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P-10 Effect of treadmill on gait variability in healthy young and old subjects
  1. B De la Cruz1,
  2. E Sarabia2,
  3. A Sánchez-Sixto3,
  4. P Floría3,
  5. J Naranjo3,
  6. FJ Berral3
  1. 1Department of Physiotherapy, Universidad de Sevilla, Seville, SPAIN
  2. 2University of CEU Cardenal Spínola, Seville, SPAIN
  3. 3Department of Sport and Computing, Universidad Pablo de Olavide, Seville, SPAIN

Abstract

Objectives Human gait is considered as a complex system and it is known that the mechanism is fundamentally non linear. Gait dynamics are governed by deterministic chaos and appropriate mathematical tools are required for analysis. Using tools from nonlinear dynamics, these studies demonstrated that this complexity is responsible for the flexible adaptations to everyday stresses placed on the human body during gait. They also established a link between the alterations of this complexity and the unhealthy states in gait. In gait, disease (e.g., idiopathic fallers) or unhealthy (e.g., physical inactivity) states may manifest with increased or decreased complexity of lower extremities walking behaviour as it was found in elderly fallers compared with healthy controls and in inactive older adults compared to those that are more active. Unhealthy state is also associated with a loss of self-similarity and long-range dependence.

So, the aim of this study was to analyse the differences in gait variability (stride interval time series) in two different conditions (spontaneous walking vs. treadmill walking) in healthy young and healthy old subjects.

Methods The stride interval time series derived from ten healthy joung males and ten healthy old males were studied in two experimental conditions: a) walking on the ground at their self-determined usual paces around an open circle circuit for 10 minutes; and b) walking on a treadmill for 10 minutes at the same pace as in situation a. A capture device enabled stride interval time series to be collected directly and stored during walking, using a simple electronic push-button mounted in the heel of the dominant foot support of an insole placed in the running shoe. The stride standard deviation (SSD), coefficient of variance (CV) and Sample Entropy (SampEn) were calculated. Significance level p < 0.05.

Results Table 1 shows mean and standard deviation for SSD, CV and SampEn for each group walking on the ground and walking on a treadmill. SD showed significant differences (p = 0.03) if we compared between ground with treatmill in young group, and SampEn and CV showed significant differences (p = 0.002 and p = 0.01) if we compared between young and old group in treadmill, respectively.

Abstract P-10 Table 1

Mean (SD) of SSD, CV and SampEn for each group walking on the ground and walking on a treadmill

Conclusion The concepts of variability and complexity, and the nonlinear tools used to measure these concepts open new vistas for research in gait dysfunction of all types. Besides, the recent modelling effort of the human locomotion provided the groundwork to better understand how motor control strategies and the mechanical constructs of the locomotion system influence the chaotic properties (complexity) of the gait. Our demostrated that treadmill induced a loss of variability in the step time series in young subjects and that older people had higher gait varibility during treadmill walking than young subjects.

Acknowledgment The authors would like to express appreciation for the support of the sponsors [Project Number = TEC2013-48439-C4-4-R]. This study has been conducted within the framework of the Research Project, which is part of the State Research, Development and Innovation Programme for the Challenges of Society and funded by the Spanish Ministry of Economics and Competitiveness.

References

  1. Hausdorff JM, Peng C-K, Ladin Z, Wei J, Goldberger AL. (1995) Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. Journal of Applied Physiology 1995;78:349–58.

  2. Buzzi UH, Stergiou N, Kurz MJ, Hageman PA, Heidel J. Nonlinear dynamics indicates ageing affects variability during gait. Clin Biomech 2003;18(5):435–43.

  3. Dingwell JB, Cusumano JP. Nonlinear time series analysis of normal and pathological human walking. Chaos 2000;10:848–63.

  4. Stergiou N, Buzzi UH, Kurz MJ, Heidel J. Nonlinear Tools in Human Movement. In: Stergiou N, editor. Innovative Analyses of Human Movement. Champaign, IL: Human Kinetics; 2004. p. 63–90.

  • Gait Variability
  • Nonlinear Dynamics
  • Treadmill.

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