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Completed

NCT Number: NCT04689685

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

The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence.

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Key information

Conditions

Diabetes Mellitus, Insulin-dependent Autoimmune Diseases Diabetes Diabetes Mellitus Diabetes Mellitus, Type 1 Endocrine System Diseases Glucose Metabolism Disorders Hyperglycemia Hypoglycemia Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism

Bern, Switzerland

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Wearing smartwatch and continous glucose sensor

Other

Patients will be wearing a smartwatch and a continuous glucose meter (CGM) over a maximum duration of 3 months in an outpatient setting.

Primary outcomes

  1. 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)

Secondary outcomes

  1. 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)

  2. 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)

  3. 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. 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)

  5. 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)

  6. 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)

  7. 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)

  8. 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).

  9. 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).

  10. 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).

  11. 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).

  12. 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).

  13. 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).

  14. 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).

  15. 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).

  16. 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).

  17. 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).

  18. 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).

  19. 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).

  20. 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).

  21. 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).

  22. 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).

  23. 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.

Sponsors and collaborators

Lead sponsor

Insel Gruppe AG, University Hospital Bern

Other

Collaborators

  • ETH Zurich
  • University of St.Gallen

Registry information

Acronym: RADAR

Important dates

Study start
2021
Primary completion
2022
Study completion
2022
First posted
Dec 30, 2020
Registry last updated
Sep 14, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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