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Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak

This study aimed to identify potential predictors of 200 m front crawl performance at the winter season peak based on the anthropometric, physiological and biomechanical domains. Twelve expert male swimmers completed an incremental 7 × 200 m step test immediately after their most important winter co...

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Detalles Bibliográficos
Autores principales: Costa, Mário J., Santos, Catarina C., Marinho, Daniel A., Silva, António J., Barbosa, Tiago M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142514/
https://www.ncbi.nlm.nih.gov/pubmed/32210037
http://dx.doi.org/10.3390/ijerph17062126
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author Costa, Mário J.
Santos, Catarina C.
Marinho, Daniel A.
Silva, António J.
Barbosa, Tiago M.
author_facet Costa, Mário J.
Santos, Catarina C.
Marinho, Daniel A.
Silva, António J.
Barbosa, Tiago M.
author_sort Costa, Mário J.
collection PubMed
description This study aimed to identify potential predictors of 200 m front crawl performance at the winter season peak based on the anthropometric, physiological and biomechanical domains. Twelve expert male swimmers completed an incremental 7 × 200 m step test immediately after their most important winter competitions. Measurements were made of: (i) height, body mass and arm span as anthropometrical parameters; (ii) velocity at a 4 mmol·L(−1) lactate concentration (V4), maximal oxygen uptake (VO(2máx)) and energy cost (C), as physiological parameters; (iii) stroke frequency (SF), stroke length (SL), stroke index (SI) and propelling efficiency (η(p)) as biomechanical indicators; and (iv) 200 m front crawl race time in official long course competitions. Spearman correlation coefficients identified V4 as the single factor having significant relationship with performance. Simple regression analysis determined V4, SI and arm span as the most relevant variables of each group. Multiple linear regression models showed that physiological factors explained better (59%) the variation in performance at this stage of the season, followed by the biomechanical (14%) ones. Therefore, V4 can be one important aspect for training control and diagnosis for those who want to achieve success in the 200 m front crawl at the winter season peak.
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spelling pubmed-71425142020-04-15 Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak Costa, Mário J. Santos, Catarina C. Marinho, Daniel A. Silva, António J. Barbosa, Tiago M. Int J Environ Res Public Health Article This study aimed to identify potential predictors of 200 m front crawl performance at the winter season peak based on the anthropometric, physiological and biomechanical domains. Twelve expert male swimmers completed an incremental 7 × 200 m step test immediately after their most important winter competitions. Measurements were made of: (i) height, body mass and arm span as anthropometrical parameters; (ii) velocity at a 4 mmol·L(−1) lactate concentration (V4), maximal oxygen uptake (VO(2máx)) and energy cost (C), as physiological parameters; (iii) stroke frequency (SF), stroke length (SL), stroke index (SI) and propelling efficiency (η(p)) as biomechanical indicators; and (iv) 200 m front crawl race time in official long course competitions. Spearman correlation coefficients identified V4 as the single factor having significant relationship with performance. Simple regression analysis determined V4, SI and arm span as the most relevant variables of each group. Multiple linear regression models showed that physiological factors explained better (59%) the variation in performance at this stage of the season, followed by the biomechanical (14%) ones. Therefore, V4 can be one important aspect for training control and diagnosis for those who want to achieve success in the 200 m front crawl at the winter season peak. MDPI 2020-03-23 2020-03 /pmc/articles/PMC7142514/ /pubmed/32210037 http://dx.doi.org/10.3390/ijerph17062126 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Costa, Mário J.
Santos, Catarina C.
Marinho, Daniel A.
Silva, António J.
Barbosa, Tiago M.
Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title_full Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title_fullStr Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title_full_unstemmed Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title_short Modelling the 200 m Front-Crawl Performance Predictors at the Winter Season Peak
title_sort modelling the 200 m front-crawl performance predictors at the winter season peak
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142514/
https://www.ncbi.nlm.nih.gov/pubmed/32210037
http://dx.doi.org/10.3390/ijerph17062126
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