14th July 2006 Vrije Universiteit Amsterdam Faculty of Human Movement Sciences Specialization: The coordination, Learning and Development of Action & Human Movement Sciences and Sport Bachelor Research Project Authors: AJH de Greef & M. Ruitenburg Supervisor: dr. C.J.C. Lamoth & drs. M. Roerdink. SUBJECT “Walking was always considered of being an automatic process, with no cognitive involvement. However, there is growing evidence that even in healthy young adults certain aspects of gait are attention demanding.” (Yogev et al., 2005). Overall, the results of the present study indicate that walking is not a completely automatic process, but does needs some attention. Secondly, this study did not confirm the findings of Lim et al. (2005) and Tecchio (2000) that paced walking on auditory cues is an automatic process with no requirements for attention. In contrast it was found that pacing did not result in a SF equal to the set frequency, but in a lower SF as in unpaced conditions. The difference between PSF and actual SF (ΔBPM) did become less compared to unpaced conditions, but there was still a significant difference between PSF and SF.

1. Effects of attention manipulation of walking
14th July 2006
Vrije Universiteit Amsterdam
Faculty of Human Movement Sciences
Specialization: The coordination, Learning and Development of Action &
Human Movement Sciences and Sport
Bachelor Research Project
Authors: AJH de Greef & M. Ruitenburg
Supervisor: dr. C.J.C. Lamoth & drs. M. Roerdink.

2. Background
“Walking was always considered of being an automatic process, with no cognitive
involvement. However, there is growing evidence that even in healthy young adults
certain aspects of gait are attention demanding.” (Yogev et al., 2005).
Skilled movements like walking are not as automatic as may seem. They still require a
certain amount of attention, for instance to maintain postural control. Although an action like
walking seems a subconscious process in normal subjects without any cognitive effort,
something as simple as keeping balance still requires complex processes that combine visual,
proprioceptive and vestibular sensory information. There are many theories about cognitive
capacity, but all show that this capacity is limited as for example in the research by Melzer &
Oddson (2004). If assumed that a certain task requires a specific portion of this capacity, this
means that there are a limited number of tasks that can be performed without deteriorating
effects in one or more of these tasks (Siu & Woollacott, 2006). Normal movement can be
affected due to Parkinson disease (PD), a Cerebral Vascular Accident (CVA) or even just
because of normal aging (Shkuratova et al 2004), because these impose a limiting constraint
due to deteriorated brain structures and increases the requirement of attention. In all these
examples, if requirements for attention exceed normal attention capacity, people can have
trouble to perform these “automatic” tasks and may have not enough attention left to perform
a second task adequately (Huxhold et al 2006).
It is questioned whether the limited resources are enough to perform a second attention
demanding task besides walking, without diminishing performance of one or both tasks even
in young, healthy adults (Yogev et al., 2005). Several studies have shown that walking still
requires attention. At least characteristics of walking as step width (SW), step length (SL) and
step time (ST) are influenced by performing a second task demanding attention. This second
task could be counting backwards (Beauchet et al 2005), performing a Stroop-test (Grabiner
& Troy 2005), performing a visual memory task (Siu and Woollacott 2006) or measured
using reaction time (RT) (Abernethy et al 2002, Regnaux et al 2005; see Woollacott and
Shumway (2002) for an overview).
Whereas cognitive tasks are suggested to interfere with walking, even in healthy
subjects, paced walking is used in revalidation programs for PD-, or CVA-patients to improve
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3. their gait pattern, see Lim et al. (2005) for an overview. When hearing rhythmic sounds near
or at the preferred step frequency (PSF), people tend to couple these percepted frequencies to
their step frequency. It is yet not clear exactly to what extent walking with auditory pacing is
an automatic process and if it really improves gait of PD-patients in non-clinical settings.
Tecchio et al (2000) using MEG showed an automatic coupling between the auditive cortex
and motoric cortex without any conscious processes. Even subconscious the auditive cortex
can distinguish rhythms and variations of a rhythm.
In this experiment, walking speed will be imposed on the subjects. By using pacing,
also the step frequency is imposed. Walking speed and step frequency will be set at PWV and
PSF, because walking at PWV and PSF does require the least amount of attention (Abernethy
et al, 2002) compared to non-preferred gait patterns. Terrier & Schutz (2003) found that when
walking unconstrained, subjects show little variability in gait (both SL and SF), as should be
the case in this study. Walking at PWV and PSF should give no problems, when assumed that
paced walking does not impose any constraints on attention. People should have no difficulty
complying to this pacing.
In the present study, both pacing and a Stroop-test are used together. The primary
question is to which degree walking is an automatic process and will not be influenced by any
dual-tasks. If walking requires attention and interferes with the dual-task or vice versa, either
performance on the dual-task will be affected, or characteristics of the gait pattern - as the
variability in step width en step length - will significantly change, or both. Beauchet (2005)
found that when adding a dual-task, people tend to walk slower, by reducing their step length
and/or frequency to decrease attentional costs. It is still possible one can direct attention
towards walking and thus no difference on walking characteristics is noticeable, but then
performance of the second task should be affected compared to execution of this task alone.
Secondly, it is questioned if paced walking on auditive cues is as automatic as is suggested by
Tecchio (2005).When pacing is assumed not to require a lot of attention, only the Stroop-test
will have higher demands on attentional capacity and limiting effects on walking
performance. It is expected that paced walking will not affect performance on a dual-task, nor
will the gait pattern show significant changes.
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4. Methods
Participants
Eleven young healthy individuals (3 females, 8 males, age: mean 23.9 yrs, SD 2.51
yrs), who were recruited from the university, volunteered to participate in the study. The
protocol was approved by the ethical committee of the faculty of Human Movement Sciences
of the Vrije Universiteit, Amsterdam and all subjects provided written consent prior to
participation in the study.
Experimental protocol
Prior to the actual experiment, the preferred walking velocity (PWV) of the
participants was determined by measuring the time needed to walk a distance of 10 meters.
The average time of three walks was used to calculate their PWV. Their preferred step
frequency (PSF) was determined by counting right-foot-contacts during one minute, while
subjects were walking on the treadmill which speed was set equal to their PWV. This
measurement was done two times during a three-minute trial and the average was taken as
their PWF.
The different experimental conditions:
- Condition 1: Dual-task only (DT).
- Condition 2: Walking only (W).
- Condition 3: Walking with dual-task (W + DT).
- Condition 4: Paced walking (PW).
- Condition 5: Paced walking with dual-task (PW + DT).
- Condition 6: Paced walking with perturbations (PW + PT)
- Condition 7: Paced walking with perturbations and dual-task (PW + PT + DT)
- Condition 8: Paced walking with perturbations (PW + PT2)
- Condition 9: Paced walking with perturbations and dual-task (PW + PT2 + DT)
The order in which the participants received the different conditions was randomized.
During the pacing conditions, conditions 5 to 9, the participant received auditive cues through
a headphone. This cues consisted of beeps alternately on the left and right ear, to indicate the
step frequency. The frequency of the beeps was equal to the PSF. In the perturbation
3

5. conditions, whether with or without dual-task, the frequency of the beeps was disturbed at
some points during the trial by changing the phase of the frequency by 90 degrees, either
positive or negative, in order to disturb the gait pattern. Subjects were informed that
perturbation could occur, but had no knowledge of the exact timing of these disturbances.
There existed a slight difference in instruction given to the participants between
conditions 7, PW+ PT + DT, and 9, PW + PT2 + DT, because of a different kind of
perturbation. In condition 7, participants were instructed to direct attention to dual-task
performance. In condition 9, they were instructed to direct attention to walking on the beeps
and give it priority over execution of the dual-task.
The Stroop-test as suggested by Stroop (1935) was used as dual-task and was
presented to the subjects on a screen in front of the treadmill. The test was adapted to the
conditions of the experimental location. Instead of 10x10 words, 10 sheets were constructed
containing 5x4, thus 20 words each. The colors of the words were red, green, blue and yellow,
the words itself were written in Dutch. The background was light grey instead of white, to get
high contrast. This was necessary, because the sheets were projected on a screen in front of
the treadmill by a beamer and a pure white background was too bright for comfortable
viewing. An example of a sheet is shown in figure 1. The grey area was scaled to a width of
approximately one meter.
Figure 1: An example sheet of a sheet of the Stroop-test. Rood = red ,geel = yellow, blauw = blue, groen = green
4

