Int J Sports Med
DOI: 10.1055/a-2550-4988
Training & Testing

Comparison of modeled lactate threshold 2 with maximal lactate steady state in running and cycling

1   Institute of Exercise Training and Sport Informatics, Section Exercise Physiology German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
2   German Research Centre of Elite Sport, German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
,
1   Institute of Exercise Training and Sport Informatics, Section Exercise Physiology German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
2   German Research Centre of Elite Sport, German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
,
1   Institute of Exercise Training and Sport Informatics, Section Exercise Physiology German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
2   German Research Centre of Elite Sport, German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
,
Lukas Zwingmann
1   Institute of Exercise Training and Sport Informatics, Section Exercise Physiology German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
,
Patrick Wahl
1   Institute of Exercise Training and Sport Informatics, Section Exercise Physiology German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
2   German Research Centre of Elite Sport, German Sport University Cologne, Cologne, Germany (Ringgold ID: RIN14926)
› Author Affiliations

Abstract

This study investigated (1) the agreement of modeled lactate threshold 2 using peak oxygen uptake, cost of locomotion, and fractional utilization of peak oxygen uptake at lactate threshold 2 with the maximal lactate steady state in running and cycling; (2) the impact of different cost of locomotion determination methods on the accuracy of the model and (3) the contributions of peak oxygen uptake, cost of locomotion, and fractional utilization of peak oxygen uptake at lactate threshold 2 to the work rate at maximal lactate steady state. Thirty-four endurance-trained athletes (27.7±6.9 y, 56.2±5.5 ml∙kg−1∙min−1) completed an incremental step test on a treadmill or a cycling ergometer. Peak oxygen uptake, cost of locomotion at lactate threshold 1, at 80% of peak oxygen uptake, and at lactate threshold 2, and fractional utilization of peak oxygen uptake at lactate threshold 2 were assessed. Two to five 30-minute constant work rate tests were performed for maximal lactate steady state determination. Moderate to good agreement was found between modeled work rate corresponding to lactate threshold 2 and the maximal lactate steady state for running and cycling (intraclass correlation coefficient≥0.698) with the smallest mean difference (±limits of agreement) for cost of locomotion determined at lactate threshold 2 with −2.0±5.2 and −0.9±6.0%, respectively. Overall, 83 and 79% of the variance in the maximal lactate steady state was explained by peak oxygen uptake, cost of locomotion determined at lactate threshold 2, and fractional utilization of peak oxygen uptake at lactate threshold 2, respectively. Peak oxygen uptake and cost of locomotion determined at lactate threshold 2 contributed the most to the regression R 2 in running (54 and 40%) and cycling (74 and 51%), while fractional utilization of peak oxygen uptake at lactate threshold 2 had the smallest contribution (4 and 5%). Based on the high accuracy of the model with the major contribution of peak oxygen uptake and cost of locomotion determined at lactate threshold 2, the work rate corresponding to the maximal lactate steady state could be improved focusing on these two variables during training.



Publication History

Received: 03 July 2024

Accepted after revision: 03 March 2025

Accepted Manuscript online:
03 March 2025

Article published online:
27 April 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 
  • References

  • 1 Péronnet F, Thibault G. Mathematical analysis of running performance and world running records. J Appl Physiol 1989; 67: 453-465
  • 2 Katch V, Henry FM. Prediction of running performance from maximal oxygen debt and intake. Med Sci Sports 1972; 4: 187-191
  • 3 Costill DL, Branam G, Eddy D, Sparks K. Determinants of marathon running success. Int Z Angew Physiol 1971; 29: 249-254
  • 4 Joyner MJ. Modeling: optimal marathon performance on the basis of physiological factors. J Appl Physiol 1991; 70: 683-687
  • 5 McLaughlin JE, Howley ET, Bassett DR. et al. Test of the classic model for predicting endurance running performance. Med Sci Sports Exerc 2010; 42: 991-997
  • 6 Støren Ø, Ulevåg K, Larsen MH. et al. Physiological determinants of the cycling time trial. J Strength Cond Res 2013; 27: 2366-2373
  • 7 Støa EM, Helgerud J, Rønnestad BR. et al. Factors influencing running velocity at lactate threshold in male and female runners at different levels of performance. Front Physiol 2020; 11: 585267
  • 8 Støren Ø, Rønnestad BR, Sunde A. et al. A time-saving method to assess power output at lactate threshold in well-trained and elite cyclists. J Strength Cond Res 2014; 28: 622-629
  • 9 Faude O, Kindermann W, Meyer T. Lactate threshold concepts: how valid are they. Sports Med 2009; 39: 469-490
  • 10 Ji S, Keller S, Zwingmann L, Wahl P. Modeling lactate threshold in young squad athletes: influence of sex, maximal oxygen uptake, and cost of running. Eur J Apply Physiol 2023; 123: 573-583
  • 11 Niemeyer M, Gündisch M, Steinecke G. et al. Is the maximal lactate steady state concept really relevant to predict endurance performance?. Eur J Apply Physiol 2022; 122: 2259-2269
  • 12 Zwingmann L, Strütt S, Martin A. et al. Modifications of the Dmax method in comparison to the maximal lactate steady state in young male athletes. Phys Sportsmed 2019; 47: 174-181
  • 13 Helgerud J. Maximal oxygen uptake, anaerobic threshold and running economy in women and men with similar performances level in marathons. Eur J Appl Physiol Occup Physiol 1994; 68: 155-161
  • 14 Beneke R. Methodological aspects of maximal lactate steady state – implications for performance testing. Eur J Apply Physiol 2003; 89: 95-99
  • 15 Iannetta D, Ingram CP, Keir DA, Murias JM. Methodological reconciliation of CP and MLSS and their agreement with the maximal metabolic steady state. Med Sci Sports Exerc 2021; 54: 622-632
  • 16 Lacour JR, Bourdin M. Factors affecting the energy cost of level running at submaximal speed. Eur J Appl Physiol 2015; 115: 651-673
  • 17 Lundby C, Montero D, Gehrig S. et al. Physiological, biochemical, anthropometric, and biomechanical influences on exercise economy in humans. Scand J Med Sci Sports 2017; 27: 1627-1637
  • 18 Barnes KR, Kilding AE. Running economy: measurement, norms, and determining factors. Sports Med Open 2015; 1: 8
  • 19 Scharhag-Rosenberger F, Meyer T, Gäßler N. et al. Exercise at given percentages of VO2max: heterogeneous metabolic responses between individuals. J Sci Med Sport 2010; 13: 74-79
  • 20 De Pauw K, Roelands B, Cheung SS. et al. Guidelines to classify subject groups in sport-science research. Int J Sports Physiol Perform 2013; 8: 111-122
  • 21 Harriss DJ, Jones C, MacSween A. Ethical standards in sport and exercise science Research: 2022 Update. Int J Sports Med 2022; 43: 1065-1070
  • 22 Bishop D, Jenkins DG, Mackinnon LT. The relationship between plasma lactate parameters, Wpeak and 1-h cycling performance in women. Med Sci Sports Exerc 1998; 30: 1270-1275
  • 23 Bentley DJ, McNaughton LR, Thompson D. et al. Peak power output, the lactate threshold, and time trial performance in cyclists. Med Sci Sports Exerc 2001; 33: 2077-2081
  • 24 Whipp BJ, Wasserman K. Oxygen uptake kinetics for various intensities of constant load work. J Appl Physiol 1972; 33: 351-356
  • 25 R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing 2022
  • 26 Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15: 155-163
  • 27 Mukaka MM. Statistics corner: a guide to appropriate use of correlation in medical research. Malawi Med J 2012; 24: 69-71
  • 28 Sabater-Pastor F, Faricier R, Metra M. et al. Changes in cost of locomotion are higher after endurance cycling than running when matched for intensity and duration. Med Sci Sports Exerc 2022; 55: 389-397
  • 29 Wahl P, Zwingmann L, Manunzio C. et al. Higher accuracy of the lactate minimum test compared to established threshold concepts to determine maximal lactate steady state in running. Int J Sports Med 2018; 39: 541-548
  • 30 Wahl P, Manunzio C, Vogt F. et al. Accuracy of a modified lactate minimum test and reverse lactate threshold test to determine maximal lactate steady state. J Strength Cond Res 2017; 31: 3489-3496
  • 31 Hunter GR, Bamman MM, Larson-Meyer DE. et al. Inverse relationship between exercise economy and oxidative capacity in muscle. Eur J Appl Physiol 2005; 94: 558-568
  • 32 Coyle EF, Sidossis LS, Horowitz JF, Beltz JD.. Cycling efficiency is related to the percentage of type I muscle fibers. Med Sci Sports Exerc 1992; 24: 782-788
  • 33 Støren Ø, Helgerud J, Støa EM, Hoff J. Maximal strength training improves running economy in distance runners. Med Sci Sports Exerc 2008; 40: 1087-1092
  • 34 Sylta Ø, Tønnessen E, Hammarström D. et al. The effect of different high-intensity periodization models on endurance adaptations. Med Sci Sports Exerc 2016; 48: 2165-2174
  • 35 Rønnestad BR, Hansen J, Hollan I, Ellefsen S. Strength training improves performance and pedaling characteristics in elite cyclists. Scand J Med Sci Sports 2015; 25: 89-98