Evidence map›Paper›PMID 37404979›Full record

ArticleFrontiers in rehabilitation sciences2023

Exercise repetition rate measured with simple sensors at home can be used to estimate Upper Extremity Fugl-Meyer score after stroke.

Veronica A Swanson, Christopher A Johnson, Daniel K Zondervan, Susan J Shaw, David J Reinkensmeyer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in rehabilitation sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Veronica A SwansonBiorobotics Laboratory, Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA, United States.
Christopher A JohnsonBiorobotics Laboratory, Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.
Daniel K ZondervanFlint Rehab, LLC, Irvine, CA, United States.
Susan J ShawDepartment of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States.
David J ReinkensmeyerBiorobotics Laboratory, Department of Mechanical and Aerospace Engineering, University of California, Irvine, Irvine, CA, United States.

Funding

RehabTouch: A mixed-reality gym for rehabilitating the hands, arms, trunk, and legs after strokeR44AG059256 · NIA · FLINT REHABILITATION DEVICES · PI ROWE, JUSTIN · 2017 to 2018
$1.5M
NIA NIH HHS R44 AG059256
6 · The paper itself

Abstract

Introduction: It would be valuable if home-based rehabilitation training technologies could automatically assess arm impairment after stroke. Here, we tested whether a simple measure-the repetition rate (or "rep rate") when performing specific exercises as measured with simple sensors-can be used to estimate Upper Extremity Fugl-Meyer (UEFM) score. Methods: 41 individuals with arm impairment after stroke performed 12 sensor-guided exercises under therapist supervision using a commercial sensor system comprised of two pucks that use force and motion sensing to measure the start and end of each exercise repetition. 14 of these participants then used the system at home for three weeks. Results: Using linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r Discussion: These results show how a simple measure-exercise rep rate measured with simple sensors-can be used to infer an arm impairment score and suggest that prediction models should be tuned separately for the clinic and home environments.

Indexed as

assessmentFugl-MeyerhomemRehabrehabilitationremotesensorsstroke

Identifiers

PMID37404979
PMCPMC10315847

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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.