Personalised sports rehabilitation analysis using a fitness enhanced model based on big data and deep learning

Introduction:
Health rehabilitation plays a vital role in contemporary healthcare in ensuring post-illness, post-injury, and post chronic condition recovery, and serving on maintaining or improving physical functions and overall quality of life and preventing long-term complications and conditions. Conventional approaches to rehabilitation have tended to involve off-the-shelf procedures and frequent clinical assessments.

Purpose:
Purpose of this study is to establish a modern health rehabilitation management model. This is through implementing the embedded technology and big data analytic in Fitness Qigong. The system incorporates wearable devices and sensors in the environment.

Results:
Physiologically, results indicated critical advances in physiological functioning following international training periods of Qigong training: increasing Heart Rate Variability (HRV) by 29.7%, decreasing breathing rate by 17.8%, improving movements efficiency by 20.3% and dropping the level of stress by 46.2% (all p < 0.001). Correlation analysis showed that improving HRV and moving with efficiency were strongly related to the intensity of practice (r = +0.84 and r = +0.90, respectively), whereas breathing rate showed a considerable negative relationship with practice intensity (r = -0.72). Comparison with Traditional Model In addition to that, the technology-enabled model exhibited further significant improvement, exceeding the traditional model increase by more than 200% HRV (+34.2% vs. +14.%) and breathing efficiency as well as adherence rates (91.5% vs. 65.8%).

Implications:
The implications of these findings emphasise the potential of technology-integrated Qigong practice to reliably, scalable and data-informed health management.

Keywords: Fitness Qigong; big data analytics; embedded technology; health management; physiological health; real-time monitoring; wearable devices.

PMID: 41020342 DOI: 10.1080/17483107.2025.2561926