Gait Dynamics in Parkinson’s Disease: Short Gait Trials “Stitched” Together Provide Different Fractal Fluctuations Compared to Longer Trials

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.

Assessing the Temporal Organization of Walking Variability: A Systematic Review and Consensus Guidelines on Detrended Fluctuation Analysis

“…Another approach that has been recently tested to mitigate the issue of short data series (but also used traditionally while handling outliers; Hausdorff et al, 1997;Herman et al, 2005;Gow et al, 2017) involves concatenating discontinuous sets of time series (Orter et al, 2019). It has been shown that, for positively correlated signals (1.5 > α > 0.5), such concatenation does not affect the scaling behavior on average (Chen et al, 2002;Gow et al, 2017), but the scaling exponent itself might not be consistent (Kirchner et al, 2014;Marmelat et al, 2018). Bartsch et al (2007) proposed a modified DFA method to obtain reliable scaling exponent α values in short time series, but such approaches require further investigation, especially in light of a new study showing that scaling exponent α values from shorter walking trials (e.g., 3 min) do not sufficiently capture the fluctuation dynamics of longer time series (Marmelat and Meidinger, 2019).…”

“…This input parameter "n" must be fixed and is generally limited to a range of window sizes, within which the linear relationship between F(n) and n is most stable. Previous studies examining this aspect have recommended the use of 16 to N/9 window size (Damouras et al, 2010), but other sizes have also been utilized (Franz et al, 2015;Gow et al, 2017;Marmelat et al, 2018). Another aspect-the sampling frequency of the 3D kinematics data-can also have a considerable impact on scaling exponent α (a lower sampling frequency may reduce the strength of long-range correlations).…”

“…Detrended fluctuation analysis (DFA) allows a characterization of the nature of long-range correlations in the stride interval of walking (Pierrynowski et al, 2005;Bashan et al, 2008;Damouras et al, 2010;Choi et al, 2015;Marmelat et al, 2018). The scaling exponent α calculated using this method is robust against non-stationarity, artifacts, and related missing data (Chen et al, 2002;Ma et al, 2010).…”

“…As a result, a scaling exponent α of ∼1 has traditionally been interpreted to represent healthy movement patterns (Hausdorff, 2007(Hausdorff, , 2009Gow et al, 2017). Movement disorders due to aging and neurological diseases, for example, Parkinson disease [PD (Frenkel-Toledo et al, 2005;Hausdorff, 2009;Marmelat et al, 2018), but also Huntington's disease (Hausdorff et al, 1997), as well as cognitive decline (Lamoth et al, 2011)], are associated with a loss of persistence (Damouras et al, 2010;Stergiou and Decker, 2011;Ota et al, 2014;Li et al, 2019) and hence lower scaling exponent α values (nearer to 0.5). The implication is that neural pathologies might adversely influence mechanisms that regulate the nature of long-range correlations in walking.…”

“…In this respect, a clear characterization of the relative values of α between populations with differing neurological statuses is needed. In addition, the interpretation of the scaling exponent α in human movement research remains ambiguous as considerable diversity in α values has been reported across similar population groups (Hausdorff et al, 1997;Kobsar et al, 2014;Kosse et al, 2016;Dotov et al, 2017;Marmelat et al, 2018). Importantly, it remains unknown whether the implementation of DFA could be critical in driving such inconsistency.…”