A new predictive equation to calculate resting metabolic rate in athletes - de Lorenzo, A.; Bertini, I.; Candeloro, N.; Piccinelli, R.; Innocente, I.; Brancati, A. (The Journal of Sports Medicine and Physical Fitness, 1999)
BACKGROUND: The purposes of the present study were: 1) to examine the accuracy and precision of seven published equations for predicting resting metabolic rate (RMR) in male athletes and 2) to develop a population-specific equation. Setting: The study occurred during a non-intensive training period. The measurements were performed at the Human Physiology laboratory. Participants: Fifty-one male athletes (22 waterpolo, 12 judo, 17 karate) who exercised regularly at least three hours per day. Measures: RMR was measured (mRMR) using indirect calorimetry (ventilated hood system). Besides, mRMR was compared with values predicted (pRMR) using equations of FAO/WHO/UNU, Harris and Benedict, Mifflin et al., Owen et al., Cunningham, Robertson and Reid, Fleisch. Statistical analyses. mRMR was compared with pRMR by means of Student's paired "t" tests, linear regression analysis and the Bland-Altman test. Relationships between mRMR and the different predictive variables were evaluated by Pearson correlation coefficients. The best subset was used to develop the predictive equation for RMR. RESULTS: mRMR was significantly underestimated by six of the seven equations in this sample of athletes. Only the Cunningham equation overestimated (+59 kcal/d) the actual RMR. Bland-Altman 95% limits of agreement were wide (+/- 200-300 kcal/d) for all equations. RMR correlated best with body surface area (r = 0.88), body weight (r = 0.84) and height (r = 0.81). The best-fit equation for the entire data included both weight and height and it was given by: RMR (kcal/d) = -857 + 9.0 (Wt in kg) + 11.7 (Ht in cm) (R2 = 0.78; SEE = 91 kcal/d; 95% IC: -226, 228). CONCLUSIONS: For an individual resting metabolic rate evaluation, the use of indirect calorimetry is recommended. In conditions where this technique cannot be used, our developed equation can predict the RMR of athletes better than any of the currently available prediction equations.