ATTENTION/

MultiCAD

82 Areferred@ subjects aged 60-91 (26 of which were identified as probably being cognitively impaired to some degree). The drivers were referred to the DMV for reexamination due to a medical condition (by physician, optometrist, ophthalmologist), a series of licensing test failures, a flagrant driving error (police referral), or some other indicator of driving impairment.

This test used MultiCAD to measure drivers' ability to rapidly detect changes in the relative motion of their own versus other vehicles. A video of suburban driving scenes was used which presented a driver's eye view of travel along an arterial route with light traffic, following a lead vehicle (that the subject was told to pay attention to) at varying distances. Subjects were required to depress the "brake" assembly whenever the vehicle directly ahead in the same lane applied its brakes or at any other time it would be advisable to stop or slow down under actual driving conditions (e.g., an adjacent-lane driver encroaches into the lane of travel). The lead vehicle brake lights were illuminated when it slowed for 12 of the angular motion sensitivity trials. For 3 other angular motion sensitivity trials, the lead vehicle=s brake lights were disabled during filming of the video, so that the subject was required to detect the change in headway without the additional brake light cue. These 3 trials were intermingled with the trials in which the brake lights were illuminated.

A gross measure was also employed, which was a count of the number of times the word Aerror@ appeared on the printout of results. This measurement ignored any varying stimulus characteristics.

Multiple linear regressions were conducted to arrive at the best linear combination of variables for predicting performance on road tests; (see On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium), and comparisons were made between cognitively impaired and cognitively non-impaired referral drivers to determine whether there were differences in performance on nondriving tests and driving tests.

California DMV Field Office

$A gross measure of the number of errors made in the driving video significantly correlated with weighted error score on the road test (r=.3462, p< .002).

$Mean brake time (in response to a lead vehicle braking with or without brake lights activated) was not significantly correlated with weighted error score on the drive test.

$The correlation between proportion of errors on trials where brake lights were visible and weighted error score on the drive test was significant (r=.2801, p<.013).

$The correlation between proportion of errors on trials where brake lights were not visible and weighted error score on road test was not significant.

$Using gross errors on the driving video, cognitively impaired referral subjects made significantly more errors (average = 7.50) than did the cognitively unimpaired referrals (average = 3.36).

$Looking at the proportion of errors for trials where the brake lights activated, cognitively impaired referrals had a significantly higher error proportion (they did not brake in 47.3% of the trials) compared to cognitively unimpaired referral subjects (who did not brake in 21% of the trials)

$Response time did not discriminate between cognitively impaired referrals and cognitively unimpaired referrals, neither did proportion of errors when the brake lights did not activate (although there were only 3 trials for this last measure).

Staplin, Gish, Decina, Lococo, and McKnight (in press)

AARP Reaction Time Test

3,238 drivers ages 65+, who applied for renewal of North Carolina driver=s license

Eight NC driver=s license offices, representing a mix of urban and rural locations in the western, central, and eastern portions of the State.

Correlational coefficient with number of crashes = 0.046 (p<0.001). Annual crash involvements increased with increasing (poorer) cognitive scores.

Stutts, Stewart, and Martell (1996)

Auto-Trails

$ 69 Areferred@ subjects aged 60-91. The drivers were referred to the DMV for reexamination due to a medical condition (by physician, optometrist, ophthalmologist), a series of licensing test failures, a flagrant driving error (police referral), or some other indicator of driving impairment.

$ 31 paid Avolunteers@ aged 56-85, recruited through signs posted at study site or word of mouth.

A modified and automated version of Trails A of Reitan=s (1958) Trail Making Test, developed by Frank Schieber (Univ of SD). 14 numbers are presented on a computer monitor arranged randomly against the background of a traffic scene, as observed by the driver through the windshield of a car. The subject must touch the numbers (touch screen display) in numerical order as rapidly and accurately as possible. Timing is done by the computer. The score used was total time, as very few subjects made errors.

(see On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium).

California DMV Field Office

A multiple linear regression model using knowledge test score, Auto Trails time, Doron Cue Recognition 2 score, MultiCAD Static Contrast Sensitivity time with the high contrast 20/80 target, and MultiCAD Static Acuity time for correct responses at 20/80 accounted for 56.4% of the variance in performance on the road test (weighted road test error score).

Janke & Eberhard (1998)

(Parts A and B)

3,238 drivers ages 65+, who applied for renewal of North Carolina driver=s license

FINDINGS (Cont=d)

A second model substituting Trails A for Trails B was almost as strong, as was a third model using Traffic Sign time, driving frequency and age.

Eight NC driver=s license offices, representing a mix of urban and rural locations in the western, central, and eastern portions of the State.

Stutts, Stewart and Martell (1996, 1997)

(Parts A and B)

105 drivers licensed in Nebraska, aged 65-88 (mean age = 71.4). 54 were females (mean age = 70.5 years); 51 were males (mean age = 72.2 years). All subjects were volunteers, and were paid $25.00 for participating. 36 had taken a driver education course in the past 10 years.

The driving performance of the subjects was evaluated using the on-street driving performance measurement (DPM) technique developed by Vanosdall and Rudisill (1979). The subjects were evaluated by a driver education expert trained in the use of the DPM technique, while they drove in their own cars. The DPM route was a 19-km circuit designed to evaluate the subjects in the situations that are most often involved in the accidents of older drivers. Therefore, their performance was evaluated at 7 intersections where they were required to make left turns at 5 intersections and right turns at the other 2 intersections. Four of the left turns were made from left-turn lanes onto four-lane divided arterial streets in suburban areas, and one was made from a left turn lane onto a two-lane one-way street in an outlying business district.

business district and residential street networks

Tarawneh, McCoy, Bishu, and Ballard (1993)

(Part A)

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist. The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance. Other behaviors demonstrated by subjects who Afailed@ the in-car exam included coasting to a near stop in the midst of traffic, drifting into other lanes of traffic, stopping abruptly without cause, simultaneously pressing the brake and accelerator while driving, delay in changing lanes when an obstacle appeared, and failure to understand why other drivers signaled them in frustration or exaggeration.

The correlation between the pass/fail outcome on the road test and performance on Trails A was significant at the p<.02 level.

