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Original Contribution |

Daily Activity Energy Expenditure and Mortality Among Older Adults FREE

Todd M. Manini, PhD; James E. Everhart, MD, MPH; Kushang V. Patel, PhD, MPH; Dale A. Schoeller, PhD; Lisa H. Colbert, PhD; Marjolein Visser, PhD; Frances Tylavsky, PhD; Douglas C. Bauer, MD; Bret H. Goodpaster, PhD; Tamara B. Harris, MD
[+] Author Affiliations

Author Affiliations: National Institute on Aging, Laboratory of Epidemiology, Demography and Biometry (Drs Manini, Patel, and Harris) and National Institute of Diabetes and Digestive and Kidney Diseases (Dr Everhart), Bethesda, Md; Departments of Nutritional Sciences (Dr Schoeller) and Kinesiology (Dr Colbert), University of Wisconsin, Madison; Institute of Health Sciences, Faculty of Earth and Life Sciences, VU University and Institute for Research in Extramural Medicine, VU University Medical Center, Amsterdam, the Netherlands (Dr Visser); Department of Biostatistics and Epidemiology, University of Tennessee, Memphis (Dr Tylavsky); Department of Medicine, University of California, San Francisco (Dr Bauer); and Division of Endocrinology and Metabolism, University of Pittsburgh Medical Center, Pittsburgh, Pa (Dr Goodpaster).

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JAMA. 2006;296(2):171-179. doi:10.1001/jama.296.2.171.
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Context Exercise is associated with mortality benefits but simply expending energy through any activity in an individual's free-living environment may confer survival advantages.

Objective To determine whether free-living activity energy expenditure is associated with all-cause mortality among older adults.

Design, Setting, and Participants Free-living activity energy expenditure was assessed in 302 high-functioning, community-dwelling older adults (aged 70-82 years). Total energy expenditure was assessed over 2 weeks using doubly labeled water. Resting metabolic rate was measured using indirect calorimetry and the thermic effect of meals was estimated at 10% of total energy expenditure. Free-living activity energy expenditure was calculated as: (total energy expenditure × 0.90) − resting metabolic rate. Participants were followed up over a mean of 6.15 years (1998-2006).

Main Outcome Measures Free-living activity energy expenditure (3 tertiles: low, <521 kcal/d; middle, 521-770 kcal/d; high, >770 kcal/d) and all-cause mortality.

Results Fifty-five participants (18.2%) died during follow-up. As a continuous risk factor, an SD increase in free-living activity energy expenditure (287 kcal/d) was associated with a 32% lower risk of mortality after adjusting for age, sex, race, study site, weight, height, percentage of body fat, and sleep duration (hazard ratio, 0.68; 95% confidence interval, 0.48-0.96). Using the same adjustments, individuals in the highest tertile of free-living activity energy expenditure were at a significantly lower mortality risk compared with the lowest tertile (hazard ratio, 0.31; 95% confidence interval, 0.14-0.69). Absolute risk of death was 12.1% in the highest tertile of activity energy expenditure vs 24.7% in the lowest tertile; absolute risks were similar to these for tertiles of physical activity level. The effect of free-living activity energy expenditure changed little after further adjustment for self-rated health, education, prevalent health conditions, and smoking behavior. According to self-reports, individuals expending higher levels of free-living activity energy were more likely to work for pay (P = .004) and climb stairs (P = .01) but self-reported high-intensity exercise, walking for exercise, walking other than for exercise, volunteering, and caregiving did not differ significantly across the activity energy expenditure tertiles.

Conclusions Objectively measured free-living activity energy expenditure was strongly associated with lower risk of mortality in healthy older adults. Simply expending energy through any activity may influence survival in older adults.

Figures in this Article

Observational studies have shown that older adults who report low physical activity levels are at elevated risk of mortality compared with those who report moderate or high levels of activity.16 These findings were based on questionnaire assessments of physical activity, which are subject to recall bias,7,8 are unable to account for free-living activity,9 and typically overestimate actual amounts of physical activity.10,11 Furthermore, self-reported physical activity does not provide accurate estimates of absolute amounts of activity (kilocalories per day) and thus cannot be evaluated to determine whether higher levels of activity-induced energy expenditure confer survival advantages.10

The most accurate and precise method of determining free-living energy expenditure uses water labeled with the stable isotopes of 2H and 18O (doubly labeled water).12 When ingested, 2H is eliminated as water and 18O is eliminated as water and carbon dioxide. The excess disappearance rate of 18O relative to 2H is a measure of the carbon dioxide production rate, a direct measure of total energy expenditure. Therefore, over a standard amount of time individuals who have less 18O relative to 2H expend more energy. After accounting for resting metabolic rate and energy from the thermic effect of meals, an objective estimate of energy expended through free-living activity is derived. This technique allows for estimation of activity in an individual's normal environment over approximately 2 weeks. This method is valid when compared with energy expenditure in respiratory chambers13 and has excellent repeatability in younger and older adults14; therefore it is considered the gold standard measure of free-living activity energy expenditure.9,12

