
9.0
12.9
Treuth et al. 2005
12
High BMI; 27 wk PP
31.9
4.1
Sed
74.4
14.3
19.2
JLP Roy et al. 2006
20
AA
32.4
5.8
Sed
83.3
15.4
18.5
JLP Roy et al. 2006
30
Caucasian
32.8
5.9
OW
81.8
13.6
16.6
Nehlsen et al. 1991
18
Control group
33.0
3.3
Sed
94.9
9.9
10.4
Tanaka et al. 1997
11
33.0
4.0
Sed
66.4
9.7
14.6
Schiller et al. 2001
14
Causcasian
34.0
4.0
Sed
63.3
10.8
17.1
Schiller et al. 2001
13
Hispanic
34.9
7.2
Sed
83.3
15.8
19.0
GR Hunter et al. 2004
39
White
35.5
7.0
Sed
74.5
13.8
18.5
GR Hunter et al. 2004
35
Black
36.0
6.8
OW
81.1
11.5
14.2
Nehlsen et al. 1991
18
Exercise group
43.7
11.3
O
76.2
17.8
23.4
Utter et al. 1998
22
Control group
44.0
3.0
Sed
56.1
15.8
28.2
Schiller et al. 2001
8
Hispanic
44.6
11.5
O
79.1
17.0
21.5
Utter et al. 1998
21
Caucasian
45.0
3.7
Sed
78.5
11.6
14.8
Tanaka et al. 1997
11
Groupl
45.0
5.0
Sed
64.2
12.3
19.2
Schiller et al. 2001
21
Caucasian
45.4
9.7
O
76.5
14.3
18.7
Utter et al. 1998
26
Group 2
48.0
7.1
OW
76.0
18.0
23.7
VK Phillips et al. 2008
20
Treadmill
48.7
10.3
O
76.3
14.1
18.5
Utter et al. 1998
22
Group 3
53.0
4.0
Sed
57.0
12.4
21.8
Schiller et al. 2001
15
Hispanic
54.0
4.5
Sed
80.5
15.2
18.9
Tanaka et al. 1997
20
54.0
5.0
Sed
66.0
18.4
27.9
Schiller et al. 2001
26
Caucasian
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age
Mean
SD
Cond.
Mean
SD
cov
(%)
Citation
Sample
Size (n) Comments
64.0
4.0
Sed
52.9
22.5
42.5
Schiller et al. 2001
18
Caucasian
64.0
4.0
Sed
64.7
16.4
25.3
Tanaka et al. 1997
16
64.0
4.0
Sed
64.7
16.4
25.3
DeVito et al. 1997
16
65.0
4.0
Sed
54.0
15.7
29.1
Schiller et al. 2001
5
Hispanic
75.2
4.6
Sed
58.7
9.6
16.4
Kent-Braun & Ng 2000
9
b. Complete age statistics are not provided
17.5
Sed
75.9
0.9
1.2
Kamon & Pandolf 1972
2
17-22
Sed
79.0
11.9
15.1
Kearney et al. 1976
14
Exercise group 1
17-22
Sed
75.5
4.4
5.8
Kearney et al. 1976
13
Exercise group 2
20-29
Sed
63.2
13.2
20.9
Schiller et al. 2001
12
Hispanic
20-29
Sed
63.5
12.0
18.9
Schiller et al. 2001
14
Caucasian
25-44
Sed
68.0
10.0
14.7
Dowdy et al. 1985
18
Group 1; VQ=32
25-44
Sed
62.6
12.4
19.8
Dowdy et al. 1985
10
Controls; VQ=32
30-39
Sed
67.3
10.8
16.0
Schiller et al. 2001
13
Hispanic
30-39
Sed
66.4
9.7
14.6
Schiller et al. 2001
14
Caucasian
35.5
Sed
69.6
9.2
13.2
Getchell 1975
11
Age range: 28-51
40-49
Sed
56.1
15.8
28.2
Schiller et al. 2001
8
Hispanic
40-49
Sed
64.2
12.8
19.9
Schiller et al. 2001
21
Caucasian
50-59
Sed
57.0
12.4
21.8
Schiller et al. 2001
15
Hispanic
50-59
Sed
66.0
18.4
27.9
Schiller et al. 2001
26
Caucasian
>60
Sed
52.9
22.5
42.5
Schiller et al. 2001
18
Caucasian
>60
Sed
54.0
15.7
29.1
Schiller et al. 2001
5
Hispanic
Females: Health or Other Issues
29.4
3.8
Preg.
81.4
14.5
17.8
Khodiguian et al. 1996
22
30 weeks pregnant
30.4
6.7
MR
65.4
13.1
20.0
Fernhall et al. 1996
29
31.7
7.2
MR-D
47.3
9.5
20.1
Fernhall et al. 1996
16
62.0
6.6
CAD
59.2
9.9
16.7
Sheldahl et al. 1996
9
63.7
5.8
COPD
26.1
7.2
27.6
Carter et al. 1994
58
Severe
64.8
6.4
COPD
39.9
8.2
20.6
Carter et al. 1994
23
Mild
65.0
5.2
COPD
33.9
8.2
24.2
Carter et al. 1994
42
Moderate
Males: Normal, Healthy, or Non-specified
a. Mean & SD statistics are provided for age
7.9
0.9
NS
44.5
11.6
26.1
Krahenbuhl et al. 1978
49
9.4
1.7
NS
50.3
8.0
15.9
Gilliam et al. 1977
35
9.5
0.7
H
59.3
11.3
19.1
Becker & Vaccaro 1983
13
Experimental Group
9.6
2.6
N
51.7
9.2
17.8
Fahey et al. 1979
7
Pubertal stage 1
10.0
0.6
H
60.7
12.0
19.8
Becker & Vaccaro 1983
13
Control Group
10.2
1.2
NS
57.8
11.4
19.7
Kanaley & Boileau 1988
10
Prepubescent
10.7
0.7
N
57.1
14.9
26.1
Fahey et al. 1979
6
Pubertal stage 2
10.8
0.4
H
60.3
10.1
16.7
Haffor et al. 1990
5
12.5
0.9
NS
85.7
14.1
16.5
Maksud & Coutts 1971
17
12.8
0.9
H
70.6
16.8
23.8
Williford et al. 1996
5
A-A
12.8
1.1
N
67.4
16.1
23.9
Boileau et al. 1977
21
Part of a test/retest
12.8
1.5
H
80.3
35.2
43.8
Williford et al. 1996
12
A-A
12.9
1.2
N
67.9
14.9
21.9
Fahey et al. 1979
6
Pubertal stage 3
13.7
0.5
NS
87.2
14.1
16.2
Kanaley & Boileau 1988
10
Pubescent
15.1
2.6
N
90.9
15.8
17.4
Fahey et al. 1979
5
Pubertal stage 4
15.4
1.8
N
80.2
12.7
15.8
Fahey et al. 1979
3
Pubertal stage 5
18.7
0.6
N
101.3
7.7
7.6
Wolfe et al. 1976
9
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age
Mean
SD
Cond.
Mean
SD
cov
(%)
Citation
Sample
Size (n) Comments
19.2
4.9
N
97.7
24.7
25.3
Burke 1977
9
Group 1
19.2
4.9
N
116.0
19.9
17.2
Burke 1977
7
Control
19.7
2.2
N
139.1
21.3
15.3
Fogel et al. 1986
210
V02.Max range-84-194
19.9
0.9
N
148.7
7.9
5.3
Harms et al. 1995
8
FM<7 kg
20.0
1.5
N
147.0
30.0
20.4
Misplaced citation
9
20.7
3.1
H
132.0
14.8
11.2
Pollock 1977
10
Lean
20.8
6.5
H
107.0
23.0
21.5
Mahler et al. 2001
14
21.0
8.5
H
92.4
23.6
25.5
Kang et al. 1997
8
21.1
1.5
NS
135.4
15.5
11.4
Kanaley & Boileau 1988
10
Adult
21.1
1.6
N
153.9
19.1
12.4
Fl Katch et al. 1974
50
Treadmill test
21.4
2.4
H
151.7
17.4
11.5
VKatch & Henry 1972
35
21.9
4.0
N
168.0
10.4
6.2
McArdle et al. 1973
15
Has 3 athletes
22.3
2.3
N
141.1
13.9
9.9
JA Davis et al. 1976
39
23.2
7.4
N
136.2
6.2
4.6
Trappe et al. 1996
15
Longitudinal study (T1)
23.8
3.4
H
123.2
26.0
21.1
Schelegle et al. 1989
20
03 sensitive
25.2
5.1
H
115.3
12.7
11.0
Schelegle et al. 1989
20
Not 03 sensitive
26.1
5.1
N
134.8
24.6
18.2
Gonzales 2002
8
27.6
5.6
N
149.0
20.8
14.0
VL Katch & Fl Katch 1973
75
30.2
9.2
N
132.0
18.8
14.2
Simon et al. 1983
5
39.1
7.4
NS
108.1
31.5
29.1
JA Davis et al 1979
7
Control
43.0
7.2
NS
105.8
15.3
14.5
JA Davis et al. 1979
97
Experimental
45.3
8.9
H
111.2
5.4
4.9
Trappe et al. 1996
15
Longitudinal study (T2)
60.0
4.7
N
71.3
13.4
18.8
Carter et al. 1994
13
62.0
6.0
N
102.4
15.9
15.5
Sheldahl etal. 1996
9
63.7
3.1
H
80.2
15.5
19.3
Kohrt et al. 1991
53
Experimental group
64.8
3.6
H
75.8
14.0
18.5
Kohrt et al. 1991
19
Control group
64.2
9.4
NS
144.0
25.0
17.4
Pollock et al. 1987
13
Ex-Athlete
68.7
5.1
H
87.0
22.5
25.9
Panton et al. 1996
19
b. Complete age statistics are not provided
3-4
H
22.8
5.3
23.2
Shuleva et al. 1990
9
33% female
5-6
H
27.1
6.1
22.5
Shuleva et al. 1990
13
23% female
8.0
N
58.7
5.1
8.7
Krahenbuhl et al. 1979
10
Group 2
8.0
N
61.5
7.4
12.0
Krahenbuhl et al. 1979
10
Group 1
8-11
N
65.9
8.0
12.1
Rowland & Cunningham
1997
11
Longitudinal study
9-12
N
78.2
9.3
11.9
Rowland & Cunningham
1998
11
Longitudinal study
10-13
N
85.5
12.3
14.4
Rowland & Cunningham
1999
11
Longitudinal study
11 - 14
N
94.4
13.1
13.9
Rowland & Cunningham
2000
11
Longitudinal study
12-15
N
105.1
16.3
15.5
Rowland & Cunningham
2000
11
Longitudinal study
18-23
NS
145.8
Seals & Mullin 1982
12
Untrained
19-47
H
124.6
24.4
19.6
Lukasi et al. 1989
16
Bruce protocol
20-29
N
94.9
15.9
16.8
Mitchell et al. 1958
36
20-35
N
123.2
16.7
13.6
Milesis et al. 1976
16
Control group
20-35
N
113.5
15.3
13.5
Milesis et al. 1976
14
Exercise group 1
20-35
N
127.5
15.5
12.2
Milesis et al. 1976
17
Exercise group 2
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age
COV
Sample
Mean
SD Cond.
Mean
SD
(%)
Citation
Size (n) Comments
20-35
N
117.5
17.2
14.6
Milesis et al. 1976
12
Exercise group 3
21 -35
N
109.5
16.4
15.0
Gettman et al. 1976
11
Controls-prisoners
22-35
N
123.0
22.2
18.0
Gettman et al. 1976
11
Group 1-prisoners
23-35
N
117.6
14.2
12.1
Gettman et al. 1976
20
Group 2-prisoners
24-35
N
114.4
16.1
14.1
Gettman et al. 1976
13
Group 3-prisoners
28.6
N
132.1
17.7
13.4
Diaz et al. 1978
7
Protocol test
30-39
N
89.4
17.3
19.4
Mitchell et al. 1958
18
40-49
N
88.8
21.3
24.0
Mitchell et al. 1959
8
45-59
N
114.0
24.0
21.1
Meyers et al. 1991
68
55-59
N
88.7
15.2
17.1
Hollenberg et al. 1998
79
60-64
N
85.8
22.1
25.8
Hollenberg et al. 1998
66
60-67
NS
83.2
7.3
8.8
Saltin & Grimby 1968
5
Ex-Ath., 10 y no train.
60-69
NS
83.0
14.0
16.9
Blackie et al. 1991
20
60-79
N
91.0
21.0
23.1
Meyers et al. 1991
64
65-69
N
78.8
20.3
25.8
Hollenberg et al. 1998
73
66.0
N
80.4
21.5
26.7
Hollenberg et al. 2006
253
68.0
N
75.8
21.6
28.5
Hollenberg & Tager 2000
419
70.0
N
62.4
15.9
25.5
Hollenberg et al. 2006
189
70-74
N
75.6
14.0
18.5
Hollenberg et al. 1998
81
70-79
NS
66.0
12.0
18.2
Blackie et al. 1991
11
75-79
N
63.6
15.7
24.7
Hollenberg et al. 1998
42
80-84
N
56.2
8.6
15.3
Hollenberg et al. 1998
189
>85
N
53.5
6.0
11.2
Hollenberg et al. 1998
4
Males: Active, Fit, or Athlete
a. Mean & SD statistics are provided for age
12.1
1.2
Act
85.3
15.6
18.3
J Davis & Oldridge 1971
6
Longitudinal study T1
13.0
0.0
Ath
93.1
18.1
19.4
GD Wells et al. 2006
8
Elite swimmers
14.0
0.0
Ath
101.6
19.3
19.0
GD Wells et al. 2006
24
Elite swimmers
14.0
1.2
Act
103.2
26.9
26.1
J Davis & Oldridge 1971
6
Longitudinal study T2
15.0
0.0
Ath
99.7
13.3
13.3
GD Wells et al. 2006
40
Elite swimmers
16.0
0.0
Ath
99.7
19.2
19.3
GD Wells et al. 2006
10
Elite swimmers
16.0
1.3
Fit
128.5
17.0
13.2
Rivera-Brown & Frontera
1998
20
Treadmill test
17.0
0.0
Ath
113.6
17.4
15.3
GD Wells et al. 2006
9
Elite swimmers
18.0
0.0
Ath
119.8
10.5
8.8
GD Wells et al. 2006
7
Elite swimmers
18.0
2.6
Ath
133.0
19.9
15.0
Rundell 1996
7
Olympic speed skaters
19.0
0.8
Ath
142.5
17.5
12.3
McMiken & Daniels 1976
8
Distance runners
19.5
2.5
Ath
156.5
13.7
8.8
Mahood et al. 2001
13
Cross-country skiers
19.8
1.0
Ath
127.0
13.0
10.2
Magel & Faukler 1967
26
Swimmers; 49 bpm
19.9
2.7
Fit
132.0
19.7
14.9
MA Sharp et al. 2002
171
Army recruits
20.4
1.8
Ath
127.9
27.4
21.4
McArdle & Magel 1979
23
Treadmill test
21.1
3.4
Fit
161.4
13.4
8.3
McArdle et al. 1978
11
Experimental group
21.2
1.6
Act
153.9
19.1
12.4
Fl Katch et al. 1974
50
Treadmill values
21.3
2.6
Act
169.0
10.7
6.3
Pollock 1977
8
Fit, good runners
21.4
2.4
Act
151.7
17.4
11.5
V Katch & Henry 1972
35
Part of the above?
21.4
2.6
Fit
167.2
8.4
5.0
McArdle et al. 1978
8
Control group
21.8
3.4
Fit
141.6
20.2
14.3
MA Sharp et al. 2002
122
Army recruits
23.0
3.0
Fit
132.9
12.4
9.3
LaFrenz et al. 2008
10
Endurance trained
24.0
2.5
Act
158.0
23.1
14.6
Glass et al. 1997
6
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age
Mean
SD
Cond.
Mean
SD
cov
(%)
Citation
Sample
Size (n) Comments
24.8
5.7
Act
144.1
27.9
19.4
Wallick et al. 1995
16
Roller skaters
25.3
5.5
Act
147.5
25.4
17.2
Astorino et al. 2011
11
24.5
4.0
Ath
173.4
14.6
8.4
Lounana et al. 2007
11
Elite amateur cyclists
25.5
3.5
Ath
174.1
24.0
13.8
Lounana et al. 2007
15
Professional cyclists
25.7
3.5
Fit
150.5
3.8
2.5
Trappe et al. 1996
18
26.0
3.0
Ath
170.0
12.0
7.1
Mahler et al. 1984
8
Olympic rowers
26.2
3.0
Ath
168.0
14.6
8.7
Pollock 1977
20
Elite runners
27.1
6.7
Act
130.4
5.1
3.9
Trappe et al. 1996
18
28.6
3.3
Act
145.7
16.4
11.3
Harms et al. 1997
7
Control case
30.4
7.4
Fit
150.8
21.1
14.0
Yuen et al. 2011
14
Active cyclists
42.4
14.0
Ath
146.5
35.0
23.9
Faria et al. 1996
16
Cross-country skiers
44.5
2.8
Ath
143.7
18.4
12.8
Hawkins et al. 2001
31
Master's athlete
46.5
6.1
Ath
122.5
24.5
20.0
Bernard et al. 1979
13
Master's sprinter
46.8
9.8
Ath
124.5
7.3
5.9
Trappe et al. 1996
10
Highly fit
47.2
3.8
Fit
121.0
2.8
2.3
Trappe et al. 1996
10
47.2
5.8
Act
123.4
21.4
17.3
Loftin et al. 1996
12
Handball players
48.7
5.9
Act
109.0
3.8
3.5
Trappe et al. 1996
18
50.5
3.5
Fit
144.0
23.4
16.3
Pollock et al. 1997
21
53.5
3.3
Ath
126.5
26.7
21.1
Hawkins et al. 2001
31
Master's athlete
53.9
2.9
Ath
131.3
19.2
14.6
Hawkins et al. 2001
34
Master's athlete
55.3
11.2
Ath
116.1
25.2
21.7
Bernard et al. 1979
13
Master's endurance
60.0
8.6
Ath
148.0
18.0
12.2
Pollock et al. 1987
11
60.2
8.8
Ath
151.4
20.0
13.2
Pollock et al. 1997
21
61.0
8.0
Ath
98.0
11.0
11.2
Proctor et al. 1998
8
62.0
8.9
Ath
116.2
17.8
15.3
MA Rogers et al. 1990
15
62.2
3.5
Ath
120.3
23.3
19.4
Hawkins et al. 2001
34
Master's athlete
62.3
2.9
Ath
84.0
14.0
16.7
Hawkins et al. 2001
13
Visit #1
64.0
6.0
Ath
135.0
25.0
18.5
Proctor et al. 1998
8
65.0
3.0
Ath
106.9
27.4
25.6
Hagberg et al. 1988
10
68.4
9.8
Fit
87.5
11.7
13.4
Trappe et al. 1996
10
Highly fit
70.4
8.8
Ath
117.4
24.7
21.0
Pollock et al. 1997
21
Followup
71.1
3.2
Ath
88.0
27.4
31.1
Hawkins et al. 2001
13
Visit #2
72.7
1.5
Ath
97.8
12.1
12.4
Wilmore et al. 1974
3
Endurance runners
76.0
4.8
Ath
93.9
27.4
29.2
Hawkins et al. 2001
8
Visit #1
82.8
4.0
Ath
73.8
23.2
31.4
Hawkins et al. 2001
8
Visit #2
b. Complete age statistics are not provided
17-26
-
Fit
117.7
12.3
10.5
Kamon & Pandolf 1972
5
18-23
Ath
173.7
Seals & Mullin 1982
12
Crew team
18-23
Ath
153.1
Seals & Mullin 1982
10
Gymnastics team
18-23
Ath
169.0
Seals & Mullin 1982
11
Swimming team
18-23
Ath
173.1
Seals & Mullin 1982
10
Wrestling team
22.0
Act
149.6
20.7
13.8
Maksud & Coutts 1971
20
18-21
-
Fit
140.1
19.3
13.8
WL Daniels et al. 1982
11
Army cadets
19-34
-
Act
122.1
13.0
10.6
Kamon & Pandolf 1972
5
40-49
-
Ath
150.9
-
-
Pollock 1974
11
Runners; 112-162
50-59
-
Ath.
139.9
-
-
Pollock 1974
5
Runners; 111-159
60-69
-
Ath.
140.0
-
-
Pollock 1974
6
Runners; 113-160
-------
Table 7. Estimates of VEMax seen in the literature (continued)
VEmax (L/min)
Age
Mean
SD
Cond.
Mean
SD
COV
(%)
Citation
Sample
Size (n) Comments
70 -75
-
Ath.
97.8
-
-
Pollock 1974
3
Runners;84-106
Males: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for Age
19.1
1.4
O
102.0
18.2
17.8
Wolfe etal. 1976
12
19.7
1.6
OW
148.0
9.3
6.3
Harms et al. 1995
8
FM>13 kg
21.5
1.9
Sed.
148.7
19.9
13.4
Wilmore et al. 1970
17
23.5
2.9
Sed.
104.8
18.4
17.6
Poole & Gaesser 1985
6
Group 3
23.8
3.6
Sed.
103.7
11.0
10.6
Poole & Gaesser 1985
6
Group 2
24.6
6.7
Sed.
109.6
26.5
24.2
Poole & Gaesser 1985
5
Group 1
29.7
2.9
Sed.
146.1
16.0
11.0
Wilmore et al. 1970
15
39.1
7.4
Sed.
108.1
31.5
29.1
JA Davis et al. 1979
7
Control group
40.5
3.1
Sed.
142.7
27.3
19.1
Wlmore et al. 1970
16
43.0
7.2
Sed.
105.8
15.3
14.5
JA Davis et al. 1979
9
Group 1
52.9
4.4
Sed.
133.5
27.5
20.6
Wlmore et al. 1970
7
Control group
61.4
5.2
Sed
95.8
22.1
23.1
MA Rogers et al. 1990
14
66.0
5.0
Sed
85.0
11.0
12.9
Hagberg et al. 1988
10
75.7
4.7
Sed
98.3
21.9
22.3
Kent-Braun & Ng 2000
9
b. Complete age statistics are not provided
20-35
Sed
123.2
16.7
13.6
Milesis et al. 1976
16
Control
20-35
Sed
113.5
15.3
13.5
Milesis et al. 1976
14
Group 1
20-35
Sed
127.5
15.5
12.2
Milesis et al. 1976
17
Group 2
20-35
Sed
117.5
17.2
14.6
Milesis et al. 1976
12
Group 3
28-39
Sed
126.9
12.2
9.6
Pollock et al. 1969
8
Control group
28-39
Sed
126.9
16.6
13.1
Pollock et al. 1969
9
Group 2
28-39
Sed
127.2
14.1
11.1
Pollock et al. 1969
10
Group 1
30-45
Sed
125.6
18.8
15.0
Pollock et al. 1972
10
Group 2
30-45
Sed
132.3
14.5
11.0
Pollock et al. 1972
12
Group 1
41.6
Sed
101.0
14.9
14.8
Getchell 1977
12
Ages 30-57
48.9
Sed
86.9
18.6
21.4
Pollock et al. 1971
16
49-65
Sed
104.9
19.1
18.2
Pollock et al. 1976
22
Group 1
49-65
Sed
108.1
26.2
24.2
Pollock et al. 1976
7
Control
60-72
Sed
75.2
17.3
23.0
Frontera et al. 1990
12
Males: Health/Other Issues
26.7
5.9
MR-D
70.4
16.7
23.7
Fernhall et al. 1996
35
26.9
6.4
MR
85.4
21.6
25.3
Fernhall et al. 1996
31
27.4
8.1
Para
69.0
16.0
23.2
Davis & Shephard 1988
15
Inactive
28.1
5.8
Para
106.0
22.0
20.8
Davis & Shephard 1988
15
Active
55.0
9.0
CHF
60.1
12.8
21.3
J Myers et al. 2012
24
Exercise Group
55.2
10.1
HT
79.9
25.2
31.5
Olivari et al. 1996
11
1 female age=36
56.0
10.0
HF
55.2
-
-
Keteyian et al. 2010
160
V02 COV=9%
57.0
7.0
CHF
54.4
12.2
22.4
J Myers et al. 2012
26
Control Group
59.0
9.0
CAD
92.7
18.0
19.4
J Milani et al. 1996
15
63.3
6.4
48.9
14.5
29.7
Mador et al. 1995
6
COPD
64.0
3.0
CAD
68.0
9.3
13.7
Sheldahl et al. 1996
10
Exercise group 2
64.0
11.0
CHF
59.0
15.0
25.4
Bowen et al. 2012
24
Mild heart problem
65.3
6.5
51.5
18.5
35.9
Carter et al. 1994
32
Mild COPD
66.3
6.2
48.3
14.2
29.4
Carter et al. 1994
57
Moderate COPD
-------
Table 7. Estimates of VE Max seen in the literature (continued)
VEmax (L/min)
Age COV Sample
Mean SD Cond. Mean SD (%) Citation Size (n) Comments
66.3 6.3 37.1 11.4 30.7 Carter et al. 1994 176 Severe COPD
68.0
5.7 CAD 68.2 6.8 10.0
Sheldahl eta al.
1996 8 Exercise group 1
69.0
3.0 CAD 74.6 10.3 13.8
Sheldahl eta al.
1996 11 Exercise group 3
Abbreviation & Symbols:
A-A Africian-Americans
Abbreviation & Symbols:
$ Females
Act
Active
S
Males
Alt
Altitude
MR
Mental Retardation
Ath
Athlete
N
Normal health
BMI
Body Mass Index (kg/m**2)
N
Sample size
Bpm
Breaths per minute
NS
Not specified (unknown)
BSA
Body Surface Area (m**2)
O
Obese
CAD
Coronary artery disease
OW
Overweight
CHF
Chronic Heart Failure
Para
Paraplegic
COPD
Chronic Obstructive Pulmonary Disease
PP
Post-partum
COV
Coefficient of Variation (SD/mean)
Preg
Pregnant
D
Down Syndrome
Sed
Sedentary
Fit
Fit or trained individuals
T
Time (followed by a label)
FM
Fat mass
VE
Ventilation rate (L/min)
H
Healthy
V02
Oxygen consumption (L/min)
Heart
Heart disease or coronary artery disease
VQ
Ventilatory Equivalent (VE/V02) (unitless)
HF
Heart Failure
Wk
Week(s)
HT
Heart Transplant recipient
1990s, and only 34 after the Millennium. About 66% of the
articles published after 1990 used females for their subjects
exclusively. It is unknown if this time/gender bias affects our
VE MAX or other ventilatory metrics. It should be noted that
all of the data in Table 7 are cross-sectional in nature, even
though some VE estimates are from "longitudinal" studies—
usually consisting of a single measurement for multiple time
periods in the same individual, often separated by years.
Basically these are treated as separate sequential cross-
sectional studies.
In general, people who are sedentary, overweight, and/
or have health problems have lower VE MAX levels than
"normal," healthy people, who have lower levels than fit,
active, or athletes. Individuals with mental issues, including
mental retardation—with or without Down syndrome—also
have lower VE MAX levels than "normals" (Baynard et al.,
2004, 2008).
Pregnant females do not have significantly different VE MAX
levels than non-pregnant females of approximately the
same age, although women who are pregnant have higher
VE recordings for rest, 25W, 50W, and 75W exercise levels
(Khodiguian et al., 1996). Even given that situation, the
authors state that there was no pregnant condition/workload
interaction in an ANOVA of all of the data, "suggesting that
pregnancy did not lead to augmentation in the ventilatory
response to increasing levels of work (Khodiguian
et al., 1996; p. 234). Thus, the impact of pregnancy on VE
is mixed, and no other similar study could be found to shed
light on the issue. It does appear that post-partum VE values
are significantly lower than pre-pregnancy values, but the
decrease becomes less over time post-delivery (Jaque-
Fortunato et al., 1996; Treuth et al., 2005). I could not find
any information regarding the length of time post-partum that
is required before pre-pregnancy VE values are attained.
There is no difference in VE MAX over the menstrual cycle for
females (Stephenson et al. 1982), nor is there any difference
in activity-specific VE in the different phases of the cycle
either at sea level or at altitude (Beidleman et al., 1999;
Bemben et al., 1995).
In attempting to address VE RES values for various age/gender
cohorts, we run into a problem. There are very little data on
resting ventilation rate reported in the literature (VE REST) and
even less on VE RESERVE (VE RES). Unlike the V02 RES metric,
there is no well-accepted approach used to derive VE RES
from VE MAX data, so there are few VE RES values reported in
the exercise physiology literature. What data I could find
on VE REST and VE RES appears in Table 8. VE RES at maximal
oxygen consumption appears to be on the order of 60-70
L/min in females without health issues, and 70-110 L/min
in males.
49
-------
Also included in that Table are resting VE data. VE REST does
not vary much with age or gender on an absolute basis, but
does on a per-BM basis. VE REST values in Table 8 appear to
be higher than resting values provided in Tables 5-6 and 5-14
of EPA's Exposure Factors Handbook (EPA, 1997b) and a
Summary Table found in Adams (1993).
There is a difference in VE values between predominately
arm-work (upper body), predominately leg-work only, or a
combination of the two work modes (Adams et al., 1998).
Arm-work only at low-to-moderate intensities require -10%
less VEthan leg-work at the same intensity; arms-only work
at high intensities elicit greater VE at any given HR than legs-
only work at equivalent workloads. The impact that static
(isometric) work alone, or in combination with various levels
of dynamic work, has on the HR—> VE relationship and VE
itself needs systematic investigation (Adams et al., 1998).
Breathing Rate
Breathing rate (fB) is an innate function of oxygen
consumption demands, including tidal volume (VT)
and ventilation rate VE. One formula for fB is that it is =
VE/ VT (McArdle et al., 2001). Breathing rate increases with
workload. Tidal volume does also, in such a matter that V,.
7 E
increases even faster. An insert presented in McArdle et al.
(2001) provides "typical values" for pulmonary ventilation
values in fit males from rest to vigorous exercise (p. 261).
The insert is reproduced here: brpm = breaths per minute.
For adult females of "normal fitness," the fB values shown
above are higher at low workloads but lower at high: 14 bpm
at rest and 40 bpm at maximum workload (Jaque-Fortunato
et al., 1996). The resting tidal volume values for females
are similar to the values depicted above, but are <2.1 for
Condition
Rest
Moderate
Exercise
Vigorous
Exercise
f V V
B T E
Pulmonary
Breathing Tidal Volume Ventilation
Rate (brpm) (L/breath) Rate (L/min)
12 0.5 6
30
50
2.5
3.0
75
150
maximum workload levels due to their smaller lung size
relative to males. The same finding with respect to higher
values for fB at rest and lower VE values at peak exercise is
seen in Stephenson et al. (1982). Tobin et al. (1983) present
resting fB values that are 16.6 ± 2.8 bpm in both males and
females. Treuth et al. (2004) report that fB at rest and at peak
exercise increases with age in girls, going from 13 to 15 bpm
at rest, and from 41 to 57 bpm at peak exercise.
Adams (1993) conducted a series of tests on respiratory
functions that involved children aged 3-6 y up to older adults
as old as 78 y. His resting values are higher than listed above
for children—between 20-26 bpm—and somewhat higher
for adolescents and adults of both genders. Measured resting
fB's for the latter groups were 13-14 bpm (Adams, 1993).
Breathing rates for walking a 2.5 mph were somewhat
higher for children and young teenagers—about 32-37 bpm,
but were significantly lower for adults and the elderly for
both genders: 23-25 bpm (Adams, 1993). His running (8>, 4
mph measured fB's were about 10% lower than the "vigorous
exercise" estimate provided above.
Table 8. Estimates of V„ „ or both V,, „ , and V,, „ seen in the same article
E.Reserve E.Rest E.Max
VE. Reserve
Ventilation Rate (L/min) Calculated
VE.Rest VE.Max VE.Reserve
Age Range Health
(Mean±SD) Status Mean SD Mean SD Mean SD Citation (n) Comments
Females, Normal, Healthy, or Not Specified
a. Mean & SD statistics are provided for age
26.2 ±10.4 N 8.9 5.3 85.1 12.9 76.2 Stephenson et al. 1982 6 Mean of all cycle
^ days
28.0 ±4.6 N 7.7 0.9 Schoene et al. 1981 6 Folicular phase
28.0 ±4.6
N
10.0
0.7
Schoene et al. 1981
6 Luteal phase
29.4 ±3.8
N
9.6
1.6
81.4
14.6
71.8
Jaque-Fortunato et al.
1996
22 Pregnant
30.4 ±4.3
N
7.6
2.6
74.9
15.0
67.3
Jaque-Fortunato et al.
1996
16 Control group
31.0 ±3.8
N
6.9
1.9
78.6
10.3
71.7
Jaque-Fortunato et al.
1996
7 Post-partum
-------
Table 8. Estimates of V„ „ or both V,, „ , and V,, „ seen in the same article (continued)
E.Reserve E.Rest E.Max v 7
VE. Reserve
Ventilation Rate (L/min) Calculated
VE.Rest VE.Max VE.Reserve
Age Range Health
(Mean±SD) Status Mean SD Mean SD Mean SD Citation (n) Comments
Females: Active, Fit, or Athlete
a. Mean & SD statistics are provided for age
22.3 ±4.8
Ath
10.7
1.1
Schoene et al. 1981
6
amen. ain.
(follicular)
22.3 ±4.8
Ath
11.6
1.1
Schoene et al. 1981
6
Amen. ath. (luteal)
27.8 ±8.4
Ath
8.8
0.6
Schoene et al. 1981
6
Mens ath.
(follicular)
27.8 ±8.4
Ath
10.7
0.7
Schoene et al. 1981
6
Mens. ath. (luteal)
Males: Normal, Healthy, or Not Specified
a. Mean & SD statistics are provided for age
8.8 ±1.1
NS
5.2
1.6
X Wang & Perry 2006
21
Video game
VE=7.9(2.1)
12.5 ±0.9
NS
13.3
2.5
85.7
14.1
72.4
Maksud & Coutts 1971
17
54.3 ± 9.2
NS
11.8
4.5
93.3
23.0
87.5
Hansen et al. 1984
77
Shipyard workers
b. Complete age statistics are not provided
18-24
N
8.6
115.2
106.6
Hansen et al. 1967
18
Sea level Group 1
18-24
N
9.2
107.7
98.5
Hansen et al. 1967
18
Sea level Group 2
18-24
N
8.9
109.8
100.9
Hansen et al. 1967
18
Sea level Group 3
18-24
N
8.8
113.2
104.4
Hansen et al. 1967
18
Sea level Group 4
65-75
N
11.6
2.9
HE Wood et al. 2010
11
Males: Active, Fit, or Athlete
21.1 ±1.7
Ath
11.6
2.6
39.5
15.4
27.9
Hopkins et al. 1998
7
VE @ 30%
V02Max
21.1 ±1.7
Ath
11.6
2.6
71.9
7.9
60.3
Hopkins et al. 1998
7
VE @ 65%
V02Max
21.1 ±1.7
Ath
11.6
2.6
146.2
26.2
135
Hopkins et al. 1998
7
VE @ 90%
V02Max
Males: Health Issues
COPD
15.0
15.0
28
7.0
13
Montes Oca et al. 1996
25
Both Genders: Normal, Healthy or Not Specified
"Young"
N
10.4
2.6
93.3
40.4
82.9
Did not record!
8
Mixed fitness
30 ±7
NS
8.0
1.7
63.8
17.2
55.8
Keyser et al. 1999
6
NWC user
Both Genders: Sedentary, Overweight, Obese, or Health Issues
40 ± 9 Sed. 7.0 1.8 46.8 18.7 39.8 Keyser et al. 1999 18 WC for 16+years
Abbreviations: Abbreviations:
Ath
Athletes
NS
Not specified
Amen
Amenorrheic
NWC
Non-wheelchair (ambulatory)
CHF
Chronic Heart Failure
SD
Standard Deviation
COPD
Chronic Obstructive Pulmonary Disease
VE
Ventilation rate (L/min)
Mens
Menstruating
VE.Max
Maximum ventilation rate (L/min)
n
Sample size
V02.Max Maximum oxygen consumption rate (L/min)
N
Normal
WC
Wheelchair user (manual)
-------
Adams' resting VE measures were slightly higher than
the 6 bpm value shown above: the measured values were
between 6.2-7.1 L/min at rest for the various age/gender
cohorts evaluated (Adams, 1993). Walking and running VE
estimates in Adams (1993) are significantly lower than the
moderate/vigorous estimates in the "typical values" table.
Adams (1993) did not contain any information on tidal
volume (VT), so comparisons could not be made for that
parameter.
In general, female VE's will be lower than those shown
above, with VE MAX's in the 75-85 L/min range for "normal
fitness" females. That is what is seen in Table 7 for most
normal, healthy adult females, although fit, active, or
athletic females between the ages of 18 and 55 y can have
considerably higher ventilation rates.
The relative ratio change from rest to vigorous exercise
in the above data is 4 for breathing rate, 6 for VT, and 25
for VE. Athletes and fit persons can have fB's as high as
60-70 breaths/min, making for a ratio change of 5.0 - 5.8 for
fB. (However, the depth of breathing at these rates is shallow,
causing VT to decrease relative to the values shown in the
McArdle et al. (2001) insert. Since VE's greater thanl60
L/min or so are uncommon—even in elite athletes—the
maximum ratio change of VE is on the order of 26-30 times
the resting rate.)
Beals et al. (1996) took the Adams (1993) data and used
cluster analysis to apportion activities into similar groupings
from a VE perspective using a "nearest neighbor" approach
(and two others methods). They then classified them into
low, moderate, and high groups for children, adult females,
and adult males. None of these groups were explicitly
defined (Beals et al., 1996). For children, moderate activities
were walking at 2-3 mph and "playing"; VE's for these
activities were in the 16-18 L/min for a 5 min duration
(mean/SD=16.7±2.8). Vigorous activities included walking
at 3-4 mph and running; VE's for those activities were 24-29
L/min (28.6±3.3) (Beals et al., 1996). The VE's for typical
activities for the same two groupings were higher for adults,
with males having higher ventilation rates than females. VE
distributions for all activities and all age/gender categories
were best fit with a log-normal or gamma distribution (Beals
et al., 1996).
If breathing with increasing workload becomes labored
or inadequate, the usual VE-to-V02 convex relationship
discussed earlier can curve downward, decreasing VQ
with increasing workload among other changes (McArdle
et al. 2001). That response is common in COPD patients,
and indicates a failure of ventilation to keep pace with
oxygen demands (McArdle et al., 2001). For more on VQ,
see Section 5.
Activity-Specific Estimates of VE (VEflCT)
Prior to 2000, OAQPS and NERL used estimates of activity-
specific ventilation rate directly in its exposure/intake
dose-rate models. Since then, we "build" activity-specific
VE ACT estimates using the METS—>V02—> VE relationships
documented in Appendix D. In order to facilitate a "quick and
dirty" evaluation of activity-specific VE estimates developed
in the APEX and SHEDS models, I wanted to include in
Appendix E a compilation of VEACT estimates appearing
in various articles, reports, and books. This was not done,
however, due to author fatigue. A definitive evaluation of
activity-specific VE's should be undertaken. Subsequently, a
"hard look" should be taken for each factor in the appropriate
METS—>V02—> VE relationship used to estimate intake dose
rate in our exposure models. That would assist in determining
and evaluating which parameter in the above relationship
affects estimated VE ACT estimates the most.
Equations for Predicting VEfl from V02fl Estimates
As just mentioned, METSact estimates are converted into
V02ACT values and then into VE ACT metrics. We no longer
use general distributions of VE, but have developed a new
set of age- and gender-specific V02—>VE equations based
upon a re-analysis of the Adams data (Graham & McCurdy,
2004). These equations are used for all activities to obtain
an estimate of VE ACT from V02ACT | METSact. For example,
for people <20 years old, we use the following equation to
estimate VE from V02 for individual i, in units of L kg"1 min1,
and that it applies to all activities undertaken by individuals
in that cohort.
Ln [E BM1], = 4.433 + (1.086 * Ln [VO, BM1] ) + ( 0.283 *
Ln [AGE, ]) + ( 0.051 * GENDER,) + {e Wiftm: 0. 0.096 }
+ {eBetween: 0.0.112}
Where: V02 BM1 = Body mass adjusted V02 in mL kg1
min1 (S.E. =0.010)
AGE, = Age of the individual in years (S.E. =
0.012)
GENDER, = Sex of the individual: -1 $ and +1
-------
There are numerous alternative equations to estimate VE
for general and specific activities available in the exercise
physiology literature, including standard textbooks on the
subject; e.g., Anderson et al. (1978), Astrand & Rodahl
(1986), McArdle et al. (2001), Nieman (1990), and
Wells (1991).
EVR: Equivalent Ventilation Rate
Prior to the development of the APEX model, and for some
versions of the pNEM model, OAQPS used the EVR form
of breathing rate: VE divided by body surface area (BSA).
The units of this metric are liters/m2 - min. This metric was
used to facilitate the linking of exposure model outputs to
the ozone clinical data that were on a per-BSAbasis. For a
discussion of the previously used approach to estimate EVR,
see Johnson (2002), Johnson & McCoy (1995), and Johnson
et al. (2005).
Nasal/Oral Patterns Associated with VE Levels
As a person undertakes more and more work, oral inhalation
becomes an ever more important source of oxygen for the
lung as nasal resistance increases with airflow (Chadha et al.,
1987; Kleimnan & Mautz, 1988). The changing patterns of
nasal/oral breathing greatly affect the "scrubbing efficiency"
of the nose, usually a very effective filter of "incidentally
inhaled toxic particles and gases" (p. 101), resulting in
more deposition of pollutants in the lung via oral breathing
(Kabel et al., 1994; Kirkpatrick et al., 1982; Vass et al.,
2003). While most humans are oral-nasal breathers at even
relatively low VE levels, the fraction of air entering through
the mouth is relatively small in most individuals (0-10%)
until 25-45 L/min V , where it increases to 50-60% or so
E'
(Kleimnan & Mautz, 1988). This alteration of nasal/oral
components of VE affects the pattern of intake dose received
and the distribution of dose in sensitive regions of the lung
(and subsequently the blood stream). Factors that determine
the type and amount of biological responses due to these
changes in dosing pattern and amount of xenobiotic material
absorbed in the body depend upon a number of chemical,
physical, and physiological parameters (Kleimnan & Mautz,
1988); they are:
1. The relative air-to-liquid phase partitioning of the
inhaled chemical entering the mucus layer lining of the
respiratory airway.
2. Residence time of the gas in each airway segment, a
direct function of VE and segment volume
3. Degree of turbulence in the airstream
4. The effective area of absorbing surface in each segment
of the airway
5. Diffusivity of the gas
6. The presence of particles in the "carrier" airstream,
which alters the site and type of deposition in the
airway (and may enhance transport of a toxic substance
deeper into the respiratory tract)
7. Modification of mucus production rate due to altered
physiological states
8. Alteration of chemical and physical properties in the
mucus "sheath" itself
9. Changes in airway volumetric dimensions due to
airway dilution/constriction due to biological responses
affected by inhalation of the irritant itself.
Data on these effects have been obtained in vivo through
animal experiments using different gases, with and without
particulate aerosols. Functional relationships between the
amounts of pollutant absorbed in the lung by nasal and oral
breathing routes and by different ventilation rates have been
developed in humans for selected pollutants (Kleimnan &
Mautz, 1988). For relatively low VE's. the ratio of dose of
pollutant reaching the lung drops for oral breathing-to-nasal
breathing from 1.8 at low VE to 1.3 at higher VE (Kleimnan &
Mautz, 1988).
Apparently, body size, body composition, and nose
volume all affect the physiological and other parameters
mentioned above for nose and mouth breathing rates, more
so than gender per se (Hall, 2005). Obviously the effects
of biological and physiological parameters on VE are
complex, but could be important in modeling intake dose
rate associated with human exposures. More work should be
undertaken on this subject to determine if nasal/oral changes
due to increasing work rates make a significant difference in
absorbed dose to target organs.
53
-------
-------
5.0
VQ: the Ratio of VE to V02
The ratio of VE-to-V02 is called the "ventilatory equivalent"
(VQ). It is fairly linear with work rate up to the ventilatory
threshold, (also known as the "anaerobic threshold") but
increases non-linearly above it (Simon et al., 1983). VQ
measurements, like its two constituents VEandV02, depend
upon the exercise protocol used to ascertain them, and
statistically significant differences in VQ have been observed
using different procedures in a number of fitness groups and
in both genders (Kamon & Pandolf, 1972). VQ is affected
by exercise mode and the specific protocol used within an
exercise mode (McArdle et al., 2001). VQ observed using a
cycling protocol generally is higher than that obtained using
a treadmill; VQ's obtained from cycling is on the order of
2.0-5.0 ratio units over a running VQ of 30.0-35.0 (about
6-17% higher: data from Kamon & Pandolf, 1972).
McArdle et al. (2001) state that this ratio in healthy people
is on the order of 20-32 (L min '/L min1, unitless in other
words) at moderate exercise levels, but those values seem
to be low compared with some of the data shown in Table 9.
VQ values at V02 MAX in the 40's are seen in the literature
(Astrand & Rodahl, 1986). Miyashita et al. (1981) present
data for 5 adult males aged 23-26 y having a VQ mean/SD
of 40.7 / 3.3 (COV=8.2%); the VQ range of the group is
37.1-44.3. The measured VQ at the anaerobic/ventilatory
threshold in that study was only about 23.5 (SD=2.3), with a
narrow range of 22.0-27.6 (Miyashita et al., 1981).
In general, VQ for sedentary individuals is higher than for
more active people (Tanaka et al., 1997). In addition, high
VQ values are a marker of inefficient ventilation due to
hyperventilation, increased dead space, and/or the "oxygen
cost of breathing."
Subjects with heart failure or other respiratory problems
have a consistently high VQ (Luks et al., 2012). The VQ
for hospital patients with heart failure was 46 ± 11 at peak
exercise (which was at a work rate 0.85
(p<0.05) seen in a number of studies reviewed by Mahon &
Cheatham (2002). VQ's for elderly females is significantly
higher than that for elderly males (Panton et al., 1996).
Some authors state that VQ is non-linear at low V02 rates,
but that non-linearity does not significantly affect intake dose
estimates in our exposure models. In some papers, VQ is
defined relative to VC02 MAX but since EPA's exposure models
do not utilize VC02 parameters, they are not reviewed here.
For one such paper, see McConnell & Davies (1992).
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
Females: Normal, Healthy, or Not Specified
a. Mean & SD statistics are provided for age
8.5
0.8
N
33.1
* Wilmore & Sigerseth 1967
20
9.8
0.7
N
34.8
* Loftin et al. 1998
19
10.4
0.5
N
31.8
* Wilmore & Sigerseth 1967
20
12.4
0.5
N
29.2
* Wilmore & Sigerseth 1967
22
55
-------
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature (continued)
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
16.9
3.0
N
32.8
*
Loftin et al. 1998
19
19.7
1.0
N
38.3
*
Lesmes et al. 1978
8
Group 1
19.7
1.6
N
40.3
*
Lesmes et al. 1978
8
Group 2
19.9
2.0
N
37.4
*
Lesmes et al. 1978
8
Group 3
20.8
1.1
N
38.1
*
McArdle et al. 1972
35
22.4
3.5
N
35.7
*
Lesmes et al. 1978
8
Group 4
62.0
6.0
N
39.2
*
Sheldahl et al. 1996
9
Controls
68.6
5.7
N
41.3
7.7
18.6
Panton et al. 1996
36
68.6
5.7
N
41.3
7.7
18.6
Panton et al. 1996
55
Both genders
68.6
5.7
N
41.3
7.7
18.6
Panton et al. 1996
55
Both genders
b. Complete age statistics are not provided.
8-11
N
39.6
2.3
5.8
Rowland 1997
9
Longitudinal Study
9-12
N
39.2
3.4
8.7
Rowland 1997
9
Longitudinal Study
10-13
N
38.0
2.3
6.1
Rowland 1997
9
Longitudinal Study
11 - 14
N
39.5
2.0
5.1
Rowland 1997
9
Longitudinal Study
12-15
N
39.4
3.9
9.9
Rowland 1997
9
Longitudinal Study
19.5
H
32.6
2.9
8.9
Rowland 1997
15
Females: Active, Fit, or Athlete
15.6
1.1
Act
36.0
4.5
12.5
Butts 1982
127
20.7
3.2
Fit
40.7
MA Sharp et al. 2002
122
Army recruits
21.2
2.3
Ath
36.9
*
McArdle et al. 1972
6
21.4
3.4
Fit
39.9
*
MA Sharp et al. 2002
155
Army recruits
23.3
3.7
Fit
34.9
*
Williford et al. 1989
10
Aerobic dancers
26.0
3.7
Ath
39.5
*
Tanaka et al. 1997
14
Endurance trained
34.0
14.6
Ath
38.0
*
Tanaka et al. 1997
21
Endurance trained
45.0
3.6
Ath
40.6
*
Tanaka et al. 1997
13
Endurance trained
54.0
4.8
Ath
43.0
*
Tanaka et al. 1997
23
Endurance trained
66.0
3.6
Ath
48.2
*
Tanaka et al. 1997
13
Endurance trained
Females:
: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for age
25.0
3.3
Sed
42.0
*
Tanaka et al. 1997
11
31.9
4.1
Sed
41.3
*
JLP Roy et al. 2006
20
A-A
32.4
5.8
Sed
39.7
*
JLP Roy et al. 2006
30
Caucasian
33.0
13.3
Sed
45.2
*
Tanaka et al. 1997
11
45.0
3.7
Sed
46.2
*
Tanaka et al. 1997
14
54.0
4.5
Sed
44.7
*
Tanaka et al. 1997
20
64.0
4.0
Sed
40.4
*
Tanaka et al. 1997
16
-------
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature (continued)
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
b. Complete age statistics are not provided.
20-29
Sed
30.2
*
Schiller et al. 2001
14
Caucasian
20-29
Sed
30.1
*
Schiller et al. 2001
12
Hispanic
30-39
Sed
31.6
*
Schiller et al. 2001
14
Caucasian
30-39
Sed
35.4
*
Schiller et al. 2001
13
Hispanic
40-49
Sed
35.7
*
Schiller et al. 2001
21
Caucasian
40-49
Sed
33.0
*
Schiller et al. 2001
8
Hispanic
50-59
Sed
36.7
*
Schiller et al. 2001
26
Caucasian
50-59
Sed
35.6
*
Schiller et al. 2001
15
Hispanic
>60
Sed
35.3
*
Schiller et al. 2001
18
Caucasian
>60
Sed
36.0
*
Schiller et al. 2001
5
Hispanic
Females: Health Issues
32.0
5.0
Preg.
McMurray et al. 1995
10
Sub-maximal
workload
62.0
6.6
CAD
38.9
*
Sheldahl etal. 1996
11
Males: Normal, Healthy, or Not Specified
a. Mean & SD statistics are provided for age
10.8
0.4
H
33.9
2.5
7.4
12.5
0.9
NS
38.8
*
Maksud & Coutts 1971
17
Age range: 11-14
19.9
0.9
NS
*
Harms et al. 1995
8
FM<7 kg
22.7
4.2
H
41.2
*
Toner et al. 1990
6
22.7
4.2
H
41.2
*
Toner et al. 1990
6
22.7
4.2
H
41.2
*
Toner et al. 1990
6
22.7
4.2
H
41.2
*
Toner et al. 1990
6
23.2
7.4
NS
29.5
*
Trappe et al. 1996
15
23.8
3.4
H
32.6
*
Schelegle et al. 1989
20
03 sensitive
25.5
5.1
H
32.1
*
Schelegle et al. 1989
20
Non 03 sensitive
45.3
8.9
H
29.4
*
Trappe et al. 1996
15
54.3
9.2
NS
37.7
6.9
18.3
Hansen et al. 1984
77
Shipyard workers
62.0
6.0
N
39.4
*
Sheldahl etal. 1996
9
Controls
64.2
9.4
N
44.2
*
Pollock et al. 1987
13
Ex-athlete
68.7
5.1
N
39.8
8.7
21.9
Panton et al. 1996
55
Both genders
68.7
5.1
N
39.8
8.7
21.9
Panton et al. 1996
55
Both genders
68.7
5.1
N
39.8
8.7
21.9
Panton et al. 1996
55
Both genders
b. Complete age statistics are not provided.
8-11
N
37.2
3.5
9.4
Rowland 1997
9
n=9; Longitudinal
Study
57
-------
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature (continued)
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
9-12
N
35.3
1.7
4.8
Rowland 1997
9
Longitudinal Study
10-13
N
34.4
2.3
6.7
Rowland 1997
9
Longitudinal Study
11 - 14
N
35.9
2.8
7.8
Rowland 1997
9
Longitudinal Study
12-15
N
34.1
2.6
7.6
Rowland 1997
9
Longitudinal Study
18-23
NS
38.3
*
Seals & Mullin 1982
12
Untrained
19-47
H
35.1
*
Lukaski et al. 1989
16
Bruce protocol
Males: Active, Fit, or Athlete
a. Mean & SD statistics are provided for age
16.0
1.3
Fit
32.1
*
Rivera-Brown et al. 1997
20
18.0
2.6
Ath
31.5
*
Rundell 1996
7
Olympic speed
skaters
19.9
2.7
Fit
37.4
*
MA Sharp et al. 2002
122
Army recruits
21.8
3.4
Fit
36.1
*
MA Sharp et al. 2002
171
Army recruits
24.8
5.7
Act
32.5
*
Wallick et al. 1995
16
VQ range: 26.2-33.'
25.7
3.5
Act
32.9
*
Trappe et al. 1996
10
27.1
6.7
Act
28.9
*
Trappe et al. 1996
18
42.4
14.0
Ath
43.3
5.1
11.8
IE Faria et al. 1996
16
Cross-country skier
46.8
9.8
Fit
31.3
*
Trappe et al. 1996
10
47.2
3.8
Ath
29.0
*
Trappe et al. 1996
10
47.2
5.8
Act
33.7
*
Loftin et al. 1996
12
Handball players
48.7
7.6
Act
29.0
*
Trappe et al. 1996
18
60.0
8.6
Ath
41.9
*
Pollock et al. 1987
11
Master's athlete
68.4
9.8
Fit
31.9
*
Trappe et al. 1996
10
b. Complete age statistics are not provided
18-23
Ath
35.7
*
Seals & Mullin 1982
12
row team
18-23
Ath
38.8
*
Seals & Mullin 1982
10
Gymnastics team
18-23
Ath
36.2
*
Seals & Mullin 1982
11
Swimming team
18-23
Ath
36.9
*
Seals & Mullin 1982
10
Wrestling team
24.3
Ath
S Robinson et al. 1976
13
Champion runners
40-49
Ath
36.8
*
Pollock 1974
11
47.9
Ath
S Robinson et al. 1976
13
Champion runners
56.6
Ath
S Robinson et al. 1976
13
Champion runners
50-59
Ath
38.5
*
Pollock 1974
5
60-69
Ath
40.8
*
Pollock 1974
6
70-75
Ath
36.5
*
Pollock 1974
3
-------
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature (continued)
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
Males: Sedentary, Overweight, or Obese
See Note 1.
a. Mean & SD statistics are provided for age
19.7
1.6
OW
Harms et al. 1995
8
FM>13 kg
23.5
1.2
Sed
27.9
3.2
11.5
Poole & Gaesser 1985
6
Group 3
23.8
3.6
Sed
32.6
2.9
8.9
Poole & Gaesser 1985
6
Group 2
24.6
6.7
Sed
36.1
5.1
14.1
Poole & Gaesser 1985
5
Group 1
39.1
7.4
Sed
38.8
6.1
15.7
JA Davis et al. 1979
7
Control
43.0
7.2
Sed
39.4
7.2
18.3
JA Davis et al. 1979
9
Group 1
b. Complete age statistics are not provided
30-
47
Sed
42.4
*
Pollock et al. 1975
9
Group 1
30-
47
Sed
42.8
*
Pollock et al. 1975
8
Group 3
30-
47
Sed
43.3
*
Pollock et al. 1975
9
Group 2
30-
47
Sed
43.3
*
Pollock et al. 1975
7
Control Group
49-
65
Sed
39.0
*
Pollock et al. 1976
7
Control Group
49-
65
Sed
42.5
*
Pollock et al. 1976
22
Group 1
Males: Health Issues
55.0
9.0
CHF
39.0
6.7
17.2
J Myers et al. 2012
24
Exercise Group
57.0
7.0
CHF
29.1
9.4
32.3
J Myers et al. 2012
26
Control Group
56.0
7.3
RP
Furuike et al. 1982
23
See Note 2
64.0
3.0
CAD
33.2
*
Sheldahl etal. 1996
9
68.0
5.7
CAD
34.7
*
Sheldahl etal. 1996
8
69.0
3.3
CAD
39.1
*
Sheldahl etal. 1996
11
Both Genders: Normal, Healthy, or Non-Specified
3-4
N
30.8
*
Shuleva et al. 1990
9
33% female
5-6
N
29.8
*
Shuleva et al. 1990
13
23% female
25.0
8.0
H
38.0
8.0
21.1
Shah et al. 1998
17
Controls (41%
female)
70+
NS
38.1
8.8
23.1
BD Johnson et al. 1991
30
70+
NS
38.1
8.8
23.1
BD Johnson et al. 1991
30
70+
NS
38.1
8.8
23.1
BD Johnson et al. 1991
30
70+
NS
38.1
8.8
23.1
BD Johnson et al. 1991
30
Both Genders: Health Issues
25.0
10.0
CF
38.0
8.0
21.1
Shah et al. 1998
32.0
9.0
CHF
38.0
7.0
18.4
Weber et al. 1982
5
Group A
50.0
13.0
CHF
45.0
17.0 37.8
Weber et al. 1982
19
Group C
59
-------
Table 9. Estimates of the ventilatory equivalent (VQ) seen in the literature (continued)
VQ@V02.Max
Age/Age
Range (y) (unitless)
Health
Mean SD Status Mean SD COV Citation Sample Size (n) Comment
53.0
14.0
CHF
38.0
15.0 39.5
Weber et al. 1982
14
Group D
55.0
17.0
CHF
40.0
8.0
20.0
Weber et al. 1982
17
Group B
62.0
7.0
COPD
43.0
6.0
14.0
JL Larson et al. 1999
12
Group 1
66.0
5.0
COPD
42.0
9.0
21.4
JL Larson et al. 1999
13
Group 2
66.0
6.0
COPD
40.0
9.0
22.5
JL Larson et al. 1999
14
Group 3
68.0
6.0
COPD
40.0
4.0
10.0
JL Larson et al. 1999
14
Group 4
Symbols & Abbreviations: Symbols & Abbreviations:
$ Females Preg Pregnant
S
Males
RP
Respiratory problems (patients)
*
Calculated from VE & V02 Max data
SD
Standard deviation
provided
Sed
Sedentary
A-A
African-American
VE
Ventilation rate (L/min)
Act
Active
V02
Oxygen Consumption (L/min)
Ath Athlete
CF Cystic Fibrosis
CHF Chronic Heart Failure
COPD Chronic Obstructive Pulmonary Disease
CV Coefficient of Variation
FM Fat Mass
Max Maximum oxygen consumption (L/min)
n Sample size
N Normal
y Years
Notes:
1. If no value is shown in the "Specified Work Rate"
column, it means that it was at V02.Max.
2. VE value shown is the mean ventilation rate
(SD=5.8; range: 20.9-1.7 L/min); the VQ range was
25.9-43.3 (unitless).
60
-------
6.0
METS Considerations
METS, the metabolic equivalent of work, can function as
a reserve metric due to its definition. METS are the ratio
between activity-specific energy expenditure (EEACT) to
RMR or oxygen consumption (V02ACT)to RMR (in V02
units, of course). Since RMR is defined to be a MET of 1.0,
METSres = METSmax - 1.0. It is a unitless metric.
As noted earlier, to simplify the estimation of activity-
specific METS, exercise physiologists have developed the
concept of a "standardized" MET (Ainsworth et al., 1993,
2000, 2011) and set it equal to 3.5 mL kg"1 min1 in oxygen
consumption units. Pettitt et al. (2007) states that this factor
was based on observations from only one person, and
cites Byrne et al. (2005) as the source for this statement.
However, the "one-person" statement is not discussed in
that source, so there is uncertainty concerning validity of the
Petitt et al. (2007) statement.
We do not use standardized METS in this paper. In fact,
examining the reasons why standardized METS should not
be used is one of the main themes of this work. In short, EE
rates based on a standardized MET under-estimates activity
specific METS (METSact) in most adults for all but the most
sedentary activities (Kozey et al., 2010; Manore et al., 1991)
because it over-estimates RMR (Bryne et al., 2005; Lee et al.,
2010a, b; McMurray et al., 2014). For example, RMR is 3.1
mL kg-1 mini in people with paraplegia, which is statistically
different than 3.5 mL kg1 min1 (Lee et al., 2010a).
Conversely, METSact are over-estimated in overweight
and obese individuals using a standardized MET because it
under-estimates RMR on a body-mass basis in those people
(Rachette et al., 1995). RMR in children and adolescents
generally is higher than 3.5 mL kg1 min1, although it
approaches that value in males around puberty (Son'kin &
Tambovtseva, 2012), further complicating use of the standard
METS concept.
In a large homogeneous sample of subjects aged 18-74
using the 3.5 value overestimates V02REST by 35%, on
average (Byrne et al., 2005). While a range of measured
V02 REST versus 3.5 was not provided in their paper,
only 14 (2%) of the 769 subjects in the study had a
resting V02> 3.5 mL kg"1 min1. These 14 subjects were
heterogeneous in age and included both genders, but all
had a low relative body weight, with a Body Mass Index
(BMI) between 16-22 kg m 2 (Byrne et al., 2005). The
authors obtained better results using the 1 kcal kg1 lr1
V02REST "constant" that is equivalent to 3.5 mL kg"1 min1.
Doing so still overestimates resting oxygen consumption
by 20%, on average; better, but still not good. A paper by
KS Hall et al. (2013) confirms that measured REE is 31.6%
on average lower than the 3.5 resting METS value, which
results in METS estimates in the Compendium that are
lower than measured METS by 71%, on average, for 60% of
walking activities (Hall et al., 2013).
McMurray et al. (2014) reviewed REE data from 197 studies
of adults that clearly show that measured REE is <1 kcal
kg1 hi in the vast majority of age/gender cohorts that were
investigated. Overall, the mean REE was 0.863 kcal kg"1 lr1
(95°° CI=0.852-0.874), or about 14% lower than that normally
used for REE (McMurray et al., 2014). REE for females was
0.839 kcal kg"1 lr1 (CI=0.825-853) and was 0.892 kcal kg"1
lr1 (0.872-0.912) for males, highlighting another problem of
using the same value for both genders.
One alternative to the standardized MET approach is
to use "corrected MET" values, which are defined in
the following webpage: (http://sites. google.com/site/
compendiumofphvsicalactivities/home). That definition
however, has its own set of problems, since it is explicitly
based on using the Harris-Benedict BMR equation (H-B
BMR) developed in 1918 (Harris & Benedict, 1918). The
corrected METS equation from the website is:
Corrected METS = Compendium METS Value * (3.5 ml kg"1
mirr1 / H-B BMR ml kg"1 min1)
In general, corrected METS will be larger than the
Compendium-METS estimates as the H-B BMR estimate is
<3.5 ml kg"1 min1. See the webpage for examples.
Activity-specific METS (METSact) are often estimated from
HR-monitoring studies using the Karvonen approach, as
clearly described in Strath et al. (2000). The Strath example
uses the 3.5 factor for V02 REST, but does not have to, since
V02 REST could have been measured directly. Their logic
steps follow:
1. Obtain activity-specific HR (HRACT). HRrest and HRMAX
have previously been measured.
2. Obtain equivalent %HRR for an activity (%HRACT).
3. hrract= [(HRact - HRrest) / (HRm xx - HRr|st)| * 100
4. Assume for an activity that: %V02 RES.ACT = %HRRACT
5. Estimate V02ACT:
6. V02 ACT = [(%V02 RESA/100)* (V02 MAX - V02 REST)]
+ V02.REST
7. Calculate METSact: METSact = (V02ACT / V02 REST)
Where HRR=Heart Rate Reserve.
61
-------
Activity-specific METS are briefly discussed in Section 11
of this report. To link the METS approach to Section 10
that is focused on moderate and vigorous physical activity
defined by accelerometers, it should be recognized that
the relationship between accelerometer counts and METS
estimates are non-linear. In general, METS estimates
decrease with accelerometer counts as counts increase
(Agiovlasitis et al., 2012). Thus, higher accelerometer counts
will underestimate activity-specific METS associated with an
undertaking.
We are most interested in using the bounded METSres metric
as a way to improve intake dose rate estimation procedures
used in our exposure models. We present what little data on
METS,, ,. is seen in the literature in Table 10. Estimates of
METS.„
, can easily be obtained by subtracting 1 from
the values shown. Many of the values seen in the Table are
from studies that reported both V02 REST and V02 MAX data.
Using the metabolic chronotrophic relationship discussed in
the next Section, V02RESERVE = METS RESERVE, thus:
((V0,MAX - vo2 REST) / V02 REST) = ((METSmax - METSrest) /
METS ).
Since METSrest = 1 by definition, ((V02 MAX - V02 REST) /
v02rest) = (METSmax "')• Rearranging and simplifying
terms, we get: METSmax = V02 MAX / V02 REST. This is the
approach used to obtain many of the data entries appearing
in Table 10.
METSmax has long been used in prescribing exercise limits
in cardiovascular patients (Bourque et al., 2009; Morris et al.,
1993; and see Appendix B). In general, these researchers first
identify people, who are mostly male, who cannot achieve
85% of their age-predicted HRMAX and then subject them to a
graded treadmill exercise test that is indexed as METS. Those
that pass the HR criterion, are then categorized into 3 groups:
<7 METS, 7.0-9.9 METS, and >10 METS. A cutpoint of
10 METS or higher predicts low mortality, even when
coronary artery disease is present (Bourque et al., 2009).
Patients in the lower two groups have a higher prevalence
of ischemia, and that is inversely related to the METS
levels attained (Bourque et al., 2009). It should be noted
that these METS levels are all quite high when compared to
the "standard" METS criteria of 3-5.9 METS for moderate
physical activity (PA) and >6 METS for vigorous PA.
There are a number of studies that confirm the "protective
role" of a higher METSmax capacity in these patients, even
in the presence of other risk factors; the risk of death from
any cause in subjects with a METSmax <5 was double that of
subjects with a METSmax of 8 or higher (Myers et al., 2002).
Their sample size was about 6,200 subjects. Their findings
are similar to those of previous researchers (Blair et al.,
1989, 1995; Ekelund et al., 1988; Franklin & Swain, 2003;
Haskell et al., 1992).
The METSmax, concept, by the way, has been used by EPA
since 2003 to "cap" youth METSmax as a part of the CHAD
database (www.epa.gov/heasd/chad.html'). The cap was
calculated from a "means-of means" analysis undertaken
in 2001, and was slightly modified in the McCurdy &
Graham (2004) report. The caps are provided—as METSmax
limits~on the CHAD webpage. In the 2004 report, we
recommended that in an exposure modeling application
using the usual algorithms, if any METSact estimate was
obtained that was >METSmax limits for the age/gender
cohort, then the METSact estimate be set at the limit. We
were fully aware that doing so may result in a "skewed"
distribution of METSact values for a particular activity
code, but data were—and still are-weak concerning those
distributions anyway, and there are no data available to us
to test the practical impact of using the maximum METSact
limit decision rule. More rigorously: METSact are always
-------
Table 10. Estimates of METS.„ seen in-or calculated from—the literature
Max
METS.Max Estimate (unitless)
Age Range
(Mean ± SD) Mean ± SD Citation Comment
Females: Normal, Healthy, or Not Specified
13.1 ±2.0
13 ± NS
Hui & Chen 2006
Calc. from VOmax & VOrest in Table 6.
14 ± NS
7.0 ±NS
Wilson et al. 1985
Calc. from VOmax & VOrest in Table 6.
24.3 ±4.2
9.7 ±NS
Frew et al. 1993
Calc. from VOmax & VOrest in Table 6.
31.1 ±8.8
11.1 ± NS
Dalleck & Kravitz 2006
Calc. from VOmax & VOrest in Table 6.
55.7 ±7.8
9.0 ±NS
Nikolai et al. 2009
Calc. from VOmax & VOrest in Table 6.
Females: Active, Fit, or Athlete
13-19
15.1 ± NS
Guidetti et al. 2000
Calc. from VOmax & VOrest in Table 6.
21.8 ±6.0
12.0 ±NS
Blanksby & Reidy 1988
Competitive dancers
27.8 ±2.6
13.1 ± NS
Frey et al. 1993
Calc. from VOmax & VOrest in Table 6.
Females: Sedentary, Overweight, Obese, or Health Issues
14 ± NS
7.1 ± NS
Wilson et al. 1985
Calc. from VOmax & VOrest in Table 6.
26.8 ±7.9
9.3 ±NS
M Lee et al. 2010a
Calc. from VOmax & VOrest in Table 6.
Males: Normal, Healthy, or Not Spec.
13.9 ±1.9
12.4 ±NS
Hui & Chen 2006
Calc. from VOmax & VOrest in Table 6.
14 ± NS
7.1 ± NS
Wlson et al. 1985
Calc. from VOmax & VOrest in Table 6.
29.2 ±6.8
8.7 ±NS
Dalleck & Kravitz 2006
Calc. from VOmax & VOrest in Table 6.
41.7 ±8.8
10.1 ±2.1
Blair et al. 1995
Measured via a maximal/resting fitness test.
59.1 ±7.6
9.0 ±NS
Nickolai et al. 2009
Calc. from VOmax & VOrest in Table 6.
Males: Active, Fit, or Athlete
23.2 ±6.3
10.7 ±NS
Blanksby & Reidy 1988
Competitive dancers
Males: Sedentary, Overweight, Obese, or Health Issues
14 ± NS
7.7 ±NS
Wlson et al. 1985
Calc. from VOmax & VOrest in Table 6.
22.5 ±4.4
9.2 ±NS
M Lee et al. 2010a
Calc. from VOmax & VOrest in Table 6.
27.4 ±8.1
7.4 ±NS
Davis & Shephard 1988
Calc. from VOmax & VOrest in Table 6.
28.1 ±5.8
9.3 ±NS
Davis & Shephard 1988
Calc. from VOmax & VOrest in Table 6.
46.8 ±9.6
11.0 ±2.2
Blair et al. 1995
From a maximal/resting fitness test.
Both Genders: General Estimates
Young
13
McArdle et al. 2001
Brochard et al. 1990 Table 9.5
Middle Age
10
McArdle et al. 2001
Brochard et al. 1990 Table 9.5
Old
7
McArdle et al. 2001
Brochard et al. 1990 Table 9.5
Very Old
4
McArdle et al. 2001
Brochard et al. 1990 Table 9.5
20-39
12
U.S. Dept. Health & HS
Table 2-4; females 1-2 METS lower
40-64
10
U.S. Dept. Health & HS
Table 2-4; females 1-2 METS lower
65-79
8
U.S. Dept. Health & HS
Table 2-4; females 1-2 METS lower
80+
5
U.S. Dept. Health & HS
Table 2-4; females 1-2 METS lower
30 (Sed.)
10
Franklin 2000
-------
Table 10. Estimates of METS.Max seen in-or calculated from-the literature (continued)
METS.Max Estimate (unitless)
Age Range
(Mean ± SD) Mean ± SD Citation Comment
30 (Athlete) 23 Franklin 2000
Both Genders: Normal, Healthy, or Not Specified
6-14 8.2 ±NS Cabrera et al. 2002 BSA<1.1
6-14 6.7 ±NS Cabrera et al. 2002 BSA=1.1-1.4
6-14 8.1 ±NS Cabrera et al. 2002 BSA>1.4
Both Genders: Sedentary, Overweight, Obese. Or Not Healthy
24.1 ±6.3 8.4 ± NS M Lee et al. 2010b Calc. from VOmax & VOrest in Table 6.
27.8 ± 5.6 6.0 ± NS PL Jacobs et al. 1997 Calc. from VOmax & VOrest in Table 6.
58.0 ± 7.0 6.6 ± NS Colberg et al. 2003 Calc. from VOmax & VOrest in Table 6.
62.9 ±10.1 5.2 ± NS Colberg et al. 2003 Calc. from VOmax & VOrest in Table 6.
Abbreviations:
HS Human Services
NS Not specified/not calculated
Sed. Sedentary
number of accelerometer participants was not proportional
to people in the different METSmax categories, so there is a
disjunct in extrapolating the data, limiting conclusions that
can be drawn from the analysis. By METSmax category, the
following METS-minute data can be obtained:
Average
Percent
METMin
of Group
METSmax
of Activity
that Partic
Category
Undertaken
in Bouts
<6.0
16.9
59.2
6.0-7.9
19.8
72.1
8.0-9.9
22.2
79.5
> 10.0
25.8
88.4
(Partic. = Participates)
Both the total MET-minutes and the average energy
expended per one MET-minute increased with METSmax
category (Jakicic et al., 2010), so the relationship is not
linear. The last column reflects the percentage of people
(both females and males) that undertook exercise for one or
more 10 min bouts with at least a 3 METS level of energy
expenditure as estimated by a RT3 accelerometer (Jakicic
et al., 2010). Thus, the higher a person's METSmax fitness
level is, the more frequent and/or the longer they participate
in higher energy expenditure activities. These are intuitively
appealing findings.
Morris et al. (1993), practicing cardiologists, developed
regression equations for males using subjects between the
ages of 18-89 y. They disaggregate their subjects into four
groups: 1,338 patients referred to them for evaluation of
possible coronary artery disease, divided into sedentary
and active patients; and, 196 volunteers who undertook the
same type of treadmill V02 tests, also divided into active
and sedentary subjects. The referral subjects did not include
severe heart patients, who were excluded for safety reasons
(Morris et al., 1993). A reproduction of their METSmax
prediction regression equations follows.
Referral patients, active:
METSmax = 18.7 - (0.15 *Age)
n=346, SEE = 3.0, r= -0.49, p<0.001
Referral patients, sedentary:
METSmax = 16.6 - (0.16 * Age)
n=253, SEE = 3.2, r= -0.43, p<0.001
Volunteers, active:
METSmax = 16.4 - (0.13 * Age)
n=122, SEE = 2.5, r= -0.58, p<0.001
Volunteers, sedentary:
METSmax = 11.9 - (0.07 * Age)
n= 74, SEE = 1.8, r= -0.47, p<0.001
These equations produce consistently lower METSmax for
sedentary individuals than active people for the two groups,
and also predict higher METSmax for volunteers than for
referral patients. Applying these equations results in pretty
high METSmax estimates at the 95% prediction interval
(e.g., 15 METS for 60 y olds, and 10 METS for 80 y old
people having possible coronary patients). These values
are considerably higher than those appearing in Table 10.
It is quite obvious that additional data on cohort-specific
METSmax limits are needed, especially if we anchor our
METSact estimates using the metabolic chromotropic
relationship. A concerted effort should be made to thoroughly
review the literature to come up a more complete database of
METSmax value than I was able to undertake.
64
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7.0
Metabolic Chronotropic Relationship
A unifying approach to linking HR and other physiological
parameters to V02 and METS is called the metabolic
chronotropic relationship (MC relationship). It also is known
as the heart rate/work rate ratio (HR/WR) (Lewather et
al., 1999; Wilkoff & Miller, 1992). The MC relationship
is functionally very similar to the Karvonen approach and
is based upon using reserve metrics to relate submaximal
exercise stages. Proponents of the relationship state that
it adjusts for age, physical fitness, and functional capacity
of an individual and appears to be unaffected by exercise
testing mode or protocol (Brubaker & Kitzman, 2011). It
often is stated in the physiological literature that energy
expenditure for a specific activity will elicit the same relative
HRR and V02RES. This seems to be particularly true when
a single exercise protocol is used for work rates lower than
the lactate threshold.
In short, the MC relationship implies that %HHR =
%METSres = %V02RES, and implies that the slope of
relationships between any two of these metrics is = 1.0
and that their intercept = 0.0 (Lewalter et al., 1999). For
instance, Dalleck & Kravitz (2006) regress %HRR and
%V02RES for different exercise rates in 48 adults of both
genders on an elliptical crosstrainer and obtained the
following relationship: %HRR = -0.7 + [1.01 * %V02RES],
R2=0.99, p<0.001 (SE not provided). The intercept was not
significantly different than 0.0 and the slope was not different
than 1.0 (Dalleck & Kravitz, 2006). Another example of
good regressions for the two metrics is shown in Hui &
Chan (2006) for Chinese youth aged 10-17. They report an
R2 of 0.92 for a treadmill equation for females of the form
%HHR = 22.9% + (0.79 * %VO, RES) [SEE=0.01%], For
males, it was %HHR = 15.7% + (0.85 * %V02RES) [R2=0.90;
SEE=0.01%], Even though these results seem to be excellent,
the authors state that "the equivalency between %HRR and
%V02R [their symbol for %V02 RES] was not confirmed in the
present study" (Hui & Chan, 2006; p. 48), perhaps because
they estimated HRMAX using the 220-Age formula; see the
discussion below regarding this way of estimating HRMAX.
A study of 6 Japanese female black-belt karate participants
aged 20 y also reported a very good relationship between
HRR and V02RES. The regression was %HRR = 5.72 + (1.01
* %V02RES) [R2=0.98; no SEE presented] (Imamura et al.,
2002). Swain (2000) and Swain et al. (1998) also develop
%HRR—>%V02 RES regressions with very highR2's.
The strong association of the two reserve metrics deteriorates
somewhat when a set of disparate activities is evaluated or
people with health issues are tested. Carvalho et al., (2008)
regressed %HRR against %V02 RES in heart failure patients
using optimized beta blocker regimes; the subsequent
regression equation had an R2 of 0.90 (SE= 1.8%). The same
results for patients without an optimized drug regime was
R2=0.83; SE=2.1%. Not bad in either case, and Carvalho et
al. (2009) found a good relationship between the two reserve
metrics in a study of heart transplant patients. The regression
equation in that study (%HRR=13.3 + [0.88* %V02RES]) had
an R2=0.89 (no SE provided). A study of older diabetics also
found a close relationship between %HRR and %V02 RES
(Colberg et al., 2003).
However, it appears that the %HRR to %V02 RES relationship
is imprecise for people with a blunted HR response to
exercise, whether it is due to physical problems or to age
(Patterson et al., 2005). In a study of heart disease patients,
both the slope and intercept of a number of %HRR—>%
V02 RES regressions are significantly different at p< 0.01
(Brawner et al., 2002). In addition, Cunlia et al. (2010,
2011) have shown that the V02 testing protocol used itself
significantly affects the %HRR—> %V02 RES relationship
in healthy individuals, depending upon how fit they were
and their prior physical activity patterns. They found that
in a number of individuals, there was a better relationship
between %HRR and %V02 MAX than between %HRR and
%V02RES (Cunlia et al., 2010).' The %HRR to %V02RES
relationship is also not very good for non-traditional exercise
protocols not involving large muscles (Rothstein & Meckel,
2000). That also is the finding of Mendez-Villanueva
et al. (2010) for arm-paddling exercise in highly-trained
surfboard riders; both the mean slope of the regression
between the two metrics and the intercept is significantly
different than 1 and 0, respectively, for arm-exercise, contrary
to what was found for lower-body exercise (Mendez-
Villanueva et al., 2010).
Like the absolute HR-to- V02 relationship, the association
between %HRR and %V02 RES is not linear over the entire
range. %HRR and %V02RES is approximately linear up to the
"gas exchange threshold" (Pettit et al., 2008), which seems to
be a new name for anaerobic threshold, but the relationship
becomes non-linear above it. (See comments below regarding
this threshold concept.) The relative variability (coefficient
of variability: COV) in the two reserve metrics is different
for people with similar age and gender characteristics: on the
order of 19-24% for V02 RES and 24-26% for HRR (Nikolai
et al., 2009). This finding does not indicate a consistently
linear relationship between HRR and V02 RES. Lounana et al.
(2007) state that predicted %V02 RES values were equivalent
to %HRR values in the 35-95% HRR range, but diverged on
either side of that range.
65
-------
This infers that a linear relationship among the three reserve
parameters exists (Lewather et al., 1999), but in reality there
is variability among persons at different relative work rates,
and variability along the work rate continuum within an
individual (Lewather et al., 1999) Thus the MCR function
is not a smooth one, but has a slope only -1.0 over the work
rate range. The inter- and intra-individual variability of the
MC relationship results in a 95th confidence interval for
the slope of 0.8-1.3 (Brubaker & Kitzman, 2011; Coman
et al., 2008). Wilkoff & Miller (1992) state that the 95th
confidence interval for the relationship is 0.79-1.33 in CEAP
patients (those having chronic heart rate issues and using the
chronotropic assessment exercise protocol) and 0.97-1.02
in "normals" using the Bruce protocol. Not only is the MC
relationship only approximately linear, it seems to be so
for most people only at exercise rates between 35-95% of
V02RES (Lounana et al., 2007). The divergence from linearity
can be about 8% for an individual at a particular work rate
depending upon the protocol used (Cunha et al., 201 la, b).
In fact, Cunha et al. (2011b) present a Table showing 25
regression equations relating %HRR (dependent variable)
to % V02RES with slope values between 0.83 and 1.08. Most
slope values are within 3% of 1.00, however. Wilkoff et
al. (1989) provide the following regression equations for
CEAP patients and "normals" that are informative: %HRR
= 4.6 + (0.94 * %METSres; R2=0.96) and %HRR = 3.4 +
(0.94 * %METSres; R2=0.98), respectively. Their relative
deviations around the 0.0 intercept were 7.7% and 5.0%,
respectively (Wilkoff et al., 1989). A study of the MC
relationship in sedentary heart transplant patients by Carvalho
et al. (2010) produced a weaker %HRR-%V02RES regression
equation (R2=0.89) and a higher intercept value (13.3). A
much more positive finding from a regression of %HRR on
% V02RES is found in Brawner et al. (2002), also involving
patients with heart disease. For three different subgroups
of that population, none of the slopes were significantly
different from the line of identity. Thus, there is a wide
variety of findings in the literature regarding the %HRR-
%V02 RES MC relationship.
Cardiologists use the MC relationship to identify people
with cardiovascular, particularly cardiac, problems, such
as myocardial ischemia (Lauer et al., 1998). In these cases,
the MC relationship is called the "cardiac chronotropic
relationship" or the "chronotropic index" (Brubaker &
Kitzman, 2007). Since it is dangerous to exercise people
with heart problems at maximal rates, HRMax is estimated
by 220-Age rather than being measured; this introduces a
lot of uncertainty into the concept. A person with an MC
relationship <0.8 (or, alternatively, <0.85) is said to have
chronotropic incompetence (CIComp), defined to be an
inability of the heart to increase its rate commensurate with
increased activity or demand (Brubaker & Kitzman, 2011;
Chin et al., 1979; Coman et al., 2008; Wilkoff & Miller,
1992). Alternative ratios have also been used (0.70, 0.75.
0.85), resulting in a fairly wide range of CIComp prevalence
rates in the general population when coupled with different
incremental dynamic exercise protocols (Brubaker &
Kitzman, 2011; Lauer etal., 1998, 1996, 1999). Lewather et
al. (1999) states than any deviation of the slope 1.0 between
HR and metabolic reserve (METS) "can be used to define
a potentially abnormal rate response to exercise" (p. 361).
Thus, while the numerical definition of CIComp is not
a settled concept, the MC relationship is receiving wide
acceptance as a method for better relating physiological
parameters over the range of exercise rates possible in
humans (Coman et al., 2008; Cunha et al., 2011b; Panton
etal., 1996; Swain etal., 1994, 1998; Swain & Leutholtz,
1997). We are considering using the MC relationship in the
future to better relate activity-specific work rates (EEA in
kcal/kg-min) that are used in the APEX and SHEDS models
to model intake dose rates of exposed persons.
With respect to oxygen consumption (V02) and metabolic
equivalents of work (METS), the MC relationship can be
stated as:
V0,ACT I V0,RES = (V0,CT " V0,REST) ' (V0,MAX " V0,REST)
- METSact I METSres - METSact I (METSmax - 1)
Where:
METSact
metsmax =
v0
vo2
vo„
2.ACT
Activity-specific METS
Maximum METS
METS Reserve (METSmax - 1.0)
Activity-specific V02
Maximal VO„
-2rest= Resting V02
Notes:| ="given" or "conditioned upon"
By definition, resting METS=1.0
There is an idea that changes in body size alter the metabolic
requirements of relative work rates rather than absolute work
rates, and this results in a constant "per kg" metric. There
is intuitive support for this concept, backed up observations
from animal and human experiments (Rowland, 2012).
Subtracting resting (basal) metabolic rate from activity-
specific energy expenditure minimizes age and gender
differences in activity-specific EE at similar work rates
(Rowland, 2011, 2013). However, it has been known for
a long time that "normalizing" work rates by a fat-free or
lean-body mass minimizes these differences even more.
See Astrand & Rodahl (1986), Boileau & Horswill (2000),
or McArdle et al. (2001), for additional information.
Some information relating METSmax, HRR, and V02 MAX
"workload intensities" for differing fitness levels is presented
in Table 11.
66
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Table 11. "WORKLOAD INTENSITY" for differing METS.Max fitness level
METS & Corresponding V02Max Percentages
Corresponding
miensuy
Level
Category
V02Reserve
& HRR Levels
%
Max METS
= 5.0
Max METS
= 8.0
METS
%V02 Max
METS
%V02 Max
Light
20-39
1.8-2.5
36-51
2.4-3.7
30-47
Moderate
40-59
2.6-3.3
52-67
3.8-5.1
48-64
Hard
60-84
3.4-4.3
68-87
5.2-6.9
65-86
Very Hard
>85
>4.4
>88
>7.0
> 87
Max METS
= 10
Max METS
= 12.0
METS
%V02 Max
METS
METS
Light
20-39
2.8-4.5
28-45
3.2-5.3
27-44
Moderate
40-59
CO
CD
CD
46-63
5.4-7.5
45-62
Hard
60-84
6.4-8.6
64-86
7.6-10.2
63-85
Very Hard
> 85
> 8.7
>87
> 85
> 86
METS Reserve Levels Associated with the Above Max METS
5
8
10
12
Light
20-39
0.8 -1.6
1.4-2.7
1.8-3.5
2.2-4.3
Moderate
40-59
1.6-2.4
2.8-4.1
3.6-5.3
4.4-6.5
Hard
60-84
2.4-3.4
4.3-5.9
5.4-7.6
6.6-9.2
Very Hard
>85
> 3.4
>6.0
>7.7
>9.3
Abbreviations:
HR Heart Rate
METS Metabolic equivalents (of work)
V02 Oxygen consumption
Source: Kesaniemi et al. (2001).
Note: Kesanniemi et al. (2001) also provide an
estimate of "maximal" workload intensity, but it simply
is the Max METS value seen in the corresponding
column at 100% of VOz & HR Reserve. Their "very
light" category was deleted;it equaled <20% of HRR &
VOzreserve (=50% HRmax).
67
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-------
8.0
Daily Total Energy Expenditure (DTEE)
In reality we would like to have estimates of intake energy
(EI) for purposes of exposure modeling, especially for dietary
exposure analyses. The equating of energy intake with energy
expenditure-plus change in body stores-is a "fundamental
property" of thermodynamics and is the basis for estimating
nutritional needs of mammals (Schoeller, 2009). In humans,
energy input is the amount of chemical energy entering the
body that can be liberated via metabolism; it is measured by
metabolizable energy. It is very difficult to estimate EI in
practice and most methods used to do so lead to systematic
errors in reporting calories consumed, especially among
overweight and obese individuals (Champagne et al., 2002).
Thus, energy expenditure is measured or estimated in order to
accurately estimate EI in the general population (Livingstone
& Black, 2003; Schoeller, 2009).
Methods used to estimate EE intake on a daily (DTEE) or
other time-basis include dietary records (diary), ex post food
frequency questionnaires, and duplicate food portion studies.
The latter studies generally are relatively accurate but are
very expensive to undertake. Studies that compare DTEE
measured by DLW (see below) with that determined by a
dietary diary approach, the second-most accurate method of
estimating EI, have found that EI is systematically under-
reported by 20-30%, on average by youth (Champagne et
al., 1998) and by 10-30% in adults (Hebert et al., 2002). In
overweight or obese females and those having a high need
for "social approval," the underestimate of EI is even higher
(Hebert et al., 2002). Studies that compare DLW measures
against food-frequency questionnaires or seven-day dietary
recall estimates show low correlations between EE and EI
measures. For example, low and statistically insignificant
correlations of 0.12-0.14 between EE and EI are reported
by Hebert et al. (2002). The only group tested that can
accurately estimate EI from a food diary compared to DLW
measures of EE, is professional dietitians (Champagne et al.,
2002). See also Trabulski & Schoeller (2001) and Tran et al.
(2000) for dietary intake and DLW comparisons.
The literature review used to obtain DTEE data in US
citizens followed the pattern explained above in the V02 and
subsequent Sections. Literature searches supplied by the EPA
Library originally identified about 1,200 papers that focused
on energy expenditure concepts in human, and their abstracts
were reviewed. A number of these were acquired and further
reviewed, and relevant references were identified as being
of potential interest. These papers (or books; hereafter
"papers") were obtained and evaluated for relevancy. At
this point, 1,617 papers were identified as being of potential
interest. All of them were obtained—in whole or in part
(abstract only) and were systematically, but briefly, reviewed
to determine which might contain DTEE data derived from
doubly labeled water (DLW) studies of US citizens. Of the
total papers, 736 (45.5%) were identified for detailed review.
Of the 881 papers that were rejected for further review at
this stage, 379 contained only resting energy data or REE
estimates using only statistical prediction equations (23.4%
of the total pursued); 207 did not use DLW to estimate daily
energy expenditure (12.8%) or were a methods comparison
study; and 174 papers used non-US subjects (10.8%). The
remaining 121 papers were rejected because they provided
redundant data (for example, DeLany et al., 2002, which
also appeared in other guises as DeLany (1998) and DeLany
et al. 2004 & 2006). Additional papers were rejected if they
presented only "mixed-gender" (i.e., did not distinguish
between females and males) data, were a review of other
studies, or provided EE data only on a per-kg basis. A
number of papers by Eliakim et al. (1996, 1997, and 2001a,
b) were rejected because they were intervention studies with
no baseline measurements.
The above procedure was supplemented by a de novo Google
Scholar search plus review of selected references cited in the
736 papers that were reviewed in detail. Frankly, I lost track
of what DTEE papers were reviewed and rejected or accepted
after that, but Tables 12-21 contain over 220 unique citations
providing over300 lines of DTEE data (and its components,
where appropriate). These studies provided data used in this
report. The data in Table 12 all come from DLW studies of
US citizens unless otherwise noted. Almost 100% of the
studies listed in Tables 12-21 are cross-sectional in nature.
Very few longitudinal studies of DTEE involving the same
set of subjects exist, and many studies purported as being
longitudinal do not provide tabular data for all of the years
(e.g., Spandano et al. 2005). Essentially they are treated as
sequentially cross-sectional samples in this Report.
69
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Table 12. Estimates of DTEE & PAEE seen in the literature
Ages DTEE PAEE
H9es> (kcal/day) (kcal/d)
Mean SD Type (n) Mean SD COV Mean SD
Females: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for Age
COV Citation
Comment
5.5
0.9
N
35
1410
263
18.7
382
239
62.6
Nguyen et al. 1996
5.5
0.4
H
13
1347
184
13.7
Fontvielle et al. 1993
From individual
data
6.4
1.0
N
12
1536
363
23.6
Johnson et al. 1996
C
6.5
2.3
H
11
1453
534
36.8
Motil et al. 1998
Controls
7.6
1.7
N
25
1648
475
28.8
485
160
33.0
Nagy et al. 1997
AA: Tanner 1
7.9
1.2
N
9
1614
401
24.8
479
164
34.2
Nagy et al. 1997
C: Tanner 2
8.1
1.0
H
11
1934
201
10.4
641
213
33.2
Dugas et al. 2008
EA
8.1
1.7
N
24
1715
428
25.0
346
254
73.4
Johnson et al. 2000
Fairly fat group; AJ
8.1
1.4
N
55
1566
399
25.5
238
312
131.1
Johnson et al. 2000
Fairly fat group; C
8.2
1.0
N
12
1574
218
13.9
372
243
65.3
Treuth et al. 1998
8.3
1.2
H
10
1640
222
13.5
351
146
41.6
Dugas et al. 2008
MA
9.7
0.8
N
123
1846
247
13.4
Bandini et al. 2002
Pre-pubertal
10.1
1.0
N
45
2002
335
16.7
722
239
33.1
Craig et al. 1996
Premenarchal
10.2
1.4
N
13
2123
206
9.7
693
112
16.2
Roemmich et al.
2000
Pre-pubertal
10.6
0.4
H
25
2314
351
15.2
860
239
27.8
DeLany et al. 2006
C
10.7
0.7
H
28
2182
246
11.3
741
167
22.5
DeLany et al. 2006
AA
10.7
0.9
N
73
2098
257
12.2
Bandini et al. 2002
Pubertal
12.3
1.0
H
13
2429
327
13.5
773
313
40.5
Calabro et al. 2013
Ages 11-14
12.6
0.7
M
53
2196
399
18.2
559
296
53.0
DeLany et al. 2004
AA & C
12.7
2.3
N
27
2304
387
16.8
Perks et al. 2000
12.8 1.9 N 18 2237 263 11.8 654 148 22.6 R°®mmich et al. Pubertal
13.2
1.8
H
9
2321
281
12.1
Wong 1994
Caucasian
14.3
1.0
N
14
2385
446
18.7
Bandini et al. 1990
18.4
0.6
N
91
2448
351
14.3
Stice et al. 2011
PAI data not
reported
22.1
4.3
H
32
2596
421
16.2
Hise et al. 2002
24.1
3.5
N
10
2224
386
17.4
731
367
50.2
Beidleman et al.
1995
24.8
6.9
N
6
1985
351
17.7
590
293
49.7
Casper et al. 1991
25.2
3.5
H
10
2368
124
5.2
Sawaya et al. 1995
9-day study
25.8
5.8
N
13
2371
397
16.7
Leenders et al. 2006
13 accel. equations
28.0
5.7
N
33
2409
574
23.8
747
408
54.6
Johannsen et al.
2008a
31.0
6.0
N
9
1993
427
21.4
610
347
56.9
Hibbert et al. 1994
PAI range: 1.34-
2.15
31.3
5.0
LM
9
2414
237
9.8
Lovelady et al. 1993
PAI range: 1.51-
2.09
31.7
4.8
N
27
2221
368
16.6
702
354
50.4
Weinsier et al. 2002
Group 1:
Maintainers
-------
"able 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages
DTEE
(kcal/day)
PAEE
(kcal/d)
Mean
SD
Type
(n)
Mean
SD
cov
Mean
SD
COV
Citation
Comment
31.8
5.5
N
20
2017
237
11.8
Walsh et al. 2004
White
31.9
4.7
N
14
1992
340
17.1
Walsh et al. 2004
Black
32.6
13.1
NS
16
1765
625
35.4
1010
400
39.6
Luke et al. 2005
33.0
6.0
N
12
2261
208
9.2
Welle et al. 1992
Control group
34.0
6.1
H
83
2008
333
16.6
524
282
53.8
Hunter et al. 2002
Premen.; ages 23-
47 y
34.0
6.3
N
14
2259
192
8.5
Amatruda et al. 1993 Ages 21-45
37.6
5.7
N
20
1959
303
15.5
491
291
59.3
Weinsier et al. 2002
Group2: Gainers
38.0
8.0
H
15
2199
215
9.8
Schoeller et al. 1997
Moderately active
39.6
5.9
H
10
2519
418
16.6
820
411
50.1
Johannsen et al.
2008b
48.0
14.0
H
20
3883
1732
44.6
Roubenoff et al.
2002
Control group
49.1
6.8
N
80
2118
404
19.1
Herbert et al. 2002
Ages 40-65
49.7
7.3
N
136
2306
455
19.7
705
323
45.8
Masse et al. 2004
PAI range: 1.2-2.5
59.4
3.5
N
34
2141
363
17.0
684
280
40.9
Bathalon et al. 2001
Restrain. Eaters
60.0
4.0
H
33
2156
329
15.3
Hays et al. 2002
PAI range: 1.22-
2.29
60.3
3.1
N
26
2268
280
12.3
805
232
28.8
Bathalon et al. 2001
Unrest. Eaters
60.8
3.1
H
29
2229
325
14.6
Vinken et al. 1999
Ages: 55-65
62.1
11.9
H
27
2282
167
7.3
Seale 2002
Age range: 41-80
64.0
5.0
H
6
2092
231
11.0
410
251
61.2
Goran & Poehlman
1992
PAI range: 1.25-
1.82
64.0
7.0
NS
37
2090
411
19.7
207
211
101.9 Starling et al. 1998a
Ages: 52-79; AA
65.0
8.0
H
37
1987
396
19.9
397
290
73.0
Carpenter et al. 1998 AA
66.0
8.0
H
96
2115
360
17.0
600
260
43.3
Brochu et al. 1999
Ages: 50-88
67.0
4.0
H
13
1447
162
11.2
682
325
47.7
Treuth et al. 1996
67.0
6.0
H
52
1946
371
19.1
469
305
65.0
Carpenter et al. 1998 C
67.6
4.1
NS
10
2065
NS
Roberts 1996
Meta-analysis
68.0
6.6
NS
43
1997
403
20.2
Tomoyasu et al.
1999
White
69.0
5.4
N
29
2233
404
18.1
711
275
38.7
Johannsen et al.
2008a
70.0
3.9
N
15
2293
682
29.7
767
558
72.8
Frisard et al. 2007
71.5
4.8
N
21
2213
429
19.4
547
360
65.8
Ades et al. 2005
73.0
3.0
H
13
2103
837
39.8
Rutgers et al. 1997
73.5
4.2
H
13
2256
215
9.5
Seale et al. 2002
Rural residents
74.0
2.0
NS
10
1852
214
11.6
Roberts 1996
Meta-analysis
74.0
4.4
H
10
1814
212
11.7
Sawaya et al. 1995
9-day study
74.0
4.4
H
10
1813
215
11.9
Vinken et al. 1999
Ages: 68-80
74.1
3.2
NS
67
1904
369
19.4
620
272
43.9
Blanc et al. 2004
AA
74.5
2.8
N
40
1892
271
14.3
568
181
31.9
Cooper et al. 2013
74.6
3.1
N
40
1839
175
9.5
436
386
88.5
Manini et al. 2009
Ages: 71-79
74.8
2.8
NS
77
1885
286
15.2
584
197
33.7
Blanc et al. 2004
AA
71
-------
Table 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages
DTEE
PAEE
(kcal/day)
(kcal/d)
Mean
SD
Type
(n)
Mean
SD
cov
Mean
SD
COV
Citation
Comment
82.0
2.8
N
40
1814
337
18.6
540
277
51.3
Cooper et al. 2013
92.0
2.0
N
49
1626
222
13.7
436
180
41.3
Johannsen et al.
2008a
93.0
3.3
N
11
1608
206
12.8
381
179
47.0
Frisard et al. 2007
b. Complete age statistics are not provided
5-10
H
19
1779
257
14.4
373
248
66.5
Trowbridge et al.
1997
AA
5-10
H
14
1780
273
15.3
403
262
65.0
Trowbridge et al.
1997
C
00
1
CD
H
27
1710
281
16.4
463
213
46.0
Treuth et al. 2003a
2 lean parents
00
i
CD
H
38
1738
290
16.7
483
265
54.9
Treuth et al. 2003a
1 lean/1 obese
parent
00
1
CD
H
23
1790
297
16.6
511
230
45.0
Treuth et al. 2003a
2 obese parents
8-12
H
196
1940
161
8.3
511
133
26.0
Bandini et al. 2004
Premenarchal
8-12
NS
90
1851
213
11.5
Bandini et al. 2013
Relatively low
active
33.4
NS
10
2315
285
12.3
Champagne et al.
2002
Non-dietitians;
26-41
36.4
NS
10
2154
332
15.4
Champagne et al.
2002
Dietitians; ages
28-45
30-69
NS
180
2190
406
18.5
Tooze et al. 2013
49-79
NS
21
2357
807
34.2
Mahabir et al. 2006
Postmenopausal
60-69
N
48
2042
343
16.8
Roberts & Dallal
2005
70-79
NS
14
1888
295
15.6
Roberts & Dallal
2005
80-89
NS
6
1382
152
11.0
Roberts & Dallal
2005
90-97
NS
9
1356
166
12.2
Roberts & Dallal
2005
Females: Active, Fit, or Athlete
a. Mean & SD are provided for Age
20.0
2.0
Fit
20
4732
191
4.0
Castellani et al. 2006 Winter military act.
21.5
1.9
Ath
10
2937
709
24.1
1355
647
47.7
Beidleman et al. 1995
23.4
4.7
At.
5
5593
2510
44.9
Trappe et al. 1997
Olympic trials
training
25.0
1.3
Fit
9
3541
718
20.3
1754
625
35.6
Ruby et al. 2002
Wildfire firefighters
26.0
3.3
Ath
9
2826
312
11.0
L.O. Shulz et al.
1992
Elite distance
runners
40.0
7.0
Act
9
2462
167
6.8
Schoeller et al. 1997
74.2
2.7
Act
39
2106
263
12.5
805
206
25.6
Manini et al. 2009
Ages: 70-79
b. Complete age statistics are not provided
8-12
Act
71
2097
249
11.9
Bandini et al. 2013
Relatively active
19-22
Ath
9
2038
298
14.6
Edwards et al. 1993
Cross-country
runners
72
-------
Table 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages DTEE PAEE
H9es> (kcal/day) (kcal/d)
Mean SD Type (n) Mean SD COV Mean SD COV Citation
Females: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for Age
8.5 2.0 OW 14 1554 319 20.5 313
8.7 0.7 OW 12 2009 316 15.7 525
10.2 0.7 OW 27 2156 338 15.7
10.2 0.7 OW 31 2308 373 16.2
275 87.9 Johnson et al. 1998
193 36.8 Treuth et al. 1998
Champagne et al.
1998
Champagne et al.
1998
10.5
13.4
15.2
29.0
0.3 OE
0.8 OW
1.8 O
4.0 O
31.3 13.0
32.0
34.6
35.2
35.6
36.0
36.0
36.5
38.0
10.0
10.6
7.4
6.9
5.8
7.0
6.1
5.0
38.5 6.1
38.6
38.7
39.5
39.8
40.8
43.8
48.0
8.1
6.0
5.2
5.0
4.5
9.2
10.0
57.5 4.2
57.8
61.2
62.1
64.0
65.0
73.5
75.5
75.5
6.6
15.3
11.9
8.0
3.5
4.2
2.8
2.8
51
20
16
OW
M
Sed
Sed
OW
OW
OW
Sed
OW
O
O
O
O
OW
OW
O
OW
OW
OW
O
OW
OW
OW
17
18
20
26
21
8
2835
3282
2963
28 2684
172 2428
2177
1969
2118
2677
2234
1960
30 2559
15 2703
18 2704
14 2452
13 2616
35 2353
47 2462
19 2752
27 3071
27 2282
37 2090
25 1999
13 2256
72 1930
80 1891
336
558
135
2602
11.9
17.0
4.6 1212
2789 440 15.8
309
433
267
342
343
428
396
191
11.5
17.8
12.3
17.4
16.2
16.0
17.7
9.7
295
339
449
361
422
611
454
11.5
12.5
16.6
14.7
16.1
26.0
18.4
1028
834
1333
980
15 2639 378 14.3 761
513
361
167
411
385
215
395
296
18.6
11.8
7.3
19.7
19.3
9.5
20.5
15.7
864
493
435
605
549
251
253
498
335
359
360
297
310
302
192
Comment
Mohawk &
Caucasian
AA
655 25.2
570 47.0
793 272 34.3
10 2593 319 12.3 673 304 45.2
24.4
30.3
37.4
34.2
Bunt et al. 2003
R.Singh et al. 2009
Bandini et al. 1990
Hibbert et al. 1994
L.O. Schulz et al.
1994
Tataranni et al. 2003
Ebersole et al. 2008
Hunter et al. 2000
Hunter et al. 2000
Walsh et al. 2004
Welle et al. 1992
Walsh et al. 2004
Schoeller et al. 1997
Johannsen et al.
2008b
Roberts et al. 2012
Kushner et al. 1995
Amatruda et al. 1993
Kushner et al. 1995
Racette et al. 1995
Staten et al. 2001
Paul et al. 2004
Pima Indians
Pima Indians
Pima Indians
2/3 were OW or O
Premenopausal C
Premenopausal AA
Black
White
47.2 Rawson et al. 2002
41.7 Rawson et al. 2002
Seale 2002
Seale 2002
60.2 Starling et al. 1998b
71.3 Nicklas etal. 1997
Seale 2002b
49.9 Manini et al. 2009
35.0 Manini et al. 2009
CALERIE Study
C
Ages 31-51
AA
(Question the PAI)
Trp64Arg Non-
Carriers
Trp64Arg Carriers
Ages: 32-82
AA
AA
AA
C
73
-------
Table 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages DTEE PAEE
H9es> (kcal/day) (kcal/d)
Mean SD Type (n) Mean SD COV Mean SD COV Citation
b. Complete age statistics are not provided
40-69 OW 206 2308 474 20.5 750
49-79 OW 25 2665 631 23.7
49-79 OW 19 2730 1185 43.4
60-69
70-79
80-89
90-97
OW 46 2061 294 14.3
OW 19 1868 402 21.5
OW 6 1748 464 26.5
OW
1766 292 16.5
799 106.5 Tooze etal. 2007
Mahabir et al. 2006
Mahabir et al. 2006
Roberts & Dallal
2005
Roberts & Dallal
2005
Roberts & Dallal
2005
Roberts & Dallal
2005
Comment
62% was OWorO
Post-menopausal
Post-menopausal
Females: Health & Other Issues
6.2 2.1 RS 14 845 251 29.7
24.5 6.9 ANP 6 1972 644 32.7 888
39.9 11.9 CP 12 1986 363 18.3 340
47.0 14.0 RA 20 2849 1075 37.7
468 52.7
601 176.8
72.9 6.1 CHD 21 2207 402 18.2 498 314 63.1
Males: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for Age
Motil et al. 1998
Casper et al. 1991
RK Johnson et al.
1997
Roubenoff et al.
2002
Ades et al. 2005
Lack of use of hand
Amenorrheic
Stable, with drugs
5.2
0.7
N
36
1554
335
21.6
454
287
63.2
Nguyen et al. 1996
5.4
0.3
H
15
1416
252
17.8
Fontvielle et al. 1993
From individual
data
5.5
0.7
N
12
1678
603
35.9
Johnson et al. 1996
C
7.4
1.6
N
22
1698
479
28.2
432
215
49.8
Nagy et al. 1997
AA: Tanner 1
7.6
1.0
H
10
1804
215
11.9
523
186
35.6
Dugas et al. 2008
MA
7.6
1.5
N
19
1799
480
26.7
355
324
91.3
Johnson et al. 2006
Fairly fat group; AJ
8.0
1.0
H
16
1893
359
19.0
588
239
40.6
Dugas et al. 2008
EA
8.3
1.6
N
20
1660
349
21.0
429
173
40.3
Nagy et al. 1997
C: Tanner 1
8.7
1.8
N
17
1783
377
21.1
307
195
63.5
Johnson et al. 2006
Fairly fat group; C
10.9
0.6
H
29
2576
330
12.8
932
239
25.6
DeLany et al. 2006
C
10.9
0.7
H
31
2572
382
14.9
1004
287
28.6
DeLany et al. 2006
AA
10.9
1.0
N
14
2174
236
10.9
712
191
26.8
Roemmich et al.
2000
Pre-puberal
12.5
1.6
N
23
2412
476
19.7
Perks et al. 2000
12.8
0.8
M
61
2651
392
14.8
758
299
39.4
DeLany et al. 2004
AA & C
12.9
2.1
H
15
2710
658
24.3
937
272
29.0
Calabro et al. 2013
Ages 10-16
13.4
1.2
n
14
2555
251
9.8
673
210
31.2
Roemmich et al.
2000
Pubertal
14.5
1.5
N
14
3109
506
16.3
Bandini et al. 1990
22.3
1.9
N
14
3494
182
5.2
Roberts et al. 1991
Sed.Occup +
<""»/¦>+1\ (A
74
-------
Table 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages
Mean
22.7
22.7
23.1
35.9
41.2
42.0
61.2
64.0
64.0
67.0
67.8
68.0
70.0
71.0
74.4
74.7
74.8
75.1
75.1
82.0
82.2
SD
2.5
3.8
2.4
27.0 4.4
13.4
9.8
16.0
15.3
7.0
8.0
8.0
6.1
6.4
68.0 6.0
69.0 5.4
69.0 7.0
70.0 6.9
71.0 4.1
7.0
5.0
4.1
3.2
2.9
3.1
3.2
3.0
3.3
92.0 2.0
DTEE PAEE
(kcal/day) (kcal/d)
Type (n) Mean SD COV Mean SD COV
H 17 3461 641 18.5
H 22 3379 1353 40.0
N 24 3356 635 18.9
N
NS
NS
H
H
H
NS
H
H
H
H
N
20 3476 880 25.3 1265 679 53.7
16
24
30
27
28
28
84
20
18
3245
3172
2892
3071
2642
2772
2755
2580
2691
565
410
548
351
537
556
511
566
547
17.4
12.9
18.9
11.4
20.3
20.1
18.5
21.9
20.3
1300 370 28.5
788 414 52.5
746
410
860
438
320
355
NS 15 2495 352 14.1
H 9 2349 300 12.8
H 17 2852 462 16.2 940
70.0 6.2 NS 39 2701 528 19.5
H
NS
H
N
NS
N
NS
NS
N
N
47
16
14
47
72
43
72
23
47
2584
2412
2971
2482
2324
2395
2521
1657
2208
506
390
476
436
214
396
209
376
19.6
0.0
13.1
19.2
18.8
8.9
15.7
12.6
17.0
832
865
737
775
666
46 2002 326 16.3 539
93.0 3.3 N 11 2052 265 12.9 551
b. Complete age statistics are not provided
5.0 N 41
308
284
544
313
243
227
196
5-10
5-10
30-69
60-69
H
58.7
78.0
41.3
2675 394 14.7 692 402 58.1
29 2970 458 15.4 1057 307 29.0
349 37.1
743 375 50.5
37.0
32.8
73.8
40.4
36.5
42.1
35.6
12 1871 260 13.9 387 249 64.3
H 17 1837 260 14.2 347 251 72.3
NS 189 2877 498 17.3
NS 14 2397 437 18.2
Citation
Roberts et al. 1995
Hise et al. 2002
Vinken et al. 1999
Johannsen et al.
2008a
Luke et al. 2005
Conway et al. 2002
Rising et al. 1994
Seale 2002
Carpenter et al. 1998
Starling et al. 1998a
Brochu et al. 1999
Vinken et al. 1999
Roberts et al. 1995
Goran & Poehlman
1992
Johannsen et al.
2008a
Roberts 1995
Roberts et al. 1996
Frisard et al. 2007
Tomoyasu et al.
1999
Carpenter et al. 1998
Roberts 1996
Seale et al. 2002
Cooper et al. 2013
Blanc et al. 2004
Manini et al. 2009
Blanc et al. 2004
Fuller et al. 1996
Cooper et al. 2013
Johannsen et al.
2008a
Frisard et al. 2007
Salbe et al. 1997
Trowbridge et al.
1997
Trowbridge et al.
1997
Tooze et al. 2013
Roberts & Dallal
2005
Comment
Same as above?
Age range: 18-28
Pima Ind.; some O
Age range: 32-82
AA
Ages: 52-79
Ages: 45-90
Ages: 60-81
PAI range: 1.25-
2.11
Meta-analysis
Same as above?
White
Rural residents
W; ages: 70-79
Ages: 70-79
Ages: 76-88
Pima Indians
C
AA
75
-------
Table 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages DTEE PAEE
H9es> (kcal/day) (kcal/d)
Mean SD Type (n) Mean SD COV Mean SD COV
70-79
80-89
90-97
NS 30 2407 374 15.5
NS
NS
1700 239 14.1
1935 156
22.5 3.3
22.6 3.5
24.5 1.8
25.0 3.0
25.0 5.0
27.1 4.2
Fit
Fit
Fit
Fit
Fit
Fit
19 4116 719
7 4878 716
10 5378 678
7 3480 220
27 3477 816
8.1
Males: Active, Fit, or Athlete
a. Mean & SD are provided for Age
20.0 2.0 Fit 30 6142 191 3.1
21.0 2.9 Act 13 3031 627 20.7
4750 531 11.2
17.5
14.7 2628 714 27.2
12.6
6.3
23.5
Citation
Roberts & Dallal
2005
Roberts & Dallal
2005
Roberts & Dallal
2005
Castellani et al. 2006
Haggerty et al. 1997
Forbes-Ewan et al.
1989
Tharion et al. 2004
Ruby et al. 2002
Hoyt et al. 2001
DeLany et al. 1989
Hoyt et al. 1991
Comment
Winter military act.
Construction
workers
Military training
Military training
Wildfire firefighters
Cold military
training
Military training
High-alt. military
train.
27.9
7.3
Act
10
4716
435
9.2 2422 375
15.5 Heil 2002
Woodland
firefighters
28.0
5.0
Fit
7
3220
280
8.7
DeLany et al. 1989
Military training
31.0
4.0
Fit
6
4558
566
12.4
Hoyt et al. 1994
High-alt. mitary
train.
45.5
4.8
Fit
13
2964
676
22.8
Lane et al. 1997
Astro.; ground-
study
45.6
4.8
Fit
13
2796
452
16.2
Lane et al. 1997
Astro.space-study
74.5
3.3
Act
43
2788
293
10.5
1079
183
17.0
Manini et al. 2009
Ages: 70-79
3. Complete age statistics are not provided
19-20
Fit
18
4281
721
16.8
Burstein et al. 1996
Wnter military
training
19-20
Fit
12
3937
551
14.0
Burstein et al. 1996
Summer military
training
\/lales: Sedentary, Overweight, or Obese
i. Mean & SD statistics are provided for Age
8.2
1.9
OW
17
1922
501
26.1
598
353
59.0
RK Johnson et al.
1998
Mohawk &
Caucasian
10.3
0.7
OW
29
2537
339
13.4
Champagne et al.
1998
AA
10.3
0.7
OW
31
2574
367
14.3
Champagne et al.
1998
C
13.7
0.7
OW
14
3332
312
9.4
R. Singh et al. 2009
14.4
1.9
O
18
3612
643
17.8
Bandini et al. 1990
35.4
13.8
O
12
3172
707
22.3
Paul et al. 2004
Pima Indians
37.0
13.0
OW
64
2985
481
16.1
Tataranni et al. 2003
Pima Indians
-------
fable 12. Estimates of DTEE & PAEE seen in the literature (continued)
Ages
DTEE
(kcal/day)
PAEE
(kcal/d)
Mean
SD
Type
(n)
Mean
SD
COV
Mean
SD
COV
Citation
Comment
47.0
11.0
OW
44
3035
335
11.0
1171
311
26.6
Paul et al. 2004
64.0
7.0
OW
28
2772
556
20.1
865
451
52.1
Starling et al. 1998
AA
66.0
4.6
OW
21
2679
591
22.1
817
472
57.8
Nicklas et al. 1997
AA
75.2
2.9
OW
74
2327
431
18.5
733
302
41.2
Manini et al. 2009
AA
75.5
3.1
OW
76
2511
390
15.5
804
273
34.0
Manini et al. 2009
3. Complete age statistics are not provided
40-69
OW
244
2899
469
16.2
893
328
36.7
Tooze et al. 2007
75% were OW or
60-69
OW
30
2851
420
14.7
Roberts & Dallal
2005
70-79
OW
34
2624
461
17.6
Roberts & Dallal
2005
80-89
OW
6
2294
357
15.6
Roberts & Dallal
2005
90-97
OW
2
1863
46
2.5
Roberts & Dallal
2005
Males: Health & Other Issues
a. Mean & SD statistics are provided for Age
35.1 11.5 CP 18 2455 622 25.3 307 453 147.6
62.0 8.0 Park 16 2214 460 20.8 339
72.9 7.9 Park 20 2237 510 22.8 521 310 59.5
RK Johnson et al.
1997
366 108.0 Toth et al. 1997a
Delikanaki et al.
2009
Notes & Abbreviations:
* Calculated as: mean DTEE/mean REE.
AA African-American
Accel. Accelerometers
Act. Activite
Al American Indian
ANP Anorexia Nervosa Patients
Astro. Astronauts
Ath. Athlete
C Caucasian
CHD Chronic Heart Disease
COV Coefficient of Variation (SD/Mean*100)
CP Cerebral palsy
DTEE Daily Total Energy Expenditure (kcal/day)
EA European-American
H Healthy
LM Lactating Mothers
Notes & Abbreviations:
M Mixed lean and obese subjects
MA Mexican-American
(n) Sample Size
N Normal
NS Not Specified
PAEE Physical Activity Energy Expenditure
Physical Activity Index (DTEE/REE; also known
PAI as PAL: Physical Activity Level)
Park Parkinson Disease patient
RA Rheumatoid Arthritis
REE Resting Energy Expenditure
Rett Syndrome (a neurodevelopmental
RH disorder)
O Obese
OW Overweight
SD Standard Deviation
Sed Sedentary
77
-------
A number of studies using fairly narrowly-defined cohorts
and/or uncommon physical problems were not included
in the tabular data. Most of these involved in-patient or
hospital-based research studies, but not all. The excluded
studies comprised infants with (1) ventricular septal defects
(Ackennan et al., 1988); (2) cyanotic congenital heart disease
(Leitch et al.. 1998); (3) pre-symptomatic cystic fibrosis
(Bronstein 1995); and pulmonary insufficiency (Denne,
2001). Finally, studies involving preterm babies still in the
hospital were also excluded; these studies were Jensen et al.
(1992) and Leitch et al. (1999). In general, disease-related
weight loss reflects a chronic whole-body energy imbalance
that is difficult to model successfully (Toth, 1999), and lias
a low probability of ever being of interest to EPA's exposure
modelers.
Even though DLW studies are treated as being the "gold
standard," in a comparison of DLW estimates of DTEE in
a 7-day whole-body respiratory chamber study of 12 young
male adults, it was found that the DLW estimates on average
were -2.5% ± 5.8% lower than the V02 chamber studies.
See information relating METSmax, HRR, and V02 MAX
chamber estimates contained in Ravussin et al. (1991). The
range of the differences was -14% to +4%, with the larger
underestimates being observed in heavier, fatter subjects
(Ravussin et al., 1991).
The units of DTEE are usually presented as kcal/d or MJ/d. If
MJ/d units are provided, they are converted to kcal/d units by
multiplying them by 239. Rarely—for some odd reason—are
only kcal/kg data presented, even though doing so probably
would reduce age/gender variability in energy expenditure
estimates. Thus, the data in Tables 11-19 are presented as
kcal/d and are not BM-nonnalized. Extreme DTEE measures
are seen in polar expeditions and in strenuous military
training, where values as high as 7,500 kcal/d were recorded,
although 3,300-4,000 kcal/d was a more common high range
(Burstein et al., 1996; Hoyt et al., 1991). Usually participants
in artic expeditions lose significant weight at even these high
DTEE rates (Stroud et al., 1993). Many studies of male-only
military training exercises report DTEE in the 4,000-5,000
kcal/d range (Tharion et al., 2005). A few of these "special
studies" are included in Table 12 for contrast. Hoyt et al.
(2001) and Tharion et al. (2005) contain good reviews of
military training studies in the U.S. and other countries that
use DLW to estimate DTEE.
There have been a number of multi-study analyses of DLW-
measured DTEE. These include purely descriptive and
meta-analytic studies. Cambridge University in England lias
compiled a descriptive analysis of 74 studies in "affluent
societies" that used DLW to estimate DTEE (Black 1996,
2000; Black et al. 1996). The studies produced 574 group
means/SD's estimates for both genders and ages between 2 y
to 90 y olds. Fifty of the studies are of non-US citizens and
are not included in Table 12. Also not included in the Table
were 24 U.S. studies that provided incomplete information,
were in units that could not be converted to kcal/d estimates,
or used a non-standard protocol. The Black et al. papers
cited above are the most synoptic review of DLW studies
that I know of. Summary data from all of the studies appears
here as Table 13. Besides DTEE data. Black and colleagues
present REE and PAI data for the same cohorts included in
their review. Their work is not a meta-analysis of EE data per
se. but could be used as an input to one.
Table 13. Group mean estimates of DTEE, REE, & PAI from BLACK (2000)
DTEE
(kcal/day)
REE
(kcal/day)
PAI
Range
Mean
SD
(%)
Mean
SD
Females
(%)
Mean
SD
(%)
n
1 -6
1315
215
16.3
860
167
19.4
1.57
0.30
19.1
21
7-12
1912
430
22.5
1147
239
20.8
1.68
0.16
9.5
24
13-17
2725
621
22.8
1601
359
22.4
1.73
0.24
13.9
26
18-29
2486
526
21.2
1482
263
17.7
1.70
0.28
16.5
89
30-39
2390
406
17.0
1434
143
10.0
1.68
0.25
14.9
76
40-64
2342
406
17.3
1386
167
12.0
1.69
0.23
13.6
47
60-74
2055
382
18.6
1267
167
13.2
1.62
0.28
17.3
24
> 75
1458
263
18.0
980
143
14.6
1.48
0.23
15.5
12
Males
1 -6
1458
239
16.4
908
191
21.0
1.64
0.39
23.8
29
7-12
2342
382
16.3
1362
239
17.5
1.74
0.22
12.6
32
13-17
3370
645
19.1
1936
359
18.5
1.75
0.19
10.9
31
18-29
3298
717
21.7
1793
286
16.0
1.85
0.33
17.8
56
30-39
3418
741
21.7
1960
430
21.9
1.77
0.31
17.5
36
78
-------
able 13. Group mean estimates of DTEE, REE, & PAI from BLACK (2000) (continued)
DTEE
REE
(kcal/day)
(kcal/day)
PAI
Age
IVl|/\n
COV
COV
COV
Range
mean
SD
(%)
Mean SD
(%)
Mean
SD
(%)
n
40-64
2749
406
14.8
1673 191
11.4
1.64
0.17
10.4
15
60-74
2629
382
14.5
1649 215
13.0
1.61
0.28
17.4
22
>75 2199 311 14.1 1434 167 11.6 1.54 0.24 15.6 34
Notes & Abbreviations:
COV: Coefficient of Variation (SD/Mean)
DTEE: Daily Total Energy Expenditure (kcal/d)
n: Sample Size
PAI: Physical Activity Index (DTEE/REE)
REE: Resting Energy Expenditure (kcal/d)
SD: Standard Deviation
Sources:
Black, A.E. (2000). "Critical evaluation of energy intake
using the Goldberg cut-off for energy intake.
Inter. J. Obesity 24: 1119-1130. Black, A.E. (1996).
"Physical activity levels from a meta-analysis of doubly
water studies for validating energy intake as measured
by dietary assessment." Nutr. Rev. 54: 170-174.
Black, A.E., Coward, W.A., et al. (1996). "Human
energy expenditure in affluent societies: an analysis
of 574 doubly-labeled water measurements." Euro. J.
Clin. Nutr. 50: 72-82.
The same is true for Brooks et al. (2004), which is a
condensation of a NIH study by the (US) National Academy
of Sciences' Institute of Medicine (IOM). The IOM looked at
approximately 80 studies (the exact number is not provided
in Brooks et al., 2004) of people with a "healthy" BMI,
which is defined to be between 18.5 and 25.0 kg/m2 for adults
and <85"' percentile for youth 3-18 y old. Their data—mean
estimates of DTEE, REE, and PA~are reproduced here as
Table 14. REE for youth 3-18 was estimated by regression
equations using weight and height as independent variables.
PAI estimates were derived by dividing group mean DTEE
by group mean REE. The mean DTEE and REE values
presented in Table 14 generally are higher than those seen
in Table 13, but the mean PAI values are lower. Most of the
differences among any of the measures, however, are <10%
or so for all of the age/gender groups used.
A true meta-analysis of DTEE/PAI data is described in
Dugas et al. (2011). They included 98 studies from both
developed and under-developed countries. The studies
reported data for 183 cohorts including almost 5,000
individuals.
DTEE, not surprisingly, is inversely related to age and
positively related to BM in both genders; there was no
association of DTEE (and PAI) with development status of
the country where the subjects resided (Dugas et al., 2011).
An informative and visually interesting discussion of DTEE
as people age appears inManini (2010). Basically both
REE and DTEE decrease over time, but the decrease in
DTEE is due more to a decrease in physical activity (PA)
rather than to a reduction in organ sizes or tissue metabolic
rates (Manini, 2010; Manini et al., 2009). There does not
seem to be a difference in DTEE (or PAI) between pre- and
post-menopausal status in older women, adjusted for age
(Tooze et al., 2007).
The Food and Nutrition Board (2005) of the Institute of
Medicine, part of the U.S. National Academies of Science,
reviewed a number of DTEE studies using DLW. None of its
data appear in this report.
Components of DTEE
DTEE for weight-stable persons is decomposed into
a limited number of components because they cannot
readily be separated. Basal, or resting, metabolism always
is identified in any typology of DTEE and it is by far the
largest component of DTEE in most individuals (Goran
& Treuth, 2001). REE accounts for 60-80% of DTEE in
sedentary people, 55-70% in "normal" individuals, and
45-60% in active people (McCurdy, 2000). REE is the
minimal metabolic activity needed to maintain bodily
functions and temperature at rest. This includes circulation
respiration transport and movement of liquids, cellular
activity, maintenance of electrolyte gradients, and central
nervous functioning (McCurdy, 2000). There are numerous
ways that REE is measured (or predicted from a variety of
anthropogenic observations), and each one has multiple
protocol variations. This report is not the place to expand
upon that complicated subject; REE data that underlies
PAI data in this report all come from direct (chamber) or
indirect calorimetry measures unless otherwise noted. For
narrowly defined cohorts, variability within the cohort
(between-person variability [COVB]) for REE is about 3.0-
6.0%, about the same as within-person variability (COVw)
of approximately 4% for individuals comprising a narrow
age/gender cohort (Black, 2000). However, these estimates
seem to be low according to data provided by Black & Cole
(2000) for specific studies—with an implied wider grouping
of subjects—that states that the mean within-person variance
over multiple days is 11.8% as compared with the mean
79
-------
ible 14 Group mean estimates of DTEE, REE, & PAI from BROOKS ET AL. (2004)
Age
DTEE (kcal/d)
REE (kcal/d)
PAI
Sample
Range (y)
Mean
Mean
Mean
Size (n)
Females
3-8
1487
1004
1.48
227
9-13
1907
1186
1.60
89
14-18
2302
1361
1.69
42
19-30
2436
1361
1.80
82
31 - 50
2404
1322
1.83
61
51 - 70
2066
1226
1.70
71
> 70
1564
1183
1.33
24
Males
3-8
1441
1035
1.39
129
9-13
2079
1320
1.56
28
14-18
3116
1729
1.80
10
19-30
3081
1769
1.74
48
31 - 50
3021
1675
1.81
59
51 - 70
2469
1524
1.63
24
>70 2238 1480 1.52 39
Notes & Abbreviations:
DTEE: Daily Total Energy Expenditure (kcal/d)
n; Sample size
PAI: Physical Activity Index (DTEE/REE)
REE: Resting Energy Expenditure (kcal/d)
Source:
Brooks, G.A., et al. (2004). "Chronicle of the Institute
of Medicine physical activity recommendation: how
a physical activity recommendation came to be
among dietary recommendations." Amer. J. Clin.
/Vufr-.79(Supp.): 921S-930S.
between-person variance of 13.0%. These are quite different
within and between variances estimates reported by the same
first author.
Infrequently, REE is divided into sleeping EE (SEE) and
"arousal" EE, where the latter is ill-defined and often
treated as a "residual" (REE-SEE) representing EE spent
in waking up or going from one state to another (Manini,
2010; Ravussin & Rising, 1992). I do not see any advantage
to making this distinction and since it cannot be accurately
measured, arousal EE is not used in this report. SEE also
is not considered further except to note that REE in babies
and infants usually is measured as the child sleeps; so SEE
plays a major role in measurements of DTEE (and PAI) for
that cohort.
Another component of DTEE usually identified is Dietary
Induced Thennogenesis, or DIT. It also is called the thermic
effect of food (TEF; Hibbert et al., 1994), but that term
is not used here. DIT is energy needed to digest food and
fluids, and is difficult to measure directly. Usually it simply
is estimated to be 5-15% of DTEE and is subtracted from
DTEE to identify non-DIT daily energy expenditure (Martin
et al., 2011; Westerterp, 2004). Frequently 10% is used as
the estimate of DIT, but careful monitoring studies indicate
that DIT values of 7.6-8.0% are reasonable, at least for
females of widely varying BMI (Hibbert et al., 1994). The
impact of using of a constant—of any value—is unexplored
in DTEE studies that I have seen in the literature. Thus, DIT
essentially has an imprecise impact on daily DTEE estimates
and their variability over time, both within an individual
and in a group of individuals. You would expect DIT to
vary by the caloric value of individual meals, macronutrient
composition (Wilson & Morely, 2003), the type of activity
taken both before and after ingesting food, the type of food
consumed (particularly protein and alcohol), and personal
characteristics, such as body composition, FFM, etc.
(Westerterp, 2004). DIT certainly is an under-defined aspect
of daily total energy expenditure.
That remainder (often calculated by DTEE - [0.10*DTEE]
- REE) is known by a number of terms: physical activity
energy expenditure (PAEE), non-exercise thennogenesis
(NEAT; see below), shivering & twitching, retained energy
(gaining weight), work EE, daily living EE, etc. (Luke et
al. 2005; McCurdy, 2000). Because there is no method
to estimate these components of DTEE in "free-living"
individuals, generally only three components of DTEE
are discussed: REE, PAEE, and DIT (treated as a constant
proportion of DTEE itself as just noted). PAEE, then, is a
catch-all term that is used in the exercise literature and is not
focused on what most people would consider to be actual
physical activity. See the Glossary of Terms for formal
definitions of PA. Because of the uncertainty in what exactly
-------
PAEE includes in any one study, the PAEE values provided
in the literature should be taken as an approximation. S. Liu
et al. MSSE (2012) discuss computational methods used to
estimate EE for physical activities.
Physical activity energy expenditure is the most variable
component of DTEE on a daily basis in most individuals,
and is largely determined by genetic factors (Westerterp &
Plasqui, 2004). PAEE greatly affects the overall (absolute)
amount of DTEE in individuals (Westerterp, 2008), and
is estimated as being from 3% to 50% of DTEE in adult
males (Rising et al., 1994). This proportion is lower in
elderly individuals and people with health problems: about
10-30% (Goran & Poehlman, 1992; Toth, 1999). PAEE
itself is often disaggregated into activity-specific EE, such
as walking, carrying tilings, gardening, etc. (Passmore &
Durnin 1955), but data on these cannot come from DLW
studies that integrate whole-body EE over time. DLW,
then, cannot describe the intensity, duration frequency, or
pattern of physical activity in a subject without some type of
additional monitoring being used (Roemmich et al., 2000).
Understanding EE from specific activities requires the use of
methods discussed below in Section 10. The vast majority of
PAEE estimates contained in tables included in this report are
calculated and not directly measured. Almost all of these are
calculated using the formula presented above, usually in the
form: PAEE = (DTEE * 0.9) - REE (e.g., Tooze et al., 2007).
NEAT is a term that is used by some Mayo Clinic researchers
to represent energy expended in an individual by all physical
activities other than sleeping, eating, and volitional sports
or exercise (Levine, 2003, 2004; Levine et al., 1999, 2000,
2006). Thus, EE expended in working, walking for transport,
watching TV, cleaning the home, etc. is considered to be
NEAT. It accounts for the most variance in daily physical
activity (Donahoo et al., 2004), which—as mentioned—is
the most variable part of DTEE. Since NEAT can only be
roughly estimated using the "factorial method" (see the
Glossary) to subtract activity-specific EE from estimated
PAEE (itself estimated as (DTEE - [REE + DIT]) ~ the
concept causes a sense of false precision in daily energy
expenditure components and is not used further in this report.
In confined respiratory chamber studies of exercise using
strict protocols, NEAT was 89-92% of PAEE for youth aged
4-19 y (Butte et al., 2007).
The above discussion assumes that subjects are weight-stable.
If weight is changing, either higher or lower for whatever
reason (e.g., energy intake >energy expenditure from eating
too much and/or not undertaking enough physical activity
or both; energy expenditure
-------
Table 15. Group mean estimates of DTEE, REE, & PAEE from BUTTE ET AL. (2000) (continued)
Age
DTEE (kcal/d)
REE (kcal/d)
PAEE (Kcal/d)
PAI
(months)
Mean
Mean
Mean
Mean
Males
0-0.9
248
226
22
1.10
1.0-1.9
320
283
37
1.13
2.0-2.9
389
333
56
1.17
3.0-3.9
454
378
76
1.20
4.0-4.9
516
417
99
1.24
5.0-5.9
574
449
125
1.28
6.0-8.9
684
506
178
1.35
9.0-11.9
765
577
188
1.33
12.0-17.9
845
643
202
1.31
18.0-24.0 944 719 225 1.31
Abbreviations:
DTEE: Daily Total Energy Expenditure
PAEE: Physical Activity Energy Expenditure
PAI: Physical Activity Index
REE: Resting Energy Expenditure
Energy expenditure changes during and after pregnancy
are pronounced. See Table 16 abstracted from Butte et al.
(2004). DTEE increases through pregnancy—although not
linearly-for all three BMI groups shown in the Table and
decreases postpartum. REE follows the same trend, but the
decrease in resting EE is relatively larger than the decrease in
DTEE, resulting in an increase of PAI during pregnancy and
a decrease after pregnancy, also for all three BMI groups.
After middle age, DTEE generally decreases-both on an
absolute and relative mass basis~as people age. The decrease
is on the order of 25-35% on an absolute basis (Wilson &
Morley, 2003). REE does not decrease as much on a per-
BM basis for cross-sectional samples; see, for example
McMurray et al. (2014). The average cross-sectional decrease
found in that report was 7.5% for both genders. The two
trends together (DTEE and REE) should result in a decrease
in PAI with aging over about 35 y old, which is seen in
Tables 12-20 (but not so much in Table 19, especially for
females). See Section 9 below for discussion of PAI.
Estimating DTEE: DLW and Other Methods
There are numerous methods that have been used over
the years to estimate DTEE or some of its components
(Bray, 1997; Ravussin & Rising, 1992; Y. Schutz et al.,
2001; Shephard & Aoyagi, 2012). These are discussed in
scores of articles and books (Acheson et al., 1980 a, b;
Chen et al., 2012;; DeLany, 2012; DeLany & Lovejoy,
1996; Leonard, 2003; Leonard et al., 1997; Levine, 2005:
Livingstone et al., 1992; Murgatroyd et al., 1993; Prentice,
1988; Prentice et al., 1991; Rosenbaum et al., 1996; and
Westerterp et al., 1988). Levine (2005) is a succinct summary
82
Source:
Butte et al. (2000). "Energy requirements derived
from total energy expenditure based on energy
deposition during the first 2 y of life." Amer. J. Clin.
Nutr. 72: 1558-1569.
of over 16 different approaches for measuring/estimating
EE, many of which are not suitable for "free-living"/"free-
ranging" conditions. A common experimental or laboratory
method is the use of a room calorimeter, with a number
of ways to estimate DTEE (and some of its components,
such as REE using indirect calorimetry, sleeping EE, and
even PAEE-with some additional information). The room
calorimeter may utilize either direct or indirect methods
to gather EE data (Levine, 2005). There also are non-
caloric methods used to measure/estimate EE data. These
include heart rate (HR) monitoring, DLW, and activity
logs/factorial approach for free-living studies (Colbert &
Schoeller, 2011). Other approaches require a subject to be
in a defined space and so are experimental (Levine, 2005).
A brief and interesting survey of EE measuring methods
beginning with Lavosier's "respiratory chamber" in 1777
and ending with accelerometry in 2006 appears in Halsey
(2011). A recent approach to estimating DTEE is via body-
worn "calorimeters," including the Personal Calorie Monitor
(Lyden et al., 2014), the Body Media armband (Lee et al.,
2014), and the Energy-Monitoring Garment (shorts with
textile electrodes: Tikkanen et al., 2014).
There are a number of devices of varying complexity
to record HR over a day and "convert" it to oxygen
consumption/EE estimates that have been applied to a wide
variety of population subgroups (Levine, 2005; Livingstone
et al., 1990, 2000). Problems with the HR approach are (1)
the relationship between activity-specific EE and HR is non-
linear; (2) there is considerable intra-individual variability
between HR for most types of activity and EE; (3) affective
and emotional factors affect the HR—>EE relationship, and
-------
these are not observable (Levine, 2005). Li et al. (1993)
provide data that indicates HR recording has a 14-18% inter-
individual COV and a 11-20% intra-individual COV in 40
30-y old females, and that there was poor agreement in EE
between group and individual calibration of the HR—>EE.
They state that only individual calibration curves (HR—>EE
relationships) should be used with the HR approach to
estimating DTEE (Li, et al., 1993). We do not provide DTEE
estimates obtained using HR monitoring in this report. We
similarly do not provide data from the activity log/factorial
approach because of subject recording/recall issues (accuracy
and precision) and the fact that activity-specific estimates of
EE usually come from the Compendium (Ainsworth et al.,
1993, 2000, 2011) or similar databases, and these are not
specific to an individual or to the rate at which that individual
is working (McCurdy et al., 2000).
Butte et al. (2010) estimated DTEE using HR and
accelerometry in free-living youth 5-18 y and compared
their estimates with DLW measures. A number of analytic
procedures were used with the non-DLW techniques to
better improve their performance in estimating DTEE.
Overall, "predicted TEE values were within 11-14% of
DLW-derived TEE in 75% of participants," but the remainder
had larger differences (Butte et al., 2010; p. 1521). This,
to me, is not very good agreement, and the non-DLW
methods visually showed an increasing variance with total
energy expended using Bland-Altman plots, meaning that
there was heterogeneity in the method's residuals. There
are numerous evaluations of different accelerometers
against DLW; the interested reader is referred to the ever-
expanding literature on the topic. One is RK Johnson et al.
(1998), who conclude with "the main finding was that the
Caltrac accelerometer was not a useful predictor of AEE
(activity energy expenditure) in the sample" (p. 1050).
The correlations between accelerometer-AEE and DLW-
estimated AEE was a non-significant r = -0.09 (p=0.63) for
a 3-d period (RK Johnson et al., 1998). Others have stated
that accelerometer activity counts do "not reflect" DTEE in
4-6 y old children (Lopez-Alarcon et al., 2004). The same is
true for adult activities, especially older adults with balance
and gait issues. Newer accelerometers seemingly do a better
job of estimating activity EE, but comparative performance
is highly dependent upon the accelerometer model used
(Mackey et al., 2011). A confounding problem is that the
Table 16. DTEE, REE, & PAI during pregnancy from BUTTE ET AL. (2004)
BMI Group DTEE (kcal/d) Mean
Baseline (Pre-pregnant)
SD REE(kcal/d) Mean SD PAI Mean SD COV (%)
Low
2348
276
1201
137
1.97
0.25
12.7
17
Normal
2434
368
1323
127
1.84
0.25
13.6
34
High
2940
421
1505
153
1.96
0.22
11.2
12
22 Weeks Pregnant
Low
2272
376
1330
121
1.72
0.28
16.3
17
Normal
2520
381
1413
142
1.78
0.28
15.7
34
High
2887
435
1393
210
1.72
0.25
14.5
12
36 Weeks Pregnant
Low
2439
485
1573
210
1.63
0.33
20.2
17
Normal
2693
372
1673
172
1.62
0.24
14.8
34
High
3020
553
2016
254
1.49
0.22
14.8
12
27 Weeks Postpartum
Low
2020
267
1254
169
1.68
0.30
17.9
17
Normal
2480
410
1323
136
1.88
0.29
15.4
34
High
2708
400
1505
171
1.77
0.19
10.7
12
Definitions & Abbreviations:
BMI: Body Mass Index (kg/m**2)
COV: Coefficient of Variation (SD/Mean)
DTEE: Daily Total Energy Expenditure
High: BMI >26.0 (kg/m**2)
Low: BMI <19.8 (kg/m**2)
n: Sample Size
Normal: BMI 19.8-26.0 (kg/m**2)
PAI: Physical Activity Index (DTEE/REE)
Definitions & Abbreviations:
REE: Resting Energy Expenditure
SD: Standard Deviation
Source:
Butte et al. (2004). "Energy requirements during
pregnancy based on total energy expenditure and
energy deposition." Amer. J. Clin. Nutr. 79: 1078-1087.
83
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manufacturers change their models frequently (because
of a bad evaluation?), and comparisons among a single
manufacturer's different models are infrequent.
There are many algorithms in existence to convert
accelerometer counts into EE estimates. Many have been
developed by exercise physiologists and published in the
literature so that the manufacturers' "black box" approach
of doing so can be openly evaluated. In general, this work
has shown that significantly different EE estimates can be
obtained using different accelerometers on the same people
and that these EE estimates also are significantly different
than DLW measurements (Calabro et al., 2013). Newer
accelerometer algorithms do a better job of replicating DLW
estimates of DTEE than older algorithms (Calabro et al.,
2013), but accelerometer-based estimates of PAEE still are
problematic (Leenders et al., 2006). Scores of accelerometer/
DLW "validation" studies are published every year. See the
work by Freedson and colleagues listed in the references for
a "taste" of these papers. Almost every academic exercise
physiology program in the U.S. has done one or more
accelerometer-EE comparisons—both in vitro (treadmill
or other controlled-work rate method) and in vivo ("free-
living"). This type of testing is a "growth industry" for
academics. (Using "accelerometer accuracy" as a keyword
phrase in a Google Scholar search comes up with 66,800
"hits" as of November 2013. Even if most of the hits are
misidentified, redundant, or irrelevant, that leaves thousands
of those types of evaluations.) See Section 10.
That leaves the doubly-labeled water (DLW) technique.
Consensus of EE-measurement practitioners is that DLW
is the most accurate method of estimating multi-day EE
currently available (Friedman & Johnson, 2002; Racette et
al., 2012; Roemmich et al., 2000; Schoeller, 1999, 2009;
Schoeller & van Santen, 1982; Schoeller et al., 1980). A
number of authors call it "the gold standard," "reference,"
or "state-of-the-art criterion method" (Eliakim et al., 1998;
RK Johnson et al., 1998; Racette et al., 1995; Westerterp &
Plasqui, 2004), and use it to evaluate other approaches to
estimating DTEE. (Leenders et al., 2006; Schoeller et al.,
1990; Schoeller & Webb, 1984; L.O. Schulz et al., 1992;
S. Schulz et al., 1989; Seale et al., 1990; Singh et al., 2009;
Staten et al., 2001; Trabulsi & Schoeller, 2001; Tran et al.,
2000; Westerterp, 1999). A review of the use of DLW to
estimate EE in ambulatory children is contained in Goran
and Sun (1998), and in Leitch & Denne (2000) for low-
weight infants.
DLW even has been adapted for use in estimating astronauts'
EE during space flight, a unique environment posing extreme
challenges on other EE-measuring devices (Gretebeck et
al., 1997; Lane etal., 1997; Stein etal., 1999). However,
DLW is not perfect, requiring a number of assumptions and
calculations to be made about total body water volume,
the isotopic elimination rates for 2H and lsO (see below),
and other specific constants used to calculate the rate of
C02 production given the DLW elimination rate. The
analytic precision of DLW is variously stated to be ± 3%
(Levine, 2005) or ± 4-5% (Black & Cole, 2000; Prentice et
al., 1996; Westerterp et al., 1988). One of the foremost DLW
scientists, Dr. D.A. Schoeller of the University of Chicago,
states that DLW has a precision of 2-8% depending on the
isotope dosage and the length of the elimination period used
(Schoeller, 1988; Trabulsi et al., 2003). Goran et al. (1994)
state that the "experimental variability" of DLW is 8.5%
on average, and is 1-21% over theoretical calculations for
individual subjects. Discussion of the technical aspects
of using the DLW technique, including the reasons for
DLW imprecision, is found in Klein et al. (1984); Racette
et al. (1994); Schoeller et al. (1986); Schoeller & Taylor
(1987); Speakman (1990, 1995); Welle (1990); and Welle &
Nair (1990).
DLW is predicated upon the fact that hydrogen and oxygen in
ingested water equilibrates with body water at different rates,
and this affects the turnover of water and the subsequent
production of C02 in the body. This in turn—w ith some
assumptions (see below)—can be used to estimate the
metabolic uptake of oxygen and the production of whole-
body EE (Bray, 1997). 2H is "lost" as water, while 180
is "lost" as both water and C02. (Cole & Coward, 1992;
Wong, 1996). The difference in elimination rates provides an
estimate of the amount of C02 expended over the sampling
time period. The expended C02 is converted to EE, and
divided by the elapsed time of the sampling time period.
The resultant DTEE estimates are daily averages from the
time of isotopes administration and whenever the urine/
saliva samples are taken. Generally the elapsed period is
anywhere from one week to three weeks. Re-dosing has to
be undertaken for longer periods (DeLany et al., 1989). The
method was developed largely by N. Lifson and colleagues in
the late 1940's (Bray, 1997).
One reason for the variability in analytic precision in DLW
estimates is that the conversion of C02 into its energy
equivalents requires that the relative amount of body
"fuel" type is known; the metric used for this is called the
respiratory quotient (RQ). (See the Glossary for a discussion
of RQ.) RQ varies by the relative amounts of carbohydrates,
fats, and proteins (amino acids) "burned" to supply energy to
the body. These proportions are quite different for different
people (Coward & Cole, 1991). A population COV forRQ
of 1.5% translates into an error of 2-3% in EE estimates
for individuals (Coward & Cole, 1991). Another reason
for precision error is that the disappearance of 2H2 in body-
water is nonlinear with time and this varies slightly with
individuals, so the "model" (called a one-point or a two-point
method) used to relate the disappearance rate is somewhat
uncertain (Coward, 1988). A third reason is that the output
rates (fluxes) of water and C02 are assumed to be constant
with water pool size, which itself is assumed to be constant
over time. Because eating and drinking is episodic, this
assumption is frequently violated. These assumptions affect
the time-dependent urine concentrations of the two isotopes,
giving rise to uncertainty regarding DTEE estimates from the
DLW approach (Coward, 1998). Finally, the DLW technique
may be inappropriate for lactating mothers because precision
of the method is compromised when water turnover is high,
as is the case for lactating women (Lovelady et al., 1993). An
-------
EE overestimation (-18%) in babies who are being weaned
from a lactating mother has also been measured with the
DLW method (Roberts et al., 1988).
Within-Subject Variation in DTEE
There have been a handful of free-living studies where DTEE
has been repeated in individuals where body weight, activity,
and physiological status remained unaltered over the two (or
sometimes 3) measurement intervals. Nine of these studies
are listed in Shetty et al. (1996), a very important FAO/
WHO/UNU report that is available on the web. Some of the
studies were undertaken in a metabolic chamber, and thus
are not truly free-living, although there was no restriction
on activity. The mean within-subject COV of the repeated-
measurement studies are "reasonably small" with a mean
COV of 8.9% for 79 individuals (Shetty et al., 1996). The
study-specific mean COV's ranged from 6.8-11.0%. For just
the free-living studies (n=7), I calculated the mean COV
to be 9.2%.
Daily Variation of DTEE within a Week
Daily variations of DTEE within a week cannot be obtained
from DLW studies unless the required urine samples are
obtained daily. While possible to do so, it is very labor
intensive and expensive. Thus, other EE-estimating methods
are used to estimate daily differences in DTEE, mostly via
heart-rate monitoring. A thorough study done in a chamber is
Ribeyre et al. (2000), where French athletes and non-athletes
aged 16-19 y of both genders (n=50) were monitored for a
week. The mean daily range of DTEE and PAEE within the
week is as follows:
Daily Percent
DTEE Daily Range COV PAEE of Day in
(kcal) (%) (%) (kcal) Exercise
Female
Non-Athlete
Female
Athlete
Male
Non-Athlete
Male Athlete 3609 -19 to+13% 17% 1052 6.4%
2174 -11 to+14% 14% 550 1.1%
2486 -17 to+11% 18% 574 5.0%
3131 -12 to+11% 12% 765 1.7%
These data are quite interesting, even though they are from
a chamber study. Female athletes expended about 14% more
EE per day on average than their non-athletic counterparts,
while male athletes spent about 15% more energy per day
than non-athletic males. However, there is wide difference in
DTEE in all four cohorts as evidenced by their daily ranges.
These daily variations are rarely captured in the literature,
and are not well captured in our exposure assessments
that inadvertently minimizes day-to-day variability even
though we have addressed daily differences in the time
spent in generalized locations (Glen et al., 2008). Linking
daily variability in individual activities and daily energy
expenditure with daily variability in locations in our exposure
models is a relatively unexplored topic, and much more work
needs to be done in this area.
Seasonal Variations in DTEE
Schoeller & Hnlicka (1996) present repeated-measures data
using the DLW technique on 6 employed females living in
an urban area over two seasons. There were no significant
seasonal differences in DTEE, REE, or PAEE estimates for
the group, and the sample was equally split on whether or
not winter EE > summer EE for the 3 metrics. They also
provide estimates of DTEE COV's seen in a review of 16
repeated-measurement studies. The COV's ranged from
2.9% to 20.2%, with a mean of 7.8% (Schoeller & Hnlicka,
1996). Shetty et al. (1996) provide COV estimates of DTEE
in weight-stable females for over a 7 months period; it was
2.0%, which seems low to me.
85
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9.0
Physical Activity Index (PAD &
Physical Activity Level (PAL)
PAI and PAL relate to the same concept: PAI = DTEE / REE
= PAL, total daily energy expended divided by basal, or
resting, metabolic rate, both in kcal/d (or kJ/d). The result is
a unitless metric. PAL seems to be the preferred acronym in
Europe and PAI in the United States. PAI will henceforth be
used in this report. McCurdy (2000) developed a "consensus"
PAI classification of activity-level categories:
Sedentary <1.55
Low Active 1.55 - 1.75
Moderately Active 1.76-2.00
Active > 2.00
These categorizations are not universally accepted by
exercise physiologists, and an alternative classification
appears in Brooks et al. (2004). Their classes are:
Sedentary 1.00-1.39
Low Active 1.40-1.59
Active 1.60-1.89
Very Active 1.90-2.50
Note that the both of the "outer" classes are defined by
a lower/upper limit; Brooks et al. (2004) do not provide
a reason for this prescribed categorization, which results
in uncertainty regarding PAI values outside of the
bounds depicted.
The FAO/WHO/UNU (see Glossary) has developed its
own PAI classifications for use in developing countries. Its
categories are:
Light 1.40 - 1.69
Moderate 1.70-1.99
Vigorous 2.00 - 2.40
I could not find any defined values for categories on either
side of those shown above (Dufour & Piperata, 2008).
Fanners in undeveloped countries, by the way, have
measured PAI's of 1.5-2.5 for males and 1.4-2.4 for females,
on average. It has been estimated that the PAI value of early
hominids was between 2.0-3.0 for subsistence fanners and
1.7-2.1 for hunters and gathers (Hayes et al, 2005b).
Another classification, based on both HR monitoring and
accelerometers, for PAI is light <1.6; moderate 1.6-2.8;
moderate 2.9-3.5; and vigorous >3.6 (Adolph et al., 2012).
Obviously there is no consensus regarding the categorization
of PAI in the literature.
Reasonable Boundaries of PAI
There have been several attempts to define reasonable
boundaries of PAI for the general population. If a person is
sleeping all day for whatever reason at an activity-specific
METS (METSa) of 0.80-1.10 (CHAD database, code
14400), her or his PAI would be close to 1.00. (The mean/
SD for sleeping in CHAD is 0.90 ± 0.10, with a median
of 0.90.) In an ambulatory population—for at least part of
the day—the lowest feasible PAI is the oft-cited "Goldberg
criterion" of 1.20, representing the minimally sustainable
ratio of total EE-to-resting metabolism for ambulatory
individuals (Black, 2000; Goldberg, 1997). However, there
is disagreement concerning that value; the UNU uses 1.27 as
"the survival" PAI (Goldberg, 1997), and other commentators
usually round that value up to 1.30.
Inactive subjects in a calorimeter have a PA=1.21, lower
that the UNU value of 1.27 (Shetty et al., 1996). Babies
often have a PAI <1.3 as shown in Table 17 particularly
at the younger ages. PAI increases significantly with age
in babies and infants of both genders and feeding method
(Butte et al., 2000).
With respect to mothers who breast-feed, lactation does not
significantly affect PAI, although mothers 18-24 months
post-delivery have a higher PAI than mothers that were
measured within 3 months of delivery. See Table 18. Note
that for both time periods, the mothers are in the "moderately
active" category using the first PAI classification schema
noted above.
Westerterp (2001) states that the upper bound of sustainable
PAL in the general population is 2.2 - 2.5. He also states that
athletes can have twice as high an upper bound due to long-
tenn exercise training and their simultaneous consumption
of carbohydrate-rich food. When athletes have a PAI >2.5,
many of them "have problems maintaining energy balance"
and thus lose weight (Westerterp, 2008). Shetty et al. (1996)
consider a PAI of 2.4 to be "the maximum sustainable way of
life for most people.
Very high PAI values have been found for participants in
strenuous athletic events that can go on for several days.
For instance. Cooper et al. (2011) provide data from 26
endurance-type events, and the uppennost PAI was 6.94
that was sustained over 10 days! Two other studies reported
PAI's of >5.0 for a three-week and a 3-month elapsed period
(Cooper et al., 2011)! S. Schulz et al. (1989) state that PAI's
of 4.3-5.3 are common during the Tour de France races,
and Westerterp et al. (1986) report PAI's of 3.6-5.2 for 3
riders in that race. A "Fitness Club" website states that
87
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Table 17. Estimates of DTEE, SMR, & PAI for infants from BUTTE ET AL. (1990)
Feeding/ DTEE SMR
Age Group (kcal/d) (kcal/d) PAI COV
(months) Mean SD Mean SD Mean SD (%)
Females
reast
3
394
72
330
31
1.20
0.22
18
6
554
112
428
36
1.30
0.24
18
9
645
98
497
53
1.25
0.16
13
12
736
120
598
48
1.24
0.15
12
18
820
160
645
74
1.26
0.27
21
24
951
151
684
84
1.39
0.28
20
ottle
3
452
93
366
29
1.20
0.19
16
6
614
91
464
33
1.32
0.15
11
9
707
98
521
43
1.36
0.19
14
12
782
129
578
65
1.40
0.29
21
18
860
174
648
69
1.38
0.28
20
24
1009
191
679
84
1.43
0.20
14
Males
Breast
3
411
93
351
36
1.18
0.27
23
6
595
112
459
50
1.28
0.20
16
9
700
105
559
62
1.29
0.25
19
12
803
151
617
62
1.27
0.18
14
18
932
134
473
45
1.37
0.24
18
24
994
134
731
65
1.31
0.10
8
Bottle
3
440
96
380
33
1.14
0.18
16
6
590
93
490
45
1.24
0.17
14
9
755
103
552
36
1.37
0.15
11
12
815
158
607
60
1.35
0.28
21
18
923
96
700
36
1.33
0.17
13
24 968 163 691 60 1.49 0.18 12
Abbreviations:
COV: Coefficient of Variation.
DTEE: Daily Total Energy Expenditure.
PAI: Physical Activity Index (DTEE/REE)
SD: Standard Deviation.
___ Resting Energy Expenditure (= Sleeping EE
in babies)
Source: Butte et al. (1990). "Energy utilization of
breast-fed and formula-fed infants." Amer. J. Clin. Nutr.
51: 350-355.
88
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Table 18. Estimates of DTEE, REE, PAEE, & PAI for lactating & non-lactating Mothers from BUTTE ET AL. (2001)
Group
Lactating
Non-Lactating
DTEE
(kcal/d)
Mean SD
2392 351
2529 497
REE
(kcal/d)
Mean SD
1331 115
1350 143
Abbreviations: See Table 17.
Note:
Lactating mothers were 3 months post-partum; n=24.
Age at delivery=30.4 (3.2) y. Non-lactating mothers
were between 18-24 months post-partum.
PAEE
(kcal/d)
Mean
SD
Mean
PAI SD
COV
(%)
1061
284
1.79
0.20
11
1135
497
1.89
0.35
19
Source:
Butte et al. (2001). "Energy requirements of lactating
women derived from doubly labeled water and milk
energy output." J. Nutr. 131: 53-58.
"normal activity limits" (PAI) are between 1.2 and 5.5. Thus,
it is physically possible to have very high PAI values for
multiple days.
A review of the PAI literature provides the following general
qualitative cutoffs from a summary of the literature.
Black (2000) contains generalized PAI data for all ages and
both genders; her data were reprinted above as Table 13.
PAI Level Descriptive Grouping
1.17 Obese adults
1.19
1.20
1.21
1.27
1.27
1.36
1.6-1.7
1.65
PAI measured in English females aged
91-97 (10% COV)
Goldberg Criterion (theoretical); chair-
bound (Shetty et al., 1996)
Minimal sustainable (from data for
ambulatory people)
FOA/WHO "survival requirement"
PAI of demented elderly
PAI measured in English males aged 91-
97 (15.4% COV)
1 4 _ 1 5 Seated occupation, little movement; little
active leisure time (Shetty et al., 1996)
1.43-1.80 Range for retired females
1.55-1.77 Range for retired males
Seated work, some moving around; little
1.75
active leisure activity (Shetty et al., 1996)
Mean of DLW studies used for evaluating
the technique (18.2% COV)
Median PAL for the developed world
(Butte et al., 2012)
PAI Level Descriptive Grouping
Median value of "standing work" (Shetty
1.8-1.9
2.0-2.4
2.5
3.1
3.5
4.0
4.5
4.7
et al., 1996)
Sustainable PAI's in active individuals
(Shetty et al., 1996)
Very active lifestyle
Athletes in training
Nordic skiers
Maximal level of activity sustainable on a
permanent basis
Dog-sledding PAI
PAI for "Tour-de-France" riders
Butte (2000) provides a table listing study-mean PAI (and
DTEE, PAEE) for youth between the ages of 3.0-18.4 y old. It
was developed from data from over 20 previous studies, many
of which appear in this report as Table 12. There is no trend in
PAI with age or gender. Using the categories presented above
from McCurdy (2000), study mean PAI's from the Butte
(2000) paper would be assigned to the following categories:
Females Frequency Males Frequency
<1.55 8 33.3% 5 21.7%
1.55- 1.75 8 33.3% 8 34.8%
1.76-2.00 7 29.2% 6 26.1%
>2.00 1 4.2% 4 17.4%
Thus the modal value of the studies for both genders are in
the 1.55-1.75 category (low active).
89
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Table 19. DTEE & PAI estimates from DLW studies reviewed in ROBERTS & DALLEL (2005)
Age Range (y) Mean DTEE (kcal/d) SD COV (%) Mean PAI SD COV (%) Sample Size (n)
Normal-Weight Females
20-29.9 3047 510 16.7 1.79 0.28 15.6 76
30- 30.9 2964 429 14.5 1.83 0.26 14.2 59
40-49.9 3048 419 13.7 1.89 0.30 15.9 8
50- 59.9 2513 401 16.0 1.75 0.22 12.6 18
60-69.9 2397 437 18.2 1.69 0.31 18.3 48
70- 79.9 2407 374 15.5 1.55 0.26 16.8 14
80- 89.9 1700 239 14.1 1.21 0.09 7.4 6
90- 96.5 1935 156 8.1 1.17 0.13 11.1 9
Overweight Females
20-29.9 2713 394 14.5 1.78 0.23 12.9 33
30- 30.9 2794 358 12.8 1.78 0.23 12.9 41
40-49.9 3032 545 18.0 1.80 0.19 10.6 14
50- 59.9 2349 368 15.7 1.68 0.26 15.5 29
60-69.9 2061 294 14.3 1.52 0.23 15.1 46
70- 79.9 1868 402 21.5 1.51 0.28 18.5 19
80- 89.9 1748 464 26.5 1.42 0.37 26.1 6
90- 96.5 1766 292 16.5 1.33 0.22 16.5 7
Normal-Weight Males
20-29.9 3047 510 16.7 1.75 0.22 12.6 48
30- 30.9 2964 429 14.5 1.78 0.21 11.8 47
40-49.9 3048 419 13.7 1.84 0.23 12.5 22
50- 59.9 2513 401 16.0 1.60 0.31 19.4 8
60-69.9 2397 437 18.2 1.61 0.18 11.2 14
70- 79.9 2407 374 15.5 1.62 0.25 15.4 30
80- 89.9 1700 239 14.1 1.17 0.15 12.8 4
90- 96.5 1935 156 8.1 1.38 0.17 12.3 6
Overweight Males
20-29.9 3224 842 26.1 1.90 0.20 10.5 10
30- 30.9 2275 753 33.1 1.81 0.30 16.6 53
40-49.9 3465 588 17.0 1.88 0.24 12.8 37
50- 59.9 3458 644 18.6 1.88 0.29 15.4 17
60-69.9 2851 420 14.7 1.71 0.29 17.0 30
70- 79.9 2624 461 17.6 1.55 0.27 17.4 34
80- 89.9 2294 357 15.6 1.47 0.16 10.9 7
90- 96.5 1863 46 2.5 1.29 0.13 10.1 2
Abbreviations: Same as Table 17.
Source:
Roberts & Dallel (2005). "Energy requirements of
aging." Public Health Nutr." 8: 1028-1036.
90
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Table 20. Estimates of DTEE, REE, PAEE, & PAI from SHETTY ET AL. (1996)
Age
Group
DTEE
(kcal/d)
Mean
SD
REE
(kcal/d)
Mean
SD
PAEE
(kcal/d)
Mean
SD
PAI
Mean SD
COV*
(%)
Females
18-29
2486
526
1482
263
1004
406
1.70
0.28
16.5
30-39
2390
406
1434
143
980
386
1.68
0.25
14.9
40-64
2342
406
1386
167
956
345
1.69
0.23
13.6
Males
18-29
3298
717
1793
287
1506
598
1.85
0.33
17.8
30-39
3418
741
1960
430
1458
598
1.77
0.31
17.5
40-64
2749
406
1673
191
1076
311
1.64
0.17
10.4
Abbreviations & Symbols See Table 17.
"Calculated by the present author.
Source:
Shetty et al. (1996). "Energy requirements of adults:
an update on basal metabolic rates (BMRs) and
physical activity levels (PALs)." Euro. J. Clin. Nutr.
50(Supp. 1).
PAI and Health Issues
Mortality relative risk (RR) has been shown to be inversely
related to PAI in 50-82 y old adults (Hamilton et al.,
2007). While the RR numbers per se are not of interest in
this report, data on the mean PAI levels by tertiles is of
interest. The PAI for the three tertiles are (low-to-high):
1.48 ± 0.01, 1.68 ± 0.01, and 1.94 ± 0.02. The mean PAI
value for the highest tertile is somewhat surprising given
age of the subjects and the "high moderate"/"active" PAI
classification scheme. A PAI of 1.94 certainly is higher than
the mean PAI's shown in Table 12 for elderly subjects. The
data in Hamilton et al. (2007) came from a 1953 English
study, which is not included in this report.
Individual (Longitudinal) Variability in PAI
The multi-day COV for PAI within individuals is reported to
be approximately 15% (Black, 2000), roughly comparable
with most of the measured COV's seen in Table 12, but the
variability in COV's seen there indicates that PAI varies
considerably within similar age/gender cohorts. Shetty et al.
(1996) state that Black et al. (1996) provide mean estimates
of \r/7/?/«-indi\ idual COV of 8.9%. Since PAI has REE
as its denominator, variability in REE contributes to total
variability in PAI. It is difficult to find COV estimates for
REE to put that component into perspective, and the only
COV estimates that I could find were for adults. Shetty et al.
(1996) provide COV estimates for REE for days, weeks, and
months. The daily estimates have an individual mean COV
of 2.0 - 3.5% depending upon the studies (3 studies, with 43
total subjects); the weekly estimates have a mean COV of
2.2 - 4.8% (5 studies, with 47 subjects); while the one study
using a monthly time span had a COV of 2.5% (Shetty et al.
1996). Shetty et al. (1996) considers the "intra-individual
variations in BMR [REE], measured over a period of days,
weeks, or even months or years, are small and probably not
significant" (p. 2 ).
On the other hand. Black (2000) states that REE itself has
a COV of 4-8.5%, and that the non-REE component of
DTEE has even more variability. (Note that the COV for the
different DLW studies itself had a multi-study cross-sectional
COV of 18.1%, quite a high observation [Black, 2000].) In
another report (Black & Cole, 2000), the mean within-person
COV for PAI is 12.3%, while the estimated between-person
variance is given as 10.7%. Obviously there is lack of
agreement among researchers on the day-to-day variability of
PAI within and among people.
Table 21. Estimates of PAI seen in the literature
Ages PAI
Mean SD Type (n) Mean SD COV Citation Comment
Females: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for Age
5.5
0.9
N
35
1.37
Nguyen et al. 1996
5.5
0.4
H
13
1.37
0.17
12.4
Fontvielle et al. 1993
From individual data
7.6
1.7
N
25
1.41
Nagy et al. 1997
AA: Tanner 1
7.9
1.2
N
9
1.41
Nagy et al. 1997
C: Tanner 2
8.1
1.0
H
11
1.66
0.22
13.3
Dugas et al. 2008
EA
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Table 21. Estimates of PAI seen in the literature (continued)
Ages PAI
Mean
SD
Type
(n)
Mean
SD
cov
Citation
Comment
8.1
1.7
N
24
1.44
Johnson et al. 2000
Fairly fat group; AA
8.1
1.4
N
55
1.34
Johnson et al. 2000
Fairly fat group; C
8.2
1.0
N
12
1.50
0.30
20.0
Treuth et al. 1998
8.3
1.2
H
10
1.40
0.12
8.6
Dugas et al. 2008
MA
9.7
0.8
N
123
1.58
Bandini et al. 2002
Pre-pubertal
10.1
1.0
N
45
1.56
Craig et al. 1996
Premenarchal
10.2
1.4
N
13
1.71
Roemmich et al. 2000
Pre-pubertal
10.6
0.4
H
25
1.77
0.29
16.4
DeLany et al. 2006
C
10.7
0.7
H
28
1.74
0.32
18.4
DeLany et al. 2006
AA
10.7
0.9
N
73
1.59
Bandini et al. 2002
Pubertal
12.3
1.0
H
13
1.47
Calabro et al. 2013
Ages 11-14
12.6
0.7
M
53
1.48
0.22
14.9
DeLany et al. 2004
AA & C
12.7
2.3
N
27
1.69
0.19
11.2
Perks et al. 2000
12.8
1.9
N
18
1.71
Roemmich et al. 2000
Pubertal
14.3
1.0
N
14
1.68
0.19
11.3
Bandini et al. 1990
18.4
0.6
N
91
1.83
Stice et al. 2011
PAI data not reported
22.1
4.3
H
32
1.65
0.25
15.2
Hise et al. 2002
24.1
3.5
N
10
1.90
Beidleman et al. 1995
24.8
6.9
N
6
1.50
0.19
12.7
Casper et al. 1991
25.2
3.5
H
10
1.77
0.32
18.1
Sawaya et al. 1995
9-day study
25.8
5.8
N
13
1.75
Leenders et al. 2006
13 accel. equations
28.0
5.7
N
33
1.70
Johannsen et al. 2008a
31.0
6.0
N
9
1.64
0.34
20.7
Hibbert et al. 1994
PAI range: 1.34-2.15
31.3
5.0
LM
9
1.76
0.16
9.1
Lovelady et al. 1993
PAI range: 1.51-2.09
31.7
4.8
N
27
1.68
0.26
15.5
Weinsier et al. 2002
Group 1: Maintainers
32.6
13.1
NS
16
1.87
Luke et al. 2005
33.0
6.0
N
12
1.67
Welle et al. 1992
Control group
34.0
6.1
H
83
1.56
Hunter et al. 2002
Premen.; ages 23-47 y
34.0
6.3
N
14
1.68
Amatruda et al. 1993
Ages 21-45
37.6
5.7
N
20
1.55
0.27
17.4
Weinsier et al. 2002
Group2: Gainers
38.0
8.0
H
15
1.64
0.19
11.6
Schoeller et al. 1997
Moderately active
39.6
5.9
H
10
1.75
Johannsen et al. 2008b
48.0
14.0
H
20
1.89
0.35
18.5
Roubenoff et al. 2002
Control group
49.7
7.3
N
136
1.70
0.30
17.6
Masse et al. 2004
PAI range: 1.2-2.5
59.4
3.5
N
34
1.72
Bathalon et al. 2001
Restrain. Eaters
60.0
4.0
H
33
1.75
0.22
12.6
Hays et al. 2002
PAI range: 1.22-2.29
60.3
3.1
N
26
1.83
Bathalon et al. 2001
Unrest. Eaters
60.8
3.1
H
29
1.81
0.23
12.7
Vinken et al. 1999
Ages: 55-65
64.0
5.0
H
6
1.44
0.20
13.9
Goran & Poehlman 1992 PAI range: 1.25-1.82
64.0
7.0
NS
37
1.51
0.25
16.6
Starling et al. 1998a
Ages: 52-79; AA
65.0
8.0
H
37
1.43
Carpenter et al. 1998
AA
66.0
8.0
H
96
1.62
Brochu et al. 1999
Ages: 50-88
67.0
6.0
H
52
1.52
Carpenter et al. 1998
C
-------
able 21. Estimates of PAI seen in the literature (continued)
Ages
Mean
SD
Type
(n)
Mean
PAI
SD
cov
Citation
Comment
67.6
4.1
NS
10
1.66
Roberts 1996
Meta-analysis
68.0
6.6
NS
43
1.62
Tomoyasu et al. 1999
White
69.0
5.4
N
29
1.72
Johannsen et al. 2008a
70.0
3.9
N
15
1.80
Frisard et al. 2007
71.5
4.8
N
21
1.56
Ades et al. 2005
74.0
2.0
NS
10
1.62
Roberts 1996
Meta-analysis
74.0
4.4
H
10
1.59
0.19
11.9
Sawaya et al. 1995
9-day study
74.0
4.4
H
10
1.59
0.18
11.3
Vinken et al. 1999
Ages: 68-80
74.1
3.2
NS
67
1.69
0.24
14.2
Blanc et al. 2004
AA
74.5
2.8
N
40
1.68
0.19
11.3
Cooper et al. 2013
74.8
2.8
NS
77
1.65
0.21
12.7
Blanc et al. 2004
AA
82.0
2.8
N
40
1.67
0.31
18.6
Cooper et al. 2013
92.0
2.0
N
49
1.50
Johannsen et al. 2008a
93.0
3.3
N
11
1.51
Frisard et al. 2007
i. Complete age statistics are not provided
5.0
N
43
1.35
0.14
10.4
Salbe et al. 1997
Pima Indians
5.0
N
19
1.37
0.12
8.8
Salbe et al. 1997
Whites
5-10
H
19
1.45
0.18
12.4
Trowbridge et al. 1997
AA
5-10
H
14
1.49
0.19
12.8
Trowbridge et al. 1997
C
00
1
CD
H
27
1.59
0.21
13.2
Treuth et al. 2003a
2 lean parents
00
1
CD
H
38
1.62
0.31
19.1
Treuth et al. 2003a
1 lean/1 obese parent
00
i
CD
H
23
1.62
0.24
14.8
Treuth et al. 2003a
2 obese parents
8-12
H
196
1.58
Bandini et al. 2004
Premenarchal
8-12
NS
90
1.50
Bandini et al. 2013
Relatively low active
30-69
NS
180
1.59
0.24
15.1
Tooze et al. 2013
49-79
NS
21
1.91
Mahabir et al. 2006
Postmenopausal
60-69
N
48
1.69
0.31
18.3
Roberts & Dallal 2005
70-79
NS
14
1.65
0.26
15.8
Roberts & Dallal 2005
80-89
NS
6
1.21
0.09
7.4
Roberts & Dallal 2005
90-97
NS
9
1.17
0.13
11.1
Roberts & Dallal 2005
Females: Active, Fit, or Athlete
a. Mean & SD are provided for Age
20.0 2.0 Fit 20 3.30
21.5 1.9 Ath 10 2.31
23.4 4.7 At. 5 3.00
25.0 1.3 Fit 9 2.50
26.0 3.3 Ath 9 1.99
40.0 7.0 Act 9 1.89
b. Complete age statistics are not provided
8-12 Act 71 1,72
Females: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for Age
5.5 0.3 OW 51 1.35
8.5 2.0 OW 14 1.25
93
0.40 12.1 Castellani et al. 2006 Winter military act.
Beidleman et al. 1995
0.45 15.0 Trappe et al. 1997 Olympic trials training
0.50 20.0 Ruby et al. 2002 Wildfire firefighters
0.30 15.1 L.O. Shulz et al. 1992 Elite distance runners
0.24 12.7 Schoeller et al. 1997
Bandini et al. 2013 Relatively active
0.14 10.4 Bunt et al. 2003 Pima Indians
RK Johnson et al. 1998 Mohawk & Caucasian
-------
Table 21. Estimates of PAI seen in the literature (continued)
Ages PAI
Mean
SD
Type
(n)
Mean
SD
cov
Citation
Comment
8.7
0.7
OW
12
1.60
0.20
12.5
Treuth et al. 1998
10.5
0.3
OE
51
1.58
0.14
8.9
Bunt et al. 2003
Pima Indians
13.4
0.8
OW
20
2.02
0.41
20.3
R.Singh et al. 2009
15.2
1.8
O
16
1.74
0.19
10.9
Bandini et al. 1990
29.0
4.0
O
5
1.95
0.45
23.1
Hibbert et al. 1994
31.3
13.0
O
9
1.58
0.15
9.5
L.O. Schulz et al. 1994
Pima Indians
32.0
10.0
OW
28
1.58
0.17
10.8
Tataranni et al. 2003
Pima Indians
34.6
10.6
M
172
1.75
0.20
11.4
Ebersole et al. 2008
2/3 were OW or O
35.2
7.4
Sed
17
1.61
Hunter et al. 2000
Premenopausal C
35.6
6.9
Sed
18
1.60
Hunter et al. 2000
Premenopausal AA
36.0
7.0
OW
26
1.73
Welle et al. 1992
38.0
5.0
Sed
8
1.44
0.23
16.0
Schoeller et al. 1997
38.5
6.1
O
10
1.56
Johannsen et al. 2008b
38.6
8.1
OW
30
1.76
0.18
10.2
Roberts et al. 2012
CALERIE Study
38.7
6.0
O
15
1.78
Kushner et al. 1995
C
39.5
5.2
O
18
1.81
Amatruda et al. 1993
Ages 31-51
39.8
5.0
O
14
1.66
Kushner et al. 1995
AA
43.8
9.2
OW
35
2.31
Staten et al. 2001
(Question the PAI)
48.0
10.0
OW
47
1.69
0.19
11.2
Paul et al. 2004
57.5
4.2
O
15
1.70
Rawson et al. 2002
Trp64Arg Non-Carriers
57.8
6.6
O
19
1.75
Rawson et al. 2002
Trp64Arg Carriers
64.0
8.0
OW
37
1.51
0.25
16.6
Starling et al. 1998b
AA
65.0
3.5
O
25
1.46
Nicklas et al. 1997
AA
75.5
2.8
OW
72
1.71
0.30
17.5
Manini et al. 2009
AA
75.5
2.8
OW
80
1.65
0.20
12.1
Manini et al. 2009
C
i. Complete age statistics are not provided
40-69
OW
206
1.75
0.56
32.0
Tooze et al. 2007
62% was OW or O
49-79
OW
25
1.97
Mahabir et al. 2006
Post-menopausal
49-79
OW
19
1.73
Mahabir et al. 2006
Post-menopausal
60-69
OW
46
1.52
0.23
15.1
Roberts & Dallal 2005
70-79
OW
19
1.51
0.28
18.5
Roberts & Dallal 2005
80-89
OW
6
1.41
0.39
27.7
Roberts & Dallal 2005
90-97
OW
7
1.33
0.22
16.5
Roberts & Dallal 2005
;emales: Health & Other Issues
24.5
6.9
ANP
6
1.96
0.34
17.3
Casper et al. 1991
Amenorrheic
39.9
11.9
CP
12
1.46
RK Johnson et al. 1997
47.0
14.0
RA
20
1.70
0.24
14.1
Roubenoff et al. 2002
Stable, with drugs
72.9 6.1 CHD 21 1.56 Ades et al. 2005
Males: Normal, Healthy, or Not-Specified
a. Mean & SD statistics are provided for Age
5.2
0.7
N
36
1.27
Nguyen et al. 1996
5.4
0.3
H
15
1.36
0.13
9.6 Fontvielle et al. 1993
From individual data
7.4
1.6
N
22
1.36
Nagy et al. 1997
AA: Tanner 1
-------
Table 21. Estimates of PAI seen in the literature (continued)
Ages PAI
Mean
SD
Type
(n)
Mean
SD
cov
Citation
Comment
7.6
1.0
H
10
1.57
0.18
11.5
Dugas et al. 2008
MA
7.6
1.5
N
19
1.43
MS Johnson et al. 2006
Fairly fat group; AA
8.0
1.0
H
16
1.58
0.19
12.0
Dugas et al. 2008
EA
8.3
1.6
N
20
1.35
Nagy et al. 1997
C: Tanner 1
8.7
1.8
N
17
1.36
MS Johnson et al. 2006
Fairly fat group; C
10.9
0.6
H
29
1.69
0.23
13.6
DeLany et al. 2006
C
10.9
0.7
H
31
1.87
0.26
13.9
DeLany et al. 2006
AA
10.9
1.0
N
14
1.75
Roemmich et al. 2000
Pre-puberal
12.5
1.6
N
23
1.74
0.22
12.6
Perks et al. 2000
12.8
0.8
M
61
1.55
0.23
14.8
DeLany et al. 2004
AA & C
12.9
2.1
H
15
1.53
Calabro et al. 2013
Ages 10-16
13.4
1.2
n
14
1.57
Roemmich et al. 2000
Pubertal
14.5
1.5
N
14
1.79
0.20
11.2
Bandini et al. 1990
22.3
1.9
N
14
1.98
0.34
17.2
Roberts et al. 1991
Sed. Occup + active
22.7
2.5
H
17
1.97
Roberts et al. 1995
Same as above?
22.7
3.8
H
22
1.63
0.31
19.0
Hise et al. 2002
23.1
2.4
N
24
1.94
0.31
Vinken et al. 1999
Age range: 18-28
27.0
4.4
N
20
1.89
Johannsen et al. 2008a
35.9
13.4
NS
16
1.94
Luke et al. 2005
41.2
9.8
NS
24
1.81
0.15
8.3
Conway et al. 2002
42.0
16.0
H
30
1.38
Rising et al. 1994
Pima Ind.; some O
64.0
7.0
H
28
1.62
Carpenter et al. 1998
AA
64.0
8.0
NS
28
1.71
0.32
18.7
Starling et al. 1998a
Ages: 52-79
67.0
8.0
H
84
1.70
Brochu et al. 1999
Ages: 45-90
67.8
6.1
H
20
1.74
0.27
15.5
Vinken et al. 1999
Ages: 60-81
68.0
6.4
H
18
1.82
Roberts et al. 1995
68.0
6.0
H
7
1.51
0.27
17.9
Goran & Poehlman,
1992
PAI range: 1.25-2.11
69.0
5.4
N
29
1.84
Johannsen et al. 2008a
69.0
7.0
NS
15
1.75
Roberts 1995
Meta-analysis
70.0
6.9
H
9
1.72
0.69
40.1
Roberts et al. 1996
Same as above?
71.0
4.1
H
17
1.75
Frisard et al. 2007
70.0
6.2
NS
39
1.74
Tomoyasu et al. 1999
White
70.0
7.0
H
47
1.63
Carpenter et al. 1998
71.0
5.0
NS
16
1.51
Roberts 1996
74.7
3.2
N
47
1.77
0.23
13.0
Cooper et al. 2013
74.8
2.9
NS
72
1.71
0.22
12.9
Blanc et al. 2004
W; ages: 70-79
75.1
3.2
NS
72
1.74
0.22
12.6
Blanc et al. 2004
Ages: 70-79
82.0
3.0
NS
23
1.50
0.20
13.3
Fuller et al. 1996
Ages: 76-88
82.2
3.3
N
47
1.68
0.21
12.5
Cooper et al. 2013
92.0
2.0
N
46
1.58
Johannsen et al. 2008a
93.0
3.3
N
11
1.58
Frisard et al. 2007
95
-------
Table 21. Estimates of PAI seen in the literature (continued)
Ages PAI
Mean SD Type (n) Mean SD COV Citation Comment
b. Complete age statistics are not provided
.0
N 41
1.35
0.13
9.6
Salbe et al. 1997
Pima Indians
5-10
H
12
1.44
0.17
11.8
Trowbridge et al. 1997
C
5-10
H
17
1.41
0.17
12.1
Trowbridge et al. 1997
AA
5.0
N
24
1.33
0.13
9.8
Salbe et al. 1997
Whites
30-69
NS
189
1.69
0.25
14.8
Tooze et al. 2013
60-69
NS
14
1.61
0.18
11.2
Roberts & Dallal 2005
70-79
NS
30
1.62
0.25
15.4
Roberts & Dallal 2005
80-89
NS
4
1.17
0.15
12.8
Roberts & Dallal 2005
90-97
NS
6
1.39
0.17
12.2
Roberts & Dallal 2005
lales: Active, Fit, or Athlete
. Mean & SD are provided for Age
20.0 2.0 Fit
30
3.40
0.50
14.7
Castellani et al. 2006
Wnter military act.
21.0 2.9 Act
13
1.78
0.25
14.0
Haggerty et al. 1997
Construction workers
24.5 1.8 Fit
7
2.80
0.50
17.9
Ruby et al. 2002
Wldfire firefighters
25.0 3.0 Fit
10
2.80
0.20
7.1
Hoyt et al. 2001
Cold military training
27.1 4.2 Fit
27
2.79
0.16
5.7
Hoyt et al. 1991
High-alt. military train
31.0 4.0 Fit
6
3.14
0.19
6.1
Hoyt et al. 1994
High-alt. mitary train.
Males: Sedentary, Overweight, or Obese
a. Mean & SD statistics are provided for Age
8.2 1.9 OW 17 1.45 RK Johnson et al. 1998
13.7 0.7 OW 14 1.99 0.32 16.1 R. Singh et al. 2009
14.4 1.9 O 18 1.68 0.19 11.3 Bandini et al. 1990
35.4 13.8 O 12 1.66 0.28 16.9 Paul et al. 2004
37.0 13.0 OW 64 1.58 0.20 12.7 Tataranni et al. 2003
47.0 11.0 OW 44 1.64 0.19 Paul et al. 2004
64.0 7.0 OW 28 1.71 0.32 18.7 Starling et al. 1998
66.0 4.6 OW 21 1.68 Nicklas et al. 1997
75.2 2.9 OW 74 1.71 0.24 14.0 Manini et al. 2009
75.5 3.1 OW 76 1.73 0.21 12.1 Manini et al. 2009
b. Complete age statistics are not provided
40-69
OW
244
1.69
0.22
13.0
Tooze et al. 2007 75%wereOWorO
60-69
OW
30
1.71
0.29
17.0
Roberts & Dallal 2005
70-79
OW
34
1.55
0.27
17.4
Roberts & Dallal 2005
80-89
OW
6
1.47
0.16
10.9
Roberts & Dallal 2005
90-97
OW
2
1.29
0.13
10.1
Roberts & Dallal 2005
lales: Health & Other Issues
. Mean & SD statistics are provided for Age
35.1
11.5
CP
18
1.52
RK Johnson et al. 1997
62.0
8.0
Park
16
1.34
Toth et al. 1997a
72.9
7.9
Park
20
1.50
Delikanaki-Skaribas et al. 2009
Mohawk & Caucasian
Pima Indians
Pima Indians
AA
AA
AA
96
-------
Table 21. Estimates of PAI seen in the literature (continued)
Notes & Abbreviations:
AA African-American
Accel. Accelerometers
Act. Activite
Al American Indian
ANP Anorexia Nervosa Patients
Astro. Astronauts
Ath. Athlete
C Caucasian
CHD Chronic Heart Disease
COV Coefficient of Variation (SD/Mean*100)
CP Cerebral palsy
DTEE Daily Total Energy Expenditure (kcal/day)
EA European-American
H Healthy
LM Lactating Mothers
M Mixed lean and obese subjects
Perhaps the clearest delineation of variability in PAI is
provided in Shetty et al. (1996):
"Thus, the 95% confidence limits on PALs [PAI's] at the
individual level, assuming a measured BMR [REE] and no
change in body weight or physical activity is on the order
of± 18.5%, representing about ± 0.3 PAL units on a mean
PAL value of 1.65 (p. 1). "
There have been very few studies of PAI changes in a cohort
of individuals monitored over a long intervening period.
One such study is contained in Cooper et al. (2013), which
provides PAI data for 40 females and 47 males monitored
in both 1999 and 2006. The subjects were in their mid-
70s in 1999 and in their low-80s in 2006. While their data
are included in Table 12, it is interesting to note that PAI
in older females did not change significantly over the 7
years (1.68—>1.67, on average), while the males registered
a significantly lower PAI in the later year (1.77—>1.68, on
average) (Cooper et al., 2013).
PAI and Physical Activity at Various Levels
There is recent discussion in the exercise physiology field
concerning how much moderate/vigorous physical activity
contributes to PAI estimates, and the form of the relationship
between PAI level and the time spent in moderate/vigorous
activity (Thompson & Batterham, 2013). A parallel concern
is how sedentary time affects PAI. The specific levels of
activities (in terms of METS) are often called "dimensions"
of PA in that literature (Thompson & Batterham, 2013).
In a clinical study of 100 males over 7 days, a high
Notes & Abbreviations:
MA Mexican-American
(n) Sample Size
N Normal
NS Not Specified
PAEE Physical Activity Energy Expenditure
Physical Activity Index (DTEE/REE; also
PAI known as PAL: Physical Activity Level)
Park Parkinson Disease patient
RA Rheumatoid Arthritis
REE Resting Energy Expenditure
Rett Syndrome (a neurodevelopmental
RH disorder)
O Obese
OW Overweight
SD Standard Deviation
Sed Sedentary
correlation was found for the time spent in activities having
a METS>3.0, and PAI, but that correlation decreases when
the same METS criterion is held for 10 minutes or more.
The association deteriorates further when >6 or >7.2 METS
activities lasting >10 minutes are considered. In fact, the
subjects spending the most time in >7.2 METS activities in
bouts of 10 minutes or more had PAI values in the 1.65-2.05
range. Significantly less time—about 50%~was spent in
those activities for subjects with PAI's >2.02 (Thompson &
Batterham, 2013).
Conversely, while spending relatively more time in sedentary
activities is generally negatively correlated with PAI, there
is not a good correlation between relative sedentary time
and time spent in activities >3 METS lasting >10 minutes
(Thompson & Batterham, 2013). Because of these counter-
intuitive findings, the authors make this conclusion:
The attainment of one threshold for a given physical
activity dimension did not automatically predict how well
an individual scored in another dimension... Thus, physical
activity is highly heterogeneous and there is no single
outcome measure that captures all the relevant information
about a given individual. We propose that future studies
need to capture (rather than ignore) the different
physiologically-important dimensions of physical activity
via generation of integrated, multidimensional physical
activity 'profiles'(p. 1, e56527).
In other words, the relationship between sedentary, moderate,
and vigorous PA is not linear with daily PAI measures.
97
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From a time use perspective, the main factor affecting PAI
is the amount of time spent in moderate-intensity exercise or
work level. Time spent in moderate and vigorous physical
activity—MVPA—is discussed in some detail in the next
Section. From Westerterp (2001):
In a multiple regression analysis with the fraction of time
spent on activities of moderate and high intensity, only
moderate activity level came out as a significant predictor
of PAL (R2 = 0.51, p<0.0001). Subjects spending relatively
more time on moderate-intensity activity and therefore less
on low-intensity activity had a higher PAL value. There was,
however, no relation between PAL value and the time spent
on just high-intensity activity, presumably because this was
limited by its nature to being relatively short (p. 539).
The dominant impact of physical activity on PAI is confirmed
in Westerterp & Plasqui, 2004). In addition, Westerterp
(2003) states that <25% of PAEE in the "average subject"
is due to high-intensity activities; the rest is due to light and
moderate-level activities.
A somewhat surprising conclusion regarding the role of
PAEE in DTEE, and PAI, is the statement by Westerterp &
Speakman (2008) that "physical activity energy expenditure
has not declined since the 1980s" in both Europe and the
U.S. (even though obesity rates have increased)! Another
surprising finding is that PAI is not significantly different
between developed countries and "third-world" countries, or
in wild terrestrial mammals (Westerterp & Speakman, 2008).
I could not find confirmation for either of these statements in
the more general DTEE/PAI literature(s).
Physical Activity and Physical Activity Index
in Asthmatics
In 2005, about 9% of children living in the U.S. are asthmatic
(Brim et al., 2008) and 3-9% of women of child-bearing age
in the U.S. have asthma (Kwon et al., 2003). Thus, they are a
significant part of the overall U.S. population.
Asthmatics have a slightly lower V02 capability than
"normal" people of the same age/gender cohort and
exercise level, especially for children (Counil et al., 1997,
2001; Fink et al., 1993). V02 can be improved in some
asthmatics, however, with a sustained and high-intensity
exercise program, but there is a drop-out issue associated
with such a program (Counil et al., 2003; Crosbie, 2012;
Dogra et al., 2011). Such improvement is not seen in youth
who have exercise-induced astluna or exercised-induced
bronchoconstriction (Carlsen et al., 2000; Fitch et al.,
1986). Active astlunatic children can achieve V02MAX levels
about 95% of non-astlunatics, but do so by lowering their
respiratory frequency (fB) and increasing their tidal volume
(VT) (Santuz et al., 1997). One of the manifestations of these
changes inbreathing pattern is a "shortness of breath" (Ritz
et al., 2010). The net impact of these adaptations and changes
is to reduce an astlunatic's "ventilatory reserve capacity"
(BD. Johnson et al., 1995). One magnification of this in adults
is to lose "elastic recoil" of the lung, leading to an increased
cost of breathing (Johnson et al, 1991).
A logical conclusion to draw from these findings is that
astlunatics would have a reduction in exercise capability,
and less desire to undertake high-energy exercise (Kosmas
et al., 2004; Lang et al., 2004). One author states that astluna
is a "barrier" to exercise" (Glazebrook et al., 2006). This, in
turn, would lead to lower fitness levels (Strunk et al., 1989),
and a tendency to avoid MVPA activities (Brasholt et al.,
2010; Shamoo et al., 1994). This would then result in lower
PAI levels in astlunatics, especially children. These were the
conclusions drawn from an early survey of astlunatics (all
ages) in Los Angeles (Lichtenstein & Wyzga, 1989). They
found that astlunatics spend much of their time indoors at
relatively low exertion levels, and when higher levels of
exertion occur, astlunatic symptoms increase (Lichtenstein
& Wyzga, 1989). This survey was described in more detail
in Roth Associates (1988). These general findings were also
seen in a survey of 136 astlunatics in Cincinnati, with more
detailed findings: <8% of astlunatics in Los Angeles and
<13% of astlunatics in Cincinnati exercised strenuously in
any hour (Lichtenstein et al., 1989). Overall, <80% of an
astlunatic's waking time was spent at
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The US Center for Disease Control and Prevention (CDC)
periodically conducts a cross-sectional survey of "Youth
Risk Behavior Survey" (YRBS), one part of which focuses
on physical activity. The 2003 YRBS study compared
asthmatic youth's MVPA rates versus youth without asthma
(SE Jones et al., 2006). There was very little difference in
PA and VPA participation rates for the two groups; in fact,
current asthmatics had slightly higher participation rates in
both VPA (65.4% v. 63.2% for non-asthmatics) and MPA
(25% v. 24.1%). While it can be argued that the actual energy
expended was different for the two groups, and thus were not
comparable, a marginally higher percentage of asthmatics
than non-asthmatics also played on a sport team (58.8% v.
56.7%) (SE Jones et al., 2006).
Westermann et al. (2008) found less physical activity in adult
asthmatic patients than in the general population using the
"Paffenbarger Physical Activity and Exercise Index" derived
from in-office visit questionnaires. In general, there were not
significant differences among asthma severity score and PA/
exercise (based on multivariate odds ratios), although there
was an issue in separating out body mass (BMI) effects from
asthma impacts (Westermann et al., 2008).
A number of non-USA studies have also not seen statistically
significant lower exercise rates—either in duration or
intensity—between asthmatic and non-asthmatic youths aged
7-16 y (Nystad, 1997; van Gent, et al., 2007). The later study
used an accelerometer for 5 days, a physical activity diary,
and a questionnaire-based "scale" filled out by the 7-10 y
old children. Three groups of participants were included:
diagnosed and undiagnosed asthmatics plus a healthy control
group (van Gent et al., 2007). They conclude: "childhood
asthma does not appear to be associated with a decreased
level of physical activity in our study population" (p. 1018).
This included both similarities in the frequency and intensity
of PA. For the record, accelerometer-based overall mean
min/d (range) of the MVPA data for the 3 groups were:
Undiagnosed
Asthma
MVPA 86 (76-95)
VPA 22 (15-25)
Diagnosed
Asthma
78 (66-90)
21 (14-28)
Healthy
Controls
78 (71-85)
20 (14-21)
These daily values were lower, but not by much, than those
obtained using the diary or questionnaire.
What accelerometer data that I could find on asthmatic's
MVPA appear in Table 24; there are not many entries there
for asthmatics. The above findings relating to the amount
of MVPA that asthmatics participate in vis-a-vis "normals"
certainly indicate that no definitive statement can be made
regarding the relative impact that asthma has on MVPA.
More definitive data on that point are needed.
99
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10.0
Time Spent Per Day in Moderate / Vigorous
Physical Activity (MVPA)
Introduction
The type of physical activity (PA) undertaken during an
exposure event obviously affects a person's intake dose rate
during that event because of breathing rate characteristics
associated with the activity (VE A). As depicted in Figure
D-4, VE A is dependent upon oxygen consumption (V02 A)
associated with the activity and other physiological
considerations that are specific to an individual. V02 A is, in
turn, predicated upon the activity-specific energy expenditure
(EEa) needed to undertake an activity. All of these parameters
are represented in our exposure models by age/gender-
specific distributions for these physiological considerations.
Age and gender are the most important determinants of
how much time is spent in moderate and vigorous physical
activity (Arroyo, 2001, 2002). It is well documented by both
objective measures and questionnaire surveys that MVPA
declines rapidly during middle adolescence, especially
for females, but the decline in both genders is significant
(Bradley et al. 2011). Time spent in MVPA keeps declining
with age, from about 35% of total non-sedentary time at
age 20 y to 20% at age 90 y (Westerterp, 2000). See also
Westerterp (2001, 2003).
Health-compromised individuals, including being overweight
and obese, participate is less MVPA—both in total amount
and less frequent participation-than healthy, normal-
weight people (Brasholt et al., 2010). Persons with eating
disorders—especially anorexia—on the other hand, often
participate in more MVPA than "normal" control subjects
(Bratland-Sanda et al., 2010). There is a strong cultural
component to time spent in MVPA and its distribution over
the day, week, and other time periods (Tudge et al., 2006).
There are data from the CDC and NHANES interviews that
some adolescents, in particular, are active in many sports and
exercise classes, much more than the "typical" teen-ager (Liu
et al., 2013). There is a correlation structure, in other words,
among the activities and activity level in teens that participate
in organized sports. Adolescents "clump" into "natural"
activity-level groupings depending upon their dominant or
most prevalent sport. For males 12-19 the natural groupings
are (1) basketball players and runners, (2) football players,
(3) bike riders and soccer players; for females 12-19 they are
(1) dancers/walkers/joggers, (2) swimmers, (3) volleyball
players, and (4) soccer players (Liu et al., 2013). Athletes
also participate in other sports and undertake more PA in
general than sedentary individuals. The so-called natural
groupings are affected by race, weight status, geographic
region of the county, and season of the year—in addition to
age and gender (Liu et al., 2013). Active people, in general,
are more likely to have a higher intake dose rate given similar
microenviromnental concentrations.
As with most biological processes and phenomena, there
are four aspects, or dimensions, of physical activity that are
important in delineating activity level: intensity, duration
frequency, and pattern. Intensity relates to the amount of
energy expended in an activity. Duration is related to how
long PA is undertaken at a specified intensity; frequency is
how often a specified PA "bout" is repeated within a longer
time period (often called an "epoch"); pattern refers to
the time pattern of specified PA "bouts" within an epoch.
Bailey et al. (1995) use the word tempo to account for the
four dimensions of physical activity (PA). There are other
temporal dimensions of MVPA associated with climate and
season of the year, weekend versus weekday patterns, and
time spent outdoors rather than indoors (Garcia et al., 1997;
Kohl III & Hobbs, 1998; Pivarnik et al., 2003).
While all physical activities have an activity-specific eneigy
expenditure (EEA) associated with them, activities that are
innately more energetic affect intake dose rate estimates the
most. The hierarch of qualitative PA's that often is used is:
resting, sedentary, light PA, moderate PA, and vigorous PA
(Pate et al., 1995). Alternate descriptors exist but that is a
representative listing. Often these descriptors are tied to an
activity-specific METS level, with some consensus—but
not unanimity—among exercise physiologists regarding
appropriate METS levels for an activity. Most often moderate
PA is defined to have a METS of 3.0-5.9 and vigorous PA
has a METS of 6.0 or higher. Other METS levels have been
used for these categories (e.g., Millward et al., 2014). In the
3/6 METS scheme, MVPA is any activity over 3 METS.
Gyinhouya & Hubert (2008) state that using a METS
level of 3 inflates estimates of the time spent in MPA and
MVPA for most people, and especially for children. This
comment highlights the difficulties of precisely defining
meaningful physical activity categories. A related issue is
how to accurately characterize the level of PA that is being
measured. As is discussed throughout this Section, there is
no good resolution of these issues: every researcher has their
own way of defining and measuring MVPA (and, of course,
sedentary and light activities).
101
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MPA
VPA
3-6
>6
40-59
>60
65-75
>75
"Somewhat hard"
"Hard'
An interesting description of MPA and VPA appears in
Haennel & Leinire (2002) that ties together a number of
exercise metrics used in this report. Data from their Table 1
(p. 67) is reproduced here:
Metric
METS
V02 & heart rate reserve (%)
Maximum heart rate (%)
Rating of perceived exertion
(RPE)
The RPE metric, also known as the Borg scale, has not been
discussed before in this report due to its subjective nature and
rather narrow use in laboratory studies (mostly measuring
V02 and associated metrics) or in exercise classes. RPE was
first developed in the 1950s. There is an extensive literature
on RPE and similar subject ratings of exertion (e.g., Borg,
1973, 1982; Herman et al., 2003). A synoptic compilation
of indices of what constitutes MPA, VPA, and MVPA is
contained in Table 22. It focuses on quantitative metrics
involving METS, V02, EE, VE, and heart rate parameters.
Except where noted when discussing the "compromises"
discussed in the footnotes, no attempt has been made to
resolve differences among the various author's metrics.
Levels of PA lower than "moderate," however defined,
are not of interest in this Report, even though most of an
individual's day is spent at relatively low levels of PA.
Non-MVPA activities obviously affects a person's daily total
energy expenditure (DTEE); see Section 8. Although MVPA
activities having >3 METS typically constitute less than 1-2
h/d, they often provide a significant proportion of a person's
daily total energy expenditure (Butte et al., 2012). For
instance, a PAL value of 1.75—which is close to the median
for people living in the U.S. and in much of the developed
world—means that -33% of DTEE is represented by PAEE,
and about half of that is accounted for by MVPA (Butte et
al., 2012). Thus, approximately 15-17% of average daily
DTEE is accounted for by MVPA activity, and this proportion
is much higher in exercisers and for some occupations (see
McCurdy, 2000).
MVPA data, combining MPA and VPA activities, are
displayed in Table 24. A separate dataset, available from
the author, provides partitioned MPA, MVPA, and VPA
estimates, so it shows more detail than what is depicted
in Table 24. No manner what data are provided, however,
problems remain about how to measure MVPA time in
free-living individuals, and how to compare the subsequent
estimates using one method to one using another approach.
The traditional—and subjective-methods that have been
used in the past to estimate time spent in MVPA are surveys
and questionnaires. More recent methods are observational
studies, heart rate monitoring, and placing motion sensors on
subjects. There are many different types of motion sensors
that have been~and are being used to estimate MVPA time,
including accelerometers and pedometers (and variants of
them, including a combination of these instruments). These
approaches and others are discussed later in this Section. As
we shall see, it is difficult to relate MPA and VPA obtained
via one method to those obtained by another approach even
in narrowly-defined age/gender cohorts.
Before discussing differing approaches to estimating
MVPA, we delve into officially recognized PA "standards"
and "guidelines" recommended by governmental and
organizational entities designed to promote healthy physical
activity behaviors and practices. Doing so puts the measured
data seen in Table 24 into perspective.
Alternative Recommendations for Moderate and
Vigorous Physical Activity
Describing the intensity, duration, frequency, and pattern of
PA is the "holy grail" of the exercise physiology field (Pate
et al., 2010). Of particular importance to that discipline is to
fully describe a population subgroup's moderate and vigorous
physical activity due to the health benefits associated with
PA (Pate et al., 2002). One reason why this is important is to
evaluate a person's or a group's adherence to recommended
levels of exercise and/or physical activity (Pate et al.,
2002). One such recommendation is the American College
of Sports Medicine (ACSM) "Quantity and Quality of
Exercise for Developing and Maintaining Cardiorespiratory,
Musculosketal, and Neuromotor Fitness in Apparently
Healthy Adults: Guidance for Prescribing Exercise"
(ACSM, 2011; Chodizo-Zajko et al., 2009). The 2011
recommendations are an update of previous ACSM guidance
issued in 1975, 1978, 1995, and 1999 (Grundy et al., 1999).
Another organization having MVPA guidelines is the
National association for Sport and Physical Education
(Graser et al., 2009). MVPA to this group is "activity of
Table 22. Alternative qualitative metrics of MVPA seen in the EXERCISE PHYSIOLOGY LITERATURE
(for adults unless otherwise noted)
Metric
METS-Based Metrics
MPA
VPA
Source of Information (See Notes also)
"Standard" METS
3.0-5.9
>6.0
Numerous sources; see text.
Less stringent ranges
3.0-4.9
>5.0
Sallis et al. 1993 (for grade-school children)
More stringent ranges
5.0-7.4
>7.5
Slight mod. of Morehouse & Miller (1976)
4.0-6.9
>7.0
Belcher et al. 2010; Van Mechelen et al. 1997
MVPA
>4
Gortmaker et al. 2012; Aaron et al. 1993
MVPA
>4.5
Crespo et al. 2013; Ekelund et al. 1997;
102
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Table 22. Alternative qualitative metrics of MVPA seen in the EXERCISE PHYSIOLOGY LITERATURE
(for adults unless otherwise noted)(continued)
Metric MPA VPA Source of Information (See Notes also)
MVPA
>4.6
Young et al. 2014 (for a 30 second epoch)
MVPA
>5.5
Ekelund et al. 1997
Oxygen Consumption (V02) Metrics
In mL / min - kg
16.5-26.6
>24.8
A compromise; see Note 1
Percent of VQ2.Max
> 60%
Atomi et al. 1986; Janz 1994
> 50%
> 70%
Livingstone et al. 1992; Maffeis et al. 1995
30-50%
> 50%
Spurr& Raina 1990
26-50%
> 51%
Slight mod. of Andersen et al. 1978
Energy Expenditure Metrics
In kcal/min-kg
0.085-0.126
>0.127
A compromise; see Note 1
In kcal/min (females)
3.5-5.4
> 5.5
Durnin 1987
In kcal/min (females)
4.0-5.9
>6.0
Durnin 1982; see Note 2
In kcal/min (males)
5.0-7.4
> 7.5
Buskirk 1960; Durnin 1967 & 1983
Breathing Rate(VE) Metrics
In L/min-kg
0.6-1.0
>1.1
A compromise; see Note 3
In L/min
20-34
> 35
Buskirk 1960
Heart Rate (HR) Metrics
Heart Rate Reserve (Note 4)
51 -60
>61
Blair & Connelly 1996; Cunningham et al. 1981
Percent of HR.Max
75 - 84%
> 85
Lost citation.
Multiple of HR.Rest
1.25-1.49
>1.5
Durant et al. 1993; Welk & Corbin 1995
Heart Rate (bpm)
140-159
>160
See Note 5
bpm
120-168
>169
Atomi et al. 1986
bpm
> 150
Cunningham et al. 1981
bpm
125-176
>177
Saris et al. 1977
3-5 y children: bpm
120-139
>140
Freedson 1989
Notes:
1. The values show are a compromise between two different conversion factors: 1 MET= 3.30-3.65 mL 02/min-kg
and 1 kcal=200-250. While close, the two methods result in different boundary values, which were (essentially)
halved in the compromise. Usually a single author will provide only a single, deterministic conversion value
(e.g., 1 MET=3.5 mL 02/min-kg); multiple values seen in the Table arise from showing the entire range of
conversion factors seen in the physiology literature.
2. Durnin 1982 also provide estimates of MVPA for energy expenditure estimates in units of kJ/kg-min, but they are
not presented here.
3. The values shown are a compromise between using two different approaches. One was based on the VE/V02
ratio (VQ), which varies between 15-49; VQ values of 30-35 are commonly measured. The other approach is
based on VE rates between 0.4-3.7 L/min-kg, and then applying commonly seen V02.Max percentages to this
range. The two methods actually produce quite different estimates; the differences were halved for the values
shown in the Table.
4. By the Metobolic Chronotrophic Relationship, %HR.Reserve = %V02.Reserve = %METS.Reserve; see Section
7.
5. Cited in: Armstrong et al. 1990, 1993, 1998; Gilliam et al. 1981; MacConnie et al. 1982; Pels & Geenen 1985;
Sallis et al. 1993.
103
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an intensity equal to brisk walking" or higher. Guidelines
or recommendations proffered by individual researchers
sometimes explicitly define what they mean by MVPA.
One researcher defines MPA as walking for 30 min/d and
VPA as any activity that "breaks or sweat" or "causes hard
breathing" for 20 min/d (Kann et al. 1993, 1996). There also
are Compendium-like (Ainsworth et al.. 1993, 2000, 2011)
definitions that simply are a listing of activities thought by
exercise physiologists to elicit MVPA. These listings include
domestic, occupational, and sports/recreational activities. See
Wilson et al. (1986). Other groups have also promulgated
physical activity recommendations, such as the President's
Council on Physical Fitness, the American Heart Association
(AHA), the American Cancer Society (Byers et al., 2002;
Doyle et al., 2006), the US Department of health and Human
Services (Brooks et al., 2004; Buchner 2014), the Centers for
Disease Control (CDC), and the World Health Organization
[WHO] (Chodizo-Zajko, 1997). There also have been joint
recommendations made by combinations of some of the
above organizations, including ACSM and AHA (Haskell
et al., 2007). A discussion of international PA guidelines is
contained in Oja et al. (2010). Blair et al. (1992, 2004), Blair
& Connelly (1996), Physical Activity Advisory Committee
(2008), and Schoenborn et al. (2011) are articles that review
how physical activity recommendations have evolved
over time.
PA guidelines for particular subgroups of the population
exist, particularly for those with special circumstances or
existing health problems. An example is PA recommendations
for people with arthritis (ACR, 2000). It recommends that
they participate in 30 min/d of low-to-moderate PA on 5
days/week. Guidelines for the elderly also exist (Elsawy &
Higgins, 2010). PA guidelines for pregnant and post-partum
females have also been proposed (Pivarnik & Mudd, 2009).
MVPA guidelines for children recommend more time in
exercise than adult-oriented recommendations, as might
be expected, but they are even less-precise about what
constitutes MVPA! CDC guidelines state that children
and adolescents 6-17 y old should undertake 60+ min/day
of "age-appropriate" physical activity, and that most of it
should consist of aerobic activity. The term age-appropriate
is not defined. See: www.cdc.gov/phvsicalactivitv/evervone/
guidelines/children.html). Vigorous-intensity PA, otherwise
not defined, should be undertaken on 3+ days/week.
Likewise, muscle-strengthening PA should be undertaken
3+ days/week, as should bone-strengthening exercises.
Both of these activities can be part of the 60+ min/day
recommendation. There is no guidance, apparently, on what
constitutes MVPA except for two examples: brisk walking
(moderate) and running (vigorous). MVPA Guidelines for
children have been "translated into pedometer-based steps/
day criteria. These guidelines are 11,000 steps/d for girls and
15,000 for boys (Alderman et al., 2012).
Because the point here is to emphasize why adherence
to PA guidelines for moderate and vigorous activity is
important and not to describe alternative recommendations
made by the various groups, we focus on the 2011 ACSM
104
guidance. These recommendations involve four types
of exercise: cardiorespiratory, resistance, flexibility, and
neuro-motor (ACSM, 2011). Occupational PA is included
in the recommendations. Only cardiorespiratory exercise is
germane here. Paraphrased "basic recommendations" for
it follows:
• Adults 18-64 y of age should get at least 150 min/week of
moderate-intensity exercise.
• This can be met through 30-60 min of moderate-intensity
exercise on 5 days/week or 20-60 min of vigorous-
intensity exercise on 3 days/week.
• One continuous session or multiple sessions of at least
10 min in duration are acceptable in accumulating the
150 min/week (this is analogous to the epoch issue
mentioned above).
• In addition, muscle-strengthening exercises should be
undertaken on 2+ days/week (CDC, 2010).
• Gradually increasing the intensity, duration, and
frequency of exercise is recommended for best adherence
and least risk of injury.
• People unable to meet these minimums can still benefit
from some activity.
Moderate-intensity is not rigorously defined in these
recommendations.
CDC has its own recommendations that are a variant on
the above, but its thrust is the same. See: www.cdc.gov/
phvsicalactivitv/evervone/guidelines/adults.html. CDC's
recommendations for older adults (65+) have a similar
scope (www, ede. gov/phvsicalactivitv/evervone/guidelines/
oldcradults.html).
It should be noted that most people in this country are
ignorant of these guidelines (Morrow Jr. et al., 2004). A 2009
paid incentive survey of 10,587 people with a 65% response
rate (quite high) indicated that <1% of the respondents were
familiar with the 2008 year moderate+ intensity guidelines
(Kay et al., 2014). Perhaps because of lack of knowledge,
very few people in the United States meet recommended
PA guidelines when their activity levels are measured by
objective monitoring. In one study of middle-aged adults,
56% of males and only 5% of females met CDC PA
guidelines using an accelerometer (Behrens et al., 2011).
Adults with intellectual disabilities or overweight/obese
adults meet PA guidelines even less frequently (Barnes et al.,
2013; Behrens et al., 2011). Mudd et al. (2008) state that only
-23% of U.S. adults attain the CDC/ACSM guidelines.
Children also do not attain PA goals when objectively
monitored (Beets et al., 2011). Pate et al. (2006) discuss
the role that schools should play in attaining the CDC
recommendations for children. Basically these articles
allocate the overall MVPA recommendations to physical
education (PE) classes, recess, and other school-based
opportunities. Their recommendations for elementary school
children are 150 min/week of PE, with 50% of it at MVPA
for 30+ min/d including 20+ minutes/d at recess on school
days (Carlson et al., 2013).
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Not many children meet these standards. A pedometer study
of children 6-10 y old indicated that they could accumulate
more than 30 minutes of MVPA in a 3h active after-school
program with a step count of about 4,600 steps. That level of
activity would assure that 93% of the children would meet
the guidelines (Beets et al., 2012). However, children in the
study did not usually attain that number of steps. Using an
accelerometer, average time spent in MVPA during the 3 h
program was 18.4 ± 11.1 minforboys and 13.3 ± 8.8 min
for girls. Only 16.9% of the boys and 6.1% of the girls met
the 30 minute daily MVPA step or accelerometer goal in
the after-school program alone (Beets et al., 2012). Thus,
additional PA outside of the program is needed. Attending
active PE classes increased the percentage of both girls and
boys meeting PA guidelines: twice as many girls and three
times as many boys meet PA goals on days with PE than
on those without phys ed. Students meeting the guidelines
did not increase out-of-school PA on days without PE, so
PA was significantly increased overall on days with PE
(Alderman et al., 2012).
As children age, they are less active and have less MVPA
time, on average. The percentage of children and adolescents
meeting a goal of 60 min/d or MVPA time decreases from
42% in children aged 6-11 down to only 8% of adolescents in
the NHANES study (Nader et al., 2008).
The messages in the above paragraphs are that (1) "official"
guidelines are generally not explicit in what is meant by
MVPA in terms of V02, VE. or energy expenditure (METS
or kcal expended), and (2) physical education (PE) classes
positively contributes to meaningful MVPA. Although not
mentioned in the above material, similar to the PE/recess
time difference in MVPA time in children participating
in moderate levels of occupational PA affects whether
or not adults meet MVPA recommendations (increasing
attainment of the guidelines by about 5-8% overall); see
Boslaugh et al. (2005).
Alternative Indicators of MVPA Seen in the
Literature
There have been many qualitative and quantitative (objective)
indicators of MVPA that have been used over the years in the
exercise physiology literature. In general, these indicators are
targeted to specific age and gender groups, and sometimes
to people with a particular health issue or weight problem.
Probably one of the most common qualitative approach to
defining MVPA activities is the METS assigned to them
in Ainsworth et al. (1993, 1997, 2000, 2011). Alternative
MVPA metrics are seen in Haennel & Lemire (2002) and in
other papers.
Estimating Non-clinical MVPA
The rest of this Section of the report (1) defines MVPA in
a manner that can be related to the CHAD database—or to
an improvement thereof, and (2) provides information on
how much time people spend time in MPA, VPA, or MVPA
activities so that APEX/SHEDS outputs can be evaluated
against objectively measured data. None of the information
reported in this Section is taken from a review paper, either
qualitative or meta-analytic; only data from original articles
are discussed here or are included in Table 24.
We begin with an overview of the methods that have been
used to estimate MVPA activities. There are many reviews
and descriptions of the methods that have been used to assess
PA in the general population or in specific sub-groups. The
most comprehensive discussions of measuring methods
commonly used are contained in textbooks, including
Montoye et al. (1996) Measuring Physical Activity and
Energy Expenditure and Welk [editor] (2002) Physical
Activity Assessments for Health-Related Research. A
number of papers contain reviews (or mini-reviews, as they
are sometimes called) of PA-measurement methods. These
include: Aaron et al. (1993); Armstrong & Welsman (2006)
focused on youth behavior; Berlin et al. (2006); Butte et al.
(2012); Cauley et al. (1987); Corderet al. (2008); Dollman
et al. (2009); Dufour (1997); Going et al. (1999); Healy,
2000; Heath et al., 1993; Intille et al. (2012); Kreshel (2002);
LaMonte et al. (2003); Liu et al. (2012); Matthews et al.
(2012);Melanson & Freedson (1996); Montoye (1988);
Norgan & Ferro-Luzzi (1978); Pate et al. (2010); Reiser &
Schlenk (2009); Shepard & Aoyagi (2012); Stanish et al.
(2006); Steele et al. (2010); Schutz et al. (2001); Troiano et
al. (2001); Trost (2001, 2007); Tiyon (2005); Tudor-Locke &
Myers (2001);Valanou et al. (2006); Ward & Evans (1995);
Washburn et al. (2000); Welk et al. (2000, 2012); and Wilbur
et al. (1989). Pate et al. (2010) also provide an interesting
discussion of the history of PA monitoring methods that have
been used over the years.
General methods used to estimate MPA and VPA in free-
living individuals are listed in tabular form below. The
information parallels the work of Pate & Sirard (2000). The
criterion approaches are considered to have high objectivity
and reliability, and include Doubly Labeled Water and Direct
Observation. The DLW method is thoroughly discussed
in the "Estimating DTEE" subsection above (p. 82). DLW
is not suitable for defining MVPA since daily DTEE
estimates cannot be disaggregated into individual activity
classes without using some other monitoring approach,
such as accelerometry or pedometry. Indirect calorimetry
that measures V02 consumption precisely is of course an
objective measurement method, but it is impractical for field
work. It therefore is not listed in the Table by Pate & Sirard
(2000), nor will it be discussed here. Indirectly calorimetry
basically is a criterion method against which many of the
other methods (except DLW) are evaluated in the laboratory.
The remaining methods include subjective means of
obtaining MVPA data, and include self-reports (mostly
questionnaires), interviews, and proxy reports by a third-
party in the case of young children and those who cannot
provide the needed data on their own. Diaries and "cell
phone-like" methods and considered to be more reliable,
even though a lot of subjective user-supplied information
accompanies this method. Camera-wearing techniques
would be included in this category, but there was only one
study where this approach was used in the exercise sciences
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(mentioned below). The last general category is objective
methods: heart rate monitoring, accelerometry, pedometry,
and combining objective techniques to provide a more
rigorous picture of MVPA activities.
Estimating the Proportion of Population Subgroups
that Undertake MVPA
There is a disjunct in the literature when trying to estimate
the proportion of any particular population subgroup that
undertakes MVPA and at what frequency. This issue is called
here the MVPA participation rate estimation problem. A
threshold issue of course is defining exactly what is meant
by MVPA. A related issue is how it is to be estimated. Before
widespread use of objective methods, the usual approach
to estimating MVPA participation rate was to ask people
what activities they participated in during some past time
period (yesterday, the last 3 days, last month, etc.), assign
a METS value to them, and count the proportion of people
undertaking the MVPA METS level of interest (usually 3.0
for MPA and 6.0 for VPA). Sometimes a sweating/breathing
hard answer was used for the indicator of MVPA (Kann et
al., 1993). In general, age-level MVPA participation rate
estimates obtained by these approaches result in very high
participation rates—over 50-60% or higher (Casperson
et al., 2000), much higher than those obtained using an
accelerometer to estimate MVPA rates. In other words,
subjective estimates are unrealistically high.
Another problem arises in trying to estimate MVPA
participation rates using information from objective methods
papers. They rarely provide an estimate of how many
subjects actually participated in MVPA, only their mean
time for doing so. Some objective studies do compare their
subject's MVPA times to the various Guidelines described
Overview of PA Field Monitoring Methods
above, but do so on a time/day basis and not on a day/epoch
frequency basis. Thus, a true estimate of MVPA participation
rates cannot be ascertained even using "objective data."
Methods of Estimating Physical Activity in
Free-living Individuals
Pate & Sirard (2000) provide a succinct overview of common
"field" PA monitoring approaches used currently and in the
past; it is abstracted here from their paper, supplemented
by information contained in Pate et al. (2010). Discussion
of each method follows in some detail, starting with direct
observation.
Direct Observation Studies
Observational studies of PA include both real-time
observations by trained researchers and ex-post reviews
of films/tapes made of the subjects. There have been a
number of protocols and instruments used for these types of
surveillance studies, usually focused on a particular cohort
and/or a specific location. For instance, trained observers
have used the "Observational System for Recording Physical
Activity" (OSRPA) in a number of studies, especially the
version used for preschool-aged children (OSRPA-P) (WH
Brown et al., 2006, 2009; Hustyi et al., 2011, 2012; Kalian
et al., 2013; Pate et al., 2008, 2013). Another frequently
used platform is the System for Observing Play and Leisure
Activity (SOPLAY) (Findholt et al., 2011; Floyd et al., 2011;
Spengler et al., 2011). There is a "Behaviors of Eating and
Activity for Child Health (BEACHES) protocol used by a
number of researchers (Nader et al., 1995; Pate et al., 2010;
Sallis et al., 1995). SOFIT, or "System for Observing Fitness
Instruction Time," was originally used for investigating PA in
gym classes (Capio et al., 2010; Heath et al., 2006; Keating et
Measurement Method Costs/Training Factors Strengths Limitations, Other Considerations
1. Objective (Criterion) Approaches
Doubly Labeled Expensive; limited A direct marker of EE: Provides only average multi-day
Water (DLW) supply of material physiological processes estimates of energy expenditure
Direct Observation Costly requires extensive Can observe PA Some subject reactivity; limited to
training patterns; nuanced small samples & only a few locations
2. Subjective Approaches: Surveys, Interviews, & Questionnaires
Self-reported Relatively low cost Large sample size Recall error; low validity; cannot
capture PA patterns
Proxy Report Moderate cost; used for As above
children & those with
cognitive issues
As above; proxies do not have full
knowledge
Interview (direct or by Moderate cost; requires Moderate sized samples Recall & validity are better; can
phone/computer) training provide verbal prompts
3. Conterminous Diary
Paper diary or fill-in Moderate cost; need data Can obtain multi- day PA High subject burden; re-activity
computer form coding/QA patterns increases with time
4. Objective Approaches (some type of motion sensing)
Heart rate monitoring Costly; need clinical Good at group level; HR is non-linear with EE; subject
facility/daily contact small sample sizes compliance & Equipment issues
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Overview of PA Field Monitoring Methods (continued)
Measurement Method Costs/Training Factors Strengths Limitations, Other Considerations
Pedometer Relatively low cost weekly Large sample sizes; Highly variable steps-to-EE
contact needed can get PA patterns with relationships in individuals
some models
Accelerometers
Moderate cost; weekly
contact needed
5. Multiple instrument Approaches
Smart Phone/similar Moderate-high cost;
device with GPS & privacy issues
accelerometers
Can get PA patterns
Can "automatically"
obtain multi-day
patterns/locations
Have count-to-EE problems; subject
compliance issues
Need provider cooperation &
approvals; can use Apps to obtain
additional info.
Abbreviations:
EE:
GPS:
PA:
QA:
Energy expenditure
Global positioning system
Physical activity
Quality assurance
al., 1999; Levin et al., 2001; McKenzie, 2002; Scruggs, 2007;
Skala et al.. 2012; and Smith et al., 2014). SOFIT lias been
modified for estimating PA during recess (Springer et al.,
2013). That version is known as SOFIT-R, but there also is a
C-SOFIT, a more computerized version of the basic approach
(Huang et al., 2012; Scruggs et al., 2003, 2005a, b). Another
surveillance method is known as the "Systematic Observation
of Play and Recreation in Communities" (SOPARC), which
lias been used in multiple communities over a number of
seasons (Floyd et al., 2002; Kaczynski et al., 2013; Price
et al., 2012; Reed et al., 2011; Ward et al., 2014). Another
direct observation approach often used focuses on the
Children's Activity Rating Scale (CARS); one example study
is described in Pulil et al. (1990). There are other papers on
CARS, but it is difficult to translate the scale into energy
expenditure estimates. (Actually this is a problem with all
observation studies.)
Many of these types of studies use an underlying system
of assigning PA intensity to an observed behavior (e.g.,
Fales, 1938; Harrison & Kielhofner, 1986; Hovell et al.,
1978; Kelder et al., 2005). Probably the most used system
is the "Children's Activity Rating Scale" (CARS), which
classifies all PA activities into 5 intensity levels, to which
a METS estimate is then assigned (R. Li et al., 1995; Pate
et al., 2010; Robertson et al., 1999). An analysis of one of
the classification scales versus accelerometer monitoring
indicates that there is a lot of overlap in the scale metrics,
so they are not unique or precise (Floro et al., 2009). On
occasion observation studies of school-based PA utilize
an accelerometer that is issued at the beginning of school
day or PE class and collected at the end of the day or class.
Carlson et al. (2014) is one such study, but there are others:
e.g., Eaton (1983). MVPA data from these studies are not
compiled in Table 24 as they are not collected on a full-day
basis. Most studies of PE classes find that a minority of time
is spent in MVPA; Skala et al. (2012), for instance, find that
38% of PE class is spent at MVPA levels. They also find
that MVPA in PE class time is higher outdoors than indoors.
Another study that finds low levels of MVPA in PE class is
Sleap & Warburton (1996).
In general, observation studies are short in duration and
confined to a single, well-delineated location (Anthbamatten
et al., 2011; Bennan et al., 1998; Bower et al., 2008; Brown
et al., 2006, 2009; Burclifield et al., 2012; Chin & Ludwig,
2013; Chung-Do et al., 2011; A Cohen et al., 2014; DA
Cohen et al., 2011; Colabianclii et al., 2011; Epstein et al.,
1984; Fitzhugh et al., 2010; Hayes et al., 2008; McKenzie,
1991, 2002; McKenzie et al.,1991, 2000; Nordstrom et al.,
1998; O'Hara et al., 1989; Reynolds et al., 2007; Sacheck
et al., 2011; Sallis et al., 1988; Scruggs, 2007; Sirard et al.,
2005). Example locations are day care centers, school yards
at recess, school gyms, playgrounds, basketball courts during
practice and/or games, trails in a park or urban greenway,
work sites, and soccer fields during a game (Nicaise et al.,
2012; Sacheck et al., 2011). Afew observational studies
occur at home (Eck et al., 1992; Elder et al., 1998; Nader et
al., 1995). One observational study of 8 y old children that
included many locations for a maximum of 4 h on 3 days
is described by Bailey et al. (1995). A post hoc attribution
of V02 to the observed activities was undertaken, which
were then classified as low, moderate, or high PA. For both
genders, 19.7% (± 3.8) of the observation time was spent in
MPA and 3.1% (± 1.0) was spent in VPA (Bailey et al., 1995).
The vast majority of VPA occurred in very short "bursts";
95% of these activities lasted less than 15 sec (Bailey et al.,
1995). The overall percentages of M/VPAare much higher
than those seen in the Arroyo data reproduced in Table 25.
Because observational studies do not include the entire day
or all locations, MVPA data are not provided in Table 24
from these studies, even if an accelerometer or pedometer
is used during the observation period to characterize MVPA
(e.g., Bruggeman, 2006; Mukeslii et al., 1990; Sacheck
et al., 2011). The Mukeslii et al. (1990) article indicates
that the correlation between direct observation and Caltrac
accelerometer monitoring was r=0.62 (p<0.001) in young
children aged 35.1 ± 3.0 months.
One long-term (3 years) observational study investigated
whether or not "tracking" of exercise occurred in children
aged 4 y at the beginning of the study (Sallis et al., 1995).
Subjects were observed twice at home and school every
6 months. Only 15% of home-based PA and only 8% of
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school-based PA was considered to be stable over time (i.e.,
could be attributed to tracking). This implies that PA cannot
be adequately described by single-point-in-time observations
(Sallis et al., 1995).
An observation study of a different type involved putting
a camera onto a subject, and having a third-party observer
translate the stored pictures into PA categories. This protocol
was tested on a few people using a "SenseCam" device by
P.Kelly et al. (2011) in the United Kingdom, but it was found
to be very expensive and burdensome.
Subjective Surveys, Questionnaires, and Interviews
(Telephone or Face-to-Face)
There are five main approaches of obtaining subjective PA
estimates: surveys or questionnaires, phone interviews,
proxy reports concerning someone else's PA, paper or
electronic diaries, and using recording devices—such as a
digital assistant or "smartphone" that can provide (some) PA
information "automatically" (Sternfield & Goldman-Rosas,
2012). Using a smartphone borders on objective data-
gathering, and will be discussed later. The first three methods
provide only retrospective (recall) information on PA, while
the latter two methods can provide data on contemporary,
event-based PA activity level (Garg et al., 2006). The first
three approaches, like direct observation studies, require
some type of subjective mapping of activity level onto an
action or behavior to 'translate" the activity undertaken into
an energy expenditure estimate (Fales, 1938). There literally
are thousands of papers and reports describing studies that
develop and describe subjective estimates of PA, including
MPA and VPA, using one of the translation approaches. Most
of them use questionnaire data for their estimates.
None of the MVPA data obtained via subjective methods
involving data provided by the subjects themselves—surveys
and questionnaires—w ill be presented in this report. One
reason is that comparisons of alternative questionnaires
on the same population provide wide estimates of exercise
time in the population surveyed (Slater et al., 1987). For
another, there simply are too many "validity" comparisons
of subjective and various objective methods that show
wide differences in the amount and type of PA estimated by
subjective approaches versus objective monitoring, even
for a recent time period (e.g., Loney et al., 2011; Masse
et al., 2012; McMurray et al., 2008; Parker et al., 2008;
Washburn et al., 2003; Westerterp 2009; Wickel et al., 2006;
Wong et al., 2006; Yore et al., 2007; Zalewski et al., 2009).
Westerterp (2009) succinctly states that questionnaire data
have low reliability and validity. This finding applies even
though subjective/objective comparative studies find that the
subjective approach supposedly provides reliable and valid
MVPA data. The statistical comparison metrics used in most
comparative evaluation papers are weak and misleading
(Ayen & Montoye, 1998; Ball et al., 2008; Colbert &
Schoeller, 2011). Often only a Pearson correlation coefficient
(r) is used to relate survey results with METS or MVPA
estimates from an accelerometer or other objective technique,
withr's in the 0.3-0.5 range for l-to-7-day comparison
periods (Ainsworth et al., 2006; Beyler et al., 2008; Burdette
108
et al., 2004; Masse et al., 2012; Miller et al., 1994; Pate et
al., 2003; Senso et al., 2014). Absolute differences in the PA
estimates obtained by the two approaches often are ignored.
This complaint generally holds true whether the metric being
compared is a "count" (accelerometer / pedometer), total
minutes of MPA/MVPA, or VPA, or energy expenditure
metrics (Aadahl and Jorgensen, 2003; Welk et al., 2014). In
addition, these "validity" studies do not normally compare
estimated bout frequencies, and are entirely silent regarding
PA patterns. For example, one study that concluded that a
subjective approach provided valid data was Epstein et al.
(1996). It included 59 children of both genders and compared
self report-derived average-daily METS estimates of total PA
versus that obtained wearing a Tritrac R3D accelerometer.
The main finding was that self-reported daily-averaged
METS was 2.26 ± 0.64 versus the 1.60 ± 0.18 actually
measured, about a 30% self-reported overestimate (Epstein et
al., 1996). In another evaluation study of a questionnaire—
the often used 3-Day Physical Activity Recall (3DPAR)—
found that only 10% of women with breast cancer met the
current PA guideline using an accelerometer, but 28% did so
using the 3DPAR (Johnson-Kozlow et al., 2007). Differences
that large are not comparable in my estimation. In general,
surveys and questionnaires consistently over-report MVPA
in overweight (and especially obese) individuals (Welk et
al., 2014).
The discussion and conclusion sections of many of these
"validity" studies are disingenuous and often do not support
their own findings. These evaluations generally conclude
that that the survey/questionnaire produces reasonable
correlations of time spent in PA as compared to some
type of objective methoid (Welk et al., 2014). Infrequently,
more sophisticated statistical approaches are used in these
comparisons, such as plotting the data in Bland-Altman
plots or using "receiver operating characteristics curves" to
evaluate the subjective method (Aadahl and Jorgensen, 2003;
Marshall et al., 2009). These more sophisticated approaches
also show large differences in the estimates of MVPA
obtained using subjective versus objective methods (Masse et
al., 2005).
As an aside. Masse et al. (2005), besides evaluating a PA
questionnaire and a PA diary against accelerometer data,
also compared the Compendium METS estimates with
accelerometer-derived activity-specific METS values. The
Spearman r for the two approaches was only 0.31, but
significant at 0.05, while the absolute difference was 1,500
METS-minutes/d on average. See also Masse et al. (2002)
and Masse et al. (1999) for similar analyses and findings.
Common surveys/questionnaires used are the Three-Day
Physical Activity Recall (3DPAR), the Seven-Day PAR
(7DPAR) (SA Adams et al., 2005; Csizmadi et al., 2014),
the Physical Activity Scale for the Elderly (PASE; Chad
et al., 2005), the Godin Long-term exercise Questionnaire
(Andrykowski et al., 2007), the National Children and
Youth Fitness Study (NCYFS), the International Physical
Activity Questionnaire (IPAQ), the Behavioral Risk Factor
Surveillance System (BFRSS) (BFRSS Coordinators,
-------
1995, 2000, 2007; Casperson & Merntt, 1995), the
National College Health Risk Behavior Study (DR Brown
& Blanton, 2002), the National Health Interview Survey
(Casperson et al., 1986, 2000), and the Youth Risk Behavior
Study (YRBS) (Bauman et al., 2009; DR Brown et al.,
2007; Demissie et al., 2014; Ottenbacher et al., 2014; Pate
et al., 1994). Both the BFRSS and the YRBS are nationally-
applicable random-probability surveys that are undertaken
every five years or so, and include other health-related
factors besides the MVPA questions (DR Brown et al., 2005).
The National Health and Nutrition examination Survey
(NHANES) in 2003-2006 had a component focused on PA
that used the ActiGraph 7164 accelerometer to measure
MVPA, so this major health survey also provided objective
MVPA data (Loprinzi et al., 2014a, b, c). There are many
other surveys and questionnaires used to estimate MVPA
time, many used only one time by a single research group
(Breslow et al., 2001; Casperson et al., 1994, 1998; Prince et
al., 200; Sallis et al., 2000).
Occasionally, surveys and/or questionnaires are used to
estimate retrospective PA as far back as 10-20 years (Bowles
et al., 2004; Dawson et al., 2003). Although most of these
studies are impossible to evaluate statistically, they generally
are felt to not produce either accurate or unbiased results
(Corder et al., 2009; Corder & van Sluijs, 2010; Dawson et
al., 2003; Garcia-Rio et al., 2012). Goran et al. (1998) state
that only 50% of a child's PA is correctly recalled after a
week. Proxy surveys, usually involving a parent or care-giver
estimating the amount and type of PA undertaken by their
children, have also been shown to be inaccurate (Coder et
al., 2012). Parents overestimate the amount of PA undertaken
by their children even when the metric simply was "active"
(>60 min/d of MVPA) versus "inactive." The rate of PA
was overestimated on 75% of the days that were evaluated
(Corder et al., 2012).
Some important insights into PA in various groups of people
can be only obtained using questionnaires. One study used
the same survey 7 times over a 14 year period to estimate
how PA changed in children over the years. It provides
information on the partitioning of variance among and
within individuals involving PA (Ridley et al., 2009). It used
the MARCA (Multimedia Activity Recall for Children and
Adolescents) 7-day questionnaire, and one aim of the study
was to characterize the amount of intra- and inter-individual
variability seen in daily PA over time, using the ICC statistic.
The inter-individual COV for MVPA was 11.7%, while the
intra-individual COV was 14.5%. Using PAL instead of
MVPA, the inter-individual COV for PAL was 52.0% while
the intra-individual COV was 83.4% (Ridley et al., 2009).
Thus, intra-individual variability was greater than inter-
individual variability for both PA metrics.
Questionnaire data have been gathered that confirm that
physical activity declines in winter (in temperate areas) and
on days with bad weather. Specifically, it was found that PA
decreased 2-4% for every 10 mm of rainfall and increased
1-2% for every 10°C increase (Belanger et al., 2009).
Although the specific proportions noted probably vary by
climate, these results are intuitive, and are consistent with
that found in the more general time use data (Graham &
McCurdy, 2004).
Another reason why using data from questionnaires-
especially telephone surveys-is problematic, is that response
rates are dropping precipitously. People just are not returning
mail surveys, and telephone surveys have very poor response
rates due to cell phone use and poor participation by the
general public having only a land line (Kempf & Remington,
2007). People don't answer their phone anymore when Caller
ID displays an unknown number. Even without Caller ID,
the high number of solicitation calls received by the average
household has made people wary of answering their phone
during "prime" survey times. The subsequent low response
rates cause a bias in the data obtained, thus questioning
the validity of data from telephone surveys (Kemp &
Remington, 2007).
Filling out a paper or electronic diary carried by a subject
to record MVPA has been undertaken since at least 1965
(Bouchard et al., 1983; Huenemann et al., 1967). Exercise
events are supposed to be recorded as they occur (Gleeson-
Kreig, 2006; Matthews, 2002; Qian et al., 2014; Schwab et
al., 1990, 1991; Sternfield et al., 2012; Washburn et al., 1990;
Whitt et al., 2004; Wickel & Eisenmann, 2006), much like a
conterminous time use diary. Both place a fairly high burden
on the subject to compile the information, and so are used
only for relatively short periods of time. Reactivity also is
a problem, where the act of recording an activity affects the
data quality (Matthews, 2002). Originally, only paper diaries
were used, but electronic data-storing devices have been
used since the 1990's, especially the Palm Digital Assistant
(Yon et al., 2006). Diaries are not to be confused with a PA
"log," which generally is filled out at the end of a day (or
longer elapsed time span) and uses fairly broad categories of
activity, such as walking, standing, and running (Buman et
al., 2011; Garcia et al., 1997; Kaczynski et al., 2011, 2012;
Matthews, 2002). Essentially, an activity log shares many of
the issues associated with a survey or questionnaire, but with
a shorter time lag between the PA and its record.
An interesting study of a PA diary is contained in Baranowski
et al. (1999). Study subjects were 165 elementary school
teachers who carried a PA diary for 7 days once each year for
3 years. The ICC statistic (assuming three different variance
structures) was used to estimate how many days of PA data
needed to be collected for a Spearman-Brown "prophesy"
formula reliability coefficient of 0.7, 0.8, or 0.9. To achieve a
0.8 coefficient that EPA used in its ICC work in the past (Xue
et al., 2004) requires that 2 weeks of 7 d PA activity records
be collected every year (Baranowski et al., 1999).
Even though we do not provide data in this Report from
PA diary surveys, a partial listing of papers describing
USA studies follows if a reader wants to pursue additional
information on the topic. There are a number of 7-days
studies: Cummings & Vandewater (2007); Eason et al.
(2000a, b); Dishman et al. (1992); Evenson & Wen (2010);
Garcia et al. (1997); Katzmarzyk et al., (1998); Kerner &
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Kurrant (2003); King et al. (2008); Sallis et al. (1988); and
Schwab et al. (1991). Shorter 3-day USA diary studies are
Bouchard et al. (1983) and Eiseninann et al. (2000).
There are a number of diary studies of MVPA undertaken in
other countries that are not further analyzed. For example,
see Bratteby et al. (1997) and Freene et al. (2011). We also
do not include any PA data obtained from the broader "time
use" field due to its lack of focus on activities associated with
differing levels of energy expenditure, and especially MVPA.
Most time use studies that classify physical activity use
too few categories (e.g., Robinson et al., 1988, 1989, 1999;
Robinson & Thomas, 1991; Wiley et al, 1991), that are then
assigned into MVPA classes or to a METS estimate (Tudor-
Locke et al. 2008, 2011a, b). Unhappily, EPA's CHAD
database has the "too-few PA categories" problem, since it
was based largely on Robinson's prior time use studies. In a
few instances, however, a "traditional" time use diary survey
lias been analyzed to estimate physical activity participation.
One such study in the United Kingdom is reported in Fisher
(2002). The same approach was undertaken in this country
using the American Time Use Survey (ATUS), an ex post
sequential diary (event) study. Data from ATUS surveys
from 2003-2014 are available on the web from the Bureau
of Labor Statistics. A deterministic METS code was mapped
to every ATUS activity code and used to identify MVPA
activities (described in Tudor-Locke et al., 2009). A number
of papers have used these PA codes to determine how much
physical activity occurs in the general population at various
intensities, including MPA and VPA. Papers describing these
analyses are Dunton et al. (2008, 2012) and Tudor-Locke et
al. (2010, 2011a). None of the data from these studies are
included in Table 24 due to subjective nature of how the
METS codes were developed and applied.
Objective Monitoring Methods: Overview
A review of the history of objective monitoring techniques
used to estimate physical activity in humans is contained in
Montoye (1988), a French paper with an English summary.
Most of its references are in English. The article covers
pedometers, force plates in shoes, and many different
types of accelerometers. A version of a pedometer was first
developed by Leonardo de Vinci over 500 years ago. Since
then, of course, there have been many innovations in design
and manufacturing of pedometers that have made them
smaller and cheaper so that they are the least expensive
way to estimate the number of steps taken by an individual
during their waking hours. Force plates in shoes, a 1950's
invention, were essentially another step-counter; this
technique is not currently mentioned in the literature. Early
accelerometers include (1) a long-term movement integrator
(LSI) developed in the 1950's and located on a subject's
wrist (FG Foster et al., 1978); (2) a 1970's "biometer," no
longer seen; (3) single-plane accelerometers developed in the
early 1960's and still used, sometimes called an "actometer"
(Buss et al., 1980); and (4) a tri-axial accelerometer, which
was first developed in the 1970's (Halverson & Waldrop,
1973). The main improvements in accelerometers over
the years relates to their reduction in size and weight, and
their ability to store ever-increasing amounts of data for
110
longer periods of time (Freedson & Miller, 2000; Montoye,
1988). Another technique that Montoye (1988) mentioned
was a radar detector that transmitted data back to a central
station. An other review of objective monitoring over the
years was published by Butte et al. (2012). It mentions
"six main categories" of PA objective methods, including
load transducers and foot-contact monitors, which are
not mentioned in this report. Other reviews of objective
monitoring in general include Chen et al. (2002), Prince et al.
(2008), and Schuna Jr., et al. (2013a).
We divide objective monitoring methods into 4 major
categories: heart rate monitors, accelerometers, pedometers,
and multiple-instruments. We briefly discuss each in turn.
Intille et al. (2012) provide insightful information regarding
the temporal trend of PA sensor development, and speculate
about what changes in sensors may be anticipated. Freedson
et al. (2012) make recommendations regarding how PA
monitors should be calibrated and used in the field, as
does Bassett Jr. et al. (2012). Chen et al. (2012) develop
recommendations regarding what information regarding
sensors should be obtained by PA researchers before making
a decision regarding what type of monitor should be used in a
study. Heil et al. (2012) do much the same tiling.
Heart Rate Monitoring
HR monitoring has long been used to assess PA (Achten
and Jeukendrup, 2003; Gilliam et al., 1981; Glagov et al.,
1970; Goldsmith & Hale, 1971). Benedict proposed the use
of HR monitoring to provide an indirect estimate of EE in
the early 1900s, and monitors to do so were developed in
the 1950s (Janz, 2002). In the past, HR monitoring was done
using a portable electrocardiogram that stored HR data, but
this approach was replaced by a chest strap monitor that
transmitted data to a nearby receiver, oftentimes located on
the subject's wrist (Janz, 2002; Pate et al., 1996). There are a
number of HR monitors available commercially.
No data using the HR monitoring method are provided in
Table 24 because of problems with subject compliance
issues, ambient interferences, and equipment failures. Also,
HR is non-linearly related to V02 at different PA levels
and activities, so reliability is an issue (RB Andrews, 1971;
Cliristensen et al., 1983). Thus, HR monitoring is non-
linearly related to energy expenditure, VE, and MVPA levels
(and time spent at different levels).
Many studies comparing HR estimates of EE with those
measuring V02 directly found large differences in group
mean EE estimates—and even larger differences in individual
estimates of EE. These studies include Allor & Pivarnik
(2000), Daucey & James (1979), Emons et al. (1992), Eston
et al. (1997), Livingstone et al. (1992), Lovelady et al.
(1993), Luke et al. (1997), Morio et al. (1997), Rachette et
al. (1995), Schulz et al. (1989), and Spurr et al. (1988). Other
studies that compared HR monitoring to V02 measurements
for specific activities found that HR estimates were within
10% or so of the group-mean V02 values (Bradfield et al.,
1969; Ceesay et al., 1989; Maffeis et al., 1995; McCrory et
al., 1997; Moore et al., 1997; Strath et al., 2000; and Treiber
et al., 1989). In most cases, the correlation between HR and
-------
V02 was high, even when the absolute differences were
quite large. It depends upon the intensity level of the PA,
personal fitness level of the subject, and—problematically—
their emotional state at the time. These factors negatively
affect reliability and validity of heart rate monitoring.
For an evaluation of HR monitoring versus the Caltrac
accelerometer, see Allor & Pivarnik (2001). There are other
comparisons and reviews of HR monitoring versus other
objective methods that might be of interest: Cole & Miller
(1973); Corder et al. (2007); Davis et al. (1971); Drenowatz
& Eisenmann (2011); Dugas (2005); Edmunds et al. (2010);
and Epstein et al. (2001). For additional information on HR
monitoring, see Appendix B.
An associated problem with HR monitoring—which
actually affects most objective monitoring approaches—is to
determine what constitutes a threshold for moderate or active
PA. Often a HR value of 160 bpm is used for "strenuous"
activities (Armstrong et al., 1991; Harro, 1997), but this
value is greatly affected by age, gender, and HR reserve of
an individual. A HR of 160 bpm corresponds approximately
to 60-70% of HR reserve in "normal" children aged 7-12
(Al-Hazzaa et al., 1994). Using a percentage of HRMAX of 65-
75% as an indicator of MPA and >75% for VPA in children
aged 4.3 ± 0.7 y, Benham-Deal (2005) found that about 20%
of their time was spent at MVPA on average. There were no
statistically significant differences in this percentage between
weekdays and weekends or among the morning, afternoon
or evening time periods (Benham-Deal, 2005). Several
weekdays and weekends should be monitored using a HR
method to obtain a representative daily average PA estimate
(Gretebeck et al., 1991).
Accelerometers
Principles and Overview
Acclerometers utilize a coupling seismic mass suspended
on one or more levers that deflects upon movement.
Piezoresistors on each bridge respond to this deflection
and a current proportional to the displacement is induced
and processed. The signals are filtered to limit the sensor
to frequencies that are associated with actual body motion.
In general there are uniaxial and triaxial accelerometers. A
good discussion of the principles behind accelerometry is
contained in Servais et al. (1984) and Welk (2005).
The first article that I found that used the term accelerometer
is Smidt et al. (1971). It cites 8 previously-published articles
that used accelerometry to estimate walking "kinematic and
kinetic information," but I did not follow up on them given
the narrow activity that was being monitored, all in a clinical
setting. Other early articles on accelerometry are Kupfer et al.
(1972), Johnson (1971), and MacCoby et al. (1965). Kupfer
et al. (1972) discusses a uniaxial accelerometer that transmits
movement data to a receiver having a 100 foot range; it was
used to monitor mental patients. Morris (1973) discusses
how using 6 well-placed uni-axial accelerometer can be used
"to completely define a person's movement in space." Thus,
in general, accelerometry was being discussed in the early
1970's. So, the idea of accelerometry has been around for a
rather long time.
Protocols have been developed and used to calibrate
and "validate" accelerometers to provide "best practice"
approaches to undertaking research in exercise science
(Bassett Jr., et al., 2012; Evenson et al., 2008; Freedson et al.,
2012). See also: Bassett, Jr. (2000); Bray et al. (1992, 1994);
Chen et al., (2005); Eslinger et al. (2005); John & Freedson
(2012); Kelly et al. (2013); Khan et al. (2010); Labyak &
Bouguignon (2002); McClain et al. (2009); Meijer et al.
(1991); Redmond & Hegge, 1985; Schaefer et al. (2014);
and Troiano (2006 & 2007), Troiano & Freedson (2010),
Troiano et al. (2012). P.J. Trost is the first author on many
overview and application articles related to accelerometers
(and pedometers); see for example, Trost (2001), and Trost et
al. (2000, 2001, 2005).
The literature on using accelerometers to estimate physical
activity levels is vast and growing. See Table 23 for an
example of the increase in publications for the ActiPAL
accelerometer, which is not the most frequently used one
in the exercise physiology literature. There are hundreds
of papers on accelerometers in general, many "validating"
different accelerometers against other objective methods or
doubly labeled water (e.g., Hageman et al., 2004). A few
Table 23. Number of publication for the ActiPAL ACCELEROMETER by year of publication
Year Journal Papers Reports / Theses Conference Presentations
2004 1 0 8
2005
0
1
11
2006
4
1
4
2007
11
1
15
2008
8
0
1
2009
12
1
7
2010
25
14
28
2011
31
3
58
2012
38
3
57
2013
27
4
60
Source: ActiPAL website: www.paltechnologies.com/bibliography
ill
-------
accelerometers have even been used as a "criterion method"
against which other accelerometers or pedometers have
been compared.
The most frequently used accelerometer in exercise studies
of "free-living" individuals in this country is the ActiGraph
monitor. There are various models of this instrument,
including the 7164, GT2M and GT3X. Before it was known
as the ActiGraph 7164, it was called the MTI7164, and
before that it was known as the AM-7164 or the CSA 7164.
There also is an ActiGraph 71256 model used infrequently
(Buman et al., 2010). The most recent version of the
ActiGraph series of accelerometers is the GT3X and GT3X+
(Feito et al., 2012; Rowlands et al., 2015). To confuse matters
more, the ActiGraph accelerometer is available in a uniaxial
and triaxial version (Oftedal et al., 2014).
The second most widely used accelerometer in the U.S. is the
Mini Mitter Company's Actical (Evenson et al., 2008). The
Actical accelerometer is not the same as the ActiGraph or
any of the other "Acti-"models; similar names are a problem
with distinguishing among accelerometers! Additional
accelerometers (or hybrid accelerometer/pedometer units)
mentioned in the literature include the Actibelt, ActiPAL,
ActiPed, Actiwatch, ActiReg, ActivTracer, Actimarker,
Biotrainer-Pre, Calcount, IDEEA, DynaPort, CSA1, the
IMS (Integrated PA Monitoring System); Kenz, Tracmore,
GENEA, GENEActiv, PASE (a PA-sensing earpiece
[Manohar et al., 2009]), Polar Activity Watch, Sense Wear
(SWA & Mini) and TRACMORE accelerometers (Balogun et
al., 1988; Barreira et al., 2013; Bassett Jr. et al., 2000; Bassett
Jr. & Strath, 2002; Benito et al., 2011; Bjornson, 2005;
Bonomi et al., 2010; Bornstein et al., 2011a; Brazeau et al.
2011a,b; Brugniaux et al., 2010; Busser et al., 1997; Calabro
et al., 2009; Conn et al., 2000; Egger et al., 2001; Eslinger et
al., 2007, 2011; Hayden-Wade et al., 2003; Hikihara et al.,
2012; Hildebrand et al., 2014; Huberty et al. 2011a; Hustvedt
et al., 2008; Jerrett et al. 2013; John et al., 2011; Kavanaugh
& Menz, 2008; McCrorie et al., 2014; Motl et al. 2012;
Nightingale et al., 2014; Patel et al., 2007; Roemmich et al.,
2007; Welch et al., 2014; Welk et al., 2014).
There are a number of comparative studies of accelerometer
performance. They have been compared against different
models of the same brand, different brands, against V02 and
HR monitoring, and against EE (via indirect calorimetry.
These comparison studies have found a number of issues
of accelerometer performance, not the least of which are
big differences found in accelerometer performance seen
within the same manufacturer's model. Accelerometers
frequently have high inter-instrument variability, in other
words (Hollowell et al., 2009). Instrument continuity over
time also is a problem as accelerometer manufacturers
change their models often (perhaps after a bad review of their
performance?), and subsequent models sometimes compare
favorably with their predecessors from the same manufacturer
but other times do not! For instance, a comparative study of
performance in three Actigraph accelerometers (the GT1M,
GT3X, and the GT3X+, found close agreement both in the
lab and in the field in their total counts of activity (Robusto &
Trost, 2012). On the other hand, a direct comparative study of
two ActiGraph models, the 7164 and the GT3X, researchers
found that the two versions do not produce comparable step
counts or estimates of MVPAtime (Cain et al., 2013a). In
fact, the same researchers reviewed 273 articles that used
ActiGraph accelerometers to estimate physical activity in
youth and concluded with this disheartening note:
Studies using [the ActiGraph] accelerometer more than
doubled from 2005-2010. Two accelerometer models were
used, as was 6 epoch lengths, 6 nonwear definitions, 13
valid day definitions, 8 minimum wear day thresholds,
12 moderate-intensity physical activity cut points, and 11
sedentary cut points.. .The increasing diversity of methods
used to process and store accelerometer data for youth
precludes comparison of results across studies. Decision
rule reporting is inconsistent, and trends indicate declining
standardization of methods [Cain et al., 2013b; p. 437],
Some studies have also compared accelerometer counts/
minute from different body locations on the same people, or
compared the step-counting function of some accelerometers
versus using a pedometer on the same people. Usually these
studies are laboratory experiments, but some are field-based
in free-living subjects. For studies that evaluate the validity
and reliability of accelerometers, see Ayen & Montoye
(1998), Balogun et al. (1998, 1989), Barriera et al. (2009,
2013), Bassett Jr. et al. (2000), Bouten et al. (1994, 1996),
Eston et al. (1998), Fehling et al. (1999), Feito et al. (2012),
Haymes & Brynes (1993), Janz (1994), Johnet al. (2011),
Kilanowski et al. (1999), Leenders et al. (2000); Louie et al.
(1999), Maliszewski et al. (1991), Matthews et al. (2000),
Melanson & Freedson (1995), Pambianco et al. (1990), Sallis
et al. (1990), Schutz et al. (1987), Swartz et al. (2000), Trost
et al. (1997, 1998), and Welk et al. (1998, 2000). This is just
a sampling of validity/reliability articles; many more could
be cited.
Comparative studies rarely provide MVPA data of the
type that we need in free-living people, so they are not
often included in Table 24. Additional comparisons of
accelerometers are contained in Beets et al. (2011), Bouchard
& Trudeau (2007), Cliff & Okely (2007), Fischer et al.
(2012), and Lee et al. (2014). Those studies that present
MPA/VPA data only as a % of total valid wear time, and said
wear time could not be determined, are also not included in
Table 24 (e.g., Epstein et al., 2005).
Accuracy of different accelerometer models and brands
vary with the type of activity chosen and the level of
effort expended. Some are more accurate at moderate or
vigorous activities, while others are more accurate at low
intensity tasks. In other words, accelerometer estimates of
PA are not linear over the entire gamut of human activities
that are undertaken, and neither are the various regression
equations developed to translate movement counts to
energy expenditure, V02, orMETS (Bassett Jr., et al., 2000;
Freedson et al., 2011 JAP; Masse et al., 2005). Walking
at different speeds is often the activity of choice in many
accelerometer model evaluations since it spans the light
PA-to moderate PA spectrum and is easy to monitor (Bassett
112
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
__ __ Accelerometer Used
Mean SD (n) Mean SD Citation (Make/Model »Protocol)» CPM
Females: Normal, Healthy, or Not Specified
3.7 0.5 21 12.7 Kelly et al. 2007 AG 3d 3200
5.2 0.4 205 31.5 15.9 Francis et al. 2011 AG 7164; 3-4 d 2296
7.1 1.9 64 55.0 28.4 Sarzynski et al. 2010 AG GT1M; 7 d 2172
7.3 0.9 48 206.0 Pate et al. 2013 CSA7164;7d
7.5
0.6
72.4
27.7
Willis et al. 2015
AG GT3x+; 4 d
7.7
1.1
79
60.4
40.8
HM Hayes et al. 2011
MTI-7164; 7 d
2172
8.0
0.2
35
106.3
41.2
Sherwood et al. 2004
AG 7164; 3 d;1200-1800
8.1
0.7
63
54.1
19.7
NC Crespo et al. 2013
AG GT1M; 7 d
8.7
0.6
229
38.6
21.2
Francis et al. 2011
AG 7164; 3-4 d
2296
8.7
1.8
60
28.7
16.8
Beets et al. 2011
AG GT1M ; >1d, 5.7h
8.7
1.8
60
39.6
20.0
Beets et al. 2011
AG GT1M; >1d, 5.7h
1952
8.7
1.8
60
14.7
11.0
Beets et al. 2011
AG GT1M ; >1d, 5.7h
8.7
1.8
60
17.6
12.4
Beets et al. 2011
AG GT1M; >1d, 5.7h
8.7
1.8
60
19.6
13.4
Beets et al. 2011
AG GT1M ;>1d, 5.7h
8.8
0.9
52
101.0
47.0
Atkins et al. 2004
CSA; 3d, 1200-1800
3200
8.9
0.8
60
83.0
42.9
Atkins et al. 2004
AG 7164; 3 d;1200-1800
9.1
0.8
61
125.5
61.7
Atkins et al. 2004
AG 7164; 3 d;1200-1800
9.2
0.9
198
66.5
30.9
Jago et al. 2004
AG 3 d; 24 h
1952
9.2
2.1
423
55.9
26.7
MW Long et al. 2013
AG 7164; 7 d
9.4
0.9
48
88.0
Hsu et al. 2014
AG GT1M
9.9
1.1
23
64.3
23.9
Olvera et al. 2010
AG GT1M
1500
10.4
1.1
23
35.4
21.9
Olvera et al. 2010
AG GT1M
1500
10.4
1.0
46
111.0
Pate et al. 2012
CSA 7164; 7d
10.6 0.6 22 128.7 45.5 Fuemmeler et al. 2011 MTI-7164; 4 d; 24 h
10.6 0.6 22 138.5 60.1 Fuemmeler et al. 2011 MTI-7164; 4 d; 24 h
10.7
4.3
230
79.0
57.0
Butte et al. 2007
Actiwatch; 3 d; 24 h
11.2
0.3
171
26.4
10.9
Francis et al. 2011
AG 7164; 3-4 d
2296
11.2
0.3
184
22.1
13.5
Janz et al. 2007
AG 7164 d
3000
11.7
0.4
229
22.5
16.6
Pate et al. 2006
MTI 7164; 7d
3000
11.7
0.4
204
136.9
83.3
CC.Johnson et al. 2008
ActiGraph
11.8
0.4
267
167.8
74.1
CC.Johnson et al. 2008
ActiGraph
11.8
0.4
291
27.1
17.1
Pate et al. 2006
MTI 7164; 7d
3000
11.8
0.5
1559
23.6
11.8
Taber et al. 2011
Actigraph; 7 d
1500
11.9
0.4
984
23.5
11.6
J.Stevens et al. 2007
AG 7164; 6 d
113
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Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
__ __ Accelerometer Used .
Mean SD (n) Mean SD Citation (Make/Model »Protocol)» CPM
11.9
0.4
216
174.8
96.8
CC.Johnson et al. 2008
ActiGraph
11.9
0.5
1043
26.9
32.3
Treuth et al. 2007
AG 7165; 7 d
1500
11.9
0.5
1043
19.2
16.1
Treuth et al. 2007
AG 7165; 7 d
1500
12.0
0.3
229
27.7
16.6
Pate et al. 2006
MTI 7164; 7d
3000
12.0
0.4
269
22.9
18.0
Pate et al. 2006
MTI 7164; 7d
3000
12.0
0.4
262
134.6
63.7
CC.Johnson et al. 2008
ActiGraph
12.0
0.4
175
25.6
77.9
CC.Johnson et al. 2008
ActiGraph
12.0
0.5
645
25.3
1.1
McMurray et al. 2008
AG; 6 d; 30 sec epoch
12.0
0.5
1576
23.7
11.7
Young et al. 2014
MTI 7164; 7d
1500
12.0
0.5
1162
99.2
99.9
Saksvig et al. 2007
AG 7164; 6 d
1500
12.0
0.5
112
113.0
41.6
Saksvig et al. 2007
AG 7164; 6 d
1500
12.1
0.5
274
20.8
18.2
Pate et al. 2006
MTI 7164; 7d
3000
12.2
0.7
273
135.7
82.4
CC.Johnson et al. 2008
ActiGraph
12.3
0.3
65
17.1
20.1
Tucker et al. 2011
Biotrainer; d
12.3
0.7
286
21.5
18.6
Pate et al. 2007
MTI 7164; 7d
3000
12.6
2.8
1235
18.6
16.8
Mark & Janssen 2009
AG 7124; 7 d
3000
12.8
1.0
48
75.0
Pate et al. 2012
CSA7164; 7d
12.8
1.3
471
50.0
24.2
Sanchez et al. 2007
CSA7164; 7d
13.3
0.4
168
31.6
18.1
Francis et al. 2011
AG 7164; 3-4 d
2296
13.3
0.6
43
46.9
28.3
Anderson et al. 2005
MTI 7164; 4d 0600-2300
1399
13.3
0.6
43
9.2
6.5
Anderson et al. 2005
MTI 7164; 4d 0600-2301
3200
13.9
0.4
984
22.0
10.8
J.Stevens et al. 2007
AG 7164; 6 d
14.0
0.9
70
49.4
23.3
Pate et al. 2003
CSA7164; 7d;0700-2400
14.0
0.5
3085
22.2
11.2
Young et al. 2014
MTI 7164; 7d
1500
14.1
0.5
1140
21.9
10.9
Lohman et al. 2008
AG 7164; 6 d
14.5
1.8
289
37.5
21.4
Sirard et al. 2010
AG 7164; 5 d
14.6
1.8
360
26.7
13.9
Hearst et al. 2012
MTI 7164; 7d
14.9
1.9
149
16.4
1.5
O'Neill et al. 2011
AG 7164; 8 d
1500
14.9
1.9
149
29.7
1.4
O'Neill et al. 2011
AG 7164; 8 d
1500
15.0
2.9
859
20.6
35.2
MW Long et al. 2013
AG 7164; 7 d
2020
15.5
1.1
48
55.0
Pate et al. 2012
CSA7164; 7d
16.2
1.1
104
34.2
19.8
Gutin et al. 2005
MTI 7164; 7d
16.3
1.2
121
35.7
25.3
Gutin et al. 2005
MTI 7164; 7d
16.7
1.2
27
38.0
18.6
Sirard et al. 2008
AG GT1M; 7 d
940
31.9
8.7
55
31.9
18.0
Whitt et al. 2003
Actisplit
114
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Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range
(years)
Mean SD (n)
36.2 7.3 51
36.5 9.2
40.0
39.0 Buchowski et al. 2009
TriTrac R3D
36.5 9.2
45.0
36.0 Buchowski et al. 2009
TriTrac R3D
36.5 9.2
49.0
34.0 Buchowski et al. 2009
TriTrac R3D
36.5 9.2
52.0
32.0 Buchowski et al. 2009
TriTrac R3D
40.6 5.6
33.8
25.2 Fuemmeler et al. 2011
MTI-7164; 4 d; 24 h
36.5 9.2
63.0
30.0 Buchowski et al. 2009
TriTrac R3D
36.5 9.2
58.0
30.0 Buchowski et al. 2009
TriTrac R3D
40.6 5.6 45 26.3 20.4 Fuemmeler et al. 2011 MTI-7164; 4 d; 24 h
42.9 10.7 504 27.5 Candelaria et al. 2012 Actigraph; 7 d
47.0 14.0 786 95.6 117.7 Welk et al. 2014 SenseWear Mini; 1 d
47.0 9.0 1150 26.0 10.0 Glazer et al. 2013 Actical; 7 d 1486
47.7 31.0 1963 16.8 Camhi et al. 2011 AGAN-7164;7d
48.1 17.1 1594 78.0 40.4 Strath et al. 2008 AG 7164; 7 d
48.1 23.5 2208 18.3 23.5 Loprinzi & Pariser2014 AG 7164; 7 d 2020
49.3 20.8 535 18.0 13.9 Loprinzi PM 2012 AG 7164; 7 d 2020
53.3 6.9 184 112.8 59.0 Jilcott et al. 2011 Actigraph; 7 d 574
65.0 5.0 19 85.0 36.0 Gonzales et al. 2011 AG GT1M; 4 d 1041
73.2 1.7 148 61.8 4.8 Gabriel et al. 2013 AG GT1M; 7 d 760
Females, as above; complete are statistics are not provided
2- 3.0 124 5.4 Grzywacz et al. 2014 Actical; 7 d 715
3- 4.0 26 6.7 6.5 Shen et al. 2013 AG GT1M; 5 d 615
3- 4.0 26 8.8 7.1 Shen et al. 2013 AG GT1M; 5 d 615
3- 5.0 192 7.0 2.0 Dowda et al. 2011 AG7164;14d 420
5.3 184 24.0 Kwon etal. 2011 AG 7164; 4 d 3000
6- 11.0 288 75.0 37.9 Gortmaker et al. 2012 AG 7164; 7 d
6- 11.0 325 78.0 36.0 Gortmaker et al. 2012 AG 7164; 7 d
8.7 184 25.0 Kwon etal. 2011 AG 7164; 4 d 3000
9- 9.0 431 173.3 46.4 Nader et al. 2008 Actigraph; 7 d
9- 9.0 431 173.3 64.3 Nader et al. 2008 Actigraph; 7d
9- 9.0 90 44.4 Trost et al. 2013 AG GT3X+; 7 d
10- 10.0 93 40.2 Trost etal. 2013 AG GT3X+; 7 d
11- 11.0 85 32.5 Trost etal. 2013 AG GT3X+; 7 d
11- 11.0 434 115.3 36.3 Nader et al. 2008 Actigraph; 7d
11- 11.0 434 112.6 53.2 Nader et al. 2008 Actigraph; 7d
115
MVPA
(m in/day)
.. or> .. Accelerometer Used
Mean SD Citation (Make/Model »Protocol)» CPM
20.8 18.1 Olvera etal.2011 AGGT1M ;2d; 8 h/d
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
Mean
SD
(n)
Mean
SD
Citation
Accelerometer Used
(Make/Model #/Protocol)Ą
CPM
11.3
184
23.0
Kwon et al. 2011
ActiGraph 7164; 4 d
3000
11-
13
43
62.2
38.6
Cradock et al. 2004
TriTrac R3D; 2d, 24 h
11 -
11.0
143
30.8
11.6
Barker et al. 2003
AG 7164; 7 d
11 -
15.0
15
98.0
62.0
Kozub & Farmer 2011
ActiGraph
11 -
15.0
23
149.0
108.0
Kozub & Farmer 2011
ActiGraph
12-
12.0
351
86.0
32.5
Nader et al. 2008
Actigraph; 7d
12-
12.0
351
73.9
45.8
Nader et al. 2008
Actigraph; 7d
12-
19.0
535
21.9
34.7
Gortmaker et al. 2012
AG 7164; 7 d
13-
13.0
149
35.9
14.8
Barker et al. 2011
AG 7164; 7 d
13.2
202
30.6
Kwon et al. 2013
Actigraph 7164; 4d
3000
15-
15.0
280
38.7
23.6
Nader et al. 2008
Actigraph; 7d
15-
15.0
280
25.5
23.3
Nader et al. 2008
Actigraph; 7d
15.3
134
28.1
Kwon et al. 2013
Actigraph 7164; 4d
3000
20-
39
920
111.4
59.6
Martin et al. 2014
AG AM-7164; 7d
760
20-
65
837
21.6
25.1
Luke et al. BMC 2011
AG 7164; 7 d
2020
20-
65
375
18.5
25.2
Luke et al. BMC 2011
AG 7164; 7 d
2020
20-
65
383
19.1
17.6
Luke et al. BMC 2011
AG 7164; 7 d
2020
22-
41
10
91.0
16.7
Calabro et al. 2009
IDEEA; 1 d
22-
41
10
150.0
16.7
Calabro et al. 2009
SenseWear; 1 d
26.2
359
12.3
16.7
Evenson & Wen 2011
AG 7164; 4 d
2020
26.2
359
111.8
57.4
Evenson & Wen 2011
AG 7164; 4 d
574
40-
59
903
104.4
65.6
Martin et al. 2014
AG AM-7164; 7d
760
60-
69
522
79.5
62.0
Martin et al. 2014
AG AM-7164; 7d
760
66-
69
106
15.2
Buman et al. 2010
AG 7164/71256; 7d
1952
66-
69
106
14.4
Buman et al. 2010
AG 7164/71256; 7d
1952
70-
79
229
10.4
Buman et al. 2010
AG 7164/71256; 7d
1952
70-
79
229
9.8
Buman et al. 2010
AG 7164/71256; 7d
1952
70+
602
44.9
46.8
Martin et al. 2014
AG AM-7164; 7d
760
80+
147
4.0
Buman et al. 2010
AG AM-7164; 7d
1952
80+
147
4.0
Buman et al. 2010
AG AM-7164; 7d
1952
Females: Health Considerations or Being Overweight/Obese
8.4
0.9
23
32.8
17.1
DuBose & McK 2014
ActiGraph GT1M
9.3
1.1
7
47.0
Hsu et al. 2014
ActiGraph GT1M
10.9
3.6
226
74.0
46.0
Butte et al. 2007
Actiwatch; 3 d; 24 h
11.9
0.5
534
24.1
41.6
Treuth et al. 2007
AG 7165; 7 d
1500
116
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (m in/day)
Mean
SD
(n)
Mean
SD
Citation
Accelerometer Used
(Make/Model #/Protocol)Ą
CPM
11.9
0.5
534
16.7
16.2
Treuth et al. 2007
AG 7165; 7 d
1500
12.0
0.6
378
20.8
0.5
McMurray et al. 2008
AG 6 d; 30 s epoch
15.7
1.1
37
27.9
17.8
Gyllen. et al. 2013
AG GT1M; 7 d
2020
27.8
6.5
27
8.8
10.3
Loprinzi et al. 2012b
AG 7164; 7 d
2020
28.9
5.6
114
14.3
43.6
Loprinzi et al. 2012b
AG 7164; 7 d
2020
41.1
15.9
21
12.6
12.2
Behrens et al. 2011
AG GT1M; 7d; 10 h/d
53.0
9.0
41
113.4
Rogers et al. 2009
AG GT1M; 7 d
1953
54.5
6.9
196
22.2
17.4
Farr et al. 2008
MTI-7164; 7 d
2225
61.1
16.5
337
8.6
11.0
Loprinzi & Pariser2014
AG 7164; 7 d
Males: Normal, Healthy, or Not Specified
3.8
0.4
21
19.4
Kelly et al. 2007
ActiGraph; 3d
3200
7.2
0.9
42
243.0
Pate et al. 2012
CSA7164; 7d
7.2
2.0
68
70.8
31.4
Sarzynski et al. 2010
AG GT1M; 7 d
2172
7.6
0.6
90.6
34.7
Willis etal. 2015
AG GT3x+; 4 d
7.7
1.3
78
69.8
30.8
HM Hayes etal. 2011
MTI-7164; 7 d
2172
8.1
0.7
50
65.2
28.0
NC Crespo et al. 2013
AG GT1M; 7 d
9.1
2.0
393
71.7
61.5
MWLong etal. 2013
AG 7164; 7 d
10.1
1.0
51
146.0
Pate et al. 2013
CSA7164; 7d
10.6
0.8
23
168.7
59.9
Fuemmeler et al. 2011
MTI-7164; 4 d; 24 h
10.6
0.8
23
145.0
51.9
Fuemmeler et al. 2011
MTI-7164; 4 d; 24 h
10.7
4.0
194
96.0
57.0
Butte et al. 2007
Actiwatch; 3 d; 24 h
11.2
0.3
184
41.6
22.0
Janz et al. 2007
ActiGraph 7164; 5 d
3000
12.0
1.0
48
88.0
Pate et al. 2013
CSA7164; 7d
12.3
0.4
56
42.1
35.1
Tucker et al. 2011
Biotrainer; 3 d
12.5
2.4
19
109.7
32.7
Holmes et al. 2008
MTI; 4 d
12.7
1.4
407
67.6
30.8
Sanchez et al. 2007
CSA7164; 9d
12.8
1.1
210
24.8
17.6
Jago et al. 2005
MTI; 3d; 24 h
12.8
2.7
1263
34.3
23.5
Mark & Janssen 2009
AG 7164; 7 d
3000
13.4
0.5
37
86.5
47.9
Anderson et al. 2005
MTI 7164; 4d, 0600-2300
1399
13.4
0.5
37
27.1
22.9
Anderson et al. 2005
MTI 7164; 4d, 0600-2300
3200
14.5
1.8
286
49.7
28.6
Sirard et al. 2010
AG 7164; 5 d
14.6
1.8
340
35.0
18.3
Hearst et al. 2012
MTI 7164; 7 d
14.7
3.0
873
36.5
50.2
MW Long etal. 2013
AG 7164; 7 d
2020
15.7
1.0
44
61.0
Pate et al. 2015
CSA7164; 7d
16.7
1.4
37
60.0
25.8
Sirard et al. 2008
AG GT1M; 7 d
940
117
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
_ _ __ Accelerometer Used .
Mean SD (n) Mean SD C.tat.on (Make/Model #/Protocol)Ą CPM
42.8 6.2 45 30.5 23.2 Fuemmeler et al. 2011 MTI-7164; 4 d; 24 h
42.8 6.2 45 29.5 18.8 Fuemmeler et al. 2011 MTI-7164; 4 d; 24 h
43.0 10.2 547 38.4
Candelaria et al. 2012 Actigraph; 7 d
46.2 16.8 1678 102.7 53.1 Strath et a 1.2008 AG 7164; 7 d
45.4 16.6 561 168.0 113.3 Welk et al. 2014 SenseWear Mini; 1 d
46.0 24.3 2364 31.9 34.0 Loprinzi & Pariser 2014 AG 7164; 7 d
46.5
38.0
1781
29.5
Camhi et al. 2011
AG AN-7164; 7 d
47.0
8.0
959
30.0
22.0
Glazer et al. 2013
Actical; 7 d
1486
47.5
24.7
611
30.4
34.6
Loprinzi PM 2012
AG 7164; 7 d
2020
65.0
5.0
19
109.0
49.0
Gonzales et al. 2011
AG GT1M; 4 d
1041
78.5
4.8
2157
90.8
60.7
Cawthon et al. 2013
SenseWear Pro; 5 d
Males, as above; complete age statistics not provided
2-
3.0
118
6.6
Grzywacz et al. 2014
Actical; 7 d
715
3-
4.0
20
9.3
15.7
Shen et al. 2013
AG GT1M; 5 d
615
3-
4.0
20
10.4
9.1
Shen et al. 2013
AG GT1M; 5 d
615
3-
5.0
177
8.1
2.1
Dowda et al. 2011
AG 7164; 14d
420
5.2
142
31.0
Kwon et al. 2011
AG 7164; 4 d
3000
6-
11.0
265
96.5
78.1
Gortmaker et al. 2012
AG 7164; 7 d
6-
11.0
319
101.2
72.1
Gortmaker et al. 2012
AG 7164; 7 d
8.7
184
40.0
Kwon et al. 2011
AG 7164; 4 d
3000
9-
9.0
555
190.8
53.2
Nader et al. 2008
Actigraph; 7d;
9- 9.0 555 184.3 68.6 Nader et al. 2008 Actigraph; 7d
9- 9.0 68 59.9 Trost et al. 2013 AG GT3X+; 7 d
10- 10.0 70 51.7 Trost et al. 2013 AG GT3X+; 7 d
9- 9.0 64 57.5 Trost et al. 2013 AG GT3X+; 7 d
11- 11.0 544 133.0 42.9 Nader et al. 2008 Actigraph; 7d
11-
11.0
544
127.0
59.5
Nader et al. 2008
Actigraph; 7d
11.3
184
41.0
Kwon et al. 2011
AG 7164; 4 d
3000
11-
13
43
82.3
42.8
Cradock et al. 2004
TriTrac R3D; 2d; 24 h
11 - 15.0 9 181.0 103.0 Kozub & Farmer 2011 ActiGraph
11 - 15.0 21 242.0 77.0 Kozub & Farmer 2011 ActiGraph
12- 12.0 416 105.3 40.2 Nader et al. 2008 Actigraph; 7d
12- 12.0 416 93.4 55.3 Nader et al. 2008 Actigraph; 7d
12- 19.0 577 39.9 55.2 Gortmaker et al. 2012 AG 7164; 7 d
12- 19.0 549 36.9 44.5 Gortmaker et al. 2012 AG 7164; 7 d
118
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
Mean
SD
(n)
Mean
SD
Citation
Accelerometer Used
(Make/Model #/Protocol)Ą
CPM
13.3
199
51.5
Kwon et al. 2013
AG 7164; 4 d
3000
15-
15.0
324
58.2
31.8
Nader et al. 2008
Actigraph; 7d
15-
15.0
324
43.2
38.0
Nader et al. 2008
Actigraph; 7d
15.3
133
40.1
Kwon et al. 2013
AG 7164; 4 d
3000
20-
39
795
151.2
88.8
Martin et al. 2014
AG AM-7164; 7d
760
20-
65
926
34.7
27.4
Lukeet al. BMC 2011
AG 7164; 7 d
2020
20-
65
386
33.4
27.5
Lukeet al. BMC 2011
AG 7164; 7 d
2020
20-
65
463
42.1
32.3
Lukeet al. BMC 2011
AG 7164; 7 d
2020
22-
41
10
89.0
16.7
Calabro et al. 2009
IDEEA; 1 d
22-
41
10
112.0
16.7
Calabro et al. 2009
SenseWear; 1 d
40-
59
899
135.1
66.7
Martin et al. 2014
AG AM-7164; 7d
760
60-
69
501
98.4
54.5
Martin et al. 2014
AG AM-7164; 7d
760
66-
69
109
21.3
Buman et al. 2010
AG 7164/71256; 7d
1952
66-
69
109
22.3
Buman et al. 2010
AG 7164/71256; 7d
1952
70-
79
182
15.3
Buman et al. 2010
AG 7164/71256; 7d
1952
70-
79
182
14.2
Buman et al. 2010
AG 7164/71256; 7d
1952
70+
646
56.8
48.1
Martin et al. 2014
AG AM-7164; 7d
760
80+
89
11.6
Buman et al. 2010
AG 7164/71256; 7d
1952
80+
89
10.7
Buman et al. 2010
AG 7164/71256; 7d
1952
Males: Health Issues, or Overweight/Obese
11.1
3.5
247
88.0
50.0
Butte et al. 2007
Actiwatch; 3 d; 24 h
15.5
2.1
18
46.2
15.9
Holmes et al. 2008
MTI; 4 d
37.8
14.0
9
34.2
22.5
Behrens et al. 2011
AG GT1M; 7d; 10 h/d
55.3
8.0
59
32.4
22.1
Farr et al. 2008
MTI-7164; 7 d
2225
57.9
19.9
396
15.8
25.9
Loprinzi & Pariser 2014
AG 7164; 7 d
80.6
5.6
743
58.6
53.2
Cawthon et al. 2013
SenseWear Pro; 5 d
Both genders
3-
80
96.7
32.0
HG Williams et al. 2008
AG 7164; 7 d
1680
3-
3.0
85.0
38.0
Edwards et al. 2013
RT-3; 3 d
1400
3.5
1.1
337
14.9
9.5
Dolinsky et al. 2011
Actical; 7 d
715
4-
118
96.0
24.4
HG Williams et al. 2008
AG 7164; 7 d
1680
4-
4.0
90.0
37.0
Edwards et al. 2013
RT-3; 3 d
1400
5-
5.0
94.0
37.0
Edwards et al. 2013
RT-3; 3 d
1400
6-
6.0
87.0
33.0
Edwards et al. 2013
RT-3; 3 d
1400
7.0
1.9
63
90.7
22.4
Eisenmann et al. 2010
MTI-7164; 4 d
2172
119
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range MVPA
(years) (min/day)
Mean
SD
(n)
Mean
SD
Citation
Accelerometer Used
(Make/Model #/Protocol)Ą
CPM
7.5
2.0
61
38.1
12.0
Eisenmann et al. 2010
MTI-7164; 4 d
2172
7-
7.0
80.0
40.0
Edwards et al. 2013
RT-3; 3 d
1400
8-
282
66.0
67.2
Butte et al. 2014
Actical; 3 d
8-
14.0
291
19.5
15.5
Dunton et al. 2012
AG GT2M; 7d
2020
9-
282
55.0
33.6
Butte et al. 2014
Actical; 3 d
9.1 1.5 76 113.4 37.0 Hennessy et al. 2010 AG 7164; 7 d
9.1 1.6 682 144.0 52.0 Kneeshaw et al. 2013 AG GT1M; 7 d
9.2
1.6
713
62.2
36.5
Tandon et al. 2014
AG GT1M; 7 d
9.6
0.8
65
25.8
6.3
Wrontniak et al. 2007
AG 7164; 7 d; 10 h/d
3200
9.9
0.7
27
307.8
96.6
Tsai et al. 2012
Actiwatch 64; 7d
700
9.9
0.9
27
265.2
82.8
Tsai et al. 2012
Actiwatch 64; 7d
700
10-
282
53.0
33.6
Butte et al. 2014
Actical ;3 d
10.0
0.7
51
49.1
26.9
Olvera et al. 2011
AG GT1M; 2 d; 8 h
11.4
0.7
198
47.5
2.0
Wilson et al. 2011
Mini-Mitter; 7 d
1500
13.3
2.1
181
27.6
21.2
Lawman & Wilson 2014
Actical; 7 d
1500
14.7
1.7
91
42.0
26.4
Matthews et al. 2013
AG GT3X; 7 d
1952
14.7
2.0
39.5
26.6
AC Long et al. 2008
Acti watch 64; 7 d
1500
14.8
0.5
130
44.2
27.1
DJ Graham et al. 2011
AG 7164; 7 d
1952
15.1
1.4
20.2
18.1
AC Long et al. 2008
Acti watch 64; 7 d
1500
21.3
2.3
34
85.7
37.0
Sisson &Tudor-L. 2008
AG 7164; 2d
21.7
4.0
35
50.3
23.8
Sisson &Tudor-L. 2008
AG 7164; 2d
32.3
8.4
45
21.0
18.6
Dixon-lbarra et al. 2013
GT1M; 4 d
2020
42.1
14.8
88
36.0
24.0
Matthews et al. 2013
AG GT3X; 7 d
1952
43.4
11.6
135
122.3
75.2
Warner et al. 2012
Actical; 7 d; 10 h/d
43.6 10.7 946 30.3
Rovniak et al. 2010; AG 7164/71256; 7 d
46.0 10.6 655 23.8 Rovniak et al. 2010; AG 7164/71256; 7 d
51.1 15.4 30 64.7
Pugh et al. 2012 AG GT3X; 7 d
53.7 14.3 20 7.5 Pugh et al. 2012 AG GT3X; 7 d
57.4
9.9
71
35.4
24.2
Banda et al. 2010
Actical; 7d
57.9
6.9
31
10.2
13.8
Dixon-lbarra et al. 2013
GT1M; 4 d
2020
58.3
10.3
139
11.8
28.8
Sloane et al. 2009
RT3; 7 d
62.0
9.0
519
14.0
J Song et al. 2010
AG GT1M; 7 d
63.5
8.3
200
14.0
19.0
Hutto et al. 2013
Actical; 4 d
1065
64.3
6.9
232
22.9
22.0
Swartz et al. 2012
AG 7164; 7 d
1952
65.6
13.2
22
11.5
11.0
Stevenson et al. 2009
AG GT1M; 7 d
120
-------
Table 24. Estimates of moderate & vigorous (MVPA) physical activity (PA) (continued)
PUBLISHED IN THE U.S. LITERATURE
Age Range
(years)
MVPA
(m in/day)
Mean
SD
(n)
Mean
SD
Citation
Accelerometer Used
(Make/Model #/Protocol)Ą
CPM
70.5
12.6
13
41.6
30.9
Erikson et al. 2013
SenseWear; 3 d; NS
71.3
8.4
84
79.7
46.7
Parker et al. 2008
AG 7164; 7 d
760
73.2
5.9
33
21.6
13.8
Dixon-lbarra et al. 2013
GT1M; 4 d
2020
75.0
7.5
28
86.2
118.5
Erikson et al. 2013
SenseWear; 3 d; NS
76.8
9.3
26
40.7
43.4
Erikson et al. 2013
SenseWear; 3 d; NS
78.8
4.2
121
14.8
17.0
J.Kerr et al. 2013
AG GT3X+; 4 d
1952
89.3
3.8
94
5.3
9.3
J.Kerr et al. 2013
AG GT3X+; 4 d
1952
Ą All studies require that the accelerometer not be worn during "wet" events (bathing, swimming, or other); 24h
studies allow it to be worn during sleeping; duration is "awake hours" unless otherwise noted.
AG=ActiGraph.
CPM=Counts per minute
d = Day(s)
NS=Not specified length of time
121
-------
Jr. et al., 2008; Lyden et al., 2011). However, Welk et al.
(2000) caution that regression equations developed in a
laboratory or clinical setting to relate accelerometer counts to
activity-specific energy expenditure estimates probably are
not applicable to field conditions. They also think that certain
accelerometers over-estimate EE at all levels of activity
(Welk et al., 2000).
Accelerometers have been evaluated against the "gold-
standard" doubly-labelled water (DLW) method of obtaining
EE on a daily basis. Most evaluations indicate that there is
"poor agreement" between physical activity measured by
accelerometers (at any level of activity) and PAEE evaluated
by the DLW technique (N.Campbell et al., 2012; J. Carter et
al., 2008). This is a near-universal finding of these types of
aggregate EE evaluation studies (e.g., Leenders et al., 2006).
Accelerometers frequently have been evaluated against
V02 monitoring (indirect calorimeter): an interesting one is
Adolph et al. (2012); there are many others. With respect
to accelerometer "validity" (evaluation) studies, some
researchers make a distinction between "convergent validity"
(represented by a correlation coefficient) and "criterion
validity" (represented by absolute estimates). Convergent
validity also is called "concurrent validity," especially when
multiple monitors are being compared (Welk et al., 2000).
Accelerometers were used in two two-year "waves" (2003-
2004 & 2005-2006) of the National Health and Nutrition
examination Surveys (NHANES). There are 20-30 papers
in the literature analyzing data from these waves, differing
mostly in which subgroups are the cohort of interest in a
particular paper (youth of different age groupings, people
with COPD, etc.). One first author, Paul D. Loprinzi of the
University of Kentucky has over ten papers on the 2003-
2006 NHANES papers himself focused on different cohorts!
Table 24 contains data from some of these papers and other
NHANES analyses, but I tried to not provide "redundant"
MVPA data in the Table (MVPA data for essentially the same
subgroup) from the NHANES papers; doing so would bias
variability estimates included in the database if some type
of meta-analysis would be done on the information. I'm
sure that some NHANES redundancy has crept into Table
24, because of the multiple authors/institutions involved in
publishing data from the same study. Oftentimes the only
difference among the studies is the accelerometer counts used
for the MPA and VPA cutoffs. None of the combined female/
male data from NHANES studies are reproduced in Table 24
(e.g., Loprinzi et al., 2011a, 2014b).
The most sophisticated analysis of the NHANES data that
exists is Metzger et al. (2008). They succinctly describe
how the MPA and VPA count cutoffs for ActiGraph 7164
accelerometer used in NHANES affected subsequent
estimates of MVPA. The NHANES study cutoffs used a
sample-size weighted average of cutoffs published in Brage
et al. (2003), Freedson et al. (1998), Leenders et al. (2003),
and Yngve et al. (2003). The count cutoff averages were
2020 CPM for MPA and 5999 for VPA. Many other count
cutpoints have been developed and used as can be seen in
122
Table 24. Accelerometer counts used for MPA and VPA in
various studies vary greatly, even for the same instrument.
More on that below.
Please note that the Table 24 presented in this report is
an abbreviated version of one that is available from the
author. The original table has separate estimates for MPA
and VPA, where available, and relates the CPM cutoffs to
METS classes, also where available. Some of the articles
that provide separate MPA and VPA estimates in min/d do
not provide MVPA estimates also, so there are more data
available on time spent per day in moderate and/or vigorous
physical activity than shown in this Report. Finally, it should
be noted that Table 24 does not contain MVPA data that were
presented only (1) graphically, such as Song, et al. (2010, or
(2) as quartiles or quintiles, etc., such as Thiese et al. (2011).
Since accelerometers are an electro-mechanical device, they
cannot come in contact with water. That obviates their use
for estimating energy expenditure or oxygen consumption
during water-contact activities or water sports. This may
significantly underestimate daily MVPA for 2% of so of all
adults whose exercise consists solely of swimming or pool-
based exercise classes (Sidney et al., 1991). Accelerometers
have also been shown to be very inaccurate for some
activities, like cycling (where hip-mounted devices do not
pick up on exercise that does not change the vertical plane
much) which leads to a systematic under-estimate of MVPA
in individuals that participate in those types of activities
(Miller et al., 2006). We have experiential knowledge of this
in an in-house experiment: a subject wore an accelerometer
on his hip while riding a bike on roads between northern
Durham and RTP and it recorded hardly any activity, whereas
when he drove a car with bad shocks on the same roads,
the accelerometer recorded very high levels of activity! We
certainly got a very misleading indication of activity-specific
energy expenditure in that trial. Swimming, water sports, and
biking are all high-METS activities, generally >7 METS,
and fairly popular in the general population. A lot of MVPA
min/d can be missed during these activities solely due to
accelerometer limitations.
Besides these problems, there are four universal issues with
accelerometers: (1) how to handle non-wear time, also known
as zero count data; (2) what temporal "epoch" should be
used to aggregate count data (recorded between 1-15 seconds
normally) before storing the information and how "bouts" of
PA are defined; (3) how to translate mechanical movement
of a lever into an estimate of energy expenditure, which then
has to be related to oxygen consumption; and (4) how many
hours per day are enough to accurately estimate total daily
physical activity (Bornstein et al., 2011a; Gabriel et al., 2010;
Herrmann et al., 2013; King et al., 2011; Troiano & Freedson,
2010; Troiano et al., 2012). These issues will be briefly
addressed below in order.
Where on the body an accelerometer is placed has a major
effect on its estimates (Abel et al., 2009b). Most of the
studies place it on or near the "non-dominant" hip, but
sometimes it is placed on an ankle or wrist. Table 24 data are
-------
supposed to be exclusively from hip-placed accelerometers,
but a few non-hip study results may have slipped into the
Table. Accelerometer placement on the body also affects
estimates of non-wear time, an issue discussed next, even if
the subject is expending energy. Subject differences in BMI,
stride length, gait speed and pattern, and activities undertaken
all affect MVPA estimates (Storti et al., 2008). In addition,
certain health considerations also affect accelerometer count-
to-energy expenditure rate metrics compared to "normals,"
another source of uncertainty in the results (Agiovlasitis et
al., 2012; Almeida et al., 2011). Of course, age and gender
are always important considerations leading to different
accelerometer results. These considerations give rise to the
large counts/steps differences for similar activities that are
discussed below.
Often accelerometer studies involve placing the subjects into
a typology of cohorts, such as age groupings, gender, health
condition, etc. For MPA/VPA studies the cohort aggregate
data include subjects engaging in no MVPA time/d being
combined with people participating in significant exercise
time. This leads to the well-recognized problem that the
mean estimate (of MVPA time/d) is a compromise statistic,
overestimating non-doer time (which is zero minutes)
and systematically underestimating those that undertake
significant amounts of PA. For instance, Strauss et al. (2001)
found that 16% of their 13 y old subjects participated in
MVPA and only 7% undertook VPA. These are MVPA doers.
If the mean MPA time was 43.8 min/d (using Anderson et
al., 2005 data; see Table 24), then 84% of the sample had 0
min/d, while doers had 274 min/d in MVPA exercise. Quite a
different MVPA picture is provided for doers and non-doers
than might be obtained by looking at just the mean. The same
holds true, even more so, for VPA. Unhappily, however,
rarely does an exercise paper include both the participation
rate and the mean time/d data to address this data issue.
The COV's associated with data in Table 24 indicate that
there is a lot of variability in the samples taken. The total
number of studies for which a COV for MVPA time can
be calculated is 226. The median COV for these studies is
60% (mean=57.8%). For "normal, healthy, or not specified"
samples having "complete" age statistics available, the
median COV for MPA time for females is 56% (n=79;
range=4-304%) and 58% for males (n=28; range=36-138%).
For samples containing people with complete age statistics
having health problems or being overweight/obese,
the median COV for female MPA time is 97% (n=ll;
range=2-305% and 67% for males (n=5; range=34-164%).
These data confirm what Saris & Binkhorst (1977) stated in
1977: "it is well known that daily physical activity varies
enormously" within the general population. The wide
range in individual COV's just presented in the parentheses
confirms this statement.
Within a reasonably compact age group there is a wide
variability in the number of minutes per day of MVPA
undertaken. For instance, in a study of elders (mean age =
71.3 ± 8.4; range 55-87), the mean MVPA estimate for the
both-gender cohort over 7+ days was 79.9 ± 46.7, with a
range of 9.6 - 220.3 min/d for individual subjects. Daily
MVPA values were of course wider than that, but were not
provided (Parker et al., 2008). The difference in the mean
range of 210 min/d is very large for the cohort.
Non-Wear Time.
How to handle zero- or near-zero count data from
accelerometers for a specified epoch time to determine non-
wear time by a subject is a major issue with accelerometers
(Choi et al., 2011, 2012; Evenson & Terry, 2009; Herrmann
et al., 2014; Semamik et al., 2010; Tudor-Locke et al.,
20 lie). There are a plethora of ways that zero counts are
treated in accelerometer studies. Usually a criterion is
established that considers zero counts to be non-wear time
if they exceed 60 consecutive minutes in duration (Brown.
BB & Werner, 2007; Loprinzi et al., 2014d; HG Williams
et al., 2008). Other less conservative decision rules have
been developed, such as excluding any zero count lasting
10 minutes or longer (Beets et al., 2011). On the other hand,
Hutto et al. (2013) state that using 120 min of consecutive
zero counts provides "dependable population-based estimates
of wear and nonwear time, and time spent being sedentary
and active in older adults wearing the Actical™ activity
monitor" (p. 120). Because accelerometers can be—and
frequently are—w orn while sleeping, there also are decision
rules that relate to what is an "effective" zero-count, such as
no non-zero readings for <10 minutes or other defined time
period (Cradock et al., 2004). Thus, a zero-count may not
really be zero per se, but some small count number that is
thought to be improbable if the subject is awake and wearing
the monitor. See Crouter et al. (2013) for more information
on these points. Non-wear time is subtracted from the daily
span of time that the accelerometer is supposed to be worn,
generally the total awake time of the subject, except for
bathing or swimming time.
Another issue is that the impact of non-wear time is not
spread out evenly over the day.
At either end of the day, nonwear time appears to distort
population estimates of all accelerometer time and physical
activity volume indicators [e.g., MVPA], but its effects are
particularly clear on population estimates of time spend in
sedentary behavior (Tudor-Locke et al., 2011; p. 693).
Rarely, however, are time-of-day specific rules used to define
non-wear time in a study.
The different decision rules used for non-wear time greatly
affect the estimates of MVPA and other levels of activity.
Non-wear time definitions affect both the absolute and
relative (% of time in the day) min/d estimates of MVPA. An
interesting simulation evaluation of elderly accelerometer
data obtained during the 2005-2006 NHANES health survey
that included accelerometer monitoring, indicates that using
different criteria of what constitutes non-wear time causes
MPA estimates to vary from 7-26 min/d and VPA from 14-40
min/d (Herrmann et al., 2013, 2014). These are large ranges
in an elderly cohort. On the other hand, in another study of
older adults with knee osteoarthritis who did not have a lot of
physical activity to begin with, different non-wear decision
123
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rules did not significantly change the observed MVPA of
14 min/d (Song et al., 2010). Three different thresholds of
minimum consecutive zero counts of 60, 90, and 120 min
were tested in that study.
Because of the uncertainty of whether or not a zero count for
a defined epoch is "real" or not, there have been a number
of accelerometer count imputation schemes devised to
substitute non-zero counts when researchers think that
physical activity was occurring but not recorded. Imputation
is a complex topic, involving a number of "nested" decision
rules and criteria. Imputation cannot really be treated in this
Report to do the topic justice. For additional information, see,
for instance, Dowda et al. (2007); Morris et al. (2006); and
Paul et al. (2008). The Morris et al. (2006) paper describes
a sophisticated approach to imputing missing data via the
modeling of wavelet-based functional mixed models; it is an
interesting paper. Perhaps the most rigorous analysis of non-
wear algorithms focused on MVPA estimates is contained in
Crespo et al. (2012).
Metzger et al. (2008) describe how imputation of missing
daily accelerometer data affects estimates of MPA and VPA,
and how imputation also affects the number of "valid"
days of data used for further analysis. (See below for more
information on the number-of-days issue.) They then use
latent class analysis to determine if "natural groupings" of
age-weighted cohorts existed in the sample with respect to
the number of MPA/VPA accumulated per day given the
missing day problem. Five classes of people were identified,
whose mean daily MVPA ranged from 134 min/d in a
miniscule 0.9% of the population to the lowest class only
averaging 5.3 min/d for 33.6% of the population. The authors
label this last group as "inactive" (Metzger et al., 2008). That
group and the next lowest, averaging about 20 min/d MVPA,
constitute about 79% of the weighted population. When daily
MVPA time/d is analyzed with respect to 10 min "bouts," the
proportion of the weighted population MVPA "bout-minutes"
indicates that the highest class had about 90 min/d in bouts
but were only 0.6% of the population; the lowest two classes,
averaging <10 min/d of MVPA constituted 93.5% of the
weighted population (Metzger et al., 2008).
Affected even more by zero count considerations are those
results reported only in percent of time above any count
cutpoint, as we would have to know wear time and epoch
length to calculate the minutes/day in MVPA. We do not
generally report percentage of time data in this Report.
One study that provided estimates of the percent of daily
time spent in MPA and VPA states that a mixed sample of
92 females/males aged 13.3 ± 2.0 y spent a mean of 16 ±
3.7% of their 14h day in MPA and another 7.1 ± 2.4% in
VPA (Strauss et al., 2001). Multiplying these percentages by
monitoring time works out to be about 138 min/d for MPA
and 58 min/d for VPA. These are relatively high estimates.
Even though decision rules used to calculate non-wear time
are important in understanding accelerometer results, we
do not report them in Table 24 or in the text, as we would
get bogged down in a lot of tedious detail. Suffice to say
that the data in Table 24 are implicitly based upon whatever
124
non-wear and valid day decision rules that authors of the
papers themselves applied in their study of daily MVPA time
and participation rates. These rules undoubtedly affect the
MVPA estimates.
Epoch Time and Activity "Bouts."
The range used for epochs in reported studies is between 5
and 60 seconds. The most commonly used epoch by far is
60 seconds. Obviously more data have to be stored for short
epoch times given a fixed daily monitoring period. This
leads to instrument storage and battery-life problems. This
issue is related to the criteria used to identify MVPA bouts,
such as x counts per 10 minutes, 20 minutes, etc. It is not to
be confused with the even longer length of time over which
the monitoring is undertaken, such as 10 h/d over 4 d or 7
days, etc. that also affects storage capacity and battery life.
Papers by Edwardson & Gorley (2010), Kuffel et al. (2011),
McClain et al. (2008), Rowlands et al. (2006), and Vale et
al. (2009) discuss the epoch length/bout issues, including
providing alternative estimates of MVPA depending upon
different algorithms used to define bout and epoch length.
Edwardson & Gorley (2010) recommend that a short epoch
length be used to realistically estimate MPA and VPA,
especially in children, whose PA occurs in "bursts." Epochs
<60 sec result in significantly higher estimates of MVPA
time/d, but with smaller bias relative to direct observation
using the Children's Activity Rating Scale (CARS) (Hislop et
al., 2012a,b). The same is true for shorter bout length.
An example from Kang et al. (2010) of how epoch length,
accelerometer counts, and bout length all interact in an
estimate of MVPA time/d:
PA bouts were defined as time intervals having
accelerometer counts >500 counts per 30-s epoch (cpe)
for at least 7 min, allowing for up to 2 min of epoch below
that threshold during the 7-min interval. Multiple time
intervals with breaks <2 min were considered as one bout
if the entire sequence of counts satisfied the count criteria
(p. 1420).
Explaining all of this in Table 24 would be a herculean task,
even if the authors of each study provided it in their papers,
which many do not.
Since epoch length directly affects MVPA count cutpoints,
reproduced here is a table from a paper by McClain et al.
(2008) that clearly relates these two parameters in a study of
5th grade students. Four different decision criteria were used
to derive epoch duration/accelerometer cutpoint. These are
identified by the papers where they were first published (first
author names only). Count cutpoints increase by epoch length
are non-linear for three of the four studies, with an apparent
inflection point at a 30 sec epoch time. (Actually the data
plot as two splines with 30 seconds as the inflection point.
Whether or not the relationship between epoch length and
counts truly is a continuous non-linear function cannot be
ascertained with the data provided.) Note also that for the last
two papers (by the same first author), age of the child affects
the count/epoch length relationship.
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MVPA Count Cutpoint for Different Epoch Lengths
Epoch Derivation
Age
5
10
Length (in seconds)
20
30
60
Treuth (2004)
N/A
250
500
1000
1500
3000
Mattocks (2007a)
N/A
298
596
1193
1790
3851
Freedson (2005)
10 y
159
318
636
955
1910
Freedson (1997)
11 y
171
343
686
1030
2060
None of the MVPA data from McClain et al. (2008) appears
in Table 24 due to the authors only presenting MVPA data
for a 30 minute PE class for 5th graders. Free-living MVPA
information for an entire day was not provided in the paper.
