Abstract
The current U.S. military operations in Afghanistan (Operation Enduring Freedom [OEF]) and Iraq (Operation Iraqi Freedom [OIF]) have been fundamentally different from other wars. In particular, the repeated exposure to explosions has increased the incidence of mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) as well as other serious injuries in combat veterans (Institute of Medicine, 2007). Estimates suggest that 15% of OEF–OIF returnees sustained an mTBI during deployment and that 19.1% of returnees from Iraq and 11.3% of returnees from Afghanistan experienced PTSD (Hoge et al., 2008). PTSD rates will increase in the coming years as symptoms continue to emerge in the postexposure phase (Friedman, n.d.). Specific mTBI symptoms include blurred vision, headaches, dizziness, impaired concentration and memory, irritability, light sensitivity, motor coordination deficiencies, and nausea (Ghajar, 2000; Heegaard & Biros, 2007). PTSD symptom clusters include reliving the experience, hyperarousal, numbing, and avoidance (American Psychiatric Association, 2000).
mTBI and PTSD affect body systems, function (including the cognitive, vision, and motor abilities), and activities (including the ability to drive safely) and, as such, societal participation (American Occupational Therapy Association [AOTA], 2008). Driving, an occupation enabler, promotes autonomy, community integration, and participation in life (AOTA, 2005). Yet, the task of driving demands appropriate cognitive, visual, perceptual, and motor skills to recognize stimuli, speedily process the information, and accommodate and react to rapid changes in the environment (Classen et al., 2009). Safe driving is therefore likely to be impaired in returning combat veterans with mTBI or PTSD. In fact, the Department of Veterans Affairs (VA) stated on January 12, 2009, that motor vehicle accidents are the primary cause of death among combat veterans during the first years after returning home, yet the return-to-driving patterns and accompanying impediments are not clear among this group of veterans. Moreover, a paucity of published empirical studies is evident in the current literature to describe driving performance issues among returning OEF–OIF veterans with mTBI. Occupational therapy practitioners must be informed, and researchers must understand the effects of these comorbidities on the occupation of driving to better support the health and participation in life of returning combat veterans. The need for our study is therefore magnified in the absence of current data to describe driving performance issues in OEF–OIF combat veterans.
A recent evidence-based review on assessing driving performance in people with TBI (all levels of severity) indicated that the on-road driving assessment is the most accurate way to determine fitness to drive (Classen et al., 2009). However, because of safety issues, risks, time, and monetary and access constraints, on-road driving assessments are not always practical. Driving simulators have the potential to be a promising addition or alternative to on-road assessments because they are safe, time efficient, cost effective, commercially available, and increasingly realistic because of advancements in technology (Lew, Rosen, Thomander, & Poole, 2009). Moreover, research indicates that driving simulators have good validity, because they can predict on-road performance (Lew et al., 2005; Shechtman, Awadzi, Classen, Lanford, & Joo, 2010).
In response to the increased prevalence of mTBI and PTSD among combat veterans from OEF–OIF and the potential influence of those conditions on their safe driving performance, the Institute of Mobility, Activity, and Participation (I–MAP) at the University of Florida initiated a research program to evaluate combat veterans’ driving performance issues. The primary aim of this project was to empirically compare the driving errors of returning combat veterans from OEF–OIF with those of healthy control participants of the same age cohort. Driving errors were described using type and number; types of errors were defined as vehicle position, lane maintenance, speed regulation, signaling, yielding, visual scanning, adjustments to stimuli and traffic signs, and gap acceptance; see the detailed description of errors in Justiss, Mann, Stav, & Velozo (2006). We used a STISM M500W (Systems Technology, Inc., Hawthorne, CA) fixed-base driving simulator to test the participants. We also determined, secondarily, the correlations among cognitive, visual, and motor functions and driving errors. Understanding driving performance deficits is a first step toward developing and testing tailored occupational therapy rehabilitation strategies to address independence and safety in driving and societal participation.
