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Residual attentional capacity amongst young and elderly during dual and triple task walking
Available online at www.sciencedirect.com
Human Movement Science 27 (2008) 496–512 www.elsevier.com/locate/humov
Residual attentional capacity amongst young and elderly during dual and triple task walking Uffe Laessoe a,b,*, Hans C. Hoeck c, Ole Simonsen d, Michael Voigt a a
Center for Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, Denmark b University College North Jutland, Aalborg, Denmark c Center for Clinical and Basic Research A/S, Denmark d Northern Orthopedic Division Aalborg Hospital, Aalborg, Denmark Available online 28 January 2008
Abstract Walking is considered an automatic function which demands little attentional resources. Thus a residual attentional capacity is available for a concurrent task (dual task). Minor age-related deficits in postural control may minimize the residual attentional capacity, however this may not be detected by a simple examination of the individuals gait performance. This study investigated the use of challenging dual task combinations to detect age related changes in gait performance. Eleven community-dwelling elderly (mean age 76 years) and 13 young subjects (mean age 26 years) participated in the study. The participants walked along a figure-of-eight track at a self-selected speed. The effect of introducing a concurrent cognitive task and a concurrent functional motor task was evaluated. Stride-to-stride variability was measured by heel contacts and by trunk accelerometry. In response to the cognitive task the elderly increased their temporal stride-to-stride variability by 39% in the walking task and by 57% in the combined motor task. These increases were significantly larger than observed for the young. Equivalent decreases in trunk acceleration autocorrelation coefficients and gait speed were found. A combination of sufficiently challenging motor tasks and concurrent cognitive tasks can reveal signs of limited residual attentional capacity during walking amongst the elderly. Ó 2007 Elsevier B.V. All rights reserved. PsycINFO classification: 2221; 2346 Keywords: Dual task; Gait variability; Elderly; Attention; Physical examination
* Corresponding author. Address: Center for Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, Denmark. Tel.: +45 96358797. E-mail address: [email protected] (U. Laessoe).
0167-9457/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.humov.2007.12.001
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1. Introduction Recently, there has been a focus on the interaction between cognitive factors and motor performance when assessing the functional capacity of a patient (Huang & Mercer, 2001). Mulder and colleagues have argued that most tests which are used to assess physical performance allow the participants to compensate for their deficits by utilizing other control strategies (e.g., visual and/or cognitive regulation of task performance). To better detect deficits a dual task assessment can be used (Mulder, Zijlstra, & Geurts, 2002). Dual task paradigms are typically used to investigate the attentional demands of a motor task and to examine the effects of concurrent cognitive or motor tasks on motor performance (Fraizer & Mitra, in press; Schmidt & Lee, 2005). The latter approach is sometimes referred to as a divided attention or ‘‘time-sharing” paradigm (Huang & Mercer, 2001). When one task is more demanding a greater proportion of the performer’s limited processing capacity must be allocated to this task in order to maintain an acceptable level of performance (Huang & Mercer, 2001). As the central processing capacity is limited, a primary task with higher attention demands will leave less residual processing capacity for a concurrent secondary task. An additional concurrent attention-demanding cognitive task or motor task may therefore exceed the available resource capacity. Dual task interference will only occur if the available central resource capacity is exceeded, resulting in impaired performance in one or both tasks (Abernethy, 1988). Dual task paradigms can be used to investigate the attentional demands on walking and to examine the effects of a concurrent cognitive or motor task on walking. The competition between the attention demands of walking and a concurrent attention-demanding task may result in gait alterations (Dubost et al., 2006). Postural control is defined as the control of the body’s position in space to maintain balance and orientation (Shumway-Cook & Woollacott, 2001). Traditionally, maintenance of postural control has been considered automatic or reflex controlled, suggesting that control of posture requires minimal attention. However, recent research has shown that there are significant attention requirements for postural control which vary depending on the postural task, the age of the individual and his or her balance abilities (Woollacott & Shumway-Cook, 2002). The organization of movement and postural control is developed through motor learning. Fitts and Posner articulated three stages of motor learning consisting of: a cognitive (verbal) stage, an associative stage (gradual decrease in errors; development of internal (sensory) reference of correctness), and an autonomous (automatic) stage (ShumwayCook & Woollacott, 2001). The first stage requires conscious attention to each part of the movement whereas the third stage leaves attentional resources for other tasks. The movements related to the first stage become slow, cautious and uncertain compared to the more competent practice of the next stages. Motor tasks which are well trained and not very demanding can be performed with little conscious attention which leaves a relatively large residual processing capacity. Maintenance of postural control during activities of daily living (e.g., walking) does not usually place high demands on attentional resources. In contrast, when sensory or motor deficits occur, the complex generation of movement may have to be restructured, and movements may then be controlled and performed at an associative or a cognitive stage. When the benefits from the movement automation are lost the postural control of the participant can be expected to be more vulnerable to cognitive distractions and additional tasks.
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It may be expected that postural control changes with age due to age-related alterations in the brain, loss of muscle mass, and decreased perception of high frequency vibrations, touch, proprioception, and pressure stimuli (Kandel, Schwartz, & Jessell, 2000; Prince, Corriveau, Hebert, & Winter, 1997). Thus it is possible that the elderly require increased conscious attention to maintain postural control. However, impaired postural control may not be revealed in a standard examination in which the residual attentional capacity is not sufficiently challenged. Walking is a very familiar and automated motor task to most people and the control of posture during this task will be expected to require little conscious attention. The quality of the gait performance is reflected in the stride-to-stride variability (Hausdorff, 2005, 2007). When the walking pattern is planned and executed in a way so that no major corrections are needed the stride variability is low. Deficits in the postural control may disturb the planning and execution of the gait and this can lead to a walking pattern that needs postural adjustments revealed as increased stride-to-stride variability (Hausdorff, 2005). This approach to the assessment of gait performance has been investigated through many studies and good support has been found for its relevance (Hausdorff, Zemany, Peng, & Goldberger, 1999; Hausdorff, Rios, & Edelberg, 2001). An increase in gait variability, in response to a dual task, may be expected amongst elderly people. A study evaluating this hypothesis showed a significant effect for elderly fallers whereas elderly non-fallers were unaffected (Springer et al., 2006). This finding conflicts with other studies which do report increased stride variability amongst elderly in response to a dual task (Beauchet et al., 2003; Hollman, Kovash, Kubik, & Linbo, 2007). It is possible that the dual task combination in the first case may not have been sufficiently challenging to reveal deterioration in the postural control capacity of the elderly non-fallers. Walking in a figure-of-eight has been shown to be a more challenging motor task than just walking in a straight path (Shkuratova, Morris, & Huxham, 2004). In the present study this task was used as the basic motor task in combination with either a cognitive task or a motor task in a dual task tests or in combination with both tasks in a triple task test. The purpose of this study was to evaluate differences in the residual attentional capacity between healthy young and elderly persons. We hypothesized that the postural control of both young and elderly would be affected when a concurrent cognitive or motor task was added to the motor task of walking in a figure-of-eight. We expected that the motor planning and gait performance would be less successful and result in increased gait variability. In addition, we hypothesized that dual and triple task performance among elderly would be characterized by an increased relative change in gait variability (i.e., the postural control of the elderly would be more vulnerable to additional tasks). This finding would be consistent with the idea that the residual processing capacity is relatively smaller in elderly compared to young. 2. Methods 2.1. Participants Eleven healthy community-dwelling elderly (mean age 76.4 years, SD: 5.0) and 13 young participants (mean age 25.6 years, SD: 2.0) were included in this study. There were four men in the group of young and one in the elderly group. Body mass index (BMI) was
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22.5 (SD: 2.0) and 25.2 (SD: 4.4), respectively. Young adults were included if they were between the ages of 20 and 31 years and had no known disease or need for medication. Elderly adults older than 70 years were recruited from community centers. They were included if they were independent in activities of daily living, and had no history of falls within the last year. They were excluded if they had: (a) musculoskeletal or neurological disorders; (b) significant pain that limited daily functions; (c) known uncorrected hearing problems; (d) known uncorrected visual or vestibular problems, or (e) cognitive impairment (Mini Mental State Examination (MMSE) < 23) (Foreman, Fletcher, Mion, & Simon, 1996). Two questionnaires were used to characterize the level of physical activity of the participants and their fear of falling: Physical Activity Scale for the Elderly (PASE) and Activityspecific Balance Confidence Scale (ABC) (Loland, 2002; Powell & Myers, 1995; Washburn, Smith, Jette, & Janney, 1993). The general physical performance of the elderly was evaluated by the ‘‘Timed Up and Go test” (TUG) (Podsiadlo & Richardson, 1991). Informed consent was obtained from all participants prior to inclusion in the study. The protocol was approved by the Ethics Committee of Region North Jutland (VN-20040034). 2.2. Procedures The protocol consisted of a simple walking test, two dual task tests and a triple task test. Participants were asked to walk along a figure-of-eight path marked on the laboratory floor (Fig. 1). Twenty full rounds were completed at a self-selected comfortable walking speed which would take 4–5 min. After the first ten rounds participants were asked to perform a concurrent cognitive task. The task combinations are referred to as ‘‘walk” (w) and ‘‘walk + cognitive task” (wc). After completing the 20 rounds the participants had a short break. Participants were then asked to walk again while performing a concurrent attention-demanding motor task for 10 rounds. A further ten rounds was then performed combining both the attention-demanding motor task and the cognitive task. These task combinations are referred to as ‘‘walk + motor task” (wm) and ‘‘walk + motor task + cognitive task” (wmc).
2 meter
Photo cell
Photo cell
5 meter Fig. 1. Schematic presentation of the walking path. The figure-of-eight path was marked by ten marker cones illustrating two half-circles at each end of the figure. The dimension of the marked figure was 5 2 meter. Participants were instructed to follow the figure on the outside of the marker cones. A set of photo cells were placed at each end of the path.
