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The effect of rhythmic musical training on healthy olderadults' gait and cognitive functionDOI:10.1093/geront/gnt050
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Citation for published version (APA):Maclean, L. M., Brown, L. J. E., & Astell, A. J. (2014). The effect of rhythmic musical training on healthy olderadults' gait and cognitive function. Gerontologist, 54(4), 624-633. https://doi.org/10.1093/geront/gnt050
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Download date:02. Jun. 2020
The effect of rhythmic musical training on healthy older adults’ gait and cognitive
function
This is a pre-copyedited, author-produced PDF of an article accepted for publication in The Gerontologist
following peer review. The final published version of the article Maclean, Brown & Astell (2014) is
available online at: http://dx.doi.org/10.1093/geront/gnt050
Lin M. Maclean1, Laura J. E. Brown, PhD
2, and Arlene J. Astell, PhD
1
1. School of Psychology and Neuroscience, University of St. Andrews, St. Andrews,
Scotland
2. Department of Psychology and Research Institute for Health and Social Change,
Manchester Metropolitan University, Manchester
Corresponding author:
Linda Maclean
School of Psychology and Neuroscience
University of St. Andrews
St Mary’s Quad
South Street
St. Andrews, KY16 9JP
Phone : 0044 (0)1334 461990
Fax: 01334 463042
Email: [email protected]
1
ABSTRACT
Purpose of the study: Older adults’ gait is disturbed when a demanding secondary cognitive
task is added. Gait training has been shown to improve older adults’ walking performance but
it is not clear how training affects their cognitive performance. This study examined the
impact on gait, in terms of cost or benefit to cognitive performance, of training healthy older
adults to walk to a rhythmic musical beat.
Design and Methods: In a mixed model design, forty-five healthy older adults over 65 years
of age (M = 71.7 years) were randomly assigned to three groups. One group received a
rhythmic musical training (MT) and their dual-task walking and cognitive performances were
compared with a group who had music playing in the background (MP) but no training, and a
third group who heard no music and received no training (NM). Outcomes in single (ST) and
dual-task (DT) conditions were step-time variability and velocity for gait and correct
cognitive responses for the cognitive task.
Results: The musical training group’s step-time variability improved in both the ST (P
< .05) and in the DT (P < .05) after training, without adversely affecting their cognitive
performance. No change was seen in the control groups.
Implications: Rhythmic musical training can improve gait steadiness in healthy older adults
with no negative impact on concurrent cognitive functioning. This could potentially enhance
‘postural reserve’ and reduce fall risk.
Key terms: attention; dual-task; musical-training; cognition
2
Introduction
The ability to carry out two tasks simultaneously is achieved by efficient allocation of
attention to both activities. Allocating attention is an executive function carried out within
working memory (Baddeley, 1986) the system that oversees aspects of higher-level cognitive
function (Coolidge & Wynn, 2005). The dual-task paradigm provides a means of examining
the allocation of attention when carrying out two tasks simultaneously, by comparing the
impact on performance relative to carrying out the two tasks singly (Baddeley & Hitch,
1974). There is almost always a ‘cost’, known as the dual task deficit (DTD) of dividing
attention between two tasks no matter how ‘simple’ they are (Smith & Kosslyn, 2007).
Walking is a well-practised motor-action that involves some element of attention, but with
increasing age, it becomes less automatic and more attention is required (Dubost et al., 2006;
Lindenberger & Baltes, 2000). This is even more pronounced when walking is combined
with a second activity such as talking. Decreased ability to allocate attention when carrying
out two tasks, such as walking and talking simultaneously may be a marker of cognitive
decline (Yogev-Seligmann, Rotem-Galili, Dickstein, Giladi & Hausdorff 2012a). It has been
suggested that healthy older adults unconsciously adopt a ‘posture first’ strategy when
simultaneously walking and carrying out a cognitively demanding task (Shumway-Cook,
Woollacott, Kerns & Baldwin, 1997). That is, they naturally default to attending more to their
gait than to the cognitive task, presumably to ensure their gait (Yogev-Seligmann et al.,
2010). As such, reduced ability to effectively allocate attention can be detrimental to posture
and balance, increasing the risk of falls (Siu, Choua, Mayrb, van Donkelaara & Woollacott,
2009).
