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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Digital Mental Health
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1479207
This article is part of the Research Topic Technological Advances in Psychiatric Treatments: A Focused Exploration of Human-Computer Interaction (HCI) and Human Factors in Digital Therapeutics View all articles

A Virtual Reality-based Self-guided Training on Identification of Negative Automatic Thoughts in Healthy Adults: A Mixed-Methods Feasibility Study

Provisionally accepted
Bixi Yang Bixi Yang 1Chenxi Liao Chenxi Liao 1Yuqing Yang Yuqing Yang 1,2Binbin Shi Binbin Shi 1Caidi Zhang Caidi Zhang 1*Chunbo Li Chunbo Li 1,3,4*
  • 1 Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
  • 2 Shanghai Yangpu Dsitrict Mental Health Center, Shanghai University of Medicine and Health Sciences, Shanghai, China
  • 3 Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai, Shanghai Municipality, China
  • 4 Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, Shanghai, China

The final, formatted version of the article will be published soon.

    Cognitive restructuring (CR) is an evidence-based psychological technique for depression. However, face-to-face CR is not easily accessible. Digital CR interventions often overlook the difficulties individuals experiencing depression encounter in identifying their negative automatic thoughts (NAT), leading to suboptimal outcomes. Virtual Reality (VR) has potential advantages in assisting the identification of NAT in CR intervention.The aim of this preliminary feasibility study is to assess the efficacy, acceptability and safety of a VRbased self-guided training on the identification of negative automatic thoughts (VR-STINAT) for depression, as well as to evaluate the user experience.In a mixed methods study, 20 healthy participants underwent VR-STINAT and completed a semistructured interview, followed by post-training homework. The VR-STINAT includes three modules: psychological education, NAT identification training in VR scenarios, and practice in personally experienced scenarios. Effectiveness was measured via Thought Record Skills Assessment of homework and Cognitive Therapy Awareness Scale. Acceptability was measured using an adapted Technology Acceptance Model and duration of training. Safety was measured via Simulator Sickness Questionnaire and self-reported negative emotions. Qualitative material was analyzed using thematic analysis.The VR-STINAT was acceptable, with an average rating of 80.68%. The accuracy of NAT identification in TRSA reached 84.55%, and CTAS correctness reached 76.67%. The majority of participants experienced minimal or no side effects, although a few (10%, 2/20) reported relatively severe fatigue and craniofacial pain. Thematic analysis reviewed four themes: effectiveness, acceptability, advantages of VR, difficulties in use and suggestions for improvement. Most participants indicated that they've learned how to identify their NAT through VR-STINAT (85%, 17/20), which was engaging (90%, 18/20) and easy to use (60%, 12/20).This study provides preliminary evidence that self-guided training for the identification of negative automatic thoughts related to depression using VR is feasible. Future studies are needed to compare the efficacy of VR with other intervention modalities in people with depression.

    Keywords: virtual reality, cognitive behavioral therapy, cognitive restructuring, Negative automatic thoughts, feasibility

    Received: 11 Aug 2024; Accepted: 31 Oct 2024.

    Copyright: © 2024 Yang, Liao, Yang, Shi, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Caidi Zhang, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    Chunbo Li, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.