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.     

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.     

Physical Activity Levels Are Low in Free‐Living Adults with Chronic Paraplegia

Objectives: To compare physical activity levels (PALs) of free‐living adults with chronic paraplegia with World Health Organization recommendations and to compare energy expenditure between persons with complete vs. incomplete paraplegia. Research Methods and Procedures: Twenty‐seven euthyroid adults (17 men and 10 women) with paraplegia (12.5 ± 9.5 years since onset; 17 with complete lesions and 10 with incomplete lesions) participated in this cross‐sectional study. Resting metabolic rate was measured by indirect calorimetry and total daily energy expenditure (TDEE) by heart rate monitoring. PAL was calculated as TDEE/resting metabolic rate. Total body water was measured by deuterium dilution and fat‐free mass (FFM) and fat mass (FM) by calculation (FFM = total body water/0.732; FM = weight ? FFM). Obesity was defined using the following percentage FM cutoffs: men 18 to 40 years >22% and 41 to 60 years >25%; and women 18 to 40 years >35% and 41 to 60 years >38%. Results: Nineteen subjects (70.4%; 13 men and six women) were obese. Fifteen subjects (56%) engaged in structured physical activity 1.46 ± 0.85 times during the observation period for a mean of 49.4 ± 31.0 minutes per session. Despite this, mean PAL of the group was 1.56 ± 0.34, indicative of limited physical activity. TDEE was 24.6% lower in subjects with complete paraplegia (2072 ± 505 vs. 2582 ± 852 kcal/d, p = 0.0372). Discussion: PAL of the group was low, indicating that persons with paraplegia need to engage in increased frequency, intensity, and/or duration of structured physical activity to achieve a PAL ≥1.75 and, thereby, to offset sedentary activities of daily living.     

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.     

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.     

Measuring Free‐Living Energy Expenditure and Physical Activity with Triaxial Accelerometry

Objective: To investigate the ability of a newly developed triaxial accelerometer to predict total energy expenditure (EE) (TEE) and activity‐related EE (AEE) in free‐living conditions. Research Methods and Procedures: Subjects were 29 healthy subjects between the ages of 18 and 40. The Triaxial Accelerometer for Movement Registration (Tracmor) was worn for 15 consecutive days. Tracmor output was defined as activity counts per day (ACD) for the sum of all three axes or each axis separately (ACD‐X, ACD‐Y, ACD‐Z). TEE was measured with the doubly labeled water technique. Sleeping metabolic rate (SMR) was measured during an overnight stay in a respiration chamber. The physical activity level was calculated as TEE × SMR^?1, and AEE was calculated as [(0.9 × TEE) ? SMR]. Body composition was calculated from body weight, body volume, and total body water using Siri's three‐compartment model. Results: Age, height, body mass, and ACD explained 83% of the variation in TEE [standard error of estimate (SEE) = 1.00 MJ/d] and 81% of the variation in AEE (SEE = 0.70 MJ/d). The partial correlations for ACD were 0.73 (p < 0.001) and 0.79 (p < 0.001) with TEE and AEE, respectively. When data on SMR or body composition were used with ACD, the explained variation in TEE was 90% (SEE = 0.74 and 0.77 MJ/d, respectively). The increase in the explained variation using three axes instead of one axis (vertical) was 5% (p < 0.05). Discussion: The correlations between Tracmor output and EE measures are the highest reported so far. To measure daily life activities, the use of triaxial accelerometry seems beneficial to uniaxial.     

Effect of calorie restriction on resting metabolic rate and spontaneous physical activity

Objective: It is unclear if resting metabolic rate (RMR) and spontaneous physical activity (SPA) decrease in weight‐reduced non‐obese participants. Additionally, it is unknown if changes in SPA, measured in a respiratory chamber, reflect changes in free‐living physical activity level (PAL). Research Methods and Procedures: Participants (N = 48) were randomized into 4 groups for 6 months: calorie restriction (CR, 25% restriction), CR plus structured exercise (CR+EX, 12.5% restriction plus 12.5% increased energy expenditure via exercise), low‐calorie diet (LCD, 890 kcal/d supplement diet until 15% weight loss, then weight maintenance), and control (weight maintenance). Measurements were collected at baseline, Month 3, and Month 6. Body composition and RMR were measured by DXA and indirect calorimetry, respectively. Two measures of SPA were collected in a respiratory chamber (percent of time active and kcal/d). Free‐living PAL (PAL = total daily energy expenditure by doubly labeled water/RMR) was also measured. Regression equations at baseline were used to adjust RMR for fat‐free mass and SPA (kcal/d) for body weight. Results: Adjusted RMR decreased at Month 3 in the CR group and at Month 6 in the CR+EX and LCD groups. Neither measure of SPA decreased significantly in any group. PAL decreased at Month 3 in the CR and LCD groups, but not in the CR+EX group, who engaged in structured exercise. Changes in SPA in the chamber and free‐living PAL were not related. Discussion: Body weight is defended in non‐obese participants during modest caloric restriction, evidenced by metabolic adaptation of RMR and reduced energy expenditure through physical activity.     

