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Objectives Dementia increases the risk of unsafe driving, but this is less apparent in preclinical stages such as mild cognitive impairment (MCI). There is, however, limited detailed data on the patterns of driving errors associated with MCI. Here, we examined whether drivers with MCI exhibited different on-road error profiles compared with cognitively normal (CN) older drivers. Design Observational. Setting and Participants A total of 296 licensed older drivers [mean age 75.5 (SD = 6.2) years, 120 (40.5%) women] recruited from the community. Method Participants completed a health and driving history survey, a neuropsychological test battery, and an on-road driving assessment including driver-instructed and self-navigation components. Driving assessors were blind to participant cognitive status. Participants were categorized as safe or unsafe based on a validated on-road safety scale, and as having MCI based on International Working Group diagnostic criteria. Proportion of errors incurred as a function of error type and traffic context were compared across safe and unsafe MCI and CN drivers. Results Compared with safe CN drivers (n = 225), safe MCI drivers (n = 45) showed a similar pattern of errors in different traffic contexts. Compared with safe CN drivers, unsafe CN drivers (n = 17) were more likely to make errors in observation, speed control, lane position, and approach, and at stop/give-way signs, lane changes, and curved driving. Unsafe MCI drivers (n = 9) had additional difficulties at intersections, roundabouts, parking, straight driving, and under self-navigation conditions. A higher proportion of unsafe MCI drivers had multidomain subtype [n = 6 (67%)] than safe MCI drivers [n = 11 (25%)], odds ratio 6.2 (95% confidence interval, 1.4–29.6). Conclusion and Implications Among safe drivers, MCI and CN drivers exhibit similar on-road error profiles, suggesting driver restrictions based on MCI status alone are unwarranted. However, formal evaluation is recommended in such cases, as there is evidence drivers with multiple domains of cognitive impairment may require additional interventions to support safe driving.
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Original Study
On-Road Behavior in Older Drivers With Mild Cognitive Impairment
Ranmalee Eramudugolla DPsych
, Md Hamidul Huque PhD
, Joanne Wood PhD
Kaarin J. Anstey PhD
School of Psychology, University of New South Wales, Randwick, NSW, Australia
Neuroscience Research Australia (NeuRA), Randwick, NSW, Australia
Queensland University of Technology (QUT), Centre for Vision and Eye Research, Institute of Health and Biomedical Innovation, Brisbane, QLD,
Mild cognitive impairment
tness to drive
older driver
cognitive decline
Objectives: Dementia increases the risk of unsafe driving, but this is less apparent in preclinical stages
such as mild cognitive impairment (MCI). There is, however, limited detailed data on the patterns of
driving errors associated with MCI. Here, we examined whether drivers with MCI exhibited different on-
road error proles compared with cognitively normal (CN) older drivers.
Design: Observational.
Setting and Participants: A total of 296 licensed older drivers [mean age 75.5 (SD ¼6.2) years, 120 (40.5%)
women] recruited from the community.
Method: Participants completed a health and driving history survey, a neuropsychological test battery,
and an on-road driving assessment including driver-instructed and self-navigation components. Driving
assessors were blind to participant cognitive status. Participants were categorized as safe or unsafe based
on a validated on-road safety scale, and as having MCI based on International Working Group diagnostic
criteria. Proportion of errors incurred as a function of error type and trafc context were compared across
safe and unsafe MCI and CN drivers.
Results: Compared with safe CN drivers (n ¼225), safe MCI drivers (n ¼45) showed a similar pattern of
errors in different trafc contexts. Compared with safe CN drivers, unsafe CN drivers (n ¼17) were more
likely to make errors in observation, speed control, lane position, and approach, and at stop/give-way
signs, lane changes, and curved driving. Unsafe MCI drivers (n ¼9) had additional difculties at in-
tersections, roundabouts, parking, straight driving, and under self-navigation conditions. A higher pro-
portion of unsafe MCI drivers had multidomain subtype [n ¼6 (67%)] than safe MCI drivers [n ¼11
(25%)], odds ratio 6.2 (95% condence interval, 1.4e29.6).
Conclusion and Implications: Among safe drivers, MCI and CN drivers exhibit similar on-road error pro-
les, suggesting driver restrictions based on MCI status alone are unwarranted. However, formal eval-
uation is recommended in such cases, as there is evidence drivers with multiple domains of cognitive
impairment may require additional interventions to support safe driving.
Ó2020 AMDA eThe Society for Post-Acute and Long-Term Care Medicine.
Cognitive impairment is a key risk factor for unsafe driving and
crashes in older adults,
and drivers with dementia have approxi-
mately 10 times the risk of failing an on-road driving test relative to
healthy older drivers.
The progressive nature of dementias, how-
ever, means that it is not always clear when and how the transition to
unsafe driving occurs. In fact, the evidence for driving impairment in
preclinical and early stages of dementia are mixed, with some studies
reporting a comparable range of performance to healthy drivers,
whereas others report higher fail rates, although less than that for
A similar pattern of ndings is evident for performance
on driving simulators, with reports of signicant impairment in
drivers with dementia,
but little or no impairment in drivers with
mild cognitive impairment (MCI).
The variability in ndings may
in part reect the use of different diagnostic criteria across studies, but
also the heterogeneity in level of impairment and function in this
group. Furthermore, MCI increases the risk of progression to demen-
tia, but the annual conversion rate is only approximately 5% to 10%,
with most remaining stable or reverting to age-normative cognition.
The authors declare no conicts of interest.
This study was funded by the National Health and Medical Research Council of
Australia (NHMRC grant #1045024). Kaarin Anstey is funded by NHMRC Research
Fellowship #1102694.
* Address correspondence to Kaarin J. Anstey, PhD, School of Psychology, Uni-
versity of New South Wales, Randwick, 2031 NSW, Australia.
E-mail address: (K.J. Anstey).
1525-8610/Ó2020 AMDA eThe Society for Post-Acute and Long-Term Care Medicine.
journal homepage:
JAMDA xxx (2020) 1e7
This group is therefore an important target for the development of
interventions and tools to support driving safety and independence.
To achieve this, detailed data on the type of driving behaviors and
difculties experienced by drivers with MCI is needed, particularly for
driving under on-road, in-trafc conditions.
Few studies have examined the pattern of driving difculties in MCI,
with most data derived from simulated driving tests.
errors and crash situations typically associated with increasing age in
healthy drivers include negotiating complex intersections, turning
against trafc, maintaining appropriate speed, and the ability to
maintain vehicle position within a trafclane.
Relative to healthy
older drivers, simulator data indicate MCI is associated with more errors
in lane positioning,
maintaining appropriate speed,
breaking speed,
and errors at stop-signecontrolled intersections.
One study using on-road assessment also reported greater errors in
lane positioning and executing turns against trafc in drivers with MCI
relative to healthy older drivers.
Although it is difcult to compare
across studies, the broad pattern of errors associated with MCI appears
similar to that reported for healthy older adults, albeit more severe.
The broad categories of MCI and dementia in the preceding studies
do not distinguish between different etiologies and patterns of neu-
rocognitive impairment. However, emerging data indicate this is an
important factor in on-road safety.
Despite variations in on-road
assessment across different jurisdictions, the reported fail rate for
healthy older drivers typically ranges from 0% to 11%,
that for early or mild Alzheimer disease ranges between 10% and
and for vascular dementia, fronto-temporal dementia, and
dementia with Lewy-bodies the fail rate is between 35% and 70%.
Consistent with this, 1 simulator study reported that drivers with
single-domain amnestic-type MCI showed less impairment than those
with multidomain amnestic-type MCI.
Studies that have examined
cognitive skills associated with on-road driving in healthy older adults
also suggest a pattern in which reduced skills in selective attention,
task switching, response inhibition, and visual perceptual abilities are
the most predictive of driving errors.
A recent study found a
similar range of overall driving safety levels among older drivers who
were cognitively healthy or had MCI,
but this study did not examine
whether specic driving skills or contexts were more affected by MCI.
Thus, there is some evidence that drivers with MCI may be hetero-
geneous with respect to on-road safety,
but data on the nature
of MCI impacts on-road driving skills is still lacking. Given the po-
tential for this group to benet from tailored interventions, this article
seeks to compare the pattern of errors incurred during a standardized
on-road driving assessment between safe and unsafe older drivers
who were identied as either cognitively healthy or MCI. We hy-
pothesized that MCI as a group would be associated with a greater
number of errors, but a similar pattern of errors, to cognitively normal
(CN) older drivers.
Participants were involved in the Driving Ageing Safety and Health
(DASH) study
that was undertaken in the Australian city of Canberra
from 2013 to 2016. Drivers aged 65 years and older with a current
valid license were recruited from the community through advertise-
ments in newspapers, community groups, and primary care providers,
as well as those referred to the Australian Capital Territory Health
Disability and Rehabilitation Service due to concerns about driving
safety. For the purposes of this study, data from participants with
known diagnoses of dementia or MMSE scores <23 were excluded
(Figure 1).
Standard Protocol Approvals and Consents
The study protocol was approved by the University Human
Research Ethics Committees (2012/643), and informed, written con-
sent was obtained from all participants before study involvement.
Following consent, participants completed a general health and
driving survey to obtain information on basic demographics, self-
reported health conditions, self-rated health,
subjective memory
instrumental activities of daily living,
and driving
Fig. 1. Participant ow in the DASH study and selection for current sample.
R. Eramudugolla et al. / JAMDA xxx (2020) 1e72
history. Participants then completed a laboratory assessment
including vision, hearing, and balance tests, and a neurocognitive
battery. Within 3 months of the laboratory assessment, participants
took part in an on-road driving assessment with a driver qualied
occupational therapist on a standard urban route in a dual-brake
Cognitive Measures
A neurocognitive test battery designed to sample performance
from a range of cognitive domains was administered by a trained
research assistant. The test battery took approximately 30 to 45 mi-
nutes within a longer laboratory session that lasted up to 2 hours
including rest breaks. Learning and memory function was assessed
using the California Verbal Learning Test,
language was assessed
using verbal uency (Controlled Oral Word Association Test
) and the
15-item Boston Naming Test,
visuospatial skills were assessed with
the Copy test from the Benton Visual Retention Test,
attention was assessed with the Victoria Stroop Part D (colored dots)
and W (nonconict words)
; and executive function was assessed
using the Victoria Stroop Test Part C (conict color-words),
Span Backwards,
Trail Making Test Part B,
and the Game of Dice
All scores were converted to z-scores for each participant based
on published age, gender, and education level stratied normative
data. For each cognitive domain, the z-scores for the component tests
were averaged to create a domain z-score. Thus scores near 0.0
represent normal performance, above zero is better, and below zero is
worse than normal performance in units of standard deviation.
Classication of MCI
A psychometric approach to classication of MCI was taken to
categorize participants as either CN or MCI.
Briey, participants were
classied as meeting International Working Group diagnostic criteria
for MCI
if they demonstrated (1) no evidence of dementia (ie, MMSE
>24 and no known diagnosis of dementia); (2) subjective memory
concern (score >24 on the Memory Complaints Questionnaire
); (3)
objective MCI (of between 1 to 2 SDs below published age and gender
stratied norms) on 1 or more of 5 neurocognitive domains: complex
attention, learning and memory, language, visuo-perceptual skills,
and executive function; and (4) preserved basic activities of daily
living or minimal impairment in complex instrumental activities of
daily living. To examine MCI by subtype, participants with MCI who
had amnestic impairment (ie, z-score less than 1.0 for Memory
domain) were classed as amnestic-type. A second category was
created for multidomain type, in which participants were categorized
as such if there were more than 1 domain with a z-score less
than 1.0.
On-Road Driving Assessment
Each participant undertook a 50-minute open-road assessment in
an automatic vehicle with dual-brake controls tted. The route was
predetermined and located in an urban area and included a range of
controlled and uncontrolled intersections, highway driving, residen-
tial areas, shopping strips, school zones, car parks, stop/give-way in-
tersections, and roundabouts. The route was approximately 20 km and
was divided into a total of 164 possible locations or maneuvers to
assist with scoring. Driving performance was scored following a
standard, validated protocol.
Scoring was conducted by a driver
trained occupational therapist who sat in the rear passenger seat of
the vehicle and scored performance using a Driver Safety Rating (DSR)
on a scale of 1 to 10 (where higher scores indicated higher safety), as
well as recording turn-by-turn errors in observation, lane position,
blind-spot checking, indication, speed control, gap selection, and
approach. A driving instructor sat in the front passenger seat and
provided turn-by-turn directions for 80% of the route, and for the
remaining 20%, the participant was asked to self-navigate to the
nearest hospital using the available road signs. A pass/fail safety cutoff
was used that was previously validated for this DSR scale,
given that
this is the typical outcome of driver licensing decisions.
driving was identied by participants whose DSR ranged from 1 to 3,
demonstrating multiple serious errors requiring either intervention
by the driving instructor to prevent a crash, or errors that would lead
to failing a local licensing test. Some measures of on-road errors varied
depending on opportunity (eg, lane change, indication) and route
changes. To ensure comparability across participants, errors of each
type (eg, observation, lane position), and errors in each trafc context
(eg, roundabout, straight driving) were converted to proportion of
errors per opportunity as reported previously.
Statistical Analysis
Participants were categorized according to on-road safety (Safe:
DSR of 4e10; Unsafe: DSR of 1e3), and MCI status (ie, MCI or CN),
producing 4 levels: CN-safe, CN-unsafe, MCI-safe, and MCI-unsafe.
Demographics, self-reported health, and cognitive measures and on-
road performance were reported in numbers with proportions or
median with interquartile ranges, as appropriate, according to the
preceding 4 levels of on-road safety and MCI status. The trafc context
of errors and different error types across various levels of on-road
safety and MCI status were compared with the CN-safe category us-
ing multinomial logistic regression models. All models were adjusted
for age, gender, and years of education. All the analysis was conducted
using Stata version 16.0 (StataCorp, College Station, TX).
Of the 296 participants, 242 were classied as CN and 54 as MCI.
Within these categories, 93% of CN and 83% of MCI were classed as safe
on the on-road assessment. Sample characteristics for each of the 4
groups are presented in Table 1. Drivers in the CN-unsafe group tended
to be older than the other groups, and those in the MCI-unsafe group
tended to report lower driving distance and frequency per week.
Physical health in the MCI-unsafe group was generally similar to that in
the CN-safe and other groups. In terms of cognition, MCI participants
had lower domain z-scores relative to the CN groups. Although MCI-safe
drivers had greater levels of impairment in attention, memory, and
language, MCI-unsafe drivers had greater levels of impairment in vi-
suospatial, executive function, and attention. MMSE scores were close
to ceiling for all groups, consistent with this testslowsensitivitytomild
impairment, and in highly educated samples.
Unsurprisingly, unsafe
drivers incurred approximately twice as many errors as safe drivers.
Approximately 75% of drivers in each category rated their own on-road
performance as being comparable to a typical driver, whereas the self-
ratings among MCI-unsafe drivers included greater numbers reporting
excellent performance, suggestive of poor insight.
Trafc Contexts of Errors
Table 2 presents the regression coefcients for the association
between safety category and errors at different trafc contexts.
Overall, there was no statistically signicant difference in errors in any
of the trafc contexts between CN-safe and MCI-safe drivers. Relative
to CN-safe drivers, both MCI-unsafe and CN-unsafe drivers were prone
to make more errors at stop/give-way signs, when executing a lane
change, and driving on curved roads. For CN drivers, unsafe driving
was also signicantly associated with errors in school zones and at
trafc pedestrian crossing. In comparison, unsafe driving for MCI
drivers was signicantly associated with errors at both trafc-light
R. Eramudugolla et al. / JAMDA xxx (2020) 1e73
and nonetrafc-light controlled intersections, roundabouts, parking,
and driving on straight roads when compared with CN-safe drivers
(Table 2,Supplementary Figure 1).
Error Types
Figure 2 displays the group (grouped by safety category and MCI
status) mean error proportions of each error type, for the driver-
instructed component (Figure 2A) and the self-navigation compo-
nent (Figure 2B). Unsafe CN drivers had the highest proportion of
errors in checking their blind-spot, for both driver-instructed and
self-navigation conditions (Figure 2C). Table 3 displays numerical
comparison to aid interpretation. Relative to CN-safe drivers,
MCI-safe drivers had a similar likelihood of making errors, except for
marginally higher brake/acceleration errors. Relative to CN-safe
drivers, CN-unsafe drivers were more likely to make errors (Table 3),
Table 1
Sample Characteristics by MCI and Driving Safety Category
CN (n ¼242) MCI (n ¼54)
Safe Unsafe Safe Unsafe
Sample size, n (%) 225 (93) 17 (7) 45 (83) 9 (17)
Age, mdn (p25, p75) 75 (70,78) 83 (81,85) 75 (70,79) 75 (74,81)
Females, n (%) 92 (40.9) 6 (35.3) 17 (37.8) 5 (55.6)
Years of education, mdn (p25, p75) 16 (13,18) 17 (14,18) 16 (13,17) 16 (10,17)
Days driven weekly, mdn (p25, p75) 6 (5,7) 6 (3,6) 6 (5,7) 4 (3,7)
Distance driven weekly (km) 200 (100,250) 100 (50,200) 120 (74,200) 80 (40,100)
Years of driving experience 55 (51,60) 65 (60,67) 56 (50,59) 60 (50,66)
Self-reported health
SF-12 Physical Scale, mdn (p25, p75) 42 (35,50) 40 (37,46) 42 (25,48) 55 (52,56)
SF-12 Mental Scale, mdn (p25, p75) 59 (52,61) 54 (46,61) 57 (53,62) 53 (44,60)
Number of health conditions 4 (2,5) 5 (4,5) 3 (2,5) 4 (2,5)
Vision impairment, n (%) 35 (15.5) 1 (5.9) 6 (13.3) 1 (11.1)
Hearing impairment, n (%) 60 (26.7) 6 (35.3) 12 (26.4) 3 (33.3)
Arthritis, n (%) 122 (54.2) 9 (52.9) 19 (42.2) 4 (44.4)
Heart disease/Heart attack, n (%) 36 (16) 3 (17.6) 12 (26.7) 0
Diabetes, n (%) 19 (8.4) 4 (23.5) 3 (6.7) 1 (11.1)
Parkinson disease, n (%) 3 (1.3) 0 1 (2.2) 0
Stroke/transient ischemic attack, n (%) 26 (11.6) 2 (11.8) 6 (13.3) 1 (11.1)
Cognition, mdn (p25, p75)
MMSE score (0e30) 29 (28,30) 29 (27,30) 29 (29,30) 28 (28,30)
Complex attention z-score 0.25 (0.2,0.6) 0.27 (0.2,0.5) 0.84 (1.4,0.2) 0.41 (1.1,0.0)
Memory z-score 0.37 (0.3,0.9) 0.05 (1.0,1.0) 0.48 (1.0,0.2) 0.13 (0.3,0.1)
Language z-score 0.24 (0.1,0.6) 0.10 (0.3,0.9) 0.18 (0.8,0.2) 0.03 (1.0,0.3)
Visuospatial z-score 0.57 (0.3,0.7) 0.62 (0.4,0.7) 0.31 (2.0,0.7) 1.17 (2.0,0.3)
Executive function z-score 0.20 (0.2,0.5) 0.00 (0.4,0.4) 0.13 (0.4,0.1) 0.62 (1.0,0.3)
On-road errors, median (25th and 75th percentile)
Total number of errors 21 (12,31) 42 (33,52) 19 (12,28) 50 (38,59)
Total number of locations 161 (159,160) 156 (152,161) 161 (160,163) 159 (154,163)
Self-rated on-road performance, n (%)
Excellent 2 (1) 0 0 2 (22)
Above average 38 (17) 1 (6) 8 (18) 2 (22)
Average (typical driver) 168 (75) 13 (77) 33 (73) 3 (33)
Below average 14 (6) 3 (18) 4 (9) 2 (22)
Note: MMSE: Mini-mental Status Exam ehigher scores are better with scores above 24 indicating no dementia. Cognitive domain z-scores ehigher scores are better with
mean normal performance at 0.0 and negative values indicating below normal performance in standard deviation units.
Table 2
Association Between MCI-Driving Safety Status and Errors Occurring in Different Trafc Contexts Using Multinomial Logistic Regression Adjusting for Age, Gender, and Years of
Trafc Contexts of Errors CN-safe CN-unsafe MCI-safe MCI-unsafe
(Ref) Coef (95% CI) PCoef (95% CI) PCoef (95% CI) P
Proportion of total errors - 10.34 (0.3 to 20.37) .04 1.42 (4.68 to 7.52) .65 12.45 (0.8 to 24.1) .04
Trafc-light controlled intersection - 2.52 (0.05 to 5.09) .05 0.39 (2.04 to 1.26) .64 5.46 (1.85 to 9.08) <.01
Nonetrafc-light controlled intersection - 2.33 (0.45 to 5.1) .10 0.55 (1.63 to 2.74) .62 5.38 (1.81 to 8.95) <.01
Stop give-way - 12.75 (7.24 to 18.26) <.001 1.38 (2.3 to 5.06) .46 16.96 (9.64 to 24.27) <.001
Roundabout - 2.46 (0.34 to 5.26) .08 0.4 (1.3 to 2.09) .65 5.46 (1.81 to 9.12) <.001
Lane change - 5.1 (2.23 to 7.98) <.01 0.15 (1.79 to 2.09) .88 5.44 (2.16 to 8.72) <.01
Merging - 1.21 (1.1 to 3.51) .31 0.6 (1.12 to 2.31) .50 -*-*
Curve driving 1 way - 4.76 (0.82 to 8.7) .02 1.54 (1.19 to 4.27) .27 11.01 (5.68 to 16.33) <.001
Curve driving dual-carriage way - 3.93 (1.95 to 5.92) <.001 1.18 (0.31 to 2.66) .12 4.6 (2.26 to 6.94) <.001
Straight driving one-way - 8.14 (0.37 to 16.66) .06 4.42 (1.11 to 9.94) .12 29.24 (15.38 to 43.11) <.001
Straight driving dual-carriageway - 2.54 (0.12 to 5.21) .06 0.18 (2.54 to 2.19) .88 5.01 (2.05 to 7.97) <.001
School zone - 4.94 (2.14 to 7.74) <.01 0.09 (1.99 to 2.17) .93 3.14 (0.18 to 6.47) .06
Trafc calmer/Pedestrian crossing - 5.71 (1.04 to 10.37) .02 0.19 (3.16 to 3.54) .91 4.29 (1.37 to 9.95) .14
Parking - 2.36 (0.77 to 5.49) .14 1.62 (4.53 to 1.29) .27 4.44 (1.06 to 7.82) .01
Pull in out - 1.79 (0.49 to 3.1) .01 0.38 (0.42 to 1.18) .35 -*-*
Reversing - 0.61 (0.65 to 1.87) .34 0.08 (0.64 to 0.8) .83 0.53 (2.2 to 1.14) .54
Turnaround maneuvers - 0.14 (1.36 to 1.08) .83 0.35 (1.09 to 0.39) .35 0.54 (3.03 to 1.95) .67
*Estimation not possible as all participants had error proportion of 0.0.
R. Eramudugolla et al. / JAMDA xxx (2020) 1e74
particularly under driver-instructed conditions: in observation, brake/
accelerator use, lane position, gap selection, and approach. Under self-
navigation conditions, CN-unsafe drivers were more likely to make
lane position and approach errors compared with CN-safe drivers.
Compared with CN-safe drivers, MCI-unsafe drivers were also
impaired on observation, brake/accelerator, lane position, and gap
selection under driver-instructed conditions. However, under self-
navigation conditions, MCI-unsafe drivers were more likely to make
errors in observation, brake/accelerator, lane position, and gap selec-
tion, when compared with CN-safe drivers, whereas CN-unsafe drivers
did not show this pattern.
Analysis of MCI Subtype and Safety
To examine the association between MCI subtypes and on-road
safety, the MCI group was further categorized in terms of whether
impairment (domain z-score less than 1.0) was evident in more
than 1 of the 5 domains (multidomain type), and whether amnestic
impairment was apparent (Memory domain z less than 1.0 )
(amnestic type). One participant was excluded due to insufcient
cognitive data. Of the 53 MCI cases analyzed, frequencies for the 4
MCI subtypes were as follows: amnestic single domain 8 (15.1%),
nonamnestic single domain 28 (53%), amnestic multidomain 4
(7.5%), and nonamnestic multidomain 13 (25%). Due to small
numbers in each of these subtypes, the sample was categorized into
the characteristics of interest: presence of any amnestic decits, and
presence of decits of any type across multiple domains. Here, 12
(22.6%) had amnestic impairment, and 17 (32.1%) had impairment
across multiple domains. Of the 44 MCI drivers classied as safe, 11
(25%) were amnestic, and 11 (25%) were multidomain, and of the 9
MCI drivers classied as unsafe, 1 (11%) was amnestic and 6 (67%)
were multidomain. Binary logistic regression was used to examine
amnestic category and multidomain category as predictors of unsafe
driving. There was no association with amnestic subtype [odds ratio
(OR) 0.33; 95% condence interval (CI), 0.04e2.80; P¼.307], but
there was a higher odds of unsafe driving in those with multidomain
subtype (OR 6.32; 95% CI 1.35e29.62; P¼.019). This remained after
adjustment for age, gender, and education (OR 7.69; 95% CI,
1.2 1e48.77; P¼.03), although the CI remains large because of the
small sample size.
We sought to characterize the on-road error prole of drivers
with MCI, and to identify unsafe on-road behaviors associated with
MCI. We found no differences between safe drivers with and without
MCI. In contrast, unsafe drivers with MCI demonstrated additional
difculties at intersections, roundabouts, parking, and driving on
straight roads than unsafe CN drivers. Unsafe MCI drivers also
demonstrated additional errors during self-navigation. This pattern
of additional errors among unsafe drivers with MCI, but not CN-
unsafe drivers, suggests that the impact of MCI is mostly revealed
under cognitively demanding trafc contexts and driving conditions.
Our ndings are consistent with simulator studies suggesting MCI is
associated with more errors in lane position,
speed control,
at intersections.
Fig. 2. (A) Mean proportion of errors to opportunity under driver-instructedconditions as a function of MCI edriver safety category and error type. (B) Mean proportion of errors to
opportunity under self-navigation conditions as a function of MCI edriver safety category and error type. (C) Mean proportion of errors to opportunity for errors in blind-spot
checking as a function of driving condition (self-navigation vs driver-instructed) and MCI-driver safety category.
R. Eramudugolla et al. / JAMDA xxx (2020) 1e75
We found that all of the drivers in our sample had the highest
proportion of errors to opportunity when required to reverse,
conduct turnaround maneuvers, and blind-spot checks. Errors in
checking the blind-spot were also more likely under self-navigation
conditions for all driver categories, suggesting that the increased
cognitive load during self-navigation compromised this activity, and
conrms prior ndings.
We also found that MCI-unsafe drivers
had similar levels of performance to CN-safe drivers in school zones,
at pedestrian crossings, indicator use, and in their approach to
trafc situations. Despite a small MCI sample, it could be speculated
that some aspects of safe driving may be preserved in this group,
possibly relating to the low speeds typically adopted in these
Although most drivers with MCI were classied as safe (83%), those
who were unsafe were also more likelyto have impairment across more
than 1 cognitive domain, and, as a group, had poorer visuospatial,
attention, and executive function than memory and language. This
cognitive prole is consistent with the nding that unsafe drivers with
MCI had greater difculties with spatially demanding tasks such as
parking, roundabouts, and intersections, as well as executively
demanding conditions such as self-navigation. Further studies are
needed on driving errors and MCI subtype to conrm these ndings,
given the low prevalence of unsafe driving and multidomain MCI in our
sample. Prior studies have reported that visuospatial impairment, along
with attentional decits, are predictive of crashes in simulated
Our ndings are also consistent with evidence that drivers
with non-Alzheimer's dementia may present with earlier and more
severe driving impairment than those with predominantly amnestic
Importantly, for safe drivers, the presence of MCI alone is not
associated with a different on-road error prole when compared with
CN drivers. To date, most studies that have examined the impact of
MCI on driving have used Clinical Dementia Rating (CDR) of 0.5 as a
denition of MCI.
However, CDR is functionally dened,
whereas MCI incorporates neuropsychological as well as subjective
decline and functional impairment.
As a result, the 2 approaches
often classify different groups
: CDR 0.5 tends to have a higher
prevalence than MCI,
and captures a wider range of functional
impairment, including mild dementia,
which may account for the
mixed ndings in the driving literature. MCI-safe drivers tended to
have impairments in only a single domain, which supports the notion
that driving decits emerge with increasing burden of cognitive
Future studies will need to examine the longitudinal
changes in cognitive prole as well as on-road driving errors to better
understand the progression from safe MCI to unsafe MCI and
Limitations of the present study include the small sample of MCI
drivers who were categorized as unsafe, and the nonerandom sam-
pling method used to obtain the cohort. Strengths of the study include
the use of a standard open-road driving assessment with detailed
characterization of error types and inclusion of a self-navigation
component. This allowed for precise analysis of the specic areas
where drivers with MCI had difculties as well areas where perfor-
mance was not affected. Further strengths include detailed neuro-
psychological testing and the application of established MCI criteria
with greater sensitivity than CDR 0.5.
Conclusions and Implications
We found no differences in the on-road driving errors of safe
drivers with and without MCI. Unsafe drivers with MCI had greater
difculties under some conditions. Drivers with impairment across
multiple domains, particularly visuospatial and executive, may need
more tailored advice, support, and re-skilling on driving under
cognitively demanding conditions. In terms of implications for prac-
tice, our data conrm previous ndings that suggest that driver re-
strictions are unwarranted based on MCI status alone, but that these
drivers may require formal driving evaluation.
We thank Ally Gunn, Stephanie Sabadas, Emily Wilford, Sidhant
Chopra, Lily ODonoughue-Jenkins, and Elizabeth Parkes who assisted
with data collection; Jasmine Price and Dereck Crooke for on-road
assessments; Rebecca Lawrence and Morgan Laird for data cleaning;
and Ray Wondal for assistance with manuscript preparation. We are
grateful to the ACT Health Driver Assessment and Rehabilitation Ser-
vice who assisted with recruitment.
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Association Between MCI-Driving Safety Status and Error Type During Assessment Using Multinomial Logistic Regression Adjusted for Age, Gender, and Years of Education
Error Types CN-safe CN-unsafe MCI-safe MCI-unsafe
(Ref) Coef (95% CI) PCoef (95% CI) PCoef (95% CI) P
Instructed driving component
Observation - 22.75 (10.15 to 35.36) <.001 1.17 (9.2 to 6.87) .78 27.71 (12.52 to 42.9) <.001
Blind-spot - 0.34 (1.66 to 2.33) .74 0.11 (1.31 to 1.09) .86 1.04 (4.07 to 1.98) .5
Indication - 10.65 (0.63 to 21.93) .06 3.7 (3.7 to 11.1) .33 9.25 (4.51 to 23.02) .19
Brake/accelerator - 30.17 (17.09 to 43.26) <.001 11.29 (0.68 to 21.89) .04 35.34 (20.5 to 50.19) <.001
Lane position - 45.54 (27.12 to 63.95) <.001 8.39 (6.15 to 22.93) .26 43.79 (24.67 to 62.91) <.001
Gap selection - 61.28 (11.13 to 111.42) .02 3.37 (40.37 to 33.63) .86 101.91 (46.71 to 157.11) <.001
Approach - 19.31 (8.82 to 29.79) <.001 3.15 (12.61 to 6.3) .51 12.02 (0.93 to 24.97) .07
Self-navigation component
Observation - 2.34 (4.22 to 8.89) .48 1.06 (3.09 to 5.21) .62 10.23 (2.84 to 17.62) .01
Blind-Spot - -*-*0.5 (1.46 to 2.46) .62 1.17 (3.4 to 1.06) .3
Indication - 1.89 (3.19 to 6.97) .47 0.71 (2.62 to 4.04) .68 0.05 (7.37 to 7.46) .99
Brake/accelerator - 5.62 (3.15 to 14.39) .21 1.34 (6.1 to 8.78) .72 16.51 (6.87 to 26.15) <.001
Lane position - 16.82 (7.72 to 25.92) <.001 5.06 (1.63 to 11.75) .14 18.55 (8.83 to 28.28) <.001
Gap selection - 4.09 (28.69 to 36.87) .81 1.87 (15.47 to 19.2) .83 25.76 (3.72 to 47.81) .02
Approach - 10.93 (1.51 to 20.36) .02 2.78 (4.07 to 9.63) .43 9.58 (1.59 to 20.75) .09
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Supplementary Fig. 1. Mean proportion of errors to opportunity as a function of trafc context and MCI-safety category.
R. Eramudugolla et al. / JAMDA xxx (2020) 1e77.e1
... Der Praxisfahrtest (On-road) gilt als "Goldstandard" zur Beurteilung der Fahrperformanz und wird häufig in Studien zur Überprüfung der Fahrsicherheit eingesetzt [10,11,13,15,16,18,28,[31][32][33][34][35][36]. Die Beurteilung der Fahrperformanz erfolgt durch spezialisierte Results Thirty studies were included in the qualitative analysis. ...
... Ergotherapeut*innen oder Fahrprüfer*innen mithilfe standardisierter Beurteilungsschemata. Die Fahrperformanz aller Teilnehmenden verschlechterte sich unter herausfordernden Bedingungen oder bei Ablenkung durch zusätzliche Aufgaben [10,35]. In einigen Studien zeigten Menschen mit kognitiven Einschränkungen insgesamt eine schlechtere Fahrperformanz als kognitiv Gesunde [11,15,33,34]. ...
... In einigen Studien zeigten Menschen mit kognitiven Einschränkungen insgesamt eine schlechtere Fahrperformanz als kognitiv Gesunde [11,15,33,34]. Andere Studien legten hingegen dar, dass kogniti-ve Einschränkungen allein kein signifikanter Prädiktor für die Fahrsicherheit sind [10,18,32,35]. ...
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Zusammenfassung Einleitung Aufgrund der hohen Prävalenz demenzieller Erkrankungen handelt es sich bei der Überprüfung der Fahrtauglichkeit von Menschen mit kognitiven Beeinträchtigungen um ein Thema von großer gesellschaftlicher Relevanz. Methodik Es wurde eine umfassende systematische Literaturrecherche zu der Forschungsfrage: „Welche evidenz-basierten Methoden eignen sich zur Beurteilung der Fahrtauglichkeit von Menschen mit Demenz (MmD) oder milden kognitiven Einschränkungen (MCI)?“ für den Zeitraum 2015–2020 durchgeführt. Die Recherche erfolgte in den Datenbanken Medline, PsycINFO, LIVIVO, PubPsych, Scopus, Cinahl und CENTRAL. Ergebnisse 30 Studien wurden in die qualitative Analyse eingeschlossen. Die Fahrtauglichkeit kann mit praxisbasierten Testverfahren wie Praxisfahrtest (On-road), Fahrsimulator und Fahrverhaltensbeurteilung im natürlichen Umfeld (Naturalistic Driving) beurteilt werden. Theoriebasierte Methoden sind: neuropsychologische Tests, fahrspezifische Testungen und Fragebögen zur Selbsteinschätzung. Die Studienlage zeigt, dass einzelne neuropsychologische Tests nicht zur Bestimmung der Fahrtauglichkeit ausreichen. Eine höhere Aussagekraft haben praxisbasierte Testverfahren, fahrspezifische Testungen sowie Kombinationen aus evidenz-basierten Methoden. Schlussfolgerungen Die Überprüfung der Fahrtauglichkeit von Menschen mit kognitiven Beeinträchtigungen sollte angesichts des progredienten Verlaufs demenzieller Erkrankungen engmaschig durch eine Kombination aus theorie- und praxisbasierter Methoden erfolgen.
... Research studies show that up to 70% of older adults with CI fail the on-road assessment (Carr et al., 2011;Chee et al., 2017;Eramudugolla et al., 2021;Fuermaier et al., 2019;Hird et al., 2016;Jacobs et al., 2017;Piersma et al., 2018;Toepper & Falkenstein, 2019). Older adults with CI have poorer speed and lane control and route-following behaviors than healthy controls, and perform worse on the on-road assessment, particularly when changing lanes, merging, navigating, and identifying landmarks or signs Chee et al., 2017;Eramudugolla et al., 2021;Fuermaier et al., 2019;Griffith et al., 2013;Hird et al., 2016;Jacobs et al., 2017;Toepper & Falkenstein, 2019;Wadley et al., 2009). ...
... Research studies show that up to 70% of older adults with CI fail the on-road assessment (Carr et al., 2011;Chee et al., 2017;Eramudugolla et al., 2021;Fuermaier et al., 2019;Hird et al., 2016;Jacobs et al., 2017;Piersma et al., 2018;Toepper & Falkenstein, 2019). Older adults with CI have poorer speed and lane control and route-following behaviors than healthy controls, and perform worse on the on-road assessment, particularly when changing lanes, merging, navigating, and identifying landmarks or signs Chee et al., 2017;Eramudugolla et al., 2021;Fuermaier et al., 2019;Griffith et al., 2013;Hird et al., 2016;Jacobs et al., 2017;Toepper & Falkenstein, 2019;Wadley et al., 2009). ...
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Background. The Trail Making Test Part B (Trails B) and Useful Field of View® (UFOV) can predict on-road outcomes in drivers with cognitive impairment (CI); however, studies have not included drivers referred for comprehensive driving evaluations (CDEs), who typically have more severe CI. Purpose. We determined the predictive ability of Trails B and UFOV on pass/fail on-road outcomes in drivers with CI (Montreal Cognitive Assessment <26) referred for CDEs. Method. Retrospective data collection from two driving assessments centers ( N = 100, mean age = 76.2 ± 8.8 years). Findings. The Trails B (area under the curve [AUC] = .70) and UFOV subtests 2 (AUC = .73) and 3 (AUC = .76) predicted pass/fail outcomes. A cut-point ≥467 ms on UFOV subtest 3 better-predicted pass/fail outcomes with 78.9% sensitivity and 73.5% specificity. In comparison, a cut-point ≥3.58 min on Trails B had lower sensitivity (73.7%) and specificity (61.8%). Implications. The UFOV subtest 3 may be more useful than the Trails B for predicting pass/fail outcomes in drivers with more severe CI referred for CDEs.
... The risk of individuals with major cognitive disorders being involved in a collision is up to 4.5 times higher than for older people without cognitive disorders (4,5). Most on-road studies (4,(6)(7)(8)(9)(10) and simulator studies (9,11) have also shown that, on average, the ability to drive is more affected in drivers with cognitive disorders than in drivers without cognitive disorders. Studies which take a naturalistic approach have also shown that drivers with cognitive disorders have poorer self-regulatory behavior than healthy older drivers (12). ...
... The risk of individuals with major cognitive disorders being involved in a collision is up to 4.5 times higher than for older people without cognitive disorders (4,5). Most on-road studies (4,(6)(7)(8)(9)(10) and simulator studies (9,11) have also shown that, on average, the ability to drive is more affected in drivers with cognitive disorders than in drivers without cognitive disorders. Studies which take a naturalistic approach have also shown that drivers with cognitive disorders have poorer self-regulatory behavior than healthy older drivers (12). ...
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Older drivers face the prospect of having to adjust their driving habits because of health problems, which can include neurocognitive disorders. Self-awareness of driving difficulties and the interaction between individual with neurocognitive disorders and natural caregiver seem to be important levers for the implementation of adaptation strategies and for the subsequent voluntary cessation of driving when the cognitive disorders become too severe. This study aims to evaluate an educational program for patient/natural caregiver dyads who wish to implement self-regulation strategies in driving activity, and to improve self-awareness of driving ability. The ACCOMPAGNE program is based on seven group workshops, which target the dyad. The workshops deal with the impact of cognitive, sensory and iatrogenic disorders on driving. They tackle questions about responsibility, and about autonomy and social life. They also provide alternative solutions aimed at maintaining outward-looking activities even if driving is reduced or stopped. A randomized controlled trial is planned to evaluate the effectiveness of the program 2 months and 6 months after inclusion, and to compare this to the effectiveness of conventional approaches. The main outcome of this trial (i.e., the implementation of self-regulated driving strategies), will be measured based on scores on the “Current Self-Regulatory Practices” subscale of the Driver Perception and Practices Questionnaire. The Driving Habits Questionnaire will be used to measure secondary outcomes (indicators of driving changes; indicators of changes in mood, quality of life and caregiver burden; and self-awareness of driving abilities). Indicators will be collected for both patients and natural caregivers. This cognitive, social and psychological program should allow older individuals with cognitive disorders to drive more safely, and help to maintain the quality of life and mood of both patient and natural caregiver despite driving limitations. The patient's care path would be optimized, as he/she would become an actor in the process of giving up driving, which will, most certainly, be needed at some point in the progress of neurocognitive disorders. This process ranges from becoming aware of driving difficulties, to implementing self-regulation strategies, through to complete cessation of driving when necessary. Clinical trial registration number NCT04493957.
... Prior studies show that older adults with CI/dementia make significantly more driving errors than healthy older drivers in adjustment to stimuli, lane maintenance, speed regulation, vehicle positioning, self-directed driving, and their overall on-road driving performance (Barco et al., 2015;Chee et al., 2017;Eramudugolla et al., 2021;Fuermaier et al., 2019;Griffith et al., 2013;Hird et al., 2016;Jacobs et al., 2017;Wadley et al., 2009). Although these studies identify challenging driving situations in older drivers with CI/dementia, they do not specifically examine the types of driving errors that contribute to pass/fail outcomes on the onroad assessment. ...
Full-text available
Older drivers with cognitive impairment (CI)/dementia make significantly more driving errors than healthy controls; however, whether driving errors are predictive of pass/fail outcomes in older drivers with CI/dementia are unclear. This study determined the driving errors that predicted failing an on-road assessment in drivers with CI. We retrospectively collected comprehensive driving evaluation data of 80 participants (76.1 ± 9.3 years) from an Ontario driving assessment center. Adjustment to stimuli (area under the curve [AUC] = 0.88), lane maintenance (AUC = 0.84), and speed regulation errors (AUC = 0.85) strongly predicted pass/fail outcomes. Worse performance on the Trails B (time) and Useful Field of View® (Subtest 2, Subtest 3, and risk index) were significantly correlated with adjustment to stimuli ( p < .05), lane maintenance ( p < .05), and speed regulation errors ( p < .05). Adjustment to stimuli, lane maintenance, and speed regulation errors may be critical indicators of failing an on-road assessment in older drivers with CI. Prioritizing these errors may help identify at-risk drivers.
... This impairment may be mild, as in mild cognitive impairment (MCI) [25,26], or serious, as in dementia, most commonly due to Alzheimer's disease (AD), interfering with instrumental activities of daily living [27,28]. The driving ability of cognitively impaired patients is of great clinical and public health importance, and extensive research has been conducted in this field [29][30][31][32][33][34]. Beyond the cognitive [35,36], and neuropsychiatric symptoms [37,38], patients with MCI or AD often present motor symptoms [10,39,40]. ...
Background Driving is a complex task requiring the integrity and the cooperation of cognition, motor, and somatosensory skills, all of which are impacted by neurological diseases.Objective Identification of neurologist’s role when assessing fitness to drive of cognitively impaired individuals.Methods We performed a systematic review of the guidelines/recommendations (G/Rs) regarding the evaluation of driving fitness of patients with mild cognitive impairment (MCI) and/or dementia. Emphasis was put on the neurological and neuropsychological aspects of the evaluation.ResultsEighteen G/Rs were included in the review (9 national guidelines, 5 recommendation papers, 3 consensus statements, and 1 position paper). All G/Rs referred to drivers with dementia and 9/18 referred to drivers with MCI. A common approach among G/Rs is the initial trichotomization of patients in safe to drive, unsafe to drive, and undetermined cases, which are referred to a second-line evaluator. First-line evaluators are general practitioners in 10/18 G/Rs; second-line evaluators are neurologists in 7/18 G/Rs. Specific neuropsychological tests are proposed in 11/18 G/Rs and relative cut-off values in 7/18. The most commonly used tests are the MMSE, TMT, and CDT. A thorough neurological examination is proposed in only 1/18 G/R.Conclusion Although extensive multi-disciplinary research has provided useful information for driving behavior of cognitively impaired individuals, we are still far from a widely accepted approach of driving ability evaluation in this increasing population. A comprehensive assessment from a multi-disciplinary team in which the neurologist plays a critical role seems to be required, although this has not yet been implemented in any G/Rs.
... 28 In a study that studied road driving behavior in older drivers with mild cognitive impairment, MCI safe drivers had higher levels of attention, memory, and speech impairment, and MCI unsafe drivers had higher levels of space-time, executive function, and attention impairment. 29 Driving simulator studies of older drivers have found that MMSE is highly predictive of driver safety. 30 In this study, the MoCA-K evaluation tool was used for cognitive function, and the MoCA-K evaluation tool is an evaluation tool similar to MMSE, which measures mild cognitive impairment. ...
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Purpose: In this study, we investigated the effect of visual and cognitive functions of elderly taxi drivers on safe driving behavior. We aimed to identify factors that interfere with safe driving in an aging Korean society in elderly taxi drivers. Participants and methods: A total of 203 elderly taxi drivers, aged >65, working at 3 companies in a single city were assessed over 4 weeks from December 1 to December 30, 2017, using the Motor-Free Visual Perception Test, Korean Montreal Cognitive Assessment, and Korean Safe Driving Behavior Measure. To examine the effects of cognitive and visual functions on driving behavior, we performed a stepwise multiple linear regression analysis (p<0.05). Results: All 4 subdomains of safe driving behaviors were significantly correlated with the cognitive subdomains of attention and abstraction and the visual perception subdomains of visual closure 1 and figure-ground. Conclusion: More systematic assessments of the relationship between driving behavior and cognitive and visual function in elderly individuals are needed.
Background Driving simulators are effective tools to evaluate the driving abilities of patients with stroke. They can introduce various driving scenarios which will greatly benefit both the assessors and drivers. However, there is still no guidelines by which driving scenarios should be introduced in the driving assessment. Objectives We conducted a systematic review to examine the utilization of driving scenarios and environments in the simulator-based driving assessment for patients with stroke. Methods A systematic review was conducted following PRISMA. We searched PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library databases in January and June 2022 to identify eligible articles published since 2010. Results Our searches identified 1,614 articles. We included 12 studies that applied driving simulators to assess the driving performance of patients with stroke. The driving scenarios were categorized into three categories – vehicle controls scenarios, hazard perception scenarios, and trajectory planning scenarios – based on a certain set of driving abilities. The most common driving scenarios are simple navigation (n = 8) and emergency stop (n = 8). The most frequently used driving area is urban (n = 9), and a variety of roads and traffic conditions were found in the included studies. Only 2 studies applied weather conditions, such as the clear and sunny condition or the windy condition. Conclusion It is recommended for future research to consider covering scenarios from the aforementioned three categories and further investigate the benefits of introducing complex weather conditions and localized traffic conditions in the driving assessment.
Objective To describe current practice and outcomes relating to fitness to drive for people with mild cognitive impairment (MCI) attending a specialist driving clinic. Methods Retrospective medical record audit from a driving fitness assessment clinic at a tertiary medical centre, South Australia, from 2015 to 2019. Results Of 100 notes audited, n = 40 had a documented diagnosis of MCI and n = 60 had subjective cognitive concerns characteristic of MCI. Participants mean age was 80.0 years (SD 6.7), and mean Mini-Mental State Examination score was 26.1 (SD 2.1). Medical practitioners completed a comprehensive initial assessment relating to medical fitness to drive, considering scores from a cognitive assessment battery and non-cognitive factors (driving history, current driving needs, vision, physical abilities and collateral from family). After the initial assessment, most participants (84%) were referred for a practical on-road assessment, before receiving a final driving recommendation. Over half of participants continued driving (51%), most with conditions, while 35% ceased driving. Outcomes for the remaining 14% are unknown as we were unable to determine whether the practical assessment (11%) or lessons (3%) were completed. Conclusions Driving outcomes for people with MCI with questionable driving capabilities are variable, with both cognitive and non-cognitive factors important in guiding medical fitness to drive recommendations. There is a need for more driving clinics to provide in-depth assessment for people with MCI who demonstrate uncertain driving capabilities and improved support for decision-making in other non-driving specialist settings.
Background: The driving behavior of patients with mild Alzheimer's disease dementia (ADD) and patients with mild cognitive impairment (MCI) is frequently characterized by errors. A genetic factor affecting cognition is apolipoprotein E4 (APOE4), with carriers of APOE4 showing greater episodic memory impairment than non-carriers. However, differences in the driving performance of the two groups have not been investigated. Objective: To compare driving performance in APOE4 carriers and matched non-carriers. Methods: Fourteen APOE4 carriers and 14 non-carriers with amnestic MCI or mild ADD underwent detailed medical and neuropsychological assessment and participated in a driving simulation experiment, involving driving in moderate and high traffic volume in a rural environment. Driving measures were speed, lateral position, headway distance and their SDs, and reaction time. APOE was genotyped through plasma samples. Results: Mixed two-way ANOVAs examining traffic volume and APOE4 status showed a significant effect of traffic volume on all driving variables, but a significant effect of APOE4 on speed variability only. APOE4 carriers were less variable in their speed than non-carriers; this remained significant after a Bonferroni correction. To further examine variability in the driving performance, coefficients of variation (COV) were computed. Larger headway distance COV and smaller lateral position COV were observed in high compared to moderate traffic. APOE4 carriers had smaller speed COV compared to non-carriers. Conclusion: The lower speed variability of APOE4 carriers in the absence of neuropsychological test differences indicates reduced speed adaptations, possibly as a compensatory strategy. Simulated driving may be a sensitive method for detecting performance differences in the absence of cognitive differences.
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Many studies use the global clinical dementia rating (CDR) of 0.5 as a criterion for mild cognitive impairment, but past studies have not fully discussed its validity. The authors developed the ABC Dementia Scale (ABC-DS) to accurately monitor the changes in activities for daily living, behavioral and psychological symptoms of dementia, and cognitive function. When we carried out a cluster analysis of ABC-DS scores of 110 individuals for whom global CDR was 0.5, there were three groups with different levels of activities for daily living and cognitive function. O'Bryant et al. proposed a new guideline to stage dementia using the CDR sum of boxes scores (CDR-SOB). We used their proposal and ABC-DS scores to evaluate the validity of CDR 0.5 as a definition of mild cognitive impairment (MCI). We concluded that the CDR-SOB scores and ABC-DS score are more accurate than global CDR of 0.5 for specifying individuals with MCI.
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Background: With population aging, drivers with mild cognitive impairment (MCI) are increasing; however, there is little evidence available regarding their safety. Objective: We aimed to evaluate risk of unsafe on-road driving performance among older adults with MCI. Method: The study was a cross-sectional observational study, set in Canberra, Australia. Participants were non-demented, current drivers (n = 302) aged 65 to 96 years (M = 75.7, SD = 6.18, 40% female) recruited through the community and primary and tertiary care clinics. Measures included a standardized on-road driving test (ORT), a battery of screening measures designed to evaluate older driver safety (UFOV®, DriveSafe, Multi-D), a neurocognitive test battery, and questionnaires on driving history and behavior. Results: Using Winblad criteria, 57 participants were classified as having MCI and 245 as cognitively normal (CN). While the MCI group had a significantly lower overall safety rating on the ORT (5.61 versus 6.05, p = 0.03), there was a wide range of driving safety scores in the CN and MCI groups. The MCI group performed worse than the CN group on the off-road screening tests. The best fitting model of predictors of ORT performance across the combined sample included age, the Multi-D, and DriveSafe, classifying 90.4% of the sample correctly. Conclusion: Adults with MCI exhibit a similar range of driving ability to CN adults, although on average they scored lower on off-road and on-road assessments. Driving specific tests were more strongly associated with safety ratings than traditional neuropsychological tests.
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Intersections are one of the most complex and cognitively demanding driving situations. Individuals with dementia and, more precisely, Alzheimer’s disease (AD), may face additional challenges negotiating intersections given the nature of their cognitive decline, which often includes deficits of attention. We developed a comprehensive evaluation scheme to assess simulated driving performance at intersections. The evaluation scheme captured all types of errors that could occur during preparation (i.e., prior to the intersection), execution (i.e., during the intersection), and recovery (i.e., after the intersection). Using the evaluation scheme, intersection behaviour in a driving simulator among 17 drivers with mild AD was compared to that of 21 healthy controls. The results indicated that across all types of intersections, mild AD drivers exhibited a greater number of errors relative to controls. Drivers with mild AD made the most errors during the preparation period leading up to the intersection. These findings present a novel approach to analyzing intersection behaviour and contribute to the growing body of research on dementia and driving.
