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Cognitive—Motor Interference in an Ecologically Valid Street Crossing Scenario

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  • Zentrum für Qualität in der Pflege

Abstract and Figures

Laboratory-based research revealed that gait involves higher cognitive processes, leading to performance impairments when executed with a concurrent loading task. Deficits are especially pronounced in older adults. Theoretical approaches like the multiple resource model highlight the role of task similarity and associated attention distribution problems. It has been shown that in cases where these distribution problems are perceived relevant to participant's risk of falls, older adults prioritize gait and posture over the concurrent loading task. Here we investigate whether findings on task similarity and task prioritization can be transferred to an ecologically valid scenario. Sixty-three younger adults (20–30 years of age) and 61 older adults (65–75 years of age) participated in a virtual street crossing simulation. The participants' task was to identify suitable gaps that would allow them to cross a simulated two way street safely. Therefore, participants walked on a manual treadmill that transferred their forward motion to forward displacements in a virtual city. The task was presented as a single task (crossing only) and as a multitask. In the multitask condition participants were asked, among others, to type in three digit numbers that were presented either visually or auditorily. We found that for both age groups, street crossing as well as typing performance suffered under multitasking conditions. Impairments were especially pronounced for older adults (e.g., longer crossing initiation phase, more missed opportunities). However, younger and older adults did not differ in the speed and success rate of crossing. Further, deficits were stronger in the visual compared to the auditory task modality for most parameters. Our findings conform to earlier studies that found an age-related decline in multitasking performance in less realistic scenarios. However, task similarity effects were inconsistent and question the validity of the multiple resource model within ecologically valid scenarios.
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published: 03 May 2018
doi: 10.3389/fpsyg.2018.00602
Frontiers in Psychology | 1May 2018 | Volume 9 | Article 602
Edited by:
Karen Zentgraf,
Goethe University Frankfurt, Germany
Reviewed by:
André Klostermann,
Universität Bern, Switzerland
Herbert Heuer,
Leibniz Research Centre for Working
Environment and Human Factors (LG),
Claudia Voelcker-Rehage
Specialty section:
This article was submitted to
Movement Science and Sport
a section of the journal
Frontiers in Psychology
Received: 03 February 2018
Accepted: 10 April 2018
Published: 03 May 2018
Janouch C, Drescher U, Wechsler K,
Haeger M, Bock O and
Voelcker-Rehage C (2018)
Cognitive—Motor Interference in an
Ecologically Valid Street Crossing
Scenario. Front. Psychol. 9:602.
doi: 10.3389/fpsyg.2018.00602
Cognitive—Motor Interference in an
Ecologically Valid Street Crossing
Christin Janouch 1, Uwe Drescher 2, Konstantin Wechsler 2, Mathias Haeger 2, Otmar Bock 2
and Claudia Voelcker-Rehage 1
1Faculty of Behavioral and Social Sciences, Institute of Human Movement Science and Health, Chemnitz University of
Technology, Chemnitz, Germany, 2Institute of Physiology and Anatomy, German Sport University Cologne, Cologne, Germany
Laboratory-based research revealed that gait involves higher cognitive processes,
leading to performance impairments when executed with a concurrent loading task.
Deficits are especially pronounced in older adults. Theoretical approaches like the
multiple resource model highlight the role of task similarity and associated attention
distribution problems. It has been shown that in cases where these distribution problems
are perceived relevant to participant’s risk of falls, older adults prioritize gait and posture
over the concurrent loading task. Here we investigate whether findings on task similarity
and task prioritization can be transferred to an ecologically valid scenario. Sixty-three
younger adults (20–30 years of age) and 61 older adults (65–75 years of age) participated
in a virtual street crossing simulation. The participants’ task was to identify suitable
gaps that would allow them to cross a simulated two way street safely. Therefore,
participants walked on a manual treadmill that transferred their forward motion to forward
displacements in a virtual city. The task was presented as a single task (crossing only)
and as a multitask. In the multitask condition participants were asked, among others,
to type in three digit numbers that were presented either visually or auditorily. We found
that for both age groups, street crossing as well as typing performance suffered under
multitasking conditions. Impairments were especially pronounced for older adults (e.g.,
longer crossing initiation phase, more missed opportunities). However, younger and older
adults did not differ in the speed and success rate of crossing. Further, deficits were
stronger in the visual compared to the auditory task modality for most parameters.
Our findings conform to earlier studies that found an age-related decline in multitasking
performance in less realistic scenarios. However, task similarity effects were inconsistent
and question the validity of the multiple resource model within ecologically valid scenarios.
Keywords: multitasking, dual-tasking, aging, walking, cognitive-motor interference, ecological validity, virtual
reality, street crossing
Many daily activities require us to manage sensory-motor tasks while we simultaneously engage in
cognitive tasks. One prominent example is pedestrian mobility, such as walking down a sidewalk
while avoiding a collision with another pedestrian, walking while screening items in a shop window,
or crossing a non-signalized street while paying attention to relevant traffic information. These
Janouch et al. Cognitive—Motor Interference While Street Crossing
activities become even more complex with the advent of portable
technologies such as smartphones or music players. In June
2013 the Pedestrian Survey Infographic published, that from
over 1,000 American respondents three out of five (60%) 18–
65 year olds stated to use smartphones while crossing a street,
even though this was considered as dangerous by 70% of these
respondents (Liberty Mutual Insurance, 2013).
Standardized laboratory paradigms have provided evidence
that sensory-motor performance decreases under dual-task
conditions, and that this decrease is especially pronounced
in older adults (Kray and Lindenberger, 2000; Verhaeghen
et al., 2003). This has been often explained by sensory-motor
and cognitive declines within the aging process (Baltes and
Lindenberger, 1997; Li and Lindenberger, 2002). Performance
decrements in dual-task situations have even been observed
with tasks that are considered to be highly automated, like gait.
It has therefore been argued that even gait requires cognitive
control and higher-level resources (Hausdorff et al., 2005; Yogev-
Seligmann et al., 2008). Especially in older adults, gait seems
to place high attentional demands and requires more cognitive
resources (Lindenberger et al., 2000; Woollacott and Shumway-
Cook, 2002; Hausdorff et al., 2008) which leads to greater dual-
task decrements in this age group (Al-Yahya et al., 2011).
Performance decrements in dual-task situations have been
interpreted in light of several theoretical positions, such as
capacity models of attention (Kahneman, 1973) or multiple
resource models (Wickens, 2002). In both types of models, two
or more tasks compete for common resources, either within a
limited attentional resource pool (Kahneman, 1973) or within
multiple resource pools (Wickens, 2002). In the latter case,
pools are thought to be devoted to different stimulus modalities,
signal codes, processing stages, and response channels (Wickens
and McCarley, 2007). Both theoretical approaches share the
idea that performance deteriorates when the competing tasks
are so complex that their combined resource demand exceeds
the available resource capacity. The multiple-resource model
additionally posits that the tasks must be similar enough in order
to compete for the same resource. The determinants of dual-
task decrements therefore are task complexity and—in case of the
multiple-resource model—task similarity.
Several studies provided evidence for the role of task
similarities. They documented interference between tasks that
share sensory modalities, processing levels or information
channels (Allport et al., 1972; Isreal et al., 1980; Duncan
et al., 1997; Talsma et al., 2006). In a street crossing context,
such interference could emerge when two tasks require to
simultaneously process similar visual signals. This is the case,
e.g., when we look for a suitable gap in traffic and concurrently
read walking directions on a mobile phone. In contrast, looking
for gaps while listening to walking directions over headphones
should cause less interference.
So far, most available knowledge about dual-task performance
came from traditional laboratory-based research that offers a high
controllability and standardization, but lacks ecological validity.
Even if real walking is required, tasks are often executed within
a laboratory surrounding and most of the applied loading tasks
are rather abstract like verbal fluency or arithmetic subtraction
tasks. For example, participants were asked to memorize word
lists while walking (walk as accurately and quickly as possible
on two narrow tracks with different path complexity/avoid
obstacles) (Lindenberger et al., 2000; Li et al., 2001). The results
revealed diminished performance when the tasks were performed
concurrently. Age-related differences were more pronounced
in the memory task than in the walking task. This result was
discussed as older adults prioritizing walking over memorizing
to protect themselves from falls, a view known as “posture first
hypothesis” (Shumway-Cook and Woollacott, 2000; Schaefer and
Schumacher, 2011; cf. Li et al., 2012 for discussion of mixed
Everyday life typically differs from traditional laboratory
paradigms in that behavior is uninstructed and volitional, with
varying and often unpredictable stimuli and with a wider range
of possible and purposeful responses. Little is known about the
transferability of laboratory outcomes to more realistic settings.
