EPA/600/R-12/013 | April 2011 | www.epa.gov/ord
United States
Environmental Protection
Agency
Data Sources Available for
Modeling Environmental
Exposures in Older Adults
Report forAPM 70 (2010): Provide program
offices and the exposure science community
with human exposure activity pattern and
exposure factor data for older adults
I
Office of Research and Development
National Exposure Research Laboratory
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Data Sources Available for Modeling
Environmental Exposures in Older Adults
Report forAPM 70 (2010): Provide program offices and the
exposure science community with human exposure activity
pattern and exposure factor data for older adults
Thomas McCurdy
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
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Disclaimer
This report has been subject to EPA's peer-review process and has been approved for publication. Mention
of registered trade names does not constitute Agency endorsement of the product. The author has no financial
interest in the outcome of this study; it was funded solely by the U.S. government at taxpayer's expense.
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Acknowledgments
Most of the research reviewed here involves studies and literature reviews conducted for or directed by the
author. Some of these efforts were made in coordination or cooperation with colleagues in other National Exposure
Research Laboratory (NERL) groups or in EPA's National Health and Environmental Effects Research Laboratory
(NHEERL) and have been so recognized and cited in this report. Melissa Smarr, student contractor at the NERL
Exposure Modeling Research Branch (EMRB) developed some of the figures used in this report; she also obtained
a lot of the raw data on older adults that appear in the many tables provided. Jennifer Hutchinson and A'ja Moore,
student contractors, also reviewed papers and abstracted relevant information for the tables.
I owe much gratitude to the staff of EPA's Research Triangle Park, NC, Library, both contract employees
and student interns from the University of North Carolina's School of Information and Library Science. Susan
Forbes, Assistant Director of the Library, and Michael Cummings were particularly helpful in obtaining articles and
overseeing literature searches. This report would be a lot less synoptic without their help. Almost all of the
references cited, even if not used directly, are available locally.
Staff of Alion Science and Technology, Inc., an EPA contractor, developed the Consolidated Human
Activity Database (CHAD) and performed a number of the analyses discussed below. Alion employees who were
involved significantly over the years include Dr. Graham Glen, Dr. Kristin Isaacs, Dr. Melissa Nysewander, and
Dr. Luther Smith. Many of the graphics used here originally were developed by Dr. Janet Burke and Dr. Stephen
Graham of EPA's Office of Air Quality Planning and Standards (OAQPS). Dr. Bernine Khan of NERL helped me
format other graphs.
This report has benefited greatly from the extensive internal peer-review comments provided by Dr. Andrew
Geller, EMRB Chief; Ross Highsmith, NERL Assistant Laboratory Director; Dr. Marsha Morgan, NERL; Dr. Stephen
Graham, OAQPS; and Dr. Kristin Isaacs, now a NERL staff member. Their comments and rewrites significantly
improved the flow and exposition of the presented material.
Thomas McCurdy
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Table of Contents
List of Tables vii
List of Figures viii
Abbreviations, Symbols, and Acronyms Used ix
Executive Summary 1
1. Introduction and Overview 3
1.A Exposure Modeling Overview and Principles 3
1.B Functional Structure of the APEX Model 7
1.C Exposure Model Evaluation 11
1.D Section 1 Concluding Comments 13
2. Adjustments to Anthropogenic and Physiological Inputs to the APEX and SHEDS Models When
Modeling Older Populations 14
2.A Conceptual Framework of Physiological Changes Resulting from Aging 14
2.BBMR 16
2.CMETS 18
2.DV02Max 18
2.EVEMax 26
2.FVQ 30
2.G HR and HRMAX 32
2.HHT 33
2.1 Section 2 Concluding Comments 33
3. Energy Expenditure, Total Daily Energy Expenditure, and Physical Activity Index 35
3.A Overview and Total Daily Energy Expenditure 35
3.B Activity-Specific EEA and Oxygen Consumption 39
3.CMETSA 40
S.DPAIorPAL 41
4. Time Use and Human Activity 42
4.A Overview 42
4.B Factors Affecting Time Use in Older Individuals 43
4.C Time Use Databases 44
4.C.1 The CHAD Database 45
4.C.2 The American Time Use Survey 46
4.C.3 Other Databases 46
4.C.4 On Vacations and Out-of-Region Time 48
4.D Examples of 24-h Time Use Data 49
4.D.1 Time Use in Specified Activities or Locations 52
4.D.2 Travel 52
4.D.3 Outdoors 55
4.E Intra- and Interindividual Variability in Time Use/Activity Data 56
5. Physical Activity, Exercise, and Aging 60
5.A Overview of the Literature 60
5.B General Estimates of Physical Activity and Inactivity in Older Adults 62
5.C Specific Estimates of Physical Activity in Older Adults 63
6. Health Considerations in Older Adults 68
6.A Impairment, Functional Limitations, and Disability 68
6.BADLand IADL 71
6.C Caregiver Time 72
6.D Cognitive Issues in Older Individuals 72
7. Exposure Impacts on Older Adults and Their Impact on the Environment 74
7.A Introduction 74
7.B Examples of Exposure Impacts on Older Adults 74
7.C Impact of Older Adults on the Environment 74
References 76
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Appendix: An Example of Available Health and Co-morbidity Information 97
AP.A Introduction and Explanation of This Material 97
AP.B Overview of the "Population" Analysis Undertaken 97
AP.C Arthritis 98
AP.C.1 Prevalence Rates for Arthritis 98
AP.C.2 Physical Activity Difficulties for People with Arthritis 100
AP.D Co-morbidity 101
AP.D.1 Dementia as the Reference Health Problem 101
AP.D.2 Arthritis as the Reference Health Problem 103
AP.D.3 Alzheimer's Disease and Dementia 103
AP.E Definitions and Concepts Used in This Appendix 104
AP.F References for This Appendix 106
VI
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List of Tables
Table 1-1. Summary of the CHAD Database 10
Table 2-1. Variables Used for Activity-Specific Metabolic and Ventilation Metrics Used in APEX and SHEDS
Exposure Models 15
Table 2-2a. Literature Reported Estimates of VO2Max for Older Adults 20
Table 2-2b. Estimates of VO2Max for Older Adults Seen in the Literature 27
Table 2-3. Estimates of VEMax for Older Adults 31
Table 3-1. Estimates of TDEE, PAEE, and/or PAI for Older Adults 37
Table 3-2. Estimates of Activity-Specific Energy Expenditure for Older Adults 40
Table 4-1. Definitions of Time Use Metrics Useful for Exposure Modeling 43
Table 4-2. Selected Activity-Location Data for Seniors in EPA'S Exposure Factors Handbook 46
Table 4-3. Selected Time-Use Data for People Aged 65+ from EPA's Exposure Factors Handbook 47
Table 4-4. Activity Diaries in CHAD for Older Adults 48
Table 4-5. Time Spent per Day in Selected Activities 49
Table 4-6. Demographic and Long-Distance Travel Characteristics in Seniors 53
Table 4-7. Local Travel Characteristics in Seniors 53
Table 4-8. 1995 Daily Trip Data for People Aged 65+ 54
Table 4-9. Percentage of Mode Choice for All Trips (1995), by Age 55
Table 4-10. Variance and Autocorrelation Statistics in the Internal EPA Study 59
Table 5-1. Physical Activity Estimates for U.S. Older Adults 65
Table 5-2. Observed Steps per Day Pedometer Counts in U.S. Seniors 66
Table AP-1. Co-morbidity Associated with Arthritis Without ADL Limitations 100
Table AP-2. Co-morbidity Associated with Different Degrees of Dementia 102
VII
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List of Figures
Figure 1-1. The individual is the unit of analysis 4
Figure 1-2. A Venn diagram of exposure 4
Figure 1-3. Exposure metrics available from an exposure time-series 5
Figure 1-4. Human exposure model principles 6
Figure 1-5. APEX/SHEDS exposure simulation process 8
Figure 1-6. Percent of people in three groups estimated to experience 1+ days with an 8-h daily maximum O3
exposure >0.07 ppm while at moderate exercise when the current 8-h daily maximum NAAQS of
0.08 ppm is just met 12
Figure 2-1. Activity-specific metabolic and ventilation metrics used in EPA exposure models 14
Figure 2-2. Conceptual framework of important relationships that affect physiological processes in the body 16
Figure 2-3. Decrease of BMR with age 17
Figure 4-1. Mean time spent outdoors by study year in adults aged 65+ years 56
Figure 4-2. Conceptual diagram of alternative decision rules used to sample single-day diaries to develop
longitudinal activity patterns 57
Figure 4-3. Daily variability in time use over 7 mo by a single individual 58
Figure 6-1. Conceptual model for modifying activity pattern data based on assessment of functional limitations
and disabilities 70
VIM
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Abbreviations, Symbols, and Acronyms
5
c?
±
ME
ACSM
ACT
ADL
ADT
AHEAD
ANOVA
APEX
ARM
AT
ATS
ATUS
BLS
BLSA
BM
BMI
BMR
BRFSS
BSA
C
C
GARB
CDC
CDS
CHAD
CHAMPS
CHAP
CO
COPD
COUT
CoUT.h
CoUT.t
C-S
Ct
CT
D
D
D&A
Female(s)
Male(s)
Used to depict the standard deviation of
the mean
Microenvironment— location having a
constant CT for a time period
American Council of Sports Medicine
Adult Changes in Thought (study)
Activities of daily living
Average daily traffic (vehicles/day)
Assets and Health Dynamics Study
(among the oldest studies)
Analysis of variance
Air Pollution Exposure Model (OAQPS
model)
Annual Performance Measure
Anaerobic threshold (L/min)
American Travel Survey
American Time Use Survey (a yearly
BLS survey)
Bureau of Labor Statistics;
U.S. Department of Labor
Baltimore Longitudinal Study of Aging
Body Mass ["weight"] (kg)
Body mass index (BM/HT^ in kg/rn^)
Basal metabolic rate (kcal/day)
Behavioral Risk Factor Surveillance
Survey
Body surface area (m^)
Calorie
Concentration (various units [e.g.,
ug/m3, ppm])
California Air Resources Board
Centers for Disease Control and
Prevention
Child Development Survey
Consolidated Human Activity Database
(www.epa.gov/chadnet1/)
Community Health Activities Model
Program for Seniors
Chicago Health and Aging Project
Carbon monoxide
Chronic obstructive pulmonary disease
Concentration outdoors (various units)
Hourly-specific outdoor concentration
(various units)
COUT for time period [t]
Cross-sectional
Concentration for time period [t]
Concentration for a specified time
period T (various units)
Dose (various units; moles/min is the
most general)
Intake dose rate (moles/min)
Diversity and autocorrelation [approach]
D/E
DIN
DT/dt
DHHS
DLW
D/R
DSM-IV
E
EGG
EE
EEa
EEai
EFH
El
EMBS
EMRB
EPA
EPESE
EPOC
EVR
FFM
FIF
GMHR
HAPEM
HDL
HEASD
H-HEPSE
HR
HRMAx
HRR
HRpEs
HRS
HT
IADL
ICC
ICF
IQ
Dose/effect relationship (individuals)
Dose fora particular time period (moles
per specified T: minute, hour, etc.)
The time rate of dose rate received
(moles/min over some specified T)
Department of Health and Human
Services
Doubly labeled water
Dose/response relationship (cohorts)
Diagnostic and Statistical Manual of
Mental Disorders, Chapter IV.
Psychological and Environmental
Problems
Exposure (various units and averaging
times
Electrocardiogram
Energy expenditure (various units)
Activity-specific energy expenditure
(kcal/min)
EEa for a particular modeled individual
EPA's Exposure Factor's Handbook
Energy intake
Engineering in Medicine and Biology
Society
Exposure Modeling Research Branch
(EPA)
U.S. Environmental Protection Agency
Established Populations for
Epidemiological Studies of the Elderly
Excess postoxygen consumption
Equivalent Ventilation Rate (L/BSA;
L/m2 for a specified time period)
Fat-free mass (kg); equal to LBM
Federal Interagency Forum on
Aging-Related Statistics
General Medical Health Rating
Hazardous Air Pollution Exposure
Model
High-density lipoprotein cholesterol
Human Exposure and Atmospheric
Sciences Division
Hispanic version of EPSE
Heart rate (beats/min)
Maximal heart rate (beats/min)
Resting heart rate (beats/min)
Heart rate reserve [HRMAx - HRR]
(beats/min)
Hours
Height (in centimeters or meters)
Independent activities of daily living
[minimal ADL for independent living]
Intraclass correlation coefficient
International Classification of
Functioning, Disability, and Health
Intelligence quotient
IX
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IQCODE
IEEE-MBS
IBM
LPA
LSOA
MDS-
COGS
ME
METS
METSa
METSMax[ll
MMSE
MPA
MVPA
NCC
NCEA
NCHS
NERL
NH
NHANES
NHEERL
NHIS
NHLBI
NHTS
NIA
NICHHD
NLTCS
NO2
NPTS
02
03
OAQPS
OAR
ORD
PA
PAEE
PAI
PAL
PC
PEFR
Informant Questionnaire for Cognitive
Decline in Elderly
International Electrical and Electronic
Engineers [a society]-Engineering in
Medical and Biological Engineering
[a section]
Lean body mass (kg); equivalent to
FFM
Light physical activity
Longitudinal Study of Aging
Minimum Data Set-Cognition Scale
Microenvironment
Metabolic equivalents of work (unitless)
Activity-specific METS (unitless)
Maximum [achievable] METS
Mini-Mental State Exam [often-used
measure of cognitive impairment]
Moderate physical activity
Moderate/vigorous physical activity
National Climatic Center
National Center for Environmental
Assessment (EPA)
National Center for Health Statistics
(National Institutes of Health)
National Exposure Research
Laboratory (EPA)
Nursing home
National Health and Nutrition
Examination Survey
National Health and Environmental
Effects Laboratory (EPA)
National Health Interview Survey
National Heart, Lung, and Blood
Institute
National Highway and Transportation
Survey
National Institute on Aging
National Institute of Child Health and
Human Development
National Long-Term Care Survey
Nitrogen dioxide
National Personal Travel Survey
Oxygen
Ozone
Office of Air Quality Planning and
Standards (EPA)
Office of Air and Radiation (EPA)
Office of Research and Development
(EPA)
Physical activity
Physical activity energy expenditure
Physical activity index (many alternative
units; generally TDEE/BMR)
Physical activity level
Personal care [activities]
Peak expiratory flow rate (L/min)
PM
PM2.5
POV
RADC
RC/AL
REE
RER
RMR
ROS
RPAHS
RQ
SD
SE
SHEDS
TDEE
TPA
TRIM
U
VA
VA
VCO2
VD
VE
VE.A
VE.MBX
VER
VE. Reserve
VMT
V02
V02.Max
VOR
VO2Peak
VO2. Reserve
VPA
VQ
VT
VT
WHAS
WHO
Particulate matter
PM >2.5 urn in average effective
diameter
Personally owned vehicle
Rush Alzheimer's Disease Center
Residential care with assisted living
Resting energy expenditure (kcal/time
period)
Respiratory exchange ratio
Resting metabolic rate [approximately
equivalent to BMR]
Religious Orders Study
Regenstrief Physical Activity and Health
Study
Respiratory quotient [VCO/VO2, both as
volumes] (unitless)
Standard deviation
Standard error
Stochastic Human Exposure and Dose
Simulation Model
Total daily energy expenditure
(generally kcal/day)
Total physical activity
Total Risk Integrated Method
Conversion factor used to relate EE to
VO2 (kcal-to-L/min)
Veterans Administration
Alveolar ventilation rate (L/min or
BM-adjusted mL/min-kg)
Carbon dioxide ventilation rate (L/min)
Dead-space volume (L)
Ventilation [breathing] rate (L/min or
mL/min-kg)
Activity-specific VE
Maximal VE, defined by an exercise
protocol (L/min or mL/min-kg)
Ventilation rate measured at rest [basal
conditions] (L/min or mL/min-kg)
Ventilatory reserve [VE Max-VE R ] (L/min
or mL/min-kg)
Vehicle miles traveled
Oxygen consumption rate (L/min or
mL/min-kg)
Maximal VO2, defined by an exercise
protocol (L/min or mL/min-kg)
VO2 measured at rest [basal conditions]
(L/min or mL/min-kg)
Peak (maximum) VO2
Oxygen consumption reserve [VO2 Max-
VO2 R] (L/min or mL/min-kg)
Vigorous physical activity
Ventilatory equivalent [VE/VO2]
(unitless)
Tidal volume (L)
Ventilatory threshold (L/min)
Women's Health and Aging Study
World Health Organization
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Executive Summary
This report, "Data Sources Available for Modeling
Environmental Exposures in Older Adults," focuses on
information sources and data available for modeling
environmental exposures in the older U.S. population,
defined here to be people 60 years and older, with an
emphasis on those aged greater than 65. The
information was gathered as part of the U.S.
Environmental Protection Agency's (EPA's) Aging
Initiative project.
In general, this report contains the same type of
information found in EPA's Exposure Factors Handbook
(e.g., NCEA, 1997a,b) but with older adults as the sole
population subgroup of interest. We envision that this
report will be used to inform exposure assessors about
the data available for modeling exposures to older
people. In addition, the data enable scientists to check
or evaluate results obtained from the modeling
assessments for older adults, such as determining
whether the distribution of ventilation (breathing) rates
seen in a particulate matter (PM) intake dose rate
assessment, for example, is realistic or not. The same
is true for their time spent in motor vehicles, outdoors,
or indoors. Intra- and interindividual variability measures
are discussed for all of these parameters, where
available. In the situation where a time-averaged
exposure model is used, the data in this report can
provide aggregate information on many of the inputs
needed for that type of model. This report can be a
useful "source book" on older adult exposure modeling,
similar to the Exposure Factors Handbook. The report is
centered on the inputs needed for two of EPA's
inhalation exposure models, the Air Pollution Exposure
(APEX) model and the Stochastic Human Exposure and
Dose Simulation (SHEDS) model.
The report also includes a review of physical
activity data available for evaluating model outputs. In
addition, the report includes discussion of how general
health status of older adults might affect exposure to
environmental contaminants and an assessment of the
interactions between exposure and possible impacts of
older people on environmental loadings. The latter
category focuses on pharmaceutical discharges into
bodies of water. The appendix provides information on
developing conditional probabilities for those individuals
that have both arthritis and one or more co-morbidities
often associated with it.
Data shortcomings and research needs are
described for each topic covered.
Finally, this report presents detailed information on
changes in time use, activity, and physiology as people
age. It is important to understand these changes
because older adults are becoming a larger proportion
of the total U.S. population, and more and more societal
resources will be directed toward their maintenance.
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1. Introduction and Overview
This report focuses on information sources and
data available for modeling environmental exposures in
U.S. older adults, defined here to be people 60 years
and older, with an emphasis on those aged 65 and
greater. This subpopulation is increasing rapidly, both in
relative and absolute terms (Administration on Aging,
2009), which makes it an ever-increasing group of
concern (or cohort) from an exposure and risk
assessment perspective. The information was gathered
as part of EPA's Aging Initiative project (Geller and
Zenick, 2005), supplemented by work directed toward
improving risk estimates for older Americans. This is a
review of the main topics needed to undertake and
evaluate exposure and intake dose rate modeling in
aging adults, in particular, the time use, physical
activity, exercise, and physiology inputs needed for the
Air Pollution Exposure model (APEX; Palma et al.,
1999) and the Stochastic Human Exposure and Dose
Simulation model (SHEDS; Burke et al., 2001). These
inputs are delineated in detail below. Related, but less
important, physiological considerations are addressed
more briefly.
This review reflects the current state of the science
regarding exposure modeling in independent-living
older adults as of the end of 2009. Thus, older adults
who are confined to a nursing home or other institution
are mentioned only briefly in this report.1 This also is
true for people suffering from dementia or other health
circumstances that preclude them from functioning
without help, even if they are still living at home.
Most of the data and citations to the literature
come from U.S. studies, although significant information
on physiology in older adults comes from non-U.S.
data. In general, people of a specified age and gender
are physiologically similar regardless of ethnic
background or where they live. There are some
physiological parameters for which ethnicity seemingly
makes a difference, but these associations are
confounded by genetics and lifestyle aspects of a
society's culture that affect selected physiological
systems. Basal metabolic rate and fitness levels are two
examples. Others will be discussed in context. Because
there is a substantial cultural component associated
with many of the nonphysiological topics covered,
particularly time use and physical activity participation,
focusing on U.S. data is a practical necessity.
1 For elderly residential types not discussed here, see, for
example, Eckert and Murrey (1984), Marans et al. (1984),
Moos and Lemke (1984), and Pruchno and Rose, 2002). The
approximate proportion of the elderly not living in their home
or other residence for two age groups is 65 to 74 years =
2.2% to 2.4% ? and 2.1% to 3.6% $ and 75+ years = 8.9% to
11.7% $ and 6.3%to 7.1% $ (Czaja, 1990). Seethe
discussions of impairment, functional limitations, and disability
for additional information.
It should be noted that the tabular data for the
most part only include subjects whose mean age is
>60 years. More information is available for subjects
whose mean age is >55 years and having a large
enough standard deviation so that a considerable
portion of the sample would be 60+ years of age. In
most cases, these data are not presented. Most readers
will feel that there is a large enough sample of data
provided here for 60+-year-aged individuals; including
slightly younger people does not alter the trends or
findings of this report but would increase its length
substantially.
1.A Exposure Modeling Overview and
Principles
This report is focused on time use, physical
activity, and physiological inputs needed for modeling
inhalation exposures and intake dose rates, such as the
APEX and SHEDS models. This subsection describes,
in general terms, the approach, algorithms, and
important variables used in both models. APEX is the
primary air exposure model used by EPA's Office of Air
Quality and Standards (OAQPS) to evaluate existing
and proposed alternative National Ambient Air Quality
Standards (NAAQS). APEX is also part of OAQPS's
TRIM (Total Risk Integrated Methodology) program
(U.S. EPA, 2008a,b), along with EPA's Hazardous Air
Pollutant Exposure Model (HAPEM). HAPEM is a
longer term exposure model that uses many of the
same activity and physiological inputs as does APEX
and SHEDS (Palma et al., 1999) but functions primarily
to evaluate exposures to hazardous air pollutants from
mobile and stationary sources of air toxics. The SHEDS
model is an umbrella term for EPA's Stochastic Human
Exposure and Dose Simulation model (Burke et al.,
2001; Zartarian et al., 2000), of which there are a series
of route-specific versions (dietary/nondietary,
pesticides, etc.). It was developed by EPA staff in
NERL's Human Exposure and Atmospheric Sciences
Division (HEASD) and staff of Alion Science and
Technology, Inc. The SHEDS model discussed here is
oriented toward modeling exposures and intake dose
rates for airborne pollutants (SHEDS-Air), but because
the activity/time use and physiological concepts are
similar in all of the SHEDS models, the findings
reported here are more widely applicable to the
modeling of all routes of exposure.
APEX and SHEDS now have similar features and
input needs. Both use EPA's CHAD for their time use
input data (McCurdy et al., 2000). CHAD, therefore, is
discussed in some detail in this report.
There are a number of important principles that
have guided exposure and intake dose modeling since
1980 (Johnson, 1995; McCurdy, 1995, 1997). In
general, these principles (15 in number and described
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Building a Realistic Person
Simulated Individual
• Home location
• Work location (if employed)
• Age
• Gender
• Ethnicity
• Employment status
• Housing characteristics
• Anthropometric parameters
(height, weight, etc.)
• Basal Metabolic Rate (BMR) >
V. .X
0 ~N
•^ Activity Diary Pools
• Personal attributes
• Day-type (e.g., weekday)
• Temperature
• Physical activity index (PAI)
(initial median estimate)
i -.-, iTS^Vv.'i^vl
~
X
Simulated
Activity
• Selected diary
' simulation peno
• Sequence of e
(microenvironm
minutes spent,
\
1
Individual
r Profile
records days in
d ""
vents
ents visited,
and activity) .
o ;
T Physiological Parameters
. METS^, METS^
!• Ventilation relationships
~
Individual Physiological
Sequence
Metabolic Equivalents (METS)
Oxygen Consumption Rate (V02)
Total Ventilation Rate (VE)
Alveolar Ventilation Rate (VA)
PAI, actual daily estimate
A
Stochastic Calculation
• Energy expended per event
•* and ventilation rates
• Both adjusted for physiological
I limits and EPOC
j
Source: Stephen Graham, OAQPS
Figure 1-1. The individual is the unit of analysis. APEX and SHEDS construct simulated populations
based on the above characteristics.
just below) apply to all groups and not just to older
adults.
(1) An individual is the unit of analysis
(Figure 1-1). Each individual has a unique dose-
response (D/R) relationship (National Research
Council, 2009), which often is called a dose-effect
(D/E) curve to distinguish it from the population-
level D/R association. D/E uniqueness results from
genetic factors; preexisting disease considerations;
age/gender differences in biology, physiology, and
time use patterns (location and activities); and
lifestage and lifestyle differences among people
(Db'rre, 1997; McCurdy, 2000). EPA's exposure
models are designed to reproduce such
uniqueness. Being older can influence greatly D/E
relationships in individuals both directly and
indirectly because of physiological changes,
immune system challenges, neurological
impairment (cognitive decline), and other physical
alterations (Hertzog et al., 2008; Jette, 2006; Kiely
etal.,2009).
(2) Location is critical to evaluating an exposure to an
environmental pollutant (often termed a "stressor")
because, by definition, exposure is the "contact
between an agent [substance or pollutant] and a
receptor [a person in our case]" (Figure 1-2).
Contact takes place at an exposure surface over an
"exposure period" (Zartarian et al., 2005),2 directly
implying a specific location. It should be noted that
there is a correlation structure to location patterns in (3)
an individual, both within and among days;
(4)
Distribution of
Stressors in Space and
Time
Distribution of
Receptors in Space
and Time
From the "Official Glossary" of the International Society of
Exposure Science
Source: Adapted from NERL Framework for Exposure Science
Figure 1-2. A Venn diagram of exposure.
locations that a person inhabits cannot be modeled
using a "random-walk" process. On the other hand,
there is day-to-day variability in locations that any
individual frequents (unless confined to bed or an
institution), so using "averaged" data does not
capture daily variability in this important exposure
variable either (Glen et al., 2008). This point is
discussed further in principles 12 and 13.
An individual is not averaged overtime or space; a
person can be in only one location at any particular
time.
A location having a constant concentration (CT) for
a specified period of time is called a
"microenvironment" (uE). Microenvironmental data
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are crucial inputs to an exposure model (locations
and concentrations), and time spent in the various
uEs vary greatly with age, gender, and lifestyle. In
the APEX and SHEDS models, locational data
come from CHAD, whereas uE concentration data
are derived from ambient measurement data or
route/pathway-specific model algorithms.
(5) An exposure event is the smallest unit of time used
in the two models and is characterized by a person
being in a unique uE, undertaking a single type of
activity and, therefore, experiencing a specific
activity-level (see below.) By definition, an event
does not cross a clock hour; longer activities are
subdivided into two or more exposure events in that
case (McCurdy et al., 2000). If any of these factors
change, a new event occurs.
(6) The event-based time pattern of concentrations
experienced by an individual is called the exposure
profile, or the exposure time-series. An example of
an exposure profile is depicted in Figure 1-3.
A number of alternative exposure metrics may be
derived from this profile, such as the number of
peak exposures over a specified concentration
level, the mean exposure level, and the time
integral of exposures over some important value.
Exposure Metrics
Tim e-Aver a ged
Instantaneous
A
•Time-Integrated
Time
Source: Duan et al., 1990, as modified by Thomas McCurdy (1996)
Figure 1-3. Exposure metrics available from an
exposure time-series.
(7) Activity level is the amount of energy expended
(EE) by an individual to complete the activity
undertaken (expressed in kcal or kJ/min/kg). Other
metrics performing the same function were used in
the past in EPA's exposure models.3 Activity level
affects how much dose is received given an
exposure. Activity levels are correlated overtime in
an individual, because prior physiological
circumstances affect subsequent ones when EE
reaches individually specific limits (Isaacs et al.,
2008). These limits are determined, in part, by an
Activity level generally was defined to be the breathing rate
(L/min) associated with the activity. The EE metric is a more
generalized approach to modeling activity level and
accommodates non-air exposure modeling (McCurdy, 2000).
individual's age, gender, fitness level, and
functional (health) limitations that may exist
(Figure 1-4).
(8) Work is defined to be activity-specific energy
expenditure. In the APEX and SHEDS models,
activity-level-specific energy expenditure (EEa) by
an individual; (EEai) is estimated by multiplying an
activity-specific relative energy value in metabolic
equivalents of work (METSa) sampled from a
literature-derived distribution by the modeled
person's basal metabolic rate (BMRj)—EEai =
BMRi * METSa. See Ainsworth et al. (1993) and
McArdle et al. (2001) for a discussion of the METS
concept. A person's BMR is dependent on age,
gender, health conditions, and lifestyle factors.
Numerous equations exist in the nutrition literature
for estimating BMRi using a multitude of
independent variables (Froehle, 2008; Mtiller et al.,
2004; Schofield, 1985; Speakman, 2005). It is
important to note that BMR in older individuals is
quite different than that in younger adults; see
Section 2.B.
(9) Given a uE exposure concentration, activity level
ultimately determines a person's intake dose rate,
the amount of material inhaled, ingested, or
absorbed into an individual (Figure 1-4). For
inhalation exposures, intake dose rate is a function
of the amount of air breathed per unit time
multiplied by the uE concentration; its units ideally
are in moles/min, but alternative metrics sometimes
are used. The magnitude of intake dose rate is
affected greatly by the amount of work being
undertaken by an exposed person at the time of
exposure. The pattern of intake dose rate
experienced overtime often is called the intake
dose profile, and is similar in appearance to the
exposure profile depicted in Figure 1-3.
(10)A relevant dose metric must be utilized to properly
address individual dose-effect (D/E) or population
dose-response (D/R) relationships (Lorenzana
et al., 2005; National Research Council, 2009).
However, in general, health effects are associated
with the time pattern of dose rate received
(Lippmann, 1989; McCurdy, 1997). Knowing this
specific pattern (abbreviated as DT/dt) enables any
longer term dose metric to be calculated, including
dose levels exceeding selected levels one or more
times in a year, the mean dose rate, and other
metrics of interest. For example, an exposure
assessment conducted for the most recent ozone
(O3) NAAQS review (U.S. EPA, 2007a) focused on
8-h peak exposures coincident with moderate or
greater exercise levels occurring within a year.
Multiple, short-term peak dose metrics like these
cannot be uniquely determined from an aggregated,
time-averaged dose metric. They only can be
modeled using an intake dose rate simulation
approach that calculates the time series of
-------
Human Exposure Model Principles
Individual
Attributes
1
Bo
Pa ran
dy
leters
i
NETS Estimates
(time series)
Non-Dietary
Ingestion
t
M inactivities
[>ermal Uptake
(mass time'1) ,
(^Oxygen Consumption
/Dietary Ingestion
>( consumption
V concentration
Inhalation
breathing rate
concentration
;Water Ingestion
consumption
concentration .
Source: Thomas McCurdy (2000) modified by Dr. Stephen Graham.
Figure 1-4. Human exposure model principles. This schematic diagram illustrates the relationship among activity level,
energy expenditure, and the intakes needed to maintain that activity level.
exposures such as those produced by the APEX
and SHEDS models.
(11)Multiple-route intake/uptake dose rates are
correlated in an individual because of the
bioenergetics of human metabolism. Basically,this
principle derives from conservation of mass and
energy (McArdle et al., 2001). In contrast, "micro-
activity" dose rate uptakes, such as nondietary
ingestion associated with
hand-to-mouth or hand-to-surface activity—of
concern with respect to environmental exposures
of children—are not directly associated with
bioenergetics but are related instead to age/gender
differences in behavioral characteristics of children
inhabiting a particular location. Thus, there is a
correlation among pathways, and it is maintained
in SHEDS-Multimedia by basing dietary and water
consumption, as well as ventilation rate, on activity
level considerations. Microactivity intake dose rate
modeling will not be considered further in this
paper. See Tulve et al. (2002) orXue et al. (2007)
for a discussion of microactivity exposure
modeling. For modeling air route exposures to
older individuals, we assume that there is no
nondietary (or dietary for that matter) ingestion
resulting from hand-to-mouth activity in that
population. This assumption can be evaluated if
data on nondietary mouthing behavior become
available for older people.
(12)There are seasonal, day-of-week (or
workday/nonworkday), and meteorological
(temperature and precipitation) differences in time
use within and among individuals (Fisher et al.,
2005; Hill, 1985). EPA exposure models maintain
the time use patterns via targeted selection of
appropriate CHAD diaries for each day of the
simulated year for each individual. This is another
reason why average time use data are deficient in
capturing and interpreting what people do in time
and space.
(13)There are day-to-day similarities and differences in
locations inhabited and activities undertaken by an
individual and among individuals within a larger
population cohort (Xue et al., 2004; Glen et al.,
2008). These similarities and differences are
affected by the contextual culture of a society,
habits, and technology. Viewed overtime, there is
a structure to these effects, resulting in longitudinal
patterns of locations visited and activities
performed in a population (Echols et al., 1999,
2001; Frazier et al., 2009; Glen et al., 2008).
Ramifications of this observation are that both
intra- and interindividual variability have to be
addressed in an exposure modeling effort, as well
as day-to-day correlations within an individual.
(14)There are long-term patterns to a person's use of
time—called "tracking"—that can be addressed
analytically to some extent in multiyear exposure
modeling (Elgethun et al., 2005, 2007). Tracking is
affected greatly by changing physiological and
functional limitations and housing pattern changes
in the aged. It is difficult to obtain information on
this subject, except in the physical activity
literature; see Section 5.
(15)Because of the inherent nature of the risk
assessment process where judgments have to be
-------
made regarding uncertain future events, including
intake dose rates associated with inhaling a
pollutant by population subgroups undertaking
multiple activities in many locations, said
assessments often use a stochastic simulation
modeling approach (Jordan et al., 1983; Ott et al.,
1988). A simulation model facilitates evaluation of
variability and uncertainty in parameters of the
model, often ignored in many exposure modeling
efforts. Uncertainty in the model structure itself,
however, only can be addressed by using a
different model and comparing output estimates
with measured data. This rarely is done because of
resource limitations.
1.B Functional Structure of the APEX
Model
How these principles are implemented in the
APEX and SHEDS-Air models is shown in Figure 1-5.
Those symbols and abbreviations not already
described above are defined in the List of
Abbreviations, Symbols, and Acronyms. Figure 1-5
depicts the event-based exposure and intake dose rate
simulation logic frequently used in the two models.
Specific applications of them may differ in the details
depicted. Major model inputs are shown outside of the
dashed-line portion of the Figure; they are
(1) environmental concentration data, (2) U.S. Census
population data, (3) CHAD time use data, and (4) daily
meteorological data for the geographical area being
modeled. This review focuses on the model processes
inside the dashed line portion. Because some of the
inputs differ between the APEX and SHEDS models,
as well as among different applications of either of the
models, it would be tediousforthe reader to continually
distinguish among the versions. The following
discussion is oriented toward a generalized ideal APEX
model.
Area of analysis and population groups of concern.
APEX usually is applied at the community- or urban-
scale level for three specified air quality conditions,
generally described by a period of time: (1) some past
time period having measured (or modeled) ambient
concentration field data, (2) current (or as is) air quality
conditions also using either measured or modeled
concentrations, and (3) some indefinite future time
when environmental concentrations just meet one or
more alternative standards being evaluated.
Comparing outputs for these three scenarios provides
a quantitative estimate of the "effectiveness" of each
scenario modeled. An example is New York City for as
is conditions in 2007 versus just attaining a specified
standard level occurring at some future time. (This
approach is called a standards objective analysis. If a
specific control scenario is evaluated, usually
compared with an alternative control approach, it is
called a standards impact assessment [Feagans,
1986]). The population groups of concern may be the
entire population or a specific portion of it; exercising
children (a small subset of U.S. children) was the focus
of EPA's recent O3 NAAQS exposure analyses (U.S.
EPA, 2007a,b). Older adults with compromised
cardiovascular systems (chronic obstructive pulmonary
disease, angina, etc.) likely will be an important
subpopulation to consider for modeling exposures in
the next PM NAAQS review.
Environmental concentration field. An
environmental concentration field, or profile, is
estimated for all outdoor locations in the selected
geographic area, often referred to as the modeling
domain. This concentration field may be measured
(monitored) and/or modeled ambient data; the latter
data usually are used for future-time air quality
scenarios. The output of this step typically is a time
series of hourly concentrations for every hour of the
day during the modeling period, usually for an entire
year. See "Sequence of Hourly Environmental
Concentrations" depicted inside of the dashed lines in
Figure 1-5.
Microenvironmental-specific concentration
estimates are developed from these hourly
concentration profiles. If a person is outdoors, the
hourly environmental concentration (C0ui h) value itself
often, but not always, is equivalent to the ambient
concentration and used for this uE for the duration of
the exposure event. In other words, a Ct may be the
same as an hourly COUTH value. Note that, if there is
within-hour variability in GOUT, then C0ui.t would be
based on the sub-hourly time period of concern, such
as 5 min used in the sulfur dioxide NAAQS review.
If a person is indoors or inside a motor vehicle,
the concentration within that uE depends on a variety
of chemical/physical factors, such as chemical
deposition and removal rates, air exchange rate, and
indoor source strengths. There have been a number of
approaches used to model these factors over the
years, but three are most commonly used: (1) solving a
mass-balance equation for the specific location;
(2) sampling from literature-derived "indoor/outdoor"
ratios specific to the uE being modeled (McCurdy,
1995); and (3) using a linear-regression-based
algorithm that relates outdoor-to-indoor concentrations
(the regression slope) with an additive term (the
regression intercept) for indoor sources.
The number of indoor locations used in EPA's
exposure models range varies with the pollutant being
analyzed, but is generally between 7 and 27 specific
locations. Usually <10 locations are used. Some
examples are home, work, school, retail
establishments, motorways, retail stores, and a
"residual" location ("other indoors"). Outdoor locations
also are subdivided, but the concentration assigned to
them may simply be the ambient concentration
estimate noted above. The output of these steps is a
-------
AREA OF ANALYSIS &
POPULATION GROUPS
OF CONCERN
ENVIRONMENTAL
CONCENTRATION FIELD
• Models, measurements,
statistical relationships
US CENSUS DATA
Population Characteristics
(Age, gender, ethnicity)
Housing Locations &
Types of Housing
Employment Locations &
Types of Jobs
CensusTract Commuting
Relationships
CHAD DIARY SELECTION
CRITERIA
• Intra- and Inter-Individual
Relations ("D")
• Lag-One Location
Correlations ("A")
• Season- of-the-year
• Day-Type
(Weekday, weekend;
Work-day, non-work-
day)
PHYSIOLOGICAL PROFILE
GENERATOR
. Anthropogenic Data (BM,HT,BMR)
• VO2 max, METS max
• "Lifestyle" Attributes (PAI mostly)
SEQUENCE OF HOURLY
ENVIRONMENTAL
CONCENTRATIONS
MICROENVIRONMENTAL
-SPECIFIC CONCENTRATION
RELATIONSHIPS
• Mass-Balance Equations
• Indoor/ Outdoor Distributions
CHAD DIARIES (TIME USE)
• Daily Sequenceof Locations flE's)
• Activities & Activity Leve(IVlETS;
EE; VQ>; VE)
• PAI
SEQUENCE OF jjE
CONCENTRATIONS
• C,
SEQUENCE OF EXPOSURE
EVENTS &DOSE RATES
. Dr/dt
• Other metrics possible $jf
MODELED- DAY WEATHER DATA
• Maximum Daily Temperature,
Precipitation Amount
DAILY AGGREGATION OF
NON-AIR DOSES
(If Desired)
Repeatfor
Each Day
in the
Period of
Analysis
to Obtain
the
Overall
Dose Rate
Profile
NATIONAL CLIMATIC DATADAILY
M.Smarr 12/07/08
Figure 1-5. APEX/SHEDS exposure simulation process.
time series of |jE concentration estimates
{C-i, C2, C3. . . CT} for all outdoor and indoor locations
that the simulated population may inhabit (see
Figure 1-5).
It is possible to model more uEs than the 7 to 27
locations noted above, but input data to calculate the
uE concentration are limited for many locations. Most
time use studies use a hierarchical locational coding
scheme, some down to individual rooms in a home, but
rarely do subjects provide data on time spent in them,
even for contemporaneous diary studies, for which
subjects are supposed to record in some manner where
they were at the time, with a new entry for every
location inhabited. Remembering specific locations in
the commonly used ex post time use recall surveys
done over the phone (e.g., "What did you do
yesterday?") is almost impossible. Misleading modeling
results would occur for specific locations using most
recall survey data for exposures in detailed uEs, as
there would be a lot of false negatives ("0 time") spent
in isolated locations of interest. Thus, only a handful of
general microenvironments are considered in most
exposure modeling efforts.
There is a lively literature on the diary versus recall
protocols used to gather time use data; see As, 1978;
Collopy, 1996; Fenstermaker, 1996; Geurts and
De Ree, 1993; Harvey, 1993; Nickols and Ayieko, 1996;
Niemi, 1993; and Stinson, 1999, among others. CHAD
contains both recall and contemporaneous diary time
use information. See Section 4 for a more detailed
discussion of time use data.
Census data. U.S. Census data are a major input to
EPA's exposure models. The data are used to define
how many people are within the modeling domain,
along with their age, gender, employment, housing, and
commuting characteristics. The proportion of people in
each 1 -year age category by gender for the population
groups of interest is derived from the Census data and
governs the number of simulations undertaken. The
Census also provides frequency distributions of work
commuting trips among every census tract in the United
States (centroid to centroid distances). These data
provide an estimate of commuting trips between any
pair of census tracts in the area being modeled (e.g.,
U.S. EPA, 2007a,b).
After characterizing the simulated population,
development of an actual pool of simulated persons
begins. Suppose that we are interested in modeling the
exposures to 45- to 65-year-old workers of both
-------
genders. A single person within that age range is
selected randomly, say, a 65-year-old female. That
person has some probability (using the Census data) of
living in a single family residence having gas heating
and cooking. A random draw from this probability
distribution will assign the person to a single housing
type based on the Census probability. Work (paid) or
nonwork status is determined from Census probabilities
for the subject's age/gender combination. If a worker,
the subject will be assigned to a work district (Census
tract) location based on Census commuting
probabilities. Thus, the simulated example person is
characterized by a specific age, gender, housing type,
and home and work locations. Additional characteristics
are sometimes used if warranted. This could include
variables, such as health status, body mass index, etc.,
all defined by population probabilities that exist in
additionally provided external data, but not in the
Census. For example, additional information is needed
to determine the proportion of asthmatics aged 65 to
69 years relative to the total population residing within
the modeling domain. Activity patterns explicit for
people having specific health conditions are
uncommon, thus judgments are used to determine the
appropriateness of available diary data for use in the
assessment (typically not available for the health
compromised). If the existing activity data do not reflect
what people having a health condition do in time and
space, then selected attributes of the diary information
have to be adjusted to better represent time use
patterns of the modeled group. Sensitivity analyses can
then be implemented to evaluate the implications of
making these modifications.
This process is repeated until the simulated
population has proportionally the same characteristics
of the Census-derived population data.
Physiological profile generator. Physiological
characteristics are needed for every simulated person
in the population pool. The main inputs required to do
so are derived from the person's anthropogenic data,
such as age, gender, weight (body mass [BM]), height
(HT), body mass index (BMI), and health status
variables that might affect a person's physiology (e.g.,
asthma, cardiovascular problems, poor fitness, etc.).
