Understanding 'monitoring' data-the association between measured stressors and athlete responses within a holistic basketball performance framework.

Mercer, RAJ; Russell, JL; McGuigan, LC; Coutts, AJ; Strack, DS; McLean, BD

Understanding 'monitoring' data-the association between measured stressors and athlete responses within a holistic basketball performance framework.

Mercer, RAJ Russell, JL McGuigan, LC Coutts, AJ Strack, DS McLean, BD

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PLoS One, 2022, 17, (6), pp. e0270409
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Mercer, RAJ
dc.contributor.author	Russell, JL
dc.contributor.author	McGuigan, LC
dc.contributor.author	Coutts, AJ
dc.contributor.author	Strack, DS
dc.contributor.author	McLean, BD
dc.date.accessioned	2022-07-26T22:01:51Z
dc.date.available	2022-06-10
dc.date.available	2022-07-26T22:01:51Z
dc.date.issued	2022
dc.identifier.citation	PLoS One, 2022, 17, (6), pp. e0270409
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/159187
dc.description.abstract	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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation.ispartof	PLoS One
dc.relation.isbasedon	10.1371/journal.pone.0270409
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	Copyright: © 2022 Mercer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
dc.subject.classification	General Science & Technology
dc.subject.mesh	Adult
dc.subject.mesh	Athletes
dc.subject.mesh	Athletic Performance
dc.subject.mesh	Basketball
dc.subject.mesh	Fatigue
dc.subject.mesh	Humans
dc.subject.mesh	Retrospective Studies
dc.subject.mesh	Travel
dc.subject.mesh	Travel-Related Illness
dc.subject.mesh	Young Adult
dc.subject.mesh	Humans
dc.subject.mesh	Fatigue
dc.subject.mesh	Retrospective Studies
dc.subject.mesh	Basketball
dc.subject.mesh	Travel
dc.subject.mesh	Adult
dc.subject.mesh	Athletic Performance
dc.subject.mesh	Young Adult
dc.subject.mesh	Athletes
dc.subject.mesh	Travel-Related Illness
dc.title	Understanding 'monitoring' data-the association between measured stressors and athlete responses within a holistic basketball performance framework.
dc.type	Journal Article
utslib.citation.volume	17
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-07-26T22:01:48Z
pubs.issue	6
pubs.publication-status	Published online
pubs.volume	17
utslib.citation.issue	6

Abstract:

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.

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http://hdl.handle.net/10453/159187