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The cognitive task was an arithmetic task, in which the participant recited out loud serial subtractions of seven. The subtraction started from a three digit number given by the investigator as an initiation of the task. Before the walking task the cognitive task was also performed for one minute while sitting on a chair as a reference measure. The functional motor task was constructed to mimic a daily-life manipulation task, like carrying a tray with a filled cup. It consisted of holding two interlocked sticks steady in front of the body. One stick was held in each hand with the elbows in 90° flexion. Each stick had a ring at the end with an inner diameter of 4 cm and the rings were interlocked. The participant was advised not to let the rings touch each other. Each stick weighed 15 g and was held 20 cm distant from the participant’s hand. The participants were asked to concentrate on the concurrent tasks rather than on the gait performance during the dual task conditions. 2.3. Equipment The figure-of-eight path was marked by 2 5 marker cones creating two half-circles at each end of the figure. The marked figure was 5 m long and 2 m wide (Fig. 1). Participants were instructed to follow the figure-of-eight around the outside of the cones so that the diameter of the path was approximately 2.5 m. Two sets of photo cells were placed 40 cm above the floor at each end of the walking path. These cells provided a timer signal when passed by a participant. To avoid the influence of initial acceleration and deceleration the participant started and ended the walking 2 m before and after the data recording began. The stride-to-stride time variability was chosen as an outcome measure since it has been widely used in studies investigating gait in the elderly (Hausdorff, 2005). A force sensing resistor (Interlink Electronics Inc.) was placed under each heel of the participant to record step and stride times. This measure was complemented with the measure of trunk acceleration autocorrelation coefficients indexing gait periodicity, which has also been used for studies in the elderly population (Moe-Nilssen & Helbostad, 2005). A tri-axial accelerometer (custom made, using two dual axis accelerometers ADXL202E from Analog Devices Inc. in a tri-axial setting) was placed on the lower back of the participant at the level of the third lumbar vertebra to evaluate trunk motion according to the protocol described by MoeNilssen (1998b). The test sessions were video and audio recorded in order to be able to analyze the cognitive task performance. The two interlocking sticks for the functional motor task were connected to an electric circuit to obtain an indication of task error, that is, when the rings at the end of the sticks were in contact with each other. All analog data were sampled with a custom made data acquisition system (MrKick, Knud Larsen Aalborg University, DK) at 1000 Hz. The signals from the sensors on the person (foot switches, accelerometer signals, and stick-signal) were transmitted from the person to the data acquisition unit by a wireless system (Telemyo 2400, Noraxon Inc., USA). 2.4. Data analysis In both walking tests the 20 rounds of figure-of-eight walking were split up into four sequences of five rounds. Only sequence two and three were included in the data analysis to avoid any confounding effects related to commencement and termination of the trial.
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Stride times were determined from heel contact signals and were defined as the time intervals between consecutive heel contact times. Because data from the left and right strides were not statistically different, only data from left side stride times were analyzed. Mean and standard deviation of the stride time for each set of five rounds was calculated and the coefficient of variance (CV%: standard deviation/mean 100) was determined. Trunk acceleration was extracted for each half round of figure-of-eight walking. The three axes of the acceleration signal were converted into a horizontal–vertical coordinate system according to the procedures described by Moe-Nilssen (1998a). Anterior–posterior accelerations from the data sequences were low pass filtered at 50 Hz and evaluated by an unbiased normalized autocorrelation procedure. The correlation values equivalent to a shift of one stride were extracted from the autocorrelation signals (Moe-Nilssen & Helbostad, 2004). These autocorrelation coefficients illustrate to which extent the acceleration pattern of the trunk varies between strides. A correlation value close to 1.0 indicates a high degree of uniformity whereas smaller values indicate greater variation. The autocorrelation coefficients were averaged for each set of five rounds to present mean stride-to-stride variability of the trunk accelerations for the sequences. Gait speed was calculated for each half-round and normalized to body height. The normalized gait speed was averaged for each sequence of five rounds. Signal processing for heel contact signals, acceleration signals and timer signals was performed using MatLab (MathWorks Inc.). The gait characteristics were presented in two ways. The performance characteristics in different task combinations were presented to allow a simple comparison of the differences in performance of the two age groups, which can be ascribed to general age-related differences in the ability to control posture. After that the magnitude of the relative differences in the performance, when shifting from one task combination to another, were presented to reveal more specific aspects of postural control. The latter measure describes the agerelated postural control vulnerability to additional load on the residual attentional capacity. The individual relative changes in gait characteristics were expressed in percent with (w) as reference for (wc), (wm) and (wmc); (wc) as reference for (wm) and (wmc); (wm) as reference for (wmc). Evaluation of the performance on the subtraction task included the total number of subtractions minus the number of subtraction mistakes made during the task and expressed as the number of subtractions per minute. The subtraction performance while sitting on a chair at the beginning of the session was used as a reference (100%). Performance in the functional motor task was evaluated as the total time of contact between the two interconnected rings over the analysis period relative to the total walking time. The longer time the rings were short-circuited, the poorer performance. The statistical analysis was performed with SPSS (ver. 12.0, SPSS Inc.) or SigmaStat (ver. 2.03, SPSS Inc.). Firstly, a two way analysis of variance (ANOVA) with a repeated measures design was performed to examine differences between the two age groups and between the four task combinations for stride variance coefficients, trunk acceleration autocorrelation coefficients and gait speed. Post hoc tests with corrections for multiple comparisons (Tukey) were conducted for the main effects that were statistically significant. Secondly, similar analyses were used to evaluate age-dependency of the relative changes in gait characteristics when comparing one task combination to another. In all statistical tests p < .05 was considered significant. Associations between gait speed and the two gait variability measures were evaluated by Pearson’s correlations.
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3. Results 3.1. Participants’ characteristics On the scales of physical activity (PASE), balance confidence (ABC), and self-estimated health the young had median (range) scores of 223 (183–341), 99 (97–100), and 10 (8–10), respectively, and the elderly had corresponding scores of 173 (140–271), 93 (56–99), and 8 (5–10). The elderly had a mean score of 7.6 (SD: 1.1) on the ‘‘Timed Up and Go test” (TUG). 3.2. Gait characteristics related to age and task interactions 3.2.1. Stride variance coefficient (Table 1 and Fig. 2) A significant age effect, F(1, 22) = 7.3, p < .05, task effect, F(3, 22) = 10.9, p < .001, and Age Task effect, F(3, 22) = 6.1, p < .01, were observed for the stride variance coefficients. Age effect: The elderly participants walked with a 61% higher stride variance coefficient than the young participants in the wc task (p < .01) and with a 75% higher stride variance coefficient in the wmc task (p < .001) (see Table 1 and Fig. 2). Task effect: Differences between tasks were only identified for the elderly participants. The stride variance coefficient was 39% higher (p < .01) when the elderly performed the cognitive task (wc) compared to the walking task (w) and 20% higher (p < .05) compared to the wm task. The variance coefficient was 61% higher (p < .001) when the elderly performed both the cognitive and the motor task (wmc) compared to the walking task (w) and 57% higher (p < .001) compared to the wm task. 3.2.2. Trunk acceleration autocorrelation (Table 1 and Fig. 3) A significant task effect, F(3, 22) = 12.2, p < .001, and Age Task effect, F(3, 22) = 4.8, p < .01, were observed for the acceleration autocorrelation coefficients (see Fig. 3). Age effect: The autocorrelations were generally lower for the elderly participants compared to the young, but this tendency was not statistically significant, F(3, 22) = 3.8, p = .06. Task effect: For the elderly the autocorrelation coefficient was 5% lower (p < .05) when they performed the cognitive task (wc) compared to the walking task (w). The autocorrelation coefficient was 10% lower (p < .001) when the elderly performed both the cognitive and the motor task (wmc) compared to the walking task (w), 5% lower (p < .05) compared to the wc task and 7% lower (p < .01) compared to the wm task. For the younger participants the autocorrelation was 4% lower (p < .05) in the wm task compared to the wc task. 3.2.3. Gait speed (Table 1 and Fig. 4) A significant age effect, F(1, 22) = 6.8, p < .05, task effect, F(3, 22) = 102.8, p < .001, and Age Task effect, F(3, 22) = 2.8, p < .05, were observed for gait speed (see Fig. 4). Age effect: The elderly participants walked with a 10% slower gait than the young participants in the w task (p < .05), with a 15% slower gait in the wc task (p < .01) and with a 14% slower gait speed in the wmc task (p < .05). Task effect: For the elderly the gait speed was 7% slower (p < .01) when they performed the cognitive task (wc) compared to the walking task (w). The gait speed was 14% lower (p < .001) when they performed the motor task (wm) compared to the walking task (w)
Task
a
Relative task interference (%)
w
wc
wm
wmc
w ? wc
w ? wm
w ? wmc
1.76 (0.47) 2.83 (1.14)**
1.81 (0.39) 2.11 (0.57)
1.97 (0.46) 3.45 (2.09)***
14 (23) 39 (24)*
17 (18) 7 (19)
23 (34) 61 (37)** 3 (3) 10 (9)**
wc ? wm
wc ? wmc
wm ? wmc
7 (26) 21 (19)*
11 (25) 19 (33)
6 (21) 57 (55)***
4 (5) 2 (7)*
3 (4) 5 (7)
1 (3) 7 (8)**
13 (8) 7 (7)*
14 (7) 14 (5)
1 (3) 8 (7)**
b
Stride variance coefficient Young 1.56 (0.29) Elderly 2.02 (0.71)
Trunk acceleration autocorrelationc Young .86 (.04) .86 (.04) Elderly .85 (.04) .80 (.06)
.82 (.04) .81 (.07)
.83 (.04) .76 (.04)
0 (4) 5 (5)*
4 (3) 4 (5)
Gait speedd Young Elderly
.65 (.07) .59 (.09)
.64 (.05) .55 (.11)*
3 (3) 7 (4)*
16 (7) 14 (6)
.77 (.06) .69 (.11)*
.74 (.05) .64 (.11)**
16 (6) 21 (6)
Mean values and standard deviations () for young (n = 13) and elderly (n = 11) walking in a figure-of-eight pattern with different task combinations. a Tasks are indicated as: w = walk, wc = walk + cognitive task, wm = walk + motor task, wmc = walk + motor task + cognitive task. b Temporal stride variability expressed as variance coefficient (SD/mean%). c Ipsi-lateral autocorrelation values for anterior–posterior accelerations (no units). d Gait speed normalized to height (m/s/height). * p < .05 significance levels for age differences. ** p < .01 significance levels for age differences. *** p < .001 significance levels for age differences.
