Abstract
Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.
Highlights
Resting metabolic rate (RMR) is an essential component of daily energy needs and accounts for approximately 60–70% of total energy expenditure in sedentary individuals (Johnstone et al, 2005)
There were no significant differences between the measured RMR and Harris-Benedict, Mifflin, Schofield, De Lorenzo, Johnstone, and Roza prediction equations in male athletes
A positive correlation value indicates that the predicted RMR is greater than the measured RMR
Summary
Resting metabolic rate (RMR) is an essential component of daily energy needs and accounts for approximately 60–70% of total energy expenditure in sedentary individuals (Johnstone et al, 2005). The accurate determination of daily energy needs is vital in maintaining an optimal body composition and developing nutritional strategies for providing body requirements (Thomas et al, 2016). Providing sufficient energy availability is one of the most critical points for boosting sports performance, physiological function, and maintaining metabolic health (Loucks et al, 2011). Accurate RMR measurement is a crucial component in determining optimal energy needs in order to prepare a comprehensive person-specific sports nutrition program (Jagim et al, 2018). An optimal nutritional strategy developed according to precise energy needs provides pre/post-training needs, decreases fatigue, and up-regulates body compositions while improving athletes’ metabolic functions (Thomas et al, 2016)
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