BMR is a very important bioenergetic parameter, as we
shall see, and it is derived from the age, gender, BM,
and HT data for each person. Although a number of
equations are available for estimating BMR, the APEX
and SHEDS models currently use the Schofield (1985)
set of equations that account for variability in age,
gender, and BM. Because of criticisms that the
Schofield (1985)-derived equations may not reflect
current population characteristics, such as the higher
BM and larger BMI4 seen in the current population
(Frankenfield et al., 2005; Livingston and Kohlstadt,
4 BMI = BM (kg)/HT2 (m), a widely used index of relative
fatness
2005), the BMR equations used in APEX and SHEDS
will change in the near future.
The variables mentioned above also affect a
person's maximal oxygen consumption rate (VO2.Maxp]),
which, in turn, places an upper limit on the amount of air
that a person can breathe at maximal exercise (VE.Maxp])
(see Blomstrand et al., 1997). Using commonly
available physiological relationships (McArdle et al.,
2001), VO2.Maxp] can be related directly to a person's
METSMax[i]- As noted above, METS are activity-specific
metabolic equivalents of work based on the ratio of
energy expenditure (EE) needed to undertake an
activity (EEA) to a person's BMR, (Ainsworth et al.,
1993, 2000). Activity-specific VO2 is a function of a
person's VO2 Maxp] and prior event work rates (EE)
undertaken (Isaacs et al., 2008).
Activity-specific METS, EE, VO2, and breathing
rate (VE) all are related to each other via well-accepted
physiological principles (Isaacs et al., 2008). However,
there is still a lot of uncertainty regarding applications of
the known principles to actual cases, with limited
knowledge concerning the relationship among fitness
level, lifestyle, and the physiological parameters
mentioned. Many of these uncertainties are amenable
to sensitivity analyses, so that implications of the
assumptions and relationships used can be addressed
quantitatively. If needed fora particular standard
assessment, alveolar ventilation (VA) can be derived
from the VE estimates; EPA staff currently are working
on defining new VE—>VA functional relationships for use
in the APEX and SHEDS models.
CHAD diary selection criteria. CHAD has 34,773
person-days of diary data available for use in the APEX
and SHEDS models. About 41% of them (14,249) are
single-day (cross-sectional) diaries. The remainder has
between 2 and 369 days of data per person (see
Table 1-1). To simulate year-long activity patterns
requires that single-day diaries be sampled multiple
times—a problem that exists with every exposure model
because of the dearth of longitudinal time use data. We
have developed a method (called the "D&A" approach)
of simulating longitudinal activity patterns based on
maintaining the intra- and interindividual variability in
time use seen in the few repeated-measures analyses
of variance that have been undertaken on multiday
surveys and replicating the day-to-day correlations
within individuals in the time spent in selected,
important locations. The method is quite complex but is
logically straight-forward and runs fast in the
simulations (see Glen et al. (2008). In essence, the
method imposes only as much habitual behavior on
individuals and the population (as a whole) that is
described in the literature. See Section 4.E for
additional discussion of the method and metrics used to
implement it.
Conflating CHAD diaries/time use data with the
physiological profiles. The crux of APEX and SHEDS
is combining simulated individually specific time use
-------
Table 1-1. Summary of the CHAD Database
Study Name
Denver MSA
Washington, DC, MSA
Cincinnati MSA
California - adolescents
California - adults
Los Angeles - elementary
Los Angeles - high school
California - children
Valdez, AK
NHAPS - A
NHAPS - B
PSID (CDS) I
Baltimore Elderly
EPA#1
RTP Unhealthy
Seattle MSA
EPA #2
PSID (CDS) II
RTI Averting Behavior
Internal EPA
EPA#1
Mother and Child
Totals
Year*
1983
1983
1986
1988
1988
1989
1990
1990
1991
1994
1994
1997
1998
2000
2001
2002
2002
2003
2003
2007
2007
2008
Diaries
805
699
2,614
183
1,579
51
43
1,200
397
4,723
4,663
5,616
391
367
1,000
1,693
197
4,782
2,907
432
369
62
34,773
Number of Days of
Data per Person
Range
1
1
1-3
1
1
3
2-3
1
1
1
1
1-2
1-24
367
8-33
5-10
197
1-2
1-6
35-69
369
31
Median
1
1
3
1
1
3
3
1
1
1
1
2
14
367
32
10
197
2
4
54
369
31
Sponsor
EPA
EPA
EPRI
GARB
GARB
API
API
GARB
Oil companies
EPA
EPA
NICHHD
EPA
EPA
EPA
EPA
EPA
NICHHD
EPA
EPA
EPA
EPA
Notes and Abbreviations:
* The last year of a multiyear study is used.
# Number (of days)
API = American Petroleum Institute
CARS = California Air Resources Board
CDS = Child Development Supplement
EPA = Environmental Protection Agency
MSA = Metropolitan Statistical Area
NICHHD = National Institute of Child
Health and Human Development
PSID = Population Study of Income Dynamics
RTI = Research Triangle Institute
RTP = Research Triangle Park
data (activity/location) and concentration patterns with
simulated activity-specific breathing rates (VEA) to
obtain intake dose rates. The first step in doing so is to
match simulated people with their appropriate diary
pool, including seasonal and daily meteorological
constraints on human activities. Day-specific National
Climatic Center (NCC) data are used to classify every
day into one of eight seasonal and meteorological
categories (four temperature classes and two
precipitation categories: "none/trace" and ">0.5" per
day). These become "diary day bins" for the model
simulations. Bin definitions are not fixed but are defined
according to the simulation objectives.
The simulations are undertaken on an event-by-
event basis, beginning at midnight on the first day of the
analysis period. For each person, a diary is selected
from the appropriate bin, and a breathing rate is
modeled for each event undertaken. This is repeated
for the daily sequence of activities, and the output is a
string of hourly averaged VE estimates developed from
event-specific EE estimates. A daily physical activity
index (PAI) is calculated from the time-weighted
average of the sum of all the event-specific EE
estimates for the day. PAI can be used to provide a
check on the physiological modeling procedure used in
APEX and SHEDS (McCurdy and Xue, 2004) and as a
10
-------
surrogate for a person's lifestyle and fitness level. In
fact, each person's median PAI can be calculated
directly from the CHAD data and could be one of the
physiological metrics used to develop the diary pools in
the first place (see above).
All of these steps use stochastic processes. The Ct
estimates are partly the result of sampling from known
or approximated distributions of mass-balance equation
parameters (or from indoor/outdoor uE relationship
data). Monte Carlo techniques are used for this
sampling. The same is true for most of the physiological
parameters needed to estimate energy expenditure,
oxygen consumption, ventilation (breathing) rate, and
alveolar ventilation rate, if needed. This stochastic
approach is used to ensure that population variability is
addressed regarding the parameters of interest.
Modeling intake or uptake dose. The second major
step in estimating exposure and dose patterns is to
combine the uE-specific concentration field with the
physiological profiles described above. The simulated
person goes through her or his day, comes in contact
with a concentration (or not) on an event-by-event
basis, and receives a dose based on the estimated
activity level. When the day is completed, the next day
is modeled for the person, continuing for every day in
the simulation period, usually a year. The entire process
is repeated for every individual in the simulated
population.
Intermediate model outputs (for inhalation
exposure analyses) are strings of 1-h averaged
exposure estimates, 1-h averaged VE estimates, and
1-h dose estimates (e.g., E * VE) for each person, plus
any aggregation of them for whatever time period is of
interest.5 This is the dose profile mentioned earlier. For
O3, for example, the main APEX output of interest is the
number of 8-h daily maximum (the highest 8 h in each
day) incidences of exposures when people, especially
children, were exercising at >27 L min"1 m"2 (this is a
body surface area normalized ventilation metric). An
illustration of this type of model output appears as
Figure 1-6; it depicts the 8-h daily maximum exposure
estimates for three population groups in 12 Metropolitan
Statistical Areas for one air quality scenario, with 2002
air quality just meeting the current O3 8-h daily
maximum standard. Five other scenarios also were
evaluated (not shown). Separate sensitivity analyses of
many of the model parameters were simulated in this
assessment, giving an estimate of confidence intervals
about the percentage values depicted in Figure 1-6,
(although not shown in the figure).6 A more thorough
The same metrics could be saved on an event-time basis,
the smallest time interval used in the models, but usually the
data are summed to an hour and saved on that basis.
6 The SHEDS model directly includes uncertainty analyses in
its simulations and provides the same type of output in
cumulative distribution format. It thus combines, in one output,
estimates of population variability and uncertainty in that
variability. OAQPS has found that approach to be difficult to
discussion of this sensitivity analysis is presented in
U.S. EPA (2007b).
Modeling Response to a Dose
The next step after modeling the dose profile is
estimating a response—adverse or not—from the time
pattern of dose rate received. The loci of the response
eventually will be at the cellular level but, currently, is at
the organ level or at a whole-body systems level, using
some type of toxicokinetic modeling approach. EPA has
funded a number of reports describing how this
approach can be used to model adverse health effects
to older adults associated with exposures to xenobiotic
substances. See Hattis and Russ (2003), Ginsberg
et al. (2005), and Krishnan and Hattis (2005) for
example risk assessment documents focused on older
people. Although dose-response and toxicokinetic
modeling are needed to explicitly define health effects
associated with intake dose rates, the topics are
discussed extensively in the scientific literature and
really are one step removed from the exposure/intake
dose modeling focus of this report.
1.C Exposure Model Evaluation
The APEX and SHEDS models have received only
a limited amount of evaluation against measured
personal monitoring data over the years. In general,
OAQPS compares some of their exposure estimates
against personal monitoring data, but usually the latter
are for longer averaging times than those of interest in
the exposure assessment. For instance, OAQPS
compared O3 exposure estimates for children against
weekly average personal monitoring data obtained for a
few weeks in 1995-1996 in two separate areas of San
Bernardino County: (1) urban Upland, CA, and (2) two
small mountain towns (Langstaff, 2007; U.S. EPA,
2007a). That was the only dataset available to the
Agency for such a comparison, even though it was
relatively old and based on a longer averaging time
(6 to 7 days) than of interest in the assessment (1- or
8-h daily exposures). The APEX model performed
reasonably well in the mid-range of the cumulative
distribution of weekly exposure estimates (20th to 70th
percentiles) but systematically overestimated the low
end of the exposure distribution and systematically
underestimated the high end (U.S. EPA, 2007a). This
phenomenon has been found in all synoptic short- to
mid-term model evaluation efforts of which the author is
aware: Burke et al. (2001), Law et al. (1997), Ott et al.
(1988), and Zartarian et al. (2000, 2006). The
overestimate of low-end exposures is not of much
interest, because health risks associated with low-end
exposures generally are not of regulatory concern
(McCurdy, 1995). The probable cause of systematically
underestimating high-end exposures results from the
models' inability to mimic repeated daily activity
explain to decisionmakers and, so, uses the two-step
approach to addressing variability and uncertainty.
11
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Popular,
Subgroup
children
asthmatic children
all people
ATLA
Figure 1-6. Percent of people in three groups—(1) all children, (2) asthmatic children, and (3) all persons—
estimated to experience 1+ days with an 8-h daily maximum O3 exposure >0.07 ppm while at moderate exercise when
the current 8-h daily maximum NAAQS of 0.08 ppm is just met.
patterns that lead to high exposures seen in the
measured data (Law et al., 1997). Thus, the main
reason for model underestimation is basically a
longitudinal time use issue, although the current D&A
procedure may reduce activity variability overtime and
improve model performance. The impact of using the
D&A approach has not been evaluated thoroughly with
respect to exposure model output distributions.
The impact that time use data per se have on
APEX exposure modeling results has received a limited
amount of sensitivity analyses (Nysewander et al.,
2009). These analyses consisted of 5,000 simulations
of seven time use variables in two urban areas, Atlanta
and Boston, using the APEX model. The locational
codes used in CHAD were collapsed to 12 aggregated
locations that accounted for all places visited by every
individual in the simulations (all 24 h were accounted
for, in other words.) A number of "impact" indices were
used to describe sensitivity: time spent in each
microenvironment, daily average and 1-hour maximum
O3 exposure estimates, and distributional tests. The
seven variables included the following.
(1) Selection of the appropriate intra- and
interindividual statistics to combine diary days into
longitudinal patterns
(2) Choice of the "key location" used to sort the above
statistics (e.g., in vehicles versus outdoor time)
(3) Differences in start and stop times for the diary day
(All events were shifted forward and backward 1 h.)
(4) Using diaries from different years to test changes in
time spent outdoors by children (There was a
5.2-min decrease per year in this time for CHAD
diaries from the 1980s to 2007.)
(5) Alternative assignments of "ambiguous location
codes" to either indoors or outdoors (e.g., travel by
boat—indoors or outdoors?)
(6) Modifying the diary "weights" used in the National
Human Activity Pattern Survey
(7) Level of detail in the diaries (Short events were
collapsed into longer durations of 2-, 5-, 10-, and
15-min durations.)
Using the exposure impact indices, differences
among the various simulations were greater than simply
selecting diaries at random, but the differences were
small: -1% to 2% versus -0.2% to 0.5%. The one
exception was age of the diary data itself (the year that
the data were obtained). Using the older diaries
increased exposure estimates by ~1.5% to 21.8%
(Nysewander et al., 2009), mostly because high-end
O3 exposures were associated with time spent
outdoors, which has decreased over the years.
However, this finding may be a result of how the diaries
themselves were coded for the different uEs, rather
than a function of age of the diary perse. More work on
understanding the impacts of age of diary data is
needed before a definitive conclusion can be made
about the topic.
It should be noted that obtaining longitudinal
personal exposure data is extremely expensive,
especially when using "active" short-term monitors (as
opposed to passive long-term "diffusion tubes" that are
based on Brownian movement). Active personal
monitoring involves attaching a monitor having a small
pump to each individual on a daily basis, usually at the
subject's home at a preselected time. Active monitoring
requires a field staff, multiple (expensive) monitors, and
detailed logistics. These types of studies also involve
collecting time use data. Needless to say, these are
12
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invasive protocols, and it is difficult to retain subjects for
periods longer than a week at a time. A monitoring
study—passive or active—reflects "the state of nature"
at the time of the study, including the unique societal
and environmental conditions present at that time.
Because these conditions generally will not be present
at some future time when environmental control
scenarios being modeled are implemented, there is
uncertainty concerning applicability of exposure/dose
relations found in the past in one area being applicable
in another area at a different time. From the modeling
perspective, the best use of monitoring data is to
"ground-truth" performance of the model itself.
A concerted sensitivity/uncertainty evaluation of
EPA's time series exposure models following the
principles advocated in Saltelli et al. (2000) would be
useful and provide insights into those variables and
parameters that significantly affect their performance.
1.D Section 1 Concluding Comments
As we shall see in subsequent sections of this
report, there are quite large differences between the
general adult population and older individuals in how
and where they as groups spend time, travel, and
undertake physical activity and how much of their
physical work capacity is spent on the normal activities
of life. There also are large differences among elders
themselves regarding these attributes. We explore
these issues further from an environmental exposure
modeling perspective. These within-group differences
result in large interindividual variability in exposure and
dose profiles in older individuals, not often addressed in
exposure modeling applications for this population
subgroup. There also is a surprising amount of
intraindividual variability in aging individual's time use
and physiology, and this rarely is addressed in current
modeling efforts. Intraindividual (within-person) data are
provided wherever possible in the following sections,
but such information is difficult to obtain.
Besides the citations provided above, there is a
wealth of general information available on older people,
including trends overtime in their health and well-being,
quality of life, lifestyle, and living accommodations
(Birren etal., 1991; Crimmins, 2004; Federal
Interagency Forum, 2006; Lawton, 1991; Simon et al.,
2001). Basically, people are living longer and are
healthier than they have been in the past but, just
recently, have gotten more overweight/obese (Zamboni
et al., 2005). U.S. Census and other projections of the
numbers of older adults that are expected in the future
indicate that they will be an ever-increasing percentage
of the total U.S. population. The projections only affect
our estimates of the numbers of people that belong to a
particular subgroup of concern but will not affect our
modeling procedures.7
There are caveats to this report. We do not discuss
certain "extra-biological" considerations that may affect
how older Americans respond to exposure to xenobiotic
substances. Some of these considerations might
moderate disease progression given an exposure. They
include religious views and practices of the aged and
their psychological makeup (Olman and Reed, 1998;
Sloan and Wang, 2005). Although important
considerations in the etiology of disease once exposed,
have no a priori data on these factors to use in our
exposure models. Similarly, possible differential
cognitive affects on exposure also are slighted, given
the lack of information on the topic. If better data
become available on these issues, we could simulate
their impact on health endpoints via our stochastic
approach. This is not a theoretical or even a
methodological problem from the modeling perspective;
in other words; it is a data input problem. The
transparency of a model, albeit complex, allows outside
interested observers to interject their own parameters to
see what happens under alternative assumptions.
In sum, the older adult population is increasing
rapidly, both in the United States and worldwide
(Goulding et al., 2003). They will become an important
population subgroup from an exposure modeling
perspective, and not just for PM. Fora discussion of the
detailed type of information that we need as inputs to
our exposure models or to evaluate their performance,
we turn to the broader literature regarding
anthropogenic and physiologic studies of older people.
7 However, appropriate physiological parameters relevant to
changing elderly body composition, such as increasing BM,
would be needed to reflect the current situation.
13
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2. Adjustments to Anthropogenic and Physiological Inputs to the APEX and
SHEDS Models when Modeling Older Populations
ABSRACT
Topic: This chapter covers the the physiological input data
required by EPA's human exposure models and identifies the
data sources available to parameterize these variables for
aging individuals.
Issue/Problem Statement: In most cases, the population
distributions of these physiological characteristics differ
between the general population and the aged and, thus, may
impact directly exposure estimates for older persons. Unique,
age-specific distributions for older adults should be
developed.
Data Available: In general, because of the extensive general
physiology literature (even for older individuals), this topic is
quite data rich.
Research Needs: The identification or collection of additional
data on maximum oxygen consumption and maximal METS in
older adults is needed, although these data are difficult to
come by because of limitations on maximal exercise testing
for this age group. The development of better age-dependent
estimates of basal metabolic rate also should be a priority.
A more detailed look at some of the anthropogenic
and physiological variables in the APEX and SHEDS
inhalation exposure models appears as Figure 2-1.
Variables depicted in this figure are listed in Table 2-1.
The structure of the modeling logic applies to all
population subgroups, but we will emphasize those
variables needed to model older people as a unique
population. Note that most of the respiratory variables
are rates (per unit time, such as L/min) and, as such,
normally are depicted with a "dot" over the "V" symbol.
However, because Microsoft Word does not allow
overstrikes, except in "equation writer," the dots are not
depicted in our discussion. This may cause some
confusion, because "V" also is used in the physiological
literature to represent "volume," such as "dead space
volume" (VD) and "tidal volume" (VT). Those metrics are
often normalized by BM and have units of L/min-kg (or
L/kg-min or L min"1 kg"1). The negative exponential
format is the one used most often in the physiological
literature.
Anthropogenic and physiological variables used in
our models follow; not all of them are depicted in
Figure 2-1 but are mentioned because of their
widespread use in the physiological literature. Our units
are all in the International System of Units (SI)
convention, except EE, where "kcal" is used (1 kcal =
1,000 calories). The SI unit is the Joule (J); 1 kcal =
4.184 kJ or 1 kJ = 0.239 kcal. Kuczmarski et al. (2000)
provides descriptive statistics from the 1988-1994
National Health and Nutrition Examination Survey
(NHANES) for a number of important anthropogenic
parameters used in our models.
2.A Conceptual Framework of
Physiological Changes Resulting from
Aging
To account for factors that affect intake dose rate
for older adults, we developed a conceptual framework
of important anthropogenic and physiological attributes
that might affect metabolic parameters used in our
exposure models; this is depicted as Figure 2-2. The
figure basically is a qualitative "path analytical"
framework of physiological relationships in people, with
a focus on attributes affecting older individuals. Not all
of the attributes are included in APEX or SHEDS, but all
can influence how a person metabolizes xenobiotic
substances following an exposure. Direct, causal
relationships are depicted with a solid line; indirect,
BMR/RMR * METS *
CHAD
BM
BSA
U
(EE -» V02)
= VO2—i
RQ/Pam9 —
Va
VD/VT
r
EVR
VQ
HT
Source: Stephen Graham
Figure 2-1. Activity-specific metabolic and ventilation metrics used in EPA exposure models.
14
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Table 2-1. Variables Used for Activity-Specific Metabolic and Ventilation Metrics Used in APEX and
SHEDS Exposure Models
Variable
A
BM
BMI
BMR
BSA
EEA
El
EVR
FFM
G
HR
HT
LBM
METSA
METSMax
PaCO2
PAI
PEFR
RQ
TDEE
U
VA
VD
VE
N/E.Max
VQ
VT
VO2
VO2Max
Definition and Source of Data
Age (years); obtained from U.S. Census data
Body mass (kg); random-sample BM from age/gender-specific NHANES distributions and assign the
"realization" to a simulated person (kg)
Body mass index (kg m"2); calculated from BM/HT2
Basal metabolic rate (kcal/time period); calculated from age/height data using (currently) the Schofield (1985)
equations (in kcal min"1 or kcal min"1 kg"1, kcal day"1, kcal 24h"1, etc., as appropriate)
Body surface area (m2); in APEX, BSA is estimated from BM using an exponential relationship reported in
Burmaster (1 998)— BSA = ea * BMb.
Activity-specific energy expenditure estimates (££A = BMR * METSA) (kcal min "' for the activity duration); CHAD
contains activity-specific distributions of METS (see below).
Energy intake (kcal per some defined time period) [We currently do not use El in our exposure models.]
Equivalent ventilation rate (L min"1 m"2); a BSA-normalized total ventilation rate (VE) [This parameter has been
used in the APEX exposure assessments for ozone and SO2.]
Fat-free mass (kg); also called lean-body mass (see LBM)
Gender; U.S. Census (female [$], male [d?]); obtained from U.S. Census data and generally treated as a
nominal variable
Heart rate (beats/min) [This variable has not been used in our exposure models to date.]
Height (m); derived distributions from NHANES age/gender-specific measurements in the overall population
Lean body mass (kg); the amount of bone and muscle mass in the body (Muscle is the primary component of
LBM by weight.) It does not include nonsubcutaneous fat. Generally, it is quantified by subtracting an estimate of
fat mass (measured indirectly by a variety of methods) from total BM. Most physiological parameters have
improved relationships with one another when normalized to LBM rather than BM alone.
Metabolic equivalents of work (unitless); sampled from activity-specific distributions in CHAD (McCurdy, 2000)
Maximum measured METS estimates (unitless); CHAD-specified and age/gender-specific
The arterial partial pressure of CO2 (torr); not currently used in our exposure models (except APEX-CO, the CO
version of APEX)
Physical Activity Index (unitless); the daily time-averaged METS estimates for an individual
(I METSA * timeA [min])/1 ,440 min ), also known as the Physical Activity Level (PAL)
Peak expiratory flow rate [L min"1]; the maximum rate of expelled airflow during a forced expiration. It is used as
an indicator of asthma or other lung diseases. Although it is believed to be a measure of large airways function,
it is an insensitive measure because it is heavily reliant on each subject's effort, which is highly variable (Cook et
al., 1989).
Respiratory quotient (unitless); the ratio of volume of CO produced (VCO2) to oxygen consumed (VO2) [Not
used in our exposure models currently]
Total Daily Energy Expenditure (kcal day"1)
A conversion factor to convert energy expenditure (kcal) into oxygen consumption (L/kcal); 1 L O2 ~ 4.85 kcal,
values between 4.69 and 5.01 are seen in the literature, depending on the foodstuffs being metabolized. Using
the 4.85 conversion, 1 kcal = 206 mL O2. APEX randomly samples from uniform distributions of 200 to 21 0 mL
9 and 210 to 220 mL ?.
Alveolar ventilation rate (L min"'); the effective ventilation rate of the alveoli in which gas exchange with blood
occurs in the pulmonary capillaries [A "dot" should be over the "V".]
Dead-space volume in the respiratory system (L); the combined volume of all air passages in the respiratory
system in which no gas exchange occurs [Values of VD come from the literature.]
Breathing rate or "minute ventilation rate" (L min"'); calculated from regression equations relating age/gender-
specific BM to VE [A "dot" should be over the "V".]
Maximum VE rate for an individual; a nonlinear relation of VO2Max (L min"') [A "dot" should be over the "V".]
Ventilatory equivalent (unitless); the ratio of VE to VO2 at any specified energy expenditure rate. It varies from
about 20 to 32 in healthy individuals, with the lower ratio being at rest. [It no longer is used in our exposure
models.]
Tidal volume (L) in the respiratory system; the volume of air that is inhaled or exhaled. VT increases greatly from
rest to maximal EE.
Activity-specific oxygen consumption rate (mL O2 min"'); estimated using a gender-specific U (EE to VO2 ratio)
[A "dot" should be over the "V because it is a rate.]
Age/gender-specific maximal oxygen consumption rate (mL min"1 kg"1); also known as VO2Peak; considered to be
"aerobic capacity" [A "dot" should be over the "V".]
15
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correlated relationships are depicted by curved dashed
lines. Important genetic factors that directly affect an
attribute are depicted by straight, lightly-dashed lines.
A plus sign on a relational line, either direct or
correlated, indicates a positive impact, whereas the
opposite is true for a negative sign. Looking at the
diagram, and beginning with age, as age increases, a
person's HT usually decreases (-); morbidity (disease)
increases (+) but possibly not as a function of age per
se; frailty increases (+); BMR decreases (-), both on an
absolute and relative-to-BM basis; physical activity
usually decreases (-); and, physiological processes of
many types decrease (-). These might include maximal
oxygen consumption, maximal breathing rate, maximal
heart rate, and body strength. The "Diff note indicates
complex relationships between the linked variables that
probably are nonlinear and that vary with gender; we
make no a priori hypothesized direction of change
between the two variables.
Those variables in Figure 2-2 that are an explicit
part of our exposure models include the anthropogenic
variables: age, HT, and BM—but not IBM or BMI. Other
explicit variables in the models are BMR, fitness—as
estimated by maximal oxygen consumption (VO2Max),
and a surrogate for "fitness"—the PAI. Frailty and
disease states could be handled in our exposure
modeling procedures by sampling from data from
people having those types of issues, where available.
Model simulations then would provide information about
the impact that the altered states have on model
results.
It should be noted that many of the factors
depicted in Figure 2-2 have been studied and shown to
be important in morbidity and mortality in older adults
(Skinner, 1970). Often, these factors are known by
more precise nomenclature than listed. One of the most
important considerations is sarcopenia, age-associated
loss of muscle (Rogers and Evans, 1993; Starling et al.,
1999a). Another is the "metabolic syndrome"
(a complex of symptoms focused on abdominal
adiposity, hypertension, high cholesterol, elevated
triglycerides, and high glucose), and hormonal changes
(Maggio etal., 2006; Metteretal., 1997; Rodriguez
et al., 2007; Schranger et al., 2007; and Skinner, 1970).
"Aging of the respiratory system" is a major issue in
limiting human activities and performance (Zeleznik,
2003). Figure 2-2 is a broad and general depiction of
important physiological and metabolic changes in
people as they age. These changes undoubtedly affect
what people can do, the activities that can be
undertaken, and where they occur. These factors, in
turn, affect exposures experienced and dose/effect
relationships in aging individuals.
What follows is a discussion of variables identified
as having (1) significant influence on exposure
modeling outcomes and (2) adequate data available for
use in EPA models. They include basal metabolic rate,
maximal oxygen consumption, METS, maximal
ventilation rate, the ventilatory equivalent, and maximal
heart rate.
2.BBMR
BMR is also known as resting metabolic rate
(RMR) or resting energy expenditure (REE). It
approximates the unavoidable loss of heat because of
cell metabolism and energy expended in maintaining
minimal bodily functions: circulation, respiration,
digestion, and involuntary muscle tone (McCurdy,
2000). Most basal energy is expended to keep the
brain, liver, and skeletal muscles functioning properly. It
has various units, depending on the application, but all
involve energy expenditure in kcal or kJ for some time
period. The most commonly used units are kcal day"1 or
kcal min"1, but BM-normalized units often are used (kcal
kg"1 min"1 or kcal kg"1 day"1). Alternative BMR units also
CONCEPTUAL FRAMEWORK FOR AGING
Height —-—»• Lean Body Mass
A f+
AGE
Fitness Lifestyle
Diseases and Illnesses
PHYSIOLOGICAL PROCESSES
A
Diff
» METABOLIC PROCESSES
Diff
Diff
:::::::::::::::::::::: GENETICS :::::::::::::::::::::
Source: Thomas McCurdy
Figure 2-2. Conceptual framework of important relationships that affect physiological processes in the body.
16
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are used; sometimes BMR is expressed as oxygen
consumption in L min"1 or ml min"1, and the "U"
conversion factor depicted in Table 2-1 is used to
convert them into EE units. Also, by definition, BMR =
1 MET (unitless). Dividing BMR by BM (BMR/BM in
units of kcal min"1 kg"1 or one of the alternative
measures) reduces the population variability of the
BMR among age and gender groups. Dividing BMR by
IBM reduces population variability BMR further,
especially in the aged (McArdle et al., 2001). These
transformations are called BM- or LBM-normalized
BMR.
There is a strong association between body
surface area (BSA) and IBM (McArdle et al., 2001).
IBM decreases significantly after 60 years of age in
both genders and for different ethnic groups, but the
rate of change is not the same for all age/ethnic/gender
group combinations (Obisesan et al., 2005).
Most studies show a significant decrease in BMR
overtime both for individuals (longitudinally) and among
older adults (cross-sectionally) (see Figure 2-3). This is
true for both U.S. (Hunter et al., 2001; Obisesan et al.,
2005) and non-U.S. studies (Goldberg et al., 1988;
Haveman-Nies et al., 1996; Kwan et al., 2004). This
decrease is seen for all the usual BMR metrics:
absolute, BM- and IBM-adjusted, and BSA- and BMI-
adjusted variations (Dupont et al., 1996). The rate of
decline is about 1% to 2% per decade (Keys et al,
1973). Reduction in BM in seniors by itself explains
about 55% of the relative decrease in BMR (Obisesan
et al., 1997). BMR is correlated positively with both
activity level (fitness) and IBM (Anderson et al., 2001).
However, other studies indicate that BMR is only
slightly lower in older than in younger adults (Das et al.,
2001). These authors state that weight gain in older
individuals—a relatively recent trend—is
"compensating" for the differences in body composition
of older people, and that the net effect is causing BMR
to be similar to or even higher in older subjects
compared to young ones (Das et al., 2001; p. 1837,
citations removed). This trend of weight gain in seniors
may affect future BMR predicting equations, as the
LBM-to-total BM ratio changes with body composition in
overweight and obese people.
Estimates of the daily intraindividual variability
range in BMR in people >65 years of age are about 6%
to 8% (Visseret al., 1995). The cross-sectional
population coefficient of variation (COV; mean/standard
deviation) for people >70 years is somewhat lower, but
sample sizes for repeated measures studies of BMR in
older individuals are small. For instance, the COV's for
females >70 years was between 2.5% and 3.0% on
average, with some individuals showing more than a
12% difference over relatively short time intervals
(Gibbons et al., 2004). The COVs for males >70 years
was 3.6% to 4.0%, with the highest individual having a
10% COV (Gibbons et al., 2004).
A table of older American's BMR values seen in
the literature is not presented here because EPA staff
(Graham and McCurdy, in preparation) have compiled
extensive U.S. data on BMR measurements. The
information will be used to develop de novo BMR
regression equations to replace the "Schofield
equations" (Schofield, 1985) currently used in APEX
and SHEDS. To provide some basic information in this
report on BMR, the following prediction equations
(in kcal day"1) are taken from Nieman (1990), who, in
turn, reproduced them from the sources noted. The
equations are for adults >18 years, unless otherwise
noted. BM has units kilograms, HT is in centimeters,
and age (A) is in years (y). Gender-specific equations
usually are presented for BMR.
From the Owens equations reproduced in Nieman
(1990])
BMR S = 879 + (10.2 * BM)
BMR ? = 795 + ( 7.2 * BM)
TJ
I
4-*
CO
§ 40H
TO
"? 35 H
DC
I 30 H
E
CO
GO
25 -
20
•
* * ^* • - »«. BJ*» *^ i^fc^» ,^j»»^ *—*^"« * » *
KAbn *»* .*"i™nii *^' *^ •
_ MBI , . * r* f ' "1. f*,, ,, ''„* ,•
-, Women • •
10 20 30 40 50 60 70
Figure 2-3. Decrease of BMR with age.
80 90 100
Source: Ruggiero et al. (2008)
17
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From the revised Harris-Benedict equations
reproduced in Nieman (1990)
BMR 3 = 88.4 + (4.8 * HT) + (13.4 * BM) -
(5.7* Age)
BMR ? = 447.6 + (3.1 * HT) + ( 9.2 * BM) -
(4.3 * Age)
From the World Health Organization (WHO)
equations depicted in Nieman (1990) for people
>60 years
BMR(? = 487 + (13.5*BM)
BMR? = 596 + (10.5*BM)
As mentioned, in the exercise physiology literature,
BMR is defined to be 1 MET (see the following section).
It also often is "fixed" at 3.5 ml kg"1 min"1 oxygen
consumption (Kwan et al., 2004; McArdle et al., 2001),
but that equivalency has been shown to be incorrect—
even as a mean population value—for seniors and
children (Kwan et al., 2004; McCurdy and Graham,
2004a). Age, gender, fitness level, and health status all
affect BMR on an absolute and relative basis. A fixed
BMR value is inconsistent with that observation and will
not be further used in this report.
The relationship between BMR and mortality in
older individuals is complex and nonlinear. Relatively
low- and high-BMR groups (compared with the mean
group) have increased mortality independent of age,
BM, BMI, total physical activity, muscle mass, strength,
diabetes status, and a number of other physiological
considerations (Ruggiero et al., 2008). These findings
come from the Baltimore Longitudinal Study of Aging
(BLSA), a comprehensive National Institute on Aging
(NIA)-funded study that began in 1958. The sample
used in the Ruggiero et al. (2008) analysis consisted of
1,227 participants enrolled in the 1958-1982 period that
were evaluated in 2000. BMR was measured every
2 years in a clinical setting, along with other
physiological and cognitive parameters. Their data are
reproduced as Figure 2-3 above.
2.C METS
METS are metabolic equivalents of work, the
unitless ratio of activity-specific energy expenditure to
basal metabolism. Thus, if an activity incurs a
20 ml kg"1 min"1 oxygen consumption (EEA in O2 units),
and BMR is 6 ml kg"1 min"1, the activity-specific METS
(METSA) is 3.3. Maximum METS (METSMAx) increases
in childhood, gradually declines in adults, and
decreases rapidly in seniors (Lai et al., 2009). The
METSMAx values for people > 65 years old are about
67% of those <50 years old and even lower in those
people who subsequently died of cardiovascular
problems (Lai et al., 2009). McArdle et al. (2001) state
that METSMAx drops from about 10.0 in middle age to
7.0 in older people and drops again to 4.0 in the very
old. Although the precise age descriptors are not
defined in McArdle et al. (2001), they can be estimated
from VO2Max data. To facilitate that work, we are
developing databases of physiological information for all
ages and both genders and will undertake meta-
analyses of that data in the future.
Data are sparse concerning METSMAx values for
older people. Papers that do discuss them are reviewed
below. It should be recognized that because of the
general low fitness levels of seniors, most of the
estimates are derived from "symptom-limited" exercise
protocols that estimate METSMAxfrom submaximal
tests. This is done to avoid severe morbidity and
mortality incidents associated with a maximal exercise
test. However, maximal exercise protocols are used in
older healthy individuals (see Section 2.D).
The estimates from Amundsen et al. (1989) are
quite low relative to younger individuals. METSMAx for
sedentary females divided into two groups was
4.5 ± 1.7 for 75.7 year-olds (n=14) and was 3.7 ±0.8 for
71.8 year-olds (n=5). The authors do not speculate as
to why the expected pattern of higher METSMAx for
younger people did not hold in this case, or why the
METSMAx values were so low. Yamazaki et al. (2004)
provide METSMAx estimates for male patients (with no
heart-related issues) tested at two Veterans
Administration (VA) hospitals. They indicated that
METSMAx was 7.0 ± 3.0 METS for males aged 65 to
75 years, declining to 6.5 ± 2.8 for 70 to 74 year-olds
and to 5.6 ± 2.5 for>75 year-olds. Sergi et al. (2009)
estimated that METSMAx for 81 females aged
70.4 ± 3.9 years was 6.1 ± 1.2, and Sagiv et al. (1989)
stated that METSMAx for 40 males aged 67± 4 years
was 9.1 ± 1.2. These scant data seem to indicate that
there are relatively large age and gender differences in
the METSMAx parameter.
CHAD, a direct input to the APEX and SHEDS
models, contains distributions of activity-specific METS
that were derived from a statistical analysis of METS
values contained in Ainsworth et al. (1993; updated by
Ainsworth et al., 2000) and other sources of
information. McCurdy et al. (2000) describe how the
METS distributions in CHAD were developed. Activity-
specific METS are discussed in Section 3.
2.D VO2Max
VO2Max is maximal oxygen consumption and is also
known as maximal aerobic power (Jones and Lindstedt,
1993). It is highly related to the genetic makeup of an
individual. McArdle et al. (2001) state that between 40%
and 90% of variability in VO2Max can be attributed to
heredity alone. VO2Max values generally are obtained
from empirical testing of the amount of oxygen
consumed by subjects undertaking a strenuous
exercise test. The estimates usually come from cycle
ergometer or treadmill tests of whole-body exercise, but
also are derived from specific tasks that mimic the real
world. There are many articles presenting VO2Max
information for children and adults but many fewer for
seniors (Conley et al., 2000; Goodman and Thomas,
2002). An early summary of cross-sectional VO2Maxdata
on older adults is presented in Smith and Gilligan
18
-------
(1989). VO2Max estimates for older people seen in the
literature are summarized in Tables 2-2a and 2-2b.
In our exposure models, VO2Max is estimated from
age/gender-specific equations using a range of "U"
coefficients (see above). EPA staff currently is
developing a database of age/gender VO2Max
observations from the exercise physiology literature to
check on the performance of these equations.
In general, there is a decline in VO2MaxWith age in
both genders on a BM-adjusted basis, regardless of the
test protocol used (Aminoff et al., 1996; Fleg, 1994;
Fleg et al., 2005; Peiffer et al., 2008; Proctor et al.,
1998; Smith and Gilligan, 1989). The reduction probably
is nonlinear with age (Wiswell et al., 2001) but often is
depicted as a linear trend. The reduction is thought to
be associated with a decrease in large muscle mass but
not in muscle metabolic capacity or morphology
(Aminoff etal., 1996; Fleg and Lakatta, 1988;
Kent-Braun and Ng, 2000; Kirkendall and Garrett Jr.,
1998). Thus, physical work capacity seemingly is not
reduced in old people—at least into their 60s—when
small muscles control. However, there also is
disagreement on this point (see Conley et al., 2000).
Females have a lower VO2Max than males, even on a
per-BM basis. Total body fat does not seem to affect
VO2 Max after adjusting for IBM (Goran et al., 2000).
Other published information indicates that aging
per se results in a decline in VO2Max in older people
(Goodman and Thomas, 2002). Reduced physical
activity, physiological aging (biological functioning), and
increased prevalence of pathological conditions
contribute to this decline. Goodman and Thomas (2002)
estimate that VO2Max declines 5% to 15% per decade
from early adulthood.
McArdle et al. (2001) state that VO2Max decreases
about 0.4 to 0.5 ml kg"1 min"1 per year in adults after
age 20 (p. 882). However, this estimate mixes active
and sedentary individuals; VO2Max decreases less in
active people than sedentary ones, especially inactive
individuals who are overweight or obese. McArdle et al.
(2001) provide the following equations for VO2Max-
VO2Max <$ [ml min"1 kg"1] = 59.48 - (0.46 *
V0
2Max ?
Age [years]) SEE = 7.12
[ml min"1 kg"1] = 57.73 - (0.54 *
Age [years]) SEE = 6.44
Other VO2 Max prediction equations for seniors exist
(Blackie et al.,1989). The SEEs of the McArdle et al.
(2001) equations are about 30% of their means, so they
are quite large, indicating a lot of variability in that
population subgroup, as seen in Tables 2-2a and 2-2b
for the various fitness categories. In fact, McArdle et al.
(2001) explicitly state that sedentary living produces
losses in functional capacity at least as great as the
effects of aging itself (p. 883).
Surveying other articles regarding the decline of
VO2 Max with age, Bonnefoy et al. (1998) estimate that it
decreases about 8.3% per decade in males after their
20s on a BM basis. That estimate is roughly consistent
with the prediction equations presented above and with
the values given in Fleg (1994). Bruce (1984) states
that the decline is about 0.4 ml min"1 kg"1 year"1, which
is somewhat lower than that indicated by the above
equations. In males, Jackson et al. (1995) state that the
decline is 0.46 ml min"1 kg"1 year"1. In females, the
decline in VO2 Max has been estimated as 0.54 ml kg"1
min"1 year"1 (Jackson et al., 1996). Larson and Bruce
(1987) state that the decline is 0.4 ml kg"1 min"1 year"1
in the healthy aged (both genders) when measured
cross-sectionally but 0.9 ml kg"1 min"1 year"1 in the
same people when analyzed longitudinally. Thus, using
cross-sectional data to describe VO2 Max change in
individuals overtime will underestimate systematically
the longitudinal impacts of aging (Wiswell et al., 2001).
The probable cause of the underestimate is that fitness
levels of individuals decrease differentially overtime,
and that is not explicitly accounted for in a cross-
sectional analysis.
Probably the most comprehensive review of
reduced maximal oxygen consumption in aging
individuals is Hawkins and Wiswell (2003). They
indicate that the decline in VO2 Max is caused by both
central and peripheral physiological adaptations,
especially reductions in maximal heart rate and IBM
(muscle). The authors distinguish between the decline
rate in inactive people (10% to 15% per decade) and
athletic people (5% to 7% per decade). The decline is
nonlinear with age, declining faster after 70 years
(Hawkins and Wiswell, 2003). They also state that
results from cross-sectional studies of VO2 Max in older
adults give quite different results than longitudinal
studies of that cohort, especially for formerly active
individuals. The reader is advised to review Hawkins
and Wiswell (2003) for an excellent and succinct
discussion of the topic. Kenney (1997) and Stamford
(1988) support the same findings. See also Pollock
(1974) and Pollock et al. (1987,1997) for measuring
VO2 Max (and VE) in current and former athletes over
extended periods of time—10 and 20 years.
Evidence exists indicating that prolonged dynamic
exercise at the same percentage of VO2 Max (~65%)
under controlled conditions represents no more of a
physiological strain in healthy older adults than in young
people (Davy et al., 1995). This finding could have been
affected by the protocol; to get the ~65% of VO2 Max
exercise level in young adults, they had to run, whereas
older adults (65 ± 2 years) had only to walk (Davy et al.,
1995). There were other testing differences that might
affect their findings. This was the only paper with this
type of finding, so its results need to be considered
carefully.
VO2 Max has been used as an indicator of
cardiorespiratory fitness and is a strong predictor of
successful cognitive functioning in older people
(Shephard, 2009), explaining more variance in cognitive
measures than "higher order" measures of cognition
(memory, speed of processing information, "executive
functioning," etc.) (see: Newson and Kemps, 2006). In
addition, a VO2 Max<30 to 35 ml kg"1 min" in older
19
-------
Table 2-2a. Literature-Reported Estimates of VO2Max for Older Adults
Age Range
(Mean
and SD)
60-69
60-69
70-79
80-89
60-69
>60
>60
60-69
60-77
61 ±3
61 ±4
62 ±3
62 ±6
62 ±7
63 ±5
63.3 ±2.9
64 ±4
64 ±6
64.0 ±3.1
65 ±5
Ethnic
Group
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Females:
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Health
Status
NS
NS
NS
NS
NS
N
N
N
N
Ath
N
N
N
Ath
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
VO2Max
Estimate
(mL/kg-min)
£15.9
16.0-22.9
23.0-35.9
36.0-40.9
£41.0
25.1
32.1
33.2
24.6
25
25.5
20.3
24.7
21.9
£12.9
13.0-20.9
21.0-32.9
33.0-36.9
>37.0
<27
28-30
31 -37
38-40
>41
£21
22-24
25-30
31 -33
>34
Citation
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
Shephard 1966
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
McArdle et al. 2001
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Baumgartner and Jackson 1999
Normal or Healthy
H
H
N
N
N
N
N
N
N
N
N
N
N
25.7 ±4.4
19.4 ±0.9
26 ±3
22.2 ±4.7
33.4 ±7.6
23.1 ±3.3
21.7±3.3
30.1 ±8.5
21.8±2.6
24.3 ±4.3
29.1 ±4.8
21.6 ±2.9
27.1 ±5.8
Flegetal. JAP 1995
Parker et al. 1996
Hagberg et al. 2003
Huntetal. 1997
Hagberg et al. 1998
Sheldahletal. 1996
Sheldahletal. 1996
Hagberg etal. 1998
Kohrtetal. 1991
Proctor et al. 2003
Farquhar and Kenney 1999
Kohrtetal. 1991
Hagberg etal. 1998
Comment
Poor cardio. fitness
Fair cardio. fitness
Average cardio. fitness
Good cardio. fitness
Excellent cardio. fitness
Untrained: Canada
Untrained: U.S.
Untrained: Scandinavia
Untrained: General
Active/Ath.: General
Untrained: U.S.
Untrained: General
Active/Ath.: General
Active/Ath.: General
Poor cardio. fitness
Fair cardio. fitness
Average cardio. fitness
Good cardio. fitness
Excellent cardio. fitness
Poor aerobic fitness
Fair aerobic fitness
Average aerobic fitness
Good aerobic fitness
Excellent aerobic fitness
Poor aerobic fitness
Fair aerobic fitness
Average aerobic fitness
Good aerobic fitness
Excellent aerobic fitness
n=12
n=14
n=9
n=15
n=22; ACE=
n=9
n=11
n=16
n=13
n=8
n=57
20
-------
Age Range
(Mean
and SD)
65.5 ±7.8
66 ±4
66.8 ± 15.9
67 ±4
67±NS
68.0 ±7.0
68.7 ±5.7
70.0 ±6.1
70.4 ±3.9
70.4 ±6.1
70.9 ±8.1
71 ±6
71.2 ±3.5
72.3 ±2.1
73 ±9
73.3 ±2.7
80-89
60 ±5
60.0 ±7.0
60 ±8
62.0 ±8.10
62 ±2
62 ±6
64 ±4
64 ±4
64 ±4
64 ±5
64.4 ±3.2
65 ±4
65 ±5
65.7 ±6.3
65.7 ±6.3
66 ±6
67.0 ±4.9
68.0 ±5.6
69.2 ± 11.0
71.1 ±5.1
71.3±4.4
71.5±4.6
71.5±4.8
72 ±8
73.5 ±5.7
75.3 ±4.6
60 ±3
61 ±8
63.3 ±2.0
64 ±7
Ethnic
Group
NS
M
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Females:
NS
NS
NS
W
NS
W
NS
NS
W
NS
M
His
NS
NS
NS
W
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Females:
NS
NS
NS
W
Health
Status
N
N
N
N
N
N
N
N
N
N
N
N
N
H
N
N
H
Sedentary,
Sed
O
O
Sed
Sed
Sed
Sed
Sed
Sed
O,OW
Sed
Sed
OW
Sed
Sed
Sed
Sed
O,OW
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
VO2Max
Estimate
(mL/kg-min)
17.0 ±5.6
32.5 ±4.7
29.4 ± 14.5
19.3 ±3.9
21.2 ±4.6
23.2 ±5.3
21. 9 ±4.2
21. 5 ±4.3
17.5 ±2.8
17.6 ±5.0
20.3 ±4.1
24.8 ±3.6
22.6 ±3.2
19.5±4.1
25.2 ±6.2
16.7 ±3.3
21.2 ± 1.3
Overweight, or Obese
22.9 ±4.1
15.0 ±2.8
21.1 ± 1.6
22.6 ±4.0
22.8 ± 1.4
23 ±3
22.4 ±4.8
22.2 ±3.1
21.5±4.7
36.3 ±8.2
22.0 ±2.2
20.7 ±2.9
20.2 ±3.6
19.9 ±3.1
19.9±3.1
23 ±3
16.2 ±3.5
12.0 ±2.3
20.3 ± 7.6
17.3 ±4.0
23.7 ±4.7
21. 5 ±5.2
16.4 ±2.7
19.1 ±3.6
26.8 ±8.3
25.0 ±4.2
Citation
Carter et al. 1994
Clausey et al. 2001
Wilund et al. 2008
Treuthetal.1995
Hollenberg and Tager2000
Pescatello et al. 1994
Panton etal. 1996
Parise et al. 2004
Sergi et al. 2009
Ainsworth etal. 1997
Simonsick et al. 2006
Stachenfeld et al. 1998
Fehling etal. 1999
Melanson et al. 1997
Stachenfeld et al. 1998
Perini et al. 2000
Fleg etal. JAP 1995
Jones etal. 1997
Jordan et al. 2005
Tanaka etal. 1998
Seals etal. 1999
Tanaka etal. 1998
Hagberg et al. 2003
Tanaka etal. 1997
Ogawa etal. 1992
Schiller etal. 2001
Nicklas et al. 2003
Turner etal. 1999
Schiller etal. 2001
Thompson et al. 1997
Kohrtetal. 1998
Kohrtetal. 1998
Hagberg et al. 2003
White etal. 1998
Kara et al. 2005
Wilund et al. 2008
Pererson et al. 2003
Audette et al. 2006
Audette et al. 2006
Ades et al. 2005
Weiss et al. 2006
Audette et al. 2006
Kent-Braun and Ng 2000
Fit, Active, or Athlete
Fit
Ath
Ath
Act
30.7 ±6.6
35.1 ±4.5
46.2 ±9.0
26 ±3
Hunt etal. 1997
Proctor etal. 1998
Hawkins etal. 2001
Hagberg et al. 2003
Comment
n=16
n=27
n=6
n=15
n=579
n=11
n=36
n=117
n=81
n=18
n=46
n=9
n=42
n=8
n=8
n=11
n=2
n=12
n=24
n=9; Sed
n=20
n=9
n=9; HRT
n=16
n=14
n=18
n=29
n=10
n=5
n=40
n=112
n=112
n=10; no HRT
n=60
n=45
n=6
n=114
n=8
n=11
n=21
n=83
n=8
n=9
n=10
n=8
n=13; visit #1
n=11; no HRT
21
-------
Age Range
(Mean
and SD)
64.6 ±3.8
65 ±3
65 ±3
65 ±4
67.0 ±5.1
72.9 ±5.5
73.2 ±5.7
VO2Max
Ethnic Health Estimate
Group Status (mL/kg-min)
NS
W
W
NS
NS
NS
NS
Ath
Ath
Ath
Ath
Fit
Fit
Ath
39.4 ±4.8
39 ±6
38 ±7
31. 5 ±2.4
45.3 ±7.2
21.0±4.3
31. 8 ±8.4
Females: Health Problems
63.7 ±5.8
64.8 ±6.4
65.0 ±5.2
72.9 ±6.1
60-67
60-69
60-79
60.0 ±8.5
61 -79
62 ±6
63 ±3
63 ±3
63 ±6
63.7 ±3.1
64 ±5
64 ±5
64.2 ±9.4
64.8 ±3.6
65 ±2
66.0 ±5.2
67 ± 1
67 ±2
68±NS
68 ±3
68.0 ±5.8
68.6 ± 10.5
68.6 ±5.1
68.7 ±4.8
69 ±2
69 ±3
70.4 ±3.8
70.7 ±7.5
71.1 ±3.8
71.4 ±6.3
71.6 ±2.4
71. 7 ±5.2
72.1 ±7.6
73.1 ±5.9
73.6 ±5.9
74 ±4
NS
NS
NS
NS
Males:
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
M
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
COPD
COPD
COPD
Cardio
Normal or Healthy
Ex-Ath.
H
N
N
N
N
N
N
N
N
N
H
Ex-Ath.
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
11.3±3.0
16.6 ±4.1
13.9 ±3.5
14.2 ±2.9
37±NS
30.4 ±8.2
24.2 ±NS
22.5 ±5.2
23.7 ±NS
34.9 ±3.3
26.5 ±3.5
35.3 ±5.4
30.1 ±8.6
27.5 ±4.2
32.9 ±5.6
41.8±2.9
45.9 ±6.7
28.3 ±4.3
31.5±2.3
30.0 ±5.9
23.9 ± 1.0
27.4 ±4.0
26.5 ±6.1
29.9 ±7.4
20.7 ±6.7
20.7 ±6.8
27.7 ±3.7
26.3 ±5.2
39 ±7
24.8±3.4
28.9 ±4.9
31.3±5.6
29.5 ±4.7
28.2 ±5.0
16.4 ±2.8
25.4 ±3.9
23.7 ±4.0
25.8 ±4.5
27.9 ±6.2
24.6 ± 5.6
Citation
Hawkins et al. 2001
Hagberg et al. 2003
Hagberg et al. 2003
Tanaka et al. 1997
McClaran et al. 1995
McClaran et al. 1995
Hawkins et al. 2001
Carter et al. 1994
Carter et al. 1994
Carter et al. 1994
Ades et al. 2005
Saltin and Grimby 1968
Flegetal. JAP 1996
Buccola and Stone 1975
Carter et al. 1994
Buccola and Stone 1975
Sheldahletal. 1996
Proctor et al. 2005
Hunt etal. 2001
Flegetal.1995
Kohrtetal. 1991
Tankersley et al. 1991
Kenney and Ho 1995
Pollock etal. 1987
Kohrtetal. 1991
Davy etal. 1995
Clausey et al. 2001
Thomas etal. 1999
Thomas etal. 1999
Hollenberg and Tager2000
Thomas etal. 1999
Ainsworth etal. 1997
Andros and Gerber 1998
Panton etal. 1996
Lost reference
Sheffield-Moore et al. 2004
Thomas etal. 1999
Fehling etal. 1999
Pescatello etal. 1994
Bonnefoy et al. 1998
Parise et al. 2004
Sabapathy et al. 2004
McAuley et al. 2007
Simonsicket al. 2006
Talbot et al. 2002
Talbot et al. 2002
Proctor et al. 2005
Comment
n=9; visit #1
n=9; HRT
n=12; no HRT
n=13
n=18; visit #1
n=18; visit #2
n=9; visit #2
n=58; severe
n=23; mild
n=42; moderate
n=21
No training in 10 years
n=26; nonobese
n=16
n=13
n=20
n=9
n=10
n=12
n=23
n=53
n=7
n=6
n=13
n=19
n=6 untrained; healthy
n=35
n=4
n=3
n=419
n=7
n=10
n=12
n=19
n=19
n=6
n=7
n=44
n=14
n=37
n=95
n=9
n=126; followup
n=56
n=27; cardio
n=140; no cardio.
n=14
22
-------
Age Range
(Mean
and SD)
74 ±5
74.7 ±2.8
80-89
60.0 ±5.1
61.1 ±6.2
61.4 ±5.2
62 ±6
63 ±3
63 ±7
63 ±5
64 ±3
65 ±2
65 ±3
65 ±5
65 ±5
66 ±5
66.4 ±5.6
66.7 ±5.4
66.7 ± 14.9
67 ±2
67.9 ±5.6
72.2 ±5.7
72.5 ±4.9
75.7 ±4.7
76 ±9
59.6 ±8.5
60-67
60.2 ±8.8
60.0 ±4.7
61.8±8.8
62.0 ±8.9
63 ±4
63 ±6
63 ±6
63.4 ±6.5
64 ±5
64 ±6
65.0 ±6.0
65 ±3
65 ±4
65 ±8
66 ±3
66 ±8
66.3 ± 11.6
67 ± 1
68 ±6
68.4 ±9.8
VO2Max
Ethnic Health Estimate
Group Status (mL/kg-min)
M
NS
NS
Males:
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
M
NS
NS
NS
NS
NS
NS
NS
NS
Males:
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
N
N
H
29 ±5
41.5±3.7
23.2 ±5.8
Sedentary, Overweight, or Obese
Sed
Sed
Sed
Sed
Sed
Sed
Sed.O
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Sed
Fit, Active, or Athlete
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Fit
Ath
Fit
Ath
Ath
Ath
Fit
Ath
Ath
Ath
Ath
Fit
32.1 ±4.4
30.1 ±5.5
33.9 ±6.4
31.0 ±6.4
27.2 ±5.1
48 ±4
26 ±5
29.6 ±4.1
28.0 ±2.4
29 ±3
29 ±5
18.8±5.1
27.0 ±2.2
28.0 ±3.6
22.9 ±5.3
25.4 ± 13.7
30 ±6
22.2 ±5.4
26.1 ±7.9
21.7±4.8
27.6 ±0.6
21.4 ±6.3
49.9 ±5.4
43±NS
50.1 ±7.0
53.5 ±5.4
44.3 ±9.8
54.0 ±6.6
47.5 ±4.3
45 ±3
42.3 ±7.4
49.6 ±5.8
36.3 ±8.2
39.9 ±4.0
43.3 ±6.3
50.0 ±4.9
45.9 ±4.6
45.9 ±4.7
46.4 ±5.1
48 ±4
36.5 ± 17.2
38 ±2
31. 2 ±6.2
40.7 ±7.3
Citation
Fleqetal. 1993
Perini et al. 2000
Flegetal.1995
Schulman etal. 1996
Katzel et al. 2001
Rogers etal. 1990
Van Pelt et al. 2001
Ogawa etal. 1992
Goldberg et al. 2000
Goldberg et al. 2000
Ehsani et al. 2003
Ho etal. 1997
Monahan etal. 2001
Tanaka et al. 2002
Ari et al. 2004
Hagberg etal. 1988
Turner etal. 1999
McAuley et al. 2007
Wilund et al. 2008
Vaitkevicius etal. 1993
White etal. 1998
Takeshima etal. 1996
Pererson et al. 2003
Kent-Braun and Ng 2000
Weiss et al. 2006
Schulman etal. 1996
Saltin and Grimby 1968
Pollock etal. 1987
Pollock etal. 1997
Wiswell et al. 2002
Rogers etal. 1990
Ogawa etal. 1992
Monahan etal. 2001
Van Pelt et al. 2001
Katzel et al. 2001
Jones et al. 2004
Proctor etal. 1998
Lost reference
Hagberg etal. 1988
Peiffer et al. 2008
Flegetal. 1995
Tankersley et al. 1991
Goldberg et al. 2000
Wilund et al. 2008
Tanaka et al. 2002
Ari et al. 2004
Trappe etal. 1996
Comment
n=16
n=12; indiv. 70-79
n=3
n=6
n=42
n=14
n=34
n=13
n=12
n=26
n=10
n=6
n=8
n=24
n=11
n=10
n=11
n=174
n=6
n=38
n=45
n=172
n=59
n=9
n=33
n=8
Current athlete
n=21
n=11
n=54
n=15
n=14
n=8
n=32
n=42
n=21
n=8
n=9
n=10
n=8
n=16
n=6
n=18
n=7
n=17
n=10
n=10
23
-------
Age Range
(Mean
and SD)
69 ±8
70.4 ±8.8
71.1 ±3.2
71.3±5.8
76.0 ±4.8
82.8 ±4.0
Ethnic
Group
NS
NS
NS
NS
NS
NS
Health
Status
Ath
Ath
Ath
Ath
Ath
Ath
VO2Max
Estimate
(mL/kg-min)
45.0 ±4.1
40.5 ±8.9
36.4 ±9.4
36.4 ±9.5
41.5±3.8
28.4 ±7.6
Males: Health Problems
64 ±3
65.3 ±6.5
65.9 ±6.0
66.3 ±6.2
66.6 ±6.7
68 ±6
69 ±3
76 ±8
60-69
60-69
60-83
60-83
60-83
60-83
61 ±4
62.5 ±3.1
63 ±3
63.5 ±3.1
63.5 ±3.0
63.5 ±3.0
63.6 ±2.7
64 ±7
64.2 ±4.0
64.4 ±2.5
64.8 ±6.6
65.0 ±2.8
65 ±2
65 ±6
65.1 ±2.9
65.3 ±4.7
66 ±3
66 ±5
66.0 ±5.1
66.2 ± 4.2
66.2 ±8.8
66.3 ±6.3
67.3 ±5.6
67.5 ±3.0
67.6 ±6.3
67.8 ±3.0
67.8 ±7.5
NS
NS
NS
NS
NS
NS
NS
M
Both
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Heart
COPD
COPD
COPD
COPD
Heart
Heart
IS
Genders
N
N
Sed
Sed
Sed
Sed
Sed
Ath
N
N
Fit
N
N
COPD
N
N
N
Sed
H
N
N
Sed
N
M
N
Sed
N
N
N
N
N
Ath
N
27.6 ±5.7
47.2 ±5.9
9.9 ±2.7
16.2 ±4.0
13.5 ±3.8
25.3 ±2.8
26.0 ±5.3
24 ±4
30.3 ± 8.2
34.5 ±6.1
24.0 ±4.1
30.9 ±7.1
30.4 ±4.9
29.8 ± 1.4
24 ±7
24.4 ±4.8
25.4 ±4.6
30.4 ±6.2
41.5±7.7
30.2 ±5.0
27.9 ±7.0
14.2 ±4.1
23.4 ±2.5
29.2 ±6.5
18.5 ±4.3
23.3 ±3.9
27.3 ±2.3
27.3 ±6.5
26.6 ±4.4
25.8 ±5.6
36.0 ±7.0
32 ± 10
32.0 ± 11.0
23.6 ±3.8
28.0 ±6.0
20.9 ±6.1
17.8 ±4.5
27.4 ±5.7
22.2 ±4.3
38.6 ±6.1
29.0 ±8.2
Citation
Vaitkevicius et al. 1993
Pollock etal. 1997
Hawkins et al. 2001
Takeshima et al. 1996
Hawkins et al. 2001
Hawkins et al. 2001
Sheldahletal. 1996
Carter etal. 1994
Montes de Oca et al. 1996
Carter etal. 1994
Carter etal. 1994
Sheldahletal. 1996
Sheldahletal. 1996
Fleqetal. 1993
Heiletal. 1995
Heiletal. 1995
Sidney and Shephard 1978
Sidney and Shephard 1978
Sidney and Shephard 1978
Sidney and Shephard 1978
Meijer et al. 2001
Marker et al. 1998
Seals etal. 1984
Marker et al. 1998
Hillman et al. 2002
DeVitoetal. 1997
Kline etal. 1987
Singh etal. 1994
DeVitoetal. 1997
Kline etal. 1987
Hays et al. 2006
Hillman etal. 2002
Scheuermann et al. 2002
Scheuermann et al. 2002
Meredith et al. 1989
Woods etal. 1998
Bell et al. 2004
Bell et al. 2001
Bell etal. 2001
Stein etal. 1999
Correia et al. 2002
Vincent et al. 2002
Simmons et al. 2000
Hays et al. 2006
Vincent et al. 2002
Arbab-Zadeh EA 2004
Chick etal. 1991
Comment
n=14
n=21: followup
n-=13: visit #2
n=72
n=8; visit #1
n=8; visit #2
n=9
n=32; mild
n=25; severe
n=57; moderate
n=176; severe
n=8
n=11
n=8
n=66
n=8
n=12
n=8
n=14
n=8
n=28
n=23
n=24
n=21
n=12
n=11
n=34
n=10
n=5; control
n=36
n=11
n=12
n=8
n=8
n=10
n=33
n=10; 60-73
n=26
n=26; nonobese
n=16
n=20
n=22; ACE=II
n=125
n=11
n=24
n=12
n=8
24
-------
Age Range
(Mean
and SD)
68.1 ±9.8
68.8 ±6.1
69 ±5
69 ±9
69.1 ±2.2
69.2 ±5.8
69.4 ±3.4
69.4 ±5.2
69.6 ±6.0
69.7 ±2.5
69.8 ±3.0
69.9 ±2.2
70 ±4
70-79
70-79
70-79
70-79
70-79
70-79
70-79
70.1 ±5.0
70.8 ±5.4
70.9 ±3.2
71.0±4.6
71.0±4.7
71. 8 ±5.4
72.1 ±3.8
73 ±5
73.6 ± 14.9
73.9 ±6.3
74 ±3
76.2 ±6.2
79 ±6
81.6 ±3.6
83.0 ±3.6
84 ±4
84.0 ±4.2
84.4 ±5.7
84 ±7
Abbreviations:
AA
ACE
Act
Ath
Cardio
COPD
VO2Max
Ethnic Health Estimate
Group Status (mL/kg-min)
NS N
NS Heart
NS N
NS N
NS Sed
NS OW
NS Fit
NS Heart
NS Cardio
NS Sed
NS Sed
NS Sed
NS Fit
NS N
NS N
NS H
NS H
NS N
NS N
NS N
NS OW
NS OW
NS Sed
M NLF
NS N
M LF
NS Sed
NS N
NS N
NS N
NS N
NS N
NS N
M Frail
NS Frail
NS Sed
NS Frail
M Frail
NS Frail
African American (black)
Angiotensin-converting enzyme
DD: deletion/deletion genotype
ID: insertion/deletion genotype
II: insertion/insertion genotype
Active (but nonathletes)
Athletes
Cardiovascular problems
Chronic obstructive pulmonary
32.6 ± 10.1
13.4 ±2.6
26.6 ±5.3
21.0±6.3
20.9 ±3.5
18.4 ± 3.3
34.3 ± 4.1
13.7 ±3.3
19±5
22.4 ±3.5
21.6 ±2.8
21. 4 ±2.5
43.7 ±9.2
26.0 ±5.8
17.8 ±3.2
18.0 ±2.4
30.2 ±5.6
22.5 ±4.6
22.2 ±5.1
30.2 ±5.9
19.7 ±3.9
21.0±5.3
27.1 ±6.3
21.3±4.3
22.6 ±3.4
17.2 ±4.3
24.9 ±2.5
31.4 ± 12.0
31. 4 ±5.7
39.0 ±6.3
21.1 ±6.8
19.8 ±6.3
16.8 ±4.0
15.2 ±2.9
16.0 ±2.3
18.3 ±3.9
15.6 ±2.7
13.6 ±2.6
12.8 ±3.8
Fit
Frail
H
Heart
His
HRT
Indiv
IS
LF
Citation
Hernandez and Franke 2005
Maldonado-M. etal. 2006
Tonino and Driscoll 1988
Bell et al. 2004
Mouletal. 1995
Vieira et al. 2007
Smith et al. 2004
Maldonado-M. etal. 2006
Ades etal. 1993
Mouletal. 1995
Arbab-Zadeh EA 2004
Mouletal. 1995
Johnson et al. 1991
Heiletal. 1995
Heiletal. 1995
Flegetal. 1995
Flegetal. 1995
Hagberg etal. 1989
Hagberg etal. 1989
Hagberg etal. 1989
Vieira et al. 2007
Vieira et al. 2007
Hernandez and Franke 2004
Morey et al. 1998
Vincent et al. 2002
Morey etal. 1998
Smith et al. 2004
Sialetal. 1996
Hernandez and Franke 2005
Hernandez and Franke 2004
Cress and Meyer 2003
Arnett et al. 2008
Cress and Meyer 2003
Carr et al. 2006
Ehsani etal. 2003
Vaitkevicius et al. 2002
Ehsani etal. 2003
Carr et al. 2006
Cress and Meyer 2003
Very active healthy exercisers
Mild-to-moderate frailty
Healthy
Heart "failure" patients
HISPANIC
Hormone replacement therapy
Data for individuals are provided
Ischemic subjects ("silent")
Low functioning: a combination of five self-reported
functional measure
Comment
n=10
n=47
n=11
n=7
n=10
n=44
n=10
n=50
n=43
n=10
n=12
n=10
n=29
n=40
n=7
n=7
n=14
n=16
n=19
n=12
n=44
n=44
n=10
n=53
n=16
n=108
n=14
n=6
n=10
n=10
n=98
n=29; 70-92 years
n=49;
n=155
n=22
n=35
n=24
n=28
n=45
25
-------
M Mixed ethnicity or mixed fitness level OW
N Normal (mostly healthy) park
NLF Not low-functioning: see LF above PD
NS Not specified Sed
O Obese W
Overweight
Parkinson's disease patients
Peripheral disease patients
Sedentary
White (Caucasian)
Notes:
a. The McArdle et al. (2001) and Baumgartner and Jackson (1999) values are "standards."
b. The 1999 values are those recommended by the American College of Sports Medicine.
c. There was no statistically significant difference in Hagberg et al. (2003) in VO2Max estimates for the HRT and non-HRT groups;
only the non-HRT group data are shown.
d. The Hagberg et al. (1998) article provides VO2Max estimates for lifestyle groups also (not presented).
e. The Sergi et al. (2009) article also provides 5th and 95th percentile values for VO2Max- The 5th value is 71 % of the median, and
the 95th value is 136% of the median.
adults is thought to be the "threshold level" for
increased mortality risk (Leaf and Reuben, 1996).
The first part of Table 2-2a consists of VO2 Max
estimates (in units of mL/kg-min) from textbooks
presenting highly aggregated data for both genders.
Standard deviations, for instance, are not available for
these estimates. The following groupings contain data
from more narrowly focused papers that provide mean
and standard deviation parameters for the samples
monitored.
A number of researchers do not believe that
VO2Max is an appropriate indicator of fitness or an
elder's ability to undertake physical work, because
many older people cannot attain true VO2Max according
to commonly accepted criteria (White et al., 1998). In
fact, they state that only "motivated" subjects, <50% of
their sample, could attain a classically defined VO2Max8-
Thus, there are issues associated with use of the metric
itself and what it indicates in older people.
Training (fitness improvements), on the other hand,
improves exercise performance and VO2Max estimates
using most metrics of maximal oxygen consumption.
Saltin and Grimby (1968) state that VO2 Max is 40%
higher in senior endurance competitors than in
sedentary individuals of the same age. In fact,
endurance training impacts remain long after exercise
stops. Ex-athletes who have not performed in
endurance events for at least 10 years before being
tested, still had VO2Max rates 20% higher than their
sedentary competitors (Saltin and Grimby, 1968). The
effect that lifestyle has on VO2.Max, especially activity
level, has been studied extensively by Hagberg and
colleagues; they also looked at differences in VO2 Max in
The three criteria are (1) hitting a plateau in oxygen
consumption with increasing work rate (defined to be a
leveling or decrease in VC>2Max over 3 consecutive minute
averages recorded at 10-s intervals), (2) a respiratory
exchange ratio (RER) of >1.10, and (3) a heart rate within
10 beats of the subject's age-predicted HRMAX (White et al.,
1998). RER approximates the "true" nonprotein respiratory
quotient of metabolism under a steady-state condition
(Astrand and Rodahl, 1986). It is measured as the ratio of
CC>2to 62 uptake of the lungs, which is obtained during the
VO2Max testing protocol.
menopausal women resulting from lifestyle and
selected genotypes (Hagberg et al., 1998).
A derived VO2 metric is VO2REs, which is equal to
VO2MAx - VO2REsT- For many physiological relationships,
VO2REs shows a more linear and stronger relationship
(higher R2) with other "reserve" metrics (heart rate
reserve [HRREs] and METSRES) than do absolute values
of the same variable (McCurdy and Graham, 2004b). It
is anticipated that we will be developing new
physiological relations based on the reserve metric
approach for use in exposure modeling efforts.
Peak expiratory flow rate (PEFR) is a less reliable
indicator of maximal airway functioning than VO2 Max, but
it is easier and less expensive to measure. This
physiological measure has been shown to positively
reflect aspects of a subject's fitness level, being higher
in elders who take frequent walks, work in the garden,
and sweat at least once a month (Cook et al., 1989).
PEFR also is positively associated with such things as
cognitive functioning (Cook et al, 1989). There are
alternative measures used to describe respiratory
functioning in elders. For a review of some of them, see
Enright et al. (1994, 1997). At the current time, our
exposure models do not use PEFR or any of the
alternatives as indicator variables of fitness or
lung/airway function in the modeled population.
2.EVEMax
The exercise physiology literature on older
indivuals does not focus much on VE Max, an important
parameter in our exposure models. To physiologists,
VO2Max is the preferred ventilation metric of choice, as it
is related more directly to metabolic processes than is
maximal minute ventilation, but to exposure assessors
VE Max is the metric that determines how much of a
pollutant enters the respiratory system. McArdle et al.
(2001) states that VEMax varies with age; at age 80, it is
40% of what it was at age 30 (p. 877). The data that
could be found on VE Max in seniors appear in Table 2-3.
VE Max for exposure modeling purposes is estimated
from VO2Max values using equations contained in
Graham and McCurdy (2005). The equations have the
following form.
26
-------
Table 2-2b. Estimates of VO2Maxfor Older Adults Seen in the Literature
Age Range
(Mean
and SD)
59.4 ±3.5
60-69
60 ±4
60.3 ±3.1
61 ±3
61 ±8
62 ±6
62 ±7
63.3 ± 2.9
64 ±4
64 ±5
64 ±8
64.0 ±3.1
65 ±8
66 ±6
66.0 ± 5.8
67 ±6
67 ±9
68.7 ± 5.7
70-79
70.0 ±8.1
70.4 ±3.9
>80
59 ±6
60 ±5
60.8 ± 4.7
61 ±2
62 ±2
62 ±6
63.2 ± 5.4
64 ±4
64 ±4
64 ±4
64 ±5
64.1 ± 6.9
64.4 ± 3.2
65 ±4
66 ±6
70.3 ± 4.7
75.3 ± 4.6
Ethnic Health
Group Status
Females
NS
NS
W
NS
NS
NS
NS
NS
NS
NS
NS
AA
NS
AA
NS
NS
W
W
NS
NS
NS
NS
NS
Females
AA
AA
NS
NS
NS
W
NS
NS
NS
W
NS
NS
M
His
W
NS
NS
: Normal or Healthy
N
N
H
N
N
N
N
N
N
N
H
N
N
N
H
N
N
N
N
N
N
N
N
VO2Max
Estimate
(L/min)
1.45 ±0.27
1.76 ±0.39
1.42 ±0.23
1.39 ±0.23
1.7 ±0.3
1.7 ±0.3
1.51 ±0.24
1.50 ±0.1 7
1.45 ±0.23
1.6 ±0.7
1.53 ±0.18
1.58 ±0.56
1.46 ±0.21
1.4 ±0.3
1.6 ±0.3
1.49 ±0.31
1.4 ±0.3
1.96 ±0.84
2.1 7 ±0.35
1 .40 ± 0.25
1.21 ±0.25
1.12±0.21
0.9±0.16
Citation
Bathalon et al. 2001
Tlusty 1969
Hays et al. 2002
Bathalon et al. 2001
Hagberg et al. 2003
Arciero et al. 1993a
Sheldahl et al. 1996
Sheldahl et al. 1996
Kohrtetal. 1991
Proctor et al. 2003
Goran and Poehlman 1992
Starling etal. 1998b
Kohrtetal. 1991
Carpenter etal. 1998
Johnson etal. 1994
Blackieetal. 1989
Carpenter etal. 1998
Starling et al. 1999b
Panton etal. 1996
Tlusty 1969
Johnson et al. 2000
Sergi et al. 2009
Tlusty 1969
: Sedentary, Overweight, or Obese
0
0
Sed
0
Sed
Sed
O.OW
Sed
Sed
Sed
O,OW
O,OW
Sed
Sed
Sed
Sed
Sed
1.6 ±0.2
1.6 ±0.2
1.32 ±0.1 2
1.6 ±0.1
1.5 ±0.1
1.4 ±0.3
1 .60 ± 0.27
1.6 ±0.4
1.46 ±0.24
1.5 ±0.4
1.51 ±0.30
1 .50 ± 0.26
1.39 ±0.1 9
1.5 ±0.2
1.4 ±0.3
1.21 ±0.20
1.47 ±0.89
Nicklas et al. 2003
Nicklas et al. 2003
Hughes etal. 1995
Tanaka etal. 1998
Tanaka et al. 1998
Hagberg et al. 2003
Nicklas etal. 1997
Tanaka etal. 1997
Ogawa etal. 1992
Schiller etal. 2001
Nicklas etal. 1995
Nicklas etal. 1997
Turner etal. 1999
Schiller etal. 2001
Hagberg et al. 2003
Hughes etal. 1995
Kent-Braun and Ng 2000
Females: Active or Athlete
61 ±8
64 ±7
65 ±3
65 ±3
NS
W
W
W
Ath
Act
Ath
Ath
2.0 ±0.3
1.7 ±0.3
2.2 ±0.3
2.1 ±0.3
Proctor etal. 1998
Hagberg et al. 2003
Hagberg et al. 2003
Hagberg et al. 2003
Comment
n=26
n=14; 63.5 years
n=33
n=34
n=9
n=75
n=9
n=11
n=16
n=13
n=6
n=37
n=57
n=37
n=81
n=81
n=52
n=35
n=36
n=14; 75 years
n=146
n=81
n=7; 81 years
n=19
n=57
n=6
n=9; Sed
n=9
n=9; HRT
n=29
n=16
n=14
n=18
n=29
n=28
n=10
n=5
n=10; no HRT
n=6
n=9
n=8
n=11; no HRT
n=9; HRT
n=12; no HRT
27
-------
Age Range
(Mean
and SD)
66 ±4
70 ±3
Ethnic
Group
NS
M
VO2Max
Health Estimate
Status (L/min)
Ath 1 .8 ± 0.4
Females: Health Issues
PD 1.12 ±0.34
Males: Normal or Healthy
59.4 ±3.6
60-69
62 ±6
63.7 ±3.1
64 ±3
64 ±5
64 ±5
64 ±7
64 ±7
64.2 ± 9.4
64.8 ± 3.6
65 ±2
65.5 ± 4.5
65.8 ± 5.4
66 ±6
66.0 ± 5.9
67 ±6
68 ±6
68.6 ±5.1
68.7 ±8.1
70-79
70 ±7
70 ±7
71 .6 ±2.4
74.7 ± 3.5
75.2 ± 4.5
>80
NS
NS
NS
NS
NS
NS
NS
AA
AA
NS
NS
NS
NS
NS
NS
W
W
NS
NS
NS
NS
W
NS
NS
NS
NS
NS
Ex-Ath. 2.60 ± 0.42
N 1.87 ±0.44
N 2.60 ± 0.24
N 2.28 ± 0.35
N 2.7 ±0.3
N 2.67 ± 0.26
H 3.11 ±0.68
N 1.9 ±0.6
N 1.74 ±0.60
Ex-Ath. 3.26 ± 0.70
N 2.20 ± 0.33
H 2.1 ±0.2
N 2.37 ± 0.40
N 2.43 ± 0.44
H 2.6 ±0.6
N 2.67 ± 0.76
N 2.27 ± 0.88
H 2.31 ± 0.67
N 1.38 ±0.31
N 1.78 ±0.46
N 1 .55 ± 0.40
N 2.1 ±0.5
N 2.5 ±0.5
N 2.1 2 ±0.33
N 1.78 ±0.38
N 1.71 ±0.37
N 1.14±0.16
Males: Sedentary, Overweight, or Obese
60 ±5
61 ±4
61 ±8
61 ±9
61.1 ±6.2
61 .4 ±5.2
61 .8 ±5.3
62.5 ± 3.4
63 ±3
63 ±3
63 ±3
63 ±5
63 ±7
64 ±3
64.8 ± 8.0
65 ±2
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Sed.O 2.7 ±0.6
Sed 1.7 ±0.3
Sed.O 2.7 ±0.4
Sed.O 2.7 ±0.5
Sed 2.73 ± 0.48
Sed 2.73 ± 0.52
Sed 2.34 ± 0.40
Sed 2.28 ± 0.47
Sed.OW 2.6 ±0.6
Sed, OW 2.6 ±0.4
Sed 2.24 ± 0.33
Sed.O 2.3 ±0.5
Sed 2.5 ± 0.7
Sed 2.36 ± 0.09
Sed 2.3 ± 0.24
Sed 2.5 ± 0.2
Citation
Tanaka et al. 1997
Ryan et al. 2000
Saltin and Grimby 1968
Tlusty 1969
Sheldahletal. 1996
Kohrtetal. 1991
Arciero et al. 1993b
Tankersley et al. 1991
Kenneyand Ho 1995
Carpenter et al. 1998
Starling et al. 1998b
Pollock etal. 1987
Kohrtetal. 1991
Scheuermann et al. 2002
Spina etal. 1997
Blackieetal. 1989
Johnson etal. 1994
Starling et al. 1998a
Starling et al. 1998a
Goran and Poehlman 1992
Panton etal. 1996
Johnson et al. 2000
Tlusty 1969
Carpenter etal. 1998
Tothetal. 1997a
Sabapathy et al. 2004
Papadakis et al. 1996
Papadakis et al. 1996
Tlusty 1969
Katzeletal. 1995
Van Pelt etal. 1998
Katzeletal. 1995
Katzeletal. 1995
Katzel et al. 2001
Rogers etal. 1990
Thomas etal. 1985
Thomas et al. 1985
Ferrara et al. 2006
Ferrara et al. 2006
Ogawa etal. 1992
Goldberg et al. 2000
Goldberg et al. 2000
Ehsani et al. 2003
Hughes etal. 1995
Ho etal. 1997
28
Comment
n=13
n=109
n=5
n=25; 65 years
n=9
n=53
n=89
n=7
n=6
n=28
n=28
n=13
n=19
n=8
n=8
n=47
n=56
n=44
n=32
n=7
n=19
n=152
n=13; 75 years
n=47
n=46
n=9
n=26
n=26
n=2; 85 years
n=26
n=19
n=73
n=71
n=42
n=14
n=44
n=45
n=9
n=13
n=13
n=26
n=12
n=10
n=4
n=6
-------
Age Range
(Mean
and SD)
66 ±5
66.4 ±5.6
66.8 ±1.8
75.7 ± 4.7
60-67
60.0 ± 8.6
60.2 ± 8.8
62.0 ± 8.9
62.3 ± 2.9
63 ±4
63.4 ±6.5
64 ±6
66 ±3
65 ±3
65 ±4
66 ±8
67 ±4
68 ±4
68.4 ±9.8
70.4 ±3.2
71.1 ±3.2
76.0 ± 4.8
82.8 ± 4.0
Ethnic
Group
NS
M
NS
NS
Males:
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NA
NS
NS
NS
NS
NS
Health
Status
Sed
Sed
Sed
Sed
VO2Max
Estimate
(L/min)
2.35 ± 0.22
2.33 ± 0.33
2.18±0.16
2.38 ± 1.71
Active, Fit, or Athlete
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Ath
Fit
Ath
Ath
Ath
Act
Act
Fit
Ath
Ath
Ath
Ath
2.68 ± NS
3.53 ± 0.40
3.5 ±0.5
3.68 ± 0.50
3.1 ±0.7
3.14 ±0.43
3.45 ± 0.39
3.0 ±0.3
3.25 ± 0.25
3.22 ± 0.36
3.49 ± 0.58
3.3 ±0.4
2.08 ± 0.37
2.12 ±0.44
2.74 ± 0.79
2.9 ±0.7
2.4 ±0.7
2.9 ±0.8
2.0 ±0.6
Males: Health Problems
62 ±8
63.3 ± 6.5
64 ±3
65.9 ± 6.0
68 ±6
69 ±3
60-69
60-69
60-83
60-83
60-83
60-83
61 ±10
62 ±7
63 ±3
63.5 ± 3.0
63.6 ± 2.7
64.2 ± 9.3
64.4 ±2.5
64.8 ± 6.6
65-80
65 ±6
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Park
COPD
Heart
COPD
Heart
Heart
Both Genders
N
N
Sed
Sed
Sed
Sed
COPD
COPD
N
N
N
N
N
N
Sed
N
1.3 ±0.6
1.43 ±0.39
2.05 ±0.21
0.7 ±0.2
1.96 ±0.22
1.91 ±0.23
1.93 ±0.58
1.77 ±0.44
1.70 ±0.26
2.09 ± 0.53
1.96 ±0.64
1.96 ±0.32
1.2 ±0.5
1.26 ±0.43
1.9 ±0.4
1.71 ±0.20
1.89 ±0.55
1.65 ±0.20
2.1 4 ±0.73
1.55 ±0.50
1.81 ±0.21
2.1 ±0.6
Citation
Hagberg et al. 1988
Turner et al. 1999
Hughes et al. 1995
Kent-Braun and Ng 2000
Saltin and Grimby 1968
Pollock etal. 1987
Pollock etal. 1997
Rogers etal. 1990
Hawkins et al. 2001
Ogawa etal. 1992
Katzel et al. 2001
Proctor etal. 1998
Tankersley et al. 1991
Hagberg et al. 1988
Peiffer etal. 2008
Goldberg et al. 2000
Sagivetal. 1989
Sagiv etal. 1989
Trappe etal. 1996
Pollock etal. 1997
Hawkins et al. 2001
Hawkins etal. 2001
Hawkins et al. 2001
Tothetal. 1997b
Mador et al. 1995
Sheldahl et al. 1996
Monies de Oca etal. 1996
Sheldahl etal. 1996
Sheldahl etal. 1996
Heiletal. 1995
Heiletal. 1995
Sidney and Shephard 1978
Sidney and Shephard 1978
Sidney and Shephard 1978
Sidney and Shephard 1978
LoRussoetal. 1993
Larson etal. 1999
Seals etal. 1984
DeVitoetal. 1997
Kline etal. 1987
DeVitoetal. 1997
Kline etal. 1987
Hays et al. 2006
Bell etal. 1998
Scheuermann et al. 2002
29
Comment
n=10
n=11
n=4
n=9
n=4
n=11
n=21
n=15
n=13: visit #1
n=14
n=42
n=8
n=6
n=10
n=8
n=18
n=20
n=20
n=10
n=21; followup
n=13; visit #2
n=8: visit #1
n=8; visit #2
n=16
n=6
n=9
n=25; severe
n=8
n=11
n=66
n=8
n=12
n=8
n=14
n=8
n=62
n=12
n=24
n=11
n=34
n=5
n=36
n=11
n=9
n=8
-------
Age Range
(Mean
and SD)
65.3 ± 4.7
66 ±5
66 ±5
66.2 ± 4.2
67 ±8
67.5 ± 7.3
68 ±6
68.8 ±6.1
69 ±8
69.4 ±5.2
69.5 ± 11.0
70-79
70-79
70-79
70-79
70-79
72.6 ± 9.5
83.0 ± 3.6
84 ±4
84.0 ± 4.2
Ethnic
Group
NS
NS
NS
NS
NS
NS
NS
NS
W
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Health
Status
Sed
COPD
COPD
Sed
N
H
COPD
Heart
N
Heart
N
N
N
N
N
N
N
Frail
Sed
Frail
VO2Max
Estimate
(L/min)
2.16 ±0.61
1.38 ±0.38
1.14±0.38
1.8 ±0.5
0.77 ± 0.35
1.62 ±0.45
1.26 ±0.45
1.06 ±0.25
1.72 ±0.56
1.08 ±0.34
1 .20 ± 0.30
1.93 ±0.58
1.77 ±0.44
1.59 ±0.55
1.68 ±0.50
1.51 ±0.57
1.08 ±0.28
1.18±0.38
1.23 ±0.37
1.09 ±0.29
Citation
Woods etal. 1998
Larson etal. 1999
Larson etal. 1999
Stein etal. 1999
LoRusso etal. 1993
Hays et al.2006
Larson etal. 1999
Maldonado-Martiii et al. 2005
Startling etal. 1998a
Maldonado- Martin et al. 2006
Barry etal. 1966
Heiletal. 1995
Heiletal. 1995
Hagberg etal. 1989
Hagberg etal. 1989
Hagberg etal. 1989
Barry etal. 1966
Ehsani et al. 2003
Vaitkevicius et al. 2002
Ehsani et al. 2003
Comment
n=33
n=13
n=14
n=16
n=20; severe
n=11
n=14
n=47
n=99
n=50
n=5
n=40
n=7
n=16
n=19
n=12; no HRT
n=3
n=22
n=35
n=24
Abbreviations:
AA African American (black) M
Act Active (but nonathletes) N
Ath Athletes NS
COPD Chronic obstructive pulmonary disease O
Fit Very active healthy exercisers OW
Frail Mild-to-moderate frailty Park
H Healthy PD
Heart Heart "failure" patients Sed
HRT Hormone replacement therapy W
Ln(VE/BM)i = b0 + (b1 * Ln[VO2/BMi]) + (b2 * [Age;]) +
(b3 * [Genden ]) + ew + eB
The within- and between-residuals (ew and eB) are
sampled from a random normal distribution of mean = 0
and the standard deviations noted below (N{0, a}).
