Evidence map›Paper›PMID 30290806›Full record

Trial reportJournal of neuroengineering and rehabilitation2018

Dissociating motor learning from recovery in exoskeleton training post-stroke.

Nicolas Schweighofer, Chunji Wang, Denis Mottet, Isabelle Laffont, Karima Bakhti, David J Reinkensmeyer, Olivier Rémy-Néris

Erratum issuedOpen access · goldAbstract readClinical Trial
In one paragraph

Trial report in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
5.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
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  6. Article
  7. Observational
  8. Review
  9. Review
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  12. Article
  13. Assessing Impairments in Visuomotor Adaptation After Stroke.Neurorehabilitation and neural repair · 2022
    Article
  14. Article
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  18. Article
  19. Observational
  20. Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Nicolas SchweighoferBiokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA. schweigh@usc.edu.ORCID 0000-0003-3362-6088
Chunji WangNeuroscience graduate Program, University of Southern California, Los Angeles, USA.
Denis MottetSTAPS, Université de Montpellier, Euromov, Montpellier, France.
Isabelle LaffontMontpellier University Hospital, Euromov, IFRH, Montpellier University, Montpellier, France.
Karima BakhtiMontpellier University Hospital, Euromov, IFRH, Montpellier University, Montpellier, France.
David J ReinkensmeyerDepartments of Mechanical and Aerospace Engineering, Anatomy and Neurobiology, University of California, Irvine, USA.
Olivier Rémy-NérisUniversité de Bretagne Occidentale, Centre hospitalier universitaire, LaTIM-INSERM UMR1101, Brest, France.
Université de Montpellier · FRUniversity of Southern California · USInserm · FRUniversity of California, Irvine · US

Funding

Optimizing the Dose of Rehabilitation After StrokeR01HD065438 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SCHWEIGHOFER, NICOLAS, WINSTEIN, CAROLEE J · 2011 to 2015
$1.9M
Optimizing sensori-motor training post-strokeR56NS100528 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SCHWEIGHOFER, NICOLAS · 2017 to 2017
$483k
NICHD NIH HHS HD065438NICHD NIH HHS R01 HD065438NINDS NIH HHS R56 NS100528
6 · The paper itself

Abstract

backgroundA large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they could potentially help to develop predictive models of the effects of motor training on recovery. However, during training with such devices, patients must become adept at using the new "tool" of the exoskeleton, including learning the new forces and visuomotor transformations associated with the device. We thus hypothesized that the changes in performance during extensive training with a passive, gravity-supporting, exoskeleton device (the Armeo Spring) will follow an initial fast phase, due to learning to use the device, and a slower phase that corresponds to reduction in overall arm impairment. Of interest was whether these fast and slow processes were related.

methodsTo test the two-process hypothesis, we used mixed-effect exponential models to identify putative fast and slow changes in smoothness of arm movements during 80 arm reaching tests performed during 20 days of exoskeleton training in 53 individuals with post-acute stroke.

resultsIn line with our hypothesis, we found that double exponential models better fit the changes in smoothness of arm movements than single exponential models. In contrast, single exponential models better fit the data for a group of young healthy control subjects. In addition, in the stroke group, we showed that smoothness correlated with a measure of impairment (the upper extremity Fugl Meyer score - UEFM) at the end, but not at the beginning, of training. Furthermore, the improvement in movement smoothness due to the slow component, but not to the fast component, strongly correlated with the improvement in the UEFM between the beginning and end of training. There was no correlation between the change of peaks due to the fast process and the changes due to the slow process. Finally, the improvement in smoothness due to the slow, but not the fast, component correlated with the number of days since stroke at the onset of training - i.e. participants who started exoskeleton training sooner after stroke improved their smoothness more.

conclusionsOur results therefore demonstrate that at least two processes are involved in in performance improvements measured during mechanized training post-stroke. The fast process is consistent with learning to use the exoskeleton, while the slow process independently reflects the reduction in upper extremity impairment.

Indexed as

Exoskeleton DeviceAdultBiomechanical PhenomenaFemaleHumansMaleMiddle AgedMovementRecovery of FunctionStrokeStroke RehabilitationUpper ExtremityExoskeletonMotor adaptationMotor learningMotor recoveryMovement analysisNeurorehabilitationRehabilitation roboticsStroke

Identifiers

PMID30290806
PMCPMC6173922
OpenAlexW2894672688

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.