Using the behaviour change wheel framework to develop a rule-based chatbot to support varenicline adherence for smoking cessation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".