Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece.
Asteras Tripolis Football Club, Tripoli, Greece.
J Sports Sci Med. 2024 Sep 1;23(1):537-547. doi: 10.52082/jssm.2024.537. eCollection 2024 Sep.
Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.
运动损伤对运动员福利和团队动态造成重大挑战，尤其是在高强度运动如足球中。本研究使用机器学习算法评估 25 名首次遭受非接触性肌肉损伤的职业男性足球运动员的生理和机械负荷变量的非接触性损伤风险。在 4 年期间，记录了所有训练和正式比赛中的外部负荷（速度、距离和加速/减速数据）和内部负荷（心率）。在损伤前 28 天和相等长度的基线期内，为每个 9 个不同指标计算了机器学习模型训练和评估特征。通过最大与平均值的偏差来量化每个工作量指标的急性波动值，同时还计算了损伤前最后四周累积工作量的变化。模型选择了七个特征作为损伤发生率的显著估计器。其中三个特征涉及急性负荷偏差（冲刺次数、包含心率和肌肉负荷的训练负荷得分以及心率在最大的 90-100%时的时间）。四个累积负荷特征是（总距离、高速和冲刺跑距离以及训练负荷得分）。肌肉损伤风险评估模型的准确率为 0.78，灵敏度为 0.73，特异性为 0.85。我们的模型使用有限的训练负荷变量在损伤风险检测方面取得了优异的性能。首次在损伤风险评估模型中包含心率相关变量，突出了生理过载作为足球肌肉损伤的一个因素的重要性。通过确定重要参数，教练可以通过控制训练和比赛期间训练负荷的激增来预防肌肉损伤。
