Improved Equations for Estimating the Resting Metabolic Rate

Abstract

New models were developed for predicting the resting metabolic rate (RMR) with sufficient accuracy for use in epidemiologic studies and for weight control of individuals. For this purpose, the RMR of 213 women and 76 men was measured, and physical measurements were taken. The RMR was regressed on correlates of RMR, avoiding harmful degrees of collinearity by rejecting interregressor correlations exceeding r=0.5. For women, the best model (R2=0.71) included the regressors age, race, weight, pulse rate, smoking, and body temperature. The best model for males (R2 = 0.81) included age, race, weight, blood pressure, smoking, time (of day the RMR was measured), and whether subjects had a meal prior to calorimetry. The models were cross validated internally and also validated using an external database. In both cases, the mean estimated RMR did not differ significantly from the measured RMR. The accuracy of the models was compared with four models reported in the literature, three of which overestimated the RMR by up to 17%. In conclusion, improved RMR prediction models have been developed, more accurate than existing models, rendering them suitable for application to epidemiological databases and for individual weight control programs.