Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: Smoking cessation use case.

Objective: This research aims to address the challenges of just-in-time adaptive interventions (JITAIs) in behaviour change by introducing an architecture that integrates both the tailoring of the message to the user profile and context, and the timing of the intervention by detecting the trigger of the behaviour. Methods: We designed a system that integrates trigger detection to determine optimal intervention moments and uses prompt engineering on a large language model (LLM) to give personalised support based on the detected trigger, the context, and personal information of the person. As a proof of concept, we applied this intervention to the domain of smoking cessation. We conducted an in-depth semi-structured interview with a domain expert to evaluate the correctness, relevancy and personalisation of the chatbot's responses. Results: An expert indicated that the support given by the chatbot is correct, personal, and tailored to the trigger and circumstances. While some suggestions were provided to further enhance the chatbot, its current capabilities were deemed effective and acceptable as a supportive tool for smoking cessation. Conclusions: An LLM with prompt engineering can be used to create a chatbot that can react to a trigger in a personalised way. Integrating both trigger detection and a generative chatbot into a JITAI is possible while ensuring privacy of the individual's personal information and circumstances.