The Effectiveness of AI Chatbots in Alleviating Mental Distress and Promoting Health Behaviors Among Adolescents and Young Adults: Systematic Review and Meta-Analysis
Open accessAbstract
Background: The prevalence of mental distress and health risk behaviors among adolescents and young adults has emerged as a pressing public health concern. Artificial intelligence (AI) chatbots have been increasingly recognized for their potential to provide scalable and accessible mental health support and health education; however, questions remain about their effectiveness in addressing the unique challenges faced by adolescents and young adults. Objective: This study aimed to synthesize evidence from randomized controlled trials (RCTs) on the effectiveness of AI chatbots in alleviating mental distress and promoting health behaviors among adolescents and young adults. Methods: Eight databases (PubMed, PsycINFO, Cochrane Library, CINAHL, Embase, Web of Science, Scopus, and IEEE Xplore) were searched for RCTs published in English between January 1, 2014, and January 26, 2025. Eligible studies assessed the effects of AI chatbots on mental distress and health behavior outcomes among adolescents and young adults (15‐39 years). Extracted data were synthesized narratively or meta-analyzed as appropriate; subgroup and meta-regression analyses were performed to explore moderators of chatbot effectiveness. Risk of bias was evaluated using the revised Cochrane risk-of-bias 2 (RoB 2) tool for randomized trials. Evidence quality was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: Out of 2495 records retrieved, 31 RCTs were included, comprising 29,637 participants; 26 studies were eligible for meta-analysis. Overall, AI chatbots demonstrated small-to-moderate effects in mitigating mental distress (standard mean difference [SMD] −0.35, 95% CI −0.46 to −0.24; P <.001) and promoting health behaviors (SMD 0.11, 95% CI 0.03 to 0.19; P =.006) in adolescents and young adults. Significant improvements were observed for depressive (SMD −0.43, 95% CI −0.62 to −0.23; P <.001), anxiety (SMD −0.37, 95% CI −0.58 to −0.17; P <.001), stress (SMD −0.41, 95% CI −0.50 to −0.31; P <.001), and psychosomatic symptoms (SMD −0.48, 95% CI −0.82 to −0.14; P =.006); negative affect (SMD −0.27, 95% CI −0.53 to −0.01; P =.04); and self-ambivalence and appearance distress (SMD −0.25, 95% CI −0.34 to −0.17; P =.01). While AI chatbots contributed to modest enhancements in life satisfaction and well-being, their impacts on positive affect and self-efficacy were limited. The effectiveness of AI chatbots varied depending on target samples, control conditions, and design features such as dialog system methods, deployment formats, and the use of reminders. User engagement emerged as a critical factor for success, with repetitive content and technical issues noted as primary barriers to adherence. Conclusions: This systematic review and meta-analysis highlights the potential of AI chatbots to address mental health challenges and promote health behaviors among adolescents and young adults. Retrieval-based dialog systems demonstrated consistent and reliable effects, while generative systems showed promise, but their overall effectiveness was inconclusive. Future research should prioritize developing safety protocols and evaluation frameworks for generative systems and validating their long-term impacts on mental health and behavior change in adolescents and young adults.
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The first systematic reviews and meta-analyses demonstrate the potential of LLM-CBs as clinical tools for mental health.
“the first systematic reviews and meta-analyses demonstrate the potential of LLM-CBs as clinical tools for mental health13,14.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [13]
The Results of Meta-Analysis report reduced mental distress with AI chatbots and a larger effect for generative chatbots, supporting their potential as mental-health tools. But the review covers mostly non-generative chatbots, and the Discussion (Limitations) says the clinical effectiveness of generative AI chatbots remains unknown. It therefore does not establish the claimed clinical potential specifically for LLM-based chatbots. (LLM, conf=0.88)
Experimental — accuracy not yet measuredExisting reviews and meta-analyses of LLM-CBs for mental health focus on applications of LLM-CBs as clinical intervention tools, often under research-protocol conditions.
“Although reviews and meta-analyses of LLM-CBs for mental health exist13,14,29,30, as well as scoping reviews on the implementation of LLM-CBs in mental health31,32, their focus is narrowed to applications of LLM-CBs as clinical intervention tools.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [13]
The Methods (Eligibility Criteria; Statistical Analysis) limit the review to randomized trials of chatbot interventions, supporting the characterization of research-protocol conditions. But the Results (Results of Systematic Review) say most chatbots were rule-based or retrieval-based and only three studies explored generative approaches; the review also covers health behaviors and nonclinical populations. It therefore does not support describing its focus as narrowly LLM-based clinical intervention tools. (LLM, conf=0.87)
Experimental — accuracy not yet measuredLLM-CBs find increasing use as mental health support tools, both for the general public and for patients in clinical interventions.
“LLM-CBs find increasing use as mental health support tools, both for the general public8–12 and for patients in clinical interventions13,14.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [13]
The Introduction (page unavailable) describes chatbot interventions for people with psychological distress and drug addiction, supporting their use in mental-health interventions. But the Results say only three reviewed studies used generative chatbot approaches, and the Discussion says most therapeutic chatbots remain rule- or retrieval-based. The paper therefore does not support the claim that LLM-based chatbots are finding increasing clinical use. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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- Authors
- Feng, X., Tian, L., Ho, G. W. K., Yorke, J., & Hui, V.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Hui, Vivian
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-11-26
- Volume
- 27
- Pages
- e79850-e79850
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC12661615
- OpenAlex
- W4416792402
- Cited by
- 51
- References
- 61
- Citations by year
- 2026: 51
- FWCI
- 5.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- AI chatbots, psychological distress, adolescents, young adults, health behavior, depression, anxiety, stress, health education, digital mental health interventions, chatbot effectiveness, risk of bias, GRADE quality of evidence
- MeSH terms
- Psychological Distress, Generative Artificial Intelligence, Adolescent, Adult, Artificial Intelligence, Health Promotion, Humans, Stress, Psychological, Health Behavior, Randomized Controlled Trials as Topic, Young Adult
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