Available literature documents marked differences between
laboratory and realistic behavior with respect to gait (Bock and
Beurskens (2010), manual grasping (Bock and Züll, 2013) and
cognitive performance (Verhaeghen et al., 1993). In a systematic
review on dual-task training effects in older adults, Wollesen and
Voelcker-Rehage (2014) found heterogeneous results regarding
the transferability of training effects to everyday situations.
Consequently, several authors cautioned against generalizing
laboratory results to real life (Chaytor and Schmitter-Edgecombe,
2003; Li et al., 2005) and questioned the extent to which especially
age-related decays apply on everyday-like behavior (Verhaeghen
et al., 2012).
Given the above considerations, it seems desirable to expand
dual-task research by using more ecologically valid paradigms,
without giving up the advantages of a laboratory setting such
as controllability and standardization. A promising approach to
do so can be seen in virtual reality (VR) settings (Lopez Maite
et al., 2016) which can provide an everyday-like, controllable
and safe surrounding that can be adapted to the need of the
experimenter. Thus, a realistic walking task (e.g., walking down
or crossing a street) can be combined with a realistic loading
task (e.g., watching for vehicles or using a smartphone) while
ambient stimuli are controlled for and relevant measures are
extracted. Indeed, several VR pedestrian street crossing studies
have been conducted recently (Dommes et al., 2014; Schwebel
et al., 2014; Morrongiello and Corbett, 2015). However, most
of these studies did not address multitasking (Dommes et al.,
2014; Schwebel et al., 2014; Morrongiello and Corbett, 2015) and
those which did rather focused on children and young adults than
older adults (Chaddock et al., 2011, 2012; Byington and Schwebel,
2013; Gaspar et al., 2014; Tapiro et al., 2016) or used only a single
loading task throughout the whole experimental block or session
(Neider et al., 2011). For example, Neider et al. (2011) confirmed
that dual-task crossing performance deteriorates in old age, but
did not evaluate performance changes of the cognitive loading
task (cell phone conversation) to control for interaction effects or
possible prioritization strategies.
The present study aims to overcome the mentioned
limitations by combining a VR street crossing task with a realistic
loading task. We posit that the ecological validity of our approach
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Janouch et al. Cognitive—Motor Interference While Street Crossing
exceeds that of earlier approaches. Specifically, loading tasks are
administered either through the visual or the auditory modality,
in order to scrutinize the validity of the multiple resource model
(Wickens, 2002) for ecologically valid settings. We hypothesized
that street crossing requires visual resources and therefore will
interfere with visually presented loading tasks more than with
auditorily presented loading tasks, particularly in older persons.
In accordance with the posture first hypothesis (Lindenberger
et al., 2000; Li et al., 2001; Schaefer and Schumacher, 2011),
we expected that age-related deficits will be less pronounced
for walking than for loading task performance, even in an
ecologically valid setting.
The study was conducted within the DFG (German Research
Foundation) Priority Program SPP 1772 “Multitasking,” In total,
134 healthy men and women between 20 and 30 (n=69) and
65 and 75 (n=65) years of age who actively participated in
traffic as drivers as well as pedestrians were recruited. Younger
participants were recruited via mailing lists from the student pool
of the Chemnitz University of Technology (Germany) and the
German Sport University Cologne. Older adults were acquired
via local newspaper advertising and (only in Chemnitz) further
via the participant pool of the Cognition, Brain, and Movement
Lab of Chemnitz University of Technology. About half of the
young and old participants were recruited and tested in Chemnitz
and the other half in Cologne. Both locations used standardized
and indentical set ups, test designs and instructions as well as
identical hardware and software.
Interested persons were screened in an initial telephone
interview for the following exclusion criteria: (a) age range
violations, (b) former or current health impairments (heart
attacks, brain injuries, strokes; motor impairments that inhibit
the participant to continuously walk for 30 min, eye diseases or
current relevant injuries), (c) obesity (Body Mass Index, BMI >
30), and (d) driving irregularity (driving a car less than once a
week). Further exclusion tests were performed on participant’s
first laboratory test session (cf. below). No person had to be
excluded based on these tests. Before testing began, participants
obtained medical clearance from their local physician and signed
an informed consent statement to our study. This experiment
was part of a larger project in which the same participants
were additionally given a car-driving test (reported in another
contribution to this issue) and a cardiovascular fitness test. The
project was approved by the Ethics Committee of the German
Sport University, Cologne.
Six participants dropped out over the study time without
giving reasons, three had to be excluded because of simulator
sickness, and one participant left the study for personal reasons.
The remaining 124 participants were subdivided with respect to
age into 61 older adults (OA) with a mean age of 69.97 (SD =
2.96) years [females: n=22; BMI =25.09 (SD =2.44); MMSE
=29.15 (SD =0.85)] and 63 young adults (YA) with a mean age
of 23.17 (SD =2.83) years [females: n=40; BMI =22.04 (SD =
2.30); MMSE =29.67 (SD =0.62)]. OA received 15 eper session
as monetary compensation (60 ein total) and YA received course
credits. Further, all participants received an individual report of
their cardiovascular fitness test as compensation.
Laboratory Screening
Normal hearing was assessed by the Freiburg speech intelligibility
test (Freiburger Sprachverständlichkeitstest) with a set cutoff
word recognition rate of 50 %. Normal vision was assessed
by the Freiburg Visual Acuity Test (FrACT; version 3.9.0)
with a cutoff score of 20/60 since driving is presumed to be
safe above that score (Keeffe et al., 2002). Lack of visual-field
deficits was confirmed by the online version of the Damato
Multifixation Campimeter (Damato and Groenewald, 2003). All
participants who used visual and hearing aids in their daily
life did so in testing as well. Normal overall cognition was
assessed by the Mini-Mental State Examination (Folstein et al.,
1975) with a cutoff score of 27/30. Finally, the Edinburgh
Handedness Inventory (Oldfield, 1971) was used to determine
hand dominance. Five participants were left handed, one was
ambidextrous but preferred the right hand for typing, and all
others were right handed.
Apparatus and Setup
Hardware for the street crossing task consisted of a non-
motorized treadmill (DRAX, Speedfit 1000c, Vibrafit R
, Solms)
and three 46′′ TV flat screens that featured a 195 degree
horizontal field of view. Treadmill speed was registered opto-
electronically, and was synchronized with a first person
perspective view of a 3D world. Thus, as participants walked
at their own pace, sped up, and slowed down, their viewpoint
in the visual 3D world moved accordingly. To reduce physical
exertion, participants were asked not to run. For safety reasons,
each participant was equipped with a drop guard and asked to
keep the non-dominant hand on the treadmill’s handrail for the
entire test duration.
Headphones (Shark Zone H10 Gaming Headset, Sharkoon
Technologies GmbH, Linden, Germany) were used to deliver
auditory stimuli and a microphone to register verbal responses.
A keypad with 2 ×3 digits was attached within easy reach of the
participants’ dominant/preferred hand to register manual typing
Software consisted of a modified, commercially available
driving simulator (Carnetsoft R
, version 8.0 Groningen, NL) that
was adapted to the needs of a street crossing task: it displayed
the 3D model of a city street from a first person perspective (see
section Street Crossing Task).
Figure 1 illustrates the set up and displays the modeled city
Street Crossing Task
The street crossing task was designed similarly to a study by
Neider et al. (2010), in which the participant’s task was to safely
cross a street presented in virtual reality. To do so, they had
to detect suitable gaps between the oncoming vehicles. In our
scenario, the street consisted of one three-meter wide lane in
each direction and was flanked by typical downtown buildings.
Vehicles traveled along both lanes at 50 km/h, which is the legal
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Janouch et al. Cognitive—Motor Interference While Street Crossing
driving speed in German towns. At the onset of each trial,
participants walked 15 m through a virtual back alley to reach the
curb of the street, stopped, watched for a suitable intervehicle gap
and then crossed. Intervehicle gaps increased during each trial
according to the sequence 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6 s, and this
sequence was repeated if participants did not cross the street yet.
Cars on the far lane reached the crossing area one second later
than those on the near lane. Pilot work yielded that this traffic
pattern allows safe crossing even for older participants. Far lane
traffic was implemented to detect possible behavioral anomalies
during the crossing process (e.g., stopping in the middle of the
street to let pass the traffic on the far lane before continuing
crossing). However, as no such anomalies occurred and analyses
of near and far lane provided equally results, only near lane
analyses will be reported. A crossing trial was completed when
participants reached the opposite walkway, when they caused an
accident or when 80 s elapsed.
Loading Task
We used two realistic loading tasks that resembled rehearsal of
a shopping list (shopping task) and smartphone usage (typing
task). Each given loading task was presented repeatedly from
trial onset until trial end to ensure that the crossing task and the
loading task could not be dealt with sequentially. Loading tasks
were presented visually on some, and auditorily on other trials.
In the shopping task, grocery products were sequentially
presented either visually on billboards across the street or
auditorily through headphones. In the typing task, three-digit
numbers were sequentially presented either on the billboards
(for 4 s each) or through headphones, for about 1.7 s each.
Participants reacted by depressing, with their preferred hand,
the corresponding numbers on a keypad that was attached to
the treadmill handrail. Task type (shopping, typing) and task
modality (visually or auditorily) varied quasi-randomly between
trials, with the constraint that each typemodality combination
was presented a total of ten times. To limit the complexity
of the present paper, we decided to focus our analyses on the
FIGURE 1 | Street crossing simulator set up.
typing task. However, it is important to note that this task was
not administered alone but rather intermixed with the shopping
task, to mimic the diversity of everyday multitasking. Possible
switching costs, resulting from such a loading task intermix will
be discussed in a car driving simulator study by Wechsler et al.
(under review).
The simulation offered three task conditions. In the control
condition “single-task crossing (STcross),” participants walked
on the treadmill and crossed the virtual street without loading
tasks. In the condition “single-task loading,” participants stood
still on the treadmill while the virtual reality display advanced
automatically and the loading tasks were displayed sequentially.
In this condition, each typemodality combination [typing
auditory (STtype_aud), typing visual (STtype_vis), shopping
auditory (STshop_aud) and shopping visual (STshop_vis)]
was presented with a total of 10 trials. In the condition
“multitask,” participants walked on the treadmill and crossed
the virtual street while concurrently engaged in a loading task
(MTtype). Again, there where 10 trials of each typemodality
combination [auditory typing task (MTtype_aud), visual typing
task (MTtype_vis), auditory shopping task (MTshop_aud), and
visual shopping task (MTshop_vis)].
Ten control trials of STCross were randomly intermixed with
ten trials each of MTtype_aud, MTtype_vis, MTshop_aud, and
MTshop_vis. The total of 50 trials was presented in blocks of
ten trials each that were characterized as active blocks in which
participants had to actually walk on the treadmill. These active
blocks alternated with passive blocks of ten single-task loading
trials each in which the participants stood still on the treadmill.
The latter blocks were formed by intermixing ten trial each
of STtype_aud, STtype_vis, STshop_aud, and STshop_vis. The
alternation of active and passive blocks was introduced to avoid
fatigue. All participants received the same sequence of trials,
which took about 40 min.
All data were collected in four sessions of about 2 h each,
scheduled one to seven days apart. The first session included
TABLE 1 | Means (M) and Standard Deviations (SD) of crossing parameters
during single-task crossing (STCross), multitask typing visually (MTtype_vis) and
multitask typing auditorily (MTtype_aud).
STCross MTtype_vis MTtype_aud
M (SD) M (SD) M (SD)
Stay Time 6.29
Speed (km/h)
Speed (km/h)
Failure (%)
Gap (#) 4.87
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Janouch et al. Cognitive—Motor Interference While Street Crossing
a screening (see above) and a familiarization phase in which
participants walked on the treadmill. This phase ended when
participants and experimenter considered starting, walking and
stopping on the treadmill to be smooth and effortless, which
took about 10–15 min for YA, and 15–20 min for OA. Afterwards,
participants received one practice trial each for STtype_vis,
STtype_aud, and STcross. MTtype was not practiced.
Street crossing performance was registered in one of the
remaining three test sessions, depending on participant’s test
order randomization. It was assured that the street crossing
experiment was never scheduled on the same day as the
cardiovascular fitness test, to avoid fatigue.
Data Reduction
The following performance measures were calculated.
Back-alley Speed (km/h): Mean velocity of walking toward
the curb. Triggers were set at trial onset, and when participants
stopped at the curb.
Stay Time (s): Length of time that participants stood still at the
curb while watching traffic. Time triggers were set when treadmill
pace dropped to 0 m/s and when treadmill pace exceeded 0 m/s
Crossing Speed (km/h): Mean velocity of crossing the street.
Triggers were set when participants left the curb to cross
(treadmill pace >0 m/s) and when they reached the opposite
Crossing Failures: Percentage of unsuccessful trials, as a result
of timeouts (i.e., participant did not complete street crossing
within 80 s) or experienced a collision (i.e., participant was hit
by a car).
Gap Number: Serial order of the gap selected for crossing.
Typing Accuracy (%): Percentage of trials on which all three
digits were typed correctly.
Typing Reaction Time (ms): Interval from stimulus onset until
typing the first digit.
Multitasking Effects, MTE: Relative performance change under
multitask conditions, with negative values indicating poorer
performance (cf. Kelly et al., 2010; Plummer and Eskes, 2015).
For Back-alley Speed, Crossing Speed, and Typing Accuracy MTE
was calculated as
MTE =Multitask performance Single task performance
Single task performacne x100%
while for Stay Time, Crossing Failure, Gap Number, and Typing
Reaction Time it was calculated as
MTE = Multitask performance Single task performance
Single task performancne x100%
Statistical Analyses
Outliers were eliminated by applying the ±3.29 SD criterion
(Tabachnick and Fidell, 2001), separately for each participant and
task. Data were then averaged across repetitions if at least seven
repetitions remained, which was the case for all 124 participants.
Each street crossing parameter was submitted to an analysis of
variance (ANOVA) with Age (OA, YA) as between-subject factor
and Condition (STcross, MTtype_aud, MTtype_vis) as within-
subject factor. Typing parameters were submitted to an ANOVA
with Age (OA, YA) as between-subject factor and Condition
(STtype, MTtype) and Task Modality (visual, auditory) as within-
subject factors.
Each MTE score was tested against zero with one-sample
t-tests in case of normal distributions, and Wilcoxon Signed
Rank tests otherwise. Further, MTE scores were submitted to
an ANOVA with Age (OA,YA) as between-subject factor and
Parameter Type (Stay Time, Walking Speed, Crossing Speed,
Failures, Gap, Typing Accuracy and Typing Reaction Time) as
well as Task Modality (visual, auditory) as within-subject factors.
Effect sizes are reported as partial eta squares. Homogeneity of
variances was determined by Mauchly-tests and, if the sphericity
assumption was violated, Greenhouse-Geisser adjustments were
applied. When omnibus ANOVA was significant, Bonferroni
post-hoc tests were conducted. All statistical analyses were
conducted with SPSS for Windows, version 25 (IBM Corp.,
Armonk, NY, USA).
Crossing Task
Table 1 and Figure 2 show descriptive data on each street
crossing parameter and Table 2 pertinent ANOVA results.
Back-alley Speed was significantly slower in OA than YA
and differed between conditions (Figure 2A). It was significantly
slower in the MTtype conditions compared to the STCross
condition but also differed significantly between both MTtype
TABLE 2 | ANOVA results for street crossing parameters.
df (Error) FSig η2
Condition 1.672 (203.929) 95.064 <0.001** 0.438
Age 1 (122) 7.038 0.009** 0.055
Condition ×Age 1.672 (203.929) 5.930 0.005** 0.046
Condition 1.009 (123.112) 18.344 <0.001** 0.131
Age 1 (122) 6.937 0.010* 0.054
Condition ×Age 1.01 (123.112) 4.855 0.029* 0.038
Condition 2 (244) 40.003 <0.001** 0.247
Age 1 (122) 0.220 0.640 0.002
Condition ×Age 2 (244) 17.677 <0.001** 0.247
Condition 1.895 (231.246) 18.423 <0.001** 0.131
Age 1 (122) 0.915 0.341 0.007
Condition ×Age 1.895 (231.246) 2.407 0.095 0.019
Condition 2 (244) 33.970 <0.001** 0.218
Age 1 (122) 9.228 0.003** 0.070
Condition ×Age 2 (244) 11.609 <0.001** 0.087
*p<0.05; **p<0.01.
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Janouch et al. Cognitive—Motor Interference While Street Crossing
conditions (slowest in MTtype_aud followed by MTtype_vis
and fastest in STCross), especially in OA (significant age ×
condition interaction). Pairwise post-hoc comparisons revealed
that age differences only emerged in the MTtype conditions
(MTtype_vis: p=0.007; MTtype_aud: p=0.002), but not in
STCross (p =0.066).
Stay time was significantly longer in OA than YA and different
between conditions (Figure 2B). It was significant longer in
MTtype_vis compared to MTtype_aud and compared to STCross
(always, p<0.001), particularly so in older persons as shown
by a significant age by condition interaction. However, post-
hoc tests revealed significant age differences in MTtype_vis only
Crossing Speed did not differ as a function of age, but
again differed between conditions (Figure 2C). Also the age
by condition interaction was significant, indicating that only
for older adults Crossing Speed was significantly affected by
condition. OA were significantly slower in both MTtype
conditions compared to STcross, but this time with the slowest
Crossing Speed in MTtype_vis that was also significantly slower
compared to the MTtype_aud condition (p=0.001).
Crossing Failure revealed a significant condition effect only
(Figure 2D). Post-hoc comparisons revealed that this effect
was driven by MTtype_vis for which Crossing Failure was
significantly higher compared to STCross (p<0.001) as well as
to MTtype_aud (p<0.001).
FIGURE 2 | Condition differences between single-task crossing (STCross), multitask typing visually (MTtype_vis) and multitask typing auditorily (MTtype_aud),
grouped by age (M and SE) for (A) Back-alley Speed; (B) Stay Time; (C) Crossing Speed; (D) Gap Number; (E) Crossing Failures.
TABLE 3 | Means (M) and Standard Deviations (SD) of typing parameters for younger (YA) and older adults (OA).
STTyping_visual MT_visual STTyping_auditory MT_auditory
M (SD) M (SD) M (SD) M (SD)
Accuracy in % 95.70 (1.17) 95.10 (1.36) 95.80 (1.13) 94.11 (3,66) 96.58 (3.51) 96.74 (7.80) 92.73 (5.05) 89.61 (8.31)
Reaction Time (s) 1.44 (0.19) 1.73 (0.18) 1.62 (0.24) 1.94 (0.22) 1.75 (0.25) 1.57 (0.21) 1.78 (0.23) 1.72 (0.21)
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Janouch et al. Cognitive—Motor Interference While Street Crossing
FIGURE 3 | Condition differences between single-task typing visual (STtype_vis) and multitask typing visual (MTtype_vis) and between single task typing auditory
(STtype_aud) and multitask typing auditory (MTtype_aud) for (A) accuracy visual; (B) accuracy auditory; (C) reaction time visual; (D) reaction time auditory.
TABLE 4 | ANOVA results for typing parameters.
df F Sig η2
Age 1 (122) 8.894 0.004** 0.067
Condition 1 (122) 51.217 <0.001** 0.296
Condition ×Age 1 (122) 6.937 <0.001** 0.054
Task Modality 1 (122) 7.992 0.005** 0.061
Task Modality ×Age 1 (122) 0.143 0.706 0.001
Condition ×Task Modality 1 (122) 36.702 <0.001** 0.231
Condition ×Age ×Task Modality 1 (122) 1.728 0.191 0.014
Age 1 (122) 9.401 0.003** 0.072
Condition 1 (122) 112.852 <0.001** 0.481
Condition ×Age 1 (122) 6.912 0.010* 0.054
Task Modality 1 (122) 1.094 0.298 0.009
Task Modality ×Age 1 (122) 145.077 <0.001** 0.543
Condition ×Task Modality 1 (122) 31.572 <0.001** 0.206
Condition ×Age ×Task Modality 1 (122) 6.283 0.014* 0.049
*p<0.05; **p<0.01.
OA selected later Gaps than YA (significant effect of Age),
and gap selection was significant earlier within STcross compared
to both MTtype conditions (significant condition effect; both
p<0.001) (Figure 2E). As indicated by the age by condition
interaction, this condition effect was only driven by OA. Pairwise
post-hoc comparisons revealed significant age differences in
MTtype_vis (p=0.001) and MTtype_aud (p<0.001).
Typing Task
Table 3 and Figure 3 show descriptive data on all typing task
parameters and Table 4 pertinent ANOVA results.
Accuracy scores for typing were significantly lower in OA than
to YA, significantly lower in MTtype conditions than to STtype
conditions and significantly lower in the auditory than the visual
task modality (Figures 3A,B). The condition by age interaction
and corresponding post-hoc tests revealed, that age differences
only occurred in the MT conditions (p<0.001). Differences
between STtype and MTtype conditions occurred for the auditory
task modality (p<0.001), while task modality differences
occurred within both conditions (significant condition ×task
modality interaction).
Reaction Time was significantly longer for OA than YA (age
effect) and longer in MTtype conditions than STtype conditions
(condition effect) (Figures 3C,D). This was particularly true for
OA as shown by the age ×condition interaction. Pairwise
comparisons revealed, that age differences only occurred in the
MTtype conditions (p<0.001) but within both task modalities
(auditory: p=0.001; visual p<0.001; significant age ×task
modality interaction).
Condition differences were found within both task modalities
but task modality differences were only present in STtype
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Janouch et al. Cognitive—Motor Interference While Street Crossing
condition (significant condition ×task modality interaction).
Pairwise comparisons for the condition ×modality ×age
interaction revealed that within the visual task modality, age
differences were present in the single task (p<0.001) as well
as the multitasks (always p<0.001). Within the auditory
task modality, age differences were only present in the STtype
condition (p<0.001).
Multitasking Effects
Table 5 shows MTE scores for crossing as well as typing
parameters, and their differences from zero. Within the visual
task modality, significant non-zero MTE scores emerged for both
age groups in all street crossing parameters and in Reaction Time.
Within the auditory task modality, significant non-zero MTE
scores in both age groups were only yielded for Walking Speed,
Gap, and Accuracy, while significant non-zero MTE for Crossing
Speed,Crossing Failure, and Reaction Time were only found in
OA. These data did not support a consistent relationship between
multitasking deficits and task modality.
Prioritization: Street Crossing-Related
MTE vs. Typing-Related MTE
The ANOVA results for MTE are depicted in Table 6, and the
pertinent post-hoc comparisons are summarized in Table 7. MTE
differed significantly between street crossing-related and typing-
related parameters. OA were more likely to produce significant
differences between street crossing- and typing-related MTE
compared to YA. Further, in the visual task modality significantly
higher MTE occurred more frequently for the street crossing-
related parameters than for the typing-related ones, especially in
OA. This was contrary to findings in the auditory task modality,
where significantly higher MTE were produced more frequently
in the typing task. However, the direction of those differences
was not consistent overall, for a given age group or for a given
These data argued against an overall or an age-dependent
prioritization of the street-crossing or the typing task. To
emphasize this lack of an overall prioritization strategy, we
plotted the Means of significant street crossing-related vs. typing-
related MTE differences, grouped by age (see Figure 4).
The aim of this study was to expand available dual-task
research by using an ecologically valid task, street crossing
in virtual reality, and by including realistic loading tasks and
stimulus modalities. The loading task was delivered via two
different task modalities (visual vs. auditory) to further provide a
theoretical contribution toward the multiple resource theory and
Mulittasking Effects were considered to identify possible general
prioritization strategies.
We expected to confirm that even in our ecologically valid
scenario the costs of multitasking increase in older age and
that this increase is more pronounced in the loading tasks as
compared to the street crossing task, in accordance with the
posture-first hypothesis (Lindenberger et al., 2000; Li et al., 2001;
Schäfer et al., 2006). Further we expected that this increase is
TABLE 5 | Means (M) and Standard Deviations (SD) of multitasking effects within
the visual task modality (MTE visual) and within the auditory task modality (MTE
auditory), and their difference from zero.
MTE visual MTE auditory
M (SD) M (SD) M (SD) M (SD)
Stay Time (s) 25.91
Back-alley Speed
Crossing Speed
Crossing Failure
Gap (#) 6.93
Accuracy 0.12
Reaction time 13.18
*p<0.05; **p<0.01.
TABLE 6 | ANOVA results for multitasking effects.
df (Error) FSig η2
Age 1 (122) 9.566 0.002** 0.073
Parameter Type 6 (154.025) 19.637 <0.001** 0.139
Parameter Type ×Age 6 (154.025) 5.296 0.016* 0.042
Task Modality 1 (122) 24.151 <0.001** 0.165
Task Modality ×Age 1 (122) 0.068 0.068 0.027
Parameter Type ×Task
6 (132.331) 18.744 <0.001** 0.133
Parameter Type ×Age
×Task Modality
6 (132.331) 6.180 0.012* 0.048
*p<0.05; **p<0.01.
more pronounced in the visual than the auditory task modality,
in accordance with the multiple-resource model (Wickens, 2002).
