Sports Medicine

, Volume 37, Issue 8, pp 647–667 | Cite as

Distribution of Power Output During Cycling

Impact and Mechanisms
  • Greg Atkinson
  • Oliver Peacock
  • Alan St Clair Gibson
  • Ross Tucker
Leading Article Pacing and Cycling

Abstract

We aim to summarise the impact and mechanisms of work-rate pacing during individual cycling time trials (TTs). Unlike time-to-exhaustion tests, a TT provides an externally valid model for examining how an initial work rate is chosen and maintained by an athlete during self-selected exercise.

The selection and distribution of work rate is one of many factors that influence cycling speed. Mathematical models are available to predict the impact of factors such as gradient and wind velocity on cycling speed, but only a few researchers have examined the inter-relationships between these factors and work-rate distribution within a TT.

When environmental conditions are relatively stable (e.g. in a velodrome) and the TT is >10 minutes, then an even distribution of work rate is optimal. For a shorter TT (≤10 minutes), work rate should be increased during the starting effort because this proportion of total race time is significant. For a very short TT (≤2 minutes), the starting effort should be maximal, since the time saved during the starting phase is predicted to outweight any time lost during the final metres because of fatigue. A similar ‘time-saving’ rationale underpins the advice that work rate should vary in parallel with any changes in gradient or wind speed during a road TT. Increasing work rate in headwind and uphill sections, and vice versa, decreases the variability in speed and, therefore, the total race time.

It seems that even experienced cyclists naturally select a supraoptimal work rate at the start of a longer TT. Whether such a start can be blunted through coaching or the monitoring of psychophysiological variables is unknown. Similarly, the extent to which cyclists can vary and monitor work rate during a TT is unclear. There is evidence that sub-elite cyclists can vary work rate by ±5% the average for a TT lasting 25–60 minutes, but such variability might be difficult with high-performance cyclists whose average work rate during a TT is already extremely high (>350 watts).

During a TT, pacing strategy is regulated in a complex anticipatory system that monitors afferent feedback from various physiological systems, and then regulates the work rate so that potentially limiting changes do not occur before the endpoint of exercise is reached. It is critical that the endpoint of exercise is known by the cyclist so that adjustments to exercise work rate can be made within the context of an estimated finish time. Pacing strategies are thus the consequence of complex regulation and serve a dual role: they are both the result of homeostatic regulation by the brain, as well as being the means by which such regulation is achieved.

The pacing strategy ‘algorithm’ is sited in the brain and would need afferent input from interoceptors, such as heart rate and respiratory rate, as well as exteroceptors providing information on local environmental conditions. Such inputs have been shown to induce activity in the thalamus, hypothalamus and the parietal somatosensory cortex. Knowledge of time, modulated by the cerebellum, basal ganglia and primary somatosensory cortex, would also input to the pacing algorithm as would information stored in memory about previous similar exercise bouts. How all this information is assimilated by the different regions of the brain is not known at present.

