The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?

Research output: Contribution to conferencePresentationResearch

Abstract


Effective energy prescription requires an accurate assessment of the athletes’ daily energy expenditure. Whilst the use of published prediction equations using RMR and an activity factor is common practice; there is little evidence to validate their use with athletic groups. This study compared measured resting metabolic rate (RMR) using indirect calorimetry to RMR using 17 prediction equations. Anthropometric and metabolic data was collected for 23 male rugby athletes and a literature review was conducted for evidence relating to the measurement and prediction of RMR in athlete populations. Paired samples t-tests and root mean square prediction error (RMSPE) were used to compare measured and predicted RMR and the Bland-Altman procedure was used to assess the bias for each prediction. While prediction equations significantly and systematically underestimated RMR in rugby players for all equations (p=0.001), there are several sources of error that need to be addressed. The validation of population-specific prediction equations in athlete groups requires standardised and accurate assessments of body composition (including fat and fat-free mass) and RMR by indirect calorimetry. While there is a strong, linear relationship between lean mass and RMR, research is also needed to identify the unique characteristics of athletes that can act as covariates.
Original languageEnglish
Publication statusPublished - 2015
EventThe 9th International Conference on Diet and Activity Methods - Brisbane, Australia
Duration: 1 Sep 20153 Sep 2015
Conference number: 9th
http://www.icdam.org/what-is-icdam/

Conference

ConferenceThe 9th International Conference on Diet and Activity Methods
Abbreviated titleICDAM
CountryAustralia
CityBrisbane
Period1/09/153/09/15
OtherThe 9th International Conference on Diet and Activity Methods (ICDAM 2015) is dedicated to improving methods and measures for nutrition, diet and physical activity. Improved methods are critical to monitoring changing physical activity patterns and food consumption, enhancing our understanding of relationships to health, assessing household food insecurity and hunger, monitoring health objectives and measuring energy balance. Topics include:

- Combining Theory and Practice
- Assessing Diets to Improve World Health
- Promoting the Appropriate use of Dietary Assessment Tools for All
- Advances in Dietary, Biochemical and Statistical Approaches
- Complementary Advances in Diet and Physical Activity Assessment Methodologies
- Expanding the Horizon, Dietary Assessment in a Multi-cultural World
- Methodological Challenges for Measuring the Achievements of International Policies
- Diet and Physical Activity Assessment: From the Individual to the Environment
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Basal Metabolism
Athletes
Technology
Population
Indirect Calorimetry
Football
Fats
Body Composition
Energy Metabolism
Sports
Prescriptions
Research Design

Cite this

MacKenzie, K. (2015). The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?. The 9th International Conference on Diet and Activity Methods, Brisbane, Australia.
MacKenzie, Kristen. / The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?. The 9th International Conference on Diet and Activity Methods, Brisbane, Australia.
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abstract = "Effective energy prescription requires an accurate assessment of the athletes’ daily energy expenditure. Whilst the use of published prediction equations using RMR and an activity factor is common practice; there is little evidence to validate their use with athletic groups. This study compared measured resting metabolic rate (RMR) using indirect calorimetry to RMR using 17 prediction equations. Anthropometric and metabolic data was collected for 23 male rugby athletes and a literature review was conducted for evidence relating to the measurement and prediction of RMR in athlete populations. Paired samples t-tests and root mean square prediction error (RMSPE) were used to compare measured and predicted RMR and the Bland-Altman procedure was used to assess the bias for each prediction. While prediction equations significantly and systematically underestimated RMR in rugby players for all equations (p=0.001), there are several sources of error that need to be addressed. The validation of population-specific prediction equations in athlete groups requires standardised and accurate assessments of body composition (including fat and fat-free mass) and RMR by indirect calorimetry. While there is a strong, linear relationship between lean mass and RMR, research is also needed to identify the unique characteristics of athletes that can act as covariates.",
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year = "2015",
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MacKenzie, K 2015, 'The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?' The 9th International Conference on Diet and Activity Methods, Brisbane, Australia, 1/09/15 - 3/09/15, .

The accurate prediction of RMR in athlete populations: which methodologies and technologies are required? / MacKenzie, Kristen.

2015. The 9th International Conference on Diet and Activity Methods, Brisbane, Australia.

Research output: Contribution to conferencePresentationResearch

TY - CONF

T1 - The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?

AU - MacKenzie, Kristen

PY - 2015

Y1 - 2015

N2 - Effective energy prescription requires an accurate assessment of the athletes’ daily energy expenditure. Whilst the use of published prediction equations using RMR and an activity factor is common practice; there is little evidence to validate their use with athletic groups. This study compared measured resting metabolic rate (RMR) using indirect calorimetry to RMR using 17 prediction equations. Anthropometric and metabolic data was collected for 23 male rugby athletes and a literature review was conducted for evidence relating to the measurement and prediction of RMR in athlete populations. Paired samples t-tests and root mean square prediction error (RMSPE) were used to compare measured and predicted RMR and the Bland-Altman procedure was used to assess the bias for each prediction. While prediction equations significantly and systematically underestimated RMR in rugby players for all equations (p=0.001), there are several sources of error that need to be addressed. The validation of population-specific prediction equations in athlete groups requires standardised and accurate assessments of body composition (including fat and fat-free mass) and RMR by indirect calorimetry. While there is a strong, linear relationship between lean mass and RMR, research is also needed to identify the unique characteristics of athletes that can act as covariates.

AB - Effective energy prescription requires an accurate assessment of the athletes’ daily energy expenditure. Whilst the use of published prediction equations using RMR and an activity factor is common practice; there is little evidence to validate their use with athletic groups. This study compared measured resting metabolic rate (RMR) using indirect calorimetry to RMR using 17 prediction equations. Anthropometric and metabolic data was collected for 23 male rugby athletes and a literature review was conducted for evidence relating to the measurement and prediction of RMR in athlete populations. Paired samples t-tests and root mean square prediction error (RMSPE) were used to compare measured and predicted RMR and the Bland-Altman procedure was used to assess the bias for each prediction. While prediction equations significantly and systematically underestimated RMR in rugby players for all equations (p=0.001), there are several sources of error that need to be addressed. The validation of population-specific prediction equations in athlete groups requires standardised and accurate assessments of body composition (including fat and fat-free mass) and RMR by indirect calorimetry. While there is a strong, linear relationship between lean mass and RMR, research is also needed to identify the unique characteristics of athletes that can act as covariates.

M3 - Presentation

ER -

MacKenzie K. The accurate prediction of RMR in athlete populations: which methodologies and technologies are required?. 2015. The 9th International Conference on Diet and Activity Methods, Brisbane, Australia.