The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions.

Coyne, JOC; Coutts, AJ; Newton, RU; Haff, GG

The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions.

Coyne, JOC Coutts, AJ Newton, RU Haff, GG

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Sports Med Open, 2022, 8, (1), pp. 53
Issue Date:: 2022-04-15

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Field	Value	Language
dc.contributor.author	Coyne, JOC
dc.contributor.author	Coutts, AJ
dc.contributor.author	Newton, RU
dc.contributor.author	Haff, GG
dc.date.accessioned	2022-07-26T22:54:34Z
dc.date.available	2022-03-13
dc.date.available	2022-07-26T22:54:34Z
dc.date.issued	2022-04-15
dc.identifier.citation	Sports Med Open, 2022, 8, (1), pp. 53
dc.identifier.issn	2199-1170
dc.identifier.issn	2198-9761
dc.identifier.uri	http://hdl.handle.net/10453/159188
dc.description.abstract	This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.
dc.format	Electronic
dc.language	eng
dc.publisher	SPRINGER
dc.relation.ispartof	Sports Med Open
dc.relation.isbasedon	10.1186/s40798-022-00433-y
dc.rights	© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
dc.rights	info:eu-repo/semantics/openAccess
dc.title	The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions.
dc.type	Journal Article
utslib.citation.volume	8
utslib.location.activity	Switzerland
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	2022-07-26T22:54:30Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	8
utslib.citation.issue	1

Abstract:

This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.

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http://hdl.handle.net/10453/159188