PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of amjepidLink to Publisher's site
 
Am J Epidemiol. 2009 June 15; 169(12): 1507–1516.
Published online 2009 April 21. doi:  10.1093/aje/kwp069
PMCID: PMC2733767

Activity Energy Expenditure and Mobility Limitation in Older Adults: Differential Associations by Sex

Abstract

In this study, the authors aimed to determine whether higher activity energy expenditure, assessed by using doubly labeled water, was associated with a reduced decline in mobility limitation among 248 older community-dwelling US adults aged 70–82 years enrolled in 1998–1999. Activity energy expenditure was calculated as total energy expenditure (assessed over 2 weeks by using doubly labeled water) minus resting metabolic rate (measured with indirect calorimetry), with adjustment for the thermic effect of food. Across sex-specific tertiles of activity energy expenditure, men in the lowest activity group experienced twice the rate of mobility limitation as men in the highest activity group (41% (n = 18) vs. 18% (n = 8)). Conversely, women in the lowest and highest activity groups exhibited similarly high rates of mobility limitation (40% (n = 16) vs. 38% (n = 15)). After adjustment for potential confounders, men with higher activity energy expenditure levels continued to show reduced risk of mobility limitation (per standard deviation (284 kcal/day): hazard ratio = 0.61, 95% confidence interval: 0.41, 0.92). Women showed no association (per standard deviation (226 kcal/day): hazard ratio = 1.34, 95% confidence interval: 0.98, 1.85). Greater energy expenditure from any and all physical activity was significantly associated with reduced risk of developing mobility limitation among men, but not among women.

Keywords: aging, disability evaluation, energy metabolism, exercise, mobility limitation, motor activity, sex

We previously demonstrated that higher energy expenditure from any physical activity is associated with lower mortality among older men and women (1). However, a highly salient dimension of health status for many older adults is not death but functional independence, particularly the ability to walk without limitation. The loss of mobility marks a critical stage in the disablement process, whereby the risk of dependence and mortality is elevated (2). Therefore, understanding how modifiable behaviors may help preserve mobility in later life is a critical step in improving the health of an aging society.

Total daily energy expenditure is composed of resting metabolic rate, postprandial thermogenesis, and activity energy expenditure (AEE). AEE can be further subdivided into 2 components, volitional exercise and nonexercise activity thermogenesis. Therefore, AEE is the energy expended during all physical activities, including those due to daily living, volitional exercise, fidgeting, and posture allocation (sitting, standing, and ambulation). AEE is highly variable between individuals (3), and a strong underlying genetic predisposition has been found (4). More importantly, however, AEE is largely affected by the physical environment and the choices individuals make in their lives (5). Thus, AEE is a modifiable health behavior.

AEE may help to regulate weight gain (6, 7) and is associated with total body fatness (8, 9), but few studies have evaluated how AEE contributes to specific health conditions likely because of the technical difficulty and expense involved in measuring free-living total energy expenditure in population-based samples. In addition, because AEE encompasses all activities an individual performs throughout the day, including planned and unintentional movement, self-report approaches to capturing this domain are inherently inadequate. Therefore, the potential health benefits of AEE have yet to be fully investigated.

In our previous work, we observed that volitional exercise (walking for exercise, bicycling, or swimming) did not distinguish older individuals with the highest AEE from those with the lowest AEE, thereby implicating everyday activity, and not simply exercise activity, as a strong contributor to reduced mortality risk (1). Since few older adults participate in organized physical activity programs or engage in intense exercise, it is important to examine the relation of overall activity to reduced onset of mobility limitation (10). To do so, we examined precursors of mobility limitation as measured by gait speed and self-reported mobility limitation to determine the longitudinal association between AEE and maintenance of mobility among adults aged 70–82 years.

MATERIALS AND METHODS

Study sample

In 1997–1998, investigators from the University of Pittsburgh and University of Tennessee, Memphis, recruited 3,075 participants aged 70–79 years from a random sample of white Medicare beneficiaries and all age-eligible, self-identified black community residents to participate in the Health, Aging and Body Composition (Health ABC) study. Eligibility criteria included self-reporting no difficulty walking one-quarter of a mile (0.4 km), climbing 10 stairs, or performing activities of daily living; no plans to leave the area for the next 3 years; and no evidence of life-threatening illnesses. The sample was approximately balanced regarding sex (51% women), and 42% of the participants were black.

An energy expenditure substudy carried out in 1998–1999 enrolled 323 participants and has been described in detail elsewhere (11, 12). Three hundred twenty-three participants, selected randomly within sex and race strata, participated in an energy expenditure substudy nested in the Health ABC study. Twenty-one participants were excluded from analysis because of failure to complete the protocol, lack of appropriate urine volume specimens, or failure of isotope or resting metabolic rate data to meet a priori quality control criteria, leaving an analytic sample of 302 participants (150 men and 152 women). Fifty-nine percent (n = 179) and 41% (n = 123) of subjects participated in the energy expenditure substudy in 1998 and 1999, respectively. Compared with the full Health ABC cohort, the energy expenditure substudy included 8% more blacks, but there were no differences in age, sex, gait speed, self-reported walking ability, or self-reported physical activity (e.g., walking, stair climbing, working, volunteering, and caregiving). Written informed consent, approved by the institutional review boards at the University of Pittsburgh and University of Tennessee, Memphis, was obtained from each participant.

Doubly labeled water protocol

Total energy expenditure was measured by using doubly labeled water. This procedure has been described in detail previously (11). Measurements were obtained at 2 visits separated by 2 weeks. At the first visit, participants ingested a 2 g/kg estimated total body water dose of doubly labeled water, composed of 1.9 g/kg estimated total body water of 10% oxygen-18 (H218O) and 0.12 g/kg estimated total body water of 99.9% deuterium (2H2O). After dosing, 3 urine samples were obtained at approximately 2, 3, and 4 hours. Two consecutive urine voids were obtained during a second visit to the laboratory, approximately 15 days after the first visit. Plasma from a 5 mL blood sample was obtained from everyone but considered for only those who had evidence of delayed isotopic equilibration likely caused by urine retention in the bladder (n = 28) (11). Urine and plasma samples were stored at −20oC until analysis by isotope ratio mass spectrometry.

Dilution spaces for doubly labeled water (2H and 18O) were calculated according to Coward (13). Total body water was calculated as the average of the dilution spaces of 2H and 18O after correction for isotopic exchange (1.041 for 2H and 1.007 for 18O). Carbon dioxide production was calculated by using the 2-point doubly labeled water method outlined by Schoeller et al. (14, 15), and total energy expenditure was derived by using Weir's equation (16) with a respiratory quotient of 0.86. All values of energy expenditure were converted to kilocalories per day, and the thermic effect of meals was assumed to be 10% of total energy expenditure (17). The intratester repeatability of total energy expenditure based on blinded, repeat, urine isotopic analysis was excellent (mean difference = 1.2% (standard deviation, 5.4), n = 16) and compared well with that in a recent review article (18).

Resting metabolic rate protocol

Resting metabolic rate was measured via indirect calorimetry on a Deltatrac II respiratory gas analyzer (Datex Ohmeda Inc., Helsinki, Finland). Detailed procedures have been described elsewhere (12). In brief, while a participant was in a fasting state and after 30 minutes of rest, a respiratory gas exchange hood was placed over the participant's head, and resting metabolic rate was measured minute by minute for 40 minutes. To avoid artifacts associated with the initial placement of the hood, only the final 30 minutes were used in subsequent calculations. Movement or sleeping during the test was noted, and those time periods were excluded from the resting metabolic rate calculation.

Activity energy expenditure

We calculated AEE as (total energy expenditure × 0.9) – resting metabolic rate to remove energy expenditure due to the thermic effect of meals and energy devoted to resting metabolism. Thus, AEE was defined as the number of calories an individual expends in any and all activities per day. Physical activity level calculated as total energy expenditure/resting metabolic rate is not presented in tabular format in this paper but was used for comparison purposes to verify the AEE results.

Gait speed and mobility limitation

Gait speed is an important determinant of difficulty performing mobility-related tasks (19) and was studied here as a precursor of mobility limitation. Gait speed was measured at the baseline doubly labeled water visit and annually over 3 years. Participants were asked to walk 20 m, from a standing start, as quickly as possible without running. Subjects were given standard instructions for completing the task. Timing started when the investigator said “go” and ended with the first footfall over the finish line. Speed was calculated by dividing the distance by the time required to complete the task. Subjects were permitted to use a walking aid if necessary. Gait speed test, as performed in this study, has been found to be extremely reliable (20).

We assessed mobility limitation by using questionnaires about walking ability that were administered semiannually, alternating between the annual clinic visits and telephone assessments. Mobility limitation was defined as 2 consecutive reports of any difficulty walking one-quarter of a mile. Follow-up was defined as the time from the second energy expenditure visit to the date that mobility limitation was first reported. The date of last contact or date of death was used to censor individuals who did not develop mobility limitation. Fifty-four subjects had reported mobility limitation prior to the energy expenditure visit and were excluded from the analysis (total analysis sample = 248). Individuals who had mobility limitation at baseline were generally in poorer health than those who either reported no difficulty or developed limitation during the follow-up. Persons reporting mobility limitation at baseline were more likely to be black (P = 0.001) and to self-report fair or poor health (P < 0.001), and they had a greater body mass index (P = 0.07), more prevalent diseases (P < 0.001), and a slower gait speed (P = 0.001) than individuals who reported no difficulty during the follow-up.

Self-reported physical activity

Physical activity over the past 7 days was assessed by an interviewer-administered questionnaire at the time of the doubly labeled water dosing. The questionnaire was modified from the College Alumnus Physical Activity Questionnaire to include tasks more applicable to older adults (21) (the questionnaire used for this study is available from the authors upon request). The reliability and validity of this type of questionnaire have previously been established in adults up to age 65 years (22, 23), and the questionnaire was designed to capture duration and intensity of both recreational activities (walking for exercise) and usual daily activities (walking for other than exercise, caregiving, climbing stairs, working for pay, and volunteering) across 7 domains. For example, the domain of walking for other than exercise included walking to work and shopping. Follow-up questions assessed intensity and duration over the past 7 days and were used to estimate energy expenditure (24). One question asked about caregiving, but only duration of the activity was assessed; thus; intensity was not included in the calculation of energy expenditure. Participants were also asked whether they performed high-intensity exercise such as bicycling, swimming, jogging, racquet sports, stair stepping, rowing, or cross country skiing, but information on duration and intensity was not collected. Self-report activities were also categorized as volitional exercise (walking for exercise and high-intensity exercise) or everyday activities to examine cumulative associations with AEE.

Other measurements

Self-reported health status (1–5 category score, excellent–poor), lean mass, body weight, and height were measured at the first energy expenditure visit. Lean mass was assessed by using dual energy x-ray absorptiometry (Hologic QDR 4500, software version 8.21; Hologic, Inc., Bedford, Massachusetts). Body weight was measured with the participant in a hospital gown with no shoes by using a calibrated balance beam scale, and height was measured with a stadiometer. Self-reported medical conditions, with confirmation by treatment and/or medication, were updated to the doubly labeled water visit in 1998–1999. These conditions included cardiovascular disease (hypertension, coronary heart disease, myocardial infarction, and stroke), lung disease (asthma, chronic bronchitis, emphysema, and chronic obstructive lung disease), diabetes, hip or knee osteoarthritis, osteoporosis, cancer, and depression. Disease conditions were summed to create a comorbidity index. Education (high school vs. no high school) and smoking behavior (current and former vs. never) were assessed during the first Health ABC annual clinic visit in 1997–1998. Cognitive status was assessed by using the Teng-modified Mini-Mental State Examination (25).

Data analysis

Preliminary data analysis revealed a strong interaction between sex and AEE on the primary outcome of self-reported mobility limitation (P = 0.006). Therefore, all data analyses were stratified by sex and were performed by considering the 248 individuals reporting, at the doubly labeled water visit, no difficulty walking one-quarter of a mile. For both descriptive and illustrative purposes, the data were categorized into sex-specific AEE tertiles. Statistical models were performed using the continuous form of AEE by computing values as standardized units (per standard deviation) within each sex. Participant characteristics, stratified by sex and tertile of AEE, were assessed by using analysis of variance for continuous variables and the χ2 statistic for categorical variables.

