4.4 Article

Are increases in skeletal muscle mass accompanied by changes to resting metabolic rate in rugby athletes over a pre-season training period?

期刊

EUROPEAN JOURNAL OF SPORT SCIENCE
卷 19, 期 7, 页码 885-892

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/17461391.2018.1561951

关键词

Metabolism; nutrition; measurement; prediction; team sport

资金

  1. Australian Postgraduate Award PhD scholarship from the Queensland University of Technology

向作者/读者索取更多资源

Optimising dietary energy intake is essential for effective sports nutrition practice in rugby athletes. Effective dietary energy prescription requires careful consideration of athletes' daily energy expenditure with the accurate prediction of resting metabolic rate (RMR) important due to its influence on total energy expenditure and in turn, energy balance. This study aimed to (a) measure rugby athletes RMR and (b) report the change in RMR in developing elite rugby players over a rugby preseason subsequent to changes in body composition and (c) explore the accurate prediction of RMR in rugby athletes. Eighteen developing elite rugby union athletes (age 20.2 +/- 1.7 years, body mass 101.2 +/- 14.5 kg, stature 184.0 +/- 8.4 cm) had RMR (indirect calorimetry) and body composition (dual energy x-ray absorptiometry) measured at the start and end of a rugby preseason similar to 14 weeks later. There was no statistically significant difference in RMR over the preseason period (baseline 2389 +/- 263 kcal center dot day(-1) post 2373 +/- 270 kcal center dot day(-1)) despite a significant increase in lean mass of +2.0 +/- 1.6 kg (P < 0.01) and non-significant loss of fat mass. The change in RMR was non-significant and non-meaningful; thus, this study contradicts the commonly held anecdotal perception that an increase in skeletal muscle mass will result in a significant increase in metabolic rate and daily energy needs. Conventional prediction equations generally under-estimated rugby athletes' measured RMR, and may be problematic for identifying low energy availability, and thus updated population-specific prediction equations may be warranted to inform practice.

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