University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism
Bern, Switzerland
NCT Number: NCT05183191
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.
Looking for future studies?
Notify Me21 year–60 year
All sexes
Interventional
Not applicable
Bern, Switzerland
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Participants arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants drive on a designated circuit using a driving simulator. Initially, a euglycaemic state (5.0-8.0 mmol/L) is kept stable and blood glucose is then progressively declined targeting at a level between 3.0-3.5 mmol/L by administering insulin. Blood glucose is kept stable in the hypoglycaemic range for 30 minutes. Thereafter, blood glucose is raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, the investigators analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression are recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively. Participants are blinded to the blood glucose values during the procedure and have to rate their symptoms and their driving performance on a 0-6 scale every 15 minutes.
Time frame: 240 minutes
The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as AUROC.
Time frame: 240 minutes
The machine learning model is developed and evaluated based on wearable data recorded in eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as AUROC.
Time frame: 240 minutes
The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity.
Time frame: 240 minutes
The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity.
Time frame: 240 minutes
Driving signals are recorded using a driving simulator.
Time frame: 240 minutes
Gaze coordinates are recorded using an eye-tracker device.
Time frame: 240 minutes
Head pose (position/rotation) are recorded using an eye-tracker device.
Time frame: 240 minutes
Heart rate is recorded using a holter-ECG device and wearables.
Time frame: 240 minutes
Heart rate variability is recorded using a holter-ECG device and wearables.
Time frame: 240 minutes
Electrodermal activity is recorded using wearables.
Time frame: 240 minutes
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 48, a higher score means more symptoms)
Time frame: Time Frame: 240 minutes
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone are measured at pre-defined time points.
Time frame: 240 minutes
Participants rate their driving performance on a 7-point Lickert Scale (lower value means poorer driving performance).
Time frame: 240 minutes
CGM values will be recorded using a CGM sensor (Dexcom G6). Venous blood glucose is considered as the reference. Accuracy will be quantified using mean absolute relative difference (MARD) from the gold-standard and using the Clarke error grid.
Time frame: 2 weeks, from screening to close out visit in each participant
Adverse Events will be recorded at each study visit.
Time frame: 2 weeks, from screening to close out visit in each participant
Serious Adverse Events will be recorded at each study visit.
Time frame: 240 minutes
Physiological response is measured using an electro-dermal activity sensor (skin conductance) and eye tracker (eye blinks). Self-reported emotional response is assessed with scales (e.g., valence, arousal, annoyance, sense of urgency).
Time frame: 240 minutes
Technology acceptance is measured with user experience questionnaires, such as the Unified Technology Acceptance and Use of Technology Questionnaire from Venkatesh et al. (2012) and free words associations.
Insel Gruppe AG, University Hospital Bern
Other
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 3
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT05308095
Abnormalities, Multiple, Autoimmune Diseases
Bern, Switzerland
View Trial DetailsNCT04569630
Abnormalities, Multiple, Autoimmune Diseases
Bern, Switzerland
View Trial DetailsNCT04035993
Autoimmune Diseases, Diabetes
Bern, Switzerland
View Trial DetailsNCT02092896
Autoimmune Diseases, Diabetes
Hvidovre, Copenhagen, Denmark
View Trial Details