Posts Tagged walking

[WEB SITE] Study Examines Exoskeleton’s Ability to Improve Walking for Stroke Patients

Conor Walsh and his graduate student, Jaehyun Bae, fine-tune an ankle-assisting exosuit. (Photo courtesy of Rolex Awards/Fred Merz)

A study published recently in Science Translational Medicine suggests that the use of a soft suit exoskeleton system helps aid in the facilitation of walking ability among ambulatory patients following a stroke.

Researchers from Harvard University’s Wyss Institute for Biologically Inspired Engineering, the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and Boston University’s (BU) College of Health & Rehabilitation Sciences: Sargent College developed the lightweight, soft, wearable ankle-assisting exosuit, and they they suggest in the study that it could help reinforce normal gait in people with hemiparesis after stroke.

The study centers on the use of the exosuit among nine participants, each of whom recently experienced a stroke, and examines the immediate improvements in walking capability that could be obtained when wearing the suit, dubbed the Restore system, according to a media release from ReWalk Robotics Ltd.

According to the release, the study concludes that improvements in paretic limb function contributed to a 20 +/- 4% reduction in forward propulsion interlimb asymmetry and a 10 +/- 3% reduction in the energy cost of walking, which is equivalent to a 32+/- 9% reduction in the metabolic burden associated with poststroke walking.  Relatively low assistance (~12% of biological torques) delivered with a lightweight and nonrestrictive exosuit was sufficient to facilitate more normal walking in ambulatory individuals after stroke.

“This foundational study shows that soft wearable robots can have significant positive impact on gait functions in patients post-stroke, and it is the result of a translational-focused multidisciplinary team of engineers, designers, biomechanists, physical therapists, and most importantly patients who volunteered for this study and gave valuable feedback that guided our research,” says Wyss Core Faculty member Conor Walsh, who is also the John L. Loeb Associate Professor of Engineering and Applied Sciences at SEAS and the Founder of the Harvard Biodesign Lab, in the release.

ReWalk is working with the Wyss Institute on the development of lightweight designs to complete clinical studies, pursue regulatory approvals, and commercialize the systems on a global scale. The first commercial application will be for stroke survivors, followed by Multiple Sclerosis patients and then additional applications.

“Exoskeletons are now a commercially available, disruptive technology that have changed the lives of many individuals in the paraplegic community,” states ReWalk CEO Larry Jasinski, in the release. “The ongoing research at the Wyss Institute on soft exosuits adds a new dimension to exoskeletons that can potentially meet the needs of individuals that have had a stroke, as well as for those diagnosed with Multiple Sclerosis, Parkinson’s disease or people who have limitations in walking.”

[Source(s): ReWalk Robotics Ltd, PR Newswire, Science Daily]

Source: Study Examines Exoskeleton’s Ability to Improve Walking for Stroke Patients – Rehab Managment

Advertisements

, , , ,

Leave a comment

[ARTICLE] Effects of dual-task and walking speed on gait variability in people with chronic ankle instability: a cross-sectional study – Full Text

Abstract

Background

Recent evidence suggests that impaired central sensorimotor integration may contribute to deficits in movement control experienced by people with chronic ankle instability (CAI). This study compared the effects of dual-task and walking speed on gait variability in individuals with and without CAI.

Methods

Sixteen subjects with CAI and 16 age- and gender-matched, able-bodied controls participated in this study. Stride time variability and stride length variability were measured on a treadmill under four different conditions: self-paced walking, self-paced walking with dual-task, fast walking, and fast walking with dual-task.

Results

Under self-paced walking (without dual-task) there was no difference in stride time variability between CAI and control groups (P = 0.346). In the control group, compared to self-paced walking, stride time variability decreased in all conditions: self-paced walking with dual-task, fast speed, and fast speed with dual-task (P = 0.011, P = 0.016, P = 0.001, respectively). However, in the CAI group, compared to self-paced walking, decreased stride time variability was demonstrated only in the fast speed with dual-task condition (P = 1.000, P = 0.471, P = 0.008; respectively). Stride length variability did not change under any condition in either group.

Conclusions

Subjects with CAI and healthy controls reduced their stride time variability in response to challenging walking conditions; however, the pattern of change was different. A higher level of gait disturbance was required to cause a change in walking in the CAI group compared to healthy individuals, which may indicate lower adaptability of the sensorimotor system. Clinicians may use this information and employ activities to enhance sensorimotor control during gait, when designing intervention programs for people with CAI.

The study was registered with the Clinical Trials network (registration NCT02745834, registration date 15/3/2016).

Background

Recurrent ankle sprains occur in up to 40% of individuals who have previously experienced a lateral ankle sprain [1, 2]. Individuals who report residual symptoms, which include repetitive episodes of ‘giving way’ and subjective feeling of ankle joint instability are termed as having chronic ankle instability (CAI) [3]. The cause of these symptoms and the high frequency of recurrent ankle sprain is not fully understood [4]. It has been suggested that the residual joint instability and the high reoccurrence rates can be attributed to loss of sensory input from articular mechano-receptors, decreased muscle strength, mechanical instability of the ankle joint, and reduced ankle range of motion [5, 6].

Recent evidence suggests that deficits in central neural sensorimotor integration can contribute to impaired movement control in people with CAI [7, 8, 9, 10, 11, 12, 13, 14]. For example, Springer et al. [8] assessed the correlation between single-limb stance postural control (Overall Stability Index) and shoulder position sense (Absolute Error Score) among people with CAI and healthy controls. Correlations between the lower and upper limbs were observed only in the healthy controls, indicating altered sensorimotor integration in the CAI group. Several studies have observed altered gait mechanism in people with CAI, which was explained by compromised central nervous system (CNS) control [9, 14, 15, 16]. It was shown that people with CAI have a typical gait pattern of increased inversion kinematics and kinetics, lateral shift of body weight, increased hip flexion during terminal swing to mid stance, reduced hip extension and increased knee flexion during terminal stance to initial swing, and slow weight transfer at the beginning and end of the stance [15, 16, 17]. Altered biomechanical strategies during gait initiation and termination tasks (e.g., reduced center of pressure displacement), have also been demonstrated in this population [9, 14]. Studies that assessed movement variability, such as knee and hip joint motions during single leg jump landing, identified differences between individuals with and without CAI, which may also indicate central motor programming deficits [10, 11, 12, 13]. Hence, further investigation of motor control adaptations may contribute to understanding the underlying neurophysiologic mechanisms of CAI.

Gait speed and other spatio-temporal parameters during daily activities should reflect behavioral goals and environmental conditions [18]. Studies revealed that walking speed has a significant effect on joint coordination pattern and gait variability [18, 19, 20]. Therefore, assessing gait variability under challenging situations such as walking at different speeds might test CNS flexibility in controlling gait [19, 20]. Moreover, based on the understanding that for many daily activities even a fully intact motor control system requires attention and cognitive resources [21], the dual-task paradigm has been used to provide insight into the demands of postural control and gait on attention. Performance of a cognitive task has been shown to decrease postural control in participants with CAI as compared to healthy controls [7, 22]. However, no previous study examined the impact of cognitive task and walking speed on gait performance in subjects with CAI.

Balance during walking is reflected by precise spatial and temporal control of foot placement. Stride to stride fluctuations in time and length are related to control of the rhythmic walking mechanism. Thus, previous research has suggested that studying gait variability is a reliable way to quantify locomotion [23]. The mechanism of adjusting movement variability is considered beneficial for coping with changes, maintaining stability, preventing injury, and attaining higher motor skills [24]. Performing a cognitive task while walking or while altering self-paced walking speed has been related to changes in gait variability in populations with neurological and musculoskeletal pathologies, as well in healthy young individuals [25, 26, 27, 28]. Yet, there is no consensus in the literature as to how to interpret these changes. Decreased variability while performing demanding gait tasks may reflect voluntary gait adaptation toward a more conservative gait pattern [26]. Alternatively, it has been suggested that increased variability may indicate CNS flexibility and adaptability to changes in task demands [29]. A possible central sensorimotor control deficit in people with CAI may constrain the ability of the CNS to adjust to different task demands; thus, affecting central control over gait variability and reducing the ability to cope with varied tasks. Consequently, testing the mechanism of adjusting gait variability as a response to complex walking conditions in people with CAI compared to healthy controls may provide more information on sensorimotor control in this population.

