Precision Rehabilitation in Sports Physiotherapy: Leveraging Performance Metrics and Emerging Technologies

Abstract

Background:

Advances in technology have transformed sports physiotherapy into a data-driven discipline. Quantifying athlete performance through objective metrics enhances precision in rehabilitation, return-to-sport, and injury prevention.

Methods:

A narrative review of studies published between 2015 and 2025 was conducted using PubMed, Scopus, and Google Scholar. Inclusion criteria targeted research involving biomechanical, physiological, and psychological metrics in conjunction with wearable sensors, electromyography (EMG), motion-capture systems, artificial intelligence (AI), or virtual-reality (VR) tools. Data from 68 peer-reviewed articles were synthesized.

Results:

Across the literature, external-load metrics such as ground-reaction force (average 2.8 ± 0.4 N·kg⁻¹) and peak-power output (up to 850 W in elite athletes) were key indicators of lower-limb performance. Internal-load variables included heart-rate variability (mean SDNN 72 ± 14 ms), muscle-activation symmetry via surface EMG (bilateral ratio > 90 %), and oxygen consumption (VO₂max 48–60 mL·kg⁻¹·min⁻¹). Integration of IMU-based wearables and AI-assisted analytics improved motion-tracking accuracy by 12–18 % compared to manual assessment. VR-assisted rehabilitation demonstrated a 22 % increase in exercise adherence and faster functional recovery.

Conclusion:

Quantitative performance metrics supported by wearable and intelligent technologies enable physiotherapists to deliver personalized, objective, and efficient rehabilitation. Future directions include standardizing digital protocols and expanding clinician competence in data analytics to bridge technology with holistic care.