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Can Driving-Simulator Training Enhance Visual Attention, Cognition, and Physical Functioning in Older Adults?

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Virtual reality offers a good possibility for the implementation of real-life tasks in a laboratory-based training or testing scenario. Thus, a computerized training in a driving simulator offers an ecological valid training approach. Visual attention had an influence on driving performance, so we used the reverse approach to test the influence of a driving training on visual attention and executive functions. Thirty-seven healthy older participants (mean age: 71.46 ± 4.09; gender: 17 men and 20 women) took part in our controlled experimental study. We examined transfer effects from a four-week driving training (three times per week) on visual attention, executive function, and motor skill. Effects were analyzed using an analysis of variance with repeated measurements. Therefore, main factors were group and time to show training-related benefits of our intervention. Results revealed improvements for the intervention group in divided visual attention; however, there were benefits neither in the other cognitive domains nor in the additional motor task. Thus, there are no broad training-induced transfer effects from such an ecologically valid training regime. This lack of findings could be addressed to insufficient training intensities or a participant-induced bias following the cancelled randomization process.
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Research Article
Can Driving-Simulator Training Enhance Visual Attention,
Cognition, and Physical Functioning in Older Adults?
Mathias Haeger ,
1
Otmar Bock ,
1
Daniel Memmert,
2
and Stefanie H¨
uttermann
2
1
Institute of Physiology and Anatomy, German Sport University Cologne, Am Sportpark M ¨
ungersdorf 6, 50933 Cologne, Germany
2
Institute of Training and Computer Science in Sport, German Sport University Cologne, Am Sportpark M¨
ungersdorf 6, 50933
Cologne, Germany
Correspondence should be addressed to Mathias Haeger; m.haeger@dshs-koeln.de
Received 8 September 2017; Revised 30 November 2017; Accepted 10 January 2018; Published 7 February 2018
Academic Editor: Jean-Francois Grosset
Copyright ©2018 Mathias Haeger et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Virtual reality oers a good possibility for the implementation of real-life tasks in a laboratory-based training or testing scenario.
us, a computerized training in a driving simulator oers an ecological valid training approach. Visual attention had an inuence
on driving performance, so we used the reverse approach to test the inuence of a driving training on visual attention and
executive functions. irty-seven healthy older participants (mean age: 71.46 ±4.09; gender: 17 men and 20 women) took part in
our controlled experimental study. We examined transfer eects from a four-week driving training (three times per week) on
visual attention, executive function, and motor skill. Eects were analyzed using an analysis of variance with repeated mea-
surements. erefore, main factors were group and time to show training-related benets of our intervention. Results revealed
improvements for the intervention group in divided visual attention; however, there were benets neither in the other cognitive
domains nor in the additional motor task. us, there are no broad training-induced transfer eects from such an ecologically
valid training regime. is lack of ndings could be addressed to insucient training intensities or a participant-induced bias
following the cancelled randomization process.
1. Introduction
Many studies strongly suggest that attention declines with
advancing age. is has been documented via several
components of visual attention, such as selective attention
[1, 2], sustained attention [1], distributed attention [3, 4], and
divided attention [2]. ere is also a decline of executive
functions in old age, which is indicated by reduced in-
hibitory control, declined working memory, or slower
cognitive exibility [5–7]. e age-related decits of exec-
utive functions [7–10] and visual attention can be amelio-
rated by practice [11, 12]. us, the purpose of our study is to
evaluate a training approach for these cognitive functions.
Cognitive functions can be trained by classic cognitive
training [13], computerized brain training programs [12],
and video games [7, 14–18] (for a corresponding review, see
[19]). A further review in healthy older adults showed that
computerized training could improve working memory,
cognitive speed of processing, or visuospatial abilities, but
not executive functions or attention [20]. e authors
conducted domain-specic analyses for cognitive functions
but neglected dierences regarding the kind of computer-
ized training. us, dierent types of programs might have
dierent eects since action video games, for example, are
probably eective based on fast reactions and management
of multiple tasks at the same time [21].
It has been suggested that cognitive training based on
video/computer games is more attractive than cognitive
training based on standard laboratory tasks, as it is diverse
and motivating rather than stereotyped and dull [22–24].
However, training benets seem to be smaller rather than
larger if compared to laboratory-type training [19, 22, 25].
One possible explanation is that trained activities were not
familiar and realistic; that is, they lacked ecological validity
[26, 27]: the cognitive benets of training regimes are
thought to increase when trained activities are similar to
situations of everyday life [28]. us, an ecologically valid
training reects everyday actions and could be helpful,
Hindawi
Journal of Aging Research
Volume 2018, Article ID 7547631, 9 pages
https://doi.org/10.1155/2018/7547631
especially for older adults, to pass demanding situations
(i.e., driving on dangerous crossroads). In such situations,
people also have to manage multiple tasks at the same time,
and studies including multitasking training have already
showed cognitive benets [14].
One possible approach for a diverse, motivating, and
ecologically valid cognitive training regime is driving in a car
simulator. It requires selective, sustained, distributed, and
divided attention as well as executive abilities, that is, cog-
nitive skills which are known to decay in older age (see above).
