Examination of stride-to-stride independence of selected lower extremity kinematic and temporal variables during treadmill walking

• Published in: Gait & posture Vol. 50; no. NA; pp. 212 - 216
• Main Authors: Yang, Hyung Suk, Atkins, Lee T., James, C. Roger
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.10.2016
• Subjects: Adult; Ankle Joint; Biomechanical Phenomena; Biomechanics; Female; Gait - physiology; Hip Joint; Humans; Knee Joint; Lower Extremity - physiology; Male; Middle Aged; Orthopedics; Single-subject analysis; Trial independence; Walking - physiology; Biomechanics; Single-subject analysis; Trial independence
• ISSN: 0966-6362; 1879-2219; 1879-2219
• DOI: 10.1016/j.gaitpost.2016.09.010

•Short-term dependence was not found in maximum angles, ROM, or stride duration.•Different methods of sampling gait variables may not affect trial dependence.•Data from an individual can fulfill the independence requirement for statistics. The purpose of this study was to examine the nature of the intra-subject independence among strides during treadmill walking. We investigated the strength of the relationships among strides sampled in different ways from a population of observed strides. Eighteen asymptomatic subjects walked on a treadmill at 1.4±0.1m/s. Maximum angles and ranges of motion from the ankle, knee and hip joints, as well as stride duration were obtained and autocorrelation coefficients (AC) for 3 lags were calculated among 12 strides sampled consecutively (CS), in order but non-adjacently (NA), and randomly (RA). Ninety-nine percent of AC values were within Bartlett's 95% confidence interval limits and thus the strides were not considered significantly autocorrelated. The results support the hypothesis that strides obtained from an individual walking on a treadmill can be statistically independent. This supports the theoretical assumption that in some circumstances humans can be modeled as random sample generators due to inherent movement variability. The ability to assess statistically clinical intervention provides objective rigor for evaluating rehabilitation outcomes.