Evidence-informed methods for predicting rehabilitation outcomes for individuals with patellofemoral pain
| dc.contributor.advisor | Ferber, Reed | |
| dc.contributor.author | Watari, Ricky | |
| dc.contributor.committeemember | Edwards, William Brent | |
| dc.contributor.committeemember | Hettinga, Blayne Alexander | |
| dc.date | 2018-06 | |
| dc.date.accessioned | 2018-04-26T22:02:45Z | |
| dc.date.available | 2018-04-26T22:02:45Z | |
| dc.date.issued | 2018-04-25 | |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | Watari, R. (2018). Evidence-informed methods for predicting rehabilitation outcomes for individuals with patellofemoral pain (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31842 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/31842 | |
| dc.identifier.uri | http://hdl.handle.net/1880/106556 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.faculty | Kinesiology | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject.classification | Rehabilitation and Therapy | en_US |
| dc.subject.classification | Engineering--Biomedical | en_US |
| dc.title | Evidence-informed methods for predicting rehabilitation outcomes for individuals with patellofemoral pain | |
| dc.type | doctoral thesis | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |