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Blood glucose individualized prediction for type 2 diabetes using iPhone application

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7 Author(s)
Salim Chemlal ; Old Dominion University, Norfolk, VA, USA ; Sheri Colberg ; Marta Satin-Smith ; Eric Gyuricsko
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Type 2 diabetes is now the most rapidly growing form of diabetes and has become increasingly common among children. This paper presents our work of implementing an individualized real time predictive system for blood glucose in type 2 diabetes in an iPhone application. The developed application, called HealthiManage, provides relevant feedback to patients at each glucose input reading comparing the measured and predicted readings, facilitating improved self-management of the disease. The application incorporates activity recognition via a built-in accelerometer on the iPhone, which monitors any physical activity and adjusts predictions accordingly. Also, a reward component interface was incorporated that is intended to enhance patient compliance and encourage mainly teenagers to take control and improve their blood glucose regulation. The individualized prediction algorithm was tested and verified with real patient data. Different physical activities were also examined and classified for an accurate activity recognition component. The designed application with its predictive model, activity recognition, and other elements provide what we believe to be helpful feedback to monitor and manage type 2 diabetes and improve patient compliance.

Published in:

2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC)

Date of Conference:

1-3 April 2011