4.3 Article

Resting metabolic rate of Indian Junior Soccer players: Testing agreement between measured versus selected predictive equations

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AMERICAN JOURNAL OF HUMAN BIOLOGY
卷 30, 期 1, 页码 -

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WILEY
DOI: 10.1002/ajhb.23066

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  1. National Institute of Nutrition, Indian Council of Medical Research [12-BS15]

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ObjectivesOwing to a dearth of research related to Resting Metabolic Rate (RMR) among adolescent athletes in India, our study aimed to document RMR among junior soccer players (JSP) and to identify suitable RMR predictive models for JSP from nine existing equations. MethodsForty Indian JSP (Boys=21, Girls=19) representing the under-12 and under-16 age categories were assessed for body composition (skinfold technique) and RMR (oxycon mobile). Two-way ANOVA and ANCOVA were used to examine the differences across age and sex. Bland-Altman plot was used to test agreement between measured vs. predicted RMR using the equations of Cunningham (, The American Journal of Clinical Nutrition, 33), Soares et al. (, European Journal of Clinical Nutrition, 47; 1998, British Journal of Nutrition, 79), Henry (, Public Health Nutrition, 8), and Patil and Bharadwaj (, Journal of Postgraduate Medicine, 59) for non-athletic populations and the equations of De Lorenzo et al. (, The Journal of Sports Medicine and Physical Fitness, 39), Wong et al. (, Singapore Medical Journal, 53), and ten Haaf & Weijs (, PloS One, 9) for adult athletes. ResultsRMR showed significant (P<.01) sex differences (Boys: 1343297.1; Girls: 1135 +/- 116.7 kcalday(-1)). While RMR values adjusted for fat-free mass (FFM) were similar across age and sex. The equation of Soares et al. (, British Journal of Nutrition, 79) for girls and Wong et al. (, Singapore Medical Journal, 53) for boys showed better RMR predictability. ConclusionsFFM explained variation in RMR across age and sex. The FFM-based Soares et al. (, British Journal of Nutrition, 79) equation for girls and body weight-based Wong et al. (, Singapore Medical Journal, 53) equation for boys are best suited for predicting RMR.

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