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Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease./
Author:	Gates, Peter.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	72 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
Contained By:	Dissertations Abstracts International83-03A.
Subject:	Biomedical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28730746
ISBN:	9798535576572

Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease.
Gates, Peter.

Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 72 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: A.

Thesis (Ph.D.)--Kent State University, 2021.

This item must not be sold to any third party vendors.

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.

ISBN: 9798535576572Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Modeling

Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease.
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245 1 0 $a Development of a Model to Predict Outcomes After Dynamic Cycling People with Parkinson's Disease.
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300 $a 72 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
500 $a Includes supplementary digital materials.
500 $a Advisor: Ridgel, Angela.
502 $a Thesis (Ph.D.)--Kent State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0101.
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650 4 $a Bioinformatics. $3 553671
650 4 $a Physiology. $3 518431
650 4 $a Standard deviation. $3 3560390
650 4 $a Exercise. $3 532868
650 4 $a Datasets. $3 3541416
650 4 $a Body mass index. $3 3562858
650 4 $a Brain research. $3 3561789
650 4 $a Gender. $3 2001319
650 4 $a Fitness equipment. $3 3563152
650 4 $a Time series. $3 3561811
650 4 $a Entropy. $3 546219
650 4 $a Gait. $3 1362772
650 4 $a Quality of life. $3 529789
650 4 $a Velocity. $3 3560495
650 4 $a Dopamine. $3 1362554
650 4 $a Age. $3 1486010
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650 4 $a Parkinsons disease. $3 3562685
650 4 $a Design. $3 518875
650 4 $a Tremor (Muscular contraction). $3 3685839
650 4 $a Posture. $3 667530
650 4 $a Heart rate. $3 3563099
653 $a Modeling
653 $a Dynamic bike
653 $a Forced exercise
653 $a Parkinson's
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