Evidence map›Paper›PMID 39109662›Full record

ArticleNeurorehabilitation and neural repair2024

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability.

Sunghoon Ivan Lee, Yunda Liu, Gloria Vergara-Díaz, Benito Lorenzo Pugliese, Randie Black-Schaffer, Mary Ellen Stoykov, Paolo Bonato

Abstract read
In one paragraph

Article in Neurorehabilitation and neural repair, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Sunghoon Ivan LeeCollege of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA.ORCID 0000-0001-5935-125X
Yunda LiuCollege of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA.
Gloria Vergara-DíazDepartment of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA.
Benito Lorenzo PuglieseDepartment of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA.
Randie Black-SchafferDepartment of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA.
Mary Ellen StoykovArm & Hands Lab, Shirley Ryan AbilityLab, Chicago, IL, USA.ORCID 0000-0002-9045-9231
Paolo BonatoDepartment of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA.

Funding

Achieving Optimal Motor Function in Stroke Survivors via a Human-Centered Approach to Design an mHealth PlatformR01EB027777 · NIBIB · UNIVERSITY OF MASSACHUSETTS AMHERST · PI LEE, SUNGHOON · 2020 to 2024
$2.4M
SCH: A Sensing Platform Monitoring Interactions with Daily Objects to Assess Real-World Motor Performance in Stroke SurvivorsR01HD114147 · NICHD · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Sunghoon Lee, Mary Ellen Stoykov · 2023 to 2026
$1.1M
NIBIB NIH HHS R01 EB027777NICHD NIH HHS R01 HD114147
6 · The paper itself

Abstract

backgroundFrequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity.

objectiveTo explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist.

methodsSeventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity.

resultsWe have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson's correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and -.68 with the Performance Time component of the Wolf Motor Function Test).

conclusionsThe findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors' motor progression throughout rehabilitation.

Indexed as

Activities of Daily LivingStrokeUpper ExtremityWearable Electronic DevicesAdultAgedBiomechanical PhenomenaFemaleHumansMaleMiddle AgedMotor ActivityMovementReproducibility of ResultsStroke RehabilitationSurvivorsdigital markerdigital phenotyperehabilitationstrokeupper limbwearable inertial devices

Identifiers

PMID39109662
PMCPMC11405131

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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.