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)


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)


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


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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


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#


0.98 (0.40) 1.37 (0.55) 0.39

(0.14 to 0.64) 0.72


0.20 (-0.03 to 0.43)

0.85


1.08 (0.57) 1.75 (0.74) 0.67

(0.33 to 1.01) 0.62


0.46 (0.14 to 0.78)

0.74

33


0.15 (0.08) 0.23 (0.12) 0.08

(0.03 to 0.13) 0.65


0.06 (0.00 to 0.12)

0.77

Accelerometry (total power in (deg/s2)2) 0.75#


0.73 (0.29) 0.90 (0.27) 0.17

(0.03 to 0.31) 0.81


0.17 (0.05 to 0.29)

0.81


0.65 (0.35) 1.05 (0.46) 0.40

(0.19 to 0.61) 0.62


0.28 (0.10 to 0.46)

0.73


0.55 (0.26) 0.77 (0.19) 0.22

(0.10 to 0.34) 0.71


0.16 (0.03 to 0.29)

0.81

Accelerometry (ratio of peak power to total power) 1.00#


1.40 (0.40) 1.53 (0.40) 0.13

(-0.08 to 0.34) 0.92


-0.09 (-0.29 to 0.11)

1.06


1.75 (0.35) 1.71 (0.50) -0.04

(-0.26 to 0.18) 1.02


-0.07 (-0.25 to 0.11)

1.04


0.27 (0.06) 0.29 (0.1) 0.02

(-0.02 to 0.06) 0.93


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)*


* 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)

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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.

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Ethic approval from Ethics Review Committee (RPA Zone) Sydney Local Health District, NSW

59

Telephone Script as initial contact to potential participants

60

Participant information sheet (Healthy Control Group)

62

Participant information sheet (Stroke Survivor Group)

64

Participant Consent forms (Healthy Control)

65

Participant Consent forms (Stroke Survivor)

66

Demographics Data Sheet

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Data Summary sheet

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Data Collection form (Healthy Control)

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Date Collection form (Stroke Survivor)

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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.)

the relationship between lower limb coordination …

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