The HEADWIND Study - Part 3

Study Overview
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Brief Summary
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To analyse driving behavior of individuals with type 1 diabetes in eu- and mild 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 classifiers.

Detailed Description
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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 hypoglycemia during driving at an early stage. During controlled eu- and hypoglycemia, participants with type 1 diabetes mellitus drive in a validated driving simulator while in-vehicle data are recorded. Based on this data, the investigators aim at building machine learning classifiers to detect hypoglycemia during driving.

Official Title
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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 3

Conditions
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Diabetes, Diabetes Mellitus, Type 1

Intervention / Treatment
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* Other: Controlled hypoglycaemic state while driving with a driving simulator


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Informed Consent as documented by signature Type 1 Diabetes mellitus 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 21-60 years HbA1c ≤ 9.0 % based on analysis from central laboratory Functional insulin treatment with insulin pump therapy or basis-bolus insulin for at least 3 months with good knowledge of insulin self-management 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, etc.) or driving performance (e.g. opioids, benzodiazepines) Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session.

Ages Eligible for Study
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Minimum Age: 21 Years
Maximum Age: 60 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention group<br> | Other: Controlled hypoglycaemic state while driving with a driving simulator<br>* Participants arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants drive on a designated circuit using a driving simulator. Initially, a euglycaemic state (5.0-8.0 mmol/L) is kept stable and blood glucose is then progressively declined targeting at a level between 3.0-3.5 mmol/L by administering insulin. Blood glucose is kept stable in the hypoglycaemic range for 30 minutes. Thereafter, blood glucose is raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, the investigators analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression are recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively. Participants are blinded to the blood glucose values during the procedure and have to rate their symptoms and their driving performance on a 0-6 scale every 15 minutes.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic accuracy of the hypoglycemia warning system using in-vehicle data to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as the area under the receiver operator characteristics curve (AUC ROC). | The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as AUROC. | 240 minutes | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic accuracy of the hypoglycemia warning system using wearable data to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as the area under the receiver operator characteristics curve (AUC ROC). | The machine learning model is developed and evaluated based on wearable data recorded in eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as AUROC. | 240 minutes | 
| Diagnostic accuracy of the hypoglycemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as sensitivity and specificity. | The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity. | 240 minutes | 
| Diagnostic accuracy of the hypoglycemia warning system using wearable data and recordings of the CGM system to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as sensitivity and specificity. | The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity. | 240 minutes | 
| Change in driving features over the glycemic trajectory. | Driving signals are recorded using a driving simulator. | 240 minutes | 
| Change of gaze coordinates over the glycemic trajectory. | Gaze coordinates are recorded using an eye-tracker device. | 240 minutes | 
| Change of head pose over the glycemic trajectory. | Head pose (position/rotation) are recorded using an eye-tracker device. | 240 minutes | 
| Change of heart rate over the glycemic trajectory | Heart rate is recorded using a holter-ECG device and wearables. | 240 minutes | 
| Change of heart rate variability over the glycemic trajectory | Heart rate variability is recorded using a holter-ECG device and wearables. | 240 minutes | 
| Change of electrodermal activity over the glycemic trajectory | Electrodermal activity is recorded using wearables. | 240 minutes | 
| Hypoglycemic symptoms over the glycemic trajectory. | Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 48, a higher score means more symptoms) | 240 minutes | 
| Time course of the hormonal response over the glycemic trajectory | Epinephrine, norepinephrine, glucagon, cortisol and growth hormone are measured at pre-defined time points. | Time Frame: 240 minutes | 
| Self assessment of driving performance over the glycemic trajectory. | Participants rate their driving performance on a 7-point Lickert Scale (lower value means poorer driving performance). | 240 minutes | 
| CGM accuracy over the glycemic trajectory | CGM values will be recorded using a CGM sensor (Dexcom G6). 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. | 240 minutes | 
| Incidence of Adverse Events (AEs) | Adverse Events will be recorded at each study visit. | 2 weeks, from screening to close out visit in each participant | 
| Incidence of Serious Adverse Events (SAEs) | Serious Adverse Events will be recorded at each study visit. | 2 weeks, from screening to close out visit in each participant | 
| Emotional response to hypoglycemia warning system | Physiological response is measured using an electro-dermal activity sensor (skin conductance) and eye tracker (eye blinks). Self-reported emotional response is assessed with scales (e.g., valence, arousal, annoyance, sense of urgency). | 240 minutes | 
| Technology acceptance of the hypoglycemia warning system | Technology acceptance is measured with user experience questionnaires, such as the Unified Technology Acceptance and Use of Technology Questionnaire from Venkatesh et al. (2012) and free words associations. | 240 minutes | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator