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.

Comparison of Actual and Predicted Resting Energy Expenditure in Skilled Endurance Athletes

“…This also coincides with the results of the study by O'Neill and colleagues [8] on the Mifflin and WHO equa-tions. At the same time, the authors note that the Harris-Benedict equation has better accuracy and can be generally used among athletes.…”

“…[3,5]. Researchers involved in validating existing equations note that the more specific the sample, the more important it is to use equations developed on similar samples [7,8].…”

Completely Plant-Based Diets That Meet Energy Requirements for Resistance Training Can Supply Enough Protein and Leucine to Maximize Hypertrophy and Strength in Male Bodybuilders: A Modeling Study

“…Energy requirements for maximal muscular hypertrophy can be calculated as total daily energy expenditure (TDEE) plus an energy surplus due to the whole-body anabolic response to overfeeding [ 54 , 55 ]. TDEE is comprised of the resting metabolic rate (RMR), which is the amount of energy required to maintain basic body functions, along with the appropriate physical activity level (PAL) factor [ 56 ]. RMR is calculated using body weight, age, and sex.…”

“…RMR is calculated using body weight, age, and sex. Based on a recent meta-analysis evaluating the accuracy of RMR prediction equations in athletes [ 56 ], a newly developed RMR prediction equation was determined to be most accurate in male bodybuilders [ 57 ]. This formula, which is shown in Equations (1)–(3), was therefore used to calculate the predicted mean TDEE for the modeled sample.…”

The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players

“…This could result in deficiencies in energy, which are associated with performance decrements, losses in FFM, and increased susceptibility to illness and/or injury [ 19 ]. Consequently, sport-specific prediction equations are strongly encouraged to better estimate energy requirements among specific athletes in their respective sports [ 18 , 20 , 21 , 22 ].…”