On the Integration of Self-tracking Data amongst Quantified Self Members

Abstract

Self-tracking, the process of recording one's own behaviours, thoughts and feelings, is a popular approach to enhance one's self-knowledge. While dedicated self-tracking apps and devices support data collection, previous research highlights that the integration of data constitutes a barrier for users. In this study we investigated how members of the Quantified Self movement-early adopters of self-tracking tools-overcome these barriers. We conducted a qualitative analysis of 51 videos of Quantified Self presentations to explore intentions for collecting data, methods for integrating and representing data, and how intentions and methods shaped reflection. The findings highlight two different intentions-striving for self-improvement and curiosity in personal data-which shaped how these users integrated data, i.e. the effort required. Furthermore, we identified three methods for representing data-binary, structured and abstract-which influenced reflection. Binary representations supported reflection-inaction , whereas structured and abstract representations supported iterative processes of data collection, integration and reflection. For people tracking out of curiosity, this iterative engagement with personal data often became an end in itself, rather than a means to achieve a goal. We discuss how these findings contribute to our current understanding of self-tracking amongst Quantified Self members and beyond, and we conclude with directions for future work to support self-trackers with their aspirations.

Beyond Abandonment to Next Steps

“…To characterize how people use self-tracking tools, Li et al introduce a five-stage model of personal informatics, which emphasizes barriers to tracking toward a goal of reflection and presumed action [ 12 ]. This model has been modified and expanded, noting people can reflect on data [ 3 ] and ultimately change habits [ 16 ] in the midst of tracking. Epstein et al characterize challenges in lived informatics [ 14 ], developing a model of tool use in everyday life that surfaces lapsing and stopping as major components [ 6 ].…”

Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies

“…We found that the literature provided a variety of perspectives on health SQ as data-driven work that users undertake to fulfill their health objectives [ 4 , 127 , 128 ]. It described how users interact with the SQ tools to define what aspects are relevant to their health conditions (eg, weight, sleep, blood pressure, and so on) [ 4 , 5 ]; set goals and track data about these health aspects for a period of time [ 7 ]; analyze the collected data to extract insights on health status [ 129 , 130 ]; adjust behaviors based on the insights and knowledge obtained from the analyzed data [ 130 ]; and control the adapted behaviors by sustaining the changes until the desired health outcomes are achieved [ 127 ]. We found that it was possible to categorize the overall content of the literature on health SQ activity or work in two ways: work on data and work with data.…”

“…The historical analysis of the activity is often needed to understand the recent situation [ 15 ]. In health SQ, the advances in computational analysis of SQ tools make building a history of work possible, which is a major facilitator to understand current health status and obtain self-knowledge [ 129 , 130 , 134 , 142 ]. Data generated from using SQ tools are also beneficial for people to evaluate their future health status.…”

Am I Coughing More Than Usual?

“…In contrast, self-trackers with a goal, knew what they were looking for in the data and strived at using automatic integration systems, allowing them to concentrate on reflection. The manual integration process is an iterative process of moving back and forth between representation creation and reflection [31].…”