Strong Relations of Elbow Excursion and Grip Strength with Post-stroke Arm Function and Activities: Should We Aim for This in Technology-supported Training?

Overview

Journal J Rehabil Assist Technol Eng

Specialty Biomedical Engineering

Date 2019 Jun 14

PMID 31191944

Citations 3

Authors

Sharon M Nijenhuis

Gerdienke B Prange-Lasonder

Judith Fm Fleuren

Jan Wagenaar

Jaap H Buurke

Johan S Rietman

Affiliations

Soon will be listed here.

Abstract

Objective: To investigate the relationships between an extensive set of objective movement execution kinematics of the upper extremity and clinical outcome measures in chronic stroke patients: at baseline and after technology-supported training at home.

Methods: Twenty mildly to severely affected chronic stroke patients participated in the baseline evaluation, 15 were re-evaluated after six weeks of intensive technology-supported or conventional arm/hand training at home. Grip strength, 3D motion analysis of a reach and grasp task, and clinical scales (Fugl-Meyer assessment (FM), Action Research Arm Test (ARAT) and Motor Activity Log (MAL)) were assessed pre- and post-training.

Results: Most movement execution parameters showed moderate-to-strong relationships with FM and ARAT, and to a smaller degree with MAL. Elbow excursion explained the largest amount of variance in FM and ARAT, together with grip strength. The only strong association after training was found between changes in ARAT and improvements in hand opening (conventional) or grip strength (technology-supported).

Conclusions: Elbow excursion and grip strength showed strongest association with post-stroke arm function and activities. Improved functional ability after training at home was associated with increased hand function. Addressing both reaching and hand function are indicated as valuable targets for (technological) treatment applications to stimulate functional improvements after stroke.

Citing Articles

Correlation between kinetic and kinematic measures, clinical tests and subjective self-evaluation questionnaires of the affected upper limb in people after stroke.

Baer R, Feingold-Polak R, Ostrovsky D, Kurz I, Levy-Tzedek S Front Neurosci. 2024; 17:1264513.

PMID: 38178833 PMC: 10765579. DOI: 10.3389/fnins.2023.1264513.

Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke.

Liao W, Hsieh Y, Lee T, Chen C, Wu C Sci Rep. 2022; 12(1):11235.

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Validity, reliability, and sensitivity to motor impairment severity of a multi-touch app designed to assess hand mobility, coordination, and function after stroke.

Molla-Casanova S, Llorens R, Borrego A, Salinas-Martinez B, Serra-Ano P J Neuroeng Rehabil. 2021; 18(1):70.

PMID: 33892763 PMC: 8066975. DOI: 10.1186/s12984-021-00865-9.

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