Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners

Author:

Sordi Ana Flávia12ORCID,Silva Bruno Ferrari1ORCID,Silva Breno Gabriel da3ORCID,Marques Déborah Cristina de Souza145,Ramos Isabela Mariano1,Camilo Maria Luiza Amaro14ORCID,Mota Jorge56ORCID,Valdés-Badilla Pablo78ORCID,Peres Sidney Barnabé2,Branco Braulio Henrique Magnani1456

Affiliation:

1. Interdisciplinary Laboratory of Intervention in Health Promotion, Cesumar University, Maringá 87050-390, Paraná, Brazil

2. Department of Physiological Sciences, State University of Maringá, Maringá 87020-900, Paraná, Brazil

3. Luiz de Queiroz College of Agriculture–ESALQ, USP Department of Exact Sciences, University of Sao Paulo, Sao Paulo 13418-900, Sao Paulo, Brazil

4. Graduate Program in Health Promotion, Cesumar University, Maringá 87050-390, Paraná, Brazil

5. Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sports, University of Porto (FADEUP), Porto 4200-450, Portugal

6. Laboratory for Integrative and Translational Research in Population Health (ITR), Porto 4050-600, Portugal

7. Department of Physical Activity Sciences, Faculty of Education Sciences, Universidad Católica del Maule, Talca 3530000, Chile

8. Sports Coach Career, School of Education, Universidad Viña del Mar, Viña del Mar 2520000, Chile

Abstract

This study aimed to investigate the resting metabolic rate (RMR) in cross-training practitioners (advanced and novice) using indirect calorimetry (IC) and compare it with predictive equations proposed in the scientific literature. Methods: A cross-sectional and comparative study analyzed 65 volunteers, both sexes, practicing cross-training (CT). Anthropometry and body composition were assessed, and RMR was measured by IC (FitMate PRO®), bioimpedance (BIA-InBody 570®), and six predictive equations. Data normality was tested by the Kolgomorov–Smirnov test and expressed as mean ± standard deviation with 95% confidence intervals (CI), chi-square test was performed to verify ergogenic resources, and a Bland–Altman plot (B&A) was made to quantify the agreement between two quantitative measurements. One-way ANOVA was applied to body composition parameters, two-way ANOVA with Bonferroni post hoc was used to compare the RMR between groups, and two-way ANCOVA was used to analyze the adjusted RMR for body and skeletal muscle mass. The effect size was determined using Cohen’s d considering the values adjusted by ANCOVA. If a statistical difference was found, post hoc Bonferroni was applied. The significance level was p < 0.05 for all tests. Results: The main results indicated that men showed a higher RMR than women, and the most discrepant equations were Cunningham, Tinsley (b), and Johnstone compared to IC. Tinsley’s (a) equation indicated greater precision in measuring the RMR in CM overestimated it by only 1.9%, and BIA and the Harris–Benedict in CW overestimated RMR by only 0.1% and 3.4%, respectively. Conclusions: The BIA and Harris–Benedict equation could be used reliably to measure the RMR of females, while Tinsley (a) is the most reliable method to measure the RMR of males when measuring with IC is unavailable. By knowing which RMR equations are closest to the gold standard, these professionals can prescribe a more assertive diet, training, or ergogenic resources. An assertive prescription increases performance and can reduce possible deleterious effects, maximizing physical sports performance.

Funder

Cesumar Institute of Science, Technology, and Innovation

FCT

Publisher

MDPI AG

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