New Predictive Resting Metabolic Rate Equations for High-Level Athletes

Freiré, Raúl Rodríguez; Pereira, Geraldo; Alcantara, Juan M. A.; Santos, Ruan; Hausen, Matheus; Itaborahy, Alex

doi:10.1249/mss.0000000000002851

Cited by 5 publications

(21 citation statements)

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“…This newly developed ( R 2 = 0.84, RMSE = 158 kcal) and cross‐validated ( R 2 = 0.71, RMSE = 200 kcal) equation was established from a large sample of elite‐level men and women athletes from a variety of Olympic sports. Further research is warranted into the agreeability of the Freire equation in other athletic populations, primarily across high‐level collegiate athletes from a variety of sports (Freire et al, 2021). Other research in men athletes have reported the Harris Benedict equation to be the most accurate prediction equation (Balci et al, 2021; Jagim et al, 2018; Oliveira et al, 2021), which is interesting as it was developed in less active individuals and also does not account for FFM.…”

Section: Discussionmentioning

confidence: 99%

“…Results of the present study are contradictory to previous findings. Prior reports have demonstrated that the Cunningham equation most accurately predicts RMR across a variety of athletes, most likely due to the fact that it was developed in athletic populations, and the model is primarily based upon FFM (Freire et al, 2021; Haaf & Weijs, 2014; Jagim et al, 2018; Kim et al, 2015; Thompson & Manore, 1996). Similarly, the equation of Tinsley et al was developed in muscular men and women physique athletes, and is also largely based on FFM (Tinsley et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…It is possible that the higher measured RMR values observed in the men from the current study may have influenced the outcome of the regression analysis and therefore, the accuracy of each prediction equation (Jagim et al, 2018). It is known that RMR is both impacted by body weight and sport‐specific body composition parameters (Freire et al, 2021). For example, the mean measured RMR of the men athletes in the current study was 2,595 kcal·d −1 , which is greater than those reported in previous literature using men athletes or active individuals (2,481 kcal·d −1 (Jagim et al, 2018) and 2,400 kcal·d −1 (Haaf & Weijs, 2014)).…”

Section: Discussionmentioning

confidence: 99%

“…Linear regression analysis assessed agreement between measured and predicted RMR by calculating the slope, intercept, and coefficient of determination ( p <0.05). Root‐mean squared error (RMSE) was also calculated as follows:

\sqrt{\sum true0 \frac{{(R M R_{p r e d i c t e d} - R M R_{m e a s u r e d})}^{2}}{n}}

The %RMSE was calculated by dividing the RMSE by the mean of measured RMR for each predictive equation (Freire et al, 2021). A %RMSE value of ≤ 10% was used to consider an equation to be an acceptable prediction method (Freire et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Resting metabolic rate values for each participant were then estimated from 10 commonly used and newly developed prediction equations: Cunningham, De Lorenzo, Freire, Harris‐Benedict, Mifflin, Nelson, Owen, Tinsley, Watson (women, only), Schofield (Cunningham, 1980; De Lorenzo et al, 1999; Freire et al, 2021; Harris & Benedict, 1918; Mifflin et al, 1990; Nelson et al, 1992; Owen et al, 1986; Schofield et al, 2019; Tinsley et al, 2018; Watson et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

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The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Fields

Magee

Jones

et al. 2022

European Journal of Sport Science

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

\sqrt{\sum true0 \frac{{(R M R_{p r e d i c t e d} - R M R_{m e a s u r e d})}^{2}}{n}}

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Fields

Magee

Jones

et al. 2022

European Journal of Sport Science

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Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

Prado‐Nóvoa,

Howard,

Laskaridou

et al. 2023

American J Hum Biol

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ObjectivesUsing equations to predict resting metabolic rate (RMR) has yielded different degrees of validity, particularly when sex and different physical activity levels were considered. Therefore, the purpose of the present study was to determine the validity of several different predictive equations to estimate RMR in female and male adults with varying physical activity levels.MethodWe measured the RMR of 50 adults (26 females and 24 males) evenly distributed through activity levels varying from sedentary to ultra‐endurance. Body composition was measured by dual X–ray absorptiometry and physical activity was monitored by accelerometry. Ten equations to predict RMR were applied (using Body Mass [BM]: Harris & Benedict, 1919; Mifflin et al., 1990 [MifflinBM]; Pontzer et al., 2021 [PontzerBM]; Schofield, 1985; FAO/WHO/UNU, 2004; and using Fat‐Free Mass (FFM): Cunningham, 1991; Johnstone et al., 2006; Mifflin et al., 1990 [MifflinFFM]; Nelson et al. 1992; Pontzer et al., 2021 [PontzerFFM]). The accuracy of these equations was analyzed, and the effect of sex and physical activity was evaluated using different accuracy metrics.ResultsEquations using BM were less accurate for females, and their accuracy was influenced by physical activity and body composition. FFM equations were slightly less accurate for males but there was no obvious effect of physical activity or other sample parameters. PontzerFFM provides higher accuracy than other models independent of the magnitude of RMR, sex, activity levels, and sample characteristics.ConclusionEquations using FFM were more accurate than BM equations in our sample. Future studies are needed to test the accuracy of RMR prediction equations in diverse samples.

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

O’Neill,

Corish,

Horner

2023

Sports Med

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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.

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New Predictive Resting Metabolic Rate Equations for High-Level Athletes

Cited by 5 publications

References 0 publications

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

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

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