Type 1 diabetes is associated with information deluge. Individuals are collecting enormous amounts of data, including glucose, insulin, carbohydrate data, yet the extent to which this information is being utilized for developing insights about self-management in diabetes is limited. The majority of patients do not utilize data visualization displays in their self-management, as most data displays are static and often display blood glucose numbers without providing additional information or insights related to the data visualization. There are a variety of new information tools and technologies that facilitate exploration and examination of multiple streams of longitudinal data using interactive visualization techniques. However most of these displays are used outside of the healthcare setting, have not been evaluated for usability or understanding, and have not had a specific focus on the patient as the user. In addition, with the advent of smartphone technology, there are many additional data streams that are relevant for understanding self-management in diabetes, that are easily passively collected, including physical activity levels and physical location, a proxy for environment. However, these additional displays are not traditionally included in data displays for diabetes. Adolescents with type 1 diabetes are the ideal population for innovating a novel personalized data visualization system that integrates large amounts clinical data with contextual data collected from mobile phones using GPS and accelerometry, provides interactive visualizations that tell a story and are engaging, and provides opportunities for greater engagement, insight, and problem solving for improving glycemic control and overall health outcomes. Success of this prototype will lead to advances in interactive health visualizations, advances in interactive technologies for engagement, and the rise of the patient as expert, working in partnership with the healthcare system to improve health.
This is a self-contained health IT research project will lead to the design and development of a patient-facing data visualization system which leverages diabetes data and contextual data from mobile technology and uses interactivity to provides greater insight about diabetes data for caregivers and adolescents with type 1 diabetes.
