Currently submitted to: JMIR Diabetes
Date Submitted: Mar 10, 2020
Open Peer Review Period: Mar 11, 2020 - Apr 27, 2020
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Diabits: Smartphone-assisted predictive monitoring of glycemia for diabetic patients
Diabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes have to monitor their glycemic levels in order to keep them in the healthy range. This task is made easier by using continuous glucose monitoring devices (CGMs) and relaying their output to modern smartphone apps, thus providing users with real-time information on their glycemic fluctuations, possibly along with added statistical data and predictions of future trends.
The present study discusses various challenges of real-time predictive monitoring of glycemia and reviews Diabits, our smartphone application that helps diabetic patients efficiently monitor and manage their blood glucose levels in real time.
Using data from continuous glucose monitors, user input, and a variety of smart wearables, Diabits applies modern machine learning techniques to create personalized patient models and predict future blood glucose fluctuations up to 60 minutes in advance. These predictions give patients an opportunity to take preemptive action in order to keep their blood glucose values within normal range. Additionally, the presence of predictive alarms and statistical insights derived from past data makes it easier for users of the app to manage their condition and have better blood glucose control.
Based on over 6.8 million actual in-app predictions for free-living users, the accuracy of Diabits predictions, evaluated using Parkes (Consensus) Error Grid, was found to be 86.89% clinically accurate (zone A) and 99.56% clinically acceptable (zones A and B) for 30-minute predictions, while the results of 60-minute predictions were 70.56% clinically accurate and 97.49% clinically acceptable. On the Ohio T1DM dataset of 6 type 1 diabetic patients that was used in the 2018 Blood Glucose Prediction Challenge, 30-minute predictions of the base Diabits model had an average root mean squared error (RMSE) of 18.68 mg/dL, which is an improvement over published state-of-the-art results for this dataset. By analyzing daily use statistics and corresponding CGM data for the 280 most long-standing users of Diabits, it was established that, under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not communicate with the app was 154.0 mg/dL, with 67.52% of time spent in the normal 70-180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 mg/dL (P < .001), while the time in range (TIR) increased to 74.28% (P < .001).
The obtained results show that Diabits accurately predicts future glycemic fluctuations, making it easier for diabetic patients to keep their blood glucose in the normal range. Furthermore, an improvement in glucose control was observed for app users on the days with higher frequency of Diabits use.
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