Longitudinal analyses were undertaken to evaluate whether AEE altered the trajectory of gait speed. Change in gait speed was examined in a series of linear mixed models, in which intercepts and slopes are permitted to differ between individuals and thus are often referred to as random effects (26). The first model included a term for time, which was calculated because of unequal spacing between the initial visit and follow-up, that was considered the age at the last clinic visit date subtracted from the age at the initial contact (baseline) divided by 365.25. This term indicates the mean annual linear change in gait speed for an average participant. In this model, the main effects of AEE, aging, and baseline age and an interaction term (AEE × time) were tested by using the following equation:

An external file that holds a picture, illustration, etc.
Object name is amjepidkwp069fx1_ht.jpg

where μ0i is the random intercept and μ1itimeit is the random slope. In this model, both the main effects and interactions of AEE with time were in their continuous form.

A second model was created to examine the effect of AEE on changes in gait speed while simultaneously adjusting for baseline covariates. Some of our covariates were chosen on the basis of previous observations that AEE levels are associated with lean mass, smoking status, self-reported health, and medical conditions and thus were accordingly added to the model. Additional covariates included race (black vs. white) and educational status.

Each model was estimated by using an unstructured error covariance matrix with Stata version 9.0 software and the xtmixed command (Stata Corporation, College Station, Texas). Goodness of fit for each model was examined with a plot of the residuals versus the primary predictor value of AEE. Scatter plots revealed no trends or correlations between the primary predictor and each outcome, and residuals were homoscedastic across the distribution of AEE values. We also found no evidence that the associations of interest were curvilinear, as demonstrated by nonsignificant quadratic terms (P > 0.25).

Cox proportional hazards models were used to test the association between AEE and onset of self-reported mobility limitation by sex. Similar to the gait speed analysis, an age-adjusted model was first estimated, with AEE predicting the onset of mobility limitation. Potential confounders were then entered in an adjusted model. The global proportional hazards assumption was confirmed by using Schoenfeld residuals (27). Cumulative incidence curves of mobility limitation were plotted by sex-specific tertiles of AEE for illustration purposes. The log-rank test was used to determine whether event rates differed across AEE tertiles.

Data from the self-reported physical activity questionnaires were nonnormally distributed. We expressed results as medians (25th and 75th percentiles) and performed a Kruskal-Wallis equality of populations rank test.

RESULTS

Table 1 lists the baseline characteristics of the total sample of individuals attending the doubly labeled water visit according to tertiles of AEE. For both men and women, body mass index and lean mass were similar across AEE tertiles, as were smoking status, mental status, educational level, self-reported health, and disease conditions.

Table 1.
Baseline Characteristics of Men and Women Enrolled in a US Study in 1997–1998, by Sex-specific Tertiles of Activity Energy Expenditure

For the gait speed analysis, data on 7 participants with fewer than 2 follow-up observations were removed from analysis. The remaining 241 participants were observed an average of 3.8 times, with a follow-up rate of 90% at year 4. Over 3 years of follow-up, gait speed declined an average of 0.37 m/second per year, and the amount of decline did not differ by sex (sex-by-study-year interaction P = 0.617). However, as illustrated in Figure 1, men in the highest tertile of AEE had a significantly faster gait speed at baseline compared with men in the lowest tertile, and these differences remained constant over time. Each standard-deviation increase in AEE translated into a 0.060 m/second increase (P = 0.008) in gait speed (Table 2). This relation was unaltered over time (Table 2). Women, on the other hand, showed a reduced association between AEE and gait speed (Figure 1), with a nonsignificant 0.029 m/second increase in gait speed for every standard-deviation increase in AEE. Adjusting for potential covariates did not alter these associations.

Table 2.
Predictors of Longitudinal Changes in Gait Speed of Men and Women Enrolled in a US Study in 1997–1998
Figure 1.
Longitudinal changes in rapid gait speed over 4 study years according to tertiles of activity energy expenditure in A) men (n = 125) and B) women (n = 116) enrolled in a US study in 1997–1998. Values are means and ...