6. Experimental setup
Participants walked on a treadmill. The movements of the markers were recorded by
using a 3D active marker motion analysis system (Optotrak 3020, Northern Digital, Ontario,
Canada), consisting of two columns of each three cameras situated behind the treadmill.
Clusters of three active markers were taped to the heel of the subjects. Figure 2 gives an
overview of the experimental setting; figure 3 is a photograph of the fysiolaboratory of the
VUMC Amsterdam. The light of the room was reduced to get a good contrast of the sheet on
the screen.
The duration of each trial was dependent of the PSF, so that in each trial 300 strides
could be recorded. Therefore, the trial for a participant with a PSF of 60 BPM – 300 steps
divided by 60 steps per minute - lasted five minutes.
Screen
Figure 2: Experimental setup top view
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7. Figure 3: Overview of the laboratory.
Performance on the Stroop-test was recorded in three ways. First, in order to count the
wrong answers given by the participant, one of the researchers marked the wrong answers.
Secondly, to be able to exclude possible mistakes of the researcher with this correction, the
answers of the participant, who spoke them aloud, were recorded using a microphone attached
to the laptop computer. Would there be any doubt about an incorrect answer, these recordings
were used to make a final decision. Thirdly, to determine the average time per word the
participant needed, the time used to complete a sheet was saved using PowerPoint. So for
each slide presented the time the participants took for it, as well as their answers were
recorded.
Experimental procedure
The procedure of the experiment was explained to the subjects and they were told that
they had to complete nine trials, some in which they had to perform a dual-task, some in
which pacing with auditive cues would be offered using a headphone and other with a
combination of these two. Which condition was up next, was told right before starting the
6

8. trial. The subjects were shown an example of a sheet of the Stroop-test. At the same time,
they were told that they would receive sheets with words as long as the trial lasted and that
they were expected to speak out the answers loud. In addition, they experienced the beeps that
they would hear, primarily so these beeps would not come as a shock the first time and
secondly to adjust volume to a loud, but comfortable setting. Finally, the purpose of this
research was explained. After these instructions, the participant signed the informed consent.
Two custom made frames containing clusters of three markers were taped on the heel
of both shoes. To determine the PWV of the participants they completed a 10-meter-walk
three times. When the participants had taken place on the treadmill and anatomical landmarks
(heel, outside ankle and metatarsalis V) were captured, the treadmill was set at their PWV and
during a three-minute walk their PSF was determined. OptoTrak was set to a sample rate of
300 Hz. Then the actual experiment began with trial one. After trial one, trial two followed
and so on until trial nine was completed, with periods of rest between the trials. These periods
were between trials, or when the participant indicated that her/she needed to rest. After the
completion of the nine trials, the experiment ended.
There were nine trials with nine different conditions; however the focus was upon the
five conditions in which there were no perturbations, thus conditions one to five of the
experimental protocol. The condition with just the Stroop-test served as a baseline condition
for dual-task performance. Data of one subject were not used for analysis because the
collected data turned out to be corrupted.
Data analysis and statistics
The raw data from OptoTrak was used to calculate the position of the heel of the foot.
The data contained the position of the six markers and were transformed to heel position using
the relative measurement of heel, ankle and metatarsalis position to the cluster of markers
placed on each foot. From this processed data, the exact time of heel strikes was determined
by using the exact moment when the minimum vertical position of the heel was reached.
Stride time intervals (ST) were determined as the difference in time between two
consecutive heel strikes of the same leg and then multiplied by the velocity of the treadmill to
get the stride length (SL). Also, the step width (SW) for each consecutive heel strike was
determined. The mean and standard deviation of these three characteristics – ST, SL, and SW
- were determined for the whole length of each trial of 300 strides. Furthermore, the real
number of strides executed by each subject was counted and the difference between actual
7

9. stride frequency and PSF was calculated. This results in four couples of characteristics
(Strides per minute (BPM) and ∆BPM, mean and SD for SL, SW and ST) for each subject
measured for the four conditions different conditions when walking, with or without the dual-
task and pacing, thus W, W +DT, PW, PW + DT.
The time per sheet was saved and used to determine the average time used for each
word. Mean and SD of the time used per word were calculated. By using the percentage errors
((total of errors/ total words completed)*100) an indication of the quality of dual-task
performance and possible differences between conditions could be given. Both time per word
and percentage errors were calculated for only the conditions DT, W + DT and PW + DT. On
the contrary, to be able to say something about a possible learning effect, time per word and
percentage errors were calculated for all the trials in which a dual-task had to be performed.
These are five conditions, DT, W + DT, PW + DT, PW + PT + DT, PW + PT2 + DT.
The characteristics calculated from the output of OptoTrak (mean and SD for SL, SW
and ST) were analyzed using a repeated measures ANOVA containing the within factors dual-
task (two levels: with or without performing a dual-task) and pacing (two levels: with or
without paced walking). The performance on the dual-task were analyzed using a repeated
measures ANOVA containing the within factors condition DT, condition W + DT and
condition PW + DT. A significance level of p < .05 is used and it was checked if the
assumption of sphericity was not violated (Mauchly’s Test) to ensure no adjustments were
necessary. If so, it is mentioned in the results. If a significant main effect was found, a paired-
samples T-test was applied to the data as a post-hoc test, to determine which of the groups
differ significantly.
Results
Dual Task
A significant difference was found between the three conditions DT, W + DT and PW
+ DT (F (2, 10) = 3.723, p = .042). Figure 4 shows the time per word increases over the
conditions. Although after applying a paired-samples T-test it did not become clear which two
groups differ significantly, there was a tendency (p = .061) to an increased time per word in
the PW+DT walking condition. This indicates that performance of the dual-task is impaired
8

10. only in the paced walking condition. The combination of just walking alone and dual-task did
not seem to result in an impairment of dual-task performance.
Time/word Strooptest % wrong answers
2.5
1.6
% wrong answers
2
1.4
time/word (sec)
1.2 PW + DT 1.5 DT DT
DT W + DT
1 W + DT
DT
W + DT
0.8 W + DT 1 PW + DT PW + DT
0.6 PW + DT
0.5
0.4
0.2
0
0 1
condition condition
Figure 4: Mean values of time/word in seconds for each of the Figure 5: Mean values of % wrong answers for each of the
conditions DT, DT + walking and DT + paced walking (PW). Error conditions DT, DT + W and DT + PW. Error bars represent SD
bars represent SD of the mean. of the mean
To exclude the possibility that participants were mainly focused on giving answers as
fast as possible and therefore gave a lot of wrong answers, figure 5 gives an overview of the
percentage of wrong given answers ((wrong answers/ words completed) * 100). There turned
out to be no significant difference between the three trials (F (2, 20) = 1.817, p = .207). As
figure 5 also shows, the average percentage of wrong given answers does not differ much
over the conditions, indicating that the average time per word can be used as an indicator of
Stroop-test performance, because time per word does not have any influence on the number of
errors.
Learning effect
To exclude the possibility that subjects performed better on the Stroop-test in the later
trials because of a possible learning effect, a the development of Stroop-test performance
throughout the sequential trials was further analyzed.
9

