Back to resultsThe HEADWIND-Study (HEADWIND)
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
About this trial
This is an interventional other trial for Diabetes focused on measuring Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Driving simulator
Eligibility Criteria
Inclusion Criteria:
- Informed Consent as documented by signature (Appendix Informed Consent Form)
- DM1 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 21-50 years
- HbA1c ≤ 8.5 % based on analysis from central laboratory
- Functional insulin treatment with insulin pump therapy (CSII) or basis-bolus insulin for at least 3 months with good knowledge of insulin self-management
- Only for the main-study: 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, statins etc.) or driving performance (e.g. opioids, benzodiazepines)
- Only for the main-study: Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session (at visit 2).
Sites / Locations
- University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Intervention group
Arm Description
Outcomes
Primary Outcome Measures
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Accuracy of the HEADWIND-model will be assessed using driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
Secondary Outcome Measures
Change of time driving over midline
Change of time over midline during driving in hypoglycemia will be compared to euglycemia
Change of swerving
Change of swerving during driving in hypoglycemia will be compared to euglycemia
Change of spinning
Change of spinning during driving in hypoglycemia will be compared to euglycemia
Defining the glycemic level when driving performance is decreased
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L) plasma-glucose level (mmol/L) when driving performance begins to be impaired will be assessed
Driving performance before and after hypoglycemia
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) driving performance before and after hypoglycemia will be assessed
Change of heart-rate
Change of heart-rate during driving in hypoglycemia will be compared to euglycemia
Change of heart-rate variability
Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia.
Change of electrodermal activity (EDA)
Change of EDA during driving in hypoglycemia will be compared to euglycemia.
Change of skin temperature
Change of skin temperature during driving in hypoglycemia will be compared to euglycemia.
CGM accuracy during hypoglycaemic state
Accuracy (MARD) of CGM Sensor (dexcom G6) in euglycemia (3.9 - 7 mmol/L), hypoglycemia (3.0 - 3.9mmol/L) and severe hypoglycemia (< 3.0 mmol/L) will be assessed based on plasma glucose measurements.
CGM time-delay during hypoglycaemic state
Time-delay (minutes) of CGM Sensor (dexcom G6) during progressive hypoglycemia will be assessed compared to plasma glucose.
Change of glucagon
Change of glucagon before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Change of growth hormone (GH)
Change of GH before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Change of catecholamines
Change of catecholamines before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Change of cortisol
Change of cortisol before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
Blood glucose at time point of hypoglycemia detection by the HEADWIND-model will be determined.
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
Time point of hypoglycemia detection by CGM will be compared to time point of hypoglycemia detection by the HEADWIND-model.
Comparison CGM and HEADWIND-model regarding glycemia
Blood glucose at time point of hypoglycemia detection by the HEADWIND- model compared to glucose value of CGM at same time point will be assessed.
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose < 3.9 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC) using only driving parameters (HEADWIND-model) will be compared to the HEADWIND-model with additional integration of CGM and physiological parameters (heart-rate, heart-rate variability, electrodermal activity (EDA), skin temperature and facial expression) (HEADWINDplus-model)
Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data
Accuracy of hypoglycemia detection using physiological data (heart-rate, heart-rate variability, skin temperature, EDA) recorded with wearable devices during the study period will be analysed using applied machine learning technology.
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using video data
Using video data recorded by a camera and a thermal camera accuracy in hypoglycaemia detection will be analysed with applied machine learning technology.
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
Using eye-tracking data recorded by a camera and an eye-tracker (to record gaze behaviour) accuracy in hypoglycemia detection will be analysed with applied machine learning technology.
Self-estimation of glucose and hypoglycemia
Correlation between self-estimated glucose values and measured blood glucose will be assessed.
Self-estimation of driving performance
Correlation between self-estimated driving performance and measured driving performance based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L). Self-estimated driving performance will be assessed on a absolute 7-point scale from 0-6 (a lower value means a better outcome).
Time point of need-to-treat
Time point of self-perceived need-to-treat (hypoglycemia) compared to time point of hypoglycemia detection by the HEADWIND-model and CGM.
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
Correlation and comparison of perceived hypoglycemia symptoms on a scale from 0-6 (0 = no symptoms, 6 = extreme symptoms) to baseline hypoglycemia awareness score. Baseline hypoglycemia awareness will be assessed using a validated questionnaire (Clarke-Score) with a score over 3 points indicating decreased hypoglycemia awareness.
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.