Method
This study was approved by the University of Florida and the VA institutional review boards. All participants provided written informed consent.
Participants
Twenty-one returning combat veterans were recruited from the North Florida/South Georgia Veterans Health System VA clinic charged with completing the nationally mandated Level-2 Comprehensive TBI Evaluation for returning combat veterans. The Comprehensive TBI Evaluation includes a detailed injury history, the Neurobehavioral Symptom Inventory, a physical examination, and a treatment plan. Only those who were diagnosed with an mTBI by the evaluating physiatrist were eligible for the trial. Inclusion criteria were as follows: history of OEF or OIF deployment; patient receiving care from the North Florida/South Georgia Veterans Health Service; diagnosis of mTBI or PTSD; ability to drive before the injury or condition; possession of a valid driver’s license or eligibility for a driver’s license; Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) score of at least 24 of 30; community dweller; potential for following driving safety recommendations (MMSE >24); (9) ability to travel to the driving evaluation testing site in Gainesville, Florida; and (10) able to participate in driving evaluation battery.
Participants were excluded if they had severe psychiatric (e.g., psychoses) or physical conditions (e.g., missing limbs), as diagnosed by the referring physicians, that would preclude full participation; had multiple psychotropic medications, as identified by the referring physicians, that could negatively affect mental or physical functioning because of side effects; had moderate or severe TBI that would preclude participation; were pregnant (no previous driving simulator studies have provided data for assessing potential risks to pregnant women); and were employed by the VA. All participants who met the inclusion and exclusion criteria gave informed consent before enrollment and completed a telephone survey in which we collected demographic, medical, and driving history information. Three combat veterans who met inclusion criteria did not complete the simulated drive, because they experienced simulator sickness (SS), resulting in termination of their participation. We removed their data and reported on a total of 18 combat veterans.
The control group was a convenience sample consisting of healthy community-dwelling participants in the I–MAP database who had been recruited and tested from 2003 to 2005. Inclusion criteria for the 20 control participants included age ≥18 yr, possession of a valid driver’s license, free of seizures in the previous year, ability to read and understand English, and visual acuity corrected to at least 20/40 in one eye (Florida’s minimum requirement).
Procedure
We scheduled an appointment with each participant during a phone interview. After obtaining informed consent, we ascertained eligibility using our criteria and collected demographic information. The certified driving rehabilitation specialist (CDRS) proceeded to conduct clinical and simulator tests. All participants’ data were collected in a standardized way; in the same order; and using the same setting, criteria, and procedures according to our IRB-approved research protocol.
Before driving in the simulator, all participants (combat veterans and control participants) were screened for SS and then completed a 5-min acclimation scenario in the simulator. The driving simulator is integrated with a 1997 Dodge Neon vehicle and built on a computerized platform with the STISIM Drive Model 500W configuration (Systems Technology, Inc.; Figure 1A). It operates with three-channel projected images on 3-ft × 6-ft screens, providing 180° field-of-view with 100% image size (Figure 1B). It has real image side-view mirrors; integrated gas, brake, and steering controls; and audio feedback providing road sounds for enhanced driving realism. The “driver” operates normal accelerator, brake, signal, and steering controls with the corresponding visual scene responding accordingly.
High-fidelity STISM M500W driving simulator and control station (2000 ANSI Lumens, Sanyo, San Diego, CA). This simulator provides a 180° forward field-of-view (FOV) and displays virtual objects behind the car. The wide FOV is accomplished by connecting three flat screens with scenes provided by three high-intensity projectors. A: View of the simulator vehicle and projected scenes. B: A driver’s view of forward and rear scenes from within the car cab.
