PDF herunterladen
Int J Sports Med 2017; 38(10): 735-740
DOI: 10.1055/s-0043-114007
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League

Daniel Weaving
1   School of Life Sciences, The University of Hull, Hull, United Kingdom of Great Britain and Northern Ireland
,
Ben Jones
2   Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom of Great Britain and Northern Ireland
,
Phil Marshall
1   School of Life Sciences, The University of Hull, Hull, United Kingdom of Great Britain and Northern Ireland
,
Kevin Till
2   Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom of Great Britain and Northern Ireland
,
Grant Abt
1   School of Life Sciences, The University of Hull, Hull, United Kingdom of Great Britain and Northern Ireland
› Institutsangaben
Weitere Informationen

Publikationsverlauf



accepted after revision 31. Mai 2017

Publikationsdatum:
07. August 2017 (online)

Auch verfügbar auf
    Lizenzen und Reprints

Abstract

This study aims to investigate the effect of training mode (conditioning and skills) on multivariate training load relationships in professional rugby league via principal component analysis. Four measures of training load (internal: heart rate exertion index, session rating of perceived exertion; external: PlayerLoad™, individualised high-speed distance) were collected from 23 professional male rugby league players over the course of one 12 wk preseason period. Training was categorised by mode (skills or conditioning) and then subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subject to a varimax rotation. Skills extracted 1 principal component, explaining 57% of the variance. Conditioning extracted 2 principal components (1st: internal; 2nd: external), explaining 85% of the variance. The presence of multiple training load dimensions (principal components) during conditioning training provides further evidence of the influence of training mode on the ability of individual measures of external or internal training load to capture training variance. Consequently, a combination of internal and external training-load measures is required during certain training modes.

 

  • References

  • 1 Abt G, Lovell R. The use of individualised speed and intensity thresholds for determining the distance run at high-intensity in professional soccer. J Sports Sci 2009; 27: 893-898
    CrossrefPubMedGoogle Scholar
  • 2 Akubat I, Patel E, Barrett S, Abt G. Methods of monitoring the training and match load and their relationship to changes in fitness in professional youth soccer players. J Sports Sci 2012; 30: 1473-1480
    CrossrefPubMedGoogle Scholar
  • 3 Alexiou H, Coutts AJ. A comparison of methods used for quantifying internal training load in women soccer players. Int J Sports Physiol Perform 2008; 3: 320-330
    CrossrefPubMedGoogle Scholar
  • 4 Bartlett MS. A note on the multiplying factors for various chi square approximations. J R Stat Soc Ser C Appl Stat 1954; 16: 296-298
    PubMedGoogle Scholar
  • 5 Bishop D. An applied research model for the sports sciences. Sports Med 2008; 38: 253-263
    CrossrefPubMedGoogle Scholar
  • 6 Borg G, Ljunggren G, Ceci R. The increase of perceived exertion, aches and pain in the legs, heart rate and blood lactate during exercise on a bicycle ergometer. Eur J Appl Physiol1985. 54: 343-349
    PubMedGoogle Scholar
  • 7 Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform 2011; 6: 311-321
    CrossrefPubMedGoogle Scholar
  • 8 Buchheit M. The 30-15 intermittent fitness test: accuracy for individualising interval training of young intermittent sport players. J Strength Cond Res 2008; 22: 365-374
    CrossrefPubMedGoogle Scholar
  • 9 Colby MJ, Dawson B, Heasman J, Rogalski B, Gabbett TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res 2014; 28: 2244-2252
    CrossrefPubMedGoogle Scholar
  • 10 Edwards S. High performance training and racing. In: The Heart Rate Monitor Book. Edwards, ed. Sacramento, CA: Feet Fleet press; 1993
    Google Scholar
  • 11 Federolf P, Reid R, Gilgien M, Haugen P, Smith G. The application of principal component analysis to quantify technique in sports. Scand J Med Sci Sports 2014; 24: 491-499
    CrossrefPubMedGoogle Scholar
  • 12 Foster C, Florhaug JA, Franklin J. et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15: 109-115
    CrossrefPubMedGoogle Scholar
  • 13 Gabbett TJ.. Relationship between accelerometer load, collisions, and repeated high-intensity-effort activity in rugby league players. J Strength Cond Res 2015; 29: 3424-3431
    CrossrefPubMedGoogle Scholar
  • 14 Gabbett TJ.. The training-injury prevention paradox: Should athletes be training smarter and harder?. Br J Sports Med 2016; 50: 273-280
    CrossrefPubMedGoogle Scholar
  • 15 Gabbett TJ, Jenkins DG, Abernethy B. Physical demands of professional rugby league training and competition using microtechnology. J Sci Med Sport 2012; 15: 80-86
    CrossrefPubMedGoogle Scholar
  • 16 Gabbett TJ, Stein JG, Kemp JG, Lorenzen C. Relationship between tests of physical qualities and physical match performance in elite rugby league players. J Strength Cond Res 2013; 27: 1539-1545
    CrossrefPubMedGoogle Scholar
  • 17 Hair J, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis. 4th ed. Upper Saddle River, NJ: Prentice-Hall; 1995
    Google Scholar
  • 18 Harriss DJ, Atkinson G.. Ethical standards in sport and exercise science research: 2016 update. Int J Sports Med 2015; 36: 1121-1124
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 19 Hopkins WG. Measures of reliability in sports medicine and science. Sports Med 2000; 30: 1-15
    CrossrefPubMedGoogle Scholar
  • 20 Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: High chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med 2016; 50: 231-236
    CrossrefPubMedGoogle Scholar
  • 21 Impellizzeri F, Rampinini E, Marcora S. Physiological assessment of aerobic training in soccer. J Sports Sci 2005; 23: 583-592
    CrossrefPubMedGoogle Scholar
  • 22 Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc 2004; 36: 1042-1047
    CrossrefPubMedGoogle Scholar
  • 23 Ioannidis JPA. Why most published research findings are false. PLoS Med 2005; 2: e124
    CrossrefPubMedGoogle Scholar
  • 24 Johnston RJ, Watsford ML, Kelly SJ, Pine MJ, Spurrs RW. Validity and interunit reliability of 10Hz and 15Hz GPS units for assessing athlete movement demands. J Strength Cond Res 2014; 28: 1649-1655
    CrossrefPubMedGoogle Scholar
  • 25 Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas 1960; 20: 141-151
    CrossrefPubMedGoogle Scholar
  • 26 Kelly SJ, Murphy AJ, Watsford ML, Austin D, Rennie M. Reliability and validity of sports accelerometers during static and dynamic testing. Int J Sports Physiol Perform 2015; 10: 106-111
    CrossrefPubMedGoogle Scholar
  • 27 Lovell TWJ, Sirotic AC, Impellizzeri FM, Coutts AJ. Factors affecting perception of effort (session rating of perceived exertion) during rugby league training. Int J Sports Physiol Perform 2013; 8: 62-69
    CrossrefPubMedGoogle Scholar
  • 28 Manzi V, Iellamo F, Impellizzei F, D’Ottavio S, Castagna C. Relation between individualized training impulses and performance in distance runners. Med Sci Sports Exerc 2009; 41: 2090-2096
    CrossrefPubMedGoogle Scholar
  • 29 Manzi V, Bovenzi A, Impellizzeri FM, Carminati I, Castagna C. Individual training-load and aerobic fitness variables in premiership soccer players during the precompetitive season. J Strength Cond Res 2013; 27: 631-636
    CrossrefPubMedGoogle Scholar
  • 30 Oxendale CL, Twist C, Daniels M, Highton J. The relationship between match-play characteristics of elite rugby league and indirect markers of muscle damage. Int J Sports Physiol Perform 2016; 11: 515-521
    CrossrefPubMedGoogle Scholar
  • 31 Rampinini E, Alberti G, Florenza M, Riggio M, Sassi R, Borges TO, Coutts AJ. Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int J Sports Med 2015; 36: 49-53
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 32 Roe G, Darrall-Jones J, Black C, Shaw W, Till K, Jones B. Validity of 10Hz GPS and timing gates for assessing maximum velocity in professional rugby union players. Int J Sports Physiol Perform 2016; 13: 1-14
    PubMedGoogle Scholar
  • 33 Soligard T, Schwellnus M, Alonso JM, Bahr R, Clarsen B, Dijkstra HP, Gabbett T, Gleeson M, Hagglund M, Hutchinson MR, Janse van Rensburg C, Khan KM, Meeusen R, Orchard JW, Pluim BM, Raferty M, Budgett R, Engebretsen L. How much is too much? (Part 1) International Olympic Committee concensus statement on load in sport and risk of injury. Br J Sports Med 2016; 50: 1030-1041
    CrossrefPubMedGoogle Scholar
  • 34 Tabachnick BG, Fidell LS. Using Multivariate Statistics. Boston, MA: Pearson Education; 2007
    Google Scholar
  • 35 Twist C, Highton J. Monitoring fatigue and recovery in rugby league players. Int J Sports Physiol Perfom 2013; 8: 467-474
    PubMedGoogle Scholar
  • 36 Weaving D, Marshall P, Earle K, Nevill A, Abt G. Combining internal- and external-training-load measures in professional rugby league. Int J Sports Physiol Perform 2014; 9: 905-912
    CrossrefPubMedGoogle Scholar
  • 37 Williams S, Trewartha G, Cross MJ, Kemp SP, Stokes KA. Monitoring what matters: A systematic process for selecting training load measures. Int J Sports Physiol Perform 2016; 11: 1-20
    CrossrefPubMedGoogle Scholar