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Lively Data, Social Fitness and Biovalue: The Intersections of Health Self-Tracking and Social Media

Authors:
Lively Data, Social Fitness and
Biovalue: The Intersections
of Health and Fitness
Self-tracking and Social Media
Deborah Lupton
IntroductIon
The fitness-tracking platform Strava calls
itself ‘The social network for athletes’ on its
Twitter account. Its Twitter feed is filled with
screenshots of the routes that members of the
platform have taken on their bicycle rides,
swims or runs, and accounts of how many
kilometres they have travelled and how fast
they have done so. These images and com-
ments contribute to the social media functions
of the site. The Strava website lists the oppor-
tunity for members to ‘socialize’ by follow-
ing friends and their activities, joining or
creating clubs and ‘pushing’ each other by
commenting on people’s data and giving
them kudos for their achievements. A new
Strava feature encourages members to upload
photos of their trips to the platform to share
with other members. These are entitled
‘Strava Stories’, and the most recent photos
are displayed at the top of members’ profiles,
framing their other data.
In my current research on people who
use platforms and apps like Strava for track-
ing their physical activity, many participants
commented on the pleasures they derived
from the social networking functions of such
software. The possibilities of recording infor-
mation about their activities, sharing these
with members of the site or with friends on
social media sites, comparing their data
with other athletes, engaging in challenges
and competitions and providing encourage-
ment to others were viewed as motivating,
encouraging and adding a social dimension
to their pursuits. These findings are echoed in
researchers’ work on self-tracking physical
fitness pursuits (Rooksby, Rost, Morrison and
Chalmers, 2014; Epstein, Jacobson, Bales,
McDonald and Munson, 2015; Stragier,
Evens and Mechant, 2015).
As the example of Strava demonstrates,
there are growing entanglements between the
practices of self-tracking human bodies and
engaging in social media networks and rela-
tionships. The expanded array of digitised
31
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devices that are available for self-tracking,
and the capacity of many of these technolo-
gies to interact with social media platforms,
has encouraged self-trackers to share the
details that they collect about themselves
with others. In addition to physical activity,
this personal information may include biom-
etrics such as heart rate, body temperature,
body weight, sleep patterns, mood, blood
glucose levels, blood pressure, menstrual and
ovulation cycles, sexual activity and preg-
nancy. As part of their general ethos of pro-
moting the sharing of personal information,
mainstream social media platforms such as
Instagram, Twitter, YouTube and Facebook
offer opportunities for people to share the
data that they generate from these self-
tracking practices, whether these are photos,
videos, maps of their movements in space or
quantitative data. In turn, some self-tracking
platforms have incorporated tailored social
media elements as part of their customised
offerings. The pleasures and affordances of
the networking capabilities of these media
promote a culture of sharing what are often
very intimate details about people’s bodies
and their movements in space. These data
have use value that extends well beyond
the individuals who generate these details
about themselves and their social networks.
As contributors to large datasets of informa-
tion about people’s activities and movements
in space, these self-trackers are imbricated
within the digital data economy.
In this chapter, I focus on these intersec-
tions, drawing out their sociocultural and
political implications. The chapter begins
with a brief review of the theoretical per-
spectives that underpin my analysis. After
describing the range of technologies that are
available for physical activity, health and
medical self-tracking, I then discuss the con-
cept of ‘social fitness’ and its broader impli-
cations. This is followed by an analysis of the
new forms of value that personal health and
medical data have attracted and the political
implications of encouraging people to par-
ticipate as socially fit citizens.
theoretIcal foundatIons
My approach here is underpinned by a per-
spective that recognises the sociomaterial
status of digital devices, software and the
data that they generate (Rogers, 2013;
Kitchin and Lauriault, 2014). Digital devices
that generate personal data participate in the
formation of digital data assemblages, in
which technologies and humans work
together to create new configurations of
information. The digital data generated by
self-tracking may be conceptualised as
‘lively’ in various ways (Lupton, 2016c).
First, these data are generated from life itself
by documenting humans’ bodies and selves.
Second, as participants in the digital data
economy, they are labile and fluid, open to
constant repurposing by a range of actors
and agencies, often in ways in which the
original generators of these data have little or
no knowledge. Third, these data are lively
due to the advent of algorithmic authority
and predictive analytics that use digital self-
tracked data to make inferences and deci-
sions about individuals and social groups.
These data, therefore, have potential effects
on the conduct of life and life opportunities.
Fourth, by virtue of their growing value as
commodities or research sources, the per-
sonal data that are derived from self-tracking
practices have significant implications for
livelihoods (those using these data in the
data-mining, insurance and data science
industries, for instance).
The recognition of the sociocultural and
political implications of the use of people’s
personal data by other actors and agencies is
also a key theoretical tenet of my discussion
here. The term ‘prosumption’ is often used to
describe users’ engagement in the digital data
economy as both consumers and producers of
digital data content (Beer and Burrows, 2010;
Ritzer, 2014). The taken-for-granted defini-
tion of social media relates to platforms or
apps that facilitate and promote prosumption:
the sharing of personal information with other
users and opportunities to comment on or
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respond to others’ content. However, any form
of personal data that is generated using digital
technologies now becomes ‘social’ by these
data’s transmission to and storage in databases
on the computing cloud. These data inevitably
enter into the circulations, flows and fluxes of
the digital data economy. As part of big data,
they contribute to social relations and social
selves and the management of social institu-
tions and have potential social effects.
Theories of surveillance are also highly
pertinent to understanding personal digital
data assemblages. The role played by digital
technologies in surveillance, or watching, of
people is a central feature of contemporary
data practices. As scholars in surveillance
studies have emphasised, there are many
different modes of dataveillance, or conduct-
ing modes of watching people using data-
generating technologies (van Dijck, 2014).
Some are covert and non-consensual: the
subjects of these modes do not realise
that they are being watched and have not
given their permission. These covert modes
include the dataveillance conducted by secu-
rity and policing agencies, some forms of
commercial collection of personal digital data
and the dataveillance that is conducted by
hackers or cybercriminals. Other modes of
dataveillance are open and voluntary. These
include the self-surveillance that people may
undertake of their own bodies and lives using
mobile and wearable devices, apps and other
software (Lupton, 2016c); the intimate sur-
veillance that they may conduct on friends
and family members as part of their social
relationships or caregiving practices (Levy,
2015); and the social surveillance that is
part of people’s interactions of social media
platforms, in which they watch each other
(Marwick, 2012).
Early versions of social media platforms
focused on the ideals of sharing and participa-
tory democracy; the free exchange of informa-
tion for the mutual benefit of all users (Beer
and Burrows, 2010; John, 2013). Several crit-
ical scholars have drawn attention to the ways
in which this participatory and communal
ethos is now harnessed to commercial, mana-
gerial and surveillance imperatives that seek
to exploit people’s prosumption activities
( van Dijck, 2013; Fuchs, 2014; van Dijck,
2014; Zuboff, 2015; Banning, 2016). Zuboff
(2015) uses the term ‘surveillance capitalism’
to refer to what she characterises as a new era
in capitalist economic systems, in which per-
sonal digital data as part of big data have taken
on immense value as commodities. As I will
go on to outline in this chapter, the value of
personal biometric data has significant impli-
cations for how self-trackers use and share
their data with others on social media and also
for how they may lose control of their data as
they enter the digital data economy.
self-trackIng and the
quantIfIed self
Self-tracking is an enterprise that involves
individuals observing and, in many cases,
recording details of their bodies and lives,
often for achieving self-knowledge, self-
reflection and self-improvement. Monitoring
and measuring details of their bodies’ physical
activities and functions is a common focus for
many self-trackers. In recent years, the prac-
tices of self-tracking using digital devices have
received growing attention from the popular
media and in academic research. While self-
tracking has taken place for millennia, new
digital technologies facilitate the collection of
ever-more detailed personal information. Self-
trackers are drawing on the capacities of new
technologies to generate increasing quantities
and diverse forms of information about their
bodies and selves (Rooksby et al., 2014;
Ruckenstein, 2014, 2015: 6; Barta and Neff,
2015; Epstein et al., 2015; Lomborg and
Frandsen, 2015; Stragier etal., 2015).
Terms other than self-tracking are used to
refer to self-monitoring practices: lifelogging,
personal analytics, personal informatics and
the quantified self. The ‘quantified self’ is a
relatively new term, but has become popularly
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used. Wired journalists Gary Wolf and Kevin
Kelly invented it in 2007 to describe the prac-
tices that they had observed among friends
and colleagues involving the use of digital
technologies to monitor and measure them-
selves. Wolf and Kelly went on to establish
the Quantified Self website (Quantified
Self, 2016) and to establish the associated
Quantified Self Labs to facilitate the develop-
ment of technologies directed at self-trackers.
Since then, the ‘quantified self’ or ‘quanti-
fying the self’ terms have been taken up in
the popular media and some of the academic
literature to refer to various methods of self-
tracking and especially those that involve
using digital devices to generate numerical
data (Lupton, 2013c). I prefer to use ‘self-
tracking’ in my work, as it is a broader term
that encompasses all the different types and
practices of self-monitoring, including col-
lecting both metrics and qualitative data.
All forms of self-tracking involve data prac-
tices, or ways of generating, engaging with,
interpreting and applying the insights devel-
oped from personal data, and data materiali-
sations, or ways of representing data (Lupton,
2016c). When self-trackers use methods such
as journal keeping or writing down numbers,
it is difficult to analyse these data for their pat-
terns. Digital data devices and apps and other
software provide the opportunity to access
and analyse personal details efficiently and
quickly. These technologies also let users
combine different data sets to identify pat-
terns in ways that were not achievable in the
past. Indeed, the difficulty now faced by self-
trackers is the overwhelming mass of data that
they may have to deal with, given the infinite
number of ways in which data sets can be
combined with the aim of generating insights.
technologIes for self-trackIng
fItness, health and medIcIne
New digital technologies are increasingly
incorporated into healthcare delivery and
health promotion initiatives. Telemedicine
and telehealth initiatives offered to patients
have emphasised self-monitoring as part of
patient self-care for chronic conditions such
as diabetes and high blood pressure for over
two decades. Well before the advent of digi-
tal technologies, patients were encouraged to
keep track of their blood glucose level or
blood pressure using technologies that they
could operate themselves, and to take note of
the readings as part of managing their condi-
tion and treatment regimen. The latest ver-
sions of telemedicine offer continuous
real-time self-monitoring, wireless data
transfer and cloud computing storage facili-
ties that reduce the expense and expertise
required for the proprietary systems that
were characteristic of earlier technologies.
Self-care strategies using digital devices have
become incorporated in the ideal of the digi-
tally engaged patient, who is willing and able
to take up these devices for self-monitoring
(Lupton, 2013a). Patient self-monitoring
technologies that are now available include
digital pills embedded with sensors that
monitor the body from within by sending
signals to a patch worn on the user’s arm.
Continuous wearable monitoring devices
measure such bodily features as physical
movement, blood glucose levels, body tem-
perature, sleep patterns, heart rate and func-
tion, lung function, blood pressure and
oxygen saturation and brain activity. Sensor
pads are available to place under mattresses
or in chairs to monitor heart rate, breathing
and body movement and mobility. These data
can then be transmitted wirelessly to health-
care providers or caregivers as part of remote
monitoring programs (for overviews of these
technologies, see Swan, 2012a, 2012b;
Lupton, 2013a, 2014b; Topol, 2015).
Health and physical activity self-
monitoring also takes place among healthy
populations using new apps and wearable
technologies. Thousands of apps for the self-
tracking of human bodies are available for
downloading. By late 2015, 160,000 health
and medical apps had been placed on the
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market, most of which (65 per cent) focus
on promoting wellness, diet and exercise,
with nearly a quarter directed at the self-
management of chronic diseases such as dia-
betes, high blood pressure and mental health
conditions. One in ten have the capability
to connect to a monitoring device or sensor,
while a third can connect to social media
networks. There are a small number of very
popular, highly-downloaded apps, such as
some of the fitness tracking or calorie count-
ing apps, period trackers or medical informa-
tion apps such as WebMD (IMS Institute for
Healthcare Informatics, 2015).
Digital devices that are available for self-
tracking include wireless body weight scales
and blood pressure monitors and wearable
trackers. The range of wearables offered by
the Fitbit, Misfit, Nike and Jawbone com-
panies include wristbands, headbands, pen-
dants or devices clipped to clothing. Many
of these wearables either interact with cus-
tomised apps or sync with platforms such
as Runkeeper, Strava and MapMyFitness.
Smartwatches also now offer biometric
tracking. For example, the new Apple Watch
includes a range of sensors, such as geolo-
cation, accelerometer, gyroscope and heart
rate monitors that facilitate self-tracking.
It offers two pre-loaded apps, Workout and
Fitness, which record the wearer’s levels of
physical activity. There is also a range of
smart clothing and sporting equipment on the
market, including shirts, helmets, bats and
balls equipped with sensor-based technolo-
gies for monitoring and measuring exercise
and sporting activities. Several self-tracking
device developers are finding ways of incor-
porating their technologies into the Internet
of Things so that these devices can interact
with other monitoring digital technologies.
For example, the Misfit company is work-
ing on integrating its wristband self-tracking
device that currently facilitates sleep and
physical fitness monitoring with Nest, ‘smart
home’ monitoring software that regulates
the home thermostat. The users’ sleep data
can then be incorporated into the home
thermostat system to regulate air temperature
based on the occupant’s sleeping patterns and
time of awakening in the morning.
Self-tracking initiatives focusing on health
and fitness are spreading from the clinic
into insurance and the workplace. Some
health insurance companies are taking up
self-tracking initiatives as part of user-based
insurance policy calculations, encouraging
their clients to upload their health and fit-
ness data to receive incentives or reduced
premiums. In the USA, these programs are
often linked to the provision of health insur-
ance by the employers, who therefore have a
financial incentive to motivate their employ-
ees to take part. The manufacturers of self-
tracking devices are approaching American
workplaces to use their technologies in their
corporate ‘wellness programs’ (Olson, 2014;
Zamosky, 2014). Children and young people
are also encouraged to engage in health and
fitness self-monitoring endeavours. Several
wearable device manufacturers offer child-
sized devices that parents can purchase as
part of nudging their children to be more
physically active. Some schools are introduc-
ing self-tracking as part of physical educa-
tion and health classes. Initiatives such as the
UNICEF Kid Power program involve recruit-
ing children and young people to monitor
their physical activity using a digital wrist-
band and use the points they earn to play
games that unlock therapeutic food packets
for malnourished children.
Personal health data
and socIal medIa
The use of social media for medical and
health-related purposes has become wide-
spread. Patient online discussion groups and
blogs written by healthcare providers or
institutions and patients are long-established
modes of digital health communication and
interaction. The role played by websites in
providing health and medical information to
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lay people (‘Dr Google’) has been well rec-
ognised for some years (Fox and Duggan,
2013; Kivits, 2013). Online searching
remains a dominant source of information
about medical conditions, with the top web-
sites, such as WebMD, receiving tens of mil-
lions of users each month (eBizMBA, 2016).
The newer social media platforms are often
represented in the medical and public health
literature as an extension of Dr Google: a
means by which lay people can continue to
seek information about health, illness and
medicine online and, importantly, easily
share their experiences with others.
Sometimes these initiatives are promoted by
patients and related lay person organisations
(Lupton, 2014a). As part of championing the
ideal of digital patient engagement (Lupton,
2013a), healthcare and public health profes-
sionals have also frequently called for lay
people to collect information on themselves
and share these on social media (Swan,
2012a; Lefebvre and Bornkessel, 2013;
Househ, Borycki and Kushniruk, 2014).
