Driving Impairments Associated with Depressive Symptomatology

DRIVING IMPAIRMENTS ASSOCIATED WITH DEPRESSIVE SYMPTOMATOLOGY

A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Master of Arts

Vivek Venugopal

August, 2009

Thesis written by Vivek Venugopal B. A., Ohio Wesleyan University, 2004 M. A., Kent State University, 2009

Approved by

Jeffrey A. Ciesla, Ph.D. Advisor

Douglas L. Delahanty, Ph.D. Interim Chair, Department of Psychology

Timothy Moerland, Ph.D. Dean, College of Arts and Sciences

TABLE OF CONTENTS

LIST OF TABLES iv

LIST OF FIGURES vi

ACKNOWLEDGMENTS vii

INTRODUCTION…………………………………………………………….. 1

METHOD………………………………………………………………...... 13

Participants…………………………………………...... 13

Procedures…………………………………………...... 13

Measures…………………………………………...... 14

Neuropsychological Tests.…………………...... 16

Driving Task……………..…………………...... 17

RESULTS……………………………………………………………………... 21

DISCUSSION………………………………………………………………… 45

REFERENCES………………………………………………………………... 52

iii

LIST OF TABLES

TABLE 1………..……………………………………………………………….. 6 THE FOUR MODES OF ATTENTIONAL PROCESSING OUTLINED IN TRICK ET AL. (2004)

TABLE 2………………………………………………………………...... 12 HYPOTHESES TESTED IN THE PRESENT STUDY

TABLE 3………………………………………………………………………... 14 SAMPLE CHARACTERISTICS

TABLE 4………………………………………………………………………… 18 A DESCRIPTION OF THE EVENTS/OBSTACLES A DRIVER ENCOUNTERS IN THE KMADS

TABLE 5………………………………………………………………………… 22 DEPRESSION, RUMINATION, SLEEP QUALITY, AND NEUROPSYCHOLOGICAL TESTS

TABLE 6………………………………………………………………………… 24 CORRELATIONS AMONG DEPRESSION, SLEEP QUALITY, RUMINATION, AND NEUROPSYCHOLOGICAL TESTS

TABLE 7………………………………………………………………………… 25 CESD SCORES PREDICTING PERFORMANCE ON THE TMTB AND LNS (OLS REGRESSION)

TABLE 8………………………………………………………………………… 26 CESD SCORES PREDICTING PERFORMANCE ON THE TMTA, GPTA, AND GPTB (OLS REGRESSION)

TABLE 9………………………………………………………………………… 28 RSQ SCORES PREDICTING PERFORMANCE ON THE TMTB AND LNS (OLS REGRESSION)

TABLE 10………………………………………………………………………… 29 RSQ SCORES PREDICTING PERFORMANCE ON THE TMTA, GPTA, AND GPTB (OLS REGRESSION)

TABLE 11………………………………………………………………………… 30 DRIVING PERFORMANCE

TABLE 12………………………………………………………………………… 31 CORRELATIONS AMONG THE VARIOUS DRIVING VARIABLES AND NEUROPSYCHOLOGICAL TESTS

TABLE 13………………………………………………………………………… 32 PREDICTING REACTION TIME BASED ON NEUROPSYCHOLOGICAL ABILITIES (OLS REGRESSION)

TABLE 14………………………………………………………………………… 34 PREDICTING AVERAGE LANE DEVIATION BASED ON NEUROPSYCHOLOGICAL ABILITIES (OLS REGRESSION)

TABLE 15………………………………………………………………………… 36 PREDICTING AVERAGE SPEED DEVIATION BASED ON NEUROPSYCHOLOGICAL ABILITIES (OLS REGRESSION)

TABLE 16………………………………………………………………………… 43 PREDICTING NUMBER OF CRASHES BASED ON NEUROPSYCHOLOGICAL ABILITIES (GzLM REGRESSION)

TABLE 17………………………………………………………………………… 44 PREDICTING DRIVING PERFORMANCE BASED ON RSQ SCORES

TABLE 18………………………………………………………………………… 47 NEUROPSYCHOLOGICAL TEST SCORES AND AGEREFERENCED NORMS

LIST OF FIGURES

FIGURE 1………………………………………………………………...... 39 HISTOGRAM OF THE NUMBER OF CRASHES VARIABLE

FIGURE 2………………………………………………………………...... 40 THE NUMBER OF CRASHES VARIABLE AND EXAMPLES OF POISSON DISTRIBUTIONS

Acknowledgments

The love, support, and guidance of many parties have contributed to the successful completion of this project. First and foremost, I thank my supervisor, Dr.

Hughes, for granting me the opportunity to participate in such an innovative and exciting research endeavor. The levity and optimism of his mentorship provided all the encouragement and reassurance I needed to overcome the many practical and technical challenges of this study. I also extend my gratitude to my advisor, Dr. Ciesla, under whose warm and able tutelage, I discovered my love for scientific inquiry. Our numerous meetings and discussions equipped me not only with many tools for research, but also a fond and intricate appreciation of their utility. Hearty thanks are due to Dr. Gunstad for sharing his wealth of technology with our team; his generosity afforded this project a new and competitive edge. I have learnt from all three of these distinguished researchers, but far less than I could have. I must also acknowledge my labmates, Katie Horsey, David

Kalmbach, and Laura Reilly, for their kind review of this manuscript. Finally, I wish to express my eternal gratitude to my parents, Rajee Venugopal and V. G. Pillai. Their love gave me the courage to dream.

vii

INTRODUCTION

Over six million motor vehicle accidents and over 40,000 fatalities have occurred

in the U.S. every year since 1993 (Research and Innovative Technology Administration,

2008). A majority of these accidents are caused by human factors such as failure to

attend to appropriate stimuli while driving (Trick, Enns, Mills, & Vavrik, 2004; Recarte

& Nunes, 2000; Rumar, 1990). A government report based on police records, baseline

data, general population surveys, and onsite technician reports of over 2000 accidents

concluded that human factors such as inattention and internal distraction were the direct

cause of accidents in at least 64% of the cases, and the probable cause in 90 – 93% of the

cases (Treat et al., 1979).

Given that depression causes impairments in neuropsychological abilities such as

attention, individuals with depression have received research consideration in recent

years as a potential risk group for motor vehicle accidents. However, the extant literature

on depressionrelated driving impairment is limited not only in volume but also in scope.

Most studies do not focus on the independent effects of depression on driving capability, but instead on the effects of antidepressant medication on driving (Brunnauer et al.,

2006; Wingen, Ramaekers, & Schmitt, 2006; Wingen, Bothmer, Langer, & Ramaekers,

2005; Ramaekers, 2003; Gerhard & Hobi, 1984). Other studies identify individuals with

depression as a high risk group for motor vehicle accidents based on epidemiological data

or survey methods (Wilson & Jonah, 1988; Donovan & Marlatt, 1982; Schmidt, Shaffer,

Zlotowitz, & Fisher, 1977; Selzer, Rogers, & Kern, 1968), though most of these studies

are either dated or fail to extricate driving impairments on account of depressionrelated

neurological deficits from confounds such as suicidal intent or comorbid alcoholism. To

the best of my knowledge, only one study (Bulmash et al., 2006) has examined actual

driving performance among unmedicated individuals with depression.

Bulmash and colleagues (2006) found that a clinically depressed outpatient group performed significantly more poorly than a nonclinical control group on a simulated

driving task, and attributed this effect to psychomotor disturbances among the depressed

group. Although their results convincingly indicate disproportionate levels of driving

impairment among their depressed sample, psychomotor disturbances may not be the

only explanation for this impairment. Firstly, because they did not obtain an objective

measure of psychomotor ability for their participants, they lacked the empirical basis to

suggest that their depressed sample exhibited psychomotor deficits or that these deficits

had caused the observed driving impairments. Secondly, since depression is typically

accompanied by a decline in cognitive abilities such as attention in addition to psychomotor functioning (Hammar, Lund, & Kayumov, 2003; Veiel, 1997), the driving

impairments Bulmash and colleagues found in their depressed sample could just as

reasonably be attributed to a decline in global cognitive abilities.

Research in the area of depressionrelated driving impairments should endeavor

not only to replicate the association between depressive symptomatology and driving

impairments, but also to identify the specific neuropsychological deficits responsible for

these impairments. Hence, it is important to assess, first and foremost, the various

neuropsychological capacities utilized while driving and, secondly, whether and to what

extent these capacities are diminished in individuals with depressive symptomatology.

The role of psychomotor functions in driving

Although safe driving entails complex psychomotor responses (Groeger, 2000),

attributing driving impairments among individuals with depression to psychomotor

disturbances is problematic for a number of reasons. Psychomotor disturbances,

observable either in the form of retardation (slowed speech and body movements) or

agitation (inability to sit still, pacing etc.), are one of the signs of depression enumerated

in the Diagnostic and statistical manual of mental disorders, fourth edition, text revision

(American Psychological Association, 2000) . However, the evidence for the prevalence

of psychomotor changes among individuals with depression remains scarce due to

contradictory findings and lack of replication. While some research (Sabbe et al., 1999;

Hartlage, Alloy, Vazquez, & Dykmna, 1993; Cornell, Suarez, & Berent, 1984) indicates

that nearly all individuals with depression exhibit some degree of psychomotor

disturbances, other studies (Parker et al., 1993; Austin et al., 1992) conclude that psychomotor changes are only observable in a proportion of this population. Moreover,

recent reviews of the research on depressionrelated neuropsychological deficits identify psychomotor ability as the neuropsychological domain least affected by depression

(Airaksinen, Larsson, Lundberg, & Forsell, 2004; Zakzanis et al., 1999). Finally, there

exists little to no empirical evidence linking deficits in psychomotor function to driving

impairment. Stolwyk and colleagues (2006) found that whereas impairments in

psychomotor speed as measured by neuropsychological tests like the Trail Making Test A

(Reitan & Wolfson, 1993) exerted little impact on driving performance, impairments in

attentional setshifting and other executive functions as measured by the Brixton Test

(Burgess & Shallice, 1997), a test that demands minimal psychomotor capability, were

significantly associated with driver error. Indeed, the most consistent thesis on driver

error across the ergonomics and psychology literatures as well as government studies is

that the vast majority of accidents are not caused by errors in motor responses, but instead by attentional lapses such as inattention (attending to thoughts and stimuli unrelated to

the driving task) and improper lookout (failure to notice the pertinent stimuli in the visual

field i.e., looking but not seeing) on the part of the driver (Klauer et al., 2006; Trick et al.,

2004; Recarte & Nunes, 2000; Rumar, 1990; Treat et al., 1979).

The role of attention in driving

In an effort to consolidate the literature on driving with the literature on attention,

Trick et al. (2004) conceptualized attentional processes as existing along two parallel dimensions or continua: automaticeffortful attention and endogenousexogenous attention. The distinction between automatic and effortful attentional processes derives from the differential demands these processes place upon one’s attentional resources

(Hartlage et al., 1993; Shiffrin & Schneider, 1984; Hasher & Zacks, 1979). Automatic processes are initiated automatically or without conscious awareness, require minimal attentional resources, and do not interfere with ongoing attentional processes. Effortful processes (also known as controlled processes) must be consciously initiated, usurp

greater attentional resources, and, consequently, are difficult to sustain in conjunction

with other effortful attentional processes. Exogenous and endogenous attentional processes, on the other hand, differ in terms of whether the attentional processes are

triggered as a result of an innate or evolutionary pattern of responding, or as a result of

learning or knowledge (Theeuwess, 1991). Exogenous processes are triggered solely by

external stimuli and, thus, show no specificity to the individual. Conversely, endogenous processes occur as a result of practice or familiarity with stimuli, and hence depend on

the experiences of the individual. Thus, exogenous processes are dominant in novel

situations in which there are no expectations about responding, while endogenous processes occur in familiar situations or novel situations in which there are clear expectations about responding.

Trick et al. (2004) combine these two dimensions of attention to arrive at four modes of attentional processing: automaticendogenous processes, automaticexogenous processes, effortfulendogenous processes, and effortfulexogenous processes (see Table

1). They explain the overlap between these two dimensions as follows. The position of an attentional process on the automaticeffortful continuum indicates the manner in which it transpires: with conscious awareness (effortful) or without (automatic). The endogenousexogenous continuum, on the other hand, explains the origin of the attentional process: is the said attentional process occurring as a result of practice or individual expectations (endogenous) or entirely as a result of the stimulus (exogenous).

Thus, automaticexogenous processes are essentially reflexive, and common to individuals regardless of personal experience. Most driving tasks are not reflexive,

Table 1. The four modes of attentional processing outlined in Trick et al. (2004)

Automatic Effortful

Exogenous • Occur without awareness. • Occur with awareness. • Are reflexive. • Common to all. • Do not interfere with other • Interfere with other tasks. tasks.

Endogenous • Occur without awareness. • Occur with awareness. • Are habitual or practiced. • Interfere with other tasks. • Do not interfere with other • Dependent on individual’s tasks. experiences or expectations. .

because they are learned. Trick et al. (2004), however, put forth the tendency of novice

drivers to reflexively turn the steering wheel in the direction they are looking or attending

to as an example of a driving behavior that is likely automaticexogenous, given that

young children exhibit a similar behavior while learning to ride tricycles. This example

also illustrates another important feature of automaticexogenous processes: they occur

even when they are maladaptive. Automaticendogenous processes are similar to

automaticexogenous processes in that they are initiated automatically, but differ in the

fact that they are triggered as a result of a learned pairing between the stimulus and the

attentional response. Most driving behaviors such as braking and steering become

automaticendogenous or habitual after a few years (Korteling, 1994). Like automatic

exogenous processes, automaticendogenous processes may also occur when undesirable.

For example, when trying to slow down, individuals transitioning from a manual to an

automatictransmission vehicle often reach for where they expect the clutch to be as

opposed to reaching for the brakes.

Effortful processes, both endogenous and exogenous, are consciously initiated by

the individual, and burden attentional resources to a greater extent than the automatic processes do. The point of distinction between effortfulexogenous and effortful

endogenous processes is that while the former occurs in an exploratory capacity, the latter

is more deliberate (Trick et al., 2004). In other words, drivers engage in effortful

exogenous processes when the attentional demands of driving are low enough that their

attention may be diverted elsewhere. For instance, while negotiating a familiar route

home, the attentional demands are so low (because they are being carried out via

automaticendogenous processes) that the driver’s attention may wander as a function of

the environment to billboards or signs; these billboards or signs may then grab effortful

attentional resources forcing the driver to consciously refocus attention on the road if

need be. By contrast, effortfulendogenous processes are activated in situations that place

explicit demands or expectations upon the driver. For instance, when trying to find a particular location in an unfamiliar area, the driver must consciously monitor the

environment to identify relevant road signs or landmarks. The operative attentional processes are effortful because they are consciously processed and endogenous because they are selfdirected.

The impact of depression on attention: Implications for driving. There exists some consensus among researchers that while individuals with depression show little to no deficits in automatic attention, they exhibit marked diminution in their ability to perform

effortful attentional tasks such as attentional setshifting (Hammar et al., 2003; Veiel,

1997; Hartlage et al., 1993). As Zakzanis and colleagues (1999) noted, individuals with

depression perform poorly on neuropsychological tests such as the Trail Making Test B

(Reitan & Wolfson, 1993) and the Letter Number Sequencing (Wechsler, 1997), because

these tests necessitate an effortful shifting of attention from one paradigm (numerical

ordering) to another (alphabetizing). It is important to note that most researchers

(Hartlage et al., 1993; Shiffrin & Schneider, 1984; Hasher & Zacks, 1979) in the field of psychology only characterize attentional processes along the continuum between

automatic and effortful, while only a few attempt to describe whether these processes are

exogenous or endogenous (Groeger, 2000; Theeuwess, 1991). However, considering that

most psychologists draw their conclusions by assessing performance on

neuropsychological tests in the laboratory, one may safely assume that these psychologists are referring to endogenous processing; testtakers receive specific

instructions and their behavior is goaldirected. Thus, individuals with depression suffer

from deficits in effortfulendogenous processing.

As discussed earlier, driving necessitates effortfulendogenous processing under

difficult driving conditions. For instance, when driving under conditions of poor

visibility, the driver must focus attention on the road in order to improve visibility

(Posner, 1980). Driving under such demanding conditions becomes hazardous in potential accident situations since the driver must quickly redirect attention from its primary focus to the source of the accident risk. Individuals with depression, since their ability to consciously switch between attentional paradigms is impaired, may not

accomplish this task quickly enough to avoid the accident. In other words, individuals

with depression may require a greater amount of time to react to an event on the road

than their healthy counterparts.

The impact of rumination on attention: Implications for driving. Conceptualized

as a style or responding to stressful situations by repetitively and passively focusing

attention on the self, negative mood and emotions, rumination has been implicated as a

mechanism through which depressive episodes are maintained (NolenHoeksema, 1991;

Morrow & NolenHoeksema, 1990). Once activated, rumination is a selfsustaining process that is robust against other mechanisms competing for attention. One theory

suggests that ruminators consciously prolong the ruminative response, because they believe, albeit erroneously, that by ruminating about their problems they might better

understand and eventually alleviate the associated distress (Lyubomirsky & Nolen

Hoeksema, 1993). Hence, it is my contention that rumination may be an effortful

endogenous process and likely occurs serially; a set of studies, though relatively small

and recent, has convincingly established the intrusive effects of rumination on the

effortfulendogenous processing of other stimuli. For instance, Watkins and Brown

(2002) sought to empirically examine this hypothesis by inducing ruminative thoughts

among a group of participants and measuring subsequent performance on a random

number generation task, a measure of effortfulendogenous processing. They reasoned

that if rumination is effortfully processed, then ruminative thoughts ought to interfere

with and consequently degrade performance on the random number generation task.

Consistent with their expectations, they found that the group engaged in rumination

performed significantly more poorly on the random number generation task than a control

group. Similarly, Davis and NolenHoeksema (2000) demonstrated that ruminators performed significantly less efficiently than nonruminators on the Wisconsin Card

Sorting Test (WCST), an empirically validated measure of attentional setshifting

(Miyake et al., 2000).

Although drivers are more likely to engage in effortfulexogenous processing

when the attentional demands of driving are relatively light, such driving conditions do

not preclude effortfulendogenous processing. In light of the pervasiveness of the

ruminative response style among individuals experiencing depressive symptoms as well

as individuals who have experienced depressive symptoms in the past (Roberts, Gilboa,

& Gotlib, 1998), I hypothesize that such individuals are likely to ruminate (i.e., engage in

effortfulendogenous processing) under easy driving conditions. Thus, even when

driving along a familiar road with limited traffic, ruminators must, due to the effortful

attentional demands of rumination, consciously switch the focus of their attention from

ruminative thoughts to events transpiring on the road. Thus, these individuals require

more time to switch from the ongoing effortful attentional process (rumination) to a new

target such as a traffic violator, and may hence be at a greater risk for accident

involvement.

Depression, attention, and driving: Summary. I propose that individuals suffering

from depressive symptoms are more likely to experience driving impairments than the

general population due to deficits in the effortful attentional capacities necessary for safe

driving. Depression related deficits in the ability to switch between different attentional

paradigms affect driving ability through two proposed mechanisms: inefficiency in

switching between the various attentional demands of the actual driving task under

difficult driving conditions, and, under easy driving conditions, due to an inability to

effectively alternate between the attentional demands of the driving task and those of

extraneous processes such as rumination.

The present study

The present study employed a driving simulator paradigm to identify the

depressionrelated neuropsychological deficits complicit in driving impairment. I aimed

to accomplish this goal in two steps: firstly, by assessing the association between

depressive symptoms and performance on a simulated driving task designed to replicate both easy and difficult driving conditions; and secondly, by determining the relationship between these symptoms and scores on pertinent neuropsychological tests. Any potential

driving impairments exhibited by individuals with depressive symptoms could then be

attributed with high specificity either to psychomotor slowing or to attentional deficits,

depending on their performance on the respective neuropsychological test. I

hypothesized that individuals experiencing high levels of depressive symptoms shall

exhibit marked deficits on neuropsychological tests assessing effortfulendogenous

attention. As a corollary, these individuals shall also perform poorly on the simulated

driving task. Specifically, individuals with depressive symptoms shall fare poorly on the

simulated driving task due to it varied attentional demands during the difficult parts, and

because they are likely ruminating in conjunction with driving during the easy parts. For

more detailed hypotheses, see Table 2.

Table 2. Hypotheses tested in the present study

Performance on neuropsychological tests Performance on simulated driving task

Hypothesis 1: higher levels of depressive Hypothesis 4: lower scores on symptomatology will be associated with neuropsychological tests of effortful lower scores on tests of effortful attention. attention will be associated with greater levels of driving impairment.

Hypothesis 2: depressive symptomatology Hypothesis 5: scores on will not be associated with performance on neuropsychological tests of tests of psychomotor speed or automatic psychomotor speed will not be attention. associated with simulated driving performance.

Hypothesis 3: higher levels of trait Hypothesis 6: higher levels of trait rumination will be associated with lower rumination will be associated with scores on tests of effortful attention. greater levels of driving impairment.

METHOD

Participants

Sixty seven students enrolled in undergraduate psychology courses at Kent State

University participated in the study in exchange for research credit. This sample included

30 males and 37 females, ranging in age from 17 years to 33 years with a mean age of 20

years; for additional information about the sample, refer to Table 3. All participants possessed a valid driver’s license, and reported at least one year of driving experience.

Procedure

Participants came into the laboratory and provided informed consent. Next, they

filled out a set of questionnaires that assessed various psychological constructs such as

depressive symptomatology, mental health history, as well as driving habits and

experience. Also, since onroad sleepiness has been known to contribute to driver

impairment (Papadakaki et al., 2008), participants also answered a questionnaire

assessing quality and pattern of sleep. After they completed these questionnaires, they performed a battery of neuropsychological tests that measured various neurological and psychomotor capacities such as attention, working memory, and psychomotor dexterity; testing occurred in a lowstimulus room to minimize distraction. Finally, they performed a driving task on a simulated car with a steering wheel and foot pedals. This driving task

Table 3. Sample Characteristics ( = 67)

Mean (SD) Median Minimum Maximum

Age 19.76 (2.29) 19 17 33

Years of education 12.70 (1.13) 12 11 16

Gender (% Male) 44.8%

Race (% White) 88.1%

Driving Experience (years) 3.85 (2.26) 3 1 16

No. of hours spent driving (per week) 6.96 (6.26) 4.5 0 35

No. of accident involvements 0.72 (0.92) 0 0 4

No. of citations 0.92 (1.15) 0 0 5

No. of moving violations 0.78 (1.22) 0 0 6

No. of nonmoving violations 0.16 (0.41) 0 0 2

ote. All statistics reported here are based on data selfreported by participants.

included a five to seven minute practice trial in order to help participants acclimate to the

equipment, followed by a 10 – 15 minute test trial along a custombuilt circuit.

