THE RELATIONSHIP BETWEEN LOWER LIMB COORDINATION …
Transcript of THE RELATIONSHIP BETWEEN LOWER LIMB COORDINATION …
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THE RELATIONSHIP BETWEEN LOWER LIMB COORDINATION AND WALKING SPEED FOLLOWING STROKE: AN OBSERVATIONAL STUDY
May Suk-Man Kwan. MHlthSc (Usyd)
Thesis submitted in fulfilment of the requirements for the degree of
Master of Applied Science by Research
Faculty of Health Sciences
The University of Sydney
February, 2017
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ABSTRACT
Even after recovery of strength, many people with stroke walk slowly, and this may be the
result of poor lower limb coordination. The broad aim of the project was to investigate the
contribution of loss of coordination to walking disability after stroke. The main research
question was ‘Is decreased lower limb coordination related to slow walking speed in people
who have regained lower limb strength after stroke?’ An observational study was
conducted including 30 people after stroke and 30 healthy controls. Inclusion criteria for
participants after stroke were recovery of lower limb strength (i.e. ≥ Grade 4) and walking
speed > 0.6 m/s without aids and in bare feet (stratified so that walking speed was evenly
represented across the range of 0.6 to >1.2 m/s). Walking speed was measured using the
10m walk test and the 6-minute walk test and reported in m/s. Coordination was measured
using the Lower Extremity Motor Coordination Test (LEMOCOT) and reported in taps/s.
The stroke participants were tested on average 25 months (SD 30) after their stroke, walked
at 0.97 (SD 0.26) m/s during the 6-minute walk test, and performed the LEMOCOT at 1.20
(SD 0.34) taps/s. The healthy controls walked at 1.43 (SD 0.30) m/s during the 6-minute
walk test, and performed the LEMOCOT at 1.85 (SD 0.36) taps/s. LEMOCOT scores for
both the affected and intact sides of the stroke group were significantly correlated with
walking speed (r = 0.42 to 0.51, p<0.05). People with stroke were operating at about two-
thirds of their age-matched counterparts in terms of both their lower limb coordination with
LEMOCOT and walking speed. In stroke group with well-recovered strength, coordination
was strongly related to walking speed, suggesting that loss of coordination may contribute
to slow walking. These findings suggest that once people after stroke have regained
sufficient strength to walk at reasonable speeds, intervention targeting coordination may
produce faster walking.
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This is to certify that the thesis entitled “THE RELATIONSHIP BETWEEN LOWER
LIMB COORDINATION AND WALKING SPEED FOLLOWING STROKE: AN
OBSERVATIONAL STUDY” submitted by May Suk-Man Kwan in fulfillment of the
requirements for the degree of Masters by Research is in a form ready for examination.
Signed _________________________
Date 17/02/2017
Dr Leanne Hassett
Senior Lecturer
Discipline of Physiotherapy,
Faculty of Health Sciences,
The University of Sydney.
Leanne Hassett
Digitally signed by Leanne Hassett DN: cn=Leanne Hassett, o=FHS, The University of Sydney, ou=Discipline of Physiotherapy, [email protected], c=AU Date: 2017.02.17 13:37:16 +11'00'
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I, May Suk-Man Kwan, certify that to the best of my knowledge, the content of this thesis
is my own work. This thesis has not been submitted for any degree or other purposes.
I certify that the intellectual content of this thesis is the product of my own work and that
all the assistance received in preparing this thesis and sources have been acknowledged.
In addition, ethical approval from the Ethics Review Committee, RPA Zone was granted
for the study presented in this thesis. Participants were required to read a participant
information document and informed consent was gained prior to data collection.
Name May Suk-Man Kwan
Signed
Date __17/02/2017______
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ACKNOWLEDGMENTS
Foremost, I would like to express my sincere gratitude to Jesus Christ, my LORD for
giving me health and the opportunity to do this research and to complete this thesis with the
support of three excellent, dedicated and renowned supervisors.
A sincere thank you to my three supervisors. Dr Leanne Hassett, who is always very
patience to keep me on track and to provide me constant guidance from the very early stage
of the research, preparation of data collection, data processing, analysis and writing.
Without Leanne motivating me and setting up time-frame for my work, I would not be able
to come to this completion of the thesis. Emeritus Professor Louise Ada’s excellent
structure skills in formatting a document, setting up tables indeed brought me to full
realisation that my skill level was not up to standard. Her sharp eyes spotted any minor
mistakes including my atrocious grammatical errors, spelling errors, and illogical
arguments of my writing. Louise always inspires me with new concepts and ideas and
giving me the joy of getting some meaningful results in research. Professor Colleen
Canning gave me expertise insightful comments and feedback especially during the time of
data analysis and writing up the thesis. My special thank you goes to Dr Mark Halaki for
his expertise in biomechanical explanation and guidance in data processing.
I would also like to thank all participants who helped with this study. By sacrificing their
time, I have been able to see the impact of coordination impairment in stroke recovery.
I am very thankful for my managers, Ms Ruth Perrott and Ms Julie Penn in giving me
permission to conduct this research study at the Royal Prince Alfred Hospital,
Physiotherapy Department and flexibility to attend meetings with my supervisors and time
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off for writing up the thesis. A special thank you to my colleagues, Ms Penelope
Simmonds, Mrs Serena Morcombe and Ms Felicity Tong to support and assist me in this
research study and being my blind assessors for some of the measurements.
At last, I thank my family and close friends. They accepted me not turning up to many
social functions and gatherings during the time I had to work very hard on the project.
Thank you for my furry friend, my cat Toby. He always sat beside me while I was sitting
for prolonged hours in front of the computer to work on data entry, data analysis and
writing.
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TABLE OF CONTENTS PAGE ABSTRACT .................................................................................................................. i STATEMENT OF AUTHORSHIP ............................................................................ iii ACKNOWLEDGMENTS ............................................................................................ v LIST OF TABLES .................................................................................................... viii LIST OF FIGURES ...................................................................................................... x PUBLICATIONS AND PRESENTATIONS ............................................................. xi CHAPTER 1: INTRODUCTION .......................................................................... 1 RATIONALE OF THE STUDY .......................................... 2 CONTRIBUTION OF IMPAIRMENTS TO WALKING
DISABILITY AFTER STROKE .......................................... 5 NATURE OF INCOORDINATION AFTER STROKE .... 10 MEASUREMENT OF COORDINATION ........................ 12 AIMS AND RESEARCH QUESTIONS ............................ 15 CHAPTER 2: METHODS .................................................................................. 17 DESIGN .............................................................................. 18 PARTICIPANTS ................................................................ 18 OUTCOME MEASURES .................................................. 22 DATA REDUCTION ......................................................... 25 DATA ANALYSIS ............................................................ 28 CHAPTER 3: RESULTS .................................................................................... 29 CHARACTERISTICS OF PARTICIPANTS ..................... 30 COORDINATION AND WALKING ................................ 31 RELATIONSHIP BETWEEN THE TWO WALKING
TESTS ................................................................................. 34
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RELATIONSHIP BETWEEN THE TWO MEASURES
OF COORDINATION ........................................................ 34 RELATIONSHIP BETWEEN COORDINATION AND
WALKING ......................................................................... 35 CHAPTER 4: DISCUSSION .............................................................................. 37 MAIN FINDINGS .............................................................. 38 LIMITATIONS OF THE STUDY ..................................... 43 CLINICAL AND RESEARCH IMPLICATIONS ............. 43 REFERENCES ........................................................................................................... 46 APPENDIX A ETHICAL CONSIDERATIONS................................................. 52 APPENDIX B TARDIEU SCALE ...................................................................... 71 APPENDIX C NOMOGRAM FOR THE LEMOCOT SCORES OF THE
DOMINANT AND NON-DOMINANT LOWER LIMBS BASED ON AGE AND SEX ...................................................... 72
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LIST OF TABLES
Table 1 Studies examining the association between weakness and walking after stroke .......................................................................... 6
Table 2 Studies examining the association between spasticity and
walking after stroke .......................................................................... 8 Table 3 Studies examining the association between loss of sensation and
walking after stroke .......................................................................... 9 Table 4 Quality of the muscle reaction (X) to stretch on the Tardieu scale 20 Table 5 Characteristics of participants ........................................................ 30 Table 6 Mean (SD) of outcomes by group and mean difference (95%CI)
between groups and proportion of the stroke group vs. healthy group ............................................................................................... 32
Table 7 Correlation between two measures of coordination – LEMOCOT
and hip, knee and ankle accelerometry ratio of peak power to total power – using Pearson’s correlation coefficient r (p value) ... 35
Table 8 Correlation between coordination and walking after stroke using
Pearson’s correlation coefficient r (p value)................................... 36 Table 9 Compare accuracy and speed requirement on each measurement . 41
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LIST OF FIGURES
Figure 1 Non-linear relationship between leg strength and usual gait speed.... .... 3 Figure 2 Photo for dorsiflexion angle to measure plantarflexor contracture .... . 21 Figure 3 Perspex sheet to measure matching task............................................. .. 22 Figure 4 LEMOCOT ......................................................................................... .. 23 Figure 5 Accelerometer positions for ankle, knee and hip from left to right .... .. 24 Figure 6 Actigraph recording from affected hip flexion/extension over 10
seconds for one stroke participant (displayed in Microsoft Excel) .... .. 26 Figure 7 Power spectrum of affected hip flexion/extension for one stroke
participant. .......................................................................................... .. 27 Figure 8 MATLAB program to determine peak frequency, peak power,
total power, ratio of peak power to total power .................................. .. 28 Figure 9 Footprints ............................................................................................ .. 45 Figure 10 Use of metronome (picture extracted from
physiotherapyexercise.com) ............................................................... .. 45 Figure 11 Exercises of treadmill-based C-Mill therapy with visually guided
stepping to a sequence of stepping targets.......................................... .. 46
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PUBLICATIONS AND PRESENTATIONS
Parts of the work presented in this thesis have been published and/or presented in the following forums:
PUBLISHED ABSTRACTS
Kwan, M.; Hassett, L.; Canning, C.; Ada L (2016). Relationship between Lower Limb Coordination and Walking Speed after Stroke. Cerebrovascular Diseases 42(1), Pages: 105-105 Meeting Abstract: P165 Kwan, M.; Hassett, L.; Canning, C.; Ada L (2016). Relationship between lower limb coordination and walking speed after stroke. International Journal Of Stroke, 11. Special Issue: SI Supplement: 1 Pages: 7-7. CONFERENCE PRESENTATION – ORAL Kwan, M.; Hassett, L.; Canning, C.; Ada L (2016). Relationship between lower limb coordination and walking speed after stroke. Smart Stroke Conference, Canberra, ACT, Australia CONFERENCE PRESENTATION - POSTER Kwan, M.; Hassett, L.; Canning, C.; Ada L (2016). Relationship between lower limb coordination and walking speed after stroke. (Poster Presentation). Asia Pacific Stroke Conference 2016, Brisbane, Queensland, Australia PAPER Kwan, M.; Hassett, L.; Canning, C.; Ada L (in preparation). Relationship between lower limb coordination and walking speed after stroke. Clinical Rehabilitation.
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Chapter 1
INTRODUCTION
RATIONALE OF THE STUDY
CONTRIBUTION OF IMPAIRMENTS TO WALKING DISABILITY
AFTER STROKE
Loss of strength
Spasticity
Loss of sensation
Loss of coordination
NATURE OF INCOORDINATION AFTER STROKE
MEASUREMENT OF COORDINATION
Clinical measures
Laboratory measures
AIMS AND RESEARCH QUESTIONS
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RATIONALE OF THE STUDY
The negative motor impairments of stroke include weakness and incoordination
and these impairments lead to limitations in activities such as standing up, walking,
reaching and manipulation (Canning, Ada, Adams, & O'Dwyer, 2004). These
impairments also contribute to participation restrictions in stroke survivors (Burke,
1988), particularly community ambulation, domestic chores, and personal care.
Several studies have investigated the contribution of weakness and incoordination
along with other impairments to activity limitation after stroke. Muscle weakness
has been shown to have a higher correlation than any other motor impairment with
physical disability after stroke in upper limb activity (Ada, Canning, & Dwyer,
2000; Canning et al., 2004; Canning, Ada, & O'Dwyer, 1999), stair climbing
(Bohannon & Walsh, 1991), and walking (Flansbjer, Downham, & Lexell, 2006;
Hsu, Tang, & Jan, 2003; Nadeau, Arsenault, Gravel, & Bourbonnais, 1999;
Sakuma et al., 2014). Therefore, strength is very important for regaining activity
since it is a pre-requisite of movement. Furthermore, interventions focussing on
addressing weakness after stroke is highly associated with improvement in
strength, as well as in activities such as walking (Ada, Dorsch, & Canning, 2006;
Pak & Patten, 2008).
Nevertheless, once leg strength has reached a certain level, any additional strength
does not appear to result in an increase in walking speed. The relationship beween
leg strength and walking speed was investigated in older adults (Buchner, Larson,
Wagner, Koepsell, & De Lateur, 1996). This study found a non-linear relationship
between leg strength and walking speed (Figure 1). Where leg strength is low
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(Areas B and C), increases in strength will result in increases in walking speed in
an almost linear fashion. However where leg strength is higher (Area A), there is
little association beween strength and walking speed.
Figure 1 Non-linear relationship between leg strength and usual gait speed in older adults (Buchner et al., 1996)
In summary, loss of strength and walking performance is significantly correlated
in people with stroke when lower limb muscle weakness is present. However,
when reasonable leg strength has been recovered and walking speed remains
slower than normal for that age group, the influence of other impairments (such as
incoordination) may become more prominent.
The terms ‘dexterity’ and ‘coordination’ have been widely used to describe the
quality of movement and both have similar definitions and are often used
interchangeably. Coordination is defined as “the ability to produce a smooth,
controlled, accurate, and rapid movement for the execution of a purposeful
movement in an accurate and effective manner” (Bourbonnais, Vanden Noven, &
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Pelletier, 1992). Whereas, dexterity has been defined as “the ability to adequately
solve any motor task precisely, quickly, rationally and deftly where flexibility with
respect to changing environment is an important feature” (Bernstein, 1991).
Coordination relies upon precise temporal and spatial patterning of onset and
offset of agonists, antagonists and synergists during movement (Bourbonnais et al.,
1992) to generate appropriate forces at the appropriate times. The speed and
accuracy of the movement is, therefore, a reflection of the level of coordination of
the movement.
Incoordination after stroke has been less extensively researched than weakness,
partially because coordination is confounded by weakness and most people after
stroke present with both weakness as well as incoordination. However, in a study
where minimal strength was required to measure coordination, incoordination was
found to make an independent contribution to upper limb activity (Canning, Ada,
& O’Dwyer, 2000). In contrast, the relationship between incoordination of the
lower limb and long-term activity limitations in walking is not clear. Many stroke
survivors who appear to have regained reasonable strength of the affected limbs
still suffer from slowness during every day walking compared to their premorbid
level. In this group, perhaps the slowness in walking speed is due to their inability
to turn their muscles on at the right time during the gait cycle, that is, they have
reduced coordination. There were no studies investigating the relationship
between coordination and walking in 2014, when the current study was planned.
The next section outlines the evidence for the contribution of motor and sensory
impairments to walking disability after stroke at the time the current study was
planned.
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CONTRIBUTION OF IMPAIRMENTS TO WALKING DISABILITY
AFTER STROKE
It is difficult to work out a primary source of walking disability after stroke in the
presence of multiple impairments such as weakness, spasticity (Johnson, Burridge,
Strike, Wood, & Swain, 2004) and sensory impairment (Sulzer, Gordon, Dhaher,
Peshkin, & Patton, 2010). It has been suggested that a major source of walking
disability may be impaired coordination as well as weakness of the lower limb
muscles (Tan & Dhaher, 2014).
Loss of strength
Given that strength is a pre-requisite for most activities, it is not surprising that
muscle weakness is highly correlated with walking performance after stroke
(Bohannon, 1986; Bohannon, 1987; Bohannon & Andrews, 1990). Table 1
contains a summary of studies that investigated the association between weakness
and walking from 1 month to 6 years post stroke.
