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.
EPIDEMIOLOGY; PUBLIC HEALTH; SPORTS MEDICINE; Humans; Prospective Studies; Artificial Intelligence; Feedback; Seasons; Machine Learning; Track and Field; Athletic Injuries/epidemiology; Athletic Injuries; Medicine (all); General Medicine
Résumé :
[en] [en] INTRODUCTION: Two-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 of Injury Risk Estimation Feedback (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 ANALYSIS: We will conduct a prospective cohort study, called Injury Prediction with Artificial Intelligence (IPredict-AI), over one 38-week athletics season (from September 2022 to July 2023) involving competitive athletics athletes licensed with the French Federation of Athletics. 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 DISSEMINATION: This 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.
Disciplines :
Mathématiques Physique, chimie, mathématiques & sciences de la terre: Multidisciplinaire, généralités & autres Orthopédie, rééducation & médecine sportive
Auteur, co-auteur :
Dandrieux, Pierre-Eddy ; Inter-university Laboratory of Human Movement Biology, EA 7424, F-42023, Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Etienne, Auvergne-Rhône-Alpes, France pierre.eddy.dandrieux@univ-st-etienne.fr ; Centre CIS, F-42023, Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, INSERM, U 1059 Sainbiose, Saint-Etienne, Auvergne-Rhône-Alpes, France
Navarro, Laurent ; Centre CIS, F-42023, Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, INSERM, U 1059 Sainbiose, Saint-Etienne, Auvergne-Rhône-Alpes, France
Blanco, David ; Physiotherapy Department, Universitat Internacional de Catalunya, Barcelona, Catalunya, Spain
Ruffault, Alexis ; Laboratory Sport, Expertise, and Performance (EA 7370), French Institute of Sport (INSEP), Paris, France ; Unité de Recherche interfacultaire Santé & Société (URiSS), Université de Liège, Liege, Belgium
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH)
Bruneau, Antoine; French Athletics Federation, Paris, France
Chapon, Joris ; Inter-university Laboratory of Human Movement Biology, EA 7424, F-42023, Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Etienne, Auvergne-Rhône-Alpes, France
Hollander, Karsten ; Institute of Interdisciplinary Exercise Science and Sports Medicine, Medical School Hamburg, Hamburg, Germany
Edouard, Pascal ; Inter-university Laboratory of Human Movement Biology, EA 7424, F-42023, Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Etienne, Auvergne-Rhône-Alpes, France ; Department of Clinical and Exercise Physiology, Sports Medicine Unit, University Hospital of Saint-Etienne, Faculty of Medicine, Saint-Etienne, Auvergne-Rhône-Alpes, France
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
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.
The software for data collection has been developed by Mines Saint-Etienne. This research is part of a doctoral scholarship funded by the University of Lyon, UJM-Saint-Etienne, Saint Etienne, France. Also, this research was funded by the Ministerio de Ciencia e Innovación (Spain) (PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033). These funding sources had no role in the design of this study and will not have any role during its execution, analysis, interpretation of the data or decision to submit results. The University Hospital of Saint-Etienne has promoted this study.
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