Furthermore, there is some evidence that visual attention
[29, 30] and working memory [31] are connected with real-life
driving performance. Although two earlier studies found little
benets of driving-simulator training for older participants’
attention, training quantity was limited to 10 ×40 minutes in
one [23] and 2 ×120 minutes in the other study [32]. Ad-
ditionally, results from Casutt and colleagues [23] only
showed eects in an overall cognitive score with the greatest
impact on simple and complex choice reaction times. It re-
mains questionable whether components of visual attention
were improved. Furthermore, task realism and benets for
visual attention were reduced by using only one central screen
to display the driving scenarios. We believe that more sub-
stantial benets of driving-simulator training are conceivable
since they already have been observed in reciprocal ap-
proaches, in which cognitive training targeting speed of
processing [11, 32, 33] or attention and dual-tasking training
[34] improved driving performance. In our approach, we
want to reevaluate the cognitive benets of driving-simulator
training using more and longer sessions as well as a larger
visual display. We reasoned that a larger display may provide
a more immersive experience and thus facilitate top-down
modulation of visual attention (i.e., upcoming moving ob-
stacles near the lane), which was recently connected with the
prefrontal cortex (PFC) [2, 35]. Furthermore, there is growing
evidence that the PFC moreover regulates the focus of at-
tention, selects information, and controls executive functions
[36]. So, we decided to expand the scope of outcome variables
and include executive functions. Besides the theoretical
connection, executive functions are known to be associated
with driving performance [37] but have not been evaluated
yet in the context of driving-simulator training.
A further aspect that has also not been evaluated before is
functional mobility as a far transfer from driving-simulation
training. We took that into consideration because driving
involves limb movements and requires complex visual
processing—two abilities associated with mobility and risk
of falls in older adults [38–40]—and also because there is an
association between cognition (i.e., executive function and
dual tasking) and gait parameters (i.e., walking speed) [41].
So, we decided to assess functional mobility by the Timed
Up-and-Go (TUG) test, an established marker of reduced
mobility [42]. From our point of view, it is an interesting
insight concerning a relationship between a computerized
training (i.e., including cognitive and coordinative aspects)
and a functional parameter, which was slightly examined
before [43].
Summing up, we reevaluated driving-simulation
training in older adults [23, 32] to extend existing
knowledge about eects on visual attention, executive
functions, and mobility. Since other computerized training
regimes showed benets on dierent cognitive functions
[19, 20, 44], we hypothesized that our ecologically valid
training (1) would improve dierent parameters of visual
attention (distributed, divided, selective, and sustained
attention); (2) would improve core executive functions
(working memory and task switching); and (3) would show
a positive far transfer from cognitive training on functional
abilities, as it is suggested in literature [43].
2. Method
2.1. Sample. Required sample size was calculated by
G-Power®3.1 as follows: earlier attention studies yielded
eect sizes ranging from f0.105 to f0.47 [14–16, 23, 32],
so we expect an eect size of f0.25. For the interaction term
of a 2 (group) ×2 (time) analysis of variance with f0.25,
alpha 0.05, and beta 0.80, correlation among repetitive
measures 0.40, G-Power yielded a required total sample
size of 40 participants. Participants were recruited by dis-
tributing yers on the university campus and, in the city,
sending them out through mailing lists, uploading them on
the Internet, and adding them to newspapers.
We initially planned a randomized controlled trial, using
a computerized block-randomized (two per block, balanced
for groups) allocation; however, we had to abort this process
after a few participants because a lot of interested people
refused their participation (i.e., they only were interested in
one: intervention or control group). Additionally, recruitment
was slow and the dropout rate was high because of the high
time demand of this study. So, we asked all respondents
personally who met our inclusion criteria to take part in the
intervention group, and if they refused because of the high
time demand, we asked them to participate in the control
group. ere was no additional investigator blinding. Finally,
we had a controlled experimental design without randomi-
zation (Figure 1).
Inclusion criteria were 65 to 80 years of age, no neu-
rological disease, MMSE >23, normal or corrected-to-
normal vision, and car driving experience of no more
than 20 hours per month within the last six months. Ex-
clusion criteria were a low MMSE score (<24), previous or
actual neurological diseases (i.e., stroke, multiple sclerosis),
and daily driving routine. ese criteria were formulated to
bring in older, not cognitive-impaired drivers with minor
driving routine. It was met by 47 respondents, of whom four
dropped out later on because of time limitations and six
because of simulator sickness. In eect, 16 men and women
were retained in the intervention and 21 in the control
group.
2.2. Training. We used a commercially available driving
simulator (Carnetsoft BV, Groningen, NL) which consists of
a computer, three rendering monitors, a steering wheel,
pedals, and a gear shift (Figure 2). e monitors have a 48
diagonal and a 100 HZ frame rate and were positioned on
laboratory tables at eye level in front of a black shroud which
2Journal of Aging Research
blocked vision of the laboratory room. Pedals and the
driver’s seat were adjusted individually for comfort. Steering
wheel, seat, pedals, and gear shift were placed mid between
the center and the left edge of the middle screen to imitate
the drivers’ position in a real car.
e Carnetsoft®software includes a curriculum with
multiple driving scenarios from which the following were
used for training: learn to drive, using a gear shift, emergency
breaking in dierent road settings, driving ecologically, no-
ticing road signs during the drive, and danger of driving after
consuming alcohol. Scenarios consist of rural areas, towns,
highways, or combinations of them and include leading or
oncoming vehicles. During the drive, a female voice gave
driving directions. In addition, participants received short
informal feedback from the instructor after each session
(i.e., driving errors). erefore, we recorded some driving
Aborted randomization (11 people
were randomized)
Enrollment (n= 47)
Intervention group
(i) Allocated and pretested (n= 24)
(ii) Dropout (n= 8), due to simulator
sickness and time eort
Control group
(i) Allocated and pretested (n= 23)
(ii) Dropout (n= 2), due to time eort
Posttest
(i) Intervention group (n= 16)
(ii) Control group (n= 21)
Analysis
(i) Including both groups (n= 37)
(ii) Except for Grid Span test (n= 36),
due to missing data in one
control participant
Figure 1: Study owchart describing participants’ allocation in both groups. Enrollment started in November 2015, and the study was
nished in September 2016.
