Describing epilepsy-related anxiety to inform the design of a virtual reality exposure therapy: Results from Phase 1 of the AnxEpiVR clinical trial
Bibliographic record
Abstract
People with epilepsy (PwE) are at a greater risk of comorbid anxiety, which is often related to the fear of having another seizure for safety or social reasons. While virtual reality (VR) exposure therapy (ET) has been successfully used to treat several anxiety disorders, no studies to date have investigated its use in this population. This paper discusses Phase 1 of the three-phase AnxEpiVR pilot study. In Phase 1, we aimed to explore and validate scenarios that provoke epilepsy/seizure-specific (ES) interictal anxiety and provide recommendations that lay the foundation for designing VR-ET scenarios to treat this condition in PwE. An anonymous online questionnaire (including open- and closed-ended questions) that targeted PwE and those affected by it (e.g., through a family member, friend, or as a healthcare professional) was promoted by a major epilepsy foundation in Toronto, Canada. Responses from n = 18 participants were analyzed using grounded theory and the constant comparison method. Participants described anxiety-provoking scenes, which were categorized under the following themes: location, social setting, situational, activity, physiological, and previous seizure. While scenes tied to previous seizures were typically highly personalized and idiosyncratic, public settings and social situations were commonly reported fears. Factors consistently found to increase ES-interictal anxiety included the potential for danger (physical injury or inability to get help), social factors (increased number of unfamiliar people, social pressures), and specific triggers (stress, sensory, physiological, and medication-related). We make recommendations for incorporating different combinations of anxiety-related factors to achieve a customizable selection of graded exposure scenarios suitable for VR-ET. Subsequent phases of this study will include creating a set of VR-ET hierarchies (Phase 2) and rigorously evaluating their feasibility and effectiveness (Phase 3).
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".