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Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework
This study examined associations between cumulative training load, travel demands and recovery days with athlete-reported outcome measures (AROMs) and countermovement jump (CMJ) performance in professional basketball. Retrospective analysis was performed on data collected from 23 players (mean±SD: a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231705/ https://www.ncbi.nlm.nih.gov/pubmed/35749466 http://dx.doi.org/10.1371/journal.pone.0270409 |
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author | Mercer, Richard A. J. Russell, Jennifer L. McGuigan, Lauren C. Coutts, Aaron J. Strack, Donnie S. McLean, Blake D. |
author_facet | Mercer, Richard A. J. Russell, Jennifer L. McGuigan, Lauren C. Coutts, Aaron J. Strack, Donnie S. McLean, Blake D. |
author_sort | Mercer, Richard A. J. |
collection | PubMed |
description | This study examined associations between cumulative training load, travel demands and recovery days with athlete-reported outcome measures (AROMs) and countermovement jump (CMJ) performance in professional basketball. Retrospective analysis was performed on data collected from 23 players (mean±SD: age = 24.7±2.5 years, height = 198.3±7.6 cm, body mass = 98.1±9.0 kg, wingspan = 206.8±8.4 cm) from 2018–2020 in the National Basketball Association G-League. Linear mixed models were used to describe variation in AROMs and CMJ data in relation to cumulative training load (previous 3- and 10-days), hours travelled (previous 3- and 10-day), days away from the team’s home city, recovery days (i.e., no travel/minimal on-court activity) and individual factors (e.g., age, fatigue, soreness). Cumulative 3-day training load had negative associations with fatigue, soreness, and sleep, while increased recovery days were associated with improved soreness scores. Increases in hours travelled and days spent away from home over 10 days were associated with increased sleep quality and duration. Cumulative training load over 3 and 10 days, hours travelled and days away from home city were all associated with changes in CMJ performance during the eccentric phase. The interaction of on-court and travel related stressors combined with individual factors is complex, meaning that multiple athletes response measures are needed to understand fatigue and recovery cycles. Our findings support the utility of the response measures presented (i.e., CMJ and AROMs), but this is not an exhaustive battery and practitioners should consider what measures may best inform training periodization within the context of their environment/sport. |
format | Online Article Text |
id | pubmed-9231705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92317052022-06-25 Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework Mercer, Richard A. J. Russell, Jennifer L. McGuigan, Lauren C. Coutts, Aaron J. Strack, Donnie S. McLean, Blake D. PLoS One Research Article This study examined associations between cumulative training load, travel demands and recovery days with athlete-reported outcome measures (AROMs) and countermovement jump (CMJ) performance in professional basketball. Retrospective analysis was performed on data collected from 23 players (mean±SD: age = 24.7±2.5 years, height = 198.3±7.6 cm, body mass = 98.1±9.0 kg, wingspan = 206.8±8.4 cm) from 2018–2020 in the National Basketball Association G-League. Linear mixed models were used to describe variation in AROMs and CMJ data in relation to cumulative training load (previous 3- and 10-days), hours travelled (previous 3- and 10-day), days away from the team’s home city, recovery days (i.e., no travel/minimal on-court activity) and individual factors (e.g., age, fatigue, soreness). Cumulative 3-day training load had negative associations with fatigue, soreness, and sleep, while increased recovery days were associated with improved soreness scores. Increases in hours travelled and days spent away from home over 10 days were associated with increased sleep quality and duration. Cumulative training load over 3 and 10 days, hours travelled and days away from home city were all associated with changes in CMJ performance during the eccentric phase. The interaction of on-court and travel related stressors combined with individual factors is complex, meaning that multiple athletes response measures are needed to understand fatigue and recovery cycles. Our findings support the utility of the response measures presented (i.e., CMJ and AROMs), but this is not an exhaustive battery and practitioners should consider what measures may best inform training periodization within the context of their environment/sport. Public Library of Science 2022-06-24 /pmc/articles/PMC9231705/ /pubmed/35749466 http://dx.doi.org/10.1371/journal.pone.0270409 Text en © 2022 Mercer et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mercer, Richard A. J. Russell, Jennifer L. McGuigan, Lauren C. Coutts, Aaron J. Strack, Donnie S. McLean, Blake D. Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title | Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title_full | Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title_fullStr | Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title_full_unstemmed | Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title_short | Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
title_sort | understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231705/ https://www.ncbi.nlm.nih.gov/pubmed/35749466 http://dx.doi.org/10.1371/journal.pone.0270409 |
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