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Distribution of Power Output During Cycling

Impact and Mechanisms

  • Leading Article
  • Pacing and Cycling
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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.

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References

  1. Atkinson G, Nevill AM. Selected issues in the design and analysis of sport performance research. J Sports Sci 2001; 19 (10): 811–27

    Article  PubMed  CAS  Google Scholar 

  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: 265–74

    Google Scholar 

  3. Craig NP, Norton KI. Characteristics of track cycling. Sports Med 2001; 31: 457–68

    Article  PubMed  CAS  Google Scholar 

  4. Noakes TD. Challenging beliefs: ex Africa semper aliquid novi. Med Sci Sports Exerc 1997; 29 (5): 571–90

    Article  PubMed  CAS  Google Scholar 

  5. Noakes TD. Maximal oxygen uptake: “classical” versus “contemporary” viewpoints: a rebuttal.Med Sci Sports Exerc 1998; 30 (9): 1381–98

    PubMed  CAS  Google Scholar 

  6. Noakes TD. Can we trust rehydration research? In: McNamee M, editor.Philosophy and the sciences of exercise, health and sport.London: Routledge, 2005: 144–68

    Google Scholar 

  7. Atkinson G, Davison R, Jeukendrup A, et al. Science and cycling: current knowledge and future directions for research. J Sports Sci 2003; 21: 767–87

    Article  PubMed  Google Scholar 

  8. Lucia A, Pardo J, Durantez A, et al. Physiological differences between professional and elite road cyclists. Int J Sports Med 1998; 19: 342–8

    Article  PubMed  CAS  Google Scholar 

  9. Mujika I, Padilla S. Physiological and performance characteritics of male professional road cyclists. Sports Med 2001; 31: 479–87

    Article  PubMed  CAS  Google Scholar 

  10. Billat VL. Use of blood lactate measurements for prediction of exercise performance and for control of training. Sports Med 1996; 22: 157–75

    Article  PubMed  CAS  Google Scholar 

  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: 636–40

    Article  PubMed  CAS  Google Scholar 

  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: 93–107

    PubMed  CAS  Google Scholar 

  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: 167–72

    Article  PubMed  CAS  Google Scholar 

  14. Lucia A, Hoyos J, Santalla A, et al. Kinetics of V̇O2 in professional cyclists. Med Sci Sports Exerc 2002; 34: 320–5

    Article  PubMed  Google Scholar 

  15. Jeukendrup AE, Jentjens RLPG. Efficacy of carbohydrate feedings during prolonged exercise: current thoughts, guidelines and direction for future research. Sports Med 2000; 29: 407–24

    Article  PubMed  CAS  Google Scholar 

  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: 782–8

    PubMed  CAS  Google Scholar 

  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): 360–4

    Article  CAS  Google Scholar 

  18. Lucia A, Gomez-Gallego F, Santiago C, et al. ACTN3 genotype in professional endurance cyclists. Int J Sports Med 2006; 27: 880–4

    Article  PubMed  CAS  Google Scholar 

  19. Yang N, Macarthur DG, Gulbin JP, et al. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 2003; 73: 627–31

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  21. Hawley JA. Designing a training program. In: Jeukendrup AE, editor. High performance cycling. Champaign (IL): Human Kinetics, 2002: 3–12

    Google Scholar 

  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: 736–41

    Article  PubMed  CAS  Google Scholar 

  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: E843–8

    PubMed  CAS  Google Scholar 

  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: 61–7

    Article  PubMed  CAS  Google Scholar 

  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: 82–98

    CAS  Google Scholar 

  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: S26–31

    Article  PubMed  Google Scholar 

  27. Jentjens RL, Jeukendrup AE. Determinants of post-exercise glycogen synthesis during short-term recovery. Sports Med 2003; 33: 117–44

    Article  PubMed  Google Scholar 

  28. Hawley JA, Schabort EJ, Noakes TD, et al. Carbohydrate loading and exercise performance: an update. Sports Med 1997; 24: 73–81

    Article  PubMed  CAS  Google Scholar 

  29. Coyle EF, Coggan AR, Hemmert MK, et al. Substrate usage during prolonged exercise following a pre-exercise meal. J Appl Physiol 1985; 59: 429–33

    PubMed  CAS  Google Scholar 

  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: 579–89

    Article  PubMed  CAS  Google Scholar 

  31. Burke LM. Nutritional practices of road cyclists. Sports Med 2001; 31: 521–32

    Article  PubMed  CAS  Google Scholar 

  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: 392–8

    Article  PubMed  CAS  Google Scholar 

  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: 200–10

    PubMed  CAS  Google Scholar 

  34. Kovacs EMR, Stegen JHCH, Brouns F. Effect of caffeinated drinks on substrate metabolism, caffeine excretion, and performance. J Appl Physiol 1998; 85: 709–15

    PubMed  CAS  Google Scholar 

  35. Graham TE. Caffeine and exercise: metabolism, endurance and performance. Sports Med 2001; 31: 765–807

    Article  Google Scholar 

  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: 797–801

    Article  PubMed  CAS  Google Scholar 

  37. Kyle CR. Selecting cycling equipment. In: Burke ER, editor. High-tech cycling. Champaign (IL): Human Kinetics, 1996: 1–43

    Google Scholar 

  38. Ryschon TW, Stray-Gundersen J. The effect of tyre pressure on the economy of cycling. Ergonomics 1993; 36: 661–6

    Article  PubMed  CAS  Google Scholar 

  39. Martin JC, Milliken DL, Cobb JE, et al. Validation of a mathematical model for road cycling power. J Appl Biomech 1998; 14: 276–91

    Google Scholar 

  40. Kyle CR. Mechanical factors affecting the speed of a bicycle.In: Burke ER, editor. Science of cycling. Champaign (IL): Human Kinetics, 1986: 123–36

    Google Scholar 

  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: 1677–85

    Article  PubMed  CAS  Google Scholar 

  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: 1665–76

    Article  Google Scholar 

  43. Swain DP. A model for optimizing cycling performance by varying power on hills and in wind. Med Sci Sports Exerc 1997; 29: 1104–8

    Article  PubMed  CAS  Google Scholar 

  44. Foster C, De Koning JJ, Hettinga F, et al. Pattern of energy expenditure during simulated competition. Med Sci Sports Exerc 2003; 35: 826–31

    Article  PubMed  Google Scholar 

  45. Atkinson G, Brunskill A. Pacing strategies during a cycling time trial with simulated headwinds and tailwinds. Ergonomics 2000; 43: 1449–60

    Article  PubMed  CAS  Google Scholar 

  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: 197–201

    PubMed  CAS  Google Scholar 

  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: 2082–8

    Article  PubMed  CAS  Google Scholar 

  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: 149–57

    Article  PubMed  Google Scholar 

  49. Bishop D, Bonetti D, Dawson B. The influence of pacing strategy on V̇O2 and supramaximal kayak performance. Med Sci Sports Exerc 2002; 34: 1041–7

    Article  PubMed  Google Scholar 

  50. Jeukendrup AE, Martin J. Improving cycling performance: how should we spend our time and money. Sports Med 2001; 31: 559–69

    Article  PubMed  CAS  Google Scholar 

  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: 1278–83

    PubMed  CAS  Google Scholar 

  52. Foster C, Snyder AC, Thompson NN, et al. Effect of pacing strategy on cycle time trial performance. Med Sci Sports Exerc 1993; 25: 383–8

    PubMed  CAS  Google Scholar 

  53. Padilla S, Mujika I, Orbananos J. Exercise intensity during competition time trials in professional road cycling. Med Sci Sports Exerc 2000; 32: 850–6

    Article  PubMed  CAS  Google Scholar 

  54. Foster C, Schrager M, Snyder AC, et al. Pacing strategy and athletic performance. Sports Med 1994; 17: 77–85

    Article  PubMed  CAS  Google Scholar 

  55. De Koning JJ, Bobbert MF, Foster C. Determination of optimal pacing strategy in track cycling. Med Sci Sports Exerc 1999; 27: 1090–5

