Equations for predicting the energy requirements of healthy adults aged y 1 3

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1 Equations for predicting the energy requirements of healthy adults aged y 1 3 Angela G Vinken, Gaston P Bathalon, Ana L Sawaya, Gerard E Dallal, Katherine L Tucker, and Susan B Roberts ABSTRACT Background: Recent studies have questioned the accuracy of using the current recommended dietary allowances (RDAs) to predict usual energy requirements in adults. Objective: We developed equations to predict adult energy requirements from simple anthropometric and laboratory measures by using the doubly labeled water method to determine each subject s total energy expenditure (TEE), which is equal to usual energy requirements in weight-stable individuals. Design: This was a cross-sectional study conducted with 93 healthy, free-living adults [44 men and 49 women; body mass index range (in kg/m 2 ): ] aged y. Body fat and fat-free mass were measured by underwater weighing, physical activity was estimated by using activity monitors, and resting energy expenditure was determined by indirect calorimetry. Information on anthropometric variables and reported strenuous activity was also collected. Results: Three regression equations were developed and verified for accuracy by using bootstrap analysis and doubly labeled water data published by other research groups. The first equation used information on only age, weight, height, and sex and had an SEE for prediction of TEE of 1.80 MJ/d. The second and third equations used different combinations of basic and laboratory data and had SEEs of 1.55 and 1.65 MJ/d, respectively. With use of the same analytic approaches, the RDAs for energy were shown to significantly underestimate usual energy needs by 10%; the extent of underestimation was significantly greater for subjects with high TEEs than for subjects with low TEEs. Conclusion: Regression equations based on doubly labeled water measurements of TEE appear to be more accurate than the current RDAs for predicting energy requirements in healthy, nonobese adults living in affluent countries. Am J Clin Nutr 1999;69: KEY WORDS Energy requirements, energy expenditure, isotopes, body composition, exercise, regression equations, doubly labeled water method, recommended dietary allowances, adults INTRODUCTION Recommended dietary allowances (RDAs) for energy are based on factorial procedures that estimate the average expected amounts of metabolizable energy intake required to sustain normal metabolic processes in addition to desirable or expected levels of physical activity in groups of healthy individuals (1, 2). RDAs for energy are predicted in a 2-step process in which resting energy expenditure (REE) is first calculated from age and body weight and then an empirically derived activity factor is applied. RDAs for energy are used for several practical purposes. For example, they form the basis for recommendations of group energy needs and food portion suggestions and provide the starting point for food aid allowances to underprivileged groups. They are also used to predict the energy requirements of individuals in weight-loss programs when an estimate of weightmaintenance energy requirements is needed as the starting point for a dietary intervention, and in clinical studies providing food to weight-stable subjects. Recently, the accuracy of the RDAs has been questioned on the grounds that measurements of total energy expenditure (TEE) obtained by using the accurate doubly labeled water technique (3, 4) in healthy, weight-stable subjects are typically considerably higher than anticipated on the basis of the RDAs (5 10). There is therefore a need for equations that can be used to predict the energy requirements of individuals and groups more accurately than the current RDAs. Two studies reported relations between TEE determined by doubly labeled water and simple laboratory measures in older adults and children (11, 12) (n = 13 and 30, respectively), which suggests the feasibility of developing a regression equation based on this approach. The numbers of subjects in those studies, however, were too small for development or cross-validation of generally applicable equations. The study described here was designed to develop and cross-validate regression equations to predict TEE and energy requirements in a larger group of healthy adults. 1 From the Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston. 2 Supported by the US Department of Agriculture, Agricultural Research Service, under contract 53-3K and by NIH grants AG12829, NIH DK46124, and 2P30DK Address reprint requests to SB Roberts, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA roberts_em@hnrc.tufts.edu. Received May 21, Accepted for publication October 21, Am J Clin Nutr 1999;69: Printed in USA American Society for Clinical Nutrition

