DiabetesHEADWINDNCT04569630
The HEADWIND Study - Part 2
A study of Diabetes and Diabetes Mellitus, Type 1, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
Trial 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… Read more about this study
- Who is it for?
- Ages 21 to 60 years
- Conditions the study is for
- Diabetes, Diabetes Mellitus, Type 1
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 22 participants
- Where
- 1 siteSwitzerland
- Study dates
- 1 Oct 2020to 28 May 2021
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 60 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- 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.
Who cannot join
- 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
Show all 15Show fewer
- 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
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism
Bern
About this study
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
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).
Read the 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).
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Being tested
- Controlled hypoglycaemic state while driving
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Car
- Medical subject headings
- Diabetes Mellitus, Diabetes Mellitus, Type 1, Hypoglycemia, Carney Complex
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Controlled hypoglycaemic state while drivingOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Intervention group | Experimental | Not described Receives: Other: Controlled hypoglycaemic state while driving |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 240 minutes
Secondary outcomes (47)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 240 minutes
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.
Measured: 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.
Measured: 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).
Measured: 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
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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)
Measured: 240 minutes
Self-estimation of glucose and hypoglycemia
Evaluation of self-estimated glucose during progressive hypoglycemia and correlation with measured blood glucose.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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)
Measured: 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)
Measured: 240 minutes
Incidence of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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
Measured: 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
Measured: Throughout the study, expected to be up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2020
- Study start1 Oct 2020
- Primary completion27 May 2021
- Study completion28 May 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for DiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 22 participants (actual)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Responsible party
- Sponsor
- Investigators
- Christoph Stettler, Prof. MD, Principal investigator, Inselspital, Bern University Hospital, University of Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04569630
- Sponsor's protocol ID
- HEADWIND 2
- Acronym
- HEADWIND
- Status last verified
- Jun 2021
- First submitted
- 17 Sep 2020
- Last updated
- 29 Jun 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Lehmann V, Zueger T, Maritsch M, Notter M, Schallmoser S, Berube C, Albrecht C, Kraus M, Feuerriegel S, Fleisch E, Kowatsch T, Lagger S, Laimer M, Wortmann F, Stettler C. Machine Learning to Infer a Health State Using Biomedical Signals - Detection of Hypoglycemia in People with Diabetes while Driving Real Cars. NEJM AI. 2024 Mar;1(3):AIoa2300013. doi: 10.1056/AIoa2300013. Epub 2024 Jan 31.
PubMed 42932738
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04569630
- Acronym / sponsor ID
- HEADWIND / HEADWIND 2
- Conditions
- Diabetes, Diabetes Mellitus, Type 1
- Keywords
- Automotive Technology, Hypoglycemia, Hypoglycaemia, Driving, Car
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 22 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Study start
- 1 Oct 2020 (actual)
- Primary completion
- 27 May 2021 (actual)
- Study completion
- 28 May 2021 (actual)
- First posted
- 30 Sep 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Primary purpose
- Other
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Intervention group | Experimental | Other: Controlled hypoglycaemic state while driving |
Who may qualify
- Ages
- 21 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (7 listed)
- 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
Key exclusion criteria (15 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Controlled hypoglycaemic state while drivingParticipants 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. | Other | Intervention group |
Primary outcome
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).
Time frame: 240 minutes
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.
47 secondary outcomes
Change of swerving
Time frame: 240 minutes
Change of spinning
Time frame: 240 minutes
Change of velocity
Time frame: 240 minutes
Change of steer
Time frame: 240 minutes
Change of brake
Time frame: 240 minutes
Change of steer torque
Time frame: 240 minutes
Change of steer speed
Time frame: 240 minutes
Defining the glycemic level when driving performance is decreased
Time frame: 240 minutes
Driving performance before and after hypoglycemia based on driving parameters (swerving, spinning, velocity, steer, brake, steer torque, steer speed)
Time frame: 240 minutes
Change of heart-rate
Time frame: 240 minutes
Change of heart-rate variability
Time frame: 240 minutes
Change of electrodermal activity (EDA)
Time frame: 240 minutes
Change of skin temperature
Time frame: 240 minutes
Change of eye movement
Time frame: 240 minutes
Change of facial expression
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
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
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
Time frame: Throughout the study, expected to be up to 12 months
CGM accuracy during the controlled hypoglycemic state
Time frame: 240 minutes
CGM time-delay during the controlled hypoglycemic state
Time frame: 240 minutes
Change of glucagon
Time frame: 240 minutes
Change of growth hormone (GH)
Time frame: 240 minutes
Change of catecholamines
Time frame: 240 minutes
Change of cortisol
Time frame: 240 minutes
Change of insulin
Time frame: 240 minutes
Glycemic level at time point of hypoglycemia detection by the HEADWIND-model
Time frame: 240 minutes
Comparison CGM and HEADWIND-model regarding time-point of hypoglycemia detection
Time frame: 240 minutes
Comparison CGM and HEADWIND-model regarding glycemia
Time frame: 240 minutes
Accuracy-comparison of HEADWIND-model and HEADWINDplus-model
Time frame: 240 minutes
Self-estimation of glucose and hypoglycemia
Time frame: 240 minutes
Self-estimation of driving performance
Time frame: 240 minutes
Time point of need-to-treat
Time frame: 240 minutes
Self-perception of hypoglycemia symptoms
Time frame: 240 minutes
Self-perception of hypoglycemia symptoms compared to baseline hypoglycemia awareness
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).
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)
Time frame: 240 minutes
Incidence of Adverse Events (AEs)
Time frame: Throughout the study, expected to be up to 12 months
Incidence of Serious Adverse Events (SAEs
Time frame: Throughout the study, expected to be up to 12 months
Pre-test perception of technology in general
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
Time frame: Throughout the study, expected to be up to 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2021.
- Derived from this study: PubMed 42932738Lehmann V, Zueger T, Maritsch M, Notter M, Schallmoser S, Berube C, Albrecht C, Kraus M, Feuerriegel S, Fleisch E, Kowatsch T, Lagger S, Laimer M, Wortmann F, Stettler C. Machine Learning to Infer a Health State Using Biomedical Signals - Detection of Hypoglycemia in People with Diabetes while Driving Real Cars. NEJM AI. 2024 Mar;1(3):AIoa2300013. doi: 10.1056/AIoa2300013. Epub 2024 Jan 31.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Christoph Stettler, Prof. MD principal investigator
Inselspital, Bern University Hospital, University of Bern
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study of Diabetes and Diabetes Mellitus, Type 1, run by Insel Gruppe AG, University Hospital Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 15 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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). (time frame: 240 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04569630, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2021.