Accuracy of resting metabolic rate prediction in overweight and obese Australian adults
Objectives: Predictive resting metabolic rate (RMR) equations in Australian populations are at least 10 years old, focused on males and do not commonly use overweight and obese weight categorisation. The aim of this study was to measure RMR via indirect calorimetry in overweight and obese Australian...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/12657 |
| _version_ | 1848748137380839424 |
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| author | Wright, T. Dawson, B. Jalleh, Geoffrey Guelfi, K. |
| author_facet | Wright, T. Dawson, B. Jalleh, Geoffrey Guelfi, K. |
| author_sort | Wright, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Objectives: Predictive resting metabolic rate (RMR) equations in Australian populations are at least 10 years old, focused on males and do not commonly use overweight and obese weight categorisation. The aim of this study was to measure RMR via indirect calorimetry in overweight and obese Australian adults to develop population specific predictive equations and compare with other well-known international equations (Mifflin–St. Jeor, Owen and WHO/FAO/UNU). Methods: Retrospective data from 278 participants (154 males, 124 females: 37% overweight, 63% obese) who had attended a weight management clinic were used to develop predictive RMR equations. These were then validated against another sample (from the same clinic) of 297 participants (150 males, 147 females: 47% overweight, 53% obese), and their accuracy compared with known standard equations. Results: For the prediction sample, weight, BMI, resting VO2 and measured RMR were significantly greater in the obese than overweight. Using the validation sample, the predictive equations met a ±10% of measured RMR criterion 42% (females), 41% (total sample) and 40% (males) of the time. Prediction accuracy was not improved by using specific overweight and obese weight category equations, or by applying the known standard equations from the literature. Conclusions: In our sample of overweight and obese adults, RMR prediction to within ±10% of the measured value was only accurate ∼40% of the time, regardless of gender and weight classification. In clinical weight management settings direct measures of RMR should be made wherever possible. |
| first_indexed | 2025-11-14T07:00:16Z |
| format | Journal Article |
| id | curtin-20.500.11937-12657 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:00:16Z |
| publishDate | 2015 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-126572017-09-13T14:59:19Z Accuracy of resting metabolic rate prediction in overweight and obese Australian adults Wright, T. Dawson, B. Jalleh, Geoffrey Guelfi, K. Objectives: Predictive resting metabolic rate (RMR) equations in Australian populations are at least 10 years old, focused on males and do not commonly use overweight and obese weight categorisation. The aim of this study was to measure RMR via indirect calorimetry in overweight and obese Australian adults to develop population specific predictive equations and compare with other well-known international equations (Mifflin–St. Jeor, Owen and WHO/FAO/UNU). Methods: Retrospective data from 278 participants (154 males, 124 females: 37% overweight, 63% obese) who had attended a weight management clinic were used to develop predictive RMR equations. These were then validated against another sample (from the same clinic) of 297 participants (150 males, 147 females: 47% overweight, 53% obese), and their accuracy compared with known standard equations. Results: For the prediction sample, weight, BMI, resting VO2 and measured RMR were significantly greater in the obese than overweight. Using the validation sample, the predictive equations met a ±10% of measured RMR criterion 42% (females), 41% (total sample) and 40% (males) of the time. Prediction accuracy was not improved by using specific overweight and obese weight category equations, or by applying the known standard equations from the literature. Conclusions: In our sample of overweight and obese adults, RMR prediction to within ±10% of the measured value was only accurate ∼40% of the time, regardless of gender and weight classification. In clinical weight management settings direct measures of RMR should be made wherever possible. 2015 Journal Article http://hdl.handle.net/20.500.11937/12657 10.1016/j.orcp.2015.07.008 Elsevier Ltd restricted |
| spellingShingle | Wright, T. Dawson, B. Jalleh, Geoffrey Guelfi, K. Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title | Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title_full | Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title_fullStr | Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title_full_unstemmed | Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title_short | Accuracy of resting metabolic rate prediction in overweight and obese Australian adults |
| title_sort | accuracy of resting metabolic rate prediction in overweight and obese australian adults |
| url | http://hdl.handle.net/20.500.11937/12657 |