Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries

dc.contributor.authorZumeta-Olaskoaga, Lore
dc.contributor.authorBender, Andreas
dc.contributor.authorLee, Dae Jin
dc.contributor.funderGobierno Vasco
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades
dc.contributor.funderAgencia Estatal de Investigación
dc.contributor.funderFondo Europeo de Desarrollo Regional
dc.contributor.funderEuropean Union
dc.contributor.funderDiputación Foral de Bizkaia
dc.contributor.rorhttps://ror.org/02jjdwm75
dc.date.accessioned2026-03-09T10:12:02Z
dc.date.issued2024-11-27
dc.description.abstractWe present a flexible modelling approach to analyse time-varying exposures and recurrent events in team sports injuries. The approach is based on the piece-wise exponential additive mixed model where the effects of past exposures (i.e. high-intensity training loads) may accumulate over time and present complex forms of association. In order to identify a relevant time window at which past exposures have an impact on the current risk, we propose a penalty approach. We conduct a simulation study to evaluate the performance of the proposed model, under different true weight functions and different levels of heterogeneity between recurrent events. Finally, we illustrate the approach with a case study application involving an elite male football team participating in the Spanish LaLiga competition. The cohort includes time-loss injuries and external training load variables tracked by Global Positioning System devices, during the seasons 2017–2018 and 2018–2019.
dc.description.peerreviewedYes
dc.description.sponsorshipThis work was supported by the Basque Government through the BERC 2022–2025 program; of the Ministry of Science, Innovation and Universities through BCAM Severo Ochoa accreditation and through SEV-2017-0718 PRE2018-084007 funding; of AEI/FEDER, UE through the PID2020-115882RB-I00 with acronym ‘S3M1P4R’ and PID2023-153222OB-I00 with acronym ‘SPHERES’; and of Provincial Council of Bizkaia through the 6/12/TT/2022/00006 and acronym ‘MATH4SPORTS’.
dc.description.statusPublished
dc.formatapplication/pdf
dc.identifier.citationZumeta-Olaskoaga, L., Bender, A., & Lee, D. J. (2025). Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries. Journal of the Royal Statistical Society Series C: Applied Statistics, 74(2), 391-405. https://doi.org/10.1093/jrsssc/qlae059
dc.identifier.doihttps://doi.org/10.1093/jrsssc/qlae059
dc.identifier.issn1467-9876
dc.identifier.officialurlhttps://academic.oup.com/jrsssc/article/74/2/391/7909398
dc.identifier.urihttps://hdl.handle.net/20.500.14417/4242
dc.issue.number2
dc.journal.titleJournal of the Royal Statistical Society, Series C (Applied Statistics)
dc.language.isoeng
dc.page.final405
dc.page.initial391
dc.page.total14
dc.publisherRoyal Statistical Society Oxford University Press
dc.relation.departmentSci Tech (Data Science)
dc.relation.entityIE University
dc.relation.projectidSEV-2017-0718
dc.relation.projectidPRE2018-084007
dc.relation.projectidPID2020-115882RB-I00
dc.relation.projectidPID2023-153222OB-I00
dc.relation.schoolIE School of Science & Technology
dc.rightsAttribution 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.keywordscumulative effect
dc.subject.keywordsfootball injuries
dc.subject.keywordsrecurrent events
dc.subject.keywordssports analytics
dc.subject.keywordssurvival analysis
dc.subject.keywordstime-varying exposures
dc.subject.odsODS 5 - Igualdad de género
dc.subject.unesco12 Matemáticas::1209 Estadística
dc.titleFlexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries
dc.typeinfo:eu-repo/semantics/article
dc.version.typeinfo:eu-repo/semantics/publishedVersion
dc.volume.number74
dspace.entity.typePublication
relation.isAuthorOfPublicationc8601ce9-af35-48fa-bdb6-9875f25e6c1f
relation.isAuthorOfPublication.latestForDiscoveryc8601ce9-af35-48fa-bdb6-9875f25e6c1f

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