@article{ref1,
title="A field-based approach to determine soft tissue injury risk in elite futsal using novel machine learning techniques",
journal="Frontiers in psychology",
year="2021",
author="Ruiz-Pérez, Iñaki and López-Valenciano, Alejandro and Hernández-Sanchez, Sergio and Puerta-Callejón, José M. and De Ste Croix, Mark and Sainz de Baranda, Pilar and Ayala, Francisco",
volume="12",
number="",
pages="e610210-e610210",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1664-1078",
doi="10.3389/fpsyg.2021.610210",
url="http://dx.doi.org/10.3389/fpsyg.2021.610210"
}