Individualized Virtual Reality for Increasing Self-Compassion: Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are common and debilitating mental disorders with severe negative repercussions at both individual and societal levels. Although virtual reality (VR) has emerged as a safe and effective tool for the treatment of anxiety disorders, studies of the therapeutic application of VR to treat depression are more limited. OBJECTIVE: The purpose of this study was to test whether a novel type of individualized VR (iVR) can be used to improve self-compassion and decrease depressive symptoms and to evaluate the usability and acceptability of this approach, as rated by participants. The iVR system was designed and developed based on the feedback obtained from a previous study, with improved appearance and feel of the avatar and enhanced graphical quality. METHODS: A total of 36 young adult participants were recruited from a university community social media site. Participants were aware that the study was investigating a treatment for depression but were not recruited based on depression diagnosis. Participants were asked to complete 2 iVR sessions, spaced 2 weeks apart. At baseline and upon completion of each iVR session, participants were asked to complete validated measures of self-compassion and depression. Upon completion of both iVR sessions, additional measures were administered to assess participants' perceptions about the perceived usability and system acceptability of the iVR approach. RESULTS: Self-compassion was assessed at the beginning of session 1 (preintervention baseline) and at the end of session 1 (postintervention assessment). Owing to COVID-19 constraints, 36% (13/36) of the participants were unable to complete the follow-up iVR session. Self-compassion was assessed again for the remaining 64% (23/36) of the participants at the end of session 2 (postintervention assessment). Within-group analyses revealed that self-compassion was significantly increased at the end of both session 1 (P=.01) and session 2 (P=.03) relative to baseline. There was also a nonsignificant trend for depressive symptoms to be low at the end of session 2 relative to baseline. Both quantitative and qualitative participant data supported the iVR approach as being acceptable and usable. CONCLUSIONS: Although these data must be treated as preliminary owing to the small sample size and potential selection bias, the data provide encouraging initial evidence that iVR might be a useful tool to enhance self-compassion and reduce depressive symptoms, highlighting the need for randomized controlled trials in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".