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Atrial fibrillation, also known as Afib or AF, is the most common irregular heart rhythm among the United States adult population. Atrial fibrillation is characterized by an abnormal fibrillation of the upper chambers of the heart, known as the atria. When left chronically untreated, this condition may lead to insufficient

Atrial fibrillation, also known as Afib or AF, is the most common irregular heart rhythm among the United States adult population. Atrial fibrillation is characterized by an abnormal fibrillation of the upper chambers of the heart, known as the atria. When left chronically untreated, this condition may lead to insufficient systemic blood flow or the formation of blood clots. Atrial fibrillation has many modifiable risk factors, meaning contributing habits and practices within the patient's control that may worsen the condition. Communication of these modifiable risk factors to patients with atrial fibrillation is important in improving patient quality of life and for reduction of disease symptoms. The motivation for this study was to convey the potential of improved disease process by lifestyle modification to patients with atrial fibrillation.
ContributorsLehman, Jessica Lynn (Author) / Ross, Heather (Thesis director) / Kelly, Lesly (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
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Description
Type 2 diabetes mellitus (T2DM) is a chronic disease affecting more than ten percent of the U.S. adults. Approximately 50 percent of people with diabetes fail to achieve glycemic targets of A1C levels below seven percent. Poor glycemic control disproportionately affects minority populations such as Korean Americans (KAs). Successful diabetes

Type 2 diabetes mellitus (T2DM) is a chronic disease affecting more than ten percent of the U.S. adults. Approximately 50 percent of people with diabetes fail to achieve glycemic targets of A1C levels below seven percent. Poor glycemic control disproportionately affects minority populations such as Korean Americans (KAs). Successful diabetes self-management requires a comprehensive approach that takes into account depression, sleep, and acculturation to achieve good glycemic control. Therefore, the purposes of this study were to: 1) describe the levels of glycemic control, depressive symptoms, sleep quality and duration, and acculturation; 2) examine an association of depressive symptoms with glycemic control; 3) identify mediational roles of sleep quality and sleep duration of less than 6 hours between depressive symptoms and glycemic control; and 4) explore a moderation role of acculturation between depressive symptoms and glycemic control in KAs with T2DM. This is a cross-sectional, descriptive correlational study. A total of 119 first generation KAs with T2DM were recruited from Korean communities in Arizona. A1C levels, the Center for Epidemiological Studies Depression Scale, the Pittsburgh Sleep Quality Index, the Suinn-Lew Asian Self-Identity Acculturation scale, the International Physical Activity Questionnaire, and the Berlin Questionnaire were measured. Descriptive statistics, multiple regression analyses, path analyses, and the Sobel tests were conducted for data analyses of this study. Poor glycemic control (A1C ≥ 7 %), high depressive symptoms (CES-D ≥ 16), poor sleep quality (PSQI > 5), and short sleep duration (< 6 hours) were prevalent among KAs with T2DM. The mean score of acculturation (2.18) indicated low acculturation to Western culture. Depressive symptoms were revealed as a significant independent predictor of glycemic control. Physical activity was negatively associated with glycemic control, while cultural identity was positively related to glycemic control. Sleep quality and sleep duration of less than 6 hours did not mediate the relationship between depressive symptoms and glycemic control. Acculturation did not moderate the association between depressive symptoms and glycemic control. Diabetes self-management interventions of a comprehensive approach that considers depressive symptoms, sleep problems, and cultural differences in minority populations with T2DM are needed.
ContributorsJeong, Mihyun (Author) / Reifsnider, Elizabeth G. (Thesis advisor) / Belyea, Michael (Committee member) / Petrov, Megan (Committee member) / Kelly, Lesly (Committee member) / Arizona State University (Publisher)
Created2017
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Description

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

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.

ContributorsMishra, Shambhavi (Co-author) / Numani, Asfia (Co-author) / Sweazea, Karen (Thesis director) / Plasencia, Jonathan (Committee member) / Economics Program in CLAS (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

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

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.

ContributorsNumani, Asfia (Co-author) / Mishra, Shambhavi (Co-author) / Sweazea, Karen (Thesis director) / Plasencia, Jon (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05