Skip to main content

Advertisement

Log in

The Quantification of Training Load, the Training Response and the Effect on Performance

  • Review Article
  • Published:
Sports Medicine Aims and scope Submit manuscript

Abstract

Historically, the ability of coaches to prescribe training to achieve optimal athletic performance can be attributed to many years of personal experience. A more modern approach is to adopt scientific methods in the development of optimal training programmes. However, there is not much research in this area, particularly into the quantification of training programmes and their effects on physiological adaptation and subsequent performance. Several methods have been used to quantify training load, including questionnaires, diaries, physiological monitoring and direct observation. More recently, indices of training stress have been proposed, including the training impulse, which uses heart rate measurements and training load, and session rating of perceived exertion measurements, which utilizes subjective perception of effort scores and duration of exercise. Although physiological adaptations to training are well documented, their influence on performance has not been accurately quantified. To date, no single physiological marker has been identified that can measure the fitness and fatigue responses to exercise or accurately predict performance. Models attempting to quantify the relationship between training and performance have been proposed, many of which consider the athlete as a system in which the training load is the input and performance the system output. Although attractive in concept, the accuracy of these theoretical models has proven poor. A possible reason may be the absence of a measure of individuality in each athlete’s response to training. Thus, in the future more attention should be directed towards measurements that reflect individual capacity to respond or adapt to exercise training rather than an absolute measure of changes in physiological variables that occur with training.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Williams JG, Eston RG. Determination of the intensity dimension in vigorous exercise programmes with particularreference to the use of the rating of perceived exertion. Sports Med 1989; 8 (3): 177–89

    Article  PubMed  CAS  Google Scholar 

  2. Budgett R. Fatigue and underperformance in athletes: the overtraining syndrome. Br J Sports Med 1998; 32 (2): 107–10

    Article  PubMed  CAS  Google Scholar 

  3. Halson SL, Jeukendrup AE. Does overtraining exist? An analysis of overreaching and overtraining research. Sports Med 2004; 34 (14): 967–81

    Google Scholar 

  4. Urhausen A, Kindermann W. Diagnosis of overtraining: what tools do we have? Sports Med 2002; 32 (2): 95–102

    Article  PubMed  Google Scholar 

  5. Budgett R, Newsholme E, Lehmann M, et al. Redefining the overtraining syndrome as the unexplained under performance syndrome. Br J Sports Med 2000 Feb 1; 34 (1): 67–8

    Article  PubMed  CAS  Google Scholar 

  6. Hopkins WG. Quantification of training in competitive sports: methods and applications. Sports Med 1991; 12 (3): 161–83

    Article  PubMed  CAS  Google Scholar 

  7. Lambert MI, Bryer L, Hampson DB, et al. Accelerated decline in running performance in a masters runner with ahistory of a large volume of training and racing. J Aging Phys Activ 2002; 10: 314–21

    Google Scholar 

  8. Borresen J, Lambert MI. Validity of self-reported training duration. Int J Sports Sci Coach 2006; 1 (4): 353–9

    Article  Google Scholar 

  9. Shephard RJ. Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 2003; 37 (3): 197–206

    Article  PubMed  CAS  Google Scholar 

  10. Buchheit M, Gindre C. Cardiac parasympathetic regulation: respective associations with cardiorespiratory fitnessand training load. Am J Physiol Heart Circ Physiol 2006; 291 (1): H451–8

    Article  CAS  Google Scholar 

  11. Achten J, Jeukendrup AE. Heart rate monitoring: applications and limitations. Sports Med 2003; 33 (7): 517–38

    Article  PubMed  Google Scholar 

  12. Arts FJ, Kuipers H. The relation between power output, oxygen uptake and heart rate in male athletes. Int J Sports Med 1994; 15 (5): 228–31

    Article  PubMed  CAS  Google Scholar 

  13. Robinson DM, Robinson SM, Hume PA, et al. Training intensity of elite male distance runners. Med Sci Sports Exerc 1991; 23 (9): 1078–82

    PubMed  CAS  Google Scholar 

  14. Karvonen J, Vuorimaa T. Heart rate and exercise intensity during sports activities: practical application. Sports Med 1988; 5 (5): 303–11

    Article  PubMed  CAS  Google Scholar 

  15. Lambert MI, Mbamba ZH, St Clair Gibson A. Heart rate during training and competition for long-distance running. J Sports Sci 1998; 16: S85–90

    Article  Google Scholar 

  16. Bagger M, Petersen PH, Pedersen PK. Biological variation in variables associated with exercise training. Int J Sports Med 2003; 24 (6): 433–40

    Article  PubMed  CAS  Google Scholar 

  17. Xu F, Rhodes EC. Oxygen uptake kinetics during exercise. Sports Med 1999; 27 (5): 313–27

    Article  PubMed  CAS  Google Scholar 

  18. Kohrt WM, Morgan DW, Bates B, et al. Physiological responses of triathletes to maximal swimming, cycling, andrunning. Med Sci Sports Exerc 1987; 19 (1): 51–5

    PubMed  CAS  Google Scholar 

  19. Swain DP, Leutholtz BC, King ME, et al. Relationship between % heart rate reserve and % V̇O2 reserve in treadmill exercise. Med Sci Sports Exerc 1998; 30 (2): 318–21

    Article  PubMed  CAS  Google Scholar 

  20. Swain DP, Leutholtz BC. Heart rate reserve is equivalent to % V̇O2 reserve, not to % V̇O2max. Med Sci Sports Exerc 1997; 29 (3): 410–4

    Article  PubMed  CAS  Google Scholar 

  21. Baldwin J, Snow RJ, Febbraio MA. Effect of training status and relative exercise intensity on physiological responsesin men. Med Sci Sports Exerc 2000; 32 (9): 1648–54

    PubMed  CAS  Google Scholar 

  22. Skinner JS, Wilmore KM, Krasnoff JB, et al. Adaptation to a standardized training program and changes in fitnessin a large, heterogeneous population: the HERITAGE Family Study. Med Sci Sports Exerc 2000; 32 (1): 157–61

    PubMed  CAS  Google Scholar 

  23. Pyne DB, Lee H, Swanwick KM. Monitoring the lactate threshold in world-ranked swimmers. Med Sci Sports Exerc 2001; 33 (2): 291–7

    PubMed  CAS  Google Scholar 

  24. Jacobs I. Blood lactate: implications for training and sports performance. Sports Med 1986; 3 (1): 10–25

    Article  PubMed  CAS  Google Scholar 

  25. Weltman A, Seip RL, Snead D, et al. Exercise training at and above the lactate threshold in previously untrained women. Int J Sports Med 1992; 13 (3): 257–63

    Article  PubMed  CAS  Google Scholar 

  26. Stegmann H, Kindermann W, Schnabel A. Lactate kinetics and individual anaerobic threshold. Int J Sports Med 1981; 2 (3): 160–5

    Article  PubMed  CAS  Google Scholar 

  27. Swart J, Jennings CL. Use of blood lactate concentration as a marker of training status. S Afr J Sports Med 2004; 16: 3–7

    Google Scholar 

  28. Jeukendrup AE, Hesselink MK. Overtraining: what do lactate curves tell us? J Sports Med 1994; 28 (4): 239–40

    Article  CAS  Google Scholar 

  29. Urhausen A, Gabriel HH, Weiler B, et al. Ergometric and psychological findings during overtraining: a long-termfollow-up study in endurance athletes. Int J Sports Med 1998; 19 (2): 114–20

    Article  PubMed  CAS  Google Scholar 

  30. Kirwan JP, Costill DL, Flynn MG, et al. Physiological responses to successive days of intense training in competitive swimmers. Med Sci Sports Exerc 1988; 20 (3): 255–9

    Article  PubMed  CAS  Google Scholar 

  31. Green JM, McLester JR, Crews TR, et al. RPE association with lactate and heart rate during high-intensity interval cycling. Med Sci Sports Exerc 2006; 38 (1): 167–72

    Article  PubMed  CAS  Google Scholar 

  32. Little T, Williams AG. Measures of exercise intensity during soccer training drills with professional soccer players. J Strength Cond Res 2007; 21 (2): 367–71

