Literature DB >> 23929692

Error augmentation enhancing arm recovery in individuals with chronic stroke: a randomized crossover design.

Farnaz Abdollahi1, Emily D Case Lazarro, Molly Listenberger, Robert V Kenyon, Mark Kovic, Ross A Bogey, Donald Hedeker, Borko D Jovanovic, James L Patton.   

Abstract

BACKGROUND: Neurorehabilitation studies suggest that manipulation of error signals during practice can stimulate improvement in coordination after stroke.
OBJECTIVE: To test visual display and robotic technology that delivers augmented error signals during training, in participants with stroke.
METHODS: A total of 26 participants with chronic hemiparesis were trained with haptic (via robot-rendered forces) and graphic (via a virtual environment) distortions to amplify upper-extremity (UE) tracking error. In a randomized crossover design, the intervention was compared with an equivalent amount of practice without error augmentation (EA). Interventions involved three 45-minute sessions per week for 2 weeks, then 1 week of no treatment, and then 2 additional weeks of the alternate treatment. A therapist provided a visual cursor using a tracking device, and participants were instructed to match it with their hand. Haptic and visual EA was used with blinding of participant, therapist, technician-operator, and evaluator. Clinical measures of impairment were obtained at the beginning and end of each 2-week treatment phase as well as at 1 week and at 45 days after the last treatment.
RESULTS: Outcomes showed a small, but significant benefit to EA training over simple repetitive practice, with a mean 2-week improvement in Fugl-Meyer UE motor score of 2.08 and Wolf Motor Function Test of timed tasks of 1.48 s.
CONCLUSIONS: This interactive technology may improve UE motor recovery of stroke-related hemiparesis.

Entities:  

Keywords:  error augmentation; haptic technology; physical therapy; robotics; stroke rehabilitation; upper extremity

Mesh:

Year:  2013        PMID: 23929692      PMCID: PMC8734943          DOI: 10.1177/1545968313498649

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  35 in total

1.  Turning a blind eye: why we don't test for blindness at the end of our trials.

Authors:  David L Sackett
Journal:  BMJ       Date:  2004-05-08

2.  Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Authors:  James L Patton; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2004-04       Impact factor: 4.538

3.  Custom-designed haptic training for restoring reaching ability to individuals with poststroke hemiparesis.

Authors:  James L Patton; Mark Kovic; Ferdinando A Mussa-Ivaldi
Journal:  J Rehabil Res Dev       Date:  2006 Aug-Sep

4.  A body-powered functional upper limb orthosis.

Authors:  T Rahman; W Sample; R Seliktar; M Alexander; M Scavina
Journal:  J Rehabil Res Dev       Date:  2000 Nov-Dec

Review 5.  Neural adaptations with chronic physical activity.

Authors:  R M Enoka
Journal:  J Biomech       Date:  1997-05       Impact factor: 2.712

6.  The Motor Activity Log-28: assessing daily use of the hemiparetic arm after stroke.

Authors:  G Uswatte; E Taub; D Morris; K Light; P A Thompson
Journal:  Neurology       Date:  2006-10-10       Impact factor: 9.910

7.  Motor impairment as a predictor of functional recovery and guide to rehabilitation treatment after stroke.

Authors:  F D Shelton; B T Volpe; M Reding
Journal:  Neurorehabil Neural Repair       Date:  2001       Impact factor: 3.919

8.  Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial.

Authors:  Steven L Wolf; Carolee J Winstein; J Philip Miller; Edward Taub; Gitendra Uswatte; David Morris; Carol Giuliani; Kathye E Light; Deborah Nichols-Larsen
Journal:  JAMA       Date:  2006-11-01       Impact factor: 56.272

9.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke.

Authors:  Peter S Lum; Charles G Burgar; Peggy C Shor; Matra Majmundar; Machiel Van der Loos
Journal:  Arch Phys Med Rehabil       Date:  2002-07       Impact factor: 3.966

10.  Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke.

Authors:  Bruce T Volpe; Daniel Lynch; Avrielle Rykman-Berland; Mark Ferraro; Michael Galgano; Neville Hogan; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2008-01-09       Impact factor: 3.919

View more
  34 in total

1.  Locomotor adaptation is influenced by the interaction between perturbation and baseline asymmetry after stroke.

Authors:  Christine M Tyrell; Erin Helm; Darcy S Reisman
Journal:  J Biomech       Date:  2015-04-22       Impact factor: 2.712

2.  Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study.

Authors:  Edward P Washabaugh; Emma Treadway; R Brent Gillespie; C David Remy; Chandramouli Krishnan
Journal:  Restor Neurol Neurosci       Date:  2018       Impact factor: 2.406

3.  Design and Validation of a Lower-Limb Haptic Rehabilitation Robot.

Authors:  Alexander R Dawson-Elli; Peter G Adamczyk
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-07       Impact factor: 3.802

4.  Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial.

Authors:  Justin B Rowe; Vicky Chan; Morgan L Ingemanson; Steven C Cramer; Eric T Wolbrecht; David J Reinkensmeyer
Journal:  Neurorehabil Neural Repair       Date:  2017-08       Impact factor: 3.919

5.  Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles.

Authors:  Mindy F Levin; Patrice L Weiss; Emily A Keshner
Journal:  Phys Ther       Date:  2014-09-11

6.  Microstructural properties of premotor pathways predict visuomotor performance in chronic stroke.

Authors:  Derek B Archer; Gaurav Misra; Carolynn Patten; Stephen A Coombes
Journal:  Hum Brain Mapp       Date:  2016-02-27       Impact factor: 5.038

Review 7.  How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR's Rehabilitation Engineering Research Centers.

Authors:  David J Reinkensmeyer; Sarah Blackstone; Cathy Bodine; John Brabyn; David Brienza; Kevin Caves; Frank DeRuyter; Edmund Durfee; Stefania Fatone; Geoff Fernie; Steven Gard; Patricia Karg; Todd A Kuiken; Gerald F Harris; Mike Jones; Yue Li; Jordana Maisel; Michael McCue; Michelle A Meade; Helena Mitchell; Tracy L Mitzner; James L Patton; Philip S Requejo; James H Rimmer; Wendy A Rogers; W Zev Rymer; Jon A Sanford; Lawrence Schneider; Levin Sliker; Stephen Sprigle; Aaron Steinfeld; Edward Steinfeld; Gregg Vanderheiden; Carolee Winstein; Li-Qun Zhang; Thomas Corfman
Journal:  J Neuroeng Rehabil       Date:  2017-11-06       Impact factor: 4.262

8.  Effects of robotically modulating kinematic variability on motor skill learning and motivation.

Authors:  Jaime E Duarte; David J Reinkensmeyer
Journal:  J Neurophysiol       Date:  2015-02-11       Impact factor: 2.714

Review 9.  Stroke Rehabilitation Using Virtual Environments.

Authors:  Michael J Fu; Jayme S Knutson; John Chae
Journal:  Phys Med Rehabil Clin N Am       Date:  2015-08-01       Impact factor: 1.784

10.  A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training.

Authors:  Edward Washabaugh; Jane Guo; Chih-Kang Chang; David Remy; Chandramouli Krishnan
Journal:  IEEE Trans Biomed Eng       Date:  2018-06-21       Impact factor: 4.538

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.