Quantifying human movement across the continuum of care: From lab to clinic to community

Gregory, Chris; Embry, Aaron E.; Perry, Lindsay; Bowden, Mark G.

doi:10.1016/j.jneumeth.2014.04.029

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Although walking speed is a robust and clinically meaningful metric that is commonly used to stratify participants in intervention studies [ 5 , 7 – 10 ], the heterogeneity of motor impairment underlying similar poststroke walking speeds appears to reduce the utility of using walking speed to identify the appropriate candidates for a targeted gait intervention [ 16 , 28 ]. These findings build on recent work calling for a quantification of the biomechanical deficits underlying walking function to guide clinical intervention [ 20 , 30 , 31 ] and support future work evaluating how different participant characteristics interact to influence the effects of poststroke locomotor training. This line of research is particularly critical for advancing the individualization of neurorehabilitation efforts in biomechanically-diverse populations.…”

Section: Discussionsupporting

confidence: 71%

“…Although the clinical utility of this finding may be limited due to the inability to directly measure gait kinetics in the clinic, several kinematic variables that can be measured in most clinical settings have been identified as being strongly linked to paretic propulsion (e.g., paretic trailing limb angle [ 32 – 34 ] and paretic and nonparetic step lengths [ 16 , 35 ]). Moreover, there is growing interest in developing low cost, wearable technology solutions to enable measurement of metrics such as propulsion outside of the laboratory [ 30 , 31 ]. Further research and development in this area appear worthwhile.…”

Section: Discussionmentioning

confidence: 99%

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Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

Awad

Reisman

Pohlig

et al. 2016

J NeuroEngineering Rehabil

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BackgroundWalking speed has been used to predict the efficacy of gait training; however, poststroke motor impairments are heterogeneous and different biomechanical strategies may underlie the same walking speed. Identifying which individuals will respond best to a particular gait rehabilitation program using walking speed alone may thus be limited. The objective of this study was to determine if, beyond walking speed, participants’ baseline ability to generate propulsive force from their paretic limbs (paretic propulsion) influences the improvements in walking speed resulting from a paretic propulsion-targeting gait intervention.MethodsTwenty seven participants >6 months poststroke underwent a 12-week locomotor training program designed to target deficits in paretic propulsion through the combination of fast walking with functional electrical stimulation to the paretic ankle musculature (FastFES). The relationship between participants’ baseline usual walking speed (UWSbaseline), maximum walking speed (MWSbaseline), and paretic propulsion (propbaseline) versus improvements in usual walking speed (∆UWS) and maximum walking speed (∆MWS) were evaluated in moderated regression models.ResultsUWSbaseline and MWSbaseline were, respectively, poor predictors of ΔUWS (R2 = 0.24) and ΔMWS (R2 = 0.01). Paretic propulsion × walking speed interactions (UWSbaseline × propbaseline and MWSbaseline × propbaseline) were observed in each regression model (R2s = 0.61 and 0.49 for ∆UWS and ∆MWS, respectively), revealing that slower individuals with higher utilization of the paretic limb for forward propulsion responded best to FastFES training and were the most likely to achieve clinically important differences.ConclusionsCharacterizing participants based on both their walking speed and ability to generate paretic propulsion is a markedly better approach to predicting walking recovery following targeted gait rehabilitation than using walking speed alone.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

Awad

Reisman

Pohlig

et al. 2016

J NeuroEngineering Rehabil

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“…The Kinect is also being used for the assess ment of postural control [30], gait [31], balance [3], static foot posture [32], body sway [33], and common motor movements [34], all with promising comparisons to three-dimensional gold standard systems. The use of the Kinect as a rehabilitation tool outside of the clinic and in the home [35][36][37] suggests the sensor's portability and supports the recommendation of previous research to incorporate assessment throughout the continuum of care [38].…”

Section: Microsoft Kinectsupporting

confidence: 64%

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Milgrom¹,

OTD²,

Foreman³

et al. 2016

J Rehabil Res Dev

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Abstract-Concurrent validity and test-retest reliability of the Microsoft Kinect in quantification of manual wheelchair pro pulsion were examined. Data were collected from five manual wheelchair users on a roller system. Three Kinect sensors were used to assess test-retest reliability with a still pose. Three sys tems were used to assess concurrent validity of the Kinect to measure propulsion kinematics (joint angles, push loop charac teristics): Kinect, Motion Analysis, and Dartfish ProSuite (Dart fish joint angles were limited to shoulder and elbow flexion). Intraclass correlation coefficients (ICCs) revealed good reliabil ity (0.87-0.99) between five of the six joint angles (neck flexion, shoulder flexion, shoulder abduction, elbow flexion, wrist flexion). ICCs suggested good concurrent validity for elbow flexion between the Kinect and Dartfish and between the Kinect and Motion Analysis. Good concurrent validity was revealed for maximum height, hand-axle relationship, and maximum area (0.92-0.95) between the Kinect and Dartfish and maxi mum height and hand-axle relationship (0.89-0.96) between the Kinect and Motion Analysis. Analysis of variance revealed sig nificant differences (p < 0.05) in maximum length between Dartfish (mean 58.76 cm) and the Kinect (40.16 cm). Results pose promising research and clinical implications for propul sion assessment and overuse injury prevention with the applica tion of current findings to future technology.

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“…26 Due to these missing links, there is great potential and opportunity for researchers to link laboratory-based measurements to clinical improvements. 28,83 …”

Section: Discussionmentioning

confidence: 99%

A systematic review of mechanisms of gait speed change post-stroke. Part 1: spatiotemporal parameters and asymmetry ratios

Wonsetler

Bowden

2017

Topics in Stroke Rehabilitation

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Background In walking rehabilitation trials, self-selected walking speed has emerged as the dominant outcome measure to assess walking ability. However, this measure cannot differentiate between recovery of impaired movement and compensation strategies. Spatiotemporal variables and asymmetry ratios are frequently used to quantify gait deviations and are hypothesized markers of recovery. Objectives The purpose of this review is to investigate spatiotemporal variables and asymmetry ratios as mechanistic recovery measures in physical therapy intervention studies post-stroke. Methods A systematic literature search was performed to identify physical therapy intervention studies with a statistically significant change in self-selected walking speed post intervention and concurrently collected spatiotemporal variables. Methodological quality was assessed using the Cochrane Collaboration’s tool. Walking speed, spatiotemporal, and intervention data were extracted. Results 46 studies met the inclusion criteria, 41 of which reported raw spatiotemporal measures and 19 reported asymmetry ratio calculations. Study interventions included: aerobic training (n=2), functional electrical stimulation (n=5), hippotherapy (n=2), motor dual task training (n=2), multidimensional rehabilitation (n=4), robotics (n=4), sensory stimulation training (n=8), strength/resistance training (n=4), task specific locomotor rehabilitation (n=9), and visually guided training (n=6). Conclusions Spatiotemporal variables help describe gait deviations, but scale to speed, so consequently, may not be an independent factor in describing functional recovery and gains. Therefore, these variables are limited in explaining mechanistic changes involved in improving gait speed. Use of asymmetry measures provides additional information regarding the coordinative requirements for gait and can potentially indicate recovery. Additional laboratory-based mechanistic measures may be required to truly understand how walking speed improves.

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Quantifying human movement across the continuum of care: From lab to clinic to community

Cited by 9 publications

References 14 publications

Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

A systematic review of mechanisms of gait speed change post-stroke. Part 1: spatiotemporal parameters and asymmetry ratios

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