Within applied sports science and medicine research, many challenges hinder the establishment and detailed understanding of athletic injury causality as well as the development and implementation of appropriate athletic injury prevention strategies. Applied research efforts are faced with a lack of variable control, while the capacity to compensate for this lack of control through the application of randomised controlled trials is often confronted by a number of obstacles relating to ethical or practical constraints. Such difficulties have led to a large reliance upon observational research to guide applied practice in this area. However, the reliance upon observational research, in conjunction with the general absence of supporting causal inference tools and structures, has hindered both the acquisition of causal knowledge in relation to athletic injury and the development of appropriate injury prevention strategies. Indeed, much of athletic injury research functions on a (causal) model-blind observational approach primarily driven by the existence and availability of various technologies and data, with little regard for how these technologies and their associated metrics can conceptually relate to athletic injury causality and mechanisms. In this article, a potential solution to these issues is proposed and a new model for investigating athletic injury aetiology and mechanisms, and for developing and evaluating injury prevention strategies, is presented. This solution is centred on the construction and utilisation of various causal diagrams, such as frameworks, models and causal directed acyclic graphs (DAGs), to help guide athletic injury research and prevention efforts. This approach will alleviate many of the challenges facing athletic injury research by facilitating the investigation of specific causal links, mechanisms and assumptions with appropriate scientific methods, aiding the translation of lab-based research into the applied sporting world, and guiding causal inferences from applied research efforts by establishing appropriate supporting causal structures. Further, this approach will also help guide the development and adoption of both relevant metrics (and technologies) and injury prevention strategies, as well as encourage the construction of appropriate theoretical and conceptual foundations prior to the commencement of applied injury research studies. This will help minimise the risk of resource wastage, data fishing, p-hacking and hypothesising after the results are known (HARK-ing) in athletic injury research.

在应用运动科学和医学研究中，许多挑战阻碍了对运动损伤因果关系的建立和详细理解，以及适当的运动损伤预防策略的开发和实施。应用研究工作面临着缺乏变量控制的问题，而通过应用随机对照试验来弥补这种控制缺乏的能力往往面临着许多与伦理或实践限制有关的障碍。这些困难导致很大程度上依赖观察研究来指导该领域的应用实践。然而，对观察性研究的依赖，加上普遍缺乏支持性因果推理工具和结构，阻碍了运动损伤相关因果知识的获取和适当损伤预防策略的制定。事实上，许多运动损伤研究都是基于（因果）模型盲观察方法，主要由各种技术和数据的存在和可用性驱动，很少考虑这些技术及其相关指标如何在概念上与运动损伤因果关系和相关性相关联。机制。在本文中，提出了这些问题的潜在解决方案，并提出了一种用于研究运动损伤病因和机制以及制定和评估损伤预防策略的新模型。该解决方案以各种因果图的构建和利用为中心，例如框架、模型和因果有向无环图 (DAG)，以帮助指导运动损伤研究和预防工作。这种方法将通过利用适当的科学方法促进对特定因果关系、机制和假设的调查，帮助将实验室研究转化为应用体育领域，并指导应用研究的因果推论，从而缓解运动损伤研究面临的许多挑战。通过建立适当的支持性因果结构来努力。此外，这种方法还将有助于指导相关指标（和技术）和伤害预防策略的开发和采用，并鼓励在开始应用伤害研究之前构建适当的理论和概念基础。这将有助于最大限度地减少运动损伤研究中资源浪费、数据钓鱼、p-hacking 和结果已知后假设 (HARK-ing) 的风险。