Muscle Spasticity Clinical Trial
Official title:
Making Training Better: Error Augmentation Motor Learning in Stroke
Deficits in upper limb (UL) functional recovery persist in a large proportion of stroke
survivors. Understanding how to obtain the best possible UL recovery is a major scientific,
clinical and patient priority. We propose that UL motor recovery may be improved by training
that focuses on remediating an individual's specific motor impairment. Our approach is based
on evidence that deficits in the control of muscle activation thresholds (spatial thresholds)
of the elbow in stroke underlie impairments such as disordered movement and spasticity. Our
novel training program focuses on improving the individual's active elbow control range using
error augmentation (EA) feedback. Since training intensity and lesion load are key factors in
motor recovery that lack guidelines, we will also investigate effects of exercise dose and
corticospinal tract (CST) injury on UL recovery.
In this multicenter, double-blind, parallel-group, randomized controlled trial (RCT),
patients with stroke will participate in an individualized intensive technology-assisted
reaching training program, based on error augmentation (EA), in order to improve voluntary
elbow function. They will practice robot-assisted reaching in a virtual reality (VR) game
setting. We will identify if intensive training with feedback aimed at expanding the range of
spatial threshold (ST) control at the elbow (experimental group) is better than intensive
training with general feedback about task success (control group). We will also determine the
patient-specific optimal therapy dose by comparing kinematic and clinical outcomes after 3, 6
and 9 weeks of intensive training, and again at 4 weeks after training to determine
carry-over effects. We will quantify the severity of the participant's motor deficit, as the
amount of cortico spinal tract (CST) injury due to the stroke (%CST injury) and relate
training gains to their %CST injury. Results of this pragmatic trial will provide essential
information for optimizing individualized post-stroke training programs and help determine
optimal patient-specific training dosing to improve motor recovery in people with different
levels of stroke severity.
This type of research involving personalized, impairment-based feedback and dose-effective
training has the potential to significantly improve rehabilitation for a greater number of
post-stroke individuals and improve the health and quality of life of Canadians.
Recovery of upper limb movement after stroke is incomplete. Stroke is a leading cause of
long-term sensorimotor disability including persistent deficits in upper limb (UL) function.
Understanding how to improve UL recovery is a major scientific, clinical and patient
priority. Yet, despite numerous studies attempting to identify the most effective
rehabilitation interventions based on established principles of motor learning and neural
plasticity, post-stroke UL recovery remains incomplete. Indeed, even with therapy, UL
sensorimotor deficits persist in a large proportion (up to 62%) of stroke survivors for >6
months leading to a high socio-economic burden.
MOTOR CONTROL DISORDERS: A consequence of the underlying control deficit after stroke is
hemiparesis, characterized by a diminished capacity to recruit agonist muscles,
unwanted/inappropriate muscle activation (i.e., spasticity, agonist and antagonist muscle
co-contraction), abnormal muscle activation timing, weakness and muscle fiber property
changes. This leads to deficits in the ability to isolate joint movement and appropriately
combine different joints to accomplish task-related functions. We have accumulated
substantial evidence suggesting that movement deficits and spasticity are associated with a
common control deficit in the specification and regulation of spatia thresholds (ST) of the
stretch reflex and other proprioceptive reflexes. STs are expressed in the spatial (angular)
rather than the temporal (latency) domain. ST regulation is a well-established mechanism of
control of stretch reflexes in animals and reflexes and movements in humans.
ST DEFINITION AND ACTION MECHANISMS: Spatial threshold (ST) is the joint angle at which
muscles begin to be recruited and postural reflexes and other reflexes begin to act. By
shifting ST, the brain resets posture-stabilizing mechanisms to a new limb or body position.
These mechanisms combine to regulate STs in multi-muscle systems according to body
configuration and task demands. Stroke results in deficits in ST regulation. Central nervous
system (CNS) injuries affecting descending and spinal mechanisms and intrinsic lead to
limitations in ST regulation. As a result, passive or active movements past the angular
threshold, ST (spasticity range), elicit abnormal reflex muscle activation. The ST is
velocity-dependent reducing the active control range in stroke patients and their ability to
make faster movements.
