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Accident Analysis and Prevention 45 (2012) 711–717 Contents lists available at SciVerse ScienceDirect Accident Analysis and Prevention j ourna l h o mepage: www.elsevier.com/locate/aap Distracted driving in elderly and middle-aged drivers Kelsey R. Thompson a , Amy M. Johnson b , Jamie L. Emerson a , Jeffrey D. Dawson b , Erwin R. Boer c , Matthew Rizzo a,a University of Iowa, Department of Neurology, 200 Hawkins Drive, Iowa City, IA 52242, USA b University of Iowa, Department of Biostatistics, 200 Hawkins Drive, Iowa City, IA 52242, USA c Entropy Control, Inc., 7945 Avenida Kirjah, LaJolla, CA 92037, USA a r t i c l e i n f o Article history: Received 24 May 2011 Received in revised form 22 September 2011 Accepted 27 September 2011 Keywords: Multitasking Distracted driving Older driver safety Serialization Instrumented vehicle Cognitive decline a b s t r a c t Automobile driving is a safety-critical real-world example of multitasking. A variety of roadway and in-vehicle distracter tasks create information processing loads that compete for the neural resources needed to drive safely. Drivers with mind and brain aging may be particularly susceptible to distraction due to waning cognitive resources and control over attention. This study examined distracted driving performance in an instrumented vehicle (IV) in 86 elderly (mean = 72.5 years, SD = 5.0 years) and 51 middle-aged drivers (mean = 53.7 years, SD = 9.3 year) under a concurrent auditory–verbal processing load created by the Paced Auditory Serial Addition Task (PASAT). Compared to baseline (no-task) driving performance, distraction was associated with reduced steering control in both groups, with middle-aged drivers showing a greater increase in steering variability. The elderly drove slower and showed decreased speed variability during distraction compared to middle-aged drivers. They also tended to “freeze up”, spending significantly more time holding the gas pedal steady, another tactic that may mitigate time pressured integration and control of information, thereby freeing mental resources to maintain situation awareness. While 39% of elderly and 43% of middle-aged drivers committed significantly more driving safety errors during distraction, 28% and 18%, respectively, actually improved, compatible with allocation of attention resources to safety critical tasks under a cognitive load. © 2011 Elsevier Ltd. All rights reserved. 1. Introduction Multitasking involves competition for limited neural resources by behavioral tasks, such that engagement in one task affects per- formance of the others. There are also global effects, wherein brain activity for concurrent tasks may be less than the sum of activi- ties for each task performed alone, even when the tasks draw on different brain systems (Just et al., 2008). Automobile driving is a safety-critical real-world example of multitasking that requires tracking locations of surrounding vehi- cles, judging when it is safe to pass or merge, and navigating, while also obeying traffic signals and controlling vehicle steering and speed. Added distracter tasks can compete for the neural resources needed to drive safely. Performance may initially improve with heightened arousal (a broad construct representing one’s overall stimulation [Hockey, 1984; Hancock and Szalma, 2007; Hanoch and Vitouch, 2004]) before it deteriorates under the added cognitive Corresponding author. Tel.: +1 319 356 8748; fax: +1 319 384 7199. E-mail addresses: [email protected] (K.R. Thompson), amy-m- [email protected] (A.M. Johnson), [email protected] (J.L. Emerson), [email protected] (J.D. Dawson), [email protected] (E.R. Boer), [email protected] (M. Rizzo). load (Teigen, 1994), ultimately increasing the risk of single-vehicle crashes (e.g., running off the road) and rear-end collisions (Eby and Kostyniuk, 2003). Modern vehicles include “traditional” distracters, such as heat- ing/cooling systems, cigarette lighters, and radios, and more modern “infotainment”, such as CD/DVD players and iPod/MP3 players (Strayer and Drews, 2007). Cell phone communication has raised particular concern among public health and policy experts, as even hands-free use may impair driving (Eby and Kostyniuk, 2003; Horberry et al., 2006; Horrey and Wickens, 2006; Shinar et al., 2005; Strayer and Drews, 2004, 2007; Watson and Strayer, 2010). Drivers using cell phones process less visual information in the driv- ing scene (Strayer and Drews, 2004), stop incompletely at stop signs (Strayer and Drews, 2007), and have delayed breaking responses (Watson and Strayer, 2010) and more rear-end collisions (Strayer and Drews, 2007). Driving, especially under distracted conditions, relies heavily on executive functions, including selective attention and working memory. These abilities decline with age (Shih, 2009), which can reduce driver control over the focus of attention, task switching (Chaparro et al., 2005; Craik and Bialystok, 2006) and vehicle con- trol (Rizzo et al., 2004), increasing the risk of crashes (Trick et al., 2004). Several studies have used verbal or auditory–visual tasks to study these effects (Horberry et al., 2006; Strayer and Drews, 2007; 0001-4575/$ see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2011.09.040

