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TRUVACE RECORD VERSION
record: TRV-2026-2736
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:16:40.575785Z
status: published
lens: trace
sector: health
headline: Chatbot‐Delivered Interventions for Improving Mental Health Among Young People: A Systematic Review and Meta‐Analysis
dek: BACKGROUND: 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. AIM: 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. METHODS: To identify eligible interventional studies, we systematically searched 11 databas…
gain_title: A meta-analysis of 11 randomized trials found chatbot-delivered interventions reduced psychological distress in people aged 10 to 24 by a small-to-moderate margin (Hedges' g = -0.28, 95% CI -0.46 to -0.10) compared with control conditions.
problem_title: The same meta-analysis of seven randomized trials found chatbot interventions produced no statistically significant improvement in young people's psychological well-being (g = 0.13, 95% CI -0.16 to 0.41), and the evidence was rated low certainty.
trace_subject: Chatbot-delivered mental health interventions for young people aged 10 to 24
gain_reading: A meta-analysis of 11 randomized trials found chatbot-delivered interventions reduced psychological distress in people aged 10 to 24 by a small-to-moderate margin (Hedges' g = -0.28, 95% CI -0.46 to -0.10) compared with control conditions.
gain_evidence: chatbot-delivered interventions significantly reduced distress (Hedge's g = -0.28, 95% CI [-0.46, -0.10])
problem_reading: The same meta-analysis of seven randomized trials found chatbot interventions produced no statistically significant improvement in young people's psychological well-being (g = 0.13, 95% CI -0.16 to 0.41), and the evidence was rated low certainty.
problem_evidence: did not have a significant effect on psychological well-being (Hedge's g = 0.13, 95% CI [-0.16, 0.41]) | the evidence was downgraded by two levels to “low” because of the presence of bias and inconsistency
quick_read: A systematic review and meta-analysis in Worldviews on Evidence-Based Nursing pooled randomized trials of chatbot-delivered mental health interventions for people aged 10 to 24. Across 11 trials, chatbots reduced psychological distress (Hedges' g = -0.28, 95% CI -0.46 to -0.10). Across seven trials, they had no significant effect on well-being (g = 0.13, 95% CI -0.16 to 0.41).

The authors searched 11 databases and search engines for studies published from January 2014 to September 2024 and found 29 interventional studies, 13 of them randomized trials. Eleven trials were usable for the meta-analysis, as two lacked sufficient outcome data. Certainty of evidence was rated moderate for distress and low for well-being.

Effects differed by design. Distress fell significantly in clinical or subclinical groups (g = -0.34) but not in nonclinical groups, and AI-based chatbots (g = -0.36) outperformed rule-based ones (g = -0.09) on distress. Only rule-based chatbots significantly improved well-being (g = 0.29). The authors describe the evidence as preliminary.
limitation: Most studies were in nonclinical populations in educational settings and developed countries, the trials carried risk of bias and substantial heterogeneity, and too few studies existed to assess long-term effects.
tag: Dual reading
key_points: Of 4,031 records screened, 29 interventional studies met the criteria; 16 were quasi-experimental and 13 were randomized trials, and 11 trials fed the meta-analysis. | Distress effect: g = -0.28 across 11 trials, and leave-one-out sensitivity analyses gave estimates between -0.23 and -0.31. | Well-being effect: g = 0.13 across seven trials, not statistically significant, with certainty rated low. | Distress fell more in clinical or subclinical groups (g = -0.34, five trials) than in nonclinical groups (g = -0.19, six trials, not significant). | Chatbots delivered through instant messengers (g = -0.29) and those using multiple communication modes (g = -0.39) cut distress; text-only chatbots did not significantly do so (g = -0.19). | Attrition ranged from 0% to 89.7% (median 23.4%), and only 14 of 29 studies included safeguards such as emergency assistance or crisis identification. | Most studies were run in educational settings (19) and high-income countries (18 of 29), with the US, China and New Zealand most represented.
rundown: The review followed PRISMA reporting and was registered with PROSPERO. Two reviewers independently screened studies and extracted data, with Cohen's kappa of 0.88 and 0.90 at the two screening stages. Risk of bias in trials was assessed with the revised Cochrane tool, and certainty with GRADE. The meta-analysis used three-level random-effects models to handle multiple effect sizes per trial.

Participants ranged from 8 to 33 years old. Twenty studies recruited nonclinical populations, five enrolled people with psychological symptoms, and four focused on young people diagnosed with physical or mental disorders. Depression and anxiety were each assessed in 14 studies. In 20 studies the chatbot was AI-based; the rest followed rules or decision trees. Cognitive behavioral therapy was used in seven studies.

On feasibility, recruitment rates ranged from 24% to 100% (median 46.9%), and 18 studies reported attrition below 30%. Participants consistently reported satisfaction, citing empathic communication, accessibility and non-judgment, but also input misunderstanding, repetitiveness and impersonal interactions. Only three studies integrated human support, and these showed lower attrition.

Heterogeneity was substantial for both outcomes (I2 of 63.94% for distress and 66.90% for well-being). The main source of bias in the trials was reliance on self-reported measures without assessor blinding. The authors recommend delivery through instant messengers, multimodal interaction, stronger privacy and security measures, longer follow-up and trials in low-income countries.
sources:
- peer_reviewed | Worldviews on Evidence-Based Nursing | https://doi.org/10.1111/wvn.70059 | 2025-07-15
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