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Predictive ability of a comprehensive incremental test in mountain bike marathon

OBJECTIVES: Traditional performance tests in mountain bike marathon (XCM) primarily quantify aerobic metabolism and may not describe the relevant capacities in XCM. We aimed to validate a comprehensive test protocol quantifying its intermittent demands. METHODS: Forty-nine athletes (38.8±9.1 years;...

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Autores principales: Ahrend, Marc-Daniel, Schneeweiss, Patrick, Martus, Peter, Niess, Andreas M, Krauss, Inga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786905/
https://www.ncbi.nlm.nih.gov/pubmed/29387445
http://dx.doi.org/10.1136/bmjsem-2017-000293
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author Ahrend, Marc-Daniel
Schneeweiss, Patrick
Martus, Peter
Niess, Andreas M
Krauss, Inga
author_facet Ahrend, Marc-Daniel
Schneeweiss, Patrick
Martus, Peter
Niess, Andreas M
Krauss, Inga
author_sort Ahrend, Marc-Daniel
collection PubMed
description OBJECTIVES: Traditional performance tests in mountain bike marathon (XCM) primarily quantify aerobic metabolism and may not describe the relevant capacities in XCM. We aimed to validate a comprehensive test protocol quantifying its intermittent demands. METHODS: Forty-nine athletes (38.8±9.1 years; 38 male; 11 female) performed a laboratory performance test, including an incremental test, to determine individual anaerobic threshold (IAT), peak power output (PPO) and three maximal efforts (10 s all-out sprint, 1 min maximal effort and 5 min maximal effort). Within 2 weeks, the athletes participated in one of three XCM races (n=15, n=9 and n=25). Correlations between test variables and race times were calculated separately. In addition, multiple regression models of the predictive value of laboratory outcomes were calculated for race 3 and across all races (z-transformed data). RESULTS: All variables were correlated with race times 1, 2 and 3: 10 s all-out sprint (r=−0.72; r=−0.59; r=−0.61), 1 min maximal effort (r=−0.85; r=−0.84; r=−0.82), 5 min maximal effort (r=−0.57; r=−0.85; r=−0.76), PPO (r=−0.77; r=−0.73; r=−0.76) and IAT (r=−0.71; r=−0.67; r=−0.68). The best-fitting multiple regression models for race 3 (r(2)=0.868) and across all races (r(2)=0.757) comprised 1 min maximal effort, IAT and body weight. CONCLUSION: Aerobic and intermittent variables correlated least strongly with race times. Their use in a multiple regression model confirmed additional explanatory power to predict XCM performance. These findings underline the usefulness of the comprehensive incremental test to predict performance in that sport more precisely.
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spelling pubmed-57869052018-01-31 Predictive ability of a comprehensive incremental test in mountain bike marathon Ahrend, Marc-Daniel Schneeweiss, Patrick Martus, Peter Niess, Andreas M Krauss, Inga BMJ Open Sport Exerc Med Original Article OBJECTIVES: Traditional performance tests in mountain bike marathon (XCM) primarily quantify aerobic metabolism and may not describe the relevant capacities in XCM. We aimed to validate a comprehensive test protocol quantifying its intermittent demands. METHODS: Forty-nine athletes (38.8±9.1 years; 38 male; 11 female) performed a laboratory performance test, including an incremental test, to determine individual anaerobic threshold (IAT), peak power output (PPO) and three maximal efforts (10 s all-out sprint, 1 min maximal effort and 5 min maximal effort). Within 2 weeks, the athletes participated in one of three XCM races (n=15, n=9 and n=25). Correlations between test variables and race times were calculated separately. In addition, multiple regression models of the predictive value of laboratory outcomes were calculated for race 3 and across all races (z-transformed data). RESULTS: All variables were correlated with race times 1, 2 and 3: 10 s all-out sprint (r=−0.72; r=−0.59; r=−0.61), 1 min maximal effort (r=−0.85; r=−0.84; r=−0.82), 5 min maximal effort (r=−0.57; r=−0.85; r=−0.76), PPO (r=−0.77; r=−0.73; r=−0.76) and IAT (r=−0.71; r=−0.67; r=−0.68). The best-fitting multiple regression models for race 3 (r(2)=0.868) and across all races (r(2)=0.757) comprised 1 min maximal effort, IAT and body weight. CONCLUSION: Aerobic and intermittent variables correlated least strongly with race times. Their use in a multiple regression model confirmed additional explanatory power to predict XCM performance. These findings underline the usefulness of the comprehensive incremental test to predict performance in that sport more precisely. BMJ Publishing Group 2018-01-26 /pmc/articles/PMC5786905/ /pubmed/29387445 http://dx.doi.org/10.1136/bmjsem-2017-000293 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Original Article
Ahrend, Marc-Daniel
Schneeweiss, Patrick
Martus, Peter
Niess, Andreas M
Krauss, Inga
Predictive ability of a comprehensive incremental test in mountain bike marathon
title Predictive ability of a comprehensive incremental test in mountain bike marathon
title_full Predictive ability of a comprehensive incremental test in mountain bike marathon
title_fullStr Predictive ability of a comprehensive incremental test in mountain bike marathon
title_full_unstemmed Predictive ability of a comprehensive incremental test in mountain bike marathon
title_short Predictive ability of a comprehensive incremental test in mountain bike marathon
title_sort predictive ability of a comprehensive incremental test in mountain bike marathon
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786905/
https://www.ncbi.nlm.nih.gov/pubmed/29387445
http://dx.doi.org/10.1136/bmjsem-2017-000293
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