2 ESTIMATION OF ENERGY REQUIREMENTS 921 TABLE 1 Subject characteristics Young men Older men Young women Middle-aged women Older women (n = 24) (n = 20) (n = 10) (n = 29) (n = 10) Age range (y) Age (y) 23.1 ± ± ± ± ± 4.4 Body weight (kg) 71.7 ± ± ± ± ± 9.9 Height (cm) ± ± ± ± ± 4.7 BMI (kg/m 2 ) 22.6 ± ± ± ± ± 3.0 Body fat (% of wt) 13.9 ± ± ± ± ± 7.2 Lean body mass (kg) 61.6 ± ± ± ± ± 3.7 Waist-to-hip ratio 0.83 ± ± ± ± ± 0.07 Reported strenuous activity (min/d) 2 21 ± 23 5 ± 7 31 ± ± 26 6 ± 7 1 x ± SD. 2 Reported activities with an energy cost >5 predicted resting energy expenditure. SUBJECTS AND METHODS Subjects The subjects were 93 adult men and women participating in 3 different studies of energy regulation (5 7; SB Roberts, unpublished observations, 1999). All subjects were either employed full-time or were retired. They were in good health as judged by a routine physical examination, blood tests, and psychologic and health-history questionnaires and none smoked or took medications known to influence energy regulation. Details of the subjects are given in Table 1. The studies were conducted at the Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, and the Clinical Research Center at the Massachusetts Institute of Technology, Boston, with permission from the respective institutes. Informed consent was obtained from all volunteers before the start of the study. Protocol TEE was measured with the doubly labeled water technique over an 8 14 d period (starting on day 1 2 of the menstrual cycle in the young women); identical procedures (13) were followed in all 3 studies. Subjects arrived at the research center the night before the start of the study, were familiarized with the study procedures, and slept in the inpatient facility overnight. In the morning, a measurement of REE was taken, followed by body-composition and anthropometric measurements. All subjects were then dosed with doubly labeled water for the measurement of TEE, kept sedentary for 5 h during isotope equilibration and collection of initial urine samples, and then allowed to return to their homes. Sixty-six subjects also received instructions on how to wear and use activity monitors (Caltrac; Muscle Dynamics Fitness Network, Torrance, CA). During the next 7 14 d the subjects maintained their usual lifestyles, during which time they collected urine specimens for the doubly labeled water measurement; the 66 subjects wore their activity monitors for the duration of the study. At the end of the study period, subjects returned to the research center for anthropometric measurements and to deliver their urine samples. Measurement of total energy expenditure At the start of each TEE measurement, subjects were given a mixed dose of doubly labeled water ( 2 H 18 2 O) containing 0.15 g H 18 2 O/kg body wt and 0.07 g 2 H 2 O/kg body wt. The dose was given early in the morning after subjects had fasted overnight and after the collection of 2 baseline urine specimens. Subjects then fasted for 4 h while hourly urine specimens were collected. Urine specimens at the second void of the day were then collected daily at home by the subjects, at about the same time of day that the isotope dose was administered. The final samples were collected at the research center when subjects returned for testing. Isotope analyses were performed by using isotope ratio mass spectrometry (SIRA-10; VG Isogas, Middlewich, United Kingdom). Samples were prepared for 1 H: 2 H analysis by using the zinc-reduction reaction and for 16 O: 18 O analysis in carbon dioxide by using the H 2 O- CO 2 equilibrator system (14). Analyses for 1 H: 2 H abundances were conducted in triplicate on each urine or standard sample and analyses for 16 O: 18 O abundances were conducted in duplicate. TEE was calculated by using standard equations (15). In these calculations, the values for respiratory quotient used to convert measured values for carbon dioxide production rate to TEE were obtained by using a food quotient. For the women, the food quotient was calculated from 7-d weighed food intake records kept by the subjects during the doubly labeled water measurement period, as described elsewhere (7). For the men, the food quotient was calculated from the diet provided to them by the research center because they were participating concomitantly in a study in which they were provided with all food and were required to maintain a constant body weight. For all subjects, food quotients were modified to take into account small changes in energy balance during the measurement period, assuming proportional changes in body weight and body fat (16). Measurement of resting energy expenditure Each subject s REE was determined by indirect calorimetry for 30 