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2021 (English)In: Lancet psychiatry, ISSN 2215-0374, E-ISSN 2215-0366, Vol. 8, no 6, p. 500-511, article id S2215-0366(21)00077-8Article in journal (Refereed) Published
Abstract [en]
BACKGROUND: Internet cognitive behavioural therapy (iCBT) is a viable delivery format of CBT for depression. However, iCBT programmes include training in a wide array of cognitive and behavioural skills via different delivery methods, and it remains unclear which of these components are more efficacious and for whom.
METHODS: We did a systematic review and individual participant data component network meta-analysis (cNMA) of iCBT trials for depression. We searched PubMed, PsycINFO, Embase, and the Cochrane Library for randomised controlled trials (RCTs) published from database inception to Jan 1, 2019, that compared any form of iCBT against another or a control condition in the acute treatment of adults (aged ≥18 years) with depression. Studies with inpatients or patients with bipolar depression were excluded. We sought individual participant data from the original authors. When these data were unavailable, we used aggregate data. Two independent researchers identified the included components. The primary outcome was depression severity, expressed as incremental mean difference (iMD) in the Patient Health Questionnaire-9 (PHQ-9) scores when a component is added to a treatment. We developed a web app that estimates relative efficacies between any two combinations of components, given baseline patient characteristics. This study is registered in PROSPERO, CRD42018104683.
FINDINGS: We identified 76 RCTs, including 48 trials contributing individual participant data (11 704 participants) and 28 trials with aggregate data (6474 participants). The participants' weighted mean age was 42·0 years and 12 406 (71%) of 17 521 reported were women. There was suggestive evidence that behavioural activation might be beneficial (iMD -1·83 [95% credible interval (CrI) -2·90 to -0·80]) and that relaxation might be harmful (1·20 [95% CrI 0·17 to 2·27]). Baseline severity emerged as the strongest prognostic factor for endpoint depression. Combining human and automated encouragement reduced dropouts from treatment (incremental odds ratio, 0·32 [95% CrI 0·13 to 0·93]). The risk of bias was low for the randomisation process, missing outcome data, or selection of reported results in most of the included studies, uncertain for deviation from intended interventions, and high for measurement of outcomes. There was moderate to high heterogeneity among the studies and their components.
INTERPRETATION: The individual patient data cNMA revealed potentially helpful, less helpful, or harmful components and delivery formats for iCBT packages. iCBT packages aiming to be effective and efficient might choose to include beneficial components and exclude ones that are potentially detrimental. Our web app can facilitate shared decision making by therapist and patient in choosing their preferred iCBT package.
FUNDING: Japan Society for the Promotion of Science.
Place, publisher, year, edition, pages
London, United Kingdom: The Lancet Publishing Group, 2021
National Category
Psychiatry
Identifiers
urn:nbn:se:liu:diva-176585 (URN)10.1016/S2215-0366(21)00077-8 (DOI)000654287100020 ()33957075 (PubMedID)2-s2.0-85107088551 (Scopus ID)
Note
Funding: Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [17K19808]; National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research FacilityNational Institute for Health Research (NIHR); NIHR Oxford Health Biomedical Research Centre [BRC-1215-20005]; National Institute on Drug Abuse (NIDA)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [K23 DA045766]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [152917]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [P50 MH119029, R01 MH111610]; NIHR Oxford Cognitive Health Clinical Research Facility; NIHR Research Professorship [RP-201708ST2-006]; NIHR Oxford and Thames Valley Applied Research Collaboration; Swiss National Science FoundationSwiss National Science Foundation (SNSF)European Commission [180083]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [019.182SG.001]
2021-06-162021-06-162022-05-23Bibliographically approved