Positive Bias in Teenage Drivers With ADHD Within a Simulated Driving Task

Abstract

Objective: Youth with ADHD exhibit positive bias, an overestimation of ability, relative to external indicators. The positive bias construct is understudied in adolescents, particularly in the domain of driving. Study is needed as youth with ADHD experience greater negative outcomes in driving relative to typically developing teens. Method: Positive bias on a driving simulator task was investigated with 172 teenagers with ADHD, combined type. Youth participated in a driving simulation task and rated driving performance afterward. Results: Compared with external ratings of driving performance, youth overestimated driving competence for specific driving behaviors as well as globally. The global rating demonstrated a greater degree of positive bias. Greater positive bias on global ratings of driving ability also predicted greater rates of risky driving behaviors during the simulator exercise independent from disruptive behavior disorder symptoms. Conclusion: Results inform prevention and intervention efforts for teenage drivers with ADHD.

Keywords

ADHD driving accidental injury positive bias

American drivers between 16 and 20 years old have, by far, the greatest injury and fatality rate compared with other age groups (National Highway Traffic Safety Administration, National Center for Statistics and Administration, U.S. Department of Transportation, 2005). Thus, adolescent drivers are a prominent public health concern (American Academy of Pediatrics, Committee on Injury, Violence, and Poison Prevention, Committee on Adolescence, 2006), and outcomes are even worse when one considers the driving records of adolescents diagnosed with ADHD (Barkley & Cox, 2007; Fischer, Barkley, Smallish, & Fletcher, 2007; Jerome, Segal, & Habinski, 2006; Thompson, Molina, Pelham, & Gnagy, 2007). Teens with ADHD exhibit higher rates of risky driving behaviors relative to peers without ADHD (Merkel et al., 2013; Narad et al., 2013). There is now clear, converging evidence from multiple prospective studies with adolescents with ADHD and comparison adolescents without ADHD that teen drivers with ADHD have more accidents (Fischer et al., 2007; Nada-Raja et al., 1997; Thompson et al., 2007; Woodward, Fergusson, & Horwood, 2000) that result in greater costs (Fischer et al., 2007; Narad et al., 2015), injuries (Woodward et al., 2000), and fatalities (Lambert, 1995). Adolescent drivers with ADHD are also more likely to be cited for a traffic citation or reckless driving, driving without a license, having hit-and-run accidents, and having a revoked or suspended license (see Barkley & Cox, 2007, for a review).

Another area of difference between youth with ADHD and those without is the degree to which a positive bias is evident. The positive bias can be defined as a reliable difference between self-assessed competence and objectively assessed competence wherein the self-assessment is more positive than the objective assessment (Hoza, Pelham, Dobbs, Owens, & Pillow, 2002; Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007). This positive bias has been found to be reliably greater in individuals with ADHD in multiple studies of children (Owens et al., 2007). However, there is currently little research on the extent to which the positive bias continues to be present in adolescent samples of youth with ADHD, and studies with such samples are needed to investigate whether this cognitive style is present within tasks important for appropriate adolescent development such as driving.

Study of positive bias is relevant to driving behavior, given that overestimations have been linked in previous research to greater misperception of driving skill and lower utilization of safety precautions such as wearing a seatbelt (DeJoy, 1989; Harré, Foster, & O’Neill, 2005; Svenson, Fischhoff, & MacGregor, 1985). Recent work has started to investigate the positive bias in young drivers with ADHD and there are some initial investigations that suggest the positive bias contributes to the adverse driving outcomes experienced by youth with ADHD. For example, ratings of behavioral competence (i.e., youth with ADHD rated their behavior as more appropriate than reported by external raters) fully mediated the relationship between ADHD symptoms and risky driving outcomes (frequency of driving illegally, frequency of traffic violations, and having a license/permit revoked or suspended; Hoza et al., 2013). Knouse, Bagwell, Barkley, and Murphy (2005) examined self-assessments of young adults with ADHD as compared with driving performance in naturalistic settings and in a driving simulator. Although the young adults with ADHD had greater rates of collisions and traffic citations and performed worse in the simulator scenarios, they provided similar self-assessments of driving skill relative to comparison young adults who did not evidence impairment in driving. Fischer et al. (2007) also reported that adolescents with ADHD appraise their own driving skills as being better than parent or objective record reports. These findings in ADHD samples represent a general perceptual style that cannot be explained by deficits in driving knowledge (Barkley, Murphy, & Kwasnik, 1996) or maturation to adulthood (Knouse et al., 2005).

There is a need to further investigate these perceptual biases in youth with ADHD. First, the majority of studies within the literature have focused on child samples (Owens et al., 2007). The degree to which positive bias persists throughout development into adolescence is currently not well documented, and samples often include a wide range of ages (Steward, Tan, Delgaty, Gonzales, & Bunner, 2014). Second, there is a need to investigate the degree to which positive bias may relate to the driving behaviors of novice drivers. Given that new drivers are by far the riskiest on the road (Mayhew, Simpson, & Pak, 2003), positive bias during this critical developmental transition may be especially impairing. Furthermore, it represents a viable candidate to target within intervention. Thus, although there is evidence that the positive bias affects driving outcomes for teenagers and young adults with ADHD (Fischer et al., 2007; Hoza et al., 2013; Knouse et al., 2005), at the present time, the evidence relies on positive bias information collected from childhood (Hoza et al., 2013), assessed with drivers who already have considerable driving experiences (Knouse et al., 2005), or driving data collected retrospectively (Fischer et al., 2007). It is necessary to investigate the degree to which the positive bias affects driving contemporaneously in novice drivers, as this information is important for informing prevention and intervention approaches for young drivers with ADHD.

In the present study, it was hypothesized as follows:

Hypothesis 1: Evidence for positive bias in adolescents with ADHD would be observed through a comparison of parent and teen ratings of ADHD symptoms and impairment.

Hypothesis 2: Evidence for positive bias would be observed within the context of a developmentally appropriate task for adolescents—driving.

Hypothesis 3: Greater positive bias would predict more adverse outcomes within simulated driving performance.

Method

Participants

Participants were 172 youth with ADHD aged 16 to 18 who were enrolled in a larger clinical trial to investigate family and behavioral supports for novice drivers (see Table 1 for participant characteristics). All participants had a learner’s permit to drive and were enrolled in a driver education training curriculum. Seventy percent of the sample was prescribed stimulant medication to treat ADHD. Parental educational level was used as an indicator of socioeconomic status with mothers attaining high school or equivalent (27.9%), an associate’s degree (20.3%), a bachelor’s degree (31.4%), and a graduate degree (20.4%), and fathers attaining below high school (1.3%), high school or equivalent (45.8%), an associate’s degree (25.5%), a bachelor’s degree (17.6%), and a graduate degree (9.8%).

Table 1.

Demographic Characteristics of Participants in the Study.

	% of sample	M (SD)
Gender	72% boys
Ethnicity	6% Hispanic/Latino
Race	85% White
	11% Black/African American
	1% American Indian
	3% Other
Taking medication for ADHD	70%
Child age		16.92 (0.68)
DBD parent inattentive		2.02 (0.59)
DBD parent hyperactive/impulsive		1.47 (0.60)
DBD parent ODD		1.26 (0.67)
DBD teacher inattentive		1.26 (0.75)
DBD teacher hyperactive/impulsive		0.74 (0.72)
DBD teacher ODD		0.53 (0.61)
DBD interviewer inattentive		1.98 (0.56)
DBD interviewer hyperactive/impulsive		1.51 (0.61)
IRS overall impairment		3.41 (1.67)

Note. DBD = Disruptive Behavior Disorder; ODD = Oppositional Defiant Disorder Factor; IRS = Impairment Rating Scale. N=160.

As part of the larger study, all teens participated in a driving simulation exercise during the second week of the 12-week driver training program. Data from the simulation were available for 160 of the teenagers (93%). Reasons for missing data included non-participation in the simulator session, drop-out from the study prior to the simulator session, or equipment malfunction. Procedures were approved by the university’s Child and Youth Institutional Review Board.

Procedures

As part of a larger clinical trial investigating driving interventions, all teens were invited to the university campus to participate in a driving simulator exercise (see description of simulator hardware and software below). On arrival to the laboratory, participants received a general overview of the simulator, rules of use, and a formal instruction to drive as they “typically would.” Participants then engaged in a 3-min orienting drive to familiarize them with the controls, environment, and ensure the equipment was functioning properly. Then, participants participated in a structured, 20-min drive wherein they were guided throughout the simulated environment by blinking directional signs. Throughout the environment, they encountered hazards they needed to negotiate, including a construction zone, icy roadway conditions, and unexpected hazards (e.g., a deer crossing the roadway). Following the simulator activity, teens provided a self-report of driving performance, an observer completed the same rating of performance, and indicators of performance were generated from the simulator activity.

Driving Simulator (Hardware and Software)

The simulator consists of a 6-degree-of-freedom electrically actuated motion platform; a front-seat real vehicle passenger cabin (a Ford Contour); driver input in the form of a steering wheel; three floor pedals; feedback buttons; an on-board Emergency-Stop switch for safety purposes; a four-screen front-projected XVGA+ (4:3, 8’ × 6’, 1280 × 960 pixel resolution) visualization system; an off-board stereo audio system, mixer, and amplifier; and a dual-core performance PC-based MS-Windows computer workstation to drive the entire simulation. The virtual simulation driving environment—approximately a 4-square-mile region—was modeled after an actual array of local neighborhoods, streets, and landmarks. As the driver negotiates the simulated environment, behavioral data on driving behavior are continually measured. This includes speed, whether complete stops are made at stop signs, and the results of interactions with roadway obstacles and hazards.

Measures

Disruptive Behavior Disorders (DBD) Rating Scale

ADHD and Oppositional Defiant Disorder (ODD) factor Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) symptoms were measured using the DBD Rating Scale (Pelham, Gnagy, Greenslade, & Milich, 1992), which was administered to the teen, the parent, and the teacher. The DBD is a measure that asks informants to rate the DSM symptoms of ADHD and ODD on a 0- to 3-point Likert-type scale (i.e., not at all, just a little, pretty much, or very much). The 0 to 3 ratings were summed across 18 ADHD symptoms and then, an average score for ratings across symptoms was calculated for each individual for the inattentive and hyperactive/impulsive symptoms. The ODD score for each individual was the average of 0 to 3 ratings for eight ODD symptoms. The DBD rating scale has acceptable psychometric characteristics (Pelham, Fabiano, & Massetti, 2005).

Impairment Rating Scale (IRS)

The IRS (Evans, Allen, Moore, & Strauss, 2005; Fabiano et al., 2006) is a rating scale that asks informants to rate the severity of the child’s problems and need for treatment and/or special services in important functional domains (i.e., relationship with peers, relationship with the teacher(s), academic progress, classroom functioning, self-esteem, overall need). There are six items on the scale and scores on the measure range from 0 (not a problem/definitely does not need treatment or special services) to 6 (extreme problem/definitely needs treatment or special services). Test–retest reliability ranged from .60 to .89 over a period of 6 months and .54 to .76 over 1 year. Ratings on the IRS predict mental health or school services, and there is evidence of convergent and discriminant validity on the measure (Fabiano et al., 2006). The average score for each of the individual domains rated were used in the analysis; scores could range from 0.0 to 6.0.

Driving Behavior Rating Scale (DBRS)

The DBRS (Barkley, Guevremont, Anastopoulos, DuPaul, & Shelton, 1993; Barkley, Murphy, O’Connell, & Connor, 2005) includes 21 items rated from 1 (not at all) to 4 (very often) with lower scores indicating worse performance. In the present study, the items that were not applicable within the scenarios presented within the simulated environment were not rated (e.g., yielding to emergency vehicles), resulting in a 17-item measure (a copy of the measure is available from the first author). The DBRS was completed by teens and the observer immediately after the simulator drive. Cronbach’s alpha for the teen self-report and observer report were .89 and .91, respectively. An inter-class correlation of .56 was obtained on occasions where two observers were present, within the “fair” range for interrater reliability (Cicchetti, 1994).

Overall performance rating

Teens and observers also provided an overall rating of simulator performance on a scale of 1 (poor) to 10 (excellent) immediately following completion of the DBRS. This rating represented an overall evaluation of performance during the simulated driving task. An inter-class correlation of .74 was obtained between two observers, within the “good” range for interrater reliability (Cicchetti, 1994).

Simulator behavior composite

During the simulator exercise, five behavioral outcomes were counted and collected electronically through the simulator program. These included the number of deer hit during a portion of the drive where animals ran onto the roadway unexpectedly, the number of cones hit when lanes narrowed within construction zones, and the number of times the car deviated from the lane. Furthermore, the maximum speed within the environment and the average speed at stop signs were also collected.

Overall, the base rate of each indicator collected during the simulator exercise was inconsistent and at times low with teens exhibiting negative behavior for (a) speed greater than 55 miles per hour in 19% of trials, (b) hitting a deer in 60% of trials, (c) hitting any cone in the construction zone in 31% of trials, (d) deviating from the lane at any point in 18% of trials, and (e) rolling through a stop sign in 41% of trials, respectively. Thus, these items were combined into a composite of risky driving on the simulator. This was done by first creating a dichotomous variable indicating whether the risky driving behavior was exhibited, or not (i.e., 0 = did not occur, 1 = occurred). These values were then summed to yield a score ranging from 0 to 5. The composite yielded a mean score of 1.7 (SD = 1.12, range = 0-5, median = 2; mode = 1). Higher scores indicated worse driving whereas lower scores represented appropriate driving behaviors. The overall composite appeared to represent a reasonable, objective indicator of negative driving behavior, as it correlated with an observer rating of teen driving behavior significantly (r = −.29, p < .05), and it did not significantly correlate with measures of unrelated constructs such as ADHD symptoms (p > .05).

Positive bias

There are multiple ways to characterize positive illusory bias (Owens et al., 2007); in the present study, it was defined as a discrepancy between self-assessment and objective indicators of competence (i.e., observer assessment) following the simulator exercise (see Owens et al., 2007).

Results

Overview of Data Analytic Approach

Teen, parent, and teacher ADHD symptom and impairment ratings were compared to investigate the presence of positive bias in an adolescent sample to replicate and extend findings from child samples (Owens et al., 2007). Then, teen self-assessments of driving outcomes were compared with the objective observer ratings as predictors of simulator-generated driving outcomes. Finally, positive bias was investigated as a predictor of adverse driving outcomes, over and above symptom ratings also correlated with driving outcomes.

Prior to these analyses, because teens were all enrolled in one of two study groups, mean comparisons on outcomes were inspected to ensure there was no treatment effect of group on outcome variables. Group differences were not expected as the study activities reported herein occurred within the first few weeks of intervention. No group effect was obtained (p > .05), so teens were collapsed into a single group for all analyses (see a similar approach in Hoza et al., 2004).

Positive Bias Within Teen Ratings of Symptoms and Impairment

Teens’ self-report ratings on the DBD indicated less severe symptom ratings of ADHD than parents’ ratings of teen ADHD. See Table 2 for t statistics, means, and standard deviations. This pattern was evident across ratings of inattention (d = 1.61), ratings of hyperactivity/impulsivity (d = 0.75), ratings of teen ODD (d = 0.83), and ratings of impairment (d = 1.25) as analyzed in paired samples t tests (for all comparisons, p < .001). Thus, within this sample of adolescents with ADHD, we replicated and extended the findings from the child literature on positive bias in ADHD to a late teen sample (Owens et al., 2007).

Table 2.

Positive Bias in Teen Self-Report of Symptoms, Impairment, and Driving Behavior.

Measure	Teen		Parent		t (df)	d
Measure	M	SD	M	SD	t (df)	d
ADHD ratings
DBD IA	1.08	0.57	2.02	0.59	16.14 (171)	1.61
DBD HI	1.03	0.55	1.47	0.60	7.32 (171)	0.75
DBD ODD	0.77	0.48	1.26	0.67	9.38 (171)	0.83
IRS total score	1.62	1.18	3.21	1.35	12.77 (171)	1.25
Driving ratings
DBRS total score	3.08	0.48	2.89	0.52	4.39 (165)	0.38
Global driving rating	5.85	1.99	4.13	1.98	9.46 (163)	0.87

Note. All teen ratings were significantly lower than parent ratings (p < .001). df = degrees of freedom; d = effect size calculated by subtracting the mean scores and dividing by the pooled standard deviation; DBD = Disruptive Behavior Disorders Rating Scale; IA = Inattention Factor; HI = Hyperactive/Impulsive Factor; ODD = Oppositional Defiant Disorder Factor; IRS = Impairment Rating Scale; DBRS = Driving Behavior Rating Scale.

Positive Bias in Teen Ratings of Simulator Performance

See Table 2 for a comparison of teens’ self-report and observer ratings of teen simulator performance. Following the driving exercise, there was evidence of positive bias with teen ratings (M = 3.08, SD = 0.48) being significantly greater than observer ratings (M = 2.89, SD = 0.52) on the DBRS, which yielded an effect size of d = 0.38. For the teen global rating, there was also a significant difference with teen ratings (M = 5.84, SD = 1.99) being significantly greater than observer ratings (M = 4.13, SD = 1.98), which yielded an effect size of d = 0.86.

Relationship Between Ratings and Objective Measures of Simulator Performance

See Table 3 for bivariate correlations between teen self-report ratings, observer ratings, and simulator-generated performance measures. Associations exist between observer ratings of teen driving performance and objective simulator-generated measures of teen driving performance; however, such associations do not exist between teen self-reported symptom ratings and the simulator measures. The correlation between the composite score of risky behavior on the simulator and the observer DBRS total score was significant (r = −.29, p < .001).

Table 3.

Bivariate Correlations Between Self-Report, Parent Report, Examiner Report, and Objective Measures of Teen Driving Simulator Performance.

		1	2	3	4	5	6	7	8	9	10	11	12	13	14	M	SD
1	Parent DBD IA	—														2.02	0.59
2	Parent DBD HI	.49***	—													1.47	0.60
3	Parent DBD ODD	.29***	.40***	—												1.26	0.67
4	Parent IRS	.25**	.31***	.51***	—											3.21	1.35
5	Teen DBD IA	.15	.01	.01	.03	—										1.08	0.57
6	Teen DBD HI	−.03	.09	.05	.06	.68***	—									1.03	0.55
7	Teen DBD ODD	.10	.14	.34***	.09	.53***	.52***	—								0.77	0.48
8	Teen IRS	−.01	.13	.17*	.18*	.34***	.35***	.21**	—							1.62	1.19
9	Examiner DBRS	−.06	−.12	−.06	−.06	−.06	.01	−.13	−.08	—						2.89	0.52
10	Examiner global driving	−.12	−.17*	−.01	−.04	−.08	−.03	−.13	−.09	.75***	—					4.13	1.98
11	Teen DBRS	−.08	−.17*	−.12	−.01	−.15*	−.11	−.12	−.13	.38***	.31***	—				3.08	0.48
12	Teen global driving	.07	−.07	.04	.18*	−.06	−.06	−.04	−.11	.31***	.31***	.57***	—			5.84	1.99
13	Positive bias DBRS	−.02	−.04	−.05	.05	−.07	−.11	.01	−.03	−.61***	−.43***	.50***	.20**	—		0.19	0.56
14	Positive bias global	.15	.08	.04	.18*	.01	−.02	.08	−.02	−.36***	−.59***	.21**	.59***	.53***	—	1.73	2.33
15	Total violations simulator	−.09	.05	.17*	−.06	.11	.07	.14	.09	−.29***	−.39***	−.17*	−.19*	.13	.17*	1.70	1.12

Note. Positive bias DBRS and global are variables created by subtracting the Examiner rating from the Teen rating. DBD = Disruptive Behavior Disorder; IA = Inattentive; HI = Hyperactive/Impulsive; ODD = Oppositional Defiant Disorder Factor; IRS = Impairment Rating Scale; DBRS = Driving Behavior Rating Scale.

p < .05. **p < .01. ***p < .001.

To investigate the relationship between others’ ratings and self-report ratings as predictors of negative driving outcome, the variable representing negative driving outcomes in the simulator was regressed onto teen and observer DBRS scores and overall evaluations of driving as well as the parent rating of ODD. Teen gender and medication status were also included within the model. The overall model was significant, R = .340, F(5, 159) = 3.97, p = .002. Examiner report of overall driving performance was a significant predictor of negative driving (B = −.542, p = .003) such that teens rated as performing globally worse on the simulator by the examiner predicted greater rates of negative driving on the simulator.

Relationship Between Positive Bias and Risky Driving

Global ratings of driving proficiency on the simulator illustrated the greatest degree of positive bias for teen drivers, and positive bias on the global ratings were most likely to be correlated with driving outcomes (Table 3). Because there were differences in ratings between the teens and observer, a regression model was constructed that examined whether positive bias uniquely predicted driving outcomes independently from symptom ratings. The variable representing negative driving outcomes exhibited in the simulator was regressed onto the global driving positive bias score and the parent rating of oppositional defiant disorder. Teen gender and medication status were also included within the model. The overall model was significant, R = .259, F(4, 157) = 2.75, p = .03. Discrepancy between the teen and observer in driving performance evaluation was the only significant predictor of negative driving (B = .075, p = .049) such that teens who evinced greater positive bias had greater rates of negative driving on the simulator.

Discussion

This study investigated the presence of the positive illusory bias in a sample of adolescents with ADHD who were novice drivers. Teen symptom and impairment ratings were collected via self-report, parent ratings, and teacher ratings. Evidence for positive bias was evident within these ratings, replicating prior work (Hoza et al., 2013; Owens et al., 2007). These novice drivers with ADHD were also asked to rate their own driving performance during a simulated driving activity, and their driving was also rated by an observer. Objective performance data were also collected during the simulated driving task. The presence of the positive bias was evident for teen drivers with ADHD, and the bias was more pronounced for global ratings of performance made by the teen relative to ratings of specific driving behaviors. Each of these major results will be addressed, briefly, in turn, and implications for treatment efforts directed toward adolescents with ADHD are discussed.

As has been well established in other samples, age groups, and contexts (Owens et al., 2007), adolescents with ADHD exhibited a positive bias with respect to ADHD symptoms and associated impairment in functioning. These results provide replication of the findings in other studies that suggested the continuance of the positive bias within individuals with ADHD (Knouse et al., 2005). Importantly, the magnitude of the discrepancy is comparable with that observed in child samples (Hoza et al., 2004), suggesting there may be little attenuation of this bias over time. The present study also illustrates the presence of the positive bias in a specific area of impaired functioning specific for adolescents with ADHD—driving performance.

An important finding of the present study was that the degree to which the positive illusory bias was present varied depending on the rating utilized. Although teens always estimated their performance to be better than that of the rater, the teens exhibited a much stronger bias when answering general questions about overall driving performance relative to specific questions about discrete driving behaviors. These results are concerning as a novice driver who overestimates ability may take more risks and also learn less from mistakes if blame is not appropriately attributed. Given that the observer ratings and the objective driving measure of risks were related to a greater extent than teen ratings of performance (see Table 3), reliance on a teen with ADHD’s self-assessments for driving performance is not advised as a sole measure. Furthermore, this positive bias independently predicted negative driving outcomes whereas symptom ratings did not. In spite of the differences in magnitude of positive bias, it is important to note that this positive bias was observed across two different raters and functional domains (i.e., parent ratings of symptoms of disruptive behavior and observer ratings of driving performance).

Intervention Implications

The results of this study have implications for intervention and support programs for youth with ADHD. The American Academy of Pediatrics (AAP; Committee on Injury, Violence, and Poison Prevention, Committee on Adolescence, 2006) charges pediatricians to provide anticipatory guidance to parents of teenagers of driving age, including informing parents of state driving laws, encouraging safety behaviors (e.g., wear seatbelts), distributing educational materials, encouraging parent–teen driving contracts, and avoiding distractions and impairments while driving (e.g., cell phones, drinking/drugging and driving). The present results suggest that clinicians working with youth with ADHD need to go beyond these general guidelines to provide more precise recommendations and monitoring strategies to families with youth with ADHD. Indeed, evaluative feedback for new drivers with ADHD and self-assessments should include detailed feedback about specific behaviors rather than general feedback such as “Slow down” or “Be careful.” Initial investigations suggest parents do not typically use such detailed feedback with new drivers with ADHD (Schatz et al., 2014), and this is a fertile area for additional treatment development. Furthermore, these findings suggest that parents and clinicians should not rely on teen self-report of driving performance; objective indicators of performance such as on-board engine performance monitors, simulator performance, in-vehicle video recorders, or collateral reports of driving should be emphasized (Fabiano et al., 2011; Farmer, Kirley, & McCartt, 2010; McDonald et al., 2015; McGehee, Raby, Carney, Lee, & Reyes, 2007). Given the overestimation of driving competence, youth with ADHD may benefit from external restrictions on driving behaviors (e.g., passenger restrictions as part of graduated driver licensing) to prevent untoward outcomes, or rewards contingent on appropriate driving behaviors may also be warranted, similar to effective approaches in other pediatric interventions where the patient may not behave in his or her own best interest (Stark, 2013). Although more intensive than the recommendations from the AAP (AAP, Committee on Injury, Violence, and Poison Prevention, Committee on Adolescence, 2006), given the poor insight into driving performance, especially as it relates to risk, these additional intervention approaches appear to be warranted.

Limitations

This study has limitations. The simulated driving environment provides a controlled situation for assessing driving performance, but the degree to which these results generalize to authentic driving situations is unstudied. In this sample of youth with ADHD, medication was not controlled as teens took their medication as typically prescribed. This limitation may be off-set, however, by prior studies that indicated the positive bias was impervious to medication, in general (Pelham et al., 2002). An additional limitation relates to the lack of a non-ADHD comparison group. Because teens without ADHD may also overestimate their driving skill, it is not possible to evaluate in this study whether positive bias is more severe within a group of adolescents with ADHD. However, given the strong literature demonstrating increased positive bias in samples of youth with ADHD relative to youth without ADHD (i.e., Owens et al., 2007), as well as studies of self-reported driving impairment (Hoza et al., 2013), one might speculate that youth without ADHD would not have as pronounced positive bias in the simulated driving situation. Certainly, this is an important question for future study. Future studies also need to replicate findings with samples that include more diversity with respect to gender, race, ethnicity, and age, as this sample was primarily comprised of White boys within a narrow age band of 16 to 18 years.

Conclusion

The positive bias was present in a sample of teens with ADHD, on measures of ADHD symptomatology and impairment as well as in a simulated driving task. These results suggest that objective monitoring technologies to measure teen behavior within driving activities may be necessary for parents and other adults to appropriately assess driving performance, and these assessments may also be necessary to provide objective feedback on performance to the teen.

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 project was supported by a research grant from the Eunice Kennedy Shiver National Institute of Child Health and Human Development R01HD058588.

Author Biographies

Gregory A. Fabiano, PhD, is Professor of Counseling, School, and Educational Psychology at the University at Buffalo, State University of New York. He studies effective assessments and interventions for youth with ADHD in homes, schools, and community settings.

Nicole K. Schatz, PhD, is currently an Assistant Research Professor at Florida International University. Her program of research focuses on effective interventions for adolescents with ADHD, addressing areas of impairment such as medication adherence, substance use prevention, and driving.

Kevin F. Hulme, PhD, is a Senior Research Associate for the Center for Engineering Design and Applied Simulation at the University at Buffalo. His primary research interest is the application of vehicle-based modeling and simulation to improve transportation safety.

Karen L. Morris, MPH, is a Project Coordinator at the Child & Family Research Group at the University at Buffalo.

Rebecca K. Vujnovic, PhD, is clinical assistant professor in the Department of Counseling, School, and Educational Psychology. She has expertise in school-based interventions for youth with ADHD.

Michael T. Willoughby, PhD, is a Fellow and Senior Research Public Health Analyst at RTI International. He is an expert on the development of self-regulation in children, with a specific interest in how regulatory problems contribute to risk for and manifestation of disruptive behavior disorders.

