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

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: Completed
• Conditions: Diabetes; Diabetes Mellitus, Insulin-Dependent
• Intervention / Treatment: Other: Wearing smartwatch and continous glucose sensor

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: Observational
• Enrollment (Actual): 40

Contacts and Locations

Study Locations

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

Participation Criteria

Eligibility Criteria

• 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

How is the study designed?

What is the study measuring?

Primary Outcome Measures

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)	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

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	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	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	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	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	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	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	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.	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	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	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	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.	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.	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.	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.	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.	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.	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.	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)	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)	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)	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)	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	User requirements for the smartwatch based dysglycemia warning system will be assessed in a semi-quantitative interview.	4-12 weeks

Collaborators and Investigators

• Sponsor: University Hospital Inselspital, Berne
• Collaborators: ETH Zurich; University of St.Gallen
• Investigators: Principal Investigator: Christoph Stettler, Prof. MD, University of Bern

Study record dates

Study Major Dates

• Study Start (ACTUAL): February 19, 2021
• Primary Completion (ACTUAL): March 28, 2022
• Study Completion (ACTUAL): March 28, 2022

Study Registration Dates

• First Submitted: December 24, 2020
• First Submitted That Met QC Criteria: December 24, 2020
• First Posted (ACTUAL): December 30, 2020

Study Record Updates

• Last Update Posted (ACTUAL): September 14, 2022
• Last Update Submitted That Met QC Criteria: September 13, 2022
• Last Verified: September 1, 2022

More Information

Terms related to this study

• Keywords: Hypoglycemia; Hyperglycemia; Smartwatch; Prediction; Wearable; Dysglycemia; Physiological data
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Immune System Diseases; Autoimmune Diseases; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 1
• Other Study ID 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