    PubMed  Google Scholar 

  33. Ozkan A, Kin-Isler A. The reliability and validity of regulating exercise intensity by ratings of perceived exertionin step dance sessions. J Strength Cond Res 2007; 21 (1): 296–300

    Article  PubMed  Google Scholar 

  34. Chen MJ, Fan X, Moe ST. Criterion-related validity of the Borg ratings of perceived exertion scale in healthyindividuals: a meta-analysis. J Sports Sci 2002; 20 (11): 873–99

    Article  PubMed  Google Scholar 

  35. Dekerle J, Baron B, Dupont L, et al. Maximal lactate steady state, respiratory compensation threshold andcritical power. Eur J Appl Physiol 2003; 89 (3): 281–8

    Article  PubMed  CAS  Google Scholar 

  36. Pringle J, Jones A. Maximal lactate steady state, critical power and EMG during cycling. Eur J Appl Physiol 2002; 88 (3): 214–26

    Article  PubMed  CAS  Google Scholar 

  37. Brickley G, Doust J, Williams CA. Physiological responses during exercise to exhaustion at critical power. Eur J Appl Physiol 2002; 88 (1): 146–51

    Article  PubMed  CAS  Google Scholar 

  38. Vanhatalo A, Doust JH, Burnley M. Determination of critical power using a 3-min all-out cycling test. Med Sci Sports Exerc 2007; 39 (3): 548–55

    Article  PubMed  Google Scholar 

  39. Jeukendrup A, van Diemen A. Heart rate monitoring during training and competition in cyclists. J Sports Sci 1998; 16: S91–9

    Article  Google Scholar 

  40. Foster C, Heimann KM, Esten PL, et al. Differences in perceptions of training by coaches and athletes. S Afr J Sports Med 2001; 8 (2): 3–7

    Google Scholar 

  41. Larsson P. Global positioning system and sport-specific testing. Sports Med 2003; 33 (15): 1093–101

    Article  PubMed  Google Scholar 

  42. Townshend AD, Worringham CJ, Stewart IB. Assessment of speed and position during human locomotion using non differential GPS. Med Sci Sports Exerc 2008; 40 (1): 124–32

    PubMed  Google Scholar 

  43. Rodriguez DA, Brown AL, Troped PJ. Portable global positioning units to complement accelerometry-basedphysical activity monitors. Med Sci Sports Exerc 2005; 37 (11 Suppl.): S572–81

    Google Scholar 

  44. Larsson P, Henriksson-Larsen K. The use of dGPS and simultaneous metabolic measurements during orienteering. Med Sci Sports Exerc 2001; 33 (11): 1919–24

    Article  PubMed  CAS  Google Scholar 

  45. Schutz Y, Herren R. Assessment of speed of human locomotion using a differential satellite global positioning system. Med Sci Sports Exerc 2000; 32 (3): 642–6

    Article  PubMed  CAS  Google Scholar 

  46. Vermeulen AD, Evans DL. Measurements of fitness in thorough bred race horses using field studies of heart rateand velocity with a global positioning system. Equine Vet J Suppl 2006; (36): 113–7

    Google Scholar 

  47. Kingston JK, Soppet GM, Rogers CW, et al. Use of a global positioning and heart rate monitoring system toassess training load in a group of thoroughbred racehorses. Equine Vet J Suppl 2006; (36): 106–9

    Article  PubMed  Google Scholar 

  48. Banister EW, MacDougall JD, Wenger HA, et al. Modeling elite athletic performance: physiological testing ofthe high-performance athlete. Campaign (IL): Human Kinetics Books; 1991: 403–25

    Google Scholar 

  49. Morton RH, Fitz-Clarke JR, Banister EW. Modeling human performance in running. J Appl Physiol 1990; 69 (3): 1171–7

    PubMed  CAS  Google Scholar 

  50. Busso T, Häkkinen K, Pakarinen A, et al. A systems model of training responses and its relationship to hormonalresponses in elite weight-lifters. Eur J Appl Physiol 1990; 61 (1): 48–54

    Article  CAS  Google Scholar 

  51. Sweet TW, Foster C, McGuigan MR, et al. Quantitation of resistance training using the session rating of perceived exertion method. J Strength Cond Res 2004; 18 (4): 796–802

    PubMed  Google Scholar 

  52. McGuigan MR, Egan AD, Foster C. Salivary cortisol responses and perceived exertion during high intensity and low intensity bouts of resistance exercise. J Sports Sci Med 2004; 3: 8–15

    Google Scholar 

  53. McGuigan MR, Foster C. A new approach to monitoring resistance training. Strength Cond J 2004; 26 (6): 42–7

    Article  Google Scholar 

  54. Egan AD, Winchester JB, Foster C, et al. Using session RPE to monitor different methods of resistance exercise. J Sports Sci Med 2006; 5: 289–95

    Google Scholar 

  55. Foster C, Daines E, Hector L, et al. Athletic performance in relation to training load. Wis Med J 1996; 95 (6): 370–4

    PubMed  CAS  Google Scholar 

  56. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15 (1): 109–15

    PubMed  CAS  Google Scholar 

  57. Edwards S. The heart rate monitor book. Sacramento (CA): Fleet Feet Press, 1993

    Google Scholar 

  58. Herman L, Foster C, Maher MA, et al. Validity and reliability of the session RPE method for monitoring exercise training intensity. S Afr J Sports Med 2006; 18 (1): 14–7

    Google Scholar 

  59. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc 1998; 30 (7): 1164–8

    Article  PubMed  CAS  Google Scholar 

  60. Impellizzeri FM, Rampinini E, Coutts AJ, et al. Use of RPE-based training load in soccer. Med Sci Sports Exerc 2004; 36 (6): 1042–7

    Article  PubMed  Google Scholar 

  61. Borresen J, Lambert MI. Quantifying training load: a comparison of subjective and objective methods. IJSPP 2008; 3: 16–30

    PubMed  Google Scholar 

  62. Day ML, McGuigan MR, Brice G, et al. Monitoring exercise intensity during resistance training using the session RPE scale. J Strength Cond Res 2004; 18 (2): 353–8

    PubMed  Google Scholar 

  63. Earnest CP, Jurca R, Church TS, et al. Relation between physical exertion and heart rate variability characteristicsin professional cyclists during the Tour of Spain. Br J Sports Med 2004; 38 (5): 568–75

    Article  PubMed  CAS  Google Scholar 

  64. Lucia A, Hoyos J, Santalla A, et al. Tour de France versus Vuelta a Espana: which is harder? Med Sci Sports Exerc 2003; 35 (5): 872–8

    Article  PubMed  Google Scholar 

  65. Stagno KM, Thatcher R, van Someren KA. A modified TRIMP to quantify the in-season training load of team sport players. J Sports Sci 2007; 25 (6): 629–34

    Article  PubMed  Google Scholar 

  66. Kuipers H, Keizer HA. Overtraining in elite athletes: review and directions for the future. Sports Med 1988; 6 (2): 79–92

    Article  PubMed  CAS  Google Scholar 

  67. Kuipers H. Training and overtraining: an introduction. Med Sci Sports Exerc 1998; 30 (7): 1137–9

    Article  PubMed  CAS  Google Scholar 

  68. Uusitalo ALT. Overtraining: making a difficult diagnosis and implementing targeted treatment. Phys Sportsmed 2001; 29 (5): 35–50

    Article  PubMed  CAS  Google Scholar 

  69. Fitz-Clarke JR, Morton RH, Banister EW. Optimizing athletic performance by influence curves. J Appl Physiol 1991; 71 (3): 1151–8

    PubMed  CAS  Google Scholar 

  70. Busso T, Carasso C, Lacour JR. Adequacy of a systems structure in the modeling of training effects on performance. J Appl Physiol 1991; 71 (5): 2044–9

    PubMed  CAS  Google Scholar 

  71. Wood RE, Hayter S, Rowbottom D, et al. Applying a mathematical model to training adaptation in a distance runner. Eur J Appl Physiol 2005; 94 (3): 310–6

