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Clinical Trial Summary

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.


Clinical Trial 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. 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. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05183191
Study type Interventional
Source University Hospital Inselspital, Berne
Contact Vera Lehmann, MD
Phone +41 (0)31 632 40 70
Email [email protected]
Status Recruiting
Phase N/A
Start date November 29, 2021
Completion date May 31, 2022

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