EPA/600/R-06/129F
May 2009
METABOLIC ALLY DERIVED HUMAN VENTILATION RATES: A
REVISED APPROACH BASED UPON OXYGEN CONSUMPTION RATES
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
ABSTRACT
The U.S. Environmental Protection Agency's (EPA's) National Center for Environmental
Assessment (NCEA) publishes the Exposure Factors Handbook and the Child-Specific Exposure
Factors Handbook to provide risk assessors with data on various factors that can impact an
individual's exposure to environmental contaminants. Both of these handbooks included
estimates of ventilation rate (VE)—the volume of air that is inhaled by an individual in a
specified time period. Previous approach to calculate VE is limited by its dependence on a
"ventilatory equivalent" which, in turn, relies on a person's fitness level. In this document, U.S.
EPA presents a revised approach in which VE is calculated directly from an individual's oxygen
consumption rate. U.S. EPA then applies this method to data provided from more recent
sources: the 1999-2002 National Health and Nutrition Examination Survey (NHANES) and
U.S. EPA's Consolidated Human Activity Database (CHAD).
Preferred Citation:
U.S. Environmental Protection Agency (EPA). (2009) Metabolically derived human ventilation rates: a revised
approach based upon oxygen consumption rates. National Center for Environmental Assessment, Washington, DC;
EPA/600/R-06/129F.
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CONTENTS
LIST OF TABLES v
LIST OF ABBREVIATIONS AND ACRONYMS vi
PREFACE vii
AUTHORS, CONTRIBUTORS, AND REVIEWERS viii
EXECUTIVE SUMMARY ix
1. BACKGROUND AND OBJECTIVES 1-1
2. DATA SOURCES 2-1
2.1. SOURCE OF BODY WEIGHT DATA: 1999-2002 NHANES 2-1
2.2. SOURCE OF BMR CALCULATION: SCHOFIELD (1985) 2-3
2.3. SOURCE OF ACTIVITY AND METS DATA: CONSOLIDATED HUMAN
ACTIVITY DATABASE (CHAD) 2-4
2.3.1. The National Human Activity Pattern Survey 2-5
3. APPROACH 3-1
3.1. STEP 1: GROUP NHANES AND NHAPS PARTICIPANTS BY AGE AND
GENDER CATEGORIES 3-2
3.2. STEP 2: CALCULATE BMR ESTIMATES FOR NHANES PARTICIPANTS 3-2
3.3. STEP 3: GENERATE A SIMULATED 24-HOUR ACTIVITY PATTERN
FOR EACH NHANES PARTICIPANT 3-2
3.4. STEP 4: GENERATE A METS VALUE FOR EACH ACTIVITY WITHIN
THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES
PARTICIPANT 3-4
3.5. STEP 5: CALCULATE ENERGY EXPENDITURE AND VO2 FOR EACH
ACTIVITY WITHIN AN INDIVIDUAL'S SIMULATED 24-HOUR
ACTIVITY PATTERN 3-6
3.6. STEP 6: CALCULATE VENTILATION RATE FOR EACH ACTIVITY
WITHIN THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH
NHANES PARTICIPANT 3-7
3.7. STEP 7: CALCULATE AVERAGE VENTILATION RATE FOR TIME
SPENT PERFORMING ACTIVITIES WITHIN SPECIFIED METS
CATEGORIES, AS WELL AS 24-HOUR AVERAGE VENTILATION RATE,
FOR EACH NHANES PARTICIPANT 3-9
3.8. STEPS: CALCULATE SUMMARY TABLES ACROSS INDIVIDUALS 3-10
4. RESULTS 4-1
4.1. STRENGTHS AND LIMITATIONS 4-11
REFERENCES R-l
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CONTENTS (continued)
APPENDIX A: EVISED VENTILATION RATE (VE) EQUATIONS FOR USE IN
INHALATION-ORIENTED EXPOSURE MODELS A-l
APPENDIX B: STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY
CODES FOR USE IN SIMULATING METS VALUES B-l
APPENDIX C: ADDITIONAL ANALYSIS TABLES C-l
APPENDIX D: RESPONSE PREPARED BY S. GRAHAM (U.S. EPA) TO PEER
REVIEWER COMMENTS ON APPENDIX A D-l
IV
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LIST OF TABLES
2-1. Numbers of individuals from NHANES 1999-2002 with available age, gender,
and body weight data, by age and gender categories 2-2
2-2. Equations from Schofield (1985) that predict BMR (MJ/day) as a function of
body weight (BW, kg) 2-3
2-3. Numbers of individuals from the NHAPS study by age and gender categories 2-7
3-1. Maximum possible METS values assigned to children, by age and gender 3-6
3-2. Estimated values, by age range, of the parameters within the multiple linear
regression model for predicting body-weight adjusted ventilation rate
(VE/BW; L/min-kg) 3-7
3-3. Estimated values, by age range, of the parameters within the mixed effects
regression model for predicting body-weight adjusted ventilation rate
(VE/BW; L/min-kg) 3-9
4-la. Descriptive statistics for daily average ventilation rate (L/min) in males, by age
category 4-3
4-lb. Descriptive statistics for daily average ventilation rate (L/min) in females, by age
category 4-4
4-2a. Average time spent per day performing activities within specified intensity
categories, and average ventilation rates associated with these activity categories,
for males according to age category 4-5
4-2b. Average time spent per day performing activities within specified intensity
categories, and average ventilation rates associated with these activity categories,
for females according to age category 4-8
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LIST OF ABBREVIATIONS AND ACRONYMS
APEX Air Pollution Exposure
BMR basal metabolic rate
CDC Centers for Disease Control and Prevention
CHAD Consolidated Human Activity Database
EE energy expenditure
EPA U.S. Environmental Protection Agency
H oxygen uptake
METS metabolic equivalent
NERL National Exposure Research Laboratory
NHANES National Health and Nutrition Examination Survey
NHAPS National Human Activity Pattern Survey
SHEDS Stochastic Human Exposure and Dose Simulation
USD A U.S. Department of Agriculture
VE ventilation rate
VC>2 oxygen consumption rate
VQ ventilatory equivalents
VI
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PREFACE
The Exposure Factors Program of the U.S. Environmental Protection Agency's (EPA's)
Office of Research and Development (ORD) has three main goals: (1) provide updates to the
Exposure Factors Handbook and the Child-Specific Exposure Factors Handbook, (2) identify
data gaps in exposure factors and needs in consultation with clients; and (3) develop companion
documents to assist clients in the use of exposure factors' data. The activities under each goal
are supported by and respond to the needs of the various program offices.
ORD's National Center for Environmental Assessment (NCEA) published the Exposure
Factors Handbook in 1997 and the Child-Specific Exposure Factors Handbook in 2008. These
documents provide summaries of available statistical data on various factors impacting an
individual's exposure to environmental contaminants. NCEA maintains these handbooks and
periodically updates them using current literature and other reliable data made available through
research. This document, Metabolically Derived Human Ventilation Rates: A Revised Approach
Based Upon Oxygen Consumption Rates, provides information that can be used to update the
ventilation rate values in the next editions of the Exposure Factors Handbook and the
Child-Specific Exposure Factors Handbook.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The National Center for Environmental Assessment (NCEA), Office of Research and
Development was responsible for the preparation of this document. The document was prepared
by Battelle under U.S. EPA Contract No. EP-C-04-027. Laurie Schuda served as Work
Assignment Manager, providing overall direction, technical assistance, and serving as
contributing author.
AUTHORS
Battelle
Bob Lordo
Jessica Sanford
Marcie Mohnson
U.S. EPA
Laurie Schuda
Jacqueline Moya
Tom McCurdy (Appendix A)
Steven Graham (Appendices A and D)
The following U.S. EPA individuals reviewed an earlier draft of this document and
provided valuable comments:
Bob Benson, U.S. EPA, Region 8
Brenda Foos, U.S. EPA, Office of Children's Health Protection
Gary Foureman, U.S. EPA, National Center for Environmental Assessment
Deirdre Murphy, U.S. EPA, Office of Air Quality Planning and Standards
Harvey Richmond, U.S. EPA, Office of Air Quality Planning and Standards
John Schaum, U.S. EPA, National Center for Environmental Assessment
Michel Stevens, U.S. EPA, National Center for Environmental Assessment
Paul White, U.S. EPA, National Center for Environmental Assessment
An external panel of experts serving as peer reviewers, along with experts representing
the general public, reviewed this document (i.e., the main document and Appendices A through
C). The peer-review panel was composed of the following individuals:
Dr. William C. Adams (Chair), Professor Emeritus, University of California, Davis
Dr. Amy Arcus-Arth, Office of Environmental Health Hazard Assessment, Cal/EPA
Dr. David W. Layton (Chair), University of California, Retired.
viii
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EXECUTIVE SUMMARY
The U.S. Environmental Protection Agency's (EPA's) National Center for Environmental
Assessment (NCEA) published the Exposure Factors Handbook and the Child-Specific Exposure
Factors Handbook to provide data on various factors impacting an individual's exposure to
environmental contaminants. The two primary purposes of these handbooks are (1) to
summarize data on human behaviors and characteristics that can affect exposure to
environmental contaminants, and (2) to recommend values for specific exposure factors when
included within an exposure assessment. NCEA maintains the Exposure Factors Handbook and
the Child-Specific Exposure Factors Handbook and periodically updates them using current
literature and other reliable data made available through research. Many program offices (e.g.,
Office of Solid Waste and Emergency Response, Office of Pesticides and Toxic Substances,
Office of Water) within U.S. EPA rely on the data from these handbooks to conduct their
exposure and risk assessments.
The Exposure Factors Handbook was first published in 1997 and the Child-Specific
Exposure Factors Handbook in 2008, and the data presented in the handbooks have been
compiled from various sources, including government reports and information presented in the
scientific literature. Among the exposure factors addressed by these handbooks are drinking
water consumption, soil ingestion, inhalation rates, dermal factors, food consumption, breast
milk intake, human activity factors, consumer product use, and residential characteristics. These
exposure factors represent the general population as well as specific target populations that may
have differing characteristics from those of the general population.
One important determinant of a person's exposure to contaminants in air is the ventilation
rate (VE), or the volume of air that is inhaled by an individual in a specified time period. VE s,
also known as breathing or inhalation rates, are given in Chapter 6 of the Exposure Factors
Handbook and the Child-Specific Exposure Factors Handbook. Ventilation rates have been
calculated in the past indirectly using estimates of "ventilatory equivalent," or the ratio of the
volume of air ventilating the lungs to the volume of oxygen consumed. Past methodologies have
not taken into account the variability in ventilatory equivalent with regard to age, gender, and
fitness level.
IX
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This document, Me tabolically Derived Human Ventilation Rates: A Revised Approach
Based Upon Oxygen Consumption Rate?, presents a revised approach that calculates VE s directly
from an individual's oxygen consumption rate (VCh) and applies this method to data provided
from more recent sources—the 1999-2002 National Health and Nutrition Examination Survey
(NHANES) and U.S. EPA's Consolidated Human Activity Database (CHAD). This new
approach considers variability due to age, gender, and activities. Data were grouped into age
categories and a simulated 24-hour activity pattern was generated by randomly sampling activity
patterns from the set of participants with the same gender and age. Each activity was assigned a
metabolic equivalent (METS) value based on statistical sampling of the distribution assigned by
CHAD to each activity code. Using statistical software, equations for METS based on normal,
lognormal, exponential, triangular, and uniform distributions were generated as needed for the
various activity codes. The METS values were then translated into energy expenditure (EE) by
multiplying the METS by the basal metabolic rate (BMR), which was calculated as a linear
function of body weight. The VO2 was calculated by multiplying EE by H, the volume of
oxygen consumed per unit of energy. VO2 was calculated both as volume per time and as
volume per time per unit body weight.
The inhalation rate for each activity within the 24-hour simulated activity pattern for each
individual was estimated as a function of VC>2, body weight, age, and gender. Following this, the
average inhalation rate was calculated for each individual for the entire 24-hour period, as well
as for four separate classes of activities based on METS value (sedentary/passive [METS less
than or equal to 1.5], light intensity [METS greater than 1.5 and less than or equal to 3.0],
moderate intensity [METS greater than 3.0 and less than or equal to 6.0], and high intensity
[METS greater than 6.0]). Data for individuals were then used to generate summary tables with
distributional data based on gender and age categories. Mean long-term inhalation rates,
presented as daily rates for children and adults, ranged from 8.76-20.93 mVday for males
(Table C-2a) and 8.53-16.20 nrVday for females (Table C-2b) with the lowest value
corresponding to children birth to <1 year and the highest value to adults 41 to <51 years. Mean
short-term inhalation rates, determined for children and adults performing various activities,
ranged from 3.Ox 10"03 - 5.8x 10"02 mVminute (Table C-6) with the lowest value corresponding to
male children birth to <1 year sleeping or napping and the highest value for male adults 51 to
<61 years of age during high-intensity activities.
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A validation exercise was conducted to compare results presented in this document with
values published in the literature using two other methodologies. These methodologies include
the use of doubly labeled water for estimating EE (Brochu et al., 2006a, b) and the use of
food-energy intakes from nationwide food intake surveys to estimate EEs (Layton, 1993;
Arcus-Arth and Blaisdell, 2007). U.S. EPA has implemented the methodology presented in this
document in Appendix A within the inhalation modules of population-based probabilistic
exposure models, including the Stochastic Human Exposure and Dose Simulation (SHEDS)
model and the Air Pollution Exposure (APEX) model. The APEX model results were then
compared with the values reported by Brochu et al. (2006a, b) and Arcus-Arth and Blaisdell
(2007) and are presented in Appendix D. Mean estimates for all of the physiological parameters
generated by APEX, including VE s, are reasonably correlated with independent measures from
the Brochu et al. (2006a, b) estimates—particularly when correcting the Brochu et al. (2006a)
ventilation estimates for children using a more appropriate estimate of ventilatory equivalents
(VQ) for children. The comparisons offer a validation of the methodology presented in this
document and show that despite the different methodologies and data sources, the resulting
APEX-derived mean ventilation estimates compare favorably to those of these two other
methods. Although the three methodologies available to estimate VE s (i.e., doubly labeled
water, food-energy consumption, and metabolically-derived VE s) have different strengths and
limitations, they complement each other in providing useful information on VE s. It should be
noted, however, that upper percentile values estimated using the metabolically derived daily VE s
methodology may be more uncertain. These values tend to equate to unusually high estimates of
caloric intake per day and are unlikely to represent an average individual.
The comprehensive analysis of VE s presented in this document for the four separate
classes of activities based on METS value (i.e., sedentary/passive, light intensity, moderate
intensity, and high intensity) for the age categories for both children and adults, males and
females, is unique and is not currently available in the literature. These estimates of VE s and
their variability with age and gender are important parameters in the estimation of the inhaled
dose and deposition of contaminants along the respiratory tract.
XI
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1. BACKGROUND AND OBJECTIVES
The U.S. Environmental Protection Agency (EPA) and its program offices conduct
various types of exposure assessment activities to characterize human exposure to toxic
chemicals. To assist in these efforts, U.S. EPA's National Center for Environmental Assessment
has developed the Exposure Factors Handbook (U.S. EPA, 1997) and the Child-Specific
Exposure Factors Handbook (U.S. EPA, 2008), documents that provide a summary of available
statistical data on various factors that can impact a person's exposure to environmental
contaminants. The two primary purposes of the handbooks are
• to summarize data on human behaviors and characteristics that can affect exposure to
environmental contaminants, and
• to recommend values for specific exposure factors when included within an exposure
assessment.
The exposure factors addressed by the handbooks include drinking water consumption, soil
ingestion, inhalation rates, dermal factors including skin area and soil adherence factors, food
consumption, breast milk intake, human activity factors, consumer product use, and residential
characteristics. Values documented in the handbooks for these exposure factors represent the
general population as well as specific target populations. The handbooks are a compilation of
information obtained from a variety of different sources and studies that are presented in a
consistent manner, while retaining much of the original formats that the individual study authors
used in their publications. Exposure assessors are the primary intended audience—with a
particular focus placed on researchers requiring data on standard factors to calculate human
exposure to toxic chemicals.
U.S. EPA maintains the handbooks and periodically updates them using current literature
and data available through U.S. EPA's research and other reliable sources. The handbooks are
available on U.S. EPA's Web site at www.epa.gov/ncea.
One important determinant of human exposure to toxic chemicals via inhalation of
contaminants in air is a person's ventilation rate (VE\ the volume of air inhaled in a specified
time period (e.g., liters per minute, hour, or day). In the scientific literature, VE is often
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abbreviated VE (with the dot above the V indicating that the abbreviation represents ventilation
"rate" rather than "volume") and has occasionally been referred to as "breathing rate" or
"inhalation rate," among other terms. Values of VE s for both adults and children are given
within Chapter 6 (Inhalation) of the handbooks and originate from several published studies,
each having considered different approaches and target populations.
In multiroute exposure assessments, U.S. EPA recognizes that metabolism "can become
the systemic organizing principle for simplifying intake/uptake dose modeling" (McCurdy,
2000). Metabolism can be quantifiably measured through energy expenditure (EE). Layton
(1993) was the first to demonstrate how EE could be used to characterize inhalation exposures
by estimating metabolically consistent VEs for different age/gender cohorts. Layton's approach,
as cited in the handbooks, calculated VE as the product of EE (expressed in energy units per unit
time—typically on a daily basis), oxygen uptake (H; the volume of oxygen consumed per energy
unit), and ventilatory equivalent (VQ; a unitless ratio of inhaled air volume to H). Layton (1993)
used a constant value for H (equal to 0.05 L O2/KJ or 0.21 L (Vkcal) and VQ (equal to 27) while
representing average daily EE by each of the following three approaches:
1) EE = average daily intake of food energy as determined from dietary survey data,
adjusting for the under-reporting of foods.
2) EE = basal metabolic rate (BMR; energy expended per day, determined as a function of
body weight) multiplied by the ratio of total daily EE to BMR that is reported in earlier
publications.
3) EE = average EE associated with different levels of physical activity that a person
experiences in an average day, as determined from time-activity survey data.
Activity-specific EEs were calculated as the product of a person's BMR, the activity's
metabolic equivalent (METS) score (i.e., a measure of the activity's metabolic rate
relative to a person's BMR), and the duration of time spent performing the activity.
Among the data sources used by Layton (1993) in these calculations are the U.S. Department of
Agriculture (USDA) 1977-78 Nationwide Food Consumption Survey, the Second National
Health and Nutrition Examination Survey (NHANES), and various exposure and activity studies
published primarily in the 1980s.
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One limitation of Layton's approach to calculating VE is its dependence on ventilatory
equivalent (VQ), which relies on an individual's fitness and EE levels. The VQ value of 27 used
in Layton (1993) may be appropriate for adults, but not necessarily for children. In addition, the
relationship between oxygen consumption and VE has been documented to be nonlinear
(Hebestreit et al., 1998, 2000), even among equally fit individuals. These limitations introduce
bias to the results. As a result, staff at U.S. EPA's National Exposure Research Laboratory
(NERL) have developed a revised approach, documented in the U.S. EPA document within
Appendix A, which calculates VE as a direct function of a person's oxygen consumption rate
(F02). U.S. EPA has implemented the methodology in Appendix A within the inhalation
modules of population-based probabilistic exposure models, including the Stochastic Human
Exposure and Dose Simulation (SHEDS) model and the Air Pollution Exposure (APEX) model.
The methodology features linear regression models that predict VE s that are normalized for body
mass and account for activity level, variability within age groups, and variation both between and
within individuals. This document presents metabolically derived human VE s that were
calculated by applying this methodology to data from such sources as the 1999-2002 NHANES
and U.S. EPA's Consolidated Human Activity Database (CHAD). The data were analyzed for
various age categories and gender. Age categories for children were based on U.S. EPA's
Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to
Environmental Contaminants (U.S. EPA, 2005). Infants under 1 year of age were grouped into
one category because of sample-size limitations.
There are two other methodologies found in the literature for estimation of VE s. One is
the doubly labeled water method for estimating EE (Brochu et al., 2006a, b), while the other uses
food-energy intakes from nationwide food intake surveys to estimate EEs (Layton, 1993;
Arcus-Arth and Blaisdell, 2007). Doubly labeled water is water in which both the oxygen and
hydrogen atoms are replaced with nonradioactive isotopes of these elements (2H2O and H218O).
The methodology is used to measure metabolic rate by measuring the disappearance rate of the
isotopes deuterium (2H) and heavy oxygen-18 (18O) in urine, saliva, or blood samples over time
following the ingestion of predetermined doses of doubly labeled water (Brochu et al., 2006a, b).
This methodology is the most accurate measurement of the total daily EEs and the stored daily
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energy cost for growth, parameters necessary for the estimation of daily VE s. Arcus-Arth and
Blaisdell (2007) used the methodology developed by Layton (1993) and updated the estimates of
VE s by using food intake data from USDA's Continuing Survey of Food Intake by Individuals
(USDA, 2000). The methodology presented in this document and the other two methodologies
found in the literature all have strengths and limitations. The doubly labeled methodology has a
disadvantage: it assumes a fixed H and ventilatory equivalent. In reality, these are known to
vary with age and fitness level. However, the doubly labeled approach provides reliable
estimates of average daily inhalation rates because direct measurements are taken for a long
period of time. The methodology is not useful, however, for estimating variability in an
individual's VE (or other parameter) over shorter time periods (i.e., VE s for various levels of
exertion).
The food-energy consumption methodology has limitations resulting from the collection
of data from a recall survey instrument. Food intake, especially for children, may be
underreported. It also relies on accurate estimates of both H and ventilatory equivalents (VQ).
One advantage of the metabolically derived VE s is that it does not require any
assumptions about VQ. The estimates derived using this methodology add to the current body of
knowledge regarding VE s. The methodology presented in this document also has some
limitations. These are described in more detailed in Section 4.1. For example, there may be
biases introduced by the assumed physical intensity or exertion associated with recorded
activities for the specific purpose of estimating EEs. In addition, the methodology requires
reliable estimates of the BMR, for which data are limited for some age groups. The
methodology also may be more appropriate for estimating activity-specific VE s rather than
long-term daily averages.
In order to provide some validation of the metabolically derived VE s presented in this
document, U.S. EPA conducted analyses comparing the three methodologies. Appendix D
provides a summary of these analyses and additional description of how U.S. EPA's APEX
model uses the methodology in Appendix A to estimate VE s. It also compares the estimates
generated by the methodology (as applied within the APEX model) with those of two other
recently reported methodologies.
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2. DATA SOURCES
The approach presented in this document for calculating metabolically based VE for a
person within a specific age and gender subpopulation uses the following information on that
person:
• Body weight;
• BMR, a measure of a person's EE while at physical and mental rest (i.e., in the absence
of activity requiring exertion), primarily to perform basic brain, liver, and skeletal muscle
function (McCurdy, 2000);
• Typical 24-hour activity pattern (i.e., types of activities performed in a given day and the
duration for which each activity was conducted); and
• METS values associated with each activity type.
After carefully identifying and evaluating various sources for these different types of
information, U.S. EPA selected the data sources below for use in this effort. Each data source
provides a specific type of information for an individual.
2.1. SOURCE OF BODY WEIGHT DATA: 1999-2002 NHANES
The Centers for Disease Control and Prevention's (CDC) National Center for Health
Statistics operates the NHANES program of studies. NHANES is designed to assess the health
and nutritional status of adults and children in the United States. Begun in the 1960s, the
NHANES program originally consisted of a periodic series of surveys focusing on different
population groups or health topics. Data collected within the NHANES originates from personal
interviews and physical examinations.
Beginning in 1999, the NHANES became a continuous, annual survey rather than the
periodic survey that it had been in the past. The survey examines a nationally representative
sample of persons each year. The CDC now releases public-use data files every 2 years. Data
used in this document originated from public-use data files labeled as "NHANES 1999-2000"
and "NHANES 2001-2002," upon CDC's recommendation that NHANES data collected from
1999 to 2002 should be considered as originating from a single survey (CDC, 2005). A total of
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21,004 individuals were represented in the combined data set, which is comprised of
(CDC, 2004):
• 1999-2000: Interview sample size = 9,965; examination sample size = 9,282
• 2001-2002: Interview sample size = 11,039; examination sample size = 10,477
U.S. EPA selected the NHANES 1999-2002 database because it is the most complete and
recent nationally representative source of body weight data for the U.S. population and for
subcategories determined by age and gender (CDC, 2000, 2002). Reported body weights were
measured by trained health professionals during an interview process using measuring equipment
that was consistent from year to year. Within this database, a total of 19,022 individuals had
recorded data for age, gender, and body weight. Table 2-1 presents the number of individuals
according to the age and gender categories considered in this document. In addition,
Tables C-la and C-lb of Appendix C presents a summary of body weight data for the
NHANES subjects represented in Table 2-1 by gender and age category.
Table 2-1. Numbers of individuals from NHANES 1999-2002 with available
age, gender, and body weight data, by age and gender categories
Age Category"
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
Total
Gender Category
Male
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
9,284
Female
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
9,738
Total
834
553
516
1,083
1,834
2,788
2,423
1,724
1,597
1,516
1,249
1,378
966
561
19,022
aAn age category labeled as "x to
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2.2. SOURCE OF BMR CALCULATION: SCHOFIELD (1985)
As noted earlier, a person's basal metabolic rate, or BMR, is a measurement of energy
required to maintain the body's normal body functions while at rest. Thus, it serves as a baseline
to which the EE of specific activities can be related. BMR is a function of such attributes as
body weight, height, age, and gender.
U.S. EPA has identified several sets of mathematical equations that researchers have
published for calculating BMR as a function of one or more attributes of a person. Each such
equation typically represented some subset of the population determined by age, gender, and
ethnic origin. Among the candidate equations were those proposed by Schofield (1985), which
express BMR (in megajoules1 per day) as a linear function of body weight (in kg) based upon a
person's gender and age category. Although these equations tend to be most representative of
primarily Caucasian individuals descended from European regions, no other source of BMR
estimates was judged to be a better representation of the general population. (Most alternative
BMR prediction equations tend to be based on small sample sizes involving a narrowly-defined
cohort of individuals.) Furthermore, the Schofield equations have been frequently cited in
refereed publications, and they are currently coded in U.S. EPA's APEX and SHEDS models.
They were used by Layton (1993) and are included in Appendix 5 A of the Exposure Factors
Handbook (U.S. EPA, 1997). U.S. EPA subsequently determined that the Schofield equations
would continue to be used for the analyses presented in this document. Table 2-2 presents these
equations.
Table 2-2. Equations from Schofield (1985) that predict BMR (MJ/day) as a
function of body weight (BW, kg)
Age Category"
Birth to <3 years
3 to <10 years
10 to <18 years
18 to <30 years
30 to <60 years
60 years and older
Male
BMR = 0.249 xBW- 0.127
BMR = 0.095 xBW + 2.110
BMR = 0.074 xBW + 2.754
BMR = 0.063 xBW + 2.896
BMR = 0.048 xBW + 3.653
BMR = 0.049 xBW + 2.459
Female
BMR = 0.244 xBW- 0.130
BMR = 0.085 xBW + 2.033
BMR = 0.056 xBW + 2.898
BMR = 0.062 xBW + 2.036
BMR = 0.034 xBW + 3.538
BMR = 0.038 xBW + 2.755
aAn age category labeled as "x to
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U.S. EPA recognizes that since the Schofield equations were derived, increased rates of
obesity, overweight incidence, and sedentariness have been observed in some sectors of the U.S.
population, especially children and adolescents (e.g., Derumeaux-Burel et al., 2004). This may
impact the representativeness of BMR predictions generated by the Schofield equations for
certain age groups, such as children under 4 years of age. U.S. EPA continues to reinvestigate
the issue of revising BMR predictions to account for more recently published information. Any
necessary revisions to the prediction process will be incorporated into the intake dose rate
modeling procedures used by the APEX and SHEDS models.
2.3. SOURCE OF ACTIVITY AND METS DATA: CONSOLIDATED HUMAN
ACTIVITY DATABASE (CHAD)
CHAD is the central source of information on activity patterns and METS values for
individuals within various age and gender categories. Available from
http://www.epa.gov/chadnetl and documented in U.S. EPA (2002), CHAD contains data from
12 preexisting human activity studies that were conducted within the U.S. at the city, state, and
national levels. It is intended for use by exposure assessors and modelers as a source of activity
data for exposure/intake dose modeling and/or statistical analysis. CHAD contains nearly
23,000 person-days of time-location-activity data representing all ages and genders and which
can be used for exposure modeling purposes (McCurdy et al., 2000).
U.S. EPA's NERL has developed and maintained CHAD since 1997. CHAD
incorporates various human activity databases that U.S. EPA has used over the years. Each of
these databases contains information on each activity undertaken by a given study subject during
a monitoring period of at least 24 hours. This activity-specific information includes the
activity's ID code (taken from the list of activity codes in Appendix B that corresponded to the
set of standardized activities that were applied across all studies within the database), location,
duration expended, and an estimate of the metabolic cost of performing the activity. Metabolic
cost is given in units of "METS" or "metabolic equivalents of work," an EE metric used by
exercise physiologists and clinical nutritionists to represent activity levels. An activity's METS
value represents a dimensionless ratio of its metabolic rate (EE) to a person's resting, or basal,
metabolic rate.
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The CHAD assigns a METS value to an activity according to the standardized ID code
that it assigned to the activity (see Appendix B). However, for most activities, it does not always
assign the same single point METS value to each occurrence of the same activity within the
database. Instead, the CHAD assigned a statistical distribution to each activity ID code
(McCurdy et al., 2000) representing the distribution of possible METS values associated with
that activity. Whenever a specific activity ID code was encountered within a study respondent's
data records, the CHAD generated a random value from the code's assigned distribution to serve
as the METS value for that particular activity. The statistical distributions that the CHAD
assigned to each activity ID code were specified in Appendix 1 of U.S. EPA (2002) and are
presented in Appendix B. The distributional forms included normal, lognormal, uniform,
triangular, and exponential distributions, as well as point estimates (i.e., when the same METS
value was to be assigned for all occurrences). For some activity codes, the CHAD occasionally
assigned a different form of the distribution to different age categories (<25 years, 25-40 years,
>40 years), in order to account for different ranges of intensity levels that may occur among
these age groups when performing the specified activity. Appendix B also lists lower and upper
bounds for certain distributions, where the lower bound was assigned in lieu of the randomly
generated METS value when the latter fell below the bound, and the upper bound was assigned
whenever the randomly generated METS value fell above the bound.
For this analysis, U.S. EPA utilized the distributions that had previously been assigned to
each activity code as specified in Appendix 1 of U.S. EPA (2002). No documentation was
available within CHAD to justify why certain distributions were assigned to a particular activity
code, why different distributions were assigned to different age categories within an activity
code, or why the age categories within these distributions were defined as they were. Section 3
presents more information on the specific approach used in this document to assign METS
values to activities prior to calculating VE.
2.3.1. The National Human Activity Pattern Survey
Many of the studies in CHAD focused their sample within a certain age range, such as
children or senior citizens, and/or a single region or city. Only one study was conducted on a
national scale: the U.S. EPA-sponsored National Human Activity Pattern Survey (NHAPS).
Conducted from 1992 to 1994 by the University of Maryland Survey Research Center, the
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NHAPS was a probability based national telephone interview survey of 9,386 respondents that
collected retrospective diary information on activities performed over a 24-hour day, along with
personal and exposure-related data (Klepeis et al., 2001). Participants were selected using a
stratified sampling approach, with stratification corresponding to the four major U.S. census
regions (Northeast, Midwest, South, West) within the 48 contiguous states (Klepeis et al., 2001).
U.S. EPA adopted the method used in the NHAPS study for assigning activity codes as the
common method for coding activities across all studies within the CHAD.
Based upon the NHAPS study's more general representation of the U.S. population
compared to the other studies within CHAD, activity data from the NHAPS study were selected
for use in characterizing activity patterns and obtaining METS values when calculating VE
estimates for this document. Within CHAD, NHAPS data records were distinguished by the type
of questionnaire that the survey provided to the study subjects. Because this discernment did not
affect the recording of information on activities performed in the previous 24-hour period and on
the duration spent performing each of these activities, all data records were utilized in the
analyses within this document regardless of the questionnaire type used. Tables 2-3 presents a
breakdown of the number of NHAPS respondents with available activity data, according to the
age and gender categories considered in this document. A total of 9,196 respondents had
available age and gender information, and, therefore, contributed information to this analysis.
(Each of these respondents contributed 24 hours worth of activity pattern data.)
One major limitation to the use of the NHAPS study data in this document was the lack
of body-weight measurements within the CHAD data records for the study respondents. When
an NHAPS respondent's data records are accessed interactively within the CHAD, the database
assigns a simulated body-weight measurement to that respondent by sampling randomly from a
lognormal distribution that is specific to the respondent's age and gender. (Details on the
lognormal distributions are not provided within U.S. EPA, 2002.) However, these simulated
body-weight measurements could not be downloaded with the other study data for use in this
document. Therefore, the NHAPS data were used only for characterizing the activity patterns of
an individual within a given age and gender category, while the CHAD also provided the
approach for assigning METS values to specific activities.
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Table 2-3. Numbers of individuals from the NHAPS study by age and
gender categories
Age Category"
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
Total
Gender Category
Male
53
67
63
184
261
234
234
755
737
588
453
354
199
59
4,241
Female
30
64
61
169
225
239
227
748
848
736
548
536
380
144
4,955
Total
83
131
124
353
486
473
461
1,503
1,585
1,324
1,001
890
579
203
9,196
aAn age category labeled as "x to
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3. APPROACH
The document in Appendix A describes an approach for estimating VE from VO2 using a
series of regression-based equations derived from data collected from 25 years of clinical studies
by Dr. William C. Adams of the University of California at Davis (Adams, 1993; Adams et al.,
1995). The multistep approach presented in this section applies these equations to the data
sources cited within Section 2 to estimate VE. An overview of the steps involved in this
approach is as follows:
1. Categorize individuals in the NHANES 1999-2002 and NHAPS data sets by age and
gender.
2. Calculate BMR for NHANES individuals as a function of body weight.
3. Obtain a simulated 24-hour activity pattern for each NHANES individual.
4. Assign a METS value to each activity represented in an NHANES individual's simulated
24-hour activity pattern.
5. Calculate EE and VO2 for each activity within an NHANES individual's simulated
24-hour activity pattern.
6. Calculate activity-specific VE values for an NHANES individual using the equations
derived in the U.S. EPA document (see Appendix A) that express VE (adjusted for body
weight) as a function of VO2 (adjusted for body weight), age, and gender.
7. Calculate average daily VE, as well as average VE for activities sharing a similar
intensity level, for each NHANES individual.
8. Summarize average VE values across individuals for each age and gender category.
Each step is further discussed in the subsequent sections of this document.
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3.1. STEP 1: GROUP NHANES AND NHAPS PARTICIPANTS BY AGE AND
GENDER CATEGORIES
Once the NHANES and NHAPS data were obtained for this analysis, the individuals
represented in each data set were grouped into age and gender categories using information
stored within the data records. Adults from 21 to 80 years were divided into six groups, each of
size 10 years (21-30 years, 31-40 years, etc.), while adults above 80 years were placed in a single
group. Children (<21 years) were divided into seven age categories according to groupings
given in U.S. EPA (2005) with the following exception: children less than 1 year old were
placed into a single group rather than further divided by age. If these children were further
segregated, the resulting age-related groups would have had insufficient sample sizes for the
analyses presented in this document.
Tables 2-1 and 2-3, in Section 2, list the age and gender categories used in this analysis,
along with the numbers of individuals within the NHANES and NHAPS data sets, respectively,
that were grouped into each category. A total of 19,022 NHANES participants and
9,196 NHAPS participants were grouped into these categories, corresponding to those
individuals having sufficient data to allow the grouping and to contribute to this analysis.
