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J Appl Physiol 85: 1063-1069, 1998;
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Vol. 85, Issue 3, 1063-1069, September 1998

Energy requirements and physical activity in free-living older women and men: a doubly labeled water study

Raymond D. Starling, Michael J. Toth, William H. Carpenter, Dwight E. Matthews, and Eric T. Poehlman

Division of Clinical Pharmacology and Metabolic Research, Department of Medicine, University of Vermont, Burlington, Vermont 05405

    ABSTRACT
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

Determinants of daily energy needs and physical activity are unknown in free-living elderly. This study examined determinants of daily total energy expenditure (TEE) and free-living physical activity in older women (n = 51; age = 67 ± 6 yr) and men (n = 48; age = 70 ± 7 yr) by using doubly labeled water and indirect calorimetry. Using multiple-regression analyses, we predicted TEE by using anthropometric, physiological, and physical activity indexes. Data were collected on resting metabolic rate (RMR), body composition, peak oxygen consumption (VO2 peak), leisure time activity, and plasma thyroid hormone. Data adjusted for body composition were not different between older women and men, respectively (in kcal/day): TEE, 2,306 ± 647 vs. 2,456 ± 666; RMR, 1,463 ± 244 vs. 1,378 ± 249; and physical activity energy expenditure, 612 ± 570 vs. 832 ± 581. In a subgroup of 70 women and men, RMR and VO2 peak explained approximately two-thirds of the variance in TEE (R2 = 0.62; standard error of the estimate = ±348 kcal/day). Crossvalidation of this equation in the remaining 29 women and men was successful, with no difference between predicted and measured TEE (2,364 ± 398 and 2,406 ± 571 kcal/day, respectively). The strongest predictors of physical activity energy expenditure (P < 0.05) for women and men were VO2 peak (r = 0.43), fat-free mass (r = 0.39), and body mass (r = 0.34). In summary, RMR and VO2 peak are important independent predictors of energy requirements in the elderly. Furthermore, cardiovascular fitness and fat-free mass are moderate predictors of physical activity in free-living elderly.

total daily energy expenditure; elderly; aerobic capacity; physical activity

    INTRODUCTION
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

IT IS UNCLEAR whether energy needs in the elderly are higher or lower than current recommendations (10, 28). Moreover, factors that predict daily energy requirements in free-living elderly are poorly defined. Traditionally, energy intake methods have been used to determine energy requirements. However, the accuracy of these techniques are questionable in the elderly because of misreporting of habitual energy intake (12, 31). To circumvent this problem, the assessment of daily total energy expenditure (TEE) is used to estimate individual energy needs. When an individual is in energy balance, the assessment of daily energy expenditure becomes a proxy measure of energy needs. Doubly labeled water (24, 32) allows an integrated measurement of daily energy expenditure and, therefore, provides a valuable tool to examine daily energy needs in free-living elderly.

Present energy requirement recommendations, based on the physical activity level (PAL) ratio [PAL = TEE/resting metabolic rate (RMR)], are estimated to be 1.51 × RMR (10). Recent results (28) based on the pooling of TEE data (12, 17, 26, 29, 30) suggest that this recommendation may underestimate energy requirements of older individuals. Although the PAL ratio may be a useful tool to estimate group energy needs, research should be directed at predicting individual energy requirements.

Another method to estimate daily energy requirements is multiple-regression analysis. This approach predicts individual daily energy requirements by using parameters related to energy needs (i.e., anthropometric, physiological, and physical activity indexes). Because energy expenditure of physical activity is the most variable component of TEE among the elderly (11, 12, 17, 26), measures of physical activity and/or aerobic fitness may allow a more accurate prediction of individual energy requirements. Evidence for this assertion is based on previous data which show a strong association between aerobic capacity and daily energy expenditure measured via questionnaire (3) and doubly labeled water (12). However, the use of multiple-regression analysis to estimate energy requirements has been limited by small numbers of subjects and the lack of cross-validation procedures to assess the accuracy of these equations in independent populations (2, 4, 15, 27, 33).

Thus the primary aim of this study was to develop and crossvalidate an equation to predict daily energy requirements from anthropometric, physiological, and physical activity indexes associated with TEE in a relatively large sample of healthy, elderly women and men. A secondary aim was to identify factors which correlate with physical activity energy expenditure (PAEE) estimated from doubly labeled water and indirect calorimetry. Because low physical activity levels are predictive of cardiovascular and metabolic disease risk (18a, 22), an understanding of factors modulating physical activity has important public health implications.

    MATERIALS AND METHODS
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

Subjects

Subjects were 99 healthy, elderly Caucasians (51 women and 48 men, ages 56-90 yr) recruited from the Burlington, VT, area via advertisements in local newspapers. A subset of these individuals were control subjects in previous studies from our laboratory examining energy metabolism in various diseased populations (7, 23, 36, 38). All participants were healthy and had no history or evidence on physical examination of 1) coronary heart disease (e.g., S-T segment depression >1 mm at rest or exercise); 2) hypertension (resting blood pressure >140/90); 3) medications that could affect cardiovascular function or metabolism; 4) diabetes; 5) body mass fluctuation of >2 kg in the past yr; 6) exercise-limiting noncardiac disease (arthritis, peripheral vascular disease, cerebral vascular disease); 7) smoking; or 8) hormone replacement therapy. No subject was regularly engaging in aerobic or resistance training (i.e., <2 days/wk). Each subject signed a consent form approved by the Institutional Review Board of the University of Vermont before participating in the study.

Testing Protocol

All subjects were tested in the morning after an overnight visit to the General Clinical Research Center (GCRC) at the University of Vermont. Subjects provided a baseline urine sample on the evening of admission (at 1600-1800) and were administered a mixed dose of doubly labeled water to measure daily TEE. After a 12-h overnight fast, each subject was awakened at 0630 for a measurement of RMR and collection of a venous blood sample and two urine samples for doubly labeled water analysis. Body composition was assessed by using dual-energy X-ray absorptiometry, and a Minnesota Leisure Time Physical Activity questionnaire was administered to each subject. Each subject returned 10 days later to provide two urine samples for doubly labeled water analysis and to complete a cycling aerobic capacity test. Specific details about data collection are provided below.

RMR. RMR was measured by indirect calorimetry by using the ventilated hood technique (19). Respiratory gas analysis was performed with the use of a Deltatrac metabolic cart (Sensormedics, Yorba Linda, CA). RMR (kcal/day) was calculated from the equation of Weir (39). The intraclass correlation and coefficient of variation for RMR, as determined by using test-retest in 17 volunteers from our laboratory, are 0.90 and 4.3%, respectively. RMR was also estimated from body mass and height by using gender-specific equations (10).

TEE. TEE was measured over a 10-day period by using the doubly labeled water (2H218O) method of Schoeller and van Santen (32). Subjects arrived at the GCRC on day 0, and a urine sample was acquired for measurement of baseline 2H and 18O enrichment. Between 1600 and 1800, a premixed dose containing ~0.12 g of 2H2O and 0.15 g of H218O/kg of estimated total body water (TBW) was given to each subject to drink (~70 ml). Two urine samples were collected the next morning (day 1), and another two urine samples were collected on the return visit to the GCRC on day 10. These urine samples were obtained between 0800 and 1200. Aliquots of the urine samples were stored frozen at -20°C in vacutainers until later analysis by isotope-ratio mass spectrometry (IRMS).

For measurement of 18O, duplicate 1-ml aliquots of urine were placed in vacutainers, which were then filled with a low pressure of CO2 and shaken at room temperature overnight. CO2 oxygen equilibrates with the water oxygen, and the 18O isotopic enrichment of the water was then measured by IRMS. For measurement of 2H enrichment, triplicate 5-µl aliquots of urine were placed in stopcock-sealed reaction vessels containing 100 mg of zinc catalyst, following the method of Wong et al. (40). Vessels were sealed under nitrogen gas and then evacuated with the water frozen. The water was reduced to hydrogen gas by heating the vessels in a block at 500°C for 30 min. 2H enrichments of the hydrogen samples were measured by IRMS on the same day they were prepared. Aliquots of the 2H218O dose were also measured for 2H and 18O enrichment after being quantitatively diluted with unlabeled water. Urine sample 2H and 18O enrichments were calculated by using the diluted dose 2H and 18O enrichments as calibrants for analysis in each study.