Eighty of the 248 subjects (32%) developed mobility limitation over an average follow-up time of 3.8 years (range: 0.25–5.14). Eighteen men (41.8%) in the lowest tertile, 9 (20.9%) men in the middle tertile, and 8 (18.6%) men in the highest tertile of AEE reported mobility limitation over follow-up. Conversely, 16 (40%), 14 (35%), and 15 (38%) women in the lowest, middle, and highest AEE tertiles, respectively, reported mobility limitation. Figure 2 shows the cumulative incidence plots for onset of mobility limitation by AEE tertile for men and women. Log-rank tests comparing event rates showed significant differences across AEE tertiles among men, but not women. Cox proportional hazards models, in which AEE was examined as a standardized continuous variable for men and women, were used to adjust for potential confounders (Table 3). Among men, for every standard-deviation increase in AEE, the risk of reporting mobility limitation decreased by approximately 40%. This association was unaltered in multivariate-adjusted models and even after adjusting for baseline gait speed. Among women, standardized AEE showed no association, in either unadjusted or adjusted models, with risk of developing mobility limitation over the follow-up. Physical activity levels expressed per standard deviation were similar for men (hazard ratio = 0.62, 95% confidence interval: 0.40, 0.94) and women (hazard ratio = 1.33, 95% confidence interval: 0.97, 1.85).

Table 3.
Risk of Developing Mobility Limitation for Men and Women Enrolled in a US Study in 1997–1998
Figure 2.
Cumulative incidence plots of mobility limitation (defined as 2 consecutive reports of having difficulty walking one-quarter of a mile (0.4 km)) in A) men (n = 129) and B) women (n = 119) enrolled in a US study in 1997–1998, ...

Self-reported physical activity was assessed to determine whether the different associations observed for men and women resulted from differences in types of volitional activities pursued. As demonstrated in Table 4, the proportion of individuals who reported performing volitional exercise activities was similar across AEE tertiles, which was true for both men and women. Participants in the higher tertiles of AEE reported more time and energy performing everyday activities, and this association was similar for both men and women.

Table 4.
Self-reported Activities of Men and Women Enrolled in a US Study in 1997–1998, by Sex and Across Tertiles of Activity Energy Expenditure (n = 248)a

DISCUSSION

AEE, as assessed by using doubly labeled water, has been largely unexplored as a predictor of health outcomes. We found, in a relatively healthy sample of older men, that higher levels of AEE were associated with faster gait speed and reduced risk of developing difficulty walking one-quarter of a mile. These results did not extend to older women. Our results support the notion that physical activity, in general, is associated with reduced prevalence and incidence of disability and mobility limitation (2830) and, more importantly, add to the existing knowledge by suggesting that cumulative energy expended in all physical activities may help maintain mobility function and reduce mobility limitation in men. Unfortunately, it is not clear why women did not demonstrate these same benefits.

Difficulty performing activities that require mobility marks a serious decline in functional health that often leads to institutionalization (31, 32) and death (2). Therefore, identifying strategies that maintain mobility is an important step in the effort to preserve functional independence with increasing age. For example, physical inactivity has been consistently associated with disability (3336), and increasing physical activity has been found to improve gait speed and functional performance (37). Additionally, increasing physical activity reduces the impact of morbid conditions, such as osteoarthritis, that are pivotal in the disablement process (38). What makes our study different from past research is that AEE encompasses not only purposeful exercise and physical activity but also spontaneous, nondirected movement (fidgeting-like activity) and activities with very low workloads. In fact, in an assessment of the type of activity that influences AEE, only low- and moderate-intensity activity showed a relation (39), presumably because the time spent on high-intensity exercise was very short. This distinction sets AEE apart from volitional exercise activity that is often studied.

Physical activity levels have long been known to be associated with reduced risk of adverse health outcomes (21, 40). In general, the magnitude of this benefit seems to be similar for both men and women (41). However, most of these studies, including our own previous work (1), have examined all-cause mortality or cause-specific mortality in relation to physical activity (4245). The mechanisms underlying the beneficial effects of physical activity on mobility may differ from those impacting mortality. The few studies in this area have predominately relied on individual self-report of physical activity, with most showing a graded reduction in mobility limitation with increasing levels of physical activity (28, 30, 46, 47). Notably, one study using a combination of pedometers and questionnaires to assess physical activity showed that women who were consistently active versus those whose activity levels were sporadic over a 14-year time period had better physical performance and a lower risk of reporting difficulty performing activities of daily living (48). Additionally, several clinical trials have demonstrated that increasing physical activity through moderate-intensity walking results in improved gait speed, with results being consistent across sex (37, 49, 50).

