Prediction of Sports Injuries by Mathematical Models

J. C. D. L. Cruz-Márquez, A. Cruz-Campos, J. C. D. L. Cruz-Campos, María Belén Cueto-Martín, M. García-Jiménez, M. T. Campos-Blasco
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引用次数: 5

Abstract

A number of different methodological approaches have been used to describe the inciting event for sports injuries. These include interviews of injured athletes, analysis of video recordings of actual injuries, clinical studies (clinical findings of joint damage are studied to understand the injury mechanism, mainly through plain radiography, magnetic resonance imaging, arthroscopy, and computed tomography scans), in vivo studies (ligament strain or forces are measured to understand ligament loading patterns), cadaver studies and simulation of injury situations, and measurement/estimation from "close to injury" situations. This chapter describes mathematical modeling approach and assesses its strengths and weaknesses in contributing to the understanding and prevention of sports injuries. This chapter demonstrates the relationship between structural measures and lower limb injuries. Sports injuries can affect any and all parts of the body depending on the particular repetitive movement performed just like any repetitive motion injury. While there are factors that raise the risk of injury, there are also elements that predispose athletes to sports injuries. Rehabilitation and preventative efforts should be centered on a thorough knowledge of risk factor etiology as well as knowledge of how such factors contribute to sports injuries. In most epidemiological studies directed toward identifying major sports injury causation factors, injured athletes have been compared with uninjured athletes through single variable techniques. However, many of the factors highlighted later in this paper through these analytical techniques either interact or are interrelated. Multivariable statistical techniques have also been used to detail risk factor interaction (Mechelen, 1992), such as discriminatory analyses and stepwise logistic regression (Dixon, 1993). In this chapter we will identify potential predictive factors that can be used in logistic regression equations, the basic concepts of this mathematical study, and equations that have been developed to what they are today.
用数学模型预测运动损伤
许多不同的方法方法已经被用来描述运动损伤的煽动事件。其中包括对受伤运动员的访谈、对实际损伤录像的分析、临床研究(研究关节损伤的临床表现,以了解损伤机制,主要通过x线平片、磁共振成像、关节镜检查和计算机断层扫描)、体内研究(测量韧带应变或受力,以了解韧带负荷模式)、尸体研究和损伤情况的模拟。以及“接近受伤”情况下的测量/估计。本章描述了数学建模方法,并评估了其优点和缺点,有助于理解和预防运动损伤。本章阐述了结构性措施与下肢损伤之间的关系。运动损伤可以影响身体的任何部位,这取决于特定的重复性运动,就像任何重复性运动损伤一样。虽然有一些因素会增加受伤的风险,但也有一些因素会使运动员容易受到运动损伤。康复和预防工作应该集中在对危险因素病因学的全面了解以及这些因素如何导致运动损伤的知识上。在大多数旨在确定主要运动损伤原因的流行病学研究中,通过单变量技术将受伤运动员与未受伤运动员进行比较。然而,本文后面通过这些分析技术强调的许多因素要么是相互作用的,要么是相互关联的。多变量统计技术也被用于详细描述风险因素的相互作用(Mechelen, 1992),如歧视性分析和逐步逻辑回归(Dixon, 1993)。在本章中,我们将确定可以在逻辑回归方程中使用的潜在预测因素,这一数学研究的基本概念,以及已经发展到今天的方程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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