JMIR Publications

ABSTRACT

Objective:

This study used the Dynamic Systems Development Method to incorporate patient and consumer sources of conversational data to develop a Technology Assisted Motivational Interviewing chatbot (TAMI), a digital agent employing machine learning models to deliver Motivational Interviewing (MI; Miller & Rollnick, 2013) for tobacco cessation. Method: During the Functional Model Iteration Phase, user-centered design interviews with smokers (n=3) informed the creation of TAMI. The Design and Build Phase involved utilization of existing datasets to guide incorporation of MI-Consistent utterances, language recognition and topic classification to guide a discussion about smoking and providing a tailored quit plan if indicated. During the Implementation Phase, user experience interviews with randomly selected participants (n=9) in a pilot trial discussed their experiences with TAMI.

Results:

User-centered design interviews indicated a desire for a chatbot that was engaging and adaptable to personal interest in quitting smoking. Inductive analysis of user experience interviews revealed that anonymity, regular reminders, and a humanized experience facilitated engagement with TAMI, but technical glitches, chatbot misunderstandings, and issues with rapport were barriers to engagement.

Conclusions:

Informed by user input and patient and consumer datasets, TAMI can employ MI skills to elicit change talk and or accurately evaluate readiness for tobacco cessation. Further development will enhance TAMI’s ability to seamlessly engage with users when discussing behavior change and assist underserved populations achieve improvements in a variety of health behavior goals.