We examined several potential reasons for the unexpected sex-specific associations. Firstly, compared with men, women may have more chronic health conditions that may limit the potential biologic effects of AEE. Secondly, women may engage in more low-intensity activities that may have only a small impact on mobility limitation. Each of these potential reasons was evaluated across AEE tertiles and found not to explain our findings. For example, women and men had a similar number of health conditions across AEE tertiles, and women were no more likely than men to perform high-intensity exercise across AEE tertiles. Since these efforts did not explain our findings, activity levels assessed by using the doubly labeled water technique, which captures all daily activities including very low intensity movements, may have sex-specific effects on health conditions of the elderly.

A limitation of this study is potential residual confounding that occurs between AEE and health status in the elderly. We do not think that AEE is completely reflective of health status because the correlation between AEE and scores on self-reported health questionnaires is very low (r < 0.15, P > 0.10). We also attempted to account for these confounding effects by adjusting for several common disease conditions along with gait speed, a parameter that captures many degenerating physiologic pathways not reflected by specific health conditions.

In conclusion, greater energy expended during all activities of the day was associated with faster gait speed and reduced risk of mobility limitation among older men. However, we found no effect among older women.

Acknowledgments

Author affiliations: Department of Aging and Geriatric Research, University of Florida, Gainesville, Florida (Todd M. Manini); Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland (Kushang V. Patel, Tamara B. Harris); Epidemiology and Data Systems Program, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland (James E. Everhart); Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin (Dale A. Schoeller); Department of Kinesiology, University of Wisconsin-Madison, Madison, Wisconsin (Lisa H. Colbert); Faculty of Earth and Life Sciences, Institute of Health Sciences VU University and Institute for Research in Extramural Medicine, VU University Medical Center, Amsterdam, the Netherlands (Marjolein Visser); Department of Biostatistics and Epidemiology, University of Tennessee, Memphis, Memphis, Tennessee (Frances Tylavsky); Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania (Anne B. Newman); San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California (Steve Cummings, Dawn C. Mackey); Department of Medicine, University of California, San Francisco, San Francisco, California (Douglas C. Bauer); and Clinical Research Branch, National Institute on Aging, Clinical Research Branch, Bethesda, Maryland (Eleanor M. Simonsick).

This work was supported by National Institute on Aging contracts NO1-AG-6-2101, NO1-AG-6-2103, and NO1-AG-6-2106. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging.

Conflict of interest: none declared.

