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...
| Publicado en: | Journal of Sports Science & Medicine Vol. 22; no. 3; pp. 476 - 488 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Hakan Gur, Journal of Sports Science & Medicine
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172028607&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172028607 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13032968 FYN jtl: Journal of Sports Science & Medicine issn: 13032968 maglogo: N pubinfo: dt: Sep2023 vid: 22 iid: 3 pid: 26030 pub: Hakan Gur, Journal of Sports Science & Medicine artinfo: ui: 172028607 172028607 172028607 10.52082/jssm.2023.476 172028607 ppf: 476 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months. aug: au: Haller, Nils Kranzinger, Stefan Kranzinger, Christina Blumkaitis, Julia C. Strepp, Tilmann Simon, Perikles Tomaskovic, Aleksandar O'Brien, James Düring, Manfred Stöggl, Thomas affil: Department of Sport and Exercise Science, University of Salzburg, Salzburg, Austria sug: subj: Soccer Injuries Risk Factors Musculoskeletal Diseases Risk Factors Risk Assessment Early Diagnosis Musculoskeletal Diseases Diagnosis Athletes, Elite In Adolescence Machine Learning Health Behavior Evaluation Athletic Performance Evaluation Biological Markers Blood Athletic Training Biomechanics Human Adolescence Questionnaires Athletic Ability Neuromuscular Control Muscle, Skeletal Physiology Hamstring Muscles Physiology Descriptive Statistics Support Vector Machine Athletic Injuries Prevention and Control Inflammation Hormones Blood Exercise Test, Muscular Athletes, Male Male Cluster Analysis ROC Curve Data Analysis Software kappa Statistic Exercise Intensity Heart Rate Recovery Funding Source Adolescent: 13-18 years Male ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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