Literature DB >> 30776997

Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke.

Anne Schwarz1,2,3, Christoph M Kanzler4, Olivier Lambercy4, Andreas R Luft1,2, Janne M Veerbeek1,2.   

Abstract

Background and Purpose- Assessing upper limb movements poststroke is crucial to monitor and understand sensorimotor recovery. Kinematic assessments are expected to enable a sensitive quantification of movement quality and distinguish between restitution and compensation. The nature and practice of these assessments are highly variable and used without knowledge of their clinimetric properties. This presents a challenge when interpreting and comparing results. The purpose of this review was to summarize the state of the art regarding kinematic upper limb assessments poststroke with respect to the assessment task, measurement system, and performance metrics with their clinimetric properties. Subsequently, we aimed to provide evidence-based recommendations for future applications of upper limb kinematics in stroke recovery research. Methods- A systematic search was conducted in PubMed, Embase, CINAHL, and IEEE Xplore. Studies investigating clinimetric properties of applied metrics were assessed for risk of bias using the Consensus-Based Standards for the Selection of Health Measurement Instruments checklist. The quality of evidence for metrics was determined according to the Grading of Recommendations Assessment, Development, and Evaluation approach. Results- A total of 225 studies (N=6197) using 151 different kinematic metrics were identified and allocated to 5 task and 3 measurement system groups. Thirty studies investigated clinimetrics of 62 metrics: reliability (n=8), measurement error (n=5), convergent validity (n=22), and responsiveness (n=2). The metrics task/movement time, number of movement onsets, number of movement ends, path length ratio, peak velocity, number of velocity peaks, trunk displacement, and shoulder flexion/extension received a sufficient evaluation for one clinimetric property. Conclusions- Studies on kinematic assessments of upper limb sensorimotor function are poorly standardized and rarely investigate clinimetrics in an unbiased manner. Based on the available evidence, recommendations on the assessment task, measurement system, and performance metrics were made with the goal to increase standardization. Further high-quality studies evaluating clinimetric properties are needed to validate kinematic assessments, with the long-term goal to elucidate upper limb sensorimotor recovery poststroke. Clinical Trial Registration- URL: https://www.crd.york.ac.uk/prospero/ . Unique identifier: CRD42017064279.

Entities:  

Keywords:  biomechanical phenomena; movement; paresis; review; stroke; upper extremity

Mesh:

Year:  2019        PMID: 30776997     DOI: 10.1161/STROKEAHA.118.023531

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  52 in total

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

Authors:  Christoph M Kanzler; Mike D Rinderknecht; Anne Schwarz; Ilse Lamers; Cynthia Gagnon; Jeremia P O Held; Peter Feys; Andreas R Luft; Roger Gassert; Olivier Lambercy
Journal:  NPJ Digit Med       Date:  2020-05-29

Review 2.  Optimizing functional outcome endpoints for stroke recovery studies.

Authors:  Mustafa Balkaya; Sunghee Cho
Journal:  J Cereb Blood Flow Metab       Date:  2019-09-14       Impact factor: 6.200

3.  Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study.

Authors:  Ze-Jian Chen; Chang He; Ming-Hui Gu; Jiang Xu; Xiao-Lin Huang
Journal:  J Healthc Eng       Date:  2021-08-19       Impact factor: 2.682

Review 4.  A review of computational approaches for evaluation of rehabilitation exercises.

Authors:  Yalin Liao; Aleksandar Vakanski; Min Xian; David Paul; Russell Baker
Journal:  Comput Biol Med       Date:  2020-03-04       Impact factor: 4.589

Review 5.  Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation.

Authors:  Ahmet Arac
Journal:  Curr Neurol Neurosci Rep       Date:  2020-06-15       Impact factor: 5.081

6.  Quantifying Pathological Synergies in the Upper Extremity of Stroke Subjects With the Use of Inertial Measurement Units: A Pilot Study.

Authors:  Miguel M C Bhagubai; Gerjan Wolterink; Anne Schwarz; Jeremia P O Held; Bert-Jan F Van Beijnum; Peter H Veltink
Journal:  IEEE J Transl Eng Health Med       Date:  2020-12-07       Impact factor: 3.316

7.  Effect of post-stroke spasticity on voluntary movement of the upper limb.

Authors:  Hadar Lackritz; Yisrael Parmet; Silvi Frenkel-Toledo; Melanie C Baniña; Nachum Soroker; John M Solomon; Dario G Liebermann; Mindy F Levin; Sigal Berman
Journal:  J Neuroeng Rehabil       Date:  2021-05-13       Impact factor: 4.262

8.  Relative independence of upper limb position sense and reaching in children with hemiparetic perinatal stroke.

Authors:  Andrea M Kuczynski; Adam Kirton; Jennifer A Semrau; Sean P Dukelow
Journal:  J Neuroeng Rehabil       Date:  2021-05-12       Impact factor: 4.262

9.  Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton.

Authors:  Florian Grimm; Jelena Kraugmann; Georgios Naros; Alireza Gharabaghi
Journal:  J Neuroeng Rehabil       Date:  2021-06-02       Impact factor: 4.262

10.  Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke.

Authors:  Monika Zbytniewska; Christoph M Kanzler; Lisa Jordan; Christian Salzmann; Joachim Liepert; Olivier Lambercy; Roger Gassert
Journal:  J Neuroeng Rehabil       Date:  2021-07-16       Impact factor: 4.262

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