Abstract
The fractal analysis of stride-to-stride fluctuations in walking has become an integral part of human gait research. Fractal analysis of stride time intervals can provide insights into locomotor function and dysfunction, but its application requires a large number of strides, which can be difficult to collect from people with movement disorders such as Parkinson’s disease. It has recently been suggested that “stitching” together short gait trials to create a longer time series could be a solution. The objective of this study was to determine if scaling exponents from “stitched” stride time series were similar to those from continuous, longer stride time series. Fifteen young adults, fourteen older adults, and thirteen people with Parkinson’s disease walked around an indoor track in three blocks: one time 15 min, five times 3 min, and thirty times 30 s. Stride time intervals were determined from gait events recorded with instrumented insoles, and the detrended fluctuation analysis was applied to each stride time series of 512 strides. There was no statistically significant difference between scaling exponents in the three blocks, but intra-class correlation revealed very low between-blocks reliability of scaling exponents. This result challenges the premise that the stitching procedure could provide reliable information about gait dynamics, as it suggests that fractal analysis of stitched time series does not capture the same dynamics as gait recorded continuously. The stitching procedure cannot be considered as a valid alternative to the collection of continuous, long trials. Further studies are recommended to determine if the application of fractal analysis is limited by its own methodological considerations (i.e., long time series), or if other solutions exists to obtain reliable scaling exponents in populations with movement disorders.
Highlights
Consecutive gait cycles during steady-state walking are not exactly identical
The value of the detrended fluctuation analysis (DFA) scaling exponent α indicates the nature of serial correlations: for stationary time series, α > 0.5 indicates statistical persistence, α < 0.5 indicates anti-persistence, and α = 0.5 indicates the absence of serial correlations
Post hoc analyses did not detect any statistically significant differences, stride time coefficient of variation (CV) were greater in the Parkinson’s disease (PD) group compared to the healthy elderly (HE) group and to the healthy young (HY) group
Summary
Consecutive gait cycles during steady-state walking are not exactly identical. Slight changes from one step to the create stride-to-stride fluctuations over time, i.e., gait variability. Gait variability refers to the magnitude of fluctuations, assessed by measures of dispersion around the central tendency (e.g., standard deviation or CV), and to the serial correlations between consecutive strides (i.e., temporal ordering of fluctuations) (Pierrynowski et al, 2005; Damouras et al, 2010; Dingwell and Cusumano, 2010). Serial correlations can be assessed using the detrended fluctuation analysis (DFA; Peng et al, 1993), which is often preferred to other methods due to its robustness to non-stationarities and its greater accuracy for relatively short time series, e.g., a few hundred cycles (Chen et al, 2002; Delignieres et al, 2006; Almurad and Delignières, 2016; Kuznetsov and Rhea, 2017).
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