Driver distraction is one major cause of road traffic accidents. In order to avoid distraction-related accidents it is important to inhibit irrelevant stimuli and unnecessary responses to distractors and to focus on the driving task, especially when unpredictable critical events occur. Since inhibition is a cognitive function that develops until young adulthood and decreases with increasing age, young and older drivers should be more susceptible to distraction than middle-aged drivers. Using a driving simulation, the present study investigated effects of acoustic and visual distracting stimuli on responses to critical events (flashing up brake lights of a car ahead) in young, middle-aged, and older drivers. The task difficulty was varied in three conditions, in which distractors could either be ignored (perception-only), or required a simple response (detection) or a complex Go-/NoGo-response (discrimination). Response times and error rates to the critical event increased when a simultaneous reaction to the distractor was required. This distraction effect was most pronounced in the discrimination condition, in which the participants had to respond to some of the distracting stimuli and to inhibit responses to some other stimuli. Visual distractors had a stronger impact than acoustic ones. While middle-aged drivers managed distractor inhibition even in difficult tasks quite well (i.e., when responses to distracting stimuli had to be suppressed), response times of young and old drivers increased significantly, especially when distractor stimuli had to be ignored. The results demonstrate the high impact of distraction on driving performance in critical traffic situations and indicate a driving-related inhibition deficit in young and old drivers.
Background/objectives: Most forms of dementia are associated with progressive cognitive and noncognitive impairments that can severely affect fitness to drive. Whether safe driving is still possible in the single case, however, is often difficult to decide and may be dependent on both severity and type of the respective dementia syndrome. Particularly in early disease stages, Alzheimer disease dementia (ADD) and different types of non-Alzheimer dementias, such as vascular dementia (VaD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), and Parkinson disease dementia (PDD), might differentially affect fitness to drive. Design: To examine the effects of severity and type of dementia on driving fitness, we conducted a systematic review with qualitative narrative synthesis, involving different driving outcomes in different forms and stages of dementia. Setting: Literature research included MEDLINE and PsycINFO databases with a focus on the most relevant and recent publications on the topic. Participants: The population of interest included older drivers in different stages of ADD and different forms of non-Alzheimer dementias (VaD, FTD, DLB, and PDD). Measurements: Narrative description of driving outcomes in the population of interest. Results: Overall, previous studies suggest that driving fitness is severely impaired in moderate and severe dementia, irrespective of the type of dementia. In milder disease stages, fitness to drive appears to be more severely impaired in non-Alzheimer dementias than in ADD, since the non-Alzheimer syndromes are not only associated with driving-relevant cognitive but noncognitive risk factors, such as behavioral or motor symptoms. Conclusions: Based on these findings, practical recommendations are presented, including a risk evaluation for driving safety, depending on severity and type of different dementia syndromes.
Background/objective: Neurodegenerative disorders impact fitness to drive of older drivers, but on-road driving studies investigating patients with different neurodegenerative disorders are scarce. A variety of driving errors have been reported in patients with Alzheimer's disease (AD), but it is unclear which types of driving errors occur most frequently. Moreover, patients with other neurodegenerative disorders than AD typically present with different symptoms and impairments, therefore different driving errors may be expected. Methods: Patients with AD (n = 80), patients with other neurodegenerative disorders with cognitive decline (i.e., vascular dementia, frontotemporal dementia, dementia with Lewy bodies/Parkinson's disease, n = 59), and healthy older drivers (n = 45) participated in a fitness-to-drive assessment study including on-road driving. Results: Patients with AD performed significantly worse than healthy older drivers on operational, tactical, visual, and global aspects of on-road driving. In patients with AD, on-road measures were significantly associated with 'off-road' measures. Patients with neurodegenerative disorders other than AD showed large overlap in the types of driving errors. Several driving errors were identified that appear to be characteristic for patients with particular neurodegenerative disorders. Conclusion: Patients from each group of neurodegenerative disorders commonly display tactical driving errors regarding lane positioning, slow driving, observation of the blind spot, and scanning behavior. Several other tactical and operational driving errors, including not communicating with cyclists and unsteady steering, were more frequently observed in patients with non-AD neurodegenerative disorders. These findings have implications for on-road and 'off-road' fitness-to-drive assessments for patients with neurodegenerative disorders with cognitive decline.
Dementia is a risk factor for unsafe driving. Therefore, an assessment strategy has recently been developed for the prediction of fitness to drive in patients with the Alzheimer disease (AD). The aim of this study was to investigate whether this strategy is also predictive of fitness to drive in patients with non-AD dementia, that is, vascular dementia, frontotemporal dementia, and dementia with Lewy bodies. Predictors were derived from 3 types of assessment: clinical interviews, neuropsychological tests, and driving simulator rides. The criterion was the pass-fail outcome of an official on-road driving assessment. About half of the patients with non-AD dementia (n=34) failed the on-road driving assessment. Neuropsychological assessment [area under the curve (AUC)=0.786] was significantly predictive of fitness to drive in patients with non-AD dementia, however, clinical interviews (AUC=0.559) and driving simulator rides (AUC=0.404) were not. The fitness-to-drive assessment strategy with the 3 types of assessment combined (AUC=0.635) was not found to significantly predict fitness to drive in non-AD dementia. Different types of dementia require different measures and assessment strategies.
Guidelines that physicians use to assess fitness to drive for dementia are limited in their currency, applicability, and rigour of development. Therefore, we performed a systematic review to determine the risk of motor vehicle collisions (MVCs) or driving impairment caused by dementia, in order to update international guidelines on driving with dementia. Seven literature databases (e.g. MEDLINE, CINAHL, Embase, etc.) were searched for all research studies after 2004, containing participants with mild, moderate, or severe dementia. From the retrieved 12,860 search results, we included nine studies in this analysis, involving 378 participants with dementia and 416 healthy controls. Two studies reported on self/informant-reported MVC risk, one revealing a 4-fold increase in MVCs per 1000 miles driven per week in three years prior, and the other showing no statistically significant increase over the same time span. We found medium to large effects of dementia on driving abilities in six of the seven recent studies that examined driving impairment. We also found that persons with dementia were much more likely to fail a road test than healthy controls (RR 10.77, 95% CI 3.00 - 38.62, z = 3.65, p < 0.001), with no significant heterogeneity (X² = 1.50, p = 0.68, I² = 0%) in a pooled analysis of four studies. Although the limited data regarding MVCs are equivocal, even mild stages of dementia place patients at a substantially higher risk of failing a performance-based road test and of demonstrating impaired driving abilities on the road.
The objective of this research was the analysis of the driving performance of drivers with Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD), in different road and traffic conditions, on the basis of a driving simulator experiment. In this experiment, healthy “control” drivers, patients with MCI, and patients with AD, drove at several scenarios at the simulator, after a thorough neurological and neuropsychological assessment. The scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of healthy and impaired drivers was analysed and compared by means of Repeated Measures General Linear Modelling techniques. A sample of 75 participants was analysed, out of which 23 were MCI patients and 14 were AD patients. Various driving performance measures were examined, including longitudinal and lateral control measures. The results suggest that the two examined cerebral diseases do affect driving performance, and there were common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, cognitively impaired drivers drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. The group of patients had difficulties in all road and traffic environments, and especially when traffic volume was high. Most importantly, both cerebral diseases appear to significantly impair reaction times at incidents. The results of this research suggest that compensatory behaviours developed by impaired drivers are not adequate to counterbalance the direct effects of these cerebral diseases on driving skills. They also demonstrate that driving impairments increase as cognitive impairments become more severe (from MCI to AD).
The areas of driving impairment characteristic of mild cognitive impairment (MCI) remain unclear. This study compared the simulated driving performance of 24 individuals with MCI, including amnestic single-domain (sd-MCI, n = 11) and amnestic multiple-domain MCI (md-MCI, n = 13), and 20 age-matched controls. Individuals with MCI committed over twice as many driving errors (20.0 versus 9.9), demonstrated difficulty with lane maintenance, and committed more errors during left turns with traffic compared to healthy controls. Specifically, individuals with md-MCI demonstrated greater driving difficulty compared to healthy controls, relative to those with sd-MCI. Differentiating between different subtypes of MCI may be important when evaluating driving safety.