min after he or she had fasted overnight for h. Before the REE measurement, subjects rested quietly for 30 min under thermoneutral conditions. They were familiarized with the calorimetry procedure before the measurement. Routine alcohol burn experiments in our laboratory showed that the calorimeter measurements were accurate to ±1%. Rates of energy expenditure were calculated from oxygen consumption and carbon dioxide production by using de Weir s equation (17). The REE of each subject was also predicted (pree) by using the FAO/WHO/UNU equations (1) on the basis of age group, sex, and body weight. Estimation of physical activity Activity monitors were worn like a pager at the waist to estimate physical activity. The monitors are accelerometers and

3 922 VINKEN ET AL TABLE 2 Summary of the energy expenditure data 1 Young men Older men Young women Middle-aged women Older women (n = 24) (n = 20) (n = 10) (n = 29) (n = 10) Measured energy expenditure TEE (MJ/d) ± 2.66 a ± 2.37 b 9.91 ± 1.76 b,c 9.33 ± 1.36 c 7.59 ± 0.90 d REE (MJ/d) 7.23 ± 0.83 a 6.21 ± 0.91 b 5.61 ± 0.30 c 5.16 ± 0.50 c,d 4.79 ± 0.43 d TEE REE (MJ/d) 6.82 ± 2.32 a 4.60 ± 1.85 b 4.31 ± 1.72 b,c 4.14 ± 1.21 b,c 2.80 ± 0.81 c PAL 1.94 ± 0.31 a 1.74 ± 0.27 b,c 1.77 ± 0.30 a,c,d 1.81 ± 0.23 a,c 1.59 ± 0.18 b,d Predicted energy expenditure TEE eq1 (MJ/d) ± ± ± ± ± 1.07 TEE eq2 (MJ/d) ± ± ± 1.16 NA 7.63 ± 0.53 TEE eq3 (MJ/d) ± ± ± 1.06 NA 7.48 ± 1.01 RDA (MJ/d) ± ± ± ± ± x ± SD. TEE, total energy expenditure; REE, resting energy expenditure; PAL, physical activity level (TEE/REE); eq1, eq2, eq3, equations 1, 2, and 3 in text, respectively; RDA, recommended dietary allowance (2); NA, not available. Means within each row with different superscript letters are significantly different, P < Significantly different from measured TEE, P < detect motion in the horizontal plane. When a subject moves, a cantilevered beam in the monitor (supported at one end) bends and emits a current proportional to the force acting on it. A small computer in the unit then plots an acceleration curve and integrates the area under that curve for the estimation of the amount of physical activity. Activity monitors predict REE by using an equation that includes age, sex, height, and weight (18) and give estimates of TEE by combining the estimate for REE with an estimate for energy expended in physical activity. Subjects were instructed how to use the monitors while they were resident in our research facility and were instructed to record readings in the morning immediately after waking and at night immediately before sleeping for 7 consecutive 24-h periods. Readings of daily energy expenditure from the activity monitors were used to develop the equations. Self-reported strenuous activity was also assessed. Sixty-four subjects reported durations and types of strenuous leisure time activity daily for 7 consecutive days, and the subset of these reported activities with an estimated energy expenditure of >5 pree (1) were judged to be truly strenuous and averaged per day. In the remaining 29 subjects, the Minnesota Leisure Time Activity Questionnaire (19) was completed; again, reported activities with an estimated energy cost of >5 pree were used for data analysis. Measurement of body composition Standard anthropometric measurements (including weight, height, and waist, hip, and thigh circumferences) were obtained on the first and last days of the study period. Body fat and fatfree mass were also determined by underwater weighing (20) with a correction for measured residual lung volume (21) on the first study day after subjects had fasted overnight. Measurements were repeated until 4 determinations of body fat content were within 1% of each other. Residual lung volume was measured by nitrogen washout [model 505 Nitralyzer; Med Science, St Louis (22)]. Statistical analysis Data are expressed as means ± SDs unless specified otherwise. Differences between subject groups were assessed by one-way analysis of variance with Tukey s honestly significant difference adjustment for multiple comparisons. Multiple regression analysis was performed to determine prediction equation 1, and stepwise multiple regression analyses were performed to determine prediction equations 2 and 3. Cross-evaluation (23) was conducted by calculating predicted TEE with prediction equation 1 and comparing this value with TEE measured by the doubly labeled water method in subjects from 3 different studies (11, 24, 25). Bootstrap analysis with 100 bootstrap samples was performed according to Efron and Tibshirani (26) to estimate the true prediction error of prediction equation 1. A bootstrap sample is a random sample of the original data set, with the same number of observations as the original data set, drawn with replacement. For prediction