Resting metabolic rate in bodybuilding: Differences between indirect calorimetry and predictive equations

Sordi, Ana Flávia; Mariano, Isabela Ramos; Silva, Bruno Ferrari; Branco, Bráulio Henrique Magnani

doi:10.1016/j.clnesp.2022.08.024

Cited by 3 publications

(7 citation statements)

References 35 publications

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“… In Korean collegiate soccer players, the Taguchi (2011) equation performed best out of five FFM-based RMR equations [ 71 ]. In groups of active resistance trained females and males, the Cunningham (1991) and the DeLorenzo (1999) equations respectively were closest to measured values of seven equations studied [ 73 ]. …”

Section: Discussionmentioning

confidence: 91%

“…In groups of active resistance trained females and males, the Cunningham (1991) and the DeLorenzo (1999) equations respectively were closest to measured values of seven equations studied [ 73 ].…”

Section: Discussionmentioning

confidence: 91%

“…Of these five studies, three [ 71 – 73 ] did not include the Ten-Haaf (2014) (age, weight, height) equation. In these studies, the key findings were as follows: In NCAA collegiate men and women athletes, all prediction equations investigated (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) were found to underestimate RMR [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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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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“…Metabolic analyzers can be used in metabolic rate analysis of individuals engaged in physical exercise, helping in determining the energy disparities associated with different physical activities. They can also monitor the impact of the workouts and dietary interventions on metabolic rate and energy use [ 49 ].…”

Section: Wearable Devices For Measuring Physiological Parameters Duri...mentioning

confidence: 99%

“…The process entails the collection of expired air from the body and analyzing the oxygen and carbon dioxide content contained. Indirect calorimetry is used in the determination of the resting metabolic rate (RMR), which is the total amount of energy used by the body when resting to ensure that basic functions continue [ 49 ]. Some of the maintained body functions at rest include breathing, blood circulation, and digestion of food.…”

Section: Wearable Devices For Measuring Physiological Parameters Duri...mentioning

confidence: 99%

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

de Beukelaar,

Mantini

2023

Bioengineering

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Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.

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Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners

Sordi,

Silva,

Silva

et al. 2024

IJERPH

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This study aimed to investigate the resting metabolic rate (RMR) in cross-training practitioners (advanced and novice) using indirect calorimetry (IC) and compare it with predictive equations proposed in the scientific literature. Methods: A cross-sectional and comparative study analyzed 65 volunteers, both sexes, practicing cross-training (CT). Anthropometry and body composition were assessed, and RMR was measured by IC (FitMate PRO®), bioimpedance (BIA-InBody 570®), and six predictive equations. Data normality was tested by the Kolgomorov–Smirnov test and expressed as mean ± standard deviation with 95% confidence intervals (CI), chi-square test was performed to verify ergogenic resources, and a Bland–Altman plot (B&A) was made to quantify the agreement between two quantitative measurements. One-way ANOVA was applied to body composition parameters, two-way ANOVA with Bonferroni post hoc was used to compare the RMR between groups, and two-way ANCOVA was used to analyze the adjusted RMR for body and skeletal muscle mass. The effect size was determined using Cohen’s d considering the values adjusted by ANCOVA. If a statistical difference was found, post hoc Bonferroni was applied. The significance level was p < 0.05 for all tests. Results: The main results indicated that men showed a higher RMR than women, and the most discrepant equations were Cunningham, Tinsley (b), and Johnstone compared to IC. Tinsley’s (a) equation indicated greater precision in measuring the RMR in CM overestimated it by only 1.9%, and BIA and the Harris–Benedict in CW overestimated RMR by only 0.1% and 3.4%, respectively. Conclusions: The BIA and Harris–Benedict equation could be used reliably to measure the RMR of females, while Tinsley (a) is the most reliable method to measure the RMR of males when measuring with IC is unavailable. By knowing which RMR equations are closest to the gold standard, these professionals can prescribe a more assertive diet, training, or ergogenic resources. An assertive prescription increases performance and can reduce possible deleterious effects, maximizing physical sports performance.

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Resting metabolic rate in bodybuilding: Differences between indirect calorimetry and predictive equations

Cited by 3 publications

References 35 publications

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

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

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners

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