ACCURACY OF RESTING METABOLIC RATE PREDICTION EQUATIONS IN ATHLETES

The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 +/- 1.4 years; 166.2 +/- 5.5 cm; 63.5 +/- 7.3 kg; 49.2 +/- 4.3 kg of fat-free mass (FFM); 23.4 +/- 4.4 body fat (BF) percent) and 28 male (20.2 +/- 1.6 years; 181.9 +/- 6.1 cm; 94.5 +/- 16.2 kg; 79.1 +/- 7.2 kg of FFM; 15.1 +/- 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p <= 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest rootmean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.