Skip to main content
Injury Epidemiology logoLink to Injury Epidemiology
. 2025 Dec 1;13:2. doi: 10.1186/s40621-025-00645-2

A contemporary examination of the effect of driver training for reducing crash risk in novice adolescent drivers: protocol for the DRIVER study, a random assignment trial

Elizabeth A Walshe 1,2,, Daniel Romer 3, Michael R Elliott 4,5, Keith S Baxelbaum 6, M Kit Delgado 2, Jeffrey P Ebert 2, Flaura K Winston 1,2
PMCID: PMC12771938  PMID: 41327408

Abstract

Background

Motor vehicle crashes and resultant fatalities remain disproportionately high among young drivers, with crash risk peaking immediately after licensure. Although graduated driver licensing laws (GDL) for young novice drivers have reduced such fatalities, driver error remains a leading cause; thus, prevention efforts that target improving skills in novice teen drivers before licensure are a strong candidate for reducing crash risk early in licensure. States with more comprehensive driver licensing laws that include mandated driver training before licensure in addition to GDL show lower crash rates post-licensure, but these effects were not determined through rigorous controlled studies of driver training. This paper describes the DRIVER study, a phase III randomized trial that tests the effectiveness of two different driver training programs in reducing young driver crash risk early in licensure in Pennsylvania, a state like many others that does not require formal training for young drivers.

Methods

Learner drivers age 16 and 17 years will be recruited and followed through the GDL learner phase and for six months post-licensure. Participants will be randomly assigned to one of three interventions: professional behind-the-wheel training (n = 333), online hazard training (n = 333), or an active control online vehicle and driver safety course, unrelated to hazard skills training (n = 333). The primary outcomes are on-road crash risk post-licensure (via kinematic hard braking events tracked through a smartphone-based app) and state license examination performance. Secondary outcomes include change in simulated driving performance from baseline to the time of license examination, self-reported and kinematically tracked risky driving behavior (e.g. cell phone use, speeding) and self-reported crashes. Participants will complete baseline surveys and cognitive assessments to determine potential moderating effects of cognitive maturation and risk-taking tendencies.

Discussion

Findings from the DRIVER study will provide insights into training effectiveness generally, and an evidence base for recommendations to policy makers, while also revealing for whom these interventions are less effective.

Trial registration

The study was registered on ClinicalTrials.gov Registry (NCT06413927) in May 2024, https://clinicaltrials.gov/study/NCT06413927 and last updated on August 11th, 2025. This protocol was developed per the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Checklist.

Keywords: Injury prevention, Teen Drivers, Motor Vehicle Crashes, Driver Training, Adolescent Development

Key Message

What is already known: States with more comprehensive driver licensing laws that include mandated behind-the-wheel (BTW) driver training before licensure in addition to GDL show lower crash rates post-licensure, but these effects were not determined through rigorous controlled studies of driver training and there may be barriers to accessing BTW training.

What this study adds: This Phase III randomized trial will test the effectiveness of BTW driver training and an evidence-based online virtual hazard skills training in reducing young driver crash risk early in licensure.

How this study might affect research, practice or policy: Findings will provide insights into training effectiveness overall, and an evidence base for recommendations to policy makers, while also advancing research by identifying individual characteristics that determine for whom each type of training is not effective, addressing key gaps in understanding individual differences in training outcomes.

Introduction

Motor vehicle crashes remain a leading cause of death and injury worldwide, with children and young people most at risk [1]. In the US, young drivers are overrepresented in crashes [2, 3], with long-term sequela including chronic disabilities leading to ongoing medical care, reduced workforce participation, and disability-related costs [4]. The US adopted graduated driver licensing laws (GDL) as the national strategy for reducing young driver crash risk. At a minimum, US GDL requires a learner permit period and restricted driving conditions during an intermediate or junior license [5, 6]. Yet, despite GDL, young driver crashes remain high [7]. Typically, crash risk peaks within the first 2–3 months after licensure, and then lowers to adult levels between 1.5–2 years post-licensure [8, 9]. Furthermore, driver error, due to a lack of skills and experience [10], accounts for approximately 76–94% of crashes [11, 12]. Thus, prevention efforts that target improving skills before licensure are promising strategies for reducing crash risk early in licensure.

Recent studies suggest that behind-the-wheel driver training (BTW) may improve safety early in licensure [9, 13]. In Ohio, a state that mandates driver education and BTW in addition to GDL for drivers under age 18 years, these young drivers had better skills at licensure and lower crash rates early in licensure, relative to those licensed at age 18 years (without BTW) [13]. Similar policies and lower crash rates have been observed in California [9]. While these studies were not randomized trial studies, and could not account for exposure (i.e. varying time spent on the road), they show some promise that skills training before licensure may be a modifiable risk factor. However, a 2022 review of state policies found that 17 states (including Pennsylvania) have no BTW requirements for young drivers, with an additional 3 states allowing for BTW to be replaced (e.g. with adult supervised practice) [14].

State endorsement of BTW for licensure has dwindled since the 1980s, since the only random assignment study of BTW to date (the DeKalb study, 1983) [15] found modest short-term protective effects of BTW on 6 month outcomes, but concluded it did not reduce crashes overall [15, 16]. The most rigorous re-analysis of these data showed a 13.1% crash reduction in the first 6 months of licensure (which we now know is the period of highest lifetime crash risk [8, 17]). Prior research shows that skill deficits underscore most of these early crashes, pointing to the potential value of training [10, 11, 1820]. While the DeKalb study was rigorous in its random assignment design, it did not capture exposure, and predates modern GDL, vehicle safety, driver training standards, as well as modern risks (e.g. cell phone use). Therefore, there is a critical need for a contemporary examination of the effectiveness of driver training for improving young driver safety in the US.

