Evidence map›Paper›PMID 25906983›Full record

Trial reportJournal of neuroengineering and rehabilitation2015

Efficacy of robot-assisted fingers training in chronic stroke survivors: a pilot randomized-controlled trial.

Evan A Susanto, Raymond Ky Tong, Corinna Ockenfeld, Newmen Sk Ho

2 registry-linked trialsAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of neuroengineering and rehabilitation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 47 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 12 pooled it
–field-weighted citation impact
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.

NCT03392493 nacompletednot on this mapstarted 2018, after this paper: background citation

The Effects on Hand Function With Robot-assisted Rehabilitation for Patients With Stroke

TypeinterventionalSponsorTaipei Medical University Shuang Ho HospitalRan2018 to 2018Enrolled25ConditionsStrokeArmsRobot-assisted hand rehabilitation, Standard treatment
NCT04296032 nacompletednot on this mapstarted 2020, after this paper: background citation

Effects of Wearable Sensor Based Virtual Reality Game on Upper Extremity Function for Patients With Stroke

TypeinterventionalSponsorTaipei Medical University Shuang Ho HospitalRan2020 to 2021Enrolled37ConditionsStrokeArmsvirtual reality game traning, standard treatment
3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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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.

Evan A SusantoInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, S.A.R., China. evan.a.susanto@connect.polyu.hk.
Raymond Ky TongInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, S.A.R., China. kytong@cuhk.edu.hk.
Corinna OckenfeldInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, S.A.R., China. corinna.ockenfeld@connect.polyu.hk.
Newmen Sk HoInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, S.A.R., China. newmen.ho@polyu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile constraint-induced movement therapy (CIMT) is one of the most promising techniques for upper limb rehabilitation after stroke, it requires high residual function to start with. Robotic device, on the other hand, can provide intention-driven assistance and is proven capable to complement conventional therapy. However, with many robotic devices focus on more proximal joints like shoulder and elbow, recovery of hand and fingers functions have become a challenge. Here we propose the use of robotic device to assist hand and fingers functions training and we aim to evaluate the potential efficacy of intention-driven robot-assisted fingers training.

methodsParticipants (6 to 24 months post-stroke) were randomly assigned into two groups: robot-assisted (robot) and non-assisted (control) fingers training groups. Each participant underwent 20-session training. Action Research Arm Test (ARAT) was used as the primary outcome measure, while, Wolf Motor Function Test (WMFT) score, its functional tasks (WMFT-FT) sub-score, Fugl-Meyer Assessment (FMA), its shoulder and elbow (FMA-SE) sub-score, and finger individuation index (FII) served as secondary outcome measures.

resultsNineteen patients completed the 20-session training (

trial registrationHKClinicalTrials.com HKCTR-1554); eighteen of them came back for a 6-month follow-up. Significant improvements (p < 0.05) were found in the clinical scores for both robot and control group after training. However, only robot group maintained the significant difference in the ARAT and FMA-SE six months after the training. The WMFT-FT score and time post-training improvements of robot group were significantly better than those of the control group.

conclusionsThis study showed the potential efficacy of robot-assisted fingers training for hand and fingers rehabilitation and its feasibility to facilitate early rehabilitation for a wider population of stroke survivors; and hence, can be used to complement CIMT.

Indexed as

Stroke RehabilitationAdultAgedEquipment DesignFemaleFingersHumansMaleMiddle AgedPhysical Therapy ModalitiesPilot ProjectsRoboticsSurvivorsTreatment Outcome

Identifiers

PMID25906983
PMCPMC4422529

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.