DETERMINATION OF TEE. After the dose of 2H218O is consumed, the 2H and 18O tracers equilibrate in the TBW pool. 2H and 18O tracers are then lost at an exponential rate from the body over the following 10 days. Rate of CO2 production, integrated over the 10-day period, was determined by subtracting the difference between the rate of 18O tracer loss (water turnover + CO2 production) and 2H tracer loss (water turnover alone) (32). Rates of 18O and 2H loss (kO and kH, respectively) from body water and the 18O and 2H dilution spaces (NO and NH, respectively) were determined from the semilogarithmic regression of the 18O and 2H enrichments vs. time. The slopes define the rates of tracer disappearance, and the intercepts define the dilution spaces (liters of water). TBW (N) in liters was calculated by taking the average of the dilution space of the 18O and 2H tracers
  <IT>N</IT> = 1/2 (<IT>N</IT><SUB>O</SUB>/<IT>c</IT><SUB>O</SUB> + <IT>N</IT><SUB>H</SUB>/<IT>c</IT>′<SUB>H</SUB>)
where cO and c'H are the sizes of the exchangeable oxygen and hydrogen pool sizes relative to TBW. Pool size based on the average of the cO and c'H pool spaces reduces error in TBW by ~1/2, which has been previously discussed (16). 18O dilution space relative to TBW, cO, was assumed to be 1.01 (24, 25, 34). 2H dilution space relative to TBW, c'H, was assumed to be 1.053 (13). This value includes all analytical methodology effects of hydrogen sample preparation and is consistent in our hands where c'H/cO ratio is 1.046 ± 0.002, averaging the results of >250 studies.

Rate of CO2 production (rCO2, mol/day) was calculated by using Eq. 3 of Speakman et al. (34)
<IT>r</IT><SUB>CO<SUB>2</SUB></SUB> = <IT>N</IT>/2.196 · (<IT>c</IT><SUB>O</SUB><IT>k</IT><SUB>O</SUB> − <IT>c</IT><SUB>H</SUB><IT>k</IT><SUB>H</SUB>)
where cH is the true hydrogen dilution space parameter (i.e., does not contain any analytical-method-induced component). Following the recommendation of Racette et al. (25), a value of 1.041 was used for cH. If a value of 1.053 had been used instead for cH, the cOkO - cHkH difference would have been decreased by ~1%, and the calculation of rCO2 would have been decreased by <= 5% (16). Assuming a respiratory quotient of the food consumed of 0.85 (5), total CO2 production was converted to oxygen consumed or daily TEE (in kcal/day) by using the Weir formula (39)
TEE = (3.044/0.85 + 1.104)<IT>r</IT><SUB>CO<SUB>2</SUB></SUB>

DETERMINATION OF PAEE. PAEE was calculated by using the following equation as previously described (12)
PAEE (kcal/day) = (0.90 × TEE) − RMR
This approach assumes that the thermic effect of feeding is 10% in the elderly (20).

Body composition. Fat and fat-free mass were measured by dual-energy X-ray absorptiometry with the use of a Lunar DPX-L densitometer (Lunar Radiation, Madison, WI). A total body scan was completed in 40 min and provided measurements of fat-free mass, fat mass, and percent body fat. In six older women from our laboratory, the coefficient of variation for body fat was 1.7% during test-retest on two occasions within 1 wk.

Aerobic capacity and leisure time activity. Peak oxygen consumption (VO2 peak) was determined during an incremental cycling test to voluntary exhaustion. Cycling cadence was 50 rpm, with a workload during the first 3 min of 25 and 50 W for the women and men, respectively. Workload was increased 25 W every 2 min until voluntary exhaustion. VO2 peak (l/min) was considered to be achieved with a respiratory exchange ratio >1.1 or a heart rate at or above the age-related predicted maximum (220 - age). Test-retest conditions (within 1 wk) for VO2 peak on nine older individuals in our laboratory have yielded an intraclass correlation of 0.94 and a coefficient of variation of 3.8%. Leisure time physical activity was measured by a structured questionnaire and interview (35). This questionnaire assessed each individual's physical activity level over the past year.

