Self-tracking practices are ubiquitous, but not every self-tracking activity is the same. For some, self-tracking only happens voluntarily and occasionally for fun, or unknowingly through devices bought for other reasons. People with a chronic disease, however, are often pushed to self-track for their own good. Others are even forced to undertake self-tracking activities, for example, by their health insurance provider. What can we learn from these different kinds of self-tracking and what is the most dominant type in today’s world?
Kevin Kelly’s view of self-tracking as ubiquitous and inevitable runs the risk of making “self-tracking” a catch-all term. We then lose the subtle and important differences between self-trackers and their lifelogging activities. Lupton’s typology of self-tracking modes (private, communal, pushed, imposed and exploited) provides a useful alternative, allowing us to critically reflect on today’s world.
Private self-tracking refers to the mode of self-tracking which is completely voluntary. It is undertaken for personal reasons that often include goals such as better health, improved control over mood and sleep, higher work productivity or more efficient time planning. In its most extreme form, it is strictly private and personalized, making it hard to scale or transfer (e.g. members of the Quantified Self (QS) movement invented their own tracking devices, databases and algorithms).
The next mode is referred to as pushed self-tracking. In this case, self-tracking could still be a voluntary act, albeit undertaken in response to an external party. The initial motive for engaging is connected to another agent. The workplace and healthcare sector have become key sites of pushed self-tracking. Although pushed self-tracking is often perceived as a voluntary act, there frequently is a clear benefit to another party. Indeed, the most effective form of coercion is one in which repression isn’t perceived as such.
The third mode refers to communal self-tracking. While we often associate self-tracking with individualism or even narcissism, many self-trackers perceive themselves as part of a community. An important goal for them is to learn each other’s data and realize of collective goals (e.g. the national genome project[5] hopes to improve population health). We see this type of communal self-tracking being advocated with regard to smart cities, environmental activism, local development or sports niches.
The fourth mode is called imposed self-tracking. In this instance, self-tracking has lost any glimpse of voluntary behavior. Self-tracking devices are foisted on people and they are forced to monitor themselves. Companies might require employees to wear specific badges that record geo-location, sound, and physical movement. Employees are “advised” to follow certain self-tracking programs, which are connected to insurance premiums and exclude employees from health benefit programs if they don’t participate, not leaving them much choice.
The last mode is exploited self-tracking. This refers to a second iteration of personal data in addition to the basic utility for the primary users, in which the data is repurposed for the benefit of other parties. As there is significant commercial value to aggregated data, the reiteration of data has become a dominant design principle for companies in all sectors. For example, retailers offer customer loyalty programs with clear-cut personal benefits for consumers, while the implicit motive is the future monetization of the underlying data.
Based on this typology, we can elaborate on some current developments and trends we have discussed recently, namely the rise of mega-ecosystems[6] and decentralized solutions. Last week, we wrote about the age of digital mega-ecosystems. Societies are increasingly aware of the excesses of the centralized digital world (e.g. platform feudalism, surveillance capitalism, privacy scandals, etc.) and these are inherently linked to the exploited and imposed modes of self-tracking. In response, developers are building alternative digital ecosystems such as decentralized stacks, decentralized funding[7], open-source software[8] and data-pricing[9], to break down the walls of the monopolistic and centralized structure of ecosystems. These decentralized alternatives are not likely to substitute today’s platforms entirely and several may co-exist as they offer services their counterparts aren’t able to deliver.
What does this imply for the different self-tracking practices? Since the origination of the Quantified Self movement in 2007, we have seen a gradual shift from the private self-tracking mode to the pushed and imposed self-tracking modes. Generally speaking, we went from the IT nerd with his private database and gadgets to the omnipresent self-tracker with his affordable and intuitive wearables, delivered to him by big tech platforms. For Kevin Kelly, this is the inevitable internal logic of technology itself. At the same time, due to the significant commercial value of data, the communal self-tracking nature of the QS movement was quickly overshadowed by the exploited self-tracking mode. The commercial value of data has been mostly captured by private data silos, perfectly symbolized by big tech companies surpassing the 1000 billion market capitalization.
Nevertheless, in light of current developments and trends, these two dominant tendencies could now face a slight reversal. To begin with, Kevin Kelly only explains the internal logic of closed ecosystems. As a consequence of open and decentralized alternatives, we might observe a reversed trend in terms of freedom and coercion. In the most optimistic view, the mere availability of realistic alternatives already implies a substantial shift in terms of self-tracking modes from an imposed to a more voluntary nature of self-tracking. Furthermore, if data ownership moves from the center to the periphery of the network, we might also encounter a reversed tendency in terms of exploited self-tracking back to self-tracking embedded in communal practices. In that case, data won’t be repurposed commercially without permission or even awareness and control over and motives for data commodification will be transferred back to the community and the users.
Decentralized ecosystems could initiate reversed trends and regain some of the lost freedom of choice and counteract the private exploitation of personal data. However, there is always the possibility that decentralized ecosystems will give rise to new unforeseen forms of coercion and exploitation. An analogy can be found in the combating of inequality. The reallocation of wealth through progressive taxes might diminish economic inequality, but at the same time enlarge the importance of social and cultural capital. Economic inequality is then partly replaced by cultural inequality, visible, for example, in the currently escalating identity politics in Europe. Nevertheless, the decentral architecture and internal logic of the system at least facilitate more degrees of freedom and flexibility to naturally deal with unforeseen consequences of social and cultural developments.
