A Framework for Quantitative Data Analysis of Big Data Collected by Activity Trackers

Abstract

Activity trackers are used to monitor the physiological activity of users, which in turn enables users to take responsibility for their health care. These trackers can collect a large amount of data from the user which is referred to as ‘big data’.  Literature suggests that the user is not always able to analyse the vast amount of data effectively to benefit from this health information trend.  The purpose of this study was to develop a framework for the analysis of big data collected from activity tracking devices to enable users to manage their health proactively. This study applied a qualitative research approach with a literature review design. The study developed a framework and six critical success factors. The critical success factors include credible and high-quality data from activity trackers; select a suitable big data software structure and storage technology; meaningful visualisation of big data; continuous monitoring of health data for early detection and treatment; communicating data trends to users while feedback must lead to behavioral improvement by the user.