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
Objective 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...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2025-02-01
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| Series: | BMJ Open Sport & Exercise Medicine |
| Online Access: | https://bmjopensem.bmj.com/content/11/1/e002331.full |
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| author | Karsten Hollander David Blanco Pascal Edouard Laurent Navarro Antoine Bruneau Alexis Ruffault Joris Chapon Pierre-Eddy Dandrieux Christophe Ley Spyridon (Spyros) Iatropoulos |
| author_facet | Karsten Hollander David Blanco Pascal Edouard Laurent Navarro Antoine Bruneau Alexis Ruffault Joris Chapon Pierre-Eddy Dandrieux Christophe Ley Spyridon (Spyros) Iatropoulos |
| author_sort | Karsten Hollander |
| collection | DOAJ |
| description | Objective 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, eβ: 0.992, 95% CI: 0.977 to 1.007; p=0.308). However, when considering athletes’ daily response rate in secondary analysis, for a response rate of at least 9%, we observed a significant association between the self-reported level of I-REF use and the injury burden (n=76, eβ: 0.981, 95% CI: 0.965 to 0.998; p=0.027).Conclusions Daily injury risk estimation feedback using machine learning was not associated with reducing injury burden. |
| format | Article |
| id | doaj-art-0f063355165d4a4791b7b961c32f1c83 |
| institution | Kabale University |
| issn | 2055-7647 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Sport & Exercise Medicine |
| spelling | doaj-art-0f063355165d4a4791b7b961c32f1c832025-02-08T17:25:08ZengBMJ Publishing GroupBMJ Open Sport & Exercise Medicine2055-76472025-02-0111110.1136/bmjsem-2024-002331Association 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 seasonKarsten Hollander0David Blanco1Pascal Edouard2Laurent Navarro3Antoine Bruneau4Alexis Ruffault5Joris Chapon6Pierre-Eddy Dandrieux7Christophe Ley8Spyridon (Spyros) Iatropoulos95 Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg, Hamburg, Germany4 Physiotherapy Department, Universitat Internacional de Catalunya, Sant Cugat del Vallès, SpainEuropean Athletics Medical & Anti Doping Commission, European Athletics Association (EAA), Lausanne, SwitzerlandU 1059 Sainbiose, Centre CIS, Mines Saint-Etienne, Univ Lyon, Univ Jean Monnet, INSERM, Saint-Etienne, FranceFrench Athletics Federation, Paris, France5 Laboratory Sport, Expertise, and Performance (EA 7370), Institut National du Sport de l`Expertise et de la Performance, Paris, FranceInter-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, France1 Inter-university Laboratory of Human Movement Biology (EA 7424), Université Jean Monnet, Lyon 1, Université Savoie Mont-Blanc, Saint-Etienne, FranceDepartment of Mathematics, University of Luxembourg, Esch-sur-Alzette, Luxembourg1 Laboratoire Interuniversitaire de Biologie de la Motricité (EA 7424), Université Jean Monnet Saint-Etienne, Lyon 1, Université Savoie Mont-Blanc, Saint-Etienne, FranceObjective 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, eβ: 0.992, 95% CI: 0.977 to 1.007; p=0.308). However, when considering athletes’ daily response rate in secondary analysis, for a response rate of at least 9%, we observed a significant association between the self-reported level of I-REF use and the injury burden (n=76, eβ: 0.981, 95% CI: 0.965 to 0.998; p=0.027).Conclusions Daily injury risk estimation feedback using machine learning was not associated with reducing injury burden.https://bmjopensem.bmj.com/content/11/1/e002331.full |
| spellingShingle | Karsten Hollander David Blanco Pascal Edouard Laurent Navarro Antoine Bruneau Alexis Ruffault Joris Chapon Pierre-Eddy Dandrieux Christophe Ley Spyridon (Spyros) Iatropoulos 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 BMJ Open Sport & Exercise Medicine |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopensem.bmj.com/content/11/1/e002331.full |
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