Table of Contents
Next Section


1. Self-Reports

(a) Driving Habits Survey
(b) Panel Data Set (medical & functional limitations, demographics)

2. Number of Observed Problems

3. Test Batteries

(a) Automated Psychophysical Test (APT)
(b) Johns Hopkins University
(c) Salisbury Eye Evaluation
(d) University of Alabama at Birmingham (UAB)
(e) University of Helsinki
(f) University of Iowa
(g) Yale University

4. Literature Review- Medical Conditions


Self Reports:

Driving Habits Survey

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

Brief survey included:

$ frequency of making trips (almost never, < once/week, 1-2 days/week, 3-6 days/week, every day);

$ average number of miles driven daily (none, <5, 5-9, 10-19, 20-49, 50+);

$ average miles driven weekly (<10, 10-19, 20-29, 30-49, 50-99, 100+);

$ average miles driven yearly (less than 1,000; 1,000-2,999; 3,000-4,999; 5,000-9,999; 10,000-14,999; 15,000-19,999; 20,000-24,999; 25,000+);

$ avoidance of driving under certain conditions (dark, heavy traffic, etc.);

$ involvement in any police-reported crashes;

$ medical conditions and medications that might impact driving.

Dependent variable: involvement in a police-reported motor vehicle crash during the three-year period immediately preceding license renewal.

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 coefficients for average annual crashes and:

trip frequency = 0.052 (p<0.003)

daily miles = 0.051 (p<.004)

weekly miles = 0.029 (p<.10)

yearly miles = 0.049 (p<.01)

There is a trend of higher crash rates for higher categories of exposure or mileage.

Note: all of the cognitive tests were significantly correlated with each other. Trails A & B and AARP Reaction Time test had highest correlations with one another, with r-values ranging from .60 to .73. Correlations of Short Blessed test with these 3 measures ranged from .44 to .46. The NC Traffic Sign Recognition test had the lowest correlations with the other measures.

Stutts, Stewart, and Martell (1996)


Self Reports:

Driving Habits Survey

121 licensed drivers forming groups composed of :

$ 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

This interview consisted of 5 multi-item questions (56 items total) eliciting information about driving problems the subject might be experiencing. The questions were concerned with driving faults, changes in driving ability, level of concern about deficits with driving skill and potential driving mishaps, difficulties with driving maneuvers, and the extent to which various factors interfered with driving.

Subject=s self appraisals were compared to collateral ratings of driving problems and to performance on a cone avoidance task.

The cone avoidance task required a subject to maneuver a test vehicle through a course of traffic cones, hitting as few as possible. On each of three trials, the participant was asked to estimate the number of cones he/she expected to hit. The difference between predicted and actual hits provides an index of subjects' abilities to estimate the difficulty of the task while taking account of their score on the previous trials. A negative score where actual hits are greater than predicted hits, suggests that the driver is overconfident and can not adequately assess task difficulty in relation to own skill level.

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

CDAM testing performed at a local Rehab Center

MVB Road test conducted by license examiners on a class 5 course

Cone Avoidance test conducted on off-road course

Results from the Driving Interview revealed that both elderly groups drove fewer miles and more often avoided demanding driving situations than the mid-age drivers. The demented elderly drove fewer miles than the normal elderly, however they did not limit their exposure to high-risk driving situations such as driving after dark and in rush hour as did the normal elderly. The demented elderly claimed that their exposure was higher than that estimated by their collaterals.

On both measures examining the accuracy of subjects' self-appraisals, the demented group was overconfident about their performance abilities. The demented subjects hit more cones than predicted in the Cone Avoidance Task and also reported significantly lower levels of driving performance problems than their collaterals did. There was no difference in self and collateral driving appraisals for either mid-age controls or the normal elderly.

Tallman, Tuokko, and Beattie (1993)


Self Reports:

Driving Habits Survey

17 subjects (age 57-97; mean age = 75); 6 females and 11 males.

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.

A questionnaire, developed specifically for this study, that included specific questions about: driving frequency, problem areas in driving (e.g., braking, turning), ratings of driving ability compared to other drivers, and restrictions in driving.

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).

Clinical tests: University Laboratory

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

The group that failed the road exam drove significantly fewer miles (mean = 2,313) than those who passed the road exam (mean = 6,188).

There were no significant differences in terms of how drivers who failed the on-exam and drivers who passed the on-exam described their driving skills; 61% of the drivers who failed the driving exam saw themselves as either Aa little better@ or Amuch better@ than their peers in various domains of driving skill (passing vehicles, entering expressway, parking, backing up, turning, steering, dealing with heavy traffic, braking) compared to 69% of the drivers who passed the exam, who also saw themselves as either Aa little better@ or Amuch better@ than their peers.

Drivers who passed the on-road exam and drivers who failed the on-road exam took similar numbers of medications on average, however, the 6 subjects who took some form of psychoactive medication (antidepressant, antimanic, antipsychotic, anxiolytic, or sedative/hypnotic agents) were all in the group who failed the on-road driving exam

Cushman (1992)


Self Reports:

Driving Habits Survey

$ 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.

9-question situational avoidance measure derived from Hennessy (1995), which asked subjects whether they avoided certain driving situations. Answers ranged from Anever@ to Aalways@ of 4-choice scale; avoidance measure took into account both number of situations avoided and strength of avoidance reported.

Three tiers of analyses were conducted in this research: (1) logistic regressions to determine what combination of tests, observations, or survey variables, with what weightings, would best predict whether a subject was a volunteer or referral; (2) multiple linear regressions were conducted to arrive at the best linear combination of variables for predicting performance on road tests; and (3) 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.

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

California DMV Field Office

Strength of avoidance was significantly correlated with group (Volunteer vs. Referral), r=.368.

Average score for referrals = 18.99

Average score for volunteers = 14.03

Strength of avoidance was also significantly correlated with age (r=.423).

Avoidance score correlated significantly with weighted error score on drive test (r=.4394, p<.000) for combined referral and volunteers, as well as for the referral group only (r=.3573, p<.001)

Amount of avoidance did not discriminate between cognitively impaired referral subjects and cognitively unimpaired referral subjects.

Janke & Eberhard (1998)


Self Reports:

Driving Habits Survey

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)

9-question situational avoidance measure derived from Hennessy (1995), which asked subjects whether they avoided certain driving situations. Answers ranged from Anever@ to Aalways@ of 4-choice scale; avoidance measure took into account both number of situations avoided and strength of avoidance reported.

An on-road driving exam was given by the project driving instructor (owner/operator of a driving school in San Francisco) based on the California Driving Performance Evaluation (DPE), and using the same scoresheet as used for the MDPE given in San Jose by these researchers. (See On-road Performance Measures of Driving Safety: California MDPE at the end of this Compendium). A weighted error score was calculated as total # of unweighted errors, plus twice the sum of critical and hazardous errors. Concentration errors were also noted.

Critical errors = errors which would in normal circumstances cause test termination (turning from improper lane, dangerous maneuver, examiner intervention needed).

Hazardous errors = dangerous maneuver or examiner intervention.

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

Avoidance of specific driving situations was not significantly correlated with weighted error score on the drive test (r=.18)

Janke and Hersch (1997)


Self Reports:

Driving Habits Survey

257 community dwelling, active drivers, ages 56-90.

137 males, 120 females

Mean age = 70 years

Objectives: (1) to examine self-reported driving avoidance in a cohort of drivers with objectively established visual and cognitive functional capabilities; (2) to examine the interrelationships among functional impairment, avoidance, and crash risk.

The Driving Habits Questionnaire (DHQ) asked questions about driving exposure and the avoidance of potentially challenging driving situations. The driving exposure question was, AHow many days/week do you drive?@ The avoidance questions were: (1) Ado you avoid driving at night?@; (2) Ado you avoid high-traffic roads?@; (3) do you avoid rush-hour traffic?@; (4) Ado you avoid high speed interstates/expressways?@; (6) Ado you avoid left-hand turns across traffic?@; and (7) Ado you avoid driving in the rain?@ Responses covered a range of 5 options from 1=never to 5=always.

At-fault crash involvement for the previous 5-year period was compiled from records obtained from the Alabama Department of Public Safety.

Visual and cognitive performance were also measured as follows:

Visual Acuity - ETDRS chart

Contrast Sensitivity - Pelli-Robson chart

Visual Field Sensitivity - Humphrey Field Analyzer

Useful Field of View - Vision Attention Analyzer

Cognitive Function - MOMSSE



$Avoidance of driving in rain: Group 1 signif less avoidance than each other group; Group 2 signif less than Group 5; Group 3 signif less than Group 5.

$Subjects with higher number of crashes in prior 5 years reported more avoidance of driving in rain (r=0.20, p=.002), making left turns (r=0.18, p=.004), and driving during rush hour (r=0.15, p=.018).

$Avoidance and at-fault crashes in subsequent 3 years could not be evaluated due to driving cessation or death in 52 subjects, most of whom were functionally impaired.

