The HEADWIND-Study
Condition: Diabetes; Diabetes Mellitus, Type 1
MeSH terms: Diabetes Mellitus; Hypoglycemia; Diabetes Mellitus, Type 1
Study type: Interventional

Summary: 
      To analyse driving behavior of individuals with type 1 diabetes in eu- and progressive
      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 neural networks
      (deep machine learning classifiers).
    

Detailed description: 
      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. Despite important
      developments in the field of diabetes technology, the problem of hypoglycaemia during driving
      persists. Automotive technology is highly dynamic, and fully autonomous driving might, in the
      end, resolve the issue of hypoglycemia-induced accidents. However, autonomous driving (level
      4 or 5) is likely to be broadly available only to a substantially later time point than
      previously thought due to increasing concerns of safety associated with this technology.
      Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the
      problem of hypoglycemia-associated traffic incidents are urgently needed.

      On the supposition that driving behaviour differs significantly between euglycaemic state and
      hypoglycaemic state, the investigators assume that different driving patterns in hypoglycemia
      compared to euglycemia can be used to generate hypoglycemia detection models using machine
      learning neural networks (deep machine learning classifiers).
    

Intervention name(s): Controlled hypoglycaemic state while driving with a driving simulator
Intervention type(s): Other
Intervention description(s): Patients will arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants will drive on a designated circuit using a driving simulator. Initially, euglycaemic state (5.0-8.0 mmol/L) will be kept stable and then blood glucose will be declined progressively targeting at a level between 2.0-2.5mmol/L by administering an insulin bolus. Glucose will be kept stable at the hypoglycaemic level for 30 minutes. Thereafter, it will be raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, we will analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression, will be recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively.
Participants will be blinded to the glucose values during the procedure. They will have to rate their symptoms and their performance on a 0-6 scale every 15 minutes.
Intervention MeSH terms: Hypoglycemic Agents
Intervention model: Single Group Assignment
Masking: None (Open Label)
Primary purpose: Other
Keywords: Automotive Technology; Hypoglycemia; Hypoglycaemia; Driving; Driving simulator