Developing methods for integrating and using continuous monitoring data for diabetics to inform self-care and shared decision-making with providers

This project will explore new ways to integrate continuous data sources and will develop novel data-science methods to promote data-driven self-management and care and shared decision-making by patients and their health care providers. The grantee will conduct a data-gathering study of individuals with type 2 diabetes (this disease was selected because of convenient access to the target population) in a community disproportionately affected by this chronic disease; integrate individuals' self-monitored data with their clinical data collected at local outpatient clinics; and use these data to develop novel science methods and to explore ways to visually depict data for individuals and their health care providers. Deliverables will include: (1) a set of novel data-science methods for identifying and visualizing trends, patterns, interrelationships, and correlations between individuals' social factors and lifestyle activities and their health outcomes; (2) a novel computing application for facilitating data-driven problem-solving and decision-making in diabetes self-management that is informed by the data-science methods; (3) a dataset that will be de-identified and made available to the broader research community; (4) presentations at scientific conferences and an article for publication in a scientific journal; and (5) evaluation of the impact of these data-science methods and information-presentation approaches on individuals' self-management and shared decision-making with their health care providers.