References

  1. 1.
    Atkinson G, Nevill AM. Selected issues in the design and analysis of sport performance research. J Sports Sci 2001; 19 (10): 811–27PubMedCrossRefGoogle Scholar
  2. 2.
    Jones SM, Passfield L. The dynamic calibration of bicycle power measuring cranks. In: Haake S, editor. The engineering of sport.Oxford: Blackwell Science, 1998: 265–74Google Scholar
  3. 3.
    Craig NP, Norton KI. Characteristics of track cycling. Sports Med 2001; 31: 457–68PubMedCrossRefGoogle Scholar
  4. 4.
    Noakes TD. Challenging beliefs: ex Africa semper aliquid novi. Med Sci Sports Exerc 1997; 29 (5): 571–90PubMedCrossRefGoogle Scholar
  5. 5.
    Noakes TD. Maximal oxygen uptake: “classical” versus “contemporary” viewpoints: a rebuttal.Med Sci Sports Exerc 1998; 30 (9): 1381–98PubMedGoogle Scholar
  6. 6.
    Noakes TD. Can we trust rehydration research? In: McNamee M, editor.Philosophy and the sciences of exercise, health and sport.London: Routledge, 2005: 144–68Google Scholar
  7. 7.
    Atkinson G, Davison R, Jeukendrup A, et al. Science and cycling: current knowledge and future directions for research. J Sports Sci 2003; 21: 767–87PubMedCrossRefGoogle Scholar
  8. 8.
    Lucia A, Pardo J, Durantez A, et al. Physiological differences between professional and elite road cyclists. Int J Sports Med 1998; 19: 342–8PubMedCrossRefGoogle Scholar
  9. 9.
    Mujika I, Padilla S. Physiological and performance characteritics of male professional road cyclists. Sports Med 2001; 31: 479–87PubMedCrossRefGoogle Scholar
  10. 10.
    Billat VL. Use of blood lactate measurements for prediction of exercise performance and for control of training. Sports Med 1996; 22: 157–75PubMedCrossRefGoogle Scholar
  11. 11.
    Lucia A, Hoyos J, Perez M, et al. Which laboratory variable is related with time trial performance time in the Tour de France? Br J Sports Med 2004; 38: 636–40PubMedCrossRefGoogle Scholar
  12. 12.
    Coyle EF, Feltner ME, Kautz SA, et al. Physiological and biomechanical factors associated with elite endurance cycling performance. Med Sci Sports Exerc 1991; 23: 93–107PubMedGoogle Scholar
  13. 13.
    Lucia A, Hoyos J, Carvajal A, et al. Heart rate responses to needed to examine if pacing interventions are affec professional road cycling: the Tour de France. Int J Sports Med 1999; 20: 167–72PubMedCrossRefGoogle Scholar
  14. 14.
    Lucia A, Hoyos J, Santalla A, et al. Kinetics of V̇O2 in professional cyclists. Med Sci Sports Exerc 2002; 34: 320–5PubMedCrossRefGoogle Scholar
  15. 15.
    Jeukendrup AE, Jentjens RLPG. Efficacy of carbohydrate feedings during prolonged exercise: current thoughts, guidelines and direction for future research. Sports Med 2000; 29: 407–24PubMedCrossRefGoogle Scholar
  16. 16.
    Coyle EF, Sidossis LS, Horowitz JF, et al. Cycling efficiency is related to the percentage of type I muscle fibers. Med Sci Sports Exerc 1992; 24: 782–8PubMedGoogle Scholar
  17. 17.
    Ahlquist LE, Bassett DR, Sufit R, et al. The effect of pedalling frequency on glycogen depletion rate in type I and type II quadriceps muscle fibers during submaximal cycling exercise. Eur J Appl Physiol 1992; 65 (4): 360–4CrossRefGoogle Scholar
  18. 18.
    Lucia A, Gomez-Gallego F, Santiago C, et al. ACTN3 genotype in professional endurance cyclists. Int J Sports Med 2006; 27: 880–4PubMedCrossRefGoogle Scholar
  19. 19.
    Yang N, Macarthur DG, Gulbin JP, et al. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 2003; 73: 627–31PubMedCrossRefGoogle Scholar
  20. 20.
    Hawley JA, Stepto NK. Adaptations to training in endurance cyclists: implications for performance. Sports Med 2001; 31: 511–20PubMedCrossRefGoogle Scholar
