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The HEADWIND Study - Part 3

Primary Purpose

Diabetes, Diabetes Mellitus, Type 1

Status
Completed
Phase
Not Applicable
Locations
Switzerland
Study Type
Interventional
Intervention
Controlled hypoglycaemic state while driving with a driving simulator
Sponsored by
Insel Gruppe AG, University Hospital Bern
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional other trial for Diabetes focused on measuring Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator

Eligibility Criteria

21 Years - 60 Years (Adult)All SexesDoes not accept healthy volunteers

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.

Sites / Locations

  • University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Intervention group

Arm Description

Outcomes

Primary Outcome Measures

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).
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

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).
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.
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.
The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity.
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.
The CGM device is in use during controlled eu- and hypoglycemia. Detection performance of hypoglycemia is quantified as sensitivity and specificity.
Change in driving features over the glycemic trajectory.
Driving signals are recorded using a driving simulator.
Change of gaze coordinates over the glycemic trajectory.
Gaze coordinates are recorded using an eye-tracker device.
Change of head pose over the glycemic trajectory.
Head pose (position/rotation) are recorded using an eye-tracker device.
Change of heart rate over the glycemic trajectory
Heart rate is recorded using a holter-ECG device and wearables.
Change of heart rate variability over the glycemic trajectory
Heart rate variability is recorded using a holter-ECG device and wearables.
Change of electrodermal activity over the glycemic trajectory
Electrodermal activity is recorded using wearables.
Hypoglycemic symptoms over the glycemic trajectory.
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 48, a higher score means more symptoms)
Time course of the hormonal response over the glycemic trajectory
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone are measured at pre-defined time points.
Self assessment of driving performance over the glycemic trajectory.
Participants rate their driving performance on a 7-point Lickert Scale (lower value means poorer driving performance).
CGM accuracy over the glycemic trajectory
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.
Incidence of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Incidence of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Emotional response to hypoglycemia warning system
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).
Technology acceptance of the hypoglycemia warning system
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.

Full Information

First Posted
December 20, 2021
Last Updated
May 4, 2022
Sponsor
Insel Gruppe AG, University Hospital Bern
Collaborators
ETH Zurich, University of St.Gallen
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1. Study Identification

Unique Protocol Identification Number
NCT05183191
Brief Title
The HEADWIND Study - Part 3
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
Study Type
Interventional

2. Study Status

Record Verification Date
May 2022
Overall Recruitment Status
Completed
Study Start Date
November 29, 2021 (Actual)
Primary Completion Date
March 3, 2022 (Actual)
Study Completion Date
March 3, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Insel Gruppe AG, University Hospital Bern
Collaborators
ETH Zurich, University of St.Gallen

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No

5. Study Description

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.
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diabetes, Diabetes Mellitus, Type 1
Keywords
Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator

7. Study Design

Primary Purpose
Other
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
11 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Intervention group
Arm Type
Experimental
Intervention Type
Other
Intervention Name(s)
Controlled hypoglycaemic state while driving with a driving simulator
Intervention Description
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 Measure Information:
Title
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).
Description
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
Secondary Outcome Measure Information:
Title
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).
Description
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
Title
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.
Description
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
Title
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.
Description
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
Title
Change in driving features over the glycemic trajectory.
Description
Driving signals are recorded using a driving simulator.
Time Frame
240 minutes
Title
Change of gaze coordinates over the glycemic trajectory.
Description
Gaze coordinates are recorded using an eye-tracker device.
Time Frame
240 minutes
Title
Change of head pose over the glycemic trajectory.
Description
Head pose (position/rotation) are recorded using an eye-tracker device.
Time Frame
240 minutes
Title
Change of heart rate over the glycemic trajectory
Description
Heart rate is recorded using a holter-ECG device and wearables.
Time Frame
240 minutes
Title
Change of heart rate variability over the glycemic trajectory
Description
Heart rate variability is recorded using a holter-ECG device and wearables.
Time Frame
240 minutes
Title
Change of electrodermal activity over the glycemic trajectory
Description
Electrodermal activity is recorded using wearables.
Time Frame
240 minutes
Title
Hypoglycemic symptoms over the glycemic trajectory.
Description
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 48, a higher score means more symptoms)
Time Frame
240 minutes
Title
Time course of the hormonal response over the glycemic trajectory
Description
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone are measured at pre-defined time points.
Time Frame
Time Frame: 240 minutes
Title
Self assessment of driving performance over the glycemic trajectory.
Description
Participants rate their driving performance on a 7-point Lickert Scale (lower value means poorer driving performance).
Time Frame
240 minutes
Title
CGM accuracy over the glycemic trajectory
Description
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
240 minutes
Title
Incidence of Adverse Events (AEs)
Description
Adverse Events will be recorded at each study visit.
Time Frame
2 weeks, from screening to close out visit in each participant
Title
Incidence of Serious Adverse Events (SAEs)
Description
Serious Adverse Events will be recorded at each study visit.
Time Frame
2 weeks, from screening to close out visit in each participant
Title
Emotional response to hypoglycemia warning system
Description
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
Title
Technology acceptance of the hypoglycemia warning system
Description
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.
Time Frame
240 minutes

10. Eligibility

Sex
All
Minimum Age & Unit of Time
21 Years
Maximum Age & Unit of Time
60 Years
Accepts Healthy Volunteers
No
Eligibility 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.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Christoph Stettler, MD
Organizational Affiliation
Inselspital, Bern University Hospital, Universität of Bern
Official's Role
Principal Investigator
Facility Information:
Facility Name
University Department of Endocrinology, Diabetology, Clinical Nutrition and Metabolism
City
Bern
Country
Switzerland

12. IPD Sharing Statement

Plan to Share IPD
Yes
IPD Sharing 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.
IPD Sharing Access Criteria
Only applications for non-commercial use will be considered and should be sent to the PI (Prof. Ch. Stettler).

Learn more about this trial

The HEADWIND Study - Part 3

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