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Twin studies with objective measurements suggest habitual physical activity (HPA) are modestly to highly heritable, depending on age. We aimed to confirm or refute this finding and identify relevant genetic variants using a candidate gene approach. HPA was measured for 14 days with a validated triaxial accelerometer (Tracmor) in two populations: (1) 28 monozygotic and 24 dizygotic same-sex twin pairs (aged 22 +/- 5 years, BMI 21.8 +/- 3.4 kg/m(2), 21 male, 31 female pairs); (2) 52 and 65 unrelated men and women (aged 21 +/- 2 years, BMI 22.0 +/- 2.5 kg/m(2)). Single nucleotide polymorphisms (SNPs) in PPARD, PPARGC1A, NRF1 and MTOR were considered candidates. Association analyses were performed for both groups separately followed by meta-analysis. Structural equation modeling shows significant familiality for HPA, consistent with a role for additive genetic factors (heritability 57 %, 95 % CI 32-74 %, AE model) or common environmental factors (47 %, 95 % CI 23-65 %, CE model). A moderate heritability was observed for the time spent on low- and high-intensity physical activity (P

BACKGROUND: Interindividual differences in response to weight loss and maintenance thereafter are ascribed to genetic predisposition and behavioral changes. OBJECTIVE: To examine whether body weight and short and long-term body weight loss were affected by candidate single nucleotide polymorphisms (SNPs) and changes in eating behavior or by an interaction between these genetic and behavioral factors. METHODS: 150 healthy subjects (39 males, 111 females) aged 20-50y with a BMI of 27-38kg/m2 followed a very low energy diet for 8-weeks, followed by a 3-month weight maintenance period. SNPs were selected from six candidate genes: ADRB2, FTO, MC4R, PPARG, PPARD, and PPARGC1A. Changes in eating behavior were determined with the Three Factor Eating Questionnaire. RESULTS: A high genetic predisposition score was associated with a high body weight at baseline and more short-term weight loss. From the six selected obesity-related SNPs, FTO was associated with increased body weight at baseline, and the effect allele of PPARGC1A was positively associated with short-term weight loss, when assessed for each SNP separately. Long-term weight loss was associated with a larger increase in dietary restraint and larger decrease in disinhibition. CONCLUSION: During long-term weight loss, genetic effects are dominated by changes in eating behavior.

Background:Free-living physical activity can be assessed with an accelerometer to estimate energy expenditure but its validity in overweight and obese subjects remains unknown.Objective:Here, we validated published prediction equations derived in a lean population with the TracmorD accelerometer (DirectLife, Philips Consumer Lifestyle, Netherlands) in a population of overweight and obese. We also explored possible improvements of new equations specifically developed in overweight and obese subjects.Design:Subjects were 11 men and 25 women (age: 41+/-7 y, BMI: 31.0+/-2.5 kg/m2). Physical activity was monitored under free-living conditions with TracmorD while total energy expenditure was measured simultaneously with doubly-labeled water. Physical activity level (PAL) and activity energy expenditure (AEE) were calculated from total energy expenditure and sleeping metabolic rate.Results:The published prediction equation explained 47% of the variance of the measured PAL (P<0.001). Physical activity level estimates were unbiased (errors [bias +/- 95%CI]: -0.02 +/- 0.28). Measured and predicted AEE/body weight were highly correlated (r2=58%, P<0.001), however the prediction model showed a significant bias of 8 kJ/kg/d or 17.4% of the average AEE/body weight. The new prediction equation of AEE/body weight developed in the obese group showed no bias.Conclusions:In conclusion, equations derived with the TracmorD allow valid assessment of PAL and AEE/body weight in overweight and obese subjects. There is evidence that estimates of AEE/body weight could be affected by gender. Equations specifically developed in overweight and obese can improve the accuracy of predictions of AEE/body weight.International Journal of Obesity accepted article preview online, 29 October 2013; doi:10.1038/ijo.2013.195.

For the same BMI, South Asians have a higher body fat percentage and an adverse metabolic profile compared with whites. The objective of the present study was to determine the metabolic profiles of South Asian and white men matched for body fat percentage in response to short-term overfeeding with a high-fat diet. A total of ten healthy non-diabetic South Asian men matched for body fat percentage with ten white men were included in the study. A weight-maintenance diet (containing 30 % fat, 55 % carbohydrate and 15 % protein) was provided for 3 d followed by 4 d of overfeeding (150 % of energy requirement) with a high-fat diet (60 % fat, 25 % carbohydrate and 15 % protein). Before and after the overfeeding period, plasma glucose, insulin, TAG, NEFA, total cholesterol and HDL-cholesterol concentrations were determined. Glucose clearance was calculated using a 2 h oral glucose tolerance test. The results revealed that in South Asian and white men, respectively, overfeeding with a high-fat diet decreased plasma TAG concentrations by 0.4 (sd 0.6) and 0.4 (sd 0.5) mmol/l (P diet= 0.008; P ethnicity= 0.24), increased HDL-cholesterol concentrations by 0.12 (sd 0.1) and 0.14 (sd 0.2) mmol/l (P diet= 0.001; P ethnicity= 0.06) and decreased glucose clearance by 48.8 (sd 53.5) and 37.2 (sd 34.2) ml/min per m2 body surface (P diet= 0.004; P ethnicity= 0.18). There was a significant interaction between diet and ethnicity with regard to the changes in total and LDL-cholesterol concentrations (P= 0.01 and 0.007, respectively), which trended towards a larger increase in South Asian subjects than in white subjects. Despite a similar body fat percentage, short-term overfeeding with a high-fat diet had more adverse effects on the lipid profile of South Asians than on that of whites.

