Baize Diane, Mériaux-Scoffier Stéphanie, Massamba Anasthase, Hureau Thomas, Reneaud Nicolas, Garcia-Gimenez Yoann, Marchand Florian, Bontemps Bastien, Corcelle Baptiste, Maléjac Vincent, Jaafar Amyn, Ippoliti Emiliano, Payet Florian, Ajarai Iliès, d'Arripe-Longueville Fabienne, Piponnier Enzo
LAMHESS UPR 6312, Université Côte d'Azur, Nice, France.
CEERIPE UR 3072, Université de Strasbourg, Strasbourg, France.
Ann Med. 2025 Dec;57(1):2494683. doi: 10.1080/07853890.2025.2494683. Epub 2025 May 8.
Reducing the incidence of hamstring strain injuries (HSIs) is a priority for soccer clubs. However, robust multifactorial predictive models are lacking and potential predictors such as sprint kinematics, performance fatigability, and psychological variables have been overlooked. Thus, the aim of this study was to develop a preliminary parsimonious multifactorial model to predict players at risk of HSI through preseason screening.
Psychological, physiological, kinematic, performance fatigability and health-related variables were collected for 120 regional and national soccer players during the 2022 preseason. HSIs were prospectively recorded over the entire soccer season. After variable selection, logistic regressions with the Wald backward stepwise method were used to refine the model. The predictive abilities of the model and of the individual variables were determined using the area under the receiver operating characteristic curve (AUC).
Twenty-nine players sustained an HSI during the follow-up period. The final model included eight variables: age, sex, HSI history, knee flexor performance fatigability, sprint performance (best sprint time and maximal theoretical velocity V), perceived vulnerability to injury, and subjective norms in soccer. While its model was preliminary, it showed good fit indices and strong predictive performance (true positive rate: 79%, AUC = .82). None of the variables evaluated independently demonstrated satisfactory performance in predicting HSI (AUC≤.65).
Using a multidisciplinary approach and measurements of only a few variables during preseason screening, the current model tends to demonstrate high accuracy in identifying soccer players at risk of HSI.
降低腘绳肌拉伤损伤（HSIs）的发生率是足球俱乐部的首要任务。然而，目前缺乏强大的多因素预测模型，诸如短跑运动学、运动疲劳性和心理变量等潜在预测因素一直被忽视。因此，本研究的目的是通过季前筛查建立一个初步的简约多因素模型，以预测有HSIs风险的球员。
在2022年季前赛期间，收集了120名地区和国家级足球运动员的心理、生理、运动学、运动疲劳性和健康相关变量。在整个足球赛季中前瞻性记录HSIs情况。在变量选择之后，使用Wald向后逐步法的逻辑回归来优化模型。使用受试者工作特征曲线（AUC）下的面积来确定模型和各个变量的预测能力。
在随访期间，有29名球员发生了HSI。最终模型包括八个变量：年龄、性别、HSI病史、膝关节屈肌运动疲劳性、短跑成绩（最佳短跑时间和最大理论速度V）、感知到的受伤易感性以及足球运动中的主观规范。虽然该模型是初步的，但它显示出良好的拟合指数和强大的预测性能（真阳性率：79%，AUC = 0.82）。独立评估的变量中没有一个在预测HSI方面表现出令人满意的性能（AUC≤0.65）。
通过多学科方法并在季前筛查期间仅测量少数变量，当前模型在识别有HSIs风险的足球运动员方面往往表现出较高的准确性。
