Validating an Adjustment to the Intermittent Critical Power Model for Elite Cyclists—Modeling W′ Balance During World Cup Team Pursuit Performances

in International Journal of Sports Physiology and Performance

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Jason C. Bartram
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Dominic Thewlis
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David T. Martin
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Kevin I. Norton
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Purpose: Modeling intermittent work capacity is an exciting development to the critical power model with many possible applications across elite sport. With the Skiba 2 model validated using subelite participants, an adjustment to the model’s recovery rate has been proposed for use in elite cyclists (Bartram adjustment). The team pursuit provides an intermittent supramaximal event with which to validate the modeling of W′ in this population. Methods: Team pursuit data of 6 elite cyclists competing for Australia at a Track World Cup were solved for end W′ values using both the Skiba 2 model and the Bartram adjustment. Each model’s success was evaluated by its ability to approximate end W′ values of 0 kJ, as well as a count of races modeled to within a predetermined error threshold of ±1.840 kJ. Results: On average, using the Skiba 2 model found end W′ values different from zero (P = .007; mean ± 95% confidence limit, –2.7 ± 2.0 kJ), with 3 out of 8 cases ending within the predetermined error threshold. Using the Bartram adjustment on average resulted in end W′ values that were not different from zero (P = .626; mean ± 95% confidence limit, 0.5 ± 2.5 kJ), with 4 out of 8 cases falling within the predetermined error threshold. Conclusions: On average, the Bartram adjustment was an improvement to modeling intermittent work capacity in elite cyclists, with the Skiba 2 model underestimating the rate of W′ recovery. In the specific context of modeling team pursuit races, all models were too variable for effective use; hence, individual recovery rates should be explored beyond population-specific rates.

Bartram and Norton are with the Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, Adelaide, SA, Australia. Bartram is also with Cycling Australia, Adelaide, SA, Australia. Thewlis is with the Centre of Orthopaedic and Trauma Research, University of Adelaide, Adelaide, SA, Australia. Martin is with Australian Catholic University, Melbourne, VIC, Australia.

Bartram (jason.bartram@mymail.unisa.edu.au) is corresponding author.
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  • 1.

    Hill AV. The physiological basis of athletic records. Lancet. 1925;206(5323):481486. doi:10.1016/S0140-6736(01)15546-7

  • 2.

    Jones AM, Vanhatalo A, Burnley M, Morton R, Poole D. Critical power: implications for determination of VO2max and exercise tolerance. Med Sci Sports Exerc. 2010;42(10):18761890. PubMed ID: 20195180 doi:10.1249/MSS.0b013e3181d9cf7f

    • Search Google Scholar
    • Export Citation
  • 3.

    Skiba PF, Chidnok W, Vanhatalo A, Jones AM. Modeling the expenditure and reconstitution of work capacity above critical power. Med Sci Sports Exerc. 2012;44(8):15261532. PubMed ID: 22382171 doi:10.1249/MSS.0b013e3182517a80

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

    Skiba PF, Clarke D, Vanhatalo A, Jones AM. Validation of a novel intermittent W′ model for cycling using field data. Int J Sports Physiol Perform. 2014;9(6):900904. PubMed ID: 24509723 doi:10.1123/ijspp.2013-0471

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Skiba PF, Jackman S, Clarke D, Vanhatalo A, Jones AM. Effect of work and recovery durations on W′ reconstitution during intermittent exercise. Med Sci Sports Exerc. 2014;46(7):14331440. PubMed ID: 24492634 doi:10.1249/MSS.0000000000000226

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

    Skiba PF, Fulford J, Clarke DC, Vanhatalo A, Jones AM. Intramuscular determinants of the ability to recover work capacity above critical power. Eur J Appl Physiol. 2015;115(4):703713. PubMed ID: 25425258 doi:10.1007/s00421-014-3050-3

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

    Bartram JC, Thewlis D, Martin DT, Norton KI. Accuracy of W′ recovery kinetics in high performance cyclists—modeling intermittent work capacity. Int J Sports Physiol Perform. 2018;13(6):724. PubMed ID: 29035607 doi:10.1123/ijspp.2017-0034

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

    Broker JP, Kyle CR, Burke ER. Racing cyclist power requirements in the 4000-m individual and team pursuits. Med Sci Sports Exerc. 1999;31(11):16771685. PubMed ID: 10589873 doi:10.1097/00005768-199911000-00026

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

    Barry N, Burton D, Sheridan J, Thompson M, Brown NA. Aerodynamic drag interactions between cyclists in a team pursuit. Sports Eng. 2015;18(2):93103. doi:10.1007/s12283-015-0172-8

    • Search Google Scholar
    • Export Citation
  • 10.

    Bartram JC, Thewlis D, Martin DT, Norton KI. Predicting critical power in elite cyclists: questioning validity of the 3-min all-out test. Int J Sports Physiol Perform. 2017;12(6):783787. PubMed ID: 27834562 doi:10.1123/ijspp.2016-0376

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

    Quod MJ, Martin DT, Martin JC, Laursen PB. The power profile predicts road cycling MMP. Int J Sports Med. 2010;31(6):397401. PubMed ID: 20301046 doi:10.1055/s-0030-1247528

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

    Paton C, Hopkins W. Ergometer error and biological variation in power output in a performance test with three cycle ergometers. Int J Sports Med. 2006;27(6):444447. PubMed ID: 16767608 doi:10.1055/s-2005-865781

    • Search Google Scholar
    • Export Citation
  • 13.

    Martin JC, Gardner AS, Barras M, Martin DT. Modeling sprint cycling using field-derived parameters and forward integration. Med Sci Sports Exerc. 2006;38(3):592. PubMed ID: 16540850 doi:10.1249/01.mss.0000193560.34022.04

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

    Lamberts RP, Swart J, Woolrich RW, Noakes TD, Lambert MI. Measurement error associated with performance testing in well-trained cyclists: application to the precision of monitoring changes in training status. Int J Sports Med. 2009;10(1):3344.

    • Search Google Scholar
    • Export Citation
  • 15.

    Housh DJ, Housh TJ, Bauge SM. A methodological consideration for the determination of critical power and anaerobic work capacity. Res Q Exerc Sport. 1990;61(4):406409. PubMed ID: 2132901 doi:10.1080/02701367.1990.10607506

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

    Hill DW. The critical power concept. A review. Sports Med. 1993;16(4):237254. PubMed ID: 8248682 doi:10.2165/00007256-199316040-00003

  • 17.

    Karsten B, Jobson SA, Hopker J, Stevens L, Beedie C. Validity and reliability of critical power field testing. Eur J Appl Physiol. 2015;115(1):197204. PubMed ID: 25260244 doi:10.1007/s00421-014-3001-z

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

    Vanhatalo A, Jones AM, Burnley M. Application of critical power in sport. Int J Sports Physiol Perform. 2011;6(1):128136. PubMed ID: 21487156 doi:10.1123/ijspp.6.1.128

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

    Passfield L, Hopker JG, Jobson S, Friel D, Zabala M. Knowledge is power: issues of measuring training and performance in cycling. J Sports Sci. 2017;35(14):14261434. PubMed ID: 27686573 doi:10.1080/02640414.2016.1215504

    • PubMed
    • Search Google Scholar
    • Export Citation
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