McClain et al. (2008) also discuss the epoch/bout issue in
previously-published papers (McClain et al., 2007a, b). Data
from McClain et al. (2007a) were not presented in Table
24 because the authors did not provide age information by
gender for their subjects. Data on adults from McClain et al.
(2007b) were not reproduced because they mixed genders
and did not provide a mean age value for them.
A number of studies aggregate counts into 10-minute (or
other length) bouts and utilize cutpoint thresholds for only
that time period (Glazer et al., 2013; Thompson et al.,
2009). One study considers moderate intensity activities
to be those between 30,000-49,999 CPM per 10 minbout;
vigorous activities are those with 50,000 counts per 10/min
bout (Bailey & Mclnnis, 2011). Data from this study are
not included in Table 24 because the authors only provided
the number of days having one or more bouts over the
cutpoints; total minutes per day spent at moderate/vigorous
level were not reported. MVPA estimates drop drastically
when a longer time period is used to define a bout. If bout
length is raised to 10 minutes from 1 minute and the cutpoint
criterion is not changed, MVPA estimates drop by 25-50%
on average (Glazer et al., 2013). Trost et al. (2002) evaluate
how many 5-, 10-, or 20-minute bouts of MVPA (>3 METS)
occur/week in 1-12 grade students using an Actigraph 7164
accelerometer. The number of weekly MVPA bouts drops
greatly as bout-length increases at all grade levels in both
genders. They also drop with grade level (Trost et al., 2002).
For example the number of weekly 5-min bouts in boys
drops from 85 in grades 1-3 to 22 in grades 10-12. The drop
in weekly bouts for grade 1-3 boys is from 85 for 5-min
bout lengths to 8 for 20-minbout lengths (Trost et al., 2002).
Similar decreases in both the number of bouts/grade and
bouts/bout-length occurs in females.
A correspondingly wide span of estimates for MVPA bouts
of 10 minutes or longer and total minutes/day is depicted
in Ham et al. (2007). These authors also provide 10-minute
bout estimates by four heart rate reserve categories (<25%;
25-44%; 45-59%; and >60%) and found that most MPA
accelerometer estimates fell into the light HR category of
<45% heart rate reserve.
Daily Monitoring Period.
Related issues to non-wear time and data imputation are how
many hours/day constitutes a "valid day" and how many
valid days out of the overall monitoring period are required
to provide an unbiased estimate of MVPA for a sample of
subjects. The criterion used by researchers for these related
topics vary widely. The criterion for having a valid day of
data often is >10 h (Cook et al., 2012; Glazer et al., 2013;
Loprinzi et al., 2014b; Rowlands et al., 2015), but other
periods are used also: 24 h is used by Cook et al. (2012).
Barnes et al. (2013) and Cradock et al. (2004) use 8 h; Trost
et al. (2013) use 9 h. Some studies use a minimum of 4 days
of valid data to be called a valid sample, with at least 8 h of
complete information per day (Francis et al., 2011; Hayes
et al., 2013). Avery loose criterion for a 7 d study is that
each individual has to have > 1 h of valid data on > 3 days
for their data to be included in subsequent analyses (Pate
et al., 2004). How a criterion like this can be considered
to provide a valid estimate of daily time spent in MVPA
is questionable. An alternative—and more sophisticated-
-criterion of non-wear time that has been used is that if 2
of the 3 vectors in a triaxial accelerometer records a zero
count for any epoch length of time < 60 sec, the data are
not considered to be valid (Burdette et al., 2004). That is a
stringent definition; usually single axis analysis of triaxial
data is not undertaken due to the increased storage capacity
needed to maintain single axis information. Sometimes a
valid day only includes a specific part of the day—say 1000-
1400 and all other hours are ignored; a valid day in that case
is not necessarily even daily awake time. How a valid day is
defined using accelerometer data involves many factors and
the possibilities are many.
A simulation study of 40 days of accelerometer data having
14 h/d of valid data is described in Herrmann et al. (2013,
2014). The 40 days were randomly sampled from a base
study of 1,200 days, not all of which had a complete 14 h/d
of valid data. From these 40 days, they repeatedly sampled
between 10 and 13 h/d from the valid days of data and
compared the absolute and "absolute percentage error" (APE)
for each sample versus the original 14 h/d data set. For MPA
and VPA, the absolute estimate of time/d fell as the number
of monitoring hours/day decreased, while the APE increased
greatly. For 10 h/d, the APE was 29.2 ± 25.9% for MPA and
41.7 ± 75.8% for VPA, while for a 13 h/d sample it was 6.4
± 11.2% for MPA and 5.6 ±24,2% (Herrmann etal., 2013).
The other h/d simulations were in between these values.
The absolute differences were about 10 min/d for MPA and
only 2 min/d for VPA; which works out to 21.2% of the
original 14 h/d data for MPA and 36.5% for VPA: quite large
differences. Thus, the number of hours per day considered
to be valid non-wear time makes a large difference in
subsequent estimates of the time spent in MPA and VPA (and
by extension to MVPA).
125
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In an interesting analysis of valid versus invalid
accelerometer data in children and adolescents, Loprinzi et
al. (2014c) found that overweight or obese youth are far more
likely (60% more so) to have invalid data than normal weight
youth, thus introducing another bias in accelerometer data
and limiting its generalizability (Loprinzi et al. 2014c).
Translating Accelerometer Counts into
Activity Estimates.
The "translation" process is most often accomplished
using the manufacturer's proprietary software to categorize
energy expended (as V02 or METS) associated with
accelerometer counts. Manufacturers usually do not provide
their algorithms used to make that conversion, so the method
used is a "black box" for users. Exercise physiologists
employing accelerometers quickly found that there was
wide variability in their PA estimates due to these unknown
translation algorithms (Calabro et al., 2009; Cliff & Okely,
2007; Freedson et al., 1997, 1998; Mark & Janssen, 2011;
Montoye et al., 1996). This has led to a "mini-industry" for
academic researchers in devising regression-based algorithms
to translate accelerometer counts into better EE, METS, or
categorical estimates, such as "moderate" and "vigorous"
PA levels (Griffiths et al., 2012; Leenders et al., 2006; Lyden
et al., 2011; Matthews, 2005; Trost et al, 2011; Trumpeter
et al., 2012; Troiano et al., 2006; Tucker et al., 2003). In
doing so, researchers themselves are forced to operationally
define what constitutes MVPA levels, and then delineate
what accelerometer count limits are used for the MVPA
levels. Most use 3-5.9 METS as moderate PA and >6 METS
as vigorous PA. As we have seen in Section 6, METS limits
themselves are problematic, being based upon an inflated
REE value (3.5 mL/kg-min). The accelerometer count bases
used for the different study MVPA estimates are provided
in Table 24 where available. Many authors do not supply
these data, however, so we have to assume that they use the
manufacturer's translating algorithms.
Another dimension of the translation issue is the difference
among subjects in step frequency, acceleration rate, gait, and
type of locomotion, all of which affect the counts-to-energy
expenditure relationships implicit in the manufacturer's
unexplained accelerometer algorithms. These considerations
certainly affect variability in individual results for similar
activities (Brage et al., 2003a, b). Non-linear relationships
between counts and forward acceleration exist in many
accelerometers, resulting in significant differences in inter-
instrument readings—about a 20% COV—for specific
motions in a mechanical test not involving humans wearing
an accelerometer (Brage et al., 2003c). There are numerous
studies that develop regression equations to "correct" for
acceleration and other differences in specific accelerometers;
for example, Brandes et al. (2012) and Calabro et al. (2009).
There are scores of these regression papers, "correcting" the
manufacturer's built-in algorithms to better predict energy
expenditure for a set of activities. Calabro et al. (2009)
developed their own algorithms that dropped the overall
average error of the Sense Wear Pro accelerometer estimates
versus treadmill results from 32% to 1.7%; however, error
improvement was activity-specific. It was -20.7% for rest,
-4.0% for coloring, -4.9% for playing computer games,
between -0.9% and +3.5% for walking on a treadmill at
3 different speeds, and -25.7% for biking (Calabro et al.,
2009). However, only the new biking algorithm produced
statistically significantly different results compared with the
built-in algorithms.
Because of the translation issue, there are a number of
clinical studies testing the various algorithms that have been
devised to translate accelerometer counts/minute (CPM)
into estimates of EE, V02, and PA categories. We are only
interested in the PA categories issue here. Some of these
studies follow.
One study developed its own unique counts/min algorithms
based on observing 7 y old subjects undertake MPA
activities and then averaged the cutpoints over their sample
(Sarzynski et al., 2010). In general, the relationship between
accelerometer counts and energy expenditure (and/or) METS
is specific to each individual and is non-linear; for this
reason, mean count values are used for age/gender-specific
cohorts (Tudor-Locke et al., 2011). It has to be realized,
however, that any cutpoint designation is a compromise for
any activity over a set of subjects.
An analysis of six alternative cutpoints used to define
MPA and VPA for a single accelerometer—an ActiGraph
GT1M—was undertaken by Crouter et al. (2013). The
lower bound cutpoints for four studies, two of which used
alternative algorithms from a single first author group are
reproduced below.
These cutpoints are highly variable for a single
accelerometer model.
Lower-Bound Cutpoints
(Counts / min)
Citation/Algorithm
MPA
VPA
Crouter (2006)
Walking/running
algorithm
Lifestyle algorithm
Crouter (2010)
Walking/running
algorithm
Lifestyle algorithm
NHANES
Mathews (3
papers)
1,588
388
297
61
2,020
760
6,774
2,826
1,126
445
5,999
5,725
126
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Freedson et al. (2005) reported an earlier evaluation of
alternative outpoints for the ActiGraph 7164, as well as the
RT3 and Actical acccelerometers. They provide the following
alternative outpoints for "youth" (children and teens) MPA
and VPA criteria for the ActiGraph 7164 monitor based on
either V02 or EE criteria.
Sample Age MPA VPA
Paper Size (n) Range Criterion (CPM) (CPM)
Treuth et
al.2004
74
13-14 VC>
3000 5200
Puyuaetal. Energy
2002 Exp.
Eston et al.
1998
30
8-10 VC>
500 4000
As can be seen from the above two tables, there is a lot of
variability in cutpoints for both the Crouter et al. (2006,
2010) and Freedson et al. (2005) papers, even for narrow age
ranges. Wide variability is also seen in Crouter et al. (2013).
Guinhouya et al. (2006) state that the various accelerometer
cutpoints used by exercise physiologists are inconsistent
and biased. Loprinzi et al. (2012c) provide an excellent
overview of the cutpoint differences used by researchers and
how they affect estimates of MVPA in children and adults. It
probably is the most comprehensive study of its type in the
literature. Another good review of how epoch length affects
MPA and VPA estimates is contained in Sirard et al. (2011).
They investigated 5 different "data reduction algorithms"
(24 different possible combinations), and found that only a
handful would not produce statistically significant differences
in MPA estimates. They repeated the analysis for VPA for
a different study time period, using a repeated measures
general linear modeling approach, and found a similar result
(Sirard et al., 2011).
In another analysis, four academic research groups devised
accelerometer count "cutpoints" for MPA and VPA for the
Actigraph GT1M uniaxial model (Actigraph LLC. Pensacola
FL) using regression analysis of treadmill and indirect
calorimetry data. For a discussion of cutpoint differences
for the GT1M and a comparison of them with output from
another accelerometer (the Kenz Lifecorder), see Abel et
al. (2009a). Data from this paper are not contained in Table
24 since results were not provided by gender, but they are
instructive concerning how much variability is seen in
accelerometer performance by the academic community.
One-minute accelerometer cutpoints—"translated" into
CPM~varied by an order-of-magnitude among the four
groups for the same model accelerometer! Data from the Abel
(2009a) paper are reproduced here.
Accelerometer:
Algorithm
MPA CPM Time
Cutpoint (Min/d)
VPA
CPM
Cut- Time
point (Min/d)
191-7525 245±106 >7526 260±102
574-4944
129± 70
>4945
155±68
1952-5724
40± 24
> 5725
60±31
2191-6892
39± 27
>6893
55±31
3285-5676
39± 24
> 5677
60±31
29± 22
52±34
GT1M:
Hendelman et
al. (All)
Swartz et al.
Freedson et al.
Hendelman et al
(Walk)
Nichols et al
Kenz
Lifecorder
Another study that compared accelerometer counts to
activity-specific oxygen consumption measures depicts the
same wide range in the relationships as noted above. The
study is Evenson et al. (2008) using 5-8 y old subjects. A
table in their paper includes data from other studies that
developed MPA and VPA cutpoints for children aged 3-16
y old. Two accelerometers were used: the ActiGraph 7164
(predecessor of the GT1M) and the Actical, which despite
the similar name, is made by another company. Aversion
of their table follows; in the "activities included" column,
play is P, other is O, R is run, and walk is W; CPM is counts
per minute.
Accelerometer/
Algorithm
Activities
Included
MPA CPM
Cutpoint
VPA CPM
Cutpoint
Subject Ages
(y)
Sample
Size
7164:
Puyua et al. (2002)
W, R, O
3200-8199
> 8200
6-16
26
Sirard et al. (2005)
Sit, P, W, R
615-1230
> 1231
3
5
As above
812-1234
> 1235
4
5
As above
891-1254
> 1255
5
6
Pate et al. (2006)
W, R
420 - 841
>841
3-5
29
Evenson et al. (2008)
W, R, O
574-1002
> 1002
5-8
33
Actical:
Puyua et al. (2004)
W, R, O
1500-6499
>6500
7-18
32
Pfeiffer et al. (2006)
Sit, P, W, R
715-1410
> 1415
3-5
18
Evenson et al. (2008)
W, R, O
508-718
>719
5-8
33
127
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A wide difference in the estimated number of MPA, MVPA,
and VPA minutes/day is seen depending upon the count
cutpoints used in a mixed-gender sample that also carried
a portable V02 indirect calorimeter. The study period was
between 5-6 h in a field setting (Strath et al., 2003a). The
following results were obtained for activities classified
as MPA and VPA according to the "standard" 3.0-3.59 /
>6.0 METS categories using 5 different cutpoints from
the literature.
Ratio
Monitor/ Ratio to
Cutpoint Min of to Indir. Min. of Indir.
Distinctions MPA Calor. VPA Calor.
Indirect
64
±41
6 ±8
Calorimetry
Freedson
(1998)
26
±19
0.41
10 ± 18
1.67
Hendelman#1
(2000)
141
±48
2.19
6± 14
1.00
Hendelman#2
(2000)
26
±15
0.41
7± 14
1.17
Nichols (2000)
29
±17
0.45
10 ± 18
1.67
Swartz (2000)
82
±39
1.28
11 ± 18
1.83
None of the count cutpoint approaches are particularly
accurate for either MVPA category compared with indirect
calorimeter measures (the ratio of the accelerometer min/d to
indirect calorimetry is shown as "Ratio to Indir.Calor.).
Another interesting comparison of cutpoint algorithms is
contained in Alhassan et al. (2012). They looked at both the
GT1M Actigraph and Actical accelerometers in a clinical,
treadmill study of children 8-16 y. Four algorithms were
evaluated for the GT1M and 3 for the Actical. None of them
were used in the Abel et al. (2009a) paper discussed above.
Their emphasis was on comparing EE estimates using the
algorithms/models and not on the cutpoints for MPA and
VPA per se. However, Alhassan et al. (2012) provided
cutpoint estimates in counts/min for three situations:
MPA
VPA
(CPM)
(CPM)
Actigraph GT1M: Treuth et al.
3200-8199
>8200
Trost et al.
3000-5199
>5200
Actical: Puyua et al.
1500-6499
>6500
Alhassan et al. (2012) also provide regression equations
to relate METS (in kcal/min) to accelerometer counts/
min for the seven model/algorithm cases studied. Most
of them are linear, but one is non-linear (the Puyua et al.,
2002 algorithm); one of the equations includes body mass
as an independent variable along with counts, and another
is based on age and an age/CPM "interactive" term. Thus,
there is variety in what the both the form and content
of the regression equations evaluated. A similar finding
has been reported by Balogun et al. (1989). Alhassan et
al. (2012) calculated the Kappa statistic to quantify the
statistical agreement between observed METS estimates
and treadmill kcal. The Kappa coefficient (K) represents the
precision of two categorizations; it has the same form as
a Spearman correlation coefficient and may be interpreted
as the percent of agreement attained given the amount of
agreement obtained by chance alone (Viera & Garrett, 2005).
For moderate PA, K varied from -0.01-0.34 for the four
Actigraph/algorithm combinations; K's of this magnitude
are considered to have poor-to-slight agreement. The K's
using the same four combinations for vigorous PA were
0.03-0.55, and three of the combinations were considered to
have slight-to-fair agreement. The Actical K analysis showed
better agreement for both moderate and vigorous PA. The K's
for the three Actical model/algorithms ranged from 0.14-0.26
for MPA and 0.27-0.34 for VPA. These agreements can be
considered to be slight-to-fair (Viera & Garrett, 2005).
Bornstein et al. (2011a, b) explicitly discuss the problems
of different accelerometer count cutpoints used to identify
MVPA levels in pre-school children. In a secondary data
analysis of 5 different studies focused on children aged 3-5
y using the ActiGraph 7164 monitor, the authors applied
the various cutpoints and obtained estimates of MVPA
that ranged from 40-269 min/d (Bornstein et al., 2011b).
They then developed regression equations to relate count
data for the ActiGraph accelerometer from one study to
the other 4 using linear regression equations: 50 different
equations in all! (There were so many because there were
2-3 equations for each study depending on the independent
variables included in the regressions. All included the MVPA
cutpoint for each study—either directly or as MVPA2 or
MVPA0 5—and also may have included age of the subject and
accelerometer wear time/day as independent variables. Even
so, the absolute percent error of the equations when taken in
pairs varied between 6.4% and 38.4%, with the median error
being 15.8% (Bornstein et al., 2011b).
With count differences like those shown above for the same
model accelerometer, a user has to wonder what really is
being measured. When accelerometer counts are translated
into minutes/day of MPA and VPA, very large differences
are seen in MPA (but smaller differences for VPA) using
the various algorithms. The potential for wide differences
in time spent in moderate and vigorous PA can be large. It
is results like these that led to my decision to only provide
accelerometer model-specific results in Table 24 for daily
time spent in MVPA. Other than undertaking the type of
analyses done by Bornstein et al. (2011b), there simply is
no rational way to reconcile the various cutpoints used to
128
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estimate time spent in PA at any level. The user, therefore,
has to be skeptical about some of the estimates presented
in Table 24. Many of the MVPA estimates in the Table
are considerably higher than those recorded in the United
Kingdom, for example, for similar age/gender samples using
the same accelerometer model (e.g., Davis & Fox, 2007).
Besides time spent in MPA and VPA, accelerometer data
often are provided for METS intervals. Abel et al. (2011)
relate accelerometer-based step rates to METS using a
"best fit" non-linear regression approach. Their equation for
females is:
METS = 0.00008048 * (Step Rate)2 288 R2 = 0.91
And for males it is:
METS = 0.00004325 * (Step Rate)2 4528 R2 = 0.79
The METS estimates were obtained from 19 subjects who
were young (29 ± 7 y) and recreationally active. Individual
resting V02 was used to calculate the step-specific METS
estimates, so the problematic use of the 3.5 ml kg1 min1
usually used by exercise physiologists as basal metabolic
rate—and the basis for the Compendium's METS estimates-
-is not an issue with their work. (The measured resting V02
was not too different than 3.5 ml kg1 min1, by the way. It
was 3.6 ml kg"1 min1 for females and 3.3 ml kg"1 min1 for
males. That is usually not the case; see McMurray et al.,
2014.) Abel et al. (2011) did not provide estimates of the
daily time spent in MPA and/or VPA so their data also do not
appear in Table 24.
Intra-Day Patterns of MVPA
Accelerometers can provide time-of-day PA data if the
time pattern of counts/min is preserved when the data are
downloaded. The information is there, but often is not
analyzed. A few intra-day analyses have been published;
one is for preschool children attending daycare (Van
Dauwenberghe et al., 2012). They found that for both
genders, MVPA is generally highest during the mid-afternoon
to early evening time period. MVPA is generally quite low at
other times. For school-age children, significant amounts of
MVPA occurs during outdoor recess and lunch periods where
the students can go outdoors, but it is still less than that
occurring after school. In a study of children in grades 6-8 in
two different types of schools (rural public and private), the
percent of daily MVPA occurring at school varied from an
average of 13.3% for males in public school up to 35.1% for
males in private school. For females, the proportions were
20.0% for rural public schools and 18.8% for private schools.
Thus, the pattern for the percent of MVPA by school location
is quite variable by gender and type of school. The actual
minutes/d recorded in this study appears in Table 24. A non-
USA study looking at intra-day variability of objectively-
measured PA is Verbestel et al. (2011); that paper is worthy
of review. Additional intra-day information on MVPA using
accelerometry appears below under locational considerations
and sports participation information.
The next few subsections of this report provide information
on using accelerometers to address various aspects of
participating in MVPA activities, such as day-of-the week and
seasonal effects, the impact of sports on MVPA levels, and
the locations where MVPA activities occur. While these are
all important factors to consider in trying to model MVPA,
there is surprising little information on many of them. One
study that does so is an EPA-funded analyses of data obtained
by Harvard University (Arroyo, 2000). The TriTrac R3D
accelerometer was used to estimate (among other tilings) the
percentage of time during a 4-day (maximum) monitoring
period in the Winter/Spring season that youth aged 11-13
spent in moderate (3.0-6.0 METS) and vigorous PA (>6.0
METS). Subjects were 251 students randomly selected from
10 schools in the Boston area (Arroyo, 2000). Only the data
for the 0700-2200 time period were analyzed, and only for
those days with <40% zero accelerometer counts. This study
provides detailed information for a number of variables, such
as day-of-the week, obesity status, and ethnicity. An abstract
of these data appeared below in Table 25. The gender, season
(February/March v. April/May), weekday v. weekend, and
day-of-the-week differences in percentage of time spent
in MPA were all statistically different (using the F-test at
a=0.05) withp vales all <0.0025. Most articles are not as
synoptic as the Arroyo (2000) article, providing data on some
of these issues but not others.
Longitudinal (multi-day) and Day-of-the-Week Effects on
MVPA Estimates.
One study that provided individual daily data was Almeida
et al. (2011). They were interested in determining how many
days of data were needed to adequately describe weekly
average energy expenditure in a sample of women with
rheumatoid arthritis (aged 50-60 y). It turns out that four
days of accelerometer monitoring were needed to predict
with 95% confidence 84% of the weekly variability in PAEE
>3 METS, which most exercise scientists use as the low
end of moderate PA. The authors also present data on the
percentage of DTEE accounted for by MVPA >3 METS.
The daily percentages varied from 9.6% (Monday) to 13.0%
(Thursday). The weekly average DTEE accounted for by
>3 METS activities was 11.4% (Almeida et al., 2011). If we
assume that a MPA METS of 3 would occur only during an 8
h period of the day, it works out to be about 27 min/d, which
does not seem to be too bad of an estimate.
Cook et al. (2012) also report daily MPA and VPA
measurements for a 7-day period; there was little difference
between weekday and weekend time spent in these levels
of physical activity for adult minority females. Shen et al.
(2013) found the same result in pre-school children across
two seasons of the year. Their two-day accelerometer study
found that the COV was 21% for time spent in MPA (Finn et
al., 2002). A third multi-day study that reported daily MVPA
frequency (but not min/d) data is Janz et al. (1995). They
found little daily variation in MVPA frequency in individuals
over the 6 days. Coleman & Epstein (1998) state that 3-4
days of accelerometer monitoring is needed in sedentary
males to capture weekday/weekend differences.
129
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Buchowski et al. (2004) is a study that reported aggregated
MVPA data on a weekday/weekend basis; adult males expend
more time in MVPA on weekends than on weekdays (about
3.4% more), but there is little weekend/weekday difference in
adult females: only 0.2% fewer minutes/d of MVPA for them
on weekends (Buchowski et al.. 2004; graphical data). Farr
et al. (2008) reported a similar finding. As usual, however,
there is contrary evidence in PA studies. Corder et al. (2013)
found a statistically significant difference in adult MPA
for both genders between weekdays and weekends. So did
Treuth et al. (2007), which reported statistically significant
MVPA differences between weekdays and weekends for both
normal-weight and overweight 11-12 y old girls. Weekdays
had more MPA and VPA minutes than weekends. The
reduction in weekend MPA time was -36% and -25% for
VPA for the group as a whole, and for each weight cohort
singly. The biggest differences in the weekday/weekend
MVPA times occurred in the morning (6:30-10:00 am) and
early afternoon (2:00-4:00 pm) periods (Treuth et al., 2007).
The data from this study appears in Table 24.
Long et al. (2013) analyzed data from the accelerometer
monitoring portion of the 2003-2005 NHANES survey for
those years. Their paper only describes analyses undertaken
on weekdays, but distinguishes between schooldays and
not, and by time-of-day on schooldays. Summary data
from their paper appear in Table 24. Males and females of
both age groups evaluated (6-lly and 12-19y) participated
in MVPA activities more on school days than on a non-
school weekday: the percentage differences were between
11.2-19.8% among the four age/gender groups (Long et al.,
2013). A difference between school- and non-school days
during the week has also been found by Panter et al. (2011)
and Ridgers et al. (2006).
One of best analyses of daily differences in MVPA time that
I came across is Metzger et al. (2008). In their classification
of the weighted adult population into 5 "natural classes,"
there were distinct daily patterns seen in the NHANES data
of 3,462 people aged 20 y or more. When total mean daily
MVPA is considered, there was very little daily difference
in time/d for the two lowest classes totaling 88.7% of the
Table 25. Time spent in MVPA categories from ARROYO (2000)
Estimated
% of Time Spent in Time
Sample MPA 3.0-6.0 METS (min/d)
Category Size (n) Mean SD Mean SD
Females 107 3.41
Estimated in VPA >6
METS
Males
Weekday
Weekend
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Obese
Non-obese
February
March
April
May
144
183
73
19
3
5
45
111
73
62
177
43
119
54
40
6.21
5.16
4.61
4.96
6.23
4.46
5.24
5.17
4.61
4.85
4.8
4.97
5.03
4.51
5.64
SD
2.31
3.91
3.87
2.70
3.53
2.52
3.94
5.05
3.43
2.70
2.56
3.24
2.99
4.02
2.95
3.57
30.7
55.9
46.4
41.5
44.6
56.1
40.1
47.2
46.5
41.5
43.7
43.2
44.7
45.3
40.6
50.8
20.8
35.2
34.8
24.3
31.8
22.7
35.5
45.5
30.9
24.3
23.0
29.2
26.9
36.2
26.6
32.1
Mean
0.09
0.23
0.18
0.16
0.10
0.36
0.23
0.33
0.12
0.16
0.14
0.54
0.16
0.18
0.16
0.17
SD
0.21
3.91
0.53
0.29
0.19
0.40
0.19
0.95
0.28
0.29
0.26
0.04
0.03
0.60
0.33
0.37
Mean
0.8
2.1
1.6
1.4
0.9
3.2
2.1
3.0
1.1
1.4
1.3
4.9
1.4
1.6
1.4
1.5
Time (min/d)
SD
1.9
35.2
4.8
2.6
1.7
3.6
1.7
8.6
2.5
2.6
2.3
0.4
0.2
5.4
3.0
3.3
Source: Arroyo (2000). "A preliminary analysis of children's physical activity data." (Project report). Cambridge MA: Harvard
University.
130
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population. The next two highest classes, constituting about
20.3% of the weighted population, had a moderate drop-
off of MVPA on the weekend. The highest class, however,
had a major drop-off of MVPA on the weekend, especially
on Sunday. They constituted only 0.9% of the weighted
population. Thus, very active people during weekdays had a
major reduction in MVPA time on weekends.
When MVPA time was classified into 10-minutes "bouts,"
an interesting change in the activity patterns occurs. While
MVPA time/d estimates fall for all population classes due
to the longer bout period (consecutive minutes of MVPA
time), there appears to be a "weekend warrior" pattern in the
third-lowest population class, the only one to increase MVPA
time/d on both Saturday and Sunday, particularly Sunday
(Metzger et al., 2008). They represent about 1.8% of the
total weighted population. This finding indicates that there
are complex patterns with respect to MVPA time/d in the
population on a daily basis and by subgroup. These patterns
will be difficult to replicate in our exposure models, although
the "D & A" approach used in our recent models could
possibly mimic some of them.
Mean Daily Estimates of MVPA and VPA
by Day-of-week
MVPA (min/d)
VPA (min/d)
Day of Week
Mean
SD
Mean
SD
Monday
25.6
27.3
0.8
3.81
Tuesday
25.4
27.9
0.8
3.62
Wednesday
25.1
28.1
0.8
5.48
Thursday
25.5
28.0
0.8
3.21
Friday
24.6
26.5
0.7
3.51
Saturday
21.4
25.0
0.7
4.51
Sunday
19.4
23.5
0.7
4.22
Data from the Metzger et al. (2008) paper follows for the
entire sample of 3,802 people; the weighted daily mean
min/d for both MPA and VPA are the metrics of concern.
All 5 "natural classes" of the population are included in
these estimates. Note the very high standard deviations for
the daily data, especially for VPA (about 570%)! The mean
estimates do not reflect the "class" differences noted above,
or the "weekend warrior" phenomenon either.
Another analysis of MVPA participation indicates that there
was a weekday/weekend difference in the percentage of the
sample undertaking >30 minutes of MVPA (Patnode et al.,
2011). The 720 subjects with an average age of 14.7 y and
in grades 6-8 were found to participate in >30 min of MVPA
more on weekdays than weekends in both genders ( : 30.9 v.
14.8%; c?: 46.lv. 31.2%).
An interesting study of MVPA participation by parent/child
pairs on non-school days found low levels of MVPA in both
groups and that only 16% of MVPA occurred when the child
and parent were in the same location (Dunton et al., 2012).
Rarely does a study provide the participation rate (percent
of the population studied) for MPA or VPA. One study that
does is Marquez et al. (2011). Using a GT1M accelerometer,
they evaluated MVPA in 148 Latino adults living in
Chicago. One-hundred percent of their sample participated
at least once in MPA during the 7 day study; however, that
percentage decreases to 88% of males and 63% of females
for VPA (Marquez et al., 2011). Data from this paper appears
in Table 24. In a study of adults aged 70-89 y, 19-22% of
the sample participated in MVPA at least once per week
(Pruitt et al., 2008).
It should be noted that pedometer studies also generally show
a higher number of steps on weekdays than on weekends, so
there is consistency between the two types of PA monitors
(Pelclova et al., 2010). There has been found a difference
among days whether or not MVPA goals are achieved, so
day-of-the-week is an important consideration in MVPA
compliance (Moore et al., 2014).
Seasonal and Weather (Temperature and Precipitation)
Impacts on MVPA
There obviously is a correlation between where MVPA
occurs and weather/seasonal considerations (Suminski et al.,
2008). Reviews of seasonality in PA, using accelerometer,
pedometer, and questionnaire data indicate that PA (and
MVPA) vary with seasons (Shephard and Aoyagi, 2009;
Taveras et al., 2005). Obtaining precise estimates of these
differences using accelerometer data, however, is difficult to
find. Buchowski et al. (2009) evaluate accelerometer-derived
time in MVPA for middle-aged females using a TriTrac R3D
model and state that there are "larger seasonal differences
during weekends than weekdays" (p. 258). They also found
more variability in MVPA among the seasons on weekends
compared to weekdays. They state that their results confirm
Pivarnik et al. (2003)'s findings (Buchowski et al., 2009). A
United Kingdom study found that overall PA increased as
daylight time increased after adjusting for rainfall; out-of-
home play accounted for 50% of this increase (Goodman et
al., 2012a). Daylight time MVPA also was investigated in
Winkler et al. (2005).
Fisher et al. (2005) described a two-season study (spring
& summer) that found relatively small—but statistically
significant—differences in MVPA behavior for the two
seasons. Using an Actigraph GT1M accelerometer, Hopkins
et al. (2011) found that all levels of PA decreased by 17
min/d from summer to fall. In another longitudinal study of
MVPA in 294 youth aged 10-17 y living in Portland, average
monthly temperature explained the most variability in MVPA
time for males, but not females (Patnode et al., 2010). Female
MVPA apparently was more affected than male MVPA by
both temperature and precipitation. This pattern was also
seen by Brodersen et al. (2005) in England. Shen et al. (2013)
found that there were statistically significant differences in
pre-school children's MPA after-school time between fall and
131
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winter for both girls and boys, but only on weekends. They
did not find a seasonality effect—or a location effect-on VPA
either at school or afterward. MVPA time for all locations and
day-of-the-week were less in winter than in the fall (Shen et
al„ 2013).
Pedometer studies of PA also indicate a seasonal and monthly
effect on the number of steps taken by adolescents, both in
this country and in Europe (Pelclova et al., 2010). However,
Rich et al. (2012) state that there currently is insufficient
evidence from accelerometry studies to conclude that
significant seasonal differences in PA is occurring.
Locational Aspects of MVPA
Only a few accelerometer studies disaggregate MVPA by
location. Those that do have shown that MVPA is generally
higher outdoors than indoors (Burdette et al., 2004; Sallis et
al., 2000; Wheeler et al., 2010), and higher during daylight
hours, on days with mean daily temperature between 20-75
°F, and on days with little or no rain (Feinglass et al., 2011;
Harrison et al., 2011). Welk et al. (2000) state that time
spent outdoors is strongly predictive of MVPA (and overall
physical activity) in children. Tran et al. (2013) provide
pedometer data that support this observation. They state that
outdoor recess resulted in more MVPA steps/period than
indoor recess, and the differences are statistically significant
at p<0.0001 (Tran et al., 2013).
A study of children aged 6-11 y found that the outdoor and
neighborhood locations had the highest average percent of
time at MVPA than any other location (Kneeshaw-Price et
al., 2013). Boys had slightly higher percentages of time spent
outdoors than girls, and children of both genders aged 6-8 y
had higher percentages than children 9-11 y. Around 43-49%
of time spent outdoors or in the neighborhood by children
(both genders) was spent in MVPA, while 31-41% of time
spent in the neighborhood was at that level (Kneeshaw-Price
et al., 2013). In a study of pre-school children in Malmo,
Sweden and Raleigh NC, the Swedish kids spent more time
outdoors (47% v. 10%), but MVPA counts were significantly
higher outdoors than indoors in both locations (Raustorp et
al., 2012). Children aged 10-12 had higher activity counts
outdoors than indoors (Stone & Faulkner, 2014). In middle-
aged adults participating in an accelerometer/GIS study, the
percent of time spent in MVPA activities away-from-home
is 5.6 times higher than that incurred at home, and about 35
times higher [sic.] on weekends than on weekdays (Ramulu
et al., 2012; n=35, mean age=38 y, both genders).
An interesting study that looks at the locational aspects
of MVPA, as well as seasonal effects, is Oreskovic et al.
(2012). The study involved 24 middle-school children in
Massachusetts for 3 seasons, and included 5 weekday and
2 weekend days per season. MVPA data were monitored
with an Actigraph GT1M accelerometer and a Forerunner
201 GPS mapping device, measuring locations at a 1 minute
interval. Results of their MVPA monitoring follow; there was
no MVPA time recorded in "car," another specific location
that was included (thank goodness!).
Percent of Total Daily MVPA Time
by Location and Season
Location Winter Spring Summer
33.8 12.3
8.8
5.1 10.2
8.5 57.4
43.8 11.1
0.0 9.0
Note: - is <0.1%
The locational impacts on MVPA in school or out seems
to depend upon gender; after-school MVPA is higher than
in-school MVPA is females, but not in males (Panter et
al., 2011). Recess time spent in MVPA seems to be highly
variable, both within an individual school and among a set
of schools in the same general area. In a "Ready for Recess"
study of 393 children aged 8-1 ly attending 12 schools that
emphasized active recesses, individuals spent between
15-63% of recess time at MVPA levels, but this was highly
dependent upon the school itself; the mean time spend
at MVPA at the various schools varied between 12-33%
(Saint-Maurice et al., 2014). Wheeler et al. (2010) found
that children aged 10-lly in Bristol, UK spent 13% of their
time outdoors, which accounted for 35% of their total MVPA
time. Most of the time spent outdoors (85%) was not in a
"greenspace" (park or other location).
Using American Time Use Survey data, Dunton et al. (2012)
report that there is a gender difference in where MPA and
VPA occurs: MVPA occurs more frequently and for a longer
duration at "someone else's house" (55%) and school (64%)
for boys, and "outdoors" (54%) and at home (49%) for girls.
Participation in Sports and Recreational Activities
We know from observational and clinical studies that
accelerometer counts are correlated with both heart rate
monitoring and V02 consumption (Coe & Pivarnik, 2001).
Thus daily accelerometry counts should be higher in people
who participate in sports and high-intensity recreational
activities than those who do not. That is seen in the data.
People who participate in high-intensity sports and physical
activities have many more min/d of MVPA than does the
rest of the population regardless of gender or age. Between
92-97% of youth (both genders) aged 7-14 that participate on
soccer teams have more than 30 min/d in MVPA just in that
activity (Leek et al., 2011). A goodly percentage even reaches
60 min/d of MVPA just in that sport: around 30% (Leek et
Home 43.1
School 12.3
Indoor Other 15.4
Park/Playground
Street/Walking 19.2
Outdoor other &
unknown
11.0
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al., 2011). The percentage drops rapidly, however, for less
intense sports; only 50-75% of youth playing organized
baseball get 30 min/d of MVPA in that sport. There is not
much difference in these percentages when the samples are
disaggregated into two age groups: 7-10y and ll-14y. The
importance of organized sports contributing to MVPA in
males, but not in females, is also discussed in Patnode et al.
(2010) and Sallis et al. (2000).
A study that evaluated the percentage of time spent in two
sports activities designed to elicit cardiovascular fitness gains
in middle-school girls using the TriTrac R3D accelerometer
indicates that MVPA was attained about 56% of the time
spent participating in sports activities (Arnett & Lutz, 2003).
Specifically, the 95th CI of time spent at MVPA for soccer
was 52.6-61.0% and 51.2-59.6% for field hockey regardless
of skill level of the girls involved. There was a definite
monotonic trend in this CI by skill level, with the highest-
ability level girls spending more time in the MVPA interval
than those of lower ability levels (Arnett & Lutz, 2003).
Wickel & Eisemnann (2007) looked at the relative and
absolute impact of organized sport on total daily PA and
MVPA in 113 6-12 y boys. The GT1M accelerometer was
used, so swimming activities were excluded from the study.
The average monitoring period was 811 min/d. Sports played
included soccer, basketball, and flag football. Participants
averaged 110 min/d in MVPA, 40 min/d of it in VPA. There
were differences in MVPA time among the different sports.
When PA time was disaggregated into time spent in organized
sports, PE class, and recess, sports accounted for 26% of
MVPA, while PE class and recess accounted for 11% and
16%, respectively. The remainder of MVPA time (47%) was
attributed to "unstructured activities" (Wickel & Eisemnann,
2007). There was a significant difference in MVPA time
between a "sport day" and a "non-sport day," as might be
expected: 125 min/d versus 95 min/d.
A study whose MVPA data do not fit into the Table 24
format is Davison & Jago (2009). It is a longitudinal study
of 96 girls who have MPA and VPA on a min/d basis when
they were 13 and 15 y old. The reason why their data do
not fit is that it applies only to girls who were active at age
13 y, meeting at least 30 min/d of MVPA originally. (We
have called these people "doers" in the past.) They were
disaggregated into two groups: those that maintained at least
that level of MVPA (n=24; 25% of the original sample) and
those that did not (n=72; 75%). The time spent per day (in
minutes) in MVPA for these groups of girls appears below:
The differences between the two groups are statistically
different at p<0.01 for both ages and PA levels. Obviously,
the maintainers were more active at age 13, and did not
reduce MVPA much over the two year period. Questionnaire
studies of longitudinal "tracking" of MVPA show much
the same pattern: there is gradual fall off in time spent
Maintained PA (n=24) Decreased PA (n=72)
Age MVPA VPA MVPA VPA
13 45.7 ±13.0 5.9 ±3.7 32.2 ±12.9 3.6 ± 3.6
15 44.1 ±12.9 5.0 ±4.3 20.3 ±10.5 1.5 ±1.9
in MVPA in most everyone, but it is less in the more
committed individuals (DeBourdeaudhuij et al., 2002).
Another analysis of MVPA tracking in children aged 10-12
also showed moderate correlations of MVPA over the years
(Dencker et al., 2013).
Health and Other Impacts on MVPA
People having health problems, intellectual disabilities, or
are obese usually have lower accelerometer counts than the
general population (Coleman et al., 1997, 1999; Cook et al.,
2012). MVPA has been shown in accelerometer studies to be
inversely proportional to adverse health conditions (COPD:
Loprinzi et al., 2014d; diabetes: Loprinzi et al., 2014b; and
the hearing-impaired elderly: Gispen et al., 2014). MVPA
is inversely related to a person's weight status (Kitzman-
Ulrich et al., 2010; Treuth et al., 2007). A study of adults with
intellectual disabilities indicates that MVPA is lower for them
than for normal-functioning individuals; the study also shows
that MVPA is less in overweight/obese (BMI > 25 kg/m2)
than non-overweight adults (Barnes et al., 2013; Dixon-Ibarra
et al., 2013). Ethnicity and type of house that the subjects
resided in were not important correlates of MVPA. Valid data
for this study was defined to be >8 h/d of non-zero count
data for 4 or more of the 7 d monitoring period; MVPA was
defined using the Freedson et al. (1998)/Matthews et al.
(2002) count criteria. Only 23.7% of the sample with "valid"
accelerometry data met the recommended PA Guidelines
(Barnes et al., 2013).
A study of adolescents with fibromyalgia (Kashikar-Zuck et
al., 2010, 2013) provides unique data for the ratio of 5-min
peak-to-mean activity levels for the sample of 104 boys &
girls aged 14.9 ± 1.8 y. The ratios were 19.0 for girls and 16.0
for boys, indicating that there is a fair amount of temporal
variability in accelerometer count measures for 5 minute
averages in the adolescent population. This ratio is probably
even higher in healthy youth as fibromyalgia is a chronic
pain condition associated with impaired physical functioning
(Kasikar-Zuck et al., 2010). Note that ratios of this magnitude
are similar to METSMAX-to-METSsrrriNG ratios seen in the
literature.
An analysis of 2003-2006 NHANES accelerometer data for
pregnant women indicates that the trimester of pregnancy did
not significantly affect time spent in MPA, MVPA, or VPA
activity levels using "Swartz MVPA count cutpoints", but
so did for the 2nd/3rd trimesters using the "Triono cutpoints"
(Evenson & Wen 2011). This finding highlights the
importance of count cutoffs in affecting findings of a study
(see above).
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A number of studies have investigated the effects that
menopause lias on time spent in MVPA. If data from these
studies are compatible with the format of Table 24, their
information appears in it. However, many of these studies
have data that do not fit into the Table's format. According
to Sternfeld et al. (2005), over 60% of women coming into
or finishing menopause have >60 min/d or MPA. Since their
ages were in the range of 48-55 y, these proportions are quite
high. Average minutes of MVPA estimates were not provided.
Readers interested in additional information on the impact
that health-related conditions has on MVPA should consult
the work of Sternfield et al. (1999, 2002, 2005), Bonen et
al. (1981), Harlow & Matanoski (1991), Kuslii et al. (1997),
Melzer et al. (2010), and Stevenson (1997). An interesting
overview of physical activity during pregnancy is contained
in Artal (1992).'
Ethnicity does not seem to affect MVPA levels significantly.
A 7 d accelerometer study found that black/while ethnicity
did not affect time/d spent in MVPA (Dong et al., 2010).
Number of days needed to adequately characterize MVPA
Variability in MVPA over days leads to the related issue of
ascertaining how many days of data are needed to reliably
characterize a cohort's mean/SD MVPA statistics given
the inherent variability in individual daily MVPA time.
Addressing this issue depends largely on the pattern of
daily PA in a group of subjects, but it also is affected by
systematic and random sampling error in the cohort sample
itself (Baranowski et al., 2008) and by which monitoring
method is used for determining MVPA. The Baranowski
article discusses inherent variability in MVPA and how many
days of data are needed to describe (with a known degree of
confidence) grouped physical activity levels given both intra-
and inter-individual variability in participation rates.The
largest source of MVPA variance estimates in healthy adults
as a whole is due to inter-individual variability (55-60% of
total variance), but intra-individual variability accounts for
30-45% of total variance (Matthews et al., 2002). In their
sample, only 1-8% of total variance is attributed to day-of-
the-week variability.
Multi-day accelerometer data rarely are reported on a daily
basis; only MPA and/or VPA time/d averaged over the entire
monitoring period is reported. Papers that analyze how
many days of data are needed to "adequately" estimate the
mean time spent in various categories of physical activity
usually use the ICC statistic and the Spearman-Brown
Prophecy formula to estimate the number of days to attain
a reliability coefficient of 0.8, a common "reliability target"
(Ridley et al., 2009). (We have done so in the past; see Xue
et al., 2004.) When MVPA time is evaluated, the number of
monitoring days needed, for either objective monitors or for
questionnaires, turns out to be between 3-9 days (Ridley et
al., 2009). There are, however, a number of issues associated
with the ICC-based procedure involving addressing inherent
variability versus bias that usually are not addressed well, so
these analyses have to be used with caution (Ridley et al.,
2009). Janz et al. (1995) state that 5 d of accelerometer data
is needed to attain a reliability coefficient of approximately
0.8 for a population-average estimate of MPA and VPA; one
fewer day was needed to attain the same reliability coefficient
with respect to sedentary activity.
At least 5-6 days of accelerometer monitoring is needed
to "minimize the intra-individual variance [in estimates
of MPA and VPA] to a reasonable degree" (Gretebeck &
Montoyne, 1992; p. 1167). Hart et al. (2011) state that 3 days
of accelerometer data are needed to "accurately predict" PA
levels in elderly people aged 55-86 over any 21 day period
using a Spearman-Brown Prophecy formula with a 0.80
reliability coefficient. Using the same reliability criterion,
Kim (2006) states that at least 4 days of monitoring was
required to account for weekday/weekend activity patterns,
as do Kim & Kim (2009) and S-Y Kim & Yun (2009). If only
average daily time spent in MVPA is of interest, then 2 days
of accelerometer data are required (using the ActiGraph-7164
model). One study of variability in PAEE indicates that 4
days of complete accelerometer data would be needed to
predict variability in METS>3 activities with a probability
>84% Almeida et al., 2011).
These findings are different from those reported in Herrmann
et al. (2014), which states that their review of a study by
Jerome et al. (2006) that applied the Spearman-Brown
prophecy (S-B.P) formula to obese subjects having at least
6 h/day of valid data, indicates that 5-6 days of data are
required to adequately address variability in the sample.
A similar theme is struck by Levin et al. (1999), who state
that 6 days of accelerometer data are needed to obtain a
0.80 reliability. According to Penpraze et al. (2006) seven
days of accelerometer data (using the 7164) are required
to attain a reliability of 80%. Hinkley et al. (2012) may
provide the key to understanding these differences in their
study of the number of days of accelerometer data needed to
reliably determine time spent in MVPA; their study focused
on preschool children and also used the S-B.P formula at 3
different reliability levels (0.7, 0.8, and 0.9). They found that
fewer days of accelerometer data were required when the
number of h/d was increased from 8 to 10 (Hinkley et al.,
2010). This is a logical finding.
The papers by Baranowski et al. (1993), Coleman & Epstein,
1998; Janz et al. (1995), Levin et al. (1999), Matthews et al.
(2001), Patterson (2000), Trost et al. (2000), Tudor-Locke
et al. (2005), and Ugrinowitsch et al. (2004) are worthy
of review concerning the question of how many days of
data are needed to reliability characterize physical activity
in the population.
Pedometers
Pedometers are an accelerometer-like step counter that
are used mostly to estimate the amount of walking that a
subject undertakes. They can be used to measure steps and
distances undertaken, and generally are worn on or around
the waist (Bassett & Strath, 2002). Apparently the first
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reported use—in one person—of a pedometer to measure
free-living activity occurred in 1926 (Stunkard, 1960). The
first multi-subject research effort to use modern miniaturized
pedometers to estimate physical activity in infants and
children that I could find is discussed in a 1968 article that
describes a 1959 study of a modified wrist watch to record
movement (RQ Bell, 1968). The wrist-watch pedometer was
placed on the feet of children aged 2 y in a pilot study of 37
children. Another early pedometer study of activity in obese
girls occurred in 1960, and found that they walked as far as
normal-weight girls during a two-week period (Stunkard &
Pestka, 1962).
Some early studies of pedometers and physical activity
used an "actometer" (Eaton & Duroski, 1986; Eaton et al,
1988; Redmond & Hegge, 1985) which does not seem to
be currently available. Studies that compared pedometer
estimates versus oxygen consumption or other measure
of energy expenditure include Eston et al. (1998) and
Hendelman et al. (2000). Reviews of pedometer studies are
contained in Clemes & Bidddle (2013) and Melanson et
al. (2004). Theoretical shortcomings of using pedometers
to estimate daily PA in children (at all activity levels) are
discussed in Eisenmann & Wickel (2005). A "steps/minute
cadence criterion" has been developed to be able to relate
pedometer data to moderate intensity physical activity but
data are rarely reported using it (Harrington et al., 2011,
2012). Reactivity to wearing a pedometer and thereby
biasing its use does not seem to be a problem (Matevey et
al., 2006), although their study may not have been able to
test all aspects of the issue (Beets, 2006).
Pedometers generally are relatively inexpensive and a
number of models exist from a number of manufacturers;
seemingly the preferred model for exercise physiologists is
the Yamax Digiwalker SW-200 due to its reliability (Bassett
& Strath, 2000; Brusseau et al., 2011; Gaydos et al., 2011;
Montoye et al., 1996). Other commercial pedometers that
have been evaluated by U.S. exercise physiologists are
the "Freestyle Pacer," "Eddie Bauer," "L.L. Bean," the
New Lifestyles NL2000, the "Stepwatch", the Omron
HJ-112, the "Stepping Meter," the Sport Brain iStepXl,
and the "Accusplit" (Bassett Jr., et al., 1996; Beets et al.,
2007; Bjornson et al., 2007; Busse et al., 2009; Cadmus-
Bertram et al., 2014; Cavanaugh et al., 2007; Clemes et al.,
2010; Crouter et al., 2003; Dauenhauser & Keating, 2011;
DeCrocker et al.,2006; Dueker et al., 2012; Foster,RC et al.,
2005; McKee et al., 2012; Nunez-Gaunaurd et al., 2013;
Oh et al., 2012; Pettee et al., 2008; Raustorp, et al., 2007;
AM Swartz et al., 2009; Swift et al., 2012; Tudor-Locke et
al., 2004). C. Tudor-Locke and colleagues have probably
written the most articles about pedometer applications on
various age-gender cohorts. See, for example, Tudor-Locke
(2001a & b, 2002, 2005, 2006, 2008). A "free" pedometer
in the iPhone—called iPedometer—exists, but has been
found to be inaccurate (Bergman et al., 2012). There even
are pedometers for the blind that provide information via
voice announcements; these brands/models are: Centrious,
TALKiNG, and Sportline Talking (Beets et al., 2007). A
review and meta-analysis of 26 pedometer studies appears in
Bravata et al. (2007).
While the SW-200 is used a lot (e.g., Bennett et al., 2006;
Fuller, 2000; Kang et al., 2009, 2012), it only counts the
number of total steps taken during an elapsed time period.
Other pedometers can record steps, distances, and an estimate
of EE expended, and save these values by time of day over
a 7-d period (Montoye et al., 1996). These attributes allow
the pedometer to estimate and store steps/minute (SPM)
data which have been shown to be reasonably correlated
with oxygen consumption/energy expenditure level in
subjects (Graser et al., 2009). Thus, the newer and more
sophisticated pedometers purportedly provide essentially
the same type of information as an accelerometer at a far
lower cost. Pedometers have the same "black box" problem
of estimating EE from steps/distance taken as does the
accelerometer's counts-to-EE (Leiper& Cralk, 1991). I
did not find any pedometer study that reported time spent
in MPA or VPA, however defined; therefore, there are no
pedometer data listed in Table 24. If daily step rates are high,
some of them occurred at higher PA levels, but the specific
time spent in MPA and/or VPA is not provided in pedometer
studies to date.
In general, there are significant differences found among
pedometers in estimating both the distances traveled and
the steps taken; there also is a significant amount of inter-
instrument variability among pedometer units for the same
model unit (Bassett Jr., et al., 1996; Beets et al., 2007). Some
of the inter-instrumental variability problem is due to weak
quality control during the manufacturing process, differences
in exactly where the pedometer is placed on the body, tilt
changes during the study, and differential sensitivity to the
type of activity undertaken (Beets et al., 2007). Gait problems
in the elderly hamper the use of pedometers and cause high
error rates when compared to oxygen consumption estimates
of energy expenditure (Cyarto et al., 2004).
There are numerous articles in the literature that
recommend a specific number of steps/d to attain and
maintain a "healthy lifestyle." They are promulgated by
both governmental and non-governmental organizations.
Since these recommendations are not tied directly to time
spent in MVPA, they are not reviewed in detail here.
Some are specific to children (Duncan et al., 2006), to
adults (Tudor-Locke & Bassett, 2004), or to people with
various disabilities. Duncan et al. (2007) state that these
recommendations should be based on percent body fat
measurements—a surrogate for fitness—to be more realistic
and apropos. I have never seen such a distinction made in
articles making pedometer recommendations.
The number of steps/d needed for "healthy body
composition" in 6-12 y children has been found to be 12,000
in girls and 15,000 in boys (Tudor-Locke et al., 2004). Colley
et al. (2012) indicate that children in that age bracket in
Canada recorded between 11,200-15,212 steps/d on average,
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not quite meeting the recommended levels. Canadian step
guidance for children aged 3-5 y is 6,000 steps/d (Gabel et
al., 2013).
In a study of elementary school children in 2nd-5th grades,
a SW-200 was worn during classroom periods, recess, and
PE classes (Barfield et al., 2004). Even though recess had
the shortest duration of the three periods, its mean steps/
minute (SPM) estimates were the highest: 43-45 SPM on
average, slightly more than PE classes, which were ~40
SPM. Classroom time only had 7-8 steps/min (Barfield et al.,
2004). Analyses of the data were not reported to determine if
the observed differences were statistically significant or not.
Steps/d were higher in elementary school children on days
with PE periods of 60 min in duration than those having 30
minutes of PE or on non-PE days (Dauenhauer & Keating,
2011). However, about half of PE class time is spent in fairly
sedentary activities (Fuller et al., 2009) as measured by a
SW-200 monitor. Erwin et al. (2012) state that a 15-minute
recess accounted for 17-44% of all school steps in elementary
school aged children. The larger the school campus, the more
MVPA occurred during outdoor recess (Cradock et al., 2007).
In another study of grade school children, steps-out-of-school
were about the same as those during the shorter school time
period (Cox et al., 2006).
Seasonal differences in the steps taken by lst-5"1 graders
using the Walk4Life pedometer are discussed in Beighle et
al. (2008). Steps taken in school versus out-of-school was not
distinguished. They found that steps/day were significantly
higher in the spring (May) than in winter (February) for
both genders. There were seasonal difference in step counts
between seasons for 3-4 y old children also (McKee et
al., 2012). In an interesting study of the number of steps
taken/day during two seasons of the school year, it was
found that there were no significant differences between
fall and winter seasons in steps taken in and on school
grounds, but there was in out-of-school steps (Beighle et
al. 2012). The pedometer used was the Walk4Life MLS
2505, and the subjects were 112 students in grades 3-5;
4 days of monitoring were collected with between 10-12
h/d of valid recorded data. The fall period was in October
2007 and the winter period was in February 2008; no snow
accumulation occurred during the February session, so severe
weather was not an issue. Another study of weather (as
reflected in seasonal differences), there was a statistically-
significant interaction among weather, month-of-the-year,
and day-type that affected PA levels (Chen & Mao, 2006;
Clemes et al., 2011). Less MVPA is undertaken during the
"cold months," but this seasonal affect is moderated by
day-of-the-week effects.
Pedometer data were analyzed to distinguish between the
number of steps/min taken during recess vrs. out-of-school
time (Beighle et al., 2006). Steps/min taken were not too
different for the 9 y old boys and girls during these two
periods. Girls had 98 SPM during recess and 90 SPM out-
of-school; boys had 108 SPM during recess and 93 SPM
out-of-school (Beighle et al., 2006). However, since the out-
of-school time was 6.6-6.8 times longer in duration compared
to recess time, out-of-school steps cumulatively were much
greater: 5,754-7,136 steps vrs. 918-1,262 steps for recess
(Beighle et al., 2006). These differences were statistically
significant. Girls spent 63% of their recess time engaged in
PA, while boys spent 78% of their time doing so (Beighle
et al., 2006). Note that none of these results are focused
on MPA, MVPA, or VPA cutpoints; they include total
cumulative steps taken for the time periods of interest.
A comparative pedometer study of children aged 10-13 y
with cerebral palsy (CP) and those developing normally
indicates that the number of steps taken in a day is inversely
proportional to severity of CP, as measured by gross motor
skills (Bjornson et al., 2007). The difference between
normal children and the most severe class of CP children
was statistically significant (6,739 steps/d versus 4,222,
on average), with a large difference in the range of steps/d
(SPD) seen in the two cohorts: 6.123-7,355 SPD for normally
developing as opposed to 3,739-4,749 SPD for youth with
cerebral palsy (Bjornson et al., 2007).
Kang et al. (2009, 2012) describe interesting simulations of
pedometer data from 23 adults, mostly female, that had a
full-year of pedometer monitoring using a Yamax SW-200.
In their first analysis, they found that 6 randomly-sampled
days were needed to describe the daily-average steps taken
in a year with an ICC of 0.8 (Kang et al., 2009). If the goal
was to reduce the mean absolute percentage error (MAPE) to
10% of the daily mean, then 14 randomly-selected days were
required. If non-random consecutive days were used, then
30 days of data were needed for the same MAPE goal (Kang
et al., 2009). In the second analysis, they looked at monthly
patterns within the same dataset, which was obtained in the
Knoxville TN area. April through October mean steps/d
were consistently higher than those during the other months:
about 1,000 steps/day on average (-10% higher) (Kang
et al., 2012). The number of pedometer monitoring days
needed per year to reduce MAPE varied somewhat by
season, from 5 days in the spring to 7 days in the summer
and fall; 6 days provided the lowest MAPE is winter (Kang
et al., 2012). Generally 4-7 days of pedometer monitoring
in "free-ranging" individuals is undertaken, but at least 7 d
are required to reliability capture daily variability in MVPA
(Clemes & Griffith, 2008).
In a study of the OMRON HJ-112 pedometer, Kim (2006)
and Kim & Kim (2009) found that to correctly account for
intra- and individual variability in personal activity patterns
and instrument variability (at a reliability coefficient of 0.8),
requires that monitoring be performed for 4 weekdays, 6
weekend days, and for 8 weekday/weekend days combined.
The population mean daily coefficient of variation (COV)
for pedometer step counts in 6-12 y old males observed in
3 countries (US, Sweden, and Australia) is 22% (Wickel et
al., 2007). Individual COV's varied between 2-88%, quite a
range. In addition, the authors found significant differences
in steps taken by day-of-the-week (weekdays>weekends) and
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seasons (summer>spring>winter). The authors state that they
found very similar patterns in college-aged students using a
total daily energy expenditure monitoring approach.
Multiple Methods to Capture MVPA
One type of multiple methods study is to apply the same
monitor to different part of the body and integrate the
measures into a composite metric (Choi et al., 2010). That
has been done frequently in monitoring method evaluation
studies and will not be considered further here. More
interesting are those applications that involve combining
two or more objective methods to estimate energy expended
by an exercising individual. Combining methods has been
shown to be more accurate in the laboratory when compared
to indirect calorimetry (Tikkanen et al.. 2014). Combining
accelerometers and pedometers is relatively common
(Kilanowski et al., 1999), and some manufacturers combine
both in one instrument (Dijkstra et al., 2008). Others combine
heart rate monitoring with accelerometry (Castiglioni
et al., 2007). One interesting study combined heart rate
monitoring, accelerometry, and an energy-measuring
garment that estimated energy expended by leg muscles
using textile electrodes (Tikkanen et al., 2014). To date,
combining monitoring approaches to estimate PA expended
seems to be confined to laboratory or relatively small-scale
investigations only.
The following combinations of non-questionnaire monitoring
methods have been reported in the peer review literature; not
all of them are U.S. studies:
1. Accelerometer and GPS: Almanza et al., 2012; Chaix
et al., 2014; Colby et al., 2014; Dunton et al., 2012;
Durand et al., 2011a,b; Evenson et al., 2009, 2013;
Frank et al., 2005; Gell & Wadsworth 2014; Hermann
et al., 2011; Hongu et al., 2013; Jerrett et al., 2013;
AP.Jones et al., 2009; Kang et al., 2013; Klinker et
al., 2014; Maddison et al., 2010; Nguyen et al. 2013;
Norland et al., 2014; Oliver et al. 2010; Oreskovic et
al., 2012; Quigg et al., 2010; Rainham et al., 2012;
Ramulu et al., 2012; Rodriguez et al., 2005, 2011;
Scheck et al., 2011; Southward et al., 2012; Troped et
al., 2007, 2010; Wheeler et al., 2010; Zenk et al., 2011.
2. Accelerometer and the use of post- hoc GIS
information: McCorrie et al., 2014; Patnode et al.,
2010; Scott et al., 2007; Wheeler et al., 2010; Wieters
et al., 2012.
3. Accelerometer with GPS and heart rate monitoring:
Panter, 2014.
4. Accelerometer and a heat flux/temperature monitor
(the Sense Wear monitor): Almeida et al., 2011.
5. Accelerometer with a heart rate monitor: Calabro et
al., 2014; Brage et al., 2004, 2005, 2007; Fudge et al.,
2007; Strath et al., 2001, 2003a,b.
6. Accelerometer with a light-level sensor to determine if
the subject is indoors or outdoors: Flynn et al., 2014;
Gehrman et al., 2004.
7. Accelerometer and direct observation in a single
location: Gao et al., 2011; Huberty et al., 2011a,b;
Mukeshi et al., 1990; Nelson et al., 2011; Pate et al.,
2004; Sacheck et al., 2011; Saint-Maurice et al., 2011,
2014; Sarkinet al., 1997; Schuna Jr. et al., 2013b, c;
Trost et al. (2008).
8. Accelerometer with a PA diary: Goodman et al., 2011,
2012b; Murphy et al., 2012; AD Stein et al., 2003.
9. Accelerometer and a "SenseCam" (an automated
picture-taking camera): J.Kerr et al., 2013.
10. Pedometer with a mobile phone- or paper-based
activity diary: Fukuoka et al., 2011; Strycker
et al., 2007
11. Pedometer and a heart rate monitor: Graser et al.,
2009; Scruggs et al., 2005.
12. Pedometer with direct observation in a single location:
Hustyi et al. 2011; Scruggs, 2007, 2013; Scruggs et
al., 2013.
13. Heart rate monitoring and GPS: Duncan, JS et
al., 2009; Fjortoft et al., 2009; Panter et al., 2014;
Worringham et al., 2011.
14. Heart rate monitoring and activity diary: Campbell et
al., 2010; Kalkwarf et al., 1989. '
15. Heart rate monitoring with direct observation: Horvat
& Franklin. 2001; O'Hara et al., 1989.
16. Observation and GIS: Suminski et al., 2008.
While not a PA-monitoring method per se. GPS units have
been attached to individuals to distinguish if a subject is
outdoors or not, and to record movement in space, mostly to
distinguish among walking, running, bicycling, and motor
vehicle travel (Cho et al., 2011; Dueker et al., 2014; Duncan.
MJ et al., 2007; Evenson et al., 2009; Maddison et al., 2009;
Rainham et al., 2008; Wiehe et al., 2008a, b). Along this line,
a gyroscope has been combined with a microphone and a
camera to record MVPA and other activities (Clarkson et al.,
2000). The microphone is used to record the specific activity
being undertaken. Apropos, EPA funded RTI to record
activities via a voice-activated recorder in 9 people who were
simultaneously hooked up to a heart rate monitor. The study
was unsuccessful for a number of technical reasons related to
failure of the HR monitoring equipment and problems with
the voice recognition software used.
There are scores of papers that evaluate accelerometers and
pedometers against self-reported estimates of MVPA, either
from questionnaires, diaries, or perceived estimates of the
"scale of work" involved in an activity (RPE). They mostly
involve method evaluation (or "validation") studies of a
particular instrument. None of them is included in the list
noted above or in Table 24.
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11.0
Activity-Specific Energy Expenditure Estimates
Papers contained in file cabinets located in E-253 contain
over 250 articles providing activity-specific energy
expenditure (EEACT) estimates. These papers can be used
to compare selected EEACT estimates with those appearing
in the three Compendium articles (Ainsworth 1993, 2000,
2011) that only provide a single METS value for each listed
activity. Some of the papers in the cabinets are incomplete,
in that only their data and first page were copied without
the rest of the article. (In the old days copying a paper was
a time-consuming and expensive, manual process so to save
time only important portions of a paper were copied.) More
papers, however, and most of the newer articles, are stored
as PDFs located on the computer in Room E253. Papers
containing EEACT estimates are noted with an "EEa" on its
entry in my "working" bibliography, so articles containing
activity-specific EE data can be searched by using "EEa"
in the "Find" function of Microsoft's Word program. These
papers include both U.S. and non-US studies, since the
relative nature of work needed to accomplish a specific
task is not necessarily (or probably) culturally-dependent.
The country of origin of most papers also is provided in the
working bibliography.
I call my bibliography a working one because the list of
authors has been truncated to (generally) only two names
and the rest are cited as "et al." This was done to save space
(lines) while still being able to find an article by the first
author's name. In fact, the second author's name is provided
only to distinguish between/among numerous articles
published in the same year by the same author. And that
happens frequently.
It would be a yeoman's job to abstract EEACT data from
these -250-300 articles and list them in a table like Table
1 for V02 MAX or Table 7 for VE. Since many of the articles
provide EEACT estimates for a number of specific activities—
and differing experimental rates of doing a single activity,
organized by age/gender cohorts-such an EEACT table might
be longer than Tables 1 and 7 combined. For instance,
Agiovlasitis et al. (2012) provide EEACT information for 6
different walking speeds for 2 groups: 12 different EEACT
estimates from one article! I just don't have time at present to
undertake a systematic review needed to compile a complete
EEact dataset from the information currently on hand. (And
there may be additional articles "out there" that have not been
systematically evaluated.)
The Compendium's single METS estimate for each activity,
which also has been carried over to the ATUS and related
databases (Tudor-Locke et al., 2009, 2010, 2011a, b),
is obviously a problem since it ignores both intra- and
inter-individual variability in EEACT. This shortcoming
is surprising given that early energy expenditure articles
depict a wide range in EEACT for individuals undertaking
common activities; see, for instance. Figure 13-1 in Astrand
& Rodahl's 1986 Textbook of Work Physiology. Other early
compilations of EEACT showing a range of measured energy
expenditure across subjects undertaking specific activities
is Durnin & Passmore Energy Work and Leisure (1967)
and Durnin & Namyslowski "Individual variations in the
energy expenditure of standardized activities" (1958). Thus,
there was ample information available to the authors of
the Compendium in 1993 on variability in EEACT among
individuals.
Sallis (1991) states that "standard lists" of METS are
inaccurate, biased toward adults, and inapplicable to children.
EE is underestimated in children if adult values are used
due to the prevailing over-estimate of REE. The "standard"
value of REE of 3.5 mL 02 kg"1 min1, which is especially
incorrect for children, is partly responsible for the METSact
under-estimates (McMurray et al., 2014). If nothing else,
Sallis (1991) posits that METS estimates for the same
activity decrease with age due to changes in gait, even though
cellular metabolic considerations associated with undertaking
common activities are not very different by age.
It was for these reasons that my 1998-9 work on fitting
METS distributions to the Compendium values involved
reviewing what EEACT data that I could find and "mapping"
them onto the highly aggregated time use codes used in
CHAD. The resultant statistical distributions of the mapped
values were then fitted and analyzed. See McCurdy (2000)
for a brief discussion of the procedure used. I had considered
then that this approach was a temporary one, and stated so
in the article. Unhappily, however, we in EPA have never
gone back with a synoptic "hard look" at the METS data for
different activities to update and improve upon what was
done earlier. Given the rather crude CHAD codes that had
(and still has) to be used relating to physical activities, it may
not really be worth the effort to redo CHAD METS codes,
but at least the issue should be investigated to determine
its impact on exposure-modeling results. One promising
effort might be to make EEACT relative to both METSmax
limits as well as the METS=1 basal rate (the "metabolic
chromotropic relationship, in other words). See Sections 6, 7,
and Appendix C.
At any rate, instead of developing a synoptic table of EEACT
to depict inherent population variability in METSact, I
will simply discuss some of the general findings regarding
METS (and/or V02 or EEACT) COV's seen in the literature
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for specific activities. The COV's presented, however, are
themselves problematic given that they assume a normal
distribution and other sampling assumptions that rarely are
discussed in the exercise physiology literature. Probably
a log-normal distribution would generally be a more
appropriate sampling distribution for EEACT, as Dr. Kristin
Isaacs has found (personal communication, 2014).
Selected EEACT COV's seen in the literature include data
presented by Agiovlasitis et al. (2012) regarding activity-
specific energy expenditure of adults with and without
Down syndrome (DS). The subjects walked at 5 different
speeds plus another that was preferred by each individual.
The COV's for DS subjects were marginally greater than
for non-DS subjects for every speed tested except the
fastest (1.5 m/s); the difference in COV's were 1-3%
larger in DS subjects. (All of the speed-specific METS
were statistically significantly higher for DS subjects than
for non-DS subjects also.) The range of the COV's was
17-24% for non-DS subjects and 18-26% for DS subjects
(Agiovlasitis et al., 2012).
Brooks. AG et al. (2004) provided EEACT estimates for 4
different household activities using 3 different metrics: (1)
absolute energy expenditure in kJ/kg-h; (2) a METS estimate
using the "standard" resting metabolic rate of 3.5 mL/kg-
min (called METS3 5); and (3) a METS estimate based on the
individuals' own measured REE (METSIND). The sample
consisted of 36 "representative females" (?) aged 35-45 y old.
EEact was measured in both the laboratory and in a subject's
own home. The home-based data follow, plus the "walking"
activity, which was measured only in the lab.
EE COV
ACT
Activity
(kJ/kg-h)
(%)
metsind
METS3.5
Window
Cleaning
14.0 ±2.5
17.9
3.8 ±0.5
3.3 ±0.6
Vacuuming
15.8 ±2.6
16.5
4.3 ±0.7
3.7 ±0.6
Sweeping
17.3 ±2.9
16.8
4.7 ±0.6
4.0 ±0.7
Lawn Mowing
22.8 ±4.6
20.2
6.2 ±1.0
5.3 ±1.0
Walking
17.4 ±3.0
17.2
4.8 ±0.7
4.1 ±0.7
Note the fact that METSIND > METS3 5 for all activities, which
conforms to the criticism leveled against the use of 3.5 mL/
kg-min as the basis for a general population basal metabolic
rate. In addition, the COV's for METSIND estimates are
lower than those for absolute EEACT (kj/kg-h) measures (data
not shown), as expected because there is less variability in
relative metrics than in absolute measures (McCurdy, 1997).
The COV's for METS3 5 are sometimes higher or lower
than those for the EEACT measure itself. This, too, indicates
another problem, generally unrecognized, with using
"standard" METS values from the Compendium: by ignoring
individual-specific REE, additional non-linear variability is
added to the relative METS concept itself. Again, using the
metabolic chronotropic relationship would improve EEACT
estimates, and make them biologically more relevant in our
exposure and intake dose rate models. It also would provide
a better theoretical basis for estimating task-specific energy
expenditure metrics for exercise physiologists.
Benden et al. (2011) provide interesting data for two
activities that you think would not have a lot of intra- and
inter-individual variability: sitting and standing. The absolute
EEact for sitting in 21 children aged 7.5 y (0.9 SD) was
0.63 kcal/min ± 0.18 for a COV of 28.6% for the sample!
The COV for standing was lower (26.4%) even though the
absolute EEACT was slightly higher: 0.72 kcal/min (0.19 SD).
What is remarkable about their data, however, is the wide
differences in individual COV's seen in the children over 10
repeat measurements for each activity. While the subject-
specific COV data are presented only graphically, it is clear
that the sitting data have much less individual variability than
the standing data. Three of the 31 children had a sitting COV
> 40% and it was 10% or less for the remainder. Individual
standing COV showed much more variability. Four children
had an EE COV <15%, 8 had a COV between
MANDINCj y
50-100%, and another 8 had an EE COV >100%
J MANDINCj
(Benden et al., 2011). These are high individual coefficients
of variation for such a low energy-expenditure activity. Intra-
individual variability of EEACT is a real problem from the
perspective of using a single METS value for an individual,
let alone a collection of similar age/gender individuals. See
also Benden et al. (2012) for a discussion of within-subject
(intra-individual) variability of using a standing desk versus
a sitting desk, obviously a follow up study of the work
discussed above.
Graves et al. (2008a) provide energy expenditure data
for 11-17 y old adolescents playing 3 active video games
(Nintendo Wii) and a sedentary video game (XBOX 360).
Thirteen male and female adolescents were involved in the
study, and each game was played for 15 minutes, in random
order, with a 5-min seated rest between games. Two EEACT
indices were measured, oxygen consumption (V02) and
heart rate (HR). As mentioned in Appendix B and Section 3,
there is a non-linear relationship between V02 and HR so we
do not normally use that metric to estimate EEACT, but HR
data are included here to make a point about using HR as an
indicator of group COV's. The V02 data were converted into
EE metrics using 1 litre of V02 = 4.9 kcal and 1 kJ = 0.239
kcal (Graves et al., 2008a). (Incidentally, while the kJ to kcal
conversion is identical to has been used in the past, we use 1
L V02 = 4.85 kcal, citing Erb [1981], Since this conversion
depends upon an assumed RQ, there is a range of V02-to-kcal
values seen in the exercise physiology literature, anywhere
from 1 L =4.71 to 1 L = 5.01 kcal. 1 L V02 = 4.85 or 4.90 are
compromises for a difficult-to-measure metric. For purposes
of this illustration, the conversion simply changes the "scale"
of the COV's, but does not affect their relative magnitudes.)
Data—means and standard deviations-from the Graves et al.
(2008a) paper follow for the activities tested. I calculated the
rounded-off COV's.
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Activity
V02 (mL/min) COV EE(J/kg-min) COV HR(beats/min) COV
Rest
250 ± 60
24.0%
84.0 ± 14.6
17.4%
70.1 ± 12.1
17.3%
XBOX 360
350 ± 70
20.0
115.8 ± 18.3
15.8
85.0 ± 11.7
13.8
Wii Bowling
550 ±170
30.1
182.1 ±41.3
22.7
103.2 ±16.7
16.2
Wii Tennis
610 ±190
31.1
200.5 ±54.0
26.9
107.0 ±15.2
14.2
Wii Boxing
820 ± 400
48.8
267.2 ± 115.8
43.3
136.7 ±24.5
17.9
A number of interesting findings from these data can
be observed. One is that the COV's for EEACT are not
identical to those for V02 ACT even those the former is a
linear transformation of the latter. That is somewhat of a
surprise and it may be due to differences in the subject's
RQ, which varies with the type of foodstuffs ingested by an
individual. See "Weir's Equation" in the Glossary of Terms
(Appendix E). In general, there is less variability in EE than
in V02. Finally, the COV's for HR vary non-monotonically
compared to COV's for V02 and EE, highlighting again the
problems with using HR as an indicator of activity level.