Symptoms during physical activity (PA) and physical inactivity are COPD. Our aim was to evaluate the validity and usability of six activity in patients with COPD against the doubly labelled water (DLW) indirect calorimetry method.Eighty COPD patients (age 68+/-6 years, FEV1 57+/-19% predicted) recruited in four centres each wore simultaneously three or six commercially available monitors validated in chronic conditions for consecutive days. A priori validity criteria were defined. These ability to explain total energy expenditure (TEE) variance through regression analysis, using TEE as the dependent variable with total body (TBW) plus several PA monitors outputs as independent variables; and with DLW measured activity energy expenditure (AEE).The Actigraph GT3X DynaPort MoveMonitor best explained the majority of the TEE variance not explained by TBW (53% and 70% respectively) and showed the most correlations with AEE (r=0.71 p<0.001, r=0.70 p<0.0001, of this study should guide users in choosing valid activity monitors for or for clinical use in patients with chronic diseases such as COPD.

BACKGROUND: An inverse relation between sleep duration and body mass index (BMI) has been shown. OBJECTIVE: We assessed the relation between changes in sleep duration and changes in body weight and body composition during weight loss. DESIGN: A total of 98 healthy subjects (25 men), aged 20-50 y and with BMI (in kg/m2) from 28 to 35, followed a 2-mo very-low-energy diet that was followed by a 10-mo period of weight maintenance. Body weight, body composition (measured by using deuterium dilution and air-displacement plethysmography), eating behavior (measured by using a 3-factor eating questionnaire), physical activity (measured by using the validated Baecke's questionnaire), and sleep (estimate by using a questionnaire with the Epworth Sleepiness Scale) were assessed before and immediately after weight loss and 3- and 10-mo follow-ups. RESULTS: The average weight loss was 10% after 2 mo of dieting and 9% and 6% after 3- and 10-mo follow-ups, respectively. Daytime sleepiness and time to fall asleep decreased during weight loss. Short (7 to <9 h) sleepers increased their sleep duration, whereas sleep duration in long sleepers (>/=9 h) did not change significantly during weight loss. This change in sleep duration was concomitantly negatively correlated with the change in BMI during weight loss and after the 3-mo follow-up and with the change in fat mass after the 3-mo follow-up. CONCLUSIONS: Sleep duration benefits from weight loss or vice versa. Successful weight loss, loss of body fat, and 3-mo weight maintenance in short and average sleepers are underscored by an increase in sleep duration or vice versa. This trial was registered at clinicaltrials.gov as NCT01015508.

Summary: Adipocyte size is a major modulator of endocrine functioning of tissue and methods allowing accurate determination of adipocyte size are important to study energy metabolism. The aim of this study was to relative shrinkage of adipocytes before and after weight loss by adipose tissue from the same subjects embedded in paraffin and plastic. healthy subjects (5 males and 13 females) aged 20-50 y with a BMI of 28- kg/m(2) followed a very low energy diet for 8 weeks. Adipose tissue taken prior to and after weight loss and were processed for paraffin and sections. Parameters of adipocyte size were determined with computer analysis. Mean adipocyte size was smaller in paraffin compared to embedded tissue both before (66 +/- 4 vs. 103 +/- 5 mum, P < 0.001) as weight loss (62 +/- 4 vs. 91 +/- 5 mum, P < 0.001). Relative shrinkage adipocytes in paraffin embedded tissue in proportion to plastic embedded was not significantly different before and after weight loss (73 and respectively). Shrinkage due to the type of embedding of the adipose be ignored when comparing before and after weight loss. Plastic adipose tissue provides more accurate and sensitive results.

BACKGROUND: Diet-induced weight loss is accompanied by adaptive ie, a disproportional or greater than expected reduction of resting rate (RMR). OBJECTIVE: The aim of this study was to investigate whether thermogenesis is sustained during weight maintenance after weight loss. Subjects were 22 men and 69 women [mean +/- SD age: 40 +/- 9 y; body (BMI; in kg/m2): 31.9 +/- 3.0]. They followed a very-low-energy diet for followed by a 44-wk period of weight maintenance. Body composition was with a 3-compartment model based on body weight, total body water dilution), and body volume. RMR was measured (RMRm) with a ventilated addition, RMR was predicted (RMRp) on the basis of the measured body RMRp (MJ/d) = 0.024 x fat mass (kg) + 0.102 x fat-free mass (kg) + 0.85. Measurements took place before the diet and 8, 20, and 52 wk after the the diet. RESULTS: The ratio of RMRm to RMRp decreased from 1.004 +/- before the diet to 0.963 +/- 0.073 after the diet (P < 0.001), and the was sustained after 20 wk (0.983 +/- 0.063; P < 0.01) and 52 wk (0.984 P < 0.01). RMRm/RMRp was correlated with the weight loss after 8 wk (P < 20 wk (P < 0.05), and 52 wk (P < 0.05). CONCLUSION: Weight loss results adaptive thermogenesis, and there is no indication for a change in thermogenesis up to 1 y, when weight loss is maintained. This trial was registered at clinicaltrials.gov as NCT01015508.

BACKGROUND: Weight loss due to a negative energy balance is considered to be accompanied by a decrease in physical activity. OBJECTIVE: The aim of this study was to investigate whether a decrease in physical activity is sustained during weight maintenance. DESIGN: Subjects were 20 men and 31 women [mean (+/-SD) age: 42 +/- 8 y; BMI (in kg/m2): 31.4 +/- 2.8]. Weight loss was achieved by an 8-wk very-low-energy diet period, followed by 44 wk of weight maintenance. Physical activity measures were total energy expenditure expressed as a multiple of sleeping metabolic rate (PALSMR) and resting metabolic rate (PALRMR), activity-induced energy expenditure divided by body weight (AEE/kg), and activity counts measured by a triaxial accelerometer. Measurements took place at 0, 8, and 52 wk. RESULTS: Body mass decreased significantly during the diet period (10.5 +/- 3.8%, P < 0.001), and this reduction was sustained after 52 wk (6.0 +/- 5.1%, P < 0.001). PALSMR and PALRMR decreased from 1.81 +/- 0.23 and 1.70 +/- 0.22, respectively, before the diet to 1.69 +/- 0.20 and 1.55 +/- 0.19 after the diet (P < 0.001) and increased again after weight maintenance to 1.85 +/- 0.27 and 1.71 +/- 0.23, respectively, compared with 8-wk measurements (P < 0.001). AEE/kg decreased from 0.043 +/- 0.015 MJ/kg at baseline to 0.037 +/- 0.014 MJ/kg after the diet (P < 0.001) and was higher after 52 wk (0.044 +/- 0.17 MJ/kg) compared with after 8 wk (P < 0.001). Activity counts decreased from 1.64 +/- 0.37 megacounts/d at baseline to 1.54 +/- 0.35 megacounts/d after the diet (P < 0.05) and were higher after 52 wk (1.73 +/- 0.49 megacounts/d) compared with 8 wk (P < 0.01). CONCLUSION: A weight loss-induced reduction in physical activity returns to baseline values when weight loss is maintained. This trial was registered at clinicaltrials.gov as NCT01015508.

