Cross-Validation of Resting Metabolic Rate Prediction Equations

Flack, Kyle D.; Siders, William A.; Johnson, L.P.; Roemmich, James N.

doi:10.1016/j.jand.2016.03.018

Cited by 67 publications

(59 citation statements)

References 37 publications

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“…Correspondingly, accuracy at the individual and group level was the greatest (accurate prediction at 70% of the sample, bias of 1.0%) among all equations included for assessment. Of importance is the observed accuracy, which was among the highest in relevant literature [3, 10, 11, 27-31]. Post hoc internal validity showed a mean difference of merely 36 kcal·day –1 , which could be described as clinically insignificant [26].…”

Section: Discussionmentioning

confidence: 99%

Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

et al. 2018

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Aim: To examine the validity of published resting energy expenditure (REE) equations in Greek adults, and if indicated, develop new cohort-specific predictive REE equations. Methods: Indirect calorimetry and anthropometric data were obtained from 226 adult volunteers of diverse age groups and body mass index ranges (18–60 years, 16.6–67.7 kg·m^–2). Measured REE was compared to preexisting prediction equations via correlation, regression, and Bland-Altman analysis. Then, cohort-specific REE equations were developed using curve estimation and nonlinear regression. To reduce type I error, presently derived equations were validated by splitting the sample into a training and validation group. Results: Preexisting equations over-predicted in-cohort REE. Equations by Livigston and Kohlstadt were most accurate at the individual level (63% accuracy), while formulas by Owen and collaborators elicited highest accuracy at the group level (–1.8% bias). Bland-Altman analysis showed proportional bias for most equations. Currently developed equations showed highest overall accuracy with 70% at the individual and group level (1.0% bias), with small differences between measured and predicted REE values (mean, 95% CI 36 [–15 to 88] kcal·day^–1). Conclusion: Data indicate currently developed equations to be the most accurate and valid for estimating REE in Greek adults. Further studies should examine the developed equations in an independent sample.

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Section: Discussionmentioning

confidence: 99%

Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

et al. 2018

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“…Prediction equations based on body weight (BW) have been utilized for over 100 years (Harris and Benedict 1918), and numerous distinct equations are presently employed (Flack et al 2016).…”

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confidence: 99%

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Tinsley

Graybeal

Moore

2019

Appl. Physiol. Nutr. Metab.

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Estimation of resting metabolic rate (RMR) is an important step for prescribing an individual’s energy intake. The purpose of this study was to evaluate the validity of portable indirect calorimeters and RMR prediction equations in muscular physique athletes. Twenty-seven males (n = 17; body mass index (BMI): 28.8 ± 2.0 kg/m2; body fat: 12.5% ± 2.7%) and females (n = 10; BMI: 22.8 ± 1.6 kg/m2; body fat: 19.2% ± 3.4%) were evaluated. The reference RMR value was obtained from the ParvoMedics TrueOne 2400 indirect calorimeter, and the Cosmed Fitmate and Breezing Metabolism Tracker provided additional RMR estimates. Existing RMR prediction equations based on body weight (BW) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Errors in RMR estimates were assessed using validity statistics, including t tests with Bonferroni correction, linear regression, and calculation of the standard error of the estimate, total error, and 95% limits of agreement. Additionally, new prediction equations based on BW (RMR (kcal/day) = 24.8 × BW (kg) + 10) and FFM (RMR (kcal/day) = 25.9 × FFM (kg) + 284) were developed using stepwise linear regression and evaluated using leave-one-out cross-validation. Nearly all existing BW- and FFM-based prediction equations, as well as the Breezing Tracker, did not exhibit acceptable validity and typically underestimated RMR. The ten Haaf and Weijs (PLoS ONE, 9: e1084602014 (2014)) and Cunningham (1980) (Am. J. Clin. Nutr. 33: 2372–2374 (1980)) FFM-based equations may produce acceptable RMR estimates, although the Cosmed Fitmate and newly developed BW- and FFM-based equations may be most suitable for RMR estimation in male and female physique athletes. Future research should provide additional external cross-validation of the newly developed equations to refine the ability to predict RMR in physique athletes.

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“…Hourly resting energy expenditure was estimated using the Harris-Benedict equation and a 13-point MET-value relative intensity energy expenditure scale. The Harris-Benedict equation has recently been validated as the most accurate for assessing resting energy expenditure in healthy adults [13]. This equation was suitable for the studied population as they reflect similar energy expenditure and lean mass as recreationally active females compared to professional female basketball, football, or volleyball athletes who have higher lean mass and greater energy expenditure.…”

Section: Dietary Intake and Physical Activitymentioning

confidence: 99%

Relationships Between Estimated Hourly Energy Balance and Body Composition in Professional Cheerleaders

Bellissimo

Licata

Nucci

et al. 2019

J. of SCI. IN SPORT AND EXERCISE

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Purpose We sought to describe and examine the interrelationships between energy intake, body composition, and estimated energy balance. Methods Using self-reported hourly food intake and formula-based energy expenditure (EE) protocols, 19 female professional cheerleaders (mean age 25.4 years) were assessed to obtain energy balance (EB) for a typical training day. Energy intake (EI) was predicted using the USDA Food Composition Database SR27, and EE was predicted using the Harris-Benedict equation plus a MET-based relative intensity activity scale. Body composition was predicted using a multi-current, 8-mode segmental bioelectrical impedance analysis system. Hourly and daily EB was calculated from EI and EE data. Results Subjects reported a 24 h EI significantly below (P < 0.001) the unadjusted predicted energy requirement (1482 kcal vs. 2199 kcal, respectively), resulting in an average negative net EB of − 720 kcal. Carbohydrate intake was significantly below the minimum recommended level (3.1 g/kg vs. 6 g/kg, P < 0.001) while protein and fat intakes met the recommended levels. Higher fat intake (g/kg) was significantly associated with a higher EI kcal/kg (r = 0.726; P < 0.001), which was significantly associated (r = − 0.55; P = 0.01) with a lower body fat percent (BF%). Using the median of BF% (20.9) as the cut point, participants with fewer hours in a negative EB had lower BF% (P = 0.043) and those with lower BF% spent more time in an EB of ± 300 kcal (P = 0.013). Conclusions These athletes reported low energy intakes that resulted in large EB deficits and/or more hours in a negative EB, which could be counterproductive for achieving a lean body composition overtime.

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Cross-Validation of Resting Metabolic Rate Prediction Equations

Abstract: At the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.

Cited by 67 publications

References 37 publications

Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations

Relationships Between Estimated Hourly Energy Balance and Body Composition in Professional Cheerleaders

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