Distracted driving in elderly and middle-aged drivers

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Page 1: Distracted driving in elderly and middle-aged drivers

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Accident Analysis and Prevention 45 (2012) 711– 717

Contents lists available at SciVerse ScienceDirect

Accident Analysis and Prevention

j ourna l h o mepage: www.elsev ier .com/ locate /aap

istracted driving in elderly and middle-aged drivers

elsey R. Thompsona, Amy M. Johnsonb, Jamie L. Emersona, Jeffrey D. Dawsonb,rwin R. Boerc, Matthew Rizzoa,∗

University of Iowa, Department of Neurology, 200 Hawkins Drive, Iowa City, IA 52242, USAUniversity of Iowa, Department of Biostatistics, 200 Hawkins Drive, Iowa City, IA 52242, USAEntropy Control, Inc., 7945 Avenida Kirjah, LaJolla, CA 92037, USA

r t i c l e i n f o

rticle history:eceived 24 May 2011eceived in revised form2 September 2011ccepted 27 September 2011

eywords:ultitaskingistracted drivinglder driver safety

a b s t r a c t

Automobile driving is a safety-critical real-world example of multitasking. A variety of roadway andin-vehicle distracter tasks create information processing loads that compete for the neural resourcesneeded to drive safely. Drivers with mind and brain aging may be particularly susceptible to distractiondue to waning cognitive resources and control over attention. This study examined distracted drivingperformance in an instrumented vehicle (IV) in 86 elderly (mean = 72.5 years, SD = 5.0 years) and 51middle-aged drivers (mean = 53.7 years, SD = 9.3 year) under a concurrent auditory–verbal processingload created by the Paced Auditory Serial Addition Task (PASAT). Compared to baseline (no-task) drivingperformance, distraction was associated with reduced steering control in both groups, with middle-ageddrivers showing a greater increase in steering variability. The elderly drove slower and showed decreased

erializationnstrumented vehicleognitive decline

speed variability during distraction compared to middle-aged drivers. They also tended to “freeze up”,spending significantly more time holding the gas pedal steady, another tactic that may mitigate timepressured integration and control of information, thereby freeing mental resources to maintain situationawareness. While 39% of elderly and 43% of middle-aged drivers committed significantly more drivingsafety errors during distraction, 28% and 18%, respectively, actually improved, compatible with allocation

safety

of attention resources to

. Introduction

Multitasking involves competition for limited neural resourcesy behavioral tasks, such that engagement in one task affects per-ormance of the others. There are also global effects, wherein brainctivity for concurrent tasks may be less than the sum of activi-ies for each task performed alone, even when the tasks draw onifferent brain systems (Just et al., 2008).

Automobile driving is a safety-critical real-world example ofultitasking that requires tracking locations of surrounding vehi-

les, judging when it is safe to pass or merge, and navigating, whilelso obeying traffic signals and controlling vehicle steering andpeed. Added distracter tasks can compete for the neural resourceseeded to drive safely. Performance may initially improve with

eightened arousal (a broad construct representing one’s overalltimulation [Hockey, 1984; Hancock and Szalma, 2007; Hanoch anditouch, 2004]) before it deteriorates under the added cognitive

∗ Corresponding author. Tel.: +1 319 356 8748; fax: +1 319 384 7199.E-mail addresses: [email protected] (K.R. Thompson), amy-m-

[email protected] (A.M. Johnson), [email protected] (J.L. Emerson),[email protected] (J.D. Dawson), [email protected]. Boer), [email protected] (M. Rizzo).

001-4575/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2011.09.040

critical tasks under a cognitive load.© 2011 Elsevier Ltd. All rights reserved.

load (Teigen, 1994), ultimately increasing the risk of single-vehiclecrashes (e.g., running off the road) and rear-end collisions (Eby andKostyniuk, 2003).

Modern vehicles include “traditional” distracters, such as heat-ing/cooling systems, cigarette lighters, and radios, and moremodern “infotainment”, such as CD/DVD players and iPod/MP3players (Strayer and Drews, 2007). Cell phone communication hasraised particular concern among public health and policy experts,as even hands-free use may impair driving (Eby and Kostyniuk,2003; Horberry et al., 2006; Horrey and Wickens, 2006; Shinar et al.,2005; Strayer and Drews, 2004, 2007; Watson and Strayer, 2010).Drivers using cell phones process less visual information in the driv-ing scene (Strayer and Drews, 2004), stop incompletely at stop signs(Strayer and Drews, 2007), and have delayed breaking responses(Watson and Strayer, 2010) and more rear-end collisions (Strayerand Drews, 2007).

Driving, especially under distracted conditions, relies heavilyon executive functions, including selective attention and workingmemory. These abilities decline with age (Shih, 2009), which canreduce driver control over the focus of attention, task switching

(Chaparro et al., 2005; Craik and Bialystok, 2006) and vehicle con-trol (Rizzo et al., 2004), increasing the risk of crashes (Trick et al.,2004). Several studies have used verbal or auditory–visual tasks tostudy these effects (Horberry et al., 2006; Strayer and Drews, 2007;
Page 2: Distracted driving in elderly and middle-aged drivers

7 lysis and Prevention 45 (2012) 711– 717

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Table 1Battery of off-road tests completed in addition to the driving assessment.

Measure Reference

Basic vision and visual perception measuresNear visual acuity (NVA) Ferris et al. (1982)Far visual acuity (FVA) Ferris et al. (1982)Contrast sensitivity (CS) Pelli et al. (1988)Judgment of line orientation (JLO) Strauss et al. (2006)Structure from motion (SFM) Rizzo et al. (1995) and Rizzo

et al. (1997)Useful field of view (UFOV) Ball and Owsley (1992)

Motor function measuresGet-up and go Podsiadlo and Richardson

(1990) and Alexander (1994)Functional reach (FR) Duncan et al. (1990)Grooved pegboard test (Pegs) Heaton et al. (1991)

Visual cognition and executive function measuresWAIS-III block design (Blocks) Wechsler (1981)Complex figure test (CFT)-copy and recall Stern et al. (1994)Benton visual retention test (BVRT) Sivan (1992)Auditory-learning verbal test (AVLT)—recall Strauss et al. (2006)Trail making test (TMT)—parts A and B Reitan and Davison (1974)Controlled oral word association (COWA) Benton and Hamsher (1978)COGSTATa Dawson et al. (2009, 2010)

12 K.R. Thompson et al. / Accident Ana

atson and Strayer, 2010) using driving simulator outcomes orerformance on a driving course free of other vehicles (Chaparrot al., 2005).

The overarching goal of the current study was to examine theffects of distracted driving in elderly individuals with mind andrain aging affecting central executive and attentional mechanismsequired for multitasking. To do so we compared the performancesf elderly and middle aged individuals engaged in real-world driv-ng in an instrumented vehicle (IV) under the effects of a controlleduditory–verbal processing load created by a version of the Paceduditory Serial Addition Task (Gronwall, 1977; Rizzo et al., 2004).he PASAT presents single digits to the participant in a pre-recordednterval and asks that each new digit be added to the one immedi-tely prior to it (see Section 2.2.1). We hypothesized (1) that elderlyrivers would perform poorer on the PASAT task itself both off-oad and on-road compared to middle-aged participants, and (2)hat performing PASAT would reduce driver control over speed andteering and increase at-fault safety errors compared to baselineriving on similar road segments without distraction, particularly

n the elderly drivers.

. Material and methods

.1. Participants

Participants were recruited from the general community as partf ongoing studies on aging and driving. Eighty-six elderly drivers46 males; 40 females) between 65 and 89 years old (mean = 72.5,D = 5.0 years) and 51 middle-aged drivers (23 males; 28 females)etween 40 and 64 years old (mean = 53.7, SD = 9.3 years) com-leted the study. All participants were currently licensed driversithout dementia or other medical conditions such as stroke,ajor psychiatric diseases, vestibular disorders, and alcoholism

r other forms of drug addiction. Use of prescription medicationsas allowed, except for stimulants, antihistamines, narcotics, anx-

olytics, anticonvulsants, and neuroleptics. Participants were notxcluded because of visual defects unless they had corrected visualcuity worse than 20/50. Informed consent was obtained in accordith institutional guidelines for human participant safety and con-dentiality.

.2. Design and procedure

Participant age, gender, and years of education were collectedrom a questionnaire during the visit. Drivers completed a batteryf visual, motor, and cognitive tests (Table 1) in addition to assess-ent of their driving ability. A composite measure, COGSTAT, is

composite score of the Controlled Oral Word Association task,s well as the auditory-learning verbal test—recall, WAIS-III blockesign, complex figure test (copy and recall), Benton visual reten-ion test, judgment of line orientation, and trail making test—part B.ach component was standardized to have mean 50 and standardeviation of 10 in a reference group, such that high values repre-ented better abilities. The components were then summed withqual weights. Driving assessment took place during an on-roadrive in an instrumented vehicle (IV).

