Evidence map›Paper›PMID 40593200›Full record

ArticleEuropean journal of applied physiology2025

Reassessment of isometric muscle force complexity under different contraction intensities, joint angles, and visual feedback conditions.

Ellen Pereira Zambalde, Carina Marconi Germer, Ricardo Gonçalves Molinari, Francesco Negro, Jakob Dideriksen, Leonardo Abdala Elias

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Article in European journal of applied physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ellen Pereira ZambaldeDepartment of Electronics and Biomedical Engineering, School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil.ORCID http://orcid.org/0000-0002-0877-3062
Carina Marconi GermerDepartment of Electronics and Biomedical Engineering, School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil.ORCID http://orcid.org/0000-0002-7323-3767
Ricardo Gonçalves MolinariDepartment of Electronics and Biomedical Engineering, School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil.
Francesco NegroDepartment of Clinical and Experimental Sciences, Università Degli Studi Di Brescia, Brescia, Italy.ORCID http://orcid.org/0000-0002-9094-8932
Jakob DideriksenDepartment of Health Science and Technology, Aalborg University, Aalborg, Denmark.ORCID http://orcid.org/0000-0001-6587-0865
Leonardo Abdala EliasDepartment of Electronics and Biomedical Engineering, School of Electrical and Computer Engineering, University of Campinas, Campinas, SP, Brazil. leoelias@unicamp.br.ORCID http://orcid.org/0000-0003-4488-3063

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 316320/2023-4Fundação de Amparo à Pesquisa do Estado de São Paulo 2017/22191-3Fundação de Amparo à Pesquisa do Estado de São Paulo 2019/01508-4
6 · The paper itself

Abstract

purposeWe employed an optimization method for the approximate entropy (ApEn) parameters (

methodsSeventeen participants performed an index finger abduction isometric force task in six contraction intensities (5-75% of maximum voluntary contraction, MVC), with and without visual feedback of the force, and in three different metacarpophalangeal (MCP) joint angles. Force variability, complexity (ApEn), and power spectrum density (PSD) were assessed, and a correlation analysis was performed between these variables.

resultsThe best ApEn (

conclusionApEn is influenced by force level and visual feedback, and it is strongly correlated with low-frequency force oscillations, which are related to the muscle's common drive.

Indexed as

Feedback, SensoryIsometric ContractionMuscle, SkeletalAdultFemaleFingersHumansMaleYoung AdultApproximate entropyComplexityForce controlRegularityVariabilityVisual feedback

Identifiers

PMID40593200

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Registered trials

None linked

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.