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

The aim of this study was to investigate the ability of a novel activity monitor designed to be minimally obtrusive in predicting free‐living energy expenditure. Subjects were 18 men and 12 women (age: 41 ± 11 years, BMI: 24.4 ± 3 kg/m²). 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‐r² = 0.55, P < 0.001), and Cnts/d (partial r² = 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‐r² = 0.31, P < 0.001) and Cnts/d (partial‐r² = 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.     

Estimating Activity‐related Energy Expenditure Under Sedentary Conditions Using a Tri‐axial Seismic Accelerometer

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 × 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/m²). Predicted AEE in the chamber was significantly related to measured AEE both within (r² = 0.81 ± 0.06, P < 0.00001) and between (r² = 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.     

Is the ArteACC Index a Valid Indicator of Free‐Living Physical Activity in Adolescents?

Objective: The principal aim of this study was to validate a proposed new index of physical activity, the activity‐related time equivalent based on accelerometry (ArteACC), in adolescents. A secondary aim was to develop regression equations for prediction of total energy expenditure (TEE) and activity energy expenditure [AEE = 0.9 × TEE ? resting metabolic rate (RMR)]. Research Methods and Procedures: RMR and energy expenditure (EE) under standardized exercises were measured by indirect calorimetry in 36 adolescents (14 to 19 years old). TEE was measured by the doubly labeled water method, and physical activity was assessed simultaneously with an accelerometer for 14 days. AEE, AEE in relation to body weight (AEE per kilogram), and activity‐related time equivalent based on energy expenditure (ArteEE = AEE/[EE reference activity ? RMR]) were calculated from laboratory and free‐living EE data. ArteACC was calculated as total activity counts/activity counts of reference activity. Results: ArteACC was significantly related to AEE per kilogram (r = 0.57; p < 0.0001) and ArteEE (r = 0.68; p < 0.001). The absolute amount of time (minutes per day) spent in physical activity was significantly lower when calculated from ArteACC than from ArteEE (p < 0.001). TEE was significantly influenced by RMR, sex, and ArteACC (r² = 0.89). AEE was significantly influenced by sex and ArteACC (r² = 0.59). Discussion: Despite an absolute difference between the two indexes, ArteEE and ArteACC, ArteACC seems to be a valid indicator of free‐living physical activity. It contributed significantly, by 3.3% and 12.5%, to the explained variations in TEE and AEE, respectively.     

Validation of the TracmorD Triaxial Accelerometer to Assess Physical Activity in Preschool Children

Objectives: To assess validity evidence of Tracmor_D to determine energy used for physical activity in 3‐4‐year‐old children. Design and Methods: Participants were randomly selected from GECKO Drenthe cohort (n = 30, age 3.4 ± 0.3 years). Total energy expenditure (TEE) was measured using the doubly labeled water method. Sleeping metabolic rate (SMR) was measured by indirect calorimetry (Deltatrac). TEE and SMR were used to calculate physical activity level (PAL) and activity energy expenditure (AEE). Physical activity was monitored using a DirectLife triaxial accelerometer, Tracmor_D with activity counts per minute (ACM) and activity counts per day (ACD) as outcome measures. Results: The best predictor for PAL was ACM with gender and weight, the best predictor for AEE was ACM alone (backward regression, R² = 0.50, P = 0.010 and R² = 0.31, P = 0.011, respectively). With ACD, the prediction model for PAL included ACD, height, gender, and sleep duration (R² = 0.48, P = 0.033), the prediction model for AEE included ACD, gender and sleep duration (R² = 0.39, P = 0.042). The accelerometer was worn for 5 days, but 3 days did not give a different estimated PAL. Conclusion: Tracmor_D provides moderate‐to‐strong validity evidence that supports its use to evaluate energy used for physical activity in 3‐4‐year‐old children.     

Energy Expenditure and Adiposity in Nigerian and African‐American Women

Objective: Obesity is a prevalent condition in industrialized societies and is increasing around the world. We sought to assess the relative importance of resting energy expenditure (REE) and activity EE (AEE) in two populations with different rates of obesity. Methods and Procedures: Women of African descent between 18 and 59 years of age were recruited from rural Nigeria and from metropolitan Chicago. Total EE (TEE) was measured using the doubly labeled water (DLW) technique and REE by indirect calorimetry; AEE was calculated as the difference between TEE and the sum of REE plus a factor for the thermic effect of food. In the analyses all EE parameters were adjusted for body size using a regression method. Comparisons were made between the groups and associations between EE and adiposity examined. Results: A total of 149 Nigerian and 172 African‐American women completed the protocol. All body size measurements were lower in the Nigerian women. Adjusted TEE and REE were higher in the Nigerian cohort but adjusted AEE did not differ significantly. Adjustment for parity, seasonality, and recent illness did not modify mean AEE or adiposity. In neither cohort was there a meaningful association between measures of AEE and adiposity. Discussion: In these cohorts of women from very different environments, AEE did not differ significantly nor was it associated cross‐sectionally with adiposity. If generalizable, these findings suggest that reduction in AEE may have less of a role in the development of obesity than anticipated. The possibility remains that variation in type and duration of activity plays a role not captured by total AEE.     

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

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

Weight-Loss Induced Changes in Physical Activity and Activity Energy Expenditure in Overweight and Obese Subjects before and after Energy Restriction

Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R² = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (–26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.     

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

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

Evaluation of low-intensity physical activity by triaxial accelerometry

Objective: To develop regression‐based equations that estimate physical activity ratios [energy expenditure (EE) per minute/sleeping metabolic rate] for low‐to‐moderate intensity activities using total acceleration obtained by triaxial accelerometry. Research Methods and Procedures: Twenty‐one Japanese adults were fitted with a triaxial accelerometer while also in a whole‐body human calorimeter for 22.5 hours. The protocol time was composed of sleep (8 hours), four structured activity periods totaling 4 hours (sitting, standing, housework, and walking on a treadmill at speeds of 71 and 95 m/min, 2 × 30 minutes for each activity), and residual time (10.5 hours). Acceleration data (milligausse) from the different periods and their relationship to physical activity ratio obtained from the human calorimeter allowed for the development of EE equations for each activity. The EE equations were validated on the residual times, and the percentage difference for the prediction errors was calculated as (predicted value ? measured value)/measured value × 100. Results: Using data from triaxial accelerations and the ratio of horizontal to vertical accelerations, there was relatively high accuracy in identifying the four different periods of activity. The predicted EE (882 ± 150 kcal/10.5 hours) was strongly correlated with the actual EE measured by human calorimetry (846 ± 146 kcal/10.5 hours, r = 0.94 p < 0.01), although the predicted EE was slightly higher than the measured EE. Discussion: Triaxial accelerometry, when total, vertical, and horizontal accelerations are utilized, can effectively evaluate different types of activities and estimate EE for low‐intensity physical activities associated with modern lifestyles.     

A Weight Reduction Program Preserves Fat‐Free Mass but Not Metabolic Rate in Obese Adolescents

Objective: To determine the effects of a multidisciplinary weight reduction program on body composition and energy expenditure (EE) in severely obese adolescents. Research Methods and Procedures: Twenty‐six severely obese adolescents, 12 to 16 years old [mean BMI: 33.9 kg/m²; 41.5% fat mass (FM)] followed a 9‐month weight reduction program including moderate energy restriction and progressive endurance and resistance training. Body composition was assessed by DXA, basal metabolic rate by indirect calorimetry, and EE by whole‐body indirect calorimetry with the same activity program over 36‐hour periods before starting and 9 months after the weight reduction period. Results: Adolescents gained (least‐square mean ± SE) 2.9 ± 0.2 cm in height, lost 16.9 ± 1.3 kg body weight (BW), 15.2 ± 0.9 kg FM, and 1.8 ± 0.5 kg fat‐free mass (FFM) (p < 0.001). Basal metabolic rate, sleeping, sedentary, and daily EE were 8% to 14% lower 9 months after starting (p < 0.001) and still 6% to 12% lower after adjustment for FFM (p < 0.05). Energy cost of walking decreased by 22% (p < 0.001). The reduction in heart rate during sleep and sedentary activities (?10 to ?13 beats/min), and walking (?20 to ?25 beats/min) (p < 0.001) resulted from both the decrease in BW and physical training. Discussion: A weight reduction program combining moderate energy restriction and physical training in severely obese adolescents resulted in great BW and FM losses and improvement of cardiovascular fitness but did not prevent the decline in EE even after adjustment for FFM.     

