Research Report
Allowing Intralimb Kinematic
Variability During Locomotor Training
Poststroke Improves Kinematic
Consistency: A Subgroup Analysis
From a Randomized Clinical Trial
Michael D. Lewek, Theresa H. Cruz, Jennifer L. Moore, Heidi R. Roth,
Yasin Y. Dhaher, T. George Hornby
Background. Locomotor training (LT) to improve walking ability in people
poststroke can be accomplished with therapist assistance as needed to promote
continuous stepping. Various robotic devices also have been developed that can
guide the lower limbs through a kinematically consistent gait pattern. It is unclear
whether LT with either therapist or robotic assistance could improve kinematic
coordination patterns during walking.
Objective. The purpose of this study was to determine whether LT with physical
assistance as needed was superior to guided, symmetrical, robotic-assisted LT for
improving kinematic coordination during walking poststroke.
Design. This study was a randomized clinical trial.
Methods. Nineteen people with chronic stroke (⬎6 months’ duration) participating in a larger randomized control trial comparing therapist- versus roboticassisted LT were recruited. Prior to and following 4 weeks of LT, gait analysis was
performed at each participant’s self-selected speed during overground walking.
Kinematic coordination was defined as the consistency of intralimb hip and knee
angular trajectories over repeated gait cycles and was compared before and after
treatment for each group.
Results. Locomotor training with therapist assistance resulted in significant improvements in the consistency of intralimb movements of the impaired limb. Providing consistent kinematic assistance during robotic-assisted LT did not result in
improvements in intralimb consistency. Only minimal changes in discrete kinematics
were observed in either group.
Limitations. The limitations included a relatively small sample size and a lack of
quantification regarding the extent of movement consistency during training sessions
for both groups.
Conclusions. Coordination of intralimb kinematics appears to improve in response to LT with therapist assistance as needed. Fixed assistance, as provided by this
form of robotic guidance during LT, however, did not alter intralimb coordination.
M.D. Lewek, PT, PhD, is Assistant
Professor, Division of Physical
Therapy, Department of Allied
Health Sciences, University of
North Carolina at Chapel Hill,
3043 Bondurant Hall, CB 7135,
Chapel Hill, NC 27599-7135
(USA). Address all correspondence
to Dr Lewek at: mlewek@med.
unc.edu.
T.H. Cruz, MS, is a graduate student, Sensory Motor Performance
Program, Rehabilitation Institute
of Chicago, Chicago, Illinois, and
Department of Biomedical Engineering, Northwestern University,
Chicago, Illinois.
J.L. Moore, PT, MPT, NCS, is Research Physical Therapist, Sensory
Motor Performance Program, Rehabilitation Institute of Chicago.
H.R. Roth, PT, MSPT, NCS, is Research Physical Therapist, Sensory
Motor Performance Program, Rehabilitation Institute of Chicago.
Y.Y. Dhaher, PhD, is Assistant Professor, Sensory Motor Performance
Program, Rehabilitation Institute
of Chicago, and Department of
Biomedical Engineering, Northwestern University.
T.G. Hornby, PT, PhD, is Assistant
Professor, Department of Physical
Therapy, University of Illinois at
Chicago, Chicago, Illinois, and
Sensory Motor Performance Program, Rehabilitation Institute of
Chicago.
[Lewek MD, Cruz TH, Moore JL,
et al. Allowing intralimb kinematic
variability during locomotor training poststroke improves kinematic
consistency: a subgroup analysis
from a randomized clinical trial.
Phys Ther. 2009;89:829 – 839.]
© 2009 American Physical Therapy
Association
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find The Bottom Line:
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August 2009
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Locomotor Training Poststroke
L
ocomotor training (LT) using a
treadmill with body-weight support (BWS) has been advocated
for improving walking in individuals
with hemiparesis poststroke.1 Specific afferent inputs provided during
LT, including maximizing lower-limb
weight bearing during stance,2 training at gait speeds approximating
normal walking speeds,3 and generating reciprocal lower-limb kinematics associated with locomotion,4,5 are thought to facilitate
locomotor recovery in individuals
with spinal cord injury (SCI) and
stroke. In individuals with substantial gait impairment, successfully
completing each step during LT often requires physical assistance,
which can be achieved in 1 of 2
ways. Commonly, therapists assist
patients manually to approximate
the lower-limb kinematic trajectories
associated with human gait. Providing such assistance is labor intensive,
however, and may result in variable,
inconsistent kinematic trajectories
during training. Increased variability
of intralimb kinematics may represent diminished coordination,6 and
deficits in consistency or stability
from stride to stride are thought to
predict gait instability and fall risk.7
In contrast, the use of clinical robotic locomotor devices can relieve
therapists of the physical effort often
required during LT by providing consistent, repetitive guidance to the
lower extremities.8 –10 The ability of
robotic devices to provide stable,
Available With
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repetitive LT is thought to supply
many of the sensory-specific cues
related to walking, which may
strengthen neural pathways associated with the production of coordinated locomotion.10,11
Contrary to the notion that consistent sensory information during LT
is critical to enhancing stepping, a
long-standing body of research has
indicated the importance of practice
variability when learning a motor
task.12 Recent data in experimental
models of SCI indicate that variable,
assist-as-needed step training improves the consistency of stepping
compared with constrained guidance through a fixed trajectory.13
Furthermore, such fixed training paradigms are thought to reduce voluntary participation14 and the central
nervous system’s ability to fully explore various movement options.13
Thus, training with robotic devices
that provide strict guidance of limb
kinematics may limit improvements
in the recovery of motor coordination by reducing movement variability, particularly compared with variable, compliant, assist-as-needed LT
paradigms.13,15
Previous work investigating the effects of robotic- versus therapistassisted training on recovery of walking function in subjects with
hemiparesis poststroke focused on
alterations in gait speed and symmetry and functional outcomes following training.16 In the present study,
we sought to determine whether LT
with therapist assistance as needed
was superior to guided, symmetrical
robotic-assisted LT at improving kinematic coordination during walking. An estimate of intralimb coordination has been quantified by other
investigators6,17 as the repeatability
or consistency of the coupling of
hip and knee kinematics during
multiple gait cycles. In the present
study, gait kinematics were assessed
in a subpopulation of individuals
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with chronic (⬎6 months’ duration)
stroke from a larger randomized
controlled study prior to and following 4 weeks of LT performed on a
treadmill. Specific analyses were performed to determine alterations in
intralimb coordination during overground walking and their relationship to functional improvements.
Secondary analysis was performed to
determine whether absolute joint
angles and excursions were altered
following LT with therapist or robotic assistance. Based on previous
work,6,17 we hypothesized that
therapist-assisted LT using variable
assistance as needed would elicit
greater improvements in intralimb
coordination than robotic-assisted
LT using fixed movement patterns.
Method
Participants
Participants recruited for the current
investigation represent an intentional subgroup analysis of the final
26 of 62 individuals recruited for a
larger randomized clinical trial, comparing functional outcomes following LT with either robotic or therapist assistance.16 Data collection for
the project presented here began
midway through the larger randomized clinical trial16 because our motion capture equipment was unavailable at the beginning of the larger
trial. Individuals with chronic (⬎6
months’ duration) hemiparesis following unilateral ischemic or hemorrhagic stroke were recruited for participation (Fig. 1). Lesion location
was confirmed by imaging (ie, magnetic resonance imaging, computed
tomography, positron emission tomography), with no evidence of
brain-stem, cerebellar, or bilateral lesions. Participants were included
only if they could walk at least 10 m
overground without physical assistance and at a self-selected gait speed
of ⬍0.8 m/s, indicative of limited
community ambulation.18 Exclusion
criteria consisted of significant cardiorespiratory or metabolic disease
August 2009
Locomotor Training Poststroke
Figure 1.
CONSORT flow diagram representing participant enrollment, allocation, and analysis throughout the study. LT⫽locomotor training.
that limited exercise participation,
history of previous orthopedic or
neurological conditions that may
limit walking ability, and size limitations of the harness-counterweight
system and robotic orthosis.16 No
participant had received botulinum
toxin therapy in the lower limbs in
the past 6 months, and all participants were prohibited from receiving physical therapy training outside
of the study. Participants with scores
of ⬍22 on the Mini Mental Status
Exam were excluded. All participants required medical clearance to
participate. Participants were randomly assigned to receive either
robotic-assisted LT (n⫽11) or
therapist-assisted LT (n⫽15) using
sealed envelopes concealed from
view. All participants were informed
August 2009
of the purpose of and procedures for
the study and signed an informed
consent form approved by the Institutional Review Board of Northwestern University prior to participation.
Training
All individuals participated in 12
sessions of LT (3 sessions per week
for 4 weeks), with up to 30 minutes
of stepping during a 1-hour session.16 Speed was gradually increased during the first session and
remained at 3.0 kmph (0.83 m/s) for
the remainder of LT. Body-weight
support was provided by a harnesscounterweight system, with up to
40% BWS at the first session and reduced as tolerated.16 Treadmill training speeds and the amount of unloading were similar between groups.16
Participants randomly assigned to
the robotic-assisted LT group used
the Lokomat,* a robotic gait trainer,
to assist the lower extremities in a
consistent, symmetrical walking pattern during treadmill stepping. The
design and control of this device
have been described previously.11
Participants were fixed to the device
with adjustable cloth straps placed
around the trunk, pelvis, and lower
extremities, with hip and knee joints
aligned with computer-controlled actuators. Spring-loaded cloth straps
were attached around the participants’ forefoot to ensure toe clearance during swing on the paretic
side. The robotic device provided
* Hocoma AG, Industriestrasse 4, CH-8604
Volketswil, Switzerland.
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Locomotor Training Poststroke
continuous assistance in kinematic
trajectories approximating “normal”
gait. Participants were given continuous visual feedback of bilateral hip
and knee torques during walking and
were encouraged to generate maximal effort in the paretic limb.
Participants randomly assigned to
the therapist-assisted LT group received manual assistance from a single therapist for limb advancement
or pelvic control. An ankle-foot orthosis (AFO) was used only if an individual was unable to step safely
without it. Manual facilitation was
provided only if a participant could
not step continuously at the required
treadmill speed (ie, an assist-asneeded paradigm) and was not provided to normalize kinematics between limbs.
Gait Analysis
Gait analysis was performed for all
participants less than 1 week prior to
the initiation of LT and was repeated
less than 1 week following the last
LT session. Eight participants (4 in
each group) required some form of
ankle bracing (7 AFOs, 1 ankle stirrup brace), and use of orthoses and
assistive devices was consistent during testing sessions. Participants ambulated at least 5 times across a 10-m
walkway at their comfortable, selfselected speed, while an 8-camera
motion capture system† recorded
the 3-dimensional (3D) movement of
25.4-mm (1-in) retroreflective markers affixed to the pelvis, thighs,
shanks, and feet. Specifically, markers were placed on the posterior sacrum, bilateral anterior-superior iliac
spine, medial and lateral femoral
condyles, medial and lateral malleoli,
and posterior heel counter of the
shoe and dorsally over the second
metatarsal head to identify segment
ends. The motion of the thighs and
shanks was tracked by 3 markers rig†
Motion Analysis Corp, 3617 Westwind Blvd,
Santa Rosa, CA 95403.
