Download PDF
Int J Sports Med 2023; 44(09): 657-663
DOI: 10.1055/a-2024-9490
Training & Testing

Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort

Olli-Pekka Nuuttila
1   Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
,
Pekka Matomäki
1   Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
,
Heikki Kyröläinen
1   Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
,
Ari Nummela
2   Finnish Institute of High Performance Sport KIHU, Jyväskylä, Finland
› Author Affiliations Funding Information The Foundation of Sports Institute —; The Finnish Sports Research Foundation —; Firstbeat Analytics Oy —; Polar Electro Oy
› Further Information
Also available at
    Permissions and Reprints

Abstract

This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running performance were investigated. Either 6-week (10-km group, n=29) or 2-week (3-km group, n=16) interval training periods were performed by recreational runners. A linear model was created for both groups based on the running speed of the first 6×3-min interval session and the test run of the preceding week (T1). The accuracy of the model was tested with the running speed of the last interval session and the test run after the training period (T2). Pearson correlation was used to analyze relationships between changes in running speeds during the tests and interval sessions. At T2, the mean absolute percentage error of estimate for 3-km and 10-km test times were 2.3% and 3.4%, respectively. The change in running speed of intervals and test runs from T1 to T2 correlated (r=0.75, p<0.001) in both datasets. Thus, the maximal sustainable effort intervals were able to predict 3-km and 10-km running performance and training adaptations with good accuracy, and current results demonstrate the potential usefulness of intervals as part of the monitoring process.

