Working Memory Development and Motor Vehicle Crashes in Young Drivers

IMPORTANCE Adolescent drivers have the highest rate of motor vehicle crashes, and among equally novice drivers, crash risk is inversely age graded. Working memory (WM), crucial to driving hazard awareness, is also age graded, with ongoing development into late adolescence. Variability in WM capacity and growth trajectory positions WM as a candidate crash risk factor for study, clinical screening, and possible preventative intervention. OBJECTIVE To test the association between crashes and differential WM development. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study used data from a longitudinal cohort of 118 community youth in Philadelphia, Pennsylvania. Working memory and other risk factors were measured annually from age 11 to 13 years (prelicensure, in 2005) to 14 to 16 years (in 2009), and again at 18 to 20 years (in 2013). In 2015, a follow-up survey of driving experience identified 84 participants who had started driving. Latent growth curve modeling was used to examine the association between variability in the baseline (intercept) and developmental trajectory (slope) of WM and the crash outcome.

are highest during the first 6 months of independent driving. 3,4 However, even among equally novice drivers, crash risk is age graded. At the outset of independent driving, drivers aged approximately 17 years have a higher crash rate than drivers aged approximately 20 years. 3 Given this age-graded risk, individual variation in development is likely associated with crash risk, but its nature and contribution to this risk is not known.
One possible developmental source of crash risk is working memory (WM), 5 which is associated with learning and other risk outcomes in adolescents. 6,7 Working memory has a protracted development into late adolescence, 8,9 with intraindividual and interindividual variability, 10 but generally demonstrates a linear growth trajectory during adolescence. 11,12 This limited-capacity system allows for in-the-moment monitoring, updating, and planning, which are important for decision making, managing complex tasks, and controlling attention in the face of distraction. 13,14 Given that driving is a complex task requiring attentional and related abilities (important for situational awareness), variability in WM development may contribute to crashes during adolescence. For example, safe driving involves scanning, monitoring, and updating information about the vehicle and environment while managing multiple subtasks (eg, adjusting speed, steering, in-vehicle controls) and distractors (eg, peer passengers and mobile phones). 15,16 Working memory development may be even more critical among novice drivers who have not yet automated the subtasks of driving. During the learning phase, adequate cognitive capacity is required to develop both the declarative and procedural memory required for safe driving. 17 Therefore, if some adolescents start driving before their WM capacity has sufficiently developed, they may be at increased risk for crashes for this reason alone.
Indeed, previous studies have shown that in typically developing teens, lower WM capacity is associated with more reckless and inattentive driving, crashes, and poorer performance on simulated driving tasks. [18][19][20][21] In addition, driving performance deteriorates when young drivers complete a secondary WM task while driving in a simulator, but is less affected in those with a higher baseline WM capacity. 22 Furthermore, variability in WM has been associated with impulsivity and substance use during adolescence, 23,24 known risk factors for reckless driving. However, prior studies were cross-sectional, often focused on driving behavior rather than crash outcomes, and rarely controlled for other known risk factors (eg, impulsivity, substance use). To our knowledge, no studies to date have longitudinally examined WM development and its association with crashes by concurrently investigating WM with personality traits and unsafe behaviors.
Young driver policy statements 25 outline the importance of considering development when pediatricians counsel families about driving, pointing to recommendations for atypical development (eg, attention-deficit/hyperactivity disorder [ADHD]) as a risk factor. To our knowledge, no evidence exists for recommendations around expected individual variation in typical neurocognitive development, including WM capacity, which co-occurs during the period of learning to drive and early licensure. Therefore, this study aims to enhance the scientific foundation for young driver policies with an initial investigation of individual variation in the trajectory of WM development in association with unsafe driving and crash risk. The question that arises for public policy and pediatric care is whether differential development in WM places some adolescents at risk of crashes due to limitations in attention and related processing skills that are critical for safe driving. This study addresses 2 gaps in the field by (1) assessing the independent prospective association of WM development with young driver crashes while (2) controlling for other risk-related traits and behaviors (impulsivity, sensation seeking, reckless driving, and substance use). We do this using data from a longitudinal trajectory study of community youth that measured WM development from early to late adolescence, as well as the associated risk-related traits and behaviors, and examine the association between these factors and crashes reported in a follow-up survey after a year or more of independent driving.

Participants
Participants were recruited from a larger longitudinal cohort of community youth in Philadelphia, Pennsylvania, the Philadelphia Trajectory Study (PTS). 11 The original longitudinal study enrolled a community sample of 387 youths aged 10 to 12 years who were assessed annually across 5 waves from 2004 to 2010, and again in 2013 and 2014 at the sixth and final wave of testing, retaining 290 participants (aged 18-20 years). We invited all participants retained at wave 6 to take part in a follow-up survey of driving experiences conducted in 2015. One hundred eighteen participants responded and provided written consent. Of these, 84 participants held a driver's license and were included in the following analysis. As a check for sample bias, the characteristics of the driver sample and the larger PTS sample were compared.

