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Can muscle typology explain the inter‐individual variability in resistance training adaptations?

Overview of attention for article published in Journal of Physiology, April 2023
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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7 news outlets
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131 X users
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3 YouTube creators

Citations

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6 Dimensions

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49 Mendeley
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Title
Can muscle typology explain the inter‐individual variability in resistance training adaptations?
Published in
Journal of Physiology, April 2023
DOI 10.1113/jp284442
Pubmed ID
Authors

Kim Van Vossel, Julie Hardeel, Freek Van de Casteele, Thibaux Van der Stede, Anneleen Weyns, Jan Boone, Silvia Salinas Blemker, Eline Lievens, Wim Derave

Abstract

Considerable inter-individual heterogeneity exists in the muscular adaptations to resistance training. It has been proposed that fast-twitch fibers are more sensitive to hypertrophic stimuli and thus that variation in muscle fiber type composition is a contributing factor to the magnitude of training response. This study investigated if the inter-individual variability in resistance training adaptations is determined by muscle typology and if the most appropriate weekly training frequency depends on muscle typology. In strength-training novices, 11 slow (ST) and 10 fast typology (FT) individuals were selected by measuring muscle carnosine with proton magnetic resonance spectroscopy. Participants trained both upper arm and leg muscles to failure at 60% 1RM for 10 weeks, whereby one arm and leg trained 3x/week, the contralateral arm and leg 2x/week. Muscle volume (MRI-based 3D segmentation), maximal dynamic strength (one-repetition maximum, 1RM) and fiber-type specific cross-sectional area (vastus lateralis biopsies) were evaluated. The training response for total muscle volume (+3 to +14%), fiber size (-19 to +22%) and strength (+17 to +47%) showed considerable inter-individual variability, but these could not be attributed to differences in muscle typology. However, ST individuals performed a significantly higher training volume to gain these similar adaptations as FT individuals. The limb that trained 3x/week had generally more pronounced hypertrophy than the limb that trained 2x/week, and there was no interaction with muscle typology. In conclusion, muscle typology cannot explain the high variability in resistance training adaptations when training is performed to failure at 60% of 1RM. KEY POINTS: This study investigated the influence of muscle typology ( = muscle fiber type composition) on the variability in resistance training adaptations and on its role in the individualization of resistance training frequency. We demonstrate that an individual's muscle typology cannot explain the inter-individual variability in resistance training induced increases in muscle volume, maximal dynamic strength and fiber cross-sectional area when repetitions are performed to failure. Importantly, slow typology individuals performed a significantly higher training volume to obtain similar adaptations compared to fast typology individuals. Muscle typology does not determine the most appropriate resistance training frequency. However, regardless of muscle typology, an additional weekly training (3x/week vs 2x/week) increases muscle hypertrophy but not maximal dynamic strength. These findings expand on our understanding of the underlying mechanisms for the large inter-individual variability in resistance training adaptations. Abstract figure legend This study investigated if muscle typology can explain the high variability in resistance training adaptations. Slow and fast typology resistance training novices were selected to participate in this study by the non-invasive measurement of muscle carnosine with proton magnetic resonance spectroscopy. After the chronic training period a high inter-individual variability was observed in changes in muscle volume, maximal dynamic strength and fiber cross-sectional area. However, this high inter-individual variability could not be explained by muscle typology for any of the outcomes. Visual abstract created with BioRender. This article is protected by copyright. All rights reserved.

X Demographics

X Demographics

The data shown below were collected from the profiles of 131 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 12%
Student > Bachelor 6 12%
Student > Ph. D. Student 5 10%
Student > Doctoral Student 3 6%
Researcher 2 4%
Other 5 10%
Unknown 22 45%
Readers by discipline Count As %
Sports and Recreations 16 33%
Biochemistry, Genetics and Molecular Biology 4 8%
Medicine and Dentistry 2 4%
Computer Science 1 2%
Arts and Humanities 1 2%
Other 2 4%
Unknown 23 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 121. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2024.
All research outputs
#349,534
of 25,658,541 outputs
Outputs from Journal of Physiology
#193
of 9,837 outputs
Outputs of similar age
#8,086
of 412,025 outputs
Outputs of similar age from Journal of Physiology
#2
of 116 outputs
Altmetric has tracked 25,658,541 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,837 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 412,025 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.