Recommendations for Mental Health Chatbot Conversations: An Integrative Review

Area of Science:

• Digital Health
• Human-Computer Interaction
• Mental Healthcare Technology

Background:

• Conversational design for mental health chatbots is crucial for user engagement and therapeutic efficacy.
• Existing guidelines for mental health chatbot design are fragmented, necessitating a synthesized approach.

Purpose of the Study:

• To identify and synthesize existing recommendations and guidelines for conversational design in mental health chatbots.
• To provide a comprehensive overview of best practices for developing effective and safe mental health chatbot interactions.

Main Methods:

• An integrative review methodology was employed to systematically search and analyze relevant literature.
• Searches were conducted across major databases (PubMed, Scopus, ACM, EBSCO) and Google Scholar, limited to the last 10 years.
• Thematic analysis was used to synthesize findings from 16 selected publications, with quality assessed using Joanna Briggs Institute Critical Appraisal Tools.

Main Results:

• Three key themes emerged: explicit knowledge of chatbot design and domain, understanding the target audience, and creating a safe user environment.
• Effective mental health chatbot conversations necessitate professional planning, target group definition, and user collaboration.
• Prioritizing a positive user experience and ensuring both technical and psychological safety are paramount.

Conclusions:

• Recommendations for mental health chatbot conversational design have become more specific and demanding.
• Co-design, deep user understanding, and domain expertise are essential for meeting high standards in mental health chatbot development.
• This review offers valuable insights for developers of mental health chatbots and independent health applications.