Research on Muscle Fiber Composition in Humans

DOI

10.2991/978-2-38476-122-7_3How to use a DOI?

Keywords

muscle composition; typing system; resistance training

Abstract

Accounting for 30%-40% of total body mass, skeletal muscles consist of multiple types of muscle fibers according to different histochemical classifications. Twitch-speed typing and myosin ATPase typing are two popular classification systems, both dividing muscle fibers into further subtypes. Type I muscle fibers are slow twitch that are suited for low-intensity and prolonged exercise, while type II muscle fibers are fast twitch that are suited for high-intensity and short-duration exercise. The proportion of muscle fiber types in an individual is determined by both genetic and environmental factors. Athletes who compete in different sports have different percentages of slow and fast twitch fibers, with endurance athletes having a higher percentage of slow-twitch fibers and power athletes having a higher percentage of fast-twitch fibers. Additionally, different resistance training can alter the proportion of muscle fiber types in the body, and it is important to follow different training plans according to their goals.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mingqi Xiao
PY  - 2023
DA  - 2023/10/05
TI  - Research on Muscle Fiber Composition in Humans
BT  - Proceedings of the 2023 2nd International Conference on Sport Science, Education and Social Development (SSESD 2023)
PB  - Atlantis Press
SP  - 11
EP  - 16
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-122-7_3
DO  - 10.2991/978-2-38476-122-7_3
ID  - Xiao2023
ER  -