A Smartphone-Gamified Virtual Reality Exposure Therapy Augmented With Biofeedback for Ailurophobia: Development and Evaluation Study
Bibliographic record
Abstract
BACKGROUND: To the best of our knowledge, no specialized research has been conducted to address ailurophobia (fear of cats) in Iran or globally. This has driven our project, along with the prevalence of ailurophobia and the absence of a gamified virtual reality exposure therapy (VRET) that incorporates affordable and easily accessible biofeedback (BF) tools. We hypothesize that a gamified VRET augmented with BF will yield more positive effects than a similar device lacking BF. OBJECTIVE: This study primarily focuses on the development and preliminary evaluation of a smartphone-gamified VRET integrated with BF, targeting animal phobia, with a specific case study on ailurophobia. The secondary objectives are using affordable and readily available BF found in devices such as smart bands and smartwatches and creating a mobile virtual reality gamified app to improve patients' adherence to treatments while simultaneously enhancing the app's accessibility, scalability, and outreach. METHODS: Evaluations encompassed 3 methods. First, we identified the tool's potential positive effects on phobia interventions, exploring 4 effects: intrinsic motivation, simulation of fearful situations, management of stressful circumstances without therapists' presence and mitigation of catastrophic thoughts, and preliminary effects on ailurophobia treatment. Participants were divided into BF and non-BF groups. Second, we gathered user preferences and opinions about the treatment. Third, we conducted heuristic evaluations using 44 heuristics from existing system usability scales assessing user interfaces, virtual reality platforms, and video games' playability. To interpret the data, mean scores; ANOVA, single factor; and ANOVA, 2-factor with replication were used. A total of 29 individuals were identified, of which 10 met the eligibility criteria or were accessible. RESULTS: The smartphone-gamified VRET augmented with BF exhibited better results on the identified effects compared with the non-BF version and contributed to normalizing encounters with cats. Moreover, 41 of the 44 heuristics achieved a percentage above 62%, indicating its potential as a therapeutic product and its ability to enhance patient adherence to treatments. Patient preferences on the treatment and its strengths and weaknesses were provided for further improvement. CONCLUSIONS: The tool has the potential to evolve into a comprehensive solution by incorporating various types of cats and their behaviors, simulating environments in which they are commonly found, and enhancing its appeal through an increased sense of adventure without inducing unrealistic fears. By adapting fear elements, the game can be tailored to treat various animal phobias. Phobia-focused games should avoid action and combat scenarios to prevent reinforcement of fear responses. After rigorous evaluation, further exploration is required to provide remote use beyond clinical settings.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".