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.     

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.     

Physical activity in aging: comparison among young, aged, and nonagenarian individuals.

Physical activity (PA) is known to decline with age; however, there is a paucity of data on activity in persons who are in their nineties and beyond. We used objective and reliable methods to measure PA in nonagenarians (>or=90 yr; n=98) and hypothesized that activity would be similar to that of aged (60-74 yr; n=58) subjects but less than in young (20-34 yr; n=53) volunteers. Total energy expenditure (TEE) was measured by doubly labeled water over 14 days and resting metabolic rate (RMR) by indirect calorimetry. Measures of PA included activity energy expenditure adjusted for body composition, TEE adjusted for RMR, physical activity level (PAL), and activity over 14 days by accelerometry expressed as average daily durations of light and moderate activity. RMR and TEE were lower with increasing age group (P<0.01); however, RMR was not different between aged and nonagenarian subjects after adjusting for fat-free mass, fat mass, and sex. Nonagenarians had a lower PAL and were more sedentary than the aged and young groups (P<0.01); however, the nonagenarians who were more active on a daily basis walked further during a timed test, indicating higher physical functionality. For all measures of activity, no differences were found between young and aged volunteers. PA was markedly lower in nonagenarians compared with young and aged adults. Interestingly, PA was similar between young volunteers and those who were in their 60s and 70s, likely due to the sedentary nature of our society, particularly in young adults.     

Effect of calorie restriction on the free-living physical activity levels of nonobese humans: results of three randomized trials

The objective of this study was to evaluate the influence of calorie restriction (CR) on free-living physical activity levels among humans. Data were from three CALERIE phase I site-specific protocols. Participants were nonobese (body mass index = 23.5-29.9 kg/m2 adults randomly assigned to 25% CR, low-calorie diet (LCD, 890 kcal/day supplement diet until 15% weight loss, then weight maintenance), or control at Pennington Biomedical Research Center (PBRC); 30% or 10% CR at Tufts University; and 20% CR or control at Washington University School of Medicine (WUSM). Activity was measured at months 0, 3, and 6 (PBRC) and at months 0, 3, 6, 9, and 12 (WUSM and Tufts). Total daily energy expenditure (TEE) by doubly labeled water and resting metabolic rate (RMR) were used to compute activity energy expenditure: AEE = TEE - RMR - 0.1 * TEE. Accelerometry and 7-day recall categorized activities by intensity. At Tufts, the 10% and 30% CR groups experienced significant decreases in AEE at months 6, 9, and 12. At month 6, a larger decrease in AEE was observed in the CR than the control group at WUSM. At months 3 and 6, larger decreases in AEE were observed in the CR and LCD groups than the control group at PBRC. Accelerometry and 7-day PAR did not consistently detect changes in activity categories. CR-associated changes in AEE were variable but, generally, reduced the energy deficit, which would reduce the expected rate of weight loss. Accelerometry and recall did not consistently explain reduced AEE, suggesting that increased muscle efficiency and/or decreased fidgeting accounted for decreased AEE. Inaccuracy of accelerometry and recall also likely negatively affected sensitivity.     

Muscle metabolic function and free-living physical activity.

We have previously shown that muscle metabolic function measured during exercise is related to exercise performance and subsequent 1-yr weight gain. Because it is well established that physical activity is important in weight maintenance, we examined muscle function relationships with free-living energy expenditure and physical activity. Subjects were 71 premenopausal black and white women. Muscle metabolism was evaluated by (31)P magnetic resonance spectroscopy during 90-s isometric plantar flexion contractions (45% maximum). Free-living energy expenditure (TEE) was measured using doubly labeled water, activity-related energy expenditure (AEE) was calculated as 0.9 x TEE - sleeping energy expenditure from room calorimetry, and free-living physical activity (ARTE) was calculated by dividing AEE by energy cost of standard physical activities. At the end of exercise, anaerobic glycolytic rate (ANGLY) and muscle concentration of phosphomonoesters (PME) were negatively related to TEE, AEE, and ARTE (P < 0.05). Multiple regression analysis showed that both PME (partial r = -0.29, <0.02) and ANGLY (partial r = -0.24, P < 0.04) were independently related to ARTE. PME, primarily glucose-6-phosphate and fructose-6-phosphate, was significantly related to ratings of perceived exertion (r = 0.21, P < or = 0.05) during a maximal treadmill test. PME was not related to ARTE after inclusion of RPE in the multiple regression model, suggesting that PME may be obtaining its relationship with ARTE through an increased perception of effort during physical activity. In conclusion, physically inactive individuals tend to be more dependent on anaerobic glycolysis during exercise while relying on a glycolytic pathway that may not be functioning optimally.     

Pubertal alterations in growth and body composition. V. Energy expenditure, adiposity, and fat distribution

We determined whether activity energy expenditure (AEE, from doubly labeled water and indirect calorimetry) or physical activity [7-day physical activity recall (PAR)] was more related to adiposity and the validity of PAR estimated total energy expenditure (TEE(PAR)) in prepubertal and pubertal boys (n = 14 and 15) and girls (n = 13 and 18). AEE, but not physical activity hours, was inversely related to fat mass (FM) after accounting for the fat-free mass, maturation, and age (partial r = -0.35, P < or = 0.01). From forward stepwise regression, pubertal maturation, AEE, and gender predicted FM (r(2) = 0.36). Abdominal visceral fat and subcutaneous fat were not related to AEE or activity hours after partial correlation with FM, maturation, and age. When assuming one metabolic equivalent (MET) equals 1 kcal. kg body wt(-1). h(-1), TEE(PAR) underestimated TEE from doubly labeled water (TEE bias) by 555 kcal/day +/- 2 SD limits of agreement of 913 kcal/day. The measured basal metabolic rate (BMR) was >1 kcal. kg body wt(-1). h(-1) and remained so until 16 yr of age. TEE bias was reduced when setting 1 MET equal to the measured (bias = 60 +/- 51 kcal/day) or predicted (bias = 53 +/- 50 kcal/day) BMR but was not consistent for an individual child (+/- 2 SD limits of agreement of 784 and 764 kcal/day, respectively) or across all maturation groups. After BMR was corrected, TEE bias remained greatest in the prepubertal girls. In conclusion, in children and adolescents, FM is more strongly related to AEE than activity time, and AEE, pubertal maturation, and gender explain 36% of the variance in FM. PAR should not be used to determine TEE of individual children and adolescents in a research setting but may have utility in large population-based pediatric studies, if an appropriate MET value is used to convert physical activity data to TEE data.     

Resistance training increases total energy expenditure and free-living physical activity in older adults.

The purpose of this study was to determine what effects 26 wk of resistance training have on resting energy expenditure (REE), total free-living energy expenditure (TEE), activity-related energy expenditure (AEE), engagement in free-living physical activity as measured by the activity-related time equivalent (ARTE) index, and respiratory exchange ratio (RER) in 61- to 77-yr-old men (n = 8) and women (n = 7). Before and after training, body composition (four-compartment model), strength, REE, TEE (doubly labeled water), AEE (TEE - REE + thermic response to meals), and ARTE (AEE adjusted for energy cost of standard activities) were evaluated. Strength (36%) and fat-free mass (2 kg) significantly increased, but body weight did not change. REE increased 6.8%, whereas resting RER decreased from 0.86 to 0.83. TEE (12%) and ARTE (38%) increased significantly, and AEE (30%) approached significance (P = 0.06). The TEE increase remained significant even after adjustment for the energy expenditure of the resistance training. In response to resistance training, TEE increased and RER decreased. The increase in TEE occurred as a result of increases in both REE and physical activity. These results suggest that resistance training may have value in increasing energy expenditure and lipid oxidation rates in older adults, thereby improving their metabolic profiles.     

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.     

Metabolic Differences in Response to a High-Fat vs. a High-Carbohydrate Diet

Energy expenditure was measured in a group of 7 subjects who received two isocaloric isonitrogenous diets for a period of 9–21 days with a 4–10-day break between diets. Diet 1 was a high-fat diet (83.5 ± 3.6% of total energy). Diet 2 was a high carbohydrate diet (83.1 ± 3.7% of total energy). Resting and postprandial resting metabolic rate were measured by open circuit indirect calorimetry 2–4 times during each metabolic period. Total energy expenditure (TEE) was measured by the doubly labeled water method over an 8–13-day period. The respiratory quotient was measured 2–4 hours after a meal during each metabolic period for the calculation of total energy expenditure by the doubly labeled water method. Levels of total T₃ (TT₃), T₃ uptake, free thyroid index and T₄ were measured at the end of each metabolic period. No significant changes in resting metabolic rate (RMR) were apparent on the two diets (1567 ± 426 kcal/d high-fat diet and 1503 ± 412 kcal/d high-carbohydrate diet n=7, p<0.15). Total energy expenditure measured in 5 subjects was significantly higher during the high-carbohydrate phase of the diet (2443 ± 422 vs. 2078 ± 482 kcal/d p<0.05). Activity estimated from TEE/RMR was greater on the high-carbohydrate diet but only approached statistical significance (p<0.06). Total T₃ was significantly lower and free thyroid index and T₃ uptake were significantly higher at the end of the high fat diet in comparison to the high-carbohydrate diet. These data suggest that individual tolerance to a high-fat diet varies considerably and may significantly lower TEE by changing levels of physical activity. The explanation for changes in thyroid hormone levels independent of changes in metabolic rate remains unclear.     

