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The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes.

Authors
  • Fields, Jennifer B1, 2
  • Magee, Meghan K2, 3
  • Jones, Margaret T2, 3, 4
  • Askow, Andrew T5
  • Camic, Clayton L6
  • Luedke, Joel7
  • Jagim, Andrew R2, 7, 8
  • 1 Exercise Science and Athletic Training, Springfield College, Springfield, MA, USA.
  • 2 Patriot Performance Laboratory, Frank Pettrone Center for Sports Performance, George Mason University, Fairfax, VA, USA.
  • 3 Kinesiology, George Mason University, Manassas, VA, USA.
  • 4 Sport, Recreation, and Tourism Management, George Mason University, Fairfax, VA, USA.
  • 5 Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL,, USA.
  • 6 Kinesiology and Physical Education, Northern Illinois University, DeKalb, IL, USA.
  • 7 Sports Medicine Department, Mayo Clinic Health System, La Crosse, WI, USA.
  • 8 Exercise & Sport Science Department, University of Wisconsin, La Crosse, WI, USA.
Type
Published Article
Journal
European journal of sport science
Publication Date
Oct 01, 2023
Volume
23
Issue
10
Pages
1973–1982
Identifiers
DOI: 10.1080/17461391.2022.2130098
PMID: 36168819
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. 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). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

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