Measures Longitudinal Measures
The longitudinal PTS measured WM on 4 tasks and a number of risk traits and behaviors, including impulsivity, sensation seeking, delay discounting, and incidences of fighting behavior and substance use dependency. These measures have been described previously. 11 A principal component analysis using data from wave 5 and 6 for the total sample of 118 participants revealed that the 2-back, Corsiblock tapping, and spatial WM tasks loaded on 1 factor (explaining 46% of the variance, Cronbach α = 0.83), while the backward digit span loaded on a separate factor alone (explaining 15% of the variance). The 2-back, Corsi-block tapping, and spatial WM scores were standardized to derive a composite score of WM at each wave (Cronbach α ranged from 0.67 to 0.73). This score measured the relative standing of each adolescent in the sample at each wave of the study.
A principal component analysis was used to reduce the risk traits and behaviors to common latent factor scores for use in the LGC model. A risk behavior score derived from wave 6 assessments comprised acting without thinking (AWT) impulsivity, sensation seeking, fighting behavior, alcohol use dependence, and marijuana use dependence. This factor was used in the LGC model to represent this set of risk behaviors near the time of independent driving.

Driving Survey
This self-report survey included items adapted from the commonly used Driver Behavior

Crash Outcome
Two questions asked whether the participant had ever been involved in a crash as a driver and, if so, how many crashes. The crash history items were recoded into a single binary crash outcome (0 = never; 1 = at least 1 crash).

Statistical Analysis
We  matches that of the rest of the larger PTS study. However, the driver sample was slightly older at   Table 2 presents the Philadelphia trajectory study measure scores recorded at wave 6 and Table 3 presents the correlations between the individual characteristics recorded at wave 6, the driving behavior factors, and crash outcome measured at follow-up. While reckless driving was correlated with crashes (r = 0.29), the driving error factor was not (r = −0.18). Crashes did not correlate with age (r = 0.16), sex (r = 0.03), or IQ (r = 0.04). On the other hand, WM at wave 6 was significantly  negatively correlated with crashes (r = −0.32; P = .003). No other individual characteristics (ie, behavior, substance use or dependency) were associated with crashes. However, IQ and marijuana use dependence were positively correlated with WM at wave 6, and so alcohol and marijuana dependence were controlled in the models.

Latent Growth Curve Model
We regressed the binary crash outcome on the latent intercept and slope of WM across waves 2 to 6 obtained from the LGC analysis, as well as the factor scores for reckless driving, driving errors, and risk behaviors (

Limitations
There are several limitations of this study. First, the crash outcome was self-reported. However, the finding that reckless driving behavior was associated with crashes is consistent with previous work 26 and provides some convergent validity for this crash outcome in this study. The crash outcome was not associated with number of years driving, which may be attributed to the fact crash rates are Relative changes in working memory from wave 2 to wave 6 assessment points for drivers with a crash history (n = 25), drivers with no crash history (n = 59), and nondrivers who responded to the follow-up survey (n = 34). highest within the first year of licensure and our outcome was a cumulative measure of crashes.
Second, the sample size may have limited the power to detect associations among the individual characteristic variables (eg, WM development and risk traits and behaviors). Future studies with more power can tease apart the relationships between these factors and the outcome, as well as consider group-based trajectory modeling and latent class analysis to further examine how different developmental trajectories of WM might exert differential influences on crash rates (in both typically and atypically developing adolescents). The association of IQ with WM should also be further examined, with additional factors such as driver training and experience that will change over time with WM development. In addition, while we used a composite score of WM from a battery of performance-based tasks, these measures did not include real-life scenarios or tasks, and so may not be generalizable to how WM would be used during driving. Furthermore, it can be argued that these results could be affected by a practice effect for better learners across waves. However, we do not believe this is likely given that there was at least a year interval between assessments.

Conclusions
This study builds on the scientific foundation for evidence-based guidelines for the counseling and management of young drivers by addressing individual variation in development. While crash statistics indicate that risk is high in young drivers, most young drivers do not crash. Substantial individual variability exists in neural and cognitive development and patterns of risk in adolescence. 10,33 Monitoring WM development across adolescence as part of routine assessment could help to identify at-risk drivers, as well as opportunities for intervention. Attention and driving skill deficits due to insufficient WM may be one of the most modifiable risk factors-via experience and skill training. However, we do not yet know how variability in WM contributes to young driver crashes. The advancement of simulated driving technology offers an opportunity to examine how WM development affects skill learning, in-the-moment performance, and risk taking while driving. 5 Encouragingly, these findings suggest that any interventions that could improve or enhance WM development during adolescence may offer a novel way to reduce the risk outcomes. By identifying the underlying mechanisms of crashes that span typical and atypical development, we can attempt to develop more broadly applicable interventions across multiple populations.