A data driven model for optimal orthosis selection in children with cerebral palsy

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review

BACKGROUND

Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine learning methods still fail to translate effectively into clinical applications. This systematic review addressed the gaps hindering the use of machine learning data analysis in the clinical assessment of cerebral palsy and stroke patients.

RESEARCH QUESTION

What are the main challenges in transferring proposed machine learning methods to clinical applications?

METHODS

PubMed, Web of Science, Scopus, and IEEE databases were searched for relevant publications on machine learning methods applied to gait analysis data from stroke and cerebral palsy patients until February the 23rd, 2023. Information related to the suitability, feasibility, and reliability of the proposed methods for their effective translation to clinical use was extracted, and quality was assessed based on a set of predefined questions.

RESULTS

From 4120 resulting references, 63 met the inclusion criteria. Thirty-one studies used supervised, and 32 used unsupervised machine learning methods. Artificial neural networks and k-means clustering were the most used methods in each category. The lack of rationale for features and algorithm selection, the use of unrepresentative datasets, and the lack of clinical interpretability of the clustering outputs were the main factors hindering the clinical reliability and applicability of these methods.

SIGNIFICANCE

The literature offers numerous machine learning methods for clustering gait data from cerebral palsy and stroke patients. However, the clinical significance of the proposed methods is still lacking, limiting their translation to real-world applications. The design of future studies must take into account clinical question, dataset significance, feature and model selection, and interpretability of the results, given their criticality for clinical translation.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

"That's frustrating": Perceptions of ankle foot orthosis provision, use, and needs among people with cerebral palsy and caregivers

BACKGROUND

Cerebral palsy (CP) affects roughly 3 per 1000 births in the United States and is the most common pediatric developmental motor disability. Ankle foot orthoses (AFOs) are commonly prescribed to provide support and improve function for individuals with CP.

OBJECTIVES

The study objective was to evaluate the lived experiences of individuals with CP and their caregivers regarding AFO access, use, and priorities. We examined experiences around the perceived purpose of AFOs, provision process, current barriers to use, and ideas for future AFO design.

STUDY DESIGN

Secondary qualitative data analysis.

METHODS

Secondary data analysis was performed on semistructured focus groups that included 68 individuals with CP and 74 caregivers. Of the focus group participants, 66 mentioned AFOs (16 individuals with CP and 50 caregivers). Deidentified transcripts were analyzed using inductive coding, and the codes were consolidated into themes.

RESULTS

Four themes emerged: 1) AFO provision is a confusing and lengthy process, 2) participants want more information during AFO provision, 3) AFOs are uncomfortable and difficult to use, and 4) AFOs can benefit mobility and independence. Caregivers and individuals with CP recommended ideas such as 3D printing orthoses and education for caregivers on design choices to improve AFO design and provision.

CONCLUSIONS

Individuals with CP and their caregivers found the AFO provision process frustrating but highlight that AFOs support mobility and participation. Further opportunities exist to support function and participation of people with CP by streamlining AFO provision processes, creating educational materials, and improving AFO design for comfort and ease of use.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton ( Exo ) gait, and (iii) the Discrepancy ( i.e. , response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R 2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), ( p < 0.042)) than the Nominal model (median R 2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R 2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill

A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.

Clinical effects of assisted robotic gait training in walking distance, speed, and functionality are maintained over the long term in individuals with cerebral palsy: a systematic review and meta-analysis

PURPOSE

To identify the short-term effects of robotic-assisted gait training (RAGT) on walking distance, gait speed and functionality of cerebral palsy (CP) patients, and to verify if the effects of RAGT are maintained in the long term.

METHODS

A systematic literature review was performed in PubMed, PEDro, CINAHL, and LILACS databases. Studies were included considering: (1) population (CP individuals); (2) study design (experimental studies); (3) type of intervention (RAGT); (4) outcome (gait parameters and function); and (5) period (short and long term).

RESULTS

This systematic review included seven articles in meta-analysis. Only walking distance, thru six minutes walking test, increased statistically after RAGT. However, RAGT demonstrated large clinical effects differences (minimal clinically important difference - MCID) in gait speed and Gross Motor Function Measure score (dimensions D and E), for CP population. After RAGT intervention, differences in short term (comparison 1) were maintained in long term (comparison 2) for all outcomes. Gait speed results were not significant.

