Sports Medicine

, Volume 33, Issue 4, pp 245–260 | Cite as

Movement Systems as Dynamical Systems

The Functional Role of Variability and its Implications for Sports Medicine
  • Keith DavidsEmail author
  • Paul Glazier
  • Duarte Araújo
  • Roger Bartlett
Leading Article


In recent years, concepts and tools from dynamical systems theory have been successfully applied to the study of movement systems, contradicting traditional views of variability as noise or error. From this perspective, it is apparent that variability in movement systems is omnipresent and unavoidable due to the distinct constraints that shape each individual’s behaviour. In this position paper, it is argued that trial-to-trial movement variations within individuals and performance differences observed between individuals may be best interpreted as attempts to exploit the variability that is inherent within and between biological systems. That is, variability in movement systems helps individuals adapt to the unique constraints (personal, task and environmental) impinging on them across different timescales. We examine the implications of these ideas for sports medicine, by: (i) focusing on intra-individual variability in postural control to exemplify within-individual real-time adaptations to changing informational constraints in the performance environment; and (ii) interpreting recent evidence on the role of the angiotensin-converting enzyme gene as a genetic (developmental) constraint on individual differences in physical performance.

The implementation of a dynamical systems theoretical interpretation of variability in movement systems signals a need to re-evaluate the ubiquitous influence of the traditional ‘medical model’ in interpreting motor behaviour and performance constrained by disease or injury to the movement system. Accordingly, there is a need to develop new tools for providing individualised plots of motor behaviour and performance as a function of key constraints. Coordination profiling is proposed as one such alternative approach for interpreting the variability and stability demonstrated by individuals as they attempt to construct functional, goal-directed patterns of motor behaviour during each unique performance. Finally, the relative contribution of genes and training to between-individual performance variation is highlighted, with the conclusion that dynamical systems theory provides an appropriate multidisciplinary theoretical framework to explain their interaction in supporting physical performance.


Stride Length Movement System Dynamical System Theory Task Constraint Movement Variability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to acknowledge the help of Maureen Hazelwood in the preparation of this manuscript, and the comments of Major David Woods, RAMC, on a previous version of this article. No sources of funding were used to assist in the preparation of this manuscript. The authors have no conflicts of interest that are directly relevant to the content of this manuscript.


