Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot : smoking cessation use case
- Author
- Kyana Bosschaerts (UGent) , Arian Kashefi (UGent) , Lieven De Marez (UGent) , Peter Conradie (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)
- Organization
- Project
- Abstract
- 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.
- Keywords
- Behavioural change interventions, JITAI, mHealth, chatbot, trigger detection, smoking cessation, conversational agents, large language models (LLMs), BEHAVIOR
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K85H4842A19AZ12VM32NBQNJ
- MLA
- Bosschaerts, Kyana, et al. “Designing a Just-in-Time Adaptive Intervention with Trigger Detection and a Generative Chatbot : Smoking Cessation Use Case.” DIGITAL HEALTH, vol. 11, 2025, doi:10.1177/20552076251381747.
- APA
- Bosschaerts, K., Kashefi, A., De Marez, L., Conradie, P., Van Hoecke, S., & Ongenae, F. (2025). Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot : smoking cessation use case. DIGITAL HEALTH, 11. https://doi.org/10.1177/20552076251381747
- Chicago author-date
- Bosschaerts, Kyana, Arian Kashefi, Lieven De Marez, Peter Conradie, Sofie Van Hoecke, and Femke Ongenae. 2025. “Designing a Just-in-Time Adaptive Intervention with Trigger Detection and a Generative Chatbot : Smoking Cessation Use Case.” DIGITAL HEALTH 11. https://doi.org/10.1177/20552076251381747.
- Chicago author-date (all authors)
- Bosschaerts, Kyana, Arian Kashefi, Lieven De Marez, Peter Conradie, Sofie Van Hoecke, and Femke Ongenae. 2025. “Designing a Just-in-Time Adaptive Intervention with Trigger Detection and a Generative Chatbot : Smoking Cessation Use Case.” DIGITAL HEALTH 11. doi:10.1177/20552076251381747.
- Vancouver
- 1.Bosschaerts K, Kashefi A, De Marez L, Conradie P, Van Hoecke S, Ongenae F. Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot : smoking cessation use case. DIGITAL HEALTH. 2025;11.
- IEEE
- [1]K. Bosschaerts, A. Kashefi, L. De Marez, P. Conradie, S. Van Hoecke, and F. Ongenae, “Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot : smoking cessation use case,” DIGITAL HEALTH, vol. 11, 2025.
@article{01K85H4842A19AZ12VM32NBQNJ,
abstract = {{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.}},
articleno = {{20552076251381747}},
author = {{Bosschaerts, Kyana and Kashefi, Arian and De Marez, Lieven and Conradie, Peter and Van Hoecke, Sofie and Ongenae, Femke}},
issn = {{2055-2076}},
journal = {{DIGITAL HEALTH}},
keywords = {{Behavioural change interventions,JITAI,mHealth,chatbot,trigger detection,smoking cessation,conversational agents,large language models (LLMs),BEHAVIOR}},
language = {{eng}},
pages = {{34}},
title = {{Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot : smoking cessation use case}},
url = {{http://doi.org/10.1177/20552076251381747}},
volume = {{11}},
year = {{2025}},
}
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