Estimating athlete metabolic rate, can we improve predictability using old and new techniques

Kristen MacKenzie, Neil King, Nuala Byrne, Gary Slater

Research output: Contribution to conferencePresentationResearch

Abstract

Introduction. Resting Metabolic Rate (RMR) prediction is important in estimating athletes’ energy requirements. RMR is often predicted using total body mass (TBM) or lean mass (LM). Allometric (exponential) prediction may be useful due to its successful application in mammals of varying size and in predicting athlete performance. Alternatively, using regional lean mass measured by dual-energy x-ray absorptiometry (DXA) may be more predictive than conventional methods due to the increased energy cost of internal organs. Methods. RMR (2392.67 ± 255.68 Kcal) and body composition were measured in 19 developing rugby players (Age 20.73 ± 2.38 yr; weight 101.23 ± 14.14 kg; lean mass 77.61 ± 7.7 kg; fat mass 20.1 ± 8.5 kg; ht 185.25 ± 8.38 cm) using indirect calorimetry and DXA respectively. Several linear predictive models were derived using the linear model B = C + BoMb with the exponential component b = 1 for a linear model, ¾ based on a literature-predicted exponential or an exponential derived from the log-log transformation of data. Combinations of regional lean mass values were used to determine individual explanatory power of RMR in forced entry linear regression analysis. The models were applied to the same body composition data to determine the correlation of predicted and measured RMR. Results. For TBM derived models, allometric scaling (proposed exponent) allometric scaling (derived exponent) and TBM (linear) were similarly predictive Adj R2 = 0.767; 0.763 and 0.761 respectively. All LM derived models were equally predictive and marginally more predictive than TBM derived models; Adj R2 = 0.797; P<0.001. Regional LM derived models provided limited benefit over models based on TBM and LM. Conclusion. Conventional linear RMR prediction models derived from LM or TBM are suitable for athletes when derived from a similar population. Further research is warranted to determine the relationship between regional LM and RMR.
Original languageEnglish
Publication statusPublished - 19 Oct 2013
Externally publishedYes
EventSports Dietitians Australia Conference : Performance Nutrition – Measurement, Manipulation, Application - Melbourne, Australia
Duration: 19 Oct 201320 Oct 2013

Conference

ConferenceSports Dietitians Australia Conference
Abbreviated titleSDA
CountryAustralia
CityMelbourne
Period19/10/1320/10/13

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body mass
prediction
rate
energy
calorimetry
fat
mammal
cost
method

Cite this

MacKenzie, K., King, N., Byrne, N., & Slater, G. (2013). Estimating athlete metabolic rate, can we improve predictability using old and new techniques. Sports Dietitians Australia Conference , Melbourne, Australia.
MacKenzie, Kristen ; King, Neil ; Byrne, Nuala ; Slater, Gary. / Estimating athlete metabolic rate, can we improve predictability using old and new techniques. Sports Dietitians Australia Conference , Melbourne, Australia.
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MacKenzie, K, King, N, Byrne, N & Slater, G 2013, 'Estimating athlete metabolic rate, can we improve predictability using old and new techniques' Sports Dietitians Australia Conference , Melbourne, Australia, 19/10/13 - 20/10/13, .

Estimating athlete metabolic rate, can we improve predictability using old and new techniques. / MacKenzie, Kristen; King, Neil; Byrne, Nuala; Slater, Gary.

2013. Sports Dietitians Australia Conference , Melbourne, Australia.

Research output: Contribution to conferencePresentationResearch

TY - CONF

T1 - Estimating athlete metabolic rate, can we improve predictability using old and new techniques

AU - MacKenzie, Kristen

AU - King, Neil

AU - Byrne, Nuala

AU - Slater, Gary

PY - 2013/10/19

Y1 - 2013/10/19

N2 - Introduction. Resting Metabolic Rate (RMR) prediction is important in estimating athletes’ energy requirements. RMR is often predicted using total body mass (TBM) or lean mass (LM). Allometric (exponential) prediction may be useful due to its successful application in mammals of varying size and in predicting athlete performance. Alternatively, using regional lean mass measured by dual-energy x-ray absorptiometry (DXA) may be more predictive than conventional methods due to the increased energy cost of internal organs. Methods. RMR (2392.67 ± 255.68 Kcal) and body composition were measured in 19 developing rugby players (Age 20.73 ± 2.38 yr; weight 101.23 ± 14.14 kg; lean mass 77.61 ± 7.7 kg; fat mass 20.1 ± 8.5 kg; ht 185.25 ± 8.38 cm) using indirect calorimetry and DXA respectively. Several linear predictive models were derived using the linear model B = C + BoMb with the exponential component b = 1 for a linear model, ¾ based on a literature-predicted exponential or an exponential derived from the log-log transformation of data. Combinations of regional lean mass values were used to determine individual explanatory power of RMR in forced entry linear regression analysis. The models were applied to the same body composition data to determine the correlation of predicted and measured RMR. Results. For TBM derived models, allometric scaling (proposed exponent) allometric scaling (derived exponent) and TBM (linear) were similarly predictive Adj R2 = 0.767; 0.763 and 0.761 respectively. All LM derived models were equally predictive and marginally more predictive than TBM derived models; Adj R2 = 0.797; P<0.001. Regional LM derived models provided limited benefit over models based on TBM and LM. Conclusion. Conventional linear RMR prediction models derived from LM or TBM are suitable for athletes when derived from a similar population. Further research is warranted to determine the relationship between regional LM and RMR.

AB - Introduction. Resting Metabolic Rate (RMR) prediction is important in estimating athletes’ energy requirements. RMR is often predicted using total body mass (TBM) or lean mass (LM). Allometric (exponential) prediction may be useful due to its successful application in mammals of varying size and in predicting athlete performance. Alternatively, using regional lean mass measured by dual-energy x-ray absorptiometry (DXA) may be more predictive than conventional methods due to the increased energy cost of internal organs. Methods. RMR (2392.67 ± 255.68 Kcal) and body composition were measured in 19 developing rugby players (Age 20.73 ± 2.38 yr; weight 101.23 ± 14.14 kg; lean mass 77.61 ± 7.7 kg; fat mass 20.1 ± 8.5 kg; ht 185.25 ± 8.38 cm) using indirect calorimetry and DXA respectively. Several linear predictive models were derived using the linear model B = C + BoMb with the exponential component b = 1 for a linear model, ¾ based on a literature-predicted exponential or an exponential derived from the log-log transformation of data. Combinations of regional lean mass values were used to determine individual explanatory power of RMR in forced entry linear regression analysis. The models were applied to the same body composition data to determine the correlation of predicted and measured RMR. Results. For TBM derived models, allometric scaling (proposed exponent) allometric scaling (derived exponent) and TBM (linear) were similarly predictive Adj R2 = 0.767; 0.763 and 0.761 respectively. All LM derived models were equally predictive and marginally more predictive than TBM derived models; Adj R2 = 0.797; P<0.001. Regional LM derived models provided limited benefit over models based on TBM and LM. Conclusion. Conventional linear RMR prediction models derived from LM or TBM are suitable for athletes when derived from a similar population. Further research is warranted to determine the relationship between regional LM and RMR.

M3 - Presentation

ER -

MacKenzie K, King N, Byrne N, Slater G. Estimating athlete metabolic rate, can we improve predictability using old and new techniques. 2013. Sports Dietitians Australia Conference , Melbourne, Australia.