Resting Metabolic Rate Prediction Equation Accuracy in Structural Firefighters

Area of Science:

• Exercise Physiology
• Occupational Health
• Metabolic Research

Background:

• Accurate estimation of resting metabolic rate (RMR) is crucial for energy balance and health management in occupational groups.
• Firefighters have unique physiological demands that may affect metabolic rate.
• The predictive accuracy of established RMR equations in this specific population is not well-defined.

Purpose of the Study:

• To evaluate the accuracy of six common RMR prediction equations in male firefighters.
• To compare the performance of Cunningham, De Lorenzo, Harris-Benedict, Mifflin-St Jeor, Nelson, and Jagim equations against measured RMR.

Main Methods:

• Twenty-six male firefighters underwent RMR measurement and body composition assessment.
• Measured RMR was compared to predicted RMR from six equations using repeated measures ANOVA.
• Linear regression analysis assessed prediction accuracy and standard error of the estimate (SEE).

Main Results:

• All tested equations, except Jagim, significantly underestimated measured RMR in firefighters (p<0.001).
• The Harris-Benedict and De Lorenzo equations showed the closest agreement with measured RMR (R²=0.696 and R²=0.675, respectively).
• The Nelson equation exhibited the highest prediction error (RMSE=412 kcal, %RMSE=18.6%).

Conclusions:

• Current RMR prediction equations generally underestimate the energy requirements of firefighters.
• The Harris-Benedict and De Lorenzo equations offer better, though imperfect, estimates for this population.
• Clinical application of these equations for firefighters requires careful consideration due to potential underestimation of energy needs.