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Bridging Audience Segmentation and Message Intervention: AI-Generated HBM-Based Messages for Diabetes Prevention
1Department of Communication, University of Macau, Taipa, China.
Journal of Health Communication
|July 23, 2026
Summary
This study bridges the health communication gap using AI to create tailored messages for type 2 diabetes prevention. Personalized, AI-generated content effectively boosted preventive intentions across different audience segments.
Area of Science:
- Health Communication
- Behavioral Science
- Artificial Intelligence
Background:
- A significant gap exists between audience segmentation and intervention development in health communication.
- Psychographic segmentation is effective, but segment-specific messages are rarely tested experimentally.
- Type 2 diabetes prevention requires tailored communication strategies to address diverse audience needs.
Purpose of the Study:
- To propose and test a pipeline integrating Health Belief Model (HBM) diagnostics with generative AI for precision health communication.
- To experimentally evaluate AI-generated, segment-specific messages for type 2 diabetes prevention.
- To explore the mechanisms linking AI personalization to increased preventive intentions.
Main Methods:
- Study 1: Identified three HBM-based psychographic segments using cluster analysis in at-risk adults (N=993).
- Study 2: Randomized experiment (N=737) testing AI-generated messages (Gemini 2.5 Pro) tailored to each HBM segment.
- Mediation analysis examined message elaboration, cognitive trust, affective trust, and preventive intention.
Main Results:
- AI-generated segment-specific messages significantly increased preventive intentions compared to a general message, both overall and within each segment.
- Message personalization via AI was associated with increased message elaboration.
- Message elaboration mediated the relationship between AI personalization and cognitive/affective trust, ultimately influencing preventive intention.
Conclusions:
- A scalable, theory-driven framework for precision health communication using AI is feasible.
- AI-driven message tailoring can effectively enhance health behavior intentions across distinct audience segments.
- Findings extend human-AI trust models, highlighting elaboration and affective trust in personalized health interventions.
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