When the cognitive load becomes very demanding, gait stability (which includes a measure
of step-to-step variability) suffers (Hausdorff, 2005). This is important because dual-task
situations, such as walking and talking, are common in everyday life. Finding ways to
3
improve older adults’ attention allocation could be beneficial both for ensuring gait stability
and enhancing ‘postural reserve’, which is an individual’s ability to respond effectively to a
postural threat (Yogev-Seligmann, Hausdorff & Giladi, 2012b).
Interventions to improve attention-allocation when walking and carrying out a cognitive task
have been developed for people with Parkinson’s disease (PD). Some have shown that
training older adults with PD to optimise their division of attention when walking and
carrying out another activity can benefit their gait (Yogev-Seligmann, Giladi, Brozgol &
Hausdorff, 2012c). Others demonstrate that rhythmic movement training increases gait
regularity and automaticity, which, in turn, is linked to increased gait safety plus a reduction
in fall risk (Bridenbaugh & Kressig, 2010). Auditory cues, sometimes embedded in music,
have been particularly successful at training older adults with PD to walk more steadily
(Rochester, Burn, Woods, Godwin & Nieuwboer, 2009; Satoh & Kuzuhara, 2008; Thaut,
McIntosh, Rice, Miller & Rathbun, 1996; Trombetti et al., 2011). Matching auditory cues to
their preferred walking pace optimises steady gait in people with PD (Arias & Cudeiro, 2008;
de Bruin et al., 2010).
Improving gait steadiness could also benefit healthy older adults who do not currently have a
gait or cognitive disorder. Training older adults to walk more steadily by making gait more
rhythmic and automatic so that it requires less mental effort could free attentional resources,
which potentially could be transferred to carrying out a secondary cognitive task (Luszcz,
2011; Moors & Houwer, 2006;Yogev-Seligmann, Hausdorff & Giladi, 2008 ).
4
Methods
Ethics
The study was approved by the University of St Andrews Teaching and Research Ethics
Committee (UTREC). Participants had the opportunity to raise questions about the study
before providing signed consent to participate.
Participants
Forty-five participants (28 women, 17 men), aged between 65-88 years (mean 71.7 years)
were recruited. Inclusion criteria were physically and cognitively healthy adults over 65
years, able to walk unassisted, living independently and speaking English as a first language.
They were assigned to one of three groups: the first 15 to a Musical Training group (MT; n =
15), the next 15 to a Music Playing group (MP; n = 15) and the final 15 to a No Music group
(NM; n = 15).
Design
The experiment used a mixed model design. The independent variables were time (pre- vs.
post-intervention training) and group (MT vs. MP vs. NM). The dependent variables for gait
were step-time variability and velocity in both single (ST) and dual task (DT) conditions. The
dependent variable for cognition was the number of Correct Cognitive Responses (CCR).
Materials
A demographic questionnaire was constructed to collect self-reported health, mood and
lifestyle measures. A battery of norm-referenced cognitive measures was assembled to
provide a baseline assessment of participants’ functioning, including memory and executive
functions (Table 1).
5
Insert Table 1 about here
Equipment
The ‘Bigfoot’ footswitches and connected software were developed in the School of
Psychology at the University of St Andrews. Bluetooth technology, housed in a PC ‘mouse’
in a box attached to the participant’s waist, was used to measure mean step-time, velocity,
number of steps and step-time variability via two footswitches which were fastened to their
heels with ‘Velcro’ strips and attached by wires to the box containing the ‘mouse’. The
‘Bigfoot’ equipment had previously been validated against video recording equipment and a
stopwatch to measure CV and velocity (Maclean & Astell, 2012).
The music chosen for training was the ‘Bluebell Polka’, which has a 2/4 rhythm (two strong
beats to each bar). The preferred walking pace of each of the participants in the Music
Training and the Music Playing Groups was matched to the music using the ‘Amazing Slow
Downer’ downloaded from www.ronimusic.com This software allowed the timing of the
selected music to be slowed down or quickened from the 100% pace set on the original
programme. The adjusted music was placed on a ‘loop’ and played continuously. The
researcher could then mute and unmute the music at will, depending on the experimental
condition.
Procedure
Participants were asked to attend wearing comfortable, flat shoes. Each participant first
completed the demographic questionnaire, cognitive test battery and BDI. Participants then
completed a short balance and mobility battery. They were then asked to walk along a 15m
indoor walkway twice at their self-selected pace. The walkway was a flat corridor marked off
6
at either end with tape indicating the 15m walk plus 1m at either end to allow for acceleration
and deceleration. The ST time and number of steps were averaged and used to adjust the pre-
selected music to the participant’s preferred walking pace.
