Transportation AccidentsDRIVENCT04980846
Design and Implementation of a Drunk Driving Detection System
A study testing Driving under the influence of alcohol with a driving simulator in people with Transportation Accidents and Drunk Driving, run by University of Bern at 1 site in Switzerland.
Trial summary
To analyse driving behavior of individuals under the influence of alcohol using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating sober and drunk driving patterns using machine learning neural… Read more about this study
- Who is it for?
- Ages 17 years and olderHealthy volunteers can take part
- Conditions the study is for
- Transportation Accidents, Drunk Driving, Alcohol Drinking
- What is being tested?
- Driving under the influence of alcohol with a driving simulator
- Type of treatment
- Pill or oral medicine
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30 participants
- Where
- 1 siteSwitzerland
- Study dates
- 15 Aug 2021to 14 Nov 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 17 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Informed consent as documented by signature.
- In possession of a Swiss or EU driving license for at least two years.
- At least driving 1'000 kilometers per year.
- No special equipment needed when driving.
- Drinks alcohol at least occasionally (moderate/social consumption).
- Fluent in (Swiss) German and no speech impairment.
- Lives in or near Bern.
Who cannot join
- Health concerns that are incompatible with alcohol consumption.
- Any potential participant currently taking illegal drugs or medications that interact with alcohol.
- Women who are pregnant or breast feeding.
- Intention to become pregnant during the course of the study.
- Teetotallers (alcohol abstinent persons).
- Alcohol misuse (excessive alcohol consumption habits/risky drinking behaviour (according to WHO definition) and/or PEth in capillary blood > 210 ng/mL at first visit.
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- Known or suspected non-compliance or drug 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 investigational drug within the 30 days preceding and during the present study.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Drunk Driving Detection System
To analyse driving behavior of individuals under the influence of alcohol using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating sober and drunk driving patterns using machine learning neural networks (deep machine learning classifiers).
Read the detailed description
Driving under the influence of alcohol (or "drunk driving") is one of the most significant causes of traffic accidents. Alcohol consumption impairs neurocognitive and psychomotor function and has been shown to be associated with an increased risk of driving accidents. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of alcohol impaired 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 drunk driving-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between sober and drunk states, the investigators assume that different driving patterns in both states can be used to generate drunk driving detection models using machine learning neural networks (deep machine learning classifiers).
- Being tested
- Driving under the influence of alcohol with a driving simulator
- Keywords
- Metabolism, Machine Learning, Automotive Technology, Driving, Driving Simulator, Alcohol biomarker, Cortisol, Phosphatidylethanol
- Medical subject headings
- Driving Under the Influence, Alcohol Drinking
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Driving under the influence of alcohol with a driving simulatorOther
Participants will drive in three different states (sober, drunk above and below the legal limit) on designated circuits using a driving simulator. After the initial sober driving session, participants are administered pre-mixed alcoholic beverages (e.g., vodka orange). Participants are expected to achieve a target BrAC of 0.35 mg/l (legal limit in Switzerland is 0.25 mg/l BrAC) before the second driving session starts. Finally, the third driving session starts when the participants' BrAC drops to 0.15 mg/l. Heart rate, skin conductance, accelerometer, eye movement, radar, facial expression, and speech will be recorded by a smart-watch, an eye-tracker, microphones and an onboard camera, respectively.
Participants will be blinded to their alcohol levels during the study. They will have to rate their symptoms and their performance via questionnaires before and after each driving session. Further, capillary blood and oral fluid samples will be collected.
| Study group | Role | What they receive |
|---|---|---|
| Intervention group | Experimental | Intervention: Other: Driving under the influence of alcohol with a driving simulator Receives: Other: Driving under the influence of alcohol with a driving simulator |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the DRIVE-model: Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect drunk driving (>= 0.25 mg/l breath alcohol concentration (BrAC)) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Accuracy of the DRIVE-model will be assessed using driving data recorded in sober and drunk driving states and driving data will be analysed using applied machine learning technology for impaired driving detection.
Measured: 480 minutes
Secondary outcomes (35)
Change of velocity
Change of velocity between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of steer
Change of steer between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of brake
Change of brake between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of steer torque
Change of steer torque between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of steer speed
Change of steer speed between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of time driving over midline
Change of time over midline between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of swerving
Change of swerving between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of spinning
Change of spinning between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of gaze behavior
Change of gaze behavior (as gaze velocity, acceleration or direction changes) between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of gaze regions of interest
Change of gaze regions of interest (as amount and duration of speedometer or front shield inspections) between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of gaze events
Change of gaze events (as amount and duration of fixations or saccades) between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Defining the alcohol concentration when driving performance is decreased
Breath alcohol concentration (mg/l BrAC) when driving performance begins to be impaired will be assessed based on significantly altered driving parameters for drunk driving above the legal limit (>= 0.25 mg/l BrAC), below the legal limit (<0.25 mg/l BAC) and sober driving.
Measured: 480 minutes
Driving performance while being sober, above and within the legal limit
Based on significantly altered driving parameters above the legal limit of drunk driving (>= 0.25 mg/l breath alcohol concentration in Switzerland) driving performance while being sober, above and within the legal limit will be assessed.
Measured: 480 minutes
Change of heart-rate
Change of heart-rate between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of heart-rate variability
Change of heart-rate variability between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of wrist accelerometer
Change of wrist accelerometer recorded by a wearable between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of electrodermal activity (EDA)
Change of EDA between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of skin temperature
Change of skin temperature between drunk driving above the legal limit (>= 0.25 mg/l BrAC), driving within the legal limit (<0.25 mg/l BrAC) and sober driving.
Measured: 480 minutes
Change of oral fluid cortisol
Change of oral fluid cortisol between before the study day, before driving, during driving in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed.
Measured: 2 weeks
Change of phosphatidylethanol (PEth) in capillary blood
Change of phosphatidylethanol in capillary blood between before the study day, before driving, during driving in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed.
Measured: 2 weeks
Change of ethylglucuronide (EtG) in capillary blood
Change of ethylglucuronide in capillary blood between before the study day, before driving, during driving in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed.
Measured: 2 weeks
Change of ethylsulfate (EtS) in capillary blood
Change of ethylsulfate in capillary blood between before the study day, before driving, during driving in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed.
Measured: 2 weeks
Change of cortisol in capillary blood
Change of cortisol in capillary blood between before the study day, before driving, during driving in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed.
Measured: 2 weeks
Accuracy-comparison of DRIVE-model and DRIVEplus-model
Diagnostic accuracy of the drunk driving warning system (DRIVING) to detect drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator characteristics curve (AUC ROC) using only driving parameters (DRIVE-model) will be compared to the DRIVE-model with additional integration of physiological parameters (heart-rate, heart-rate variability, electrodermal activity (EDA), skin temperature, accelerometer, facial expression, gaze behavior, and radar) (DRIVEplus-model)
Measured: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator characteristics curve using physiological data
Accuracy of drunk driving detection using physiological data (heart-rate, heart-rate variability, skin temperature, EDA, accelerometer) recorded with wearable devices during the study period will be analysed using applied machine learning technology.
Measured: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) 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 drunk driving detection will be analysed with applied machine learning technology.
Measured: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) 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 drunk driving detection will be analysed with applied machine learning technology.
Measured: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using audio data
Using audio data recorded by microphones accuracy in drunk driving detection will be analysed with applied machine learning technology.
Measured: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using radar sensor data
Using radar sensor data (directed on the body of the driver) accuracy in drunk driving detection will be analysed with applied machine learning technology.
Measured: 480 minutes
Self-estimation of alcohol concentrations
Correlation between self-estimated alcohol concentrations and measured breath alcohol concentrations will be assessed.
