Self-tracking, social fitness and biovalue

I have just completed a chapter for the forthcoming volume The Sage Handbook of Social Media. The chapter addresses the intersections of self-tracking for health and medical purposes with social media platforms and rationales. As I argue in the chapter, the expanded array of digitised devices that are available for self-tracking and the capacity of many of these technologies to interact with social media platforms have encouraged self-trackers to share the details that they collect about themselves with others. I begin with a description of self-tracking and the sociomaterial theoretical foundations on which the chapter rests. This is followed with an overview of the technologies that are available for health and medical self-tracking and for self-trackers to share their data. The discussion section of the chapter presents an analysis of the new forms of value that personal health and medical data have attracted in the digital data economy, and the moral and political repercussions of encouraging people to participate as socially fit citizens. The chapter ends with outlining key questions for further research.

The full pre-print of the chapter is available here.

Who owns your personal health and medical data?

09/01/15 -- A moment during day 1 of the 2-day international Healthcare and Social Media Summit in Brisbane, Australia on September 1, 2015. Mayo Clinic partnered with the Australian Private Hospitals Association (APHA), a Mayo Clinic Social Media Health Network member to bring this first of it's kind summit to Queensland's Brisbane Convention & Exhibition Centre. (Photo by Jason Pratt / Mayo Clinic)

Presenting my talk at the Mayo Clinic Social Media and Healthcare Summit (Photo by Jason Pratt / Mayo Clinic)

Tomorrow I am speaking on a panel at the Mayo Clinic Healthcare and Social Media Summit on the topic of ‘Who owns your big data?’. I am the only academic among the panel members, who comprise of a former president of the Australian Medical Association, the CEO of the Consumers Health Forum, the Executive Director of a private hospital organisation and the Chief Executive of the Medical Technology Association of Australia. The Summit itself is directed at healthcare providers, seeking to demonstrate how they may use social media to publicise their organisations and promote health among their clients.

As a sociologist, my perspective on the use of social media in healthcare is inevitably directed at troubling the taken-for-granted assumptions that underpin the jargon of ‘disruption’, ‘catalysing’, ‘leveraging’ and ‘acceleration’ that tend to recur in digital health discourses and practices. When I discuss the big data phenomenon, I evoke the ‘13 Ps of big data‘ which recognise their social and cultural assumptions and uses.

When I speak at the Summit, I will note that the first issue to consider is for whom and by whom personal health and medical data are collected. Who decides whether personal digital data should be generated and collected? Who has control over these decisions? What are the power relations and differentials that are involved? This often very intimate information is generated in many different ways – via routine online transactions (e.g. Googling medical symptoms, purchasing products on websites) or more deliberately as part of people’s contributions to social media platforms (such as PatientsLikeMe or Facebook patient support pages) or as part of self-tracking or patient self-care endeavours or workplace wellness programs. The extent to which the generation of such information is voluntary, pushed, coerced or exploited, or indeed, even covert, conducted without the individual’s knowledge or consent, varies in each case. Many self-trackers collect biometric data on themselves for their private purposes. In contrast, patients who are sent home with self-care regimes may do so reluctantly. In some situations, very little choice is offered people: such as school students who are told to wearing self-tracking devices during physical education lessons or employees who work in a culture in which monitoring their health and fitness is expected of them or who may be confronted with financial penalties if they refuse.

Then we need to think about what happens to personal digital data once they are generated. Jotting down details of one’s health in a paper journal or sharing information with a doctor that is maintained in a folder in a filing cabinet in the doctor’s surgery can be kept private and secure. In this era of using digital tools to generate and archive such information, this privacy and security can no longer be guaranteed. Once any kind of personal data are collected and transmitted to the computing cloud, the person who generated the data loses control of it. These details become big data, part of the digital data economy and available to any number of second or third parties for repurposing: data mining companies, marketers, health insurance, healthcare and medical device companies, hackers, researchers, the internet empires themselves and even national security agencies, as Edward Snowden’s revelations demonstrated.

Even the large institutions that are trusted by patients for offering reliable and credible health and medical information online (such as the Mayo Clinic itself, which ranks among the top most popular health websites with 30 million unique estimated monthly visitors) may inadvertently supply personal details of those who use their websites to third parties. One recent study found that nine out of ten visits to health or medical websites result in data being leaked to third parties, including companies such as Google and Facebook, online advertisers and data brokers because the websites use third party analytic tools that automatically send information to the developers about what pages people are visiting. This information can then be used to construct risk profiles on users that may shut them out of insurance, credit or job opportunities. Data security breaches are common in healthcare organisations, and cyber criminals are very interested in stealing personal medical details from such organisations’ archives. This information is valuable as it can be sold for profit or used to create fake IDs to purchase medical equipment or drugs or fraudulent health insurance claims.

In short, the answer to the question ‘Who owns your personal health and medical data?’ is generally no longer individuals themselves.

My research and that of others who are investigating people’s responses to big data and the scandals that have erupted around data security and privacy are finding that concepts of privacy and notions of data ownership are beginning to change in response. People are becoming aware of how their personal data may be accessed, legally or illegally, by a plethora of actors and agencies and exploited for commercial profit. Major digital entrepreneurs, such as Apple CEO Tim Cook, are in turn responding to the public’s concern about the privacy and security of their personal information. Healthcare organisations and medical providers need to recognise these concerns and manage their data collection initiatives ethically, openly and responsibly.

A cultural analysis of the ‘3D selfie’

Image credit: 3D Printed Heroes – photograph by Maurizio Pesce. Available under a CC BY 2.0 license. Image available at:

Image credit: 3D Printed Heroes – photograph by Maurizio Pesce. Available under a CC BY 2.0 license. Image available at:

I recently completed a book chapter on what has been termed ‘3D selfies’: replicas of people that have been fabricated using computer assisted design files and 3D printing machines. Here is the abstract (the full chapter can be accessed as a preprint here):

A new form of representing selfhood and embodiment has emerged in the wake of the development of 3D printing technologies. This is the 3D printed self replica, a fabrication using digital 3D body scans of people that produces a material artefact of a person’s entire body or parts thereof. The technologies to generate these artefacts are rapidly moving into a range of leisure domains, including sporting events, shopping centres, airports, concerts and amusement parks as well as fan cultures and marketing programs. 3D printed self replicas can even be fabricated at home using a software package developed for the Xbox Kinect game box and a home 3D printer. As I argue in this chapter, there are deeper implications of these artefacts for the ways in which we understand not only the body, selfhood and social relations and the engagement of people in leisure cultures but also people’s entanglements with personal digital data. The 3D self replica as a case study offers an opportunity to think through some of these intersections. As personal digital data ‘made solid’, these artefacts offer new ways of thinking about the ways in which digital data can be employed to represent bodies/selves and become biographical objects, mementos and signifiers of important or intimate events in people’s lives. Their use provides insights into data practices, or how people interact with and make sense of digital data in an era in which such ‘lively’ data are ceaselessly collected about them.

Edited book ‘Beyond Techno-Utopia: Critical Approaches to Digital Health Technologies’ now out

Last year I guest-edited a special issue of the open-access sociology journal Societies that focused on critical perspectives on digital health technologies. The collection includes my editorial and another article I contributed (on the topic of apps as sociocultural artefacts), as well as eight other articles from scholars based in the UK, Australia, Finland, the USA and Sweden. Individual contributions may be accessed on the journal’s website here, and now the whole collection is available as an open access book PDF (or can be purchased as a hard copy), both available here.

The following outline of the special issue/book’s contents, an edited excerpt taken from my editorial, provides an overview of its contents.

The articles in this special issue build on a well-established literature in sociology, science and technology studies and media and cultural studies that has addressed the use of digital technologies in health and medicine… Several of these topics are taken up in the articles published in this special issue. All the authors use social and cultural theory to provide insights into the tacit assumptions, cultural meanings and experiences of digital health technologies. The articles cover a range of digital health technologies: devices used for the self-tracking of body metrics (Ruckenstein; Till; Rich and Miah; Lupton); social media platforms for discussing patients’ experiences of chronic disease (Sosnowy) and experiences of pregnancy and early motherhood (Johnson); health and medical apps (Till; Johnson; Christie and Verran; Lupton); telehealthcare systems (Hendy, Chrysanthaki and Barlow); and a digital public health surveillance system (Cakici and Sanches). While some articles focus on globalised digital media (Cakici and Sanches; Rich and Miah; Till; Lupton), others engage more specifically with a range of sociocultural groups, contexts and locations. These include Aboriginal people living in a remote region of Australia (Christie and Verran) and Australian mothers in urban Sydney (Johnson) as well as research participants in Helsinki, Finland (Ruckenstein), the United States (Sosnowy) and England (Hendy, Chrysanthaki and Barlow).

Understandings and experiences of selfhood and embodiment as they are generated and experienced via digital health devices are central preoccupations in the articles by Ruckenstein, Rich and Miah, Till, Lupton, Sosnowy and Johnson. Ruckenstein’s study of self-trackers found that they often conceptualised their bodies and their physical activities in different ways when these were being monitored and rendered into digital data. The data that were generated by these devices proved to be motivational and to give value to some activities (like housework) that otherwise lacked value or new meaning to functions such as sleep (which when digitised and quantified became viewed as a competence). Ruckenstein found that the digital data tended to be invested with greater validity than were other indicators of bodily wellbeing or activity, such as the individual’s physical sensations.

All of the above authors comment on the ways in which digital health devices such as wearable self-tracking devices, social media platforms, apps and patient support websites work as disciplinary tools. They invite users to conform to the ideals of healthism (privileging good health above other priorities) and the responsible self-management and self-monitoring of one’s health and body, including avoiding exposure to risk. Rich and Miah use the concept of “public pedagogy” to describe the socio-political dimensions of digital health technologies as they are employed to educate people about their bodies and promote self-management. As Johnson notes, for women who are pregnant or have the care of young children, this sphere of responsibility is extended to the bodies of others: the foetus or child. And as Till’s article emphasises, when employees are “encouraged” to engage in self-tracking, the ethos of responsibility extends from personal objectives to those of employers.

Ruckenstein, Till and Sosnowy also highlight the digital labour involved for people who engage with social media or self-tracking apps as part of their personal health or fitness practices. Sosnowy’s interviews with women with multiple sclerosis who blog about their condition emphasise the work involved in such engagement as an “active patient”. Till’s analysis of digital exercise self-tracking points to the appropriations of people’s labour by other actors for commercial reasons.

The article by Hendy, Chrysanthaki and Barlow moves in a somewhat different direction. Using ethnographic cases studies, they look at the managerial issues involved with implementing telehealthcare in English social and health care organisations. Their focus, therefore, is not on the recipients or targets of digital health technologies but rather those who are attempting to institute programs as part of their work as managers. These authors’ contribution highlights the messiness of introducing new systems and practices into large organisations, and the resistances that may emerge on the part of both workers and the targets of telehealthcare programs. Cakici and Sanches’ article also takes an organisational perspective in addressing a European Commission co-funded project directed at syndromic surveillance, or the use of secondary sources to detect outbreaks and patterns in diseases and medical conditions. Digital data are increasingly being use as part of syndromic surveillance: Google Flu Trends is one such example. Cakici and Sanches’ analysis highlights the role played by human decision-making and the affordances of digital technologies in structuring what kinds of data are retrieved for syndromic surveillance and how they are interpreted.

While there are as yet few detailed ethnographic accounts of how people are implementing, adopting or resisting contemporary digital health technologies, there are even fewer that investigate the use of these technologies by members of cultural groups outside the global North. The article by Christie and Verran takes a much-needed diversion from perspectives on white, privileged groups to Aboriginal people living in a remote part of Australia. As they argue, the concepts on health, illness and the body that are held by this cultural group differ radically from the tacit assumptions that are invested in mainstream health and medical apps. Any app that is developed to assist in health literacy that is targeted at this group must incorporate culturally-appropriate modes of communication: positioning people within their cultural and kinship networks of sociality, for example, rather than representing them as atomised actors.

The articles collected here in this special issue have gone some way in offering a critical response to digital health technologies, but they represent only a beginning. Many more compelling topics remain to be investigated. These include research into the ways in which lay people and healthcare professionals are using (or resisting the use) of social media, apps and self-monitoring devices for medicine and health-related purposes; the implications for medical power and the doctor-patient relationship; how citizen science and citizen sensing are operating in the public health domain; the development of new digital health technologies; the implications of big data and data harvesting in medicine and healthcare; the spreading out of health-related self-tracking practices into many social domains; the unintended consequences and ethical aspects of digital technology use and their implications for social justice; and data security and privacy issues.

Digitised children’s bodies

This is an excerpt from the pre-print version of a chapter I have written on the topic of ‘digital bodies’. The full pre-print can be accessed here.

The sociomaterialist perspective has been taken up by several scholars writing about children’s bodies, particularly within cultural geography, but also by some sociologists and anthropologists (Prout, 1996; Horton and Kraftl, 2006a, 2006b; Lee, 2008; Woodyer, 2008). Researchers using a sociomaterialist approach have conducted studies on, for example, children’s use of asthma medication (Prout, 1996), the surveillant technologies that have developed around controlling children’s body weight in schools (Rich et al., 2011), children’s sleep and the objects with which they interact (Lee, 2008), the interrelationship of objects with pedagogy and classroom management of students’ bodies (Mulcahy, 2012) and sociomaterial practices in classrooms that lead to the inclusion or exclusion of children with disabilities (Söderström, 2014). Outside sociomaterialist studies, young children’s interactions with digital technologies have attracted extensive attention from social researchers, particularly in relation to topics such as the potential for cyber-bullying, online paedophilia and for children to become unfit and overweight due to spending too much time in front of screens (Holloway et al., 2013). However few researchers thus far have directed their attention to the types of digital technologies that visually represent children’s bodies or render their body functions, activities and behaviours into digital data; or, in other words, how children’s bodies become digital data assemblages.

From the embryonic stage of development onwards, children’s bodies are now routinely monitored and portrayed using digital technologies. A plethora of websites provide images of every stage of embryonic and foetal development, from fertilisation to birth, using a combination of digital images taken from embryo and foetus specimens and digital imaging software  (Lupton, 2013). 3/4D ultrasounds have become commodified, used for ‘social’ or ‘bonding’ purposes. Many companies offering 3/D ultrasounds now come to people’s homes, allowing expectant parents to invite family and friends and turn a viewing of the foetus into a party event. This sometimes involves a ‘gender reveal’ moment, in which the sonographer demonstrates to all participants, including the parents, the sex of the foetus . Some companies offer the service of using 3D ultrasound scan files to create life-sized printed foetus replica models for parents.

The posting to social media sites such as Facebook, Twitter, Instagram and YouTube of the foetus ultrasound image has become a rite of passage for many new parents and often a way of announcing the pregnancy. Using widgets such as ‘Baby Gaga’, expectant parents can upload regular status updates to their social media feeds automatically that provide news on the foetus’s development. While a woman is pregnant, she can use a range of digital devices to monitor her foetus. Hundreds of pregnancy apps are currently on the market, including not only those that provide information but others that invite users to upload personal information about their bodies and the development of their foetus. Some apps offer a personalised foetal development overview or provide the opportunity for the woman to record the size of her pregnant abdomen week by week, eventually creating a time-lapse video. Other apps involve women tracking foetal movements or heart beat. Bella Beat, for example, is a smartphone attachment and app that allows the pregnant women to hear and record the foetal heart beat whenever she likes and to upload the audio file to her social media accounts.

YouTube has become a predominant medium for the representation of the unborn entity in the form of ultrasound images and of the moment of birth. Almost 100,000 videos showing live childbirth, including both vaginal and Caesarean births, are available for viewing on that site, allowing the entry into the world of these infants to be viewed by thousands and, in the case of some popular videos, even millions of viewers. Some women even choose to live-stream the birth so that audiences can watch the delivery in real time. Following the birth, there are similar opportunities for proud parents to share images of their infant online on social media platforms. In addition to these are the growing number of devices on the market for parents to monitor the health, development and wellbeing of their infants and young children. Apps are available to monitor such aspects as infants’ feeding and sleeping patterns, their weight and height and their development and achievements towards milestones. Sensor-embedded baby clothing, wrist or ankle bands and toys can be purchased that monitor infants’ heart rate, body temperature and breathing, producing data that are transmitted to the parents’ devices. Smartphones can be turned into baby monitors with the use of apps that record the sound levels of the infant.

As children grow, their geolocation, educational progress and physical fitness can be tracked by their parents using apps, other software and wearable devices. As children themselves begin to use digital technologies for their own purposes, they start to configure their own digital assemblages that represent and track their bodies. With the advent of touchscreen mobile devices such as smartphones and tablet computers, even very young children are now able to use social media sites and the thousands of apps that have been designed especially for their use (Holloway et al., 2013). Some such technologies encourage young children to learn about the anatomy of human bodies or about nutrition, exercise and physical fitness, calculate their body mass index, collect information about their bodies or represent their bodies in certain ways (such as manipulating photographic images of themselves). These technologies typically employ gamification strategies to provide interest and motivation for use. Some involve combining competition or games with self-tracking using wearable devices. One example is the Leapfrog Leapband, a digital wristband connected to an app which encourages children to be physically active in return for providing them with the opportunity to care for virtual pets. Another is the Sqord interactive online platform with associated digital wristband and app. Children who sign up can make an avatar of themselves and use the wristband to track their physical activity. Users compete with other users by gaining points for moving their bodies as often and as fast as possible.

In the formal educational system there are still more opportunities for children’s bodies to be monitored measured and evaluated and rendered into digitised assemblages. Programmable ‘smart schools’ are becoming viewed as part of the ‘smart city’, an urban environment in which sensors that can watch and collect digital data on citizens are ubiquitous (Williamson, 2014). The monitoring of children’s educational progress and outcomes using software is now routinely undertaken in many schools, as are their movements around the school. In countries such as the USA and the UK, the majority of schools have CCTV cameras that track students, and many use biometric tracking technologies such as RFID chips in badges or school uniforms and fingerprints to identify children and monitor their movements and their purchases at school canteens (Taylor, 2013; Selwyn, 2014). A growing number of schools are beginning to use wearable devices, apps and other software for health and physical education lessons, such as coaching apps that record children’s sporting performances and digital heart rate monitors that track their physical exertions (Lupton, 2015).

We can see in the use of digital technologies to monitor and represent the bodies of children a range of forms of embodiment. Digitised data assemblages of children’s bodies are generated from before birth via a combination of devices that seek to achieve medical- or health-related or social and affective objectives. These assemblages may move between different domains: when, for example, a digitised ultrasound image that was generated for medical purposes becomes repurposed by expectant parents as a social media artefact, a way of announcing the pregnancy, establishing their foetus as new person and establishing its social relationships. Parents’ digital devices, and later those of educational institutions and those of children themselves when they begin to use digital devices, potentially become personalised repositories for a vast amount of unique digital assemblages on the individual child, from images of them to descriptions of their growth, development, mental and physical health and wellbeing, movements in space, achievements and learning outcomes. These data assemblages, containing as they do granular details about children, offer unprecedented potential to configure knowledges about individual children and also large groups of children (as represented in aggregated big data sets).


Holloway D, Green L and Livingstone S. (2013) Zero to Eight: Young Children and Their Internet Use. London: LSE London, EU Kids Online.

Horton J and Kraftl P. (2006a) Not just growing up, but going on: Materials, spacings, bodies, situations. Children’s Geographies 4(3): 259-276.

Horton J and Kraftl P. (2006b) What else? some more ways of thinking and doing ‘Children’s Geographies’. Children’s Geographies 4(1): 69-95.

Lee N. (2008) Awake, asleep, adult, child: An a-humanist account of persons. Body & Society 14(4): 57-74.

Lupton D. (2013) The Social Worlds of the Unborn, Houndmills: Palgrave Macmillan.

Lupton D. (2015) Data assemblages, sentient schools and digitised health and physical education (response to Gard). Sport, Education and Society 20(1): 122-132.

Mulcahy D. (2012) Affective assemblages: body matters in the pedagogic practices of contemporary school classrooms. Pedagogy, culture and society 20(1): 9-27.

Prout A. (1996) Actor-network theory, technology and medical sociology: an illustrative analysis of the metered dose inhaler. Sociology of Health and Illness 18(2): 198-219.

Rich E, Evans J and De Pian L. (2011) Children’s bodies, surveillance and the obesity crisis. In: Rich E, Monaghan LF and Aphramor L (eds) Debating Obesity: Critical Perspectives. Houndsmills: Palgrave Macmillan, 139-163.

Selwyn N. (2014) Data entry: towards the critical study of digital data and education. Learning, Media and Technology: 1-19.

Söderström S. (2014) Socio-material practices in classrooms that lead to the social participation or social isolation of disabled pupils. Scandinavian Journal of Disability Research online first.

Taylor E. (2013) Surveillance Schools: Security, Discipline and Control in Contemporary Education, Houndmills: Palgrave Macmillan.

Williamson B. (2014) Smart schools in sentient cities. dmlcentral.

Woodyer T. (2008) The body as research tool: embodied practice and children’s geographies. Children’s Geographies 6(4): 349-362.

‘Eating’ digital data

My previous post drew on Donna Haraway’s concept of companion species to theorise the ways in which we engage with our personal digital data assemblages. The work of Annemarie Mol offers an additional conceptual framework within which to understand digital data practices at a more detailed level, while still retaining the companion species perspective.

Mol has developed a framework that incorporates elements of enquiry that can be mapped onto the topic of digital data practices. These include the following: understanding language/discourse and its context and effects; tracing the development and use of objects of knowledge as they become objects-in-practice; acknowledging the dynamic nature of processes and the ‘endless tinkering’ that is involved in processes; incorporating awareness of the topologies or sites and spaces in which phenomena are generated and used; and finally, directing attention at the lived experiences or engagements in which practices and objects are understood and employed (see also Mol, 2002, 2008; Mol and Law, 2004).

If this approach is applied to digital data practices and their configurations, then focusing attention on the language that is employed to describe digital data, viewing digital data as objects that have both discursive and material effects and that constantly changing, recognising the process of tinkering (experimenting, adapting) that occur in relation to digital data and the spaces in which these processes take place are all important to developing an understanding of the ontology of digital data and our relationship with them.

Mol’s (2002) concept of ‘the body multiple’ in medicine has resonances with the Haraway’s cyborg ontology. This concept recognises that the human body is comprised of many different practices, sites and knowledges. While the body itself is not fragmented or multiple, the phenomena that make sense of it and represent it do so in many different ways so that the body is lived and experienced in different modes. So too the digital data assemblages that are configured by human users’ interactions with digital technologies are different versions of people’s identities and bodies that have material effects on their ways of living and conceptualising themselves. Part of the work of people’s data practices is negotiating the multiple bodies and selves that these digital data assemblages represent and configure.

Mol’s writings on human subjectivity also have implications for understanding data practices and interpretations. In her essay entitled ‘I eat an apple’, Mol points out that once a foodstuff has been swallowed, the human subject loses control over what happens to the content of the food in her body as the processes of digestion take place. As she notes, the body is busily responding to the food, but the individual herself has no control over this: ‘Her actorship is distributed and her boundaries are neither firm nor fixed’ (Mol, 2008: 40). The eating subject is able to choose what food she decides to eat, but after this point, her body decides how to deal with the components of the food, selecting certain elements and discarding others.

This raises questions about human agency and subjectivity. In the statement ‘I eat an apple’ is the agency in the ‘I’ or in the apple? Humans may grow, harvest and eat apples, but without foodstuffs such as apples, humans would not exist. Furthermore, once the apple is chewed and swallowed, it then becomes part of and absorbed into the eater’s body. It is impossible to determine what is human and what is apple (Mol, 2008: 30) The eating subject, therefore, is semi-permeable, neither completely closed off nor completely open to the world.

Mol then goes on to query at what stage the apple becomes part of her, and whether the category of the human subject might recognise the apple as ‘yet another me, a subject in its own right’ (Mol, 2008: 40). Apples themselves have been shaped by years of cultivation by humans into the forms in which they now exist. In fact they may be viewed as a form of Haraway’s companion species. How then do we draw boundaries around the body/self and the apple? How is the human subject to be defined?

To extend Mol’s analogy, the human subject may be conceptualised as both data-ingesting and data-emitting in an endless cycle of generating data, bringing the data into the self, generating yet more data. Data are absorbed into the body/self and then become new data that flow out of the body/self into the digital data economy. The data-eating/emitting subject, therefore, is not closed off but is open to taking in and letting out digital data. These data become part of the human subject but, as data assemblages also represent the individual in multiple ways that have different meanings based on their contexts and uses. Just as eating an apple has many meanings, depending on the social, cultural, political, historical and geographical contexts in which this act takes place, generating and responding to digital data about oneself are highly contingent acts. If digital data are never ‘raw’ but rather are always ‘cooked’ (that is, always understood and experienced via social and cultural processes), and may indeed be ‘rotted’ or spoilt in some way (Boellstorff, 2013), can we also understand them as ‘eaten’ and ‘digested’?

Haraway and Mol both emphasise the politics of technocultures. Haraway’s cyborg theorising was developed to explain her socialist feminist principles. In all of her work she emphasises the importance of paying attention as critical scholars to the exacerbation of socioeconomic disadvantage and inequalities that may be outcomes of these relationships. Mol similarly notes the political nature of technologies. In her ‘I eat an apple’ essay, for example, she comments about her distaste for Granny Smith apples, once imported from Chile and therefore associated in her mind with repressive political regimes. As she notes, while she may eat this type of apple and while it may nourish her body as other apples do, she is unable to gain sensory pleasure from it.

Data science writings on big data often fail to acknowledge the political dimensions of digital data. They do not see how data are always already ‘cooked’, or how their flavour or digestibility are influenced by their context. Just as ‘eating apples is variously situated’ (Mol, 2008: 29) in history, geography, culture, social relations and politics, resulting in different flavours and pleasures, so too eating data is contextual. Like Haraway’s cyborg figuration (see her interview with Gane, 2006), the digital data assemblage may be viewed both as a product of global enterprise and capitalism and as representing possibilities for radical creative and political possibilities.

Using Mol’s concepts of the eating subject, we might wonder: What happens when we ingest/absorb digital data about ourselves? Do we recognise some data as ‘food’ (appropriate for such ingestion) and others as ‘non-food’ (not appropriate in some way for our use)? Are some data simply indigestible (our bodies/selves do not recognise them as us and cannot incorporate them)? How are the flavours and tastes of digital data experienced, and what differentiates these flavours and tastes?


Boellstorff, T. (2013) Making big data, in theory. First Monday, 18 (10). <;, accessed 8 October 2013.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Mol, A. (2002) The Body Multiple: Ontology in Medical Practice. Durham, NC: Duke University Press.

Mol, A. (2008) I eat an apple. On theorizing subjectivities. Subjectivity, 22, 28-37.

Mol, A. & Law, J. (2004) Embodied action, enacted bodies: the example of hypoglycaemia. Body & Society, 10, 43-62.

Personal digital data as a companion species

While an intense interest in digital data in popular and research cultures is now evident, we still know little about how humans interacting with, making sense of and using the digital data that they generate. Everyday data practices remain under-researched and under-theorised. In attempting to identify and think through some of the ways in which critical digital data scholars may seek to contribute to understandings of data practices, I am developing an argument that rests largely on the work of two scholars in the field of science and technology studies: Donna Haraway and Annemarie Mol. In this post I begin with Haraway, while my next post will discuss Mol.

Haraway’s work has often attempted ‘to find descriptive language that names emergent ontologies’, and I use her ideas here in the spirit of developing new terms and concepts to describe humans’ encounters with digital data. Haraway emphasises that humans cannot be separated from nonhumans conceptually, as we are constantly interacting with other animals and material objects as we go about our daily lives. Her writings on the cyborg have been influential in theory for conceptualising human and computer technological encounters (Haraway, 1991). In this work, Haraway drew attention to the idea that human ontology must be understood as multiple and dynamic rather than fixed and essential, as blurring boundaries between nature and culture, human and nonhuman, Self and Other. She contends that actors, whether human or nonhuman, are never pre-established; rather they emerge through relational encounters (Bhavnani and Haraway, 1994). The cyborg metaphor encapsulates this idea, not solely in relation to human-technology assemblages but to any interaction of humans with nonhumans.

This perspective already provides a basis for thinking through the emergent ontologies that are the digital data assemblages that are configured by humans’ interactions with the software and hardware that generate digital data about them. Haraway’s musings on human and nonhuman animal interactions (Haraway, 2003, 2008, 2015) also have resonance for how we might understand digital data-human assemblages. Haraway uses the term ‘companion species’ to describe the relationships that the human species has not only with other animal species but also with technologies. Humans are companion species with the nonhumans with which they live alongside and engage, each species learning from and influencing the other, co-evolving. Haraway refers to companion species as ‘post-cyborg entities, acknowledging the development of her thinking since her original cyborg exegesis.

This trope of companion species may be taken up to think about the ways in which humans generate, materialise and engage with digital data. Thrift has described the new ‘hybrid beings’ that are comprised of digital data and human flesh. Adopting Haraway’s companion species trope allows for the extension of this idea by acknowledging the liveliness of digital data and the relational nature of our interactions with these data. Haraway has commented in a lecture that she has learnt

through my own inhabiting of the figure of the cyborg about the non-anthropomorphic agency and the liveliness of artifacts. The kind of sociality that joins humans and machines is a sociality that constitutes both, so if there is some kind of liveliness going on here it is both human and non-human. Who humans are ontologically is constituted out of that relationality.

This observation goes to the heart of how we might begin to theorise the liveliness of digital data in the context of our own aliveness/liveliness, highlighting the relationality and sociality that connect them.

Like companion species and their humans, digital data are lively combinations of nature/culture. Digital data are lively in several ways. They are about life itself (details about human’s and other living species), they are constantly generated and regenerated as well as purposed and repurposed as they enter into the digital knowledge economy, they have potential impacts on humans’ and other species’ lives via the assumptions and inferences that they are used to develop and they have consequences for livelihoods in terms of their commercial and other value and effects.

Rather than think of the contemporary digitised human body/self as posthuman (cf. Haraway’s comments on posthumanism in her interview with Gane, 2006), the companion species perspective develops the idea of ‘co-human’ entities. Just as digital data assemblages are comprised of specific information points about people’s lives, and thus learn from people as algorithmic processes manipulate this personal information, people in turn learn from the digital data assemblages of which they are a part. The book choices that Amazon offers the, the ads that are delivered to them on Facebook or Twitter, the returns that are listed from search engine queries or browsing histories, the information that a fitness trackers provides about their heart rate or calories burnt each day are all customised to their digitised behaviours. Perusing these data can provide people with insights about themselves and may structure their future behaviour.

These aspects of digital data assemblages are perhaps becoming even more pronounced as the Internet of Things develops and humans become just one node in a network of smart objects that configure and exchange digital data with each other. Humans move around in data-saturated environments and they are able to wear personalised data-generating devices on their bodies, including not only their smartphones but objects such as sensor-embedded wristbands, clothing or watches. The devices that we carry with us literally are our companions: in the case of smartphones regularly touched, fiddled with and looked at throughout the day. But in distinction from previous technological prostheses, these mobile and wearable devices are also invested with and send out continuous flows of personal information. They have become the repositories of communication with others, geolocation information, personal images, biometric information and more. They also leak these data outwards as they are transmitted to computing cloud servers. All this is happening in real-time and continuously, raising important questions about the security and privacy of the very intimate information that these devices generate, transmit and archive (Tene and Polonetsky, 2013).

The companion species trope recognises the inevitability of our relationship with our digital data assemblages and the importance of learning to live together and to learn from each other. It suggests both the vitality of these assemblages and also the possibility of developing a productive relationship, recognising our mutual dependency. We may begin to think about our digital data assemblages as members of a companion species that have lives of their own that are beyond our complete control. These proliferating digital data companion species, as they are ceaselessly configured and reconfigured, emerge beyond our bodies/selves and into the wild of digital data economies and circulations. They are purposed and repurposed by second and third parties and even more actors beyond our reckoning as they are assembled and reassembled. Yet even as our digital data companion species engage in their own lives, they are still part of us and we remain part of them. We may interact with them or not; we may be allowed access to them or not; we may be totally unaware of them or we may engage in purposeful collection and use of them. They have implications for our lives in a rapidly growing array of contexts, from the international travel we are allowed to undertake to the insurance premiums, job offers or credit we are offered.

If we adopt Haraway’s companion species trope, we might ask the following: What are our affective responses to our digital data companion species? Do we love or hate them, or simply feel indifferent to them? What are the contexts for these responses? How do we live with our digital data companion species? How do they live with us? How do our lives intersect with them? What do they learn from us, and what do we learn from them? What is the nature of their own lives as they move around the digital data economy? How are we influenced by them? How much can we domesticate or discipline them? How do they domesticate or discipline us? How does each species co-evolve?


Bhavnani, K.-K. & Haraway, D. (1994) Shifting the subject: a conversation between Kum-Kum Bhavnani and Donna Haraway, 12 April 1993, Santa Cruz, California. Feminism & Psychology, 4, 19-39.

Gane, N. (2006) When we have never been human, what is to be done?: Interview with Donna Haraway. Theory, Culture & Society, 23, 135-58.

Haraway, D. (1991) Simians, Cyborgs and Women: the Reinvention of NatureLondon: Free Association.

Haraway, D. (2003) The Companion Species Manifesto: Dogs, People, and Significant Otherness. Chicago: Prickly Paradigm.

Haraway, D. (2008) When Species Meet. Minneapolis: The University of Minnesota Press.

Tene, O. & Polonetsky, J. (2013) Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology & Intellectual Property, 11, 239-73.

The thirteen Ps of big data

Big data are often described as being characterised by the ‘3 Vs’: volume (the large scale of the data); variety (the different forms of data sets that can now be gathered by digital devices and software); and velocity (the constant generation of these data). An online search of the ‘Vs’ of big data soon reveals that some commentators have augmented these Vs with the following: value (the opportunities offered by big data to generate insights); veracity/validity (the accuracy/truthfulness of big data); virality (the speed at which big data can circulate online); and viscosity (the resistances and frictions in the flow of big data) (see Uprichard, 2013 for a list of even more ‘Vs’).

These characterisations principally come from the worlds of data science and data analytics. From the perspective of critical data researchers, there are different ways in which big data can be described and conceptualised (see the further reading list below for some key works in this literature). Anthropologists Tom Boellstorff and Bill Maurer (2015a) refer to the ‘3 Rs’: relation, recognition and rot. As they explain, big data are always formed and given meaning via relationships with human and nonhuman actors that extend beyond data themselves; how data are recognised qua data is a sociocultural and political process; and data are susceptible to ‘rot’, or deterioration or unintended transformation as they are purposed and repurposed, sometimes in unintended ways.

Based on my research and reading of the critical data studies literature, I have generated my own list that can be organised around what I am choosing to call the ‘Thirteen Ps’ of big data. As in any such schema, this ‘Thirteen Ps’ list is reductive, acting as a discursive framework to organise and present ideas. But it is one way to draw attention to the sociocultural dimensions of big data that the ‘Vs’ lists have thus far failed to acknowledge, and to challenge the taken-for-granted attributes of the big data phenomenon.

  1. Portentous: The popular discourse on big data tends to represent the phenomenon as having momentous significance for commercial, managerial, governmental and research purposes.
  2. Perverse: Representations of big data are also ambivalent, demonstrating not only breathless excitement about the opportunities they offer but also fear and anxiety about not being able to exert control over their sheer volume and unceasing generation and the ways in which they are deployed (as evidenced in metaphors of big data that refer to ‘deluges’ and ‘tsunamis’ that threaten to overwhelm us).
  3. Personal: Big data incorporate, aggregate and reveal detailed information about people’s personal behaviours, preferences, relationships, bodily functions and emotions.
  4. Productive: The big data phenomenon is generative in many ways, configuring new or different ways of conceptualising, representing and managing selfhood, the body, social groups, environments, government, the economy and so on.
  5. Partial: Big data can only ever tell a certain narrative, and as such they offer a limited perspective. There are many other ways of telling stories using different forms of knowledges. Big data are also partial in the same way as they are relational: only some phenomena are singled out and labelled as ‘data’, while others are ignored. Furthermore, more big data are collected on some groups than others: those people who do not use or have access to the internet, for example, will be underrepresented in big digital data sets.
  6. Practices: The generation and use of big data sets involve a range of data practices on the part of individuals and organisations, including collecting information about oneself using self-tracking devices, contributing content on social media sites, the harvesting of online transactions by the internet empires and the data mining industry and the development of tools and software to produce, analyse, represent and store big data sets.
  7. Predictive: Predictive analytics using big data are used to make inferences about people’s behaviour. These inferences are becoming influential in optimising or limiting people’s opportunities and life chances, including their access to healthcare, insurance, employment and credit.
  8. Political: Big data is a phenomenon that involves power relations, including struggles over ownership of or access to data sets, the meanings and interpretations that should be attributed to big data, the ways in which digital surveillance is conducted and the exacerbation of socioeconomic disadvantage.
  9. Provocative: The big data phenomenon is controversial. It has provoked much recent debate in response to various scandals and controversies related to the digital surveillance of citizens by national security agencies, the use and misuse of personal data, the commercialisation of data and whether or not big data poses a challenge to the expertise of the academic social sciences.
  10. Privacy: There are growing concerns in relation to the privacy and security of big data sets as people are becoming aware of how their personal data are used for surveillance and marketing purposes, often without their consent or knowledge and the vulnerability of digital data to hackers.
  11. Polyvalent: The social, cultural, geographical and temporal contexts in which big data are generated, purposed and repurposed by a multitude of actors and agencies, and the proliferating data profiles on individuals and social groups that big data sets generate give these data many meanings for the different entities involved.
  12. Polymorphous: Big data can take many forms as data sets are generated, combined, manipulated and materialised in different ways, from 2D graphics to 3D-printed objects.
  13. Playful: Generating and materialising big data sets can have a ludic quality: for self-trackers who enjoy collecting and sharing information on themselves or competing with other self-trackers, for example, or for data visualisation experts or data artists who enjoy manipulating big data to produce beautiful graphics.

Critical Data Studies – Further Reading List

Andrejevic, M. (2014) The big data divide, International Journal of Communication, 8,  1673-89.

Boellstorff, T. (2013) Making big data, in theory, First Monday, 18 (10). <;, accessed 8 October 2013.

Boellstorff, T. & Maurer, B. (2015a) Introduction, in T. Boellstorff & B. Maurer (eds.), Data, Now Bigger and Better! (Chicago, IL: Prickly Paradigm Press), 1-6.

Boellstorff, T. & Maurer, B. (eds.) (2015b) Data, Now Bigger and Better! Chicago, IL: Prickly Paradigm Press.

boyd, d. & Crawford, K. (2012) Critical questions for Big Data: provocations for a cultural, technological, and scholarly phenomenon, Information, Communication & Society, 15 (5),  662-79.

Burrows, R. & Savage, M. (2014) After the crisis? Big Data and the methodological challenges of empirical sociology, Big Data & Society, 1 (1).

Cheney-Lippold, J. (2011) A new algorithmic identity: soft biopolitics and the modulation of control, Theory, Culture & Society, 28 (6),  164-81.

Crawford, K. & Schultz, J. (2014) Big data and due process: toward a framework to redress predictive privacy harms, Boston College Law Review, 55 (1),  93-128.

Gitelman, L. & Jackson, V. (2013) Introduction, in L. Gitelman (ed.), Raw Data is an Oxymoron. Cambridge, MA: MIT Press, pp. 1-14.

Helles, R. & Jensen, K.B. (2013) Making data – big data and beyond: Introduction to the special issue, First Monday, 18 (10). <;, accessed 8 October 2013.

Kitchin, R. (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.

Kitchin, R. & Lauriault, T. (2014) Towards critical data studies: charting and unpacking data assemblages and their work, Social Science Research Network. <;, accessed 27 August 2014.

Lupton, D. (2015) ‘Chapter 5: A Critical Sociology of Big Data’ in Digital Sociology. London: Routledge.

Lyon, D. (2014) Surveillance, Snowden, and Big Data: Capacities, consequences, critique, Big Data & Society, 1 (2). <;, accessed 13 December 2014.

Madden, M. (2014) Public Perceptions of Privacy and Security in the post-Snowden Era, Pew Research Internet Project: Pew Research Center.

McCosker, A. & Wilken, R. (2014) Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation, Visual Studies, 29 (2),  155-64.

Robinson, D., Yu, H., and Rieke, A. (2014) Civil Rights, Big Data, and Our Algorithmic Future. No place of publication provided: Robinson + Yu.

Ruppert, E. (2013) Rethinking empirical social sciences, Dialogues in Human Geography, 3 (3),  268-73.

Tene, O. & Polonetsky, J. (2013) A theory of creepy: technology, privacy and shifting social norms, Yale Journal of Law & Technology, 16,  59-134.

Thrift, N. (2014) The ‘sentient’ city and what it may portend, Big Data & Society, 1 (1). <;, accessed 1 April 2014.

Tinati, R., Halford, S., Carr, L., and Pope, C. (2014) Big data: methodological challenges and approaches for sociological analysis, Sociology, 48 (4),  663-81.

Uprichard, E. (2013) Big data, little questions?, Discover Society,  (1). <;, accessed 28 October 2013.

van Dijck, J. (2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology, Surveillance & Society, 12 (2),  197-208.

Vis, F. (2013) A critical reflection on Big Data: considering APIs, researchers and tools as data makers, First Monday, 18 (10). <;, accessed 27 October 2013.

Medical diagnosis apps – study findings

Over 100,000 medical and health apps for mobile digital devices have now been listed in the Apple App Store and Google Play. They represent diverse opportunities for lay people to access medical information and track their body functions and medical conditions. As yet, however, few critical social researchers have sought to analyse these apps.

In a study I did with Annemarie Jutel we undertook a sociological analysis of medical diagnosis apps, and two articles have now been published from the study. Annemarie is a sociology of diagnosis expert and we were interested in investigating how these apps represented the process of diagnosis. We drew on the perspective that apps are sociocultural artefacts that draw on and reproduce tacit norms and assumptions. We argue that from a sociological perspective, digital devices such as health and medical apps have significant implications for the ways in which the human body is understood, visualised and treated by medical practitioners and lay people alike, for the doctor-patient relationship and the practice of medicine.

In one article, published in Social Science & Medicine, we focused on self-diagnosis apps directed at lay people. We undertook a search using the terms ‘medical diagnosis’ and ‘symptom checker’ for apps that were available for download to smartphones in mid-April 2014 in the Apple App Store and Google Play. We found 35 self-diagnosis apps that claimed to diagnose across a range of conditions (we didn’t include apps directed at diagnosis of single conditions). Some have been downloaded by tens or hundreds of thousands, and the case of WebMD and iTriage Health, millions of smartphone owners.

Our analysis suggests that these apps inhabit a contested and ambiguous site of meaning and practice. The very existence of self-diagnosis apps speaks to several important dimensions of contemporary patienthood and healthcare in the context of a rapidly developing ecosystem of digital health technologies. They also participate in the quest for patient ‘engagement’ and ‘empowerment’ that is a hallmark of digital health rhetoric (or what I call ‘digital patient engagement’).

Self-diagnosis apps, like other technologies designed to give lay people the opportunity to monitor their bodies and their health states and engage with the discourses of healthism and control that pervade contemporary medicine We found that app developers combined claims to medical expertise in conjunction with appeals to algorithmic authority to promote their apps to potential users. While the developers also used appeals to patient engagement as part of their promotional efforts, these were undermined by routine disclaimers that users should seek medical advice to effect a diagnosis. While the cautions that are offered on the apps that they are for ‘entertainment purposes only’ and not designed to ‘replace a diagnosis from a medical professional’ may be added for legal reasons, they detract from the authority that the app may offer and indeed call into question why anyone should use it.

In our other article, published in the new journal Diagnosis, we directed attention at diagnosis apps that are designed for the use of medical practitioners as well as lay people. We analysed 176 such apps that we found in Google Play and the Apple App Store in December 2013. While 36 of these were directed at lay people, the remainder were for medical practitioners. The Diagnosis article mainly concentrates on the latter, given that our other article was about the self-diagnosis apps for lay people.

Our research suggests that these apps should be used with great caution by both lay people and practitioners. The lack of verifiable information provided about the evidence or expertise used to develop these apps is of major concern. The apps are of very variable quality, ranging from those that appear to have the support and input of distinguished medical experts, specialty groups or medical societies to those that offer little or nothing to support their knowledge claims. While at one end of the spectrum we can see apps as a delivery system for information which has been subject to the conventional forms of academic review, at the other extreme, we see apps developed by entrepreneurs with interests in many topics outside medicine, with little input from medical sources, or with inadequate information to ascertain what the sources might be. The lack of information provided by many app developers also raises questions about how users can determine the presence of conflicts of interest and commercial interests that might determine content.

Managing and materialising data as part of self-tracking

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. In a context in which digital data are culturally represented as liquid entities that require management and containment, part of the project of managing the contemporary body is that of containment of the data that one’s body produces. As discursive representations of self-tracking and the quantified self frequently contend, personal data are profligate: it is only right that one should seek first, to collect these data, and second, to manage and discipline the data by aggregating them, representing them visually, and making sense of them.

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. They thus represent a type of selfhood that is distributed between different and constantly changing data sets. Self-tracking assemblages are constantly created and recreated when information about individuals is derived via digital technologies and then reassembled for various purposes.

Bodies and selves are always multiple, in whatever context they find themselves. However for self-trackers, this multiplicity is foregrounded in ways that may not have occurred in previous eras. If they are reviewing their personal data regularly, they cannot fail to be confronted with the shifting data assemblages that serve to represent their bodies and their selves. Part of the data practices in which they are invited to engage as part of self-tracking culture, therefore, is the negotiation and sense-making around the hybridity and vitality of their data assemblages.

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. An important element of self-tracking practices for many people is the visualisation or presentation of their personal data. The notion that data can be beautiful and aesthetically pleasing when presented in appropriate formats pervades data science in general: Gregg (2015) refers to this phenomenon as the ‘data spectacle’. The generation of digital data visualisations can be, variously, acts of work, creative expression and the presentation or performance of selfhood, with the latter an element in particular of self-tracking practices.

In the ‘show-and-tell’ ethos of the Quantified Self movement, finding compelling visual modes to demonstrate the patterns in one’s data is a central feature. The Quantified Self website is full of demonstrations by members of their data, including videos of their ‘show-and-tell’ presentations and still images of their visualisations. Collecting and aggregating personal data, therefore, are part of a range of practices involving self-knowledge and self-expression. By showing one’s data to others in a visually interesting and explanatory graphic, a self-tracker is achieving both self-knowledge and self-expression. Self-tracking becomes performative, both for the insights that a self-tracker may achieve about her or his life but also in terms of the aesthetics of the data that she or he may be able to curate.

The aesthetic elements of data visualisations involve affective responses that may include both pleasure and anxiety (McCosker and Wilken 2014). Indeed McCosker and Wilken (2014) refer to the tendency in data visualisation circles towards the fetishising and sublimity of ‘beautiful data’ as part of exerting mastery over the seemingly unlimited and thus overwhelming amounts of big digital datasets. Extending this logic, the physical materialising of digital data in the form of a 2D or 3D data materialisation may offer a solution to the anxieties of big data. When it is one’s personal data drawn from one’s own flesh that is being manifested in a material digital data object, this may provoke a sense of mastery over what may be experienced as a continually data-emitting subjectivity. The liquidity, flows and force of personal digital data become frozen in time and space, offering an opportunity to make sense of one’s data.


Gregg, M. (2015) Inside the data spectacle. Television & New Media, 16 (1), 37-51.

McCosker, A. and Wilken, R. (2014) Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation. Visual Studies, 29 (2), 155-164.