Anja Carlsohn, Friederike Scharhag-Rosenberger, Michael Cassel, Frank Mayer

• Background: Athletes may differ in their resting metabolic rate (RMR) from the general population. However, to estimate the RMR in athletes, prediction equations that have not been validated in athletes are often used. The purpose of this study was therefore to verify the applicability of commonly used RMR predictions for use in athletes. Methods: The RMR was measured by indirect calorimetry in 17 highly trained rowers and canoeists of the German national teams (BMI 24 ± 2 kg/m2, fat-free mass 69 ± 15 kg). In addition, the RMR was predicted using Cunningham (CUN) and Harris-Benedict (HB) equations. A two-way repeated measures ANOVA was calculated to test for differences between predicted and measured RMR (α = 0.05). The root mean square percentage error (RMSPE) was calculated and the Bland-Altman procedure was used to quantify the bias for each prediction. Results: Prediction equations significantly underestimated the RMR in males (p < 0.001). The RMSPE was calculated to be 18.4% (CUN) and 20.9% (HB) in the entire group. The bias wasBackground: Athletes may differ in their resting metabolic rate (RMR) from the general population. However, to estimate the RMR in athletes, prediction equations that have not been validated in athletes are often used. The purpose of this study was therefore to verify the applicability of commonly used RMR predictions for use in athletes. Methods: The RMR was measured by indirect calorimetry in 17 highly trained rowers and canoeists of the German national teams (BMI 24 ± 2 kg/m2, fat-free mass 69 ± 15 kg). In addition, the RMR was predicted using Cunningham (CUN) and Harris-Benedict (HB) equations. A two-way repeated measures ANOVA was calculated to test for differences between predicted and measured RMR (α = 0.05). The root mean square percentage error (RMSPE) was calculated and the Bland-Altman procedure was used to quantify the bias for each prediction. Results: Prediction equations significantly underestimated the RMR in males (p < 0.001). The RMSPE was calculated to be 18.4% (CUN) and 20.9% (HB) in the entire group. The bias was 133 kcal/24 h for CUN and 202 kcal/24 h for HB. Conclusions: Predictions significantly underestimate the RMR in male heavyweight endurance athletes but not in females. In athletes with a high fat-free mass, prediction equations might therefore not be applicable to estimate energy requirements. Instead, measurement of the resting energy expenditure or specific prediction equations might be needed for the individual heavyweight athlete.…