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The HEADWIND Study - Part 3

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ClinicalTrials.gov Identifier: NCT05183191
Recruitment Status : Recruiting
First Posted : January 10, 2022
Last Update Posted : January 10, 2022
Sponsor:
Collaborators:
ETH Zurich
University of St.Gallen
Information provided by (Responsible Party):
University Hospital Inselspital, Berne

Brief Summary:
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.

Condition or disease Intervention/treatment Phase
Diabetes Diabetes Mellitus, Type 1 Other: Controlled hypoglycaemic state while driving with a driving simulator Not Applicable

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 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.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 11 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Other
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 3
Actual Study Start Date : November 29, 2021
Estimated Primary Completion Date : May 30, 2022
Estimated Study Completion Date : May 31, 2022

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Hypoglycemia

Arm Intervention/treatment
Experimental: Intervention group Other: Controlled hypoglycaemic state while driving with a driving simulator
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.




Primary Outcome Measures :
  1. Diagnostic accuracy of the hypoglycemia warning system using in-vehicle data to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as the area under the receiver operator characteristics curve (AUC ROC). [ 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.


Secondary Outcome Measures :
  1. Diagnostic accuracy of the hypoglycemia warning system using wearable data to detect hypoglycemia (blood glucose <3.9mmol/L) quantified as the area under the receiver operator characteristics curve (AUC ROC). [ 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.

  2. Diagnostic accuracy of the hypoglycemia warning system using in-vehicle data and recordings of the continous glucose monitoring (CGM) system to detect hypoglycemia (blood glucose <3.9mmol/L) 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.

  3. Diagnostic accuracy of the hypoglycemia warning system using wearable data and recordings of the CGM system to detect hypoglycemia (blood glucose <3.9mmol/L) 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.

  4. Change in driving features over the glycemic trajectory. [ Time Frame: 240 minutes ]
    Driving signals are recorded using a driving simulator.

  5. Change of gaze coordinates over the glycemic trajectory. [ Time Frame: 240 minutes ]
    Gaze coordinates are recorded using an eye-tracker device.

  6. Change of head pose over the glycemic trajectory. [ Time Frame: 240 minutes ]
    Head pose (position/rotation) are recorded using an eye-tracker device.

  7. Change of heart rate over the glycemic trajectory [ Time Frame: 240 minutes ]
    Heart rate is recorded using a holter-ECG device and wearables.

  8. Change of heart rate variability over the glycemic trajectory [ Time Frame: 240 minutes ]
    Heart rate variability is recorded using a holter-ECG device and wearables.

  9. Change of electrodermal activity over the glycemic trajectory [ Time Frame: 240 minutes ]
    Electrodermal activity is recorded using wearables.

  10. Hypoglycemic symptoms over the glycemic trajectory. [ Time Frame: 240 minutes ]
    Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 48, a higher score means more symptoms)

  11. Time course of the hormonal response over the glycemic trajectory [ Time Frame: Time Frame: 240 minutes ]
    Epinephrine, norepinephrine, glucagon, cortisol and growth hormone are measured at pre-defined time points.

  12. Self assessment of driving performance over the glycemic trajectory. [ Time Frame: 240 minutes ]
    Participants rate their driving performance on a 7-point Lickert Scale (lower value means poorer driving performance).

  13. CGM accuracy over the glycemic trajectory [ 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.

  14. Incidence 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.

  15. Incidence 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.

  16. Emotional response to hypoglycemia warning system [ 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).

  17. Technology acceptance of the hypoglycemia warning system [ 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.



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Ages Eligible for Study:   21 Years to 60 Years   (Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

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

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
  • Women who are pregnant or breastfeeding
  • Intention to become pregnant during the study
  • Lack of safe contraception, defined as: Female participants of childbearing potential, not using and not willing to continue using a medically reliable method of contraception for the entire study duration, such as oral, injectable, or implantable contraceptives, or intrauterine contraceptive devices, or who are not using any other method considered sufficiently reliable by the investigator in individual cases.
  • Other clinically significant concomitant disease states as judged by the investigator (e.g., renal failure, hepatic dysfunction, cardiovascular disease, etc.)
  • Known or suspected non-compliance, 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
  • Previous enrolment into the current study
  • Enrolment of the investigator, his/her family members, employees and other dependent persons
  • Total daily insulin dose >2 IU/kg/day.
  • Specific concomitant therapy washout requirements prior to and/or during study participation
  • Physical or psychological disease is likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator (especially coronary heart disease or epilepsy).
  • Current treatment with drugs known to interfere with metabolism (e.g. systemic corticosteroids, etc.) or driving performance (e.g. opioids, benzodiazepines)
  • Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05183191


Contacts
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Contact: Vera Lehmann, MD +41 (0)31 632 40 70 vera.lehmann@insel.ch

Locations
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Switzerland
University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism Recruiting
Bern, Switzerland
Contact: Christoph Stettler, Prof.    +41316324070    christoph.stettler@insel.ch   
Sponsors and Collaborators
University Hospital Inselspital, Berne
ETH Zurich
University of St.Gallen
Investigators
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Principal Investigator: Christoph Stettler, MD Inselspital, Bern University Hospital, Universität of Bern
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Responsible Party: University Hospital Inselspital, Berne
ClinicalTrials.gov Identifier: NCT05183191    
Other Study ID Numbers: HEADWIND 3
First Posted: January 10, 2022    Key Record Dates
Last Update Posted: January 10, 2022
Last Verified: December 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: Any requests for raw data will be reviewed by the HEADWIND scientific study board comprising the principal investigator (PI) and Co-PI as well as senior researchers leading the involved research groups at Inselspital Bern, ETH Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI (Prof. Ch. Stettler). Applications should outline the purpose for the raw-data transfer. Any data that can be shared will need approval from the HEADWIND scientific study board and a Material Transfer Agreement in place. All data shared will be de-identified.
Supporting Materials: Study Protocol
Analytic Code
Access Criteria: Only applications for non-commercial use will be considered and should be sent to the PI (Prof. Ch. Stettler).

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University Hospital Inselspital, Berne:
Automotive Technology
Hypoglycemia
Hypoglycaemia
Driving
Driving simulator
Additional relevant MeSH terms:
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Diabetes Mellitus
Hypoglycemia
Diabetes Mellitus, Type 1
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases
Autoimmune Diseases
Immune System Diseases
Hypoglycemic Agents
Physiological Effects of Drugs