Gender = 1 $ and -1 $.
The equation for VE Max, as well as for activity-
specific VE estimates, for individuals aged 61 + years in
Graham and McCurdy (2005) is
Ln(VE BIW1)61+ = 2.449 + (1.044 * Ln[ VO2
EM'1]) + (0.268 * [Age]) + (0.030 * [Gender]) +
ew(0.068) + eB(0.106) R2 = 0.89; p=0.003,
where
eB is the between-individual variability (interindividual)
residual, and ew is the within-individual variability
(intraindividual) residual.
Less complicated, more direct VE Max equations for
females and males are seen in Tlusty (1969).
VE Max ? = 120.6-(1.103* Age) R2 = 0.42
VE Max
-------
Table 2-3. Estimates of VE.Max for Older Adults
Age Range
(Mean ± SD)
64.6 ±3.9
66 ±4
73.2 ±5.7
61.4 ±5.2
63.3 ±6.4
64 ±3
65.3 ±6.5
66 ±5
66.3 ±6.2
66.3 ±6.3
68 ±6
69 ±3
75.7 ±4.7
Health
Status
60-69
62 ±6
62 ±7
65.5 ±7.8
67±NS
70-79
NS
N
N
N
N
NS
63.7 ±5.8
64 ±4
64.8 ±6.4
65.0 ±5.2
75.2 ±4.6
COPD
Sed
COPD
COPD
Sed
Ath
Ath
Ath
60-67
60-69
60.0 ±4.7
62 ±6
64.2 ± 9.4
68±NS
68.4 ±9.8
70-79
Ex-Ath.
NS
N
N
Ex-Ath.
N
Fit
NS
Sed
COPD
Heart
COPD
Sed
COPD
COPD
Heart
Heart
Sed
VE.Max Estimate
(L/min)
Citation
Females: Normal, Healthy, or Not-Specified
56 ± 14 Blackieetal. 1991
59.2 ±9.9 Sheldahletal. 1996
58.3 ±10.3 Sheldahletal. 1996
42.6 ±16.5 Carter et al. 1994
47.0 ±12.2 Hollenberg and Tager 2000
48 ± 12 Blackieetal. 1991
Females: Sedentary or Health Issues
26.1 ±7.2 Carter et al. 1994
64.7 ±16.4 DeVitoetal. 1997
39.9 ±8.2 Carter et al. 1994
33.9 ±8.2 Carter et al. 1994
58.7 ± 9.6 Kent-Braun and Ng 2000
Females: Athletes
80.3 ±9.0
86.7 ±20.2
61.2 ±13.5
Hawkins et al. 2001
Tanaka et al. 1997
Hawkins et al. 2001
Males: Normal, Healthy, or Non-specified
83.2 ± 7.3 Saltin and Grimby 1968
83 ±14 Blackieetal. 1991
71.3 ±13.4 Carter et al. 1994
102.4±15.9 Sheldahletal. 1996
144 ±25 Pollock etal. 1987
75.8 ±21.6 Hollenberg and Tager 2000
87.5 ±11.7 Trappe etal. 1996
66 ± 12 Blackieetal. 1991
Males: Sedentary or Health Issues
95.8 ± 22.1 Rogers et al. 1990
48.9 ± 14.5 Mador et al. 1995
68.0 ±9.3 Sheldahletal. 1996
51.5 ± 18.5 Carter etal. 1994
85 ±11 Hagberg etal. 1988
48.3 ± 14.2 Carter etal. 1994
37.1 ±11.4 Carter etal. 1994
68.2 ±6.8 Sheldahletal. 1996
74.6 ±10.3 Sheldahletal. 1996
98.3 ±21.9 Kent-Braun and Ng 2000
Males: Athletes
Comment
n=20
n=9
n=11
n=16
n=579
n=20
n=58; severe
n=16
n=23; mild
n=42; moderate
n=9
n=9; visit #1
n=13
n=9; visit #2
n=5;10 years no training
n=20
n=13
n=9
n=13
n=419
n=10
n=11
n=14
n=62
n=9
n=32; mild
n=10
n=57; moderate
n=176; severe
n=8
n=11
n=9
60.0 ±8.6
60.2 ±8.8
61 ±8
62.0 ±8.9
62.3 ±2.9
Ath
Ath
Ath
Ath
Ath
148± 18
151.4±20.0
98 ± 11
116.2±17.8
84 ± 14
Pollock etal. 1987
Pollock etal. 1997
Proctor etal. 1998
Rogers etal. 1990
Hawkins et al. 2001
n=11
n=21
n=8
n=15
n=13; visit #1
31
-------
Age Range
(Mean ± SD)
64 ±6
65 ±3
70.4 ±8.8
71.1 ±3.2
76.0 ±4.8
82.8 ±4.0
Health
Status
Ath
Ath
Ath
Ath
Ath
Ath
VE.Max Estimate
(L/min)
135 ±25
106.9±27.4
117.3±24.7
88.0 ±27.4
93.9 ± 27.4
73.8 ± 23.2
Citation
Proctor et al. 1998
Hagberg etal. 1988
Pollock etal. 1997
Hawkins etal. 2001
Hawkins et al. 2001
Hawkins et al. 2001
Comment
n=8
n=10
n=21; followup
n=13; visit #2
n=8; visit #1
n=8; visit #2
Both Genders
61 ± 10
63 ±3
63.5 ±3.0
64 ±7
64.2 ±4.0
65.1 ±2.9
67 ±8
69.1 ±NS
70-79
70-79
70-79
COPD
N
N
COPD
N
N
COPD
N
N
N
N
49 ± 21 LoRusso et al. 1993
67.2 ±16.4 Seals etal. 1984
50.0 ±10.0 Lost citation
44.2 ± 14.1 Singh etal. 1994
53.8 ±6.0 DeVito etal. 1997
60.5 ± 25.7 Meredith et al. 1989
34 ± 18 LoRusso et al. 1993
69.2 ±15.4 James etal. 1997
51.1 ±18.8 Hagberg etal.1989
57.3 ±15.0 Hagberg etal.1989
53.5 ±22.6 Hagberg etal.1989
n=62
n=24
n=11
n=10
n=5
n+10
n=20; severe
n=10
n=16
n=19
n=12
Abbreviations:
Ath Athlete
COPD Chronic Obstructive Pulmonary Disease
Heart Heart disease or coronary artery disease
N Normal health
n Sample size
NS Not specified (unknown)
Sed Sedentary
of the subsets. VQMAX at VO2 Max for the three groups
was 32.2 ± 4.4, 34.6 ± 4.2, and 35.5 ± 6.6. Large COVs
for the groups indicate that there is substantial
variability in VQ data, 13.7%, 12.1%, and 18.5%,
respectively. Statistical testing of the means or the SDs
was not presented. The high variability in VQ is but one
of the reasons this parameter no longer is used in
APEX and SHEDS exposure/intake dose models.9
Panton et al. (1996) provide VQMAX estimates for
68.6 ± 5.7 year-old females and 68.7 ± 5.1 year-old
males. The VQMAX estimates are 41.3 ± 7.7 and 39.8 ±
8.7, respectively. Besides the absolute values being
relatively high, the COVs are quite large, being 18.6%
for females and 21.8% for males. This magnitude of
Firstly, VQ is not measured often, so there is a lack of
empirical data on the parameter. Secondly, VQ is not stable
over time in an individual or among individuals of the same
age/gender cohort. Most importantly, VQ varies nonlinearly
with VO2, and the increasing slope of VE with VO2 was not
acknowledged explicitly in EPA's older models. The older
exposure/intake dose rate model runs also systematically
underestimated VQ at higher levels of energy
expenditure/oxygen consumption (a VQ of 27 often was used,
but values as high as 40 often are recorded), which biased
estimated VE rates downward, subjects aged 66 ± 5 years
was 36 ± 4, and the VQMAX for athletes aged 65 ± 3 years was
33 ± 4 (n=10 for both groups). These differences were not
significantly different using analysis of variance (ANOVA) and
author-identified "appropriate contrasts."
cross-sectional variability is rarely accounted for in
human exposure/intake dose rate models.
Additional VQMAX data for older males are provided
in Hagberg et al. (1988). The VQMAX for sedentary VQ is
also a marker of the ventilatory threshold (VT), which is
another term often used for the aerobic threshold
(where VE increases, but VO2 does not, for an increase
in work undertaken). VT in seniors is about 50% to 60%
of VO2 Max, a higher proportion than seen in young
adults (Thomas et al., 1985). VT is correlated in a
U-shaped fashioned with VO2 Max- VT seen in older
males in another study was between 56% and 61% of
VO2 Max, regardless of the subject's fitness level
(Takeshima et al., 1996).
2.GHRandHRMAx
Heart rate in an individual associated with a
particular work load and HRMAX itself are other
physiological traits that largely are inherited from a
person's parents (McArdle et al., 2001).10 There are
numerous VO2 Max prediction equations based on
HRMAX, either by itself or in conjunction with other
independent variables (such as age, gender, and
32
10 Maximum heart rate shows about an "86% genetic
determination" (McArdle et al., 2001; p. 236). In another place
in the same book, they state that heritability explains about
50% of variability in HRMAX, so obviously there is an
"unsettled" relationship between genetics and maximum
physiological parameters.
-------
fitness level), and there seems to be a relatively tight
linear relationship between heart rate reserve (HRRES),
which is HRMAx - HRREST, and VO2REs.
HRMax declines with age, closely related to activity
level and fitness of an individual. The following
estimated mean HRMax values are provided in Sharkey
(1984) for older males of differing fitness levels.
Age
60
65
70
Below
Average
158
152
147
Average
Fitness
172
169
165
Above
Average
175
173
170
The average COV for these estimates is about 8%
or so, which is low for cross-sectional data in general.
Because the resting heart rate (HRREST) decreases for
fit people, often quite dramatically, the impact of fitness
level on HRRES is even larger than the above age-
related HRMax declines might suggest. All of these
factors affect the "stroke volume" of the heart, blood
flow and distribution among body organs, and the
"oxygen extraction efficiency" from the blood (McArdle
etal.,2001).
One general HRMax-tc-age relationship seen often
in the literature is HR Max = 220 - Age (years). However,
this approximation does not apply to fit individuals, who
show a smaller HRMax reduction with age than predicted
by this formula (McArdle et al., 2001). The decrease in
HRMax in the older adults can be reversed to some
extent by training.
Because the absolute HR-to-VO2 relationship for
given workloads is highly individualistic and is greatly
affected by how the work is performed (arm work
versus leg or whole-body work), we do not use HR
metrics in our intake dose modeling procedures. They
are mentioned here because of the hypothesized
relationships among HR, cardiovascular disease in the
aged, and PM concentrations often seen in the
epidemiological literature (e.g., Pope and Dockery,
2006; Zanobetti and Schwartz, 2009). Literally
hundreds of citations could be provided on this point,
but the point remains that neither APEX or SHEDS
uses HR as a physiological input variable because of
the highly individualistic nature of its relationship to
other important physiological parameters.
2.HHT
HT in meters is an input to our BMR-estimating
equation but plays no role in the physiological modeling
procedures used in our exposure models. In
longitudinal studies of height measurements in seniors,
HT decreases at an accelerating rate after about
45 years, especially in females (Sorkin et al.,1999). In
one longitudinal study of 1,068 males and 390 females,
the following decreases were measured in centimeters
per year.
Age Group (years) 60-69 70-79 80-89 90-94
Females -0.22 -0.29 -0.47 -0.34
Males -0.14 -0.19 -0.31 -0.58
It should be noted that the sample size for both
genders in the 90 to 94-years group is only three
individuals with a short "follow-up" longitudinal period
also. Gender difference in the slopes is statistically
significant for all age groups <90 years (and >50 years
[not shown]).
Additional information is available on height/age
change rates, but is not reviewed here.
2.I Section 2 Concluding Comments
Attention has been given to the types of
anthropogenic and physiologic variables used in the
APEX and SHEDS models. As mentioned, there is a lot
of variability in seniors for some of the important
physiological variables discussed. In general, "older
individuals possess impressive plasticity in physiologic,
structural, and performance characteristics . . . even
into the 9th decade of life" (McArdle et al., 2001;
879-880). In particular, when modeling air intake dose
rates in exposed individuals, it is important to address
differences in fitness in older Americans. Information on
fitness is very difficult to determine a priori; it may have
to be simulated based on the PAI data in CHAD. That
subject will be addressed below under "Physical
Activity."
There are other physiological considerations that
apply to exposure modeling because they affect the
ability to participate in exercise or travel outside the
home. These include muscle mass, heart rate, strength,
mobility, and the like.11 Heart rate change is one
physiological component that has been shown to be
associated with environmental exposures, especially to
small-sized PM (Stein et al., 2009; Schlesinger et al.,
2006). For instance, Adar et al. (2007b) state that HR
variability is associated negatively with fine particulate
exposure in 44 older people wearing an
electrocardiogram (EGG) recorder, both for short-term
and daily exposures. Exposure to fine particulates
reduces parasympathetictone. Although HR change
because of age is not a factor in APEX or SHEDS yet, it
could be added. As discussed, exercise scientists treat
HR rather superficially, normally using the very broad
HRMax = 220 - Age [years] equation to relate heart rate
to age. This relationship would have to be made more
rigorous if heart rate impacts associated with fine
particulates or any other environmental pollutant are to
be modeled explicitly.
Physiologic variables of secondary interest do
affect some of the major parameters discussed above,
but often the relationships among them are tenuous and
33
Organ mass in the elderly, particularly the brain, kidneys,
liver, and spleen, decreases with age, but heart mass has less
of a change (He et al. 2009). The clinical importance of this
decrease is not understood at the present time.
-------
difficult to quantify. Additional research on these topics
would need to be undertaken if they are to be added to
our exposure models. BMI, for instance, although not
used directly in the models mentioned (its two
constituents, BM and HT, are used), is an important
metric of concern from a health effects perspective
(Stevens et al., 1998). A report undertaken as part of
EPA's Aging Initiative by Abt Associates (Marriott et al.,
2008) provides information about BMI in the older
population. The proportion of seniors who are
overweight hovers around 35% to 42% for all 5-year
age groups between 65 and 85, while those categorized
as obese decrease from 38% to 17% for the same age
classes (Marriott et al., 2008; p. 6). An overweight older
population is a relatively new phenomenon. Because
being overweight or obese (especially) affects intake
dose rates, these conditions probably also affect heath
effects associated with airborne exposures.
A review of seniors' BMI data was conducted in
2009 by the Centers for Disease Control and
Prevention (CDC, 2009b). CDC provided the following
age-adjusted percentage estimates by body weight
categories based on four BMI cutoffs (in kg m"2):
(1) underweight (BMI <18.5); (2) healthy weight (18.5 <
BMI < 25.0); (3) overweight (25.0 < BMI < 30.0); and
(4) obese (BMI > 30.0).
Weight Categorical Descriptors
[Percent of age group population and (standard error)]
Under- Over-
weight Healthy weight Obese
65-74 years
>75 years
1.1 (0.2)
3.5(0.5)
30.2(1.1)
42.0(1.3)
38.3(1.2)
37.5(1.3)
30.4(1.1)
16.9(1.0)
These CDC estimates are similar to those provided
above by Abt Associates. When undertaking an
exposure analysis, subjects who provided diary data in
CHAD could be assigned to the above categories and
modeled accordingly. However, doing so requires that
one or more of the physiological parameters in APEX or
SHEDS would have to be distinguished somehow on
the categories used and no published parameter
relationship has been identified to do so. Perhaps that
type of information will become available in the future.
Thus, at the present time BMI can be used only
descriptively and not as an operative variable in EPA's
older Americans' exposure modeling efforts.
34
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3. Energy Expenditure, Total Daily Energy Expenditure, and
Physical Activity Index
ABSTRACT
Topic: This chapter discusses physical activity in older adults
from an energy-expenditure perspective.
Issue /Problem Statement: Energy expenditure (EE)
decreases with age, and thus exposure model algorithms that
base intake doses on EE-derived ventilation rates should be
validated with age-specific EE data.
Data Available: The literature covering different mean energy
expenditure metrics in seniors is relatively data-rich, and
these data are mostly useful in validating EPA's physical
activity algorithms. Activity-specific energy expenditure data in
older individuals, however, are scarce.
Research Needs: The identification or collection of activity-
specific EE and METS data in older people is needed. The
ventilation algorithms in EPA's exposure models should be
refined to account for age-dependent changes in both
maximal and activity-specific EE.
3.A Overview and Total Daily Energy
Expenditure
Because of the commonly identified systematic
biases associated with estimates of energy intake (El,
in kilocalories), energy expenditure metrics are used in
the APEX and SHEDS-Air models. Discussions of
problems in estimating El in older adults are contained
in Johnson et al. (1994), Tooze et al. (2007), and Young
et al. (1992). Basically, El has been shown to be
underreported by approximately 40% of the
respondents, particularly in low-income seniors (Tooze
et al., 2007). The most accurate estimate of daily El in
subjects of any age is to measure total daily energy
expenditure (TDEE) in an individual. This is because
daily El is equivalent to TDEE, given the condition that
a person is neither losing nor gaining weight. For some
persons, this assumption generally is valid from a
practical perspective (i.e., there may be minimal
changes in body weight within a relatively short
timeframe, perhaps a year or less, generally the longest
time period of analyses used in EPA's exposure
modeling assessments). This assumption of stable
weight may not be applicable for people having certain
health conditions or for children and adolescents. There
subsequently will be greater uncertainty in representing
their body weight and attendant energy expenditures
than for weight-stable persons. It would be very difficult
to model intake dose rates for weight-changing
individuals, given the importance that BM plays in many
of the physiological relationships found in APEX and
SHEDS, so EPA modelers so far have assumed that
BM of a simulated individual does not change for the
year modeled (McCurdy et al., 2000). This restriction
can be lifted at the expense of a considerable increase
in model running time and assumptions regarding the
time trajectory of weight changes, so modeling
exposures and intake dose for a weight-changing
individual is a practical matter, not a conceptual issue.
TDEE is estimated using a variety of techniques
(Goldsmith etal., 1967; Schultz et al., 1989), but the
doubly labeled water (DLW) method is considered to be
the most widely accepted (the "gold standard") for
TDEE measures (Sawaya et al., 1995, 1996; Starling
et al., 1998a,b). The DLW method actually provides
estimates of EE for a multiday period, but they are
averaged over the number of elapsed days since
drinking the isotope-labeled water to obtain a daily
average estimate.12 In the APEX and SHEDS models,
TDEE is calculated as the sum of activity-specific EEs
obtained by multiplying activity-specific METS by the
time spent in each activity (McCurdy, 2000). See
Section 2 for more information on these parameters.
TDEEi = I (EEA), where EEA = METSA * timeA * BMRi
The units of TDEEi, EEA, and BMRi are kilocalories
(kcal; but popularly called calories [C] in this country).
METSA is a unitless metric. All of the energy (kcal)
values are converted using the "U" parameter within the
exposure models to oxygen consumption (VO2)
associated with the activity's energy expenditure. The
units of VO2 are either L min"1 or ml kg"1 min"1.
The main source of METSA estimates is from the
Ainsworth et al. (1993, 2000) compendium. Additional
METSA data are found in Jette et al. (1990). Many
articles can be found on activity-specific estimates and
how they were developed, but not in older individuals.
See Section 3.B.
TDEEj also can be obtained from multiplying the
simulated person's PAIj by BMRi. Basically, PAI is the
subject's daily averaged METS for all activities
undertaken during the day. This approach essentially
follows the "factorial method" used by exercise
physiologists and clinical nutritionists to estimate TDEE
in individuals (Roberts and Rosenberg, 2006). There
are scores of prediction equations relating TDEE in the
aged to both BMR and BM; see Carpenter et al. (1995)
for a survey of more than 20 such equations. The
"pooled mean" COV for PAI for older males is 22.5%
(versus 12.3% for all age groups). Thus, there is
considerable relative variability within the older
population, probably resulting from variability in health
Although DLW is considered to be the most accurate
means of estimating multiple-day EE, calculating DLW
involves using specific regression-based equations and
assumptions involving fractionated water loss, the rate of CO
production per litre of oxygen consumed (the Weir equation),
and the respiratory quotient (Surrao et al. 1998). Thus, the
DLW measure itself is not without uncertainty. Note that the
time period used in a DLW study usually is 7 days, but it
varies between 5 and 14 days in different studies.
35
-------
status and physical/mental functioning. See Section 6
for more information on that topic.
There are numerous articles on energy
requirements of various population groups, including
seniors, mostly oriented toward minimum food intake
needed to survive. There also are articles on energy
requirements needed to "thrive" and to avoid nutrition-
related health problems. Roberts (1996) is a
comprehensive article of that type. Probably the single
most relevant review of TDEE in older people is
Roberts and Rosenberg (2006). They state that TDEE
usually is divided into three major categories: (1) basal
metabolism (see Section 2), (2) physical activity, and
(3) the thermic effect of feeding. The thermic effect of
feeding generally is about 10% of TDEE and is never
directly measured (Rogers and Rosenberg, 2006).
Essentially, it is treated as a fixed component of TDEE,
and, for this reason, we also ignore it here. Thus, the
category of TDEE that is most important from an
exposure and dose modeling perspective is physical
activity (PA).
Section 5 focuses entirely on PA in seniors. In this
section, specific types of PA of interest are discussed
from an energy expenditure perspective that is
described by oxygen consumption, METS, or kcal, all
on a per unit time (minute) basis. When aggregated
over a day, total EE from physical activity is known as
PAEE (physical activity energy expenditure); Table 3-1
provides estimates of PAEE in older adults that is seen
in the literature.
In our modeling efforts, we use TDEE to check on
how realistic are the intake dose rate output
distributions in APEX and SHEDS, which are developed
from the highly disaggregated physiological processes
depicted in Figure 2-1. If there is systematic error
associated with the use of and parameterization of the
variables depicted in the modeling logic, the
subsequent exposure and dose estimates likely would
be biased. Calculating TDEE in the model simulations
and comparing them with distributions found in literature
values provide an independent, albeit indirect, check on
the intake dose modeling calculations. Thus, TDEE
plays an important role in our modeling efforts and has
been used by OAQPS to evaluate APEX model
performance. Estimates of TDEE in older individuals
appear in Table 3-1.
We note that the intraindividual variation in TDEE
is quite large. Based on theoretical error analysis of
experimental variation of the DLW method, the COV for
TDEE should be about 6%, but the observed variation
is double that, about 12% (Goran, 1995). With respect
to cross-sectional relative variability, Black and Cole
(2000) report that the "pooled mean" COV for TDEE is
11.8% for all age groups and 16.3% for male subjects
65 to 74 years old. A longitudinal study that only
presents cross-sectional data by age groups is Sunman
et al. (1991). In this study, which began in 1952, TDEE
was assessed after 24 years, when the male college
graduates were in their late 60s, and again when they
were in their mid-70s. The group was divided into
former athletes and "controls." The Sunman et al.
(1991) mean ± SD data are reproduced here; the
weekly PAEE was divided by seven to obtain the "daily"
estimate.
Athletes
1976
1984
Controls
1976
1984
TDEE
(kcal day"1)
1968 ±923
1850 ±802
1992 ±708
1618 ±660
PAEE
(kcal day"1)
232 ± 229
238 ± 226
190 ±27
229 ± 234
147
80
66
35
Age
68.5 ±7.7
75.1 ±5.3
69.8 ±8.8
77.1 ±7.1
A few cross-sectional statistics from their study of
interest indicate (1) a much larger TDEE decline in
controls (19%) than in former athletes (6%);
(2) relatively large TDEE COVs exist in both groups,
ranging from 36% to 47%; (3) very large COVs in PAEE
occurs in both groups, being 95% to 120% of the mean;
and (4) the relatively narrow proportion of PAEE-to-
TDEE in both groups, 9.5% to 14.1%. Both the TDEE
and PAEE estimates are lower than many of the values
contained in Table 3-1. Perhaps this is a reflection of
when the study was done, in the late 1970s and early
1980s, before the rapid increase in body mass and BMI
occurred in the U.S. population. It is unfortunate that the
authors, Sunman et al. (1991), did not analyze their
data longitudinally on a per-person basis.
TDEE decreases with age, as expected, because
BMR (about 50% to 70% of TDEE in most adults) and
PAEE both decrease with age (Roberts and Dallal,
2005). This trend holds for TDEE adjusted by BM or by
IBM. Roberts and Dallal (2005) provide an extensive
table of TDEE and PAI for seniors by decade of age,
which is included in Table 3-1. Their information comes
from a National Academy of Sciences database of
doubly labeled water studies, but it is not otherwise
identified. One important age-related phenomenon is
that seniors have greater fluctuations in total body and
fat mass following under- and overeating events relative
to that of younger adults. This results in a greater
imbalance between daily El and EE because of reduced
compensation from adaptive changes in EE (Roberts
and Rosenberg, 2006). Older people also have a
reduced ability to oxidize fat in meeting the fuel
requirements of living and, thus, have an increased
potential to become overweight. However, there are
contraindicatory effects in postprandial EE that
minimize this problem in older adults (Roberts and
Rosenberg, 2006). The topic is complex, and the data
available on the subject are not definitive.
The distributional parameters of our model outputs
should be evaluated to see whether or not the
intraindividual variability in modeled TDEE
approximates the values seen in the literature. Too
narrow or overly wide modeled COVs would provide
insight into the sampling procedures used in the
exposure models. That has not been done to date in
36
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Table 3-1. Estimates of TDEE, PAEE, and/or PAI for Older Adults
Age
Range
(years)
Mean
Age
(years)
Sample
Size
(number) Gender
TDEE
(kcal/day)
PAI
Reference
Comment
Normal Weight Individuals
60-69
70-79
80-89
90-97
48 ?
14 $
14 ?
30 3
6 ?
4 $
9 ?
6 $
2042 ± 343
2397 ± 437
1888 ±295
2407 ± 374
1382 ± 152
1700 ±239
1356 ± 166
1935 ± 156
1.69
1.61
1.55
1.62
1.21
1.17
1.17
1.38
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
SD for PAI: 0.31
SD for PAI: 0.18
SD for PAI: 0.26
SD for PAI: 0.25
SD for PAI: 0.09
SD for PAI: 0.15
SD for PAI: 0.13
SD for PAI: 0.17
Overweight Individuals
60-69
70-79
80-89
90-97
55-65
32-82
41 -80
56-70
52-79
52-79
57-70
61 -77
60-77
NS
60-81
NS
56-90
NS
NS
66-81
NS
NS
67-82
NS
68-80
67-82
70-79
70-79
70-79
60.8 ±3.1
61 ±4
61 ±4
61.2±15.3
62 ±8
62.1±11.9
64 ±5
64 ±7
64 ±8
65 ±5
66.8 ± 3.7
67 ±4
67 ±5
67.6 ±4.1
67.8 ±6.1
69 ±6
69 ±7
69 ±8
70.0 ± 6.9
71 ±5
71 .2 ±5.0
71 .5 ±4.8
72.9 ±6.1
73 ±3
73 ±3
73 ±6
73.5 ± 4.2
74 ±2
74.0 ± 4.4
74.1 ±4.1
74.1 ±3.2
74.2 ± 2.7
74.5 ± 3.3
46 ?
30 3
19 ?
34 $
6 ?
6 $
7 ?
2 <$
29 ?
28 B
9 B
27 $
16 3
27 ?
6 ?
37 ?
28 3
1 3
15 B
13 ?
13 B
10 ?
20 3
50 B
15 3
99 B
9 3
16 3
20 B
21 ?
21 ?
10 ?
13 ?
12 B
13 ?
10 ?
10 ?
14 3
67 ?
39 ?
43 J'
2061 ± 294
2851 ± 420
1 868 ± 402
2624 ± 461
1 748 ± 464
2294 ± 357
1 766 ± 292
1863 ±46
2229 ± 325
3071 ± 351
221 4 ±460
2282 ± 167
2092 ± 231
2090 ± 41 1
2772 ± 556
2675 ± 394
1764 ±531
1447 ± 162
2349 ± 545
2065 ± NS
2580 ± 566
2543 ± 449
2495 ± 352
2379 ± 556
2349 ± 300
241 2 ± NS
1 840 ± 395
221 3 ±429
2207 ± 402
2201 ± 354
21 03 ± 837
1870 ±347
2256 ±215
1852 ±214
1813±215
2971 ± 390
1 904 ± 369
21 06 ±263
2788 ± 293
1.52
1.71
1.51
1.55
1.41
1.47
1.33
1.29
1.81
1.65
1.72
1.42
1.51
1.71
1.50
1.66
1.74
1.75
1.68
1.72
1.51
1.80
1.62
1.59
1.69
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Roberts and Dallal 2005
Vinkenetal. 1999
Meijeret al. 2001
Meijer et al. 2001
Seale 2002
Tothetal. 1997a
Seale 2002
Goran and Poehlman 1992
Starling et al. 1998a
Starling et al. 1998a
Goran and Poehlman 1992
Hunter etal. 2000
Treuthetal. 1996
Tothetal. 1997c
Roberts 1996
Vinkenetal. 1999
Tothetal. 1997
Roberts 1996
Starling et al. 1998a
Roberts etal. 1996
Roberts 1996
Leaf and Reuben 1996
Ades etal. 2005
Ades etal. 2005
Roberts 1996
Rutgers etal. 1997
Tothetal. 1997
Seale et al. 2002b
Roberts 1 996
Vinkenetal. 1999
Seale et al. 2002b
Blanc etal. 2004
Manini etal. 2009
Manini etal. 2009
SD for PAI: 0.23
SD for PAI: 0.29
SD for PAI: 0.28
SD for PAI: 0.27
SD for PAI: 0.37
SD for PAI: 0.16
SD for PAI: 0.22
SD for PAI: 0.13
H; SDforPAI:0.23
H; PAI Range:1. 4-2.0
H; not retired subset
Overweight
Parkinson's disease
Overweight
H; PAI: 1.3-1.8
AA; SD for PAI: 0.25
AA; SD for PAI: 0.32
H: PAI: 1.3-2.1
Healthy
Healthy
Noncathectic HP
Note 2 (P&W 1 995)
H; SD for PAI:0.27
Healthy controls
Note 2 (Roberts 1992)
SD of PAI: 0.28
H; PAI SD: 0.69
Note 2 (P&W 1 995)
Sedentary; note 1
Normal (control)
CHD & limitations
Note2(Reilly 1993)
Healthy
Cachectic HP
Overweight
Note 2 (Sawaya 1 993)
H; SDfor PAI:0.18
Overweight
B; PAI SD: 0.24
Active; normal
Active; normal
37
-------
Age
Range
(years)
71 -79
70-79
70-79
70-79
70-79
70-79
70-79
76-88
NS
76-88
52-79
52-79
48-94
48-94
56-90
70-79
70-79
71 -79
70-79
70-79
70-79
70-79
70-79
70-79
70-79
Mean
Age
(years)
74.6 ±3.1
74.8 ± 2.8
74.8 ± 2.9
75.1 ±3.2
75.1 ±3.1
75.2 ± 2.7
75.5 ± 3.2
82 ±3
65 ±5
82 ±3
64 ±8
64 ±7
67 ±4
67 ±9
66 ±11
69 ±8
71 .5 ±4.8
72.9 ±6.1
74.2 ± 2.7
74.5 ± 3.3
74.6 ± 3.1
74.6 ± 3.2
74.8 ±2. 10
74.8 ± 2.8
75.1 ± 3.2
75.1 ±3.1
75.2 ± 2.7
75.5 ± 3.2
Sample
Size
(number) Gender
40 ?
77 ?
72 3
72 3
43 3
43 3
40 ?
23 3
16 3
23 3
37 ?
28 3
15 ?
32 ?
35 3
99 B
21 ?
21 ?
39 ?
43 3
40 ?
67 ?
72 3
77 ?
72 3
43 <$
43 3
40 ?
TDEE
(kcal/day)
1839 ± 175
1885 ±286
2324 ± 436
2521 ± 396
2395 ±214
2044 ± 280
21 99 ±335
1 657 ± 209
TDEE/BM
(kcal/kg-d)
38 ±14
30.8 ±4.9
PAEE
(kcal/d)
207 ± 21 1
410 ±320
682 ± 325
1 21 1 ± 429
874 ± 244
71 9 ± 377
547 ± 360
498 ±314
805 ± 206
1 079 ± 1 83
436 ± 61
620 ± 272
865 ± 284
584 ± 1 97
775 ±313
737 ± 83
467 ± 115
350 ± 66
PAI
1.65
1.71
1.74
1.50
1.50
1.51
1.71
1.68
1.69
1.74
1.65
1.71
Reference
Manini et al. 2009
Blanc et al. 2004
Blanc et al. 2004
Blanc et al. 2004
Manini etal. 2009
Manini etal. 2009
Manini etal. 2009
Fuller etal. 1996
Tanaka et al. 2002
Fuller etal. 1996
Starling etal. 1998b
Starling etal. 1998b
Treuthetal. 1996
Starling etal. 1999
Starling etal. 1999
Starling etal. 1998a
Ades et al. 2005
Ades et al. 2005
Manini et al. 2009
Manini et al. 2009
Manini etal. 2009
Blanc etal. 2004
Blanc et al. 2004
Blanc et al. 2004
Blanc et al. 2004
Manini etal. 2009
Manini et al. 2009
Manini et al. 2009
Comment
Normal
B; PAI SD: 0.21
W; PAI SD: 0.22
W; PAI SD: 0.22
Normal
Less active; normal
Less active; normal
W: PAI SD: 0.2
Sedentary
White; normal
SD for PAI: 0.25
SD for PAI: 0.32
Whites
Whites
Normal (control)
CHD & limitations
Active; normal
Active; normal
Normal
AA; note 3
B
W
AA
Normal
Less active; normal
Less active; normal
Abbreviations
AA African-American (black)
B Both genders
CHD Coronary heart disease
? Females
H Healthy
HP Heart patients
3 Males
NS Not specified
SD Standard deviation
W White (Caucasian)
Notes
(1) Data were provided for a 48 h-period; the TDEE estimate is 1/2 of it. The authors provide data for individuals and group means. The
weighted means are estimated to be 5 = 1781 (n=15) and 3 = 2018 (n=5).
(2) Roberts et al., 1995 is a review of previous papers on TDEE in the aged. One of them is Roberts et al. 1992 in the references. P&Wis
Pannemans & Westerterp, 1995 Brit. J. Nutr. 73: 571-581, which is not in the references. Reilly, 1993, is in Brit. J. Nutr. 69: 21-27.
Sawaya 1993 is Sawaya et al., Amer. J. Clin. Nutr. 62: 338-344.
(3) PAEE also is supplied in units of kcal/day-kg.
38
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APEX or SHEDS modeling applications. We are not
addressing full variability within and among individuals if
the resultant TDEE COVs are too narrow.
3.B Activity-Specific EEA and Oxygen
Consumption
As just mentioned, the APEX and SHEDS models
use activity-specific estimates of EE (EEA) to estimate
intake dose rates via inhalation exposure. If METS-
derived estimates of EEA are simply random sampled
from an approximate of METS, there is still a possibility
that unrealistic estimates of activity-specific oxygen
consumption (VO2A) and VEA could result, because
work cannot be maintained at a constant level for long
periods of time. If work exceeds approximately 50% of
VO2MAx, the body uses anaerobic physiological
processes to meet its energy demands. Doing so incurs
an oxygen debt that ultimately reduces breathing
efficiency, that is VO2 and VE are increased to do the
same amount of work. This is known as the oxygen cost
of breathing. During prolonged exercise, a person's VO2
will approach VO2 MAX, resulting in fatigue. Once
prolonged exercise ceases, the accumulated oxygen
debt has to be repaid. Therefore, both VO2 and VE will
be higher after the work stops than the subsequent
activity's nominal EEA would dictate. This oxygen
needed to repay the debt now is called "excess post-
oxygen consumption (EPOC). See Hagberg et al.
(1980a,b) for more information on both fatigue and
EPOC.
EPA's modeling group and its collaborators have
developed a method to account for fatigue and EPOC in
its exposure/intake dose models (Isaacs et al., 2008).
Not much model adjustment is needed to address
EPOC for most individuals, so the biggest impact on
intake dose rate modeling is to account for fatigue by
lowering both VO2 A and VE A appropriately when
sequential prolonged exercise occurs. It should be
noted that EEA (or the METSA value on which it is
based) is not adjusted directly; the effective change in
EEA is accounted for by an oxygen debt correction to
VO2.A and breathing rate.
There are a number of articles presenting EEAdata
for older population cohorts, usually for walking (at
different rates) and cycling. Sometimes, other activities
are measured, but they are quite limited in breadth.
What data are available are shown in Table 3-2. When
BMR and EEAs are both supplied for an individual,
METS estimates for specific activities can be calculated
for them. This is how METS estimates themselves
generally are calculated. However, when group
mean/standard deviation data are the only information
presented, the subsequent METS estimates are biased
and not very useful (Haveman-Nies et al., 1996).
One good example of older American's EEA data
(as VO2) is Leaf and MacRae (1995). They tested 20
subjects (15 $ and 5 3) having a mean age of 71.2 ±
4.5 years (range: 65 to 81 years). Although individual
data are provided—quite rare actually—group mean
data only are discussed here. The subjects walked on a
treadmill at a rate of 2 ± 0.4 mi h"1, where VO2 was
measured by indirect calorimetry (a face mask
recording a number of respiratory parameters). Work
undertaken on the treadmill was converted from ergs
into EE using American College of Sports Medicine
(ACSM) equations and, then, into METS. The estimated
group EEs worked out to be a METS of 3.4 ± 0.4 for a
2.0 ± 0.4 mph pace. They then allowed the subjects to
walk outside at their own pace on a track, and the
average measured speed for the group was 3.0 ±
0.4 mph, faster than the treadmill speed. Calculated
METS for this "self-selected, customary walking speed"
(Leaf and MacRea, 1995; p. 101) is approximately 4.4 ±
0.5 METS. Both METS estimates are considered to be
in the "moderate" exercise range of 3 to 6 METS for all
but the very active (and younger) athletes (see Welk,
2002). The METS compendium states that walking at
3.0 mph on a firm, level surface expends 3.5 METS
(Ainsworth et al., 1993), very close to the treadmill
exercise estimate.
Malatesta et al. (2003) compared the energy cost
of walking in three small, mixed-gender samples (n=10
in each case). Their "G80" group was 81.6 ± 3.3 years
old on average and used 0.229 ± 0.030 mL O2 kg"1 m"1
at their preferred walking speed of 1.14 m s"1 (about
1.6 mph). The "G65" group was 65.3 ± 2.5 years old on
average and used 0.205 ± 0.020 mL O2 kg" m"1 of
energy at their preferred speed of 1.35 ± 0.08 m s"1
(about 3 mph). Both groups were slower and burned
more energy on average to accomplish the task
(walking at their preferred speed) than the youngest
group (age = 24.6 ± 2.6 years, EE of 0.179 ±0.020 mL
kg"1 m ). Data were not presented to be able to
calculate average METS for this activity, but the
preferred speeds were similar to those seen in the Leaf
and MacRea (1995) study.
There is one article that apportions EEs of specific
activities as a percentage of TDEE using the "factorial"
method of estimating TDEE (Morio et al., 1997). Twelve
"free-living" females and males aged 71.1 ± 2.7 years
participated in a study that estimated TDEE three
different ways: (1) DLW, (2) the factorial method, and
(3) a HR-to-EE relationship. The subjects were
instructed to record their activities in a diary every
5 min.
The proportion of TDEE spent in the following
activities was estimated for the study subjects from the
factorial method.
Sleep Rest Sit Stand Walk Recreation
20%
20%
3%
1%
27%
28%
32%
20%
13%
13%
5%
18%
The factorial method's estimates were not
statistically different than the DLW estimates on
39
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Table 3-2. Estimates of Activity-Specific Energy Expenditure for Older Adults
Activity Descriptor
Sitting
Walking
Activity Descriptor
Lying
Sitting
Sitting
Sitting
Sitting
Standing
Standing
Standing
Walking @38 m/min
Walking @38 m/min
Walking @38 m/min
Walking @64 m/min
Walking @64 m/min
Walking @64 m/min
Walking @ 2.5 mph
Activity Descriptor
Free level walking
Free level walking
Activity Descriptor
Walking
Mean Age
72.0 ± 4.0
72.0 ± 4.0
Mean
Age
68 ±5
66 ±3
66 ±3
66 ±3
68 ±5
66 ±3
66 ±3
66 ±3
66 ±3
66 ±3
66 ±3
66 ±3
66 ±3
66 ±3
68 ±5
Mean
Age
60-80
60-80
Mean
Age
72.0±4.0
n
28
29
n
6
13
14
13
6
13
14
13
13
14
13
13
14
13
6
n
21
43
n
29
Gender
9
5
Gen
3
2
9
5
3
2
2
2
2
2
2
2
2
2
<3
Gen
c?
2
Gen
2
METS
1 .29 ± 0.09
4.74 ±0.82
Reference
Vooripset al. 1993
Vooripset al. 1993
Energy Expenditure (EE) Units
EE
(kcal/min)
1.37 ±0.1 5
1.2 ±0.2
1.0 ±0.1
1.1 ±0.1
1 .47 ± 0.21
1.3±0.2
1.2 ±0.2
1.2 ±0.2
3.2 ±0.8
2.9 ±0.5
3.2 ±0.6
4.3 ± 1.1
3.9 ±0.5
4.1 ±0.5
4.51 ± 0.34
Reference
Galloway and Zanni 1 980
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Galloway and Zanni 1 980
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Thompson et al. 1997
Galloway and Zanni 1980
Oxygen Consumption Units
VO2/BM
(mL/Kg-
Min)
11.9± 1.9
11. 8 ±1.6
Reference
Waters etal. 1983
Waters etal. 1983
Oxygen Consumption Units
VO2
(mL/Min)
16
Reference
(Misplaced)
Comment
Comment
Healthy
Overweight
Overweight
Overweight
Healthy
Overweight
Overweight
Overweight
Overweight
Overweight
Overweight
Overweight
Overweight
Overweight
Healthy
Comment
Comment
average, but there was wide variability among the
individual factorial/DLW comparisons (Morio et al.,
1997).
3.C METSA
There is little direct data on METS for older
individuals. The METS compendium and its update
(Ainsworth et al., 1993, 2000) essentially assume that
METS apply to both genders and all ages. The only
supplied caveats to their use are (1) they represent
averages of EE seen among individuals undertaking the
same task (and do not, therefore, represent population
variability inherent in undertaking the work), and (2) the
estimates are "not intended to be used for adults with
major neuromuscular handicaps or other conditions that
would significantly alter their mechanical or metabolic
efficiency" (Ainsworth et al., 1993; p. 73). The last
caveat almost certainly applies to a significant portion of
seniors, although that group is not explicitly identified in
the article. To accomplish a fixed workload, METSA
should be adjusted upward for elders to indicate the
increase in energy expenditure needed to accomplish
that workload. In addition, METSMAx values are lower in
older people. This is because older adults have muscle
atrophy, diminished balance, and less IBM, making
them less efficient in accomplishing work than the
younger people on which most METS estimates are
based. These factors increase EEA and VO2A for
selected relatively-high VO2Max activities for seniors, or
40
-------
at least many of them. Further, when considering the
fact that seniors have a relatively lower BMR, the
METSA needed to accomplish the same amount of work
as younger people has to be higher in the aged (or the
time needed to complete a fixed task has to increase).
Data on healthy seniors being able to accomplish a
specific task at lower METSA than health-compromised
older people (postmyocardial infarction patients)
indicates that the hypothesized needed adjustment to
"standard" METSA estimates is logical for older adults
(Woolf-May and Ferrett, 2008).
Regardless of the precise applicability of METSA in
seniors, it has been found that older people who cannot
exercise at a METS of 5 "generally indicates a higher
mortality group," compared with those with an exercise
capacity of >5 METS (this essentially is a METSMax
criterion). Elders capable of exercise at METS >5 have
an excellent long-term prognosis of survival, even in
seniors suffering from coronary disease (Franklin, 2007;
Franklin et al., 2003; Shaw and Mieres, 2008). Thus,
METSA capability can be used as a marker of fitness in
the aged. The reason for an increased VO2 A in older
individuals for a particular workload seems to be that
VO2 kinetics are reduced because of slow adaptation of
muscle blood flow and oxygen delivery (DeLorey and
Babb, 1999; DeLorey et al., 2004, 2005, 2007).
3.D PAI or PAL
PAI in the "free-living" population (all ages) ranges
from 1.2 to 2.2 (Black et al., 1996), but estimates over
2.5 are not uncommon in active people (Goldberg,
1997), including seniors. A United Nations report
recommends that the PAI for people >65 years should
be at least 1.5 to "prevent accelerated changes in
muscle and bone" (Dupont et al., 1996). Estimates of
PAI for seniors seen the literature are summarized in
Table 3-1. Most are >1.5 until the age of 80 years old,
when there is a dramatic decline. A few of the group
means for the younger seniors are close to being
labeled "moderately active" (a PAI between 1.75 and
1.99) in our exposure modeling scheme (McCurdy,
2000). That also holds true for selected ethnic groups in
the 70 to 79-year age range (Blanc et al., 2004).
Roberts et al. (1996) performed a meta-analysis of
574 DLWstudies and provide the following summary
data for PAI values in seniors.
Females
Males
65-74 years
75+ years
65-74 years
75+ years
1.62 ±0.28
1.48 ±0.23
1.61 ±0.28
1.54 ±0.24
These estimates fall in the same range as those
reported by Roberts and Dallal (2005) data in Table 3-1,
but we note that the first author is the same for both
studies.
41
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4. Time Use and Human Activity
ABSTRACT
Topic: This section discusses human activity patterns in older
individuals.
Issue /Problem Statement: The pattern and distribution of
time spent in different microenvironments and activities is
markedly different in seniors than in younger adults. In EPA's
exposure models, microenvironment determines encountered
concentration, whereas activity determines ventilation (and
possibly food intake). Thus, time use has a large impact on
exposure estimates.
Data Available: There exists a data-rich literature on where
older Americans' spend their time, on average, but
distributional data for their time is scanty. There also is a
moderate amount of cross-sectional event- or diary-based
information available on seniors from a number of time-use
surveys. However, longitudinal time-use information is scant.
Research Needs: More data should be collected and
identified for parameterizing EPA's longitudinal diary
assembly algorithms specifically for older populations.
4.A Overview
The intent of this section is to provide general
information on seniors' locational and activity data from
the time use literature. Specific data on these items that
are used in our exposure models come from CHAD and
other diary data. However, that information needs to be
put into perspective to check model performance and
the diaries used to estimate exposure. We attempt to do
that here. Most of the available older adult time use
data in the general literature are not sequentially event
based. The data generally are time-averaged, indicating
the number of minutes or the proportion of time spent
per day in selected activities. There is very little
published location information provided for older adults.
EPA uses time use data in its event-based
(sequential) exposure models, although it generally
calls it human activity or activity-pattern data.13 Time
use data has been collected and used by many
disciplines, including sociology, economics, urban and
transportation planning, epidemiology, women's
studies, psychology, sleep clinics, physiologists, and
exposure modelers (Committee on National Statistics,
2000). There are two basic approaches to gathering
sequential time use data: (1) the ex post recall interview
survey ("What did you do yesterday?")14 and (2) the
13 Remember from Section 1 that an event occurs in a single
location (uE), constitutes a single activity, and a single EE. If
any of these factors change, then a new event occurs.
14 Also known as the "day reconstruction method" (Kahneman
et al., 2004). This method has been used to obtain time use
data in 40 community-based elders with neurodegenerative
disease, including Parkinson's, dementia, and Alzheimer's
(and other less common mental problems). Restricted time
use patterns were found for both discretionary and obligatory
activities, as expected (Lomax et al., 2004). These elders,
who were English, undertook mostly passive activities, such
as day-time sleeping and watching television, and they rarely
left their houses. The same findings have been obtained for
contemporaneous time budget diary approach, where
the subjects record activities as they undertake them
(As, 1978; Gershuny and Sullivan, 1998; Niemi, 1993;
Stafford, 2009). There are advantages and
shortcomings associated with either approach, but the
diary approach usually provides more information on
more events than the recall approach (McCurdy and
Graham, 2003; Robinson, 1988, 1989; Robinson and
Silvers, 2000). CHAD contains time use information
from both types of studies.
Selected aspects of time use by seniors has been
studied extensively by sociologists, economists, and
epidemiologists because the use of time reflects,
among other things, functional capabilities, including
working potential, interactions with others, and health
impacts, of that subpopulation (Lawton, 1999;
Singleton, 1999). In fact, the congruence between
actual and desired time use is an important concept in
the psychology of aging (Calderon, 2001; Seleen,
1982). Life satisfaction is increased when older people
can do what they want to do, without restriction or
compromise. Probably that is true of everyone, but may
be more important (and is more studied) in older
people. It is called the congruence theory of life
satisfaction (Seleen, 1982). Part of the congruence
theory is "transport mobility" by seniors, shown to be
closely linked to independence, well-being, and quality
of life (Spinney et al., 2009).
On the other hand, it often is difficult to use
sociologically oriented older adult time use data
because of its emphasis on the social context of
activities, in the first instance, and its dichotomization of
most major activities into work and nonwork categories,
in the second. Both sociology and economics usually
disaggregate time use into obligatory and discretionary
activities without regard to locations (Gauthier and
Smeeding, 2001; Lawton et al., 1986). Obligatory
actions are paid work, eating, shopping, housework,
cooking, sleeping, etc. Discretionary actions are
socializing, leisure pursuits, rest and relaxation, and
passive or active recreation. Travel often is assigned to
one of these two general actions based on its purpose,
not where it occurs. These data have limited usefulness
for exposure modeling purposes.
One of the most prolific time use researchers is
sociologist Dr. John Robinson now of the University of
Maryland. He has worked with both EPA and
California's Air Resources Board (GARB) to obtain
exposure-relevant time use data, including the National
Human Activity Pattern Study (NHAPS; Klepeis et al.,
1996, 2001; Robinson, 1989; Robinson and Blair, 1995;
Robinson and Silvers, 2000; Robinson et al., 1996;
Robinson and Thomas, 1991; and Robinson et al.,
elderly stroke victims in Australia (McKenna et al., 2008). We
could not find a similar U.S. study, but the observations
probably apply in this country as well.
42
-------
1989). The NHAPS and California time use studies are
in CHAD, and selected daily aggregated data from both
are discussed below.
It should be noted that obtaining time use
information is sometimes difficult for selected older
people because of physical or cognitive difficulties,
although many researchers feel that it is no more
difficult to obtain reliable and valid activity data from
elders than for other population subgroups (Lawton,
1999). More importantly, it is the educational and
reading ability of subjects, along with health status, that
gives rise to response inconsistencies. When cognitive
problems arise with a particular older person, a proxy
time budget frequently is obtained (Lawton, 1999).
A sequential structured interview of "yesterday's events"
seems to be the preferred method used to obtain
activity data from older people (Klumb and Maier, 2007;
Lomax et al., 2004). There is an "age effect" in
obtaining convergent and reliable time use information
that has to be addressed when obtaining data from the
very old (Klumb and Baltes, 1999).
There are many dimensions of the use of time by
people that are important for exposure modeling. They
are outlined in Table 4-1. As mentioned earlier, the
"event" (E) is the basis for locating a simulated person
in time and space. Other important dimensions for
exposure modeling are frequency (F), duration (D), and
pattern (P). Many of the other dimensions follow from
the usual weekday/weekend (or workday/nonworkday)
arrangement of life, captured by the sequence (S) and
cycle (C) dimensions. To date, seasonal or yearly
estimates of exposure (related to the T and TTOT
metrics) have been utilized, but additional time
dimensions can be accommodated easily.
4.B Factors Affecting Time Use in Older
Individuals
Despite many cultural differences among
countries, in general, time use by older adults is similar
across developed countries, especially for "non-
discretionary activities," such as work, sleeping, and
eating (McGrath and Tschan, 2004). Time spent in
these activities by the aged has not changed much over
the years (Bittman and Goodin, 2000; Gauthierand
Smeeding, 2001). However, time use has changed for
discretionary activities that are important in how older
Americans view themselves and relate to society
(Altergott, 1988).
Time use changes over the life cycle (biological
lifestage) because of age (perse), disability, and health
status. But it also changes because of "social" (family
role) lifestage factors (Vadarevu and Stopher, 1996).
The latter include marriage, parenthood,
Table 4-1. Definitions of Time Use Metrics Useful for Exposure Modeling
(After: J.E. McGrath and F. Tschan (2004), Temporal Matters, Washington, DC:
American Psychological Association.)
Event (e)
t
et
T
Coupled events
Cycle
Frequency (F)
Duration (Dx)
Location (Lt)
Pattern (Px)
Proportional Duration (%DX)
Rhythm (R.ex)
Sequence (S)
Trend
An observed state/activity with a homogeneous "value" related to the subject matter; e's
are numbered sequentially with a subscript (i) for each T period (e.g., e1, e2, ...en). The
class of events having a defined commonality with one or more ex's regardless of
temporal sequence is designated as Ex
A minimum unit of time; in exposure modeling it is 1— >60 minutes
Duration of an event (in minutes); in exposure modeling, et never crosses a clock hour;
longer events are subdivided into two or more clock hours
A longer period time that is the summation of all applicable t's of interest; in exposure
modeling, T generally is a day. Longer time periods are also of interest designated TTOT,
which is the sum of all T's of interest
A series of events that occur sequentially in a causal manner (i.e., ex=i always proceeds
ex=2). The coupled events may consist of a sequence of events that are always temporally
related to each other.
A systematic temporal pattern showing a rise and fall in some property of an event et such
that it returns to its original value recurrently. Each cycle has phase and magnitude
properties. A cycle is a "rhythm" with approximately equal subject-specific values of
magnitudes.
Frequency of a class of events (E) = number of ex's per T
Total duration of ex's occurring within T
The location where an event occurs; the same event may occur in more than one location.
Thus, there are parallel metrics for location as for events: frequency, duration, proportional
duration, and sequence.
The temporal pattern of a series of events of the same class { ex=i;j, ex=i;j+i . . . ex=i;n}
DX/TMOO
A regular pattern of ex=i overtime period T. It can be defined in terms of periodicity
(sequence), frequency, or rate. It generally refers to the pattern of evenly spaced
occurrence of ex=j's that are approximately equal in duration.
The order of events in a class (ex=i/i, ex=1/2, ... ex=1/n) or for all events (ei, e2, ...en )
A temporal pattern of events of a given class ex=j that shows a systematic directionality
over time
43
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employment/retirement status, household income,
children living nearby, and other considerations (e.g.,
the "empty nest" syndrome) (McGrath and Tschan,
2004). The largest impact on time use by the aged is
caused by decreased mobility, especially in those of
very advanced age (referred to commonly as the "old
old"). When seniors become dependent on others for
transportation and personal care, they begin spending a
large proportion of time in passive activities (Lawton,
1991).
Most older adult single-person households consist
of a female living alone. Females 65+ years are four
times more likely to be widowed than males of the same
age. They also are 20% more likely to be divorced. This
disparity in household structure increases with age
(Rosenbloom, 2004b). By 75 years, 50% of females live
alone versus 23% of males. By 85 years, the proportion
is 86% for females and 41% for males (Rosenbloom,
2004b). These factors certainly affect time use by older
persons.
Retirement obviously affects elders' time use (Kim
and Hong, 1998; Mancini and Orthner, 1982); Piekkola
and Leijola, 2004; Rosenkoetter et al., 2001). They
have more "leisure," among other things. This does not
mean that they stay home and do nothing. Partly, this is
a definition problem, in that the word leisure has many
meanings that vary by age and gender (Lawton, 1999;
Little, 1984). For older people, leisure usually means
discretionary or nonobligatory activities (Lawton, 1999).
One type of leisure is voluntary work. In recent years,
"unpaid productive activities" (voluntary work) have
increased greatly,15 which results in seniors being
"socially productive" for years beyond their
(paid/housework) working life (Altergott and McCreedy,
1993). In general, retired males spend more time in
active leisure and in the traveling associated with it than
do females of the same age. The same is true for
passive leisure. The only leisure activity that older
females devote more time to than older males is
"creative leisure," such as knitting, making things, and
art-making (Altergott, 1988). These are statistically
significant differences.
The total amount of leisure (both active and
passive) peaks at about 8 h day" for individuals in the
65 to 74 age range (Altergott, 1988). After that age
range, active leisure decreases greatly for males, but
not females. Travel for leisure does not change much
with increasing age, however. Older females spend
significantly more time than males of the same age in
"obligatory" activities, especially housework (Altergott,
1988; Bryson, 2008; Henderson et al., 1996). These
gender differences could have exposure impacts
resulting from differences in time spent outdoors, in
motor vehicles, or indoors if pollutant sources are
present.
Hospitalization obviously affects locational aspects
of daily living for older people (Boyd, 2005). Because
EPA does not estimate exposures to environmental
contaminants inside hospitals or other health-related
institutions, we ignore those locations in our analyses
and do not provide any data for time use or
"participation rates" for them. (They are not ignored for
"free-living" individuals visiting one of these facilities,
however.) It should be noted that hospitalization can
affect 30% of more of older females (>65 years) in a
given 18-mo period, so the potential subpopulation size
for institutionalized people is large. Hospitalization also
is an independent predictor of a decline in activities of
daily living (ADLs), which greatly affect an older
person's ability to live alone, with numerous
psychological and locational dimensions (Boyd, 2005).
Data are available from Europe and Canada to put
the U.S. time use information into perspective; see
European Commission (2003), Horgas et al. (1998), the
series of articles by Leech and co-workers (1996-2006),
and the work by Zuzanek and colleagues (1988-1999).
Articles can be found for other countries also (e.g.,
Japan [see Ujimoto, 1988, 1990, 1993]).
Literature on the aged, especially when focused on
people having functional limitations and the frail,
distinguishes between basic activities that are needed
to survive at a minimal level of independence and those
that require more social engagement. There are
locational aspects of both types of activities. The more
basic activities are discussed in Section 6.
4.C Time Use Databases
We review here some of the time use information
sources that may be used to model exposures in older
persons or serve as a check on model performance
regarding time use by that group. The broader time use
(activity) literature, in general, does not provide
information on where activities occur (i.e., their
location), and, when it does, it is not presented in a way
that we can use for exposure modeling purposes
(Robinson, 1977; Robinson and Godbey, 1999). In
other words, it is difficult to identify habitues—people
who actually inhabit a specified location of interest in
most time use databases. To emphasize this point,
Robinson and Thomas (1991) directly state that most
activity information cannot be used to estimate where
people spend time.16 Some locational data on where
15
National statistics from 1993 indicated that 43% of people
aged 65-74 years participate in volunteer work, whereas 36%
of those 75+ years do so (Kim and Hong, 1998).
An extended quote from Robinson and Thomas (1991)
succinctly highlights this issue from just one locational
perspective, time spent outdoors.
[There is an] unexpectedly wide range of activities that
are performed in outdoor locations near the home. It brings
home the difficultly that analysts fare in predicting locations
from activities. This cross-tabulation of activities by location
does show that most of the types of activities that one expects
to be outdoor activities by location are, in fact, the ones most
likely to be performed outdoors. Thus, among household
activities (which take up more than half the time spent
outdoors near the home), yard work (15%) and plant/pet care
44
-------
older Americans spend time are contained in EPA's
Exposure Factors Handbook (NCEA, 1997a) and are
reproduced here in Table 4-2. Gender-specific data are
not available.
Aggregate data on time use distributions by seniors
abstracted from various tables in the Exposure Factors
Handbook are provided in Table 4-3. The data are from
EPA's NHAPS surveys (see Table 1-1) and are not
gender-specific. Much of this information also appears
in Tsang and Klepeis (1996, 1997). The information is
arranged under four main categories:
(1) bathing/showering, useful for estimating dermal and
inhalation to water contaminants; (2) motor-vehicle
oriented locations, useful for estimating inhalation
exposures to air toxics and other gaseous pollutants;
(3) outdoors, for estimating exposures to any ambient
pollutant; and (4) potential high-exposure-generating
activities.
It should be noted that the Exposure Factors
Handbook provides time use data other than that
reproduced here. The time units for those data are in
hours per week, hours per month, and minutes per
month and do not fit easily into the minute per day units
used in Table 4-3 without making the assumption that
daily time is simply the monthly value divided by 29 to
31 days/mo. As already noted, there is a wide variety of
time use by seniors on a daily basis, and assuming
equal daily usage is not consistent with the longitudinal
data that are available (see Section 4.E). Another
caveat associated with Table 4-3 is that Tsang and
Klepeis (1996) used a number of recedes for both
locations (called NEWLOC) and activities (NEWACT). It
is not clear from either Tsang and Klepeis (1986) or the
Exposure Factors Handbook precisely what recedes
were used to develop the activity/locations used for the
distributions noted in our Table 4-3, so there is
unresolved uncertainty then, regarding the breakdowns
in that table. (For more detail on the activities/locations
mentioned, see Tables 27 and 28 in Tsang and Klepeis,
1996).
Regardless of the precise distributional outpoints
depicted in Table 4-3, most of the distributions are very
(16%) are the activities that fall mainly into the outdoor
category. However, almost as much 'indoor-type' as outdoor-
type housework activity is done outdoors—such as cooking
outside (1%), cleaning carpets and other household objects
outside (5%), putting laundry out to dry or other clothes care
(2%), repairing appliances/other household objects outside
(11%), and performing household management tasks outside
(6%).
As expected, one also finds a fair amount of outdoor
time near the home . . . spent on sports activities (3%), on
play activities with children (2%), on meals (2%), and on
relaxing (3%). But more outdoor time is on hobby activities
(5%), and watching TV (6%) than on any of the 'usual' outdoor
activities. Six percent of home outdoor/yard time is even spent
sleeping and 7% doing paid work, which further illustrates how
little these 'usual outdoor' activities take up the time that
people spend outdoors near the home (p. 36).
"heavy tailed" (skewed to the right). Normally, a log-
normal distribution approximates that type of data, with
most doers or habitues spending a little time doing the
activity and a few doing it a lot. Combined with the
generally low participation rates in many of the
activities, only a minority of older adults will experience
exposures "at the high-end tail of the distribution."
However, these are the very same people that our
environmental standards are supposed to protect
(Jordan etal., 1983).
We attempted to provide the same type of data
seen in Table 4-3 for the California adult study (Wiley
et al., 1991 b), but were unable to determine the
proportion of senior "doers" or habitues from data
shown in that report. Without that, a participation rate
could not be determined, nor could the mean
doer/habitue time be calculated. The reader is referred
to Table 3.3 in Wiley et al. (1991 b) for additional
information on time use by persons 65+ years in
California, but for our purposes the data are insufficient
for checking on outputs from the APEX/SHEDS
exposure models.
4.C.1 The CHAD Database
CHAD has been introduced in Section 1. A lot of
the early material in CHAD is associated with Dr. John
Robinson, because he was funded by EPA to
investigate the relationships between time use and
potential exposures to smoking and environmental
contaminants (Robinson, 1988; Robinson and Thomas,
1991; Thomas and Behar, 1989). This published work,
however, is very general with respect to older adults,
who rarely are discussed as a separate subgroup. This
is also true of Robinson (1977) and Robinson and
Godbey (1999). The actual diaries that come from the
EPA-funded study, called NHAPS, are part of CHAD
(see Table 1-1), as is his GARB data (Robinson et al.,
1989; Wiley et al., 1991 a,b). Therefore, individual diary
data from these studies can be part of our current
modeling work, if desired. Aggregate data from NHAPS
have been extensively discussed by others (Kleipis
et al., 1996, 2001; Shadwick et al., 1999; Tsang and
Kleipis, 1996, 1997).
The CHAD database includes 5,742 person-days of
diary data for people aged 60 or older, 38% of it being a
single day per individual (see Table 4-4). This is not a
large dataset to represent the wide range of activities in
older Americans. Approximately 3,400 of those days
were added as part the Aging Initiative program, a
tangible result of the Agency's Aging Initiative work.
Additional diary days of data are being pursued.
As EPA researchers have shown analytically, there
are contextual factors that affect time use by seniors
and others, such as day of the week, seasons of the
year, special times of the year, social class, educational
levels, etc. (Graham and McCurdy, 2004; McCurdy and
Graham, 2003). Overall, in the CHAD database, people
>64 years old spend about 65 min/day outdoors, but
variability in this population group is large; the COV is
45
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Table 4-2. Selected Activity-Location Data for Seniors in EPA's Exposure Factors Handbook
Location/Activity Combination**
"Autoplaces" (locations containing motor vehicles)
In an internal combustion vehicle
In another type of vehicle
Outdoors-physical activity
Outdoors-other
Nonresidential Indoor Locations
Restaurants/bars
Shopping & undertaking errands
Working
Residential Locations
Working
Cooking
Other activities, including kitchen
Not defined as being in a specific location
Physical activity
Social & cultural activities
Eating & leisure activities
Sleeping
Mean "Doer" Time for Peo
CARB Data
% Doers
17
71
3
15
55
26
46
9
3
59
82
7
43
98
100
(min/day)
53
89
53
104
101
99
76
336
195
69
119
48
114
394
502
pie Aged 65+ (rounded)*
NHAPS Data
% Doers
7
78
<0.5
19
58
28
50
10
2
77
91
13
70
97
100
(min/day)
57
80
277
81
140
74
69
341
297
65
119
51
122
312
509
Notes:
'Related data also appear in Robinson and Thomas (1991), which is the source of the EFH data.
"These are selected combined location and activity pairs that are called microenvironments in both papers, but this is
an inaccurate use of that term as used by exposure modelers. A microenvironment is a location having a constant
concentration for the period of time inhabited by a person of interest (a habitue). A doer is a person who undertakes
an activity.
120% for "habitues" (those people who actually go
outdoors). Only 57% of older Americans went outside
on the days they were surveyed. Both the time spent
outdoors and the "participation rate" by seniors are
lower than that for adults <65 years old and children, on
average, but the differences are not large, even though
they are statistically significant at p=0.05. When gender
differences in the time spent outdoors are investigated,
older females are outside less than males (60 versus
118 min/day on average), and this difference is
significant (p<0.001) (Graham and McCurdy, 2004).
Older adults spend less time in motor vehicles
(86 min/day on average) than other adults, about
105 min/day, and also have a lower participation rate
for that location (Graham and McCurdy, 2004).
4.C.2 The American Time Use Survey
A potentially good source of older adult diary data
is the Bureau of Labor Statistics' (BLS) American Time
Use Survey (ATUS). There is 1 day of data per person
in the survey. See Abraham et al. (2006), Hamermesh
et al. (2005), Herz and Devens (2001), Krantz-Kent and
Stewart (2007), Russell et al. (2007), and Schwartz
(2002) for information on this database. This survey is
large and ongoing, with between 12,250 and 14,000
person-days of information being obtained each year
since 200417. Approximately 17% of the diary-days in
ATUS are for people aged 65+ years. There are some
structural problems with the ATUS data from an
exposure modeling perspective, but we have
conditional plans to address them and attempt to use
the database for our work (George and McCurdy,
2011). Until that effort is undertaken, we cannot utilize
the ATUS data in our models. Some papers that have
been published using the ATUS data are discussed in
Section 4.D.
4.C.3 Other Databases
Another large activity pattern study is the
Multinational Time Use Study (MTUS). There are many
articles and books describing this study (Gershuny,
2000, 2004, 2005, 2009), and its "raw" data are
available on the Web to registered users. Although the
ATUS began in 2003 with 20,720 ex post diaries, but the
number per year fell after that. The number of diaries per year
are as follows: 13,973 in 2004, 13,038 in 2005, 12,943 in
2006, 12,248 in 2007, and 12,723 in 2008. Thus, there are
85,645 days of diary days available for the 2003 to 2008 time
period, the largest and most recent source of U.S. time use
data available for analysis from any source.
46
-------
Table 4-3. Selected Time Use Data for People Aged 65+ Years from EPA's Exposure Factors Handbook
Activity on the Diary Day
Bathing/showering
Taking a shower
Time spent in bathroom after a shower
Taking/giving a bath
Time spent in bathroom after a bath
Total time spent in the shower or bath
Total time in bathroom after either/both
Motor-vehicle oriented locations
Gas/service station (cumulative)
Ditto, per visit
Outdoors at a gas/service station
Alongside of a road with heavy traffic
Outdoors: near street/neighborhood
Outdoor in a parking lot
Waiting at a bus/train stop
Inside a vehicle in heavy traffic
Traveling in a car
Traveling in a truck/pickup
Traveling in other trucks
Traveling in a bus
Traveling on a train/subway/rapid transit
Inside a vehicle (cumulative)
In a parking garage/indoor lot
Traveling: bike, skateboard, roller skates
Outdoors
Walking to car: driveway/parking lot
Other outdoor time (walk or run)
Other outdoors
Outdoor cleaning
Construction site
Outdoor playing (cumulative)
Playing on grass
School or playground
Park or golf course
Pool, lake, or river
Farm
Outdoor recreation (cumulative)
Outdoors at home or in yard
Outdoors-at-home (cumulative)
Outdoors near-a-vehicle
Doer
Sample
Size (n)
408
409
139
133
567
548
16
67
16
31
122
13
11
139
812
90
9
27
9
907
18
7
373
143
128
164
6
4
3
7
55
25
17
32
401
502
342
Potential high-exposure activities/locations
Near frying, grilling, or "bar-b-queing"
In a bar/nightclub, restaurant
Smokers are present
96
270
340
Percent
Doers
(Calc.)**
30.2
30.3
10.3
9.9
42.0
40.6
1.2
5.0
1.2
2.3
9.0
1.0
0.8
10.3
60.2
6.7
0.7
2.0
0.7
67.2
1.3
0.5
27.7
10.6
9.5
12.2
0.4
0.3
0.2
0.5
4.1
1.9
1.3
2.4
29.7
37.2
25.4
7.1
20.0
25.2
Distribution of Time by Selected Percentiles
(Rounded) Spent in the Activity (min/day)
5%
5
0
5
0
5
0
5
3
5
2
2
1
5
5
10
12
18
20
10
10
0
23
0
2
12
30
60
30
30
5
20
30
5
5
10
5
4
3
20
30
25%
10
4
10
5
10
4
10
5
10
4
20
10
20
15
30
30
25
45
10
35
3
25
2
15
45
60
300
45
30
30
30
60
50
30
45
36
10
5
45
100
50%
10
5
15
10
15
10
18
10
18
20
40
25
30
30
60
49
60
73
24
60
5
35
5
30
95
120
460
60
121
60
120
115
85
171
90
110
30
10
63
240
75%
20
10
20
15
20
15
55
15
55
45
75
60
40
60
110
105
99
130
120
120
15
110
10
60
203
173
540
60
121
95
300
277
160
375
180
210
60
20
100
540
90%
30
20
40
35
30
20
180
15
180
60
120
180
45
121
165
185
186
435
690
190
45
205
15
121
420
300
560
60
121
150
510
480
360
495
302
375
120
30
178
798
95%
60
30
60
35
30
30
240
40
240
121
190
465
45
121
225
265
186
460
690
258
90
205
30
121
510
350
560
60
121
150
570
510
495
600
465
485
205
120
255
880
99%
60
45
61
60
60
60
240
120
240
121
270
465
45
121
405
453
186
570
690
460
90
205
88
121
610
510
560
60
1231
150
735
525
495
735
660
735
510
121
520
1205
Source*
15-21
15-23
15-26
15-28
15-30
15-32
15-106
15-39
15-106
15-43
15-104
15-105
15-128
15-44
15-121
15-122
15-124
15-125
15-129
1 5-1 33
15-45
15-127
15-46
15-47
15-140
15-72
1 5-1 07
15-80
15-64
15-108
15-109
15-110
15-112
15-86
15-120
1 5-1 32
15-134
15-34
1 5-1 39
1 5-1 41
Notes:
The Table number in the EHF containing the data from the NHAPS survey. Data from other sources are included in the Handbook, but are not
reproduced here.
"Calculated (calc.) using a total number of people aged 65+ (1,349) given in Tsang and Klepeis, 1996
47
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Table 4-4. Activity Diaries in CHAD for Older Adults
Age Range
Females
60-64
65-69
70-74
75-79
80-84
85-89
>90
Total
Males
60-64
65-69
70-74
75-79
80-84
85-89
>90
Total
Grand Total
Total Number
of Diary Days
647
589
681
592
345
201
19
3074
342
1083
362
384
260
193
44
2668
5742
Unknown Gender
>90
1
Number of Diar
1 Day
372
331
296
186
129
38
13
1365
266
218
156
107
53
20
12
832
2197
1
2 Days
18
12
2
0
0
0
0
32
6
6
1
0
0
0
0
13
45
j Days Available per Individual
3 or 4 Days
18
9
9
4
4
5
0
49
16
10
7
3
3
1
0
40
89
5 to 9 Days
1
2
2
3
3
2
0
13
0
1
1
7
3
3
0
15
28
10+ Days
7
14
25
25
13
11
1
96
1
6
12
18
18
11
2
68
164
Percent
with
Only 1 day
57.5
56.2
43.5
31.4
37.5
18.9
68.4
44.4
77.8
20.1
43.1
27.9
20.4
10.4
27.3
31.2
38.3
United States is included in this essentially historic time
use database, the published papers describing it do not
focus on seniors or even provide descriptive information
on time use patterns for them. The same comment
applies to the American Heritage Time Use Study
(Allard et al., 2007; Merz and Stolze, 2008; Tudor-
Locke et al., 2007), which contains data only on time
use patterns of U.S. citizens.
4.C.4 On Vacations and Out-of-Region Time
There is no exposure model that correctly handles
time spent by a modeled population outside of their
region, either on vacation (short-term or long) or during
work or leisure travel by the modeled subjects. Modeled
subjects never leave the analyzed region, in other
words. The main reason for this is a lack of time use
data for vacations and for multiday travel, mostly
because the ex post survey is done at the home
location. If no one is there, they cannot be surveyed. If
a diary is used to obtain sequential time use data, many
subjects object to using it on vacation and become
noncompliant. Even if vacation time use data are
collected, there usually is not any way to determine if a
person is away from home in the CHAD or other
databases except by deduction, if a person sleeps in a
hotel or motel, then he or she probably is outside of the
"home" region. Even that may not hold for all
circumstances, and sleeping in "another's home"
(a code used in many studies, but not in ATUS) could
occur anywhere.
Assuming a person is in their own region all the
time will overestimate exposures to pollutants particular
to that region, of course. (And exposures experienced
in another region are completely ignored.) Although this
is a problem with all exposure modeling efforts, it may
be a particularly important one for retired seniors
because many of them spend significant time away
from their primary residence (Stalvey et al., 1999). They
visit children (locally or in another region), "temporarily
migrate" to another area, or just "travel around" (see
Section 4.D.2, for instance).
It is difficult to both model vacation/out-of-region
behavior and obtain data to go about doing so. One
interesting article that quantifies the size of temporary
migration is Smith and House (2006). Their focus is on
seniors in Florida. From a random-probability telephone
survey of people 55+ years old in Florida over a 3-year
period, they identified those who spent 1 mo or more
per year out of state. They were called "temporary
residents" if they were nonpermanent Florida residents.
Nonresidents who spent <1 mo in Florida were
excluded. They classified the residents as "stayers" if
48
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they spent <1 mo out of state or "sunbirds" if they left
during the hot months (or any other time of the year).
"Snowbirds" were those temporary residents who came
into the state during the winter months. The number of
temporary residents in Florida was very large and
seasonal, as expected, and most were >65 years old
(Smith and House, 2006).
How should exposure modelers capture this time
use phenomenon? How can a risk assessment account
for doses received or not received outside of the
modeling area of interest? These questions are not
addressed in any published environmental
exposure/dose/risk assessment report. These questions
are something to be aware of, and all health risk
assessments should contain caveats regarding these
essentially time use issues.
4.D Examples of 24-h Time Use Data
Time use data for seniors include different
emphases, age/gender combinations, and formats.
Therefore, it is difficult to summarize the information
succinctly. Selected data from U.S. studies with multiple
categories of time use are summarized here. Articles
that present useful data for only one activity type of
importance to exposure modeling are reviewed under
the specific categories that follow. Locational
information for multiple or single categories of time use
is only occasionally provided, however. Participation
rate is the percent of the sample actually undertaking a
specific activity on the sampled day, and these people
are called a "habitue" when location considerations are
being discussed and a "doer" when a specific activity is
undertaken.
Czaja (1990) provides mean estimates of the time spent
in various activities in a 24-h day, which are reprinted
here as Table 4-5. The Czaja (1990) data are
reproduced (in more readable form) from Moss and
Lawton (1982), so the data are not very current. The
estimates probably are similar to those cited below
under Lawton et al. (1986) because the mean age of
the sample is the same (76.2 years). The time use data
are from 426 people living independently and from 164
people living with others or in a facility. Czaja (1990)
provides the location ("environmental context") for
waking hours only, and 82% of all waking-time activities
occur in the home or yard.
G0rtz (2006) provides data on time use by U.S.
seniors, but combines 31 activity types into six major
categories, none of which are particularly useful from an
exposure modeling perspective. Therefore, no
information is abstracted from this study.
Kelly et al. (1986) provide graphical—and at a
small scale, at that—data on the percentage of elders
aged 65-74 and 75+ that participate in various activities
that are of interest to us: overall activity level, travel,
exercise and sport, and outdoor recreation. Because of
its format, specific statistics from this paper are not
Table 4-5. Time Spent per Day in Selected Activities
(from Czaja et al. 1990)
Activity
1 . Obligatory activities
Personal and health
care
Eating
Cooking
Helping others
Housework
Shopping
2. Discretionary
activities
Social interaction
Religious activities
(non-service)
Reading
Watch TV
Listen to radio
Recreation and
hobbies
Rest and relaxation
Sleep
3. "Gap" and minor
Summation
Unknown mean time
Time Spent in the
Listed Activity
(minutes per 24-h day)
Independent
Subjects
53
77
69
10
68
22
112
10
59
205
28
44
128
456
26
1367
73
Impaired
Subjects
71
77
45
7
38
13
110
7
52
210
33
32
200
452
40
1387
53
Notes:
"Impaired subjects" are recipients of in-home services (n=91) or
are people awaiting entry to a long-term care facility. Impaired
subjects is the heading used in Czaja (1990), but not in the source
article (Moss and Lawton, 1982).
The means are "statistically adjusted" to account for age, gender,
education, ethnicity, income, and household consumption (Moss
and Lawton, 1982).
abstracted here. As expected, participation in all of
these items decreases with age for both genders.
Females participate less than males, except for "overall
activity level" and "travel," where 75+ aged females
participate more frequently than 75+ aged males.
Knipscheeret al. (1988) provide information on
time use (hours per day) in older adults, disaggregated
into two age groups of interest to us: (1) 65 to 74 years
and (2) 75+ years. Their data, however, are not very
useful; for one thing, there is no locational information,
and the activities are grouped into "productive" and
"nonproductive" categories. Productive activities include
houseork, helping others, volunteering, and "going out."
Nonproductive activities are leisure, mass media,
television news watching, and newspaper reading
(Knipscheeret al., 1988).
49
-------
The most promising-sounding article on elders'
time use is entitled "How do older Americans spend
their time?" (Krantz-Kent and Stewart, 2007). It is based
on data from the 2003 and 2004 ATUS surveys. It
provides complete daily data for a number of activities
and work status, but none on locations, by gender for
two age categories that we are interested in: (1) 65 to
69 years and (2) 70+ years (other age groupings are
included also). We cannot get very much useful
exposure information from the article, however,
because travel is assigned to its purpose, travel for a
large number of household-related purposes is
assigned to "household work," and travel for paid work
is assigned to working. The household travel category
is particularly troublesome because it includes travel for
obtaining governmental and civic services, consumer
purchases, obtaining professional and personal care,
and a number of other purposes (including "not
elsewhere classified"). Participation rate data to
determine "doer" time also are not provided. Probably
the most useful information contained in the article is
the differences in time use spent in selected activities
by employment status, employed full time, employed
part time, and not employed.
For the "leisure and sports" category, the following
mean hoursfaverage" day information is provided for
older females and males (Krantz-Kent and Stewart,
2007). The category is broad, including socializing,
communicating, watching television, sports, exercise,
recreation, relaxing and thinking, and reading. Most of
these activities are quite passive and have low
Table 4-5. Time Spent per Day in energy expenditures,
usually resulting in a low dose rate even if an exposure
occurs.
Females (h/day) Males (h/day)
Age 60-64 65-69 70+ 60-64 65-69 70+
Employed 3.8 4.0 3.6 4.1 5.7 7.6
full-time
Employed 4.4 4.9 6.1 3.9 6.0 8.1
part-time
Not 6.1 6.5 7.2 4.1 5.9 8.1
employed
Another ATUS-based paper is by Waidmann et al.
(2006). It provides information from 2003-2005 "waves"
of the survey. They aggregated data for everyone
65 years and over, with a sample size of 7,932 for the
3 years. The participation rate and doer time from their
study follows (from their Tables 4 and 5). The travel
time estimates are lower than those provided by
Gossen and Purvis (2006); see Section 4.D.1 for more
information on that activity.
Activity
Travel
Cleaning
Work/volunteering
Physical recreation
Participation
Rate (%)
72.4
33.2
18.3
17.9
Time Spent
(min/day)
77
84
282
76
Lawton et al. (1986) provide participation rate and
time "allocations" for selected activities and a few
general locations. The sampled mean age was
76.2 years; the standard deviation (SD) was not
provided. "Recreation" is one of the discretionary
activities depicted, and 35% of the 535 people sampled
from a wide variety of housing types participated in it.
"Doer" time was 118.2 min/day. The category is not well
defined and probably includes both active and passive
leisure. (If it were entirely active, it would have been
reviewed below in "physical activity.") They also report
travel as an activity, and 50% of the people participated
fora doer time of 67.8 min/day. These values both are
reasonably consistent with those shown in Table 4-3
("inside a vehicle, cumulative").
With respect to locations, called "environmental
contexts" by Lawton et al. (1986), the choices were "at
home," "in yard," and "away from home." Travel
locations probably were included in that last category.
The most useful coded location from an exposure
modeling perspective is time spent in the yard; 41% of
the elders expended 148.1 min/day on average in that
location (Lawton et al., 1986). These are quite high
numbers and could lead to high exposures to ambient
pollutants. Lawton's estimates are quite close to
"cumulative outdoor-at home" time shown in Table 4-3,
where 37.2% of the seniors do so on any 1 day for a
median of 110 min/day.
Linn et al. (1999) provide time use data on 30
COPD subjects aged 56 to 83 years old enrolled in a
study of heath effects associated with living in a city
with high particulate concentrations. The subjects
maintained a paper diary for 4 consecutive days on two
occasions; the minimum time block used in the paper
diary was 20 min. Most of the subjects spent the
majority of time indoors and were sedentary. "Physical
activity time was appreciable but was of low intensity,
as judged either from diary reports or from recorded
heart rates" (Linn etal., 1999; p. PM-113). Selected
data from their paper follows (their Tables 4 and 5).
Group Mean Time Spent in the Categories Shown
Percent clock hours
away from home
Min/time period
outdoors
Min/time period in
vehicles
Min/time period
active
Midnight-
6 a.m.
0.5
0.4
0.2
4.8
6 a.m.-
Noon
7.5
19
10
72
Noon- 6p.m.-
6p.m. Midnight
23.5 7.0
45
27
95
10
24
Note: "Active" time use was based on a self-described
qualitative term that used a 0-100 visual analog scale
developed by Linn et al. (1999). The subjects looked at
the scale and "coded" each activity according to their
impression of how much work (energy expenditure)
they expended in undertaking it. There are a number of
similar scales used in the exercise physiology literature
(e.g., the Borg et al. articles), and the Linn et al. (1999)
50
-------
scale data are not consistent with them. It is difficult to
know what to do with the Linn et al. (1999) data.
The percent of clock hours away from home in Linn
et al. (1999) includes those diary hours with one or
more 20-min periods that were coded away from home.
Therefore, it includes partial and whole hourly blocks of
time. There is uncertainty about just how much clock
time the whole/partial blocks relate to exactly. The
authors also provide the following locational data in
their Table 5 (p. PM-113). The monitored week is a
4-day time period with concomitant personal particulate
and home monitoring data. Coded time use information
is available for it and a "reference" week, which was not
monitored (Linn et al., 1999). The time spent in both
locations was shown to be statistically significantly
different using a repeated-measures ANOVA. The main
effects of week and order were nonsignificant for all
items, but the interaction terms were statistically
different at p<0.05.
Group Mean Time/Activity Data for Two Time Periods
Min/day outdoors
Min/day in vehicles
Monitored
Week
62.4
36.0
Reference
Week
86.4
55.2
The Linn et al. (1999) data were included in the
Frazier et al. (2009) analysis of intra- and interindividual
variability in time spent in three general locational
categories. That analysis was sponsored by EPA and
so also should be considered to be an output of our
Aging Initiative program. Its findings are discussed in
section 4.E.
Ott (1989) provides an early description of time use
data in modeling exposures, but older individuals are
not a prominent subgroup of concern in this article.
Pruchno and Rose (2002) provide selected
summary information on time use by a group of frail
elders in Cleveland, OH. Some of the 123 people
included lived in a nursing home (n=45), whereas
others lived in an assisted living facility (n=51) or "in the
community" with the support of home health services
(n=27). They all participated in a 1-day "yesterday"
interview using 15-min blocks, but age of the
participants was not provided. The data provided in the
article are in both the obligatory/discretionary dichotomy
favored by transportation planners (and some
geographers) and by sociologists. They also use an
"environmental context," but the only category of
interest in it to us is "time away from home." That datum
and travel time estimates from Table 1 in Pruchno and
Rose (2002) follow.
Mean Minutes/Day Spent by Frail Elders and
Participation Rate by Housing Type
Nursing Assisted Home Stat. Sign.
Home Living (Assisted) Differences
Travel 11 26 32 Cols. 1 and 3
time
Participation 17.8 43.1 55.6 Not tested
percent
95
145 Cols. 1 and 3
Away from 36
"home"
Participation 17.8 43.1 63.0 Not tested
rate
As can be seen, time use by frail individuals is
different for the diverse housing types, sometimes
significantly so. The participation rate data are
particularly informative and indicate that most of the
people who are away from home also travel, but that
time spent in the two categories is quite different. See
the discussion of time use by health-compromised
seniors contained in the Frazier et al. (2009) paper
described in Section 4.E.
A paper by Vadarevu and Stopher (1996) is
interesting as it emphasizes the importance of "life
cycle" in affecting individual and family activities. They
use the term in the way that we have defined "life
stage," and we will use the latter term here. One of their
life stage groups is "older families." Although
participation rate in "mandatory" activities is similar
among the five life stage groups depicted, except the
unemployed adult group, there are big differences
among them for "optional" activities, such as social
engagements, recreation, eating out, etc. Older families
socialize almost twice as much (not significant based on
an ANOVA analysis) as the other life stage groups and
about four times as much for recreation (significant at
p=0.01). Time (hours per day) spent in recreation also
is significantly different (against the population mean,
using a z statistic from multiple pair-wise comparisons
[Vadarevu and Stopher, 1996]). Some of the differences
found in the older family life stage group undoubtedly
result from most of them not working compared with the
other groups (except for nonworking adults—
unemployed and retired, of course).
Verbrugge et al. (1996) provide time use and other
information from the Baltimore Longitudinal Study of
Aging (BLSA), one of the most important studies of
older adults in this country. They provide estimates of
variability in time use due to cross-sectional versus
longitudinal (within individual) effects, so data from the
paper are provided in Section 4.E.
There are a few articles on the time use of nursing
home residents, but because that location is so specific
and may never be a focus of EPA exposure modeling, it
is mentioned only in passing in this review. However,
one article is of interest. Smith et al. (1986) asked 60
people aged 78 years on average (range: 65 to
99 years) living in a nursing home to keep an activity
diary for 2 days separated by 2 weeks. Locations were
51
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not recorded. The sample spent their days in this
manner (as a percent of daily time): sleep 40%, "daily
living tasks" 20%, leisure time (recreation) 27%, rest
7%, and work 6% (Smith et al., 1986).
4.D.1 Time Use in Specified Activities or
Locations
There is a large literature on time-averaged time
use data for specific activities or locations that cannot
be used in an event-based exposure model but could
be used to evaluate its performance. Basically, the idea
would be to determine if the frequency, duration, and
pattern of activities/locations output by the model are
compatible with the extant data on them. The data
would be used essentially as a "control total" to check
individual activity estimates coming from APEX or
SHEDS. Data on "physical activities" is provided in
Section 5; this section presents data on specific
nonexercise activity/locations seen in nonsequential
time use papers.
Kelly et al. (1986) discuss a survey taken in Peoria,
IL, of "leisure activities," which include cultural, social,
community participation, and home-based activities
(plus travel and outdoor activities, which are discussed
below). Data are provided on the percentage of older
people aged 65 to 74 and 75+ by gender participating in
the various activities, as well as for younger age
categories (see just below).
Relative Participation by Age and Gender (approximate)
Age
Cultural Events
Family Leisure
Social Activities
Community Activity
Home-Based
Exercise/Sport
Outdoor Recreation
Travel
Females
65-74
67%
80%
93%
58%
90%
75+
60%
55%
84%
61%
75%
Males
65-74
41%
78%
69%
55%
80%
75+
41%
90%
90%
42%
56%
15% 3% 40% 5%
2% 1% 16% 2%
55% 40% 66% 30%
Although overall activity level in older adults of both
genders decreases, as can be seen, there are
significant differences among the main activity types.
Health and physical ability rather than age perse seem
to be the most important factors in understanding
age/gender differences in activity participation rates.
However, there are fairly large decreases in most of the
activities listed between the 65 to 74 age groups and
those aged 75+ (Kelly et al., 1986). The large decrease
in "outdoor-" and "indoor-productive" activities, walking,
and active leisure has been seen in other countries for
the same two age groups, although they seem to be
more active than U.S. seniors overall (Dallosso et al.,
1988).
Robinson and Caporaso (2009) published an
analysis of ATUS data for people aged 65+ (as well as
for two other age groups). The authors categorize
activity data into four main groups: (1) contracted time,
(2) committed time, (3) personal care, and (4) free time.
Contracted time focuses on working and commuting to
it. The average number of hours per week for seniors in
this category is quite low, 7.1 for males and 3.8 for
females (SD or SE estimates are not provided).
Committed time includes housework, child care, and
shopping. Mean time spent in this category is
31.0 h/week $ and 20.8 h/week <$. Except for the
"obligatory" personal care time (sleeping, eating, and
grooming), the free time category includes everything
else. One interesting category is "fitness activities."
Older females spend only 1.1 h/week, on average, in
fitness tasks, whereas older males do not spend much
more: 2.2 h/week. Total travel time is a modest
5.5 h/week $ and 6.4 h/week for males (Robinson and
Caporaso, 2009).
4.D.2 Travel
Most travel information that is gathered relates to
urban area commuting patterns by working-age
individuals (Frusti et al., 2002). Since 2000, more
information is being obtained on travel by the aged and
other "special population groups." The main sources of
data available are the 1995 American Travel Survey
(ATS), the 1995 Nationwide Personal Transportation
Survey (NPTS), and the 2001 National Household
Travel Survey (NHTS). The ATS focuses on long-
distance travel >100 miles one way) and its data are
abstracted in Table 4-6 from Georggi and Pendyala
(2003). Some of the demographic data in the table are
interesting. Note the rather large increase in single-
person households between the ages of 65-74 and
ages 75+, mostly widowed females. The proportion of
workers drops between the two age groups, as
expected. Car ownership drops, as does the use of
private vehicles for long-distance travel; the number of
trips drops almost in half. Mean trip length, on the other
hand, increases. This increase is probably related to the
relative increase in airplane usage (Georggi and
Pendyala, 2003). Additional long-distance travel data
appear in Mallett (1999), but its information is not as
useful to us.
A very informative analysis of older Americans'
travel patterns is Giuliano et al. (2003). The data come
from the 1995 NPTS. Selected data are abstracted in
Table 4-7. Gender or work/nonwork breakdowns are not
provided. The authors provide graphs of trips by
purpose by time-of-day, but these are difficult to
quantify because of their format. For most people, work
trips occur in the 6:40 a.m. to 6:20 p.m. time period,
with many fewer work trips for the 75+ years age group
than for the 65 to 74 years age group. The vast majority
of all trips occur between 6:00 a.m. and 7:00 p.m.
(Giuliano et al., 2003). Okola (2002) corroborates this
observation and provides some data on 75+ year olds.
52
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Table 4-6. Demographic and Long-Distance Travel
(L-DT) Characteristics in Seniors
Table 4-7. Local Travel Characteristics in Seniors
Demographic Characteristics
Females
Single-person household
Married
Widowed
Employment Status
Full-time worker
Part-time worker
Not working
Transportation-Related
Own 1+ vehicles
Mean L-DT trips/year
None
1-4
5-9
10+
Mode choice for L-DT
Personal vehicle
Airplane
Bus
Train
Mean trip length (mi)
Age Ranges (years)
65-74
55%
36%
65%
20%
12%
7%
80%
81%
3.9
40%
33%
15%
12%
77%
15%
5%
1%
480
75+
62%
55%
45%
45%
4%
3%
91%
70%
2
58%
27%
10%
4%
70%
19%
9%
1%
510
Source: Georggi and Pendyala (2003)
She also provides graphical data on weekday/weekend
travel splits by shopping, eating out, and socializing, but
all ages are included, not just older cohorts.
Hu and Reuscher (2004) analyze the 2001 NHTS
information and provide limited data for older
individuals. Daily mean trips per person and person-
miles of travel by gender are provided for almost a
20-year period, 1983-2001, using a number of national
studies. The trend in both measures approximately
doubles for the total period.
Mean Daily Travel Statistics for 1983-2001 for
People >65 Years Old
Trips/person
Miles traveled
1983
O j"1
¥ 0
1.5 2.2
10.2 14.8
1990
? s
2.2 2.8
15.3 22.5
1995
? $
3.0 3.9
19.2 31.7
2001
? $
3.1 3.8
23.5 32.9
The mean time spent in POVs was about
55 min/day for people aged 65+ in 2001 (Hu and
Reuscher, 2004).
Frazier et al. (2009) provide descriptive statistical
information on the time spent in a motor vehicle in a
sample of health-compromised older individuals living in
two very different communities, Los Angeles and
Baltimore. Multiple days of data are available for each
Characteristic
Own 1+ Vehicles
Mean Daily Trips Data
All Trips
Number
Total distance (miles)
Time in travel (min)
Nonwork Trips
Number
Total distance (miles)
Time in travel (min)
Modal Split
POV driver
POV passenger
Bus or train passenger
Walking
Trip Length by Purpose (miles)
Shopping
Personal business
Social/recreational
Time in Travel by Purpose (min)
Shopping
Personal business
Social/recreational
Age Ranges (years)
65-74
91%
3.5
22.3
52.9
3.2
20.2
48.2
72%
21%
1.50%
5.40%
4.8
5.8
7.6
12.4
14.1
17.2
75+
72%
2.4
13.6
36.3
2.3
12.9
34.5
62%
28%
1.90%
7.00%
4.7
7.6
6.3
13.5
14.5
16.4
Source: Giuliano et al. (2003)
subject (see Section 4.E for a fuller description of the
analysis). The mean time spent in travel in Baltimore
was 20.0 ± 47.2 min day"1 for females (range: 0 to 375)
and 27.8 ± 65.3 min day"1 for males (range: 0 to 450).
The mean estimates for Los Angeles were 74.4 ±
72.9 min day"1 for females (range: 0 to 360) and 53.1 ±
50.4 min day"1 for males (range: 2 to 200). Note the
wide range and the high coefficients of variability in both
areas.
Gossen and Purvis (2006) provide travel time data
for 1990 and 2000 for working and nonworking 65 to
99 year olds by gender. Participation rate information,
however, is not provided. Surprisingly, travel time
dropped between 1990 and 2000.
Time Spent in Travel, by Doers (min/day)
Year
1990
2000
Workers
c? ?
Nonworkers
•^ n
O V
82.5
51.6
67.9
45.8
102.2
83.7
95.0
72.3
Females' travel time is less than males in both
working categories and years, and differences for
nonworkers by gender are significant (at p<0.05) for
53
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both of the years presented. Gender differences are not
significant for the working group (Gossen and Purvis,
2006; Table 2). Ethnicity did not account for significant
differences among the groups either.
With respect to travel during the day, the 2001
NHTS indicates that 23% of nonwork-related travel
during peak congestion periods is by retired seniors,
and only 0.2% of all travel by seniors is by public transit.
Total trips per day for seniors, however, are less than
that for workers (Hildebrand, 2003; Collia etal., 2003).
Driver involvements in crashes per 1,000 licensed
drivers decrease with age—beginning after age
19!—even up to 85 years old, for both genders, with
females having a slightly lower rate than males at any
age (Ferguson and Braitman, 2006). However, crashes
per million miles traveled by age increases after age 60
years, with males having a slightly lower rate. The
obvious reason for these results is that, although many
seniors keep their driver's licenses as they get older,
they drive many fewer miles (Ferguson and Braitman,
2006). Up to about age
54 years, females travel fewer miles than males (but
make more daily trips); this pattern seems to continue
after age 55 years. The proportion of trips made in cars
(POVs) as a driver versus as a passenger decreases
with age in both females and females.
Approximate Proportion (%) of POV Trips as
65-69
o ^
¥ o
Driver/Passenger
Age Ranges
70-74 75-79
9 $ 9 $
80-84
9 $
85+
Driver 42 90 41 89 30 82 32 78 30 67
Passenger 58 10 59 11 70 18 68 22 70 23
Source: Rosenbloom (2004b), Figure 2
There are exposure and intake dose implications
for the above differences, because drivers work harder
than passengers (about twice as hard; METS = 1.0 for
being a passenger versus 2.0 for driving [Ainsworth
et al., 1993]), and drivers often drive alone but
passengers cannot (thus, there are more trips per
person).
Rosenbloom (2004a) provides detailed information
on mobility of seniors in an article titled "good news and
bad news." Total 1995 daily trips and total vehicle miles
traveled (VMT) by older Americans are depicted in
Table 4-8, by age and gender cohorts (Rosenbloom,
2004b). The vast majority of the number of trips and
miles of travel undertaken are nonwork related, more so
in females than males. These are not unexpected
findings. There does not seem to be a trend with
Table 4-8.1995 Daily Trip Data (Means) for People Aged 65+
Daily Travel Statistics for 65+ Year-Old Persons
Trips/driver
VMT/driver
Mean trip length (miles)
Time in travel (minutes)
1983
1.7
9.8
5.9
Total Trip Rates (Number per
Age Groups
65-69
70-74
75-79
80-84
85+
1990 1995
2.3 2.9
14.8 19.6
6.6 6.7
31.0 43.0
% Change
1983-1995
77
99
13
Day) by Age and Gender
Females
Trips
3.7
3.4
2.9
2.4
1.3
Percent
Nonwork
94.6
94.1
96.6
95.8
100.0
Males
Trips
4.4
4.2
3.5
3.4
2.1
Percent
Nonwork
86.4
90.5
94.3
100.0
95.2
Total Miles of Travel per Day by Age and Gender
65-69
70-74
75-79
80-84
85+
Females
Miles
24.9
20.6
16.4
13.0
7.3
Percent
Nonwork
92.8
97.1
96.3
97.7
98.6
Males
Miles
37.4
34.5
23.8
19.0
13.1
Percent
Nonwork
85.6
90.1
91.6
97.4
100.0
Source: Rosenbloom (2004) Transportation in an Aging Society (Table 3)
54
-------
55
increasing age in either of these metrics, and statistical
testing for age trend (or gender, for that matter) was not
reported in Rosenbloom (2004b). However, there is an
overall temporal trend in the data over the years; all
metrics indicate that the trend in travel by the aged was
up for the 1983-1995 time period. Overall trips taken
per year for 65+ year old drivers increased 77%
between 1983 and 1995. VMT increased even more, by
99%. Mean trip length also increased for this time span,
but not significantly so. Travel time increased greatly
between 1990 and 1995, but data are not available for
older people in 1983.
Table 4-9 provides additional information on modal
choice by seniors, but specific gender data are not
provided. The mode choice depends, in part, on the
type of trip undertaken, and there is not an obvious
trend in modal choice by age in the table. According to
Rosenbloom (2004b), there is no statistical difference in
the use of private vehicles (on average, at least) for
total trips among the various ages depicted in Table 4-9
(even for persons <65 years old).
Rosenbloom (2004b) also provides age and
gender data on the percent of 1995 trips taken by their
purpose, using the following categories:
family/personal, medical, recreational/social, religious,
shopping, work-related, and other. The proportion of
work-related trips drops significantly after 65 years of
age, as expected. Medical trips increase, but it does not
appear that there are concomitant increases in the
other categories to account for the decrease in work
trips. Statistical testing of these data is not provided in
Rosenbloom (2004b).
An article by Purcher and Renne (2003) compares
the 2001 NHTS travel data with the 1995 NPTS data
analyzed in the Rosenbloom articles reviewed above.
Purcher and Renne (2003) do not disaggregate their
data by gender. "There are few differences between the
findings of the 1995 NPTS and the 2001 NHTS
regarding the impact of age on travel behavior"
(Purcher and Renne, 2003; p. 70).
More recent articles on travel by seniors could not
be found. Clearly more information on this topic is
needed, especially regarding average daily travel (ADT)
and VMT, to ascertain what differential impacts travel
activity will have on future exposure profiles in the aged.
This research area seems to be underaddressed.
Because EPA is focusing a lot of research on near-
roadway and motor vehicle exposures, not having
better information on these issues may bias exposure
estimates for older individuals.
4.D.3 Outdoors
As mentioned, most studies of seniors (or even
younger people) do not provide information on where
activities occur. Graham and McCurdy (2004) provide
some information on the time spent outdoors by older
Americans in an analysis of CHAD data. For 65+ year-
old individuals, 57% went outdoors on the day they
were surveyed, for an average of 118 min day"1. The
range for habitues was 1 to 1,015 min day"1, and the
COVwas a relatively high 110%, indicating a lot of
interindividual variability in time spent in that location by
seniors. Frazieret al. (2009) provide information for the
mean time spent outdoors by health-compromised older
people in two communities (see Sections 4.C and 4.D
for more information on this study). There is quite a
large difference in this time for the two locations,
probably because of their very different climates
(Frazier et al., 2009). Including those who did not go
outside on any of the 4 to 24 days that were monitored,
the subjects spent a mean of 62.7 ± 62.2 min day"1
outdoors in Los Angeles (range: 0 to 360 min day"1) and
21.7 ± 51.8 min day"1 (range: 0 to 490 min day"1) in
Baltimore. There was a lot of day-to-day variability in
both samples in the time spent outdoors.
In both areas, there are large and statistically
significant differences in the time spent outdoors by
gender and by season of the year (Frazier et al., 2009).
This study is discussed in greater detail in Section 4.E,
as it is one of the few that addressed both intra- and
interindividual variability in time use data.
An analysis of the time spent outdoors by adults
65+ was undertaken by Nyswander et al. (2009).
A figure from their report is reprinted here as Figure 4-1.
A clear decrease in the time spent outdoors is seen in
the CHAD database when the data are plotted by year
of the study undertaken. The studies include both
national probability ex post surveys and localized time
Table 4-9. Percentage of Mode Choice for All Trips (1995), by Age
Modal Percentage of All Trips Taken
Age
Range
65-69
70-74
75-79
80-84
85+
Private Vehicle
Driver
71.5
67.6
63.3
57.6
49.3
Passenger
18.6
21.8
25.1
31.4
32.2
Total
90.1
89.4
88.4
89.0
81.5
Other Mode
Public
Transit
1.7
1.5
2.1
1.6
2.3
Taxi
0.2
0.2
0.3
0.2
0.9
Walk
4.5
5.5
5.9
5.3
11.0
Bicycle
0.2
0.2
<0.1
0.3
0.0
Other
Misc.
3.4
3.2
3.4
3.6
4.4
Source: Rosenbloom (2004), Transportation in an Aging Society (Table 4).
55
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56
120
100
80
| 40
20
-20
1980
1985
1990
1995
2000
2005
2010
2015
Yea
Source: Nyswander et al. 2009
Figure 4-1. Mean time spent outdoors by study year in adults aged 65+ years.
use diary studies, so the trend line could not be
evaluated statistically. The decrease is striking, an
approximately threefold reduction over the 15+-year
time period. The Baltimore study just discussed would
be quite close to the trend line seen in Figure 4-1,
although the Los Angeles data would be much higher
than it.
Additional data on the time spent by seniors
outdoors could not be found; this is another under-
analyzed aspect of time use/activity pattern information.
4.E Intra- and Interindividual Variability in
Time Use/Activity Data
As discussed in Section 1, multiple-day exposure
modeling requires that some type of "decision rule" be
invoked to combine time use data from different
individuals to represent a single individual (Xue et al.,
2004). EPA recently has developed a D&A approach to
modeling longitudinal activity patterns from cross-
sectional data (where "D" stands for diversity and "A" is
a calculated autocorrelation coefficient; more on these
metrics shortly) that is described in Glen et al. (2008).
Prior to the D&A method, four different decision rules
were used to obtain longitudinal time use patterns:
(1) repeat the same pattern, (2) randomly draw from
different patterns, (3) a mixture of the two approaches,
and (4) a "conditional probability" approach that
essentially followed Markov-chain sampling (Xue et al.,
2004). These decision rules resulted in widely different
longitudinal time use patterns in the modeled
population. An abstraction of the four rules and the
pattern obtained from using a D&A approach follows
(see Figure 4-2).
Development of the D&A approach started with an
analysis of a large longitudinal time use study, which
indicated that the intraclass correlation coefficient (ICC)
could be used to compute the reliability of capturing the
within- and between-individual variability seen in
outdoor and in-home locations, two important general
locations from an exposure viewpoint (Xue et al., 2004).
An ICC is calculated from a repeated-measures
analysis of variance (ANOVA) and is defined to be
equal to aB2/(aB2 + aw2), where the subscripted as
present explained between-person (B) and within-
person (W) variances, respectively. The ICC metric
often is used in the exercise physiology field to
determine how many days of data adequately capture
population variability in time spent in exercise. Some of
these studies are Baranowski and de Moor (2000);
Baranowski et al. (1999); Matthews et al. (2001); and
Trost et al. (2000). Although there is variety in the
recommended number of days of data needed to
reliability estimate intra- and interindividual variability in
the time spent in exercise, Baranowski and de Morr
(2000) concluded that 28 days of data spread over four
seasons of the year were needed.
Xue et al. (2004) used the ICC logic with a
reliability coefficient of 0.8 in their analysis of school
children's locational preferences and also determined
that 28 days of time use data spread over the year were
needed to capture longitudinal stability in the mean
observed within- and between-individual variability in
the time spent outdoors. Less data were needed to
obtain reliable estimates of the individual mean
18
estimate of indoor time. The analysis also calculated
1-day "lag" autocorrelations for time spent in the same
two locations. The r (Pearson product-moment
Although the literature on applying reliability calculations to
the elderly is exiguous, Jacelon and Imperio (2005) looked at
the issue to determine how much longitudinal data were
needed to adequately "explain" elderly activity patterns. They
state that "the optimum length of time for recording diaries is
between 1 and 2 weeks" (p. 995). One week of data had
insufficient "depth," whereas subjects become noncompliant
after 2 weeks (Jacelon and Imperio, 2005). Tudor-Locke et al.
(2005) evaluated the number of days needed to estimate
weekly steps per day in adults using a pedometer. A 3-day
monitoring period achieved a reliability coefficient of 0.8+.
56
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57
Always use same diary
Pick new diary every day
Pick new diary every season
D and A Method
(before autocorrelation)
D and A Method
(after autocorrelation)
:.-.•• • V." -v ' ••••.;.•..,.••. ••••
V ~'*' •". '.' -. '••••'• • •••'••• .tfV
1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154
Time
Figure 4-2. Conceptual diagram of alternative decision rules used to sample single-day diaries to develop longitudinal
activity patterns.
correlations) for outdoors was about 0.36, whereas the
r for indoors was 0.45. The last statistic seems to be
low, but, surprisingly, there is a lot of day-to-day
variability in even that location (Xue et al., 2004).
Using the above (scanty) information and the ICC
logic, the APEX/SHEDS exposure modeling team
developed a "scaled rank-order "Diversity" (D) statistic,
which has the same formula as the ICC. The method is
described fully in Glen et al. (2008). Basically, the
modeler inputs a "target" D&A for one or more important
parameters into an APEX or SHEDS model simulation,
and age/gender-specific cross-sectional daily activity
data are ranked to replicate those values. A test of the
logic indicated that the obtained D&A values were very
close to those requested based on 25 simulations of
10,000 persons each. The requested and obtained D&A
values are within 5% or so for simulation periods
>60 days (Glen et al., 2008), which is on the low end of
the time period of analysis used in EPA's time series
exposure models.
Since early 2008, the D&A approach has been used to
develop longitudinal activity patterns for the event-
based APEX and SHEDS models. To ground the
approach in reality, EPA is developing a library of ICC
values that are seen in those longitudinal time
use/activity studies that calculate them. To date, we
have not found any "independent" (non-EPA) study
focused on older adult ICCs and only a few focused on
adults in general. One in-house EPA study combined
data from three different longitudinal diary studies and
calculated ICCs for two locations (outdoors and
indoors) and two activities (travel and "hard work").
Hard work is a self-reported activity that involved "heavy
breathing and/or sweating." Besides the ICC statistic,
we also calculated "A" from the original data, and the
D&A metrics that would be obtained from the rank-order
procedure used during a modeling effort. The data have
been presented in poster format at the 2009 ATUS
conference in College Park, MD (Isaacs et al., 2009).
Findings of this work are reproduced in Table 4-10.
Only one of the individuals in the study was >65 years,
so the data probably are not representative of older
populations. The table is presented here to delineate
the variability in D&A statistics caused by gender,
temperature classes (a surrogate for seasonal
considerations), and wo rkday/n on workday distinctions.
The D&A values shown in Table 4-10 are similar to
those seen in seniors, as evaluated by Frazier et al.
(2009).
Another EPA project to delineate ICCs in the
population was focused explicitly on health-
compromised seniors living in two communities,
Baltimore and Los Angeles (Frazier et al., 2009). This
study's findings regarding travel and outdoor time spent
by seniors have been mentioned a few times above.
The Baltimore time use data came from an EPA project
described in Williams et al., 2000a,b,c). The sample
included 26 individuals aged 65 to 89 years, and 69% of
the sample had hypertension or coronary heart disease.
Between 4 and 24 days of time use data were obtained
for the subjects. The Los Angeles data came from a
study of 30 individuals aged 56 to 83 years with
clinically diagnosed COPD, and it is described in Linn
et al. (1999). Time use data were obtained for two
4-day periods in that study.
57
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58
Examples of Day-to-Day Variability For a Single Subject (M)
Time Spent Outdoors
12WMW
tang i,
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07/05107
Date
Figure 4-3. Daily variability in time use over 7 mo by a single individual.
The location data from both studies were collapsed
into three categories: (1) time spent indoors and (2)
outdoors and (3) in a motor vehicle. Data for time spent
outdoors and in a vehicle were provided above. Time
spent indoors constitutes most of seniors' daily time
use, being about 1,324 to 1,388 min day"1 in the two
areas and both genders. There is some variability in
time spent indoors by season of the year and day of the
week, but not much (Frazier et al., 2009). There is
some time-of-day variability in the time spent indoors,
with less being spent between noon and 6:00 p.m., but
the differences are not large.
ICCs were calculated in the Frazier et al. (2009)
paper using different models. They are listed below,
where the vertical symbol (|) means "given" (e.g.,
season|gender, a conditional variable).
Location/Model
Independent Variables
Baltimore
Outdoors
Gender only 0.14
Season | Gender 0.13
Time of day | Gender 0.09
Day of the week | Gender 0.15
In a Vehicle
Gender only 0.30
Season | Gender 0.30
Time of day | Gender 0.19
Day of the week | Gender 0.32
Los
Angeles
0.35
0.38
0.26
0.37
0.17
0.17
0.11
0.18
As seen, the ICCs are affected somewhat in
conditional form, but not greatly, and the overall pattern
is not consistent. Increasing specificity through the use
of conditional variables does not always provide a
58
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Table 4-10. Variance and Autocorrelation Statistics in the Internal EPA Study
Characteristic
All days and subjects
Males
Females
"Cool" days (max. temp. <50F)
"Warm" days (max. temp. >50F)
Day type: workday
Day type: nonworkday
All days and subjects
Males
Females
"Cool" days (max. temp. <50F)
"Warm" days (max. temp. >50F)
Day type: workday
Day type: nonworkday
ICCs and Ds for Specified Locations and Activities
Outdoors
ICC
0.16
0.14
0.07
0.20
0.09
0.19
0.11
D
0.38
0.22
0.27
0.26
0.24
0.31
0.14
Indoors
ICC
0.26
0.36
0.08
0.37
0.12
0.12
0.12
D
0.33
0.54
0.09
0.37
0.24
0.21
0.21
Travel
ICC
0.14
0.36
0.05
0.23
0.10
0.45
0.09
D
0.31
0.46
0.18
0.37
0.24
0.47
0.24
Hard Work
ICC
0.18
-0.01
0.15
0.21
0.01
0.20
0.06
D
0.22
0.15
0.24
0.31
0.20
0.25
0.07
"Raw" and "Ranked" Autocorrelation (A) Estimates
Raw
0.22
0.24
0.35
0.33
0.39
0.78
0.60
Rank
0.31
0.22
0.18
0.18
0.20
0.07
0.18
Raw
0.23
0.25
0.37
0.23
0.45
0.56
0.59
Rank
0.34
0.16
0.25
0.19
0.23
0.05
0.24
Raw
0.12
0.17
0.15
0.20
0.34
0.30
0.38
Rank
0.19
0.08
0.11
0.09
0.09
0.01
0.08
Raw
0.17
0.22
0.16
0.14
0.35
0.53
0.43
Rank
0.19
0.20
0.21
0.14
0.14
-0.12
0.18
Abbreviations
A: The lag-one Pearson product-moment correlation coefficient
D: The rank-ordered ICC-like "diversity" coefficient
ICC: The intraclass correlation coefficient
max temp: The maximum daily temperature in degrees Fahrenheit
Source: Isaacs et al. (2009) "Statistical properties of longitudinal time-activity data" (poster)
higher ICC. ICC values for seniors are about the same
as those seen for younger adults in the EPA analysis
(Table 4-10).
The relationship between ICC values and the ratio
of within-person to between-person variance
"explained" (aw2/aB2) is a nonlinear one, exponentially
decreasing with an increasing ICC. At ICCs on the
order seen in the Frazier et al. (2009) and Isaacs et al.
(2009) analyses, the within-to-between ratio is on the
order of 2-5. Thus, the analyses indicate that a lot of
variability in human locations and activities is explained
by within-person variability. Most exposure models
ignore within-person variability, which means that there
is a systematic bias downward in output estimates from
these models, especially at the "high end" of the
exposure distribution, which is of most interest to
EPA.This is why we are using the D&A procedure in the
first place.
It is hoped that new longitudinal human activity
data will come along, so that we can obtain additional
ICC estimates to further test and refine the D&A
approach.
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5. Physical Activity, Exercise, and Aging
60
ABSTRACT
Topic: This chapter discusses the impact of exercise on the
health of the older person and on estimates of physical activity
in the aging population.
Issue /Problem Statement: Estimates of physical activity in
older adults are required for validation and assessment of the
EE-based algorithms in EPA's exposure models.
Data Available: This topic is extremely data rich.
Research Needs: Futher assessement of EPA's current
algorithms is needed, with the goal of determining how well a
realistic population distribution of physical activity is being
reproduced for older adults.
5.A Overview of the Literature
There are thousands of articles on the impact of
PA and exercise on the aging process in seniors.19
Hundreds are being added every year. We have had to
cull the shear magnitude of the available information to
a manageable amount by focusing on the following.
• U.S. studies (because of the cultural component of
PA)
• "Free-ranging" individuals living independently at
home (Institutionalized or nursing-home residents are
not discussed, with minor exceptions.)
• Studies that use objective measurement techniques
to ascertain the frequency, duration, and intensity of
PA20
This last focus means that limited attention is given
to the extensive "epidemiological" research in the
seniors that rely on questionnaires for their estimates of
PA. Questionnaire-derived data are by far the most
extensive information available on the topic, and not
using it means that a lot of older adult PA information is
ignored, including some very long-term longitudinal
studies. Citations for many of them are provided in
Section 5.F. Examples are the Harvard Alumni Study
(ongoing for more than 50 years; see the many citations
for I.-M. Lee and R.S. Paffenbarger (1991-2001) and
19 To check our literature searches for completeness, on
December 2, 2009, Google Scholar was accessed using
"physical activity in the elderly." 124,326 citations were
provided in 0.4 seconds! The first 150 citations of U.S. studies
of independently living subjects were reviewed. 85% of them
provided relevant PA data on the elderly. Of those, over 90%
of the articles were on hand and were evaluated for this
report—not all of them are listed here. Therefore, we are
confident that we have most of the relevant literature on the
subject. What is more interesting is the shear number of
articles on the topic. Elderly PA research is a major subject of
interest that seemingly receives a lot of funding by U.S.
Federal agencies.
20 Other common acronyms used in the PA field are light PA
(LPA), moderate PA(MPA), vigorous or, sometimes, "heavy"
PA (VPA), and the sum of moderate and vigorous PA
(MVPA). In general, there is an exponential decrease in the
amount of time spent in the LPA, MPA, and VPA categories,
respectively, with the elderly spending very little time in the
last category.
Sesso et al., 1999, 2000, 2003); the Physicans' Health
Study (Lee et al., 1997); the Baltimore Longitudinal
Study of Aging (Talbot et al., 2003); the Normative
Aging Study (O'Connor et al., 1995); the older San
Francisco Longshoremen's Study (Paffenbarger et al.,
1978); the Framingham Study (Paffenbarger et al.,
1984); the Nurses' Health Study (Garcia-Aymerich et
al., 2009); the older University of Pennsylvania Study
(Paffenbarger et al., 1966); the LIFE Study (Rejeski
et al., 2005); the Cardiovascular Health Study (Geffken
et al., 2001); the Iowa 65+ Rural Health Study (Cerhan
et al., 1997); the Iowa Women's Health Study (Sinner
et al., 2006); the Health, Aging and Body Composition
Study (Colbert et al., 2004); the Rancho Bernado Study
(Greendale et al., 2003; McPhillips et al., 1989); the
Honolulu Heart Program (Donahue et al., 1988); the
Honolulu-Asia Aging Study (Taafe et al., 2008); the
Minnesota Heart Study (Steffen et al., 2006); the San
Antonio Longitudinal Study of Aging (Dergance et al.,
2005); the Health and Retirement Study (Chung et al.,
2009); the Women's' Health Initiative (Masse et al.,
1998), the Women's Health and Aging Study
(Simonsick et al., 2005); and the many PACE articles
using the Physical Activity Scale for the Elderly (Allison
etal., 1998; Chad etal., 2005; Martin etal., 1999; Vieira
etal.,2007).
Epidemiological studies usually take questionnaire-
derived estimates of PA participation in specific
activities and convert them into PAEE, as mentioned in
Section 3. Usually, this is done using METS estimates
from the compendium (Ainsworth et al., 1993) or similar
source. The METS estimates then often are assigned to
low, medium, and vigorous PA levels and compared
with various PA normative standards developed by
health professionals or government organizations
(Chodzko-Zajko et al., 2009; Crespo et al. 1996,1999;
Kruskall et al., 2004; Masse et al., 1998). There are a
number of rater-oriented issues associated with this
approach (Masse et al., 2005a,b). The
questionnaire/assignment approach has been shown to
overestimate by varying amounts the amount of PA
undertaken when compared with a simultaneously
monitored "objective" measurement method using an
accelerometer, indirect calorimeter, or pedometer
(Harris et al., 2009a; Masse et al., 2005a). A succinct
evaluation of the epidemiologic approach is contained
in Shephard (2003). See also Harada et al. (2001),
Sallis and Owen (1999), especially their chapter on
"Measuring Physical Activity;" Tryon (1991); and
Westerterp (2009). A more accessible review of self-
reported PA methods is contained in Sallis and Saelens
(2000), which includes mention of eight previous
reviews on the topic.
The older adult PA literature emphasizes the
positive benefits of physical activity, exercise, and
"fitness" in the aged (Frankel at al., 2006; President's
Council on Physical Fitness and Sports, 1998; Stewart,
60
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2005). The list of overall benefits is extensive, and
includes the following attributes, many of which are
related.
• Preventing or slowing of osteoporosis (decrease in
bone mass and density via the enlargement of
interstitial spaces), which, in turn, causes frailty and
an increase in falling, particularly in females (Allison
and Keller, 1997; Gregg et al., 1998; Hughes et al.,
2004)
• Slowing the rate of decrease in VO2 Max or even
reversal of this functional decrease via concerted
exercise in older people (Ades et al., 1996; Allison
and Keller, 1997; Fleg etal., 1995; Shephard, 2009)
[This improves endurance and reduces heart-related
issues (see items 4 and 9 below).]
• Reducing physical/mental unhealthy days for
arthritics (Abell et al., 2005; Hamer et al., 2009)
• Preventing coronary heart disease and
"cardiovascular events" (Berlin and Colditz, 1990;
Geffken et al., 2001; Mason et al., 2002;
Paffenbarger, 1988; Sacco et al., 1998) [If the same
amount of PA is undertaken (in kcal per time period),
it appears that vigorous PA confers "greater
cardioprotective benefits than exercise of a moderate
intensity," using METS > 6 as the cut point (Swain
and Franklin, 2006).]
• Reducing preinfarction angina incidents in older
people having existing acute myocardial infarction;
also reducing stroke rates (Abbott et al., 1994; Abete
et al., 2001; Sallis and Owen, 1999)
• Slowing muscle mass loss and weakness (Allison
and Keller, 1997; Buchneret al., 1997; Fiatrone and
Evans, 1993; Koopman and van Loom, 2009)
[Muscle loss is known as sarcopenia (Stewart, 2005).
Improving muscle mass reduces falls and fractures
and improves physical capacity (Ades
et al., 2003). It makes for increased gait stability
(Brach et al., 2001). It slows disability prevalence in
seniors (Berk et al., 2006). It reduces back pain by
strengthening muscles that stabilize the spine and
maintaining flexibility (Sallis and Owen, 1999).
Overall, there is better physical functioning in older,
exercising subjects (Brach et al., 2004a,b).]
• Maintaining weight and decreasing obesity rates
(Sallis and Owen, 1999; Van Pelt et al., 1998)
• Reducing high-density lipoprotein (HDL) cholesterol
(Casperson etal., 1991; Reaven etal., 1990)
• Reducing hypertension and reducing blood pressure
(Bassett et al., 2002; Reaven et al., 1991) [PA also
reduces "vascular stiffness (Havlik et al., 2003; Seals
et al., 2006).]
• Increasing joint flexibility (Birrer, 1989)
• Increasing longevity (Paffenbarger and Lee, 1996;
Manini et al., 2006; Morey et al., 2002; Sallis and
Owen, 1999)
• Reducing prostrate and breast cancer incidence
(Cerhan et al., 1997; John et al., 2003) [This finding
is not universal, and no protective effect has been
found in a number of studies (Moore
et al., 2000), and a credible biological mechanism for
this finding cannot be derived (McTiernan et al.,
1996). Probably those people who exercise more
also have other lifestyle patterns that have a real
effect on cancer etiology, although tobacco smoking
is usually accounted for in these studies, so that
"lifestyle" factor already is controlled for.]
• Reducing age/gender-specific morbidity and mortality
rates (Kushi et al., 1997; Sherman et al., 1994;
Trolle-Lagerros et al., 2005) [There is another term
used for this attribute, "the compression of morbidity"
(Fries, 1996). This refers to an increasing age of
onset of disability and age of death, a shortening of
the period when the person is disabled/frail/totally
dependent before death (von Bonsdorff et al., 2009).]
• Preventing or delaying the onset of diabetes
(Hawkins etal., 2009)
• Reducing the risks of cognitive impairment,
Alzheimer's, and dementia (Laurin et al., 2001; Sumic
et al., 2007; Weuve et al., 2004) [PA also reduces the
rate of cognitive decline in older females (Yaffe et al.,
2001). The incidence of Parkinson's disease was
also lower in active older individuals (Thacker et al.,
2008).]
• Improving general quality of life and decreasing
depression (Schechtman et al., 2001; Strawbridge et
al., 2002)
• Reducing the risk of lung cancer in females who are
current or former smokers (Sinner et al., 2006)
• Reducing the risk of rectal cancer in both genders
(Slattery et al., 2003) [Stolzenberg-Solomon et al.
(2008), however, find no association between PA and
pancreatic cancer, although there is a positive
association between adiposity and pancreatic
cancer.]
• Preventing early onset of ADL and IADL limitations
(Stewart, 2005)
• Reducing the risk of gallstone disease (Storti et al.,
2005)
Because of these perceived benefits of PA and
exercise, there are a number of articles on how much
PA should be undertaken by older individuals in various
age/gender categories. The recommendations are quite
specific and generally involve prescriptions for the
intensity, duration, and frequency of specified activities
(Birrer, 1989; Chodzko et al., 2009 [the American
College of Sports Medicine's "position stand"]; Haskell
et al., 2007; Jordan et al., 2005; Thompson et al.,
2003). Usually these recommendations take the form of
a minimum number of minutes per week that should be
spent in moderate and/or vigorous activity (see below),
but a Canadian goal is for older adults to expend
1,000 kcal/week in moderate leisure-time PA (Sawatzky
et al., 2007), a more precise objective. The
epidemiological literature often compares its findings
with one or more of these normative standards (e.g.,
Dergance et al., 2005). A summary of many
61
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recommendations for PA is contained in Sallis and
Owen (1999).
Not all researchers, however, question whether all
of the attributed benefits of exercise and PA in seniors
are "real," as the following quote makes clear. "Late-life
exercise clearly improves strength, aerobic capacity,
flexibility, and physical function. Existing scientific
evidence, however, does not support a strong argument
for late-life exercise as an effective means of reducing
disability" (Keysorand Jette, 2001).
Physical inactivity is, of course, the flip side of PA.
Data indicate that inactivity is an independent factor for
certain physical impairments in older people,
particularly hip fracture (Coupland et al., 1993; Sallis
and Owen, 1999). A sedentary lifestyle is considered to
be a major contributor to the leading causes of death in
adults, and about 15% of newly diagnosed chronic
health conditions result from sedentary lifestyle alone
(Stewart, 2005). There are some studies showing that
physical inactivity is a better measure of PAEE in
seniors, and that, if sedentary activities are reduced, the
benefits of PA and exercise listed above can be
achieved without the need for high-intensity (vigorous)
activity (Meijer et al., 2001). This approach intuitively
seems to be more relevant to older people because it is
relatively more difficult for them to undertake vigorous
activities because of all the other changes that occur in
their VO2 Max, muscle mass and strength, etc., which are
a function of aging per se regardless of lifestyle and
fitness level.
There are national goals for reducing inactivity in
the aged population. The U.S. Public Health Service set
a goal in 1991 that the proportion of adults >65 years
old who engage in "no leisure-time physical activity"
should be reduced to 22% by 2000 (Public Health
Service, 1991).
We probably should mention that there are some
risks associated with PA in seniors, particularly
musculoskeletal injury and sudden cardiac arrest
(Haskell et al., 2009). The negative issues of PA are
minimized in the literature, probably because of the
relatively low energy expenditure levels of activities
undertaken as PA by older adults, mostly walking for
exercise. Stress from walking and low-level EE
activities generally is not intensive enough to invoke
severe adverse health repercussions.
5.B General Estimates of Physical Activity
and Inactivity in Older Adults
Hawkins et al. (2009) report on an accelerometer
study that was part of the 2003-2004 NHANES cycle.
This was the first time that accelerometers were used in
an NHANES survey, and valid data (>4 days with at
least 10 h/day of wearing time) were obtained from
2,688 adults. Only accelerometer count data were
provided, so the estimates in Hawkins et al. (2009) are
difficult to put into perspective, and no sample size (n)
was provided for the number of people aged >60 years,
even though count data were provided for them by age,
gender, and ethnicity. That data, in activity counts in
thousands, are reproduced here. The counts are about
50% to 67% of those seen in the 40 to 59-year age
group for all the subgroups depicted (AA = African
Americans; C = Caucasians; and H=Hispanics). The PA
categories were defined above.
Accelerometer Counts per Day in Thousands for
Persons >60 Years Old
Females
AA C H
Total PA (TPA) 145 159 156
LPA 125 128 130
MVPA 21 31 26
Males
AA C H
171 182 212
135 132 149
37 51 62
Statistical testing for significant differences among
the various subgroups depicted were not provided
uniformly or discussed in Hawkins et al. (2009). Female
LPA is 80% to 86% of TPA and male LPA is 70% to
80% of TPA, with ethnic differences seen in the count
data.
CDC has data on physical inactivity in seniors from
its Behavioral Risk Factor Surveillance System
(BRFSS), a random probability telephone survey of the
noninstitutionalized U.S. population. In 1995, CDC
published state-specific data on older adults' inactivity
for the 1987 to 1992 time period; overall inactivity levels
decreased from 43% in 1987 to 39% in 1992 (BRFSS
Coordinators, 1995). For 1992, the range in inactivity
among the states' older people varied from 27.2% in
Colorado to 62.5% in Mississippi (BRFSS Coordinators,
1995). Thus, in general, the United States is far from
attaining its goal of having a senior inactivity goal of
22%.
Macera and Pratt (2000) and Macera et al. (2005)
have published BRFSS data from 1998 and 2001,
respectively. Weighted, age-adjusted PA participation
data for 1998 follows (Macera and Pratt, 2000).
Females
65-74 75+
Males
65-74 75+
Inactive
Insufficient PA
Meets PA Recom.2
36.1%
37.7%
26.2%
48.6%
30.7%
20.8%
31.4%
37.5%
31.1%
41.4%
28.1%
30.5%
The 2001 data have a different format, with MPA
and VPA prevalence being shown separately, and the
categories are not mutually exclusive. Thus, only the
"Inactive" and "Meets-PA Recommendations" data from
Macera et al. (2005) are described here.
Males
65-74 75+
Females
65-74 75+
Inactive
Meets PA
Recom.
24.5%
36.1%
39.6%
26.9%
21.4%
45.7%
29.7%
38.4%
The most recent national data that was found on
the topic is contained in CDC (2007). They provide the
Meets the Surgeon General's minimum recommended
levels of PA, 30+ min/day for 5 days/week at moderate
intensity or 20 min/day for 3 days/week at vigorous PA.
62
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following PA estimates for citizens 65+ years old for the
country as a whole and by state. The overall U.S.
estimates are as follows.
Inactive 23.7%
Insufficient PA 36.9%
Meets PA Recommendations 39.3%
The proportion of people aged 65+ that meet
recommended PA guidelines varies from 27.7% in
Kentucky to 52.3% in Alaska (CDC, 2007).
Another way that CDC presents PA data on
seniors is contained in CDC (2009a). The percentage of
two older age/gender groups that engage in regular
LTPA in January through March 2009 follows (with 95%
confidence intervals).
65-74 Females 34.0% (26.1 - 42.0)
Males 39.1% (28.2-50.1)
75+ Females 13.1% (6.4-19.9)
Males 20.8% (11.2-30.4)
These participation rate estimates are hard to
reconcile with other LTPA data provided by CDC
seemingly for the same year (CDC, 2009b). Gender is
not distinguished; numbers in parentheses are the
standard errors of the estimate (SE). (Because group-
specific sample sizes were not provided, SEs could not
be converted into SDs.)
Inactive
Some activity
Regular LTPA
65-74
46.0% (1.1)
27.9% (2.0)
26.1% (1.1)
75+
56.0% (1.4)
25.5% (1.2)
18.5% (2.0)
CDC 2009b also provides estimates for the
frequency of vigorous LTPA bouts (defined to be at
least 10 min of heavy sweating and/or a large increase
in breathing or heart rate) per week. The percentages of
seniors attaining different numbers of vigorous bouts
per week (with standard errors) are shown below.
Never
<1
1-2
3-4
>5
65-74 76.5% (1.0) 0.8% (0.2) 5.5% (0.6) 8.3% (0.7) 8.8% (0.7)
75+ 86.0% (1.0) 0.9% (0.3) 3.6% (0.5) 4.6% (0.6) 4.9% (0.6)
These data seem to indicate that, although the
LTPA exercisers are a small proportion of the two age
categories' population, they undertake vigorous activity
on multiple days in a week; the median number of days
of LTPA for "doers" is 3 to 4 days w"1.
It should be noted that national data on U.S. PA in
seniors, or for anyone else, have to be used with
caution. In one analysis of PA prevalence contained in
three different National Center for Health Statistics
(NCHS; affiliated with CDC) surveys with random-
probability designs, there was a 10-fold difference for
essentially the same time period in the national
estimates for a specific cohort (Slater et al., 1987).
Perhaps the U.S. data have improved in consistency
since then, but there are quite profound differences in
PA estimates seen in the data reproduced above also.
As expected, a retrospective study of 127 older
people aged >65 years wearing an accelerometer found
that seasonal and daily weather variations in the
amount of activity counts are correlated positively with
daily maximum temperature, sunshine, and day length
(Sumukadas et al., 2009). Other weather variables were
tested (precipitation and wind speed), but those
associations with PA were not statistically significant.
Washburn et al. (1990b) placed an accelerometer
on older people (23 males aged 72.9 ± 3.9 years and
22 females aged 72.9 ± 6.5 years) for 3 consecutive
weekdays. The percent of time spent by the subjects in
three general PA categories is shown just below.
Lying and "sitting around"
Standing, performing light
work
Walking, undertaking
sports/rec. activities
Females
50.5 ± 10.7
34.7 ± 12.8
9.9 ± 4.5
Males
53.9 ± 14.9
26.8 ± 12.6
14.4 ± 9.1
Consistent with the time use data measured in
studies reviewed by Washburn et al. (1990a,c), older
females were more active overall than older males but
not for vigorous PA. The difference between the time
spent in the last category, walking etc., was the only
statistically significant gender difference (Washburn
etal., 1990c).
Most of the data reviewed above and in the next
section are cross-sectional. There are very few studies
of PA participation in the same person overtime. One
longitudinal study in Germany found that sports
participation and exercise drops off with age much
slower than indicated in a parallel cross-sectional study
(Breuer and Wicker, 2009). This is caused by the
mixing together doers and nondoers in cross-sectional
studies, which "balance out" individual trends,
especially for those older individuals who purposively
exercise more in retirement than when working. The
modeling of individuals in APEX and SHEDS by
assigning them to "lifestyle" groups using the PAI index
from CHAD is used to minimize the mixing of physical
activity doers and nondoers. If better longitudinal data
on PA become available, we could do a more rigorous
job of focusing on truly active individuals, who are
expected to receive a larger intake dose rate than
sedentary people. Although their overall better fitness
level might protect them better against xenobiotic
"assaults" from an exposure, the underlying etiology of
effects are characterized better when lifestyle factors in
exposed individuals are considered explicitly,
everything else being equal.
5.C Specific Estimates of Physical Activity
in Older Adults
Gauthierand Smeeding (2000, 2001) provide PA
data for two U.S. age groups (65 to 74 years and 75+
years) at four points over a 28-year time period (1965,
1975, 1985, and 1993). The sampling size, "frame," and
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sampling approaches varied, so direct comparisons
among the years are speculative. Sample sizes
increased monotonically over the years for both
genders and age categories (9—>298 and 13—>510 for
65 to 74 year-old males and females, respectively).
There were no 75+ year-old people in the 1965 sample,
and its sample size increased monotonically after that
(29—>172 for males and 65—>349 for females). Even so,
there is a remarkable amount of similarity among the
four sampling periods. The only category of interest to
us in this study probably is "sports & fitness." There
seems to be a trend in time spent in this activity in
males 65 to 74 years old, from 0.1 h/day in 1975 to
0.7 h/day in 1993. For males aged 75+, this time varied
from 0.1 h/day in 1975 to 0.3 to 0.4 h/day in the more
recent time periods (Gauthierand Smeeding, 2001;
Table 2). There did not seem to be a trend in
undertaking sports and fitness for females aged 65 to
74, varying between 0.1 and 0.3 h/day for the four time
points. Neither was there a trend for females aged 75+;
the time in this category varied between 0.0 and
0.2 h/day over the years (Gauthierand Smeeding,
2001).
Iso-Ahola et al. (1994) of the University of
Maryland evaluated changes that take place among the
types of activities undertaken by seniors aged 64 years
or older. The authors evaluated, among other things,
the numbers of people beginning, ceasing, or
maintaining specific activities as they aged. They
calculated a "replacement rate" in percent for the
activities (a negative rate indicates that seniors ceased
participating in an activity faster than others in the group
adopted it). The replacement rate was -4% for exercise-
oriented activities, -26% for outdoor recreation, and -
82% for team sports (Iso-Ahola et al., 1994). There
were large increases in the replacement rate for
hobbies and home-based activities, such as board
games, television viewing, listening to radio and/or
music, reading books, etc.
Katz and Morris (2007) provide selected
time/participation rate data for 375 older women who
had rheumatoid arthritis. Their sample included women
who could have been as young as 38 (mean=60 years,
SD=13.2), so we do not review their information here.
More than 80% of their sample spent less than
60 min/day in physical recreation, and 31% had no time
in that activity, so it was a rather sedentary group.
Roberts (1995) conducted a dietary study of
sedentary older males (n=18; 68.0 ± 6.4 years) and
asked them to keep a diary on how many minutes per
day they spent doing "strenuous" physical activity and
activity requiring 5+ METS. Details were not provided
on exactly how these levels were defined. The sample
indicated that they spent 29.1 ± 35.6 min day"1 in
strenuous activity and 4.3 ± 7.6 min day"1 in 5+ METS
activities (Roberts, 1995). Note that the COV for both
activity metrics is >1.0.
Useful quantitative information found regarding PA
data for seniors is presented in Table 5-1. It is a scanty
database, especially given the number of articles that
are published every year on the subject, most of which,
as mentioned above, are based on questionnaires or
other subjective information.
Pedometers are becoming an ever-increasing
objective PA measure of choice because they are much
cheaper than accelerometers to acquire and operate
(Harris et al., 2009a; Schneider et al., 2003, 2004).
Pedometer step-counts generally decrease with age,
although this relationship is moderated by general
health conditions, disability, BMI, and exercise
"efficiency" (Harris et al., 2009b; Tudor-Locke et al.,
2009a,b). Although there is a general agreement
between pedometer and accelerometer estimates on
the amount of physical activity, particularly walking,
undertaken by seniors, there is a lot of variability in
pedometer outputs over a 24-h period when the same
subject wears multiple units. Compared with a
"criterion" pedometer (the Yamax Digi-Walker SW-200),
some pedometers overestimated the number of steps
by 45%, whereas others underestimated it by 25%
(Schneider et al., 2004). Accuracy can be a problem,
therefore. On the other hand, intrainstrument reliability
(Cronbach's a >0.80) of most instruments is good
(Schneider etal., 2003).
Pedometers, when compared with other objective
measures of PA, even the criterion pedometer, usually
underestimate the number of steps taken by seniors by
a considerable percent. In a study of a Yamax
pedometer in seniors dwelling in a nursing home (NH)
and a community (CD), mostly females in their 70s
(NH=79.4 ± 8.2 years; CD=70.6 ± 5.5), steps were
undercounted by 25% to 74% at a slow pace, 13% to
38% at a moderate pace, and 7% to 46% at a fast pace
(Cyarto et al., 2004). At all three paces for the CD
cohort, the Yamax pedometer was considered to be
inaccurate for quantifying total physical activity in older
adults (Cyarto et al., 2004) but could be used for
estimating step counts in that group. Thus, step counts
are not an accurate measure of total PA in seniors, and
pedometer data must be used with caution.
A study in Oregon of 5-day pedometer counts
found that females aged 60 to 69 years took 3,888 ±
2,572 steps per day on average (n=98), and older
females took only slightly fewer steps, 3,773 ± 3,051
(n=53). There was no statistically significant difference
in steps taken per day over the 5 days, although there
was a significant difference between weekdays and
weekends (Stryker et al., 2007, but this last analysis
was done using the entire sample of 270 women aged
40 years and older, so it may not be accurate for older
cohorts). Note that the step-count COVs for the two age
groups are relatively large and increases for the older
group compared with the younger one (66.2% to
80.9%).
Secondary data on observed step counts in U.S.
seniors is provided by Tudor-Locke et al. (2009a). They
are reproduced here in Table 5-2. The type of
pedometer used in each study is not provided in the
article, so the interested reader will have to obtain the
original citation for that datum. The authors indicate that
64
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Table 5-1. Physical Activity Estimates for U.S. Older Adults
Age
(Mean ± SD)
G
Ethnic
Group
Health
Status
Exercise Time by Level (h/week)
MPA
MVPA
Vigor
Citation
Comments
57.1 ±4.3
56.2 ±4.1
68.4 ±9.4
71 .3 ±8.4
F
M
B
B
Mixed
Mixed
Mixed
NS
Normal
Normal
NS
NS
5.0 ±6.7
5.7 ±6.7
2.5± 3.9
9.3± 5.4
0.6±2.0
0.7± 1.8
Young etal. 1994
Young etal. 1994
Wilcox et al. 2006
Parker 2008 JAPA
n=161; 7-day recall
n=196; 7-day recall
n=538; survey
n=84; accelerometer
65-74(1975)
65-74(1985)
65-74(1993)
F
F
F
NS
NS
NS
NS
NS
NS
0.7 ?
2.1 ?
1.4 ?
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
n=134; survey: see note
n=227; survey: see note
n=510; survey: see note
75+
75+
75+
F
F
F
NS
NS
NS
NS
NS
NS
0.0 ?
1.4 ?
0.7 ?
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
n=65; survey: see note
n=1 14; survey: see note
n=349; survey: see note
65-74 (1 975)
65-74 (1 985)
65-74 (1 993)
M
M
M
NS
NS
NS
NS
NS
NS
0.7 ?
2.8 ?
4.9 ?
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
n=81 ; survey: see note
n=173; survey: see note
n=298; survey: see note
75+
75+
75+
M
M
M
NS
NS
NS
NS
NS
NS
0.7 ?
2.8 ?
2.1 ?
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
Gauthier and Smeeding
2000
n=29; survey: see note
n=87; survey: see note
n=172; survey: see note
Percent
Inactive
Mod.
Act.
Very
Act.
73.5 ± NS
73.5 ±NS
M
M
Mixed
Mixed
Pros
NS
27
36
41
39
32
24
Cerhan 1997
Cerhan 1997
n=71: recall; diagnosed
prostrate cancer
n=979: recall; no known
prostrate cancer
Daily Time in Exercise (h / d)
None
>0 -<2
>2
65-74
65-74
60.0 ±13.2
76.2 ±NS
F
F
F
B
White
Black
Mixed
Mixed
NS
NS
RA
NS
36
53
30.6
65
64.2
5.2
Kaminoto in Shepard
2002
Kaminoto in Shepard
2002
Katz and Morris 2007
Lawton 1 986
BRFSS: 1994-1 996; "no
LTPA last month"
BRFSS: 1994-1 996; "no
LTPA last month"
n=375: recall; diagnosed
RA
n=525; recall; doers
(35%) mean=13.8 h/week
Percent Reporting Participation in "Sports, Exercise, and Recreation"
65+
65+
F
M
Mixed
Mixed
NS
NS
15
21.3
Russell 2007
Russell 2007
n=491 ; ATUS recall; doer
mean=69 min/day
n=479: ATUS recall; doer
mean=104 min/day
65
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Abbreviations
Act: Active
AJPH: American Journal of Public Health
B: Both genders
F: Females
IJAHD: International Journal of Aging and Human Development
JAPA: Journal of Aging & Physical Activity
LTPA: Leisure time physical activity
M: Males
MC: Medical Care
MENH: Medicine Exercise Nutrition Health
Note
1: Time in "sports and fitness"= "active sport" and "walking"
Mod.: Moderate exercise level; moderately
MVPA: Moderate and vigorous physical activity
n: Number of subjects (sample size)
NS: Not specified
Pros: Prostrate cancer cases
RA: Rheumatoid arthritis cases
Rheum.: Rheumatology
SD: Standard deviation
Vigor.: Vigorous exercise level
?: Unknown
Table 5-2. Observed Steps per Day Pedometer Counts in U.S. Seniors
Age
Range
(years)
60+
<65
50-75
60-75
65+
65-69
70-74
75+
75-79
80-84
85+
Mean
Age
(years)
60.9
64.1
65.6
68.4
71.3
72.3
72.4
74.0
74.2
77.0
83.7
Sample
Size
(number)
29
93
26
45
82
47
84
214
150
89
149
590
46
28
No.
of
Days
Obs.
7
7
14
?
7
7
7
7
7
7
7
7
7
7
7
7
7
7
6
7
Steps/Day
Mean
5143
5314
6813
4027
3766
5481
8088
5085
5233
3536
3912
3810
4728
5285
2895
3536
3653
2688
9982
2015
SD
2459
2316
2955
2515
2805
3629
2941
4794
2982
2281
2757
2444
3641
2170
2281
1388
983
2925
1538
Comment
Gender not given
See note
Postmenopausal
Females
64% Female
57% Female
Females
See note
67% Female
71% Female
Gender not given
See note
67% Female
Females/median
84% Female
89% Female
See note
See note
75% Female
See note
Reference
Payn et al. 2008
Croteau et al. 2005
Krumm et al. 2006
Jensen et al. 2004
Tudor-Locke 2009b
Yamakawa 2004
Woolfetal. 2008
Croteau et al 2005
Parker et al. 2008
Swartz et al. 2007
Strath et al. 2007
Croteau et al. 2005
Rowe et al. 2007
King et al. 2003
Fitzpatrick et al. 2008
Sarkisian et al. 2007
Croteau et al. 2005
Croteau et al. 2005
Cavanaugh et al. 2007
Croteau et al. 2005
Source: Tudor-Locke et al. (2009a). Inter. J. Behav. Nutr. Phys. Act.:59 doi:10.1186/1479-5868-6-59. See it for
the full citations since they were not checked for this report and are not in the references.
Note: The sample size per age category is not provided. The overall sample = 76 (87% female). The age range
for the entire sample is 60-90. Mean step counts for everyone = 4041 (2824).
there is a "clear decline" in age-stratified steps, but
study-specific age groupings were broad and did not
allow them to compare results qualitatively. There also
was a very broad range of steps in the healthy older
adults. Their range of PA behaviors was as high as
9,000 steps/day (Tudor-Locke et al., 2009a). In general,
step counts in older adults are considerably less than
those in younger adults. Bassett and Strath (2002)
reviewed the pedometer literature and provide data
from one non-U.S. study that indicated that the mean
number of steps per day declined from 11,900 in males
aged 25 to 35 to 6,700 in males aged 65 to 74. The
corresponding estimate for females of the same age
brackets was 9,300 down to 7,300. The values seen in
Table 5-2 for U.S. citizens are considerably lower than
those estimates.
Step count data from other countries indicate that
U.S. seniors are less active than those in other
"developed" areas. For example, a Swiss study found
that females aged 65 to 74 took on average 7,300 ±
3,300 steps/day and males of the same age had 6,700
± 3,000 steps/day (Sequeira et al., 1995). Comparable
data are available for a number of other countries, but
are not reviewed here.
66
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It should be noted that there are seasonal
variations in PA for older people, as there are for others
(Shephard and Aoyagi, 2009). However, there is very
little quantitative information collected and published on
seasonal differences in physical activities, except those
that are highly "seasonally-dependent," such as skiing,
ice skating, and (sometimes) outdoor sport
participation, golf, etc. Exposure assessments done for
specific seasons should take the differential
participation rates of activities into account, but
obtaining data to do so will be difficult. The CHAD
database, being calendar-day specific can help in this
respect, but explicit season-of-the-year analyses of
CHAD like those contained in Graham and McCurdy
(2004) should be undertaken to see if additional
seasonal differences for PA in seniors can be
discovered in CHAD.
67
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6. Health Considerations in Older Adults
ABSTRACT
Topic: This chapter discusses the wide variety of health
issues encountered by seniors.
Issue/Problem Statement: Both normal and pathological
health changes in the aging population have the potential to
impact exposure estimates.
Data Available: This topic is extremely data rich.
Research Needs: At this point in time, very little health
information has been systematically included in EPA's
exposure models. This is a large potential area of research.
The models should be refined to consider the changes in both
physiology and time-location-activity patterns that result from
health impairments.
6.A Impairment, Functional Limitations,
and Disability
Depending on the pollutant, some of the Agency's
NAAQS reviews have focused on population subgroups
with preexisting diseases or activity limitations.
Examples are the O3 NAAQS review that evaluated
exposures to (among other subgroups) asthmatic
children, the SO2 review that focused on exercising
asthmatic adults, and the CO NAAQS assessment that
estimated exposures to adults with cardiovascular
disease (particularly angina). In all three examples,
intake dose rate considerations were of paramount
concern. To date, however, neither EPA's OAQPS nor
NERL have undertaken an exposure modeling
assessment of older individuals having impairments,
functional limitations, or disabilities that would limit their
human activity patterns (and, therefore, affect their
exposures to environmental contaminants). We explore
these issues in this section, with emphasis on how an
exposure assessment might be structured to account
for health concerns that limit activity in older adults.
There are many measures of impairment,
functional limitations (which include "frailty"), and
disability in the literature, and it is difficult to clearly
distinguish among them for our purposes (Guccione
etal., 1994; Jette, 2006; Stuck etal., 1999). There "is
no consensus about how to define these concepts or
which are the best health or function indicators for
population surveys" [of disability] (Parker and
Thorslund, 2007; p. 151). The language of
"disablement" [sic] is in a state of flux and has been
since the early 1990s (Jette, 2006). That is because
there are both medical and social components of
disability, and each discipline has its own concepts and
terminology.
For exposition purposes, we make the following
preliminary distinctions among chronic medical
conditions, functional limitations, and disability; they
follow Boult et al. (1994), but also include information
from Guralnik and Simonsick (1993), Jette (2006), and
the series of articles by Newman et al. (2003, 2005,
2006). Having two or more of these conditions is called
a co-morbidity1.
Chronic medical conditions
• Arthritis and osteoporosis
• Cancer
• Cerebro-vascular disease
• Chronic pain (generalized and pervasive)
• Coronary disease (myocardial infarction,
cardiovascular disease, angina, stroke)
• Diabetes2
• Hypertension
• Neurodegenerative disease (Alzheimer's, dementia,
Parkinson's)
• Obesity
• Pulmonary disease/respiratory problems (COPD,
asthma, emphysema)
Chronic medical conditions also are known as
"active pathology" and involve the disruption of normal
cellular processes and/or homeostatic efforts of the
organism to regain a "normal" state. Chronic impacts
also can be the result of normal cellular senescence,
which is defined to be an "active, genetically
programmed process that responds to an inductive
signal: in this case, perhaps telomere shortening"
(Sedivy, 1998).
Impairment is used to describe a loss or
abnormality at the tissue, organ, or whole-body system
level. Active pathology usually causes an impairment,
but not all impairments are associated with an active
pathology (Jette, 2006).
A functional limitation is a restriction in activities
undertaken by a person. A disability, on the other hand,
is a physical or mental limitation in a societal context
(Jette, 2006). It is "the gap between a person's intrinsic
capabilities and demands created by the social and
physical environment" (Jette, 2006). Two people with
the same medical condition may have widely varying
limitations and/or disabilities, depending on the
individuals' lifestyle behaviors, personal attitudes, and
social context (Jette, 2006).
Functional limitations/disability
• ADL
• IADL
• Discretionary physical activity limitations (exercise)
• Leisure-time and social restraints and limitations3
1 See the appendix for a pilot examination of co-morbidity
delineating the probability of having (1) arthritis and another
medical condition or (2) arthritis and experiencing an active
lifestyle.
2 This probably affects physiology and metabolism rather than
exposures per se.
3 Verghese et al. (2003) lists a number of specific leisure and
physical activities that may be considered representative of
elderly interests. Most are sedentary indoor activities, but a
few are outdoor activities, such as walking, bicycling, and
playing team games. Others are quite (relatively) energetic
68
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• Limitations on occupational and/or other role
activities
A number of these concepts have been
incorporated into WHO'S International Classification of
Functioning, Disability and Health (International
Classification of Functioning, Disability and Health
[ICF]) framework. For example, it is recognized that
dementia is a major cause of functional limitations and
disability in the aged (Aguero-Torres et al, 1998), and
that condition will be very difficult to model in an EPA
exposure assessment because of the lack of identified
time-use information for people with dementia. One
limitation of the current CHAD database is that activity
patterns that have been collected from older adults may
include some from individuals with dementia, but these
are not identified because that condition was not
included in the survey questionnaire.
There is a "feedback loop" between activity,
especially physical activity, and dementia; more active
people have less prevalence of dementia and
Alzheimer's (Morey et al., 1998; Yaffe et al., 2001). See
Section 5.A on the benefits of physical activity in older
individuals.
From an exposure perspective, chronic health
conditions may affect the type of activities undertaken,
where they occur, and certain physiological parameters
of the people so afflicted (Jones and Killian, 2000).
Thus, chronic health conditions would affect both
exposures and intake/uptake dose rates in an exposure
modeling effort. One potential way to proceed would be
to alter person-specific activity information in CHAD
diaries to mimic the impact of disability on individual
activity patterns. The following logic paradigm of chronic
medical conditions/impairment/limitations that might be
used for altering activity data in an exposure model
(Figure 6-1) is based on Boult et al. (1994). See
Johnson and Wolinsky (1993) for an alternative
conceptual model. The paradigm proceeds from health
conditions to curtailed activities.
To more fully develop this model, it will be
necessary to define and understand what is meant by
"altered" and "extremely curtailed" activities. This is
difficult to do because the literature often focuses solely
on very basic human activities to delineate functional
limitations and disability, and these generally are
considered to be "personal care" (PC) activities in
CHAD and other exposure-oriented databases. PC
activities are marginalized in most exposure
assessments, ignoring many disparate actions
subsumed under that category (e.g., sleeping, dressing,
showering and bathing, putting on makeup, sexual
activity). Thus, there is lack of specificity in the
exposure modeling databases vis-a-vis the impairment
and disability paradigm.
Northwestern University has undertaken a number
of studies to estimate how gender and ethnicity affect
but probably occur indoors: dancing, playing a musical
instrument, doing housework, climbing stairs, participating in
group exercise, and swimming.
disability prevalence rates (Dunlop et al., 1997, 2002,
2007). The data come from the Longitudinal Study of
Aging (LSOA) and the Health and Retirement Study
(HRS). An analysis of 6-year change in disability in
LSOA seniors (n=1,644 $, whose average age at
baseline = 77.3 years [range = 70 to 99], and 1,133 3,
whose average age was 76.9 years in the beginning
[range = 70 to 96]), found the following increased
proportion of people who could not perform the
following functions.
Function
Walking
Bathing
Transferring4
Dressing
Toileting
Feeding
Females
38.2%
29.9%
23.0%
14.1%
11.5%
8.2%
Males
33.5%
29.7%
18.8%
12.7%
9.1%
6.3%
Perhaps a better way to depict disability in older
adults is to provide the median age and interquartile
[IQ] range) for the onset of limitation in ADL activities.
This is done in Dunlop et al. (1997). The onset ages are
shown below.
Functional
Limitations
Walking
Bathing
Transferring
Dressing
Toileting
Feeding
Median Percentage
of ADL Onset and IQ
Range for Females
83.7 (75.6-90.0) %
86.3(80.0-91.7)%
89.4(81.1-96.4)%
91.7(85.3-99.6)%
91.0(86.8-100.1)%
99.3(91.2-102.4)%
Median Percentage of
ADL Onset and IQ
Range for Males
85.1 (77.2-92.6)%
87.9(81.1-93.9)%
91.9(83.2-96.6)%
92.7 (85.7-98.3) %
96.2 (87.2-98.2) %
102.3(92.5-104.6)%
Dunlop et al. (2002) provide data on the prevalence of
functional limitations for many chronic conditions in
adults aged approximately 76 years grouped by age
and ethnicity (black and white). Although there are
some ethnic differences, in general, almost all of the
subjects had at least one chronic condition at the
beginning (baseline) of the study, between 86.7% and
90.2% for females and 82.1% and 83.2% for males.
The most prevalent conditions after arthritis are
cardiovascular disease (especially hypertension), about
53%; vision impairment, about 17%; hearing
impairment, about 16%; followed by cancer, obesity,
diabetes, incontinence, and osteoporosis, in that order
(Dunlop et al., 2002). The authors state that current
moderate functional limitations are the strongest
predictor of future severe functional limitations, which
seems logical.
Dunlop et al. (2007) provide similar data from the
1998 HRS survey for 65+ year-old adults. Arthritis
prevalence rates are discussed in the appendix. The
second-highest chronic condition is hypertension, with a
large and significant difference between blacks (61.6%)
and the other ethnicities evaluated (Hispanics and
Getting in/out of a bed; getting up from a chair.
69
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Chronic
Medical
Conditions
Impairment
Functional
Limitations
Disability
Altered
Activities
Extremely
Curtailed
Activities
Figure 6-1. Conceptual model for modifying activity pattern data based on assessment of functional limitations
and disabilities.
70
whites, both in the 45% to 46% range). These
conditions are followed by heart disease and diabetes,
varying among the various ethnic groups (Dunlop et al.,
2007). The authors provide prevalence rates for
functional limitations, both physical functions
(prevalence rates vary from 26% for whites to 44% for
Hispanics, who were interviewed in Spanish rather than
English [31%]) and IADL disability (6% for whites to
15% for Hispanics interviewed in Spanish). Some of
these disabilities likely affected time/activity patterns
and,thus, exposures.
One of the most often used working definitions of
disability in the aged is "functional limitation," a
"limitation in one or more major life activities" (Heath
and Fentem, 1997). The 1992 National Health Interview
Survey (NHIS-92) found that, although 15% of adults
are disabled, the percentage of disabled increases
greatly with age. The percent of older adults with one or
more activity limitation is approximated below.
Age Range % Disabled
65-69
70-74
75-84
85+
36
32
42
56
In addition, people older than 70 years have two
disabling conditions on average (Heath and Fentem,
1997). Co-morbidity is a major problem in the aged, and
obtaining reliable population estimates of the numbers
of persons with one or more functional limitations is
difficult. An important data gap in EPA's understanding
is the number and characterization of population
subgroups with co-morbidities and how these affect
activity patterns and/or intake dose rates. As a pilot
investigation, we attempt to define the relative
percentages of older adults with arthritis who also have
another health or cognitive condition because arthritis is
one of the most common health problems among older
adults. That attempt, which is considered to be a
preliminary investigation, is explained in the appendix.
A recent estimate regarding limitations on
"undertaking usual activities" for persons with one or
more chronic conditions is provided in Adams et al.
(2009). It contains extensive survey data from the 2008
NHIS. The proportion of people aged 65 to 74 years
who have one or more limitations is 27% ± 0.8% (mean
± standard error), and, for elders aged 75+ years, it is
43% ±1% (Adams et al., 2009).
There is reasonable consistency overtime in the
type and prevalence of functional disability in a
Women's Health and Aging Study (WHAS) sample of
108 females aged 65 to more than 85 years old. These
women completed a weekly symptom survey, and their
responses were significantly and positively associated
for up to 23 weeks. There does not seem to be an age
or disability pattern in an analysis of nine [age *
disability] classes (65 to 74, 75 to 84, and 85+ years
times disability in 1,2,3 "domains") (Rathouz et al.,
1998). The authors "found substantial evidence for
internal validity and test-retest reliability of 20 self-
reported measures of functioning" (Rathouz et al., 1998;
p. 772). Measures made <12 weeks apart provide
redundant information on chronic conditions, but
measures made at intervals of 24 weeks or longer
begin to show substantial within-subject variability
(Rathouz etal., 1998).
It should be recognized that functional limitations
can change for the better (Crimmins et al., 2009; Jette,
2006; Parker and Thorslund, 2007 Seidel et al., 2009).
In the Established Populations for Epidemiological
Studies of the Elderly (EPESE) study, 18% of those
who lost mobility regained it over 4 years. In the
National Long-Term Care Survey (NLTCS), 18% of
elders with one or more ADL disabilities had no
disabilities after 2 years (Fried and Guralnik, 1997). The
overall prevalence of disability in older adults also is
declining by about 1% per year, probably because of
improvements in medical technology and healthy
behavioral changes (Cutler, 2001). In longitudinal
studies of the aged, both the number of years with
disability-free life and life expectancy are increasing
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overtime (Crimmins et al., 2009). Although the number
of elders living in a nursing home is increasing in
absolute numbers, the relative proportion of people
living in one is declining about 0.7% per year (Cutler,
2001). This decline is occurring even in the 85+ year old
age group.
An interesting and important consideration in
understanding health changes in older adults is the
interplay of mortality and morbidity patterns with
demographic change (Parker and Thorsland, 2007).
There are three alternative explanations with varying
degrees of optimism for the future of healthy living in
the aged.
(1) "Expansion of morbidity:" This reflects the medical
paradox that, as expected life span increases,
morbidity increases in the "added" years.
(2) "Compression of morbidity:" Decline in morbidity
rates are greater than increases in life expectancy,
and overall morbidity decreases.
(3) "Dynamic equilibrium:" Longer survival results in an
increase in morbidity, but medical interventions and
improved lifestyle slow the progression of chronic
diseases, decreasing the duration of severe
disability.
Parker and Thorsland (2007) state that trend
studies using disability measures related to impairment
and functional limitations often present a skewed
picture of the overall health status in older adults. Thus,
it is difficult to obtain a clear picture of
morbidity/mortality patterns in seniors that could serve
to put an environmental exposure modeling into
perspective. There are many overviews of mortality
rates for older people in the literature (Crimmins et al.,
2009). An interesting one is Rudman (1989), which
provides age-specific death rates by disease and
disability classes.1 There are many articles available on
the general topic; it is a subject in and of itself.
There are numerous articles on what are good
predictors of future ADL and IADL limitations in older
individuals. Current fitness level (generally VO2 MAX),
anaerobic fitness, amount of physical activity
undertaken, cognitive ability (speed of response to
certain tasks), and obesity, all have been investigated
for their usefulness in predicting impairment and
disability. For more information on this topic, see Fuller
et al. (1996), Jette et al. (1990), Lee and Skerrett
(2001), Meijer et al. (2001), and Sunman et al. (1991).
A health-related issue that is examined only
indirectly in this report is the increasing obesity rates in
older people (Elia, 2001; Himes, 2000; Sharkey et al.,
2006). In Section 2, we addressed some of the
physiological impacts of obesity, such as the alterations
in basal metabolism, fitness levels, maximum oxygen
consumption, and maximal ventilation rate (Lee and
Skerrett, 2001). In Section 3, we addressed the impact
of obesity on activity-specific energy expenditure and
1 He also has data on change in ADL rates and change in
physiological functions referenced to age 30 by decade of life.
The exposition in Rudman (1989) is very clear.
total daily energy expenditure. In Section 5, we
mentioned the impact of senior obesity on exercise and
physical activity levels. There are little or no data on
how obesity affects older individuals' time use
(Section 4) and responses to environmental exposures
(Section 7).
6.B ADL and IADL
The gerontology literature uses the ADL and IADL
concepts to distinguish between basic self-care
activities (ADL) and tasks considered necessary for
independent living in the general community (IADL).
ADL activities include bathing, dressing, moving from
bed to a seat, using the toilet, and eating by one's self
(Guralink and Simonsick, 1993). These are basic
activities, and not being able to perform them are the
most frequently assessed indicators of physical
disability. The list was originally compiled to assess
physical capability in a long-term care or rehabilitation
setting, but it now is used widely in surveys of
community-dwelling populations (also sometimes called
"free-ranging" people). For additional information, see
Guralink and Simonsick (1993), Galasko et al. (2005),
Katz (1983), and the series of McAuley articles listed in
the references (McAuley et al., 1999, 2004, 2005a,b).
IADL activities include talking on the phone,
shopping, food preparation, housekeeping, doing
laundry, walking or otherwise being mobile, using
transportation, taking medications, and handling
finances (Guralink and Simonsick, 1993). Although
there is consensus in factors comprising ADLs and
lADLs, how they are measured varies in the survey
instruments used, wording of questions, sampling
protocols, etc. Thus, estimates of noninstitutionalized
people "failing" one or more ADL or IADL vary widely,
and it is difficult to ascertain trends in these metrics
overtime. In the 1980s between 5% and 8% of people
aged 65+ living in the community could not perform all
ADLs without assistance (Guralink and Simonsick,
1993). There is a marked difference among areas of the
United States in the percentage of older adults needing
assistance with ADLs, even for the same age groups,
and the proportion increases with age. The percentages
approximately double or the 75 to 84 year-old "cohort"
compared with those aged 65 to 74 years, and double
again in the 85+ year-oldgroup (Guralink and
Simonsick, 1993).
The doubling rate phenomenon applies to at least
three of the lADLs, (1) preparing meals, (2) shopping,
and (3) doing light housework, but only for the age 65 to
74 to age 75 to 84 pairs. From age 75 to 84, the percent
of older people dependent on help for one or more of
the three lADLs mentioned more than doubles. In fact,
the ratio for 85+ is about three times that of the 75 to
84 years group (Guralink and Simonsick, 1993). The
number of activities for which help is needed also
increases with age, as well as the percentage of people
needing help. The age-adjusted mortality rate also
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increases with the number of ADLs affected (Guralink
and Simonsick, 1993).
Adams et al. (2009), mentioned above, also
provide estimates of the proportion of older individuals
having one or more ADL and IADL limitations. Their
estimates (mean and standard error) are as follows.
Age
65-74
75+
ADLs
3.4 ± 0.3%
10.0 ±0.6%
lADLs
6.9 ± 0.5%
19.2 ±0.8%
These estimates are somewhat lower than
Guralink and Simonsick's (1993) values, but the rate of
increase between the two groups is about three times,
which is somewhat larger than the "doubling time" ratio
mentioned there.
There are important exposure implications of the
trends in the IADL data, in that they basically alter the
time-use patterns of affected seniors. However, we
have no information in CHAD or in any other time use
database that provides explicit information on IADL
problems. ATUS may be able to address the issue most
directly by comparing the lack of time spent in certain
activities by seniors vis-a-vis younger cohorts. Hence,
the only way to simulate exposures for older people
with IADL restrictions would be to alter the activity
patterns of diaries already collected. There is no study
that explicitly provides data on how those activity
patterns would change, so there would be a lot of
uncertainty with doing so.
It should be noted that there is a major effort
underway to develop technologies to monitor seniors'
movement in the residential environment, including a
number of ADL activities. The rationale is that seniors
with disabilities could remain at home longer if they
could be "watched" to see if they were still ambulatory.
The 2008 Institute of Electrical and Electronics
Engineers-Engineering in Medicine and Biology Society
(IEEE-EMBS) conference has a number of articles on
using "microtechnology" to monitor basic activities in
older people, including those having falling problems.
See Bang et al. (2008), wearable sensors to monitor
ADLs; Bas et al. (2008), "fish-eye" camera to assess in-
home activity; Lim et al. (2008), "pressure sensors" to
recognize different activities; Min et al. (2008), wearable
wireless sensors to monitor early morning activities;
Uhrikova and Nugent (2008), "computer vision"
techniques to augment home-based activities tracked
by sensors; as well as the more commonly used
accelerometers to monitor movement (Guralink, 2008;
Narayanan et al. 2008). The above approaches would
be closer to those "objective techniques" used to
monitor physical activity in older individuals, as
mentioned in Section 5. They also could be used to
obtain time use data in this population, although no
proposal to do so has been published to date.
6.C Caregiver Time
A relatively new theme in the time use/activity
literature is the amount of time spent by seniors and
others on providing care for other older persons. In
some cases, the old are taking care of the "old old" [sic].
Because this activity probably does not have unique
environmental exposure implications, we only mention it
is passing. See the following articles for additional time
use implications of elder-care: Clipp and Moore (1995),
Mancini and Blieszner (1989), Moss et al. (1993), and
Russell et al. (2007).
6.D Cognitive Issues in Older Individuals
Another subject that must be investigated only
briefly in this report is the decline in cognitive function
seen in seniors, except where it becomes the root
cause of functional limitations and physical activity, as
discussed in Section 6.B. There is extensive literature
on the origins and impacts of cognitive functioning on
chronic health conditions and vice versa, "successful
aging," and related subjects. Just a few of them are the
series of articles that are part of the MacArthur Studies
of Successful Aging (Seeman and Chen, 2002; Seeman
etal., 1994,2005).
Dementia, Alzheimer's, and Parkinson's are the
more well-known cognitive problems, but less severe
cognitive issues also affect what the aged can do and
often result in the impairment, functional dependence,
and disability issues discussed above (Aquero-Torre
et al., 1998). There is a strong link between cognitive
problems and the ability to function (Galasko et al.,
2005).2 Investigations into this link focus on ADLs and
lADLs for the most part, but more general, exposure-
related activities of interest would certainly be affected.
There is much intraindividual variability in cognitive
function seen in longitudinal studies of seniors and
other age cohorts (Salthouse, 2007). In fact,
intraindividual variability in cognitive "scores" are about
50% of the interindividual variability in the same
age/gender cohort (Salthouse et al., 2006). These
findings are based on relatively short-term repetitions of
cognitive tests and would be even greater if the time
interval between testing would increase. The
implications of this variability on exposure assessment
in older adults are unknown at the present time, but, if
data become available to link cognitive and functional
limitations, and these are shown to have an exposure
impact, then we would have to devise a means of
modifying activity patterns in older individuals overtime
in a stochastic manner (Figure 6.1). These
modifications would be marked in older adults with
cognitive impairments who are "confined" (voluntarily or
There also is a "feedback loop" between functional
limitations and chronic health conditions that affect cognitive
performance (and, importantly, depression and other
psychological factors). See Samuelsson et al. (2009),
Scarmeas et al. (2001), and many of the "unused" citations
listed below for more information on this topic.
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involuntarily) to an indoor space with little or no pattern of indoor/outdoor relationships and indoor
interaction with the ambient environment.3 If EPA can sources. Until then, we can be aware only of the
obtain data sufficient to assign these older adults to a potential impact of cognitive issues on activities,
specific location or a series of locations, modeled locations, and exposures.
exposure to older adults could focus on the temporal
3 New drug theraphies are being developed that can affect
this state of affairs by improving cognition, changing the
progression of Alzheimer's, and increasing mobility
(Roundtree et al., 2009). Undoubtedly these therapies will
proliferate in the future.
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7. Exposure Impacts on Older Adults and Their Impact on the Environment
ABSTRACT
Topic: This chapter discusses both environmental exposure
impacts on older adults and the impacts of older adults on the
environment.
Issue /Problem Statement: Older adults may be more
sensitive to exposures, while the increased medical resources
required by an aging population may have an environmental
inpact.
Data Available: Information available on the exposure
impacts on adults and vice versa is low to moderate.
Research Needs: Although these topics may have no bearing
on research needs for exposure modeling per se, the
increased sensitivity of seniors to environmental pollutants is
extremely significant in risk assessment. The continued
investigation of this sensitivity can be used in conjunction with
exposure estimates for the aged to provide improved
estimates of risk.
7.A Introduction
This section is intended to consider both
environmental exposure impacts on older adults and
the impacts of older adults on the environment. These
"mirror" impacts have to be understood, so they can be
compared to those in other age cohorts. Environmental
impacts on seniors are discussed extensively—in much
74 more detail than is possible here—in the environmental
epidemiological literature. See the sampling of this
literature contained in the bibliography attached to this
section. Much of this material is concerned with
exposures to particulate air pollution in health-
compromised older people. Those with
cardiorespiratory limitations are the prime "sensitive"
group1 for both PM (all size fractions) and CO (Chen
et al., 2004; Delfino et al., 1998; Liao et al., 1999; Puett
et al., 2009). NERL has conducted a number of
monitoring studies of elders' particulate exposure; see
the partial list of papers and EPA reports by Williams
and colleagues.
The impacts of older people on the environment
have received not much attention until recently. EPA
and CDC together have provided leadership in this
area. The nonphysical environmental literature is almost
silent on the issue of impacts by seniors on nonhome or
noninstitutional environments (except for their impact on
the need for more beds, nurses, geriatric facilities, etc.).
7.B Examples of Exposure Impacts on
Older Adults
Adar et al.(2007a) show that exhaled nitric oxide is
increased in 44 seniors subjects aged 62 to 94 (of
mixed ethnicity and both genders) after riding in a diesel
bus. Nitric oxide is a general marker for pulmonary
oxidative stress and inflammation and is probably most
associated with PM in diesel exhaust. Other chemicals
associated with mobile sources also likely played a role
in the effect. For example, another transportation-
related air pollutant, O3, causes oxidative stress in lung
cells and is associated with adverse health effects in
seniors (Alexeeff et al., 2008).
Radon is a gas that often reaches high levels
inside of residences located in regions with naturally
occurring radon in soil. Because older people spend
more time at home than younger people, they are
thought to be more vulnerable to exposure to this gas
than younger adults (Briggs et al., 2003).
There are a few studies on cumulative exposure to
pollutants, such as lead, that have a long elimination
rate time constant such that intake doses are
sequestered in the body faster than they can be
removed. Dose rate is not as important as total
accumulative dose overtime for these pollutants (Nie
et al., 2009; Peters et al., 2009; Weuve et al., 2009).
Mercury, asbestos, and some environmental
carcinogens may be additional examples of this type of
pollutant, given the assumption made in EPA's cancer
risk assessment procedures for a 70-year exposure
period (Samet and Utell, 1991). It will be important to
distinguish between cumulative body burden and
constant exposure when accounting for results, such as
the association of Parkinson's disease with dietary
consumption assessment and long-term consumption of
pesticides in well water (Gatto et al., 2009).
There is a growing body of literature suggestive
that exposures to a wide variety of toxic chemicals in
the earliest stages of life, even in the womb and
infancy, may initiate neurological changes that
ultimately result in Alzheimer's, Parkinson's, and other
neurodegenerative diseases (Lau and Rogers, 2004?).2
This "developmental origins of health and disease"
hypothesis was based originally on heart disease and
diabetes studies. Some of the toxic exposures that have
been implicated include lead, mercury, pesticides,
persistent organic compounds, and polychlorinated
biphenyls (Stein, et al., 2008). Many of these chemicals
have cumulative effects. The exposure modeling
implications for these types of chemicals probably
would affect neonate and infant exposure assessments
more than they would an older population exposure
assessment, however.
7.C Impact of Older Adults on the
Environment
There was very little in the peer-reviewed literature
on this subject at the end of 2009, but EPA raised this
as an issue in the development of its Aging Initiative
"Sensitive" is the term used in Section 109 of the Clean Air
Act to identify susceptible people.
Other adverse health effects mentioned in the literature are
obesity, hypertension, elevated blood lipids, and the
"metabolic syndrome."
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(U.S. EPA, 2004). There are a number of PowerPoint
presentations about Pharmaceuticals used by seniors
and other age groups getting into the environment (e.g.,
Krewski et al., 2009) that can be accessed on the Web,
and many of them are available on EPA's Aging
Initiative Web site (www.epa.gov/aging/index.htm).
EPA, CDC, and the California EPA seem to be the main
source of information on "chemicals in the
environment." See for instance, Sykes (2009), "Prudent
disposal of unused medications: why it matters to our
aging population" and "Discarded drugs as
environmental contaminants" (Ruhoy and Daughton,
2009). The Agency has studied the problem of unused
Pharmaceuticals in some depth (U.S. EPA, 2009a) and
has proposed effluent guidelines for them (U.S. EPA,
2009b). It would be difficult to try to expand on this topic
because it is new and rapidly changing. The interested
reader can access the EPA Web site provided above
and the accompanying links for more information on the
impact of discarded Pharmaceuticals and other
chemicals (mostly by seniors) on the environment,
particularly on water body ecosystems. See Daughton
and Ruhoy (2009) also.
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APPENDIX
An Example of Available Health and Co-morbidity Information
AP.A Introduction and Explanation of
This Material
This appendix primarily is a preliminary
evaluation of the literature on the health of older adults
focused on two health conditions: (1) arthritis and
(2) co-morbidity involving dementia as the "reference"
illness. This appendix is not considered to be a final
product but, rather, a first attempt at defining a complex
subject. Definitions, concepts, abbreviations, and
acronyms used in this appendix appear as Section
AP.5. The data come entirely from U.S. studies, except
for a few oriented toward physiological relationships,
which are universal. There are some references on
health effects on the elderly provided in Section AP.6,
but they are not exhaustive on the topic.
A quantitative "summary" of how the information
reviewed in this appendix might be used in an exposure
assessment follows. It takes the form of recommended
joint-probability distributions for assigning "arthritis" and
"co-morbidity | dementia" (the | line is to be read at
"given" [i.e., a conditional probability]) to an individual
chosen out of 65+ year-old individuals included in the
CHAD database. Because the data reviewed generally
were not disaggregated by gender, the probabilities
apply to both females and males. Because it is unlikely
that EPA would estimate exposures to residents of a
"group home" (of any type), nursing home, or hospital,
the probability estimates apply only to community-
dwelling seniors living in a private residence.
Although distribution parameters are provided,
the way we would do it in practice would be to give the
data to a statistician (Bayesian, preferably) and have
her/him fit distributions until the best practical fit would
be obtained. Therefore, the following information should
be considered to be "seed values" for such an analysis.
Note that the uniform distribution bounds are smaller
than those seen in the literature. Where multiple values
were provided in the literature, the lowest and highest
estimates were removed and defined to be the range
depicted. This range, of course, would be treated
explicitly in a statistical distribution-fitting. For the
Weibull distribution, the g is the scale parameter and the
p is the shape parameter. A "small" value suggests that
its implied variance would be <10% of the mean or
scale parameter. The conditional probability estimates
developed from this review are as follows.
Arthritis | Dementia
(Confined)
Arthritis | Mild
Cognitive Problems
Arthritis | Very
Active Lifestyle
Arthritis | Unknown
Cognitive Condition
65-79 Point estimate (40%)
80+ Uniform (50%-60%)
65-74 Uniform (50%-70%)
75+ Uniform (50%-75%)
65+ Point estimate (35%)
65-69 Weibull ($=57%; (3=small)
70-74 Weibull ($=55%; (3=small)
75-79 Uniform (53%-70%)
80+ Weibull ($=50%; (3=small)
AP.B Overview of the "Population"
Analysis Undertaken
We ultimately are interested in undertaking
exposure analyses of older adults that makes practical
distinctions among important factors that result in
differential exposures and intake dose rates, changes in
metabolism and subsequent adverse health effects, and
differential health risks. EPA's modeling focus is on
exposure and intake dose rate changes, and that will be
the subject of the preliminary work that follows. To
better model exposures and intake dose rates requires
that we evaluate and understand differences in human
activity patterns and ("whole body") physiology31. Our
models already disaggregate people into age and
gender subgroups but include other disaggregating
factors as well, depending on the environmental hazard
of interest. Examples are exercising asthmatic adults as
a susceptible subpopulation group for sulfur dioxide
exposures; older people with angina as the "sensitive"
group of concern for CO exposures; and, exercising
children and outdoor workers for O3 exposures. With
respect to seniors as a general subpopulation group,
there long has been concern about their exposures to
PM—either with respect to different size fractions or to
chemical species absorbed on the particles and
aerosols. EPA has not, however, formally evaluated PM
exposures to that cohort in its NAAQS-setting process
to date.
Defined to be those physiological parameters that are
needed and used (either as an input or as a "pre-input"
predictor variable) in NERL's SHEDS-Air and OAQPS's APEX
(TRIM-Expos) time-series exposure models. They include
basal metabolic rate; oxygen consumption factors (maximum,
rest, and reserve); ventilation rate (maximum, rest, and
reserve); oral/nasal breathing rate distributions; and activity-
specific parameters, including METS, oxygen consumption
(and decreases in same because of fatigue), ventilation, and
alveolar ventilation rate. These parameters are dependent, in
part, on anthropogenic considerations that also are needed in
the models, including age, gender, "fitness" level (as
estimated by an individual's PAI), and BMI. To a lesser extent,
predictors also could include race (ethnic group), height, lean
body mass, and percent body fat.
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AP.C Arthritis
Three different types of disorders frequently are
subsumed under the term arthritis; (1) osteoarthritis,
(2) rheumatoid arthritis, and (3) septic arthritis. Authors
on the subject often do not distinguish among the three
types, probably because many of their studies use self-
reporting by their subjects, who may not know precisely
what type of arthritis they have. This probably is the
reason for some of the differences in the prevalence
rates seen below.
AP.C.1 Prevalence Rates for Arthritis
People with chronic arthritis have problems
undertaking many activities, especially those involving
movement and dexterity. This is especially true for
people with rheumatoid arthritis (Backman, 2006). They
engage in fewer types of leisure activities (recreation
and hobbies), especially among less-well educated
people. Thus, people suffering from this health problem
are of interest to us from a population-cohort
perspective, in that their "macro-activities" may differ
from the rest of the "healthy" aged population, and they
would be treated as a subpopulation group in Agency
exposure analyses.
There are numerous estimates of the
percentage of the population 65 years old with arthritis.
Probably the most definitive is the CDC, National
Center for Health Statistics (NCHS) presentation of data
from the biennial NHIS. Hereafter, these data are cited
just as NHIS with the year denoted by the last year of
the survey; thus, NHIS 2002 covers the 2001-2002 time
period, and NHIS 2004 is for the 2003-2004 time
period. NHIS 2002 indicates that 31% of older males
have arthritic symptoms as do 39% of older females.
NHIS 2004 increases these estimates to 43% and 55%,
respectively.
Another source of information on arthritis is the
Federal Interagency Forum on Aging-Related Statistics,
hereafter cited as FIF. There is no discussion in FIF
(2006) of why the estimates increase so dramatically
between the two time periods. A number of reasons
could be proposed: a slightly older population, different
populations sampled, different questions, different
criteria, or varying definitions of the chronic condition.
The two FIF reports do use different labels for arthritis:
"arthritic symptoms" in FIF 2004 versus "arthritis" in FIF
2006.
Other estimates of chronic arthritis found in the
literature are listed below in an abbreviated format.
Bean et al.
(2004)
Dunlop et al.
(2002)
Dunlop et al.
(2007)
Lyketsos et al.
(2005)
Schmader et al.
(1998)
Song et al.
(2006)
CDC:
National
LSOA
MRS
Cache
County
Durham
EPESE
MRS
1998-2000
70 years: 60%
76±6 years black ?: 71%
76±5 years white $: 58%
76±5 years black $: 54%
76±5 years white $: 42%
65 years black: 60%
65 years white: 53%
00
65 years Hispanic: 45%
65 years: 54%
>65 years: 69%
65 years: 57%
Bruce et al. (2005) compare arthritis rates for
runners and "healthy" controls in a 14-year longitudinal
study of the benefits of aerobic exercise to diminish
musculoskeletal pain. The runners were members of
the "Fifty-Plus Runners' Association" from across the
United States (all 50 states; n=492, 83%; mean age =
61.6 years), and the community controls were from a
random sample of subjects enrolled in Stanford
University's Lipid Research Clinics Study (n=374, 56%;
mean age = 65.1); thus, makeup of the two samples is
quite different. The proportion of subjects suffering from
arthritis in the runner's group is 35% versus 41% for the
community controls. This difference is not statistically
significant (using a t-test at a=0.05).
Baseline data from a clinical exercise
intervention study provides some comparative arthritis
data for relatively low income community-dwelling
people who attend two outpatient health clinics for
medical care operated by an urban hospital (Clark
et al., 2003). The centers are linked to the Regenstrief
Medical Records System (RMRS), and this was used
as the sampling frame to randomly select older patients
at the centers. The researchers used the RMRS data
and an interviewer-administered survey to determine
each subject's chronic disease state. The people
selected who decided to participate were actually less
healthy at the baseline than those who did not
participate. Participants were aged 63.7 years on
average, were 67.5% black, and 22.8% had arthritis
(among other chronic diseases). Nonparticipants were
aged 63.1 years on average, were 55.9% black, and
18% had arthritis. These relative arthritis estimates are
much lower than those provided above.
In a paper describing an unusual approach to
gathering health and activity data, Clark (1999)
describes a focus-group study (8 to 10 people at a time)
that eventually included 771 individuals who were
selected randomly from the RMRS system, as was
32
These are for Hispanics who were interviewed in Spanish,
their native language. The prevalence rate was higher, 53%,
for Hispanics who were interviewed in English.
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used above. The RMRS data came from seven primary
care centers in and around Indianapolis. The study itself
used the Regenstrief Physical Activity and Health
Survey (RPAHS) as the instrument forgathering data.
Gender and ethnic group breakdowns of the survey
takers are not provided. The percentage of RPAHS
respondents >70 years old with arthritis was 44.1%.
Clark compared this estimate to the 1984 NHIS, which
showed a 55.0% incidence of arthritis in the 70+
year-old population.
Dunlop et al. (2002) report data on changes in
functional limitations in older individuals over a 6-year
period using data from the Longitudinal Study of Aging
(LSOA). The LSOA is a prospective survey of
community-dwelling people 70 years old when first
interviewed in the 1984 NHIS. The proportion of people
with arthritis in 1990 who did not have a functional
limitation in 1984 is 53.1% (n=4,206; mean age = 76.4 ±
5.3). The age/race breakdown is as follows.
Black:
Black:
White:
70.7%
53.6%
57.9%
(n=308)
(n=168)
(n=1,303)
In a review of HRS findings, Feinglass et al.
(2005) indicates that 44.1% of elders have arthritis,
defined as answering "yes" to questions involving
(1) diagnoses of arthritis, rheumatism, bursitis, and
tendonitis; and (2) having pain, stiffness, or swelling
sometimes in the joints. In those people—whose
average age is only 56 years—68% were overweight or
obese, and they had about two chronic medical
conditions totaf( = 2.1 ± 1.2).
Another longer term study of seniors is reported
by Gill and Gahbauer (2005). This paper describes a
sample 552 people 70 years old that had no baseline
disability in four essential activities of daily living:
(1) bathing, (2) dressing, (3) walking inside the house,
and (4) getting out of a chair). They were members of
the "Participating Events Project," but details as to their
location and other project details are not provided.
A monthly telephone interview of study participants
provided information on new and chronic disability
rates, and the paper reports data for those who
completed interviews at 54 mo after the study began.
The median age of the sample by this time was
81.5 years (range: 75 to 101); 67.2% were female, and
89.7% were white. About 46.2% of them had arthritis.
Another underexplained study of community-
dwelling people in an unnamed location is reported in
Ho et al. (2002). Because the researchers are from the
University of South Carolina in Columbia, study
subjects probably are located nearby. An interviewer-
administrator questionnaire was used to ascertain the
participant's physical, vision, cognitive, nutritive, and
hearing functioning. Multiple specific health items were
included within each category. If a subject had difficulty
on half of the items included within any one of these
functional categories, they were identified at being "at
risk for frailty." The Strawbridge protocol was used in
this regard (Strawbridge et al., 2000). Of the 78
participants, 47.4% (37) were identified as being at
"high risk" (mean age = 74.1 ± 6.1; 100% white), with
the remainder (52.6% [42]; mean age = 69.8 ± 7.8; 95%
white) being labeled as "low risk." About 68.8% of the
high-risk group had arthritis, as did 73.7% of the low
risk group. Analyses of statistically significant
differences were not reported for any data presented.
Another prospective cohort study representative
of the community-dwelling U.S. population 70 years old
is reported in Holroyd-Leduc (2004). The study is the
Asset and Health Dynamics Among the Oldest Old
(AHEAD), and it is a supplement to the HRS study. The
proportion of 6,506 subjects with self-reported arthritis
is a surprisingly low 25%, given ages of the
respondents, 40% between 70 and 74 years, 29% aged
75 to 79 years, and 31%. 80 years. About 63% of the
respondents were female, and 86% were white.
A study proving both cross-sectional and
longitudinal data on arthritis is discussed in Janssen
(2006), but few details regarding it are reported; the
reader is referred to other papers. The data come from
the Cardiovascular Health Study (CDS) sponsored by
the National Heart, Lung, and Blood Institute (NHLBI).
The cross-sectional (C-S) part of the study included
5,036 people of varying ages, whereas 3,694 people
contributed data to the longitudinal (L) part (see below).
Almost all of the participants were white, 94.7% and
95.1% for the two parts, respectively. "Prevalent
arthritis" was self-reported by 50.9% cross-sectional
participants and by 44.6% of the longitudinal subjects.
The age distributions of the two parts are similar.
Ages C-S Percentages L Percentages
65-70
71-76
83-89
>90
42.7%
32.7%
18.2%
6.4%
46.2%
33.0%
16.1%
4.7%
Apparently there were no participants between
the ages of 77 and 82. No statistical analyses of the
data are provided in Janssen (2006).
A study of residents of a particular continuing-
care retirement community, called Air Force Villages, is
discussed in Royall et al. (2005). The sample consists
of 547 randomly selected retirees 60 years old living in
the community (noninstitutionalized). The mean age is
77.9 years ± 4.9, with a range of 60 to 100. About 58%
were female. The proportion of residents with arthritis
was 61.2%.
An important study of arthritis prevalence from
the national perspective is described in Shih et al.
(2005). It uses data on people "free of ADL limitations"
from the 1998 and 2000 HRS interviews who have self-
reported arthritis using this question: "Have you ever
had, or has a doctor ever told you that you have,
arthritis or rheumatism?" (a fairly broad question). The
number of HRS respondents who responded "yes" was
3,451, which is 45.6% of the 7,758 HRS participants
provided in Song et al. (2006). (The total was not
provided in Shih et al., 2005!) A majority of them had
99
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one or more physical limitations and did not participate
in regular vigorous physical activity. A high proportion
had other chronic conditions (see Table AP-1). See the
discussion of this study below.
Table AP-1. Co-morbidity Associated with
Arthritis Without ADL Limitations
Percent of
sample
Mean age
Percent female
African-
American
10.7
73.3
68.3
Hispanic
5.1
73.3
64.0
White
84.2
73.8
61.5
Percentage of People with Arthritis Having Other
Medical Conditions
Diabetes
Heart disease
Lung disease
Serious illness
23.0
70.3
7.3
74.5
18.8
57.9
5.9
65.0
12.9
62.4
10.9
62.7
Source: Shih et al. (2005). "Racial Differences in
Activities of Daily Living limitation in Older Adults:
A National Cohort Study." Arch. Phys. Med. Rehab.
86: 1521-1526.
There was a study undertaken somewhere in
California (otherwise undefined) entitled "Community
Health Activities Model Program for Seniors"
(CHAMPS) that ascertained arthritis status information
from 249 community-dwelling residents who
subsequently participated in an exercise program
(Stewart et al., 2001). The mean age of the sample was
74.1 years ± 5.6), with a range of 65 to 90 years; about
64% of them were female, and 92% were white. Almost
59% of the sample had self-reported "arthritis or joint
problems."
In a random-digit telephone survey of residents
.60 years old in two counties in southern New Mexico as
part of a 3-year study of the health needs of
southwestern U.S. residents, the University of Texas-
El Paso asked a number of health-related questions
(Tomaka et al., 2006). The total sample size was 755;
72% were white or "Anglo," and 23% were Hispanic.
The average age of the sample is 71.1 years, with a
range of 60 to 92 years. Fifty-seven percent of the
Hispanic and Caucasian respondents (separately)
stated that they had arthritis.
A study of multiple chronic conditions in Seattle
older people provides lower arthritis prevalence rates
than most of the studies reviewed here. The data are
from the Adult Changes in Thought (ACT) study, which
is a population-based prospective cohort evaluation
conducted by the University of Washington's
Alzheimer's Disease Patient Registry. The study
population was sampled from Group Health
Cooperative members aged 65+ years in the Seattle
area from 1994 to 1996 (L. Wang et al., 2002). A total of
2,578 people at baseline did not have dementia; their
age breakdown was 65 to 69 years (23%), 70 to
74 years (30%), 75 to 80 years (24%), 81-84 years
(15%), and 85 years (8%). Most of the respondents
were white, 91%, and 4% were black. The proportion of
the sample with arthritis was 26%.
Wilcox et al. (2006) describe an evaluation of
community programs designed to increase physical
activity in older adults. Participants in this program
could be as young as 50 years, and 35% of the sample
was between 50 and 64 years of age. The average age
was 68.4 years ± 9.4), and 80.6% were female. There
were two different programs evaluated, but their
proportion of participants with self-diagnosed arthritis
was not statistically different, so their data are
combined. About 61% of the sample had arthritis.
In an intervention study of improving balance
among 72 reclusive independent living center residents,
the analysts found that 69.4% of them had arthritis at
baseline in the
three groups studied (Wolf et al., 1997). (There was not
a statistically significant difference among the three
groups experiencing different intervention approaches,
with the range being 62.5% to 75.0%). The mean age of
the sample was 76.9 years (SD: 5.7), and 83.3% were
female.
A study that provides estimates for rheumatoid
arthritis, a more severe type of arthritis having a more
complex etiology, is Corrada et al. (2006). They report
on a longitudinal, large-scale, population-based study of
seniors in Leisure World, Laguna Hills, CA. This is a
retirement community and 13,451 people participated in
the study for 13 years on average. The age of study
participants varied between 44 and 101 years at entry,
with a mean of 73.5 years. Overall, 5.9% of them had
rheumatoid arthritis, and this percentage changed only
marginally with BMI. The prevalence of rheumatoid
arthritis by BMI category was as follows.
Underweight (BMI <18.5) 5.8%
Normal (BMI 18.5-24.9) 5.7%
Overweight (BMI 25-29.9) 6.3%
Obese (BMI >30) 6.4%
AP.C.2 Physical Activity Difficulties for People with
Arthritis
A quote from Shih et al. (2005) succinctly
places the issue of activity limitations caused by arthritis
into perspective.
"The prevalence of arthritis increases with age,
affecting approximately 60% of people 65 years and
older [cites MMWR 51: 948-950 (2002)]. Arthritis is also
among the principal sources of restricted activity and
bed disability days every year [cites Collins Vital Health
Stat 10 194: 1-89 (1997)], and a major reason for
limitations in activities of daily living (ADL)....
Numerous national population-based studies indicated
substantially more activity or functional limitations
among minorities compared with white Americans,
disproportionate to differences in arthritis prevalence.
African and Hispanic minorities with arthritis
100
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consistently have higher rates of activity limitations"
(p. 1521).
Data from Shih et al. (2005) on limitations follow
for people 65 years old with arthritis but no ADL
limitations at baseline.
African
American Hispanic
White
380
73.3
68.3%
74.5%
64.7%
179
73.3
. 64.0%
65.0%
63.4%
2,982
73.8
61.5%
62.7%
54.4%
Characteristic
Sample size
Mean age
Percentage
One+ physical limits
Lack of VPA
"VPA" is vigorous physical activity; the term
"vigorous" is age-adjusted and includes participating in
sports, heavy housework, or having a physical laboring
job for at least three times a week over the past 12 mo.
Additional information, if any, should be
evaluated on this issue. We did not have time to
undertake any more work on the subject.
AP.D Co-morbidity
AP.D.1 Dementia as the Reference Health Problem
There are a number of studies that provide data
on co-morbidity, defined to be multiple health and/or
mental conditions, adverse health problems, or
disabilities in a single individual. However, their frame of
reference or population groups covered are very
different. Some studies focus on people with dementia
and provide data on the proportion of people in differing
dementia classifications that have one or more chronic
health conditions. Two studies of this type are Lyketsos
et al. (2005) and Schmaderet al. (1998). See
Table AP-2.
Their population groups are quite different with
respect to ethnic makeup, location of the study,
methods of classifying dementia, and residential living
arrangements of the subjects. Lyketsos et al. (2005)
reports on data from the Cache County, UT, Study, and
its subjects are almost entirely white people, some of
whom live in nursing homes. Cognitive classification
was done using the Modified Mini-Mental State Exam
(MMSE) or the Informant Questionnaire for Cognitive
Decline in Elderly (IQCODE). Medical conditions were
ascertained using self-reports and the Johns Hopkins'
General Medical Health Rating (GMHR) procedure
assigned by a geriatric psychiatrist based on direct and
nurse (proxy) interviews. Schmader et al. (1998)
presents data from community-dwelling individuals in
Durham, NC, who are part of a long-term
epidemiological study conducted by Duke University.
Dementia status was ascertained using a neuro-
psychological battery of tests that included the MMSE.
The health data came from information in that paper;
the reader is referred to other papers for details.
Selected information from the two papers is reproduced
in Table AP-2.
In the two studies, dementia classification
significantly affected co-morbidity for stroke in both
studies, for arthritis in the Durham study (but not in
Cache County), and for "serious physical illness" in the
Cache County (not reported in Durham). The authors
do not specifically define what is included in that term,
but it was based on the GMHR procedure.
A study listed on Table AP-2 focuses on older
Mexican-Americans who are participating in a
longitudinal study entitled "Hispanic Established
Population for Epidemiological Study of the Elderly"
(H-HEPSE), funded by the National Institute on Aging
(Raji et al., 2005). The study population comes from five
southwestern States, and data have been collected
over an 8-year period (1993-2001). The data depicted
come from the baseline, 1993-1994. Cognitive
capability is defined using the MMSE scale, and
disabilities are based on responses to seven items on a
modified version of the Katz ADL scale. Medical
conditions were assessed by self-report based on a
doctor's diagnoses of a condition. There are no
statistically significant differences in medical conditions
(that were evaluated) experienced by the two cognitive-
functioning groups, except for stroke.
S. Wang et al. (1997) provide dementia-
referenced co-morbidity estimates for residents of a
large long-term care institution in Massachusetts. The
average age of the residents is 86.7 years ± 7.1. The
proportion of residents having heart disease, both the
"non-demented independently functioning" and those
with dementia, is much greater than in the previously
mentioned studies. Otherwise, the relative co-morbidity
estimates are in line with those cited above. The Katz
ADL scale and the MMSE tests were used to classify
the residents into the two classes. The residents were
evaluated for 3 to 6 years, and a distinction was made
in the paper between people who were admitted with
dementia and those who required total care during the
period of evaluation, but these two groups were
combined into one group for our Table AP-2.
Another study depicted in the table is
Fillenbaum et al. (2005), which, like the Schmader et al.
(1998) study, is part of Duke University's long-term,
community-based study of residents in five North
Carolina counties. See the above discussion of how
dementia was defined. Of the co-morbidity health status
indicators, only the percentage of prescription drugs
taken was statistically significantly different, with
subjects having "incident dementia" taking fewer drugs
on average than subjects with no dementia. That
observation is consistent with L. Wang et al. (2006)
data but is inconsistent with the Lyketsos et al. (2005)
data.
Estimates of co-morbidity with respect to
dementia class are found in L. Wang et al. (2006). For
dementia-free people 65 years old, 16% had coronary
heart disease, and 6% had cerebrovascular disease,
compared with 26% and 14%, respectively, for seniors
101
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Table AP-2. Co-morbidity Associated with Different Degrees of Dementia (in percentages)
Study citation
Cognitive Condition
Lyketsosetal., 2005
Normal
CIND
Dementia
Schmaderet al., 1998
Intact
Percent of sample
Mean age
Age SD
Percent female
Percent white
46.2
79.3
6.3
54.8
99.4
32.4
82.4
7.5
53.8
100.0
21.4
83.9
6.3
64.4
99.3
Impaired
Demented
Fillenbaum et al., 2005
None
Incident
Dementia
Raji et al., 2005
High
Low
Wangetal., 1997
None
Demented
58.3 22.5
77.3 80.1
5.2 6.7
63.0 67.0
47.0 74.0
19.2
83.1
6.3
72.0
61.0
77.1
72.3
6.2
62.1
38.3
22.9
74.9
6.4
62.4
36.2
62.9
71.7
5.8
57.1
0.0
37.1
75.0
7.1
56.5
0.0
24.8
86.7
7.1
69.2
75.2
86.0
5.5
86.0
People in the dementia categories having other medical conditions
Mean # of conditions
# Conditions SD
3.7
2.3
4.1
2.4
4.1
2.5
2.3
1.3
2.4
1.5
2.1
1.3
Mean # of prescribed meds.
Pres. Medications SD
4.5
3.4
5.2
4.4
6.2
4.7
Notel
Note2
4.5
2.6
3.0
2.1
Percentage of people in the various categories having other medical
conditions (if specified)
Arthritis
Diabetes
Hypertension
Heart disease
Stroke
Thyroid disease
Lung disease
56.1
13.4
40.9
21.5
52.4
18.2
41.7
22.8
50.3
19.6
37.1
21.8
70.0
19.0
58.0
33.0
10.0
13.0
16.0
74.0
25.0
55.0
38.0
19.0
9.0
9.0
58.0
20.0
44.0
33.0
26.0
7.0
16.0
20.6
59.9
12.8
7.2
23.4
52.9
10.6
7.8
37.1
20.4
7.2
3.6
38.8
21.7
6.6
6.1
Ml=84.6
26.8
26.9
Ml=70.9
39.1
16.5
Serious physical illness
Chronic Pain
High cholesterol
22.1
19.6
17.3
28.9
23.2
14.0
34.5
15.9
12.4
o
ro
Notes and abbreviations:
CIND = Cognitive impairment but no dementia
Ml = Myocardial infarction
SD = Standard deviation
Note 1: Percentage of sample taking: 0 prescription drugs-24.6; 1-4 drugs-61.3; 5+ drugs-27.2%
Note 2: Percentage of sample taking: 0 prescription drugs-38.3; 1-4 drugs-56.0; 5+ drugs-5.7%
-------
with dementia (both statistically significant at p<0.001
using a Wilcoxon rank sum test at a=0.05.
Dementia obviously affects what people do,
especially their leisurely activities. We could uncover
only one study that looked explicitly at leisure activities
in people who eventually developed dementia,
Verghese et al. (2003). They classified people as
having probable, possible, or mixed vascular dementia
using two schemes developed by the Alzheimer's
Disease and Related Disorders Association and the
Alzheimer's Disease Research Centers of California.
The frequency of participation by subjects with
dementia was classified as being "frequent" if the
person undertook the activity at least several times per
week and "rare" otherwise. There was no information
presented on the intensity, duration, or actual frequency
of the participation rate. The percentage of people with
dementia who frequently participated in selected
activities33 that might affect environmental exposures
follows.
Playing a musical instrument 3.2%
Dancing 20.2%
Housework 68.5%
Walking 84.7%
Climbing stairs 64.5%
Bicycling 5.6%
Swimming 12.9%
Team games 3.2%
Group exercises 29.0%
I could not find any other paper on this topic.
AP.D.2 Arthritis as the Reference Health Problem
Seniors with arthritis have other chronic
conditions that may affect their exposures, physiology,
or metabolism, usually at statistically significant higher
rates than people without arthritis. Song et al. (2006)
provide such data from the MRS; the data that follow
comes from the subset of 7,758 people aged. 65 who
did not have any ADL disability at baseline. In terms of
the percent of older adults with arthritis, 15.4% also
have diabetes, 26.2% have heart disease, 11.3 % have
pulmonary disease, 20.2% are obese, and 7.1% have
had a stroke. All of these conditions were statistically
significant higher than in seniors without arthritis, using
a x2 test at an a=0.05, except for stroke (6.5% for
seniors without arthritis).
AP.D.3 Alzheimer's Disease and Dementia
This section relates to AP.4.A, but the focus
there was dementia and other health problems. We
focus here on Alzheimer's as a type of dementia.
Bennett et al. (1999) provide estimates of the
proportion of older individuals having Alzheimer's in
three different longitudinal panel studies, called
"cohorts" in the paper. All three studies used the MMSE
test to define Alzheimer's. The proportion of cohorts
diagnosed with Alzheimer's varies widely among the
studies. One cohort is from the Chicago Health and
Aging Project (CHAP), a population-based study in a
biracial community; the average age of its participants
is 79.6 years ± 7.4; 52% were female, and 51% were
African-American. One-third of this cohort had
Alzheimer's. The second cohort is from the Religious
Orders Study (ROS), a longitudinal study of people over
65 who served as clergy (priests, nuns, and brothers) in
nine U.S. States. The mean age of this cohort was
76.6 years ± 7.0; 60.3% were female, and <1% were
African-American; 10.8% had dementia. The final study
evaluated was the Chicago-based Rush Alzheimer's
Disease Center (RADC) tertiary diagnostic and
treatment clinic. The mean age of this cohort was
77.1 years ± 6.0; 65.2% were female, 17.5% were
African-American, and 89.0% of this cohort had
Alzheimer's. The high percentage for the RADC
population is to be expected, because the facility treats
Alzheimer's patients.
Many older patients with Alzheimer's need full-
time care that must be provided by some type of
institution. (To avoid "double-counting" in our exposure
models, these people should be "removed"
quantitatively from the U.S. Census data on residences
and "placed" into the institutionalized category.) Sloane
et al. (2005) undertook a study of people with varying
degrees of dementia who already were in two types of
institutions for their malady. It was a longitudinal cohort
study of 1,252 residents with dementia in 106
"residential care/assisted living" facilities (RC/AL), often
known to the public as "group homes," and 40 nursing
homes (NH) in four States. Dementia classifications
were accomplished using the "Minimum Data Set
Cognition Scale" (MDS-COGS), roughly equivalent to
the MMSE. Other instruments were used to
classification depression, behavioral problems, and
social withdrawal. RC/AL units had statistically
significantly more cases of mild dementia (70.6%) than
NHs (50.7%; p<0.001), but the type of test used is not
provided). Conversely, NHs had more cases of
moderate or severe dementia.
Hospitalization rates for patients staying in
either type of facility were not significantly different:
12.6% for RC/ALs versus 10.1% for NHs, but, for those
residents of an RC/AL who then transferred from the
facility, the hospitalization rate was 29.2%. It is clear
that worsening dementia was partially responsible for
the hospitalization and subsequent relocation to a
higher level-of-care facility. Between 22% and 25% of
residents of an RC/AL unit will be transferred to a NH
per year. In a repeated measures Poisson regression
model, the per-year rate in worsening morbidity of
residents of either facility who stay within it is 21 % to
24%; for increasing ADL dependency, it is 4% to 6% for
people with mild dementia and about 1% for moderate
or severe dementia.
With respect to having a METS value substantially different
than a "sitting" METS score (i.e., a METS of 2.0 or higher).
103
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AP.E Definitions and Concepts Used in
This Appendix
3MS—Modified MMSE
ACT—Adult Changes in Thought study, a long-term
longitudinal study of aging and dementia in Seattle
AD—Alzheimer's Disease. Criteria listed in the joint
National Institute of Neurological and Communicative
Diseases and the Stroke-Alzheimer's Disease and
Relation Disorders Association are used to define it.
Other dementias are defined using DSM-IV criteria.
ADL—Activities of daily living. There are many versions
of this scale, with different items included and different
ways of scoring each dimension. Most include the
following factors, which often are scored from 0 to 4,
with 0 = complete dependency, cannot perform, to
4 = can perform independently. The scores usually are
summed across all the dimensions to obtain the overall
rating.
Daily Activity Dimensions
Engage in social activities
Household responsibilities
Personal care
Meals/feeding
Incontinence
Mobility
Mental acuity
Memory
Cognitive symptoms
AHEAD—Assets and Health Dynamics Among the
Oldest Old, a random-probability interview survey of
adults in the United States (see Wray et al., 2005a,b)
AI/AN—American Indian/Alaska native
APOE—Apoli-proprotein-E allele (genotype)
Arthritis—Inflammation of the joints and its effects. In
its acute form, arthritis is marked by pain, inflammation,
redness, and swelling, mostly in the joints. The impact
of arthritis is to limit movement. It often involves the
breakdown of cartilage surrounding bones of a joint.
The rubbing of bone against itself, gives rise to arthritis,
generally a chronic condition. There are three principal
forms: (1) osteoarthritis, (2) rheumatoid arthritis, and
(3) septic arthritis. The most common joint disorder is
osteoarthritis, having the symptoms listed above, but it
sometimes involves a bone spur. The cartilage
becomes rough because of wear over the years,
leading to pain, etc. Rheumatoid arthritis affects
females more than it does males, and it is an
autoimmune disorder. The connective tissue adjacent to
a joint becomes inflamed, and the immune defense
system works to reduce it but functions improperly,
thickening joint membranes and eroding cartilage and, if
it continues, bones, associated tendons, and ligaments.
Bursitis and tendonitis often are included as subsets of
arthritis or rheumatism (or both). HRS asked two
questions related to arthritis: (1) Has a doctor ever told
you, or have you ever had, arthritis or rheumatism
(bursitis/tendonitis included)? and (2) Do you
sometimes have pain, stiffness, or swelling in your
joints?
BLSA—Baltimore Long-Term Study on Aging (Johns
Hopkins)
BMI—Body mass index (weight in kilograms/height in
m**2)
Bronx Aging Study—See Verghese et al., 2003. The
study uses the following tests to define dementia: the
Blessed Test (Blessed Information-Memory-
Concentration Test), the Wechsler Adult Intelligence
Scale (verbal and performance IQ), the Fuld Object-
Memory Evaluation test, and the Zung Depression
Scale.
Cache County Study (Utah)—Described in Lyketsos
et al. (2005)
CAD—Coronary artery disease
California Verbal Learning Test (CVLT)—A test of
verbal-free cued recall, a sensitive test for cognitive
deficits associated with abnormal aging (Swan et al.,
1998)
CASI—Cognitive Ability Screening Instrument, a
screening test for cognitive function using a structured
interview
CC—Cardiac conditions: Myocardial infarction,
congestive heart failure, stroke, high blood pressure,
and diabetes
Center for Epidemiological Studies Depression
Scales (CES-D)—Described in Song et al. (2006)
CHAP—Chicago Health and Aging Project
CHD—Coronary heart disease
CHF—Congestive heart failure
CHS—Cardiovascular Health Study (Newman et al.,
2005)
Chronic bronchitis—Chronic inflammation of bronchi
resulting in cough, sputum production, and progressive
dyspnea
Chronic disability—A disability lasting or expected to
last at least 90 days according to a protocol that was
established by the National Long-Term Care Survey
(Gill and Gahbauer, 2006)
CIND—Cognitive impairment, nondementia
Color Trails Making Test—A test of visual attention
and scanning and graphomotor skills (Swan et al.,
1998)
Co-morbidity—Multiple health and/or mental
conditions, adverse health problems, or disabilities
COPD—Chronic obstructive pulmonary disease
104
-------
CVD—Cerebrovascular disease
Dementia—A general term that includes Alzheimer's
disease, vascular dementia, and "mixed dementia" (see
Older Adults, 1986)
Disability—Limitation in performance of socially
defined roles and tasks within a sociocultural and
physical environment (Vette, 2006)
DSRS—Dementia Severity Rating Scale: An 11-item
scale of signs and symptoms associated with dementia
(Lyketsosetal.,2005)
Emphysema—A chronic pulmonary disease
characterized by loss of lung function because of
destruction of alveolar or terminal bronchiole walls with
resultant enlargement of airspaces in the lung. The
total epithelial surface for gas exchange is reduced.
EPESE—Established Populations for Epidemiological
Studies of the Elderly; see Fried and Guralink (1997)
ERT—Estrogen replacement therapy
Functional limitation—Limitation in performance at the
level of the whole organism or person (Vette, 2006)
General Medical Health Rating (GMHR)—used by the
Johns Hopkins Hospital (Lyketsos et al., 1999)
HABCS—Health, Aging, and Body Composition Study
(Newman et al. 2003, 2005, 2006)
HBP—High blood pressure
Health and Retirement Study (HRS)—A national
probability study of noninstitutionalized older adults
undertaken by the University of Michigan and
sponsored by the National Institute of Aging
Heart problems—A general term usually including
heart attacks, coronary heart disease (CHD), angina,
and congestive heart failure (CHF)
IADL—Instrumental ADLs (Song et al., 2006). lADLs
include physical limitations (four tasks using lower and
upper extremities: [1] walking several blocks,
[2] climbing several flights of stairs without resting,
[3] pushing/pulling large objects, and [4] lifting/carrying
>10 Ib) and task limitations (five specific tasks:
[1] preparing hot meals, [2] going grocery shopping,
[3] using a telephone, [4] taking medications, and
[5] managing money).
ICD-9—International Classification of Diseases,
version 9
ICF—The International Classification of Functioning,
Disability, and Health, World Health Organization,
Switzerland (2001) (supersedes the ICIDH)
ICIDH—The International Classification of Impairments,
Disabilities, and Handicaps, World Health Organization,
Switzerland (1980)
ICL—Institute for Continued Learning
IFG—Impaired fasting glucose
IGT—Impaired glucose tolerance
Impairment—Anatomical, physiological, mental, or
emotional abnormalities (ICF definitions; Jette, 2006)
Informant Questionnaire for Cognitive Decline in
Elderly (IQCODE)—Cited in Jorm et al. (2007)
Iowa Screening Battery for Mental Disease—Three
tests assessing time orientation, visuospatial skills,
visual memory, and associative word fluency
LOSA—Longitudinal Study on Aging. Part of the
National Health Interview Study sponsored by NIH and
evaluated by the National Center for Health Statistics; it
basically is a subset of people aged 70 or older in the
1984 baseline period who were reinterviewed at 2-year
intervals.
LTC—Long-term care (facility)
MCI—Mild cognitive impairment
Metabolic Syndrome—A complex of health conditions
having the following symptoms: abdominal adiposity,
elevated triglycerides, low HDL-C, HBP, and high
fasting blood glucose
Ml—Myocardial infarction
Mild cognitive impairment—A nondemented elderly
person with isolated cognitive and minimal functional
impairment (Royall et al., 2005)
MMSE—Mini-Mental State Exam (see also SMS).
A 30-point test including questions on time and place
orientation, registration, attention, calculations, recall,
language, and visual construction. A score <23 signifies
significant cognitive impairment (Swan et al., 1998).
Modified Mini-Mental State Exam (3MS)—A MMSE
having itself two versions: one for sensory impaired and
another for not impaired individuals
MVPA—Moderate or vigorous physical activity
NGT—Normal glucose tolerance (tolerant)
NH—Nursing home
NHLBI—National Heart, Lung, and Blood Institute
NHIS—National Health Interview Survey
NLTCS—National Long-Term Care Survey
NMAPS—New Mexico Aging Process Study
NMF—No More Falls program
Obesity—BMI > 30 kg/m**2
Overweight—>25 but <30 BMI
PA—Physical activity
105
-------
Physical disability—In the MRS/AHEAD study, it is
measured by the sum of any difficulty (Y/N; 1/0) on
10 PA/ADL tasks. These include ADL (transferring,
dressing, bathing, toileting, and eating), mobility (lower
body) activities (walking across a room, walking several
blocks, and climbing stairs), and strength (upper body)
activities (pushing furniture and lifting 10 Ib). It seems
very similar to the IADL above.
Pulmonary diseases—Considered to be chronic
bronchitis and emphysema
RADC—Rush Alzheimer's Disease Center
RC/AL—Residential care with assisted living
ROS—Religious Orders Study
RPAHS—Regenstrief Physical Activity and Health
Survey
RVPA—Regular vigorous physical activity, including
sports, heavy housework, and physical labor job more
than three times per week (Song et al., 2006)
SPB—Systolic blood pressure
SPPARCS—Study of the Physical Performance and
Related Changes in Sonoman's project (Johns
Hopkins)
TIA—Transient ischemic attack
Underweight—<18.5 BMI
Wechsler Adult Intelligence Scale—A digit/symbol
substitution test (Swan et al., 1998)
Western Collaborative Group Study—A longitudinal
study of SBP over 30 years. It began in the early 1960s
as a prospective cardiovascular epidemiology study at
10 California corporations (Swan et al., 1998).
WHAS II—Women's Health and Aging Study
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