Personalized machine learning approach to injury monitoring in elite volleyball players
| Autor: | Stephan van der Zwaard, Arno Knobbe, Arie-Willem de Leeuw, Rick van Baar |
|---|---|
| Přispěvatelé: | Physiology, AMS - Musculoskeletal Health, AMS - Sports, Motor learning & Performance |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Adult
Cumulative Trauma Disorders 030209 endocrinology & metabolism Physical Therapy Sports Therapy and Rehabilitation Machine learning computer.software_genre Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine Injury prevention Injury risk Humans Orthopedics and Sports Medicine Training load Wearable technology Retrospective Studies biology business.industry Athletes Trauma research 030229 sport sciences General Medicine biology.organism_classification Volleyball Physical load Elite Athletic Injuries Artificial intelligence Psychology business computer |
| Zdroj: | European journal of sport science, 22(4), 511-520. Taylor and Francis Ltd. European Journal of Sport Science, 22(4), 511-520. Informa UK Limited de Leeuw, A-W, van der Zwaard, S, van Baar, R & Knobbe, A 2022, ' Personalized machine learning approach to injury monitoring in elite volleyball players ', European journal of sport science, vol. 22, no. 4, pp. 511-520 . https://doi.org/10.1080/17461391.2021.1887369 |
| ISSN: | 1746-1391 |
| DOI: | 10.1080/17461391.2021.1887369 |
| Popis: | We implemented a machine learning approach to investigate individual indicators of training load and wellness that may predict the emergence or development of overuse injuries in professional volleyball. In this retrospective study, we collected data of 14 elite volleyball players (mean ± SD age: 27 ± 3 years, weight: 90.5 ± 6.3 kg, height: 1.97 ± 0.07 m) during 24 weeks of the 2018 international season. Physical load was tracked by manually logging the performed physical activities and by capturing the jump load using wearable devices. On a daily basis, the athletes answered questions about their wellness, and overuse complaints were monitored via the Oslo Sports Trauma Research Center (OSTRC) questionnaire. Based on training load and wellness indicators, we identified subgroups of days with increased injury risk for each volleyball player using the machine learning technique Subgroup Discovery. For most players and facets of overuse injuries (such as reduced sports participation), we have identified personalized training load and wellness variables that are significantly related to overuse issues. We demonstrate that the emergence and development of overuse injuries can be better understood using daily monitoring, taking into account interactions between training load and wellness indicators, and by applying a personalized approach.Highlights With detailed, athlete-specific monitoring of overuse complaints and training load, practical insights in the development of overuse injuries can be obtained in a player-specific fashion contributing to injury prevention in sports.A multi-dimensional and personalized approach that includes interactions between training load variables significantly increases the understanding of overuse issues on a personal basis.Jump load is an important predictor for overuse injuries in volleyball. |
| Databáze: | OpenAIRE |
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