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Objective The causes of protein malnutrition and body composition changes in chronic kidney disease (CKD) are poorly understood. Alterations to metabolic rate caused by CKD may be a contributor. Using the doubly labeled water technique and indirect calorimetry, we set out to determine whether reduced glomerular filtration rate is associated with alterations to total energy expenditure (TEE) and resting energy expenditure (REE). We also aimed to determine whether TEE in patients with CKD can be easily predicted from a physical activity questionnaire. Methods In a prospective, observational study we evaluated 80 patients (52 men; mean age 56.7 ± 16.2 years) with CKD ranging from stage 1 to stage 5 with estimated glomerular filtration rate (eGFR) calculated by the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI). TEE was measured using doubly labeled water isotope excretion over 14 days (TEEDLW), REE by indirect calorimetry (REEIndirectCal) and physical activity level using the Stanford 7-day recall questionnaire. Results eGFR did not correlate with TEEDLW and REEIndirectCal. Findings with weight-adjusted energy measures were similar. REEIndirectCal and TEEDLW were significantly lower in patients whose eGFR was Conclusion There was no direct relationship between reduced renal function and metabolic rate. Differences in energy metabolism at lower levels of glomerular filtration rate are more likely to be due to factors such as age, body composition, and physical activity.

Total daily energy expenditure (“total expenditure”, MJ/d) reflects daily energy needs and is a critical variable in human health and physiology, yet it is unclear how daily expenditure changes over the life course. Here, we analyze a large, globally diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 yr. We show that total expenditure is strongly related to fat free mass in a power-law manner and identify four distinct metabolic life stages. Fat free mass-adjusted daily expenditure accelerates rapidly in neonates (0-1yr) to ~46% above adult values at ~1 yr, declines slowly throughout childhood and adolescence (1-20 yr) to adult levels at ~20 yr, remains stable in adulthood (20-60 yr) even during pregnancy, and declines in older adults (60+ yr). These changes in total expenditure shed new light on human development and aging and should help shape nutrition and health strategies across the lifespan.

PURPOSE: Accurate measurement of energy expenditure (EE) using doubly labeled water depends on the estimate of total body water (TBW). The aims of this study were to 1) assess the accuracy of a new approach for estimating TBW and EE during high-energy turnover and 2) assess the accuracy of day-to-day assessment of EE with this new approach.METHODS: EE was measured in six healthy subjects (three male) for 5 consecutive days using three doubly labeled water methods: 1) the plateau, 2) slope-intercept, and 3) overnight-slope method, with whole-room indirect calorimetry as reference method. Urine samples were collected every evening and morning. High EE (physical activity level of >2.5) was achieved by cycling 4 h·d-1.RESULTS: Physical activity level was 2.8 ± 0.1. TBW values were 41.9 ± 6.1, 38.4 ± 5.7, and 40.4 ± 5.8 L for the plateau, slope-intercept, and overnight-slope methods, respectively. The overnight-slope method showed the highest accuracy in estimated CO2 production, when compared with indirect calorimetry over the complete 5-d period (mean ± SD difference, 0.9% ± 1.6%). The plateau method significantly overestimated CO2 production by 4.7% ± 2.6%, whereas the slope-intercept method underestimated CO2 production (-3.4% ± 2.3%). When CO2 production was assessed per day, the overnight-slope method showed an average difference of 9.4% ± 4.5% to indirect calorimetry.CONCLUSIONS: The overnight-slope method resulted in a more accurate estimation of CO2 production and EE compared with the plateau or slope-intercept method over a 5-d period in high physical activity conditions. Day-to-day determination of EE using the overnight-slope method was more accurate than diet recall and several standard prediction equations in athletes.

We determined the total energy expenditure (TEE) of healthy overweight or obese people, and those with impaired glucose tolerance and/or impaired fasting glycemia (IGT/IFG), or type 2 diabetes (T2DM) using the doubly-labeled water method. As a second purpose, we compared the measured TEE with the target energy intake recommended in the treatment guidelines for diabetes. The participants were normal glucose tolerance (NGT), and IGT/IFG (n=11) and T2DM (n=9) patients, who were 50-59 y and had a body mass index >25 kg/m2. The median TEE/body mass (BM) values were 32.6, 33.3, and 34.4 kcal/kg BM and the TEE/target BM values (target BM: BM at a BMI of 22 kg/m2) were 43.7, 50.2, and 46.5 kcal/kg target BM for each group, respectively, and did not differ significantly among them. Obese Japanese participants with T2DM in this study had lower TEE/BM than previously studied in non-obese participants with T2DM. In IGT/IFG or T2DM patients, if 30 kcal/kg target BM was used as the energy coefficient, on the basis of the treatment guidelines, the difference between TEE and the target energy intake would be -1,174±552 kcal (-38±11%). When 35 kcal/kg target BM was used as the energy coefficient, the difference between TEE and the target energy intake would be -877±542 kcal (-27±13%). Thus, the energy coefficients used to estimate target energy intake during lifestyle modification in obese/overweight patients with T2DM are considered to be quite low during the first step of diet therapy.

Background Longer-term feeding studies suggest that a low-carbohydrate diet increases energy expenditure, consistent with the carbohydrate-insulin model of obesity. However, the validity of methodology utilized in these studies, involving doubly labeled water (DLW), has been questioned. Objective The aim of this study was to determine whether dietary energy requirement for weight-loss maintenance is higher on a low- compared with high-carbohydrate diet. Methods The study reports secondary outcomes from a feeding study in which the primary outcome was total energy expenditure (TEE). After attaining a mean Run-in weight loss of 10.5%, 164 adults (BMI ≥25 kg/m2; 70.1% women) were randomly assigned to Low-Carbohydrate (percentage of total energy from carbohydrate, fat, protein: 20/60/20), Moderate-Carbohydrate (40/40/20), or High-Carbohydrate (60/20/20) Test diets for 20 wk. Calorie content was adjusted to maintain individual body weight within ± 2 kg of the postweight-loss value. In analyses by intention-to-treat (ITT, completers, n = 148) and per protocol (PP, completers also achieving weight-loss maintenance, n = 110), we compared the estimated energy requirement (EER) from 10 to 20 wk of the Test diets using ANCOVA. Results Mean EER was higher in the Low- versus High-Carbohydrate group in models of varying covariate structure involving ITT [ranging from 181 (95% CI: 8–353) to 246 (64–427) kcal/d; P ≤0.04] and PP [ranging from 245 (43–446) to 323 (122–525) kcal/d; P ≤0.02]. This difference remained significant in sensitivity analyses accounting for change in adiposity and possible nonadherence. Conclusions Energy requirement was higher on a low- versus high-carbohydrate diet during weight-loss maintenance in adults, commensurate with TEE. These data are consistent with the carbohydrate-insulin model and lend qualified support for the validity of the DLW method with diets varying in macronutrient composition. This trial was registered at clinicaltrials.gov as NCT02068885.

Physical activity questionnaires (PAQ) could be suitable tools in free-living people for measures of physical activity, total and activity energy expenditure (TEE and AEE). This meta-analysis was performed to determine valid PAQ for estimating TEE and AEE using doubly labelled water (DLW). We identified data from relevant studies by searching Google Scholar, PubMed and Scopus databases. This revealed thirty-eight studies that had validated PAQ with DLW and reported the mean differences between PAQ and DLW measures of TEE (TEEDLW − TEEPAQ) and AEE (AEEDLW − AEEPAQ). We assessed seventy-eight PAQ consisting of fifty-nine PAQ that assessed TEE and thirty-five PAQ that examined AEE. There was no significant difference between TEEPAQ and TEEDLW with a weighted mean difference of –243·3 and a range of –841·4 to 354·6 kJ/d, and a significant weighted mean difference of AEEDLW – AEE PAQ 414·6 and a range of 78·7–750·5. To determine whether any PAQ was a valid tool for estimating TEE and AEE, we carried out a subgroup analysis by type of PAQ. Only Active-Q, administered in two seasons, and 3-d PA diaries were correlated with TEE by DLW at the population level; however, these two PAQ did not demonstrate an acceptable limit of agreement at individual level. For AEE, no PAQ was correlated with DLW either at the population or at the individual levels. Active-Q and 3-d PA diaries were identified as the only valid PAQ for TEE estimation. Further well-designed studies are needed to verify this result and identify additional valid PAQ.

The goal of professional soccer academies is to develop players who can progress and represent their first team or that can be sold for financial gain. As an academy player transitions through the academy pathway (i.e. from under, U9 to U23), they undergo distinct phases of growth and maturation that result in anatomical (e.g. an increase in stature and body mass), physiological (e.g. an increase in growth hormone, insulin-like growth factor 1, testosterone and thyroid hormones) and metabolic changes (e.g. increased glycolytic capabilities). The collective result of such changes are likely to influence such daily energy requirements. However, unlike professional adult players, the body composition, physical loading patterns and associated daily energy expenditure have not yet been quantified in academy soccer players. A detailed understanding of such parameters is fundamental in order to promote growth and maturation whilst also maximising each players physical and technical development. With this in mind, the aim of this thesis was to determine the energy requirements of male academy soccer players from the English Premier League of different chronological and biological ages. The aim of Study 1 (Chapter 4) was to assess body composition and resting metabolic rate in a cohort of academy soccer players. In a cross-sectional design, ninety-nine males from a Category One English Premier League academy (U12-U23 age-groups) underwent assessments of body composition (dual-energy X-ray absorptiometry, DXA) and resting metabolic rate (indirect calorimetry). Fat-free mass increased between the U12 (31.6 ± 4.2 kg) and U16 (56.3 ± 5.3 kg) age-groups after which no further increases occurred. Resting metabolic rate in the U12 (1655 ± 195 kcalday-1), U13 (1720 ± 205 kcalday-1) and U14 (1846 ± 218 kcalday-1) age-groups was significantly lower than the U15 (1957 ± 128 kcalday-1), U16 (2042 ± 155 kcalday-1), U18 (1875 ± 180 kcalday-1) and U23 (1941 ± 197 kcalday-1) age-groups. These data provide the first report of differences in body composition and resting metabolic rate in academy soccer players (as assessed via DXA and indirect calorimetry, respectively) and demonstrate that the growth and maturation occurring between U12-U16 significantly increases resting energy requirements. Having quantified body composition and resting metabolic rate in Study 1 (Chapter 4), the aim of Study 2 (Chapter 5) was to determine the typical weekly training and match loading patterns of academy soccer players. Over the course of an entire competitive season, weekly training and match load was quantified using global positioning system technology in 76 soccer players from a Category One English Premier League academy (U12-U18 age-groups). Weekly training and match volume (i.e. duration and total distance) was similar in the U12 (329 ± 29 min; 19.9 ± 2.2 km), U13 (323 ± 29 min; 20.0 ± 2.0 km) and U14 (339 ± 25 min; 21.7 ± 2.0 km) age-groups, but was lower than the U15 (421 ± 15 min; 26.2 ± 2.1 km), U16 (427 ± 20 min; 25.9 ± 2.5 km) and U18 (398 ± 30 min; 26.1 ± 2.6 km) age-groups. Weekly high-speed running and sprint distance (i.e. intensity) were comparable in the U12 (220 ± 95 m and 6 ± 9 m respectively), U13 (331 ± 212 m and 6 ± 27 m) and U14 (448 ± 193 m and 21 ± 29 m) age-groups was similar, but less than the U15 (657 ± 242 m and 49 ± 98 m), U16 (749 ± 152 m and 95 ± 55 m) and U18 (979 ± 254 m and 123 ± 56 m) age-groups. These data provide the first 4 report to objectively assess accumulative training and match loads in academy soccer players and demonstrate that the absolute loads are progressive in nature throughout the academy pathway. Given the progressive changes in body composition (Study 1, Chapter 4), resting metabolic rate (Study 1, Chapter 4) and physical loading patterns (Study 2, Chapter 5), the aim of Study 3 (Chapter 6) was to quantify the total daily energy expenditure of academy soccer players with different chronological and biological ages. Energy expenditure (doubly labelled water) and energy intake (remote food photographic method) was assessed over a 14-day in-season period in 24 soccer players from a Category One English Premier League academy (U12/13, n=8; U15, n=8; U18, n=8). U18 players presented with greater total energy expenditure (3586 ± 487 kcalday-1) than both the U15 (3029 ± 262 kcalday-1) and U12/13 players (2859 ± 265 kcalday-1), though no differences were evident between the U12/13 and U15 age-groups. Within age-groups, no differences were apparent between energy intake and total energy expenditure, whilst U18 players (3180 ± 279 kcalday-1) reported a higher energy intake than both the U15 (2821 ± 338 kcalday-1) and U12/13 (2659 ± 187 kcalday-1) players. In some individuals (evident in all age-groups) total energy expenditure was greater than that previously observed in adult English Premier League soccer players (3566 ± 585 kcalday-1). In summary, the data presented in this thesis provides the first report to simultaneously quantify body composition, resting metabolic rate, physical loading patterns and total daily energy expenditure of academy soccer players. Importantly, these data demonstrate that players’ absolute daily energy expenditure progressively increase as they become more biologically mature, likely a reflection of increased fat-free mass and physical loading patterns. From a practical perspective, these data will assist in developing population specific sport nutrition guidelines.

Background Appropriate energy intake (EI) is essential to prevent frailty. Because self-reported EI is inaccurate and has systematic errors, adequate biomarker calibration is required. This study examined the association between doubly labeled water (DLW)-calibrated EI and the prevalence of frailty among community-dwelling older adults. Method A cross-sectional study was performed using baseline data of 7,022 older adults aged ≥65 years in the Kyoto-Kameoka Study. EI was evaluated using a validated food frequency questionnaire (FFQ), and calibrated EI was obtained from a previously established equation using the DLW method. Physical and comprehensive frailty were defined by the Fried phenotype (FP) model and the Kihon Checklist (KCL), respectively. We used multivariable-adjusted restricted cubic spline logistic regression analysis. Results The prevalence of physical frailty was 14.8% and 13.6% in women and men, respectively. The spline models showed significant reverse J-shaped or U-shaped relationships between the prevalence of physical or comprehensive frailty against the DLW-calibrated EI, respectively. The lowest prevalence of both types of frailty was found at 1,900–2,000 kcal/d in women and 2,400–2,500 kcal/d in men, which corresponded to approximately 40 kcal/d/kg IBW (ideal body weight = 22 × height2) with DLW-calibrated EI. Uncalibrated EI underestimated approximately 20% compared with calibrated EI; underestimated EI were attenuated by calibration approach. Conclusions This study suggests that low EI has a greater detrimental effect compared with excessive EI, particularly on physical frailty. Using biomarkers to calibrate EI holds promise for providing accurate energy requirements to establish guidelines used in public health and clinical nutrition.

Objective: This study aimed to determine whether different measures of habitual physical activity (PA) at baseline predict weight change, weight compensation, and changes in energy intake (EI) during a 24-week supervised aerobic exercise intervention. Methods: Data from 108 participants (78 women; 48.7 [SD: 11.6] years; BMI 31.4 [SD: 4.6] kg/m2), randomly assigned to either the moderate-dose exercise group (8 kcal/kg of body weight per week) or the high-dose exercise group (20 kcal/kg of body weight per week) of the Examination of Mechanisms of Exercise-induced Weight Compensation (E-MECHANIC) trial, were analyzed. Moderate-to-vigorous PA (MVPA), steps per day, and PA energy expenditure (PAEE) were measured with SenseWear armbands (BodyMedia, Pittsburgh, Pennsylvania), and total activity energy expenditure and EI were estimated with doubly labeled water, all over 2 weeks, before and toward the end of the intervention. Multiple linear regression models, adjusted for sex, exercise group, and baseline value of the outcome, were used. Results: Baseline habitual MVPA levels predicted weight change (β = −0.275; P = 0.020), weight compensation (β = −0.238; P = 0.043), and change in EI (β = −0.318; P = 0.001). Associations between baseline PAEE and outcomes were comparable, whereas steps per day and, importantly, total activity energy expenditure (via doubly labeled water) did not significantly predict change in weight-related outcomes. Conclusions: While acknowledging substantial variability in the data, on average, lower baseline habitual MVPA and PAEE levels were associated with less weight loss from exercise, higher compensation, and increased EI.

Background/Objectives: The winter holiday season in the US, which spans mid-November to mid-January, contributes to over half of annual body weight gain. Although self-reported data has linked this weight change to both increased energy intake and reduced physical activity, objective techniques have never been used and thus the actual cause of holiday weight gain is controversial. Here, we aimed to determine changes in components of energy balance leading to the holiday weight gain. Methods: Body weight change was compared between the pre-holiday (mid-September to mid-November) and the holiday period (mid-November to early January). Total energy expenditure (TEE) was measured using doubly labeled water during holiday time (early to mid-December). Subjective (ratings) and physiological (appetite-regulating hormones) measures of appetite, eating-away-from-home frequency, and incentive salience of food pictures were also evaluated. Results: In 23 obese adults (87% female), body weight change during the holidays (0.41 ± 0.42 kg) was significantly higher (P=0.02) than the body weight change during the pre-holiday period (−0.86 kg ± 0.42 kg). The TEE was unchanged during the two periods, suggesting no role of energy expenditure on weight gain. However, participants reported lower satisfaction after a meal pre-load which was significantly correlated with increased body weight during the holiday period. An increase in number of episodes of eating at sit-down restaurants was also reported during that period. Overall, these changing behaviors were supported by a non-significant increase in energy intake (+80 kcal/day, P=0.07) observed during the study holiday period. Conclusion: We conclude that a decrease in energy expenditure does not result in the weight increase, but that an increase in food intake is the more likely cause. Our data imply that compromised internal satiety mechanisms in presence of external food cues and diet related behavioral variables during the holidays may influence this weight gain.