The ST cognitive condition was a one-minute seated test in which participants performed a
Serial 7s test from a randomly generated three-digit number. They were allowed to practise
and only the second score was recorded. The ST test was counter-balanced between the start
and end of the experiment, for each group, to minimize possible practice effects during the
DT.
In the pre-intervention DT participants in all groups walked at their own pace whilst
subtracting 7s out loud (DT) from an initial three digit figure, which was randomly generated
for each DT condition. The DT walking conditions were carried out twice so that participants
could practice the secondary cognitive task, with data from the second task recorded. The
initial ST walking conditions were considered to be practice for the other pre and post–
intervention ST walking conditions. All of the cognitive tasks (both ST and DT, practice and
recorded) started with different 3-digit numbers.
Musical Training
The Musical Training Group was instructed to walk in time to the adjusted music.
Participants were not told that the music had been adjusted to suit their preferred walking
pace. Each participant walked up and down the 15m until they felt that they were walking
naturally in time to the music. If clarification was required, they were told to walk in time to
the music, until they were no longer thinking about the music or the walking. Participants in
the MT group walked an average of 4 times until they felt they were walking comfortably to
the music ‘without having to think about it’. The post-intervention conditions consisted of a
7
ST 15m walk with the adjusted music playing, followed by a DT 15m walk with the adjusted
music playing.
The Music Playing Group carried out all the same conditions as the Musical Training Group
without being trained to walk to the music. This group was told that there would be music
playing in the background, but were not instructed to walk to the music. The same
intervention (15 metres x 4 times) was used for this group as for the MT Group and, like the
MT group, these participants were not told that the music had been adjusted to suit their
preferred walking pace.
The No Music Group completed all the same conditions as the other two groups but there
was no music playing throughout the experiment.
Each group completed the walking conditions in the same order to ensure everyone
experienced the 4 x 15 metre walks at the same point in the experiment. The main gait
parameters measured over 15m were: mean step-time (ms) variability, expressed as
Coefficient of Variation (CV = M/SD) in ms and velocity expressed as m/s. The main
cognitive parameters for each DT were: participants’ correct responses per second (calculated
as the total number of responses divided by the time of each walk, multiplied by the
proportion of correct answers to the total number of answers, thereby accounting for number
of errors made) and number of steps taken per cognitive response, as a measure of the pace at
which participants simultaneously walked and counted.
Statistical analyses
Responses to the questionnaires and standardised cognitive tests were scored and compared
between groups using parametric analyses. Descriptive statistics indicated that the gait data
were non-parametric. The Kruskal-Wallis was used to test for group effect and the Mann-
Whitney (post-hoc) test was used to test for differences between pairs of conditions across the
8
groups. Wilcoxon signed-rank (post-hoc) test was used to compare results between pairs of
conditions within groups. The statistical significance for the experiment was set at 0.05.
Effect sizes were calculated and reported as r values. Using G*Power Version 3.1, we
calculated that 45 participants would be needed in this experiment, giving 0.8 power to detect
a large effect size of 0.5.
Results
The 45 older adults were healthy, with the majority reporting only one age-related health
condition plus very small numbers of hospital visits in the past twelve months alongside high
self-reported general satisfaction with their health (Table 2). Eighteen out of the forty-five
participants had never smoked, 29/45 reported a moderate level of alcohol intake (less than 7
units of alcohol per week) and 16/45 had a normal Body Mass Index (BMI: Table 2). Forty-
one out of forty-five, walked every day and 43/45 self-reported being cognitively and
physically active more than three times a week (Table 2). On the mobility measures, 12 out of
the 45 healthy older adults, 4 in the MT group, 3 in the MP group and 5 in the NM group,
chose not to attempt the heel-to-toe walk. This has previously been taken as an indicator of a
fear of falling (Nakamura, Holm &Wilson, 1999),but closer inspection of the falls history of
these 12 participants did not support this concern. Indeed, 9 of the 12 had had no falls in the
previous year, 1 had had one fall and 2 had more than one fall. Additionally, another 8 people
of the remaining 33 reported one or more falls in the past 12 months but they did not decline
to complete the heel-to-toe walk.
