Evidence map›Paper›PMID 39852611›Full record

ArticleSports (Basel, Switzerland)2025

A Muscle Physiology-Based Framework for Quantifying Training Load in Resistance Exercises.

Frank Imbach, Stéphane Perrey, Thomas Brioche, Robin Candau

Abstract read
In one paragraph

Article in Sports (Basel, Switzerland), 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Frank ImbachSeenovate, 34000 Montpellier, France.ORCID 0000-0001-8175-8515
Stéphane PerreyEuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès, 34000 Montpellier, France.ORCID 0000-0002-8741-629X
Thomas BriocheDMeM, University of Montpellier, INRAE, 34000 Montpellier, France.ORCID 0000-0002-6726-9800
Robin CandauDMeM, University of Montpellier, INRAE, 34000 Montpellier, France.ORCID 0000-0002-5930-8750

Funding

Agence Nationale de la Recherche et de la Technologie 2018/0653
6 · The paper itself

Abstract

backgroundObjective training load (TL) indexes used in resistance training lack physiological significance. This study was aimed to provide a muscle physiology-based approach for quantifying TL in resistance exercises (REs).

methodsFollowing individual torque-velocity profiling, fifteen participants (11 healthy males, stature: 178.36 ± 3.95 cm, and body mass (BM): 77.48 ± 7.74 kg; 4 healthy females, stature: 169.25 ± 5.03 cm, and body mass: 60.62 ± 3.91 kg) performed isokinetic leg extension exercise sessions at low, moderate, and high intensities (LI, MI, and HI, respectively). Systemic and local physiological responses were measured, and sessions were volume-equated according to the "volume-load" (VL) method.

resultsSignificant differences were found between sessions in terms of mechanical work (p<0.05 and p<0.001, for LI-MI and MI-HI, respectively), averaged normalised torque (p<0.001), mechanical impulse (p<0.001), and rate of force development (RFD, p<0.001 for LI-MI). RFD was mainly impacted by the accumulation of repetitions. Muscle function impairments mainly occurred at low intensities-long series, and high intensities, supported by greater RFD rate decay and changes in electromyographic activity. Therefore, accounting for muscle fatigue kinetics within objective TL indexes and using dimension reduction methods better described physiological responses to RE.

conclusionsA generic equation of muscle fatigue rise could add value to TL quantification in RE. Considering other training-related information and TL indexes stands essential, applicable to field situations and supports the multidimensional facet of physiological responses to RE.

Indexed as

force–velocity profilingmodellingmuscle fatiguephysiological responsesprincipal component analysisrate of force developmentstrength training

Identifiers

PMID39852611
PMCPMC11768794

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