Evidence map›Paper›PMID 29161264›Full record

ArticlePloS one2017

Proprioceptive assessment in clinical settings: Evaluation of joint position sense in upper limb post-stroke using a robotic manipulator.

Sara Contu, Asif Hussain, Simone Kager, Aamani Budhota, Vishwanath A Deshmukh, Christopher W K Kuah, Lester H L Yam, Liming Xiang, Karen S G Chua, Lorenzo Masia and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
3.3field-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.

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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 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

11 authors at 3 institutions in 1 country.

Sara ContuRobotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.ORCID http://orcid.org/0000-0001-9235-9675
Asif HussainRobotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
Simone KagerNUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
Aamani BudhotaRobotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
Vishwanath A DeshmukhCentre for Advanced Rehabilitation Therapeutics, Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Christopher W K KuahCentre for Advanced Rehabilitation Therapeutics, Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Lester H L YamCentre for Advanced Rehabilitation Therapeutics, Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Liming XiangSchool of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.
Karen S G ChuaCentre for Advanced Rehabilitation Therapeutics, Department of Rehabilitation Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
Lorenzo MasiaRobotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
Domenico CampoloRobotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.
Nanyang Technological University · SGTan Tock Seng Hospital · SGNational University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proprioception is a critical component for motor functions and directly affects motor learning after neurological injuries. Conventional methods for its assessment are generally ordinal in nature and hence lack sensitivity. Robotic devices designed to promote sensorimotor learning can potentially provide quantitative precise, accurate, and reliable assessments of sensory impairments. In this paper, we investigate the clinical applicability and validity of using a planar 2 degrees of freedom robot to quantitatively assess proprioceptive deficits in post-stroke participants. Nine stroke survivors and nine healthy subjects participated in the study. Participants' hand was passively moved to the target position guided by the H-Man robot (Criterion movement) and were asked to indicate during a second passive movement towards the same target (Matching movement) when they felt that they matched the target position. The assessment was carried out on a planar surface for movements in the forward and oblique directions in the contralateral and ipsilateral sides of the tested arm. The matching performance was evaluated in terms of error magnitude (absolute and signed) and its variability. Stroke patients showed higher variability in the estimation of the target position compared to the healthy participants. Further, an effect of target was found, with lower absolute errors in the contralateral side. Pairwise comparison between individual stroke participant and control participants showed significant proprioceptive deficits in two patients. The proposed assessment of passive joint position sense was inherently simple and all participants, regardless of motor impairment level, could complete it in less than 10 minutes. Therefore, the method can potentially be carried out to detect changes in proprioceptive deficits in clinical settings.

Indexed as

Feedback, SensoryFemaleHandHumansJointsMaleMiddle AgedMusculoskeletal SystemProprioceptionRoboticsShoulderStrokeStroke RehabilitationUpper Extremity

Identifiers

PMID29161264
PMCPMC5697829
OpenAlexW2768825941

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.