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The Use of Visual Feedback to Improve Temporal Gait Asymmetry Post-Stroke
by
Jessica Noelle Powers
A thesis submitted in conformity with the requirements for the degree of Master of Science
Rehabilitation Sciences Institute University of Toronto
© Copyright by Jessica Noelle Powers 2018
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The Use of Visual Feedback to Improve Temporal Gait Asymmetry Post-
Stroke
Jessica Noelle Powers
Master of Science
Rehabilitation Sciences Institute University of Toronto
2018
Abstract
Temporal gait asymmetry (TGA) is common post-stroke and particularly resistant to
conventional therapy. Therefore new rehabilitation approaches are required. One approach
may be visual feedback (FB) during gait training. Optimal parameters for providing visual FB
during motor skill acquisition are well established for healthy adults but remain largely
unknown for motor learning in people with stroke. This study aimed to determine the effect of
visual displays and frequencies for visual FB about TGA during an overground walking practice
session and its effects during a retention session. Participants received feedback at 50% or
100% frequency in one of two display formats (A and B). The largest effect sizes were seen in
the group that received Display B at 100%, and 50%. Individuals that received FB during practice
showed significant changes in TGA at the retention while those individuals who did not receive
feedback showed no changes at retention.
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Acknowledgments
First, I would like to thank my thesis supervisor, Dr. Kara Patterson, at the Rehabilitation
Sciences Institute at the University of Toronto. Her encouragement, understanding and
seemingly limitless patience with my learning curve made this journey possible. Thank you to
my co-supervisor, Dr. Dina Brooks, for sharing her optimism and experience. Thank you both for
your support in perusing a career in physiotherapy and for helping make it a reality. Thank you
to my committee members, Dr. Avril Mansfield and Dr. George Mochizuki, for sharing their
unique insights and pushing me to broaden my knowledge. I am also grateful to my lab mates
who were always willing to lend a hand and share an idea.
To my friends and family, to whom I cannot express enough gratitude. For celebrating my
successes, no matter how small, and giving me the courage to tackle daunting challenges. For
lending an ear and talking it out. For finding me when I was lost and reminding me where I was
heading. I am especially grateful to my parents, for their unfaltering confidence in me.
Finally, a special thanks to the participants who so graciously volunteered their time to my
research and whom this work would not be possible without.
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Table of Contents
Contents
Acknowledgments........................................................................................................................... iii
Table of Contents ............................................................................................................................ iv
List of Tables ................................................................................................................................... vi
List of Figures ................................................................................................................................. vii
List of Appendices ......................................................................................................................... viii
List of Abbreviations ....................................................................................................................... ix
Overview .....................................................................................................................................1
Literature Review ........................................................................................................................3
2.1 Stroke Overview ...................................................................................................................3
2.1.1 Stroke in Canada ......................................................................................................3
2.2 Course of Recovery ..............................................................................................................4
2.2.1 General Course of Recovery ....................................................................................4
2.2.2 Recovery of Gait .......................................................................................................4
2.3 Gait .......................................................................................................................................5
2.3.1 Normal Gait ..............................................................................................................5
2.3.2 Neural Control of Walking .......................................................................................7
2.3.3 Common Gait Deviations Post-Stroke .....................................................................8
2.4 Motor Learning ................................................................................................................. 17
2.4.1 Motor Learning in Healthy Adults ......................................................................... 20
2.4.2 Motor Learning in Adults with Stroke ................................................................... 25
2.5 Summary ........................................................................................................................... 30
2.6 Study Objectives ............................................................................................................... 31
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Study: Use of Visual Feedback to Improve Temporal Gait Asymmetry Post-Stroke ............... 32
3.1 Introduction ...................................................................................................................... 32
3.2 Methods ............................................................................................................................ 35
3.2.1 Design .................................................................................................................... 35
3.2.2 Participants ........................................................................................................... 35
3.2.3 Testing Protocol .................................................................................................... 36
3.2.4 Baseline Measurements ....................................................................................... 36
3.2.5 Gait measures ....................................................................................................... 37
3.2.6 Overground walking training with feedback ........................................................ 37
3.2.7 Visual Feedback about TGA .................................................................................. 38
3.2.8 Data Processing ..................................................................................................... 41
3.2.9 Data Analysis ......................................................................................................... 41
3.3 Results ............................................................................................................................... 44
3.4 Discussion.......................................................................................................................... 51
3.5 Conclusion ......................................................................................................................... 55
General Discussion ................................................................................................................... 56
4.1 Limitations......................................................................................................................... 62
4.2 Future directions ............................................................................................................... 64
References .................................................................................................................................... 65
Appendix A .................................................................................................................................... 83
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List of Tables
Table 3.1 Participant characteristics ………………………………………………………………………………………. 53
Table 3.2 TGA outcomes at retention ………………………………………………………………………………………57
Table 3.3 Spatiotemporal gait parameters of individuals who showed change in TGA at
retention gait .............................................................................................................................. 58
Table 3.4 Effect size of feedback conditions …………………..……………………………………………………… 59
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List of Figures
Figure 3.1 Phases of the gait cycle …………………………………………………………………………………………. 17
Figure 3.2 Visual feedback display A ………………………………………………………………………………………. 49
Figure 3.3 Visual feedback display B ………………………………………………………………………………………. 49
Figures 3.4 Single subject analysis of motor learning curves ………………………………………………….. 55
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List of Appendices
Participant instructions for visual feedback display A and B
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List of Abbreviations
ADL Activities of Daily Living
CMSA Chedoke McMaster Stroke Assessment
CPG Central Pattern Generator
DLS Double Limb Support
EMG Electromyography
GC Gait Cycle
GRF Ground Reaction Force
HRQoL Health Related Quality of Life
HS Heel Strike
IC Initial Contact
IQR Interquartile Range
KP Knowledge of Performance
KP Knowledge of Results
LE Lower Extremity
MoCA Montreal Cognitive Assessment
NDT Neurodevelopmental Technique
NIHSS National Institute of Health Stroke Scale
RAS Rhythmic Auditory Stimulation
SD Standard Deviation
x
SLS Single Limb Support
TGA Temporal Gait Asymmetry
TO Toe Off
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Overview
Mobility challenges are common after stroke, with almost 80% of people still experiencing
impaired walking function 3 months post-stroke1. Recovery of walking function is one of the
most commonly stated goals of patients2, however 22% of those who experience walking
impairments do not regain any walking function by the end of rehabilitation3. Many of those
that do regain walking function have continued gait related impairments or deviations such as
decreased velocity4,5, difficulty with balance6, increased energy cost7 and spatiotemporal gait
asymmetry 8. These persistent gait impairments can contribute to a decline in mobility9,10,
increased dependence and reduced health related quality of life (HRQoL)11.
Rehabilitation is often discontinued for patients when they no longer exhibit motor
improvements, or “plateau”, and further improvements are deemed unlikley12. However,
improvements seen in chronic stroke patients with the use of novel interventions13–17 rebuts
the supposed motor recovery plateau. It has been suggested that the plateau is not indicative
of limited capacity for recovery, but rather a neuromuscular adaptation to motor
interventions18. New interventions are required to address persistent gait impairments and
enable patients to overcome the motor recovery plateau. One approach may be to apply the
principles of motor learning to post-stroke gait rehabilitation.
Principles of the use of augmented visual feedback to facilitate motor skill learning were
applied in this study. Feedback is often provided during rehabilitation therapy, however, not in
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the most effective format19. The purpose of this study was to investigate the effects of visual
feedback on post-stroke gait, specifically, temporal gait asymmetry (TGA). This study employed
a motor learning experimental paradigm that included a single acquisition session where visual
feedback was given during overground walking practice and a retention session 24 hours later
where TGA was assessed during overground walking without feedback.
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Literature Review
2.1 Stroke Overview
2.1.1 Stroke in Canada
Stroke is the leading cause of disability in Canada, with 50,000 new strokes each year20. There
are 412,000 survivors living with the effects of stroke, and this is expected to rise to between
666,000 and 738,000 by the year 203821 due to Canada’s growing and aging population. Every
individual experiences the effects of stroke differently, which can manifest both physically and
cognitively. Some physical effects of stroke include muscle weakness, spasticity, and loss or
change in sensation causing challenges with movement, mobility, and balance22. Cognitive
effects of stroke can include difficulty with thinking, memory and emotions23,24. Rehabilitation
after stroke is aimed at mitigating physical and cognitive impairments, and increasing
independence and health related quality of life (HRQoL)25.
Despite rehabilitation efforts, many survivors of stroke never fully recover. Approximately 36%
experience significant disability related to stroke impairments that persists 5 years post-stroke26
and more than 40% require ongoing assistance with activities of daily living (ADLs)27. With the
estimated number of individuals experiencing the effects of stroke in Canada expected to rise
substantially over the next years these outcomes are of increasing concern.
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2.2 Course of Recovery
2.2.1 General Course of Recovery
Recovery after stroke is the re-emergence of structures and functions as they existed previous
to neurologic injury. Recovery is highly individual and is related to a variety of factors such as
site and size of the lesion in the brain, severity of initial motor impairment, and age28–33.
Recovery is generally rapid during the sub-acute phase and tapers to a plateau in the chronic
phase34–38. The largest improvements are seen during the first 3 months following the stroke.
These rapid improvements are seen in all areas including upper extremity function, walking and
ability to perform ADLs39–41. These improvements continue up to 6 months although at a less
significant rate. At around six months, recovery begins to plateau with little if any
improvements seen, especially after one year3,37–39. However, there is evidence from numerous
studies of improved function in the chronic phase of stroke recovery42–45 and the capacity for
continued neuroplasticity has been demonstrated46. There are a variety of interventions
administered at least one year post-onset that have improved daily functioning 47–49, and
motor recovery of the upper and lower extremities (LE)13–16,18.
2.2.2 Recovery of Gait
Impaired walking function is common after stroke, with half of survivors having no walking
function immediately following stroke3and almost 80% still experiencing walking dysfunction
after the first 3 months1. Improvement in walking, as measured by the Barthel index for
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walking, occurs in the first 11 weeks3, with little change seen after 6 months of onset3,39,41,50.
The speed of gait recovery can be linked to the initial degree of paresis and walking ability (e.g.
independence, velocity, endurance), with faster recovery seen in those with milder paresis and
greater walking ability in the first week post-stroke3,10,51,52. Of those whose walking function
was affected at onset, 64% will regain walking independence3,53, 14% will be able to walk with
assistance, and 22% will not recover walking function3 by the end of rehabilitation. It is
important to note that the studies reporting these outcomes used functional scales, which
measure level of assistance required while walking (e.g. dependent versus independent), and
do not consider other features of gait impacted by stroke, such as velocity, endurance,
symmetry and loading. While it is believed that very little improvement is seen after 6 months,
there is increasing evidence for improving gait velocity and endurance at the chronic stage9,54,55.