3.2. STEP 2: CALCULATE BMR ESTIMATES FOR NHANES PARTICIPANTS
As noted in Section 2, body-weight data were available for individuals in the NHANES
data set (originating from data collected during the survey's medical examinations) but not for
NHAPS participants; therefore, BMR estimates could be calculated only for the
19,022 NHANES individuals. The Schofield equations given in Table 2-2 of Section 2 were
used to calculate these estimates as a function of age, gender, and body weight. However, the
approach in Appendix A assumes that BMR is expressed in kcal/min, while the Schofield
equations calculate BMR in MJ/day. Given that 1 MJ equals 238.846 kcal, BMR was converted
from MJ/day to kcal/min as follows: BMR (kcal/min) = 0.16587 x [BMR (MJ/day)].
3.3. STEP 3: GENERATE A SIMULATED 24-HOUR ACTIVITY PATTERN FOR
EACH NHANES PARTICIPANT
Table 2-3 of Section 2 gives the number of NHAPS participants within each age/gender
category. Each of these participants had activity pattern data available for a single 24-hour
5-2
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monitoring period. For a given age/gender category, let TV correspond to the number of NHAPS
participants in that category, as given in Table 2-3. Each participant in this category was then
assigned a unique group ID number from 1 to N.
For each of the 19,022 individuals in the NHANES data set, the following procedure was
performed to generate a simulated 24-hour activity pattern for that individual:
• The individual's age/gender category was noted.
• Twenty (20) random integers were generated, with replacement, from the set of integers
ranging from 1 to TV (i.e., N= number of NHAPS participants within the individual's
age/gender category). The number of random integers to select (20) was arbitrarily
determined.
• For each random integer that was generated, data on the recorded 24-hour activity pattern
(activity ID codes and the duration of time spent performing each activity) were obtained
for the NHAPS participant whose group ID number within the given age/gender category
matched the random integer. This resulted in assigning a "simulated" set of activity data
to the NHANES individual that represented a total of 20 x 24 = 480 hours. (Because an
integer could occur multiple times within the generated set of 20 random integers, a given
set of 24-hour activity pattern data could likewise be represented multiple times within
the simulated set of activity data.)
• The different activity ID codes were identified in this simulated set, and for each code,
the duration of time (in minutes) spent performing that activity was totaled across all
records within this set. This total duration was then divided by 28,800 (i.e., the number
of minutes in 480 hours) to estimate the proportion of this total time that is represented by
the given activity. The proportions associated with each activity were then each
multiplied by 24 to yield a simulated number of hours that the given NHANES individual
was deemed to perform the activity within a 24-hour period.
Note that activities could not be assigned to NHANES participants based on prior knowledge of
their preferences and lifestyles because this information was unavailable. Furthermore, because
no body weight data were available on NHAPS participants, it is not possible to account for body
weight in assigning an activity pattern to NHANES participants.
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3.4. STEP 4: GENERATE A METS VALUE FOR EACH ACTIVITY WITHIN THE
SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES
PARTICIPANT
Once a simulated 24-hour activity pattern was assigned to a given NHANES individual, it
was necessary to assign a METS value to each activity ID code represented within that activity
pattern. METS values were assigned following the same approach used in the CHAD. As first
noted in Section 2.3, the CHAD has assigned statistical distributions to each activity ID code.
Appendix B lists these statistical distributions. While most activity ID codes were assigned a
single distribution, a few codes were assigned different distributions for different age ranges,
apparently to account for different ranges of intensity levels that may occur among different age
groups performing the same type of activity.
As is done in the CHAD, for each activity ID code encountered within the simulated
24-hour activity pattern for an NHANES individual, a METS value was assigned to that activity
by randomly sampling from the statistical distribution that CHAD has assigned to that code (and,
when necessary, to the age range in which the individual falls). The procedure developed to
generate random numbers from each of the distribution types represented within Appendix B
used random number generator functions available within the SAS® System (SAS, 2005). These
functions yield the following:
• RANEXP, a random number from a standard exponential distribution (scale
parameter = 1).
• RANNOR, a random number from a standard normal distribution (mean = 0, standard
deviation =1).
• RANTRI, a random number from a triangular distribution on the interval (0, 1) with
parameter H, a number between 0 and 1 which represents the distribution's modal value.
• RANUNI, a random number from a uniform distribution on the interval (0, 1).
The random number generation procedure depended not only on the particular distributional
form (e.g., uniform, normal, lognormal, exponential, triangular), but, also, on specific parameters
associated with the distribution, such as the mean (mean), standard deviation (std), minimum
(min\ and maximum (max), which are specified along with the distributions in Appendix B. The
exp denotes the exponentiation function, log denotes the natural logarithmic function, and sqrt
3-4
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denotes the square root function. Then random numbers for the distributions in Appendix B
were generated as follows:
• Exponential distribution: METS = min + std x RANEXP
• Lognormal distribution: METS = exp {log [mean21 sqrt (mean2 + std2)]}
+ sqrt {log[l+ (std/mean)2]} x RANNOR
• Normal distribution: METS = mean + std x RANNOR
• Triangular distribution: The generated METS value depends on the value of the mode of
the triangular distribution, which equals 3 x mean - min - max.
- If mode = min, then METS = max - sqrt [(1 - RANUNI) x (max - min)
x (max - mode)]
- If mode = max, then METS = min + sqrt [RANUNI x (max - min)
x (mode - min)]
- If min < mode < max, then METS = min + (max - min) x RANTRI,
where the value ofH used to determine RANTRI equals (mode - min)/(max -
min).
• Uniform distribution: METS = min + (max - min) x RANUNI.
Whenever an activity ID code's distribution was specified as a "point estimate," the
distribution consisted of a single value that occurred with 100% probability. Therefore, for such
an activity ID code, the METS value was always assigned to equal this single value.
The distributions for some activity ID codes were accompanied by a specified lower and
upper bound (see Appendix B). In these situations, the lower bound was assigned in lieu of the
randomly generated METS value when the latter fell below the bound, and the upper bound was
assigned whenever the randomly-generated METS value fell above the bound.
In November 2003, the CHAD incorporated a new feature which identified "maximum
possible METS values" that could be assigned to children aged 16 years and younger when
performing an activity that is 5 minutes or more in duration. This feature was implemented due
to U.S. EPA's finding that a child does not experience a METS value above a certain threshold
(McCurdy and Graham, 2004). Table 3-1 presents these maximum possible values by age and
gender. When METS values were generated from the statistical distributions specified in
5-5
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Appendix B, those values exceeding the maximum specified in Table 3-1 were replaced by the
maximum.
Table 3-1. Maximum possible METS values assigned to children, by age and
gender
Age (years)
6 and younger
7
8
9
10
11
12
13
14
15
16
Gender
Males
7.2
7.7
8.2
8.7
9.2
9.8
10.5
11.1
11.8
12.6
13.4
Females
6.4
6.8
7.3
7.7
8.2
8.7
9.3
10.0
10.6
11.3
12.2
Source: http ://oaspub .epa. gov/chad/recent_additions$. startup
3.5. STEP 5: CALCULATE ENERGY EXPENDITURE AND VO2 FOR EACH
ACTIVITY WITHIN AN INDIVIDUAL'S SIMULATED 24-HOUR ACTIVITY
PATTERN
Once the METS values were generated, EE (expressed in kcal/min) associated with a
given activity was calculated by multiplying the activity's assigned METS value by the BMR
value assigned to the individual within Step 2: EE = BMR x METS. This calculation was done
for each activity ID code encountered within an individual's simulated 24-hour activity pattern.
Once the set of activity-specific EE values were obtained for a given NHANES
individual, activity-specific values of the VO2 (expressed in L O2/min) were calculated from
these values according to the approach given in the document in Appendix A. VO2 was
calculated as the product of EE (kcal/min) and H, the volume of oxygen consumed per unit of
energy (L O2/kcal): VO2 = EE x H.
In each application of this equation, the value of His obtained by randomly sampling
from the uniform distribution over the interval (0.20, 0.22) for males and (0.19, 0.21) for
females. (These two distributions were obtained from Table A-l of Appendix A and differ
slightly from the distribution given in McCurdy, 2000. For a given gender, the specified uniform
-------
distribution did not differ according to age.) VO2 values were normalized by body weight by
dividing VO2 by the individual's body weight (in kg).
3.6. STEP 6: CALCULATE VENTILATION RATE FOR EACH ACTIVITY WITHIN
THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES
PARTICIPANT
Within this step, the multiple linear regression model presented in Section 2 of Appendix
A was applied to data on each activity within the simulated 24-hour activity pattern of an
NHANES participant in order to predict that person's VE (expressed in L/min), adjusted for body
weight, when performing the given activity. For each activity within an individual's activity
pattern, the model predicted VE as a function of VO2 estimated within Step 5 (also after adjusting
for body weight), age, and gender. The multiple linear regression model took the following
form: log (VEIBW) = b0 + b1>< log (VO2IBW) + b2 x log (age) + b3 x gender + e where "fog"
indicates the natural logarithmic transformation, BWcorresponds to the individual's body weight
(kg), age denotes the individual's age (in years), and gender equals -1 for males and +1 for
females. The term represents random deviation between the actual and predicted value of the
left-hand side of the equation for individuals having the same age, gender, and (VO2/BW) value
and is assumed to originate from a normal distribution with a mean of 0 and a standard
deviation of o. Estimated values of the intercept and slope parameters (bo, bi, b2, and b3) o
and were provided for specified age ranges and are given in Table 3-2. These age ranges were
determined based on prior usage (such as in Johnson, 2002) and on what would result in a best fit
of the regression model, as noted in Appendix A.
Table 3-2. Estimated values, by age range, of the parameters within the
multiple linear regression model for predicting body-weight adjusted
ventilation rate (VE/BW; L/min/kg)
Age
<20 years
20-33 years
34-60 years
>60 years
bt
4.4329
3.5718
3.1876
2.4487
bt
1.0864
1.1702
1.1224
1.0437
b2
-0.2829
0.1138
0.1762
0.2681
b3
0.0513
0.0450
0.0415
-0.0298
o
0.1444
0.1741
0.1727
0.1277
Source: Table A-3 of Appendix A.
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For each activity within an individual's simulated 24-hour activity pattern, the predicted
values of VE/BWwere determined as follows:
• The following information was entered into the regression equation: the ratio of the
individual's calculated VO2 for that activity to the individual's body weight, the
individual's age and gender codes, and estimates of the intercept and slope parameter
(bo, bj, b2, and b3, from Table 3-2) that are relevant to the individual's age.
• For the model's random error term , a random number was generated from a normal
distribution with mean zero and standard deviation equal to the estimate given in
Table 3-2 for o. This random number was then substituted for the error term in the
regression equation.
• The equation was then calculated, and the result was exponentiated.
The predicted value of VE that is unadjusted for body weight was determined by multiplying this
result by the individual's body weight.
In developing this step, U.S. EPA had also considered an alternate form of the regression
model for predicting VE/BW, which was also presented in Appendix A. In this alternate model,
called a mixed-effects regression model., the random error term of the multiple linear regression
model is divided into two additive components, Sb and sw, representing between-person and
within-person variability, respectively: log (VEIBW) = b0 + b} x log (VO^BW) + b2
x log (age) + bs x gender + (e\, + sw), where all other terms are as defined in the multiple linear
regression model. Both e\, and sw are assumed to originate from normal distributions with mean
0, but with different standard deviations Ob and ow, respectively. Estimated values of the
intercept and slope parameters (bo, bi, b2, and bs), Ob, and ow are given in Table 3-3 for the same
age ranges given in Table 3-2. Note that because the two models differ in their random
component, their parameter estimates differ as well.
Appendix A provides more details on the derivation of the multiple linear-regression
model and the mixed-effects regression model, along with their parameter estimates. Upon
observing how VE estimates compare between the two methods, which is discussed in Section 4,
the multiple linear-regression model was used as the basis for the VE estimates presented in this
document.
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Table 3-3. Estimated values, by age range, of the parameters within the
mixed effects regression model for predicting body-weight adjusted
ventilation rate (VE/BW; L/min-kg)
Age
<20 years
20-33 years
34-60 years
>60 years
Bo
4.3675
3.7603
3.2440
2.5828
bt
1.0751
1.2491
1.1464
1.0840
b2
-0.2714
0.1416
0.1856
0.2766
b3
0.0479
0.0533
0.0380
-0.0208
ob
0.0955
0.1217
0.1260
0.1064
ow
0.1117
0.1296
0.1152
0.0676
Source: Table A-3 of Appendix A.
3.7. STEP 7: CALCULATE AVERAGE VENTILATION RATE FOR TIME SPENT
PERFORMING ACTIVITIES WITHIN SPECIFIED METS CATEGORIES, AS
WELL AS 24-HOUR AVERAGE VENTILATION RATE, FOR EACH NHANES
PARTICIPANT
Once values of VE and VE/BWwere predicted for each reported activity ID code within
an individual's simulated 24-hour activity pattern (Step 6), an average daily VE was calculated
for the individual, both across the entire 24-hour activity pattern, as well as within specified
activity categories that were determined by level of intensity (based on assigned METS values).
Within the individual's simulated 24-hour activity pattern, each activity was classified into one
of four activity categories that were, in turn, associated with intensity level:
• Sedentary/Passive Activities: Activities with METS values no higher than 1.5.
• Light Intensity Activities: Activities with METS values exceeding 1.5, but no higher
than 3.0.
• Moderate Intensity Activities: Activities with METS values exceeding 3.0, but no higher
than 6.0.
• High Intensity Activities: Activities with METS values exceeding 6.0.
(These categories were defined based on general information in the scientific literature on how
researchers have grouped activities according to intensity level.) Within an activity category, let
A represent the number of activities within the individual's simulated 24-hour activity pattern
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that fall within the category, and let T equal the total duration of time (in minutes) that the
individual spent performing these A activities. Let VE,I represent the individual's VE calculated
in Step 6 for the /th activity within this activity category, and let Tt correspond to the duration of
time spent by the individual performing this activity (/' = 1, ..., A). Then the individual's average
daily VE for that METS activity group was calculated as a weighted average of the
activity-specific VE values, with weights corresponding to time spent performing the activities:
For each NHANES individual, this average VE statistic was calculated within each of the four
activity categories, as well as across all activities within the individual's simulated 24-hour
activity pattern. The latter average was calculated using the same formula as above, with A
equaling the total number of activities within the 24-hour activity pattern, and T equaling
1,440 minutes (i.e., the total number of minutes in a 24-hour period). These average daily VE
values were adjusted for body weight by dividing by the individual's body weight.
3.8. STEP 8: CALCULATE SUMMARY TABLES ACROSS INDIVIDUALS
For each age and gender category noted in Tables 2-1 and 2-3, individual-specific
average VE values from Step 7 were summarized across individuals for each of the four METS
activity categories, for a 24-hour period, and for sleeping and napping activities only
(i.e., activity code 14500). These summaries corresponded to weighted descriptive statistics,
with the weights corresponding to the individuals' 4-year sampling weights stored within the
NHANES 1999-2002 database. The descriptive statistics, which were calculated using the
UNIVARIATE procedure within the SAS® System, included the mean, maximum, and selected
percentiles of the observed distribution among the 19,022 NHANES participants.
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4. RESULTS
This section presents tables that summarize the results of the eight-step statistical
technique described in Section 3, which predict ventilatory rate from simulated 24-hour activity
data assigned to individuals represented within the NHANES 1999-2002 data base (Section 2).
The results in this section were generated using Version 9 (Release 9.1.3) of the SAS® System
(SAS, 2005). Appendix C provides supplemental summary tables that provide more detailed
information that accompanies the results presented in this section.
The multiple linear regression model in Section 3.6 was used to predict ventilatory rate as
a function of FCb, age, and gender for each activity assigned to each individual in the NHANES
data set. Section 3.6 also cites a mixed effects model which differed from the multiple linear
regression model in how its random component was specified (i.e., the multiple linear regression
model included a single random error term, while the mixed effects model separated random
error into two additive terms that represented between-individual and within-individual
variability). The extent to which predictions differed between the two types of models was
minimal; the median percentage change in the mixed effect regression model prediction relative
to the multiple linear regression model prediction was a two percentage point decline. The
multiple linear regression model predicted higher ventilatory rate estimates 53% of the time
compared to the mixed effect regression model, and this percentage did not deviate much
between the two genders or among different METS categories. Because no model tended to
consistently produce higher predictions compared to the other, the choice of models was not
expected to impact the types of summaries presented in this section. (It should be noted,
however, that if the prediction process did not incorporate a realization of the random error
term(s), then the multiple linear regression model led to higher ventilatory rate predictions
compared to the mixed effect regression model more frequently—about 62% of the time.)
Descriptive statistics presented in tables within this section and Appendix C include the
observed mean and selected percentiles of the analyzed data. These statistics were selected to
characterize the central tendency and the general range of the predicted data distribution. While
no parametric distributional assumptions were placed on the observed data distributions before
these statistics were calculated, the 4-year sampling weights assigned to the individuals within
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NHANES 1999-2002 were used to weight each individual's data values in the calculations of
these statistics.
Table C-l in Appendix C contains descriptive statistics on body weight and BMR for the
NHANES individuals by gender and age category. This table serves to summarize the reported
body weights of the individuals represented in these analyses, as well as the outcome of the
BMR calculations (using the Schofield equations and conversion to kcal/min), both of which
enter into calculation of EE, VO2, and VE. Sample sizes within each age/gender category were
provided in Table 2-1 of Section 2.
Tables 4-la and 4-lb summarize daily averageFg., both adjusted and unadjusted for body
weight, by age category for males and females, respectively. The daily average VEs entering
into these summaries, in L/min, were calculated in Step 7 (Section 3.7). The summaries
represent an average rate taken over a 24-hour period (and, therefore, its typical activity pattern)
among individuals in the specified category. Table C-2, in Appendix C, presents the same
information, but expressed in m3/day, as is currently done in the Exposure Factors Handbook.
As noted in Section 3.7, average daily VE was also calculated for each of four groups of
activities defined according to specified ranges of METS values representing sedentary/passive
activity, light intensity, middle intensity, and high intensity activities. In addition, average VE
was calculated for the period of time when an individual is sleeping or napping. This activity
occurs more than any other and represents the lowest intensity activity. Thus, while sleeping and
napping are included within the sedentary/passive activity category for this data analysis, it is
also treated as a separate activity in the calculations. Table 4-2a (for males) and Table 4-2b (for
females) summarize average VE, both adjusted and unadjusted for body weight, within each
activity category by gender and age category. These results are presented in L/min, representing
an average rate while performing the activity.
Tables 4-2a and 4-2b also summarize the number of NHANES participants whose
simulated 24-hour activity pattern included activities falling within the specified category, as
well as the average number of hours per day (across individuals) that individuals spent
performing these activities within their simulated activity patterns.
4-2
-------
Table 4-la. Descriptive statistics for daily average ventilation rate (L/min) in males, by age category
Age Category
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(VE; L/min)
Mean
6.08
9.37
9.19
8.78
9.32
10.64
11.95
13.07
14.09
14.54
14.52
12.46
11.35
10.52
Percentiles
5th
3.32
6.76
6.56
7.24
7.00
7.92
8.75
8.81
9.72
10.18
10.41
9.66
9.10
8.30
10th
3.96
7.23
7.09
7.55
7.42
8.41
9.31
9.42
10.39
10.79
11.16
10.07
9.45
8.73
25th
4.97
8.09
7.94
7.91
8.15
9.22
10.06
10.76
11.78
12.15
12.22
11.03
10.18
9.60
50th
6.04
9.11
9.16
8.74
9.09
10.27
11.55
12.62
13.77
14.30
14.17
12.22
11.27
10.35
75th
7.24
10.43
10.07
9.47
10.23
11.68
13.31
14.75
15.98
16.59
16.08
13.57
12.20
11.33
90th
8.28
11.82
11.30
10.16
11.50
13.57
15.23
17.06
18.59
18.55
18.76
15.12
13.49
12.51
95th
8.81
12.43
12.30
10.70
12.31
14.73
16.23
18.84
20.07
19.70
20.20
16.32
14.18
12.98
Maxi-
mum
11.84
16.83
19.56
13.56
17.34
19.82
27.23
30.15
28.28
31.93
26.51
19.51
17.03
15.72
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW: L/min-kg)
Mean
0.76
0.82
0.66
0.49
0.31
0.20
0.16
0.16
0.17
0.17
0.17
0.14
0.14
0.14
Percentiles
5th
0.63
0.67
0.54
0.36
0.22
0.14
0.12
0.11
0.11
0.12
0.11
0.12
0.12
0.12
10th
0.65
0.71
0.57
0.39
0.24
0.15
0.13
0.12
0.12
0.12
0.12
0.12
0.12
0.12
25th
0.70
0.76
0.61
0.43
0.26
0.17
0.14
0.13
0.14
0.14
0.14
0.13
0.13
0.13
50th
0.75
0.81
0.65
0.48
0.30
0.19
0.16
0.16
0.16
0.16
0.17
0.14
0.14
0.14
75th
0.81
0.88
0.70
0.54
0.35
0.22
0.18
0.18
0.19
0.19
0.19
0.15
0.15
0.15
90th
0.87
0.95
0.76
0.61
0.38
0.25
0.19
0.21
0.22
0.22
0.21
0.17
0.16
0.16
95th
0.90
1.03
0.78
0.64
0.40
0.27
0.21
0.22
0.24
0.23
0.23
0.18
0.17
0.17
Maxi-
mum
1.03
1.20
0.94
0.75
0.56
0.35
0.27
0.36
0.32
0.32
0.30
0.22
0.22
0.19
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table 4-lb. Descriptive statistics for daily average ventilation rate (L/min) in females, by age category
Age Category
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body
Weight
(VE; L/min)
Mean
5.92
9.24
8.85
8.45
8.62
9.33
9.44
10.12
10.40
11.25
11.24
9.02
8.36
7.74
Percentiles
5th
3.36
6.31
6.19
6.86
6.94
7.27
6.85
7.05
7.69
8.41
8.56
7.22
6.87
6.38
10th
3.81
7.03
6.99
7.21
7.19
7.72
7.37
7.41
8.20
8.73
9.00
7.48
7.08
6.57
25th
4.75
7.81
7.90
7.78
7.65
8.36
8.18
8.29
9.04
9.83
9.77
8.18
7.56
7.04
50th
5.84
9.05
8.75
8.35
8.30
9.08
9.17
9.79
10.20
11.03
11.04
8.97
8.21
7.65
75th
6.79
10.17
9.69
9.04
9.32
10.10
10.43
11.54
11.33
12.47
12.36
9.66
9.00
8.24
90th
8.09
12.12
10.82
9.74
10.51
11.29
11.89
13.42
12.85
13.83
13.84
10.69
9.80
8.92
95th
8.79
12.93
11.36
10.37
11.35
12.09
12.70
14.68
14.20
14.82
14.73
11.21
10.55
9.68
Maxi-
mum
18.23
17.20
15.98
13.71
14.46
18.46
20.91
20.99
19.64
24.92
17.85
14.12
12.29
11.76
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW: L/min-kg)
Mean
0.79
0.83
0.66
0.48
0.30
0.17
0.15
0.14
0.14
0.15
0.15
0.12
0.12
0.12
Percentiles
5th
0.63
0.68
0.57
0.33
0.19
0.13
0.11
0.10
0.10
0.10
0.11
0.10
0.10
0.10
10th
0.67
0.70
0.58
0.37
0.21
0.14
0.12
0.11
0.11
0.11
0.11
0.10
0.10
0.10
25th
0.72
0.77
0.62
0.41
0.25
0.15
0.13
0.12
0.12
0.13
0.13
0.11
0.11
0.11
50th
0.78
0.82
0.66
0.48
0.30
0.17
0.14
0.14
0.14
0.15
0.15
0.12
0.12
0.12
75th
0.86
0.90
0.70
0.53
0.34
0.19
0.16
0.16
0.16
0.17
0.17
0.13
0.13
0.14
90th
0.92
0.98
0.74
0.61
0.38
0.22
0.19
0.18
0.19
0.20
0.19
0.15
0.15
0.15
95th
0.96
1.02
0.77
0.64
0.40
0.24
0.20
0.19
0.21
0.21
0.21
0.16
0.16
0.15
Maxi-
mum
1.11
1.20
0.86
0.77
0.52
0.33
0.25
0.28
0.30
0.29
0.28
0.19
0.23
0.20
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. VE was estimated using the multiple linear regression model in Section 3.6.
-------
Table 4-2a. Average time spent per day performing activities within
specified intensity categories, and average ventilation rates associated with
these activity categories, for males according to age category
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
Sleep or Nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
13.5
12.6
12.1
11.2
10.2
9.4
8.7
8.4
8.1
7.9
8.0
8.3
8.5
9.2
3.08
4.50
4.61
4.36
4.61
5.26
5.31
4.73
5.16
5.65
5.78
5.98
6.07
5.97
0.38
0.40
0.33
0.24
0.15
0.10
0.07
0.06
0.06
0.07
0.07
0.07
0.07
0.08
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
15.0
14.3
14.6
14.1
13.5
13.8
13.2
12.4
12.3
12.3
13.1
14.5
15.9
16.6
3.18
4.62
4.79
4.58
4.87
5.64
5.76
5.11
5.57
6.11
6.27
6.54
6.65
6.44
0.40
0.41
0.34
0.25
0.16
0.10
0.08
0.06
0.07
0.07
0.07
0.08
0.08
0.09
4-5
-------
Table 4-2a. Average time spent per day performing activities within
specified intensity categories, and average ventilation rates associated with
these activity categories, for males according to age category (continued)
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
Light-Intensity Activities (1.5 < METS < 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
5.3
5.5
5.5
6.6
7.6
7.5
7.1
6.1
5.7
6.1
5.6
5.5
5.0
4.9
7.94
11.56
11.67
11.36
11.64
13.22
13.41
12.97
13.64
14.38
14.56
14.12
13.87
13.76
0.99
1.02
0.84
0.63
0.38
0.25
0.18
0.16
0.16
0.17
0.17
0.16
0.17
0.18
Moderate-Intensity Activities (3.0 < METS < 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
3.7
4.0
3.8
3.2
2.7
2.3
3.3
5.2
5.7
5.4
5.0
3.7
2.9
2.3
14.49
21.35
21.54
21.03
22.28
26.40
29.02
29.19
30.30
31.58
32.71
29.76
29.29
28.53
1.80
1.88
1.55
1.17
0.74
0.49
0.39
0.36
0.36
0.37
0.38
0.34
0.36
0.38
4-6
-------
Table 4-2a. Average time spent per day performing activities within
specified intensity categories, and average ventilation rates associated with
these activity categories, for males according to age category (continued)
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
High-Intensity Activities (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
183
164
162
263
637
1,111
968
546
567
487
452
490
343
168
0.2
0.3
0.1
0.3
0.3
0.4
0.4
0.3
0.4
0.3
0.4
0.4
0.4
0.3
27.47
40.25
40.45
39.04
43.62
50.82
53.17
53.91
54.27
57.31
58.42
54.13
52.46
53.31
3.48
3.52
2.89
2.17
1.41
0.95
0.71
0.66
0.64
0.66
0.68
0.62
0.65
0.72
aAn individual's VE for the given activity category equals the weighted average of the individual's
activity-specific VE s for activities falling within the category, estimated using the multiple linear
regression model in Section 3.6, with weights corresponding to the number of minutes spent performing
the activity. Numbers in these two columns represent averages, calculated across individuals in the
specified age category, of these weighted averages. These are weighted averages, with the weights
corresponding to the 4-year sampling weights assigned within NHANES 1999-2002.
4-7
-------
Table 4-2b. Average time spent per day performing activities within
specified intensity categories, and average ventilation rates associated with
these activity categories, for females according to age category
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
Sleep or Nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
13.0
12.6
12.1
11.1
10.3
9.6
9.1
8.6
8.3
8.3
8.1
8.4
8.6
9.1
2.92
4.59
4.56
4.18
4.36
4.81
4.40
3.89
4.00
4.40
4.56
4.47
4.52
4.49
0.39
0.41
0.34
0.24
0.15
0.09
0.07
0.06
0.06
0.06
0.06
0.06
0.07
0.07
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
14.1
14.3
14.9
14.3
14.0
14.2
13.6
12.6
12.3
12.2
12.7
14.3
15.4
16.5
3.00
4.71
4.73
4.40
4.64
5.21
4.76
4.19
4.33
4.75
4.96
4.89
4.95
4.89
0.40
0.43
0.36
0.25
0.16
0.10
0.07
0.06
0.06
0.06
0.07
0.07
0.07
0.08
4-8
-------
Table 4-2b. Average time spent per day performing activities within specified
intensity categories, and average ventilation rates associated with these activity
categories, for females according to age category (continued)
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
Light-Intensity Activities (1.5 < METS < 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
6.0
5.6
5.8
6.3
7.3
7.6
7.0
6.4
6.5
6.6
6.5
6.2
6.0
5.3
7.32
11.62
11.99
10.92
11.07
12.02
11.08
10.55
11.07
11.78
12.02
10.82
10.83
10.40
0.98
1.05
0.90
0.62
0.38
0.23
0.17
0.15
0.15
0.16
0.16
0.15
0.16
0.17
Moderate-Intensity Activities (3.0 < METS < 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
3.9
4.0
3.3
3.4
2.6
2.0
3.3
4.8
5.0
5.0
4.6
3.3
2.5
2.1
13.98
20.98
21.34
20.01
21.00
23.55
23.22
22.93
22.70
24.49
25.24
21.42
21.09
20.87
1.87
1.90
1.60
1.14
0.72
0.44
0.36
0.33
0.32
0.33
0.34
0.29
0.31
0.33
4-9
-------
Table 4-2b. Average time spent per day performing activities within specified
intensity categories, and average ventilation rates associated with these activity
categories, for females according to age category (continued)
Age Category
# NHANES
Participants
Reporting
Activity
Average
Duration
(hr/day) Spent
at Activity
Ventilation Rate During this Activity"
Unadjusted for Body
Weight (L/min)
Adjusted for Body
Weight (L/min-kg)
High-Intensity Activities (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
79
55
130
347
707
1,170
887
796
687
515
424
465
304
188
0.2
0.2
0.2
0.2
0.2
0.3
0.2
0.3
0.2
0.3
0.3
0.3
0.3
0.3
24.19
36.48
37.58
34.53
39.39
46.56
44.09
45.68
44.44
46.98
47.35
40.02
40.64
41.88
3.26
3.38
2.80
1.98
1.33
0.88
0.70
0.65
0.61
0.65
0.63
0.54
0.59
0.67
aAn individual's VE for the given activity category equals the weighted average of the individual's activity-specific
VE s for activities falling within the category, estimated using the multiple linear regression model in Section 3.6,
with weights corresponding to the number of minutes spent performing the activity. Numbers in these two columns
represent averages, calculated across individuals in the specified age category, of these weighted averages. These
are weighted averages, with the weights corresponding to the 4-year sampling weights assigned within NHANES
1999-2002.
4-10
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Additional descriptive statistics to accompany the results in Tables 4-2a and 4-2b can be
found in Table C-3 through Table C-7 in Appendix C. These five tables summarize the
following:
• Duration of time spent performing activities (hr/day)
• Average VE (L/min and m3/min), unadjusted for body weight
• Average VE (L/min/kg and m3/min/kg), adjusted for body weight
4.1. STRENGTHS AND LIMITATIONS
The major strengths of the approach applied in this document (and detailed in
Appendix A) are that it accounts for differences in VE that occur due to activity level, the effect
of age and gender, and variation between individuals. The approach yields an estimate of VE
that is a function of F02 rather than an indirect measure of oxygen consumption such as VQ.
(While other researchers have estimated VE given VQ, the appropriate value of VQ to use can
depend on an individual's work rate, and thus, can introduce bias and additional variability.) The
primary sources of input data to this approach, the NHANES and NHAPS data sets, are each
nationally representative data sets with a large sample size, even within the age and gender
categories considered in this document, thereby allowing for improved characterization of body
weight and activity patterns that can represent everyone in an age/gender subpopulation.
However, in the prediction of VE from F02, there is, admittedly, limited data available in the
literature on diverse groups of people varying in age, gender, body weight, and other relevant
factors.
By simulating an individual's 24-hour activity pattern based on information for a
subpopulation with the same age and gender range, this approach attempted to address the
correlation that is present between an individual's BMR measure and the METS values
associated with the activities that the individual performs. However, because the NHAPS
database within CHAD does not include body weight, information on both METS values and
BMR were not available for an individual that would allow a more rigorous characterization,
such as taking into account correlation among the incidence and duration of certain activity
4-11
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types. This was one limitation of the analysis outcome. While other data sources within CHAD,
which did include body weight were considered, they were deemed to have limited target
populations that would have limited the ability to infer findings to larger populations.
To determine an individual's BMR, EPA utilized a series of equations proposed by
Schofield (1985) that predicted BMR as a function of body weight, gender, and age category.
While this set of equations was deemed to represent the general U.S. population more completely
than any other available BMR estimating procedures, there are some limitations associated with
their use. For example, the equations are based on studies that have become considerably dated
in recent years, and, therefore, may be less representative of current populations (especially in
young children). Some researchers question the extent of uncertainty that may be introduced by
expressing BMR as a strictly linear function of body weight, as the Schofield equations do.
Despite these limitations, the derivation and application of the Schofield equations have been
subject to independent technical review in scientific publications. They remain the best available
tool for U.S. EPA in calculating BMR from body weight for the general population.
The simulated 24-hour activity pattern assigned to an NHANES participant is likely to
contain a greater variety of different types of activities than one person may typically experience
in a day. Furthermore, a particular activity may be represented within an activity pattern for a
shorter duration than what may be typical for a person. The durations of different intensity
levels summarized in Table C-3 of Appendix C across the simulated activity patterns appear to
be within reason for each age category.
The approach does not specifically account for uncertainty that is introduced by assigning
a random METS value to an activity that originates from a pre-specified statistical distribution.
In addition, a potential bias may be introduced if this distribution is not appropriate in reality for
a given activity, although the CHAD identified appropriate distributions based upon a review of
the exercise physiology and clinical nutrition literature. The METS randomization process
allows for different METS values to be assigned to the same activity being performed by the
same individual at a given moment in time. There is both variability associated with this METS
randomization process as well as variability in METS values that is present from one individual
to another. The variability in METS values that is present from one individual to another
confounds the variability associated with the METS randomization process.
4-12
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By using the NHANES sampling weights in the calculation of the statistics in this
document, the goal of this effort was to generate statistics that could represent national estimates.
In the calculation, use of the sample weights is preferable to ignoring them. However, because
the 24-hour activity pattern assigned to each NHANES individual was simulated using activity
information from the NHAPS study, the observed distribution of VE values across individuals
can only approximate a national distribution. In addition, because the simulated 24-hour activity
patterns are limited to the set of activities reported within the NHAPS database, and because
each simulated pattern represented an average of multiple patterns observed within the NHAPS
database, an individual's true activity pattern in any given 24-hour period may be more variable
than that considered in this exercise. Furthermore, because the simulated activity profiles did not
consider possible limits on the "maximum possible METS value" that would account for
previous activities, VE s may be overestimated as a result.
Data from the NHAPS were used to characterize activity levels for individuals in the U.S.
population. Because the NHAPS was conducted over 10 years ago, it may not accurately portray
activity profiles in certain subpopulations, especially those seeing greater trends toward
overweight incidence and obesity (e.g., children and adolescents). In addition, the growing
sedentary nature of the population as a whole may be affecting the continued relevance of
NHAPS activity data to the contemporary U.S. population. METS distributions also may not be
adequately characterized when activities are conducted by children, due to the more frequent and
sudden movement by children from one activity to another compared to other subpopulations.
Lastly, the survey's practice of retrospectively providing activity information may result in less
accurate and less detailed information than studies that use prospective, real-time diaries that
subjects would complete after participation in each activity. While U.S. EPA recognizes and
considered these limitations, the several advantages associated with the NHAPS data remained
important, and, therefore, led to accepting the data for use in this analysis.
