Sean T. Osis

Gender and Age-Related Differences in Bilateral Lower Extremity Mechanics during Treadmill Running

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to their male counterparts. Thus, a comprehensive understanding of the sex-related differences in running kinematics is necessary. However, previous studies have either used discrete time point variables and inferential statistics and/or relatively small subject numbers. Therefore, the first purpose of this study was to use a principal component analysis (PCA) method along with a support vector machine (SVM) classifier to examine the differences in running gait kinematics between female and male runners across a large sample of the running population as well as between two age-specific sub-groups. Bilateral 3-dimensional lower extremity gait kinematic data were collected during treadmill running. Data were analysed on the complete sample (n = 483: female 263, male 220), a younger subject group (n = 56), and an older subject group (n = 51). The PC scores were first sorted by the percentage of variance explained and we also employed a novel approach wherein PCs were sorted based on between-gender statistical effect sizes. An SVM was used to determine if the sex and age conditions were separable and classifiable based on the PCA. Forty PCs explained 84.74% of the variance in the data and an SVM classification accuracy of 86.34% was found between female and male runners. Classification accuracies between genders for younger subjects were higher than a subgroup of older runners. The observed interactions between age and gender suggest these factors must be considered together when trying to create homogenous sub-groups for research purposes.

Gender differences in gait kinematics in runners with iliotibial band syndrome

Atypical running gait biomechanics are considered a primary factor in the etiology of iliotibial band syndrome (ITBS). However, a general consensus on the underpinning kinematic differences between runners with and without ITBS is yet to be reached. This lack of consensus may be due in part to three issues: gender differences in gait mechanics, the preselection of discrete biomechanical variables, and/or relatively small sample sizes. Therefore, this study was designed to address two purposes: (a) examining differences in gait kinematics for male and female runners experiencing ITBS at the time of testing and (b) assessing differences in gait kinematics between healthy gender‐ and age‐matched runners as compared with their ITBS counterparts using waveform analysis. Ninety‐six runners participated in this study: 48 ITBS and 48 healthy runners. The results show that female ITBS runners exhibited significantly greater hip external rotation compared with male ITBS and female healthy runners. On the contrary, male ITBS runners exhibited significantly greater ankle internal rotation compared with healthy males. These results suggest that care should be taken to account for gender when investigating the biomechanical etiology of ITBS.

Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running?

Recently, a principal component analysis (PCA) approach has been used to provide insight into running pathomechanics. However, researchers often account for nearly all of the variance from the original data using only the first few, or lower-order principal components (PCs), which are often associated with the most dominant movement patterns. In contrast, intermediate- and higher-order PCs are generally associated with subtle movement patterns and may contain valuable information about between-group variation and specific test conditions. Few investigations have evaluated the utility of intermediate- and higher-order PCs based on observational cross-sectional analyses of different cohorts, and no prior studies have evaluated longitudinal changes in an intervention study. This study was designed to test the utility of intermediate- and higher-order PCs in identifying differences in running patterns between different groups based on three-dimensional bilateral lower-limb kinematics. The results reveal that differences between sex- and age-groups of 128 runners were observed in the lower- and intermediate-order PCs scores (p<0.05) while differences between baseline and following a 6-week muscle strengthening program for 24 runners with patellofemoral pain were observed in the higher-order PCs scores (p<0.05), which exhibited a moderate correlation with self-reported pain scores (r=-0.43; p<0.05).

Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners.

Accelerometers are increasingly used tools for gait analysis, but there remains a lack of research on their application to running and their ability to classify running patterns. The purpose of this study was to conduct an exploratory examination into the capability of a tri-axial accelerometer to classify runners of different training backgrounds and experience levels, according to their 3-dimensional (3D) accelerometer data patterns. Training background was examined with 14 competitive soccer players and 12 experienced marathon runners, and experience level was examined with 16 first-time and the same 12 experienced marathon runners. Discrete variables were extracted from 3D accelerations during a short run using root mean square, wavelet transformation, and autocorrelation procedures. A principal component analysis (PCA) was conducted on all variables, including gait speed to account for covariance. Eight PCs were retained, explaining 88% of the variance in the data. A stepwise discriminant analysis of PCs was used to determine the binary classification accuracy for training background and experience level, with and without the PC of Speed. With Speed, the accelerometer correctly classified 96% of runners for both training background and experience level. Without Speed, the accelerometer correctly classified 85% of runners based on training background, but only 68% based on experience level. These findings suggest that the accelerometer is effective in classifying athletes of different training backgrounds, but is less effective for classifying runners of different experience levels where gait speed is the primary discriminator.