    Article  PubMed  Google Scholar 

  72. Lambert MI, Borresen J. A theoretical basis of monitoring fatigue: a practical approach for coaches. Int J Sports Sci Coach 2006; 1 (4): 371–88

    Article  Google Scholar 

  73. Jones AM, Carter H. The effect of endurance training on parameters of aerobic fitness. Sports Med 2000; 29 (6): 373–86

    Article  PubMed  CAS  Google Scholar 

  74. Mayes R, Hardman AE, Williams C. The influence of training on endurance and blood lactate concentration during submaximal exercise. Br J Sports Med 1987; 21 (3): 119–24

    Article  PubMed  CAS  Google Scholar 

  75. Urhausen A, Gabriel H, Kindermann W. Blood hormones as markers of training stress and overtraining. Sports Med 1995; 20 (4): 251–76

    Article  PubMed  CAS  Google Scholar 

  76. Filaire E, Bernain X, Sagnol M, et al. Preliminary results on mood state, salivary testosterone: cortisol ratio and team performance in a professional soccer team. Eur J Appl Physiol 2001; 86 (2): 179–84

    Article  PubMed  CAS  Google Scholar 

  77. Smith DJ, Roberts D. Effects of high volume and/or intense exercise on selected blood chemistry parameters. Clin Biochem 1994; 27 (6): 435–40

    Article  PubMed  CAS  Google Scholar 

  78. Angeli A, Minetto M, Dovio A, et al. The overtraining syndrome in athletes: a stress-related disorder. J Endocrinol Invest 2004; 27 (6): 603–12

    PubMed  CAS  Google Scholar 

  79. Beard J, Tobin B. Iron status and exercise. Am J Clin Nutr 2000; 72 (2 Suppl.): 594S–7S

    PubMed  CAS  Google Scholar 

  80. Wilkinson JG, Martin DT, Adams AA, et al. Iron status in cyclists during high-intensity interval training and recovery. Int J Sports Med 2002; 23 (8): 544–8

    Article  PubMed  CAS  Google Scholar 

  81. Hawley JA, Stepto NK. Adaptations to training in endurance cyclists: implications for performance. Sports Med 2001; 31 (7): 511–20

    Article  PubMed  CAS  Google Scholar 

  82. Hawley JA. Adaptations of skeletal muscle to prolonged, intense endurance training. Clin Exp Pharmacol Physiol 2002; 29 (3): 218–22

    Article  PubMed  CAS  Google Scholar 

  83. Horowitz JF, Klein S. Lipid metabolism during endurance exercise. Am J Clin Nutr 2000; 72 (2 Suppl.): 558S–63S

    PubMed  CAS  Google Scholar 

  84. Schmitt B, Fluck M, Decombaz J, et al. Transcriptional adaptations of lipid metabolism in tibialis anterior muscle of endurance-trained athletes. Physiol Genomics 2003; 15 (2): 148–57

    PubMed  CAS  Google Scholar 

  85. Busso T, Denis C, Bonnefoy R, et al. Modeling of adaptations to physical training by using a recursive least squares algorithm. J Appl Physiol 1997; 82 (5): 1685–93

    PubMed  CAS  Google Scholar 

  86. Busso T, Benoit H, Bonnefoy R, et al. Effects of training frequency on the dynamics of performance response to asingle training bout. J Appl Physiol 2002; 92 (2): 572–80

    PubMed  Google Scholar 

  87. Taha T, Thomas SG. Systems modelling of the relationship between training and performance. Sports Med 2003; 33 (14): 1061–73

    Article  PubMed  Google Scholar 

  88. Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Sports Exerc 2003; 35 (7): 1188–95

    Article  PubMed  Google Scholar 

  89. Hellard P, Avalos M, Millet G, et al. Modeling the residual effects and threshold saturation of training: a case studyof Olympic swimmers. J Strength Cond Res 2005; 19 (1): 67–75

    PubMed  Google Scholar 

  90. Tremblay MS, Copeland JL, Van Helder W. Effect of training status and exercise mode on endogenous steroid hormones in men. J Appl Physiol 2004; 96 (2): 531–9

    Article  PubMed  CAS  Google Scholar 

  91. Lehmann M, Foster C, Keul J. Overtraining in endurance athletes: a brief review. Med Sci Sports Exerc 1993; 25 (7): 854–62

    Article  PubMed  CAS  Google Scholar 

  92. Heck AL, Barroso CS, Callie ME, et al. Gene-nutrition interaction in human performance and exercise response. Nutrition 2004; 20 (7-8): 598–602

    Article  PubMed  CAS  Google Scholar 

  93. Bell GJ, Syrotuik D, Martin TP, et al. Effect of concurrent strength and endurance training on skeletal muscle propertiesand hormone concentrations in humans. Eur J Appl Physiol 2000; 81 (5): 418–27

    Article  PubMed  CAS  Google Scholar 

  94. Avalos M, Hellard P, Chatard JC. Modeling the trainingperformance relationship using a mixed model in elite swimmers. Med Sci Sports Exerc 2003; 35 (5): 838–46

    Article  PubMed  Google Scholar 

  95. Al Ani M, Munir SM, White M, et al. Changes in R-R variability before and after endurance training measuredby power spectral analysis and by the effect of isometric muscle contraction. Eur J Appl Physiol Occup Physiol 1996; 74 (5): 397–403

    PubMed  CAS  Google Scholar 

  96. Bouchard C, Rankinen T. Individual differences in response to regular physical activity. Med Sci Sports Exerc 2001; 33 (6 Suppl.): S446–51

    Google Scholar 

  97. Rice T, An P, Gagnon J, et al. Heritability of HR and BP response to exercise training in the HERITAGE Family Study. Med Sci Sports Exerc 2002; 34 (6): 972–9

    Article  PubMed  Google Scholar 

  98. Wilmore JH, Stanforth PR, Gagnon J, et al. Heart rate and blood pressure changes with endurance training: the HERITAGE Family Study. Med Sci Sports Exerc 2001; 33 (1): 107–16

    PubMed  CAS  Google Scholar 

  99. Skinner JS, Jaskolski A, Jaskolska A, et al. Age, sex, race, initial fitness, and response to training: the HERITAGE Family Study. J Appl Physiol 2001; 90 (5): 1770–6

    PubMed  CAS  Google Scholar 

  100. Macarthur DG, North KN. Genes and human elite athletic performance. Hum Genet 2005; 116 (5): 331–9

    Article  PubMed  CAS  Google Scholar 

  101. MacArthur DG, North KN. ACTN3: A genetic influence on muscle function and athletic performance. Exerc Sport Sci Rev 2007; 35 (1): 30–4

    Article  PubMed  Google Scholar 

  102. Brooks GA, Fahey TD, White TP, et al. Exercise physiology: human biogenetics and its applications. Columbus (OH): McGraw-Hill Companies, Inc., 2000

    Google Scholar 

  103. Yang N, MacAruthur J. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 2003; 73: 627–31

    Article  PubMed  CAS  Google Scholar 

  104. Saunders CJ, September AV, Xenophontos SL, et al. No association of the ACTN3 gene R577X polymorphismwith endurance performance in Ironman triathlons. Ann Hum Genet 2007; 71 (6): 777–81

    Article  PubMed  CAS  Google Scholar 

  105. Mujika I, Busso T, Lacoste L, et al. Modeled responses to training and taper in competitive swimmers. Med Sci Sports Exerc 1996; 28 (2): 251–8

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The research undertaken in this study is funded in part by the University of Cape Town, Discovery Health, National Research Foundation, Ernst & Ethel Eriksen Foundation and Deutscher Akademischer Austausch Dienst. The authors have no conflicts of interest that are directly related to the content of this review.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Ian Lambert.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Borresen, J., Lambert, M.I. The Quantification of Training Load, the Training Response and the Effect on Performance. Sports Med 39, 779–795 (2009). https://doi.org/10.2165/11317780-000000000-00000

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/11317780-000000000-00000

Keywords

Navigation