Age-related decline in RMR in physically active men: relation to exercise volume and energy intake

We tested the hypothesis that resting metabolic rate (RMR) declines with age in physically active men (endurance exercise > or =3 times/wk) and that this decline is related to weekly exercise volume (h/wk) and/or daily energy intake. Accordingly, we studied 137 healthy adult men who had been weight stable for > or =6 mo: 32 young [26 +/- 1 (SE) yr] and 34 older (62 +/- 1 yr) sedentary males (internal controls); and 39 young (27 +/- 1 yr) and 32 older (63 +/- 2 yr) physically active males (regular endurance exercise). RMR was measured by indirect calorimetry (ventilated hood system) after an overnight fast and approximately 24 h after exercise. Because RMR is related to fat-free mass (FFM; r = 0.76, P < 0.001, current study), FFM was covaried to adjust RMR (RMR(adj)). RMR(adj) was lower with age in both the sedentary (72.0 +/- 2.0 vs. 64.0 +/- 1.3 kcal/h, P < 0.01) and the physically active (76.6 +/- 1.1 vs. 67.9 +/- 1.2 kcal/h, P < 0.01) males. In the physically active men, RMR(adj) was related to both exercise volume (no. of h/wk, regardless of intensity; r = 0.56, P < 0.001) and estimated energy intake (r = 0.58, P < 0.001). Consistent with these relations, RMR(adj) was not significantly different in subgroups of young and older physically active men matched either for exercise volume (h/wk; n = 11 each) or estimated energy intake (kcal/day; n = 6 each). These results indicate that 1) RMR, per unit FFM, declines with age in highly physically active men; and 2) this decline is related to age-associated reductions in exercise volume and energy intake and does not occur in men who maintain exercise volume and/or energy intake at a level similar to that of young physically active men.     

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.     

Estimation of activity related energy expenditure and resting metabolic rate in freely moving mice from indirect calorimetry data

Physical activity (PA) is a main determinant of total energy expenditure (TEE) and has been suggested to play a key role in body weight regulation. However, thus far it has been challenging to determine what part of the expended energy is due to activity in freely moving subjects. We developed a computational method to estimate activity related energy expenditure (AEE) and resting metabolic rate (RMR) in mice from activity and indirect calorimetry data. The method is based on penalised spline regression and takes the time dependency of the RMR into account. In addition, estimates of AEE and RMR are corrected for the regression dilution bias that results from inaccurate PA measurements. We evaluated the performance of our method based on 500 simulated metabolic chamber datasets and compared it to that of conventional methods. It was found that for a sample time of 10 minutes the penalised spline model estimated the time-dependent RMR with 1.7 times higher accuracy than the Kalman filter and with 2.7 times higher accuracy than linear regression. We assessed the applicability of our method on experimental data in a case study involving high fat diet fed male and female C57Bl/6J mice. We found that TEE in male mice was higher due to a difference in RMR while AEE levels were similar in both groups, even though female mice were more active. Interestingly, the higher activity did not result in a difference in AEE because female mice had a lower caloric cost of activity, which was likely due to their lower body weight. In conclusion, TEE decomposition by means of penalised spline regression provides robust estimates of the time-dependent AEE and RMR and can be applied to data generated with generic metabolic chamber and indirect calorimetry set-ups.     

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.     

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.     

Physical activity level and its clinical correlates in chronic obstructive pulmonary disease: a cross-sectional study

Background

Decreased physical activity is associated with higher mortality in subjects with COPD. The aim of this study was to assess clinical characteristics and physical activity levels (PALs) in subjects with COPD.

Methods

Seventy-three subjects with COPD (67 ± 7 yrs, 44 female) with one-second forced expiratory volume percentage (FEV₁%) predicted values of 43 ± 16 were included. The ratio of total energy expenditure (TEE) and resting metabolic rate (RMR) was used to define the physical activity level (PAL) (PAL = TEE/RMR). TEE was assessed with an activity monitor (ActiReg), and RMR was measured by indirect calorimetry. Walking speed (measured over 30-meters), maximal quadriceps muscle strength, fat-free mass and systemic inflammation were measured as clinical characteristics. Hierarchical linear regression was applied to investigate the explanatory values of the clinical correlates to PAL.

Results

The mean PAL was 1.47 ± 0.19, and 92% of subjects were classified as physically very inactive or sedentary. The walking speed was 1.02 ± 0.23 m/s, the quadriceps strength was 31.3 ± 11.2 kg, and the fat-free mass index (FFMI) was 15.7 ± 2.3 kg/m², identifying 42% of subjects as slow walkers, 21% as muscle-weak and 49% as FFM-depleted. The regression model explained 45.5% (p < 0.001) of the variance in PAL. The FEV₁% predicted explained the largest proportion (22.5%), with further improvements in the model from walking speed (10.1%), muscle strength (7.0%) and FFMI (3.0%). Neither age, gender nor systemic inflammation contributed to the model.

Conclusions

Apart from lung function, walking speed and muscle strength are important correlates of physical activity. Further explorations of the longitudinal effects of the factors characterizing the most inactive subjects are warranted.     

Relationship among diastolic intraventricular pressure gradients, relaxation, and preload: impact of age and fitness

Diastolic intraventricular pressure gradients (IVPGs) are a measure of the ability of the ventricle to facilitate its filling using diastolic suction. We assessed 15 healthy young but sedentary subjects, aged <50 yr (young subjects; age, 35 +/- 9 yr); 13 healthy but sedentary seniors, aged >65 yr with known reductions in ventricular compliance (elderly sedentary subjects; age, 70 +/- 4 yr); and 12 master athletes, aged >65 yr, previously shown to have preserved ventricular compliance (elderly fit subjects; age, 68 +/- 3 yr). Pulmonary capillary wedge pressure (PCWP) and echocardiography measurements were performed at baseline, during load manipulation by lower body negative pressure at -15 and -30 mmHg, and after saline infusion of 10 and 20 ml/kg (elderly) or 15 and 30 ml/kg (young). IVPGs were obtained from color M-mode Doppler echocardiograms. Baseline IVPGs were lower (1.2 +/- 0.4 vs. 2.4 +/- 0.7 mmHg, P < 0.0001), and the time constant of pressure decay (tau(0)) was longer (60 +/- 10 vs. 46 +/- 6 ms, P < 0.0001) in elderly sedentary than in young subjects, with no difference in PCWP. Although PCWP changes during load manipulations were similar (P = 0.70), IVPG changes were less prominent in elderly sedentary than in young subjects (P = 0.02). Changes in stroke volume and IVPGs during loading manipulations correlated (r = 0.96, P = 0.0002). PCWP and tau(0) were strong multivariate correlates of IVPGs (P < 0.001, for both). IVPG response to loading interventions in elderly sedentary and elderly fit subjects was similar (P = 0.33), despite known large differences in ventricular compliance. The ability to regulate IVPGs during changes in preload is impaired with aging. Preserving ventricular compliance during aging by lifelong exercise training does not prevent this impairment.     

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.     

Energy requirements and physical activity in free-living older women and men: a doubly labeled water study

Determinants of daily energy needs and physicalactivity are unknown in free-living elderly. This study examineddeterminants of daily total energy expenditure (TEE) andfree-living physical activity in older women(n = 51; age = 67 ± 6 yr) and men(n = 48; age = 70 ± 7 yr) by usingdoubly labeled water and indirect calorimetry. Usingmultiple-regression analyses, we predicted TEE by using anthropometric,physiological, and physical activity indexes. Data were collected onresting metabolic rate (RMR), body composition, peak oxygen consumption(O_{2 peak}),leisure time activity, and plasma thyroid hormone. Data adjusted forbody composition were not different between older women and men,respectively (in kcal/day): TEE, 2,306 ± 647 vs. 2,456 ± 666;RMR, 1,463 ± 244 vs. 1,378 ± 249; and physical activity energyexpenditure, 612 ± 570 vs. 832 ± 581. In a subgroup of 70 womenand men, RMR andO_{2 peak}explained approximately two-thirds of the variance in TEE(R² = 0.62;standard error of the estimate = ±348 kcal/day). Crossvalidation ofthis equation in the remaining 29 women and men was successful, with nodifference between predicted and measured TEE (2,364 ± 398 and2,406 ± 571 kcal/day, respectively). The strongest predictors ofphysical activity energy expenditure(P < 0.05) for womenand men were O_{2 peak}(r = 0.43), fat-free mass(r = 0.39), and body mass(r = 0.34). In summary, RMR andO_{2 peak} are importantindependent predictors of energy requirements in the elderly.Furthermore, cardiovascular fitness and fat-free mass are moderatepredictors of physical activity in free-living elderly.

