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

Abstract We 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.

Authors

Institutions

Publication Details

Journal
Journal of the Royal Statistical Society Series C (Applied Statistics)
Published
2024-11-27
DOI
https://doi.org/10.1093/jrsssc/qlae059
Citations
1
Primary Topic
Sports injuries and prevention
Type
article
Field-Weighted Citation Impact
0.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Lore Zumeta‐Olaskoaga, Andreas Bender, Dae‐Jin Lee
1 citations
Journal of the Royal Statistical Society Series C (Applied Statistics)
Sports injuries and prevention
0.48
article

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

Lore Zumeta‐Olaskoaga, Andreas Bender, Dae‐Jin Lee
article en
1 citations

Abstract

Abstract We 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.

Journal of the Royal Statistical Society Series C (Applied Statistics)Vol. 74(2)
IE University (ES), University of the Basque Country (ES), Basque Center for Applied Mathematics (ES), Munich Center for Machine Learning, Ludwig-Maximilians-Universität München (DE)
Ministerio de Ciencia, Innovación y Universidades, Eusko Jaurlaritza, Basque Center for Applied Mathematics, Agencia Estatal de Investigación
Gender equality
Openalex Percentile: Top 34%
Sports injuries and prevention
0.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Flexible modelling of time-varying exposures and recurrent events to analyse training load effects in team sports injuries — Lore Zumeta‐Olaskoaga, Andreas Bender, et al. · Journal of the Royal Statistical Society Series C (Applied Statistics) (2024) | TGRS Research Map | TGRS