2.3 Gait
2.3.1 Normal Gait
Gait is a cyclical and complex sequence of movements that carries an individual from one point
to another by propelling the body forward, while maintaining upright stability 56. Human
locomotion is dependent on stance stability despite constantly changing posture57, propulsion
through muscle and joint activation58,59, shock absorption to minimize impact60, and energy
conservation61, for performance efficiency. These four variables enable locomotion to occur
through alternation of body weight support and limb propulsion between the limbs. A single
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set of support and propulsion actions of one limb is called a gait cycle(GC)56.
A GC can then be further divided into phases: stance and swing. A GC can be analyzed spatially
or temporally. Spatial characteristics of gait include step length (i.e., the distance traveled by
one limb in a step) and step width (i.e., distance between two feet in a step), but for the
purposes of this thesis, the primary focus will be on temporal factors. Stance is the period in
which one limb maintains contact with the ground to support the body, while swing is the
period in which one limb moves through space in a forward motion to advance position. Swing
begins when the foot is lifted and the toes no longer have any contact with the ground,
commonly referred to as toe-off (TO), and ends when the same foot makes contact again with
the heel, commonly referred to as heel strike (HS) or initial contact (IC). The distance between
HS of one foot to HS of the opposite foot is the step length, while stride length is the distance
from HS of one foot to the next consecutive HS of the same foot. Stance begins at IC and ends
at TO, during which ground contact is maintained56.
The stance phase can be further divided into three parts. At the time of HS, the opposite limb
still has contact with the ground to facilitate the transfer of weight to the other limb. This is a
period where the body is supported by both limbs called initial double limb support (DLS). Once
support has been transferred to the limb that has just initiated floor contact, the opposite limb
enters swing phase, and no longer has contact with the floor. At this point, the body is being
supported by only one limb, and is called single limb support (SLS). When the opposite limb
completes swing phase and HS occurs, this marks the end of SLS and the beginning of DLS56.
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Figure 3.1 Phases of the gait cycle. Adapted from Bajawa ZH, Wootton RJ, Warfield CA.
Principles and Practice of Pain Medicine, 3rd Edition. (McGraw Hill Education, 2016).62
2.3.2 Neural Control of Walking
The underlying processes controlling human locomotion are believed to be a combination of
central pattern generators (CPG), descending neural pathways and peripheral sensory
feedback63,64. However, much of this knowledge was ascertained from the study of animal
models, leaving the exact nature of human locomotor control debated64–67. The CPG is a
neuronal circuit within the spinal cord that operates independently of descending neural input
able to produce the rhythmical motor patterns through flexor and extensor motor neuron
activation69. While the CPG is able to produce the rhythmical basis of locomotion, supraspinal
and peripheral sensory information is required for creating a flexible motor pattern63,64, capable
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of spatial and temporal adaptation to extrinsic environmental factors. It is believed that the
importance and role of this descending and peripheral information for the control of
locomotion is greater in humans than the animal models of CPG frequently studied64.
Neural control of walking can be impacted by lesions to various structures of the central
nervous system that disrupt the automaticity and patterning of gait. Injury to the motor cortex,
and descending motor pathways can result in paresis and hypertonicity70, while injury to
somatosensory cortex can interfere with sensory feedback71. These neurologic injures lead to
the gait deviations observed in post-stroke gait.
2.3.3 Common Gait Deviations Post-Stroke
Factors that contribute to gait deviations post-stroke have been characterized using
electromyography (EMG), kinetic, kinematic, and spatiotemporal measures. The most widely
reported spatiotemporal measure of gait after stroke is velocity, which is typically decreased
compared to neurotypical gait72–75. This slower gait is also characterized by decreased cadence
and longer gait cycle74–77. Other spatiotemporal characteristics of gait such as step length, swing
and stance time, and double support time may change after stroke, and can exhibit interlimb
differences77.
The earliest EMG studies of post-stroke gait identified atypical motor performance of the
paretic and nonparetic side, with marked decrease in EMG activation on the paretic side,
increased activation on the nonparetic side, and atypical timing of EMG onset78–80. As is the
case with many characteristics of stroke, there is great interindividual variation in muscle
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activation patterns, although there are some patterns that are exhibited more often74,80,81.
Knutsson and Richards80 attempted to identify common EMG patterns across individuals,
resulting in three classifications. Type I (spastic gait) demonstrates an early activation of the calf
muscles of the paretic limb during stance phase due to hyperactive stretch reflexes as the knee
is extended at initial contact. Type II (paretic gait) is evidenced by a decrease in muscle
activation of two or more muscle groups of the paretic limb. Type III is characterized by atypical
co-activation of muscle groups, but not an increase or decrease in muscle activity. While these
classifications are limited in profiling the diversity of post-stroke gait82, the general pattern
holds that atypical amplitude and timing are common characteristics of post-stroke EMG
activity74,81,83–85.
Post-stroke gait also exhibits kinetic and kinematic abnormalities, compared to that of a healthy
individual. Slower gait in healthy individuals is marked by smaller joint excursions and force
production86,87 and similar deviations are seen in the typically reduced speed of post-stroke gait
88–90. The most common kinematic deviations of the paretic limb are increased ankle plantar
flexion at initial contact, knee hyperextension at weight acceptance and stance phase,
decreased knee flexion and dorsiflexion through swing phase, hip hiking or circumduction, and
increased external rotation of the hip and ankle74,83,89,91. Furthermore, ground reaction force
(GRF) is significantly reduced in the paretic limb compared to the nonparetic limb92,93, where
the reduced propulsive force of the paretic limb translates to smaller steps lengths of the
nonparetic limb94. While the deviations described are common in post-stroke gait, individuals
may exhibit some or none of these deviations, due to interindividual differences.
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2.3.3.1 Temporal Gait Asymmetry
Asymmetry is a prominent feature of post-stroke gait. Theoretically, asymmetry can be
expressed using almost any bilateral variable of interest including kinematic variables (e.g. peak
knee flexion angle of the paretic and nonparetic limbs), kinetic variables (e.g. GRF of the paretic
and nonparetic limb during stance) and spatiotemporal variables (e.g. step length). Central to
this thesis is temporal gait asymmetry (TGA).
Gait asymmetry has been described as a ‘compensation’ for the motor impairment that enables
gait function95,96. Compensation is considered the emergence of new motor patterns of end
effectors (i.e., body parts such as the leg and foot) to carry out functions executed by different
motor patterns previous to stroke97 Motor impairment of the LE is associated with TGA6,92
suggesting that at least in some cases it is a compensation adopted by the individual in order to
have some walking function. However, motor impairment it does not explain all the variance
observed in TGA post-stroke and there are examples of the presence of TGA in individuals with
higher level of motor recovery. Thus, it is plausible that factors other than motor impairment
contribute to the TGA and this may be particularly true in the specific case of asymmetrical
walkers with milder motor impairment 5. For example, TGA may be directly caused by brain
lesions that impair the ability to produce regular and rhythmic timing of gait events. Therefore,
while TGA may be a compensation for motor impairment of the affected leg in some
individuals, in others it may be a direct result of impairments to the systems involved in
locomotor pattern generation. Therefore, for the purposes of this thesis, TGA will be referred to
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as a g ‘deviation’, rather than a ‘compensation’, since TGA is a deviation from a typical gait
pattern, that may be a result of compensation for impairment or of impairments directly.
Temporal asymmetry, the specific focus of this thesis, is characterized by unequal amounts of
time spent in specific phases of gait (e.g. swing time, stance time) between right and left limbs.
Temporal asymmetry can be quantified by ratios of the left and right limb values. There are also
established cutoffs to determine whether a ratio value represents asymmetric gait as compared
to healthy older adults98. For example, a swing time ratio (larger value/smaller value) of greater
than 1.06 is considered temporally asymmetric98. Other common temporal deviations include
greater stance time and SLS of the nonparetic limb, and increased overall DLS with terminal DLS
of the paretic limb being greater74–77. One potential explanation for this temporal asymmetry is
motor impairment of the affected limb that leads to difficulty executing swing and,
consequently, increased amount of time spent in the swing phase77,92. Other studies have found
that spasticity of the affected ankle plantarflexors is a primary determinant of TGA, suggesting
that greater hip and knee flexor movements are used during the swing phase to accomplish
foot clearance, consequently increasing the duration of swing of the paretic limb92,99. Another
possible factor causing temporal asymmetry is sensory impairment of the affected limb, which
suggests that better sensation leads to increased confidence in landing the affected foot
terminating swing phase, and therefore shorter swing phase of the affected limb, compared to
those who have more severe sensory impairments92.
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2.3.3.2 Consequences of Temporal Gait Asymmetry
Temporal gait asymmetries can lead to secondary consequences that can impede mobility and
decrease independence. One consequence may be musculoskeletal injury of the nonparetic
lower limb. The high ground reaction forces repetitively applied through the unaffected limb
has been positively correlated with TGA 68. The nonparetic limb also spends more time in SLS
and less time in swing than the paretic limb, consequently, more time is spent weightbearing
on the nonparetic limb 68,75,100. One possible explanation for this could be a reluctance to bear
weight on the paretic limb, leading to the increased swing of the nonparetic limb and delayed
initiation of foot contact, to reduce the amount of time spent on the paretic limb in SLS,
subsequently, more time is spent in stance on the nonparetic limb than the paretic limb. This
type of repetitive and prolonged loading associated with asymmetric gait has been linked to
joint pain and degeneration. Findings in the unilateral amputee population, who also show
asymmetrical gait by nature of their prosthetic limb, show that spending more time on the
intact limb than the prosthetic limb subjects the intact limb to greater stress that may lead to
joint pain and degeneration of the limb 101,102. Improved gait symmetry may reduce repetitive
loading in the unaffected limb, and decrease the risk of developing musculoskeletal injuries.
Temporal gait asymmetry also has repercussions for the affected limb. Decreased bone
density, or osteoporosis, may occur after prolonged non-use of the affected limb103. Load
bearing of the limb helps to maintain bone density and bone health. The reduced muscle
activity and decreased weight bearing activity of the affected limb leads to loss of bone mineral
density and increased risk of injury 8,103–105 with the risk of hip/femoral neck fracture increasing
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two fold after stroke106. Reducing immobility of the affected limb and improving symmetrical
weight-bearing during walking could reduce bone mineral density loss and reduce the risk of
injury103,107,108.
Increased energy expenditure, despite decreased capacity for ambulation7,109, is another
consequence of post-stroke gait asymmetry. A study examining the metabolic costs of walking
in healthy individuals indicates that, compared to symmetrical walking, there is a substantial
increase in energy costs when asymmetrical walking is induced using a dual-belt treadmill109.
Similarly, another study on energy expenditure during walking showed that, compared to
healthy controls where gait is automatic, individuals with stroke had a higher oxygen uptake
during walking and therefore a greater demand for energy during asymmetric gait7. This
discrepancy in energy expenditure indicates that the altered gait pattern as a result of a stroke
compromises the efficiency of ambulation 7. Furthermore, a study in the amputee population
with asymmetric gait showed that increased gait symmetry resulted in decreased energy
demands 110. It is reasonable to apply this same concept to individuals with asymmetric gait
due to stroke. To reduce energy inefficiencies due to TGA, new methods of improving gait
symmetry are required.
Furthermore, post-stroke gait asymmetries are associated with challenges with dynamic
balance control111. Balance and gait deficits are linked to post-stroke falls112–114 and loss of
balance while walking is reported as the most common activity when the fall occured91.
Consequences of post-stroke falls can include reduced ambulation, increased sedentary
behavior and decreased community participation115,116. To decrease the risk of these secondary
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consequences, gait rehabilitation focusing on improved symmetry requires more careful
consideration.
2.3.3.3 Treatment of Gait Disorders Post-Stroke
Gait Training
Post-stroke rehabilitation has a large focus on gait training, with 25% to 45% of physical therapy
time spent practicing gait, especially at the very beginning of rehabilitation117. Common
interventions used to improve walking ability post-stroke are Bobath framework,
neurodevelopmental techniques (NDT), strength training, treadmill training, and intensive
mobility training118. The most commonly used measures of functional ambulation are self-
selected walking speed and endurance, measured by the six minute walk test (6MWTor similar
test), therefore the effectiveness of many gait training programs are evaluated based on these
measures. Bobath and the related yet separate NDT approaches are commonly used in stroke
rehabilitation119–121 and while it does not show superior outcomes compared to other
interventions122–124, it has shown improvements in temporal gait parameters such as SLS
time125. Strength training of the lower extremity (LE) is proven to increase muscle strength of
the limb, which has benefits such as improving bone mineral density, but does not have a direct
effect on improved gait42,45,126. A number of meta-analyses examining the effectiveness of
treadmill training (with or without body weight support) largely agree that there is no
significant difference in gait improvements when compared to or combined with other
physiotherapy interventions (e.g. stretching, strengthening, overground gait training)118,127.
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However, some studies of treadmill training in people with chronic stroke demonstrated
increased gait velocity after training128,129. Finally, there is evidence suggesting that intensive
mobility training (which includes a combination of aerobic training, functional strength training
and dynamic walking tasks) is a superior approach to improving endurance and walking speed
post-stroke and has additional benefits of improving cardiorespiratory fitness, bone density,
balance confidence and strength 118,130–132.
Common gait training practices, show some improvements in gait ability after stroke, though
the target is often increased velocity and endurance123,133. These interventions are largely
ineffective at improving other temporal gait parameters, with the majority of asymmetric
stroke patients showing no improvement in these measures during rehabilitation134,135.
Although some measures of functional ambulation improved with conventional rehabilitation
strategies, given the potential long-term consequences of persistent temporal asymmetry, a
greater focus should be placed on improving this particular post-stroke gait deviation.
Interventions for TGA
Conventional rehabilitation does not result in improved TGA136, though some experimental
interventions have shown promise for improving TGA. One such intervention is split belt
treadmill training 135,137–139, where patients can practice walking with the belts at different
speeds, to facilitate the movement of the paretic limb. While this treatment has shown to be
effective for short-term improvement in spatiotemporal gait parameters (up to 3 months post-
treatment)137, the equipment is not readily accessible in clinical settings due to high costs140.
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Rhythmic auditory stimulation (RAS) is another therapeutic approach that shows improvement
in gait asymmetries is rhythmic auditory stimulation(RAS)123,141. This treatment shows improved
asymmetry in spatial characteristics like step length, velocity, as well as temporal variables such
as swing time, although not to the same extent as spatial variables123,141. For example, velocity
and step length improved by 164% and 88%, respectively, while swing symmetry improved by
32% six weeks after RAS training. Although it is important to note that the control group, which
received NDT/Bobath therapy, also showed 16% improvement in swing symmetry at six
weeks123. This finding highlights the view that temporal asymmetry can be changed, however
interventions that specifically target this gait parameter may be required.
Advances in treatment of post-stroke asymmetrical gait are evident123,135, however, they have
limitations: feasibility in a clinical setting, lack of effectiveness for altering temporal
characteristics, compared to spatial characteristics and, does not address underlying factors.
New approaches that address these issues are necessary. One option for improving and
creating new interventions for TGA is through the application of motor learning principles,
specifically augmented feedback. Motor learning principles for individuals with stroke may
differ from those established in healthy adults due to altered sensorimotor capabilities71.
Augmented feedback is currently used during rehabilitation, although parameters for its
application (e.g. format and frequency) remain unclear. Optimized application of feedback
principles during post-stroke gait training, targeted at improving TGA, may enhance gait
outcomes and help reduce the risk of long-term negative consequences.
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2.4 Motor Learning
Motor learning is defined as “a set of processes associated with practice or experience leading
to relatively permanent changes in capability for skilled movement” 142. Practice can be defined
as repeated attempts to produce a movement that is outside an individual’s current
capability143. During practice, processes and conditions are applied to the task and environment
that alter the interaction the individual has with them, facilitating the search for task solution
and leading to acquisition of a skilled action. The term skilled action has come to mean high
levels of effectiveness and efficiency in movement, encompassing accuracy, consistency,
reliability, and automatic economical execution of movement144. In healthy adults, practice
conditions have been established that optimize acquisition and result in the relatively
permanent ability to produce the skilled action142.
There are theories of motor learning to explain the mechanism of skill acquisition. Schmidt’s
Schema Theory is based on closed loop motor control, in which sensory information about an
ongoing movement is compared with the stored memory of the intended movement that is
used as a reference to detect error and adjust movement accordingly63,145. However, this theory
does not account for the production of novel movements or movements that occur in the
absence of sensory feedback63. Newell’s Ecological Theory proposes that motor learning is
based on a search for optimal strategies for task solution during practice, congruous with the
environmental and task restraints. This search for a task solution is facilitated by complex
perception/cognition/action processes resulting from the interaction between the individual
with the task and environment63,146. Newell’s theory accounts for more factors that are
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essential to motor learning, though it is still fairly new and yet to be systematically investigated
relative to specific motor skill acquisition examples63.
Other theories explain skill acquisition with respect to the process of learning over time. Fitts
and Posner147 propose a three-stage model for motor learning that focuses on the provision of
cognitive resources and degree of attentional demand of producing a skilled movement.
Initially the new task requires a high attentional focus as the task is investigated and various
strategies are attempted to produce the skilled movement. This ‘cognitive stage’ results in high
variability and large improvements in performance as an effective strategy for producing the
movement is developed. Once the best movement strategy has been selected, the movement is
refined for optimal performance, displaying less variability, smaller improvements and reduced
attentional demand. The final stage of motor learning in this theory, the ‘autonomous stage’,
requires a much lower degree of attention as the automaticity of the task increases. During this
stage, more attention is diverted to external factors that may affect performance such as
scanning the environment for features that could affect the task or preforming another task
simultaneously.
Another three-stage model originally proposed by Bernstein148 and adapted by Vereijken,
Newell and colleagues149, the systems theory, suggests that the central component of motor
learning is the control of the degrees of freedom. The theory suggests that learning a motor
skill begins with a reduction in the degrees of freedom to simplify the task and learn the general
movement, called the ‘novice stage’. The ‘advanced stage’ follows in which more degrees of
freedom are released, resulting in more precision and greater ability to respond to task and
19
environmental demands. In the third stage, the ‘expert stage’, all the degrees of freedom have
been released, increasing the efficiency of execution under a variety of demands by modifying
characteristics of the movement.
A two-stage model of motor skill acquisition has been proposed by Gentile150,151, which
describes the goals of the learner at each stage. During the first stage the learner’s goal is to
understand the task, explore movement strategies and discern characteristics of the
environment that could affect the task performance. During the second stage the learner’s
primary goal is to refine the movement, including consistent and efficient task execution, as
well as the ability to adapt movement to meet changing task and environmental demands.
Fitts and Posner’s model of motor learning stages is particularly suited for rehabilitation in
patient populations as therapy begins early in recovery, which may be equated to the cognitive
stage of the model. Employing the principles of motor learning that are ideally suited to this
stage of the learning process may optimize rehabilitation outcomes. The use of augmented
feedback during the cognitive stage, or early in rehabilitation, is a good illustration of such a
principle152. Frequent provision of feedback during the early stages aids the learner in
understanding the task and creates a reference for correct movement patterns. As the learner
approaches the autonomous stage, feedback should be more precise and less frequent so that
skills can be refined without becoming dependent on the extrinsic source of information about
their movement145,151.
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2.4.1 Motor Learning in Healthy Adults
Motor learning can be measured directly through neural activity during performance (e.g.
electroencephalography153, functional magnetic resonance imaging154), but is often inferred
from indirect measures of performance, such as movement outcome and movement
characteristics. A common experimental paradigm used to evaluate the effectiveness of the
application of various principles applied to practice is the use of a retention design, which
includes an acquisition and retention phase142,155. During the acquisition phase, the skill is
practiced under specific conditions by manipulating one element of practice to varying degrees
in two or more groups (e.g. control group and experimental group). After a set amount of time,
where the skill is not directly practiced under the outlined conditions, the retention phase
occurs. During retention, the skill is performed under the same conditions by both groups, and
performance is measured. Motor learning is said to have occurred if there is improved
performance during the retention phase. The group that performed better during retention
would indicate the optimal condition to apply to practice that lead to more permanent ability
to produce the skilled action.
Elements of practice can be manipulated to aid in the acquisition of a motor skill. These
elements can include level of practice, practice design, and provision of feedback. Level of
practice refers to the amount of practice, which could be measured in time or number of
repetitions, where the amount of practice required is dependent on the stage of the learner63.
It has been shown that larger improvements are seen early on with lots of practice, then
smaller increments of improvement are observed as there is less and less room for
21
improvement156,157. The rate of improvement at any point of practice is linearly related (on a
log scale) to the amount left to improve142.
Repetition of a skill within practice can be organized to be constant or variable, such that the
same skill is practiced repeatedly or variations thereof, are practiced during a session142,145.
Variable practice leads to greater ability to perform the skill outside the range that it was
practiced. This practice designs may seem to make the acquisition of a skill more challenging,
but leads to better retention performance158,159. When repetitions within practice are varied,
they can be scheduled to optimize learning, by performing repetitions in blocked random order;
this is a form of contextual interference142,160. Using a blocked practice design (low contextual
interference), repetitions of one variation of the skill to be learned are performed, before
moving onto another variation, and so on. A random practice design (high contextual
interference) entails variations of the skill randomly distributed throughout the practice
session. Shea and Morgan161 discovered that high contextual interference leads to poor
performance during practice, but leads to better performance during retention tests. Blocked
practice design also leads to poor transfer of performance in novel contexts, whereas the same
context dependency is not as evident when the skill is practiced with high contextual
interference162–164. Therefore, practice design that includes variations of the skill randomly
distributed throughout practice leads to optimal performance in novel contexts when practice
has concluded.
The manner in which the skill is practiced is also a contributing factor in motor skill acquisition.
The skill may be practiced in its entirety, initiation to completion of the task, every repetition.
22
Conversely, a skill can be practiced in parts, wherein the skill is broken down into segments to
be practiced individually, then combined142. This is most effective when a task can be naturally
divided into parts, such as reaching, grasping and drinking from a cup165,166. Other tasks, where
transition between segments is an integral part of its performance, are ideally practiced in
whole rather than in parts167,168. Gait is a prime example of such a task, in that a patient could
practice bearing weight on a single limb to improve stance stability, however the shifting of
weight from the opposite limb and weight acceptance before achieving SLS are integral to
completing a gait cycle.
During training of a skill, instruction about the movement are often provided to guide the
learner’s performance and can be delivered as explicit or implicit information142,160. Where
explicit instructions inform the learner of how to carry out the movement with rules and
knowledge about the movement itself, implicit instructions do not declare such rules, allowing
the learner to make discoveries about the movement outcome independently169. In healthy
learners, copious evidence supports that providing explicit information about the movement,
requiring conscious awareness of performance, actually degrades learning169–171. Conversely,
implicit learning allows the learner to discover correct movement patterns and use sensory
information to guide movement with less attentional demand, rather than focusing on specific
movement goals which may hinder automatic control of movement172. Similarly, attentional
focus (internal versus external) is an aspect of practice that has a similar effect, where external
foci (focus on movement outcome) is superior in facilitating skill acquisition, compared to
internal foci (focus on body movements)173,174. While there are numerous studies proposing
that the optimal focus of attention is dependent on the stage of the learner173,175–177, a review
23
by Wulf178, provides strong evidence that external attentional focus is superior, regardless of
learning stage179–181. It may be surmised that similar to the effects of explicit knowledge,
internal foci demand too much conscious awareness, interfering with the automaticity of the
movement. Gallwey & Kriegel182 provide a rather simplistic yet appropriate explanation for this
effect: “doing rather than thinking about doing”.
Feedback During Motor Learning
Feedback during motor learning is generally classified into two types: intrinsic and extrinsic
feedback142,160,183. Intrinsic feedback, also called task-intrinsic feedback, refers to any source of
information inherent to the movement and environment: visual, auditory, tactile and
proprioception142,184. Extrinsic, or augmented feedback, is often utilized during practice to
enhance the learner’s intrinsic feedback and provide motivation to continue working toward
the movement goal185–187. The broad use of the term augmented feedback can be used to
describe any source of information provided to the learner originating from an external source
and will be referred to in the remainder of this section, as simply feedback. While feedback is
not always necessary and can even hinder motor skill acquisition, it is often used to enhance
motor learning160 presented in appropriate format (e.g. type, frequency, content, mode).
There are two main categories of feedback, knowledge of results (KR) and knowledge of
performance (KP). KR is information about the outcome of the movement, whereas KP is
information about the movement characteristics150, both of which have advantages depending
on the goal of the skill160. As KR is a focus of this thesis, further discussion of feedback will be in
24
reference to KR. The content of KR can be information about correct aspects of performance or
errors; which is superior is still largely debated and may be dependent upon the role of the
feedback (e.g. direct external attention, provide motivation) 160. KR can take a variety of forms;
for example, the information about the movement outcome can be qualitative (e.g.
right/wrong, long/short), quantitative (e.g. numerically, graphical representation) or a
combination thereof (e.g. error occurrence and the magnitude of error); the latter of which
provides more guidance in how to correct the movement in future trials142.
Feedback can be provided in differing amounts and various schedules, which can impact the
effectiveness in improving performance and retention of a motor skill. Feedback can be
provided in real time during the repetition (i.e., concurrent feedback), or it can be provided
once the repetition is complete, called terminal feedback183. There is a vast body of literature
supporting the use of terminal feedback and its benefits. Concurrent feedback can be effective
under certain learning conditions where intrinsic feedback is not always readily available;
however, more often, concurrent feedback has a negative effect on learning. Concurrent
feedback can give rise to dependency upon external information, such that practice
performance improves, but retention of the skill when the feedback is no longer available is
degreaded188–190. It is proposed that this is due to a ‘blocking’ effect that the augmented
feedback has on the learner’s inherent feedback, whereby the skill is not truly learned, as
determined by retained ability to perform at a later period. A similar effect is seen when
evaluating frequency of feedback (e.g. how often the feedback is provided)191–193. It is largely
accepted that less than 100% feedback is best for skill retention183. The same type of
25
dependency on external information and lack of retained skilled performance can be seen
when feedback is provided after every trial.
It is clear that many variables of practice can be manipulated in order to optimize skill motor
learning, such as schedule, type, organization and use of feedback. Generally speaking, in
healthy adults, practice sessions that are variable with high contextual interference are ideal for
skill retention and transferability161. Whether repetitions of the task are practiced in parts, or as
a whole, is dependent upon the nature of the task. To reduce constrained motor control and
reduce attentional demand, implicit instruction and an external focus of attention should be
used. Based on the literature reviewed, it is apparent that augmented feedback in the form of
knowledge of results is beneficial for skill acquisition, with less feedback (<100%) showing
better retention performance. The field of motor learning is vast and very well studied,
although it remains unclear whether these same principles of practice can be effectively applied
to neurological populations such as people with stroke.
2.4.2 Motor Learning in Adults with Stroke
The field of motor learning is concerned with the acquisition of movement skills, but can be
defined in the context of recovery and rehabilitation as the reacquisition of movement skills63.
Rehabilitation is “fundamentally a process of relearning how to move”194. Due to altered
neurological structures and communication as a result of stroke, the principles of motor
learning applied to neurotypical learners may not be the most effective approach for skill
reacquisition in this population. In recent years, various aspects of practice (or therapy
26
sessions) during post-stroke motor re-learning have received increased attention19,195. Many of
these studies focus on motor tasks of the UE196–198, rather than the LE199,200, though some
studies investigating motor learning principles applied to gait will be reviewed in this section.
Optimal scheduling of practice for people with chronic hemiparesis may not be congruent to
that of healthy adults, in that variations of a task randomly distributed throughout practice may
only improve some aspects of motor performance. A study by Hanlon201 compared the effects
of random and blocked practice of a reach and grasp task among people with unilateral stroke.
The random practice group completed 10 trials per session, where the primary task was
alternated with three variations, creating high contextual interference. The blocked practice
group performed 10 trials of the primary task in blocks of 5 trials (low contextual interference),
with a five-minute rest between blocks. Unlike studies of practice schedule with neurotypical
individuals, the practice type did not have an effect on task performance during acquisition.
There was no difference in the number of trials required to meet success criterion (completion
of the task three consecutive times), nor the time required to successfully complete the task;
although the random practice group subjectively reported that it was more difficult to perform
the task. At retention trials, the random practice group required fewer trials to meet criterion,
however the blocked practice group improved in this respect from the first to second retention
trial. Interestingly, time to complete successful trials was not different between the groups at
retention trials. These finding suggest that random practice providing high contextual
interference leads to superior retention of some aspects of performance (e.g. number of trials
required to complete task) of an upper extremity task after stroke but not others (e.g. time to
complete task).
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Principles regarding type of task training (whole vs part) are consistent with those of
neurotypical adults, in that training type is dependent on the task being trained. Motor learning
principles developed from work with neurotypical adults has shown that part task training is
ideal for tasks that can naturally be divided into parts166, while whole task practice is better for
tasks in which transition segments are imperative to the performance of the task168.
Hochstenbach and Mulder202 suggest that due to the motor, perceptual, cognitive and
behavioral effects of stroke, total movement patterns are remembered better than isolated
actions. There is limited evidence that part-task training aids in learning a serial task (e.g. sit to
stand)203, but is not as effective for learning a continuous task (e.g. walking)204, suggesting that
part/whole task training principles align with those developed in neurotypical adults. The
influence of implicit and explicit information during motor learning is also consistent with that
of healthy adults. Boyd and colleagues198,205 have done extensive work examining the effect of
explicit information on implicit learning of a motor-sequence of finger tapping. They have
confirmed that explicit information interferes with motor learning after stroke198 similar to
neurotypical adults171. Further, the magnitude of capacity for implicit learning is greater in
those with mild strokes, compared to those with moderate strokes195. It is again important to
note that these studies used an upper extremity task. In regard to focus of attention during task
performance, findings in the stroke population are again consistent with healthy adults, in that
internal focus of attention is superior. Studies examining the effect of attention foci on an
upper extremity task concluded that external focus of attention was superior206,207, which was
also found to be the case in a study evaluating the automaticity of a LE stepping task208.
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Feedback
Augmented feedback during motor learning is important for individuals with neurologic injury
like stroke, since intrinsic feedback may be limited due to disruption of neural networks71. This
means that intrinsic feedback about movement (e.g. proprioception and tactility) may be
unreliable and/or may be interpreted incorrectly71. Feedback is often provided during therapy
sessions in the form of verbal, visual or manual guidance209, however the content of which is
usually motivational and reinforcing, rather than providing information about a movement19. It
has been suggested that both KR (knowledge of results) and KP (knowledge of performance)
are effective for improving hemiparetic arm motor performance, although KP may have more
lasting benefits and lead to better movement quality197,210,211. The optimal mode of feedback
remains unknown, however it is apparent that people with stroke are able to use a variety of
feedback sources (e.g. visual212 or auditory211). Very few studies have investigated optimal
parameters for feedback scheduling after stroke and the evidence available is inconclusive. A
study that used terminal visual feedback about an upper extremity timed tracing task at a
frequency of 100% or a relative frequency of 67%, found that feedback condition did not affect
accuracy or consistency of the task at retention when feedback was no longer provided 212.
Similarly, a study using a grip force production task providing feedback about actual force
production at either 100% or 50% frequencies, found no significant difference between groups
at no feedback retention213. It is apparent that there still remains a large gap in the literature
regarding the provision of augmented feedback during the relearning of an upper extremity
motor task.
29
While some principles for feedback of the upper extremity remain ambiguous, the provision of
augmented feedback for LE motor tasks has received considerably less attention, especially a
bilateral task that was previously automatic such as gait. Stanton and colleagues199 conducted a
review examining the use of biofeedback (visual, auditory, tactile), and the effect it has on
lower limb activities such as standing up, weight shifting and walking. Feedback provided
included information about weight distribution between limbs, muscle activity, spatiotemporal
gait parameters, and joint angles which was provided via visual, auditory or tactile mediums.