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idly affixed to thermoplastic shells,
which, in turn, were wrapped securely around each limb segment.
Data Management
The marker trajectories were identified and low-pass filtered (6 Hz) to
track the 3D motion of the pelvis and
lower-limb segments using EvaRT
software.† Relative positions and intersegmental joint angles (eg, hip,
knee, and ankle angles) were calculated using a rigid body analysis19
and normalized to a stride cycle using OrthoTrak 6.2.4.†
The consistency of intralimb coordination between the hip and knee
joints was quantified by calculating
the average coefficient of correspondence (ACC) as described by FieldFote and Tepavac.17 The ankle joint
was not assessed because some participants used AFOs to restrict ankle
kinematics. The ACC uses a vector
coding technique to analyze the
sagittal-plane hip and knee angles on
an angle-angle plot. The difference
between successive frames (one
frame equals 1% of the gait cycle) on
the phase plane is represented by a
vector whose length is calculated by:
(1)
l i ⫽ 冑x i2 ⫹ y i2
where, xi and yi are the change in
hip angle and knee angle from frame
i to frame i⫹1, respectively. Consequently, there are 100 l values for
each stride. Determination of the direction of the vector at each frame is
calculated from the sine and cosine
of the angle between l and the x-axis
for each frame:
(2)
cos ␪ i ⫽
xi
li
sin ␪ i ⫽
yi
li
and
(3)
Number 8
Then the mean cos(␪) and sin(␪) for
a given frame over multiple steps
was calculated. The mean vector angle for each frame can then be evaluated by:
(4)
冑
a i ⫽ cos ␪ i2 ⫹ sin ␪ i2 ,
where cos ␪i and sin ␪i are the mean
cos(␪) and sin(␪) for each i frame.
The ai values are a measure of dispersion across strides of the hip and
knee angle pair at each percent of
the gait cycle. Values equal to 1
would indicate that there is perfect
consistency across all steps for that
given frame. To represent all the ai
values as a single variable, the mean
of the a values for all i frames was
calculated to represent the hip and
knee ACC. Individuals who are unimpaired walking at their selfselected speeds exhibit a hip and
knee ACC of .9417 to .97.6 A representative example of the hip and
knee angle-angle plot and the steps
involved in the calculation of hip and
knee ACC in an individual poststroke
is shown in Figure 2. Overall, an average of 15 strides (SD⫽8) on the
involved side and an average of 16
strides (SD⫽9) on the uninvolved
side per participant were analyzed.
In addition to hip and knee ACC,
secondary outcome measures included alterations in spatiotemporal
gait parameters (gait speed, cadence,
and stride length) and changes in discrete joint kinematics, as well as the
extent of limb circumduction during
walking. Gait speed was calculated
during the gait analysis session as the
speed of the sacral marker in the
direction of forward progress. Cadence and stride length were determined from the OrthoTrak software.
Hip, knee, and ankle joint angles
were calculated for both the stance
phase (ie, peak hip and knee extension and ankle plantar flexion) and
the swing phase (ie, peak hip and
knee flexion and ankle dorsiflexion),
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Locomotor Training Poststroke
Figure 2.
Representative example of data obtained before locomotor training (LT) for a participant enrolled in the robotic-assisted LT group.
The left column represents the nonparetic-side hip (A) and knee (C) angles plotted against percentage of gait cycle for all steps, with
an angle-angle plot for the hip and knee (E). The bottom graph represents the a values calculated for each percentage of the stride
cycle, with the hip and knee average coefficient of correspondence (ACC) value (G). Similarly, the right column shows the
more-variable paretic-side hip (B) and knee (D) angles with the respective angle-angle plot (F) and a values (H) used to calculate hip
and knee ACC.
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Locomotor Training Poststroke
Table 1.
Demographic Characteristics of Participants Who Completed Locomotor Training (LT)
Therapist-Assisted
LT Group
(nⴝ9)
Robotic-Assisted
LT Group
(nⴝ10)
P
53⫾6
52⫾12
.87
Sex, male/female
4/5
4/6
.85
Race, white/other
4/5
5/5
.81
3/6
5/5
.46
65⫾68
45⫾56
.49
Characteristic
Age (y), X⫾SD
Side of paresis, right/left
Duration of stroke (mo), X⫾SD
Table 2.
Mean (SD) Values for Hip and Knee Average Coefficient of Correspondence Before
and After Locomotor Training (LT) for the Robotic- and Therapist-Assisted LT Groups
Therapist-Assisted
LT Group
Side
Robotic-Assisted
LT Group
Before LT
After LT
P
Before LT
After LT
P
Involved
.80 (.12)
.84 (.09)
.02
.79 (.10)
.81 (.10)
.57
Uninvolved
.88 (.11)
.87 (.09)
.58
.88 (.09)
.89 (.09)
.33
in addition to maximum flexion to
extension excursion. Finally, we calculated limb circumduction as the
maximum lateral deviation of the
heel during swing with respect to
the position of the ipsilateral heel
during consecutive stance phases
(immediately prior to and following
the measured swing phase).20
Data Analysis
All statistical analyses were performed with SPSS, version 15.0.‡ The
effect of LT on the consistency of
intralimb coordination was evaluated with a 2-group (robotic-assisted
LT and therapist-assisted LT) ⫻
2-session (pretraining and posttraining) analysis of variance (ANOVA)
for repeated measures for session.
Post hoc testing of within-group differences for comparison of pretraining and posttraining values was
performed using paired t tests.
Group data are reported as mean
(SD), with ␣⫽.05. The relationship
between gait speed and intralimb coordination was assessed with Pear‡
SPSS Inc, 233 S Wacker Dr, Chicago, IL
60606.
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son correlation coefficients. Secondary measures of peak joint angle and
excursions are presented using descriptive statistics (ie, mean and SD),
but pretraining and posttraining values were not compared, as these
were not primary outcomes. In addition, a stepwise linear regression
analysis was used to determine the
relative contributions of cadence
and stride length to changes in walking speed. Cadence and stride length
for each group were compared before and after training with paired t
tests.
Results
A total of 26 participants were enrolled in the study. During the training period, 6 participants could not
complete the study (5 in the
therapist-assisted LT group and 1 in
the robotic-assisted LT group). Dropouts were secondary to an orthopedic injury related (n⫽1) or unrelated
(n⫽1) to training, difficulty with
transportation (n⫽1), fear of falling
(n⫽1), or self-reported exercise intolerance (n⫽2). There was no difference in dropout rates between
Number 8
groups (␹2⫽2.10, P⫽.147). In addition, data collection procedures on 1
participant in the therapist-assisted
LT group resulted in unusable kinematic data, yielding a total of 10 participants in the robotic-assisted LT
group and 9 participants in the
therapist-assisted LT group for data
analysis. Demographic characteristics of these 19 participants are provided in Table 1.
The mean and standard deviation of
pretraining and posttraining intralimb consistency of movement (hip
and knee ACC) are shown in Table 2
for each group. Prior to training,
both groups had significantly less
consistency of the relative hip and
knee movement on the paretic side
compared with the nonparetic side
(paretic side: .79 [.11]; nonparetic
side: .88 [.10]; ANOVA: P⬍.001).
Furthermore, higher gait speeds
were significantly associated with
larger hip and knee ACC values
(R⫽.785, P⬍.001). Changes in
paretic- and nonparetic-side hip
and knee ACC following LT are
shown in Figure 3. Following training, between-group analysis demonstrated that the therapist-assisted LT
group did not exhibit a significantly
greater increase in paretic-side hip
and knee ACC than the roboticassisted LT group (P⫽.53, effect
size⫽0.30). However, a withingroup analysis showed that the
therapist-assisted LT group demonstrated an increase in consistency of
relative paretic-side hip and knee
movement (P⫽.02), whereas the
robotic-assisted LT group did not
(P⫽.57). Neither the therapistassisted LT group (P⫽.58) nor the
robotic-assisted LT group (P⫽.33)
demonstrated a significant change in
nonparetic hip and knee ACC as a
result of training.
Overall, the participants walked at
0.43 (0.22) m/s prior to training and
0.47 (0.24) m/s following training.
The participants in the roboticAugust 2009
Locomotor Training Poststroke
assisted LT group did not demonstrate a significant increase in selfselected walking speed following
training (0.01-m/s increase, P⫽.45;
Tab. 3). In contrast, the participants
in the therapist-assisted LT group significantly increased their selfselected walking speed after training
(0.06-m/s increase, P⫽.05). A moderate effect size (0.58) was observed
for walking speed. For cadence and
stride length, there were no significant changes in either variable
within groups (all P⬎.24). To determine the contribution of each variable (eg, stride length, cadence) to
increases in gait speed in the
therapist-assisted LT group, a stepwise, linear regression revealed a significant
relationship
(R2⫽.896,
P⬍.001) between the change in
stride length (␤⫽.946) and improved
gait speed. After including the
change in stride length into the analysis, the change in cadence did not
meet criteria for inclusion (ie,
P⬍.05) in the model. Additionally,
there was no association between
changes in gait speed and change in
hip to knee ACC from before to after
training (R⫽.146, P⫽.55).
Maximum joint angles and excursions of the hip, knee, and ankle
joints also are shown in Table 3. Although training altered the variability of intralimb kinematics of the paretic limb, these data generally
indicate only small mean increases
(⬍3°) in joint angles and excursions
throughout the gait cycle following
LT with either therapist or robotic
assistance. Likewise, neither group
substantially altered limb circumduction, with only small changes (⬍0.2
cm) observed in either group following LT.
Discussion
This study indicates that improvements in intralimb coordination (hip
and knee ACC) can occur following
4 weeks of LT with therapist assistance but not robotic assistance.
August 2009
Figure 3.
Graph depicts the mean change in paretic- and nonparetic-side hip and knee average
coefficient of correspondence (ACC) from before to after locomotor training (LT) for the
robotic-assisted LT group (filled circle) and the therapist-assisted LT group (open circle).
The error bars represent the 95% confidence interval. Although there was no betweengroup difference for the paretic side (P⫽.53), a significant within-group difference was
observed for the therapist-assisted LT group only (P⫽.02).
Consistent with the results from
the larger study,16 improvements in
walking speed also were evident, although only in the therapist-assisted
LT group. In contrast, clinically
meaningful changes in peak joint kinematics or excursions did not appear to be present.
Investigations of improvements in
acquisition and retention of novel
motor tasks following variable and
consistent practice have been performed in subjects who were neurologically intact. In general, variable
practice conditions appear to decrease motor performance during
learning compared with constant
task practice, although better retention or transfer is observed with
more-variable practice.21,22 In contrast, few data are available regarding
the effects of variable or consistent
practice conditions for improving
locomotor function in individuals
poststroke.
Significant between-group differences
in robotic- versus therapist-assisted
LT were not revealed, further indicating the general efficacy of locomotor
training.
Nevertheless,
robotic-assisted LT did not elicit improvements in hip and knee ACC.
Instead, intralimb coordination as
quantified using the hip and knee
ACC, was improved with therapistassisted training and might suggest
that mechanically imposed practice
of movement consistency during
training does not, by itself, improve
intralimb coordination (ie, consistency of hip and knee trajectories).
The extent of error allowed by the
patient during training may influence changes in intralimb coordination. During robotic-assisted LT, hip
and knee joints were rigidly “guided”
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Table 3.
Spatiotemporal Patterns and Joint Kinematics Before and After Locomotor Training
(LT) for the Robotic- and Therapist-Assisted LT Groupsa
Therapist-Assisted
LT Group
Variable
Before LT
After LT
Robotic-Assisted
LT Group
Before LT
After LT
Spatiotemporal patterns
Self-selected gait speed (m/s)
0.43 (0.26)
0.49 (0.20)
0.43 (0.20)
0.44 (0.22)
Cadence (steps/min)
69 (26)
71 (27)
72 (24)
72 (24)
Stride length (cm)
73 (18)
78 (26)
67 (17)
70 (18)
Joint kinematics
Hip
Peak flexion (swing) (°)
25 (8.0)
Peak extension (stance) (°)
⫺0.7 (12)
Net excursion (°)
25 (13)
26 (7.4)
28 (6.2)
26 (5.7)
⫺7.8 (7.7)
⫺4.7 (4.6)
27 (14)
20 (5.6)
21 (6.1)
28 (15)
31 (9.9)
33 (8.7)
0.7 (12)
Knee
Peak flexion (swing) (°)
29 (17)
Peak extension (stance) (°)
Net excursion (°)
2.0 (8.5)
27 (21)
⫺0.9 (11)
29 (20)
8.5 (11)
22 (11)
8.7 (13)
24 (13)
Ankle
Peak dorsiflexion (swing) (°)
Peak plantar flexion (stance) (°)
Net excursion (°)
Circumduction (cm)
11 (6.2)
9.1 (4.5)
9.5 (7.6)
9.0 (8.0)
1.2 (9.5)
⫺0.9 (9.0)
⫺3.1 (9.4)
⫺4.5 (9.6)
8.7 (8.9)
10 (7.6)
13 (5.3)
14 (6.4)
4.0 (3.5)
4.0 (3.1)
4.5 (2.9)
4.7 (3.9)
a
Values represent mean (SD). Hip extension, knee extension (ie, hyperextension), and ankle
plantar-flexion joint angles are negative; hip flexion, knee flexion, and ankle dorsiflexion joint angles
are positive.
through a given movement trajectory, thereby minimizing movement
errors. In contrast, although therapists can encourage consistent kinematic movements associated with
locomotion, they cannot minimize
error to the same extent. The resultant variability in lower-limb trajectories with therapist assistance may allow the patient to explore various
solutions to accomplish the locomotor task and adapt to a more consistent locomotor trajectory.13
Although the paretic-side hip and
knee ACC of the therapist-assisted LT
group increased significantly, there
was no significant change in hip and
knee ACC for the nonparetic limb of
either group. The nonparetic limb
exhibited somewhat less-consistent
movements than what has been re836
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ported for individuals who were unimpaired (eg, ACC values⫽.94 –
.97).6,17 Nevertheless, the hip and
knee ACC for the nonparetic limb
was similar to values reported by
Daly and colleagues6 for the nonparetic limb of their participants. Unfortunately, they did not report
training-induced changes to the nonparetic limb, although it appears
from our data that LT with either
robotic or therapist assistance is unable to significantly alter the nonparetic limb’s hip and knee ACC.
A limitation to the present study is
that we did not quantify the extent
of movement consistency during
training sessions for either group,
and we are unsure of the extent
of error allowance or consistency
achieved with therapist assistance.
Number 8
However, the lower-limb trajectories
during robotic-assisted treadmill
stepping are extremely consistent,
with very little error in kinematic
trajectories.23 It is unlikely that
therapist-assisted LT provides consistency of movement similar to that of
robotic-assisted treadmill stepping,
where joint kinematic trajectories
are so tightly controlled.11
Although the allowance of errors
during training may facilitate improvements in locomotor consistency in animal models of SCI13 and
in the current data in individuals
with hemiparesis poststroke, the
type of feedback that facilitates improvement in coordination still is
uncertain. Specifically, participants
who received robotic-assisted LT
were provided visual feedback of
their relative hip and knee torques
during walking (eg, kinetics) to increase volitional effort during stepping,14,24 but they did not improve
their hip and knee ACC. A previous
study in individuals without neurological injury indicated that continuous physical guidance with visual
feedback during learning of a novel
task may limit retention and transfer,25 particularly compared with
subjects who received no guidance
and a reduced schedule of feedback.
