Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet

Authors:
  • PHPatrick S. Harty
    Patrick S. Harty
  • MSMatthew T. Stratton
    Matthew T. Stratton
  • MSMadelin R. Siedler
    Madelin R. Siedler
  • RSRobert W. Smith
    Robert W. Smith
  • BJBaylor A. Johnson
    Baylor A. Johnson
  • JDJacob R. Dellinger
    Jacob R. Dellinger
  • AWAbegale D. Williams
    Abegale D. Williams
  • SWSarah J. White
    Sarah J. White
  • MBMarqui L. Benavides
    Marqui L. Benavides
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Abstract
Rodriguez, C, Harty, PS, Stratton, MT, Siedler, MR, Smith, RW, Johnson, BA, Dellinger, JR, Williams, AD, White, SJ, Benavides, ML, and Tinsley, GM. Comparison of indirect calorimetry and common prediction equations for evaluating changes in resting metabolic rate induced by resistance training and a hypercaloric diet. J Strength Cond Res 36(11): 3093–3104, 2022—The ability to accurately identify resting metabolic rate (RMR) changes over time allows practitioners to prescribe appropriate adjustments to nutritional intake. However, there is a lack of data concerning the longitudinal utility of commonly used RMR prediction equations. The purpose of this study was to evaluate the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutritional intervention and supervised resistance exercise training program. Twenty resistance-trained men completed the study. The protocol lasted 6 weeks, and subjects underwent RMR assessments by indirect calorimetry (IC) preintervention and postintervention to obtain reference values. Existing RMR prediction equations based on body mass (BM) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC. Null hypothesis significance testing was also performed, and Bland-Altman analysis was used alongside linear regression to assess the degree of proportional bias. Body mass and FFM increased by 3.6 ± 1.7 kg and 2.4 ± 1.6 kg, respectively. Indirect calorimetry RMR increased by 165 ± 97 kcal·d−1, and RMR:FFM increased by 5.6 ± 5.2%. All prediction equations underestimated mean RMR changes relative to IC, with magnitudes ranging from 75 to 155 kcal·d−1, while also displaying unacceptable levels of negative proportional bias. In addition, no equation demonstrated equivalence with IC. Common RMR prediction equations based on BM or FFM did not fully detect the increase in RMR observed with resistance training plus a hypercaloric diet. Overall, the evaluated prediction equations are unsuitable for estimating RMR changes in the context of this study.

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