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Objectives To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI) -enabled blended care in type 2 diabetes mellitus (T2DM), and to examine preference heterogeneity by digital experience and socioeconomic status (SES). Design Cross-sectional discrete choice experiment (DCE). Setting 12 community health centres in Jiaozuo and Puyang, Henan Province, China. Data were collected between June and August 2025. Participants 423 adults diagnosed with T2DM for at least 6 months, recruited using consecutive convenience sampling from routine follow-up appointments. Of 769 participants who completed the survey, 346 were excluded following prespecified data quality criteria (retention rate: 55. 0%). Outcome measures Outcome measures included preference weights and WTP (in Chinese Yuan, ¥) for five DCE attributes: monthly subscription fee, recommendation source, feedback modality, in-person follow-up frequency and expert oversight, estimated using mixed logit models. Simulated uptake probabilities for tailored service packages across four user profiles were computed. Results Among 423 participants, 80. 6% had never used AI tools. Price was the dominant driver of choice (62. 7% relative attribute importance). Profound preference heterogeneity emerged across subgroups: rural residents (n=78) were highly price-sensitive but preferred physician endorsement (WTP ¥17. 58 (US2. 55), 95% CI ¥5. 98 to ¥29. 17) ; female participants (n=224) valued guideline recommendations (WTP ¥18. 45 (US2. 67), 95% CI ¥7. 81 to ¥29. 40) ; and diabetes app users (n=34) were the least price-sensitive but showed a negative preference for AI instant feedback, instead preferring human dietitian feedback. Expert oversight carried a consistent negative WTP across all profiles. Targeting tailored service bundles to intended subgroups increased uptake by 8–16 percentage points compared with non-targeted bundles. Conclusions A ‘digital experience paradox’ exists whereby digitally experienced users view human interaction as a premium service, while underserved groups rely on specific trust markers such as physician endorsement. To avoid widening the digital divide, AI-enabled blended diabetes care must move beyond standardised models towards configurable, equity-driven service pathways.
Sun et al. (Fri,) studied this question.