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Int J Sports Med 2022; 43(11): 949-957
DOI: 10.1055/a-1821-6179
Training and Testing

Predictions of the Distance Running Performances of Female Runners Using Different Tools

Lucie Lerebourg 
1   CETAPS EA3832, Université de Rouen UFR Sciences et Techniques des Activités Physiques et Sportives, Mont-Saint-Aignan, France
,
Brice Guignard
1   CETAPS EA3832, Université de Rouen UFR Sciences et Techniques des Activités Physiques et Sportives, Mont-Saint-Aignan, France
,
Maxime L’Hermette
1   CETAPS EA3832, Université de Rouen UFR Sciences et Techniques des Activités Physiques et Sportives, Mont-Saint-Aignan, France
,
Eric Held
2   Clinique Mathilde 2, Orthodynamica, Rouen, France
,
Jérémy Bernard Coquart
1   CETAPS EA3832, Université de Rouen UFR Sciences et Techniques des Activités Physiques et Sportives, Mont-Saint-Aignan, France
3   Univ. Lille, Univ. Artois, Univ. Littoral Côte d'Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, Lille, France
› Author Affiliations Funding The authors are grateful to the French Athletics Federation (Fédération Française d’Athlétisme) for data diffusion, and the Orthodynamica Center at Mathilde Hospital 2 for funding.
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Abstract

This study examined the validity and compared the precision and accuracy of a distance-time linear model (DTLM), a power law and a nomogram to predict the distance running performances of female runners. Official rankings of French women (“senior” category: between 23 and 39 years old) for the 3000-m, 5000-m, and 10,000-m track-running events from 2005 to 2019 were examined. Performances of runners who competed in the three distances during the same year were noted (n=158). Mean values and standard deviation (SD) of actual performances were 11.28±1.33, 19.49±2.34 and 41.03±5.12 for the 3000-m, 5000-m, and 10,000-m respectively. Each performance was predicted from two other performances. Between the actual and predicted performances, only DTLM showed a difference (p<0.05). The magnitude of the differences in these predicted performances was small if not trivial. All predicted performances were significantly correlated with the actual ones, with a very high correlation coefficient (p<0.001; r>0.90), except for DTLM in the 3000-m, which showed a high correlation coefficient (p<0.001; r>0.895). Bias and 95% limits of agreement were acceptable because, whatever the method, they were≤–3.7±10.8% on the 3000-m, 1.4±4.3% on the 5000-m, and -2.5±7.4% on the 10,000-m. The study confirms the validity of the three methods to predict track-running performance and suggests that the most accurate and precise model was the nomogram followed by the power law, with the DTLM being the least accurate.

Key words

estimation - modeling - time to exhaustion - validity - women athletes - track-and-field


Publication History

Received: 23 October 2021

Accepted: 05 April 2022

Accepted Manuscript online:
08 April 2022

Article published online:
27 June 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Coquart JB, Mercier D, Tabben M. et al. Influence of sex and specialty on the prediction of middle-distance running performances using the Mercier et al.’s nomogram. J Sports Sci 2015; 33: 1124-1131
    CrossrefPubMedSearch in Google Scholar
  • 2 Tabben M, Bosquet L, Coquart JB. Effect of performance level on the prediction of middle-distance-running performances using a nomogram. Int J Sports Physiol Perform 2016; 11: 623-626
    CrossrefPubMedSearch in Google Scholar
  • 3 Blythe DAJ, Király FJ. Prediction and quantification of individual athletic performance of runners. PloS One 2016; 11: e0157257
    CrossrefPubMedSearch in Google Scholar
  • 4 Coquart J, Bosquet L. Precision in the prediction of middle distance-running performances using either a nomogram or the modeling of the distance-time relationship. J Strength Cond Res 2010; 24: 2920-2926
    CrossrefPubMedSearch in Google Scholar
  • 5 Zinoubi B, Vandewalle H, Driss T. Modeling of running performances in humans: comparison of power laws and critical speed. J Strength Cond Res 2017; 31: 1859-1867
    CrossrefPubMedSearch in Google Scholar
  • 6 Nimmerichter A, Novak N, Triska C. et al. Validity of treadmill-derived critical speed on predicting 5000-meter track-running performance. J Strength Cond Res 2017; 31: 706-714
    CrossrefPubMedSearch in Google Scholar
  • 7 Vandewalle H. Modelling of running performances: comparisons of power-law, hyperbolic, logarithmic, and exponential models in elite endurance runners. BioMed Res Int 2018; 2018: 8203062
    CrossrefPubMedSearch in Google Scholar
  • 8 Gamelin FX, Coquart JM, Ferrari N. et al. Prediction of one-hour running performance using constant duration tests. J Strength Cond Res 2006; 20: 735-739
    PubMedSearch in Google Scholar
  • 9 Hill DW, Vingren JL, Nakamura FY. et al. Relationship between speed and time in running. Int J Sports Med 2011; 32: 519-522
    Thieme ConnectPubMedSearch in Google Scholar
  • 10 Katz JS, Katz L. Power laws and athletic performance. J Sports Sci 1999; 17: 467-476
    CrossrefPubMedSearch in Google Scholar
  • 11 Zinoubi B, Vandewalle H, Zbidi S. et al. Estimation of running endurance by means of empirical models: A preliminary study. Sci Sports 2019; 34: 24-29
    CrossrefPubMedSearch in Google Scholar
  • 12 Vandewalle H. A nomogram of performances in endurance running based on logarithmic model of péronnet-thibault. Am J Eng Res 2017; 6: 78-85
    PubMedSearch in Google Scholar
  • 13 Lerebourg L, Guignard B, Racil G. et al. Prediction of distance running performances of female runners using nomograms. Int J Sports Med 2021. Online ahead of print. doi: 10.1055/a-1673-6829
    PubMed
  • 14 Billat LV, Koralsztein JP, Morton RH. Time in human endurance models. From empirical models to physiological models. Sports Med 1999; 27: 359-379
    CrossrefPubMedSearch in Google Scholar
  • 15 Morton RH, Hodgson DJ. The relationship between power output and endurance: a brief review. Eur J Appl Physiol 1996; 73: 491-502
    CrossrefPubMedSearch in Google Scholar
  • 16 Hughson RL, Orok CJ, Staudt LE. A high velocity treadmill running test to assess endurance running potential. Int J Sports Med 1984; 5: 23-25
    Thieme ConnectPubMedSearch in Google Scholar
  • 17 Hill AV. Muscular Movement in Man: The Factors Governing Speed and Recovery from Fatigue. New York: McGraw-Hill,. 1927
    PubMedSearch in Google Scholar
  • 18 Scherrer J, Monod H. Le travail musculaire local et la fatigue chez lʼhomme. J Physiol (Paris) 1966; 52: 419-501
    PubMedSearch in Google Scholar
  • 19 Ettema JH. Limits of human performance and energy-production. Int Z Angew Physiol Einschl Arbeitsphysiol 1966; 22: 45-54
    PubMedSearch in Google Scholar
  • 20 Monod H, Scherrer J. The work capacity of a synergic muscular group. Ergonomics 1965; 8: 329-338
    CrossrefPubMedSearch in Google Scholar
  • 21 Di Prampero PE. The concept of critical velocity: a brief analysis. Eur J Appl Physiol 1999; 80: 162-164
    CrossrefPubMedSearch in Google Scholar
  • 22 Kennelly AE. An approximate law of fatigue in the speeds of racing animals. Proceedings of the American Academy of Arts and Sciences; 1906. 42: 275-331
    Search in Google Scholar
  • 23 Vandewalle H, Zinoubi B, Driss T. Modelling of running performances: comparison of power laws and Endurance Index. In: Communication to the 14th Annual Conference of the Society of Chinese Scholars on Exercise Physiology and Fitness (SCSEPF) academia.edu 2015; 22-23
    PubMedSearch in Google Scholar
  • 24 Mercier D, Léger L, Desjardins M. Nomogramme pour prédire la performance, le VO2max et l’endurance relative en course de fond. Méd Sport 1984; 58: 181-187
    PubMedSearch in Google Scholar
  • 25 Tokmakidis SP, Léger L, Mercier D. et al. New approaches to predict VO2max and endurance from running performances. J Sports Med Phys Fitness 1987; 27: 401-409
    PubMedSearch in Google Scholar
  • 26 Coquart J, Alberty M, Bosquet L. Validity of a nomogram to predict long distance running performance. J Strength Cond Res 2009; 23: 2119-2123
    CrossrefPubMedSearch in Google Scholar
  • 27 Vandewalle H, Vautier JF, Kachouri M. et al. Work-exhaustion time relationships and the critical power concept. A critical review. J Sports Med Phys Fitness 1997; 37: 89-102
    PubMedSearch in Google Scholar
  • 28 Hopkins WG, Edmond IM, Hamilton BH. et al. Relation between power and endurance for treadmill running of short duration. Ergonomics 1989; 32: 1565-1571
    CrossrefPubMedSearch in Google Scholar
  • 29 Smyth B, Muniz-Pumares D. Calculation of critical speed from raw training data in recreational marathon runners. Med Sci Sports Exerc 2020; 52: 2637-2645
    CrossrefPubMedSearch in Google Scholar
  • 30 Emig T, Peltonen J. Human running performance from real-world big data. Nat Commun 2020; 11: 4936
    CrossrefPubMedSearch in Google Scholar
  • 31 Harriss DJ, MacSween A, Atkinson G. Ethical Standards in Sport and Exercise Science Research: 2020 Update. Int J Sports Med 2019; 40: 813-817
    Thieme ConnectPubMedSearch in Google Scholar
  • 32 Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15: 155-163
    Search in Google Scholar
  • 33 Cohen J. Statistical power analysis. Curr Dir Psychol Sci 1992; 1: 98-101
    CrossrefPubMedSearch in Google Scholar
  • 34 Munro BH. Correlation. Stat Methods Health Care Res 1997; 224-245
    PubMedSearch in Google Scholar
  • 35 Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. The lancet 1986; 327: 307-310
    CrossrefPubMedSearch in Google Scholar
  • 36 Giavarina D. Understanding bland altman analysis. Biochem Medica 2015; 25: 141-151
    CrossrefPubMedSearch in Google Scholar
  • 37 Di Prampero PE, Atchou G, Brückner JC. et al. The energetics of endurance running. Eur J Appl Physiol 1986; 55: 259-266
    CrossrefPubMedSearch in Google Scholar
  • 38 Péronnet F, Thibault G. Physiological analysis of running performance: revision of the hyperbolic model. J Physiol (Paris) 1987; 82: 52-60
    PubMedSearch in Google Scholar
  • 39 Lerebourg L, Coquart JB. Changes in performances/characteristics of French female runners over the last 12 years. Res Sports Med 2021; 185-195
    CrossrefPubMedSearch in Google Scholar
  • 40 Hanley B, Williams EL. Successful pacing profiles of olympic men and women 3,000 m steeplechasers. Front Sports Act Living 2020; 2: 21
    CrossrefPubMedSearch in Google Scholar
  • 41 Bunker RP, Thabtah F. A machine learning framework for sport result prediction. Appl Comput Inform 2019; 15: 27-33
    CrossrefPubMedSearch in Google Scholar
  • 42 Bosquet L, Léger L, Legros P. Methods to determine aerobic endurance. Sports Med 2002; 32: 675-700
    CrossrefPubMedSearch in Google Scholar