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Long before the COVID-19 pandemic, the education technology narrative asserted that "New tools hold great promise for teaching and learning, but only when they are used effectively, appropriately and intentionally". In this dissertation, I explored how early childhood education teachers construct their understanding of technology integration in a technology-infused social studies methods course. The preservice teachers came into the course expressing their attitudes towards technology as positive, negative, or mixed. In addition, they articulated their self-perceptions of technology skill as confident, unconfident, or both confident and unconfident. As they continued in this technology infused methods course, they experienced technology as a way to understand, engage, explore/examine, reflect, collaborate/share, and extend their knowledge about social studies. As they experienced different technologies throughout the course, preservice teachers recognized technology as a space for building a community of learners to share and generate knowledge and build learner agency. They also identified technology as a multimodal tool to support learning that simulates realistic experiences, provides creative outlets, and expands opportunities for learning. The preservice teachers described engaging with technology passively, actively, and critically by noticing, extending, and evaluating technology. In their lesson plans, they conceptualized using technology with children in many of the same ways they engaged in technology in the course. Specifically, they created experiences that encouraged young children to explore/examine, collaborate/share, and extend their learning. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Introduction: Hypoglycemia is the most common acute adverse complication of glucose lowering therapy among patients with diabetes and is associated with poor outcomes, including death. A recent study reported that the rate of hypoglycemia-related hospitalization in the United States increased by 11.7% (from 94 to 105 admissions per 100,000 person years) between 1991 and 2011 and that total annual costs increased to $1.6 billion. Despite these concerns, diabetes care quality established a decade ago has primarily focused on prevention and reducing rates of complications such as hypoglycemia. Although these efforts have been successful and the proportion of patients achieving HbA1c levels below a 7% threshold improved significantly, consequences of these changes may include increased hospital admission rates of hypoglycemia, which now exceed rates of hyperglycemia. Overall, the three studies presented provide an understanding of the risks of hypoglycemia and multifaceted interventions that reduce the rate of hypoglycemia in the hospital setting and U.S. population. Study One: Study one is a literature review of multifaceted interventions that have used an educational component to reduce the rate of hypoglycemia in inpatient settings. The literature search used a combination of search terms, which include ‘inpatient hypoglycemia’, ‘inpatient low glucose’ ‘educational prevention’, and ‘educational intervention’. Fifteen articles were selected for the review. Three articles focused on the safety and efficacy of insulin order sets, three articles implemented structured insulin order sets and protocols, seven studies focused on computerized insulin order sets and alert systems and two studies focused on root cause analysis and alert systems. The review suggested a need for randomized control trials and interventions that target at-risk populations, interventions that focus on the root causes of hypoglycemia and interventions that use technological enabled education tools (i.e. mobile health). The review also found that education in combination of multifaceted intervention that have used technology have been successful in reducing the rates of hypoglycemia. Study Two: Study two is a pilot randomized control study that examined the risk factors of hypoglycemia and provided a tailored feedback intervention to address those risk factors in the hospital setting. In this RCT study, a convenient sample of diabetes patients (N=85) were recruited in Atlanta. Baseline surveys were conducted to administer survey instrument. Nine months after recruitment, electronic medical reviews were conducted to determine whether the intervention reduced the incidence of hypoglycemia. Univariate and multivariate logistic regression analysis was conducted to determine the predisposing risk factors of hypoglycemia. Logistic regression analysis was also conducted to determine whether the intervention reduced the incidence of hypoglycemia. Results indicated that insulin use and older age (60+) were significant predictors of hypoglycemia occurrence. Results also indicated that the intervention reduced the incidence nine months post recruitment (P=0.046). Ongoing clinician education regarding insulin use and dosing and educational tools and resources targeted for an older population could continue to lower the rate of hypoglycemia occurrence. Study Three: Study three is a cross-sectional analysis of data on 1,642 older adults (≥ 65 years) with diabetes from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018 who had an HbA1c measurement. The study examined glycemic control levels among older adults with diabetes mellitus by health status and to estimate the potential of overtreatment with hypoglycemia causing medications (insulin or sulfonylureas). A weighted proportion of survey participants with glycemia that was poorly, moderately or tightly controlled across health status categories was conducted. Weighted proportions of participants whose glycemia was tightly controlled and were treated with insulin or sulfonylureas was also conducted. Logistic regression analysis was conducted to assess linear trends in participants with tightly controlled glycemia, their health status and patterns of treatment during the five NHANES surveys. Results indicated that during the 10 years there were no significant changes in proportion with an HbA1c level less than 7% who had complex/intermediate or very complex/poor health (P=0.444). There were significant changes in the proportion with an HbA1c Conclusion: Collectively, the three studies in this dissertation describe risk factors for hypoglycemia and multifaceted interventions that have used an educational component to reduce the rate of hypoglycemia in the hospital setting and the U.S. population. Implications suggest that there is a shift toward determining the root causes/risk factors of hypoglycemia and that there is a need to educate, individualize and tailor treatment to the patient to successfully reduce the rates of hypoglycemia. However, there are gaps in clinician education on insulin use and other hypoglycemia causing medications, which can lead to overtreatment. Therefore, there is a need in the assistance of a diverse and specialized diabetes management team that can include education and effective communication in the physician workflow. Furthermore, overtreatment measures, technological advancements (i.e. mobile health, smartphones) and improvements in electronic medical records to flag hypoglycemic events can further reduce hypoglycemic events and overall healthcare costs.

