Exploring Virtual Reality for Body Image Assessment and Psychological Interventions in Individuals With Obesity: a Comprehensive Review
Bibliographic record
Abstract
INTRODUCTION: Individuals living with obesity often experience body image (BI) disturbances, which can negatively affect their quality of life and treatment outcomes. Virtual reality (VR) has emerged as a promising tool for enhancing psychological interventions, but no comprehensive review has specifically focused on VR-based studies addressing BI disturbances in this population. METHODS: This comprehensive review examined studies utilizing VR for the assessment and treatment of BI disturbances in individuals with obesity. Twelve studies met the inclusion criteria. RESULTS: Studies were categorized into three groups: (i) VR in psychological interventions for individuals with obesity, (ii) VR interventions following metabolic and bariatric surgery, and (iii) VR-based full-body illusion experiments. The primary clinical application was experiential cognitive therapy, which demonstrated greater efficacy in reducing negative BI compared with standard cognitive behavioral therapy and other treatments. Studies involving post-metabolic and bariatric surgery adults also supported VR's efficacy in reducing BI dissatisfaction, though long-term benefits were inconsistent. Full-body illusion experiments suggested that VR can help modify distorted body perceptions. However, most studies were conducted by the same research group, focused exclusively on women, and were limited to specific geographical regions, primarily Italy. CONCLUSION: While preliminary results suggest that VR is a promising tool for treating BI disturbances in individuals with obesity, the field remains under-researched. Notably, no studies have explored VR's potential as an assessment tool in this population. Future studies should include more diverse populations, investigate long-term outcomes, and explore potential barriers to clinical implementation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".