OBJECTIVE To examine relationships between percent body fat (%BF) and total energy expenditure (TEE) on permethrin exposure among Army National Guard (ARNG) Soldiers wearing permethrin-treated uniforms. METHODS ARNG members (n = 47) participated in a 9-day study. Repeated body composition (height, weight, %BF) measurements and daily urine samples, analyzed for permethrin and N,N-diethyl-meta-toluamide (DEET) metabolites, were collected. TEE was determined via doubly labeled water protocol. Linear mixed and regression models were used for analyses. RESULTS Neither %BF nor TEE were significantly associated with permethrin or DEET biomarkers. However, a significant interaction effect (F = 10.76; P = 0.0027) between laundering history and %BF was observed; 10% higher %BF was significantly associated with 25% higher permethrin biomarker concentrations among those wearing uniforms washed less than or equal to 25 (compared with more than 25) times. CONCLUSIONS Uniform laundering history significantly affects the association between %BF and permethrin-treated uniform exposure.

PURPOSE To examine the shape of the relationship between physical activity (PA) and total energy expenditure (TEE) and to explore the role of energy balance status (negative, stable, positive) in influencing this association. METHODS Cross-sectional. Participants were 584 older adults (50-74 yrs.) participating in the Interactive Diet and Activity Tracking in AARP study. TEE was assessed by doubly labeled water and PA by accelerometer. The relationship between PA and TEE was assessed visually and using non-linear methods (restricted cubic splines). Percent weight change (>3%) over a six-month period was used as a proxy measurement of energy balance status. RESULTS TEE generally increased with increasing deciles of PA averaging 2354 (SD = 351) kcal/d in the bottom decile to 2693 (SD = 480) kcal/d in the top decile. Cubic spline models showed an approximate linear association between PA and TEE (linear relation p < 0.0001, curvature p = 0.920). Results were similar in subgroup analyses for individuals classified as stable or positive energy balance. For those in negative energy balance, TEE was generally flat with increasing deciles of PA averaging 2428 (SD = 285) kcal/d in the bottom decile to 2372 (SD = 560) kcal/d in the top decile. CONCLUSION Energy balance status appears to play an important role in the relationship between PA and TEE. When in a positive energy balance, the relationship between TEE and PA was consistent with an additive model, however, when energy balance was negative, TEE appears to be consistent with a constrained model. These findings support PA for weight gain prevention by increasing TEE; however, the effect of PA on TEE during periods of weight loss may be limited. An adequately powered, prospective study is warranted to confirm these exploratory findings.

Background: Contemporary energy expenditure data are crucial to inform and guide nutrition policy in older adults to optimize nutrition and health.Objective: The aim was to determine the optimal method of estimating total energy expenditure (TEE) in adults (aged >= 65 y) through 1) establishing which published predictive equations have the closest agreement between measured resting metabolic rate (RMR) and predicted RMR and 2) utilizing the RMR equations with the best agreement to predict TEE against the reference method of doubly labeled water (DLW).Methods: A database consisting of international participant-level TEE data from DLW studies was developed to enable comparison with energy requirements estimated by 17 commonly used predictive equations. This database included 31 studies comprising 988 participant-level RMR data and 1488 participant-level TEE data. Mean physical activity level (PAL) was determined for men (PAL = 1.69, n = 320) and women (PAL = 1.66, n = 668). Bland-Altman plots assessed agreement of measured RMR and TEE with predicted RMR and TEE in adults aged >= 65 y, and subgroups of 65-79 y and >= 80 y. Linear regression assessed proportional bias.Results: The Ikeda, Livingston, and Mifflin equations most closely agreed with measured RMR and TEE in all adults aged >= 65 y and in the 65-79 y and >= 80 y subgroups. In adults aged >= 65 y, the Ikeda and Livingston equations overestimated TEE by a mean +/- SD of 175 +/- 1362 kJ/d and 86 +/- 1344 kJ/d, respectively. The Mifflin equation underestimated TEE by a mean +/- SD of 24 +/- 1401 kJ/d. Proportional bias was present as energy expenditure increased.Conclusions: The Ikeda, Livingston, or Mifflin equations are recommended for estimating energy requirements of older adults. Future research should focus on developing predictive equations to meet the requirements of the older population with consideration given to body composition and functional measures.

Water and energy are essential for the human body. The doubly labeled water (DLW) method measures water turnover (WT) and total energy expenditure (TEE), which serves as a benchmark for the adequate intake (AI) of water and estimated energy requirements (EER). The objective of the current study was to examine the association of WT and TEE with physical activity and body composition in Japanese preschool children. We included 41 preschool children (22 girls, 19 boys) aged 3–6 in this study. WT, TEE, and fat-free mass (FFM) were obtained using DLW. Physical activity was measured using a triaxial accelerometer and categorized as light (LPA; 1.5–2.9 Metabolic equivalents, METs) and of moderate-to-vigorous intensity (MVPA; ≥3.0 METs). Exercise duration (Ex) was defined as ≥4.0 METs of physical activity. WT and TEE moderately positively correlated with Ex, but not with LPA. WT moderately positively correlated with BW and FFM while TEE strongly. We established predictive equations for WT and TEE using body weight (BW), FFM, step count, and Ex to guide the AI of water and EER in Japanese preschool children. We found that FFM and step count are the determinants of TEE, and that BW and Ex are the determinants of WT in preschool children.

The Dietary Reference Intakes 2020 divided the older population into those aged 65-74 y and those over 75 y old. However, physical activity level in each age group was not specified. This study examined age-related differences in physical activity level among healthy Japanese older people, and the effect of lifestyles on these differences. In total, 70 people (22 men, 48 women) aged 65-85 y old participated in this study. Total energy expenditure was measured using the doubly labeled water method, and basal metabolic rate using expired gas concentration and volume. The Physical Activity Scale for the Elderly and a triaxial accelerometer were used to assess physical activities. Physical activity level was significantly higher among 65-74 y old (median 1.86) than those over 75 y old (1.76). However, the Physical Activity Scale for the Elderly did not show any significant differences between the age groups. The duration of physical activity with 3.0-5.9 metabolic equivalents was longer for both locomotive and household activities among 65-74 y old than those over 75 y old. Younger participants walked a median of 6,364 steps a day, compared with 4,419 steps for older people. The 65-74 y old participants involved in paid work or who habitually exercised, and those over 75 y old taking more than 40 min a day of moderate to vigorous physical activity, and walking more than the median level for their sex and age group had significantly higher physical activity levels.

Introduction Exercise usually results in less weight loss than expected. This suggests increased energy intake and/or deceased expenditure counteract the energy deficit induced by exercise. The aim of this study was to evaluate changes in components of daily energy expenditure (doubly labeled water and room calorimetry) after 24 wk of exercise training with two doses of aerobic exercise. Methods This was an ancillary study in 42 (29 women, 13 men) sedentary, middle-age (47.8 ± 12.5 yr) individuals with obesity (35 ± 3.7 kg·m-2) enrolled in the Examination of Mechanisms of Exercise-induced Weight Compensation study. Subjects were randomized to three groups: healthy living control group (n = 13), aerobic exercise that expended 8 kcal·kg-1 of body weight per week (8 KKW, n = 14), or aerobic exercise that expended 20 kcal per kilogram of weight per week (20 KKW, n = 15). Total daily energy expenditure (TDEE) was measured in free-living condition by doubly labeled water and in sedentary conditions in a metabolic chamber over 24 h (24EE). Energy intake was calculated over 14 d from TDEE before and after the intervention using the intake-balance method. Results Significant weight loss occurred with 20 KKW (-2.1 ± 0.7 kg, P = 0.04) but was only half of expected. In the 20 KKW group free-living TDEE increased by ~4% (P = 0.03), which is attributed to the increased exercise energy expenditure (P = 0.001), while 24EE in the chamber decreased by ~4% (P = 0.04). Aerobic exercise at 8 KKW did not induce weight change, and there was no significant change in any component of EE. There was no significant change in energy intake for any group (P = 0.53). Conclusions Structured aerobic exercise at a dose of 20 KKW produced less weight loss than expected possibly due to behavioral adaptations leading to reduced 24EE in a metabolic chamber without any change in energy intake.

RATIONALE This study determines if saliva collection procedures for the doubly labeled water (DLW) method, used for measuring total energy expenditure (TEE), are comparable to urine and plasma collection. Both the cavity ring-down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS) analyses techniques are compared. METHODS Saliva specimens were collected from participants for the DLW method. The specimens were collected under different conditions: after consumption of tap water, after chewing gum, and during exposure to conditions of high and low relative humidity. The isotopes in saliva were compared with simultaneous plasma and urine collection. TEE calculated from saliva and analyzed using CRDS was compared to that of plasma analyzed using IRMS. RESULTS The within-individual variances were not significantly different between the saliva specimens (0.4‰) and plasma (0.3‰). After the oral dose of DLW, the saliva specimens displayed a shorter equilibration time to urine. When participants consumed 500 mL of tap water, the enrichment of saliva specimens reached a new plateau value faster than urine. Saliva collection exposed to high ambient humidity conditions was slightly less enriched as compared to low-humidity conditions while urine enrichment was unaffected. In contrast, whereas the within-individual effects of gum chewing during saliva collection on 18 O were unaffected, the abundance of 2 H in saliva was slightly lower after chewing the gum. The within-individual difference between TEE calculated from saliva and that calculated from plasma analyzed using IRMS did not differ from zero, and the standard deviation was not different from that predicted by a propagation of error analysis based on analytical error alone. CONCLUSIONS Our findings support using saliva specimens for the DLW method. The analysis of plasma and urine, however, requires reducing the memory effect resulting from contaminants. Also, it should be performed in a manner that minimizes exposure to air where specimens may be exposed to evaporation or contamination from water vapor during sampling.

Background The Goldberg cutoffs are used to decrease bias in self-reported estimates of energy intake (EISR). Whether the cutoffs reduce and eliminate bias when used in regressions of health outcomes has not been assessed. Objective We examined whether applying the Goldberg cutoffs to data used in nutrition studies could reliably reduce or eliminate bias. Methods We used data from the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE), the Interactive Diet and Activity Tracking in American Association of Retired Persons (IDATA) study, and the National Diet and Nutrition Survey (NDNS). Each data set included EISR, energy intake estimated from doubly labeled water (EIDLW) as a reference method, and health outcomes including baseline anthropometric, biomarker, and behavioral measures and fitness test results. We conducted 3 linear regression analyses using EISR, a plausible EISR based on the Goldberg cutoffs (EIG), and EIDLW as an explanatory variable for each analysis. Regression coefficients were denoted ${\hat{\beta }_{\rm SR}}$, ${\hat{\beta }_{\rm G}}$, and ${\hat{\beta }_{\rm DLW}}$, respectively. Using the jackknife method, bias from ${\hat{\beta }_{\rm SR}}$ compared with ${\hat{\beta }_{\rm DLW}}$ and remaining bias from ${\hat{\beta }_{\rm G}}$ compared with ${\hat{\beta }_{\rm DLW}}$ were estimated. Analyses were repeated using Pearson correlation coefficients. Results The analyses from CALERIE, IDATA, and NDNS included 218, 349, and 317 individuals, respectively. Using EIG significantly decreased the bias only for a subset of those variables with significant bias: weight (56.1%; 95% CI: 28.5%, 83.7%) and waist circumference (WC) (59.8%; 95% CI: 33.2%, 86.5%) with CALERIE, weight (20.8%; 95% CI: -6.4%, 48.1%) and WC (17.3%; 95% CI: -20.8%, 55.4%) with IDATA, and WC (-9.5%; 95% CI: -72.2%, 53.1%) with NDNS. Furthermore, bias significantly remained even after excluding implausible data for various outcomes. Results obtained with Pearson correlation coefficient analyses were qualitatively consistent. Conclusions Some associations between EIG and outcomes remained biased compared with associations between EIDLW and outcomes. Use of the Goldberg cutoffs was not a reliable method for eliminating bias.

In the tropical island of Mauritius, the rise in obesity has accelerated in the past decades, and could be contributed by low physical activity and increased sedentary behavior. The study objectives were to generate the first dataset of total energy expenditure (TEE), to estimate physical activity in Mauritian children, and to explore differences due to gender and ethnicity.Subjects/methods: The doubly labeled water (DLW) technique was used to evaluate TEE over 14 days in 56 Mauritian school children (aged 7–11 years) belonging to the two main ethnic groups: Indian (South Asian descent) and Creole (African/Malagasy descent). Physical activity level (PAL) was calculated as the ratio of TEE and resting energy expenditure (using Schofield equations), and daily step counts were measured by accelerometry. Anthropometry and body composition were also assessed.Results: TEE measured by DLW was lower in Mauritian children (by ~155 kcal/d) than that predicted using FAO/WHO/UNU equations for children of the same sex, age, and body size. Furthermore, TEE, as well as PAL and step counts, also differed according to gender (lower in girls than in boys) and to ethnicity (lower in Indians than in Creoles) even after adjusting for differences in body weight and body composition.Conclusion: These results in Mauritian children provide the first dataset of objectively measured TEE, from which physical activity is estimated as PAL, and complemented by step counts measurements. They suggest potential gender and ethnic differences in TEE and physical activity that need consideration in developing strategies to counter sedentary behavior and obesity.

Wearable physical activity (PA) monitors have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established research methods has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors and a PA log against doubly-labelled water (DLW) during British Army Officer Cadet (OC) training. For 10 days of training, twenty (10 male and 10 female) OCs (mean ± SD: age 23 ± 2 years, height 1.74 ± 0.09 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK) on the dominant wrist, wore one commercially-available monitor (Fitbit SURGE, USA) on the non-dominant wrist and completed a self-report PA log. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analysed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement (LoA) were employed to compare TEE from each estimation method to DLW. Average daily TEE from DLW was 4112 ± 652 kcal·day against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal day ) while Fitbit tended to underestimate (-656 ± 683 kcal·day ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day ). Wearable physical activity monitors provide a cheaper and more practical method for estimating free-living TEE than DLW in military settings. The GENEActiv accelerometer demonstrated good validity for assessing daily TEE and would appear suitable for use in large-scale, longitudinal military studies.

BACKGROUND: Low-carbohydrate diets have been reported to significantly increase human energy expenditure when measured using doubly labeled water (DLW) but not by respiratory chambers. Although DLW may reveal true physiological differences undetected by respiratory chambers, an alternative possibility is that the expenditure differences resulted from failure to correctly estimate the respiratory quotient (RQ) used in the DLW calculations. OBJECTIVE: To examine energy expenditure differences between isocaloric diets varying widely in carbohydrate and to quantitatively compare DLW data with respiratory chamber and body composition measurements within an energy balance framework. DESIGN: DLW measurements were obtained during the final 2 wk of month-long baseline (BD; 50% carbohydrate, 35% fat, 15% protein) and isocaloric ketogenic diets (KD; 5% carbohydrate, 80% fat, 15% protein) in 17 men with a BMI of 25–35 kg/m(2). Subjects resided 2 d/wk in respiratory chambers to measure energy expenditure (EE(chamber)). DLW expenditure was calculated using chamber-determined RQ either unadjusted (EE(DLW)) or adjusted (EE(DLWΔRQ)) for net energy imbalance using diet-specific coefficients. Accelerometers measured physical activity. Body composition changes were measured by dual-energy X-ray absorptiometry (DXA) which were combined with energy intake measurements to calculate energy expenditure by balance (EE(bal)). RESULTS: After transitioning from BD to KD, neither EE(chamber) nor EE(bal) were significantly changed (∆EE(chamber )= 24 ± 30 kcal/d; P = 0.43 and ∆EE(bal )= −141 ± 118 kcal/d; P = 0.25). Similarly, physical activity (−5.1 ± 4.8%; P = 0.3) and exercise efficiency (−1.6 ± 2.4%; P = 0.52) were not significantly changed. However, EE(DLW) was 209 ± 83 kcal/d higher during the KD (P = 0.023) but was not significantly increased when adjusted for energy balance (EE(DLWΔRQ) = 139 ± 89 kcal/d; P = 0.14). After removing 2 outliers whose EE(DLW) were incompatible with other data, EE(DLW) was marginally increased during the KD by 126 ± 62 kcal/d (P = 0.063) and EE(DLW∆RQ) was only 46 ± 65 kcal/d higher (P = 0.49). CONCLUSIONS: DLW calculations failing to account for diet-specific energy imbalance effects on RQ erroneously suggest that low-carbohydrate diets substantially increase energy expenditure. This trial was registered at clinicaltrials.gov as NCT01967563.

Malnutrition is a severe problem among elderly residents living in long-term care facilities. A simple and accurate estimation for total daily energy expenditure (TEE) is required in order to provide them with an adequate amount of food. The purpose of this study was to validate a physical activity assessment tool for estimating TEE among elderly residents. The data of 58 subjects aged 69-99 y were analyzed in this study. The one-day physical activity recall was filled out by facility staff for a typical day. TEE was measured by the doubly labeled water (DLW) method (TEEDLW), and predicted by one-day physical activity recall using the basal metabolic rate (pTEEBMR) and body weight (pTEEBW). The TEEDLW, pTEEBMR and pTEEBW were 1,129±196 kcal/d, 1,186±251 kcal/d and 1,326±236 kcal/d, respectively. In a regression model, body weight, movement means and sex explained 39.0% of the variance in the difference between pTEEBMR and TEEDLW (p

BACKGROUND: Individuals with overweight or obesity commonly underreport energy intake (EI), but it is unknown if the tendency to underreport persists in formerly obese individuals who lose significant weight and maintain their weight loss over long periods of time. OBJECTIVE: Assess the accuracy of self-reported EI in successful weight loss maintainers (WLM) compared with controls of normal body weight (NC) and controls with overweight/obesity (OC). METHODS: Participants for this case-controlled study were recruited in 3 groups: WLM [n = 26, BMI (in kg/m(2)) 24.1 ± 2.3; maintaining ≥13.6 kg weight loss for ≥1 y], NC (n = 33, BMI 22.7 ± 1.9; similar to current BMI of WLM), and OC (n = 32, BMI 34.0 ± 4.6; similar to pre–weight loss BMI of WLM). Total daily energy expenditure (TDEE) was measured over 7 d using the doubly labeled water (DLW) method, and self-reported EI was concurrently measured from 3-d diet diaries. DLW TDEE and self-reported EI were compared to determine accuracy of self-reported EI. RESULTS: WLM underreported EI (median, interquartile range) (–605, –915 to –314 kcal/d) to a greater degree than NC (–308, –471 to –68 kcal/d; P < 0.01) but not more than OC (–310, –970 to 18 kcal/d; P = 0.21). WLM also showed a greater degree of relative underreporting (–25.3%, –32.9% to –12.5%) compared with NC (–14.3%, –19.6% to –3.1%; P = 0.02) but not OC (–11.2%, –34.1% to –0.7%; P = 0.16). A greater proportion of WLM was classified as underreporters (30.8%) than NC (9.1%; P = 0.05) but not OC (28.1%; P = 1.00). CONCLUSIONS: WLM underreported EI in both absolute and relative terms to a greater extent than NC but not OC. These findings call into question the accuracy of self-reported EI in WLM published in previous studies and align with recent data suggesting that WLM rely less on chronic EI restriction and more on high levels of physical activity to maintain weight loss. This trial was registered at clinicaltrials.gov as NCT03422380.

Background The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. Objectives The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. Methods Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. Results There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. Conclusions Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

Life history, brain size and energy expenditure scale with body mass in mammals but there is little conclusive evidence for a correlated evolution between life history and energy expenditure (either basal/resting or daily) independent of body mass. We addressed this question by examining the relationship between primate free-living daily energy expenditure (DEE) measured by doubly labeled water method (n = 18 species), life history variables (maximum lifespan, gestation and lactation duration, interbirth interval, litter mass, age at first reproduction), resting metabolic rate (RMR) and brain size. We also analyzed whether the hypometabolic primates of Madagascar (lemurs) make distinct energy allocation tradeoffs compared to other primates (monkeys and apes) with different life history traits and ecological constraints. None of the life-history traits correlated with DEE after controlling for body mass and phylogeny. In contrast, a regression model showed that DEE increased with increasing RMR and decreasing reproductive output (i.e., litter mass/interbirth interval) independent of body mass. Despite their low RMR and smaller brains, lemurs had an average DEE remarkably similar to that of haplorhines. The data suggest that lemurs have evolved energy strategies that maximize energy investment to survive in the unusually harsh and unpredictable environments of Madagascar at the expense of reproduction.

BACKGROUND: To inform the interpretation of dietary data in the context of sex differences in diet-disease relations, it is important to understand whether there are any sex differences in accuracy of dietary reporting.OBJECTIVE: To quantify sex differences in self-reported total energy intake (TEI) compared with a reference measure of total energy expenditure (TEE).METHODS: Six electronic databases were systematically searched for published original research articles between 1980 and April 2020. Studies were included if they were conducted in adult populations with measures for both females and males of self-reported TEI and TEE from doubly labeled water (DLW). Studies were screened and quality assessed independently by 2 authors. Random-effects meta-analyses were conducted to pool the mean differences between TEI and TEE for, and between, females and males, by method of dietary assessment.RESULTS: From 1313 identified studies, 31 met the inclusion criteria. The studies collectively included information on 4518 individuals (54% females). Dietary assessment methods included 24-h recalls (n = 12, 2 with supplemental photos of food items consumed), estimated food records (EFRs; n = 11), FFQs (n = 10), weighed food records (WFRs, n = 5), and diet histories (n = 2). Meta-analyses identified underestimation of TEI by females and males, ranging from -1318 kJ/d (95% CI: -1967, -669) for FFQ to -2650 kJ/d (95% CI: -3492, -1807) for 24-h recalls for females, and from -1764 kJ/d (95% CI: -2285, -1242) for FFQ to -3438 kJ/d (95% CI: -5382, -1494) for WFR for males. There was no difference in the level of underestimation by sex, except when using EFR, for which males underestimated energy intake more than females (by 590 kJ/d, 95% CI: 35, 1,146).CONCLUSION: Substantial underestimation of TEI across a range of dietary assessment methods was identified, similar by sex. These underestimations should be considered when assessing TEI and interpreting diet-disease relations.

ObjectiveTo determine the effects of diets varying in carbohydrate to fat ratio on total energy expenditure.DesignRandomized trial.SettingMulticenter collaboration at US two sites, August 2014 to May 2017.Participants164 adults aged 18-65 years with a body mass index of 25 or more.InterventionsAfter 12% (within 2%) weight loss on a run-in diet, participants were randomly assigned to one of three test diets according to carbohydrate content (high, 60%, n=54; moderate, 40%, n=53; or low, 20%, n=57) for 20 weeks. Test diets were controlled for protein and were energy adjusted to maintain weight loss within 2 kg. To test for effect modification predicted by the carbohydrate-insulin model, the sample was divided into thirds of pre-weight loss insulin secretion (insulin concentration 30 minutes after oral glucose).Main outcome measuresThe primary outcome was total energy expenditure, measured with doubly labeled water, by intention-to-treat analysis. Per protocol analysis included participants who maintained target weight loss, potentially providing a more precise effect estimate. Secondary outcomes were resting energy expenditure, measures of physical activity, and levels of the metabolic hormones leptin and ghrelin.ResultsTotal energy expenditure differed by diet in the intention-to-treat analysis (n=162, P=0.002), with a linear trend of 52 kcal/d (95% confidence interval 23 to 82) for every 10% decrease in the contribution of carbohydrate to total energy intake (1 kcal=4.18 kJ=0.00418 MJ). Change in total energy expenditure was 91 kcal/d (95% confidence interval −29 to 210) greater in participants assigned to the moderate carbohydrate diet and 209 kcal/d (91 to 326) greater in those assigned to the low carbohydrate diet compared with the high carbohydrate diet. In the per protocol analysis (n=120, PConclusionsConsistent with the carbohydrate-insulin model, lowering dietary carbohydrate increased energy expenditure during weight loss maintenance. This metabolic effect may improve the success of obesity treatment, especially among those with high insulin secretion.Trial registrationClinicalTrials.govNCT02068885.

Unintentional weight loss is a major frailty component; thus, assessing energy imbalance is essential for institutionalized elderly residents. This study examined prediction errors of the observed energy intake (OEI) against the actual energy intake obtained from the doubly labeled water (DLW) method and clarified the relationship between the energy gap obtained by subtracting total energy expenditure (TEE) from OEI and subsequent weight changes in elderly residents in long-term care facilities. Overall, 46 participants were recruited in Japan. TEE was measured using the DLW method, and OEI was calculated from recipes and plate waste simultaneously over a 14–15-day period at baseline. The total energy intake (TEIDLW) was determined on the basis of DLW and weight changes during the DLW period. The weight was longitudinally tracked monthly for 12 months in the 28 residents who still lived at the facilities. OEI was higher than TEIDLW by a mean of 232 kcal/day (15.3%) among 46 residents at baseline. The longitudinal data of 28 residents showed that the energy gap tended to be correlated with the slope of weight change (ρ = 0.337, p = 0.080) and the median value was significantly lower in the weight loss group (152 kcal/day) than in the weight gain group (350 kcal/day) (p < 0.05). In conclusion, weight loss could occur at Japanese long-term care facilities even if the difference obtained by subtracting TEE from OEI was positive because OEI was overestimated by more than 200 kcal/day.

