Use of dual energy X-ray absorptiometry (DXA) to evaluate changes in body composition and the association with performance changes in skeleton athletes.

Research output: Contribution to conferenceAbstract

Abstract

Introduction: Body composition contributes to explosive performance by influencing the power to body mass ratio (Cronin & Hansen, 2005). Measurement errors are seldom considered when evaluating body composition changes determined with DXA. We assessed the reliability of DXA in an applied setting, quantified body composition changes across a training year, and investigated their association with physical performance. Methods: Forty-eight athletes [12 skeleton (7 elite; 5 talent squad), 8 rugby union, 14 swimming, 14 athletics] underwent two DXA scans, typically within 48 hours. Typical error of the measurement (TEM; %) for total body mass (TBM), non-bone non-fat (lean) mass (LM), fat mass (FM), and leg lean mass (LLM) were calculated. Elite skeleton athletes underwent three further scans (representing two training blocks and the competition season) alongside physical tests (countermovement jump and leg press). Training block 1 focussed on hypertrophy, whereas block 2 involved higher velocity, sprint-based training. Body composition changes between each scan were calculated. Relationships between changes in body composition and performance were tested using Pearson correlation coefficients. Results: TEMs for all participants were: TBM, 0.6%; LM, 0.9%; FM, 3.9%; and LLM, 1.2%. Mean (± SD) changes in TBM for elite skeleton athletes were 1.6 ± 2.7%, 1.1 ± 2.0% and 0.7 ± 2.3% across training block 1, block 2, and the competition season, respectively. LM and LLM increased across block 1 (LM, 3.0 ± 2.4%; LLM, 2.7 ± 2.6%) with decreases occurring across block 2 (LM, -0.9 ± 2.6%; LLM, -1.6 ± 2.4%) and the competition season (LM, -1.6 ± 3.1%; LLM, 1.1 ± 2.4%). FM decreased across both training blocks (block 1, -4.3 ± 8.5%; block 2, -3.0 ± 7.2%) and increased across the competition season (12.9 ± 7.4%). Significant relationships (p < 0.05) were found between changes in LM and performance (jump, r = 0.61; leg press, r = 0.40) and between changes in LLM and jump performance (r = 0.65). Discussion: Many body composition changes observed in this study were above the TEM even with a less stringently controlled scanning protocol than previously suggested (Nana et al., 2012). Therefore, DXA was able to detect real body composition changes, which appear to reflect the emphases of each training block and the nature of the competition season. Relationships between changes in LM and performance demonstrated the important influence of body composition on strength and power indices. References:Cronin, J. & Hansen, K. (2005). J Strength Cond Res, 19, 349-57. Nana, A., Slater, G., Hopkins, W. & Burke, L. (2012). Med Sci Sports Ex, 44, 189-9.

Conference

Conference19th European College of Sport Science Congress
CountryUK United Kingdom
CityAmsterdam
Period2/07/145/02/16

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Photon Absorptiometry
Body Composition
Skeleton
Athletes
Leg
Fats
Sports
Aptitude
Football
Hypertrophy

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Use of dual energy X-ray absorptiometry (DXA) to evaluate changes in body composition and the association with performance changes in skeleton athletes. / Colyer, Steffi; Roberts, Simon; Robinson, Jonathan; Thompson, Dylan; Stokes, Keith; Bilzon, James; Salo, Aki.

2014. Abstract from 19th European College of Sport Science Congress, Amsterdam, UK United Kingdom.

Research output: Contribution to conferenceAbstract

@conference{53ae51c93d9041f9aa180dcf5247b986,
title = "Use of dual energy X-ray absorptiometry (DXA) to evaluate changes in body composition and the association with performance changes in skeleton athletes.",
abstract = "Introduction: Body composition contributes to explosive performance by influencing the power to body mass ratio (Cronin & Hansen, 2005). Measurement errors are seldom considered when evaluating body composition changes determined with DXA. We assessed the reliability of DXA in an applied setting, quantified body composition changes across a training year, and investigated their association with physical performance. Methods: Forty-eight athletes [12 skeleton (7 elite; 5 talent squad), 8 rugby union, 14 swimming, 14 athletics] underwent two DXA scans, typically within 48 hours. Typical error of the measurement (TEM; {\%}) for total body mass (TBM), non-bone non-fat (lean) mass (LM), fat mass (FM), and leg lean mass (LLM) were calculated. Elite skeleton athletes underwent three further scans (representing two training blocks and the competition season) alongside physical tests (countermovement jump and leg press). Training block 1 focussed on hypertrophy, whereas block 2 involved higher velocity, sprint-based training. Body composition changes between each scan were calculated. Relationships between changes in body composition and performance were tested using Pearson correlation coefficients. Results: TEMs for all participants were: TBM, 0.6{\%}; LM, 0.9{\%}; FM, 3.9{\%}; and LLM, 1.2{\%}. Mean (± SD) changes in TBM for elite skeleton athletes were 1.6 ± 2.7{\%}, 1.1 ± 2.0{\%} and 0.7 ± 2.3{\%} across training block 1, block 2, and the competition season, respectively. LM and LLM increased across block 1 (LM, 3.0 ± 2.4{\%}; LLM, 2.7 ± 2.6{\%}) with decreases occurring across block 2 (LM, -0.9 ± 2.6{\%}; LLM, -1.6 ± 2.4{\%}) and the competition season (LM, -1.6 ± 3.1{\%}; LLM, 1.1 ± 2.4{\%}). FM decreased across both training blocks (block 1, -4.3 ± 8.5{\%}; block 2, -3.0 ± 7.2{\%}) and increased across the competition season (12.9 ± 7.4{\%}). Significant relationships (p < 0.05) were found between changes in LM and performance (jump, r = 0.61; leg press, r = 0.40) and between changes in LLM and jump performance (r = 0.65). Discussion: Many body composition changes observed in this study were above the TEM even with a less stringently controlled scanning protocol than previously suggested (Nana et al., 2012). Therefore, DXA was able to detect real body composition changes, which appear to reflect the emphases of each training block and the nature of the competition season. Relationships between changes in LM and performance demonstrated the important influence of body composition on strength and power indices. References:Cronin, J. & Hansen, K. (2005). J Strength Cond Res, 19, 349-57. Nana, A., Slater, G., Hopkins, W. & Burke, L. (2012). Med Sci Sports Ex, 44, 189-9.",
author = "Steffi Colyer and Simon Roberts and Jonathan Robinson and Dylan Thompson and Keith Stokes and James Bilzon and Aki Salo",
year = "2014",
language = "English",
note = "19th European College of Sport Science Congress ; Conference date: 02-07-2014 Through 05-02-2016",

}

TY - CONF

T1 - Use of dual energy X-ray absorptiometry (DXA) to evaluate changes in body composition and the association with performance changes in skeleton athletes.

AU - Colyer, Steffi

AU - Roberts, Simon

AU - Robinson, Jonathan

AU - Thompson, Dylan

AU - Stokes, Keith

AU - Bilzon, James

AU - Salo, Aki

PY - 2014

Y1 - 2014

N2 - Introduction: Body composition contributes to explosive performance by influencing the power to body mass ratio (Cronin & Hansen, 2005). Measurement errors are seldom considered when evaluating body composition changes determined with DXA. We assessed the reliability of DXA in an applied setting, quantified body composition changes across a training year, and investigated their association with physical performance. Methods: Forty-eight athletes [12 skeleton (7 elite; 5 talent squad), 8 rugby union, 14 swimming, 14 athletics] underwent two DXA scans, typically within 48 hours. Typical error of the measurement (TEM; %) for total body mass (TBM), non-bone non-fat (lean) mass (LM), fat mass (FM), and leg lean mass (LLM) were calculated. Elite skeleton athletes underwent three further scans (representing two training blocks and the competition season) alongside physical tests (countermovement jump and leg press). Training block 1 focussed on hypertrophy, whereas block 2 involved higher velocity, sprint-based training. Body composition changes between each scan were calculated. Relationships between changes in body composition and performance were tested using Pearson correlation coefficients. Results: TEMs for all participants were: TBM, 0.6%; LM, 0.9%; FM, 3.9%; and LLM, 1.2%. Mean (± SD) changes in TBM for elite skeleton athletes were 1.6 ± 2.7%, 1.1 ± 2.0% and 0.7 ± 2.3% across training block 1, block 2, and the competition season, respectively. LM and LLM increased across block 1 (LM, 3.0 ± 2.4%; LLM, 2.7 ± 2.6%) with decreases occurring across block 2 (LM, -0.9 ± 2.6%; LLM, -1.6 ± 2.4%) and the competition season (LM, -1.6 ± 3.1%; LLM, 1.1 ± 2.4%). FM decreased across both training blocks (block 1, -4.3 ± 8.5%; block 2, -3.0 ± 7.2%) and increased across the competition season (12.9 ± 7.4%). Significant relationships (p < 0.05) were found between changes in LM and performance (jump, r = 0.61; leg press, r = 0.40) and between changes in LLM and jump performance (r = 0.65). Discussion: Many body composition changes observed in this study were above the TEM even with a less stringently controlled scanning protocol than previously suggested (Nana et al., 2012). Therefore, DXA was able to detect real body composition changes, which appear to reflect the emphases of each training block and the nature of the competition season. Relationships between changes in LM and performance demonstrated the important influence of body composition on strength and power indices. References:Cronin, J. & Hansen, K. (2005). J Strength Cond Res, 19, 349-57. Nana, A., Slater, G., Hopkins, W. & Burke, L. (2012). Med Sci Sports Ex, 44, 189-9.

AB - Introduction: Body composition contributes to explosive performance by influencing the power to body mass ratio (Cronin & Hansen, 2005). Measurement errors are seldom considered when evaluating body composition changes determined with DXA. We assessed the reliability of DXA in an applied setting, quantified body composition changes across a training year, and investigated their association with physical performance. Methods: Forty-eight athletes [12 skeleton (7 elite; 5 talent squad), 8 rugby union, 14 swimming, 14 athletics] underwent two DXA scans, typically within 48 hours. Typical error of the measurement (TEM; %) for total body mass (TBM), non-bone non-fat (lean) mass (LM), fat mass (FM), and leg lean mass (LLM) were calculated. Elite skeleton athletes underwent three further scans (representing two training blocks and the competition season) alongside physical tests (countermovement jump and leg press). Training block 1 focussed on hypertrophy, whereas block 2 involved higher velocity, sprint-based training. Body composition changes between each scan were calculated. Relationships between changes in body composition and performance were tested using Pearson correlation coefficients. Results: TEMs for all participants were: TBM, 0.6%; LM, 0.9%; FM, 3.9%; and LLM, 1.2%. Mean (± SD) changes in TBM for elite skeleton athletes were 1.6 ± 2.7%, 1.1 ± 2.0% and 0.7 ± 2.3% across training block 1, block 2, and the competition season, respectively. LM and LLM increased across block 1 (LM, 3.0 ± 2.4%; LLM, 2.7 ± 2.6%) with decreases occurring across block 2 (LM, -0.9 ± 2.6%; LLM, -1.6 ± 2.4%) and the competition season (LM, -1.6 ± 3.1%; LLM, 1.1 ± 2.4%). FM decreased across both training blocks (block 1, -4.3 ± 8.5%; block 2, -3.0 ± 7.2%) and increased across the competition season (12.9 ± 7.4%). Significant relationships (p < 0.05) were found between changes in LM and performance (jump, r = 0.61; leg press, r = 0.40) and between changes in LLM and jump performance (r = 0.65). Discussion: Many body composition changes observed in this study were above the TEM even with a less stringently controlled scanning protocol than previously suggested (Nana et al., 2012). Therefore, DXA was able to detect real body composition changes, which appear to reflect the emphases of each training block and the nature of the competition season. Relationships between changes in LM and performance demonstrated the important influence of body composition on strength and power indices. References:Cronin, J. & Hansen, K. (2005). J Strength Cond Res, 19, 349-57. Nana, A., Slater, G., Hopkins, W. & Burke, L. (2012). Med Sci Sports Ex, 44, 189-9.

M3 - Abstract

ER -