Measured: 480 minutes
Self-estimation of driving performance
Correlation between self-estimated driving performance and measured driving performance based on significantly altered driving parameters in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed. Driving performance will be assessed with two questionnaires. Driving performance will be assessed with on an absolute 5-point scale from 0-5 (a higher value means higher driving performance).
Measured: 480 minutes
Self-estimation of workload
Correlation between self-estimated workload and measured driving performance based on significantly altered driving parameters in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed. Workload will be assessed an absolute 20-point scale from 0-20 (a higher value means higher workload).
Measured: 480 minutes
Self-estimation of sleepiness levels
Correlation between self-estimated sleepiness levels, measured driving performance, and measured breath alcohol concentrations based on significantly altered driving parameters in sober state, above the legal limit (>= 0.25 mg/l BrAC), and under the legal limit (< 0.25 mg/l BrAC) will be assessed. Sleepiness will be assessed on an absolute 7-point scale from 0-6 (a lower value means less sleepiness).
Measured: 480 minutes
Incidence of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Measured: 3 weeks
Incidence of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Measured: 3 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jul 2021
- Study start15 Aug 2021
- Primary completion14 Nov 2021
- Study completion14 Nov 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Transportation AccidentsStudies 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
- 30 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Bern
- Sponsor type
- University, hospital or other
- Collaborators
- University of St.Gallen, ETH Zurich
- Responsible party
- Sponsor
- Investigators
- Wolfgang Weinmann, Prof. Dr., Principal investigator, University of Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04980846
- Sponsor's protocol ID
- DRIVE
- Acronym
- DRIVE
- Status last verified
- Jul 2021
- First submitted
- 22 Jul 2021
- Last updated
- 3 Jan 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Jan 2022. 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 Drunk Driving Detection System
- ClinicalTrials.gov ID
- NCT04980846
- Acronym / sponsor ID
- DRIVE / DRIVE
- Keywords
- Metabolism, Machine Learning, Automotive Technology, Driving, Driving Simulator, Alcohol biomarker, Cortisol, Phosphatidylethanol
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 30 participants (actual)
- Lead sponsor
- University of BernUniversity, hospital or other
- Collaborators
- University of St.Gallen, ETH Zurich
- Study start
- 15 Aug 2021 (actual)
- Primary completion
- 14 Nov 2021 (actual)
- Study completion
- 14 Nov 2021 (actual)
- First posted
- 28 Jul 2021 (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 under the influence of alcohol using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating sober and drunk driving patterns using machine learning neural networks (deep machine learning classifiers).
Detailed description
Driving under the influence of alcohol (or "drunk driving") is one of the most significant causes of traffic accidents. Alcohol consumption impairs neurocognitive and psychomotor function and has been shown to be associated with an increased risk of driving accidents. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of alcohol impaired 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 drunk driving-associated traffic incidents are urgently needed.
On the supposition that driving behaviour differs significantly between sober and drunk states, the investigators assume that different driving patterns in both states can be used to generate drunk driving 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: Driving under the influence of alcohol with a driving simulatorIntervention: Other: Driving under the influence of alcohol with a driving simulator |
Who may qualify
- Ages
- 17 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (7 listed)
- Informed consent as documented by signature.
- In possession of a Swiss or EU driving license for at least two years.
- At least driving 1'000 kilometers per year.
- No special equipment needed when driving.
- Drinks alcohol at least occasionally (moderate/social consumption).
Key exclusion criteria (9 listed)
- Health concerns that are incompatible with alcohol consumption.
- Any potential participant currently taking illegal drugs or medications that interact with alcohol.
- Women who are pregnant or breast feeding.
- Intention to become pregnant during the course of the study.
- Teetotallers (alcohol abstinent persons).
