Evidence map›Paper›PMID 42671543›Full record

ArticleEuropean journal of applied physiology2026

Effects of squat load and velocity on neuromuscular control strategies.

Xingchen Zhang, Yao Li, Yuling Fang, Danyang Kou, Hanbing Wu, Yuan Gao, Lian Duan

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Article in European journal of applied physiology, 2026. 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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1 · What the graph read from it

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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.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xingchen ZhangSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.ORCID http://orcid.org/0009-0004-0497-9073
Yao LiSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.
Yuling FangSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.
Danyang KouSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.
Hanbing WuSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.
Yuan GaoSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China. gaoyuan1107@ysu.edu.cn.
Lian DuanSchool of Physical Education, Yanshan University, Qinhuangdao, Hebei, China. dl1984@ysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study investigated the effects of velocity and load on lower limb neuromuscular synergy during the concentric phase of the squat to inform resistance training design.

methodsSurface electromyography (sEMG) signals from lower limb muscles were collected during squats performed under two loads (40% 1RM, 60% 1RM) and two velocities (fast, slow). Non-negative matrix factorization extracted muscle synergies, and functional networks were constructed based on sEMG frequency band coherence.

resultsTwo synergies were identified during slow squats, compared to four during fast squats. Increased load resulted in higher activation weight and delayed peak activation of agonist muscles, while antagonist activation decreased during fast squats. Beta-band connectivity was stronger during slow squats, whereas Gamma-band connectivity dominated during fast squats. Global efficiency in the Gamma band peaked under both fast-light and slow-heavy conditions, revealing a significant interaction. For the Beta-band clustering coefficient, only the main effect of velocity was significant.

conclusionsVelocity determines synergy architecture (integrated for stability vs. decoupled for power), while load modulates gain and timing within this framework. For stability, slow training with 60% load is recommended; for explosive power, fast training with 40% load is optimal for maximizing acceleration.

Indexed as

Muscle functional networkMuscle synergyResistance trainingSquatSurface electromyography

Identifiers

PMID42671543

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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.