The HEADWIND Study - Part 4

Insel Gruppe AG, University Hospital Bern10 enrolled

Overview

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.

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.

Study Type

INTERVENTIONAL

Allocation

Purpose

OTHER

Masking

NONE

Enrollment

Conditions

Diabetes Diabetes Mellitus, Type 1

Interventions

Controlled hypoglycaemic state while drivingOTHER

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.

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 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

Locations (1)

University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism

Bern, Switzerland

Outcomes

Primary Outcomes

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.

Time frame: 240 minutes

Secondary Outcomes

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.

Time frame: 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.

Time frame: 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.

Time frame: 240 minutes

Change in driving features over the glycaemic trajectory.

Driving signals are recorded using a driving simulator.

Time frame: 240 minutes

Data from ClinicalTrials.gov

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