Article RETRACTED due to malpractice Risk Management in Diagnostic Wearable Devices for Sports Injury Prevention
DOI:
https://doi.org/10.47197/retos.v82.118085Palabras clave:
Risk Management, Wearable Devices, Sports InjuriesResumen
Abstract
Introduction: The present study focused on enhancing risk management for sports injuries in professional football players through the use of wearable diagnostic devices (WDDs). By integrating biomechanical and physiological monitoring, the research sought to identify key risk factors and develop predictive models for proactive injury prevention.
Objective: This study aimed to evaluate and optimize risk management strategies using WDDs to prevent injuries among professional football players, with a focus on analyzing workload ratios, movement dynamics, and physiological markers to predict and mitigate injury risks.
Methodology: Biomechanical and physiological data were gathered from 120 professional football players (aged 18–35 years) during an eight-week period. Monitored indicators encompassed the acute:chronic workload ratio (ACWR), deceleration rate (Dec_rate), peak acceleration (Acc_peak), skin temperature (Skin_temp), and heart rate variability index (HRV-RMSSD). Data analysis involved statistical approaches, including the Cox proportional hazards model, alongside machine learning techniques such as Random Forest, XGBoost, and LSTM.
Results: Injured players displayed markedly elevated ACWR (1.45 ± 0.28 vs. 1.17 ± 0.24; p = 0.001), higher skin temperature (37.2°C vs. 36.8°C; p = 0.009), and reduced HRV-RMSSD (28.9 ± 9.4 vs. 39.5 ± 10.1; p = 0.002) relative to non-injured counterparts. The Cox model pinpointed ACWR (HR = 2.29; p < 0.001) and Dec_rate (HR = 1.63; p = 0.002) as primary injury predictors, with HRV-RMSSD showing a protective role (HR = 0.76; p = 0.018). XGBoost outperformed other models, yielding 0.88 accuracy and 0.94 AUC. Sensitivity analysis revealed that elevating ACWR from 1.2 to 1.5 at 37.2°C skin temperature increased injury probability by ~2.3 times, indicating nonlinear interactions. A composite risk index achieved ~86% accuracy in detecting high-risk scenarios.
Conclusions: Concurrent tracking of mechanical and physiological parameters via WDDs enables robust injury risk prediction and management. These insights support the creation of intelligent wearable systems for real-time, proactive prevention of sports injuries in professional football.
Keywords: Risk Management, Wearable Devices, Sports Injuries, Biomechanical Data, Machine Learning
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