The accuracy of resting metabolic rate prediction equations in athletes
The purpose of this study was to determine the accuracy of five different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7± 1.4 yrs.; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of Fat-Free Mass; 23.4 ± 4.4 BF%) and twenty-eight male (20.2 ± 1.6 yrs.; 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 one day of metabolic testing. Assessments comprised RMR measurements via indirect calorimetry and body composition analyses via 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 five 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 of the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In males, the Harris-Benedict equation was found to be the best prediction formula with the lowest root mean square prediction error (RMSPE) value of 284 kcals. In females, the Cunningham equation was found to be the best prediction equation with the lowest RMSE value of 110 kcals. RMR prediction equations consistently appear to underestimate RMR in male and female athletes. The Harris-Benedict equation appears to be most accurate for male athletes while the Cunningham equation may be better suited for female athletes.