  21. 21.
    Hawley JA. Designing a training program. In: Jeukendrup AE, editor. High performance cycling. Champaign (IL): Human Kinetics, 2002: 3–12Google Scholar
  22. 22.
    Stepto NK, Hawley JA, Dennis SC, et al. Effects of different interval-training programs on cycling time-trial performance. Med Sci Sports Exerc 1999; 31: 736–41PubMedCrossRefGoogle Scholar
  23. 23.
    Pilegaard H, Terzis G, Halestrap A, et al. Distribution of the lactate/H+ transporter isoforms MCT1 and MCT4 in human skeletal muscle. Am J Physiol 1999; 276: E843–8PubMedGoogle Scholar
  24. 24.
    Garcia-Roves PM, Terrados N, Fernandez SF, et al. Macronutrients intake of top level cyclists during continuous competition. Int J Sports Med 1998; 19: 61–7PubMedCrossRefGoogle Scholar
  25. 25.
    Garcia-Roves PM, Terrados N, Fernandez SF, et al. Comparison of dietary intake and eating behaviour of professional road cyclists during training and competition. Int J Sports Nutr 2000; 10: 82–98Google Scholar
  26. 26.
    Saris WHM, Vanerpbaart MA, Brouns F, et al. Study on food-intake and energy expenditure during extreme sustained exercise: the Tour de France. Int J Sports Med 1989; 10: S26–31PubMedCrossRefGoogle Scholar
  27. 27.
    Jentjens RL, Jeukendrup AE. Determinants of post-exercise glycogen synthesis during short-term recovery. Sports Med 2003; 33: 117–44PubMedCrossRefGoogle Scholar
  28. 28.
    Hawley JA, Schabort EJ, Noakes TD, et al. Carbohydrate loading and exercise performance: an update. Sports Med 1997; 24: 73–81PubMedCrossRefGoogle Scholar
  29. 29.
    Coyle EF, Coggan AR, Hemmert MK, et al. Substrate usage during prolonged exercise following a pre-exercise meal. J Appl Physiol 1985; 59: 429–33PubMedGoogle Scholar
  30. 30.
    Jeukendrup AE, Raben A, Gijsen A, et al. Glucose kinetics during prolonged exercise in highly trained human subjects: effect of glucose ingestion. J Physiol 1999; 515: 579–89PubMedCrossRefGoogle Scholar
  31. 31.
    Burke LM. Nutritional practices of road cyclists. Sports Med 2001; 31: 521–32PubMedCrossRefGoogle Scholar
  32. 32.
    Walsh RM, Noakes TD, Hawley JA, et al. Impaired high-intensity cycling performance time at low levels of dehydration. Int J Sports Med 1994; 15: 392–8PubMedCrossRefGoogle Scholar
  33. 33.
    Below PR, Mora-Rodriguez R, Gonzalez-Alonso J, et al. Fluid and carbohydrate ingestion independently improve performance during 1h of intense exercise. Med Sci Sports Exerc 1995; 27: 200–10PubMedGoogle Scholar
  34. 34.
    Kovacs EMR, Stegen JHCH, Brouns F. Effect of caffeinated drinks on substrate metabolism, caffeine excretion, and performance. J Appl Physiol 1998; 85: 709–15PubMedGoogle Scholar
  35. 35.
    Graham TE. Caffeine and exercise: metabolism, endurance and performance. Sports Med 2001; 31: 765–807CrossRefGoogle Scholar
  36. 36.
    Shing CM, Jenkins DG, Stevenson L, et al. The influence of bovine colostrums supplementation on exercise performance in highly trained cyclists. Br J Sports Med 2006; 40: 797–801PubMedCrossRefGoogle Scholar
  37. 37.
    Kyle CR. Selecting cycling equipment. In: Burke ER, editor. High-tech cycling. Champaign (IL): Human Kinetics, 1996: 1–43Google Scholar
  38. 38.
    Ryschon TW, Stray-Gundersen J. The effect of tyre pressure on the economy of cycling. Ergonomics 1993; 36: 661–6PubMedCrossRefGoogle Scholar
  39. 39.
    Martin JC, Milliken DL, Cobb JE, et al. Validation of a mathematical model for road cycling power. J Appl Biomech 1998; 14: 276–91Google Scholar
  40. 40.
    Kyle CR. Mechanical factors affecting the speed of a bicycle.In: Burke ER, editor. Science of cycling. Champaign (IL): Human Kinetics, 1986: 123–36Google Scholar