Hunt, Morris, Edwards, and Wilson (1993)

(Part B)

$ 47 normal/nondemented elderly (mean age 72.9)

$ 29 middle-aged/nondemented controls (mean age 40.6)

$ 45 cognitively impaired drivers (mean age 73.3)

$ 28 with mild dementia

$ 8 with moderate dementia

$ 9 with cognitive impaired but not meeting the criteria for dementia

Two operational level dependent measures were collected using the Computerized Driving Assessment Module (CDAM): simulator brake reaction time and simulator steering accuracy. The CDAM consists of an automobile seat, dashboard with speedometer, brake and gas pedals, steering wheel, computer monitor for display of instructions, and a double arc of light-emitting diodes (LEDs) set at eye-level subtending 190° of visual field which generate stimuli for steering tasks.

Strategical level measures were related to the accuracy of subjects' self appraisals and comprised the Cone Avoidance Task and a comparison between self-ratings and collateral ratings of driving problems. The cone avoidance task required a subject to maneuver a test vehicle through a course of traffic cones, hitting as few as possible.

Cognitive battery given at Clinic for Alzheimer=s Disease and Related Disorders (University Hospital, Vancouver B.C),

Cone Avoidance test conducted on off-road course.

There was no significant correlation between these two tests and performance on the motor vehicle branch test, or on stopping distance or cone avoidance.

Tallman, Tuokko, and Beattie (1993)

(Part B)

S=s were primarily recruited through local news media; some were referred by physicians to a driver evaluation service

S=s were administered the following measures:

$ Snellen visual acuity test

$ A continuous performance task (CPT) that used a single letter as a target

$ Simple reaction time

$ 15 items from Boston Naming Test

$ Trail Making Part B

$ Wechsler Memory Test (Mental control and Orientation subtests)

$ A recognition memory test

$ A word fluency test (F-A-S)

$ Embedded Figures Test

$ Visual Search Test

$ Motor Free Visual Perception Test

Dependent measures included the DMV official summary of driver records for each subject and a standard driving assessment that included paper and pencil tests and a behind-the-wheel examination.

Laboratory (Univ. Of Rochester School of Medicine and Dentristy)

1 of the 6 below-standard S=s had been involved in a road traffic accident in the past 3 years, and another had a conviction for failure to yield while turning left.

Cushman (1988)

(Part B)

8 S=s were referred from local mental disorder clinics or from local physicians because of possible dementia and associated driving problems.

9 S=s were community residents who did not have suspected dementia or driving problems.

An on-road driving assessment was performed with the subject driving with a certified driving examiner in a dual-brake vehicle. Simple maneuvers were first performed in a parking lot, then subjects joined the flow of traffic and traveled over a prescribed route in moderate to heavy traffic. Subjects were scored on the basis of errors or omissions that correspond to points on the State of New York road test exam; higher scores indicate poorer performance. Therefore a total score was used as well as a determination of whether the subject met or exceeded state standards ("pass") or failed to meet standards ("fail"). In addition, a pass/fail rating was given for the subjects' performance in steering control, braking, acceleration, judgment in traffic, observation skills, and turning skills (particularly left turning).

On-road driving evaluation: parking lot and in-traffic (moderate to heavy traffic situations)

Results of the driving exam indicated that eight subjects passed, eight failed (scored 19 or more errors on the on-road exam), and one could not complete the exam because of poor vision. The analyses conducted in this study compared the subjects who met the driving exam standards with the eight who did not. There was no significant difference in average age of subjects who passed the exam compared to those who failed. Drivers who failed drove significantly fewer miles, however. The below standard group took significantly longer to complete the Trail Making Part B test (mean = 266 s for S=s who failed road test; mean =117 s for S=s who passed road test, t(4.5)=3.21, p=0.027).

Of the 8 persons referred for possible dementia, 5 failed the road test, 2 passed the test, and 1 was unable to complete the evaluation.

Cushman (1992)

(a derivative Trails B procedure)

$ passenger vehicle drivers

$ fuel truck drivers

$ transit bus operators

$ long distance tractor trailer drivers

$ emergency response trainees

$ race car drivers

Can be administered one-on-one or in a group. Uses a (proprietary) windows-based scoring program to assess accident risk (high or low), and a narrative about the person=s strengths and weaknesses.

(3)MARTA -Metropolitan Atlanta Rapid Transit Authority

Results were interpreted to show that errors on WayPoint were directly related to (1) technical errors on a closed course (a high speed drive circuit), (2) directly related to line-of-travel errors, and (3) positively correlated with lap speed. In addition, technical errors were correlated with WayPoint Focus, a measure of a person=s ABig Picture.@

Crash frequency was obtained through self-report in some validation studies and through driver record files in other validation studies.

WayPoint

101 licensed drivers (39 females and 62 males) age 72-90 (mean age = 78.3) who were members of a preexisting study cohort engaged in longitudinal studies of a community-dwelling cohort of older people (at Buck Center for Research in Aging)

The scoring system determines (1) channel capacity or information-processing rate, defined as the average speed per exercise on the first administration over two of the exercises and (2) high vs low risk of preventable and non preventable collisions, reflecting the driver=s situational awareness.

Novato, Marin County California; Buck Center for Research in Aging

$Average time per exercise on the first administration of WayPoint was strongly related to road test weighted errors (r=.37) as was channel capacity (r=.35). These correlations were significant.

$A substantial but not significant correlation was found between overall average time per exercise on the second administration of WayPoint and weighted road test errors (r=.31).

$The difference in number of errors from WayPoint 1 to WayPoint 2 was not significant, but the difference in time (5.2 s less on WayPoint 2) was significant.

$Two of the 3 cognitively impaired subjects failed to improve their time scores from the first administration to the second.

$A multiple regression model using 98 subjects using age, average time per exercise on WayPoint 1, Perceptual Response Time (Part 1 of the UFOV), and average number of cognitive domains on the MMSE in which subjects made 1 error yielded a significant prediction of weighted error score on the drive test (Multiple R = .484, adjusted R² =0.202.

$Substituting channel capacity for WayPoint average time reduced the number of subjects to 92, and yielded a multiple R of .475 and adjusted R² of 0.190.

