Evidence map›Paper›PMID 42213698›Full record

ArticlePloS one2026

A comparative analysis of co-contraction indices using synthetic EMG data: Implications for selection and interpretation.

Hannah D Carey, Friedl De Groote, Andrew Sawers

Abstract readComparative Study
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

3 authors.

Hannah D CareyDepartment of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, Illinois, United States of America.ORCID https://orcid.org/0000-0002-9430-427X
Friedl De GrooteDepartment of Movement Sciences, KU Leuven, Leuven, Vlaams-Brabant, Belgium.
Andrew SawersDepartment of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, Illinois, United States of America.ORCID https://orcid.org/0000-0002-3493-304X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Co-contraction-the simultaneous activation of opposing muscles-influences movement efficiency, joint stability, and motor learning. While consensus exists for EMG acquisition and processing, comparable guidance for quantifying co-contraction is lacking. This study evaluated the behavior and interrelationships of six commonly used co-contraction indices (CCIs) to develop practical recommendations for their selection, calculation, and interpretation. Synthetic EMG-like signals were generated and used to evaluate CCI behavior across a range of conditions that would be difficult to achieve experimentally. Based on their formulas and observed behavior, CCIs were sorted into three categories: shape-based, amplitude-driven, and temporal indices. Shape-based CCIs increase when two EMG signals have similar shapes, regardless of amplitudes. Amplitude-driven CCIs increase when activation is high in both muscles. Temporal CCIs increase as the duration of EMG overlap increases, regardless of signal shape or amplitude. Correlation analyses showed stronger associations within-category than between-category, supporting the proposed classification scheme. CCI behavior yielded three principal findings, each paired with a practical recommendation. First, EMG amplitude normalization techniques altered co-contraction estimates, and the effect varied by index. Researchers should therefore test whether their conclusions hold across normalization methods. Second, because CCIs differ in scale and theoretical maxima, their values are not directly comparable across indices. Comparisons should instead focus on relative trends interpreted within each index's bounds. Third, each CCI category was sensitive to different EMG features (e.g., amplitude versus shape). The choice of CCI should therefore align with hypothesized differences in EMG signals-use shape-based CCIs when waveform similarity is of interest, and amplitude-driven CCIs when differences in activation magnitude are expected. These results provide initial guidance for selecting, calculating, and interpretating CCIs, and they establish a framework for testing the robustness of these theoretical findings using experimental EMG from diverse tasks, muscle pairs, and populations.

Indexed as

ElectromyographyMuscle ContractionMuscle, SkeletalHumans

Identifiers

PMID42213698
PMCPMC13221005

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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.