More-recent data obtained during
learning of a novel 3D upper-limb
task indicate that mechanical guidance through the required trajectory
that combined proprioceptive and
visual input was slightly inferior to a
simple visual demonstration that involved no limb movement.26 Comparative data of the effect of visual,
kinematic, or kinetic feedback and
error in improving locomotor function in individuals poststroke are
lacking, however, and further investigation is needed.27
As a secondary measure, gait speed
also improved significantly following LT, but only in the therapistassisted LT group by an average of
August 2009
Locomotor Training Poststroke
0.06 m/s. Results from the regression
analysis indicated that gait speed increases were determined by changes
in stride length, although stride
length did not increase significantly
after training. The magnitudes of
changes in gait speed in both groups
were lower than those observed in
the larger randomized training trial,
although relative differences in gait
speed
improvements
between
groups were consistent between
studies.16 Although this change in
gait speed in the therapist-assisted LT
group may be relatively small, such
an improvement is thought to constitute a “small meaningful change”28
according to recently established estimates obtained, in part, in subjects
with subacute (1–5 months’ duration) stroke (see Fulk and Echternach,29 however, for changes in
acute stroke). The relatively smaller
sample size may have contributed to
nonsignificant differences between
LT groups, and studies with larger
sample sizes are needed to reveal differences in walking speed16 as well
as kinematic coordination between
groups.
Additional secondary measures of
joint kinematic data indicate very little changes in joint angle kinematics
on the paretic extremity. Importantly, kinematic parameters of interest were peak angles during specific
gait phases and net joint excursions
and did not take into account alterations in angular velocities, which
may be present with changes in gait
speed, particularly in the therapistassisted LT group. Accordingly, although increased hip and knee
ACC values were observed in the
therapist-assisted LT group, it is unclear whether this response to training is beneficial or detrimental. Increasing movement consistency has
traditionally been viewed as a favorable outcome, as excessive movement variability has been purported
to indicate inappropriate motor control and decreased movement effiAugust 2009
ciency.30 –32 In the therapist-assisted
LT group, however, the relative lack
of changes in discrete kinematic data
points suggests that although the
movements became more similar after training, individuals were not repeating “correct” patterns. Therefore, the therapist-assisted training
may reinforce impaired gait patterns
that have been ingrained in the
chronic stages poststroke.
Possible reasons for the relative lack
of changes in absolute joint angular
excursions in both groups may be
multifactorial. To begin, it is possible
that training intensities were not
high enough to elicit alterations in
kinematic variables. This is particularly relevant for the robotic-assisted
LT group, which may have allowed
participants to walk more “passively” than those in the therapistassisted LT group due to the continuous physical guidance from the
Lokomat. Walking with robotic assistance has been shown in ambulatory
subjects with incomplete SCI to reduce metabolic costs of stepping
compared with therapist-assisted
stepping.14 Reduced volitional effort
during training may limit the possibility of enhanced learning of skilled
movements.33 Providing feedback
during training, as performed in our
study, however, mitigates these effects.14 Nevertheless, as we did not
measure “effort” during LT, it is possible that the capacity for passive
walking in the robotic-assisted LT
group contributed to a lack of improvements in hip and knee ACC
and gait speed. Furthermore, the
protocol used for the present study
required the treadmill speed during
LT to be limited by the device used,
with maximal speeds of 3.0 km/h
used in the present study. Although
training speeds were greater than
the self-selected speed of all participants and similar to those of previous studies of LT in individuals poststroke,34 our inability to further
increase stepping speed during train-
ing may have reduced walking intensity, even during therapist-assisted
LT, when passive assistance was limited. Stepping at higher speeds may
have required greater joint excursions35 and has been shown to elicit
greater changes in locomotor ability
(ie, walking speed, spatiotemporal
parameters) than walking at slower
speeds.34,36 There is, however, little
evidence to support improvements
in kinematic patterns as a result of
increased training speeds, although
we cannot exclude this possibility.
Alternatively, our data may suggest
that the ambulatory individuals with
chronic stroke recruited for this
training
program
had
wellestablished abnormal gait patterns
that could not be modified in 4
weeks of treadmill training with therapist or robotic assistance. Altering
these persistent, learned gait deviations may require training programs
that begin more acutely,37 consist of
longer, more-frequent sessions, continue for a longer period of time,38
or offer a different training method.
Other researchers, however, have
used LT for a 12-week period (eg,
control group described by Daly et
al39) and also did not show a change
in gait kinematics. These data, therefore, may indicate that much-longer
training is necessary to elicit changes
poststroke or that simply providing
appropriate repetitions for task practice may be an insufficient stimulus
for evoking substantial changes in
kinematic trajectories during walking. Perhaps additional sensorimotor
stimuli may be necessary to elicit
changes during walking in individuals with chronic stroke. An enhanced physical stimulus, such as
the addition of neuromuscular or reflex electrical stimulation during locomotor training, for instance, may
evoke substantial changes to joint kinematics in individuals poststroke39
and those with incomplete SCI.17
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Locomotor Training Poststroke
Another potential limitation of this
study is the relatively small sample
size tested. Although we were able
to document changes in intralimb
coordination following therapistassisted LT, we did not find differences between groups. Additionally,
it appeared that kinematic changes
were not appreciably altered. Nevertheless, other researchers34,39,40 have
used equivalent or even smaller sample sizes to demonstrate that LT with
specific modalities or protocols has
the ability to elicit changes following stroke. Recruitment of substantially larger sample sizes may reveal
significant differences in relative hipknee coordination between groups,
although the differences in the observed magnitude of altered gait
kinematics may not be clinically
meaningful.
