Variation In Total Energy Expenditure Research Articles

Greater male variability in daily energy expenditure develops through puberty.

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decreasing GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages.

The metabolic cost of physical activity in mice using a physiology-based model of energy expenditure

ObjectivePhysical activity is a major component of total energy expenditure (TEE) that exhibits extreme variability in mice. Our objective was to construct a general, physiology-based model of TEE to accurately quantify the energy cost of physical activity. MethodsSpontaneous home cage physical activity, body temperature, TEE, and energy intake were measured with frequent sampling. The energy cost of activity was modeled considering six contributors to TEE (basal metabolic rate, thermic effect of food, body temperature, cold induced thermogenesis, physical activity, and body weight). An ambient temperature of 35 °C was required to remove the contribution from cold induced thermogenesis. Basal metabolic rate was adjusted for body temperature using a Q10 temperature coefficient. ResultsWe developed a TEE model that robustly explains 70–80% of the variance in TEE at 35 °C while fitting only two parameters, the basal metabolic rate and the mass-specific energy cost per unit of physical activity, which averaged 60 cal/km/g body weight. In Ucp1−/− mice the activity cost was elevated by 60%, indicating inefficiency and increased muscle thermogenesis. The diurnal rhythm in TEE was quantitatively explained by the combined diurnal differences in physical activity, body temperature, and energy intake. Incorporating body temperature into human basal metabolic rate measurements significantly reduced the inter-individual variation. ConclusionsThe physiology-based model of TEE allows quantifying the energy cost of physical activity. While applied here to mice, the model should be generally valid across species. Due to the effect of body temperature, we suggest that basal metabolic rate measurements be corrected to a reference body temperature, including in humans. Having an accurate cost of physical activity allows mechanistic dissection of disorders of energy homeostasis, including obesity.

Variability in energy expenditure is much greater in males than females

In mammals, trait variation is often reported to be greater among males than females. However, to date, mainly only morphological traits have been studied. Energy expenditure represents the metabolic costs of multiple physical, physiological, and behavioral traits. Energy expenditure could exhibit particularly high greater male variation through a cumulative effect if those traits mostly exhibit greater male variation, or a lack of greater male variation if many of them do not. Sex differences in energy expenditure variation have been little explored. We analyzed a large database on energy expenditure in adult humans (1494 males and 3108 females) to investigate whether humans have evolved sex differences in the degree of interindividual variation in energy expenditure. We found that, even when statistically comparing males and females of the same age, height, and body composition, there is much more variation in total, activity, and basal energy expenditure among males. However, with aging, variation in total energy expenditure decreases, and because this happens more rapidly in males, the magnitude of greater male variation, though still large, is attenuated in older age groups. Considerably greater male variation in both total and activity energy expenditure could be explained by greater male variation in levels of daily activity. The considerably greater male variation in basal energy expenditure is remarkable and may be explained, at least in part, by greater male variation in the size of energy-demanding organs. If energy expenditure is a trait that is of indirect interest to females when choosing a sexual partner, this would suggest that energy expenditure is under sexual selection. However, we present a novel energetics model demonstrating that it is also possible that females have been under stabilizing selection pressure for an intermediate basal energy expenditure to maximize energy available for reproduction.

The relationship of sleep duration and quality to energy expenditure and physical activity in children.

Shorter sleep duration has been linked to the risk for obesity in children. The pathways linking sleep duration and quality to the risk of obesity are unclear, particularly the effect of sleep on energetics. We investigated the relationship between sleep duration, quality and timing in children, to the basal metabolic rate (BMR), total energy expenditure (TEE) and physical activity (PA). Fifty nine children in two age-groups (5-11 and 12-18 years) underwent evaluation of body composition (DXA), BMR in a room calorimeter, free-living TEE by doubly labelled water method, sleep and PA (7-day Actiheart monitor) during school break. Sleep duration contributed to the variance in BMR (β = 0.11, P = .009) after adjusting for age-group, sex, lean and fat mass, but not to the variance in TEE. Late sleep timing was related to lower PA. In the younger age-group, children who met recommended sleep duration on ≥50% of the 7 days had higher light PA (P = .03) and lower sedentary time (P = .009). Suboptimal sleep is associated with lower BMR, lower PA, and higher sedentary behaviours in young children. Prospective studies are needed to confirm if insufficient sleep duration or late sleep timing contribute to obesity risk by increasing sedentary behaviours and decreasing BMR.

Training-induced changes in daily energy expenditure: Methodological evaluation using wrist-worn accelerometer, heart rate monitor, and doubly labeled water technique.

IntroductionWrist-mounted motion sensors can quantify the volume and intensity of physical activities, but little is known about their long-term validity. Our aim was to validate a wrist motion sensor in estimating daily energy expenditure, including any change induced by long-term participation in endurance and strength training. Supplemental heart rate monitoring during weekly exercise was also investigated.MethodsA 13-day doubly labeled water (DLW) measurement of total energy expenditure (TEE) was performed twice in healthy male subjects: during two last weeks of a 12-week Control period (n = 15) and during two last weeks of a 12-week combined strength and aerobic Training period (n = 13). Resting energy expenditure was estimated using two equations: one with body weight and age, and another one with fat-free mass. TEE and activity induced energy expenditure (AEE) were determined from motion sensor alone, and from motions sensor combined with heart rate monitor, the latter being worn during exercise only.ResultsWhen body weight and age were used in the calculation of resting energy expenditure, the motion sensor data alone explained 78% and 62% of the variation in TEE assessed by DLW at the end of Control and Training periods, respectively, with a bias of +1.75 (p <.001) and +1.19 MJ/day (p = .002). When exercise heart rate data was added to the model, the combined wearable device approach explained 85% and 70% of the variation in TEE assessed by DLW with a bias of +1.89 and +1.75 MJ/day (p <.001 for both). While significant increases in TEE and AEE were detected by all methods as a result of participation in regular training, motion sensor approach underestimated the change measured by DLW: +1.13±0.66 by DLW, +0.59±0.69 (p = .004) by motion sensor, and +0.98±0.70 MJ/day by combination of motion sensor and heart rate. Use of fat-free mass in the estimation of resting energy expenditure removed the biases between the wearable device estimations and the golden standard reference method of TEE and demonstrated a training-induced increase in resting energy expenditure by +0.18±0.13 MJ/day (p <.001).ConclusionsWrist motion sensor combined with a heart rate monitor during exercise sessions, showed high agreement with the golden standard measurement of daily TEE and its change induced by participation in a long-term training protocol. The positive findings concerning the validity, especially the ability to follow-up the change associated with a lifestyle modification, can be considered significant because they partially determine the feasibility of wearable devices as quantifiers of health-related behavior.

Accelerometer Data Processing and Energy Expenditure Estimation in Preschoolers.

To assess the capacity of different acceleration metrics from wrist accelerations to estimate total energy expenditure (TEE) and activity energy expenditure (AEE) using doubly labeled water in preschool children. Thirty-nine preschoolers (5.5 ± 0.1 yr) were included. Total energy expenditure was measured using doubly labeled water during 14 d, and AEE was then calculated using a predicted basal metabolic rate. Participants wore a wGT3X-BT accelerometer on their nondominant wrist for ≥5 d. We derived the following metrics from raw accelerations: raw ActiGraph activity counts using the normal filter and the low-frequency extension; and alternate summary metrics, such as the Euclidian norm minus 1g (ENMO), Euclidian norm of the high-pass-filtered accelerations (HFEN), the bandpass-filtered accelerations, the HFEN plus Euclidean norm of low-pass filtered accelerations minus 1g (HFEN+) and the mean amplitude deviation. Alternate summary metrics explained a larger proportion of the variance in TEE and AEE than ActiGraph's activity counts (counts, 7-8 and 25% of TEE and AEE; alternate summary metrics, 13%-16% and 35%-39% of TEE and AEE). Adjustments for body weight and height resulted in an explanation of 51% of AEE by ENMO. All of the metrics adjusted for fat mass and fat-free mass explained up to 84% and 67% of TEE and AEE, respectively. ENMO and the other alternate summary metrics explained more of the variance in TEE and AEE than the ActiGraph's activity counts in 5-yr-old children, suggesting further exploration of these variables in studies on physical activity and energy expenditure in preschoolers. Our results need confirmation in other populations with wider age groups and varying body compositions.