equation 2, stepwise regression analysis was performed with 100 bootstrap samples to determine the retention of variables in a set that included age, sex, height, weight, activity monitor activity, percentage body fat, reported minutes of strenuous activity, and REE. Statistical analyses were done with SPSS (version 7.5 for WINDOWS; SPSS Inc, Chicago) and SYSTAT (version 7.01 for WINDOWS; SPSS Inc). RESULTS Shown in Table 2 is a summary of the doubly labeled water and other measured and reported energy expenditure data for subjects divided into 5 groups by age and sex. Young subjects had significantly higher TEEs and REEs than older subjects and men had significantly higher TEEs and REEs than women. The TEE and REE of middle-aged women was not significantly different from that of young women, but TEE in these 2 groups was significantly higher than that in older women. In addition, young men had a significantly higher physical activity level (equal to the ratio of TEE to REE) than older men and than all age groups of women, and the older men had a significantly higher physical activity level than the older women. Measured PAL ranged from 1.21 to 2.57 in individual subjects with a mean of This value is significantly higher than the mean adult energy requirements of 1.55 and 1.67 times metabolic rate as proposed by the FAO/WHO/UNU (1) and the National Research Council (2). Three different best-fitting regression equations were developed from different combinations of basic and laboratory data to

4 ESTIMATION OF ENERGY REQUIREMENTS 923 FIGURE 1. Regression of total energy expenditure (TEE) measured by the doubly labeled water method versus TEE predicted by using equation 1 (r 2 = 0.64, SEE = 1.80 MJ/d, P = 0.001), equation 2 (r 2 = 0.78, SEE = 1.55 MJ/d, P = 0.001), equation 3 (r 2 = 0.76, SEE = 1.65 MJ/d, P = 0.001), and the current recommended dietary allowances [RDAs (2)] (r 2 = 0.65, SEE = 1.74 MJ/d, P = 0.001). Also shown are plots of the residuals versus predicted TEE. Regression lines were not significantly different from the line of identity except for the plot of measured TEE versus TEE predicted by the RDAs (slope = 1.25 compared with 1.0 for the line of identity; 95% CI: 1.05, 1.44). Mean residuals are indicated by the dashed line at 0 MJ/d and 80th percentiles by the dotted lines. For TEE predicted by the RDAs, the mean difference between measured and predicted TEE is shown by the dashed line at 0 MJ/d and the 80th percentiles by the dotted lines. predict TEE. The first equation included variables that can be assessed in any field setting: TEE eq1 = (0.073 age) + ( wt) + ( ht) (1.363 sex) (1) where TEE eq1 is predicted TEE in MJ/d, age is in y, wt is body weight in kg, ht is standing height in cm, and sex is 0 for men and 1 for women [r 2 = 0.64, SEE = 1.80 MJ/d (430 kcal/d), P < 0.001]. Plots of TEE eq1 versus TEE measured by the doubly labeled water method and of TEE eq1 versus the residuals (the difference between measured and predicted TEE) are shown in Figure 1. The plot of predicted TEE versus the residuals shows that the 80% limit of agreement was 0 ± 2.25 MJ/d, indicating that in 80% of the cases individual TEE could be predicted within ± 2.25 MJ/d of measured TEE. Bootstrap analysis was done to estimate prediction error. We used 100 bootstrap samples, estimated a model on each sample by using the same variables used to estimate the model for prediction equation 1, and then applied each fitted model to the original data set to give 100 estimates of prediction error. The overall estimate of prediction error was the average of these 100 estimates. This bootstrap analysis showed that the optimism, or amount by which the average residual mean squared error (MSE) underestimated the true residual MSE of equation 1, was Adding this value to the original residual MSE, the true residual MSE of equation 1 was 3.60, which gives a true SEE (square root of the residual MSE) of 1.90 MJ/d for equation 1. This small change from 1.80 indicates that equation 1 can be expected to give similar SEEs in other groups of comparable subjects. The validity of this equation was also evaluated by using published TEE data on 30 nonobese, free-living men and women (11, 24, 25). Reported values for age, weight, height, and sex in those studies were used to calculate TEE by using equation 1 and the values were compared with data on measured TEE also given in the reports. The equation accounted for 66% of the variance in TEE. A plot of TEE eq1 versus measured TEE in this data set and a plot of TEE eq1 versus the residuals showed that 80% prediction limits were from 1.8 to 1.8 MJ/d (Figure 2). A second regression equation was also developed with the intention of deriving an improved equation by fitting a model with minutes of reported strenuous activity, body composition, physical activity by activity monitor, and REE added to age, sex, weight, and height: TEE eq2 = (0.612 activity monitor activity) ( percentage body fat) + (1.149 REE) (2) where TEE eq2 is predicted TEE in MJ/d and activity monitor activity and REE are also in MJ/d [r 2 = 0.78, SEE = 1.55 MJ/d (370 kcal/d), P < 0.001]. Plots of TEE eq2 versus measured TEE and of the residuals versus TEE eq2 are shown in Figure 1. The plot of the residuals versus TEE eq2 showed that the 80% limit of agreement was 0 ± 1.95 MJ/d, indicating that in 80% of the cases, individual TEE could be predicted within ±1.95 MJ/d of true

