Towards Precision Behavioral Medicine with IoT: Iterative Design and Optimization of a Self-Management Tool for Type 1 Diabetes

Internet of Things (IoT) technologies are revolutionizing healthcare, providing many so-called “smart health” opportunities, ranging from remote monitoring of health statistics to self-management of chronic conditions. This paper describes an IoT-based approach to the management intervention of type 1 diabetes (T1D), which is a major chronic disease with significant economic and social impact worldwide. Specifically, we focus on the structure, functionality, and development process of MyDay, which is an IoT-based, multi-faceted self-management problem solving tool for pediatric T1D patients. By leveraging IoT technologies, MyDay can connect with various devices to integrate traditionally paper-documented physiological data (e.g., blood glucose values) in real-time with psychosocial and contextual data, such as mood, stress, and social activities. By integrating relevant—but heterogeneous—data sources, MyDay can create personalized feedback for self-awareness of factors associated with diabetes self-management patterns and promote data sharing and problem solving. Iterative user-centered design cycles were used throughout the development of MyDay to document and/or troubleshoot feasibility and technical stability, optimize feedback for effective health communication through data visualization, identify barriers to app use, optimize assessment, and evaluate capability of the app as a problem solving tool. Each iterative design round identified technical and design issues that were addressed in subsequent rounds by incorporating user input and expertise. An in-vivo case study and one-month pilot study of the system indicated high feasibility and use of our IoT-based MyDay tool.