Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79% for 60-minute).In addition, the model provides competitive performance in providing effective prediction horizon (P H ef f ) with minimal time lag both in a simulated patient dataset (P H ef f = 29.0±0.7 for 30-min and P H ef f = 49.8±2.9 for 60-min) and in a real patient dataset (P H ef f = 19.3±3.1 for 30-min and P H ef f = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.

The Squawk virtual machine is a small Java TM virtual machine (VM) written mostly in Java that runs without an operating system on a wireless sensor platform. Squawk translates standard class file into an internal pre-linked, position independent format that is compact and allows for efficient execution of bytecodes that have been placed into a read-only memory. In addition, Squawk implements an application isolation mechanism whereby applications are represented as object and are therefore treated as first class objects (i.e., they can be reified). Application isolation also enables Squawk to run multiple applications at once with all immutable state being shared between the applications. Mutable state is not shared. The combination of these features reduce the memory footprint of the VM, making it ideal for deployment on small devices.Squawk provides a wireless API that allows developers to write applications for wireless sensor networks (WSNs), this API is an extension of the generic connection framework (GCF). Authentication of deployed files on the wireless device and migration of applications between devices is also performed by the VM. This paper describes the design and implementation of the Squawk VM as applied to the Sun TM Small Programmable Object Technology (SPOT) wireless device; a device developed at Sun Microsystems Laboratories for experimentation with wireless sensor and actuator applications.

The study has shown that recent severe stressors are associated with poorer glycemic control. Positive life events were associated with fair or improved glycemic control. This study has its limitations, and future studies should be prospective in design. While it is not always possible to avoid stress, learning to recognize and cope with stressors may help individuals with diabetes maintain good glycemic control and improve overall quality of life.

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This work was supported by EPSRC EP/P00993X/1 and President's Ph.D. Scholarship at Imperial College London. T. Zhu and L. Kuang have equal contribution.