Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

Abstract

Background: Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

Objective: The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

Data sources: A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

Eligibility criteria: Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

Analysis: A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

Results: Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I² range: 80-93%) except the Ten-Haaf (p = 0.48, I² = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

Conclusion: Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

Prospero registration: CRD42020218212.

Fig. 1

PRISMA flowchart. RMR resting metabolic rate

Fig. 2

Forest plot containing all equations analysed in meta-analysis listed in chronological order. CI confidence interval, ES effect size, FFM fat free mass, FM fat mass, LBM lean body mass

Fig. 3

Flow chart to guide choice of equation for predicting resting metabolic rate (RMR) in an athlete. Ideally an equation developed in or validated in athletes of similar characteristics (considering age, sex, body composition) should be used. If not available, the equations shown (which demonstrated no overall mean bias in the present meta-analysis and have been validated for some athlete groups) could be considered. Under each equation, the key characteristics of athletes that have been studied (including sport and mean body weight), mean bias and precision (where available) are reported. This information should be considered when selecting an equation to best match the athlete/s of interest. Studies that met below average best practice RMR measurement guidelines are shown in red text and should be interpreted more cautiously. Full details of each equation, the population(s) it was developed and validated in along with references are shown in Table 2 and Supplementary Document 7 (see ESM). Created in Biorender.com. LBM lean body mass, RMR resting metabolic rate