背景:在手球中,额平面的运动学似乎是下肢损伤发展的最重要因素之一。膝关节外翻角度是预防伤害的基本轴,通常使用2D系统进行测量,例如Kinovea软件(版本0.9.4。).计算机视觉等技术进步有可能彻底改变运动医学。然而,在临床实践中使用计算机视觉之前,必须评估计算机视觉的有效性和可靠性。这项研究的目的是分析基于计算机视觉的Beta版应用程序的重测和评分者间可靠性以及并发有效性,以测量精英手球运动员的膝盖外翻角度。
方法:对42名优秀手球运动员的膝关节外翻角度进行测量。拍摄了单腿蹲下时的正面照片,两名考官在基线和一周随访时通过基于计算机视觉的beta应用程序测量角度,以计算测试重测和评估者间的可靠性。第三位检查者使用2DKinovea软件评估膝关节外翻角度以计算并发有效性。
结果:优秀手球运动员膝关节外翻角度为158.54±5.22°。两位考官的重测可靠性都很好,显示出类内相关系数(ICC)为0.859-0.933。评估者间的可靠性显示出中等的ICC:0.658(0.354-0.819)。应用测量的标准误差在1.69°和3.50°之间,最小可检测变化在4.68°和9.70°之间。并行效度很强,r=0.931;p<0.001。
结论:与Kinovea软件相比,基于计算机的智能手机应用程序在测量膝关节外翻角度方面表现出出色的重测和评分者间可靠性以及强大的并发有效性。
BACKGROUND: In handball, the kinematics of the frontal plane seem to be one of the most important factors for the development of lower limb injuries. The knee valgus angle is a fundamental axis for injury prevention and is usually measured with 2D systems such as Kinovea software (Version 0.9.4.). Technological advances such as computer vision have the potential to revolutionize sports medicine. However, the validity and reliability of computer vision must be evaluated before using it in clinical practice. The aim of this
study was to analyze the test-retest and inter-rater reliability and the concurrent validity of a beta version app based on computer vision for the measurement of knee valgus angle in elite handball athletes.
METHODS: The knee valgus angle of 42 elite handball athletes was measured. A frontal photo during a single-leg squat was taken, and two examiners measured the angle by the beta application based on computer vision at baseline and at one-week follow-up to calculate the test-retest and inter-rater reliability. A third examiner assessed the knee valgus angle using 2D Kinovea software to calculate the concurrent validity.
RESULTS: The knee valgus angle in the elite handball athletes was 158.54 ± 5.22°. The test-retest reliability for both examiners was excellent, showing an Intraclass Correlation Coefficient (ICC) of 0.859-0.933. The inter-rater reliability showed a moderate ICC: 0.658 (0.354-0.819). The standard error of the measurement with the app was stated between 1.69° and 3.50°, and the minimum detectable change was stated between 4.68° and 9.70°. The concurrent validity was strong r = 0.931; p < 0.001.
CONCLUSIONS: The computer-based smartphone app showed an excellent test-retest and inter-rater reliability and a strong concurrent validity compared to Kinovea software for the measurement of the knee valgus angle.