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Iranian Journal of Psychiatry، جلد ۲۰، شماره ۴، صفحات ۵۲۳-۵۴۴
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عنوان انگلیسی Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia: A Meta-Analysis of Randomized Controlled Trials
چکیده انگلیسی مقاله Abstract   Objective: This study aimed to evaluate the effectiveness of fully automated Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) interventions in reducing insomnia severity through a systematic review and meta-analysis of randomized controlled trials (RCTs). Method: A systematic search was conducted across multiple databases, including PubMed, PsycINFO, Web of Science, Scopus, and Google Scholar, to identify RCTs evaluating fully automated dCBT-I. Eligible studies were included those assessing adults diagnosed with insomnia using validated criteria or scales, utilizing digital delivery platforms, and reporting quantitative insomnia severity outcomes. A meta-analysis was performed using a random-effects model, with standardized mean differences (SMDs) and 95% confidence intervals (CIs) as the primary effect measures. Subgroup and sensitivity analyses were conducted to explore sources of heterogeneity. Results: A total of 49 RCTs involving 20,118 participants were included. Fully automated dCBT-I significantly reduced insomnia severity compared to control conditions (WMD: -3.42; 95% CI: -4.35 to -2.48; P < 0.001). Subgroup analyses revealed greater effectiveness in studies using rigorous diagnostic criteria, as well as among U.S.-based populations. Despite substantial heterogeneity (I² > 98%), sensitivity analyses confirmed the robustness of findings. Funnel plot asymmetry suggested minor potential publication bias, though Egger’s test did not confirm significant bias (P = 0.494). Conclusion: Fully automated dCBT-I programs effectively reduce insomnia severity, offering a scalable, accessible solution to overcome barriers in traditional CBT-I delivery. However, variability in study methodologies and the predominance of studies from high-income countries highlight the need for further research. Future directions include incorporating objective sleep measures, assessing long-term outcomes, and adapting interventions to diverse cultural and economic contexts. Fully automated dCBT-I holds transformative potential for addressing insomnia on a global scale.
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نویسندگان مقاله | Abouzar Nazari
Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.


| Ali Mirzakhani
Student Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.


| Gholamreza Garmaroudi
Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.


| Mohsen Amani
Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
نشانی اینترنتی https://ijps.tums.ac.ir/index.php/ijps/article/view/4140
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