Energy Expenditure Determined by Self-Reported Physical Activity Is Related to Body Fatness

BUCHOWSKI, MACIEJ S., KAREN M. TOWNSEND, KONG Y. CHEN, SARI A. ACRA, AND MING SUN. Energy expenditure determined by self-reported physical activity is related to body fatness. Obes. Res. 1999;7:23–33. Objective : Activity self-reports are a commonly used tool in assessing daily physical activity (PA) and associated energy expenditure (EE). This study examined the effect of relative body fatness (%BF) on differences between self-reported and measured duration and associated EE in healthy adults. Research Methods and Procedures: Men and women (n= 115, age 38±9 years), ranging in %BF from 7.9% to 58.9%, spent two separate days (normal and exercise) in a whole-room indirect calorimeter where EE was measured. While in the room calorimeter, subjects reported the type, intensity, and duration of each performed PA. The Compendium of Physical Activity was used to calculate the energy cost of each reported activity. The EE of all self-reported activities (EE_r) was categorized into four intensity levels, synchronized, and compared with EE from the room calorimeter (EE_m). Results : With increasing %BF, subjects significantly overestimated duration of more strenuous activities (≥4.5), while underestimating moderate activities (2.5 to 4.4 metabolic equivalents (METs)). Misreporting of duration and/or intensity caused an overestimation or underestimation of PA-associated EE at these levels. Reported EE sleep was lower than measured EE sleep, although both had similar durations. As a result, total EE_r was similar to EE_m. Discussion : Individual variability of daily total PA and associated EE generated from self-reports in adults is high. Persons with a higher %BF report duration and/or intensity of moderate to high levels of PA with lower accuracy than leaner individuals. We conclude using the Compendium of Physical Activity is not suitable for the accurate estimation of self-reported EE of AA in adults with a higher %BF.     

Measurement of Human Daily Physical Activity

Objectives: To validate a new device, Intelligent Device for Energy Expenditure and Activity (IDEEA), for the measurement of duration, frequency, and intensity of various types of human physical activity (PA). Research Methods and Procedures: The ability of IDEEA to identify and quantify 32 types of PA, including the most common daily exercise and nonexercise PA, was tested in 76 subjects: Subjects included males (N = 33) and females (N = 43) ranging in age from 13 to 72 years with a mean body mass index (BMI) of 24.7 kg/m² (range: 18.4 to 41.0) [43 females: 13 to 72 years old and BMI 18.4 to ～41.0 kg/m² (mean = 24.7 kg/m² ); 33 males: 15 to ～72 years old and BMI 21.0 to ～38.4 kg/m² (mean = 25.9 kg/m²)]. Postures, limb movements, and jumping were tested using a timed protocol of specific activities. Walking and running were tested using a 60‐meter track, on which subjects walked and ran at 6 self‐selected speeds. Stair climbing and descending were tested by timing subjects who climbed and descended a flight of stairs at two different speeds. Results: Correct identification rates averaged 98.9% for posture and limb movement type and 98.5% for gait type. Pooled correlation between predicted and actual speeds of walking and running was high (r = 0.986, p ≤ 0.0001). Discussion: IDEEA accurately measured duration, frequency, type, and intensity of a variety of daily PAs.     

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.     