Dwight Hennessy, PhD, is Associate Professor in the Department of Psychology at Buffalo State College, State University of New York. He is an expert in traffic safety and research, particularly in the area of driver aggression.

Kemper E. Lewis, PhD, is Professor and Chair of the Department of Mechanical and Aerospace Engineering at the University at Buffalo.

Julie Owens, PhD, is Professor and Co-director of the Center for Intervention Research in Schools at Ohio University. Her research areas of expertise include school mental health and the study of positive bias and self-perceptions for youth with ADHD.

William E. Pelham, Jr., PhD, ABPP, is Distinguished Professor of Psychology, Psychiatry & Behavioral Health, and Public Health in the Department of Psychiatry at Florida International University. Dr. Pelham is an internationally recognized expert in the assessment and treatment of ADHD in children and adolescents.

References

American Academy of Pediatrics, Committee on Injury, Violence, and Poison Prevention, Committee on Adolescence. (2006). The teen driver. Pediatrics, 118, 2570-2581.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Barkley

R. A.

Cox

(2007). A review of driving risks and impairments associated with attention-deficit/hyperactivity disorder and the effects of stimulant medication on driving performance. Journal of Safety Research, 38, 113-128.

Barkley

R. A.

Guevremont

D. C.

Anastopoulos

A. D.

DuPaul

G. J.

Shelton

T. L.

(1993). Driving-related risks and outcomes of attention deficit hyperactivity disorder in adolescents and young adults: A 3- to 5-year follow-up survey. Pediatrics, 92, 212-218.

Barkley

R. A.

Murphy

K. R.

Kwasnik

(1996). Motor vehicle driving competencies and risks in teens and young adults with attention deficit hyperactivity disorder. Pediatrics, 98, 1089-1095.

Barkley

R. A.

Murphy

K. R.

O’Connell

Connor

D. F.

(2005). Effects of two doses of methylphenidate on simulator driving performance in adults with attention-deficit hyperactivity disorder. Journal of Safety Research, 36, 121-131.

Cicchetti

D.V.

(1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6, 284–290.

DeJoy

D. M.

(1989). The optimism bias and traffic accident risk perception. Accident Analysis & Prevention, 21, 333-340.

Evans

S. W.

Allen

Moore

Strauss

(2005). Measuring symptoms and functioning in youth with ADHD in middle schools. Journal of Abnormal Child Psychology, 33, 695-706.

10.

Fabiano

G. A.

Hulme

Linke

S. M.

Nelson-Tuttle

Pariseau

M. E.

Gangloff

. . . Buck

M. M.

(2011). The Supporting a Teen’s Effective Entry to the Roadway (STEER) program: Feasibility and preliminary support for a psychosocial intervention for teenage drivers with ADHD. Cognitive and Behavioral Practice, 18, 267-280.

11.

Fabiano

G. A.

Pelham

W. E.

Waschbusch

Gnagy

E. M.

Lahey

B. B.

Chronis

A. M.

. . . Burrows-MacLean

(2006). A practical impairment measure: Psychometric properties of the Impairment Rating Scale in samples of children with attention-deficit/hyperactivity disorder and two school-based samples. Journal of Clinical Child & Adolescent Psychology, 35, 369-385.

12.

Farmer

C. M.

Kirley

B. B.

McCartt

A. T.

(2010). Effects of in-vehicle monitoring on the driving behavior of teenagers. Journal of Safety Research, 41, 39-45.

13.

Fischer

Barkley

R. A.

Smallish

Fletcher

(2007). Hyperactive children as young adults: Driving abilities, safe driving behavior, and adverse driving outcomes. Accident Analysis & Prevention, 39, 94-105.

14.

Harré

Foster

O’Neill

(2005). Self enhancement, crash-risk optimism and the impact of safety advertisements on young drivers. British Journal of Social Psychology, 96, 215-230.

15.

Hoza

Gerdes

A. C.

Hinshaw

S. P.

Arnold

L. E.

Pelham

W. E.

Molina

B. S. G.

. . . Wigal

(2004). Self-perceptions of competence in children with ADHD and comparison children. Journal of Consulting and Clinical Psychology, 72, 382-391.

16.

Hoza

McQuade

J. D.

Murray-Close

Shoulberg

Molina

B. S. G.

Arnold

L. E.

. . . Hechtman

(2013). Does childhood positive self-perceptual bias mediate adolescent risky behavior in youth from the MTA study? Journal of Consulting and Clinical Psychology, 81, 846-858.

17.

Hoza

Pelham

W. E.

Dobbs

Owens

J. S.

Pillow

D. R.

(2002). Do boys with attention-deficit/hyperactivity disorder have positive illusory self-concepts? Journal of Abnormal Psychology, 111, 268-278.

18.

Jerome

Segal

Habinski

(2006). What we know about ADHD and driving risk: A literature review, meta-analysis, and critique. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 15, 105-125.

19.

Knouse

L. E.

Bagwell

C. L.

Barkley

R. A.

Murphy

K. R.

(2005). Accuracy of self-evaluation in adults with ADHD: Evidence from a driving study. Journal of Attention Disorders, 8, 221-234.

20.

Lambert

N. M.

(1995). Analysis of driving histories of ADHD subjects. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration.

21.

Mayhew

D. R.

Simpson

H. M.

Pak

(2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis & Prevention, 35, 683-691.

22.

McDonald

C.C.

Kandadal

Loeb

Seacrist

T.S.

Lee

Y.C.

Winston

F.K.

(2015). Simulated driving assessment (SDA) for teen drivers: Results from a validation study. Injury Prevention, 21, 145-152.

23.

McGehee

D. V.

Raby

Carney

Lee

J. D.

Reyes

M. L.

(2007). Extending parental mentoring using an event-triggered video intervention in rural teen drivers. Journal of Safety Research, 38, 215-227.

24.

Merkel

R. L.

Nichols

J. Q.

Fellers

J. C.

Hidalgo

Martinez

L. A.

Putziger

. . . Cox

D. J.

(2013). Comparison of on-road driving between young adults with and without ADHD. Journal of Attention Disorders. Advance online publication. doi:10.1177/1087054712473832

25.

Nada-Raja

Langley

J. D.

McGee

Williams

S. M.

Begg

D. J.

Reeder

A. I.

(1997). Inattentive and hyperactive behaviors and driving offenses in adolescence. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 515-522.

26.

Narad

Garner

A. A.

Antonini

T. N.

Kingery

K. M.

Tamm

Calhoun

H. R.

Epstein

J. N.

(2015). Negative consequences of poor driving outcomes reported by adolescents with and without ADHD. Journal of Attention Disorders. Advance online publication. doi:10.1177/1087054715575063

27.

Narad

Garner

A. A.

Brassell

B. A.

Saxby

Antonini

T. N.

O’Brien

K. M.

. . . Epstein

J. N.

(2013). Impact of distraction on the driving performance of adolescents with and without attention-deficit/hyperactivity disorder. JAMA Pediatrics, 167, 933-938.

28.

National Highway Traffic Safety Administration, National Center for Statistics and Administration, U.S. Department of Transportation. (2005). Traffic safety facts 2005: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Washington, DC: U.S. Department of Transportation.

29.

Owens

J. S.

Goldfine

M. E.

Evangelista

N. M.

Hoza

Kaiser

N. M.

(2007). A critical review of self-perceptions and the positive illusory bias in children with ADHD. Clinical Child and Family Psychology Review, 10, 335-351.

30.

Pelham

W. E.

Fabiano

G. A.

Massetti

G. M.

(2005). Evidence-based assessment of attention-deficit/hyperactivity disorder in children and adolescents. Journal of Clinical Child & Adolescent Psychology, 34, 449-476.

31.

Pelham

W. E.

Gnagy

E. M.

Greenslade

K. E.

Milich

(1992). Teacher ratings of DSM-III-R symptoms of the disruptive behavior disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 31, 210-218.

32.

Pelham

W. E.

Hoza

Pillow

D. R.

Gnagy

E. M.

Kipp

H. L.

Greiner

A. R.

Fitzpatrick

(2002). Effects of methylphenidate and expectancy on children with ADHD: Behavior, academic performance, and attributions in a summer treatment program and regular classroom settings. Journal of Consulting and Clinical Psychology, 70, 320-335.

33.

Schatz

N. K.

Fabiano

G. A.

Morris

K. L.

Shucard

J. M.

Leo

B. A.

Bieniek

(2014). Parenting behaviors during risky driving by teens with attention-deficit/hyperactivity disorder. Behavior Therapy, 45, 168-176.

34.

Stark

L. J.

(2013). Introduction to the special issue on adherence in pediatric medical conditions. Journal of Pediatric Psychology, 38, 589-594.

35.

Steward

K. A.

Tan

Delgaty

Gonzales

M. M.

Bunner

(2014). Self-awareness of executive functioning deficits in adolescents with ADHD. Journal of Attention Disorders. Advance online publication. doi:10.1177/1087054714530782

36.

Svenson

Fischhoff

MacGregor

(1985). Perceived driving safety and seatbelt usage. Accident Analysis & Prevention, 17, 119-133.

37.

Thompson

A. L.

Molina

B. S. G.

Pelham

Gnagy

E. M.

(2007). Risky driving in adolescents and young adults with childhood ADHD. Journal of Pediatric Psychology, 32, 745-759.

38.

Woodward

L. J.

Fergusson

D. M.

Horwood

L. J.

(2000). Driving outcomes of young people with attentional difficulties in adolescence. Journal of the American Academy of Child & Adolescent Psychiatry, 39, 627-634.