Impacts of Glucose Forecasting
|ClinicalTrials.gov Identifier: NCT04217369|
Recruitment Status : Not yet recruiting
First Posted : January 3, 2020
Last Update Posted : January 3, 2020
|Condition or disease||Intervention/treatment||Phase|
|Diabetes Mellitus, Type 1 Diabetes Mellitus, Type 2||Device: Diabits Predictions||Not Applicable|
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.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||90 participants|
|Intervention Model:||Parallel Assignment|
|Intervention Model Description:||Participants will be assigned randomly at the time of recruitment into two groups: a treatment group and a control group. Both groups will be given the app, but the control group will not have predictions enabled.|
|Masking:||None (Open Label)|
|Primary Purpose:||Supportive Care|
|Official Title:||Impacts of Estimated Future Glucose Values on Health|
|Estimated Study Start Date :||May 4, 2020|
|Estimated Primary Completion Date :||December 1, 2020|
|Estimated Study Completion Date :||December 1, 2020|
No Intervention: Control
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.
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.
Device: Diabits Predictions
Participants will be able to view predictions of future blood glucose. These predictions will indicate where the participant's blood glucose will travel over the next hour given that the participant's state does not change. Based on this, the participant is expected, but not required to make decisions about their activity, food, and insulin, in order to maintain blood glucose in a healthy range. The intervention does not require a specific method of glucose management, or event that a participant takes any action after viewing a prediction, the intervention is simply to display the prediction.
- Time in Range [ Time Frame: One day ]The proportion of time in a single day in which a given participant's blood glucose is within a predetermined target blood glucose range.
- BGL variability (SD) BGL variability (SD) [ Time Frame: 1 day ]The standard deviation from the mean of the participant's blood glucose
- BGL Variability (ADRR) [ Time Frame: 14 days ]Average Daily Risk Range, a measure of variability which assesses two weeks of data to identify risk of out-of-range events.
- Time below range [ Time Frame: 1 day ]A proportion of time where the participant's blood glucose is below 3.9 mmol/L, and is below 3.0 mmol/L, respectively
- Change is HbA1c [ Time Frame: 14 days ]A calculated metric which is an analogue to a laboratory HbA1c measure, to assess the likelihood of measurable changes in HbA1c
- Laboratory HbA1c [ Time Frame: 90 days ]A blood test which measures hemoglobin A1c, an indicator of long term tissue damage