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Published on: June 7, 2024
RMR Estimation Model Accuracy Using Air Displacement Plethysmography-Derived Body Composition Measures in Young
Ronald Otterstetter1, Brian Miller1,2, Mark Fridline3
1a School of Sport Science & Wellness Education, The University of Akron , Akron , Ohio.
Objective:
Predictive equations derived from regression techniques based on large samples are extensively utilized in estimating resting metabolic rate (RMR). Body composition assessments utilize model equations to estimate RMR. However, the agreement of these predictive models with indirect calorimetery (IC) has come into question. Our aim is to investigate the agreement of RMR estimation models using air displacement plethysmography (ADP) measures against a gas exchange IC system (RMR-C).
Methods:
Sixty-six participants (25 men, 41 women) completed the study. RMR measurements were obtained from IC and ADP within 10 minutes of one another. IC RMR estimates were tested against 9 other validated models using ADP measures via analysis of variance (ANOVA) techniques with multiple comparisons testing and Bland-Altman analysis.
Results:
Based on the ANOVA, the Nelson (1992) model underpredicted RMR compared to IC (p < 0.001). The Doré et al. (1982) model was the best predictor of RMR compared to the IC measures (p = 0.907).
Discussion:
The current RMR estimation model using ADP measures underpredicts total caloric needs. The Doré et al. (1982) model more accurately predicted RMR in the entire sample.