SS protocol, fully described elsewhere, was applied to ensure maximum comfort among participants (Shechtman et al., 2007). After driving the acclimation scenario (which provided a less complex visual representation of the road environment), participants drove the actual simulated road course, which included negotiating eight intersections (six left turns and two right turns) for a duration of approximately 15 min. The SS Questionnaire (SSQ; Kennedy, Fowlkes, Berbaum, & Lilienthal, 1992) was administered before, during, and after the drive; in addition, trained project staff observed the participant for signs of SS. The drive was terminated if a threshold on the SSQ was exceeded or by CDRS discretion. Sitting in the passenger seat of the vehicle, the CDRS recorded the driving errors by type and number. The driving errors were vehicle positioning, visual scanning, speed regulation, lane maintenance, signaling, adjustment to stimuli, and gap acceptance, all of which are fully described in Justiss et al. (2006). All participants were paid $100.00 for their participation.
Measurement
Demographic information—age, gender, race, and level of education—was obtained from all participants. For combat veterans, we also collected duration of mTBI or PTSD, presence of other health conditions, and information on their primary and secondary exposures leading to mTBI. Performance metrics included cognitive tests for combat veterans and control participants; that is, MMSE (range = 1–30, 30 indicating intact cognition, ≤24 indicating impaired cognition; Folstein et al., 1975); Trailmaking Test Part B (Trails B; >180 s indicating cognitive decline; Reitan, 1958); vision tests, including visual acuity (for both eyes and each eye individually) and peripheral field testing using the Optec 2500 (Stereo Optical, Inc., Chicago).
Visual acuity scores range from 20/15 to 20/200; the latter category indicates legal blindness. Visual acuity of 20/40 in one eye is required to pass the driving test for licensure in Florida. Combat veterans also completed visual tests for contrast sensitivity and depth perception, both of which were measured as impaired or intact.
Visual attention tests for combat veterans included the Useful Field of View (UFOV; Edwards et al., 2005). The UFOV consists of three subtests and a category indicating risk for motor vehicle crashes. Subtest 1 measures processing speed, Subtest 2 measures divided attention, and Subtest 3 measures selective attention. The threshold exposure duration for correct performance of each of the tasks is 16–500 ms; when participants exceed 500 ms in a subtest, they are not allowed to continue to the next subtest. The category for crash risk consists of a composite of the first three subtests and is called the UFOV Risk Index; scores range from 1 to 5 (1 indicates the lowest crash risk, and 5 indicates the highest crash risk; Edwards et al., 2005). A recent evidence-based literature review suggested that the UFOV Subtest 2 (divided attention) should be considered a predictor of on-the-road driving performance in people with moderate to severe TBI (Classen et al., 2009). Motor function—specifically, lower-limb mobility, coordination, and trunk stability—was measured with the Rapid Pace Walk (RPW; Staplin, Gish, & Wagner, 2003), which assesses the person while he or she walks a 20-ft distance. Taking >7.5 s to complete the walk has been associated with a greater likelihood of at-fault crashes in older adult studies (Staplin et al., 2003).
Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale. Scores range from 0 to 60; higher scores indicate clinical depression and psychological distress. The cutoff score for evidence of depressive symptoms is ≥16 (Radloff & Locke, 1986).
The CDRS recorded driving errors on the basis of the type and number in each of the seven categories (Justiss et al., 2006). The measures used in our clinical battery have acceptable levels of reliability and validity, are frequently cited in driving studies, and have psychometric details described in our previous publications and the primary studies (see Classen et al., 2007; Edwards et al., 2005; Folstein et al., 1975; Justiss et al., 2006; Kennedy et al., 1992; Radloff & Locke, 1986; Reitan, 1958; Shechtman et al., 2007).
Analysis
We used SPSS 17.0 for all data analysis procedures (SPSS, Inc., Chicago). We provided summary statistics (frequency, mean [M], standard deviation [SD]) for all data. To determine between-group differences, we conducted a Fisher’s exact test (with n < 5 in any cell) for nominal variable comparisons; two-tailed independent sample t tests for continuous data adjusted on the basis of Levin’s test of (in)equality; and Mann–Whitney U test for nonparametric data. We conducted a partial correlation (one-tailed) to control for the effect of age and gender on driving errors. All other comparisons (two-tailed) were considered significant at the .05 α level. Because of the exploratory nature of this study, we did not adjust for multiple comparisons.
