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At the Olympic level, optimally distributing training intensity is crucial for maximizing performance. Purpose: The authors evaluated the effect of training-intensity distribution on anaerobic power as a substitute for 1500-m speed-skating performance in the 4 y leading up to an Olympic gold medal. Methods: During the preparation phase of the speed-skating season, anaerobic power was recorded periodically (n = 15) using the mean power (in watts) with a 30-s Wingate test. For each training session in the 4 wk prior to each Wingate test, the volume (in hours), training type (specific, simulation, nonspecific, and strength training), and the rating of perceived exertion (RPE; CR-10) were recorded. Results: Compared with the 8 lowest, the 7 highest-scoring tests were preceded by a significantly (P < .01) higher volume of strength training. Furthermore, the RPE distribution of the number of nonspecific training sessions was significantly different (P < .01). Significant (P < .05) correlations highlighted that a larger nonspecific training volume in the lower intensities RPE 2 (r = .735) and 3 (r = .592) was associated positively and the medium intensities RPE 4 (r = −.750) and 5 (r = −.579) negatively with Wingate performance. Conclusion: For the subject, the best results were attained with a high volume of strength training and the bulk of nonspecific training at RPE 2 and 3, and specifically not at the adjoining RPE 4 and 5. These findings are surprising given the aerobic nature of training at RPE 2 and 3 and the importance of anaerobic capacity in this middle-distance event.

The authors are with the Leiden Inst of Advanced Computer Science (LIACS), Leiden University, Leiden, the Netherlands. Orie and Hofman are also with Team Jumbo-Visma Ice, ‘s Gravenmoer, the Netherlands. Hofman is also with PACA SMA Aalsmeer, Aalsmeer, the Netherlands.

Meerhoff (rensmeerhoff@gmail.com) is corresponding author.
  • 1.

    Seiler KS, Kjerland GO. Quantifying training intensity distribution in elite endurance athletes: is there evidence for an “optimal” distribution? Scand J Med Sci Sports. 2006;16(1):4956. PubMed ID: 16430681 doi:

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

    Seiler S. What is best practice for training intensity and duration distribution in endurance athletes? Int J Sports Physiol Perform. 2010;5(3):276291. PubMed ID: 20861519 doi:

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

    Esteve-Lanao J, Foster C, Seiler S, et al. Impact of training intensity distribution on performance in endurance athletes. J Strength Cond Res. 2007;21(3):943949. PubMed ID: 17685689

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

    Stöggl TL, Sperlich B. The training intensity distribution among well-trained and elite endurance athletes. Front Physiol. 2015;6:295.

  • 5.

    de Koning JJ, Foster C, Lampen J, Hettinga F, Bobbert MF. Experimental evaluation of the power balance model of speed skating. J Appl Physiol. 2005;98(1):227233. PubMed ID: 15591304 doi:

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

    Hofman N, Orie JNM, Hoozemans MJM, Foster C, de Koning JJ. Wingate test is a strong predictor of 1500 m performance in Elite speed skaters. Int J Sports Physiol Perform. 2017;12(10):12881292. PubMed ID: 28253027 doi:

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

    Orie JNM, Hofman N, de Koning JJ, Foster C. Thirty-eight years of training distribution in Olympic speed skaters. Int J Sports Physiol Perform. 2014;9(1):9399. PubMed ID: 24408352 doi:

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

    Day ML, McGuigan MR, Brice G, Foster C. Monitoring exercise intensity during resistance training using the session RPE scale. J Strength Cond Res. 2004;18(2):353358. PubMed ID: 15142026

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Abe D, Yoshida T, Ueoka H, Sugiyama K, Fukuoka Y. Relationship between perceived exertion and blood lactate concentrations during incremental running test in young females. BMC Sports Sci Med Rehabil. 2015;7:5. PubMed ID: 25973209 doi:

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

    Yu H, Chen X, Zhu W, Cao C. A quasi-experimental study of Chinese top-level speed skaters’ training load: threshold versus polarized model. Int J Sports Physiol Perform. 2012;7(2):103112. PubMed ID: 22634959 doi:

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

    Foster C, Daines E, Hector L, Snyder AC. Athletic performance in relation to training load. Wis Med J. 1996;95(6):370374. PubMed ID: 8693756

  • 12.

    Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998;30(7):11641168. PubMed ID: 9662690 doi:

  • 13.

    Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109115. PubMed ID: 11708692

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

    Foster C, Heimann KM, Esten PL, Brice G, Porcari JP. Differences in perceptions of training by coaches and athletes. S Afr J Med Sci. 2001;8(2):37.

    • Search Google Scholar
    • Export Citation
  • 15.

    Knobbe A, Orie J, Hofman N, van der Burgh B, Cachucho R. Sports analytics for professional speed skating. Data Min Knowl Discov. 2017;31(6):18721902. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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