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Internet-based cognitive behavior therapy for depression, social anxiety disorder, and panic disorder: Effectiveness and predictors of response in a teaching clinic
San Francisco VA Med Ctr, CA USA; Univ Calif San Francisco, CA USA.
Karolinska Inst, Sweden.
Karolinska Inst, Sweden.
Karolinska Inst, Sweden.
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2021 (English)In: Behaviour Research and Therapy, ISSN 0005-7967, E-ISSN 1873-622X, Vol. 136, article id 103767Article in journal (Refereed) Published
Abstract [en]

Though therapist-guided Internet-based cognitive behavior therapy (ICBT) appears to be efficacious for depression, social anxiety disorder, and panic disorder, relatively little is known about real-world settings and predictors of treatment effects derived from cognitive-behavioral theory. We examined treatment effectiveness and predictors of improvement in a prospective cohort study where patients took part in 10 weeks of ICBT for depression (n = 114), social anxiety disorder (n = 150), or panic disorder (n = 106) at a teaching clinic. Patients self-reported symptoms before, during, and after treatment. Effect sizes were large for improvement in the primary symptom domain of each treatment group: depression (d = 1.48), social anxiety disorder (d = 1.01), and panic disorder (d = 1.15). In ICBT for depression, having no previous experience of psychological treatment (r = 0.21), and more frequent baseline negative automatic thoughts (r = 0.20) predicted larger improvement. In ICBT for panic disorder, having more baseline safety behaviors (r = 0.25) predicted larger improvement. Predictors remained significant when baseline symptoms were included in the statistical models. We conclude that ICBT can be effective in a real-world teaching clinic, and that patients with greater deficits at baseline benefit the most.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 136, article id 103767
Keywords [en]
Cognitive behavior therapy; Digital health; Internet; Personalized medicine; Predictors
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:liu:diva-173425DOI: 10.1016/j.brat.2020.103767ISI: 000608125700016PubMedID: 33249272Scopus ID: 2-s2.0-85097227145OAI: oai:DiVA.org:liu-173425DiVA, id: diva2:1529957
Note

Funding Agencies|Advanced Womens Health Fellowship at the San Francisco VA Medical Center

Available from: 2021-02-20 Created: 2021-02-20 Last updated: 2022-05-23Bibliographically approved

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Andersson, Gerhard

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CiteExportLink to record
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Citation style
  • apa
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Output format
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