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DOI: 10.1055/a-2605-0048
Erweiterte Realität (XR) in der Behandlung von Übergewicht und Adipositas: Eine Delphi-Befragung zu Status Quo und Potenzial
Extended Reality (XR) for the treatment of overweight and obesity: a delphi poll on status quo and potential
Zusammenfassung
Ziel der Studie
In der Therapie von Übergewicht und Adipositas kann erweiterte Realität (XR) als unterstützendes Mittel für die Verhaltensänderung dienen. Diese Arbeit untersucht, wie Expert*innen den Mehrwert von XR einschätzen und welche kritischen Faktoren sie für Marktdurchdringung und Nutzung sehen.
Methodik
Eine Delphi-Befragung mit N=42 Expert*innen bewertete Mehrwert und Akzeptanz von 1) virtuellen Übungssituationen, 2) virtueller Körperexposition und 3) XR zur Motivationssteigerung in zwei Runden. Zudem wurden Preisvorstellungen marktverfügbarer Produkte geschätzt und kritische Faktoren im Freitext erhoben.
Ergebnisse
Der Mehrwert aller 3 Anwendungen wurde als mittel bis hoch eingeschätzt, wobei insbesondere nutzerfreundliche und stabile technische Umsetzung, therapeutische Begleitung und kohärente Therapiekonzepte betont wurden. Die Preisvorstellungen lagen im marktüblichen Bereich.
Schlussfolgerung
XR kann laut Expert*innen die Therapie von Übergewicht und Adipositas wirksam unterstützen. Erforderlich sind jedoch zielgruppengerechte, evidenzbasierte virtuelle Umgebungen.
abstract
Purpose
Extended reality (XR) can be used as a supportive tool for behavior change in the treatment of overweight and obesity. This study investigates how experts assess the added value of XR for the treatment of overweight and obesity, and identifies critical factors for market penetration and application.
Methods
A Delphi survey of experts was conducted. In two rounds, N=42 experts assessed the added value and acceptance of practitioners and patients of 1) virtual exposure and exercise situations, 2) virtual body exposure and 3) the use of XR to increase motivation. In addition, the panel estimated realistic prices for products on the market and commented critical factors for use.
Results
The added value of all three areas of application was rated as medium to high, with particular reference being made to the relevance of user-friendly and stable technical implementation, therapeutic support and coherent therapy concepts. Price expectations were within the usual market range.
Conclusion
According to experts, XR can effectively support the treatment of overweight and obesity. However, target group-oriented, evidence-based virtual environments are required.
Publication History
Article published online:
15 September 2025
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Literatur
- 1 AWMF. Interdisziplinäre Leitlinie der Qualität S3 zur Prävention und Therapie der Adipositas. In. 5.0 ed; 2024
- 2 Wienrich C, Döllinger N, Hein R. Behavioral Framework of Immersive Technologies (BehaveFIT): How and Why Virtual Reality can Support Behavioral Change Processes. Frontiers in Virtual Reality 2021; 2
- 3 Carl E, Stein AT, Levihn-Coon A. et al. Virtual reality exposure therapy for anxiety and related disorders: A meta-analysis of randomized controlled trials. J Anxiety Disord 2019; 61: 27-36
- 4 Behrens SC, Streuber S, Keizer A. et al. How immersive virtual reality can become a key tool to advance research and psychotherapy of eating and weight disorders. Frontiers in Psychiatry 2022; 13
- 5 Al-Rasheed A, Alabdulkreem E, Alduailij M. et al. Virtual Reality in the Treatment of Patients with Overweight and Obesity: A Systematic Review. Sustainability 2022; 14
- 6 de Carvalho MR, Dias TRS, Duchesne M. et al. Virtual Reality as a Promising Strategy in the Assessment and Treatment of Bulimia Nervosa and Binge Eating Disorder: A Systematic Review. Behav Sci (Basel) 2017; 7
- 7 Ferrer-Garcia M, Gutiérrez-Maldonado J, Riva G. Virtual Reality Based Treatments in Eating Disorders and Obesity: A Review. Journal of Contemporary Psychotherapy 2013; 43: 207-221
- 8 Harris NM, Lindeman RW, Bah CSF. et al. Eliciting real cravings with virtual food: Using immersive technologies to explore the effects of food stimuli in virtual reality. Front Psychol 2023; 14: 956585
- 9 Manzoni GM, Cesa GL, Bacchetta M. et al. Virtual Reality-Enhanced Cognitive-Behavioral Therapy for Morbid Obesity: A Randomized Controlled Study with 1 Year Follow-Up. Cyberpsychol Behav Soc Netw 2016; 19: 134-140
- 10 Max SM, Schag K, Giel KE. et al. Behavioural biases in the interaction with food objects in virtual reality and its clinical implication for binge eating disorder. Eat Weight Disord 2023; 28: 46
- 11 Schroeder PA, Mayer K, Wirth R. et al. Playing with temptation: Stopping abilities to chocolate are superior, but also more extensive. Appetite 2023; 181: 106383
- 12 Power D, Jones A, Keyworth C. et al. Emotional Eating Interventions for Adults Living With Overweight and Obesity: A Systematic Review and Meta-Analysis of Behaviour Change Techniques. J Hum Nutr Diet 2025; 38: e13410
- 13 Scarpina F, Serino S, Keizer A. et al. The Effect of a Virtual-Reality Full-Body Illusion on Body Representation in Obesity. J Clin Med 2019; 8
- 14 Gemesi K, Döllinger N, Weinberger N-A. et al. Virtual body image exercises for people with obesity – results on eating behavior and body perception of the ViTraS pilot study. BMC Medical Informatics and Decision Making 2025; 25
- 15 Korbanka TA, Schild S, Mack I. et al. Reflection of Therapy Progress in Virtual Reality for Individuals Affected by Obesity: A Pilot Study. Eating and Weight Disorders under review.
- 16 Mack I, Reiband N, Etges C. et al. The Kids Obesity Prevention Program: Cluster Randomized Controlled Trial to Evaluate a Serious Game for the Prevention and Treatment of Childhood Obesity. J Med Internet Res 2020; 22: e15725
- 17 Vorgrimler D, Wübben D. Die Delphi-Methode und ihre Eignung als Prognoseinstrument. Statistisches Bundesamt Wirtschaft und Statistik 2003; 8: 763-774
- 18 RStudio Team. RStudio: Integrated Development for R. Posit Software, PBC 2024. Version 2024.12.1+563 [R Package]
- 19 Wickham H, François R, Henry L. et al. dplyr: A Grammar of Data Manipulation. Posit Software, PBC 2023. Version 1.1.4 [R Package]
- 20 Lenth R. emmeans: Estimated Marginal Means, aka Least-Squares Means. Posit Software, PBC 2024. Version 2024.12.1+563 [R Package]
- 21 Wickham H. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. Posit Software, PBC 2023. Version 2024.12.1+563 [R Package]
- 22 R Core Team. grid: The Grid Graphics Package. Posit Software, PBC 2024. Version 2024.12.1+563 [R Package]
- 23 R Core Team. Pinheiro J, Bates D. et al. nlme: Linear and Nonlinear Mixed Effects Models. Posit Software, PBC 2023. Version 2024.12.1+563 [R Package]
- 24 Wickham H, Girlich M. tidyr: Tidy Messy Data. Posit Software, PBC 2023. Version 2024.12.1+563 [R Package]
- 25 Mayring P, Fenzl T. Qualitative Inhaltsanalyse. In Handbuch Methoden der empirischen Sozialforschung 2019; 633-648