Determinants of Free-Living Energy Expenditure in Normal Weight and Obese Women Measured by Doubly Labeled Water

Total free-living energy expenditure (TEE) was measured in 9 normal weight controls and 5 obese women using the doubly labeled water (DLW) method. Resting energy expenditure (REE) and the thermic effect of food (TEF) were measured by indirect calorimetry and the energy cost of physical activity (PA) calculated by deduction, in order to quantify the components and identify determinants of free-living TEE. Although REE was quantitatively the major component of TEE in both groups, PA best explained the variability, contributing 76% to the variance in free-living TEE. The obese women had elevated values for TEE (12397+/-2565 vs. 8339+/-1787 kJ/d, mean+/-SD; p<0.00S), compared with the control women. PA (5071+/-2385 vs. 2552+/-1452; p<0.0S) and REE (6393+/-678 vs. 5084+/-259; p<0.000S) were also raised in the obese, whereas TEF was not significantly different between the groups, accounting for 7.6% of energy expenditure for the obese and 8% for the control subjects. Body weight was the single best determinant of mean daily free-living TEE across both groups. We conclude that PA and body weight are the main determinants of free-living TEE .     

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.     

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.     

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.     

Mitochondrial DNA variation in human metabolic rate and energy expenditure

The role of climate in driving selection of mtDNA as Homo sapiens migrated out of Africa into Eurasia remains controversial. We evaluated the role of mtDNA variation in resting metabolic rate (RMR) and total energy expenditure (TEE) among 294 older, community-dwelling African and European American adults from the Health, Aging and Body Composition Study. Common African haplogroups L0, L2 and L3 had significantly lower RMRs than European haplogroups H, JT and UK with haplogroup L1 RMR being intermediate to these groups. This study links mitochondrial haplogroups with ancestry-associated differences in metabolic rate and energy expenditure.     

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.     

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.     

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.     

Effects of chronic oral rimonabant administration on energy budgets of diet-induced obese C57BL/6 mice

The endocannabinoids have been recognized as an important system involved in the regulation of energy balance. Rimonabant (SR141716), a selective inverse agonist of cannabinoid receptor 1 (CB1), has been shown to cause weight loss. However, its suppressive impact on food intake is transient, indicating a likely additional effect on energy expenditure. To examine the effects of rimonabant on components of energy balance, we administered rimonabant or its vehicle to diet-induced obese (DIO) C57BL/6 mice once daily for 30 days, by oral gavage. Rimonabant induced a persistent weight reduction and a significant decrease in body fatness across all depots. In addition to transiently reduced food intake, rimonabant-treated mice exhibited decreased apparent energy absorption efficiency (AEAE), reduced metabolizable energy intake (MEI), and increased daily energy expenditure (DEE) on days 4-6 of treatment. However, these effects on the energy budget had disappeared by days 22-24 of treatment. No chronic group differences in resting metabolic rate (RMR) or respiratory quotient (RQ) (P > 0.05) were detected. Rimonabant treatment significantly increased daily physical activity (PA) levels both acutely and chronically. The increase in PA was attributed to elevated activity during the light phase but not during the dark phase. Taken together, these data suggested that rimonabant caused a negative energy balance by acting on both energy intake and expenditure. In the short term, the effect included both reduced intake and elevated PA but the chronic effect was only on increased PA expenditure.     

Renal excretion of 8-oxo-7,8-dihydro-2(')-deoxyguanosine: degradation rates of RNA and metabolic rate in humans

Renally excreted 8-oxo-7,8-dihydro-2(')-deoxyguanosine (oxo(8)dG) is a potential marker of oxidative DNA damage by reactive oxygen species. Whole-body degradation rates of t- and rRNA are potential indicators of the resting metabolic rate (RMR). Excretion rates of oxo(8)dG and degradation rates of t- and rRNA were determined in healthy non-smoking adults and children. RMR (indirect calorimetry; 14 children, 16 adults), total energy expenditure (TEE; doubly labelled water technique; 4 children, 6 adults), and lean body mass (LBM; dual-energy X-ray absorptiometry; 14 children, 16 adults) were also measured. Degradation of t- and rRNA (micromol/d/kg LBM; 4 children, 6 adults) was highly correlated with RMR (kJ/d/kg LBM), r=0.867 (p<0.005) and 0.959 (p<0.001), respectively. Excretion of oxo(8)dG (pmol/d/kg LBM; 14 children, 16 adults) was not significantly correlated with RMR (p>0.05). Neither excretion of oxo(8)dG nor degradation of RNA was significantly correlated with TEE (kJ/d/ kg LBM) (p>0.05). In healthy subjects further factors, other than the metabolic rate, seem to influence the excretion rate of oxo(8)dG. The degradation rates of t- and rRNA seem to be appropriate indicators of the RMR.     

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.