  1. 1.
    Beek PJ, Meijer OG. On the nature of ‘the’ motor-action controversy. In: Meijer OG, Roth K, editors. Complex movement behaviour: the motor-action controversy. Amsterdam: Elsevier Science, 1988: 157–85CrossRefGoogle Scholar
  2. 2.
    Davids K, Button C, Bennett SJ. Coordination and control of movement in sport: an ecological approach. Champaign (IL): Human Kinetics, 2003Google Scholar
  3. 3.
    Kelso JAS. Dynamic patterns: the self-organisation of brain and behaviour. Cambridge (MA): MIT Press, 1995Google Scholar
  4. 4.
    Davids K, Williams AM, Button C, et al. An integrative modeling approach to the study of intentional movement behaviour. In: Singer RN, Hausenblas H, Jannelle C, editors. Handbook of sport psychology. 2nd ed. New York: John Wiley & Sons, 2001: 144–73Google Scholar
  5. 5.
    van Emmerik REA, van Wegen EEH. On the functional aspects of variability in postural control. Exerc Sport Sci Rev 2002: 30: 177–83PubMedCrossRefGoogle Scholar
  6. 6.
    Thelen E, Smith LB. A dynamic systems approach to the development of cognition and action. Cambridge (MA): MIT Press, 1994Google Scholar
  7. 7.
    Muchisky M, Gershkoff-Cole L, Cole E, et al. The epigenetic landscape revisited: a dynamical interpretation. In: Rovee-Collier C, Lipsitt LP, editors. Advances in infancy research. Norwood (NJ): Ablex, 1996: 121–59Google Scholar
  8. 8.
    Newell KM, Liu Y-T, Mayer-Kress G. Time scales in motor learning and development. Psychol Rev 2001; 108(1): 57–82PubMedCrossRefGoogle Scholar
  9. 9.
    Piek JP. The role of variability in early motor development. Inf Behav Dev 2003; 25: 452–65CrossRefGoogle Scholar
  10. 10.
    Newell KM. Change in movement and skill: learning, retention and transfer. In: Latash ML, Turvey MT, editors. Dexterity and its development. Mahwah (NJ): Erlbaum, 1996: 393–430Google Scholar
  11. 11.
    Newell KM, Corcos DM. Issues in variability and motor control. In: Newell KM, Corcos DM, editors. Variability and motor control. Champaign (IL): Human Kinetics, 1993: 1–12Google Scholar
  12. 12.
    Riley MA, Turvey MT. Variability and determinism in motor behavior. J Motor Behav 2002; 34: 99–125CrossRefGoogle Scholar
  13. 13.
    Williams AM, Davids K, Williams JG. Visual perception and action in sport. London: Routledge, Taylor & Francis, 1999Google Scholar
  14. 14.
    Iberall AS. A field and circuit thermodynamics for integrative physiology. I. Introduction to the general notions. Am J Physiol 1977; 233: R171–80PubMedGoogle Scholar
  15. 15.
    Prigogine I, Stengers I. Order out of chaos. New York: Bantam Books, 1984Google Scholar
  16. 16.
    Kugler PN, Kelso JAS, Turvey MT. On the control and coordination of naturally developing systems. In: Kelso JAS, Clark JE, editors. The development of movement control and coordination. New York: Wiley, 1982: 5–78Google Scholar
  17. 17.
    Kugler PN, Turvey MT. Information, natural law, and the self-assembly of rhythmic movement. Hillsdale (NJ): Lawrence Erlbaum Associates, 1987Google Scholar
  18. 18.
    Haken H. Synergetics: an introduction: non-equilibrium phase transitions and self-organisation in physics, chemistry and biology. Berlin: Springer-Verlag, 1983Google Scholar
  19. 19.
    Kugler PN, Kelso JAS, Turvey MT. On the concept of coordinative structures as dissipative structures: I. Theoretical lines of convergence. In: Stelmach GE, Requin J, editors. Tutorials in motor behavior. Amsterdam: North-Holland, 1980: 3–47CrossRefGoogle Scholar
  20. 20.
    Clark JE. On becoming skillful: patterns and constraints. Res Q Exerc Sport 1995; 66: 173–83PubMedGoogle Scholar
  21. 21.
    Newell KM. Constraints on the development of coordination. In: Wade MG, Whiting HTA, editors. Motor development in children: aspects of coordination and control. Dordrecht: Martinus Nijhoff, 1986: 341–60CrossRefGoogle Scholar
  22. 22.
    Bernstein NA. The coordination and regulation of movements. Oxford: Pergamon Press, 1967Google Scholar
  23. 23.
    Vereijken B, van Emmerik REA, Whiting HTA, et al. Free(z)ing degrees of freedom in skill acquisition. J Motor Behav 1992; 24: 133–42CrossRefGoogle Scholar
  24. 24.
    Newell KM, Vaillancourt D. Dimensional change in motor learning. Hum Mov Sci 2001; 20: 695–715PubMedCrossRefGoogle Scholar
  25. 25.