Glossary

Abbreviations

AEE
activity energy expenditure
Health ABC
Health, Aging and Body Composition

References

1. Manini TM, Everhart JE, Patel KV, et al. Daily activity energy expenditure and mortality among older adults. JAMA. 2006;296(2):171–179. [PubMed]
2. Hirvensalo M, Rantanen T, Heikkinen E. Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population. J Am Geriatr Soc. 2000;48(5):493–498. [PubMed]
3. Ravussin E, Lillioja S, Anderson TE, et al. Determinants of 24-hour energy expenditure in man. Methods and results using a respiratory chamber. J Clin Invest. 1986;78(6):1568–1578. [PMC free article] [PubMed]
4. Joosen AM, Gielen M, Vlietinck R, et al. Genetic analysis of physical activity in twins. Am J Clin Nutr. 2005;82(6):1253–1259. [PubMed]
5. Levine JA, Kotz CM. NEAT—non-exercise activity thermogenesis—egocentric & geocentric environmental factors vs. biological regulation. Acta Physiol Scand. 2005;184(4):309–318. [PubMed]
6. Levine JA, Eberhardt NL, Jensen MD. Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science. 1999;283(5399):212–214. [PubMed]
7. Levine JA, Lanningham-Foster LM, McCrady SK, et al. Interindividual variation in posture allocation: possible role in human obesity. Science. 2005;307(5709):584–586. [PubMed]
8. Esparza J, Fox C, Harper IT, et al. Daily energy expenditure in Mexican and USA Pima Indians: low physical activity as a possible cause of obesity. Int J Obes Relat Metab Disord. 2000;24(1):55–59. [PubMed]
9. Rising R, Harper IT, Fontvielle AM, et al. Determinants of total daily energy expenditure: variability in physical activity. Am J Clin Nutr. 1994;59(4):800–804. [PubMed]
10. US Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Government Printing Office; 2000.
11. Blanc S, Colligan AS, Trabulsi J, et al. Influence of delayed isotopic equilibration in urine on the accuracy of the (2)H(2)(18)O method in the elderly. J Appl Physiol. 2002;92(3):1036–1044. [PubMed]
12. Blanc S, Schoeller DA, Bauer D, et al. Energy requirements in the eighth decade of life. Am J Clin Nutr. 2004;79(2):303–310. [PubMed]
13. Coward WA. Calculation of pool sizes and flux rates. In: Prentice AM, editor. The Doubly Labeled Water Method: Technical Recommendations for Use in Humans. Report of an IDECG Expert Working Group. Vienna, Austria: AERA; 1990. pp. 45–53.
14. Schoeller DA, Ravussin E, Schutz Y, et al. Energy expenditure by doubly labeled water: validation in humans and proposed calculation. Am J Physiol. 1986;250(5 pt 2):R823–R830. [PubMed]
15. Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol. 1982;53(4):955–959. [PubMed]
16. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109(1–2):1–9. [PubMed]
17. Bloesch D, Schutz Y, Breitenstein E, et al. Thermogenic response to an oral glucose load in man: comparison between young and elderly subjects. J Am Coll Nutr. 1988;7(6):471–483. [PubMed]
18. Elia M, Ritz P, Stubbs RJ. Total energy expenditure in the elderly. Eur J Clin Nutr. 2000;54(suppl 3):S92–S103. [PubMed]
19. Guralnik JM, Ferrucci L, Pieper CF, et al. Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci. 2000;55(4):M221–M231. [PubMed]
20. Simonsick EM, Gardner AW, Poehlman ET. Assessment of physical function and exercise tolerance in older adults: reproducibility and comparability of five measures. Aging (Milan) 2000;12(4):274–280. [PubMed]
21. Paffenbarger RS, Jr, Wing AL, Hyde RT. Physical activity as an index of heart attack risk in college alumni. Am J Epidemiol. 1978;108(3):161–175. [PubMed]
22. Ainsworth BE, Leon AS, Richardson MT, et al. Accuracy of the College Alumnus Physical Activity Questionnaire. J Clin Epidemiol. 1993;46(12):1403–1411. [PubMed]
23. Washburn RA, Smith KW, Goldfield SR, et al. Reliability and physiologic correlates of the Harvard Alumni Activity Survey in a general population. J Clin Epidemiol. 1991;44(12):1319–1326. [PubMed]
24. Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc. 1993;25(1):71–80. [PubMed]
25. Teng EL, Chui HC. The Modified Mini-Mental State (3MS) examination. J Clin Psychiatry. 1987;48(8):314–318. [PubMed]
26. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963–974. [PubMed]
27. Schoenfeld D. Partial residuals for the proportional hazard regression model. Biometrika. 1982;69:239–241.
28. Brach JS, Simonsick EM, Kritchevsky S, et al. The association between physical function and lifestyle activity and exercise in the Health, Aging and Body Composition study. J Am Geriatr Soc. 2004;52(4):502–509. [PubMed]
29. Patel KV, Coppin AK, Manini TM, et al. Midlife physical activity and mobility in older age: the InCHIANTI study. Am J Prev Med. 2006;31(3):217–224. [PMC free article] [PubMed]
30. Visser M, Simonsick EM, Colbert LH, et al. Type and intensity of activity and risk of mobility limitation: the mediating role of muscle parameters. J Am Geriatr Soc. 2005;53(5):762–770. [PubMed]
31. Foley DJ, Ostfeld AM, Branch LG, et al. The risk of nursing home admission in three communities. J Aging Health. 1992;4(2):155–173. [PubMed]
32. Mor V, Wilcox V, Rakowski W, et al. Functional transitions among the elderly: patterns, predictors, and related hospital use. Am J Public Health. 1994;84(8):1274–1280. [PubMed]
33. Buchner DM, Beresford SA, Larson EB, et al. Effects of physical activity on health status in older adults. II. Intervention studies. Annu Rev Public Health. 1992;13:469–488. [PubMed]
34. Hubert HB, Bloch DA, Oehlert JW, et al. Lifestyle habits and compression of morbidity. J Gerontol A Biol Sci Med Sci. 2002;57(6):M347–M351. [PubMed]
35. Spirduso WW, Cronin DL. Exercise dose-response effects on quality of life and independent living in older adults. Med Sci Sports Exerc. 2001;33(6 suppl):S598–S608. discussion S609–S610. [PubMed]
36. Stuck AE, Walthert JM, Nikolaus T, et al. Risk factors for functional status decline in community-dwelling elderly people: a systematic literature review. Soc Sci Med. 1999;48(4):445–469. [PubMed]
37. LIFE Study Investigators. Pahor M, Blair SN, et al. Effects of a physical activity intervention on measures of physical performance: results of the lifestyle interventions and independence for Elders Pilot (LIFE-P) study. J Gerontol A Biol Sci Med Sci. 2006;61(11):1157–1165. [PubMed]
38. Messier SP, Loeser RF, Miller GD, et al. Exercise and dietary weight loss in overweight and obese older adults with knee osteoarthritis: the Arthritis, Diet and Activity Promotion Trial (ADAPT) Arthritis Rheum. 2004;50(5):1501–1510. [PubMed]
39. Westerterp KR. Pattern and intensity of physical activity. Nature. 2001;410(6828):539. [PubMed]
40. Paffenbarger RS, Jr, Hyde RT, Wing AL, et al. The association of changes in physical-activity level and other lifestyle characteristics with mortality among men. N Engl J Med. 1993;328(8):538–545. [PubMed]
41. Oguma Y, Sesso HD, Paffenbarger RS, Jr, et al. Physical activity and all cause mortality in women: a review of the evidence. Br J Sports Med. 2002;36(3):162–172. [PMC free article] [PubMed]
42. Gregg EW, Cauley JA, Stone K, et al. Relationship of changes in physical activity and mortality among older women. JAMA. 2003;289(18):2379–2386. [PubMed]
43. Wannamethee SG, Shaper AG, Walker M. Changes in physical activity, mortality, and incidence of coronary heart disease in older men. Lancet. 1998;351(9116):1603–1608. [PubMed]
44. Wannamethee SG, Shaper AG, Walker M. Physical activity and mortality in older men with diagnosed coronary heart disease. Circulation. 2000;102(12):1358–1363. [PubMed]
45. Weller I, Corey P. The impact of excluding non-leisure energy expenditure on the relation between physical activity and mortality in women. Epidemiology. 1998;9(6):632–635. [PubMed]
46. Koster A, Patel KV, Visser M, et al. Joint effects of adiposity and physical activity on incident mobility limitation in older adults. J Am Geriatr Soc. 2008;56(4):636–643. [PubMed]
47. Koster A, Penninx BW, Newman AB, et al. Lifestyle factors and incident mobility limitation in obese and non-obese older adults. Obesity (Silver Spring) 2007;15(12):3122–3132. [PubMed]
48. Brach JS, FitzGerald S, Newman AB, et al. Physical activity and functional status in community-dwelling older women: a 14-year prospective study. Arch Intern Med. 2003;163(21):2565–2571. [PubMed]
49. Ettinger WH, Jr, Burns R, Messier SP, et al. A randomized trial comparing aerobic exercise and resistance exercise to a health education program on physical disability in older people with knee osteoarthritis. The Fitness Arthritis and Seniors Trial (FAST) JAMA. 1997;277(1):25–31. [PubMed]
50. Kovar PA, Allegrante JP, MacKenzie CR, et al. Supervised fitness walking in patients with osteoarthritis of the knee. A randomized, controlled trial. Ann Intern Med. 1992;116(7):529–534. [PubMed]

Articles from American Journal of Epidemiology are provided here courtesy of Oxford University Press