Alexithymia and Bipolar Disorder: Virtual Reality Could Be a Useful Tool for the Treatment and Prevention of These Conditions in People with a Physical Comorbidity
Bibliographic record
Abstract
Background: Alexithymia, a predictor in chronic illnesses, like cardiovascular and bipolar disorder (CD–BD), could be improved with a virtual reality (VR) cognitive remediation program. This secondary analysis of a previous randomized controlled trial (RCT) evaluates alexithymia improvement and its factors in an experimental group versus a control group, exploring extensions to individuals with comorbid non-psychiatric chronic conditions. Methods: A feasibility cross-over RCT (ClinicalTrials.gov NCT05070065) enrolled individuals aged 18–75 with mood disorders (BD, DSM-IV), excluding those with relapses, epilepsy, or severe eye conditions due to potential risks with VR. Alexithymia levels were measured using the Toronto Alexithymia Scale with 20 items (TAS-20). Results: The study included 39 individuals in the experimental group and 25 in the control group, with no significant age or sex differences observed. Significantly improved alexithymia scores were noted in the experimental group compared to controls (F = 111.9; p < 0.0001) and in subgroups with chronic non-psychiatric comorbidities (F = 4.293, p = 0.048). Scores were particularly improved for difficulty in identifying feelings (F = 92.42; p < 0.00001), communicating feelings (F = 61.34; p < 0.00001), and externally oriented thinking (F = 173.12; p < 0.00001). Conclusions: The findings highlight alexithymia enhancement in BD, even with comorbid non-psychiatric chronic diseases. Given its impact on BD progression and related conditions, like CD, developing and evaluating VR-based tools in this context is suggested by these findings.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".