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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

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 repre...

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Autores principales: Dandrieux, Pierre-Eddy, Navarro, Laurent, Blanco, David, Ruffault, Alexis, Ley, Christophe, Bruneau, Antoine, Chapon, Joris, Hollander, Karsten, Edouard, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193054/
https://www.ncbi.nlm.nih.gov/pubmed/37192797
http://dx.doi.org/10.1136/bmjopen-2022-069423
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author Dandrieux, Pierre-Eddy
Navarro, Laurent
Blanco, David
Ruffault, Alexis
Ley, Christophe
Bruneau, Antoine
Chapon, Joris
Hollander, Karsten
Edouard, Pascal
author_facet Dandrieux, Pierre-Eddy
Navarro, Laurent
Blanco, David
Ruffault, Alexis
Ley, Christophe
Bruneau, Antoine
Chapon, Joris
Hollander, Karsten
Edouard, Pascal
author_sort Dandrieux, Pierre-Eddy
collection PubMed
description 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.
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spelling pubmed-101930542023-05-19 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 Dandrieux, Pierre-Eddy Navarro, Laurent Blanco, David Ruffault, Alexis Ley, Christophe Bruneau, Antoine Chapon, Joris Hollander, Karsten Edouard, Pascal BMJ Open Sports and Exercise Medicine 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. BMJ Publishing Group 2023-05-16 /pmc/articles/PMC10193054/ /pubmed/37192797 http://dx.doi.org/10.1136/bmjopen-2022-069423 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Sports and Exercise Medicine
Dandrieux, Pierre-Eddy
Navarro, Laurent
Blanco, David
Ruffault, Alexis
Ley, Christophe
Bruneau, Antoine
Chapon, Joris
Hollander, Karsten
Edouard, Pascal
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Sports and Exercise Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193054/
https://www.ncbi.nlm.nih.gov/pubmed/37192797
http://dx.doi.org/10.1136/bmjopen-2022-069423
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