Key words

running - endurance training - interval training - perceived effort


Publication History

Received: 07 October 2022

Accepted: 01 February 2023

Accepted Manuscript online:
01 February 2023

Article published online:
01 June 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Noakes TD, Myburgh KH, Schall R. Peak treadmill running velocity during the VO2max test predicts running performance. J Sports Sci 1990; 8: 35-45
    CrossrefPubMedGoogle Scholar
  • 2 Grant S, Craig I, Wilson J. et al. The relationship between 3 km running performance and selected physiological variables. J Sports Sci 1997; 15: 403-410
    CrossrefPubMedGoogle Scholar
  • 3 Roecker K, Schotte O, Niess AM. et al. Predicting competition performance in long-distance running by means of a treadmill test. Med Sci Sports Exerc 1998; 30: 1552-1557
    CrossrefPubMedGoogle Scholar
  • 4 Lourenço TF OC, da Silva F, Tessutti LS. et al. Prediction of 3000-m running performance using classic physiological respiratory responses. Int J Kinesiol Sports Sci 2018; 6: 18
    CrossrefPubMedGoogle Scholar
  • 5 Vesterinen V, Nummela A, Äyrämö S. et al. Monitoring training adaptation with a submaximal running test under field conditions. Int J Sports Physiol Perform 2016; 11: 393-399
    CrossrefPubMedGoogle Scholar
  • 6 Vesterinen V, Hokka L, Hynynen E. et al. Heart rate-running speed index may be an efficient method of monitoring endurance training adaptation. J Strength Cond Res 2014; 28: 902-908
    CrossrefPubMedGoogle Scholar
  • 7 Düking P, Van Hooren B, Sperlich B. Assessment of peak oxygen uptake with a Smartwatch and its usefulness for training of runners. Int J Sports Med 2022; 43: 642-647
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Altini M, Amft O. Estimating running performance combining non-invasive physiological measurements and training patterns in free-living. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI: IEEE, 2018: 2845–2848 Available from: https://ieeexplore.ieee.org/document/8512924/
    PubMed
  • 9 Capostagno B, Lambert MI, Lamberts RP. A systematic review of submaximal cycle tests to predict, monitor, and optimize cycling performance. Int J Sports Physiol Perform 2016; 11: 707-714
    CrossrefPubMedGoogle Scholar
  • 10 Lamberts RP, Swart J, Noakes TD. et al. A novel submaximal cycle test to monitor fatigue and predict cycling performance. Br J Sports Med 2011; 45: 797-804
    CrossrefPubMedGoogle Scholar
  • 11 Bellenger CR, Karavirta L, Thomson RL. et al. Contextualizing parasympathetic hyperactivity in functionally overreached athletes with perceptions of training tolerance. Int J Sports Physiol Perform 2016; 11: 685-692
    CrossrefPubMedGoogle Scholar
  • 12 Sangan HF, Hopker JG, Davison G. et al. The self-paced submaximal run test: Associations with the graded exercise test and reliability. Int J Sports Physiol Perform 2021; 16: 1865-1873
    CrossrefPubMedGoogle Scholar
  • 13 Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle: Part I: Cardiopulmonary emphasis. Sports Med 2013; 43: 313-338
    CrossrefPubMedGoogle Scholar
  • 14 Seiler S, Hetlelid KJ. The impact of rest duration on work intensity and RPE during interval training. Med Sci Sports Exerc 2005; 37: 1601-1607
    CrossrefPubMedGoogle Scholar
  • 15 Rønnestad BR, Ellefsen S, Nygaard H. et al. Effects of 12 weeks of block periodization on performance and performance indices in well-trained cyclists: Block periodization in well-trained cyclists. Scand J Med Sci Sports 2014; 24: 327-335
    CrossrefPubMedGoogle Scholar
  • 16 Nuuttila OP, Nummela A, Kyröläinen H. et al. Physiological, perceptual, and performance responses to the 2-week block of high- versus low-intensity endurance training. Med Sci Sports Exerc 2022; 54: 851-860
    CrossrefPubMedGoogle Scholar
  • 17 Nuuttila OP, Nummela A, Korhonen E. et al. Individualized endurance training based on recovery and training status in recreational runners. Med Sci Sports Exerc 2022; 54: 1690-1701
    CrossrefPubMedGoogle Scholar
  • 18 Riegel PS. Athletic records and human endurance: A time-vs.-distance equation describing world-record performances may be used to compare the relative endurance capabilities of various groups of people. Am Sci 1981; 69: 285-290
    PubMedGoogle Scholar
  • 19 Billat LV, Koralsztein JP, Morton RH. Time in human endurance models: From empirical models to physiological models. Sports Med 1999; 27: 359-379
    CrossrefPubMedGoogle Scholar
  • 20 Coquart JBJ, Alberty M, Bosquet L. Validity of a nomogram to predict long distance running performance. J Strength Cond Res 2009; 23: 2119-2123
    CrossrefPubMedGoogle Scholar
  • 21 Alvero-Cruz J, Carnero E, García M. et al. Predictive performance models in long-distance runners: A narrative review. Int J Environ Res Public Health 2020; 17: 8289
    CrossrefPubMedGoogle Scholar
  • 22 Santos-Concejero J, Granados C, Irazusta J. et al. OBLA is a better predictor of performance than Dmax in long and middle-distance well-trained runners. J Sports Med Phys Fitness 2014; 54: 553-558
    PubMedGoogle Scholar
  • 23 da Silva DF, Simões HG, Machado FA. vVO2max versus Vpeak, what is the best predictor of running performances in middle-aged recreationally-trained runners?. Sci Sports 2015; 30: e85-e92
    CrossrefPubMedGoogle Scholar
  • 24 Slattery KM, Wallace LK, Murphy AJ. et al. Physiological determinants of three-kilometer running performance in experienced triathletes. J Strength Cond Res 2006; 20: 47-52
    PubMedGoogle Scholar
  • 25 Abad CCC, Barros RV, Bertuzzi R. et al. 10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables. J Hum Kinet 2016; 51: 193-200
    CrossrefPubMedGoogle Scholar
  • 26 Hopkins WG, Hewson DJ. Variability of competitive performance of distance runners. Med Sci Sports Exerc 2001; 33: 1588-1592
    CrossrefPubMedGoogle Scholar
  • 27 Ramsbottom R, Williams C, Fleming N. et al. Training induced physiological and metabolic changes associated with improvements in running performance. Br J Sports Med 1989; 23: 171-176
    CrossrefPubMedGoogle Scholar
  • 28 Legaz Arrese A, Serrano Ostáriz E, Jcasajús Mallén JA. et al. The changes in running performance and maximal oxygen uptake after long-term training in elite athletes. J Sports Med Phys Fitness 2005; 45: 435-440
    PubMedGoogle Scholar
  • 29 Paavolainen L, Häkkinen K, Hämäläinen I. et al. Explosive-strength training improves 5-km running time by improving running economy and muscle power. J Appl Physiol (1985) 1999; 86: 1527-1533
    CrossrefPubMedGoogle Scholar
  • 30 da Silva DF, Ferraro ZM, Adamo KB. et al. Endurance running training individually guided by HRV in untrained women. J Strength Cond Res 2019; 33: 736-746
    CrossrefPubMedGoogle Scholar
  • 31 Smith TP, Coombes JS, Geraghty DP. Optimising high-intensity treadmill training using the running speed at maximal O2 uptake and the time for which this can be maintained. Eur J Appl Physiol 2003; 89: 337-343
    CrossrefPubMedGoogle Scholar
  • 32 Seiler S, Sylta Ø. How does interval-training prescription affect physiological and perceptual responses?. Int J Sports Physiol Perform 2017; 12: 80-86
    CrossrefPubMedGoogle Scholar
  • 33 de Koning JJ, Foster C, Bakkum A. et al. Regulation of pacing strategy during athletic competition. PLoS One 2011; 6: e15863
    CrossrefPubMedGoogle Scholar
  • 34 Marcora S. Perception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungs. J Appl Physiol (1985) 2009; 106: 2060-2062
    CrossrefPubMedGoogle Scholar