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This randomized controlled trial will evaluate the effectiveness of a 12-week, home-based telerehabilitation program for adults with fibromyalgia (FM) combining AI-guided exercise with Pain Neuroscience Education (PNE). Fifty participants will be randomized (1:1) to the digital intervention or usual care. The primary outcome is change in pain intensity from baseline to post-intervention (week 13 ± 7 days), with secondary outcomes including physical function and health-related quality of life. Outcome assessors, the principal investigator, and the study statistician will be blinded to group allocation until database lock.
Full description
This randomized controlled trial aims to evaluate the effectiveness of a 12-week tele-rehabilitation program that integrates artificial intelligence (AI)-guided exercise, computer vision, and pain neuroscience education (PNE) for individuals with fibromyalgia (FM). The study seeks to determine whether this technology-driven approach can reduce pain intensity, improve physical function, and enhance quality of life in a population that frequently experiences limited benefit from conventional treatment strategies.
Intervention
Participants in the intervention group will use a tele-rehabilitation platform that applies AI and computer vision to deliver personalized exercise programs. The AI system monitors performance in real time and adapts exercises to individual capacities and progress. In parallel, participants will attend weekly sessions of pain neuroscience education, designed to improve their understanding of pain processing within the nervous system and to promote more effective self-management through cognitive and behavioral strategies.
The control group will receive standard care for fibromyalgia, which typically includes pharmacological management (e.g., analgesics, antidepressants, anticonvulsants) alongside general recommendations for physical activity and self-care. Comparison between the groups will allow assessment of the added value of combining AI-guided tele-rehabilitation with PNE.
Study Design Participants will be randomly assigned (1:1) to intervention or control group. The intervention will last 12 weeks, during which participants in the intervention group will complete three exercise sessions per week and one educational session weekly. All sessions will be conducted remotely through a user-friendly platform accessible via smartphones or computers. Outcome assessments will be conducted at baseline and immediately after completion of the 12-week program.
Masking: Participants and investigators will remain blinded to group allocation. Outcome assessors and the statistician will also remain blinded until database lock
Objectives
The primary objective is to determine whether the AI-guided exercise and PNE program reduces pain intensity and improves physical function compared with standard care. Secondary objectives include evaluating changes in quality of life, psychological well-being, and treatment adherence. Findings will provide evidence on whether technology-enabled rehabilitation can serve as an accessible, effective alternative for fibromyalgia management.
Rationale
Conventional therapies for fibromyalgia, such as pharmacological treatment and standard physical therapy, often provide suboptimal relief. Tele-rehabilitation represents a promising strategy to deliver personalized, supervised exercise at scale, particularly for patients in underserved or remote areas. When combined with pain neuroscience education, this approach addresses both physical and cognitive aspects of pain, with the potential to improve outcomes beyond current standards of care.
This study aligns with broader public health goals to improve access to effective interventions for chronic pain. Its results may inform future clinical guidelines and support the integration of AI-driven tele-rehabilitation into routine practice for fibromyalgia and related chronic pain conditions.
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50 participants in 2 patient groups
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Central trial contact
Romualdo Ordoñez-Vega, MsC; Marco A Morales-Osorio, PhD
Data sourced from clinicaltrials.gov
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