The DeKalb study also did not account for other known individual risk factors, such as cognitive development and impulsive traits. Even among typically developing youth, executive function and attentional skills, and associated brain regions, show dramatic maturation during adolescence (when many adolescents learn to drive) before stabilizing by age 18–19 or later [21]. These executive functions, particularly working memory, have been linked to driving performance and outcomes in young drivers [2225]. Risk-related impulsive personality traits that peak in adolescence in association with executive function have also been associated with crash risk [2629]. However, we hypothesize that sufficient skills training may offset developmental risk factors, given the youngest new drivers in Ohio who completed driver training had the lowest crash rates [13].

Despite the potential benefits of BTW, access is uneven due to cost and location barriers [14, 30]. Alternatively, validated digital training programs for improving safety–critical skills and reducing crashes have the potential to overcome these barriers. Specifically, three free virtual training programs in hazard awareness and mitigation (RAPT and ACT) [3133], and attention maintenance skill training (FOCAL) [34] have been packaged as an omnibus driver education program (ACCEL) [35] that has shown promise in licensed young novice drivers [36]. Online driver's education programs can eliminate the need for travel to a physical driving school, benefiting individuals in remote or rural areas where access to such facilities is limited. However, this intervention has not been tested in the pre-licensure phase for efficacy in reducing crash risk post-licensure.

Objectives

The primary objective of the DRIVER (DRIVer Education Research) study is to quantify the extent to which modern online driver training and BTW prior to licensure can add protection beyond GDL against crash risk early in licensure. The secondary objective is to quantify the effect of driver training on skill acquisition. The study aims and outcomes are summarized in Table 1. We hypothesize that both ACCEL and BTW training will reduce crash risk and improve driving skills compared to the control group. More specifically, we hypothesize that ACCEL will show greater improvement in crash risk and skills than BTW on the theory that ACCEL is more targeted to the critical reasons for crashes among novice adolescent drivers. However, we expect BTW to perform better on PA license examination outcomes since this test measures skills directly trained by BTW. We also hypothesize that those with weakness in cognitive and attentional control will benefit most from the training interventions in the key outcomes, and those with personality differences and risky driving practices linked to crash risk may be more resistant to the effects of driver training.

Table 1.

Summary of study aims and associated outcomes

Study Aim Primary Outcome Secondary Outcome
Aim 1. Determine the effect of driver training (ACCEL or BTW) compared to GDL alone on post-licensure crash risk Hard Braking Events per 100 miles driven via Way to Drive monitoring app

• Phone Use while Driving (minutes per hour of driving)

• Speeding (proportion of time > 10 miles over posted speed limit)

• Self-reported crash involvement

• Self-reported risky driving behavior

Aim 2. Determine the effect of driver training (ACCEL or BTW) compared to GDL alone on skill acquisition Pass rate on the first attempt at the state licensing examination

• Virtual Driving Assessment (VDA) Performance at T2

• Change in T1-T2 VDA performance

• Number of license exam attempts

Aim 3. Identify age-related risk factors for crashes that may moderate the response to training Interactions with Aim 1 & 2 outcomes Interactions with Aim 1 & 2 outcomes

Trial design

This trial uses random assignment with three parallel groups and follows an individually randomized group-treatment (IRGT) design. Participants are enrolled early in their learner permit and followed through licensure, and up to 6 months post-licensure, with the primary end point of crash risk at 2 months post-licensure. Participation length will vary with permit duration (range 8 to 18 months). After baseline, participants are randomized 1:1:1 to one of three groups (n = 333 per group): (i) professional BTW; (ii) ACCEL, a modern online driving skills training program; (iii) an active control online program covering vehicle safety topics without skills training. Participants in the BTW group will be clustered by instructor to account for variation in driver teaching style. Participants will be monitored remotely through the learner and post-licensure phases using surveys and a smartphone application capturing driving kinematics to quantify exposure and crash risk. At licensure, driving skill will be measured using three methods: the virtual driving assessment (VDA), an online hazard skills test, and results from the license examination. The primary endpoint is on-road kinematic crash risk (the rate of hard braking events) at 2 months post-licensure, with continued follow-up through 6 months post-licensure. Kinematic risky driving (KRD) events such as hard-braking events, have previously been validated as behavioral proxies for elevated crash risk [3742]. See Fig. 1 and Table 2 for participant flow through the study and all procedures and measures through the trial.

Fig. 1.

Fig. 1

Flow diagram of participant progression through the trial, with participation estimated between 8–18 months (with varying learner/intervention phase)

Table 2.

Outline of the schedule of enrollment, interventions, and assessments

Timepoint Enrollment Baseline Allocation Intervention Post-intervention Post-Licensure
-t1 t1 0 m1 m2 m3 m4 m5 m6 t2 m8 m10 m12
Enrollment: X
 Eligibility Screen X
 Informed Consent/Assent X
 Allocation to Trial Arm X
Interventions:
 Active Control Online Education X X X X X X
 ACCEL Online Driver Skills Training X X X X X X
 Behind-the-Wheel Training X X X X X X
Assessments:
 Virtual Driving Assessment X X
 Penn Neurocognitive Battery X
 Day-of-Performance Factors X
 Driving Intake (status) X
 Driving History (crashes/citations) X X X X X X X X X X X
 Personality Scales X
 Demographic & Household Factors X X
 Adolescent Health Risk Factors X
 Way to Drive Monitoring
 Driving Practice Quality Questionnaire X X X X X X X
 Hazard Skills Test X
 State Licensing Examination X
 Driver Behavior Surveys X X X X

This table outlines the schedule of study procedures and provides an approximate length of participation, however, the intervention phase length will vary, and some drivers may need to retake the state license examination, extending the time before post-licensure follow-up. T1 and T2 = study assessment timepoints before and after the intervention learner permit phase; m = month

Methods & analysis

This study is registered at ClinicalTrials.gov as of May 2024 (NCT06413927). Enrollment started in August 2024 and data collection is ongoing with an expected primary completion in December 2027, or until all final data collection is complete.

