The HEADWIND Study - Part 4

Not Applicable

Completed

• Conditions: Diabetes; Diabetes Mellitus, Type 1
• Interventions: Other: Controlled hypoglycaemic state while driving

• Registration Number: NCT05308095
• Lead Sponsor: Insel Gruppe AG, University Hospital Bern

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.

Study Timeline

• Study First Submitted: 2022-03-24
• Study First Submitted QC: 2022-03-24
• Study First Posted: 2022-04-01
• Study Start: 2022-04-13
• Primary Completion: 2022-06-23
• Study Completion: 2022-06-23
• Status Verified: 2022-12
• Last Update Submitted: 2022-12-20
• Last Update Posted: 2022-12-21

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 10

Inclusion Criteria

• Informed consent as documented by signature
• Type 1 Diabetes mellitus as defined by WHO for at least 1 year or confirmed C-peptide negative (<100pmol/l with concomitant blood glucose >4 mmol/l)
• Age between 21-60 years
• HbA1c ≤ 9.0 %
• Functional insulin treatment with good knowledge of insulin self-management
• Passed driver's examination at least 3 years before study inclusion. Possession of a valid, definitive Swiss driver's license.
• Active driving in the last 6 months.

Exclusion Criteria

• Contraindications to the drug used to induce hypoglycaemia (insulin aspart), known hypersensitivity or allergy to the adhesive patch used to attach the glucose sensor.
• Pregnancy or intention to become pregnant during the course of the study, lactating women or lack of safe contraception
• Other clinically significant concomitant disease states as judged by the investigator
• Physical or psychological disease likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator
• Renal failure
• Hepatic dysfunction
• Coronary heart disease
• Other cardiovascular disease
• Epilepsy
• Drug or alcohol abuse
• Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc. of the participant
• Participation in another study with an investigational drug within the 30 days preceding and during the present study
• Total daily insulin dose >2 IU/kg/day
• Specific concomitant therapy washout requirements prior to and/or during study participation
• Current treatment with drugs known to interfere with metabolism or driving performance

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Intervention group	Controlled hypoglycaemic state while driving	-

Primary Outcome Measures

Name	Time	Method
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).	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.

Secondary Outcome Measures

Name	Time	Method
Change of heart rate over the glycaemic trajectory	240 minutes	Heart rate is recorded using a holter-ECG device and a wearable.
Number of driving mishaps over the glycaemic trajectory.	240 minutes	Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Diagnostic accuracy of the hypoglycaemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycaemia quantified as sensitivity and specificity.	240 minutes	The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as sensitivity and specificity.
Time course of the hormonal response over the glycaemic trajectory	240 minutes	Epinephrine, norepinephrine, glucagon, cortisol and growth hormone will be measured at pre-defined time points.
Number of Adverse Events (AEs)	2 weeks, from screening to close out visit in each participant	Adverse Events will be recorded at each study visit.
Number of Serious Adverse Events (SAEs)	2 weeks, from screening to close out visit in each participant	Serious Adverse Events will be recorded at each study visit.
Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia quantified as sensitivity and specificity.	240 minutes	The CGM device is in use during controlled eu- and hypoglycaemia. Detection performance of hypoglycaemia is quantified as sensitivity and specificity.
Change of head pose over the glycaemic trajectory.	240 minutes	Head pose (position/rotation) is recorded using an eye-tracker device.
Hypoglycaemic symptoms over the glycaemic trajectory.	240 minutes	Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 6, a higher score means more symptoms)
Self assessment of driving performance over the glycaemic trajectory.	240 minutes	Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
CGM accuracy over the glycaemic trajectory	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.
Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.	240 minutes	Accuracy will be quantified using mean absolute relative difference from the intended hypoglycaemic range.
Change in driving features over the glycaemic trajectory.	240 minutes	Driving signals are recorded using a driving simulator.
Change of gaze coordinates over the glycaemic trajectory.	240 minutes	Gaze coordinates are recorded using an eye-tracker device.
Change of electrodermal activity over the glycaemic trajectory	240 minutes	Electrodermal activity is recorded using a wearable.
Change of cognitive performance over the glycaemic trajectory.	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).
Emotional response to the hypoglycaemia warning system	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).
Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).	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.
Change of heart rate variability over the glycaemic trajectory	240 minutes	Heart rate variability is recorded using a holter-ECG device and a wearable.
Technology acceptance of the hypoglycaemia warning system	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.

Trial Locations

Locations (1)

University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism; 🇨🇭 Bern, Switzerland