.2.1. PASATPASAT requires participants to add serial pairs of randomized

igits so that each digit is added to the preceeding one (Gronwall,977). We used a standardized version of the task recorded by aative English speaker who was also a local radio announcer, as in

ur previous research (e.g., Rizzo et al., 2004). A list of 37 serial num-ers is read to the participant at a rate of 2.4 s between each number.

ncorrect responses and ommissions were totaled to yield the num-er of errors out of 36. This task was completed during the off-road

a COGSTAT is a composite score of several of the above measures: JLO, Blocks,CFT-Copy and Recall, BVRT, AVLT-Recall, TMT B-A, and COWA.

cognitive testing as well as on-road during the driving assessment.PASAT performance activates brain areas for auditory perceptionand processing and speech production (Lockwood et al., 2004),placing simultaneous demands on sustained attention, workingmemory, and processing speed, similar to the demands of engagedconversation (Rizzo et al., 2004; Tombaugh, 2006).

2.2.2. On-road driving assessmentTrained research assistants proctored the on-road drive in the

IV ARGOS (automobile for research in ergonomics and safety) alongrural and urban streets around Iowa City. Measurements werecollected during both a baseline (off-task) segment and while per-forming the PASAT on US-218, a rural four-lane interstate freewaywith a speed limit of 65 mph. Participants were informed of thespeed limit and instructed to drive as they normally would on thefreeway. Both scenarios were performed on road segments withsimilar traffic density and road profile (straight). The PASAT drivingsegment length (approximately 1.3 miles) immediately precededthe slightly longer baseline segment (1.5 miles). Video data from theIV drive was reviewed by a certified driving instructor to determineat-fault safety errors. Errors were defined by the Iowa Departmentof Transportation, as described by Dawson et al. (2009, 2010). Forexample, crossing the center line, speeding, or stopping in the inter-section at a traffic signal would all be counted as an “at-fault” errors.In this study, all driving safety errors counted equally.

Speed, gas pedal position, and steering wheel position data werecaptured at approximately 10 Hz during the drive in the IV. Thesedata were used to calculate several electronic vehicle control mea-sures within the baseline and PASAT segments for each driver.Specifically, “Mean MPH” was the average speed and “SD MPH”was the standard deviation of speed. Higher speed variability indi-cates that a driver was not closely controlling vehicle speed. “PedalHold” was the 25th percentile of the duration (seconds) that thedriver held the gas pedal in the same position, within a small tol-erance (i.e., less than 1% change) (Fig. 1). Smaller pedal hold valuesindicate more frequent pedal adjustments, which drivers normallymake to maintain a constant speed whereas longer pedal hold indi-

cates fewer pedal adjustments and less control over speed. Lateralacceleration captures swerves to the left and right, but becausethese may average to zero, the standard deviation of the lateralaccelerometer (“SD lateral acceleration”) was used. Higher lateral
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K.R. Thompson et al. / Accident Analysis and Prevention 45 (2012) 711– 717 713

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Fig. 1. A single gas pedal-hold time (PHT) calculation is depicted for two participants. Dashed lines represent actual pedal position. The solid line shows the time interval overwhich the gas pedal position changed <0.01 compared to pedal position at a specific time (at the vertical time-reference bar). In these examples, PHT is approximately 2 s fora middle-aged driver at baseline (top) and 11 s for a multitasking elderly driver (bottom). The within-person summary of PHT used in our analyses was the 25th percentileof the PHT values calculated at every specific time, as the vertical reference bar slides across a driver’s time series data. Note that the PHT is calculated at every time point.T ing to

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his means that as the time-reference bar slides across the flat regions correspondhe 25th percentile, rather than a central value such as the 50th percentile or the m5th percentile was made a priori, rather than being based on empirical sensitivity.

cceleration variability tends to result from stronger steering cor-ections, indicating greater deviation from a central lane positionnd/or shorter time to line crossing.

Three electronic measures quantified magnitude, rate, and fre-uency of steering corrections. These were standard deviation ofhe steering wheel position (“SD Steering”) in degrees, “Steeringev. ≥6◦” and “Steering Dev. ≥10◦”, the number of times that the

teering wheel crossed respective thresholds of ±6◦ and ±10◦ fromhe mean steering wheel position in a driving segment (McGeheet al., 2004). The 6◦ threshold captures small corrections whereashe 10◦ threshold captures larger corrections that are generallynly observed during lane changes or during attempts to correctndesirable time to line crossing. Note that all three of these steer-

ng measures fall under the “time domain” (Diggle, 1990). We alsoalculated a “frequency domain” metric, the “steering frequency.”o do so we partitioned the steering frequency signal into theutput of a high-pass filter (comprising high frequency or fast steer-ng corrections above 0.5 Hz) and the output of a low-pass filtercomprising low frequency or slow steering changes below 0.5 Hz).Steering frequency” was a ratio calculated from dividing the stan-ard error of the high-pass portion by the standard error of the

ow-pass portion.

.3. Statistical analysis

We first analyzed PASAT task performance in the IV and inhe lab, comparing age groups using the Wilcoxon rank-sum testnd testing for association between IV-based and lab-based perfor-ance using Spearman correlation estimates. We then calculatedeans and standard deviations of nine measures of safety and vehi-

le control within each segment and age group. These measuresave been described above and include mean MPH, SD MPH, gas

edal hold, SD lateral acceleration, SD steering, steering frequency,teering deviations greater than 6◦ and 10◦, as well as at-faultafety errors. Between-group comparisons within each segment,gain using the Wilcoxon rank-sum test, were performed. The

high PHT’s, there are many high PHT values that are nearly identical. Hence, using helps remove the redundancy caused by these repeated values. This choice of the

difference between driving performance measures during PASATversus baseline driving indexed the effects of distraction. Withingroup comparisons used the one-sample t-test to determine if theresult differed from zero, while across group comparisons used theWilcoxon rank sum test. Finally, linear regression assessed non-standardized cognitive predictors of the mean and variance of MPH,steering variance, and the variance of lateral acceleration within thePASAT segment among elderly controls. A similar analysis of thesesame four outcomes as a difference between PASAT and baselinewithin the elderly group was also performed.

3. Results

3.1. PASAT performance

Elderly participants performed worse on the PASAT (mea-sured by number of errors) compared to the middle-aged group.This was true both in the lab (p < 0.001; elderly mean = 13.34vs. middle-aged mean = 8.37) and on the road (p < 0.001; elderlymean = 16.57 vs. middle-aged mean = 12.07). PASAT performancein the lab was significantly correlated with PASAT performanceon the road for both elderly (r = 0.516, p < 0.001) and middle-aged(r = 0.555, p < 0.001) groups. However, the ratio of on-road PASATto lab PASAT performance did not differ significantly between thetwo groups (p = 0.509) indicating that performance on the sec-ondary auditory–verbal distracter task was similarly reduced inboth groups in the context of real-world driving.

3.2. On-road performance

Table 2 displays between-group comparisons within each drivesegment, as well as comparisons of basic demographics. Table 3

presents the comparisons within and between groups of the changein performance from baseline to PASAT. Overall, elderly driversshowed more driving safety errors than middle-aged driversduring the PASAT segment (p = 0.013) with a trend (p = 0.081) for
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714 K.R. Thompson et al. / Accident Analysis and Prevention 45 (2012) 711– 717

Table 2Mean at-fault safety errors, speed, and steering control during baseline andthe PASAT, with comparisons between middle-aged and elderly drivers via theWilcoxon-rank sum test.

Middle-aged drivers Elderly drivers p-ValuesMean (SD) Mean (SD)

At-fault safety errorsBaseline 0.30 (0.61) 0.65 (1.09) 0.082PASAT 218 0.76 (1.05) 1.19 (1.17) 0.021

Mean MPHBaseline 61.65 (7.52) 60.00 (5.34) 0.005PASAT 218 61.00 (6.86) 59.09 (5.27) 0.005

SD MPHBaseline 1.62 (1.16) 1.94 (0.91) 0.006PASAT 218 2.09 (1.72) 2.33 (1.32) 0.012

Gas pedal holdBaseline 2.19 (1.56) 3.12 (7.76) 0.882PASAT 218 2.19 (1.32) 4.09 (8.31) 0.164

SD lateral accelerationBaseline 0.04 (0.004) 0.04 (0.006) 0.586PASAT 218 0.04 (0.005) 0.04 (0.006) 0.550

SD steering (degrees)Baseline 1.95 (0.47) 2.21 (2.98) 0.583PASAT 218 2.93 (0.50) 2.64 (1.85) <0.001

Steering frequencyBaseline 0.48 (0.11) 0.53 (0.13) 0.029PASAT 218 0.39 (0.09) 0.53 (0.16) <0.001

# Steering dev. ≥6◦

Baseline 3.86 (4.97) 3.24 (4.81) 0.552PASAT 218 12.98 (7.89) 8.84 (8.58) 0.001

# Steering dev. ≥10◦

Baseline 0.00 (0.00) 0.13 (0.61) 0.124PASAT 218 0.43 (1.08) 0.65 (3.38) 0.322

Demographics

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Table 4Linear regression analysis of cognitive, motor, and visual predictors of the meandifference in speed, speed variability, and lateral acceleration variability betweenthe baseline and PASAT segments for elderly drivers.

