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Using the behaviour change wheel framework to develop a rule-based chatbot to support varenicline adherence for
Nadia Minian1,2,3,4,5, Kamna Mehra1, Jonathan Rose1,6
1INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada.
Digital Health
|September 25, 2025
Summary
A new rule-based chatbot, ChatV, was developed to improve varenicline adherence for smoking cessation. This theory-driven approach enhances medication support by integrating behavior change principles.
Area of Science:
- Digital Health
- Behavioral Science
- Pharmacology
Background:
- Varenicline is effective for smoking cessation, but non-adherence is a major obstacle.
- Conversational agents can support medication adherence, but generative AI risks limit reliability.
- Rule-based chatbots offer a transparent, theory-driven alternative for patient support.
Purpose of the Study:
- To develop ChatV, a rule-based chatbot designed to enhance varenicline adherence.
- To ground the chatbot in the Behaviour Change Wheel framework for evidence-based support.
Main Methods:
- A three-step development process was employed.
- Identified adherence determinants using the Theoretical Domains Framework and qualitative interviews.
- Selected intervention components based on Behaviour Change Techniques (BCTs) Taxonomy v1 and APEASE criteria.
Main Results:
- Eleven key behavior change domains were identified.
- Refined to nine theoretical domains and 21 BCTs as core components of ChatV.
- The APEASE criteria guided the selection of intervention components.
Conclusions:
- This study presents a structured, theory-informed method for developing healthcare chatbots.
- ChatV demonstrates a model for rule-based conversational agents supporting medication adherence.
- Integrating behavior change principles enhances the potential of digital health tools.
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