We assessed the ability of the Polar activity recorder (AR) to measure energy expenditure (EE) during military training. Twenty-four voluntary male conscripts participated in the study and wore an AR on the non-dominant wrist 24 h a day for 7 d. The AR analyzed and stored the frequency of hand movements (f_hand) into memory at 1 min intervals. The relationship between f_hand and EE was studied over a 7 d period of military training using the doubly labeled water (DLW) technique. In addition, the relationship between f_hand and EE was analyzed during walking and running on a treadmill with an indirect calorimeter (IC), and f_hand was measured during a supervised 45 min field march test where the conscripts carried combat gear. EE was expressed as physical activity level (PAL), total energy expenditure (TEE), and activity-induced energy expenditure adjusted for body mass (AEE/BM). Over the 7 d period, f_hand alone explained 46% of inter-individual variation in PAL(DLW). After inclusion of body height and mass in the model used to predict PAL(DLW) from f_hand, a very high positive correlation and a low standard error of estimate (SEE) were observed between the AR and DLW techniques: for TEE r = 0.86 (p < 0.001), the SEE was 6.3%, and for AEE/BM r = 0.84 (p < 0.001), the SEE was 12.8%. In the treadmill exercise, f_hand correlated highly with PAL(IC) (r = 0.97 +/- 0.02). In the 45 min field march test, the AR measured similar f_hand as on the treadmill at the same speed. In conclusion, the wrist-worn AR can be regarded as a reliable and valid method for assessing EE during intensive training.

Obesity is a serious health problem because of its co-morbidities. The solution, implying weight loss and long-term weight maintenance, is conditional on: (i) sustained satiety despite negative energy balance, (ii) sustained basal energy expenditure despite BW loss due to (iii) a sparing of fat-free mass (FFM), being the main determinant of basal energy expenditure. Dietary protein has been shown to assist with meeting these conditions, since amino acids act on the relevant metabolic targets. This review deals with the effects of different protein diets during BW loss and BW maintenance thereafter. Potential risks of a high protein diet are dealt with. The required daily intake is 0.8-1.2 g/kg BW, implying sustaining the original absolute protein intake and carbohydrate and fat restriction during an energy-restricted diet. The intake of 1.2 g/kg BW is beneficial to body composition and improves blood pressure. A too low absolute protein content of the diet contributes to the risk of BW regain. The success of the so-called 'low carb' diet that is usually high in protein can be attributed to the relatively high-protein content per se and not to the relatively lower carbohydrate content. Metabolic syndrome parameters restore, mainly due to BW loss. With the indicated dosage, no kidney problems have been shown in healthy individuals. In conclusion, dietary protein contributes to the treatment of obesity and the metabolic syndrome, by acting on the relevant metabolic targets of satiety and energy expenditure in negative energy balance, thereby preventing a weight cycling effect.

Increased gluconeogenesis (GNG) has been suggested to contribute to protein-induced satiety via modulation of glucose homoeostasis. The objective was to determine GNG and appetite in healthy human subjects after a high-protein v. a normal-protein diet and to assess whether GNG contributes to protein-induced satiety. A total of twenty-two healthy subjects (ten men and twelve women: age 23 (sem 1) years, BMI 22.1 (sem 0.5) kg/m2) received an isoenergetic high-protein (30/0/70 % of energy from protein/carbohydrate/fat) or normal-protein diet (12/55/33 % of energy from protein/carbohydrate/fat) for 1.5 d in a randomised cross-over design. Appetite ratings were measured using visual analogue scales (VAS); endogenous glucose production and GNG were measured via infusion of [6,6-2H2]glucose and ingestion of 2H2O. Moreover, fasting glucose and beta-hydroxybutyrate concentrations were measured. Glycogen stores were lowered at the start with a glycogen-lowering exercise test. During the high-protein compared with the normal-protein diet, GNG was increased and appetite was suppressed (GNG: 148 (sem 7) v. 133 (sem 6) g/24 h, P < 0.05; and 24 h area under the curve for hunger: 694 (sem 46) v. 1055 (sem 52) mm VAS x 24 h, P < 0.001; fullness: 806 (sem 59) v. 668 (sem 64) mm VAS x 24 h, P < 0.05; desire to eat: 762 (sem 48) v. 1004 (sem 66) mm VAS x 24 h, P < 0.001). There was no correlation between appetite ratings and GNG. Glucose concentration was lower (4.09 (sem 0.10) v. 4.89 (sem 0.06) mmol/l, P < 0.001) and beta-hydroxybutyrate concentration was higher (1349 (sem 139) v. 234 (sem 25) mumol/l, P < 0.001) after the high-protein compared with the normal-protein diet. In conclusion, after a high-protein diet, GNG was increased and appetite was lower compared with a normal-protein diet; however, these were unrelated to each other. An increased concentration of beta-hydroxybutyrate may have contributed to appetite suppression on the high-protein diet.

The 9th Stock Conference acknowledged the complex background of genetic, cultural, environmental and evolutionary factors of obesity. Gene-environment interactions underlie the flexibility in body-weight and body-fat regulation, illustrated by the hunter-gatherers' feast and famine lifestyle, the variation in physical activity over the lifespan being highest at reproductive age, the variation in energy intake through 'eating in the absence of hunger', while running the risk of exceeding the capacity of triacylglyceride storage, leading to lipotoxicity and metabolic problems. Perinatal metabolic programming for obesity via epigenetic changes in response to a 'Western diet' results in production of lipid-poor milk and metabolically efficient pups, contributing to the perpetuation of obesity throughout generations. Evolutionary insight from comparative physiology and ecology indicates that over generations activity-induced energy expenditure has remained the same compared to wild mammals, that energy balance might be dependant on protein balance, while the function of taste changed from detection of poison or energy to social drinking and social behaviour. At present, the impact of assortative mating on obesity prevalence is unambiguously positive. The complexity that appeared can only be fully appreciated by setting the data into the context of our evolutionary history.

Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.International Journal of Obesity advance online publication, 17 May 2011; doi:10.1038/ijo.2011.99.

BACKGROUND: Physical activity may be defined broadly as "all bodily actions produced by the contraction of skeletal muscle that increase energy expenditure above basal level." Physical activity is a complex construct that can be classified into major categories qualitatively, quantitatively, or contextually. The quantitative assessment of physical activity using wearable monitors is grounded in the measurement of energy expenditure. Six main categories of wearable monitors are currently available to investigators: pedometers, load transducers/foot-contact monitors, accelerometers, HR monitors, combined accelerometer and HR monitors, and multiple sensor systems. BEST PRACTICES: Currently available monitors are capable of measuring total physical activity as well as components of physical activity that play important roles in human health. The selection of wearable monitors for measuring physical activity will depend on the physical activity component of interest, study objectives, characteristics of the target population, and study feasibility in terms of cost and logistics. FUTURE DIRECTIONS: Future development of sensors and analytical techniques for assessing physical activity should focus on the dynamic ranges of sensors, comparability for sensor output across manufacturers, and the application of advanced modeling techniques to predict energy expenditure and classify physical activities. New approaches for qualitatively classifying physical activity should be validated using direct observation or recording. New sensors and methods for quantitatively assessing physical activity should be validated in laboratory and free-living populations using criterion methods of calorimetry or doubly labeled water.

BACKGROUND: With the same BMI, age, and sex, Asians were reported to have a higher body fat percentage than whites. OBJECTIVE: This study aimed to determine the difference in body composition and its effect on dietary and 24-h fat oxidation between Asians and whites when they were fed a diet that contained 30% of energy as fat. DESIGN: Seventeen Asians (8 men) were matched with 17 whites (8 men) for BMI, age, and sex. Physical activity was measured for 7 d with an accelerometer. During the last 3 d of the activity measurement, subjects were given a diet to maintain energy balances. Energy expenditure and substrate use were measured for 24 h in a respiration chamber. Dietary fat oxidation was determined from the percentage recovery of deuterium in the urine after a breakfast meal that contained deuterated palmitic acid. Body composition was calculated with a 3-compartment model from body mass, body volume (hydrodensitometry), and total body water (deuterium dilution). RESULTS: Asians had 5% higher body fat than that of whites (28.1 +/- 7.3% compared with 23.0 +/- 6.9%, respectively; P = 0.03). The fat-free mass index tended to be lower in Asians than in whites (16.3 +/- 1.6 compared with 17.0 +/- 1.7 kg/m(2), respectively; P = 0.07). Dietary fat oxidation as a percentage of fat consumed was 11.7 +/- 3.6% compared with 10.8 +/- 4.5% (P = 0.50) for Asians and whites, respectively. In Asians and whites, the 24-h fat oxidation as a percentage of total energy expenditure was 17.7 +/- 6.9% compared with 19.2 +/- 5.1% (P = 0.63), respectively; carbohydrate oxidation was 68.0 +/- 6.8% compared with 66.1 +/- 5.1% (P = 0.51), respectively; and protein oxidation was 14.3 +/- 2.2 compared with 14.7 +/- 1.6% (P = 0.61), respectively. CONCLUSION: Dietary and 24-h fat oxidation were not different between Asians and whites despite differences in body composition. This study was registered in the public trial registry at www.ccmo.nl as NL31217.068.10.

This study investigated which aspects of the individuals' activity behavior determine the physical activity level (PAL). Habitual physical activity of 20 Dutch adults (age: 26-60 years, body mass index: 24.5+/-2.7 kg/m(2)) was measured using a tri-axial accelerometer. Accelerometer output was used to identify the engagement in different types of daily activities with a classification tree algorithm. Activity behavior was described by the daily duration of sleeping, sedentary behavior (lying, sitting, and standing), walking, running, bicycling, and generic standing activities. Simultaneously, the total energy expenditure (TEE) was measured using doubly labeled water. PAL was calculated as TEE divided by sleeping metabolic rate. PAL was significantly associated (P<0.05) with sedentary time (R=-0.72), and the duration of walking (R=0.49), bicycling (R=0.77), and active standing (R=0.62). A negative association was observed between sedentary time and the duration of active standing (R=-0.87; P<0.001). A multiple-linear regression analysis showed that 75% of the variance in PAL could be predicted by the duration of bicycling (Partial R(2)=59%; P<0.01), walking (Partial R(2)=9%; P<0.05) and being sedentary (Partial R(2)=7%; P<0.05). In conclusion, there is objective evidence that sedentary time and activities related to transportation and commuting, such as walking and bicycling, contribute significantly to the average PAL.

OBJECTIVE: To validate an FFQ designed to estimate energy intake in children against doubly labelled water (DLW). To investigate how quality control and standard beverage portion sizes affect the validity of the FFQ. DESIGN: Thirty healthy children, aged 4-6 years, participated. Total energy expenditure (EE) was measured by the DLW method during an observation period of 15 d. At the end of this period parents filled out an FFQ designed to assess the child's habitual energy intake (EI) of the preceding four weeks. SETTING: Validation study in The Netherlands. SUBJECTS: Thirty healthy children (fifteen boys and fifteen girls), aged 4-6 years. RESULTS: Mean EI (6117 (sd 1025) kJ/d) did not differ significantly from mean EE (6286 (sd 971) kJ/d; P = 0.15); the mean EI:EE ratio was 0.98. The Pearson correlation coefficient between EI and EE was 0.62. The Bland-Altman plot showed no systematic bias and a constant bias close to zero. Less intensive quality control of the FFQ maintained the mean EI:EE ratio and decreased the correlation slightly. Using standard instead of individually measured beverage portion sizes decreased the mean EI:EE ratio, but maintained the correlation. CONCLUSIONS: It can be concluded that the developed FFQ is a valid instrument to estimate mean energy intake in a group of 4- to 6-year-old children and performs reasonably well to rank the subjects with respect to energy intake. It is therefore a useful instrument to estimate energy intake in children in surveys and epidemiological studies in The Netherlands.

