mHealth Environments for Chronic Disease Management

mHealth Environments for Chronic Disease Management

Eleni I. Georga (University of Ioannina, Greece), Athanasios N. Papadopoulos (University of Ioannina, Greece) and Dimitrios I. Fotiadis (University of Ioannina, Greece & Institute of Molecular Biology and Biotechnology, Greece)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/978-1-5225-2237-9.ch038

Abstract

The management of chronic diseases requires the continuous monitoring and control of an extensive set of medical and lifestyle parameters affecting the health status of patients. The purpose of this chapter is to present an overview of the state of the art in wearable medical systems and mobile self-management support interventions in the daily care of Chronic Obstructive Pulmonary Disease (COPD) and Type 1 and Type 2 diabetes. In both cases, research and commercial approaches to the integration of specialized sensors in a wearable smart module are presented and their ability to provide real-time estimations for crucial parameters is emphasized. Moreover, special emphasis is placed on mobile self-monitoring applications which are progressively enhanced with decision support, pattern recognition and predictive capabilities which can be used by the patient. The way in which mobile health technology can improve health outcomes is discussed and future research directions are described.
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Background

Mobile devices, such as smartphones and tablets are becoming increasingly popular in the provision of services in various fields. In the last 10 years, they have become a central part in the provision of healthcare services as well thanks to a series of unique features, such as:

  • 1.

    Provision of significant computational capabilities and native applications (Andoid, iOS, Windows Phone) which provide Application Programming Interfaces (APIs)

  • 2.

    Incorporation of internet and other connectivity options (e.g. Bluetooth)

  • 3.

    Availability of inbuilt sensors (accelerometers, cameras, GPS, etc.) and associated data analysis algorithms (e.g. heart rate extraction)

  • 4.

    Availability of a great variety of apps as well as app-enabled peripheral devices (connected health and lifestyle gadgets)

  • 5.

    Tendency of people to carry and use mobile phones and smartphones everytime and everywhere

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