In order to assess the impact of these limitations, a validation exercise was conducted to
compare results presented in this document with values published in the literature using two
other methodologies. These methodologies include the use of doubly labeled water for
estimating EE (Brochu et al., 2006a, b) and the use of food-energy intakes from nationwide food
intake surveys to estimate EEs (Layton, 1993; Arcus-Arth and Blaisdell, 2007). Appendix D
shows how the results from the metabolically derived methodology compares with the values
4-13
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reported by Brochu et al. (2006a, b) and Arcus-Arth and Blaisdell (2007). Mean estimates for all
of the physiological parameters generated by APEX including VE s are reasonably correlated
with independent measures from the Brochu et al. (2006a, b) estimates, particularly when
correcting the Brochu et al. (2006a) ventilation estimates for children using a more appropriate
estimate of VQ for children. The results of this exercise suggest that despite the different
methodologies and data sources, the resulting APEX-derived mean ventilation estimates
compare favorably to those of these two other methods. It should be noted, however, that upper
percentile values may be more uncertain. These values tend to equate to unusually high
estimates of caloric intake per day and are unlikely to represent an average individual. Although
the three methodologies available to estimate VE s (i.e., doubly labeled water, food-energy
consumption, and metabolically-derived VE s) have different strengths and limitations, they
complement each other in providing useful information on VE s.
4-14
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REFERENCES
Adams, WC; Shaffrath, JD; Ollison, WM. (1995) The relation of pulmonary ventilation and heart rate in leg work
alone, arm work alone, and in combined arm and leg work. Paper WA-84A.04 presented at the 88th Annual Air &
Waste Management Association Conference. 21 June 1995.
Adams, WC. (1993) Measurement of breathing rate and volume in routinely performed daily activities. California
Environmental Protection Agency, Air Resources Board. Final Report, Contract No. A033-205. Available online at
http://www.arb.ca.gov/research/apr/past/a033-205.pdf
Arcus-Arth, A; Blaisdell, RJ. (2007) Statistical distributions of daily breathing rates for narrow age groups of infants
and children. Risk Anal 27(1):97-110.
Brochu, P; Ducre-Robitaille, J; Brodeur, J. (2006a) Physiological daily inhalation rates for free-living individuals
aged 1 month to 96 years, using data from doubly labeled water measurements: a proposal for air quality criteria,
standard calculations and health risk assessment. Hum Ecol Risk Assess 12(4):675-701.
Brochu, P; Ducre-Robitaille, J; Brodeur, J. (2006b) Physiological daily inhalation rates for free-living pregnant and
lactating adolescents and women aged 11 to 55 years, using data from doubly labeled water measurements for use in
health risk assessment. Hum Ecol Risk Assess 12(4):702-735.
CDC (Centers for Disease Control and Prevention). (2000) National Health and Nutrition Examination Survey
(NHANES) 1999-2000. U.S. Department of Health and Human Services, National Center for Health Statistics
(NCHS), Hyattsville, MD. Available online at http://www.cdc.gov/nchs/about/major/nhanes/nhanes99_00.htm
CDC (Centers for Disease Control and Prevention). (2002) National Health and Nutrition Examination Survey
(NHANES) 1999-2000. U.S. Department of Health and Human Services, National Center for Health Statistics
(NCHS), Hyattsville, MD. Available online at http://www.cdc.gov/nchs/about/major/nhanes/nhanes01-02.htm.
CDC (Centers for Disease Control and Prevention). (2005) Analytic and reporting guidelines The national health
and nutrition examination survey (NHANES). National Center for Health Statistics, Centers for Disease Control
and Prevention, Hyattsville, MD. December 2005. Available online at
http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf.
CDC (Centers for Disease Control and Prevention). (2004) NHANES analytic guidelines. National Center for
Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD. June 2004. Available online at
http://www.cdc.gov/nchs/data/nhanes/nhanes_general_guidelines June_04.pdf.
Derumeaux-Burel, H; Meyer, M; Morin, L; et al. (2004) Prediction of resting energy expenditure in a large
population of obese children. Am J Clin Nutr 80(6): 1544-1550.
Hebestreit, H; Staschen, B; Hebestreit, A. (2000) Ventilatory threshold: a useful method to determine aerobic fitness
in children? Med Sci Sports Exerc 32(11): 1964-1969.
Hebestreit, H; Kriemler, S; Hughson, RL; et al. (1998) Kinetics of oxygen uptake at the onset of exercise in boys
and men. J Appl Physiol 85(5):1833-1841.
Klepeis, NE; Nelson, WC; Ott, WR; et al. (2001) The national human activity pattern survey (NHAPS): a resource
for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11(3):231-252.
Johnson, T. (2002) A guide to selected algorithms, distributions, and databases used in exposure models developed
by the office of air quality planning and standards. Prepared for the U. S. Environmental Protection Agency, Office
of Research and Development, RTF, NC. Prepared by TPJ Environmental, Chapel Hill, NC. Available online at
http://www.epa.gov/ttn/fera/data/human/report052202.pdf.
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Layton, DW. (1993) Metabolically consistent breathing rates for use in dose assessments. Health Phys 64(l):23-36.
McCurdy, TR. (2000) Conceptual basis for multi-route intake dose modeling using an energy expenditure approach.
J Expo Anal Environ Epidemiol 10:86-97.
McCurdy, TR; Glen, G; Smith, L; et al. (2000) The National Exposure Research Laboratory's consolidated human
activity database. J Expo Anal Environ Epidemiol 10:566-578.
McCurdy TR; Graham, SE. (2004) Analyses to understand relationships among physiological parameters in children
and adolescents aged 6-16. U.S. Environmental Protection Agency, Office of Research and Development, National
Exposures Research Laboratory, Research Triangle Park, N. Carolina. EPA/600/X-04/092.
SAS (2005) SAS OnlineDoc® 9.1.3. Cary, NC: SAS Institute, Inc. Available online at
http://support.sas.com/onlinedoc/913/docMainpage.jsp.
Schofield, WN. (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin
Nutr. 39(Suppl.):5-41.
USDA (Department of Agriculture). (2000) Continuing survey of food intake by individuals (CSFII) 1994-96, 98.
U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD. Available from the National
Technical Information Service (NTIS), Springfield Va, PB2000-500027.
U.S. EPA (Environmental Protection Agency. (1997) Exposure factors handbook. Office of Research and
Development, National Center for Environmental Assessment, Research Triangle Park, NC. Available online at
http://www.epa.gov/ncea/pdfs/efh/front.pdf.
U.S. EPA (Environmental Protection Agency). (2002) CHAD user's guide: extracting human activity information
from CHAD on the PC. Prepared by ManTech Environmental Technologies and modified March 22 2002 by
Science Applications International Corporation for the National Exposure Research Laboratory, U.S. Environmental
Protection Agency. Research Triangle Park, NC. Available online at
http://www.epa.gov/chadnetl/reports/CHAD_Manual.pdf.
U.S. EPA (Environmental Protection Agency). (2005) Guidance on selecting age groups for monitoring and
assessing childhood exposures to environmental contaminants. Risk Assessment Forum, Washington, DC.
EPA/630/P-03/003F. November 2005. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid= 146583.
U.S. EPA (Environmental Protection Assessment). (2008) Child-specific exposure factors handbook (CSEFH).
National Center for Environmental Assessment, Washington, DC, EPA/600/R-06/096F, October, 2008. Available
online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid= 199243.
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APPENDIX A:
Revised Ventilation Rate (F#) Equations for Use
in Inhalation-Oriented Exposure Models
by
S. Graham and T. McCurdy
EPA/600/X-05/008
-------
Disclaimer to Appendix A
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
Abstract to Appendix A
Using data compiled from 32 clinical exercise studies, algorithms were developed to
estimate body mass-normalized ventilation rate (VE, L/min-kg) for 4 age groups (<20, 20-<34,
34-<61, 61+ years of age) and both genders. The algorithms account for differences in
ventilation rate due to activity level, variability within age groups, and variation both between
and within individuals. A multiple linear regression (MLR) model was first used to estimate
significant explanatory parameters (p<0.01) following natural log (Ln) transformation of body
mass (BM) normalized oxygen consumption rate (VO2). Log transformed age (Ln(age)), gender
(-1 for males, 1 for females), and Ln(VC>2/BM) served as independent variables and regressed on
multiple VE measurements that were collected during incremental exercise to obtain regression
parameter estimates. The (MLR) model showed marginal statistical improvement (R2 +5%) in
comparison with a previous simple linear regression model for estimating VE, however the MLR
can estimate population VE with one-half the equations formerly used and can be used to address
uncertainty in VE estimations. A mixed-effects regression (MER) model was then constructed
utilizing the independent variables as fixed parameters and retaining individuals and study of
origin as random effects variables. The MER model was used to allocate the random error (e) to
between-person residuals distributions (inter-individual variability) and within-person residuals
distributions (intra-individual variability). Predictive equations were executed for 5,000
iterations at a given age (e.g., 5 year olds) or age group classification (e.g., 45-55 years old) and
estimated ventilation rates for each model were compared at their respective 50th, 95th and 99th
percentiles. U.S. EPA's Air Pollution Exposure (APEX) model was used to estimate population
ventilation rates using a variety of ventilation algorithms for comparison with the MLR and
MER at individual years in age. VE estimations from the MLR and MER algorithms were
similar across all ages and provided reasonable ventilation rates at all percentiles and ages,
suggesting either approach is reasonable for stochastic modeling exercises where simulation of
activity-specific person-oriented ventilation rates is desired.
A-ii
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Keywords / Acronyms
APEX
BMR
BM
BMI
BSA
CHAD
EE
EVR
F,-
HR
HT
LBM
METS
NAAQS
NERL
OAQPS
RQ
SHEDS
•
V,
V,
CO,
VD
VT
VQ
Air Pollution Exposure model (OAQPS)
Basal metabolism rate
Body mass
Body mass index
Body surface area
Consolidated Human Activity Database
Energy expenditure
Equivalent ventilation rate [VE/BSA]
Conversion Factors
Heart rate
Height
Lean Body Mass (equivalent to fat-free mass)
Metabolic equivalents of work
National Ambient Air Quality Standard
National Exposure Research Laboratory
Office of Air Quality Planning and Standards
partial pressure of arterial carbon dioxide
• •
Respiratory quotient (Vco^ IV0^ )
Stochastic Human Exposure and Dose Simulation model (NERL)
Alveolar ventilation rate (due to formatting issues, VA is used in report)
Carbon dioxide expiration rate
Dead space volume of the lung
Total ventilation rate (due to formatting issues, VE is primarily used here)
Tidal volume of the lung
Oxygen consumption rate (due to formatting issues, VO2 is primarily used here)
• •
Ventilatory equivalent (VE IV0 )
A-iii
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Acknowledgments
The authors are indebted to a number of people who invested time in improving this
report. Special thanks are due our OAQPS colleagues who shared their expertise in human
exposure modeling and risk assessment which helped focus our efforts; they are, in particular:
John Langstaff, Ted Palma, and Harvey Richmond. Gratitude is also due to Ted Johnson of TOJ
Environmental, who provided us with information on past practices regarding uptake dose
modeling. Finally, we thank our U.S EPA colleague, Dr. James Starr who reviewed this report
and discussed ventilation issues with us.
A-iv
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Table of Contents
1. Introduction A-l
2. Methods A-3
Data Set Description A-3
Statistical Analysis A-3
Algorithm Evaluation A-5
3. Results and Discussion A-6
Statistical Analysis A-6
Extrapolation Issues and Assumptions A-9
Performance Evaluation A-11
4. Recommendations A-15
5. Future Research A-l5
6. References A-16
Attachment Al A-199
Attachment A2 A-31
Attachment A3 A-355
List of Tables
Table A-l. Parameter estimates used to estimate activity-specific VC>2 for males and
females of different age groups A-5
Table A-2. Parameter and residuals distribution estimates derived from two different
statistical techniques and reported from Johnson (2002) for use in predictive
equation (1) or (2) A-7
Table A-3. Ventilation parameter estimates (bi), standard errors (se), and residual
distributions standard deviation estimates (e;) using Adams data and
assuming equation (3) or (4) A-9
Table A-4. Residual distributions standard deviation estimates (eb and ew) using
data characterized by percentage of maximum VC>2 (VC^m) assuming
equation (3) A-10
Table A-5. Residual distributions standard deviation estimates (eb and ew) using
data categorized by percentage of maximum VC>2 (VC^m) assuming
equation (4) A-10
Table A-6. Recommended inhalation rates (L/min) from U.S. EPA (1997) Table 5-23 A-12
Table Al -1. Total subj ects for each study, gender, and exercise ergometry used A-20
A-v
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List of Figures
Figure A-1. Pathways for estimating various ventilation parameters and metrics A-1
Figure A-2. Ventilatory quotient (VQ) as a function of age during exercise A-8
Figure A-3. Estimated ventilation rates (VE, L/min) for females (left) and males
(right) while performing low-level (top), moderate (middle), and
vigorous (bottom) activities A-13
Figure A-4. Estimated population ventilation rates (VE, L/min) for 20,000 persons
using APEX and the mixed effects regression (MER) algorithm
(Equation 4 and Table 3) A-14
Figure A2-1. Relationship between total ventilation rate (VE) and oxygen
consumption rate (VO2) during exercise A-33
Figure A2-2. Relationship between body mass normalized total ventilation rate
(VE/BM) to oxygen consumption rate (VO2/BM) during exercise A-33
Figure A2-3. Relationship between the natural logarithm of total ventilation rate
Ln(Vfi) and oxygen consumption rate Ln(VC>2) during exercise A-34
Figure A2-4. Relationship between body mass normalized total ventilation rate
Ln(VE/BM) to oxygen consumption rate Ln(VO2/BM) during exercise A-34
Figure A3-1. Comparison of selected percentiles of estimated event-based
ventilation rates from 20,000 person APEX model simulation using
different ventilation algorithms A-37
Figure A3-2. Comparison of estimated event-based ventilation rate percentiles from
20,000 person APEX model simulation using mixed effects regression
(MER-left) and Johnson (2002) (right) ventilation algorithms A-3 8
Figure A3-3. Percent difference of estimated event-based ventilation rate percentiles
from 20,000 person APEX model simulation using mixed effects
regression (MER-left) and Johnson (2002) (right) ventilation algorithms ...A-39
A-vi
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1. Introduction
The use of population-based probabilistic exposure models in risk assessments has
increased over the past few decades, largely due to their ability to simulate human activities more
realistically than previous models that used mostly static but conservative estimates of
physiologic parameters such as ventilation rate (VE, commonly in L min"1). Some of the early,
more advanced human exposure models were developed by U.S. EPA's Office of Air Quality
Planning and Standards (OAQPS) in the 1980s, each containing an inhalation dose metric since
their inception (Johnson, 1995; McCurdy, 1994a, 1995). The first series of these models were
known as NAAQS Exposure Model NEM and probabilistic NEM (pNEM) models. The
ventilation algorithm became more detailed over time, culminating with equivalent ventilation
rate (EVR; VE normalized to body surface area (BSA)) and alveolar ventilation rate (VA)
estimations used by a number of the pNEM models that are described in numerous
OAQPS-sponsored papers and reports (Johnson, 2002; Johnson and Adams, 1994; Johnson and
Capel, 2002; Johnson et al., 1995, 1996; Johnson and McCoy, 1995; McCurdy, 1994b; and
McCurdy, 1997a). More recently, the National Exposure Research Laboratory (NEJAL) has
developed the Stochastic Human Exposure and Dose Simulation (SJrtEDS) model, essentially
adopting the ventilation algorithm used in OAQPS's Air Pollution Exposure (APEX) model,
itself a variant of the pNEM models. The impact of using advanced procedures for dose rate
metrics has been evaluated by McCurdy (1997b, c); however an integrated approach for
estimating multiple ventilation parameters has not yet been developed.
To estimate inhalation exposure and dose in these fairly complex models, a standard but
flexible algorithm is required. One that not only addresses variability in breathing rates but can
simulate differences in the site of action of pollutants within the respiratory system (e.g., ozone,
particulate matter deposition) and variable chemical uptake characteristics (e.g., absorption
across the alveolar membrane versus total absorption). Using current U.S. EPA exposure model
approaches for approximating ventilation rates and considering the need to address ventilation
for multiple classes of pollutants, a framework of activity-specific ventilation parameters was
constructed and is depicted in Figure A-l.
BMR * METS * F1 = V0o —,
(EE •* VO2)
CHAD
i- BM
RQ/Pa
VD/VT
BSA-
I
EVR
-VQ
HT
Figure A-1. Pathways for estimating various ventilation parameters and metrics.
A-l
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Central to the framework is the U.S. EPA's Consolidated Human Activity Database
(CHAD), a database of nearly 23,000 person-days of time-location-activity data useful for
exposure modeling purposes (McCurdy et al., 2000). Distributions of metabolic equivalents
(METS) are assigned in CHAD to every activity that respondents participated in. These METS
distributions have been developed from a review of the exercise physiology and clinical nutrition
literatures (McCurdy, 2000) and represent the ratio of the energy needed for the activity
performed to the energy needed to sustain life (basal metabolism). The METS are fundamental
to simulating an individual's breathing rate while the person is performing a variety of activities
(e.g., running, walking, sleeping).
To estimate activity-specific ventilation rates, first a prediction equation for basal
metabolic rate (BMR, in kilocalories min"1) is used to estimate the simulated individual's resting
metabolic rate from their body mass (BM), or from BM and height (HT) together. Then activity-
specific METS (METSA) are sampled via Monte-Carlo techniques and multiplied by a person's
estimated BMR to obtain a single realization of the energy expenditure rate (EE, kilocalories
min"1). This rate of energy expenditure is retained over the duration of the activity (termed here
as an "event"), which can be as short as 1 minute or as long as one hour (due to the structure of
CHAD).
Thus mathematically, event-specific EE for an individual (EEE;) is defined as:
EEE/=BMR/* METSA
Estimated EEE; can then be converted to an activity-specific oxygen consumption rate
(VO2E;) using a gender-specific relationship expressed as a uniform distribution (F^-,
L-CVkilocalorie) (McCurdy, 2000) as follows:
V02E/= EEEi*Fn
VO2, however, is not the final physiological process to be simulated since most air pollution
clinical studies do not use it as the end-point ventilation metric. Most of these studies use VE or
EVR, and some exposure models, particularly OAQPS's APEX model for carbon monoxide
(APEX-CO), need VA (commonly in L min"1) for their inhalation modeling approach. By
definition, VA is a fraction of VE and is important in estimating respiratory uptake of gases
(e.g., 62, CO, CO2) and chemicals that likely act as gases (e.g., benzene, 1-3-butadiene
[Lin, et al., 2001]). Regardless, all three mentioned ventilation metrics (VE, EVR, VA) can be
obtained from VO2, either directly or indirectly, thus VO2 is fundamental to the development of
each of these ventilation algorithms.
The pathway from VO2 to VE can be direct or indirect, with the indirect approach itself
having a few options: from VO2 to VA and then to VE, or from VO2 to VE using the ventilatory
quotient (VQ or alternatively, the ventilatory equivalent). VQ is simply the unitless ratio of VE
to VO2 when both metrics are in the same units. This ratio is non-linear with work rate however,
varying between 20 and 32 in healthy people at low-to-moderate work rates while higher at more
extreme exercise levels (McArdle et al., 1991). While there are nuances among the many ways
A-2
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that VE and EVR have been estimated over the years, in general the approach taken has been the
VQ pathway depicted in Figure A-l. VA has been estimated by Johnson (2002) using a direct
relationship between VC>2 and VA originally described by Galletti (1959). For a more complete
discussion of how ventilation rate has been modeled by OAQPS, see Section 9 of Johnson
(2002).
This NERL Research Report describes an approach to estimating VE directly from VC>2
using a series of regression-based equations derived from 25 years of clinical studies conducted
by Dr. William C. Adams of the University of California at Davis. Much of the work cited
above has been predicated upon past work and data provided by Dr. Adams, particularly Adams
(1993) and Adams et al. (1995). OAQPS and NERL at different times acquired independent
(non-overlapping) data sets from his laboratory at the University of California-Davis. These data
have been extensively analyzed by OAQPS contractors, particularly Ted Johnson of TRJ
Environmental (and previously with IT Technology). In addition to the citations noted above
regarding analysis of Dr. Adams' data, see also Johnson et al. (1998).
OAQPS requested that staff in the Exposure Modeling Research Branch of NERL review
the literature on calculating VA since a previous review of the algorithms used in pNEM/CO
indicated that a constant in the equation possibly varied non-linearly with exercise rate. That
review has not been completed as of this date, but as an outgrowth of this work NERL staff
decided to first investigate a VE algorithm for use in both the APEX and SHEDS inhalation
modules. It is this work that is described below.
2. Methods
Data Set Description
The data set acquired is listed and briefly described in communication memos authored
by Dr. Adams and provided in Attachment Al. Data from 32 panel studies collected over a
25-year period by the same laboratory were obtained in electronic format. The number of
subjects included within these studies was nearly one-thousand, undoubtedly one of the largest
datasets of its kind. The data set used was a Microsoft ® Excel (.xls) file obtained from a disk
labeled "Converted Adams Data". The file used in this analysis (adam2.xls) was considered as
the raw data file, since also on this disc was included an ASCII text version of the file and the
memo from Dr. Adams describing the data set.
The raw data required physical manipulation and mathematical transformation to allow
for statistical analyses. Details of the procedures used as part of this research are described
further in Attachment A2. Briefly, due to the format of the original study data sets, a file was
created containing a single vector for each individual ventilation parameter. Data were then
screened for erroneous and potentially extreme values. Ventilation parameters (VE and VO2)
were normalized to body mass and followed with a natural logarithm (Ln) transformation.
Statistical Analysis
All statistical analyses were performed using SAS® software, version 8.2.1 (SAS
Institute, Gary, NC). Parameters considered useful in model simulations (i.e., those that could
capture a significant degree of variability and are consistent with current exposure modeling
A-3
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structure) were first evaluated for statistical significance (p<0.01) using an analysis of variance
(ANOVA). Then, a simple linear regression (SLR) model was developed of the form yt = b0 +
bjXj + Sj to estimate parameter coefficients for use in predictive equations:
Ln(VE/BM)i = b0 + (^ * Ln(VO2/BMi)) + 61 Eq. (1 )
where b0 = the regression intercept, b} = the regression slope coefficient, and et representing
individual variability in ventilation rate. The coefficient of determination (R2) was used in
evaluating the regression model since it represents the proportion of total variance of the
dependent variable "explained" by the independent variables.
The approach was modified slightly for predictive purposes to reflect additional test
factors contributing to variance in the ventilation rate. The model presented here was given as
Equation 9-6 in Johnson (2002) and interpreted as follows, where bo = the intercept and bi = the
slope regression coefficient:
Ln(VE/BM)i = b0 + (b., * Ln(VO2/BMi)) + ebi + ewi Eq. (2)
It was assumed here that the predictive regression equation represents a mixed-effects
regression (MER) model containing both fixed and random effects variables. VC>2 was
considered a fixed parameter and subject and study were random effects variables used to
estimate the between-person (inter-individual variability) residuals distribution (et,) and within-
person (intra-individual variability) residuals distribution (ew) rather than simply random error
(e) alone. Each of the residuals are normally distributed, with a mean of 0 and an estimated
standard deviation of
-------
Modification of the age groupings originally developed by Johnson (2002) was
performed to determine if the statistical performance of the predictive equations could be
improved. Criteria for the model development included individual regression coefficient
significance (p- or lvalue), total model explanatory power (R2), and stability of the regression
coefficients. For this last criterion, it was desired that coefficients neither greatly increase nor
decrease in the individual regression equations compared with previous coefficient estimates
while expanding/compressing age classifications. Age groupings were varied by one-year
increments until the evaluation criteria described above was optimized, that is, models containing
the greatest R2, with statistically significant coefficients that varied minimally were retained.
Algorithm Evaluation
Each of the algorithms for estimating ventilation were evaluated using one or both
methods described below to determine the range possible outcomes for individuals and a
population. Selected evaluations for the MLR and MER (using equations 3 and 4, respectively)
are presented in the main text, while additional evaluations are provided in Attachment A3.
Ventilation rates were first estimated using Crystal Ball™ software (Decisioneering, Inc.,
Denver Colorado). Age- and gender-specific body weights for simulated individuals were
estimated by probabilistic sampling of distributions provided by Burmaster and Crouch (1997).
Basal metabolic rate was estimated using age- and gender-specific equations presented in
Schoefield (1985), with age itself being sampled from uniform distributions within the age
groupings used in our analyses. Activity-specific VO2 was generated using METS distributions
for low, moderate, and vigorous intensity activities combined with the unit conversions given in
Table A-l. Ventilation rates were estimated for 5,000 hypothetical persons within each age (or
age grouping) and gender category using predictive equations (3) and (4) and their respective
parameters. To estimate variability in ventilation rates, each of the residuals distributions were
probabilistically sampled while the intercept and coefficients held as constants, thus each
estimated ventilation rate is representative of one activity performed by one hypothetical
individual. Median (p50), 95th (p95), and 99th (p99) percentiles of the hypothetical population
distribution of estimated ventilation rates were compiled by age. The output represents the
possible range of expected ventilation rates across the population at a moment in time.
Table A-1. Parameter estimates used to estimate activity specific VO2 for males and females
of different age groups.1
Age group
Child
(0-18yrs)
Adult
(>18yrs)
Gender
Male
Female
Male
Female
METS-Activity Level'1
Low
N{2.0,0.34}
N{1. 5,0.26}
N{2.5,0.43}
N{2.0,0.34}
Moderate
N{5.0,0.85}
N{4.5,0.77}
N{6.5,1.1}
N{5.0,0.85}
Vigorous
N{9.0,1.5}
N{8.0,1.4}
N{10,1.7}
N{9.0,1.5}
Conversion Factors
Energy to
Oxygen
(L-C-2/kcal)
11(0.20-0.22}
U{0.19-0.21}
11(0.20-0.22}
U{0.19-0.21}
Unit
(MJ/kcal)/
(m in/day)
239/1440
Distribution type and parameters used: N=normal {arithmetic mean, standard deviation}; U=Uniform
{min,max}.
It was assumed that the relative standard deviation of the METS for each distribution was 17% (see
McCurdy and Graham, 2004)
A-5
-------
A second method for evaluation was conducted using OAQPS's APEX model, version
4.0 (see U.S. EPA, 2005 for details on the model algorithms). Twenty thousand individuals were
simulated for one day to allow for the comparison of selected ventilation algorithms developed
as would be used in an actual exposure model. Activity-specific ventilation rates were generated
by APEX using human activity diaries from CHAD and the general approach described above
and outlined in Figure A-l. Diaries in CHAD are at a minimum disaggregated to hourly
components, that is, the maximum time step for an activity or location inhabited could be one
hour, thus up to 24 events in a day. However much of the data are further divided such that
within one hour there may be multiple activities performed or multiple locations inhabited,
upwards to 1 minute in duration. Since every simulated individual had multiple estimations for
ventilation rate depending on their activities performed (generally ranging from 30-40 events in a
day), distributions were first calculated for each person followed by an estimate of the population
distribution at each age (generally between 1 and 400 persons were simulated for each year of
age). The median (p50), 95th (p95), and 99th (p99) percentiles and maximum ventilation rates
estimated with the APEX model represent the variability in the mid-upper range of ventilation
rates for individuals within a population. It should be noted that the maximum for all individuals
is the same as the 99th percentile unless there was more than 99 events (rare if occurs at all).
3. Results and Discussion
Statistical Analysis
Both age and gender were used in the development of several regression equations
derived from the Adams data set and summarized in Table 9-1 of Johnson (2002); however
significance of these variables was not reported there. An analysis of variance was performed
here on VE, utilizing the 4 age groups (i.e., <18, 18-44, 45-64, >65 years old) and two genders as
classification variables indicated by Johnson (2002). VO2 normalized to body mass was
included as an additional independent variable. Age group, gender, their interaction term (age
group by gender), and VC>2 were each significant explanatory parameters (all p<0.003).
Results of the simple linear regression analysis, the simple mixed model addressing fixed
and random effects, and parameter coefficients reported by Johnson (2002) assuming equations
(1) or (2) are presented in Table A-2. Regression model intercept and slope were statistically
significant parameters in each of the regression models.
There were marginal differences between the simple regression coefficients and the
simple mixed model coefficients developed in this work; both the intercepts and slopes were
systematically lower for the simple regression. The results from the simple mixed model and
Johnson (2002) were nearly identical with the most notable differences seen in the residuals
distributions, albeit at a minimal level.
Following this single variable model comparison, age and gender were investigated as
additional independent variables for use in a multiple linear regression model. Gender was
already deemed significant based on the ANOVA and, since its use as a parameter in a multiple
linear regression would halve the number of equations needed for ventilation simulations, was to
be included as a parameter in the regression model. For age, it was hypothesized that it would
A-6
-------
have a statistically significant effect on the relationship between VE and VC>2, not just among the
different age groups but also within a given age group. Figure A-2 shows the relationship
between VQ and age, with the most notable variation of VQ for those under age 18. These data
(age<18) were not analyzed by Johnson (2002) due to lack of availability. Age, when included
in a preliminary multiple regression model, was determined to be a significant explanatory
parameter for both genders where age<18 and for males only within the other age groups (data
not shown here). Estimated coefficients for the females, although not statistically significant,
were generally consistent with those of the males.
When VQ was plotted by age (Figure A-2), it was observed that a few of the subjects
contained excessive VQ values, such that further culling of the data set was warranted.
Observations of VQ in excess of 50 were removed based on a review of the relevant literature
undertaken as part of the work documented by McCurdy and Graham (2004). Based on this
criterion, 13 data points were removed. No single subject had more than one data point
removed. The impact of the additional culling was negligible (not reported).
Table A-2. Parameter and residuals distribution estimates derived from two different
statistical techniques and reported from Johnson (2002) for use in predictive
equation (1) or (2).
Age
group
<18
18-44
45-64
65+
n
315
288
1473
3145
60
641
45
317
Gender
F
M
F
M
F
M
F
M
Method3
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
bo
3.214
3.263
seb0
0.089
0.050
Ln(VO,/BM)
bi
0.941
0.950
seb1
0.022
0.012
Residuals
eb
ew
0.1609
0.1427
0.0735
R2
0.8504
Not Performed
3.054
3.180
0.103
0.052
0.913
0.941
0.026
0.012
0.1715
0.1600
0.0722
0.8069
Not Performed
4.021
4.358
4.357
3.758
3.983
3.991
3.360
3.462
3.454
3.824
4.019
4.018
2.687
2.958
2.956
3.686
3.731
3.730
0.040
0.034
0.023
0.022
0.239
0.153
0.060
0.047
0.297
0.143
0.090
0.055
1.182
1.276
1.276
1.130
1.194
1.197
0.998
1.023
1.021
1.117
1.166
1.165
0.846
0.908
0.908
1.060
1.071
1.071
0.011
0.009
0.007
0.006
0.055
0.034
0.016
0.012
0.068
0.032
0.023
0.013
0.1736
0.1351
0.1351
0.1176
0.1182
0.1826
0.1219
0.1228
0.1382
0.1395
0.1401
0.1152
0.1106
0.0774
0.0769
0.1584
0.1172
0.1107
0.1073
0.1112
0.0960
0.0920
0.0886
0.0341
0.0338
0.1280
0.1092
0.1082
0.0632
0.0632
0.8790
0.8965
0.8498
0.8884
0.7820
0.8729
SLR: simple linear regression model (PROC REG in SAS) when using equation (1); MER: mixed
effects regression model (PROC MIXED in SAS) when using equation (2); Johnson: data reported in
Johnson (2002) for use with equation (2)
A-7
-------
VQ
70 i
60
50:
40;
30-
20
10
0 10 20 30 40 50 60 70 80 90
Age
Figure A-2. Ventilatory quotient (VQ) as a function of age during exercise.
To determine optimum age groups for the final multiple linear regression model, the
boundary values of the age groups—i.e., the youngest and oldest age groups determined by
Johnson (2002) (<18 and 65+ years of age, respectively) were first evaluated. Based on the
criteria described above, the lower and upper age groups were redefined to be <20 years old and
>60 years old. Two "inner" age groupings (20 to <34; 34 to <61) were also optimized based on
their fit with each other and with the lower and upper boundaries. The group comprising ages
34 to <61 could have been further subdivided (e.g., 34 to <45, 45 to <61 groups provided a good
statistical fit based on the semi-quantitative criteria), however the regression coefficients for the
intercept and age variables were dramatically altered for the 34-<45 age group (decreased and
increased, respectively) in comparison with the other age groups. It is not apparent whether this
response is physiologically representative of this age group, or that it is a function of the data set
itself; therefore, the larger age grouping was retained.
Final ventilation parameter estimates for use in equations (3) or (4) following age group
optimization are presented in Table A-2. Slightly improved explanatory power was achieved
with the new models (as measured by the multiple linear regression model, about 90% of total
variance is now explained) compared with the earlier analyses (on average 85%). Each of the
regression models and all estimated coefficients were statistically significant (p<0.01) except
where noted.
A-8
-------
Table A-3. Ventilation parameter estimates (b,), standard errors (se), and residual
distributions standard deviation estimates (e,) using Adams data and assuming
equation (3) or (4).
Age
group
<20
20-<34
34-<61
61 +
Ln(VO2/BM)
n
1085
3646
1083
457
Method3
MLR
MER
MLR
MER
MLR
MER
MLR
MER
bo
4.4329
4.3675
3.5718
3.7603
3.1876
3.2440
2.4487
2.5826
se b0
0.0579
0.0650
0.0792
0.1564
0.1271
0.2578
0.3646
0.7013
b.
1 .0864
1.0751
1.1702
1 .2491
1.1224
1.1464
1 .0437
1 .0840
se b1
0.0097
0.0087
0.0067
0.0061
0.0120
0.0088
0.0195
0.0122
Ln(age)
b2
-0.2829
-0.2714
0.1138
0.1416
0.1762
0.1856
0.2681
0.2766
se b2
0.0124
0.0190
0.0243
0.0493
0.0335
0.0674
0.0834
0.1652
Gender
b3
0.0513
0.0479
0.0450
0.0533
0.0415
0.0380b
-0.0298
-0.02081°
seb3
0.0045
0.0077
0.0031
0.0061
0.0095
0.0172
0.0100
0.0149
Residuals
eb ew
0.1444
0.0955 0.1117
0.1741
0.1217 0.1296
0.1727
0.1260 0.1152
0.1277
0.1064 0.0676
R2
0.9250
0.8927
0.8925
0.8932
MLR: multiple linear regression model (PROC REG in SAS) when using equation (3); MER: mixed-effects regression
(PROC MIXED in SAS) when using equation (4); b p=0.0286; c p = 0.1656.
Extrapolation Issues and Assumptions
Prior to algorithm evaluation, an analysis of the residuals distributions was first
undertaken in a manner that mimicked the way the equations would be applied in human
exposure modeling simulations. Note that all of the data were collected while individuals were
performing exercise; however exposure modelers will commonly extrapolate the data to activity
situations outside of the sample collection range. For example, when estimating a typical
person's daily exposure, there is not a significant time spent exercising but more spent
performing less strenuous activities such as sleeping. Since resting measurements were not
collected by Dr. Adams for most of his subjects, an evaluation of the data bracketed by percent
of maximum VC>2 (VC^m) was decidedly appropriate in determining whether the data could be
extrapolated downward to reasonably simulate low energy-expenditure activities. Typically VC>2
reserve (VO2res) is used; however, this was not measured in the Adams' studies. A tripartite
categorization of the measured VC>2 for a step relative to the VC^m of each subject was
undertaken using <33.3%, 33.3-66.6%, >66.6% of VC^m as the category boundary values. This
categorization has been done previously based on intervals of low, moderate, and vigorous
exercise and recently summarized from the exercise physiology literature (McCurdy and
Graham, 2004). Residuals distributions were estimated using the multiple linear regression and
mixed models as was done above [equations (3) and (4)], but now accounting for the tripartite
categorization.
Residuals for the MLR model using equation (3) and the tripartite categorization (Table
A-4) were generally lower at the lower and moderate level exercise levels compared with the
estimated total residuals in Table A-3. This indicates there is less variability in ventilation rate at
the low and moderate exercise levels.
A-9
-------
Table A-4. Residual distributions standard deviation estimates (eb and ew) using data
categorized by percentage of maximum VO2 (VO2m) assuming equation (3).
Age
Group
<20
20-<34
34-<61
61 +
<33.3% V02m
6|
0.1233
0.1486
0.1954
0.0974
X
123
127
74
9
n
2.0
1.9
1.8
1.9
33.3-66.6% V02m
6|
0.1007
0.1184
0.1568
0.1144
X
179
428
144
78
s
2.5
2.9
3.2
2.7
>66.6% V02m
6|
0.1523
0.1734
0.1592
0.1344
X
137
521
139
67
s
2.8
4.1
3.5
3.4
x is the number of subjects in given age group and tripartite categorization where measurements were
collected.
n is the average number of V02 samples subjects had within each age group and tripartite
categorization.
For the mixed model, between-person residuals (eb) were generally higher and the within-
person variability was lower for all age groups using the tripartite breakdown (Table A-5)
compared to the residuals distributions estimated using all of the data combined (Table A-3).
This indicates that there is greater variability in ventilation between persons and less variability
within a person than would be simulated when an individual is performing low-level activities.
One may expect this to occur intuitively since the tripartite breakdown basically restricts the total
number of measurements for an individual while the number of individuals for the most part has
remained the same. There was a small difference in the total number of subjects in each exercise
category because some of the individuals did not attain a level of exercise >66.6% VC^m;
however, this was not the principal reason for the observed residual differences since
consistently even fewer individuals were measured at exercise <33.3% VC^m (Tables 4 and 5).
In addition, more measurements were consistently obtained for exercise >66.6% VC^m on
average per person than at the low or moderate levels of exercise.
Table A-5. Residual distributions standard deviation estimates (eb and ew) using data
categorized by percentage of maximum VO2 (VO2m) assuming equation (4).
Age Group
<20
20-<34
34-<61
61 +
<33.3% V02m
eb
0.1217
0.1291
0.1522
0.1244
ew
0.0506
0.0728
0.0938
0.0164
33.3-66.6% V02m
eb
0.0951
0.1088
0.1444
0.1112
ew
0.0456
0.0524
0.0581
0.0362
>66.6% V02m
eb
0.1637
0.2190
0.1936
0.1422
ew
0.0741
0.0740
0.0710
0.0563
Numbers of individuals and samples collected per individual are the same as indicated in Table A-4.
These results in Tables A-4 and A-5 imply that activity-level specific equations may be
warranted to better simulate an individual's ventilation rate over all ranges of exercise levels.
However, given the sample size of the data set analyzed, further subclassification of the data
would likely lead to greater instability of the regression coefficients and prevent reasonable
A-10
-------
ventilation estimations for all exercise levels, age groups, or genders. Using the data provided in
Table A-3 and implementation of either equation (3) or equation (4) should not have a large
impact on a population-based exposure analyses.
It should be noted that in extrapolating lower than the age range of the original data
(e.g., <3.6 years old), it is assumed that regression equations are suitable for these children and
infants. The trend for VQ illustrated in Figure A-2 is likely to be continued upward for younger
children and infants due to the anticipated reduction in efficiency (i.e., underdevelopment) of
their respiratory systems. However, since the natural log for age <1 is negative [i.e., ln(l)=0; for
x
-------
depending primarily on age. Values at older ages are compressed, possibly biased by the small
number of persons simulated (10-50 persons for each year in age 80 to 90; 1-10 for each year in
age >90). Rarely did the upper percentile ventilation rate exceed 100 L/min, the majority of
simulated persons performed activities requiring less than 50 L/min, with most breathing about
10 L/min throughout the day.
Results are also compared to those summarized by U.S. EPA (1997), but much of the
data presented here are in fact approximations to that report utilizing similar approaches.
Table 5-6 in U.S. EPA (1997) contains somewhat comparable data disaggregated by age and
gender, adults only, for average inhalation rates. The origin of the U.S. EPA (1997) data,
however, is Adams (1993), which is used extensively in this report. Recommended inhalation
rates from Table 5-23 in U.S. EPA (1997), based on measured and approximated data, are
presented in Table A-6 and are assumed to be reflective of "average" or likely inhalation rates
and are generally comparable to the medians reported here in Figures 3 and 4.
Table A-6. Recommended inhalation rates (L/min) from U.S. EPA (1997) Table 5-23.
Children
Adults
Rest
5.0
6.7
Sedentary
6.7
8.3
Low
16.7
16.7
Medium
20
26.7
High
31.7
53.3
A-12
-------
Percentlles of Estimated VE for Females Performing Low-Exertion Activities Using Selected
Parameter Distributions
Percentlles of Estimated VE for Males Performing Low-Exertlon Activities Using Selected
Parameter Distributions
I 20
Percentiles of Estimated VE for Females Performing Moderate-Exertion Activities Using
Selected Parameter Distributions
Percentiles of Estimated VE for Males Performing Moderate-Exertion Activities Using Selected
Parameter Distributions
•>c»
•„<&>•
^
*?*,**#***•-
*„*'
Percentiles of Estimated VE for Females Performing Vigorous-Exertion Activities Using
Selected Parameter Distributions
«p50-MLR
)5-MLR
)9-MLR
)50-MER
)95-MER
)99-MER
Percentiles of Estimated VE for Males Performing Vigorous -Exertion Activities Using Selected
Parameter Distributions
~*-
Figure A-3. Estimated ventilation rates (VE, Urn in) for females (left) and males (right) while
performing low-level (top), moderate (middle), and vigorous (bottom) activities.
Median (p50), 95th (p95) and 99th (p99) percentiles are given for a 5,000 person
simulation for each of the multiple parameter regression models.
A-13
-------
p50 V, using 20,000 persons from APEX and using Mixed Model
p95 VE using 20,000 persons from APEX and using Mixed Model
p99 VE using 20,000 persons from APEX and using Mixed Model
Figure A-4. Estimated population ventilation rates (VE, L/min) for 20,000 persons using APEX
and the mixed effects regression (MER) algorithm (Equation 4 and Table A-3). The
full distribution of the median (p50-top), 95th (p95-middle) and 99th (p99-bottom)
percentiles are represented for each age.
A-14
-------
4. Recommendations
We recommend that for inhalation exposure modeling purposes, the regression equation
coefficients listed in Table A-3 be used with equation (3) or (4) to estimate activity-specific body
mass-adjusted VE for simulated individuals in the age groups listed. Estimated regression
coefficients and output from each of the algorithms were very similar, however gender within the
MER algorithm was not considered statistically significant for the older age group compared
with the MLR.
To obtain estimates of VE in units of L min"1, the antilog of the predicted value multiplied
by the subject's body mass (BM in kg) would be taken. Ages less than one year old are not to be
approximated (i.e., persons with age2 to VA is considered as a direct linear proportionality (i.e., a constant value of 19.63)
and estimated independently from VE. A preliminary literature review indicates that the
approximation is reasonable and may be linear for low to moderate exercise levels, but at a
minimum, there is variability in VA at all exercise levels that is not accounted for by the point
estimate used to modify VC>2. Further investigation is needed to determine if the VC>2 to VA
relationship is maintained for vigorous activity levels. In addition, the lack of a direct
computational link with VE potentially can lead to simulated values of VA in excess of VE, a
physiological impossibility.
One potential method would be to estimate VA from VE by using another physiological
relationship: the ratio of dead space volume-to-tidal volume (Vo/Vr, see Figure A-l).
Physiological dead space is the volume of the lung that does not take part in gas exchange and is
comprised of basic anatomic dead space (e.g. volume of trachea and bronchioles) and areas of
lung with reduced functionality (e.g., damaged alveolar regions, increased dead space due to
bronchiole expansion during exercise). Tidal volume is the total amount of air breathed upon
inspiration, not all of which comes in contact with the alveolar region of the lung due to the
presence of physiologic dead space. It has been found that VD/VT does not remain constant over
varying exercise levels, with VT increasing at a greater rate than VD during increasing exercise
level. The effect of this non-linear relationship in simulating VA (does VA increase linearly with
increasing VC>2 at all exercise levels?) has not yet been determined. The relationships of VE,
A-l 5
-------
VD/VT, and VC>2 with VA and other ventilation parameters (e.g., the respiratory quotient or RQ)
will be explored in greater detail and integrated in a second report.
6. References
Adams WC. 1993. Measurement of Breathing Rate and Volume in Routinely Performed Daily
Activities. Davis CA: University of California.
Adams WC, Shaffrath JD and Ollison WM. 1995. The relation of pulmonary ventilation and
heart rate in leg work alone, arm work alone, and in combined arm and leg work. Paper WA-
84 A. 04 presented at the Annual Meeting of the Air & Waste Management Assoc.
Baba R, Mori E, et al. 2002. Simple exponential regression model to describe the relation
between minute ventilation and oxygen uptake during incremental exercise. Nagoya J Med
Sci. 65: 95-102.
Burmaster DE and Crouch EAC. 1997. Lognormal distributions for body weight as a fucntion of
age for males and females in the United States, 1976-1980. Risk Anal. 17(4): 499-505.
Galletti PM. 1959. Les echanges respiratoires pendant 1'exercice musculaire. HelvPhysiol
Acta. 17:34-61.
Johnson T. 1995. Recent advances in the estimation of population exposure to mobile source
pollutants. JExp Anal Environ Epidemiol. 5:551-571.
Johnson T. 2002. A Guide to Selected Algorithms, Distributions, and Databases Used in
Exposure Models Developed by the Office of Air Quality Planning and Standards. Chapel
Hill: TRJ Environmental.
Johnson T and Adams WC. 1994. An algorithm for determining maximum sustainable
ventilation rate according to gender, age, and exercise duration. Unpublished paper.
Johnson T and Capel J. 2002. User's Guide: Software for Estimating Ventilation (Respiration)
Rates for Use in Dosimetry Models. Chapel Hill NC: TRJ Environmental.
Johnson T, Capel J, McCoy M, and Warnasch J. 1996. Estimation of Ozone Exposures
Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of
NEM. Durham NC: IT Corporation.
Johnson T and McCoy M. 1995. A Monte Carlo Approach to Generating Equivalent
Ventilation Rates in Population Exposure Assessments. Washington DC: American
Petroleum Institute (API Publication # 4617).
Johnson T, McCoy Jr. M, and Ollison W. 1995. A Monte Carlo approach to generating
equivalent ventilation rates in population exposure assessments." Paper 95-TA42.05
presented at the annual meeting of the Air & Manage. Waste Assoc.; San Antonio TX.
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Johnson T and Mihlan G. 1998. Analysis of clinical data provided by Dr. William Adams and
revisions to proposed probabilistic algorithm for estimating ventilation rate in the 1998
version of pNEM/CO. Memo to A. Rosenbaum, Systems Applications International.
Lin Y-S, Smith TJ, et al. 2001. Human physiologic factors in respiratory uptake of 1,3-
butadiene. Environ Health Perspect. 109(9):921-926.
McArdle WD, Katch FI, and Katch VL. 1991. Exercise Physiology. 3rd Ed. Philadelphia: Lea
& Febiger.
McCurdy T. 1994a. Human exposure to ambient ozone, pp. 85-127 in: DJ. McKee (ed.)
Tropospheric Ozone. Ann Arbor MI: Lewis Publishers.
McCurdy T. 1994b. Repackaging Adams (1993) breathing rate data. U.S. EPA memo,
Research Triangle Park NC (April 20).
McCurdy T. 1995. Estimating human exposure to selected motor vehicle pollutants using the
NEM series of models: Lessons to be learned. J Exp Anal Environ Epidemiol. 5: 533-550.
McCurdy T. 1997a. Comparison of cumulative inhaled ozone dose estimates using a
disaggregated, sequential approach and alternative recommended approaches. Paper
presented at the 7th Annual Meeting of the International Society of Exposure Analysis.
McCurdy T. 1997b. Human activities that may lead to high inhaled intake doses in children
aged 6-13. Environ Tox Pharm. 4: 251-260.
McCurdy T. 1997c. Modeling the dose profile in human exposure assessments: ozone as an
example. Rev Tox: In Vivo Tox Risk Assess. 1: 3-23.
McCurdy T. 2000. Conceptual basis for multi-route intake dose modeling using an energy
expenditure approach. J Expos Anal Environ Epidemiol. 10:86-97.
McCurdy T. 2001. Research Note: Analyses to understand relationships among physiological
parameters in children and adolescents aged 6-16. RTF: U.S. Environmental Protection
Agency (being reviewed).
McCurdy T, Glen G, Smith L, and Lakkadi Y. 2000. The National Exposure Research
Laboratory's Consolidated Human Activity Database. J Exp Anal Environ Epidemiol. 10:
566-578.
McCurdy TR and Graham SE. 2004. Analyses to understand relationships among physiological
parameters in children and adolescents aged 6-16. EPA/600/X-04/092.
Nieman DC. 1999. Exercise Testing and Prescription. A Health-Related Approach. 4th edition.
Mayfield Publishing Company, Mountain View, CA.
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Schofield WN. 1985. Predicting basal metabolic rate, new standards and review of previous
work. Human Nutrition: Clinical Nutrition. 39C(Suppl):5-41.
U.S. EPA. 1997. Exposure Factors Handbook. National Center for Environmental Assessment,
Office of Research and Development, Washington D.C. EPA/600/P-95/002Fa.
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464
U.S. EPA. 2005. Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model
Documentation (TRIM.Expo / APEX, Version 4) Volume I: User's Guide. Office of Air
Quality Planning and Standards, US EPA. November 2005.
http://www.epa.gov/ttn/fera/data/apex/APEX4UG120505.pdf
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Attachment A1 to Appendix A
First memo from Dr. Adams to T. McCurdy describing major data set
21 August 1998
Dear Tom:
Enclosed is a diskette which includes the electronic data base containing data my graduate
students and I have collected over the last 25 years on a large number of subjects of varying ages
that includes VE, VO2, and other physiological data that should be very useful for estimating VE
and respiratory intake dose. It is in an Excel (5.0a) spread sheet format, as well as an ASCII
format, blank delimited file with headings.
A description of the subjects for which data were potentially available was detailed in a list of 37
studies (pages 5-8) in my proposal dated 28 April 1998. Table Al-1 details the 31 studies for
which valid physiologic data were available, together with the total number of subjects, their
gender, and whether they were tested on a cycle ergometer or on a motor driven treadmill.
Missing study numbers from the original proposal list denotes that no valid body composition
and multi-stage VO2max data were available. In Study 21,16 male subjects exercised on a cycle
ergometer (21.1), while 22 male subjects exercised on a treadmill (21.2).
The total number of subjects with multi-stage, steady-state corresponding VC>2 and VE values,
including those at VO2max, was 521 males and 224 females. Most were obtained on a cycle
ergometer test (262 males and 158 females), with the remainder on a treadmill, utilizing a
walking and/or running protocol. In addition, steady-state VC>2 and VE values at several
submaximal workloads on the treadmill were available on 211 other subjects as described in
Study 30, above. Time at each work level was usually two or three minutes, except at the
maximal work level, which sometimes was as short as 15 sec. (with the physiologic data
extrapolated to per minute values). A variety of progressive increment protocols were used on
both the cycle ergometer and the treadmill. However, each (except for Study 30) was designed
to obtain at least near steady-state physiologic response at progressively intensified work rates
ranging from light, or moderate, through very heavy, ending with voluntary exhaustion.
In the electronic data base, the array of data for each subject is arranged horizontally in the
following order:
1. study ID number (l=Study 1, 2=Study 2, etc.)
2. subj ect ID numb er
3. subject gender (0=male, l=female)
4. subject age (years)
5. special characteristics of the subject (e.g., 1= trained athlete, 2= trained non-athlete, 3=
normally active, 4= sedentary, and 5= obese)
6. subject height (cm)
7. subject body mass (kg)
8. subject lean body mass (kg)
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9. machine used (1= cycle ergometer, 2= treadmill)
10. total test time (min)
11. observed VO2max (1/min, STPD) for the test
12. for each step of the test for each subject, the following sequence was used:
a. cumulative test time at end of step
b. machine setting (cycle ergometer in Watts, treadmill in speed (m/min) and percent
grade)
c. VE (1/min BTPS) measured during the last minute of each step
d. VC>2 (1/min, STPD) measured during the last minute of each step
e. HR (b/min) measured during the last minute of each step
Table A1-1. Total subjects for each study, gender, and exercise ergometry used.
Study
1
2
5
6
7
8
9
10
12
13
14
16
18
19
20
21.1
21.2
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Total
Total Subjects
148
42
60
12
4
6
10
8
10
8
32
10
25
15
39
16
22
17
13
37
13
21
40
11
211
20
10
40
10
6
12
28
956
Males
148
42
30
6
0
6
10
8
10
8
0
10
25
0
18
16
22
9
13
37
13
11
20
0
105
0
10
20
6
3
6
14
626
Females
0
0
30
6
4
0
0
0
0
0
32
0
0
15
21
0
0
8
0
0
0
10
20
11
106
20
0
20
4
3
6
14
330
Cycle Ergometer Tests
Males
0
0
0
0
0
6
10
8
10
8
0
10
25
0
18
16
0
9
13
37
13
0
20
0
0
0
10
20
6
3
6
14
262
Females
0
0
0
0
4
0
0
0
0
0
32
0
0
15
21
0
0
8
0
0
0
0
20
11
0
0
0
20
4
3
6
14
158
Treadmill Tests
Males
148
42
30
6
0
0
0
0
0
0
0
0
0
0
0
0
22
0
0
0
0
11
0
0
105
0
0
0
0
0
0
0
364
Females
0
0
30
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
106
20
0
0
0
0
0
0
172
Consistent units of measurement for all entries were used throughout the file. For machine
setting, two columns were needed for treadmill tests, one each for speed and percent grade, while
A-20
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only one (work rate in Watts) was required for Quinton electronically braked cycle ergometer
tests. A Monark cycle ergometer was used in Studies 9 and 33-37. Calibration of the Monark
device displayed on the ergometer itself only accounts for braking force produced by the
flywheel friction strap, and does not include internal friction produced in the drive train.
Therefore, work rate values displayed on the ergometer were converted to Watts and then
increased by 9% in order to obtain corrected values (E. Harman, Medicine and Science in Sports
and Exercise 21(4):487, 1989).
Quality assurance of the basic data, including that from handwritten records and computer print
outs, was initiated by my review of each subject's data. Where apparent spurious data appeared,
or notably aberrant subject responses were identified, they were eliminated from transfer to the
electronic data base. I also noted any missing data for any subject, so that it was clear to the
graduate student transferring the data which were valid and what data were missing. The
graduate student transferring data to the electronic data base was thoroughly trained as to what
data were to be entered and the format that they were to be entered in. After data were entered
for a study, the graduate student read the data appearing on the original data record for each
subject's protocol, while another graduate student verified that what was being said was what
appeared on the spreadsheet. Errors identified by this procedure proved to be relatively small in
number, non-systematic, and easily correctable. I have great confidence that the data furnished
you are a valid representation of what appears in our original handwritten or computer print-out
records.
A list of subjects who participated in more than one study is given below in ascending Study
Number (and subject number) for the first study they participated in, and then the other
study(ies), with their subject number(s), that they participated in.
Study 1
Subject #2 also subject #2 in study 2.
Subject #6 also subject #10 in study 18.
Subject #25 also subject #7 in study 2, and #3 in study 5.
Subject #29 also subject #18 in study 18.
Subject #30 also subject #23 in study 18.
Subject #43 also subject #3 in study 18.
Subject #52 also subject #2 in study 18.
Subject #54 also subject #17 in study 18.
Subject #55 also subject #20 in study 2.
Subject #56 also subject #19 in study 2, and #5 in study 19.
Subject #60 also subject #13 in study 2.
Subject #61 also subject #19 in study 18.
Subject #63 also subject #18 in study 2, and #5 in study 8.
Subject #69 also subject #16 in study 18.
Subject #88 also subject #21 in study 18.
Subject #89 also subject #14 in study 18.
Subject #91 also subject #22 in study 18.
Subject #97 also subject #11 in study 18.
A-21
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Study 2
Subject #17 also subject #6 in study 18.
Subject #32 also subject #30 in study 5.
Subject #33 also subject #26 in study 5.
Subject #34 also subject #1 in study 8.
Subject #35 also subject #3 in study 8.
Study 5
Subject #18 also subject #1 in study 6.
Subject #19 also subject #3 in study 6.
Subject #21 also subject #6 in study 6, #1 in study 9, #1 in study 12, #2 in study 20, #17 in
study 21.2, and #23 in study #25.
Subject #27 also subject #2 in study 9.
Subject #43 also subject #10 in study 6.
Subject #48 also subject #11 in study 6.
Study 9
Subject #9 also subject #15 in study 21.2.
Study 10
Subject #1 also subject #7 in study 13, #3 in study 20, and #34 in study 25.
Subject #2 also subject #4 in study 13 and #1 in study 20.
Subject #7 also subject #8 in study 13.
Study 12
Subject #10 also subject #5 in study 20.
Study 13
Subject #2 also subject #5 in study 16.
Study 20
Subject #7 also subject #16 in study 21.1 and #8 in study 25.
Study 21.1
Subject #3 also subject #3 in study 24 and #33 in study 25.
Study 21.2
Subject #18 also subject #18 in study 25.
Study 23
Subject #1 also subject #10 in study 28.
Subject #5 also subject #12 in study 24.
Study 24
Subject #13 also subject #21 in study 25.
A-22
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Study 28
Subject #12 also subject #3 in study 32.
Subject #28 also subject #20 in study 31.
Study 31
Subject #10 also subject #40 in study 33.
Subject #15 also subject #3 in study 34.
Study 32
Subject #2 also subject #12 in study 33.
Study 33
Subject #3 also subject #7 in study 34.
Subject #7 also subject #4 in study 35.
Subject #9 also subject #10 in study 34, and #5 in study 35.
Subject #35 also subject #4 in study 34.
Study 34
Subject #1 also subject #2 in study 35.
Study 35
Subject #3 also subject #3 in study 36.
Study 36
Subject #12 also subject #26 in study 37.
I believe that this final report letter contains additional information beyond the electronic data
base that you wanted and clarifies the format that was used. If you have questions, however,
please do not hesitate to give me a call or drop me a note by FAX. I look forward to hearing from
you and working with you and Ted on developing a publishable paper or two.
Best regards,
William C. Adams
Professor
A-23
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Second memo from Dr. Adams to T. McCurdy describing additional data from
study #30
8 October 2001
Dear Tom:
Pursuant to the U.S. EPA Order for Supplies and Services, No. 1D-5590-NATX, approved for
the period, 1 August - 1 November 2001, I believe that I have now completed all professional
services stipulated. Specifically, it was requested that I provide certain "raw" data on a group of
children and adolescents who were part of the subject pool utilized in a California State Air
Resources Board sponsored study, entitled: Measurement of Breathing Rate and Volume in
Routinely Performed Daily Activities (Adams, 1993). The professional services stipulated
included: 1) providing a complete listing of all variables that were obtained during the study in
accordance with the attached Statement of Work; 2) the development of an electronic data base
of selected physiological information for children and adolescents from the aforementioned
study, again in accordance with the attached Statement of Work; and 3) the submittance of a
transcribed data file for the aforementioned study in ASC II format, together with a description
of data quality objectives that were established in accordance with the attached Statement of
Work.
The subject pool of interest included 132 individuals, half female and half male, including 12
young children, age 3.6-5.8 yrs., 80 children, age 6.0-12.9 yrs., and 40 adolescents, age 13.2-18.9
yrs. All subjects were apparently healthy. In all cases, subject identification, including age and
gender, as well as body weight, height, and activity habitus, were obtained. Body composition, as
assessed by gender/age specific skinfold formulae, were used to calculate lean body mass. All
subjects completed a laboratory treadmill walk (usually three different speeds, i.e., steps) and
jog (ranging form 1 to 3 different speeds) protocol. The treadmill grade was horizontal
throughout. Each subject completed a laboratory resting protocol (40 of the children did only
sitting and standing, while the others also rested in a lying position). The 12 young children each
did two spontaneous play protocols of 20 minutes duration, while 40 children also did two
spontaneous play protocols, but of 30 minutes duration. The other 40 children did a single
spontaneous play protocol of 35 minutes duration. The 40 adolescent subjects were not asked to
perform a spontaneous play protocol. In addition, each subject (or their parent/guardian)
completed an 11-item health history questionnaire.
Enclosed is a 3.75 ZIP disk which includes the electronic data base containing data described in
general above. It is in an Excel (5.0a) spread sheet format produced on a Macintosh Performa
6214CD hard drive, as well as an ASCII format, blank delimited file with headings. Consistent
units of measurement for all entries were used throughout this file. In the electronic data base,
the array of data for each subject is separated into five distinct files: 1) active (treadmill)
protocol; 2) resting protocol; 3) spontaneous play protocol; 4) health history responses to
selected questions; and 5) predicted VO2max values from measured submaximal HR and VO2
values contained in File #1. Details of what items, variables, time periods, etc., and their order,
which are arranged horizontally in each file, is as we agreed on via my FAX of 22 August 2001,
A-24
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with minor modifications we agreed on by phone the next day. The order for each file is given
below:
ACTIVE (File #1)
1. File ID number (#1)
2. subject ID number (same number for each subject as identified for Study #30 in 1998
data base)
3. subj ect gender (0=male, l=female)
4. subject age (years)
5. special characteristics of the subject (viz., 1= trained athlete, 2= trained non-athlete, 3=
normally active, 4= sedentary, and 5= obese)
6. subj ect height (cm)
7. subject body mass (kg)
8. subject lean body mass (kg)
9. machine used (1= cycle ergometer, 2= treadmill) - NOTE: TREADMILL ONLY
USED IN THIS STUDY.
10. total test time (min)
11. observed VO2max (1/min, STPD) for the test - NOTE: VO2max NOT MEASURED IN
THIS STUDY.
12. for each step of the test for each subject, the following sequence was used:
a. cumulative test time at end of step
b. machine setting (two columns: one for treadmill in speed (m/min) and one for
percent grade). The latter was always zero.
c. VE (1/min BTPS) measured during the last two minutes of each step
d. VO2 (1/min, STPD) measured during the last two minutes of each step
e. HR (b/min) measured during the last two minutes of each step
RESTING (File #2)
1. File ID number (#2)
2. subject ID number (same number for each subject as identified for Study #30 in 1998
data base)
3. subject's body surface area in square meters; from measured body height and body mass,
using the standard DuBois and DuBois formula
4. for each resting posture for each subject, the following sequence was used:
a. VE (1/min BTPS) measured during the 5 minutes of each test
b. VO2 (1/min, STPD) measured for the 5 minute of the test
c. average of five HR (b/min) measurements taken each minute of the 5 minute test
d. average of five breathing frequency (breaths/min) measurements taken each
minute of the 5 minute test
SPONTANEOUS PLAY (File #3)
1. File ID number (#3)
2. subject ID number (same number for each subject as identified for Study #30 in 1998
data base)
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3. for each 5 minutes data collection period for each subject, the following sequence was
used:
a. VE (1/min BTPS) measured during the 5 minutes
b. average of five HR (b/min) measurements taken each minute of the 5 minute
period
c. average of five breathing frequency (breaths/min) measurements taken each
minute of the 5 minute period. NOTE: Because these data were obtained on a tape
cassette that rather routinely malfunctioned, valid data were obtained in only
-75% of the subject 5-minute time periods
d. activity intensity rating by the technician. NOTE: There was some confusion
among the technicians as to what they were to indicate in the comments column;
e.g., any problems with the equipment, what the subject was playing, and/or an
estimation of the intensity of activity. The occasional noted problems with
equipment were dealt with as described on pp. 38-39 of the CARB Final Report
(Adams, 1993). While the play activity was occasionally recorded, it was not
systematic (i.e., estimated at between 15-20%). Intensity of play was recorded
-55% of the time. The intensity scale devised and used for the first time in the
enclosed data base was: 1 = standing, or just "hanging out"; 2 = moderate
intensity, i.e., walking, swinging an implement, kicking or throwing a ball, etc.;
and 3 = vigorous, or very active. Ratings of 1.5 and 2.5 were used to indicate
activity intensity somewhere in-between the absolute number categories. The
mean value for each 5-minute period was near 2.0, moderate, which closely
agrees with the observed VE estimated intensity discussed on p. 110 of the CARB
Final Report.
HEALTH HISTORY (File #4)
1. File ID number (#4)
2. subject ID number (same number for each subject as identified for Study #30 in 1998
data base)
3. Re question #1, how often do you exercise? Numerals in column 3 correspond to which
of 5 choices were circled.
4. Re question #2, describe the intensity of your exercise. Numerals in column 4 correspond
to which of 5 choices were circled. In six cases, two adjoining numbers (e.g., 2 and 3)
were circled, and the mean entered (in this case, 2.5).
5. Re question #3, what types of exercise do you engage in? Numerals in column 5
correspond to which of 9 choices were circled. No one circled No. 1 (none). Most
subjects circled more than one choice, which is reflected by the numerals 2 through 8 in
column 5 for each subject. If the subject circled 9 (other), the following numerals were
entered in column 5 to indicate which other activities they engaged in (10, play; 11,
dance; 12, horseback riding; 13, gymnastics; 14, rollerblading; 15, karate; 16, ice skating;
17, aerobics (high impact); 18, aerobics (machines at fitness club); 19, hockey; and 20,
boxing
6. Re question #7, any medical complaints? 1 = yes; 2 = no. If yes, 1 was not entered, but
what "caused" the yes answer was entered in column 6 as follows: 3, asthma; 4, ear, 5,
scoliosis; 6, cerebral palsy; 7, allergies
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7. Re question #11, do you have, or have you ever had, any of the following? Numerals
from 1 through 12 in column 7 indicate that only one choice was circled. If more than one
choice was indicated, higher numbers were used as follows: 13, choices 7, 9, and 10; 14,
choices 9, 10, and 11; and 15, choices 10 and 11.
A-27
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PREDICTION OF VO2MAX FROM SUBMAXIMAL MEASURED HR AND VO2 VALUES
OBTAINED FROM FILE #1 (File #5)
1. File ID number (#5)
2. subject ID number (same number for each subject as identified for Study #30 in 1998
data base)
3. subject body mass (kg)
4. subject age (years)
5. estimated HRmax
6. VO2max Y intercept
7. VO2max b exponent
8. predicted VO2max (1/min)
9. predicted VO2max (1/min/kgBM)
The rationale for predicting percent VO2max at any given percent HRmax is developed in brief on
p. 403 of McArdle et al.'s exercise physiology text (4th ed., 1996) and in more detail in Astrand
and Rodahl's Textbook of Work Physiology (2nd ed., 1977), pp. 344-348. Using data from both
sources, I calculated very closely similar submaximal % VO2max values as a function of %
HRmax values (i.e., never more than 2%, and usually the same or only 1% difference). To get a
clear visual perspective overview of the estimated VO2max prediction from measured
submaximal HR and VO2, see Fig. 10-4 (line A), p. 346, in Astrand and Rodahl. To use this
procedure, it is first necessary to obtain a valid HRmax value which decreases an average of 1
b/min each year of age from 10 years on. The best data I'm aware of on young children and
adolescents that had HR and VO2 measured in both submaximal and maximal treadmill exercise
is that of Astrand (Experimental Studies of Physical Working Capacity in Relation to Sex and
Age, 1952, Ejnar Munksgaard, Copenhagen). Between the ages of 4 and 10 years, there was no
significant relationship between HRmax and age for either sex, averaging 205 b/min. Thereafter,
up to 33 years, there was the now widely accepted decrease of 1 b/min per year of age for both
males and females, with 10 year-old boys and girls averaging 210 b/min. Accordingly, in File #5,
the estimated HRmax m column 5 is 205 b/min for subjects less than 10 years of age and 220
minus age in years for subjects 10 to 18.9 years of age. The y intercept and b exponent values for
predicting VO2max were obtained by calculating, via simple regression analyses, individual
subject values from measured submaximal HR and VO2 values taken from File #1. Predicted
VO2max (in 1/min), given in column 8 for each subject, was obtained by multiplying the b
exponent value (column 7) times the estimated HRmax value (column 5) for each subject, and
then subtracting their y intercept value (column 6). Each subject's VO2max value in ml/min/kg
(column 9) was calculated by dividing the column 8 value by body mass (column 3).
Accuracy of the data in the enclosed electronic files began with data management and quality
control procedures employed in the original CARB study, and which are described in detail on
pages 38-39 of the Final Report (Adams, 1993). In summary, very few problems were
encountered in the acquisition of active and resting protocol data. Accuracy assurance
procedures for the transfer of the data from handwritten records to master data sheets, and
A-28
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subsequently to electronic spreadsheet data bases, is described in the aforementioned Final
Report. The retrospective quality control program for all field protocol data bases, including
spontaneous play, revealed that 5 children needed to repeat a protocol. Elimination of aberrant
bits of data obtained during the play protocols (due to the result of momentary saliva blockage in
the Harvard respirometer, Heart Watch heart rate artifacts, etc.), which rarely included more than
one or two 1-min "glitches" in any one protocol, were part of the aforementioned quality control
program. When this was done, the remaining data for the 5-min period was used to calculate an
average for the full time period (i.e., 20, 30, or 35 min). A significant number of play protocols
(-35%) were completed with incomplete, or no, fB data. This occurred because there was no
way to determine whether the expiration electronic pulse from the Harvard respirometer was
being recorded on the tape cassette until after the protocol was completed. However, since these
were random occurrences, and fB was not of such prime concern as HR and VE, these protocols
were not repeated.
Per the Statement of Work for this project, to ensure that an accurate translation of the data was
accomplished, all data entries were checked by me. The data quality objectives described in
detail below were developed before data were translated to the enclosed electronic data files.
These objectives were applied against 100% of the entries transcribed, including file column
headings, for the first 500 datum points. In each of the five files, this objective was met, and
double-checking procedures described in detail below were employed to achieve the highest
accuracy possible. I have great confidence that the data furnished you are a valid representation
of what appears in our original CARB study computer data files and the original handwritten
records used to transfer data to electronic files for the first time in this project.
The specific procedures used for each of the five files differed somewhat and are described in
detail here. For the active file, a copy of the 1998 Excel file was made and all data not from
Study #30 for the 132 subjects of interest were deleted. A search of the original 1998 Excel file
was done, and a print out of these data obtained (i.e., pp. 14-18, 36-40, 58-62, and 80-84). All
entries in the 2001 file were double-checked against the 1998 print-out for the first 12 subjects,
and for subjects 13, 45, 46, 58, 59, 66, 107, 131, 132, 150, 151, and 152. Finally, the values on
the last page of data for all subjects was verified. In no case was any difference seen.
Formulation of the file for the resting protocol (#2) was initiated by transferring data from
summary CARB study electronic data files (in a similar, but not exact format for each subject) to
the present electronic data file. Individual data values for all variables in each posture were
double-checked against a print-out of the 1998 data for the first 12 subjects, and for every 10
subjects thereafter. In no case was any difference seen. As a further cross-check, I then
calculated entire group (N = 132) means for each posture in the present file, and compared these
values to weighted tabular mean values in the original Final Report, and found no difference
greater than 0.7%, i.e., within the range of rounding error.
Formulation of the file for the play protocol (#3) entailed entering VE, HR, and breathing
frequency data from handwritten data summary sheets. All values were double-checked
immediately after entry for each time period (4 to 7) for each subject (N=92). In addition, I then
calculated an entire group mean for each time period, and compared these values to weighted
tabular mean values in the original Final Report. Again, I found very close agreement. Intensity
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-------
values available for each time period for each subject were entered from handwritten data
acquisition sheets into the electronic data base (File #3). As I entered them, I double-checked
these values against that read from the data sheet, and that the adjacent HR and breathing
frequency data were for the correct subject and time period.
Procedures used for establishing the health history data base from handwritten responses to a
questionnaire, together with how data was entered in each column, are described above. The data
were typed directly into the electronic file (#4) for each subject from the handwritten responses
on the questionnaire. The numerical values entered were double-checked for each question (#s 1,
2, 3, 7, and 11) for each subject immediately after each subject's data entry.