Gait biomechanics in the era of data science

Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.

Kinematic gait patterns in healthy runners: A hierarchical cluster analysis

Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

A novel method to evaluate error in anatomical marker placement using a modified generalized Procrustes analysis.

As biomechanical research evolves, a continuing challenge is the standardization of data collection and analysis techniques. In gait analysis, placement of markers to construct an anatomical model has been identified as the single greatest source of error; however, there is currently no standardized approach to quantifying these errors. The current study applies morphometric methods, including a generalized Procrustes analysis (GPA) and a nearest neighbour comparison to quantify discrepancies in marker placement, with the goal of improving reliability in gait analysis. An extensive data-set collected by an Expert (n = 340) was used to evaluate marker placements performed by a Novice (n = 55). Variances identified through principal component analysis were used to create a modified GPA to transform anatomical data, and scaled coordinates from the Novice data-set were then scored against the Expert subset. The results showed quantitative differences in marker placement, suggesting that, although training improved consistency, systematic biases remained.

Gait Biomechanics and Patient-Reported Function as Predictors of Response to a Hip Strengthening Exercise Intervention in Patients with Knee Osteoarthritis

Objective Muscle strengthening exercises have been shown to improve pain and function in adults with mild-to-moderate knee osteoarthritis, but individual response rates can vary greatly. Predicting individuals who respond and those who do not is important in developing a more efficient and effective model of care for knee osteoarthritis (OA). Therefore, the purpose of this study was to use pre-intervention gait kinematics and patient-reported outcome measures to predict post-intervention response to a 6-week hip strengthening exercise intervention in patients with mild-to-moderate knee OA. Methods Thirty-nine patients with mild-to-moderate knee osteoarthritis completed a 6-week hip-strengthening program and were subgrouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in Knee injury Osteoarthritis Outcome Score (KOOS). Predictors of responder subgroups were retrospectively determined from baseline patient-reported outcome measures and kinematic gait parameters in a discriminant analysis of principal components. A 3–4 year follow-up on 16 of the patients with knee OA was also done to examine long-term changes in these parameters. Results A unique combination of patient-reported outcome measures and kinematic factors was able to successfully subgroup patients with knee osteoarthritis with a cross-validated classification accuracy of 85.4%. Lower patient-reported function in daily living (ADL) scores and hip frontal plane kinematics during the loading response were most important in classifying High-Responders from other sub-groups, while a combination of hip, knee, ankle kinematics were used to classify Non-Responders from Low-Responders. Conclusion Patient-reported outcome measures and objective biomechanical gait data can be an effective method of predicting individual treatment success to an exercise intervention. Measuring gait kinematics, along with patient-reported outcome measures in a clinical setting can be useful in helping make evidence-based decisions regarding optimal treatment for patients with knee OA.

Kinematic gait patterns and their relationship to pain in mild-to-moderate hip osteoarthritis

BACKGROUND Mild-to-moderate hip osteoarthritis is often managed clinically in a non-surgical manner. Effective non-surgical management of this population requires characterizing the specific impairments within this group. To date, a complete description of all lower extremity kinematics in mild-to-moderate hip osteoarthritis patients has not been presented. The aim of the present study is to describe the lower extremity gait kinematics in mild-to-moderate hip osteoarthritis patients and explore the relationship between kinematics and pain. METHODS 22 subjects with mild-to-moderate radiographic hip osteoarthritis (Kellgren-Lawrence grade 2-3) and 22 healthy age and BMI matched control subjects participated. Kinematic treadmill walking data were collected across all lower extremity joints. A two-way repeated measures analysis of variance estimated mean differences in gait kinematics between groups. Correlations between gait kinematics and pain were assessed using a Spearman correlation coefficient. FINDINGS Hip osteoarthritis subjects hiked their unsupported hemi-pelvis 1.40° (P<0.001) more than controls and tilted their pelvis 4.65° more anteriorly (P=0.01). Osteoarthritis subjects walked with 4.30° more peak hip abduction (P<0.001), 8.57° less peak hip extension (P<0.001), and 10.54° more peak hip external rotation (P<0.001). Kinematics were related to pain in the ankle frontal plane only (r=-0.43, P<0.05). INTERPRETATION Individuals with mild-to-moderate hip osteoarthritis demonstrate altered gait biomechanics not related to pain. These altered biomechanics may represent effective therapeutic targets by clinicians working with this population. Understanding the underlying patho-anatomic changes that lead to these biomechanical changes requires further investigation.

Validation of a Torso-Mounted Accelerometer for Measures of Vertical Oscillation and Ground Contact Time During Treadmill Running

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.