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Table 1 Gait characteristics in different task combinations
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U. Laessoe et al. / Human Movement Science 27 (2008) 496–512 Temporal stride-to-stride variability in different task combinations 6 Young Elderly
Variance coefficient (%)
5
#
§
4
3
*
* 2
1
0 w
wc
wm
wmc
Fig. 2. Stride variance coefficients presented as mean and SD for different task combinations; walking (w), walking with cognitive task (wc), walking with a functional motor task (wm), and walking with a functional motor task and cognitive task (wmc). Significant difference between age groups. # wc significantly different from w and wm for the elderly. § wmc significantly different from w and wm for the elderly. Trunk acceleration stride variability in different task combinations 1.0
Autocorrelation coefficient
Young Elderly
#
¤
§
0.8
0.0
w
wc
wm
wmc
Fig. 3. Stride-to-stride autocorrelation coefficients for trunk accelerations presented as mean and SD for different task combinations; walking (w), walking with a cognitive task (wc), walking with functional a motor task (wm) and walking with a functional motor task and a cognitive task (wmc). # wc significantly different from w for the elderly. § wmc significantly different from w, wc, and wm for the elderly. wm significantly different from wc for the young.
and 7% slower (p < .01) compared to the wc task (p < .001). When they performed both the cognitive and the motor task (wmc) the gait speed was 21% slower (p < .001) compared to the walking task, (w), 14% slower (p < .001) compared to the wc task and 8% lower
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Gait speed in different task combinations
Normalized gait speed (m s-1 height-1)
1.0 Young Elderly
0.8
*
§
*
¤
*
# #
0.6
# # 0.0 wc
w
wm
wmc
Fig. 4. Self-selected gait speed presented as mean and SD for different task combinations; walking (w), walking with a cognitive task (wc), walking with a functional motor task (wm), and walking with a functional motor task and cognitive task (wmc). Significant difference between age groups. # All tasks within the group of elderly were significantly different from each other. § wm significantly different from w and wc for the young. wmc significantly different from w and wc for the young.
(p < .05) compared to the wm task. For the young the gait speed was 16% slower (p < .001) in the wm task compared to the walking task (w) and 13% slower (p < .001) compared to the wc task. When they performed both the cognitive and the motor task (wmc) the gait speed was 16% slower (p < .001) compared to the walking task (w) and 14% slower (p < .001) compared to the wc task. The gait speed and the stride-to-stride variance coefficients were highly correlated for the elderly in all task combinations (p < .01) (Table 2). The gait speed was also correlated to the acceleration autocorrelation in the tasks wc and wmc (p < .05). No significant correlations were found for the young participants.
Table 2 Correlation between gait speed and gait variability measures Gait speed (w) Young Stride variance coefficient Trunk acceleration autocorrelation
.17 .01
Elderly Stride variance coefficient Trunk acceleration autocorrelation
.94 .21
a
**
a
Gait speed (wc)
Gait speed (wm)
Gait speed (wmc)
.25 .52
.39 .15
.47 .30
.90** .63*
.77** .42
.85 .80
** **
Tasks are indicated as: w = walk, wc = walk + cognitive task, wm = walk + motor task, wmc = walk + motor task + cognitive task. * p < .05. ** p < .01.
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3.3. Relative changes in gait characteristics at introduction of more complex task compositions The magnitude of the relative differences in gait performance when comparing one task combination with another are presented to describe the age-related postural control vulnerability to different task demands. 3.3.1. Stride variance coefficient (Table 1 and Fig. 5A) A significant effect from Age Task combination, F(5, 22) = 7.2, p < .001, was seen in the relative changes in stride variance coefficients. The elderly increased their variance coefficients more than the young when comparing walking to walking with a cognitive task (w/wc) and to walking with both a motor and cognitive task (w/wmc). The influence from the cognitive task was even more pronounced when it was added to the combination of walking and the concurrent motor task (wm/wmc) (see Fig. 5). In contrast to the elderly, the young had higher stride variance coefficients while walking with a functional motor task compared to walking with a cognitive task (wc/wm). 3.3.2. Trunk acceleration autocorrelation (Table 1 and Fig. 5B) The changes in autocorrelation coefficients showed differences between the two age groups when comparing task composition which corresponded to the changes in temporal stride variance. A significant effect from Age Task combination was seen, F(5, 22) = 6.3, p < .001. When the cognitive task was added to normal walking (w/wc) the elderly had a decrease in autocorrelation coefficient which the young did not have. In a comparison between walking and the combination of walking, motor task, and cognitive task (w/wmc) the elderly had a larger autocorrelation decrease than the young. When the cognitive task was added to walking with a concurrent functional motor task (wm/wmc) the elderly had a decrease while the young had an increase. In contrast to the decrease in autocorrelation coefficient for the elderly the young had an increase when comparing walking with a functional motor task to walking with a cognitive task (wc/wm). 3.3.3. Gait speed (Table 1 and Fig. 5C) A significant Age Task combination effect was also seen in the gait speed, F(5, 22) = 5.2, p < .001. The introduction of a cognitive task mainly affected the gait speed of the elderly. When the cognitive task was added to normal walking (w/wc) the elderly decreased their gait speed more than the young and when it was added to walking with a concurrent motor task (wm wmc) the difference in decrease was even more pronounced. Both groups had a slower gait while walking with a functional motor task compared to walking with a cognitive task (wc/wm) but the decrease in gait speed was larger for the elderly. 3.4. Task characteristics The subtraction performance was evaluated relative to the individual performance while seated. Both the young and elderly demonstrated a reduced performance in the
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Relative differences in gait parameters between different task combinations
A Change in stride variance coefficient %
120 100
Young Elderly
80
**
*
**
60
*
40 20 0 -20 wc-w
wm-w
wmc-w
wmc-wc
wmc-wm
-40 wm-wc
-60
B Change in autocorr. coefficient %
10
5
*
**
0
-5
-10
-15 wc-w
wm-w
-20
C
**
*
wm-wc
wmc-wc
wmc-wm
wmc-w
0
Change in gait speed %
-5
-10
-15
* *
**
-20
-25
wc-w
wm-w
wm-wc
wmc-wc
wmc-wm
wmc-w
-30
Fig. 5. The relative changes in: (A) stride variance coefficient, (B) trunk acceleration autocorrelation coefficient, and (C) gait speed are presented to compare the vulnerability to concurrent tasks between young and elderly. The differences in gait characteristics are expressed in percent with (w) as reference for (wc), (wm), and (wmc); (wc) as reference for (wm), and (wmc); (wm) as reference for (wmc). p < .05; p < .01; significance levels for age differences.
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cognitive task while walking compared to baseline, but this tendency was not statistically significant. When the cognitive task was combined with a walking task (wc) and when it was combined with a walking and a functional motor task (wmc) the subtraction performance was 88% (SD: 21) and 97% (SD: 23) for the young and 90% (SD: 14) and 96% (SD: 13) for the elderly, respectively. Compared to the young participants the elderly had a significant increase in fault time (decrease in functional performance) in response to the concurrent cognitive task, F(1, 22) = 5.1, p < .05. The fault time in the task combination without a cognitive task (wm) and in the task combination with a cognitive task (wmc) was 0.9% (SD: 0.7) and 1.1% (SD: 1.2) for the young and 2.1% (SD: 2.2) and 6.0% (SD: 6.1) for the elderly, respectively. 4. Discussion This study showed a higher relative increase in gait variability in response to increased attentional demands in the elderly compared to younger individuals. An age dependency of gait variability with concurrent tasks suggests that the elderly have a lower residual attentional capacity compared to the younger participants. This finding is in agreement with the initial hypothesis of this study. The observation that the elderly were more disturbed by the cognitive task compared to the young suggests that the gait of the elderly is less automated. 4.1. Methodological considerations Gait speed and gait variability changed in parallel in response to the different task combinations and the two factors correlated strongly in the elderly group. It could be questioned whether the increased gait variability should be seen as a result of slower gait speed. It is well known that many gait cycle characteristics are velocity dependent (Moe-Nilssen & Helbostad, 2004). For example, higher stride time variability has been found in overground walking when participants have been asked to reduce their walking speed (Frenkel-Toledo et al., 2005). In the present study, however, the decrease in gait speed was not a requested artificial modification but rather occurred as a natural response to the challenge of a given task. Reduced gait speed has been shown to relate to fear of falling and gait speed decrease has been shown to occur during dual task activities (Maki, 1997; Springer et al., 2006). A natural reduction in gait speed is thought to reflect a compensation strategy which is used during walking when stability is challenged (Hollman et al., 2007) in order to maintain postural control and avoid increased gait variability. In the present study gait speed and variability measures were not correlated in the young participants. A strategy of decreasing gait speed may have been sufficient to keep the gait pattern undisturbed and avoid increased gait variability in the younger participants. Walking in a figure-of-eight induces increased gait variability compared to normal walking because the steps must be constantly adjusted to follow the track. However, this does not affect the results of this study as the data from only full or half tracks of the figure-of-eight were used and only intra-participant gait changes were analyzed. The subtraction capacity of the individuals was disturbed by the motor tasks giving a poorer subtraction performance relative to the single-task reference. Similarly, the functional task of manipulating sticks with both hands was performed with more errors when the cognitive task was introduced. Even though there may have been some learning effect
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in relation to these tasks, the relative changes indicate that the participants did not prioritize the secondary tasks exclusively on behalf of the attention they paid to the performance of the gait function and no trade-off was seen between demands of gait and demands of the dual/triple task. Several studies indicate that the elderly have less overall central processing capacity or they have difficulties shifting/distributing their attentional resources between different task demands especially when multi-tasking becomes more complex (Woollacott & ShumwayCook, 2002). This view is supported by the findings in the present study as the elderly displayed a poorer performance in the additional tasks as well as in gait performance. The choice of a relevant gait performance outcome measure is not trivial (Danion, Varraine, Bonnard, & Pailhous, 2003). In this study, two different outcome measures were used to evaluate the gait variability. The heel contacts provide only temporal information, but this information has proven to be very relevant for gait variability assessment (Hausdorff, 2005). Measures of trunk acceleration combine both kinetic and kinematic information. The interpretation of acceleration signals is not trivial, but when evaluating the signals in an autocorrelation procedure the measure becomes relevant for evaluating the variability of the gait (Moe-Nilssen & Helbostad, 2004, 2005). Both methods revealed a larger increase in gait variability amongst the elderly with the introduction of a cognitive task. Thus any argument for selecting one method in preference to the other should be based on their clinical applicability. The heel contacts can be troublesome to place in the shoes of the elderly and they are vulnerable to mechanical damage as they are exposed to high forces. An accelerometer is easy to apply, but the data handling and interpretation is challenging. Commercial accelerometer products are available and further development will probably facilitate the use of this method. 4.2. Choice of a relevant basic motor task for dual task assessment This study showed that gait variability increased in the elderly in response to a cognitive task consistent with previous observations (Beauchet et al., 2003; Dubost et al., 2006; Hollman et al., 2007). However, no clear consensus exists regarding this issue. In contrast to the present results, the group of Hausdorff (Hausdorff, Edelberg, Mitchell, Goldberger, & Wei, 1997; Springer et al., 2006; Yogev et al., 2005) demonstrated that elderly non-fallers do not necessarily have significant changes in stride variability in response to dual tasks in normal overground walking. In a recent study by Springer et al. (2006) it was shown that swing time variability increased amongst elderly fallers but not amongst elderly non-fallers, when a cognitive task was introduced. Methodological differences may account for these conflicting observations. The characteristics of the cognitive task should be considered carefully when designing the dual task activity (Beauchet, Dubost, Aminian, Gonthier, & Kressig, 2005). In addition, the characteristics and the difficulty of the basic motor task on which the load of the cognitive task is added should be considered with caution. Simple overground walking is regarded as a well known task to the participants – a task which to a large extent is automated. Little attention is required for this task and therefore residual attentional resources may be available for a secondary task. When walking in a figure-of-eight the steps have to be adjusted to let the gait pattern curve alternately from left to right. This motor task has been shown to result in a reduction in walking speed, stride length and cadence for both young and elderly (Shkuratova et al., 2004). Thus,
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walking in a figure-of-eight may be considered more challenging than simply walking in a straight path. A more complex motor task may require more attention and the residual attentional capacity will be minimized. In this case, the competition for attention to perform different tasks is more likely to occur. The findings of the present study support this view since the additional cognitive task disturbed the gait pattern of both the elderly and the young. In other words the attentional demands of figure-of-eight walking were large enough to reveal a dual task effect which might not have been shown in a simple walking task. The introduction of the functional motor task (manipulating sticks) resulted in a significant reduction of gait speed in both groups, but it introduced no significant changes in variability of gait. However, it should be noted that age-related differences in all gait parameters were most obvious when the combination of walking and functional motor task served as an even larger basic motor load for the introduction of the cognitive task. 4.3. Clinical applications Impaired balance is a risk factor for falls which highlights the importance of assessing postural control in the elderly (AGS Panel on Falls Prevention, 2001). It has been suggested that proactive control mechanisms, as seen in gait performance should be assessed to obtain a complete understanding of a person’s ability to control balance (Woollacott & Tang, 1997). However, early signs of insufficiency in control of posture can be disguised when a person is allowed to use compensatory strategies (Mulder et al., 2002). In the assessment of postural control it therefore becomes relevant to use a dual task testing approach in order to reveal whether a person compensates by directing excessive attention to the control of posture and movements. Postural control in a dual task setting is especially relevant to daily life situations. When an elderly person has to get from one side of the street to the other he/she is facing a difficult task combination. It is a challenging motor task to descend from the pavement, cross the street as quickly as possible and adjust the steps in order to ascend the other pavement. In addition, it is a cognitive challenge to keep an eye on the traffic or the traffic light and to handle some anxiety which might add to the cognitive load. Additionally, the loads on postural control become even higher when a grandchild has to be led by one hand and groceries have to be carried in the other. Therefore clinicians should seek clinical tests which can mimic daily-life situations in a standardized way. The findings of this study indicate that a dual task test adjusted to a sufficiently challenging level is relevant for revealing deterioration in postural control. A dual task combination consisting of a motor task of walking in a figure-of-eight and a cognitive arithmetic task could serve this purpose. 5. Conclusion The gait of both young participants and community-dwelling elderly participants between 70 and 80 years of age became slower and more variable when a concurrent cognitive task was performed while walking in a figure-of-eight. However, the gait characteristics of the elderly were more affected by a concurrent attention-demanding task. This suggests that the maintenance of postural control is more attention-demanding for the elderly. A combination of sufficiently challenging motor tasks and concurrent cognitive
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tasks may be used to reveal early signs of deterioration in the ability to control posture amongst elderly. Acknowledgements The study was financially supported by Center for Clinical and Basic Research A/S (CCBR), The National Danish Research Foundation, Department of Health Science and Technology, Aalborg University and University College of Health, Aalborg. Statistical assistance was provided by M. Hoejbjerre, Center for Health Statistics, Aalborg University. References Abernethy, B. (1988). Dual-task methodology and motor skills research: Some applications and methodological constraints. Journal of Human Movement Study, 14, 101–132. AGS Panel on Falls Prevention (2001). Guideline for the prevention of falls in older persons American Geriatrics Society, British Geriatrics Society, and American Academy of Orthopaedic Surgeons Panel on Falls Prevention. Journal of the American Geriatrics Society, 49, 664–672. Beauchet, O., Dubost, V., Aminian, K., Gonthier, R., & Kressig, R. W. (2005). Dual-task-related gait changes in the elderly: Does the type of cognitive task matter?. Journal of Motor Behavior 37, 259–264. Beauchet, O., Kressig, R. W., Najafi, B., Aminian, K., Dubost, V., & Mourey, F. (2003). Age-related decline of gait control under a dual-task condition. Journal of the American Geriatrics Society, 51, 1187–1188. Danion, F., Varraine, E., Bonnard, M., & Pailhous, J. (2003). Stride variability in human gait: The effect of stride frequency and stride length. Gait & Posture, 18, 69–77. Dubost, V., Kressig, R. W., Gonthier, R., Herrmann, F. R., Aminian, K., Najafi, B., et al. (2006). Relationships between dual-task related changes in stride velocity and stride time variability in healthy older adults. Human Movement Science, 25, 372–382. Foreman, M. D., Fletcher, K., Mion, L. C., & Simon, L. (1996). Assessing cognitive function. Geriatric Nursing, 17, 228–232. Fraizer, E. V., & Mitra, S. (in press). Methodological and interpretive issues in posture-cognition dual-tasking in upright stance. Gait & Posture. Frenkel-Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Effect of gait speed on gait rhythmicity in Parkinson’s disease: Variability of stride time and swing time respond differently. Journal of Neuroengineering and Rehabilitation, 2, 23. Hausdorff, J. M. (2005). Gait variability: Methods, modeling and meaning. Journal of Neuroengineering and Rehabilitation, 2, 19. Hausdorff, J. M. (2007). Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking. Human Movement Science, 26, 555–589. Hausdorff, J. M., Edelberg, H. K., Mitchell, S. L., Goldberger, A. L., & Wei, J. Y. (1997). Increased gait unsteadiness in community-dwelling elderly fallers. Archives of Physical Medicine and Rehabilitation, 78, 278–283. Hausdorff, J. M., Rios, D. A., & Edelberg, H. K. (2001). Gait variability and fall risk in community-living older adults: A 1-year prospective study. Archives of Physical Medicine and Rehabilitation, 82, 1050–1056. Hausdorff, J. M., Zemany, L., Peng, C., & Goldberger, A. L. (1999). Maturation of gait dynamics: Stride-tostride variability and its temporal organization in children. Journal of Applied Physiology, 86, 1040–1047. Hollman, J. H., Kovash, F. M., Kubik, J. J., & Linbo, R. A. (2007). Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait & Posture, 26, 113–119. Huang, H. J., & Mercer, V. S. (2001). Dual-task methodology: Applications in studies of cognitive and motor performance in adults and children. Pediatric Physical Therapy, 13, 133–140. Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (2000). Principles of neural science (4th ed.). New York: McGrawHill. Loland, N. W. (2002). Reliability of the physical activity scale for the elderly. European Journal of Sport Science, 2.
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Maki, B. E. (1997). Gait changes in older adults: Predictors of falls or indicators of fear. Journal of the American Geriatrics Society, 45, 313–320. Moe-Nilssen, R. (1998a). A new method for evaluating motor control in gait under real-life environmental conditions Part 1: The instrument. Clinical Biomechanics (Bristol., Avon.), 13, 320–327. Moe-Nilssen, R. (1998b). A new method for evaluating motor control in gait under real-life environmental conditions Part 2: Gait analysis. Clinical Biomechanics (Bristol., Avon.), 13, 328–335. Moe-Nilssen, R., & Helbostad, J. L. (2004). Estimation of gait cycle characteristics by trunk accelerometry. Journal of Biomechanics, 37, 121–126. Moe-Nilssen, R., & Helbostad, J. L. (2005). Interstride trunk acceleration variability but not step width variability can differentiate between fit and frail older adults. Gait & Posture, 21, 164–170. Mulder, T., Zijlstra, W., & Geurts, A. (2002). Assessment of motor recovery and decline. Gait & Posture, 16, 198–210. Podsiadlo, D., & Richardson, S. (1991). The timed ‘‘Up & Go”: A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39, 142–148. Powell, L. E., & Myers, A. M. (1995). The activities-specific balance confidence (ABC) scale. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 50A, M28–M34. Prince, F., Corriveau, H., Hebert, R., & Winter, D. A. (1997). Gait in the elderly. Gait & Posture, 5, 128–135. Schmidt, R. A., & Lee, T. D. (2005). Motor control and learning. A behavioral emphasis (4th ed.). Champaigne IL: Human Kinetics. Shkuratova, N., Morris, M. E., & Huxham, F. (2004). Effects of age on balance control during walking. Archives of Physical Medicine and Rehabilitation, 85, 582–588. Shumway-Cook, A., & Woollacott, M. H. (2001). Motor control: Theory and practical applications. Philadelphia: Williams and Wilkins. Springer, S., Giladi, N., Peretz, C., Yogev, G., Simon, E. S., & Hausdorff, J. M. (2006). Dual-tasking effects on gait variability: The role of aging, falls, and executive function. Movement Disorders, 21, 950–957. Washburn, R. A., Smith, K. W., Jette, A. M., & Janney, C. A. (1993). The Physical Activity Scale for the Elderly (PASE): Development and evaluation. Journal of Clinical Epidemiology, 46, 153–162. Woollacott, M., & Shumway-Cook, A. (2002). Attention and the control of posture and gait: A review of an emerging area of research. Gait & Posture, 16, 1–14. Woollacott, M. H., & Tang, P. F. (1997). Balance control during walking in the older adult: Research and its implications. Physical Therapy, 77, 646–660. Yogev, G., Giladi, N., Peretz, C., Springer, S., Simon, E. S., & Hausdorff, J. M. (2005). Dual tasking, gait rhythmicity, and Parkinson’s disease: Which aspects of gait are attention demanding? European Journal of Neuroscience, 22, 1248–1256.