Energy Expenditure in Humans and Other Primates: A New Synthesis

This review examines the proximate, ecological, and evolutionary determinants of energy expenditure in humans and primates, with an emphasis on empirical measurements of total energy expenditure (TEE). Body size is the main proximate determinant of TEE, both within and between species; physical activity, genetic variation, and endocrine regulation explain substantially less of the variation in TEE. Basal metabolism is the single largest component of TEE, far exceeding the cost of physical activity, digestion, growth and reproduction, and thermoregulation in most instances. Notably, differences in physical activity do not generally result in corresponding differences in TEE, undermining the utility of activity-based factorial estimates of TEE. Instead, empirical measurements of energy expenditure in humans and other primates suggest that the body adapts dynamically to long-term changes in physical activity, maintaining TEE within an evolved, and relatively narrow, physiological range.

Evaluation of Actiheart and a 7 d activity diary for estimating free-living total and activity energy expenditure using criterion methods in 1·5- and 3-year-old children

Accurate and easy-to-use methods to assess free-living energy expenditure in response to physical activity in young children are scarce. In the present study, we evaluated the capacity of (1) 4d recordings obtained using the Actiheart (mean heart rate (mHR) and mean activity counts (mAC)) to provide assessments of total energy expenditure (TEE) and activity energy expenditure (AEE) and (2) a 7d activity diary to provide assessments of physical activity levels (PAL) using three sets of metabolic equivalent (MET) values (PALTorun, PALAdolph and PALAinsworth) in forty-four and thirty-one healthy Swedish children aged 1·5 and 3 years, respectively. Reference TEE, PALref and AEE were measured using criterion methods, i.e. the doubly labelled water method and indirect calorimetry. At 1·5 years of age, mHR explained 8% (P=0·006) of the variation in TEE above that explained by fat mass and fat-free mass. At 3 years of age, mHR and mAC explained 8 (P=0·004) and 6 (P=0·03)% of the variation in TEE and AEE, respectively, above that explained by fat mass and fat-free mass. At 1·5 and 3 years of age, average PALAinsworth values were 1·44 and 1·59, respectively, and not significantly different from PALref values (1·39 and 1·61, respectively). By contrast, average PALTorun (1·5 and 3 years) and PALAdolph (3 years) values were lower (P<0·05) than the corresponding PALref values. In conclusion, at both ages, Actiheart recordings explained a small but significant fraction of free-living energy expenditure above that explained by body composition variables, and our activity diary produced mean PAL values in agreement with reference values when using MET values published by Ainsworth.

Validity of hip-mounted uniaxial accelerometry with heart-rate monitoring vs. triaxial accelerometry in the assessment of free-living energy expenditure in young children: the IDEFICS Validation Study

One of the aims of Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS) validation study is to validate field measures of physical activity (PA) and energy expenditure (EE) in young children. This study compared the validity of uniaxial accelerometry with heart-rate (HR) monitoring vs. triaxial accelerometry against doubly labeled water (DLW) criterion method for assessment of free-living EE in young children. Forty-nine European children (25 female, 24 male) aged 4-10 yr (mean age: 6.9 ± 1.5 yr) were assessed by uniaxial ActiTrainer with HR, uniaxial 3DNX, and triaxial 3DNX accelerometry. Total energy expenditure (TEE) was estimated using DLW over a 1-wk period. The longitudinal axis of both devices and triaxial 3DNX counts per minute (CPM) were significantly (P < 0.05) associated with physical activity level (PAL; r = 0.51 ActiTrainer, r = 0.49 uniaxial-3DNX, and r = 0.42 triaxial Σ3DNX). Eight-six percent of the variance in TEE could be predicted by a model combining body mass (partial r(2) = 71%; P < 0.05), CPM-ActiTrainer (partial r(2) = 11%; P < 0.05), and difference between HR at moderate and sedentary activities (ModHR - SedHR) (partial r(2) = 4%; P < 0.05). The SE of TEE estimate for ActiTrainer and 3DNX models ranged from 0.44 to 0.74 MJ/days or ∼7-11% of the average TEE. The SE of activity-induced energy expenditure (AEE) model estimates ranged from 0.38 to 0.57 MJ/day or 24-26% of the average AEE. It is concluded that the comparative validity of hip-mounted uniaxial and triaxial accelerometers for assessing PA and EE is similar.

Activity Counts From Accelerometers Do Not Add Value to Energy Expenditure Predictions in Sedentary Overweight Individuals During Weight Loss Interventions

Knowing the total energy expenditure (TEE) of overweight adults is important for prescribing weight loss interventions. However, objective measurements of TEE may not always be readily available and can be expensive. This study aimed to investigate the validity of RT3 accelerometers in predicting the TEE of sedentary overweight adults, and to identify any sensitivity to anthropometric changes. The analysis used data from a 12-week weight loss study. At baseline and 12-week, TEE was predicted using RT3 accelerometers during whole room calorimeter stays. Bias between 2 methods was compared at and between the baseline and 12-week measurement points. Multiple regression analyses of TEE data were conducted. Predicted and measured values for TEE were not different at baseline (P = .677) but were significantly different after weight loss (P = .007). However, the mean bias between methods was small (<100 kcal/d) and was not significantly different between 2 time-points. RT3 activity counts explained an additional 2% of the variation in TEE at 12-week but not at baseline. RT3 accelerometers are not sensitive to body composition changes and do not explain variation in TEE of overweight and obese individuals in a sedentary environment.

Energetics during reproduction: A doubly labeled water study of lactating baboons

Understanding the costs and regulation of reproduction in primates requires understanding the separate but linked effects of energy availability and total energy expenditure (TEE). We compared variation in TEE and energy intake (EI) between two periods, early lactation and after the resumption of sexual cycling, for eight females from two groups of normally reproducing colony-living baboons (Papio h. anubis). Total energy expenditure was assessed using the doubly labeled water method. TEE was correlated with maternal mass both during early lactation and after the resumption of cycling. TEE after the resumption of cycling was positively related to infant growth rates; mothers with rapidly growing infants had higher energy expenditure. TEE was however unrelated to maternal rank and only weakly associated with reproductive parameters such as delay to conception. EI in early lactation was related to infant mass and interbirth intervals, but unrelated to infant growth or reproductive parameters once cycling had resumed. Energy availability (EA; the difference between intake and expenditure) differed significantly between subordinate and dominant females during early lactation, was highly variable among individuals as a function of body composition, and is suggested to follow a nonlinear relationship as a complex function of social status, lactation stage, infant growth, and female fertility. Thus, as a consequence of reduced energy availability, subordinate females in this captive context may experience reproductive delays even though the total energy expenditure after the return of cycling was similar between high and low ranking females.

Estimation of Free‐Living Energy Expenditure Using a Novel Activity Monitor Designed to Minimize Obtrusiveness

The aim of this study was to investigate the ability of a novel activity monitor designed to be minimally obtrusive in predicting free-living energy expenditure. Subjects were 18 men and 12 women (age: 41 +/- 11 years, BMI: 24.4 +/- 3 kg/m(2)). The habitual physical activity was monitored for 14 days using a DirectLife triaxial accelerometer for movement registration (Tracmor(D)) (Philips New Wellness Solutions, Lifestyle Incubator, the Netherlands). Tracmor(D) output was expressed as activity counts per day (Cnts/d). Simultaneously, total energy expenditure (TEE) was measured in free living conditions using doubly labeled water (DLW). Activity energy expenditure (AEE) and the physical activity level (PAL) were determined from TEE and sleeping metabolic rate (SMR). A multiple-linear regression model predicted 76% of the variance in TEE, using as independent variables SMR (partial-r(2) = 0.55, P < 0.001), and Cnts/d (partial r(2) = 0.21, P < 0.001). The s.e. of TEE estimates was 0.9 MJ/day or 7.4% of the average TEE. A model based on body mass (partial-r(2) = 0.31, P < 0.001) and Cnts/d (partial-r(2) = 0.23, P < 0.001) predicted 54% of the variance in TEE. Cnts/d were significantly and positively associated with AEE (r = 0.54, P < 0.01), PAL (r = 0.68, P < 0.001), and AEE corrected by body mass (r = 0.71, P < 0.001). This study showed that the Tracmor(D) is a highly accurate instrument for predicting free-living energy expenditure. The miniaturized design did not harm the ability of the instrument in measuring physical activity and in determining outcome parameters of physical activity such as TEE, AEE, and PAL.

