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Int J Sports Med 2024; 45(05): 359-368
DOI: 10.1055/a-2207-2578
Training & Testing

Reliability of Anaerobic Contributions during a Single Exhaustive Knee-extensor Exercise

Gabriel Luches-Pereira 
1   School of Physical Education and Sport of Ribeirão Preto, University of Sao Paulo, Ribeirão Preto, Brazil
,
Carlos Augusto Kalva-Filho 
2   Human Movement Research Laboratory (MOVI-LAB), São Paulo State University, Bauru, Brazil
,
Danilo Rodrigues Bertucci
3   Department of Sport Sciences, Institute of Health Sciences, Federal University of Triangulo Mineiro, Uberaba, Brazil
,
Carlos Dellavechia de Carvalho 
4   Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
,
Ricardo Augusto Barbieri
5   Department for Life Quality Studies, University of Bologna - Rimini Campus, Rimini, Italy
,
Marcelo Papoti
1   School of Physical Education and Sport of Ribeirão Preto, University of Sao Paulo, Ribeirão Preto, Brazil
› Author Affiliations Funding Information Fundação de Amparo à Pesquisa do Estado de São Paulo — the grant numer 16/12781-5; 16/09339–9
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Abstract

The total anaerobic contribution (AC[La-]+PCr) is a valid and reliable methodology. However, the active muscle mass plays an important role in the AC[La-]+PCr determination, which might influence its reliability. Thus, this study aimed to investigate the effects of two exhaustive intensities on the reliability of the AC[La-]+PCr during a one-legged knee extension (1L-KE) exercise. Thirteen physically active males were submitted to a graded exercise to determine the peak power output (PPO) in the 1L-KE. Then, two constant-load exercises were conducted to task failure at 100% (TTF100) and 110% (TTF110) of PPO, and the exercises were repeated on a third day. The blood lactate accumulation and the oxygen uptake after exercise were used to estimate the anaerobic lactic and alactic contributions, respectively. Higher values of AC[La-]+PCr were found after the TTF100 compared to TTF110 (p=0.042). In addition, no significant differences (p=0.432), low systematic error (80.9 mL), and a significant ICC (0.71; p=0.004) were found for AC[La-]+PCr in the TTF100. However, an elevated coefficient of variation was found (13.7%). In conclusion, we suggest the use of the exhaustive efforts performed at 100% of the PPO with the 1L-KE model, but its elevated individual variability must be carefully considered in future studies.

Key words

high-intensity effort - anaerobic metabolism - MAOD - reliability - performance


Publication History

Received: 13 June 2023

Accepted: 08 November 2023

Accepted Manuscript online:
08 November 2023

Article published online:
12 February 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Noordhof DA, De Koning J, Foster C. The maximal accumulated oxygen deficit method. Sport Med 2010; 40: 285-302
    CrossrefPubMedGoogle Scholar
  • 2 Bangsbo J, Gollnick P, Graham T. et al. Anaerobic energy production and O2 deficit-debt relationship during exhaustive exercise in humans. J App Physiol 1990; 422: 539-559
    PubMedGoogle Scholar
  • 3 Andersson EP, McGawley KJ. A comparison between different methods of estimating anaerobic energy production. Front Physiol 2018; 9: 82
    CrossrefPubMedGoogle Scholar
  • 4 Krogh A, Lindhard J. The changes in respiration at the transition from work to rest. The J Physiol 1920; 53: 431-439
    CrossrefPubMedGoogle Scholar
  • 5 Noordhof D, Vink A, De Koning J. et al. Anaerobic capacity: effect of computational method. Int J Sports Med 2011; 32: 422-428
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Medbø JI, Mohn A-C, Tabata I. et al. Anaerobic capacity determined by maximal accumulated O2 deficit. J App Physiol 1988; 64: 50-60
    CrossrefPubMedGoogle Scholar
  • 7 Gastin P. Quantification of anaerobic capacity. Scand J Med Sci Sports 1994; 4: 91-112
    CrossrefPubMedGoogle Scholar
  • 8 Bertuzzi R, Franchini E, Ugrinowitsch C. et al. Predicting MAOD using only a supramaximal exhaustive test. Int J Sports Med 2010; 31: 477-481
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Zagatto AM, Bertuzzi R, Miyagi W. et al. MAOD determined in a single supramaximal test: A study on the reliability and effects of supramaximal intensities. Int J Sports Med 2016; 37: 700-707
    Article in Thieme ConnectPubMedGoogle Scholar
  • 10 Miyagi WE, De Poli RDAB, Papoti M. et al. Anaerobic capacity estimated in a single supramaximal test in cycling: Validity and reliability analysis. Sci Rep 2017; 7: 42485
    CrossrefPubMedGoogle Scholar
  • 11 Zagatto AM, Nakamura FY, Milioni F. et al. The sensitivity of the alternative maximal accumulated oxygen deficit method to discriminate training status. J Sports Sci 2017; 35: 2453-2460
    CrossrefPubMedGoogle Scholar
  • 12 Brisola GMP, Miyagi WE, da Silva HS. et al. Sodium bicarbonate supplementation improved MAOD but is not correlated with 200-and 400-m running performances: a double-blind, crossover, and placebo-controlled study. Appl Physiol Nutr Metab 2015; 40: 931-937
    CrossrefPubMedGoogle Scholar
  • 13 Bertuzzi R, Kiss MA, Damasceno M. et al. Association between anaerobic components of the maximal accumulated oxygen deficit and 30-second Wingate test. Braz J Med Biol Res 2015; 48: 261-266
    CrossrefPubMedGoogle Scholar
  • 14 Campos EZ, Kalva-Filho CA, Gobbi RB. et al. Anaerobic Contribution Determined in Swimming Distances: Relation with Performance. Front Physiol 2017; 8: 755
    CrossrefPubMedGoogle Scholar
  • 15 Zagatto AM, Miyagi WE, Sousa FA. et al. Relationship between anaerobic capacity estimated using a single effort and 30-s tethered running outcomes. PLoS One 2017; 12: e0172032
    CrossrefPubMedGoogle Scholar
  • 16 Foresti YF, Higino WP, de Carvalho CD. et al Can hypoxic alter the anaerobic capacity measured by a single exhaustive exercise?. Int J Sports Med 2022; 44: 961-968
    PubMedGoogle Scholar
  • 17 Redkva PE, Miyagi WE, Milioni F. et al. Anaerobic capacity estimated by the sum of both oxygen equivalents from the glycolytic and phosphagen pathways is dependent on exercise mode: Running versus cycling. PLoS One 2018; 13: e0203796
    CrossrefPubMedGoogle Scholar
  • 18 Billat VL, Sirvent P, Py G. et al. The concept of maximal lactate steady state: A bridge between biochemistry, physiology and sport science. Sports Med 2003; 33: 407-426
    CrossrefPubMedGoogle Scholar
  • 19 Bergman BC, Wolfel EE, Butterfield GE. et al. Active muscle and whole body lactate kinetics after endurance training in men. J App Physiol 1999; 87: 1684-1696
    CrossrefPubMedGoogle Scholar
  • 20 Bangsbo J. Oxygen deficit: a measure of the anaerobic energy production during intense exercise?. Can J Appl Physiol 1996; 21: 350-363
    CrossrefPubMedGoogle Scholar
  • 21 Luches-Pereira G, Kalva-Filho CA, Papoti M. Anaerobic Contributions Are Influenced by Active Muscle Mass and The Applied Methodology in Well-Controlled Muscle Group. Int J Exerc Sci 2022; 15: 599-615
    PubMedGoogle Scholar
  • 22 Andersen P, Adams RP, Sjogaard G. et al. Dynamic knee extension as model for study of isolated exercising muscle in humans. J App Physiol 1985; 59: 1647-1653
    CrossrefPubMedGoogle Scholar
  • 23 Kalva-Filho CA, Barbieri RA, de Andrade VL. et al. A prototype for dynamic knee extension: Construction, force characterization and electromiographic responses. Braz J Mot Behav 2020; 14: 97-109
    CrossrefPubMedGoogle Scholar
  • 24 Arifin WN. 2023. Sample size calculator (web). Available at http://wnarifin.github.io Accessed [6 November 2023]
    PubMed
  • 25 Bonett DG. Sample size requirements for estimating intraclass correlations with desired precision. Stat Med 2002; 21: 1331-1335
    CrossrefPubMedGoogle Scholar
  • 26 van Melick N, Meddeler BM, Hoogeboom TJ. et al. How to determine leg dominance: The agreement between self-reported and observed performance in healthy adults. PloS one 2017; 12: e0189876
    CrossrefPubMedGoogle Scholar
  • 27 Foster C, Florhaug JA, Franklin J. et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15: 109-115
    PubMedGoogle Scholar
  • 28 Rannou F, Nybo L, Andersen JE. et al. Monitoring Muscle Fatigue Progression during Dynamic Exercise. Med Sci Sports Exerc 2019; 51: 1498-1505
    CrossrefPubMedGoogle Scholar
  • 29 Kuipers H, Rietjens G, Verstappen F. et al. Effects of stage duration in incremental running tests on physiological variables. Int J Sports Med 2003; 24: 486-491
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Karlsson J, Saltin B. Lactate, ATP, and CP in working muscles during exhaustive exercise in man. J App Physiol 1970; 29: 598-602
    CrossrefPubMedGoogle Scholar
  • 31 Beneke R, Beyer T, Jachner C. et al. Energetics of karate kumite. Eur J Appl Physiol 2004; 92: 518-523
    CrossrefPubMedGoogle Scholar
  • 32 Hopkins W, Marshall S, Batterham A. et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41: 3-13
    CrossrefPubMedGoogle Scholar
  • 33 Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15: 155-163
    CrossrefPubMedGoogle Scholar
  • 34 Hopkins WG. Measures of reliability in sports medicine and science. Sport Med 2000; 30: 1-15
    CrossrefPubMedGoogle Scholar
  • 35 Beckerman H, Roebroeck M, Lankhorst G. et al. Smallest real difference, a link between reproducibility and responsiveness. Qual Life Res 2001; 10: 571-578
    CrossrefPubMedGoogle Scholar
  • 36 Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 2005; 19: 231-240
    CrossrefPubMedGoogle Scholar
  • 37 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud 2010; 47: 931-936
    CrossrefPubMedGoogle Scholar
  • 38 Zhang J, Iannetta D, Alzeeby M. et al. Exercising muscle mass influences neuromuscular, cardiorespiratory, and perceptual responses during and following ramp-incremental cycling to task failure. Am J Physiol Regul Integr Comp Physiol 2021; 321: R238-R249
    CrossrefPubMedGoogle Scholar
  • 39 Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sport Med 2001; 31: 211-234
    CrossrefPubMedGoogle Scholar
  • 40 Laursen PB, Francis GT, Abbiss CR. et al. Reliability of time-to-exhaustion versus time-trial running tests in runners. Med Sci Sports Exerc 2007; 39: 1374-1379
    CrossrefPubMedGoogle Scholar
  • 41 Billat V, Renoux JC, Pinoteau J. et al. Reproducibility of running time to exhaustion at VO, in subelite runners. Med Sci Sports Exerc 1994; 26: 254-257
    CrossrefPubMedGoogle Scholar
  • 42 Kalva-Filho C, Araújo M, Silva A. et al. Determination of VO2-intensity relationship and MAOD in tethered swimming. Int J Sports Med 2016; 37: 687-693
    Article in Thieme ConnectPubMedGoogle Scholar
  • 43 Ferguson C, Rossiter HB, Whipp BJ. et al. Effect of recovery duration from prior exhaustive exercise on the parameters of the power-duration relationship. J App Physiol 2010; 108: 866-874
    CrossrefPubMedGoogle Scholar
  • 44 Hansen AK, Fischer CP, Plomgaard P. et al. Skeletal muscle adaptation: Training twice every second day vs. training once daily. J App Physiol 2005; 98: 93-99
    CrossrefPubMedGoogle Scholar