The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes (RADAR)

September 13, 2022 updated by: University Hospital Inselspital, Berne
The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Completed

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Intervention / Treatment

Intervention / Treatment

A process or action that is the focus of a clinical study. Interventions include drugs, medical devices, procedures, vaccines, and other products that are either investigational or already available. Interventions can also include noninvasive approaches, such as education or modifying diet and exercise.

Detailed Description

Prior research has investigated the general potential of data analytics and artificial intelligence to infer blood glucose levels from a variety of data sources. In this study patients with insulin-dependent diabetes mellitus will be wearing a continuous glucose meter (CGM) and a smartwatch for a maximum duration of 3 months in an outpatient setting. The gathered data will be used to develop a non-invasive and wearable based dysglycemia detection and warning system using artificial intelligence.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Actual)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
40

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Switzerland
      • Bern, Switzerland
        • Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

18 years and older (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Adult patients with insulin-dependent diabetes mellitus treated with multiple daily insulin injections or continuous subcutaneous insulin infusion

Description

Inclusion Criteria:

  • Informed consent as documented by signature
  • Age ≥ 18 years
  • Diabetes mellitus treated with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII)

Exclusion Criteria:

  • Smartwatch cannot be attached around the wrist of the patient
  • Known allergies to components of the Garmin smartwatch or the Dexcom G6 system
  • Pregnancy, intention to become pregnant or breast feeding
  • Cardiac arrhythmia (e.g. atrial flutter or fibrillation, AV-reentry tachycardia, AV-block > grade 1)
  • Pacemaker or ICD (implantable cardioverter defibrillator)
  • Treatment with antiarrhythmic drugs or beta-blockers
  • Drug or alcohol abuse
  • Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc. of the participant
  • Physical or psychological disease likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as the area under the receiver operator characteristics curve (AUC-ROC)
Time Frame: 4-12 weeks
Accuracy of the RADAR-model will be assessed using machine learning technology and physiological data recorded by the smartwatch compared to continuous glucose measurements (ground truth)
4-12 weeks

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR-model will be assessed using machine learning technology and physiological data recorded by the smartwatch compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting severe hypoglycemia (glucose < 3.0 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR-model will be assessed using machine learning technology and physiological data recorded by the smartwatch compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR-model will be assessed using machine learning technology and physiological data recorded by the smartwatch compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting dysglycemia (glucose > 13.9mmol/L and glucose < 3.9 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR+-model will be assessed using machine learning technology and wearable based data (physiological, time, fasting glucose, and motion) compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR+-model will be assessed using machine learning technology and wearable based data (physiological, time, fasting glucose, and motion) compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR+model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting severe hypoglycemia (glucose < 3.0 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR+-model will be assessed using machine learning technology and wearable based data (physiological, time, fasting glucose, and motion) compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of the RADAR+ model: Diagnostic accuracy of wearable based data (physiological, time, fasting glucose, and motion) in detecting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR+-model will be assessed using machine learning technology and wearable based data (physiological, time, fasting glucose, and motion) compared to continuous glucose measurements (ground truth)
4-12 weeks
Accuracy of RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting glucose levels quantified as the mean absolute error.
Time Frame: 4-12 weeks
Accuracy of the RADAR forecasts will be assessed using machine learning technology, historical continuous glucose measurements data, and historical wearable based data (physiological, time, fasting glucose, and motion) compared to future continuous glucose measurements (ground truth).
4-12 weeks
Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting dysglycemia (glucose>13.9mmol/L and glucose<3.9 mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR forecasts will be assessed using machine learning technology, historical continuous glucose measurements data, and historical wearable based data (physiological, time, fasting glucose, and motion) compared to future continuous glucose measurements (ground truth).
4-12 weeks
Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR forecasts will be assessed using machine learning technology, historical continuous glucose measurements data, and historical wearable based data (physiological, time, fasting glucose, and motion) compared to future continuous glucose measurements (ground truth).
4-12 weeks
Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting mild hypoglycemia (glucose < 3.9mmol/L) quantified as AUC-ROC
Time Frame: 4-12 weeks
Accuracy of the RADAR forecasts will be assessed using machine learning technology, historical continuous glucose measurements data, and historical wearable based data (physiological, time, fasting glucose, and motion) compared to future continuous glucose measurements (ground truth).
4-12 weeks
Accuracy of the RADAR-forecast model: Diagnostic accuracy of CGM data in combination with wearable based data (physiological, time, fasting glucose, and motion) in forecasting severe hyperglycemia (glucose < 3.0mmol/L) quantified as AUC-ROC.
Time Frame: 4-12 weeks
Accuracy of the RADAR forecasts will be assessed using machine learning technology, historical continuous glucose measurements data, and historical wearable based data (physiological, time, fasting glucose, and motion) compared to future continuous glucose measurements (ground truth).
4-12 weeks
Change of sleep pattern in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Sleep pattern will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Change of heart rate in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Heart rate will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Change of heart rate variability (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Heart rate variability will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Change of skin temperature (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Skin temperature will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Change of electrodermal activity (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Electrodermal activity will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Change of stress level (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time Frame: 4-12 weeks
Stress level will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Influence of sleep duration on daily time in glycemic target range (3.9 - 10 mmol/L)
Time Frame: 4-12 weeks
Sleep duration will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Influence on stress-level on daily time in glycemic target range (3.9 - 10 mmol/L)
Time Frame: 4-12 weeks
Stress level will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Influence on activity (number of steps and stairs climbed per day) on daily time in glycemic target range (3.9 - 10 mmol/L)
Time Frame: 4-12 weeks
Number of steps and stairs climbed per day will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
Influence of movement on daily time in glycemic target range (3.9 - 10.0 mmol/l)
Time Frame: 4-12 weeks
Movement will be recorded by the smartwatch and glucose values are measured with the continuous glucose meter (CGM).
4-12 weeks
24. Analysis of user requirements for smartwatch based dysglycemia warning systems
Time Frame: 4-12 weeks
User requirements for the smartwatch based dysglycemia warning system will be assessed in a semi-quantitative interview.
4-12 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
University Hospital Inselspital, Berne

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
ETH Zurich
University of St.Gallen

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Principal Investigator: Christoph Stettler, Prof. MD, University of Bern

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
February 19, 2021

Primary Completion (ACTUAL)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
March 28, 2022

Study Completion (ACTUAL)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
March 28, 2022

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
December 24, 2020

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
December 24, 2020

First Posted (ACTUAL)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
December 30, 2020

Study Record Updates

Last Update Posted (ACTUAL)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
September 14, 2022

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
September 13, 2022

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
September 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • RADAR

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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