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.

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

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 University Hospital Bern, Swiss Federal Institute of Technology (ETH) Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI. 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.