The goal of this study was to investigate whole-body kinematic adaptations when running on an unstable, irregular, and compliant surface in comparison to running on asphalt. We hypothesised that the gait pattern (H1) and its stride-to-stride variability (H2) would be affected by the unstable surface but that variability related to some movement features would be reduced over multiple testing days indicative of gait optimisation (H3). Fifteen runners ran on a woodchip and asphalt track on five testing days while their whole-body movements were captured using inertial motion capture and examined using joint angle and principal component analysis. Joint angles and stride-to-stride variability in eight principal running movements were subjected to surface by day analyses of variance. The woodchip track compared to asphalt resulted in (H1) a more crouched gait pattern including more leg flexion and forward trunk lean and (H2) higher stride-to-stride variability in most investigated principal running movements. However, (H3) stride-to-stride variability did not systematically change over testing days. Running on an unstable, irregular, and more compliant surface leads to the adoption a gait pattern and control strategy that are more robust against disturbances caused by the surface but may pose certain risks for overuse injury in trail runners.
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http://dx.doi.org/10.1080/14763141.2023.2222022 | DOI Listing |
J Biomech
February 2025
School of Human Kinetics, University of Ottawa, Ottawa, Canada. Electronic address:
Stride-to-stride fluctuations are natural in gait. These fluctuations are marked by inter-individual variability, suggesting that different fluctuation strategies (i.e.
View Article and Find Full Text PDFGait Posture
December 2024
School of Physical & Health Education, Nipissing University, 100 College Drive, Box 5002, North Bay, Ontario P1B 8L7, Canada. Electronic address:
Background: Spatiotemporal and kinematic variables during gait undergo characteristic changes with aging. However, the relationships between these domains, and how these change with aging, have not been extensively investigated.
Research Question: How does age affect relationships between spatiotemporal and joint/segment range-of-motion variables during treadmill gait?
Methods: In this cross-sectional study, a motion capture system tracked 60 participants (20-80 years old), walking at self-selected and slow speeds on a treadmill.
Sensors (Basel)
November 2024
School of Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, Canada.
Stride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This study investigated age-related differences in linear and nonlinear gait measures estimated from a smartphone accelerometer (SPAcc) in an unconstrained, free-living environment.
View Article and Find Full Text PDFJ Exp Zool A Ecol Integr Physiol
October 2024
School of Health Sciences, Cleveland State University, Cleveland, Ohio, USA.
Quadrupedal animals traveling on arboreal supports change aspects of locomotion to avoid slipping and falls. This study compares locomotor biomechanics in two small mammals: first, the gray short-tailed opossum (Monodelphis domestica) predominantly trots, which is a symmetrical gait. The second species, the Siberian chipmunk (Tamias sibiricus), primarily bounds or half-bounds.
View Article and Find Full Text PDFSensors (Basel)
October 2024
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, 81377 Munich, Germany.
Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnoses of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns.
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