de Leeuw Arie-Willem, van der Zwaard Stephan, van Baar Rick, Knobbe Arno
Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands.
Department of Human Movement Sciences, Faculty of Behavioral and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Eur J Sport Sci. 2022 Apr;22(4):511-520. doi: 10.1080/17461391.2021.1887369. Epub 2021 Feb 25.
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 ), 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. 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.
我们采用了一种机器学习方法来研究训练负荷和健康状况的个体指标，这些指标可能预测职业排球运动员过度使用损伤的出现或发展。在这项回顾性研究中，我们收集了14名精英排球运动员（平均±标准差年龄：27±3岁，体重：90.5±6.3千克，身高：1.97±0.07米）在2018年国际赛季24周期间的数据。通过手动记录所进行的体育活动以及使用可穿戴设备捕捉跳跃负荷来跟踪身体负荷。运动员每天回答有关其健康状况的问题，并通过奥斯陆体育创伤研究中心（OSTRC）问卷监测过度使用损伤的主诉。基于训练负荷和健康指标，我们使用机器学习技术“子群发现”为每位排球运动员确定了损伤风险增加的日子的子群。对于大多数运动员和过度使用损伤的方面（如 ），我们已经确定了与过度使用问题显著相关的个性化训练负荷和健康变量。我们证明，通过日常监测，考虑训练负荷和健康指标之间的相互作用，并采用个性化方法，可以更好地理解过度使用损伤的出现和发展。通过对过度使用损伤主诉和训练负荷进行详细的、针对运动员个体的监测，可以以运动员个体的方式获得有关过度使用损伤发展的实际见解，有助于预防运动损伤。一种包括训练负荷变量之间相互作用的多维个性化方法显著增加了对个人过度使用问题的理解。跳跃负荷是排球过度使用损伤的重要预测指标。