INTERVENTION APPROACH: Our approach is designed to increase the reflex-free range of elbow
motion in stroke. Adaptation of elbow movement to a new load (i.e., the ability to correct
errors) in patients with chronic stroke was substantially improved when movement was made
within the active control range (where spasticity did not affect muscle contraction) compared
to when the reflex-free range was not identified. Accordingly, the potential for motor
learning may be improved by considering the range of impaired elbow movement in properly
designed trials. To avoid eliciting movements made with abnormal muscle activation patterns
and other compensations (bad plasticity), training programs will be tailored to the movement
capacity of the individual and incorporate approaches that quantify and enlarge the joint
range made with typical muscle activation patterns. In this proposal, we will use a robot and
a novel VR learning interface to manipulate the ability to produce controlled movement at the
elbow, which is a common impairment in people with moderate to severe stroke. The proposed
personalized training approach focuses on providing specific feedback to increase an
individual's ST regulation range.
ERROR AUGMENTATION FEEDBACK (EA): Error Augmentation feedback will be used to increase the
active control ST zone of the elbow. EA uses intrinsic error-driven learning to enhance the
CNS's ability to take advantage of kinematic redundancy and find meaningful motor task
solutions. Specifically, subjects are provided with feedback that enhances their motor
errors. Manipulation of error signals has been shown to stimulate UL sensorimotor improvement
in both healthy and stroke subjects with greater learning gains occurring when errors are
larger. EA feedback will be used to dynamically remap the active elbow control range. Visual
feedback about elbow angle will be modified, to make it seem as if the elbow moves less than
in reality. Thus, when the actual elbow moves, the subject perceives the elbow as having
moved less and attempts to correct the error by extending the elbow further. The active
control range will be expanded by having subjects working near or just at the limit of their
ST range. Remapping of the perception/action relationship will occur when the afferent
feedback becomes associated with a greater elbow angle. Given the key role of errors in motor
learning, artificially increasing the performance error via EA will increase each
individual's active control range and cause learning to occur more quickly.
IMPACT ON REHABILITATION: Results will build knowledge that can guide clinicians and their
patients in identifying the best type of training for UL functional recovery - an essential
component of reintegration into daily life activities. Findings can also support a paradigm
shift in clinical practice, encouraging rehabilitation practitioners to consider personalized
intervention options for improving outcomes. Increasing therapeutic options can also
contribute to personalized care that is tailored to the patient's particular needs and lead
to better functional outcomes.
Objective 1 - Determine the effectiveness of personalized exercise using EA to expand the
range of active elbow control in post-stroke subjects. Hypothesis 1: Intrinsic feedback about
movement error at the elbow will lead to dynamic remapping of muscle-level control
mechanisms, and improve the range of active elbow joint movement. Hypothesis 2: Subjects
practicing with EA will be able to incorporate the greater elbow joint range into functional
reaching movements, as reflected in better clinical outcomes.
Objective 2 - Determine the patient-specific optimal dose of intensive exercise to maximize
arm motor recovery. Hypothesis 3: Increased training dose will lead to better kinematic and
clinical outcomes and better motor learning.
Objective 3 - Relate the amount of CST damage to UL recovery based on kinematic and clinical
measures. Hypothesis 4: Greater CST damage will be correlated with poorer motor learning and
clinical outcomes.
Two groups, training duration,
TRAINING DURATION: 54 subjects will perform ~30 min/session of target reaching with their
affected arm. To control for intensity, practice will be extended to the time needed to
complete 138 reaches/session with 6-10s between reaches. Sessions will be done 3 times/wk for
9 wks (i.e., 27 sessions, 810 mins, 3,726 trials) - considered to be high-intensity exercise
as recommended by the Stroke Recovery & Rehabilitation Roundtable. Kinematic and clinical
measures will be made before (PRE), after 3 (POST3), 6 (POST6), and 9 wks (POST9) and after a
4 wk follow-up (FOLL-UP).
SAMPLE SIZE: The Minimal Clinical Important Difference (MCID) of the primary outcome measure
(ST) was used to compute the sample size. The MCID of the ST was determined to be 18.07°
using an anchor-based method (change in FMA> MCID 5.25). Considering an α level of 5% and a
95% power (effect size=1.39) to detect differences using a mixed model ANOVA (G*Power
3.1.9.4), the minimal sample size is 21/group. Sample size was increased to 27 per group
considering a drop-out rate of 25% given the need to attend multiple training/evaluation
sessions for a final cohort of 54 subjects.
STATISTICAL ANALYSIS: We will relate changes in motor behavior to initial clinical status
(PRE) and to post-treatment changes (POST) at 3 time points (POST1, POST2, POST3), and at
follow-up (FOLL-UP). Statistical approaches are based on intention-to-treat analysis.
Descriptive/distribution analysis will highlight main demographic and clinical
characteristics and control for differences in the baseline prognostic indicators between
groups. For Obj. 1-3, we will use a repeated measures mixed model ANOVA for primary and
secondary outcomes where the model includes one between-subject factor - group with 2 levels
(EA, no EA) and one within-subject factor - time (5 levels), using normalized change scores
(i.e., POST-PRE/PRE; FOLL-UP-PRE/PRE). We will consider changes in the primary and secondary
outcomes significant if their 95% confidence intervals (CI) exceed MCIDs for each measure. To
control for %CST injury as a potential confounding factor, we will run a parallel ANCOVA
using %CST as a covariate. This will increase the statistical power and adjust for baseline
group differences estimating an unbiased difference on primary outcomes. This study design
has been used in our previous RCT. For the active arm workspace, significance will be
indicated by a >10% change of PRE-test area, based on an increase of the TSRT of at least
18°. For elbow range of motion, a significant change will be 15% of the Pre-test range. For
secondary outcomes, MCID values will be used when known. For measures for which MCIDs are not
known, we will consider a minimally significant change as >15% of the pre-test value.
Multiple linear regression analysis on pooled data will identify relationships between
subjects with different levels of initial clinical impairment (%CST injury) and primary and
secondary outcome measures. All analyses will consider sex as a confounding factor. While men
have a higher age-adjusted stroke incidence, women experience more severe strokes and have
higher short-term mortality. Better understanding of the influence of sex on therapeutic
interventions can lead to improved stroke management. For all models, residual plots will be
examined to verify linearity, normality and homoscedasticity. Co-linearity will be assessed
based on tolerance, variation of inflation and eigenvalues. Partial correlation and
standardized (beta) coefficients will be examined to demonstrate which explanatory variables
have a greater effect on the dependent variable in the multiple regression models. For each
outcome, variability will be estimated based on 95%CIs. Missing data will be checked for
non-random patterns.
TRIAL MANAGEMENT: Daily trial management will be the responsibility of the steering committee
(Levin, Archambault, Piscitelli). Randomization will be done by Levin. Trial coordination and
data handling will be done by Piscitelli. The team has complementary expertise directly
relevant to the proposal and extensive experience conducting stroke research. A former
patient (GG) who has participated in our previous studies will help assess the feasibility
and acceptability of the technology and the protocol, including clinical and kinematic tests.
Piscitelli will coordinate the trial, help supervise students and take care of daily
management. Prevost (Clinical Research Coordinator) will recruit and assess patients from 3
centers within CRIR. Levin and Wien have expertise in imaging and Feldman in motor control.
Wein is a Stroke Neurologist at the MNI where he has conducted several RCTs. Trivino
(physiotherapist) has participated in several clinical research projects at the JRH using
technology-supported rehabilitation in patients with stroke. Berman (rehabilitation engineer)
designed the robotic/VR intervention and conducted the initial feasibility studies with
Levin. We will disseminate findings to stroke teams at CRIR affiliated hospitals through
in-service presentations and discuss problems of UL measurement and management. Diagnostic
imaging tools and motor control knowledge will be shared with researchers, clinicians and
patients. Feasibility of incorporating the developed technology into clinical settings will
be evaluated with clinicians Trivino and Wein and patient GG.
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