Prediction of Visceral Adipose Tissue Using Air Displacement Plethysmography in Children

Objective: To determine the ability of air displacement plethysmography (ADP) to predict visceral adipose tissue (VAT) volume in children. Research Methods and Procedures: Fifty‐five (33 boys/22 girls) white children 13 to 14 years old were studied. Anthropometric measures were collected for body mass, stature, BMI, and waist‐to‐hip ratio (WHR), and body fat percentage was estimated from triceps and subscapular skinfolds, bioelectrical impedance analysis, and ADP. VAT volume was determined using magnetic resonance imaging, using a multiple slice protocol at levels L1 to L5. Results: Boys had significantly (p ≤ 0.05) less VAT volume than girls [645.1 (360.5) cm³ vs. 1035.8 (717.3) cm³]. ADP explained the greatest proportion of the variance in VAT volume compared with the other anthropometric measures. Multiple regression analysis indicated that VAT volume was best predicted by ADP body fat percentage in boys [r² = 0.81, SE of the estimate (SEE) = 160.1, SEE coefficient of variation = 25%] and by WHR and BMI in girls (r² = 0.80, SEE = 337.71, SEE coefficient of variation = 33%). Discussion: Compared with the other anthropometric measures, ADP explains the greatest proportion of the variance in VAT volume in children 13 to 14 years old. For boys, ADP is the tool of choice to predict VAT volume, yet using the more simply collected measures of BMI and WHR is recommended for girls. However, large SE of the estimates remained, suggesting that if precision is needed, there is no surrogate for direct imaging of VAT.     

Repeatability of energy metabolism and resistance to dehydration in the invasive slug Limax maximus

Standard metabolic rate (SMR) and resistance to body dehydration (BD) are important physiological traits that have an effect on water balance and the amount of energy available for activity and production, and thus could contribute to variation in life history traits expressed across a range of environments. Few studies have tested whether SMR and BD show consistent between‐individual variation in molluscs. Significant repeatability of SMR and BD indicates that the traits might be heritable and therefore a possible target for natural selection, so describing the repeatability of SMR and BD is important in studies of phenotypic variability. Here, we studied energy metabolism (body mass‐corrected SMR) and the change in the scaling relationship of SMR and body mass in response to time between measurements in the giant garden slug Limax maximus. Limax maximus is one of the most invasive terrestrial molluscs, with a wide geographical distribution, and is considered an important pest of horticultural and agricultural crops. Our results show that L. maximus follows the expected relationship of increasing SMR with increasing mass, but the scaling exponent varies through time and is different from that described for other gastropods. We also found significant inter‐individual variation in VCO_{2 Mean}, VCO_{2 Min}, VCO_{2 Max}, and BD (τ=0.25, 0.29, 0.24, 0.22, p<0.05, respectively), and significant repeatability of body mass (τ=0.90). To our knowledge, this is the first comprehensive analysis of the repeatability of body mass‐corrected SMR and BD in terrestrial slugs. Our results suggest that energy metabolism and water balance could potentially respond to selection.     

Effects of Energy Expenditure and Ucp1 on Photoperiod‐Induced Weight Gain in Collared Lemmings

Objective: To determine the role of total energy expenditure (TEE) and its components in the ability of collared lemmings to increase weight in response to a decrease in photoperiod. Research Methods and Procedures: Energy expenditure was measured by 24‐hour indirect calorimetry concurrent with food‐intake studies. TEE and resting and nonresting energy expenditure (REE and NREE, respectively) were adjusted for body weight by analysis of covariance (ANCOVA). Uncoupling protein 1 (Ucp1) mRNA levels from interscapular brown adipose tissue were determined by Northern blot. Results: TEE and REE of lemmings exposed to a short photoperiod for 10 days were significantly lower than that of lemmings exposed to a long photoperiod (p < 0.05), whereas NREE was not significantly different (p = 0.44). Ucp1 mRNA levels in interscapular brown adipose tissue were 50% lower in short‐ vs. long‐photoperiod lemmings (p < 0.01). Ucp1 mRNA levels were positively related to REE (r² = 0.79, p < 0.01). After adjustment of REE for differences in Ucp1 mRNA levels, there was no longer a significant difference attributable to photoperiod treatment (p = 0.54). Discussion: The results of this study indicate that the increase in body mass that occurs when collared lemmings are exposed to a short photoperiod may be primarily fueled by a decrease in REE and is correlated with a decrease in Ucp1 mRNA levels.     

