Examining the Success Factors for Mobile Applications for Self-Management of Diabetic Treatment in a South African Context

Mainoti, Ganizani F.; Isabirve, Naomi

doi:10.1109/oi.2018.8535910

Cited by 3 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis indicated that healthier patients with better diabetes management may adopt a technological tool more easily than those with poorly controlled diabetes. Given that perceived risks/concerns and technology anxiety negatively affect adoption of a new technology, [34][35][36] nontechnology adopters might be less likely to try a new technological method for DSMES. This finding is consistent with those of previous studies.…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting Adoption of a Technology-Based Tool for Diabetes Self-Management Education and Support Among Adult Patients with Type 2 Diabetes in South Texas

Baek

Lin

Bolin

et al. 2021

The Science of Diabetes Self-Management and Care

View full text Add to dashboard Cite

Purpose The purpose of this study is to describe a novel computerized diabetes education tool and explore factors influencing self-selection and use among primarily Hispanic patients diagnosed with type 2 diabetes in south Texas. Methods Study participants included 953 adult patients with type 2 diabetes enrolled in a diabetes education program between July 1, 2016, and June 30, 2017. Participants were asked to choose either a new technology-based diabetes education tool with a touch-screen device or a traditional face-to-face education method. Multivariate logistic regression analysis was applied to identify factors associated with adopting the computerized diabetes education tool among the patients. Results When comparing technology-based tool adopters and nonadopters, several demographic and health-related factors differentiated technology use in bivariate analyses. The multivariate logistic regression model showed that Hispanic patients were less likely to choose a technology-based tool. Patients who perceived their health status as excellent/good were more likely to adopt the technologic education method than those with fair/poor perceived health status. A1C level was negatively associated with self-selection of technology. Conclusions Specific demographic and health-related characteristics are significant contributing factors to patients’ adoption of a technology-based diabetes education tool. Health care providers can utilize these findings to target and refer specific patients to a computerized diabetes education tool for more effective diabetes care and to optimize technology adoption success.

show abstract

Section: Discussionmentioning

confidence: 99%

Factors Affecting Adoption of a Technology-Based Tool for Diabetes Self-Management Education and Support Among Adult Patients with Type 2 Diabetes in South Texas

Baek

Lin

Bolin

et al. 2021

The Science of Diabetes Self-Management and Care

View full text Add to dashboard Cite

show abstract

“…Many research options are under developments in which Bi-hormonal artificial pancreas using switching Economic predictive control model which consist of insulin subsystem and glucose subsystem can be predictively controlled by switching PID controller. Another model which is based on a nonlinear control system based on (TS) Fuzzy model which consists of an effective and exact representation of a complex nonlinear system with a compact set of variable [18][19][20][21][22][23][24].By the above methods successful implementation of artificial pancreas or insulin pumps depends upon exact calculations of activity monitoring, measurement of glucose, carbohydrate intake in the body. Basic structure of artificial pancreas will be explained by Figure no.6.…”

Section: E Artificial Pancreasmentioning

confidence: 99%

“…With this strong relationship between insulin and glucose contents. We can develop a mathematical model with the help of artificial intelligence or closed loop control system with the help of switching PID predictive control [18][19][20][21][22][23][24]. Also, we can make model of nonlinear differential equations to develop artificial pancreas.…”

Section: Introductionmentioning

confidence: 99%

Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

2019

IJRTE

View full text Add to dashboard Cite

Recently, the entire world is facing another noncommunicable disease known as Diabetics. Basic reasons behind this disease is not unique , but certainly may be due to an increase in economic burden, less activity, change in lifestyle, improper food intake and increase in the level of stress. This disease has its effects all over the ages in the world from child to old age people. Hence its treatment is a big challenge to the social and economic growth of every nation. Plenty of research is undergoing in this area to improve health care which requires certain modifications and validations by clinical trials. Advancement in artificial intelligence is able to provide unique solutions in the future. The basic motivation behind this paper is to study the entire innovation taking place in recent years in the area of diabetic prevention and detection. Continuous monitoring of blood glucose level by invasive and non-invasive sensor technology, diet monitoring system, activity monitoring, intelligent foot and artificial pancreas for diabetics is the major focus of this study. Also, these studies focus on complete medical healthcare solution by using artificial intelligence. This study will surely help to identify future scope in the area of diabetic and its prevention models which can act as a future of diabetic health care

show abstract

Factor Influenced the Acceptance of e-Health from Health Care Personnel and Patient Perspective: Systematic Literature Review

Putra

Hidayanto

2022

2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

View full text Add to dashboard Cite

Examining the Success Factors for Mobile Applications for Self-Management of Diabetic Treatment in a South African Context

Cited by 3 publications

References 25 publications

Factors Affecting Adoption of a Technology-Based Tool for Diabetes Self-Management Education and Support Among Adult Patients with Type 2 Diabetes in South Texas

Factors Affecting Adoption of a Technology-Based Tool for Diabetes Self-Management Education and Support Among Adult Patients with Type 2 Diabetes in South Texas

Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

Factor Influenced the Acceptance of e-Health from Health Care Personnel and Patient Perspective: Systematic Literature Review

Contact Info

Product

Resources

About