Abstract
INTRODUCTION: Increased stride-to-stride time variability has been observed in dual task situations and among elderly fallers [1]. Variability is therefore often regarded as an indicator of poor gait performance. However, some degree of movement variability is perfectly normal. From a synergistic point of view elemental and performance variables may represent good and bad components of variability [2].
In this study we propose that the gait pattern can be seen as an on-going movement synergy in which each stride is corrected by the next stride (elemental variables) to ensure a steady gait (performance variable). AIM: The aim of this study was to evaluate stride time synergy and to identify good and bad stride variability in relation to walking during dual task.
METHODS: Thirteen healthy young participants walked along a 2x5 meter figure-of-eight track at a self-selected comfortable speed. Stride time was measured by heel contacts and the stride-to-stride difference (s-t-s) was evaluated. Each s-t-s was plotted against the following s-t-s in a coordinate system. Variability was evaluated in diagonal directions in the plot; i.e. good variance was evaluated with respect to a straight line with a positive slope going through the mean of the strides, and bad variance with respect to a similar line with a negative slope. The general variance coefficient (CV%) was also computed. The effect of introducing a concurrent cognitive task (dual task: counting backwards in sequences of 7) was evaluated.
RESULTS: The variance coefficient (CV%) increased significantly from 1.59 to 1.90 (p<0.05) when shifting from single to dual task. With respect to the synergy approach, the good/bad variance ratio during single task was: 2.53 (CI95%: 2.07-3.00). When shifting to dual task the good/bad ratio was 2.28 (CI95%: 1.99-2.57) (p=0.21).
CONCLUSION: The good/bad variability in the stride-to-stride time differences was larger than 1.0 indicating a synergy pattern. Gait synergy was found fairly robust and complementary to CV in presence of an additional cognitive load. These preliminary findings suggest that a synergy perspective on gait variability may provide a new approach to gait assessment.
References
[1] Hausdorff JM. Gait variability: methods, modelling and meaning. J Neuroengineering Rehabil 2005 Jul 20;2:19.
[2] Latash ML. Synergy. New York: Oxford University Press; 2008.
In this study we propose that the gait pattern can be seen as an on-going movement synergy in which each stride is corrected by the next stride (elemental variables) to ensure a steady gait (performance variable). AIM: The aim of this study was to evaluate stride time synergy and to identify good and bad stride variability in relation to walking during dual task.
METHODS: Thirteen healthy young participants walked along a 2x5 meter figure-of-eight track at a self-selected comfortable speed. Stride time was measured by heel contacts and the stride-to-stride difference (s-t-s) was evaluated. Each s-t-s was plotted against the following s-t-s in a coordinate system. Variability was evaluated in diagonal directions in the plot; i.e. good variance was evaluated with respect to a straight line with a positive slope going through the mean of the strides, and bad variance with respect to a similar line with a negative slope. The general variance coefficient (CV%) was also computed. The effect of introducing a concurrent cognitive task (dual task: counting backwards in sequences of 7) was evaluated.
RESULTS: The variance coefficient (CV%) increased significantly from 1.59 to 1.90 (p<0.05) when shifting from single to dual task. With respect to the synergy approach, the good/bad variance ratio during single task was: 2.53 (CI95%: 2.07-3.00). When shifting to dual task the good/bad ratio was 2.28 (CI95%: 1.99-2.57) (p=0.21).
CONCLUSION: The good/bad variability in the stride-to-stride time differences was larger than 1.0 indicating a synergy pattern. Gait synergy was found fairly robust and complementary to CV in presence of an additional cognitive load. These preliminary findings suggest that a synergy perspective on gait variability may provide a new approach to gait assessment.
References
[1] Hausdorff JM. Gait variability: methods, modelling and meaning. J Neuroengineering Rehabil 2005 Jul 20;2:19.
[2] Latash ML. Synergy. New York: Oxford University Press; 2008.
| Original language | English |
|---|---|
| Title of host publication | Conference proceedings. The International Society of Electrophysiology and Kinesiology. ISEK Congress 2012 |
| Place of Publication | Australien |
| Publication date | 2012 |
| Publication status | Published - 2012 |
| Event | The International Society of Electrophysiology and Kinesiology - Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 18 Jul 2012 → 21 Jul 2012 Conference number: XIX |
Conference
| Conference | The International Society of Electrophysiology and Kinesiology |
|---|---|
| Number | XIX |
| Location | Brisbane Convention & Exhibition Centre |
| Country/Territory | Australia |
| City | Brisbane |
| Period | 18/07/12 → 21/07/12 |