Improving energy expenditure estimation by using a triaxial accelerometer

Chen, Kong Y., and Ming Sun. Improving energyexpenditure estimation by using a triaxial accelerometer.J. Appl. Physiol. 83(6):2112-2122, 1997.In our study of 125 subjects (53 men and 72 women) for two 24-h periods, we validated energy expenditure (EE),estimated by a triaxial accelerometer (Tritrac-R3D), by using awhole-room indirect calorimeter under close-to-normal livingconditions. The estimated EE was correlated with the measured total EEfor the 2 days (r = 0.925 andr = 0.855;P < 0.001) and in minute-by-minuteEE (P < 0.01). Resting EE formulatedby the Tritrac was found to be similar to the measured values[standard errors of estimation (SEE) = 0.112 W/kg;P = 0.822]. The Tritrac significantly underestimated total EE, EE for physical activities, EEof sedentary and light-intensity activities, and EE for exercise suchas stepping (all P < 0.001). Wedeveloped a linear and a nonlinear model to predict EE by using theacceleration components from the Tritrac. Predicted EE wassignificantly improved with both models in estimating total EE, totalEE for physical activities, EE in low-intensity activities,minute-by-minute averaged relative difference, and minute-by-minute SEE(all P < 0.05). Furthermore, withour generalized models and by using subjects' physical characteristics and body acceleration, EE can be estimated with higher accuracy (averaged SEE = 0.418 W/kg) than with the Tritrac model.

     

Validation and Calibration of Physical Activity Monitors in Children

Objective: This study was designed to validate accelerometer-based activity monitors against energy expenditure (EE) in children; to compare monitor placement sites; to field-test the monitors; and to establish sedentary, light, moderate, and vigorous threshold counts. Research Methods and Procedures: Computer Science and Applications Actigraph (CSA) and Mini-Mitter Actiwatch (MM) monitors, on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children, 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AEE) computed as EE − RMR was regressed against counts to derive threshold counts. Results: The mean correlations between EE or AEE and counts were slightly higher for MM-hip (r = 0.78 ± 0.06) and MM-leg (r = 0.80 ± 0.05) than CSA-hip (r = 0.66 ± 0.08) and CSA-leg (r = 0.73 ± 0.07). CSA and MM performed similarly on the hip (inter-instrument r = 0.88) and on the lower leg (inter-instrument r = 0.89). Threshold counts for the CSA-hip were <800, <3200, <8200, and ≥8200 for sedentary, light, moderate, and vigorous categories, respectively. For the MM-hip, the threshold counts were <100, <900, <2200, and ≥2200, respectively. Discussion: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.     

Leptin Levels Are Associated with Fat Oxidation and Dietary‐Induced Weight Loss in Obesity

Objective: To examine the relationship between fasting plasma leptin and 24‐hour energy expenditure (EE), substrate oxidation, and spontaneous physical activity (SPA) in obese subjects before and after a major weight reduction compared with normal weight controls. To test fasting plasma leptin, substrate oxidations, and SPA as predictive markers of success during a standardized weight loss intervention. Research Methods and Procedures: Twenty‐one nondiabetic obese (body mass index: 33.9 to 43.8 kg/m²) and 13 lean (body mass index: 20.4 to 24.7 kg/m²) men matched for age and height were included in the study. All obese subjects were reexamined after a mean weight loss of 19.2 kg (95% confidence interval: 15.1–23.4 kg) achieved by 16 weeks of dietary intervention followed by 8 weeks of weight stability. Twenty‐four‐hour EE and substrate oxidations were measured by whole‐body indirect calorimetry. SPA was assessed by microwave radar. Results: In lean subjects, leptin adjusted for fat mass (FM) was correlated to 24‐hour EE before (r = ?0.56, p < 0.05) but not after adjustment for fat free mass. In obese subjects, leptin correlated inversely with 24‐hour and resting nonprotein respiratory quotient (r = ?0.47, p < 0.05 and r = ?0.50, p < 0.05) both before and after adjustments for energy balance. Baseline plasma leptin concentration, adjusted for differences in FM, was inversely related to the size of weight loss after 8 weeks (r = ?0.41, p = 0.07), 16 weeks (r = ?0.51, p < 0.05), and 24 weeks (r = ?0.50, p < 0.05). Discussion: The present study suggests that leptin may have a stimulating effect on fat oxidation in obese subjects. A low leptin level for a given FM was associated with a greater weight loss, suggesting that obese subjects with greater leptin sensitivities are more successful in reducing weight.     

Comparison of Methods for Achieving 24‐Hour Energy Balance in a Whole‐Room Indirect Calorimeter

Objective: To evaluate and compare methods for achieving 24‐hour energy balance in a whole‐room indirect calorimeter. Research Methods and Procedures: Twenty‐four‐hour energy expenditure (EE) for 34 healthy adults (16 women, 18 men) was measured in a calorimeter during a prestudy day and on a subsequent nonconsecutive assessment day (AD). Several methods for estimating EE on the AD using activity factors or regression equations with data available before the AD [anthropometrics, body composition, resting metabolic rate (RMR), sleeping metabolic rate (SMR) on prestudy day, 24‐hour EE on prestudy day] were compared for predictive accuracy. Results: Use of a 24‐hour calorimeter stay gave the smallest mean absolute error (119 ± 16 kcal/d) and smallest single maximum error (361 kcal/d). However, several other methods were only slightly, and not significantly, less accurate (e.g., mean absolute error = 131 ± 17, 140 ± 20, and 141 ± 22 kcal/d and greatest error = 384, 370, and 593 kcal/d for anthropometric, RMR, and SMR regression equations, respectively). Fat‐free mass alone and SMR with a simple activity factor were seen to be less accurate. Discussion: Our results indicate that there may be some improvement in achieving 24‐hour energy balance in a metabolic chamber by using a preceding 24‐hour calorimeter stay; that only slightly less accurate predictions can be obtained using a combination of anthropometric, body composition, and/or RMR measurements; and that there is little or no advantage in using SMR from a previous overnight calorimeter stay.     

Seasonal and sex variation in physical activity levels among agro-pastoralists in Nepal

Considerable attention has been devoted to variation in levels of energy expenditure between and within populations; these are commonly evaluated following international guidelines for grading light, moderate, and heavy physical activity levels (PAL). This study presents activity profiles by season and sex for subsistence agro-pastoralists in Nepal, comparing data for a sample of 20 men observed four times across the year with previously published data on women. Total energy expenditure (TEE) was estimated from direct minute-by-minute observation (totaling 1,679 h for men, 3,601 h for women) and measures of the energy cost of single tasks (117 for men, 168 for women). PAL were calculated and graded as multiples of predicted basal metabolic rate (BMR). Despite an explicitly egalitarian organization of labor, men achieved higher PAL than women (P < .0001), although according to international gradings, both men and women assume moderately heavy PAL in the winter and very heavy PAL in the monsoon. PAL were 1.88 and 2.22 × BMR for men in respective seasons (P < .005; TEE, 11.8 MJ/d and 13.9 MJ/d) and 1.77 and 2.0 × BMR for women (TEE, 9.1 MJ/d and 10.5 MJ/d). High TEE values result from time-consuming work in subsistence tasks, most of which are of moderate energy cost. Results show that the international guideline (FAO/WHO/UNU [1985]) for grading levels of energy expenditure, which adopts discrepant sex-specific values to define thresholds for moderate or heavy PAL, can mask significant gender variation. Male/female ratios of PAL values are suggested instead for population-level comparisons. © 1996 Wiley-Liss, Inc.     

Body mass, body composition and sleeping metabolic rate before, during and after endurance training

Metabolic rate, more specifically resting metabolic rate (RMR) or sleeping metabolic rate (SMR), of an adult subject is usually expressed as a function of the fat-free mass (FFM). Chronic exercise is thought to increase FFM and thus to increase RMR and SMR. We determined body mass (BM), body composition, and SMR before, during, and after an endurance training programme without interfering with energy intake. The subjects were 11 women and 12 men, aged 37 (SD 3) years and body mass index 22.3 (SD 1.5) kg · m^–2. The endurance training prepared subjects to run a half marathon competition after 44 weeks. The SMR was measured overnight in a respiration chamber. Body composition was measured by hydrostatic weighing. Measurements were performed at 0, 8, 20, 40, and 90 weeks after the start of the training. The BM had decreased from a mean value of 66.6 (SD 6.9) to 65.6 (SD 6.7) kg (P<0.01), fat mass (FM) had decreased from 17.1 (SD 3.9) to 13.5 (SD 3.6) kg (P<0.001), and FFM had increased from 49.5 (SD 7.3) to 52.2 (SD 7.6) kg (P<0.001) at 40 weeks. Mean SMR before and after 40 weeks training was 6.5 (SD 0.7) and 6.2 (SD 0.6) MJ · day^–1 (P<0.05). The decrease in SMR was related to the decrease in BM (r=0.62,P=0.001). At 90 weeks, when most subjects had not trained for nearly a year, BM and SMR were not significantly different from the initial value while FM and FFM had not changed since week 40 of training. In conclusion, it was found that an exercise induced increase in FFM did not result in an increase in SMR. There was an indication of the opposite effect, a decrease in SMR in the long term during training, possibly as a defence mechanism of the body in the maintenance of BM.