Perceived ease of use of the early hypoglycaemia warning system (EWS)
Perceived ease of use of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Perceived usefulness of the EWS
Perceived usefulness of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Perceived enjoyment during EWS usage
Perceived enjoyment during EWS usage will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Intention to adopt the EWS
Intention to adopt the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Intention to continuously use the EWS
Intention to continuously use the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Reception of recommendations of the EWS
Reception of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Processing of recommendations of the EWS
Processing of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Perceived understandability of the recommendations of the EWS
Perceived understandability of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Perceived familiarity of the recommendations of the EWS
Perceived familiarity of the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Cognitive and emotional trust in the recommendations of the EWS
Cognitive and emotional trust in the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Full Information
NCT ID
NCT04035993
First Posted
June 5, 2019
Last Updated
June 2, 2021
Sponsor
Insel Gruppe AG, University Hospital Bern
Collaborators
ETH Zurich, University of St.Gallen
1. Study Identification
Unique Protocol Identification Number
NCT04035993
Brief Title
The HEADWIND-Study
Acronym
HEADWIND
Official Title
The HEADWIND Study: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an in Vehicle Hypoglycaemia Warning System in Diabetes
Study Type
Interventional
2. Study Status
Record Verification Date
June 2021
Overall Recruitment Status
Completed
Study Start Date
October 7, 2019 (Actual)
Primary Completion Date
July 2, 2020 (Actual)
Study Completion Date
July 6, 2020 (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 progressive 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 neural networks (deep 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. Despite important developments in the field of diabetes technology, the problem of hypoglycaemia during driving persists. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of hypoglycemia-induced accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of hypoglycemia-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between euglycaemic state and hypoglycaemic state, the investigators assume that different driving patterns in hypoglycemia compared to euglycemia can be used to generate hypoglycemia detection models using machine learning neural networks (deep machine learning classifiers).
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
26 (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
Patients will arrive in the morning after an overnight fast. During the controlled hypoglycaemic state, participants will drive on a designated circuit using a driving simulator. Initially, euglycaemic state (5.0-8.0 mmol/L) will be kept stable and then blood glucose will be declined progressively targeting at a level between 2.0-2.5mmol/L by administering an insulin bolus. Glucose will be kept stable at the hypoglycaemic level for 30 minutes. Thereafter, it will be raised again and kept stable for another 30 minutes at an euglycaemic level between 5.0-8.0mmol/L. During the procedure, we will analyse counterregulatory hormones. Heart rate, skin conductance, CGM values, eye movement and facial expression, will be recorded by a smart-watch, a CGM device, an eye-tracker and an onboard camera, respectively.
Participants will be blinded to the glucose values during the procedure. They will have to rate their symptoms and their performance on a 0-6 scale every 15 minutes.
Primary Outcome Measure Information:
Title
Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose <3.9mmol/l and <3.0mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Description
Accuracy of the HEADWIND-model will be assessed using driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
Time Frame
240 minutes
Secondary Outcome Measure Information:
Title
Change of time driving over midline
Description
Change of time over midline during driving in hypoglycemia will be compared to euglycemia
Time Frame
240 minutes
Title
Change of swerving
Description
Change of swerving during driving in hypoglycemia will be compared to euglycemia
Time Frame
240 minutes
Title
Change of spinning
Description
Change of spinning during driving in hypoglycemia will be compared to euglycemia
Time Frame
240 minutes
Title
Defining the glycemic level when driving performance is decreased
Description
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L) plasma-glucose level (mmol/L) when driving performance begins to be impaired will be assessed
Time Frame
240 minutes
Title
Driving performance before and after hypoglycemia
Description
Based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) driving performance before and after hypoglycemia will be assessed
Time Frame
240 minutes
Title
Change of heart-rate
Description
Change of heart-rate during driving in hypoglycemia will be compared to euglycemia
Time Frame
240 minutes
Title
Change of heart-rate variability
Description
Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia.
Time Frame
240 minutes
Title
Change of electrodermal activity (EDA)
Description
Change of EDA during driving in hypoglycemia will be compared to euglycemia.
Time Frame
240 minutes
Title
Change of skin temperature
Description
Change of skin temperature during driving in hypoglycemia will be compared to euglycemia.
Time Frame
240 minutes
Title
CGM accuracy during hypoglycaemic state
Description
Accuracy (MARD) of CGM Sensor (dexcom G6) in euglycemia (3.9 - 7 mmol/L), hypoglycemia (3.0 - 3.9mmol/L) and severe hypoglycemia (< 3.0 mmol/L) will be assessed based on plasma glucose measurements.
Time Frame
240 minutes
Title
CGM time-delay during hypoglycaemic state
Description
Time-delay (minutes) of CGM Sensor (dexcom G6) during progressive hypoglycemia will be assessed compared to plasma glucose.
Time Frame
240 minutes
Title
Change of glucagon
Description
Change of glucagon before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Time Frame
240 minutes
Title
Change of growth hormone (GH)
Description
Change of GH before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Time Frame
240 minutes
Title
Change of catecholamines
Description
Change of catecholamines before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Time Frame
240 minutes
Title
Change of cortisol
Description
Change of cortisol before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (< 3.9mmol/L), serious hypoglycemia (< 3mmol/L) and after hypoglycemia will be assessed.
Time Frame
240 minutes
Title
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
Description
Blood glucose at time point of hypoglycemia detection by the HEADWIND-model will be determined.
Time Frame
240 minutes
Title
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
Description
Time point of hypoglycemia detection by CGM will be compared to time point of hypoglycemia detection by the HEADWIND-model.
Time Frame
240 minutes
Title
Comparison CGM and HEADWIND-model regarding glycemia
Description
Blood glucose at time point of hypoglycemia detection by the HEADWIND- model compared to glucose value of CGM at same time point will be assessed.
Time Frame
240 minutes
Title
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
Description
Diagnostic accuracy of the hypoglycaemia warning system (HEADWIND) to detect hypoglycaemia (blood glucose < 3.9 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC) using only driving parameters (HEADWIND-model) will be compared to the HEADWIND-model with additional integration of CGM and physiological parameters (heart-rate, heart-rate variability, electrodermal activity (EDA), skin temperature and facial expression) (HEADWINDplus-model)
Time Frame
240 minutes
Title
Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data
Description
Accuracy of hypoglycemia detection using physiological data (heart-rate, heart-rate variability, skin temperature, EDA) recorded with wearable devices during the study period will be analysed using applied machine learning technology.
Time Frame
240 minutes
Title
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using video data
Description
Using video data recorded by a camera and a thermal camera accuracy in hypoglycaemia detection will be analysed with applied machine learning technology.
Time Frame
240 minutes
Title
Diagnostic accuracy in detecting hypoglycemia (blood glucose < 3.9 mmol/l and < 3.0 mmol/l) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
Description
Using eye-tracking data recorded by a camera and an eye-tracker (to record gaze behaviour) accuracy in hypoglycemia detection will be analysed with applied machine learning technology.
Time Frame
240 minutes
Title
Self-estimation of glucose and hypoglycemia
Description
Correlation between self-estimated glucose values and measured blood glucose will be assessed.
Time Frame
240 minutes
Title
Self-estimation of driving performance
Description
Correlation between self-estimated driving performance and measured driving performance based on significantly altered driving parameters in serious hypoglycemia (< 3.0 mmol/L) compared to euglycemia (5.5mmol/L). Self-estimated driving performance will be assessed on a absolute 7-point scale from 0-6 (a lower value means a better outcome).
Time Frame
240 minutes
Title
Time point of need-to-treat
Description
Time point of self-perceived need-to-treat (hypoglycemia) compared to time point of hypoglycemia detection by the HEADWIND-model and CGM.
Time Frame
240 minutes
Title
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
Description
Correlation and comparison of perceived hypoglycemia symptoms on a scale from 0-6 (0 = no symptoms, 6 = extreme symptoms) to baseline hypoglycemia awareness score. Baseline hypoglycemia awareness will be assessed using a validated questionnaire (Clarke-Score) with a score over 3 points indicating decreased hypoglycemia awareness.
Time Frame
240 minutes
Title
Incidence of Adverse Events (AEs)
Description
Adverse Events will be recorded at each study visit.
Time Frame
5 weeks
Title
Incidence of Serious Adverse Events (SAEs)
Description
Serious Adverse Events will be recorded at each study visit.
Time Frame
5 weeks
Title
Perceived ease of use of the early hypoglycaemia warning system (EWS)
Description
Perceived ease of use of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Perceived usefulness of the EWS
Description
Perceived usefulness of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Perceived enjoyment during EWS usage
Description
Perceived enjoyment during EWS usage will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Intention to adopt the EWS
Description
Intention to adopt the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Intention to continuously use the EWS
Description
Intention to continuously use the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Reception of recommendations of the EWS
Description
Reception of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Processing of recommendations of the EWS
Description
Processing of recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Perceived understandability of the recommendations of the EWS
Description
Perceived understandability of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Perceived familiarity of the recommendations of the EWS
Description
Perceived familiarity of the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
Title
Cognitive and emotional trust in the recommendations of the EWS
Description
Cognitive and emotional trust in the recommendations of the EWS will be assessed via questionnaire based self-reports (questionnaire for user interaction satisfaction) measured on the 9-point Likert scale from strongly disagree to strongly agree with a scale range from 0 to 9 and with higher values representing a better outcome. The total score will be averaged.
Time Frame
Throughout the study, expected to be up to 12 months
10. Eligibility
Sex
All
Minimum Age & Unit of Time
21 Years
Maximum Age & Unit of Time
50 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria:
Informed Consent as documented by signature (Appendix Informed Consent Form)
DM1 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 21-50 years
HbA1c ≤ 8.5 % based on analysis from central laboratory
Functional insulin treatment with insulin pump therapy (CSII) or basis-bolus insulin for at least 3 months with good knowledge of insulin self-management
Only for the main-study: 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, statins etc.) or driving performance (e.g. opioids, benzodiazepines)
Only for the main-study: Patients not capable of driving with the driving simulator or patients experiencing motion sickness during the simulator test driving session (at visit 2).
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Christoph Stettler, Prof. MD
Organizational Affiliation
Inselspital, Bern University Hospital, University of Bern
Official's Role
Principal Investigator
Facility Information:
Facility Name
University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism
City
Bern
Country
Switzerland
12. IPD Sharing Statement
Learn more about this trial
The HEADWIND-Study
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