The approximate METS levels for the activities, based on
the mean resting values, are—in the order listed: 1.5, 2.3,
2.5, and 2.4 METS (Graves et al., 2008b), so the activities
investigated are low-EEACT undertakings.
Graves (2010) expands upon their previous work by having
a wider age range of subjects (of both genders) undertake
Wii and treadmill activities. They present mean/SD data for
V02, EE, HR, and METS metrics, where 1 MET=the V02
resting value. Their data are presented below; the EE and HR
data will not be discussed further. Age statistics for the three
cohorts are: adolescents: 15.8 ± 1.3 y (n=14), young adults:
28.2 ± 4.6 y (n=15), and older adults: 57.6 ± 6.7 y (n=13). I
calculated the COV's (as percentages) from their data. Older
adults did not try the treadmill jogging task. The differences
between V02 COV's and METS COV's are striking, but
there is less variability in COV metrics among the three age
groups (except for "treadmill jogging"). Note that using the
"standard" breakdown of METS into PA categories, Wii
aerobics is a MPA task, as is treadmill walking. Treadmill
jogging would be considered to be a VPA task.
While it is interesting to review the exercise physiology
literature and provide explicit data on activity-specific COV's
for METS and/or EEact (or V02ACT) sample statistics, I
feel that the above information is sufficient to make the
point that there is considerable population variability in the
amount of work needed to undertake even fairly narrowly-
defined tasks. This variability is ignored using a single-
point estimate of activity-specific METS (or V02, HR, and
EEact). Sample-specific COV's addresses inter-individual
(among individuals) variability. Except for the Benden et al.
(2011) paper discussed above, I did not uncover any paper
that explicitly addressed within-subject or intra-individual
variability in undertaking a specific task, but my review of
the literature on that topic was not synoptic. Longitudinal
variability in EEACT within an individual certainly exists, as
anyone knows from past personal experience, but it is rarely
reported by exercise physiologists, even if measured.
VO, (L/min)
Adolescents
Young Adults
Older Adults
Activity
Mean (SD)
COV
Mean (SD)
COV
Mean (SD)
COV
Rest Handheld
0.35 (0.07)
20.0%
0.31 (0.05)
16.1%
0.32 (0.07)
21.9%
Gaming Wi
0.36 (0.09)
25.0
0.34 (0.06)
17.6
0.32 (0.07)
21.9
Balance Wii
0.59 (0.11)
18.6
0.58 (0.13)
22.4
0.57 (0.17)
29.8
Yoga Wi Muscle
0.60 (0.10)
16.7
0.57 (0.14)
24.6
0.57 (0.16)
28.1
Conditioning
0.74 (0.12)
16.2
0.73 (0.17)
23.3
0.68 (0.22)
32.4
Wii Aerobics Treadmill
1.09 (0.17)
15.6
1.09 (0.22)
20.2
0.96 (0.29)
30.2
Walking Treadmill
1.20 (0.26)
21.7
1.35 (0.26)
19.3
1.17 (0.47)
40.2
Jogging
2.21 (0.43)
19.5
2.44 (0.35)
14.3
Not only is there considerable variability in activity-specific oxygen consumption required for each task,
their COV's are also quite different by both activity and age group. (COV's for each task would probably be
reduced if both gender- and age-specific data were provided.) The METS data provided in Graves et al.
(2010) follows; again, I calculated the COV's.
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METS (Unitless)
Adolescents Young Adult Older Adult
Activity
Mean/SD
cov
Mean/SD
COV
Mean/SD
COV
Handheld Gaming
1.0 ±0.1
10.0
1.1 ±0.1
10.0
1.1 ±0.3
30.0
Wii Balance
1.7 ±0.4
23.5
1.9 ±0.5
26.3
1.9 ±0.5
26.3
Wii Yoga
1.7 ±0.3
17.6
1.9 ±0.4
21.1
1.9 ±0.4
21.0
Wii Muscle Conditioning
2.2 ± 0.4
18.1
2.4 ± 0.4
16.7
2.3 ±0.6
26.1
Wii Aerobics
3.2 ±0.7
21.8
3.6 ±0.8
22.2
3.2 ±0.8
25.0
Treadmill Walking
3.5 ±0.5
14.3
4.5 ±1.0
22.2
4.0 ±1.5
37.5
Treadmill Jogging
6.5 ±1.5
23.1
8.0 ±1.2
15.0
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12.0
Human Exposure Modeling Research
Needs
Although the current APEX and SHEDS exposure models
are quite sophisticated in their general approach and
procedures, there are a number of areas that could be
improved or expanded upon. Besides better addressing
both inter-and intra-individual variability (uncertainty)
for most physiological parameters and other deterministic
"constants" used in the overall modeling effort—which are
not addressed here except where noted—the biggest areas of
needed improvements in my view are those involving human
activity/physiology data inputs to the models. The main
focus here is on the air route of exposure/intake dose rate,
but many of the comments affect the modeling of all routes
of exposure.
In rough order of priority, they are:
1. Expansion of the longitudinal time-activity
database, especially coordinated with locational
and activity-specific measurements data, is needed.
At the current time, this information can best be
obtained via "smart-phones" having built-in GPS
and accelerometry monitoring capabilities. As the
review in Section 11 on accelerometers indicates,
however, there are (surmountable) problems with
accelerometer information (and, to lesser extent) GPS
data-gathering that also need to be addressed. Perhaps
by the time that EPA will be able to undertake such
a data-gathering effort, these essentially technical
(engineering) problems will have been solved. In
general, using a protocol where smart-phone subjects
are "automatically" queried when "unusual" changes
in GPS/accelerometer data occur using real-time
algorithms that "monitor" the smart-phone—including
getting no signal for a specified interval-will obviate
many missing data problems. The subjects would be
"automatically" queried, asking them to text what
is going on in order to provide a usable signal on a
real-time basis. Receiving location/activity data on
essentially a contemporaneous basis can obviate some
of the problems with the technology. It is recommended
that at least 7 consecutive days of complete 24 h data
be collected from each study individual at least 4 times
per year to obtain adequate longitudinal human activity
coverage (Xue et al., 2004). Doing so also expands data
on the correlation structure of time spent in selective
locations that would improve the D & A procedure used
to develop cohort-specific longitudinal activity patterns
(Glen et al., 2008).
2. Especially important is obtaining better time-
activity data for "susceptible groups of individuals"
with pre-existing health conditions that make them
particularly vulnerable to airborne insults. Examples
are asthmatics, people with COPD, people with
cardiovascular disease, overweight and obese people
(especially children), and other health-compromised
groups (that may have pollutant-specific issues). For
instance, the prevalence of overweight/obese children
and adolescents more than doubled during the 1990-
2000 time period (Jolliffee, 2004). Their metabolic
and physiological makeup is sufficiently different from
the general population to warrant treating them as a
separate cohort in EPA's exposure/intake dose models.
Another important group of individuals that should be
focused on are active people, especially children and
adolescents, and outdoor workers: these cohorts are
important with respect to setting many of the NAAQS
standards, especially ozone. Their activity pattern data
are under-represented in CHAD.
3. Rigorous characterization of the "feedback loop" that
certainly exists between microenvironment-specific
pollutant concentrations and respiratory parameters is
needed. These parameters include breathing rate (fB),
dead-space and tidal volume (VD & VT), ventilation
rate (VE), and alveolar ventilation (VA), which is a
function of these parameters (Valcke & Krishnan,
2011). Doing so means that a modeled subject's
physiological parameters will have to be "dynamic"
in that they potentially will be altered "on the fly," so
to speak, to reflect exogenous impacts on a receptor.
While conceptually important, it is not expected that
this feedback-loop capability will affect intake dose
rate metrics significantly except in very highly polluted
locations frequented by highly active people.
4. One aspect of this feedback loop improvement is to
develop a systematic procedure to differentiate between
nasal and oral routes of intake dose rate as inhalation
rates increase due to increasing activity-specific energy
expenditure. These different routes obviously affect
how much material gets into the lung and other organs
given the same amount of exogenous material in the
environment. To date, our exposure models have
ignored this issue.
5. Addressing mitigating behavior should become
a priority. This issue may have a lot of impact
on exposure estimates, and is focused on better
addressing the "mitigating behavior" issue, where
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a person may alter her or his time use patterns due
to identified environmental problems existing in a
microenvironment or larger geographic location.
One dimension of the issue is EPA's Air Quality
Index" (AQI) used to provide the public with timely
information on potential "code red", etc. days that
may affect (some) people's health status. The AQI is
designed to inform the public and affect change in
sensitive people's near-term activity/location (behavior)
choices. There is a lot of uncertainty regarding the real
impact of these types of programs (Mansfield et al,
2003, 2004, 2006a b), so more information is needed
to characterize what people do differently on "bad air"
days. Probably this shortcoming affects mostly ozone
and particulate matter exposure assessments. This type
of information probably could most effectively be
obtained via expansion of the longitudinal time-activity
database (Need #1), especially focused on asthmatics,
people with COPD, and/or people with cardiovascular
disease issues.
6. Characterizing the systematic variability in
physiological parameters due to circadian and other
"rhythms of life" in humansis is needed. These rhythms
can have a significant impact on intake dose rates. This
phenomenon has been recognized since the 1860's (E.
Smith, 1861). Linn (1991) states that lung function and
symptoms in asthmatics show a significant circadian
variation, and this variation increases with asthma
severity. The magnitude of the circadian variability
differs among individuals (Linn, 1991). There also is
a circadian rhythm associated with resting metabolic
rate (REE), V02MAX, VE MAX in many individuals on
(mostly) a daily basis, which causes—among other
impacts—daily variability in total daily energy
expenditure (DTEE) seen in longitudinal studies
of energy expenditure (Reilly et al., 1997, 2000).
Circadian rhythms might affect the temporal timing of
physiological peak characteristics, such as VE MAX. A
peak VE value coinciding with a peak environmental
concentration would result in a significantly larger
intake dose rate than might be expected if daily
average physiologic estimates were used. In addition,
total daily dose-received estimates are affected by
these rather short-term alterations in a person's basic
metabolic states.
7. There also is a weekly pattern to human activities that
significantly affects DTEE in some individuals; see
Section 8. There also are longer-term rhythmic patterns
in people that could affect intake dose rate over time.
Some obvious examples are pregnant or lactating
females, and even fecund females going through their
monthly cycles (Reilly et al., 2000). Other similar
longer-term patterns in physiology (and metabolism)
parameters should be investigated and incorporated into
exposure model algorithms where appropriate.
8. Better characterizing activity-specific energy
expenditure (EEACT) definitely is needed, as the material
in Section 11 indicates. In essence, this means that
we need better METSact, or equivalent, distributions,
including estimates that are valid for a longer period
of time than normally used by exercise physiologists
in their testing protocols. In this manner, the fatigue/
EPOC procedure used in our exposure models can
be evaluated and improved, if needed (Isaacs et al.,
2007). The new METS (or oxygen consumption)
estimates should be based on a person's individual
basal metabolic rate instead of the 3.5 mL/kg-min
"standard" factor (see McMurray et al., 2014). These
individual METSact estimates should be analyzed
in such a manner that population distributions of
METSact can be developed. Combined with individual
METSact data over multiple measurements will allow
the characterization of intra-individual variability in
the METSact distributions as well as characterization
of inter-individual variability in them. The ICC statistic
should be useful in this endeavor.
9. Actually, new EEACT estimates should be based upon
the metabolic chronotropic relationship and reserve
physiological measurements and concepts as discussed
in Section 7. Doing so would considerably reduce
uncertainty about METSact estimates currently
provided in the literature, including (1) age/gender-
specific basal energy expenditure values (and their
predictive equations), (2) V02MAX estimates, and (3)
METSmax measures. Using reserve physiological
parameters reduces the impact that age/gender/health
status has on most of the physiological processes that
we currently address in the APEX and SHEDS models.
10. Providing a linked "micro-activity" and "macro-
activity" (time use) database for comprehensive
multi-media exposure assessment is needed. This
means that locational and time-of-day data needs
to be developed for such events as hand-to-mouth
frequencies in children, water ingestion rates by
location and time-of-day, and better temporal and
locational data on babies crawling behavior, and so
forth (Xue et al., 2007, 2009). In that matter, micro-
activities could be tied directly to EPA's CHAD
database, as was recommended in 1998 by an expert
panel (Pechan Associates, 2001). The few papers that
provide information that could become the basis for
micro- and macro-activity diary estimates include
Kissel et al. (1996) and Shepherd-Banigan et al
(2014). There may be more papers on this topic, but
identifying them would require a separate literature
search and compilation. Time constraints do not permit
such a search.
144
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APPENDIX A
Physiological Testing Protocols with
an Emphasis on V02MAX
Much of the information contained in this report comes
from clinical studies of exercise and respiratory physiology
from four general classes of subjects. These are: (1)
athletes or "trained" individuals, usually with the aim of
improving performance; (2) participants in a clinical study
oriented toward a lifestyle modification or other type of
intervention, especially getting people to exercise more and/
or eating a more healthy diet; (3) people with some type of
health problem; and (4) the general population in order to
establish relevant fitness or other physiological benchmarks.
Additional physiological data comes from (1) work-place
studies of activity-specific work rates, mostly focused on
employee fitness to do the job and programs to improve it;
(2) studies of energy expended by participants undertaking a
particular type of recreational activity or exercise regime; and
(3) dietary and nutrition studies, where basal and daily total
energy expenditure data are collected. Usually some type of
data on HR, V02, and VE is provided for study participants in
these research efforts. Rarely, however, does a single study
report data for all of these parameters, even if they were all
collected. Thus, obtaining a complete picture of physiological
parameters used in our exposure models requires the analyst
to combine information from disparate studies. That is what
is done in this report.
Exercise Testing Fundamentals
There appear to be three phases during an exercise
physiology test as workload increases from low to maximum
intensity, which is the usual progression: I-Aerobic
metabolism, II-Transition, Ill-Anaerobic metabolism. As
work increases in Phase I, an increasing amount of 02 is
extracted by muscle and other tissues. This produces more
C02 than is expired. V02, VE, and HR all increase linearly
with workload. Blood lactate (lactic acid: LA) remains level,
and the respiratory quotient (R: VC02/V02) remains steady
at about 0.7-0.8. Phase II begins in individuals around
40-60% of V02MAX depending upon their fitness level. V02
and HR continue to rise linearly, but LA doubles (to around
4 mmol/L, on average) and C02 increases above linearity.
The "respiratory center" is stimulated to increase VE and
the combination of increased VE and C02 greatly increases
VC02; thus R increases greatly. At this level, the rise in VE
and VC02 is greater than the increase in V02.
The ratio of VE/V02 is called the respiratory quotient, or RQ,
and it increases non-linearly above the point of inflection
between VE and V02. HR also increases non-linearly. The
threshold at which the non-linear rise in RQ occurs is called
the "aerobic threshold" in Skinner & McLellan (1980), and
seems to be called the "ventilatory threshold" in more recent
articles. See below for more on this.
Phase III occurs at the "anaerobic threshold," around 65-
70% of V02MAX in most people, but it can be as high as 90%
in very fit individuals, who can sustain high work levels
for longer periods of time, mostly because they have low
LA levels above the anaerobic threshold, contrary to the
general population. In general, LA levels increase above
the anaerobic threshold, V02 increases to maintain the
respiratory muscles, and VE increases even more rapidly
to support the increased "metabolic cost of breathing."
Thus, VQ increases sharply above the anaerobic threshold,
as does HR.
Of course, a number of other physiological changes occur
during the workload transition, such as: muscle fiber
composition and oxidation (removal of electrons in the
mitochondria, lactate dehydrogenase (the "Krebs cycle"),
anaerobiosis, and blood flow from the heart to exercising
muscles (i.e., cardiopulmonary system functioning, etc.).
These factors are not explicitly considered in our exposure
models, and so are slighted here.
Anaerobic Threshold / Ventilatory Threshold
As one author states: "despite the popularity of the concept of
'anaerobic threshold (AT), the noninvasive detection criteria
[to determine it] remain subjective, and invasive validations
of AT ignore differences in lactate concentrations in arterial,
mixed venous, venous and capillary blood samples" (Yeh
et al. 1983). There have been scores of subjective criteria
developed for defining the AT and Yeh et al. (1983) lists
10 of them. After applying a number of subjective and
objective criteria of AT, including looking at concentrations
of lactate from both venous and arterial blood samples, the
authors conclude:
This study shows that while anaerobic metabolism does
occur during exercise, a threshold phenomenon is not
detectable with the invasive methods. In addition, when
current noninvasive methods to determine AT are used,
there is an unacceptably large range of AT values for
individual subjects when determined by different reviewers.
Without the support of reliable invasive methods for
assessing AT, the development of computerized noninvasive
assessment techniques are on a unstable foundation (Yeh et
al., 1983; p. 1185).
The average difference among the four reviews of the
subjective AT data was about 50% of the between-
subject range of values, which Yeh et al. (1983) considers
to be too large.
A-l
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There is a specifically-named anaerobic threshold test called
the Friel FATT test (Yuen et al., 2011). The Friel test is
based upon a HR deflection point and a rating of perceived
exertion (RPE) of 17 on the Borg Scale (Borg, 1973, 1986).
This test is a good example of the difficulties in using
exercise physiology information. Yuen et al, 2011) states
that the Fiell FATT test is described differently in a number
of publications, even by Dr. Friel himself, so they used one
particular version appearing in Friel 2004 (Yuen et al., 2011;
p. 173.) There were significant differences in estimating AT
and V02 MAX depending upon the protocol used.
It also is interesting to note that although the AT is normally
stated to be around 60-70% of V02MAX in "normal," healthy
subjects, and higher in trained individuals, studies have been
done which measured "end-exercise oxygen cost" in both
children and adults at 125% of their estimated AT (Mahon &
Cheatham, 2001; Zanconato etal., 1991). Multiplying that by
the AT range estimate, means that people were exercising at
75-88% of their V02MAX, quite a high—but possible—level,
at least for short periods of time.
In more recent articles, ventilatory threshold (VT or VT) is
substituted for anaerobic threshold (McArdle et al., 2001).
Again, there is difficulty determining a precise point for this
threshold, so there is uncertainty concerning the concept
and its relationship to lactic acid accumulation. VT decreases
with age in children and adolescents both on an absolute and
relative sense. As a percent of V02MAX, VT decreased from
about 75% in male youth to around 65%; the relative change
in female youth was from 70% to 55% (Mahon & Cheatham,
2002). However, that finding apparently is not universal, as
other researchers find no difference in relative VT between
children and adults (Mahon & Cheatham, 2002), while others
find no trend by age and gender (Washington et al., 1988).
Most studies seem to agree that endurance training results in
an increased relative VT; increases in VT as a percent of V02
MAX on the order of 20% are not uncommon due to training
(Mahon & Cheatham, 2001).
Whole-body V02 MAX testing has been used as a marker of
fitness since A.V. Hill developed it in 1923 (Akalan et al.,
2008). A brief history of V02MAX testing is contained in Yoon
et al. (2007). There probably are as many exercise testing
protocols used to ascertain V02 MAX as there are laboratories
doing the testing. For a review of fitness tests, see Burke
(1976), lamb (1984), JN Myers (1994), Nieman (1990),
Robergs & Burnett (2003), and Rowland (1996). Most of
them use either a treadmill or bicycle ergometer to elicit V02
MAX, but arm-cranking (JA Davis et al., 1976; Kang et al.,
1997; Washburn & Seals, 1984), bench-stepping (Olson et al.,
1995), aerobic dancing (Olson et al., 1995), stair climbing,
wheelchair ergometers (Keyser et al., 1999), running in water
(McComb et al., 2006), repetitive lifting of heavy boxes
(Nindl et al. 1998), lifting handweights (Robertson et al.,
1990), one-mile walking time (Weiglein et al., 2011). and
rowing protocols (Carey et al., 1974) have also been used.
Activity-specific, sub-maximal, V02 measurements also are
obtained using the same protocols.
A-2
V02 MAX itself often is predicted using non-maximal testing,
either estimating oxygen consumption directly, or by
measuring sub-maximal HR, which then is "extrapolated"
or extended to obtain an estimate of maximal work rate,
which in turn is used as an estimate of V02 MAX. One common
approach is based on regression analyses of occupationally-
specific activities or sub-maximal treadmill work rates,
especially in heart failure patients and the elderly (e.g.,
Pescatello et al., 1994; Ribisl & Kachadorian, 1969). We do
not provide V02 MAX estimates obtained in this matter due
to the unstated uncertainty attending these procedures. For
examples of this type of prediction equation, see George et
al. (1993, 1997), Kline et al. (1987), Morris et al. (2009), and
Peate et al. (2002),. Malek et al. (2004) provides a detailed
review of eight V02MAX prediction equations for active,
trained individuals. These extrapolating techniques are
different than the V02 MAX-predicting equation discussed later,
which generally are based on anthropogenic characteristics
divorced from an exercise protocol.
There also are multiple protocols that have been developed
for each method or machine, such as a treadmill (Wilkoff &
Miller, 1992). Each has its own name, such as "Fox running
protocol," the "Astrand protocol," "Balke protocol," " Bruce
protocol," "Costill/Fox protocol," the "modified Naughton
protocol," etc. (Diaz et al., 1978; Falls & Humphrey, 1973;
Kang et al., 2001; lockwood et al., 1997; Panton et al.,
1996). Even then, there are variants of these protocols used
by different labs, so there are a wide variety of ways that
have been used to measure V02 MAX over the years. V02 MAX
differences of 10-13% have been seen in the same set of
subjects using alternative exercise protocols (McArdle et
al., 2001), and walking versus running on a treadmill with
everything else being equal the "Bruce protocol" has been
shown to result in statistically significant different V02 MAX
estimates for the same sample (Ward et al.,1998). Another
discussion of statistically significant differences among
V02 MAX estimates using the various protocols appears in
Kang et al. (2001). Other investigations have shown that
the differences are not significant at a=0.05 (McArdle et
al., 2001). Investigations into the reliability of duplicate
testing of the same subjects a number of times indicates that
the SEest of at least some of the measurement protocols is
fairly small: about 2.4% (Taylor et al., 1955). A study of 3
different treadmill protocols used on 144 children aged 6-15
indicated no statistically significant differences among the
three tests on a gender-specific basis (Skinner et al. 1971).
Apparently, the data are inconsistent with respect to the
impact of different protocols on estimated maximal oxygen
consumption measurements.
Treadmill testing generally provides higher V02 MAX estimates
than the cycle ergometer. This is true for youth (Boileau et
al., 1977; Davis et al., 1997; FI Katch et al., 1974; lukaski
et al., 1989) as well as adults (Kamon & Pandolf, 1972;
Robertson et al. 1990; AC Snyder et al., 1993). In adults, the
differences in V02 MAX obtained by these two procedures has
been measured to be +11% (range: 4-22%) for males and
+7% (range: -1% to +17%) of varying fitness levels (Kamon
& Pandolf, 1972).
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Reproducibility of treadmill measures of V02 MAX over
relatively short periods of time using the same protocol
is relatively high (using a reliability coefficient, which
apparently is a standard Pearson product-moment correlation
coefficient): 0.52-0.99, and averaging about 0.90 (Freedson
& Goodman, 1993). These test/retest r's are similar to those
seen in Kirkeberg et al. (2011), who also provide a COV in
the 2-3% range for V02 MAX estimates for 3 different exercise
durations.
An example of a well-described V02 MAX procedure comes
from Iwamoto et al. (1994). They call it a "standard open
circuit" treadmill protocol where V02 is measured by a
mouthpiece and a nose clip to minimize nasal breathing.
The treadmill grade starts at 0% and increases 2.5% in slope
every 2 minutes. Data are collected in the last 20 seconds of
each condition to assure a "steady state" estimate. Treadmill
speed is adjusted for "pre-determined" (by questionnaire)
fitness level of the subject: 3.5 mph for low fit people—a
walking speed—and 6.0 mph for higher fit people—running
speed. Subjects are exercised until voluntary exhaustion
(Iwamoto et al., 1994). It should be noted that most labs do
not vary treadmill speed by fitness level; everyone runs at the
same treadmill speed (Kang et al., 2001).
One of the clearest descriptions of successful attainment of
V02 max used for both "normal, healthy" and overweight/
obese subjects of both genders is contained in Wood et al.
(2010). They declare a measurement of V02MAX to be valid if
at least 3 of the following 5 criteria are achieved during the
last 30 s of the last completed graded treadmill test:
1. <50% increase in V02 of that expected for the change
in mechanical work (this is based upon past experience)
2. HR is within ±11 bpm of the subject's age-predicted
maximum (220-Age)
3. RER> 1.15
4. Peak blood lactate > 8 mmol L1
5. RPE (Rating of Perceived Exertion) >18 (see the
Glossary for a discussion of RPE)
There also are "pre-conditions" associated with a valid
V02 MAX testing protocol that have been followed since
the mid-1970's, such as no strenuous work on the prior
day, testing in the morning after a good night's sleep, and
testing in a "low-anxiety environment," etc. (Shephard,
1987). A number of these attributes have currently been
relaxed, especially the hour rest period in a thermally-neutral
environment prior to the test.
There is one aspect of V02 MAX testing that is quite subjective
and which influences the resulting estimate: verbal
encouragement by testing staff for the subject to "keep
going and work harder." Oftentimes the nature and extent
of this encouragement is not fully described in the protocol,
and so is not well documented. The presence or absence
of encouragement has been shown to result in significantly
higher V02 MAX values for untrained subjects but not for
competitive runners (Moffatt et al., 1994). One cardiologist
states that RER (C02 as a volume / 02 as a volume) is "the
most definitive and objective clinically available measure of
physiological level of effort during exercise" (Brubaker &
Kitzman, 2011; p. 1012). The range in RER is <0.85 at rest
and >1.20 during intense, exhaustive exercise; a value <1.05
indicates that a peak work load was not obtained (Brukaker
& Kitzman, 2001).
In general, no manner how measured, the main criterion
of whether or not V02 MAX is attained is that the measured
oxygen consumption shows no further rise with increasing
work load (Kemper & Verschuur, 1980; Nieman, 1990;
Robergs, 2001; Sanchez-Otero et al., 2014; Taylor et al.,
1955), but that criterion is not universally defined. Some labs
use <10% difference in V02 between workloads; others use a
tighter definition: < 50 mL/min increase with a 1% increase
in treadmill grade (Ardestani et al., 2011). The Wood et al.
(2010) criterion mentioned above is another example. Since
some people—and especially children—never see a leveling
off of V02 with increasing work load, secondary attainment
criteria are often used in those cases (McArdle et al., 2001).
One secondary criterion is that HR of the exercising person
is >95% of HRmax—measured or predicted, and there are
other relative levels also seen, such as attaining ± 10 bpm
of age-predicted maximal heart rate (Ardestani et al., 2011).
A third criterion is that the respiratory gas exchange ratio is
>1.00, (or, alternatively: 1.10), while a fourth is that VE/BM be
>1.6 (Kemper & Verschuur, 1980). If a leveling off of V02
does not occur during the exercise test, some physiologists
state that the performance is limited by (1) local muscular
factors rather than central circulatory dynamics, (2) anaerobic
substrate metabolism. In fact, the presence or absence of
a plateau may be associated with anaerobic capacity level
itself, so this criterion may be "circular" in practice. In many
cases, when a plateau is not seen, the term V02 PEAK is used
instead of V02MAX (McArdle et al., 2001). That distinction is
not universally made either, and many contemporary authors
use V02 PEAK even if a plateau in V02 is attained. That is why
we use the two terms interchangeably in this report. Howley
et al. (1995) is a succinct review of the criteria used by
exercise physiologists to ascertain if V02 PEAK/MAX is attained
or not. A similar paper by Huggett et al. (2005), focused
on the elderly, also is informative. The interested reader is
referred to those papers, but there are many other papers
and books that discuss the maximum oxygen consumption
testing process.
Given all of the nuances of the tests and the criteria used
to ascertain attainment of V02 MAX, we take the pragmatic
viewpoint that the measured and reported V02 MAX value is an
indicator of "true" maximal oxygen capacity of an individual,
even though uncertainty exists regarding what exactly is
being measured. If we have a choice, I present treadmill V02
MAX instead of bicycle ergometry data. The data in Table 1
certainly indicates a wide variety in the mean and standard
deviation of V02MAX estimates for relatively similar age/
gender groups. These differences are most certainly at least
partly due to different subjects being tested, but also to the
protocol used to assess the parameter.
A-3
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There are a number of V02 MAX prediction equations in
the literature using only age, gender, and/or body mass
as independent variables. Additional prediction equations
have been formulated using some of these anthropometric
measures plus sub-maximal exercise data (see, for instance:
Magaria et al., 1965 and McAuley et al., 2011). These types
of prediction equations also are not reviewed in this report.
The reader is directed to Armstrong & Welsman (1994,
1997), Armstrong et al. (1999), Bonen et al. (1979), Mahar
et al. (2011), and McMurray et al. (1998), for a discussion
of V02 MAX prediction equations in children and adolescents.
V02 MAX prediction equations for older groups appears in
Bradsford and Howley (1977), Darby & Pohlman (1999),
Dolenger et al. (1994); Fleg (1994), Fleg et al. (2005), and
Peterson et al. (2003). Other citations could be provided
regarding V02MAX prediction equations using anthropometric
inputs, but these should suffice to indicate the number of
citations available in the exercise physiology literature.
We focused on V02 and V02 MAX measuring methods and
protocols in this Appendix, but alternative metrics of energy
expenditure are used in the clinical nutrition and exercise
physiology fields. There are a number of books and articles
describing these alternatives besides the oft-cited Exercise
Physiology (5th Ed.) book by McArdle et al. (2001). Some of
these are:
T.A. Baumgartner and A.S. Jackson (1999). Measurement
for Evaluation in Physical Education and Exercise Science
(6th Ed.).
H.J. Montoye, et al. (1996). Measuring Physical Activity
and Energy Expenditure.
M.J. Safrit and T.M. Wood (1995). Introduction to
Measurement in Physical Education and Exercise Science.
G.J. Welk [ed.] (2002). Physical Activity Assessments for
Health-Related Research.
For additional information regarding oxygen consumption
measurement, please see one of these books.
A-4
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APPENDIX B
Examples of the Three Types of General Metrics
with a Focus on Heart Rate
Most physiological studies include some type of heart rate
(HR) measurement, for one or more of these conditions: basal
or resting state: HR|(, an absolute metric; activity-specific
heart rate, also an absolute metric: HRA (or as %HRMAX,
which is a one-sided relative metric); or maximal HR, an
absolute metric: HRMAX. Occasionally, data on a percentage
of HR reserve (HRR) are presented, and this is a two-sided
relative metric. Non-relative HR measures have units of beats
per minute (bpm orb min1).
There is a problem with using absolute levels of HR with
respect to understanding oxygen consumption impacts
associated with any HR level. That is due to deviation from
linearity in the absolute HR—> V02 association at both low-
and high-intensity work rates (Acten & Jeukendrup, 2003;
Shakerian et al., 2012). Since it is V02 that more closely
approximates energy expenditure, non-linearity with HR
is problematic with respect to its use in predicting activity-
specific or maximal EE (and consequently, METS). For
example, V02 MAX predicted from submaximal HR estimates
are 10-20% higher than actual measured V02 MAX (Acten
& Jeukendrup, 2003), probably too high an "error rate" for
our models.
The non-linearity is due to many factors. One is that the
hydration status of the individual affects the distribution
of blood to working muscles as V02 as HR increases,
causing a disassociation of the HR/VO, relationship
(Astrand & Rodahl, 1986). There are also non-linear
relationships between cardiac output and (1) V02, (2)
arterial/venous oxygen differences, and (3) stroke volume
(Astrand, 1980). The change in the cardiac output/stroke
volume relationship is called "cardiovascular drift" (Acton
& Jeukendrup, 2003). In addition, HR—> V02 is quite
individualistic, and is inconsistently reproducible over time
even in the same individual (Acten & Jeukendrup, 2003).
The HR—>V02 relationship is greatly affected by the type
of work performed, the relative amounts of arm versus leg
(large muscle mass) movement involved, and the subject's
emotional state at the time of exercise (Armstrong, 1998;
Louhevaara et al., 1990). Thus, absolute values of HR have a
lot of uncertainty concerning relationships of interest to us.
The first relative HR metric: a "one-sided" characterization
of %HRmax, shows better associations and more stability
vis-a-vis other physiological parameters than HR alone.
With respect to %V02 MAX and %HRMAX, an association
sometimes seen in the literature. A regression between these
two metrics in elderly subjects had a R2 of 0.71 and a 10% SE
(Panton et al., 1996). A good relationship between %HRMAX
and %V02MAX was also found in obese males (Eizadi, et al..
2011); the regression equation developed from their study
of 34 middle-aged obese males (BMI >30) was %V02MAX =
-58.4 +[1.61* %HRmax] with an R2 of 0.81 (no SE provided).
A third "good regression" relationship was found for
patients with spinal cord injury (Jacobs et al., 1997). Their
equation is %HRMAX = 0.28 + [0.72 * %V02MAX ] (R2=0.85,
no SE provided). On the other hand, a study of motorcross
riders—working at a vigorous rate—indicates that a HR of
> 90% HRmax can elicit anywhere from 70-95% of V02 MAX
(Burr et al., 2010), a fairly wide range. Davis and Covertino
(1975) equate a 70% HR,l ;: work load to a 55-60% V02MAX
response. Stated more generally, a %HRMAX estimate is
between 5-10% greater than the %V02 MAX value for the same
relative work rates (Kolirt et al., 1998; Simmons et al., 2000).
Londeree & Ames (1976) and Londeree et al. (1995) provide
an overview of %V02 MAX —> %HRMAX regressions seen in the
exercise literature.
It is difficult to succinctly summarize the relationship
between the %HRMAX and %V02 MAX metrics based on these
findings. They track closely for some cohorts and protocols
but not for others (Meyer et al., 1999). Using these two
metrics in an exposure model could produce quite unstable
oxygen consumption estimates.
Infrequently, the relationship between %V02 MAX, a one-sided
metric, and HRR, a two-sided metric, is addressed Scharff-
Olsen et al., 1992). Interestingly, Brawner et al. (2002) state
that %HRR is a better estimator of %V02 MAX than it is of
%V02RES, but that finding is contrary to a number of other
studies (Dalleck & Kravitz, 2006; Swain et al., 1998; Swain
& Franklin, 2000a,b; Swain & Leutholz, 1997). Jakcic et
al. (1995) also found excellent agreement between %HRR
and %V02 MAX, but only between workloads of 40-70% of
V02 MAX. Contrarily, in a study of young female gymnasts,
%HRR could not be used to accurately estimate %V02 MAX,
even though the two metrics were correlated (Guidetti et
al., 1999). In a cross-section exercise study of breast cancer
survivors, Kirham (2010) found that there was a 13% COV
for %V02 MAX at the same %HRR percentage. In a study
of asthmatic patients, Molanorouzi & Mojtaba (2011)
regressed the two metrics for HRR values ranging between
10% and 80% of HRR and found that the regression line
was %V02MAX = 0.66 + [14.8 * %HRR ] with an R2=0.55
(no S.E. provided). Given the lower R2, there is considerable
scatter in the data (no statistics provided).
There are only a few studies that relate V02 RES, a bounded
reserve metric, to the one-sided HR r l. metric. Swain &
7 PEAK
Franklin (2002a) review a number of aerobic training studies
in cardiac patients and show rather large variability in HRpEAK
B-l
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(and HRR) to V02 RES percentages. They also provide a
regression equation for the HRpEAK-to-%V02 relationship
using both REE-corrected and uncorrected measures,
citing another 2002 Swain & Franklin paper (2002b) for
it. Their regression equation was %V02RES = (1.667 * %
HRpEAK) - 70% (no statistics reported). However when
Swain & Franklin (2002b) was read, the equation's basis
was not provided, only a citation to Swain et al. (1994) as
the source of the relation. Swain et al. (1994) did not include
the regression cited! Thus, since I could not find another
paper using the formula, or anything like it, its validity and
usefulness is not verified.
The statistical connection between the two-sided HRR and
V02 RES metrics were discussed in Section 7 of the main
report under the metabolic chronotropic relationship. The
relationship is fundamental in associating the various reserve
forms of important physiological parameters used in our
exposure models. What follows is a discussion of the role
that HRR plays in exercise prescription programs, especially
for people with cardiovascular problems.
Even with the non-linearity problem between heart rate
and V02 reserve metrics, cardiologists and other medical
disciplines use HRR in their work on improving physical
fitness of patients with health problems. To operationalize
the concept, they have developed the "Kavonen approach"
to prescribing exercise rates in diabetics, subjects with past
heart failure, and the elderly (Azarbal et al., 2004; Madden et
al., 2009; Skidmore et al., 2008). This approach is based on
the "Kavonen formula" (Karvonen et al., 1957):
Prescribed exercise level desired = (HREX - HRr) / HRR
Where: HREX = HR of the exercise undertaken
HRr = Resting HR
HRR = HR„ay - HRd
MAX R
The formula usually is used in this form: HREX = HRr + [ %
exercise level desired * HRR ] (McArdle et al., 2001). It has
been "institutionalized" by the American College of Sports
Medicine in its guidance to practitioners on setting exercise
testing limits for individuals (ACSM, 2001). Thus, it enjoys
wide use (Geddes et al., 2009; Hepple et al., 1997). It has
been found to be equally applicable in overweight and obese
people and normal weight individuals (Miller et al., 1993).
The ACSM Guidelines are increasing being used in exercise
prescription programs for healthy individuals also. Athletes
normally train at a relatively high proportion of their HRR,
e.g., 85%, for a specified period of time (Patterson et al.,
2005). Lower relative rates are prescribed for improving or
maintaining fitness in sedentary people: 50%, for example
(Patterson et al., 2005). For older persons, exercising at 30-
45% of HRR is a sufficient training stimulus (Badenhop et
al., 1983). In fact an exercise program at 35% of HRR for a
moderate period of time provided similar improvements in
aerobic capacity in sedentary people aged 65-75 in an 85%
HRR program for shorter periods of time (Belman & Glasser,
1991). However, questions have been raised about how the
Guidelines are being used to prescribe appropriate exercise
levels in health-compromised people. Dalleck and Kravitz
(2006) state that the Guidelines are often misapplied and
misinterpreted, and cite specific studies. (None of the papers
cited above are on their misapplication/misinterpreted list,
however.) The ACSM Guidelines are based on an assumed
RMR of 3.5 mL kg1 min1 (Hultgren & Burke, 1980) which
we know is not universally applicable.
One major problem with the Karoven formula is that HRR
often is not explicitly measured, but is estimated, particularly
in health-compromised groups. HRMAX is not measured in
these people due to potential adverse consequences, but is
estimated by one of a number of formulae developed by
regressing HR on age. One such formula is: HRMAX = 210 -
[0.8 * Age] (Suurnakki et al., 1991). A more commonly used
one is HRMAX = 220 - Age formula (ACSM, 2001; Carvalho
et al., 2008; Lui et al., 2011; Robergs & Landwehr, 2002).
Carvalho et al. (2008) have shown that predicted versus
measured HRMAX estimates are good for healthy, young
adult subjects of both genders using the 220-Age equation
(98.6 ± 2.2%), but are inaccurate for similar subjects with
pre-existing heart failure conditions (65.4 ± 11.1%). Note
the wide disparity between the COV's in these groups: 2.2%
in healthy people versus 17.0% in heart failure people. This
raises the issue of differential uncertainty among cohorts
regarding predicted HRMAX estimates. Graves et al. (2012)
state that using 220-Age significantly underestimates HRMAX
in the healthy elderly; this is confirmed by Whaley et al.
(1994). Nelson et al. (2010) state that the prediction equation
is inaccurate for all 10-year age cohorts between the ages
of 30 and 69. Gulati et al. (2010) state that the formula
systematically overestimates HRMAX for females in general,
and older females in particular. Finally, Robergs & Landwehr
(2002) state "research spanning more than two decades
reveals the large error inherent in the estimation of HRMAX.
The formula HRMAX=220-Age has no scientific merit for use
in exercise physiology and related fields" (ISSN 1097-9751).
There are a number of other HR,l : -cstiinating formulae
in existence, as noted below, but they will not be discussed
further here.
A positive consideration for using the HRR metric in exercise
studies is that it is relatively stable as people age. A study of
15,247 males aged 40-59 had HRR values between 101 ± 9
and 138 ± 5 in four HRR quartiles, while the range was 103
± 7 to 139 ± 6 for 12,212 males aged 20-39. Decreases in
HRMAX with age apparently are matched by decreases in HRr.
Their COV's for the two age groups are similar also: 4.1%
for younger males and 4.5% for older individuals (Cheng
et al., 2002). Another positive is that there is not a gender
difference in the %HRR and %V02 RES relationship, at least
for adolescents (Eklund et al., 2001).
Other cardiologists disagree that HR|( decreases with age;
see Brubaker & Kitzman (2011.) With "an inexorable" (and
highly predictable) decrease in HRMAX, they believe that HRR
will also decease with age (Brubaker & Kitzman, 2011).
B-2
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Because of the above concerns about the HRR metric, how it
varies with age, and its relationship with V02 RES, no tabular
HRR data will be presented in this report. A list of papers
containing measured HRR, or its component parameters
follows for specific population groups. See the list of
References for a complete citation for the papers cited below.
Papers that present measured HRres data are:
Cheng et al. (2002) Med. Sci. Sports Exer.
Kasser & Bruce (1969) Circulation
Papers that present measured (rather than estimated using
one of the formulas) HR, l ;: and resting heart rate (HRr), but
not HRR data per se. for a set of subjects are listed below. In
a pinch, these data could be used to estimate group-means
and an approximate SD for the HRR metric by subtraction,
or more rigorously, by calculating those statistics using a
formal meta-analysis approach. Neither option is explored in
this report.
Billinger et al., 2012
Blanksby & Reidy, 1988
Dalleck & Kravitz, 2006
Davis & Shephard, 1988
Detollenaere et al., 1993
Dunn et al., 1999
Edwards, 1974
Iwamoto et al., 1994
Nikolai et al., 2009
Noahetal., 2011
Pettitt et al., 2008
Robinson, 1938
Sidney etal., 1992; 1998
Skidmore et al., 2008
Szymanski & Satin, 2012
Finally, HRR papers that estimate HRMAX in their subjects
using a formula but measure HRr follow.
HR= 200 (a constant for youth aged 6-18 y):
Stratton, 1996
HRMAXusing a 220 -Ageformula:
ACSM, 2001
Azarbal et al., 2004
Carvalho et al., 2008
Graves et al., 2012
Hui & Chan, 2006
Mahonetal., 2010
Miller etal., 1993
HRMAXusing a 210- (0.8 *Age) formula:
Suurnakki et al., 1991
HRMAXusing a 208 - (0.7 *Age) formula
Mahon etal., 2010
Tanaka et al. (2001)
HRMAXusing a 206 - (0.88 * Age) formula
Gulati et al. (2010): for females only
Tanaka et al. (2001)
Miller et al. (1993) provide information on the performance
of 6 different HRMAX formulae, some in a format never
seen in any other paper, three of which apply solely to
obese individuals. See that paper for more information on
alternative HRMAX formulae. The most widely-used formula,
HRmax = 220 - Age, "often leads to an underestimation [of
HRmax] for ages <40 y and overestimation for ages over 40"
(Kirham, 2010: p. 23).
We have discussed heart rate at some length because there
is a lot of information on all three types of metrics for that
parameter, and because its associations with V02 have been
fairly well studied. We are more interested in reserve metrics,
however, for V02, VE, and METS. Discussion of those
metrics are contained in the main body of this report.
B-3
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APPENDIX C
Background on Reserve Metrics
There seems to be three main sources of the reserve concept
used over the years by different disciplines, with little
communication among them. (Scientific balkanization?)
The earliest work that we found using the reserve concept
seems to be by exercise physiologists in Finland (Karvonen
et al., 1957). That paper cites studies in the 1950's, but they
apparently do not use reserve terminology, so genesis of the
concept seems to be Karvonen et al. (1957) itself. In their
paper, the authors state that HR of an activity (HRA, in this
case, exercise) should be expressed as a percentage of HRR:
HRa/( HR,i ;: - HRr) * 100. There is a short discussion in
Karvonen et al. (1957) that HR and V02 are highly correlated
based on work reported by others, but there is no information
given in the paper on the explicit functional relationship
between HRR and V02RES. Swain (2000) states that Davis
& Covertino (1975) made the case that %HRR = %V02
RES, which they call net V02. Thus, the reserve concept was
identified and evaluated many years ago.
Current use of the oxygen consumption reserve by exercise
physiologists is discussed thoroughly by Swain and
colleagues in a series of articles. One of the most succinct is
Swain (2000). An excerpt from that paper follows.
Recent research has resulted in a number of recommended
changes in how fitness professionals should prescribe target
workloads and calculate the energy cost of exercise. The
principal changes are in the use of oxygen consumption
reserve (V02R) as an alternative to percentage of maximal
oxygen consumption (V02MAX) for prescribing exercise
intensity, the use of net V02 rather than gross V02 for the
calculation of caloric expenditure during exercise ... Several
recent studies have shown that there is a discrepancy
between the exercise intensity at given percentages
of HRR and V02MAX, but that HRR and V02R yield
equivalent exercise intensities. The use of V02R in exercise
prescription provides more accurate target workloads,
especially for individuals with a low fitness level. (Swain,
2000; p. 17).
Work by Swain et al. that analytically evaluated problems
with using either the absolute or relative maximal values of
HR (HRm xx. %HRmax) and V02 (V02 MAX, % V02 MAX) are
discussed in Swain & Leutholz (1997); Swain & Franklin
(2002a, 2002b); and Swain et al. (1994, 1998). Other
discussions of the reserve concept are found in Brawner et al.
(2002), Franklin et al. (2000), and Pollock et al. (1997).
Swain (2000) states that equivalency between %HRR and
%Vo2max is not expected on "theoretical grounds" because
the concepts are not consistent at resting conditions. The
discrepancy is smaller for highly fit people (Belman &
Gaesser, 1991; Pantonet al., 1996).
Hypothesis testing that %HRR = %V02R was conducted
by Swain & Leutholz (1997). The sample involved 33
(mean V02max=3.33 ± 0.12 L/min) and 30 $ subjects (mean
VO2max=2.02 ± 0.08) who were 18-40 y old. Regressions of
%HRR on %V02R were not significantly different than 0.0
for the intercept (-0.1 ±0.6 %HHR units) and 1.0 for the slope
(1.0 ± 0.01). The mean correlation between the two measures
was 0.991 ± 0.001. The regressions and other statistics seem
to be averages of individually-based regressions. Swain et al.
(1998) is very similar to the Swain & Leutholz 1997 paper,
with a slightly smaller sample size. The averaged regression
(explicitly this time) was %HRR = (1.03*%VO2R) +1.5 with
anR2=0.990 ± 0.009. The mean intercept was 1.5 ± 0.6 and
the mean slope was 1.03 ± 0.01.
The disparity between %HRR and %V02R is greater at low
intensities than high, and in fit versus non-fit individuals
(Brawner et al (2002). They investigated 3 groups of
health-compromised subjects: people with myocardial
infarction (MF; n=65); patients having a previous heart
failure (HF; n=72); and subjects only having suspected
risk factors for heart problems but with no overt symptoms
at the time of the study (RF; n=42). Subject ages were in
the 53-62 range (means) and included both genders. In
regressing %HRR on %V02R for the 3 groups, none had
a statistically different slope from 1.00 (at p<0.05). The
intercepts were significantly different than 0.0 for the HF and
RF groups. The regressions, without SE's being provided)
are: MI group--%HRR=(0.96*%VO2R)-1.9; R2=0.95); HF
group~%HRR=(0.97*%VO2R)-5.9; R2=0.90); RF group-
%HRR=(1.01*%VO2R)-4.7; R2=0.95). The difference in the
intercepts "suggests that %HRR is not equal to %V02R" (p.
420). The authors did not have measured HR at rest, and it
was assumed to be 3.5 mL/min-kg.
The second use of the reserve logic first involves
occupational work physiologists in Finland (Ilmarinen 1980,
1984; Karpansalo et al., 2002, 2003; Suurnakki et al., 1991)
and other European countries. These researchers focus on
"stress" (work load) and "strain" of occupational activities
(Oja et al., 1977). Strain is the impact of the work load on
the cardiovascular and/or respiratory systems. Percent HRR
is often used to estimate job-related strain (Suurnakki et al.,
1991). Prior to using HRR, strain was estimated by using
C-l
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percentage of maximal V02, HR, or (even) ventilation rate
(VE) (Astrand, 1960, 1967). The "%Max" approach is still
being used, as we shall see.
The third historic users of the reserve concept are
cardiologists, who want to improve their patients'
cardiovascular system performance without exceeding
their exercise capacity (Renlund et al., 1996; Wilkoff &
Miller, 1992). Doing so, of course, could result in serious
complications and even death. These patients include cardiac
transplant recipients, people with chronic heart failure,
coronary artery disease, hypertension and/or vascular stiffing
(Renlund and Gerstenblith, 1987). The intent generally is to
increase cardiac output, including heart rate, at increasing
work rates. Often the patients can only perform at 62-68%
of V02MAX levels seen in similar, but healthy, age/gender
cohorts (Renlund et al., 1996).
The METS approach can easily be placed on a reserve
logic basis. METS have been discussed since World War
II, although origin of the idea is attributed to Dill (1936)
and use of the term is attributed to Gagge et al. (1941), who
predicated it upon body-surface area (BSA) heat loss. They
defined 1 met to be the metabolism (thermal activity) of a
subject resting in a sitting position on a kilocalories m2 h1
basis (Gagge et al., 1941). Since then a MET is defined to be
RMR in a prone position.
The concept of making activity-specific energy expenditure
relative to a person's resting (lying down) rate actually was
discussed by E. Smith in 1861! METS-like values were
presented by Smith for 29 activities, many involving tasks
that are no longer common, but some that are still undertaken
(Smith, 1861). His values are lower than those presented in
the METS Compendium for similar activities (Ainsworth et
al., 1991).
Less frequently seen in the literature is the METS reserve
(METSres) metric (McCurdy & Graham, 2004; Wilkoff et
al., 1989). METSres is the difference between a person's
METSmax and their resting METS, which is equal to 1.
Thus, METSres = METSmax - 1. Thus, it is easy to calculate
METSres when METSmax is known.
The Wilkoff et al. (1989) paper contains the first use of the
METSres that I could find in the literature, although it is
called the "metabolic reserve." However, that paper does
not describe how METSrest, as they term it, was measured.
It does not appear that V02 at rest was actually measured
anytime during their multi-day exercise protocol, but was
estimated from HR itself using the ACSM formula; thus
the depicted relationship in Wilkoff et al. (1989) between
HRR and METSres is tautological. The paper also does not
describe how activity-specific METS were defined—by using
the 3.5 mL kg"1 min1 value or by using a "measured" basal
rate. I give the authors credit for using the term "METSres"
first, but it is unclear how they operationally defined it.
Rarely is there information available on the population
distribution of METSmax, but Kokkinos et al. (2010) provides
one for a large sample of male patients participating in a
Veterans Administration study. There is no information
available on how representative is the distribution in the
overall population, but perhaps the age/ METSmax categories
are of some interest. The authors had previously shown that
survivors over an 8.1 y time period had a higher METSmax
than non-survivors; 6.3 ± 2.4 METS to 5.3 ± 2.0 (Kokkinos
et al., 2010). Using the 3.5 mL kg"1 min1 factor for 1 MET,
the population "baseline" breakdown of METSmax is:
Fitness
Category
(METSmax
Class)
Age
(Mean
SD)
Sample Percent Mean
Size of Total METSmax
(n) Group (%) (Unitless)
<4.0
72.4 ±5.3
1,083
20.3
3.2 ±0.7
4^
i
tn
o
72.4 ±5.3
1,226
22.9
4.7 ±0.3
5.1 -6.0
71.6 ±5.0
886
16.6
5.6 ±0.3
o
I
CD
70.8 ±4.5
835
15.7
6.6 ±0.3
o
CO
I
70.7 ±5.0
486
9.1
7.6 ±0.3
00
I
CD
b
70.4 ±4.7
355
6.7
8.6 ±0.3
>9.1
69.4 ±4.0
463
8.7
11.0± 1.8
There is a clear decrease in METSmax with age in the sample,
and a pretty good decrease in the proportion of the study
population in the higher METSmax categories. The sample
size is large: 5,334 males (Kokkinos et al., 2010).
A related reserve-like approach was developed in the 1960's
by Bink (1962) and Bonjer (1962). The concept is that the
amount of energy expenditure that can be maintained by
an individual can be estimated if the aerobic capacity (as
measured by V02 MAX) and elapsed time of the activity itself
is known. This concept was called the physical working
capacity of an individual. We actually used that approach
in our early exposure models, citing Bink (1962) and Erb
(1981). While not a reserve metric per se, it approaches it as
well as factoring in the diminished work capacity over time.
This limitation is also used in our current models in a method
developed by Issacs et al. (2007) that has been successfully
tested against the Bink/Erb approach.
The reserve concept is also used in non-human genera, where
it is called "aerobic scope." A brief literature search of that
term indicates that thousands of articles discuss aerobic
scope in species as diverse as birds, lizards, snakes, fish, and
non-human mammals. See, for example, Bishop (1999): "The
maxima oxygen consumption and aerobic scope of birds and
mammals: getting to the heart of the matter." I did not find
any article that applied the term to humans.
C-2
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APPENDIX D
Background Material on Exposure Modeling
This is a slightly modified reprint of Section 1 of Data
Sources Available for Modeling Environmental exposures in
Older Adults)
D-l
-------
D.l Exposure Modeling Overview and Principles
This report is focused 011 time use, physical activity, and
physiological inputs needed for modeling inhalation
exposures and intake dose rates, such as the APEX and
SHEDS models. This subsection describes, in general
terms, the approach, algorithms, and important variables
used in both models. APEX is the primary air exposure
model used by EPA's Office of Air Quality and Standards
(OAQPS) to evaluate existing and proposed alternative
National Ambient Air Quality Standards (NAAQS). APEX
is also part of OAQPS's TRIM (Total Risk Integrated
Methodology) program (U.S. EPA, 2008a, b), along with
EPA's Hazardous Air Pollutant Exposure Model (HAPEM).
HAPEM is a longer term exposure model that uses many
of the same activity and physiological inputs as does APEX
and SHEDS (Palma et al., 1999) but functions primarily to
evaluate exposures to hazardous air pollutants from mobile
and stationary sources of air toxics. The SHEDS model is
an umbrella term for EPA's Stochastic Human Exposure and
Dose Simulation model (Burke et al., 2001; Zartarian et al.,
2000), of which there are a series of route-specific versions
(dietary/nondietary, pesticides, etc.). It was developed by
EPA staff in NERL's Human Exposure and Atmospheric
Sciences Division (HEASD) and staff of Alion Science
and Technology, Inc. The SHEDS model discussed here is
oriented toward modeling exposures and intake dose rates for
airborne pollutants (SHEDS-Air), but because the activity/
time use and physiological concepts are similar in all of the
SHEDS models, the findings reported here are more widely
applicable to the modeling of all routes of exposure.
APEX and SHEDS now have similar features and input
needs. Both use EPA's CHAD for their time use input data
(McCurdy et al., 2000). CHAD, therefore, is discussed in
some detail in this report.
There are a number of important principles that have guided
exposure and intake dose modeling since 1980 (Johnson,
1995; McCurdy, 1995, 1997). In general, these principles (15
in number and described just below) apply to all groups and
not just to older adults.
1. An individual is the unit of analysis
(Figure D-l). Each individual has a unique dose-
response (D/R) relationship (National Research
Council, 2009), which often is called a dose-effect
(D/E) curve to distinguish it from the population-
level D/R association. D/E uniqueness results from
genetic factors; preexisting disease considerations; age/
gender differences in biology, physiology, and time
use patterns (location and activities); and lifestage
and lifestyle differences among people (Dorre,
1997; McCurdy, 2000). EPA's exposure models are
designed to reproduce such uniqueness. Being older
can influence greatly D/E relationships in individuals
both directly and indirectly because of physiological
changes, immune system challenges, neurological
Building a Realistic Person
©^
r q
Simulated Individual
Home location
Work location (f employed)
Age
Gender
Ethnicity
Employment status
Housing characteristics
Anthropometric parameters
(height, weight, etc.)
Basal Metabolic Rate (BMR)
©
V
Activity Diary Pools
Personal attributes
Day-type (e.g., weekday)
Temperature
Physical activity index (PM)
(initial median estimate)
&
©
Individual Physiological
Sequence
Metabolic Equivalents (Mblb)
Oxygen Consumption Rate (V02)
Total Ventilation Rate (VE)
Alveolar Ventilation Rate (VA)
PAI, actual daily estimate
Simulated Individual
Activity Profile
• Selected diary records days in
simulation period
• Sequence of events
(microenvironments visited,
minutes spent, and activity)
Physiological Parameters
1 METSfiax, M ETSjy^
»Ventilation relationships
©
Stochastic Calculation
• Energy expended per event
and ventilation rates
> Both adjusted for physiological
limits and EPOC
Source: Stephen Graham, OAQPS
Figure D-l. The individual is the unit of analysis. APEX and SHEDS construct simulated populations based on the
above characteristics.
-------
Distribution of
Stressors in Space and
Time
Exposure
Distribution of
Receptors in Space
\ and Time y
^ Exposure
Metrics
intaneous
T ime-Averaged
Peak
Time-Integrated
Time
Source: Duan et al., 1990, as modified by Thomas McCurdy (1996)
Figure D-3. Exposure metrics available from an exposure
time-series.
Source: Adapted from NERL Framework for Exposure Science
Figure D-2. A Venn diagram of exposure.
impairment (cognitive decline), and other physical
alterations (Hertzog et al.. 2008; Jette, 2006; Kiely et
al., 2009).
2. Location is critical to evaluating an exposure to an
enviromnental pollutant (often termed a "stressor")
because, by definition, exposure is the "contact
between an agent [substance or pollutant] and a
receptor [a person in our case]" (Figure D-2). Contact
takes place at an exposure surface over an "exposure
period" (Zartarianet al., 2005), 1 directly implying
a specific location. It should be noted that there
is a correlation structure to location patterns in an
individual, both within and among days; locations that
a person inhabits cannot be modeled using a "random-
walk" process. On the other hand, there is day-to-day
variability in locations that any individual frequents
(unless confined to bed or an institution), so using
"averaged" data does not capture daily variability in
this important exposure variable either (Glen et al.,
2008). This point is discussed further in principles
12 and 13.
3. An individual is not averaged over time or space;
a person can be in only one location at any
particular time.
4. A location having a constant concentration (CT) for a
specified period of time is called a "microenvironment"
(|iE). Microenvironmental data are crucial inputs to
an exposure model (locations and concentrations).
and time spent in the various |iEs vary greatly
with age, gender, and lifestyle. In the APEX and
SHEDS models, locational data come from CHAD,
whereas |iE concentration data are derived from
ambient measurement data or route/pathway-specific
model algorithms.
5. An exposure event is the smallest unit of time used in
the two models and is characterized by a person being
in a unique |iE. undertaking a single type of activity
and, therefore, experiencing a specific activity-level
(see below.) By definition, an event does not cross
a clock hour; longer activities are subdivided into
two or more exposure events in that case (McCurdy
et al., 2000). If any of these factors change, a new
event occurs.
6. The event-based time pattern of concentrations
experienced by an individual is called the exposure
profile, or the exposure time-series. An example of an
exposure profile is depicted in Figure D-3. A number
of alternative exposure metrics may be derived from
this profile, such as the number of peak exposures over
a specified concentration level, the mean exposure
level, and the time integral of exposures over some
important value.
7. Activity level is the amount of energy expended (EE)
by an individual to complete the activity undertaken
(expressed in kcal or kJ/min/kg). Other metrics
performing the same function were used in the past in
EPA's exposure models.2 Activity level affects how
much dose is received given an exposure. Activity
levels are correlated over time in an individual, because
prior physiological circumstances affect subsequent
ones when EE reaches individually specific limits
(Isaacs et al., 2008). These limits are determined, in
1 From the "Official Glossary" of the International Society
of Exposure Science
2 Activity level generally was defined to be the breathing
rate (L/min) associated with the activity. The EE metric
is a more generalized approach to modeling activity
level and accommodates non-air exposure modeling
(McCurdy, 2000).
D-3
-------
Human Exposure
Individual
Attributes
I
Ti me-Location-
Activity Pattern
(time series)
Body
Parameters
METS Estimates
(time series)
Dietary Ingestion
consumption
concentration
(^Oxygen Consumption^)
Inhalation
breathing rate
concentration
Non-Dietary
Ingestion
t
Microactivities
T
Dermal Uptake
(mass time1)
Ca oric Intake
Liquid Intake
Water Ingestion
consumption
concentration
Source: Thomas McCurdy (2000) modified by Dr. Stephen Graham.
Figure D-4. Human exposure model principles. This schematic diagram illustrates the relationship among activity level,
energy expenditure, and the intakes needed to maintain that activity level.
part, by an individual's age, gender, fitness level, and
functional (health) limitations that may exist
(Figure F-4).
8. Work is defined to be activity-specific energy-
expenditure. In the APEX and SHEDS models,
activity-level-specific energy expenditure (EEa) by
an individual i (EEai) is estimated by multiplying an
activity-specific relative energy value in metabolic
equivalents of work (METSa) sampled from a
literature-derived distribution by the modeled person's
basal metabolic rate (BMRi)—EEai =
BMRi * METSa. See Ainsworth et al. (1993) and
McArdle et al. (2001) for a discussion of the METS
concept. A person's BMR is dependent on age, gender,
health conditions, and lifestyle factors. Numerous
equations exist in the nutrition literature for estimating
BMRi using a multitude of independent variables
(Froehle, 2008; Miiller et al., 2004; Schofield, 1985;
Speakman, 2005). It is important to note that BMR in
older individuals is quite different than that in younger
adults; see Section 2.B.
9. Given a uE exposure concentration, activity level
ultimately determines a person's intake dose rate, the
amount of material inhaled, ingested, or absorbed
into an individual (Figure F-4). For inhalation
exposures, intake dose rate is a function of the amount
of air breathed per unit time multiplied by the j.lE
concentration; its units ideally are in moles/min, but
alternative metrics sometimes are used. The magnitude
of intake dose rate is affected greatly by the amount
of work being undertaken by an exposed person at
the time of exposure. The pattern of intake dose rate
experienced over time often is called the intake dose
profile, and is similar in appearance to the exposure
profile depicted in Figure F-3.
10. A relevant dose metric must be utilized to properly
address individual dose-effect (D/E) or population
dose-response (D/R) relationships (Lorenzana
et al., 2005; National Research Council, 2009).
However, in general, health effects are associated with
the time pattern of dose rate received (Lippmann,
1989; McCurdy, 1997). Knowing this specific pattern
(abbreviated as DT/dt) enables any longer term
dose metric to be calculated, including dose levels
-------
exceeding selected levels one or more times in a year,
the mean dose rate, and other metrics of interest. For
example, an exposure assessment conducted for the
most recent ozone (03) NAAQS review (U.S. EPA,
2007a) focused on 8-h peak exposures coincident with
moderate or greater exercise levels occurring within a
year. Multiple, short-term peak dose metrics like these
cannot be uniquely determined from an aggregated,
time-averaged dose metric. They only can be modeled
using an intake dose rate simulation approach that
calculates the time series of exposures such as those
produced by the APEX and SHEDS models.
11. Multiple-route intake/uptake dose rates are correlated
in an individual because of the bioenergetics of human
metabolism. Basically, this principle derives from
conservation of mass and energy (McArdle et al.,
2001). In contrast, "micro-activity" dose rate uptakes,
such as nondietary ingestion associated with
hand-to-mouth or hand-to-surface activity—of concern
with respect to environmental exposures of children—
are not directly associated with bioenergetics but are
related instead to age/gender differences in behavioral
characteristics of children inhabiting a particular
location. Thus, there is a correlation among pathways,
and it is maintained in SHEDS-Multimedia by basing
dietary and water consumption, as well as ventilation
rate, on activity level considerations. Microactivity
intake dose rate modeling will not be considered
further in this paper. See Tulve et al. (2002) or Xue et
al. (2007) for a discussion of microactivity exposure
modeling. For modeling air route exposures to older
individuals, we assume that there is no nondietary (or
dietary for that matter) ingestion resulting from hand-
to-mouth activity in that population. This assumption
can be evaluated if data on nondietary mouthing
behavior become available for older people.
12.There are seasonal, day-of-week (or workday/
nonworkday), and meteorological (temperature and
precipitation) differences in time use within and among
individuals (Fisher et al., 2005; Hill, 1985). EPA
exposure models maintain the time use patterns via
targeted selection of appropriate CHAD diaries for each
day of the simulated year for each individual. This is
another reason why average time use data are deficient
in capturing and interpreting what people do in time
and space.
13. There are day-to-day similarities and differences in
locations inhabited and activities undertaken by an
individual and among individuals within a larger
population cohort (Xue et al., 2004; Glen et al., 2008).
These similarities and differences are affected by the
contextual culture of a society, habits, and technology.
Viewed over time, there is a structure to these effects,
resulting in longitudinal patterns of locations visited
and activities performed in a population (Echols et al.,
1999, 2001; Frazier et al., 2008; Glen et al., 2008).
Ramifications of this observation are that both intra-
and interindividual variability have to be addressed
in an exposure modeling effort, as well as day-to-day
correlations within an individual.
14. There are long-term patterns to a person's use of
time—called "tracking"—that can be addressed
analytically to some extent in multiyear exposure
modeling (Elgethun et al., 2003, 2007). Tracking
is affected greatly by changing physiological and
functional limitations and housing pattern changes in
the aged. It is difficult to obtain information on this
subject, except in the physical activity literature; see
Section 5.
15. Because of the inherent nature of the risk assessment
process where judgments have to be made regarding
uncertain future events, including intake dose rates
associated with inhaling a pollutant by population
subgroups undertaking multiple activities in many
locations, said assessments often use a stochastic
simulation modeling approach (Jordan et al., 1983;
Ott et al., 1988). A simulation model facilitates
evaluation of variability and uncertainty in parameters
of the model, often ignored in many exposure
modeling efforts. Uncertainty in the model structure
itself, however, only can be addressed by using a
different model and comparing output estimates with
measured data. This rarely is done because of resource
limitations.
I). 2 Functional Structure of the APEX Model
How these principles are implemented in the APEX and
SHEDS-Air models is shown in Figure D-5. Those symbols
and abbreviations not already described above are defined
in the List of Abbreviations, Symbols, and Acronyms.
Figure D-5 depicts the event-based exposure and intake dose
rate simulation logic frequently used in the two models.
Specific applications of them may differ in the details
depicted. Major model inputs are shown outside of the
dashed-line portion of the Figure; they are
(1) environmental concentration data, (2) U.S. Census
population data, (3) CHAD time use data, and (4) daily
meteorological data for the geographical area being modeled.
This review focuses on the model processes inside the dashed
line portion. Because some of the inputs differ between
the APEX and SHEDS models, as well as among different
applications of either of the models, it would be tediousfor
the reader to continually distinguish among the versions. The
following discussion is oriented toward a generalized ideal
APEX model.
Area of analysis and population groups of concern. APEX
usually is applied at the community- or urban-scale level for
three specified air quality conditions, generally described by
a period of time: (1) some past time period having measured
(or modeled) ambient concentration field data, (2) current
(or as is) air quality conditions also using either measured
or modeled concentrations, and (3) some indefinite future
time when environmental concentrations just meet one or
more alternative standards being evaluated. Comparing
outputs for these three scenarios provides a quantitative
D-5
-------
estimate of the "effectiveness" of each scenario modeled.
An example is New York City for as is conditions in 2007
versus just attaining a specified standard level occurring
at some future time. (This approach is called a standards
objective analysis. If a specific control scenario is evaluated,
usually compared with an alternative control approach, it is
called a standards impact assessment [Feagans, 1986]). The
population groups of concern may be the entire population
or a specific portion of it; exercising children (a small subset
of U.S. children) was the focus of EPA's recent 03 NAAQS
exposure analyses (U.S. EPA, 2007a, b). Older adults with
compromised cardiovascular systems (chronic obstructive
pulmonary disease, angina, etc.) likely will be an important
subpopulation to consider for modeling exposures in the next
PM NAAQS review.
Environmental concentration field. An environmental
concentration field, or profile, is estimated for all outdoor
locations in the selected geographic area, often referred to
as the modeling domain. This concentration field may be
measured (monitored) and/or modeled ambient data; the latter
data usually are used for future-time air quality scenarios.
The output of this step typically is a time series of hourly
concentrations for every hour of the day during the modeling
period, usually for an entire year. See "Sequence of Hourly
Environmental Concentrations" depicted inside of the dashed
lines in Figure D-5.
Microenvironmental-specific concentration estimates
are developed from these hourly concentration profiles. If a
person is outdoors, the hourly environmental concentration
(C0UTh) value itself often, but not always, is equivalent to the
ambient concentration and used for this |iE for the duration
of the exposure event. In other words, a Ct may be the same
as an hourly C0UTh value. Note that, if there is within-hour
variability in C0UT, then C0UTt would be based on the sub-
hourly time period of concern, such as 5 min used in the
sulfur dioxide NAAQS review.
If a person is indoors or inside a motor vehicle, the
concentration within that |iE depends on a variety of
chemical/physical factors, such as chemical deposition and
removal rates, air exchange rate, and indoor source strengths.
There have been a number of approaches used to model these
factors over the years, but three are most commonly used: (1)
solving a mass-balance equation for the specific location;
(2) sampling from literature-derived "indoor/outdoof' ratios
specific to the |iE being modeled (McCurdy, 1995); and
(3) using a linear-regression-based algorithm that relates
outdoor-to-indoor concentrations (the regression slope) with
an additive term (the regression intercept) for indoor sources.
The number of indoor locations used in EPA's exposure
models range varies with the pollutant being analyzed, but
is generally between 7 and 27 specific locations. Usually
<10 locations are used. Some examples are home, work,
school, retail establishments, motorways, retail stores, and a
"residual" location ("other indoors"). Outdoor locations also
are subdivided, but the concentration assigned to them may
simply be the ambient concentration estimate noted above.
The output of these steps is a time series of |iE concentration
estimates {Cp C2, C3. . . CT} for all outdoor and indoor
locations that the simulated population may inhabit (see
Figure D-5).
It is possible to model more |iEs than the 7 to 27 locations
noted above, but input data to calculate the |iE concentration
are limited for many locations. Most time use studies use
a hierarchical locational coding scheme, some down to
individual rooms in a home, but rarely do subjects provide
data on time spent in them, even for contemporaneous diary
studies, for which subjects are supposed to record in some
manner where they were at the time, with a new entry for
every location inhabited. Remembering specific locations
in the commonly used ex post time use recall surveys done
over the phone (e.g., "What did you do yesterday?") is almost
impossible. Misleading modeling results would occur for
specific locations using most recall survey data for exposures
in detailed |iEs. as there would be a lot of false negatives ("0
time") spent in isolated locations of interest. Thus, only a
handful of general microenvironments are considered in most
exposure modeling efforts.
There is a lively literature on the diary versus recall protocols
used to gather time use data; see As, 1978; Collopy, 1996;
Fenstermaker, 1996; Geurts and
De Ree, 1993; Harvey, 1993; Nickols and Ayieko, 1996;
Niemi, 1993; and Stinson, 1999, among others. CHAD
contains both recall and contemporaneous diary time use
information. See Section 4 for a more detailed discussion of
time use data.
Census data. U.S. Census data are a major input to
EPA's exposure models. The data are used to define how
many people are within the modeling domain, along with
their age, gender, employment, housing, and commuting
characteristics. The proportion of people in each 1-year age
category by gender for the population groups of interest is
derived from the Census data and governs the number of
simulations undertaken. The Census also provides frequency
distributions of work commuting trips among every census
tract in the United States (centroid to centroid distances).
These data provide an estimate of commuting trips between
any pair of census tracts in the area being modeled (e.g., U.S.
EPA, 2007a, b).
After characterizing the simulated population, development
of an actual pool of simulated persons begins. Suppose
that we are interested in modeling the exposures to 45- to
65-year-old workers of both genders. A single person
within that age range is selected randomly, say, a 65-year-
old female. That person has some probability (using
the Census data) of living in a single family residence
having gas heating and cooking. A random draw from this
probability distribution will assign the person to a single
housing type based on the Census probability. Work (paid)
or nonwork status is determined from Census probabilities
for the subject's age/gender combination. If a worker, the
subject will be assigned to a work district (Census tract)
location based on Census commuting probabilities. Thus,
-------
the simulated example person is characterized by a specific
age, gender, housing type, and home and work locations.
Additional characteristics are sometimes used if warranted.
This could include variables, such as health status, body
mass index, etc., all defined by population probabilities that
exist in additionally provided external data, but not in the
Census. For example, additional information is needed to
determine the proportion of asthmatics aged 65 to 69 years
relative to the total population residing within the modeling
domain. Activity patterns explicit for people having specific
health conditions are uncommon, thus judgments are used
to determine the appropriateness of available diary data for
use in the assessment (typically not available for the health
compromised). If the existing activity data do not reflect
what people having a health condition do in time and space,
then selected attributes of the diary information have to be
adjusted to better represent time use patterns of the modeled
group. Sensitivity analyses can then be implemented to
evaluate the implications of making these modifications.
This process is repeated until the simulated population has
proportionally the same characteristics of the Census-derived
population data.
Physiological profile generator. Physiological characteristics
are needed for every simulated person in the population
pool. The main inputs required to do so are derived from the
person's anthropogenic data, such as age, gender, weight
(body mass [BM]), height (HT), body mass index (BMI), and
health status variables that might affect a person's physiology
(e.g., asthma, cardiovascular problems, poor fitness, etc.).
BMR is a very important bioenergetic parameter, as we
shall see, and it is derived from the age, gender, BM, and
HT data for each person. Although a number of equations
are available for estimating BMR, the APEX and SHEDS
models currently use the Schofield (1985) set of equations
that account for variability in age, gender, and BM. Because
of criticisms that the Schofield (1985)-derived equations
may not reflect current population characteristics, such as the
higher BM and larger BMI3 seen in the current population
(Frankenfield et al., 2005; Livingston and Kohlstadt, 2005),
the BMR equations used in APEX and SHEDS will change in
the near future.
The variables mentioned above also affect a person's
maximal oxygen consumption rate (V02 Max[i]), which, in turn,
places an upper limit on the amount of air that a person can
breathe at maximal exercise (VEMax[i]) (see Blomstrand et al.,
1997). Using commonly available physiological relationships
(McArdle et al., 2001), V02 can be related directly to
a person's METSMax[i]. As noted above, METS are activity-
specific metabolic equivalents of work based on the ratio
of energy expenditure (EE) needed to undertake an activity
3 BMI = BM (kg)/HT2 (m), a widely used index of
relative fatness
(EEa) to a person's BMR. (Ainsworth et al., 1993, 2006).
