Ignite Creation Date:
2025-12-24 @ 2:14 PM
Ignite Modification Date:
2025-12-25 @ 2:35 PM
Study NCT ID:
NCT05308095
Status:
COMPLETED
Last Update Posted:
2022-12-21
First Post:
2022-03-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The HEADWIND Study - Part 4
Sponsor:
Insel Gruppe AG, University Hospital Bern
Organization:
Study Overview
Official Title:
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 IV
Status:
COMPLETED
Status Verified Date:
2022-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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.
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. 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.
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.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: