Ruddy Joshua D, Cormack Stuart J, Whiteley Rod, Williams Morgan D, Timmins Ryan G, Opar David A
School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia.
Aspetar Orthopaedic and Sports Medicine Hospital, Doha, Qatar.
Front Physiol. 2019 Jul 9;10:829. doi: 10.3389/fphys.2019.00829. eCollection 2019.
Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries.
在团队运动中，受伤是常有的事，会给运动员及其体育组织带来重大的经济、身体和心理后果。因此，大量研究试图找出与受伤风险相关的因素，这在制定预防受伤和降低风险策略时很重要。有多种方法可用于识别受伤风险因素。然而，理解不同统计方法之间的细微差别存在困难，可能导致从数据中得出错误的推断和决策。因此，本叙述性综述旨在：（1）概述确定受伤风险的常用方法；（2）强调与受伤相关的关联和预测之间的差异；（3）描述统计建模的进展以及当前与预测运动中受伤相关的证据。基于本叙述性综述中讨论的要点，研究人员和从业者都需要仔细考虑与受伤风险相关的不同类型变量，以及如何解释与这些不同变量相关的分析。在对受伤风险进行建模时，还有许多其他重要的考虑因素，如数据转换方法、模型验证和性能评估。考虑到这些技术因素，研究人员和从业者应考虑将他们对受伤病因的观点从还原论转变为复杂性观点。同时，应利用采用还原论方法的研究为识别受伤风险的复杂方法提供信息并加以实施。然而，目前体育损伤研究的一个局限性是难以获取大量的受伤数据，因此有人呼吁向研究人员提供数据，以便分析和结果能够被复制和验证。这样的合作努力将有助于防止从虚假数据中得出错误的推断，并有助于开发基于合理科学依据的干预措施。这些努力将朝着提高识别受伤风险能力的正确方向迈出一步，这反过来将有助于改善风险降低，最终预防受伤。
