18Background: Previous research has demonstrated clear associations between specific

Please cite this article as: Mason DL, Preece SJ, Bramah CA, Herrington LC, Reproducibility of kinematic measures of the thoracic spine, lumbar spine and pelvis during fast running, Gait and Posture (2014), http://dx

Background: Aberrant frontal-plane hip and pelvis kinematics have been frequently observed in runners with patellofemoral pain (PFP). Gait retaining interventions have been shown to improve running kinematics and may therefore be beneficial in runners with PFP. Purpose: To investigate whether a 10% increase in the running step rate influences frontal-plane kinematics of the hip and pelvis as well as clinical outcomes in runners with PFP. Study Design: Case series; Level of evidence, 4. Methods: Runners with PFP underwent a 3-dimensional gait analysis to confirm the presence of aberrant frontal-plane hip and/or pelvis kinematics at baseline. A total of 12 participants with frontal-plane hip and/or pelvis kinematics 1 standard deviation above a reference database were invited to undergo the gait retraining intervention. Running kinematics along with clinical outcomes of pain and functional outcomes were recorded at baseline, 4 weeks after retraining, and 3 months. Gait retraining consisted of a single session where step rate was increased by 10% using an audible metronome. Participants were asked to continue their normal running while self-monitoring their step rate using a global positioning system smartwatch and audible metronome. Results: After gait retraining, significant improvements in running kinematics and clinical outcomes were observed at 4-week and 3-month follow-up. Repeated-measures analysis of variance with post hoc Bonferroni correction ( P < .016) showed significant reductions in peak contralateral pelvic drop (mean difference [MD], 3.12° [95% CI, 1.88°-4.37°]), hip adduction (MD, 3.99° [95% CI, 2.01°-5.96°]), and knee flexion (MD, 4.09° [95% CI, 0.04°-8.15°]) as well as significant increases in self-reported weekly running volume (MD, 13.78 km [95% CI, 4.62-22.93 km]) and longest run pain-free (MD, 6.84 km [95% CI, 3.05-10.62 km]). Friedman test with a post hoc Wilcoxon signed-rank test showed significant improvements on a numerical rating scale for worst pain in the past week and the Lower Extremity Functional Scale. Conclusion: A single session of gait retraining using a 10% increase in step rate resulted in significant improvements in running kinematics, pain, and function in runners with PFP. These improvements were maintained at 3-month follow-up. It is important to assess for aberrant running kinematics at baseline to ensure that gait interventions are targeted appropriately. Registration: NCT03067545 (ClinicalTrials.gov identifier)

20Previous research into running has demonstrated consistent patterns in pelvic, lumbar and 21 thoracic motions between different human runners. However, to date, there has been limited 22 attempt to explain why observed coordination patterns emerge and how they may relate to 23 centre of mass (CoM) motion. In this study, kinematic data were collected from the thorax, 24 lumbar spine, pelvis and lower limbs during over ground running in n=28 participants. These

The gait cycle is frequently divided into two distinct phases, stance and swing, which can be accurately determined from ground reaction force data. In the absence of such data, kinematic algorithms can be used to estimate footstrike and toe-off. The performance of previously published algorithms is not consistent between studies. Furthermore, previous algorithms have not been tested at higher running speeds nor used to estimate ground contact times. Therefore the purpose of this study was to both develop a new, custom-designed, event detection algorithm and compare its performance with four previously tested algorithms at higher running speeds. Kinematic and force data were collected on twenty runners during overground running at 5.6m/s. The five algorithms were then implemented and estimated times for footstrike, toe-off and contact time were compared to ground reaction force data. There were large differences in the performance of each algorithm. The custom-designed algorithm provided the most accurate estimation of footstrike (True Error 1.2 ± 17.1 ms) and contact time (True Error 3.5 ± 18.2 ms). Compared to the other tested algorithms, the custom-designed algorithm provided an accurate estimation of footstrike and toe-off across different footstrike patterns. The custom-designed algorithm provides a simple but effective method to accurately estimate footstrike, toe-off and contact time from kinematic data.