  41. 41.
    Broker JP, Kyle CR, Burke ER. Racing cyclist power requirements in the 4000-m individual and team pursuits. Med Sci Sports Exerc 1999; 31: 1677–85PubMedCrossRefGoogle Scholar
  42. 42.
    Bassatt JR, Kyle CR, Passfield L, et al. Comparing cycling world hour records, 1967–1996: modelling with empirical data. Med Sci Sports Exerc 1999; 31: 1665–76CrossRefGoogle Scholar
  43. 43.
    Swain DP. A model for optimizing cycling performance by varying power on hills and in wind. Med Sci Sports Exerc 1997; 29: 1104–8PubMedCrossRefGoogle Scholar
  44. 44.
    Foster C, De Koning JJ, Hettinga F, et al. Pattern of energy expenditure during simulated competition. Med Sci Sports Exerc 2003; 35: 826–31PubMedCrossRefGoogle Scholar
  45. 45.
    Atkinson G, Brunskill A. Pacing strategies during a cycling time trial with simulated headwinds and tailwinds. Ergonomics 2000; 43: 1449–60PubMedCrossRefGoogle Scholar
  46. 46.
    Robinson S, Robinson DL, Mountjoy RJ, et al. Influence of fatigue on the efficiency of men during exhausting runs. J Appl Physiol 1958; 12: 197–201PubMedGoogle Scholar
  47. 47.
    Billat VL, Slawinski J, Danel M, et al. Effect of free versus constant pace on performance and oxygen kinetics in running. Med Sci Sports Exerc 2001; 33: 2082–8PubMedCrossRefGoogle Scholar
  48. 48.
    Thompson KG, MacLaren DPM, Lees A, et al. The effects of changing pace on metabolism and stroke characteristics during high-speed breaststroke swimming. J Sports Sci 2004; 22: 149–57PubMedCrossRefGoogle Scholar
  49. 49.
    Bishop D, Bonetti D, Dawson B. The influence of pacing strategy on V̇O2 and supramaximal kayak performance. Med Sci Sports Exerc 2002; 34: 1041–7PubMedCrossRefGoogle Scholar
  50. 50.
    Jeukendrup AE, Martin J. Improving cycling performance: how should we spend our time and money. Sports Med 2001; 31: 559–69PubMedCrossRefGoogle Scholar
  51. 51.
    Palmer GS, Hawley JA, Dennis SC, et al. Heart rate responses during a 4-d cycle stage race. Med Sci Sports Exerc 1994; 26: 1278–83PubMedGoogle Scholar
  52. 52.
    Foster C, Snyder AC, Thompson NN, et al. Effect of pacing strategy on cycle time trial performance. Med Sci Sports Exerc 1993; 25: 383–8PubMedGoogle Scholar
  53. 53.
    Padilla S, Mujika I, Orbananos J. Exercise intensity during competition time trials in professional road cycling. Med Sci Sports Exerc 2000; 32: 850–6PubMedCrossRefGoogle Scholar
  54. 54.
    Foster C, Schrager M, Snyder AC, et al. Pacing strategy and athletic performance. Sports Med 1994; 17: 77–85PubMedCrossRefGoogle Scholar
  55. 55.
    De Koning JJ, Bobbert MF, Foster C. Determination of optimal pacing strategy in track cycling. Med Sci Sports Exerc 1999; 27: 1090–5Google Scholar
  56. 56.
    Van Ingen Schenau GJ, Dekoning JJ, De Groot G. Optimisation of sprinting performance in running, cycling and speed skating. Sports Med 1994; 17: 259–75CrossRefGoogle Scholar
  57. 57.
    Hirvonen J, Rekunen S, Rusko H, et al. Breakdown of high energy phosphate compounds and lactate accumulation during short supramaximal exercise. Eur J Appl Physiol 1987; 56: 253–9CrossRefGoogle Scholar
  58. 58.
    Nikolopousos V, Arkinstall MJ, Hawley JA. Pacing strategy in simulated cycle time-trials is based on perceived rather than actual distance. J Sci Med Sport 2001; 4: 212–9CrossRefGoogle Scholar
  59. 59.
    Albertus Y, Tucker R, Gibson AS, et al. Effect of distance feedback on pacing strategy and perceived exertion during cycling. Med Sci Sports Exerc 2005; 37 (3): 461–8PubMedCrossRefGoogle Scholar
  60. 60.