$Eliminating age and using Waypoint average time, MMSE error areas and PRT yielded a multiple R of .462, and adjusted R2 of 0.188. Substituting channel capacity for average WayPoint time yielded Multiple R of .451, adjusted R2 of 0.176.

Using only WayPoint 1 average time and PRT as predictors of weighted error score on the road test yielded multiple R = .428; adjusted R²=0.166.

Janke and Hersch (1997)

Digit Symbol Subscale of Wechsler Adult Intelligence Scale

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The Digit Symbol subscale of Wechsler Adult Intelligence Scale was given to participants prior to the on-road drive test. This symbol substitution task demands rapid switching of attention between different sources of information. In the WAIS booklet, four rows of blank squares are presented, with each square having above it a randomly assigned number form 1 to 9. At the top of the page is a Akey row@ that pairs each number, in order, with a different abstract symbol. Following practice trials, the subject must fill in each blank square with the symbol corresponding to its number. The subject is instructed to do this as quickly as possible. After 90 seconds, the test is terminated, and the subjects score is the number of squares filled in correctly. The Digit Symbol test taps visuomotor coordination, fine motor speed, speed of mental operation, visual short-term memory, and visual incidental learning.

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist. The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance. Other behaviors demonstrated by subjects who Afailed@ the in-car exam included coasting to a near stop in the midst of traffic, drifting into other lanes of traffic, stopping abruptly without cause, simultaneously pressing the brake and accelerator while driving, delay in changing lanes when an obstacle appeared, and failure to understand why other drivers signaled them in frustration or exaggeration.

The correlation between the pass/fail outcome on the road test and performance on the Digit Symbol Test was significant at the p<.007 level.

Hunt, Morris, Edwards, and Wilson (1993)

Washington University Attention Switching Task

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist. The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance. Other behaviors demonstrated by subjects who Afailed@ the in-car exam included coasting to a near stop in the midst of traffic, drifting into other lanes of traffic, stopping abruptly without cause, simultaneously pressing the brake and accelerator while driving, delay in changing lanes when an obstacle appeared, and failure to understand why other drivers signaled them in frustration or exaggeration.

All 5 CDR 1 subjects who failed the road test performed poorly on the attention switching task. The correlation between the pass/fail outcome on the road test and performance on the attention switching task was significant at the p<.0001 level.

Hunt, Morris, Edwards, and Wilson (1993)

visual field-integrity loss and attentional visual field-integrity loss

26-39, 40-51, 52-69, and 70+.

The perimeter looks like half a large globe 2.5 ft in diameter. The subject is seated looking into the globe, to view a small spot of red light at the far end of the globe. For the Standard Visual Field test, subject focuses eyes on red spot and releases a button each time a green light is flashed. Test spots (green lights) are presented 5 times at 8 different distances from the red focal light along each of 5 meridia (e.g., spokes of a wheel). The meridia stretched to the upper right (rear-view mirror location), the far left and the far right, and the lower left and lower right (where lane boundaries would be seen in one=s side vision). For the Attentional Visual Field test, the red fixation light blinked on and off, irregularly. In addition to pressing a button each time a green light appeared, the subject was required to count and remember how many times the red fixation-light blinked.

$ Pelli-Robson Low-Contrast Acuity Test (measures loss in low contrast acuity; ability to see objects and borders)

$ Smith-Kettlewell Low-Luminance Card (measures high-contrast near-acuity loss and low-contrast near-acuity loss)

$ Berkeley Glare Tester (measures low-contrast near acuity loss, and low-contrast near-acuity loss in the presence of glare)

$ Modified Synemen Perimeter (measures standard visual field-integrity loss and attentional visual field-integrity loss)

$ Visual Attention Analyzer (measures loss in UFOV, the area of the visual field in which useful information can be rapidly extracted from a complex visual display)

Drivers also completed a Driving Habits Survey measuring level of restriction (never, sometimes, often or always) for night driving, rain or fog, sunrise or sunset, driving alone, left turns, and heavy traffic.

Roseville

Standard field: S=s rated test as face valid (clear instructions, safety-related, and fair in requiring driver license applicants to pass similar sensory tests to get full driving privileges).

Attention field: clarity of instructions rated high, but safety-relatedness and fairness of requirement to pass were not rated as high as for sensory tests. Regression analyses showed that S=s who performed more poorly on attentional tests tended to rate them more negatively.

$For all age groups combined, test score was not significantly associated with total prior 3-year crash involvement when considered in isolation.

$Standard field integrity was excellent for all age groups, but, when an attentional task was added, the S=s age 70+ showed a marked deterioration. They also showed high variability in attentional field-integrity loss.

$Poor perf. on the standard field test showed small, but statistically significant predictive value for S=s aged 26-39 and 70+.

$After adjusting for gender, age, and exposure, the standard field test explained 1.9% of the variance for all age groups, and the attention field test explained 1.6% of the variance. Approx. 5% of the variation in reported level of self-restriction was explained by test performance or age (the worse the visual perf. or the older the driver, the more restriction). There was no significant association between vision score and avoiding heavy traffic or driving alone for standard field, or for heavy traffic for attentional field. 11.4% of the variation for age 70+ S=s was accounted for by the avoidance of left turns and low attention-field scores.

Hennessy (1995)

Visual Attention Analyzer (UFOV)

As of 12/8/95, 48 subjects ages 64-84 were screened. Results presented here are for these 48; however as of 10/11/96, 55 subjects were screened, and their results strengthen the trend.

In Progress Study: AEnhancing Mobility in the Older Driver through Improving the Useful Field of View.@

A model 2000 Visual Attention Analyzer was used to measure the detection, localization and identification of suprathreshold targets in complex displays. The size of a person=s UFOV is determined by manipulating three variables: target presentation duration, the competing attentional demands of the central and peripheral task, and the salience of the peripheral target. Three subtests provide a measure of the percentage reduction of a maximum 35 degree radius field. During the first subtest (measuring processing speed capability and vigilance), the test participant must identify a centrally located object which varies in duration, by pressing an icon of a truck or a car (whichever was presented) on the touch-screen display. The second subtest (measuring divided attention capabilities) requires the same identification, in addition to locating a simultaneously presented peripheral target of varying eccentricity. A third subtest (measuring selective attention capabilities) required the same two responses required for subtests 1 and 2 while the peripheral target is embedded in distractors. The composite measure of UFOV reduction is recorded as a percentage ranging from 0% to 90%, and the basis for the loss can be determined by considering the percentages of loss on the three subtests.

