Predicting Daily Recovery During Long-Term Endurance Training Using Machine Learning Analysis

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aut.relation.endpage3290
aut.relation.issue11
aut.relation.journalEuropean Journal of Applied Physiology
aut.relation.startpage3279
aut.relation.volume124
dc.contributor.authorRothschild, Jeffrey A
dc.contributor.authorStewart, Tom
dc.contributor.authorKilding, Andrew E
dc.contributor.authorPlews, Daniel J
dc.date.accessioned2024-11-11T23:53:48Z
dc.date.available2024-11-11T23:53:48Z
dc.date.issued2024-06-20
dc.description.abstractPURPOSE: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. METHODS: Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. RESULTS: Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5-23.6 and 0.05-0.44 for AM PRS and HRV change, respectively). CONCLUSION: At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.
dc.identifier.citationEuropean Journal of Applied Physiology, ISSN: 1439-6319 (Print); 1439-6327 (Online), Springer, 124(11), 3279-3290. doi: 10.1007/s00421-024-05530-2
dc.identifier.doi10.1007/s00421-024-05530-2
dc.identifier.issn1439-6319
dc.identifier.issn1439-6327
dc.identifier.urihttp://hdl.handle.net/10292/18278
dc.languageeng
dc.publisherSpringer
dc.relation.urihttps://link.springer.com/article/10.1007/s00421-024-05530-2
dc.rightsOpen 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.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCycling
dc.subjectHRV
dc.subjectNutrition
dc.subjectRunning
dc.subjectSleep
dc.subjectTraining load monitoring
dc.subjectTriathlon
dc.subjectCycling
dc.subjectHRV
dc.subjectNutrition
dc.subjectRunning
dc.subjectSleep
dc.subjectTraining load monitoring
dc.subjectTriathlon
dc.subject42 Health Sciences
dc.subject4207 Sports Science and Exercise
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectBrain Disorders
dc.subjectBehavioral and Social Science
dc.subjectPrevention
dc.subjectNutrition
dc.subjectCardiovascular
dc.subject1106 Human Movement and Sports Sciences
dc.subjectSport Sciences
dc.subject3202 Clinical sciences
dc.subject3208 Medical physiology
dc.subject4207 Sports science and exercise
dc.subject.meshHumans
dc.subject.meshMachine Learning
dc.subject.meshMale
dc.subject.meshHeart Rate
dc.subject.meshEndurance Training
dc.subject.meshAdult
dc.subject.meshFemale
dc.subject.meshPhysical Endurance
dc.subject.meshSleep
dc.subject.meshHumans
dc.subject.meshSleep
dc.subject.meshHeart Rate
dc.subject.meshPhysical Endurance
dc.subject.meshAdult
dc.subject.meshFemale
dc.subject.meshMale
dc.subject.meshMachine Learning
dc.subject.meshEndurance Training
dc.titlePredicting Daily Recovery During Long-Term Endurance Training Using Machine Learning Analysis
dc.typeJournal Article
pubs.elements-id558168
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