Chatbot-Delivered Psychotherapy for Adults With Depressive and Anxiety Symptoms: A Systematic Review and Meta-Regression.

Journal: Behavior therapy
PMID:

Abstract

Although psychotherapy is a well-established treatment for depression and anxiety, chatbot-delivered psychotherapy is an emerging field that has yet to be explored in depth. This review aims to (a) examine the effectiveness of chatbot-delivered psychotherapy in improving depressive symptoms among adults with depression or anxiety, and (b) evaluate the preferred features for the design of chatbot-delivered psychotherapy. Eight electronic databases were searched for relevant randomized controlled trials. Meta-analysis and random effects meta-regression was conducted using Comprehensive Meta-Analysis 3.0 software. Overall effect was measured using Hedges's g and determined using z statistics at significance level p < .05. Assessment of heterogeneity was done using χ and I tests. A meta-analysis of 11 trials revealed that chatbot-delivered psychotherapy significantly improved depressive symptoms (g = 0.54, 95% confidence interval [-0.66, -0.42], p < .001). Although no significant subgroup differences were detected, results revealed larger effect sizes for samples of clinically diagnosed anxiety or depression, chatbots with an embodiment, a combination of types of input and output formats, less than 10 sessions, problem-solving therapy, off-line platforms, and in different regions of the United States than their counterparts. Meta-regression did not identify significant covariates that had an impact on depressive symptoms. Chatbot-delivered psychotherapy can be adopted in health care institutions as an alternative treatment for depression and anxiety. More high-quality trials are warranted to confirm the effectiveness of chatbot-delivered psychotherapy on depressive symptoms. PROSPERO registration number: CRD42020153332.

Authors

  • Shi Min Lim
    National University Hospital, National University Health System.
  • Chyi Wey Claudine Shiau
    Tan Tock Seng Hospital, National Healthcare Group.
  • Ling Jie Cheng
    Saw Swee Hock School of Public Health, National University of Singapore.
  • Ying Lau
    Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore. Electronic address: nurly@nus.edu.sg.