Mobile health data: investigating the data used by an mHealth app using different mobile app architectures

Mobile Health (mHealth) has come a long way in the last forty years and is still rapidly evolving and presenting many opportunities. The advancements in mobile technology and wireless mobile communication technology contributed to the rapid evolution and development of mHealth. Consequently, this evolution has led to mHealth solutions that are now capable of generating large amounts of data that is synchronised and stored on remote cloud and central servers, ensuring that the data is distributable to healthcare providers and available for analysis and decision making. However, the amount of data used by mHealth apps can contribute significantly to the overall cost of implementing a new or upscaling an existing mHealth solution. The purpose of this research was to determine if the amount of data used by mHealth apps would differ significantly if they were to be implemented using different mobile app architectures. Three mHealth apps using different mobile app architectures were developed and evaluated. The first app was a native app, the second was a standard mobile Web app and the third was a mobile Web app that used Asynchronous JavaScript and XML (AJAX). Experiments using the same data inputs were conducted on the three mHealth apps. The primary objective of the experiments was to determine if there was a significant difference in the amount of data used by different versions of an mHealth app when implemented using different mobile app architectures. The experiment results demonstrated that native apps that are installed and executed on local mobile devices used the least amount of data and were more data efficient than mobile Web apps that executed on mobile Web browsers. It also demonstrated that mobile apps implemented using different mobile app architectures will demonstrate a significant difference in the amount of data used during normal mobile app usage.

Grantor

Department of Computer Science

Year dc:date.issued

2018

Author dc:creator

• Faker, Faizel

Advisors dc:contributor.advisor

• Suleman, Hussein
• deRenzi Brian

• computer science

Handle dc:identifier.uri

http://hdl.handle.net/11427/31242

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/31242