Completed

NCT Number: NCT04035993

The HEADWIND-Study

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).

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Key information

Conditions

Diabetes Autoimmune Diseases Diabetes Mellitus Diabetes Mellitus, Type 1 Endocrine System Diseases Glucose Metabolism Disorders Hypoglycemia Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases

Age range

21 year–50 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism

Bern, Switzerland

About this study

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).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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
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).

Treatment and study plan

Controlled hypoglycaemic state while driving with a driving simulator

Other

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.

Primary outcomes

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.

Secondary outcomes

Change of time driving over midline

Time frame: 240 minutes

Change of time over midline during driving in hypoglycemia will be compared to euglycemia
Change of swerving

Time frame: 240 minutes

Change of swerving during driving in hypoglycemia will be compared to euglycemia
Change of spinning

Time frame: 240 minutes

Change of spinning during driving in hypoglycemia will be compared to euglycemia
Defining the glycemic level when driving performance is decreased

Time frame: 240 minutes

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
Driving performance before and after hypoglycemia

Time frame: 240 minutes

Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) driving performance before and after hypoglycemia will be assessed
Change of heart-rate

Time frame: 240 minutes

Change of heart-rate during driving in hypoglycemia will be compared to euglycemia
Change of heart-rate variability

Time frame: 240 minutes

Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia.
Change of electrodermal activity (EDA)

Time frame: 240 minutes

Change of EDA during driving in hypoglycemia will be compared to euglycemia.
Change of skin temperature

Time frame: 240 minutes

Change of skin temperature during driving in hypoglycemia will be compared to euglycemia.
CGM accuracy during hypoglycaemic state

Time frame: 240 minutes

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.
CGM time-delay during hypoglycaemic state

Time frame: 240 minutes

Time-delay (minutes) of CGM Sensor (dexcom G6) during progressive hypoglycemia will be assessed compared to plasma glucose.
Change of glucagon

Time frame: 240 minutes

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.
Change of growth hormone (GH)

Time frame: 240 minutes

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.
Change of catecholamines

Time frame: 240 minutes

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.
Change of cortisol

Time frame: 240 minutes

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.
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model

Time frame: 240 minutes

Blood glucose at time point of hypoglycemia detection by the HEADWIND-model will be determined.
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection

Time frame: 240 minutes

Time point of hypoglycemia detection by CGM will be compared to time point of hypoglycemia detection by the HEADWIND-model.
Comparison CGM and HEADWIND-model regarding glycemia

Time frame: 240 minutes

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.
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model

Time frame: 240 minutes

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)
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

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.
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

Using video data recorded by a camera and a thermal camera accuracy in hypoglycaemia detection will be analysed with applied machine learning technology.
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

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.
Self-estimation of glucose and hypoglycemia

Time frame: 240 minutes

Correlation between self-estimated glucose values and measured blood glucose will be assessed.
Self-estimation of driving performance

Time frame: 240 minutes

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).
Time point of need-to-treat

Time frame: 240 minutes

Time point of self-perceived need-to-treat (hypoglycemia) compared to time point of hypoglycemia detection by the HEADWIND-model and CGM.
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness

Time frame: 240 minutes

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.
Incidence of Adverse Events (AEs)

Time frame: 5 weeks

Adverse Events will be recorded at each study visit.
Incidence of Serious Adverse Events (SAEs)

Time frame: 5 weeks

Serious Adverse Events will be recorded at each study visit.
Perceived ease of use of the early hypoglycaemia warning system (EWS)

Time frame: Throughout the study, expected to be up to 12 months

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.
Perceived usefulness of the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Perceived enjoyment during EWS usage

Time frame: Throughout the study, expected to be up to 12 months

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.
Intention to adopt the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Intention to continuously use the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Reception of recommendations of the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Processing of recommendations of the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Perceived understandability of the recommendations of the EWS

Time frame: Throughout the study, expected to be up to 12 months

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.
Perceived familiarity 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 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.
Cognitive and emotional trust in 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 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.

Sponsors and collaborators

Lead sponsor

Insel Gruppe AG, University Hospital Bern

Other

Collaborators

ETH Zurich
University of St.Gallen

Registry information

Official study 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

Acronym: HEADWIND

Important dates

Study start: 2019
Primary completion: 2020
Study completion: 2020
First posted: Jul 29, 2019
Registry last updated: Jun 8, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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