Over the last 5 years, wearable technology – comprising devices whose embedded sensors and analytic algorithms can track, analyze and guide wearers’ behavior – has increasingly captured the attention of venture capitalists, technology startups, established electronics companies and consumers. Drawing on ethnographic fieldwork conducted 2 years running at the Consumer Electronics Show and its Digital Health Summit, this article explores the vision of technologically assisted self-regulation that drives the design of wearable tracking technology. As key artifacts in a new cultural convergence of sensor technology and self-care that I call ‘data for life’, wearables are marketed as digital compasses whose continuous tracking capacities and big-data analytics can help consumers navigate the field of everyday choice making and better control how their bites, sips, steps and minutes of sleep add up to affect their health. By offering consumers a way to simultaneously embrace and outsource the task of lifestyle management, I argue, such products at once exemplify and short-circuit cultural ideals for individual responsibility and self-regulation.
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The seed for this article was a short piece in the MIT Technology Review titled “Obamacare meets wearable technology” (Schüll, 2014).
The US government’s Obama administration has taken a keen interest in the power of big data to transform health care. The US Department of Health and Human Services, the US National Institutes of Health’s Office of Behavioral and Social Sciences Research, and government-funded entities such as the National Science Foundation and the Robert Wood Johnson Foundation have invested in mHealth (or “mobile health”) initiatives as a way to address wide-scale population health problems. Projects include smoker cessation apps, health text messaging, digital tools for the management of diabetes or for medication compliance, and the like. Market research shows that over one third of doctors recommend health or medical apps for their patients (MobiHealthNews, 2014). (See also Goetz, 2010; Topol, 2012, 2015).
For more on the rise of chronic disease, see the accounts of historians of medicine Weisz (2014), Armstrong (2014) and Greene (2007) who writes of “a shift in the basic conception of chronic disease from a model of inexorable degeneration to a model of surveillance and early detection” (p. 84). For analyses of the idea of “lifestyle” see Friedman (1994), Giddens (1991) and Dumit (2012).
In 1980, the sociologist Robert Crawford described an early version of lifestyle management linked to the simultaneous depoliticization and privatization of health then taking place in America: collective struggles for wellbeing were being replaced by an emphasis on individual self-care in the form of lifestyle modification (1980, p. 365). Solutions to bad diet, for instance, were located “within the realm of individual choice”, in the ability to resist advertising and overcome bad habits (p. 368).
Unlike acute diseases that arise suddenly, lifestyle diseases pose “a more sinister threat, another type of mortal hazard with slower effects that go stealthily into the blood one cancerous bacon sandwich or poisonous drink at a time, potential killers by degrees that might catch up with us later in life” (Blastand and Speigelhafter, 2014).
Scholars of the “Internet of things” (Halpern et al, 2013) and “sensor society” (Andrejevic and Burdon, 2014) have called attention to the importance of sensor technology to contemporary life. Dramatic increases in the sensitivity and sophistication of sensors along with decreases in their size means they can be loaded into clothing, pillboxes, toothbrushes and smartphones – which are becoming wearable tracking devices in themselves. Algorithms operating on the tracked data “can analyze data along multiple lines – time, frequency, episode, cycle and systemic variables”, writes Swan (2013) , a science and technology innovator and philosopher, and in this way detect “elements that are not clear in traditional time-linear data”: patterns, cycles, exceptions, the emergence of new trends, episodic triggers, variability, correlations and early warning signs (p. 90).
Founded by two former editors of Wired magazine in 2007, Quantified Self currently claims 45 000 members in 40 countries. In online forums and in meetings around the world, quantified selfers share their attempts to experiment with diet and meditation, monitor drug side effects, correlate hormone levels with mood fluctuations and relationship dynamics, or even evaluate semantic content in daily email correspondence for clues to stress and unhappiness. Social studies of quantified self include Lupton, 2015, 2013a, 2016; Albrechtslund, 2013; Boesel (see her blog, http://www.thesocietypages.org/cyborgology/author/whitneyerinboesel/), Mackenzie, 2008; Nafus and Neff, 2016; Nafus and Sherman, 2014; Oxlund, 2012; Pantzar and Ruckenstein, 2015; Ruckenstein, 2014; Potts, 2010; Schüll, forthcoming; Till, 2014; Berson, 2015; Watson, 2013. While journalists typically cast those who live by numbers as narcissistic and obsessive in their zeal for personal data, digital health pundits hold them up as beacons of a sensible tracking future. At the same time, they recognize that mass-market users are not as responsive to quantification as the typical QS member and that technology must be designed in a way that makes it “automated, easy, inexpensive, and comfortable” (Swan, 2013, p. 93).
“People living with chronic conditions”, the authors of the report write, “are significantly more likely to track a health indicator or symptom” (Fox and Duggan, 2013, p. 2). They go on to note that two-thirds of US adults are considered overweight or obese and half are living with at least one chronic condition – most often high blood pressure and diabetes (ibid., p. 6).
According to recent reports by industry analysts, a third of people discontinue tracking within the first 6 months (Ledger, 2014; Ledger and McCaffrey, 2014). Nafus and Sherman (2014) have shown how trackers frequently switch between devices, interrupting data streams and amounting to a form of “soft resistance”.
Dumit (2012) has observed (personal communication) that what I call ‘data for life’ is becoming a part of the “drugs for life” agenda; although “changes in lifestyle such as exercising more and watching one’s diet are rendered secondary” to the administration of pharmaceuticals (p. 127), drug companies are increasingly looking to self-tracking technology to help solve the problem of medication compliance. As in the case of diabetes, it is suggested that ongoing glucose monitoring, exercise and diet be combined with a lifetime of drug-taking.
One of Fitbit’s chief officers chairs the newly formed Health and Fitness Technology Division of the Consumer Electronics Association, which oversees the presence of digital health technology at each year’s Consumer Electronics Show.
Although comical to some ears, the HAPIfork is not marketed in jest. Many journalists have mocked the product, including Stephen Colbert who called the fork an “un-American” product because of its effort to slow consumption. Some have critiqued the fork for addressing a first-world problem.
‘Big data’ has come to mean many things. Typically the phrase characterizes the continuous collection of data streams and the convergence of multiple streams and types of data such that previously undetectable patterns can be discerned – with the right tools. Some definitions of big data include the novel analytic tools that are brought to bear on vast data sets, such as advanced mining techniques, predictive modeling, dynamic systems modeling and new machine learning algorithms.
It should be noted that personal data streams can be bought, sold, transferred and mined for insight in comparison with those of others, and in this sense holds value as a kind of bioeconomic capital or “biocapital” that government-sponsored researchers or multinational corporations can harness and exploit (Rabinow and Rose, 2006, p. 203; Beer and Burrows, 2013; Lupton, 2014; Till, 2014).
Data collected by individuals for their own health or fitness projects can be recombined with that of others to draw population-wide correlations and inferences (Cukier and Mayer-Schönberger, 2013; Mayer-Schönberger and Cukier, 2013, Chapter 6). Steve Downs of the Robert Wood Johnson Foundation has commented that personal informatics “creates new opportunities to roll data up on an aggregate level and really look at the population, bringing the potential to find really interesting connections among the data” (RWJF Website).
Koopman (2014) suggests the term “infopower” as a way to extend Foucault’s concept of biopower, noting that “if biopower in its first functioning made heavy use of technologies of statistics and recordkeeping, then those very technologies have in the century since developed a gravity of their own in part due to the contributions of electrification, digitization, and other processes at the heart of our contemporary information societies” (p. 89). The concept of biopower, he suggests, “cannot fully exhaust the new modes of information surveillance, aggregation, and distribution in our midst” (p. 106; see also Koopman, 2015, n.p.).
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Thanks to Paul Gardner, Richard Fadok, Linda Hogle and Rayna Rapp for their close readings and helpful suggestions as I worked to develop my initial ideas into a full-length article, and to Colin Koopman and three anonymous reviewers for their valuable revision pointers.
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Schüll, N. Data for life: Wearable technology and the design of self-care. BioSocieties 11, 317–333 (2016). https://doi.org/10.1057/biosoc.2015.47
- digital health
- wearable technology
- big data