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Personalized Wellness at Scale: A Framework for Designing AI-Driven Feedback in mHealth
0
Zitationen
2
Autoren
2026
Jahr
Abstract
This research-in-progress paper examines how AI agents can enhance personalization and feedback in mHealth wellness applications, addressing long-standing scalability challenges. While rule-based systems provide a feedback mechanism that reduces the burden on healthcare professionals, they lack adaptability and nuance. We argue that AI agents can shift aspects of wellness feedback from human experts to intelligent systems while improving engagement and responsiveness. Drawing on prior literature, we are developing a design framework mapping five feedback categories to AI capabilities, along with a phased evaluation plan incorporating expert review, prototype-based usability testing with end users and healthcare professionals, and a planned longitudinal follow-up study. This framework supports IT workforce development by providing mHealth designers with guidance for responsibly adopting AI agents as development tools.
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