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The limited sample size in gait studies has hampered progress in the field. This challenge could be addressed through multicenter studies, thereby leveraging data sets from different laboratories. This study compared 3-dimensional lower-extremity running kinematics between the Biomechanics and Motor Control Laboratory, Federal University of ABC (Brazil), and the Running Injury Clinic, University of Calgary (Canada). Three-dimensional lower-extremity kinematics from 23 male runners were collected from each laboratory using comparable instrumentation and experimental procedures. The 3-dimensional hip, knee, and ankle angles were compared within and between centers using root-mean-square deviation. Two-sample t tests Statistical Parametric Mapping tested the hypothesis that the data from both laboratories were not different. The sagittal plane hip, knee, and ankle angles were similar between laboratories, while notable differences were observed for frontal (hip and ankle) and transverse (hip and knee) plane angles. The average interlaboratory root-mean-square deviation (2.6°) was lower than the intralaboratory root-mean-square deviation (Biomechanics and Motor Control = 4.8°, Running Injury Clinic = 5.6°), with the ankle transverse angle displaying the smallest, and the knee transverse angle displaying the largest variability. This study demonstrates the potential of combining gait kinematics data from different laboratories to increase sample size, but frontal and transverse plane data should be considered with caution.

Altered gait variability occurs in those with patellofemoral pain and may be relevant to pain progression. We examined gait kinematic and coordination variability between individuals with acute and chronic patellofemoral pain and healthy controls. Eighty-three patellofemoral pain runners (37 men and 46 women) and 142 healthy controls (52 men and 90 women) ran on a treadmill while 3-dimensional lower limb kinematic data were collected. Patellofemoral pain runners were split into acute (n = 22) and chronic (n = 61) subgroups based on pain duration (< and ≥3 mo, respectively). Approximate entropy assessed continuous hip, knee, and ankle kinematic variability. Vector coding calculated coordination variability for select joint couplings. Variability measures were compared between groups using 1-way analysis of variance and post hoc comparisons with Cohen d effect sizes. The chronic patellofemoral pain subgroup displayed higher frontal plane knee kinematic variability compared with controls (P = .0004, d = 0.550). No statistically significant effects for any coordination variability couplings were identified. Minimal differences in gait variability were detected between those with acute and chronic patellofemoral pain and healthy controls.

The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the "typical" data (4-14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect "atypical" data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age-grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age-grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age-grade performance scores.

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the 'competitive' (n = 20) or 'recreational' group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, 'typical' running patterns that may help explain differences in injury mechanisms.

The purpose of this study was to validate measures of vertical oscillation (VO) and ground contact time (GCT) derived from a commercially-available, torso-mounted accelerometer compared with single marker kinematics and kinetic ground reaction force (GRF) data. Twenty-two semi-elite runners ran on an instrumented treadmill while GRF data (1000 Hz) and three-dimensional kinematics (200 Hz) were collected for 60 s across 5 different running speeds ranging from 2.7 to 3.9 m/s. Measurement agreement was assessed by Bland-Altman plots with 95% limits of agreement and by concordance correlation coefficient (CCC). The accelerometer had excellent CCC agreement (> 0.97) with marker kinematics, but only moderate agreement, and overestimated measures between 16.27 mm to 17.56 mm compared with GRF VO measures. The GCT measures from the accelerometer had very good CCC agreement with GRF data, with less than 6 ms of mean bias at higher speeds. These results indicate a torso-mounted accelerometer provides valid and accurate measures of torso-segment VO, but both a marker placed on the torso and the accelerometer yield systematic overestimations of center of mass VO. Measures of GCT from the accelerometer are valid when compared with GRF data, particularly at faster running speeds.

There is evidence to suggest that navicular drop measures are associated with specific lower-extremity gait biomechanical parameters. The aim of this study was to examine the relationship between navicular drop and a) rearfoot eversion excursion, b) tibial internal rotation excursion, c) peak ankle inversion moment, and d) peak knee adduction moment during the stance phase of running. Sixteen able-bodied men having an average age of 28.1 (SD=5.30) years, weight of 81.5 (SD=10.40) kg, height of 179.1 (SD=5.42) cm volunteered and ran barefoot at 170 steps/minute over a force plate. Navicular drop measures were negatively correlated with tibial internal rotation excursion (r=-0.53, P=.01) but not with rearfoot eversion excursion (r=-0.19; P=.23). Significant positive correlations were found between navicular drop and peak knee adduction moment (r=.62, P<.01) and peak ankle inversion moment (r=.60, P<.01). These findings suggest that a low navicular drop measure could be associated with increasing tibial rotation excursion while high navicular drop measure could be associated with increased peak ankle and knee joint moments. These findings indicate that measures of navicular drop explained between 28% and 38% of the variability for measures of tibial internal rotation excursion, peak knee adduction moment and peak ankle inversion moments.