Diabetes Mellitus, Insulin-DependentRADARNCT04689685
The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
An observational study of Diabetes Mellitus, Insulin-Dependent and Diabetes, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
Trial summary
The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetes Mellitus, Insulin-Dependent, Diabetes
- What is being tested?
- Wearing smartwatch and continous glucose sensor
- How many people
- 40 participants
- Where
- 1 siteSwitzerland
- Study dates
- 19 Feb 2021to 28 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients with insulin-dependent diabetes mellitus treated with multiple daily insulin injections or continuous subcutaneous insulin infusion
Who may be able to join
- Informed consent as documented by signature
- Age ≥ 18 years
- Diabetes mellitus treated with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII)
Who cannot join
- 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
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- 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
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Bern
About this study
The study RADAR aims at developing a wearable based dysglycemia detection and warning system for patients with diabetes mellitus using artificial intelligence.
Read the 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.
- Conditions
- Diabetes Mellitus, Insulin-Dependent, Diabetes
- Being tested
- Wearing smartwatch and continous glucose sensor
- Keywords
- Smartwatch, Wearable, Physiological data, Prediction, Dysglycemia, Hyperglycemia, Hypoglycemia
- Medical subject headings
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Hyperglycemia, Hypoglycemia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 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
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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 4-12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2020
- Study start19 Feb 2021
- Primary completion28 Mar 2022
- Study completion28 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus, Insulin-DependentStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Responsible party
- Sponsor
- Investigators
- Christoph Stettler, Prof. MD, Principal investigator, University of Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04689685
- Sponsor's protocol ID
- RADAR
- Acronym
- RADAR
- Status last verified
- Sep 2022
- First submitted
- 24 Dec 2020
- Last updated
- 14 Sep 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 36805169
Study at a glance
From the registry record, last updated by the sponsor on 14 Sep 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The RADAR Study - Wearable-Based Dysglycemia Detection and Warning in Diabetes
- ClinicalTrials.gov ID
- NCT04689685
- Acronym / sponsor ID
- RADAR / RADAR
- Conditions
- Diabetes Mellitus, Insulin-Dependent, Diabetes
- Keywords
- Smartwatch, Wearable, Physiological data, Prediction, Dysglycemia, Hyperglycemia, Hypoglycemia
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Study start
- 19 Feb 2021 (actual)
- Primary completion
- 28 Mar 2022 (actual)
- Study completion
- 28 Mar 2022 (actual)
- First posted
- 30 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients with insulin-dependent diabetes mellitus treated with multiple daily insulin injections or continuous subcutaneous insulin infusion
Key inclusion criteria (3 listed)
- Informed consent as documented by signature
- Age ≥ 18 years
- Diabetes mellitus treated with multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII)
Key exclusion criteria (9 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Wearing smartwatch and continous glucose sensorPatients will be wearing a smartwatch and a continuous glucose meter (CGM) over a maximum duration of 3 months in an outpatient setting. | Other | – |
Primary outcome
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)
23 secondary outcomes
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: 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: 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: 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: 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: 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: 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 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-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-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-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-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
Change of sleep pattern in dysglycemia (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time frame: 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
Change of heart rate variability (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time frame: 4-12 weeks
Change of skin temperature (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time frame: 4-12 weeks
Change of electrodermal activity (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time frame: 4-12 weeks
Change of stress level (< 3.9 mmol/l and > 13.9 mmol/l) compared to eugylcemia.
Time frame: 4-12 weeks
Influence of sleep duration on daily time in glycemic target range (3.9 - 10 mmol/L)
Time frame: 4-12 weeks
Influence on stress-level on daily time in glycemic target range (3.9 - 10 mmol/L)
Time frame: 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
Influence of movement on daily time in glycemic target range (3.9 - 10.0 mmol/l)
Time frame: 4-12 weeks
24. Analysis of user requirements for smartwatch based dysglycemia warning systems
Time frame: 4-12 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Sep 2022.
- Derived from this study: PubMed 36805169Lehmann 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Christoph Stettler, Prof. MD principal investigator
University of Bern
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes Mellitus, Insulin-Dependent and Diabetes, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 14 Sep 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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) (time frame: 4-12 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04689685, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Sep 2022.