Evidence map›Paper›PMID 33754890›Full record

ArticleNeurorehabilitation and neural repair2021

Fluid Cognitive Abilities Are Important for Learning and Retention of a New, Explicitly Learned Walking Pattern in Individuals After Stroke.

Margaret A French, Matthew L Cohen, Ryan T Pohlig, Darcy S Reisman

Open access · greenAbstract read
In one paragraph

Article in Neurorehabilitation and neural repair, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

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  5. Human-AI Cooperation in Healthcare and Rehabilitation.Delaware journal of public health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Margaret A FrenchUniversity of Delaware, Newark, DE, USA.ORCID 0000-0002-3581-7374
Matthew L CohenUniversity of Delaware, Newark, DE, USA.
Ryan T PohligUniversity of Delaware, Newark, DE, USA.
Darcy S ReismanUniversity of Delaware, Newark, DE, USA.ORCID 0000-0002-6546-0863
University of Delaware · US

Funding

Behavioral and Neurophysiologic Process of Locomotor Learning After StrokeR01HD078330 · NICHD · UNIVERSITY OF DELAWARE · PI REISMAN, DARCY S. · 2014 to 2025
$3.2M
Robotic Gait Training and Evaluation SystemS10RR028114 · NCRR · UNIVERSITY OF DELAWARE · PI HIGGINSON, JILL S · 2011 to 2011
$404k
Factors impacting locomotor learning following strokeF31NS111806 · NINDS · UNIVERSITY OF DELAWARE · PI FRENCH, MARGARET · 2019 to 2021
$43k
NCRR NIH HHS S10 RR028114NICHD NIH HHS R01 HD078330NINDS NIH HHS F31 NS111806
6 · The paper itself

Abstract

backgroundThere is significant variability in poststroke locomotor learning that is poorly understood and affects individual responses to rehabilitation interventions. Cognitive abilities relate to upper extremity motor learning in neurologically intact adults, but have not been studied in poststroke locomotor learning.

objectiveTo understand the relationship between locomotor learning and retention and cognition after stroke.

methodsParticipants with chronic (>6 months) stroke participated in 3 testing sessions. During the first session, participants walked on a treadmill and learned a new walking pattern through visual feedback about their step length. During the second session, participants walked on a treadmill and 24-hour retention was assessed. Physical and cognitive tests, including the Fugl-Meyer-Lower Extremity (FM-LE), Fluid Cognition Composite Score (FCCS) from the NIH Toolbox -Cognition Battery, and Spatial Addition from the Wechsler Memory Scale-IV, were completed in the third session. Two sequential regression models were completed: one with learning and one with retention as the dependent variables. Age, physical impairment (ie, FM-LE), and cognitive measures (ie, FCCS and Spatial Addition) were the independent variables.

resultsForty-nine and 34 participants were included in the learning and retention models, respectively. After accounting for age and FM-LE, cognitive measures explained a significant portion of variability in learning (

conclusionsCognitive abilities appear to be an important factor for understanding locomotor learning and retention after stroke. This has significant implications for incorporating locomotor learning principles into the development of personalized rehabilitation interventions after stroke.

Indexed as

Stroke RehabilitationAgedAptitudeCognitionFemaleHumansIntelligenceLearningMaleMiddle AgedPsychomotor PerformanceRetention, PsychologyStrokeWalkingcognitionlocomotor learningstroke

Identifiers

PMID33754890
PMCPMC8122051
OpenAlexW3136271021

What OpenQuestion holds

Textmetadata
LicenceTDM
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.