Mobile Phone-Based Pattern Recognition and Data Analysis for Patients with Type 1 Diabetes

Abstract

Background: Persons with type 1 diabetes who use electronic self-help tools, most commonly blood glucose meters, record a large amount of data about their personal condition. Mobile phones are powerful and ubiquitous computers that have a potential for data analysis, and the purpose of this study is to explore how self-gathered data can help users improve their blood glucose management. Subjects and Methods: Thirty patients with insulin-regulated type 1 diabetes were equipped with a mobile phone application for 3-6 months, recording blood glucose, insulin, dietary information, physical activity, and disease symptoms. The data were analyzed in terms of usage of the different modules and which data processing and visualization tools could be constructed to support the use of these data. Results: Eighteen patients (denoted ''adopters'') recorded complete data for over 80 consecutive days, up to 247 days. Among those who withdrew or did not use the application extensively, the most common reasons given were outdated or difficult-touse phone. Data analysis using period finding and scale-space trends was found to yield significant patterns for most adopters. Pattern recognition methods to predict low or high blood glucose were found to be performing poorly. Conclusions: Minimally intrusive mobile applications enable users with type 1 diabetes to record data that can provide datadriven feedback to the user, potentially providing relevant insight into their disease.

A qualitative study on the use of personal information technology by persons with spinal cord injury

“…Similar to previous finding in the illness and disability literature [21][22][23][24][25][26][27], including SCI [14][15][16][17][18][19][20], the use of IT in our sample was found to have implications for the management of their health. Specifically, participants commented that they sought information online with regards to accessing information regarding SCI, which has been found to be this population's preferred source for health-related information [33].…”

“…This is best illustrated through a number of studies examining how access to personal IT can promote health in those with diabetes mellitus. Studies have shown that access to mobile phones, text messaging, online video-conferencing and social networking can improve the health of diabetics by offering peer support [21], by improving blood glucose monitoring and control [22][23][24][25], providing information resources [26,27], supporting patients' feelings of self-efficacy [25], and improving selfmanagement of health [23,24,26].…”

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review (Preprint)

“…The 18 studies included in this review consist of 5 qualitative studies [40][41][42][43][44], 5 experimental studies [31][32][33][34][35], 3 observational studies [28][29][30], 4 cross-sectional studies [36][37][38][39] and 1 mixed methods [45]. Five of the studies were smaller samples drawn from parent clinical trials [29,32,38,40,41].…”

“…Skrosveth et al [30] explored which methods of diabetes data analysis could be realistically used to provide meaningful feedback for the user. A mobile diary application was developed for adults with T1DM to log insulin doses and dietary intake with options for the user to comment upon these and a screen to visualize each of the following variables BG levels, insulin dosing and dietary intake.…”

Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

“…Skrøvseth et al conducted a study to identify how self-gathered data can help users to improve their blood glucose management [61]. The participants were equipped with a mobile phone application, recording blood glucose, insulin, dietary information, physical activity and disease symptoms in a minimally intrusive way.…”