Gear shi
Steering
wheel
157 cm
60 cm
36 cm
130° 120°
70 cm
60 cm
139 cm
80 cm
Figure 2: Top view of the driving simulator including tables, monitors, seat, gear shift, and steering wheel. Pedals are under the table (not
shown).
Journal of Aging Research 3
parameters (i.e., velocity and lane-adherence) as well as errors
(i.e., missing a stop sign) and reported those afterwards.
Additionally, we yielded information for safer driving
(i.e., scanning the upcoming lane for potential danger).
Training took four weeks with three training sessions of about
50–60 minutes per week. It began with simple scenarios
whose diculty level increased gradually in three main steps.
So, the rst session was for familiarization of our participants
with the car dynamics of the driving simulator (i.e., using
pedals and gear shift). e following ve sessions had more
complex requirements: participants had to enter motorways,
overtake slow driving cars, or drive over a longer period.
During those scenarios we increased trac (i.e., approaching
cars and slower cars ahead), driving durations, and com-
plexity of scenarios (i.e., from a rural area to a city including
pedestrians). Finally, the last six sessions consisted of a ran-
domized order of more challenging tasks: participants should
drive while paying attention to trac signs (i.e., we presented
additional signs on the left and right display), long highway
driving sessions including a lot of trac as well as trac jams,
and brake-reaction tasks. During these scenarios, we addi-
tionally recorded reaction times (i.e., brake reaction or seeing
trac signs) to inform participants about their results in each
session (see above: feedback). A single training session took
place during the week between 9 a.m. and 4 p.m., depending
on availability of our participants.
2.3. Outcome Measures. e following test battery was ad-
ministered before and after training in the intervention
group and four weeks apart in the control group:
(i) e Precue task [45, 46] is a measure of
distributed/spatial attention. Participants respond
after a correct, false, or neutral cue to visual stimuli
presented on the right or left side of a central
xation point. Performance is quantied as a mean
reaction time for correct, false, and neutral cues.
(ii) e D2-Attention task [47] is a measure of selective
and sustained attention. In a computerized version
of this test, participants watch a sequence of items
from the list {ddd d’ d” d’ ppp p’ p” p’} and
have to select all instances of a letter “d” followed by
two dashes (dd” d’) over a time period of six
minutes. Performance is quantied as the number
of correct answers minus the number of errors.
(iii) e Attention Window task [4, 48] is a measure of
multistream divided attention [2]. Participants
watch a sequence of two simultaneously presented
patterns, each consisting of four objects, dark or light
grey triangles and circles. Following the presentation
of each pair, they are asked to indicate the number of
light grey triangles in both patterns, without being
pressed for time. Successive pattern pairs vary quasi-
randomly in the number of light grey triangles and in
the distance from a central xation point. Perfor-
mance is quantied as percentage of correct re-
sponses to both patterns in a pair on each axis
(diagonal, horizontal, and vertical).
(iv) e Grid Span task [49–51] is a measure of spatial
working memory which is related to executive
control. Participants watch a sequence of crosses in
a 4 ×4 grid and are asked to replicate the sequence
immediately thereafter. Sequence length increases
from trial to trial, and performance is quantied as
length of last correctly replicated sequence.
(v) e Switching task [52] is a measure of executive
exibility. Participants watch a sequence of small
and large fruits and vegetables and have to indicate
either the size (task A) or their nature (fruit versus
vegetable; task B). In single blocks, only one task is
asked for; in switching blocks, the task sequence
AABBAABB and so on is asked for. Performance is
quantied as mean reaction time in each task.
(vi) e PAQ-50+ [53] is a retrospective physical ac-
tivity assessment, covering the preceding four
weeks.
(vii) e Timed Up-and-Go task [42, 54] is a functional
test of gait and balance. Participants stand up from
a chair upon command, walk three meters, turn,
and walk back to sit down again, all with their
habitual velocity. Performance is quantied as
mean completion time across three test repetitions.
(viii) e Mini-Mental State Examination (MMSE) [55]
was administered as a screening tool on pretests
only.
2.4. Procedure. e study was preapproved by the Ethics
Committee of the German Sport University Cologne, and all
participants signed an informed consent before testing
started. Each participant was tested individually. During an
initial interview, participants were informed about the test
battery. Control persons were told that a follow-up evalu-
ation will check whether performing the test battery had
lasting eects, and training persons were told about the
driving intervention and its possible eects. In sum, testing
took approximately one and a half hours. Each session took
place during the week between 9 a.m. and 5 p.m., and we
tried to maintain the individual testing times from pre- to
posttesting.
First, MMSE and PAQ-50+ were completed in the form
of an interview. Next, computerized versions of Precue, D2-
Attention, Grid Span, Switching, and Attention Window
tasks were administered in a randomized order, which was
the same during pre- and posttest. Finally, the TUG was
conducted three times using a manually operated clock and
a chair without armrest.
We preregistered our study in the Open Science
Framework (OSF) but unfortunately had to change two
methodological aspects later on. Due to slow participant
recruitment and limited availability of laboratory space, we
had to give up randomized group assignment and cancel the
retention test twelve weeks after training.
2.5. Statistics. Data from the D2-Attention task, each axis of
the Attention Window task, the Grid Span task, and TUG
4Journal of Aging Research
were submitted to a 2 (group: intervention and control) ×2
(time: pre and post) analysis of variance (ANOVA) with
repeated measures on the latter factor. For the Switching
task, we used a 2 (group) ×2 (time) ×3 (trial: single, non-
switching, and switching) ANOVA with repeated measures
on the latter two factors. For the Precue task, we conducted
a 2 (group) ×2 (time) ×3 (cue: correct, neutral, and false)
ANOVA with repeated measures on the latter two factors.
Furthermore, we conducted the Mauchly test for sphericity
for the Switching task and the Precue task and used
a GreenhouseGeisser correction in case of a sphericity
violation. Regarding this number of tests, we conducted
a BonferroniHolm correction for multiple testing to ad-
just pvalues. Training benets should emerge as signicant
group ×time interactions in these analyses. Interaction
eects (i.e., group ×time) represent our primary outcome;
main eects of these tests are secondary outcome.
As further secondary outcome, we analyzed participants’
characteristics using an independent t-test, a two-
dimensional (group and education) chi-square test, and
a 2 (group) ×2 (time) ANOVA to reveal dierences between
our groups.
3. Results
Table 1 shows the demographic characteristics of all
participants (n37) included in data analysis. None of the
scores diered signicantly between groups at the pretest.
Furthermore, there were no group- or time-dependent
eects in subjective physical activity (F
(1,35)
0.96,
MSE 1516.202, p>0.05).
As expected, the Precue task yielded with Greenhouse
Geisser correction (χ
2
(2)
7.01, p0.03,ε0.843) a signi-
cant main eect for cue (F
(1.69,59.01)
19.75, MSE 786.476,
p<0.01). ere were no other signicant dierences, notably
not for group ×time ×cue (F
(1.70,59.62)
0.79, MSE 523.931,
p>0.05). e D2-Attention task yielded a signicant main
eect for time (F
(1,35)
13.25, MSE 418.892, p<0.01), but no
other signicant eects, in particular not for group ×time
(F
(1,35)
0.00, MSE 418.892, p>0.05). For the Attention
Window task, we found a signicant group ×time interaction
for the horizontal axis (F
(1,35)
4.46, MSE 0.003, p0.04,
η
2
0.113); however, this eect did not remain after
Bonferroni–Holm correction. ere was also neither a sig-
nicant eect for the other two axes (diagonal: F
(1,35)
0.14,
MSE 0.008, p>0.05, η
2
0.004; vertical: F
(1,35)
0.01,
MSE 0.007, p>0.05, η
2
0.000) nor any other eects.
Figure 3 illustrates that, in the Attention Window task,
accuracy on the horizontal axis increased from pre to post in
the intervention group but decreased in the control group;
this tendency was absent on the other two axes.
ere were no signicant eects on the Grid Span task,
notably no signicant group ×time interaction (F
(1,34)
1.86,
MSE 0.627, p>0.05). e Switching task yielded with
GreenhouseGeisser correction (χ
2
(2)
17.03, p<0.01,ε0.717)
signicant eects for trial (F
(1.44,50.22)
37.39, MSE 12091.056,
p<0.01) and trial ×time (F
(1.61,56.33)
3.71, MSE 5061.153,
p0.04); but again after BonferroniHolm correction the
trial ×time interaction did not remain signicant (p>0.01).
Finally, the Timed Up-and-Go task yielded no signicance,
notably not for group ×time (TUG: F
(1,35)
3.72, MSE 0.291,
p>0.05). Table 2 summarizes all outcome scores. Further
statistical outcomes are presented in an additional table as
Supplementary Material (Table S1).
4. Discussion
We evaluated a four-week training program in a driving
simulator with a wide eld of view, administering three
sessions of about one hour duration per week. Outcome
measures comprised visual attention, executive functions,
and physical abilities. Because of recruitment problems and
a 28% dropout rate in the intervention group due to simulator
sickness, only 16 participants completed in the intervention
group and 21 completed in the control group. We found
signicant benets of training for divided visual attention
along the horizontal axis; however, statistical signicance
disappeared after correction for multiple testing. Further-
more, we did not nd any signicant eects neither for other
cognitive measures nor for functional mobility. e lack of
more substantial training benets cannot be attributed to
group dierences regarding demographic, cognitive, or
physical baseline scores (Tables 1 and 2).
First, we will discuss our results in relation to other
driving-simulator studies. Casutt and colleagues [15] de-
scribed a signicant training benet on overall cognitive
performance, but regarding visual attention, those authors
presented only descriptive statistics with small eects
(d0.13–0.31) for selective attention, eld of vision, and
divided attention. Roenker and colleagues [32] described no
Table 1: Mean values (and standard deviation) of demographic characteristics, MMSE, and PAQ-50+ scores in the intervention and control
group.
Intervention Control Statistics
Age (years) 70.25 (±3.77) 72.38 (±4.17) t
(35)
1.61, p>0.05
Gender (men/women) 8/8 9/12
Education (1/2) 8/8 9/12 c
2
(1,N37)
0.19, p>0.05
Driving time (hours per month) 10.63 (±7.21) 9.88 (±8.39) t
(35)
0.02, p>0.05
MMSE (score) 28.63 (±1.09) 28.62 (±1.20) t
(35)
0.28, p>0.05
PAQ-50+ (MET/week) Pre 126.37 (±67.60) 125.35 (±66.84) T: F
(1,35)
2.66, p>0.05, η
2
0.071
GT: F
(1,35)
0.96, p>0.05, η
2
0.027Post 102.50 (±39.06) 119.40 (±65.76)
Statistics included t-tests, chi-square test (for education: 1 A level, 2 O level), and ANOVA (G group eects, T time eects) to analyze group dierences.
Journal of Aging Research 5
training benets on the Useful Field of View (UFOV®), a test
of various aspects of visual attention and perception. e
present study is therefore in line with earlier work since
training had no substantial benets for most cognitive
functions. In a rst analytical step, we found benets for the
horizontal component of divided visual attention, which did
not remain after a further statistical correction. However, we
calculated an eect size for that component (overall eect
size for divided attention, η
2
0.039; conversion according
to [56, 57]) as d0.403, which is slightly more than the value
reported by Casutt et al. [23]. We attribute this stronger
eect in our study to the dramatically wider eld of view of
our simulator (see Introduction).
Our results can also be compared to those on video-
game training. Action video games, characterized by
moving objects, fast responses, and multiple tasks [21, 58],
led to improvements in selective [16] and sustained at-
tention [14], but this was not necessarily the case for other
types of video games: a review of computerized cognitive
training in older adults conrmed training benets for
visuospatial abilities but not for attention [20]. However,
the authors of this review did not dierentiate, for example,
between computerized cognitive training (e.g., [12]) and
video games (e.g., [15]). Especially, in the case of visual
attention, a more detailed dierentiation would be needed
since action video games appeared to be more eective than
“slower” video games [21]. So, for action video games,
a recent review showed moderate benets for older adults in
attention and visuospatial abilities [44]. We assume that, in
our study, improvements of attention were limited since
fast responses and multitasking occurred less frequently
than in action video games.
0
0.1
Intervention Control
Group
Pre
Post
0.2
Accuracy
0.3
0.4
0.5
0
0.1
Intervention Control
Group
Pre
Post
0.2
Accuracy
0.3
0.4
0.5
0
0.1
Intervention Control
Group
Pre
Post
0.2
Accuracy
0.3
0.4
0.5
Figure 3: Response accuracy in the Attention Window test, plotted separately for the three axes and for the intervention group and the
control group. Boxes indicate across-participant means and error bars the pertinent standard errors. (a) Diagonal. (b) Horizontal.
(c) Vertical.
Table 2: Mean values and standard deviation of pre- and posttest scores in the intervention and control group as well as statistical results
(T time, C cue, G group, Tr trial).
Test Intervention Control Statistics
Precue “false” (ms) Pre 371.63 (±64.96) 393.75 (±71.48) T: F
(1,35)
0.15, p>0.05, η
2
0.004
TG: F
(1,35)
0.05, p>0.05, η
2
0.001
C: F
(1.69,59.01)
19.75, p<0.00, η
2
0.361
CG: F
(1.69,59.01)
1.68, p>0.05, η
2
0.046
TC: F
(1.70,59.62)
0.46, p>0.05, η
2
0.013
GTC: F
(1.70,59.62)
0.79, p>0.05, η
2
0.022
Post 374.38 (±76.03) 391.77 (±77.68)
Precue “neutral” (ms) Pre 362.88 (±61.16) 377.48 (±60.41)
Post 355.16 (±50.90) 372.71 (±66.04)
Precue “correct” (ms) Pre 358.92 (±68.31) 357.26 (±41.64)
Post 348.70 (±53.34) 359.86 (±63.12)
D2 (score) Pre 141.13 (±37.81) 134.29 (±36.76) T: F
(1,35)
13.25, p<0.01, η
2
0.275
GT: F
(1,35)
0.00, p>0.05, η
2
0.000Post 158.75 (±36.67) 151.62 (±31.74)
Grid Span (score) Pre 5.63 (±0.72) 4.75 (±0.91) T: F
(1,34)
0.09, p>0.05, η
2
0.003
GT: F
(1,34)
1.86, p>0.05, η
2
0.052Post 5.31 (±1.14) 4.95 (±1.00)
Switching “single” (ms) Pre 839.75 (±120.60) 808.43 (±84.38) T: F
(1,35)
0.82, p>0.05, η
2
0.023
TG: F
(1,35)
0.24, p>0.05, η
2
0.007
Tr: F
(1.44,50.22)
37.39, p<0.00, η
2
0.517
TrG: F
(1.44,50.22)
0.02, p>0.05, η
2
0.000
TTr: F
(1.61,56.33)
3.71, p0.04, η
2
0.096
GTTr: F
(1.61,56.33)
0.43, p>0.05, η
2
0.012
Post 790.35 (±105.56) 758.51 (±122.16)
Switching “nonswitch” (ms) Pre 884.69 (±157.93) 840.80 (±102.71)
Post 855.24 (±123.44) 836.59 (±156.47)
Switching “switch” (ms) Pre 951.24 (±146.64) 905.39 (±153.74)
Post 940.00 (±144.86) 932.36 (±174.00)
TUG (s) Pre 8.70 (±1.40) 8.52 (±1.67) T: F
(1,35)
0.57, p>0.05, η
2
0.016
GT: F
(1,35)
3.72, p>0.05, η
2
0.096Post 8.37 (±1.41) 8.67 (±1.75)
Data from the attention window test are presented in Figure 2.
6Journal of Aging Research
Regarding our results, we have to reject our rst hy-
pothesis that there is a broad impact on older people’s
visual attention from our driving-simulator training. is
is partly in line with previous driving-simulator studies
and “slower” video games.
Unlike earlier driving-simulator studies, earlier video game
research also evaluated the eects of training on executive
functions. Improvements of working memory [14] and task
switching [59] were reported, but a generalized eect on ex-
ecutive functioning is still under discussion [19, 20]. However,
a recent review described moderate eects on executive
functions from action video games [44]. us, in executive
functions, the same dierentiation like in visual attention might
be necessary. We found no eects of driving-simulator training
in executive functions, and our data are therefore in agreement
with the more pessimistic views. In view of these results, we also
had to reject our second hypothesis that driving-simulator
training would induce transfer eects on executive functions.
e use of engaging and ecologically valid training regimes
seems not enough to ensure improved executive functions.
Possibly, training has to specically address those functions, as
was the case in the studies by Anguera et al. [14] and Montani
et al. [59], since the transfer of training benets to unpracticed
tasks may be limited [60].
