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Review
. 2025 Oct;81(10):6169-6182.
doi: 10.1111/jan.16762. Epub 2025 Jan 22.

Recommendations for Mental Health Chatbot Conversations: An Integrative Review

Heidi Nieminen  1 , Anna-Kaisa Vartiainen  2 , Raymond Bond  3 , Emilia Laukkanen  4 , Maurice Mulvenna  3 , Lauri Kuosmanen  1
Affiliations
Review

Recommendations for Mental Health Chatbot Conversations: An Integrative Review

Heidi Nieminen et al. J Adv Nurs. 2025 Oct.

Abstract

Aim: To identify and synthesise recommendations and guidelines for mental health chatbot conversational design.

Design: Integrative review.

Methods: Suitable publications presenting recommendations or guidelines for mental health conversational design were included. The quality of included publications was assessed using Joanna Briggs Institute Critical Appraisal Tools. Thematic analysis was conducted.

Data sources: Primary searches limited to last 10 years were conducted in PubMed, Scopus, ACM Digital Library and EBSCO databases including APA PsycINFO, CINAHL, APA PsycArticles and MEDLINE in February 2023 and updated in October 2023. A secondary search was conducted in Google Scholar in May 2023.

Results: Of 1684 articles screened, 16 publications were selected. Three overarching themes were developed: (1) explicit knowledge about chatbot design and domain, (2) knowing your audience and (3) creating a safe space to engage. Results highlight that creating pleasant and effective conversations with a mental health chatbot requires careful and professional planning in advance, defining the target group and working together with it to address its needs and preferences. It is essential to emphasise the pleasant user experience and safety from both technical and psychological perspectives.

Conclusion: Recommendations for mental health chatbot conversational design have evolved and become more specific in recent years. Recommendations set high standards for mental health chatbots. To meet that, co-design, explicit knowledge of the user needs, domain and conversational design is needed.

Implications for the profession and/or patient care: Mental health professionals participating in chatbot development can utilise this review. The results can also inform technical development teams not involving healthcare professionals directly.

Impact: Knowledge of developing mental health chatbot conversations appears scattered. In mental health chatbots, features that enhance the chatbot's ability to meet users' needs and increase safety should be considered. This review is useful for developers of mental health chatbots and other health applications used independently.

Reporting method: This integrative review was reported according to PRISMA guidelines, as applicable.

Patient or public contribution: No patient or public contribution.

Keywords: chatbots; conversational design; health; mental health.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA flowchart describing the study identification and selection.
FIGURE 2
FIGURE 2
Main themes (above) and subthemes (below) developed in thematic analysis of recommendations and guidelines for mental health chatbot conversation design.
See this image and copyright information in PMC

References

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    1. * Cameron, G. , Cameron D., Megaw G., et al. 2018. Best Practices for Designing Chatbots in Mental Healthcare: A Case Study on iHelpr. Proceedings of the 32nd International BCS Human Computer Interaction Conference, 1–5. 10.14236/ewic/HCI2018.129. - DOI
    1. * De Souza, P. M. , da Pires I. C., Motti V. G., et al. 2022. “Design Recommendations for Chatbots to Support People with Depression.” Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems, 1–11. 10.1145/3554364.3559119. - DOI
    1. * Han, H. J. , Mendu S., Jaworski B. K., Owen J. E., and Abdullah S.. 2023. “Preliminary Evaluation of a Conversational Agent to Support Self‐Management of Individuals Living With Posttraumatic Stress Disorder: Interview Study With Clinical Experts.” JMIR Formative Research 7: e45894. 10.2196/45894. - DOI - PMC - PubMed
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