A Mobile Telehealth Framework for Diabetes Care

碩士 === 國立成功大學 === 醫學資訊研究所 === 100 === Telehealth has been an increasing popular research domain recently, with multiple technological developments for many aspects of medicine. However, much remains to be done before telehealth can be deployed at a large scale in a real clinical environment. This r...

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Bibliographic Details
Main Authors: I-HenTsai, 蔡亦恒
Other Authors: Vincent S. Tseng
Format: Others
Language:en_US
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/54571237876446983192
Description
Summary:碩士 === 國立成功大學 === 醫學資訊研究所 === 100 === Telehealth has been an increasing popular research domain recently, with multiple technological developments for many aspects of medicine. However, much remains to be done before telehealth can be deployed at a large scale in a real clinical environment. This research puts forward a framework utilizing mobile platforms to provide diabetes care. This framework uses the portability of mobile platforms and wireless network access, to provide better self-care services for diabetes and to provide ubiquitously accessible self-care advice for diabetics. As a routine of the self-care process, diabetes patients must make several blood glucose measurements every day. Yet, the pain of blood sampling and monotonous recording taking process is often detrimental to diligent self-care for diabetic patients. We proposed a new automated self-care concept in this research in attempt to boost patients’ initiative in good self-care practice. We focus on designing and provisioning a much more streamlined and simplified self-care process for daily blood glucose records. In addition, we also provide support for the management of long-term records and feedback for daily self-care. The implemented framework has been deployed in a 2-month clinical trial to test its feasibility. Pre- and post-trial questionnaires completed by participating patients indicate good acceptance of this system and better understanding/control of their own condition. Data mining has been applied to the collected clinical data, and high confidence rules corresponding to possible clinical outcomes has been discovered.