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dc.contributor.advisor | Mahapatra, Radha | |
dc.contributor.advisor | Nerur, Sridhar | |
dc.creator | Murad, Mohammad Moinul Islam | |
dc.date.accessioned | 2023-06-30T17:13:46Z | |
dc.date.available | 2023-06-30T17:13:46Z | |
dc.date.created | 2022-12 | |
dc.date.issued | 2022-12-20 | |
dc.date.submitted | December 2022 | |
dc.identifier.uri | http://hdl.handle.net/10106/31468 | |
dc.description.abstract | **Please note that the full text is embargoed until 12/19/2024** ABSTRACT: Information Technology (IT) has radically changed our daily lives and it has the potential to help us adopt healthy behaviors and improve health outcomes. This two-part study investigates the influence of IT on health behaviors in population health management.
Crises lead to severe uncertainty, high-risk perceptions, and vulnerability among people. Crisis communication through social media platforms influences people to undertake recommended behaviors that mitigate crisis consequences. Political leaders utilize Twitter to deliver crisis messages that offer mental support and empower local communities to spawn resilience and adaptability with emergent collective behaviors necessary to respond to the crisis. The primary objective of this study is to investigate the COVID-19-related tweets posted by political leaders using computational linguistics to examine the effects of crisis communication on crisis outcomes (e.g., confirmed cases). We observed that the contents of the crisis messages from political leaders have changed in consistent with the progress of the COVID-19 crisis. We also found that while tweets with analytic, authentic, and tone from the past week affected the confirmed cases in the following week, surprisingly, tweets with clout are not significantly associated with crisis outcomes. We further analyzed several significant properties of the network of political leaders on Twitter. The findings demonstrate that the network of political leaders on Twitter is relatively dense and well-connected. A few nodes are highly dominant and have power law distribution. Our study detected twenty-three communities of political leaders and observed evidence of political polarization in the network. We find two large communities representing the Republican and Democratic parties at the national level. The remaining communities are reasonably well-balanced in size and center at the state level. Our findings have greater implications for leaders deploying social media during a crisis.
While chronic diseases pose tremendous challenges for patients, physicians, and care providers, lack of its management incurs exorbitant costs and can cause early death. Diabetes is a highly prevalent chronic disease that leads to health complications and comorbidity. Medically underserved populations (MUP) are relatively at higher risk of diabetes due to cultural, economic, and social barriers. Studies show that IT-enabled self-management is critical to avoid or slow the consequences of diabetes. This study investigates how to improve compliance with diabetes using IT-enabled self-management among MUPs. We designed and developed a user-centered mHealth app reflecting the needs and characteristics of the target population. To achieve this end, we used design science research methodology and articulated design principles based on the relevant theories and dominant literature to inform the design. The study contributes to reducing health disparity, a long-standing societal problem, and caters valuable insights to improve population health management. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.subject | Behavior change | |
dc.subject | Information technology | |
dc.title | Examining the Influence of Information Technology on Health Behaviors and Health Outcomes | |
dc.type | Thesis | |
dc.date.updated | 2023-06-30T17:13:46Z | |
thesis.degree.department | Information Systems and Operations Management | |
thesis.degree.grantor | The University of Texas at Arlington | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy in Information Systems | |
dc.type.material | text | |
local.embargo.terms | 2024-12-01 | |
local.embargo.lift | 2024-12-01 | |
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