[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"athletic-injury\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:athletic-injury":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":20,"enrollmentInfo":21,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":25,"conditions":26,"keywords":29,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":5},"100659964","multimodal-machine-learning-for-predicting-reduced-participation-in-elite-youth-football-100659964",false,"NCT07870460","Multimodal Machine Learning for Predicting Reduced Participation in Elite Youth Football","Machine Learning-Based Short-Term Prediction of Reduced Participation Within the Competitive Microcycle in Elite Youth Football Players Using Multimodal Data","PREDICT-YOUTH","Inclusion Criteria:\n\n* Registered player in the U16 or U19 squad of an elite football academy (SK Sigma Olomouc).\n* At least three years of structured football training within an organized academy programme.\n* Regular active participation in microcycle team training and competitive league matches.\n* Written informed assent by the player and written informed consent by a parent or legal guardian (for minor participants).\n* Monitoring compliance rate \\> 80% during the initial 14-day passive run-in period.\n\nExclusion Criteria:\n\n* Pre-existing severe musculoskeletal injury, orthopedic surgery, or neurological disorder preventing full participation at baseline.\n* Known cardiovascular, metabolic, or systemic disease contraindicating maximal athletic exertion.\n* Inability or refusal to wear continuous monitoring sensors (Catapult GNSS unit during training, WHOOP strap during sleep).\n* Prolonged absence from the squad due to non-medical administrative reasons (transfer, academic leave, disciplinary suspension).",true,"MALE","15 Years","19 Years",{"count":22,"type":23},30,"ESTIMATED","OBSERVATIONAL","This study protocol describes a prospective longitudinal observational study designed to evaluate the accuracy of machine learning models in predicting the short-term (24-72 h) risk of reduced participation (training modification or absence due to health complaints, injury, or illness) among elite youth football players (categories U16 to U19).\n\nFull participation in training and matches is a key determinant of athletic development and squad availability in elite youth football. Conventional injury surveillance typically reduces athlete availability to a binary time-loss state and overlooks the continuum on which athletes remain active despite emerging fatigue, minor musculoskeletal complaints, or functional limitations.\n\nIn this study, continuous multimodal monitoring integrates external training and match locomotion (Catapult Vector 10 Hz GNSS \u002F 100 Hz IMU), nocturnal autonomic nervous regulation (WHOOP 5.0 wrist-worn photoplethysmography evaluating resting heart rate, heart rate variability RMSSD, and sleep architecture), daily subjective wellness (AMS Hooper Index: sleep quality, stress, fatigue, mood, and muscle soreness), weekly neuromuscular screening (VALD ForceDecks countermovement jumps and NordBord hamstring strength), and anthropometrics with biological maturation offset.\n\nParticipation status is adjudicated daily by designated coaching and medical staff. The primary objective is to develop, compare, and internally validate short-term prediction models for the onset transition from full participation to reduced participation, formulated as a single one-step discrete-time hazard read coherently across 24-, 48-, and 72-hour operational horizons. The study aims to evaluate explainable machine learning models (Survival XGBoost, GAMM with SHAP\u002FLIME interpretation) to support training load management and injury risk mitigation within elite academies.",[27,28],"Athletic Injury","Fatigue Recovery",[30,31,32,33,34,35,36,37],"youth footbal","injury prevention","machine learning","multimodal monitoring","catapult GPS","WHOOP HRV","VALD dynamometry","reduced participation","RECRUITING","2026-10-06",{"date":41,"type":42},"2026-10-13","ACTUAL",{"date":44,"type":42},"2026-07-15",{"date":46,"type":23},"2028-06-30",{"name":48,"class":49},"Palacky University","OTHER"]