Posts Tagged walking

[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

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[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

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[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.

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[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.

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[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.

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[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

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[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

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[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.

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[Abstract] Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

Abstract

Objective: This study aimed to determine the effectiveness of current interventions to improve real-world walking for people with stroke and specifically whether benefits are sustained.

Data sources: EBSCO Megafile, AMED, Cochrane, Scopus, PEDRO, OTSeeker and Psychbite databases were searched to identify relevant studies.

Review methods: Proximity searching with keywords such as ambulat*, walk*, gait, mobility*, activit* was used. Randomized controlled trials that used measures of real-world walking were included. Two reviewers independently assessed methodological quality using the Cochrane Risk of Bias Tool and extracted the data.

Results: Nine studies fitting the inclusion criteria were identified, most of high quality. A positive effect overall was found indicating a small effect of interventions on real-world walking (SMD 0.29 (0.17, 0.41)). Five studies provided follow-up data at >3–6 months, which demonstrated sustained benefits (SMD 0.32 (0.16, 0.48)). Subgroup analysis revealed studies using exercise alone were not effective (SMD 0.19 (–0.11, 0.49)), but those incorporating behavioural change techniques (SMD 0.27 (0.12, 0.41)) were.

Conclusions: A small but significant effect was found for current interventions and benefits can be sustained. Interventions that include behaviour change techniques appear more effective at improving real-world walking habits than exercise alone.

Source: Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

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[Abstract] Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

Abstract

Objective: This study aimed to determine the effectiveness of current interventions to improve real-world walking for people with stroke and specifically whether benefits are sustained.

Data sources: EBSCO Megafile, AMED, Cochrane, Scopus, PEDRO, OTSeeker and Psychbite databases were searched to identify relevant studies.

Review methods: Proximity searching with keywords such as ambulat*, walk*, gait, mobility*, activit* was used. Randomized controlled trials that used measures of real-world walking were included. Two reviewers independently assessed methodological quality using the Cochrane Risk of Bias Tool and extracted the data.

Results: Nine studies fitting the inclusion criteria were identified, most of high quality. A positive effect overall was found indicating a small effect of interventions on real-world walking (SMD 0.29 (0.17, 0.41)). Five studies provided follow-up data at >3–6 months, which demonstrated sustained benefits (SMD 0.32 (0.16, 0.48)). Subgroup analysis revealed studies using exercise alone were not effective (SMD 0.19 (–0.11, 0.49)), but those incorporating behavioural change techniques (SMD 0.27 (0.12, 0.41)) were.

Conclusions: A small but significant effect was found for current interventions and benefits can be sustained. Interventions that include behaviour change techniques appear more effective at improving real-world walking habits than exercise alone.

 

Source: Interventions to improve real-world walking after stroke: A systematic review and meta-analysis

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