Thus far studies of users that are not members of any tracking community are relatively few. HCI studies on this subject constitute a fair part of all of them. Nonetheless, they have a variety of research aims, and I will discuss them by grouping them accordingly.
Some of the research focuses on the self-trackers’ interaction with the technology and the collected data. Elsden et al. (2015) explored the notion of quantified past and the
importance of historical self-tracking data among some self-trackers. They described how participants performed data-work - ‘the language and “work” that is required to qualify and make sense of one’s data’ (Elsden et al. 2015, 29). Yang and colleagues (2015) looked at how users evaluate the accuracy of self-tracking technologies. Found that users’
satisfaction with accuracy (trueness and precision) varied as self- tracking devices did not satisfactorily attend to differences between different user characteristics (e.g., physical characteristics, bodily movements, activities types, and lifestyle), as well as their goals.
Also, users did not have adequate knowledge of how these devices evaluate the tracked activity and, therefore, did not know how to assess data and measurement quality. Authors identified seven types of problems that users encountered as they sought to assess the accuracy of their tracking devices. Harrison et al. (2015) interviewed people who were
27 given a self-tracking device about the issues they encountered. Users complained about accuracy, social aspects of self-tracking were not always satisfactory and aesthetic, and comfort demands were often unmet. Authors discuss the workarounds that some people engaged in.
Other, the studies that are closest to the interest of the present thesis interviewed people using activity trackers who were not part of any community, in order to understand what made them interested in self-tracking and what drives their usage. Rooksby et al. (2014) interviewed people, who self-tracked physical activity. Participants frequently used apps for short periods to assist them in achieving long-term goals. Unlike the QSers, who are interested in collection and analysis of data, they perceived tracking as ‘prospective rather than retrospective’ (Rooksby et al., 2014, p. 1171) and did not look at historical data.
Based on these findings, Rooksby et al. (2014) introduced the term ‘lived informatics,’
underscoring that ‘people are using information and finding its meaning in their day-to-day lives’, as opposed to Li’s et al. (2010) personal informatics, where rational users require validated and analysed data to act.
Fritz et al. (2014) conducted interviews with people using activity trackers. They found that users lose their initial enthusiasm over time. Nonetheless, participants were attached to numerical goals and data, noting that technologies motivated them to be more active and made them make durable behavioural changes with some participants rewarding themselves for some device-related achievements. Also, users often became dependent on the devices and felt irritated when they forgot to wear them and could not account for their activities. Only some of the participants engaged in the social features of their devices, with those engaging in them feeling more motivated and mainly sharing data with
strangers. As self-tracking technologies are more prevalent among young and middle-aged adults, research into the older population is limited.
28 HCI studies on self-tracking users offer valuable insights into different aspects of
engagement in self-tracking. More often than not they use qualitative methods, such as interviews, to gather their data, and thus can provide more in-depth information about the motivations, use, and end of use. What is often neglected, however, is an exploration of the practices, as the discussed studies mostly rely on experiences and evaluations.
Moreover, HCI research is geared towards utilitarian goals of improving the experience of self-tracking technology use. Whilst, I am interested in engagement in self-tracking in order to understand this endeavour better.
Nonetheless, some HCI studies on non-QS users offer some insights particularly relevant to this thesis. Because of their interest in differences in technology use, HCI studies have explored a wider variety of users, in particular new and less enthusiastic users.
For example, Rapp and Cena (2016) provide a valuable study exploring the first-time users of tracking whom they refer to as ‘naïve’ users. These users find the self-tracking technologies as requiring too much effort, posing questions about privacy and their data visualisations too abstract. The ‘naïve’ users wanted the tool to provide more explanations and recommendations, and because they were not getting them they found the use lacking rewards. While this study does not provide insight into how these user concerns would be addressed by technology developers, it shows a different way that self-tracking technologies can be perceived as opposed to long-time, enthusiastic users that are usually being studied.
Lazar et al. (2015) looked at engagement and abandonment. They argue that at least half of people who own some kind of activity trackers are not using them anymore.
Participants in their study lost interest in self-tracking technologies as they did not provide
29 useful information. The information was perceived as suggestion rather than prescription as users were not adhering to it.
Clawson et al. (2015) looked at Craigslist to determine the reasons behind self-tracking device abandonment. They distinguish several kinds of abandonment: happy abandonment – when users switch to a new technology; social switching – when a different device was preferred by someone in family or friends and abandonment when technology no longer fitted with their health needs or was simply found as not useful. The abandonment was most often sparked by the fact that self-tracking technology in use did not meet users.
The findings of these studies suggest that self-tracking data is not universally
understandable and useful. Moreover, they point to the agency of users as they pick and choose what data to use and react to, and also decide to stop using the technology
altogether. These findings stand in contrast to the idea of ‘datafied power’ found in some of the conceptual works on self-tracking where self-tracking users are seen as controlled by the data they collect (Ruckenstein & Schüll, 2017).
Thus, these HCI studies inspired and informed my research, as I aimed to open up the scope even further and, therefore, focused on another of so far neglected user groups which I referred to as everyday self-trackers. As the studies discussed above show, different user groups use the technologies and react to them differently. By focusing on everyday self-trackers as opposed to other more studied user group – Quantified Self community – I aim to show that self-tracking can be perceived and done differently than what studies on Quantified Self allow to imagine.