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

Abstract

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.

Full text

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 f�0.105 to f�0.47 [14–16, 23, 32],
so we expect an eect size of f�0.25. For the interaction term
of a 2 (group) ×2 (time) analysis of variance with f�0.25,
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 {d”d’d d’ d” d’’ p”p’p p’ p” p’’} and
have to select all instances of a letter “d” followed by
two dashes (d”d” 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 Greenhouse–Geisser correction in case of a sphericity
violation. Regarding this number of tests, we conducted
a Bonferroni–Holm 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 (n�37) 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, p�0.03,ε�0.843) 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, p�0.04,
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
Greenhouse–Geisser correction (χ
2
(2)
�17.03, p<0.01,ε�0.717)
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,
p�0.04); but again after Bonferroni–Holm 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
(d�0.13–0.31) 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,N�37)
�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
G∗T: 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 d�0.403, 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
T∗G: 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
C∗G: F
(1.69,59.01)
�1.68, p>0.05, η
2
�0.046
T∗C: F
(1.70,59.62)
�0.46, p>0.05, η
2
�0.013
G∗T∗C: 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
G∗T: 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
G∗T: 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
T∗G: 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
Tr∗G: F
(1.44,50.22)
�0.02, p>0.05, η
2
�0.000
T∗Tr: F
(1.61,56.33)
�3.71, p�0.04, η
2
�0.096
G∗T∗Tr: 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
G∗T: 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)
References
[1] L. P. McAvinue, T. Habekost, K. A. Johnson et al., “Sustained
attention, attentional selectivity, and attentional capacity
across the lifespan,” Attention, Perception, and Psychophysics,
vol. 74, no. 8, pp. 1570–1582, 2012.
[2] T. P. Zanto and A. Gazzaley, “Attention and ageing,” in e
Oxford Handbook of Attention, A. C. Nobre and S. Kastner,
Eds., pp. 927–971, Oxford University Press, Oxford, UK, 2014.
[3] K. Ball, C. Owsley, and B. Beard, “Clinical visual perimetry
underestimates peripheral eld problems in older adults,”
Clinical Vision Sciences, vol. 5, no. 2, pp. 113–125, 1990.
[4] S. H¨uttermann, O. Bock, and D. Memmert, “e breadth of
attention in old age,” Ageing Research, vol. 4, no. 1, p. e10,
2012.
[5] J. Kray, K. Z. H. Li, and U. Lindenberger, “Age-related
changes in task-switching components: the role of task un-
certainty,” Brain and Cognition, vol. 49, no. 3, pp. 363–381,
2002.
[6] T. Strobach, Executive Functions Modulated by Context,
Training, and Age, Humboldt-Universit¨
at zu Berlin, Berlin,
Germany, 2014.
[7] A. Diamond, “Executive functions,” Annual Review of Psy-
chology, vol. 64, no. 1, pp. 135–168, 2013.
[8] T. Strobach, T. Salminen, J. Karbach, and T. Schubert,
“Practice-related optimization and transfer of executive
functions: a general review and a specic realization of their
mechanisms in dual tasks,” Psychological Research, vol. 78,
no. 6, pp. 836–851, 2014.
[9] S. Enriquez-Geppert, R. J. Huster, and C. S. Herrmann,
“Boosting brain functions: improving executive functions
with behavioral training, neurostimulation, and neurofeed-
back,” International Journal of Psychophysiology, vol. 88, no. 1,
pp. 1–16, 2013.
[10] B. Wollesen and C. Voelcker-Rehage, “Training eects on
motor–cognitive dual-task performance in older adults,”
European Review of Aging and Physical Activity, vol. 11, no. 1,
pp. 5–24, 2014.
[11] J. D. Edwards, E. G. Vald´
es, C. Peronto et al., “e ecacy of
insight cognitive training to improve useful eld of view
performance: a brief report,” Journals of Gerontology Series B:
Psychological Sciences and Social Sciences, vol. 70, no. 3,
pp. 417–422, 2013.
