The HEADWIND-Study

Diabetes Clinical Trial

— 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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04035993
• Other study ID #: HEADWIND
• Status: Completed
• Phase: N/A
• Start date: October 7, 2019
• Est. completion date: July 6, 2020

Study information

• Verified date: June 2021
• Source: University Hospital Inselspital, Berne
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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).

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).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 26
• Est. completion date: July 6, 2020
• Est. primary completion date: July 2, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 21 Years to 50 Years

Eligibility

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).

Related Conditions & MeSH terms

• Diabetes
• Diabetes Mellitus
• Diabetes Mellitus, Type 1
• Hypoglycemia

Intervention

Other:
• Controlled hypoglycaemic state while driving with a driving simulator
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.

Locations

Country	Name	City	State
Switzerland	University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism	Bern

Sponsors (3)

Lead Sponsor	Collaborator
University Hospital Inselspital, Berne	ETH Zurich, University of St.Gallen

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	240 minutes
Secondary	Change of time driving over midline	Change of time over midline during driving in hypoglycemia will be compared to euglycemia	240 minutes
Secondary	Change of swerving	Change of swerving during driving in hypoglycemia will be compared to euglycemia	240 minutes
Secondary	Change of spinning	Change of spinning during driving in hypoglycemia will be compared to euglycemia	240 minutes
Secondary	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	240 minutes
Secondary	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	240 minutes
Secondary	Change of heart-rate	Change of heart-rate during driving in hypoglycemia will be compared to euglycemia	240 minutes
Secondary	Change of heart-rate variability	Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia.	240 minutes
Secondary	Change of electrodermal activity (EDA)	Change of EDA during driving in hypoglycemia will be compared to euglycemia.	240 minutes
Secondary	Change of skin temperature	Change of skin temperature during driving in hypoglycemia will be compared to euglycemia.	240 minutes
Secondary	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.	240 minutes
Secondary	CGM time-delay during hypoglycaemic state	Time-delay (minutes) of CGM Sensor (dexcom G6) during progressive hypoglycemia will be assessed compared to plasma glucose.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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)	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	Self-estimation of glucose and hypoglycemia	Correlation between self-estimated glucose values and measured blood glucose will be assessed.	240 minutes
Secondary	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).	240 minutes
Secondary	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.	240 minutes
Secondary	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.	240 minutes
Secondary	Incidence of Adverse Events (AEs)	Adverse Events will be recorded at each study visit.	5 weeks
Secondary	Incidence of Serious Adverse Events (SAEs)	Serious Adverse Events will be recorded at each study visit.	5 weeks
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months
Secondary	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.	Throughout the study, expected to be up to 12 months