Evidence map›Paper›PMID 42798508›Full record

ArticleFrontiers in physiology2026

The influence of muscle mass on the coordination required for efficient movement.

Ameur Latreche, Stephanie A Ross, Taylor J M Dick, Nicolai Konow, Andrew A Biewener, James M Wakeling

Abstract read
In one paragraph

Article in Frontiers in 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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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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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ameur LatrecheDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.
Stephanie A RossFaculty of Kinesiology, University of Calgary, Calgary, AB, Canada.
Taylor J M DickSchool of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia.
Nicolai KonowDepartment of Biological Sciences University of Massachusetts, Lowell, MA, United States.
Andrew A BiewenerOrganismic and Evolutionary Biology Harvard University Cambridge, Cambridge, MA, United States.
James M WakelingDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.

Funding

Muscle Mass: a Critical but Missing Component in Muscle Modeling and SimulationR01AR080797 · NIAMS · HARVARD UNIVERSITY · PI Andrew A Biewener · 2023 to 2026
$1.4M
NIAMS NIH HHS R01 AR080797
6 · The paper itself

Abstract

Muscle efficiency decreases with increasing size, largely due to a relative decrease in its mechanical output. Muscle mechanical output depends on its activation, strain, and strain rate and thus varies between different muscles within a limb during locomotion. Distinct muscle coordination patterns are required for efficient cycling, and so we would expect that the coordination patterns for efficient cycling or indeed locomotion would change across animal sizes. We tested whether muscle coordination would change with muscle size using data derived from human cycling: this paradigm allowed for controlled changes in both crank torque and cadence, allowing the multifactorial problem of muscle power output to be decomposed. We used kinematic and pedal data from 12 cyclists undergoing steady pedalling at cadences from 80 to 140 r.p.m. and generated musculoskeletal simulations of their movements. We introduced novel multisegment muscle models in the simulation that incorporated the internal muscle mass and thus accounted for the scaling effects of muscle tissue inertia. We solved the simulations for the muscle activity that was required to minimise the metabolic cost during cycling for each condition. The masses of the muscle models were scaled across five orders of magnitude. The predicted muscle activations were classified by Principal Component analysis to identify whether the coordination of muscle activity was modulated across models with different sized muscles. Analysis of variance revealed significant changes in coordination at the large-scale factors. This study shows how the coordination may be influenced by muscle inertia under scaling scenarios due to the non-linear effects of the inertial mass within the muscle tissues.

Indexed as

Hill-type muscle modelmass gravitymuscle coordinationmuscle inertiamusculoskeletal simulation

Identifiers

PMID42798508
PMCPMC13612335

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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.