CONCLUSIONS

Evidence from the present study demonstrated that RAGT can be an important intervention to improve gait parameters and functionality, in children with CP, that are maintained over long-term.Implications for RehabilitationRobotic-assisted gait training (RAGT) is a beneficial treatment for children with cerebral palsy (CP).RAGT improvements in walking distance are maintained over the long-term in children with CP.RAGT demonstrated large clinical effect differences in gait speed and functionality in CP population.

The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke

Robotic devices can provide physical assistance to people who have suffered neurological impairments such as stroke. Neurological disorders related to this condition induce abnormal gait patterns, which impede the independence to execute different Activities of Daily Living (ADLs). From the fundamental role of the ankle in walking, Powered Ankle-Foot Orthoses (PAFOs) have been developed to enhance the users’ gait patterns, and hence their quality of life. Ten patients who suffered a stroke used the actuation system of the T-FLEX exoskeleton triggered by an inertial sensor on the foot tip. The VICONmotion capture system recorded the users’ kinematics for unassisted and assisted gait modalities. Biomechanical analysis and usability assessment measured the performance of the system actuation for the participants in overground walking. The biomechanical assessment exhibited changes in the lower joints’ range of motion for 70% of the subjects. Moreover, the ankle kinematics showed a correlation with the variation of other movements analyzed. This variation had positive effects on 70% of the participants in at least one joint. The Gait Deviation Index (GDI) presented significant changes for 30% of the paretic limbs and 40% of the non-paretic, where the tendency was to decrease. The spatiotemporal parameters did not show significant variations between modalities, although users’ cadence had a decrease of 70% of the volunteers. Lastly, the satisfaction with the device was positive, the comfort being the most user-selected aspect. This article presents the assessment of the T-FLEX actuation system in people who suffered a stroke. Biomechanical results show improvement in the ankle kinematics and variations in the other joints. In general terms, GDI does not exhibit significant increases, and the Movement Analysis Profile (MAP) registers alterations for the assisted gait with the device. Future works should focus on assessing the full T-FLEX orthosis in a larger sample of patients, including a stage of training.

A Decision Support System to Facilitate Identification of Musculoskeletal Impairments and Propose Recommendations Using Gait Analysis in Children With Cerebral Palsy

The identification of musculoskeletal impairments from gait analysis in children with cerebral palsy is a complex task, as is formulating (surgical) recommendations. In this paper, we present how we built a decision support system based on gait kinematics, anthropometrics, and physical examination data. The decision support system was trained to learn the association between these data and the list of impairments and recommendations formulated historically by experienced clinicians. Our aim was 2-fold, train a computational model that would be representative of data-based clinical reasoning in our center, and support new or junior clinicians by providing pre-processed impairments and recommendations with the associated supportive evidence. We present some of the challenges we faced, such as the issues of dimensionality reduction for kinematic data, missing data imputations, class imbalance and choosing an appropriate model evaluation metric. Most models, i.e., one model for each impairments and recommendations, achieved a weighted Brier score lower than 0.20, and sensitivity and specificity greater than 0.70 and 0.80, respectively. The results of the models are accessible through a web-based application which displays the probability predictions as well as the (up to) 5 best predictors.

Predicting walking response to ankle exoskeletons using data-driven models

Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy

Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children's 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children's gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.

RESULTS OF ORTHOSES USED ON AMBULATORY PATIENTS WITH BILATERAL CEREBRAL PALSY

Objective:

To investigate the impact of ankle-foot orthoses (AFO) on subjects diagnosed with bilateral cerebral palsy (CP) using the gait index and temporal data parameters.

Methods:

Twenty-four subjects, 14 male and 10 female, with a mean age of 11 (5-17 years old), underwent a comprehensive gait analysis under both barefoot (BF) and braced walking conditions. All children had been wearing the orthoses for at least 2 months before the gait analysis.

Results:

The overall values for the left and right Gait Profile Scores (GPS) did not show statistically significant variations when comparing the same individuals with and without orthoses. Gait velocity increased by 19.5% (p < 0.001), while the cadence decreased by 4% with use of orthosis, although it was not statistically significant (p > 0.05). The stride and the step lengths on both the right and left sides, however, resulted in statistically significant increases, when wearing AFO.

Conclusion:

AFO, prescribed for assistance by professionals without using gait data, did not significantly affect the gait index (GPS), but improved temporal data. The determination of quantitative clinical parameters for the prescription of orthotics in patients with bilateral CP, as well as orthotics that meet the specific requirements are points to be addressed in the future to obtain more significant effects. Level of evidence III, Case control study.

A study on the efficacy of AFO stiffness prescriptions

PURPOSE

Ankle foot orthosis (AFO) stiffness is a key characteristic that determines how much support or restraint an AFO can provide. Thus, the goal of the current study is twofold: (1) to quantify AFO prescriptions for a group of patients; (2) to evaluate what impact these AFO have on the push-off phase.

METHOD

Six patients were included in the study. Three patients were prescribed an AFO for ankle support and three patients were prescribed an AFO for ankle and knee support. Two types of AFO - a traditional polypropylene AFO (AFOPP) and a novel carbon-selective laser sintered polyamide AFO (AFOPA), were produced for each patient. AFO ankle stiffness was measured in a dedicated test rig. Gait analysis was performed under shod and orthotic conditions.

RESULTS

Patient mass normalized AFOPP stiffness for ankle support ranged from 0.042 to 0.069 N·m·deg^-1·kg^-1, while for ankle and knee support it ranged from 0.081 to 0.127 N·m·deg^-1·kg^-1. On the group level, the ankle range of motion and mean ankle velocity in the push-off phase significantly decreased in both orthotic conditions, while peak ankle push-off power decreased non-significantly. Accordingly, on the group level, no significant improvements in walking speed were observed. However, after patient differentiation into good and bad responders it was found that in good responders peak ankle push-off power tended to be preserved and walking speed tended to increase.

CONCLUSIONS

Quantification of AFO stiffness may help to understand why certain orthotic interventions are successful (unsuccessful) and ultimately lead to better AFO prescriptions. Implications for rehabilitation AFO ankle stiffness is key characteristic that determines how much support or restraint an AFO can provide. In a typical clinical setting, AFO ankle stiffness is not quantified. AFO has to meet individual patient's biomechanical needs. More objective AFO prescription and more controlled AFO production methods are needed to increase AFO success rate.

Physical Therapists' Use of Evaluation Measures to Inform the Prescription of Ankle-Foot Orthoses for Children with Cerebral Palsy

Aims: To examine how physical therapists (PTs) use evaluation measures to guide prescription and re-assessment of ankle-foot orthoses (AFOs) for children with CP. Methods: PTs in Canada who work with children with CP were invited to complete an online survey. Survey questions examined PT evaluation and interpretation of findings at initial AFO prescription and re-assessment. Closed-ended responses were analyzed using descriptive statistics, and a conventional content analysis examined responses to open-ended questions. Results: Sixty responses from ten provinces were analyzed. Three themes emerged from the open-ended responses, which were supported by closed-ended responses. (1) Focus on impairment-level measures. Although evaluation primarily involved observational, non-standardized measures of impairments and gait pattern, most respondents also considered participation-level constructs. (2) Lack of confidence/knowledge. Respondents reported a moderate level of confidence concerning decision-making about AFO type and characteristics. 3) Inconsistent practices between therapists, possibly reflecting the paucity of available evidence or individualization of the prescription. Conclusions: Non-standardized, observational assessment methods, and impairment-level constructs appear to guide AFO prescription decisions. Integrating current knowledge into practice, developing best practice guidelines, and developing standardized tools to assess the effects of AFOs on participation may promote confidence, consistency, and improved outcomes.

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.

Clinician perspectives and experiences in the prescription of ankle-foot orthoses for children with cerebral palsy

PURPOSE

Physiotherapists, orthotists, and physicians are involved in the prescription of ankle-foot orthoses (AFOs) for children with cerebral palsy (CP); however, little is known about how prescription decisions are made in practice. Therefore, the study objective was to identify current AFO prescription and clinical decision-making practices for children with CP in Canada.

METHODS

Focus groups were conducted in five pediatric rehabilitation facilities, with 32 clinicians. Semi-structured interviews focused on the goals and types of AFOs used, referral and follow-up processes, and clinical evaluation measures. Interpretive Description was used as a framework for analysis. Transcribed dialogue was imported into NVivo 11 for data coding and analysis. Three researchers participated in coding to establish categories and themes.

RESULTS

Categories included: what is made, how it is used, and factors that either support or challenge outcomes. Strengths and challenges of the current prescription process were discussed, including funding, communication, and technology to enhance clinical evaluation. Throughout the interviews, the theme of prescription as a collaborative, iterative, and individualized process emerged.

CONCLUSIONS

Processes, strengths, and challenges associated with AFO prescription in Canada were identified. This is a first step toward the development of guidelines to help clinicians improve AFO prescription for children with CP.

What's New in the Management of Foot Deformities in Children With Cerebral Palsy

BACKGROUND

Foot deformities have been frequently reported in cerebral palsy (CP), and numerous diagnostic modalities and treatment options have recently been developed to achieve a better level of management for children with CP.

METHODS

A thorough search of the English literature, published between January 2013 and March 2016, was performed. A summary of the new findings that had not previously described was reported. The review included recent advances regarding clinical and gait evaluation, orthotic management, botulinum toxin A treatment, and surgical correction.

RESULTS

The review summarized new findings reported in 46 articles and abstracts that were published between January 2013 and March 2016. Older articles were included and cited when an original description was mentioned, or when a change or development of some findings was discussed.

CONCLUSIONS

Foot deformity forms an essential part of evaluating children with CP. Dramatic advances have been achieved in gait assessment, conservative management, and surgical correction. Promising results have been reported with the goal to reach a higher level of orthopaedic care and optimize the functional potentials for children with CP.

LEVEL OF EVIDENCE

Level IV-literature review.

Simulated impacts of ankle foot orthoses on muscle demand and recruitment in typically-developing children and children with cerebral palsy and crouch gait

Passive ankle foot orthoses (AFOs) are often prescribed for children with cerebral palsy (CP) to assist locomotion, but predicting how specific device designs will impact energetic demand during gait remains challenging. Powered AFOs have been shown to reduce energy costs of walking in unimpaired adults more than passive AFOs, but have not been tested in children with CP. The goal of this study was to investigate the potential impact of powered and passive AFOs on muscle demand and recruitment in children with CP and crouch gait. We simulated gait for nine children with crouch gait and three typically-developing children with powered and passive AFOs. For each AFO design, we computed reductions in muscle demand compared to unassisted gait. Powered AFOs reduced muscle demand 15-44% compared to unassisted walking, 1-14% more than passive AFOs. A slower walking speed was associated with smaller reductions in absolute muscle demand for all AFOs (r2 = 0.60-0.70). However, reductions in muscle demand were only moderately correlated with crouch severity (r2 = 0.40-0.43). The ankle plantarflexor muscles were most heavily impacted by the AFOs, with gastrocnemius recruitment decreasing 13-73% and correlating with increasing knee flexor moments (r2 = 0.29-0.91). These findings support the potential use of powered AFOs for children with crouch gait, and highlight how subject-specific kinematics and kinetics may influence muscle demand and recruitment to inform AFO design.

Dynamic motor control is associated with treatment outcomes for children with cerebral palsy

AIM

To estimate the impact of dynamic motor control on treatment outcomes in children with cerebral palsy.

METHOD

We used multiple regression on a retrospective cohort of 473 ambulatory children with cerebral palsy who underwent conservative treatment, single-level orthopaedic surgery, single-event multi-level orthopaedic surgery, or selective dorsal rhizotomy. Outcomes included gait pattern, gait speed, energy cost of walking, and the Pediatric Outcomes Data Collection Instrument. Explanatory variables considered were pre-treatment levels of each outcome, treatment group, prior treatment, age, and dynamic motor control computed from surface electromyography using synergy analysis. Effect sizes were estimated from the adjusted response.

RESULTS

Pre-treatment levels had effect sizes 2 to 13 times larger than the next largest variable. Individuals with milder pre-treatment involvement had smaller gains or actual declines. Dynamic motor control was significant in all domains except energy cost. The effect size of dynamic motor control was second only to pre-treatment level, and was substantially larger than the effect size of treatment group for outcomes where both were significant (gait pattern 2:1, gait speed 4:1). The effect of dynamic motor control was independent of treatment group.

INTERPRETATION

Dynamic motor control is an important factor in treatment outcomes. Better dynamic motor control is associated with better outcomes, regardless of treatment.