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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
PubMed
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.

Keywords:
Behaviour change wheelbehaviour change techniqueschatbotmedication adherencesmoking cessationtheoretical domains frameworkvarenicline

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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.