Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season

Abstract

IntroductionTwo-thirds of athletes (65%) have at least one injury complaint leading to participation restriction (ICPR) in athletics (track and field) during one season. The emerging practice of medicine and public health supported by electronic processes and communication in sports medicine represents an opportunity for developing new injury risk reduction strategies. Modelling and predicting the risk of injury in real-time through artificial intelligence using machine learning techniques might represent an innovative injury risk reduction strategy. Thus, the primary aim of this study will be to analyse the relationship between the level ofInjuryRiskEstimationFeedback (I-REF) use (average score of athletes’ self-declared level of I-REF consideration for their athletics activity) and the ICPR burden during an athletics season.Method and analysisWe will conduct a prospective cohort study, calledInjuryPrediction withArtificialIntelligence (IPredict-AI), over one 38-week athletics season (from September 2022 to July 2023) involving competitive athletics athletes licensed with theFrenchFederation ofAthletics. All athletes will be asked to complete daily questionnaires on their athletics activity, their psychological state, their sleep, the level of I-REF use and any ICPR. I-REF will present a daily estimation of the ICPR risk ranging from 0% (no risk for injury) to 100% (maximal risk for injury) for the following day. All athletes will be free to see I-REF and to adapt their athletics activity according to I-REF. The primary outcome will be the ICPR burden over the follow-up (over an athletics season), defined as the number of days lost from training and/or competition due to ICPR per 1000 hours of athletics activity. The relationship between ICPR burden and the level of I-REF use will be explored by using linear regression models.Ethics and disseminationThis prospective cohort study was reviewed and approved by the Saint-Etienne University Hospital Ethical Committee (Institutional Review Board: IORG0007394, IRBN1062022/CHUSTE). Results of the study will be disseminated in peer-reviewed journals and in international scientific congresses, as well as to the included participants.

Relationship exercise motivation levels on quality of life during retirement from elite sport

“…Second, the loss of training motivation in retired athletes is also caused by injuries suffered while they were athletes (Pillay et al, 2023). Every athlete experiences at least one injury during training or a match (Dandrieux et al, 2023). The injuries experienced will result in trauma that often results in the impact until retirement from competitive sports (Vasenina et al, 2023).…”