Activity-specific V02 is a function of a person's V02 and
prior event work rates (EE) undertaken (Isaacs et al., 2008).
Activity-specific METS, EE, V02, and breathing rate (VE)
all are related to each other via well-accepted physiological
principles (Isaacs et al., 2008). However, there is still a
lot of uncertainty regarding applications of the known
principles to actual cases, with limited knowledge
concerning the relationship among fitness level, lifestyle,
and the physiological parameters mentioned. Many of these
uncertainties are amenable to sensitivity analyses, so that
implications of the assumptions and relationships used can be
addressed quantitatively. If needed for a particular standard
assessment, alveolar ventilation (VA) can be derived from the
VE estimates; EPA staff currently are working on defining
new VE—>VA functional relationships for use in the APEX and
SHEDS models.
CHAD diary selection criteria. CHAD has 34,773 person-
days of diary data available for use in the APEX and SHEDS
models. About 41% of them (14,249) are single-day (cross-
sectional) diaries. The remainder has between 2 and 369 days
of data per person (see
Table D-l). To simulate year-long activity patterns requires
that single-day diaries be sampled multiple times—a
problem that exists with every exposure model because of
the dearth of longitudinal time use data. We have developed
a method (called the "D&A" approach) of simulating
longitudinal activity patterns based on maintaining the
intra- and interindividual variability in time use seen in
the few repeated-measures analyses of variance that have
been undertaken on multiday surveys and replicating the
day-to-day correlations within individuals in the time
spent in selected, important locations. The method is quite
complex but is logically straight-forward and runs fast in the
simulations (see Glen et al. [2008]). In essence, the method
imposes only as much habitual behavior on individuals and
the population (as a whole) that is described in the literature.
See Section 4.E for additional discussion of the method and
metrics used to implement it.
Conflating CHAD diaries/time use data with the
physiological profiles.
The crux of APEX and SHEDS is combining simulated
individually specific time use data (activity/location) and
concentration patterns with simulated activity-specific
breathing rates (VE.A) to obtain intake dose rates. The
first step in doing so is to match simulated people with
their appropriate diary pool, including seasonal and daily
meteorological constraints on human activities. Day-specific
National Climatic Center (NCC) data are used to classify
every day into one of eight seasonal and meteorological
categories (four temperature classes and two precipitation
categories: "none/trace" and ">0.5" per day). These
become "diary day bins" for the model simulations. Bin
definitions are not fixed but are defined according to the
simulation objectives.
D-7
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Table D-l. Summary of the CHAD Database
Number of Days of
Data per Person
Study Name
Year*
Diaries
Range
Median
Sponsor
Denver MSA
1983
805
1
1
EPA
Washington, DC, MSA
1983
699
1
1
EPA
Cincinnati MSA
1986
2,614
1-3
3
EPRI
California - adolescents
1988
183
1
1
CARB
California - adults
1988
1,579
1
1
CARB
Los Angeles - elementary
1989
51
3
3
API
Los Angeles - high school
1990
43
2-3
3
API
California - children
1990
1,200
1
1
CARB
Valdez, AK
1991
397
1
1
Oil companies
NHAPS-A
1994
4,723
1
1
EPA
NHAPS-B
1994
4,663
1
1
EPA
PSID (CDS) 1
1997
5,616
1-2
2
NICHHD
Baltimore Elderly
1998
391
1-24
14
EPA
EPA# 1
2000
367
367
367
EPA
RTP Unhealthy
2001
1,000
8-33
32
EPA
Seattle MSA
2002
1,693
5-10
10
EPA
EPA #2
2002
197
197
197
EPA
PSID (CDS) II
2003
4,782
1-2
2
NICHHD
RTI Averting Behavior
2003
2,907
1-6
4
EPA
Internal EPA
2007
432
35-69
54
EPA
EPA#1
2007
369
369
369
EPA
Mother and Child
2008
62
31
31
EPA
Totals 34,773
Notes and Abbreviations:
* The last year of a multiyear
study is used.
MSA = Metropolitan Statistical Area
# Number (of days)
NICHHD = National Institute of Child
Health and Human Development
API = American Petroleum
PSID = Population Study of Income
Institute
Dynamics
CARB = California Air
Resources Board
RTI = Research Triangle Institute
CDS = Child Development
Supplement
RTP = Research Triangle Park
EPA= Environmental
Protection Agency
The simulations are undertaken on an event-by-event basis,
beginning at midnight on the first day of the analysis period.
For each person, a diary is selected from the appropriate bin,
and a breathing rate is modeled for each event undertaken.
This is repeated for the daily sequence of activities, and the
output is a string of hourly averaged VE estimates developed
from event-specific EE estimates. A daily physical activity
index (PAI) is calculated from the time-weighted average of
the sum of all the event-specific EE estimates for the day.
PAI can be used to provide a check on the physiological
modeling procedure used in APEX and SHEDS (McCurdy
and Xue, 2004) and as a surrogate for a person's lifestyle
and fitness level. In fact, each person's median PAI can be
calculated directly from the CHAD data and could be one of
the physiological metrics used to develop the diary pools in
the first place (see above).
All of these steps use stochastic processes. The Ct estimates
are partly the result of sampling from known or approximated
distributions of mass-balance equation parameters (or from
indoor/outdoor |iE relationship data). Monte Carlo techniques
are used for this sampling. The same is true for most of
the physiological parameters needed to estimate eneigy
D-8
-------
expenditure, oxygen consumption, ventilation (breathing)
rate, and alveolar ventilation rate, if needed. This stochastic
approach is used to ensure that population variability is
addressed regarding the parameters of interest.
Modeling intake or uptake dose. The second major step in
estimating exposure and dose patterns is to combine the |iE-
specific concentration field with the physiological profiles
described above. The simulated person goes through her or
his day, comes in contact with a concentration (or not) on
an event-by-event basis, and receives a dose based on the
estimated activity level. When the day is completed, the next
day is modeled for the person, continuing for every day in
the simulation period, usually a year. The entire process is
repeated for every individual in the simulated population.
Intermediate model outputs (for inhalation exposure analyses)
are strings of 1-h averaged exposure estimates, 1-h averaged
VE estimates, and 1-h dose estimates (e.g., E * VE) for each
person, plus any aggregation of them for whatever time
period is of interest. This is the dose profile mentioned earlier.
For 03, for example, the main APEX output of interest is the
number of 8-h daily maximum (the highest 8 h in each day)
incidences of exposures when people, especially children,
were exercising at >27 L min-1 m-2 (this is a body surface
area normalized ventilation metric). An illustration of this
type of model output appears as Figure F-6; it depicts the
8-h daily maximum exposure estimates for three population
groups in 12 Metropolitan Statistical Areas for one air quality
scenario, with 2002 air quality just meeting the current 03
8-h daily maximum standard. Five other scenarios also
were evaluated (not shown). Separate sensitivity analyses
of many of the model parameters were simulated in this
assessment, giving an estimate of confidence intervals about
the percentage values depicted in Figure F-6, (although not
shown in the figure). A more thorough discussion of this
sensitivity analysis is presented in U.S. EPA (2007b).
Modeling Response to a Dose
The next step after modeling the dose profile is estimating
a response—adverse or not—from the time pattern of dose
rate received. The loci of the response eventually will be at
the cellular level but, currently, is at the organ level or at a
whole-body systems level, using some type of toxicokinetic
modeling approach. EPA has funded a number of reports
describing how this approach can be used to model adverse
health effects to older adults associated with exposures to
xenobiotic substances. See Hattis and Russ (2003), Ginsberg
et al. (2005), and Krishnan and Hattis (2005) for example risk
assessment documents focused on older people. Although
dose-response and toxicokinetic modeling are needed to
explicitly define health effects associated with intake dose
rates, the topics are discussed extensively in the scientific
literature and really are one step removed from the exposure/
intake dose modeling focus of this report.
I). 3 Exposure Model Evaluation
The APEX and SHEDS models have received only a limited
amount of evaluation against measured personal monitoring
data over the years. In general, OAQPS compares some of
their exposure estimates against personal monitoring data, but
usually the latter are for longer averaging times than those
of interest in the exposure assessment. For instance, OAQPS
compared 03 exposure estimates for children against weekly
average personal monitoring data obtained for a few weeks in
1995-1996 in two separate areas of San Bernardino County:
(1) urban Upland, CA, and (2) two small mountain towns
(Langstaff, 2006; U.S. EPA, 2007a). That was the only
dataset available to the Agency for such a comparison, even
though it was relatively old and based on a longer averaging
time (6 to 7 days) than of interest in the assessment (1- or 8-h
daily exposures). The APEX model performed reasonably
well in the mid-range of the cumulative distribution of
weekly exposure estimates (20th to 70th percentiles) but
systematically overestimated the low end of the exposure
distribution and systematically underestimated the high end
(U.S. EPA, 2007a). This phenomenon has been found in all
synoptic short- to mid-term model evaluation efforts of which
the author is aware: Burke et al. (2001), Law et al. (1997),
Ott et al. (1988), and Zartarian et al. (2000, 2006). The
overestimate of low-end exposures is not of much interest,
because health risks associated with low-end exposures
generally are not of regulatory concern (McCurdy, 1995).
The probable cause of systematically underestimating high-
end exposures results from the models' inability to mimic
repeated daily activity patterns that lead to high exposures
seen in the measured data (Law et al., 1997). Thus, the main
reason for model underestimation is basically a longitudinal
time use issue, although the current D&A procedure may
reduce activity variability over time and improve model
performance. The impact of using the D&A approach has not
been evaluated thoroughly with respect to exposure model
output distributions.
The impact that time use data per se have on APEX exposure
modeling results has received a limited amount of sensitivity
analyses (Nysewander et al., 2009). These analyses consisted
of 5,000 simulations of seven time use variables in two
urban areas, Atlanta and Boston, using the APEX model.
The locational codes used in CHAD were collapsed to 12
aggregated locations that accounted for all places visited by
every individual in the simulations (all 24 h were accounted
for, in other words.) A number of "impact" indices were used
to describe sensitivity: time spent in each microenvironment,
daily average and 1-hour maximum 03 exposure estimates,
and distributional tests. The seven variables included the
following. 180
1. Selection of the appropriate intra- and
interindividual statistics to combine diary days into
longitudinal patterns
2. Choice of the "key location" used to sort the above
statistics (e.g., in vehicles versus outdoor time)
3. Differences in start and stop times for the diary day
(All events were shifted forward and backward 1 h.)
-------
M.Smarr 12/07/08
NATIONAL CLIMATIC DATADAILY
AREA OF ANALYSIS &
POPULATION GROUPS
OF CONCERN
SEQUENCE OF HOURLY
ENVIRONMENTAL
CONCENTRATIONS
Repeatfor
Each Day
in the
Period of
Analysis
to Obtain
the
Overall
Dose Rate
Profile
DAILY AGGREGATION OF
NON-AIR DOSES
(If Desired)
ENVIRONMENTAL
CONCENTRATION FIELD
• Models, measurements,
statistical relationships
SEQUENCE OF |jE
CONCENTRATIONS
SEQUENCE OF EXPOSURE
EVENTS &DOSE RATES
• Dr/dt
• Other metrics possible (^f
MODELED- DAY WEATHER DATA
Maximum Daily Temperature,
Precipitation Amount
MICROENVIRONMENTAL
-SPECIFIC CONCENTRATION
RELATIONSHIPS
• Mass-Balance Equations
• Indoor/ Outdoor Distributions
CHAD DIARIES (TIME USE)
Daily Sequenceof Locations |(iE's)
Activities & Activity Leve(IMETS;
EE; VQ; VE)
PAI
PHYSIOLOGICAL PROFILE
GENERATOR
• Anthropogenic Data (BM,HT,BMR)
• VO^ max, METS max
• "Lifestyle" Attributes (PAI mostly)
US CENSUS DATA
Population Characteristics
(Age, gender, ethnicity)
Housing Locations &
Types of Housing
Employment Locations &
Types of Jobs
CensusTract Commuting
Relationships
CHAD DIARY SELECTION
CRITERIA
• Intra-and InteFlndividual
Relations ("D")
• Lag-One Location
Correlations ("A")
• Season- of-the-year
• Day-Type
(Weekday, weekend;
Work-day, non-work-
day)
Figure D-5. Logic flowchart of the APEX model.
4. Using diaries from different years to test changes in
time spent outdoors by children (There was a
5.2-min decrease per year in this time for CHAD
diaries from the 1980s to 2007.)
5. Alternative assigmnents of "ambiguous location codes"
to either indoors or outdoors (e.g., travel by boat—
indoors or outdoors?)
6. Modifying the diary "weights" used in the National
Human Activity Pattern Survey
7. Level of detail in the diaries (Short events were
collapsed into longer durations of 2-, 5-, 10-, and
15-min durations.)
Using the exposure impact indices, differences among the
various simulations were greater than simply selecting diaries
at random, but the differences were small: -1% to 2% versus
-0.2% to 0.5%. The one exception was age of the diary data
itself (the year that the data were obtained). Using the older
diaries increased exposure estimates by -1.5% to 21.8%
(Nysewander et al., 2009), mostly because high-end O,
exposures were associated with time spent outdoors, which
lias decreased over the years. However, this finding may be
a result of how the diaries themselves were coded for the
different |iEs. rather than a function of age of the diary per se.
More work on understanding the impacts of age of diary data
is needed before a definitive conclusion can be made about
the topic.
It should be noted that obtaining longitudinal personal
exposure data is extremely expensive, especially when
using "active" short-term monitors (as opposed to passive
long-term "diffusion tubes" that are based on Brownian
movement). Active personal monitoring involves attaching
a monitor having a small pump to each individual on a daily
basis, usually at the subject's home at a preselected time.
Active monitoring requires a field staff, multiple (expensive)
monitors, and detailed logistics. These types of studies also
involve collecting time use data. Needless to say, these
are invasive protocols, and it is difficult to retain subjects
for periods longer than a week at a time. A monitoring
study—passive or active—reflects "the state of nature"
at the time of the study, including the unique societal and
enviromnental conditions present at that time. Because these
conditions generally will not be present at some future time
when enviromnental control scenarios being modeled are
implemented, there is uncertainty concerning applicability
of exposure/dose relations found in the past in one area
being applicable in another area at a different time. From the
modeling perspective, the best use of monitoring data is to
"ground-truth" performance of the model itself.
A concerted sensitivity/uncertainty evaluation of EPA's time
series exposure models following the principles advocated
in Saltelli et al. (2000) would be useful and provide insights
into those variables and parameters that significantly affect
their performance.
D-10
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WASH
children \ 1
gsthmauc children
H.-JUS
Population
Subgroup
all people
ATLA
Figure D-6, Percent of people in three groups—(1) all children, (2) asthmatic children, and (3) all persons—estimated to
experience 1+ days with an 8-h daily maximum 03 exposure >0.07 ppm while at moderate exercise when the current 8-h
daily maximum NAAQS of 0.08 ppm is just met.
D-ll
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APPENDIX E
Supplemental Material
This Appendix consists of three independent supplements
focused on abbreviations and symbols used in this report; a
glossary of terms used; and a table of common conversion
factors used in the exercise physiology and clinical nutrition
literatures.
E-l. Abbreviations & Symbols Used in this Synthesis
Symbol
9
6
>
<
a
P
a2
M
ME
ACSM
ADL
AIHC
ANOVA
APEX
AT
ATP
ATPD
ATPS
atm
a-vOz Diff
BF
BLSA
BM
BMI
BMR
bpm
BRFSS
BSA
BTPS
C
C
cal
Definition
Female(s)
Male(s)
For relationships between two variables:
Approximately equal to
Greater than or equal to
Less than or equal to
Alpha (level of significance: probability of rejecting a true H0)
Beta (power of the test: probability of rejecting a false H0 when it is
false)
Variance
Mu; a prefix=10-6; in exposure assessment it means "micro"
Microenvironment: a location with Ct for a specified time period
American Council of Sports Medicine
Activities of daily living
American Industrial Health Council
Analysis of variance
Air Pollution Exposure Model (an OAQPS exposure model)
Anaerobic threshold
ATP: Adenosine triphosphate
Ambient temperature and pressure, dry.
Ambient temperature and pressure, saturated with water vapor
Standard atmospheric pressure
Difference in oxygen content between arterial and mixed venous blood.
Body fat
Baltimore Longitudinal Study of Aging
Body mass [commonly: "weight"]
Body mass index [BM/HT2]
Basal metabolic rate (functionally identical to REE or RMR)
Heart rate
Behavioral Risk Factor Surveillance Survey
Body Surface Area
Body temperature and ambient barometric pressure, saturated with
water vapor
Calorie [English units]
Degrees Celisius (Centigrade)
Calorie, a measure of work
Units
Alternate Units
L/min
L min-
Pascals (Pa) bar
kg
kg
kg/m2
kcal/d
beats/min
m2
kg rrr2
kcal kg-1 d-1
beats min-1
1,000 kcal Calorie
E-l
-------
E-l. Abbreviations & Symbols Used in this Synthesis (continued)
Symbol Definition Units Alternate Units
CASAC Chemical Abstracts Service
CASAC Clean Air Scientific Advisory Committee
cc Cubic centimeter
CC Closing capacity
CDC Centers for Disease Control and Prevention
Cdyn Dynamic compliance
cfd Cumulative frequency distribution
CHAD Consolidated Human Activity Database (www.epa.gov/chadnet1)
CHD Coronary heart disease
CI Confidence Interval
cm Centimeter
CNS Central nervous system
CO Carbon monoxide
COz Carbon dioxide
COLD Chronic obstructive lung disease
COPD Chronic obstructive pulmonary disease
COV Coefficient of variation (= standard deviation/mean) unitless
C-S Cross-sectional [a study type focused on a single time period]
Ct Concentration at time period "t" [air medium] |jg/m3 jjg nr3
CV Coefficient of variation (= standard deviation/mean) [CV=COV] unitless
d day
D Dose (various units and time periods)
D, Intake dose
Dl Diffusing capacity of the lung mL/min mL min-1
D, Intake dose rate [air media] moles/min moles min-1
. "Diversity & Autocorrelation" [an approach to developing exposure
cohorts]
DIT Dietary induced thermogenesis (EE expended to digest food) kcal
DLW Doubly labeled water [having a chemical composition of 2H21sO] L
D/E Dose-effect relationships [for an individual]
D/R Dose-response relationship [for a population]
Dlt/dt Time pattern of intake dose rate moles/min2 moles min-2
E Exposure [various units and averaging times] jjg-min/m3 ppm min
ECG Electrocardiogram
EE Energy expenditure [various units and averaging times] kcal kcal d-1
EEa Activity-specific energy expenditure kcal kcal d-1
EE/bm EE per body mass kcal/kg-min kcal kg-min-1
EE,C„. EE per fat-free body mass kcal/kgFFM kcal kgc -min-1
/ffm ~ 3 min aFFM
EE,. EE per lean body mass kcal/kgLBM kca| kg -min-1
/lbm " 3 min aLBM
EELV End-expiratory lung volume %TLC
EFH EPA's Exposure Factors Handbook
El Energy intake
ELV Effective lung volume
EMRB Exposure Modeling Research Branch (a part of HEASD)
EPA US Environmental Protection Agency
EPOC Excess post-oxygen consumption kcal
ERV Expiratory reserve volume L/min-m2
est. Estimate, estimated
EVR Equivalent ventilation (breathing) rate [Ve/BSA] L/min-m2 L min-1 nr2
Ex Exercise: planned, structured, & purposeful physical activity
°F Degrees Fahrenheit
E-2
-------
E-l. Abbreviations & Symbols Used in this Synthesis (continued)
Symbol
fc
FEF
FEFt
FEVt
FEV,
FFM
fR
FRC
ft
FVC
Gaw
h
2H
2h218o
HbOz
H0
HEASD
HDL
HR
HRa
HRmax
HRpeak
HRr
HRR
HT
IADL
IC
ICC
IEEE-MBS
IRV
IVC
J
°K
kcal
kg
km
K-S
L
LAT
LBM
LPA
m
MAX
METS
METSa
metsmax
metsres
min
mo
MPA
MVPA
MVR
Definition
Heart rate
Forced expiratory flow
FEF for a specified "t" time
Forced expiratory volume in "t" time
Forced expiratory volume in one second
Fat-free mass
Breathing (ventilation) rate
Functional residual capacity
Foot: a measure of length=12 inches
Forced vital capacity
Airway conductance
Hour
An isotope of hydrogen: deuterium
Chemical formula for DLW
Oxyhemoglobin
A hypothesis subjected to statistical testing
Human Exposure and Atmos. Sci. Division (a part of NERL)
High-density lipoprotein cholesterol
Heart rate
Activity-specific heart rate
Maximal heart rate
Peak heart rate (functionally identical to HRMAX)
Resting heart rate
Heart rate reserve [ HRMAX - HRr ]
Height
Indep. Act. of daily living [min. ADL for non-institutional living]
Inspiratory capacity
Intraclass correlation coefficient
Inter. Elect. & Electron. Engineers; Med. & Biol. Section
Inspiratory reserve volume
Inspiratory vital capacity
Joule, a unit of work or mechanical energy
Degrees Kelvin (1K=273 °C)
Kilocalorie
Kilogram; a measure of mass
Kilometer; a measure of distance
Kolmorgov-Smirnoff "non-parametric" test of two distributions
Liter
Lactic acid threshold
Lean body mass [=fat-free mass]
Light physical activity [a category of activity]
Meter: measure of length
A subscript denoting "maximum" or "maximal"
Metabolic Equivalents of work (unitless) [METS at rest=1.0]
Activity-specific METS (unitless)
Maximal achievable or measured METS (unitless)
METS reserve [METSMAX - 1.0] (unitless)
Minute
Month
Moderate physical activity
Moderate & vigorous physical activity
Minute ventilation rate [VE]
Units
bpm
L
kg
L/min
L
Alternate Units
L min-
bpm
bpm
bpm
bpm
m
beats min1
beats/min
beats min1
beats/min
cm
cal
kg
E-3
-------
E-l. Abbreviations & Symbols Used in this Synthesis (continued)
Symbol
MW
n
N
NAAQS
NAPAP
NCEA
NCHS
NEAT
NEM
NERL
NHANES
NHAPS
NHEERL
NHIS
NHLBI
NIA
NICHHD
NIH
NO
no2
NOx
ns
OAQPS
OAR
OEL
ORD
OSHA
P
P
PA
PAEE
PAEE
PAI
PAL
pao2
PaOz
PE
PEFR
PEFV
PEL
PEM
PFI
PM
pmZ5
pNEM
po2
ppb
pphm
PPm
PWC
Q
Definition
Maximal voluntary ventilation
Sample size
Newton
National Ambient Air Quality Standard
National Acid Precipitation Assessment Program
National Center for Environmental Assessment (a part of EPA)
National Center for Health Statistics (a part of NIH)
Non-exercise activity thermogenesis
NAAQPS Exposure Model
National Exposure research Laboratory (a part of EPA)
National Health and Nutrition Examination Study
National Human Activity Pattern Survey
National Health and Environmental Effects Lab. (a part of EPA)
National Health Interview Survey
National Heart, Lung, and Blood Institute
National Institute on Aging
National Institute of Child Health and Human Development
National Institute of Health
Nitric oxide
Nitrogen dioxide
Nitrogen oxides
Not significant: associated with a statistical test at some specified
a level
Molecular oxygen
Ozone
Office of Air Planning and Standards (a part of OAR/EPA)
Office of Air and Radiation (a part of EPA)
Occupational Exposure Limit
Office of Research and Development (a part of EPA)
Occupational Safety and Health Administration
Probability
Pressure
Physical activity
Physical activity energy expenditure
Physical Activity Index [various definitions; generally TDEE/BMR]
Physical Activity Level (identical to PAL)
Physical Activity Level (identical to PAI)
Arterial partial pressure of oxygen
Alveolar partial pressure of oxygen
Physical education; generally as in "class" of structured PA
Peak expiratory flow rate
Peak expiratory flow volume
Permissible Exposure Level
Personal exposure monitor
Personal Fitness Index
Particulate matter (particles or aerosols of varying sizes)
PM with a mean aerodynamic diameter of 2.5 microns or less
Probabilistic NAAQS Exposure Model
Pratial oxygen pressure
Parts per billion
Parts per hundred million
Parts per million
Physical working capacity
Cardiac output (blood flow)
Units Alternate Units
kcal
unitless
L/min
L min-
L/min L min-1
L
mL/min mL min-
E-4
-------
E-l. Abbreviations & Symbols Used in this Synthesis (continued)
Symbol
Definition
Units
Alternate Unil
Q
Capillary perfusion
r
Pearson "product-moment" correlation coefficient
rs
Spearman rank-order correlation coefficient
R
Gas exchange ratio (also known as RQ)
unitless
Raw
Airway resistance
REE
Resting energy expenditure (functionally identical to BMR)
kcal/d
kcal kg-
1 d-1
REL
Recommended Exposure Limit
RER
Respiratory exchange ratio
unitless
RES
A subscript denoting "reserve" [MAX - MIN (orREST)]
RH
Relative humidity
RMR
Resting metabolic rate (functionally identical to BMR)
kcal/d
kcal kg-
1 d-1
RQ
Respiratory quotient [VC0/V02, as volumes]
unitless
RR
Respiratory rate [ V,]
L/ min
L min1
RV
Residual volume
L
SD
Standard deviation of the mean
SE
Standard error of the estimate [SE=SD/Vn]
sec
Second
SEE
Sleeping energy expenditure
kcal
Sgaw
Specific airway conductance
SHEDS
Stochastic Human Exposure and Dose Simulation model
SI
Systeme Internationale d'Unites (international system of scientific units)
STP
Standard Temperature and Pressure
STPD
Standard Temperature and Pressure, Dry
SV
Stroke volume
mL
t An index of time, used as a subscript generally
TDEE
Total daily energy expenditure
kcal
te
Time for one exhaled breath
sec
T,
Time for one inhaled breath
sec
"'"total
Time it takes for one complete breathing cycle (Ttotal= T, + TE
sec
TLC
Total lung capacity
L
TLV
Threshold limit value
TM
Trade Mark
TRIM
Total Risk Integrated Method (an OAQPS risk modeling approach)
TSP
Total suspended particulates
Tv
Total volume
L
TWA
Time-weighted average concentration
jjg/min
ppm
U
Conversion factor between EE and VOz (kcal<->L/min)
vA
Alveolar ventilation rate
L/min
L min1
VAT
Ventilatory anerobic threshold
L/min
L min1
VC
Vital Lung capacity
L
vco2
Carbon dioxide ventilation rate produced during respiration
mL/min
mL min
-1
vD
Dead-space volume
L
vE
Ventilation (breathing) rate = minute ventilation rate
L/min
L min1
^E/BM
Ventilation rate per body mass
L/kgBM-min
' S
m
O)
_i
min1
^E/LBM
Ventilation rate per lean body mass
L/kgLBM~min
^ ^9|_BM
1 min1
VEA
Activity-specific ventilation rate
L/min
L min1
^E.A/BM
Activity-specific ventilation rate per body mass
L/kgBM-min
' S
m
O)
_i
min1
^E.MAX
Maximal ventilation rate (defined by an exercise protocol)
L/min
L min1
^E.MAX/BM
Maximal ventilation rate on a per body mass basis
L/kgBM-min
m
O)
_i
min1
VER
Resting ventilation rate ("basal" or resting conditions)
L/min
L min1
-------
E-l. Abbreviations & Symbols Used in this Synthesis (continued)
Symbol
^E.R/BM
V,RES
^E.RES/BM
V,
VL
VT
vo2
vo„,D„
vo2
vo2
vo2
vo2
vo2
vo2
vo2
vo2
vo„
VO:
vo.
2.RES
2.RES/BM
VPA
VQ
VT
VT/V,
VT
w
w
w
170
WHO
Definition
Resting ventilation rate on a body mass basis
Ventilation rate reserve [VEMAX - VER ]
Ventilation rate reserve on a per body mass basis[VEMAX - VER ]
Inspired ventilation rate
Lung volume
Total volume of the pulmonary system (lungs & conducting airways)
Oxygen uptake or consumption rate
Oxygen consumption per body mass
Oxygen consumption per lean body mass
Activity-specific oxygen consumption rate
Activity-specific oxygen consumption rate per body mass
Maximal oxygen consumption rate (defined by an exercise protocol)
Maximal oxygen consumption rate per body mass
Peak oxygen consumption rate (functionally = V02MAX)
Peak oxygen consumption rate per body mass (functionally = V02MAX/
BM )
Resting oxygen consumption rate
Resting oxygen consumption rate per body mass
Oxygen consumption rate reserve [V02MAX - V02R ]
Oxygen consumption rate reserve per body mass [VO
Vigorous physical activity [a category of activity]
Ventilatory equivalent [VE / V02 ]
Tidal volume of the lungs per breath
Mean inspiratory flow; an index of "respiratory drive"
Ventilatory (anaerobic) threshold
Week
Watt
Work performed at a heart rate of 170 bpm
World Health Organization (part of the United Nations)
Year
- VO
2.MAX/BM 2.R/BM ¦
Units
L/kgBM-min
L/min
L/kgBM-min
L/ min
L
L
mL/min
mL/k9BM-
min
mL/kgLBM-
min
mL/min
mL/k9BM-
min
mL/min
mL/k9BM-
min
mL/min
mL/k9BM-
min
mL/min
mL/k9BM-
min
mL/min
mL/k9BM-
min
unitless
L/breath
L/sec
L/ min
W
Alternate Units
L kgn,;1 min-1
& BM
L min1
L kg -1 min1
& BM
L min1
mL min-1
mL kg -1 min-1
aBM
mL kg, -1 min-1
^LBM
mL min-1
mL kg -1 min-1
& BM
mL min1
mL kg -1 min-1
& BM
mL min-1
mL kg -1 min-1
& BM
mL min-1
mL kg -1 min1
& BM
mL min-1
mL kg ~1 min1
L breath-1
L sec1
L min1
J/sec
E-6
-------
E-2 Glossary of Terms Used in this Synthesis
A
oooooooooooooooooooooooooooooooooooooooc^oooooooooooooooooooo
Absorbed Dose: The amount of a chemical or substance
penetrating an absorption barrier~the exchange boundaries of
the skin, lung, or digestive tract-through an uptake process
via either a physical or biological process per specified time
period; that process often is diffusion through a resisting
boundary layer (IPCS, 2000).
Absorption Barrier: Any of the exchange barriers of the
body that allow differential diffusion across a boundary (e.g.,
the lung).
Activity: From an exposure perspective, a specific action
related to a human behavior or task that may result in an
exposure to the substance of interest. Recently, activities
have been further differentiated into "macro-activities" and
"micro-activities." Data on activities often are developed
from a time-use, or human-activity, study.
Activity Pattern: A series of events describing what a person
does (the activity), her or his activity "level" associated with
the activity (the estimated energy expended), and where
the activity occurs (location or the "microenviromnent").
This information is gathered via a time/activity study using
various techniques.
Acute Exposure: A vague term that loosely relates to a
short-term, high-peak exposure even, usually < 24h in
duration (IPCS, 2000).
Adiposity: The amount of body fat, presented either as
a weight (mass) or as a percentage of total body mass.
Adiposity is estimated by skinfold measurements,
bioimpedance analysis, underwater densitometry, and body
scanning by x-rays or computerized tomography. "Excess"
adiposity is a measure of obesity.
Adolescence: (1) The state or process of growing up. (2) The
period of life from puberty to maturity terminating legally at
the age of majority (Webster's, 1984).
Adolescent: (1) A human that is between 11-21 years old
(Bar-Or & Baranowski, 1994).
Aerobic: (1) Using or requiring oxygen, said of an organism
(International). (2) Denoting an environment in which free
oxygen is present (International, 1986).
Aerobic Exercise: Activity in which the amount of oxygen
consumed increases directly with the amount of physical
exertion (Solomon, 1984). Aerobic activity involves an
associated increase in respiratory rate, heart rate, stroke
volume, systolic blood pressure, and coronary blood flow
(Leon, 1989).
Aerobic Fitness: The capacity to accomplish endurance
activities that depend largely on aerobic metabolism
(Leger, 1996).
Aerobic Power: A synonym for maximal oxygen
consumption/uptake [see].
Aerobic Training: Training that improves the efficiency
of the aerobic energy-producing systems and which often
improves cardiorespiratory endurance (Nieman 1999).
Aerobic Scope: The ratio of V02 MAX to V02 R, which is
approximately equal to V02 MAX / V02 BASAL and HRMAX / HRr.
It is also known as the "metabolic scope" (Rowland, 1989).
This term is rarely seen in the newer literature; the term used
now is V02 RESERVE-
Agent: A chemical, physical, mineralogical, or biological
entity that may cause deleterious effects in an exposed
organism (IPCS, 2000).
Age-Predicted HR^ (A/P HRmx) : A formula that
is widely used in the exercising physiology literature to
estimate HRMAX at maximal voluntary exercise (exertion).
The most common formula used is: A-P HRMAX = (220 -
Age), in units of bpm. One alternative version uses 226 in
lieu of 220; see, for example, Dishman (1994). There are
other definitions used.
Air Exchange Rate: The rate at which outside air replaces
indoor air in a given space (IPCS, 2000).
Airways: Air passages in the "respiratory tree" that includes
the pharynx, larynx, trachea, and the lung.
Allometric Equation: The equation used to "describe" the
relationship between a physiological attribute and body mass
(BM) or other body parameter (Rowland, 1996). It takes the
general form: Y = a * BMb
Where: Y = An attribute of interest (e.g., basal metabolism)
a = a constant of proportionality
b = the power parameter needed to make "a" a constant
Taking logs of both sides gives the linear-
transformed equation:
Log Y = log a + (b * log BM )
There is regularity in the value of exponent b for classes
of physiological attributes, especially those dealing with
flow rates in the body: cardiac output, minute volume
(ventilation rate), basal metabolic rate, oxygen consumption
glomerular filtration rate, and food/water consumption (IPG,
1992). Many of these rates are directly associated with the
amount of energy expended by the organism. See: "Energy
Expenditure" and "Total Daily Energy Expenditure."
Biological rates tend to maintain proportionality with BM°75
(IPG, 1992), but using lean body mass (LBM) often shows
less variability in the relationship.
The BM exponent "b" for volumes and capacities of the
body, such as blood volume, organ sizes, and lung volume,
tend to maintain proportionality with BM1 (BM) in large
and small mammals (IPG, 1992). A lively literature exists
on cross-species scaling, on both empirical and theoretical
grounds. BM°67 (BM2 3) is another metric often used and
analyzed. See Rowland (1996) for a succinct discussion of
scaling in humans.
E-7
-------
Allometry: (1) The measure and study of relative growth of a
part with respect to the entire organism (Webster's, 1984). (2)
The study of the regular variation in features of anatomy and
physiology as a function of overall body size (IPG, 1992).
(3) The study of the variation in physiological "attribute"
of mammals~and the consequences of that variation~as
function of body mass or other body parameter [body surface
area is sometimes used] (IPG, 1992). Some of the "attributes"
that are investigated include heart rate, basal metabolism, and
blood flow.
Alveolar: Pertaining to an alveolus, which is an air cell, one
of the terminal saclike dilations of the alveolar ducts in the
lung (Stedman's, 1982).
Alveolar Air: Literally, air present in the pulmonary alveoli
that participates in gas exchange with blood in the pulmonary
capillaries. This air cannot be obtained for analyses, so the
last portion of air expelled in a deep expiration approximates
it in composition-however, this expelled air comes from
the respiratory bronchioles and alveolar ducts, as well as the
alveoli (Morehouse & Miller, 1976). Thus, deeply expired air
does not equal alveolar air.
Alveolar Ventilation: The process in which gas exchange
with blood occurs (Dorland's, 1988).
Alveolar Ventilation Rate (VA): The rate at which the total
ventilation volume is involved in gas exchange with the
blood. Alveolar ventilation is < total ventilation because
when a tidal volume of gas leaves the alveolar spaces, the
last part does not get expelled from the body but occupies the
dead space, to be re-inspired with the next inspiration. Thus,
the volume of alveolar gas actually expelled completely is
equal to tidal volume minus dead space volume (EPA, 1989).
Aveoli: Tiny air sacs in the lungs through whose walls gases
such as oxygen and carbon dioxide diffuse in an out of blood
(Fahey et al., 2007).
Alveolus: One of the numerous thin-walled polyhedral
formations that line the walls of the alveolar sacs that open
into the alveolar ducts at the termination of a respiratory
bronchiole in the lung; it is the ultimate respiratory unit
where gas exchange takes place (International, 1986).
Ambient Air: Air external to a structure, building, or other
air-flow barrier to which the public has legal access.
Anaerobic: Denoting an oxygen-free environment
(International, 1986); occurring in the absence of oxygen.
Anaerobic Activities: Physical activity where energy
is provided for muscular function without oxygen. This
results in an increase in muscle lactic acid formation from
breakdown of stored carbohydrates (glycogen). Anaerobic
metabolism accounts for most of the energy expended during
the first few minutes of prolonged dynamic activity, during
short-duration high-intensity dynamic activity, during static
activity, and is progressively stimulated when intensity
of dynamic exercise exceeds ~ 70% of maximal aerobic
capacity [(V02MAX) (Leon, 1989).
Anaerobic Energy System: See "non-oxidative
energy system."
Anaerobic Threshold (AT): (1) The level of exercise at
which anaerobic production of energy through glycolysis
leads to the rapid accumulation of lactic acid in the blood
(Lamb, 1984). (2) The AT for an individual represents the
maximal workload where production and elimination of
lactate are in equilibrium; it is the upper limit of an almost
exclusively aerobic metabolism that permits exercise lasting
for hours at a lactate level of approximately 2 mmol L min1
(Guidetti et al. 2008). (3) The fraction of V02MAX that can be
maintained during an endurance event (Luks et al., 2012). It
also is known as the ventilatory threshold (VT) [see] or the
lactate threshold [see], but recent work distinguished among
these terms.
Anaerobic Training: Training that improves the efficiency
of the anaerobic energy-producing systems and which
often increases muscular strength and tolerance for acid-
base imbalances produced during high-intensity efforts
(Nieman, 1999).
Asthma: (1) A condition marked by recurrent attacks of
paroxysmal dyspnea, with wheezing due to spasmodic
contraction of the bronchi. Some cases of asthma are allergic
manifestations in sensitized persons (atopic allergy), while
others are provoked by a variety of factors, including
rigorous exercise (exercise-induced asthma), irritants, and
psychological stress (Dorland's, 1988). See "Bronchial
Asthma," "Bronchitic Asthma," "Essential Asthma",
"Extrinsic Asthma," and "Intrinsic Asthma." (2) A chronic
inflammatory disorder of the airways, characterized by
recurrent episodes of wheezing, breathlessness, chest
tightness, and coughing. These episodes are usually
associated with widespread, but variable airflow obstruction.
(Strunk, 2002).
Asthmatic Bronchitis: A condition characterized by
the clinical features of both asthma and bronchitis
(International, 1986).
Atopic: (1) Pertaining to atopy, which is a genetic
predisposition toward developing immediate hyper-
sensitivity reactions against common environmental
antigens or substances. It occurs in about 10% of the general
population (Dorland's, 1988). (2) Clinical hyperreactivity of
the airways associated with asthma and allergies (EPA, 1989).
ATPD: Ambient temperature and pressure, dry.
ATPS: Ambient temperature and pressure, saturated with
water vapor. These are conditions existing in a water
spirometer, used for lung functional testing. Lung volume
measures at ATPS will be approximately 8-10% smaller than
when measured at BTPS (Shephard, 1967).
Average Exposure: Instantaneous exposures averaged over a
time period (Duan ,et al.,1990).
Averaging Time: The time period over which any function is
measured, often yielding a time-weighted average.
a-02 Diff.: Difference in oxygen content between arterial and
mixed venous blood.
-------
B
000000000000000000000000000000000000000000000000000000000<>
Basal Metabolism: (1) State of minimal metabolic activity
(energy expenditure) associated with maintenance of body
function at its normal temperature and at mental and physical
rest (International, 1986). (2) Alternatively, the caloric
requirements of the fasting body, at physical and mental
rest, and at room temperature (20 ° C). It corresponds to
the unavoidable loss of heat due to cell metabolism and the
energy expended in maintaining minimal bodily functions:
circulation, respiration, digestion, and involuntary muscle
tone (Diem & Lentner, 1970). Basal metabolism varies with
age, gender, and body size, and is highly correlated with fat-
free body mass (Andersen et al., 1978).
Basal Metabolic Rate (BMR): The rate at which BMR
occurs, in units of L min1 when oxygen consumption is
measured, or in kcal min1 (or kJ min1) when energy is
measured directly. Generally BMR is measured as oxygen
consumption using an indirect calorimeter. It often is
"normalized" on a body mass (BM) basis (kcal kg"1 min1)
or—in past decades—on a body surface area basis in units
of either kcal min1 m2 or kJ min1 m2. It is functionally— but
not mathematically- equivalent to the "resting metabolic
rate" (RMR) or the "resting energy expenditure" (REE),
but there are differences in the standard protocols used to
ascertain these metrics. Consequently, the measurements are
not identical in terms of energy expended.
Bias: Any systematic departure from "true values" (IPCS,
2000). Also known as "systematic error." Bias can take
many forms: instrumental error, measurement error, faulty
assumptions, "publication bias," "interpretation bias,"
"sampling bias," "selection bias" (also known as Berkson's
error), etc.
Bimodal distribution: A distribution of values having two
modes of high frequency separated by a region of lower
values (Last, 1983).
Bioavailability: State of being absorbed and available
to interact with metabolic processes of an organism
(IPCS, 2000).
Biological marker (biomarker): Ameasureable posterior
indicator of exposure to an exogenous chemical, metabolite,
or product of biological interaction between the chemical and
some target molecule or cell (IPCS, 2000).
Biologically effective dose: The amount of a contaminant
that reaches cells or target site where an interaction with a
membrane surface or an adverse effect occurs (IPCS, 2000).
Body Build: A combination of body weight and fat content
of a person.
Body Composition: A health-related component of physical
fitness that relates to the relative amount of muscle, fat, bone,
and other vital tissues in a person (Nieman, 1999).
Body Mass Index (BMI): One of the anthropometric
measures of body mass. BMI = weight/height2 The units
usually seen are in kg m2. BMI also is known as Quetelet's
Index (Last, 1983), but this term is rarely seen in the current
literature. For the general adult population, overweight
is defined to be a BMI>25 and <30.0. For adult Asians,
overweight is >23.0 an <25.0; for adult Pacific Islanders
overweight is defined to be >26.0 and <32.0. Obese adults are
defined to be those people with a BMI >30.0 for the general
population; > 25.0 for Asian adults; and >32.0 for Pacific
Islanders.
Overweight and obesity for youth (children, adolescents,
and those <20 y of age) is defined by BMI values for age/
gender-specific percentiles; overweight is > 85th percentile
and obese is >95th percentile of the BMI distributions.
(This approach alters the BMI benchmark over time, as the
BMI distributions change—mostly increasing in the general
population (Kuczmarski & Flegal, 2000).
Body Surface Area (BSA): One of the anthropometric
factors used to normalize" or scale ventilatory measures.
There are a number of alternative measures of BSA that have
been developed over the years; for example, see Dubois
& Dubois, (1916): BSA = Mass0 425 * Height0725 * 71.84.
The units are cm2, obtained from: kg * cm * cm/kg). BSA
is roughly proportional to BMR, and the ratio of BSA-to-
BMR is approximately constant across species. BSA also
is approximately proportional to an animal's body weight
(mass) raised to the % power.
bpm: Beats per minute (a heart rate metric).
brpm: Breaths per minute (a breathing rate metric).
Breath-by-Breath - A method for measurement of
respiratory gas exchange in a breath during which expired
gas volume and simultaneously measured expired gas
concentration are collected, integrated and reported.
Breathing Pattern: A general term designating
characteristics of ventilatory activity, such as frequency of
breathing (fR), tidal volume (VT), and shape of the volume-
time curve associated with specific human activities (OAQPS
Staff, 1988).
Breathing Rate (fR): The number of breaths taken per
minute (bpm, breaths min1).
Breathing Zone: Air within the vicinity of an organism from
which inspired air is drawn, generally the area around the
nose and mouth (EPA, 1992).
Bronchi: The first subdivisions of the trachea, which conduct
air to and from the bronchioles of the lungs.
Bronchial: Pertaining to the airways of the lung below
the larynx that lead to the alveolar region of the lungs
(EPA, 1989).
Bronchial Asthma: Asthma associated with an allergy in
persons with a constricted airway (International, 1986).
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Bronchiole: One of the finer subdivisions of the bronchial
(trachea) tubes, less than 1 millimeter in diameter, and
having no cartilage in its wall.
Bronchitic Asthma: An asthmatic disorder accompanying
bronchitis (International, 1986).
BTPS: Body temperature and ambient barometric pressure,
saturated with water vapor. This is the "reference condition"
for most pulmonary functional tests associated with clinical
status of exercise and/or exposure to air pollutants. For
humans, the normal temperature is 37 °C, the pressure is
based on the barometric pressure, and the partial pressure of
water vapor is 47 torr.
c
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C x T: Pollutant concentration multiplied by time; an
index or metric of exposure that often is incorrectly used
as an index or metric of dose. Since most effects are better
described by the time profile of intake dose received, C x
T has limited usefulness in explaining exposure-response
relationships. See "Exposure Profile" and "Dose Profile." C *
T is used with "Area Under the Curve" logic (deleted here),
and does not explain adverse health effects in a target organ
for most chemicals.
Calorie: A unit of heat energy; the amount of heat required
to raise temperature of 1 gram of water at 15 °C by 1 °C. 1
cal = 4.186 kJ = 3.968 x 10 3 BTU, but other conversions are
seen in the literature.
Calorimetry: Methods use to estimate the rate of energy
expenditure in a person undertaking work or at rest.
Direct Calorimetry: A method that estimates
energy expenditure by a direct measure of total
body heat production.
Indirect Calorimetry: A method of estimating energy
expenditure by measuring inspired and exhaled
respiratory gases (02 and C02).
Capillary: (1) The smallest type of vessel; it distributes
blood to all parts of the body. Usually used in reference to a
blood or lymphatic capillary vessel. (2) The tiny, thin-walled
blood vessels interposed between the arteries and veins in
which materials are exchanged between blood and tissues or,
in the lung, between blood and alveolar air (Morehouse &
Miller, 1976).
Carbon Dioxide Output (VC02): The amount of C02
exhaled from the body into the atmosphere per unit time,
expressed in milliliters (mL) or liters (L) per minute. A
normal adult value at rest is 200 ml/min, and increases
with exercise (Luks et al., 2012). It is not the same as C02
production by metabolic processes, but are the same in the
steady state and low energy expenditure.
Carbon Monoxide: An odorless, colorless, toxic gas with a
strong affinity for hemoglobin and cytochrome; it reduces
oxygen absorption capacity, transport, and utilization in the
blood stream (EPA, 1989).
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Carboxyhemoglobin: A fairly stable union of carbon
monoxide with hemoglobin that interferes with the normal
transfer of carbon dioxide and oxygen during circulation
of blood. Increasing levels of carboxyhemoglobin result
in various degrees of asphyxiation, including death
(EPA, 1993).
Cardiac Index: Heart rate divided by body surface area
(HR/BSA) in units of beats min1 m2 (Rowland, 1989). This
index is not often used currently by exercise physiologists.
Cardiac Output (Q): The blood flow or volume of blood
passing through the heart per unit time measured in liters
min1. It is estimated as heart rate (HR) * stroke volume (SV)
(Lamb, 1984). Because it is a rate, a "dot" should be placed
over Q to distinguish it from blood volume in L, also denoted
as Q in some papers (Diem & Lentner, 1970).
Cardiac Reserve: Ability of the heart to increase its blood
output by increasing heart rate (HR) or stroke volume (SV),
or both (Morehouse & Miller,1976).
Cardiorespiratory Endurance Capacity: A synonym for
maximal oxygen consumption/uptake [see].
Cardiorespiratory Fitness (CRF): (1) One aspect of
"physical fitness" that, in practice, is defined only by the
recommended levels of activity (exercise) needed to maintain
it (it is a tautology, in other words). Attributes included in
CRF recommendations are: (a) frequency, duration, and
intensity of training, and (b) the mode of activity that should
be pursued (ACSM, 1990). CRF is also known as "aerobic
fitness" [see]. (2) A health-related component of physical
fitness that relates to ability of the circulatory and respiratory
systems to supply oxygen during sustained physical activity
(Nieman, 1990).
Cardiovascular: Pertaining to the heart and blood vessels
(Lamb 1984).
Cell: The smallest membrane-bound protoplasmic body,
consisting of a nucleus and its surrounding cytoplasm,
capable of independent reproduction (OTA 1986).
Cellular permeability: Ability of gases to enter and leave
cells; a sensitive indicator of injury to deep-lung cells.
Central Nervous System (CNS): The brain and the
spinal cord.
CHAMPS: Children's Activity and Movement in
Preschool Study.
Chronic: (1) Of or characterized by an extended duration,
and typically by slow development or a pattern of recurrence
(International, 1986). (2) Referring to a health-related state
lasting a long time (Last 1983). (3) Referring to prolonged
or long-term exposure, often with reference to low-intensity
concentration levels (Last, 1983).
Chronic Bronchitis: (1) Chronic inflammation of bronchi
resulting in cough, sputum production and progressive
dyspnea (International, 1986). (2) A long-continued form of
bronchitis, often with a tendency to reoccur after a quiescent
period. It is due to repeated attacks of acute bronchitis or to a
chronic general disease (Dorland's, 1988).
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Chronic Disease: A disorder or disease of long duration
or frequent recurrence that often is characterized by slowly
progressing seriousness.
Chronic Exposure: (1) A vague term that loosely relates to a
low-level, long-term exposure profile. Used by some analysts
to represent exposures lasting greater than six months to
a lifetime (IPCS, 2000). (2) Multiple exposures—over
some specified level—occurring over a long period of time
(IPCS, 2000). A persistent, recurring, or long-term exposure.
Chronic exposure [dose really] to a substance is thought
to result in a health effect-such as cancer-that is delayed
in its onset, often occurring long after exposure has ceased
(CMA,n.d.). However, see "Dose Profile."
Chronic Intake: A vague term relating to a long time period
over which a substance crosses the outer exchange boundary
of an organism (IPCS, 2000). See "Dose Profile."
Chronic Obstructive Pulmonary Disease (COPD): A
general term for a pulmonary condition of uncertain etiology
characterized by persistent slowing of airflow during forced
expiration; also known as COLD, where lung is substituted
for pulmonary. A more specific term should be used, such
as chronic obstructive bronchitis or chronic obstructive
emphysema (OAQPS Staff, 1988).
Cilia: Motile, often hair-like extensions of a cell surface.
Ciliary Action: Movements of cilia in the upper respiratory
tract, which move mucus and foreign material upward
(EPA, 1993).
Ciliated Epithelial Cell: A cell with cilia that lines the
tracheobronchial region of the lung. The beating of the cilia
moves mucus and substances (such as inhaled particles
trapped on/in the mucus) upwards and out of the lung,
thereby contributing significantly to lung clearance.
Circadian Rhythm: Fluctuation in biological variables
that are repetitive and cyclical over a solar or 24 h day. The
most prominent rhythms from a performance and/or energy
expenditure viewpoint are those of body temperature and
wakefulness. Isometric strength of back and leg muscles,
anaerobic power, and exercise performance also seem to
follow a circadian rhythm. V02 MAX, however, in general,
does not (Reilly & Garrett, 1998).
Citric Acid (Krebs) Cycle: A major biochemical pathway
in cells, involving terminal oxidation of fatty acids and
carbohydrates. It yields a major portion of energy needed for
essential body functions and is the major source of carbon
dioxide. It also serves to regulate the synthesis of a number
of compounds required by a cell.
Clara Cell: A nonciliated cell in the epithelium of the
respiratory tract.
Clearance: (1) Removal of a solute or substance from a
specific volume of blood per unit of time (Dorland's, 1988).
(2) Removal of insoluble particles or other substances
that are deposited on epithelial surfaces of the lung
(Lippmann, 1989).
Clinical: Of or pertaining to direct observation or
experimentation on human subjects. In our context, it
means direct and controlled (1) physiological or metabolic
experiments, or (2) exposure-effect observations on humans
in a laboratory or experimental chamber.
Coefficient of Variation (CV, COV): Ratio of the standard
deviation of a sample to its mean, when the sample is
measured on a ratio scale, randomly sampled, and is
normally distributed.
Congenial: A condition that is present at birth (OTA, 1986).
Cohort: (1) A group of individuals sharing a statistical
characteristic for a epidemiologic or other study of disease
(Dorland's, 1988). (2) A taxonomic category approximately
equivalent to a division order, or suborder in a population
classification (Dorland's, 1988). (3) A group of people within
a population who are assumed to have similar exposures.
Cohort Study: A study of a group of persons sharing a
common experience (e.g., exposure to a substance) within
a defined time period; this experiment is used to determine
if an increased risk of a health effect (disease) is associated
with that exposure (EPA, 1989).
Chronic obstructive lung disease (COLD): see "Chronic
Obstructive Pulmonary Disease."
Community Exposure: A general term to depict the
situation in which people in a sizeable area are subjected to
ambient pollutant concentrations; this term is ambiguous.
Compartments: (1) Representation in a model of a
particular tissue or organ group with anatomical significance.
(2) All tissues, organs, cells, and/or fluids for which the rate
of uptake and loss of a substance (chemical) is sufficiently
similar as to preclude further kinetic resolution (Dietz et
al., 1983).
Compound: A substance with its own distinct properties,
formed by the chemical combination of two or more
elements in fixed proportion (EPA, 1993).
Concentration: The amount of a substance of interest
that is contained or dissolved in a specified amount/
quantity of another substance. The amount of material in air
(IPCS, 2000).
Concentration-Effect Relationship (Curve): A
mathematical or graphical association or causal relationship
between an ambient concentration of a contaminant or
substance and a specified biological effect in an individual
(Duffus, 2000).
Concentration-Response Relationship (Curve): A
mathematical or graphical association or causal relationship
between an ambient concentration of a contaminant or
substance and a specified biological effect in a population
(Duffus, 2000).
Concentration Ratio: The ratio of concentration of a
substance in a tissue or organ compared with that which is
found in surrounding tissue/organ(s) under equilibrium or
steady-state conditions (IPCS, 2000).
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Confidence Interval (CI): A range of values from a sample
that bracket a point estimate assuming a random sample,
normal distribution, and a ratio measurement (IPCS, 2000).
For a 95% CI, it means that there is a 95% probability that
the true value is contained in the range (with an a = 0.05).
Confidence Limit: The lower and upper specific values for
the confidence interval.
Congenital Abnormality: Any abnormality, genetic or non-
genetic, that is present at birth (OTA, 1986).
Concentration: (1) Quantity of a substance per unit volume
or weight (Stedman's, 1982). (2) Ratio of the mass or volume
of a solute to the mass or volume of a solution or solvent
(Dorland's, 1988). Usually designated as "c".
Confounder (Confounding variable): A variable that can
cause or prevent an outcome of interest which is not an
intermediate variable, and is not associated with the factor
under investigation (Last, 1983).
Consistent, Consistency: The property of a measurement or
estimate that conforms to themselves over time or repeated
trials; see "reliability."
Consolidated Human Activity Database (CHAD): A
data base of daily human activity patterns (time-sequenced
activity information) for U.S. residents that was developed
for, and is maintained on EPA's web site (www.epa.gov/
chadnel 1/). As of 2012, CHAD contains 34,773 person-
days of data, about half of which is a single diary-day of
information for an individual. Additional data are included
on an ad hoc basis as they become available.
Contact Rate: The rate per unit time that a boundary of an
organism comes into contact with a medium.
Continuous Exposure: An exposure profile in which no
concentration level falls to zero or to some value below a
level of interest (for a specified time period).
Control Group: A group of subjects observed in the absence
of a condition or exposure agent for comparison with
subjects having the condition or exposure (EPA, 1989).
Coronary Blood Vessels: Blood vessels that supply blood to
the heart muscle (Morehouse & Miller, 1976).
Correlation: A change association between two or more
entities (variables) characterized by a linear relationship.
Correlation Coefficient (r): A measure of association
indicating the degree to which two of more samples fit a
linear association, assuming random probability sampling,
a normal distribution, and an a=0.05. Also called a Person
product-moment correlation coefficient. See also "Spearman
Rank Correlation Coefficient."
Critical Pathways: Environmental or other pathways by
which a significant amount of a substance moves from a
source to a receptor of concern.
Critical Receptor: A specified, or identified, receptor of a
substance that is most adversely affected by receiving a dose
of the substance.
Critical Tissue: Tissue that shows adverse effects at the
lowest dose, with no reference to severity of the effects
(International, 1986).
Cross-sectional Study: A study or analysis having samples
for only one point in time.
Cumulative Frequency Distribution: A statistical
distribution where sampled values are ranked in a specific
order, generally lowest-to-highest.
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Daily Energy Expenditure: A measure of the amount of
energy expended by a person (or living organism) on a daily
basis to support basic metabolism and dietary processes, and
to undertake all other physical activities, including work. An
alternative term for "Total Daily Energy Expenditure."
Dead Space Fraction (VD/VT): Aunitless measure of the
physiological dead space of the lungs and represents the
fraction of inspired air that does not exchange gas with
capillary blood. In normal individuals, the value is generally
between 0.25-0.35 and decreases with progressive exercise.
(If it does not, it is a marker of either pulmonary vascular or
interstitial lung disease.) Luks, et al. (2012).
Dead Space Volume (VD): The combined volume of all air
passages in which no gas exchange occurs; these include the
trachea, bronchi, and bronchioles down to~but not including-
-respiratory bronchioles (Morehouse & Miller, 1976). About
a third of every resting breath, or about 150 mL, is exhaled
exactly as it came into the body. Because of dead space,
taking deep breaths more slowly (e.g., ten 500 mL breaths
per minute) is more effective than taking shallow breaths
quickly (e.g., twenty 250 mL breaths per minute). Although
the amount of gas per minute is the same (5 L/min), a large
proportion of the shallow breaths is dead space, and does not
allow oxygen to get into the blood.
There are several components that go into dead space. These
include anatomical dead space (gas in the conducting areas
of the respiratory system, such as the mouth and trachea,
where the air doesn't come to the alveoli of the lungs),
physiological dead space (the anatomical dead space plus
the alveolar dead space), and alveolar dead space (the area
in the alveoli that does not exchange air because there is not
enough blood flowing through the capillaries for exchange
to be effective). Alveolar dead space is normally very small
(less than 5 mL) in healthy individuals, but can increase
dramatically in heart or lung disease.
Demographic Group: A group of people within a population
that share one or more defined demographic characteristics,
such as gender, age, ethnicity, household income, working
status, health impairment, or housing type. These groups
usually are defined differently depending upon the health end
point of interest, the pollutant, and the time or spatial area of
interest. Often it is called simply as a "cohort."
Dermal adsorption: The process by which materials come
in contact with the skin surface and are then retained and
adhered to the epithelial epidermis without being taken into
the body. (EPA, 1992).
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Dermal Exposure: The contact of an organism's external
membrane, generally the skin, with a chemical substance or
physical agent via any medium.
Dermally absorbed dose: The amount of substance from a
dermal exposure that is absorbed into the body.
Deterministic: In statistics and modeling, a variable taking
on a single, unchanging value.
Deterministic Model: A mathematical model in which
its parameters and variables are not subject to random
processes. The underlying system defined by the model is
entirely defined by its initial conditions (IPCS, 2000).
Diary Study: A "field" study in which subjects are asked to
record general of specific activities, such as foods consumed,
time uses undertaken, locations frequented, etc. as they are
done. Compare with "Recall Survey."
Dietary Induced Thermogenesis (DIT): The amount of
energy needed to process the digestion of food above that
needed for basal metabolism (Nieman, 1990). Often it is
simply defined to be some proportion of DTEE, usually
10%, but that is a questionable assumption due to individual
differences and the type of food ingested (Nieman, 1990). It
also is known as: the "Thermic effect of food" (TEF).
Diffusion: (1) Movement of a chemical substance from
areas of high concentration to areas of low concentration.
Biologically, diffusion is an important means for toxicant
deposition for gases and very small particles in the
pulmonary region of the lungs (EPA, 1989). (2) The process
by which molecules or other particles intermingle as a result
of their random thermal motion (EPA, 1993).
Direct Exposure: Exposure to a receptor or subject who
comes into contact with a chemical or substance in the
same medium in which it was released into the environment
(IPCS, 2000).
Disease: Any deviation from, or interruption of, the normal
structure or function of any part, organ, or system of the
body that is manifested by a set of symptoms or signs and
woes etiology, pathology, and prognosis may be known or
unknown (Dorland's, 1988).
Distribution: In biology and toxicology: the transport of
a substance through the body by a physical means, such
as active transport or diffusion. It is dependent upon the
chemical properties of the toxicant or its metabolites and~to
some extent~on the route of exposure and physiological state
of the body (EPA, 1988).
In statistics: A set of sampled values or measurements
derived from a specific population that represents the range
and array of data for the measured quantity (EPA, 1995).
Distribution-free: A method of statistical testing a
hypothesis, or establishing a confidence interval, that
does not depend upon form of the underlying distribution.
Generally applied to variables not following a normal
distribution (Last, 1983).
Diurnal: Having a repeating pattern or cycle 24 hours long.
Doer: A person who participates in a specific type of time
use or activity.
Dosage: "Dose Rate" [see].
Dose: (1) Inspired air concentration per unit time. (2)
Presence of a pollutant [substance] inside a target (Duan,
et al., 1990). (3) Quantity of a substance (contaminant)
absorbed across an exchange boundary of a receptor organ
and available for metabolic interactions (EPA, 1992). See
also: "Applied Dose," "Biologically Effective Dose," "Intake
Dose," and "Internal Dose." (4). The quantity of energy or
xenobiotic substance available for interaction with metabolic
processes or biological receptors after it crosses the outer
boundary of an organism (RPA, 2003). (5). The amount of
agent that enters a target during a specified time interval by
crossing a contact boundary (Zartarian et al., 1997). (6) In
pharmacology, the quantity of a drug or other material to be
administered at one time (IPCS, 2000).
Dose-Effect Relationship (D/E): A correlative relationship
between a dose of a substance or agent and the biological
response (effect) in an individual (not a population). This
often is confused with "Dose-Response Relationship," which
should only be applied to a population. A linear dose-effect
relationship between dose and biological response follows
a straight line. In other words, the rate of change (slope) in
the effect is the same at any dose. A linear dose response
is written mathematically as follows: if E represents the
expected, or average, effect and D represents dose, then E =
a * D, where a is the slope, also called the linear coefficient.
Dose Membrane: A barrier that resists the flow of an agent
after it crosses a contact boundary (Zartarian, et al., 1997).
Dose Metric: A specific description of the dose received
by a receptor or target organ during a specified time period.
A fully-specified dose metric includes an estimate of the
magnitude or intensity of the substance, an averaging period,
and a "profile" of the dose received over time. The time
pattern of dose rate received is a fully-specified dose metric.
Dose Profile: The time pattern of intake dose received by
a target organ or system. For inhaled substances, it is the
sequential pattern of concentration for a specified time period
times the intake, or inhalation, rate (e.g., c * t * VE).
Dose Rate: (1) Dose per unit time (and, sometimes, per
body mass), sometimes called "dosage." Often dose rate is
expressed on a body-weight basis, such as mg kg1 day1; dose
rate also is expressed as an average over a time period (EPA,
1992). (2) The quantity of material absorbed across a unit
area of an exchange boundary per unit time.
Dose-Response Curve: A curve on a graph based on
responses occurring in a population as a result of a series
of stimuli intensities or doses. A visual representation of a
D/R relationship.
Dose-Response Relationship (D/R): (1) A correlative
relationship between a dose of a substance or agent and
the proportion of a population that experiences a specified
effect. It is developed by integrating across individual
dose-effect relationships for a specified effect level. (2)
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The relationship between magnitude of applied or internal
dose and a specified biological response. [In an individual,
this is called "dose-effect"; see the above.] Response can
be expressed as measured or observed incidence ["effect"
in an individual], percent response in populations, or the
probability of occurrence of a response in a population (EPA,
1992). A linear dose-response relationship between dose and
biological response for a population follows a straight line.
In other words, the rate of change (slope) in the population
response is the same at any dose level. A general linear
dose response is written mathematically as follows: if R
represents the expected, or average, population response and
D represents dose, then R = a * D, where a is the slope, also
called the linear coefficient.
Dosimetry: (1) Accurate determination of dose (Stedman's,
1982). (2) Determination of absorbed dose in a substance
by measuring chemical reactions (International 1986).
(3) Estimating the amount of substance delivered to or
absorbed by a specific target site (Miller et al., 1989). (4) The
modeling of the amount, rate, and distribution of a substance
in the body, especially as it pertains to producing a particular
(specified) biological effect (EPA, 1989).
Doubly Labeled Water (DLW): The "gold standard"
for measuring Total Daily Energy Expenditure [see]. It is
based upon the following relationships: C02 production is
estimated from the difference in turnover rates of two tracer
isotopes~2H and 180~inthe body's water pool. 2H is lost
from the body entirely as water (urine, sweating, insensible
water loss, etc.) while 180 is lost both as water and C02. The
difference between the elimination rates of the two isotopes
is therefore an estimate of C02 production rate. That, plus
an assumed (or measured) respiratory quotient (RQ) allows
one to predict total daily oxygen consumption. Daily total
energy expenditure is estimated from C02 by using the Weir
Equation [see].
Duration: A measure of the length of time associated with
a specified event of interest; e.g.: an exposure, dose intake,
exercise period.
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Effective Dose: A dose metric that simply is the product
of concentration, exposure duration, and ventilation rate
(Adams, Savin and Christo, 1981). ED = c * t * VE. Its units
often are ppm-liters per elapsed time period (min, hour,
day) or could simply be a concentration metric (|ig) for the
applicable time period.
Energy: The capacity to perform work, produce force, or
generate heat (McArdle et al., 1991).
Energy Cost of Breathing: The oxygen consumption
requirements of breathing itself needed to generate sufficient
pressure (force) in the respiratory system to move blood
to the locomotor muscles (McArdle et al., 2001). This
is also known as the "Oxygen Cost of Breathing." At
maximal exercise, it may be as high as 15% of total oxygen
consumption needed for the workload. This cost is lower
relative to V02MAX in fit individuals than in "normals."
Energy Expenditure: (1) The amount of energy needed
to maintain life and perform work (undertake physical
activity). Energy is expended in humans in three general
ways: (a) to maintain body temperature and those
involuntary muscular contractions needed for circulation
and respiration-this is resting [but not basal] metabolism;
(b) to digest and assimilate food-also known as dietary
induced thennogenesis; and (c) to support muscular
activity (Montoye et al.,1996). There are a number of
ways to measure energy expenditure in humans. The
unit of energy used in nutritional studies generally is
the kilocalorie (kcal). It is equivalent to 4.185 kJ "and
corresponds to the consumption of about 239 mL of oxygen"
(Andersen et al., 1978).
Energy Metabolism: Metabolic activity associated with
energy production or utilization (International, 1986).
Epidemiology: Study of the distribution and associations
of health-related states or events in specified populations
(Last, 1983).
EPOC: Excess post-exposure 02 consumption; it also is
known as recovery 02. See also "Oxygen Debt."
Ergometer: An apparatus for measuring the amount of work
performed by a subject, generally as oxygen consumed; the
stationary bike ergometer is one example (Morehouse &
Miller, 1976).
Essential Asthma: Asthma of unknown or not apparent
cause; also known as "true asthma." (Dorland's, 1988).
Event: (1) In exposure modeling, a time period <1 clock h
characterized by a specific activity, "activity-level" (energy
expenditure or breathing level), exposure level (intensity),
and location. If any of these characteristics change, the
event changes, even if a person stays in the same location
and undertakes the same general activity. (2) An observed
state of activity (action) having a specific time duration of
measureable discrete units (t) within a larger temporal period
T. An event has a measurable intensity on some property of
interest that occurs. (3) Frequency of events is the number
of times a specified event occurs with a specified T. Pattern
of events occur if a series of similar event types are seen in
T. If t between events is regular, the events have periodicity;
otherwise the events have an irregular pattern. Periodic event
that occur over multiple T's have rhythm. Adapted from
McGrath & Tschan (2004).
Excess Metabolism of Exercise: (1) Increase in metabolic
activity during exercise and recovery from it, over that used
during sleep (International, 1986). (2) Amount by which
the oxygen consumed (or C02 eliminated) during exercise
and recovery exceeds the corresponding rates during sleep
(Dorland's, 1986).
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Excess Post-Oxygen Consumption (EPOC): The oxygen
consumption to "make up" oxygen—the "oxygen deficit"--
needed for anaerobic process associated with moderate-to-
high physical activity. See also "oxygen debt."
Exercise: (1) A subset of physical activity that is planned,
structured, and repetitive and has as an objective to improve
or maintain a person's physical fitness (Kohl et al., 2000).
(2) Any and all activity involving generation of force
by activated muscles which results in a disruption of a
homeostatic state (ACSM, 1990).
Exercise Conditioning: Repeated exercise with sufficient
intensity and duration to increase a participant's strength and
endurance (Morehouse & Miller, 1976).
Exercise Intensity: A specific level of maintenance of
muscular activity that can be quantified in terms of power,
isometric force sustained, or velocity (American College of
Sports Medicine as cited in McArdle, et al., 2001).
Exercise Training: Repeated exercise that improves
economy of movement that increases performance
(Morehouse & Miller, 1976).
Exergonic: Characterized or accompanied by the release of
energy; said of chemical reactions that release free energy so
that their products have lower free energy than the reactants
(Dorland's, 1988).
Exogenous: Produced or otherwise originating outside of an
organism (International, 1986).
Expected Value (E[x]): The mean value of a cumulative
normal distribution random sample.
Expiration: Act of breathing out, or expelling air from the
lungs (Dorland's, 1988). The time it takes for one expiration
is known as T„.
E
Expiratory Reserve Volume (ERV): The maximal volume
that can be exhaled from the lung's resting end-tidal
expiratory position. See also "functional residual capacity."
Expired Ventilation (VE): The amount of air in the lungs
that is expired per breathing event.
Expired Ventilation Rate (VE): The rate at which expired
ventilation occurs; it also is known as pulmonary ventilation
in L min1 BTPS. VE approximates Vp and by definition
V =f * V
E R T
Exposure: (1) Contact between a target and a pollutant
[substance/agent] at an exposure boundary (Duan et al.,
1990). (2) Proximity and/or contact with a disease agent
[substance] in such a manner that transmission of the agent
to the organism of interest may occur (Last, 1983). (3)
Exposure is quantified as the amount of agent/substance
available at the exchange boundary of the receptor organism
per specified time period (EPA, 1989).
Exposure Assessment: Measurement or estimation of the
magnitude, frequency, duration, pattern and route of exposure
of a target~an individual or a population~to substances in
the environment for a specified time period. An exposure
assessment also describes the nature of exposure and the size
and nature of the exposed populations (EPA, 1989).
Exposure Concentration: Concentration of a chemical or
pollutant in a transport or carrier medium at the point of
contact with a receptor of interest (EPA, 1992).
Exposure Duration: Length of time that contact with a
chemical or pollutant occurs; total time that an individual is
exposed to a chemical being evaluated (EPA, 1997).
Exposure-Effect Relationship: Exposure-Response
Relationship: The association between a fully-specified
exposure metric and the distribution of adverse effects in a
person (receptor of interest).
Exposure Event: (1) The joint set of occurrences in which
the contact boundary of a receptor of interest intersects a
medium having agent concentrations of interest during a
time interval of interest (Zartarian et al., 1997). (2) In the
APEX, pNEM, and NEM series of exposure models, it is a
varying period of time between 1 minute and the next clock
hour where a subject is located in a single microenvironment
that is characterized by a constant concentration level
and an activity-specific activity level (energy expenditure
level). If any of these parameters change, a new exposure
event occurs.
Exposure Factor: A "point estimate" or a distribution
of values for any unknown quantity of interest used to
undertake an exposure assessment. Generally these factors
appear in a "sanctioned" handbook, such as EPA's Exposure
Factors Handbook or AIHC's Exposure Factors Sourcebook.
Exposure Frequency: The number of times and exposure of
interest occurs in a specified time period.
Exposure Level: Concentration of a contaminant to which
an individual or a population is exposed.
Exposure Limit: Suggested or mandatory limit, standard,
or restriction implemented by some authority to ensure that
possible receptors are not exposed to concentrations of a
substance, usually in a specified location, that can cause
some unwanted effect (CMA, n.d.). It is thought that the limit
will result in minimal or no adverse (health or other) effects.
A fully specified limit will describe the level (magnitude or
intensity), duration, frequency, and pattern of exposure that
should be avoided.
Exposure Medium: See "Media" / "Medium."
Exposure Metric: A specific description of the exposure
experienced by a receptor or organism during a specified
time period. A fully specified exposure metric includes an
estimate of the magnitude or intensity of the substance, an
averaging period, and a "profile" of the exposure experienced
over a specified time period. The "Exposure Profile" is an
example of a fully-specified exposure metric.
Exposure Monitoring: The actual measuring or monitoring
of substances in microenvironments and/or at or near
individuals as they undertake personal activities in various
microenvironments. See "Microenvironmental Monitoring"
and "Personal Monitoring."
Exposure Pattern: See "Exposure Profile."
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Exposure Pathway: The physical course that a substance
takes between its source and an exposed receptor [organism]
(CMA, n.d.).
Exposure Profile: The record of instantaneous exposures
over a time period; a function of time (Duan et al., 1990).
Some authors label it the "time course of exposure." A fully
described exposure profile provides information concerning
the intensity (magnitude), duration and time pattern of
exposure experienced by a receptor.
Exposure-Response Relationship: The association between
a fully-specified exposure metric and the probability of
an adverse effect in a population, which is estimated by
integrating across individual exposure-effect relationships.
Exposure Route: The manner in which a substance or
chemical enters into, or is absorbed by, an exposed receptor
or organism upon first contact; the main routes of exposure
include inhalation ingestion, and dermal absorption (CMA,
n.d.). A substance may enter by all three routes within some
specified time period of analysis.
Exposure Scenario: A set of facts, assumptions, and/or
inferences about how a particular exposure event occurs
that assists an exposure assessor in evaluating, estimating,
modeling, and/or otherwise quantifying exposure(s) to a
specified receptor or population (CMA, n.d.).
Exposure Surface: A target surface where an agent is
present. Examples include a/an: stomach wall lining, lung
surface, exterior of an eyeball, skin surface, leaf, wall, and a
conceptual surface over an open mouth (IPCS, 2000).
External Dose: A vague term that approximates "exposure."
It is confusing and should not be used.
Extrinsic Asthma: Asthma caused by an enviromnental
factor (Dorland's, 1988).
F
00000000000000C<>000000000000000000000000000000000000<>00000
Factorial Method: An approach used by clinical nutritionists
and exercise physiologists to estimate the energy expended
by a specific activity (EEA) or a set of activities. Basically it
is simply the multiplication of time spent in an activity by an
estimate of the oxygen consumption or energy expenditure
associated with that activity—usually generalized from
clinical measurement studies. An example is time in activity
A times METSA. The basis of EPA exposure models using
the METS distributions in CHAD is the factorial approach
of method.
FAO: Food and Agricultural Organization (part of the
United Nations).