Bryn Mawr Rehabilitation Center (Pennsylvania)

Results for 9 drivers who received training (8-to-12-sessions) with the UFOV protocol showed improved UFOV scores, but only 2 drivers improved on-road performance.

Principal Investigator - Tom Kalina

Visual Attention Analyzer (UFOV)

18% had 4 or more crashes over the prior 5 year period

$Subjects received the following sensory tests:

$Subjects received comprehensive eye exam resulting in a primary diagnosis (cataract, age-related maculopathy, glaucoma, diabetic retinopathy)

$Mental status was assesses using the MOMSSE, with composite score ranging from 0-28. Lower scores = higher functioning. Scores greater than 9 = cognitive impairment.

$Visual Attention was measured with the Vision Attention Analyzer:

$AOn the road@ exposure was estimated using questionnaire data on number of days/week subjects drove and annual number of miles driven. Subjects were asked if anyone had ever suggested they limit or stop driving.

Dependent variable: Motor vehicle crash occurrence during the 3 years following clinic assessment, obtained from Alabama Department of Public Safety. Person-years to first crash was calculated from enrollment date; Person-miles of travel was calculated by multiplying person-years times reported annual mileage.

mology clinic

$56 S=s had at least 1 crash in the 3-year follow-up period, and 11 of these had 2 or more.

$Estimated annual crash rate was 7.4 per 100 person-years of driving and 7.1 per million person-miles of travel.

$Crash involvement in prior 5-year period was significantly associated with increased crash risk (Risk ratio = 2.0)

$S=s who reported that someone had suggested they limit or stop driving were no more likely to be involved in a crash.

$Impaired UFOV was the only visual processing variable associated with increased crash risk.

$Significant, independent associations with crash risk in 3-year follow-up were found only for:

$UFOV reduction of > 40%: RR=2.3; 95% CI = 1.27 - 4.29)

$Driving < 7 days/week: 48% decreased crash risk (95% CI = 0.27 - 1.01).

$Dx of diabetic retinopathy (5X greater risk, 95% CI = 1.13 - 21.8).

$Dx of glaucoma: (RR=5.20, 95% CI = 1.19-22.72). Relationship for glaucoma and crashes stronger for males (RR=9.81) than females (RR=5.14).

$Of UFOV component scores, speed of processing (subtest 1) and selective attention (subtest 3) were NOT associated with crash occurrence. Impairment in divided attention (subtest 2) was significantly associated with a 2.3 fold increased risk of crashing (95% CI = 1.24 - 4.38, p=0.01).

$For every 10 points of UFOV reduction, S=s had 16% increase in crash risk.

$Estimates are that 24% of older driver crashes are due to UFOV reduction >40%.

Owsley, Ball, McGwin, Sloane, Roenker, White, and Overley (1998).

Visual Attention Analyzer (UFOV)

$Subjects received the following sensory tests:

$Subjects received comprehensive eye exam resulting in a primary diagnosis (cataract, age-related maculopathy, glaucoma, diabetic retinopathy)

$Mental status was assesses using the MOMSSE, with composite score ranging from 0-28. Lower scores = higher functioning. Scores greater than 9 = cognitive impairment.

$Visual Attention was measured with the Vision Attention Analyzer (composite scores from 0 - 90% reduction).

$@on-the-road@ driving exposure was estimated, using subjects= responses to a questionnaire that asked how many days per week and how many miles per year they drove.

$Presence vs absence (self-reported) of common chronic medical conditions was determined through interview questions.

FINDINGS (Cont=d)

NOTE: although medication information was not collected in this study, Glynn et al. (1991) reported that the use of topical eye medications in elderly patients with glaucoma increased their risk of falling (an adverse mobility outcome).

University of Alabama, Birmingham

$No significant differences between cases and controls were found with respect to driving habits (exposure).

$The odds ratio for a case driver having one or more chronic diseases was 2.2 (95% CI=1.1 to 4.5).

$Case drivers were 2.1 times more likely to receive a score of greater than 9 on the MOMSSE as compared to control drivers.

$Univariate analyses showed that older drivers involved in injurious crashes were more likely to have impairments in stereoacuity (OR=2.2); visual field sensitivity (OR=2.6 for central and 2.4 for peripheral); and UFOV reductions (OR =5.3 for 23 to 40% reduction; 16.3 for 41 to 60 % reduction; and 22.0 for greater than 60% reduction).

$ Univariate analyses for common eye diseases in the elderly showed that of the 4 conditions considered, only glaucoma and macular degeneration had significantly elevated point estimates. Case drivers were 3.6 times more likely to report a diagnosis of glaucoma compared to controls; case drivers were 3.3 times more likely to have macular degeneration.

$ Only 2 variables were independently associated with crash risk in the multivariate analyses: UFOV and glaucoma.

$ UFOV reductions of 22.5-40%, 41-60%, and >60% were associated with 5.2, 16.5, and 21.1-fold increased risk of an injurious crash, respectively compared to those with reductions of <22.5%.

$Cases were 3.6 times more likely to report glaucoma than were controls.

Owsley, McGwin, Ball, K. (1998).

Visual Attention Analyzer (UFOV)

S=s were recruited from the larger sample of drivers participating in the larger study (Ball et al., 1993); those with poor visual acuity were excluded (since those w/ acuity worse than 20/50 uniformly fail the first subtest of the UFOV).

$ MOMSSE (total performance score)

$ Trail-Making Part A (time to complete)

$ Trail Making Part B (time to complete)

$ Wechsler Memory Scale - Visual Reproduction Subtest /WMS-VR (total raw score)

$Rey-Osterrieth Complex Figures Test (accuracy scores for copy trial and immediate recall trial)

$UFOV/Visual Attention Analyzer (composite % reduction; impaired/fail > 40% vs unimpaired/pass < 40%

FINDINGS (Cont=d)

$Each measure significantly associated with crash status; however, sensitivity & specificity less than that achieved with UFOV reduction score.

