NCT04569630

The HEADWIND Study - Part 2

Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Hypoglycaemia Warning System in Diabetes - The HEADWIND Study Part 2

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 22 (actual)
• Sponsor: Insel Gruppe AG, University Hospital Bern · Academic / Other
• Sex: All
• Age: 21 Years – 60 Years
• Healthy volunteers: Not accepted

Summary

To analyse driving behavior of individuals with type 1 diabetes in eu- and progressive hypoglycaemia while driving in a real car. Based on the driving variables provided by the car 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).

Conditions

• Diabetes
• Diabetes Mellitus, Type 1

Interventions

Type	Name	Description
OTHER	Controlled hypoglycaemic state while driving	Participants will drive on a designated circuit with a real car on a test track accompanied by a driving instructor. Driving data will be recorded in 4 subsequent glycemic states using an adapted hypoglycemic clamp protocol: euglycemia (d1, 5-8 mmol/l), progressive hypoglycaemia (d2, declining from 4.5 to 2.5 mmol/l), stable hypoglycemia (d3, 2.0-2.5 mmol/l), and again in euglycaemia (d4, 5-8 mmol/l). Patients will be blinded to their glucose levels.

Timeline

• Start date: 2020-10-01
• Primary completion: 2021-05-27
• Completion: 2021-05-28
• First posted: 2020-09-30
• Last updated: 2021-06-29

Locations

1 site across 1 country: Switzerland