    Firth M. From high-tech to low-tech: another look at time-trail pacing strategy. Coaching News 1998; 3: 7–10Google Scholar
  61. 61.
    Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med 1970; 2: 92–8PubMedGoogle Scholar
  62. 62.
    Atkinson G, Peacock O, Law M. Acceptability of power variation during a simulated hilly time trial. Int J Sports Med 2007; 28: 157–63PubMedCrossRefGoogle Scholar
  63. 63.
    Di Prampero PE, Cortili G, Mognoni P, et al. Equation of motion of a cyclist. J Appl Physiol 1979; 47: 201–6PubMedGoogle Scholar
  64. 64.
    Davies CT. Effect of air resistance on the metabolic cost and performance of cycling. Eur J Appl Physiol Occup Physiol 1980; 45: 245–54PubMedCrossRefGoogle Scholar
  65. 65.
    Kyle CR. The mechanics and aerodynamics of cycling. In: Burke ER, Newsom MM, editors. Medical and scientific aspects of cycling. Champaign (IL): Human Kinetics, 1988: 235–51Google Scholar
  66. 66.
    Olds TS, Norton KI, Craig NP. Mathematical model of cycling performance. J Appl Physiol 1993; 75: 730–7PubMedGoogle Scholar
  67. 67.
    Olds TS, Norton KI, Lowe EL, et al. Modelling road-cycling performance. J Appl Physiol 1995; 78: 1596–611PubMedGoogle Scholar
  68. 68.
    Schoberer E. Operating instructions for the SRM training system. Welldorf, 1994Google Scholar
  69. 69.
    Atkinson G, Peacock O, Passfield L. Variable versus constant power strategies during cycling time trials: prediction of time savings using an up-to-date mathematical model. J Sports Sci 2007; 25: 1001–9PubMedCrossRefGoogle Scholar
  70. 70.
    Gaesser GA, Poole DC. The slow component of oxygen uptake kinetics in humans. In: Holloszy JO, editor. Exercise and sport sciences reviews. Baltimore (MD): Williams and Wilkins, 1996: 24, 35–70PubMedCrossRefGoogle Scholar
  71. 71.
    Lucia A, Hoyos J, Chicharro JL. Physiology of professional road cycling. Sports Med 2001; 31 (5): 325–37PubMedCrossRefGoogle Scholar
  72. 72.
    Liedl MA, Swain DP, Branch JD, et al. Physiological effects of constant vs variable power during endurance cycling. Med Sci Sports Exerc 1999; 31: 1472–7PubMedCrossRefGoogle Scholar
  73. 73.
    Fitts RH. Cellular mechanisms of muscle fatigue. Physiol Rev 1994; 74: 49–94PubMedCrossRefGoogle Scholar
  74. 74.
    Cherry PW, Lakomy HKA, Nevill ME, et al. Constant external work cycle exercise: the performance and metabolic effects of all out and even paced strategies. Eur J Appl Physiol 1997; 75: 22–7CrossRefGoogle Scholar
  75. 75.
    Palmer GS, Noakes TD, Hawley JA. Effects of steady-state versus stochastic exercise on subsequent cycling performance. Med Sci Sports Exerc 1997; 25: 684–7Google Scholar
  76. 76.
    Palmer GS, Borghouts LB, Noakes TD, et al. Metabolic and performance responses to constant-load vs variable intensity exercise in trained cyclists. J Appl Physiol 1999; 87: 1186–96PubMedGoogle Scholar
  77. 77.
    Noakes TD, St Clair Gibson A. Logical limitations to the “catastrophe” models of fatigue during exercise in humans. Br J Sports Med 2004; 38: 648–9PubMedCrossRefGoogle Scholar
  78. 78.
    St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004; 38: 797–806CrossRefGoogle Scholar
  79. 79.
    Noakes TD, St Clair Gibson A, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans. Summary and conclusions. Br J Sports Med 2005; 39: 120–4PubMedCrossRefGoogle Scholar
  80. 80.
    St Clair Gibson A, Lambert EV, Lambert MI, et al. Exercise and fatigue-control mechanisms. Int J Sports Med 2001; 2: 1–14Google Scholar
  81. 81.
    Nielsen B, Hales JR, Strange S, et al. Human circulatory and thermo regulatory adaptations with heat acclimation and exercise in a hot, dry environment. J Physiol 1993; 460: 467–85PubMedGoogle Scholar
  82. 82.
    Gonzalez-Alonso J, Teller C, Andersen SL, et al. Influence of body temperature on the development of fatigue during prolonged exercise in the heat. J Appl Physiol 1999; 86: 1032–9PubMedGoogle Scholar
  83. 83.
    Nielsen B, Savard G, Richter EA, et al. Muscle blood flow and muscle metabolism during exercise and heat stress. J Appl Physiol 1990; 69: 1040–6PubMedGoogle Scholar
  84. 84.
    Nybo L, Nielsen B. Hyperthermia and central fatigue during prolonged exercise in humans. J Appl Physiol 2001; 91: 1055–60PubMedGoogle Scholar
  85. 85.
    Todd G, Butler JE, Taylor JL, et al. Hyperthermia: a failure of the motor cortex and the muscle. J Physiol 2005; 563: 621–31PubMedCrossRefGoogle Scholar
  86. 86.
    Tatterson AJ, Hahn AG, Martin DT, et al. Effects of heat stress on physiological responses and exercise performance in elite cyclists. J Sci Med Sport 2000; 3: 186–93PubMedCrossRefGoogle Scholar
  87. 87.
    Marino FE, Mbambo Z, Kortekaas E, et al. Advantages of smaller body mass during distance running in warm, humid environments. Pflugers Arch 2000; 441: 359–67PubMedCrossRefGoogle Scholar
  88. 88.
    Marino FE, Lambert MI, Noakes TD. Superior performance of African runners in warm humid but not in cool environmental conditions. J Appl Physiol 2004; 96: 124–30PubMedCrossRefGoogle Scholar
  89. 89.
    Tucker R, Rauch L, Harley YXR, et al. Impaired exercise performance in the heat is associated with an anticipatory reduction in skeletal muscle recruitment. Pflugers Arch 2004; 448: 422–30PubMedCrossRefGoogle Scholar
  90. 90.
    Cheung SS, Sleivert GG. Lowering of skin temperature decreases isokinetic maximal force production independent of core temperature. Eur J Appl Physiol 2004; 91: 723–8PubMedCrossRefGoogle Scholar
  91. 91.
    Marino FE. Anticipatory regulation and avoidance of catastrophe during exercise-induced hyperthermia. Comp Biochem Physiol B Biochem Mol Biol 2004; 139: 561–9PubMedCrossRefGoogle Scholar
  92. 92.
    Morrison S, Sleivert GG, Cheung SS. Passive hyperthermia reduces voluntary activation and isometric force production. Eur J Appl Physiol 2004; 91: 729–36PubMedCrossRefGoogle Scholar
  93. 93.
    Peltonen JE, Rantamäki J, Niittymäki SPT, et al. Effects of oxygen fraction in inspired air on rowing performance. Med Sci Sports Exerc 1995; 27: 573–9PubMedGoogle Scholar
  94. 94.
    Peltonen JE, Rantamäki SPT, et al. Effects of oxygen fraction in inspired air on force production and electromyogram activity during ergometer rowing. Eur J Appl Physiol 1997; 76: 495–503CrossRefGoogle Scholar
  95. 95.
    Brosnan MJ, Martin DT, Hahn AG, et al. Impaired interval exercise responses in elite female cyclists at moderate simulated altitude. J Appl Physiol 2000; 89: 1819–24PubMedGoogle Scholar
  96. 96.
    Havemann L, West SJ, Goedecke J, et al. Fat adaptation fol lowed by carbohydrate-loading compromises high-intensity sprint performance. J Appl Physiol 2005; 100 (1): 194–202PubMedCrossRefGoogle Scholar
  97. 97.
    Rauch HG, St Clair Gibson A, Lambert EV, et al. A signalling role for muscle glycogen in the regulation of pace during prolonged exercise. Br J Sports Med 2005; 39: 34–8PubMedCrossRefGoogle Scholar
  98. 98.
    Ansley L, Robson PJ, St Clair Gibson A, et al. Anticipatory pacing strategies during supramaximal exercise lasting longer than 30 s. Med Sci Sports Exerc 2004; 36 (2): 309–14PubMedCrossRefGoogle Scholar
  99. 99.
    Baden DA, Warwick-Evans LA, Lakomy J. Am I nearly there? The effect of anticipated running distance on perceived exertion and attentional focus. J Sports Exerc Psychol 2004; 27: 215–31Google Scholar