    Sparrow WA. Energetics of human activity. Champaign (IL): Human Kinetics, 2000Google Scholar
  26. 26.
    Rushton SK, Wann JP. Weighted combination of size and dispartity: a computational model for timing a ball catch. Nat Neurosci 1999; 2: 186–90PubMedCrossRefGoogle Scholar
  27. 27.
    Lewontin R. It ain’t necessarily so: the dream of the human genome and other illusions. London: Granta Books, 2000Google Scholar
  28. 28.
    Turvey MT. Coordination. Am Psychol 1990; 45: 938–53PubMedCrossRefGoogle Scholar
  29. 29.
    Kay B. The dimensionality of movement trajectories and the degrees of freedom problem: a tutorial. Hum Mov Sci 1988; 7: 343–64CrossRefGoogle Scholar
  30. 30.
    Latash ML, Scholz JP, Schöner G. Motor control strategies revealed in the structure of motor variability. Exerc Sport Sci Rev 2002; 30: 26–31PubMedCrossRefGoogle Scholar
  31. 31.
    Gelfand IM, Latash ML. On the problem of adequate language in movement science. Motor Control 1998; 2: 306–13PubMedGoogle Scholar
  32. 32.
    Latash ML. There is no motor redundancy in human movements. There is motor abundance. Motor Control 2000; 4: 259–61PubMedGoogle Scholar
  33. 33.
    Kelso JAS, Schöner G. Self-organisation of coordinative movement patterns. Hum Mov Sci 1988; 7: 27–46CrossRefGoogle Scholar
  34. 34.
    Goldfield EC. Emergent forms: origins and early development of human action and perception. Oxford: Oxford University Press, 1995Google Scholar
  35. 35.
    Hasan Z, Thomas JS. Kinematic redundancy. In: Binder MD, editor. Progress in brain research. Amsterdam: Elsevier, 1999: 379–87Google Scholar
  36. 36.
    Arutyunyan GH, Gurfinkwl VS, Mirskii ML. Investigation of aiming at a target. Biophysics 1968; 14: 1162–7Google Scholar
  37. 37.
    Scholz JP, Schöner G, Latash ML. Identifying the control structures of multijoint coordination during pistol shooting. Exp Brain Res 2000; 135: 382–404PubMedCrossRefGoogle Scholar
  38. 38.
    Brisson TA, Alain C. Should common optimal movement patterns be identified as the criterion to be achieved? J Motor Behav 1996; 28: 211–23CrossRefGoogle Scholar
  39. 39.
    Schöellhorn WI, Bauer HU. Identifying individual movement styles in high performance sports by means of self-organizing Kohonen maps. In: Riehle HJ, Vieten M, editors. Proceedings of the XVI Annual Conference of the International Society for Biomechanics in Sport; 1998 Jul 7–12: Konstanz, Germany. Konstanz: International Society for Biomechanics in Sport. 1998Google Scholar
  40. 40.
    HU, Schöellhorn W. Self-organizing maps for the analysis of complex movement patterns. Neural Processing Lett 1997; 5: 193–9CrossRefGoogle Scholar
  41. 41.
    Davids K, Savelsbergh GJP, Bennett SJ, et al. Interceptive actions in sport: information and movement. London: Routledge, 2002Google Scholar
  42. 42.
    Koenig G, Tamres M, Mann RW. The biomechanics of the shoe-ground interaction in golf. In: Cochran AJ, Farrally FR, editors. Science and golf II: proceedings of the 1994 Scientific Congress of Golf. London: E & FN Spon, 1994: 40–5Google Scholar
  43. 43.
    Button C, Davids K. Interacting intrinsic dynamics and intentionality requires coordination profiling. In: Thomson J, Grealey M, editors. Studies in perception and action. Mahway (NJ): Erlbaum Associates, 1999: 314–8Google Scholar
  44. 44.
    Barnes C, Mercer G. Disability culture: assimilation or inclusion? In: Albrecht G, Seelman KD, Bury M, editors. Handbook of disability studies. London: Sage Publications, 2001: 515–34CrossRefGoogle Scholar
  45. 45.
    Latash ML, Anson JG. What are ‘normal movements’ in atypical populations? Behav Brain Sci 1996; 19: 55–106CrossRefGoogle Scholar
  46. 46.
    Jeka JJ, Lackner JR. The role of haptic cues from rough and slippery surfaces in human postural control. Exp Brain Res 1995; 103: 267–76PubMedCrossRefGoogle Scholar
  47. 47.
    Davids K, Kingsbury D, George K, et al. Interacting constraints and the emergence of postural behavior in ACL-deficient subjects. J Motor Behav 1999; 31: 358–66CrossRefGoogle Scholar
  48. 48.
    Riley MA, Balasubramaniam R, Turvey MT. Recurrence quantification of analysis of postural fluctuations. Gait Posture 1999; 9: 65–78PubMedCrossRefGoogle Scholar
  49. 49.
    Riley MA, Mitra S, Stoffregen TA, et al. Influences of body lean and vision of unperturbed postural sway. Motor Control 1997; 1: 229–46Google Scholar
  50. 50.
    Riley MA, Balasubramaniam R, Mitra S, et al. Visual influences on centre of pressure dynamics in upright posture. Ecological Psychology 1998; 10: 65–91CrossRefGoogle Scholar
  51. 51.
    van Emmerik REA, van Wegen EEH. On variability and stability in human movement. J Appl Biomech 2000; 16: 394–406Google Scholar
  52. 52.
    Lee DN, Aronson E. Visual proprioceptive control of standing in human infants. Percept Psychophys 1974; 15: 529–32CrossRefGoogle Scholar
  53. 53.
    Lee DN, Lishman R. Visual proprioceptive control of stance. J Hum Mov Stud 1975; 1: 87–95Google Scholar
  54. 54.
    Schumway-Cook A, Woollacott MH. The growth of stability: postural control from a developmental perspective. J Motor Behav 1985; 17: 131–47Google Scholar
  55. 55.
    Crichton KJ, Fricker PA, Purdam CR, et al. Injuries to the pelvis and lower limb. In: Bloomfield J, Fricker PA, Fitch KD, editors. Textbook of science and medicine in sport. London: Blackwell Scientific Publications, 1992: 381–419Google Scholar
  56. 56.
    Carter ND, Jenkinson TR, Wilson D, et al. Joint position sense and rehabilitation in the anterior cruciate ligament deficient knee. Br J Sports Med 1997; 31: 209–12PubMedCrossRefGoogle Scholar
  57. 57.
    Barrett DS. Proprioception and function after anterior cruciate reconstruction. J Bone Joint Surg 1991; 73: 833–7Google Scholar
  58. 58.
    Corrigan J, Cashman W, Brady M. Proprioception in the cruciate deficient knee. J Bone Joint Surg 1992; 74B: 247–50Google Scholar
  59. 59.
    Palmieri RM, Ingersoll CD, Stone MB, et al. Center-of-pressure parameters used in the assessment of postural control. J Sports Rehabil 2002; 11: 51–66Google Scholar
  60. 60.
    Le Clair K, Riach C. Postural stability measures: what to measure and for how long. Clin Biomech 1996; 11: 176–8CrossRefGoogle Scholar
  61. 61.
    Ekdahl C, Jarnlo GB, Andersson SI. Standing balance in healthy subjects. Evaluation of a quantitative test battery on a force platform. Scand J Rehabil Med 1989; 21: 187–95PubMedGoogle Scholar
  62. 62.
    Dijkstra TMH, Schöner G, Gielen CCAM. Temporal stability of the action-perception cycle for postural control in a moving visual environment. Exp Brain Res 1994; 97: 477–86PubMedCrossRefGoogle Scholar
  63. 63.
    Dijkstra TMH, Schöner G, Gielen CCAM. Frequency dependence of the action-perception cycle for postural control in a moving visual environment: relative phase dynamics. Biol Cyberns 1994; 71: 489–501CrossRefGoogle Scholar
  64. 64.
    Rogers MW. Disorders of posture, balance and gait in Parkinson’s disease. Clin Geriatr Med 1996; 12: 825–45PubMedGoogle Scholar
  65. 65.
    Horak FB, Nutt JG, Nashner LM. Postural inflexibility in Parkinsonian subjects. J Neurol Sci 1992; 111: 46–58PubMedCrossRefGoogle Scholar
  66. 66.
    Schieppati M, Hugon M, Grasso A, et al. The limits of equilibrium in young and elderly normal subjects and in Parkinsonians. Electroencephalogr Clin Neurophysiol 1994; 93: 286–98PubMedCrossRefGoogle Scholar
  67. 67.
    Riccio GE. Information in movement variability about the qualitative dynamics of posture and orientation. In: Newell KM, Corcos DM, editors. Variability and motor control. Champaign (IL): Human Kinetics, 1993: 317–57Google Scholar
  68. 68.
    Riccio GE, Stoffregen TA. Affordances as constraints on the control of stance. Hum Move Sci 1988; 7: 265–300CrossRefGoogle Scholar
  69. 69.
    Johnston TD, Edwards L. Genes, interactions and the development of behaviour. Psychol Rev 2002; 109: 26–34PubMedCrossRefGoogle Scholar
  70. 70.
    Barsh GS, Farooqi IS, O’Rahilly S. Genetics of body-weight regulation. Nature 2000; 404: 644–51PubMedGoogle Scholar
  71. 71.
    Rankinen T, Perusse L, Gagnon J, et al. Angiotensin-converting enzyme ID polymorphism and fitness phenotype in the HERITAGE Family Study. J Appl Physiol 2000; 88: 1029–35PubMedGoogle Scholar
  72. 72.
    Fox PW, Hershberger SL, Bouchard TJ. Genetic and environmental contributions to the acquisition of a motor skill. Nature 1996; 384: 356–8PubMedCrossRefGoogle Scholar
  73. 73.
    Feitosa MF, Gaskill SE, Rice T, et al. Major gene effects on exercise ventilatory threshold: the HERITAGE Family Study. J Appl Physiol 2002; 93: 1000–6PubMedGoogle Scholar
  74. 74.