Results
Table 1 shows the descriptive statistics and group differences (combat veterans vs. control participants) for age, gender, race, and education; clinical tests, such as vision (right and left peripheral fields, use of corrective lenses, and visual acuity); and cognition (Trails B and MMSE). When comparing the participant groups, we found significant differences for age (combat veterans were younger), gender (more men and fewer women among the combat veterans), race (greater racial diversity among combat veterans), education (combat veterans had less education), and MMSE scores (combat veterans fared more poorly).
Descriptive Statistics and Mean Differences for Combat Veterans and Healthy Control Participants on Demographics, Vision, and Cognition
Note. M = mean; SD = standard deviation; ns = not significant
Hispanic or Latino, African-American, or American Indian/Alaskan Native.
Table 2 shows the descriptive statistics and group differences for the seven types of driving errors. Control participants made more signaling errors (t [19] = −2.138, p = .046, SE = 0.395), but combat veterans made more errors in speed regulation—specifically, overspeeding (t [17.3] = 4.095, p = .001, SE = 0.708)—and adjustment to stimuli (t [17] = 2.380, p = .029, SE = 0.140).
Descriptive Statistics and Mean Differences for Combat Veterans and Healthy Control Participants on Driving Errors
Overspeeding, defined as driving >10 miles per hour (mph) over the speed limit, is associated with being young and male (underspeeding is defined as driving >10 mph under the speed limit). We wanted to clarify the effect of age and gender on speed regulation and adjustment to stimuli, so we conducted a partial correlation for all participants and then for each participant group. Age and gender had no effect on adjustment to stimuli in either group. For all participants, however, we observed that age (M = 31.00, SD = 6.76) was inversely correlated (r = –.482, p = .001) with overspeeding, as was gender (r = –.319, p = .026). When controlling for gender, the unique contribution of age to speed regulation increased from 23% (R 2 = 23.2) to 25% (R 2 = 24.9, p = .001), suggesting that age alone can explain 25% of the variance in speed regulation among all participants.
Conducting the partial correlation for combat veterans only, we observed that age (M = 27.42, SD = 5.63) was marginally and inversely correlated (r = –.384, p = .058) with overspeeding, but gender was not significant. In controlling for gender, as expected, the unique contribution of age to overspeeding increased from 14.7% to 22.1% (R 2 = .221, p = .029), suggesting that younger age, not gender, explains more of the overspeeding variance in the combat veteran sample. The partial correlation for control participants shows neither age (M = 34.24, SD = 6.12) nor gender to be correlated with overspeeding. Therefore, the pilot data show that younger age, but not gender, is a significant predictor of overspeeding in combat veterans.
Table 3 shows descriptive statistics for combat veterans on primary combat exposures, secondary injuries, and other significant health conditions as well as additional clinical tests, including contrast sensitivity RPW, and the UFOV. Eighty-three percent of combat veterans had mortar exposure, 72% were exposed to explosive devices, and 61% were exposed to propelled grenades and sniper fire. Interestingly, 39% reported being injured in motor vehicle accidents, whereas <30% were injured by either falling or flying debris. Almost 80% of the combat veterans had combined diagnoses of mTBI (mean duration = 2.51 yr, SD = 1.86) and PTSD (mean duration = 1.93 yr, SD = 1.07), and many reported headaches (72%), affective disorders (72%), sleep disorders (67%), and hearing problems (50%). The mean number of health conditions was 7.17 (SD = 2.66).
Descriptive Statistics (n = 18) for Combat Veterans on Primary Exposures, Secondary Injuries, Health Conditions, and Other Clinical Variables
Note. M = mean; SD = standard deviation; UFOV = useful field of view.
Some participants did not specify length of time of having the health condition.
Forty-four percent of combat veterans had impaired depth perception, and 11% had impaired contrast sensitivity. Contrast sensitivity (r = .59, p = .02) and depth perception were correlated with adjustment-to-stimuli errors, although marginally (r = .45, p = .06). The mean for RPW was within functional limits at 5.28 s (SD = 1.27) for this group. On the UFOV, most combat veterans had a low risk for on-road crashes; group mean scores in the UFOV subtests indicated that combat veterans could adequately complete each of the tests. Not surprisingly, the UFOV 3, a measure of selective attention, was correlated with adjustment-to-stimuli errors (r = .49, p = .039 for response to the environmental triggers).
To address our secondary aim, we examined the correlations between the independent variables that had no missing data (i.e., age, right peripheral field, left peripheral field, acuity in right and left eyes, Trails B time, MMSE score) and the seven driving errors for all participants. Age was inversely correlated with speed regulation (r = –.556, p < .0001) and gap acceptance (r = –.412, p = .010) and positively correlated with signaling errors (r = .343, p = .035). Left peripheral field was inversely correlated with adjustment-to-stimuli errors (r = –.354, p = .034). Acuity of the left eye was correlated with visual scanning (r = .361, p = .030) errors. Trails B was correlated with lane maintenance (r = .408, p = .039) errors and total number of driving errors (r = .422, p = .032).
Discussion
This project empirically described the driving errors (type and number) of 18 returning OIF–OEF combat veterans diagnosed with mTBI and, for most, concurrent PTSD and compared their performance with that of an age cohort of 20 healthy control participants. We also determined, secondarily, the correlations of clinical tests of cognition, vision, and motor performance with driving errors.
The combat veterans group was younger than the control participants and had a higher percentage of men, was less educated, and had greater cognitive impairment (as measured by the MMSE). Previous research in healthy volunteers has shown inclination to adventure and risk-taking behaviors to be more evident among younger male participants (Brown & Groeger, 1988). Likewise, people with cognitive impairment, such as those with mTBI, also show impairment in executive functions (i.e., decision making, judgment, problem solving) during driving (Classen et al., 2009). These factors may partly explain why combat veterans made more speed regulation (overspeeding) and more adjustment-to-stimuli errors than the control participants. Note, too, that combat veterans are trained to engage in an aggressive form of defensive driving suitable to combat zones, such as not stopping for other road users, speeding, and driving in the middle of the road (T. Hundermarck, personal communication, October 1, 2008).
Although age, but not gender, helped explain overspeeding in combat veterans, it showed no significance with regard to adjustment-to-stimuli errors. Our pilot data therefore cannot explicitly quantify whether these driving errors are a function of cognitive decline (because of mTBI), PTSD, other comorbidities, defensive driving strategies, or a combination of all the preceding factors. Overspeeding and adjustment-to-stimuli errors are considered critical driving errors (Classen, Shechtman, Awadzi, Joo, & Lanford, 2010; Shechtman et al., 2010) as opposed to more benign errors (such as signaling) and must be examined prospectively in on-road studies, more critically, and more in depth.
Most of the combat veterans had been exposed to blasts from mortar, explosive devices, propelled grenades, and sniper fire. Other causes for injuries included in-combat motor vehicle accidents or being struck by falling or flying debris from explosives. It is not surprising, then, that almost 80% of our sample had a diagnoses of mTBI and PTSD, the signature syndromes of OEF–OIF; most also reported sleep disorders, headaches, hearing problems or impairments, and affective disorders such as clinical depression or anxiety disorder. The mean number of health conditions (7.17) is more than expected for a young group of civilians, but it is perhaps not that surprising for soldiers returning from combat.
