User Experience and usability of a new virtual reality set-up to treat eating disorders: a pilot study
Bibliographic record
Abstract
Virtual Reality (VR) has progressively emerged as an effective tool for wellbeing and health in clinical populations. VR effectiveness has been tested before in Anorexia Nervosa (AN) with full-body illusion. It consists in the embodiment of patients with AN into a different virtual body to modify their long-term memory of the body as a crucial factor for the onset and maintenance of this disorder. We extended this protocol using the autobiographical recall emotion-induction technique, in which patients recall an emotional episode of their life related to their body. In this pilot study, we aimed to test the usability and User Experience (UX) of this VR-based protocol. Five Italian women with AN were embodied in a virtual body resembling their perceived body size from an ego- and an allocentric perspective while remembering episodes of their life related to their body. High levels of embodiment were reported while embodied in a virtual body resembling their real perceived body size for ownership (p<0.0001), agency (p=0.04), and self-location (p=0.023). Negative affective state increase after session 2 (p=0.012), and positive affective state increase after session 4 (p=0.006) (PANAS). However, further iteration of the VR system is needed to improve the user experience and usability of the system.
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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 | 0.006 |
| 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 | 0.000 | 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".