Padulo, J.; Rampichini, S.; Borrelli, M.; Buono, D.M.; Doria, C.; Esposito, F. Gait Variability at Different Walking Speeds. J. Funct. Morphol. Kinesiol.2023, 8, 158.
Padulo, J.; Rampichini, S.; Borrelli, M.; Buono, D.M.; Doria, C.; Esposito, F. Gait Variability at Different Walking Speeds. J. Funct. Morphol. Kinesiol. 2023, 8, 158.
Padulo, J.; Rampichini, S.; Borrelli, M.; Buono, D.M.; Doria, C.; Esposito, F. Gait Variability at Different Walking Speeds. J. Funct. Morphol. Kinesiol.2023, 8, 158.
Padulo, J.; Rampichini, S.; Borrelli, M.; Buono, D.M.; Doria, C.; Esposito, F. Gait Variability at Different Walking Speeds. J. Funct. Morphol. Kinesiol. 2023, 8, 158.
Abstract
Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body seg-mental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, has not been clarified whether GV is associated with walking speed to exploit effective better coordination level. Fourteen male stu-dents (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, 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 Apple Watch S. 7 (AppleTM), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 minutes each one, at SS-20% / SS+20% of SS, with 5 minutes of passive recovery in-between. Heart rate (HR) was monitored beat-to beat and nor-malized as HR%MAX, while the rate of perceived exertion (RPE) (CR-10) 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 with higher walking speed (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 un-changed (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), 3.36 ± 1.09 % (high variability) at 5.94 ± 0.70 km·h-1 (P = 0.001). These results indi-cate that while the metabolic demand and kinematics variables changing 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.
Keywords
human locomotion; symmetry; gait analysis; metabolic demand; kinematic analysis
Subject
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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