Estimate 95% CI p-Values

Mean MPHContrast sensitivity (CS) 4.651 (−0.031, 9.334) 0.052FVA (LogMAR) −6.973 (−13.063, −0.883) 0.025

SD MPHFunctional reach (FR) −0.118 (−0.232, −0.004) 0.043

SD lateral accelerationCFT-Copya 0.0003 (0.0000, 0.0005) 0.018COGSTAT 0.00002 (0.00000, 0.00003) 0.042FVAb (LogMAR) −0.008 (−0.015, −0.002) 0.011

a

Education 15.63 (2.31) 15.70 (2.72) 0.666Miles/Week 162.86 (157.64) 151.33 (191.01) 0.347Gender 23 Males 46 Males 0.383

ore safety errors during baseline driving. To examine effects ofistraction, the error counts for baseline driving were subtractedrom driving errors while performing PASAT. Forty-three percentf middle-aged participants and 39% of elderly participants made

ignificantly more driving safety errors during PASAT compared toaseline driving, with no between-groups differences (p = 0.437). Inddition, 39% of middle aged drivers and 33% of elderly drivers didot differ in safety errors during the PASAT compared to baseline

able 3ean individual differences between the PASAT and baseline segments in at-fault

afety errors, speed, and steering control, both within elderly and middle-agedrivers and between-groups. Between group tests were performed using theilcoxon-rank sum test, while p-values of variable means were found using the

ne-sample t-test.

Segment performance differences (PASAT—baseline)

Middle-aged drivers Elderly drivers Between groupsMean (SD) Mean (SD) p-Values

At-fault safety errors 0.47 (1.16)b 0.54 (1.24)c 0.437Mean MPH −0.65 (1.91)a −0.91 (3.27)a 0.617SD MPH 0.47 (1.05)b 0.38 (1.36)a 0.672Gas pedal hold −0.004 (1.44) 0.965 (3.84)a 0.027SD lateral acceleration 0.004 (0.003)c 0.003 (0.004)c 0.049SD steering (degrees) 0.98 (0.51)c 0.42 (1.29)b <0.001Steering frequency −0.087 (0.11)c 0.004 (0.12) <0.001# Steering dev. ≥6◦ 9.12 (6.61)c 5.60 (9.04)c 0.005# Steering dev. ≥10◦ 0.43 (1.08)b 0.52 (3.11) 0.195

a p < 0.05.b p < 0.01.c p < 0.001.

CFT-Copy = complex figure test-copy.b FVA = far visual acuity.

driving segments, while 18% of middle-aged drivers and 28% ofelderly drivers actually improved (i.e., made fewer safety errors).

Elderly drivers drove slower than the middle-aged drivers dur-ing both PASAT (p = 0.005) and baseline (p = 0.005). They alsomade fewer steering deviations of ≥6◦ compared to the middle-aged drivers during the PASAT (p = 0.005). Both groups sloweddown during PASAT (elderly p = 0.013; middle-aged p = 0.019) andshowed greater speed variability (i.e., larger standard deviation ofspeed) during the PASAT compared to baseline (both p < 0.001).PASAT affected five of the seven electronic-based measures (allbut Mean MPH and SD MPH) differently in the two age groups(Table 3). Elderly drivers showed a greater increase in gas pedalhold (p = 0.027) and steering frequency (p < 0.001) from baselineto PASAT, while middle-aged drivers showed more significantincreases in lateral acceleration variability (p = 0.049), steering vari-ability (p < 0.001), and number of steering deviations greater than6◦ (p = 0.005) from baseline to PASAT.

3.3. Neuropsychological predictors

Neuropyschological outcomes were investigated as predictorsof the mean and variance of MPH, steering variance, and the vari-ance of lateral acceleration both within PASAT alone and as adifference between PASAT and baseline. Significant outcomes forthe difference are reported in Table 4, with several cognitive andvisual measures predicting greater change in mean speed (CS, FVA),speed variability (FR), and lateral acceleration variability (CFT-copy,COGSTAT, and FVA) for elderly drivers. Likewise, several cognitiveand visual tests significantly predicted vehicle control measuresof performance (i.e., speed, steering) during PASAT among elderlydrivers (Table 5). Slower drivers had significantly greater vision lossas measured by UFOV and CS, while drivers with greater speed vari-

ability had significantly lower performance scores on the functionalreach (FR) and get-up & go motor tests.

Table 5Linear regression analysis of motor and visual predictors of mean MPH and speedvariability (standard deviation) within the PASAT segment for elderly drivers.

Estimate 95% CI p-Values

Mean MPHUFOVa—total loss −0.007 (−0.013, −0.001) 0.015Contrast sensitivity (CS) 11.423 (4.120, 18.726) 0.003

SD MPHFunctional reach (FR) −0.150 (−0.260, −0.041) 0.008Get-up & go 0.096 (−0.015, 0.208) 0.090

a UFOV = useful field of view.

