Between-day reliability and minimum detectable change of the Conventional Gait Model 2 and Plug-in Gait Model during running

Classification of the foot kinematics during gait and the characteristics of the knee and hip kinematics in individuals with pronated foot

Overuse injuries are often caused by pronated foot and the associated abnormal lower-extremity kinematics during dynamic activities. Various patterns of foot kinematics are observed among individuals with pronated feet during dynamic activities, resulting in different dynamic kinematics of the proximal joint. This study aimed to identify the foot kinematic patterns during gait among individuals with pronated feet and evaluate the relationship between these foot kinematic patterns and the hip and knee kinematics. A three-dimensional motion capture system was used to collect data regarding the foot, knee, and hip kinematics during the stance phase of gait of 42 individuals with pronated feet. A hierarchical cluster analysis method was used to identify the optimal number of clusters based on the foot kinematics, including navicular height (NH) at initial contact and dynamic navicular drop (DND). The differences in the cluster and demographic variables were examined. One-dimensional statistical parametric mapping was used to evaluate the differences in the time histories of the NH, knee, and hip kinematics during the stance phase. Three subgroups were identified on the basis of the NH and DND: Cluster 1, moderate NH at initial contact and larger DND; Cluster 2, highest NH at initial contact and smaller DND; and Cluster 3, lowest NH at initial contact and smaller DND. The hip adduction angle of Cluster 1 was significantly higher than that of Cluster 3 from the 0% to 51% stance phases. Further longitudinal studies are needed to clarify the relationship between identified subgroups and the development of overuse injuries.

Reliability of Xsens IMU-Based Lower Extremity Joint Angles during In-Field Running

The Xsens Link motion capture suit has become a popular tool in investigating 3D running kinematics based on wearable inertial measurement units outside of the laboratory. In this study, we investigated the reliability of Xsens-based lower extremity joint angles during unconstrained running on stable (asphalt) and unstable (woodchip) surfaces within and between five different testing days in a group of 17 recreational runners (8 female, 9 male). Specifically, we determined the within-day and between-day intraclass correlation coefficients (ICCs) and minimal detectable changes (MDCs) with respect to discrete ankle, knee, and hip joint angles. When comparing runs within the same day, the investigated Xsens-based joint angles generally showed good to excellent reliability (median ICCs > 0.9). Between-day reliability was generally lower than the within-day estimates: Initial hip, knee, and ankle angles in the sagittal plane showed good reliability (median ICCs > 0.88), while ankle and hip angles in the frontal plane showed only poor to moderate reliability (median ICCs 0.38-0.83). The results were largely unaffected by the surface. In conclusion, within-day adaptations in lower-extremity running kinematics can be captured with the Xsens Link system. Our data on between-day reliability suggest caution when trying to capture longitudinal adaptations, specifically for ankle and hip joint angles in the frontal plane.

KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries

The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner's anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test-retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test-retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners' body segments using elastic bands and the volunteers' movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test-retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies.