Literature DB >> 28526482

Reducing Trunk Compensation in Stroke Survivors: A Randomized Crossover Trial Comparing Visual and Force Feedback Modalities.

Bulmaro Adolfo Valdés1, Andrea Nicole Schneider2, H F Machiel Van der Loos3.   

Abstract

OBJECTIVE: To investigate whether the compensatory trunk movements of stroke survivors observed during reaching tasks can be decreased by force and visual feedback, and to examine whether one of these feedback modalities is more efficacious than the other in reducing this compensatory tendency.
DESIGN: Randomized crossover trial.
SETTING: University research laboratory. PARTICIPANTS: Community-dwelling older adults (N=15; 5 women; mean age, 64±11y) with hemiplegia from nontraumatic hemorrhagic or ischemic stroke (>3mo poststroke), recruited from stroke recovery groups, the research group's website, and the community.
INTERVENTIONS: In a single session, participants received augmented feedback about their trunk compensation during a bimanual reaching task. Visual feedback (60 trials) was delivered through a computer monitor, and force feedback (60 trials) was delivered through 2 robotic devices. MAIN OUTCOME MEASURES: Primary outcome measure included change in anterior trunk displacement measured by motion tracking camera. Secondary outcomes included trunk rotation, index of curvature (measure of straightness of hands' path toward target), root mean square error of hands' movement (differences between hand position on every iteration of the program), completion time for each trial, and posttest questionnaire to evaluate users' experience and system's usability.
RESULTS: Both visual (-45.6% [45.8 SD] change from baseline, P=.004) and force (-41.1% [46.1 SD], P=.004) feedback were effective in reducing trunk compensation. Scores on secondary outcome measures did not improve with either feedback modality. Neither feedback condition was superior.
CONCLUSIONS: Visual and force feedback show promise as 2 modalities that could be used to decrease trunk compensation in stroke survivors during reaching tasks. It remains to be established which one of these 2 feedback modalities is more efficacious than the other as a cue to reduce compensatory trunk movement.
Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Rehabilitation; Robotics; Stroke; Therapy, Computer-assisted; Upper extremity

Mesh:

Year:  2017        PMID: 28526482     DOI: 10.1016/j.apmr.2017.03.034

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  7 in total

Review 1.  Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review.

Authors:  Xiaoyi Wang; Yan Fu; Bing Ye; Jessica Babineau; Yong Ding; Alex Mihailidis
Journal:  J Med Internet Res       Date:  2022-06-13       Impact factor: 7.076

2.  Effects of virtual reality in post-stroke aphasia: a systematic review and meta-analysis.

Authors:  Yun Cao; Xing Huang; Binlong Zhang; Georg S Kranz; Danli Zhang; Xiaolin Li; Jingling Chang
Journal:  Neurol Sci       Date:  2021-04-09       Impact factor: 3.307

3.  Robotics-assisted visual-motor training influences arm position sense in three-dimensional space.

Authors:  Bulmaro A Valdés; Mahta Khoshnam; Jason L Neva; Carlo Menon
Journal:  J Neuroeng Rehabil       Date:  2020-07-14       Impact factor: 4.262

4.  Kinect-based assessment of proximal arm non-use after a stroke.

Authors:  K K A Bakhti; I Laffont; M Muthalib; J Froger; D Mottet
Journal:  J Neuroeng Rehabil       Date:  2018-11-14       Impact factor: 4.262

5.  Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors.

Authors:  Siqi Cai; Xuyang Wei; Enze Su; Weifeng Wu; Haiqing Zheng; Longhan Xie
Journal:  J Neuroeng Rehabil       Date:  2020-04-28       Impact factor: 4.262

6.  Dual-Task Gait Stability after Concussion and Subsequent Injury: An Exploratory Investigation.

Authors:  David R Howell; Scott Bonnette; Jed A Diekfuss; Dustin R Grooms; Gregory D Myer; Julie C Wilson; William P Meehan
Journal:  Sensors (Basel)       Date:  2020-11-05       Impact factor: 3.576

7.  Perceptions of Existing Wearable Robotic Devices for Upper Extremity and Suggestions for Their Development: Findings From Therapists and People With Stroke.

Authors:  Ahmed Elnady; W Ben Mortenson; Carlo Menon
Journal:  JMIR Rehabil Assist Technol       Date:  2018-05-15
  7 in total

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