DiabetesNCT05308095
The HEADWIND Study - Part 4
A study of Diabetes and Diabetes Mellitus, Type 1, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
Trial summary
To analyse driving behavior of individuals with type 1 diabetes in eu- and mild hypoglycaemia while driving in a real car. Based on the in-vehicle variables, the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycaemic driving patterns using machine learning classifiers. Read more about this study
- Who is it for?
- Ages 21 to 60 years
- Conditions the study is for
- Diabetes, Diabetes Mellitus, Type 1
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10 participants
- Where
- 1 siteSwitzerland
- Study dates
- 13 Apr 2022to 23 Jun 2022
- Phase
- Interventional
- 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 21 to 60 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- Informed consent as documented by signature
- Type 1 Diabetes mellitus as defined by WHO for at least 1 year or confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- Age between 21-60 years
- HbA1c ≤ 9.0 %
- Functional insulin treatment with good knowledge of insulin self-management
- Passed driver's examination at least 3 years before study inclusion. Possession of a valid, definitive Swiss driver's license.
- Active driving in the last 6 months.
Who cannot join
- Contraindications to the drug used to induce hypoglycaemia (insulin aspart), known hypersensitivity or allergy to the adhesive patch used to attach the glucose sensor.
- Pregnancy or intention to become pregnant during the course of the study, lactating women or lack of safe contraception
- Other clinically significant concomitant disease states as judged by the investigator
- 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
- Renal failure
- Hepatic dysfunction
Show all 15Show fewer
- Coronary heart disease
- Other cardiovascular disease
- Epilepsy
- 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
- Participation in another study with an investigational drug within the 30 days preceding and during the present study
- Total daily insulin dose >2 IU/kg/day
- Specific concomitant therapy washout requirements prior to and/or during study participation
- Current treatment with drugs known to interfere with metabolism or driving performance
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism
Bern
About this study
Official title: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Hypoglycaemia Warning System in Diabetes The HEADWIND Study Part IV
To analyse driving behavior of individuals with type 1 diabetes in eu- and mild hypoglycaemia while driving in a real car. Based on the in-vehicle variables, the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycaemic driving patterns using machine learning classifiers.
Read the detailed description
Hypoglycaemia is among the most relevant acute complications of diabetes mellitus. During hypoglycaemia physical, psychomotor, executive and cognitive function significantly deteriorate. These are important prerequisites for safe driving.
Accordingly, hypoglycaemia has consistently been shown to be associated with an increased risk of driving accidents and is, therefore, regarded as one of the relevant factors in traffic safety. Therefore, this study aims at evaluating a machine-learning based approach using in-vehicle data to detect hypoglycaemia during driving.
During controlled eu- and hypoglycaemia, participants with type 1 diabetes mellitus drive in a driving school car on a closed test-track while in-vehicle data is recorded. Based on this data, the investigators aim at building machine learning classifiers to detect hypoglycemia during driving.
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Being tested
- Controlled hypoglycaemic state while driving
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Car
- Medical subject headings
- Diabetes Mellitus, Diabetes Mellitus, Type 1, Hypoglycemia, Carney Complex
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Controlled hypoglycaemic state while drivingOther
Participants will drive on a designated circuit with a real car on a test track accompanied by a driving instructor. Initially, a euglycaemic state (5.0 - 8.0 mmol/L) is established and blood glucose is then declined to hypoglycaemia (3.0 - 3.5 mmol/L) by administering insulin. Thereafter, blood glucose is raised again to euglycaemia (5.0 - 8.0mmol/L). During the procedure, driving data is recorded. Additionally, eye movement, head pose, facial expression, heart rate, skin conductance, and CGM values are recorded throughout the glycemic trajectory. Participants are blinded to the blood glucose values during the procedure.
| Study group | Role | What they receive |
|---|---|---|
| Intervention group | Experimental | Not described Receives: Other: Controlled hypoglycaemic state while driving |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as AUROC.
Measured: 240 minutes
Secondary outcomes (20)
Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
The machine learning model is developed and evaluated based on wearable data recorded in eu- and hypoglycaemia. Detection performance of hypoglycemia is quantified as AUROC.
Measured: 240 minutes
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycaemia quantified as sensitivity and specificity.
The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as sensitivity and specificity.
Measured: 240 minutes
Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia quantified as sensitivity and specificity.
The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as sensitivity and specificity.
Measured: 240 minutes
Change in driving features over the glycaemic trajectory.
Driving signals are recorded using a driving simulator.
Measured: 240 minutes
Change of gaze coordinates over the glycaemic trajectory.
Gaze coordinates are recorded using an eye-tracker device.
Measured: 240 minutes
Change of head pose over the glycaemic trajectory.