Patients and population

The trial aims to enroll up to 1200 adolescent learner drivers with a goal of achieving 1000 evaluable participants, accounting for 20% attrition. Participants will primarily be recruited from primary care clinics in the Children’s Hospital of Philadelphia (CHOP) Network in the southeastern region of Pennsylvania (PA). The CHOP Network serves a heterogenous patient population: among patients age 16–17 years that visited a clinic at least once in the past 2 years, 33.7% are insured via Medicaid and the Children’s Health Insurance Program (CHIP); 49.4% are female sex; and approximately 47.9% are non-white. Potentially eligible adolescents will be identified through the electronic health record (under a waiver of HIPAA authorization for screening). Other eligible learner drivers who attend other Primary Care clinics and/or who respond to other recruitment and advertising strategies (e.g., study fliers) and who agree to travel to study locations for in-person procedures may also be enrolled.

Eligibility criteria

The eligibility criteria for this study include the following: participants must be aged 16–17 years, reside in PA, hold an active PA Learner’s permit with less than 30 h of driving practice (state requires 65 h before licensure), and have never taken a state driving licensing examination, and plan to get a license and to have access to a vehicle to drive after licensure. In addition, participants must have a personal cellphone, access to a smartphone/computer with internet access, and the ability to read and write in English to complete study tasks. Of note, prior research indicates that 95% of adolescents possess smartphones, with no significant differences by demographics [43]. Non-PA residents, non-English speaking participants, and those already enrolled in other learner driver studies will be ineligible.

Intervention descriptions

All trial participants will receive care as usual, including information to access a Parents Supervised Driving Program from the Pennsylvania Department of Transportation (PennDoT), as well as information on how to access the PennDoT recommended smartphone app for logging practice hours (RoadReady App). In addition, one group will receive professional BTW with a local driving school instructor (6 h: 4 × 1.5 h lessons), another group will receive ACCEL online hazard skills training (~ 2 h), and another will receive an online active control vehicle and driver safety program (~ 2 h, with no skill training). While the ACCEL intervention is shorter than BTW instruction, this program is designed to be highly interactive and cognitively engaging, focusing specifically on the skill deficits that cause young driver crashes, and thus potentially offsetting the shorter duration. While the online programs can be completed in one sitting, participants can also take breaks and resume where they left off. Adherence (progress and duration) will be tracked via the e-learning platform (ScormCloud) event log and via a quiz and test at the end of the training. For BTW, driving instructors will fill out a brief post-lesson survey to confirm lesson content covered and completion.

In-person behind-the-wheel driver training (BTW)

Professional BTW will be carried out by a third-party accredited driver training school, and their partners. The driving school was chosen for its dual certification for training and license testing and is audited every 6–12 months, giving further support for the integrity of the intervention. The standardized curriculum used for the 6 h of training meets Pennsylvania and national standards, as reviewed by subject matter experts external to the study team from the PA Department of Education, Ohio Traffic Safety Office, and representatives of the American Driver and Traffic Safety Education Association (ADTSEA). Certified PA driving instructors will deliver the training, and school leadership will oversee fidelity through supervision, observations and end-of-lesson surveys completed in REDCap. Staff will also complete brief REDCap surveys (onboarding, post-lesson, offboarding) to monitor student progression and curriculum adherence.

Online driver skills training (ACCEL)

ACCEL [35] is an evidence-based online training program developed by Donald Fisher and colleagues at the University of Massachusetts Amherst. Designed to improve hazard anticipation, recognition, and attention maintenance, prior studies have shown ACCEL improves driving performance and reduces crash risk [3133, 36]. For this trial, ACCEL was adapted for remote delivery to teen learner drivers by CHOP’s Department of Global Pediatric Education, with input from the CHOP Parent Family Education office to enhance health literacy. Pilot testing with local high school students informed revisions to language and graphics. The final version was reviewed and approved by the original developer, Donald Fisher, and takes about 2 h to complete. In order to track remote adherence, progress and performance, this interactive training is hosted on SCORMCloud (by Rustici Software LLC), an online e-learning platform that can be accessed from any computer with internet access.

Active control: online vehicle and driver safety education

The active control group will complete an online video-based education program (~ 2 h) that covering vehicle maintenance and occupant safety (i.e., car seats, seat belts) but no driving skills training content. A short quiz at the end will confirm compliance. This program is also hosted on SCORMCloud which automatically tracks adherence, progress, and completion.

Variables and outcome measures

Table 1 provides a summary of the study aims and outcomes. Table 2 summarizes all study procedures and measures.

Primary outcomes

There are two primary outcomes. First, crash risk in the first 2 months of licensure (Aim 1 and 3) will be measured as the rate of hard braking events per 100 miles driving, captured via the Way to Drive smartphone app. Monitoring of hard braking events will continue through 6 months to assess crash risk at 6 months also. The app, provided by Cambridge Mobile Telematics, deploys the same algorithms as leading usage-based automobile insurance programs to measure risky driving behaviors (phone use during trips, speeding, and other kinematic risky driving measures) [38]. Second, driver skills at licensure (Aim 2 and 3) will be measured as the pass rate on the first attempt of the state licensure examination. While the state licensing exam assesses a limited set of procedural driving skills, it provides a standardized, real-world benchmark of basic driving competence and is a relevant proximal outcome for novice drivers.

Secondary outcomes

Secondary outcomes include measures of risky driving behavior post-licensure. Self-reported crash involvement captured in follow-up surveys at 2-, 4- and 6-months post-licensure will provide a secondary outcome of crash risk. Self-reported risky driving behavior will also be captured in follow-up surveys. Other indicators of risky driving behavior post-licensure captured from the Way to Drive app include phone use while driving (number of minutes per hour driving) and speeding (proportion of driving at speed > 10 miles over the speed limit). Secondary outcomes of driver skills at the time of licensure will be measured via driver performance on the state licensing examination and change in performance class (pre- vs post-intervention) on the validated VDA [44, 45]. The number of license examination attempts and skills on the on-road examination will also be recorded as a secondary outcome.

Other measures

At baseline, participants complete: (i) a validated VDA [44, 45]; (ii) executive function tasks of attention and working memory via the validated University of Pennsylvania Computerized Neurocognitive battery (Penn CNB [21, 46]: specifically the Penn Continuous Performance Test, the Letter N-Back test, Penn Go/No-Go task, and the Penn Abstraction and Working Memory task); (iii) a day-of performance factors survey (sleep/restfulness, caffeine and medication intake, and mood via the Positive and Negative Affect Schedule scale [47]); (iv) two impulsive personality scales: acting-without-thinking from the Eysenck Personality Inventory, Impulsivity Subscale [48]; and Temporal Discounting or Delay Discounting via a standard monetary reward task for children [49] (v) a Sensation Seeking scale (4-items form of the Zuckerman’s Sensation Seeking Scale [50]); (vi) a survey of demographic and household factors (vehicle access and features, living situation and household financial status indicators); (vii) items from the adolescent health risk questionnaire (risk behaviors, relationship with parents); (vii) a driving history survey (crashes and citations). In addition, demographic data from the electronic health record will be collected: age, sex, child opportunity index (COI) scores as neighborhood measures of financial status and opportunity [51].

During the intervention phase (i.e., learner permit to licensure), participants will receive monthly surveys to ask about their learning to drive (via the driving practice quality questionnaire [52]) and crashes and citations. Participants’ driving exposure (a confounder) will also be passively monitored via the Way to Drive app. This also has an algorithm that predicts likely crash events, which will be analyzed retrospectively as an exploratory outcome. Intervention adherence will also be tracked (via driving instructor progress reports and online e-learning platform data for online intervention progress and completion).

At the end of the learning/intervention phase, at the point of licensing examination, all participants will complete the following: (i) a second VDA; (ii) PA state licensing examination; (iii) online hazard skills test; (iv) self-report surveys of driving experience (crashes and citations), driving habits and behavior (modified driver behavior questionnaire (mDBQ) [22, 27, 53]; (vi) aggressive driving scale [54, 55]; Youth Risk Behavior Survey [55] Driving items, driving confidence scale [56]); (vii) driving practice quality questionnaire and any other ways they learned to drive beyond the interventions prescribed; and (viii) updated demographic and household factor information (update since T1). Participants who do not pass the state licensing exam initially will be given the opportunity to retake the exam for free and to continue in the trial. All participants who receive a license and remain for post-licensure phase will continue to have their driving trips monitored by the Way to Drive and complete surveys at 2, 4, and 6 months, capturing driving behaviors, confidence, crash history, and vehicle access (as at T2 and noted above). We will use multiple imputation and sensitivity analyses to address missing data, and we will report adherence rates for each intervention and for survey completion.

Sample size

The target sample size for this randomized controlled trial is 1000 evaluable participants, with approximately 333 in each study arm (one group may include 334). This is based on the primary hypothesis of a reduction in hard braking events with driver training. The trial is powered to detect a 20% reduction in hard-braking events, a threshold identified by the U.S. Department of Transportation as predictive of reduced crash risk [57]. Additionally, it is designed to detect differences in state licensure exam pass rates between groups ranging from 15 to 19%, as well as an absolute increase of 10% to 13% in the proportion of drivers assigned to safer skill categories based on VDA classification. These thresholds reflect meaningful improvements in driving safety. A priori power analysis accounted for up to 20% attrition while maintaining 80% power to detect clinically meaningful effects, resulting in a worst-case scenario of 266 participants per arm. Retention strategies and participant replacement procedures are expected to mitigate attrition, leading to a final sample size of n = 800–1,000.

Recruitment

Adolescent learner drivers will be recruited through multiple channels. Patients identified by the Electronic Health Record (EHR) that meet the age criteria and who have an upcoming clinical visit will be contacted by the study team in advance or approached on site at the Primary care clinic visit, to share information about the study and eligibility criteria. Additional candidates may be identified through retrospective review of Adolescent Health Questionnaires indicating intent to drive within 12 months (conducted during annual adolescent wellness visits). The study team will also utilize CHOP recruitment services, the Recruitment Enhancement Core (REC), to expand outreach through the EHR (EPIC). Adolescents from other CHOP primary care clinics or who respond to study fliers will also be contacted about the study. Following an initial recruitment call, written parent informed consent and child assent will be obtained, along with HIPAA authorization, via the secure REDCap electronic consent platform. Once enrolled, participants will be scheduled for baseline study visit procedures, before allocation.

Allocation

After enrollment and baseline assessments, trial participants will be randomized into one of three cohorts in a 1:1:1 ratio so that there will be approximately 333 subjects in each arm, stratified by equal-size age categories (16 and 17) per arm. Randomization will be automatically conducted via the OnCore Clinical Trial Management System (CTMS). Allocation will be made by randomly generating block sizes of 6 and assigning individuals within an age category using the block assignments as they become available for randomization; this will be repeated until all individuals within an age group have been assigned to one of the three intervention arms.

Blinding

Subjects cannot be blinded to the intervention arm assignment due to the nature of the interventions (e.g., online versus behind the wheel training). For analysis, all primary outcomes are assessed in a way that is blind to intervention assignment. The Way to Drive app provides hard-braking events via automated data coding algorithms. Similarly, VDA performance is determined based on previously validated algorithms to identify driver categories [44, 45]. State license exams are conducted by independent examiners who are blinded to intervention group assignment. This is standard procedure, per state third-party testing regulations. Research staff administer the baseline surveys and other procedures before participants are allocated to an intervention arm. The cognitive battery at baseline will be electronically delivered, with accuracy and reaction time automatically recorded and scored by Penn CNB (external to the study team).

Data collection and management

The secure REDCap platform will be used for survey delivery, data storage, and collecting participant progress reports from driving school staff, as well as communication logs and study visit notes. CHOP’s CTMS will support enrollment, randomization, and participant tracking. The Department of Bioinformatics and Health Information (DBHI) and Arcus (data management and archiving service) at CHOP will provide expert data management for this trial. They will receive, manage, and link the data sets that will be honest brokered before analysis. This ensures that the research team conducting the main study analyses does not have access to any personally identifying information in the analysis dataset, while still being able to conduct the necessary analyses for the study.

Statistical methods

Primary and secondary outcomes will be analyzed by intention-to-treat, with each outcome assessed with a separate model. Results will be interpreted based on both statistical significance (α = 0.05) and effect size, emphasizing clinically meaningful differences. As a secondary analysis, baseline covariates will be included in the model, including age at enrollment, sex, and initial VDA class. Additional factors will be examined as potential confounders (e.g., time between first VDA and licensure) and mediators (e.g., number of practice trips, miles driven, and self-reported practice quality).

To address any non-compliance in the intervention, we will use instrumental variable (IV) analysis [58], which treats the randomization assignment as the instrument. We will also consider principal stratification [59], which can relax some of the assumptions about instrumental variable analysis such as no effect of intervention assignment independent of intervention. Both of these methods attempt to estimate intervention effects among young drivers who would comply with intervention assignment regardless of assigned intervention arm. Compliance will be defined as completing more than 2/3 of the training; in the BTW arm, this would entail completing two of the three lessons. In the ACCEL and active control arms, this would entail completion of at least 2/3 of the content. In follow-up analyses, we will include the proportion of training received as a continuous measure, operationalized as the percentage of lessons completed in the assigned intervention, and utilize the mediation analyses, treating training received as a form of mediation. In all analyses we will consider log or power transformations to reduce skewness in the outcome.

Attrition is expected to be modest given the trial's retention strategies, for an estimated final sample size between 800 and 1000 participants. Participants lost before or during the intervention phase may be replaced. Participants who drop out after intervention but before the licensure exam will be encouraged to complete a second VDA. Data for available outcomes to this point will be considered for these participants, and outcomes that can be feasibly imputed will be included in analysis. For participants who fail their initial licensure exam, the study will fund a second attempt and if they pass, they will continue in the trial and follow-up data will be collected. For the rare individuals who fail the second attempt, if they go on to pass on a subsequent attempt (not provided by the study) within the study window, their follow-up data will be collected post-licensure. If the participant does not pass their licensure exam within the study window, they will not be replaced; instead, data for available outcomes will be considered for analyses, with other outcomes imputed as feasible using the data sources noted above.

Monitoring and oversight

The data and safety monitoring plan for this study will comprise monitoring by the PIs and Co-investigators, IRB oversight, and an independent Safety Monitoring Committee (SMC) for monitoring the trial. The risk for this clinical trial is considered minimal. However, to ensure an added layer of protection for our study and its participants, an external Safety Monitoring Committee (SMC) was appointed to review and monitor the study procedures, patient enrollment, and number and nature of adverse events. All significant adverse events will be reviewed by SMC to determine if additional safety measures should be initiated. No interim analyses of the proposed research aims are planned. Given the nature of the intervention and the primary outcomes being measured post-licensure, interim analyses are not feasible or informative. However, we will conduct ongoing data quality checks and monitoring.

Harms

Participants are informed during consent of the potential for simulator sickness while using the VDA, although this event is expected to be rare (previously, < 2% experience this during the VDA). Symptoms are monitored during the virtual driving assessment, and participants may discontinue if discomfort occurs. Given the minimal risk procedures, serious adverse events are not expected. Any unanticipated problems will be reported to the SMC and CHOP IRB. Other adverse events will be tracked and documented.

Ethics and dissemination

This study was initially reviewed approved by the CHOP IRB on 02/17/2023 (IRB 21–019445). Results will be published in peer-reviewed journals and disseminated at national and international meetings, with possible media press releases. We will follow the Consolidated Standards of Reporting Trials (CONSORT) guidelines for reporting the results of parallel arm trials.

Changes to protocol

To meet recruitment targets, recruitment strategies were expanded to include email campaigns to eligible patients, social media, and community fliers.

Patient and public involvement statement

The ACCEL training was refined for health literacy with input from CHOP’s Parent Family Education Office and pilot tested through cognitive interviews with local teens. Their feedback informed language and instructional design improvements.

