Skip to main content
2024 NSCA National Conference Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Back
Rate ePoster

Body Composition

(45) A HIGHER RESTING METABOLIC RATE RELATIVE TO BODY MASS IS ASSOCIATED WITH LOWER BODY FAT PERCENTAGE IN ATHLETES

Thursday, July 11, 2024
12:00 PM – 1:30 PM East Coast USA Time
Location: Exhibit Hall
Abstract Details:

Background: Resting metabolic rate (RMR) accounts for 60-70% of total daily energy expenditure, and therefore, plays a pivotal role in energy balance and weight management. Varying degrees of metabolic activity may predispose certain athletes to excess body fat or conversely, to difficulties with gaining weight. Expressing RMR relative to body mass and fat-free mass (FFM) may help identify athletes with high or low RMR values.

Purpose: The purpose of the current study was to examine relationships between resting metabolic rate (RMR) and body fat percentage (BF%) when expressed relative to body mass and FFM.

Methods: One-hundred and ninety male (n=98; Age: 20.1 ± 1.6 yr.; Body Mass: 92.7 ± 17.5 kg; Height: 181.6 ± 6.2 cm, Body Mass Index: 28.0 ± 4.7 kg/m2) and female (n=92; Age: 19.4 ± 1.1 yr.; Body Mass: 65.2 ± 11.0 kg; Height: 168.0 ± 6.6 cm, Body Mass Index: 23.0 ± 3.6 kg/m2) collegiate athletes completed testing during the pre-season period (within 6 weeks of the competitive season) during the 2016-2019 competitive sport seasons. Athletes from baseball (n = 8) cross country (n = 14), football (n = 62), track and field (n = 29), wrestling (n = 17), soccer (n = 43), swimming/diving (n = 6), volleyball (n = 9), and tennis (n = 2) were represented. Body composition was assessed using air displacement plethysmography and RMR was assessed using indirect calorimetry during a single morning of testing in a climate-controlled laboratory setting (temperature range: 22-24 °C, and relative humidity range: 37-44%). Pearson correlation coefficients were used to examine relationships between BF%, and body mass, FFM, RMR, RMR/kg, and RMR/kg of FFM. Correlation coefficients were interpreted as very weak: < 0.20, weak: 0.20–0.39, moderate: 0.40–0.59, strong:0.60–0.79, or very strong: >0.80. Linear regression analysis was used to determine which predictor variables (i.e., body mass, FFM, RMR/kg, RMR/kg of FFM) best predicted BF%. Statistical significance was determined as p < 0.05.

Results: Weak relationships between body weight (r = 0.16; (p = 0.026), FFM (r = -0.26; p< 0.001), and BF% were observed. Moderate and strong relationships were observed between RMR/kg of FFM and BF% (r = 0.37; p< 0.001) and between RMR/kg and BF% (r = -0.51; p< 0.001), respectively (Figure 1). RMR/kg and RMR/kg of FFM were the strongest (p< 0.001) predictors of BF%.

Conclusions: Athletes with a higher RMR relative to body mass had a lower BF%, whereas athletes with a higher RMR relative to FFM had a higher BF%. PRACTICAL APPLICATIONS: Athletes with a higher relative RMR per body mass have a lower BF%, potentially as a result of increased metabolic activity and higher total daily energy expenditure. Therefore, athletes with a lower BF% may require an additional increase in energy intake per kilogram of body weight to help with weight gain, if desired. Future research should assess if baseline measures of relative RMR influence training adaptations and weight changes over time.

Acknowledgements: None