Human Movement Science 27 (2008) 496–512 www.elsevier.com/locate/humov
Residual attentional capacity amongst young and elderly during dual and triple task walking Uffe Laessoe a,b,*, Hans C. Hoeck c, Ole Simonsen d, Michael Voigt a a
Center for Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, Denmark b University College North Jutland, Aalborg, Denmark c Center for Clinical and Basic Research A/S, Denmark d Northern Orthopedic Division Aalborg Hospital, Aalborg, Denmark Available online 28 January 2008
Abstract Walking is considered an automatic function which demands little attentional resources. Thus a residual attentional capacity is available for a concurrent task (dual task). Minor age-related deficits in postural control may minimize the residual attentional capacity, however this may not be detected by a simple examination of the individuals gait performance. This study investigated the use of challenging dual task combinations to detect age related changes in gait performance. Eleven community-dwelling elderly (mean age 76 years) and 13 young subjects (mean age 26 years) participated in the study. The participants walked along a figure-of-eight track at a self-selected speed. The effect of introducing a concurrent cognitive task and a concurrent functional motor task was evaluated. Stride-to-stride variability was measured by heel contacts and by trunk accelerometry. In response to the cognitive task the elderly increased their temporal stride-to-stride variability by 39% in the walking task and by 57% in the combined motor task. These increases were significantly larger than observed for the young. Equivalent decreases in trunk acceleration autocorrelation coefficients and gait speed were found. A combination of sufficiently challenging motor tasks and concurrent cognitive tasks can reveal signs of limited residual attentional capacity during walking amongst the elderly. Ó 2007 Elsevier B.V. All rights reserved. PsycINFO classification: 2221; 2346 Keywords: Dual task; Gait variability; Elderly; Attention; Physical examination
* Corresponding author. Address: Center for Sensory-Motor Interaction (SMI), Aalborg University, Aalborg, Denmark. Tel.: +45 96358797. E-mail address: [email protected] (U. Laessoe).
0167-9457/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.humov.2007.12.001
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1. Introduction Recently, there has been a focus on the interaction between cognitive factors and motor performance when assessing the functional capacity of a patient (Huang & Mercer, 2001). Mulder and colleagues have argued that most tests which are used to assess physical performance allow the participants to compensate for their deficits by utilizing other control strategies (e.g., visual and/or cognitive regulation of task performance). To better detect deficits a dual task assessment can be used (Mulder, Zijlstra, & Geurts, 2002). Dual task paradigms are typically used to investigate the attentional demands of a motor task and to examine the effects of concurrent cognitive or motor tasks on motor performance (Fraizer & Mitra, in press; Schmidt & Lee, 2005). The latter approach is sometimes referred to as a divided attention or ‘‘time-sharing” paradigm (Huang & Mercer, 2001). When one task is more demanding a greater proportion of the performer’s limited processing capacity must be allocated to this task in order to maintain an acceptable level of performance (Huang & Mercer, 2001). As the central processing capacity is limited, a primary task with higher attention demands will leave less residual processing capacity for a concurrent secondary task. An additional concurrent attention-demanding cognitive task or motor task may therefore exceed the available resource capacity. Dual task interference will only occur if the available central resource capacity is exceeded, resulting in impaired performance in one or both tasks (Abernethy, 1988). Dual task paradigms can be used to investigate the attentional demands on walking and to examine the effects of a concurrent cognitive or motor task on walking. The competition between the attention demands of walking and a concurrent attention-demanding task may result in gait alterations (Dubost et al., 2006). Postural control is defined as the control of the body’s position in space to maintain balance and orientation (Shumway-Cook & Woollacott, 2001). Traditionally, maintenance of postural control has been considered automatic or reflex controlled, suggesting that control of posture requires minimal attention. However, recent research has shown that there are significant attention requirements for postural control which vary depending on the postural task, the age of the individual and his or her balance abilities (Woollacott & Shumway-Cook, 2002). The organization of movement and postural control is developed through motor learning. Fitts and Posner articulated three stages of motor learning consisting of: a cognitive (verbal) stage, an associative stage (gradual decrease in errors; development of internal (sensory) reference of correctness), and an autonomous (automatic) stage (ShumwayCook & Woollacott, 2001). The first stage requires conscious attention to each part of the movement whereas the third stage leaves attentional resources for other tasks. The movements related to the first stage become slow, cautious and uncertain compared to the more competent practice of the next stages. Motor tasks which are well trained and not very demanding can be performed with little conscious attention which leaves a relatively large residual processing capacity. Maintenance of postural control during activities of daily living (e.g., walking) does not usually place high demands on attentional resources. In contrast, when sensory or motor deficits occur, the complex generation of movement may have to be restructured, and movements may then be controlled and performed at an associative or a cognitive stage. When the benefits from the movement automation are lost the postural control of the participant can be expected to be more vulnerable to cognitive distractions and additional tasks.
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It may be expected that postural control changes with age due to age-related alterations in the brain, loss of muscle mass, and decreased perception of high frequency vibrations, touch, proprioception, and pressure stimuli (Kandel, Schwartz, & Jessell, 2000; Prince, Corriveau, Hebert, & Winter, 1997). Thus it is possible that the elderly require increased conscious attention to maintain postural control. However, impaired postural control may not be revealed in a standard examination in which the residual attentional capacity is not sufficiently challenged. Walking is a very familiar and automated motor task to most people and the control of posture during this task will be expected to require little conscious attention. The quality of the gait performance is reflected in the stride-to-stride variability (Hausdorff, 2005, 2007). When the walking pattern is planned and executed in a way so that no major corrections are needed the stride variability is low. Deficits in the postural control may disturb the planning and execution of the gait and this can lead to a walking pattern that needs postural adjustments revealed as increased stride-to-stride variability (Hausdorff, 2005). This approach to the assessment of gait performance has been investigated through many studies and good support has been found for its relevance (Hausdorff, Zemany, Peng, & Goldberger, 1999; Hausdorff, Rios, & Edelberg, 2001). An increase in gait variability, in response to a dual task, may be expected amongst elderly people. A study evaluating this hypothesis showed a significant effect for elderly fallers whereas elderly non-fallers were unaffected (Springer et al., 2006). This finding conflicts with other studies which do report increased stride variability amongst elderly in response to a dual task (Beauchet et al., 2003; Hollman, Kovash, Kubik, & Linbo, 2007). It is possible that the dual task combination in the first case may not have been sufficiently challenging to reveal deterioration in the postural control capacity of the elderly non-fallers. Walking in a figure-of-eight has been shown to be a more challenging motor task than just walking in a straight path (Shkuratova, Morris, & Huxham, 2004). In the present study this task was used as the basic motor task in combination with either a cognitive task or a motor task in a dual task tests or in combination with both tasks in a triple task test. The purpose of this study was to evaluate differences in the residual attentional capacity between healthy young and elderly persons. We hypothesized that the postural control of both young and elderly would be affected when a concurrent cognitive or motor task was added to the motor task of walking in a figure-of-eight. We expected that the motor planning and gait performance would be less successful and result in increased gait variability. In addition, we hypothesized that dual and triple task performance among elderly would be characterized by an increased relative change in gait variability (i.e., the postural control of the elderly would be more vulnerable to additional tasks). This finding would be consistent with the idea that the residual processing capacity is relatively smaller in elderly compared to young. 2. Methods 2.1. Participants Eleven healthy community-dwelling elderly (mean age 76.4 years, SD: 5.0) and 13 young participants (mean age 25.6 years, SD: 2.0) were included in this study. There were four men in the group of young and one in the elderly group. Body mass index (BMI) was
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22.5 (SD: 2.0) and 25.2 (SD: 4.4), respectively. Young adults were included if they were between the ages of 20 and 31 years and had no known disease or need for medication. Elderly adults older than 70 years were recruited from community centers. They were included if they were independent in activities of daily living, and had no history of falls within the last year. They were excluded if they had: (a) musculoskeletal or neurological disorders; (b) significant pain that limited daily functions; (c) known uncorrected hearing problems; (d) known uncorrected visual or vestibular problems, or (e) cognitive impairment (Mini Mental State Examination (MMSE) < 23) (Foreman, Fletcher, Mion, & Simon, 1996). Two questionnaires were used to characterize the level of physical activity of the participants and their fear of falling: Physical Activity Scale for the Elderly (PASE) and Activityspecific Balance Confidence Scale (ABC) (Loland, 2002; Powell & Myers, 1995; Washburn, Smith, Jette, & Janney, 1993). The general physical performance of the elderly was evaluated by the ‘‘Timed Up and Go test” (TUG) (Podsiadlo & Richardson, 1991). Informed consent was obtained from all participants prior to inclusion in the study. The protocol was approved by the Ethics Committee of Region North Jutland (VN-20040034). 2.2. Procedures The protocol consisted of a simple walking test, two dual task tests and a triple task test. Participants were asked to walk along a figure-of-eight path marked on the laboratory floor (Fig. 1). Twenty full rounds were completed at a self-selected comfortable walking speed which would take 4–5 min. After the first ten rounds participants were asked to perform a concurrent cognitive task. The task combinations are referred to as ‘‘walk” (w) and ‘‘walk + cognitive task” (wc). After completing the 20 rounds the participants had a short break. Participants were then asked to walk again while performing a concurrent attention-demanding motor task for 10 rounds. A further ten rounds was then performed combining both the attention-demanding motor task and the cognitive task. These task combinations are referred to as ‘‘walk + motor task” (wm) and ‘‘walk + motor task + cognitive task” (wmc).
