THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED PHYSICAL REHABILITATION OF ATHLETES AFTER COVID-19 INFECTION
Abstract
Athletes recovering from COVID-19 often face persistent symptoms such as reduced exercise capacity, fatigue, and muscle weakness, complicating their safe return to sport. Standard rehabilitation protocols may not adequately address these individualized recovery needs.
Objective: to evaluate the effectiveness of artificial intelligence (AI)–based personalized rehabilitation protocols compared to standard post-COVID-19 rehabilitation in athletes, using clinical outcomes and real-world sensor data.
Methods: a prospective, observational study included 200 competitive and recreational athletes (ages 18–40) from a variety of sports. Participants were randomized to receive either AI-powered personalized rehabilitation—driven by machine learning models and wearable device data—or standard, guideline-based protocols. Functional exercise capacity (Six-Minute Walk Test), fatigue severity, muscle strength, pulmonary function, return-to-play time, and quality of life were assessed at baseline, week 4, and week 8. Statistical analyses included repeated-measures ANOVA, multivariate regression, and ROC curve evaluation.
Results: by week 8, the AI-based group showed significantly greater improvements in functional exercise capacity (+74.5 m vs +51.3 m; p = 0.002), reduced fatigue (–2.3 vs –1.5 points; p = 0.01), higher muscle strength gains, and shorter return-to-play time (36.1 vs 43.5 days; p < 0.001). Subgroup analysis revealed endurance athletes achieved the greatest functional gains. ROC analysis confirmed high discriminatory performance of the AI protocol (AUC = 0.83) for early return-to-play.
Conclusion: aI-driven personalized rehabilitation provides superior clinical outcomes and accelerates recovery in athletes post–COVID-19, supporting its integration into modern sports medicine
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