[12] C. Peretz, A. D. Korczyn, E. Shatil, V. Aharonson,
S. Birnboim, and N. Giladi, “Computer-based, personalized
cognitive training versus classical computer games: a ran-
domized double-blind prospective trial of cognitive stimu-
lation,” Neuroepidemiology, vol. 36, no. 2, pp. 91–99, 2011.
[13] L. Bherer, A. F. Kramer, M. S. Peterson, S. Colcombe,
K. Erickson, and E. Becic, “Training eects on dual-task
performance: are there age-related dierences in plasticity
of attentional control?,” Psychology and Aging, vol. 20, no. 4,
pp. 695–709, 2005.
[14] J. A. Anguera, J. Boccanfuso, J. L. Rintoul et al., “Video game
training enhances cognitive control in older adults,” Nature,
vol. 501, no. 7465, pp. 97–101, 2013.
[15] C. Basak, W. R. Boot, M. W. Voss, and A. F. Kramer, “Can
training in a real-time strategy videogame attenuate cognitive
decline in older adults?,” Psychology and Aging, vol. 23, no. 4,
pp. 765–777, 2008.
[16] P. Belchior, M. Marsiske, S. M. Sisco et al., “Video game
training to improve selective visual attention in older adults,”
Computers in Human Behavior, vol. 29, no. 4, pp. 1318–1324,
2013.
[17] C. S. Green and D. Bavelier, “Action video game modies
visual selective attention,” Nature, vol. 423, no. 6939,
pp. 534–537, 2003.
[18] C. S. Green and D. Bavelier, “Action video game experience
alters the spatial resolution of vision,” Psychological Science,
vol. 18, no. 1, pp. 88–94, 2007.
[19] A. M. Kueider, J. M. Parisi, A. L. Gross, and G. W. Rebok,
“Computerized cognitive training with older adults: a sys-
tematic review,” PLoS One, vol. 7, no. 7, article e40588, 2012.
[20] A. Lampit, H. Hallock, M. Valenzuela et al., “Computerized
cognitive training in cognitively healthy older adults: a sys-
tematic review and meta-analysis of eect modiers,” PLoS
Medicine, vol. 11, no. 11, p. e1001756, 2014.
[21] B. Hubert-Wallander, C. S. Green, and D. Bavelier,
“Stretching the limits of visual attention: the case of action
video games,” Wiley Interdisciplinary Reviews: Cognitive
Science, vol. 2, no. 2, pp. 222–230, 2010.
[22] J. C. Binder, J. Z¨
ollig, A. Eschen et al., “Multi-domain training
in healthy old age: hotel plastisse as an iPad-based serious
game to systematically compare multi-domain and single-
domain training,” Frontiers in Aging Neuroscience, vol. 7,
p. 137, 2015.
[23] G. Casutt, N. eill, M. Martin, M. Keller, and L. J¨ancke, “e
drive-wise project: driving simulator training increases real
driving performance in healthy older drivers,” Frontiers in
Aging Neuroscience, vol. 6, pp. 1–14, 2014.
[24] M. N. Lees, J. D. Cosman, J. D. Lee, and N. Fricke, “Translating
cognitive neuroscience to the driver’s operational environ-
ment: a neuroergonomic approach,” American Journal of
Psychology, vol. 123, no. 4, pp. 391–411, 2010.
[25] K. Murphy and A. Spencer, “Playing video games does not
make for better visual attention skills,” Journal of Articles in
Support of the Null Hypothesis, vol. 6, no. 1, pp. 1–20, 2009.
[26] N. Chaytor and M. Schmitter-Edgecombe, “e ecological
validity of neuropsychological tests: a review of the literature
on everyday cognitive skills,” Neuropsychology Review, vol. 13,
no. 4, pp. 181–197, 2003.
[27] C. Deniaud, V. Honnet, B. Jeanne, and D. Mestre, “e
concept of “presence” as a measure of ecological validity in
driving simulators,” Journal of Interaction Science, vol. 3, no. 1,
p. 1, 2015.
[28] D. Moreau and A. R. A. Conway, “e case for an ecological
approach to cognitive training,” Trends in Cognitive Sciences,
vol. 18, no. 7, pp. 334–336, 2014.
[29] O. J. Clay, V. G. Wadley, J. D. Edwards, D. L. Roth,
D. L. Roenker, and K. K. Ball, “Cumulative meta-analysis of
the relationship between useful eld of view and driving
performance in older adults: current and future implications,”
Optometry and Vision Science, vol. 82, no. 8, pp. 724–731,
2005.
[30] H. Sakai, Y. Uchiyama, M. Takahara et al., “Is the useful eld
of view a good predictor of at-fault crash risk in elderly
8Journal of Aging Research

Japanese drivers?,” Geriatrics and Gerontology International,
vol. 15, no. 5, pp. 659–665, 2015.
[31] S. M. Cox, D. J. Cox, M. J. Koer et al., “Driving simulator
performance in novice drivers with autism spectrum disorder:
the role of executive functions and basic motor skills,” Journal
of Autism and Developmental Disorders, vol. 46, no. 4,
pp. 1379–1391, 2015.
[32] D. L. Roenker, G. M. Cissell, K. K. Ball, V. G. Wadley, and
J. D. Edwards, “Speed-of-processing and driving simulator
training result in improved driving performance,” Human
Factors: e Journal of the Human Factors and Ergonomics
Society, vol. 45, no. 2, pp. 218–233, 2003.
[33] J. D. Edwards, C. Myers, L. A. Ross et al., “e longitudinal
impact of cognitive speed of processing training on driving
mobility,” e Gerontologist, vol. 49, no. 4, pp. 485–494, 2009.
[34] N. D. Cassavaugh and A. F. Kramer, “Transfer of computer-
based training to simulated driving in older adults,” Applied
Ergonomics, vol. 40, no. 5, pp. 943–952, 2009.
[35] A. Gazzaley and A. C. Nobre, “Top-down modulation:
bridging selective attention and working memory,” Trends in
Cognitive Sciences, vol. 16, no. 2, pp. 129–135, 2012.
[36] A. H. Lara and J. D. Wallis, “e role of prefrontal cortex in
working memory: a mini review,” Frontiers in Systems
Neuroscience, vol. 9, no. 173, pp. 1–7, 2015.
[37] J. Adrian, V. Postal, M. Moessinger, N. Rascle, and A. Charles,
“Personality traits and executive functions related to on-road
driving performance among older drivers,” Accident Analysis
and Prevention, vol. 43, no. 5, pp. 1652–1659, 2011.
[38] E. E. Freeman, B. Muñoz, G. Rubin, and S. K. West, “Visual
eld loss increases the risk of falls in older adults: the Salisbury
eye evaluation,” Investigative Opthalmology and Visual Sci-
ence, vol. 48, no. 10, pp. 4445–4450, 2007.
[39] C. Owsley and G. J. McGwin, “Association between visual
attention and mobility in older adults,” Journal of the
American Geriatrics Society, vol. 52, no. 11, pp. 1901–1906,
2004.
[40] J. R. Reed-Jones, S. Dorgo, M. K. Hitchings, and J. O. Bader,
“Vision and agility training in community dwelling older
adults: incorporating visual training into programs for fall
prevention,” Gait and Posture, vol. 35, no. 4, pp. 585–589,
2012.
[41] G. Yogev-Seligmann, J. M. Hausdor, and N. Giladi, “e role
of executive function and attention in gait,” Movement Dis-
orders, vol. 23, no. 3, pp. 329–342, 2008.
[42] R. W. Bohannon, “Reference values for the timed up and go
test: a descriptive meta-analysis,” Journal of Geriatric Physical
erapy, vol. 29, no. 2, pp. 64–68, 2006.
[43] M. E. Kelly, D. Loughrey, B. A. Lawlor, I. H. Robertson,
C. Walsh, and S. Brennan, “e impact of cognitive training
and mental stimulation on cognitive and everyday func-
tioning of healthy older adults: a systematic review and meta-
analysis,” Ageing Research Reviews, vol. 15, pp. 28–43, 2014.
[44] P. Wang, H.-H. Liu, X.-T. Zhu, T. Meng, H.-J. Li, and
X.-N. Zuo, “Action video game training for healthy adults:
a meta-analytic study,” Frontiers in Psychology, vol. 7, no. 907,
pp. 1–13, 2016.
[45] M. A. Posner, “Orienting of attention,” Quarterly Journal of
Experimental Psychology, vol. 32, no. 1, pp. 3–25, 1980.
[46] J. T. Coull, “Neural Correlates of attention and arousal: insight
from electrophysiology, functional neuroimaging and Psy-
chopharmacology,” Progress in Neurobiology, vol. 55, no. 4,
pp. 343–361, 1998.
[47] R. Brickenkamp, Test d2 Aufmerksamkeits-Belastungs-Test
Manual 9. ¨uberarb, Hogrefe, G¨ottingen, Germany, 2002.
[48] S. H¨uttermann, D. Memmert, D. J. Simons, and O. Bock,
“Fixation strategy inuences the ability to focus attention on
two spatially separate objects,” PLoS One, vol. 8, no. 6, article
e65673, 2013.
[49] S. Hale, N. S. Rose, J. Myerson et al., “e structure of working
memory abilities across the adult life span,” Psychology and
Aging, vol. 26, no. 1, pp. 92–110, 2011.
[50] A. Miyake, N. P. Friedman, M. J. Emerson, A. H. Witzki, and
A. Howerter, “e unity and diversity of executive functions
and their contributions to complex “Frontal Lobe” tasks:
a latent variable analysis,” Cognitive Psychology, vol. 41, no. 1,
pp. 49–100, 2000.
[51] A. Baddeley, “Working memory: theories, models, and
controversies,” Annual Review of Psychology, vol. 63, no. 1,
pp. 1–29, 2012.
[52] J. Karbach and J. Kray, “How useful is executive control
training? Age dierences in near and far transfer of task-
switching training,” Developmental Science, vol. 12, no. 6,
pp. 978–990, 2009.
[53] C. Huy and S. Schneider, “Instrument f¨ur die erfassung der
physischen aktivit¨
at bei personen im mittleren und h¨
oheren
erwachsenenalter: entwicklung, pr¨ufung und anwendung des
“German-PAQ-50+”,” Zeitschrift f¨
ur Gerontologie und Ger-
iatrie, vol. 41, no. 3, pp. 208–216, 2008.
[54] B. R. Greene, A. O’Donovan, R. Romero-Ortuno, L. Cogan,
C. N. Scanaill, and R. A. Kenny, “Quantitative falls risk as-
sessment using the timed up and go test,” IEEE Transactions
on Biomedical Engineering, vol. 57, no. 12, pp. 2918–2926,
2010.
[55] M. F. Folstein, S. E. Folstein, and P. R. McHugh, “Mini-mental
state; a practical method for grading the cognitive state of
patients for the clinician,” International Journal of Geriatric
Psychiatry, vol. 12, no. 3, pp. 189–198, 1975.
[56] J. Cohen, Statistical Power Analysis for the Behavioral Sciences,
p. 567, New York, NY, USA, Lawrence Erlbaum Associates,
1988.
[57] R. Rosenthal, “Parametric measures of eect size,” in e
Handbook of Research Synthesis, H. Cooper and L. V. Hedges,
Eds., pp. 231–244, Russell Sage Foundation, New York, NY,
USA, 1994.
[58] C. S. Green and D. Bavelier, “Learning, attentional control,
and action video games,” Current Biology, vol. 22, no. 6,
pp. R197–R206, 2012.
[59] V. Montani, M. D. F. De Grazia, and M. Zorzi, “A new
adaptive videogame for training attention and executive
functions: design principles and initial validation,” Frontiers
in Psychology, vol. 5, pp. 1–12, 2014.
[60] J. Karbach and P. Verhaeghen, “Making working memory
work: a meta-analysis of executive-control and working
memory training in older adults,” Psychological Science,
vol. 25, no. 11, pp. 2027–2037, 2014.
[61] D. E. R. Warburton, C. W. Nicol, and S. S. D. Bredin, “Health
benets of physical activity: the evidence,” Canadian Medical
Association Journal, vol. 174, no. 6, pp. 801–809, 2006.
Journal of Aging Research 9