Using the behaviour change wheel framework to develop a rule-based chatbot to support varenicline adherence for smoking cessation

Introduction Varenicline is one of the most effective smoking cessation medications; however, non-adherence remains a significant barrier to successful quitting. Conversational agents have the potential to support medication adherence in home and community settings. However, generative AI models pose risks due to hallucinations, making them less reliable for this purpose. Rule-based chatbots provide a more transparent, theory-driven approach to patient support. Thus, we developed ChatV, a rule based chatbot grounded in the Behaviour Change Wheel framework, to enhance varenicline adherence. Methods ChatV was developed using a three-step process. First, we identified core determinants of varenicline adherence through a rapid review and qualitative interviews with healthcare providers and patients using the Theoretical Domains Framework. Second, we identified the intervention options through group discussions. Third, we identified intervention components using Behaviour Change Techniques (BCTs) Taxonomy v1. We applied the Acceptability, Practicability, Effectiveness, Affordability, Safety, and Equity (APEASE) criteria to determine the final intervention components. Results We identified 11 key domains relevant to behaviour change, including knowledge, beliefs about capabilities and consequences, memory, attention and decision-making processes, reinforcement, intentions, goals, social influences, environmental context and resources, behaviour regulation, and skill. Applying the APEASE criteria, we refined these to nine theoretical domains and identified 21 BCTs as core components of ChatV. Conclusion This study demonstrates a structured, theory-informed approach to chatbot development for medication adherence. By integrating evidence-based behaviour change principles with practical considerations, ChatV offers a model for designing rule-based conversational agents in healthcare.