Evidence map›Paper›PMID 33192438›Full record

ArticleFrontiers in neurorobotics2020

Robotic Exoskeleton Gait Training During Acute Stroke Inpatient Rehabilitation.

Karen J Nolan, Kiran K Karunakaran, Kathleen Chervin, Michael R Monfett, Radhika K Bapineedu, Neil N Jasey, Mooyeon Oh-Park

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05241457 (Achieving Meaningful Clinical Benchmarks With Ekso Gait Training During Acute Stroke Inpatient Rehabilitation.), which is not on this map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 8% 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.

NCT05241457 completednot on this mapstarted 2022, after this paper: background citation

Achieving Meaningful Clinical Benchmarks With Ekso Gait Training During Acute Stroke Inpatient Rehabilitation.

TypeobservationalSponsorSunnyview Rehabilitation HospitalRan2022 to 2026Enrolled40ConditionsStroke, Stroke, Acute, Cerebrovascular Accident, Cerebrovascular Accident, AcuteArmsEkso gait training, Standard physical therapy
3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
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  11. Robot-assisted gait training in stroke.Turkish journal of physical medicine and rehabilitation · 2024
    Review
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  13. Enhanced Rehabilitation Outcomes of Robotic-Assisted Gait Training with EksoNR Lower Extremity Exoskeleton in 19 Stroke Patients.Medical science monitor : international medical journal of experimental and clinical research · 2023
    Observational
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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

7 authors at 4 institutions in 1 country.

Karen J NolanCenter for Mobility and Rehabilitation Engineering, Kessler Foundation, West Orange, NJ, United States.
Kiran K KarunakaranCenter for Mobility and Rehabilitation Engineering, Kessler Foundation, West Orange, NJ, United States.
Kathleen ChervinCenter for Mobility and Rehabilitation Engineering, Kessler Foundation, West Orange, NJ, United States.
Michael R MonfettDepartment of Physical Medicine and Rehabilitation, Rutgers - New Jersey Medical School (NJMS), Newark, NJ, United States.
Radhika K BapineeduDepartment of Physical Medicine and Rehabilitation, Rutgers - New Jersey Medical School (NJMS), Newark, NJ, United States.
Neil N JaseyDepartment of Physical Medicine and Rehabilitation, Rutgers - New Jersey Medical School (NJMS), Newark, NJ, United States.
Mooyeon Oh-ParkCenter for Mobility and Rehabilitation Engineering, Kessler Foundation, West Orange, NJ, United States.
Kessler Institute for Rehabilitation · USKessler Foundation · USChildren's Specialized Hospital · USRutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is the leading cause of severe disability in adults resulting in mobility, balance, and coordination deficits. Robotic exoskeletons (REs) for stroke rehabilitation can provide the user with consistent, high dose repetition of movement, as well as balance and stability. The goal of this intervention study is to evaluate the ability of a RE to provide high dose gait therapy and the resulting effect on functional recovery for individuals with acute stroke. The investigation included a total of 44 participants. Twenty-two participants received RE gait training during inpatient rehabilitation (RE+SOC Group), and a matched sample of 22 individuals admitted to the same inpatient rehabilitation facility-receiving conventional standard of care treatment (SOC group). The effect of RE training was quantified using total distance walked during inpatient rehabilitation and functional independence measure (FIM). The total distance walked during inpatient rehabilitation showed a significant difference between the SOC and RE+SOC groups. RE+SOC walked twice the distance as SOC during the same duration (time spent in inpatient rehabilitation) of training. In addition, the average change in motor FIM showed a significant difference between the SOC and RE+SOC groups, where the average difference in motor FIM was higher in RE+SOC compared to the SOC group. The results suggest that RE provided increased dosing of gait training without increasing the duration of training during acute stroke rehabilitation. The RE+SOC group increased their motor FIM score (change from admission to discharge) compared to SOC group, both groups were matched for admission motor FIM scores suggesting that increased dosing may have improved motor function.

Indexed as

dosingexoskeletonfunctional independence measuregaitrehabilitationstrokewearable robotics

Identifiers

PMID33192438
PMCPMC7661791
OpenAlexW3097121166

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.