Interest in and Predictors of Engagement With a Virtual Reality Intervention Among People With Chronic Pain: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Although chronic pain (CP) is highly prevalent, current modalities are not sufficient to address the needs of people living with this condition. Pharmacological treatments for CP can have severe side effects and increased likelihood of patients overdosing or developing addiction. Behavioral treatments are often indicated for the treatment of CP, but barriers to treatment are common. Virtual reality (VR)-based interventions have shown promise as an effective and potentially accessible form of treatment for CP. However, previous research on VR interventions for people living with CP has not often included diverse populations, including racial and ethnic minority groups and people with low socioeconomic status. OBJECTIVE: This study aimed to gauge the interest of patients with CP in participating in a hypothetical study of at-home VR for CP and to identify predictors of interest. Patients were recruited from a low socioeconomic and racially and ethnically diverse community. METHODS: A total of 48 participants living with CP were recruited from an electronic medical record database, a research participant database, and a pain clinic, and they completed surveys about demographics, pain levels, technology use, and knowledge of VR. Bivariate testing was used to determine which, if any, of the aforementioned variables were associated with interest in a hypothetical study of at-home VR for CP. Stepwise logistic regression models predicting interest were built based on bivariate testing. Finally, we used a thematic analysis framework to analyze an additional open-ended question about reasons for interest in participating in a VR intervention for CP. RESULTS: Despite low technology use and little knowledge and experience with VR, results showed high interest (42/48, 88%) among patients in participating in a hypothetical study of at-home VR for CP. More frequent email use and using Facebook demonstrated nonsignificant trends toward interest in participating in a VR clinical trial for pain (P=.06 for email use and P=.06 for Facebook use). In stepwise multivariate models controlling for pain score, Facebook use was predictive of being somewhat or very interested in participating in a VR clinical trial for pain (P=.047). Open-ended responses tended to cite the novelty of VR and desperation for pain relief as reasons for participants' interest. CONCLUSIONS: We found high interest in participating in a clinical trial of VR despite low use of technology and low knowledge of VR. Future fully powered studies should seek to confirm the effectiveness of VR treatments for people with CP, especially people from lower socioeconomic, and racially and ethnically diverse backgrounds.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".