Sex Difference in Running Stability Analyzed Based on a Whole-Body Movement: A Pilot Study

Effects of Different Wearable Resistance Placements on Running Stability

Stability during running has been recognized as a crucial factor contributing to running performance. This study aimed to investigate the effects of wearable equipment containing external loads on different body parts on running stability. Fifteen recreational male runners (20.27 ± 1.23 years, age range 19-22 years) participated in five treadmill running conditions, including running without loads and running with loads equivalent to 10% of individual body weight placed on four different body positions: forearms, lower legs, trunk, and a combination of all three (forearms, lower legs, and trunk). A tri-axial accelerometer-based smartphone sensor was attached to the participants' lumbar spine (L5) to record body accelerations. The largest Lyapunov exponent (LyE) was applied to individual acceleration data as a measure of local dynamic stability, where higher LyE values suggest lower stability. The effects of load distribution appear in the mediolateral (ML) direction. Specifically, running with loads on the lower legs resulted in a lower LyE_ML value compared to running without loads (p = 0.001) and running with loads on the forearms (p < 0.001), trunk (p = 0.001), and combined segments (p = 0.005). These findings suggest that running with loads on the lower legs enhances side-to-side local dynamic stability, providing valuable insights for training.

The application of the Lyapunov Exponent to analyse human performance: A systematic review

Variability is a normal component of human movement, allowing one to adapt to environmental perturbations. It can be analysed from linear or non-linear perspectives. The Lyapunov Exponent (LyE) is a commonly used non-linear technique, which quantifies local dynamic stability. It has been applied primarily to walking gait and appears to be limited application in other movements. Therefore, this systematic review aims to summarise research methodologies applying the LyE to movements, excluding walking gait. Four databases were searched using keywords related to movement variability, dynamic stability, LyE and divergence exponent. Articles written in English, using the LyE to analyse movements, excluding walking gait were included for analysis. 31 papers were included for data extraction. Quality appraisal was conducted and information related to the movement, data capture method, data type, apparatus, sampling rate, body segment/joint, number of strides/steps, state space reconstruction, algorithm, filtering, surrogation and time normalisation were extracted. LyE values were reported in supplementary materials (Appendix 2). Running was the most prevalent non-walking gait movement assessed. Methodologies to calculate the LyE differed in various aspects resulting in different LyE values being generated. Additionally, test-retest reliability, was only conducted in one study, which should be addressed in future.

Age and Visual Contribution Effects on Postural Control Assessed by Principal Component Analysis of Kinematic Marker Data

Postural control, the ability to control the body's position in space, is considered a critical aspect of health outcomes. This current study aimed to investigate the effects of age and visual contribution on postural control. To this end, principal component analysis (PCA) was applied to extract movement components/synergies (i.e., principal movements, PMs) from kinematic marker data of bipedal balancing on stable and unstable surfaces with eyes closed and open, pooled from 17 older adults (67.8 ± 6.6 years) and 17 young adults (26.6 ± 3.3 years), one PCA-analysis for each surface condition. Then, three PCA-based variables were computed for each PM: the relative explained variance of PM-position (PP_rVAR) and of PM-acceleration (PA_rVAR) for measuring the composition of postural movements and of postural accelerations, respectively, and the root mean square of PM-acceleration (PA_RMS) for measuring the magnitude of neuromuscular control. The results show the age and visual contribution effects observed in PM₁, resembling the anteroposterior ankle sway in both surface conditions. Specifically, only the greater PA₁_rVAR and PA₁_RMS are observed in older adults (p ≤ 0.004) and in closed-eye conditions (p < 0.001), reflecting their greater need for neuromuscular control of PM₁ than in young adults and in open-eye conditions.

Walking Stability and Risk of Falls

Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic data of 43 healthy older adults (69.8 ± 8.5 years, 36 females) into a set of principal movements (PMs), showing different movement components/synergies working together to accomplish the walking task goal. Then, the largest Lyapunov exponent (LyE) was applied to the first five PMs as a measure of stability, with the interpretation that the higher the LyE, the lower the stability of individual movement components. Next, the fall risk was determined using two functional motor tests-a Short Physical Performance Battery (SPPB) and a Gait Subscale of Performance-Oriented Mobility Assessment (POMA-G)-of which the higher the test score, the better the performance. The main results show that SPPB and POMA-G scores negatively correlate with the LyE seen in specific PMs (p ≤ 0.009), indicating that increasing walking instability increases the fall risk. The current findings suggest that inherent walking instability should be considered when assessing and training the lower limbs to reduce the risk of falling.

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called "principal movements" (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM₁, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM₃, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.