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Int J Sports Med 2019; 40(06): 397-403
DOI: 10.1055/a-0858-9900
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Predictive Ability of a Laboratory Performance Test in Mountain Bike Cross-country Olympic Athletes

Patrick Schneeweiss
1   Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Philipp Schellhorn
1   Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Daniel Haigis
1   Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Andreas Niess
1   Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
,
Peter Martus
2   Institute for Clinical Epidemiology and Applied Biometry, University of Tuebingen, Tuebingen
,
Inga Krauss
1   Department of Sports Medicine, Medical Clinic, University of Tuebingen, Tuebingen
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accepted 06. Februar 2019

Publikationsdatum:
01. April 2019 (online)

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Abstract

Mountain bike Cross-Country Olympic (XCO) has an intermittent performance profile, underlining the importance of anaerobic metabolism. Traditional performance tests in cycling primarily quantify aerobic metabolism and inadequately meet the demands in XCO. The aim was therefore to validate a specific test that quantifies these requirements by means of an XCO race.

Twenty-three competitive XCO athletes (17.9±3.6 years) performed a previously developed performance test and an XCO race within one week. Correlations between individual anaerobic threshold (IAT), 4 mmol lactate threshold (LT4), maximal aerobic power (MAP), maximal effort time trials (TT) for 10–300 s and mean power output of the race (POR) were calculated. In addition, a multiple regression model of the predictive value of the test was calculated.

Variables correlated significantly (p<.01) with POR: IAT (r=.81), LT4 (r=0.79), MAP (r=0.91), TT10 (r=0.75), TT30 (r=0.85), TT60 (r=0.84) and TT300 (r=0.86). In the regression model, sex and body mass were set influencing variables (R²adj.=0.70), whereby MAP had the highest correlation with POR and significantly improved the predictive value of the model (R²adj.=0.86).

The high correlation of collected performance variables with POR indicated the MTB-PT’s additional benefit for performance testing in XCO because it is specific but very feasible.

Key words

off-road cycling - performance testing - mountain biking - competition - validity - XCO
 

  • References

  • 1 Ahrend M-D, Schneeweiss P, Martus P, Niess AM, Krauss I. Predictive ability of a comprehensive incremental test in mountain bike marathon. BMJ Open Sport Exerc Med 2018; 4: e000293
    CrossrefPubMedGoogle Scholar
  • 2 Ahrend M-D, Schneeweiss P, Theobald U, Niess AM, Krauss I. Comparison of laboratory parameters of a mountain bike specific performance test and a simulated race performance in the field. J Sci Cycling 2016; 5: 3-9
    PubMedGoogle Scholar
  • 3 Allen H, Coggan A. Training and Racing with a Power Meter. 2nd ed. VeloPress; 2010
    Google Scholar
  • 4 Baron R. Aerobic and anaerobic power characteristics of off-road cyclists. Med Sci Sports Exerc 2001; 33: 1387-1393
    CrossrefPubMedGoogle Scholar
  • 5 Capelli C, Rittveger J, Bruseghini P, Calabria E, Tam E. Maximal aerobic power and anaerobic capacity in cycling across the age spectrum in male master athletes. Eur J Appl Physiol 2016; 116: 1395-1410
    CrossrefPubMedGoogle Scholar
  • 6 Costa V, De-Oliveira F. Physiological variables to predict performance in cross-country mountain bike races. J Exerc Physiol Online 2008; 11: 14-24
    PubMedGoogle Scholar
  • 7 Dickhuth HH, Huonker M, Münzel T, Drexler H, Berg A, Keul J. Individual anaerobic threshold for evaluation of competitive athletes and patients with left ventricular dysfunction. In Bachl TG, Löllgen H. (eds.). Advances in Ergometry. Berlin, Heidelberg, New York: Springer Verlag; 1991: 176-179
    Google Scholar
  • 8 Gardner AS, Stephens S, Martin DT, Lawton E, Lee H, Jenkins D. Accuracy of SRM and power tap power monitoring systems for bicycling. Med Sci Sports Exerc 2004; 36: 1252-1258
    CrossrefPubMedGoogle Scholar
  • 9 Gavin TP, Van Meter JB, Brophy PM, Dubis GS, Potts KN, Hickner RC. Comparison of a field-based test to estimate functional threshold power and power output at lactate threshold. J Strength Cond Res 2012; 26: 416-421
    CrossrefPubMedGoogle Scholar
  • 10 Gregory J, Johns DP, Walls JT. Relative vs. absolute physiological measures as predictors of mountain bike cross-country race performance. J Strength Cond Res 2007; 21: 17-22
    CrossrefPubMedGoogle Scholar
  • 11 Harriss DJ, Macsween A, Atkinson G. Standards for ethics in sport and exercise science research: 2018 update. Int J Sports Med 2017; 38: 1126-1131
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 12 Hinkle DE, Wiersma W, Jurs S. Applied Statistics for the Behavioral Sciences, International Edition. 5th ed Andover: Cengage Learning EMEA; 2009
    Google Scholar
  • 13 Impellizzeri FM, Marcora SM. The physiology of mountain biking. Sports Med 2007; 37: 59-71
    CrossrefPubMedGoogle Scholar
  • 14 Impellizzeri FM, Marcora SM, Rampinini E, Mognoni P, Sassi A. Correlations between physiological variables and performance in high-level cross-country off-road cyclists. Br J Sports Med 2005; 39: 747-751
    CrossrefPubMedGoogle Scholar
  • 15 Impellizzeri FM, Rampinini E, Sassi A, Mognoni P, Marcora S. Physiological correlates to off-road cycling performance. J Sports Sci 2005; 23: 41-47
    CrossrefPubMedGoogle Scholar
  • 16 Inoue A, Sa Filho AS, Mello FC, Santos TM. Relationship between anaerobic cycling tests and mountain bike cross-country performance. J Strength Cond Res 2012; 26: 1589-1593
    CrossrefPubMedGoogle Scholar
  • 17 Kuipers H, Verstappen FT, Keizer HA, Geurten P, van Kranenburg G. Variability of aerobic performance in the laboratory and its physiologic correlates. Int J Sports Med 1985; 6: 197-201
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 18 Medicine ACoS. ACSM's Guidelines for exercise . Testing and Prescription. Riebe D, Ehrman JK, Liguori G, Magal M. (eds.) 10th ed Philadelphia: Wolters Kluwer Health; 2018
    Google Scholar
  • 19 Miller MC. Validity of using functional threshold power and intermittent power to predict cross-country mountain bike race outcome. J Sci Cycling 2014; 3: 16-20
    PubMedGoogle Scholar
  • 20 Miller MC, Macdermid PW, Fink PW, Stannard SR. Performance and physiological effects of different descending strategies for cross-country mountain biking. Eur J Sport Sci 2017; 17: 279-285
    CrossrefPubMedGoogle Scholar
  • 21 Novak AR, Bennett KJM, Fransen J, Dascombe BJ. A multidimensional approach to performance prediction in Olympic distance cross-country mountain bikers. J Sports Sci 2018; 36: 71-78
    CrossrefPubMedGoogle Scholar
  • 22 Paton CD, Hopkins WG. Tests of cycling performance. Sports Med 2001; 31: 489-496
    CrossrefPubMedGoogle Scholar
  • 23 Prins L, Terblanche E, Myburgh KH. Field and laboratory correlates of performance in competitive cross-country mountain bikers. J Sports Sci 2007; 25: 927-935
    CrossrefPubMedGoogle Scholar
  • 24 Quod MJ, Martin DT, Martin JC, Laursen PB. The power profile predicts road cycling MMP. Int J Sports Med 2010; 31: 397-401
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 25 Roecker K, Schotte O, Niess AM, Horstmann T, Dickhuth HH. Predicting competition performance in long-distance running by means of a treadmill test. Med Sci Sports Exerc 1998; 30: 1552-1557
    CrossrefPubMedGoogle Scholar
  • 26 Roecker K, Striegel H, Dickhuth HH. Heart-rate recommendations: Transfer between running and cycling exercise?. Int J Sports Med 2003; 24: 173-178
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 27 Stapelfeldt B, Schwirtz A, Schumacher YO, Hillebrecht M. Workload demands in mountain bike racing. Int J Sports Med 2004; 25: 294-300
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 28 Stevens CJ, Dascombe BJ. The reliability and validity of protocols for the assessment of endurance sports performance: an updated review. Meas Phys Educ Exerc Sci 2015; 19: 177-185
    CrossrefPubMedGoogle Scholar
  • 29 Theobald U. Leistungsanforderungen und trainingsmittel in der radsportdisziplin mountainbike cross-country. Leistungssport 2015; 1: 20-24
    PubMedGoogle Scholar
  • 30 UCI Cycling Regulations. Part 4. Mountain Bike.; URL http://www.uci.ch/mm/Document/News/Rulesandregulation/17/29/73/4MTB-E-1.01.2018-Final_English.pdf
    PubMed
  • 31 Whyte GP, George K, Shave R, Middleton N, Nevill AM. Training-induced changes in maximum heart rate. Int J Sports Med 2008; 29: 129-133
    Artikel in Thieme ConnectPubMedGoogle Scholar