Accepted for/Published in: JMIR Human Factors
Date Submitted: May 4, 2024
Date Accepted: Apr 20, 2025
User and provider experiences with health education chatbots: A Qualitative Systematic Review
ABSTRACT
Background:
Chatbots present a transformative opportunity within health education and behavior change interventions. However, their optimal deployment warrants a comprehensive understanding of user and healthcare provider experiences, an area where systematic reviews of qualitative evidence are currently lacking.
Objective:
This qualitative systematic review aimed to synthesize insights into patient and healthcare provider perceptions of chatbots designed specifically for health education and behavior change.
Methods:
We searched the PubMed, Cochrane, and Science Direct databases for English peer-reviewed qualitative and mixed-methods studies published before October 1, 2023 that explored user experiences with chatbots for health education and behavior change. Methodological quality was appraised using the Joanna Briggs Institute Critical Appraisal Checklist. Key data were extracted, and findings were synthesized following meta-aggregation principles.
Results:
Our analysis included 27 studies from ten countries and revealed the potential of chatbots to increase health literacy and support positive behavioral change. These included 241 participants (112 female, 71 male, 20 LGBTQ+ youth) in primary qualitative studies and 10.802 participants in mixed-method studies, of which 657 were involved exclusively in the qualitative components of these studies. User satisfaction varied, with personalization and privacy emerging as vital considerations. Theoretical underpinnings, including the health belief model and transtheoretical model, have been shown to significantly influence chatbot effectiveness.
Conclusions:
Chatbots hold substantial promise for health education and behavior change. To maximize their potential and foster widespread acceptance, personalization, data privacy, and robust theoretical integration are essential. Further research on these aspects will drive continued advancements in integrating and optimizing chatbots within healthcare landscapes. Clinical Trial: Protocol registration: This systematic review was registered with the Open Science Framework (OSF) to ensure the transparency and reproducibility of our research methodology. Registration Type: Generalized Systematic Review Registration Registered: November 3, 2023 Date Created: November 3, 2023 OSF Registration Link: https://doi.org/10.17605/OSF.IO/4PX23 Associated Project Link: osf.io/sdmbt Internet Archive Link: https://archive.org/details/osf-registrations-4px23-v1
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Copyright
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