The HEADWIND Study - Part 3

Insel Gruppe AG, University Hospital Bern11 enrolled

Overview

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.

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Enrollment

Conditions

Diabetes Diabetes Mellitus, Type 1

Interventions

Controlled hypoglycaemic state while driving with a driving simulatorOTHER

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.

Eligibility

Sex: ALLMin age: 21 YearsMax age: 60 Years

Medical Language ↔ Plain English

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.

Locations (1)

University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism

Bern, Switzerland

Outcomes

Primary Outcomes

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.

Time frame: 240 minutes

Secondary Outcomes

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.

Time frame: 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.

Time frame: 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.

Time frame: 240 minutes

Change in driving features over the glycemic trajectory.

Driving signals are recorded using a driving simulator.

Time frame: 240 minutes

Data from ClinicalTrials.gov

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