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Table 1 Studies examining the association between weakness and walking after stroke
Study Participants
Muscles Walking parameter Pearson correlation between strength and walking*
Bohannon and Andrews (1990)
N=17 Age=59(11) yr Time=51 (42) d since stroke
Knee extensors
Walking speed r= 0.54 to 0.51
Bohannon (1986)
N=20 Age=61(8) yr Time=68(47) d since stroke
Hip flexors Hip extensors Hip abductors Knee flexors Knee extensors Ankle dorsiflexor Ankle plantarflexors
Speed r=0.25 r=0.60 r=0.42 r=0.47 r=0.36 r=0.56 r=0.47
Bohannon and Walsh (1991)
N=20 Age=68(14) yr Time=48(81) d since stroke
Hip flexors Hip extensors Knee flexors Knee extensors Ankle dorsiflexors
Stair climbing score r=0.75 r=0.74 r=0.85 r=0.84 r=0.79
Bohannon and Walsh (1992)
N=14 Age=68(11) yr Time=55(93) d since stroke
Knee extensors
Walking speed r=0.67 Fast speed
r=0.76 Dorsch, Ada, Canning, Al-Zharani, and Dean (2012)
N=60 Age=69(11) yr Time=1-6yr since stroke
Hip flexors Hip extensors, Hip adductors Hip abductors Hip internal rotators Hip external rotators Knee flexors Knee extensors Ankle dorsiflexors Ankle plantarflexors Ankle invertors Ankle evertors
Walking speed r=0.59 r=0.54 r=0.54 r=0.49 r=0.55 r=0.47 r=0.55 r=0.52 r=0.71 r=0.54 r=0.50 r=0.57
Flansbjer et al. (2006)
N=50 Age=58(6.4) yr Time= 6-46 mths since stroke
Knee extensors Knee flexors
Walking speed r=0.61 r=0.61 Fast speed r=0.67 r=0.65
Kim and Eng (2003)
N=20 A=61.2(8.4) yr Time= 4(3) yr since stroke
Hip flexors Hip extensors Knee flexors knee extensors Ankle dorsiflexors Ankle plantarflexors
Walking speed r=0.57 r=0.35 r=0.56 r=0.41 r=0.33 r=0.85
Hsu et al. (2003)
N=26 Age=54.2 (10.9) yr Time=10.3 (12) mth since stroke
Hip flexors + knee extensors
Walking speed r=0.75 Fast speed r=0.85
Nadeau et al. (1999)
N=16 A=47.9(15.6) yr Time=43.9(36.5) mth since stroke
Hip flexors Ankle plantarflexors
Fast walking speed r=0.84 r=0.63
Correlations in bold are significant at p<0.05
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The strength of the hip extensors was significantly associated with walking in all
of the four studies that investigated them (Bohannon, 1986; Bohannon & Walsh,
1991; Dorsch et al., 2012; Kim & Eng, 2003) and the strength of hip flexors was
significantly associated with walking in four of the six studies that investigated
them (Bohannon & Walsh, 1991; Dorsch et al., 2012; Kim & Eng, 2003; Nadeau
et al., 1999). The association between the other hip muscles groups such as
abductors, adductors and rotators and walking is less clear. The strength of the
knee extensors was significantly associated with walking in seven of the eight
studies that investigated them (Bohannon & Andrews, 1990; Bohannon & Walsh,
1991; Bohannon & Walsh, 1992; Dorsch et al., 2012; Flansbjer et al., 2006; Hsu et
al., 2003; Kim & Eng, 2003) and the strength of knee flexors was significantly
associated with walking in four of the five studies that investigated them
(Bohannon, 1986; Bohannon & Walsh, 1991; Dorsch et al., 2012; Flansbjer et al.,
2006). The strength of the ankle plantarflexors was significantly associated with
walking in three of the five studies that investigated them (Bohannon, 1986;
Bohannon & Walsh, 1991; Dorsch et al., 2012) and the strength of ankle
dorsiflexors was significantly associated with walking in all of the four studies that
investigated them (Bohannon, 1986; Dorsch et al., 2012; Kim & Eng, 2003;
Nadeau et al., 1999). The association between the other ankle muscles groups, ie,
invertors and evertors, with walking is less clear.
In summary, it appears that the strength of all the main lower limb extensors and
flexors make a contribution to walking ability. It is likely that all muscle groups
are important; however, there are few studies of strength of the hip
abductors/adductors, hip internal/external rotators or ankle invertors/evertors. It
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should be noted that most studies have tested people with stroke who have strength
ranging from very weak to strong. Given the findings of Buchner et al (1996), it is
not surprising that walking ability correlates with lower limb muscle strength
when strength deficits are present.
Spasticity
There are few studies investigating the relationship between spasticity and walking,
and those that do exist show variable results. Knee extensor spasticity has not
been shown to be associated with walking speed (Bohannon & Andrews, 1990).
Plantarflexor spasticity has been shown to be associated with both comfortable and
fast walking speed in one study (Hsu et al., 2003), while another study shown no
association of plantarflexor spasticity with walking speed (Nadeau et al., 1999).
Table 2 Studies examining the association between spasticity and walking after stroke
Study Participants Muscles (spasticity) Pearson correlation between spasticity and walking
Bohannon and Andrews (1990)
N=17 Age=59(11)yr Time=51 (42)d since stroke
Knee extensors
Speed r=-0.20 to 0.26
Hsu et al. (2003)
N=26 Age=54.2 (10.9) Time=10.3 (12)mth since stroke
Ankle plantarflexors Comfortable walking speed r=0.53 Fast speed r=0.58
Nadeau et al. (1999)
N=16 A=47.9(15.6)yr Time=43.9(36.5)mth since stroke
Ankle plantarflexors Fast speed r=0.22
Correlations in bold are significant at p<0.01
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Loss of sensation
Loss of sensation can be a disabling impairment after stroke. The majority of
studies investigating the contribution of loss of sensation to disability after stroke
have examined the upper limb, where loss of sensation is associated with poor
performance in daily activities such as matching and reaching tasks (Dukelow,
Herter, Bagg, & Scott, 2012) . In the lower limbs, there have been only two
studies investigating the direct contribution of loss of sensation to walking
disability after stroke. One study found that the lower the score for sensation, the
slower the walking (Hsu et al., 2003). In contrast, a study (Lin, Hsu, & Wang,
2012) found that ankle joint position sense did not appear to make a contribution
to walking speed in people who are able to walk independently after stroke (Table
3).
Table 3 Studies examining the association between loss of sensation and walking after stroke
Study Participants Impairments Pearson correlation between sensation and walking
Hsu et al. (2003)
N=26 Age=54.2 (10.9) Time=10.3 (12)mth since stroke
Motor and sensation score
Comfortable walking speed r=0.40 Fast speed r=0.40
Lin et al. (2012) N=35 Age=61.1(15.3)yr Time=57.1(58.2)mth
Ankle joint position sensation
Walking speed r=0.39
Correlations in bold are significant at p<0.01
Loss of coordination
There are very few studies of the relationship between incoordination and walking
after stroke. The focus of attention to date has been on the association between
coordination within the affected lower limb and walking. In one study, EMG of
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knee and ankle extensor activation was monitored during walking in 13 people
after stroke who were not selected based on muscle strength. The duration of the
stance phase was shorter on the affected side with increased co-activation of knee
and ankle extensors in people with stroke compared with healthy controls (Dyer et
al., 2014). However, in this study, no correlation was found between co-activation
levels and walking performance measured by gait speed, despite variations in
participant speeds from 0.5 to 1.3 m/s.
Within a limb, the various muscles and joints must act in a well organised and
coordinated manner to function efficiently, thus intralimb coordination is
necessary for walking. A study which analysed hip, knee and ankle intralimb
coordination in stroke population found that disrupted movement patterns existed
bilaterally at the hip/knee level, however at the knee/ankle level the disrupted
movement pattern only appeared on the affected side. Hence reduction of
intralimb coordination of the lower limb in community-dwelling stroke population
appears to be closely associated with a slower walking speed since stroke
individuals walked significantly slower (0.39 m/s) than healthy individuals (1.29
m/s) in this study (Giannini & Perell, 2005).
In summary, there is strong evidence that weakness is associated with walking
disability after stroke but little evidence that spasticity or loss of sensation are.
There are few studies that give us insight into the role that incoordination plays in
walking disability, especially in those who have made a good recovery of strength.
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NATURE OF INCOORDINATION AFTER STROKE
Incoordination after stroke may be the result of disruption of muscle activation
patterns in temporal and spatial contexts. Muscle co-activation patterns have been
found to be altered after stroke. One study aimed to quantify lower limb weakness
and coordination in chronic stroke participants in a standing position (Neckel,
Pelliccio, Nichols, & Hidler, 2006). While standing on the intact leg, participants
generated maximum isometric contractions of the affected leg about a given joint
while torques at joints secondary to the desired exertion were also recorded. In
this context, abnormal patterns of lower limb muscle activation are considered to
reflect incoordination. The stroke participants were weaker than controls in most
muscle groups and used similar strategies to generate maximum torque in seven
out of eight joint movements tested. Secondary torques were produced in
directions that were consistent with both the mechanical demands of the task and
the physical properties of the muscles of the leg. For example, when participants
were asked to generate maximum knee extension torque, secondary hip and ankle
flexion torques were produced which are consistent with mechanical demands of
the tasks. However, during maximal hip abduction, stroke participants generated
secondary torques in their hip flexors, compared to hip extensors in control
participants. In addition, stroke participants showed evidence of co-contraction of
antagonistic muscle groups, in particular, during knee extension as well as ankle
dorsiflexion and plantarflexion. Although the abnormal co-activation patterns
observed in the stroke group are likely to reflect incoordination, it is difficult to
tease out the contribution of weakness of the lower limb muscles to the observed
abnormal patterns of muscle activation.
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Disruption of muscle activation patterns in temporal and spatial contexts during
task performance may also reflect incoordination after stroke. Kautz and Brown
(1998) tested a chronic stroke group and a control group using a lower limb pedal
ergometer set up, monitoring EMG activity of affected knee flexor and extensor
muscles. During the knee flexion phase of cycling, the stroke group demonstrated
prolonged activation of knee extensors as well as early initiation and termination
of knee flexors. During the knee extension phase, the opposite was observed, i.e.,
early initiation and termination of knee extensors. Furthermore, the extent of these
abnormalities was correlated with reduced motor performance of the affected leg,
(i.e., reduced mechanical output by the affected leg). In addition, these muscle
activation abnormalities during bilateral pedalling were exacerbated when
compared with a mechanically equivalent unilateral pedalling condition (Kautz &
Patten, 2005) .
It is notable that the two tasks used to date to gain insights into the nature of
incoordination of the lower limb after stroke have been constrained spatially, i.e.,
during isometric force production tasks the joint being tested is constrained and in
ergometer pedalling the foot is constrained. Nevertheless, both timing and spatial
muscle activation abnormalities have been observed in people after stroke which is
likely to impact on the speed and accuracy of movement.
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MEASUREMENT OF COORDINATION
Clinical measures
There is difficulty in measuring coordination as it cannot be measured directly in
situations where weakness is a prominent impairment, such as after stroke. Most
measures of coordination measure activity in a manner that reflects coordination,
i.e., the accuracy and speed of the activity.
The Nine Hole Peg Test (NHPT) is commonly used to measure upper limb
coordination. It is administered by taking pegs from a container, one by one, and
placing them into the holes on the board, as quickly as possible. Once all nine
pegs are placed into the holes on the board, then the pegs are removed from the
holes, one by one, and replaced in the container. The time taken to perform this
task is measured. The task requires accuracy and speed. It is a relatively
inexpensive test and can be administered quickly and has high interrater reliability
(Oxford Grice et al., 2003), strong correlation with grip strength (Beebe & Lang,
2009; Sunderland, Tinson, Bradley, & Hewer, 1989) and normative data has been
established for healthy adults in both genders (Oxford Grice et al., 2003).
The Box and Block test is another clinical measure of upper limb activity. It is
quick and easy to administer. It aims to quantify upper limb activity limitations as
the ability to grasp, transport, and release small blocks. Individuals are asked to
move as many one-inch blocks across the center of the test box in one minute.
Performance is determined by the number of blocks moved in one minute. Times
are compared to established norms, with better performance indicated by a higher
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number of blocks moved. The Box and Block test is validated with normative data
for healthy elderly community dwelling individuals (Desrosiers, Bravo, Hébert,
Dutil, & Mercier, 1994) and considered more appropriate to measure coordination
function than the Nine Hole Peg Test after stroke (Lin, Chuang, Wu, Hsieh, &
Chang, 2010).
The Fugl-Meyer Assessment of motor recovery after stroke is a clinical and
research tool for evaluating changes in motor impairment following stroke
(Gladstone, Danells, & Black, 2002). It is divided into 5 domains: motor function,
sensory function, balance, joint range of motion, and joint pain. The motor
domain includes items measuring movement, coordination, and reflex action about
the shoulder, elbow, forearm, wrist, hand, hip, knee, and ankle. The coordination
component of the Fugl-Meyer Assessment includes tremor, dysmetria and time
taken to complete the finger-nose and heel-shin test in the upper limb and lower
limb. The coordination component of Fugl-Meyer Assessment scale has high
reproducibility (Crow & Harmeling-van der Wel, 2008), and the finger-nose and
heel-shin test are the most specific to coordination.
The Lower Extremity Motor Coordination Test (LEMOCOT) is a reliable and
valid clinical measure of lower limb coordination in healthy (Pinheiro, Scianni,
Ada, Faria, & Teixeira-Salmela, 2014) and stroke populations (de Menezes et al.,
2015b; Desrosiers, Rochette, & Corriveau, 2005). During this test, participants are
seated and instructed to touch two standardized targets placed 30 cm apart on the
floor with their big toe, as fast and as accurately as possible during a 20-second
period. The LEMOCOT score is calculated as the number of times the subject
15
accurately touches the two targets. From a study conducted in healthy individuals,
the reference values for the LEMOCOT scores of the non-dominant and dominant
lower limbs for age and sex have been established (Pinheiro et al., 2014). It is
financially affordable, easy to make and quick to set up in the clinical setting. The
movement involved to complete the LEMOCOT requires inter-joint coordination
between hip, knee and ankle joints without the need for balance.
Laboratory measures
Accelerometery has been used to measure coordination in several studies. Upper
limb acccelerometery has been used for tracking activity over time (Lang, Bland,
Bailey, Schaefer, & Birkenmeier, 2013) and it is able to distinguish between upper
limb activity in healthy and stroke participants (Bailey & Lang, 2013) as well as
between the intact and affected limb after stroke (Seitz, Hildebold, & Simeria,
2011). Accelerometery has also been used to describe the characteristics of gait
after stroke using trunk acceleration during walking (Mizuike, Ohgi, & Morita,
2009).
Whilst to date the use of accelerometery has been predominately to measure and
monitor performance over time, there may be a more specific role in measuring
coordination. Recognising the limitations of measuring coordination at the
activity level, the use of accelerometry may be a way to measure coordination of
each joint individually, i.e., a more direct measure of coordination that is not
activity based. Accelerometers can measure single joint rapid alternating
movement in a single plane and may be useful for obtaining quantitative
16
measurement of coordination that is joint specific.
AIMS AND RESEARCH QUESTIONS
In summary, there is a significant association between lower limb muscle strength
and walking performance in people after stroke especially when muscle strength
remains low. When reasonable lower limb strength has been recovered and
walking speed remains slower than normal for that age group, influence of other
impairments (such as incoordination) may become more prominent. Therefore,
the broad aim of the study was to investigate the contribution of loss of
coordination to walking disability after stroke. The specific research questions
were:
1. Is decreased lower limb coordination related to slow walking speed in
people who have regained lower limb strength after stroke?
2. Are fast reciprocal movements at the hip, knee and ankle joints measured
using an accelerometer a useful way to quantify lower limb coordination?
17
Chapter 2
METHOD
DESIGN
PARTICIPANTS
OUTCOME MEASURES
DATA REDUCTION
DATA ANALYSIS
18
DESIGN
A quantitative observational study examining the relationship between walking
speed and lower limb coordination after stroke compared with healthy adults was
conducted in a tertiary metropolitan hospital in Australia. This study was
approved by the Human Research Ethics Committee (RPAH Zone) of the Sydney
Local Health District. This study was registered in a public registry
(ANZCTR12614000856617). Informed consent was gained before data
collection.
PARTICIPANTS
Stroke participants were recruited from past or present patients who were
attending a physiotherapy service at a metropolitan hospital in Sydney, Australia.
Patients were eligible if they were between 18 and 85 years old; had a stroke at
least 6 months previously; hip, knee and ankle strength were graded ≥4/5 on
Manual Muscle Testing (Daniels, 1986); were able to walk unaided at a speed ≥
0.6 m/s; and were able to follow verbal instructions. Stratified sampling of 10-m
Walk Test speed was used to select eligible participants in order to represent a
range of walking speeds. Speed was divided into six categories (0.60-0.72 m/s;
0.73-0.84 m/s; 0.85-0.96 m/s; 0.97-1.08 m/s; 1.09-1.2 m/s; and > 1.2 m/s) and five
participants were recruited within each category. Healthy controls were recruited
from the community. Healthy controls were eligible if they were between 18-85
years old; were able to walk independently unaided ≥1.2m/s; and had no weakness
in the hip, knee or ankle muscles with manual muscle test.
