title: Cross-Validation of Resting Metabolic Rate Prediction Equations.
abstract: Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence the accuracy of RMR predictions will help to revise existing, or develop new and improved, equations.
  
  
  
  Our aim was to test the validity of RMR predicted in healthy adults by the Harris-Benedict, World Health Organization, Mifflin-St Jeor, Nelson, Wang equations, and three meta-equations of Sabounchi.
  
  
  
  Predicted RMR was tested for agreement with indirect calorimetry.
  
  
  
  Men and women (n=30) age 18 to 65 years from Grand Forks, ND, were recruited and included for analysis during spring/summer 2014. Participants were nonobese or obese (body mass index range=19 to 39) and primarly white.
  
  
  
  Agreement between measured (indirect calorimetry) and predicted RMR was measured.
  
  
  
  The methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated-measures analysis of variance was used to test for bias in RMR predicted from each equation vs the measured RMR.
  
  
  
  Bias (mean±2 standard deviations) was lowest for the Harris-Benedict (-14±378 kcal/24 h) and World Health Organization (-25±394 kcal/24 h) equations. These equations also predicted RMR that were not different from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 standard deviation limits of agreement were moderate or large for all equations tested, ranging from 314 to 445 kcal/24 h. Prediction bias was inversely associated with the magnitude of RMR and with fat-free mass.
  
  
  
  At the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.
authors.forename: Kyle D
authors.initials: KD
authors.lastname: Flack
authors.forename: William A
authors.initials: WA
authors.lastname: Siders
authors.forename: LuAnn
authors.initials: L
authors.lastname: Johnson
authors.forename: James N
authors.initials: JN
authors.lastname: Roemmich
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001481:Basal Metabolism; D015992:Body Mass Index; D002153:Calorimetry, Indirect / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000378:metabolism*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D055815:Young Adult
keywords: Cross-validation; Lean body mass; Prediction accuracy; Prediction equations; Resting metabolic rate (RMR)