Health & Physical Activity
Introduction: Financial and personal costs limit the applicability of direct and indirect calorimetric resting metabolic rate (RMR) assessments during health counseling. Therefore, regression-based equations are often used in preventive settings in which their accuracy could be very limited by the i...
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Dynamic Media Sales Verlag
2018
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oai:doaj.org-article:0eda3b00247749cd9a5f8322f56d285b2021-11-16T19:01:41ZHealth & Physical Activity0344-59252510-526410.5960/dzsm.2018.348https://doaj.org/article/0eda3b00247749cd9a5f8322f56d285b2018-10-01T00:00:00Zhttps://www.germanjournalsportsmedicine.com/archive/archive-2018/issue-10/resting-metabolic-rate-the-applicability-of-predictive-equations-as-an-alternative-to-indirect-calorimetry/https://doaj.org/toc/0344-5925https://doaj.org/toc/2510-5264Introduction: Financial and personal costs limit the applicability of direct and indirect calorimetric resting metabolic rate (RMR) assessments during health counseling. Therefore, regression-based equations are often used in preventive settings in which their accuracy could be very limited by the impact of habitual physical activity (PA) and sedentary behavior (SB). This study analyzed the applicability of four equations and the potential of PA and SB as explanatory variables.Methods: RMR of 35 non-obese men was assessed using indirect calorimetry. Harris-Benedict-, Mifflin- (factors: bodyweight, height, age), Cunningham-, and Owen equations (factor: fat free mass; FFM) were applied. PA (leisure-, commuting-, household- and work activities) and SB were assessed using a validated questionnaire.Results: RMR was 2061.5537.4 kcal/day and correlated with weight, height, FFM and PA. Only age and PA correlated with RMR after adjusting for bodyweight (1.060.25 kcal/kg/h). Whereas all equations were associated with calorimetric RMR (p<.05), only Harris Benedict formula did not lead to a significant underestimation (-153.4479.0 kcal/day; p>.05).Discussion: In our sample,Harris-Benedict was the only applicable equation. However, the accuracy of individual results was very limited. PA but not SB seems to beassociated with RMR in non-obese men. PA hould be consideredas an additional factor for more accurate predictive equations in preventive settings. KEY WORDS: Sedentary Behavior, Resting Energy Expenditure, Physical ActivityEngeroff TBerk DStücher KBanzer WDynamic Media Sales VerlagarticleSports medicineRC1200-1245DEENDeutsche Zeitschrift für Sportmedizin, Vol 69, Iss 10 (2018) |
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DOAJ |
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DOAJ |
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DE EN |
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Sports medicine RC1200-1245 |
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Sports medicine RC1200-1245 Engeroff T Berk D Stücher K Banzer W Health & Physical Activity |
| description |
Introduction: Financial and personal costs limit the applicability of direct and indirect calorimetric resting metabolic rate (RMR) assessments during health counseling. Therefore, regression-based equations are often used in preventive settings in which their accuracy could be very limited by the impact of habitual physical activity (PA) and sedentary behavior (SB). This study analyzed the applicability of four equations and the potential of PA and SB as explanatory variables.Methods: RMR of 35 non-obese men was assessed using indirect calorimetry. Harris-Benedict-, Mifflin- (factors: bodyweight, height, age), Cunningham-, and Owen equations (factor: fat free mass; FFM) were applied. PA (leisure-, commuting-, household- and work activities) and SB were assessed using a validated questionnaire.Results: RMR was 2061.5537.4 kcal/day and correlated with weight, height, FFM and PA. Only age and PA correlated with RMR after adjusting for bodyweight (1.060.25 kcal/kg/h). Whereas all equations were associated with calorimetric RMR (p<.05), only Harris Benedict formula did not lead to a significant underestimation (-153.4479.0 kcal/day; p>.05).Discussion: In our sample,Harris-Benedict was the only applicable equation. However, the accuracy of individual results was very limited. PA but not SB seems to beassociated with RMR in non-obese men. PA hould be consideredas an additional factor for more accurate predictive equations in preventive settings. KEY WORDS: Sedentary Behavior, Resting Energy Expenditure, Physical Activity |
| format |
article |
| author |
Engeroff T Berk D Stücher K Banzer W |
| author_facet |
Engeroff T Berk D Stücher K Banzer W |
| author_sort |
Engeroff T |
| title |
Health & Physical Activity |
| title_short |
Health & Physical Activity |
| title_full |
Health & Physical Activity |
| title_fullStr |
Health & Physical Activity |
| title_full_unstemmed |
Health & Physical Activity |
| title_sort |
health & physical activity |
| publisher |
Dynamic Media Sales Verlag |
| publishDate |
2018 |
| url |
https://doaj.org/article/0eda3b00247749cd9a5f8322f56d285b |
| work_keys_str_mv |
AT engerofft healthampphysicalactivity AT berkd healthampphysicalactivity AT stucherk healthampphysicalactivity AT banzerw healthampphysicalactivity |
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