Personal tracking as lived informatics

Abstract

This paper characterises the use of activity trackers as 'lived informatics'. This characterisation is contrasted with other discussions of personal informatics and the quantified self. The paper reports an interview study with activity tracker users. The study found: people do not logically organise, but interweave various activity trackers, sometimes with ostensibly the same functionality; that tracking is often social and collaborative rather than personal; that there are different styles of tracking, including goal driven tracking and documentary tracking; and that tracking information is often used and interpreted with reference to daily or short term goals and decision making. We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people's outlook on their future.

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

“…This fits with what Rooksby et al (2014) referred to as "diagnostic tracking": users keeping track of behaviors and outcomes to determine causal chains.…”

“…They therefore propose an alternative model of PI, based on a lived informatics perspective (Rooksby, Rost, Morrison, & Chalmers, 2014). The lived informatics model highlights the messy reality of lapses and interruptions of tracking activities.…”

“…Three additional articles reported prevalence for some insights, but not all. Many of the other articles reported prevalence of other matters (e.g., barriers to usage, Li et al, 2010; motives for usage, Rooksby et al, 2014) but not on insights or data-driven behavior change. Choe, Lee, and Schraefel (2015) reported prevalence of different types of insight, but the taxonomy they used for insights is based on the type of fact participants extract …”

“…Articles about existing PI users got their participants from the QS forum (Choe et al, 2014, forums related to specific PI products (Li et al, 2010), or by targeting a larger audience (e.g., via flyers or Amazon's Mechanical Turk) and then selecting on PI use (Epstein et al, 2015;Fritz, Huang, Murphy, & Zimmermann, 2014;Li et al, 2011;Rooksby et al, 2014). Other articles mostly used student populations, although some used colleagues (Fujinami, 2010;Park et al, 2015;Snyder et al, 2015), reports of their own experiences (Wac, 2014), or a convenience sample obtained through a snowball technique or by recruiting from their direct social network De Maeyer & Jacobs, 2013;Kay et al, 2012;Khovanskaya et al, 2013).…”

“…Most of the reported insights of this type are found in work about existing PI users, for example, "P35 realized that driving and drinking coffee were triggers for his panic attacks, and eliminated coffee altogether from his diet, which resulted in a decrease in frequency and severity of the attacks" (Choe et al, 2014(Choe et al, , p. 1150, "P36 … attributed the changes in his heart rate to a vacation" (Choe et al, 2015, p. 33), "She understood her blood sugar fluctuations better and she was able to set appropriate food and physical activity goals." (Li et al, 2011, p. 409), "P3 felt he was able to use trackers to show that his stomach problem resulted from combining medication with particular foods" (Rooksby et al, 2014(Rooksby et al, , p. 1168. Even for existing PI users, though, this type of insight seems to be relatively rare: Rooksby et al (2014) noted that of the 22 participants interviewed, only two reported what the authors call "diagnostic tracking," which relates to insights about how one factor affects another.…”