用机器学习预测精英青少年足球的损伤和疾病:3个月的综合监测方法。

IF 2.4 2区 医学 Q2 SPORT SCIENCES Journal of Sports Science and Medicine Pub Date : 2023-09-01 DOI:10.52082/jssm.2023.475
Nils Haller, S. Kranzinger, C. Kranzinger, Julia C. Blumkaitis, Tilmann Strepp, P. Simon, Aleksandar Tomaskovic, J. O’Brien, Manfred Düring, T. Stöggl
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引用次数: 0

摘要

寻找能够提供伤病早期迹象的监测工具有助于更好地保护球员。本研究的目的是i)确定我们监测方法的可行性和依从性,ii)确定与即将发生的疾病和损伤相关的变量。我们采用了一套全面的监测工具,包括外部负荷和身体健康数据、问卷调查、血液、神经肌肉、腿筋、髋关节外展肌和髋关节内收肌性能测试,这些测试在三个月的时间里对18岁以下的精英学院足球运动员进行了测试。25名运动员(年龄16.6±0.9岁,身高178±7 cm,体重74±7 kg,最大摄氧量59±4 ml/min/kg)参加研究。除了评估监测方法的依从性外,还使用线性支持向量机(SVM)分析数据以预测疾病和损伤。该方法是可行的,没有因监测过程而受伤或辍学。调查问卷的依从性在开始时很高,在研究结束时逐渐下降。在疾病预测、疾病判定和损伤预测三个分类任务中,SVM的分类结果最好。对于损伤预测,测试数据集中的四种损伤中有一种被检测到,96.3%的数据点(即受伤和非受伤)被正确检测到。对于疾病预测和确定,线性支持向量机在测试数据集中只检测到一种疾病。然而,该模型对损伤和疾病的预测精度较低,有相当多的假阳性。结果表明,整体监测方法具有预测疾病和损伤的可能性。需要额外的数据点来改进预测模型。在实际应用中,这可能会导致对何时应该保护球员免受伤害和疾病的过度谨慎的建议。
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Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months.
The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.
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来源期刊
CiteScore
5.60
自引率
6.20%
发文量
56
审稿时长
4-8 weeks
期刊介绍: The Journal of Sports Science and Medicine (JSSM) is a non-profit making scientific electronic journal, publishing research and review articles, together with case studies, in the fields of sports medicine and the exercise sciences. JSSM is published quarterly in March, June, September and December. JSSM also publishes editorials, a "letter to the editor" section, abstracts from international and national congresses, panel meetings, conferences and symposia, and can function as an open discussion forum on significant issues of current interest.
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