Insert Table 2 about here
9
The participants’ self-reported cognitive function was supported by their performance on the
battery of cognitive measures (Table 2). All 45 participants were in the normal range of
global cognitive function as measured by the MMSE and their pre-morbid IQs suggested they
were an above average sample. None was suffering from depression as measured by the BDI
(Table 2).
Cognition
Before training the three groups performed similarly at counting backwards in 7s both singly
and when walking and counting (Table 3). Although the intervention training did not improve
the MT group’s cognitive performance in the dual task, as it was expected to do, the MT
group’s DT cognitive scores did not decrease as a result of the musical training’s impact on
gait. The MP and NM groups also showed no change in their DT cognitive performance after
the intervention.
Gait
In the dual-task condition, all three groups showed a significant dual-task deficit (DTD) in
velocity in the pre-training conditions (Table 3; pre-training MT group P < .05, r = -.52, MP
group P < .001, r = -.62, NM group < .001, r = -.62. In the post-training conditions the MP
group P < .001, r = -.61 and the NM group < .001, r = -.59 had a DTD but the MT group’s
post-training DT speed (M = 1.08) was not significantly different from the ST post-training
performance (M = 1.09) P > 0.05. That is, after intervention training, the MP and the NM
groups’ dual-task ‘cost’ was that both groups walked more slowly when performing the gait
and cognitive tasks together, relative to when they walked without counting, but the MT
group who, after training, showed no DT deficit speed, relative to the same ST condition.
Gait stability was examined by looking at CV of step-time variability between groups. At
baseline, i.e. before training, the MT group’s CV (Mdn = .130) was significantly higher
10
(more unsteady) in both the ST (U = 53.5, P < .05, r = -.44) and DT (Mdn = .280: U = 49.5,
P < .05, r = -.48) conditions than the MP group (Mdn = .050) (Table 3). The MT group’s gait
was not significantly less steady than the NM group at baseline. After training, the step-time
variability of the Musical Training group (Mdn = 0.130) improved significantly in the ST
(Mdn = 0.060; T = 0, P < .05, r = -.62) whereas there was no significant change in CV in
either the MP or NM groups’ dual-task step-time variability compared to the ST (Table 3).
The CV of the MT group in the DT condition also improved significantly after training (T =
16.5, P < .05, r = - .41; Table 3), whereas there was no change in either of the other two
groups. The improvement in the MT group was such that after training, their gait became
steadier in the DT (Mdn = 0.07) than it had been in the ST at baseline (Mdn = 0.13), when the
walking task was performed alone. The significant improvement in DT CV was produced at
no ‘cost’ to DT cognition, i.e. there was no decline in performance on the secondary
cognitive task in the DT condition (Table 3).
Insert Table 3 about here
Attention Allocation
The magnitude of pre- to post-intervention change in step-time variability (CV) in the ST and
DT conditions was compared between groups (Table 3). This revealed a significant group
difference in the DT H (2) = 8.14, P < .05. Post hoc tests revealed that the MT group’s CV
improved significantly more from pre- to post-training DT performance than either the MP
group (U = 58, P < .05, r = -.34,) or the NM group (U = 55, P <.05, r = -.36; Figure 1).
Insert Figure 1 about here
11
To assess whether musical training freed up attention during the DT, we examined the
percentage improvement or decline associated with the MT group’s ST and DT pre- and post-
intervention training performances to provide a proxy measure of mental effort. The MT
group’s gait variability in the ST (pre- to post-training) improved by 38.9% after musical
training whereas it improved by 53.1% in the DT after musical training, with no concurrent
detrimental effect to the performance in the cognitive task (Figure 2). If the amount of effort
applied to gait is constant across the ST and DT, then the difference between the two
conditions, 14.2%, could be seen as a measure of the participants’ mental effort in the DT
that went on the secondary cognitive task (Figure 2).
Insert Figure 2 about here
The MT group’s pre-training gait in the DT was 43.7% less steady than in the ST (DTD).
After training, it was only 26.7% less steady in the DT than in the ST (DTD). The steadier
gait in the DT post-training suggests that 17% mental effort (the difference between ST-DT
pre-training and ST-DT post-training performances) was ‘released’ by the musical training
and re-directed back to gait. (Figure 2).