ABSTRACT Background: Prediabetes is a precursor condition to type 2 diabetes mellitus. Previous research and clinical trials have shown that the onset of type 2 diabetes could be delayed or prevented through structured life style modifications such as dietary changes, modest weight loss and moderate-intensity exercise. This study examines U.S adults of different ethnicities that include non-Hispanic white, non-Hispanic black and Mexican Americans and whether their awareness of diabetes risk is associated with their participation in diabetes risk reduction behavior, a combination of physical activity, weight control and fat/calories intake. Methods: The 2007-2008 National Health and Nutrition Examination Survey, NHANES, was used to conduct a cross-sectional study of 4083 U.S. adults who were 20 years old and above and were aware of their diabetes risk. The association between the awareness of one’s diabetes risk and the adoption of diabetes risk reduction behavior were examined in present of other risk factors such as age, gender, ethnicity, education, annual family income, BMI, hypertension, mean systolic blood pressure, mean diastolic blood pressure, total cholesterol, LDL, HDL and triglyceride levels. Males and females were examined separately for all analyses performed. Cross tabulation was conducted and p-values were calculated by the Pearson’s chi-square test for the categorical variables which include gender, ethnicity, education, annual family income, adiposity and hypertension. One Way ANOVA and Tukey post hoc tests were conducted for the continuous variables which include age, mean systolic blood pressure, mean diastolic blood pressure, total cholesterol, LDL, HDL and triglyceride levels. Univariate and multivariate analyses were performed to determine the association between the main independent variable, awareness of one’s diabetes risk, and the dependent variable, adoption of diabetes risk reduction behavior, controlling for other risk factors. A p-value of Results: After controlling for age, gender, race, education, annual family income, BMI, hypertension, mean systolic blood pressure, mean diastolic blood pressure, total cholesterol, LDL, HDL, and triglycerides, results from the multivariate analysis showed that subjects who were aware of their diabetes risk were more likely to adopt diabetes risk reduction behavior (OR= 1,734, 95 % CI=1.217-2.470). Females and non-Hispanic blacks, who were aware of their diabetes risk, were also more likely to adopt diabetes risk reduction behavior compared to males, non-Hispanic whites and Mexican Americans. An increase in the levels of education, annual family income and BMI was also associated with the adoption of diabetes risk reduction behavior. Stratification according to gender and ethnicity, showed that Mexican American males and females were more likely to engage in diabetes risk reduction behavior compared to non-Hispanic whites and non-Hispanic blacks (Mexican American males: OR: 2.496, CI: 0.792-7.868; Mexican American females: OR: 2.830, CI: 0.917-8.736). Conclusion: This study provides useful insights for health care providers and public health professionals who are developing health promotion and prevention interventions to address pre diabetes before it develops into type 2 diabetes. This study also allows the development of tailored interventions for specific genders and ethnic groups that are at risk. Results of this study indicate that Mexican Americans and females (in general) are more likely to adopt diabetes risk reduction behavior. Therefore, physicians and health care providers should develop culturally, linguistically and gender- specific education materials and programs for this particular gender and ethnic group. This in turn, may reduce the overall increasing prevalence of diabetes, reduce racial and gender disparities and may have a positive impact on the overall health of the U.S. population.

Ad hoc wireless sensor networks are emerging as an important technology for applications such as environmental monitoring, battlefield surveillance and infrastructure security. Centralized processing in sensor networks works well for small-scale systems, but for large systems, it becomes time-intensive, inefficient, insecure and non-scalable. In contrast, in-field or in-network distributed processing results in faster detection of phenomena, faster query response, and a scalable, robust network. Since the computation and communication in distributed networks is local, the network becomes more energy efficient and the communication cost is also lower. While most research so far has focused on the network aspects of these systems (e.g., routing, scheduling, etc.), the capacity for scalable, in-field information processing is potentially their most important attribute. Networks that can infer the phenomenological structure of their environment can use this knowledge to improve both their sensing performance and their resource usage. These intelligent networks would require much less a priori design, and be truly autonomous. This thesis presents a distributed algorithm for inferring the global topological connectivity of an environment through a simple self-organization algorithm based on Hebbian learning. The application considers sensors distributed over an environment with a network of tracks. Vehicles of various types move on these tracks according to rules unknown to the sensor network. Each sensor node infers the local topology of the track network by comparing its observations with those from neighboring sensors, using a method similar to Hebbian learning in neural networks. The complete topology of the network emerges from the distributed fusion of these local views. The system’s performance is evaluated based on its similarity to the actual underlying network. Ongoing research focuses on using the inferred topology for intelligent scheduling of nodes to enhance network lifetime without loss of performance.