The present study was designed to compare the effects of dual-task and walking speed on gait variability in individuals with and without CAI. Previous reports, including a meta-analysis, indicated that simple postural tasks do not always discriminate between participants with CAI and those without [6, 8, 30]. Consequently, we hypothesized that gait variability among individuals with and without CAI will be similar during “normal” self-paced walking, whereas gait will vary under complex walking conditions.[…]

Continue —> Effects of dual-task and walking speed on gait variability in people with chronic ankle instability: a cross-sectional study | BMC Musculoskeletal Disorders | Full Text

Fig. 1 Stride time variability results of the two groups under all gait conditions. CAI- chronic ankle instability, SP- self-paced, DT- dual task

, , , , , ,

Leave a comment

[ARTICLE] Near-Infrared Spectroscopy in Gait Disorders – Is it Time to Begin? – Full Text

Walking is a complex motor behavior with a special relevance in clinical neurology. Many neurological diseases, such as Parkinson’s disease and stroke, are characterized by gait disorders whose neurofunctional correlates are poorly investigated. Indeed, the analysis of real walking with the standard neuroimaging techniques poses strong challenges, and only a few studies on motor imagery or walking observation have been performed so far. Functional near-infrared spectroscopy (fNIRS) is becoming an important research tool to assess functional activity in neurological populations or for special tasks, such as walking, because it allows investigating brain hemodynamic activity in an ecological setting, without strong immobility constraints. A systematic review following PRISMA guidelines was conducted on the fNIRS-based examination of gait disorders. Twelve of the initial yield of 489 articles have been included in this review. The lesson learnt from these studies suggest that oxy-hemoglobin levels within the prefrontal and premotor cortices are more sensitive to compensation strategies reflecting postural control and restoration of gait disorders. Although this field of study is in its relative infancy, the evidence provided encourages the translation of fNIRS in clinical practice, as it offers a unique opportunity to explore in depth the activity of the cortical motor system during real walking in neurological patients. We also discuss to what extent fNIRS may be applied for assessing the effectiveness of rehabilitation programs.

Walking is one of the most fundamental motor functions in humans,13 often impaired in some focal neurological conditions (ie, stroke), or neurodegenerative diseases, such as Parkinson’s disease (PD).4 Worldwide almost two thirds of people over 70 years old suffer from gait disorders, and because of the progressively ageing population, an increasing pressure on health care systems is expected in the coming years.5

Although the physiological basis of walking is well understood, pathophysiological mechanisms in neurological patients have been poorly described. This is caused by the difficulty to assess in vivo neuronal processes during overt movements.

During the past 20 years, functional magnetic resonance imaging (fMRI) has been the preferred instrument to investigate mechanisms underlying movement control6 as well as movement disorders.7 fMRI allows measuring the blood oxygenation level-dependent (BOLD) signal that, relying on variations in deoxy-hemoglobin (deoxyHb) concentrations, provides an indirect measure of functional activity of the human brain.8 Patterns of activation/deactivation and connectivity across brain regions can be detected with a very high spatial resolution for both cortical and subcortical structures. This technique, however, is characterized by severe limitations and constraints about motion artifacts and only small movements are allowed inside the scanner. This entails dramatic compromises on the experimental design and on the inclusion/exclusion criteria. Multiple solutions have been attempted to overcome such limitations. For instance, many neuroimaging studies have been performed on the motor imagery,9,10 but imaging can be different from subject to subject,11 and imagined walking and actual walking engage different brain networks.12 Other authors have suggested the application of virtual reality,13 and there have been a few attempts to allow an almost real-walking sequence while scanning with fMRI.14,15Additional opportunities to investigate the mechanisms sustaining walking control include the use of surrogate tasks in the scanner as proxy of walking tasks,16 or to “freeze” brain activations during walking using positron emission tomography (PET) radiotracers, which allow the retrospective identification of activation patterns, albeit with some uncertainties and low spatial and temporal resolution.12

Therefore, until now there has not been an ecological way to noninvasively assess neurophysiological correlates of walking processes in gait disorders.

