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Self-tracking Modes: Reflexive Self-Monitoring and Data Practices
Deborah Lupton, News & Media Research Centre, Faculty of Arts & Design,
University of Canberra
Paper for the ‘Imminent Citizenships: Personhood and Identity Politics in the Informatic
Age’ workshop, 27 August 2014, ANU, Canberra
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Abstract
The concept of ‘self-tracking’ (also referred to as life-logging, the quantified self,
personal analytics and personal informatics) has recently begun to emerge in
discussions of ways in which people can voluntarily monitor and record specific
features of their lives, often using digital technologies. There is evidence that the
personal data that are derived from individuals engaging in such reflexive self-
monitoring are now beginning to be used by actors, agencies and organisations beyond
the personal and privatised realm. Self-tracking rationales and sites are proliferating as
part of a ‘function creep’ of the technology and ethos of self-tracking. The detail offered
by these data on individuals and the growing commodification and commercial value of
digital data have led government, managerial and commercial enterprises to explore
ways of appropriating self-tracking for their own purposes. In some contexts people are
encouraged, ‘nudged’, obliged or coerced into using digital devices to produce personal
data which are then used by others. This paper examines these issues, outlining five
modes of self-tracking that have emerged: private, communal, pushed, imposed and
exploited. The analysis draws upon theoretical perspectives on concepts of selfhood,
citizenship, biopolitics and data practices and assemblages in discussing the wider
sociocultural implications of the emergence and development of these modes of self-
tracking.
Biographical Note
Deborah Lupton is Centenary Research Professor in the News & Media Research Centre,
Faculty of Arts & Design, University of Canberra. Her latest books are Medicine as
Culture, 3rd edition (Sage, 2012), Fat (Routledge, 2013), Risk, 2nd edition (Routledge,
2013), The Social Worlds of the Unborn (Palgrave Macmillan, 2013), The Unborn Human
(editor, Open Humanities Press, 2013) and Digital Sociology (Routledge, 2015).
Deborah’s current research interests are in the critical sociology of big data, self-
tracking cultures, the digitisation of children, academic work in the digital era and
critical digital health studies.
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Introduction
The concept of ‘self-tracking’ (also referred to as life-logging, the quantified self,
personal analytics and personal informatics) has recently begun to emerge in
discussions of ways in which people can monitor and record specific features of their
lives. Some self-trackers collect data on only one or two dimensions of their lives and
for a short time. Others may do so for hundreds of phenomena and for long periods.
Some self-trackers simply collect information about themselves as a way of
remembering and recording aspects of their lives. Others take an approach which is
more specifically goal-oriented, seeking to discern patterns and reflect on and make
meaning out of the information they choose to collect. Once the data are collected, self-
tracking practices typically incorporate organisation, analysis, interpretation and
representation of the data (such as producing statistics or graphs and other data
visualisations) to make sense of them, and efforts to determine how these data can offer
insights for the user’s life.
Monitoring features of one’s life and reflecting upon them in the quest for self-
knowledge are not new practices. Traditional self-tracking practices have included ages-
old strategies such as journaling and diary-keeping. In the contemporary era many
people engage in self-tracking using a range of methods. A Pew Research Center (Fox &
Duggan, 2013) survey of Americans’ self-tracking practices found that a high proportion
– almost 70 per cent – did so, either for themselves or a loved one, with weight, diet or
exercise routines the most frequently monitored. The majority of these people did not
use digital technologies for monitoring: rather they kept track simply in their heads or
using pen-and-paper. However the recent focus on self-tracking in both popular forums
and the academic literature centres on using digital technologies for monitoring aspects
of one’s life. Mobile digital devices connected to the internet, devices and environments
that are fitted with digital sensors and the possibilities for data archiving and sharing
that are afforded by computing cloud technologies have contributed to the ever more
detailed measurement and monitoring of people’s activities, bodies and behaviours in
real time and the analysis, presentation and sharing of these data.
It is on the new digitised strategies for self-tracking that I focus here, particularly
as these technologies are raising a number of new issues concerning the use of personal
data, surveillance and citizenship. Self-tracking at first glance appears to be a highly
specialised subculture, confined to the chronically ill, obsessives, narcissists or
computer geeks. Many portrayals of self-tracking represent it as a voluntary and private
practice, undertaken for purely personal reasons. This form of participatory self-
surveillance is often represented as distinct from and in opposition to covert forms of
surveillance or those that are imposed upon people. So too the ‘personal’ or ‘small data’
that are consciously collected in self-tracking are frequently positioned as different
from the anonymous ‘big data’ sets that are generated as part of people’s routine
interactions with digital platforms, social media and the sensor-embedded spaces in
which they move. However the concept and practices of self-tracking are now
dispersing rapidly into multiple social domains. There is evidence of ‘function creep’, or
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the move of self-tracking practices from private and participatory self-surveillance to
collective and imposed surveillance.
A body of literature is beginning to emerge that examines the social, cultural and
political dimensions of the phenomenon of self-tracking (Lupton, 2012a, 2013a, 2013b,
2014a; Nafus, 2014; Nafus & Sherman, 2014; Pantzar & Ruckenstein, 2014;
Ruckenstein, 2014; Whitson, 2013). As yet, however, there has been no sustained
examination of the spreading out of self-tracking cultures and practices from the purely
personal into multiple social domains. This paper examines these issues, focusing on
five modes of self-tracking.
Technologies of self-tracking
Digitised self-tracking has attracted a high level of attention from developers and
entrepreneurs seeking to capitalise on the practice. The technologies themselves are
viewed as a major source of potential revenue for digital developers and entrepreneurs,
who are taking a keen interest in how best to produce technologies to market to self-
trackers, and often attend Quantified Self meetups and conferences (Boesel, 2013b;
Nafus & Sherman, 2014). It has been estimated that the shipment of wearable
technologies will exceed 485 million annually by 2018, and that in 2013 61 per cent of
the wearable technologies market was occupied by sporting and physical activity
personal tracking devices (ABIResearch, 2013). Tens of thousands of self-tracking apps
are available for downloading to smartphones and iPod devices. Smartphones
themselves include sensors such as GPS, gyroscopes and accelerometers that can be
employed for self-tracking and iPod Nanos come already equipped with fitness tracking
apps such as Nike+ and a pedometer.
Perhaps the most public face of self-tracking is the Quantified Self website. The
‘quantified self’ term was invented in 2007 by two Wired magazine editors, Gary Wolf
and Kevin Kelly. They went on to establish the official website (Quantified Self, 2014)
and its associated Quantified Self Labs, a collaboration of users and tool makers who are
interested in working together to share expertise and experiences of self-tracking. The
Quantified Self website provides discussion forums, supports regional meetings of
members and two annual international conferences and publishes a blog in which
various aspects of self-tracking and developing ‘self knowledge though numbers’ (the
group’s motto) are explained and the strategies and findings of members about their
own self-tracking efforts are publicised.
The Quantified Self website (Quantified Self guide to self-tracking tools, 2014)
lists over 500 self-tracking tools, including in addition to geolocation, health, fitness,
weight, sleep, diet and mood or feeling tracking apps, services and devices that are able
to record social interactions, emails, networks and social media status updates and
comments. Other listed tools also allow users to track their meditation practices,
television watching, computer use and driving habits, financial expenses, time use,
beneficial habits and work productivity, and to monitor local environmental conditions,
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progress towards learning or the achievement of personal goals (see also the Personal
Informatics website for another long list of tools (Personal informatics tools, 2014)).
A number of ‘smart’ objects have been developed that provide capacities for self-
monitoring. Cars can now monitor driving habits and drowsiness, alerting drivers if
they are at risk of falling asleep at the wheel. Mattresses can monitor sleep patterns and
body temperature, chairs can sense physical movements and ‘smart’ shoes and clothing
can record activity and other physical data. The concept of ‘self’-tracking may be
extended well beyond the envelope of the individual body. People can use sensor-based
technologies to monitor not only their own habits, bodies and behaviours but those of
intimate others (such as their children) and companion animals as well as
environmental conditions such as air temperature, humidity, light conditions, gases, air
quality and pollution and their home’s use of energy. Some of these smart objects can
now exchange data with each other, so that, for example, users’ smart home thermostat
system can now read the sleep data from their wearable device to ensure that the
heating is switched on as soon as people begin to wake in the morning (Olson & Tilley,
2014).
The digital data that are continually generated by individuals have become
invested with symbolic and commercial value and status (Andrejevic, 2013; Andrejevic
& Burdon, in press; Kitchin, 2014a, 2014b; Lupton, 2015). The collection and analysis of
personal data via self-tracking practices are now becoming increasingly advocated and
implemented in many social contexts and institutions, including the workplace,
education, medicine and public health, insurance, marketing and commerce, the
military, citizen science and urban planning and management. The growing
commodification and commercial value of digital data sets and their use in these
domains are blurring the boundaries between small and big data, the private and the
public. The personal data that people collect about themselves are now often
represented as offering contributions to the aggregation of big data sets. People are now
encouraged, ‘nudged’, obliged or coerced into using digital devices for monitoring
aspects of their lives to produce personal data which can then be used for the purposes
of others.
Modes of self-tracking
I have developed a typology of the five distinctive modes of self-tracking that have
emerged in recent times. These are private, pushed, communal, imposed and exploited
self-tracking. There are intersections and recursive relationships between each of these
self-tracking positions. However there are also observable differences related to the
extent to which the self-tracking is taken up voluntarily and the purposes to which the
data thus created are put.
Private self-tracking
A major feature and attraction of self-tracking for many practitioners is using the
information they collect on themselves to achieve self-awareness and optimise or
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improve their lives. The data and the knowledge contained therein are represented as
enabling self-tracking practitioners to achieve better health, higher quality sleep,
greater control over mood swings, improved management of chronic conditions, less
stress, increased work productivity, better relationships with others and so on. In many
cases this is all self-initiated and voluntary, as part of the quest for self-knowledge and
self-optimisation and as an often pleasurable and playful mode of selfhood.
Private self-tracking, as espoused in the Quantified Self’s goal of ‘self knowledge
through numbers’, is undertaken for purely personal reasons and the data are kept
private or shared only with limited and selected others. Portrayals of self-tracking in the
popular media often focus on this mode, with regular references to the ‘narcissism’ or
‘self-experimentation’ that self-tracking supposedly involves (Lupton, 2013a). The
private self-tracking mode is often articulated in accounts that seek to define the self-
tracking phenomenon. According to the Quantified Self Institute, a research body that is
part of the Hanze University of Applied Sciences in the Netherlands and associated with
the Quantified Self founders, self-tracking ‘is a functionally “selfish” activity, which is a
result of a personal motivation. “Me and my data”, that is the point of the Quantified Self’
(de Groot, 2014, no page number given). Similarly the online Oxford Dictionaries
definition defines self-tracking as: ‘The practice of systematically recording information
about one’s diet, health, or activities, typically by means of a smartphone, so as to
discover behavioural patterns that may be adjusted to help improve one’s physical or
mental well-being’ ("Self-tracking," 2014).
Research investigating the motives of self-trackers has found that they are often
involved for private and personal reasons. One study of American self-trackers (Li, Dey,
& Forlizzi, 2010) found that the reasons the participants gave for engaging in self-
monitoring were related to curiosity about what their data would reveal, an interest in
quantitative data and numbers in general as part of being a ‘geek’, an interest in
experimenting with new tools for self-tracking, acting on a suggestion from another
person, and trigger events, such as suffering from sleep problems, wanting to lose
weight or developing an illness. Another study analysed 52 videos of meet-up talks
posted on the Quantified Self website (Choe, Lee, Lee, Pratt, & Kientz, 2014). The
researchers found that members of the largest group of self-trackers were monitoring
health-related factors such as physical activity, food consumption, weight and mood.
Another group was interested in tracking their work productivity and cognitive
performance. A third group was identified, comprised of people who wanted to have
new life experiences through self-tracking as part of experimenting. Indeed the term
‘self-experimentation’ was used frequently across the speakers as relating to finding
meaning knowledge about themselves that they could use for self-optimisation.
There is a strong emphasis on personal experience in the Quantified Self
community. People who discuss their self-tracking practices in Quantified Self forums
are encouraged to talk about ‘What I did, how I did it and what I learned’. In this and
other self-tracking circles the concept of ‘n=1’ is often articulated, conveying the idea
that collecting data is a personal enterprise that is limited to the individual. Not only do
self-trackers make choices about what data about themselves are important to collect,
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they make sense of and use data in highly specific and acculturated ways. They seek to
make connections between diverse sets of data: how diet, meditation or caffeine affect
their concentration, for example, or how their mood is influenced by exercise, sleep
patterns or geographical location, or the specific interactions of all of these variables.
Indeed the very idiosyncrasy or uniqueness of many self-trackers’ interests and
consequent self-tracking data practices means that their data may not be interesting or
valuable to others as it is not easily transferrable (Nafus & Sherman, 2014).
Pushed self-tracking
Pushed self-tracking departs from the private self-tracking mode in that the initial
incentive for engaging in self-tracking comes from another actor or agency. Self-
monitoring may be taken up voluntarily, but in response to external encouragement or
advocating rather than as a wholly self-generated and private initiative.
In a growing number of forums self-tracking is advocated as a means for
achieving behavioural change in target groups to achieve better health or other
outcomes. This approach is referred to in computing science research as ‘persuasive
computing’, or using digital technologies to ‘nudge’ people into behaviour change
(Purpura, Schwanda, Williams, Stubler, & Sengers, 2011; Rooksby, Rost, Morrison, &
Chalmers, 2014). Advocates for pushed self-tracking are particularly evident in the
patient self-care, health promotion and preventive medicine literature. Arguments for
persuading people to self-track such bodily features as their body weight and physical
activity level, and in the case of patients with chronic illnesses, such aspects as blood
glucose level and blood pressure are becoming increasingly common in this literature
(see, for example, MacLeod, Tang, & Carpendale, 2013; Rabin & Bock, 2011; Swan, 2009,
2012). In this context the personal data that are generated from self-tracking are
represented as pedagogical and motivational, a means of encouraging self-reflection or
emotional responses such as fear, guilt or shame that will lead to the advocated
behaviour changes (Lupton, 2012b, 2013b), or else as a form of self-care that allows
people with chronic conditions to reduce their interactions with healthcare providers
and become ‘digitally engaged’ (Lupton, 2013c).
The workplace has become a key site of pushed self-tracking, where financial
incentives or the importance of contributing to ‘team spirit’ and productivity may be
offered for participating. Many employers are turning to the use of digital self-tracking
technologies (‘digital wellness tools’) as part of workplace health promotion programs
or ‘wellness programs’, particularly in the US, where employers pay for health insurance
coverage of their employees and it is therefore in their financial interests to promote
good health among their workers. Wearable technology manufacturers such as Fitbit
are brokering deals with employers and insurance companies to sell their fitness and
activity trackers and data analytics software as part of these wellness programs (Olson
& Tilley, 2014). Mobile apps and software programs that remind employees to get up
from their desks and take exercise breaks and to help them manage stress and sleep
better are becoming more often used in the workplace (Zamosky, 2014).
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Insurance companies are beginning to develop other ways of incorporating self-
tracking data into the calculation of risks and resultant premiums that are offered to
customers. Motor vehicle insurers led the way with their telematic devices attached to
car engines to monitor driving practices as part of ‘usage-based’ insurance that
calculates customised premiums using these data as well as demographic information
(NAIC, 2014). Health and life insurance companies in the US and elsewhere are also
directly offering consumers the opportunity to use self-tracking devices for health and
fitness. For example Wellness & Prevention, a health insurance subsidiary of the
Johnson & Johnson company, has developed a proprietary app, Track Your Health, that
is offered solely to their customers. Track Your Health incorporates data from several
third party apps and uploads these data to the company’s platform. Customers can also
enter their data manually into the platform or use data collected by their smartphone on
their physical activity. They can then view their data to monitor their progress towards
health- or fitness-related goals (Comstock, 2014).
Communal self-tracking
While self-tracking, in its very name and focus on the ‘self’ may appear to be an
individualistic practice, many self-trackers view themselves as part of a community of
trackers (Boesel, 2013a; Lupton, 2013a; Nafus & Sherman, 2014; Rooksby, et al., 2014).
They use social media, platforms designed for comparing and sharing personal data and
sites such as the Quantified Self website to engage with and learn from other self-
trackers. Some attend meet-ups or conferences to engage face-to-face with other self-
trackers and share their data and evaluations of the value of different techniques and
devices for self-tracking. Indeed one of the founders of the Quantified Self, Gary Wolf,
has contended from the beginning that self-tracking need not be a purely solipsistic
enterprise: ‘The excitement in the self-tracking movement right now comes not just
from the lure of learning things from one's own numbers but also from the promise of
contributing to a new type of knowledge, using this tool we all build’ (Wolf, 2009, p. no
page number given).
This drive towards ‘sharing your numbers’ fits into the wider discourse of
content creation and sharing personal details and experiences with others that
underpins many activities on Web 2.0 social media platforms (Beer, 2013; John, 2013).
However the focus on personal motivation and individual benefit is often still apparent
in these discussions of the communal nature of self-tracking. While there is constant
reference among members of the Quantified Self movement to the ‘Quantified Self
community’, this community largely refers to sharing personal data with each other, or
learning from others’ data or self-tracking or data visualisation methods so that one’s
own data practices may be improved. Several commentators have begun to refer to ‘the
quantified us’ as a way of articulating how the small data produced by self-trackers may
be usefully incorporated into large data sets to ‘get more meaning out of our data’
(Ramirez, 2013, p. no page number given). As this suggests, the concept of quantified us
still focuses firmly on the individual’s agenda. The idea is to draw on others’ pooled data
to further one’s own interests and goals: ‘Quantified Self can provide added value, when
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you start sharing your data online and other self-trackers share their data as well. All
this combined data provide an enormous amount of extra information for you’ (de
Groot, 2014, p. no page number given).
Another portrayal of communal self-tracking is that which is frequently
championed in discourses on citizen science, environmental activism, healthy cities and
community development. These initiatives, sometimes referred to as ‘citizen sensing’
(Gabrys, 2014), are a form of crowdsourcing. They involve the use of data that
individuals collect on their local environs, such as air quality, traffic levels or crime
rates. The concepts of the ‘healthy city’ and the ‘smart city’ are beginning to come
together in some attempts to use the digitised sensing and monitoring technologies for
health promoting purposes (Kamel Boulos & Al-Shorbaji, 2014; Kamel Boulos et al.,
2011). One example is the initiative announced by New York University in 2014,
involving its collaboration with the developers of a new residential area in that city,
Hudson Yards, to create a ‘quantified community’ in the interests of efficiency and
residents’ health and wellbeing. Information on such factors as pedestrian traffic, air
quality, energy production and consumption and health and physical activity levels of
residents was to be routinely collected as part of this project (Anuta, 2014).
These data may be used in various ways. Sometimes they are simply part of
gathering collective data at the behest of local agencies but they are also sometimes
used in political efforts to challenge governmental policy and agitate for improved
services or planning. The impetus may come from grassroots organisations or
encouraged upon citizens as top-down initiatives from governmental organisations as
part of community development.
Imposed self-tracking
What I call ‘imposed self-tracking’ is the foisting of the use of self-tracking devices upon
individuals by others primarily for these others’ benefit. One example is the
productivity self-tracking devices that are becoming a feature of many workplaces as
employers seek to identify the habits of staff members in the interests of collecting data
that will assist in maximising worker efficiency or reduce costs. Some companies,
including those in the banking, technology, pharmaceutical and healthcare industries,
require their employees to wear badges equipped with RFID chips and other sensors
that can record sound, geo-location and physical movement to monitor such aspects of
the wearers as tone of voice, posture and who they speak to and for how long (Lohr,
2014).
Another example of imposed self-tracking is the use of digital self-tracking
devices and apps in school-based health and physical education. Some physical
education teachers are beginning to require their students to wear such devices as
heart-rate monitors to determine whether they are fully participating in set exercise
activities and to compare their exertions with other students (Lupton, submitted). In
these contexts people often have little choice over whether they engage in self-tracking
practices. School students must follow the directions of their teachers and wearing
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tracking devices may be required as part of workers’ productivity monitoring and
linked to pay and promotion opportunities (Lohr, 2014).
At its most coercive, imposed self-tracking is used in programs involving
monitoring of location and drug use for probation and parole surveillance, drug
addiction programs and family law and child custody monitoring. Digital cellular
monitoring devices allow radio frequency monitoring of offenders who are serving at-
home sentences. In some criminal justice systems global positioning technologies are
also used to track parolees’ movements. Several self-tracking devices to monitor alcohol
use have been developed for use in programs for alcohol addiction and policing. The
secure continuous remote alcohol monitoring device is used to provide alcohol testing
(via the wearer’s sweat) through the wearing of a bracelet or anklet. Some such
monitoring devices combine a number of biometric tracking and surveillance
technologies. For example the Soberlink company has developed digital mobile alcohol
breath-testing devices that combine alcohol-monitoring with facial recognition
technologies for authenticating identity. They send text messages to clients to remind
them to test their breath and send the data to designated contacts. These devices are
marketed to criminal justice, family law and addiction treatment agencies.
Exploited self-tracking
I use the term ‘exploited self-tracking’ to refer to the ways in which individuals’
personal data (whether collected purely for their own purposes or as part of pushed,
communal or imposed self-tracking) are repurposed for the (often commercial) benefit
of others. The notion of personal data as commodities is now frequently articulated in
commercial circles. Opportunities to use these data are viewed as valuable in informing
companies about consumer habits and preferences. For example market research
companies use self-tracking apps issued to their research subjects to gauge their habits
and responses to brands. Research subjects are issued with an app that has often been
developed specifically for this purpose which is able to send them messages throughout
the day asking them to answer such questions as ‘How do you feel right now?’, ‘What
did you have for lunch today’ or ‘How did you sleep last night?’ and which use
smartphone sensors to collect such features as the geo-location of users. The Datarella
company, for example, has developed an app called Explore, described as ‘your personal
coach’, which is formatted to ask questions of the user throughout the day as a means of
generating individual data for the user’s personal use. The company also sells the data
to businesses as a way of generating information about customers and clients.
Self-tracking is often marketed to consumers as a way for them to benefit
personally, whether by sharing their information with others as a form of communal
self-tracking or by earning points or rewards. Customer loyalty programs, in which
consumers voluntarily sign up to have their individual purchasing habits logged by
retailers in return for points or rewards is one example. Their data are used by the
retailers to gather data about their customers, learn more about purchasing habits
generally and to target the individual with promotions, special offers and advertising.
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The personal data that are uploaded by participants in these activities, therefore, are
used by third parties for commercial gain.
Some retailers are beginning to use wearable devices as part of their customer
rewards schemes. One example is the ‘Balance Rewards for Healthy Choices’ program
offered by Walgreens, America’s largest pharmacy retailing chain. As part of a customer
loyalty program people are offered the opportunity to ‘earn points for your healthy
choices’ to save money on products and ‘take advantage of great, exclusive offers for
members’. They can do so by recording details of their physical activity, chronic disease
management or progress towards a health-related goal such as losing weight or ceasing
smoking and syncing the data collected by digital fitness trackers or uploading data to
the Walgreens’ platform or customised app (Walgreens, 2014).
The intersections of self-tracking modes
There are intersections and blurring between the various modes of self-tracking that I
have identified here. The private mode of self-tracking can merge with communal self-
tracking when the focus is encouraging people to achieving community development or
other collective goals via self-tracking data. This representation of self-tracking portrays
it as a civic duty in producing small data that is valuable not only or simply for personal
use but also for the purposes of others in one’s community. Reflexive self-monitoring is
still a feature of this mode when it involves sharing data with other self-trackers, as in
Quantified Self forums, but some versions of communal self-tracking incorporate
notions of participatory democracy, citizenship and community. Indeed the concept of
what I call ‘self-tracking citizenship’ involves a distribution of subjectivity that
incorporates technologies and the data they gather as part of its ethos and practice
(Gabrys, 2014).
The overlapping of self-tracking modes is apparent in platforms such as
PatientsLikeMe and similar websites that have been established to promote the sharing
of experiences between patients who have the same medical condition. The overt
objective of these platforms is to provide a place where patients can talk to each other,
exchange information and provide support, and some offer self-tracking tools for users
to monitor their symptoms and therapies as well. Here the reflexive monitoring subject
is the patient who digitally tracks their symptoms, illness experiences and therapies
(private self-tracking), but also shares these data with other patients for mutual benefit
(communal self-tracking). The data generated on these websites are also used by the
developers and by third-parties such as medical researchers and pharmaceutical
companies who are given access to the data, sometimes on payment of a fee. In some
cases these third-party uses of the data may be viewed as benefiting the patient
community; when new therapies are tested, for example. But in other cases only the
developers and third-parties benefit by harvesting the patients’ data for commercial
gain (Lupton, 2014c). This is a form of exploited self-tracking.
There is a fine line between pushed self-tracking and imposed self-tracking.
While some elements of self-interest may still operate and a discourse of ‘choice’ may be
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employed, people may have little option of opting out. In the case of workplace wellness
programs involving self-tracking of physical activity or body weight, for instance,
employees may be given the option of wearing the devices and allowing employers to
view their personal data. However failure to participate may lead to higher health
insurance premiums enforced by an employer, as is happening in some workplaces in
the US (Olson, 2014; Olson & Tilley, 2014). In these contexts the use of self-tracking
devices becomes imposed upon the user where they otherwise might not have chosen
to engage in self-tracking or to share their personal data with others.
Discussion
Self-tracking cultures have emerged in a sociocultural context in which various
rationales, discourses, practices and technologies are converging. These include the
following: concepts of the self that value self-knowledge, self-awareness and self-
entrepreneurialism; a moral and political environment in which taking responsibility
for one’s life as an individual rational actor is privileged and promoted; the ability of
digital technologies to monitor an increasing array of aspects of human bodies,
behaviours, habits and environments; the emergence of the digital data knowledge
economy, in which both small data and big data are valued for their insights and have
become tradeable commodities; and the realisation on the part of government,
managerial and commercial actors and agencies that the data derived from self-tracking
can be mobilised for their own purposes.
Self-tracking may be theorised as a practice of selfhood that conforms to cultural
expectations concerning the importance of self-awareness, reflection and taking
responsibility for managing, governing oneself and improving one’s life chances. A
Foucauldian perspective as articulated in the work of theorists on contemporary
selfhood (Elliott, 2013; Rose, 1990, 2007a) can readily be adopted to theorise the modes
and ethics of selfhood that are demonstrated in self-tracking cultures. What might be
described as ‘the reflexive monitoring self’ (Lupton, 2014b) in the context of digitised
tracking technologies is an aggregation of practices that combine regular and
systemised information collection, interpretation and reflection as part of working
towards the goal of becoming. Underpinning these efforts are the notion of an ethical
incompleteness and a set of moral obligations concerning working on the self that are
central to contemporary ideas about selfhood and citizenship (Foucault, 1988). The
idealised reflexive monitoring subject as represented in popular forums and some of the
academic literature focusing on the benefits of self-tracking is highly rational, motivated
and data-centric. Underpinning this ideal is the belief that the self-knowledge that will
eventuate will allow self-trackers to exert greater control over their destinies.
The self-tracking phenomenon offers an exemplar of the ways in which digital
technologies participate in the configuration of selfhood, embodiment and social
relations and locate the individual within digitised networks and economies. Bodies are
increasingly digitised in a multitude of ways (Lupton, 2015; O'Riordan, 2011), including
digital self-tracking devices recording personal information. A feedback loop is
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established, in which personal data are produced from digital technologies which then
are used by the individual to assess her or his activities and behaviour and modify them
accordingly (Lupton, 2012b). Discourses on self-tracking therefore also reveal notions
of the value of data and the importance of creating data that are about oneself. Self-
tracking is portrayed as a means by which the hidden patterns in one’s life that are
otherwise undiscernible may be not only identified, but most importantly, acted upon
(Lupton, 2014b).
Unlike the ‘passive’ forms of personal data collection that are characteristic of
many other forms of transactional user engagement with online technologies, self-
tracking is an ‘active’ and purposeful data practice. Self-tracking may thus be further
conceptualised as a data practice that produces data assemblages. A data assemblage is
a complex sociotechnical system composed of many actors whose central concern is the
production of data (Kitchin, 2014b, p. 24). In the case of self-tracking, these data
assemblages are configured via systems of thought, forms of knowledge, business or
government models, human users, practices, devices and software, and also sometimes
by networks of other users and agents other than the self-tracker who seek to make use
of the data for their own purposes. Given the ways in which digital data are generated,
stored, managed and used, once they are digitised, the array of practices that began as
personal and private tend to become inextricably imbricated within these networks and
economies.
The use and ownership of personal data by actors and agencies other than the
individual who generates these data are beginning to have major implications for social
discrimination and justice issues. The algorithms constructed by software coders bring
digital data together in certain ways that result in ‘algorithmic identities’ that are
configured on the behalf of users (Cheney-Lippold, 2011). These algorithmic identities
can have material effects. Like the use of biometric technologies for the authentication
of identity (Ajana, 2013; Lyon, 2002, 2008; Pugliese, 2010) or employing big digital data
sets to predict individuals’ behaviours and exclude certain individuals and groups from
access to goods and services or identify them as security risks (Andrejevic, 2013, 2014;
Crawford & Schultz, 2014), self-tracking data can be mobilised as surveillant
technologies in ways that further entrench the social disadvantage of marginalised
groups. This use of personal data may again take place without people having any
control or even knowledge of how the data are analysed and employed. An ‘algorithmic
authority’ is exerted, in which the decisions made by software coders play a dominant
role in shaping individuals’ life chances (Cheney-Lippold, 2011). People are gradually
realising how the data that are collected on them when they use the internet or
customer loyalty programs are becoming used for commercial purposes (Andrejevic,
2014; The Wellcome Trust, 2013). Post-Snowden and the mass media coverage of the
documents he released, they have been apprised of the ways in which digital data are
used by national security agencies for the mass surveillance of their own citizens,
including not only those data derived from mobile phone and social media but also the
personal data that are generated by the use of apps (Ball, 2014).
14
Like many other forms of digital data, self-tracking data have a vitality and social
life of their own, circulating across and between a multitude of sites (Beer, 2013; Lash,
2006; Lyon & Bauman, 2013). Few self-trackers who use digital technologies, other than
the most technically adept who are able to craft their own digital self-tracking tools and
silo their data, are able to avoid this circulation and re-use of their personal data.
Shifting forms of selfhood are configured via these digital data assemblages, depending
on the context in and purpose for which they are assembled. As the digital data
produced by self-tracking are constantly generated and the combinations of data sets
that may be brought together on individuals are numerous, personal data assemblages
are never stable or contained. They represent a ‘snap-shot’ of a particular moment in
time and a particular rationale of data practice. The data assemblages are always
mutable, dynamic, responsive to new inputs and interpretations (Lupton, 2015). They
thus represent a type of selfhood that is distributed between different and constantly
changing data sets. To gain meaning from these data sets, self-trackers or third parties
who seek to use their data must engage in sense-making that can interpret these data
and gain some purchase on their mutating forms.
Self-tracking cultures and practices, in their focus identifying and making sense
of the characteristics of individual lives, may be viewed as an element in contemporary
biopolitical governance and economies. The movement of self-tracking cultures into
commercial, managerial and government domains combines the rationalities of
biocapital with those of the digital data economy. The personal data that are generated
from self-tracking may be conceptualised as a form of ‘lively capital’. This term has
previously been employed to describe the increasing incorporation of the life sciences
into market regimes (Sunder Rajan, 2012). I would argue, however, that just as other
forms of human life have become commodified and invested with monetary value, so
too have the digital data assemblages that are configured on human bodies via self-
tracking. Indeed the value that is attributed to personal digital data assemblages
combine two forms of value: that related to the digital data economy and that emerging
from the capitalisation of the human body. Biocapital involves the derivation of value
from biological entities such as human bodies (Rose, 2007a, 2007b) while the digital
data economy positions digital data objects as valuable. Many self-tracking practices
involve the rendering of bodily attributes and dispositions into digital data. They
produce value in terms of the intimate bio-digital knowledges that they generate on
individuals, and therefore self-tracking practices may be described as generating digital
biocapital. These data are forms of ‘lively capital’ both because they are generated from
life itself and because as digital data they are so labile, recursive and fluid.
Beyond the biopolitical dimension of self-tracking, it can also be theorised as a
new kind of politics; namely data politics. Some self-trackers engage with practices of
data collection in critical and resistant ways, seeking to exert greater control over the
ways in which their personal data are collected, archived and used. They are attempting
to generate and control their own algorithmic identities, in other words. These practices
are in response to a growing awareness of the ways in which personal data are
structured, archived and appropriated by commercial, government or surveillance
15
agencies. This issue of ‘controlling my data’ frequently comes up for discussion on the
Quantified Self website and in their meetups and conferences. The project of reflexive
self-monitoring for many self-trackers involves reflection not only on the uses to which
personal data can be put by oneself but on the validity of the data, whether the kinds of
data they collect are appropriate for their purposes, how best to display or visualise
their data and how best to share their data with others and convey the insights they
garner from the data. Beyond these reflexive data practices, some self-trackers confront
the next level of data use: where their personal data are algorithmically generated and
stored, how they are harvested by other actors, what these actors do with their data and
how one can gain access to one’s personal data.
Nafus and Sherman (2014) contend that self-tracking is an alternative data
practice that is a form of soft resistance to algorithmic authority and the harvesting of
individuals’ personal data. They argue that self-tracking is nothing less than ‘a
profoundly different way of knowing what data is, why it is important, who gets to
interpret it, and to what ends’ (2014, p. 1785). However I would contend that this kind
of soft resistance is evident only in practices of private and communal self-tracking. The
other modes I have here outlined allow less space for soft resistance. It is difficult for
self-trackers to avoid the exploitation of their personal data by other actors or agencies.
While a small minority of technically-proficient self-trackers are able to devise their
own digital technologies for self-tracking, the vast majority must rely on the
commercialised products that are available. In most cases the personal data that they
generate using these technologies become the property of the developers.
Many people express powerlessness in the face of the authority of the internet
empires to collect, own and harvest their personal data (Andrejevic, 2014). Sometimes
self-trackers agree to the use of their personal data as an unavoidable part of accepting
the terms and conditions of self-tracking devices, apps and platforms (although to what
extent users actually read through the fine-print on these documents is not known) or
customer loyalty schemes. In other cases their data may be accessed for the purposes of
others without their knowledge or consent. The developers of many health and fitness
apps, for example, do not provide privacy policies or fail to inform users that their data
are available to third parties (Ackerman, 2013; Sarasohn-Kahn, 2014). The security of
personal data that have been uploaded to digital platforms is not always failsafe, as
several reports have demonstrated, and may be accessed by unknown third parties for
their own purposes (Ackerman, 2013; Barcena, 2014). The vitality of digital data and
the many different ways in which digital data may be repurposed by different actors
and agencies cannot be predicted, and therefore, are not amenable to control.
As humans increasingly become nodes in the Internet of Things, generating and
exchanging digital data with other sensor-equipped objects, self-tracking practices will
become unavoidable for many people, whether they are taken up voluntarily or pushed
or imposed upon them. The evidence outlined in this paper suggests a gradually
widening scope for the use of self-tracking that is likely to expand as a growing number
of agencies and organisations realise the potential of the data that are produced from
these practices.
16
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