The doubly labeled water method captures any form of physical activity ranging from purposeful exercise to simple fidgeting, whereas physical activity questionnaires generally address basic volitional activities (eg, household chores, walking, and vigorous exercise). Although this method can address whether higher levels of free-living activity energy expenditure are associated with mortality risk, to our knowledge no studies have been conducted to determine whether free-living activity energy expenditure assessed in this way is related to longevity among older adults. The purpose of this study was to determine the association of free-living activity energy expenditure, measured using doubly labeled water coupled with resting metabolic rate, with all-cause mortality in a group of high-functioning, community-dwelling older adults.

Study Sample

In 1997-1998, investigators from the University of Pittsburgh (Pittsburgh, Pa) and the University of Tennessee (Memphis) recruited 3075 participants aged 70 to 79 years from a random sample of white Medicare beneficiaries and all age-eligible black community residents to participate in the Health, Aging, and Body Composition (Health ABC) study. Eligibility criteria included self-reporting no difficulty in walking a distance of 0.4 km or climbing at least 10 stairs, independently performing activities of daily living, plans to live in the area for the next 3 years, and no evidence of life-threatening illnesses. The sample was approximately balanced for sex (51% women) and 42% of participants were black. Participants self-designated race/ethnicity from a fixed set of options (Asian/Pacific Islander, black/African American, white/Caucasian, Latino/Hispanic, do not know, other). Blacks were oversampled to ensure adequate numbers to examine whether results varied by race because of differences such as body composition between blacks and whites.

An energy expenditure substudy carried out in 1998-1999 enrolled 323 individuals and has been described in detail elsewhere.15,16 A total of 323 individuals were contacted based on random selection within sex and race and were asked to participate in a study on energy expenditure. Individuals who volunteered were paid. Twenty-one individuals were excluded from this analysis because of failure to complete the protocol, lack of appropriate urine volume specimens, or lack of isotope or resting metabolic rate data to meet a priori criteria, leaving an analytic sample of 302 individuals (150 men and 152 women). Sixty percent (n = 179) and 40% (n = 123) of individuals 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, mobility function (measured as gait speed), self-reported walking ability, or self-reported physical activity (eg, walking, stair climbing, working, volunteering, and caregiving). Written, informed consent was obtained from each participant and was approved by the institutional review boards at the University of Pittsburgh and the University of Tennessee.

Doubly Labeled Water Protocol

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

Dilution spaces for 2H and 18O were calculated according to the method by Coward.17 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 using the 2-point doubly labeled water method outlined by Schoeller et al13,18 and total energy expenditure was derived using the equation by Weir19 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.20 The within-subject repeatability of total energy expenditure was based on blinded, repeated, urine isotopic analysis and was excellent (mean [SD], 1.2% [5.4%]; n = 16) and compared well with rates given in a review article.14

Resting Metabolic Rate Protocol

Resting metabolic rate was measured via indirect calorimetry using a Deltatrac II respiratory gas analyzer (Datex Ohmeda Inc, Helsinki, Finland) and has been described in detail elsewhere.16 While in a fasting state and after 30 minutes of rest, a respiratory gas exchange hood was placed over the individual's head and the resting metabolic rate was measured for 40 minutes. To avoid a gas exchange created by 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 values were excluded from the resting metabolic rate calculation.

Free-Living Activity Energy Expenditure

Free-living activity energy expenditure was expressed in 2 ways.21 Activity energy expenditure was calculated as (total energy expenditure × 0.90) − resting metabolic rate; removing energy expenditure from the thermic effect of meals and subtracting energy devoted to basal metabolism. Activity energy expenditure was defined as the amount of kilocalories an individual expends in any activity per day. Physical activity level was calculated as total energy expenditure/resting metabolic rate. The division of total energy expenditure by resting metabolic rate, a major determinate of which is lean mass, adjusts for differences in body composition (in part reflecting weight and sex).22 This formula was adopted by the Food and Agriculture Organization, the World Health Organization, and the United Nations University.23 These agencies have developed physical activity level categories (sedentary: 1.40-1.69; active, 1.70-1.99; vigorous activity, 2.00-2.40) but they were not used in this study because of the paucity of data to support their validity in older adults. Activity energy expenditure and physical activity level are highly correlated (r = 0.91) but offer different advantages (eg, simplicity of expression and inherently controlling for differences in body composition, respectively).