Procedures used for predicting each subject's VO2max from submaximal HR and VO2 data (the
latter obtained from File #1), together with how data was entered in each column, are described
above. The data for columns 1-4 were transferred directly from File #1 and a mean, with
standard deviation, calculated for each column which matched those previously calculated in File
#1. The individual submaximal FIR and VO2 values entered into a STATVIEW simple
regression analysis were each double-checked before each individual analysis was done. The
resultant y intercept and b exponent values were written on a printout of the subjects'
submaximal HR and VO2 values, with each set double-checked as they were entered in the File
#5 Excel spread sheet. In addition to recalculating all values for the first 10 subjects, any subject
who had a predicted VO2max value < 33 or > 66 ml/min/kg was double-checked. In no case was
an error found. Please note that 18 subjects only had 3 sets of submaximal values (i.e., all at three
walking speeds). In all but 4 cases (subjects # 3, 29, 108, and 142), the spread of observed HR
and VO2 values was sufficient (in my estimation) to obtain valid predicted VO2max values. Thus,
I recommend deleting the predicted VO2max values for these four subjects. If this is done, the
mean VO2max for the group is 47.63 ml/min/kg, a value that I consider highly likely in a group
of healthy children and adolescents of probable slightly greater fitness than the average
population.
I believe that this final report letter contains additional information beyond the electronic data
base that you wanted and clarifies the format and procedure that were used. If you have
questions, however, please do not hesitate to give me a call or drop me a note by FAX. I look
forward to hearing from you and working again with you in the future.
Best regards,
William C. Adams, Ph.D.
A-30
-------
Attachment A2 to Appendix A
Data Set Manipulation. The data file needed significant manipulation to facilitate statistical
analysis. Principally, the row and column structure of the file had to be altered to put them into
proper alignment. Row headings were scattered within rows of the data set due to two different
test protocols (cycle and treadmill) that required different parameter measurements. In addition,
within-person measurements for the same parameter (e.g., total ventilation or VE) over multiple
stages of the test (VEi, VE2, VE3, etc.) were carried across the dataset in multiple columns. It was
desired to have the multiple measurements as a single vector for a given parameter. Therefore,
the following changes were made to the data set:
• 11 separate data sets were created in Excel by the 11 heading groupings within the raw
data set (more than one study could be combined under previous headers)
• A master list of parameters was created such that the 11 data sets could be combined
under one heading having 102 unique designations. Specific changes made were:
o Parameter heading for step 14 was removed since there were no parameters
supplied for this step (e.g. VEU, VO2 14, etc.).
o Common data were receded into vectors having a common descriptor. Originally
identical names were not used to describe the same parameter at different steps
(e.g., the speed parameter for the cycle ergonometer used "spd" for steps under 10
(e.g., spdl) and "sp" for steps >9 (e.g., sp!3). It was assumed that "sp"="spd",
and for grade, "gr"="grd").
o Removed inconsistent coding. Spdl2 on one instance was mislabeled as Spdl 1 in
Study #1. This was corrected.
o Cleaned up variable name conventions. Both "Age" and "LBM" parameters
contained a space after the label characters. This space was removed.
• These 11 Excel data sets were combined in S AS to create a S AS data set
(adams.sasTdbat).
• In SAS, multiple measurements for a parameter (e.g., VEI, VE2, VE3, etc.) were combined
under a single vector (e.g., VE) to create a second SAS data file: adams2.sas7dbat. A new
variable was created to account for the multiple measurements for a given parameter
termed 'step' (e.g., step=l is for where VE and VO2 were first recorded; step=2 for the
second measurement of VE, etc.).
• This data set contained a total of 19 variables:
o Step Step or stage measurement taken within an individual
o Age Subjects age in years (yrs)
o BM Body mass (kg)
o Char A characteristic of an individual acting as a surrogate for fitness level
• 1= Trained athlete
• 2= Trained non-athlete
• 3= Active individual
• 4= Sedentary individual
• 5= Obese
o ET Cumulative test time at the end of each step (min)
o Gend Gender: $ = -1; = 1
A-31
-------
o Grd Grade on treadmill (in percent)
o HR Heart rate (bpm or beats min"1)
o HT Height (cm)
o LBM Lean body mass (kg)
o Mach Machine used: Cycle Ergometer = 1; Treadmill = 2
o VO2 VO2 (L min'1 BTPS)
o Spd Speed of the Subject on Treadmill (m min"1)
o stud Study number
o Subj Subject number
o TT Total test time (min)
o VE VE (L min'1)
o VO2m Observed or estimated VO2 maximum for the test (L min"1 STPD)
o Wk Watts (power setting for the cycle ergometer)
Maximum VO2 (VO2m) was reported for all of the studies but one. Study 30 contained estimates
of VO2m for some of the data (individuals < 18.9 years old) however the study also contained 79
individuals where VO2m was neither measured nor estimated. The method reported by Adams
(see Appendix B) to estimate VO2m for the younger individuals was duplicated here for the
missing data. Briefly, maximum heart rate (HRm) was estimated using an equation provided in
Nieman (1999) (i.e., HRm=220-age). A simple linear regression analysis followed for each
individual (of the formy=mx+b) where HR measurements were regressed on concomitant VO2.
The slope (m) and intercept (b) estimates were then used to approximate VO2m from the HRm
estimate and added to the final data set.
Quality Assurance. Data values—mostly VE, VO2, and BM, since these were the principal
analytical parameters— were spot-checked by hand from the original Excel spreadsheet to both
newly created SAS data sets. No errors were found in either of the SAS data sets. The number
of individuals in the newly created data sets was each 956, equivalent to that reported by Dr.
Adams upon transfer of the data set (in Appendix A) and the total number of measurements of
VE and VO2 for individuals >18 years old was equivalent (n=5,681) to that reported by Johnson
(2002).
A simple plot of the body mass-normalized total ventilation versus the body mass-normalized
oxygen consumption revealed that two individuals (i.e., stud=l subj=25 step=8; stud=31 subj=9
step=8) had exceptionally large oxygen consumption levels during one sample collection. These
data were considered to be questionable, and upon inspection seemed to be the result of a
misplaced decimal point (30.8 and 28.5 should be 3.08 and 2.85, respectively).
Data were replaced in the SAS data sets to reflect this assumption rather than delete the
datapoints altogether, even though there is no direct evidence that the decimal was misplaced.
Due to the number of samples for a given parameter in the data set (>5,000), the impact of this
change on the analyses presented here is negligible. The new dataset was saved as
'adams3.sas7dbat' (from data set 'adams.sasTdbat') and 'adams4.sas7dbat'(from data set
'adams2.sas7dbat').
A-32
-------
Data Transformation. Figure A2-1 shows the relationship between total ventilation and oxygen
consumption rates. In general, the relationship is non-linear and exhibits greater variability
among individuals at higher oxygen consumption rates (i.e., the data are heteroscadistic), similar
to findings of other researchers (e.g., Baba et al. 2002). Normalization of VE and VO2 by body
mass is commonly done to account for a portion of the variability inherent between the two
physiological measures (Figure A2-2).
Figure A2-1. Relationship between total ventilation
rate (VE) and oxygen consumption rate (VO2)
during exercise.
Figure A2-2. Relationship between body
mass normalized total ventilation rate
(VE/BM) to oxygen consumption rate
(VO2/BM) during exercise.
Due to the non-linear relationship between VE and VO2, a number of the parameters were
transformed by taking the natural logarithm (Ln) of the variable. These include:
natural log of VE
natural log of VO2
natural log of body mass normalized VE
natural log of body mass normalized VO2
ventilatory equivalent or VE / VO2
natural log of age
Ln(VO2)
Ln(VE/BM)
Ln(VO2/BM)
VQ
Ln(age)
A logarithmic transformation directly applied to the parameters allows for a significant reduction
in the dispersion (Figure A2-3 compared to Figure A2-1), and when used in combination with
body mass normalization, yields a mostly linear relationship having a more balanced dispersion
across the range of oxygen consumption rates (Figure A2-4), that is, it better demonstrates a
degree of homoscadisticity. It should be noted that this linearity and balanced dispersion was
also demonstrated among different age groups investigated in the body of the report.
A-33
-------
Figure A2-3. Relationship between the natural
logarithm of total ventilation rate Ln(VE) and
oxygen consumption rate Ln(VO2) during
exercise.
Figure A2-4. Relationship between the
natural logarithm of body mass
normalized total ventilation rate
Ln(VE/BM) and oxygen consumption rate
Ln(VO2/BM) during exercise.
A-34
-------
Attachment A3 to Appendix A
Selected ventilation algorithms were evaluated using the APEX model by adjusting the
ventilation.txt file (see U.S. EPA, 2005). 20,000 persons were simulated for one day using the
algorithms described in the main body text and parameters in Tables 2 and 3. Model output was
nearly 800,000 event-based ventilation rates, typically around 40 events per individual simulated.
Figure A3-1 presents the mid to upper range percentiles based on these 800,000 events to
encompass the possible maximum ventilation rates generated by each simulation. Algorithms
evaluated included the following:
MLR: multiple linear regression algorithm using equation 3 and parameters from Table A-3.
MER: mixed-effects regression model using equation 4 and parameters from Table A-3.
MLR+MER: regression coefficients from MLR coupled with variance components estimated
from the MER model.
Johnson: Johnson (2002) regression model using equation 2 and parameters from Table A-2.
SMER: a simplified mixed effects regression model using equation 2 and parameters derived for
all age groups from the Adams data set as follows:
Females
Males
bo
4.1017
3.9332
Ln(VO,/BM)
bi
1.1904
1.1638
Residuals
eb ew
0.1408
0.1445
0.1186
0.1277
Results are very similar for each of the algorithms, not surprisingly since they were for the most
part derived from the same data set. At any given percentile, ventilation rates increase rapidly
with age for those less than 20 years old, stabilize from ages 20 to about 60, then gradually
decline with further increases in age. Increased variability at ages greater than 75 is also evident,
a function of both the limited amount of data available for the development of the algorithm and
the limited number of persons simulated at these ages from the population of 20,000. At each of
the percentiles, the Johnson (2002) algorithm generated lower ventilation estimates for persons
under age 5, a function of the method of the algorithm derivation, whereas the intercept was
modified based on published literature VE/VO2 relationships while the residuals were assumed
the same as those greater than 18 years of age. When considering a simple mixed effects
regression (SMER) algorithm, flattening out of the percentiles occurs across the ages, mostly due
to elevation of ventilation rates of young children that resulted from ignoring age as an
independent variable in development of the regression parameters.
Figure A3-2 presents the full range of percentiles for the event-based ventilation rates generated
from the APEX model using the mixed effects regression (MER) model and the Johnson (2002)
model. Results are very similar, however at young ages (<5 years old), the Johnson (2002)
model estimates lower ventilation rates at both the lower and upper percentiles. The percent
difference between the two model estimates is large, ranging from about 40-120% lower (Figure
A3-3). The lower percentiles (min, pi, p5) for all ages >5 are moderately different, the Johnson
(2002) ventilation estimates are less than the MER by about 20-40% for ages 10-45, then 10-
A-35
-------
20% greater than the MER estimates for ages above 45. The MER algorithm estimates higher
ventilation rates for persons above age 60 by about 20% considering the upper percentiles (p95,
p99, max), with greater differences at age 90 and older (20-60%).
A-36
-------
Figure A3-1. Comparison of selected percentiles of estimated event-based ventilation rates from 20,000 person APEX model simulation
using different ventilation algorithms.
max VE from events of 20,000 person APEX simulation
p99 VE from events of 20,000 person APEX simulation
1 80
20<^
X. DL
+
X JyHf D X •» .- •
0 10 20 30 40 50
age (yeare)
60 708090100
p95 VE from events of 20,000 person APEX simulation
40 50 60
age (years)
70 80 90 1 00
10 20 30
40 50
age (years)
60 70 80 90 100
p50 VE from events of 20,000 person APEX simulation
40 50 60
age (years)
-------
Figure A3-2. Comparison of estimated event-based ventilation rate percentiles from 20,000 person APEX model simulation using mixed
effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.
Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation
Lower Percentiles
Johnson Model Estimates of VE from for 799,355 events from 20,000 person APEX simulation
Lower Percentiles
oo
oo
Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation
Upper Percentiles
Johnson Model Estimates of VE from for 799,355 events from 20,000 person APEX simulation
Upper Percentiles
70 80 90 100
-------
Figure A3-3. Percent difference of estimated event-based ventilation rate percentiles from 20,000 person APEX model simulation using
mixed effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.
oo
VO
nson+MER)*200
ER
nson
ren
t°A
o
• max
• p99
Ap95
Op50
opi
omin
10 20 30 40 50 60
Age (years)
70
80
90
100
-------
APPENDIX B
STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY CODES
FOR USE IN SIMULATING METS VALUES
-------
LIST OF TABLES
B-l. METS Distributions assigned to activity ID codes within CHAD B-4
B-2. Activity codes whose METS Distributions were assigned to those codes
encountered in the NHAPS Database but having no METS Distribution assigned
by CHAD B-ll
B-2
-------
Table B-l documents the activity ID codes included in the CHAD, along with the
statistical distributions underlying the METS values that CHAD has assigned to each code.
These distributions have been documented in Appendix 1 of the CHAD User's Guide (U.S.
EPA, 2002).
The last two columns of Table B-l indicate when limits were placed on the METS values
generated by the specified distribution. For a given activity ID code, the CHAD randomly
generates a METS value from the specified distribution. If "Truncate Left Tail?" equals "Y,"
then any METS value falling below the distribution's specified minimum was set to equal the
minimum. Likewise, if "Truncate Right Tail?" equals "Y," then any METS value falling above
the distribution's specified maximum was set to equal the maximum. Truncation of the left and
right tails occurred with the normal and lognormal distributions, while truncation of the right tail
only occurred with the exponential distribution. In such situations, more METS observations
tend to occur at the minimum and/or maximum values. Note that truncation did not affect the
initial random generation of METS values (i.e., randomization did not occur on truncated
distributions).
Activity ID codes followed by "*" in Table B-l were encountered within the NHAPS
data set.
A total often activity ID codes that did not have a METS distribution assigned to them
within CHAD were encountered in the NHAPS data set. These codes, listed in Table B-2, were
occupation-related activity codes that appeared to represent subcodes to code 10000 (general
work and other income-producing activities). Such subcodes may have required knowledge of
the individual's occupation in order to assign the proper METS distribution to the activity.
Because the occupation of the NHAPS participants was not specified in the activity data records
within CHAD, the available information within CHAD was not sufficient to assign a METS
distribution to these subcodes as CHAD would have done. Therefore, for each of these codes, it
was necessary to identify an activity that was "similar" in description to the code and assign that
activity's METS distribution to the code. Table B-2 specifies the activity whose METS
distribution was assigned to each of these ten codes.
B-3
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
Activity
Description
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Work,
general
Breaks
General
household
activities
Prepare
food
Prepare and
clean-up
food
Indoor
chores
Clean-up
food
Clean house
Activity
ID
Code
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10300*
11000
11100*
11110
11200
11210*
11220*
Age(a)
Occupation(b)
ADMIN
ADMSUP
FARM
HSHLD
MACH
PREC
PROF
PROTECT
SALE
SERV
TECH
TRANS
Distribution
Type
LogNormal
LogNormal
LogNormal
LogNormal
Uniform
Triangle
Triangle
Triangle
Triangle
Triangle
Triangle
LogNormal
Uniform
Triangle
LogNormal
Exponential
Exponential
Uniform
Exponential
Mean
1.7
1.7
7.5
3.6
5.3
3.3
2.9
2.9
2.9
5.2
3.3
3.3
1.8
4.7
2.6
2.8
3.4
2.5
4.1
Median
1.7
1.7
7.0
3.5
5.3
3.3
2.7
2.7
2.7
5.3
3.3
3.0
1.8
4.6
2.5
2.5
3.0
2.5
3.5
Std
Dev
0.3
0.3
3.0
0.8
0.7
0.4
1.0
1.0
1.0
1.4
0.4
1.5
0.4
1.3
0.5
0.9
1.4
0.1
1.9
Min
1.4
1.4
3.6
2.5
4.0
2.5
1.2
1.2
1.2
1.6
2.5
1.3
1.0
1.5
2.0
1.9
2.0
2.3
2.2
Max
2.7
2.7
17.0
6.0
6.5
4.5
5.6
5.6
5.6
8.4
4.5
8.4
2.5
8.0
4.0
4.0
5.0
2.7
5.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
B-4
-------
Outdoor
chores
Clean
outdoors
Care of
clothes
Wash
clothes
Build a fire
Repair,
general
Repair of
boat
Paint home
/ room
Repair /
maintain car
Home
repairs
Other
repairs
Care of
plants
Care for
pets/animals
Other
household
11300*
11310
11400*
11410
11500
11600
11610
11620
11630*
11640
11650*
11700*
11800*
11900*
Normal
Exponential
Exponential
Point Est.
Point Est.
Normal
Point Est.
Exponential
Triangle
Exponential
Uniform
Uniform
Uniform
Exponential
5.0
5.3
2.2
2.0
2.0
4.5
4.5
4.9
3.5
4.7
4.5
3.5
3.3
6.6
5.0
4.5
2.0
2.0
2.0
4.5
4.5
4.5
3.4
4.5
4.5
3.5
3.3
5.5
1.0
2.7
0.7
1.5
1.4
0.4
0.7
1.4
0.9
0.1
3.6
2.0
2.6
1.5
2.0
2.0
2.0
4.5
3.5
3.0
4.0
2.0
2.0
3.0
3.0
7.0
6.0
4.0
2.0
2.0
8.0
4.5
6.0
4.5
6.0
7.0
5.0
3.5
9.0
Y
Y
Y
Y
Y
Y
Y
Y
Y
B-5
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Child care,
general
Care of baby
Care of child
Help / teach
Talk /read
Play indoors
Play outdoors
Medical
care-child
Other child
care
Obtain goods
and services,
general
Dry clean
Shop / run
errands
Shop for food
Shop for
clothes or
household
goods
Run errands
Obtain
personal care
service
Obtain
medical
service
Obtain
government /
financial
services
Obtain car
services
Other repairs
Activity
ID
Code
12000
12100*
12200*
12300*
12400*
12500*
12600*
12700*
12800*
13000
13100*
13200
13210*
13220*
13230*
13300*
13400*
13500*
13600*
13700*
Age(a)
Occupation^
Distribution
Type
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Triangle
Uniform
Triangle
Triangle
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Mean
3.1
3.3
3.3
2.8
2.8
2.8
4.5
3.2
3.0
3.8
3.3
3.7
3.9
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Median
3.0
3.3
3.3
2.8
2.8
2.8
4.5
3.2
3.0
3.7
3.3
3.6
3.8
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Std
Dev
0.7
0.1
0.1
0.1
0.1
0.1
0.3
0.1
0.3
0.8
0.4
0.8
0.8
0.6
0.6
0.6
0.6
0.6
0.6
0.6
Min
2.5
3.0
3.0
2.5
2.5
2.5
4.0
3.0
2.5
2.0
2.5
2.0
2.2
2.3
2.5
2.5
2.5
2.5
2.5
2.5
Max
5.0
3.5
3.5
3.0
3.0
3.0
5.0
3.3
3.5
6.0
4.0
6.0
6.0
4.5
4.5
4.5
4.5
4.5
4.5
4.5
Truncate
Left
Tail?
Y
Truncate
Right
Tail?
Y
B-6
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Other
services
Personal
needs and
care, general
Shower,
bathe, pers.
hygiene
Shower, bathe
Personal
hygiene
Medical care
Help and care
Eat
Sleep or nap
Dress, groom
Other
personal
needs
General educ.
and pro.
training
Attend
full-time
school
Attend
day-care
Attend K- 12
Attend
college or
trade school
Adult
education and
special
training
Attend other
classes
Activity
ID
Code
13800*
14000
14100
14110*
14120*
14200*
14300*
14400*
14500*
14600*
14700*
15000
15100*
15110
15120
15130
15140
15200*
Age(a)
Occupation^
Distribution
Type
Uniform
Uniform
Normal
Uniform
Uniform
Uniform
LogNormal
Uniform
LogNormal
Point Est.
Triangle
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Mean
3.5
2.0
2.0
3.0
1.8
1.8
3.1
1.8
0.9
2.5
2.0
1.9
2.1
2.3
2.1
2.0
1.8
2.2
Median
3.5
2.0
2.0
3.0
1.8
1.8
3.0
1.8
0.9
2.5
2.0
1.8
2.1
2.3
2.1
2.0
1.8
2.2
Std
Dev
0.6
0.6
0.3
0.6
0.4
0.4
0.7
0.1
0.1
0.4
0.7
0.4
0.4
0.4
0.3
0.2
0.5
Min
2.5
1.0
1.0
2.0
1.0
1.0
2.5
1.5
0.8
2.5
1.0
1.4
1.4
1.5
1.4
1.4
1.4
1.4
Max
4.5
3.0
4.0
4.0
2.5
2.5
5.0
2.0
1.1
2.5
2.9
4.0
2.8
3.0
2.8
2.5
2.2
3.0
Truncate
Left
Tail?
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
B-7
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Do
homework
Use library
Other
education
General
entertainment
/ social
activities
Attend sports
events
Participate in
social,
political, or
religious
activities
Practice
religion
Watch movie
Attend theater
Visit
museums
Visit
Attend a party
Go to bar /
lounge
Other
entertainment
/ social events
Leisure,
general
Leisure,
general
Leisure,
general
Sports and
active leisure
Activity
ID
Code
15300*
15400*
15500*
16000
16100*
16200
16210*
16300*
16400*
16500*
16600*
16700*
16800*
16900*
17000
17000
17000
17100
Age(a)
20
30
40
20
Occupation^
Distribution
Type
Point Est.
Uniform
Uniform
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
LogNormal
LogNormal
Uniform
LogNormal
Normal
Normal
LogNormal
Mean
1.8
2.3
2.8
2.2
2.7
1.7
1.7
1.3
1.7
2.5
1.5
3.3
3.3
3.8
5.7
5.0
4.5
5.7
Median
1.8
2.3
2.8
2.0
2.7
1.7
1.7
1.3
1.7
2.5
1.5
3.0
3.0
3.8
5.0
5.0
4.5
5.0
Std
Dev
0.4
0.7
1.1
0.8
0.2
0.2
0.2
0.4
0.3
0.3
1.4
1.4
1.3
3.0
2.0
1.4
3.0
Min
1.8
1.5
1.5
1.0
1.4
1.4
1.4
1.0
1.0
2.0
1.0
1.5
1.5
1.5
1.4
1.0
1.7
1.4
Max
1.8
3.0
4.0
6.0
4.0
2.0
2.0
1.6
2.3
2.9
1.9
8.0
8.0
6.0
16.0
9.0
7.3
16.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
B-8
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Sports and
active leisure
Sports and
active leisure
Participate in
sports
Participate in
sports
Participate in
sports
Hunting,
fishing,
hiking
Hunting,
fishing,
hiking
Hunting,
fishing,
hiking
Golf
Golf
Golf
Bowling /
pool / ping
pong / pinball
Yoga
Participate in
outdoor
leisure
Participate in
outdoor
leisure
Participate in
outdoor
leisure
Play,
unspecified
Play,
unspecified
Activity
ID
Code
17100
17100
17110*
17110*
17110*
17111
17111
17111
17112
17112
17112
17113
17114
17120
17120
17120
17121
17121
Age(a)
30
40
20
30
40
20
30
40
20
30
40
20
30
40
20
30
Occupation^
Distribution
Type
Normal
Normal
LogNormal
LogNormal
LogNormal
Normal
Normal
Normal
Uniform
Uniform
Uniform
Uniform
Triangle
LogNormal
LogNormal
Point Est.
LogNormal
LogNormal
Mean
5.0
4.5
3.6
3.6
3.4
5.6
5.8
4.7
3.8
3.8
3.5
3.0
3.1
4.2
4.2
3.5
4.2
4.2
Median
5.0
4.5
3.2
3.2
3.0
5.6
5.8
4.7
3.8
3.8
3.5
3.0
3.2
3.9
3.9
3.5
3.9
3.9
Std
Dev
2.0
1.4
1.9
1.9
1.7
2.1
2.4
1.8
1.0
1.0
0.9
0.6
0.6
1.5
1.5
1.5
1.5
Min
1.0
1.7
1.4
1.4
1.4
1.4
1.0
1.1
2.0
2.0
2.0
2.0
1.4
2.0
2.0
0.0
2.0
2.0
Max
9.0
7.3
10.0
10.0
9.0
9.8
10.6
8.3
5.5
5.5
5.0
4.0
4.0
9.0
9.0
0.0
9.0
9.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
B-9
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Play,
unspecified
Passive,
sitting
Exercise
Exercise
Exercise
Walk, bike, or
jog (not in
transit)
Walk, bike, or
jog (not in
transit)
Walk, bike, or
jog (not in
transit)
Create art,
music, work
on hobbies
Create art,
music, work
on hobbies
Create art,
music, work
on hobbies
Participate in
hobbies
Create
domestic
crafts
Create art
Perform
music / drama
/dance
Perform
music / drama
/dance
Activity
ID
Code
17121
17122*
17130*
17130*
17130*
17131
17131
17131
17140
17140
17140
17141*
17142*
17143*
17144*
17144*
Age(a)
40
20
30
40
20
30
40
20
30
40
20
30
Occupation^
Distribution
Type
Point Est.
Uniform
LogNormal
Normal
Normal
LogNormal
Normal
Normal
Normal
Normal
Normal
Triangle
Triangle
Uniform
Normal
Normal
Mean
3.5
1.5
5.8
5.7
4.7
5.8
5.7
4.7
5.3
5.2
3.8
2.8
2.0
2.5
5.3
5.2
Median
3.5
1.5
5.5
5.7
4.7
5.5
5.7
4.7
5.3
5.2
3.8
2.7
1.9
2.5
5.3
5.2
Std
Dev
0.2
1.8
1.8
1.2
1.8
1.8
1.2
1.8
1.7
1.0
0.8
0.4
0.3
1.8
1.7
Min
0.0
1.2
1.8
2.1
2.3
1.8
2.1
2.3
1.7
1.7
1.8
1.5
1.5
2.0
1.7
1.7
Max
0.0
1.8
11.3
9.3
7.1
11.3
9.3
7.1
8.9
8.9
5.8
5.0
3.0
3.0
8.9
8.9
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
B-10
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Perform
music / drama
/dance
Play games
Use of
computers
Recess and
physical
education
Other sports
and active
leisure
Other sports
and active
leisure
Other sports
and active
leisure
Participate in
passive
leisure
Watch
Watch adult
at work
Watch
someone
provide
childcare
Watch
personal care
Watch
education
Watch
organizational
activities
Watch
recreation
Activity
ID
Code
17144*
17150*
17160*
17170
17180
17180
17180
17200
17210
17211
17212
17213
17214
17215
17216
Age(a)
40
20
30
40
Occupation^
Distribution
Type
Normal
Triangle
Uniform
Uniform
LogNormal
Normal
Normal
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Mean
3.8
3.3
1.6
5.0
6.6
6.0
4.8
1.3
1.5
0.0
0.0
0.0
0.0
0.0
2.7
Median
3.8
3.2
1.6
5.0
5.9
6.0
4.8
1.3
1.5
0.0
0.0
0.0
0.0
0.0
2.7
Std
Dev
1.0
0.6
0.2
1.7
3.2
2.0
1.4
0.3
0.2
0.0
0.0
0.0
0.0
0.0
0.8
Min
1.8
2.4
1.2
2.0
2.0
2.0
2.0
1.0
1.2
1.2
1.2
1.2
1.2
1.2
1.4
Max
5.8
5.0
2.0
8.0
17.4
10.0
7.6
2.3
1.8
0.0
0.0
0.0
0.0
0.0
4.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
B-ll
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Listen to
radio /
recorded
music / watch
T.V.
Listen to
radio
Listen to
recorded
music
Watch TV
Read, general
Read books
Read
magazines /
not
ascertained
Read
newspaper
Converse /
write
Converse
Write for
leisure /
pleasure /
paperwork
Think and
relax
Other passive
leisure
Other leisure
Travel,
general
Travel during
work
Travel
to/from work
Travel for
child care
Activity
ID
Code
17220
17221*
17222*
17223*
17230
17231*
17232*
17233*
17240
17241*
17242*
17250*
17260
17300
18000
18100*
18200*
18300*
Age(a)
Occupation^
Distribution
Type
LogNormal
Uniform
Uniform
Point Est.
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
LogNormal
LogNormal
LogNormal
LogNormal
Mean
1.2
1.2
1.9
1.0
1.3
1.3
1.3
1.3
1.4
1.4
1.4
1.2
1.9
1.5
2.3
2.3
2.3
2.3
Median
1.2
1.2
1.9
1.0
1.3
1.3
1.3
1.3
1.4
1.4
1.4
1.2
1.9
1.5
2.0
2.0
2.0
2.0
Std
Dev
0.4
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.2
0.2
1.3
1.3
1.3
1.3
Min
0.9
1.0
1.5
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.5
1.2
1.0
1.0
1.0
1.0
Max
2.3
1.3
2.3
1.0
1.6
1.6
1.6
1.6
1.8
1.8
1.8
1.3
2.3
1.8
7.0
7.0
7.0
7.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
B-12
-------
Table B-l. METS Distributions assigned to activity ID codes within CHAD
(continued)
Activity
Description
Travel for
goods and
services
Travel for
personal care
Travel for
education
Travel for
organ.
activity
Travel for
event / social
act
Travel for
leisure
Travel for
active leisure
Travel for
passive
leisure
Activity
ID
Code
18400*
18500*
18600*
18700*
18800*
18900
18910*
18920*
Age(a)
Occupation^
Distribution
Type
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
Mean
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
Median
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Std
Dev
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
Min
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.8
Max
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
Truncate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
Truncate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
Y
aAge Group ("20" = <25 years; "30" = 25-39 years; "40" = >40 years)
bOccupation (activity ID code = 1000 only): ADMIN = executive/administrative/managerial; PROF = professional;
TECH = technicians; SALE = sales; ADMSUP = administrative support; HSHLD = private household;
PROTECT = protective services; SERV = service; FARM = farming/forestry/fishing; PREC = precision
production/craft/repair; MACH = machine operators/assemblers/inspectors; TRANS = transportation and material
moving; LABOR = handling/equipment cleaners/helpers/laborers
* Activity ID codes encountered within the NHAPS data set.
B-13
-------
Table B-2. Activity codes whose METS Distributions were assigned to those
codes encountered in the NHAPS Database but having no METS
Distribution assigned by CHAD
Codes Encountered in the NHAPS Data with
No METS Distribution Assigned by CHAD
Activity
Code
10111
10112
10113
10114
10115
10116
10117
10118
10120
10200
Activity Description
Work for professional/union
organizations
Work for special interest identity
organizations
Work for political party and civic
participation
Work for volunteer/helping
organizations
Work of/for religious groups
Work for fraternal organizations
Work for child/youth/family
organizations
Work for other organizations
Work, income-related only
Unemployment
Activity Code Whose METS Distribution Was
Assigned to the Code in the First Column
Activity
Code
10000
(PROF)
16200
16200
14300
16200
16200
12800
10000
(ADMIN)
16900
13500
Activity Description
Work and other income producing
activities, general — professional
positions
Participate in social, political, or
religious activities
Participate in social, political, or
religious activities
Help and care
Participate in social, political, or
religious activities
Participate in social, political, or
religious activities
Other child care
Work and other income producing
activities, general — executive,
administrative, and managerial
positions
Other entertainment/social events
Obtain government/financial services
B-14
-------
APPENDIX C
ADDITIONAL ANALYSIS TABLES
-------
LIST OF TABLES
C-la. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES
male participants, by age group C-3
C-lb. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES
female participants, by age group C-4
C-2a. Descriptive statistics for daily average ventilation rate (mVday) in males, by age
category C-5
C-2b. Descriptive statistics for daily average ventilation rate (m3/day) in females, by age
category C-6
C-3. Descriptive statistics for duration of time (hr/day) spent performing activities
within the specified activity category, by age and gender categories C-7
C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body
weight, while performing activities within the specified activity category, by age
and gender categories C-12
C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body
weight, while performing activities within the specified activity category, by age
and gender categories C-17
C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body
weight, while performing activities within the specified activity category, by age
and gender categories C-22
C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body
weight, while performing activities within the specified activity category, by age
and gender categories C-27
C-2
-------
Table C-la. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES male participants,
by age group
Age Category
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Body Weight (kg)
Mean
8.0
11.4
13.9
18.5
31.8
56.4
76.5
83.8
87.1
88.4
89.0
87.6
82.4
75.4
Percentiles
5th
4.8
9.1
11.1
13.4
19.9
32.8
54.3
56.8
61.0
64.0
62.6
63.4
60.6
57.9
10th
5.5
9.8
11.7
14.5
21.9
35.2
57.6
60.9
65.6
67.7
67.4
66.7
64.4
61.8
25th
6.7
10.3
12.5
16.0
24.8
43.3
63.9
69.5
73.9
76.7
76.6
76.1
72.5
67.0
50th
8.1
11.3
13.8
17.8
29.6
53.8
72.2
80.8
83.4
85.4
86.6
85.7
81.0
74.6
75th
9.4
12.3
15.3
20.2
36.3
65.7
83.6
93.7
96.3
97.8
99.6
97.1
92.0
82.0
90th
10.4
13.2
16.2
23.3
45.4
79.9
102.8
108.7
112.6
111.8
110.5
111.2
101.1
91.6
95th
10.8
13.7
17.2
25.2
50.0
92.5
111.2
123.4
126.7
121.2
120.3
119.0
108.8
100.5
Maxi-
mum
13.4
16.1
23.3
42.0
86.9
143.6
176.0
196.8
193.3
188.3
179.0
162.8
132.7
111.8
BMR (kcal/min)
Mean
0.31
0.45
0.55
0.64
0.85
1.15
1.33
1.35
1.30
1.31
1.30
1.12
1.08
1.02
Percentiles
5th
0.18
0.35
0.44
0.56
0.66
0.86
1.08
1.07
1.09
1.12
1.07
0.92
0.90
0.88
10th
0.21
0.38
0.46
0.58
0.70
0.89
1.11
1.12
1.13
1.14
1.12
0.95
0.93
0.91
25th
0.26
0.40
0.50
0.60
0.74
0.99
1.18
1.21
1.19
1.22
1.20
1.03
1.00
0.95
50th
0.31
0.45
0.55
0.63
0.82
1.12
1.28
1.32
1.27
1.29
1.29
1.10
1.07
1.01
75th
0.37
0.49
0.61
0.67
0.91
1.26
1.42
1.45
1.37
1.38
1.39
1.20
1.16
1.07
90th
0.41
0.52
0.65
0.72
1.04
1.44
1.60
1.62
1.50
1.50
1.48
1.31
1.23
1.15
95th
0.42
0.54
0.69
0.75
1.11
1.59
1.73
1.74
1.61
1.57
1.55
1.38
1.29
1.22
Maxi-
mum
0.53
0.64
0.94
1.01
1.57
2.22
2.62
2.54
2.14
2.11
2.03
1.73
1.49
1.32
o
Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
The numbers of male NHANES participants with data entering into these statistics are given in Table 2-1.