University of Alabama, Birmingham

$Older drivers who were more visually and/or cognitively impaired tend to report more avoidance and less exposure.

$Relationships between mental status and the avoidance items were weaker than those between visual function and avoidance, with the exception of mental status, which showed the strongest relationship with driving alone compared to all other functional measures.

$5 functionally groups were defined based on the number of vision problems and UFOV score:

Group 1 (unimpaired) = 0 vision problems & unimpaired UFOV

Group 2 = 1-2 vision problems; unimpaired UFOV

Group 3 = 0 vision problems; impaired UFOV

Group 4= 1-2 vision problems; impaired UFOV

Group 5 (most impaired) = 3-4 vision problems and impaired UFOV (> 40)

$All groups reported a similar level of avoidance of night driving.

$Avoidance of heavy traffic: Group 1 (not impaired) reported signif less avoidance than Groups 4 and 5 (most impaired); Group 3 significantly less than Groups 4 & 5

$Avoiding rush hour: Group 5 reported signif more avoidance than all other Groups

$Avoiding high speed roads: Groups 4 and 5 signif more avoidance than Group 1.

$Avoiding driving alone: Groups 4 and 5 signif more avoidance than Group 1.

$Avoidance of left turns: differed signif across all 5 groups

Ball, Owsley, Stalvey, Roenker, Sloane, and Graves (1998).


Self Reports:

Panel Data Set

(medical & functional limitations, demographics)

Remaining eligible drivers in 1993 (507 female drivers and 375 male drivers) who participated in the Iowa 65+ Rural Health Study from 1981-1993. The study included all noninstitutionalized individuals in two counties age 65+.

[The resulting sample was 6,553 female person-years and 5,414 male person-years]

Survey data from in-home and telephone interviews included demographic attributes, onset of medical conditions, symptoms and ailments, functional status, physical functioning, physical activities, vision, drug use, cognitive abilities, and annual miles driven. The survey data were linked to crash files maintained by the Iowa DMV.



Single model for both genders:

-annual miles driven

-living alone

-experiencing back pain

study limitations that may inhibit generalization to other populations: (1) study area included 2 rural counties (so, effects of other factors such as traf.mix, geom. design, travel speed found in urban areas unknown); (2) residents were affluent (effects impacts of income and employment status).

Data analysis conducted at Oak Ridge National Laboratory

Risk factors that determine the probability of an older female being involved in a crash:

-increasing annual mileage increases the odds ratio (of a crash): from 6,000 to 12,000 mi/yr the increase is 1.5x; from 6,000 to 18,000 the increase is 2.3x; from 6,000 to 24,000 the increase is 3.4 x)

-older females who have difficulty extending their arms above their shoulders have an increased probability of being involved in a crash (e.g., an older female with difficulty extending arms over shoulder is more than twice as likely to be crash involved than another female with no difficulty, given that both drive 6,000 mi/yr).

-living alone
-persistent back pain
-difficulty seeing friend across street

Risk factors that determine the probability of an older male being involved in a crash:

-annual miles driven
-living alone
-being employed
-having a history of glaucoma
-having impaired cognitive ability (low score on word recall test)
-persistent back pain
-using anti-depression drugs (doubles the crash probability and is the single most influential risk factor other than the amount of driving)

Hu, Trumble, Foley, Eberhard, and Wallace (1998)


Number of Observed Problems

$ 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.

The nondriving test administrator noted Aproblems@ or disabilities manifested by a subject during the testing process. Observable problems included: tremors, stiffness, difficulty in understanding test instructions, and impaired balance.

Three tiers of analyses were conducted in this research: (1) logistic regressions to determine what combination of tests, observations, or survey variables, with what weightings, would best predict whether a subject was a volunteer or referral; (2) multiple linear regressions were conducted to arrive at the best linear combination of variables for predicting performance on road tests; and (3) 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.

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

California DMV Field Office

Referral group performed significantly worse than the volunteer group (Average no. Observed problems for referrals = 0.41, for volunteers = 0.00).

The only subjects noted to have observable problems were in the referral group; although the observer could have been biased as she was not blinded to group, she was aware of potential bias and tried to guard against it.

(Note: this variable was not significantly related to age)

A model using number of observed problems plus Pelli-Robson errors with a cut-point of p=.80 of being a referral, gave specificity of 97 percent (32 of 33 volunteers classified correctly) with sensitivity of 71.4 percent (70 of 98 referrals in the model correctly classified).

The cognitively impaired group had significantly more observed errors (average = 0.85) than the cognitively nonimpaired group (average = 0.19)

The correlation between number of observable problems and weighted error score on the road test was significant when referrals and volunteers were combined (r=.3944, p<.000) and for the referral group only (r=.3185, p<.001)

Janke & Eberhard (1998)


Test Batteries:

Automated Psychophysical Test (APT)

360 drivers age 62+, currently licensed and driving, divided into 2 groups:

249 drivers referred to licensing agencies for reexam (Aincident-involved@), by police, family, courts, physicians, & licensing personnel. Mean age = 80.6. 55.9% of the group was male. Subjects with physical problems such as stroke, severe arthritis, or loss of consciousness were excluded.

111 drivers not previously referred for reexamination, obtained by solicitations through senior citizens groups (Aincident-free@). Subjects were paid $ 50.00 for taking the test. Mean age = 75.2. 60.3% of the group was male.

Objective: To examine the relationships between age-related psychophysical deficiencies of drivers and deficient driving performance. Independent Variables: 22 measures were assessed using a PC (battery takes 30-60 minutes):


Static Visual Acuity

.30 .18
Low Contrast Acuity .23 .18
Dynamic Visual Acuity .24 .21
Range of Attention (UFOV)    
Simple Response .26  
Choice Response    
Simple Image .34 .31
Complex Image .34 .24
Selective Attention .30 .29
Divided Attention .15 .36/.36 (dist)
Perceptual Speed .28 .24
Motion Detection .24 .36
Field Dependence .12 .24
Information Processing    
Digit Matching .18 .42
Figure Matching .25 .33
Missing Pattern NS .39  
Short Term Memory    
Digit Matching .34 .30
Figure Matching NS .22
Delayed Short Term Memory    
Digit Matching .30 .33
Simple Reaction Time    
Abstract Image .28  
Meaningful Image .30  
Choice Reaction Time    
Abstract Image .35 .24
Meaningful Image .31 .39

Visual Tracking .


33 (dist)


Dependent Variable: presence or absence of deficiency in driving performance, operationalized as observed incidents of deficient driving resulting in referrals to State licensing authority for reexamination.

Pontiac, MI

Phoenix and Tucson, AZ

$APT performance generally scored in terms of time (mean time on individual exercise for correct responses) and error (proportion of responses that were incorrect). For visual acuity measure, correctness measure was level of acuity. For visual tracking measure, correctness was distance error averaged across exercises.

$All correlations between APT performance (both time and error) and observed driving deficiency (presence vs absence) were significant (p<.01, 2-tail), and positive (longer time and more errors were related to presence of driving deficiency). See correlations in procedures section.

$The cognitive abilities error measures were most highly correlated with driving performance. The 3 information processing measures and the measure of delayed short-term memory showed fairly strong correlations between accuracy and safe driving.

$Attentional and perceptual measures (speed and accuracy) showed small to moderate correlations with driving perf. Signif. but low correlations found between driving perf. and static, dynamic, & low contrast visual acuity accuracy scores. Both speed and accuracy of psychomotor abilities showed small to moderate correlations w/ driving perf.

$Analysis of total score distributions for incident-involved and incident-free drivers showed a high degree of accuracy in identifying the most deficient of the incident-involved drivers. A score below +6 identified @ 80% of the incident-involved drivers, and included 20% of the incident-free drivers.

McKnight and McKnight (accepted by AAP, 1998)


Test Batteries:

Johns Hopkins University

300 drivers over age 65 who visit their primary care physician for medical care will be evaluated. Drivers will be selected in 2 age groups: age 66-75 and age 76+

In Progress Study

The purpose of the study is to characterize the cognitive functioning of older drivers, and to identify a short screening test that can be easily implemented in a physician=s office.

General Cognitive Function:

Modified Mini-Mental State Examination (10 min). Will include the standard modification (Folstein et al., 1975), plus (1) inclusion of both serial sevens subtraction and spelling Aworld@ backwards; (2) digit span forward and backwards; (3) adding change; (4) naming the current and previous 4 presidents ; (5) a 10-item picture confrontation naming task; (6) an additional sentence repetition item; (7) 2 additional construction figures to copy.

Visual Information Processing:

Visual Reproduction (10 min). Test of immediate visual memory (Wechsler, 1987), where subject will view a stimulus line drawing for 10 s and must draw the design from memory after the stimulus is taken away. Four stimulus cards are presented, one at a time.

Motor-Free Visual Perception Test (10 min). Will assess visual discrimination, figure-ground extraction, visual closure, visual memory, and spatial relationships. Subjects choose the appropriate response from among 4 choices for each item.


Trail-Making Test (5 min). Test of visual conceptual and visuomotor tracking involving motor speed and switching attention. Parts A and B will be given.

Brief Test of Attention (15 min). This is a brief test of auditory selective attention with 2 conditions, each with 10 trials (Schretlen and Bobholz, 1992). A tape recorder presents a series of digits and letters on each trial. The same series of digits and letters is presented for each condition. In one condition, subjects must count the number of digits presented. In the other condition, they must count the number of letters presented. The total number of correct counts is tallied across conditions.

Questionnaire data will also be obtained.

Johns Hopkins University (general medical setting)

Not reported to date

Principal Investigator: Penelope Keyl


Test Batteries:

Salisbury Eye Evaluation

2,320 community-dwelling persons between age 65-84 in the Salisbury, MD Metropolitan area

Mean age = 72.1 years

42% male, 58% female

27% African American

73% Caucasian

In Progress Study

Salisbury Eye Evaluation (SEE) includes 4 population-based studies aimed at studying risk factors for age-related eye disease and the relationship between visual impairment and disability in an aging population.

Each participant is administered a 2-hour in-home interview including mental status, medical history, diet history, and Activities of Daily Vision questionnaire.

S=s then underwent a 4- to 5-hour clinic examination that included:

Refraction (Humphrey Autorefractor)

Visual Acuity (ETDRS Chart)

Contrast Sensitivity (Pelli-Robson Chart)

Glare Sensitivity (Brightness Acuity tester)

Stereoacuity (Randot Circles Test)

Visual Fields (81-point, single-intensity test strategy on Humphrey Field Analyzer; tests points over a 60 degree radius field with a single target intensity of 24 dB)

- Assessment of Reading, Face Recognition, and Mobility
- Ocular Disease Assessment
- ADLs
-General health

Dependent measures will come from State Accident Reports (retrospectively 5 yrs, prospectively 4-5 yrs)


Salisbury, MD; in-home interviews and clinic evaluations

Report on visual function in Investigative Ophthalmology & Visual Sciences, (1997), but no data avail on ability of tests to predict driving performance.

Vision Characteristics:


mean = .01 logMar (20/20) +/- 1 line

Contrast sensitivity:

mean = 1.6 +/- 0 .2

Humphrey visual fields:

mean 17.6 +/- 2.3 points missed

Randot Stereoacuity test:

mean = 1.92 +/- 0.5 log arc sec.

UFOV: 39% of participants had UFOV loss greater than 40%

Principal Investigator:

Gary S. Rubin, Lions Vision Center

Baltimore, MD

Rubin, West, Munoz, Bandeen-Roche, Zeger, Schein, Fried (1997)


Test Batteries:

University of Alabama at Birmingham

Multivariate Model:

- Mental Status Exams
- Number of Health Conditions
- Comorbid Medical Condition Score
- Physical Activity
- Falls Efficacy Scale
- Mobility & Balance
- Depression
- Life Satisfaction
- Cataract/no Cataract
- Cataract Symptom Score
- Global Measure of Vision
- Visual Acuity
- Contrast Sensitivity
- Disability Glare
- Visual Field Sensitivity
- Useful Field of View
- Visual Performance Tasks of Everyday Life
- Age
- Gender

Older drivers age 55-90 randomly selected from age and crash stratified cohort residing in Jefferson County, AL

Group 1=older adults who have cataract surgery (n=144)

Group 2= older adults with cataract who do not have surgery (n=137)

Group 3= older adults with good eye health (n=105)

In progress study (Project 2 of Roybal Center) Improving Visual Function: Impact on Driving

The project is an intervention evaluation study to determine how improvement in vision impacts crashes and driving habits.

Other Objectives:

To determine the natural progression of crash frequency and driving habits in a group of older adults who, at the outset of the project, are in good eye health.

To determine whether certain factors serve as effect modifiers, thus altering the relationship between improvement in visual sensory function and crash frequency/driving habits.

Prospective study where all subjects are assessed once annually, with the 1st visit before cataract surgery, then annually after surgery for 2 years. Crash data from 5 years prior to enrollment and 3 years following enrollment are obtained from Alabama Dept. of Public Safety.


Roybal Center

University of Alabama at Birmingham

The mental status exams were not scored as of the date of the progress report.

The following bivariate associations were statistically significant:

One or more at-fault crashes are associated with:

$ Reduction in UFOV of 40% or more
$ African American Race
$ History of Falling
$ Not using a beta-blocking drug
$ Self-reported difficulty in visual tasks
$ Cataract
$ Acuity loss
$ Contrast sensitivity loss
$ Increased disability glare
$ Visual field loss

Findings indicate that functional measures are of greater relevance than specific medical conditions in the identification of at-risk elderly drivers.

Principal Investigator

Cynthia Owsley, Richard Sims

Advisory Committee Meeting No. 4 (January, 1996)

A3B13 Newsletter (July,1997)



Test Batteries:

University of Alabama at Birmingham


-Race (African American, Caucasian)

-Use of Beta-Blocking Drugs

-Use of Alpha-Blocking Drugs

-Use of Diuretic Drugs

-Positive Urinary Opiates

-Falls in Past 2 Years

-Reduced UFOV

-Performance Oriented Mobility Assessment (Poma)


-Medical Diagnoses

-Disordered Sleep


-Alcohol Consumption

-Grip Strength

-Supine and Sitting Blood Pressure

- Reported and Documented Hearing Impairment

-Walking Time

-Driving Exposure

-Mental Status

-Comorbidity Status

174 drivers ages 55-90 (mean age 71.1), residing in Jefferson County, AL.

Case drivers has at least 1 state-recorded at-fault crash in the 6 years preceding the assessment (n=99)

Controls had no state-recorded at-fault crashes in the prior 6 years (n=75)

Objective: to explore associations between a history of at-fault vehicle crashing in older subjects (between 1985-1991) and several medical and functional variables collected on them in 1991.

7 Questionnaires and 10 physical examination/performance measures were employed to assess medical and functional domains. Lists of drivers and number of crashes for each driver were made available by the AL Dept. of Public Safety.


1-Medical History (medical diagnoses, self-reported health, prior hospitalizations & nursing home admissions, and number of falls in prior 2 years)

2-Current prescriptions and over-the-counter meds (CHS procedures--systolic Hypertension in the Elderly Program & Cardiovascular Health Study)

3-Depression Scale (Center for Epidemiological Studies)

4-Short Michigan Alcoholism Screening Test & estimate of weekly alcohol consumption

5-Self-reported hearing function (Hearing Handicap Inventory in the elderly)

6-Physical functioning in ADL=s & IADL=s (CHS & Supplement on Aging from Nat=l Health Interview Survey)

7-Driving exposure and avoidance of situations

Physical Exams/Performance Measures:

1-Supine, sitting & standing blood pressures

2-urine screens for amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine, opiates, & phencyclidine

3-POMA (balance and gait: chair stand, balance eyes closed, neck turning, reaching up, bending over, sternal nudge, initiation of gait, gait height, path deviation, turning while walking)

4-Timed 15 ft walk

5-Bilateral hand grip strength (Jamar dynamometer)


7-Hearing (Welch-Allyn audioscope)

8-Acuity (ETDRS chart)

9-Contrast Sensitivity (Pelli-Robson)


UAB at Birmingham

At univariate level, crash-involvement was significantly associated with Black Race (p=0.002), difficulty reaching out (p=.042), not using a beta blocker (p<0.001) not using an alpha blocker (p<0.031), not using a diuretic (0.050), having positive urinary opiates (p=0.040), falling in prior 2 years (p=0.004), failing UFOV (p=0.001), older age (p=0.018), poorer visual acuity (p=0.001), poorer contrast sensitivity (p=0.032), poorer performance on MOMSSE (p=0.024). Low POMA scores were marginally significant (p=0.077), suggesting worse balance and gait among drivers who crashed. No significant difference between cases and controls for driving exposure.

All non-collinear variables that were significant at the univariate level were entered into logistic regression models. The following variables provided the best fit of the data:

$UFOV reduction of 40% or more (OR = 6.1, CI = 2.9-12.7, p<0.001),

$African American race (OR = 6.6, CI=1.7-26.2, p<0.007) (NOTE: only 14.9% of sample was African American & only 4 were non-crashers. Thus there were too few control subjects to provide sufficient info about this assoc.)

$Not taking a beta-blocking drug (OR = 4.3, CI=1.2-15.0, p=0.23)

$Having fallen in prior 2 years (OR= 2.6, CI=1.1-6.1, p=0.025).

Failure to find assoc. between crashes and use of benzodiazepines, antidepressants, or narcotics may reflect low utilization of drugs in sample.

Sims, Owsley, Allman, Ball, and Smoot (1998)


Test Batteries:

University of Helsinki

Five cohorts:

novice drivers getting their license in 1997

drivers age 35, in 1997

drivers age 50, in 1997

drivers age 60, in 1997

drivers age 70, in 1997

Samples of 300 drivers will be drawn from driver record files

In Progress Study 1997-2001: ADriving Skills and Abilities in Novice and Elderly Drivers: a 5-Year Follow-Up Study@

This program will include a test of a new system to predict elderly drivers= driving ability and include support for maintained mobility. Drivers will include 5 age groups, including drivers who will be 60 years old and 70 years old in 1997. Samples will be tested in driving schools (laboratory tests of visual acuity, contrast sensitivity, visual fields, cognitive and attentional tests, plus a battery for coherence of information processing developed by prof. V. Virsu, Dept. Of Psych) and will take a driving exam in their own vehicle. Driving exam includes detailed observation of critical driving behavior, used in present finnish driving exam. Drive test also includes feed back and supportive analysis of mobility needs and optimum means to fulfill them. Educational materials developed for elderly people by a concurrent program at Traffic Safety Organisation of Finland will also be used. Half the sample will then be undergo test and support sessions every year. Accidents and exposure will be evaluated. A sample will be tested in instrumented vehicles where measures will be made of distance and lane keeping performance, crossing management, merging onto a major road, and divided attention when doing in-vehicle tasks and using navigation information.

20 driving schools in Finland


Program directed at the Traffic Research Unit by professor Heikki Summala, funded in part by the Ministry of Transport

Heikki Summala, Ph.D.

Professor of Traffic Psychology

Dept. Of Psychology

Traffic Research Unit

P.O. Box 13 (Meritullinkatu 1 A)

00014 University of Helsinki



Test Batteries:

University of Iowa

39 licensed drivers:

$ 21 with Alzheimer Disease, recruited from a registry in the Alzheimer=s Disease Research Center of the Dept. Of Neurology, Univ. Of Iowa, Iowa City. Diagnosis relied on recommendations made under Dept. of Health and Human Services Task Force on AD (NINCDS-ADRDA)

$ 18 controls without AD, recruited from volunteers in the local community



Dependent Measure:

Driving performance is measured in Iowa Driving Simulator. Crashes or near misses can occur to 4 events on 2-lane highway (e.g., slower moving or stopped lead vehicles). Also, headway, lane deviations, abrupt braking, and potential injury severity are measured.

Objectives: To determine fitness to drive for neurological patients. Three goals: (1) to test hypothesis that drivers w/ AD are more at risk for crashes than controls w/o dementia of similar ages; (2) to determine what specific driver safety errors preceded a crash; and (3) to determine how such unsafe events are predicted by visual and cognitive factors sensitive to decline in aging and AD.

Cognitive Tests:

$ Temporal Orientation (date, day, time of day)

$ Information & Block Design (from WAIS-R)

(Verbal and nonverbal intellectual capacity)--age scaled scores are reported

$ Benton Visual Retention Test (BVRT)--sensitive to early mental decline

$ Controlled Oral Word Assoc. (COWA)--S=s must generate as many words as possible that begin w/ a certain letter within a 1-min time limit. Good detector or early abnormal decline

$ Benton Van Allen Facial Recognition Test (Faces)--measures visuoperceptual capacity (scores corrected for age & educ. level)

$ Rey-Osterreith Complex Figures Test (CFT)-Copy (S=s must copy complex geometric figure. Visuoconstructional ability, independent of memory function)

$ Trail Making A & B (measures executive function)--scale score equivalent of test raw score is reported

$ WAIS-R Digit Span, Forward & Backward--immediate and working memory (age scaled scores reported)

Visual Perception Tests:

$ Far & near visual acuity

$ Dynamic visual acuity

$ Static spatial contrast sensitivity (Pelli-Robson Chart)

$ Motion direction discrimination using random dot cinematograms

$ 3-D structure from motion (SFM)--using orthographic projections of spatially random dots on a mathematical model of a rotating cube or square

Attentional Tests:


$ Starry Night Test (visual and sensory function and attention over a spatial array over time).

University of Iowa, CDC Study

$ 6 of 21 (29%) S=s with AD had crashes, vs. 0 of 18 controls

$ Odds Ratio (OR) Estimates and exact P values (Fisher) of predictors of crashes follows:

Variable OR P
CFT-Copy <20
57.61 <.001
$3-D SFM>15 44.94 <.001
$Trails B<3 30.19 <.001
40.78 <.001
Correct <4
12.30 .01
Total loss> 50%
18.13 .002
$Faces <40 58.53 <.001
Digit <10
10.04 .02
20.14 .004
24.56 .002
$Starry Night<1 29.83 .001
$COWA<30 24.56 .002
$Alzheimer Disease 8.91 .02
$Age>70 y 0.74 >.99
$Sex, M 3.17 .39


$For UFOV, among 15 S=s with total UFOV loss >50%, 6 had at least 1 crash, while none of the 23 S=s w/ total UFOV loss <50% had any crashes.

$In development of multivariate model, after adjusting 1st step for results of Rey-Osterreith Complex Figures Test (the most significant), no other factors were significant.

$Crashes were Alooked but didn=t see,@ reacted too slow, and evasion of primary hazard and colliding w/ secondary hazard.

Rizzo and Dingus (1996)

Rizzo, Reinach, McGehee, and Dawson (1997)


Test Batteries:

Yale University

125 community-living cohort of older persons who are active drivers, followed longitudinally (ages 77+)

In progress study. In this study currently under conduct, a test battery was used to assess multiple domains of visual, cognitive, and physical abilities potentially relevant to driving. Tests were included only if they were brief, required little or no equipment, and could be administered by a trained interviewer to facilitate future in-office use.

Test battery included:


Near Acuity - Rosenbaum Card
Far Acuity - Graham Field Chart
Central Visual Fields - Amsler Grid
Peripheral Visual Fields - Manual Assessment
Contrast Sensitivity - Pelli-Robson Chart


General Cognitive - MMSE, Traffic Signs

Awareness - Aware of Driving/Memory Problems; Confidence in Driving Ability; Prediction of memory

Verbal Memory - WMS-R Logical Memory Subtests

Visual Memory -WMS-R Visual Reproduction Subtests

Visuospatial - Hooper Visual Organization; Embedded Figures; Visual Imagery

Attention - Number Cancellation

Psychomotor - Symbol-Digit; Number Connection

Reaction Time - Manual Assessment

Executive - Trails B; Visual Distractors; Problem Solving


Range of Motion (ROM)/Strength - Manual Muscle Test; Dynamometer

Sensory - Examination of Light Touch, Vibration, Proprioception, Stereognosis

Coordination/Dexterity - Ball Drop; Finger Tap, Tweezer Test

Foot Problems - Manual Examination

Physical Performance - Rapid Pace Walk

Outcomes included the self-report of a crash, moving violation, or being stopped by police, during the previous 5.75 years.

In-house (Yale University School of Medicine) by trained interviewers

Initial results (prospective findings not available yet):

The following factors were associated with adverse events in bivariate analysis

Initial multivariate results (Risk Ratio and probability):

Near Acuity (<20/40 bilateral) RR=1.9, p=0.024

Contrast Sensitivity (1.35) RR=1.5, p=0.078

Number Cancellation (48 correct) RR=2.0, p=0.006

Hooper VOT (16 correct) RR=1.5, p=0.059

Neck ROM (unable both directions) RR=2.2, p=0.001

Hand ROM (unable to touch crease) RR=1.8, p=0.015

Tweezer Test (>15.8 s) RR=1.6, p=0.038

Of the 125 subjects still driving at the time of the interview, 50 (40%) reported an adverse event in the previous 5.75 yrs. The factors independently associated with adverse events in multivariate analyses adjusting for driving frequency were: near vision worse than 20/40 (Risk Ratio 11.9; 95% Confidence Interval 1.3, 109.1); limited neck range of motion (rr=6.1, CI=1.7,22.0); and poor performance on a visual attention task (48 correct on number cancellation), RR=3.0, CI=1.2, 7.8.

If none of these factors were present, 21% reported events; if one was present, 45% reported events; if two or three were present, 87% reported events.

Principal Investigator:

Rich Marottoli


Literature Review: Medical Conditions and Their Direct and Indirect Impact on Driving Performance


In Progress Study: Medical Fitness and Crash Risk (Project 476). Goal of study: to identify shortcomings in current Ontario driver medical standards and review practices that may not stand up to judicial scrutiny due to a lack of scientific evidence to justify the setting of such standards. Four main objectives: (1) assess and compare medical standards and medical review practices in selected progressive jurisdictions with those in Ontario; (2) critique program evaluation studies in selected jurisdictions; (3) comment on landmark legal decisions or impending court challenges within the jurisdictions; (4) review the scientific literature on medical conditions and their direct and indirect impact on driving performance. Regarding objective (4), literature on performance and epidemiological studies was reviewed for 7 medical conditions: diabetes with and without insulin; organic brain disorders; seizure disorders; sleep disorders; monocular vision and restricted vision.

Sponsored by Ministry of Transportation in Ontario.

Draft report submitted 10/4/96; conclusions and recommendations not released. Not available until end of 1997.

MacGregor, Smiley, Dooley, and Tasca (1996)