Computer-Assisted Avatar-Based Treatment for Dysfunctional Beliefs in Depressive Inpatients: A Pilot Study.

avatar therapy cognitive therapy depression modification of dysfunctional beliefs virtual avatar

Journal

Frontiers in psychiatry
ISSN: 1664-0640
Titre abrégé: Front Psychiatry
Pays: Switzerland
ID NLM: 101545006

Informations de publication

Date de publication:
2021
Historique:
received: 13 10 2020
accepted: 15 06 2021
entrez: 2 8 2021
pubmed: 3 8 2021
medline: 3 8 2021
Statut: epublish

Résumé

Dysfunctional cognitions are a crucial part of depression. Cognitive therapy aims to modify dysfunctional beliefs. Typically, dysfunctional beliefs are questioned, and patients are trained to think of alternative functional beliefs. We developed a computer-assisted, avatar-based adjunct for cognitive therapy that aims to reduce dysfunctional beliefs and symptom severity. Besides, it aims to promote alternative functional beliefs. In a randomized controlled trial with 34 patients diagnosed with major depression currently undergoing inpatient treatment at the university psychiatric hospital in Regensburg, Germany, participants were randomly assigned to receive either treatment as usual (TAU) or computer-assisted avatar-based treatment for dysfunctional beliefs (CAT-DB) in addition to TAU. In CAT-DB participants are faced with a virtual avatar expressing their personal dysfunctional beliefs. Participants are asked to contradict these and express alternative functional beliefs. Assessments of conviction of dysfunctional beliefs, functional beliefs and symptom severity were done shortly before the intervention (pre-treatment), right after the intervention (post-treatment) and 14 days later (follow-up). The reduction in conviction of dysfunctional beliefs and symptom severity, and the increase in conviction of alternative functional beliefs at post-treatment and follow-up were significantly greater for the group receiving CAT-DB. Our study provides an indication in favor of the effectiveness of CAT-DB for depressive patients. It is a simple tool that could support classical cognitive therapy. Further studies at different centres, with larger sample sizes and varying therapeutic contexts are required to prove the effectiveness of our intervention.

Identifiants

pubmed: 34335319
doi: 10.3389/fpsyt.2021.608997
pmc: PMC8319718
doi:

Types de publication

Journal Article

Langues

eng

Pagination

608997

Informations de copyright

Copyright © 2021 Kocur, Dechant, Wolff, Nothdurfter, Wetter, Rupprecht and Shiban.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Martin Kocur (M)

Chair for Media Informatics, University of Regensburg, Regensburg, Germany.

Martin Dechant (M)

Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.

Christian Wolff (C)

Chair for Media Informatics, University of Regensburg, Regensburg, Germany.

Caroline Nothdurfter (C)

Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.

Thomas C Wetter (TC)

Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.

Rainer Rupprecht (R)

Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.

Youssef Shiban (Y)

Department for Clinical Psychology, Private University of Applied Sciences Göttingen, Göttingen, Germany.

Classifications MeSH