Authors
Andrew R. Jagim, [1] Sports Medicine, Mayo Clinic Health System, Onalaska, WI, USA, [2] Exercise & Sport Science, University of Wisconsin–La Crosse, La Crosse, WI, USA, [3] Patriot Performance Laboratory, Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA, USA
Olivia Iausly, Sports Medicine, Mayo Clinic Health System, Onalaska, WI, USA
Joel Luedke, Olmsted Medical Center – Sports Medicine, Rochester, MN, USA
Jacob Erickson, Sports Medicine, Mayo Clinic Health System, Onalaska, WI, USA
Jennifer B. Fields, [1] Patriot Performance Laboratory, Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA, USA, [2] Department of Nutritional Sciences, University of Connecticut, Storrs, CT, USA
Annette Zapp, OSU Tactical Fitness and Nutrition, Department of Kinesiology, Applied Health and Recreation, Oklahoma State University, Stillwater, OK, USA
Drew E. Gonzalez, Tactical Athlete Research Unit, Department of Kinesiology and Sport Management, Texas A&M University, College Station, TX, USA
Margaret T. Jones, [1] Patriot Performance Laboratory, Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA, USA, [2] Sport, Recreation, and Tourism Management, George Mason University, Fairfax, VA, USA
International Journal of Exercise Science 19(2): 1-14, 2026.
Abstract
It remains unclear whether predictive resting metabolic rate (RMR) equations accurately predict RMR in firefighters. The purpose of this study was to examine the accuracy of six RMR prediction equations (Cunningham, De Lorenzo, Harris-Benedict, Mifflin-St Jeor, Nelson, and Jagim) in firefighters. Male firefighters (n=26; [mean ± SD] age: 38.2 ± 7.6 y; height: 180.9 ± 6.8 cm; body mass: 92.0 ± 15.6 kg; BMI: 28.1 ± 4.4 kg·m-2) participated in annual fitness and health evaluations including RMR determination and body composition assessment. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05) and to determine standard error of the estimate (SEE). All prediction equations significantly underestimated RMR (all, p<0.001), except the Jagim equation, which significantly overestimated RMR (p<0.001). Equations with the closest agreement to measured RMR were the Harris-Benedict (R² = 0.696, p = 0.004, RMSE = 314 kcals, %RMSE = 14.2%) and the DeLorenzo (R² = 0.675, p<0.001, RMSE = 242 kcals, %RMSE = 10.9%). The Nelson equation yielded the highest RMSE (412 kcals, %RMSE = 18.6%). The variance in equations ranged from an SEE = 173 kcal·d−1 (Harris-Benedict) to an SEE = 215 kcal·d−1 (Cunningham), accounting for 70% and 53% of the variance in RMR, respectively. RMR prediction equations underestimate the energy requirements of firefighters; thus, caution should be exercised when interpreting values.
Recommended Citation
Jagim, Andrew R.; Iausly, Olivia; Luedke, Joel; Erickson, Jacob; Fields, Jennifer B.; Zapp, Annette; Gonzalez, Drew E.; Jones, Margaret T. (2026) “Resting Metabolic Rate Prediction Equation Accuracy in Structural Firefighters,” International Journal of Exercise Science, 19(2):1-14.