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Efficacy of internet-based cognitive-behavioral therapy for depression in adolescents: A systematic review and meta-analysis

Authors :
Yanan Wu
E. Fenfen
Yan Wang
Meng Xu
Simin Liu
Liying Zhou
Guihang Song
Xue Shang
Chaoqun Yang
Kehu Yang
Xiuxia Li
Source :
Internet Interventions, Vol 34, Iss , Pp 100673- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Objective: Internet-based cognitive behavior therapy (ICBT) may provide an accessible alternative to face-to-face treatment, but the evidence base in adolescents is limited. This systematic review and meta-analysis aims to comprehensively assess the efficacy of ICBT in addressing depression among adolescents. Methods: Four electronic databases were searched on June 8, 2023. Randomized controlled trials (RCTs) evaluating the efficacy of ICBT for depression in adolescents were included. The quality of the studies was assessed using the risk of bias tool recommended by the Cochrane Handbook. Furthermore, the GRADE approach was employed to gauge the certainty of the obtained evidence. Meta-analysis was conducted using RevMan 5.4, and Egger's test was implemented through Stata for assessment of potential publication bias. Results: A total of 18 RCTs involving 1683 patients were included. In comparison to control groups like attention control, waiting list, and treatment as usual, our meta-analysis findings elucidate a significant reduction in depression scores (SMD = −0.42, 95 % CI: [−0.74, −0.11], p .05). Conclusion: Results provide evidence of the efficacy of ICBT to reduce depressive and anxiety symptoms in adolescents. These research findings are of vital significance for the establishment of evidence-based treatment guidelines in the digital era. Trial registration: PROSPERO registration: CRD42021277562.

Details

Language :
English
ISSN :
22147829
Volume :
34
Issue :
100673-
Database :
Directory of Open Access Journals
Journal :
Internet Interventions
Publication Type :
Academic Journal
Accession number :
edsdoj.f1425bf58b4b4ed3abd6c7e2289e2b77
Document Type :
article
Full Text :
https://doi.org/10.1016/j.invent.2023.100673