Conclusion

This study is the first random assignment trial of driver training conducted in four decades. It compares behind-the-wheel training, online hazard skill training, and an active control online intervention for reducing crash risk early in licensure. The inclusion of real-world outcome measures ensures the findings will have practical relevance for improving driving safety. By integrating cognitive and personality measures, the study aims to identify for whom each type of training is effective and for whom it is not, addressing key gaps in understanding individual differences in training outcomes. In addition, follow-up surveys in the licensed phase provide some measures of self-reported crashes, driving confidence, aggression, and driver behaviour habits. While locus of control and crash beliefs were not included in our surveys, we recognize their potential value and will consider incorporating them in future follow-up studies or exploratory analyses. In addition, while the study is limited to English speaking individuals in one region, the results nonetheless have the potential to inform evidence-based policies and contribute to the design of targeted, multifaceted driver training programs that enhance safety and effectiveness.

Acknowledgements

The authors would like to acknowledge Jamillah Millner, Shukai Cheng, Alexander Gonzalez, and Joshua Fischer for their contributions to the study set-up, and our partners in the CHOP Primary Care clinics as well as The Possibilities Project at CHOP. The authors also express special thanks to Don Fisher for sharing and critically informing and reviewing the redesign of the ACCEL intervention, Roman Catholic High School students for their feedback, and the CHOP Global Education team for executing the redesign.

The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Author contributions

EAW, DR, FKW, ME, KSB, and MKD conceptualized the study design. Funding was obtained by EAW, DR and FKW. EAW drafted the manuscript. DR, FKW, ME, KSB, MKD and JE provided feedback on the manuscript. All authors approved the final version.

Funding

This work was supported by the Eunice Kennedy Shriver Institute of Child Health and Human Development of the National Institutes of Health (NIH) under award number R01HD108249, as well as funds provided by the Children’s Hospital of Philadelphia (CHOP), The Annenberg Public Policy Center at the University of Pennsylvania, and a gift from New Jersey Manufacturers (NJM) Insurance Group to CHOP. The funders have no role in the design of the study and will not have any role in its execution, data management, analysis and interpretation or on the decision to submit results for publication.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board at the Children’s Hospital of Philadelphia.

Consent for publication

Not applicable.

