Data tracking is becoming a popular practice in very different domains ranging from sports to health, work productivity and learning, among others.In Human Computer Interaction this trend has become an area of study that has been labelled as personal informatics, lifelogging, and Quantified Self, among other names. The most prominent feature of these technologies is that they enable data collection about people’s states, practices and habits in an easy and handy way. Usually, the technologies that allow automatic data collection register quantitative information, but there are also systems which also allow the input of qualitative data or that focus exclusively on the qualitative aspects.
The fields in which QS devices have gone mainstream are sports, sleep, nutrition and health, to summarize them in few categories. In sports, current well-known products focused on tracking physical activity are Nike+ and its fuelband, Fitbit, Jawbone Up24,
Adidas Mycoach, RunKeeper, Moves and Striiv. Dealing with sleep, some interesting initiatives are Lark, Beddit, Sleep Cycle and Sleep as Android. In the case of nutrition, usually it is associated to weight management. Withings smart body analyzer and Aria and Lose it! ,although there are also cases strictly focused on food intake such as MyFitnessPal. Self-monitoring tools have been widely used in healthcare of chronic conditions, such as diabetes. Dexcom G4 Platinum and Glooko are good examples of devices that seek to improve people’s self-management of their condition. Closely related to health and wellbeing, stress management is another area that is receiving increasing attention. Airo, Stress Check and Empatica Analytics are cases that monitor stress with the aim of helping people improve their wellbeing.
All these systems and tools are oriented towards behavior understanding. Usually, the extensive amount of data collected by these tools is presented in a visual way in order to make easier the detection of trends and patterns that otherwise would require a huge memory. The visualization of information can help users’ understand their habits and behavior and therefore, improve awareness and self-reflection.
In some cases, self-monitoring applications might encourage behavior change by including some sort of coaching system based on recommendations and personalized plans. When dealing with medical conditions, the data gathered by these systems is used as means for a more efficient communication between healthcare providers and patients, as well as, in chronic cases for allowing a certain degree of self-management.
The scattering of data on different systems has lead to the appearance of mashup platforms that combine different user datasets. This is specially interesting in order to develop data visualizations that explore relationships between different types of data or simply visual dashboards that provide a general overlook of the users’ performance. Some applications that go in this direction are mem:o and Argus.
Despite the scarcity of open appis that allow exporting the data automatically, there is a trend towards open models that recognize users’ right to access and use their data. Quite probably, as far as business models are redefined, more data will be open and available. Certainly, this will bring interesting questions and debates about data ownership and third party use.
Allowing users to access and manipulate their data involves reconsidering their role, since in many cases, they are supposed to keep a quite passive role that consists in watching the data presented by the self-monitoring tool. Hopefully, in a near future we will see the appearance of tools that enable a more active role of the users so they can truly achieve QS original intent, reaching self-understanding through numbers.