     

A prospective study of overweight, physical activity, and depressive symptoms in young women

This study examined the prospective associations of BMI, physical activity (PA), changes in BMI, and changes in PA, with depressive symptoms. Self-reported data on height, weight, PA, selected sociodemographic and health variables and depressive symptoms (CESD-10) were provided in 2000 and 2003 by 6,677 young adult women (22-27 years in 2000) participating in the Australian Longitudinal Study on Women's Health (ALSWH). Results of logistic regression analyses showed that the odds of developing depressive symptoms at follow-up (2003) were higher in women who were overweight or obese in 2000 than in healthy weight women, and lower in women who were active in 2000 than in sedentary women. Changes in BMI were significantly associated with increased odds of depressive symptoms at follow-up. Sedentary women who increased their activity had lower odds of depressive symptoms at follow-up than those who remained sedentary. Increases in activity among initially sedentary young women were protective against depressive symptoms even after adjusting for BMI changes. These findings indicate that overweight and obese young women are at risk of developing depressive symptoms. PA appears to be protective against the development of depressive symptoms, but does not attenuate the depressive symptoms associated with weight gain. However, among initially sedentary young women, even small increases in PA over time may reduce the odds of depressive symptoms, regardless of weight status.     

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.     

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.     

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.     

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.     

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.     

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.     

Pattern and cost of weight gain in previously obese women

Weight gain is common among postobese individuals, providing an opportunity to address the cost of weight regain on energy expenditure. We investigated the energy cost of weight regain over 1 yr in 28 women [age 39.5 +/- 1.3 (SE) yr; body mass index 24.2 +/- 0.5 kg/m(2)] with recent weight loss (>12 kg). Body composition, total energy expenditure (TEE) using doubly labeled water, resting metabolic rate (RMR), and thermic effect of a meal (TEM) were assessed at 0 and 12 mo. Metabolizable energy intake (MEI) was calculated from TEE and change in body composition. Fourteen women had a weight gain of 13.2 +/- 2.1 kg. Twelve-month cumulative excess MEI, calculated as the intake in excess of TEE at month 0, was 749 +/- 149 MJ. Of this, 462 +/- 83 MJ (62%) were stored as accrued tissue, and 287 +/- 72 MJ (38%) was increased TEE. Expressed per kilogram of body weight gain, the energy cost of weight gain was calculated to be 54.8 +/- 4.6 MJ/kg. Interestingly, weight regain time courses fell into three distinct patterns, possibly requiring varying countermeasures.     

Separate and combined effects of exercise training and weight loss on exercise efficiency and substrate oxidation

Perturbations in body weight have been shown to affect energy expenditure and efficiency during physical activity. The separate effects of weight loss and exercise training on exercise efficiency or the proportion of energy derived from fat oxidation during physical activity, however, are not known. The purpose of this study was to determine the separate and combined effects of exercise training and weight loss on metabolic efficiency, economy (EC), and fat oxidation during steady-state moderate submaximal exercise. Sixty-four sedentary older (67 +/- 0.5 yr) overweight to obese (30.7 +/- 0.4 kg/m(2)) volunteers completed 4 mo of either diet-induced weight loss (WL; n = 11), exercise training (EX; n = 36), or the combination of both interventions (WLEX; n = 17). Energy expenditure, gross efficiency (GE), EC, and proportion of energy expended from fat (EF) were determined during a 1-h submaximal (50% of peak aerobic capacity) cycle ergometry exercise before the intervention and at the same absolute work rate after the intervention. We found that EX increased GE by 4.7 +/- 2.2%. EC was similarly increased by 4.2 +/- 2.1% by EX. The addition of concomitant WL to EX (WLEX) resulted in greater increases in GE (9.0 +/- 3.3%) compared with WL alone but not compared with EX alone. These effects remained after adjusting for changes in lean body mass. The proportion of energy derived from fat during the bout of moderate exercise increased with EX and WLEX but not with WL. From these findings, we conclude that exercise training, either alone or in combination with weight loss, increases both exercise efficiency and the utilization of fat during moderate physical activity in previously sedentary, obese older adults. Weight loss alone, however, significantly improves neither efficiency nor utilization of fat during exercise.     

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 .