Fat-free Mass (FM): Body mass devoid of all extractable fat
(FFM=BM-FM) (McArdle et al., 2001). It differs from lean
body mass (LBM) in that the latter does not include essential
fat, which is about 3% of total body mass.
Fatigue: Diminished capacity for work caused by previous
work; usually used for subjective sensations (Morehouse &
Miller, 1976).
Fick Equation: An equation developed by German
physiologist Adolph Fick in 1870 that describes the
relationship among cardiac output (stroke volume), the
difference between arterial and venous blood, and oxygen
consumption (McArdle et al., 2001). 02 = HR x SV x
(a-02 Diff.).
Fit(ness): See "Physical Fitness".
Flow Volume Curve: Graph of instantaneous forced
expiratory flow recorded at the mouth, against corresponding
lung volume. When recorded over the full vital capacity, the
curve includes maximum expiratory flow rates at all lung
volumes in the vital capacity range and is called a maximum
expiratory flow-volume curve (MEFV). A partial expiratory
flow-volume curve (PEFV) is one which describes maximum
expiratory flow rate over a portion of the vital capacity only
(EPA, 1989).
Frailty: A medical syndrome with multiple causes and
contributes that is characterized by diminished strength,
endurance, and reduced physiologic function that increases
an individual's vulnerability for developing increased
depency and/or death. (Gordon et al., 2013; p. 8.)
Frequency of Exposure: The number of times some
specified exposure event occurs within a specified time
period (CMA). The term usually is used when the specified
exposure event [of some specified magnitude or duration]
occurs on an intermittent basis. A relevant example is EPA's
O, NAAQS standard; it is designed to reduce the number of
daily 8h exposures to 0.08 ppm or higher O, concentrations-
-i.e., the frequency of exposure to the O, level specified-
during the "ozone season" [generally April-September],
G
ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
Gas Exchange: As regards the lung, it is the exchange of
gases between alveoli and capillaries; often used to denote
movement of 02 into pulmonary capillary blood as CO,
enters the alveoli from blood (OAQPS Staff, 1988).
Gas Exchange Ratio (R): See "Respiratory Quotient."
Gas Exchange Ventilatory Threshold (GET): An indirect
and non-invasive index of the transition from aerobic to
anaerobic metabolism (Lind et al., 2005).
Gastrointestinal: Pertaining to the intestines and stomach.
Gender: A person's self-representation as male or female;
it is shaped by enviromnental factors and by experience.
The term refers to socially-influenced behaviors (Arbuckle,
2005), but is used in this report to represent "sex" to
distinguish it from sex as a type of activity.
Geometric Mean: An estimate of the average of a log-
normal distribution. Specifically, it is the nth root of the
product of n observations.
Geometric Standard Deviation: A measure of variability
of a log-normal distribution. It is the antilogarithm of the
standard deviation of the logarithms of the observations.
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H
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Habitue: A person who enters into, or is within, a particular
or specific location or microenvironment.
Haldane Transformation: A relationship between inspired
and expired air developed in the 1920's and used to "correct"
V02 E expired air flow rates—which is what is measured—
to estimate V02 (inspired air flow rates, which is what is
desired, but not readily measureable. Subsequent analyses
by others have shown that the average difference between
measured and estimated V02 E using the Transformation is
about 0.8%, with a higher bias at higher work rates (McArdle
etal., 2001).
Health: Physical, mental, and social well-being; not just the
absence of disease (McArdle, et al., 2001).
Heart Rate (HR, fc): The number of heart beats (complete
pulsations) per time specified period, e.g., beats per minute.
The fc term is used most often by exercise physiologists
(Norgan, 1996).
HRm AX: Maximum HR that can be sustained for a
specified activity and time period, following a defined
exercise protocol. Some definitions link it to the heart
rate at V02MAX orVOPEAK.
hRr: Heart rate at rest. Also denoted as
HRrest (inbpm).
HRres: Heart rate reserve (inbpm); it is equal to:
(HRmax - HRrest)-
HRRkst : Heart rate at rest (inbpm).
Heart Rate Recovery (HRR|,tov| RV): - HRR is defined as
the decay in heart rate over the first one minute of exercise
recovery and it relates to the degree of sympathetic and
parasympathetic neuronal control. The average normal Heart
Rate Recovery is 28 beats per minute, and a HRR of less
than 12 beats per minute is indicative of patient risk. This
parameter is useful in assessing patients with congestive
heart failure, coronary artery disease and angina. It is
effectively used in evaluating the physiological response to
cardiac rehabilitation and pharmaceutical or medical device
intervention.
Heart Rate Reserve (HRR): The difference between
maximum (or peak) heart rate and resting heart rate. Often
a theoretical HRmx is used (HRmx = 220-Age is the most
common estimated, but others are seen in the literature)
instead of a measured rate (Luks et al., 2012).
Heart Rate Reserve Percent (%HRR):The percent or ratio
of the actual heart rate at a level of work to the maximum
heart rate. (How close an individual is to achieving their
max heart rate). The percent heart rate reserve is calculated
as follows: [(HRstage - HRrest)/(HRpeak - HRrest)] X
100, where HRstage is the observed heart rate at any point
in exercise, HRpeak is the actual observed HR at the peak
level of exercise performed (i.e., not a theoretical value),
and HRrest is the observed resting heart rate. In other words,
percent heart rate reserve is the difference between the heart
rate at any point in exercise and the heart rate at rest divided
by the difference between the maximally observed heart rate
and the heart rate at rest with the result multiplied by 100 to
equal percent.
The exception to this formula is in maximal exercise stress
testing, where HRpeak is a theoretical value based on the
formula 220 minus the patient's age in years (220 - age
in years).
Hemoglobin (Hb): The red, respiratory protein of the red
blood cells, hemoglobin transports oxygen from the lungs
to the tissues as oxyhemoglobin (Hb02) and returns carbon
dioxide to the lungs as hemoglobin carbamate, completing
the respiratory cycle (EPA, 1989). Hemoglobin's affinity
for CO is 200 times greater than that of 02 (Haymes &
Wells, 1986).
Homeostasis: (1) State of equilibrium in the body or organ
with respect to various functions and chemical composition
of fluids and tissues (Stedman's, 1982). (2) The process by
which body or organ equilibrium is maintained (Stedman's,
1982), which generally is achieved by negative feedback
mechanisms (Dorland's,1988).
Hormesis: A toxic substance that causes a "stimulation"
of a bodily reaction or process at low doses, but inhibits
responses at subsequent higher doses (Calabrese & Baldwin
1998). Hormesis is an inappropriate concept when discussing
a population dose-response relationship, by definition.
Hormetic Agent: An agent or condition that causes toxicity
at high doses but shows net "beneficial effects" at very low
doses (Hart and Frame, 1996).
Human Population Biology: The study of human variety
at every level of organization within, between, and among
populations. Emphasis is placed on understanding the
development, causes, and evolution of that variety and
the biosocial effects of it (Harrison, 1996). It is associated
with population genetics, enviromnental physiology, bio-
demography, and sociobiology.
Human physiology: Study of phenomena associated with
the functioning of humans (International, 1986).
Hyperventilation: Over-ventilation; increased rate of air
exchange relative to metabolic carbon dioxide production
so that alveolar carbon dioxide pressure tends to fall below
normal (EPA, 1989). Pulmonary ventilation that is increased
out of proportion to metabolic requirements (Morehouse &
Miller, 1976).
Hypoxia: Any state in which oxygen in the lungs, blood,
and/or tissues is abnormally low relative to that of a normal
man resting at sea level (OAQPS Staff, 1988).
I
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ICC: Intraclass correlation coefficient [see].
Index (Indices) of Exposure: See: "Average Exposure,"
"Exposure Profile," "Peak Exposure," "Integrated Exposure."
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Indirect Exposure: Exposure not resulting from direct
contact with a substance in the environmental medium into
which it is first emitted. Eating food that is contaminated by a
chemical substance originally emitted into the air or water in
an example of an indirect exposure.
Indirect Exposure Assessment: An approach used to
model or estimate human exposures by combining data on
microenviromnental concentrations and human activity
information for the same time period; the joint set of these
two data bases results in an exposure estimate for a particular
time period.
Individual (Intra-individual) Variation: Variation of
biological variables within an individual (Last, 1983).
Indoor Air: Air inside of a structure, building, or other
space that often can be regulated or "conditioned" by a
mechanical means.
Inhalation: (1) Drawing of air and other substances into
the lungs via the nasal or oral respiratory route (Dorland's,
1988). (2) Inspiration (Stedman's, 1982).
Inhalation Exposure: An exposure to a substance of interest
associated with inhalation or respiration (the intake of air into
the oral/nasal boundary).
Inhaled Dose: The amount of an inhaled substance that
is available for interaction with metabolic processes or
biologically significant receptors after crossing over the outer
boundary of an organism (EPA, 1997).
Intake: Process by which a substance penetrates the outer
boundary of an organism without passing an absorption
barrier; examples are: ingestion and inhalation (EPA, 1992).
Intake Dose: (1) Amount of a substance or material that is
inhaled, ingested, or absorbed by an organism (EPA, 1992).
(2) The dose resulting from an agent crossing a contact
boundary without subsequently diffusing through a resisting
boundary layer (Zartarian et al., 1997).
Intake Dose Rate: Intake dose per unit time period.
Intake Rate: The rate at which a carrier medium crosses
a contact boundary (Zartarian et al., 1997). For ingestion,
the intake rate is simply the amount of food containing the
contaminant of interest that an individual ingests during some
specific time period (units of mass/time). For inhalation, the
intake rate is the rate at which contaminated air is inhaled.
Factors that affect dermal exposure are the amount of
material that comes into contact with the skin, and the rate at
which the contaminant is absorbed (EPA, 1997).
Integrated Dose: The amount of a substance entering the
target during a specified time period; it is the integral of
instantaneous dose over time.
Integrated Exposure: The integral of instantaneous
exposures time (Duan et al., 1990).
Intensity: As often used in exposure assessment, it is a
synonym for concentration or energy level ("magnitude") for
a specified time period.
Interindividual Variation: Variation of biological
parameters among individuals in a population (Last, 1983).
Intermediate Variable: Available that occurs in a causal
pathway from an independent to a dependent variable,
and that is statistically associated with both variables
(Last, 1983).
Intermittent Exposures: An exposure profile that includes
"gaps" or respites in which concentration of the substance
of interest goes to zero or to some value below a level of
interest (for a specified time period).
Internal Dose: (1) The amount of a substance penetrating
across an absorption or exchange boundary of an organism
(EPA, 1992). It approximates "Intake Dose." (2) In exposure
assessments, the amount of a substance penetrating the
absorption barriers (e.g., skin,, lung tissue, gastrointestinal
tract) of an organism through either physical or biological
processes: "absorbed dose" [see] (EPA. 1997).
Intraclass Correlation Coefficient (ICC): The ratio of
between-group variability to the total amount of variability
(between + within) variability "explained" by a statistical
procedure. ICC = cB / (cB + aw ). The same concept holds
for between-individual variability and within-individual
variability. The statistical procedures used include one- and
two-way (or repeated-measures) AVOVA, and even a three-
way ANOVA (Safrit & Wood, 1989). An ICC varies between
0 and 1, with a low value indicating a lot of within-individual
(group) variance relative to between-group variance.
The statistic is often used in estimating the reliability of
repeated observations for an instrument (or person) in
a sample containing multiple instruments (or persons);
it takes the design of A-measures from n subjects
(Safrit & Wood, 1989).
Intrinsic Asthma: Asthma attributed to pathophysiologic
disturbances and not to environmental factors
(Dorland's, 1988).
K
0000000000000000000000000000000000000000000000000000000000000
Kilocalorie: The amount of heat required to raise the
temperature of 1 kilogram of water 1C (Morehouse &
Miller, 1976). This is sometimes known as a "large calorie."
1 kcal = 1,000 Cal = 4,186 J = 4.186 kJ = 3.968 BTU. These
equivalencies vary in the literature.
Krebs Cycle: See "Citric Acid Cycle."
K-S test: The Kolmogorov-Smirnov "non-parametric"
statistical test of two distributions where sample values
are ordered as a cumulative frequency distribution [see].
The sample assumes random-sampling from an identical
population, but does not assume that the data, which can
be interval, ordinal, or ratio observations, are normally
distributed.
000O
Lactate: The anionic (containing an anion, or
negatively charged ion) form of lactic acid in the blood
(Dorland's, 1988).
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Lactate Threshold (Lr): The highest oxygen consumption
(exercise or work intensity) with <1.0 mM / L increase in
blood lactate concentration above the pre-exercise level
(McArdle et al., 2001). Lj, is also expressed as mg dL1 of
whole blood, and sometimes as a volume percent of whole
blood (vol%). 1.0 mML1 = 9.0%vol%. By convention,
blood lactate concentration usually is expressed simply as
millimoles (mM) (McArdle et al., 2001).
Lj, varies significantly with respect to physical fitness.
Training increases the V02 level at which Lj, occurs. In
untrained persons, it is about 67% of V02 MAX. In Trained
"normals," Lj, is on the order of 80% of V02 MAX, and in
people with coronary artery disease, Lj, may be 100% of V02
MAX (McArdle et al., 2001).
Lactic Acid: (1) A metabolic intermediate involved in
many biochemical processes (Dorland's, 1988). (2) End
product of glycolysis, which provides energy anaerobically
in skeletal muscle during heavy exercise and which can
be oxidized aerobically in the heart for energy production
(Dorland's, 1988).
Lactic Acid Threshold (LAT): The 02 uptake level above
which lactate acid accumulates [see Lj,]. It used to be thought
that it was the point in the oxygen consumption/work rate
relationship where there is hyperventilation relative to
V02 but not to C02 elimination. That is now known as the
Ventilatory Threshold [se].
Lazarev & Brusilouskaza's Rule: A "rule" of exposure
where c * tb = constant toxic effect = k.
Lean Body Mass (LBM): Technically, LBM refers to the
mass of muscle, water, bone, and "essential fat" in the body.
Essential fat is the small percentage of non-sex-specific fat
contained in the central nervous system, bone marrow, and
internal organs. Essential fat approximates 3% of total body
mass (McArdle et al., 2001). It often is used interchangeably
with fat-free mass (FFM), but they differ in the amount of
essential fat in the body.
Leisure: Any activity chosen primarily for its own sake;
thus, freedom of choice and intrinsic meaning to the
individual are the defining factors (Kelly et al., 1986).
Lifestage: A period of time in a person's life associated
with the life course of development as related to life cycle
of the family. With respect to the former, it includes such
stages as neonate, baby (infant), toddler, pre-schooler, school
child, teenager, young adult, etc. Viewed in that context, the
lifestage progression is one from absolute dependence on a
caregiver to relative independence and autonomy. Conditions
of aging can, of course reverse this pattern; frailty and
sickness can lead back to complete dependence on caregivers
for existence. The family role dimension relates to such
socio-cultural attributes as intra-familial relationships, child
care responsibilities, household and individual consumption
patterns (economic dimension), residential mobility and
"space consumption" needs (residential housing needs),
career stage (work-family relationships), sexual roles and
relationships, and transgenerational roles. Sometimes
the emphasis is placed on periods of transitions between
relatively stable lifestages, which often result in stress and
role identification problems.
Lifestyle: (1) Those components of daily behavior that are
systematic and regular over a specified period of time. The
term encompasses behavioral factors, such as time use (work,
leisure, sleeping patterns), dietary intake (nutrition), personal
habits (smoking, drug, and alcohol use), patterns of physical
activity and exercise (exercise habits, physical fitness),
health-promoting behaviors in general (including taking
action to prevent or detect disease, or for improving health
and well-being), and psychological and social considerations
(the form/intensity of social interaction, psychological
health, sexual health). Other attributes that sometimes are
included are locational considerations of where a person
chooses to live (condominiums, single-family subdivisions,
rural locations, isolated areas, etc.). Thus, the concept has a
number of dimensions and complex attributes. See: Harrison,
1996). (2). The pattern of living as expressed in a person's
activities, interests and opinions
Light Physical Activity: A phrase with many meanings,
in that the energy expenditure levels associated with it
are defined in highly variable ways (heart rate, % of heart
rate reserve, V02 consumption, and % of V02 reserve,
% of VO, „ ,, METS, etc.). Most of the definitions are
2 Peak' ' 7
laboratory or investigator-dependent, with little attempt
at standardization of the metrics used. Perhaps the most
rigorous definition might be the lower third of V02 reserve.
Linear Dose-Response Relationship: A relationship
between dose of a substance and the frequency or
severity of biological response in a population that varies
proportionately with the amount of dose (IRIS, 1999).
Linear Model: A mathematical or statistical model where
the dependent variable Y varies as a linear function of one or
more independent variables or factors. For one independent
variable (x), the most reduced form of the equation is Y =
b*x. For a statistical version of this simple model, Y = a +
b*x + e, where: a = an "intercept constant," and e represents
random variation, or error (Last, 1983).
Location: With respect to human activity modeling or
monitoring, a three-dimensional space that is occupied for
some known period of time by a habitue. When it has a
constant environmental concentration for a specified period
of time, it is known as a microenvironment.
Log-normal Distribution: A distribution of data such that
Y=log X is normally distributed. It is a "skewed" distribution
having regular parameters in log space.
Log-Transformation: Taking a logarithm of a sampled
quantity in order, generally, to makes its association with one
or more other sampled quantities linear so that usual (linear)
statistical tests can be used on the data. They generally are
used when one variable takes on a wide range of values,
but with diminishing association with one or more other
variables as the sampled values increase or decrease. The
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assumption of random-probability sampling still holds,
however. The data are usually characterized by a geometric
mean and geometric standard deviation.
Long-term: A vague term that relates to a relatively long
time period.
Longitudinal Study: A study or analysis with observations
or samples taken at multiple time points.
Lower Respiratory Tract: That part of the respiratory tract
below the larynx (EPA, 1989).
Lung: (1) One of a pair of highly elastic cone-shaped
organs of respiration occupying the thoracic cavity and
surrounded by a pleural sac (International, 1986). (2) Either
of the pair of organs that aerate blood. The right lung has
a superior, middle, and inferior lobes; while the left lung
only has the first and third lobes just mentioned. Each lobe
is subdivided into 2-5 bronchopulmonary segments. The
lung consists of an external serous coat (visceral layer of
the pleura), subserous areolar tissue, and lung parenchyma.
The latter is made up of lobules, which are bound together
by connective tissue. A primary lobule consists of terminal
bronchiole, respiratory bronchioles, and alveolar ducts,
which communicate with many alveoli. Each alveolus is
surrounded by a network of capillary blood vessels at the
interface of which gas exchange occurs (Dorland's,1988).
The average lung surface area in a normal adult human is 70-
90 m2 (Astrand & Rodahl, 1986).
Lung Volume (VL): Volume of the lung, including volume of
the conducting airways.
M
^000000000000000000000000000000000000000000000000000000
Macro-activity: With respect to human activity modeling or
monitoring, the general activity or pursuit that an individual
is engaged in for a specified period of time. See also:
Micro-activity.
Macro-Activity Data: Information on where a person
is and what she/he is doing for a specified period of
time. This information includes an identification of the
microenviromnent being occupied, the general activity being
undertaken, and the energy expenditure level (or heart rate,
breathing rate, etc.) of the activity being engaged in (or
relative energy level, such as METS).
MAX (max): Maximum; often used as a subscript.
Maximal Aerobic Capacity (V02MAX): The maximal
oxygen consumption rate recorded for an individual
following a, generally more progressive, protocol.
Maximal Expiratory Flow: See Maximal Ventilation Rate.
Maximal Heart Rate (HRmx): The highest heart rate value
measured during an all-out effort to the point of exhaustion
(Nieman 1999).
Maximal Heart Rate Reserve (HRR): The difference
between maximal and resting heart rates (Nieman, 1999).
Maximal Oxygen Uptake/Consumption (V02MAX): The
maximal capacity for oxygen consumption during maximal
exertion; also known as aerobic power and cardiorespiratory
endurance capacity (Nieman 1999). See V02MAX. It is
associated with the peak rate of oxygen delivery to the
working muscle, which in turn is dependent upon capacity of
the lungs and the cardiovascular system to transfer oxygen in
the body (Blomqvist, 1978).
For short periods of time, V02 MAX is a relatively stable
and reproducible individual characteristic although it does
change over the years and is affected by health status, body
size age, sex, and habitual level of physical activity of the
individual (Blomqvist, 1978). There is not much difference
in V02 MAX, on either an absolute or relative (body mass)
basis, in prepubertal children but there is for subsequent
ages due to body composition changes at puberty; level of
physical activity also decreases in most females at that time
(Blomqvist, 1978). The COV for V02 MAX for healthy, similar
age/gender cohorts is 10-15%, but larger relative variations
have been measured.
Maximal Ventilation Rate (VE MAX): A surrogate for the
maximal (inspired) breathing rate needed to sustain a
person's maximal oxygen consumption rate.
Maximal Voluntary Ventilation (MW): The volume
of air breathed by a subject during voluntary maximum
hyperventilation [rapid deep breathing] for some specified
time period. Its units are in liters at BTPS. MW is also
known as maximal breathing capacity, now an obsolete term.
Individuals vary greatly with respect to MW, partly due to
motivational factors. MW for college aged subjects are 70-
120 L/min $ and 100-180
-------
not a theoretical peak value), and METR is the MET value
while the patient is at rest (MET=1). This is simplified to:
METSreserve = [(METSact -1)( METSmax - 1)] * 100.
Metabolism: (1) Totality of chemical processes occurring
in a living organ, especially those associated with exchange
of matter and energy between a cell and its enviromnent
(International, 1986). (2) Physical and chemical processes
by which a living organized substance is produced and
maintained (Dorland's, 1988). (3) Aerobic metabolism
is dependent upon the presence of oxygen; also called
respiratory metabolism (International, 1986). Anaerobic
metabolism occurs in the absence of molecular oxygen.
MET(S): Metabolic equivalents of work. It is the ratio of
an activity-specific metabolic rate to a person's resting (or
basal) metabolic rate. (1) One MET approximates 1 kcal kg1
hour1 of energy expended in an adult, but this equivalency
masks important age and gender differences. It approximates
3.3-3.5 ml of 02 uptake kg1 min1, which is often used
as the resting state oxygen consumption rate for humans,
but is not really justified by measurement data. There are
comprehensive lists of the METS associated with common
human activities, including Durnin & Passmore (1967),
Ainsworth et al. (1993), and Montoye et al. (1996). Most of
the data are for young adults, and generally underestimate
relative energy expended by children and overestimate
energy expended by the elderly to accomplish a task.
Micro-activity: Skin-surface-including the mouth-contact
with an object [toys, furniture, materials, surfaces, liquids,
another part of the subject's skin itself, another person's skin
surface, etc.] by an individual that occurs as part of engaging
in a more general activity (macro-activity).
Micro-activity Data: Quantified information on the
frequency, duration, intensity, and pattern of skin surface
contact with the hand or object. This information should
include the nature of the skin-to-object contact [pressure,
motion, area and location of the body surface touched] as
well as characteristics of the surface area itself [surface type,
texture, and absorbing capacity].
Microenvironment: A physical 3-dimensional space
that is treated as a well-characterized, relatively
homogenous location with respect to a chemical or
substance concentration for a specified time period
(adapted from EPA, 1992).
Microenvironmental Model/Method: A predictive
exposure assessment approach to estimating sequential
exposures experience by an individual passing through a
series of microenviromnents, as defined by the individual's
"actual" or estimated human activity information. Usually,
the time period of interest is an entire day, or a series of days
that are "strung together" using daily activity information.
See "Activity Pattern," "Microenvironment," and "Indirect
Exposure."
Microenvironmental Monitoring: (1) The monitoring, or
measuring, of one or more specified substances in a micro-
environment via some type of media-specific sampling
device. The device may be "active" [flow through the
sampling train is mechanically regulated] or "passive"
[flow rate is not controlled, and the sampling rate is
greatly affected by deposition and Brownian movement].
Microenviromnental monitoring procedures are independent
of whether or not a potential receptor inhabits the space that
is being investigated.
Minute Ventilation: The volume of air expired per minute
(International, 1988).
Minute Ventilation Rate: See "V"
Minute Volume (MV): The minute volume of breathing
(MV); a product of tidal volume (VT) times the respiratory
frequency (fR) in one minute; synonymous with
minute ventilation.
Mitochondria: Intracellular structures containing enzymes
used in the chemical reactions that convert food energy to a
form that the body can utilize (Fahey et al., 2007).
Model: (1) Theoretical propositions on a domain of
reality (Becker, 1989). (2) An abstract representation of
the relationship among logical, analytical, or empirical
components of a system (Last, 1983). A model usually
consists of the mathematical structure and particular
constants or parameters associated with the structure. A
model may be deterministic or stochastic (Last, 1988). (3)
A representation or simulation of an actual situation or
natural system. The output (end result) of a model is an
estimate or prediction resulting from entering a set of input
quantities into and "running" (exercising) the mathematical
relationships that constitute the model's structure.
Moderate Physical Activity: A phrase with many meanings,
in that the energy expenditure levels associated with it
are defined in highly variable ways (heart rate, % of heart
rate reserve, V02 consumption, and % of V02 reserve,
% of VO, „ ., METS, etc.). Most of the definitions are
2 Peak' 7 7
laboratory or investigator-dependent, with little attempt at
standardization of the metrics used. One rigorous definition
might be the middle third of V02 reserve (McCurdy &
Graham, 2004).
Moderate and Vigorous Physical Activity (MVPA):
Moderate (MPA) and vigorous physical activity
(VPA) combined.
National Ambient Air Quality (NAAQS): Federal and
nationally-applicable air standards that are established by
the EPA under Section 109 of the Clean Air Act after a
lengthy review and comment period involving Agency and
independent scientists, the general public, interested parties
[generally, enviromnentalists and industrialists]. State air
regulatory agencies, and the political administration currently
in office.
Nasopharyngeal: Relating to the nose, nasal cavity, and
the pharynx.
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National Exposure Research Laboratory (NERL): A
group within EPA's Office of Research and Development
(ORD) that is focused on the measurement and monitoring
of human exposures, including ambient processes that lead to
these exposures.
National Human Activity Pattern Survey (NHAPS):
A random-probability telephone survey of "yesterday's"
activity pattern of continental US residents that was
conducted by the University of Maryland's Social Research
Center under contract to NERL in 1992-1994. NHAPS
contains 9,386 individual person-days of activity data, and it
is part of the CHAD database.
Neonatal: Newly born. In humans, it is considered to be up
to 6 weeks of age (EPA, 1989).
Non-Exercise Activity Thermogenesis (NEAT): All non-
exercise physical activity, such as fidgeting and squirming
(Levine et al., 2000).
Non-oxidative Energy System: The anaerobic system that
supplies energy to muscle cells through the breakdown of
muscle stores of glucose and glycogen. This is also known
as the anaerobic system or the lactic acid system because
chemical reactions take place without oxygen and produce
lactic acid (Fahey et al., 2007).
Non-parametric: Data that are not necessarily normally
distributed; said to be distribution-free. The term often is
used to denote a large category of statistical tests which do
not require an assumption of normally-distributed data or a
population (Blalock, 1960).
Normal Workload: A light or moderate load in which a
person's oxygen intake is adequate to supply the needs of the
body (Morehouse & Miller, 1976).
Null Hypothesis (HQ): An precisely-stated hypothesis the
truth of which is examined by a statistical test having a
specified level of significance, generally an a of 0.05, which
is the probability of rejecting HQ if it is true (also called
Type I error).
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Obesity: (1) A bodily-state above normal adiposity at which
health problems are likely to occur. Various criteria have
been used to identify obesity, such as "excess" body mass
on a per-age, height, or BMI bases, or on "excess" adiposity
(Bar-Or & Baranowski, 1994). See "Adiposity" and "BMI".
(2) An excess accumulation of body fat. Alternative measures
from Summerfield (1990) are: (a) for male children: body
mass is >25% fat, as estimated by the skinfold method,
(b) for female children: body mass is >32% fat, and (c) for
others: weight-for-height is >20% of the ideal.
Objective (Monitoring) Method: A means of obtaining
interval or ratio data directly from a subject by some
type of methods that does not involve a subjective
decision, or observation, by the subject regarding
the measurement quantity.
Occupational Exposure Limit (OEL): A generic term
denoting a variety of values and standards, some of them
enforceable by law. They generally are time-weighted
average concentrations-most often for airborne substances-
to which a worker can be exposed during one or more defined
time periods [e.g., 15 min, 1 h, 8 h] (EPA,1989).
Onset of Blood Lactate Accumulation (OBLA): The point
in the blood lactate concentration that shows a systematic
increase = 4.0 mM (McArdle et al., 2001). They also
state that researchers of equate the OBLA with the lactate
threshold (Lj,), but the two terms represent operationally
different (and precise) points in blood lactate/exercise
intensity levels. See Figure 14.5 in McArdle et al. (2001).
Oronasal: Breathing through the nose and mouth
simultaneously. This is the typical human breathing pattern
at moderate-to-high (heavy) levels of exercise or work.
Nasal-only breathing is the norm at rest or at low levels of
exercise or work, although some people are predominately
nasal breathers even at high exertion rates. (However, other
people are predominately oral breathers at any exertion rate.)
(Samet, et al., 1993).
Outcome: All possible results that stem from exposure to a
causal factor or to an intervention (Last, 1983).
Overload: (1) Impairment of lung clearance of a pollutant
[substance] at high lung burdens (Vincent & Donaldson,
1990). (2) A heavy workload in which oxygen uptake
is inadequate to meet the requirement (Morehouse &
Miller, 1976).
Oxidant: A chemical compound that has the ability to
remove electrons from another chemical species, thereby
oxidizing it; also, a substance containing oxygen that reacts
in air to produce a new substance, or one formed by the
action of sunlight on oxides of nitrogen and hydrocarbons
(EPA, 1992).
Oxidation: (1) An ion or molecule undergoes oxidation by
donating electrons. (2) The removal of hydrogen or electrons
from a compound. In biological oxidation, oxygen does
not directly combine with the substance "being oxidized,"
but combines with hydrogen to form water (Morehouse &
Miller, 1976).
Oxygen Cost (of Breathing): The amount of 02 needed to
sustain breathing itself. At rest (~6 L min-1) the oxygen cost
is ~2%. As ventilation increases, the energy cost per liter
ventilation increases rapidly, as does the oxygen cost-up to
-10% at 50 L min-1 or so (Astrand & Rodahl,1986).
Oxygen Debt: (1) Delayed return of oxygen uptake (V02)
to a resting level after the cessation of exercise [work]
(Astrand & Rodahl, 1986). (2) Oxygen consumed in excess
of the resting (post-exercise) 02 requirement (McArdle et
al., 2001).
Oxygen Consumption (V02):. Oxygen taken into the
body and used in tissues. In the physiology literature, it is a
volume if shown without an overstrike over the "V', and as
a rate (per minute) with an overstrike. The terms "Oxygen
E-22
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Intake," "Oxygen Utilization," and "Oxygen Uptake,"
generally are used as synonyms for oxygen consumption
(Montoye et al, 1996).
Oxygen Deficit: (1) The difference between the oxygen
requirement and the oxygen intake during performance of the
task (Morehouse & Miller, 1976). (2) The difference between
the total 02 consumed during exercise (work) and the total
that would have been consumed had a steady rate of aerobic
metabolism been reached immediately at the start of the
exercise (McArdle et al., 1991).
Oxygen Consumption Reserve (V02 R|:s): The difference
between maximal oxygen consumption (V02 MAX) and
oxygen consumption at rest in an individual (V02 R):
vo2RES = vo
2.MAX "
VO,R.V02MAXis about 10-15 times
higher than V02 R is nonnally-active and fit individuals on
a group mean basis, and even greater is specific persons
(Blomqvist, 1978).
Oxygen Intake: See "Oxygen consumption."
Oxygen Pulse: Oxygen pulse is an indirect measurement of
stroke volume. It is defined as the oxygen uptake per heart
beat and is measured by dividing the oxygen uptake in one
minute over heart rate (V02/HR). As stroke volume increase,
so does 02 pulse. It is the amount of oxygen extracted by the
tissues of the body from the 02 carried by the blood pumped
from the heart in each stroke. The term is derived from the
Fick equation (Luks et al., 2012).
Oxygen Pulse/Oxygen Saturation: The amount of 02
combined with hemoglobin, expressed as a percentage of
the 02 capacity of that hemoglobin. Oxygen Uptake: See
"oxygen consumption."
Ozone (03): A reactive oxidant gas produced naturally in
trace amounts in the earth's atmosphere; it is composed of
three oxygen atoms. Most of the earth's atmospheric O, is
found in the stratosphere.
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Pa02: Arterial partial pressure of oxygen.
PA02: Alveolar partial pressure of oxygen.
Parameter: In mathematics, a constant; in statistics and
epidemiology, a measurable characteristic of a population
that may take on varying values (Last, 1983).
Pathway: See "Exposure Pathway."
PE: Physical education: generally, a period of time during
a school or college day in which students undertake one or
more physical activities in a relatively structured way. The
activities often are moderate-to-vigorous in nature, involving
individual or group sports events.
Peak Exposure: The maximum instantaneous exposure for a
specified time period (Duan et al., 1990).
Peak Oxygen Consumption (V02 PEAK or VO, mx): As
a rate—overstrike over the "V"—it is the maximal V02
rate of an individual at the point where the individual
stops an exercise fitness test. Occasionally, physiologists
define VO, as the above when "objective" criteria for
attaining V02 MAX has not been attained. Most generally,
this distinction is not made, and the two terms are treated
synonymously.
Percent Body Fat (%BF): The percentage of body weight
(mass) that is fat, estimated by skin-fold measurements,
bioelectrical impedance, or by displacement of water or air
by immersion in a tank of water or sealed air chamber.
Perfusion: Passage of blood or other fluid through blood or
lymph vessels or any part of body (International, 1986).
Personal Exposure Measurement: A concentration
measurement collected from an individual's immediate
enviromnent using active or passive devices ((IPCS, 2000).
Personal Exposure Monitor (PEM): A personal exposure
measurement device worn on or near a contact boundary
(Zartarian et al., 1997).
Personal Monitoring: (1) Monitoring, or the measurement
of one or more specific substances in, on, or immediately
near a specified living receptor via some type of media-
specific sampling device. The device may be "active"
[flow rate through the sampler is mechanically regulated]
or "passive" [flow rate is not controlled, and the sampling
rate is greatly affected by the physics of deposition and
Browning movement]. Compare with "Microenvironmental
Monitoring;" personal monitoring moves with the receptor
as he/she/it enters and leaves the various microenviromnents
that are encountered over the sampling period.
Pharynx: The irregularly-shaped cavity into which the nose
and mouth open. The larynx is below. Pharynx is the medical
term for throat. Air and food passages cross in the pharynx.
Photochemical oxidants: Primary ozone, nitrogen dioxide,
and peroxyacetyl nitrate, with lesser amounts of other
compounds, formed as products of atmospheric reactions
involving organic pollutants, nitrogen oxides, oxygen, and
sunlight (EPA, 1993).
Photochemical smog: Air pollution caused by chemical
reaction of various airborne chemicals in sunlight
(EPA, 1993).
Physical Activity (PA): (1) Any bodily movement produced
by skeletal muscles that results in an expenditure of energy
above the resting level (Baranowski et al., 1992; Kohl
et al., 1988). Exercise is a major component of physical
activity. (2) Naturally occurring body movement (Bar-Or &
Baranowski, 1994). (3) Dynamic or static skeletal muscle
exertion that increases the body's energy expenditure and
results in cardiorespiratory adjustments. Dynamic PA
involves body movements through rhythmic contraction and
relaxation of large skeletal muscle groups. Static [isometric]
activity consists of increased muscular tension against a fixed
resistance with no change in fiber length (Leon, 1989). It lias
both physiological and behavioral aspects.
Often physical activity is further described by the level
of activity, such as light, moderate, and vigorous. It also
is divided into source or type of physical activity, such as
occupational, domestic, leisure-time, physical educational,
or recreational.
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Physical Activity Index (PAI): Daily Total Energy
Expenditure (in kcal/kg) / Basal / Resting Metabolic
Rate (in kcal/kg). Thus is a unitless metric that is used to
characterize a person relative daily energy expenditure
vis-a-vis normative rates for sedentary, low-active, active,
etc. individuals.
Physical Activity Level (PAL): Identical to PAI, used
mostly by European exercise physiologists.
Physical Fitness: (1) The ability to carry out daily tasks
with vigor and alertness, without undue fatigue and with
ample energy to undertake leisure time activities and to meet
"energy emergencies" (Kohl et al., 1988). (2) The ability to
do physical activity or to perform physical work; a measure
of a person's "functional capacity" (Solomon, 1984). (3)
The ability to perform moderate-to-vigorous levels of
physical activity without undue fatigue, and the capability
of maintaining such activity throughout life (ACSM, 1990).
(4) The ability to maintain internal equilibria as closely as
possible to the resting state during strenuous exercise and to
quickly restore any disturbed equilibriums (Astrand, 1956).
There are alternative definitions; see: Pate, 1988). V02MAX
is considered by many to be an objective measure of fitness,
and timed distance runs are used as a surrogate for fitness in
field settings (Pate, 1991; Pate et al., 1990). Again, there are
alternative field "measures" of fitness.
Sometimes physical fitness is disaggregated into "health-
related fitness" and "skill-related fitness." The former
includes cardio-respiratory endurance, body composition,
and musculosketal considerations (flexibility, strength, and
muscular endurance). Skill-related fitness includes activity-
specific factors, such as agility, balance, coordination, speed,
power, and reaction time (Nieman, 1990).
Physical Fitness Index (PFI): A measure of 02 consumption
per body weight (mL/kg).
Physical Working Capacity (PWC): It is the maximal
rate of oxygen utilization in aerobic metabolic processes. It
also is known as "functional capacity," "cardiorespiratory
fitness," or "maximal aerobic power" (Simons-Norton
et al., 1988).
PWC-170 (or PWC170): Physical working capacity of an
individual, in units of kilopond-meters min1 (kp/min), or
body mass-adjusted kp/min, at a heart rate of 170 beats per
minute (bpm).
Physiology: Study of or the normal functioning of a living
organism (International, 1986).
Point-of-Contact Exposure: An exposure estimate
expressed as the product of concentration of a substance
in an exposure medium, the duration of contact, and body
surface area of the receptor in contact with the substance; a
typical unit for this metric is mg m 2 h1 (CMA, n.d.). It is a
surrogate estimate of dose received for those substances that
produce toxicity directly at the point of contact with the body
[skin or mouth].
Point-of-contact Exposure Measurement: An approach
to quantifying exposure by taking measurements of
concentration over time at or near the point of contact
between the substance and the receptor surface of interest
while the exposure is occurring (IPCS, 2000).
Pollutant: (1) Substance in a medium to which the target is
exposed (Duan et al., 1990). (2) An undesirable modification
of a medium by a substance that is toxic, results in an
adverse effect on health, or is offensive (Last, 1983).
Population: The complete set from which a sample is drawn.
Population Variability: The concept of differences in
susceptibility of individuals within a population to toxicants
due to variations such as genetic differences in metabolism
and response of biological tissue to chemicals (EPA, 1989).
Portal-of-Entry Effect: A biological response to a toxicant
at its site of entry into the body (EPA, 1989).
Post-Exposure Period: The time period subsequent to the
last exposure to a substance but within the period of analysis
(Vincent & Donaldson, 1990mod).
Potential (Human) Exposure: A potential exposure
situation arises when two conditions are present: (1) valid
information, usually analytical environmental data, indicates
that a contaminant of public health concern exists in one
or more environmental media [i.e., air, water, soil, food];
and, (2) that there is an identified route of exposure between
the medium/media and human receptors: i.e., drinking
contaminated water, breathing contaminated air, having
contact with contaminated soil/pesticides/etc., or eating
contaminated food (ATSDR,1999).
Power: (1) Work performed per unit time (Lamb, 1984). (2)
The rate of performing work; the derivative of work with
respect to time; the product of force and velocity (McArdle et
al., 2001). See also "statistical power."
Precision: The quality of being exactly or sharply defined
(Webster's Ninth, 1974).
Probability: (1) Limit of the relative frequency of an event
in a sequence of n random trials as n approaches infinity; the
limit of: [number of occurrences of an event]/n (Last, 1983).
(2) A measure, ranging from 0 to 1, of the degree of belief in
a hypothesis or statement (Last, 1983).
Probability Density Function: (1). A function whose
value at a particular point describes the relative probability
that an uncertain value will be near that point (Feagans
& Biller, 1981). (2).The derivative of a cumulative
distribution function.
Probability Distribution: A distribution giving the
probability of any value x as a function of x (Kendall &
Buckland, 1971).
Probability Encoding: An explicit, precise, and formal
technique for quantifying expert judgments on well-defined,
but uncertain quantities (Feagans & Biller, 1981).
Probability Sampling: Any method of selection of a sample
based on probability theory.
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Probabilistic Analysis: A general term; one definition is
the calculation and expression of health risk using one or
more of a number of possible risk metrics to estimate the
likelihood of an (adverse) effect of interest. Probabilistic
risk results delineate a range of possible outcomes and
their likelihood; they are often presented as a frequency
distribution that quantitatively depicts variability of the
estimate. The uncertainty regarding this distribution may also
be depicted. See "Uncertainty" and "Variability."
Probit Model: A dose-response model that can be derived
under the assumption that individual tolerance is a random
variable following a log-normal distribution (EPA, 1989).
Pulmonary: Pertaining to the lungs (Dorland's, 1988). Often
used with function as in pulmonary function.
Pulmonary Compliance: The volume change per unit of
pressure change for the lungs, thorax, or the lung-thorax
system. The distensibility of the lungs or thorax (EPA, 1989).
Pulmonary Edema: An accumulation of excessive amounts
of fluid in the lung extravascular tissue and air spaces.
Pulmonary Measurements: Measurements of the volume
of air moved during a normal or forced inspiration or
expiration. Specific lung volume measurements are
defined independently.
Pulmonary Region: The area of the respiratory system
consisting of the respiratory bronchioles and alveoli where
gas exchange occurs (EPA, 1989).
Pulmonary Ventilation: Total exchange of air and gas
between the lungs and air needed for aerobic energy
metabolism, usually measured in liters per minute
(Dorland's, 1988). It is measured by V( or by VE. which are
not exactly equal (Astrand & Rodahl, 1986).
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Quetelet Index: See "Body Mass Index."
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Random Sample: A sample that is arrived at by selecting
sample units such that each possible unit has a fixed and
determinate probability of selection (Last, 1983).
Rating of Perceived Exertion (RPE): The subjective
effect, discomfort, strain, and fatigue during exercise of
other physical activity (Robertson & Noble, 1997). The
most common format of the RPE is as a categorical scalar;
5 different versions are listed in the citation. The researcher
most associated with the concept is Gunnar Borg, a Swedish
exercise psychologists (Borg, 1973). There are 3 different
Borg Scales, with the 15-point RPE Scale probably being the
one most used.
Reaction: (1) See "Response." (2)Aprocess inwhicha
substance is changed chemically (International, 1986).
Reactivity: (1) Tendency of a substance to undergo chemical
change. (2) In a human study, it is a "change in behavior due
to being monitored" (Beets (2006).
Recall Survey: A study design that asks subjects to
"subjectively" recall some type of past event or activity.
There are many forms of this survey, using different time
period, activities participated in, locations visited, foods
eaten pollutants encountered, etc.
Receptor: Any living organism or non-living entity,
substance, or material that is exposed to a pollutant
of interest.
Reliable, Reliability: (1) A quantity that is sound and
dependable (stable) over repeated measurements. (2)
Consistency of response across (a) multiple trials of a
single administration of a test or instrument [this is internal
consistency], or (b) across multiple administrations [test/
re-test stability or reliability] (Patterson, 2000). It should be
estimated via an intra-class correlation coefficient from an
analysis of variance, however, and not by r. (3) Repeatable
and reproducible are synonyms when used as a noun, but not
in their verb form: repeatability and reproducibility. Note that
the term does not refer to the quality of the measurement or
estimate, but to the process of performing something more
than once (IPCS, 2000).
Relative Aerobic Strain (RAS): The unitless ratio of the
oxygen consumption needed to perform a specific task to a
person's maximum oxygen consumption, usually multiplied
by 100 to change it into a percent ((Oja et al., 1977). It is
used by industrial physiologists as a measure of "strain," or
activity-long work rates.
Reserve: A quantity available beyond what normally
is needed; a surplus of potential use in extra-ordinary
circumstances (International, 1986). In a number of "reserve"
physiological metrics, it is the difference between the
maximal measurement and that occurring at rest, or basal
conditions.
Residual volume (RV): that volume of air remaining in the
lungs after maximal exhalation. The method of measurement
should be indicated in the text or, when necessary, by
appropriate qualifying symbols. RV = FRC - ERV. RV also
is used to denote "total lung capacity ratio," equal to RV/
TLC. RV used this way expresses the percentage of total lung
capacity occupied by residual volume; this varies somewhat
with age, but ordinarily should be no more than 20 to 30%.
Resistance Training: Training designed to increase strength
power, and muscle endurance (Nieman, 1999).
Respiration: (1) The totality of the processes of gaseous
exchange between tissues of the body and its enviromnent;
the process of breathing (International, 1986). (2) Exchange
of 02, and C02 between atmosphere and cells, including
inspiration and expiration [ventilation], the diffusion of
oxygen from pulmonary alveoli to the blood, and the trans-
port of 02 to and C02 from body cells (Dorland's, 1988). (3)
The exergonic metabolic processes in living cells by which
molecular 02 is taken in organic substances are oxidized,
free energy is released, and oxidized products [C02, H20,
etc.] are given off by cells (Dorland's, 1988).
Respiratory Cycle: See "Respiration Rate."
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Respiratory Frequency (fR): Breathing rate in breaths min1.
At rest, fR ~ 10-20 breaths min1, but it can be between 6-31
bpm in adults (Bendcertit, 2000). Also known as breathing
frequency (f).
Respiratory Quotient (RQ, R): Ratio of the volume of
carbon dioxide produced (C02) divided by the volume
of oxygen consumed (02) by an organism, an organ, or a
tissue during a given period of time (C02 02_1). Respiratory
quotients are measured by comparing the composition of
an incoming and an outgoing medium, such as inspired and
expired gas, inspired gas and alveolar gas, or arterial and
venous blood. This ratio reflects the metabolic exchange
of the gases in the body's tissues and is dictated by the
percentage of carbohydrate, fat, and amino acids used in
energy production by the cells. Carbohydrate metabolism
yields an RQ of 1, whereas proteins and fats yield RQs
of 0.8 - 0.9 and 0.7, respectively. A normal mixture of
fat and carbohydrate metabolism yields an RQ of around
0.8. Except in malnourishment, protein is seldom used for
energy metabolism.
See McArdle et al. (2001) for calculation formula for RQ
based on C02 and 02flow rates (p. 1120). RQ changes with
the degree of work (physical activity) undertaken. At rest, RQ
usually is 0.75-0.81 but increases close to 1.00 when only
carbohydrates—the "preferred fuel" for heavy exercise-are
used (Nieman 1990). RQ actually can go above 1.00 during
recovery due to the buffering of lactic acid; RQ's above 1.15
indicate that maximal exertion has occurred.
Sometimes the phrase "respiratory exchange ratio" (RER)
is used to designate the ratio of carbon dioxide output to
the oxygen uptake by the lungs, with "respiratory quotient"
being restricted to the actual metabolic carbon dioxide output
and oxygen uptake by the tissues. Using this definition,
respiratory quotient and respiratory exchange ratio are
identical only in the steady state, a condition which implies
constancy of the oxygen and carbon dioxide stores.
Respiratory Rate (RR): The frequency of a complete cycle
of a breath; includes inhalation and exhalation [in L min1].
See: "fR." The time it takes for one breathing cycle is Ttotal,
which equals T( + TE. In general, TE > T( (Benchetit, 2000).
Respiratory System: The lungs, air passages, and breathing
muscles that supply oxygen to the body and carries off
carbon dioxide (Fahey et al., 2007).
Response: (1) An action or movement due to a stimulus
(Dorland's, 1988). (2) Any organic process elicited by
a stimulus, either muscular, glandular, biochemical, or
immunochemical reaction (International, 1986).
Rest: Repose, inactivity (Dorland's, 1988).
Resting Energy Expenditure (REE): Assumed to be
functionally identical to basal metabolic rate [see]
Resting Metabolic Rate (RMR): Assumed to be
functionally identical to basal metabolic rate [see].
Retention: Used to refer to the amount of an inhaled
material that remains in the lung [pulmonary retention] or
to the amount of a toxicant dose that remains in the body or
body compartment for a specified period of time (EPA. 1989).
Route of Entry: The means by which a substance enters the
body: ingestion, inhalation, dermal. See "Exposure Route"
and "Route of Exposure."
Route of Exposure: (1) The mechanism by which the
medium reaches a target (Duan et al., 1990). (2) The
means by which a toxic substance (agent) gains access
to an organism: ingestion; inhalation; dermal absorption;
intravenous, subcutaneous, intramuscular, and intraperitoneal
administration.
s
ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
Sarcopenia: The involuntary loss of skeletal muscle that
occurs with advancing age (Cesari et al., 2005).
Saturation: (1) Having all chemical affinities satisfied
(Dorland's, 1988). (2) Unable to hold in solution any
more of a given substance (Dorland's, 1988). (3) State of
a solution in which a specified substance cannot dissolve
or vaporize because it is in equilibrium (adapted from
International, 1986).
Sensitivity Analysis: A technique that evaluates the
sensitivity of an output variable to possible variation in
the input variables of a given model. The main purposes
of sensitivity analysis are to (a) quantify the influence of
input variables on the outputs variable, and (b) understand
the "bounds" of the model output. Sensitivity of the output
variable of a given mathematical model depends on the
model's mathematical relationships and on plausible values
of its input variables. For a given model, sensitivity of
the output variable with respect to each input variable is
computed and compared, usually in a sequential manner
by changing one variable at a time and keeping all other
variables held fixed at their nominal vales (correlated input
variables, however, must be varied together in a logical
fashion. Varying several input parameters at the same time
often highlights interaction effects in a model which are not
obvious during "one at a time" variation (IPCS, 2000).
Screening Study (Analysis, Assessment): A [risk]
assessment using tentative or preliminary data. The results of
such an assessment are not viewed as an absolute indicator
of risk, but are viewed as an indicator of the relative
importance of the various factors that give rise to risk: such
as pollution sources, source-receptor geometry, the nature
of the substances involved, and the patterns of exposure
experienced. Most urban air toxic risk assessments to date
are considered to be screening~or "scoping"-studies, useful
mostly to point out where additional scientific and analytical
work is needed before a definitive risk assessment can be
undertaken (adapted loosely from EPA, 1989). Obviously
"screening study" is a vague term that should be used
with caution.
Scoping Study: See "Screening Study"
E-26
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Sedentary / Sedentarism / Sedentary Person: A person
who expends <10% of his or her waking daily total energy
in moderate or vigorous activities [> 4 METS] (Bernstein et
al. (1999).
Sensitive: (1) Able to respond to stimuli; often used to
mean unusually responsive or responding quickly or acutely
(Dorland's, 1988). (2) Quality or state of possessing a low
threshold to a stimulus (International, 1986).
Sensitive Person/Population: A person/people who
respond-often hyper-respond~to a pollutant exposure that
would not affect most other people; a pre-existing illness
often affects a person's sensitivity to an exposure (Lebowitz,
1991). Compare with "Susceptible Persons/Populations."
Sensitization: A condition in which response to later
stimuli is greater than response to an original stimuli
(International, 1986).
Sex: The classification of living things into generally two
categories (female or male) according to the reproductive
organs and functions associated with the subject's
chromosomal complement. (Arbuckle, 2005). An activity
undertaken for physical gratification, enjoyment, and/or
procreation. See "Gender" also.
Short-Term: A vague term that relates to a relatively short
time period.
Short-Term Exposure: Multiple or continuous exposure
to a substance for a short period of time, usually one week
(IRIS, 1999).
SI: Systeme Internationale d'Unites: the International
System of scientific units, adopted by the World Health
Organization as the official units of measurement for
phenomenon inherent in the physiological, medical, and
other health sciences.
Solubility: Quality or fact of being soluble, which means the
susceptibility of being dissolved in the matrix in which the
substance is located (adapted from Dorland's, 1988).
Spearman Rank-Order Correlation Coefficient (rs): A
measure of linear association between the rank-order of two
or more sampled variables that can be used for nominal and/
or interval scaled-data with a =0.05. The metric still assumes
random probability sampling, but this assumption often is
violated.
Spirometry: The measurement of air volumes of the lungs;
examples: tidal volume and reserve volume (EPA, 1989). It
usually involves the timed collection of exhaled air during
the forced vital capacity (FVC) maneuver.
Spirometer: A mechanical device, including bellows or
other sealed, moving parts, that collects and stores gases
to provide a graphical or electronic record of lung volume
changes over time (EPA, 1989). It usually is used to collect a
timed sample of exhaled air during the forced vital capacity
(FVC) maneuver.
Standard Deviation (SD): An index of dispersion around a
mean of measurements in a sample, equal to V Variance. The
positive square root of the sample variance.
Standard Error (SE): Standard deviation of the sampling
distribution of a statistic for random samples of n size, equal
to SD / Vn.
Standard Temperature and Pressure (STP): Defined to
be O °C, 760 millimeters of mercury (760 torr). Formula are
presented in McArdle et al. (2001) to convert atmospheric
temperature and pressures to STP based on Charles' and
Boyle's laws (p. 1117).
Standard Temperature and Pressure Dry (STPD)
conditions: These are the conditions of a volume of gas at O
°C and 760 torr, without water vapor. An STPD volume of a
given gas contains a known number of moles of that gas.
Statistic: (1) A function of one or more random variable
that does not depend upon any unknown parameters. (2) A
summary value calculated from a sample of observations
(Kendall & Buckland, 1971).
Statistical Power (1-0): (1) The probability of being able
to detect an effect is there is one (IPCS, 2000); (2) the
probability of rejecting the tested hypothesis when it is false
(when the alternative hypothesis—HA—is true); (3) the
probability of correctly rejecting HQ when it is false; it equals
1- the probability of rejecting HQ .
Statistically Significant Effect: In the analysis of data,
an effect that results in a difference between a study group
sample and a control group population that is unlikely to
arise by chance alone~the "chance" usually is specified
in the statistical test used to test the null hypothesis of no
effect, such as a=0.05, or a 5% probability of being wrong
(EPA, 1989).
Steady State Exercise - Steady state exercise is a
characteristic of physiological systems in which its functional
demands are being met such that its output per unit time
becomes constant. It is a level of exercise intensity at which
the patient is in steady state. To reach that exercise intensity,
the subject must first pass through a period of dynamic
exercise to reach the steady state level.
Steady-State Exposure: Exposure to air pollutants whose
concentration remains constant for a period of time;
generally this is an unrealistic exposure profile.
Stochastic: The property of varying in some manner that can
be described with a statistical function [i.e., follows some
type of known probability function; a narrow sense is that the
variability is random in nature, such as a normal probability
distribution].
Stochastic Model: A mathematical model which includes
one or more stochastic variables or parameters. Estimates
made using this type of model therefore do not give single-
point estimates, but a distribution of possible estimates [with
some specified probability].
Stoichiometry: (1) The application of the laws of (a) definite
proportion and (b) conservation of matter and energy to
chemical activity. (2) A quantitative relationship among
constituents in a substance, especially those undergoing
physical or chemical change.
E-27
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Stratification: The division of a population into two or more
subgroups for sampling or analysis purposes.
Strength: The ability of muscle to exert force
(Nieman 1999).
Stroke Volume (SV): the amount of blood pumped per heart
beat, in liters or milliliters beat1.
Subchronic: A period of time that is intermediate between
acute and chronic (CMA, n.d.). This term is vague and
should not be used.
Subchronic Exposure: (1) A vague term used by some
cancer risk assessors to denote an exposure to a substance
that spans no more than ten percent of the exposed
organism's lifetime (EPA, 1992). (2) An exposure of
intermediate duration between acute and chronic (IPCS,
2000). (3) Multiple or continuous exposures lasting for
approximately ten percent of an experimental species
lifetime, usually over a three-month period (EPA, 1997).
Substance: Any material of a specified nature but
of no shape or dimension, as a chemical or tissue
(International, 1986).
Subjective Interpretation of Probability: The view that
probability is a measure of the degree of belief~or quantified
judgement~of an individual, where that individual is
willing to make choices in a well-defined situation (Feagans
& Biller, 1981). See also: "Frequency Interpretation of
Probability" and "Probability Encoding."
Susceptibility: (1) Condition of being susceptible,
or liable to the effects of substances, toxins, or other
influences; lacking capacity to respond effectively to
pathogens (International, 1986). (2) Preexisting biological
characteristics that lead to an enhanced response to a dose
or exposure. Susceptible individuals, when sufficiently
dosed (exposed) become sensitive to further doses
[exposures]; susceptibility may be specific or non-specific
(Lebowitz, 1991).
Susceptible Person/Population: A person with a pre-
existing disease that makes them susceptible [see].
Synergistic Effect: (1) Any effect of two chemicals
[substances] acting together which is greater than the simple
sum of their effects when acting alone (Duffus, 2000).
(2) Joint effects of two or more agents, such as drugs that
increase each other's effectiveness when taken together
(SRA, 1999).
System: (1) A complex of anatomically-related structures
that perform a specific function (International, 1986). (2) A
method of arrangement whereby separate parts or functions
work together as a unit (International, 1986).
Systemic: Pertaining to or affecting the body as a whole or
acting in a portion of the body other than the site of entry,
used to refer generally to non-cancer effects.
Systematic Error: A reproducible inaccuracy caused by
faulty, equipment, calibration, or measuring technique
(IPCS, 2000).
E-28
T
ooo<><><>o<><><><><><>oo<><>oo<><><><>oo
Tachypnea: Very rapid breathing.
Target Heart Rate (THR): The heart rate for an individual
undertaking an exercise test that is estimated to attain a
specific exercise intensity. That intensity itself is defined in
a number of ways (oxygen consumption, %maximal oxygen
consumption, etc.), as is the method used to convert this
intensity into heart rate. There are a number of methods used
to do so, but probably the most rigorous is to equate%V02
RES that is desired with %HRres and translate that into THR
(Kirham, 2008).
Target Organ Dose: The amount of a potentially toxic
substance reaching the organ chiefly affected by that
substance (Duffus, 2000).
Target Population: (1) The collection of individuals,
items, measurements, etc., about which we want to make
inferences. The term is sometimes used to indicate the
population from which a sample is drawn and sometimes to
denote any "reference" population about which inferences
are required. (2) The group of persons for whom an
intervention is planned (Last, 1983).
Temporally-Averaged Exposure: The temporally-integrated
exposure divided by duration of the time interval of interest
(Zartarian et al., 1997).
Temporally-Integrated Exposure: The integral of
instantaneous "point" exposures over a specified time period
(Zartarian et al., 1997).
Thermic Effect of Food: See "dietary induced
thennogenesis."
Thorax: Part of the human body between the neck and
diaphragm, partially enclosed by ribs; the chest.
Threshold: (1) The minimum amount of stimulus
(concentration level) required to elicit a particular response
(adapted from International, 1986). (2) The level at which a
physiological or psychological effect begins to be produced
(EPA, 1989).
Threshold Dose: The lowest dose level at which a specified
(measurable) biological effect is observed and below which it
is not observed.
Tidal Volume (VT): The volume of air inhaled or exhaled
with each breath during breathing; usually defined for a state
of quiet breathing.
Time-Activity Pattern: The phrase used in the exposure
measurement and modeling field for "time use data." The
daily sequential pattern of activities in which an individual
engages in, including: the length of time spent performing
each activity, the location (microenviromnent) where the
activity occurs, and some type of "activity-level indicator"
indicating how much energy is being expended in the
activity (e.g., breathing rate, oxygen consumption heart
rate, accelerometer counts). EPA's CHAD database contains
22,968 person-days of time-activity pattern information.
Frequently, these data are aggregated to the proportion of a
-------
day spent doing activity x in microenvironment v at activity
level z. but doing so destroys the correlated nature of human
activity and exposure events.
Time-of-Life: The stage of life that a living receptor is
in, which may affect its' response to dose received; an
obvious example is that teratogenic effects can only occur in
receptors that are in utero (CMA, n.d.).
Time Pattern/Time Profile: A continuous record of the
time series of instantaneous point exposures/doses/intakes
estimates for a specified time period (Zartarian et al., 1997).
An example is the "time pattern of dose rate received".
Time Use Data: Data on what a subject does in
time and space for a specified time period, i.e., their
time-activity pattern.
Time-Weighted Average: The average of a quantity over a
specified time period.
Tissue: An aggregation of cells and intercellular matter that
subserves a united function (International, 1986).
Total Daily Energy Expenditure (DTEE): The total
amount of energy expended by a living organism on a daily
basis. It is the sum of metabolic, dietary, physical activity
(work, movement, fidgeting/shivering, etc.)-related energy
expenditures over some specified time period (Bar-Or &
Baranowski, 1994).
Total Exposure: See "Total Human Exposure."
Total Fluid Intake: Consumption of all types of fluids
including tap water, milk, soft drinks, alcoholic beverages,
and water intrinsic to purchased foods (EPA, 1997).
Total Human Exposure: An exposure assessment-
monitoring or modeling-that accounts for all exposures
a person lias to a specific substance, regardless of the
environmental medium or route of entry [inhalation,
ingestion, and dermal absorption]. Sometimes total exposure
is used incorrectly to refer to exposure to all pollutants in
an enviromnent; total exposure to more than one pollutant
should be stated explicitly as such (IPCS, 2000).
Total Suspended Particulates (TSP): Solid and liquid
particles present in the atmosphere.
Total Ventilation: The total volume of air breathed in a
specified time period (International, 1986).
Trachea: A cartilaginous air tube extending from the larynx
into the thorax, where it divides into two branches.
Tracheobronchial Region: The area of the lungs including
the trachea~windpipe~and conducting airways-bronchi,
bronchioles, and terminal bronchioles (EPA, 1989).
Tracking: A person's stability over time in undertaking
physical activity, often measured by ranked relative
categories of exercise level (e.g., the top 25% quartile)
(Anderssen et al., 2005).
Training: A regime in which people undergo a structured,
often supervised, set of exercises over weeks or months (Bar-
Or & Baranowski 1994).
Transfer (Media): The movement of an agent or chemical
substance from one enviromnental media to another.
Transformation: (1) Change of chemical state, form, or
structure (Dorland's, 1988). (2) The conversion-through
chemical or physical processes~of one or more compounds
into other compounds. These transformations may occur in
many media [ambient air; water; soil; etc.].
Transport: The movement of an agent or chemical substance
within a medium, either the enviromnent [e.g., air] or within
the body [e.g., blood].
TTotal: The time that it takes for one breathing cycle.
T T+T
Total = I E
Type I Error: The probability of rejecting HQ when it
is true (a).
Type II Error: The probability of accepting HQ when it is
false ((3). (3=1- power.
u
ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo
jli: Mu, a prefixused as "micro"; see "n" in the M Section.
Uncertain: Indefinite; indeterminate; not certain to occur;
problematical; not known beyond doubt; not clearly defined;
variable (Webster's). Lack of knowledge (Bogen, 1995).
Uncertainty: (1) The quality or state of being uncertain
(Webster's). (2) In cancer risk assessments, the lack of
precise scientific data regarding a phenomenon, relationship,
or endpoint. This lack requires that assumptions and "best"
scientific judgments be used in critical portions of the
risk assessment [e.g., hazard identification, dose-response
relationships], resulting in a [high] degree of uncertainty
regarding risk estimates (EPA, 1989 (3) A probability
estimate of the statistical confidence limits associated with
an estimated or measured value. (4) A lack of confidence
in the prediction of a risk assessment that may result
from natural variability in natural processes, imperfect or
incomplete knowledge, or errors in conducting an assessment
(IPCS, 2000).
Uncertainty Analysis: A process in which the sources of
uncertainty in an estimate are identified, and an estimate
made of the magnitude and direction of the resulting
error: (a) qualitative-utilizes descriptive methods; (b)
semi-quantitative-uses simple mathematical techniques
such as sensitivity analyses; (c) quantitative-uses more
complex mathematical techniques such as Monte Carlo
analysis (AIHA, 2000). (2) A detailed examination of the
systematic and random errors of a measurement or estimate;
an analytical process to provide information regarding
uncertainty (SRA, 1999).
UNU: United Nations University.
Upper Bound: A plausible upper limit to the "true value" of
a quantity; it usually is not a true statistical confidence limit
(IRIS, 1999).
Upper Respiratory Tract: The structures that conduct air
into the lungs, including the nasal cavity, mouth, pharynx,
and larynx (EPA, 1989).
E-29
-------
Uptake: (1) Absorption and assimilation of a substance by
an organ or tissue (International, 1986). (2) Process by which
a chemical [substance] crosses an absorption boundary and is
absorbed (EPA, 1992).
V
00000000000000000000
Valid, Validity: (1) Supported by objective truth or
accepted authority; sound and sufficient. A test or
experimental procedure that measures what it purports to:
it is sufficient. (Last, 1983 via IPCS, 2000). (2) Validity has
two components: relevance and reliability; objectivity is a
component of reliability (Safrit & Wood, 1989). (3) There are
4 aspects of validity (Baumgartner & Jackson, 1999):
Logical validity: the instrument measures the capacities
that it is intended to measure.
Concurrent validity: a measure of an instrument's
correlation with a specified criterion (generally using r).
Predictive validity: the value of an instrument to predict
its performance on a criterion measure.
Construct validity: used in an abstract sense; the
instrument measures what is desired, but that cannot
be directly measured itself but can be addressed
statistically via hypothesis tests.
Other authors break down essentially the same concepts
but use different words; Morrow et al. (2000), for instance,
distinguishes among "content-related," "criterion-related,"
and construct-related validity.
Variable: Any quantity that varies, taking on different
numerical values (Last, 1983).
Variance: An indicator of the variability inherent in a set
of observations—a sample—equal to the sum of squared
deviations from the mean divided by the degrees of freedom
in the sample (IPCS, 2000).
Variation: A divergence in a developing organism from the
usual or normal range of structural constitution that may not
adversely affect organ health or survival (EPA, 1989).
Variability: Heterogeneity in a population parameter
or variable.
Vascular: Pertaining to blood vessels.
VC02: C02 production during respiration [in mL min1].
Ventilation: In respiratory physiology, the process of
gaseous exchange between the blood and enviromnent via
the lungs (International, 1986). See "Pulmonary Ventilation,"
"Alveolar Ventilation" "Total Ventilation," "Minute
Ventilation," "Respiration," and "Expired Ventilation."
In indoor air pollution, the exchange of air in a room or
structure with ambient ["fresh"] air [or air from another
room or structure]; in a general sense, it also means the
circulation of air.
Ventilation, Dead Space (VD): Ventilation per minute of the
physiologic dead space [volume of gas not involved in gas
exchange with the blood], at body temperature and pressure,
saturated conditions. It is defined by the following equation:
VD(PaC02 - PEC02)/(PaC02 - P,C02)
Ventilation Perfusion Ratio (VA/Q): Ratio of the alveolar
ventilation rate to the blood perfusion volume flow through
the pulmonary parenchyma, such as pulmonary blood flow
or right heart cardia output; this ratio is a fundamental
determinant of the oxygen and carbon dioxide pressure of
the alveoli gas and of the end-capillary blood. Throughout
the lungs, the local ventilation/perfusion ratios vary, and,
consequently, the local alveolar gas and end-capillary blood
compositions also vary (EPA, 1993).
Ventilation Rate (VE): The" breathing rate" (in L/min)
needed to support oxygen consumed for a particular activity.
It actually is defined to be the breathing rate (fR) times Tidal
Volume (VT).
VE MAX: Maximum VE for a person undergoing a
strenuous (for them) exercise protocol [inL min1].
Ventilatory Anaerobic Threshold (VAT): A point in
an incremental exercise test where V,. increases out of
proportion to V02. It also is known as the ventilatory
threshold [VT]. See Hebestreit et al. (2000). There are a
number of different indicators of VAT now used; it is a
marker of physiological fitness. It often is defined to be
the point on the V02"curve" where VQ (V| /V02). R, and
PET02 increase while V02/VC02 decreases or remains
constant (Hansen et al. [1984]). McArdle et al. (2001) state:
"the term ventilator threshold (VT) describes the point at
which pulmonary ventilation increases disproportionally
with oxygen consumption during graded exercise" [p. 291],
At this exercise intensity, pulmonary ventilation on longer
links tightly to oxygen demand at the cellular level. It often
is defined to be identical to the lactate threshold and the
anaerobic threshold per se. There is no universal method of
estimating VAT; three different methods are often used, and
they provide similar—but not exact -estimates, within 7%
of one another or less, around 71% of V02MAX (Fleg et al.,
2000). However, other cardiologists state VAT in healthy
individuals is approximately 40-60% of V02 MAX, and in
trained endurance athletes it can be as high as 80% (Mezzani
et al., 2009).
Ventilatory Equivalent: The ratio of minute ventilation
(MV) to oxygen consumption, defined as VE/V02 [VE V02-1].
This ratio in healthy people is on the order of 20-32
[L min '/L min'-unitless] at moderate exercise levels
(McArdle, et al., 1991). It is higher at more extreme exercise
levels, and values in the 40's are possible for short periods
of time <5 min (Astrand & Rodahl, 1986). High values are
a marker of inefficient ventilation due to hyperventilation,
increased dead space, and/or the "oxygen cost of breathing."
Subject with heart failure or other problems have a high VQ
(Luks et al., 2012).
E-30
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Ventilatory Reserve (VR): The difference between the
maximum minute ventilation reached by a subject at
peak exercise (VE MAX) and her or his maximum voluntary
ventilation (MW); it is also known as the breathing reserve
(Luks et al., 2012). VE. for instance, is only 60-85% of MW
at VOX
. (McArdle et al., 2001).
Ventilatory Scope: The ratio of VE MAX . E BASAL.
: ^E.BASAL* ^
approximately equal to VE MAX /V E REST (Rowland, 1989).
Ventilatory Threshold: The point in a progressive exercise
test where lactic acidosis begins to develop; it is also
known as the "anaerobic threshold" (Luks et al, 2012) and
"ventilatory anaerobic threshold." It is about 2/3 of the
way through a good maximal effort, and minute ventilation
increases at a higher rate than V02 at that point (Luks et al.,
2012). It approximates—but is not identical to~the lactate
threshold [see]. It also known as the anaerobic threshold
(Barstow & Mole, 1991).
Vigorous Physical Activity (VPA): A phrase with many
meanings, in that the energy expenditure levels associated
with it are defined in highly variable ways (heart rate, % of
heart rate reserve, V02 consumption, and % of V02 reserve.
% of V0o
, METS, etc.). Most of the definitions are
laboratory or investigator-dependent, with little attempt
at standardization of the metrics used. Perhaps the most
rigorous definition might be to the lactate threshold, the
ventilator threshold, etc. McCurdy & Graham (2004) defined
it to be the highest third of the V02 reserve distribution.
V02: Oxygen uptake (a rate) during respiration [in mL
min1]. V02 = Vj * %02, where %02 = 20.93%, by definition
in the normal case. V02, as a volume—not used in this
report—is the amount of oxygen consumed in a fixed time
period.
V02 MAX: Maximal V02 needed to complete a specified
physical activity [in L min1]. Also known as the maximal
aerobic capacity, maximal 02 consumption and maximal 02
intake. There are many functional definitions of V02 MAX.
Typical definitions are: (a) the amount of 02 consumption
associated with a plateau in the V02 uptake [in L kg1 min1
or mL min1] curve as workload (exercise) is increased,
(b) "the point at which V02 shows no further increase [or
increases only slightly] with additional workload," and (c)
a quantitative measure of the person's maximum capacity
for the aerobic resynthesis of ATP (McArdle et al., 1991).
Because of problems with defining the term and investigator-
specific protocols used to determine V02 MAX, many exercise
physiologists are now using the "peak" term V02 PEAK instead
of the time-honored V02 MAX.
1. The V02 measure that is associated with a RER >1.0
(Whaley et al., 1995).
2. The highest V02 that is observed during the final
minute of a stress test at voluntary exertion, with
a HR>90% of the age-predicted maximum, and a
RER>1.0 (Jackson et al., 1995). The terms often are
used interchangeably, although some authors state
that V02 PEAK is lower than V02 MAX due to a more
liberal allowance of test cessation of the fitness test
before criteria for V02 MAX has been reached (Cowan et
al., 2009).
V02 Reserve (V02 R|,s): The difference (in consistent units)
between V02 MAX and V02 REST, which itself is V02 at "basal"
(or resting) metabolic conditions.
w
0<>0<>0<><><><>0<>0«<><><>0<>0«0<><><>0<>«0<><><>0<>0«<><><>0<>0«<><><>0<>
W170: Work accomplished at a heart rate of 170 beats
per minute.
Weir's Equation: The formula used to estimate energy
expenditure (EE in kcal/min) from measures of pulmonary
ventilation and expired oxygen percentage, developed
in 1949 by J.B. Weir. It is accurate to within ± 1% of the
traditional Respiratory Quotient (RQ) method (McArdle
et al., 2001). The formula actually assumes that protein
breakdown accounts for a fixed 12.5% of energy produced
by a person which is a reasonable assumption (but rather
inflexible). Observations of relative protein consumption
from around the world indicates that it accounts for 10-14%
by weight (Weir, 1949). Weir's basic equation is:
EE = V,
Vc
E (STPD)
* (1.044 - [0.0499 * %0, ])
" e
-------
E-3. Table of Conversion Factors Used in this Synthesis
Chosen
"Basis"
Factor
Citation
Alternatives
Citations
1 lo2=
4.85 kcal
Erb (1981)
4.69-5.01
Stegemann (1981)
4.69-5.05
Freedson & Goodman (1993)
4.71
Daly et al. (1985)
4.74-4.95
Cotes (1975)
4.78-4.94
Solomon et al. (1982)
4.825
Leger et al. (1980); Sinclair (1971)
4.83
Brown (1973)
4.87
Schulz et al. (1989)
4.8735
Park et al. (2008)
4.90
Christensen et al. (1983); Croonen & Binkhorst (1974)
4.84
Weir equation direct, using a RQ=0.855
4.86
McArdle et al. (2001); RQ=0.85
4.69
As above; RQ=0.71
5.05
As above; RQ=1.00
1 lo2 =
20.5 kJ
Emons et al. 1992)
20.19
Lovelady et al. (1993); McCrory et al. (1997)
20.35
Brage et al. (2004)
20.92
Cunningham et al. (1981)
1 kcal
4.184 kJ
Handbook of
Physics1
4.175
Brun et al. (1985)
4.186
Astrand & Rodahl (1986); Diem & Lentner (1970)
4.192
Lee & Paffenbarger (2000)
1 kcal
210 mL Oz
200
Females; range: 190-210
210
Males; range: 200-220
206
Reciprocal of Erb (1981)
1 kJ
0.2389 kcal
Montoye (1975)
0.239
Montoye et al. (1996)
0.2395
Brun et al. (1985)
1 MJ
239 kcal
Durnin (1987)
1 MJ/d
0.6944 kJ/min
Using Durnin (1987)
694.44 J/min
Using Durnin (1987)
41.667 kJ/h
Using Durnin (1987)
1 MJ/d
9.958 kcal/h
Using Durnin (1987)
0.166 cal/min Using Durnin (1987)
E-32
-------
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