Plasma thyroid hormones. Plasma thyroxine (T4), free T4, and triiodothyronine (T3) concentrations were measured by using commercially available enzymatic methods (Baxter, Cambridge, MA), whereas free T3 concentration was determined via an analog assay (Diagnostics Products, Los Angeles, CA).

Statistical Analyses

All data are expressed as means ± SD. Potential differences between women and men for daily TEE and its components were examined by using independent t-tests. Comparison of VO2 peak between women and men was completed after normalizing for fat-free mass by using analysis of covariance (37). Significance was accepted at the P < 0.05 level. Data for women and men were pooled for all subsequent analyses if no differences were detected for daily TEE and its components after adjustment for body composition.

Pearson product-moment correlations were calculated to examine relationships among TEE, PAEE, and other selected independent variables for all 99 women and men. To account for the influence of body composition on PAEE, correlations between PAEE and various independent variables were performed accordingly. 1) PAEE was correlated with various independent variables by using partial correlation analyses to account for the influence of fat and fat-free mass, as previously suggested (8). 2) PAEE was indexed as the ratio of measured TEE to RMR (i.e., PAL ratio) and correlated with various independent variables to account for the influence of body weight.

By using stepwise regression analysis (9), an equation was developed to determine the relative contribution of selected independent variables to the variation in TEE. Independent variables introduced into the analysis 1) needed a physiological rationale to be included, and 2) correlated significantly with TEE from Pearson product tests. Additionally, a moderate effect size (R2 = 0.50-0.75) was hypothesized, and a subject-to-independent variable ratio of 6:1 was maintained during stepwise regression analyses, as suggested previously (14). This prediction equation was generated on a randomly selected two-thirds of women (n = 36) and men (n = 34). The remaining one-third of women (n = 15) and men (n = 14) served as a cross-validation group. The accuracy of the prediction equation was determined by comparing 1) predicted and measured TEE in the cross-validation group by using a paired t-test; and 2) the correlation coefficient (r) of predicted and measured TEE vs. the multiple R obtained from the prediction equation by an independent z-test (18). The reliability of our prediction was also assessed by calculating an intraclass correlation coefficient.

    RESULTS
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

There were no differences in TEE, RMR, and PAEE between men and women after adjustment for body composition differences. Measured PAL was also not significantly different between women and men. Pooled gender data for physical characteristics and energy expenditure are presented in Tables 1 and 2, respectively. Pearson correlations between TEE and other selected dependent variables for all subjects (n = 99) are displayed in Table 3. Measured and predicted RMR, fat-free mass, VO2 peak, body mass, and percent body fat were significantly correlated with TEE.

                              
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  		Table 1.   Descriptive characteristics of subjects

                              
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  		Table 2.   Daily total energy expenditure, resting metabolic rate, physical activity energy expenditure, and physical activity level ratio for 99 women and men

                              
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  		Table 3.   Pearson product correlations between TEE and other independent variables for 99 women and men

There were no differences in physical characteristics between validation and cross-validation groups, and a Levene's test demonstrated equal variances between groups for all data (see Table 4). Stepwise regression analysis was performed on a randomly selected sample of 70 women and men, and predicted RMR, VO2 peak, body mass, percent body fat, fat-free mass, and gender were entered into the model based on their strong associations with TEE from simple correlations. The following equation accounted for the largest amount of variance (R2 = 0.62) in TEE
TEE(kcal/day) = [1.95 · predicted RMR(kcal/day)] 
+ [217.3 · <A><AC>V</AC><AC>˙</AC></A><SC>o</SC><SUB>2 peak</SUB> (l/min)] − 825.5
The standard error of the estimate (SEE) for this equation was ±348 kcal/day. No difference was detected between women and men for the mean residuals (i.e., measured minus predicted TEE); this demonstrates that this equation predicts TEE accurately for both genders. Cross-validation procedures on this equation demonstrated no significant difference between predicted and measured TEE for the 29 women and men (see Fig. 1). However, the correlation coefficient between the measured and predicted TEE in the cross-validation group (r = 0.61) was significantly different from the multiple R of the regression equation developed from the validation group (R = 0.79); this indicates that the equation was less precise in this independent cohort. Moreover, the intraclass correlation coefficient was 0.80 for our prediction equation with a SEE of ±460 kcal/day in the cross-validation group.

                              
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  		Table 4.   Descriptive characteristics for validation and cross-validation groups


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  		Fig. 1.   Relationship between predicted and measured daily total energy expenditure (TEE) for cross-validation group of 15 women and 14 men. Values are group means ± SD. SEE, standard error of estimate. No significant difference was present between predicted and measured TEE (P > 0.05).

Simple correlations between PAEE and various dependent variables for all subjects are shown in Table 5. Independent of the method for indexing PAEE, low to moderate correlations were demonstrated with fat-free mass, body mass, VO2 peak, and leisure time activity score.

                              
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  		Table 5.   Pearson product correlations between PAEE and various independent variables for 99 women and men

    DISCUSSION
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

This study was prompted by the paucity of data regarding energy requirements and physical activity in free-living elderly. Our relatively large numbers of subjects allowed for the determination of TEE by using several independent predictors while maintaining a favorable subject-to-variable ratio for multiple-regression analysis. Regression data demonstrate that predicted RMR and VO2 peak explain 62% of the variance in TEE. Furthermore, this equation accurately predicts mean TEE in an independent group of women and men with a SEE of ±460 kcal/day, although prediction on an individual basis was less precise. Moreover, cardiovascular fitness and fat-free mass are moderate predictors of PAEE in free-living elderly.

Prediction and Validation of TEE Equation

An understanding of factors regulating TEE and energy requirements in the elderly are important public health concerns. Energy requirements of the elderly are estimated at 1.51 times RMR on the basis of the factorial approach (10). It is suggested that present energy intake guidelines underestimate the energy needs of the elderly and that a PAL ratio of 1.62 to 1.68 may be more appropriate (28). PAL results for women (1.63 ± 0.27) and men (1.73 ± 0.28) in the present study confirm these findings. However, indexing energy requirements by using the PAL ratio assumes PAL is a constant function of an individual's RMR. Failure to account for variation in physical activity among individuals may introduce significant error into the prediction of energy requirements.

To address this problem, we used multiple-regression analyses to predict individual energy requirements from anthropometric and physiological indexes including measures of physical activity. Previous studies examining energy needs in the elderly have relied on small sample sizes (6, 13, 17, 26, 29, 30). Moreover, investigators who have used regression analyses to predict energy requirements have not crossvalidated their equations in independent cohorts (2, 4, 15, 27, 33). We attempted to account for these experimental drawbacks in the present design.

Our results show that 62% of the variance in TEE is explained by predicted RMR (10) and VO2 peak, with a SEE of ±348 kcal/day. Others demonstrate that 74% of the variance in TEE, as measured by doubly labeled water, is explained by VO2 peak and leisure time physical activity, with a SEE of ±217 kcal/day (12). Moreover, after correcting for fat-free mass by using regression procedures, a significant partial correlation persists between TEE and VO2 peak (r = 0.24, P < 0.05) in the present study. This finding provides additional evidence that aerobic fitness level is an important predictor of TEE, independent of its covariance with fat-free mass. Collectively, these studies demonstrate that VO2 peak and other indexes of physical activity are important predictors of TEE.

It is logical to assume that an individual may be more physically active because of a greater fitness level, although being more physically active may improve an individual's aerobic fitness. Nevertheless, an individual with a higher absolute VO2 peak would work at a lower percentage of individual maximum aerobic capacity for a similar quantity of physical activity compared with an individual with a lower VO2 peak. Theoretically, an elderly person with a higher aerobic fitness should be able to complete a greater quantity of work throughout the day, resulting in greater energy expenditure and requirements. Because maximal aerobic capacity cannot be easily measured in all of the elderly, predicted aerobic capacity from submaximal exercise data and other more direct measures of physical activity (i.e., axial/triaxial accelerometers, pedometers, and age-specific activity questionnaires) may be useful in quantifying the contribution of physical activity to variation in TEE. Overall, identifying other physiological (e.g., sympathetic nervous system activity), environmental (e.g., socioeconomic status, education level), and physical activity indexes may help explain a greater amount of variance in TEE and increase the precision of this predictive model (e.g., ±100 kcal/day).