Background & aims:Although accurate assessment of energy intake (EI) is critical in diabetes care, underreporting of EI on dietary records (DR) is often an issue. However, few studies have examined EI with doubly labeled water (DLW) in patients with diabetes mellitus. We aimed to investigate the impact of sex and obesity on the dissociation of DR from total energy expenditure (TEE) evaluated with DLW in patients with type 2 diabetes., Methods:Fifty-two patients with type 2 diabetes aged 60–79 years were enrolled for the Clinical Evaluation of Energy Requirements in Patients with Diabetes Mellitus (CLEVER-DM) study at a single university hospital. TEE was measured over 14 days by the DLW method as standard. EI was calculated by assessment of 3-day DR by registered dietitians., Results:The mean difference between EI and TEE was 238 ± 412 kcal/day (~10% of TEE). Neither EI nor TEE was significantly different between obese (body mass index (BMI) ≥25 kg/m 2) and non-obese (BMI, Conclusions:EI calculated by 3-day DR may underestimate habitual intake, which is assumed to be equal to TEE measured by the DLW method except in non-obese women with diabetes., Clinical trial registration number:UMIN000023051.

Among 626 participants of the Men’s Lifestyle Validation Study (2011–2013), we evaluated the validity and reproducibility of a self-administered 152-item semiquantitative food frequency questionnaire (SFFQ) using two 7-day dietary records (7DDRs), 4 Automated Self-Administered 24-hour dietary recalls (ASA24s), four 24-hour urine samples, 1 doubly labeled water measurement (repeated in 104 participants), and 2 fasting blood samples, collected over 15 months. Compared with 7DDRs, SFFQs underestimated energy intake, macronutrients, and sodium intake but overestimated some micronutrients. The mean of the Spearman correlation coefficients was 0.66 (range, 0.38–0.88) between 46 energy-adjusted nutrients estimated from 7DDRs and the final SFFQ, deattenuated for within-person variation in the 7DDRs. These deattenuated correlations were similar using ASA24s as the comparison. Relative to biomarkers, SFFQs underestimated energy, sodium, and protein intakes, as well as the sodium:potassium ratio. The energy-adjusted correlations between the final SFFQ and the biomarkers were slightly lower than the correlations between the SFFQ and 7DDRs. Using the method of triads to calculate validity coefficients, the median validity coefficient between SFFQ and true intake was 0.65 and 0.69 using 7DDRs and ASA24s, respectively, as the third method. These data indicate that this SFFQ provided reasonably valid estimates for a wide range of nutrients when evaluated by multiple comparison methods.

Accurate estimation of energy requirements is crucial for health maintenance and prevention of malnutrition in older adults. This study aimed to assess the accuracy of predictive equations for estimating energy requirements in older adults and to test the validity of new predictive equations for this age group. This is a cross-sectional study including 38 Brazilian community-dwelling older adults aged 60–84 years, who had their total energy expenditure measured by doubly labeled water (TEEDLW). The energy expenditure was compared to the Institute of Medicine (Dietary Reference Intake (DRI)) and Vinken et al. previous predictive equations and three predictive models developed in a modeling sample. The agreement was assessed using intra-class correlation coefficient, Bland–Altman plots, and Lin’s concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured energy expenditure. The mean (standard deviation) TEEDLW was 2656.7 (405.6) kcal/day for men and 2168.9 (376.9) for women. Vinken et al. and both DRI equations presented moderate to good degree of agreement, while the developed models vary from fair to very good agreement in comparison to DLW. The accuracy rate was the same for both DRI equations and Vinken et al. equation (60.53%). The new equations developed in this study had accuracy in predicting TEE for Brazilian older adults varying from 43.11% to 73.68%. The results corroborate the use of previous predictive equations for estimating energy requirements in Brazilian older adults. Further studies have the potential to explore the use of the developed models to assess energy needs in this population.

It is suggested that nutrient densities are less affected by measurement errors than absolute intake estimates of dietary exposure. We compared the validity of absolute intakes and densities of protein (kJ from protein/total energy (kJ)), potassium, and sodium (potassium or sodium (in mg)/total energy (kJ)) assessed by different dietary assessment methods. For 69 Dutch subjects, two duplicate portions (DPs), five to fifteen 24-h dietary recalls (24 hRs, telephone-based and web-based) and two food frequency questionnaires (FFQs) were collected and compared to duplicate urinary biomarkers and one or two doubly labelled water measurements. Multivariate measurement error models were used to estimate validity coefficients (VCs) and attenuation factors (AFs). This research showed that group bias diminished for protein and sodium densities assessed by all methods as compared to the respective absolute intakes, but not for those of potassium. However, the VCs and AFs for the nutrient densities did not improve compared to absolute intakes for all four methods, except for the AF of sodium density (0.71) or the FFQ which was better than that of the absolute sodium intake (0.51). Thus, using nutrient densities rather than absolute intakes does not necessarily improve the performance of the DP, FFQ, or 24 hR.

Forager-horticulturalist children do not spend more calories than industrialized children, but they do spend calories differently., Children’s metabolic energy expenditure is central to evolutionary and epidemiological frameworks for understanding variation in human phenotype and health. Nonetheless, the impact of a physically active lifestyle and heavy burden of infectious disease on child metabolism remains unclear. Using energetic, activity, and biomarker measures, we show that Shuar forager-horticulturalist children of Amazonian Ecuador are ~25% more physically active and, in association with immune activity, have ~20% greater resting energy expenditure than children from industrial populations. Despite these differences, Shuar children’s total daily energy expenditure, measured using doubly labeled water, is indistinguishable from industrialized counterparts. Trade-offs in energy allocation between competing physiological tasks, within a constrained energy budget, appear to shape childhood phenotypic variation (e.g., patterns of growth). These trade-offs may contribute to the lifetime obesity and metabolic health disparities that emerge during rapid economic development.

Objectives Usual physical activity (PA) is a complex exposure and typical instruments to measure aspects of PA are subject to measurement error, from systematic biases and biological variability. This error can lead to biased estimates of associations between PA and health outcomes. We developed a calibrated physical activity measure that adjusts for measurement error in both self-reported and accelerometry measures of PA in adults from the US Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a community-based cohort study. Design Total energy expenditure (TEE) from doubly labeled water and resting energy expenditure (REE) from indirect calorimetry were measured in 445 men and women aged 18–74 years in 2010–2012, as part of the HCHS/SOL Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS). Measurements were repeated in a subset (N = 98) 6 months later. Method Calibration equations for usual activity-related energy expenditure (AEE = 0.90 × TEE-REE) were developed by regressing this objective biomarker on self-reported PA and sedentary behavior, Actical accelerometer PA, and other subject characteristics. Results Age, weight and height explained a significant amount of variation in AEE. Actical PA and wear-time were important predictors of AEE; whereas, self-reported PA was not independently associated with AEE. The final calibration equation explained fifty percent of variation in AEE. Conclusions The developed calibration equations can be used to obtain error-corrected associations between PA and health outcomes in HCHS/SOL. Our study represents a unique opportunity to understand the measurement characteristics of PA instruments in an under-studied Hispanic/Latino cohort.

When the doubly labeled water (DLW) method is used to measure total daily energy expenditure (TDEE), isotope measurements are typically performed using isotope ratio mass spectrometry (IRMS). New technologies, such as off-axis integrated cavity output spectroscopy (OA-ICOS) provide comparable isotopic measurements of standard waters and human urine samples, but the accuracy of carbon dioxide production (V̇co2) determined with OA-ICOS has not been demonstrated. We compared simultaneous measurement V̇co2 obtained using whole-room indirect calorimetry (IC) with DLW-based measurements from IRMS and OA-ICOS. Seventeen subjects (10 female; 22 to 63 yr) were studied for 7 consecutive days in the IC. Subjects consumed a dose of 0.25 g H218O (98% APE) and 0.14 g 2H2O (99.8% APE) per kilogram of total body water, and urine samples were obtained on days 1 and 8 to measure average daily V̇co2 using OA-ICOS and IRMS. V̇co2 was calculated using both the plateau and intercept methods. There were no differences in V̇co2 measured by OA-ICOS or IRMS compared with IC when the plateau method was used. When the intercept method was used, V̇co2 using OA-ICOS did not differ from IC, but V̇co2 measured using IRMS was significantly lower than IC. Accuracy (~1–5%), precision (~8%), intraclass correlation coefficients ( R = 0.87–90), and root mean squared error (30–40 liters/day) of V̇co2 measured by OA-ICOS and IRMS were similar. Both OA-ICOS and IRMS produced measurements of V̇co2 with comparable accuracy and precision compared with IC.

This study aimed to examine the total energy expenditure (TEE) and physical activity level (PAL) of elite open-water swimmers. Our study group included 5 world-class competitive open-water swimmers. TEE was measured using the doubly labeled water method for 1 week. The TEE was 4549 ± 1185 kcal/day. The PAL was 3.22 ± 0.46. Our results may provide a reference to optimize energy requirement support.

We present a study of hydration in ALS patients and its effects on survival. This was a multicenter study over 48 weeks in 80 ALS patients who underwent 250 individual measurements using doubly labeled water (DLW). Total body water (TBW) and water turnover (a surrogate for water intake) were 3.4% and 8.6% lower, respectively, in patients compared to age- and gender-matched healthy controls, and both significantly decreased over study duration. In 20% of patients, water turnover measured over 10 d was 2 standard deviations below the mean value in healthy controls. In a separate clinic cohort of 208 patients, water intake estimated from a de novo equation created from common clinical endpoints was a prognostic indicator of survival. Regardless of nutritional state assessed by BMI, survival was two-fold longer in the group above the median for estimated water intake, suggesting that hydration may be a more important predictor of survival than malnutrition. Risk factors for poor hydration were identified. Water intake equations recommended by US Centers for Medicare and Medicaid Services in healthy elderly were inaccurate for use in ALS patients. We developed equations to estimate TBW and water intake in ALS patients for use in clinics to accurately estimate hydration and improve clinical care.

Summary Background & aims Accurate estimation of energy expenditure in older people is important for nutritional support. The current literature contains controversial or inconsistent data regarding the resting metabolic rate (RMR, or basal metabolic rate) in older adults, including the relationship between the RMR and ethnicity. Little information about the RMR in healthy Asian older adults is available. This study was performed to examine the RMR in healthy Japanese older adults and compare it with previously established 16 equations. Methods Thirty-two community-dwelling, healthy, and active elderly Japanese adults were enrolled (age, 64–87 years; 14 men, 18 women; mean height, 154.9 ± 8.9 cm; mean weight, 53.5 ± 9.1 kg; mean body mass index, 22.2 ± 2.5 kg/m 2 ). The RMR was measured by indirect calorimetry. The measured RMR was compared among 16 equations. Correlation analysis, a paired t test, and a Bland–Altman plot were used to assess the agreement among the equations. Results The average RMR was 1132 ± 178 kcal/day with 2233 ± 437 kcal/day average total energy expenditure (TEE) measured by doubly labeled water (DLW). The smallest bias was established by De Lorenzo et al.'s equation as bias ±1.96SD = 4 ± 121 kcal/day. De Lorenzo et al. and Ikeda et al.'s equations had no significant average bias both in men and women (P > 0.05). The 1.96SD of bias in six equations was within 160 kcal/day. In contrast, residuals between the measured and predicted RMR were largely correlated with the RMR in four equations. A sex-related difference in the mean bias was observed in many equations. Conclusion Although the average Japanese healthy older adult has a shorter stature and lower weight than older adults in the Western population, the current data suggest that a similar predictive equation for the RMR can be applied to both Japanese and Western older adults. This study demonstrate that the De Lorenzo et al.'s or Ikeda's equation may be useful for estimating RMR in the community-dwelling, healthy, and active elderly Japanese adults without any systematic bias.

Objective Activity energy expenditure (EE) plays an important role in healthcare, therefore, accurate EE measures are required. Currently available reference EE acquisition methods, such as doubly labeled water and indirect calorimetry, are complex, expensive, uncomfortable, and/or difficult to apply on real time. To overcome these drawbacks, the goal of this paper is to propose a model for computing EE in real time (minute-by-minute) from heart rate and accelerometer signals. Approach The proposed model, which consists of an original branched model, uses heart rate signals for computing EE on moderate to vigorous physical activities and a linear combination of heart rate and counts per minute for computing EE on light to moderate physical activities. Model parameters were estimated from a given data set composed of 53 subjects performing 25 different physical activities (light-, moderate- and vigorous-intensity), and validated using leave-one-subject-out. A different database (semi-controlled in-city circuit), was used in order to validate the versatility of the proposed model. Comparisons are done versus linear and nonlinear models, which are also used for computing EE from accelerometer and/or HR signals. Main results The proposed piecewise model leads to more accurate EE estimations ([Formula: see text], [Formula: see text] and [Formula: see text] J kg-1 min-1 and [Formula: see text], [Formula: see text], and [Formula: see text] J kg-1 min-1 on each validation database). Significance This original approach, which is more conformable and less expensive than the reference methods, allows accurate EE estimations, in real time (minute-by-minute), during a large variety of physical activities. Therefore, this model may be used on applications such as computing the time that a given subject spent on light-intensity physical activities and on moderate to vigorous physical activities (binary classification accuracy of 0.8155).

Background: Calorie restriction (CR) retards aging and increases longevity in many animal models. However, it is unclear whether CR can be implemented in humans without adverse effects on body composition.Objective: We evaluated the effect of a 2-y CR regimen on body composition including the influence of sex and body mass index (BMI; in kg/m2) among participants enrolled in CALERIE-2 (Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy), a multicenter, randomized controlled trial.Design: Participants were 218 nonobese (BMI: 21.9-28.0) adults aged 21-51 y who were randomly assigned to 25% CR (CR, n = 143) or ad libitum control (AL, n = 75) in a 2:1 ratio. Measures at baseline and 12 and 24 mo included body weight, waist circumference, fat mass (FM), fat-free mass (FFM), and appendicular mass by dual-energy X-ray absorptiometry; activity-related energy expenditure (AREE) by doubly labeled water; and dietary protein intake by self-report. Values are expressed as means ± SDs.Results: The CR group achieved 11.9% ± 0.7% CR over 2-y and had significant decreases in weight (-7.6 ± 0.3 compared with 0.4 ± 0.5 kg), waist circumference (-6.2 ± 0.4 compared with 0.9 ± 0.5 cm), FM (-5.4 ± 0.3 compared with 0.5 ± 0.4 kg), and FFM (-2.0 ± 0.2 compared with -0.0 ± 0.2 kg) at 24 mo relative to the AL group (all between-group P < 0.001). Moreover, FFM as a percentage of body weight at 24 mo was higher, and percentage of FM was lower in the CR group than in the AL. AREE, but not protein intake, predicted preservation of FFM during CR (P < 0.01). Men in the CR group lost significantly more trunk fat (P = 0.03) and FFM expressed as a percentage of weight loss (P < 0.001) than women in the CR group.Conclusions: Two years of CR had broadly favorable effects on both whole-body and regional adiposity that could facilitate health span in humans. The decrements in FFM were commensurate with the reduced body mass; although men in the CR group lost more FFM than the women did, the percentage of FFM in the men in the CR group was higher than at baseline. CALERIE was registered at clinicaltrials.gov as NCT00427193.

It has been hypothesized that the increased risk of obesity among African Americans may be partially explained by low energy expenditure (EE) and impaired fat oxidation. Twelve White adults without obesity were pair-matched by age, sex and body mass index (BMI) to a sample of 12 African Americans. Resting EE (REE), 24-h EE, 24-h RQ, Sleep EE, Sleep RQ, and spontaneous physical activity were measured in a respiratory chamber; and free-living total daily EE (TDEE) and physical activity EE were measured using doubly labeled water. There were no race differences in age, body composition, 24-h RQ, sleep RQ, or spontaneous or free-living physical activity; however, Whites had significantly higher REE (p = 0.02), 24-h EE (p = 0.02), Sleep EE (p = 0.005); but not TDEE (p = 0.30) compared to African Americans after adjustment for FFM. African Americans may have a higher risk for obesity because of lower energy expenditure.

Background Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established. Objective We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method. Methods A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices. Results On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw. Conclusions Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

Accurate assessments of a target population&rsquo, s energy intake (EI) are essential to prevent poor nutritional status. However, self-reported dietary records (DRs) or food frequency questionnaires (FFQs) are not always accurate, thereby requiring validation and calibration studies. This study aimed to validate the EI estimated by a FFQ using the doubly labeled water (DLW) method. Participants were 109 Japanese older adults (50 women and 59 men) aged 65&ndash, 88 years. The EI was obtained by a DR and 47-item FFQ over 1 year. The total energy expenditure (TEE) was measured by DLW for ~2 weeks. EI was significantly lower than TEE (p &lt, 0.01), ratios of EI assessed by DR and FFQ against TEE were 0.91 &plusmn, 0.17 and 0.82 &plusmn, 0.22, respectively. TEE was significantly and moderately correlated with the EI estimated by the DR (r = 0.45, p &lt, 0.01) and FFQ (r = 0.37, p &lt, 0.01). Furthermore, the EI correlation coefficients estimated by DR and the FFQ in this study were not significantly different (p = 0.46). The EI/TEE ratio was significantly and negatively correlated with the body mass index (BMI). In conclusion, EI estimated with a DR or FFQ modestly correlated with TEE, and calibrating EI with a developed equation in this study can attenuate the underestimation of EI.

Introduction: Evaluation of total energy expenditure (TEE) and physical activity level (PAL) is important for treatment of patients with type 2 diabetes mellitus (T2DM). However, the validity of accelerometers (ACC) and physical activity questionnaires (PAQ) for estimating TEE and PAL remains unknown in elderly populations with T2DM. We evaluated the accuracy of TEE and PAL results estimated by an ACC (TEEACC and PALACC) and a PAQ (TEEPAQ and PALPAQ) in elderly patients with T2DM. Methods: Fifty-one elderly patients with T2DM (aged 61–79 years) participated in this study. TEEACC was calculated with PALACC using a triaxial ACC (Active style Pro HJA-750c) over 2 weeks and predicted basal metabolic rate (BMR) by Ganpule’s equation. TEEPAQ was estimated using predicted BMR and the PALPAQ from the ­Japan Public Health Center Study-Long questionnaire. We compared the results to TEEDLW measured with the doubly labeled water (DLW) method and PALDLW calculated with BMR using indirect calorimetry. Results: TEEDLW was 2,165 ± 365 kcal/day, and TEEACC was 2,014 ± 339 kcal/day; TEEACC was strongly correlated with TEEDLW (r = 0.87, p < 0.01) but significantly underestimated (–150 ± 183 kcal/day, p < 0.05). There was no significant difference in TEEPAQ and TEEDLW (–49 ± 284 kcal/day), while the range of difference seemed to be larger than TEEACC. PALDLW, PALACC, and PALPAQ were calculated to be 1.71 ± 0.17, 1.69 ± 0.16, and 1.78 ± 0.24, respectively. ­PALACC was strongly correlated with PALDLW (r = 0.71, p < 0.01), and there was no significant difference between the 2 values. PALPAQ was moderately correlated with PALDLW (r = 0.43, p < 0.01) but significantly overestimated. Predicted BMR was significantly lower than the BMR ­measured by indirect calorimetry (1,193 ± 186 vs. 1,262 ± 155 kcal/day, p < 0.01). Conclusions: The present ACC and questionnaire showed acceptable correlation of TEE and PAL compared with DLW method in elderly patients with T2DM. Systematic errors in estimating TEE may be improved by the better equation for predicting BMR.

Purpose Little data are available on doubly labeled water (DLW) assessed total daily energy expenditure (TDEE) during extreme endurance exercise. Doubly labeled water is considered the gold standard to measure TDEE, but different calculations are being used, which may have a large impact on the results. The aim of the current study was to measure TDEE during the Giro d'Italia and apply two different calculation methods.Methods Seven male cyclists (age, 28 5 yr; body mass index, 22.1 2.1 kgm(-2)) completed the 24-d professional cycling race Giro d'Italia in which a total distance of 3445 km was covered, including 10 mountain stages. Total daily energy expenditure was measured over the entire duration of the race, with the ingestion of DLW at three different time points. To calculate TDEE and body composition, the isotope dilution space was calculated using two different techniques, the plateau and intercept technique.Results The %fat mass at baseline was 7.8% and 16.8% with the plateau and intercept technique respectively and did not significantly change over the course of the race. Total daily energy expenditure was on average 32.3 +/- 3.4 MJd(-1) using the plateau technique versus 28.9 +/- 3.2 using the intercept technique, resulting in an average physical activity level (PAL) of 4.37 +/- 0.43 versus 3.91 +/- 0.39, respectively. The dilution space ratio was on average 1.030 with the plateau and 1.060 with the intercept technique.Conclusions Given that the observed dilution space ratio with the plateau technique is similar as the expected ratio from literature and the % fat mass of 7.8% is more realistic for the athletes being studied, we propose the application of the plateau rather than the intercept method, when using DLW during extreme endurance exercise.

Background and objectives: There is a lack of studies investigating associations of physical activity level (PAL) and activity energy expenditure (AEE) using the doubly-labeled water (DLW) method with body composition and physical fitness in young children. Thus, we aimed to examine cross-sectional associations of PAL and AEE with body composition indices and physical fitness components in Swedish preschool children. Materials and methods: PAL was calculated as total energy expenditure measured using DLW divided by the predicted basal metabolic rate in 40 children aged 5.5 (standard deviation 0.2) years. AEE was calculated as total energy expenditure minus basal metabolic rate and the thermic effect of food, and divided by fat-free mass. Body composition was assessed using the 3-component model by combining measurements based on isotope dilution and air-displacement plethysmography. Physical fitness (muscular strength, motor fitness, and cardiorespiratory fitness) was evaluated using the PREFIT test battery. Multiple linear regression models were conducted. Results: PAL and AEE were negatively associated with body mass index, percent body fat, and fat mass index (PAL: standardized &beta, &minus, 0.35, &minus, 0.41, and &minus, 0.45, all p &lt, 0.036, AEE: standardized &beta, 0.44, &minus, 0.44, and &minus, 0.47, all p &lt, 0.006, respectively). Furthermore, PAL and AEE were positively associated with the standing long jump test (PAL: standardized &beta, 0.37, p = 0.017, 0.38, p = 0.014). There were no statistically significant associations found regarding PAL or AEE with fat-free mass index or any other physical fitness test. Conclusions: Greater PAL and AEE at the age 5.5 were significantly associated with body fatness and improved lower-body muscular strength. Therefore, increasing physical activity, and thus energy expenditure, at young ages may be beneficial for preventing overweight/obesity. However, further studies with larger sample sizes are needed to confirm the results.

Weight loss and worsening of nutritional state is a frequent downfall of acute hospitalization in older people. It is usually accepted that acute inflammation is responsible for hypercatabolism. However, several studies suggest, on the contrary, a reduction in resting energy expenditure (REE). This study aimed to obtain a reliable measure of REE and total energy expenditure (TEE) in older patients hospitalized for an acute episode in order to better assess patients&rsquo, energy requirements and help understand the mechanisms of weight loss in this situation. Nineteen hospitalized older patients (mean age 83 years) with C-reactive protein (CRP) level &gt, 20mg/L were recruited. REE and TEE were measured using gold standard methods of indirect calorimetry and doubly labeled water (DLW), respectively. REE was then compared to data from a previous study on aged volunteers from nursing homes who were free of an acute stressor event. Energy requirements measured by DLW were confirmed at 1.3 &times, REE. Energy intake covered the needs but did not prevent weight loss in these patients. TEE was not increased in hospitalized patients and was not influenced by inflammation, while the relationship between REE and inflammation was uncertain. Our results suggest that lean mass remains the major determinant of REE in hospitalized older people and that weight loss may not be explained solely by a state of hypercatabolism.

Author(s): Gurven, Michael D; Trumble, Benjamin C; Stieglitz, Jonathan; Yetish, Gandhi; Cummings, Daniel; Blackwell, Aaron D; Beheim, Bret; Kaplan, Hillard S; Pontzer, Herman | Abstract: ObjectivesResting metabolic rate (RMR) reflects energetic costs of homeostasis and accounts for 60 to 75% of total energy expenditure (TEE). Lean mass and physical activity account for much RMR variability, but the impact of prolonged immune activation from infection on human RMR is unclear in naturalistic settings. We evaluate the effects of infection on mass-corrected RMR among Bolivian forager-horticulturalists, and assess whether RMR declines more slowly with age than in hygienic sedentary populations, as might be expected if older adults experience high pathogen burden.Materials and methodsRMR was measured by indirect calorimetry (Fitmate MED, Cosmed) in 1,300 adults aged 20 to 90 and TEE was measured using doubly labeled water (n = 40). Immune biomarkers, clinical diagnoses, and anthropometrics were collected by the Tsimane Health and Life History Project.ResultsTsimane have higher RMR and TEE than people in sedentary industrialized populations. Tsimane RMR is 18 to 47% (women) and 22 to 40% (men) higher than expected using six standard prediction equations. Tsimane mass-corrected TEE is similarly elevated compared to Westerners. Elevated leukocytes and helminths are associated with excess RMR in multivariate regressions, and jointly result in a predicted excess RMR of 10 to 15%. After age 40, RMR declines by 69 kcal/decade (p l .0001). Controlling for lean mass and height accounts for 71% of age-related RMR decline, and adding indicators of infection minimally affects the age slope. The residual level of age-related decline from age 40 is 1.2% per decade.ConclusionHigh pathogen burden may lead to higher metabolic costs, which may be offset by smaller body mass or other energy-sparing mechanisms.

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.