OBJECTIVES: To estimate the energy cost of resting (RMR), sitting and standing for urban Indian adults and compare these estimates with the reported values. DESIGN: Energy costs were measured using oxylog while body fat was estimated using equipment (HBF300, OMRON Corporation, Japan) that works on the principle of bioelectrical impedance, for 24 men and 40 women, aged 20-50 y, engaged in sedentary activities. SETTINGS: Agharkar Research Institute, Pune, India. RESULTS: Mean energy cost (kJ/min) of resting (RMR), sitting and standing were significantly (P<0.01, for all) higher for men (4.01+/-0.42, 5.0+/-0.72 and 5.74+/-0.69, respectively) than women (3.54+/-0.28, 4.03+/-0.41 and 4.35+/-0.52, respectively). Gender difference increased with the level of activity, from 13% for RMR to 32% for standing. These differences reduced when adjusted (using analysis of covariance) for body weight and became non-significant on adjusting for fat-free mass (FFM) in the case of RMR and sitting activity. The measured values of energy cost (absolute and per kg weight) for these activities were similar to African subjects but lower compared to Asian or European subjects for both sexes. The stepwise regression analysis done separately by sexes showed weight (29%) in men and body mass index (44%) in women to be the best predictors of RMR, while regression analysis for combined sexes indicated FFM and height as predictors of RMR (r(2)=56%, P<0.01). If means to estimate body fat were not available, RMR could best be predicted with BMI and sex as predictors (r(2)=55%; P<0.01). This was mainly due to the fact that the sex differences in our population were more prominent in FFM than that in BMI. Our observations thus indicate the need to develop prediction equations separately for different populations owing to differences in their body compositions, especially in fat mass (FM) or FFM. CONCLUSION: The energy costs of activities were associated with body composition, especially with absolute fat-free mass, which may vary even with the same body fat percentage. Therefore, there is a need to develop separate prediction equations for different communities.
OBJECTIVES: To estimate the energy cost of resting (RMR), sitting and standing for urban Indian adults and compare these estimates with the reported values. DESIGN: Energy costs were measured using oxylog while body fat was estimated using equipment (HBF300, OMRON Corporation, Japan) that works on the principle of bioelectrical impedance, for 24 men and 40 women, aged 20-50 y, engaged in sedentary activities. SETTINGS: Agharkar Research Institute, Pune, India. RESULTS: Mean energy cost (kJ/min) of resting (RMR), sitting and standing were significantly (P<0.01, for all) higher for men (4.01+/-0.42, 5.0+/-0.72 and 5.74+/-0.69, respectively) than women (3.54+/-0.28, 4.03+/-0.41 and 4.35+/-0.52, respectively). Gender difference increased with the level of activity, from 13% for RMR to 32% for standing. These differences reduced when adjusted (using analysis of covariance) for body weight and became non-significant on adjusting for fat-free mass (FFM) in the case of RMR and sitting activity. The measured values of energy cost (absolute and per kg weight) for these activities were similar to African subjects but lower compared to Asian or European subjects for both sexes. The stepwise regression analysis done separately by sexes showed weight (29%) in men and body mass index (44%) in women to be the best predictors of RMR, while regression analysis for combined sexes indicated FFM and height as predictors of RMR (r(2)=56%, P<0.01). If means to estimate body fat were not available, RMR could best be predicted with BMI and sex as predictors (r(2)=55%; P<0.01). This was mainly due to the fact that the sex differences in our population were more prominent in FFM than that in BMI. Our observations thus indicate the need to develop prediction equations separately for different populations owing to differences in their body compositions, especially in fat mass (FM) or FFM. CONCLUSION: The energy costs of activities were associated with body composition, especially with absolute fat-free mass, which may vary even with the same body fat percentage. Therefore, there is a need to develop separate prediction equations for different communities.
Authors: Jennifer L Miles-Chan; Delphine Sarafian; Jean-Pierre Montani; Yves Schutz; Abdul Dulloo Journal: PLoS One Date: 2013-05-31 Impact factor: 3.240
Authors: Francisco J Amaro-Gahete; Guillermo Sanchez-Delgado; Juan M A Alcantara; Borja Martinez-Tellez; Francisco M Acosta; Elisa Merchan-Ramirez; Marie Löf; Idoia Labayen; Jonatan R Ruiz Journal: PLoS One Date: 2019-06-12 Impact factor: 3.240
Authors: Herman Pontzer; David A Raichlen; Brian M Wood; Audax Z P Mabulla; Susan B Racette; Frank W Marlowe Journal: PLoS One Date: 2012-07-25 Impact factor: 3.240