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 |
|---|---|---|
| Driving under the influence of alcohol with a driving simulatorParticipants will drive in three different states (sober, drunk above and below the legal limit) on designated circuits using a driving simulator. After the initial sober driving session, participants are administered pre-mixed alcoholic beverages (e.g., vodka orange). Participants are expected to achieve a target BrAC of 0.35 mg/l (legal limit in Switzerland is 0.25 mg/l BrAC) before the second driving session starts. Finally, the third driving session starts when the participants' BrAC drops to 0.15 mg/l. Heart rate, skin conductance, accelerometer, eye movement, radar, facial expression, and speech will be recorded by a smart-watch, an eye-tracker, microphones and an onboard camera, respectively. Participants will be blinded to their alcohol levels during the study. They will have to rate their symptoms and their performance via questionnaires before and after each driving session. Further, capillary blood and oral fluid samples will be collected. | Other | Intervention group |
Primary outcome
Accuracy of the DRIVE-model: Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect drunk driving (>= 0.25 mg/l breath alcohol concentration (BrAC)) quantified as the area under the receiver operator characteristics curve (AUC ROC).
Time frame: 480 minutes
Accuracy of the DRIVE-model will be assessed using driving data recorded in sober and drunk driving states and driving data will be analysed using applied machine learning technology for impaired driving detection.
35 secondary outcomes
Change of velocity
Time frame: 480 minutes
Change of steer
Time frame: 480 minutes
Change of brake
Time frame: 480 minutes
Change of steer torque
Time frame: 480 minutes
Change of steer speed
Time frame: 480 minutes
Change of time driving over midline
Time frame: 480 minutes
Change of swerving
Time frame: 480 minutes
Change of spinning
Time frame: 480 minutes
Change of gaze behavior
Time frame: 480 minutes
Change of gaze regions of interest
Time frame: 480 minutes
Change of gaze events
Time frame: 480 minutes
Defining the alcohol concentration when driving performance is decreased
Time frame: 480 minutes
Driving performance while being sober, above and within the legal limit
Time frame: 480 minutes
Change of heart-rate
Time frame: 480 minutes
Change of heart-rate variability
Time frame: 480 minutes
Change of wrist accelerometer
Time frame: 480 minutes
Change of electrodermal activity (EDA)
Time frame: 480 minutes
Change of skin temperature
Time frame: 480 minutes
Change of oral fluid cortisol
Time frame: 2 weeks
Change of phosphatidylethanol (PEth) in capillary blood
Time frame: 2 weeks
Change of ethylglucuronide (EtG) in capillary blood
Time frame: 2 weeks
Change of ethylsulfate (EtS) in capillary blood
Time frame: 2 weeks
Change of cortisol in capillary blood
Time frame: 2 weeks
Accuracy-comparison of DRIVE-model and DRIVEplus-model
Time frame: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator characteristics curve using physiological data
Time frame: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using video data
Time frame: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
Time frame: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using audio data
Time frame: 480 minutes
Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using radar sensor data
Time frame: 480 minutes
Self-estimation of alcohol concentrations
Time frame: 480 minutes
Self-estimation of driving performance
Time frame: 480 minutes
Self-estimation of workload
Time frame: 480 minutes
Self-estimation of sleepiness levels
Time frame: 480 minutes
Incidence of Adverse Events (AEs)
Time frame: 3 weeks
Incidence of Serious Adverse Events (SAEs)
Time frame: 3 weeks
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 3 Jan 2022.
- Study WebsiteLink provided by the sponsor.
- Individual participant dataSharing planned: No
Related trials for Transportation Accidents
Active, not recruiting
Transportation Safety in Adolescence
Automobile Accident
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.
Wolfgang Weinmann, Prof. Dr. principal investigator
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 testing Driving under the influence of alcohol with a driving simulator in people with Transportation Accidents and Drunk Driving, run by University of 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 9 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 3 Jan 2022. 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 DRIVE-model: Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect drunk driving (>= 0.25 mg/l breath alcohol concentration (BrAC)) quantified as the area under the receiver operator characteristics curve (AUC ROC). (time frame: 480 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 3 Jan 2022.