ArticleBMJ open sport & exercise medicine2022
Assessing the cumulative effect of long-term training load on the risk of injury in team sports.
Article in BMJ open sport & exercise medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.Journal of sports science & medicine · 2026Pooled it
- Data-Driven External-Load Analytics: Integrating Cluster Analysis and ACWR Monitoring in Elite Handball.Journal of human kinetics · 2026Article
- Time-Varying Associations Between Physical Activity and Injury Risk Among Children.Paediatric and perinatal epidemiology · 2026Article
- Incidence and risk factors of spondylolysis in youth football: a longitudinal study among male Norwegian players.BMJ open sport & exercise medicine · 2026Article
- Dose-response and time-lagged effect of daily training load on athlete well-being during an international rugby series.Biology of sport · 2025Article
- Physical, psychosocial and dual-career loads as risk factors for injuries and illnesses in elite handball players: a 45-week prospective cohort study.Frontiers in sports and active living · 2025Article
- A new statistical approach to training load and injury risk: separating the acute from the chronic load.Biology of sport · 2024Article
- Integrating Deloading into Strength and Physique Sports Training Programmes: An International Delphi Consensus Approach.Sports medicine - open · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports. Methods: First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice. Results: DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure. Conclusion: DLNM can be used to assess the cumulative effect of training load on injury risk.
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