Virtual Reality to Improve Pain Management and Mental Health in Stroke Survivors With Chronic Pain: Study Protocol for a Feasibility Randomized Controlled Trial on Virtual Reality-Acceptance and Commitment Therapy
Bibliographic record
Abstract
Background: Studies suggest that 40% to 65% of stroke survivors develop chronic poststroke pain (CPSP), which severely affects their quality of life and mental health. Empirical evidence suggests that existing treatments often fall short, underscoring the need for innovative, integrative interventions. Virtual reality (VR) seems to provide valuable tools in stroke rehabilitation. Also, contextual-behavioral psychological approaches, such as acceptance and commitment therapy (ACT), offer promising pain management and mental health resources, which seem to be feasible in VR formats. However, their combined application in CPSP remains unexplored. Objective: This study protocol describes the VR-ACT study, which will test the feasibility and preliminary efficacy of an 8-week VR-ACT program for CPSP. Methods: This pilot randomized controlled trial (N=30) will compare a VR-based ACT intervention with a sham VR control. The study will follow a mixed methods approach. Quantitative outcomes include pain intensity, psychological symptoms, and quality of life (via self-report measures), and brain network connectivity of the Triple Network (via functional magnetic resonance imaging). Feasibility will be evaluated through adherence, engagement, and acceptability. Qualitative feedback will be collected postintervention. Results: This study was funded by the Portuguese Foundation for Science and Technology in February 2025. Data collection is expected to start in December 2025 and end in June 2026. Results are expected to be published in the fall/winter of 2026/2027. Conclusions: This trial is expected to support the hypothesis that a VR-delivered ACT program is a feasible, acceptable, and potentially effective tool to support pain self-management and mental health in patients with CPSP, thereby laying the groundwork for larger multicenter trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".