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).
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).
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
22
Participants will drive on a designated circuit with a real car on a test track accompanied by a driving instructor. Driving data will be recorded in 4 subsequent glycemic states using an adapted hypoglycemic clamp protocol: euglycemia (d1, 5-8 mmol/l), progressive hypoglycaemia (d2, declining from 4.5 to 2.5 mmol/l), stable hypoglycemia (d3, 2.0-2.5 mmol/l), and again in euglycaemia (d4, 5-8 mmol/l). Patients will be blinded to their glucose levels.
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Bern, Switzerland
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).
Accuracy of the HEADWIND-model will be assessed using real car driving data recorded in progressive hypoglycemia and driving data will be analysed using applied machine learning technology for hypoglycemia detection.
Time frame: 240 minutes
Change of swerving
Change of swerving during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Change of spinning
Change of spinning during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Change of velocity
Change of velocity during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Change of steer
Change of steer during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Change of brake
Change of brake during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
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Change of steer torque
Change of steer torque during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Change of steer speed
Change of steer speed during driving in hypoglycemia (\< 3.9mmol/L) will be compared to euglycemia (5.5 mmol/L). Driving parameters will be recorded by the study car.
Time frame: 240 minutes
Defining the glycemic level when driving performance is decreased
Plasma-glucose level (mmol/L) when driving performance begins to be impaired will be assessed based on significantly altered driving parameters in serious hypoglycemia (\< 3.0 mmol/L) compared to euglycemia (5.5mmol/L).
Time frame: 240 minutes
Driving performance before and after hypoglycemia based on driving parameters (swerving, spinning, velocity, steer, brake, steer torque, steer speed)
Based on significantly altered driving parameters in serious hypoglycemia (\< 3.0 mmol/L) driving performance based on swerving, spinning, velocity, steer, brake, steer torque and steer speed, before and after hypoglycemia will be assessed
Time frame: 240 minutes
Change of heart-rate
Change of heart-rate during driving in hypoglycemia will be compared to euglycemia. Change of heart-rate will be measured with a holter-ecg and wearable devices.
Time frame: 240 minutes
Change of heart-rate variability
Change of heart-rate variability during driving in hypoglycemia will be compared to euglycemia. Heart-rate variability will be measured with a holter-ecg and wearable devices.
Time frame: 240 minutes
Change of electrodermal activity (EDA)
Change of EDA during driving in hypoglycemia will be compared to euglycemia. EDA will be measured with wearable devices.
Time frame: 240 minutes
Change of skin temperature
Change of skin temperature during driving in hypoglycemia will be compared to euglycemia. Change of skin temperature will be measured with wearable devices and a thermal camera.
Time frame: 240 minutes
Change of eye movement
Change of eye movement and gaze behaviour during driving in hypoglycemia will be compared to euglycemia. Eye movement of the participant will be recorded by a camera and an eye-tracker.
Time frame: 240 minutes
Change of facial expression
Change of facial expression during driving in hypoglycemia will be compared to euglycemia. Facial expression will be recorded by a camera.
Time frame: 240 minutes
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
Accuracy of dysglycemia 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: Throughout the study, expected to be up to 12 months
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.
Time frame: Throughout the study, expected to be up to 12 months
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.
Time frame: Throughout the study, expected to be up to 12 months
CGM accuracy during the controlled hypoglycemic state
Accuracy (mean absolute relative difference, MARD) of CGM Sensor (Dexcom G6) in euglycemia (3.9 - 10 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
CGM time-delay during the controlled hypoglycemic state
Time-delay (minutes) of CGM Sensor (Dexcom G6) during progressive hypoglycemia (hypoglycemic clamp) will be assessed compared to plasma glucose.
Time frame: 240 minutes
Change of glucagon
Change of glucagon before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (\< 3.9mmol/L), severe hypoglycemia (\< 3mmol/L) and after hypoglycemia will be assessed.
Time frame: 240 minutes
Change of growth hormone (GH)
Change of GH before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (\< 3.9mmol/L), severe hypoglycemia (\< 3mmol/L) and after hypoglycemia will be assessed.
Time frame: 240 minutes
Change of catecholamines
Change of catecholamines before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (\< 3.9mmol/L), severe hypoglycemia (\< 3mmol/L) and after hypoglycemia will be assessed.
Time frame: 240 minutes
Change of cortisol
Change of cortisol before driving, during driving in euglycemia (5.5mmol/L), in hypoglycemia (\< 3.9mmol/L), severe hypoglycemia (\< 3mmol/L) and after hypoglycemia will be assessed.
Time frame: 240 minutes
Change of insulin
Insulin levels will be measured 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
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.
Time frame: 240 minutes
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.
Time frame: 240 minutes
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.
Time frame: 240 minutes
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 and \< 3.0 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 the additional integration of physiological parameters, video and eye tracker data, in particular heart-rate, heart-rate variability, electrodermal activity (EDA), skin temperature and facial expression (HEADWINDplus-model)
Time frame: 240 minutes
Self-estimation of glucose and hypoglycemia
Evaluation of self-estimated glucose during progressive hypoglycemia and correlation with measured blood glucose.
Time frame: 240 minutes
Self-estimation of driving performance
Evaluation of self-estimated driving-performance in severe 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 better outcome).
Time frame: 240 minutes
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.
