Self-guided internet-delivered cognitive behavior therapy (ICBT) for obsessive-compulsive symptoms: A randomized controlled trial.


Journal

Journal of anxiety disorders
ISSN: 1873-7897
Titre abrégé: J Anxiety Disord
Pays: Netherlands
ID NLM: 8710131

Informations de publication

Date de publication:
Aug 2019
Historique:
received: 29 10 2018
revised: 23 06 2019
accepted: 26 06 2019
pubmed: 14 7 2019
medline: 1 7 2020
entrez: 14 7 2019
Statut: ppublish

Résumé

Internet-delivered cognitive behavior therapy (ICBT) for obsessive-compulsive disorder (OCD) has been demonstrated to be efficacious across multiple clinical trials. However, most of these interventions include clinician support, and many individuals with OCD prefer to manage their own symptoms. Self-guided ICBT overcomes this problem, but to date the efficacy of self-guided interventions has only been studied in uncontrolled trials. The present study aims to examine the efficacy and acceptability of ICBT for OCD symptoms when delivered in a self-guided format using a randomized controlled trial design. In the present study, 190 participants were randomized to either a self-guided ICBT condition or a waitlist control group. 140 participants completed the baseline assessment, initiated treatment, and were included in the analyses. The between-group effect size at post-treatment was large on the self-report version of the Yale-Brown Obsessive-Compulsive Scale (d = 1.05; 95% CI 0.89-1.21). Twenty-seven percent of the ICBT condition met conservative criteria for clinically significant change at post-treatment, which increased to thirty-eight percent at three-month follow-up. Participants rated the program as highly acceptable. The results indicate that self-guided ICBT may be a viable treatment option for some individuals with OCD symptoms.

Identifiants

pubmed: 31301476
pii: S0887-6185(18)30429-8
doi: 10.1016/j.janxdis.2019.102111
pii:
doi:

Types de publication

Journal Article Randomized Controlled Trial

Langues

eng

Sous-ensembles de citation

IM

Pagination

102111

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Bethany M Wootton (BM)

University of Technology Sydney, NSW, Australia; Macquarie University, Sydney, NSW, Australia. Electronic address: bethany.wootton@uts.edu.au.

Eyal Karin (E)

Macquarie University, Sydney, NSW, Australia.

Nick Titov (N)

Macquarie University, Sydney, NSW, Australia.

Blake F Dear (BF)

Macquarie University, Sydney, NSW, Australia.

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Classifications MeSH