Approximately half of the sample had impaired depth perception, and 11% had impaired contrast sensitivity. Although depth perception approached significance in relation to adjustment-to-stimuli errors, we detected a significant and moderate correlation between impaired contrast sensitivity and adjustment-to-stimuli errors. Depth perception and contrast sensitivity, as tested in crash-involved older adults, are particularly important for safe driving (Owsley, Stalvey, Wells, Sloane, & McGwin, 2001). The mobility (walking speed and balance) of the sample was good, given that group members could negotiate a 20-ft walk in 5.28 s (7.25 s is associated with increased risk for on-road crashes). Although the UFOV 3 was moderately correlated with adjustment-to-stimuli errors, the selective attention of the group, as determined by the UFOV, was completed within the appropriate time limit. Two (of 18) combat veterans were identified, on the basis of the UFOV risk category, as having moderate risk for on-road crashes. This percentage is high—more than 10% if the numbers hold true with a larger sample and certainly reason for concern and a rationale for testing a larger sample of the combat veterans.
Interestingly, in combat veterans, an inverse relationship was observed between age and speeding as well as gap acceptance errors. This finding is consistent with the current younger driver literature, which identifies adventure-seeking and risk-taking behaviors among younger male drivers (Brown & Groeger, 1988). For vision, our data show that an impaired left peripheral field is correlated with adjustment-to-stimuli errors and that the acuity of the left eye was also correlated with visual scanning errors. These findings may reveal that left (vs. right) peripheral field and left (vs. right) eye visual acuity are necessary abilities for drivers who live in countries where people drive on the right side of the road to potentially prevent adjustment-to-stimuli and visual scanning errors. Trails B, a proxy measure for processing speed and divided attention, was the only clinical test that was moderately correlated with lane maintenance and the total number of driving errors. Consequently, one can expect an increase in drifting out of driving lanes, making excessively wide turns, and encroaching on perpendicular traffic among drivers who have cognitive impairments such as slowed processing speed or limited divided attention. Unfortunately, we did not have Trails B data for the control participants to detect a group effect.
The generalization of this study to the population of returning combat veterans with mTBI and PTSD is limited because of the small sample and the specific demographics. In comparing this sample with control participants, however, we identified critical driving errors made by combat veterans in a driving simulator. The patterns we found are consistent with anecdotal evidence (T. Hundermarck, personal communication, October 1, 2008) and the methods used in precombat simulated driving training. In addition, although neither age nor gender accounted for adjustment-to-stimuli errors among combat veterans, age but not gender helped explain the significance of overspeeding among combat veterans. Future research will determine whether these critical errors are evident in combat veterans when they drive on the road; if they are more or less prevalent among combat veterans without mTBI and PTSD; and if they are more or less prevalent in a socioeconomically matched control group. Nevertheless, the critical errors identified in our sample point to alerting occupational therapists—whether generalists or driving rehabilitation specialists—to consider referring combat veterans with mTBI and PTSD, especially if they are young, for a formal driving evaluation. At the least, occupational therapists should discuss safe driving behaviors with this group or their family members. These steps are particularly important given that the leading cause of death for returning combat veterans is on-road crashes.
Conclusion
In comparing combat veterans with mTBI and PTSD with healthy control participants, we identified that combat veterans (who were younger than the control participants) are making critical errors while driving in a simulator. If simulator performance accurately reflects driving behavior, as we suspect (Classen et al., 2010; Shechtman et al., 2010), then we can expect increased morbidity and mortality from automobile crashes among returning combat veterans. Clearly, research efforts are needed to test interventions to reduce driving fatalities among those who have served our country in war; however, knowledge of the critical driving errors identified in this study may position occupational therapists to prevent adverse outcomes among combat veterans with mTBI and PTSD.
Footnotes
Acknowledgment
This project, VA 00066678, received funding from the Malcom Randall Veterans Affairs Medical Center. We acknowledge the Institute for Mobility, Activity and Participation for providing infrastructure.