Time frame: 240 minutes
Self-perception of hypoglycemia symptoms
Correlation of perceived hypoglycemia symptoms on a scale from 0-6 (0 means better outcome) to measured blood glucose.
Time frame: 240 minutes
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
Correlation and comparison of perceived hypoglycemia symptoms on a scale from 0-6 (0 means better outcome) to baseline hypoglycemia awareness (Clarke-Score and Gold-Score, for both tests a score of higher or equal to 4 points indicates impaired awareness of hypoglycemia).
Time frame: 240 minutes
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).
Driving mishaps and interventions will be assessed by the driving instructor using an assessment questionnaire with 4 questions on a 7 point Likert scale (lower value means worse outcome)
Time frame: 240 minutes
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)
Driving performance will be assessed by the driving instructor using an assessment questionnaire with a score from 1 to 7 (7 means the best outcome)
Time frame: 240 minutes
Incidence of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Time frame: Throughout the study, expected to be up to 12 months
Incidence of Serious Adverse Events (SAEs
Serious Adverse Events will be recorded at each study visit.
Time frame: Throughout the study, expected to be up to 12 months
Pre-test perception of technology in general
Perception of technology in general will be assessed via questionnaire based self-reports (technology readiness index) measures on the 5-point Likert Scale ranging from "strongly disagree" to "strongly agree" with a scale ranging from -2 to 2 with higher values representing a better outcome (after inversion of negative items). The total score will be averaged across participants and used individually to support the interview responses when necessary.
Time frame: Throughout the study, expected to be up to 12 months
Pre-test experience with in-vehicle voice assistants (IVAs) and technology in general
Pre-test experience with IVAs and technology in general will be assessed via questionnaire based self-reports (questionnaire of technology use and acceptance). The constructs Performance expectancy, Effort expectancy, Social influence, Facilitating conditions, Hedonic motivation, and Behavioural intention are measured on the 7-point Likert scale from "strongly disagree" to "strongly agree" with a scale range from -3 to 3 with higher values representing a better outcome. The construct Use is measured on the 7-point Likert scale ranging from "never" to "always" with a scale range from -3 to 3. The total score will be averaged per construct and across participants and used individually to support the interview responses when necessary.
Time frame: Throughout the study, expected to be up to 12 months
Direct comparison between IVA's prompts and the behavioral responses
Direct comparison of conversational turns between IVA and patient during the ecological momentary assessment and the hypoglycaemia support.
Time frame: 240 minutes
Self-report of blood sugar level while driving (i.e. ecological momentary assessment)
Comparison of perceived blood sugar level to measured blood glucose, perceived blood sugar level between drives (see outcome 21), and baseline hypoglycemia awareness (Clarke-Score and Gold-Score, for both tests a score of higher or equal to 4 points indicates impaired awareness of hypoglycemia).
Time frame: 240 minutes
Comparison of cognitive trust in competence and session alliance with IVA to warning type
Cognitive trust in competence with IVA will be assessed via questionnaire based self-reports (Cognitive trust in competence construct from Trust and adoption of recommendations agents questionnaire), measured on the 7-point Likert scale from "strongly disagree" to "strongly agree" with a scale range from -3 to 3 and with higher values representing a better outcome. Session alliance with IVA will be assessed via questionnaire based self-reports (item from Session Alliance Inventory), measured on the 6-point Likert scale from "not at all" to "completely" with a scale range from 0 to 5 and with higher values representing a better outcome. The questionnaire will be submitted after delivering IVA's support intervention and will be compared with the type of warning delivered (i.e. disclosure vs no disclosure).
Time frame: 240 minutes
General user experience of the early hypoglycaemia warning system (EWS)
General user experience of the EWS will be assessed via questionnaire based self-reports (questionnaire for User experience questionnaire and van der Laan scale) measured on an analogue scale with adjective at its extremes (e.g. easy to learn-hard to learn, boring-exciting, good-bad, etc.) with a scale range from 0 to 100. The scores will be averaged for each scale across participants and used individually to support the interview responses when necessary.
Time frame: Throughout the study, expected to be up to 12 months
Acceptance and use of the EWS
Acceptance and use of the EWS will be assessed via questionnaire based self-reports (questionnaire of technology use and acceptance) measured on the 7-point Likert scale from "strongly disagree" to "strongly agree" with a scale range from -3 to 3 with higher values representing a better outcome. The total score will be averaged per construct and across participants and used individually to support the interview responses when necessary.
Time frame: Throughout the study, expected to be up to 12 months
Cognitive trust in competence and emotional trust in the recommendations from IVA
Cognitive trust in competence and emotional trust in the recommendations from IVA will be assessed via questionnaire based self-reports (Cognitive trust in competence and emotional trust constructs from Trust and adoption of recommendations agents questionnaire) measured on the 7-point Likert scale from "strongly disagree" to "strongly agree" with a scale range from -3 to 3 and with higher values representing a better outcome. The total score will be averaged per construct and across participants and used individually to support the interview responses when necessary
Time frame: Throughout the study, expected to be up to 12 months
Perceived working alliance with IVA
Perceived working alliance with the IVA will be assessed via questionnaire based self-reports (session alliance inventory) measured on the 6-point Likert scale from "not at all" to "completely" with a scale range from 0 to 5 and with higher values representing a better outcome. The total score will be averaged per construct and across participants and used individually to support the interview responses when necessary
Time frame: Throughout the study, expected to be up to 12 months