To our knowledge, the present study is the first to assess the accuracy of a TEE prediction equation in an independent sample of the elderly the (i.e., crossvalidation) who had TEE measured by doubly labeled water. Results from the cross-validation group demonstrate that our equation accurately predicts TEE on a group basis, as evidenced by the absence of a difference between predicted and measured TEE (see Fig. 1). The precision is less than desired on an individual basis, as reflected by a SEE of 460 kcal/day in the cross-validation group and by the significant difference between the multiple R of the original equation and the r value of measured vs. predicted TEE in the cross-validation group. Nevertheless, cross-validation results demonstrate that our equation is internally consistent, but the equation may not be universally applicable because it was not developed in a random group of elderly individuals selected from the general population.

Determinants of PAEE

A secondary aim of this study was to examine potential determinants of PAEE in free-living elderly. This is the most understudied component of TEE, which is normally estimated from activity diaries and motion detectors but can be more objectively quantified from doubly labeled water and indirect calorimetry data. Low levels of physical activity are most predictive of cardiovascular and metabolic disease risk (18a, 22); therefore, identifying determinants of PAEE has important public health implications.

PAEE is both a behavioral characteristic and a physiological attribute. It includes volitional activity during structured exercise, as well as nonstructured movement such as fidgeting. Because PAEE is influenced by body mass and composition, correlations are presented in Table 5 on an absolute and adjusted basis. Because there is no consensus on how to adjust PAEE for differences in metabolic size, we present several adjustment procedures that are currently used in other studies. Briefly, we adjusted PAEE 1) for fat and fat-free mass by using partial correlation procedures, as previously suggested (8), and 2) by using the PAL ratio which divides TEE by RMR.

Absolute levels of PAEE are related to fat-free mass, body mass, VO2 peak, and leisure time activity in the present study. As with TEE, it is logical to postulate a close association between aerobic fitness and volitional participation in physical activity. Previous work (12) in 13 elderly individuals shows that PAEE is strongly correlated with VO2 peak (r = 0.52) and leisure time activity score (r = 0.83). Moderate correlations between PAEE and VO2 peak in the present study are probably reflective of differences between a predominantly behavioral characteristic like PAEE and a physiological attribute such as VO2 peak. That is, factors other than physiological indexes (e.g., socioeconomic status, availability of recreational facilities, seasonality) may be related to PAEE. We also show a low correlation between PAEE and the Minnesota Leisure Time Activity Survey (r = 0.21), which demonstrates that this structured questionnaire does not accurately reflect PAEE in the elderly, as previously suggested (12). After adjusting PAEE for body weight and composition, correlations were similar or slightly attenuated. At this point, we conclude that our ability to predict PAEE in free-living elderly is modest. Given the importance of physical activity in the determination of cardiovascular and metabolic disease risk, future research is needed to determine other factors associated with PAEE in older women and men.

Summary. The primary aim of this study was to attempt the first large-scale prediction of daily TEE from various anthropometric, physiological, and physical activity indexes in a relatively large group of healthy, elderly women and men. Our equation explains 62% of the variance in daily TEE by using predicted RMR and peak aerobic capacity with a SEE of ~350 kcal/day. Crossvalidation demonstrates that this equation works accurately on a group mean basis in a randomly selected, independent group of healthy elderly individuals; however, this equation is less precise on an individual basis. Future studies that use other physiological, environmental, and physical activity indexes are needed to determine what factors may explain more variation in daily TEE and improve the prediction of energy needs within ±100 kcal/day. Moreover, free-living physical activity, determined from doubly labeled water and indirect calorimetry, is moderately associated with cardiovascular fitness and fat-free mass in the elderly.

    ACKNOWLEDGEMENTS

This study was supported by National Institutes of Health Grants AG-07857, AG-00564, DK-52752, F32 AG-05791 (to R.D. Starling), and RR-00109 (to the University of Vermont General Clinical Research Center) and by American Association of Retired Persons grants (to E. T. Poehlman).

    FOOTNOTES

Address for reprint requests: E. T. Poehlman, Univ. of Vermont, Dept. of Medicine, Given Bldg. C-247, Burlington, VT 05405 (E-mail: EPOEHLMA{at}ZOO.UVM.EDU),

Received 22 September 1997; accepted in final form 14 May 1998.

    REFERENCES
Top
Abstract
Introduction
Materials & Methods
Results
Discussion
References

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J APPL PHYSIOL 85(3):1063-1069
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