The 24-hour respiratory quotient predicts energy intake and changes in body mass

To define the relationship between the respiratory quotient (RQ) and energy intake (EI) and to determine the impact of spontaneous locomotor activity (LMA) in the development of diet-induced obesity (DIO), we fed C57BL/6 mice a high-fat diet (HFD) for either 4 days or 17 wk and analyzed them using indirect calorimetry. Importantly, changes in body mass during calorimetry (DeltaM(b)) significantly covaried with RQ and EI; adjusting the data for DeltaM(b) permitted an analysis of the energy-balanced state. The 24-h RQ strongly predicted 24-h EI, and the slope of this relationship was diet dependent (HFD or chow) but independent of the HFD feeding period. Early-stage DIO was characterized by dark-period hyperphagia and fat storage, offset by greater light-period lipid oxidation; later stage DIO mice had a milder hyperphagia and lower substrate flexibility. Consequently, whereas 24-h RQ equaled the food quotient of the HFD in both early- and late-stage DIO, the range of RQ values was negatively correlated with, and mostly explained by, 24-h EI only in late-stage DIO. Lean and early-stage DIO mice had similar LMA values that were reduced in late-stage DIO. However, LMA significantly explained variance in total energy expenditure (EE) in only early-stage DIO mice. This indicated that the link between LMA and EE was a transient adaptive response to early DIO, whereas the later loss of LMA did not explain body weight gain in C57BL/6 DIO mice.

Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer

Accelerometers are often used to quantify the acceleration of the body in arbitrary units (counts) to measure physical activity (PA) and to estimate energy expenditure. The present study investigated whether the identification of types of PA with one accelerometer could improve the estimation of energy expenditure compared with activity counts. Total energy expenditure (TEE) of 15 subjects was measured with the use of double-labeled water. The physical activity level (PAL) was derived by dividing TEE by sleeping metabolic rate. Simultaneously, PA was measured with one accelerometer. Accelerometer output was processed to calculate activity counts per day (AC(D)) and to determine the daily duration of six types of common activities identified with a classification tree model. A daily metabolic value (MET(D)) was calculated as mean of the MET compendium value of each activity type weighed by the daily duration. TEE was predicted by AC(D) and body weight and by AC(D) and fat-free mass, with a standard error of estimate (SEE) of 1.47 MJ/day, and 1.2 MJ/day, respectively. The replacement in these models of AC(D) with MET(D) increased the explained variation in TEE by 9%, decreasing SEE by 0.14 MJ/day and 0.18 MJ/day, respectively. The correlation between PAL and MET(D) (R(2) = 51%) was higher than that between PAL and AC(D) (R(2) = 46%). We conclude that identification of activity types combined with MET intensity values improves the assessment of energy expenditure compared with activity counts. Future studies could develop models to objectively assess activity type and intensity to further increase accuracy of the energy expenditure estimation.

Validity of the ActiReg system in assessing energy requirement in chronic obstructive pulmonary disease patients

Malnutrition and weight loss are common in patients with chronic obstructive pulmonary disease (COPD) and effective nutritional support relies on accurate assessment of energy requirement. This could only be performed by measuring energy expenditure using objective methods. The aim of this study was to examine the validity of the ActiReg system in assessing energy requirement in non-hospitalized patients with severe COPD, using doubly labelled water (DLW) as criterion method. Total energy expenditure (TEE) was assessed from 14 days DLW analysis in 13 patients. During the first 7 days TEE was simultaneously assessed using the ActiReg system, combining measured resting energy expenditure (REE) with physical activity monitoring. A difference of -88 (782) kJ d(-1) (P = 0.69) was observed between the ActiReg system and DLW. REE explained 52% of the variation in TEE from DLW. Adding physical activity energy expenditure from the ActiReg system (PAEE(AR) = TEE(AR)-REE) increased the explained variation in TEE from DLW with 16%. The ActiReg system is valid in assessing energy requirement in non-hospitalized patients with severe COPD. The unique feature of being able to discriminate within both the low intensity activity range and moderate-to-high intensity activity range makes the ActiReg system a valuable tool in clinical nutritional support.

Estimating energy requirements: regression based prediction equations or multiples of resting metabolic rate

Energy requirements have traditionally been determined based on multiples of resting metabolic rate (RMR), known as Physical Activity Levels (PAL). With more data from doubly labelled water studies alternative approaches for estimating energy requirements have been suggested. Statistical analysis reveals that body weight explains more of the variance in total energy expenditure (TEE) than does RMR. The explanation for this phenomenon is that body weight contributes to the variance of both RMR and the other major determinant of TEE, i.e. physical activity related energy expenditure. Thus, in effect, the regression-based approach provides a more physiological appropriate model for TEE. Its major departure from tradition, difference from current adult proposals, and time taken for acceptance are the disadvantages of the regression-based approach.

Ability of the Actiwatch Accelerometer to Predict Free‐Living Energy Expenditure in Young Children

To determine whether activity counts obtained with the Actiwatch monitor are associated with total expenditure and body composition in young children. Actiwatch activity monitors were tested in 29 children 4 to 6 years old under field conditions over eight days. Total energy expenditure (TEE) was assessed with the doubly labeled water (DLW) technique. Correlation analyses were used to identify variables related to energy expenditure and percentage body fat. Multiple linear regression analyses were used to examine the variance in TEE and percentage body fat explained by activity counts after adjusting for relevant covariates. Both average total daily activity counts (658,816 +/- 201,657) and the pattern of activity were highly variable among subjects. TEE was significantly related to lean body mass (r = 0.45) and age (r = 0.48; p < 0.05 for both). Activity counts alone were not associated with TEE. In multiple linear regression analyses, TEE was independently associated with only lean body mass. Percentage fat mass was independently associated with body weight, being a girl, and being white, but not with average total activity counts. Activity counts obtained with the Actiwatch under free-living conditions do not reflect TEE in 4- to 6-year-old children and are not correlated with percentage fat mass. Therefore, average total activity counts obtained with the Actiwatch may be of limited value in identifying children at risk for becoming obese.

Seasonal Variation in Total Energy Expenditure and Physical Activity in Dutch Young Adults

The impact of season on energy expenditure and physical activity is not well quantified. This study focused on summer-winter differences in total energy expenditure (TEE) and physical activity. Twenty-five healthy Dutch young adults, living in an urban environment, were measured in the summer season and the winter season. TEE was measured using doubly labeled water, and sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. Subsequently, the physical activity level (PAL = TEE/SMR) and activity-related energy expenditure [(0.9 x TEE) - SMR] were calculated. Maximal mechanical power (Wmax) was determined with an incremental test on a cycle ergometer. Body composition was measured with hydrostatic weighing and deuterium dilution using Siri's three-compartment model. There was no difference in TEE between seasons. PAL was higher in summer than in winter (1.87 +/- 0.22 vs. 1.76 +/- 0.18; p < 0.001), and the difference was higher for men than for women (0.20 +/- 0.14 vs. 0.05 +/- 0.16; p = 0.04). The difference in PAL between seasons was dependent on the initial activity level. There was a strong linear relation (R2 = 0.48) between PAL and physical fitness (Wmax/fat-free mass), but Wmax/fat-free mass did not change between seasons in response to the lower PAL in winter. The extent of the changes in PAL is of physiological significance, and seasonality in physical activity should be taken into account when studying physical activity patterns or relationships between physical activity and health.

Invited Commentary: OPEN Questions

The papers by Subar et al. (1) and Kipnis et al. (2) in this issue of the Journal provide a unique set of data and a useful contribution to nutritional epidemiology. The Observing Protein and Energy Nutrition (OPEN) Study they report on was based on a reasonable sample of participants, had a remarkably high completion rate, and is the largest known study to use doubly labeled water as the standard for comparison. In addition, the measurements of doubly labeled water, which can be highly error prone (3), appear to have been made with a high degree of technical precision. The study also has several limitations inherent to using this approach, including that it enabled evaluation of only total energy and one specific nutrient, protein. Furthermore, the results cannot be readily generalized to other food frequency questionnaires because the values for total energy intake estimated by use of their questionnaire were substantially lower than typically provided by other full-length questionnaires in current use. In addition, although great expense was devoted to biomarker measurements, only 2 days of 24-hour diet recalls were used, many fewer days of assessment than are included in usual questionnaire validation studies. A more serious flaw in the design of the OPEN Study (1, 2) is failure to include a realistic measure of the withinperson variability of their standard, doubly labeled water plus urinary nitrogen. This measure should have been obtained for a sample of participants by collecting a replicate measure a number of months apart, say roughly 6 to 12, which would be consistent with the 1-year time frame of their questionnaire. In their study, the only measure of reproducibility was for 25 participants, without any interval between the replicates; the authors report a coefficient of variation of 5.1 percent total energy expenditure, suggesting little within-person variation. However, this is likely to underestimate biologic variability substantially in relation to the time frame of the dietary questionnaire. Increasing variability given a greater time interval would be expected, for example, because persons are likely to change their physical activity with time because of differences in seasons and other reasons (the fact that the authors note no average seasonal variation in total energy expenditure is not relevant to individual variability). This phenomenon is well documented in a quantitative review of the reproducibility of total energy expenditure measured by using doubly labeled water in 25 studies with repeated measurements (4). In this report, Black and Cole found that, overall, the within-person coefficient of variation was of similar magnitude to the betweenperson variation whether or not the studies involved an intervention, and it clearly increased with increasing interval between repeated measurements. They estimated that the within-person coefficient of variation was approximately twice as great at an interval of 1 year compared with having no interval between replicates. The lack of a realistic measure of variability in their standard method (1, 2), which is surprising for a study focusing on error, seriously limits the value of the findings and is likely to have led to misleading conclusions. As one example, Subar et al. (1) emphasize the importance of “intake-related bias” associated with their dietary questionnaire or 24-hour recalls, meaning that the ratio of intake measures divided by biomarker measures is inversely correlated with the biomarker measures. Because any unaccounted-for variation (random within-person error) in the biomarker would appear in the denominator of the ratio and the numerator of the biomarker variable, the inverse correlation could just be all or part artifact.

Total energy expenditure in the elderly.

Chronic diseases and disabilities increase with age, affecting more than 60% of those over 75 y, and limiting activities in about half of them. Therefore, total energy expenditure (TEE) and its components are assessed separately in health and disease. An analysis of 568 doubly labelled water measurements in 'healthy' subjects (184 measurements in subjects over 65 years) suggests that there is a decrease of 0.69 and 0.43 MJ/day/decade respectively in men (standard weight 75 kg) and women (standard weight 67 kg). Physical activity (PA) accounted for 46% of the decrease in TEE, basal metabolic rate (BMR) for 44% of the decrease and thermogenesis (T) for the remaining 10%. TEE was found to be 10.79+/-2.09 and 8.62+/-1.49 MJ/day in 150 men and 100 women aged over 60 y, respectively. Of the total variance in TEE, measured with doubly labelled water over a 2 week period, 69% was considered to be due to differences between individuals, and 31% to differences within individuals. The variance due to PA plus T was threefold greater than that due to BMR. Physiological factors were far more important than methodological factors in influencing measurements of TEE, BMR and PA+T. An analysis of 136 measurements of TEE (doubly labelled water and bicarbonate-urea methods) in free-living elderly patients suffering from a variety of diseases suggests a frequent decrease in TEE, which may occur despite an increase in BMR. This is largely due to a reduction is PA (eg up to approximately 50% reduction), but in some cases it is also due to a reduction in BMR (loss of body weight). More comprehensive information is required about TEE and its components, partly because of a probable selection bias in recruitment of subjects participating in specific tracer studies, and partly because of the variable effects of different diseases and factors that operate at different times in the course of the same disease.