Twitter has been used to facilitate informa-
tion sharing among people interested in spe-
cific health and medical topics or conditions,
including lay people as well as healthcare pro-
fessionals and officials and pharmaceutical
and medical device companies. Condition-
specific Facebook pages promote the inter-
action of patients with each other. Patients
can upload videos to YouTube about their
illness experiences. Many examples of surgi-
cal techniques are available on that platform,
including demonstrations by medical profes-
sionals, tand also videos made by lay people
seeking to display ‘do-it-yourself’ surgery.
Several patient-oriented platforms, such as
PatientsLikeMe, Treato and CureTogether,
offer opportunities for patients to join
condition-specific communities and to record
their symptoms and treatments. These data
are then aggregated to provide information
for users about trends among other members
of the site. Some initiatives have been devel-
oped to encourage people to use sensor tech-
nologies to track their local environment as
part of citizen science projects. There are also
opportunities for people to use social media
to rank and rate their medical care providers.
The Patient Opinion platforms in the UK and
Australia, for example, encourage patients to
provide feedback on the care that they receive
in their national healthcare systems. In the
USA, platforms like ZocDoc (with associ-
ated apps for mobile devices) help patients
find doctors and dentists in their local area
and make appointments online, as well as
read reviews of doctors by other users.
People who track their biometric data for
health-promoting purposes often use social
media. Facebook is one major social media
platform in which self-trackers share their
latest information with others. They can
post regular updates on exercise routines
and achievements with Facebook friends or
contribute to specialised pages that have been
set up to establish communities around spe-
cific exercise or sporting interests. Twitter,
Instagram and Tumblr offer further oppor-
tunities for people to share their information
and images with other. They can employ a
relevant hashtag to draw attention to their
data and contribute to a community of peo-
ple with shared interests. More specialised
platforms, such as Fitocracy, Daily Mile and
Strava, encourage users to share their fitness
achievements with other members, empha-
sising the competitive dimension of com-
paring numbers. The weight-loss platform
Extra Pounds combines ‘body logs’ with
access to support groups, while PumpUp
focuses on users uploading selfies to docu-
ment their fitness achievements and shar-
ing healthy recipes with each other. Several
health and fitness self-tracking app develop-
ers have worked on making the social media
elements of their apps more prominent and
easy to use. A survey of health and medical
apps published in 2015 found that recent
apps were more likely, when compared
with apps available two years ago, to offer the
functions of connecting to another device or
wearable or social media (Comstock, 2015).
The MapMyFitness app, for example, is now
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integrated with Facebook, and users can
create or join groups of other users to share
routes and training plans, organise events and
compare progress.
Patient self-care, voluntary self-tracking
and social media use are coming together
in some initiatives. In medicine and health
informatics (a field devoted to understand-
ing the best ways of generating, storing and
using health and medical information) the
term ‘patient generated data’ has begun to be
employed (Huba and Zhang, 2012). This term
describes the various ways in which patients
or their lay caregivers produce information
about the patients outside the clinic setting,
through self-tracking efforts, using remote
monitoring self-care devices or uploading
material to social media platforms or as part
of routine transactions online. For their part,
healthcare providers and public health work-
ers use social media as part of their work-
ing lives. Healthcare professionals employ
LinkedIn, Twitter and closed Facebook pages
to join specialised groups, discuss cases with
each other and provide advice. Hundreds
of YouTube channels, Facebook pages and
Twitter accounts have been set up by hospi-
tals and healthcare organisations as part of
public relations strategies.
The phrase ‘social fitness’ is used to refer
to these practices of sharing personal data to
facilitate motivation and achieving personal
goals. This is particularly clearly outlined on
the Strava website. As is contended on the
website, ‘Strava lets you experience what we
call social fitness – connecting and compet-
ing with each other via mobile and online
apps’ and thereby providing ‘motivation and
camaraderie’. The overt and broader mean-
ing of social fitness has its roots in physical
exercise training or weight loss regimes and
encouraging people to join groups as a means
of mobilising the support of others to achieve
their goals. The importance of social networks
as part of motivation in health promotion has
frequently been employed in psychological
models of behaviour change (see, for exam-
ple, Bandura, 2004). In recent times, the
field of persuasive computing has employed
these types of models in designing interven-
tions for behavioural change relate to health
(Purpura, Schwanda, Williams, Stubler and
Sengers, 2011). When it is employed more
specifically to online health and fitness self-
tracking, the newest version of social fitness
refers to sharing personal data and engaging
in online communities for the same ends.
Social fitness has become integral to corpo-
rate ‘wellness programs’. Some workplaces
have instituted competitions requiring par-
ticipating employees to upload and display to
all other workmates data they have collected
on their bodily movements and weight loss
using self-tracking devices as part of efforts
to motivate them to achieve higher fitness
levels (Zamosky, 2014). The Jawbone com-
pany (Jawbone, 2016) offers the ‘Up Group’
package to employers seeking to institute
wellness programs. On its website, Jawbone
argues that promoting competition between
different teams in an organisation will moti-
vate individuals to exercise more, sleep bet-
ter and eat healthier food. Team members
are encouraged to view the data on their
apps showing how their team is performing
against other teams and to upload supportive
comments and emoticons to motivate other
team members.
The Quantified Self website (Quantified
Self, 2016) provides an online social medium
by which people interested in self-tracking
can share experiences. A Facebook page
and Twitter account also link to the web-
site. The co-founders of the Quantified Self
movement have asserted from its incep-
tion that one of its integral elements is to
develop community among its members and
encourage them to share with other members
details of their self-tracked data and the les-
sons that they have learnt from self-tracking.
In his first article on the Quantified Self for
Wired magazine, Gary Wolf (2009) stated
that self-tracking involves the sharing of data
and collaboration on ways of using them.
An important dimension of the Quantified
Self movement, meetups and conferences is
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the ‘show-and-tell’ mode of communication.
Many ‘show-and-tell’ videos appear on the
Quantified Self website. They typically fea-
ture an individual standing in front of a group
talking about their self-tracking experiences.
The Quantified Self also includes numerous
examples of data visualisations that members
have generated, allowing them to share these
visualisations with other members. In several
blog posts on the Quantified Self website,
Gary Wolf (for example, Wolf, 2014) has
argued for the importance of ‘our data’, or the
pool of aggregated data collected from self-
trackers. Other commentators who champion
the notion of ‘the quantified us’ privilege the
ways in which aggregated personal data sets
can contribute to self-tracking communities.
Writing in Wired magazine, Jordan and Pfarr
(2014) claim that: ‘Ultimately the Quantified
Us can help people take better care of them-
selves, more often – and feel more connected
to each other in the process.’
I have developed a typology of the five
distinctive modes of self-tracking that have
emerged in recent times. This identifies
the different uses of self-tracking data and
the diverse range of actors and agencies
involved (Lupton, 2016b, 2016c). When self-
monitoring is self-initiated and purely vol-
untary, responding to self-directed objec-
tives and goals, it conforms to the mode that
I entitle ‘private self-tracking’. This mode is
perhaps the most public and well-known face
of self-tracking, particularly in portrayals of
the Quantified Self phenomenon. ‘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. People may take
up self-tracking voluntarily, but initially in
response to external encouragement or advo-
cating. ‘Imposed self-tracking’ involves the
imposition of self-tracking practices upon
individuals by other actors and agencies pri-
marily for these others’ benefit. ‘Communal
self-tracking’ describes the voluntary sharing
of a tracker’s personal data with other people
as a central feature of self-tracking practice.
‘Exploited self-tracking’ refers to the ways
in which other actors and agencies repurpose
people’s personal data from self-tracking
practices. As this section has outlined, the
private, pushed, imposed and communal
modes are apparent in self-tracking practices
for fitness, health and medical purposes. The
data generated from all of these modes can
be exploited by other actors and agencies, as
I detail in the next section.
the exPloItatIon of
Personal health data
Many commercial, research and managerial
uses of the types of personal health and
medical data that are uploaded to social
media sites have been identified. Health
insurance companies, government bodies,
pharmaceutical and medical technology
companies, healthcare organisations, self-
tracking device developers and entrepre-
neurs, researchers and employers are finding
ways of exploiting people’s personal data
from self-tracking.
Healthcare, pharmaceutical and biotech-
nology companies frequently use social and
other digital media for marketing and public
relations purposes, or ‘building their brands’
(Belby, 2015). This takes place in a variety
of ways, from the traditional explicit type of
marketing, such as sponsoring banner ads
and conferences, to the covert, like attempt-
ing to influence social media discussions on
platforms such as Facebook or Twitter. Some
pharmaceutical companies employ research-
ers to harvest data from blogs written by
people about an illness or medical condition
they have or Twitter exchanges by patients
about their conditions and therapies. Using
this material, the companies’ employees then
attempt to influence conversation threads
(Robinson, 2014). Healthcare professionals
and organisations employ patient-generated
data for purposes such as health profiling
for targeting treatment and illness prevention
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strategies. In the USA, the Affordable Care
Act promotes payment systems that reward
keeping patients out of the hospital system.
Hospitals have begun to invest in big data
technologies to construct new risk models
of patients. Information about patients is
entered into hospital data systems, including
biometric and treatment details from their
medical records and patient self-monitoring
activities at home and demographic charac-
teristics such as race or ethnicity, age and
gender. These data are used to predict which
patients are more likely to require readmis-
sion for medical care (Humphries, 2013).
Health promoters have experimented with
using social media sites to disseminate infor-
mation about preventive health, collect data
about people’s health-related behaviours and
attempt to ‘nudge’ members of target groups
to change their behaviour. Public health pro-
fessionals employ various social media tools
to disseminate information about health
risks and disease outbreaks and to collect
data on the incidence of illness and disease.
One example is the digital tool HealthMap.
The software can search the web for disease
reports from online news reports, blogs,
social media platforms and official reports
and can represent disease outbreaks visually
on a digital map. It includes an app that users
can download to their mobile digital devices
so that they can identify what region is expe-
riencing an outbreak of infectious disease or
report cases that are then followed up.
Crowdsourcing health and medical data is
often championed in discussions of how such
big data can benefit not only the patients
themselves who contribute their informa-
tion to such sites, but also other patients with
the same condition, healthcare providers,
caregivers and commercial bodies such as
pharmaceutical and medical device compa-
nies. Patient support and opinion platforms
have become increasingly commercialised.
The data generated by people contributing
their experiences of illness and treatments on
many of these platforms are now routinely
harvested by the developers and on-sold to
third parties (Lupton, 2014a). For example,
the Treato website landing page boasts that
membership of the site is over two million
patients (Treato, 2016). The site focuses on
harvesting patients’ accounts of drug thera-
pies across the spectrum of social media and
other digital platforms, including seeking
out accounts of how well drugs work, their
side-effects and why patients may switch
one brand for another. Treato provides free
access to the general data that are collected,
but also offers a more targeted service to
pharmaceutical companies that incurs fees.
The PatientsLikeMe site also offers members
the opportunity to enrol directly into clinical
trials of new pharmaceuticals.
Data privacy and security problems
have emerged in relation to the generation
and sharing of personal biometric details.
People’s very intimate bodily details that they
upload to apps and social media sites may
be disclosed to others without their knowl-
edge or consent. It has been demonstrated
that many developers of health and fitness
tracking apps fail to secure the personal data
uploaded to the apps and that these apps often
leak personal data in covert ways (Adhikari,
Richards and Scott, 2014; Huckvale, Prieto,
Tilney, Benghozi and Car, 2015). There are
also many privacy threats involved with
uploading personal health and medical infor-
mation to social media platforms, includ-
ing the misuse of the data, accidental data
releases, disclosures to third parties and user
profiling across sites (Li, 2015). Data-mining
and data brokering companies and advertis-
ers have a vested interest in health and medi-
cal data. Some of these agencies use the data
to construct detailed profiles or predictive
analytics that may be used to make decisions
about people’s eligibility for employment,
insurance and credit. Profiles of people with
conditions such as sexually transmissible
diseases, HIV/AIDS, cancer and mental ill-
nesses and who have been victims of sex-
ual assault are regularly developed by data
miners and brokers for sale to advertising
agencies, potential employers and financial
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LiveLy data, sociaL fitness and biovaLue
571
institutions (Pasquale, 2014). If used in these
ways, the kinds of personal data that are gen-
erated by self-tracking and shared on social
media may, therefore, have significant impli-
cations for people’s life chances (Crawford
and Schultz, 2014; Rosenblat, Wikelius,
boyd, Gangadharan and Yu, 2014).
The exploitation of personal health and
medical data is taking place in legal ways,
but also on the part of hackers and cyber-
criminals, who seek to access illegally these
data for financial gain. Criminal breaches
of medical digital data sets are becoming
common. Such data can be used in identity
theft, fraudulent health insurance claims and
to gain access to pharmaceuticals and medi-
cal equipment (McCarthy, 2013; Humer and
Finkle, 2014). In early 2015, for example,
confidential information about 80 million
patients was accessed illegally on the data-
base of the American healthcare provider
Anthem Inc., which had failed to encrypt this
information (Symons, 2015).
dIscussIon: dIgItIsed bodIes,
surveIllance and bIocaPItal
Self-tracking for health and medical purposes
is just one way, among a plethora of others,
to render fleshly bodies into digital data. The
digitised data assemblages that are config-
ured via self-tracking technologies are the
newest forms of ‘informatic bodies’ (Waldby,
2000). Accounts of self-tracking for health
and medical purposes are beginning to
describe a data entity generated from an indi-
vidual’s different personal data sets. Thus,
writing recently for Nature, Kish and Topol
(2015) describe the ‘external wisdom of the
body’ that these data sets comprise, while
Swan (2013) makes reference to the ‘extended
exoself’ configured by self-tracking data.
These metaphors draw on age-old representa-
tions of human bodies as machines and, more
lately, as computerised information systems
(Lupton, 2012b, 2013c). In responding to the
new affordances of the digitised human as
part of the Internet of Things, these meta-
phors represent the human body as an entity
that constantly generates digital data and
pushes these data out into the increasingly
interconnected digital world of smart things
and simultaneously incorporates these data
back as the individual responds to their data
and makes changes to their lives.
The data that self-tracking practices gener-
ate have different forms of value for different
actors and agencies. For the individual self-
tracker, these data are opportunities to acquire
self-knowledge, engage in self-reflection and
optimise their lives. Self-trackers often seek
to make meaning from their data. The prac-
tice is not simply about collecting data, as
this suggests, but also attempting to engage
with such issues as what should be done with
these data, how they should be presented
and interpreted, and what the implications
are for self-trackers’ identity and future life
prospects and success (Nafus and Sherman,
2014; Ruckenstein, 2014; Lupton, 2016c). In
so doing, self-trackers are engaging in volun-
tary self-surveillance. The process of mean-
ing making may be facilitated by engaging in
data sharing practices. For those who partici-
pate in the communal mode of self-tracking,
these data offer a means of entering into
exchanges of personal information for the
mutual benefit of other users or the opportu-
nity to contribute to aggregated big data sets
that promise to reveal insights that may be of
use to themselves and others (Barta and Neff,
2015; Lupton, 2016c). When self-trackers
engage in these practices, they are inviting
the surveillance of others and are thus engag-
ing in social surveillance.
When self-tracking practices enter into
social media platforms they draw on deeply
felt desires for becoming part of communities,
connecting and sharing with others, exchang-
ing common experiences and opening private
details about oneself to others in the interests
of altruism, creating social bonds and soli-
darity, contributing to stocks of new knowl-
edge as well as learning more about oneself
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(Barta and Neff, 2015; Banning, 2016). As
I argued in my introductory remarks, self-
trackers can gain much pleasure from their
participation in social surveillance, enjoying
feeling part of a community of like-minded
people and the often playful dimensions of
demonstrating their achievements to others,
motivating and supporting others or engag-
ing in competitive endeavours with other
members (Whitson, 2013; Lomborg and
Frandsen, 2016).
The skilful manipulation and portrayal of
personal data is a key factor of this type of
social surveillance. The ‘show and tell’ pres-
entations on the Quantified Self and other
self-tracking forums are often very complex
and aesthetically appealing, conforming to
the appeal of the ‘data spectacle’ (Gregg,
2015). The pleasures of ‘showing and tell-
ing’ in these formats, therefore, include
engaging in the opportunity to let other inter-
ested people know about the insights about
oneself that the tracker has garnered, as well
as displaying prowess in making these data
beautiful or easy to understand. Such com-
munications of personal data seek to attract
the interest and attention of other people
and in the communal and sharing ethos that
is an integral dimension of the Quantified
Self movement. They have strong performa-
tive dimensions in revealing both how a pre-
senter’s self-tracking efforts have improved
their lives and also how adept they are at
manipulating self-tracking technologies and
data materialisations and thereby facilitating
the sharing of their data with others (Nafus
and Sherman, 2014; Barta and Neff, 2015;
Lupton, 2016c).
Indeed, it could be argued that yet another
type of surveillance is demonstrated in self-
trackers’ engagements on social media: that
of ludic surveillance, involving these pleasur-
able elements of competition and play. As part
of the general ‘ludification’ of many aspects
of social life and selfhood (Frissen, Lammes,
de Lange, de Mul and Raessens, 2015), self-
tracking of human bodies has become pro-
gressively oriented towards these elements as
a way of engaging users and maintaining their
participation. This is even evident in apps that
allow people to monitor, measure and then
share details of their pregnancies (Lupton and
Thomas, 2015; Thomas and Lupton, 2015) or
sexual activities (Lupton, 2015).
As outlined in the Introduction, the
uploading and sharing of personal informa-
tion that occurs on online platforms, how-
ever, is no longer confined to a community
of like-minded individuals seeking to help
each other. As knowledge in the form of digi-
tal data has become increasingly valuable for
commercial, research and managerial pur-
poses, the participatory, sharing and playful
ethos of social media has become commodi-
fied and exploited by a multitude of actors and
agencies. The progressive commercialisation
of the sharing economy has implications
for digitised self-tracking practices. Actors
and agencies other than those collecting
their personal information have compelling
reasons for wanting to access and harvest
these data. The data generated by practices
of voluntary self-surveillance, social surveil-
lance and ludic surveillance are now often
available to the surveillance and exploitation
of these actors and agencies. Unlike previous
forms of self-tracking, in which personal data
could be kept private, digitised data assem-
blages cannot easily be protected from the
gaze or use by others.
As the digital data economy has expanded,
and entrepreneurs, researchers, managers,
businesses and cybercriminals have recog-
nised the possibilities of digital data, personal
information such as that uploaded to health
and medical social media sites has taken on
a new form of commercial value – that of
‘biovalue’ (Lupton, 2014c, 2016c). Biovalue
is produced from the surplus commercial
value that is attributed to biological objects
such as human body tissues, cells and organs
(Waldby, 2000; Mitchell and Waldby, 2006;
Rose, 2007a). Just as this human fleshly mat-
ter is now a commodity traded for financial
profit, the data about human bodies, their
functions and behaviours that are generated
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LiveLy data, sociaL fitness and biovaLue
573
by self-tracking devices have become valu-
able entities. The fact that these data are
about ‘life itself’ has become exploitable in
new ways. The biovalue that is generated by
personal digital data is yet another form of
surveillance capitalism.
The incorporation of biometric self-
tracked data into the digital data economy
and the subsequent exploitation of these data
is an instance of biopolitics and biopower. As
originally outlined by Foucault (for example,
1988, 1991, 2008), biopolitics and biopower
are exercised in and through human bod-
ies. They are modes of power relations that
rely on concepts of human subjectivity and
embodiment in which practices of self-care,
self-knowledge and self-optimisation are
privileged (Lupton, 1995; Rose and Novas,
2005; Rose, 2008). The social media plat-
forms that have been developed to promote
the uploading of these data, the sharing
of these data and their subsequent entry into
the digital data knowledge economy provide
routes for the translation of biodata into a
new form of biovalue that generates digital
biocapital.
When thinking about the entanglements of
biometrics, biovalue and biopolitics, the con-
cept of social fitness takes on an expanded
form of meaning. Programs directed at pre-
ventive medicine, patient self-care and health
promotion have traditionally relied on a dis-
course of ideal citizenship that melds private
objectives with the public good, the self with
the community. Notions of idealised ‘bio-
citizenship’ (Rose and Novas, 2005) in the
contemporary neoliberal state emphasise
self-responsibility and the entrepreneurial
management and optimisation of one’s life,
including promoting and maintaining good
health and physical fitness (Lupton, 1995;
Petersen and Lupton, 1996; Rose, 2007b,
2008). When digital technologies designed
for self-tracking biometric data enter into this
field, these ideals are elaborated in response,
configuring the ideal of the ‘socially fit citi-
zen’, a new, digitised form of biocitizen-
ship. More recent forms of self-administered
health promotion and patient self-care have
focused attention on the ways in which digi-
tal technologies can be employed to achieve
good health, supporting the notion of the
digitally engaged patient. The discourses of
‘patient empowerment’ and ‘engagement’
and the importance of people taking respon-
sibility for their health recur in such accounts
(see, for example, Swan, 2009; Househ etal.,
2014). In these programs, morality, responsi-
bility, accountability and concepts of health
and illness are inextricably intertwined
(Lupton, 2012a; 2013a, 2013b, 2016a).
These practices are emphasised to the exclu-
sion of other ways of caring for human sub-
jects, such as collective or state-supported
initiatives (McGregor, 2001).
When the meaning of social fitness is
expanded to incorporate the use of new digi-
tal media for facilitating social interactions
and networks, these moral meanings also
enter into digitised sharing and participation
systems. Idealised biocitizens are socially
fit because they are appropriately socially-
engaged using technologies such as social
media, as well as performing the attributes
of healthy and responsible citizens who are
willing to share their data for the interests of
others or to provide encouraging feedback
about other people to help motivate them.
Socially fit biocitizens take steps to employ
self-tracking devices and technologies as well
as social media to contribute to personalised
digital data assemblages that enable them to
manage and optimise their bodily health and
wellbeing. They may encourage others on
social media by engaging in communal self-
tracking, or contribute their personal data to
large data sets that are then used to develop
new knowledges about health and medicine.
In this discourse, the ‘self-knowledge’ cham-
pioned by the Quantified Self movement and
other advocates for self-tracking should not
only benefit the individual: it should also
contribute to collective knowledge stores.
The affordances of new digital media and
their intersections with the digital data econ-
omy, however, complicate and extend the
BK-SAGE-BURGESS ET AL-170281-Chp31.indd 573 24/10/17 12:33 PM
The SAGe hAndbook of SociAl MediA
574
potentialities and consequences of social fit-
ness. There is a fine line between voluntary,
pushed and imposed self-tracking. When
financial incentives are offered, or social
pressures are brought to bear on individuals
to engage in self-tracking, there is often little
room to make a choice. In the case of employ-
ees who are encouraged to engage in fitness
self-tracking as part of corporate wellness
programs, or children who are handed heart
rate monitors in physical education lessons,
encouragement can quickly become imposi-
tion. People who do not participate or who
fail to meet pre-set targets for fitness or other
health- or productivity-related data may be
discriminated against with social disapproval
or financial penalties. Given that the impetus
in these instances does not come from the indi-
vidual but from those in positions of power,
this also raises the question of how effective
such practices are in helping people achieve
self-knowledge or change their lives for the
better. Forcing change on people is likely to
be far less effective than allowing them to
make a choice (Frakt and Carroll, 2014).
All three of these modes can be taken up in
communal or exploited self-tracking modes.
The possibilities of the types of use of per-
sonal health and medical information that
I have outlined are rarely raised by actors
and agencies who are seeking to encourage
(nudge or even coerce) people to engage in
self-tracking. The ways in which personal
value that self-trackers gain from develop-
ing their self-knowledge in the quest for self-
optimisation is expropriated for the profit of
others or used to discriminate against people
tend not to be highlighted in the discourses of
sharing and caring that recur in social fitness
discussions. Self-trackers may not be aware of
who is viewing or using the types of personal
information that they share on social media.
future dIrectIons
Important questions emerge from the analy-
sis I have presented here. If moral precepts
are brought to bear on people to encourage
them to engage in self-tracking and personal
data sharing as socially fit biocitizens by
government, research and commercial agen-
cies, what are the attendant precepts that
should be highlighted by these agencies to
alert or warn people of the potential uses
(both legal and illicit, both overt and covert)
of the highly intimate personal data that are
produced by these practices? What are the
ethics and values that need to be confronted
and assessed in this situation? What responsi-
bilities should be borne by those who advo-
cate for the socially fit citizen? To what
extent will it become increasingly difficult
for people to opt out of participating in social
fitness programs, and what are the possible
harms that may eventuate from this form of
pushed or coerced self-tracking? What are
the ways in which self-tracking can be used
for radical activist purposes that challenge
existing norms and assumptions concerning
personal responsibility for health status?
How will the expansion of the Internet of
Things contribute to the generation of
increasingly detailed information on people’s
bodies and everyday practices and the
exchange of these data between humans and
between inanimate ‘smart’ objects? All of
these questions provide the basis for future
critical analysis of the entanglements of
social media with self-tracking technologies.
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The promotion of active travel is deemed a crucial component of the transition to sustainable urban mobility. Several barriers hinder its policy implementation and uptake. Some evidence suggests that capacity building could be a useful tool for deepening sustainability efforts. However, a clear framework for understanding the dimensions of capacity building for active travel is lacking. Most research and findings use cases within a Global North context, constricting implications and transferability to the Global South, especially to African cities. This study responds to the dearth of scholarly work exploring Global South cases and fills a knowledge gap regarding capacity building in the case of active travel. Through a literature review, this study examines the dimensions of capacity building that are necessary to improve active travel in selected African countries. We focus on multilevel trans-portation governance, with highlights from five (5) African cities. Our findings suggest that the literature and policies on transport in Africa have key dimensions for capacity building for active travel but lack the introduction of key instruments and strategic pathways to meet these requirements for improved sustainable mobility. We propose a thematic guiding framework under three (3) levels of governance for integrating capacity building for active travel policies and implementation at institutional, individual, and environmental levels.
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Chapter
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