Estimation of resting metabolic rate in professional soccer players: A cross-sectional study comparing traditional predictive equations and a preliminary machine learning model against indirect calorimetry.

Abstract

Resting metabolic rate (RMR) is a major component of total daily energy expenditure and varies according to age, sex, and body composition. Although indirect calorimetry (IC) is the gold standard, predictive equations are widely used in practice. This study evaluated the agreement between twelve traditional RMR equations and IC in professional soccer players and explored a preliminary machine learning approach. Forty male professional soccer players (22.5 ± 4.4 years) were assessed. RMR measured by IC was compared with twelve predictive equations. A support vector regression (SVR) model was developed using anthropometric variables and evaluated under internal validation. All equations showed poor concordance with IC (intraclass correlation coefficient [ICC]: -0.094 to 0.030) and overestimated RMR (8.38% to 36.38%). The SVR model achieved a mean absolute error of 169.3 kcal·day-1 and root mean square error (RMSE) of 190.7 kcal·day-1. Its prediction error was lower than the RMSE and average bias of traditional equations, indicating improved individual-level accuracy. However, it explained a limited proportion of variance (R2 = 0.169). Traditional equations showed poor agreement with indirect calorimetry in this sample of soccer players. These findings highlight the risks of relying on conventional predictive equations in professional athletes. Preliminary results suggest that machine learning models may improve estimation under internal validation, providing a proof of concept for data-driven approaches in this field. However, their predictive capacity remains limited, and external validation in larger and independent cohorts is required.