ArticleBMJ open sport & exercise medicine2025
Association between the use of daily injury risk estimation feedback (I-REF) based on machine learning techniques and injuries in athletics (track and field): results of a prospective cohort study over an athletics season.
Article in BMJ open sport & exercise medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of natural language processing and large language models in sports injury assessment and rehabilitation decision-making: a scoping review.Frontiers in medicine · 2026Pooled it
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- Effect of an unsupervised multidomain intervention integrating education, exercises, psychological techniques and machine learning feedback, on injury risk reduction in athletics (track and field): protocol of a randomised controlled trial (I-ReductAI).BMJ open sport & exercise medicine · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstractObjective: To analyse the association between the level of use of injury risk estimation feedback (I-REF) provided to athletes and the injury burden during an athletics season. Method: We conducted a prospective cohort study over a 38-week follow-up period on athletes competing at the French Federation of Athletics. Athletes completed daily questionnaires on their athletics activity, psychological state, sleep, self-reported level of I-REF use, and injuries. I-REF provided a daily estimation of the injury risk for the next day, ranging from 0% (no risk of injury) to 100% (maximum risk of injury). The primary outcome was the injury burden during the follow-up, defined as the number of days with injury per 1000 hours of athletics activity. A negative binomial regression model was used to analyse the association between self-reported I-REF use and the injury burden. Results: Of the 897 athletes who met the inclusion criteria, 112 (38% women) were included in the analysis. The mean daily response rate of the follow-up was 37%±30%. The primary analysis found no significant association between the self-reported I-REF use and the injury burden (n=112, Conclusions: Daily injury risk estimation feedback using machine learning was not associated with reducing injury burden.
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