$Model statistically significant (p<.0001, OR = 33.92, 95% CI = 16.54, 69.50)

$Classification success = 85.4%, with sensitivity of 86.3% and specificity of 84.3%. (same ad MODEL 3 continuous UFOV score)

University of Alabama, Birmingham

$Model statistically significant (p<.01)

$MOMSSE score & Trails A time uniquely accounted for differences in outcome of crash status.

$Classification success = 58.6% overall, correctly identifying 57.3% of crashers (sensitivity) and 60.0% of non crashers (specificity).

$Model statistically significant (p<.001)

$UFOV added unique info., accounting for crash status.

$Classification success = 77.4% overall, with sensitivity of 76.6% and specificity of 78.3%.

$Model 2 significantly better than Model 1 (p<.001)

$Model statistically significant (p<.001).

$Classification success = 85.4%, with sensitivity of 86.3% and specificity of 84.3%.

$No significant difference between Model 3 and Model 2.

$Estimated probability of crashing w/ UFOV score of 20 = 22%; for UFOV score of 60 = 81%.

Goode, Ball, Sloane, Roenker, Roth, Myers, and Owsley (1998).

Visual Attention Analyzer (UFOV)

Subjects had valid AL licenses and drove at least 1,000 mi/yr

A model 2000 Visual Attention Analyzer was used to measure the detection, localization and identification of suprathreshold targets in complex displays. The size of a person=s UFOV is determined by manipulating three variables: target presentation duration, the competing attentional demands of the central and peripheral task, and the salience of the peripheral target. Three subtests provide a measure of the percentage reduction of a maximum 35 degree radius field.

Subjects also completed Driving Habits Questionnaire to obtain measures of self-reported accident frequency and a composite measure of driving avoidance.

University Vision Laboratory

$UFOV was related to measures of peripheral vision (central and peripheral sensitivity loss) and to night acuity.

$Only the mental status total score and UFOV were significantly related to state-reported accidents. Only the UFOV was related to traffic citations.

$Eye health alone was not linked to accidents, and visual function alone was not significantly related to accidents.

$There were significant zero-order correlations between UFOV and accident frequency (r=.36, p<0.004) and between mental status and accidents (r=0.34, p<.02).

$S=s who failed the UFOV had 4.2 times more accidents than those who passed. S=s with high MOMSSE composite mental scores experienced 3.5 times more accidents than those with scores less than 10. Together these variables predicted accident frequency, accounting for 20% of the variance R²=0.20, f(2,49) =6.01, p<0.005.

$For intersection accidents, S=s who failed the UFOV had 15.6 times more intersection accidents than S=s who passed. S=s with high MOMSSE scores had 6.3 times more intersection accidents. Together, these variables predicted 29% of the variance in intersection accidents, R=.54, F(2,49) =9.8, p<0.001.

$For intersection accidents, MOMSSE: 25 correct rejections, 11 false alarms, 5 underpredictions (for no accident S=s) and 9 misses and 3 hits for accident-involved S=s. UFOV: 26 correct rejections, 14 false alarms, 1 miss, 11, hits.

Owsley, Ball, Sloane, Roenker, and Bruni (1991)

Visual Attention Analyzer (UFOV)

55-59; 60-64; 65-69; 70-74; 75-79; 80-84; and 85+.

FINDINGS (Cont=d)

As a predictor, UFOV resulted in 142 hits, 18 misses, 25 false-positives, and 109 correct rejections. Of the 25 false-positives, 19 were S=s who reported avoiding driving in general, avoided driving alone, and/or avoided left turns, thus minimizing their driving exposure. Removing these people from the data set increases the correlation between UFOV and crash frequency from r=0.52 to r=0.62.

UFOV had high sensitivity (89%) and high specificity (81%); mental status had sensitivity and specificity values of 61% and 62%, respectively.

University Vision Laboratory

$LISREL modeling program was used to evaluate IV in terms of whether they directly influence a DV, or operate indirectly through other IV=s.

$Central and peripheral vision accounted for 30% of the UFOV variance. Mental status had a significant direct effect on UFOV and crash frequency, but its effect on crash frequency was indirect, because removal of its direct effect from the model only sightly reduced the crash frequency accounted for (28% to 27%). If UFOV is entirely removed from the model, the remaining visual variables jointly account for 5% of the crash frequency. Adding the mental status to the eye status variables accounts for 16% of the variance in crash frequency. Inclusion of UFOV maximizes the prediction of crash frequency.

Ball, Owsley, Sloane, Roenker, and Bruni (1993)

Visual Attention Analyzer (UFOV)

26-39, 40-51, 52-69, and 70+.

$ Pelli-Robson Low-Contrast Acuity Test (measures loss in low contrast acuity; ability to see objects and borders)

$ Smith-Kettlewell Low-Luminance Card (measures high-contrast near-acuity loss and low-contrast near-acuity loss)

$ Berkeley Glare Tester (measures low-contrast near acuity loss, and low-contrast near-acuity loss in the presence of glare)

$ Modified Synemen Perimeter (measures standard visual field-integrity loss and attentional visual field-integrity loss

$ Visual Attention Analyzer (measures loss in UFOV, the area of the visual field in which useful information can be rapidly extracted from a complex visual display)

FINDINGS (Cont=d)

$Of drivers age 52-69, less than 7% failed the UFOV test.

$Approximately 7% of the variation in reported level of self-restriction was explained by total UFOV and divided attention test performance (or age), and 5% for PRT (or age)--the worse the visual performance or the older the driver, the more restriction. UFOV subtests were the only measure associated with the avoidance of heavy traffic. Also avoided by 70+ age S=s with poor UFOV: total amount of driving, driving in rain and fog, avoiding parallel parking, driving alone, driving at sunrise or sunset, and making left turns.

Roseville

$S=s rated clarity of instructions high, but safety-relatedness and fairness of requirement to pass were rated lower than sensory tests. Regression analyses showed that S=s who performed more poorly on attentional tests tended to rate them more negatively.

$For all age groups combined, test score was not significantly associated with total prior 3-year crash involvement when considered in isolation.

$S=s aged 70+ showed high variability in visual divided attention ability and PRT. There was a very small percentage of drivers age 70+ with very good total UFOV.

$Test scores had small but significant predictive value (2.9%) for S=s age 70+.

$After adjusting for gender, age, and exposure, total UFOV scores explained 0.9% of the variance in crash involvement, PRT explained 0.9% and divided attention explained 0.9%.

$Association with crashes for S=s in the 70+ age group was even stronger, with total UFOV accounting for 4.1% of the variation in crashes, PRT accounting for 4.1% of the crashes, and divided attention accounting for 4.3% of the crashes in the oldest age group. UFOV not predictive of crashes in the 3 younger age groups.

$Of 285 S=s age 70+, 84 (29%) scored poorly. 36 of the 285 S=s had an accident, and of the 36, 13 (36%) scored poorly on the UFOV. Thus UFOV sensitivity = 36%, specificity=71%, positive predictive accuracy=15.5%. For citation occurrence, sensitivity=28%, specificity=70%, positive predictive accuracy=12%.

Hennessy (1995)

Visual Attention Analyzer (UFOV)

$ 26 percent of the sample were between 50-64,

$ 54 percent were between 65-74,

$ 20 percent were over 75.

Participants were active drivers who had (generally) been pre-screened for risk in the insurance underwriting process. Also, participants who came in for testing appeared confident in their driving abilities.

Insurance and motor vehicle department records provided information about the following variables: at-fault accidents, non-fault accidents, non-accident claims, violations and convictions, miles driven, age, gender and marital status.

Testing rooms in hotels in 15 cities throughout Connecticut, Florida, and Illinois

The low correlation was explained by the possibility that because participants were recruited through their insurance company (as opposed to being recruited through an eye clinic and offered a detailed eye exam, as were the subjects in the Ball et al. [1991] study), drivers who were less confident in their driving skills may have elected not to participate for fear that their insurance rates could be affected.

Brown, Greaney, Mitchel, and Lee (1993)

Visual Attention Analyzer (UFOV)

101 licensed drivers (39 females and 62 males) age 72-90 (mean age = 78.3) who were members of a preexisting study cohort engaged in longitudinal studies of a community-dwelling cohort of older people (at Buck Center for Research in Aging)

Concentration errors = subject unable to proceed to field office at end of test, or drove past the street on which the field office was located and did not recognize their error.

Novato, Marin County California; Buck Center for Research in Aging

The correlation between performance on Module 1 of the UFOV (here called APRT@) and road test performance was moderately high, but did not reach significance (r= 0.27).

PRT was (after WayPoint) the best predictor of weighted error score, with a unique contribution of R² of 3%, after adjustment for age.

Janke and Hersch (1997)

Visual Attention Analyzer (UFOV)

Recruitment is focused on older drivers over the age of 80 in the attempt to identify a higher percentage of drivers who qualify for the training protocol.

In Progress Study: AUFOV Training Intervention.@

Washington University

An additional 20 participants are scheduled to participate in 1996-1997.

Principal Investigator: Linda Hunt, O.T.

Visual Attention Analyzer (UFOV)

Older drivers in the Chicago area are recruited through GEICO insurance records, based on past driving record to ensure inclusion of individuals with a range of crash involvement.

In Progress Study: AUFOV Training Intervention.@

Data collection includes UFOV, visual acuity, contrast sensitivity, and visual sensitivity. Subjects are randomly assigned to the training or no training groups; crash involvement is tracked subsequent to training, along with mobility and continued driving histories.

Rehab Institute of Chicago

Data collection continues through 1996-1997.

Principal Investigator: Christie Rom, O.T.

Visual Attention Analyzer (UFOV)

During the 1st 2 years of study (1993-1995) 456 older drivers were screened for attentional difficulties with UFOV. 129 s=s with restriction of 35% or more in attentional field were identified, and 71 completed the training study. Individuals with < 30% UFOV reduction were recruited as control S=s (n=25)

Visual acuity and contrast sensitivity were assessed to ensure that poor visual attentional performance was not caused by poor visual function

S=s were divided into 1 of 2 training groups:

Participants were assessed on several visual, attentional, and driving tasks; then training proceeded, and S=s were re-assessed on the same measures. These included:

$UFOV

$Simple RT to simulated brake lights (Doron L-225 Driving Simulator)

$Complex RT to Doron simulator stimuli

$15-mi open road driving evaluation (1-mile warm up, plus 2 loops of a 7-mile urban/suburban route)

A dangerous maneuver composite was created from 17 high traffic roadways, consisting of 6 left unprotected turns, 9 entrances to high traffic road from stop sign, and 2 opportunities for inappropriate stopping in traffic to turn right.

Laboratory and on-road, in-traffic Evaluations

$UFOV scores significantly improved across testing sessions for only the UFOV-trained subjects (average = 24.44 point improvement).

$No significant differences were found across testing sessions for Simple Reaction Time.

$For Complex RT, only the UFOV-trained group significantly improved their scores (average improvement = 0.287 sec, or 23 feet)

$On the on-road driving evaluation, both the Simulator and UFOV-trained group improved their global ratings across test sessions; there was no change in the control groups= global rating.

$For turning (turning into the correct lane), stop position (positioning vehicle at stops in order to see clearly but not obstructing traffic flow) and signals (signaling 100-150 ft in advance of a turn) composites, only the simulator-trained group significantly improved from pre- to post-training test.

$No group by pre/post interactions were found for the other composites, but general improvement was found for all groups from pre to post test. This reflects comfort and familiarity on second drive through the route.

$For the dangerous maneuvers composite, only the UFOV-trained group demonstrated a significant reduction in the number of dangerous maneuvers from pre to post test.

$Simulator training was effective in some areas of specific instruction and demonstration; UFOV training did not transfer to driving skills that reflect the mechanical operation of the vehicle, but improved items that measured critical search and judgment abilities in visually cluttered and cognitively demanding situations.

Roenker, Cissell, and Ball (Submitted)

MultiCAD

82 Areferred@ subjects aged 60-91 (26 of which were identified as probably being cognitively impaired to some degree). The drivers were referred to the DMV for reexamination due to a medical condition (by physician, optometrist, ophthalmologist), a series of licensing test failures, a flagrant driving error (police referral), or some other indicator of driving impairment.

For threats intersecting from the periphery at approximately a 30-degree angle of eccentricity (1 trial), the measures of effectiveness were (1) mean reaction time for correct response to a pedestrian stepping of the curb and entering the driver=s path; and (2) percent error.

Multiple linear regressions were conducted to arrive at the best linear combination of variables for predicting performance on road tests (see On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium), and comparisons were made between cognitively impaired and cognitively non-impaired referral drivers to determine whether there were differences in performance on nondriving tests and driving tests.

California DMV Field Office

Response time to targets at 15 and 30 degrees did not discriminate between cognitively impaired referrals and cognitively unimpaired referrals.

Staplin, Gish, Decina, Lococo, and McKnight (in press)

Rules of the Road

105 drivers licensed in Nebraska, aged 65-88 (mean age = 71.4). 54 were females (mean age = 70.5 years); 51 were males (mean age = 72.2 years). All subjects were volunteers, and were paid $25.00 for participating. 36 had taken a driver education course in the past 10 years.

The driving performance of the subjects was evaluated using the on-street driving performance measurement (DPM) technique developed by Vanosdall and Rudisill (1979). The subjects were evaluated by a driver education expert trained in the use of the DPM technique, while they drove in their own cars. The DPM route was a 19-km circuit designed to evaluate the subjects in the situations that are most often involved in the accidents of older drivers. Therefore, their performance was evaluated at 7 intersections where they were required to make left turns at 5 intersections and right turns at the other 2 intersections. Four of the left turns were made from left-turn lanes onto four-lane divided arterial streets in suburban areas, and one was made from a left turn lane onto a two-lane one-way street in an outlying business district.

business district and residential street networks

Of interest is the finding that whether or not a subject had taken a driver education course within the past 10 years had a very small correlation with on-road driving performance. Most of the subjects who had taken the course had taken it more than 5 years ago; therefore, the findings are not applicable to older drivers who have taken the course in the past 5 years.

Tarawneh, McCoy, Bishu, and Ballard (1993).

Rules of the Road

$ 102 Areferred@ subjects aged 60-91 (34 of which were identified as probably being cognitively impaired to some degree). 47% of the noncognitively impaired referred drivers had visual impairment noted on their record, and 24% of the cognitively impaired had a visual disability noted). The drivers were referred to the DMV for reexamination due to a medical condition (by physician, optometrist, ophthalmologist), a series of licensing test failures, a flagrant driving error (police referral), or some other indicator of driving impairment.

$ 33 paid Avolunteers@ aged 56-85, recruited through signs posted at study site or word of mouth.

12-item multiple-choice written test with 4 alternatives per item. Items selected from DMV=s standard renewal knowledge test.

(See On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium).

California DMV Field Office

Although the cognitively impaired group had more knowledge test errors (average = 3.76) than the cognitively unimpaired group (average = 2.14), the difference was not significant.

Janke & Eberhard (1998)

Rules of the Road

8 S=s were referred from local mental disorder clinics or from local physicians because of possible dementia and associated driving problems.

9 S=s were community residents who did not have suspected dementia or driving problems.

An on-road driving assessment was performed with the subject driving with a certified driving examiner in a dual-brake vehicle. Simple maneuvers were first performed in a parking lot, then subjects joined the flow of traffic and traveled over a prescribed route in moderate to heavy traffic. Subjects were scored on the basis of errors or omissions that correspond to points on the State of New York road test exam; higher scores indicate poorer performance. Therefore a total score was used as well as a determination of whether the subject met or exceeded state standards ("pass") or failed to meet standards ("fail"). In addition, a pass/fail rating was given for the subjects' performance in steering control, braking, acceleration, judgment in traffic, observation skills, and turning skills (particularly left turning).

On-road driving evaluation: parking lot and in-traffic (moderate to heavy traffic situations)

Of the 8 persons referred for possible dementia, 5 failed the road test, 2 passed the test, and 1 was unable to complete the evaluation.

Cushman (1992)

Traffic Sign Recognition

$ 102 Areferred@ subjects aged 60-91 (34 of which were identified as probably being cognitively impaired to some degree). 47% of the noncognitively impaired referred drivers had visual impairment noted on their record, and 24% of the cognitively impaired had a visual disability noted). The drivers were referred to the DMV for reexamination due to a medical condition (by physician, optometrist, ophthalmologist), a series of licensing test failures, a flagrant driving error (police referral), or some other indicator of driving impairment.

$ 33 paid Avolunteers@ aged 56-85, recruited through signs posted at study site or word of mouth.

Two-part written traffic-sign test. Part 1 presented pictures of traffic signs and asked whether it meant that the driver should perform a certain action (e.g., Awatch for hazards@). Part 2 presented several traffic sign shapes embedded in complex abstract drawings, and subject were to indicate the number of sign shapes of a particular type hidden in the drawing.

(See On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium).

California DMV Field Office

Traffic sign error score did not discriminate between cognitively impaired referral subjects (average error score = 8.67) and cognitively unimpaired referral subjects (average error score = 7.96)

Janke & Eberhard (1998)

Traffic Sign Recognition

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist (Principal Investigator). The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

$Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance.

$Traffic sign interpretation during the road test was scored as Aunsafe@ when the subject required verbal cues or physical assistance to comply with the sign=s intent. Three prohibitive signs (no right turn, no left turn, no u turn) were tested. Ability to interpret an Aactive@ sign (merging traffic) correlated significantly (p<0.01) with three Aprohibitive@ signs as follows: no right turn = 0.553; no left turn = 0.402; no u turn = 0.621.

$All 5 CDR 1 subjects who failed the road test performed poorly on the pre-driving traffic sign recognition test.

$The correlation between the pass/fail outcome on the road test and performance on the Traffic Sign Recognition test was significant at the p<.0002 level.

$The authors noted that visual form detection may be impaired in mild senile dementia of the Alzheimer type (SDAT), while visual acuity remains intact; this may contribute to the difficulty some subjects experienced with sign recognition, since the signs were symbols (form) rather than letters (acuity). There was no association between acuity and driving performance in this study.

