期刊论文详细信息
Frontiers in Psychology
A Field-Based Approach to Determine Soft Tissue Injury Risk in Elite Futsal Using Novel Machine Learning Techniques
Alejandro López-Valenciano1  José M. Puerta-Callejón2  Sergio Hernández-Sánchez3  Pilar Sainz de Baranda4  Iñaki Ruiz-Pérez5  Francisco Ayala6  Mark De Ste Croix7 
[1] Centre for Sport Studies, King Juan Carlos University, Madrid, Spain;Department of Computer Systems, University of Castilla-La Mancha, Albacete, Spain;Department of Pathology and Surgery, Physiotherapy Area, Miguel Hernandez University of Elche, Alicante, Spain;Department of Physical Activity and Sport, Faculty of Sports Sciences, University of Murcia, Murcia, Spain;Department of Sport Sciences, Sports Research Centre, Miguel Hernández University of Elche, Elche, Spain;Ramón y Cajal Postdoctoral Fellowship, Department of Physical Activity and Sport, Faculty of Sports Sciences, University of Murcia, Murcia, Spain;School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom;
关键词: injury prevention;    modeling;    screening;    decision-making;    algorithm;    decision tree;   
DOI  :  10.3389/fpsyg.2021.610210
来源: Frontiers
PDF
【 摘 要 】

Lower extremity non-contact soft tissue (LE-ST) injuries are prevalent in elite futsal. The purpose of this study was to develop robust screening models based on pre-season measures obtained from questionnaires and field-based tests to prospectively predict LE-ST injuries after having applied a range of supervised Machine Learning techniques. One hundred and thirty-nine elite futsal players underwent a pre-season screening evaluation that included individual characteristics; measures related to sleep quality, athlete burnout, psychological characteristics related to sport performance and self-reported perception of chronic ankle instability. A number of neuromuscular performance measures obtained through three field-based tests [isometric hip strength, dynamic postural control (Y-Balance) and lower extremity joints range of motion (ROM-Sport battery)] were also recorded. Injury incidence was monitored over one competitive season. There were 25 LE-ST injuries. Only those groups of measures from two of the field-based tests (ROM-Sport battery and Y-Balance), as independent data sets, were able to build robust models [area under the receiver operating characteristic curve (AUC) score ≥0.7] to identify elite futsal players at risk of sustaining a LE-ST injury. Unlike the measures obtained from the five questionnaires selected, the neuromuscular performance measures did build robust prediction models (AUC score ≥0.7). The inclusion in the same data set of the measures recorded from all the questionnaires and field-based tests did not result in models with significantly higher performance scores. The model generated by the UnderBagging technique with a cost-sensitive SMO as the base classifier and using only four ROM measures reported the best prediction performance scores (AUC = 0.767, true positive rate = 65.9% and true negative rate = 62%). The models developed might help coaches, physical trainers and medical practitioners in the decision-making process for injury prevention in futsal.

【 授权许可】

CC BY   

【 预 览 】
附件列表
Files Size Format View
RO202107168419538ZK.pdf 2292KB PDF download
  文献评价指标  
  下载次数:8次 浏览次数:1次