Evidence map›Paper›PMID 40662463›Full record

SynthesisWorldviews on evidence-based nursing2025

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

Jiaying Li, Yan Li, Yule Hu, Dennis Chak Fai Ma, Xiaoxiao Mei, Engle Angela Chan, Janelle Yorke

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Worldviews on evidence-based nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026
    Article
  7. Article
  8. From imaginary friends to artificial companions: growing up with AI.European child & adolescent psychiatry · 2026
    Article
  9. Review
  10. Review
  11. Article
  12. Digital Psychiatry with Chatbot: Recent Advances and Limitations.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025
    Review
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jiaying LiSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0002-4973-211X
Yan LiSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0002-5311-9190
Yule HuSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0002-0802-2859
Dennis Chak Fai MaSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0003-0013-1855
Xiaoxiao MeiSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0003-2721-5784
Engle Angela ChanSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0003-4411-6200
Janelle YorkeSchool of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.ORCID https://orcid.org/0000-0002-1344-5944

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

aimThis 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.

methodsTo 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.

resultsWe 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. LINKING EVIDENCE TO ACTION: 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.

Indexed as

Mental HealthAdolescentGenerative Artificial IntelligenceHumanschatbotconversational agentmental healthpsychological distresspsychological well‐beingyoung people

Identifiers

PMID40662463
PMCPMC12261465

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.