Low Plasma Leptin Concentration and Low Rates of Fat Oxidation in Weight‐Stable Post‐Obese Subjects

Objective: A low resting metabolic rate for a given body size and composition, a low rate of fat oxidation, low levels of physical activity, and low plasma leptin concentrations are all risk factors for body weight gain. The aim of the present investigation was to compare resting metabolic rate (RMR), respiratory quotient (RQ), levels of physical activity, and plasma leptin concentrations in eight post‐obese adults (2 males and 6 females; 48.9 ± 12.2 years; body mass index [BMI]: 24.5 ± 1.0 kg/m²; body fat 33 ± 5%; mean ± SD) who lost 27.1 ± 21.3 kg (16 to 79 kg) and had maintained this weight loss for ≥2 months (2 to 9 months) to eight age‐ and BMI‐matched control never‐obese subjects (1 male and 7 females; 49.1 ± 5.2 years; BMI 24.4 ± 1.0 kg/m²; body fat 33 ± 7%). Research Methods and Procedures: Following 3 days of weight maintenance diet (50% carbohydrate and 30% fat), RMR and RQ were measured after a 10‐hour fast using indirect calorimetry and plasma leptin concentrations were measured using radioimmunoassay. Levels of physical activity were estimated using an accelerometer over a 48‐hour period in free living conditions. Results: After adjustment for fat mass and fat‐free mass, post‐obese subjects had, compared with controls, similar levels of physical activity (4185 ± 205 vs. 4295 ± 204 counts) and similar RMR (1383 ± 268 vs. 1430 ± 104 kcal/day) but higher RQ (0.86 ± 0.04 vs. 0.81 ± 0.03, p < 0.05). Leptin concentration correlated positively with percent body fat (r = 0.57, p < 0.05) and, after adjusting for fat mass and fat‐free mass, was lower in post‐obese than in control subjects (4.5 ± 2.1 vs. 11.6 ± 7.9 ng/mL, p < 0.05). Discussion: The low fat oxidation and low plasma leptin concentrations observed in post‐obese individuals may, in part, explain their propensity to relapse.     

The Val103Ile Polymorphism of Melanocortin‐4 Receptor Regulates Energy Expenditure and Weight Gain

Objective: The melanocortin‐4 receptor (MC4R) regulates energy intake. On the basis of animal studies, it may also regulate energy expenditure. Research Methods and Procedures: The effect of the Val103Ile polymorphism of the MC4R gene on energy metabolism was studied in 229 middle‐aged nondiabetic subjects (Group 1, age 51.2 ± 9.8 years, BMI 26.8 ± 4.5 kg/m²) and on weight gain in 1013 elderly subjects (Group 2, age 69.9 ± 2.9 years, BMI 27.4 ± 4.1 kg/m²) during a 3.5‐year follow‐up study. In Group 1, insulin sensitivity, energy expenditure, and substrate oxidation were measured with the hyperinsulinemic euglycemic clamp combined with indirect calorimetry. Results: In Group 1, the Val103Ile genotype was associated with high rates of energy expenditure (63.42 ± 13.40 in eight subjects with the Val103Ile genotype vs. 59.86 ± 7.33 J/kg per minute in 221 subjects with the Val103Val genotype, p = 0.007), high rates of glucose oxidation (8.90 ± 6.15 vs. 6.07 ± 4.38 μmol/kg per minute, p = 0.020), and low levels of free fatty acids (0.45 ± 0.18 vs. 0.56 ± 0.23 mM, p = 0.029) in the fasting state, and with high rates of glucose oxidation during the clamp (18.88 ± 4.63 vs. 17.60 ± 3.24 μmol/kg per minute, p = 0.031). In Group 2, the 103Ile allele was associated with an increase in weight gain during the follow‐up (0.78 ± 3.98 vs. ?0.82 ± 3.98 kg, p = 0.038). Discussion: The Val103Ile polymorphism of the MC4R gene is associated with energy expenditure in humans. Furthermore, it may associate with glucose oxidation, free fatty acid levels, and weight gain.     

Physical activity-related energy expenditure with the RT3 and TriTrac accelerometers in overweight adults

