Impacts of Glucose Forecasting

NCT04217369 | Unknown

• 1 other identifier: 2019
• Study Type: interventional
• Target: 90
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2

Outcome Measures

Primary Outcomes (1)

• Time in Range

  The proportion of time in a single day in which a given participant's blood glucose is within a predetermined target blood glucose range.

  One day

Secondary Outcomes (5)

• BGL variability (SD) BGL variability (SD)

  1 day

• BGL Variability (ADRR)

  14 days

• Time below range

  1 day

• Change is HbA1c

  14 days

• Laboratory HbA1c

  90 days

Study Arms (2)

Control

NO INTERVENTION

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

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

Interventions

Diabits Predictions DEVICE

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.

Intervention

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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)

You may not qualify if:

• 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

Sponsors & Collaborators

• Bio Conscious Technologies Ltd: lead

MeSH Terms

Conditions

Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: December 26, 2019
• First Posted: January 3, 2020
• Study Start: May 4, 2020
• Primary Completion: December 1, 2020
• Study Completion: December 1, 2020
• Last Updated: January 3, 2020

Record last verified: 2020-01

Data Sharing

• IPD Sharing: Will not share