Page 5: Distracted driving in elderly and middle-aged drivers

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K.R. Thompson et al. / Accident Ana

. Discussion

The introduction of a controlled auditory–verbal processing loadn the form of the PASAT produced noticeable effects on driverehicle control. Distractions (e.g., conducting a conversation with

passenger or on a cell phone) decrease the number of constantinor adjustments drivers normally make to maintain lane posi-

ion and speed compliance. During periods of distraction drivers areess able to monitor and control lane position and speed compliancehus causing greater drifts in lane position and greater changes inpeed. Consequently, when adjustments over vehicle controls areade, they are larger in amplitude, as the driver is forced to make

arger steering corrections (e.g., greater than 6◦) or depress theccelerator pedal with greater force (Brumby et al., 2009; Horberryt al., 2006; Rizzo et al., 2004).

We observed greater variability in speed and steering amongoth elderly and middle-aged drivers during PASAT. Middle-agedrivers showed greater increase in lateral acceleration variabil-

ty from baseline to PASAT than the elderly, consistent with theirreater steering variability and speed (greater steering angle atigher speed yields greater lateral acceleration). The increase inteering deviations ≥6◦exhibited during PASAT was greater amongiddle-aged drivers and fits with their greater steering variability.owever, they did not show a significant increase in steering devi-tions ≥10◦, a signature of corrective actions to avert lane crossingrrors. Elderly drivers, on the other hand, exhibited a greater steer-ng frequency during both baseline and PASAT than middle-agedrivers. Because steering frequency is a ratio between fast steeringorrections and gradual steering corrections, a larger ratio wouldndicate more high rate corrective steering behavior. However, onhe whole, these corrections did not make the elderly drivers asafe as the middle aged drivers. As noted above, the elderly drivershowed more driving safety errors than middle-aged drivers dur-ng the PASAT segment (p = 0.013) with a trend (p = 0.081) for moreafety errors during baseline driving.

Our findings suggest that elderly drivers exhibited a more time-ressured and vigilant steering strategy during the PASAT (loweragnitude steering corrections at higher rates) compared to a more

onfident or relaxed strategy in the middle-aged drivers (higheragnitude steering corrections at lower rates). Further details are

eeded to determine whether these differences reflect greater con-dence in driving ability and less susceptibility to PASAT amonghe middle-aged. For example, had lane position been availablen the IV, we could have examined further whether middle-agedrivers allowed greater lateral deviations and exhibited less pres-ured response to the same lateral deviation or time to line crossing.

One reason that elderly drivers may have been able to maintain more vigilant lateral position control may be that they serializedhe tasks of speed control, lane position control, and PASAT. Elderlyrivers held their gas pedal steady for significantly longer periodsf time during PASAT distraction than middle-aged drivers did. Its possible to temporarily ignore the speed maintenance task whileistracted to focus on the more safety-critical task of lateral positionontrol. Such serialization of tasks reduces the set of simultaneousasks from three to two, which mitigates the burden on attentionalhifting, a mechanism known to degrade with age (Cosman et al.,n press). Given that the reduction in PASAT performance duringriving among elderly drivers was similar to that of middle-agedrivers suggests that elderly drivers did not also simplify the PASATask by adopting a more serial strategy such as skipping every othernswer.

These findings fit with recent evidence of passive serialization

ith time-constrained multitasking in elderly drivers (Boer et al.,

011). Multitasking involves serial switching between informationources. Task performance often involves multiple subtask compo-ents (e.g., steering, pedal control, visual search). While subtasks

nd Prevention 45 (2012) 711– 717 715

may be performed together in acquired and highly over-learnedtasks such as automobile driving, emerging evidence suggests thatperformance decomposes with cognitive aging. In this vein, elderlydrivers show serialization of vehicle control during intersectionnegotiation (Boer et al., 2011). Rather than the primarily parallelcontrol of pedals and steering wheel observed in younger drivers,they often perform these control subtasks serially. For example,during right turns they perform most of their steering while stand-ing still instead of while accelerating as younger drivers do (Boeret al., 2011). This behavior may reduce the need for rapid timepressured integration and control of information and free mentalresources that can be deployed to maintain situation awarenessand avoid safety errors, as in the current study in which drivershad to allocate cognitive resources to control their vehicles duringan auditory–verbal processing load.