5 924 VINKEN ET AL FIGURE 2. Regression of total energy expenditure (TEE) measured by the doubly labeled water method versus TEE predicted by using the validation data set (11, 24, 25) with equation 1 (r 2 = 0.66, SEE = 1.50 MJ/d, P = 0.001). Also shown is a plot of the residuals versus predicted TEE. The regression line was not significantly different from the line of identity. Mean residuals are indicated by the dashed line at 0 MJ/d and 80th percentiles by the dotted lines. TEE. Bootstrap analysis was performed to evaluate equation 2 because there were no data available in the literature to perform a direct validation. Again, 100 bootstrap samples were generated, and stepwise regression with backward elimination was performed on each sample by using the same methods as in the development of equation 2 to check the stability of the model. In 67 of 100 bootstrap samples, the same model with the same set of independent variables (percentage body fat, REE, and activity monitor activity) was kept in the final regression model. Activity monitor activity was one of the independent variables in 96 bootstrap samples, percentage body fat in 92 samples, and REE in 75 samples. All other independent variables tried in the original stepwise regression were retained as significant independent variables in <50% of the samples (between 17 and 47 times), indicating that the model produced from the original data set was reasonably stable and would be found in different comparable samples regularly. Because the activity monitor data were such strong predictors of TEE in prediction equation 2, and are a measure that is feasible to incorporate in field studies, we evaluated a third model including the independent variables of equation 1 (age, sex, height, and weight) and activity monitor data. The equation from this analysis was as follows: TEE eq3 = (0.033 age) + (0.040 wt) ( ht) (1.684 sex) + (0.76 activity monitor activity) (3) where TEE eq3 is predicted TEE in MJ/d (r 2 = 0.76, SEE = 1.65 MJ/d, P < 0.001). This model was less precise than equation 2, but more precise than equation 1, indicating that by adding a measure of energy expenditure to a field study, the accuracy of prediction of average individual energy requirements could be somewhat improved. Plots of TEE eq3 versus measured TEE and of the residuals versus TEE eq3 are shown in Figure 1. The plot of the residuals versus TEE eq3 shows that the 80% limit of agreement was 0 ± 2.04 MJ/d, indicating that in 80% of cases individual TEE could be predicted within ± 2.04 MJ/d of true TEE. Data on measured TEE and predicted TEE by equations 1, 2 and 3, and energy requirements calculated by using the RDAs are summarized in Table 2 (2). As expected, there were no significant differences between measured TEE and predicted TEE calculated by using equation 1, 2, or 3 in any of the age and sex groups. There were, however, significant differences between the RDAs (2) and measured TEE in 4 of the 5 groups (all except the older women), with measured TEE being higher than predicted TEE. These differences in 4 of the 5 groups were due to differences in energy expended in physical activity because, although the RDAs underpredict TEE, the predicted REEs on which the RDAs are based were actually higher than measured REE (Figure 3). Plots of measured TEE versus TEE predicted from the RDAs and of the difference between measured and predicted TEE versus predicted TEE are shown in Figure 1. The RDAs underestimated measured TEE, with increasing underestimation with increasing TEE. In the validation data set (11, 24, 25), the RDA underestimated measured TEE as well (Figure 4). DISCUSSION In view of the suspected inaccuracy of current RDAs for predicting usual energy needs in healthy adults (5 10), we investigated whether equations could be developed to provide improved estimates of energy requirements based on simple measures that FIGURE 3. Regression of measured resting energy expenditure (REE) versus REE predicted by the recommended dietary allowances [RDAs (2)] (r 2 = 0.87, SEE = 0.41 MJ/d, P = 0.001). The regression line was not significantly different from the line of identity.

