@article{cornoExploringVirtualReality2026,

title = {Exploring Virtual Reality for Body Image Assessment and Psychological Interventions in Individuals With Obesity: a Comprehensive Review},

author = {G. Corno and Á. Zamora and S. Bouchard and R. Baños and A. Baillot and J. Monthuy-Blanc},

url = {https://www.scopus.com/pages/publications/105024457568?origin=resultslist},

doi = {10.1111/obr.70051},

issn = {14677881 (ISSN)},

year  = {2026},

date = {2026-01-01},

journal = {Obesity Reviews},

volume = {27},

number = {4},

publisher = {John Wiley and Sons Inc},

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. © 2025 The Author(s). Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.},

keywords = {adult, Anxiety disorder, Assertiveness, Bariatric Surgery, body dysmorphic disorder, body image, body image assessment, body image avoidance questionnaire, body image disturbances, body satisfaction scale, bulimia, Clinical research, cognitive behavioral therapy, cognitive therapy, Depression, electroconvulsive therapy, exercise, feeding behavior, female, follow up, gastric banding, geography, group therapy, human, Humans, Italy, low calorie diet, male, obesity, overnutrition, physical activity, procedures, psychology, psychotherapy, quality of life, review, social background, systematic review, Therapy, Three-Factor-Eating-Questionnaire, training, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{monthuy-blanc_when_2023,

title = {When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles},

author = {J. Monthuy-Blanc and U. Faghihi and M. N. G. Fardshad and G. Corno and S. Iceta and M. -J. St-Pierre and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169079324&doi=10.3390%2fjcm12165172&partnerID=40&md5=2241ae85a40c73e19f30c5c3d10b514a},

doi = {10.3390/jcm12165172},

issn = {20770383},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Clinical Medicine},

volume = {12},

number = {16},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {Background: The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis. Methods: A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach. Results: This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles. Conclusions: This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions. © 2023 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {adult, aged, article, body dissatisfaction, bulimia, causal reasoning, Cluster Analysis, controlled study, coronavirus disease 2019, feeding behavior, female, health survey, human, intuitive eating, Machine learning, major clinical study, male, online analysis, pandemic, self report},

pubstate = {published},

tppubtype = {article}

}