New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-validation Study.

PURPOSE

The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes.

METHODS

The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, while the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, Root Mean Square Error, Simple Linear Regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ± 10%, respectively) of measured RMR. Multiple linear regressions models were employed to develop the new predictive equations based on traditional predictors (Equation 1) and the stepwise method (Equation 2).

RESULTS

The new Equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal.d-1), RMSE (200 and 192 kcal.d-1), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Harris & Benedict's equation showed the worst performance for males (mean difference = 406 kcal.d-1; RMSE = 473 kcal.d-1). Compared to measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; p ≤ 0.05), mainly in males.

CONCLUSIONS

The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Harris & Benedict's equation should not be used in high-level male athletes.