Personalized Understanding of Blood Glucose Dynamics via Mobile Sensor Data
This work addresses diabetes management for patients and clinicians by providing personalized analytical tools, but it is incremental as it builds on existing CGM technology with added mobile data.
The paper tackled the problem of understanding blood glucose dynamics in diabetics by augmenting continuous glucose monitor data with mobile sensor inputs like GPS and activity classifications from a single patient over 9 months, resulting in a method to detect lifestyle events correlated to blood glucose changes and tools for visualization and notifications.
Continuous Blood Glucose (CGM) monitors have revolutionized the ability of diabetics to manage their blood glucose, and paved the way for artificial pancreas systems. In this paper we augment CGM data with sensor input collected by a smart phone and use it to provide analytical tools for patients and clinicians. We collected GPS data, activity classifications, and blood glucose data with a custom iOS application over a 9 month period from a single free-living type-1 diabetic patient. This data set is novel in terms of it's size, the inclusion of GPS data, and the fact that it was collected non-intrusively from a free-living patient. We describe a method to measure the occurrence of lifestyle \textit{events} based on GPS and activity data, and show that they can capture instances of food consumption and are therefore correlated to changes in blood glucose. Finally, we incorporate these event representations into our system to create useful visualizations and notifications to aid patients in managing their diabetes.