Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review

Frankenfield, David C.; Roth-Yousey, Lori; Compher, Charlene

doi:10.1016/j.jada.2005.02.005

Cited by 615 publications

(609 citation statements)

References 34 publications

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“…Many prediction equations are available for estimating metabolic rates, but their applicability to other ethnic groups is uncertain (Hayter and Henry 1993;Frankenfield et al, 2005). In the present study, FAO/WHO/UNU equations overestimated BMR by more than 0.45 MJ/day on average, with a prediction error comparable to previously published reports.…”

Section: Discussionsupporting

confidence: 58%

“…Many equations have been developed to estimate BMR or SMR from body size measurements (Cunningham, 1991;Frankenfield et al, 2005), which can be helpful when actual metabolic measurements are not available. Their accuracy and applicability to specific ethnic groups must be considered.…”

Section: Introductionmentioning

confidence: 99%

“…The body size of Japanese differs from that of other ethnicities (Popkin and Doak, 1998;WHO, 1998). Most equations currently available apply to Caucasians (Frankenfield et al, 2005). Studies have found that they are not applicable to nonwhite groups (Liu et al, 1995;Case et al, 1997;Yamamura and Kashiwazaki, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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Introduction: Basal metabolic rate (BMR) or sleeping metabolic rate (SMR) is the largest component of total energy expenditure (EE). An accurate prediction of BMR or SMR is needed to accurately predict total EE or physical activity EE for each individual. However, large variability in BMR and SMR has been reported. Objectives: This study was designed to develop prediction equations using body size measurements for the estimation of both SMR and BMR and to compare the prediction errors with those in previous reports. Methods: We measured body size, height, weight and body composition (fat mass and fat-free mass) from skinfold thickness in adult Japanese men (n ¼ 71) and women (n ¼ 66). SMR was determined as the sum of EE during 8 h of sleep (SMR-8h) and minimum EE during 3 consecutive hours of sleep (SMR-3h) measured using two open-circuit indirect human calorimeters. BMR was determined using a human calorimeter or a mask and Douglas bag. Results: The study population ranged widely in age. The SMR/BMR ratio was 1.0170.09 (range 0.82-1.42) for SMR-8h and 0.9470.07 (range 0.77-1.23) for SMR-3h. The prediction equations for SMR accounted for a 3À5% larger variance with 2-3% smaller standard error of estimate (SEE) than the prediction equations for BMR. Discussion: SMR can be predicted more accurately than previously reported, which indicates that SMR interindividual variability is smaller than expected, at least for Japanese subjects. The prediction equations for SMR are preferable to those for BMR because the former exhibits a smaller prediction error than the latter.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

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Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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“…However, we thought the use of prediction equations for BMR would generate the present result. Many prediction equations are available for estimating BMR, but their applicability to other ethnic groups is uncertain (Hayter and Henry, 1993;Frankenfield et al, 2005). Ganpule et al (2007) suggested recently that the use of FAO/WHO/UNU equations overestimated BMR among Japanese when compared with measured BMR.…”

Section: Discussionmentioning

confidence: 99%

Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire

et al. 2007

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Objective: To measure total energy expenditure (TEE) for normal healthy Japanese by the doubly labelled water (DLW), and to compare the physical activity level (PAL) among categories classified by the categories used in daily reference intake (DRI), Japan and the International Physical Activity Questionnaire (IPAQ). Subjects and methods: A total of 150 healthy Japanese men and women aged 20-to 59-year-old living in four districts of Japan. TEE was measured by the DLW method, and the PAL was calculated from TEE divided by basal metabolic rate. Simultaneously with TEE measurement, the PAL was assessed employing the categories used in DRI, Japan and IPAQ. Results: The average TEE and PAL were 10.7871.67 MJ/day and 1.7270.22 for males and 8.3771.30 MJ/day and 1.7270.27 for females, respectively. The subjects in the highly active categories assessed by both DRI and IPAQ showed significantly higher PAL compared with less active categories. However, PALs among light and moderate categories by DRI, and insufficient and sufficiently active by IPAQ were not significantly different. Conclusions: In developed countries, highly active subjects could be assessed by a simple questionnaire. However, the questionnaire should be improved to clarify the sedentary to moderately active subjects by assessing carefully very light to moderate physical activity.

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“…However, the agreement of equations based on sex, weight and age (Schofield, 1985;Henry, 2002) and height (Cole and Henry, 2002) with measured REE across different gender, age, and body mass index (BMI) groups was only moderate for men and poor for women (Ramirez-Zea, 2005). One explanation may be that body composition has not been taken into account in these as well as in most other REE prediction equations (for review see Frankenfield et al (2005)). …”

Section: Introductionmentioning

confidence: 99%

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

et al. 2006

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Objective: There are considerable differences in published prediction algorithms for resting energy expenditure (REE) based on fat-free mass (FFM). The aim of the study was to investigate the influence of the methodology of body composition analysis on the prediction of REE from FFM. Design: In a cross-sectional design measurements of REE and body composition were performed. . In a normal range of FFM, REE predicted from FFM by different methods showed only small differences. The variance in REE explained by FFM varied from 69% (FFM BIA ) to 75% (FFM DXA ) and was only 46% for body weight. Conclusion: Differences in slopes and intercepts of the regression lines between REE and FFM depended on the methods used for body composition analysis. However, the differences in prediction of REE are small and do not explain the large differences in the results obtained from published FFM-based REE prediction equations and therefore imply a population-and/or investigator specificity of algorithms for REE prediction.

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Comparison of Predictive Equations for Resting Metabolic Rate in Healthy Nonobese and Obese Adults: A Systematic Review

Cited by 615 publications

References 34 publications

Interindividual variability in sleeping metabolic rate in Japanese subjects

Interindividual variability in sleeping metabolic rate in Japanese subjects

Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire

Influence of methods used in body composition analysis on the prediction of resting energy expenditure

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