Competing interests

Dr. Winston has an intellectual property and financial interest in Diagnostic Driving, Inc. Diagnostic Driving, Inc., created the virtual driving assessment system used in CHOP clinics and this trial. This research will utilize proprietary data provided by Diagnostic Driving, Inc. through its virtual driving assessment product, Ready-Assess™ (diagnosticdriving.com). This potential conflict of interest is managed under a conflict-of-interest management plan from CHOP and the University of Pennsylvania. The authors have no other relationships or activities that could appear to have influenced the submitted work.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Global Status Report on Road Safety 2023 Made possible by funding from bloomberg Philanthropies.
  • 2.Highway Traffic Safety Administration N, Department of transportation U. Traffic safety facts: young driver–2022 Data.; 2024.
  • 3.Centers for Disease Control and Prevention (CDC). WISQARS—Web-based Injury Statistics Query and Reporting System. U.S. Department of health and human services, centers for disease control and prevention, national center for injury prevention and control. Preprint posted online 2022.
  • 4.Babaie M, Joulani M, Ranjbar Hameghavandi MH, et al. Risk of permanent medical impairment after road traffic crashes: a systematic review. Chin J Traumatol. 2023;26(5):267–75. 10.1016/j.cjtee.2022.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Masten SV, Thomas FD, Korbelak KT, Peck RC, Blomberg RD. Meta-analysis of graduated driver licensing laws.; 2015. www.ntis.gov
  • 6.Williams AF. Graduated driver licensing (GDL) in the United States in 2016: a literature review and commentary. J Safety Res. 2017;63:29–41. 10.1016/j.jsr.2017.08.010. [DOI] [PubMed] [Google Scholar]
  • 7.McCartt AT, Teoh ER. Tracking progress in teenage driver crash risk in the United States since the advent of graduated driver licensing programs. J Safety Res. 2015;53:1–9. 10.1016/j.jsr.2015.01.001. [DOI] [PubMed] [Google Scholar]
  • 8.Curry AE, Pfeiffer MR, Durbin DR, Elliott MR. Young driver crash rates by licensing age, driving experience, and license phase. Accid Anal Prev. 2015;80:243–50. 10.1016/j.aap.2015.04.019. [DOI] [PubMed] [Google Scholar]
  • 9.Chapman EA, Masten S, Browning KK. Crash and traffic violation rates before and after licensure for novice California drivers subject to different driver licensing requirements. J Safety Res. 2014;50:125–38. 10.1016/j.jsr.2014.05.005. [DOI] [PubMed] [Google Scholar]
  • 10.Mcknight AJ, Mcknight AS. Young novice drivers: careless or clueless? Accid Analy Prevent. 2003;35:921–5. [DOI] [PubMed] [Google Scholar]
  • 11.Curry AE, Hafetz J, Kallan MJ, Winston FK, Durbin DR. Prevalence of teen driver errors leading to serious motor vehicle crashes. Accid Anal Prev. 2011;43(4):1285–90. 10.1016/j.aap.2010.10.019. [DOI] [PubMed] [Google Scholar]
  • 12.Seacrist T, Maheshwari J, Sarfare S, Chingas G, Thirkill M, Loeb HS. In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study. Traffic Inj Prev Published online. 2021. 10.1080/15389588.2021.1979529. [DOI] [PubMed] [Google Scholar]
  • 13.Walshe EA, Romer D, Wyner AJ, et al. Licensing examination and crash outcomes postlicensure in young drivers. JAMA Netw Open. 2022;5(4):E228780. 10.1001/jamanetworkopen.2022.8780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Walshe EA, Romer D, Aagaard N, Winston FK. Variation in young driver training requirements by state. JAMA Netw Open. 2024;7(6):e2417551. 10.1001/jamanetworkopen.2024.17551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Wynne-Jones JD, Hurst P. The AA Driver Training Evaluation. Ministry of Transport: Published online; 1984. [Google Scholar]
  • 16.Lund AK, Williams AF, Zador P. High school driver education: further evaluation of the Dekalb County study. Accid Anal Prev. 1986;18(4):349–57. 10.1016/0001-4575(86)90048-5. [DOI] [PubMed] [Google Scholar]
  • 17.Mayhew DR, Simpson HM, Pak A. Changes in collision rates among novice drivers during the first months of driving. Accid Anal Prev. 2003; 35: 683–691. (Accessed 1 June 2021). https://www-sciencedirect-com.proxy.library.upenn.edu/science/article/pii/S1054139X11006112?via%3Dihub [DOI] [PubMed]
  • 18.Braitman KA, Kirley BB, McCartt AT, Chaudhary NK. Crashes of novice teenage drivers: characteristics and contributing factors. J Safety Res. 2008;39(1):47–54. 10.1016/j.jsr.2007.12.002. [DOI] [PubMed] [Google Scholar]
  • 19.Horswill MS, McKenna FP. Drivers’ hazard perception ability: Situation awareness on the road. In: A cognitive approach to situation awareness: theory, measurement and application. Ashgate publishing; 2004:155–174. (Accessed 1 June 2021). https://www.researchgate.net/publication/271767727_Drivers’_hazard_perception_ability_Situation_awareness_on_the_road
  • 20.Klauer SG, Guo F, Simons-Morton BG, Ouimet MC, Lee SE, Dingus TA. Distracted driving and risk of road crashes among novice and experienced drivers. N Engl J Med. 2014;370(1):54–9. 10.1056/nejmsa1204142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Satterthwaite TD, Wolf DH, Erus G, et al. Functional maturation of the executive system during adolescence. J Neurosci. 2013;33(41):16249–61. 10.1523/JNEUROSCI.2345-13.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Walshe EA, Winston FK, Betancourt LM, Khurana A, Arena K, Romer D. Working memory development and motor vehicle crashes in young drivers. JAMA Netw Open. 2019;2(9):e1911421. 10.1001/jamanetworkopen.2019.11421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Walshe EA, Ward McIntosh CM, Romer D, Winston FK. Executive function capacities, negative driving behavior and crashes in young drivers. Int J Environ Res Public Health. 2017. 10.3390/ijerph14111314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Zhang H, Guo Y, Yuan W, Li K. On the importance of working memory in the driving safety field: a systematic review. Accid Anal Prev. 2023;187:107071. 10.1016/j.aap.2023.107071. [DOI] [PubMed] [Google Scholar]
  • 25.Romer D, Lee YC, McDonald CC, Winston FK. Adolescence, attention allocation, and driving safety. J Adolesc Health. 2014;54(5 SUPPL.):S6–15. 10.1016/j.jadohealth.2013.10.202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zhang X, Qu X, Tao D, Xue H. The association between sensation seeking and driving outcomes: a systematic review and meta-analysis. Accid Anal Prev. 2019;123:222–34. 10.1016/j.aap.2018.11.023. [DOI] [PubMed] [Google Scholar]
  • 27.Walshe EA, Winston FK, Romer D. Rethinking cell phone use while driving: isolated risk behavior or a pattern of risk-taking associated with impulsivity in young drivers? Int J Environ Res Public Health. 2021. 10.3390/ijerph18115640. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Dunlop SM, Romer D. Adolescent and young adult crash risk: sensation seeking, substance use propensity and substance use behaviors. J Adolesc Health. 2010;46(1):90–2. 10.1016/j.jadohealth.2009.06.005. [DOI] [PubMed] [Google Scholar]
