Evidence map›Paper›PMID 39556594›Full record

ArticlePloS one2024

Using a tablet to understand the spatial and temporal characteristics of complex upper limb movements in chronic stroke.

Devin Sean Austin, Makenna J Dixon, Joanna E Hoh, Duncan Thibodeau Tulimieri, Joshua G A Cashaback, Jennifer A Semrau

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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.

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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Devin Sean AustinGraduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.
Makenna J DixonDepartment of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States of America.
Joanna E HohGraduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.ORCID 0000-0002-8835-9696
Duncan Thibodeau TulimieriGraduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.
Joshua G A CashabackGraduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.
Jennifer A SemrauGraduate Program in Biomechanics and Movement Science (BIOMS), University of Delaware, Newark, Delaware, United States of America.ORCID 0000-0002-2812-7789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic devices are commonly used to quantify sensorimotor function of the upper limb after stroke; however, the availability and cost of such devices make it difficult to facilitate implementation in clinical environments. Tablets (e.g. iPad) can be used as devices to facilitate rehabilitation but are rarely used as assessment tools for the upper limb. The current study aimed to implement a tablet-based Maze Navigation Task to examine complex upper-limb movement in individuals with chronic stroke. We define complex upper-limb movement as reaching movements that require multi-joint coordination in a dynamic environment. We predicted that individuals with stroke would have more significant spatial errors, longer movement times, and slower speeds compared to controls with increasing task complexity. Twenty individuals with chronic stroke who had a variety of arm and hand function (Upper extremity Fugl-Myer 52.8 ± 18.3) and twenty controls navigated eight pseudorandomized mazes on an iPad using a digitizing stylus. The task was designed to elicit reaching movements engaging both the shoulder and elbow joints. Each maze became increasingly complex by increasing the number of 90° turns. We instructed participants to navigate each maze as quickly and accurately as possible while avoiding the maze's boundaries. Sensorimotor behavior was quantified using the following metrics: Error Time (time spent hitting or outside boundaries), Peak Speed, Average Speed, and Movement Time, Number of Speed Peaks. We found that individuals with stroke had significantly greater Error Time for all maze levels (all, p < 0.01), while both speed metrics, Movement Time and Number of Speed Peaks were significantly lower for several levels (all, p < 0.05). As maze complexity increased, the performance of individuals with stroke worsened only for Error Time while control performance remained consistent (p < 0.001). Our results indicate that a complex movement task on a tablet can capture temporal and spatial impairments in individuals with stroke, as well as how task complexity impacts movement quality. This work demonstrates that a tablet is a suitable tool for the assessment of complex movement after stroke and can serve to inform rehabilitation after stroke.

Indexed as

MovementStrokeUpper ExtremityAdultAgedChronic DiseaseComputers, HandheldFemaleHumansMaleMiddle AgedRoboticsStroke Rehabilitation

Identifiers

PMID39556594
PMCPMC11573164

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.