Designing a Smartphone-Based Virtual Reality App for Relaxation: Qualitative Crossover Study
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence supports the use of virtual reality (VR) in mental health care, with one potential application being its use to assist individuals with relaxation exercises. Despite studies finding support for the potential of VR to effectively aid in relaxation, its implementation remains limited outside of specialized clinics. Known barriers are insufficient knowledge regarding VR operation, lack of availability of VR relaxation apps tailored to local health care systems, and cost concerns. Unfortunately, many VR relaxation apps are designed exclusively for stand-alone headsets, limiting accessibility for a broad audience. OBJECTIVE: We aimed to design an accessible, smartphone-based VR relaxation app based on user preferences. This paper describes the assessment of 2 stand-alone VR relaxation apps and the resulting smartphone-based VR relaxation app design. METHODS: Overall, 30 participants (n=23, 77% women; n=7, 23% men) took part in 2 separate VR sessions, assessing 1 of the 2 VR relaxation apps (Flowborne and Calm Place) in each session. After each session, participants were presented with open-ended questions to assess their experiences via a web-based survey tool. These questions explored positive and negative features, shortcomings, and suggestions for improvements while also allowing space for additional remarks concerning the 2 VR relaxation apps. Three of the authors analyzed the responses using inductive thematic analysis, a process comprising 6 phases. RESULTS: Across both the apps, 5 recurring themes and 13 recurring subthemes were identified in the participants' answers: audio (music and sounds, guidance), visuals (content, realism, variation and dynamics in the environment), features (language, options, feedback and instructions, duration, exercise), implementation (technical aspects, cybersickness, acceptability and usability), and experience. We analyzed the participants' findings and conducted a literature review, which served as the basis for developing the app. The resulting app is a Dutch-language, smartphone-based VR relaxation app, with customization options including 3 types of relaxation exercises, 2 guiding voices, and 3 different environments. Efforts have been made to ensure maximum variation and dynamism in the environments. Calming music and nature sounds accompany the exercises. The efficacy and effectiveness of the resulting app design were not assessed. CONCLUSIONS: This study provides insights into key features of VR relaxation apps, which were subsequently used for the development of a novel smartphone-based VR relaxation app. Further research concerning the effectiveness of this app, along with a broader evaluation of the efficacy and user feedback for smartphone-based VR relaxation apps, is needed. More generally, there is a clear need for more research on the impact of interactivity, biofeedback, and type of environment in VR relaxation.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".