Effectiveness of Virtual Reality-Based Interventions for Managing Chronic Pain on Pain Reduction, Anxiety, Depression and Mood: A Systematic Review
Bibliographic record
Abstract
(1) Background: Patients diagnosed with chronic pain suffer from long-term pain, which negatively affects their daily lives and mental health. Virtual reality (VR) technologies are considered a therapeutic tool to manage pain perception and mental health conditions. This systematic review aimed to appraise the efficacy of VR in improving pain intensity, anxiety, depression and mood among patients with chronic pain; (2) Methods: Five electronic databases were systematically searched using the terms representing VR and chronic pain. Quality assessment was conducted using Cochrane Collaboration's tool and Newcastle-Ottawa scale; (3) Results: Seventeen peer-reviewed articles were included in this review. It was found that VR was able to reduce pain intensity in patients with phantom limb pain, chronic headache, chronic neck pain and chronic low-back pain. The effects of VR on the improvement of anxiety, depression and mood were not determined due to the inadequate amount of clinical evidence; (4) Conclusions: VR, especially immersive VR, improves pain outcomes and its effects may vary depending on the approach and study design. More research is still needed to investigate the clinical use of VR in patients with chronic pain.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".