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Effects of attention manipulation of walking




                                                                    14th ...
Background


       “Walking was always considered of being an automatic process, with no cognitive
       involvement. Ho...
their gait pattern, see Lim et al. (2005) for an overview. When hearing rhythmic sounds near
or at the preferred step freq...
Methods


Participants
       Eleven young healthy individuals (3 females, 8 males, age: mean 23.9 yrs, SD 2.51
yrs), who ...
conditions, whether with or without dual-task, the frequency of the beeps was disturbed at
some points during the trial by...
Experimental setup
          Participants walked on a treadmill. The movements of the markers were recorded by
using a 3D ...
Figure 3: Overview of the laboratory.



          Performance on the Stroop-test was recorded in three ways. First, in or...
trial. The subjects were shown an example of a sheet of the Stroop-test. At the same time,
they were told that they would ...
stride frequency and PSF was calculated. This results in four couples of characteristics
(Strides per minute (BPM) and ∆BP...
only in the paced walking condition. The combination of just walking alone and dual-task did
not seem to result in an impa...
Time per word                                                                             % wrong answers

               ...
Figure 8: Mean values for step frequency with either non-paced)   Figure 9: Mean values for ∆BPM (difference between measu...
Figure 10: Mean values for stride-to-stride interval with either        Figure 11: Mean values for stride-to-stride interv...
Figure 12: Mean values for stride length with either non-paced        Figure 13: Mean values for stride length variability...
Step width and step width variability
          Mean step width values did not change significantly between the conditions...
Tabel 1: Results of the repeated measures ANOVA containing the within factors dual-task
(two levels: with or without perfo...
Conclusion & Discussion


       The first purpose of this study was to examine the effect of a dual-task (DT) on
walking,...
involvement, like automatic processes. Altogether these results indicate that because of a
dual-task performance some char...
and started to walk as if no pacing was present. When realizing they missed the paced times
completely, suddenly gave more...
Literature
        Abernethy, B., Hanna, A., Plooy, A. (2002). The attentional demands of preferred and
non-preferred gait...
Shkuratova, N., Morris, M.E., Huxham, F. (2004). Effects of age on balance control
during walking. Archives of Physical Me...
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Effects of attention manipulation of walking - Bachelor research project

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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.

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Effects of attention manipulation of walking - Bachelor research project

  1. 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. 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 1
  3. 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. 2
  4. 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. 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. 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 5
  7. 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. 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. 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. 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. 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. 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). 11
  13. 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. 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. 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. 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* 15
  17. 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. 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. 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
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