Evidence map›Paper›PMID 34683094›Full record

ArticleJournal of personalized medicine2021

Comparative Assessment of Robotic versus Classical Physical Therapy Using Muscle Strength and Ranges of Motion Testing in Neurological Diseases.

Zoltán Zsigmond Major, Calin Vaida, Kinga Andrea Major, Paul Tucan, Emanuela Brusturean, Bogdan Gherman, Iosif Birlescu, Raul Craciunaș, Ionut Ulinici, Gábor Simori and 5 more

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 14% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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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

15 authors at 4 institutions in 3 countries.

Zoltán Zsigmond MajorNeurophysiology Department, National Center for Spinal Disorders, Királyhágó u. 1, 1126 Budapest, Hungary.
Calin VaidaResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0003-2822-9790
Kinga Andrea MajorSecond ICU, Neurosurgery Department, Cluj County Emergency Clinical Hospital, Strada Clinicilor 3-5, 400000 Cluj-Napoca, Romania.
Paul TucanResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Emanuela BrustureanNeurology Department, Municipal Clinical Hospital Cluj-Napoca, 400139 Cluj-Napoca, Romania.
Bogdan GhermanResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Iosif BirlescuResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0002-4026-2318
Raul CraciunașNeurology Department, Municipal Clinical Hospital Cluj-Napoca, 400139 Cluj-Napoca, Romania.
Ionut UliniciResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Gábor SimoriNeurology Department, Municipal Clinical Hospital Cluj-Napoca, 400139 Cluj-Napoca, Romania.
Alexandru BanicaResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Nicoleta PopResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Alin BurzResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Giuseppe CarboneDIMEG, University of Calabria, Via Pietro Bucci, 87036 Rende, Italy.ORCID 0000-0003-0831-8358
Doina PislaResearch Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.ORCID 0000-0001-7014-9431
Technical University of Cluj-Napoca · ROSpitalul Clinic Județean de Urgență Cluj-Napoca · RONational Center for Spinal Disorders · HUUniversity of Calabria · IT

Funding

European Regional Development Fund ID P_37_215, MySMIS code 103415European Social Fund POCU/380/6/13/123927
6 · The paper itself

Abstract

The use of robotic systems in physical rehabilitation protocols has become increasingly attractive and has been given more focus in the last decade as a result of the high prevalence of motor deficits in the population, which is linked to an overburdened healthcare system. In accordance with current trends, three robotic devices have been designed, called ParReEx Elbow, ParReEx Wrist, and ASPIRE, which were designed to improve upper-limb medical recovery (shoulder, elbow, forearm, and wrist). The three automated systems were tested in a hospital setting with 23 patients (12 men and 11 women) suffering from motor deficits caused by various neurological diseases such as stroke, Parkinson's disease, and amyotrophic lateral sclerosis (ALS). The patients were divided into three groups based on their pathology (vascular, extrapyramidal, and neuromuscular). Objective clinical measures, such as the Medical Research Council (MRC) scale, goniometry, and dynamometry, were used to compare pre- and post-rehabilitation assessments for both robotic-aided and manual physical rehabilitation therapy. The results of these tests showed that, with the exception of a few minor differences in muscular strength recovery, the robotic-assisted rehabilitation methods performed equally as well as the manual techniques, though only minor improvements were validated during short-term rehabilitation. The greatest achievements were obtained in the goniometric analysis where some rehabilitation amplitudes increased by over 40% in the vascular group, but the same analysis returned regressions in the neuromuscular group. The MRC scale analysis returned no significant differences, with most regressions occurring in the neuromuscular group. The dynamometric analysis mostly returned improvements, but the highest value evolution was 19.07%, which also in the vascular group. While the results were encouraging, more research is needed with a larger sample size and a longer study period in order to provide more information regarding the efficacy of both rehabilitation methods in neurological illnesses.

Indexed as

dynamometrygoniometrymotor neuron diseaseMRC scaleParkinson’sphysical therapyrobotic rehabilitationstroke

Identifiers

PMID34683094
PMCPMC8541455
OpenAlexW3202138319

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.