The prediction of athlete resting metabolic rate – is it time to reassess the method?

Research output: Contribution to conferencePosterResearch

6 Downloads (Pure)

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 - 9 Sep 2016
EventThe 17th International Congress of Dietetics Granada 2016 - Granada, Spain
Duration: 7 Sep 201610 Sep 2016
Conference number: 17th
http://www.icdgranada2016.com/

Conference

ConferenceThe 17th International Congress of Dietetics Granada 2016
Abbreviated titleICD
CountrySpain
CityGranada
Period7/09/1610/09/16
Internet address

Fingerprint

Basal Metabolism
Athletes
Indirect Calorimetry
Football
Fats
Body Composition
Energy Metabolism
Population
Sports
Prescriptions
Research Design

Cite this

MacKenzie, K. (2016). The prediction of athlete resting metabolic rate – is it time to reassess the method?. Poster session presented at The 17th International Congress of Dietetics Granada 2016, Granada, Spain.
MacKenzie, Kristen. / The prediction of athlete resting metabolic rate – is it time to reassess the method?. Poster session presented at The 17th International Congress of Dietetics Granada 2016, Granada, Spain.
@conference{e07dca0c8ae24f699f24dc1f01228424,
title = "The prediction of athlete resting metabolic rate – is it time to reassess the method?",
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.",
author = "Kristen MacKenzie",
year = "2016",
month = "9",
day = "9",
language = "English",
note = "The 17th International Congress of Dietetics Granada 2016, ICD ; Conference date: 07-09-2016 Through 10-09-2016",
url = "http://www.icdgranada2016.com/",

}

MacKenzie, K 2016, 'The prediction of athlete resting metabolic rate – is it time to reassess the method?' The 17th International Congress of Dietetics Granada 2016, Granada, Spain, 7/09/16 - 10/09/16, .

The prediction of athlete resting metabolic rate – is it time to reassess the method? / MacKenzie, Kristen.

2016. Poster session presented at The 17th International Congress of Dietetics Granada 2016, Granada, Spain.

Research output: Contribution to conferencePosterResearch

TY - CONF

T1 - The prediction of athlete resting metabolic rate – is it time to reassess the method?

AU - MacKenzie, Kristen

PY - 2016/9/9

Y1 - 2016/9/9

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 - Poster

ER -

MacKenzie K. The prediction of athlete resting metabolic rate – is it time to reassess the method?. 2016. Poster session presented at The 17th International Congress of Dietetics Granada 2016, Granada, Spain.