Measures

CESD: Center for Epidemiological Studies Depression Scale (Radloff, 1977).

The CESD is a depression scale widely used to selfreport the presence and persistence of depressive symptoms. This version of the CESD consists of 20 items which are scored

on a scale from one to four points, such that higher scores indicate greater levels of

depressive symptoms. The CESD has good internal consistency (α = 0.85) and

satisfactory testretest reliability (r = 0.51 to 0.67) over a 2 to 8week period (Radloff).

In the present sample, the CESD achieved high internal consistency (α = .90).

PSQI: Pittsburgh Sleep Quality Index (Buysse et al., 1988). The PSQI is a self

report instrument designed to assess the quality and pattern of sleep in adults along seven

dimensions: subjective sleep quality, sleep latency, sleep duration, habitual sleep

efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction.

Responses are scored on a Likerttype scale from zero to three, such that a three reflects

the negative extreme on the scale. The internal consistency of the PSQI is good (α =

0.83; Buysse et al.). In the present sample, the PSQI achieved acceptable internal

consistency (α = .74).

RSQ: Response Style Questionnaire, Rumination Scale (olenHoeksema &

Morrow, 1991). The RSQ is a selfreport inventory designed to assess the presence and persistence of ruminative coping strategies in response to depressed mood, and consists

of 22 items on a Likerttype scale ranging from one (almost never) to four (almost

always). Higher scores on this questionnaire indicate greater propensity for rumination in

response to depressed mood. In the present sample, the RSQ achieved excellent internal

consistency (α = .94).

DESK: Driving Experiences, Skill, and Knowledge. Designed specifically for this

study, the DESK assesses driving experience and history including traffic and speeding

violations, citations, and drivingrelated accidents. It also provides information regarding

driving habits and attitudes.

Neuropsychological tests

TMT – A and – B: Trail Making Test A and B (Reitan & Wolfson, 1993). Trail

Making Test A (TMTA) is similar to the children's game “Connect the Dots” and

measures psychomotor speed and visual scanning. Participants are asked to connect a

series of 25 numbered dots in ascending order as quickly as they can (e.g. 123, etc.).

Trail Making Test B (TMTB) adds a setshifting component and requires participants to

alternate between numbers and letters in ascending order (e.g. 1A2B, etc.). Thus,

while the TMTA necessitates primarily automaticendogenous attentional processing,

the TMTB assesses effortfulendogenous attentional abilities.

LS: Letter umber Sequencing (Wechsler, 1997). This test is a measure of

effortful attention and working memory. First, participants are read a string of numbers

and letters. Next, they are asked to order the numbers and letters in a specific manner: all

the numbers in ascending order followed by the letters in alphabetical order. For

example, if the string was 1JA7, participants would be asked to generate 17AJ.

Strings increase in length with each trial.

GPT: Grooved Pegboard (Klove, 1963). This task is a measure of speeded fine

motor abilities and manual dexterity. Participants are asked to place small metal pegs

into a 5 by 5 grid as quickly as they can. On the first trial, participants use their dominant

hand, and their nondominant hand on the second trial.

WRAT 3: Wide Range Achievement Test – 3 Reading Subtest (Wilkinson, 1993).

This test is a measure of reading ability, and will provide an estimate of premorbid

intellectual ability for the proposed study. Participants are asked to read increasingly

difficult words from a page.

Driving Task

The STISIM Driving Simulator. The STISIM Driving Simulator (Build 2.08.03) by Systems Technology Inc. is a personal computer based, interactive driving simulator

software package, and has been configured to control: (a) a high fidelity steering wheel

with two analog levers for left/right turn indication; (b) an analog pedal set consisting of

an analog brake pedal and another pedal for gas; and (c) a 46” High Definition LCD

television.

The Kent Multidimensional Assessment Driving Simulation (KMADS). The K

MADS is a roughly 7 mile long driving scenario (or driving course) that was developed specifically for this study; psychometric properties such as testretest reliability and internal consistency of the KMADS are under investigation in a concurrent study. The

KMADS provides an opportunity to measure diving performance in a number of environments including a quiet suburb, a country road, a small town, and a busy city, each with its own speed limit restrictions and lane configurations. The KMADS also contains a variety of sporadic events or obstacles the driver must avoid (see Table 4).

Finally, randomly positioned throughout the scenario are 8 university signs as well as 7 distractor signs of the same color, design, and font as the university signs. Drivers are

Table 4. A description of the events/obstacles a driver encounters in the KMADS

Events: Type Location

1. Dog runs out from behind parked cars Reaction Time Suburb

2. Bicycle runs stop sign Reaction Time Suburb

3. Left turn against speedy oncoming traffic Crash Country

4. Vehicle ahead starts braking Crash Country unexpectedly

5. Van hidden behind construction backs Reaction Time Country out into driver’s lane

6. Child crosses street Crash Small Town

7. Cab pulls out from a parked position and Reaction Time City into driver’s lane

8. Amber Light Dilemma: signal changes Reaction Time City from green to yellow as the driver approaches the intersection; driver must come to a halt or hit a crossing pedestrian

9. Challenging right turn at busy Crash City intersection

10. Driver sees an ambulance with Crash City emergency flashes on approaching in the rearview mirror.

ote. Reaction Time events are designed to measure the delay in responding to an obstacle. Crash events, like reaction time events, increase the likelihood of a crash, but do not provide information regarding the driver’s reaction time.

instructed to count the number of university signs they spot throughout the scenario, and

report this number to an experimenter at the end of the drive. This task serves to increase

the cognitive load on drivers because it necessitates effortfulendogenous information processing (Trick et al., 2004; Hammar et al., 2003), and is ecologically valid because it

engages them in a visual search similar to one they would perform if looking for

landmarks along an unfamiliar route. In a pilot study, most participants completed the K

MADS in approximately ten to fifteen minutes when obeying the prescribed speed limits.

Practice trial scenario. The scenario for the practice trial is shorter than K

MADS and does not include the events listed in Table 4. However, it affords drivers an

opportunity to become comfortable with the simulator and practice the tasks they shall be

required to perform during the test trial such as accelerating, decelerating, following the

speed limit signs, navigating turns, stopping at stop signs/traffic signals, and maintaining

lane position. The practice scenario is a little over 3 miles long, and took most participants in the pilot study approximately 57 minutes to complete when adhering to

the prescribed speed limits.

Driving variables. The KMADS yielded four indices of driving performance:

reaction time to obstacles, average lane deviation, average speed deviation, and number

of crashes. Reaction time to obstacles is the time delay between the presentation of an

obstacle and the driver’s first response, either in the form of braking or steering around

the obstacle. For instance, when the “dog runs out” event is triggered, the driver may

either step on the brakes to slow down, turn the steering wheel to swerve around the dog,

or perform some combination thereof. The reaction time recorded, in this case, is the

delay (in seconds) between the dog’s appearance on the screen and the driver’s braking or steering response, whichever occurs first. In the present sample, the five reaction time events described in Table 4 achieved high internal consistency (α = .88). The average lane deviation is an index of the lateral distance between the driver’s lane position and the ideal lane position (along the center of the lane), and represents the average of the lane deviations measured continuously (every 100 feet) along three 2500 footlong epochs in the suburb, country, and city respectively. The average speed deviation is measured in the same manner, and is an index of speedlimit compliance i.e., the difference between the posted speed limit and the driver’s speed. Note that, in contrast to rest of the scenario, the epochs which measure speed and lane deviation exert relatively low attentional demands on the driver. Visual stimulation, in terms of buildings, ongoing traffic, and other distractions, are reduced to a minimum along these parts. Drivers must do little else than monitor speed and lane position, and are hence more likely to engage in extraneous mental processes such as rumination in conjunction with driving along these epochs. Number of crashes indicates the number of times the driver’s car came in contact with another vehicle, pedestrian, or static object such as a building or a tree.

RESULTS

Preliminary Analyses

The univariate distributions of all continuous study variables were examined for

normality, and outliers greater than three standard deviations away from the mean were

excluded listwise from all further analyses. Table 5 summarizes the descriptive statistics

for the depression, sleep quality, and rumination measures, as well as performance on the

various neuropsychological tests. Correlational analyses revealed that depression,

rumination, and sleep quality were all significantly associated with each other (Table 6).

Scores on a number of neuropsychological tests were also significantly associated with

each other (Table 6). TMTA and TMTB scores were significantly correlated with each

other, as were scores on the GPTA and GPTB. Finally, scores on the LNS exhibited a

significant correlation with scores on the TMTB, while no such relationship emerged between the LNS and the TMTA. These results indicate that the LNS and the TMTB

measure similar effortfulattentional processes.

The impact of depression on neuropsychological test performance

Multiple hierarchical regression analyses were run to investigate the association between depressive symptoms and performance on the various neuropsychological tests.

Scores on the respective neuropsychological tests were the dependent variables (DVs) in

each of the following models. Age, gender, WRAT 3, and PSQI scores were entered in

Table 5. Depression, rumination, sleep quality, and neuropsychological tests ( = 63)

Mean (SD) Median Minimum Maximum

CESD 14.55 (9.51) 12 0 41

PSQI 15.44 (3.12) 5 1 15

RSQ 19.90 (7.26) 19 11 42.90

TMTA (seconds) 26.63 (8.87) 24.53 14.16 51.88

TMTB (seconds) 55.19 (12.46) 54.25 31.57 85.72

LNS (no. of correct trials) 11.60 (3.22) 11 3 18

WRAT 3 (raw score) 33.06 (3.88) 33 19 41

GPTA (seconds) 70.20 (8.64) 70.53 54.16 88.63

GPTB (seconds) 75.43 (11.67) 74.91 54.32 103.91

ote. CESD = Center for Epidemiological Studies – Depression Scale; PSQI = Pittsburgh Sleep Quality Index; RSQ = Response Style Questionnaire; TMTA = Trail making test – part A; TMTB = Trail making test – part B; WRAT3 = Wide Range Achievement Test 3 – Reading Subtest; LNS = Wechsler memory scale, letter number sequencing subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand

Step 1 of the model, whereas depressive symptoms as determined by CESD scores were

added in Step 2. Normalquantile plots of the residuals as well as residualversusfitted

plots were examined, and revealed that all regression models met the assumption of

normally distributed errors with constant variance (Neal & Simons, 2007). Further, pairwise correlations between all predictors were examined to assess multicollinearity.

With regard to the TMTB, results showed that the final regression model

including all predictors was significant, F (5, 57) = 3.09, p < .05, but explained only

about 21% of the variance in TMTB scores (see Table 7). WRAT 3 scores were a

significant predictor, ( β = – .424, p < .01), such that participants with lower WRAT 3 scores took longer to complete the TMTB. PSQI scores were also a significant predictor, ( β = – .307, p < .05); participants reporting greater levels of sleep disturbances took longer to complete the TMTB. Adding CESD scores did not significantly improve model fit. Similarly, the final regression model predicting LNS scores was also significant, F (5, 57) = 3.24, p < .05. Together, age, gender, WRAT 3 scores, PSQI scores, and CESD scores explained approximately 22% of the variance in LNS scores

(see Table 7). Participants with higher WRAT 3 scores were significantly more likely to score higher on the LNS test ( β = .420, p < .01). CESD scores were not a significant predictor of performance on the LNS. Thus, contrary to hypothesis 1 (see Table 1), depressive symptoms were not associated with performance on neuropsychological tests of effortfulattention. However, consistent with hypothesis 2, there was no association between depressive symptomatology and performance on tests of automatic attention, psychomotor speed, or manual dexterity. The regression models (Table 8) predicting performance on the TMTA, GPTA, and GPTB were all nonsignificant.

The impact of rumination on neuropsychological test performance

Multiple hierarchical regression analyses were run to investigate the association between rumination and performance on the various neuropsychological tests. Scores on

Table 6. Correlations among depression, sleep quality, rumination, and neuropsychological tests (=63)

CESD PSQI RSQ TMTA TMTB LNS WRAT 3 GPTA GPTB

CESD 1.00

PSQI .56** 1.00

RSQ .73** .51** 1.00

TMTA .14 .10 .31* 1.00

TMTB .01 .16 .11 .28* 1.00

LNS .13 .01 .17 .24 .32* 1.00

WRAT 3 .09 .10 .21 .06 .37** .36** 1.00

GPTA .23 .17 .23 .10 .09 .05 .01 1.00

GPTB .22 .18 .27* .18 .10 .09 .02 .72** 1.00

ote. CESD = Center for Epidemiological Studies – Depression Scale; PSQI = Pittsburgh Sleep Quality Index; RSQ = Response Style Questionnaire; TMTA = Trail making test – part A; TMTB = Trail making test – part B; WRAT 3 = Wide Range Achievement Test 3 – Reading Subtest; LNS = Wechsler memory scale, letter number sequencing subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand.

* p < .05, ** p <.01

Table 7. CESD scores predicting performance on the TMTB and LS

TMTB LNS Predictor β t R2 β t R2 Step 1 .188* .213** Age .056 .454 .136 1.127 Gender .038 .326 .045 .379 WRAT 3 .424 3.520** .420 3.453** PSQI .307 2.026* .095 .643

Step 2 .026 .009 CESD .201 1.360 .115 .796

ote. CESD = Center for Epidemiological Studies – Depression Scale; PSQI = Pittsburgh Sleep Quality Index; TMTB = Trail making test – part B; WRAT3 = Wide Range Achievement Test 3 – Reading Subtest; LNS = Wechsler memory scale, letter number sequencing subtest

* p < .05, ** p <.01

Table 8. CESD scores predicting performance on the TMTA, GPTA, and GPTB

Predictor TMTA GPTA GPTB β t R2 β t R2 β t R2 Step 1 .050 .068 .074 Age .119 1.918 .305 .305 .102 1.803 Gender .162 1.281 .150 1.210 .228 1.854 WRAT 3 .037 .281 .016 1.125 .013 1.098 PSQI .007 1.044 .128 1.860 .002 .011

Step 2 .012 .010 .034 CESD .137 1.883 .121 1.819 .229 1.510

ote. CESD = Center for Epidemiological Studies – Depression Scale; PSQI = Pittsburgh Sleep Quality Index; TMTA = Trail making test – part A; WRAT3 = Wide Range Achievement Test 3 – Reading Subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand.

the respective neuropsychological tests were the DVs in each of the following models.

Age, gender, WRAT 3 scores, PSQI scores, and CESD scores were entered in Step 1 of the model, whereas level of rumination as determined by RSQ scores was added in Step

2. Normalquantile plots of the residuals as well as residualversusfitted plots were examined, and revealed that all regression models met the assumptions of normally distributed errors with constant variance. Further, pairwise correlations between all predictors were examined to assess multicollinearity. Results revealed no significant associations between RSQ scores and any of the neuropsychological tests (see Tables 9 and 10). Thus, contrary to hypotheis3 (Table 2), higher levels of trait rumination were not associated with impaired performance on tests of effortfulattention.

Driving Performance

Descriptive statistics for the driving variables are available in Table 11. As Table

12 indicates, a number of the driving variables were significantly associated with each

other. Reaction time was significantly correlated with all three other driving variables,

and thus emerged as a fairly global index of driving performance. As expected, reaction

time and number of crashes exhibited the strongest association; the longer a participant

needed to react to an obstacle, the lesser the amount of time available to successfully

avoid it. Reaction time was also significantly associated, though to a lesser degree, with

average lane deviation and average speed deviation. Thus, drivers with slower reaction

times also exhibited impairments in monitoring the simulated vehicle’s speed and lane position. Finally, average speed deviation was significantly associated with number of

Table 9. RSQ scores predicting performance on the TMTB and LS (OLS Regression)

TMTB LNS Predictor β t R2 β t R2 Step 1 .164* .139 Age .058 1.482 .042 1.344 Gender .046 .380 .045 .366 WRAT 3 .400 3.144** .373 2.877** PSQI .204 1.381 .026 .171 CESD .005 .023 .044 .214

Step 2 .003 .000 RSQ .098 .458 .002 .010

ote. CESD = Center for Epidemiological Studies – Depression Scale; RSQ = Response Style Questionnaire; PSQI = Pittsburgh Sleep Quality Index; TMTB = Trail making test – part B; WRAT3 = Wide Range Achievement Test 3 – Reading Subtest; LNS = Wechsler memory scale, letter number sequencing subtest

* p < .05, ** p <.01

Table 10. RSQ scores predicting performance on the TMTA, GPTA, and GPTB (OLS Regression)

Predictor TMTA GPTA GPTB β t R2 β t R2 β t R2 Step 1 .095 .093 .109 Age .095 .736 .120 1.928 .095 1.738 Gender .132 1.010 .151 1.185 .211 1.671 WRAT 3 .067 .493 .037 1.278 .029 1.220 PSQI .112 .705 .033 1.201 .042 1.255 CESD .141 .634 .106 1.515 ..139 1.682

Step 2 .044 .005 .006 RSQ .396 1.673 .120 1.553 .139 1.650

ote. . CESD = Center for Epidemiological Studies – Depression Scale; RSQ = Response Style Questionnaire; PSQI = Pittsburgh Sleep Quality Index; TMTA = Trail making test – part A; WRAT3 = Wide Range Achievement Test 3 – Reading Subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand. 29

Table 11. Driving performance (=63)

Mean (SD) Median Minimum Maximum

Reaction Time (seconds) 1.53 (.41) 1.45 0.73 2.5 Average Speed Deviation (miles/hr) 5.26 (2.65) 4.50 2.18 17.08 Average Lane Deviation (feet) 2.13 (.59) 1.93 1.18 3.85 Number of crashes 1.34 (1.09) 1 0 5

crashes, indicating that drivers who faced trouble regulating the speed of the simulated vehicle were more likely to crash it.

The impact of neuropsychological abilities on simulated driving

Reaction time, average speed deviation, and average lane deviation. Multiple

hierarchical regression analyses were run to predict impairments in simulated driving based on level of depressive symptoms and performance on the various

neuropsychological tests. The respective driving variables served as the DVs in each of

the following models. To control for the effects of realworld driving experience on

simulated driving, familiarity with driving (operationalized as the number of hours spent

driving every week) was included along with age, PSQI scores, and CESD scores in Step

1 of the models. The specific neuropsychological test in question was added in Step 2.

Normalquantile plots of the residuals as well as residualversusfitted plots were

examined, and revealed that all regression models met the assumptions of normally

distributed errors with constant variance. Further, pairwise correlations between all predictors were examined to assess multicollinearity. Neither depressive symptoms nor

Table 12. Correlations among the various driving variables and neuropsychological tests

TMTA TMTB LNS GPTA GPTB RT Crashes Avg.SD Avg.LD

TMTA 1.00 TMTB .28* 1.00 LNS .22 .33* 1.00 GPTA .12 .10 .01 1.00 GPTB .20 .11 .12 .72** 1.00 RT .02 .18 .17 .09 .18 1.00 Crashes .06 .22 .10 .11 .02 .48** 1.00 Avg.SD .16 .16 .33* .04 .09 .32* .27* 1.00 Avg.LD .06 .04 .17 .07 .06 .37** .15 .19 1.00

ote. TMTA = Trail making test – part A; TMTB = Trail making test – part B; LNS = Wechsler memory scale, letter number sequencing subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand; RT = Reaction Time; Crashes = No. of crashes; Avg.SD = Average speed deviation; Avg.LD = Average lane deviation

* p < .05, ** p <.01

Table 13. Predicting reaction time based on neuropsychological abilities (OLS Regression)

Predictor β t R2 Predictor β t R2

Step 1 .039 Step 1 .039 Age .123 .880 Age .115 .854 HpwDriven .049 .360 HpwDriven .019 .141 PSQI .181 1.127 PSQI .186 1.164 CESD .020 .121 CESD .043 .267 Step 2 .002 Step 2 .014 TMTB .048 .347 LNS .126 .906

Predictor β t R2 Predictor β t R2

Step 1 .039 Step 1 .039 Age .105 1.774 Age 1.103 .758 HpwDriven .060 1.439 HpwDriven 1.056 .416 PSQI .180 1.121 PSQI .189 1.174 CESD .009 1.055 CESD 1.006 .039 Step 2 .002 Step 2 .005 TMTA .046 .329 GPTA .070 .507

Continued on next page 32

Table 13. (continued)

Predictor β t R2

Step 1 .039 Age 1.103 .636 HpwDriven 1.056 .425 PSQI .189 1.109 CESD 1.006 .184 Step 2 .034 GPTB .070 1.414

ote. PSQI = Pittsburgh Sleep Quality Index; CESD = Center for Epidemiological Studies – Depression Scale; TMTA = Trail making test – part A; TMTB = Trail making test – part B; LNS = Wechsler memory scale, letter number sequencing subtest; GPTA = Grooved pegboard test, dominant hand; GPTB = Grooved pegboard, nondominant hand; HpwDriven = No. of hours spent driving every week (selfreported)

Table 14. Predicting average lane deviation based on neuropsychological abilities (OLS Regression)

Predictor β t R2 Predictor β t R2

Step 1 .046 Step 1 .046 Age .070 .511 Age .071 .539 HpwDriven .142 1.077 HpwDriven .148 1.097 PSQI .095 1.600 PSQI .094 1.591 CESD .096 1.603 CESD .101 1.629 Step 2 .000 Step 2 .001 TMTB .008 .057 LNS .029 .213

Predictor β t R2 Predictor β t R2

Step 1 .046 Step 1 .046 Age .080 .601 Age .063 .474 HpwDriven .128 .953 HpwDriven .150 1.138 PSQI .097 1.614 PSQI .104 1.658 CESD .083 1.520 CESD .108 1.680 Step 2 .004 Step 2 .007 TMTA .068 1.505 GPTA .087 .645

Continued on next page

Table 14. (Continued)

Predictor β t R2

Step 1 .046 Age .064 .483 HpwDriven .145 1.098 PSQI .094 1.591 CESD .110 1.677 Step 2 .003 GPTB .057 .420

Table 15. Predicting average speed deviation based on neuropsychological abilities (OLS Regression)

Predictor β t R2 Predictor β t R2

Step 1 .097 Step 1 .097 Age .240 _1.816 Age .006 .043 HpwDriven .102 .803 HpwDriven .201 1.207 PSQI .288 1.882 PSQI .208 1.112 CESD .220 1.436 CESD .008 2.060 Step 2 .015 Step 2 .001 TMTB .128 .974 LNS .027 2.201

Predictor β t R2 Predictor β t R2

Step 1 .097 Step 1 .097 Age .207 1.598 Age .193 1.502 HpwDriven .097 .742 HpwDriven .084 .655 PSQI .290 1.880 PSQI .303 1.971 CESD .214 1.372 CESD .194 1.259 Step 2 .000 Step 2 .014 TMTA .008 .063 GPTB .121 .926

Continued on next page

Table 15. (Continued)

Predictor β t R2

Step 1 .097 Age .215 1.662 HpwDriven .099 .772 PSQI .291 1.893 CESD .230 1.461 Step 2 .004 GPTB .068 .520

neuropsychological test performance was significantly associated with simulated driving.

