Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


Currently submitted to: JMIR Diabetes

Date Submitted: Mar 10, 2020
Open Peer Review Period: Mar 11, 2020 - Apr 27, 2020
(closed for review but you can still tweet)

NOTE: This is an unreviewed Preprint

Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).

Peer-review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer-Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.

Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.

Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.

Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Diabits: Smartphone-assisted predictive monitoring of glycemia for diabetic patients

  • Stan Kriventsov; 



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.


Please cite as:

Kriventsov S

Diabits: Smartphone-assisted predictive monitoring of glycemia for diabetic patients

JMIR Preprints. 10/03/2020:18660

DOI: 10.2196/preprints.18660


Download PDF

Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.