2 meter
Photo cell
Photo cell
5 meter Fig. 1. Schematic presentation of the walking path. The figure-of-eight path was marked by ten marker cones illustrating two half-circles at each end of the figure. The dimension of the marked figure was 5 2 meter. Participants were instructed to follow the figure on the outside of the marker cones. A set of photo cells were placed at each end of the path.
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The cognitive task was an arithmetic task, in which the participant recited out loud serial subtractions of seven. The subtraction started from a three digit number given by the investigator as an initiation of the task. Before the walking task the cognitive task was also performed for one minute while sitting on a chair as a reference measure. The functional motor task was constructed to mimic a daily-life manipulation task, like carrying a tray with a filled cup. It consisted of holding two interlocked sticks steady in front of the body. One stick was held in each hand with the elbows in 90° flexion. Each stick had a ring at the end with an inner diameter of 4 cm and the rings were interlocked. The participant was advised not to let the rings touch each other. Each stick weighed 15 g and was held 20 cm distant from the participant’s hand. The participants were asked to concentrate on the concurrent tasks rather than on the gait performance during the dual task conditions. 2.3. Equipment The figure-of-eight path was marked by 2 5 marker cones creating two half-circles at each end of the figure. The marked figure was 5 m long and 2 m wide (Fig. 1). Participants were instructed to follow the figure-of-eight around the outside of the cones so that the diameter of the path was approximately 2.5 m. Two sets of photo cells were placed 40 cm above the floor at each end of the walking path. These cells provided a timer signal when passed by a participant. To avoid the influence of initial acceleration and deceleration the participant started and ended the walking 2 m before and after the data recording began. The stride-to-stride time variability was chosen as an outcome measure since it has been widely used in studies investigating gait in the elderly (Hausdorff, 2005). A force sensing resistor (Interlink Electronics Inc.) was placed under each heel of the participant to record step and stride times. This measure was complemented with the measure of trunk acceleration autocorrelation coefficients indexing gait periodicity, which has also been used for studies in the elderly population (Moe-Nilssen & Helbostad, 2005). A tri-axial accelerometer (custom made, using two dual axis accelerometers ADXL202E from Analog Devices Inc. in a tri-axial setting) was placed on the lower back of the participant at the level of the third lumbar vertebra to evaluate trunk motion according to the protocol described by MoeNilssen (1998b). The test sessions were video and audio recorded in order to be able to analyze the cognitive task performance. The two interlocking sticks for the functional motor task were connected to an electric circuit to obtain an indication of task error, that is, when the rings at the end of the sticks were in contact with each other. All analog data were sampled with a custom made data acquisition system (MrKick, Knud Larsen Aalborg University, DK) at 1000 Hz. The signals from the sensors on the person (foot switches, accelerometer signals, and stick-signal) were transmitted from the person to the data acquisition unit by a wireless system (Telemyo 2400, Noraxon Inc., USA). 2.4. Data analysis In both walking tests the 20 rounds of figure-of-eight walking were split up into four sequences of five rounds. Only sequence two and three were included in the data analysis to avoid any confounding effects related to commencement and termination of the trial.
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Stride times were determined from heel contact signals and were defined as the time intervals between consecutive heel contact times. Because data from the left and right strides were not statistically different, only data from left side stride times were analyzed. Mean and standard deviation of the stride time for each set of five rounds was calculated and the coefficient of variance (CV%: standard deviation/mean 100) was determined. Trunk acceleration was extracted for each half round of figure-of-eight walking. The three axes of the acceleration signal were converted into a horizontal–vertical coordinate system according to the procedures described by Moe-Nilssen (1998a). Anterior–posterior accelerations from the data sequences were low pass filtered at 50 Hz and evaluated by an unbiased normalized autocorrelation procedure. The correlation values equivalent to a shift of one stride were extracted from the autocorrelation signals (Moe-Nilssen & Helbostad, 2004). These autocorrelation coefficients illustrate to which extent the acceleration pattern of the trunk varies between strides. A correlation value close to 1.0 indicates a high degree of uniformity whereas smaller values indicate greater variation. The autocorrelation coefficients were averaged for each set of five rounds to present mean stride-to-stride variability of the trunk accelerations for the sequences. Gait speed was calculated for each half-round and normalized to body height. The normalized gait speed was averaged for each sequence of five rounds. Signal processing for heel contact signals, acceleration signals and timer signals was performed using MatLab (MathWorks Inc.). The gait characteristics were presented in two ways. The performance characteristics in different task combinations were presented to allow a simple comparison of the differences in performance of the two age groups, which can be ascribed to general age-related differences in the ability to control posture. After that the magnitude of the relative differences in the performance, when shifting from one task combination to another, were presented to reveal more specific aspects of postural control. The latter measure describes the agerelated postural control vulnerability to additional load on the residual attentional capacity. The individual relative changes in gait characteristics were expressed in percent with (w) as reference for (wc), (wm) and (wmc); (wc) as reference for (wm) and (wmc); (wm) as reference for (wmc). Evaluation of the performance on the subtraction task included the total number of subtractions minus the number of subtraction mistakes made during the task and expressed as the number of subtractions per minute. The subtraction performance while sitting on a chair at the beginning of the session was used as a reference (100%). Performance in the functional motor task was evaluated as the total time of contact between the two interconnected rings over the analysis period relative to the total walking time. The longer time the rings were short-circuited, the poorer performance. The statistical analysis was performed with SPSS (ver. 12.0, SPSS Inc.) or SigmaStat (ver. 2.03, SPSS Inc.). Firstly, a two way analysis of variance (ANOVA) with a repeated measures design was performed to examine differences between the two age groups and between the four task combinations for stride variance coefficients, trunk acceleration autocorrelation coefficients and gait speed. Post hoc tests with corrections for multiple comparisons (Tukey) were conducted for the main effects that were statistically significant. Secondly, similar analyses were used to evaluate age-dependency of the relative changes in gait characteristics when comparing one task combination to another. In all statistical tests p < .05 was considered significant. Associations between gait speed and the two gait variability measures were evaluated by Pearson’s correlations.
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3. Results 3.1. Participants’ characteristics On the scales of physical activity (PASE), balance confidence (ABC), and self-estimated health the young had median (range) scores of 223 (183–341), 99 (97–100), and 10 (8–10), respectively, and the elderly had corresponding scores of 173 (140–271), 93 (56–99), and 8 (5–10). The elderly had a mean score of 7.6 (SD: 1.1) on the ‘‘Timed Up and Go test” (TUG). 3.2. Gait characteristics related to age and task interactions 3.2.1. Stride variance coefficient (Table 1 and Fig. 2) A significant age effect, F(1, 22) = 7.3, p < .05, task effect, F(3, 22) = 10.9, p < .001, and Age Task effect, F(3, 22) = 6.1, p < .01, were observed for the stride variance coefficients. Age effect: The elderly participants walked with a 61% higher stride variance coefficient than the young participants in the wc task (p < .01) and with a 75% higher stride variance coefficient in the wmc task (p < .001) (see Table 1 and Fig. 2). Task effect: Differences between tasks were only identified for the elderly participants. The stride variance coefficient was 39% higher (p < .01) when the elderly performed the cognitive task (wc) compared to the walking task (w) and 20% higher (p < .05) compared to the wm task. The variance coefficient was 61% higher (p < .001) when the elderly performed both the cognitive and the motor task (wmc) compared to the walking task (w) and 57% higher (p < .001) compared to the wm task. 3.2.2. Trunk acceleration autocorrelation (Table 1 and Fig. 3) A significant task effect, F(3, 22) = 12.2, p < .001, and Age Task effect, F(3, 22) = 4.8, p < .01, were observed for the acceleration autocorrelation coefficients (see Fig. 3). Age effect: The autocorrelations were generally lower for the elderly participants compared to the young, but this tendency was not statistically significant, F(3, 22) = 3.8, p = .06. Task effect: For the elderly the autocorrelation coefficient was 5% lower (p < .05) when they performed the cognitive task (wc) compared to the walking task (w). The autocorrelation coefficient was 10% lower (p < .001) when the elderly performed both the cognitive and the motor task (wmc) compared to the walking task (w), 5% lower (p < .05) compared to the wc task and 7% lower (p < .01) compared to the wm task. For the younger participants the autocorrelation was 4% lower (p < .05) in the wm task compared to the wc task. 3.2.3. Gait speed (Table 1 and Fig. 4) A significant age effect, F(1, 22) = 6.8, p < .05, task effect, F(3, 22) = 102.8, p < .001, and Age Task effect, F(3, 22) = 2.8, p < .05, were observed for gait speed (see Fig. 4). Age effect: The elderly participants walked with a 10% slower gait than the young participants in the w task (p < .05), with a 15% slower gait in the wc task (p < .01) and with a 14% slower gait speed in the wmc task (p < .05). Task effect: For the elderly the gait speed was 7% slower (p < .01) when they performed the cognitive task (wc) compared to the walking task (w). The gait speed was 14% lower (p < .001) when they performed the motor task (wm) compared to the walking task (w)
Task
a
Relative task interference (%)
w
wc
wm
wmc
w ? wc
w ? wm
w ? wmc
1.76 (0.47) 2.83 (1.14)**
1.81 (0.39) 2.11 (0.57)
1.97 (0.46) 3.45 (2.09)***
14 (23) 39 (24)*
17 (18) 7 (19)
23 (34) 61 (37)** 3 (3) 10 (9)**
wc ? wm
wc ? wmc
wm ? wmc
7 (26) 21 (19)*
11 (25) 19 (33)
6 (21) 57 (55)***
4 (5) 2 (7)*
3 (4) 5 (7)
1 (3) 7 (8)**
13 (8) 7 (7)*
14 (7) 14 (5)
1 (3) 8 (7)**
b
Stride variance coefficient Young 1.56 (0.29) Elderly 2.02 (0.71)
Trunk acceleration autocorrelationc Young .86 (.04) .86 (.04) Elderly .85 (.04) .80 (.06)
.82 (.04) .81 (.07)
.83 (.04) .76 (.04)
0 (4) 5 (5)*
4 (3) 4 (5)
Gait speedd Young Elderly
.65 (.07) .59 (.09)
.64 (.05) .55 (.11)*
3 (3) 7 (4)*
16 (7) 14 (6)
.77 (.06) .69 (.11)*
.74 (.05) .64 (.11)**
16 (6) 21 (6)
Mean values and standard deviations () for young (n = 13) and elderly (n = 11) walking in a figure-of-eight pattern with different task combinations. a Tasks are indicated as: w = walk, wc = walk + cognitive task, wm = walk + motor task, wmc = walk + motor task + cognitive task. b Temporal stride variability expressed as variance coefficient (SD/mean%). c Ipsi-lateral autocorrelation values for anterior–posterior accelerations (no units). d Gait speed normalized to height (m/s/height). * p < .05 significance levels for age differences. ** p < .01 significance levels for age differences. *** p < .001 significance levels for age differences.
