Datafication of Health: From Self-tracking to soft resistance
Updated: Feb 14
Ruckenstein, Minna, and Natasha Dow Schüll. "The datafication of health." Annual review of anthropology 46 (2017): 261-278.
"Datafication," or the conversion of aspects of life into quantitative data, is a growing phenomenon in the area of health where it is applied in practices ranging from data-driven medical research to self-care tracking. As Ruckenstein and Dow Schüll (2019) note in their review of the topic, data may give the impression of objectivity, but they are imbued with power.
Health data are commonly collected through self-tracking applications marketed to support health and wellbeing, sometimes by promoting the idea that individuals who share their data are doing so for the greater public good. Scholars have argued that people who engage in self-tracking and »data donating« practices perform unpaid and invisible "digital labour" (Lupton 2016).
The process of datafication has led to a divide between "data rich" social actors (such as governments, institutions and commercial enterprises) and "data poor" individual citizens (Andrejevic 2014). This distinction highlights the asymmetric relations between those who collect, store and use data and those who are the source of data. But Ruckenstein and Dow Schüll also point to nuances within this distinction: individuals may exercise different levels of control over their data and moreover, lack of data (such as medical records) can make people even more vulnerable than when their data is exploited.
In the process of datafication, everyday activities are converted into data flows that can be monitored at a distance at all times. In the arena of health, data-gathering technologies give access to healthcare professionals (and anyone else who might have access) to the most intimate domains of people's lives that were previously inaccessible to them. Critical scholars have argued that such digitalization adds a new layer to surveillance society. The so-called »dataveillance« (Van Dijck 2014) is not carried out from any singular point, but has become distributed among multiple interested parties such as caregivers, insurance companies, pharmacies and so on.
Algorithms that are based on the collected data are aiming to decipher emerging patterns and, for example through corporate wellness programs, influence whole populations. As Ruckenstein and Dow Schüll (2019: 264) write,
Digital tracking tools and algorithms are used not only to detect and predict but also to shape and modify behavior.
However, people's relationship with self-tracking technologies appears to be rather ambivalent. Tracking may turn empowerment into an obligation to perform healthy citizenship, and so people may respond to technological nudges, even if well-meaning, through »soft resistance« (Nafus and Sherman 2014). This may involve using devices for entertainment rather than health, being selective regarding what data is collected and shared, or employing strategies such as those shown in this short film (thanks to David Prendergast for sharing it with me):
Finally, Ruckenstein and Dow Schüll note that most of the social science literature on datafication of health is focused on North America, the United Kingdom, Australia and Northern Europe, and most literature on data self-care in particular is on wealthy, educated cosmopolitan citizens. Broadening the geographic and demographic frame of research would offer new insights into datafication of health, including how different settings and actors reshape, repurpose or even re-engineer new technologies. Additionally, the authors call for applied insights of social research on data and data activism. This is necessary because
If the conversation in health technology innovation does not address the questions of data for whom, when and why, then it will be a failure of social justice and abuse of the trust that people have placed in the institutions of health care. (Neff 2013, p. 121).
Data activism would mean linking health data to broader ethical concerns and drawing on data in ways that would address issues of social imbalances of health and medical regimes. In the words of Ruckenstein and Dow Schüll (2019, p. 272), it is important to understand that
Health, considered from the standpoint of data activism, is a societal rather than individual issue, its meaning as political as it is existential.
Andrejevic, Mark. 2014. "Big data, big questions| the big data divide." International Journal of Communication 8: 17.
Lupton, Deborah. 2016. The quantified self. John Wiley & Sons.
Nafus, Dawn, and Jamie Sherman. 2014. "Big data, big questions| this one does not go up to 11: the quantified self movement as an alternative big data practice." International journal of communication 8: 11.
Neff, Gina. 2013. "Why big data won't cure us." Big data 1(3): 117-123.
Van Dijck, José. 2014. "Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology." Surveillance & society 12(2): 197-208.