• Research in Community and Public Health Nursing
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• v.15 no.1
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• pp.29-35
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• 2004

Purpose: The purpose of this study was to investigate the computer input frequency of blood glucose self testing in type2 diabetic patients, for three months. Method: 39 participants were recruited from the endocrinology outpatient department of a tertiary care hospital in an urban city. The computer input frequencies were measured by patients' log in and input of http://www.biodang.com. Glycosylated haemoglobin was determined by a high performance liquid chromatography technique and fasting blood glucose was analyzed by the glucose oxidase method. Diabetes knowledge was measured by a 19 item diabetes knowledge test. Results: The computer input frequency of before breakfast blood glucose was 33.5 for three months. The total blood glucose input frequency tended to be lower in female, aged patients, middle school graduates, those who have no spouse and job, those who have no insulin treatment, obese subjects, and hyperglycemia patients, than in their counterparts. The diabetic knowledge was positively correlated with the computer input frequency of blood glucose self testing. Conclusion: An educational program should be developed to increase the computer input frequency of blood glucose self testing in type2 diabetic patients.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.11
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• pp.337-346
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• 2023

There is a need to develop an app for a caregiver health management that can provide continuous management in response to changes over time, because elderly people have low digital utilization capabilities, difficulty maintaining regular and continuous self-management. Based on this need, this study designed an app with a user-friendly UI and simple structure for the elderly. The app developed in this study supports regular management of health data such as blood pressure, blood sugar, and heart rate, as well as specific information on physical, disease, cognitive, communication, and environment in the care field. The app developed in this study supports care services by automatically entering data through integration with health management devices, automatically analyzing and visually representing recorded data to understand trends and volatility, and adding scalability to connect with various health management and medical support platforms. The effectiveness and satisfaction of the developed app were confirmed to be significant in the field verification results.