-------
Table C-lb. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES female participants,
by age group
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Body Weight (kg)
Mean
7.4
11.1
13.3
18.2
30.9
55.6
65.2
72.4
74.7
76.6
77.0
75.5
70.3
63.9
Percentiles
5th
4.6
8.8
11.0
13.3
18.9
35.6
46.2
47.5
51.0
51.3
53.1
51.7
46.8
45.2
10th
4.9
9.1
11.2
14.2
20.6
38.1
47.8
51.4
54.6
54.2
56.2
55.9
52.0
47.4
25th
6.3
9.9
12.0
15.5
23.3
45.0
54.3
58.3
60.7
60.7
62.8
63.8
59.4
54.5
50th
7.5
10.9
13.1
17.4
28.1
53.1
61.3
69.0
69.7
72.7
73.6
73.1
68.5
62.6
75th
8.6
12.1
14.4
19.5
36.2
62.4
72.5
82.5
84.0
87.5
87.7
83.9
80.3
71.4
90th
9.6
13.1
15.6
23.0
44.7
75.3
89.9
98.3
103.8
102.8
104.6
99.9
91.8
79.4
95th
10.4
13.8
16.8
26.9
50.4
86.2
96.2
109.6
112.8
117.2
113.4
109.2
97.7
91.4
Max
20.2
18.9
22.7
38.6
87.0
134.4
156.4
159.1
191.1
182.8
150.1
138.7
127.6
120.0
BMR (kcal/min)
Mean
0.28
0.43
0.52
0.59
0.76
1.00
1.04
1.07
1.01
1.02
1.01
0.93
0.90
0.86
Percentiles
5th
0.16
0.33
0.42
0.52
0.60
0.81
0.83
0.83
0.87
0.88
0.86
0.78
0.75
0.74
10th
0.18
0.35
0.43
0.54
0.63
0.83
0.87
0.87
0.89
0.89
0.89
0.81
0.78
0.76
25th
0.23
0.38
0.46
0.56
0.67
0.90
0.93
0.93
0.93
0.93
0.93
0.86
0.83
0.80
50th
0.28
0.42
0.51
0.58
0.74
0.97
1.01
1.03
0.98
1.00
1.00
0.92
0.89
0.85
75th
0.33
0.47
0.56
0.61
0.84
1.06
1.11
1.17
1.06
1.08
1.07
0.99
0.96
0.91
90th
0.37
0.51
0.61
0.66
0.95
1.18
1.27
1.33
1.17
1.17
1.17
1.09
1.04
0.96
95th
0.40
0.54
0.66
0.72
1.02
1.28
1.35
1.45
1.22
1.25
1.22
1.15
1.07
1.03
Max
0.80
0.74
0.90
0.88
1.56
1.73
1.95
1.97
1.66
1.62
1.43
1.33
1.26
1.21
o
Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
The numbers of female NHANES participants with data entering into these statistics are given in Table 2-1.
-------
Table C-2a. Descriptive statistics for daily average ventilation rate (m3/day) in males, by age category
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(F£;m3/day)
Mean
8.76
13.49
13.23
12.65
13.42
15.32
17.22
18.82
20.29
20.93
20.91
17.94
16.35
15.15
Percentiles
5th
4.77
9.73
9.45
10.42
10.08
11.41
12.60
12.69
14.00
14.66
14.98
13.92
13.10
11.95
10th
5.70
10.41
10.20
10.87
10.69
12.11
13.41
13.57
14.97
15.54
16.07
14.50
13.61
12.57
25th
7.16
11.65
11.43
11.40
11.73
13.27
14.48
15.49
16.96
17.50
17.60
15.88
14.67
13.82
50th
8.70
13.11
13.19
12.58
13.09
14.79
16.63
18.18
19.83
20.60
20.41
17.60
16.23
14.90
75th
10.43
15.02
14.49
13.64
14.73
16.81
19.16
21.23
23.02
23.89
23.16
19.54
17.57
16.31
90th
11.93
17.03
16.27
14.63
16.56
19.54
21.94
24.57
26.77
26.71
27.01
21.78
19.43
18.02
95th
12.69
17.89
17.71
15.41
17.72
21.21
23.38
27.14
28.90
28.37
29.09
23.50
20.42
18.68
Max
17.05
24.24
28.17
19.52
24.97
28.54
39.21
43.42
40.72
45.98
38.17
28.09
24.53
22.63
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW; m3/day-kg)
Mean
1.09
1.19
0.95
0.70
0.44
0.28
0.23
0.23
0.24
0.24
0.24
0.21
0.20
0.20
Percentiles
5th
0.91
0.96
0.78
0.52
0.32
0.21
0.17
0.16
0.16
0.17
0.16
0.17
0.17
0.17
10th
0.94
1.02
0.82
0.56
0.34
0.22
0.18
0.17
0.18
0.18
0.18
0.18
0.18
0.18
25th
1.00
1.09
0.87
0.61
0.38
0.25
0.20
0.19
0.20
0.20
0.20
0.19
0.19
0.19
50th
1.09
1.17
0.94
0.69
0.43
0.28
0.23
0.22
0.23
0.23
0.24
0.20
0.20
0.20
75th
1.16
1.26
1.01
0.78
0.50
0.32
0.25
0.26
0.27
0.28
0.27
0.22
0.21
0.22
90th
1.26
1.37
1.09
0.87
0.55
0.36
0.28
0.30
0.31
0.32
0.30
0.24
0.23
0.23
95th
1.29
1.48
1.13
0.92
0.58
0.38
0.30
0.32
0.34
0.34
0.34
0.25
0.24
0.25
Max
1.48
1.73
1.36
1.08
0.81
0.50
0.39
0.51
0.46
0.47
0.43
0.32
0.31
0.28
o
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-2b. Descriptive statistics for daily average ventilation rate (m /day) in females, by age category
Age Category
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(F£;m3/day)
Mean
8.53
13.31
12.74
12.16
12.41
13.44
13.59
14.57
14.98
16.20
16.18
12.99
12.04
11.14
Percentiles
5th
4.84
9.08
8.91
9.87
9.99
10.47
9.86
10.15
11.07
12.10
12.33
10.40
9.90
9.19
10th
5.48
10.12
10.07
10.38
10.35
11.11
10.61
10.67
11.80
12.58
12.96
10.77
10.20
9.45
25th
6.83
11.24
11.38
11.20
11.01
12.04
11.78
11.93
13.02
14.16
14.08
11.78
10.89
10.13
50th
8.41
13.03
12.60
12.02
11.95
13.08
13.20
14.10
14.68
15.88
15.90
12.92
11.82
11.02
75th
9.78
14.64
13.96
13.01
13.42
14.54
15.02
16.62
16.32
17.95
17.81
13.90
12.96
11.87
90th
11.65
17.45
15.58
14.03
15.13
16.25
17.12
19.32
18.51
19.91
19.93
15.40
14.11
12.85
95th
12.66
18.62
16.37
14.93
16.34
17.41
18.29
21.14
20.45
21.35
21.22
16.15
15.20
13.94
Max
26.26
24.77
23.01
19.74
20.82
26.58
30.11
30.23
28.28
35.89
25.70
20.34
17.70
16.93
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW; m3/day-kg)
Mean
1.14
1.20
0.95
0.69
0.43
0.25
0.21
0.21
0.21
0.22
0.22
0.18
0.18
0.18
Percentiles
5th
0.91
0.97
0.82
0.48
0.28
0.19
0.16
0.14
0.14
0.15
0.15
0.14
0.14
0.14
10th
0.97
1.01
0.84
0.54
0.31
0.20
0.17
0.16
0.15
0.16
0.16
0.15
0.14
0.15
25th
1.04
1.10
0.89
0.60
0.36
0.22
0.19
0.18
0.18
0.19
0.18
0.16
0.16
0.16
50th
1.13
1.18
0.96
0.68
0.43
0.24
0.21
0.20
0.20
0.21
0.21
0.17
0.17
0.18
75th
1.24
1.30
1.01
0.77
0.49
0.28
0.24
0.23
0.23
0.25
0.24
0.19
0.19
0.20
90th
1.33
1.41
1.07
0.88
0.55
0.31
0.27
0.26
0.27
0.28
0.28
0.21
0.21
0.21
95th
1.38
1.46
1.11
0.92
0.58
0.34
0.28
0.28
0.30
0.31
0.30
0.22
0.23
0.22
Max
1.60
1.73
1.23
1.12
0.75
0.47
0.36
0.40
0.43
0.41
0.40
0.27
0.34
0.28
o
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified
activity category, by age and gender categories
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
13.51
12.61
12.06
11.18
10.18
9.38
8.69
8.36
8.06
7.89
7.96
8.31
8.51
9.24
12.63
11.89
11.19
10.57
9.65
8.84
7.91
7.54
7.36
7.15
7.29
7.65
7.80
8.48
12.78
12.15
11.45
10.70
9.75
8.94
8.08
7.70
7.50
7.30
7.51
7.78
8.02
8.64
13.19
12.34
11.80
10.94
9.93
9.15
8.36
8.02
7.77
7.58
7.69
8.01
8.27
8.97
13.53
12.61
12.07
11.18
10.19
9.38
8.67
8.36
8.06
7.88
7.96
8.30
8.53
9.25
13.88
12.89
12.39
11.45
10.39
9.61
9.03
8.67
8.36
8.17
8.23
8.60
8.74
9.54
14.24
13.13
12.65
11.63
10.59
9.83
9.34
9.03
8.59
8.48
8.48
8.83
8.99
9.74
14.46
13.29
12.75
11.82
10.72
9.95
9.50
9.23
8.76
8.68
8.66
9.01
9.10
9.96
15.03
13.79
13.40
12.39
11.24
10.33
10.44
9.77
9.82
9.38
9.04
9.66
9.89
10.69
12.99
12.58
12.09
11.13
10.26
9.57
9.08
8.60
8.31
8.32
8.12
8.40
8.58
9.11
12.00
11.59
11.45
10.45
9.55
8.82
8.26
7.89
7.54
7.58
7.36
7.67
7.85
8.35
12.16
11.88
11.68
10.70
9.73
8.97
8.44
7.99
7.70
7.75
7.53
7.88
8.01
8.53
12.53
12.29
11.86
10.92
10.01
9.27
8.74
8.26
7.98
7.99
7.81
8.15
8.26
8.84
12.96
12.63
12.08
11.12
10.27
9.55
9.08
8.59
8.28
8.31
8.11
8.40
8.55
9.10
13.44
12.96
12.34
11.38
10.54
9.87
9.39
8.90
8.59
8.63
8.43
8.68
8.89
9.34
13.82
13.16
12.57
11.58
10.74
10.17
9.79
9.20
8.92
8.93
8.73
8.93
9.19
9.73
14.07
13.31
12.66
11.75
10.91
10.31
10.02
9.38
9.17
9.13
8.85
9.09
9.46
10.04
14.82
14.55
13.48
12.23
11.43
11.52
11.11
10.35
10.22
10.02
9.29
9.80
10.34
10.55
o
-------
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activity
category, by age and gender categories (continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
14.95
14.27
14.62
14.12
13.51
13.85
13.21
12.41
12.31
12.32
13.06
14.49
15.90
16.58
13.82
13.22
13.52
13.01
12.19
12.39
11.39
10.69
10.73
10.56
11.47
12.96
14.22
15.13
14.03
13.33
13.67
13.18
12.45
12.65
11.72
11.06
10.98
11.00
11.86
13.24
14.67
15.45
14.49
13.76
14.11
13.54
12.86
13.06
12.32
11.74
11.61
11.67
12.36
13.76
15.25
15.92
14.88
14.25
14.54
14.03
13.30
13.61
13.08
12.39
12.24
12.30
13.03
14.48
15.94
16.64
15.44
14.74
15.11
14.53
13.85
14.30
13.97
13.09
12.98
12.95
13.72
15.16
16.65
17.21
15.90
15.08
15.60
15.26
14.82
15.41
14.83
13.75
13.63
13.67
14.38
15.72
17.11
17.70
16.12
15.38
15.77
15.62
15.94
16.76
15.44
14.16
14.05
13.98
14.76
16.24
17.46
18.06
17.48
16.45
17.28
17.29
19.21
18.79
18.70
15.35
15.58
15.48
15.95
17.50
18.47
18.76
14.07
14.32
14.86
14.27
13.97
14.19
13.58
12.59
12.29
12.22
12.66
14.25
15.38
16.48
12.86
13.02
13.81
12.88
12.49
12.38
11.80
10.97
10.91
10.78
11.08
12.89
13.66
14.87
13.05
13.25
13.95
13.15
12.74
12.76
12.17
11.29
11.14
11.08
11.40
13.16
14.20
15.09
13.53
13.73
14.44
13.56
13.22
13.34
12.79
11.88
11.61
11.56
12.08
13.68
14.76
15.80
14.08
14.31
14.81
14.23
13.82
14.05
13.52
12.60
12.24
12.18
12.64
14.22
15.41
16.59
14.54
14.88
15.32
14.82
14.50
14.82
14.29
13.21
12.91
12.82
13.30
14.86
16.05
17.15
15.08
15.36
15.78
15.43
15.34
15.87
15.08
13.75
13.50
13.40
13.89
15.38
16.62
17.71
15.49
15.80
16.03
15.85
16.36
16.81
15.67
14.19
13.90
13.79
14.12
15.69
16.94
18.07
16.14
16.40
16.91
17.96
18.68
19.27
16.96
16.24
15.18
15.17
15.80
17.14
17.90
19.13
o
oo
-------
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activity
category, by age and gender categories (continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Light Intensity Activities (1.5 < METS < 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
5.30
5.52
5.48
6.60
7.62
7.50
7.13
6.09
5.72
6.07
5.64
5.49
4.96
4.86
2.97
2.68
3.06
3.86
5.07
4.48
4.37
3.15
2.80
2.97
3.21
3.50
3.45
3.54
3.25
2.89
3.26
4.25
5.57
5.59
4.97
3.50
3.12
3.41
3.44
3.82
3.75
3.71
3.71
3.37
3.85
5.16
6.63
6.75
6.00
4.20
3.70
3.92
4.03
4.58
4.29
4.17
4.52
4.31
4.58
6.20
7.63
7.67
7.02
5.08
4.64
4.82
4.79
5.29
4.81
4.74
7.29
8.23
7.58
8.26
8.72
8.51
8.29
8.49
8.34
8.56
7.59
6.41
5.59
5.39
8.08
9.04
8.83
9.31
9.78
9.19
9.43
9.96
9.87
10.19
8.94
7.40
6.26
6.33
8.50
9.73
9.04
9.70
10.12
9.63
10.03
10.47
10.49
10.79
9.75
7.95
6.59
6.59
9.91
10.90
9.92
10.74
11.59
10.91
11.50
12.25
12.10
12.68
12.09
10.23
9.90
7.56
6.00
5.61
5.78
6.25
7.27
7.55
6.98
6.42
6.51
6.56
6.52
6.23
5.96
5.30
3.49
2.83
3.20
3.78
4.63
4.89
4.60
3.66
4.06
3.99
4.09
4.40
4.22
3.67
3.70
2.94
3.54
4.10
5.46
5.62
5.08
4.09
4.33
4.30
4.42
4.74
4.51
3.96
4.26
3.46
4.29
4.79
6.33
6.75
5.91
4.84
5.06
4.97
5.19
5.47
5.24
4.63
5.01
4.39
5.33
5.84
7.17
7.67
6.85
5.82
5.98
5.90
6.05
6.23
5.92
5.16
8.43
8.28
7.48
7.86
8.34
8.55
7.96
8.18
8.14
8.40
7.95
6.96
6.63
6.00
9.31
9.03
8.46
8.84
9.42
9.27
9.16
9.56
9.46
9.75
9.12
7.67
7.46
6.70
9.77
9.39
8.74
9.38
9.79
9.57
9.57
10.14
9.93
10.18
9.43
8.17
7.91
7.01
10.53
10.57
9.93
10.32
11.06
10.85
12.29
12.11
13.12
11.83
11.58
11.13
9.43
8.78
o
-------
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activity
category, by age and gender categories (continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Moderate Intensity Activities (3.0 < METS < 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.67
4.04
3.83
3.15
2.66
2.35
3.35
5.24
5.69
5.40
5.00
3.73
2.87
2.35
0.63
0.45
0.59
0.55
0.65
0.88
1.13
1.15
1.26
1.21
1.29
1.62
1.56
1.32
0.97
0.59
0.76
0.75
0.92
1.09
1.42
1.58
1.65
1.55
1.63
1.97
1.83
1.45
1.74
1.14
1.23
1.30
1.65
1.66
2.19
2.52
2.84
2.39
2.72
2.81
2.28
1.79
4.20
5.29
4.74
3.80
2.68
2.30
3.45
6.01
6.67
6.46
5.68
3.70
2.86
2.29
5.20
6.06
5.37
4.52
3.57
3.02
4.37
7.15
7.75
7.57
6.75
4.67
3.45
2.85
5.80
6.61
5.82
5.11
4.36
3.62
5.24
7.95
8.45
8.40
7.60
5.45
3.95
3.28
6.21
6.94
6.15
5.32
4.79
3.89
5.59
8.39
8.90
8.85
8.01
6.01
4.31
3.61
7.52
7.68
7.40
6.30
5.95
5.90
6.83
9.94
9.87
10.52
9.94
7.45
5.44
4.37
3.91
4.02
3.27
3.35
2.57
2.01
3.26
4.80
5.00
5.05
4.58
3.31
2.48
2.06
0.53
0.52
0.50
0.70
0.65
0.89
1.27
1.62
1.71
1.75
1.71
1.65
1.19
1.01
0.74
0.73
0.78
0.89
0.95
1.08
1.48
1.94
2.06
2.00
2.13
1.97
1.36
1.25
1.10
1.08
1.22
1.61
1.82
1.45
2.21
2.78
3.09
2.97
3.10
2.56
1.82
1.55
4.87
5.14
4.01
3.88
2.66
1.96
3.39
5.37
5.41
5.48
4.79
3.34
2.48
1.99
5.77
6.10
4.88
4.71
3.41
2.51
4.24
6.42
6.60
6.66
5.98
4.01
2.99
2.51
6.27
7.00
5.35
5.29
3.95
3.03
4.74
7.19
7.31
7.50
6.89
4.61
3.64
3.07
6.54
7.37
5.57
5.65
4.32
3.28
5.07
7.52
7.58
7.97
7.14
5.01
4.01
3.44
7.68
8.07
6.93
7.58
6.10
4.96
6.68
9.21
9.59
10.16
8.97
6.90
5.63
4.68
o
o
-------
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activity
category, by age and gender categories (continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.20
0.31
0.10
0.27
0.32
0.38
0.40
0.33
0.38
0.34
0.41
0.37
0.39
0.32
0.00
0.01
0.00
0.02
0.01
0.03
0.03
0.02
0.03
0.03
0.03
0.03
0.01
0.02
0.00
0.01
0.01
0.03
0.01
0.04
0.04
0.05
0.07
0.05
0.05
0.05
0.03
0.03
0.01
0.03
0.03
0.04
0.03
0.10
0.14
0.11
0.14
0.09
0.13
0.13
0.10
0.08
0.14
0.22
0.05
0.13
0.13
0.21
0.27
0.27
0.28
0.23
0.34
0.28
0.29
0.25
0.28
0.56
0.14
0.33
0.38
0.47
0.53
0.45
0.51
0.50
0.59
0.49
0.57
0.47
0.50
0.78
0.25
0.75
1.10
1.03
0.99
0.69
0.83
0.78
0.87
0.80
0.90
0.71
0.59
0.93
0.33
1.16
1.50
1.34
1.29
0.85
1.03
1.00
1.13
1.08
1.11
0.88
0.96
1.52
0.48
1.48
3.20
2.35
2.59
1.95
1.77
2.40
1.95
2.21
2.06
1.76
0.17
0.22
0.15
0.19
0.24
0.30
0.24
0.26
0.25
0.26
0.34
0.32
0.29
0.26
0.03
0.03
0.00
0.01
0.02
0.03
0.01
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.05
0.05
0.01
0.02
0.03
0.04
0.03
0.05
0.05
0.04
0.04
0.04
0.05
0.03
0.09
0.09
0.03
0.05
0.06
0.08
0.08
0.10
0.09
0.09
0.12
0.10
0.10
0.09
0.14
0.18
0.08
0.10
0.12
0.19
0.18
0.19
0.19
0.20
0.28
0.23
0.25
0.21
0.21
0.35
0.16
0.22
0.26
0.40
0.34
0.36
0.33
0.36
0.50
0.46
0.43
0.38
0.33
0.40
0.48
0.46
0.67
0.66
0.51
0.56
0.52
0.55
0.74
0.68
0.60
0.59
0.40
0.43
0.65
0.73
0.98
0.96
0.60
0.67
0.72
0.68
0.85
0.89
0.71
0.71
0.58
0.48
1.01
1.43
1.71
3.16
1.61
1.40
1.40
1.49
1.58
1.77
1.24
1.23
o
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories
Age Category
Average Ventilation Rate (L/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sleep or nap (Activity ID = 14500)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.08
4.50
4.61
4.36
4.61
5.26
5.31
4.73
5.16
5.65
5.78
5.98
6.07
5.97
1.66
3.11
3.01
3.06
3.14
3.53
3.55
3.16
3.37
3.74
3.96
4.36
4.26
4.20
1.91
3.27
3.36
3.30
3.39
3.78
3.85
3.35
3.62
4.09
4.20
4.57
4.55
4.49
2.45
3.78
3.94
3.76
3.83
4.34
4.35
3.84
4.23
4.73
4.78
5.13
5.17
5.23
3.00
4.35
4.49
4.29
4.46
5.06
5.15
4.56
5.01
5.53
5.57
5.81
6.00
5.90
3.68
4.95
5.21
4.86
5.21
5.91
6.09
5.42
5.84
6.47
6.54
6.68
6.77
6.68
4.35
5.90
6.05
5.54
6.01
6.94
6.92
6.26
6.81
7.41
7.74
7.45
7.65
7.36
4.77
6.44
6.73
5.92
6.54
7.81
7.60
6.91
7.46
7.84
8.26
7.93
8.33
7.76
7.19
10.02
8.96
7.67
9.94
11.49
12.82
11.17
10.86
10.84
11.81
12.27
10.50
9.98
2.92
4.59
4.56
4.18
4.36
4.81
4.40
3.89
4.00
4.40
4.56
4.47
4.52
4.49
1.54
3.02
3.00
2.90
2.97
3.34
2.78
2.54
2.66
3.00
3.12
3.22
3.31
3.17
1.72
3.28
3.30
3.20
3.17
3.57
2.96
2.74
2.86
3.23
3.30
3.35
3.47
3.49
2.27
3.76
3.97
3.62
3.69
3.99
3.58
3.13
3.31
3.69
3.72
3.78
3.89
3.82
2.88
4.56
4.52
4.10
4.24
4.66
4.26
3.68
3.89
4.25
4.41
4.38
4.40
4.39
3.50
5.32
5.21
4.71
4.93
5.39
5.05
4.44
4.54
4.95
5.19
4.99
5.11
4.91
4.04
5.96
5.76
5.22
5.67
6.39
5.89
5.36
5.28
5.66
6.07
5.72
5.67
5.61
4.40
6.37
6.15
5.73
6.08
6.99
6.63
6.01
5.77
6.25
6.63
6.37
6.06
6.16
8.69
9.59
9.48
7.38
8.42
9.39
12.25
9.58
8.10
8.97
8.96
9.57
7.35
8.27
o
to
-------
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.18
4.62
4.79
4.58
4.87
5.64
5.76
5.11
5.57
6.11
6.27
6.54
6.65
6.44
1.74
3.17
3.25
3.47
3.55
4.03
4.17
3.76
3.99
4.65
4.68
5.02
5.26
5.09
1.99
3.50
3.66
3.63
3.78
4.30
4.42
3.99
4.42
4.92
5.06
5.31
5.55
5.37
2.50
3.91
4.10
4.07
4.18
4.79
4.93
4.33
4.86
5.37
5.50
5.85
5.96
5.82
3.10
4.49
4.69
4.56
4.72
5.43
5.60
5.00
5.45
6.02
6.16
6.47
6.59
6.43
3.80
5.03
5.35
5.03
5.40
6.26
6.43
5.64
6.17
6.65
6.89
7.12
7.18
7.01
4.40
5.95
6.05
5.58
6.03
7.20
7.15
6.42
6.99
7.46
7.60
7.87
7.81
7.57
4.88
6.44
6.71
5.82
6.58
7.87
7.76
6.98
7.43
7.77
8.14
8.22
8.26
7.90
7.09
9.91
9.09
7.60
9.47
11.08
13.45
10.30
9.98
10.53
10.39
10.86
9.92
9.13
3.00
4.71
4.73
4.40
4.64
5.21
4.76
4.19
4.33
4.75
4.96
4.89
4.95
4.89
1.60
3.26
3.34
3.31
3.41
3.90
3.26
3.04
3.22
3.60
3.78
3.81
4.07
3.93
1.80
3.44
3.53
3.49
3.67
4.16
3.56
3.19
3.45
3.82
4.00
4.02
4.13
4.10
2.32
3.98
4.19
3.95
4.04
4.53
4.03
3.55
3.77
4.18
4.36
4.34
4.41
4.39
2.97
4.73
4.67
4.34
4.51
5.09
4.69
4.00
4.24
4.65
4.87
4.81
4.89
4.79
3.58
5.30
5.25
4.84
5.06
5.68
5.32
4.63
4.80
5.19
5.44
5.30
5.42
5.25
4.11
5.95
5.75
5.29
5.88
6.53
6.05
5.38
5.33
5.74
6.06
5.86
5.89
5.71
4.44
6.63
6.22
5.73
6.28
7.06
6.60
6.02
5.79
6.26
6.44
6.29
6.15
6.12
9.59
9.50
9.42
7.08
8.31
9.07
11.82
9.22
7.70
8.70
8.30
8.18
7.59
7.46
o
-------
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Light Intensity Activities (1.5 < METS < 3.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
7.94
11.56
11.67
11.36
11.64
13.22
13.41
12.97
13.64
14.38
14.56
14.12
13.87
13.76
4.15
8.66
8.52
9.20
8.95
9.78
10.01
9.68
10.63
11.16
11.08
11.07
11.17
11.02
5.06
8.99
9.14
9.55
9.33
10.26
10.54
10.18
11.05
11.81
11.58
11.74
11.68
11.71
6.16
9.89
9.96
10.23
10.20
11.34
11.53
11.25
11.99
12.95
12.97
12.69
12.73
12.56
7.95
11.42
11.37
11.12
11.26
12.84
12.95
12.42
13.33
14.11
14.35
13.87
13.69
13.75
9.57
12.91
13.02
12.28
12.79
14.65
14.95
14.04
14.83
15.61
15.90
15.37
14.96
14.70
10.76
14.39
14.66
13.40
14.60
16.42
16.95
16.46
16.46
17.39
17.96
16.91
16.23
16.03
11.90
15.76
15.31
14.00
15.60
18.65
18.00
17.74
18.10
18.25
19.37
17.97
16.89
16.72
15.50
21.12
18.98
19.65
21.83
26.86
29.07
27.22
25.50
23.01
25.48
20.54
20.02
20.71
7.32
11.62
11.99
10.92
11.07
12.02
11.08
10.55
11.07
11.78
12.02
10.82
10.83
10.40
3.79
8.59
8.74
8.83
8.51
9.40
8.31
7.75
8.84
9.64
9.76
8.87
8.84
8.69
4.63
8.80
9.40
9.04
9.02
9.73
8.73
8.24
9.30
10.00
10.17
9.28
9.23
8.84
5.73
10.03
10.27
9.87
9.79
10.63
9.64
9.05
9.96
10.67
10.87
9.85
9.94
9.36
7.19
11.20
11.69
10.69
10.79
11.76
10.76
10.24
10.94
11.61
11.79
10.64
10.74
10.29
8.73
12.94
13.17
11.74
11.98
13.09
12.27
11.67
11.93
12.66
12.97
11.67
11.69
11.37
9.82
15.17
15.63
12.85
13.47
14.66
13.80
13.40
13.11
13.85
14.23
12.62
12.52
12.06
10.80
15.80
16.34
13.81
14.67
15.82
14.92
14.26
13.87
14.54
14.87
13.21
13.01
12.63
16.97
20.22
23.61
16.43
22.22
22.10
21.40
21.46
17.40
17.67
17.94
17.40
17.59
16.05
o
-------
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Moderate Intensity Activities (3.0 < METS < 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
14.49
21.35
21.54
21.03
22.28
26.40
29.02
29.19
30.30
31.58
32.71
29.76
29.29
28.53
7.41
14.48
15.37
16.31
16.36
19.33
20.30
19.65
21.40
22.58
22.36
22.47
22.81
22.45
8.81
15.88
16.71
17.16
17.23
20.45
21.69
20.97
22.70
24.44
24.01
24.04
23.92
23.36
11.46
18.03
18.42
18.72
19.34
22.60
24.52
24.16
25.08
27.21
27.95
26.05
26.14
25.47
14.35
20.62
20.82
20.55
21.64
25.41
27.97
27.92
29.09
30.44
31.40
29.22
28.78
28.19
16.95
24.06
24.07
22.94
25.00
29.19
31.74
33.00
34.10
35.11
36.96
32.27
32.04
31.03
20.08
26.94
26.87
25.60
27.59
33.77
38.15
38.79
39.60
40.28
41.66
36.93
35.65
33.44
22.50
28.90
29.68
27.06
29.50
36.93
42.14
43.11
43.48
44.97
45.77
39.98
37.32
35.52
30.54
39.87
50.93
34.88
43.39
55.02
67.35
71.71
57.69
63.36
70.48
52.26
44.86
41.11
13.98
20.98
21.34
20.01
21.00
23.55
23.22
22.93
22.70
24.49
25.24
21.42
21.09
20.87
7.91
15.62
14.21
15.26
15.98
18.16
16.60
15.56
16.87
17.60
18.83
16.90
16.86
16.51
9.00
16.30
15.57
16.32
16.83
19.47
17.61
16.68
17.57
18.88
19.80
17.70
17.61
17.53
11.15
17.92
18.17
17.84
18.47
20.83
19.62
18.98
19.50
20.79
21.78
19.22
18.87
19.09
13.53
20.14
21.45
19.76
20.39
23.04
22.39
21.94
21.95
23.94
24.30
20.86
20.68
20.62
16.32
23.51
23.92
21.61
22.98
25.38
26.13
26.02
24.81
27.41
28.11
23.22
22.85
22.51
19.41
27.09
27.61
23.83
26.06
28.42
30.28
30.02
28.94
30.79
31.87
25.72
24.94
24.59
22.30
29.25
28.76
25.89
28.08
31.41
31.98
32.84
31.10
33.58
35.02
27.32
26.35
26.01
40.87
34.53
37.58
32.86
43.13
42.42
52.47
54.18
47.27
50.67
46.18
35.45
34.41
29.27
o
-------
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
High Intensity (METS > 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
27.47
40.25
40.45
39.04
43.62
50.82
53.17
53.91
54.27
57.31
58.42
54.13
52.46
53.31
15.07
28.33
28.15
29.46
30.66
34.31
35.96
33.55
37.79
38.31
38.95
36.28
36.99
35.35
17.26
31.68
29.74
31.35
32.76
36.84
38.33
37.95
40.36
42.47
41.57
39.51
39.50
39.17
20.63
34.66
34.45
34.01
35.77
41.53
43.51
44.83
45.43
48.29
48.65
45.17
44.12
45.51
27.79
39.80
40.57
37.80
41.94
49.12
50.51
51.51
52.05
55.20
55.90
52.41
49.95
50.93
32.47
44.34
46.17
43.23
49.52
57.40
59.33
61.63
61.21
64.45
65.95
60.81
58.95
61.18
38.41
51.62
51.90
48.93
56.58
66.25
71.45
72.38
71.42
75.61
78.57
71.96
67.56
69.55
42.24
55.92
55.06
52.22
62.40
72.92
83.03
82.07
77.35
84.39
86.46
75.23
76.45
77.05
57.90
60.66
92.01
66.17
89.86
122.91
129.88
111.94
103.88
110.28
140.74
102.16
97.34
96.76
24.19
36.48
37.58
34.53
39.39
46.56
44.09
45.68
44.44
46.98
47.35
40.02
40.64
41.88
12.36
25.94
28.99
27.00
28.59
31.06
28.69
28.84
30.27
31.04
31.54
27.56
28.49
28.48
13.26
26.24
30.51
28.21
30.13
33.76
30.61
31.18
32.93
34.02
34.82
30.63
30.08
30.09
17.15
30.42
32.33
29.98
33.66
38.76
36.51
36.65
37.02
38.35
39.38
34.59
34.25
34.35
22.45
36.11
36.43
33.33
38.02
45.34
42.71
43.10
42.23
45.61
45.69
38.71
39.56
41.38
29.27
41.97
40.81
37.63
44.08
52.90
50.23
52.22
50.45
54.06
54.07
45.30
46.98
47.57
35.59
47.28
48.07
43.22
50.48
60.81
58.15
61.93
59.54
61.52
62.30
50.81
51.96
55.58
40.67
48.64
51.36
44.72
54.60
66.32
63.44
68.91
65.26
67.40
68.75
56.42
54.07
58.33
74.55
76.97
73.01
56.62
82.88
102.37
108.83
107.89
89.51
88.72
84.40
71.34
75.25
72.12
o
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, while
performing activities within the specified activity category, by age and gender categories
Age Category
Average Ventilation Rate (L/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sleep or nap (Activity ID = 14500)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.38
0.40
0.33
0.24
0.15
0.10
0.07
0.06
0.06
0.07
0.07
0.07
0.07
0.08
0.28
0.30
0.25
0.16
0.10
0.07
0.05
0.04
0.04
0.04
0.05
0.05
0.05
0.06
0.30
0.31
0.26
0.17
0.11
0.07
0.05
0.04
0.04
0.05
0.05
0.05
0.06
0.06
0.34
0.35
0.29
0.20
0.13
0.08
0.06
0.05
0.05
0.05
0.05
0.06
0.06
0.07
0.38
0.38
0.33
0.24
0.15
0.09
0.07
0.06
0.06
0.06
0.06
0.07
0.07
0.08
0.43
0.44
0.36
0.28
0.17
0.11
0.08
0.07
0.07
0.07
0.08
0.08
0.08
0.09
0.46
0.49
0.40
0.31
0.20
0.13
0.09
0.08
0.08
0.09
0.09
0.09
0.09
0.10
0.50
0.52
0.44
0.35
0.22
0.14
0.10
0.08
0.09
0.09
0.09
0.09
0.10
0.11
0.67
0.63
0.54
0.48
0.30
0.21
0.15
0.13
0.13
0.14
0.14
0.12
0.13
0.12
0.39
0.41
0.34
0.24
0.15
0.09
0.07
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.28
0.31
0.26
0.14
0.09
0.06
0.04
0.04
0.03
0.04
0.04
0.04
0.05
0.05
0.30
0.33
0.27
0.16
0.10
0.07
0.05
0.04
0.04
0.04
0.04
0.05
0.05
0.06
0.34
0.36
0.29
0.20
0.12
0.07
0.06
0.04
0.04
0.05
0.05
0.05
0.06
0.06
0.39
0.41
0.33
0.23
0.15
0.09
0.07
0.05
0.05
0.06
0.06
0.06
0.06
0.07
0.43
0.46
0.39
0.28
0.18
0.10
0.08
0.06
0.06
0.07
0.07
0.07
0.07
0.08
0.48
0.52
0.43
0.32
0.21
0.12
0.09
0.07
0.08
0.08
0.08
0.08
0.08
0.09
0.52
0.54
0.45
0.35
0.23
0.13
0.10
0.08
0.08
0.09
0.09
0.08
0.09
0.10
0.74
0.66
0.49
0.52
0.30
0.18
0.15
0.10
0.11
0.11
0.13
0.10
0.13
0.12
o
-------
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.40
0.41
0.34
0.25
0.16
0.10
0.08
0.06
0.07
0.07
0.07
0.08
0.08
0.09
0.30
0.32
0.27
0.18
0.11
0.08
0.05
0.05
0.05
0.05
0.05
0.06
0.07
0.07
0.32
0.33
0.29
0.19
0.12
0.08
0.06
0.05
0.05
0.06
0.06
0.06
0.07
0.08
0.35
0.36
0.31
0.21
0.14
0.09
0.07
0.06
0.06
0.06
0.06
0.07
0.08
0.08
0.39
0.40
0.34
0.25
0.16
0.10
0.08
0.06
0.07
0.07
0.07
0.08
0.08
0.09
0.44
0.45
0.37
0.29
0.18
0.12
0.09
0.07
0.07
0.08
0.08
0.08
0.09
0.09
0.47
0.49
0.41
0.33
0.21
0.13
0.09
0.08
0.08
0.09
0.09
0.09
0.09
0.10
0.50
0.52
0.45
0.35
0.22
0.14
0.10
0.08
0.09
0.09
0.09
0.09
0.10
0.11
0.66
0.62
0.51
0.45
0.29
0.20
0.13
0.12
0.12
0.13
0.14
0.11
0.11
0.11
0.40
0.43
0.36
0.25
0.16
0.10
0.07
0.06
0.06
0.06
0.07
0.07
0.07
0.08
0.30
0.34
0.29
0.16
0.10
0.07
0.05
0.04
0.04
0.04
0.05
0.05
0.06
0.06
0.32
0.35
0.30
0.18
0.11
0.07
0.06
0.05
0.04
0.05
0.05
0.05
0.06
0.07
0.35
0.38
0.32
0.21
0.13
0.08
0.06
0.05
0.05
0.05
0.06
0.06
0.07
0.07
0.40
0.42
0.35
0.25
0.16
0.09
0.07
0.06
0.06
0.06
0.07
0.07
0.07
0.08
0.45
0.47
0.39
0.28
0.19
0.11
0.08
0.07
0.07
0.07
0.08
0.07
0.08
0.09
0.48
0.51
0.42
0.33
0.21
0.12
0.10
0.07
0.08
0.08
0.08
0.08
0.09
0.09
0.52
0.54
0.44
0.36
0.23
0.13
0.10
0.08
0.08
0.09
0.09
0.08
0.09
0.10
0.72
0.64
0.48
0.49
0.29
0.17
0.14
0.10
0.11
0.11
0.12
0.10
0.15
0.11
o
oo
-------
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Light Intensity Activities (1.5 < METS < 3.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.99
1.02
0.84
0.63
0.38
0.25
0.18
0.16
0.16
0.17
0.17
0.16
0.17