They found that biofeedback had a moderate effect on these LE activities immediately after
training, when compared with conventional therapy, suggesting that this type of feedback is
superior to conventional therapist feedback (e.g. verbal/socio-affective feedback)199 in the
short-term. However, the long-term effects of this intervention are unclear due to limited data
and biofeedback is not commonly used in stroke rehabilitation214. There is a scarcity of
information about the use of terminal feedback of any kind during gait training. This is
concerning since the interventions described above are not easily implemented in the clinical
setting. Previous work by Patterson and colleagues indicates that concurrent visual feedback
can result in immediate changes in TGA during treadmill training. The visual feedback provided
was a symmetry plot that presented information about the individual’s TGA as a ratio of right
over left stance time, where the goal was to move the symmetry plot into the target range in
the center of the display. Four of six participants who received feedback improved their
symmetry in response to this visual feedback. However, concurrent feedback typically requires
special equipment that can measure movement while it is occurring and customized programs
that can generate feedback based on measures in real time. Furthermore, while the use of
30
concurrent feedback has shown promise in improving some aspects of LE activities, it may also
lead to dependency and the blocking effect described in healthy adults, limiting capacity for
true learning to occur188. Thus there is a need to investigate the effects of terminal augmented
feedback for gait training.
2.5 Summary
With the incidence of stroke in Canada on the rise, and less than ideal stroke rehabilitation
outcomes, it is important to investigate effective rehabilitation strategies. Better post-stroke
rehabilitation outcomes will lead to decreased dependence and increased HRQoL. Persisting
gait deviations, such a TGA, can affect mobility and can have long-term negative consequences
like musculoskeletal injury, bone density loss, challenges with balance and inefficient energy
expenditure. TGA often persists after conventional rehabilitation therapy, and current
experimental interventions, which have shown some promise for improving TGA, are often not
feasible in the clinical setting. Clearly new approaches for treating TGA are needed, one of
which may be to incorporate the principles of motor learning in overground gait training.
While motor learning is well studied, and principles are firmly established for skill acquisition in
healthy adults, motor learning as it applies to individuals post-stroke is still being investigated.
This is especially true for motor learning of a bilateral lower extremity task which was
previously automatic, such as walking. The principles that could be applied to practice of a LE
task to enhance skill acquisition remains largely unknown. Further knowledge of how these
principles apply to motor learning of the LE after stroke can aid the design of gait training
programs that effectively improve TGA.
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2.6 Study Objectives
This study aims to investigate the use of visual feedback provided to individuals with stroke
during an overground walking practice session to improve TGA at a retention session. The initial
primary objective of this study was to investigate the effect of different frequencies of
augmented visual feedback on temporal gait asymmetry. Based on preliminary data using the
first visual feedback display it was determined that a second objective was necessary; to
investigate the effect of different visual displays of augmented feedback on temporal gait
asymmetry.
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Study: Use of Visual Feedback to Improve Temporal Gait Asymmetry Post-Stroke
3.1 Introduction
Walking is frequently affected by stroke and improved walking function is a commonly stated
rehabilitation goal2. Post-stroke gait features a number of spatiotemporal deviations including
decreased velocity, increased double support time and asymmetries between the limbs74,98.
Although some improvements are made with rehabilitation, gait is still considerably slower and
more asymmetric at discharge compared to healthy older adults6.
One deviation in particular, temporal gait asymmetry (TGA) occurs in 55% of individuals with
stroke and is described as spending unequal amounts of time on each leg during walking6. If left
untreated, TGA may have long-term negative consequences such as challenges with balance
control111, inefficient energy expenditure109, loss of bone mineral density in the paretic limb103,
and risk of musculoskeletal injury in the nonparetic limb102. Unfortunately, TGA does not
respond to conventional rehabilitation and there is some evidence that TGA may worsen over
time5,98,100.
Some experimental interventions show promise for improving TGA, including split belt treadmill
training215and rhythmic auditory stimulation141. While improvements in TGA have been made
with split belt treadmill training, this type of intervention is not always feasible in a clinical
setting due to high equipment costs215. Rhythmic auditory stimulation has also shown
therapeutic promise, although greater improvements are seen in other gait characteristics such
33
as step length and velocity compared to improvements in TGA141. While these experimental
interventions show some improvement in temporal symmetry, they are either not readily
accessible in the clinical setting, or not as effective for improving TGA compared to spatial
symmetry.
Overground gait training is commonly used in clinical practice and research216 and has the
benefit of repeated practice under conditions that closely approximate normal walking,
including terrain, sensory cues, weight bearing, and velocity closer to normal walking speed.
Conventional overground gait training post-stroke improves velocity and endurance216,217, but
practical and effective overground gait training strategies are required to address temporal
asymmetry in post-stroke gait.
One potential approach to improve the effectiveness of overground gait training for TGA is to
apply concepts of motor learning, defined as the relatively permanent capability to produce a
skilled movement as a result of practice or experience142. In the context of post-stroke
rehabilitation, motor recovery can be viewed as the process of relearning motor skills such as
symmetrical gait194. These parallels between motor recovery and motor (re)learning may
facilitate improvement in walking function – especially in producing more symmetrical gait.
One motor learning principle of particular interest to post-stroke gait rehabilitation is
augmented feedback, defined as any information that is not immediately available to the
learner from the body’s own movement 63. Augmented feedback is of specific interest since
sensorimotor impairments caused by stroke may impede the feedback inherent to the learner’s
movement71. The retention test is a common paradigm used to evaluate the effects of motor
34
learning concepts such as augmented feedback provided during practice of a motor task. At the
retention test, the motor task that was practiced is evaluated after a period without practice
and any improvements in performance are considered evidence of motor learning218. Studies
using such retention tests have demonstrated that augmented feedback can improve motor
learning in healthy adults191,219. Furthermore, feedback can be given to the learner during a
practice session at different intervals but in general, less frequent feedback (e.g. feedback after
every 3rd attempt) is typically better for skill retention220.
Some motor learning principles have been investigated for upper limb recovery post-stoke221
however, the optimal conditions for the delivery of augmented feedback particularly for the re-
learning of a bilateral lower limb activity like symmetrical walking has not been examined.
Some motor learning principles have been investigated for upper limb recovery post-stoke221
however, the optimal conditions for the delivery of augmented feedback particularly for the re-
learning of a bilateral lower limb activity like symmetrical walking has not been examined.
Therefore, the overall aim of this study is to investigate the use of visual feedback provided to
individuals with stroke during an overground walking practice session to improve TGA at a
retention session. The initial primary objective of this study was to investigate the effect of
different frequencies of augmented visual feedback on temporal gait asymmetry. Based on
preliminary data using the first visual feedback display it was determined that a second
objective was necessary; to investigate the effect of different visual displays of augmented
feedback on temporal gait asymmetry.
35
3.2 Methods
3.2.1 Design
This study used a cross-sectional observational design that employed an acquisition and
retention protocol142 to test the effect of 2 different visual feedback displays providing
information about the amount of time spent on each leg during walking and 3 frequencies of
visual feedback on TGA. Participants were randomly allocated into one of three feedback
frequencies: 100% feedback (after every trial), ~50% feedback (after every second trial), or 0%
feedback. Participants assigned to feedback groups (i.e., 50% or 100%) received feedback in the
form of Display A or Display B. Therefore, there were five feedback condition groups: no
feedback, 50% Display A (50A), 100% Display A (100A), 50% Display B (50B) and 100% Display B
(100B).
3.2.2 Participants
Individuals with first occurrence of stroke >6 months since onset who exhibited temporally
asymmetric gait (swing ratio >1.0698) were enrolled in this study. Participants were recruited
from the local community and screened for eligibility prior to enrollment. All participants were
over 18 years of age and able to ambulate independently with or without a walking aid.
Exclusion criteria for participation were: visual neglect, inability to comprehend and/or follow
multi step instructions, history of multiple strokes, other neurological or musculoskeletal
conditions that affected gait. This study was approved by the University Health Network
Research Ethics Board and all participants provided written informed consent.
36
3.2.3 Testing Protocol
Participants underwent a baseline assessment to characterize clinical presentation and gait.
Following the baseline assessment, participants were randomly assigned to a feedback
condition (NoFB, 50A, 100A, 50B, 100B). Participants then practiced the overground walking
task during the acquisition session (up to 25 trials), and returned to complete the retention test
(10 trials).
3.2.4 Baseline Measurements
Clinical measures were recorded to characterize type and severity of impairments. Motor
impairment of the leg and foot was measured using the Chedoke-McMaster Stroke Assessment
(CMSA), which has established validity and reliability222. CMSA is measured using a 7-point scale
with lower scores indicating more severe impairment. The National Institute of Health Stroke
Scale (NIHSS) is a valid and reliable scale for measuring neurologic impairment, with higher
scores indicating more severe impairment223. Plantar cutaneous sensation was measured using
a monofilament examination (Touch-Test Sensory Evaluator, North Coast Medical, Inc., Morgan
Hill, CA) on the heel and base of the fifth metatarsal of both feet. The monofilament was
pressed against the target area until the filament bent, then the participant was asked whether
they could feel it. Sham trials were randomly included throughout the testing, in which the
monofilament did not touch the foot, but the participant was asked if they could feel it.
Cutaneous sensation was considered intact if the participant responded correctly to 2 out of 3
trials. This process was repeated for each monofilament of decreasing thickness, until the
37
participant was no longer able to correctly identify 2 out of 3 trials, marking the individual’s
threshold for plantar cutaneous sensation. Each monofilament thickness was marked by a
score, where a higher score (i.e., thicker monofilament) indicated a reduced cutaneous
sensation. The Montreal Cognitive Assessment (MoCA) is a screening tool which covers eight
cognitive domains224 and was used to characterize level of cognitive impairment. The MoCA is
both sensitive and reliable for detecting mild cognitive impairment in stroke225.
3.2.5 Gait measures
Spatiotemporal parameters of gait were measured using a pressure sensitive mat. The Zeno
Walkway (ProtoKinetics, Havertown, PA, USA) is 488 cm long and 61 cm wide mat with 18432
1.27 cm x 1.27 cm embedded sensors. The scan rate was 120 Hz. The pressure sensitive mat
recorded footfall events, which were processed using PKMAS software to calculate spatial-
temporal parameters. Similar technology, such as GAITRite, has been used to measure spatial-
temporal parameters of gait among persons with stroke which demonstrates good test-retest
reliability226,227. Walking trials were 30 seconds of steady gait (i.e., constant mean velocity),
which typically included 2-3 passes over the pressure sensitive mat, at the participant’s
preferred gait velocity.
