Designing virtual reality exposure scenarios to treat anxiety in people with epilepsy: Phase 2 of the AnxEpiVR clinical trial
Bibliographic record
Abstract
Introduction: Anxiety in people with epilepsy (PwE) is characterized by distinct features related to having the condition and thus requires tailored treatment. Although virtual reality (VR) exposure therapy is widely-used to treat a number of anxiety disorders, its use has not yet been explored in people with epilepsy. The AnxEpiVR study is a three-phase pilot trial that represents the first effort to design and evaluate the feasibility of VR exposure therapy to treat epilepsy-specific interictal anxiety. This paper describes the results of the design phase (Phase 2) where we created a minimum viable product of VR exposure scenarios to be tested with PwE in Phase 3. Methods: Phase 2 employed participatory design methods and hybrid (online and in-person) focus groups involving people with lived experience (n = 5) to design the VR exposure therapy program. 360-degree video was chosen as the medium and scenes were filmed using the Ricoh Theta Z1 360-degree camera. Results: Our minimum viable product includes three exposure scenarios: (A) Social Scene—Dinner Party, (B) Public Setting—Subway, and (C) Public Setting—Shopping Mall. Each scenario contains seven 5-minute scenes of varying intensity, from which a subset may be chosen and ordered to create a customized hierarchy based on appropriateness to the individual’s specific fears. Our collaborators with lived experience who tested the product considered the exposure therapy program to 1) be safe for PwE, 2) have a high level of fidelity and 3) be appropriate for treating a broad range of fears related to epilepsy/seizures. Discussion: We were able to show that 360-degree videos are capable of achieving a realistic, immersive experience for the user without requiring extensive technical training for the designer. Strengths and limitations using 360-degree video for designing exposure scenarios for PwE are described, along with future directions for testing and refining the product.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".