11. Time per word % wrong answers
1.6 3
% wrong answers
1.4
time/word (sec)
2.5
1.2 trial 1 trial 4
trial 2 trial 1 trial 1
trial 3 trial 5 2
1 trial 2 trial 2
trial 5
0.8 trial 3 1.5 trial 3
trial 2 trial 3 trial 4
trial 1
0.6 trial 4 trial 4
1
trial 5 trial 5
0.4
0.5
0.2
0 0
trials trials
Figure 6: Mean values of time/word for five sequential trials in Figure 7: Mean values of % wrong answers for five sequential
which a dual-task was performed. Error bars represent SD of the trials in which a dual-task was performed. Error bars represent
mean. SD of the mean.
There was no significant difference between the five trials for the average time per
word (F (4, 10) =1.028, p = .405). This finding is confirmed by figure 6, where no tendency
can be discovered. Figure 7 shows no tendency in making fewer errors in the later trials. This
is confirmed by the finding that there is no significant difference between the five trials for %
wrong answers (F (4, 10) = .581, p = .678) indicating that there seemed to be no learning
effect.
Walking velocity
Participants walked on the treadmill with an average velocity of 4.95 km/h (SD = .46
km/h) and the average PSF was 57 BPM (SD = 2.79 BPM).
Step frequency
The results show that in the paced walking conditions subjects significantly increased
their step frequency (F (1, 10) = 6.659, p = .027). It also became clear that because of dual-
task implementation subjects significantly decreased their step frequency
(F (1, 10) = 11.188, p = .007) (Figure 8).
10

12. Figure 8: Mean values for step frequency with either non-paced) Figure 9: Mean values for ∆BPM (difference between measured
or paced walking and with either with or without dual-task. and actual step frequency with either non-paced or paced
walking and with either with or without dual-task
The results for ∆BPM confirm this. Because of paced walking the magnitude of
∆BPM of participants significantly decreased (F (1, 10) = 6.658, p= .027). As figure 9 shows
the difference between the step frequency and their preferred step frequency became smaller
as a result of pacing, although the absolute value was still smaller than their PSF (indicated by
a negative ∆BPM value). As a result of dual-task performance the magnitude of ∆BPM
significantly increased (F (1, 10) = 11.215, p = .007).
Stride-to-stride interval and stride-to-stride interval variability
Mean stride-to-stride-interval values decreased significantly because of pacing (F (1,
10) = 8.853, p = .014) indicating that participants needed less time to complete one stride.
When performing a dual-task participants needed significantly more time to complete one
stride (F (1, 10) = 13.493, p = .004).
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13. Figure 10: Mean values for stride-to-stride interval with either Figure 11: Mean values for stride-to-stride interval variability
non-paced or paced walking and with either with or without with either non-paced or paced walking and with either with or
dual-task. without dual-task.
Stride-to-stride-interval variability increased significantly by both dual-task performance (F
(1, 10) =7.703, p = .020) as well as by paced walking (F (1, 10) = 11.391, p = .007). There
also turned out to be an interaction effect (F (1, 10) = 6.847, p = .026), indicating that the
increase in stride-to-stride interval variability due to dual-task performance is strengthened by
paced walking.
Stride length and stride length variability
Mean stride length values increased significantly by performing a dual-task (F (1, 10)
= 12.875, p = .005) and decreased by paced walking (F (1, 10) = 8.486, p = .015).
12

14. Figure 12: Mean values for stride length with either non-paced Figure 13: Mean values for stride length variability with either
paced walking and with either with or without dual-task. non-paced or paced walking and with either with or without
dual-task.
For stride length variability an interaction effect was found (F (1, 10) = 7.617, p =
.020). Figure 13 shows that the influence of a dual-task is only present in the paced walking
condition.
13

15. Step width and step width variability
Mean step width values did not change significantly between the conditions. There
was no interaction effect (see Table 1 and figure 14).
Figure 14: Mean values for step width with either non-paced or Figure 15: Mean values for step width variability with either
paced walking and with either with or without dual-task. non-paced or paced walking and with either with or without
dual-task.
Results of step width variability on the other hand show that there is significantly
more variability in the non-pacing condition (F (1, 10) = 6.885, p = .025). There turned out to
be a significant interaction effect (F (1, 10) = 14.196, p = .004), indicating that the influence
of dual-task performance is only present without pacing (Figure 15).
14

16. Tabel 1: Results of the repeated measures ANOVA containing the within factors dual-task
(two levels: with or without performing a dual-task) and pacing (two levels: with or without
paced walking).
Pacing Dual-Task Interaction
PSF
Mean difference 1.047 -1.166
F 6.659 11.188 2.123
p .027* .007* .176
Delta PSF
Mean difference 1.047 -1.167
F 6.658 11.215 2.128
p .027* .007* .175
Stride-to-stride
interval
Mean difference -.023 ± .008 .024 ± .006
F 8.853 13.493 1.954
p .014* .004* .192
Stride-to-stride
variability
Mean difference 10.384 ± 3.077 8.367 ± 3.015
F 11.391 7.703 6.847
p .007* .020* .026*
Stride length
Mean difference -.030 ± .010 .031 ± .009
F 8.486 12.875 1.915
p .015* .005* .197
Stride length
variability
F 4.701 4.532 7.617
p .055 .059 .020*
Step width
F 1.325 .174 3.615
p .276 .863 .086
Step with variability
Mean difference -.837 ± .319
F 6.885 3.938 14.196
p .025* .075 .004*
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17. Conclusion & Discussion
The first purpose of this study was to examine the effect of a dual-task (DT) on
walking, what should give an indication about the automaticity of walking. If a DT was
introduced and this interferes with the attention needed for walking, it is suggested that this
results in changed walking characteristics such as step width (SW), step length (SL) and step
frequency (SF), or dual-task performance, or both.
The results of the present study showed that dual-task performance was only impaired
by pacing. The performance on the Stroop-task did not significantly worsen due to walking
compared to standing. This suggests that walking alone does not require more attention, or at
least that it does not interfere with the Stroop-test. This is consistent with findings by
Regnaux et al (2005) who found no difference in reaction time (RT) in healthy subjects when
responding to an electrical cue when standing or walking.
Siu & Woollacott (2006) found that young healthy subjects directed all their attention
to maintain postural control, even when instructed to perform the dual-task at full capacity.
When instructed to focus all attention to postural control, performance on dual-task did
diminish. Therefore, they concluded that instruction does not have an effect on postural
control. This suggests that certain basic attentional tasks like postural control can get
subconscious priority over others even when instructed otherwise. This could explain the
absence of any difference in the dual-task conditions with or without walking, because
subjects were instructed to do the Stroop-test at their best and no conscious attention was
given to walking.
Because dual-task performance did not worsen when walking then, if walking is an
automatic process, characteristics of gait pattern should not change as well. But as the results
show, this seemed not to be the case. Stride frequency decreased as a result of dual-task
implementation. This finding is consistent with Beauchet et al (2005) who also found that the
introduction of a DT decreases SF. When walking speed remains constant, a decreased stride
frequency needs to result in increased stride length values. The results of the present study
indeed found increased stride lengths with a decease of stride frequency. An increase in mean
stride-to-stride interval is logical as well, when reasoning that when stride length increases, it
will take more time to complete one stride. It also turned out that stride-to-stride interval
variability increase. According to Terrier & Schultz (2003) higher variability is associated
with major attention involvement while low variability is associated with low attention
16

18. involvement, like automatic processes. Altogether these results indicate that because of a
dual-task performance some characteristics of the gait pattern change. It can be concluded that
walking is not as automatic as sometimes thought, which is congruent with studies of
Regnaux et al. (2005), Abernathy et al. (2002) and Beauchet et al. (2005).
The second purpose of this study was to confirm findings of Lim et al. (2005) about
paced walking with auditory cues. As Tecchio et al. (2000) suggested, paced walking with
auditory cues is an automatic process which should not require a lot of attention. If so, there
should not be any effects of paced walking on dual-task performance as well as on the gait
pattern.
The results of Stroop-test performance already suggested that paced walking requires
attention, because there turned out to be a tendency to need more time per word in the paced
walking condition, which is an indication of impaired performance. This impairment can not
be a result of walking, because as explained before, walking alone did not have any effects on
Stroop-test performance. Did the gait pattern change as well? Results showed that SF
increased as a result of pacing. Together with the findings of ∆BPM, which indicates that
participants were walking with a SF smaller than their PSF in the unpaced conditions, this
suggests that as a result of pacing, participants changed their SF. It also means that in the
unpaced conditions their SF was not equal with their PSF, as should be the case. Walking
unconstrained should result in a SF equal to the PSF, but it did not. Either walking on a
treadmill imposes a constraint on the subjects that has an effect on their gait, or the method
used to determine PSF was inadequate. Or both, but in this experiment the method used was
probably not accurate enough. It is likely that there is a difference between walking on a
treadmill and just walking unconstrained and people have to get familiar to walking on a
treadmill. Because PSF was determined during a three-minute walk and it seems reasonable
that participants have to adapt to walking on a treadmill, these three minutes practice was not
enough. Some subjects mentioned that it felt like, even when the speed was still lower than
their PWV, the treadmill was set at a much higher velocity than they normally walked. This
experience became less during the progress of the experiment.
The increase in SF resulted in decreased stride-length values. These decreased stride
length values resulted in a logical way in decreased stride-to-stride intervals. Only in the
paced walking conditions, performance of the Stroop-test did result in increased stride length
variability. Perhaps the subjects could not follow the auditory cues constantly due to the DT
17

19. and started to walk as if no pacing was present. When realizing they missed the paced times
completely, suddenly gave more attention to the pacing and thus increasing SF and thus
decreasing SL and increasing stride-to-stride interval.
Besides these findings, also stride-to-stride interval variability increased due to paced
walking. As explained before, higher variability is associated with major attention
involvement (Terrier & Schultz, 2003). Altogether, these changes in gait parameters as well
as the findings that Stroop-test tends to diminish because of paced walking, results in the
statement that paced walking on auditory cues is not an automatic process, but requires a
significant amount of attention. This finding is not congruent with the studies of Lim et al.
(2005) and Tecchio et al. (2000). Furthermore, to evaluate a possible learning effect by
comparing performance on dual-task (percentage wrong answers and average time/word)
through the trials is incorrect, because for each participant there is a specific order in which
the different conditions are performed. Although it still could be possible there was a learning
effect on Stroop-test, the results are already indicating impairment in performance on dual-
task in the pacing condition. Correcting for a learning effect would only strengthen these
results.
Overall, the results of the present study indicate that walking is not a completely
automatic process, but does needs some attention. Secondly, this study did not confirm the
findings of Lim et al. (2005) and Tecchio (2000) that paced walking on auditory cues is an
automatic process with no requirements for attention. In contrast it was found that pacing did
not result in a SF equal to the set frequency, but in a lower SF as in unpaced conditions. The
difference between PSF and actual SF (∆BPM) did become less compared to unpaced
conditions, but there was still a significant difference between PSF and SF.
18

20. Literature
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variability while backward counting among healthy young adults. Journal of
NeuroEngineering and Rehabilitation, 2:26.
Grabiner, M.D., Troy, K.L. (2005). Attention demanding tasks during treadmill
walking reduce step width variability in young adults. Journal of NeuroEngineering and
Rehabilitation, 2:25.
Huxhold, O., Li, S.C., Schmiedek, F., Lindenberger, U. (2006). Dual-tasking postural
control: Aging and the effects of cognitive demand in conjunction with focus of attention.
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