SCH: INT: Personalized Proactive Diabetes Self-Management and Social Networking for American Indians

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The epidemic of diabetes in American Indian (AI) communities is a serious public health challenge. As daily diabetes care is primarily handled by the patients and their families, the effectiveness of diabetes control is largely impacted by self-care strategies and behaviors. This project proposes a proactive diabetes self-care mobile platform based on the unique socio-economic, cultural, and geographical status of AI patients living in a reservation community in the upper Midwest. The mobile platform connects AI diabetic patients to their medical devices, healthcare team and similar patients, and offers personalized prediction, recommendation, and social networking regarding diabetes care. It transforms diabetes management from the traditional reactive and hospital-centered care to preventive, proactive, evidence-based, and person-centered care. This project will also provide valuable research opportunities for both graduate and undergraduate students, especially from underrepresented populations.The goal of the project is to develop an integrated, accessible, cost-effective solution for improved diabetes self-management and social networking for AI patients. Considering the quasi-ubiquitous use of cellphones in most AI communities, a cellphone-based platform is proposed to provide smart and personalized service. A biocultural ontology is developed to model the unique characteristics of AI patients. Based on a user's ontological profile, through ontology-based reasoning and machine learning approaches, personalized prediction and recommendation are delivered to the AI patient. Semantics-based social matching is applied as a technical and intellectual framework for connecting AI patients with similarities to form online peer support communities. Personalized self-managed trust and privacy mechanisms are developed to establish secure user relationships. To improve system usability, a multimodal interface is implemented to deliver information with appropriate input and output modalities to a user based on the user's physical and social context.