BACKGROUND: 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. OBJECTIVE: 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. DESIGN: Predicted RMR was tested for agreement with indirect calorimetry. PARTICIPANTS/ SETTING: 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. MAIN OUTCOME MEASURE: Agreement between measured (indirect calorimetry) and predicted RMR was measured. STATISTICAL ANALYSIS: 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. RESULTS: 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. CONCLUSIONS: 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. Published by Elsevier Inc.
BACKGROUND: 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. OBJECTIVE: 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. DESIGN: Predicted RMR was tested for agreement with indirect calorimetry. PARTICIPANTS/ SETTING: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. MAIN OUTCOME MEASURE: Agreement between measured (indirect calorimetry) and predicted RMR was measured. STATISTICAL ANALYSIS: 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. RESULTS: 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. CONCLUSIONS: 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. Published by Elsevier Inc.
Authors: George Thom; Konstantinos Gerasimidis; Eleni Rizou; Hani Alfheeaid; Nick Barwell; Eirini Manthou; Sadia Fatima; Jason M R Gill; Michael E J Lean; Dalia Malkova Journal: J Nutr Sci Date: 2020-05-26
Authors: Rebecca T McLay-Cooke; Andrew R Gray; Lynnette M Jones; Rachael W Taylor; Paula M L Skidmore; Rachel C Brown Journal: Nutrients Date: 2017-09-13 Impact factor: 5.717