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.

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.

Any requests for raw data will be reviewed by the HEADWIND scientific study board comprising the principal investigator (PI) and Co-PI as well as senior researchers leading the involved research groups at Inselspital Bern, ETH Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI (Prof. Ch. Stettler). Applications should outline the purpose for the raw-data transfer. Any data that can be shared will need approval from the HEADWIND scientific study board and a Material Transfer Agreement in place. All data shared will be de-identified.

Only applications for non-commercial use will be considered and should be sent to the PI (Prof. Ch. Stettler).