This report summarises the present state-of-the-art of Self-Quantification in health and fitness applications.
With advances in Self-Quantification applications and systems, it is now possible to capture and record data about nearly all aspects of human health and fitness, including mental, emotional, physical, social and spiritual dimensions. By analysing these numbers, people have a better understanding of their health status and their relationship to the world around them. Furthermore, huge advances in sensor technology – in conjunction with widespread availability of wireless networks – have helped self-trackers to collect data whenever and wherever they want.
The amount of data that is being captured is growing at exponential rates. This large volume of data needs to be taken into consideration by health and biomedical informaticians, as such data are difficult to manage in the context of organising, accessing, using, sharing, and analysing in aggregate form. There are clear implications for the use of high capacity broadband to transmit health data.
This report aims to summarise the present state-of-the-art of Self-Quantification in health and fitness applications. The report begins by providing a classification of selected tools and data flows in Self-Quantification systems. It also identifies key directories with more extensive examples of tools currently available for public use. Next, it highlights Open mHealth and Health Level 7 (HL7) standards for dealing with the problem of data isolation. Finally, it profiles three types of big-data analytical tools. The report concludes with a summary of the main challenges facing Self-Quantification systems, and offers some possible solutions.