Hunt, Morris, Edwards, and Wilson (1993)

Traffic Sign Recognition

3,238 drivers ages 65+, who applied for renewal of North Carolina driver=s license

Eight NC driver=s license offices, representing a mix of urban and rural locations in the western, central, and eastern portions of the State.

Correlational coefficient with number of crashes = 0.05 (p<0.001). Annual crash involvements increased with increasing (poorer) cognitive scores.

Stutts, Stewart, and Martell (1996)

Traffic Sign Recognition

101 licensed drivers (39 females and 62 males) age 72-90 (mean age = 78.3) who were members of a preexisting study cohort engaged in longitudinal studies of a community-dwelling cohort of older people (at Buck Center for Research in Aging)

Paper-and-pencil test consisting of 12 factually oriented questions requiring a subject to check an alternative corresponding to the meaning of each pictured sign, and one judgmentally oriented question, where an intersection displays a Ano left turn@ and two Ado not enter@ signs on the through path, and the subject must check the alternative corresponding to what they could do (turn right).

Concentration errors = subject unable to proceed to field office at end of test, or drove past the street on which the field office was located and did not recognize their error.

Novato, Marin County California; Buck Center for Research in Aging

The correlation between traffic sign errors and weighted error score on the drive test was not significant (r=0.07)

Janke and Hersch (1997)

Logical Memory Subscale of Wechsler Memory Scale

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist. The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance. Other behaviors demonstrated by subjects who Afailed@ the in-car exam included coasting to a near stop in the midst of traffic, drifting into other lanes of traffic, stopping abruptly without cause, simultaneously pressing the brake and accelerator while driving, delay in changing lanes when an obstacle appeared, and failure to understand why other drivers signaled them in frustration or exaggeration.

The correlation between the pass/fail outcome on the road test and performance on the Logical Memory test was significant at the p<.0009 level.

Hunt, Morris, Edwards, and Wilson (1993)

Boston Naming Test

$ Healthy elderly controls (n=13); mean age = 73.5; CDR score =0

$ Subjects with very mild dementia (n=12) ; mean age = 72.5; CDR score = 0.5

$ Subjects with mild dementia (n=13); mean age = 73.4; CDR score = 1.0

Dementia severity measured w/ Washington University=s Clinical Dementia Rating

The in-vehicle, on-road driving ability of participants was scored independently by a driving instructor (blind to study design and dementia status of the subjects), and an unblinded occupational therapist. The vehicle was a standard model car w/ automatic transmission and equipped with dual brake pedals. Each subject drove for 1 hour on a pre-designed route using urban streets and highways, that included common driving situations (stop signs, traffic signals, left turns at intersections, entering and exiting an interstate highway, changing lanes, merging, diagonal and parallel parking). Subjects drove in low volume conditions. A gestalt Apass/fail@ rating was given by each observer in the vehicle.

Washington University Alzheimer=s Disease Research Center.

Five subjects--all in the CDR 1 stage--@failed@ the in-car on-road test. There was 100% agreement between the driving instructor and principal investigator in their pass/fail ratings for all 38 drivers. The ability to follow the driving instructor=s directions, the demonstration of appropriate decision-making (>judgment=) in traffic, and interpretation of traffic signs were highly correlated with overall driving performance. Other behaviors demonstrated by subjects who Afailed@ the in-car exam included coasting to a near stop in the midst of traffic, drifting into other lanes of traffic, stopping abruptly without cause, simultaneously pressing the brake and accelerator while driving, delay in changing lanes when an obstacle appeared, and failure to understand why other drivers signaled them in frustration or exaggeration.

The correlation between the pass/fail outcome on the road test and performance on the Boston Naming Test was significant at the p<.003 level. The correlation between driving performance and word fluency was not significant. Performance on the aphasia battery correlated significantly with driving performance (p<.0001). The authors note that road test performance depended, in part, on the ability to follow verbal commands. Language impairment in SDAT may interfere w/ the ability to understand commands or advice from other passengers, rendering copilots ineffective in ensuring or extending driving competency in demented drivers.

Hunt, Morris, Edwards, and Wilson (1993)

Mattis Organic Mental Status Syndrome Examination (MOMSSE)

Subjects had valid AL licenses and drove at least 1,000 mi/yr

$ -General fund of information (e.g., How many weeks are in a year?)

$ Verbal Abstraction (e.g., How are a poem and statue alike?)

$ Attention (forward and backward digit span)

$ Memory (orientation, verbal memory, reproduction of design from memory)

$ Language (e.g., test for objects, body parts, double and triple commands, reading silently and aloud)

$ Construction (draw a clock, cube copying)

Accident information was obtained on all subjects from the Alabama Department of Public Safety. Data obtained for each subject included total number of accidents in the last five years and the total number of convictions for violations of traffic laws.

University of Alabama at Birmingham

$ 34 s=s were predicted to have no accidents. 25 S=s had no accidents on record, but 9 S=s did

$ 19 S=s were predicted to have 1 or more accidents. 11 had no accidents; of the 8 who had accidents, 5 had fewer than predicted by MOMSSE.

Owsley, Ball, Sloane, Roenker, and Bruni (1991)

Mattis Organic Mental Status Syndrome Examination (MOMSSE)

Age Groups: 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+

UAB Laboratory

Mental status had sensitivity (.61) and specificity (.62) values that were Amarkedly@ less than those for UFOV (.89) and (.81), respectively.

Ball, Owsley, Sloane, Roenker, and Bruni (1993)

Mini-Mental State Evaluation (MMSE)

105 drivers licensed in Nebraska, aged 65-88 (mean age = 71.4). 54 were females (mean age = 70.5 years); 51 were males (mean age = 72.2 years). All subjects were volunteers, and were paid $25.00 for participating. 36 had taken a driver education course in the past 10 years.

business district and residential street networks

MMSE showed a significant correlation to performance on the driving task (correlation = 0.24, p<0.01).

Tarawneh, McCoy, Bishu, and Ballard (1993).

(brief form)

half were applicants (community group) to the state-home-based long-term care program; half were nursing home residents

The brief MMSE included: (1) orientation to time, (2) orientation to place, (3) memorizing and repeating three nonrelated items (house, bus, dog), and (4) spelling Aworld@ backward.

Community-care setting