6 ESTIMATION OF ENERGY REQUIREMENTS 925 FIGURE 4. Regression of total energy expenditure (TEE) measured in the validation data set (11, 24, 25) versus TEE predicted by the recommended dietary allowances [RDAs (2)]. Predicted TEE is significantly lower than measured TEE (P < 0.01). could be obtained in any field or laboratory setting. This approach was explored previously in 2 small groups and in a larger compilation of data from several laboratories. Goran and Poehlman (11) studied the association between TEE (equal to energy requirements in weight-stable subjects) and REE, physical activity, body composition, and energy intake in 13 elderly men and women and concluded that TEE was significantly related to maximal oxygen consumption and leisure time physical activity. However, the small number of subjects in that study and the use of maximal oxygen consumption, which is sensitive to the testing protocol and unquantifiable factors such as the motivational behavior of the investigators (27), limit the widespread applicability of those equations. Another study, conducted by the same group, showed an association between TEE and fat-free mass, heart rate, and REE in children (12), but again the number of subjects was small (n = 30). Black et al (23) showed a high correlation between TEE and weight, height, and age in large groups of adult men and women, but did not cross-validate the equations to demonstrate their widespread applicability or report any laboratory data that could be used to develop more accurate equations. In the current study we used data from 93 healthy men and women to develop equations for predicting TEE based on both demographic data and simple laboratory measures. Prediction equation 1, which used data on age, sex, height, and weight, was comparable in terms of its precision to the equation developed by Black et al (23) and was shown to be valid for similar populations in a cross-validation conducted by using published TEE values from other research groups. Although the SEE of the equation, 1.80 MJ/d, was relatively large and comparable with the SEE for the relation of TEE to energy requirements predicted by using current RDAs (1.74 MJ/d), there was no demonstrable bias in predicted energy requirements over the range of TEEs tested. In contrast, the RDAs for energy were shown to generally underpredict true TEE in our 93 subjects, with the extent of underestimation increasing with increasing TEE. This underestimation was not found in REE, suggesting that the multiplication factor in the RDAs to allow for physical activity is too low, especially in physically active subjects. The reason the current RDAs may underestimate usual energy requirements is unknown. We speculated previously (5, 6), however, that the use of theoretical predictions of usual activity patterns to generate energy RDAs (1, 2) may be an important factor. This is because such predictions are more are more likely to underestimate total activity than to overestimate it because of the difficulty of accounting for activity (and hence energy expenditure) unrelated to specific tasks. To investigate whether more precise estimates of TEE could be obtained by using information on physical activity and body composition, we developed 2 additional equations. Equation 2 included the variables REE, percentage body fat, and physical activity as determined by using activity monitors. The agreement of equation 2 with TEE measured by doubly labeled water was improved over that of equation 1, with an SEE of 1.55 MJ/d. Equation 3 used data on age, sex, height, weight, and physical activity as determined by using activity monitors and yielded an SEE of 1.65 MJ/d. Both equations 2 and 3 thus somewhat improved the estimate of TEE over equation 1 and, as with equation 1, predicted TEE with equivalent accuracy over the range of TEE measured. There are several potential uses for these new equations. In particular, equation 1 may provide an alternative to the current RDAs for estimating group energy requirements in healthy adults when the mean BMI of the group is within the range of Equations 2 and 3 can also be used for the same purpose when more information than just age, weight, and height is available and will provide slightly more precise values. In addition, all 3 equations may be useful in research studies requiring estimations of individual energy requirements as a starting point for other measurements. For example, studies needing to provide weight-maintenance diets as a starting point for nutrition interventions may be more accurate if they make use of equations such as those described here rather than the current RDAs. In addition, equations