19
In order to describe the groups: age, gender, and dominant side were collected for
healthy control participants; and age, gender, type of stroke, side of hemiparesis,
and months since stroke were collected for stroke participants. Measures were
taken over one session, 1-2 hours long. First, measures of walking speed were
taken, followed by measures of coordination of three lower limb joints in random
order. The order of the three joints being tested was randomised for each
participant using three opaque envelopes with either ‘Hip’, ‘Knee’, or ‘Ankle’
written inside. The researcher held the three envelopes fanned out in her hand and
each participant selected the first, second and third envelope to determine the
testing order. In addition, other motor impairments likely to affect walking in the
stroke participants (i.e., spasticity and contracture of the plantarflexor muscles and
lower limb proprioception) were measured.
Spasticity of both left and right plantarflexor muscles was measured using the
Modified Tardieu Scale (Ansari et al., 2013). The Modified Tardieu Scale was
selected because it is better at differentiating spasticity from contracture than the
Ashworth scale (Patrick & Ada, 2006). Stroke participants lay fully supported in
the supine position and were instructed to relax. The assessor dorsiflexed the
ankle to ascertain the range of movement by moving it slowly (V1), then moving it
as fast as possible within the ascertained range (V3) and rated the quality of the
muscle reaction (X) to stretch on the Tardieu scale from 0 to 4 (Table 4; appendix
B).
20
Table 4 Quality of the muscle reaction (X) to stretch on the Tardieu scale
Grade Description
0 No resistance throughout the course of the passive movement.
1 Slight resistance throughout the course of the passive movement, with no clear catch at a precise angle.
2 Clear catch at a precise angle, interrupting the passive movement, followed by a release.
3 Fatigable clonus (<10 seconds when maintaining pressure) occurring at a precise angle.
4 Infatigable clonus (>10 seconds when maintaining pressure) occurring at a precise angle.
Measurement of the plantarflexor muscle contracture was selected as simultaneous
decreased length of the gastrocnemius muscle fascicles and increased stiffness in
dorsiflexion at the joint occur in stroke survivors (Gao, Grant, Roth, & Zhang,
2009). Ankle dorsiflexion was measured with the stroke participants seated on a
chair with their feet on the ground, knees flexed to 90º and a weight of 4 kilograms
placed on top of the knee. The head of fibula, lateral malleolus and 5th
metatarsalphalangeal joint were marked using a whiteboard marker. The examiner
slid the foot back until just before the heel lifted off the ground, producing a
torque-controlled measure of the passive range of dorsiflexion which constituted
the operational definition of maximum passive ankle dorsiflexion. The researcher
then took a photo of this position in the sagittal plane (Figure 2). This procedure
was carried out for both ankles and the photos were then analysed by an assessor
blinded to the side of hemiplegia. The angle between the vertical and the lower
leg (described by a line from the lateral malleolus to the head of the fibula) was
measured. The difference in dorsiflexion range between the intact and affected
ankle in degrees was used as the measure of contracture.
21
Figure 2 Photo for dorsiflexion angle to measure plantarflexor contracture
Proprioception was measured using a validated lower-extremity matching task
(DeDominico G, 1987; Lord, Menz, & Tiedemann, 2003) which involves position
sense of knee, ankle, and big toe. Stroke participants were blindfolded and seated
with both knees at 90 degrees flexion with a vertical clear perspex acrylic sheet
(60×60×1 cm) inscribed with a protractor placed between their legs (Figure 3).
The examiner moved the unaffected knee randomly to 5 angles between 20-60
degrees flexion and instructed participants to move the affected leg to align their
great toes. Participants were told, “If the Perspex sheet was not there, your toes
would be touching”. The lines on the protractor were two degrees apart so that
matching could be made to an accuracy of one degree. The angular difference
between the big toes on the protractor was recorded. Participants practiced for two
trials prior to the five tests. The mean error in degrees in matching the two toes
for the five tests was used to determine impairment in proprioception.
22
Figure 3 Perspex sheet to measure proprioception using the matching task (Lord et al., 2003)
OUTCOME MEASURES
The primary outcomes were walking speed and lower limb coordination.
Measures of walking speed were the 10-m Walk Test and 6-minute Walk Test,
both reported in m/s. Measures of lower limb coordination were the Lower
Extremity Motor Coordination Test (LEMOCOT) reported in taps/s, and fast
reciprocal movements at the hip, knee and ankle joints measured using an
accelerometer and reported as peak frequency, peak power, total power, ratio of
peak power to total power.
The 10-m Walk Test was conducted along a 14 m straight walking track.
Participants walked unaided in bare feet and were instructed to “walk as fast as
you can but stay safe”. Familiarisation trials were allowed to ensure participants
understood the test before the time in seconds and number of steps taken were
recorded over the middle 10 metres. The time in seconds was converted to speed
(m/s).
23
The 6-min Walk Test was conducted along a 26 or 32 metre walking track in bare
feet. The instructions followed standard instructions according to Guidelines for
the 6-min Walk Test ("American Thoracic Society statement: Guidelines for the
Six-Minute Walk Test," 2002) and the distance walked was recorded. The distance
walked was converted to speed (m/s).
The Lower Extremity Motor Coordination Test (LEMOCOT) (Desrosiers et al.,
2005) was conducted with the participant seated on a standard dining room height
chair. Participants were instructed to touch and move between two targets 30 cm
apart with their big toe as accurately and as many times as they could in 20 s
(Figure 4). Each participant was given familiarisation trials prior to testing to
ensure participants understood the procedure of the test. The test began with the
unaffected side for the stroke participants and the dominant side for the healthy
control participants with standard encouragement. The number of accurate
touches was recorded by the assessor and taps/s was calculated for each side.
Figure 4 LEMOCOT
24
An accelerometer (Actigraph GT3X, sampling at 30Hz) was used to measure fast
reciprocal movements of hip flexion/extension, knee flexion/extension and ankle
plantarflexion/dorsiflexion. The participant was seated on an adjustable plinth and
the accelerometer was attached firmly below the joint being tested (i.e. the distal
portion of the anterior thigh for hip flexion/extension; the distal portion of the
anterior lower leg for knee flexion/extension and the dorsal aspect of the forefoot
for ankle plantarflexion/dorsiflexion) (Figure 5). For each reciprocal movement
(e.g. hip flexion/extension) the participant was instructed to move as fast as
possible for 10 s with verbal encouragement and no restriction of range of
movement. Each participant completed two 10 s trials at each joint with 30 s rest
between the two trials on each leg, starting with the unaffected side or dominant
side for the stroke or healthy control participants respectively. The time of testing
in hours, minutes, and seconds were recorded to enable data extraction at a later
time.
Figure 5 Accelerometer positions for ankle (left), knee (middle) and hip (right)
25
DATA REDUCTION
Raw accelerometer data was downloaded using the ActiLife 6 (ActiGraph)
software program and exported into Microsoft Excel for initial data processing and
graphing of the 10 seconds of rapid alternating movement (Figure 6). The 10 s of
data identified in excel was then imported into the MATLAB software program
for analysis. The MATLAB program (MathWorks) is a multi-paradigm numerical
computing environment and fourth-generation programming language. MATLAB
is useful for linear algebra and for solving algebraic and differential equations and
for numerical integration. The accelerometer data underwent a spectral analysis in
MATLAB and the power spectrum was graphed (Figure 7). From this, the peak
frequency, peak power and total power (Figure 7) were extracted. The ratio of
peak power to total power was then calculated. A program was developed within
MATLAB to automatically return these variables (Figure 8).
26
Figure 6 Actigraph recording from affected hip flexion/extension over 10 seconds for one stroke participant (displayed in Microsoft Excel)
27
Figure 7 Power spectrum of affected hip flexion/extension for one stroke participant.
28
Figure 8 MATLAB program to determine peak frequency, peak power, total power, ratio of peak power to total power.
Peak frequency was examined as a reflection of the temporal regularity of
acceleration of the fast reciprocal movements. Peak power was examined as a
reflection of the amplitude of the fast reciprocal movements. Ratio of peak power
to total power was examined as a reflection of the regularity of the acceleration
compared with the total acceleration. The higher the resulting number, the more
regular the acceleration, i.e., the more coordinated the fast reciprocal movements.
DATA ANALYSIS
All group results presented in text and tables are described as means (SD) or mean
differences (95% CI). Data was normally distributed, thus independent t-tests
were used to study between-group differences and presented as mean differences
(95% CI). Pearson product moment correlation analyses were used to determine
the relationship between the two coordination measures and also between the two
coordination measures and the two measures of walking speed.
A=DETREND(A,'CONSTANT');
NFFT=2^NEXTPOW2(LENGTH(A));
Y=FFT(A,NFFT)/LENGTH(A);
FS=30;
F=FS/2*LINSPACE(0,1,NFFT/2+1);
PLOT(F,2*ABS(Y(1:NFFT/2+1)))
POWER_A=2*ABS(Y(1:NFFT/2+1));
MAX_POWER=MAX(POWER_A);
AREA_POWER=SUM(POWER_A)*F(2);
RATIO_POWER_AREA=MAX_POWER/AREA_POWER;
PEAK FREQ=F(POWER A==MAX POWER);
29
Chapter 3
RESULTS
CHARACTERISTICS OF PARTICIPANTS
COORDINATION AND WALKING
RELATIONSHIP BETWEEN THE TWO MEASURES OF
COORDINATION
RELATIONSHIP BETWEEN COORDINATION AND WALKING
30
CHARACTERISTICS OF PARTICIPANTS
Thirty community dwelling people with stroke who were aged 65 years old (SD 15)
participated (Table 5). They were on average 25 months (SD 30) from the time of
stroke and 10 (33%) were right side affected. They had little spasticity,
contracture or proprioceptive loss. Thirty healthy controls who were aged 60
years old (SD 15) also participated. There was no statistically significant
difference in age between stroke and healthy participants, t(58)=2.00, p=0.18 (2-
tailed).
Table 5. Characteristics of participants
Characteristic Stroke (n = 30)
Healthy (n = 30)
Age (yr), mean (SD) 65 (15) 60 (15)
Gender, n males (%) 18 (60) 8 (27)
Time since stroke (mth) , mean (SD) 25 (30) n/a
Affected side, n right (%) 10 (33) n/a
Type of stroke, n ischaemic (%) 28 (93) n/a
Spasticity (Tardieu 0-4), mean (SD) 0.2 (0.8) n/a
Contracture (intact-affected, deg), mean (SD) 1 (6) n/a
Proprioception (intact-affected, deg), mean (SD) 3 (1) n/a
31
COORDINATION AND WALKING
Table 6 presents the walking and coordination variables for both groups. Walking
speed in both the 6-min Walk Test and 10-m Walk Test in the stroke group was
significantly slower; about 2/3 of the healthy control group. Similarly, the
affected side LEMOCOT score of the stroke group was about 2/3 of the non-
dominant side of the healthy control group, while the intact LEMOCOT score of
the stroke group was 4/5 of the dominant side of the control group.
The peak frequency of the rapidly alternating movements at the hip, knee and
ankle was similar between groups. The peak power and total power were less for
the stroke group, especially on the affected side compared to the healthy control
non-dominant side. The ratio of peak power to total power was similar between
groups.
32
Table 6 Mean (SD) of outcomes by group and mean difference (95%CI) between groups and proportion of the stroke group vs. healthy group
Characteristic Groups Difference between groups
Stroke (n = 30)
Healthy (n = 30)
Difference Proportion
Walking (10-m Walk Test)
Speed (m/s) 1.00 (0.26) 1.57 (0.31) 0.57 (0.42 to 0.72) 0.64
Step length (m) 0.52 (0.11) 0.66 (0.08)
0.14 (0.09 to 0.19)
0.79
Frequency (steps/min) 116 (17) 143 (17)
27 (18.21 to 35.79)
0.81
Walking (6-min Walk Test)
Speed (m/s) 0.97 (0.26) 1.43 (0.30)
0.46 (0.31 to 0.61)
0.68
Coordination
LEMOCOT (taps/s) (affected or non-dominant) 1.20 (0.34) 1.85 (0.36)
0.64 (0.47 to 0.83)
0.65
LEMOCOT (taps/s) (intact or dominant) 1.56 (0.37) 1.90 (0.35)
0.34 (0.15 to 0.53)
0.82
Accelerometry (peak frequency in Hz) 0.93#
Hip (affected or non-dominant)
2.4 (0.6) 2.6 (0.6) 0.2
(-0.11 to 0.51) 0.9
Hip (intact or dominant) 2.4 (0.5) 2.7 (0.6)
0.30 (0.01 to 0.59)
0.9
Knee (affected or non-dominant)
1.7 (0.5) 2.0 (0.6) 0.3
(0.01 to 0.59) 0.9
Knee (intact or dominant) 1.8 (0.5) 2.0 (0.5)
0.2 (-0.06 to 0.46)
0.9
Ankle (affected or non-dominant)
6.2 (3.4) 5.8 (3.5) -0.4
(-2.18 to 1.38) 1.1
Ankle (intact or dominant) 5.8 (3.5) 6.0 (3.6)
0.2 (-1.63 to 2.03)
1.0
Accelerometry (peak power in (deg/s2)2/Hz) 0.73#
Hip (affected or non-dominant)
0.98 (0.40) 1.37 (0.55) 0.39
(0.14 to 0.64) 0.72
Hip (intact or dominant) 1.09 (0.33) 1.29 (0.55)
0.20 (-0.03 to 0.43)
0.85
Knee (affected or non-dominant)
1.08 (0.57) 1.75 (0.74) 0.67
(0.33 to 1.01) 0.62
Knee (intact or dominant) 1.33 (0.56) 1.79 (0.68)
0.46 (0.14 to 0.78)
0.74
33
Ankle (affected or non-dominant)
0.15 (0.08) 0.23 (0.12) 0.08
(0.03 to 0.13) 0.65
Ankle (intact or dominant) 0.20 (0.09) 0.26 (0.14)
0.06 (0.00 to 0.12)
0.77
Accelerometry (total power in (deg/s2)2) 0.75#
Hip (affected or non-dominant)
0.73 (0.29) 0.90 (0.27) 0.17
(0.03 to 0.31) 0.81
Hip (intact or dominant) 0.74 (0.18) 0.91 (0.29)
0.17 (0.05 to 0.29)
0.81
Knee (affected or non-dominant)
0.65 (0.35) 1.05 (0.46) 0.40
(0.19 to 0.61) 0.62
Knee (intact or dominant) 0.75 (0.35) 1.03 (0.36)
0.28 (0.10 to 0.46)
0.73
Ankle (affected or non-dominant)
0.55 (0.26) 0.77 (0.19) 0.22
(0.10 to 0.34) 0.71
Ankle (intact or dominant) 0.69 (0.26) 0.85 (0.23)
0.16 (0.03 to 0.29)
0.81
Accelerometry (ratio of peak power to total power) 1.00#
Hip (affected or non-dominant)
1.40 (0.40) 1.53 (0.40) 0.13
(-0.08 to 0.34) 0.92
Hip (intact or dominant) 1.51 (0.36) 1.42 (0.41)
-0.09 (-0.29 to 0.11)
1.06
Knee (affected or non-dominant)
1.75 (0.35) 1.71 (0.50) -0.04
(-0.26 to 0.18) 1.02
Knee (intact or dominant) 1.84 (0.33) 1.77 (0.37)
-0.07 (-0.25 to 0.11)
1.04
Ankle (affected or non-dominant)
0.27 (0.06) 0.29 (0.1) 0.02
(-0.02 to 0.06) 0.93
Ankle (intact or dominant) 0.29 (0.07) 0.29 (0.1)
0 (-0.04 to 0.04)
1.00
#Mean proportion of stroke group versus healthy group for each accelerometry
variable averaged across the three joints.
34
RELATIONSHIP BETWEEN THE TWO WALKING TESTS
There were high correlations between the 10-m Walking speed and the 6-min
Walk Test both in the stroke group (r=0.91; p<0.01) and in the healthy controls
(r=0.91; p<0.01).
RELATIONSHIP BETWEEN THE TWO MEASURES OF
COORDINATION
The relationships between the two measures of coordination; the LEMOCOT and
the ratio of peak power to total power for the hip, knee and ankle rapidly
alternating movements are presented in Table 7. The affected hip and ankle was
significantly correlated with the LEMOCOT score in the stroke group. Similarly
in the healthy control group, the non-dominant hip and non-dominant ankle was
significantly correlated with the LEMOCOT score as well as the dominant hip.
The accelerometry knee measure was not significantly correlated with the
LEMOCOT score in either group.
35
Table 7 Correlation between two measures of coordination – LEMOCOT and hip, knee and ankle accelerometry ratio of peak power to total power – using Pearson’s correlation coefficient r (p value)
Accelerometry ratio of peak power to total power
Relationship to LEMOCOT
Stroke Healthy
Affected Intact Non-dominant
Dominant
Hip (affected or non-dominant) 0.51 (0.004)* 0.41 (0.03)*
Hip (intact or dominant) 0.33 (0.08) 0.43 (0.02)*
Knee (affected or non-dominant) -0.07 (0.73) -0.05 (0.82)
Knee (intact or dominant) -0.17 (0.37) 0.16 (0.41)
Ankle (affected or non-dominant) 0.45 (0.013)* 0.46 (0.01)*
Ankle (intact or dominant) 0.12 (0.52) 0.28 (0.14)
* p < 0.05
RELATIONSHIP BETWEEN COORDINATION AND WALKING
Walking speed in both the 10-m Walk Test and 6-min Walk Test was significantly
correlated with the LEMOCOT score for both the affected and the intact sides in
the stroke group. However, walking speed was not significantly correlated with
any of the accelerometry measures except a negative correlation occurred on the
non-dominant knee with 10-m Walk on healthy group (Table 8). In the control
group, only the LEMOCOT result for the dominant side was significantly
correlated with the 6-min Walk Test.
36
Table 8 Correlation between coordination and walking after stroke using Pearson’s correlation coefficient r (p value)
Accelerometry ratio of peak power to total power
Relationship to walk tests
Stroke Healthy
10-m Walk Test
6-min Walk Test
10-m Walk Test 6-min Walk Test
LEMOCOT (affected or non-dominant)
0.42 (0.02)* 0.50 (0.01)* 0.25 (0.18) 0.326 (0.079)
LEMOCOT (intact or dominant)
0.51 (0.004)* 0.51 (0.005)* 0.45 (0.013)* 0.50 (0.005)*
Hip (affected or non-dominant) 0.31 (0.38) 0.34 (0.06) -0.92 (0.628) -0.35 (0.855)
Hip (intact or dominant) 0.17 (0.09) 0.35 (0.06) 0.329 (0.076) 0.42 (0.021)
Knee (affected or non-dominant) -0.05 (0.79) -0.02 (0.92) -0.43 (0.018)* -0.353 (0.56)
Knee (intact or dominant) -0.07 (0.71) -0.12 (0.54) -1.06 (0.579) -0.14 (0.943)
Ankle (affected or non-dominant) 0.28 (0.14) 0.30 (0.11) 0.31 (0.872) 0.140 (0,462)
Ankle (intact or dominant) 0.04 (0.84) 0.07 (0.73) 0.09 (0.636) 0.268 (0.152)
*p <0.05
37
Chapter 4:
DISCUSSION
MAIN FINDINGS
LIMITATIONS OF THE STUDY
CLINICAL AND RESEARCH IMPLICATIONS
38
MAIN FINDINGS
This quantitative observational study has shown a strong positive relationship
between walking speed and lower limb coordination measured using the
LEMOCOT in people with stroke who have recovered good strength in their lower
limb muscles. Thus, people after stroke with good coordination walk faster and
those with poor coordination walk slower. However, using the accelerometer to
measure single joint lower limb coordination did not show a relationship between
walking speed and coordination. This finding is more likely due to the
measurement deficiencies rather than reflecting no relationship between
coordination and walking speed. Overall, the results indicate that without other
obvious motor impairments, coordination is likely to explain a good part of the
slowness in walking after stroke.
Regaining walking speed after stroke is very important to enable safe community
ambulation including crossing the street (Andrews et al., 2010). Walking speed is
a useful clinical measure (Lord, McPherson, McNaughton, Rochester, &
Weatherall, 2004) for stroke population as improvements in it are linked to better
function and quality of life (Schmid et al., 2007) and it enables categorisation of
walking ability (Bowden, Balasubramanian, Behrman, & Kautz, 2008). Perry,
Garrett, Gronley, and Mulroy (1995) developed functional walking categories in
the stroke population according to walking speed. According to the functional
walking categories, the stratified walking speeds of 0.6m/s to >1.2m/s in our study
represent a good range of community walkers from limited community ambulation
to full community ambulation.
39
The two walking measures used in our study; 10-m Walk Test and 6-min Walk
test, are commonly used in stroke rehabilitation and are free and easy to conduct
with excellent inter-rater (Wolf et al., 1999) and test-retest reliability (Flansbjer,
Holmback, Downham, Patten, & Lexell, 2005) in the chronic stroke population.
In our study, we found a high correlation (r=0.91; p<0.01) between walking speed
during the 10-m Walk Test and the 6-min Walk Test. This finding is similar to a
study by Flansbjer that shows high correlation between fast walking speed and 6-
min Walk Test (r=0.95). Together these studies show that either 10-m Walk Test
or 6-min Walk Test would be a suitable measure for quantifying walking speed in
the clinical setting.
Two measurements of lower limb coordination were used in this study, the
LEMOCOT and fast reciprocal movements at the hip, knee and ankle joints
measured using an accelerometer. The LEMOCOT is a reliable and valid measure
of coordination in healthy (Pinheiro et al., 2014) (Appendix C) and stroke (de
Menezes et al., 2015a) populations. From the study conducted in healthy
individuals, the reference values for the LEMOCOT scores of the non-dominant
and dominant lower limbs according to age and sex were established (Pinheiro et
al., 2014). In our study, the LEMOCOT scores for the healthy group were higher
than the predicted reference scores in the same age category for both the non-
dominant (127%) and dominant (125%) sides. This difference may be attributable
to differences between the Brazilian population in the Pinheiro et al. (2014) study
and the Australian population in our study.
40
A study conducted in a Brazilian stroke population (de Menezes et al., 2015b)
compared the LEMOCOT score of the affected lower limb with the healthy
reference values for the non-dominant lower limb (Pinheiro et al., 2014). Similar
to our findings, they showed reduced coordination of the affected lower limb in
well recovered stroke survivors (Fugl-Meyer lower limb section score >23), with
stroke survivors demonstrating 75% of the healthy reference values. This study
combined with the results from our study demonstrates that people who have
recovered good strength after stroke can still have coordination problems affecting
their lower limb.
Although using the LEMOCOT to measure lower limb coordination in our study
demonstrated a relationship with walking speed, our other measurement of
coordination using the accelerometer did not. The accelerometer was set up to
measure single joint coordination at hip, knee and ankle joints which has not
previously been done. Our rationale for this choice was to look at a second
method of measuring coordination that could still be used in a clinical setting.
However, our method for measuring coordination with the accelerometer only
incorporated the temporal aspect of coordination without accounting for the spatial
aspect. Therefore, our participants could minimise the displacement of each
movement at the joint tested to ensure they moved as fast as possible over the 10
second test with the encouragement of the assessor. This is confirmed from the
accelerometer data (Table 6) where on average participants in both groups were
able to move at a similar peak frequency (stroke participants’ average peak
frequency was 93% of healthy controls), however participants in the stroke groups
41
moved through a smaller amplitude (stroke participants average peak power was
73% of healthy controls).
This preference of speed over displacement can be explained by the speed-
displacement phenomenon first proposed by Fitts (Fitts, 1954). His experiments
on upper limb tapping and transfer tasks found that movement amplitudes and
speed could be varied within certain limits without much effect on performance
but that performance began to fall off outside these limits. The speed-
displacement trade-off phenomenon has been demonstrated in a study on an upper
limb tracking task in people after stroke (Ada, O'Dwyer, Green, Yeo, & Neilson,
1996), which assessed coordination using a tracking task that required skilled
interaction of elbow joint flexors and extensors to match slow and fast targets.
Performance deteriorated as the target moved faster. The slower target produced
greater spatial accuracy.
The characteristics of the measures (LEMOCOT and accelerometer) used in the
current study in terms of speed and accuracy are shown in Table 9. The
LEMOCOT requires both speed and accuracy, whereas the accelerometer
measurement in this study only requires speed.
Table 9 Compare accuracy and speed requirement on each measurement
Measurement Speed (temporal) Accuracy (spatial)
LEMOCOT Alternating movements as fast as possible
Must touch the targets
Accelerometer Alternating movements as fast as possible
No targets
42
Further to the speed- displacement trade-off phenomenon, the speed-accuracy
trade-off phenomenon was cited in a study that found changes in planned
movement time have an important effect on accuracy (Dean, Wu, & Maloney,
2007). Each participant attempted to touch small targets and earned a reward only
if they hit the target, and the amount of reward depended on the duration of the
movement. The faster the participant moved, the smaller the chances of hitting the
target. The slower the participant moved, the less reward the subject would earn
by hitting the target. Results showed that planned movement time but not actual
movement time indeed determined accuracy and concluded that planned
movement time, not actual movement time, determines the trade-off between
speed and accuracy.
The speed-accuracy trade-off is the adaptation to poor muscle control to improve
accuracy in stroke survivors. In the current study, the accelerometer set up
without a set target to demand spatial accuracy meant that the participant could
focus on moving as fast as possible at each single joint. However, the best
measures of coordination would include both temporal and spatial aspects.
Therefore, it may be useful to modify the set-up of the rapid single joint
movements to add targets so the spatial and temporal aspects of coordination are
tested.
Another possible explanation why the accelerometer measurement of coordination
did not correlate with walking speed but the LEMOCOT did is that the
accelerometer measured single joint movements, whereas both walking tests and
the LEMOCOT measured inter-joint movements.
43
LIMITATIONS OF THE STUDY
A limitation of this study is that it is a single site study with a sample of
convenience and as such may not be representative of all stroke survivors with
well recovered strength. However, our recruitment process involved stratifying
for walking speed, thus increasing the representativeness of our sample. A second
limitation of our study is that we did not get an exact measurement of participants’
strength and as such, we therefore cannot definitively confirm that strength deficits
did not have some effect on their walking speed. A study that measured the
relationship of combined leg strength of four muscle groups (knee extensor, knee
flexor, ankle plantar flexor, ankle dorsiflexor) and walking speed on 409 adults
aged 60-96 years showed a non-linear relationship between leg strength and gait
speed. Once the summed lower limb strength reached 275 Nm, any further
increase in strength did not influence walking speed (Buchner et al., 1996).
Therefore it is unlikely that minor strength deficits would influence walking speed
since the stroke participants in our study had a good recovery of lower limb
strength. Future studies incorporating an objective measure of all motor
impairments including muscle strength could help to tease out the relative
contributions of coordination and other impairments to slowness in walking speed.
CLINICAL AND RESEARCH IMPLICATIONS
The LEMOCOT is financially affordable, easy to make and quick to set up in the
clinical setting. The movement involved to complete the LEMOCOT requires
inter-joints coordination between hip knee and ankle joints which is similar to the
majority of mobility tasks in everyday living such as walking. As such, the
reliability and validity of the LEMOCOT demonstrated in other studies and the
44
positive findings from our study would suggest it is also a useful measurement tool
for lower limb coordination in a clinical setting and for research studies in the
stroke population. In future research, the LEMOCOT can be used as an outcome
measure to reflect the effectiveness of an intervention to improve coordination in
stroke population.
It has been proven that interventions involving repetitive task-specific practice
and/or auditory cueing appear to be the most promising approaches to restore gait
coordination (Hollands, Pelton, Tyson, Hollands, & van Vliet, 2012) and walking
speed in the stroke population. As such, just training single joint coordination
may not be effective to improve walking. However, given the issue with our setup
of the accelerometer, it may still be worthwhile investigating the role of single
joint lower limb coordination training with temporal and spatial targets in people
after stroke with good recovery of strength. In future studies, it may be
worthwhile to investigate and compare the measurement and training between
inter-joint and single joint coordination in stroke population.
Walking speed is an independent predictor of survival in older adults (Studenski et
al., 2011) with people over 65 years with slower walking speeds having a higher
mortality rate (Liu et al., 2016). Therefore, improvement in walking speed is
important for community ambulation in people with stroke to maintain their ability
to live independently and their participation in the community and to reduce the
risk of death. Our research suggests that clinically in order to improve walking
speed; rehabilitation needs to include coordination exercises that challenge both
temporal and spatial parameters. An example of an exercise which achieves this is
45
using markers such as footprints to set the spatial parameters (i.e. step length) and
auditory cues such as a metronome to set the temporal parameters (i.e. cadence)
(Figures 9 and 10). A recent systematic review incorporating seven studies found
training strategies incorporating cueing of cadence via a metronome or music
demonstrated greater effect than walking training alone for increasing walking
speed (by 0.23 m/sec); stride length (by 0.21 m); cadence (by 19 steps/min) and
symmetry (15%) (Nascimento, de Oliveira, Ada, Michaelsen, & Teixeira-Salmela,
2015).
Figure 9 Footprints placed on the ground provide targets to train spatial accuracy.
Figure 10 Use of metronome to train cadence (picture from www.physiotherapyexercise.com)
46
Newer interventions/technologies such as treadmills with virtual reality combined
enable both temporal and spatial cues to be trained simultaneously (e.g.
(Timmermans et al., 2016); Figure 11). Nevertheless, the cost of this advanced
technology may not be affordable in some clinical settings. Future studies can be
conducted to evaluate the effectiveness of incorporating both spatial and temporal
cues either using basic strategies or newer technologies in walking training in
people after stroke.
Figure 11 Exercises of treadmill-based C-Mill therapy with visually guided stepping to a sequence of stepping targets (Timmermans et al., 2016)
In conclusion, this study has shown a strong positive relationship between walking
speed and lower limb coordination measured using the LEMOCOT in people with
stroke who have recovered good strength in their lower limb muscles.
Incorporating coordination exercises with temporal and spatial elements in
walking retraining may be able to improve walking speed and improve ability to
participate in the community in people with stroke. The use of footprints,
metronome or the innovative option of the treadmill with virtual reality are good
examples for providing temporal and spatial cues during walking training and
warrant further investigation.
47
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APPENDIX A
ETHICAL CONSIDERATIONS
Ethic approval from Ethics Review Committee (RPA Zone) Sydney Local Health District, NSW
Telephone Script as initial contact to potential participants
Participant information sheet (Healthy Control Group)
Participant information sheet (Stroke Survivor Group)
Participant Consent forms (Healthy Control)
Participant Consent forms (Stroke Survivor)
Demographics Data Sheet
Data Summary sheet
Data Collection form (Healthy Control)
Date Collection form (Stroke Survivor)
53
ETHICAL CONSIDERATIONS
Ethical approval was obtained from Ethical Review Committee (RPA Zone) of Sydney Local Health District, NSW for this study.
Informed consent
The procedures and measurements involved were explained to potential participants and they were given a copy of the Participant Information Sheet. They were asked whether they wished to participate and the right to withdraw at any time was explained to all participants. Written informed consent was obtained from the participants prior to admission to the study.
Participants information
Information collected from the participants from the hospital clinical records. Information was entered a spreadsheet in a coded, de-identified format. All paper information was stored in a locked cupboard in the office at Physiotherapy Department at Royal Prince Alfred Hospital.
Risks and benefits to participants
All participants were informed that there may be no direct benefit to participants as a result of participating in this study. On completion of the measurements, participants were provided a brief explanation of the results. There were no payments for participating in this study.
Privacy, confidentiality and disclosure of information
All information about participants in the studies remained confidential. All paper information and measurement sheets were stored at the physiotherapy department in a locked filing cabinet in a locked office. All information entered on computer was stored on a password protected computer. On completion of data collection, all data was de-identified.
54
Ethic approval from Ethics Review Committee (RPA Zone) Sydney Local Health District, NSW
55
56
57
58
59
Telephone Script as initial contact to potential participants
60
Participant information sheet (Healthy Control Group)
61
62
Participant information sheet (Stroke Survivor Group)
63
64
Participant Consent forms (Healthy Control)
65
Participant Consent forms (Stroke Survivor)
66
Demographics Data Sheet
67
68
Data Summary sheet
69
Data Collection form (Healthy Control)
70
Date Collection form (Stroke Survivor)
71
APPENDIX B
TARDIEU SCALE
For each muscle group, reaction to stretch is rated at a specified stretch velocity with two
parameters, X and Y, however, in this study only X parameters has been used for analysis.
Velocity of stretch
V1: As slow as possible (minimizing stretch reflex).
V2: Speed of the limb segment falling under gravity.
V3: As fast as possible (faster than the rate of the natural drop of the limb segment under
gravity).
Vl is used to measure the passive range of motion. Only V2 or V3 are used to measure
spasticity.
Quality of the muscle reaction (X)
0: No resistance through the course of the passive movement.
1: Slight resistance throughout the course of the passive movement with no clear catch at a
precise angle.
2: Clear catch at a precise angle, interrupting the passive movement, followed by release.
3: Fatigable clonus (<10 s when maintaining pressure) occurring at a precise angle.
4: Unfatigable clonus ( >10 s when maintaining pressure) occurring at a precise angle.
Angle of muscle reaction (Y)
Measured relative to the position of minimal stretch of the muscle (corresponding to angle
0) for all joints except hip, where it is relative to the resting anatomic position.
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APPENDIX C
Nomogram for the LEMOCOT scores of the dominant and non-dominant lower limbs based on age and sex (Pinheiro et al.)