In general, people with insulin-treated diabetes have poor glycaemic control, mainly related to difficulties in optimizing the insulin doses. To address this problem, Dianovator develops a personalised and situation-based decision support for insulin dosing. The system is software-based and may be implemented on a hardware platform such as a smartphone or a separate device.
The purpose of the project is that the app should be ready for use in a proof-of-concept study in a subsequent project. The system communicates with external third-party sensors (mainly continuous glucose sensors (CGM) but possibly also heart rate monitors and other relevant body-worn sensors), and calculates the future trend in the glucose level using individualized and situation-specific models.
The forecast is presented to the user in a graphical interface on the screen. At activities affecting the glucose level, interactive advice on suitable corrections to the therapy, necessary to maintain a stable and normal glucose level, are given, and at the risk of dangerously low or high values, the user is prompted in advance, assuring that corrective actions can be undertaken in time.
The Dianovator system is adaptive – learning and individualized; it learns and adapts to the conditions specific to the user. This increases the possibilities of optimising the therapy outcome in comparison to existing approaches, and enables the user to reduce time spent with dangerously high and low glucose values, with implied improved quality of life and reduced risk of developing short and long term complications.
The app development project is conducted in cooperation between the companies Dianovator AB and AK2 AB together with the endocrinology department at SUS, Lund. We believe that the Dianovator system could be a vehicle for patient empowerment and a healthier and more qualitative life with less anxiety and fewer short and long term complications.
Project follow up August 2015, in brief
How has the project worked out?
Have you come any further (new results, conclusions, news?
Based on the funding by Vinnova, an Android app was developed. The app communicates with third-party continuous glucose sensors by Bluetooth and cooperates with a designated cloud service to improve performance related to data analysis. In the graphical user interface, the user may follow the current glucose level, as well as the expected development for the upcoming next hours. When administrating insulin, the user may simulate different doses in advance in the app to see the expected effect, thereby enhancing the his/her ability to make a sound decision regarding the appropriate insulin dose to administer. Our expectation is that the users will reduce the amount of high and low glucose values by utilizing the decision support provided by the app, with improved quality of life as additional outcome.
How would you like to further develop the project?
The next stage is to validate the functionality and performance in a clinical trial.