Resting metabolic rate of cyclists estimated by mathematical equations and obtained by indirect calorimetry - Cocate, P. G.; Alfenas, R. C. G.; Pereira, L. G.; Marins, J. C. B.; Bressan, J.; Cecon, P. R. (Revista Brasileira de Medicina do Esporte, 2009)
The resting metabolic rate (RMR) can be determined by indirect calorimetry (IC). However, the clinical estimation of this parameter has been done using mathematical equations, which were developed in studies involving non-athletes. Although some authors have indicated that such equations do not estimate correctly the RMR, they have been constantly used to estimate such parameter and to prescribe diets, including for athletes. The objective of this study was to compare the RMR determined by IC with the ones estimated using the equations proposed by Harris & Benedict (HB), Schofield, FAO/WHO/UNU and Henry & Rees (HR), in 15 male cyclists, aged 24.4±3.68 years, body mass index of 21.97±1.46 kg/m2 and VO2max of 70.00±5.32 mL(kg.min)-1. Student's t test (when data presented normal distribution) and Mann-Whitney (when data did not present normal distribution) were used to compare the RMR determined by IC and the ones estimated by the equations. Probability values below 0.05 were considered statistically significant. Pearson bivariate correlation coefficients were calculated to evaluate the relationship between RMR and fat free mass (FFM). It was verified that the RMR was significantly underestimated in 23.5%, 16.7%, 16.8% and 16.9%, respectively by the equations proposed by HR, FAO/WHO/UNU, Schofield and HB in comparison to RMR obtained by IC. RMR values were significantly correlated with FFM (r=0.551, p=0.03). These results confirm that the equations evaluated in this study are not appropriate to estimate the RMR of athletes. Such results can be explained by the fact that athletes have more FFM and possibly a greater ß- adrenergic receptors response to the sympathetic nervous system, which in turn leads to RMR higher than in sedentary people. Therefore, we created an equation considering the FFM and age variables to better estimate athletes´ RMR. However, this equation should be validated in a study involving a greater number of cyclists before it can be clinically used.