Chatbot-Delivered Interventions for Improving Mental Health Among Young People: A Systematic Review and Meta-Analysis

The characteristics, application, and effectiveness of chatbots in improving the mental health of young people have yet to be confirmed through systematic review and meta-analysis.

This systematic review aims to evaluate the effectiveness of chatbot-delivered interventions for improving mental health among young people, identify factors influencing effectiveness, and examine feasibility and acceptability.

To identify eligible interventional studies, we systematically searched 11 databases and search engines covering a publication period of January 2014 to September 2024. Meta-analyses and subgroup analyses were performed on randomized controlled trials to investigate the effectiveness of chatbot-delivered interventions and potential influencing factors. Narrative syntheses were conducted to summarize the feasibility and acceptability of these interventions in all the included studies.

We identified 29 eligible interventional studies, 13 of which were randomized controlled trials. The meta-analysis indicated that chatbot-delivered interventions significantly reduced distress (Hedge's g = -0.28, 95% CI [-0.46, -0.10]), but did not have a significant effect on psychological well-being (Hedge's g = 0.13, 95% CI [-0.16, 0.41]). The observed treatment effects were influenced by factors including sample type, delivery platform, interaction mode, and response generation approach. Overall, this review demonstrates that chatbot-delivered interventions were feasible and acceptable.

This review demonstrated that chatbot-delivered interventions had positive effects on psychological distress among young people. Chatbot-delivered interventions have the potential to supplement existing mental health services provided by multidisciplinary healthcare professionals. Future recommendations include using instant messenger platforms for delivery, enhancing chatbots with multiple communication methods to improve interaction quality, and refining language processing, accuracy, privacy, and security measures.