Clinical trial NCT04035993: The HEADWIND-Study
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
Overall status: COMPLETED
Phase: NA
Conditions: Diabetes
Diabetes Mellitus, Type 1
Keywords: Automotive Technology
Hypoglycemia
Hypoglycaemia
Driving
Driving simulator
Lead sponsor: Insel Gruppe AG, University Hospital Bern
Sponsor class: OTHER
Collaborators: name: ETH Zurich
name: University of St.Gallen
First posted: 2019-07-29
Enrollment: 26
Sex: ALL
Minimum age (years): 21
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

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).

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).

Primary outcomes:
measure: 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).
timeFrame: 240 minutes
Secondary outcomes:
measure: Change of time driving over midline
timeFrame: 240 minutes
measure: Change of swerving
timeFrame: 240 minutes
measure: Change of spinning
timeFrame: 240 minutes
measure: Defining the glycemic level when driving performance is decreased
timeFrame: 240 minutes
measure: Driving performance before and after hypoglycemia
timeFrame: 240 minutes
measure: Change of heart-rate
timeFrame: 240 minutes
measure: Change of heart-rate variability
timeFrame: 240 minutes
measure: Change of electrodermal activity (EDA)
timeFrame: 240 minutes
measure: Change of skin temperature
timeFrame: 240 minutes
measure: CGM accuracy during hypoglycaemic state
timeFrame: 240 minutes
measure: CGM time-delay during hypoglycaemic state
timeFrame: 240 minutes
measure: Change of glucagon
timeFrame: 240 minutes
measure: Change of growth hormone (GH)
timeFrame: 240 minutes
measure: Change of catecholamines
timeFrame: 240 minutes
measure: Change of cortisol
timeFrame: 240 minutes
measure: Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
timeFrame: 240 minutes
measure: Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
timeFrame: 240 minutes
measure: Comparison CGM and HEADWIND-model regarding glycemia
timeFrame: 240 minutes
measure: Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
timeFrame: 240 minutes
measure: 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
timeFrame: 240 minutes
measure: 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
timeFrame: 240 minutes
measure: 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
timeFrame: 240 minutes
measure: Self-estimation of glucose and hypoglycemia
timeFrame: 240 minutes
measure: Self-estimation of driving performance
timeFrame: 240 minutes
measure: Time point of need-to-treat
timeFrame: 240 minutes
measure: Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
timeFrame: 240 minutes
measure: Incidence of Adverse Events (AEs)
timeFrame: 5 weeks
measure: Incidence of Serious Adverse Events (SAEs)
timeFrame: 5 weeks
measure: Perceived ease of use of the early hypoglycaemia warning system (EWS)
timeFrame: Throughout the study, expected to be up to 12 months
measure: Perceived usefulness of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Perceived enjoyment during EWS usage
timeFrame: Throughout the study, expected to be up to 12 months
measure: Intention to adopt the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Intention to continuously use the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Reception of recommendations of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Processing of recommendations of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Perceived understandability of the recommendations of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Perceived familiarity of the recommendations of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Cognitive and emotional trust in the recommendations of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: University Department of Endocirnology, Diabetology, Clinical Nutrition and Metabolism
Study officials:
affiliation: Inselspital, Bern University Hospital, University of Bern
name: Christoph Stettler, Prof. MD
role: PRINCIPAL_INVESTIGATOR
References: