Bridging Audience Segmentation and Message Intervention: AI-Generated HBM-Based Messages for Diabetes Prevention

Health communication often faces a segmentation-intervention gap: psychographic segmentation identifies meaningful audience profiles, but these profiles are rarely translated into experimentally tested segment-specific messages. This study proposes and tests a segmentation-to-intervention pipeline that integrates Health Belief Model (HBM) diagnostics with generative artificial intelligence (GAI) for type 2 diabetes prevention. In Study 1 (N = 993), a two-step cluster analysis identified three HBM-based psychographic segments among at-risk adults. In Study 2 (N = 737), a randomized experiment tested AI-generated messages produced by Gemini 2.5 Pro to match each segment’s HBM profile. Compared with a general message, AI-generated segment-specific messages increased preventive intentions at the aggregate level and within all three segments, suggesting broad persuasive benefits across psychologically distinct groups. Mediation analysis showed that AI personalization was associated with greater message elaboration, which in turn was linked to cognitive trust, affective trust, and preventive intention. Exploratory path results further suggested a direct association between elaboration and affective trust, extending existing models of human-AI trust. These findings demonstrate the feasibility of a scalable, theory-driven framework for precision health communication.