Clinical trial NCT04689685: The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
Official title: The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Diabetes Mellitus, Insulin-Dependent
Diabetes
Keywords: Smartwatch
Wearable
Physiological data
Prediction
Dysglycemia
Hyperglycemia
Hypoglycemia
Lead sponsor: Insel Gruppe AG, University Hospital Bern
Sponsor class: OTHER
Collaborators: name: ETH Zurich
name: University of St.Gallen
First posted: 2020-12-30
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
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

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

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.

Eligibility criteria: 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

Primary outcomes:
measure: 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)
timeFrame: 4-12 weeks
Secondary outcomes:
measure: Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting hypoglycemia (glucose < 3.9 mmol/L) quantified as AUC-ROC
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: Accuracy of the RADAR model: Diagnostic accuracy of wearable based physiological data in detecting severe hyperglycemia (glucose > 13.9mmol/L) quantified as AUC-ROC
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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.
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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
timeFrame: 4-12 weeks
measure: 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.
timeFrame: 4-12 weeks
measure: Change of sleep pattern in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Change of heart rate in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Change of heart rate variability (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Change of skin temperature (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Change of electrodermal activity (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Change of stress level (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
timeFrame: 4-12 weeks
measure: Influence of sleep duration on daily time in glycemic target range (3.9 - 10 mmol/L)
timeFrame: 4-12 weeks
measure: Influence on stress-level on daily time in glycemic target range (3.9 - 10 mmol/L)
timeFrame: 4-12 weeks
measure: Influence on activity (number of steps and stairs climbed per day) on daily time in glycemic target range (3.9 - 10 mmol/L)
timeFrame: 4-12 weeks
measure: Influence of movement on daily time in glycemic target range (3.9 - 10.0 mmol/l)
timeFrame: 4-12 weeks
measure: 24. Analysis of user requirements for smartwatch based dysglycemia warning systems
timeFrame: 4-12 weeks
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Study officials:
affiliation: University of Bern
name: Christoph Stettler, Prof. MD
role: PRINCIPAL_INVESTIGATOR
References: