Symptom Dimensions of Disruptive Behavior Disorders in Adolescent Drivers

Abstract

Objective: Adolescents with disruptive behavior disorder, including ADHD, are more likely to engage in risky driving practices and, consequently, are more likely to be involved in a motor vehicle crash (MVC) than their non-ADHD peers. It is unclear whether symptoms of inattention, hyperactivity/impulsivity, or oppositional defiant disorder (ODD) increase risk of poor driving outcomes. Method: A total of 41 participants (16-19 years old) reported their ADHD and ODD symptoms and risky driving practices (errors and violations). History of citations and MVCs were acquired from state records. Relative predictive utility of symptom dimensions was assessed using multiple regressions. Results: Inattention solely predicted driving variables of interest: Greater levels of inattention were predictive of more citations, MVCs, and self-reported errors and violations. Conclusion: Findings suggest that symptoms of inattention play a primary role in driving-related problems among adolescents. Implications for future research and practice are discussed.

Keywords

ADHD symptom dimensions oppositional defiant disorder driving outcomes novice drivers

According to the most recent estimates available, motor vehicle crashes (MVCs) accounted for approximately one in three overall deaths and an estimated 362,000 injuries in the 16- to 19-year age group in 2007 (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2007). Factors contributing to increased risk of MVCs among adolescents include inexperience, distraction, and risky driving practices (Shope & Bingham, 2008). However, it is of more than casual interest that adolescents with ADHD have been identified as a subset of adolescents at significantly greater risk for adverse driving outcomes (Barkely, 2004; Thompson, Molina, Pelham, & Gnagy, 2007). Furthermore, most of what is known about the impact of ADHD on driving outcomes is the product of research focusing on adults. Thus, our study sought to extend previous work (Kass, Beede, & Vodanovich, 2010; Richards, Deffenbacher, & Rosen, 2002; Thompson et al., 2007) by using a diverse community sample of novice, adolescent drivers with and without clinically diagnosed ADHD and examining the relative contribution of ADHD symptom dimensions in increasing the risk of poor driving outcomes and reducing safe driving behavior.

Recently, the National Highway Traffic Safety Administration (NHTSA) identified driver inattention as the primary cause of MVCs (NHTSA, 2010). Prior to this, the detrimental effects of driver inattention had been well documented in nonclinical populations (Knowles & Tay, 2002; Sussman, Bishop, Madnick, & Walter, 1985; Trick, Enns, Mills, & Vavrik, 2004; Underwood, 2007) as well as in at-risk populations such as older adults (see Anstey, Wood, Lord, & Walker, 2005, for a comprehensive review) and among individuals who had experienced a traumatic brain injury (Galaski, Ehle, & Bruno, 1990; Lengenfelder, Schultheis, Al-Shihabi, Mourant, & DeLuca, 2002). Along this vein, novice drivers with attention deficits, such as ADHD, are at even greater risk for poor driving outcomes than their typically developing peers (Nada-Raja et al., 1997; Thompson et al., 2007). At present, however, it is unclear as to why adolescents with ADHD are at increased risk for MVCs and moving traffic violations.

ADHD is characterized by two symptom dimensions: inattention and hyperactivity/impulsivity. To the best of our knowledge, no study to date has taken a dimensional approach to understanding the influence of both symptom dimensions on driving outcomes among adolescent drivers with ADHD. Rather, previous studies have simply examined differences in driving outcomes and performance across ADHD and non-ADHD samples. However, because these symptom dimensions occur along a continuum in the general population (Stavrinos & Barton, 2011; Stavrinos & Schwebel, 2011), it seems plausible they could cause impairment within the context of driving even when they are not clinically significant.

Furthermore, given the high comorbidity rates of ADHD with other disruptive behavior disorders such as oppositional defiant disorder (ODD; Faraone, Biederman, Weber, & Russell, 1998) and conduct disorder (CD; Larson, Russ, Kahn, & Halfon, 2011) and given that they are also risk factors for adverse driving outcomes (Nada-Raja et al., 1997; Thompson et al., 2007), it is especially important to investigate the relative contribution of these symptoms (inattention, hyperactivity/impulsivity, and ODD/CD) to the poor driving outcomes experienced by adolescents with ADHD.

To the best of our knowledge, only one study has taken a dimensional approach to understanding the influence of both symptom dimensions of ADHD on driving outcomes (Kass et al., 2010), providing preliminary evidence that dimensions of ADHD may be differentially related to driving behaviors and driving history. In a small sample of college students between 20 and 53 years of age, greater levels of inattention were related to increased attentional lapses in a simulated driving scenario. In addition, greater symptoms of inattention correlated with following lead vehicles too closely. Although individuals with greater symptoms of hyperactivity/impulsivity deviated less from the simulated highway centerline, hyperactivity/impulsivity was not uniquely related to lane positioning as inattention was also related to this variable. In terms of driving history, symptom dimensions of ADHD were not significantly correlated with self-reported moving violations or number of crashes. These findings corroborate previous studies (Barkley, Murphy, & Kwasnik, 1996; Fischer, Barkley, Smallish, & Fletcher, 2007) and extend them by providing evidence that inattention may be more strongly associated with driving problems. However, findings suggesting that both symptom dimensions of ADHD are unrelated to driving history are inconsistent with those reported by Jerome, Segal, and Habinski (2006).

Kass and colleagues (2010) attempted to examine the relationship between symptoms of ADHD and adult driving problems. However, their use of correlations could not provide insight into the separate contributions of these dimensions. Furthermore, it is unclear as to whether the lack of a unique relationship between hyperactivity/impulsivity and driving outcomes in this sample was a result of their use of an adult sample of individuals with ADHD. Develop-mentally, symptoms of hyperactivity/impulsivity tend to become less pronounced whereas attention regulation difficulties continue to be impairing as individuals with ADHD age (Biederman, 2005). It could be that in a younger sample, symptoms of hyperactivity/impulsivity may play a greater role in driving outcomes.

One study’s findings are consistent with the proposition that symptoms of hyperactivity/impulsivity play a more significant role than inattention in predicting poor driving outcomes of adolescents with a history of ADHD (Thompson et al., 2007). In a sample of novice drivers with ADHD, symptoms of hyperactivity/impulsivity were examined as a potential mediator of the relationship between childhood ADHD diagnosis and self-reported traffic citations and collisions (Thompson et al., 2007). In this study, present symptoms of hyperactivity/impulsivity significantly mediated childhood ADHD diagnosis and self-reported traffic citations and crashes. Of note, although hyperactivity/impulsivity significantly predicted risky driving and alcohol-impaired driving, the relationship was no longer significant when symptoms of CD were included in their model. This suggests that the more severe behavioral symptoms associated with CD are better predictors of particularly risky driving behaviors than are symptoms of ADHD. Also in this study, the differential relationship between ADHD symptom dimensions and driving outcomes was examined in an exploratory analysis. Counter to their hypotheses and existing literature, symptoms of inattention were predictive of fewer traffic citations and crashes. Of note, the two symptom dimensions were highly correlated, violating the statistical assumption of multicollinearity among predictor variables. For this reason, the authors cautioned against interpreting this relationship.

This study sought to extend previous work by using a diverse sample of novice drivers, including adolescents with and without a history of ADHD. In particular, we aimed to better understand the relationship between symptoms of ADHD and adverse driving outcomes, rather than simply focusing on examining differences across individuals with and without ADHD. Given that symptoms of ADHD present along a continuum in the general population (Stavrinos & Barton, 2011; Stavrinos & Schwebel, 2011), we reasoned that using a sample that included both individuals with and without ADHD would provide greater variability in terms of symptom presentation and would likely circumvent problems of multicollinearity encountered by others (Thompson et al., 2007) and provide greater generalizability of study findings. Methodologically, we improve on earlier studies by using an objective measure of negative driving outcomes (rather than relying on entirely self-report). This entailed acquiring Department of Motor Vehicle (DMV) records of crashes and citations. We supplemented this information by also using a self-report measure of risky driving practices. Furthermore, because symptoms of ADHD are often comorbid with oppositional behavior and because oppositional behavior is related to risky driving, we also included a measure of ODD. We hypothesized that symptom dimensions of inattention, hyperactivity/impulsivity, and ODD would predict different negative driving outcomes. Specifically, because driver inattention has been identified as the leading cause of MVCs by NHTSA (2010), we hypothesized that inattention would significantly account for DMV-reported crashes. In terms of risky driving practices, we hypothesized that greater symptoms of inattention would predict more driving errors (which are due to failure in planned driving actions and are thus not deliberate violations of driving regulations), whereas greater symptoms of hyperactivity/impulsivity and ODD would predict greater driving violations (which represent deliberate engagement in risky driving).

Method

Participants

The study population consisted of 41 participants between 16 and 19 years of age (M = 17.18, SD = .88), half with a childhood diagnosis of ADHD–combined type (ADHD-C) and all of whom had participated in a larger study examining the effect of cell phone distraction on driving performance on adolescents with and without ADHD-C (Stavrinos et al., 2011). Participants with ADHD-C were recruited via local behavioral assessment clinics, pediatrician offices, and community advertisements and flyers. This approach of recruiting from multiple clinics and the community provided a more representative mix of ADHD adolescents, thereby improving generalizability of study findings. The community sample was recruited via advertisements and flyers. Inclusionary criteria included being between 16 and 19 years of age and having a valid learner’s permit or driver’s license, a cell phone with text messaging capabilities, and access to a computer. For inclusion in the ADHD group, there was a requirement that the adolescent be prescribed psychostimulant medication for ADHD, without specification of a particular type of psychostimulant or required dosage. This requirement was part of a larger study protocol in which adolescents with ADHD were asked to participate in a simulated drive while on and off their typically prescribed psychostimulant medication. Exclusion criteria included physical disabilities (e.g., visual or hearing impairment, use of a wheelchair) that prohibited full participation in the experimental protocol. A total of 72 individuals (25 ADHD/47 control) were screened. Approximately 60% of those screened were included in the study. The following were reasons that interested individuals were not included in the sample: scheduling conflicts, no license or permit, no cell phone with text messaging capability, not currently taking ADHD medications, and no computer at home.

The sample was a “novice” group of drivers, who had held a driver’s permit for an average of 1.80 years (range = 4.47 months to 3.90 years). The participants were primarily male (73.2%) and Caucasian (85.4%) with ADHD-C and control samples were matched on gender, race, and time since licensure. This project was approved by the university Institutional Review Board for Human Use.

Measures

Disruptive behavior disorder symptoms

Adolescents completed the Adult Behavior Rating Scale–Self-Report of Current Behavior form (ABRS; Barkley, 1997b), a 26-item measure of ADHD and ODD symptom frequency. The ABRS was developed specifically to reflect the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria for ADHD and ODD diagnosis and comprises two 9-item subscales, Inattention (e.g., easily distracted) and Hyperactivity/Impulsivity (e.g., difficulty awaiting turn) and one 8-item scale measuring symptoms of ODD (e.g., actively defies adults or rules). Adolescents rated their behaviors during the last 6 months on a 4-point Likert-type scale ranging from 0 (never or rarely) to 3 (very often). The responses were summed for each scale, yielding scores with a possible range from 0 to 27 for the Inattentive (Cronbach’s α = .84) and Hyperactive/Impulsive scale (Cronbach’s α = .87). In addition, a total ADHD score was derived by summing the Inattention and Hyperactivity/Impulsivity scales with a possible range from 0 to 54 (Cronbach’s α = .89). The ODD scale scores ranged from 0 to 24 (Cronbach’s α = .91).

Risky driving practices

Participants self-reported individual driving practices on the short version of the Driving Behavior Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). Items from the DBQ were divided into two 8-item subscales, Errors (e.g., trying to pass someone I didn’t notice was signaling to turn right) and Violations (e.g., tailgating to “signal” to the driver of the car in front of me to go faster), based on whether the risky driving behavior addressed in the question was intentional (i.e., a violation) or simply a mistake (i.e., an error). Participants rated their driving behaviors within the last year on a scale from 0 (never) to 5 (nearly all the time; Cronbach’s α = .76 and .67 for violations and errors, respectively).

Real-world driving outcomes

Driving records of all participants were acquired from the State of Alabama DMV. These records provided documentation of each participant’s involvement in a MVC (without determination or mention of fault) and traffic citations for moving. Given the comparatively short time frame study participants had been licensed, we were not surprised by the low number of these events. For this reason, the two variables were combined which provided a measure of real-world driving outcomes.

Procedure

Prospective participants were screened by phone to ensure each met the project’s inclusionary criteria. During the telephone screening process, information describing project logistics (e.g., estimated appointment length, compensation, etc.) was also provided. Adolescent assent and parental consent were acquired, in person, when each participant and their parent(s) presented at the research laboratory for the scheduled session. Parental consent was required because the State of Alabama considers individuals below 19 years of age to be minors. Following completion of questionnaires, adolescents were debriefed on the dangers of distracted driving.

Data Analytic Plan

Preliminary analyses were conducted prior to the main analyses. Differences in ADHD and ODD symptoms and driving outcomes across the ADHD and community sample were assessed with t tests. Zero-order correlations among the variables of interest were also assessed to identify the most appropriate predictor variables for the primary analyses. Next, a series of three multiple regressions were used to determine the relative contribution of predictor variables identified via preliminary analyses in predicting driving behaviors (DBQ errors and violations) and outcomes (DMV citations/collisions). In addition, ADHD diagnosis (community = 1, ADHD = 2) was entered in the model to assess differences in driving outcomes across the two samples. In particular, we hypothesized that symptoms of inattention would positively predict errors whereas hyperactivity/impulsivity and ODD symptoms would positively predict violations. We hypothesized that both symptom dimensions would positively predict DMV citations/collisions.

Results

Preliminary Analyses

ADHD and community samples differed in terms of total ADHD [t(39) = −2.30, p < .05] and ODD [t(39) = −2.20, p < .05] severity (Table 1). However, t tests suggested that the two samples did not differ significantly on any of the driving outcome variables (Table 1).

Table 1.

Descriptive Statistics by Diagnostic Group.

	M (SD)_ADHD	M (SD)_Community
1. ADHD total symptoms	15.64 (9.62)*	10.24 (4.81)*
2. Oppositional defiant disorder	6.45 (5.01)*	3.81 (2.58)*
3. DBQ violations	6.63 (5.51)	8.86 (4.10)
4. DBQ errors	5.23 (4.13)	6.64 (5.52)
5. DMV tickets and collisions	0.33 (0.66)	0.22 (0.54)

Note: DBQ = Driving Behavior Questionnaire; DMV = Department of Motor Vehicles. ADHD symptoms and DBQ responses are based on self-report.

p < .05.

Bivariate correlations among variables of interest are depicted in Table 2. Symptoms of ADHD (inattention and hyperactivity/impulsivity) were significantly correlated with one another (r = .52). Although greater symptoms of ODD related significantly to both symptom dimensions of ADHD, they were correlated more strongly with hyperactivity/impulsivity than with inattention. There was a strong relationship between inattention symptoms and all driving outcome variables of interest. This suggests that greater levels of inattention were related to an increase in risky driving (errors and violations) as well as outcomes (rs ranging from .41 to .63). Symptoms of hyperactivity/impulsivity were related significantly to more DMV citations/collisions (medium strength correlation; r = .38). The relationship between hyperactivity/impulsivity and self-reported violations was marginally significant (r = .27, p = .08). Symptoms of ODD were not significantly related to any of the driving outcome variables.

Table 2.

Descriptive Statistics and Bivariate Correlations of Predictor and Outcome Variables.

	M (SD)	1	2	3	4	5
1. Inattention	10.70 (6.82)	—
2. Hyperactivity/impulsivity	9.95 (7.82)	.52*	—
3. Oppositional defiant disorder	6.60 (5.31)	.47*	.63*	—
4. DBQ violations	7.72 (4.95)	.41*	.28	.23	—
5. DBQ errors	5.47 (3.72)	.55*	.12	.25	.49*	—
6. DMV tickets and collisions	0.28 (0.60)	.63*	.37*	.21	.44*	.30

Note: DBQ = Driving Behavior Questionnaire; DMV = Department of Motor Vehicles. ADHD symptoms and DBQ responses are based on self-report.

p < .05.

Relationship Between ADHD Symptom Dimensions and Driving Safety

Given that ADHD symptoms are present along a continuum in community samples (Bauermeister et al., 2007; Cohen & Cohen, 1984; Faraone et al., 1998) and because our ADHD and community samples did not differ significantly on any of the dependent variables, the entire sample was used in the primary analyses. Importantly, symptoms of ADHD did not violate the assumption of multicollinearity (i.e., r = .9 and above; Tabachnick & Fidell, 2007). Symptoms of ODD were not included in the analyses because they were not correlated with the dependent variables in the preliminary analyses.

