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arXiv · 2610.05357

Optimizing AI-Driven Messaging for Type 2 Diabetes Management: Insights from Patient Preference Elicitation

Angela Mastrianni·Defne Levine·Katerina Andreadis·Lynn Xu·Priscilla D'Antico·Antoinette Schoenthaler·Devin Mann
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Abstract

Generative AI (GenAI) allows for improved user experience within conversational agents for diabetes management by supporting dynamic, context-aware conversations. In this study, we elicited patient preferences for the communication style of a GenAI-based conversational agent (uMatter) developed to support diabetes management. We conducted an online survey with 125 individuals with type 2 diabetes. The survey included a discrete choice experiment to evaluate participant preferences for different types of messaging attributes. The survey also elicited participant perceptions and feedback on the messages from uMatter. We found significant preference heterogeneity for the inclusion of emojis within the messages. Additionally, qualitative findings indicated that participants had different desired personas and communication styles for the conversational agent. We propose strategies from recent human-computer interaction and natural language processing research that can be used to design GenAI-based conversational agents that align with the communication preferences of patients.

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Angela Mastrianni, Defne Levine, Katerina Andreadis, Lynn Xu, Priscilla D'Antico, Antoinette Schoenthaler, Devin Mann. 2026-10-04. Optimizing AI-Driven Messaging for Type 2 Diabetes Management: Insights from Patient Preference Elicitation. https://arxiv.org/abs/2610.05357

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