Background:Air-displacement plethysmography (ADP) may be a valid and practical technique to assess body composition in a clinical setting.Objective:This study aimed to assess longitudinal changes in body composition using ADP and to compare it with the deuterium dilution technique.Design:The study was a 6-months dietary intervention, consisting of four phases. The first month, subjects were fed in energy balance (phase I). This was followed by 1 month with an energy intake of 33% of energy requirements (phase II), followed by 2 months at 67% of energy requirements (phase III) and 2 months of ad libitum intake (phase IV). Body composition was assessed using ADP (Bod Pod) and deuterium dilution at baseline and at the end of each phase. The baseline analysis included 111 subjects (88 female). Sixty-one subjects (50 female) completed all measurements and were included in the longitudinal analysis.Results:At baseline, the fat mass (FM) as assessed with the Bod Pod was on average 2.3+/-4.2 kg (mean+/-2 s.d.) higher than that assessed with deuterium dilution. The difference in FM between techniques increased significantly with increasing FM (R(2)=0.23; P<0.001). Both techniques showed significant changes in FM over time P<0.001). On average, FM as assessed with the Bod Pod was 2.0 kg higher than with deuterium dilution (P<0.001). During phase II, there was a significant interaction between time and method, meaning that the Bod Pod showed a larger decrease in FM than deuterium dilution.Conclusions:The Bod Pod was able to detect all changes in the body composition, but consistently measured a higher FM than deuterium dilution.International Journal of Obesity advance online publication, 23 November 2010; doi:10.1038/ijo.2010.250.

In rats, oligofructose has been shown to stimulate satiety hormone secretion, reduce energy intake and promote weight loss. The present study aimed to examine the effect of oligofructose supplementation on appetite profiles, satiety hormone concentrations and energy intake in human subjects. A total of thirty-one healthy subjects (ten men and twenty-one women) aged 28 (sem 3) years with a BMI of 24.8 (sem 0.3) kg/m2 were included in a randomised double-blind, cross-over study. The subjects received 10 g oligofructose, 16 g oligofructose or 16 g placebo (maltodextrin) daily for 13 d, with a 2-week washout period between treatments. Appetite profile, active glucagon-like peptide 1 (GLP-1) and peptide YY3-36 (PYY) concentrations and energy intake were assessed on days 0 and 13 of the treatment period. Time x treatment interaction revealed a trend of reduction in energy intake over days 0-13 by oligofructose (P = 0.068). Energy intake was significantly reduced (11 %) over time on day 13 compared with day 0 with 16 g/d oligofructose (2801 (sem 301) v. 3217 (sem 320) kJ, P < 0.05). Moreover, energy intake was significantly lower with 16 g/d oligofructose compared with 10 g/d oligofructose on day 13 (2801 (sem 301) v. 3177 (sem 276) kJ, P < 0.05). Area under the curve (AUC) for GLP-1 on day 13 was significantly higher with 16 g/d oligofructose compared with 10 g/d oligofructose (45 (sem 4) v. 41 (sem 3) pmol/l x h, P < 0.05). In the morning until lunch, AUC0-230 min for PYY on day 13 was significantly higher with 16 g/d oligofructose compared with 10 g/d oligofructose and placebo (409 (sem 35) v. 222 (sem 19) and 211 (sem 20) pg/ml x h, P < 0.01). In conclusion, 16 g/d and not 10 g/d oligofructose may be an effective dose to reduce energy intake, possibly supported by higher GLP-1 and PYY concentrations.

Two types of relatively high-protein diets, with a normal or low proportion of carbohydrates, have been shown effective for weight loss. The objective was to assess the significance of the presence or absence of carbohydrates and the proportion of fat in high-protein diets for affecting appetite suppression, energy expenditure, and fat oxidation in normal-weight subjects in energy balance. Subjects (aged 23 (sd 3) years and BMI 22.0 (sd 1.9) kg/m2) were stratified in two groups. Each was offered two diets in a randomised cross-over design: group 1 (n 22) - normal protein (NP; 10, 60 and 30 % energy (En%) from protein, carbohydrate and fat), high protein (HP; 30, 40 and 30 En%); group 2 (n 23) - normal protein (NP-g; 10, 60 and 30 En%), high protein, carbohydrate-free (HP-0C; 30, 0 and 70 En%) for 2 d; NP-g and HP-0C were preceded by glycogen-lowering exercise (day 1). Appetite was measured throughout day 2 using visual analogue scales (VAS). Energy expenditure (EE) and substrate oxidation (respiratory quotient; RQ) were measured in a respiration chamber (08.00 hours on day 2 until 07.30 hours on day 3). Fasting plasma beta-hydroxybutyrate (BHB) concentration was measured (day 3). NP-g and NP did not differ in hunger, EE, RQ and BHB. HP-0C and HP v. NP-g and NP, respectively, were lower in hunger (P < 0.05; P < 0.001) and RQ (P < 0.01; P < 0.001) and higher in EE (P < 0.05; P = 0.07) and BHB (P < 0.05; P < 0.001). Hunger and RQ were lower with HP-0C than HP (693 (sd 208) v. 905 (sd 209) mm VAS x 24 h, P < 0.01; 0.76 (sd 0.01) v. 0.81 (sd 0.02), P < 0.01); BHB was higher (1349 (sd 653) v. 332 (sd 102) mumol/l; P < 0.001). DeltaHunger, DeltaRQ, and DeltaBHB were larger between HP-0C-NP-g than between HP-NP ( - 346 (sd 84) v. - 107 (sd 52) mm VAS x 24 h, P < 0.01; - 0.09 (sd 0.00) v. - 0.05 (sd 0.00), P < 0.001; 1115 (sd 627) v. 104 (sd 42) mumol/l, P < 0.001). In conclusion, appetite suppression and fat oxidation were higher on a

BACKGROUND/AIMS: Obesity is a predisposing factor for chronic illnesses such as type 2 diabetes, heart disease and cancer. In chronic kidney disease (CKD), the effect of obesity on mortality is reversed. Obese patients appear protected. Two ideas have been advanced to explain this 'reverse epidemiology'. First, obesity may buffer patients from wasting. Second, fat may sequester uraemic toxins leading to a systematic error in the prescription of dialysis. Our aim was to use data on the scaling of daily energy expenditure, fat and lean tissue mass to predict the pattern of variation in mortality with obesity under the contrasting hypotheses. METHODS: We used data on daily energy demands measured using the doubly labelled water technique and body composition collected on a cohort of 503 individuals to model the expected impacts of wasting and fat sequestration/underdialysis on mortality. RESULTS: A model predicting mortality due to wasting replicated the mortality pattern of the obesity paradox. However, quantitatively the beneficial effect of being fat was predicted to be much larger than that observed in the actual CKD population. Similar results were found for the fat sequestration/underdialysis hypothesis, but in this case the discrepancy was smaller. DISCUSSION: These models tend to support the fat sequestration and underdialysis idea more than the wasting hypothesis. In part (or in whole) this may be because of inadequacies in the model construction which are currently based on rather crude assumptions. Refinement of the models may enable better tests between alternative ideas for the obesity paradox.

BACKGROUND: Associations between body composition and the energy expended on basal metabolism and activity are complex and age dependent. OBJECTIVE: The objective was to examine associations between body composition and daily (DEE), basal (BEE), and activity energy expenditure (AEE) throughout the adult life span. DESIGN: A cross-sectional study was conducted in 529 adults aged 18-96 y. DEE was measured by using doubly labeled water, BEE by using respirometry, and body composition by isotope dilution. AEE was calculated as DEE - BEE, and physical activity level (PAL) was calculated as DEE/BEE. RESULTS: Up to age 52 y, fat-free mass (FFM) and fat mass (FM) were positively associated with age in men, but no significant effect was observed in women. No effects of age on DEE and AEE were observed. The average DEE in men (14.1 MJ/d) was 27% greater than that in women (10.7 MJ/d). PAL averaged 1.84 in men and 1.75 in women. Above and including the age of 52 y, FFM, FM, DEE, BEE, and AEE were all negatively associated with greater age. The effect of age on AEE was greater than on BEE; consequently, PAL by the age of 95 y was only 1.36. PAL and AEE were both unrelated to FFM (both age adjusted). CONCLUSIONS: PAL and AEE were not associated with age in subjects aged <52 y. AEE, BEE, and PAL were all negatively associated with age in subjects aged >/=52 y. An absence of a relation between age-adjusted PAL and FFM suggested that greater physical activity was not associated with higher FFM in the elderly.

Objective:To study whether eating or physical-activity (PA) habits differ between obese and non-obese monozygotic (MZ) co-twins independent of genetic effects.Methods:Rare MZ pairs discordant for obesity (n=14, body mass index difference 5.2+/-1.8 kg m(-2)) and weight-concordant control pairs (n=10, 1.0+/-0.7 kg m(-2)), identified through a population-based registry of 24-28-year-old twins (n=658 MZ pairs), completed 3-day food and PA diaries and eating behavior questionnaires. Each twin was asked to compare his/her own eating and PA patterns with the co-twin's behavior by structured questionnaires. Accuracy of energy intake was validated by doubly labeled water.Results:Non-obese co-twins consistently reported that their obese twin siblings ate more food overall, consumed less healthy foods and exercised less than the non-obese co-twins do. However, no differences in energy intake (9.6+/-1.0 MJ per day vs 9.8+/-1.1 MJ per day, respectively) in the food diaries or in the mean PA level (1.74+/-0.02 vs 1.79+/-0.04, respectively) in the PA diaries were found between obese and non-obese co-twins. A considerable underreporting of energy intake (3.2+/-1.1 MJ per day, P=0.036) and overreporting of PA (1.8+/-0.8 MJ per day, P=0.049) was observed in the obese, but not in the non-obese co-twins.Conclusions:On the basis of rare MZ twin pairs discordant for obesity, the co-twin assessments confirmed substantial differences in eating and PA behavior between obese and non-obese persons. These may be overlooked in population studies using food and PA diaries because of considerable misreporting by the obese.International Journal of Obesity advance online publication, 15 December 2009; doi:10.1038/ijo.2009.251.

It is estimated that Asia will be the home of more than 100 million people with type 2 diabetes by the year of 2025. This region combines a high proportion of the world's population with rapidly rising diabetes prevalence rates. The increase in diabetes in Asia differs from that reported in other parts of the world: it has developed in a shorter time, in a younger age group, and in people with lower body-mass index (BMI). Studies reported that for the same BMI, Asians have a higher body fat percentage, a prominent abdominal obesity, a higher intramyocellular lipid and/or a higher liver fat content compared to Caucasians. These characteristics may contribute to a higher predisposition to insulin resistance at a lesser degree of obesity than Caucasians. The differences in body composition are more pronounced depending on the region. For the same BMI, among three major ethnic groups in Asia, Asian Indians have the highest body fat, followed by Malay and Chinese. Lower insulin sensitivity is already observed in Asian Indian adolescents with a higher body fat and abdominal obesity compared to Caucasian adolescents. In general, Asian adolescents share the same feature of body composition such as higher body subcutaneous fat, lower appendicular skeletal muscle and lower gynoid fat compared to Caucasian adolescents. This unfavourable body composition may predispose to the development of insulin resistance at later age. Genetics may play a role and the interaction with environmental factors (changes in lifestyle) could increase the risk of developing the metabolic syndrome.

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 present study examined whether activity energy expenditure related to body mass (AEE/kg) is associated with maximal aerobic fitness (VO(2max)), energy balance, and body mass index (BMI) during the 2 hardest weeks of the military basic training season (BT). An additional purpose was to study the accuracy of the pre-filled food diary energy intake. Energy expenditure (EE) with doubly labeled water, energy intake (EI), energy balance, and mis-recording was measured from 24 male conscripts with varying VO(2max). AEE/kg was calculated as (EE x 0.9-measured basal metabolic rate)/body mass. The reported EI was lower (P<0.001) than EE (15.48 MJ/day) and mis-recording of the pre-filled diary was -20%. The negative energy balance (-6+/-26%) was non-significant; however, the variation was high. The subjects with a low VO(2max), a high BMI, and a negative energy balance were vulnerable to low AEE/kg. However, in the multivariate regression analysis only BMI remained in the model, explaining 33% of the variation in AEE/kg. During wintertime BT, AEE/kg is affected by energy balance, VO(2max), and BMI. From these three factors, overweight limits high-level training the most. Furthermore, an optimal energy balance facilitates physical performance and enables high training loads to be sustained during the BT season.

BACKGROUND: High-protein diets have been shown to increase energy expenditure (EE). Objective: The objective was to study whether a high-protein, carbohydrate-free diet (H diet) increases gluconeogenesis and whether this can explain the increase in EE. DESIGN: Ten healthy men with a mean (+/-SEM) body mass index (in kg/m(2)) of 23.0 +/- 0.8 and age of 23 +/- 1 y received an isoenergetic H diet (H condition; 30%, 0%, and 70% of energy from protein, carbohydrate, and fat, respectively) or a normal-protein diet (N condition; 12%, 55%, and 33% of energy from protein, carbohydrate, and fat, respectively) for 1.5 d according to a randomized crossover design, whereas EE was measured in a respiration chamber. Endogenous glucose production (EGP) and fractional gluconeogenesis were measured via infusion of [6,6-(2)H(2)]glucose and ingestion of (2)H(2)O; absolute gluconeogenesis was calculated by multiplying fractional gluconeogenesis by EGP. Body glycogen stores were lowered at the start of the intervention with an exhaustive glycogen-lowering exercise test. RESULTS: EGP was lower in the H condition than in the N condition (181 +/- 9 compared with 226 +/- 9 g/d; P < 0.001), whereas fractional gluconeogenesis was higher (0.95 +/- 0.04 compared with 0.64 +/- 0.03; P < 0.001) and absolute gluconeogenesis tended to be higher (171 +/- 10 compared with 145 +/- 10 g/d; P = 0.06) in the H condition than in the N condition. EE (resting metabolic rate) was greater in the H condition than in the N condition (8.46 +/- 0.23 compared with 8.12 +/- 0.31 MJ/d; P < 0.05). The increase in EE was a function of the increase in gluconeogenesis (DeltaEE = 0.007 x Deltagluconeogenesis - 0.038; r = 0.70, R(2) = 0.49, P < 0.05). The contribution of Deltagluconeogenesis to DeltaEE was 42%; the energy cost of gluconeogenesis was 33% (95% CI: 16%, 50%). CONCLUSIONS: Forty-two percent of the increase in energy expenditure after the H diet was explained by the increase in gluconeogenesis. The

Dietary protein plays a role in body weight regulation, partly because of its effects on appetite. The objective was to compare the effects of high or normal casein-, soy-, or whey-protein breakfasts on appetite, specific hormones, amino acid responses and subsequent energy intake. Twenty-five healthy subjects (mean+/-SEMBMI:23.9+/-0.3 kg/m2; age:22+/-1 years) received standardized breakfasts: custards with either casein-, soy, or whey-protein with either 10/55/35 (normal) or 25/55/20 (high)En% protein/carbohydrate/fat in a randomized, single-blind design. Appetite profile (Visual Analogue Scales) and amino acid concentrations were determined for 4 h whereas plasma glucose, insulin, active Glucagon-like Peptide 1 (GLP-1), and active ghrelin concentrations were determined for 3 h; the sensitive moment for lunch was determined. Subjects returned for a second set of experiments and received the same breakfasts, ad lib lunch was offered 180 min later; energy intake (EI) was assessed. At 10En%, whey decreased hunger more than casein or soy (p <0.05), coinciding with higher leucine, lysine, tryptophan, isoleucine, and threonine responses (p<0.05). At 25En% there were no differences in appetite ratings. Whey triggered the strongest responses in concentrations of active GLP-1 (p<0.05) and insulin (p<0.05) compared with casein and/or soy. There were no differences in EI. In conclusion, differences in appetite ratings between different proteins appeared at a normal concentration; at 10En% whey-protein decreased hunger more than casein- or soy-protein. At 25En% whey-protein triggered stronger responses in hormone concentrations than casein- or soy-protein. The results suggest that a difference in appetite ratings between types of protein appears when certain amino acids are above and below particular threshold values.

Activity-related energy expenditure (AEE) is difficult to quantify, especially under sedentary conditions. Here, a model was developed using the detected type of physical activity (PA) and movement intensity (MI), based on a tri-axial seismic accelerometer (DynaPort MiniMod; McRoberts B.V., The Hague, the Netherlands), with energy expenditure for PA as a reference. The relation between AEE (J/min/kg), MI, and the type of PA was determined for standardized PAs as performed in a laboratory including: lying, sitting, standing, and walking. AEE (J/min/kg) was calculated from total energy expenditure (TEE) and sleeping metabolic rate (SMR) as assessed with indirect calorimetry ((TEE x 0.9) - SMR). Subsequently, the model was validated over 23-h intervals in a respiration chamber. Subjects were 15 healthy women (age: 22 +/- 2 years; BMI: 24.0 +/- 4.0 kg/m(2)). Predicted AEE in the chamber was significantly related to measured AEE both within (r(2) = 0.81 +/- 0.06, P < 0.00001) and between (r(2) = 0.70, P < 0.001) subjects. The explained variation in AEE by the model was higher than the explained variation by MI alone. This shows that a tri-axial seismic accelerometer is a valid tool for estimating AEE under sedentary conditions.Obesity (2009) doi:10.1038/oby.2009.55.

