NCT04689685

Brief Summary

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

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
40

participants targeted

Target at P25-P50 for all trials

Timeline
Completed

Started Feb 2021

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

December 24, 2020

Completed
6 days until next milestone

First Posted

Study publicly available on registry

December 30, 2020

Completed
2 months until next milestone

Study Start

First participant enrolled

February 19, 2021

Completed
1.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 28, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

March 28, 2022

Completed
Last Updated

September 14, 2022

Status Verified

September 1, 2022

Enrollment Period

1.1 years

First QC Date

December 24, 2020

Last Update Submit

September 13, 2022

Conditions

Diabetes Mellitus, Insulin-DependentDiabetes

Keywords

SmartwatchWearablePhysiological dataPredictionDysglycemiaHyperglycemiaHypoglycemia

Outcome Measures

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)

    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 Outcomes (23)

  • Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC

    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

    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

    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

    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

    4-12 weeks

  • +18 more secondary outcomes

Interventions

Wearing smartwatch and continous glucose sensorOTHER

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

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

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

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism

Bern, Switzerland

Location

Related Publications (1)

  • Lehmann V, Foll S, Maritsch M, van Weenen E, Kraus M, Lagger S, Odermatt K, Albrecht C, Fleisch E, Zueger T, Wortmann F, Stettler C. Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data. Diabetes Care. 2023 May 1;46(5):993-997. doi: 10.2337/dc22-2290.

    PMID: 36805169DERIVED

MeSH Terms

Conditions

Diabetes Mellitus, Type 1Diabetes MellitusHyperglycemiaHypoglycemia

Condition Hierarchy (Ancestors)

Glucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System DiseasesAutoimmune DiseasesImmune System Diseases

Study Officials

  • Christoph Stettler, Prof. MD

    University of Bern

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 24, 2020

First Posted

December 30, 2020

Study Start

February 19, 2021

Primary Completion

March 28, 2022

Study Completion

March 28, 2022

Last Updated

September 14, 2022

Record last verified: 2022-09

Locations

CH(1)