In Regression 1, inattention and hyperactivity/impulsivity were entered as independent variables; DBQ violations were entered as the dependent variable. ADHD symptoms and diagnosis, together, explained 31.3% of the variance in the DBQ Violations scale, F(3, 40) = 5.61, p < .01. Inspection of individual betas suggested that greater inattention significantly predicted higher DBQ violation scores. In contrast, symptoms of hyperactivity/impulsivity were not significantly predictive of DBQ violation scores. There was a significant, inverse relationship between diagnosis and DBQ violations, indicating that the community sample reported greater levels of driving violations. Regression 2 included DBQ errors as the dependent variable. In this analysis, ADHD symptoms and diagnosis explained 37.5% of the variance of DBQ errors scores, F(3, 40) = 7.40, p < .01. Only inattention was significantly predictive of DBQ errors with greater inattention being predictive of higher DBQ errors. Finally, Regression 3 included DMV citations/collisions as the dependent variable. ADHD symptoms explained 40.7% of the variance in citations/collisions, F(3, 40) = 7.79, p < .001. Consistent with the other regressions, inattention was the only significant predictor in the model. Table 3 provides a summary of individual standardized betas for all three regressions.

Table 3.

Summary of Standardized Coefficients for Regressions.

	Regression
Predictor variables	Violations β (p)	Errors β (p)	Tickets/collisions β (p)
Inattention	0.36 (.04)	0.68 (.00)	0.61 (.00)
Hyperactivity/impulsivity	0.09 (.61)	−0.24 (.12)	0.04 (.81)

Discussion

This study is the first to determine which of the ADHD symptom dimensions, inattention or hyperactivity/impulsivity, are most predictive of risky driving and adverse driving outcomes. Our results suggest that inattention is the only significant predictor of driving problems and reduced driving safety. Although symptoms of hyperactivity/impulsivity correlated significantly with driving violations and DMV citations and collision records, these symptoms did not uniquely predict such poor driving outcomes when symptoms of inattention were included in the model. This finding is in contrast to our hypothesis that symptom dimensions of ADHD would differentially predict different forms of driving problems. Specifically, we hypothesized that symptoms of inattention would predict greater self-reported driving errors whereas hyperactivity/impulsivity would predict greater self-reported violations and DMV citations and collisions.

Inattention as a key predictor of self-reported driving errors is consistent with a correlational study which demonstrated that symptoms of inattention correlated with various simulated driving behaviors that decreased driving safety (Kass et al., 2010). These unsafe behaviors included following a lead vehicle too closely and attentional lapses. However, symptoms of hyperactivity/impulsivity were not uniquely correlated with any driving outcome. Although our findings differ from those reported by Kass et al. (2010) in that symptoms of ADHD were correlated with both self-reported driving history and official DMV records, they are consistent with previous research (Jerome et al., 2006). Differences between Kass’s findings and ours may be explained by the fact that Kass’s study population consisted of a sample of primarily female undergraduate college students (83%). Given that ADHD is more prevalent among males (American Psychiatric Association, 2000), our study sample was primarily male (approximately 67%). Due to our relatively small sample size, gender differences were not examined. Future studies should consider gender differences in their investigations.

In addition, our findings are inconsistent with those of Thompson and coworkers (2007) who reported that symptoms of hyperactivity explained the relationship between ADHD diagnosis and citations and collisions. Specifically, they noted that when both symptom dimensions were included in the model, greater levels of inattention predicted fewer traffic citations and collisions. However, we interpret this finding as a by-product of the high correlation between the hyperactivity/impulsivity and inattention variables which violated the assumption of multicollinearity. Because Thompson and coworkers did not analyze the relationship between inattention and driving outcomes without hyperactivity/impulsivity in their model, we are unable to determine whether the counterintuitive relationship between inattention and poor driving outcomes would have persisted had there not been a problem with multicollinearity.

It is likely that by including the community sample in our analyses, we were able to include both symptom dimensions (inattention and hyperactivity/impulsivity) and eliminate the threat of multicollinearity between the two variables. Moreover, in our sample, symptoms of inattention were principle predictors of negative driving outcomes rather than symptoms of hyperactivity/impulsivity. Given current theories of ADHD, it is not entirely surprising that symptoms of inattention were primarily predictive of driving problems. For example, Barkley (1997a) posited that deficits in executive functioning may play a central role in the etiology of ADHD, as it involves “higher order” cognitive processes essential for reaching long-term goals. A meta-analysis across studies found convincing evidence of deficits in response inhibition, working memory (particularly spatial working memory), and planning among individuals with ADHD (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). They also explored the role of symptoms of ADHD (inattention and hyperactivity/impulsivity) in relation to deficits in executive functioning and found that weaknesses in executive domains are primarily associated with symptoms of inattention, and that hyperactivity/impulsivity was not independently associated with executive functioning deficits (Willcutt et al., 2005). Our findings taken together with the existing literature suggest that adolescents with attention difficulties may be at increased risk for poor driving outcomes due to weaknesses in certain domains of executive functioning. Future studies could investigate these relationships by examining whether deficits in executive functioning mediate the relationship between symptoms of inattention and driving risk.

Though not a focus of our study, it is interesting that adolescents with ADHD in our study did not report greater engagement in risky driving practices or greater frequency of MVCs and/or citations than their nonclinical counterparts as has been reported in previous work (Jerome et al., 2006). Also, unlike that reported by Nada-Raja et al. (1997), symptoms of ODD were not significantly related to poor driving outcomes in our sample. A possible explanation for this discrepancy across studies may be attributable to the younger age of our participants compared with those studied by Nada-Raja et al. In addition, our sample may have been so young by comparison that they had not had enough driving experience for differences in driving outcomes to emerge. Nonetheless, Thompson et al. (2007) documented a greater number of traffic citations and MVCs among their sample of novice drivers with ADHD. Given Thompson et al.’s appreciably larger sample size (>300 participants) and the finding that their effect sizes were small to medium, it is likely that our sample was not large enough to detect statistically significant differences. Our methods should be replicated with larger samples. Yet another explanation for the lack of differences across ADHD and non-ADHD groups could relate to the medication status of our ADHD sample. As part of the larger study protocol, only adolescents with ADHD who were prescribed psychostimulant medication in the community were included in the study. Thus, these adolescents’ ADHD symptoms were presumably well managed by their medications. Psychostimulant medication may have ameliorated the negative effects of ADHD on driving behaviors and outcomes, in effect “normalizing” their driving behaviors and outcomes to that of adolescents without the condition. Future investigations should compare the driving outcomes of adolescents with ADHD who are and are not being treated with psychostimulant medication with adolescents without ADHD.

Limitations

Our findings must be considered preliminary due to the comparatively small size of our sample. In addition, our reliance on self-reporting of ADHD symptoms is an inherent limitation because children with clinical ADHD often underreport their own ADHD symptoms (Barkley, Fischer, Smallish, & Fletcher, 2002). Despite the possibility that symptoms of ADHD were underreported by our participants, there were significant relationships among symptom dimensions and outcomes of interest. Future studies will benefit from the use of multiple informants, including parents and teachers, when assessing the relationship between symptoms of ADHD and driving outcomes. A final limitation is that study sample was self-referred. Thus, it is not clear whether the study findings would generalize to the overall adolescent driver population.

Strengths

Our study is the first to assess the relative contribution of the symptom dimensions of ADHD to poor driving outcomes often experienced by individuals diagnosed with the condition. Also, our focus on adolescent drivers, a group already known to be at increased risk for MVCs and motor vehicle–related injury, fills a major gap in the literature. In addition, our inclusion of a community sample of adolescents allows for the assessment of how these symptoms may impact individuals who exhibit moderate levels of ADHD. Methodologically, the present study includes a self-report of risky driving practices and an objective measure of driving outcomes. Finally, our findings, although preliminary, provide a significant contribution to the literature by examining the differential relationship between the two symptom dimensions of ADHD. Specifically, our use of regression techniques allowed for the evaluation of the unique contribution of each symptom dimension in predicting outcomes.