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Table 1 Gait characteristics in different task combinations
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U. Laessoe et al. / Human Movement Science 27 (2008) 496–512 Temporal stride-to-stride variability in different task combinations 6 Young Elderly
Variance coefficient (%)
5
#
§
4
3
*
* 2
1
0 w
wc
wm
wmc
Fig. 2. Stride variance coefficients presented as mean and SD for different task combinations; walking (w), walking with cognitive task (wc), walking with a functional motor task (wm), and walking with a functional motor task and cognitive task (wmc). Significant difference between age groups. # wc significantly different from w and wm for the elderly. § wmc significantly different from w and wm for the elderly. Trunk acceleration stride variability in different task combinations 1.0
Autocorrelation coefficient
Young Elderly
#
¤
§
0.8
0.0
w
wc
wm
wmc
Fig. 3. Stride-to-stride autocorrelation coefficients for trunk accelerations presented as mean and SD for different task combinations; walking (w), walking with a cognitive task (wc), walking with functional a motor task (wm) and walking with a functional motor task and a cognitive task (wmc). # wc significantly different from w for the elderly. § wmc significantly different from w, wc, and wm for the elderly. wm significantly different from wc for the young.
and 7% slower (p < .01) compared to the wc task (p < .001). When they performed both the cognitive and the motor task (wmc) the gait speed was 21% slower (p < .001) compared to the walking task, (w), 14% slower (p < .001) compared to the wc task and 8% lower
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Gait speed in different task combinations
Normalized gait speed (m s-1 height-1)
1.0 Young Elderly
0.8
*
§
*
¤
*
# #
0.6
# # 0.0 wc
w
wm
wmc
Fig. 4. Self-selected gait speed presented as mean and SD for different task combinations; walking (w), walking with a cognitive task (wc), walking with a functional motor task (wm), and walking with a functional motor task and cognitive task (wmc). Significant difference between age groups. # All tasks within the group of elderly were significantly different from each other. § wm significantly different from w and wc for the young. wmc significantly different from w and wc for the young.
(p < .05) compared to the wm task. For the young the gait speed was 16% slower (p < .001) in the wm task compared to the walking task (w) and 13% slower (p < .001) compared to the wc task. When they performed both the cognitive and the motor task (wmc) the gait speed was 16% slower (p < .001) compared to the walking task (w) and 14% slower (p < .001) compared to the wc task. The gait speed and the stride-to-stride variance coefficients were highly correlated for the elderly in all task combinations (p < .01) (Table 2). The gait speed was also correlated to the acceleration autocorrelation in the tasks wc and wmc (p < .05). No significant correlations were found for the young participants.
Table 2 Correlation between gait speed and gait variability measures Gait speed (w) Young Stride variance coefficient Trunk acceleration autocorrelation
.17 .01
Elderly Stride variance coefficient Trunk acceleration autocorrelation
.94 .21
a
**
a
Gait speed (wc)
Gait speed (wm)
Gait speed (wmc)
.25 .52
.39 .15
.47 .30
.90** .63*
.77** .42
.85 .80
** **
Tasks are indicated as: w = walk, wc = walk + cognitive task, wm = walk + motor task, wmc = walk + motor task + cognitive task. * p < .05. ** p < .01.
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3.3. Relative changes in gait characteristics at introduction of more complex task compositions The magnitude of the relative differences in gait performance when comparing one task combination with another are presented to describe the age-related postural control vulnerability to different task demands. 3.3.1. Stride variance coefficient (Table 1 and Fig. 5A) A significant effect from Age Task combination, F(5, 22) = 7.2, p < .001, was seen in the relative changes in stride variance coefficients. The elderly increased their variance coefficients more than the young when comparing walking to walking with a cognitive task (w/wc) and to walking with both a motor and cognitive task (w/wmc). The influence from the cognitive task was even more pronounced when it was added to the combination of walking and the concurrent motor task (wm/wmc) (see Fig. 5). In contrast to the elderly, the young had higher stride variance coefficients while walking with a functional motor task compared to walking with a cognitive task (wc/wm). 3.3.2. Trunk acceleration autocorrelation (Table 1 and Fig. 5B) The changes in autocorrelation coefficients showed differences between the two age groups when comparing task composition which corresponded to the changes in temporal stride variance. A significant effect from Age Task combination was seen, F(5, 22) = 6.3, p < .001. When the cognitive task was added to normal walking (w/wc) the elderly had a decrease in autocorrelation coefficient which the young did not have. In a comparison between walking and the combination of walking, motor task, and cognitive task (w/wmc) the elderly had a larger autocorrelation decrease than the young. When the cognitive task was added to walking with a concurrent functional motor task (wm/wmc) the elderly had a decrease while the young had an increase. In contrast to the decrease in autocorrelation coefficient for the elderly the young had an increase when comparing walking with a functional motor task to walking with a cognitive task (wc/wm). 3.3.3. Gait speed (Table 1 and Fig. 5C) A significant Age Task combination effect was also seen in the gait speed, F(5, 22) = 5.2, p < .001. The introduction of a cognitive task mainly affected the gait speed of the elderly. When the cognitive task was added to normal walking (w/wc) the elderly decreased their gait speed more than the young and when it was added to walking with a concurrent motor task (wm wmc) the difference in decrease was even more pronounced. Both groups had a slower gait while walking with a functional motor task compared to walking with a cognitive task (wc/wm) but the decrease in gait speed was larger for the elderly. 3.4. Task characteristics The subtraction performance was evaluated relative to the individual performance while seated. Both the young and elderly demonstrated a reduced performance in the
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Relative differences in gait parameters between different task combinations
A Change in stride variance coefficient %
120 100
Young Elderly
80
**
*
**
60
*
40 20 0 -20 wc-w
wm-w
wmc-w
wmc-wc
wmc-wm
-40 wm-wc
-60
B Change in autocorr. coefficient %
10
5
*
**
0
-5
-10
-15 wc-w
wm-w
-20
C
**
*
wm-wc
wmc-wc
wmc-wm
wmc-w
0
Change in gait speed %
-5
-10
-15
* *
**
-20
-25
wc-w
wm-w
wm-wc
wmc-wc
wmc-wm
wmc-w
-30
Fig. 5. The relative changes in: (A) stride variance coefficient, (B) trunk acceleration autocorrelation coefficient, and (C) gait speed are presented to compare the vulnerability to concurrent tasks between young and elderly. The differences in gait characteristics are expressed in percent with (w) as reference for (wc), (wm), and (wmc); (wc) as reference for (wm), and (wmc); (wm) as reference for (wmc). p < .05; p < .01; significance levels for age differences.
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cognitive task while walking compared to baseline, but this tendency was not statistically significant. When the cognitive task was combined with a walking task (wc) and when it was combined with a walking and a functional motor task (wmc) the subtraction performance was 88% (SD: 21) and 97% (SD: 23) for the young and 90% (SD: 14) and 96% (SD: 13) for the elderly, respectively. Compared to the young participants the elderly had a significant increase in fault time (decrease in functional performance) in response to the concurrent cognitive task, F(1, 22) = 5.1, p < .05. The fault time in the task combination without a cognitive task (wm) and in the task combination with a cognitive task (wmc) was 0.9% (SD: 0.7) and 1.1% (SD: 1.2) for the young and 2.1% (SD: 2.2) and 6.0% (SD: 6.1) for the elderly, respectively. 4. Discussion This study showed a higher relative increase in gait variability in response to increased attentional demands in the elderly compared to younger individuals. An age dependency of gait variability with concurrent tasks suggests that the elderly have a lower residual attentional capacity compared to the younger participants. This finding is in agreement with the initial hypothesis of this study. The observation that the elderly were more disturbed by the cognitive task compared to the young suggests that the gait of the elderly is less automated. 4.1. Methodological considerations Gait speed and gait variability changed in parallel in response to the different task combinations and the two factors correlated strongly in the elderly group. It could be questioned whether the increased gait variability should be seen as a result of slower gait speed. It is well known that many gait cycle characteristics are velocity dependent (Moe-Nilssen & Helbostad, 2004). For example, higher stride time variability has been found in overground walking when participants have been asked to reduce their walking speed (Frenkel-Toledo et al., 2005). In the present study, however, the decrease in gait speed was not a requested artificial modification but rather occurred as a natural response to the challenge of a given task. Reduced gait speed has been shown to relate to fear of falling and gait speed decrease has been shown to occur during dual task activities (Maki, 1997; Springer et al., 2006). A natural reduction in gait speed is thought to reflect a compensation strategy which is used during walking when stability is challenged (Hollman et al., 2007) in order to maintain postural control and avoid increased gait variability. In the present study gait speed and variability measures were not correlated in the young participants. A strategy of decreasing gait speed may have been sufficient to keep the gait pattern undisturbed and avoid increased gait variability in the younger participants. Walking in a figure-of-eight induces increased gait variability compared to normal walking because the steps must be constantly adjusted to follow the track. However, this does not affect the results of this study as the data from only full or half tracks of the figure-of-eight were used and only intra-participant gait changes were analyzed. The subtraction capacity of the individuals was disturbed by the motor tasks giving a poorer subtraction performance relative to the single-task reference. Similarly, the functional task of manipulating sticks with both hands was performed with more errors when the cognitive task was introduced. Even though there may have been some learning effect
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in relation to these tasks, the relative changes indicate that the participants did not prioritize the secondary tasks exclusively on behalf of the attention they paid to the performance of the gait function and no trade-off was seen between demands of gait and demands of the dual/triple task. Several studies indicate that the elderly have less overall central processing capacity or they have difficulties shifting/distributing their attentional resources between different task demands especially when multi-tasking becomes more complex (Woollacott & ShumwayCook, 2002). This view is supported by the findings in the present study as the elderly displayed a poorer performance in the additional tasks as well as in gait performance. The choice of a relevant gait performance outcome measure is not trivial (Danion, Varraine, Bonnard, & Pailhous, 2003). In this study, two different outcome measures were used to evaluate the gait variability. The heel contacts provide only temporal information, but this information has proven to be very relevant for gait variability assessment (Hausdorff, 2005). Measures of trunk acceleration combine both kinetic and kinematic information. The interpretation of acceleration signals is not trivial, but when evaluating the signals in an autocorrelation procedure the measure becomes relevant for evaluating the variability of the gait (Moe-Nilssen & Helbostad, 2004, 2005). Both methods revealed a larger increase in gait variability amongst the elderly with the introduction of a cognitive task. Thus any argument for selecting one method in preference to the other should be based on their clinical applicability. The heel contacts can be troublesome to place in the shoes of the elderly and they are vulnerable to mechanical damage as they are exposed to high forces. An accelerometer is easy to apply, but the data handling and interpretation is challenging. Commercial accelerometer products are available and further development will probably facilitate the use of this method. 4.2. Choice of a relevant basic motor task for dual task assessment This study showed that gait variability increased in the elderly in response to a cognitive task consistent with previous observations (Beauchet et al., 2003; Dubost et al., 2006; Hollman et al., 2007). However, no clear consensus exists regarding this issue. In contrast to the present results, the group of Hausdorff (Hausdorff, Edelberg, Mitchell, Goldberger, & Wei, 1997; Springer et al., 2006; Yogev et al., 2005) demonstrated that elderly non-fallers do not necessarily have significant changes in stride variability in response to dual tasks in normal overground walking. In a recent study by Springer et al. (2006) it was shown that swing time variability increased amongst elderly fallers but not amongst elderly non-fallers, when a cognitive task was introduced. Methodological differences may account for these conflicting observations. The characteristics of the cognitive task should be considered carefully when designing the dual task activity (Beauchet, Dubost, Aminian, Gonthier, & Kressig, 2005). In addition, the characteristics and the difficulty of the basic motor task on which the load of the cognitive task is added should be considered with caution. Simple overground walking is regarded as a well known task to the participants – a task which to a large extent is automated. Little attention is required for this task and therefore residual attentional resources may be available for a secondary task. When walking in a figure-of-eight the steps have to be adjusted to let the gait pattern curve alternately from left to right. This motor task has been shown to result in a reduction in walking speed, stride length and cadence for both young and elderly (Shkuratova et al., 2004). Thus,
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walking in a figure-of-eight may be considered more challenging than simply walking in a straight path. A more complex motor task may require more attention and the residual attentional capacity will be minimized. In this case, the competition for attention to perform different tasks is more likely to occur. The findings of the present study support this view since the additional cognitive task disturbed the gait pattern of both the elderly and the young. In other words the attentional demands of figure-of-eight walking were large enough to reveal a dual task effect which might not have been shown in a simple walking task. The introduction of the functional motor task (manipulating sticks) resulted in a significant reduction of gait speed in both groups, but it introduced no significant changes in variability of gait. However, it should be noted that age-related differences in all gait parameters were most obvious when the combination of walking and functional motor task served as an even larger basic motor load for the introduction of the cognitive task. 4.3. Clinical applications Impaired balance is a risk factor for falls which highlights the importance of assessing postural control in the elderly (AGS Panel on Falls Prevention, 2001). It has been suggested that proactive control mechanisms, as seen in gait performance should be assessed to obtain a complete understanding of a person’s ability to control balance (Woollacott & Tang, 1997). However, early signs of insufficiency in control of posture can be disguised when a person is allowed to use compensatory strategies (Mulder et al., 2002). In the assessment of postural control it therefore becomes relevant to use a dual task testing approach in order to reveal whether a person compensates by directing excessive attention to the control of posture and movements. Postural control in a dual task setting is especially relevant to daily life situations. When an elderly person has to get from one side of the street to the other he/she is facing a difficult task combination. It is a challenging motor task to descend from the pavement, cross the street as quickly as possible and adjust the steps in order to ascend the other pavement. In addition, it is a cognitive challenge to keep an eye on the traffic or the traffic light and to handle some anxiety which might add to the cognitive load. Additionally, the loads on postural control become even higher when a grandchild has to be led by one hand and groceries have to be carried in the other. Therefore clinicians should seek clinical tests which can mimic daily-life situations in a standardized way. The findings of this study indicate that a dual task test adjusted to a sufficiently challenging level is relevant for revealing deterioration in postural control. A dual task combination consisting of a motor task of walking in a figure-of-eight and a cognitive arithmetic task could serve this purpose. 5. Conclusion The gait of both young participants and community-dwelling elderly participants between 70 and 80 years of age became slower and more variable when a concurrent cognitive task was performed while walking in a figure-of-eight. However, the gait characteristics of the elderly were more affected by a concurrent attention-demanding task. This suggests that the maintenance of postural control is more attention-demanding for the elderly. A combination of sufficiently challenging motor tasks and concurrent cognitive
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tasks may be used to reveal early signs of deterioration in the ability to control posture amongst elderly. Acknowledgements The study was financially supported by Center for Clinical and Basic Research A/S (CCBR), The National Danish Research Foundation, Department of Health Science and Technology, Aalborg University and University College of Health, Aalborg. Statistical assistance was provided by M. Hoejbjerre, Center for Health Statistics, Aalborg University. References Abernethy, B. (1988). Dual-task methodology and motor skills research: Some applications and methodological constraints. Journal of Human Movement Study, 14, 101–132. AGS Panel on Falls Prevention (2001). Guideline for the prevention of falls in older persons American Geriatrics Society, British Geriatrics Society, and American Academy of Orthopaedic Surgeons Panel on Falls Prevention. Journal of the American Geriatrics Society, 49, 664–672. Beauchet, O., Dubost, V., Aminian, K., Gonthier, R., & Kressig, R. W. (2005). Dual-task-related gait changes in the elderly: Does the type of cognitive task matter?. Journal of Motor Behavior 37, 259–264. Beauchet, O., Kressig, R. W., Najafi, B., Aminian, K., Dubost, V., & Mourey, F. (2003). Age-related decline of gait control under a dual-task condition. Journal of the American Geriatrics Society, 51, 1187–1188. Danion, F., Varraine, E., Bonnard, M., & Pailhous, J. (2003). Stride variability in human gait: The effect of stride frequency and stride length. Gait & Posture, 18, 69–77. Dubost, V., Kressig, R. W., Gonthier, R., Herrmann, F. R., Aminian, K., Najafi, B., et al. (2006). Relationships between dual-task related changes in stride velocity and stride time variability in healthy older adults. Human Movement Science, 25, 372–382. Foreman, M. D., Fletcher, K., Mion, L. C., & Simon, L. (1996). Assessing cognitive function. Geriatric Nursing, 17, 228–232. Fraizer, E. V., & Mitra, S. (in press). Methodological and interpretive issues in posture-cognition dual-tasking in upright stance. Gait & Posture. Frenkel-Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Effect of gait speed on gait rhythmicity in Parkinson’s disease: Variability of stride time and swing time respond differently. Journal of Neuroengineering and Rehabilitation, 2, 23. Hausdorff, J. M. (2005). Gait variability: Methods, modeling and meaning. Journal of Neuroengineering and Rehabilitation, 2, 19. Hausdorff, J. M. (2007). Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking. Human Movement Science, 26, 555–589. Hausdorff, J. M., Edelberg, H. K., Mitchell, S. L., Goldberger, A. L., & Wei, J. Y. (1997). Increased gait unsteadiness in community-dwelling elderly fallers. Archives of Physical Medicine and Rehabilitation, 78, 278–283. Hausdorff, J. M., Rios, D. A., & Edelberg, H. K. (2001). Gait variability and fall risk in community-living older adults: A 1-year prospective study. Archives of Physical Medicine and Rehabilitation, 82, 1050–1056. Hausdorff, J. M., Zemany, L., Peng, C., & Goldberger, A. L. (1999). Maturation of gait dynamics: Stride-tostride variability and its temporal organization in children. Journal of Applied Physiology, 86, 1040–1047. Hollman, J. H., Kovash, F. M., Kubik, J. J., & Linbo, R. A. (2007). Age-related differences in spatiotemporal markers of gait stability during dual task walking. Gait & Posture, 26, 113–119. Huang, H. J., & Mercer, V. S. (2001). Dual-task methodology: Applications in studies of cognitive and motor performance in adults and children. Pediatric Physical Therapy, 13, 133–140. Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (2000). Principles of neural science (4th ed.). New York: McGrawHill. Loland, N. W. (2002). Reliability of the physical activity scale for the elderly. European Journal of Sport Science, 2.
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