DiaSense (Blood Glucose Self-Monitoring Mobile Application)

Self-monitoring of blood glucose (SMBG) has been recommended for diabetes patients in
order to enable maintenance of a more constant glucose level. Although SMBG is an integral
component in diabetes self-management, patients who are not using insulin do not see the
value in their own self-monitoring efforts. Non-insulin- treated (NIT) patients are unable to
understand and make use of their SMBG data to manage their disease which consequently led
to disengagement from SMBG over time. Obtaining personalized education may help patient
to improve his or her ability to identify relevant responses to blood glucose results.
Additionally, SMBG is more effective for NIT patients when it has concomitant behavioral
education to support lifestyle changes guided by SMBG data.

This research aims to develop a mobile SMBG tool for facilitating personalised education by
interpreting SMBG data in the context of behavioral practice. A mobile application
(DiaSense) that enables users to keep a log of their glucose measurements and capture their
eating behaviors using photo capture and annotation as textual tags to categorize their food
intake by food name has been developed. The system, hence, provides the NIT patients the
ability to view the correlation of food intakes and glucose measurements. The behavioral
practice focused in this research is eating behavior as meal-related SMBG is theoretically
proven could lead to better glucose regulation. Ultimately, the proposed application could
help to enhance the value of SMBG to NIT patients by facilitating them to understand how
their eating behaviors affect their glucose levels and eventually obtain knowledge on the
measures to improve their glucose control.

What’s next
To apply image processing techniques to identify food taken through photos captured and
calories of the food. In addition, to predict the impact of food taken on the control of blood
glucose using artificial intelligence techniques.

Project Team
CHIEW Thiam Kian
LUM Su Lyn, Angeline
CHUAH Jia Yiing

1. Angeline Lum, Thiam Kian Chiew. (2014) Personalized Mobile Health Monitor to
Improve Healthcare for Diabetic Patients. In 3rd International Conference on Computer
Science and Computational Mathematics 2014 (ICCSCM 2014).

Project Funding
University of Malaya Research Programme (RP003E-13ICT)

Contact Person
CHIEW Thiam Kian
Department of Software Engineering
Faculty of Computer Science and Information Technology
Email: tkchiew@um.edu.my