    Kevles DJ, Hood L. The code of codes: scientific and social issues in the human genome project. Boston (MA): Harvard University Press, 1992Google Scholar
  75. 75.
    Hopkins WG. Genes and training for athletic performance [online]. Available from URL: [Accessed 2003 Jan 23]
  76. 76.
    Alvarez R, Terrados N, Ortolano R, et al. Genetic variation in the renin-angiotensin system and athletic performance. Eur J Appl Physiol 2000; 82: 117–20PubMedCrossRefGoogle Scholar
  77. 77.
    Montgomery HE, Clarkson P, Barnard M, et al. Angiotensin-converting enzyme gene insertion/deletion polymorphism and response to physical training. Lancet 1999; 353: 541–5PubMedCrossRefGoogle Scholar
  78. 78.
    Myerson S, Hemingway H, Budget R, et al. Human angiotensin I-converting enzyme gene and endurance performance. J Appl Physiol 1999; 87: 1313–6PubMedGoogle Scholar
  79. 79.
    Nazarov IB, Woods DR, Montgomery HE, et al. The angiotensin converting enzyme I/D polymorphism in Russian athletes. Eur J Hum Genet 2001; 9: 797–801PubMedCrossRefGoogle Scholar
  80. 80.
    Taylor RR, Mamotte CDS, Fallon K, et al. Elite athletes and the gene for angiotensin-converting enzyme. J Appl Physiol 1999; 87: 1035–7PubMedGoogle Scholar
  81. 81.
    Williams AG, Rayson MP, Jubb M, et al. Physiology: the ACE gene and muscle performance. Nature 2000; 403: 614PubMedCrossRefGoogle Scholar
  82. 82.
    Woods DR, Humphries SE, Montgomery HE. The ACE I/D polymorphism and human physical performance. Trends Endocrinol Metab 2000; 11: 416–20PubMedCrossRefGoogle Scholar
  83. 83.
    Woods DR, Hickman M, Jamshidi Y, et al. Elite swimmers and the D allele of the ACE I/D polymorphism. Hum Genet 2001; 108: 230–2PubMedCrossRefGoogle Scholar
  84. 84.
    Woods DR, World M, Rayson MP, et al. Endurance enhancement related to the human angiotensin I-converting enzyme I-D polymorphism is not due to differences in the cardiorespiratory response to training. Eur J Appl Physiol 2002; 86: 240–4PubMedCrossRefGoogle Scholar
  85. 85.
    Jones A, Montgomery HE, Woods DR. Human performance: a role for the ACE genotype? Exerc Sport Sci Rev 2002; 30: 184–90PubMedCrossRefGoogle Scholar
  86. 86.
    Sonna LA, Sharp MA, Knapik JJ, et al. Angiotensin-converting enzyme genotype and physical performance during US Army basic training. J Appl Physiol 2001; 91: 1355–63PubMedGoogle Scholar
  87. 87.
    Montgomery HE, Marshall R, Hemingway H, et al. Human gene for physical performance. Nature 1998; 393: 221–2PubMedCrossRefGoogle Scholar
  88. 88.
    Gayagay G, Yu B, Hambly B, et al. Elite endurance athletes and the ACE I allele: the role of genes in athletic performance. Hum Genet 1998; 103: 48–50PubMedCrossRefGoogle Scholar
  89. 89.
    Folland J, Leach B, Little T, et al. Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload. Exp Physiol 2000; 85: 575–9PubMedCrossRefGoogle Scholar
  90. 90.
    Hagberg JM, Ferrell RE, McCole SD, et al. V̇O2max is associated with ACE genotype in postmenopausal women. J Appl Physiol 1998; 85: 1842–6PubMedGoogle Scholar
  91. 91.
    Bouchard C, Daw EW, Rice T, et al. Familial resemblance for V̇O2max in the sendentary state: the HERITAGE family study. Med Sci Sport Exerc 1998; 30: 252–8CrossRefGoogle Scholar
  92. 92.
    Jones RS. Almost like a whale: the origin of the species updated. London: Anchor Press, 1999Google Scholar
  93. 93.
    van Geert P. Dynamic systems of development: change between complexity and chaos. New York: Harvester Wheatsheaf, 1994Google Scholar
  94. 94.
    Oyama S. The ontogeny of information: developmental systems and evolution. 2nd ed. Durham (NC): Duke University Press, 2000Google Scholar
  95. 95.
    Yates FE. Self-organizing systems. In: Boyd C, Noble D, editors. Logic of life. Oxford: Oxford University Press, 1993: 189–218Google Scholar

Copyright information

© Adis Data Information BV 2003

Authors and Affiliations

  • Keith Davids
    • 1
    Email author
  • Paul Glazier
    • 2
  • Duarte Araújo
    • 3
  • Roger Bartlett
    • 4
  1. 1.School of Physical EducationUniversity of OtagoDunedinNew Zealand
  2. 2.School of Sport, P.E. and RecreationUniversity of Wales InstituteCardiffWales
  3. 3.Faculty of Human KineticsTechnical University of LisbonLisbonPortugal
  4. 4.Centre for Sport and Exercise ScienceSheffield Hallam UniversitySheffieldEngland

Personalised recommendations