Impacts of Glucose Forecasting

Bio Conscious Technologies

Status: Unknown

Conditions: Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2

Treatments: Device: Diabits Predictions

Study type: Interventional

Funder types: Industry

Identifiers

NCT04217369

2019

Details and patient eligibility

About

This is a study of individuals with type I or type II diabetes. It is meant to test the effect of using the Diabits app on a participant's blood glucose control. The Diabits app is a diabetes management app which integrates with your continuous glucose monitor (CGM) and presents not only your current blood glucose trend, but also an estimate of your blood glucose values up to 60 minutes into the future. The Diabits app has been available in the USA and Canada for the past two years and was validated for predictive accuracy at BC Children's Hospital in Canada in 2017 with a predicted accuracy of 94.9%. Diabits app users in North America have shown some improvements in their individual time in range (TIR) and HbA1c values. This study aims to validate those results in a clinical setting. The study will randomise a total of 90 participants into using the Diabits app with or without the glucose forecasting enabled to help determine if the glucose forecasting (or predictions) can help participants make better treatment decisions and improve not only measurements of glucose such as time in range and HbA1c, but also reduce anxiety and improve quality of life with diabetes.

Full description

This study focuses on the impact of the Diabits app, a smartphone application which assists a person with diabetes to better manage their blood glucose. The Diabits app connects to a user's continuous glucose monitor (CGM) such as an Abbott Freestyle Libre, and predicts what the user's blood glucose will be over the next 60 minutes, updating every 5 minutes. The Diabits app displays that prediction to the user so that the user may make proactive decisions about insulin and food, with the goal of achieving more stable blood glucose control.

The study will evaluate the impact which having access to blood glucose predictions has on a user's time in range (TIR). The hypothesis is that having access to blood glucose predictions will improve the user's ability to stay within their target blood glucose range by 5% within a given 24 hour period. This is a randomized controlled trial. A control group with Abbott Freestyle Libre CGMs will be recruited, and given a version of the Diabits app which does not display any blood glucose forecasts. A treatment group, also with Abbott Freestyle Libres, will be given the Diabits app with glucose forecasts enabled. Both groups will be asked to use the app as their primary diabetes management tool for the duration of the study.

The study will run for 3 months. At the beginning of the study the 90 participants will be separated evenly into two groups. A blood test will be done to measure HbA1c and each participant will complete a number of surveys. At the end of the study the same activities will occur. Following conclusion, results will be evaluated to determine what differences develop between the control and treatment groups.

Enrollment: 90 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• An individual with type I or type II diabetes treated with > 1 insulin injections
• Currently using a smartphone compatible with the Diabits app
• Are willing to, or already wear a CGM which is compatible with the Diabits app
• Willing and able to provide informed consent, to have their data collected, and to complete questionnaires that are included as part of the protocol.
• HbA1c between 7.5 - 10 % ( 86 - 58 mmol/mol)

Exclusion criteria

• Patients who are pregnant or are planning to become pregnant during the duration of the study.
• Patients with major psychiatric illness or severe end-stage diabetes complications.
• Patients who use sensor-augmented pumps with automated features such as hybrid closed loop or predictive insulin suspension. This includes those who use non-commercial software such as DIY closed loop.
• Patients with prior use of the Diabits app

Trial design

Primary purpose: Supportive Care

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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90 participants in 2 patient groups

Control

No Intervention group

Description:

A control arm. The participant will be given a version of the Diabits app without predictions of blood glucose enabled. They will then use the Diabits app as if they were using their usual companion app to manage their diabetes for the duration of the study.

Intervention

Experimental group

Description:

The intervention arm. The participants in this arm will be provided with a version of the Diabits app which provides predictions of where their blood glucose will be one hour into the future, based on historic data and user inputs. The participant will then manage their blood glucose using these predictions for the duration of the study.

Treatment:

Device: Diabits Predictions