The Influence of Muscle Fiber Typology on the Pacing Strategy of 200-m Freestyle Swimmers

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $112.00

1 year online subscription

USD  $149.00

Student 2 year online subscription

USD  $213.00

2 year online subscription

USD  $284.00

Purpose: To determine the influence of muscle fiber typology (MFT) on the pacing strategy of elite swimmers competing in the 200-m freestyle event. Method: The top 3 career-best performances from 25 elite 200-m freestyle swimmers were analyzed—12 women (1:58.0 [0:01.3] min:s) and 13 men (1:48.4 [0:02.5]). Muscle carnosine concentration was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus muscles and expressed as a carnosine aggregate z score (CAZ score) relative to an age- and gender-matched nonathlete control group to estimate MFT. Linear regression models were employed to examine the influence of MFT on the percentage of overall race time spent in each 50-m lap. Results: Swimmers with a higher CAZ score spent a greater percentage of race time in lap 3 compared with swimmers with a lower CAZ score (0.1%, 0.0% to 0.2%; mean, 90% confidence interval, P = .02). For every 1% increase in the percentage of race time spent in lap 1, the percentage of race time spent in lap 3 decreased by 0.4% for swimmers with a higher CAZ score (0.2% to −0.5%, P = .00, r = −.51), but not for swimmers with a lower CAZ score (−0.1%, −0.3% to 0.1%, P = .28, r = −.18). The percentage of race time spent in lap 4 decreased by 0.8% for higher-CAZ-score swimmers (−0.5% to −1.0%, P = .00, r = −.66) and by 0.9% for lower-CAZ-score swimmers (−0.6% to −1.3%, P = .00, r = −.65) when lap 1 percentage increased by 1%. Conclusion: MFT may influence the pacing strategy of swimmers in the 200-m freestyle event, which provides an avenue for maximizing individualized pacing strategies of elite swimmers.

Mallett, Bellinger, and Minahan are with Griffith Sports Science, Griffith University, Gold Coast, QLD, Australia; Mallett is also with the School of Allied Health Sciences at the university. Mallett, Bellinger, and McGibbon are with the Queensland Academy of Sport, Nathan, QLD, Australia. Derave and Lievens are with the Dept of Movement and Sports Sciences, Ghent University, Ghent, Belgium. Kennedy and Rice are with Qscan Radiology, Gold Coast, QLD, Australia.

Mallett (adam.mallett@griffithuni.edu.au) is corresponding author.
  • 1.

    Foster C, De Koning JJ, Hettinga F, et al. . Pattern of energy expenditure during simulated competition. Med Sci Sports Exerc. 2003;35(5):826831. PubMed ID: 12750593 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Abbiss CR, Laursen PB. Describing and understanding pacing strategies during athletic competition. Sports Med. 2008;38(3):239252. PubMed ID: 18278984 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    McGibbon KE, Shephard ME, Osborne MA, Thompson KG, Pyne DB. Pacing and performance in swimming: differences between individual and relay events. Int J Sports Physiol Perform. 2020;15(8):10591066. PubMed ID: 32283539 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Thompson K. Pacing: Individual Strategies for Optimal Performance. Champaign, IL: Human Kinetics; 2014.

  • 5.

    Mytton GJ, Archer DT, Gibson ASC, Thompson KG. Reliability and stability of performances in 400-m swimming and 1500-m running. Int J Sports Physiol Perform. 2014;9(4):674679. PubMed ID: 24231408 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Skorski S, Faude O, Caviezel S, Meyer T. Reproducibility of pacing profiles in elite swimmers. Int J Sports Physiol Perform. 2014;9(2):217225. PubMed ID: 23689199 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Figueiredo P, Zamparo P, Sousa A, Vilas-Boas JP, Fernandes RJ. An energy balance of the 200 m front crawl race. Eur J Appl Physiol. 2011;111(5):767777. PubMed ID: 20978781 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Fédération Internationale de Natation. Lastest-results. http://www.fina.org/latest-results. Published 2020. Accessed September 2019.

  • 9.

    Escobar DS, Hellard P, Pyne DB, Seifert L. Functional role of movement and performance variability: adaptation of front crawl swimmers to competitive swimming constraints. J Appl Biomech. 2018;34(1):5364. PubMed ID: 28952848 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Parkhouse WS, McKenzie DC, Hochachka PW, Ovalle WK. Buffering capacity of deproteinized human vastus lateralis muscle. J Appl Physiol. 1985;58(1):1417. PubMed ID: 3968004 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Tesch PA, Thorsson A, Fujitsuka N. Creatine phosphate in fiber types of skeletal muscle before and after exhaustive exercise. J Appl Physiol. 1989;66(4):17561759. PubMed ID: 2732167 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Pette D. Metabolic heterogeneity of muscle fibres. J Exp Biol. 1985;115(1):179189. PubMed ID: 4031763

  • 13.

    Harber MP, Gallagher PM, Creer AR, Minchev KM, Trappe SW. Single muscle fiber contractile properties during a competitive season in male runners. Am J Physiol Regul Integr Comp Physiol. 2004;287(5):R1124R1131. PubMed ID: 15142838 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Pette D, Spamer C. Metabolic properties of muscle fibers. Fed Proc. 1986;45:29102914. PubMed ID: 3536589

  • 15.

    Bex T, Baguet A, Achten E, Aerts P, De Clercq D, Derave W. Cyclic movement frequency is associated with muscle typology in athletes. Scand J Med Sci Sports. 2017;27(2):223229. PubMed ID: 26864556 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Costill DL, Daniels J, Evans W, Fink W, Krahenbuhl G, Saltin B. Skeletal muscle enzymes and fiber composition in male and female track athletes. J Appl Physiol. 1976;40(2):149154. PubMed ID: 129449 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Gerard ES, Caiozzo VJ, Rubin BD, Prietto CA, Davidson DM. Skeletal muscle profiles among elite long, middle, and short distance swimmers. Am J Sports Med. 1986;14(1):7782. PubMed ID: 3752351 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Baguet A, Everaert I, Hespel P, Petrovic M, Achten E, Derave W. A new method for non-invasive estimation of human muscle fiber type composition. PLoS One. 2011;6(7):e21956. PubMed ID: 21760934 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    MacMillan EL, Bolliger CS, Boesch C, Kreis R. Influence of muscle fiber orientation on water and metabolite relaxation times, magnetization transfer, and visibility in human skeletal muscle. Magn Reson Med. 2016;75(4):17641770. PubMed ID: 25982125 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    RCoreTeam. R: a language and environment for statistical computing. https://www.R-project.org/. Published 2017. Accessed July 2020.

  • 21.

    Pyne DB, Trewin CB, Hopkins WG. Progression and variability of competitive performance of Olympic swimmers. J Sports Sci. 2004;22(7):613620. PubMed ID: 15370491 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Robertson EY, Pyne DB, Hopkins WG, Anson JM. Analysis of lap times in international swimming competitions. J Sports Sci. 2009;27(4):387395. PubMed ID: 19214862 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Garland SW. An analysis of the pacing strategy adopted by elite competitors in 2000 m rowing. Br J Sports Med. 2005;39(1):3942. PubMed ID: 15618339 doi:

All Time Past Year Past 30 Days
Abstract Views 595 595 51
Full Text Views 20 20 2
PDF Downloads 25 25 3