The aforesaid regression models failed to explain significant variance in reaction time, average speed deviation, and average lane deviation (for more detailed statistical analyses, refer to Tables 13 through 15). Thus, consistent with hypothesis 5 (Table 2), scores on neuropsychological tests of psychomotor speed such as the TMTA, GPTA, and GPTB were not associated with driving performance. However, contrary to hypothesis4, there was no association between driving performance and neuropsychological measures of effortful attention such as the TMTB or the LNS.

umber of crashes. Unlike the other driving variables, the number of crashes variable, by definition, represents count data. Such data indicate the number of times a particular event or behavior, crashing your car in the driving simulation, occurred (Neal

& Simons, 2007). These data were thus constrained to nonnegative integer values.

Additionally, given the low base rate of this behavior in the present sample, these data

also exhibited substantial positive skew (see Figure 1). Neal and Simons (2007) warn

against applying traditional OLS regression models to such variables, because the OLS

model assumption of normally distributed, homoskesdastic residuals are likely violated

with count data. Instead, they recommend the use of generalized linear models (GzLM), because these models allow residuals to follow a number of distributions such as Poisson, binomial, and negative binomial distributions, in addition to the normal distribution.

In GzLMs, the shape and range of the DV distribution determines model

specification. For the number of crashes DV, the Poisson and negative binomial models

are natural choices since both the range (zero to infinity) as well as the shape (positive

25 20 15 Frequency 10 5 0 0 1 2 3 4 5 Number of Crashes

Figure 1. Histogram of the number of crashes variable. The distribution is comprised solely of nonnegative integer values, and exhibits substantial positive skew.

skew with most data at or near zero) of these distributions are consistent with non negative count data (Wu, 2005; Cameron & Trivedi, 1998). However, given that the mean and variance of the number of crashes data were fairly comparable (see Table 11), the Poisson model was deemed more appropriate; the mean and variance of the Poisson distribution are both equal to a single parameter, lambda (λ). Figure 2 provides examples of Poisson distributions with λ = 1.5, λ = 1.25, and λ = 1.75 alongside a spikeplot of the number of crashes DV. Visual inspection of these distributions reveals that they approximate the number of crashes data well.

.4 Lambda = 1.5 .4 .3 .3 .2 .2 Fraction Fraction .1 .1 0 0

0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 Number of Crashes poisson

Lambda = 1.75 .4 Lambda = 1.25 .3 .3 .2 Fraction .2 Fraction .1 .1 0 0

0 2 4 6 8 10 0 2 4 6 8 10 poisson poisson

Figure 2. The number of crashes variable and examples of Poisson distributions. Poisson distributions with three different parameters are presented here: Lambda (λ) = 1.5 (top right), λ = 1.25 (bottom l eft), and λ = 1.75 (bottom right). Visual inspection of these distributions reveals that they approximate the number of crashes data well.

Therefore, GzLMs with Poisson reference distributions were run to predict the number of

crashes from level of depressive symptoms and neuropsychological ability. In all

following models, age, number of hours spent driving per week, PSQI scores, CESD

scores, and the respective neuropsychological test scores were entered as predictors. In

order to adjust for the heteroskedasticity of residuals inherent in these models, deviance

residuals which normalize and scale the residuals were inspected instead. Normal

quantile plots as well as residualversusfitted plots revealed that the deviance residuals

were normal and homoskedastic. However, as Table 16 indicates, neither depressive

symptoms nor neuropsychological ability were significant predictors of number of

crashes. Likelihood ratio χ 2 tests for all GzLMs revealed that the full models were not significantly different from the reduced models with no predictors.

The impact of rumination on simulated driving

The impact of rumination on driving performance was assessed via OLS regression for the reaction time, average lane deviation, and average speed deviation variables, where as a GzLM with a Poisson reference distribution was used to predict the number of crashes variables. Age, familiarity with driving, CESD scores, and PSQI scores were included as predictors in addition to RSQ scores in all of following models.

Also, given the association between TMTB and RSQ scores in the present sample,

TMTB scores were included in these models to assess the unique effects of both variables. Normalquantile plots of the residuals (or deviance residuals in case of the model with number of crashes as the DV) as well as residualversusfitted plots were

examined, and revealed that all regression models met the assumptions of normally distributed errors with constant variance. Further, pairwise correlations between all predictors were examined to assess multicollinearity. Results revealed that RSQ scores were not significant in predicting driving performance (see Table 17 for details). Thus, contrary to hypothesis 6 (Table 2), trait rumination was unrelated to driving impairment.

Table 16. Predicting number of crashes based on neuropsychological abilities (GzLM Poisson Regression)

Predictor b Z p Predictor b z p Age .025 0.25 .805 Age .010 0.10 .920 HpwDriven .013 0.82 .412 HpwDriven .017 1.04 .301 PSQI .012 0.26 .792 PSQI .012 0.28 .780 CESD .012 0.92 .355 CESD .011 0.78 .435 TMTB .008 1.15 .249 LNS .026 1.15 .455

Predictor b Z p Predictor b z p Age .012 0.12 .903 Age .001 0.01 .994 HpwDriven .015 0.95 .342 HpwDriven .013 0.84 .402 PSQI .014 0.31 .753 PSQI .011 0.24 .810 CESD .012 0.84 .400 CESD .013 1.01 .313 TMTA .006 0.44 .657 GPTA .009 0.68 .494

Predictor b Z p Age .006 0.06 .955 HpwDriven .014 0.88 .382 PSQI .014 0.33 .745 CESD .013 0.90 .366 GPTB .001 0.05 .959

Table 17. Predicting driving performance based on RSQ scores

DV: Reaction Time (OLS Regression) DV: Avg. Lane Deviation (OLS Regression) Predictor B t p Predictor b t p Age .038 0.83 .410 Age .036 0.53 .598 HpwDriven .003 0.40 .694 HpwDriven .013 1.05 .299 PSQI .028 1.21 .232 PSQI .011 0.31 .757 CESD .002 0.21 .835 CESD .001 0.01 .990 RSQ .003 0.43 .670 RSQ .006 0.64 .522

DV: Avg. Speed Deviation (OLS Regression) DV: No. of crashes (Poisson Regression) Predictor b T P Predictor b z p Age .456 1.59 .118 Age .004 0.04 .968 HpwDriven .042 0.82 .417 HpwDriven .015 0.92 .355 PSQI .183 1.27 .209 PSQI .032 0.68 .499 CESD .107 1.91 .061 CESD .001 0.03 .974 RSQ .058 1.30 .199 RSQ .014 0.99 .324

ote. PSQI = Pittsburgh Sleep Quality Index; CESD = Center for Epidemiological Studies – Depression Scale; HpwDriven = No. of hours spent driving every week (selfreported); RSQ = Response Style Questionnaire

DISCUSSION

The present study sought to explore the relationship between depressive symptoms and neuropsychological abilities necessary for optimal driving such as psychomotor speed, as well as automatic and effortful attention. I hypothesized, in light of the evidence for depressionrelated deficits in effortful attention, that individuals with high levels of depressive symptoms would exhibit significant impairments in neuropsychological tests assessing effortful attention such as the TMTB and LNS, while showing little to no diminution in their psychomotor abilities as measured by the TMTA, the GPTA, and GPTB. Secondly, I proposed that these deficits in effortful attention would manifest as errors in a simulated driving task via two mechanisms: (a) an increased number of crashes or slowed reaction time due to an inability to efficiently switch between the varied attentional demands of the driving task during cognitively demanding driving conditions; and (b) an inability to monitor speed and lane position under comparatively easy driving conditions on account of distraction from the cognitively effortful task of rumination. Contrary to hypotheses, however, no association emerged between depressive symptoms and scores on the TMTB or the LNS, just as there was no relationship between rumination and performance on these neuropsychological tests of effortful attention. Finally, no relationship emerged between driving ability, neuropsychological functioning, and depressive symptoms. The methodological and statistical limitations of the study as well as the sample characteristics offer some explanations for these null findings.

Prevalence of depressive symptoms in the study sample

Since participants in the present study were relatively wellfunctioning college students, they reported limited psychological distress or dysfunction. For instance, few participants endorsed clinically remarkable levels of depressive symptoms. While psychiatric patients in the reference sample for the CESD scored a mean of 24.42

(Radloff, 1977), depression scores in the present sample, by contrast, were considerably low and positively skewed ( M = 14.55, Mdn = 12). Similarly, in comparison to the

entirely nonclinical sample in the present study, the depressed group which exhibited

driving impairments in the Bulmash et al. (2006) study comprised solely of clinically

depressed outpatients recruited from a neuropsychiatric clinic. As expected, this

depressed outpatient group displayed substantially higher levels of depressive symptoms

than the present sample: the Beck Depression Inventory (Beck et al., 1961) or BDI scores

of the depressed group ranged from 15 to 55, with a mean of 27.4. As Bulmash et al.

noted, the generalizability of their findings may be confined to samples with a “… similar

range and level of [depressive] symptom intensity” (p. 217). Thus, the lack of

association between depressive symptoms and neuropsychological and driving

impairments in the present study may represent further evidence that such deficits are a

function of depressive severity. A growing body of research suggests that the strength of

the relationship between depressive symptoms and neuropsychological impairments

increases with depressive severity, such that individuals with minor levels of depression

show little to no neuropsychological deficits (Airaksinen et al., 2004; Naismith et al.,

2003; Boone et al., 1995; Austin et al., 1992). Finally, the limited range of depressive

Table 18. europsychological test scores and agereferenced norms

Present Study Normative Data

Spreen et al. (1998) Trail Making Test Mean (SD) Mean (SD) Age Mean (SD) Age PartA 26.6 (8.9) 25.7 (8.8) 1519 27.4 (9.6) 2029 PartB 55.2 (12.5) 49.8 (15.2) 1519 58.7 (15.9) 2029

Letter Number Sequencing Test Tulsky et al. (2000) Mean (SD) Mean (SD) Age 11.6 (3.2) 10.8 (3.28) 1624

Grooved Pegboard Test Ryan et al. (1987) Ruff et al. (1993) Mean (SD) Mean (SD) Age Mean (SD) Age Dominant 70.2 (8.6) 69.7 (11.5) 2130 62.0 (7.8) 2534 Nondominant 75.4 (11.7) 74.5 (10.) 2130 67.0 (9.3) 2534

symptoms in the present sample may have undermined the study’s power to detect statistically significant relationships between depression and other variables.

Statistical Power

Just as the depressive symptoms variable suffered from range restriction, so too did several other pertinent study variables. For instance, scores on the RSQ ( M = 19.97,

SD = 7.42) in this sample were comparable to those typically achieved by nondepressed populations both in community (NolenHoeksema, 2000) as well as college settings (Just

& Alloy, 1997). Similarly, sample scores on most neuropsychological tests appeared to mirror agereferenced norms for healthy, neurologically unimpaired adults (see Table

18). Mean TMTA, TMTB, GPTA, GPTB, and LNS scores in the present sample were all within 1 standard deviation of the mean scores achieved by respective normative samples of comparable age (Tulsky & Zhu, 2000; Spreen & Strauss, 1998; Ruff &

Parker, 1993; Ryan, Morrow, Bromet, & Parkinson, 1987). These limitations in sample variability may have attenuated the true strength of the relationships examined in the present study, thus leading to statistically nonsignificant observed associations (Sackett,

Laczo, & Arvey, 2002; Sackett & Yang, 2000).

Another factor that may have diminished the statistical power of the present study involves sample size. Applied statisticians have offered several rulesofthumb to aid in deciding adequate sample sizes for multiple regression analyses, most of which prescribe some function of the casestopredictors ratio as the appropriate number (Harris, 1975;

Schmidt, 1971). However, Green (1991) noted that most of these rulesofthumb are not

informed by power analysis, and, hence, suggested his own ruleofthumb based on power analytic strategies outlined in Cohen (1988). As per Green, the sample size ( )

required to detect a medium effect size (f2 = .15; η 2 = .059) at 80% power and an alpha level (α) of 0.05 is governed by the formula: > 50 + 8 m, where m represents the number of predictors. Assuming that the effects examined in the present study are comparable in size to those (η 2 = .087) reported by Bulmash et al. (2006) in their study of depressionrelated driving impairments, Green’s guidelines suggest that adequate statistical power (.80) for the five to sixpredictor outlined in Tables 13 through 17 may only be achieved with sample sizes in the range from 90 to 98. Similarly, Gpower, a computerized poweranalysis program, stipulates a minimum sample size of 92 98 to achieve a medium effect size at 80% power for a model with five six predictors

(Erdfelder, Faul, & Buchner, 1996). Thus, the present sample size ( = 63) seems

insufficient to detect statistically significant results, especially since most regression

models fitted in the present study had an even lower on account of listwise exclusion of

cases with missing data.

Other limitations and future directions

Although the methodological sophistication of the present study forms the basis of its scientific value, the novelty of these techniques may have imposed some limitations on the findings. For instance, since the present study constitutes the first experimental application of KMADS, the study’s only measure of driving performance, little is known about its psychometric properties. Though KMADS was pilottested for several weeks

before the present study began, these tests were aimed primarily at assessing such characteristics as completion time, difficulty level, and userfriendliness. Therefore, a thorough investigation of the reliability and validity of this instrument is warranted before one can justify its use in drawing meaningful inferences about actual, onroad driving capability. For instance, a comparison of participants’ performance on the KMADS with their legal driving records (number of points applied etc.) may elucidate the extent to which KMADS approximates onroad driving. Though information on traffic infractions such as citations, violations, and accident involvements were available in the present study, these data were largely unusable for two reasons: first, participants self reported this information, and as such the veracity of these reports could not be verified; and second, only five of the participants in the present study endorsed such a history, thus, limiting the power of any statistical comparisons with KMADS scores.

Finally, the cognitive demands of the KMADS also require further investigation.

One implication of the low base rate of driver errors in the present study could be that the attentional demands of the driving task were not as challenging as anticipated. Secondly,

I hypothesized that participants with depressive symptoms may ruminate in conjunction with driving during less attentionally draining portions of the course. However, because no state measure of rumination was obtained during the driving task, this hypothesis may not be accurate. Conceivably, the driving task served as a pleasant distraction from negative mood and cognitions even among participants with depressive symptoms.

Future studies may therefore benefit from preceding the driving task with a negative mood or rumination inducing activity, because such a manipulation would help model the

mental state of drivers with depressive symptoms. For instance, an ecologically valid mood manipulation task would involve asking participants to think about a painful memory or event while listening to sad music. Individuals with a ruminative response style are known to unwittingly enhance negative mood by listening to sad music, and may do so while driving.

With careful attention to the methodological pitfalls outlined above, future

research in this area can prove highly fruitful in identifying the nature and extent of

driving hazards faced by individuals with depression. The steadfast commitment of

researchers such as Lana Trick and John Groeger to the scientific study of the complex,

cognitive processes involved in driving may soon provide answers to these questions

while raising awareness about the undeniable role of driverfactors in motor vehicle

accidents. Further, such studies may highlight the implications of depressionrelated

neuropsychological deficits not only for safe driving, but also for other equally important

domains such as the workplace safety of surgeons and machine operators.

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