Multitasking with cognitive-linguistic tasks, such as we used,can impact gait in older individuals with and without neurologicaldisease (LaPointe et al., 2010), suggesting a potential common riskfactor for falls and car crashes. One mechanism for managing safetyrisks posed by multitasking is to reduce the amount of informationflow (e.g., by slowing down in a car, as our drivers did during mul-titasking); another is to control attention so that safety critical taskcomponents are assigned higher priority. Drivers control allocationof attention during multitasking to meet specific performance crite-ria: for example, when performing a non-safety-critical secondarytask (such as cell phone dialing), they will usually return attentionto steering control to maintain lane position (Janssen and Brumby,2010). A theory of concurrent multitasking (Salvucci and Taatgen,2008) posits threads of processing coordinated by a serial proce-dural resource and executed across other available resources (e.g.,perceptual and motor) to help explain dual-choices in complexreal-world domains such as driving and driver distraction. EEG datasuggest that the lateral prefrontal cortex plays a role in controllingrisk-taking behavior, task switching, and multitasking during fastdriving (Jäncke et al., 2008). Evidence of “serialization of behavior”,as in the gas pedal control data in this study of older drivers, sug-gests breakdown in these processes caused by neurodegenerationassociated with cognitive aging.

Overall, elderly drivers made significantly more at-fault safetyerrors during multitasking than middle-aged drivers, despite nosignificant differences between the groups at baseline. This sug-gests some effect of age on driving performance when a secondarytask is introduced. The results are compatible with other reportsof age-related decrements in multitasking performance (Craik andBialystok, 2006; Shih, 2009) and distracted driving (Chaparro et al.,2005; Horberry et al., 2006; Strayer and Drews, 2004). Furthermore,the elderly drivers drove slower than the middle-aged drivers, abehavior that mitigates trade-offs between secondary task perfor-mance and driving safety in older drivers (Horberry et al., 2006).

A more challenging on-road drive would likely have producedgreater numbers of at-fault safety errors and discriminated moreefficiently between the older and middle-aged drivers. However,the magnitude of on-road driving challenges in this study wasconstrained by the ethical obligation to protect participants andexperimenters in the IV. PASAT was administered on a relativelystraight freeway segment during the day under good weather andtraffic conditions. Driving on the freeway in such favorable cir-cumstances may be a more “automated” process, especially inexperienced elderly drivers (Just et al., 2008; Strayer and Drews,2004). This would allow drivers to perform a complex secondarytask such as the PASAT without as much detriment to the pri-mary task of driving safely. The effects of distraction on driving

in more dangerous circumstances, such as busy intersections orhigh-traffic scenarios, are more safely studied using the controlledenvironment of a driving simulator, a trade-off between naturalisticcontext and experimental control and driver safety.
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In this study, 39% of elderly drivers and 43% of middle-agedrivers made more at-fault safety errors when driving with an

ncreased auditory–verbal processing load. The remaining driversither had no change in performance and some actually improvedi.e., made fewer safety errors) compared to baseline. The rela-ionship among measures of performance, workload, and stressn a task such as automobile driving and multitasking is complexSzalma, 2009), and it is possible that the PASAT produced sufficientrousal for some drivers to improve their driving performance onhe road. Arousal theory invokes the inverted-U profile of perfor-

ance attributed to Yerkes and Dodson (1908) and Hebb (1955),hich states that performance improves as arousal increases until

eaching a certain threshold, at which point it decreases againTeigen, 1994). Thus performance declines with under stimulationnd increases with workload until it breaks down. According toesource theory, vigilance depends on cognitive resources, whichary with workload, stress, task and target modality, complexity,alience, event rate, and spatiotemporal uncertainty (Baker, 1959);igilance is restored by rest breaks or novel stimuli (Hull, 1943;ancock and Szalma, 2007).

Watson and Strayer (2010) found similar performance effectso ours when they tested driving performance in a simulatorn participants who concurrently performed an operation spanOSPAN) task. In most cases, driving performance decreased whenhe OSPAN task was administered. However, in 2% of the study pop-lation, performance increased during the OSPAN task. The authorsub such persons “supertaskers,” and suggest that they may haveome sort of superior multitasking ability. While we found a ten-old higher percentage of drivers that improved their performanceuring the PASAT task than Watson and Strayer did in their studyf multitasking, it would be difficult to invoke “supertasking” inur middle aged drivers and elderly driver drivers with age relatedognitive declines.

We did not find any significant neuropsychological predictorsf driving errors among the elderly drivers; the relatively lowrror count may have contributed. However, various cognitivend visual measures did predict vehicle control measures suchs speed and lateral acceleration. Specifically, poorer vehicle con-rol during PASAT in the elderly group was associated with lowercores on tests of visual cognition and memory (CFT-copy, COG-TAT), reduced motor control (lower scores on FR and get-up &o tests), and poorer vision (FVA, CS). While the breadth of ourtudy increased the chance of Type I errors, these findings appearo support the role of neurocognitive tests in the prediction of driverafety in advancing age (Barrash et al., 2010; Aksan et al., in press;nderson et al., in press).

ole of the funding source

This study was supported by awards AG 17717 and AG 15071rom the National Institute on Aging. The funding source hado role in study design, data collection/analysis, or report writ-

ng/submission.

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