The Use of Virtual Reality in the Rehabilitation of Chronic Nonspecific Neck Pain
Bibliographic record
Abstract
OBJECTIVES: There has been a major interest in using virtual reality (VR) as a pain-management tool. This systematic review evaluated the literature on the use of VR in the treatment of chronic nonspecific neck pain (CNNP). METHODS: Electronic database searches were conducted in Cochrane, Medline, PubMed, Web of Science, Embase, and Scopus between inception and November 22, 2022. Search terms used were synonyms of "chronic neck pain" and "virtual reality." Inclusion criteria were as follows: chronic neck pain patients or pain lasting longer than 3 months; nonspecific neck pain; adult population; VR intervention; and functional and/or psychological outcomes. Study characteristics, quality, participant demographics, and results were independently extracted by 2 reviewers. RESULTS: VR interventions demonstrated significant improvement in patients experiencing CNNP. Scores in the visual analogue scale, the Neck Disability Index, and range of motion were significantly improved compared with baseline but not better than gold standard kinematic treatments. DISCUSSION: Our results suggest that VR is a promising tool for chronic pain management; however, there is a lack of VR intervention design consistency, objective outcome measures, follow-up reporting, and large sample sizes. Future research should focus on designing VR interventions to serve specific, individualized movement goals as well as combining quantifiable outcomes with existing self-report measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".