  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.