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. Questions about walking for exercise, other walking, climbing stairs, working for pay, and volunteering were assessed for both duration and intensity. Another question asked about caregiving but only the duration of the activity was assessed. The duration and intensity level of these activities were used to estimate energy expenditure with established metabolic equivalent values for each activity.24 An additional question asked whether participants performed high-intensity exercise such as bicycling, swimming, jogging, racquet sports, climbing stairs, rowing, or cross-country skiing but information on duration and intensity were not collected.

Mortality

Vital status was ascertained by telephone contact every 6 months over an 8-year period (1998-2006). Date of death was verified with death certificates and survival time was defined as the time of the second energy expenditure visit to the date of death or date of last contact. There were too few deaths to assess cause-specific mortality.

Other Measurements

Self-reported health status (category score of 1-5 corresponding with categories of excellent to poor), body fat, body weight, and height were measured at the first energy expenditure visit. Body fat was assessed using dual-energy x-ray absorptiometry (QDR-4500, version 8.21, Hologic Inc, Bedford, Mass) and calculated as the ratio of body fat mass to total mass (percentage of body fat). Body weight was measured while the individual was wearing a hospital gown (no shoes) with a calibrated balance beam scale. Height was measured with a stadiometer. The following self-reported medical conditions were confirmed by treatment and/or medication: cardiovascular disease (hypertension, coronary heart disease, myocardial infarction, and stroke), lung disease, diabetes, hip or knee osteoarthritis, osteoporosis, cancer, and depression and were updated at the doubly labeled water visit in 1998-1999. Education (<high school, high school graduate, and >high school), smoking behavior (never, former, or current), and sleep duration were assessed during the first Health ABC annual clinic visit in 1997-1998.

Data Analysis

Participant characteristics were assessed using analysis of variance for continuous variables and the χ2 statistic for categorical variables. Because self-reported physical activity was not normally distributed, these data were expressed with medians and interquartile ranges and analyzed using the Kruskal-Wallis equality-of-populations rank test. Cox proportional hazard models were used to test the association between activity energy expenditure (activity energy expenditure and physical activity level) and all-cause mortality. We first tested for a continuous association by entering activity energy expenditure and physical activity level into the models as standardized units (per SD). To further evaluate linearity, we added a quadratic term (activity energy expenditure2 or physical activity level2) to each model to test for curvilinear associations but these were removed because they were not statistically significant. Activity energy expenditure and physical activity level were also coded into tertiles with the lowest tertile serving as the reference group. Model 1 was estimated after adjusting for age, sex, race, and study site. Model 2 was adjusted for the factors in model 1 plus weight, height, percentage of body fat, and sleep duration. Model 3 was adjusted for the factors in model 2 plus self-reported health, education, smoking status and history, cardiovascular disease, lung disease, diabetes, hip or knee osteoarthritis, osteoporosis, cancer, and depression. The proportional hazards assumption was confirmed in all independent variables using Schoenfeld residuals.25 Kaplan-Meier survival plots were analyzed using the log-rank test for trend, using the expected and observed survivor functions for each group over the entire follow-up time to calculate a test χ2 statistic. Stata statistical software version 9.0 (StataCorp, College Station, Tex) was used for all analyses. Results were considered statistically significant at P≤.05.

Table 1 lists descriptive baseline characteristics for all participants and by tertile of activity energy expenditure. There were no differences in the distribution of age, race, educational level achieved, smoking status, self-reported fair or poor health, and total number of diseases between the activity energy expenditure tertiles. Prevalence of individual health conditions was similar across the activity energy expenditure tertiles. Women had lower levels of activity energy expenditure than men (mean [SD], 576 [251] kcal/d vs 769 [289] kcal/d; P<.01) but similar levels of physical activity (mean [SD], 1.68 [0.25] vs 1.72 [0.23]; P = .13). Persons in the highest tertile of activity energy expenditure were more likely to be from Pittsburgh and had a higher body weight, body mass index (calculated as weight in kilograms divided by height in meters squared), and lower percentage of body fat. Except for percentage of body fat (P = .56), all differences remained unchanged when adjusting for the sex imbalance across activity energy expenditure tertiles.

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Participants by Tertile of Activity Energy Expenditure*

Over an average of 6.15 years (range, 0.21-7.53 years) of follow-up, 32 men (35.6 per 1000 person-years) and 23 women (23.9 per 1000 person-years) died with an overall cumulative mortality of 18.2%, which was similar to mortality patterns in the Health ABC cohort overall (43.2 per 1000 person-years for men and 27.3 per 1000 person-years for women).

The effects of activity energy expenditure and physical activity level as continuous variables with an added quadratic term were evaluated first. There was no evidence of a curvilinear association and therefore the quadratic term was removed from the model ([activity energy expenditure2], P = .95; [physical activity level2], P = .91 using model 1 covariates). When expressed as SD units in model 2 (Table 2), higher levels of activity energy expenditure and physical activity were associated with lower mortality risk (every 287 kcal/d for activity energy expenditure: hazard ratio [HR], 0.68 [95% confidence interval {CI}, 0.48-0.96]; every 0.24 for physical activity level: HR, 0.66 [95% CI, 0.47-0.93]). The estimates changed little when factors related to mortality risk were added to model 3.

Table Graphic Jump LocationTable 2. Free-Living Activity Energy Expenditure and Risk of All-Cause Mortality

Unadjusted and adjusted Kaplan-Meier survival estimates for activity energy expenditure and physical activity level stratified by tertiles are plotted in the Figure. Cox proportional hazard modeling was used to adjust for potential confounders (Table 2).There was no activity energy expenditure or physical activity level tertile interaction by sex (P = .58 and P = .78, respectively) or race (P = .61 and P = .48, respectively). Compared with the lowest activity energy expenditure tertile, those in the highest tertile had a lower risk of mortality (model 2: HR, 0.31; 95% CI, 0.14-0.69). Similar results were observed with physical activity level (HR, 0.40; 95% CI, 0.19-0.81). The absolute risk of mortality was 12.1% in the highest tertile of activity energy expenditure, 17.6% in the middle, and 24.7% in the lowest tertile. For physical activity level, risk of mortality was 12.0% in the highest tertile, 17.8% in the middle, and 24.7% in the lowest. The effects changed little after adjusting for smoking status, educational level, self-rated health, and prevalent health conditions.

Figure. Kaplan-Meier Survival Plots and Mortality Rates by Tertiles of Free-Living Activity Energy Expenditure and Physical Activity Level
Graphic Jump Location

The log-rank and trend tests were used to determine the equality of survivor functions between the tertiles. To calculate activity energy expenditure in kcal/d: (total energy expenditure × 0.09) − resting metabolic rate. To calculate physical activity level: total energy expenditure/resting metabolic rate.

Self-reported physical activity was assessed to determine whether individuals with higher levels of free-living activity energy expenditure reported more physical activity (Table 3). The proportion of participants reporting high-intensity exercise, walking for exercise, other walking, volunteering, or caregiving did not differ across the activity energy expenditure tertiles. However, participants with higher free-living activity energy expenditure were significantly more likely to report climbing stairs and working for pay. Additionally, individuals with higher free-living activity energy expenditure self-reported longer duration and expended a greater number of kilocalories in total activity.

Table Graphic Jump LocationTable 3. Self-Reported Activities Across Free-Living Activity Energy Expenditure Tertiles*

Several sensitivity analyses were performed using standardized units of activity energy expenditure (per SD of activity energy expenditure using model 3 covariates). After excluding individuals who died within the first year of follow-up, the strength and direction of the effect of activity energy expenditure on mortality was comparable with the original associations shown in Table 2 (after subtracting 4 deaths: HR, 0.75; 95% CI, 0.52-1.08). To test whether the effects were influenced by extreme activity levels, individuals with low-activity energy expenditure (20th percentile, after subtracting 15 deaths: HR, 0.79; 95% CI, 0.51-1.22) and high-activity energy expenditure (80th percentile, after subtracting 10 deaths: HR, 0.57; 95% CI, 0.31-0.97) were removed the analysis. Finally, data from individuals with 2 or more prevalent health conditions were analyzed to determine whether the effects remained in older adults with comorbidities (after subtracting 22 deaths: HR, 0.73; 95% CI, 0.47-1.13). Despite losing statistical power, results from these sensitivity analyses were consistent with the results in Table 2.

Higher free-living activity energy expenditure demonstrated a strong association with lower risk of mortality among older adults. The major strength of this study is the direct measurement of both total energy expenditure and resting metabolic rate resulting in an objective evaluation of free-living activity energy expenditure in a biracial sample of older adults.

The association of physical activity and mortality has been studied more often in younger adults than in older adults (>70 years). Investigation of modifiable risk factors for disease or disability in older adults is important for this growing segment of the population that contributes disproportionately to health care costs.26 The few longitudinal studies of older adults that use self-reported physical activity questionnaires suggest a substantial protective effect against premature mortality (HR range, 0.50-0.80) when comparing the highest with the lowest activity group.13,27 For example, Gregg et al27 showed that women aged 70 years or older experienced a 32% lower risk of mortality (HR, 0.68; 95% CI, 0.59-0.78) by being in the highest quintile of total activity. Although these studies have shaped our current understanding of whether physical activity is associated with lower mortality risks, they typically quantified general activities such as walking,2 vigorous exercise,28 or regularity of activity,1 which ignore large components of energy expenditure such as usual daily activity.29 The protective effects shown in Table 2, using the gold standard method for measuring activity energy expenditure, appear stronger than those derived from self-reported physical activity but are comparable with other objective measures of physical fitness.30,31 This likely reflects a more accurate activity assessment with the doubly labeled water method, which avoids known misclassification biases that occur with self-reported physical activity questionnaires.9 Therefore, this study suggests that earlier reports may have underestimated the benefits of physical activity among older adults, although this implication should be tested in future studies.

Our study suggests that any activity energy expenditure in older adults can help lower mortality risks, seemingly contradicting reports that exercise needs to be performed at a specific intensity.32 Unfortunately, the method of ascertaining free-living activity energy expenditure in this study does not provide guidance on the intensity or type of activity that may be important for public health recommendations. Results from the physical activity questionnaire suggest that the proportion of individuals who reported high-intensity exercise and walking for exercise (both in terms of duration and intensity) was similar across tertiles of free-living activity energy expenditure. Interestingly, individuals who worked for pay and climbed stairs were more likely to be in the high-activity energy expenditure tertiles. Another way to apply this information is to use the well-established metabolic equivalent values for physical activity. We found that for every 287 kcal/d in free-living activity energy expenditure, there is approximately a 30% lower risk of mortality. Using the average body weight in our sample of 76 kg and a metabolic equivalent value of 3.0, we estimated that individuals who perform 1 1/4 hours of activity per day will expend 287 kcal/d. Activities that meet a metabolic equivalent value of 3.0 include household chores (vacuuming the carpet, mopping the floor, washing windows, etc), child/adult care, lawn work, walking at a pace of 2.5 mph, and nonsitting work or volunteering. In support of this calculation, the total self-reported activity duration was approximately 30 and 60 minutes longer on average in the second and third tertiles of activity energy expenditure, respectively, compared with the lowest tertile of activity energy expenditure. Most importantly, this accumulation is from usual daily activities that expend energy and not necessarily from volitional exercise.

An unavoidable limitation in observational studies of physical activity is that unhealthy individuals who are at high risk of mortality are also less likely to be active. This bias is less likely in the current study because investigators from the Health ABC study specifically recruited high-functioning older adults who did not have mobility disabilities and thus would likely be capable of physical activity. Furthermore, adjustments for prevalent health conditions and self-reported health helped reduce this potential bias. Individual diseases as well as the total number of health conditions were not significantly different across the energy expenditure tertiles and the proportion of those self-reporting fair or poor health were slightly but not significantly worse in the lowest tertile of energy expenditure (Table 1). Finally, we also performed several sensitivity analyses to determine whether the results were influenced by extreme activity levels and comorbidity. The reduction in sample size caused some of these strategies to yield nonsignificant results but overall patterns suggest that higher levels of activity energy expenditure are beneficial for individuals with comorbidity and who have low to moderate activity levels.

Higher levels of physical activity are associated with reductions in coronary heart disease,33 cancer incidence,27 falls,34 and physical disability.35 The exact underlying mechanisms conferring protection are as yet unknown but these are likely to differ between younger and older adults. For example, biological aging is thought to be associated with increased oxidative stress, which contributes to higher levels of inflammation,36 both of which are reduced with exercise.37,38 More research is needed to elucidate underlying mechanisms of how activity energy expenditure can protect older adults from premature mortality.

Free-living activity energy expenditure is influenced by body weight, age, sex, and sleep duration.12 Those individuals who weigh more and are performing the same activity as their counterparts who weigh less expend more kilocalories.39 Physical activity level is total energy expenditure as a function of resting metabolic rate, the latter being solely influenced by lean mass. This expression accounts for differences in body weight, which also corrects for sex differences in average energy costs (men vs women in our sample, 1.72 vs 1.68; P = .13).22 Nonetheless, we continued to statistically adjust for both weight and sex to account for their influence on mortality risk. In terms of other confounders, activity energy expenditure and physical activity level are equally affected by sleep duration because those who sleep less have a greater opportunity to expend more kilocalories through activity. Adjustment for sex, age, body weight, and sleep duration revealed a stronger effect of higher levels of activity energy expenditure, suggesting that controlling for these confounders refined the association.

Both activity energy expenditure and physical activity level decrease with age,14,22 which prevented us from using the categories created by the Food and Agriculture Organization, the World Health Organization, and the United Nations University that do not recognize age-related changes in physical activity level. For example, using these established categories, 57% of our sample were sedentary, 33% were active, and only 10% were vigorously active. Additionally, the established categories revealed no benefits of energy expenditure in the moderate range. Therefore, further work to establish appropriate physical activity level categories suitable for older adults is needed.

Because the doubly labeled water method directly measures carbon dioxide production over an extended period of normal activity, it is considered the most accurate estimate of free-living activity energy expenditure. It is, however, expensive on an individual basis and requires special expertise limiting investigation to smaller sample sizes. Nonetheless, the strong protective effect observed with 302 participants demonstrates that a more accurate measurement substantially reduced the sample size needed to detect meaningful effects compared with what would have been required with a simply performed but less precise questionnaire assessment. This relatively small sample size limited our ability to assess cause-specific mortality but additional events will eventually allow examination of specific diseases and investigation into a potential threshold of activity energy expenditure that mediates these mortality associations.

In conclusion, we evaluated mortality risk with an accurate and objective measure of free-living activity energy expenditure. Free-living activity energy expenditure revealed a strong association with mortality risk suggesting that previous self-reported measurements may have underestimated the benefits of higher levels of physical activity in older adults. Efforts to increase or maintain free-living activity energy expenditure will likely improve the health of older adults.

Corresponding Author: Todd M. Manini, PhD, National Institute on Aging, 7201 Wisconsin Ave Gateway Bldg, Suite 3C309, Bethesda, MD 20892 (maninit@mail.nih.gov).

Author Contributions: Dr Manini had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Manini, Everhart, Harris.

Acquisition of data: Schoeller, Visser, Tylavsky, Harris.

Analysis and interpretation of data: Manini, Everhart, Patel, Schoeller, Colbert, Bauer, Goodpaster, Harris.

Drafting of the manuscript: Manini, Everhart, Harris.

Critical revision of the manuscript for important intellectual content: Everhart, Patel, Schoeller, Colbert, Visser, Tylavsky, Bauer, Goodpaster, Harris.

Statistical analysis: Manini, Everhart, Patel.

Obtained funding: Everhart, Harris.

Administrative, technical, or material support: Schoeller, Goodpaster, Harris.

Study supervision: Everhart, Harris.

Financial Disclosures: None reported.

Funding\Support: This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute on Aging contracts N01-AG-6-2106, N01-AG-6-2101, and N01-AG-6-2103 with additional support from the National Institute of Diabetes and Digestive and Kidney Diseases.

Role of the Sponsor: The National Institute on Aging Intramural Research Program designed the Health ABC study, supervised its conduct, and participated in the data collection. The National Institute of Diabetes and Digestive and Kidney Diseases designed the energy expenditure substudy. The authors, the Health, Aging, and Body Composition (Health ABC) publications committee, and representatives from the National Institute on Aging reviewed and approved the manuscript.

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PubMed
Coward WA.Calculation of pool sizes and flux rates. In: Doubly Labeled Water Method: Technical Recommendations for Use in Humans. Vienna, Austria: American Educational Research Association; 1990
Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method.  J Appl Physiol. 1982;53:955-959
PubMed
Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism.  J Physiol. 1949;109:1-9
PubMed
Bloesch D, Schutz Y, Breitenstein E, Jequier E, Felber JP. Thermogenic response to an oral glucose load in man: comparison between young and elderly subjects.  J Am Coll Nutr. 1988;7:471-483
PubMed
Prentice AM, Goldberg GR, Murgatroyd PR, Cole TJ. Physical activity and obesity: problems in correcting expenditure for body size.  Int J Obes Relat Metab Disord. 1996;20:688-691
PubMed
Black AE, Coward WA, Cole TJ, Prentice AM. Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements.  Eur J Clin Nutr. 1996;50:72-92
PubMed
Series WTR. Energy and Protein Requirements: Report of a Joint FAP/WHO/UNU Expert Consultation. Geneva, Switzerland: World Health Organization; 1985
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;25:71-80
PubMed   |  Link to Article
Schoenfeld D. Partial residuals for the proportional hazard regression model.  Biometrika. 1982;69:239-241
Link to Article
Rice DP, Fineman N. Economic implications of increased longevity in the United States.  Annu Rev Public Health. 2004;25:457-473
PubMed   |  Link to Article
Gregg EW, Cauley JA, Stone K.  et al.  Relationship of changes in physical activity and mortality among older women.  JAMA. 2003;289:2379-2386
PubMed   |  Link to Article
Bijnen FC, Feskens EJ, Caspersen CJ, Nagelkerke N, Mosterd WL, Kromhout D. Baseline and previous physical activity in relation to mortality in elderly men: the Zutphen Elderly Study.  Am J Epidemiol. 1999;150:1289-1296
PubMed   |  Link to Article
Weller I, Corey P. The impact of excluding non-leisure energy expenditure on the relation between physical activity and mortality in women.  Epidemiology. 1998;9:632-635
PubMed   |  Link to Article
Blair SN, Kampert JB, Kohl HW III.  et al.  Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women.  JAMA. 1996;276:205-210
PubMed   |  Link to Article
Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues.  Med Sci Sports Exerc. 1999;31:(11 suppl)  S646-S662
PubMed   |  Link to Article
Pate RR, Pratt M, Blair SN.  et al.  Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine.  JAMA. 1995;273:402-407
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Walker M. Changes in physical activity, mortality, and incidence of coronary heart disease in older men.  Lancet. 1998;351:1603-1608
PubMed   |  Link to Article
Gregg EW, Pereira MA, Caspersen CJ. Physical activity, falls, and fractures among older adults: a review of the epidemiologic evidence.  J Am Geriatr Soc. 2000;48:883-893
PubMed
Ferrucci L, Izmirlian G, Leveille S.  et al.  Smoking, physical activity, and active life expectancy.  Am J Epidemiol. 1999;149:645-653
PubMed   |  Link to Article
Chung HY, Kim HJ, Kim JW, Yu BP. The inflammation hypothesis of aging: molecular modulation by calorie restriction.  Ann N Y Acad Sci. 2001;928:327-335
PubMed   |  Link to Article
Goto S, Radak Z, Nyakas C.  et al.  Regular exercise: an effective means to reduce oxidative stress in old rats.  Ann N Y Acad Sci. 2004;1019:471-474
PubMed   |  Link to Article
Pedersen BK, Bruunsgaard H, Ostrowski K.  et al.  Cytokines in aging and exercise.  Int J Sports Med. 2000;21:(suppl 1)  S4-S9
PubMed   |  Link to Article
Prentice AM, Black AE, Coward WA.  et al.  High levels of energy expenditure in obese women.  BMJ. 1986;292:983-987
PubMed   |  Link to Article

Figures

Figure. Kaplan-Meier Survival Plots and Mortality Rates by Tertiles of Free-Living Activity Energy Expenditure and Physical Activity Level
Graphic Jump Location

The log-rank and trend tests were used to determine the equality of survivor functions between the tertiles. To calculate activity energy expenditure in kcal/d: (total energy expenditure × 0.09) − resting metabolic rate. To calculate physical activity level: total energy expenditure/resting metabolic rate.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Participants by Tertile of Activity Energy Expenditure*
Table Graphic Jump LocationTable 2. Free-Living Activity Energy Expenditure and Risk of All-Cause Mortality
Table Graphic Jump LocationTable 3. Self-Reported Activities Across Free-Living Activity Energy Expenditure Tertiles*

References

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PubMed   |  Link to Article
Hakim AA, Petrovitch H, Burchfiel CM.  et al.  Effects of walking on mortality among nonsmoking retired men.  N Engl J Med. 1998;338:94-99
PubMed   |  Link to Article
Bijnen FC, Caspersen CJ, Feskens EJ, Saris WH, Mosterd WL, Kromhout D. Physical activity and 10-year mortality from cardiovascular diseases and all causes: the Zutphen Elderly Study.  Arch Intern Med. 1998;158:1499-1505
PubMed   |  Link to Article
Morgan K, Clarke D. Customary physical activity and survival in later life: a study in Nottingham, UK.  J Epidemiol Community Health. 1997;51:490-493
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Kaplan GA, Seeman TE, Cohen RD, Knudsen LP, Guralnik J. Mortality among the elderly in the Alameda County Study: behavioral and demographic risk factors.  Am J Public Health. 1987;77:307-312
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Stessman J, Maaravi Y, Hammerman-Rozenberg R, Cohen A. The effects of physical activity on mortality in the Jerusalem 70-Year-Olds Longitudinal Study.  J Am Geriatr Soc. 2000;48:499-504
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Levin S, Jacobs DR Jr, Ainsworth BE, Richardson MT, Leon AS. Intra-individual variation and estimates of usual physical activity.  Ann Epidemiol. 1999;9:481-488
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Durante R, Ainsworth BE. The recall of physical activity: using a cognitive model of the question-answering process.  Med Sci Sports Exerc. 1996;28:1282-1291
PubMed   |  Link to Article
Lamonte MJ, Ainsworth BE. Quantifying energy expenditure and physical activity in the context of dose response.  Med Sci Sports Exerc. 2001;33:(6 suppl)  S370-S378
PubMed   |  Link to Article
Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions.  Res Q Exerc Sport. 2000;71:(2 suppl)  S1-S14
PubMed   |  Link to Article
Lichtman SW, Pisarska K, Berman ER.  et al.  Discrepancy between self-reported and actual caloric intake and exercise in obese subjects.  N Engl J Med. 1992;327:1893-1898
PubMed   |  Link to Article
Schutz Y, Weinsier RL, Hunter GR. Assessment of free-living physical activity in humans: an overview of currently available and proposed new measures.  Obes Res. 2001;9:368-379
PubMed   |  Link to Article
Schoeller DA, Ravussin E, Schutz Y, Acheson KJ, Baertschi P, Jequier E. Energy expenditure by doubly labeled water: validation in humans and proposed calculation.  Am J Physiol. 1986;250:R823-R830
PubMed
Elia M, Ritz P, Stubbs RJ. Total energy expenditure in the elderly.  Eur J Clin Nutr. 2000;54:(suppl 3)  S92-S103
PubMed   |  Link to Article
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;92:1036-1044
PubMed
Blanc S, Schoeller DA, Bauer D.  et al.  Energy requirements in the eighth decade of life.  Am J Clin Nutr. 2004;79:303-310
PubMed
Coward WA.Calculation of pool sizes and flux rates. In: Doubly Labeled Water Method: Technical Recommendations for Use in Humans. Vienna, Austria: American Educational Research Association; 1990
Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method.  J Appl Physiol. 1982;53:955-959
PubMed
Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism.  J Physiol. 1949;109:1-9
PubMed
Bloesch D, Schutz Y, Breitenstein E, Jequier E, Felber JP. Thermogenic response to an oral glucose load in man: comparison between young and elderly subjects.  J Am Coll Nutr. 1988;7:471-483
PubMed
Prentice AM, Goldberg GR, Murgatroyd PR, Cole TJ. Physical activity and obesity: problems in correcting expenditure for body size.  Int J Obes Relat Metab Disord. 1996;20:688-691
PubMed
Black AE, Coward WA, Cole TJ, Prentice AM. Human energy expenditure in affluent societies: an analysis of 574 doubly-labelled water measurements.  Eur J Clin Nutr. 1996;50:72-92
PubMed
Series WTR. Energy and Protein Requirements: Report of a Joint FAP/WHO/UNU Expert Consultation. Geneva, Switzerland: World Health Organization; 1985
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;25:71-80
PubMed   |  Link to Article
Schoenfeld D. Partial residuals for the proportional hazard regression model.  Biometrika. 1982;69:239-241
Link to Article
Rice DP, Fineman N. Economic implications of increased longevity in the United States.  Annu Rev Public Health. 2004;25:457-473
PubMed   |  Link to Article
Gregg EW, Cauley JA, Stone K.  et al.  Relationship of changes in physical activity and mortality among older women.  JAMA. 2003;289:2379-2386
PubMed   |  Link to Article
Bijnen FC, Feskens EJ, Caspersen CJ, Nagelkerke N, Mosterd WL, Kromhout D. Baseline and previous physical activity in relation to mortality in elderly men: the Zutphen Elderly Study.  Am J Epidemiol. 1999;150:1289-1296
PubMed   |  Link to Article
Weller I, Corey P. The impact of excluding non-leisure energy expenditure on the relation between physical activity and mortality in women.  Epidemiology. 1998;9:632-635
PubMed   |  Link to Article
Blair SN, Kampert JB, Kohl HW III.  et al.  Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women.  JAMA. 1996;276:205-210
PubMed   |  Link to Article
Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues.  Med Sci Sports Exerc. 1999;31:(11 suppl)  S646-S662
PubMed   |  Link to Article
Pate RR, Pratt M, Blair SN.  et al.  Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine.  JAMA. 1995;273:402-407
PubMed   |  Link to Article
Wannamethee SG, Shaper AG, Walker M. Changes in physical activity, mortality, and incidence of coronary heart disease in older men.  Lancet. 1998;351:1603-1608
PubMed   |  Link to Article
Gregg EW, Pereira MA, Caspersen CJ. Physical activity, falls, and fractures among older adults: a review of the epidemiologic evidence.  J Am Geriatr Soc. 2000;48:883-893
PubMed
Ferrucci L, Izmirlian G, Leveille S.  et al.  Smoking, physical activity, and active life expectancy.  Am J Epidemiol. 1999;149:645-653
PubMed   |  Link to Article
Chung HY, Kim HJ, Kim JW, Yu BP. The inflammation hypothesis of aging: molecular modulation by calorie restriction.  Ann N Y Acad Sci. 2001;928:327-335
PubMed   |  Link to Article
Goto S, Radak Z, Nyakas C.  et al.  Regular exercise: an effective means to reduce oxidative stress in old rats.  Ann N Y Acad Sci. 2004;1019:471-474
PubMed   |  Link to Article
Pedersen BK, Bruunsgaard H, Ostrowski K.  et al.  Cytokines in aging and exercise.  Int J Sports Med. 2000;21:(suppl 1)  S4-S9
PubMed   |  Link to Article
Prentice AM, Black AE, Coward WA.  et al.  High levels of energy expenditure in obese women.  BMJ. 1986;292:983-987
PubMed   |  Link to Article

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