  100. 100.
    Ulmer H-V. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experentia 1996; 52: 416–20CrossRefGoogle Scholar
  101. 101.
    Paterson S, Marino FE. Effect of deception of distance on prolonged cycling performance. Perc Mot Skills 2004; 98: 1017–26CrossRefGoogle Scholar
  102. 102.
    Lambert EV, St Clair Gibson A, Noakes TD. Complex system model of fatigue: integrative homeostatic control of peripheral physiological systems during exercise in humans. Br J Sports Med 2005; 39: 52–62PubMedCrossRefGoogle Scholar
  103. 103.
    St Clair Gibson A, Lambert EV, Rauch LHG, et al. The role of information processing between the brain and peripheral physiological systems in pacing and perception of effort. Sports Med 2006; 36 (8): 705–22CrossRefGoogle Scholar
  104. 104.
    Craig AD. How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 2002; 3: 655–66PubMedGoogle Scholar
  105. 105.
    Critchley HD. The human cortex responds to an interoceptive challenge. Proc Natl Acad Sci USA 2004; 101: 6333–4PubMedCrossRefGoogle Scholar
  106. 106.
    Williamson JW, McColl R, Lathews D, et al. Hypnotic manipulation of effort sense during dynamic exercise: cardiovascular responses and brain activation. J Appl Physiol 2001; 90: 1392–9PubMedGoogle Scholar
  107. 107.
    Thornton JM, Guz A, Murphy K, et al. Identification of higher brain centres that may encode the cardiorespiratory response to exercise. J Physiol 2001; 533: 823–36PubMedCrossRefGoogle Scholar
  108. 108.
    St Clair Gibson A, Baden DA, Lambert MI, et al. The conscious perception of the sensation of fatigue. Sports Med 2003; 33: 167–76CrossRefGoogle Scholar
  109. 109.
    Eichenbaum H. A cortical-hippocampal system for declarative memory. Nat Rev Neurosci 2000; 1: 41–50PubMedCrossRefGoogle Scholar
  110. 110.
    Miller EK. The prefrontal cortex and cognitive control. Nat Rev Neurosci 2000; 1: 59–65PubMedCrossRefGoogle Scholar
  111. 111.
    Critchley HD, Melmed RN, Featherstone G, et al. Brain activity during biofeedback relaxation: a functional neuroimaging investigation. Brain 2001; 124: 1003–12PubMedCrossRefGoogle Scholar
  112. 112.
    Graziano MSA, Taylor CSR, Moore T. Complex movements ovoked by microstimulation of precentral cortex. Neuron 2002; 34: 841–51PubMedCrossRefGoogle Scholar
  113. 113.
    Latash ML. Neurophysiological basis of movement. Champaign Sport and Exercise Sciences, Henry Cotton Campus, Liver (IL): Human Kinetics, 1998Google Scholar
  114. 114.
    Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci 2005; 6 (10): 755–65PubMedCrossRefGoogle Scholar
  115. 115.
    Gibbon J. Scalar expectancy theory and Weber’s law in animal timing. Psychol Rev 1977; 84: 279–325CrossRefGoogle Scholar
  116. 116.
    Salinas E, Sejnowski TJ. Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2001; 2: 539–50PubMedCrossRefGoogle Scholar
  117. 117.
    Fiorillo CD, Tobler PN, Schultz W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 2003; 299: 1898–902PubMedCrossRefGoogle Scholar

Copyright information

© Adis International Limited 2007

Authors and Affiliations

  • Greg Atkinson
    • 1
  • Oliver Peacock
    • 2
  • Alan St Clair Gibson
    • 3
  • Ross Tucker
    • 4
  1. 1.School of Sport and Exercise SciencesHenry Cotton Campus, Liverpool John Moores UniversityEngland
  2. 2.The School for HealthUniversity of BathEngland
  3. 3.Psychology and Sport SciencesNorthumbria UniversityEngland
  4. 4.Department of Human BiologyMRC UCT Research Unit for Exercise Science and Sports Medicine, University of Cape TownSouth Africa

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