Evidence map›Paper›PMID 34778722›Full record

ArticleApplied system innovation2021

Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient.

Heather A Feldner, Christina Papazian, Keshia M Peters, Claire J Creutzfeldt, Katherine M Steele

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Applied system innovation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06574737 (Use of Surface Electromyography as a Tools to Predict Upper Extremity Recovery Function After Stroke), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 24% 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.

NCT06574737 recruitingnot on this mapstarted 2024, after this paper: background citation

Use of Surface Electromyography as a Tools to Predict Upper Extremity Recovery Function After Stroke

Typeobservational_patient_registrySponsorUniversitas PadjadjaranRan2024 to 2025Enrolled30ConditionsUpper Extremity Paresis, Muscle Contracture Upper Arm, Stroke
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Breaking Barriers in Emerging Biomedical Applications.Entropy (Basel, Switzerland) · 2022
    Review
  4. Review
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

5 authors at 2 institutions in 1 country.

Heather A FeldnerDepartment of Rehabilitation Medicine, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-3659-730X
Christina PapazianDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Keshia M PetersDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Claire J CreutzfeldtHarborview Medical Center, Seattle, WA 98104, USA.
Katherine M SteeleDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-4128-9387
University of Washington · USHarborview Medical Center · US

Funding

NCCIH Supplement to NCATS/CTSA Program for KL2 Scholars - M. SoddersKL2TR002317 · NCATS · UNIVERSITY OF WASHINGTON · PI Christy Michelle McKinney · 2017 to 2026
$14.5M
SCH: Ubiquitous rehabilitation to improve movement after neurologic injury R01EB021935 · NIBIB · UNIVERSITY OF WASHINGTON · PI STEELE, KATHERINE M · 2015 to 2018
$1.6M
NCATS NIH HHS KL2 TR002317NIBIB NIH HHS R01 EB021935
6 · The paper itself

Abstract

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant's perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

Indexed as

acute carecase studyneurorehabilitationstrokesub-acute rehabilitationsurface electromyographyupper extremity

Identifiers

PMID34778722
PMCPMC8589300
OpenAlexW3160766152

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.