Another point of criticism pertains to the software used:
the simulated driving tasks were possibly not dicult
enough to challenge participants’ executive and attentional
abilities. At last, we had only six sessions including complex
situations that might be not enough for experienced drivers.
ere should be more visual stimulation to facilitate top-
down modulation, which forms an important part in visual
attention and executive functions [2, 36]. Further studies
should also record participants’ training sessions to analyze
the training progress. us, it would be possible to regulate
the training process individually.
In view of functional mobility, we observed no training
benets for participants’ as assessed by the TUG test. We again
conclude that our ecologically valid training regimes are no
guarantee for a strong transfer of training benets to untrained
abilities. So, taking into account that there were also no cognitive
training benets, we rejected our third hypothesis of a positive
far transfer from cognitive training on functional abilities.
ere are also a few more methodological aspects that
should be discussed. First, our recruited older participants were
healthy, physically active, and still able to drive a car. Regarding
this, benets might only occur in more inactive people [61],and
further studies should also control whether participants ad-
ditionally use their car during the intervention. Secondly, we
used an inactive control group and aborted our randomization
process. erefore, possible dierences in motivation, expert
knowledge (i.e., in computerized training), or individual ar-
rangements of physical activities (i.e., an inactive control group
has more leisure time) could aect our measurements. Further
studies should take these points into account.
5. Limitations
We preregistered our study protocol in the Open Science
Framework (OSF) but have to indicate some methodological
changes. First, we encountered substantial recruitment prob-
lems because of the time and eort involved in participating. As
a consequence, we had to cancel the planned randomization
and instead assigned the rst 24 participants to the intervention
group. Second, six participants from the intervention group
dropped out because of simulator sickness. ird, only a few
participants were willing to undergo follow-up testing, and we
therefore had to cancel that part of our study. Possibly, research
with older participants became so popular in recent years that
the willingness of older persons to contribute to yet another
study has been overstrained. As a consequence of these
methodological issues, there could also be a bias based on
group-related dierences: possibly, our intervention group was
more familiar with a computerized training, more motivated,
or there occurred group dierences in other driving-related
traits. Regarding the last point, we also missed to analyze
personality traits (e.g., motivation, self-ecacy, and driving
behavior) which could further explain dierences between our
groups. We also should have chosen fewer (in view of mul-
tiplicity of analyses) and perhaps other cognitive (i.e., a driving-
related dual task) or functional tests (i.e., leg/hand coordina-
tion task). At last, we did not save results from individual
training sessions for a further analysis (i.e., to detect learning
curves); in this regard, it would also be benecial to evaluate
motivation during the training process since our tasks were too
easy and motivation possibly dropped as time passes.
6. Conclusion
We found no evidence that our diverse and realistic driving-
simulator training would improve attention, executive
functions, and functional mobility. e only marked training
benet was the one on the horizontal component of divided
attention, probably because this component was specically
trained in our horizontally wide display; however, it did not
remain after statistical corrections. Perhaps, this lack of
ndings in our study could be based on a range of meth-
odological aspects. For example, a more complex training
including fast reactions and an individual training progress
might be more stimulating for visual attention and executive
functions. So, in view of further diverse and ecologically valid
interventions, the training process and other methodological
aspects should be reected.
Ethical Approval
All procedures were approved by the Ethics Committee of
the German Sport University Cologne in accordance with
the 1964 Helsinki Declaration and its later amendments.
Consent
All participants signed an informed consent before their
testing started.
Conflicts of Interest
e authors declare that they have no conicts of interest
with respect to the authorship or publication of this article.
Journal of Aging Research 7
Acknowledgments
anks are due to Katharina Reingen and Franziska Gliese
for their assistance in testing and training.
Supplementary Materials
Table S1: Mean values (±standard deviation), 95% con-
dence interval (CI), and statistics for intervention (int) and
control (con) groups. (Supplementary Materials)
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Journal of Aging Research 9

Supplementary resource (1)

... With three-dimensional (3D) VR driving simulators, researchers can create a safe and replicable stimulus, thereby enabling the empirical exploration of responses to interface designs and VR conditions [9]. To simulate real driving performance, it is important that the control device be as similar as possible to real-world circumstances [10]. It was found from the current electric-vehicle designs that seniors employed two main types of control device: a handlebar or a joystick ( Figure 1a,b). ...
... This phenomenon seems to correspond to our fifth hypothesis that there would be an interaction between navigation performance, age, gender, task type, and control device, as supported by the results of both this research and previous studies [1,22]. A possible reason is another correlative effect, such as familiarity, a learning effect [23,24] for the task types, or since the young group adapted to the task types (symmetrical straight route and asymmetrical curved route) faster than the elder group did, which might have eliminated any task effect in the young group [10]. ...
Article
Full-text available
The application of virtual reality in a driving simulation is not novel, yet little is known about the use of this technology by senior populations. The effects of age, gender, control device (joystick or handlebar), and task type on wayfinding proficiency using a virtual reality (VR) driving simulation were explored. The driving experiment model involved 96 randomly recruited participants, namely, 48 young people and 48 seniors (split evenly by gender in each group). Experiment results and statistical analyses indicated that, in a VR driving scenario, task type significantly affected VR driving performance. Navigational scores were significantly higher for the straight (easy/symmetrical straight route) task than those for the curved (difficult/asymmetrical curved route) task. The aging effect was the main reason for the significant and interacting effects of gender and control device. Interactions between age and gender difference indicated that the young group exhibited better wayfinding performance than the senior group did, and in the young group, males had better performance than that of females. Similarly, interactions between age and control device indicated that the handlebar control-device type resulted in better performance than the joystick device did in the young group, but no difference was found in the senior group due to age or learning effects. Findings provide an understanding of the evaluation of the interface designs of navigational-support systems, taking into consideration any effects of age, gender, control device, and task type within three-dimensional VR games and driving systems. With a VR driving simulator, seniors can test-drive inaccessible products such as electric bicycles or cars by using a computer at home.
... Studies of virtual game-based executive function training for dementia and MCI patients show improvement on clinical measures over long-term training regimens [85,103]. Other projects targeted sensory-motor aspects of aging, such as decreased physical activity [3,4,86], fall risk [57 59], and driving performance [33]. In most cases, these studies found that virtual augmentation to training improved motor stability and cognitive performance over time. ...
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Executive functions (EF) are a collection of cognitive domains governing task initiation, motor planning, attention, and goal-oriented action. Difficulties with EF have marked impacts on adaptive living skills, learning outcomes, and quality of life for people with cognitive and psychosocial disabilities, as well as the broader population. While there is considerable research interest in EF training intervention for disabled populations, very few studies explore metacognitive intervention for people with cognitive disabilities. Metacognition comprises conscious beliefs and strategies around task management and goal setting. Metacognitive awareness has been shown to mediate the effects of executive function on self-regulated learning. Metacognitive interventions have also shown promise in general education, military training, and medical practice. We present a virtual reality experience deploying agent-based modeling to support explicit metacognitive strategy instruction for undergraduate students of all neurotypes. Our results support that explicit instructional material explaining executive function and metacognition in relation to problem-solving experiences influenced participant self-concept and awareness of personal traits and cognitive processes.
... Studies of virtual game-based executive function training for dementia and MCI patients show positive improvement on clinical measures over long-term training regimens (Stavros et al., 2010, Zaccarelli et al., 2013. Other projects targeted sensory-motor aspects of aging, such as decreased physical activity (Anderson-Hanley et al., 2012, Anderson-Hanley et al., 2018, Stein et al., 2014, fall risk (Mirelman et al., 2013, Montero-Odasso and Speechley, 2018, Morganti et al., 2016, and driving performance (Haeger et al., 2018). In most cases, these studies found that virtual augmentation to training improved motor stability and cognitive performance over time. ...
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Virtual Reality (VR) and other game-like experiences are popular intervention platforms in neurocognitive rehabilitation research. Executive Functions (EF), the cognitive processes that regulate attention and goal-oriented action, are recognized as a domain of concern in several congenital and acquired neurocognitive conditions (e.g.: ADHD, autism, addiction, cognitive decline, traumatic brain injury, and stroke). VR-based simulations of real-world tasks have shown potential for rehabilitation in independent functioning. The custom nature of such projects makes cross-intervention analysis difficult and complicates development of best-practices. We have designed a toolkit for building virtual interactions that can consistently replicate traditional cognitive tests (such as the Wisconsin Card Sorting Task and Multitasking Task) as well as extend to more complex tasks in any virtual context. Analysis of participant performance data between traditional tasks and these VR replications may indicate the toolkit can successfully replicate traditional measures while also extending into more complex contexts.
... Moreover, the relationship between visuospatial skills and driving performance seems to show positive mutual effects in which the former improves the latter and vice versa [55,56]. Previous research demonstrated a positive relationship between simulated driving and visuospatial abilities in older drivers [57]. Improvements in spatial cognition were observed even in people with different degrees of cognitive impairment and dementia after the repetitive use of driving simulation [48]. ...
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The way people represent and transform visuospatial information affects everyday activities including driving behavior. Mental rotation and perspective taking have recently been found to predict cognitive prerequisites for fitness-to-drive (FtD). We argue that the relationship between general cognitive status and FtD is mediated by spatial transformation skills. Here, we investigated the performance in the Mental Rotation Test (MRT) and the Perspective-Taking Test (PT) of 175 male active drivers (aged from 18 to 91 years), by administering the Montreal Cognitive Assessment (MoCA) to measure their global cognitive functioning. All participants were submitted to a computerized driving assessment measuring resilience of attention (DT), reaction speed (RS), motor speed (MS), and perceptual speed (ATAVT). Significant results were found for the effect of global cognitive functioning on perceptual speed through the full mediation of both mental rotation and perspective-taking skills. The indirect effect of global cognitive functioning through mental rotation was only found to significantly predict resilience of attention whereas the indirect effect mediated by perspective taking only was found to significantly predict perceptual speed. Finally, the negative effect of age was found on each driving measure. Results presented here, which are limited to male drivers, suggest that general cognitive efficiency is linked to spatial mental transformation skills and, in turn, to driving-related cognitive tasks, contributing to fitness-to-drive in the lifespan.
... The former three functions (inhibition, updating, shifting) are often summarized under the umbrella term ''executive functions'' (see Miyake et al., 2000;Miyake and Friedman, 2012;Bock et al., 2019b). More recently, these functions have been discussed to play an important role in driving and accident risk among older adults, and particularly when simultaneously being involved in a cognitively demanding task (Mathias and Lucas, 2009;Asimakopulos et al., 2012;Harada et al., 2013;Karthaus and Falkenstein, 2016;Eramudugolla et al., 2017;Walshe et al., 2017;Haeger et al., 2018). For example, inhibiting non-relevant or distracting information during car driving is essential to keep attention focused on the road. ...
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Action video games have attracted increasing attention from both the public and from researchers. More and more studies found video game training improved a variety of cognitive functions. However, it remains controversial whether healthy adults can benefit from action video game training, and whether young and older adults benefit similarly from action video game training. In the present study, we aimed to quantitatively assess the action video game training effect on the cognitive ability of adults and to compare the training effects on young and older adults by conducting a meta-analysis on previous findings. We systematically searched video game training studies published between January 1986 and July 2015. Twenty studies were included in the present meta-analysis, for a total of 313 participants included in the training group and 323 participants in the control group. The results demonstrate that healthy adults achieve moderate benefit from action video game training in overall cognitive ability and moderate to small benefit in their abilities in specific cognitive domains. In contrast, young adults gain more benefits from action video game training than older adults in both overall cognition and specific cognitive domains. Age, education, and some methodological factors, such as the session duration, session number, total training duration, and control group type, modulated the training effects. These meta-analytic findings provide evidence that action video game training may serve as an efficient way to improve the cognitive performance of healthy adults. We also discussed several directions for future action video game training studies.
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A prominent account of prefrontal cortex (PFC) function is that single neurons within the PFC maintain representations of task-relevant stimuli in working memory. Evidence for this view comes from studies in which subjects hold a stimulus across a delay lasting up to several seconds. Persistent elevated activity in the PFC has been observed in animal models as well as in humans performing these tasks. This persistent activity has been interpreted as evidence for the encoding of the stimulus itself in working memory. However, recent findings have posed a challenge to this notion. A number of recent studies have examined neural data from the PFC and posterior sensory areas, both at the single neuron level in primates, and at a larger scale in humans, and have failed to find encoding of stimulus information in the PFC during tasks with a substantial working memory component. Strong stimulus related information, however, was seen in posterior sensory areas. These results suggest that delay period activity in the PFC might be better understood not as a signature of memory storage per se, but as a top down signal that influences posterior sensory areas where the actual working memory representations are maintained.
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Previous studies have shown that individuals with autism spectrum disorder (ASD) demonstrate poorer driving performance than their peers and are less likely to obtain a driver's license. This study aims to examine the relationship between driving performance and executive functioning for novice drivers, with and without ASD, using a driving simulator. Forty-four males (ages 15-23), 17 with ASD and 27 healthy controls, completed paradigms assessing driving skills and executive functioning. ASD drivers demonstrated poorer driving performance overall and the addition of a working memory task resulted in a significant decrement in their performance relative to control drivers. Results suggest that working memory may be a key mechanism underlying difficulties demonstrated by ASD drivers and provides insight for future intervention programs.
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1. Older individuals frequently report difficulty in everyday activities requiring the use of peripheral vision. However, standard perimetry measurements commonly reveal only a minor age-associated loss in the visual field. 2. The relationship between older patients' reported problems in these everyday activities and visual field measurements was addressed by testing both young and older observers on three tasks: Goldmann perimetry, Octopus automated perimetry and performance on a task to assess the 'functonal' of 'useful' field of view. This task consisted of visual localization of a target under conditions designed to simulate the types of situations older indidviduals describe as difficult. 3. Each patient's age and performance on all three tasks were entered into a hierarchical regression analysis as potential predictors for the frequency of reported difficulties on visual tasks relating to visual search, mobility, and speed of visual processing (as assessed by survey questions). only performance on the visual localization task proved to be a significant predictor for survey responses related to these activities. Performance on the localization task showed specifity as a predictor in that it did not predict other age-related difficulties such as light sensitivity and susceptebility to glare. 4. Thus standard perimetric techniques underestimate the severity of many older adults' functional loss in the visual field. While older adults typically show some sensitivity losses throughout the field, assessments of functional vision with our task reveal a dramatic (3-fold) reduction in the visual field for many older individuals relative to their younger counterparts. Assessments of the useful field of view, along with standard clinical evaluation, may help to delineate the visual functions necessary for the performance of routine activities dependent on peripheral vision, such as driving.
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This pilot study aims to find a way to measure ‘presence’ as a proxy for ecological validity in driving simulators. The underlying assumption is that a person experiencing a strong sense of presence in the virtual environment will react as if it were real. We measure ‘presence’ through the ‘attention’ given to the driving task. We hypothesize that the greater the attention given to the primary driving task, the more the subject will experience spatial presence. ‘Attention’ was varied by adding a second task and oncoming traffic; we then analyzed behavioral measures of driving performance and subjective ‘presence’. The main result is a lack of congruence between subjective and behavioral measures. Although behavioral differences were observed between the various experimental conditions, there was no significant difference in subjective measures of presence. One explanation for this result could be that in all experimental conditions the driving activity did not require high-level cognitive processes, and was instead based on bottom-up attentional processes. Many of the processes involved in driving seem to be automatic, and this study argues for the concomitant use of subjective measures (such as questionnaires) and objective measures to assess presence in driving simulators. Furthermore, the development of a sensitive measure of presence seems to require more challenging scenarios in terms of controlled attention, cognitive involvement and more specifically, the emotions induced by the media. Participants are clearly aware that they are not exposed to any physical danger when using the simulator and the problem of their motivation must be taken into consideration. Another major problem is to establish the extent to which they are absorbed in the simulated driving task. A significant challenge for future research is the emotional validity of driving.
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Finding effective training interventions for declining cognitive abilities in healthy aging is of great relevance, especially in view of the demographic development. Since it is assumed that transfer from the trained to untrained domains is more likely to occur when training conditions and transfer measures share a common underlying process, multi-domain training of several cognitive functions should increase the likelihood of such an overlap. In the first part, we give an overview of the literature showing that cognitive training using complex tasks, such as video games, leisure activities, or practicing a series of cognitive tasks, has shown promising results regarding transfer to a number of cognitive functions. These studies, however, do not allow direct inference about the underlying functions targeted by these training regimes. Custom-designed serious games allow to design training regimes according to specific cognitive functions and a target population's need. In the second part, we introduce the serious game Hotel Plastisse as an iPad-based training tool for older adults that allows the comparison of the simultaneous training of spatial navigation, visuomotor function, and inhibition to the training of each of these functions separately. Hotel Plastisse not only defines the cognitive functions of the multi-domain training clearly, but also implements training in an interesting learning environment including adaptive difficulty and feedback. We propose this novel training tool with the goal of furthering our understanding of how training regimes should be designed in order to affect cognitive functioning of older adults most broadly.