  • 29.Romer D, Betancourt LM, Brodsky NL, Giannetta JM, Yang W, Hurt H. Does adolescent risk taking imply weak executive function? A prospective study of relations between working memory performance, impulsivity, and risk taking in early adolescence. Dev Sci. 2011;14(5):1119–33. 10.1111/j.1467-7687.2011.01061.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Dong X, Wu JS, Jensen ST, Walshe EA, Winston FK, Ryerson MS. Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy. Accid Anal Prev. 2023. 10.1016/j.aap.2023.107198. [DOI] [PubMed] [Google Scholar]
  • 31.Muttart JW, Agrawal R, Ebadi Y, Samuel S, Fisher DL. Evaluation of a training intervention to improve novice drivers’ hazard mitigation behavior on curves. In: Proceedings of the Ninth International driving symposium on human factors in driver assessment, training, and vehicle design. The University of Iowa; 2017:136–143. 10.17077/drivingassessment.1626
  • 32.Pollatsek A, Narayanaan V, Pradhan A, Fisher DL. Using eye movements to evaluate a PC-based risk awareness and perception training program on a driving simulator. Human Fact J Human Fact Ergon Soc. 2006;48(3):447–64. 10.1518/001872006778606787. [DOI] [PubMed] [Google Scholar]
  • 33.Thomas FD, Rilea SL, Blomberg RD, Peck RC, Korbelak KT. Evaluation of the safety benefits of the risk awareness and perception training program for novice teen drivers.; 2016. (Accessed 24 Feb 2022). www.ntis.gov
  • 34.Taylor TGG, Masserang KM, Pradhan AK, et al. Long term effects of hazard anticipation training on novice drivers measured on the open road. Proc Int Driv Symp Hum Factors Driv Assess Train Veh Des. 2011:187–194. (Accessed 1 June 2021). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180240/ [PMC free article] [PubMed]
  • 35.Fisher DL, Knodler M, Samuel S, et al. Accelerating teen driver learning : anywhere, anytime training.; 2017. (Accessed 1 June 2021). www.aaafoundation.org
  • 36.NHTSA. Evaluation of an updated version of the risk awareness and perception training program for young drivers.; 2017. (Accessed 1 June 2021)
  • 37.Delgado MK, Ebert J, Xiong A, McDonald C. Behavioral characteristics of young drivers observed to engage in risky driving in a national smartphone-based auto insurance program. Soc Adv Viol Injury Res. 2021. 10.17645/mac.v4i3.536. [Google Scholar]
  • 38.Ebert JP, Xiong RA, Patel A, Abdel-Rahman D, McDonald CC, Kit Delgado M. Validation of a smartphone telematics algorithm for classifying driver trips. Transp Res Interdiscip Perspect. 2024. 10.1016/j.trip.2024.101109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Delgado MK, Ebert JP, Xiong RA, et al. Feedback and financial incentives for reducing cell phone use while driving: a randomized clinical trial. JAMA Netw Open. 2024;7(7):e2420218. 10.1001/jamanetworkopen.2024.20218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Ebert JP, Xiong RA, Khan N, et al. A randomized trial of behavioral interventions yielding sustained reductions in distracted driving. Proc Natl Acad Sci U S A. 2024. 10.1073/pnas.2320603121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Ebert JP, Yan R, Friday S, et al. Behavioral interventions for increasing seat belt use and decreasing distracted driving using telematics: a national randomized trial. Am J Public Health. 2025;115(5):758–68. 10.2105/AJPH.2024.307980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.McDonald CC, Rix K, Ebert JP, et al. Handheld cellphone use and risky driving in adolescents. JAMA Netw Open. 2024;7(10):e2439328. 10.1001/jamanetworkopen.2024.39328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Pew Research Center. Teens, Social media & technology 2022: 10; 2022. www.pewresearch.org
  • 44.Walshe EA, Elliott MR, Cheng S, et al. Driving skills at licensure and time to first crash. Pediatrics. 2023. 10.1542/peds.2022-060817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Walshe EA, Elliott MR, Romer D, et al. Novel use of a virtual driving assessment to classify driver skill at the time of licensure. Transp Res Part F Traffic Psychol Behav. 2022;87:313–26. 10.1016/j.trf.2022.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Moore TM, Reise SP, Gur RE, Hakonarson H, Gur RC. Psychometric properties of the Penn Computerized Neurocognitive Battery. Neuropsychology. 2015;29(2):235–46. 10.1037/neu0000093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Watson D, Clark LA, Tellegen A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Personal Soc Psychol. 1988;54:1063. [DOI] [PubMed] [Google Scholar]
  • 48.Khurana A, Romer D, Betancourt LM, Hurt H. Modeling trajectories of sensation seeking and impulsivity dimensions from early to late adolescence: universal trends or distinct sub-groups? J Youth Adolesc. 2018;47(9):1992–2005. 10.1007/s10964-018-0891-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Romer D, Duckworth AL, Sznitman S, Park S. Can adolescents learn self-control? Delay of gratification in the development of control over risk taking. Prev Sci. 2010. 10.1007/s11121-010-0171-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Hoyle RH, Stephenson MT, Palmgreen P, Pugzles Lorch E, Donohew RL. Reliability and validity of a brief measure of sensation seeking. Pers Individ Differ. 2002;32(3):401–14. 10.1016/S0191-8869(01)00032-0. [Google Scholar]
  • 51.Acevedo-Garcia D, Noelke C, McArdle N, et al. Racial and ethnic inequities in children’s neighborhoods: evidence from the new child opportunity index 2.0. Health Aff. 2020;39(10):1693–701. 10.1377/hlthaff.2020.00735. [DOI] [PubMed] [Google Scholar]
  • 52.Mirman JH, Curry AE, Fisher-Thiel M, Winston F, Durbin D. Development of the Driving Practice Quality Assessment for Parents and Teens. Preprint posted online September: Society for Research in Child Development; 2012. [Google Scholar]
  • 53.Reason J, Manstead A, Stradling S, Baxter J, Campbell K. Errors and violations on the roads: a real distinction? Ergonomics. 1990;33(10–11):1315–32. 10.1080/00140139008925335. [DOI] [PubMed] [Google Scholar]
  • 54.Glendon AI, Dorn L, Matthews G, Gulian E, Davies DR, Debney LM. Reliability of the driving behaviour inventory. Ergonomics. 1993;36(6):719–26. [Google Scholar]
  • 55.Redfield RR, Bunnell R, Greenspan A, et al. Youth risk behavior surveillance—United States, 2019. Vol 69.; 2020. (Accessed 30 August 2020). https://www.cdc.gov/healthyyouth/data/yrbs/index.htm
  • 56.Wohleber RW, Matthews G. Multiple facets of overconfidence: implications for driving safety. Transp Res Part F Traffic Psychol Behav. 2016;43:265–78. 10.1016/j.trf.2016.09.011. [Google Scholar]
  • 57.Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ. Comparing real-world behaviors of drivers with high versus low rates of crashes and near crashes. Published online February 2009.
  • 58.Muller C, Winship C,, Morgan S. Instrumental variables regression. In: Best H, Wolf C, eds. Sage Publications Ltd.; 2014:277–300.
  • 59.Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55. 10.1080/01621459.1996.10476902. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No datasets were generated or analysed during the current study.


Articles from Injury Epidemiology are provided here courtesy of BMC

RESOURCES