Discussion
As predicted, the gait of the musical training (MT group) became steadier after the
intervention training, as demonstrated by a significant improvement in CV between pre- and
post-training conditions in both the single and dual tasks and no DTD in velocity after
intervention training. In other words, gait velocity after training did not decline in the DT
relative to the ST. These two results for gait cannot be attributed simply to hearing music, as
there was no change in the gait of the MP group who had music playing in the background as
they walked and counted. The results also cannot be explained as a practice effect as there
12
was no improvement in the gait of the NM group who completed the walking and counting
conditions with no music playing. Moreover, the only change in gait between ST and DT, pre
to post-training, across the three groups, was the improvement in the MT group’s DT step-
time variability (CV), which became steadier in the DT than in the initial baseline ST. At
baseline the MT group’s CV gait was less steady in both the ST and DT pre-training stages
than the other two groups, reflecting the variable nature of gait. This is also seen in the non-
normal distribution of scores found in all of the gait conditions, which reflect the variability
of gait speed and steadiness between individuals (Hausdorff, 2005).
The results indicated that the MT group was a group of older community-dwellers, with no
known physical or cognitive impairments, randomly selected and allocated to experimental
groups and, as such, constitute a representative sample. Taken together, these results suggest
that musical intervention training improves gait stability, whilst having music playing
(controlling for the musical rhythm) and simply walking (controlling for the music) as
interventions have no effect on steadiness of gait. We anticipated that if musical training
improved gait stability by making it more automatic, this would free up controlled attentional
processing, which could in turn be transferred to performance of the more difficult cognitive
task (Schneider & Shiffrin, 1997). Between the pre- and post-training conditions, there was
no significant difference in the cognitive performance of the MT group, demonstrating
neither decline nor improvement as their gait became steadier.
Finding a way to quantifying the mental effort exerted on either the walking or the secondary
cognitive task is a major challenge (Yogev-Seligmann et al., 2012a). It may be possible to
examine this by measuring the magnitude of change between pairs of conditions. Looking at
the group that showed significant change after training (MT group), we can make two
observations about the magnitude of change between pairs of conditions. Firstly, the
difference between the ST pre- and ST post-training conditions suggests that the musical
13
training ‘saves’ 38.9% mental effort when walking. The size of improvement between the
DT pre and DT post-training conditions suggest that 53.1% of mental effort is saved by
musical training when walking and counting. The difference between the two ‘savings’
indicates that 14.2% of mental effort was used to count in the DT. Secondly, the
improvement between the ST pre-intervention (walking only) and the DT pre-intervention
(walking and counting) and ST post-intervention (walking to music) and DT post-
intervention (walking and counting to music) gait variability demonstrates that the MT group
had 17% extra mental effort to spare, due to the musical training, which could have been used
for the cognitive task but wasn’t. This suggests that the freed-up attention (17%) was not
automatically allocated to the cognitive task but possibly, unconsciously, re-directed to the
primary walking task.
There are two possible explanations for the Musical Training group’s correct cognitive
responses (CCR) remaining unaffected by the musical training. Firstly, during the DT, the
MT group participants, had reached their cognitive performance ceiling (Tehan & Mills,
2007) and no amount of freed-up attention could have increased the number of cognitive
responses produced by them. We suggest, therefore, that the extra attention freed by the
intervention training, for the MT group, was unconsciously allocated back to gait. An
alternative suggestion is that the MT group, being cognitively healthy, would naturally adopt
a posture first strategy in any situation where gait was threatened. In this case, it is possible
that, because the cognitive task was demanding, the threat to gait was maintained in the DT
and any extra attention, freed-up by the musical training and which might have produced an
improvement in CCRs, was, instead, unconsciously allocated to improve gait stability. This
added attention to gait could be what was observed in the MT group’s improved DT post-
intervention CV.
14
We also anticipated that the cognitive performance of the two groups who did not receive
music training would remain unchanged and this was the case. Since their gait did not
become any steadier either, this suggests that they were unaffected by the experimental
intervention.
Current understanding of gait suggests that it depends on the integrity of various aspects of
cognition, particularly the higher-level executive functions of which attention-allocation is
one (van Iersel, Kessels, Bloem, Verbeek & Olde-Rikkert, 2008). Working memory regulates
the attentional flow, which allows gait to be either consciously under control (‘effortful’) or
unconsciously automatic (‘effortless’) (Schneider & Shiffrin, 1997; Stuart-Hamilton, 2012).
The MT group’s improved gait performance suggests that rhythmic training may tap into the
ability of the working memory to flexibly process demands for attention and allocate it
according to a changing situation, for example when a secondary cognitive task becomes
more demanding (Baddeley, 1996; Yogev-Seligmann et al., 2008). Our findings support the
suggestion that walking while listening to music may render gait more ‘automatic’ for
healthy older adults who would usually unconsciously favour gait in a DT.
The performance outcomes from this group of physically and cognitively healthy older adults
have relevance both for other healthy older adults and those with impairment in gait and/or
cognition. In the first case, ‘postural reserve’ is an important factor affecting task
prioritisation during gait and cognition and which also decreases with age and disease.
Improving healthy older adults’ gait by making it more rhythmic through musical training,
whilst maintaining cognitive capability, could result in enhanced postural reserve. Secondly,
if musical training frees up attention, this could be beneficial to older adults with cognitive
impairment, who naturally adopt a ‘posture second’ strategy, whereby they allocate attention
equally to gait and cognition, thus increasing their risk of falling (Bloem, Grimbergen, van
Dijk & Munneke, 2006). Therefore, musical training to make gait more rhythmic could be a
15
preventative intervention for fall risk for older adults who are adversely affected by ‘walking
when talking’ (Lundin-Olsson, Nyberg & Gustafson, 1997).
This experiment was conducted in a controlled environment over a flat surface, with healthy
older adults, but it has the potential to be applicable to in a more ecologically valid and
complex environment, such as outdoors or in the home. Further research is required to
establish the practical application and therapeutic value to practitioners of these findings.
FUNDING
This work was supported by a doctoral studentship awarded to the first author as part of grant
number ES/G008779/1 from the Economic and Social Research Council held by the third
author.
16
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21
Table 1
Description of Baseline Cognitive and Mood Tests
Cognitive Test Description
Mini-Mental State Examination (MMSE)
(Folstein, Folstein & McHugh, 1975)
The MMSE provides a brief measure of
global cognitive function that is scored out of
30 with norm-referenced adjustments for
participants over 80 years of age (Spreen &
Strauss, 1998).
Trail Making Test A and B (TMT A & TMT
B: AITB, 1944 (Reitan & Wolfson, 1993)
TMT A and B provide a measure of
executive functions assessed through speed
in seconds to complete both parts (Delta
TMT – B minus A).
Symbol Digits Modalities Test (SDMT)
(Smith, 1982)
SDMT provides a measure of processing
speed where participants match as many
numbers to symbols as possible in 90
seconds.
Digit Span Forwards and Backwards (DS/F
& DS/B) (Thorndike, Hagen & Sattle, 1987)
These tasks provide a measure of Working
Memory by repeating short strings of
numbers both forwards and backwards. The
score is based on the number of items
correctly repeated
22
Immediate and Delayed Story Recall
(Wechsler, 1987)
Story Recall provides a measure of episodic
memory by asking participants to recall
immediately and after a 30-mintue delay
story as much detail as possible from a story
read aloud to them. Scoring comprises total
number of ideas correctly recalled
immediately out of 25 and the percentage of
ideas recalled after the delay.
National Adult Reading Test - 2nd
Edition
(Nelson & Willison, 1991)
The NART provides a measure of estimated
pre-morbid intelligence through reading a list
of progressively difficult and phonetically
irregular English words. The total number of
errors is converted to a Full Scale IQ
equivalent.
Beck Depression Index (BDI) (Beck, Steer &
Brown, 1996)
The BDI screens for the presence of clinical
depression, where scores above 13 indicate
clinically significant problems.
23
Table 2
Participant Demographic Characteristics and Physical and Cognitive Functions at Baseline
Characteristics Music
Training
(MT)
Group #
Music Playing
(MP)
Group #
No Music
(NM) Group
#
Number of Participants
Gender (male/female)
Age (years)
Self-reported chronic medical
conditions
Self-reported nights in hospital in
previous year
Self-reported falls in previous year
Never Smoked
Less than 7 units of alcohol a week
BMI indicates overweight or obese
Walking every day
Cognitive activities at least 3
times/week
Exercise at least 3 times a week
Satisfied with health status
Number of complete stand-up/sit -
downs
Time taken in Heel-To-Toe Walk
15
4/11
73.2 (±5.36)
1.67
1.13
1.33
3/15
7/15
9/15
14/15
14/15
15/15
14/15
9.35 (± 2.12)*
42.32 (± 9.87)
15
6/9
69.1 (±3.37)
1.53
1.07
1.2
6/15
6/15
11/15
13/15
15/15
13/15
13/15
12.17 (±2.89)
48.84 (± 19.48)
15
7/8
72.9 (±6.490
2
1.07
0.53
9/15
11/15
9/15
14/15
14/14
15/15
10/15
11.83 (±4.02)
55.45(±18.61)
24
Number who completed Heel-To-Toe
Walk
Balance on one leg time (seconds)
Mini Mental State Examination
Beck’s Depression Inventory
Estimated Pre-Morbid IQ
Digit Score/Forwards
Digit Score/Backwards
Delta/Trail Making Test
Symbol Digit Modalities Test
Immediate Recall (Story)
Delayed Recall (% of Immediate
Recall)
10/14
15.54 (±10.5)
28.4 (±1.18)
1.2 (±1.38)
119.6 (±4.32)
10.6 (±2.09)
6.87 (±1.8)
50.5 (±28.3)
39.9 (±7.2)
11.1 (±3.8)
94.3 (±14.6)
12/15
20.99 (±9.9)
29.1 (±1.28)
1.33 (±1.98)
117.33 (±5.52)
10.4 (±2.7)
8.2 (±2.4)
48.1 (±25.4)
43.6 (±10.8)
11.4 (±2.92)
102.8 (±13.9)
10/15
19.29(±11.06)
28.7 (± 1.83)
0.47 (±0.74)
117.5 (±7.72)
10.6 (±2.6)
8.6 (±2.9)
34.1 (±19.3)
47.0 (±9.3)
9.9 (±5.3)
83.9 ±26.6)**
#Values are mean (± Standard Deviation)
* Significant difference between the MT and the MP group, p < .05
**Significant difference between NM and MP groups, p < .05
25
Table 3.
Group Performance in Gait and Cognition Tasks – Single and Dual Task, Pre and Post-Intervention Training
Music Training Group (n = 15)
Median (range)
Music Playing Group (n = 15)
Median (range)
No Music Group (n = 15)
Median (range)
Single Task Dual Task Single Task Dual Task Single Task Dual Task
Parameter
Pre-
Trg
Post-
Trg
Pre-
Trg
Post-
Trg
Pre-Trg
Post-
Trg
Pre-Trg
Post-
Trg
Pre-Trg
Post-
Trg
Pre-Trg
Post-
Trg
Velocity 1.22
(.77)
1.09
(.87)
1.06‡
(1.15)
1.08
(.81)
1.38
(.59)
1.36
(.53)
1.15‡‡
(.83)
1.16‡‡
(.85)
1.25
(.88)
1.31
(.75)
0.986‡‡
(.97)
1.12‡‡
(.98)
Step-time
variability
(CV)
0.130†
(.62)
0.060*
(.61)
0.282§
(.99)
0.070*
(.45)
0.050
(.31)
0.060
(.26)
0.070
(.27)
0.080
(.43)
0.060
(.43)
0.060
(.42)
0.060
(.90)
0.170
(.69)
Correct
Cognitive
Responses
0.324
(1.15)
0.352
(1.15)
0.323
(1.28)
0.415
(.74)
0.399
(.77)
0.343
(.65)
0.361
(.56)
0.348
(.65)
0.296
(.60)
‡ Significant change from ST to DT, P < .05
‡‡ Significant changes from ST to DT, P < .001
† Significant difference between MT’s ST pre-training CV and MP group’s ST pre-training CV, P < .05
§ Significant difference between MT’s DT pre-training CV and MP and NP groups’ DT pre-training CV, P < .01
* Significant change from ST pre to post-training, P < .05
* * Significant change from DT pre to post-training, P < .05
26
Figure 1. Changes across groups between median DT pre and post-training step-time
variability
*Change significant at P<.050
ms = milliseconds
CV = Coefficient of Variation (Standard Deviation/Mean)
*
*
27
Figure 2. The musical training group’s median step-time variability in ST and DT, pre and
post-training walking conditions.
CV = Coefficient of Variation (Standard Deviation/Mean)
ms = milliseconds
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Single Task Pre-training
Single Task Post-training
Dual Task Pre-training
Dual Task Post-training
Med
ian
Ste
p-t
ime
va
ria
bil
ity
(CV
) in
ms