Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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This project concerns justification for why partner dance, particularly ballroom dance, should be a part of the Arizona public-school curriculum. It consists of a review of peer-reviewed scientific research on the subject, as well as interviews conducted with local experts on dance. Moreover, a sample curriculum is supplied that should provide guidance on how to implement a ballroom dance program in the K-12 system. The goal of this paper is to empower educators to create ballroom dance programs in their schools, with the ultimate plan to help develop students into better citizens.
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.
Young adults with type one diabetes mellitus (T1DM) face unique challenges in managing their chronic disease. While simultaneously navigating major life transitions and becoming fully responsible for their diabetes-self management behaviors (DSMB), social support can be an integral part of disease management. Many young adults enroll in college where student organizations are prevalent including diabetes related social groups on some campuses, which provide a rich source of social support for students with diabetes as they transition to greater independence in diabetes management. This study used descriptive analysis and personal network analysis (PNA) to investigate which aspects of being a part of a diabetes related social group and personal networks, in general, are pertinent to successful diabetes management, measured by a Diabetes Self-Management Questionnaire (DSMQ) among 52 young adults with T1DM. The majority of respondents indicated that since being a part of College Diabetes Network (CDN) or another diabetes-related social group, they increased time spent paying attention to, and felt more empowered to make changes to their diabetes management routine, and they were able to generally take better care of their diabetes. Half of respondents noticed their health improved since joining, and over half felt less burdened by their diabetes. Though no personal network measures were highly correlated with higher Diabetes Self-Management Scores, the degree to which health matters were discussed within their personal network was the most associated personal network measure. Our findings help contextualize the ways in which young adults’ DSMB are influenced by participation in diabetes- related social groups, as well as introduce the use of personal network analysis in gauging important aspects of social capital and support in young adults with chronic disease.
Chronic diseases place a financial burden on the United States and claim the lives of nearly 2 million Americans every year. Among the chronic diseases that plague American people, type 2 diabetes is particularly prevalent and injurious. Thus, action is warranted to improve prevention and management of this disease. Nutrition plays a significant role in prevention and management of type 2 diabetes and other chronic diseases. Registered dietitians, as nutrition experts, are qualified to use medical nutrition therapy as a method of prevention and treatment for chronic diseases using a nutritional approach. However, there is no consensus as to which eating pattern is the most efficacious. The aim of this review of research was to examine how plant-based eating patterns impact chronic disease conditions, with an emphasis on type 2 diabetes mellitus, as compared to omnivorous eating patterns. A literature search was conducted through the ASU Library, PubMed, and CINAHL using terms related to plant-based diets and chronic diseases, such as type 2 diabetes. The results revealed that a plant-based eating pattern may be beneficial in the prevention and treatment of certain chronic diseases, such as type 2 diabetes. Specifically, adults who have type 2 diabetes and consume a plant-based diet may exhibit enhanced glycemic control as evidenced by less insulin resistance, increased incretin and insulin secretion, greater insulin sensitivity, and improved HbA1c levels. There is sufficient evidence for registered dietitians to recommend a plant-based approach to patients with type 2 diabetes who would like to achieve enhanced glycemic control.
Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.
