Matching Items (3)
Description
Sticking to healthy behaviors is difficult. The lack of long-term behavior maintenance negatively impacts health outcomes and increases healthcare costs. Current methods for improving behavior maintenance yield varying and often limited results. This dissertation designs and tests quantitative methods for identifying behavioral strategies associated with long-term maintenance the long-term maintenance of three different health behaviors. Data were collected from three settings: mindfulness through a commercial app, walking from a randomized controlled trial, and pill-taking from a commercial app-based intervention. Novel pattern-detection methodologies were employed to measure temporal consistency and identify key behavioral strategies.
For mindfulness and walking behaviors, the impact of individual phenotypes on long-term behavior maintenance was analyzed. For medication adherence, the optimal window of time in which pills should be taken was empirically determined, and the impact of consistent timing on long-term medication adherence was analyzed. To perform these analyses, robust and regularized models, panel data models, statistical tests, and clustering algorithms were used. For mindfulness meditation, both consistent and inconsistent phenotypes were associated with long-term engagement. In the walking intervention, those with a consistent phenotype experienced greater increases in walking after the study than inconsistent individuals. However, the effect of consistency was strongest for individuals who either exercised less than 10 or more than 30 minutes per day. Lastly, in the medication adherence incentive program, consistently taking medication within 55 minutes of the goal time had the strongest association with future adherence.
This dissertation demonstrates that certain phenotypes are more advantageous than others for long-term maintenance and interventions. Temporal consistency is likely helpful for maintaining behaviors that offer delayed physical benefits, such as regular walking or medicating for chronic illnesses, but less helpful for cognitive behaviors like mindfulness, which can provide more immediate satisfaction. When designing interventions, the nature of the behavior and observable phenotypes should be taken into consideration. Generally, focusing on consistency is likely to contribute to long-term success; however, this is individual and context dependent. Future research should investigate this further by examining the relationship between behavioral phenotypes and psychological measurement tools to gain a deeper understanding of the successful maintenance of healthy behaviors.
ContributorsFowers, Rylan (Author) / Stetcher, Chad (Thesis advisor) / Chung, Yunro (Thesis advisor) / Ghasemzadeh, Hassan (Committee member) / Arizona State University (Publisher)
Created2023
Description
The proposed research is motivated by the colon cancer bio-marker study, which recruited case (or colon cancer) and healthy control samples and quantified their large number of candidate bio-markers using a high-throughput technology, called nucleicacid-programmable protein array (NAPPA). The study aimed to identify a panel of biomarkers to accurately distinguish between the cases and controls. A major challenge in analyzing this study was the bio-marker heterogeneity, where bio-marker responses differ from sample to sample.
The goal of this research is to improve prediction accuracy for motivating or similar studies. Most machine learning (ML) algorithms, developed under the one-size-fits-all strategy, were not able to analyze the above-mentioned heterogeneous data. Failing to capture the individuality of each subject, several standard ML algorithms tested against this dataset performed poorly resulting in 55-61% accuracy. Alternatively, the proposed personalized ML (PML) strategy aims at tailoring the optimal ML models for each subject according to their individual characteristics yielding best highest accuracy of 72%.
ContributorsShah, Nishtha (Author) / Chung, Yunro (Thesis advisor) / Lee, Kookjin (Thesis advisor) / Ghasemzadeh, Hassan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Computational and systems biology are rapidly growing fields of academic study, but unfamiliar researchers are impeded by a lack of accessible, programming-optional, modelling tools. To address this gap, I developed BioSSA, a web framework built on JavaScript and D3.js which allows users to explore a small library of curated biophysical models as well as create and simulate their own reaction network. The mathematical foundation of BioSSA is the Stochastic Gillespie Algorithm, which is widely used in mathematical modeling and biology to represent chemical reaction systems. SGA is particularly well-suited as an introductory modelling tool because of its flexibility, broad applicability, and its ability to numerically approximate systems when analytical solutions are not available. BioSSA is freely available to the community and further improvements are planned.
ContributorsRamirez, Daniel (Author) / Ghasemzadeh, Hassan (Thesis director) / Liu, Li (Committee member) / Lu, Mingyang (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2023-05