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Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease

Gates, Peter
http://orcid.org/0000-0003-1633-661X
http://rave.ohiolink.edu/etdc/view?acc_num=kent1625846829132496

Abstract Details

2021, PHD, Kent State University, College of Arts and Sciences / School of Biomedical Sciences.
INTRODUCTION. The number of individuals with Parkinson’s Disease (PD) in the US is projected to rise to 1,238,000 by 2030. Current pharmacological medications do not slow disease progression and only provide symptom management. Some types of exercise have been shown to reduce motor symptoms and potentially reduce the need for increasing medication dose. Previous research from our lab has shown that high-cadence dynamic cycling, which mimics the entropic cadence of tandem cycling, improves motor symptom presentation by at least 30%. However not all participants show the same improvement. The goal of this study was to investigate how individual characteristics affect responses to dynamic cycling. METHODS. Three previously collected datasets from 2015 and 2019 were used to construct linear models, to test how age, medication dosage, and body mass index (BMI) affected motor symptoms. The change in Unified Parkinson’s Disease Rating Scale part III (UPDRS), a measure of motor symptom severity for PD, was used as the outcome variable. We also defined a new term, “effort”, as the percent of time a participant overtook the motor during each session. RESULTS. Our preliminary linear mixed model of change in UPDRS (R2 = 0.31, p = 0.09) showed a significant contribution by effort (p = 0.04) and pre UPDRS (p = 0.01), with age not being a significant contributor (p = 0.06). The multiple regression model of change in UPDRS showed no significant contributors (p > 0.05) and did not significantly explain variance in change in UPDRS (R2 = 0.24, p = 0.32). BMI (p = 0.006) and age (p = 0.04) were significant contributors in our linear mixed model of effort (R2 = 0.63, p = 0.04), however only BMI remained significant (p = 0.04) in a similar multiple linear regression model of effort (R2 = 0.69, p.= 0.02). A follow-up multiple linear regression model of post UPDRS (R2 = 0.81, p < 0.001) showed pre UPDRS as the sole significant contributor (p < 0.001), and no significant contributions to our follow-up multiple linear regression model of mean effort (R2 = 0.16, p = 0.11) was found. DISCUSSION. Given that pre UPDRS was the sole significant contributor in our linear regression models of post and change in UPDRS, it is likely that a moderating or mediating effect is present in the measured variables. Post hoc analysis showed a moderating effect of BMI on mean effort, where the effect of effort on post UPDRS decreased with increasing BMI. Further studies are needed to better understand how a self-adapting dynamic bike can be programmed to adapt to each user and to maximize beneficial outcomes.
Angela Ridgel (Advisor)
71 p.
Biomedical Research
modeling; bioinformatics; dynamic bike; forced exercise; parkinsons;

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Citations

  • Gates, P. (2021). Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1625846829132496

    APA Style (7th edition)

  • Gates, Peter. Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease. 2021. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1625846829132496.

    MLA Style (8th edition)

  • Gates, Peter. "Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease." Doctoral dissertation, Kent State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1625846829132496

    Chicago Manual of Style (17th edition)

kent1625846829132496
123
© 2021, some rights reserved.Creative Commons License Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease by Peter Gates is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Kent State University and OhioLINK.