Head pose (position/rotation) is recorded using an eye-tracker device.
Measured: 240 minutes
Change of heart rate over the glycaemic trajectory
Heart rate is recorded using a holter-ECG device and a wearable.
Measured: 240 minutes
Change of heart rate variability over the glycaemic trajectory
Heart rate variability is recorded using a holter-ECG device and a wearable.
Measured: 240 minutes
Change of electrodermal activity over the glycaemic trajectory
Electrodermal activity is recorded using a wearable.
Measured: 240 minutes
Hypoglycaemic symptoms over the glycaemic trajectory.
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 6, a higher score means more symptoms)
Measured: 240 minutes
Change of cognitive performance over the glycaemic trajectory.
Cognitive performance will be assessed using the Trail Making B Test (lower time in seconds means better performance) and using the Digital Symbol Substitution Test (higher score means better performance).
Measured: 240 minutes
Time course of the hormonal response over the glycaemic trajectory
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone will be measured at pre-defined time points.
Measured: 240 minutes
Self assessment of driving performance over the glycaemic trajectory.
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Measured: 240 minutes
Number of driving mishaps over the glycaemic trajectory.
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Measured: 240 minutes
CGM accuracy over the glycaemic trajectory
CGM values will be recorded using a CGM sensor. Venous blood glucose is considered as the reference. Accuracy will be quantified using mean absolute relative difference (MARD) from the gold-standard and using the Clarke error grid.
Measured: 240 minutes
Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.
Accuracy will be quantified using mean absolute relative difference from the intended hypoglycaemic range.
Measured: 240 minutes
Number of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Measured: 2 weeks, from screening to close out visit in each participant
Number of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Measured: 2 weeks, from screening to close out visit in each participant
Emotional response to the hypoglycaemia warning system
Physiological response will be measured using an electro-dermal activity sensor (skin conductance) and eye tracker (eye blinks). Self-reported emotional response will be assessed with scales (e.g., valence, arousal, annoyance, sense of urgency).
Measured: 240 minutes
Technology acceptance of the hypoglycaemia warning system
Technology acceptance will be measures with user experience questionnaires, such as the Unified Technology Acceptance and Use of Technology Questionnaire and free words associations.
Measured: 240 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2022
- Study start13 Apr 2022
- Primary completion23 Jun 2022
- Study completion23 Jun 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 DiabetesStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 10 participants (actual)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- Swiss Federal Institute of Technology, University of St.Gallen
- Responsible party
- Sponsor
- Investigators
- Christoph Stettler, Prof. MD, Principal investigator, Inselspital, Bern University Hospital, University of Bern, Switzerland, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern, Switzerland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05308095
- Sponsor's protocol ID
- HEADWIND 4
- Status last verified
- Dec 2022
- First submitted
- 24 Mar 2022
- Last updated
- 21 Dec 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Any requests for raw data will be reviewed by the HEADWIND scientific study board comprising the principal investigator (PI) and Co-PI as well as senior researchers leading the involved research groups at University Hospital Bern, Swiss Federal Institute of Technology (ETH) Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI. Applications should outline the purpose for the raw-data transfer. Any data that can be shared will need approval from the HEADWIND scientific study board and a Material Transfer Agreement in place. All data shared will be de-identified.
Publications
Berube C, Maritsch M, Lehmann VF, Kraus M, Feuerriegel S, Zuger T, Wortmann F, Stettler C, Fleisch E, Kocaballi AB, Kowatsch T. Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving. JMIR Hum Factors. 2024 Apr 18;11:e46967. doi: 10.2196/46967.
PubMed 38635313
Study at a glance
From the registry record, last updated by the sponsor on 21 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Hypoglycaemia Warning System in Diabetes The HEADWIND Study Part IV
- ClinicalTrials.gov ID
- NCT05308095
- Acronym / sponsor ID
- HEADWIND 4
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Car
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 10 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- Swiss Federal Institute of Technology, University of St.Gallen
- Study start
- 13 Apr 2022 (actual)
- Primary completion
- 23 Jun 2022 (actual)
- Study completion
- 23 Jun 2022 (actual)
- First posted
- 1 Apr 2022 (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.
To analyse driving behavior of individuals with type 1 diabetes in eu- and mild hypoglycaemia while driving in a real car. Based on the in-vehicle variables, the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycaemic driving patterns using machine learning classifiers.
Detailed description
Hypoglycaemia is among the most relevant acute complications of diabetes mellitus. During hypoglycaemia physical, psychomotor, executive and cognitive function significantly deteriorate. These are important prerequisites for safe driving.
Accordingly, hypoglycaemia has consistently been shown to be associated with an increased risk of driving accidents and is, therefore, regarded as one of the relevant factors in traffic safety. Therefore, this study aims at evaluating a machine-learning based approach using in-vehicle data to detect hypoglycaemia during driving.
During controlled eu- and hypoglycaemia, participants with type 1 diabetes mellitus drive in a driving school car on a closed test-track while in-vehicle data is recorded. Based on this data, the investigators aim at building machine learning classifiers to detect hypoglycemia during driving.
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Intervention group | Experimental | Other: Controlled hypoglycaemic state while driving |
Who may qualify
- Ages
- 21 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (7 listed)
- Informed consent as documented by signature
- Type 1 Diabetes mellitus as defined by WHO for at least 1 year or confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- Age between 21-60 years
- HbA1c ≤ 9.0 %
- Functional insulin treatment with good knowledge of insulin self-management
Key exclusion criteria (15 listed)
- Contraindications to the drug used to induce hypoglycaemia (insulin aspart), known hypersensitivity or allergy to the adhesive patch used to attach the glucose sensor.
- Pregnancy or intention to become pregnant during the course of the study, lactating women or lack of safe contraception
- Other clinically significant concomitant disease states as judged by the investigator
- 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
- Renal failure
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 |
|---|---|---|
| Controlled hypoglycaemic state while drivingParticipants will drive on a designated circuit with a real car on a test track accompanied by a driving instructor. Initially, a euglycaemic state (5.0 - 8.0 mmol/L) is established and blood glucose is then declined to hypoglycaemia (3.0 - 3.5 mmol/L) by administering insulin. Thereafter, blood glucose is raised again to euglycaemia (5.0 - 8.0mmol/L). During the procedure, driving data is recorded. Additionally, eye movement, head pose, facial expression, heart rate, skin conductance, and CGM values are recorded throughout the glycemic trajectory. Participants are blinded to the blood glucose values during the procedure. | Other | Intervention group |
Primary outcome
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
Time frame: 240 minutes
The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as AUROC.
20 secondary outcomes
Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
Time frame: 240 minutes
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycaemia quantified as sensitivity and specificity.
Time frame: 240 minutes
Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia quantified as sensitivity and specificity.
Time frame: 240 minutes
Change in driving features over the glycaemic trajectory.
Time frame: 240 minutes
Change of gaze coordinates over the glycaemic trajectory.
Time frame: 240 minutes
Change of head pose over the glycaemic trajectory.
Time frame: 240 minutes
Change of heart rate over the glycaemic trajectory
Time frame: 240 minutes
Change of heart rate variability over the glycaemic trajectory
Time frame: 240 minutes
Change of electrodermal activity over the glycaemic trajectory
Time frame: 240 minutes
Hypoglycaemic symptoms over the glycaemic trajectory.
Time frame: 240 minutes
Change of cognitive performance over the glycaemic trajectory.
Time frame: 240 minutes
Time course of the hormonal response over the glycaemic trajectory
Time frame: 240 minutes
Self assessment of driving performance over the glycaemic trajectory.
Time frame: 240 minutes
Number of driving mishaps over the glycaemic trajectory.
Time frame: 240 minutes
CGM accuracy over the glycaemic trajectory
Time frame: 240 minutes
Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.
Time frame: 240 minutes
Number of Adverse Events (AEs)
Time frame: 2 weeks, from screening to close out visit in each participant
Number of Serious Adverse Events (SAEs)
Time frame: 2 weeks, from screening to close out visit in each participant
Emotional response to the hypoglycaemia warning system
Time frame: 240 minutes
Technology acceptance of the hypoglycaemia warning system
Time frame: 240 minutes
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 21 Dec 2022.
- Derived from this study: PubMed 38635313Berube C, Maritsch M, Lehmann VF, Kraus M, Feuerriegel S, Zuger T, Wortmann F, Stettler C, Fleisch E, Kocaballi AB, Kowatsch T. Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving. JMIR Hum Factors. 2024 Apr 18;11:e46967. doi: 10.2196/46967.
- Individual participant dataSharing planned: Yes. Any requests for raw data will be reviewed by the HEADWIND scientific study board comprising the principal investigator (PI) and Co-PI as well as senior researchers leading the involved research groups at University Hospital Bern, Swiss Federal Institute of Technology (ETH) Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI. Applications should outline the purpose for the raw-data transfer. Any data that can be shared will need approval from the HEADWIND scientific study board and a Material Transfer Agreement in place. All data shared will be de-identified.
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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
Inselspital, Bern University Hospital, University of Bern, Switzerland, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern, Switzerland
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study of Diabetes and Diabetes Mellitus, Type 1, 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 7 inclusion and 15 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 21 Dec 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?
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC). (time frame: 240 minutes). 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 NCT05308095, 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 21 Dec 2022.