3.2.6 Overground walking training with feedback
Participants walked across the pressure sensitive mat during the training session, which
recorded spatiotemporal parameters used to generate the feedback provided.
38
3.2.6.1 Acquisition Session
During the acquisition session participants practiced overground walking across the pressure
sensitive mat in trials of 30 seconds. The goal was to complete 25 trials during the acquisition
session but this was adjusted according to participant tolerance. Rest breaks were provided
when necessary. During each trial, the pressure sensitive mat recorded footfall events that
were used to generate feedback. Feedback was provided to the participant at the end of the 30
second walking trials in accordance with the assigned frequency of feedback.
3.2.6.2 Retention Session
Participants returned for the retention session 24 hours after the acquisition session. This
session consisted of 10 30-second walking trials over the pressure sensitive mat. During this
session, no feedback was provided to any participants and temporal gait parameters were
recorded.
3.2.7 Visual Feedback about TGA
During the acquisition session, visual feedback was provided to participants on a tablet (Galaxy
Tab E, Samsung, Suwon, South Korea) in one of two formats: Display A (Figure 3.1) or Display B
(Figure 3.2). The visual feedback was displayed using Excel for Android (Microsoft, Redmond,
WA, USA). Display A was generated using the right and left swing times (seconds) collected by
the pressure sensitive mat on the previous walking trial. The swing time values were entered
into an Excel spreadsheet with a formula that calculated the TGA ratio (equation here). This
TGA ratio was then plotted in Excel using the scatter plot function which was then displayed to
39
the participant. Similarly, Display B was generated using the left and right swing times (percent
of gait cycle) collected by the pressure sensitive mat on the previous trial. These values were
entered into an Excel spreadsheet, which then generated a bar graph of the time spent on each
limb and was displayed to the participant. The full script for the instructions are included in
Appendix A but are briefly described in Figure 3.1 and Figure 3.2.
40
Figure 3.2 Display A - a visual analog scale with a marker (solid black line) indicating
amount of time spent on one leg compared to the other during walking. The
participant was instructed to move the marker closer to the center of the display, and
into the target zone (shaded area), by spending more even amounts of time on each
leg during the subsequent trials.
Figure 3.3 Display B - a bar graph with two bars: time spent on left side, time spent on
right side. The participant was instructed to make the left and right bars even height
by spending more even amounts of time on each leg during the subsequent trials.
41
3.2.8 Data Processing
PKMAS software was used to automatically identify footfalls and impressions made by walking
aids on the pressure sensitive mat. Each pass on the mat was then edited manually to remove
any partial footfalls. After this processing, PKMAS software automatically calculated
spatiotemporal parameters. This processing was completed following the baseline clinical gait
assessment and after each walking trial that required feedback during the acquisition session.
After the retention session, all remaining trials were processed and calculated variables were
exported to an excel file for further statistical analysis and to create individual motor learning
curves. Swing time symmetry was calculated by dividing the right and left swing times (sec.),
with the greater value as the numerator98. Other variables of interest included step length,
velocity, cadence, swing and stance time as percent of gait cycle and initial and terminal
support as percent of gait cycle.
3.2.9 Data Analysis
Descriptive Statistical Analysis
Statistical analysis was carried out with R Project 3.4.1 software (Vienna, Austria)228 and
Microsoft Excel 2016 software. Mean and standard deviation (SD) was generated for each
feedback group for demographic variables and median and interquartile range (IQR) were
generated for clinical variables (CMSA leg scores, MoCA scores, NIHSS, scores). One-way
ANOVA and Kruskal-Wallis tests were used for normally and non-normally distributed variables,
42
respectively, to compare demographic and baseline variables between groups. An alpha level of
0.05 was used as the threshold for statistical significance for all analyses.
Motor learning Curves and Single Subject Analysis
A motor learning curve for each participant was produced by plotting the TGA (left y-axis) and
velocity (right y-axis) of each trial (x-axis) during acquisition and retention sessions. Single
subject analysis, specifically the 2 standard deviation band method, was used to determine TGA
outcome at retention for each participant. The motor learning paradigm closely parallels the A-
B-A single subject study design with a behaviour (in this case TGA) measured during a baseline
period, and intervention period (acquisition) and then during withdrawl of the intervention
(retention)229. The 2SD band method is appropriate for single subject analysis when baseline
data is stable and therefore sensitive to variability across phases230, with 2 consecutive data
points outside 2SD indicating a significant change in performance across phases231. The 2SD
band method was selected for analysis in this study because baseline TGA was expected to be
reasonably stable as participants had not yet received treatment, and the multiple measures at
retention were suitable for assessing change. The mean and 2SD of baseline TGA, as measured
during clinical assessment, was calculated and used to create a band to which TGA during each
trial of the retention session was compared. If the majority of retention trials (i.e. 6/10) had a
TGA below 2 SD from the baseline TGA, the individual was classified as exhibiting a significant
change in TGA. Participants that had <6 retention trials below 2SD band were categorized as ’no
change’.
43
Comparison of Spatiotemporal Gait Parameters at Baseline and Retention
In order to determine what aspects of gait changed to accomplish greater symmetry, paired t-
tests were used to look for differences in gait parameters between baseline and retention in
participants that were classified as having ‘changed’ symmetry. Measures of interest in this
study were swing time (% of gait cycle), stance time (% of gait cycle), initial double support time
(% of gait cycle), terminal double support time (% of gait cycle), step length (cm), of the paretic
and nonparetic limbs, along with cadence (steps/min) and velocity (m/s). These spatiotemporal
variables commonly deviate from normative values in asymmetrical post-stroke gait6,232, and
were therefore of interest to determine how they change in order to achieve improved TGA.
These parameters were used in previous work to investigate change in gait symmetry within an
individual and indicated that some of these parameters changed as symmetry improved with
increased walking speed233.
Effect Size
Effect size (Cohen’s d) was calculated to determine the effectiveness of each feedback
condition (50A, 100A, 50B, 100B) compared to the control group based on mean change in TGA
from baseline to retention.
44
3.3 Results
Participants
Sixteen participants (5 females, 11 males) with a mean (SD) age of 65.2(6.6) years, who were
6.2(5.4) years post-stroke participated. All participants exhibited TGA at clinical assessment
with a mean TGA ratio of 1.32 (0.17) and performed walking trials using a walking aid as
required (8 participants used an aid). Demographic data for individual participants and group
means are presented in Table 1. The groups were not statistically significantly different on age
(F(4,15) = 0.13, p = 0.97), time post-stroke (H(4,15) = 4.93, p = 0.29), CMSA score of the leg
(H(4,15) = 2.78, p = 0.60), MoCA (F(4,15) = 0.13, p = 0.97), NIHSS score (H(4,15) = 1.55, p = 0.82),
velocity (F(4,15) = 0.86, p = 0.52).
45
Tab
le 3
.1 P
arti
cip
ant
char
acte
rist
ics
Gro
up
P
arti
cip
ant
ID
B
asel
ine
TGA
A
ge
(yea
rs)
Sex
(M/F
) Ti
me
Po
st-
Stro
ke
(yea
rs)
CM
SA
(leg
) M
oC
A
NIH
SS
Gai
t ve
loci
ty
(m/s
)
Co
ntr
ol
1 1.
47
75
M
3.9
6
27
0
1.04
(n
=4)
2 1.
32
60
M
2.9
5
20
3
0.93
3 1.
24
56
M
5.3
7
29
1
0.78
4 1.
39
61
M
2.2
5
28
2
0.85
G
rou
p m
ean
(sd
) 1.
36(0
.1)
63.0
(8.3
)
3.6(
1.3)
5.
5(1.
8)
27.5
(7.0
) 1.
5(2.
5)
0.90
(0.1
)
50
A
5 1.
45
83
M
6.7
4
28
0
0.63
(n
=2)
6 1.
25
66
M
2.6
5
25
2
0.91
G
rou
p m
ean
(sd
) 1.
35(0
.1)
74.5
(12
.02
)
4.6(
2.9)
4.
5(1.
0)
26.5
(3.0
) 1.
0(2.
0)
0.77
(0.2
)
100A
7
1.30
44
F
6.6
6
30
1
0.91
(n
=4)
8 1.
12
60
M
1.6
6
28
1
0.83
9 1.
15
81
F 8.
7
6
20
1 0.
61
10
1.
70
59
M
5.2
2
23
8
0.61
G
rou
p m
ean
(sd
) 1.
31(0
.3)
61(7
.0)
5.
5(3.
0)
6.0(
3.0)
25
.5(8
.8)
1.0(
5.3)
0.
74(0
.2)
50
B
11
1.17
69
M
1.
5
5
27
1 0.
89
(n=3
) 12
1.
24
58
M
0.8
6
27
2
0.84
13
1.34
56
F
4.5
4
25
4
0.79
G
rou
p m
ean
(sd
) 1.
25(0
.1)
61(7
.0)
2.
2(2.
0)
5.0(
2.0)
27
(2.0
) 2.
0(3.
0)
0.81
(0.1
)
100B
14
1.
62
68
F 23
.9
6
29
2 0.
92
(n=3
) 15
1.
28
68
M
2.4
5
22
4
0.78
16
1.09
64
F
21.0
) 5
23
1
0.79
G
rou
p m
ean
(sd
) 1.
33(0
.3)
66.7
(1.9
)
15.8
(11
.7)
5.3(
0.6)
24
.5(3
.8)
2.3(
1.6)
0.
83(0
.08)
Sam
ple
Mea
n (
sd)
1.32
(0.1
) 64
.2(6
.6)
6.
2(5.
44)
5.
0(1.
0)
27.0
(5.0
) 1.
5(1.
8)
0.81
(0.1
)
46
Motor Learning Curves and Single Subject Analysis
Figures 3.3-1 to 3.3-16 represent performance and retention of each participant. Half of
participants that received feedback (6/12) during practice showed improved symmetry at
retention. Severity of TGA was categorized as mild-moderate (≤1.5) or severe (>1.5), based on a
similar categorization used in a previous study examining TGA6. Fourteen participants had mild
temporal asymmetry, and two had severe asymmetry. Of those that showed improved TGA at
retention 4 had mild-moderate asymmetry and 2 had severe asymmetry.
47
1.
.
2.
.
3.
.
4.
.
5.
.
6.
.
7.
.
8.
.
9.
.
10.
.
48
11. 12.
13. 14.
.
15.
.
16.
.
Figure 3.4 Single subject analysis of TGA ratio (black line + circle marker), compared to
2SD (shaded band) of baseline asymmetry (white dashed line within shaded band),
and velocity (m/s) (grey lines + square marker) across trials for acquisition and 24 hour
retention session. Participants 1-4 did not receive feedback and participants 5-16
received feedback.
49
Participants classified as ‘change’ (i.e., exhibiting improved symmetry at retention) and ‘no
change’ with the 2SD band method are summarized in Table 3.2.
Table 3.2 TGA outcome at retention
Group Change No Change Total
Control 0 4 4 50A 1 1 2 100A 1 3 4 50B 1 2 3 100B 3 0 3
Total 6 10 16
Comparison of Spatiotemporal Parameters of Baseline Gait to Retention Gait
Mean values for spatiotemporal parameters of participants who showed change in symmetry
(i.e., improved TGA) at retention (n=6) are summarized in Table 3.3. Paired t-test of each
parameter (baseline-retention) indicated that the ‘change’ group exhibited significant changes
in swing time of the nonparetic limbs (t(5)=-3.99,p= 0.010) and stance time nonparetic limbs
(t(5)=3.99,p=0.010).
50
Table 3.3 Spatiotemporal parameters of individuals who showed change in TGA at retention gait (n=6)
Parameters Baseline Retention
Velocity (m/sec.)* 0.51 (0.16) 0.52 (0.1) Cadence (steps/min.)* 77.1 (12.9) 75.9 (11.2)
Paretic Limb
Swing Time (%)* 31.9 (4.9) 31.5 (4.0) Stance Time (%)* 68.1 (4.9) 68.5 (4.0)
Initial Double Support Time (%)* 18.6 (5.0) 17.5 (4.3) Terminal Double Support Time (%)* 25.1 (5.9) 24.1 (6.7)
Step Length (cm)* 39.2 (9.0) 40.3 (6.4)
Nonparetic Limb Swing Time (%)* 23.8 (5.6) 26.8 (6.1)
Stance Time (%)* 76.2 (5.6) 73.2 (6.1) Initial Double Support Time (%)* 25.3 (6.1) 24.0 (6.7)
Terminal Double Support Time (%)* 18.7 (4.9) 17.5 (4.3) Step Length (cm)* 40.1 (9.1) 42.6 (7.5)
Note. Values are mean (SD) * p = < .05
51
Effect Size
All FB conditions had at least one participant demonstrate a change in TGA at retention.
However, Cohen’s d values for Display B were larger compared to Display A for both 50% and
100% feedback frequencies. Feedback condition 100B had the largest effect size and a mean
difference of change in TGA at least twice as large as all other feedback conditions.
Table 3.4 Effect size (Cohen’s d)
Group Mean Difference Pooled SD Effect Size
50A (n=2) 0.05 0.09 0.54 100A (n=4) -0.04 0.11 -0.37
50B (n=3) 0.07 0.08 0.86 100B (n=3) 0.14 0.12 1.23
3.4 Discussion
Providing augmented feedback during a single session of overground walking can significantly
change TGA in some individuals with post-stroke TGA. In contrast, participants who performed
overground walking with no feedback exhibited no improvement in TGA. With respect to
feedback display and frequency, both displays and frequencies induced change in TGA in at
least one participant and 3 out of 4 display-frequency combinations had at least a moderate
effect size (e.g. d>0.5). However, Display B with 100% feedback was associated with a change in
TGA in 100% of participants that received it and it also had the largest effect size of all the
feedback display-frequency combinations. Thus, the effect of augmented feedback on TGA may
52
be influenced by the format in which information about TGA is presented and the frequency at
which it is given.
Both frequencies of feedback for Display B had a large effect on the change in TGA compared to
those in the control group. Display A had a moderate effect on TGA when feedback was
provided for 50% of trials. Meanwhile, receiving Display A for 100% of trials had a small
negative effect, wherein mean change in TGA for the group was worse compared to the control
group. It is suspected that Display A was misinterpreted by participants and therefore did not
have a great effect on TGA. During the acquisition session multiple participants verbalized
finding Display A difficult to understand. Other participants described the way they were
interpreting the feedback at some point during or after the acquisition session, which was
different from its intended meaning. In these cases participants’ statements indicated their
interpretation of feedback as indicating their position spatially on the mat (i.e., walking in the
middle of the mat versus to one side or the other) rather that the intended meaning which was
the temporal symmetry pattern. For example, one participant said, “I was trying to walk closer
to the middle of the mat, to move the line on the display”. This was somewhat surprising since
previous work by Patterson220 used a similar visual display during a single treadmill training
session which was associated with reduced temporal asymmetry and no such misinterpretation
was observed. However, in this case the feedback was provided concurrently which may have
facilitated the correct interpretation of feedback display. In previous post-stroke motor
learning studies, a variety of visual information is presented as augmented feedback, however a
study by Sackley and Lincoln used a display similar to Display B of the current study, to evaluate
its effect on weight bearing symmetry during quiet standing234. Two vertical bars represented
53
the weight distribution of each limb on force plates, moving up and down in accordance with
weight bearing of the left and right side, where the goal was to bring the bars within 5% of each
other. While this visual feedback intervention resulted in improved weight bearing symmetry
and reduced postural sway, the feedback was concurrent, and the population was individuals
with subacute stroke234. The design of visual displays has not been well studied, although
abstract visualizations such as the plots used to design Display A (target area/shaded band) and
Display B (bars representing distribution) has proven to be effective for motor learning in
healthy adults and individuals with stroke220,234,235. Future work could investigate visual
feedback design factors that enhance interpretation and hence effectiveness of visual feedback,
as the current state of the knowledge is very limited.
Previous work on the use of visual feedback for motor learning in healthy adults suggests that
the stage of the learner and the complexity of the task dictate the frequency at which feedback
should be provided. In the early phases of learning, frequent feedback aids the learner in
understanding the task and exploring movement patterns145,193. It is also suggested that
frequent feedback is optimal for the learning of complex motor tasks236. It is possible that the
100% frequency schedule resulted in more participants with changed TGA, compared to those
that received feedback at 50% frequency, due to the learning stage of the participants and the
nature of the task. Participants were in the early stages of learning the novel bilateral
coordination task and therefore more frequent feedback may have facilitated the search for a
movement pattern that resulted in greater symmetry more successfully than those who
received less feedback.
54
It is important to note that TGA with visual feedback did not appear to be accompanied by a
change in velocity at retention. This is positive since it implies that altering one component of
the gait pattern (i.e. temporal symmetry) does not necessarily result in a negative trade-off on
other aspects of gait (i.e. forward progression). Furthermore, reduced TGA at retention seemed
to be achieved by an increase in nonparetic swing time indicating that participants spent more
time in SLS of the paretic limb in order to make time spent on each limb more equal. This is an
encouraging outcome since the intended effect of the feedback was to improve TGA through
increased use of the paretic limb which was recommended by previous work on the use of
feedback to improve TGA220. Increased time spent in SLS on the paretic limb is likely associated
with increased weight bearing which is beneficial for maintaining bone mineral density and
reducing risk of hip fracture after stroke103. Similarly, decreased overall stance time of the
nonparetic limb means reduced weight bearing on the nonparetic limb which lessens the
impact that repetitive and prolonged loading can have on the joints and reduce the risk of
musculoskeletal injury102. Improved symmetry of swing and stance time of the nonparetic limb
may also lead to more efficient energyexpenditure109. The impact of continued overground gait
training with visual feedback on TGA and these secondary negative consequences are
important issues to investigate further.
Improved symmetry was not associated with velocity or severity of TGA at baseline. One
limitation of the 2SD band method is that the threshold used to judge change in TGA is not the
same across individuals. Those with greater variability (i.e. larger SD) at baseline had a much
larger 2SD band to which TGA at retention was compared to those with less variability at
55
baseline. The range of SD for the participants that were classified as change in TGA was 0.01 –
0.07 whereas the SD range for the group that did not change in TGA was 0.02 – 0.09.
3.5 Conclusion
The main finding of this study is that TGA changes significantly in some individuals post-stroke
after receiving a single session of overground walking with augmented visual feedback.
However, the effect may be influenced by the format in which the FB is provided. It also
appears that both 50% and 100% FB results in improved TGA at retention but 100% has a bigger
effect size. Results from this study, will inform longer-term TGA gait training studies, using
display format B and providing feedback for 100% of trials. An overground gait training program
that improves post-stroke TGA may reduce the risk of long-term negative consequences
associated with TGA, increase mobility, decrease dependence and improve HRQoL.
56
General Discussion
Conventional gait training improves some aspects of post-stroke gait such as velocity and
endurance123, however, more effective interventions for treating post-stroke gait asymmetry
are required. Previous work has shown improvement in spatial variables with experimental
interventions, but do not improve temporal asymmetry to the same extent141,215. As TGA can
lead to long-term negative consequences68,103,109, it is important that effective interventions
that target TGA are developed. The current work investigates one such approach, using
augmented feedback and the principles of motor learning to improve TGA.
Responsiveness of TGA to intervention
Temporally asymmetric gait has been considered resistant to intervention237, and some
consider it a neuromuscular compensation necessary for optimizing walking function238.
However, the current work demonstrates that changes can be made to temporal parameters of
asymmetric gait after just one practice session and that these changes do not compromise
other functional measures of gait such as velocity. All participants included in the study were in
the chronic stage of stroke recovery, when improvements in walking are said to be limited3, and
yet half of participants who received feedback retained changes to their walking pattern 24
hours later in response to the feedback provided. The findings of this study suggest that more
temporally symmetric gait can be achieved in chronic stroke in response to interventions
targeted at changing temporal gait parameters and does not compromise other features of gait
considered important for functional ambulation.
57
It is possible that responsiveness of TGA to visual feedback was impacted by individual
underlying sensorimotor and cognitive impairments. Although there were no differences in
CMSA, NIHSS, MoCA and plantar cutaneous scores between groups, the individualized nature of
impairments among people with stroke causes no surprise that individuals of varying degrees of
motor function and neurologic impairment were able to change TGA in response to the
feedback. It is reasonable to surmise that those with greater somatosensory impairment would
receive less reliable intrinsic feedback during practice about the result of the movement, and
more challenges in executing the movement71. Nonetheless, there appears to be no trend in
severity of impairment and responsiveness of TGA to visual feedback. Cognitive impairment has
also been linked to degraded motor control and learning239, yet the median MoCA scores of
those who had changed symmetry (median (IQR), 25.5(5.5)) at retention is remarkably similar
to those that did not respond to feedback (26(4.8)). However, the range of the level of
impairments is limited due to the small sample size and a future study with a larger sample size
may better detect the effect of different impairments on the use of visual feedback to improve
TGA in people with stroke.
Another potential factor that may impact responsiveness of TGA to feedback is lesion location.
Lesion location was self-reported by 8 participants to the best of their knowledge (3 basal
ganglia, central sulcus, internal capsule, pons, frontal lobe, middle cerebral artery, posterior
base) and was unknown for the other 8 participants. It is long known that the cerebellum plays
important roles in motor control and learning154. There is evidence from fMRI studies of
sequential motor skills that the cerebellum is particularly active during the early phase of
58
learning240, when the movement is explored and adapted147. A lesion to the cerebellum could
impede motor relearning and interfere with the coordination of movement. It is possible that a
lack of responsiveness to the intervention, especially during the early stages of relearning the
task, is due to injury to the cerebellum. However, no participants in the sample reported lesion
to the cerebellum. Another lesion site that may impede relearning of temporally symmetric
gait, is the putamen. There is evidence that damage to the putamen is associated with the
presence of TGA in the chronic stage241. A lesion to this location may cause TGA to be
particularly resistant to interventions, at least over one practice session. Interestingly, 3 of the 6
participants who did not respond to feedback, reported basal ganglia as lesion location.
However, imaging studies of brain activation patterns during motor tasks indicate that, despite
injury to these areas, motor learning is possible through recruitment of secondary motor areas
such as supplementary and premotor areas242,243.
Display Format
The current work demonstrated that augmented feedback in the form of visual information
about temporal asymmetry can improve TGA after one overground practice session. The study
specifically investigated the effectiveness of different visual displays and the optimal frequency
at which it should be provided during practice. The results suggest that the format the
information is presented in may play a role in its effectiveness. Both displays presented
information about the amount of time spent on one leg compared to the other during a 30
second walking trial, but Display B resulted in twice as many participants exhibiting changed
TGA at the retention session compared to Display A. It seems likely that this is due, at least in
59
part, to misinterpretation of the information presented in Display A by some participants, and
statements to that effect were made by multiple participants. They expressed directly, or
implied through various comments that they were using the information presented in Display A
to adjust where they were walking on the mat (e.g. closer to the middle of the mat), rather than
adjusting the time spent on each leg during the trial. This is despite repeated verbal instructions
about how to interpret the display with respect to timing of events in the gait cycle. It is
possible that participants perceived Display A to be a literal representation of the mat, with the
red band indicating where on the mat they were aiming to walk and the black line representing
their actual location on the mat. Participants viewed the feedback while standing or sitting at
the end of the mat looking down the length of it, which may have also contributed to Display A
being perceived as a representation of the width of the mat. Furthermore, the goal of the task
was to move the marker across the display, which is inherently a spatial task, and perhaps
another possible reason for misinterpretation of the information provided in Display A.
Meanwhile, Display B seemed to be more intuitive and participants expressed no uncertainty
about the meaning of the information presented. It is possible that Display B was more easily
understood since each bar was a direct measure of time was spent on each leg, and it was more
apparent which one was larger. It may be assumed that those that improved TGA, understood
that the display was presenting temporal information about their walking, since improved TGA
was achieved by decreasing stance time of the nonparetic limb; the intended effect of the
feedback.
60
Feedback Frequency
One visual display appeared more effective than the other, and the same is true of the
frequency at which it was provided, again with double the amount of participants showing
improved TGA receiving feedback after every trial (100%), opposed to receiving feedback after
every other trial (~50%). In addition, Display B given at 100% frequency also had the largest
effect size. This is opposite to feedback frequency principles established for healthy adults
where too much feedback can degrade motor learning191, although the larger amount of
feedback may be more beneficial for people with stroke since it supplements intrinsic feedback
that may be otherwise compromised71. It is also possible that the greater need for feedback is
only present in the early stages in learning the task, then, similar to healthy adults, less
feedback may be recommended as the learner has a good understanding of the task and the
new focus is to refine the movement pattern147. Future work examining the effect of feedback
frequency in different stages of relearning could optimize gait outcomes after stroke.
Implications for Rehabilitation
This type of intervention using augmented feedback shows promise for gait training targeting
temporal asymmetry in people with chronic stroke, although it is not necessarily feasible in a
clinical setting at this stage largely due to equipment costs. Some pieces of equipment used,
such as a tablet and laptop, are often common-place in clinics, although the pressure sensitive
mat used to record spatiotemporal gait parameters used to generate the feedback is quite
costly. Many clinics may not have funds to support this type of equipment, although they are
61
being adopted in some settings associated with large research and teaching hospitals in large
urban settings. An alternative method for recording spatiotemporal gait parameters that is
more cost effective (e.g. accelerometers/tablet application), could make this type of
intervention feasible for implementing in therapy.
However, some findings from the current work may be relevant to clinical practice once tested
further in a larger clinical trial, such as the frequency of feedback to provide during gait training
after stroke. The results from this work suggest that more feedback (e.g. 100%) during the early
stages of relearning a task may better facilitate the process, specifically in gait training for
temporal gait asymmetry. Therapists using feedback (e.g. visual or verbal feedback) during gait
training targeting temporal asymmetry, may better facilitate the learning process by providing
feedback after every repetition. This study indicates that one practice session leads to short-
term retention of changes to symmetry, however, future work could investigate the impact of
more practice sessions and longer follow up periods to determine the long-term retention of
these changes. Furthermore, this study evidenced that interpretation of feedback can change
from learner to learner and may not be interpreted by the learner in the intended way. It is
suggested that therapists confirm that patients understand and interpret the information
represented in the visual display in the intended way. Providing concise instructions that
outline the visual information may have a large impact on whether the feedback is effective
during therapy. Further work investigating the interpretation and comparison of visual displays
could inform the design a display that effectively conveys visual information for relearning a
walking task in people with chronic stroke.
62
4.1 Limitations
One limitation of this study is the small sample size and uneven groups sizes, leading to poor
generalizability. Part of the challenge in enrolling participants, was finding participants who fit
the inclusion criteria, especially temporally asymmetric gait. Twenty-four people with a first
occurrence of stroke were screened, of which seventeen people exhibited temporally
asymmetric gait. One participant with TGA was unable to complete the study due to the time
sensitive nature of the study visits; acquisition and retention sessions required travel to
Toronto Rehabilitation Institute on two consecutive days. Many other potential participants
were contacted, but declined participation; indicating that they would be unable to meet the
specific time demands of the study visits. Small group sizes were due to a protocol change in
which Display B was added as a feedback condition, when it became evident that Display A was
ineffective, likely due to misinterpretation of the information presented. Although the
statistical analysis lacks power, single subject analysis enables conclusions about the effects of a
condition based on the response of a single participant to be drawn. Analysis by group in this
type of size sample is also made possible by the effect size calculation, that takes into account
the small sample size. It is important to note that the small sample size of the current study
limits the conclusions that can be drawn. The findings from this study will inform the next of
several steps towards an eventual large clinical trial study. The next step is to test Display B
provided at 100% frequency compared to a control group receiving no feedback, to support the
format and frequency of visual feedback that should be used to improve post-stroke TGA.
63
Another limitation of the study is the baseline TGA measure that was used to determine change
in symmetry at retention. Many participants showed great variability of TGA during the 30
second walking trial, which is common in post-stroke gait244. However, this variability created
different standards for improvement for each individual using the 2SD band method. Since
improved TGA was defined by the majority of retention trials having a TGA ratio less than 2SD
of baseline, those that showed more variation at baseline were required to improve by a larger
margin. Meanwhile, it would be more likely for those that showed little variation at baseline, to
improve TGA. Currently, there is no criteria for selecting the most appropriate analysis method
for single subject research designs230. However, the 2SD band method was used in this work
because it assumes stable baseline data (i.e., TGA prior to intervention), is sensitive to changes
across phases (i.e., baseline, acquisition, retention) and the multiple measures at retention lent
itself well to assessing change (i.e., ≥6 trials below 2SD).
The retention test paradigm used in this study consisted of one practice session (25 trials) and
one retention session (10 trials) 24 hours later. Many post-stroke motor learning studies
employ a similar protocol of one acquisition and one retention session180,212, however multiple
practice sessions are also common245,246 and are closer to the reality of clinical practice in stroke
rehabilitation. The use of one practice session may be seen as a limitation, though there is
evidence that one overground gait training session can induce short-term changes to
spatiotemporal parameters in people with stroke247. The shorter testing protocol used in the
current study showed changes in TGA over a single session and allowed for proof of concept to
inform a larger gait training study. This suggests that further training using these methods could
leader to greater changes in TGA.
64
4.2 Future directions
The findings from the current work demonstrate that visual FB can lead to improvements in
TGA after one training session and provides insight for the development of future gait training
programs. A larger randomized controlled trial using Display B to provide visual information
about TGA at a frequency of 100% is recommended to investigate the optimal frequency at
which FB should be provided. A future clinical trial should be designed with frequency and
intensity of training comparable to that used in other studies on gait rehabilitation with chronic
stroke, with multiple follow up sessions. For example, training may take place over a 4 week
period, 2-3 session per week, each session consisting of 25 trials with follow-ups at 1 and 3
months post training 43,200,248. Follow-up will aid in determining longer-term retention of
improvements to TGA following gait training with visual FB. A gait training program for
improving TGA that employs the principles of motor learning, as they apply to individuals with
stroke, may help reduce long-term negative consequences, decrease dependence and increase
HRQoL.
65
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Appendix A
General Instructions for Acquisition Session
Today you will be performing a series walking tasks. You will be asked to walk across the grey
mat on the floor, turn around at the marker on other side and walk back across the mat, if time
permits. You will be asked to perform this task in blocks of 30 seconds, up to 25 trials. You will
have a rest after each trial. Does this sound okay to you?
Display A
After every trial (for 100% condition, or every other trial for 50% condition), a tablet will be
shown to you which will show you the amount of time you spend on each leg when you were
walking. For example, if you spend equal amounts of time on both legs, then the marker will be
displayed in the center. If you spend more time on your right leg while walking, then the marker
will be on the right side of the screen. The more time you spent on the right leg the further to
the right the marker will be. If you spend only slightly more time on your right leg then the
marker will be closer to the center. The same is true for the left side.
This red band is the target area for your walking. A marker inside this area indicates an
improvement in your walking pattern, compared to when we first began. So after you get the
feedback, your goal is to try to walk so that the marker moves into the red band which is the
target area. The closer the marker is to the center of the display, the better. Would you mind
explaining it to me in your own words to make sure that we both understand?
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Display B
After each trial, a tablet will be shown to you which will show you the amount of time you
spent on each leg while you were walking. Each bar is showing the amount of time spent on
your right and left legs. Or you can think of it as the amount of time spent with your right and
left foot on the ground.
The dark blue bar on the left represents the amount of time spent on your left leg (or foot)
while walking, and the light blue bar on the right side represents the amount of time spent on
your right leg (or foot) while walking. If the bar on the left is taller than the bar on the right,
then this means that you are spending more time on your left leg while you were walking. If the
right bar is much taller than the left bar, then you are spending a lot more time on your right
leg (or foot). The same is true if the right bar is taller.
Your goal is to make the right and left bars equal height, by evening out the amount of time
spent on each leg while walking. Would you mind explaining it to me in your own words to
make sure that we both understand?