Preference of Virtual Reality Games in Psychological Pressure and Depression Treatment: Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) can be used to build many different scenes aimed at reducing study-related stress. However, only few academic experiments on university students for preference testing have been performed. OBJECTIVE: This study aims to assess the preference of VR games for stress and depression treatment using a discrete choice experiment (DCE). METHODS: A total of 5 different attributes were selected based on the depression therapy parameters and attributes related to VR: (1) treatment modality; (2) therapy duration; (3) perceived remission rate; (4) probability of adverse events; and the (5) monthly cost of adding treatment to a discrete choice experiment. By comparing different attributes and levels, we could draw some conclusions about the depression therapy testing preference for university students; 1 university student was responsible for VR scene development and 1 for participant recruitment. RESULTS: The utility value of different attributes for "0% Probability of adverse events" was higher than others (99.22), and the utility value of VR treatment as the most popular treatment method compared with counseling and medicine treatment was 80.95. Three parameter aspects (different treatments for depression) were statistically significant (P<.001), including "0%" and "50%" of "Probability of adverse events" and "¥500" (a currency exchange rate of ¥1 [Chinese yuan]=US $0.15 is applicable) of "The monthly cost of treatment." Most individuals preferred 12 months as the therapy duration, and the odds ratio of "12 months" was 1.095 (95% CI 0.945-1.270) when compared with the reference level (6 months). Meanwhile, the cheapest price (¥500) of depression therapy was the optimum choice for most students. CONCLUSIONS: People placed great preference on VR technology psychological intervention methods, which indicates that VR may have a potential market in the treatment of psychological problems. However, adverse events and treatment costs need to be considered. This study can be used to guide policies that are relevant to the development of the application of VR technology in the field of psychological pressure and depression treatment.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".