The HEADWIND Study - Part 4

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 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
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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 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.

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 IV

Conditions
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Diabetes, Diabetes Mellitus, Type 1

Intervention / Treatment
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* Other: Controlled hypoglycaemic state while driving


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 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. 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. 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 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

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<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 240 minutes | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 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. | 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. | 240 minutes | 
| Change in driving features over the glycaemic trajectory. | Driving signals are recorded using a driving simulator. | 240 minutes | 
| Change of gaze coordinates over the glycaemic trajectory. | Gaze coordinates are recorded using an eye-tracker device. | 240 minutes | 
| Change of head pose over the glycaemic trajectory. | Head pose (position/rotation) is recorded using an eye-tracker device. | 240 minutes | 
| Change of heart rate over the glycaemic trajectory | Heart rate is recorded using a holter-ECG device and a wearable. | 240 minutes | 
| Change of heart rate variability over the glycaemic trajectory | Heart rate variability is recorded using a holter-ECG device and a wearable. | 240 minutes | 
| Change of electrodermal activity over the glycaemic trajectory | Electrodermal activity is recorded using a wearable. | 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) | 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). | 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. | 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). | 240 minutes | 
| Number of driving mishaps over the glycaemic trajectory. | Any driving mishaps, accidents and interventions by the driving instructor will be documented. | 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. | 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. | 240 minutes | 
| Number of Adverse Events (AEs) | Adverse Events will be recorded at each study visit. | 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. | 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). | 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. | 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, Car