Standardized Measurement of Quality of Upper Limb Movement After Stroke: Consensus-Based Core Recommendations From the Second Stroke Recovery and Rehabilitation Roundtable

Kwakkel, Gert; Wegen, Erwin E. H. van; Burridge, Jane; Winstein, Carolee J.; Dokkum, Liesjet E.H. van; Murphy, Margit Alt; Levin, Mindy F.; Krakauer, John W.; Lang, Catherine E.; Keller, Thierry; Kitago, Tomoko; Nordin, Nurdiana; Pomeroy, V; Veerbeek, Janne M.; Wijck, Frederike van

doi:10.1177/1545968319886477

Cited by 124 publications

(179 citation statements)

References 41 publications

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“…Most importantly, the high variability of clinimetric properties across behavioral tasks and sensor-based metrics motivates the need for a methodology to select metrics for a specific assessment task, starting from a large set of potential metrics that should be narrowed down to a clinically relevant core set 13,21,26 . Existing approaches to select core sets of metrics commonly rely on the consensus from a group of selected experts, which can lead to bias and is often not task-dependent 21,[27][28][29][30] . Moreover, existing data-driven selection procedures (e.g., regression-based methods such as LASSO), are rarely tailored to the specific requirements of digital health metrics, where often no accurate ground truth about the targeted impairments is available [31][32][33] .…”

Section: Introductionmentioning

confidence: 99%

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

et al. 2020

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Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.

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Section: Introductionmentioning

confidence: 99%

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

et al. 2020

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“…Kinematic analysis using optoelectronic systems provide precise and detailed measurements of movement performance and quality. A resent task force for stroke recovery metrics has recommended both 2D planar reaching and 3D functional reach-to-grasp task for measuring behavioral restitution of the upper limb [13]. The measurements in 3D using functional tasks are of particular interest since they provide quantifiable metrics on the quality of movement during a natural task that is well known to the performer [14,15].…”

Section: Introductionmentioning

confidence: 99%

Upper limb kinematics during the first year after stroke: the stroke arm longitudinal study at the University of Gothenburg (SALGOT)

Thrane

Sunnerhagen

Murphy

2020

J NeuroEngineering Rehabil

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Background Reduction of compensation and improved movement quality indicate recovery after stroke. Since clinical measures alone are often inadequate to distinguish between behavioral recovery and compensation, kinematic analysis of functional tasks has been recommended. Objective To quantify longitudinal changes and residual deficits in movement performance and quality during the first year after stroke using kinematic analysis of drinking task. Methods A total of 56 participants with first ever stroke causing upper extremity impairment were extracted from a non-selected stroke unit cohort (Stroke Arm Longitudinal Study at the University of Gothenburg-SALGOT). Participants needed to able to perform the drinking task with the more-affected arm at least on 2 occasions out of 6 (3 days, 10 days, 4 weeks, and 3, 6, and 12 months) during the first year to be included. A cohort of 60 healthy individuals was used as reference. Longitudinal changes were analyzed using linear mixed models. Results Movement time, number of movement units, peak angular velocity of the elbow, peak hand velocity, and trunk displacement improved significantly over the first 3 months with a peak at 6 months. Movement time and peak hand velocity reached levels comparable to healthy at 3 months, but number of movement units, peak elbow angular velocity, trunk displacement, and arm abduction remained different from healthy over the first year after stroke. Conclusions Even when the recovery patterns of kinematics follow the known nonlinear pattern, not all kinematic measures reach the levels in par with healthy controls at one year post stroke. Since the number of movement units, peak angular velocity, trunk displacement, and arm abduction remained impaired over the first year, they might be the most suited measures to distinguish behavioral recovery from compensation strategies. Trial registration ClinicalTrials: NCT01115348. 4 May 2010. Retrospectively registered.

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“…Recent consensus-based core recommendations from the Second Stroke Recovery and Rehabilitation Roundtable include the use of standardized kinematic and kinetic outcomes as measurements of sensorimotor recovery in stroke trials targeting upper extremity function. 123 A similar principle and further investigations would be helpful to further unveil the impact of VR interventions on poststroke locomotor recovery.…”

Section: Limitationsmentioning

confidence: 97%

Virtual reality training enhances gait poststroke: a systematic review and meta‐analysis

Ghai

Ghai²,

Lamontagne

2020

Annals of the New York Academy of Sciences

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Virtual reality (VR)-based interventions are gaining widespread attention for managing neurological disorders such as stroke. A metastatistical consensus regarding the intervention is strongly warranted. In this study, we attempt to address this gap in the literature and provide the current state of evidence for the effects of VR on gait performance. We conducted both between-and within-group meta-analyses to provide a state of evidence for VR. Moreover, we conducted a search adhering to PRISMA guidelines on nine databases. Out of 1866 records, 32 studies involving a total of 809 individuals were included in this review. Considering all included studies, significant enhancements in gait parameters were observed with VR-based interventions compared with conventional therapy. A between-group meta-analysis reported beneficial significant medium effects of VR training on cadence (Hedge's g = 0.55), stride length ((STrL; Hedge's g = 0.46), and gait speed (Hedge's g = 0.30). Similarly, a within-group metaanalysis further revealed positive medium effects of VR on cadence (Hedge's g = 0.76), STrL (Hedge's g = 0.61), and gait speed (Hedge's g = 0.69). Additional subgroup analyses revealed beneficial effects of joint application of VR and robot-assisted gait training on gait speed (Hedge's g = 0.50). Collectively, findings from this review provide evidence for the effectiveness of VR-based gait training for stroke survivors.

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Standardized Measurement of Quality of Upper Limb Movement After Stroke: Consensus-Based Core Recommendations From the Second Stroke Recovery and Rehabilitation Roundtable

Cited by 124 publications

References 41 publications

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments

Upper limb kinematics during the first year after stroke: the stroke arm longitudinal study at the University of Gothenburg (SALGOT)

Virtual reality training enhances gait poststroke: a systematic review and meta‐analysis

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