In accordance with our first expectation, we found that
street crossing as well as typing performance suffered under
multitasking conditions, and that impairments were more
pronounced in older adults. When multitasking, older adults
slowed down more than young ones when approaching the
curb and when crossing the street, waited longer at the curb,
and therefore selected a later gap for crossing. This is in line
with previous street crossing studies which also found longer
approach durations (Banducci et al., 2016), longer preparation
durations (Neider et al., 2010, 2011; Chaddock et al., 2011, 2012;
Byington and Schwebel, 2013; Gaspar et al., 2014; Banducci et al.,
2016) and more missed crossing opportunities (Stavrinos et al.,
2011; Byington and Schwebel, 2013). Our findings are also in
line with traditional laboratory studies, which found stronger
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Janouch et al. Cognitive—Motor Interference While Street Crossing
TABLE 7 | Post-hoc comparisons between street crossing-related (rows) and typing-related (columns) multitasking effects, separately for each age group (YA; OA) and
task modality.
MTE (%) Visual task modality Auditory task modality
Reaction Time Reaction Time Accuracy Accuracy Reaction Time Reaction Time Accuracy Accuracy
Back-alley Speed <0.001** <0.001** 0.003** <0.001** <0.001** <0.001** <0.001** <0.001**
Stay Time 0.002** <0.001** 0.041*<0.001** <0.001**
Crossing Speed <0.001** <0.001** <0.001** 0.001** <0.001** <0.001**
Crossing Failure <0.001**
Gap <0.001** 0.002**
*p<0.05; **p<0.01. Bold, Street crossing MTE >Typing MTE; Italic, Typing MTE >Street crossing MTE.
effects of multitasking on gait speed for older than young persons
(Lindenberger et al., 2000; Hausdorff et al., 2008).
The age-related decrement of multitasking abilities manifested
not only in four of our five street-crossing parameters, but also
in both loading-task parameters. This conforms earlier findings
about differential effects of age on loading-task performance
(Lindenberger et al., 2000; Li et al., 2012), and extends them to
an ecologically valid scenario.
Surprisingly, we did not find age-related decrements in
Crossing Speed, even though a reduced walking speed in OA
compared to YA under dual-task conditions has been reported in
dual-task gait studies before (Lindenberger et al., 2000; Hausdorff
et al., 2008). In contrast, Neider et al. (2011) reported even
smaller crossing durations (i.e., faster crossing speeds) in OA
as compared to YA and interpreted this finding as a greater
perceived urge in OA to avoid (virtual) collisions. In our study,
both age groups crossed the street very quickly (around 6 km/h)
which means that especially OA accelerated their regular walking
speed (4.6–4.9 km/h; Samson et al., 2001) and invested more
motor (and cognitive) resources in order to complete the crossing
as fast as possible. Especially in a demanding multitasking
situation, this additional invest of resources might exceed the
limits of their processing capacity.
In accordance with our second expectation, multitasking had
more pronounced effects on the loading tasks than on the street-
crossing task, but this was the case for only about half of the
age ×modality ×parameter combinations (cf. Table 5). For
the other half, multitasking had more pronounced effects on
the street-crossing task. This heterogeneity persists even when
only the two crossing parameters with the closest link to posture
and gait are considered, namely, Back-alley Speed and Crossing
Speed. From this we conclude that the posture-first hypotheis
may not be applicable unconditionally (Li et al., 2012), and in
ecologically valid scenarios. These heterogeneous results could
either indicate implicit, individual prioritization strategies or a
limitation due to task difficulties. Thus, participants may either
have not perceived a risk to their health out of the virtual reality
which would limit the extent to what VR scenarios transmit a
real life impression or might have been limited by ceiling effects
within some tasks. Overall, it appears that realistic loading tasks
can be motivating enough to override older persons’ concerns
about postural stability. However, it has to be mentioned that
in our study, participants were allowed to keep one hand to the
treadmill’s handrail which might have influenced participant’s
perceived postural control. In this vein, Lövdén et al. (2005)
revealed that older adults’ navigation performance improves
when holding on to a handrail. Thus, future studies should
systematically investigate the influence of additionl support and
might also assess general as well as test set up-related anxiety
scores such as fear of falls.
Inconsistent with our third expectation, effects of multitasking
were not consistently more pronounced in the visual compared
to the auditory modality (cf. Tables 3,4). More pronounced
multitasking effects in the visual than auditory condition are
in accordance with an earlier virtual-driving study where visual
and auditory loading tasks were presented blockwise (Chaparro
et al., 2005). In our study, however, stronger effects of the visual
modality were observed for only a part of the age ×parameter
combinations. This was most striking for Stay Time, for which the
visual task modality (in YA as well as in OA) caused the highest
overall MTE of all parameters, implicating that within this phase
of the crossing process, vision might play an indispensable role.
However, for other combinations, both modalities yielded similar
effects or the auditory modality even yielded stronger effects e.g.,
for Back Alley Speed which was surprisingly more effected by
the auditory task modality. We therefore found no unequivocal
support for the multiple resource model in our ecologically
valid scenario. Possibly, our multitasking scenario was complex
enough to give participants a choice exactly what resources they
allocated to the task at hand. As a consequence, participants’
strategic choices could have upset any strict relationship between
task modality and multitasking effects.
The lack of a consistent relationship between task modality
and multitasking performance is particularly striking for our
loading task: multitasking effects on Typing Reaction Time were
more pronounced in the visual modality, while those on Typing
Accuracy were stronger in the auditory modality. We contribute
this particular dissimilarity to the fact that in the German
language, the ten’s and one’s of numbers are spoken in reversed
order (e.g., “two hundred and five-and-forty” instead of “two
hundred and forty-five”). If participants pressed the keys in the
same order in which digits were spoken, this would have reduced.
In conclusion, our findings confirm that age-related deficits
of multitasking exist even in ecologically valid scenarios, and
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Janouch et al. Cognitive—Motor Interference While Street Crossing
FIGURE 4 | Multitasking Effects (MTE) distribution (Means in %) grouped by age (YA vs. OA) and task type (street crossing related vs. typing related).
Frontiers in Psychology | 10 May 2018 | Volume 9 | Article 602
Janouch et al. Cognitive—Motor Interference While Street Crossing
document that those deficits can emerge in both concurrent
tasks. However, our findings provide no unequivocal support
for the posture-first hypothesis and for the multiple-resource
model. We attribute this lack of support to motivational and to
strategic factors, which are controlled for in traditional laboratory
paradigms but play a major role in realistic behavior.
CJ performed the acquisition of data and data analyses and wrote
the drafts of the manuscript. CV-R as a senior author as well
as OB contributed in terms of data selection, data analysis as
well as in editing the manuscript and providing additional ideas
at how to interpret our data. KW, UD and MH supported the
data collection as well as the data analysis and proof read the
This research was supported by a grant within the Priority
Program, SPP 1772 from the German Research Foundation
(Deutsche Forschungsgemeinschaft, DFG), grants BO 649/22-1
and VO 1432/19-1 and by a grant of the European Social Fund
and the Free State of Saxony (CJ).
We wish to thank Wim Van Winsum and Thomas Kesnerus
for the modification of the simulator software and constant
support. We thank Rieke Schmale, Gisli Barthelmess, Sandra
Goertz, Verena Herzoff, Clara Rentz, Damaris Wallmeroth,
Jacqueline Kasemir, Lorenz Baumjohann, Martin Rebro.
Karim Aly, Stella Henn, Vincent Drießen, Isarn Babel, Maria
Andrea Neubert, Malte Liebl-Wachsmuth, and Karolina
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Conflict of Interest Statement: The authors declare that the research was
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Copyright © 2018 Janouch, Drescher, Wechsler, Haeger, Bock and Voelcker-Rehage.
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Frontiers in Psychology | 12 May 2018 | Volume 9 | Article 602
... In summary, the aim of the present study was to examine the differential effects and the interrelating role of executive functions, motor coordinative fitness, and cardiovascular fitness on different cognitive-motor multitasking outcomes in a more ecologically valid cognitive-motor multitasking scenario, i.e., street crossing. This scenario was implemented in virtual reality, as already described in Janouch et al. 54 , and similar as in other studies on street crossing [55][56][57] . We expected that a higher multitasking performance would be associated with a higher level of executive functions, and/or motor coordinative fitness, and/or cardiovascular fitness, depending on the cognitive and/or motor demands of the street crossing outcomes (hypothesis I). ...
... The latter was assessed in two everyday-like virtual scenarios: car driving and street crossing. Findings regarding the structure of executive functions in young and older adults 65 , age-related cognitive-motor interference in driving 51 , and street crossing performance 54 have already been published elsewhere. ...
... Participants were asked to indicate products that were displayed twice with a clearly spoken "yes". To limit the degree of complexity of the present communication and because earlier analyses revealed that the typing tasks exhibited stronger task interference than the shopping tasks 54 , we analyzed only the typing task. Nevertheless, since the task is necessary to explain the design, we mention it briefly in this section. ...
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Age-related decline in cognitive-motor multitasking performance has been attributed to declines in executive functions and physical fitness (motor coordinative fitness and cardiovascular fitness). It has been suggested that those cognitive and physical resources strongly depend on lifestyle factors such as long-term regular physical activity and cognitive engagement. Although research suggests that there is covariation between components of executive functions and physical fitness, the interdependence between these components for cognitive-motor multitasking performance is not yet clear. The aim of the study was to examine the contribution and interrelationship between executive functions, motor coordinative fitness, and cardiovascular fitness on street crossing while multitasking. We used the more ecologically valid scenario to obtain results that might be directly transferable to daily life situation. Data from 50 healthy older adults (65–75 years, 17 females, recruited in two different cities in Germany) were analyzed. Participants’ executive functions (composite score including six tests), motor coordinative fitness (composite score including five tests), and cardiovascular fitness (spiroergometry), as well as their street crossing performance while multitasking were assessed. Street crossing was tested under single-task (crossing a two-line road), and multitask conditions (crossing a two-line road while typing numbers on a keypad as simulation of mobile phone use). Street crossing performance was assessed by use of cognitive outcomes (typing, crossing failures) and motor outcomes (stay time, crossing speed). Linear mixed-effects models showed beneficial main effects of executive functions for typing ( p = 0.004) and crossing failures ( p = 0.023), and a beneficial main effect of motor coordinative fitness for stay time ( p = 0.043). Commonality analysis revealed that the proportion of variance commonly explained by executive functions, motor coordinative fitness, and cardiovascular fitness was small for all street crossing outcomes. For typing and crossing failures (cognitive outcomes), the results further showed a higher relative contribution of executive functions compared to motor coordinative fitness and cardiovascular fitness. For stay time (motor outcome), the results correspondingly revealed a higher relative contribution of motor coordinative fitness compared to executive functions and cardiovascular fitness. The findings suggest that during cognitive-motor multitasking in everyday life, task performance is determined by the components of executive functions and physical fitness related to the specific task demands. Since multitasking in everyday life includes cognitive and motor tasks, it seems to be important to maintain both executive functions and physical fitness for independent living up to old age.
... It has been documented that the outcome of typical laboratory paradigms may not be transferable to many everyday-life situations [46, 50,51]. The present study, therefore, used a more realistic cognitive-motor multitasking scenario, i.e., street crossing, implemented in virtual reality, as already described in [52], and similar as in [53]- [55]]. ...
... It is well known that when older adults perform multiple tasks simultaneously, performance decrements occur in at least one of those tasks. This cognitive-motor interference has already been demonstrated in various laboratory situations [56,57], and in ecologically valid conditions such as street crossing [52][53][54][55]. The phenomenon is explained by limited cognitive resources, which must be shared between tasks. ...
... The latter was assessed in two everyday-like virtual scenarios: car driving and street crossing. Findings regarding the structure of executive functions in young and older adults [66], age-related cognitive-motor interference in driving [49] and street crossing performance [52] have already been published elsewhere. ...
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Here, we investigated whether age-related cognitive-motor interference is influenced by individual’s levels of executive functions and physical fitness in a more ecological valid scenario. Participants ( N = 61, 65–75 years) crossed a virtual street on a treadmill without getting hit by a car during single- and multitasking (typing numbers). Outcomes were cognitive and motor performance parameters. Further, participants’ executive functions, motor coordinative, and cardiovascular fitness were tested. Linear mixed-effects models showed beneficial main effects of executive functions for the cognitive parameters typing ( p = .004) and crossing failures ( p = .023), and a beneficial main effect of motor coordinative fitness for the motor parameter stay time ( p = .043). We found interaction effects between condition and executive functions for typing ( p = .044) with higher benefits during multi- than single-tasking. Commonality analysis revealed an increasing relative contribution of executive functions on cognitive parameters and of motor coordinative fitness on motor parameters from single- to multitasking. Additionally, the proportion of variance commonly explained by executive functions and physical fitness was small for both cognitive and motor parameters. Findings substantiate the benefits of maintaining cognitive functioning and physical fitness up to old age.
... However, such testing has limited ecological validity (i.e., minimal resemblance to everyday life demands) as it focuses on each motor or cognitive sub-domain in isolation, with little regard for 'real-life' demands and responses. Contrary to this 'isolated' approach, dual-task paradigms that assess concurrent motor-cognitive tasks such as texting [11] or calculating [12] while walking more closely replicate real-life activities, although such paradigms have been criticized for insufficient ecological validity [13][14][15] since they are often conducted in laboratory settings or entail non-functional tasks. We contend that a more valid evaluation of motor (specifically gait) and cognitive functions should be performed within the context of tasks that are relevant to older adult's 'real-life' interests and activities, and should be documented via simultaneous recordings of both cognitive and motor performance. ...
... Assessment of an older person's performance within simulations of daily life settings by means of laboratorybased virtual reality (VR) may overcome many of the above-mentioned barriers. VR enables objective and accurate measurement of behavior in ecologically valid and safe environments, while controlling the delivery of stimuli and maintaining standardized measurement protocols [15][16][17]. Despite this potential, studies that compared virtual and real-world performance of complex daily activities are scarce. ...
... participants can pause at will) while walking on flat surfaces with no perturbations. In line with our findings, Janouch et al. [15] showed that age did not affect walking speed when performing a virtual street crossing task. ...
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Background: Virtual reality (VR) enables objective and accurate measurement of behavior in ecologically valid and safe environments, while controlling the delivery of stimuli and maintaining standardized measurement protocols. Despite this potential, studies that compare virtual and real-world performance of complex daily activities are scarce. This study aimed to compare cognitive strategies and gait characteristics of young and older healthy adults as they engaged in a complex task while navigating in a real shopping mall and a high-fidelity virtual replica of the mall. Methods: Seventeen older adults (mean (SD) age = 71.2 (5.6) years, 64% males) and 17 young adults (26.7 (3.7) years, 35% males) participated. In two separate sessions they performed the Multiple Errands Test (MET) in a real-world mall or the Virtual MET (VMET) in the virtual environment. The real-world environment was a small shopping area and the virtual environment was created within the CAREN™ (Computer Assisted Rehabilitation Environment) Integrated Reality System. The performance of the task was assessed using motor and physiological measures (gait parameters and heart rate), MET or VMET time and score, and navigation efficiency (cognitive performance and strategy). Between (age groups) and within (environment) differences were analyzed with ANOVA repeated measures. Results: There were no significant age effects for any of the gait parameters but there were significant environment effects such that both age groups walked faster (F(1,32) = 154.96, p < 0.0001) with higher step lengths (F(1,32) = 86.36, p < 0.0001), had lower spatial and temporal gait variability (F(1,32) = 95.71-36.06, p < 0.0001) and lower heart rate (F(1,32) = 13.40, p < 0.01) in the real-world. There were significant age effects for MET/VMET scores (F(1,32) = 19.77, p < 0.0001) and total time (F(1,32) = 11.74, p < 0.05) indicating better performance of the younger group, and a significant environment effect for navigation efficiency (F(1,32) = 7.6, p < 0.01) that was more efficient in the virtual environment. Conclusions: This comprehensive, ecological approach in the measurement of performance during tasks reminiscent of complex life situations showed the strengths of using virtual environments in assessing cognitive aspects and limitations of assessing motor aspects of performance. Difficulties by older adults were apparent mainly in the cognitive aspects indicating a need to evaluate them during complex task performance.
... Recently VR exergames for adults that monitor and train physical activity have been developed for: posture (Solis-Escalante et al., 2019;Carr et al., 2020;Imaoka et al., 2020), falls (Chen et al., 2020;Liston et al., 2021), dual-task walking (Kizony et al., 2017;Janouch et al., 2018;Kafri et al., 2021). As well as assessing cognitive health and providing comfort in patients with neurological diseases and communication difficulties (e.g., Stasolla et al., 2021). ...
... On the other hand cognitive impairment causes variation in the magnitude of cognitive-motor integration, which in turn causes variation in posture and spatial aspects of gait (Mahoney and Verghese, 2020). Hence, several exergames successfully assessed cognitive memory domains while walking and performing realistic dailylife activities within VR environments (Kizony et al., 2017;Janouch et al., 2018;Kafri et al., 2021). ...
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A major concern of public health authorities is to also encourage adults to be exposed to enriched environments (sensory and cognitive-motor activity) during the pandemic lockdown, as was recently the case worldwide during the COVID-19 outbreak. Games for adults that require physical activity, known as exergames, offer opportunities here. In particular, the output of the gaming industry nowadays offers computer games with extended reality (XR) which combines real and virtual environments and refers to human-machine interactions generated by computers and wearable technologies. For example, playing the game in front of a computer screen while standing or walking on a force plate or treadmill allows the user to react to certain infrastructural changes and obstacles within the virtual environment. Recent developments, optimization, and minimizations in wearable technology have produced wireless headsets and sensors that allow for unrestricted whole-body movement. This makes the virtual experience more immersive and provides the opportunity for greater engagement than traditional exercise. Currently, XR serves as an umbrella term for current immersive technologies as well as future realities that enhance the experience with features that produce new controllable environments. Overall, these technology-enhanced exergames challenge the adult user and modify the experience by increasing sensory stimulation and creating an environment where virtual and real elements interact. As a therapy, exergames can potentially create new environments and visualizations that may be more ecologically valid and thus simulate real activities of daily living that can be trained. Furthermore, by adding telemedicine features to the exergame, progress over time can be closely monitored and feedback provided, offering future opportunities for cognitive-motor assessment. To more optimally serve and challenge adults both physically and cognitively over time in future lockdowns, there is a need to provide long-term remote training and feedback. Particularly related to activities of daily living that create opportunities for effective and lasting rehabilitation for elderly and sufferers from chronic non-communicable diseases (CNDs). The aim of the current review is to envision the remote training and monitoring of physical and cognitive aspects for adults with limited mobility (due to disability, disease, or age), through the implementation of concurrent telehealth and exergame features using XR and wireless sensor technologies.
... Cognitive-motor dual-task (CMDT) studies usually compare a single-task condition, such as a postural or walking task, with a dual-task condition, which combines the same motor task with a synchronous cognitive task. By comparing performance in both conditions, we can then calculate dual-task costs (DTCs: single-task minus dual-task performance divided by single-task performance) which indicate the degree of performance decline in one or both tasks due to limited cognitive capacity (Li and Lindenberger, 2002;Marusic et al., 2015;Janouch et al., 2018;Wollesen et al., 2018). CMDT studies have shown that as cognitive load increases during the manipulation of sensory information, older adults show greater postural performance decrements compared to their younger counterparts (Redfern et al., 2001;Doumas et al., 2008). ...
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Background: Hearing impairments are associated with reduced walking performance under Dual-task (DT) conditions. Little is known about the neural representation of DT performance while walking in this target group compared to healthy controls or younger adults. Therefore, utilizing the Mobile Brain/Body Imaging approach (MoBI), we aim at gaining deeper insights into the brain dynamics underlying the interaction of cognitive and motor processes during different DT conditions (visual and auditory) controlling for age and the potential performance decrements of older adults with hearing impairments. Methods: The cross-sectional study integrates a multifactorial mixed-measure design. Between-subject factors grouping the sample will be age (younger vs. older adults) and hearing impairment (mild vs. not hearing impaired). The within-subject factors will be the task complexity (single- vs. DT) and cognitive task modality (visual vs. auditory). Stimuli of the cognitive task will vary according to the stimulus modality (visual vs. auditory), presentation side (left vs. right), and presentation-response compatibility (ipsilateral vs. contralateral). Analyses of DT costs and underlying neuronal correlates focus either on gait or cognitive performance. Based on an a priori sample size calculation 96 (48 healthy and 48 mildly hearing impaired) community-dwelling older adults (50–70 years) and 48 younger adults (20–30 years) will be recruited. Gait parameters of speed and rhythm will be captured. EEG activity will be recorded using 64 active electrodes. Discussion: The study evaluates cognitive-motor interference (CMI) in groups of young and older adults as well as older adults with hearing impairment. The underlying processes of the interaction between motor and cognitive tasks will be identified at a behavioral and neurophysiological level comparing an auditory or a visual secondary task. We assume that performance differences are linked to different cognitive-motor processes, i.e., stimulus input, resource allocation, and movement execution. Moreover, for the different DT conditions (auditory vs. visual) we assume performance decrements within the auditory condition, especially for older, hearing-impaired adults. Findings will provide evidence of general mechanisms of CMI (ST vs. DT walking) as well as task-specific effects in dual-task performance while over ground walking.
... For example, the SeeMe virtual interactive shopping environment measures executive function by asking participants to purchase specific products and stay within an allocated budget (Nir-Hadad et al., 2017). Other real-world scenarios include driving (Liu et al., 1999), street crossing (Janouch et al., 2018), and classroom performance (Mühlberger et al., 2020). Many of these VR measurements have been validated in comparison with more traditional assessments, and in some cases outperform traditional tests (Diaz-Orueta et al., 2020). ...
... For example, the SeeMe virtual interactive shopping environment measures executive function by asking participants to purchase specific products and stay within an allocated budget (Nir-Hadad et al., 2017). Other real-world scenarios include driving (Liu et al., 1999), street crossing (Janouch et al., 2018), and classroom performance (Mühlberger et al., 2020). Many of these VR measurements have been validated in comparison with more traditional assessments, and in some cases outperform traditional tests (Diaz-Orueta et al., 2020). ...
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Virtual reality (VR) has become a readily available consumer technology, strengthening its promise as a research tool for health psychology. We identify five key strengths of VR‐based research: data collection, realism, experimental control, adaptability, and mobility (DREAM). We review how these advantages allow researchers to investigate behavioral, psychological, and social processes related to health and well‐being in novel ways, by using VR as both a stimulus and a measurement tool. We also describe challenges facing VR research and potential strategies researchers can use to mitigate them. In addition to reviewing existing research, we hope to inspire researchers to consider ways in which VR might be used in future to augment their own research programs or answer currently impracticable research questions in health psychology.
... Older pedestrians' behavior has been investigated since the late nineties. Several investigation methods have been used (for an overview, see Cavallo et al., 2019), including accident analyses (e.g., Fontaine & Gourlet, 1997), observational studies (e.g., Oxley et al., 1997;Knoblauch et al., 1996), roadside simulations (e.g., Zivotofsky et al., 2012), and laboratory experiments (Holland & Hill, 2010;Janouch et al., 2018;Oxley et al., 2005;Zito et al., 2015). Laboratory studies have used videos or generated images projected on screens of different sizes as stimuli, together with tasks pertaining to judgments as to whether street crossing is possible (including taking one step forward) or walking on a treadmill. ...
Interactive pedestrian simulators have become a valuable research tool for investigating street-crossing behavior and developing solutions for improving pedestrian safety. There are two main kinds of pedestrian simulators: one uses a technology based on rear-projection screens (Cave Automatic Virtual Environment, or CAVE), the other a head-mounted display (HMD). These devices are used indiscriminately, regardless of the research objective, and it is not yet known whether they are equally effective for studying street crossing. The present study was aimed at comparing the street crossing behavior and subjective evaluations of younger and older adult pedestrians when they are using a CAVE-like or HMD-based (HTC Vive Pro) pedestrian simulator. Thirty younger adults and 25 older adults performed 36 street-crossing trials (combining different speeds, two-way traffic conditions, and gap sizes) on each of the two types of simulators. The results indicated that participants in the HMD condition crossed the street significantly more often (58.6 %) than in the CAVE condition (42.44%) and had shorter safety margins. The most striking difference pertained to crossing initiation, which occurred considerably earlier (1.78 s) in the HMD condition than in the CAVE condition. Synchronization of crossing initiation with oncoming traffic was not as good in the CAVE condition because visual information in front of the pedestrian was missing due to the absence of ground projection. In both simulators, older adults caused more collisions than did younger ones, had shorter safety margins, and a slower crossing speed. Hence, the HMD reproduced classical age-related differences in most street-crossing behaviors already found on the CAVE. Usually observed speed effects were also found for both simulators. Neither cybersickness nor any adverse effects on stereoacuity or postural balance were found for either simulator. The HMD produced a higher level of presence and preference than the CAVE did. These findings provide evidence that HMDs have a clear potential for studying pedestrian behaviour.
... This may also have affected the cognitive task demand of auditory trials (cf. also Janouch et al., 2018 ). With regard to these results communication modality of in-vehicle devices should be cautiously considered to not increase cognitive demands and distractibility of the driver. ...
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Driving is a complex cognitive-motor task that requires the continuous integration of multisensory information, cognitive processes, and motor actions. With higher age, driving becomes increasingly challenging as a result of naturally declining neurophysiological resources. Performing additional subtasks, such as conversations with passengers or interactions with in-vehicle devices (e.g., adjusting the radio), may further challenge neurocognitive resources that are required to maintain driving performance. Based on declining brain physiological resources and inferior neurocognitive functioning, older adults (OA) may show higher brain activation and larger performance decrements than younger adults (YA) when engaging in additional subtasks during driving. Age differences, however, may further vary for different neurocognitive task demands, such that driving performance of OA might be particularly affected by certain subtasks. In this study, we hence investigated the brain functional correlates of age differences in driving behavior during concurrent subtask performance in YA and OA. Our final sample consisted of thirty younger (21.80 ± 1.73y, 15 female) and thirty older (69.43 ± 3.30y, 12 female) regular drivers that drove along a typical rural road (25 - 30 min) in a driving simulator and performed three different concurrent subtasks that were presented auditorily or visually: typing a 3-digit number (TYPE), comparing traffic news and gas station prices (working memory, WM), and stating arguments (ARG). We measured variability in lateral car position, velocity, and following distance to a frontal lead car as the standard deviation from 0 to 15 s after subtask onset. Brain activity was continuously recorded using functional near-infrared spectroscopy over the dorsolateral prefrontal cortex. Both YA and OA particularly varied in their lateral position during TYPE with a more pronounced effect in OA. For YA, in contrast, ARG led to higher variability in velocity compared to TYPE and WM, whereas OA showed no task-specific differences. Substantiating our behavioral findings, OA revealed the largest brain functional response to TYPE, while YA demonstrated a very distinct activation during ARG and smaller hemodynamic responses to TYPE and WM. Brain activity in the DLPFC was, overall, not significantly, but small to moderately related to certain behavioral performance parameters (mainly lateral position). We conclude that both OA and YA are vulnerable to distractive subtasks while driving. Age differences, however, seem to largely depend on neurocognitive task demands. OA may be at higher risk for accidents when performing visuo-motor subtasks (e.g., interacting with navigational systems) during driving while YA may be more (cognitively) distracted when talking to passengers.
It is well established that car driving performance suffers when the driver concurrently engages in a distracting activity, such as talking on a cell phone. The present study investigates whether the effects of driver distraction are short-lived, or rather persist for some time. Age-related differences are evaluated as well. Sixty-three young and 61 older adults were tested in a driving simulator. They were asked to follow a lead car that drove at a constant speed, and to concurrently engage in a pseudorandom sequence of distracting tasks (typing, reasoning, memorizing). When the lead car braked, participants had to brake as well to prevent a collision. The stimulus onset asynchrony between the braking task and the last preceding distraction was 11.49 ± 1.99 s. Each person was tested once in a multitasking condition (as described above), and once in a control condition without distracting tasks. Outcome measures quantified distance keeping and lane keeping while participants braked to the lead car. We found that braking responses differed significantly between conditions; this difference could be interpreted as a combination of performance deficits and compensatory strategies in the multitasking condition compared to the control condition. We also found significant differences between age groups, which could be interpreted similarly. Differences between age groups were less pronounced in the multitasking than in the control condition. All observed effects were associated with participants’ executive functioning. Our findings confirm that distractions have an impact on braking responses, and they document for the first time that this impact can persist for about 11.5 s. We attribute this persistence to a task set effect, and discuss the practical relevance of our findings.
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Objective: A fully immersive, high-fidelity street-crossing simulator was used to examine the effects of texting on pedestrian street-crossing performance. Background: Research suggests that street-crossing performance is impaired when pedestrians engage in cell phone conversations. Less is known about the impact of texting on street-crossing performance. Method: Thirty-two young adults completed three distraction conditions in a simulated street-crossing task: no distraction, phone conversation, and texting. A hands-free headset and a mounted tablet were used to conduct the phone and texting conversations, respectively. Participants moved through the virtual environment via a manual treadmill, allowing them to select crossing gaps and change their gait. Results: During the phone conversation and texting conditions, participants had fewer successful crossings and took longer to initiate crossing. Furthermore, in the texting condition, smaller percentage of time with head orientation toward the tablet, fewer number of head orientations toward the tablet, and greater percentage of total characters typed before initiating crossing predicted greater crossing success. Conclusion: Our results suggest that (a) texting is as unsafe as phone conversations for street-crossing performance and (b) when subjects completed most of the texting task before initiating crossing, they were more likely to make it safely across the street. Application: Sending and receiving text messages negatively impact a range of real-world behaviors. These results may inform personal and policy decisions.
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The relevance of dual-task walking to everyday ambulation is widely acknowledged, and numerous studies have demonstrated that dual-task interference can significantly impact recovery of functional walking in people with neurological disorders. The magnitude and direction of dual-task interference is influenced by the interaction between the two tasks, including how individuals spontaneously prioritize their attention. Therefore, to accurately interpret and characterize dual-task interference and identify changes over time, it is imperative to evaluate single and dual-task performance in both tasks, as well as the tasks relative to each other. Yet, reciprocal dual-task effects are frequently ignored. The purpose of this perspective paper is to present a framework for measuring treatment effects on dual-task interference, specifically taking into account the interactions between the two tasks and how this can provide information on whether overall dual-task capacity has improved or a different attentional strategy has been adopted. In discussing the clinical implications of using this framework, we provide specific examples of using this method and provide some explicit recommendations for research and clinical practice.
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Objective Pedestrian injuries represent a pediatric public health challenge. This systematic review/meta-analysis evaluated behavioral interventions to teach children pedestrian safety. Methods Multiple strategies derived eligible manuscripts (published before April 1, 2013, randomized design, evaluated behavioral child pedestrian safety interventions). Screening 1,951 abstracts yielded 125 full-text retrievals. 25 were retained for data extraction, and 6 were later omitted due to insufficient data. In all, 19 articles reporting 25 studies were included. Risk of bias and quality of evidence were assessed. Results Behavioral interventions generally improve children’s pedestrian safety, both immediately after training and at follow-up several months later. Quality of the evidence was low to moderate. Available evidence suggested interventions targeting dash-out prevention, crossing at parked cars, and selecting safe routes across intersections were effective. Individualized/small-group training for children was the most effective training strategy based on available evidence. Conclusions Behaviorally based interventions improve children’s pedestrian safety. Efforts should continue to develop creative, cost-efficient, and effective interventions.
The ability to perform two tasks simultaneously has become increasingly important as attention-demanding technologies have become more common in daily life. This type of attentional resources allocation is commonly called "divided attention". Because of the importance of divided attention in natural world settings, substantial efforts have been made recently so as to promote an integrated, realistic assessment of functional abilities in dual-task paradigms. In this context, virtual reality methods appear to be a good solution. However to date, there has been little discussion on validity of such methods. Here, we offer a comparative review of conventional tools used to assess divided attention and of the first virtual reality studies (mostly from the field of road and pedestrian safety). The ecological character of virtual environments leads to a better understanding of the influence of dual-task settings and also makes it possible to clarify issues such as the utility of hands-free phones. After discussing the theoretical and clinical contributions of these studies, we discuss the limits of virtual reality assessment, focusing in particular: (i) on the challenges associated with lack of familiarity with new technological devices; (ii) on the validity of the ecological character of virtual environments; and (iii) on the question of whether the results obtained in a specific context can be generalized to all dual-task situations typical of daily life. To overcome the limitations associated with virtual reality, we propose: (i) to include a standardized familiarization phase in assessment protocols so as to limit the interference caused by the use of new technologies; (ii) to systematically compare virtual reality performance with conventional tests or real-life tests; and (iii) to design dual-task scenarios that are independent from the patient's expertise on one of the two tasks. We conclude that virtual reality appears to constitute a useful tool when used in combination with more conventional tests.
The aim of this study was to compare parents' expectations for their children crossing streets with children's actual crossing behaviours and determine how accurately parents judge their own children's pedestrian behaviours to be. Using a fully immersive virtual reality system interfaced with a 3D movement measurement system, younger (7-9 years) and older (10-12 years) children's crossing behaviours were assessed. The parent viewed the same traffic conditions and indicated if their child would cross and how successful she/he expected the child would be when doing so. Comparing children's performance with what their parents expected they would do revealed that parents significantly overestimated the inter-vehicle gap threshold of their children, erroneously assuming that children would show safer pedestrian behaviours and select larger inter-vehicle gaps to cross into than they actually did; there were no effects of child age or sex. Child and parent scores were not correlated and a logistic regression indicated these were independent of one another. Parents were not accurate in estimating the traffic conditions under which their children would try and cross the street. If parents are not adequately supervising when children cross streets, they may be placing their children at risk of pedestrian injury because they are assuming their children will select larger (safer) inter-vehicle gaps when crossing than children actually do. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to