    Google Scholar 

  56. Van Ingen Schenau GJ, Dekoning JJ, De Groot G. Optimisation of sprinting performance in running, cycling and speed skating. Sports Med 1994; 17: 259–75

    Article  Google Scholar 

  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: 253–9

    Article  CAS  Google Scholar 

  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: 212–9

    Article  Google Scholar 

  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): 461–8

    Article  PubMed  Google Scholar 

  60. Firth M. From high-tech to low-tech: another look at time-trail pacing strategy. Coaching News 1998; 3: 7–10

    Google Scholar 

  61. Borg G. Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med 1970; 2: 92–8

    PubMed  CAS  Google Scholar 

  62. Atkinson G, Peacock O, Law M. Acceptability of power variation during a simulated hilly time trial. Int J Sports Med 2007; 28: 157–63

    Article  PubMed  CAS  Google Scholar 

  63. Di Prampero PE, Cortili G, Mognoni P, et al. Equation of motion of a cyclist. J Appl Physiol 1979; 47: 201–6

    PubMed  Google Scholar 

  64. Davies CT. Effect of air resistance on the metabolic cost and performance of cycling. Eur J Appl Physiol Occup Physiol 1980; 45: 245–54

    Article  PubMed  CAS  Google Scholar 

  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: 235–51

    Google Scholar 

  66. Olds TS, Norton KI, Craig NP. Mathematical model of cycling performance. J Appl Physiol 1993; 75: 730–7

    PubMed  CAS  Google Scholar 

  67. Olds TS, Norton KI, Lowe EL, et al. Modelling road-cycling performance. J Appl Physiol 1995; 78: 1596–611

    PubMed  CAS  Google Scholar 

  68. Schoberer E. Operating instructions for the SRM training system. Welldorf, 1994

    Google Scholar 

  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: 1001–9

    Article  PubMed  CAS  Google Scholar 

  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, 35–70

    Article  PubMed  CAS  Google Scholar 

  71. Lucia A, Hoyos J, Chicharro JL. Physiology of professional road cycling. Sports Med 2001; 31 (5): 325–37

    Article  PubMed  CAS  Google Scholar 

  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: 1472–7

    Article  PubMed  CAS  Google Scholar 

  73. Fitts RH. Cellular mechanisms of muscle fatigue. Physiol Rev 1994; 74: 49–94

    Article  PubMed  CAS  Google Scholar 

  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: 22–7

    Article  CAS  Google Scholar 

  75. Palmer GS, Noakes TD, Hawley JA. Effects of steady-state versus stochastic exercise on subsequent cycling performance. Med Sci Sports Exerc 1997; 25: 684–7

    Google Scholar 

  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: 1186–96

    PubMed  CAS  Google Scholar 

  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: 648–9

    Article  PubMed  CAS  Google Scholar 

  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: 797–806

    Article  Google Scholar 

  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: 120–4

    Article  PubMed  CAS  Google Scholar 

  80. St Clair Gibson A, Lambert EV, Lambert MI, et al. Exercise and fatigue-control mechanisms. Int J Sports Med 2001; 2: 1–14

    Google Scholar 

  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: 467–85

    PubMed  CAS  Google Scholar 

  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: 1032–9

    PubMed  CAS  Google Scholar 

  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: 1040–6

    PubMed  CAS  Google Scholar 

  84. Nybo L, Nielsen B. Hyperthermia and central fatigue during prolonged exercise in humans. J Appl Physiol 2001; 91: 1055–60

    PubMed  CAS  Google Scholar 

  85. Todd G, Butler JE, Taylor JL, et al. Hyperthermia: a failure of the motor cortex and the muscle. J Physiol 2005; 563: 621–31

    Article  PubMed  CAS  Google Scholar 

  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: 186–93

    Article  PubMed  CAS  Google Scholar 

  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: 359–67

    Article  PubMed  CAS  Google Scholar 

  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: 124–30

    Article  PubMed  Google Scholar 

  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: 422–30

    Article  PubMed  CAS  Google Scholar 

  90. Cheung SS, Sleivert GG. Lowering of skin temperature decreases isokinetic maximal force production independent of core temperature. Eur J Appl Physiol 2004; 91: 723–8

    Article  PubMed  Google Scholar 

  91. Marino FE. Anticipatory regulation and avoidance of catastrophe during exercise-induced hyperthermia. Comp Biochem Physiol B Biochem Mol Biol 2004; 139: 561–9

    Article  PubMed  CAS  Google Scholar 

  92. Morrison S, Sleivert GG, Cheung SS. Passive hyperthermia reduces voluntary activation and isometric force production. Eur J Appl Physiol 2004; 91: 729–36

    Article  PubMed  Google Scholar 

  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: 573–9

    PubMed  CAS  Google Scholar 

  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: 495–503

    Article  CAS  Google Scholar 

  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: 1819–24

    PubMed  CAS  Google Scholar 

  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): 194–202

    Article  PubMed  CAS  Google Scholar 

  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: 34–8

    Article  PubMed  CAS  Google Scholar 

  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): 309–14

    Article  PubMed  Google Scholar 

  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: 215–31

    Google Scholar 

  100. Ulmer H-V. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experentia 1996; 52: 416–20

    Article  CAS  Google Scholar 

  101. Paterson S, Marino FE. Effect of deception of distance on prolonged cycling performance. Perc Mot Skills 2004; 98: 1017–26

    Article  CAS  Google Scholar 

  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: 52–62

    Article  PubMed  CAS  Google Scholar 

  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): 705–22

    Article  Google Scholar 

  104. Craig AD. How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 2002; 3: 655–66

    PubMed  CAS  Google Scholar 

  105. Critchley HD. The human cortex responds to an interoceptive challenge. Proc Natl Acad Sci USA 2004; 101: 6333–4

    Article  PubMed  CAS  Google Scholar 

  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: 1392–9

    PubMed  CAS  Google Scholar 

  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: 823–36

    Article  PubMed  CAS  Google Scholar 

  108. St Clair Gibson A, Baden DA, Lambert MI, et al. The conscious perception of the sensation of fatigue. Sports Med 2003; 33: 167–76

    Article  Google Scholar 

  109. Eichenbaum H. A cortical-hippocampal system for declarative memory. Nat Rev Neurosci 2000; 1: 41–50

    Article  PubMed  CAS  Google Scholar 

  110. Miller EK. The prefrontal cortex and cognitive control. Nat Rev Neurosci 2000; 1: 59–65

    Article  PubMed  CAS  Google Scholar 

  111. Critchley HD, Melmed RN, Featherstone G, et al. Brain activity during biofeedback relaxation: a functional neuroimaging investigation. Brain 2001; 124: 1003–12

    Article  PubMed  CAS  Google Scholar 

  112. Graziano MSA, Taylor CSR, Moore T. Complex movements ovoked by microstimulation of precentral cortex. Neuron 2002; 34: 841–51

    Article  PubMed  CAS  Google Scholar 

  113. Latash ML. Neurophysiological basis of movement. Champaign Sport and Exercise Sciences, Henry Cotton Campus, Liver (IL): Human Kinetics, 1998

    Google Scholar 

  114. Buhusi CV, Meck WH. What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci 2005; 6 (10): 755–65

    Article  PubMed  CAS  Google Scholar 

  115. Gibbon J. Scalar expectancy theory and Weber’s law in animal timing. Psychol Rev 1977; 84: 279–325

    Article  Google Scholar 

  116. Salinas E, Sejnowski TJ. Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2001; 2: 539–50

    Article  PubMed  CAS  Google Scholar 

  117. Fiorillo CD, Tobler PN, Schultz W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 2003; 299: 1898–902

    Article  PubMed  CAS  Google Scholar 

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Atkinson, G., Peacock, O., St Clair Gibson, A. et al. Distribution of Power Output During Cycling. Sports Med 37, 647–667 (2007). https://doi.org/10.2165/00007256-200737080-00001

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