Performance prediction, pacing profile and running pattern of elite 1-h track running events
Sport Sciences for Health - 2022
Tóm tắt
This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times. The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern. Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PLV20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed. The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.
Từ khóa
Tài liệu tham khảo
Jones AM, Vanhatalo A (2017) The, “critical power” concept: applications to sports performance with a focus on intermittent high-intensity exercise. Sports Med 47:65–78
Joyner MJ (2017) Physiological limits to endurance exercise performance: influence of sex. J Physiol 595:2949–2954
Katz JS, Katz L (1999) Power laws and athletic performance. J Sports Sci 17:467–476
Tucker R, Lambert MI, Noakes TD (2006) An analysis of pacing strategies during men’s world-record performances in track athletics. Int J Sports Physiol Perform 1:233–245
Jones AM, Kirby BS, Clark IE, Rice HM, Fulkerson E, Wylie LJ et al (2021) Physiological demands of running at 2-hour marathon race pace. J Appl Physiol 130:369–379
Billat LV, Koralsztein JP, Morton RH (1999) Time in human endurance models. Sports Med 27:359–379
Bosquet L, Duchene A, Lecot F, Dupont G, Leger L (2006) Vmax estimate from three-parameter critical velocity models: validity and impact on 800 m running performance prediction. Eur J Appl Physiol 97:34–42
Bundle MW, Hoyt RW, Weyand PG (2003) High-speed running performance: a new approach to assessment and prediction. J Appl Physiol 95:1955–1962
Poole DC, Burnley M, Vanhatalo A, Rossiter HB, Jones AM (2016) Critical power. Med Sci Sports Exerc 48:2320–2334
Jones AM, Vanhatalo A, Burnley M, Morton RH, Poole DC (2010) Critical power: implications for determination of VO2max and exercise tolerance. Med Sci Sports Exerc 42:1876–1890
Smyth B, Muniz-Pumares D (2020) Calculation of critical speed from raw training data in recreational marathon runners. Med Sci Sports Exerc 52:2637–2645
Gamelin FX, Coquart JM, Ferrari N, Vodougnon H, Matran R, Leger L et al (2006) Prediction of one-hour running performance using constant duration tests. J Strength Cond Res 20:735–739
Bosquet L, Léger L, Legros P (2002) Methods to determine aerobic endurance. Sports Med 32:675–700
Vandewalle H (2018) Modelling of running performances: comparisons of power-law, hyperbolic, logarithmic, and exponential models in elite endurance runners. Biomed Res Int 2018:8203062
Casado A, Hanley B, Jiménez-Reyes P, Renfree A (2020) Pacing profiles and tactical behaviors of elite runners. J Sport Health Sci. https://doi.org/10.1016/j.jshs.2020.06.011
Díaz JJ, Fernández-Ozcorta EJ, Torres M, Santos-Concejero J (2019) Men vs. women world marathon records’ pacing strategies from 1998 to 2018. Eur J Sport Sci 19:1297–1302
Kennelly AE (1906) An approximate law of fatigue in the speeds of racing animals. Proc Am Acad Arts Sci 42:275–331
Savaglio S, Carbone V (2000) Scaling in athletic world records. Nature 404:244
Zinoubi B, Vandewalle H, Zbidi S, Driss T (2019) Estimation of running endurance by means of empirical models: a preliminary study. Sci Sports 34:24–29
Vandewalle H (2017) Mathematical modeling of the running performances in endurance exercises: comparison of the models of Kennelly and Péronnet-Thibaut for World records and elite endurance runners. Am J Eng Res 6:317–323
Kosmidis I, Passfield L (2015) Linking the performance of endurance runners to training and physiological effects via multi-resolution elastic net. arXiv preprint arXiv:150601388. http://arxiv.org/abs/1506.01388
Zinoubi B, Vandewalle H, Driss T (2017) Modeling of running performances in humans: comparison of power laws and critical speed. J Strength Cond Res 31:1859–1867
Muniz-Pumares D, Karsten B, Triska C, Glaister M (2019) Methodological approaches and related challenges associated with the determination of critical power and curvature constant. J Strength Cond Res 33:584–596
Abbiss CR, Laursen PB (2008) Describing and understanding pacing strategies during athletic competition. Sports Med 38:239–252
Tucker R (2009) The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br J Sports Med 43:392–400
Hanley B (2015) Pacing profiles and pack running at the IAAF World Half Marathon Championships. J Sports Sci 33:1189–1195
Thiel C, Foster C, Banzer W, De Koning J (2012) Pacing in Olympic track races: competitive tactics versus best performance strategy. J Sports Sci 30:1107–1115
Padulo J, Annino G, Migliaccio GM, D’ottavio S, Tihanyi J (2012) Kinematics of running at different slopes and speeds. J Strength Cond Res 26:1331–1339
Clermont CA, Osis ST, Phinyomark A, Ferber R (2017) Kinematic gait patterns in competitive and recreational runners. J Appl Biomech 33:268–276
Ogueta-Alday A, Morante JC, Gómez-Molina J, García-López J (2018) Similarities and differences among half-marathon runners according to their performance level. PLoS ONE 13:e0191688
Schache AG, Blanch PD, Rath DA, Wrigley TV, Starr R, Bennell KL (2001) A comparison of overground and treadmill running for measuring the three-dimensional kinematics of the lumbo–pelvic–hip complex. Clin Biomech 16:667–680
Bishop D, Gjenkins D, Howard A et al (1998) The critical power function is dependent on the duration. Int J Sports Med 19:125–129
Gorostiaga EM, Sánchez-Medina L, Garcia-Tabar I (2021) Over 55 years of critical power: fact or artifact? Scand J Med Sci Sports. https://doi.org/10.1111/sms.14074
Triska C, Karsten B, Beedie C, Koller-Zeisler B, Nimmerichter A, Tschan H (2018) Different durations within the method of best practice affect the parameters of the speed–duration relationship. Eur J Sport Sci 18:332–340
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Quart Appl Math 2:164–168
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11:431–441
Jones AM, Burnley M, Black MI, Poole DC, Vanhatalo A (2019) The maximal metabolic steady state: redefining the “gold standard.” Physiol Rep 7:e14098
Krouwer JS (2008) Why Bland–Altman plots should use X, not (Y+X)/2 when X is a reference method. Stat Med 27:778–780
Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160
García-Manso JM, Martín-González JM, Vaamonde D, Silva-Grigoletto MED (2012) The limitations of scaling laws in the prediction of performance in endurance events. J Theor Biol 300:324–329
Vandewalle H, Vautier JF, Kachouri M, Lechevalier JM, Monod H (1997) Work-exhaustion time relationships and the critical power concept. A critical review. J Sports Med Phys Fit 37:89–102
Mitchell EA, Martin NRW, Bailey SJ, Ferguson RA (2018) Critical power is positively related to skeletal muscle capillarity and type I muscle fibers in endurance-trained individuals. J Appl Physiol 125:737–745
Péronnet F, Thibault G (1989) Mathematical analysis of running performance and world running records. J Appl Physiol 67:453–465
Chinnasamy C, St Clair Gibson A, Micklewright D (2013) Effect of spatial and temporal cues on athletic pacing in schoolchildren. Med Sci Sports Exerc 45:395–402
Williams KR, Snow R, Agruss C (1991) Changes in distance running kinematics with fatigue. J Appl Biomech 7:138–162
Hanley B, Mohan AK (2014) Changes in gait during constant pace treadmill running. J Strength Cond Res 28:1219–1225
Hunter I, Smith GA (2007) Preferred and optimal stride frequency, stiffness and economy: changes with fatigue during a 1-h high-intensity run. Eur J Appl Physiol 100:653–661
Enomoto Y, Kadono H, Suzuki Y, Chiba T, Koyama K (2008) Biomechanical analysis of the medalists in the 10,000 metres at the 2007 World Championships in Athletics. New Stud Athl 3:61–66
Azevedo RDA, Silva-Cavalcante MD, Lima-Silva AE, Bertuzzi R (2021) Fatigue development and perceived response during self-paced endurance exercise: state-of-the-art review. Eur J Appl Physiol 121:687–696
Krzysztof M, Mero A (2013) A kinematics analysis of three best 100 m performances ever. J Hum Kinet 36:149–160
Weyand PG, Sternlight DB, Bellizzi MJ, Wright S (2000) Faster top running speeds are achieved with greater ground forces not more rapid leg movements. J Appl Physiol 89:1991–1999
Weyand PG, Sandell RF, Prime DNL, Bundle MW (2010) The biological limits to running speed are imposed from the ground up. J Appl Physiol 108:950–961