Conclusion
A 4-week LT program with variable
assistance as needed (therapistassisted) LT significantly increased
intralimb coordination and walking
speed. In contrast, robotic-assisted
LT did not facilitate such improvements. Although traditional therapies have advocated the assistance
and facilitation of movement of the
impaired extremities of people with
neurological injury in an effort to
ensure the quality of movement,41
our results contribute to a body of
evidence that suggests mechanically
or passively minimizing errors may
not be the optimal strategy to improve motor coordination or function in individuals with chronic
hemiparesis. Thus, attempts to provide consistent, precise kinematic
trajectories of the lower limbs during
assisted stepping, whether applied
with therapist assistance or through
automated technology, may not be
optimal. Rather, providing assistance
only as needed and allowing some
variability during task practice,15,42
or even augmenting error,15 may facilitate greater improvements in motor coordination and function for am838
f
Physical Therapy
Volume 89
bulatory individuals with chronic
stroke.
It is unclear whether there is a
positive effect on functional outcomes from improving intralimb
coordination without eliciting substantial alterations in discrete joint
kinematics. Further research is
needed to determine more-optimal
training parameters, including duration, intensity, and timing, following
stroke to optimize both outcomes related to impairments and functional
limitations. In addition, the efficacy
of additional physical (eg, electrical
stimulation)6 or other therapeutic
interventions to “normalize” kinematic patterns needs to be more
thoroughly investigated.
Dr Lewek, Ms Moore, Dr Dhaher, and Dr
Hornby provided concept/idea/research design. Dr Lewek, Ms Cruz, and Dr Hornby
provided writing. Dr Lewek, Ms Cruz, Ms
Moore, Ms Roth, and Dr Hornby provided
data collection. Dr Lewek, Ms Cruz, Ms
Moore, and Dr Hornby provided data analysis. Dr Lewek, Ms Roth, and Dr Dhaher provided project management. Dr Dhaher and
Dr Hornby provided fund procurement and
facilities/equipment. Ms Roth and Dr Hornby
provided participants. Ms Cruz, Ms Moore,
and Ms Roth provided consultation (including review of manuscript before submission).
This study was approved by the Northwestern University Institutional Review Board.
Portions of these results were presented at
the Combined Sections Meeting of the
American Physical Therapy Association; February 14 –18, 2007; Boston, Massachusetts.
Funding for this project was provided by the
National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes
of Health (grant F32AR053447), and the National Institute on Disability and Rehabilitation Research (grants H133G040065 and
H133B031127).
This article was received June 17, 2008, and
was accepted April 7, 2009.
DOI: 10.2522/ptj.20080180
Number 8
References
1 Visintin M, Barbeau H, Korner-Bitensky N,
Mayo NE. A new approach to retrain gait
in stroke patients through body weight
support and treadmill stimulation. Stroke.
1998;29:1122–1128.
2 Harkema SJ, Hurley SL, Patel UK, et al. Human lumbosacral spinal cord interprets
loading during stepping. J Neurophysiol.
1997;77:797– 811.
3 Beres-Jones JA, Harkema SJ. The human
spinal cord interprets velocity-dependent
afferent input during stepping. Brain.
2004;127(pt 10):2232–2246.
4 de Leon RD, Hodgson JA, Roy RR, Edgerton VR. Locomotor capacity attributable
to step training versus spontaneous recovery after spinalization in adult cats. J Neurophysiol. 1998;79:1329 –1340.
5 Pang MY, Yang JF. The initiation of the
swing phase in human infant stepping: importance of hip position and leg loading.
J Physiol (Lond). 2000;528:389 – 404.
6 Daly JJ, Sng K, Roenigk K, et al. Intra-limb
coordination deficit in stroke survivors
and response to treatment. Gait Posture.
2007;25:412– 418.
7 Hausdorff JM. Gait variability: methods,
modeling and meaning. J Neuroeng Rehabil. 2005;2:19.
8 Hesse S, Werner C, Uhlenbrock D, et al.
An electromechanical gait trainer for restoration of gait in hemiparetic stroke patients: preliminary results. Neurorehabil
Neural Repair. 2001;15:39 –50.
9 Colombo G, Wirz M, Dietz V. Driven gait
orthosis for improvement of locomotor
training in paraplegic patients. Spinal
Cord. 2001;39:252–255.
10 Hesse S, Uhlenbrock D, Sarkodie-Gyan T.
Gait pattern of severely disabled hemiparetic subjects on a new controlled gait
trainer as compared to assisted treadmill
walking with partial body weight support.
Clin Rehabil. 1999;13:401– 410.
11 Colombo G, Joerg M, Schreier R, Dietz V.
Treadmill training of paraplegic patients
using a robotic orthosis. J Rehabil Res
Dev. 2000;37:693–700.
12 Schmidt RA, Lee T. Motor Control and
Learning: A Behavioral Emphasis. 4th
ed. Champaign, IL: Human Kinetics Inc;
2005.
13 Cai LL, Fong AJ, Otoshi CK, et al. Implications of assist-as-needed robotic step training after a complete spinal cord injury on
intrinsic strategies of motor learning.
J Neurosci. 2006;26:10564 –10568.
14 Israel JF, Campbell DD, Kahn JH, Hornby
TG. Metabolic costs and muscle activity
patterns during robotic- and therapistassisted treadmill walking in individuals
with incomplete spinal cord injury. Phys
Ther. 2006;86:1466 –1478.
15 Patton JL, Stoykov ME, Kovic M, MussaIvaldi FA. Evaluation of robotic training
forces that either enhance or reduce error
in chronic hemiparetic stroke survivors.
Exp Brain Res. 2006;168:368 –383.
August 2009
Locomotor Training Poststroke
16 Hornby TG, Campbell DD, Kahn JH, et al.
Enhanced gait-related improvements after
therapist- versus robotic-assisted locomotor training in subjects with chronic
stroke. a randomized controlled study.
Stroke. 2008;39:1786 –1792.
17 Field-Fote EC, Tepavac D. Improved intralimb coordination in people with incomplete spinal cord injury following training
with body weight support and electrical
stimulation. Phys Ther. 2002;82:707–715.
18 Perry J, Garrett M, Gronley JK, Mulroy SJ.
Classification of walking handicap in
the stroke population. Stroke. 1995;26:
982–989.
19 Grood ES, Suntay WJ. A joint coordinate
system for the clinical description of threedimensional motions: application to the
knee. J Biomech Eng. 1983;105:136 –144.
20 Chen G, Patten C, Kothari DH, Zajac FE.
Gait differences between individuals with
post-stroke hemiparesis and non-disabled
controls at matched speeds. Gait Posture.
2005;22:51–56.
21 Shea CH, Kohl RM. Specificity and variability of practice. Res Q Exerc Sport. 1990;
61:169 –177.
22 Heitman RJ, Pugh SF, Kovaleski JE, et al.
Effects of specific versus variable practice
on the retention and transfer of a continuous motor skill. Percept Mot Skills. 2005;
100(3 pt 2):1107–1113.
23 Hidler J, Wisman W, Neckel N. Kinematic
trajectories while walking within the
Lokomat robotic gait-orthosis. Clin Biomech (Bristol, Avon). 2008;23:1251–1259.
24 Banz R, Bolliger M, Colombo G, et al. Computerized visual feedback: an adjunct to
robotic-assisted gait training. Phys Ther.
2008;88:1135–1145.
25 Winstein CJ, Pohl PS, Lewthwaite R. Effects of physical guidance and knowledge
of results on motor learning: support
for the guidance hypothesis. Res Q Exerc
Sport. 1994;65:316 –323.
August 2009
26 Liu J, Cramer SC, Reinkensmeyer DJ.
Learning to perform a new movement
with robotic assistance: comparison of
haptic guidance and visual demonstration.
J Neuroeng Rehabil. 2006;3:20.
27 van Vliet PM, Wulf G. Extrinsic feedback
for motor learning after stroke: what is
the evidence? Disabil Rehabil. 2006;28:
831– 840.
28 Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance
measures in older adults. J Am Geriatr
Soc. 2006;54:743–749.
29 Fulk GD, Echternach JL. Test-retest reliability and minimal detectable change of
gait speed in individuals undergoing rehabilitation after stroke. J Neurol Phys Ther.
2008;32:8 –13.
30 Barak Y, Wagenaar RC, Holt KG. Gait characteristics of elderly people with a history
of falls: a dynamic approach. Phys Ther.
2006;86:1501–1510.
31 Candau R, Belli A, Millet GY, et al. Energy
cost and running mechanics during a
treadmill run to voluntary exhaustion in
humans. Eur J Appl Physiol Occup
Physiol. 1998;77:479 – 485.
32 Furuya S, Kinoshita H. Expertisedependent modulation of muscular and
non-muscular torques in multi-joint arm
movements during piano keystroke. Neuroscience. 2008;156:390 – 402.
33 Lotze M, Braun C, Birbaumer N, et al. Motor learning elicited by voluntary drive.
Brain. 2003;126(pt 4):866 – 872.
34 Sullivan KJ, Knowlton BJ, Dobkin BH. Step
training with body weight support: effect
of treadmill speed and practice paradigms
on poststroke locomotor recovery. Arch
Phys Med Rehabil. 2002;83:683– 691.
35 Lamontagne A, Fung J. Faster is better:
implications for speed-intensive gait training after stroke. Stroke. 2004;35:2543–2548.
36 Pohl M, Mehrholz J, Ritschel C, Ruckriem
S. Speed-dependent treadmill training in
ambulatory hemiparetic stroke patients: a
randomized controlled trial. Stroke. 2002;
33:553–558.
37 Norrie BA, Nevett-Duchcherer JM, Gorassini MA. Reduced functional recovery by
delaying motor training after spinal cord
injury. J Neurophysiol. 2005;94:255–264.
38 Smith JL, Smith LA, Zernicke RF, Hoy M.
Locomotion in exercised and nonexercised cats cordotomized at two or twelve
weeks of age. Exp Neurol. 1982;76:
393– 413.
39 Daly JJ, Roenigk KL, Butler KM, et al. Response of sagittal plane gait kinematics
to weight-supported treadmill training
and functional neuromuscular stimulation
following stroke. J Rehabil Res Dev.
2004;41:807– 820.
40 Teixeira-Salmela LF, Nadeau S, McBride I,
Olney SJ. Effects of muscle strengthening
and physical conditioning training on temporal, kinematic and kinetic variables during gait in chronic stroke survivors. J Rehabil Med. 2001;33:53– 60.
41 Bobath B. Adult Hemiplegia: Evaluation
and Treatment. 3rd ed. Newton, MA: Butterworth-Heinemann; 1990.
42 Kahn LE, Zygman ML, Rymer WZ, Reinkensmeyer DJ. Robot-assisted reaching exercise promotes arm movement recovery
in chronic hemiparetic stroke: a randomized controlled pilot study. J Neuroengineering Rehabil. 2006;3:12.
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