Implications

The implications of our findings extend beyond clinically impaired populations. For example, investigations of community samples suggest that among the symptom dimensions of ADHD, problems with attention are the most common (Graetz, Sawyer, Hazell, Arney, & Baghurst, 2001). Taken together, our findings suggest that a sizable portion of the adolescent population may be at risk for impaired driving due to difficulties with attention. As teaching youth how to drive often becomes the responsibility of parents and/or drivers education teachers, our findings suggest that adults in mentoring roles be encouraged to closely monitor their student drivers for signs of driving-based attention difficulties during the learning-to-drive process. Signs of inattention include, but are not limited to, things such as lane deviation, looking away from the roadway for inappropriate periods, and engaging in secondary tasks while driving (e.g., talking on a cell phone). If adolescents exhibit inattentive behaviors while being taught to drive, caregivers may consider restricting in-vehicle distractions including the elimination of electronic devices and passengers. In addition, when making decisions regarding readiness to drive, caregivers may also be well-advised to make inquiries of the youth’s schoolteachers regarding possible signs of attention difficulties the adolescent might be exhibiting in the classroom because such attention difficulties may generalize to the driving setting.

Finally, future investigations should focus on the development and validation of interventions capable of mitigating attention difficulties among adolescent drivers. For example, Epstein and Tsal (2009) described how computerized training programs may improve cognitive functioning of children with attention difficulties. Given that our study does not provide insight into what type of attention difficulties (e.g., selective attention, vigilance) may increase risk for poor driving performance and outcomes among adolescents with attention difficulties, future investigations may benefit from including neuropsychological measures of attention in their study designs so as to better understand the underlying mechanisms of these relationships.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported through a grant from the University of Alabama at Birmingham University Transportation Center - US Department of Transportation, Research and Innovative Technology Administration Award Number DTR06G0048. We would like to thank the members of the UAB Translational Research for Injury Prevention (TRIP) Laboratory for their efforts in the data collection process as well as Dr. Tim Elliott for his thoughtful review of an earlier version of this manuscript

Author Biographies

Annie A. Garner is a doctoral student in Clinical/Medical Psychology at the University of Alabama at Birmingham and is completing her pre-doctoral internship at Cincinnati Children’s Hospital and Medical Center. Her research program focuses on exploring areas of impairment associated with ADHD, including driving.

Ashley Gentry received her BS in Psychology from the University of Alabama at Birmingham (UAB) in 2010. Ashley worked as a mental health technician at a behavioral hospital for three years as an undergraduate where she gained experience working with children with various behavioral difficulties, including ADHD, in an inpatient setting. Ms. Gentry also volunteered as a research assistant during her time at UAB, where she worked on two projects investigating distracted driving.

Sharon C. Welburn is an undergraduate research assistant in the Translation Research for Injury Prevention (TRIP) Laboratory. She has focused on studying the effects of driver distraction transportation related injury, with an emphasis on the impact of distractions on young adults, as well as the possible effect that distracted driving has on cardiovascular health. Ms. Welburn earned her BS in psychology at the University of Alabama at Birmingham in May 2011.

Philip R. Fine is founding director/principal investigator of the University of Alabama at Birmingham’s Injury Control Research Center; the UAB University Transportation Center; and the Southern Consortium for Injury Biomechanics. A UAB faculty member since 1975, he is Professor of Medicine and Professor of Epidemiology. Dr. Fine has authored or co-authored more than 100 contributions to the scientific literature and has been Principal Investigator or Co-Principal Investigator of peer-reviewed research grants that have brought more than $48 million dollars to UAB during his career.

Crystal A. Franklin serves as Program Administrator at the University of Alabama at Birmingham University Transportation Center (UAB UTC). She earned her MPH in Epidemiology from the University of Alabama at Birmingham and her undergraduate degree from Huntingdon College. Ms. Franklin performs a number of administrative and research tasks for the UAB UTC and has experience in research design, participant recruitment, data collection, data management, presentation of findings and coordinating graduate and undergraduate student participation in research.

Despina Stavrinos is Assistant Professor in the Department of Medicine and Department of Psychology at the University of Alabama at Birmingham. She earned her BS in psychology from the University of Alabama and her MA and PhD in developmental psychology from the University of Alabama at Birmingham. Dr. Stavrinos’ research focuses on the cognitive aspects of transportation-related pediatric injury, with a particular emphasis on the impact of distraction on pedestrians and teen drivers. She has received funding from multiple federal agencies.

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