University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism
Bern, Switzerland
NCT Number: NCT05308095
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.
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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 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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Participants will drive on a designated circuit with a real car on a test track accompanied by a driving instructor. Initially, a euglycaemic state (5.0 - 8.0 mmol/L) is established and blood glucose is then declined to hypoglycaemia (3.0 - 3.5 mmol/L) by administering insulin. Thereafter, blood glucose is raised again to euglycaemia (5.0 - 8.0mmol/L). During the procedure, driving data is recorded. Additionally, eye movement, head pose, facial expression, heart rate, skin conductance, and CGM values are recorded throughout the glycemic trajectory. Participants are blinded to the blood glucose values during the procedure.
Time frame: 240 minutes
The machine learning model is developed and evaluated based on in-vehicle data generated in eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as AUROC.
Time frame: 240 minutes
The machine learning model is developed and evaluated based on wearable data recorded in eu- and hypoglycaemia. Detection performance of hypoglycemia is quantified as AUROC.
Time frame: 240 minutes
The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as sensitivity and specificity.
Time frame: 240 minutes
The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia 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) is recorded using an eye-tracker device.
Time frame: 240 minutes
Heart rate is recorded using a holter-ECG device and a wearable.
Time frame: 240 minutes
Heart rate variability is recorded using a holter-ECG device and a wearable.
Time frame: 240 minutes
Electrodermal activity is recorded using a wearable.
Time frame: 240 minutes
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 6, a higher score means more symptoms)
Time frame: 240 minutes
Cognitive performance will be assessed using the Trail Making B Test (lower time in seconds means better performance) and using the Digital Symbol Substitution Test (higher score means better performance).
Time frame: 240 minutes
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone will be measured at pre-defined time points.
Time frame: 240 minutes
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Time frame: 240 minutes
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Time frame: 240 minutes
CGM values will be recorded using a CGM sensor. 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: 240 minutes
Accuracy will be quantified using mean absolute relative difference from the intended hypoglycaemic range.
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 will be measured using an electro-dermal activity sensor (skin conductance) and eye tracker (eye blinks). Self-reported emotional response will be assessed with scales (e.g., valence, arousal, annoyance, sense of urgency).
Time frame: 240 minutes
Technology acceptance will be measures with user experience questionnaires, such as the Unified Technology Acceptance and Use of Technology Questionnaire 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 IV
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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.
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