Evidence map›Paper›PMID 38257646›Full record

ArticleSensors (Basel, Switzerland)2024

Wearable Sensor to Monitor Quality of Upper Limb Task Practice for Stroke Survivors at Home.

Na Jin Seo, Kristen Coupland, Christian Finetto, Gabrielle Scronce

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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.

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

12 citing papers in PubMed, 1 synthesis or guideline 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.

Na Jin SeoDepartment of Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0001-6446-5905
Kristen CouplandDepartment of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA.
Christian FinettoDepartment of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA.
Gabrielle ScronceDepartment of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0002-3861-1371

Funding

Treating Depression and Enhancing Locomotor Recovery Post-strokeP20GM109040 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI KAUTZ, STEVEN A. · 2014 to 2024
$23.5M
Technology DevelopmentP2CHD101913 · NICHD · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2020 to 2024
$4.1M
NICHD NIH HHS P2C HD101913NIGMS NIH HHS P20 GM109040NIH HHS NIH/NICHD P2C HD101913NIH HHS NIH/NIGMS P20 GM109040
6 · The paper itself

Abstract

Many stroke survivors experience persistent upper extremity impairment that limits performance in activities of daily living. Upper limb recovery requires high repetitions of task-specific practice. Stroke survivors are often prescribed task practices at home to supplement rehabilitation therapy. A poor quality of task practices, such as the use of compensatory movement patterns, results in maladaptive neuroplasticity and suboptimal motor recovery. There currently lacks a tool for the remote monitoring of movement quality of stroke survivors' task practices at home. The objective of this study was to evaluate the feasibility of classifying movement quality at home using a wearable IMU. Nineteen stroke survivors wore an IMU sensor on the paretic wrist and performed four functional upper limb tasks in the lab and later at home while videorecording themselves. The lab data served as reference data to classify home movement quality using dynamic time warping. Incorrect and correct movement quality was labeled by a therapist. The home task practice movement quality was classified with an accuracy of 92% and F1 score of 0.95 for all tasks combined. Movement types contributing to misclassification were further investigated. The results support the feasibility of a home movement quality monitoring system to assist with upper limb rehabilitation post stroke.

Indexed as

StrokeWearable Electronic DevicesActivities of Daily LivingHumansSurvivorsUpper ExtremityWristaccelerometerhome monitoringinertial measurement unit (IMU)machine learningrehabilitationremote monitoringstrokeupper extremitywearable sensor

Identifiers

PMID38257646
PMCPMC10821060

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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.