Browsing by Author "Watari, Ricky"

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    Evidence-informed methods for predicting rehabilitation outcomes for individuals with patellofemoral pain
    (2018-04-25) Watari, Ricky; Ferber, Reed; Edwards, William Brent; Hettinga, Blayne Alexander
    Patellofemoral pain is a very common musculoskeletal complaint and exercise interventions are the treatment of choice for this condition. However, 15% to 40% of patients present a poor response to rehabilitation and identifying objective measures that can help screen patients who are more likely to present successful results after rehabilitation is important for the optimization of treatment strategies. Therefore, the overarching purpose of this thesis is to develop evidence-based methods for predicting the outcome of exercise treatment in young recreational runners with patellofemoral pain. We found that a classification model using data from conventional motion capture system was able to distinguish between treatment responders and non-responders with 78% of accuracy. To make this classification model more accessible in a clinical setting, we tested whether pelvic acceleration patterns during running could be clustered into homogeneous sub-groups of individuals with patellofemoral pain. We identified two clusters for females and one cluster for males, indicating the clinical utility of this approach for the identification of patient sub-groups. The next study developed a classification model based on pelvic acceleration data to classify patients according to treatment response, achieving an 85% classification accuracy and showing a more clinically accessible approach. Finally, we tested the equivalency of marker-based and inertial measurement unit-based segment acceleration data when applied to a random classification problem in order to understand if the latter classification model could be applied using wearable devices. Overall, the findings indicated a 35% likelihood of decrease in performance of classifiers when the input data were crossed over from different sources. Therefore, a new classification model would have to be developed using data from wearable sensors to facilitate the implementation of this method in a clinical setting. We conclude that the outcome of exercise intervention protocols for the treatment of patellofemoral pain can be predicted using baseline gait analysis data with systems that can be applied in a laboratory setting and has the potential of being translated to a clinical setting as well.
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    Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study
    (2018-04-19) Watari, Ricky; Osis, Sean T; Phinyomark, Angkoon; Ferber, Reed
    Abstract Background Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data. Methods A hierarchical cluster analysis was used to determine sub-groups of similar running profiles among 110 PFP subjects, separately for males (n = 44) and females (n = 66), using pelvic acceleration data (reduced with principal component analysis) during treadmill running acquired with optical motion capture system. In a secondary analysis, peak joint angles were compared between clusters (α = 0.05) to provide clinical context and deeper understanding of variables that separated clusters. Results The results reveal two distinct running gait sub-groups (C1 and C2) for female subjects and no sub-groups were identified for males. Two pelvic acceleration components were different between clusters (PC1 and PC5; p < 0.001). While females in C1 presented similar acceleration patterns to males, C2 presented greater vertical and anterior peak accelerations. All females presented higher and delayed mediolateral acceleration peaks than males. Males presented greater ankle eversion (p < 0.001), lower knee abduction (p = 0.007) and hip adduction (p = 0.002) than all females, and lower hip internal rotation than C1 (p = 0.007). Conclusions Two distinct and homogeneous kinematic PFP sub-groups were identified for female subjects, but not for males. The results suggest that differences in running gait patterns between clusters occur mainly due to sex-related factors, but there are subtle differences among female subjects. This study shows the potential use of pelvic acceleration patterns, which can be acquired with accessible wearable technology (i.e. accelerometers).
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    ItemOpen Access
    Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study.
    (BMC Musculoskelet Disord, 2018-04-19) Watari, Ricky; Osis, Sean T; Phinyomark, Angkoon; Ferber, Reed
    Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data.