DiabetesHEADWINDNCT04035993
The HEADWIND-Study
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 progressive hypoglycaemia using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns… Read more about this study
- Who is it for?
- Ages 21 to 50 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
- 26 participants
- Where
- 1 siteSwitzerland
- Study dates
- 7 Oct 2019to 6 Jul 2020
- 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 50 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- Informed Consent as documented by signature (Appendix Informed Consent Form)
- DM1 as defined by WHO for at least 1 year or is confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- Subjects aged between 21-50 years
- HbA1c ≤ 8.5 % based on analysis from central laboratory
- Functional insulin treatment with insulin pump therapy (CSII) or basis-bolus insulin for at least 3 months with good knowledge of insulin self-management
- Only for the main-study: Passed driver's examination at least 3 years before study inclusion. Possession of a valid Swiss driver's license. Active driving in the last 6 months before the study.
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
- Women who are pregnant or breastfeeding
- Intention to become pregnant during the study
- Lack of safe contraception, defined as: Female participants of childbearing potential, not using and not willing to continue using a medically reliable method of contraception for the entire study duration, such as oral, injectable, or implantable contraceptives, or intrauterine contraceptive devices, or who are not using any other method considered sufficiently reliable by the investigator in individual cases.
- Other clinically significant concomitant disease states as judged by the investigator (e.g., renal failure, hepatic dysfunction, cardiovascular disease, etc.)
- Known or suspected non-compliance, drug or alcohol abuse
Show all 15Show fewer
- 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
- Previous enrolment into the current study
- Enrolment of the investigator, his/her family members, employees and other dependent persons
- Total daily insulin dose >2 IU/kg/day.
- Specific concomitant therapy washout requirements prior to and/or during study participation
- Physical or psychological disease is likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator (especially coronary heart disease or epilepsy).
- Current treatment with drugs known to interfere with metabolism (e.g. systemic corticosteroids, statins etc.) or driving performance (e.g. opioids, benzodiazepines)
- Only for the main-study: Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session (at visit 2).
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism
Bern
About this study
Official title: The HEADWIND Study: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an in Vehicle Hypoglycaemia Warning System in Diabetes
To analyse driving behavior of individuals with type 1 diabetes in eu- and progressive hypoglycaemia using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns using machine learning neural networks (deep 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. Despite important developments in the field of diabetes technology, the problem of hypoglycaemia during driving persists. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of hypoglycemia-induced accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of hypoglycemia-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between euglycaemic state and hypoglycaemic state, the investigators assume that different driving patterns in hypoglycemia compared to euglycemia can be used to generate hypoglycemia detection models using machine learning neural networks (deep machine learning classifiers).
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Being tested
- Controlled hypoglycaemic state while driving with a driving simulator
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator
- Medical subject headings
- Diabetes Mellitus, Diabetes Mellitus, Type 1, Hypoglycemia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Controlled hypoglycaemic state while driving with a driving simulatorOther
Patients will arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants will drive on a designated circuit using a driving simulator. Initially, euglycaemic state (5.0-8.0 mmol/L) will be kept stable and then blood glucose will be declined progressively targeting at a level between 2.0-2.5mmol/L by administering an insulin bolus. Glucose will be kept stable at the hypoglycaemic level for 30 minutes. Thereafter, it will be raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, we will analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression, will be recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively.
Participants will be blinded to the glucose values during the procedure. They will have to rate their symptoms and their performance on a 0-6 scale every 15 minutes.
| Study group | Role | What they receive |
|---|---|---|
| Intervention group | Experimental | Not described Receives: Other: Controlled hypoglycaemic state while driving with a driving simulator |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Accuracy of the HEADWIND-model will be assessed using driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
Measured: 240 minutes
Secondary outcomes (38)
Change of time driving over midline
Change of time over midline during driving in hypoglycemia will be compared to euglycemia
Measured: 240 minutes
Change of swerving
Change of swerving during driving in hypoglycemia will be compared to euglycemia
Measured: 240 minutes
Change of spinning
Change of spinning during driving in hypoglycemia will be compared to euglycemia
Measured: 240 minutes
Defining the glycemic level when driving performance is decreased
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L) plasma-glucose level (mmol/L) when driving performance begins to be impaired will be assessed
Measured: 240 minutes
Driving performance before and after hypoglycemia
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) driving performance before and after hypoglycemia will be assessed
Measured: 240 minutes
Change of heart-rate
Change of heart-rate during driving in hypoglycemia will be compared to euglycemia
Measured: 240 minutes
Change of heart-rate variability
Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia.
Measured: 240 minutes
Change of electrodermal activity (EDA)
Change of EDA during driving in hypoglycemia will be compared to euglycemia.
Measured: 240 minutes
Change of skin temperature
Change of skin temperature during driving in hypoglycemia will be compared to euglycemia.
Measured: 240 minutes
CGM accuracy during hypoglycaemic state
Accuracy (MARD) of CGM Sensor (dexcom G6) in euglycemia (3.9 - 7 mmol/L), hypoglycemia (3.0 - 3.9mmol/L) and severe hypoglycemia (< 3.0 mmol/L) will be assessed based on plasma glucose measurements.
Measured: 240 minutes
CGM time-delay during hypoglycaemic state
Time-delay (minutes) of CGM Sensor (dexcom G6) during progressive hypoglycemia will be assessed compared to plasma glucose.
Measured: 240 minutes
Change of glucagon
Change of glucagon before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Measured: 240 minutes
Change of growth hormone (GH)
Change of GH before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Measured: 240 minutes
Change of catecholamines
Change of catecholamines before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Measured: 240 minutes
Change of cortisol
Change of cortisol before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Measured: 240 minutes
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
Blood glucose at time point of hypoglycemia detection by the HEADWIND-model will be determined.
Measured: 240 minutes
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
Time point of hypoglycemia detection by CGM will be compared to time point of hypoglycemia detection by the HEADWIND-model.
Measured: 240 minutes
Comparison CGM and HEADWIND-model regarding glycemia
Blood glucose at time point of hypoglycemia detection by the HEADWIND- model compared to glucose value of CGM at same time point will be assessed.
Measured: 240 minutes
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose < 3.9 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC) using only driving parameters (HEADWIND-model) will be compared to the HEADWIND-model with additional integration of CGM and physiological parameters (heart-rate, heart-rate variability, electrodermal activity (EDA), skin temperature and facial expression) (HEADWINDplus-model)
Measured: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data
Accuracy of hypoglycemia detection using physiological data (heart-rate, heart-rate variability, skin temperature, EDA) recorded with wearable devices during the study period will be analysed using applied machine learning technology.
Measured: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using video data
Using video data recorded by a camera and a thermal camera accuracy in hypoglycaemia detection will be analysed with applied machine learning technology.
Measured: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
Using eye-tracking data recorded by a camera and an eye-tracker (to record gaze behaviour) accuracy in hypoglycemia detection will be analysed with applied machine learning technology.
Measured: 240 minutes
Self-estimation of glucose and hypoglycemia
Correlation between self-estimated glucose values and measured blood glucose will be assessed.
Measured: 240 minutes
Self-estimation of driving performance
Correlation between self-estimated driving performance and measured driving performance based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L). Self-estimated driving performance will be assessed on a absolute 7-point scale from 0-6 (a lower value means a better outcome).
Measured: 240 minutes
Time point of need-to-treat
Time point of self-perceived need-to-treat (hypoglycemia) compared to time point of hypoglycemia detection by the HEADWIND-model and CGM.
Measured: 240 minutes
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
Correlation and comparison of perceived hypoglycemia symptoms on a scale from 0-6 (0 = no symptoms, 6 = extreme symptoms) to baseline hypoglycemia awareness score. Baseline hypoglycemia awareness will be assessed using a validated questionnaire (Clarke-Score) with a score over 3 points indicating decreased hypoglycemia awareness.
Measured: 240 minutes
Incidence of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Measured: 5 weeks
Incidence of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Measured: 5 weeks
Perceived ease of use of the early hypoglycaemia warning system (EWS)
Perceived ease of use of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Perceived usefulness of the EWS
Perceived usefulness of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Perceived enjoyment during EWS usage
Perceived enjoyment during EWS usage will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Intention to adopt the EWS
Intention to adopt the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Intention to continuously use the EWS
Intention to continuously use the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Reception of recommendations of the EWS
Reception of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Processing of recommendations of the EWS
Processing of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Perceived understandability of the recommendations of the EWS
Perceived understandability of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Perceived familiarity of the recommendations of the EWS
Perceived familiarity of the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Cognitive and emotional trust in the recommendations of the EWS
Cognitive and emotional trust in the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Measured: Throughout the study, expected to be up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2019
- Study start7 Oct 2019
- Primary completion2 Jul 2020
- Study completion6 Jul 2020
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
- 26 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, Inselspital, Bern University Hospital, University of Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04035993
- Sponsor's protocol ID
- HEADWIND
- Acronym
- HEADWIND
- Status last verified
- Jun 2021
- First submitted
- 5 Jun 2019
- Last updated
- 8 Jun 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Berube C, Lehmann VF, Maritsch M, Kraus M, Feuerriegel S, Wortmann F, Zuger T, Stettler C, Fleisch E, Kocaballi AB, Kowatsch T. Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial. JMIR Hum Factors. 2024 Jan 9;11:e42823. doi: 10.2196/42823.
PubMed 38194257
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The HEADWIND Study: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an in Vehicle Hypoglycaemia Warning System in Diabetes
- ClinicalTrials.gov ID
- NCT04035993
- Acronym / sponsor ID
- HEADWIND / HEADWIND
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 26 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Study start
- 7 Oct 2019 (actual)
- Primary completion
- 2 Jul 2020 (actual)
- Study completion
- 6 Jul 2020 (actual)
- First posted
- 29 Jul 2019 (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 progressive hypoglycaemia using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating eu- and hypoglycemic driving patterns using machine learning neural networks (deep 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. Despite important developments in the field of diabetes technology, the problem of hypoglycaemia during driving persists. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of hypoglycemia-induced accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of hypoglycemia-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between euglycaemic state and hypoglycaemic state, the investigators assume that different driving patterns in hypoglycemia compared to euglycemia can be used to generate hypoglycemia detection models using machine learning neural networks (deep machine learning classifiers).
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 with a driving simulator |
Who may qualify
- Ages
- 21 to 50 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (6 listed)
- Informed Consent as documented by signature (Appendix Informed Consent Form)
- DM1 as defined by WHO for at least 1 year or is confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
- Subjects aged between 21-50 years
- HbA1c ≤ 8.5 % based on analysis from central laboratory
- Functional insulin treatment with insulin pump therapy (CSII) or basis-bolus insulin for at least 3 months 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
- Women who are pregnant or breastfeeding
- Intention to become pregnant during the study
- Lack of safe contraception, defined as: Female participants of childbearing potential, not using and not willing to continue using a medically reliable method of contraception for the entire study duration, such as oral, injectable, or implantable contraceptives, or intrauterine contraceptive devices, or who are not using any other method considered sufficiently reliable by the investigator in individual cases.
- Other clinically significant concomitant disease states as judged by the investigator (e.g., renal failure, hepatic dysfunction, cardiovascular disease, etc.)
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 driving with a driving simulatorPatients will arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants will drive on a designated circuit using a driving simulator. Initially, euglycaemic state (5.0-8.0 mmol/L) will be kept stable and then blood glucose will be declined progressively targeting at a level between 2.0-2.5mmol/L by administering an insulin bolus. Glucose will be kept stable at the hypoglycaemic level for 30 minutes. Thereafter, it will be raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, we will analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression, will be recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively. Participants will be blinded to the glucose values during the procedure. They will have to rate their symptoms and their performance on a 0-6 scale every 15 minutes. | Other | Intervention group |
Primary outcome
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Time frame: 240 minutes
Accuracy of the HEADWIND-model will be assessed using driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
38 secondary outcomes
Change of time driving over midline
Time frame: 240 minutes
Change of swerving
Time frame: 240 minutes
Change of spinning
Time frame: 240 minutes
Defining the glycemic level when driving performance is decreased
Time frame: 240 minutes
Driving performance before and after hypoglycemia
Time frame: 240 minutes
Change of heart-rate
Time frame: 240 minutes
Change of heart-rate variability
Time frame: 240 minutes
Change of electrodermal activity (EDA)
Time frame: 240 minutes
Change of skin temperature
Time frame: 240 minutes
CGM accuracy during hypoglycaemic state
Time frame: 240 minutes
CGM time-delay during hypoglycaemic state
Time frame: 240 minutes
Change of glucagon
Time frame: 240 minutes
Change of growth hormone (GH)
Time frame: 240 minutes
Change of catecholamines
Time frame: 240 minutes
Change of cortisol
Time frame: 240 minutes
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
Time frame: 240 minutes
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
Time frame: 240 minutes
Comparison CGM and HEADWIND-model regarding glycemia
Time frame: 240 minutes
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
Time frame: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data
Time frame: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using video data
Time frame: 240 minutes
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
Time frame: 240 minutes
Self-estimation of glucose and hypoglycemia
Time frame: 240 minutes
Self-estimation of driving performance
Time frame: 240 minutes
Time point of need-to-treat
Time frame: 240 minutes
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
Time frame: 240 minutes
Incidence of Adverse Events (AEs)
Time frame: 5 weeks
Incidence of Serious Adverse Events (SAEs)
Time frame: 5 weeks
Perceived ease of use of the early hypoglycaemia warning system (EWS)
Time frame: Throughout the study, expected to be up to 12 months
Perceived usefulness of the EWS
Time frame: Throughout the study, expected to be up to 12 months
Perceived enjoyment during EWS usage
Time frame: Throughout the study, expected to be up to 12 months
Intention to adopt the EWS
Time frame: Throughout the study, expected to be up to 12 months
Intention to continuously use the EWS
Time frame: Throughout the study, expected to be up to 12 months
Reception of recommendations of the EWS
Time frame: Throughout the study, expected to be up to 12 months
Processing of recommendations of the EWS
Time frame: Throughout the study, expected to be up to 12 months
Perceived understandability of the recommendations of the EWS
Time frame: Throughout the study, expected to be up to 12 months
Perceived familiarity of the recommendations of the EWS
Time frame: Throughout the study, expected to be up to 12 months
Cognitive and emotional trust in the recommendations of the EWS
Time frame: Throughout the study, expected to be up to 12 months
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 8 Jun 2021.
- Derived from this study: PubMed 38194257Berube C, Lehmann VF, Maritsch M, Kraus M, Feuerriegel S, Wortmann F, Zuger T, Stettler C, Fleisch E, Kocaballi AB, Kowatsch T. Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial. JMIR Hum Factors. 2024 Jan 9;11:e42823. doi: 10.2196/42823.
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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
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 6 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 8 Jun 2021. 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 HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC). (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 NCT04035993, 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 8 Jun 2021; AzTrials last refreshed its copy on 31 Oct 2026.