Clinical trial NCT04569630: The HEADWIND Study - Part 2
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 2
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Diabetes
Diabetes Mellitus, Type 1
Keywords: Automotive Technology
Hypoglycemia
Hypoglycaemia
Driving
Car
Lead sponsor: Insel Gruppe AG, University Hospital Bern
Sponsor class: OTHER
Collaborators: name: ETH Zurich
name: University of St.Gallen
First posted: 2020-09-30
Enrollment: 22
Sex: ALL
Minimum age (years): 21
Maximum age (years): 60
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 while driving in a real car. Based on the driving variables provided by the car 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
* 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

Primary outcomes:
measure: Accuracy of the HEADWIND-model: Diagnostic accuracy of the hypoglycemia warning system (HEADWIND) in detecting hypoglycemia (blood glucose < 3.9 and < 3.0 mmol/l) quantified as the area under the receiver operator characteristics curve (AUC ROC).
timeFrame: 240 minutes
Secondary outcomes:
measure: Change of swerving
timeFrame: 240 minutes
measure: Change of spinning
timeFrame: 240 minutes
measure: Change of velocity
timeFrame: 240 minutes
measure: Change of steer
timeFrame: 240 minutes
measure: Change of brake
timeFrame: 240 minutes
measure: Change of steer torque
timeFrame: 240 minutes
measure: Change of steer speed
timeFrame: 240 minutes
measure: Defining the glycemic level when driving performance is decreased
timeFrame: 240 minutes
measure: Driving performance before and after hypoglycemia based on driving parameters (swerving, spinning, velocity, steer, brake, steer torque, steer speed)
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: Change of eye movement
timeFrame: 240 minutes
measure: Change of facial expression
timeFrame: 240 minutes
measure: Diagnostic accuracy in detecting hypoglycemia (blood glucose <3.9 mmol/l and <3.0 mmol/l) and hyperglycemia (blood glucose >13.9 mmol/l and >16.7 mmol/l) quantified as the area under the receiver operator characteristics curve using physiological data
timeFrame: Throughout the study, expected to be up to 12 months
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: Throughout the study, expected to be up to 12 months
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: Throughout the study, expected to be up to 12 months
measure: CGM accuracy during the controlled hypoglycemic state
timeFrame: 240 minutes
measure: CGM time-delay during the controlled hypoglycemic 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: Change of insulin
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: 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
timeFrame: 240 minutes
measure: Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
timeFrame: 240 minutes
measure: Driving mishaps and interventions by the driving instructor in euglycaemia (5-8 mmol/l), hypoglycaemia (< 3.9 mmol/l) and severe hypoglycaemia (< 3.0 mmol/l).
timeFrame: 240 minutes
measure: Direct comparison of driving performance scores assessed by the driving instructor in euglycemia (5-8 mmol/l), hypoglycaemia (<3.9 mmol/l) and severe hypoglycaemia (< 3.0 mmol/l)
timeFrame: 240 minutes
measure: Incidence of Adverse Events (AEs)
timeFrame: Throughout the study, expected to be up to 12 months
measure: Incidence of Serious Adverse Events (SAEs
timeFrame: Throughout the study, expected to be up to 12 months
measure: Pre-test perception of technology in general
timeFrame: Throughout the study, expected to be up to 12 months
measure: Pre-test experience with in-vehicle voice assistants (IVAs) and technology in general
timeFrame: Throughout the study, expected to be up to 12 months
measure: Direct comparison between IVA's prompts and the behavioral responses
timeFrame: 240 minutes
measure: Self-report of blood sugar level while driving (i.e. ecological momentary assessment)
timeFrame: 240 minutes
measure: Comparison of cognitive trust in competence and session alliance with IVA to warning type
timeFrame: 240 minutes
measure: General user experience of the early hypoglycaemia warning system (EWS)
timeFrame: Throughout the study, expected to be up to 12 months
measure: Acceptance and use of the EWS
timeFrame: Throughout the study, expected to be up to 12 months
measure: Cognitive trust in competence and emotional trust in the recommendations from IVA
timeFrame: Throughout the study, expected to be up to 12 months
measure: Perceived working alliance with IVA
timeFrame: Throughout the study, expected to be up to 12 months
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Study officials:
affiliation: Inselspital, Bern University Hospital, University of Bern
name: Christoph Stettler, Prof. MD
role: PRINCIPAL_INVESTIGATOR
References: