Effectiveness of tele-rehabilitation using AI-guided exercise and pain neuroscience education for fibromyalgia (FIBROIA): Protocol for a randomized controlled trial.

Abstract

Fibromyalgia (FM) is a chronic condition characterized by widespread pain and cognitive dysfunction, with pharmacological treatments offering limited efficacy. Although Pain Neuroscience Education (PNE) and therapeutic exercise are evidence-based interventions, accessibility and adherence remain major challenges particularly in underserved regions such as Latin America. This trial investigates the effectiveness of a 12-week tele-rehabilitation program (FIBROIA) that integrates Artificial Intelligence (AI)-guided exercise with PNE to enhance access to comprehensive multimodal care. This multicentre, randomized, assessor-blinded, parallel-group controlled trial will enroll fifty adults meeting the 2016 ACR criteria for FM. Participants will be randomly assigned (1:1) to either the intervention group or enhanced usual care. The intervention consists of three personalized exercise sessions per week delivered through the Rehbody AI platform, which provides real-time biomechanical feedback, along with a weekly PNE module designed to reconceptualize pain. The primary outcome is the change in pain intensity, measured using the Visual Analogue Scale (VAS), at week 13 (±7 days), immediately following completion of the 12-week program. Secondary outcomes include the Fibromyalgia Impact Questionnaire-Revised (FIQ-R), lower-limb strength assessed by the 30-Second Sit-to-Stand test, and health-related quality of life measured with the EQ-5D-3L. Statistical analyses will follow an intention-to-treat (ITT) framework. The FIBROIA protocol addresses the urgent need for scalable, evidence-based interventions in resource-limited settings. By combining AI-driven biomechanical feedback with cognitive reappraisal through PNE, this study seeks to reduce fear-avoidance behaviors and improve exercise adherence. This integrative approach aims to overcome the limitations of passive tele-rehabilitation by simulating asynchronous professional supervision, thereby ensuring both safety and technical precision in movement execution. If effective, this protocol will offer a robust, technology-enabled framework for remote FM management. The results could help establish a new clinical standard for accessible, patient-centered rehabilitation, bridging the gap between high-level evidence and real-world practice across diverse socioeconomic contexts. ClinicalTrials.gov NCT06672419.