Objective: The objective was to evaluate two accelerometers, the RT3 and the TriTrac‐R3D for their ability to produce estimates of physical activity‐related energy expenditure (PAEE) in overweight/obese adults. Research Methods and Procedures: PAEE estimates from both accelerometers were obtained in two experiments. In Experiment 1, 13 overweight/obese subjects (BMI 34.2 ± 6.4 kg/m²) were monitored over 2 weeks in everyday life, PAEE being simultaneously measured by the doubly labeled water method (DLW). In Experiment 2, 8 overweight/obese subjects (BMI 34.3 ± 5.0 kg/m²) and 10 normal‐weight subjects (BMI 20.8 ± 2.1 kg/m²) were monitored during a treadmill walking protocol, PAEE being simultaneously measured by indirect calorimetry. Results: In Experiment 1, there was no significant difference between methods in mean PAEE (DLW: 704 ± 223 kcal/d, RT3: 656 ± 140 kcal/d, TriTrac‐R3D 624 ± 419 kcal/d). The relative difference between methods (accelerometer vs. DLW) was ?17.1% ± 16.7% for the RT3 and ?20.0 ± 44.6% for the TriTrac‐R3D. Correlation for PAEE between RT3 and DLW was higher than between TriTrac‐R3D and DLW (r = 0.67, p < 0.05 and r = 0.36, p = 0.25, respectively). The 95% confidence interval (CI) (kcal/d) of the mean difference between methods was large, amounting to ?385 to 145 for the RT3 and ?887 to 590 for the TriTrac‐R3D. In Experiment 2, both accelerometers were sensitive to the changes in treadmill speed, with no significant difference in mean PAEE between methods in overweight/obese subjects. Conclusions: Although both accelerometers did not provide accurate estimates of PAEE at individual levels, the data suggest that RT3 has the potential to assess PAEE at group levels in overweight/obese subjects.     

Estimation of Energy Expenditure from Physical Activity Measures: Determinants of Accuracy

Objective: To describe the determinants, specifically age, body mass index, percentage of body fat, and physical activity (PA) level, associated with over‐ and underestimation of energy expenditure (EE) using PA records and the Stanford Seven‐Day Physical Activity Recall (7DR) compared with doubly labeled water (DLW). Research Methods and Procedures: We collected PA measures on 24 males eating a controlled diet designed to maintain body weight, and we determined EE from DLW and estimated EE from PA records and 7DR. Results: Absolute differences in the estimation of EE between DLW and PA assessment methods were greater for the 7DR (30.6 ± 9.9%) than PA records (7.9 ± 3.2%). In PA records, overestimation of EE was greater with older age and higher body fatness; EE was overestimated by 16.7% among men 50 years and older compared with only 5.3% among men <40 years of age. For percentage of body fat, EE was overestimated by 19.7% among men with a percentage of body fat ≥30% compared with only 5.6% among men with a percentage of body fat <25%. A trend for less overestimation of EE with higher levels of PA (measured by DLW/basal metabolic rate [BMR]) also was observed in the PA records. In the 7DR, the estimates of EE varied widely and no trends were observed by age, percentage of body fat, and PA levels. Discussion: Estimation of EE from the 7DR is considerably more variable than from PA records. Factors related to age and percentage of body fat influenced the accuracy of estimated EE in the PA record. Additional studies are needed to understand factors related to accurate reporting of PA behaviors, which are used to estimate EE in free‐living adults.     

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.     

Differences in daily energy expenditure in lean and obese women: the role of posture allocation

Objective: A low resting metabolic rate (RMR) is considered a risk factor for weight gain and obesity; however, due to the greater fat‐free mass (FFM) found in obesity, detecting an impairment in RMR is difficult. The purposes of this study were to determine the RMR in lean and obese women controlling for FFM and investigate activity energy expenditure (AEE) and daily activity patterns in the two groups. Methods and Procedures: Twenty healthy, non‐smoking, pre‐menopausal women (10 lean and 10 obese) participated in this 14‐day observational study on free‐living energy balance. RMR was measured by indirect calorimetry; AEE and total energy expenditure (TEE) were calculated using doubly labeled water (DLW), and activity patterns were investigated using monitors. Body composition including FFM and fat mass (FM) was measured by dual energy X‐ray absorptiometry (DXA). Results: RMR was similar in the obese vs. lean women (1601 ± 109 vs. 1505 ± 109 kcal/day, respectively, P = 0.12, adjusting for FFM and FM). Obese women sat 2.5 h more each day (12.7 ± 3.2 h vs. 10.1 ± 2.0 h, P < 0.05), stood 2 h less (2.7 ± 1.0 h vs. 4.7 ± 2.2 h, P = 0.02) and spent half as much time in activity than lean women (2.6 ± 1.5 h vs. 5.4 ± 1.9 h, P = 0.002). Discussion: RMR was not lower in the obese women; however, they were more sedentary and expended less energy in activity than the lean women. If the obese women adopted the activity patterns of the lean women, including a modification of posture allocation, an additional 300 kcal could be expended every day.     

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.     

Development and Validation of a Measurement System for Assessment of Energy Expenditure and Physical Activity in Prader-Willi Syndrome

CHEN, KONG Y., MING SUN, MERLIN G. BUTLER, TRAVIS THOMPSON, AND MICHAEL G. CARLSON. Development and validation of a measurement system for assessment of energy expenditure and physical activity in Prader—Willi syndrome. Obes Res. Objective: The morbid obesity associated with Prader—Willi syndrome (PWS) may result from either excessive energy intake or reduced energy expenditure (EE). In this report, we describe the development and validation of an Activity—Energy Measurement System (AEMS) to measure EE and physical activity components in an environment approximating free-living conditions. Research Methods and Procedures: The AEMS consists of a live-in, whole-room indirect calorimeter equipped with a novel force platform floor system to enable simultaneous measurements of EE, physical activity, and work efficiency during spontaneous activities and standardized exercises. Free-living physical activity and estimated free-living EE are measured using portable triaxial accelerometers individually calibrated in each subject during their stay in the AEMS. Results: Representative data from two PWS patients and two matched control (CTR) subjects displayed EE during their inactive lifestyles. Discussion: This combination of methods will allow the quantification of daily EE and its components, the amount and energy cost of physical activity, and the relationships between body composition and EE, in order to determine their roles in the development and maintenance of the morbid obesity in PWS.     

Validity of Physical Activity Intensity Predictions by ActiGraph,Actical, and RT3 Accelerometers

Objective: Accelerometers are promising tools for characterizing physical activity (PA) patterns in free‐living persons. To date, validation of energy expenditure (EE) predictions from accelerometers has been restricted to short laboratory or simulated free‐living protocols. This study seeks to determine the capabilities of eight previously published regression equations for three commercially available accelerometers to predict summary measures of daily EE. Methods and Procedures: Study participants were outfitted with ActiGraph, Actical, and RT3 accelerometers, while measurements were simultaneously made during overnight stays in a room calorimeter, which provided minute‐by‐minute EE measurements, in a diverse subject population (n = 85). Regression equations for each device were used to predict the minute‐by‐minute metabolic equivalents (METs) along with the daily PA level (PAL). Results: Two RT3 regressions and one ActiGraph regression were not significantly different from calorimeter measured PAL. When data from the entire visit were divided into four intensity categories—sedentary, light, moderate, and vigorous—significant (P < 0.001) over‐ and underpredictions were detected in numerous regression equations and intensity categories. Discussion: Most EE prediction equations showed differences of <2% in the moderate and vigorous intensity categories. These differences, though small in magnitude, may limit the ability of these regressions to accurately characterize whether specific PA goals have been met in the field setting. New regression equations should be developed if more accurate prediction of the daily PAL or higher precision in determining the time spent in specific PA intensity categories is desired.     

Influence of Short‐Term Consumption of the Caffeine‐Free,Epigallocatechin‐3‐Gallate Supplement,Teavigo, on Resting Metabolism and the Thermic Effect of Feeding

Green tea is purported to promote weight loss. Resting metabolic rate (RMR) and the thermic effect of feeding (TEF) are significant components of total daily energy expenditure and are partially determined by the sympathetic nervous system via catecholamine‐mediated stimulation of β‐adrenergic receptors. Epigallocatechin‐3‐gallate (EGCG: the most bioactive catechin in green tea) inhibits catechol‐O‐methyltransferase, an enzyme contributing to the degradation of catecholamines. Accordingly, we hypothesized that short‐term consumption of a commercially available EGCG supplement (Teavigo) augments RMR and TEF. On two separate occasions, seven placebo or seven EGCG capsules (135 mg/capsule) were administered to 16 adults (9 males, 7 females, age 25 ± 2 years, BMI 24.6 ± 1.2 kg/m² (mean ± s.e.)). Capsules (three/day) were consumed over 48 h; the final capsule was consumed 2 h prior to visiting the laboratory. Energy expenditure (ventilated hood technique) was determined at rest and for 5 h following ingestion of a liquid meal (caloric content: 40% RMR). Contrary to our hypothesis, RMR was not greater (P = 0.10) following consumption of EGCG (6,740 ± 373 kJ/day) compared with placebo (6,971 ± 352). Similarly, the area under the TEF response curve (Δ energy expenditure) was also unaffected by EGCG (246,808 ± 23,748 vs. 243,270 ± 22,177 kJ; P = 0.88). EGCG had no effect on respiratory exchange ratio at rest (P = 0.29) or throughout the TEF measurement (P = 0.56). In summary, together RMR and TEF may account for up to 85% of total daily energy expenditure; we report that short‐term consumption of a commercially available EGCG supplement did not increase RMR or TEF.