Acceptability, Usability, and Insights Into Cybersickness Levels of a Novel Virtual Reality Environment for the Evaluation of Depressive Symptoms: Exploratory Observational Study
Bibliographic record
Abstract
BACKGROUND: There is a clear need for enhanced mental health assessment, depressive symptom (DS) evaluation being no exception. A promising approach to this aim is using virtual reality (VR), which entails the potential of adding a wider set of assessment domains with enhanced ecological validity. However, whilst several studies have used VR for both diagnostic and treatment purposes, its acceptance, in particular how exposure to virtual environments affects populations with psychiatric conditions remains unknown. OBJECTIVE: This study aims to report on the acceptability, usability, and cybersickness levels of a pilot VR environment designed for the purpose of differentiating between individuals with DSs. METHODS: The exploratory study, conducted in Italy, included 50 healthy controls and 50 young adults with mild-to-moderate DSs (without the need for a formal diagnosis). The study used an observational design with approximately 30 minutes of VR exposure followed by a self-report questionnaire battery. The battery included a questionnaire based on the Theoretical Framework of Acceptability, the System Usability Scale as well as the Simulator Sickness Questionnaire. RESULTS: Results indicate that the majority found VR acceptable for the purposes of mental health screening and treatment. However, for diagnostics, there was a clear preference for VR to be used by mental health professionals as a supplementary tool, as opposed to a stand-alone solution. In practice, following exposure to the pilot VR environment, generally, good levels of acceptability and usability were reported, but areas in need of improvement were identified (such as self-efficacy). Self-reported cybersickness levels were comparable to literature averages but were considerably higher among those with DSs. CONCLUSIONS: These findings raise questions about the potential interplay between underlying somatic symptoms of depression and VR-induced cybersickness and call for more attention from the scientific community both in terms of methodology as well as potential clinical and theoretical implications. Conclusively, user support indicates a potential for VR to aid mental health assessment, but further research is needed to understand how exposure to virtual environments might affect populations with varying severity and other forms of psychiatric symptoms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/ISRCTN16396369.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".