Differences in reproducibility of gait variability and fractal dynamics according to walking duration

Gait Variability at Different Walking Speeds

Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body segmental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, it has not been clarified whether GV is associated with walking speed, a clarification needed to exploit effective better bilateral coordination level. For this aim, fourteen male students (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, and body height 1.78 ± 0.05 m) took part in this study. After three days of walking 1 km each day at a self-selected speed (SS) on asphalt with an Apple Watch S. 7 (AppleTM, Cupertino, CA, USA), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 min at each one, SS - 20%/SS + 20%/ SS, with 5 min of passive recovery in-between. Heart rate (HR) was monitored and normalized as %HRmax, while the rate of perceived exertion (RPE) (CR-10 scale) was asked after each trial. Kinematic analysis was performed, assessing the Contact Time (CT), Swing Time (ST), Stride Length (SL), Stride Cycle (SC), and Gait Variability as Phase Coordination Index (PCI). RPE and HR increased as the walking speed increased (p = 0.005 and p = 0.035, respectively). CT and SC decreased as the speed increased (p = 0.0001 and p = 0.013, respectively), while ST remained unchanged (p = 0.277). SL increased with higher walking speed (p = 0.0001). Conversely, PCI was 3.81 ± 0.88% (high variability) at 3.96 ± 0.47 km·h-1, 2.64 ± 0.75% (low variability) at SS (4.94 ± 0.58 km·h-1), and 3.36 ± 1.09% (high variability) at 5.94 ± 0.70 km·h-1 (p = 0.001). These results indicate that while the metabolic demand and kinematics variables change linearly with increasing speed, the most effective GV was observed at SS. Therefore, SS could be a new methodological approach to choose the individual walking speed, normalize the speed intensity, and avoid a gait pattern alteration.

Comparing adaptive fractal and detrended fluctuation analyses of stride time variability: Tests of equivalence

BACKGROUND

Fractal analyses quantify self-similarities in stride-to-stride fluctuations over different time scales. Fractal exponents can be measured with adaptive fractal analysis (AFA) or detrended fluctuation analysis (DFA), though measurements obtained with the algorithms have not been directly compared.

RESEARCH QUESTION

Are stride time fractal exponents measured with AFA and DFA algorithms equivalent?

METHODS

Data from 50 participants with Parkinson's Disease (n = 15), age-similar healthy adults (n = 15) and healthy young adults (n = 20) were analyzed in this cross-sectional, observational study. Participants completed 6-min walks at self-selected speeds overground on a straight walkway and on a treadmill. Stride times were measured with inertial measurement units. Fractal exponents in stride time data were processed using AFA and DFA algorithms and compared with two one-sided tests of equivalence. Mixed ANOVAs were used to compare exponents between groups and conditions.

RESULTS

Fractal exponents computed with AFA and DFA were equivalent neither in the overground (0.796 & 0.830, respectively, p = .587) nor treadmill conditions (0.806 & 0.882, respectively, p = .122). Fractal exponents measured with DFA were higher than when measured with AFA. Standard errors were 22% lower when measured with AFA. Additionally, a group × condition interaction was statistically significant when fractal exponents were processed with the AFA algorithm (F(2,47) = 11.696, p < .001), whereas the group × condition interaction was not statistically significant when DFA exponents were compared (F(2, 47) = 2.144, p = .129).

SIGNIFICANCE

AFA and DFA do not produce equivalent estimates of the fractal exponent α in stride time dynamics. Estimates of the fractal exponent α obtained with AFA or DFA algorithms therefore should not be used interchangeably. Standard errors were lower when derived with AFA. Fractal exponents calculated with AFA may be more sensitive to conditions that influence stride time fractal dynamics than are measures calculated with DFA.

Relationship between gait complexity and pain attention in chronic low back pain

ABSTRACT

Clinical models of chronic low back pain (cLBP) highlight the role of excessive attention to pain and kinesiophobia on the origin of disability. At the motor control level, various mechanisms are involved in the impairments observed in patients with cLBP. We aimed to assess the role of maladaptative attentional behaviors by using a complex systems approach and a visual display as a distraction during walking. Sixteen patients with cLBP with no previous surgery or significant leg pain and 16 healthy matched controls were included. Patients walked on a treadmill at preferred walking speed with and without distraction. Stride time (ST) fractal complexity was assessed using detrended fluctuation analysis. A two-way analysis of variance with repeated measures on distraction was performed on fractal exponents. We found a significant group × distraction interaction effect on fractal complexity of ST series (F(1,30) = 9.972, P = 0.004). Post hoc analysis showed that, without distraction, patients with cLBP had significantly lower ST complexity than controls, but when distracted, they regained gait complexity, recovering the level of controls. Our results suggest that excessive attention to pain causes loss of complexity and adaptability in cLBP and explain alterations of motor control with pain. Fractal analysis seems to be a promising method to explore movement variability and individual adaptability in musculoskeletal disorders.