0.18
0.79
0.84
0.68
0.44
0.27
0.18
0.14
0.12
0.12
0.13
0.13
0.14
0.14
0.15
0.83
0.86
0.72
0.48
0.29
0.19
0.14
0.13
0.13
0.13
0.13
0.14
0.15
0.16
0.90
0.92
0.76
0.54
0.32
0.21
0.16
0.14
0.14
0.15
0.15
0.15
0.16
0.17
0.97
1.01
0.83
0.63
0.38
0.24
0.18
0.15
0.16
0.16
0.16
0.16
0.17
0.18
1.07
1.10
0.89
0.71
0.44
0.28
0.20
0.17
0.18
0.18
0.18
0.18
0.18
0.20
1.17
1.22
1.00
0.79
0.49
0.31
0.22
0.19
0.20
0.20
0.20
0.19
0.19
0.21
1.20
1.30
1.03
0.87
0.53
0.33
0.23
0.21
0.21
0.21
0.22
0.20
0.20
0.22
1.43
1.48
1.18
1.08
0.71
0.44
0.33
0.29
0.28
0.33
0.29
0.27
0.26
0.25
0.98
1.05
0.90
0.62
0.38
0.23
0.17
0.15
0.15
0.16
0.16
0.15
0.16
0.17
0.79
0.85
0.73
0.45
0.25
0.16
0.13
0.12
0.11
0.11
0.12
0.12
0.12
0.13
0.82
0.87
0.76
0.48
0.27
0.17
0.14
0.12
0.12
0.12
0.13
0.12
0.13
0.14
0.88
0.95
0.82
0.54
0.31
0.20
0.15
0.13
0.13
0.14
0.14
0.13
0.14
0.15
0.96
1.04
0.89
0.60
0.38
0.22
0.17
0.15
0.15
0.16
0.16
0.14
0.16
0.16
1.05
1.14
0.96
0.70
0.44
0.25
0.19
0.16
0.18
0.18
0.18
0.16
0.17
0.18
1.18
1.25
1.04
0.78
0.50
0.28
0.21
0.18
0.19
0.20
0.20
0.17
0.19
0.20
1.23
1.27
1.10
0.83
0.54
0.31
0.22
0.19
0.20
0.22
0.21
0.18
0.20
0.21
1.65
1.64
1.26
1.02
0.71
0.40
0.29
0.23
0.27
0.28
0.26
0.24
0.28
0.23
o
-------
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Moderate Intensity Activities (3.0 < METS < 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
1.80
1.88
1.55
1.17
0.74
0.49
0.39
0.36
0.36
0.37
0.38
0.34
0.36
0.38
1.39
1.41
1.21
0.80
0.50
0.36
0.28
0.24
0.24
0.25
0.26
0.27
0.29
0.31
1.49
1.50
1.28
0.88
0.55
0.38
0.30
0.26
0.26
0.27
0.28
0.28
0.31
0.32
1.62
1.65
1.40
1.00
0.62
0.42
0.33
0.30
0.30
0.31
0.31
0.31
0.33
0.35
1.78
1.82
1.54
1.12
0.71
0.47
0.38
0.34
0.34
0.35
0.37
0.34
0.36
0.38
1.94
2.01
1.66
1.31
0.83
0.55
0.43
0.40
0.40
0.41
0.43
0.37
0.39
0.42
2.18
2.33
1.84
1.56
0.96
0.64
0.49
0.47
0.47
0.47
0.48
0.40
0.42
0.45
2.28
2.53
2.02
1.68
1.04
0.68
0.52
0.51
0.52
0.52
0.55
0.42
0.44
0.47
3.01
3.23
2.29
2.10
1.43
1.06
0.71
0.82
0.76
0.72
0.76
0.57
0.55
0.53
1.87
1.90
1.60
1.14
0.72
0.44
0.36
0.33
0.32
0.33
0.34
0.29
0.31
0.33
1.47
1.52
1.27
0.79
0.46
0.32
0.27
0.24
0.21
0.22
0.24
0.22
0.24
0.25
1.52
1.62
1.31
0.85
0.51
0.34
0.28
0.25
0.23
0.24
0.25
0.24
0.25
0.27
1.67
1.73
1.44
0.96
0.60
0.38
0.31
0.28
0.27
0.28
0.28
0.26
0.27
0.30
1.85
1.87
1.58
1.11
0.71
0.43
0.35
0.32
0.30
0.32
0.33
0.28
0.30
0.33
2.01
2.02
1.75
1.31
0.84
0.49
0.41
0.36
0.35
0.38
0.38
0.32
0.34
0.37
2.25
2.24
1.92
1.45
0.94
0.55
0.46
0.42
0.41
0.44
0.44
0.35
0.38
0.40
2.40
2.37
2.02
1.56
1.01
0.61
0.49
0.45
0.46
0.49
0.49
0.37
0.41
0.42
2.83
3.24
2.59
1.93
1.37
0.99
0.65
0.66
0.71
0.62
0.64
0.51
0.68
0.52
o
to
o
-------
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Average Ventilation Rate (L/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Average Ventilation Rate (L/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
High Intensity (METS > 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.48
3.52
2.89
2.17
1.41
0.95
0.71
0.66
0.64
0.66
0.68
0.62
0.65
0.72
2.70
2.52
2.17
1.55
0.94
0.63
0.48
0.45
0.44
0.44
0.45
0.44
0.47
0.50
2.93
2.89
2.34
1.66
1.03
0.70
0.53
0.47
0.47
0.48
0.48
0.47
0.50
0.54
3.10
3.22
2.58
1.81
1.19
0.79
0.60
0.54
0.53
0.55
0.55
0.53
0.55
0.60
3.46
3.57
2.87
2.11
1.38
0.91
0.69
0.64
0.62
0.63
0.64
0.61
0.63
0.70
3.81
3.91
3.20
2.50
1.59
1.09
0.80
0.75
0.73
0.74
0.77
0.70
0.72
0.80
4.14
4.11
3.43
2.73
1.83
1.27
0.92
0.85
0.85
0.86
0.91
0.79
0.85
0.94
4.32
4.34
3.54
2.98
1.93
1.36
1.00
0.97
0.93
0.94
1.02
0.85
0.91
0.99
5.08
4.86
4.30
3.62
2.68
1.98
1.94
1.27
1.23
1.77
1.31
1.08
1.04
1.35
3.26
3.38
2.80
1.98
1.33
0.88
0.70
0.65
0.61
0.65
0.63
0.54
0.59
0.67
2.53
2.57
2.20
1.36
0.89
0.59
0.45
0.42
0.38
0.38
0.39
0.36
0.39
0.45
2.62
2.75
2.31
1.51
0.97
0.63
0.50
0.46
0.42
0.44
0.43
0.40
0.44
0.48
2.89
2.97
2.48
1.69
1.12
0.71
0.57
0.55
0.50
0.52
0.51
0.45
0.50
0.54
3.23
3.24
2.81
1.90
1.33
0.85
0.69
0.63
0.59
0.64
0.61
0.53
0.58
0.63
3.63
3.71
3.12
2.19
1.52
1.01
0.79
0.73
0.71
0.76
0.75
0.61
0.68
0.77
3.96
4.16
3.35
2.50
1.72
1.18
0.92
0.88
0.83
0.88
0.85
0.72
0.78
0.93
4.08
4.87
3.48
2.99
1.81
1.31
1.00
0.94
0.90
0.95
0.93
0.80
0.83
0.97
5.02
4.88
3.88
3.24
2.22
2.05
1.50
1.30
1.55
1.61
1.37
1.11
1.26
1.22
o
to
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories
Age Category
Daily Ventilation Rate (m3/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sleep or nap (Activity ID = 14500)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.1E-03
4.5E-03
4.6E-03
4.4E-03
4.6E-03
5.3E-03
5.3E-03
4.7E-03
5.2E-03
5.7E-03
5.8E-03
6.0E-03
6.1E-03
6.0E-03
1.7E-03
3.1E-03
3.0E-03
3.1E-03
3.1E-03
3.5E-03
3.6E-03
3.2E-03
3.4E-03
3.7E-03
4.0E-03
4.4E-03
4.3E-03
4.2E-03
1.9E-03
3.3E-03
3.4E-03
3.3E-03
3.4E-03
3.8E-03
3.9E-03
3.3E-03
3.6E-03
4.1E-03
4.2E-03
4.6E-03
4.6E-03
4.5E-03
2.5E-03
3.8E-03
3.9E-03
3.8E-03
3.8E-03
4.3E-03
4.3E-03
3.8E-03
4.2E-03
4.7E-03
4.8E-03
5.1E-03
5.2E-03
5.2E-03
3.0E-03
4.3E-03
4.5E-03
4.3E-03
4.5E-03
5.1E-03
5.2E-03
4.6E-03
5.0E-03
5.5E-03
5.6E-03
5.8E-03
6.0E-03
5.9E-03
3.7E-03
4.9E-03
5.2E-03
4.9E-03
5.2E-03
5.9E-03
6.1E-03
5.4E-03
5.8E-03
6.5E-03
6.5E-03
6.7E-03
6.8E-03
6.7E-03
4.4E-03
5.9E-03
6.1E-03
5.5E-03
6.0E-03
6.9E-03
6.9E-03
6.3E-03
6.8E-03
7.4E-03
7.7E-03
7.5E-03
7.6E-03
7.4E-03
4.8E-03
6.4E-03
6.7E-03
5.9E-03
6.5E-03
7.8E-03
7.6E-03
6.9E-03
7.5E-03
7.8E-03
8.3E-03
7.9E-03
8.3E-03
7.8E-03
7.2E-03
l.OE-02
9.0E-03
7.7E-03
9.9E-03
1.1E-02
1.3E-02
1.1E-02
1.1E-02
1.1E-02
1.2E-02
1.2E-02
1.1E-02
l.OE-02
2.9E-03
4.6E-03
4.6E-03
4.2E-03
4.4E-03
4.8E-03
4.4E-03
3.9E-03
4.0E-03
4.4E-03
4.6E-03
4.5E-03
4.5E-03
4.5E-03
1.5E-03
3.0E-03
3.0E-03
2.9E-03
3.0E-03
3.3E-03
2.8E-03
2.5E-03
2.7E-03
3.0E-03
3.1E-03
3.2E-03
3.3E-03
3.2E-03
1.7E-03
3.3E-03
3.3E-03
3.2E-03
3.2E-03
3.6E-03
3.0E-03
2.7E-03
2.9E-03
3.2E-03
3.3E-03
3.3E-03
3.5E-03
3.5E-03
2.3E-03
3.8E-03
4.0E-03
3.6E-03
3.7E-03
4.0E-03
3.6E-03
3.1E-03
3.3E-03
3.7E-03
3.7E-03
3.8E-03
3.9E-03
3.8E-03
2.9E-03
4.6E-03
4.5E-03
4.1E-03
4.2E-03
4.7E-03
4.3E-03
3.7E-03
3.9E-03
4.2E-03
4.4E-03
4.4E-03
4.4E-03
4.4E-03
3.5E-03
5.3E-03
5.2E-03
4.7E-03
4.9E-03
5.4E-03
5.1E-03
4.4E-03
4.5E-03
5.0E-03
5.2E-03
5.0E-03
5.1E-03
4.9E-03
4.0E-03
6.0E-03
5.8E-03
5.2E-03
5.7E-03
6.4E-03
5.9E-03
5.4E-03
5.3E-03
5.7E-03
6.1E-03
5.7E-03
5.7E-03
5.6E-03
4.4E-03
6.4E-03
6.1E-03
5.7E-03
6.1E-03
7.0E-03
6.6E-03
6.0E-03
5.8E-03
6.2E-03
6.6E-03
6.4E-03
6.1E-03
6.2E-03
8.7E-03
9.6E-03
9.5E-03
7.4E-03
8.4E-03
9.4E-03
1.2E-02
9.6E-03
8.1E-03
9.0E-03
9.0E-03
9.6E-03
7.3E-03
8.3E-03
o
to
to
-------
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.2E-03
4.6E-03
4.8E-03
4.6E-03
4.9E-03
5.6E-03
5.8E-03
5.1E-03
5.6E-03
6.1E-03
6.3E-03
6.5E-03
6.6E-03
6.4E-03
1.7E-03
3.2E-03
3.2E-03
3.5E-03
3.6E-03
4.0E-03
4.2E-03
3.8E-03
4.0E-03
4.7E-03
4.7E-03
5.0E-03
5.3E-03
5.1E-03
2.0E-03
3.5E-03
3.7E-03
3.6E-03
3.8E-03
4.3E-03
4.4E-03
4.0E-03
4.4E-03
4.9E-03
5.1E-03
5.3E-03
5.6E-03
5.4E-03
2.5E-03
3.9E-03
4.1E-03
4.1E-03
4.2E-03
4.8E-03
4.9E-03
4.3E-03
4.9E-03
5.4E-03
5.5E-03
5.8E-03
6.0E-03
5.8E-03
3.1E-03
4.5E-03
4.7E-03
4.6E-03
4.7E-03
5.4E-03
5.6E-03
5.0E-03
5.4E-03
6.0E-03
6.2E-03
6.5E-03
6.6E-03
6.4E-03
3.8E-03
5.0E-03
5.4E-03
5.0E-03
5.4E-03
6.3E-03
6.4E-03
5.6E-03
6.2E-03
6.7E-03
6.9E-03
7.1E-03
7.2E-03
7.0E-03
4.4E-03
6.0E-03
6.1E-03
5.6E-03
6.0E-03
7.2E-03
7.1E-03
6.4E-03
7.0E-03
7.5E-03
7.6E-03
7.9E-03
7.8E-03
7.6E-03
4.9E-03
6.4E-03
6.7E-03
5.8E-03
6.6E-03
7.9E-03
7.8E-03
7.0E-03
7.4E-03
7.8E-03
8.1E-03
8.2E-03
8.3E-03
7.9E-03
7.1E-03
9.9E-03
9.1E-03
7.6E-03
9.5E-03
1.1E-02
1.3E-02
l.OE-02
l.OE-02
1.1E-02
l.OE-02
1.1E-02
9.9E-03
9.1E-03
3.0E-03
4.7E-03
4.7E-03
4.4E-03
4.6E-03
5.2E-03
4.8E-03
4.2E-03
4.3E-03
4.8E-03
5.0E-03
4.9E-03
4.9E-03
4.9E-03
1.6E-03
3.3E-03
3.3E-03
3.3E-03
3.4E-03
3.9E-03
3.3E-03
3.0E-03
3.2E-03
3.6E-03
3.8E-03
3.8E-03
4.1E-03
3.9E-03
1.8E-03
3.4E-03
3.5E-03
3.5E-03
3.7E-03
4.2E-03
3.6E-03
3.2E-03
3.4E-03
3.8E-03
4.0E-03
4.0E-03
4.1E-03
4.1E-03
2.3E-03
4.0E-03
4.2E-03
3.9E-03
4.0E-03
4.5E-03
4.0E-03
3.6E-03
3.8E-03
4.2E-03
4.4E-03
4.3E-03
4.4E-03
4.4E-03
3.0E-03
4.7E-03
4.7E-03
4.3E-03
4.5E-03
5.1E-03
4.7E-03
4.0E-03
4.2E-03
4.6E-03
4.9E-03
4.8E-03
4.9E-03
4.8E-03
3.6E-03
5.3E-03
5.3E-03
4.8E-03
5.1E-03
5.7E-03
5.3E-03
4.6E-03
4.8E-03
5.2E-03
5.4E-03
5.3E-03
5.4E-03
5.3E-03
4.1E-03
6.0E-03
5.8E-03
5.3E-03
5.9E-03
6.5E-03
6.0E-03
5.4E-03
5.3E-03
5.7E-03
6.1E-03
5.9E-03
5.9E-03
5.7E-03
4.4E-03
6.6E-03
6.2E-03
5.7E-03
6.3E-03
7.1E-03
6.6E-03
6.0E-03
5.8E-03
6.3E-03
6.4E-03
6.3E-03
6.1E-03
6.1E-03
9.6E-03
9.5E-03
9.4E-03
7.1E-03
8.3E-03
9.1E-03
1.2E-02
9.2E-03
7.7E-03
8.7E-03
8.3E-03
8.2E-03
7.6E-03
7.5E-03
o
to
oo
-------
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Light Intensity Activities (1.5 < METS < 3.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
7.9E-03
1.2E-02
1.2E-02
1.1E-02
1.2E-02
1.3E-02
1.3E-02
1.3E-02
1.4E-02
1.4E-02
1.5E-02
1.4E-02
1.4E-02
1.4E-02
4.1E-03
8.7E-03
8.5E-03
9.2E-03
9.0E-03
9.8E-03
l.OE-02
9.7E-03
1.1E-02
1.1E-02
1.1E-02
1.1E-02
1.1E-02
1.1E-02
5.1E-03
9.0E-03
9.1E-03
9.5E-03
9.3E-03
l.OE-02
1.1E-02
l.OE-02
1.1E-02
1.2E-02
1.2E-02
1.2E-02
1.2E-02
1.2E-02
6.2E-03
9.9E-03
l.OE-02
l.OE-02
l.OE-02
1.1E-02
1.2E-02
1.1E-02
1.2E-02
1.3E-02
1.3E-02
1.3E-02
1.3E-02
1.3E-02
7.9E-03
1.1E-02
1.1E-02
1.1E-02
1.1E-02
1.3E-02
1.3E-02
1.2E-02
1.3E-02
1.4E-02
1.4E-02
1.4E-02
1.4E-02
1.4E-02
9.6E-03
1.3E-02
1.3E-02
1.2E-02
1.3E-02
1.5E-02
1.5E-02
1.4E-02
1.5E-02
1.6E-02
1.6E-02
1.5E-02
1.5E-02
1.5E-02
1.1E-02
1.4E-02
1.5E-02
1.3E-02
1.5E-02
1.6E-02
1.7E-02
1.6E-02
1.6E-02
1.7E-02
1.8E-02
1.7E-02
1.6E-02
1.6E-02
1.2E-02
1.6E-02
1.5E-02
1.4E-02
1.6E-02
1.9E-02
1.8E-02
1.8E-02
1.8E-02
1.8E-02
1.9E-02
1.8E-02
1.7E-02
1.7E-02
1.5E-02
2.1E-02
1.9E-02
2.0E-02
2.2E-02
2.7E-02
2.9E-02
2.7E-02
2.5E-02
2.3E-02
2.5E-02
2.1E-02
2.0E-02
2.1E-02
7.3E-03
1.2E-02
1.2E-02
1.1E-02
1.1E-02
1.2E-02
1.1E-02
1.1E-02
1.1E-02
1.2E-02
1.2E-02
1.1E-02
1.1E-02
l.OE-02
3.8E-03
8.6E-03
8.7E-03
8.8E-03
8.5E-03
9.4E-03
8.3E-03
7.8E-03
8.8E-03
9.6E-03
9.8E-03
8.9E-03
8.8E-03
8.7E-03
4.6E-03
8.8E-03
9.4E-03
9.0E-03
9.0E-03
9.7E-03
8.7E-03
8.2E-03
9.3E-03
l.OE-02
l.OE-02
9.3E-03
9.2E-03
8.8E-03
5.7E-03
l.OE-02
l.OE-02
9.9E-03
9.8E-03
1.1E-02
9.6E-03
9.1E-03
l.OE-02
1.1E-02
1.1E-02
9.8E-03
9.9E-03
9.4E-03
7.2E-03
1.1E-02
1.2E-02
1.1E-02
1.1E-02
1.2E-02
1.1E-02
l.OE-02
1.1E-02
1.2E-02
1.2E-02
1.1E-02
1.1E-02
l.OE-02
8.7E-03
1.3E-02
1.3E-02
1.2E-02
1.2E-02
1.3E-02
1.2E-02
1.2E-02
1.2E-02
1.3E-02
1.3E-02
1.2E-02
1.2E-02
1.1E-02
9.8E-03
1.5E-02
1.6E-02
1.3E-02
1.3E-02
1.5E-02
1.4E-02
1.3E-02
1.3E-02
1.4E-02
1.4E-02
1.3E-02
1.3E-02
1.2E-02
1.1E-02
1.6E-02
1.6E-02
1.4E-02
1.5E-02
1.6E-02
1.5E-02
1.4E-02
1.4E-02
1.5E-02
1.5E-02
1.3E-02
1.3E-02
1.3E-02
1.7E-02
2.0E-02
2.4E-02
1.6E-02
2.2E-02
2.2E-02
2.1E-02
2.1E-02
1.7E-02
1.8E-02
1.8E-02
1.7E-02
1.8E-02
1.6E-02
o
to
-------
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Moderate Intensity Activities (3.0 < METS < 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
1.4E-02
2.1E-02
2.2E-02
2.1E-02
2.2E-02
2.6E-02
2.9E-02
2.9E-02
3.0E-02
3.2E-02
3.3E-02
3.0E-02
2.9E-02
2.9E-02
7.4E-03
1.4E-02
1.5E-02
1.6E-02
1.6E-02
1.9E-02
2.0E-02
2.0E-02
2.1E-02
2.3E-02
2.2E-02
2.2E-02
2.3E-02
2.2E-02
8.8E-03
1.6E-02
1.7E-02
1.7E-02
1.7E-02
2.0E-02
2.2E-02
2.1E-02
2.3E-02
2.4E-02
2.4E-02
2.4E-02
2.4E-02
2.3E-02
1.1E-02
1.8E-02
1.8E-02
1.9E-02
1.9E-02
2.3E-02
2.5E-02
2.4E-02
2.5E-02
2.7E-02
2.8E-02
2.6E-02
2.6E-02
2.5E-02
1.4E-02
2.1E-02
2.1E-02
2.1E-02
2.2E-02
2.5E-02
2.8E-02
2.8E-02
2.9E-02
3.0E-02
3.1E-02
2.9E-02
2.9E-02
2.8E-02
1.7E-02
2.4E-02
2.4E-02
2.3E-02
2.5E-02
2.9E-02
3.2E-02
3.3E-02
3.4E-02
3.5E-02
3.7E-02
3.2E-02
3.2E-02
3.1E-02
2.0E-02
2.7E-02
2.7E-02
2.6E-02
2.8E-02
3.4E-02
3.8E-02
3.9E-02
4.0E-02
4.0E-02
4.2E-02
3.7E-02
3.6E-02
3.3E-02
2.3E-02
2.9E-02
3.0E-02
2.7E-02
2.9E-02
3.7E-02
4.2E-02
4.3E-02
4.3E-02
4.5E-02
4.6E-02
4.0E-02
3.7E-02
3.6E-02
3.1E-02
4.0E-02
5.1E-02
3.5E-02
4.3E-02
5.5E-02
6.7E-02
7.2E-02
5.8E-02
6.3E-02
7.0E-02
5.2E-02
4.5E-02
4.1E-02
1.4E-02
2.1E-02
2.1E-02
2.0E-02
2.1E-02
2.4E-02
2.3E-02
2.3E-02
2.3E-02
2.4E-02
2.5E-02
2.1E-02
2.1E-02
2.1E-02
7.9E-03
1.6E-02
1.4E-02
1.5E-02
1.6E-02
1.8E-02
1.7E-02
1.6E-02
1.7E-02
1.8E-02
1.9E-02
1.7E-02
1.7E-02
1.7E-02
9.0E-03
1.6E-02
1.6E-02
1.6E-02
1.7E-02
1.9E-02
1.8E-02
1.7E-02
1.8E-02
1.9E-02
2.0E-02
1.8E-02
1.8E-02
1.8E-02
1.1E-02
1.8E-02
1.8E-02
1.8E-02
1.8E-02
2.1E-02
2.0E-02
1.9E-02
1.9E-02
2.1E-02
2.2E-02
1.9E-02
1.9E-02
1.9E-02
1.4E-02
2.0E-02
2.1E-02
2.0E-02
2.0E-02
2.3E-02
2.2E-02
2.2E-02
2.2E-02
2.4E-02
2.4E-02
2.1E-02
2.1E-02
2.1E-02
1.6E-02
2.4E-02
2.4E-02
2.2E-02
2.3E-02
2.5E-02
2.6E-02
2.6E-02
2.5E-02
2.7E-02
2.8E-02
2.3E-02
2.3E-02
2.3E-02
1.9E-02
2.7E-02
2.8E-02
2.4E-02
2.6E-02
2.8E-02
3.0E-02
3.0E-02
2.9E-02
3.1E-02
3.2E-02
2.6E-02
2.5E-02
2.5E-02
2.2E-02
2.9E-02
2.9E-02
2.6E-02
2.8E-02
3.1E-02
3.2E-02
3.3E-02
3.1E-02
3.4E-02
3.5E-02
2.7E-02
2.6E-02
2.6E-02
4.1E-02
3.5E-02
3.8E-02
3.3E-02
4.3E-02
4.2E-02
5.2E-02
5.4E-02
4.7E-02
5.1E-02
4.6E-02
3.5E-02
3.4E-02
2.9E-02
o
to
-------
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, while
performing activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min) - Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min) - Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
High Intensity (METS > 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
2.7E-02
4.0E-02
4.0E-02
3.9E-02
4.4E-02
5.1E-02
5.3E-02
5.4E-02
5.4E-02
5.7E-02
5.8E-02
5.4E-02
5.2E-02
5.3E-02
1.5E-02
2.8E-02
2.8E-02
2.9E-02
3.1E-02
3.4E-02
3.6E-02
3.4E-02
3.8E-02
3.8E-02
3.9E-02
3.6E-02
3.7E-02
3.5E-02
1.7E-02
3.2E-02
3.0E-02
3.1E-02
3.3E-02
3.7E-02
3.8E-02
3.8E-02
4.0E-02
4.2E-02
4.2E-02
4.0E-02
4.0E-02
3.9E-02
2.1E-02
3.5E-02
3.4E-02
3.4E-02
3.6E-02
4.2E-02
4.4E-02
4.5E-02
4.5E-02
4.8E-02
4.9E-02
4.5E-02
4.4E-02
4.6E-02
2.8E-02
4.0E-02
4.1E-02
3.8E-02
4.2E-02
4.9E-02
5.1E-02
5.2E-02
5.2E-02
5.5E-02
5.6E-02
5.2E-02
5.0E-02
5.1E-02
3.2E-02
4.4E-02
4.6E-02
4.3E-02
5.0E-02
5.7E-02
5.9E-02
6.2E-02
6.1E-02
6.4E-02
6.6E-02
6.1E-02
5.9E-02
6.1E-02
3.8E-02
5.2E-02
5.2E-02
4.9E-02
5.7E-02
6.6E-02
7.1E-02
7.2E-02
7.1E-02
7.6E-02
7.9E-02
7.2E-02
6.8E-02
7.0E-02
4.2E-02
5.6E-02
5.5E-02
5.2E-02
6.2E-02
7.3E-02
8.3E-02
8.2E-02
7.7E-02
8.4E-02
8.6E-02
7.5E-02
7.6E-02
7.7E-02
5.8E-02
6.1E-02
9.2E-02
6.6E-02
9.0E-02
1.2E-01
1.3E-01
1.1E-01
l.OE-01
1.1E-01
1.4E-01
l.OE-01
9.7E-02
9.7E-02
2.4E-02
3.6E-02
3.8E-02
3.5E-02
3.9E-02
4.7E-02
4.4E-02
4.6E-02
4.4E-02
4.7E-02
4.7E-02
4.0E-02
4.1E-02
4.2E-02
1.2E-02
2.6E-02
2.9E-02
2.7E-02
2.9E-02
3.1E-02
2.9E-02
2.9E-02
3.0E-02
3.1E-02
3.2E-02
2.8E-02
2.8E-02
2.8E-02
1.3E-02
2.6E-02
3.1E-02
2.8E-02
3.0E-02
3.4E-02
3.1E-02
3.1E-02
3.3E-02
3.4E-02
3.5E-02
3.1E-02
3.0E-02
3.0E-02
1.7E-02
3.0E-02
3.2E-02
3.0E-02
3.4E-02
3.9E-02
3.7E-02
3.7E-02
3.7E-02
3.8E-02
3.9E-02
3.5E-02
3.4E-02
3.4E-02
2.2E-02
3.6E-02
3.6E-02
3.3E-02
3.8E-02
4.5E-02
4.3E-02
4.3E-02
4.2E-02
4.6E-02
4.6E-02
3.9E-02
4.0E-02
4.1E-02
2.9E-02
4.2E-02
4.1E-02
3.8E-02
4.4E-02
5.3E-02
5.0E-02
5.2E-02
5.0E-02
5.4E-02
5.4E-02
4.5E-02
4.7E-02
4.8E-02
3.6E-02
4.7E-02
4.8E-02
4.3E-02
5.0E-02
6.1E-02
5.8E-02
6.2E-02
6.0E-02
6.2E-02
6.2E-02
5.1E-02
5.2E-02
5.6E-02
4.1E-02
4.9E-02
5.1E-02
4.5E-02
5.5E-02
6.6E-02
6.3E-02
6.9E-02
6.5E-02
6.7E-02
6.9E-02
5.6E-02
5.4E-02
5.8E-02
7.5E-02
7.7E-02
7.3E-02
5.7E-02
8.3E-02
l.OE-01
1.1E-01
1.1E-01
9.0E-02
8.9E-02
8.4E-02
7.1E-02
7.5E-02
7.2E-02
o
to
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
-------
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, while performing
activities within the specified activity category, by age and gender categories
Age Category
Daily Ventilation Rate (m3/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sleep or nap (Activity ID = 14500)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.8E-04
4.0E-04
3.3E-04
2.4E-04
1.5E-04
9.8E-05
7.1E-05
5.8E-05
6.1E-05
6.5E-05
6.6E-05
6.9E-05
7.5E-05
8.0E-05
2.8E-04
3.0E-04
2.5E-04
1.6E-04
l.OE-04
6.7E-05
4.7E-05
3.8E-05
3.8E-05
4.4E-05
4.5E-05
5.1E-05
5.5E-05
6.1E-05
3.0E-04
3.1E-04
2.6E-04
1.7E-04
1.1E-04
7.2E-05
5.2E-05
4.2E-05
4.3E-05
4.7E-05
4.9E-05
5.4E-05
5.8E-05
6.4E-05
3.4E-04
3.5E-04
2.9E-04
2.0E-04
1.3E-04
8.1E-05
6.1E-05
4.8E-05
5.0E-05
5.4E-05
5.5E-05
6.0E-05
6.4E-05
7.1E-05
3.8E-04
3.8E-04
3.3E-04
2.4E-04
1.5E-04
9.4E-05
6.9E-05
5.6E-05
6.0E-05
6.4E-05
6.4E-05
6.8E-05
7.3E-05
7.8E-05
4.3E-04
4.4E-04
3.6E-04
2.8E-04
1.7E-04
1.1E-04
8.0E-05
6.6E-05
7.0E-05
7.4E-05
7.6E-05
7.6E-05
8.3E-05
8.8E-05
4.6E-04
4.9E-04
4.0E-04
3.1E-04
2.0E-04
1.3E-04
9.0E-05
7.6E-05
8.0E-05
8.6E-05
8.6E-05
8.6E-05
9.3E-05
9.7E-05
5.0E-04
5.2E-04
4.4E-04
3.5E-04
2.2E-04
1.4E-04
9.8E-05
8.3E-05
8.6E-05
9.2E-05
9.3E-05
9.3E-05
9.9E-05
1.1E-04
6.7E-04
6.3E-04
5.4E-04
4.8E-04
3.0E-04
2.1E-04
1.5E-04
1.3E-04
1.3E-04
1.4E-04
1.4E-04
1.2E-04
1.3E-04
1.2E-04
3.9E-04
4.1E-04
3.4E-04
2.4E-04
1.5E-04
9.0E-05
6.9E-05
5.5E-05
5.6E-05
6.0E-05
6.1E-05
6.1E-05
6.6E-05
7.2E-05
2.8E-04
3.1E-04
2.6E-04
1.4E-04
8.9E-05
5.9E-05
4.4E-05
3.5E-05
3.4E-05
3.9E-05
3.9E-05
4.3E-05
4.7E-05
5.1E-05
3.0E-04
3.3E-04
2.7E-04
1.6E-04
9.7E-05
6.5E-05
4.7E-05
3.8E-05
3.7E-05
4.1E-05
4.2E-05
4.6E-05
5.1E-05
5.6E-05
3.4E-04
3.6E-04
2.9E-04
2.0E-04
1.2E-04
7.5E-05
5.7E-05
4.5E-05
4.5E-05
4.8E-05
5.0E-05
5.2E-05
5.6E-05
6.3E-05
3.9E-04
4.1E-04
3.3E-04
2.3E-04
1.5E-04
8.7E-05
6.7E-05
5.4E-05
5.4E-05
5.7E-05
5.9E-05
5.9E-05
6.4E-05
7.0E-05
4.3E-04
4.6E-04
3.9E-04
2.8E-04
1.8E-04
l.OE-04
8.0E-05
6.5E-05
6.5E-05
7.0E-05
7.1E-05
6.7E-05
7.4E-05
7.9E-05
4.8E-04
5.2E-04
4.3E-04
3.2E-04
2.1E-04
1.2E-04
9.3E-05
7.4E-05
7.6E-05
8.4E-05
8.3E-05
7.6E-05
8.4E-05
9.1E-05
5.2E-04
5.4E-04
4.5E-04
3.5E-04
2.3E-04
1.3E-04
l.OE-04
8.2E-05
8.2E-05
9.0E-05
8.8E-05
8.1E-05
9.0E-05
9.6E-05
7.4E-04
6.6E-04
4.9E-04
5.2E-04
3.0E-04
1.8E-04
1.5E-04
9.8E-05
1.1E-04
1.1E-04
1.3E-04
l.OE-04
1.3E-04
1.2E-04
o
to
-------
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Sedentary & Passive Activities (METS < 1.5 — Includes Sleep or Nap)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
4.0E-04
4.1E-04
3.4E-04
2.5E-04
1.6E-04
l.OE-04
7.7E-05
6.2E-05
6.6E-05
7.1E-05
7.2E-05
7.6E-05
8.2E-05
8.6E-05
3.0E-04
3.2E-04
2.7E-04
1.8E-04
1.1E-04
7.7E-05
5.5E-05
4.7E-05
4.6E-05
5.4E-05
5.5E-05
6.1E-05
6.7E-05
7.1E-05
3.2E-04
3.3E-04
2.9E-04
1.9E-04
1.2E-04
8.0E-05
6.0E-05
4.9E-05
5.0E-05
5.7E-05
5.8E-05
6.4E-05
7.0E-05
7.5E-05
3.5E-04
3.6E-04
3.1E-04
2.1E-04
1.4E-04
8.8E-05
6.8E-05
5.5E-05
5.7E-05
6.2E-05
6.3E-05
6.9E-05
7.5E-05
8.0E-05
3.9E-04
4.0E-04
3.4E-04
2.5E-04
1.6E-04
l.OE-04
7.6E-05
6.1E-05
6.5E-05
7.0E-05
7.1E-05
7.5E-05
8.1E-05
8.6E-05
4.4E-04
4.5E-04
3.7E-04
2.9E-04
1.8E-04
1.2E-04
8.5E-05
6.9E-05
7.4E-05
7.8E-05
7.9E-05
8.1E-05
8.8E-05
9.2E-05
4.7E-04
4.9E-04
4.1E-04
3.3E-04
2.1E-04
1.3E-04
9.5E-05
7.7E-05
8.2E-05
8.6E-05
8.8E-05
8.9E-05
9.4E-05
9.9E-05
5.0E-04
5.2E-04
4.5E-04
3.5E-04
2.2E-04
1.4E-04
l.OE-04
8.2E-05
8.6E-05
9.1E-05
9.2E-05
9.4E-05
9.8E-05
1.1E-04
6.6E-04
6.2E-04
5.1E-04
4.5E-04
2.9E-04
2.0E-04
1.3E-04
1.2E-04
1.2E-04
1.3E-04
1.4E-04
1.1E-04
1.1E-04
1.1E-04
4.0E-04
4.3E-04
3.6E-04
2.5E-04
1.6E-04
9.7E-05
7.5E-05
6.0E-05
6.0E-05
6.5E-05
6.7E-05
6.6E-05
7.2E-05
7.8E-05
3.0E-04
3.4E-04
2.9E-04
1.6E-04
9.9E-05
7.1E-05
5.3E-05
4.3E-05
4.0E-05
4.4E-05
4.6E-05
5.2E-05
5.5E-05
6.3E-05
3.2E-04
3.5E-04
3.0E-04
1.8E-04
1.1E-04
7.5E-05
5.7E-05
4.5E-05
4.2E-05
4.8E-05
5.1E-05
5.4E-05
6.0E-05
6.5E-05
3.5E-04
3.8E-04
3.2E-04
2.1E-04
1.3E-04
8.3E-05
6.3E-05
5.1E-05
5.1E-05
5.5E-05
5.7E-05
5.9E-05
6.5E-05
7.0E-05
4.0E-04
4.2E-04
3.5E-04
2.5E-04
1.6E-04
9.5E-05
7.4E-05
5.9E-05
5.9E-05
6.3E-05
6.5E-05
6.6E-05
7.1E-05
7.7E-05
4.5E-04
4.7E-04
3.9E-04
2.8E-04
1.9E-04
1.1E-04
8.5E-05
6.7E-05
6.9E-05
7.3E-05
7.6E-05
7.2E-05
7.8E-05
8.6E-05
4.8E-04
5.1E-04
4.2E-04
3.3E-04
2.1E-04
1.2E-04
9.6E-05
7.5E-05
7.8E-05
8.3E-05
8.3E-05
7.8E-05
8.8E-05
9.3E-05
5.2E-04
5.4E-04
4.4E-04
3.6E-04
2.3E-04
1.3E-04
l.OE-04
8.0E-05
8.3E-05
9.1E-05
9.0E-05
8.4E-05
9.2E-05
9.6E-05
7.2E-04
6.4E-04
4.8E-04
4.9E-04
2.9E-04
1.7E-04
1.4E-04
9.9E-05
1.1E-04
1.1E-04
1.2E-04
l.OE-04
1.5E-04
1.1E-04
o
to
oo
-------
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Light Intensity Activities (1.5 < METS < 3.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
9.9E-04
l.OE-03
8.4E-04
6.3E-04
3.8E-04
2.5E-04
1.8E-04
1.6E-04
1.6E-04
1.7E-04
1.7E-04
1.6E-04
1.7E-04
1.8E-04
7.9E-04
8.4E-04
6.8E-04
4.4E-04
2.7E-04
1.8E-04
1.4E-04
1.2E-04
1.2E-04
1.3E-04
1.3E-04
1.4E-04
1.4E-04
1.5E-04
8.3E-04
8.6E-04
7.2E-04
4.8E-04
2.9E-04
1.9E-04
1.4E-04
1.3E-04
1.3E-04
1.3E-04
1.3E-04
1.4E-04
1.5E-04
1.6E-04
9.0E-04
9.2E-04
7.6E-04
5.4E-04
3.2E-04
2.1E-04
1.6E-04
1.4E-04
1.4E-04
1.5E-04
1.5E-04
1.5E-04
1.6E-04
1.7E-04
9.7E-04
l.OE-03
8.3E-04
6.3E-04
3.8E-04
2.4E-04
1.8E-04
1.5E-04
1.6E-04
1.6E-04
1.6E-04
1.6E-04
1.7E-04
1.8E-04
1.1E-03
1.1E-03
8.9E-04
7.1E-04
4.4E-04
2.8E-04
2.0E-04
1.7E-04
1.8E-04
1.8E-04
1.8E-04
1.8E-04
1.8E-04
2.0E-04
1.2E-03
1.2E-03
l.OE-03
7.9E-04
4.9E-04
3.1E-04
2.2E-04
1.9E-04
2.0E-04
2.0E-04
2.0E-04
1.9E-04
1.9E-04
2.1E-04
1.2E-03
1.3E-03
l.OE-03
8.7E-04
5.3E-04
3.3E-04
2.3E-04
2.1E-04
2.1E-04
2.1E-04
2.2E-04
2.0E-04
2.0E-04
2.2E-04
1.4E-03
1.5E-03
1.2E-03
1.1E-03
7.1E-04
4.4E-04
3.3E-04
2.9E-04
2.8E-04
3.3E-04
2.9E-04
2.7E-04
2.6E-04
2.5E-04
9.8E-04
l.OE-03
9.0E-04
6.2E-04
3.8E-04
2.3E-04
1.7E-04
1.5E-04
1.5E-04
1.6E-04
1.6E-04
1.5E-04
1.6E-04
1.7E-04
7.9E-04
8.5E-04
7.3E-04
4.5E-04
2.5E-04
1.6E-04
1.3E-04
1.2E-04
1.1E-04
1.1E-04
1.2E-04
1.2E-04
1.2E-04
1.3E-04
8.2E-04
8.7E-04
7.6E-04
4.8E-04
2.7E-04
1.7E-04
1.4E-04
1.2E-04
1.2E-04
1.2E-04
1.3E-04
1.2E-04
1.3E-04
1.4E-04
8.8E-04
9.5E-04
8.2E-04
5.4E-04
3.1E-04
2.0E-04
1.5E-04
1.3E-04
1.3E-04
1.4E-04
1.4E-04
1.3E-04
1.4E-04
1.5E-04
9.6E-04
l.OE-03
8.9E-04
6.0E-04
3.8E-04
2.2E-04
1.7E-04
1.5E-04
1.5E-04
1.6E-04
1.6E-04
1.4E-04
1.6E-04
1.6E-04
l.OE-03
1.1E-03
9.6E-04
7.0E-04
4.4E-04
2.5E-04
1.9E-04
1.6E-04
1.8E-04
1.8E-04
1.8E-04
1.6E-04
1.7E-04
1.8E-04
1.2E-03
1.2E-03
l.OE-03
7.8E-04
5.0E-04
2.8E-04
2.1E-04
1.8E-04
1.9E-04
2.0E-04
2.0E-04
1.7E-04
1.9E-04
2.0E-04
1.2E-03
1.3E-03
1.1E-03
8.3E-04
5.4E-04
3.1E-04
2.2E-04
1.9E-04
2.0E-04
2.2E-04
2.1E-04
1.8E-04
2.0E-04
2.1E-04
1.7E-03
1.6E-03
1.3E-03
l.OE-03
7.1E-04
4.0E-04
2.9E-04
2.3E-04
2.7E-04
2.8E-04
2.6E-04
2.4E-04
2.8E-04
2.3E-04
o
to
VO
-------
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (m3/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS < 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
1.8E-03
1.9E-03
1.5E-03
1.2E-03
7.4E-04
4.9E-04
3.9E-04
3.6E-04
3.6E-04
3.7E-04
3.8E-04
3.4E-04
3.6E-04
3.8E-04
1.4E-03
1.4E-03
1.2E-03
8.0E-04
5.0E-04
3.6E-04
2.8E-04
2.4E-04
2.4E-04
2.5E-04
2.6E-04
2.7E-04
2.9E-04
3.1E-04
1.5E-03
1.5E-03
1.3E-03
8.8E-04
5.5E-04
3.8E-04
3.0E-04
2.6E-04
2.6E-04
2.7E-04
2.8E-04
2.8E-04
3.1E-04
3.2E-04
1.6E-03
1.7E-03
1.4E-03
l.OE-03
6.2E-04
4.2E-04
3.3E-04
3.0E-04
3.0E-04
3.1E-04
3.1E-04
3.1E-04
3.3E-04
3.5E-04
1.8E-03
1.8E-03
1.5E-03
1.1E-03
7.1E-04
4.7E-04
3.8E-04
3.4E-04
3.4E-04
3.5E-04
3.7E-04
3.4E-04
3.6E-04
3.8E-04
1.9E-03
2.0E-03
1.7E-03
1.3E-03
8.3E-04
5.5E-04
4.3E-04
4.0E-04
4.0E-04
4.1E-04
4.3E-04
3.7E-04
3.9E-04
4.2E-04
2.2E-03
2.3E-03
1.8E-03
1.6E-03
9.6E-04
6.4E-04
4.9E-04
4.7E-04
4.7E-04
4.7E-04
4.8E-04
4.0E-04
4.2E-04
4.5E-04
2.3E-03
2.5E-03
2.0E-03
1.7E-03
l.OE-03
6.8E-04
5.2E-04
5.1E-04
5.2E-04
5.2E-04
5.5E-04
4.2E-04
4.4E-04
4.7E-04
3.0E-03
3.2E-03
2.3E-03
2.1E-03
1.4E-03
1.1E-03
7.1E-04
8.2E-04
7.6E-04
7.2E-04
7.6E-04
5.7E-04
5.5E-04
5.3E-04
1.9E-03
1.9E-03
1.6E-03
1.1E-03
7.2E-04
4.4E-04
3.6E-04
3.3E-04
3.2E-04
3.3E-04
3.4E-04
2.9E-04
3.1E-04
3.3E-04
1.5E-03
1.5E-03
1.3E-03
7.9E-04
4.6E-04
3.2E-04
2.7E-04
2.4E-04
2.1E-04
2.2E-04
2.4E-04
2.2E-04
2.4E-04
2.5E-04
1.5E-03
1.6E-03
1.3E-03
8.5E-04
5.1E-04
3.4E-04
2.8E-04
2.5E-04
2.3E-04
2.4E-04
2.5E-04
2.4E-04
2.5E-04
2.7E-04
1.7E-03
1.7E-03
1.4E-03
9.6E-04
6.0E-04
3.8E-04
3.1E-04
2.8E-04
2.7E-04
2.8E-04
2.8E-04
2.6E-04
2.7E-04
3.0E-04
1.9E-03
1.9E-03
1.6E-03
1.1E-03
7.1E-04
4.3E-04
3.5E-04
3.2E-04
3.0E-04
3.2E-04
3.3E-04
2.8E-04
3.0E-04
3.3E-04
2.0E-03
2.0E-03
1.7E-03
1.3E-03
8.4E-04
4.9E-04
4.1E-04
3.6E-04
3.5E-04
3.8E-04
3.8E-04
3.2E-04
3.4E-04
3.7E-04
2.3E-03
2.2E-03
1.9E-03
1.5E-03
9.4E-04
5.5E-04
4.6E-04
4.2E-04
4.1E-04
4.4E-04
4.4E-04
3.5E-04
3.8E-04
4.0E-04
2.4E-03
2.4E-03
2.0E-03
1.6E-03
l.OE-03
6.1E-04
4.9E-04
4.5E-04
4.6E-04
4.9E-04
4.9E-04
3.7E-04
4.1E-04
4.2E-04
2.8E-03
3.2E-03
2.6E-03
1.9E-03
1.4E-03
9.9E-04
6.5E-04
6.6E-04
7.1E-04
6.2E-04
6.4E-04
5.1E-04
6.8E-04
5.2E-04
o
OJ
o
-------
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, while performing
activities within the specified activity category, by age and gender categories (continued)
Age Category
Daily Ventilation Rate (m3/min-kg) - Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
Daily Ventilation Rate (m3/min-kg) - Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Max
High Intensity (METS > 6.0)
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.5E-03
3.5E-03
2.9E-03
2.2E-03
1.4E-03
9.5E-04
7.1E-04
6.6E-04
6.4E-04
6.6E-04
6.8E-04
6.2E-04
6.5E-04
7.2E-04
2.7E-03
2.5E-03
2.2E-03
1.5E-03
9.4E-04
6.3E-04
4.8E-04
4.5E-04
4.4E-04
4.4E-04
4.5E-04
4.4E-04
4.7E-04
5.0E-04
2.9E-03
2.9E-03
2.3E-03
1.7E-03
l.OE-03
7.0E-04
5.3E-04
4.7E-04
4.7E-04
4.8E-04
4.8E-04
4.7E-04
5.0E-04
5.4E-04
3.1E-03
3.2E-03
2.6E-03
1.8E-03
1.2E-03
7.9E-04
6.0E-04
5.4E-04
5.3E-04
5.5E-04
5.5E-04
5.3E-04
5.5E-04
6.0E-04
3.5E-03
3.6E-03
2.9E-03
2.1E-03
1.4E-03
9.1E-04
6.9E-04
6.4E-04
6.2E-04
6.3E-04
6.4E-04
6.1E-04
6.3E-04
7.0E-04
3.8E-03
3.9E-03
3.2E-03
2.5E-03
1.6E-03
1.1E-03
8.0E-04
7.5E-04
7.3E-04
7.4E-04
7.7E-04
7.0E-04
7.2E-04
8.0E-04
4.1E-03
4.1E-03
3.4E-03
2.7E-03
1.8E-03
1.3E-03
9.2E-04
8.5E-04
8.5E-04
8.6E-04
9.1E-04
7.9E-04
8.5E-04
9.4E-04
4.3E-03
4.3E-03
3.5E-03
3.0E-03
1.9E-03
1.4E-03
l.OE-03
9.7E-04
9.3E-04
9.4E-04
l.OE-03
8.5E-04
9.1E-04
9.9E-04
5.1E-03
4.9E-03
4.3E-03
3.6E-03
2.7E-03
2.0E-03
1.9E-03
1.3E-03
1.2E-03
1.8E-03
1.3E-03
1.1E-03
l.OE-03
1.4E-03
3.3E-03
3.4E-03
2.8E-03
2.0E-03
1.3E-03
8.8E-04
7.0E-04
6.5E-04
6.1E-04
6.5E-04
6.3E-04
5.4E-04
5.9E-04
6.7E-04
2.5E-03
2.6E-03
2.2E-03
1.4E-03
8.9E-04
5.9E-04
4.5E-04
4.2E-04
3.8E-04
3.8E-04
3.9E-04
3.6E-04
3.9E-04
4.5E-04
2.6E-03
2.7E-03
2.3E-03
1.5E-03
9.7E-04
6.3E-04
5.0E-04
4.6E-04
4.2E-04
4.4E-04
4.3E-04
4.0E-04
4.4E-04
4.8E-04
2.9E-03
3.0E-03
2.5E-03
1.7E-03
1.1E-03
7.1E-04
5.7E-04
5.5E-04
5.0E-04
5.2E-04
5.1E-04
4.5E-04
5.0E-04
5.4E-04
3.2E-03
3.2E-03
2.8E-03
1.9E-03
1.3E-03
8.5E-04
6.9E-04
6.3E-04
5.9E-04
6.4E-04
6.1E-04
5.3E-04
5.8E-04
6.3E-04
3.6E-03
3.7E-03
3.1E-03
2.2E-03
1.5E-03
l.OE-03
7.9E-04
7.3E-04
7.1E-04
7.6E-04
7.5E-04
6.1E-04
6.8E-04
7.7E-04
4.0E-03
4.2E-03
3.4E-03
2.5E-03
1.7E-03
1.2E-03
9.2E-04
8.8E-04
8.3E-04
8.8E-04
8.5E-04
7.2E-04
7.8E-04
9.3E-04
4.1E-03
4.9E-03
3.5E-03
3.0E-03
1.8E-03
1.3E-03
l.OE-03
9.4E-04
9.0E-04
9.5E-04
9.3E-04
8.0E-04
8.3E-04
9.7E-04
5.0E-03
4.9E-03
3.9E-03
3.2E-03
2.2E-03
2.0E-03
1.5E-03
1.3E-03
1.5E-03
1.6E-03
1.4E-03
1.1E-03
1.3E-03
1.2E-03
o
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table.
Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
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APPENDIX D
RESPONSE PREPARED BY S. GRAHAM (U.S. EPA)
TO PEER-REVIEW COMMENTS ON APPENDIX A
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TABLE OF CONTENTS
1. Purpose D-4
2. APEX Model Description D-4
3. Ventilation Algorithm Evaluation D-5
4. Comparison of APEX Estimates with Brochu et al. (2006a, b) D-6
5. Results of Comparison of APEX Estimates with Brochu et al. (2006a, b) D-6
6. Concluisons of Brochu et al. (2006a, b) Comparison D-ll
7. Comparison of APEX Estimates with Arcus-Arth and Blaisdell (2007) D-12
8. Results of Compariso of APEX Estimates with Arcus-Arth and Brochu et al.
(2006a, b) D-13
9. Issues D-13
10. Conclusions of Arcus-Arth and Blaisdell (2007) Comparison D-13
11. References to Appendix D D-15
D-2
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LIST OF FIGURES
D-l. Body mass comparison D-7
D-2. Mean resting energy expenditure comparison D-8
D-3. Comparison of body mass normalized mean basal energy expenditure (BEE-kg) D-8
D-4. Comparison of mean total daily energy expenditure (EE) D-9
D-5. Comparison of mean physical activity level (PAL) D-10
D-6. Comparison of mean daily ventilation rate (VE) D-10
D-7. Comparison of body mass normalized mean daily ventilation rate (VE-kg) D-ll
D-8. Comparison of body mass normalized mean ventilation rates (VE-kg) correcting
Brochu et al. (2006a) results with child appropriate VQ estimates D-l2
D-9. Comparison of mean daily ventilation rate (VE) in children D-14
D-10. Comparison of body mass normalized mean ventilation rates (Vs-kg) in children D-14
D-3
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1. Purpose
This appendix addresses comments provided by external peer reviewers on Appendix A
of this document. In particular, clarification of how U.S. EPA's Air Pollutants Exposure
(APEX) model can be used to estimate ventilation rates is provided, and model ventilation
estimates are compared with recently published estimates of ventilation rate.
2. APEX Model Description
U.S. EPA (2008a, b) provides more details on the approach to estimating ventilation rates
using APEX. To summarize, the model is designed to estimate exposure to air pollutants and
inhalation dose, accounting for the expected variability in both human behavior and physiology
through consideration of important influential characteristics. One noteworthy feature of the
model is its ability to estimate a time-series of exposure and dose for simulated individuals by
correlating the time-series of microenvironmental concentration with the time series of
activity-specific ventilation rates that could be as short as 1 minute in duration.
Briefly, any number of individuals can be simulated in a model run to estimate exposure
and exposure-related metrics (e.g., ventilation rate, body mass). Personal attributes of a
simulated individual are first estimated (e.g., body mass, basal energy expenditure [BEE]) using
either measurement data distributions (e.g., body mass distributions from CDC) or equations
derived from measurement data and reported in the peer-reviewed literature (e.g., EE from
Schoefield, 1985). Human time-location-activity diaries in U.S. EPA's Consolidated Human
Activity Database (CHAD) (McCurdy et al., 2000; U.S. EPA, 2002) are used to generate activity
profiles for the simulated individuals for periods as short as one day upwards to a year. Each
activity has a distribution of Metabolic Equivalents of work (METS) (e.g., point, lognormal,
normal) that is sampled to estimate activity-specific METS for the individual performing the
given activity. That combined with body mass dependent EE and a conversion for energy
expenditure to oxygen consumption (e.g., H'm Brochu et al., 2006a) result in activity-specific
VC>2 (typically in L-CVmin). These data are used as inputs to the regression equations reported
in Appendix A (along with age, gender, and body mass) to estimate ventilation rate (VE, L/min).
There are greater details in the approach such as those regarding adjusting the MET time-series
to account for fatigue therefore regulating the duration of vigorous activities and excess
D-4
-------
postexercise oxygen consumption whereby oxygen consumption is increased to repay oxygen
debt that may have incurred from vigorous activities. See U.S. EPA (2008a, b) for more details.
3. Ventilation Algorithm Evaluation
Two recent publications were identified as potentially useful in the evaluation of the
ventilation algorithm. Brochu et al. (2006a) presents data for ventilation rates, body mass, and
energy expenditure for comparison, derived from data reported in studies that used the
doubly-labeled water (DLW) method to estimate energy expenditure. Important reported subject
characteristics include both genders, ages from 1 month to 96 years, and disaggregated by body
mass (overweight/obese, normal body mass). For example, overweight individuals were defined
as having body mass indexes (BMI) above the 97th percentiles for infants and toddlers <3 years,
>85th percentile for children under 20, and BMI >25 for adults above 19 years old. Estimates of
energy expended were combined with a fixed oxygen uptake factor (H = 0.21) and a fixed
ventilatory equivalent (VQ = "VVVO2 = 27), while also accounting for stored daily energy cost
for growth up to age 24, although this cost is generally only about 2% of total energy expended
for ages above 1 year old. The DLW measurement generally extended from 7-21 days, resulting
in time-averaged metrics that provide reasonable estimates for the mean (e.g., mean daily
ventilation rate), but are not useful for estimating variability in an individuals ventilation rate (or
other parameter) over shorter time periods. Reported data are averages for several age groupings
(e.g., 1~<2, 2-<5, 5-<7, etc.) with derived percentiles assuming a normal distribution.
Arcus-Arth and Blaisdell (2007) provide ventilation estimates for children <19 years of age
using energy intake (El, or calories consumed) and body mass data provided from the U.S.
Department of Agriculture's (USDA) Continuing Survey of Food Intake for Individuals (CSFII),
adjusted for underreporting of food intakes (x 1.2 for children >8 years) and stored energy in
infants alone (<1 years old). Two-day daily average Els were combined with H (i.e., 0.22 for
infants, 0.21 for non infants) and VQ (i.e., 33.5 for children 0-8, 30.6 for boys 9-18, 31.5 for
girls 9-18 years old). Again, time-averaging of the data provide reasonable estimates of the
mean, but offer no variability in ventilation estimates for time periods of shorter duration.
Furthermore, data for both genders are combined and reported by age, with gender differences
reported only for aggregated age groups (males and females, 9-18 years old). Additional
gender-specific age groupings are reported for comparison with other literature values, however
D-5
-------
are limited in number to be of great use here. An APEX model simulation was performed to
generate estimates of relevant parameters for comparison with some of the estimates reported in
these two publications.
4. Comparison of APEX Estimates with Brochu et al. (2006a, b)
A 14-day simulation was performed (i.e., the median of 7-21 days for the DLW data) for
comparison with the time averaged Brochu et al. (2006a) data. Twenty-five thousand persons
were simulated by APEX to generate a reasonable number of persons within each year of age
and other potential categorical variables (e.g., 100-200, although some age groups have only
1-5 persons). It is important when comparing the two types of data to have them as similar as
possible, particularly since age and body mass are important influential variables. Body mass
and height estimates were used to calculate BMIs for use in identifying normal versus
overweight individuals as was done for the Brochu et al. (2006a) data. Percentiles for BMI of
children ages 2-20 were obtained from CDC (2000). Children <2 years old were classified as
overweight based on whether or not they exceeded the 97th percentile for 2-year olds of the same
gender (there were no data reported by CDC for ages less than 2 years), while children aged
3-20 years were classified as overweight if they exceeded the 85th percentile. Adults were
classified as overweight if their BMI > 25.0. A total of 9,613 normal-weight individuals were
simulated by APEX and used for the following analysis. Multi-day parameter estimates (e.g.,
ventilation rates, physical activity level, or PAL) were time averaged across the 14-day
simulation period, yielding a mean daily value for each person to best represent the DLW time
averaging done in Brochu et al. (2006a).
5. Results of Comparison of APEX Estimates with Brochu et al. (2008a, b)
Figure D-l compares the mean body mass (BM, kg) estimates of APEX simulated
individuals by one year age intervals with the mean body mass reported in Table 2, page 684 of
Brochu et al. (2006a) for several age groupings of normal-weight individuals. There are no
apparent differences in the population reported by Brochu et al. (2006a) and the APEX simulated
population regarding body mass.
D-6
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80
70
60
350 +
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• APEX Females
o Brochu et al. (2006) Females
A APEX Males
a Brochu et al. (2006) Males
10 20 30 40 50 60
Age (years)
70
80
90
100
Figure D-l. Body mass comparison.
Figure D-2 compares the mean basal energy expenditure (BEE, Kcal/day) estimates of
APEX simulated individuals by one year age intervals with the mean daily BEE reported in
Table Web-3, page 3 of Brochu et al. (2006b) for several age groupings of normal-weight
individuals. Little differences exist in the population reported by Brochu et al. (2006b) and the
APEX simulated population regarding BEE.
Figure D-3 compares the mean body mass normalized basal energy expenditure (BEE-kg,
Kcal/day-kg) estimates of APEX simulated individuals by one year intervals and those reported
in Table Web-3m, page 3 of Brochu et al. (2006b) for several age groupings of normal-weight
individuals. Little difference exists in the population reported by Brochu et al. (2006b) and the
APEX simulated population regarding BEE-kg.
Figure D-4 compares the mean daily energy expenditure (EE, Kcal/day) estimates of
APEX simulated individuals by one year age intervals and those reported in Table Web-3, page 3
of Brochu et al. (2006b) for several age groupings of normal-weight individuals. Little
difference exists in the population reported by Brochu et al. (2006b) and the APEX simulated
population regarding EE for ages less than 15, APEX estimates of EE are higher by about 10%
for both genders at greater ages.
D-7
-------
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1800
1600
8 1400
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A APEX Males
A Brochu et al. (2006) Males
30
40
50
Age (years)
60
70
80
90
100
Figure D-2. Mean resting energy expenditure comparison.
70
eo
s 50
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-------
4000
3500
1 3000
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3 2500
2000
w 1500
1000
500
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• APEX Females
o Brochu et al. (2006) Females
A APEX Males
a Brochu et al. (2006) Males
10 20 30 40 50 60
Age (years)
70
80
90
100
Figure D-4. Comparison of mean total daily energy expenditure (EE).
Figure D-5 compares the mean physical activity levels (PAL, unitless) estimated by
APEX simulated individuals by one year age intervals and those reported in Table Web-3, page 3
of Brochu et al. (2006b) for several age groupings of normal-weight individuals. Little
difference exists in the population reported by Brochu et al. (2006b) and the APEX simulated
population regarding PAL for ages between 10 and 20. APEX estimates of PAL are higher for
both genders at other ages when compared with similar age PAL estimates from Brochu et al.
(2006a, b), most notably at ages above 64.
Figure D-6 compares the mean daily ventilation rate (VE, m3/day) estimates of APEX
simulated individuals by one year age intervals with those reported in Table 2, page 684 of
Brochu et al. (2006a) for several age groupings of normal-weight individuals. The results were
mixed for several ages and both genders when comparing those reported by Brochu et al. (2006a)
and the APEX simulated population. APEX estimations were higher for children (age <11) and
the elderly (age >64), while Brochu et al. (2006a) estimates were generally higher for males age
12-40 and females age 18-40. For the young children, this may be a function of the fixed VQ
used by Brochu et al. (2006 a, b) (see below).
D-9
-------
2.5
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• APEX Females
o Brochu et al. (2006) Females
A APEX Males
a Brochu et al. (2006) Males
0.0
0 10 20 30
40 50 60
Age (years)
70 80 90 100
Figure D-5. Comparison of mean physical activity level (PAL).
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20
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10 20 30
40 50 60
Age (years)
70 80 90 100
Figure D-6. Comparison of mean daily ventilation rate (VE).
D-10
-------
Figure D-7 compares the body mass normalized mean daily ventilation rate (Vs-kg,
m3/day-kg) estimates of APEX simulated individuals by one year age intervals with those
reported in Table 2, page 684 of Brochu et al. (2006a) for several age groupings of
normal-weight individuals. The two largest differences appear for children of both genders less
than age 10 (Brochu et al., [2006a] estimates are systematically lower than APEX estimates) and
ages between 16-33 (APEX estimates are lower than Brochu et al., [2006a]). Body mass
normalized ventilation rates also appear to be slightly higher using APEX for ages above 64,
both genders.
• APEX Females
o Brochu et al. (2006) Females
AAPEXMAIes
A Brochu et al. (2006) Males
*|^^
20
30
40 50 60
Age (years)
70
80
90
100
Figure D-7. Comparison of body mass normalized mean daily ventilation
rate V
6. Issues
One principal issue identified as responsible for some of the noted differences in
ventilation estimates is in the VQ used by Brochu et al. (2006a). A single value of 27 was used
in estimating ventilation rates for both children and adults, however it is widely recognized that
while a VQ of 27 may be a reasonable approximation for estimating mean ventilation rates of
adults, it is not appropriate for use in estimating mean ventilation rates in children. With this in
D-ll
-------
mind, the Brochu et al. (2006a) ventilation estimates were modified here using the VQ estimates
offered by Arcus-Arth and Blaisdell (2007). Figure D-8 illustrates the comparison of APEX
body mass normalized mean daily ventilation rates with that of Brochu et al. (2006a) corrected
ventilation estimates. The body mass normalized ventilation estimates for children are more
similar to those generated by APEX when correcting the Brochu et al. (2006a) VQ parameter.
• APEX Females
o Brochu et al. (2006) Females - Modified VQ
AAPEXMAIes
A Brochu et al. (2006) Males - Modified VQ
0.0
20
30
40 50 60
Age (years)
70 80 90 100
Figure D-8. Comparison of body mass normalized mean ventilation rates
(Vfi-kg) correcting Brochu et al. (2006a) results with child appropriate
VQ estimates.
7. Conclusions of Brochu et al. (2006a, b) Comparison
Mean estimates for all of the physiological parameters generated by APEX including
ventilation rates are reasonably correlated with independent measures from the Brochu et al.
(2006a, b) estimates, particularly when correcting the Brochu et al. (2006a) ventilation estimates
for children using a more appropriate estimate of VQ for children.
D-12
-------
8. Comparison of APEX Estimates with Arcus-Arth and Blaisdell (2007)
A 2-day model simulation was performed using APEX to generate ventilation estimates
(total ventilation and body mass normalized) for children to compare with results of Arcus-Arth
and Blaisdell (2007). Table II (page 102) of Arcus-Arth and Blaisdell (2007) provided daily
ventilation estimates, while Table III (page 103) provided body mass normalized ventilation
rates. APEX ventilation estimates were time-averaged to generate mean daily values, and since
the data reported in Arcus-Arth and Blaisdell (2007) were not separated by gender (outside of
broad age categories), the APEX estimates were also combined to provide a mean estimate for
each year of age (0-18). Body mass was also not used as a categorical variable in Arcus-Arth
and Blaisdell (2007), therefore all APEX simulated individuals were used, regardless of whether
they could be classified as overweight or of normal weight. In addition, data were obtained from
Tables 3 and 4 of Brochu et al. (2006a) for a few age categories and considering both estimates
for normal and overweight individuals (there were no combined data available). The Brochu et
al. (2006 a, b) results have been corrected for VQ as noted above using VQ estimates of
Arcus-Arth and Blaisdell (2007).
9. Results of Comparison of APEX Estimates with Arcus-Arth and Brochu et al.
(2006a, b)
Figures D-9 and D-10 illustrate ventilation rate estimates from the APEX simulation,
along with relevant data for children (ages 0-18) obtained from the two papers. Mean daily
ventilation estimates (Figure D-9) are quite similar at each year of age, with slightly higher
estimates by Arcus-Arth and Blaisdell (2007) at ages 9 and above, particularly when compared
with APEX ventilation estimates. When ventilation rate is normalized by body mass
(Figure D-10), the largest difference occurs at ages less than 4, whereas APEX estimates are
higher than Arcus-Arth (2007), possibly influenced by differences in body mass between the two
sample populations, particularly between ages 0 and 1.
10. Conclusions of Arcus-Arth and Blaisdell (2007) Comparison
Ventilation estimates are remarkably similar for children for all three sources of data,
particularly when considering the differences in the type of input data used and the varied
approaches of APEX, Brochu et al. (2006 a, b), and Arcus-Arth and Blaisdell (2007).
D-13
-------
18
16
14
12
10
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6 j
4
2
n
A /" ^
A A / A \ A /
J x
X — X _• —
/** O O ^ O
/ • -'*"*'
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10
Age (years)
12
14
16
18
Figure D-9. Comparison of mean daily ventilation rate (V^) in children.
1.0
0.8
0.6 4
ro
o 0.4
1 °-2
0.0
-«-- APEX Mean
o Brochu et al. (2006) Normals Mean - Modified VQ
-X— Arcus-Arth and Blaiedell (2007) Mean
A Brochu et al. (2006) OW/OB Mean - Modified VQ
3-
O O
Ht.
o o o
A A A
10
Age (years)
12
14
16
18
Figure D-10. Comparison of body mass normalized mean ventilation
rates (Vs-kg) in children.
D-14
-------
11. References to Appendix D
Arcus-Arth, A; Blaisdell, J. (2007). Statistical distributions of daily breathing rates for narrow age groups of infants
and children. Risk Anal 27(1):97-110.
Brochu, P; Ducre-Robitaille, J-F ; Brodeur, J. (2006a) Physiological daily inhalation rates for free-living individuals
aged 1 month to 96 years, using data from doubly labeled water measurements: a proposal for air quality
criteria, standard calculations and health risk assessment. Hum Ecol Risk Assess 12(4):675-701.
Brochu, P; Ducre-Robitaille, J-F; Brodeur, J. (2006b) Supplemental Material for Physiological daily inhalation rates
for free-living individuals aged 1 month to 96 years, using data from doubly labeled water measurements: a
proposal for air quality criteria, standard calculations and health risk assessment. Hum Ecol Risk Assess
12(4): 1-12.
CDC (Centers for Disease Control and Prevention). (2000). CDC Growth Charts: United States. BMI-for-age
charts. National Center for Health Statistics, Hyattsville. Available online at
http://www.cdc.gov/nchs/about/maior/nhanes/growthcharts/datafiles.htm.
McCurdy, T; Glen, G; Smith, L; et al. (2000) The National Exposure Research Laboratory's consolidated human
activity database. J Expo Anal Environ Epidemiol 10:566-578.
Schofield, W.N. 1985. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr.
Clin. Nutr., 39C (suppl. 1): 5-41.
U.S. EPA Environmental Protection Agency) (2002) Consolidated Human Activities Database. Office of Research
and Development, National Exposure Research Laboratory, Washington, DC. Available online at
http://www.epa.gov/chadnetl/.
U.S. EPA (2008a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model Documentation
(TRIM.Expo / APEX, Version 4) Volume I: User's Guide. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. June 2006. Available online at
http://www.epa.gov/ttn/fera/human apex.html.
U.S. EPA (2008b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model Documentation
(TRIM.Expo / APEX, Version 4) Volume II: Technical Support Document. Office of Air Quality Planning
and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. June 2006. Available
online at http://www.epa.gov/ttn/fera/human apex.html.
D-15
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