Reporting health data in waiting rooms with mobile technology: Patient expectation and confirmation

Objectives: Hospitals and medical staff use digital devices such as mobile phones and tablets to treat patients. Prior research has examined patient-reported outcomes, and the use of medical devices to do diagnosis and prognosis of patients, but not whether patients like using, and intend to use in future, mobile devices to self-report medical data. We address this research gap by developing a theoretical model based on the expectancy confirmation model (ECM) and testing it in an empirical study of patients using mobile technology to self-report data. Design: This study adopts a non-interventional cross-sectional research design. Randomly-selected patients provided data via survey and physical measurements. The target population comprises adults visiting a healthcare laboratory to get their blood drawn. Materials and methods: We surveyed 190 randomly-selected patients waiting for treatment in the clinic. They were surveyed at two points in time - before and after their blood was drawn - on their demographic characteristics, research variables concerning their use of mobile devices to provide medical information, and perceived clinical data (blood pressure, height and weight). The research model was tested using structural equation modeling. Results: The study found strong support for the research model, with seven of eight hypotheses being supported. Both self-disclosure effort and feedback expectation positively affect both perceived feedback quality and confirmation. Contrary to expectations, perceived feedback quality was not found to affect confirmation. Perceived feedback quality, along with confirmation, was found to positively affect satisfaction, which was found to affect intention to disclose medical data through mobile technology. Conclusions: The study\u27s findings support the proposed path from feedback expectation and self-disclosure effort to confirmation to satisfaction to disclosure intention. Although perceived feedback does not affect confirmation, it affects satisfaction. Overall, we believe the results provide novel insights to both scientific research community and practitioners about using mobile technologies for self-reporting medical data

Assessment of Ubiquitous Healthcare Information Systems Benefits

Health is a fully individualized concern, and is therefore inherently mobile. Thus, Ubiquitous Healthcare Information Systems can enable a much-needed patient-centered care environment. This paper presents a decision support system that makes use of system dynamics methodologies in order to assess the improved health benefits that may be realized within the context of ubiquitous healthcare information systems in support of managing diabetes

A Smart Consumer-empowered Diabetes Education System (SCEDES): Integrating Human Wellbeing and Health Care in the Community Environment

Uncontrolled diabetes creates an increasing burden on individuals, their families, and the society in the UnitedStates. The expected total annual costs of diabetes, including both direct costs and the lost productivity, are expectedto be 20% of the Gross Domestic Products by 2016. In this paper, a community-based infrastructure is proposed toprovide consumers, both diabetics and pre-diabetics, with a Smart Consumer-empowered Diabetes EducationSystem (SCEDES) that will not only build consumer self-efficacy but also promote their self-management, selflearning,and support from peers and health care professionals. The system architecture and key implementationfeatures are addressed. Challenging issues and research directions are also discussed for further investigation

Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities from Thursday, October 26, 2023

The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Symposium of Student Research, Scholarship & Creative Activities on October 26, 2023.https://corescholar.libraries.wright.edu/celebration_abstract_books/1001/thumbnail.jp

New research directions on disparities in obesity and type 2 diabetes

Obesity and type 2 diabetes disproportionately impact U.S. racial and ethnic minority communities and lowâ income populations. Improvements in implementing efficacious interventions to reduce the incidence of type 2 diabetes are underway (i.e., the National Diabetes Prevention Program), but challenges in effectively scalingâ up successful interventions and reaching atâ risk populations remain. In October 2017, the National Institutes of Health convened a workshop to understand how to (1) address socioeconomic and other environmental conditions that perpetuate disparities in the burden of obesity and type 2 diabetes; (2) design effective prevention and treatment strategies that are accessible, feasible, culturally relevant, and acceptable to diverse population groups; and (3) achieve sustainable health improvement approaches in communities with the greatest burden of these diseases. Common features of guiding frameworks to understand and address disparities and promote health equity were described. Promising research directions were identified in numerous areas, including study design, methodology, and core metrics; program implementation and scalability; the integration of medical care and social services; strategies to enhance patient empowerment; and understanding and addressing the impact of psychosocial stress on disease onset and progression in addition to factors that support resiliency and health.This report discusses a workshop convened by the National Institutes of Health to understand how to (1) address socioeconomic and other environmental conditions that perpetuate disparities in the burden of obesity and type 2 diabetes; (2) design effective prevention and treatment strategies that are accessible, feasible, culturally relevant, and acceptable to diverse population groups; and (3) achieve sustainable health improvement approaches in communities with the greatest burden of these diseases.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154507/1/nyas14270_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154507/2/nyas14270.pd