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

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.

Resting Metabolic Rate in Female Rugby Players: Differences in Measured Versus Predicted Values.

ABSTRACT: O'Neill, JERG, Walsh, CS, McNulty, SJ, Gantly, HC, Corish, ME, Crognale, D, and Horner, K. Resting metabolic rate in female rugby players: differences in measured versus predicted values. J Strength Cond Res 36(3): 845-850, 2022-This study investigated (a) the accuracy of resting metabolic rate (RMR) prediction equations in female rugby players and (b) factors that might explain poor prediction accuracy in some individuals. Resting metabolic rate was assessed in 36 female elite and subelite rugby players (age: 18-35 years, fat-free mass (FFM): 43-63 kg, fat mass %: 15-41%). After pretest standardization (24-hour exercise avoidance and 12-hour overnight fast), RMR was measured by indirect calorimetry and compared with predicted values determined by Harris-Benedict, Cunningham, Ten Haaf, Jagim and Watson equations. Body composition was assessed by air displacement plethysmography, muscle damage indicated by creatine kinase, and risk of low energy availability (LEA) by LEA in Females Questionnaire. Measured RMR was 1,651 ± 167 kcal·d-1. The Cunningham, Ten Haaf, and Watson (body mass) predicted values did not differ from measured (p > 0.05), while all other predicted values differed significantly (p < 0.001). Individually, prediction accuracy to within 10% varied widely depending on the equation used (range 44% [n = 16] to 86% [n = 31]). Three of the 5 individuals whose values were outside 10% of the measured value using the best performing Ten Haaf FFM equation could be explained by muscle damage or LEA. These measures may be useful to assist in understanding why measured RMR may be lower or higher than predicted in some athletes. Overall, the Ten Haaf equations showed the best accuracy, suggesting these equations may be most suitable for this population. The findings demonstrate the importance of considering the population studied when determining the most appropriate prediction equation to use.