Functional near-infrared spectroscopy (fNIRS) is becoming an important research tool to assess functional activity in special populations (neurological and psychiatric patients)17 or for special tasks.1821 fNIRS is a noninvasive optical imaging technique that, similarly to fMRI, measures the hemodynamic response to infer the underlying neural activity. Optical imaging is based on near-infrared (650-1000 nm) light propagation into scattering tissues and its absorption by 2 major chromophores in the brain, oxy-hemoglobin (oxyHb) and deoxyHb, which show specific absorption spectra depending on the wavelength of the photons.22 Typically, an fNIRS apparatus is composed of a light source that is coupled to the participant’s head via either light-emitting diodes (LEDs) or through fiber-optical bundles with a detector that receives the light after it has been scattered through the tissue. A variation of the optical density of the photons measured by detectors depends on the absorption of the biological tissues (Figure 1A). Using more than one wavelength and applying the modified Beer-Lambert law, it is possible to infer on the changes of oxyHb and deoxyHb concentrations.23 fNIRS has a number of definite advantages compared to fMRI, its major competitor: (a) it does not pose immobility constrains,25 (b) is portable,26 (c) allows recording during real walking,27 (d) allows long-lasting recordings, (e) it does not produce any noise, (f) it makes possible the investigation of brain activity during sleep,28 (f) it allows to obtain a richer picture of the neurovascular coupling as it measures changes in both oxyHb and deoxyHb concentration with high temporal resolution (up to milliseconds). High temporal resolution is usually not mandatory for the investigation of the hemodynamic response whose dynamic takes at least 3 to 5 seconds, but it can be useful for the study of transient hemodynamic activity like the initial dip29 or to detect subtle temporal variations in the latency of the hemodynamic response across different experimental conditions.19,21,30 The major drawback of fNIRS in comparison to fMRI is its lower spatial resolution (few centimeters under the skull) and its lack of sensitivity to subcortical regions.18,19 However, this might be considered a minor limitation, as there is a large body of evidence suggesting that (a) cortical mechanisms take place in walking,31 (b) the organization of the motor system is distributed along large brain regions,32and (c) the function of subcortical structures is mirrored in the cerebral cortex.33

figure

Figure 1. Illustration of penetration depth of near-infrared light into the tissue in a probe configuration used to investigate motor performances during walking task (upper row). The picture shows brain reconstruction from a high-resolution anatomical MRI. The spheres placed over the skull correspond to vitamin E capsules employed during the MRI to mark the positions of the optodes and to allow the coregistration of the individual anatomy together with the optode position. In this illustration, only the photons propagation from one source (S) to one detector (D) have been simulated. The yellow-red scale indicates the degree of sensitivity74 for the considered source-detector pair to the head/brain structures. (A, B, and C) Lower row: Examples of fNIRS experimental device used for assessing brain activity during real walking tasks. These fNIRS approaches included either commercial device, such as (A) wireless portable fNIRS system (NIRx; Germany) or support systems for treadmill walking activity with body weight support24 (B) or with free movement range (C).

Continue —> Near-Infrared Spectroscopy in Gait Disorders – Feb 14, 2017

, , , , , , ,

Leave a comment

[Abstract] Changes in lower limb muscle activity after walking on a split-belt treadmill in individuals post-stroke

Abstract

Background: There is growing evidence that stroke survivors can adapt and improve step length symmetry in the context of split-belt treadmill (SBT) walking. However, less knowledge exists about the strategies involved for such adaptations. This study analyzed lower limb muscle activity in individuals post-stroke related to SBT-induced changes in step length.

Methods: Step length and surface EMG activity of six lower limb muscles were evaluated in individuals post-stroke (n=16) during (adaptation) and after (after-effects) walking at unequal belt speeds.

Results: During adaptation, significant increases in EMG activity were mainly found in proximal muscles (p⩽0.023), whereas after-effects were observed particularly in the distal muscles. The plantarflexor EMG increased after walking on the slow belt (p⩽0.023) and the dorsiflexors predominantly after walking on the fast belt (p⩽0.017) for both, nonparetic and paretic-fast conditions. Correlation analysis revealed that after-effects in step length were mainly associated with changes in distal paretic muscle activity (0.522⩽ r ⩽0.663) but not with functional deficits. Based on our results, SBT walking could be relevant for training individuals post-stroke who present shorter paretic step length combined with dorsiflexor weakness, or individuals with shorter nonparetic step length and plantarflexor weakness.

Source: Changes in lower limb muscle activity after walking on a split-belt treadmill in individuals post-stroke

, , , , , , ,

Leave a comment

[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

, , , , , , ,

Leave a comment

[CASE STUDY] The effect of a powered ankle foot orthosis on walking in a stroke subject: a case study – Full Text PDF

Abstract.

[Purpose] Standing and walking are impaired in stroke patients. Therefore, assisted devices are required to restore their walking abilities. The ankle foot orthosis with an external powered source is a new type of orthosis. The aim of this study was to evaluate the performance of a powered ankle foot orthosis compared with unpowered orthoses in a stroke patient.

[Subjects and Methods] A single stroke subject participated in this study. The subject was fitted with three types of ankle foot orthosis (powered, posterior leg spring, and carbon ankle foot orthoses). He was asked to walk with and without the three types of orthoses, and kinetic and kinematic parameters were measured.

[Results] The results of the study showed that the moments applied on the ankle, knee, and hip joints increased while walking with the powered ankle foot orthosis.

[Conclusion] As the powered ankle foot orthosis influences the moments of the ankle, knee, and hip joints, it can increase the standing and walking abilities of stroke patients more than other available orthoses. Therefore, it is recommended to be used in rehabilitation programs for stroke patients.

Full Text PDF

, , ,

Leave a comment

[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery.
In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

, , , , , , ,

Leave a comment

[Poster] Design and Evaluation of a Novel Mechanical Device to Improve Hemiparetic Gait: A Case Report

To evaluate a novel gait training device, the Gait Propulsion Trainer (GPT), with respect to its ability to increase propulsion forces generated by the paretic leg in a person with hemiparesis.

Source: Design and Evaluation of a Novel Mechanical Device to Improve Hemiparetic Gait: A Case Report – Archives of Physical Medicine and Rehabilitation

, , , , , ,

Leave a comment

[Abstract] Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression.

Abstract

Objective

To assess the influence of dosing parameters and patient characteristics on the efficacy of aerobic exercise (AEX) post-stroke.

Data Sources

A systematic review was conducted using Pubmed, MEDLINE, CINAHL, PEDro and Academic Search Complete.

Study Selection

Studies were selected that compared AEX to a non-aerobic control group among ambulatory persons with stroke.

Data Extraction

Extracted outcome data included: peak oxygen consumption during exercise testing (VO2peak), walking speed and walking endurance (6-minute walk test). Independent variables of interest were: AEX mode (seated or walking), AEX intensity (moderate or vigorous), AEX volume (total hours), stroke chronicity and baseline outcome scores.

Data Synthesis

Significant between-study heterogeneity was confirmed for all outcomes. Pooled AEX effect size estimates (AEX change – control change) from random effects models were: VO2peak, 2.2 mL/kg/min [95% CI: 1.3, 3.1]; walking speed, 0.06 m/s [95% CI: 0.01, 0.11]; and 6-minute walk test distance, 29 m [95% CI: 15, 42]. From meta-regression, greater VO2peak effect sizes were significantly associated with higher AEX intensity and higher baseline VO2peak. Greater effect sizes for walking speed and the 6-minute walk test were significantly associated with a walking AEX mode. In contrast, seated AEX did not have a significant effect on walking outcomes.

Conclusions

AEX significantly improves aerobic capacity post-stroke, but may need to be task specific to impact walking speed and endurance. Higher AEX intensity is associated with better outcomes. Future randomized studies are needed to confirm these results.

Source: Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression – Archives of Physical Medicine and Rehabilitation

, , , , , , , ,

Leave a comment

[ARTICLE] Functional electrical stimulation versus ankle foot orthoses for foot-drop: a meta-analysis of orthotic effects – Full Text PDF

ABSTRACT

Objective: To compare the effects on walking of Functional Electrical Stimulation (FES) and Ankle Foot Orthoses (AFO) for foot-drop of central neurological origin, assessed in terms of unassisted walking behaviours compared with assisted walking following a period of use (combined-orthotic effects).

Data Sources: MEDLINE, AMED, CINAHL, Cochrane Central Register of Controlled Trials, Scopus, REHABDATA, PEDro, NIHR Centre for Reviews and Dissemination and clinicaltrials.gov. plus reference list, journal, author and citation searches.

Study Selection: English language comparative Randomised Controlled Trials (RCTs).

Data Synthesis: Seven RCTs were eligible for inclusion. Two of these reported different results from the same trial and another two reported results from different follow up periods so were combined; resulting in five synthesised trials with 815 stroke participants. Meta-analyses of data from the final assessment in each study and three overlapping time-points showed comparable improvements in walking speed over ten metres (p=0.04-0.95), functional exercise capacity (p=0.10-0.31), timed up-and-go (p=0.812 and p=0.539) and perceived mobility (p=0.80) for both interventions.

Conclusion: Data suggest that, in contrast to assumptions that predict FES superiority, AFOs have equally positive combined-orthotic effects as FES on key walking measures for foot-drop caused by stroke. However, further long-term, high-quality RCTs are required. These should focus on measuring the mechanisms-of-action; whether there is translation of improvements in impairment to function, plus detailed reporting of the devices used across diagnoses. Only then can robust clinical recommendations be made.

Full Text PDF

 

, , , , , , , , ,

Leave a comment

%d bloggers like this: