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Completed

NCT Number: NCT05308095

The HEADWIND Study - Part 4

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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Key information

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Controlled hypoglycaemic state while driving

Other

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.

Primary outcomes

  1. 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).

    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.

Secondary outcomes

  1. Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (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.

  2. 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.

    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.

  3. Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia 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.

  4. Change in driving features over the glycaemic trajectory.

    Time frame: 240 minutes

    Driving signals are recorded using a driving simulator.

  5. Change of gaze coordinates over the glycaemic trajectory.

    Time frame: 240 minutes

    Gaze coordinates are recorded using an eye-tracker device.

  6. Change of head pose over the glycaemic trajectory.

    Time frame: 240 minutes

    Head pose (position/rotation) is recorded using an eye-tracker device.

  7. Change of heart rate over the glycaemic trajectory

    Time frame: 240 minutes

    Heart rate is recorded using a holter-ECG device and a wearable.

  8. Change of heart rate variability over the glycaemic trajectory

    Time frame: 240 minutes

    Heart rate variability is recorded using a holter-ECG device and a wearable.

  9. Change of electrodermal activity over the glycaemic trajectory

    Time frame: 240 minutes

    Electrodermal activity is recorded using a wearable.

  10. Hypoglycaemic symptoms over the glycaemic trajectory.

    Time frame: 240 minutes

    Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 6, a higher score means more symptoms)

  11. Change of cognitive performance over the glycaemic trajectory.

    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).

  12. Time course of the hormonal response over the glycaemic trajectory

    Time frame: 240 minutes

    Epinephrine, norepinephrine, glucagon, cortisol and growth hormone will be measured at pre-defined time points.

  13. Self assessment of driving performance over the glycaemic trajectory.

    Time frame: 240 minutes

    Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).

  14. Number of driving mishaps over the glycaemic trajectory.

    Time frame: 240 minutes

    Any driving mishaps, accidents and interventions by the driving instructor will be documented.

  15. CGM accuracy over the glycaemic trajectory

    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.

  16. Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.

    Time frame: 240 minutes

    Accuracy will be quantified using mean absolute relative difference from the intended hypoglycaemic range.

  17. Number of Adverse Events (AEs)

    Time frame: 2 weeks, from screening to close out visit in each participant

    Adverse Events will be recorded at each study visit.

  18. Number of Serious Adverse Events (SAEs)

    Time frame: 2 weeks, from screening to close out visit in each participant

    Serious Adverse Events will be recorded at each study visit.

  19. Emotional response to the hypoglycaemia warning system

    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).

  20. Technology acceptance of the hypoglycaemia warning system

    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.

Sponsors and collaborators

Lead sponsor

Insel Gruppe AG, University Hospital Bern

Other

Collaborators

  • Swiss Federal Institute of Technology
  • University of St.Gallen

Registry information

Official study 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

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Apr 1, 2022
Registry last updated
Dec 21, 2022

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.

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