The role of dietary protein in weight loss and weight maintenance encompasses influences on crucial targets for body weight regulation, namely satiety, thermogenesis, energy efficiency, and body composition. Protein-induced satiety may be mainly due to oxidation of amino acids fed in excess, especially in diets with "incomplete" proteins. Protein-induced energy expenditure may be due to protein and urea synthesis and to gluconeogenesis; "complete" proteins having all essential amino acids show larger increases in energy expenditure than do lower-quality proteins. With respect to adverse effects, no protein-induced effects are observed on net bone balance or on calcium balance in young adults and elderly persons. Dietary protein even increases bone mineral mass and reduces incidence of osteoporotic fracture. During weight loss, nitrogen intake positively affects calcium balance and consequent preservation of bone mineral content. Sulphur-containing amino acids cause a blood pressure-raising effect by loss of nephron mass. Subjects with obesity, metabolic syndrome, and type 2 diabetes are particularly susceptible groups. This review provides an overview of how sustaining absolute protein intake affects metabolic targets for weight loss and weight maintenance during negative energy balance, i.e., sustaining satiety and energy expenditure and sparing fat-free mass, resulting in energy inefficiency. However, the long-term relationship between net protein synthesis and sparing fat free mass remains to be elucidated. Expected final online publication date for the Annual Review of Nutrition Volume 29 is July 17, 2009. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

The capacity to perform physical activity largely depends on physical fitness. Muscle fiber-type distribution (Muscle(FTD)) is associated with physical fitness and may influence the capacity to perform physical activity. The purpose of this study was to determine whether habitual physical activity in daily life (PA(DL)) and Muscle(FTD) are related. Thirty-eight healthy non-athletes (31 women, 7 men) were recruited. PA(DL) was measured twice for 14 days using a tri-axial accelerometer for movement registration (Tracmor). From Tracmor output, the proportion of time subjects were physically active at low, moderate, and high intensities was determined (%Low, %Moderate, and %High, respectively). A total activity index (PA(index)) and sub-scores on work, leisure-time and sports were obtained using the Baecke questionnaire. Muscle(FTD) was determined using immuno-fluorescence against respective myosin heavy chain isoforms. No relationship was observed between PA(DL) and Muscle(FTD). %Low, %Moderate, and %High, as well as PA(index) and its sub-scores, were not related to Muscle(FTD) either. The time spent on sports was associated with the proportion of type I and II(X) fibers (P=0.06 and P<0.01, respectively). In conclusion, Muscle(FTD) probably cannot explain why some people are more prone to engaging in physical activities than others. AD - Department of Human Biology, Maastricht University, Maastricht, The Netherlands.

BACKGROUND: An increase in the protein content of a diet results in an increase in satiety and energy expenditure. It is not clear to what extent a specific type of protein has such effects. OBJECTIVE: The objective was to compare the effects of 2 diets with either 25% or 10% of energy from casein (25En% and 10En% casein diets), as the only protein source, on energy expenditure, substrate balance, and appetite profile. DESIGN: During a 36-h stay in a respiration chamber, 24 healthy subjects [12 men and 12 women, body mass index (in kg/m(2)) of 22.4 +/- 2.4, age 25 +/- 7 y] received isoenergetic diets according to subject-specific energy requirements: 25En% diet (25%, 20%, and 55% of energy as protein, fat, and carbohydrate, respectively) and 10En% diet (10%, 35%, and 55% of energy as protein, fat, and carbohydrate, respectively) in a randomized crossover design. Three days before the diets began, the subjects consumed a similar diet at home. Energy expenditure, substrate oxidation, and appetite scores were measured. RESULTS: The 25En% casein diet resulted in a 2.6% higher 24-h total energy expenditure (9.30 +/- 0.24 compared with 9.07 +/- 0.24 MJ/d; P < 0.01) and a higher sleeping metabolic rate (6.74 +/- 0.16 compared with 6.48 +/- 0.17 MJ/d; P < 0.001) than did the 10En% casein diet. With the 25En% casein diet, compared with the 10En% casein diet, the subjects were in positive protein balance (0.57 +/- 0.05 compared with -0.08 +/- 0.03 MJ/d; P < 0.0001) and negative fat balance (-0.83 +/- 0.14 compared with 0.11 +/- 0.17 MJ/d; P < 0.0001), whereas positive carbohydrate balances were not significantly different between diets. Satiety was 33% higher with the 25En% casein diet than with the 10En% casein diet (P < 0.05). CONCLUSION: A 25En% casein diet boosts energy expenditure, protein balance, satiety, and negative fat balance, which is beneficial to body weight management.

OBJECTIVE: The aim of this study was to develop models for the detection of type, duration, and intensity of human physical activity using one triaxial accelerometer. METHODS: Twenty subjects (age = 29 +/- 6 yr, BMI = 23.6 +/- 3.2 kg.m) performed 20 selected activities, including walking, running, and cycling, wearing one triaxial accelerometer mounted on the lower back. Identification of activity type was based on a decision tree. The decision tree evaluated attributes (features) of the acceleration signal. The features were measured in intervals of defined duration (segments). Segment size determined the time resolution of the decision tree to assess activity duration. Decision trees with a time resolution of 0.4, 0.8, 1.6, 3.2, 6.4, and 12.8 s were developed, and the respective classification performances were evaluated. Multiple linear regression was used to estimate speed of walking, running, and cycling based on acceleration features. RESULTS: Maximal accuracy for the classification of activity type (93%) was reached when the segment size of analysis was 6.4 or 12.8 s. The smaller the segment size, the lower the classification accuracy achieved. Segments of 6.4 s gave the highest time resolution for measuring activity duration without decreasing the classification accuracy. The developed models estimated walking, running, and cycling speeds with a standard error of 0.20, 1.26, and 1.36 km.h, respectively. CONCLUSIONS: This study demonstrated the ability of a triaxial accelerometer in detecting type, duration, and intensity of physical activity using models based on acceleration features. Future studies are needed to validate the presented models in free-living conditions.