predicting usual energy needs also can be used to screen for inaccurate records in dietary surveys by providing a value for expected energy intake against which reported values can be compared. In summary, we showed the feasibility and improved accuracy of predicting usual energy requirements from simple equations rather than from current RDAs in healthy, nonobese adults aged y. Additional research is needed to develop equivalent equations suitable for other groups, including children and adolescents, obese adults, adults with chronic diseases, and persons in developing countries. We thank the subjects for participating in this study and Paul Fuss and Megan McCrory for their invaluable help. REFERENCES 1. FAO/WHO/UNU. Energy and protein requirements. Report of a joint FAO/WHO/UNU expert consultation. World Health Organ Tech Rep Ser 1985; National Research Council. Recommended dietary allowances. 10th ed. Washington, DC: National Academy Press, Schoeller DA. Measurement of energy expenditure in free-living humans by using doubly labeled water. J Nutr 1988;118: Roberts SB. Use of the doubly labeled water method for measurement of energy expenditure, total body water, water intake, and metabolizable energy intake in humans and small animals. Can J Physiol Pharmacol 1989;67: Roberts SB, Heyman MB, Evans WJ, Fuss P, Tsay R, Young VR. Dietary energy requirements of young adult men, determined by using the doubly labeled water method. Am J Clin Nutr 1991;54: Roberts SB, Young VR, Fuss P, et al. What are the dietary energy needs of elderly adults? Int J Obes 1992;16:

7 926 VINKEN ET AL 7. Sawaya AL, Saltzman E, Fuss P, Young VR, Roberts SB. Dietary energy requirements of young and older women determined by using the doubly labeled water method. Am J Clin Nutr 1995;62: Black AE, Bingham SA, Johansson G, Coward WA. Validation of dietary intakes of protein and energy against 24 hour urinary N and DLW energy expenditure in middle-aged women, retired men and post-obese subjects: comparisons with validation against presumed energy requirements. Eur J Clin Nutr 1997;51: Jones PJ, Martin LJ, Su W, Boyd NF. Canadian recommended nutrient intakes underestimate true energy requirements in middle-aged women. Can J Public Health 1997;88: Schoeller DA, Fjeld CF. What have we learned from the doubly labeled water method? Annu Rev Nutr 1991;11: Goran MI, Poehlman ET. Total energy expenditure and energy requirements in healthy elderly persons. Metabolism 1992;41: Goran MI, Carpenter WH, Poehlman ET. Total energy expenditure in 4- to 6-yr-old children. Am J Physiol 1993;264:E Tarasuk V, Beaton GH. Menstrual-cycle patterns in energy and macronutrient intake. Am J Clin Nutr 1991;53: Wong WW, Lee LS, Klein PD. Deuterium and oxygen-18 measurements on microliter samples of urine, plasma, saliva, and human milk. Am J Clin Nutr 1987;45: Roberts SB, Young VR, Fuss P, et al. Energy expenditure and subsequent nutrient intakes in overfed young men. Am J Physiol 1990;259:R Dallal GE, Roberts SB. DLW: a computer program for the calculation of total energy expenditure in doubly labeled water ( 2 H 2 18 O) studies. Comp Biomed Res 1991;24: de Weir JBV. New method for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949;109: Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990;51: Taylor HL, Jacobs DR, Schucker B, Knudsen J, Leon AS, DeBacker G. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis 1978;31: Siri WE. Body composition from fluid spaces and density: analysis of methods. In: Brozek J, Henschel A, eds. Techniques for measuring body composition. Washington, DC: National Academy of Sciences, 1961: Wilmore JH, Vodak PA, Parr RB, Girandola RN, Billing JE. Further simplification of a method for determination of residual lung volume. Med Sci Sports Exerc 1980;12: Darling RC, Cournand A, Richards DW. Studies on the intrapulmonary mixture of gases. III. An open-circuit method for measuring residual air. J Clin Invest 1940;19: 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: Livingstone MB, Prentice AM, Coward WA, et al. Simultaneous measurement of free-living energy expenditure by the doubly labeled water method and heart-rate monitoring. Am J Clin Nutr 1990;52: Seale JL, Rumpler WV, Conway JM, Miles CW. Comparison of doubly labeled water, intake-balance, and direct- and indirect-calorimetry methods for measuring energy expenditure in adult men. Am J Clin Nutr 1990;52: Efron B, Tibshirani R. An introduction to the bootstrap. London: Chapman & Hall, 1993: Fairshter RD, Walters J, Salness K, Fox M, Minh VD, Wilson AF. A comparison of incremental exercise tests during cycle and treadmill ergometry. Med Sci Sports Exerc 1983;15: