Executive Functioning as a Predictor of Adverse Driving Outcomes in Teen Drivers With ADHD

Abstract

Objective:

The present study examined the association between executive functioning (EF) and risky driving behaviors in teens with ADHD.

Method:

Teens diagnosed with ADHD (n = 179; M_age = 17.4 years) completed two 15-min drives in a fixed-base driving simulator. EF was assessed using parent- and self-report Behavior Rating Inventory of Executive Functioning (BRIEF-2), a temporal reproduction task, and a Go/No-Go task (GNG). Driving outcomes included known predictors of crashes: count of long (>2 s) off-road glances, standard deviation (SD) of lane position (SDLP), mean speed, and SD speed. Generalized linear mixed models, controlling for intelligence and driving experience, were conducted.

Results:

Higher rates of GNG commission errors predicted higher rates of long off-road glances. Lower parent-rated EF and increased rates of GNG omission errors predicted SDLP. Higher rates of GNG commission errors also predicted faster average driving speed.

Conclusion:

Heterogeneity in EF is associated with differences in teen ADHD risky driving behaviors.

Keywords

ADHD executive function driving simulated driving performance

Driving is a complicated task that, when performed poorly, can lead to motor vehicle crashes (MVC), injuries, and fatalities. The risk for fatal MVCs is highest in teens ages 16 to 19, who are approximately three times more likely to be involved in a driving-related fatality than drivers ages 20 and older (Insurance Institute for Highway Safety (IIHS), (2022). Several factors contribute to higher MVC rates among teens. Specifically, compared to more experienced drivers in real-world and simulated driving, novice teen drivers demonstrate greater deviations in lane position (i.e., swerving; Kass et al., 2007; Yang et al., 2006), more variability in speed (Parmet et al., 2015), and more frequent rapid accelerations and decelerations, sharp turns, and corrective swerving (Gershon et al., 2018; Simons-Morton et al., 2019), each of which has been associated with an increase in crashes (e.g., Horswill, 2016; McKnight & McKnight, 2003; Simons-Morton et al., 2012) . Novice teen drivers also exhibit higher rates of long (>2 s) glances away from the roadway compared to experienced drivers (Chan et al., 2010; Wikman et al., 1998), which are associated with over three times more lane departures (Green, 1999) and are the primary cause of most MVCs (Beanland et al., 2013; Y. C. Lee et al., 2007).

A potentially critical mechanism associated with teen risky driving behavior is executive functioning (EF). EF refers to a set of top-down cognitive processes including inhibition, working memory, and cognitive flexibility (task switching) that serve as the foundation for higher-order cognitive skills such as selective and sustained attention, temporal perception (Mäntylä et al., 2007; Ogden et al., 2014), problem-solving, reasoning, planning, and self-control (Diamond, 2013). For simplicity, we will refer to these core EF skills and the related cognitive skills they serve collectively as EF-associated skills. EF-associated skills play a crucial role in driving (Groeger, 2000). However, EF development is a protracted process that proceeds from early childhood into young adulthood, undergoing age-related functional differentiation and improvements in performance (Diamond, 2013; Ferguson et al., 2021). Importantly, this means that EF-associated skills are not fully developed during the early driving years (i.e., 16–19 years of age). Hence, under-developed EF-associated skills may contribute to the risk for adverse driving behaviors and outcomes in teen drivers.

Within teen drivers, those who exhibit deficits in EF-associated skills on neuropsychological tasks or behavioral rating scales relative to their peers are at risk for worse driving outcomes including crashes, tickets, speeding, and lane deviations (Ledger et al., 2019; Walshe et al., 2017; Zicat et al., 2018). Teens diagnosed with ADHD are particularly likely to exhibit EF-associated deficits. ADHD-related EF deficits have been well documented on both performance-based tasks and real-world behavioral rating scales across the lifespan (Boonstra et al., 2005; Willcutt et al., 2005). Further, almost all ADHD-related theoretical models (e.g., dual-pathway, (Sonuga-Barke, 2003), behavioral inhibition (Barkley, 1997), and working memory (Kofler et al., 2008)) posit impairments in EF-associated cognitive functions as a core feature of ADHD.

Given ADHD-related impairments in EF-associated skills, it is not surprising that teen drivers with ADHD are at greater risk than non-ADHD teen drivers for adverse driving outcomes and indices of risky behaviors. Teen drivers with ADHD are twice as likely as their peers to be involved in MVCs (Aduen et al., 2020; Klauer et al., 2017), be at fault for MVCs, receive violations (e.g., speeding, non-seatbelt use), receive points on their license, have suspended or revoked licenses, and require entry into remedial driving courses, demonstrate greater variability in speed and lane position (indicating poor vehicle control), and exhibit more long glances away from the roadway (Garner, 2019; Kingery et al., 2015). Long glances away from the roadway have been shown to be causal in contributing to higher crash rates not only in neurotypical teens, but also in teens with ADHD, as an intervention targeting reductions in long glances away from the roadway produced lower rates of crashes/near crashes (Epstein et al., 2022).

Only a few studies have examined the unique contributions of EF-associated deficits on poor driving outcomes in drivers with ADHD. In adult drivers with ADHD, Stroop interference scores predicted standard deviation of lane position (SDLP; Bioulac et al., 2020). In samples of young drivers including teens and young adults diagnosed with ADHD and a community control (CC) group, Barkley et al. (2002) found a factor score comprised of faster reaction times and more commission errors on the Conners’ Continuous Performance Test (CPT) was associated with Department of Motor Vehicle (DMV) reported crashes, while Aduen et al. (2020) reported that higher reaction time variability and more omission errors on the CPT predicted crashes and near crashes.

Notably, though long glances away from the roadway, lane position variability, and speed have been demonstrated to be key driving indices associated with crash risk, no studies have examined associations between EF-associated deficits and these critical driving indices in teens with ADHD. The goal of the present study is to examine the association between EF-associated deficits, as measured by performance-based tasks and real-world behavioral ratings, and adverse driving indices associated with crash or near-crash events in teen drivers with ADHD. We hypothesize that for teen drivers with ADHD specific EF-associated deficits will be selectively associated with theoretically-related driving indices during simulated driving. While global EF performance might demonstrate more broad associations across adverse or risky driving indices, individual cognitive skills may exhibit specific associations. Namely, worse inhibitory control, which plays a pivotal role in suppressing eye gaze to peripheral, and potentially distracting, stimuli (Luna et al., 2008), and worse temporal duration judgment, which is susceptible to the effects of attentional and cognitive demands (Block et al., 2010), may be uniquely associated with long off-road glances, particularly under the heightened cognitive load of performing secondary tasks while driving. Meanwhile, difficulties with attentional control resulting in attentional lapses may be more closely related to maintaining proper lane positioning.

Methods

Participants and Recruitment

Teens were recruited for enrollment in a randomized clinical trial examining the effectiveness of a driver’s training program targeting long glances away from the roadway (FOCAL+; Epstein et al., 2022) between December 21, 2016 and March 4, 2020 using radio, social media, and print advertisements. This trial was approved by the Cincinnati Children’s Hospital Medical Center (CCHMC) IRB. Teens and their caregivers provided written informed consent for trial participation.

Since this study only used data collected at the baseline visit, we were able to include 179 teens in these analyses irrespective of whether they met full inclusion/exclusion criteria for the clinical trial. The only relevant inclusionary criteria for the current sample included being aged 16 to 19 years-old, having a valid driver’s license, self-report of at least 3 hr of unsupervised driving per week, and a confirmed diagnosis of ADHD (any diagnostic subtype) based on caregiver- and teen-report on the clinician-administered Kiddie Schedule for Schizophrenia and Affective Disorders for School Aged Children (KSADS) interview (J. Kaufman et al., 2013). Teens were excluded if they were unable to discern secondary task stimuli in the driving simulator without glasses, could not stop ADHD medication on the day of driving assessment, had a history of multiple head traumas or lost consciousness for >30 min, or were unable to have their eye gaze adequately captured by eye tracking equipment. Teens were also excluded if they reported motion sickness on the Georgia Tech Simulator Sickness Screening Survey (GTSSSS; Gable & Walker, 2013) following a 2-min simulated drive. Specifically, teens completed a brief 17-question survey to assess symptoms of simulator sickness (e.g., queasiness, dizziness, disorientation) before and after a 2-min simulated drive. Each symptom was rated on a 0 (Not at all) to 10 (Severely) scale. If any single rating on the post-drive GTSSS was ≥5 more than pre-drive GTSSSS, or if any three of the ratings on the post-drive GTSSSS were ≥3 more than the pre-drive GTSSSS, the participant was excluded.

Procedure

Teens and caregivers interested in participating in the trial consented to an eligibility assessment to determine inclusionary and exclusionary criteria. At this visit, parents and teens completed an assessment battery which included diagnostic interview, IQ testing, neurocognitive tasks, and behavioral ratings. In addition, teens completed a driving evaluation prior to randomization (i.e., two simulated 15-min drives while wearing eye-tracking glasses). Participants taking ADHD medication were requested to not take ADHD medication on the day of their visit and this was confirmed at the visit

Measures

Intellectual Functioning

Teens’ intellectual functioning was assessed using the Kaufman Brief Intelligence Test, Second Edition (KBIT-2), a brief screening measure that provides an estimate of a children’s verbal and nonverbal intellectual abilities (A. S. Kaufman & Kaufman, 2004). The Composite Full-Scale IQ score is a standard score representing the teen’s overall intellectual functioning in the verbal and nonverbal domains.

Executive Functioning Measures

Teen EF was assessed using the Behavior Rating Inventory of Executive Functioning, Second Edition (BRIEF-2; Gioia et al., 2015). Caregivers completed the BRIEF-2 parent-report while teens completed the BRIEF-2 self-report. Individual subscale scores reflect various aspects of executive functioning (e.g., Inhibit, Emotional Control, Working Memory, and Self-Monitor) within the context of everyday functioning. A Global Executive Composite (GEC) index summary score incorporates all eight clinical scales on the BRIEF-2, with higher scores indicating greater EF difficulty. Age- and sex-adjusted T-scores were computed for both caregiver- and teen self-reported GEC indices. Higher GEC scores indicate greater levels of executive dysfunction.

The temporal reproduction task (Barkley, 1998) involves 20 presentations of a visual stimulus (i.e., a light bulb) on a computer screen for one-half, one, two, three, or 4 s. Following each presentation, the participant is asked to reproduce the duration of stimulus presentation by holding down the spacebar for the same duration as the stimulus presented. Absolute discrepancy scores are computed by comparing estimations of the interval to the actual stimulus duration, with greater discrepancy scores representing greater temporal processing deficits.

A go/no-go task (GNG) assessed inhibitory control and sustained attention. This task consists of 300 stimuli which appear on the computer screen, one at a time, each for approximately 500 ms with an inter-stimulus interval of 2,500 ms (total task length: 15 min). Participants are instructed to press the space bar for every stimulus except for the target stimulus (i.e., the letter “X”). The event rate, or percentage of trials when non-target stimuli (e.g., other letters) appear, was 90%. Percentage of omission errors (percentage of go-trials in which teen did not respond), an indication of attentional lapses, and percentage of commission errors (i.e., percentage of no-go trials that teen responded), an indication of inhibitory control, were computed.

Driving Simulator

To assess driving performance, participants drove in a driving simulator with an integrated eye-tracking system. The driving simulator (STISIM Model 400, Systems Technology Inc., Hawthorne, CA) consisted of three driving displays offering a 135-degree driver field-of-view with integrated rearview and side mirrors, full-sized steering wheel with haptic-based resistance and vibration, pedals, and car seat. The simulated roadway consisted of straight and curved two-lane roads in urban and suburban settings. Participants first completed a 5-min acclimation drive consisting of a highway drive with both straight and curving road sections with other vehicles present. Teens were instructed to drive as they normally would and to follow all road rules including posted speed limits (55 mph). Participants then completed two 15-min assessment drives with the same road conditions and instructions as the acclimation drives. When completing the assessment drives, the participant engaged in one secondary task per minute (or 14 per drive). During the secondary task, the driver was alerted with an auditory and visual cue (i.e., a letter on the dashboard). Within 20 s, they were asked to identify how many roads started with the target letter on a map displayed on a computer screen in the center console. The secondary task map was displayed on a 12″ wide × 7″ high screen separate from the driving displays and situated where a vehicle’s center console would be situated. Depending on where the participant adjusted the driver’s seat (i.e., distance from seat to screen ranged from 47″ to 55), the visual angle ranged from 21.9° × 12.9° when the seat was fully forward to 16.5° × 9.1° when fully extended. While completing the simulated driving, teens wore binocular eye-tracking glasses with embedded accelerometry and gyroscope sensors to detect head and eye movements (Tobii Glasses 2, Tobii Pro, Reston, VA). Using visual mapping from the eye tracking data, a forward roadway gaze area was defined. For each 20 ms epoch, gaze analysis software (Tobii Pro Lab; Version 1.98.1) determined whether the gaze was off the forward roadway. Eye gaze data was summarized by calculating the number of long (≥2 s) glances away from the roadway during each drive. Images of the driving simulator setup and forward roadway gaze area are provided in the Supplemental Materials. Driving speed (in miles per hour) and lateral lane position (in feet) were sampled continuously (i.e., every 17 ms) for the duration of each 15-min drive and were summarized by calculating the mean and standard deviation for speed and the SDLP for each drive.

