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The HEADWIND Study - Part 4

Primary Purpose

Diabetes, Diabetes Mellitus, Type 1

Status
Completed
Phase
Not Applicable
Locations
Switzerland
Study Type
Interventional
Intervention
Controlled hypoglycaemic state while driving
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, Car

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 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

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 hypoglycaemia warning system using in-vehicle data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
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

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

Full Information

First Posted
March 24, 2022
Last Updated
December 20, 2022
Sponsor
Insel Gruppe AG, University Hospital Bern
Collaborators
Swiss Federal Institute of Technology, University of St.Gallen
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1. Study Identification

Unique Protocol Identification Number
NCT05308095
Brief Title
The HEADWIND Study - Part 4
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 IV
Study Type
Interventional

2. Study Status

Record Verification Date
December 2022
Overall Recruitment Status
Completed
Study Start Date
April 13, 2022 (Actual)
Primary Completion Date
June 23, 2022 (Actual)
Study Completion Date
June 23, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Insel Gruppe AG, University Hospital Bern
Collaborators
Swiss Federal Institute of Technology, University of St.Gallen

4. Oversight

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

5. Study Description

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.

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, Car

7. Study Design

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

8. Arms, Groups, and Interventions

Arm Title
Intervention group
Arm Type
Experimental
Intervention Type
Other
Intervention Name(s)
Controlled hypoglycaemic state while driving
Intervention Description
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 Outcome Measure Information:
Title
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).
Description
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
Secondary Outcome Measure Information:
Title
Diagnostic accuracy of the hypoglycaemia warning system using wearable data to detect hypoglycaemia quantified as the area under the receiver operating characteristics curve (AUROC).
Description
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
Title
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.
Description
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
Title
Diagnostic accuracy of the hypoglycaemia warning system using wearable data and recordings of the CGM system to detect hypoglycaemia quantified as sensitivity and specificity.
Description
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
Title
Change in driving features over the glycaemic trajectory.
Description
Driving signals are recorded using a driving simulator.
Time Frame
240 minutes
Title
Change of gaze coordinates over the glycaemic trajectory.
Description
Gaze coordinates are recorded using an eye-tracker device.
Time Frame
240 minutes
Title
Change of head pose over the glycaemic trajectory.
Description
Head pose (position/rotation) is recorded using an eye-tracker device.
Time Frame
240 minutes
Title
Change of heart rate over the glycaemic trajectory
Description
Heart rate is recorded using a holter-ECG device and a wearable.
Time Frame
240 minutes
Title
Change of heart rate variability over the glycaemic trajectory
Description
Heart rate variability is recorded using a holter-ECG device and a wearable.
Time Frame
240 minutes
Title
Change of electrodermal activity over the glycaemic trajectory
Description
Electrodermal activity is recorded using a wearable.
Time Frame
240 minutes
Title
Hypoglycaemic symptoms over the glycaemic trajectory.
Description
Hypoglycemic symptoms are rated using a validated questionnaire (minimum score = 0, maximum score = 6, a higher score means more symptoms)
Time Frame
240 minutes
Title
Change of cognitive performance over the glycaemic trajectory.
Description
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
Title
Time course of the hormonal response over the glycaemic trajectory
Description
Epinephrine, norepinephrine, glucagon, cortisol and growth hormone will be measured at pre-defined time points.
Time Frame
240 minutes
Title
Self assessment of driving performance over the glycaemic trajectory.
Description
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Time Frame
240 minutes
Title
Number of driving mishaps over the glycaemic trajectory.
Description
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Time Frame
240 minutes
Title
CGM accuracy over the glycaemic trajectory
Description
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
Title
Accuracy of our protocol to induce hypoglycaemia in achieving the intended hypoglycaemic range.
Description
Accuracy will be quantified using mean absolute relative difference from the intended hypoglycaemic range.
Time Frame
240 minutes
Title
Number 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
Number 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 the hypoglycaemia warning system
Description
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
Title
Technology acceptance of the hypoglycaemia warning system
Description
Technology acceptance will be measures with user experience questionnaires, such as the Unified Technology Acceptance and Use of Technology Questionnaire 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 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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Christoph Stettler, Prof. MD
Organizational Affiliation
Inselspital, Bern University Hospital, University of Bern, Switzerland, Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern, Switzerland
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 University Hospital Bern, Swiss Federal Institute of Technology (ETH) Zurich, and University of St. Gallen. Only applications for non-commercial use will be considered and should be sent to the PI. 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.

Learn more about this trial

The HEADWIND Study - Part 4

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