Data Analysis

To examine our hypotheses, generalized linear mixed models were used to evaluate which EF task performance indices predicted adverse driving indices. Analyses were performed using SPSS Version 28.01.1 (IBM, 2022). Only data from the secondary task condition, during which executive functioning demands were highest, was included in the analyses. Models were analyzed separately for each outcome variable—number of long (>2 s) glances off the roadway, SDLP, mean driving speed, and standard deviation of driving speed. These variables were included in the models as repeated measures, with two drives corresponding to the two 15-min simulated drives. Predictor variables in all models included parent and teen BRIEF-2 GEC scores, temporal reproduction discrepancy scores, and omission and commission error percentages on the GNG. Teens’ driving experience, measured in months, was included in all models as a covariate to account for its known effects on driving outcomes (Gershon et al., 2018). In addition, to control for that the effects of general intellectual functioning on driving performance, IQ was also included as a covariate. Given that all teens in the study met diagnostic criteria for ADHD, symptoms of ADHD were high and negatively skewed leaving little variability for meaningful analyses and were thus not included in the models. The compound symmetry covariance structure was chosen for the analysis models as it most consistently provided the best model fit as indicated by Bayesian information criterion (BIC). Missing data was handled using fully conditional specification multiple imputation. Pooled results from 10 imputations were used in the analyses. To correct for testing across four driving outcomes, a Bonferroni-corrected significant p-value threshold of .0125 (.05/4 = .0125) was used.

Results

Sample demographic characteristics and descriptive statistics for all predictor and outcome variables are presented in Table 1. Bivariate correlations are presented in Table 2. Notably, inter-correlations between predictors were weak. On the secondary task, mean accuracy on the secondary task was 66.6% (SD = 0.13), with a 99.68% response rate, indicating participants were fully engaged in the task.

Table 1.

Characteristics of Study Sample (n = 179) and Descriptive Statistics for Predictor and Outcome Variables.

	n	%
Male	78	43.6
Race
White	156	87
Black or African American	12	7
Asian	2	1
Other	8	4
Unknown	1	1
Ethnicity
Latino(a)	6	3
On stimulant medication	120	67
Diagnostic presentations^a
ADHD, combined	69	38.8
ADHD, predominantly inattentive	109	61.2
Co-occurring disorders^a
Oppositional defiant	11	6.2
Generalized anxiety	33	18.5
Mood (major depression/dysthymia)	18	10.1
Post-traumatic stress	9	5.1
	M	SD	Range
Age (years)	17.40	0.92	16.11–19.92
Age at licensure	16.63	0.70	15.62–19.24
Months licensed	9.23	9.26	0.20–46.98
Rating Scales
BRIEF GEC T-score, Parent-report	65.93	8.19	45–86
BRIEF GEC T-score, Teen-report	66.45	9.52	40–91
Cognitive tasks
K-BIT2 Full Scale IQ	102.02	13.45	71–136
Mean temporal reproduction discrepancy (ms)	0.52	0.22	0.15–1.28
Go/No-Go Task % omission errors	0.02	0.04	0.00–0.31
Go/No-Go Task % commission errors	0.44	0.16	0.07–0.86
Driving behaviors per 15-min simulated drive^b
Number of long off-road glances	21.88	13.12	0–54
SD lane position (ft)	1.15	0.32	0.62–2.53
Mean speed (mph)	49.33	3.31	35.92–63.90
SD speed (mph)	2.00	1.31	0.09–10.67

Note. BRIEF GEC = behavior rating inventory of executive functioning, global executive composite; K-BIT2 = Kaufman brief intelligence test, second edition.

Determined from KSADS interview. Of note, the KSADS interview for one participant was lost after study completion.

Averaged across secondary task periods of two simulated drives.

Table 2.

Correlation Matrix of Predictor Variables and Driving Behaviors.

	IQ	Driving Exp.	Parent GEC	Teen GEC	Temp. Discrep.	GNG % Omiss.	GNG % Comm	LG off road	SD lane Position	Mean Speed	SD Speed
IQ	-	0.02	0.07	0.06	−0.25***	−0.10	−0.04	0.24**	−0.008	0.01	−0.21**
Driving Exp.		-	−0.15*	0.09	0.01	−0.08	0.01	−0.22**	−0.07	0.13	−0.02
Parent GEC			-	0.18*	0.08	0.20**	0.02	0.13	0.31***	−0.01	0.12
Teen GEC				-	0.08	0.13	0.04	−0.03	0.24**	0.04	0.14⁺
Temp. Discrep.					-	0.11	0.12	−0.06	0.10	0.11	0.09
GNG % Omiss.						-	0.07	0.02	0.36***	−0.07	0.16*
GNG % Comm.							-	0.15 ⁺	0.12	0.20**	0.08
LG Off Road^a								-	0.46***	0.01	0.04
SD Lane Position^a									-	0.29***	0.43***
Mean Speed^a										-	0.11
SD Speed^a											-

Note. Driving Exp. = months of driving experience; Parent GEC = parent-rated behavior rating inventory of executive Functioning, global executive composite T-scores; Teen GEC = teen-rated behavior rating inventory of executive functioning, global executive composite T-scores; Temp. Discrep. = mean temporal reproduction discrepancy score; GNG % Omiss. = percentage of omission errors on Go/No-Go task; GNG % Comm = percentage of commission errors on Go/No-Go task; LG Off Road = number of long glances off the roadway; SD = standard deviation.

Averaged across both simulated drives.

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

Results from the repeated measures linear mixed models are presented in Table 3. A higher percentage of GNG commission errors was associated with a greater number of long off-road glances. Further, higher teen IQ was significantly associated with more long glances away from the roadway while more driving experience was significantly associated with fewer number of long glances away from the roadway. Parent-reported BRIEF-2 GEC scores significantly predicted SDLP, such that greater EF impairment was associated with higher SDLP. Higher percentages of omission errors on the GNG also significantly predicted higher SDLP. Regarding speed, higher percentage of GNG commission errors were significantly associated with faster average driving speeds and lower teen IQ was significantly associated with greater speed variability.

Table 3.

Repeated Measures Linear Mixed Models of EF Scores on Driving Indices.

	B	SE	t	p	Pseudo R²
Long off-road glances					.12
Driving Exp.	−0.29	0.09	−3.11	.002
IQ	0.22	0.07	3.32	<.001
Parent GEC	0.12	0.11	1.10	.27
Teen GEC	−0.05	0.09	−0.54	.59
Temporal Discr.	−0.99	3.98	−0.25	.81
% Omission	4.73	23.90	0.20	.84
% Commission	12.81	5.10	2.51	.012
SD lane position					.13
Driving Exp.	−0.01	0.002	−0.42	.67
IQ	0.00	0.002	0.12	.91
Parent GEC	0.008	0.003	2.90	.004
Teen GEC	0.005	0.002	2.23	.03
Temporal Discr.	0.06	0.10	0.54	.59
% Omission	2.33	0.61	3.85	<.001
% Commission	0.16	0.13	1.25	.21
Mean speed					.06
Driving Exp.	0.04	0.03	1.61	.11
IQ	0.007	0.02	0.38	.70
Parent GEC	0.004	0.03	0.14	.89
Teen GEC	0.008	0.02	0.32	.75
Temporal Discr.	1.46	1.07	1.37	.17
% Omission	−7.20	6.40	−1.13	.26
% Commission	3.58	1.37	2.61	.01
SD speed					.08
Driving Exp.	0.00	0.01	0.01	.99
IQ	−0.02	0.01	−2.80	.006
Parent GEC	0.01	0.01	1.13	.26
Teen GEC	0.02	0.01	1.55	.12
Temporal Discr.	0.03	0.42	0.07	.95
% Omission	3.30	2.52	1.31	.19
% Commission	0.44	0.54	0.82	.42

Note. Significant predictors are emphasized in bold. GEC = Behavior Rating Inventory of Executive Functioning, Global Executive Composite score; Driving Exp. = months of driving experience; Temporal Discr. = mean temporal reproduction absolute discrepancy score; % Omission = Go/No-Go task percentage of omission errors; % Commission = Go/No-Go task percentage of commission errors.

Discussion

The present study examined how different aspects of EF-associated skills uniquely relate to different risky driving indices associated with adverse driving outcomes in teens with ADHD.

Long Off-Road Glances

Consistent with previous literature, more driving experience was associated with fewer long (>2 s) glances off the road. Research has consistently demonstrated that crash rate declines as a function of driving experience in novice drivers (Curry et al., 2015; Gershon et al., 2018). Importantly, the present study reaffirms that reductions in long off-road glances as driving experience increases is a likely mechanism for this trend, particularly in drivers with ADHD. Though other studies have demonstrated associations between driving experience and long off-road glances (e.g., Chan et al., 2010), the present study extends that knowledge by finding that for teen drivers with ADHD, driving experience was specifically associated with the number of long off-road glances, and not with any other adverse driving behavior. Further, this ameliorating effect of driving experience was influential on the time scale of months (standard deviation of driving experience = 9.26 months). These results, together with the findings of other studies demonstrating that crash incidence rates decrease substantially over the first few months of driving (e.g., Gershon et al., 2018) and recent results from a randomized controlled trial which found that reductions in long off-road glances led to lower rates of MVCs (Epstein et al., 2022), emphasize the critical role long off-road glances on MVC risk.

The only EF measure that predicted long glances away from the roadway was GNG commission errors, an indicator of inhibitory control. This is consistent with previous research identifying an association between inhibitory control and off-road glances (Hoekstra-Atwood et al., 2014). Perhaps comparable to commission errors being indicative of difficulty suppressing a prepotent response, long off-road glances may be indicative of difficulty suppressing or ceasing one’s response to a secondary task. Indeed, inhibitory control is a necessary component in behavioral self-regulation (Diamond, 2013) and in modulating voluntary visual saccades, especially toward peripheral stimuli (Luna et al., 2008).

We had posited that another contributory factor was that temporal processing deficits might lead to underestimation of off-road glance durations thereby causing long glances away from the roadway. Our theoretical rationale for this was that patients with ADHD demonstrate temporal processing deficits (Marx et al., 2022) along with higher rates of long glances away from the roadway (Kingery et al., 2015). However, in this study, we found that temporal processing was not predictive of the number of long off-road glances. While temporal processing as measured in this study may not be the primary mechanism leading to long glances away from the roadway, there are also other explanations for our lack of association. For example, the tendency to underestimate duration may be particularly strong during high cognitive load tasks (Block et al., 2010), such as the secondary task teens performed while driving. However, the cognitive task used to measure temporal processing in the present study was completed outside of the simulator and in the absence of any cognitive load. Thus, teens’ accuracy in this low cognitive load task might not be representative of their ability to accurately judge time under the much higher cognitive load of engaging in secondary tasks while driving.

It is also possible that other cognitive factors that were not assessed in the present study contribute to long glances away from the roadway. In particular, difficulties with task-switching may contribute to difficulty disengaging from secondary tasks. This notion is supported by multiple empirical studies. J. Y. Lee and Lee (2019) demonstrated that a computational model of task-switching behavior accurately reproduced drivers’ observed off-road glance patterns. In this model, task-switching was largely driven by individuals’ tendency to switch attention from one task to another at “subtask boundaries” (i.e., the break points between subtasks of a larger task) and the tendency to persevere with a subtask the closer individuals were to the subtask boundary (J. Y. Lee & Lee, 2019). In other words, a driver responding to text while driving is likely to continue typing until they have finished the sentence they are working on (a subtask boundary) before glancing back to the road, especially if they are almost done with typing. Specific to ADHD, Caldani et al. (2020) found that school-age children with ADHD had significantly greater difficulty shifting their visual spatial attention away from a previously cued stimulus after being cued to a different stimulus than peers without ADHD. Importantly, this finding may be extended to deficits in driving performance, such as impaired re-orientation of glances to hazards on the road after directing visual attention to a secondary task.

Finally, higher IQ was associated with more long off-road glances. Such a result is surprising, given that Barkley et al. (2002) found that IQ was significantly positively associated with driving knowledge and performance, and uncorrelated with self-reported or DMV-reported adverse driving outcomes (e.g., crashes, tickets, license suspensions) in drivers with ADHD. One possible explanation for the finding in the present study is that teens with higher IQ were perhaps more motivated to successfully complete the secondary task during the simulated drives. This idea is supported by the findings of Calero et al. (2007), who demonstrated that children with high IQ exhibited higher self-motivation to complete a computer task in the presence of distracters than children with average IQ. Thus, a potentially stronger motivation to identify all the streets that started with the target letter during the secondary task during driving simulation in teens with higher IQ, coupled with the tendency for individuals to persevere with a task when they are closer to a subtask boundary (i.e., identifying the last street), might have contributed to more long glances to the secondary task during the simulated drives in the present study.

Standard Deviation of Lane Position

SDLP is a commonly used measure of car weaving/swerving that serves as a marker for vehicular control (Verster & Roth, 2011). In the present study, we found that parent ratings of teens’ global EF skills were significant predictors of SDLP, with teens with higher levels of EF deficits having increased lane variability during simulated driving. This finding is consistent with a recent study by Narad et al. (2020), which found that higher GEC scores were associated with greater SDLP in both teens with traumatic brain injury (TBI) and uninjured teens. Importantly, this relationship was significant regardless of whether or not the teens were distracted by a secondary task while driving (Narad et al., 2020), suggesting that overall efficient use of EF is crucial to maintaining lane position. Indeed, maintaining a central and consistent lane position requires a variety of driving skills working in tandem, such as maintaining attention to the roadway, managing secondary tasks, detecting hazards, inhibiting distractions, and maintaining appropriate speeds. It may be that a broad, general measure of EF ability, such as that measured by the BRIEF GEC, best predicts how a teen will do with managing a complex driving environment.

In addition to overall EF, the present study found that higher percentages of omission errors on the GNG also predicted more variation in lane position. Omission errors, in which the individual fails to respond to a stimulus to which they were meant to respond, are considered to represent poor attention control and/or attentional lapses (Cheyne et al., 2009). Theoretical models and neuroimaging research have highlighted an important role of EF in mediating sustained attention (Fortenbaugh et al., 2017). For example, the resource control theory posits that increased attentional lapses with longer time completing a task is not the result of depleted attentional resources, but rather due to degradation in EF control over the allocation of attentional resources (Thomson et al., 2015). Therefore, within the context of driving, worse EF modulation of attentional resource allocation (i.e., more attentional lapses on the GNG) could result in worse allocation of attention to different driving-related tasks (checking speed, checking mirrors, hazard detection, maintaining lane positioning), leading to poor maintenance of lane position.

Mean and Standard Deviation of Speed

Faster driving speeds have well known relations to crash risk and were therefore of interest. In the present study, faster average driving speeds were associated with higher rates of commission errors on the GNG. No other measure of EF predicted driving speed, suggesting that it was inhibitory control (as indicated by commission errors) by itself that had a role in speeding. This result is consistent with previous research identifying associations between speeding and commission errors in non-clinical samples of young drivers (Hatfield et al., 2017; O’Brien & Gormley, 2013). Further, reduced inhibitory control has also been found to be associated with increased speeding in young novice drivers in the presence of peers (Jongen et al., 2013). Together, these results are consistent with poor impulse control being associated with greater risk-taking and sensation seeking behavior, especially in teens (Romer, 2010).

Increases in speed variability are also associated with increases in crash risk. Surprisingly, no cognitive measure predicted speed variability. Such a result is unexpected given EF and related cognitive skills’ presumed importance in monitoring speed, particularly related to updating mental representations of the vehicle’s current speed and inhibiting impulses to drive faster. Instead, we found that IQ predicted speed variability. Our finding that higher IQ was associated with less speed variability is consistent with the results of Guinosso et al. (2016), who also found that higher IQ was associated with less variability in speed, as well as less variability in acceleration. One possible explanation for this association posited by Guinosso et al. (2016) is that higher intelligence may result in less required cognitive resource allocation to secondary tasks while driving, allowing for more cognitive resources to be devoted to driving-specific tasks such as maintaining one’s speed.

Limitations

Limitations of the present study include that while it comprehensively examined both real-word behavioral ratings and performance-based measures of EF-associated skills, we did not assess some aspects of EF such as cognitive flexibility or working memory, which are also likely important to driving performance. Further, additional cognitive processes served by EF might also be important to assess. For instance, the Posner task, which assesses orienting of visual spatial attention (Posner, 1980), might better allow for examining the role of attentional disengagement in long off-road glances and subsequent crash risk. Another limitation is that driving was assessed within the context of two short drives in a simulator. Driving simulators are widely used in research to assess driving performance and provide a fully controllable environment without any true risk to participants. However, studies have identified small, but important differences in simulator versus naturalistic driving performance, perhaps due to the heightened real-world risk in naturalistic driving (e.g., Engström et al., 2005; Zöller et al., 2019). Further, the relatively short intervals of the simulated drives (15 min) might not capture the full extent of driving performance deficits, as the average U.S. driver spends a little more than an hour behind the wheel each day (Tefft, 2022). Lastly, while the simulated drives incorporated both urban and suburban roads, as well as both straight and curved roads, there were no changes to daylight or weather conditions, which also influence driving behaviors (e.g., Das et al., 2019; Garay-Vega et al., 2007) .

Conclusions

The present study investigates how unique components of EF-associated skills relate to specific aspects of driving performance within a large sample of teen drivers with ADHD, an especially at-risk group for adverse driving outcomes. Results demonstrated that with regards to EF-associated skills, long off-road glances are selectively predicted by inhibitory control, while variability of lane position is predicted by attentional lapses and parent-rated global EF. Interestingly, driving experience was significantly associated with long off-road glances but none of the other adverse driving indices, highlighting a potentially critical and specific pathway for driving experience in reducing crash risk. Lastly, IQ exhibited contradictory associations, such that higher IQ was significantly associated with more long off-road glances, but less variability in speed. Together, these results emphasize that EF and cognition plays a crucial role in driving metrics that are associated with crash risk, and that specific aspects of driving performance are associated with specific EF-associated skills. Importantly, these results also provide some understanding as to why some drivers with ADHD experience negative driving outcomes while others do not.

Supplemental Material

sj-docx-1-jad-10.1177_10870547231197210 – Supplemental material for Executive Functioning as a Predictor of Adverse Driving Outcomes in Teen Drivers With ADHD

Supplemental material, sj-docx-1-jad-10.1177_10870547231197210 for Executive Functioning as a Predictor of Adverse Driving Outcomes in Teen Drivers With ADHD by James D. Lynch, Leanne Tamm, Annie A. Garner, Amina A. Avion, Donald L. Fisher, Adam W. Kiefer, James Peugh, John O. Simon and Jeffery N. Epstein in Journal of Attention Disorders

Footnotes

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Adam Kiefer is a co-inventor of University of North Carolina-owned intellectual property related to eye tracking and performance and is a co-founder of a company (Elipsys LLC).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grant #R01HD084430 from the National Institute of Child Health and Human Development and grant #2UL1TR001425 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the Department of Transportation or the Volpe National Transportation Systems Center.

ORCID iDs

James D. Lynch

Leanne Tamm

Annie A. Garner

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

James Lynch, MA is a clinical psychology doctoral candidate at the University of Cincinnati. He has authored and co-authored publications related to ADHD, executive functioning, and evidence-based care for ADHD and Autism.

Jeff Epstein, PhD is an Endowed Chair and Professor of Pediatrics in the Division of Behavioral Medicine and Clinical Psychology at Cincinnati Children’s Hospital Medical Center. He is also the Director of the Cincinnati Children’s Center for ADHD. He has published numerous empirical papers on a variety of ADHD-related topics including ADHD-related driving risk, and the promotion of evidence-based ADHD care in community settings.

Leanne Tamm, PhD, is a Professor of Pediatrics, University of Cincinnati College of Medicine, in the Center for ADHD at the Cincinnati Children’s Hospital Medical Center whose research interests focus on numerous aspects of the functioning of children with ADHD and autism spectrum disorder including executive function, intervention development, treatment efficacy, brain–behavior relationships, and early intervention.

Adam Kiefer, PhD is an Assistant Professor in the Department of Exercise and Sport Science at the University of North Carolina at Chapel Hill where he co-directs the Simulation, Training, Analytics and Rehabilitation (STAR) Heel Per-formance Laboratory. Dr. Kiefer has extensive research experience in the areas of perception, motor skill learning and eye tracking.

Donald L. Fisher, PhD, is a Principal Technical Advisor at the U.S. Department of Transportation’s Volpe National Trans-portation Systems Center and was previously on the faculty at the University of Massachusetts Amherst. His research interests have focused on identifying mechanisms predicting motor vehicle crash risk using simulation and eye tracking as well as developing training programs to target these mechanisms.

Annie A. Garner, Ph.D. is an Associate Professor of Psychology at the ADHD Laboratory at Saint Louis University. Her research focuses on ADHD impairments, including driving, across the lifespan.

Amina A. Avion, M.S. is a clinical psychology doctoral candidate at Saint Louis University. Her research focuses on ADHD-related driving impairment, ADHD treatments, and exploring the neurocognitive underpinnings of pediatric ADHD.

John O. Simon, M.A., is a senior specialist in data management in the Division of Behavioral Medicine and Clinical Psychology Center for ADHD at Cincinnati Children’s Hospital Medical Center. His interests include display design, database creation and management, and statistical analyses.

James Peugh, PhD, is Research Professor of Pediatrics and the Director of Quantitative Services in the Division of Behavioral Medicine and Clinical Psychology at Cincinnati Children’s Hospital Medical Center. His publications include Monte Carlo simulation testing and advanced statistical analysis tutorials.

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