Twitter and health

Surprisingly little research by sociologists or media studies researchers has investigated how Twitter is used to discuss health and medical issues. Yet there are many interesting issues and topics to explore.

The Healthcare Hashtag Project operated by Symplur, a healthcare social media analytics company, provides a publicly available online resource that demonstrates the diversity of health and medical topics that are discussed on Twitter. When I checked the website in early May 2016, the Project had identified close to 13,000 healthcare topics, over 10,000 hashtags related to healthcare and almost 4,000 contributors to these discussions on Twitter. The diseases that were receiving attention on Twitter on that day included breast cancer, migraine, brain tumours, lymphoma, heart disease, diabetes, lung cancer, attention deficit disorder and leukaemia (these were the top ten trending diseases in order).

The site also shows the ‘influencers’ in the Twitter discussions it documents as well as the latest tweets related to the hashtags it collects. This information demonstrates the sheer diversity of actors who engage in discussions about medical conditions and healthcare on Twitter. The top ten (by mentions) ‘influencers’ for the hashtag #BCSM (denoting ‘breast cancer social media’) were a clinical professor in surgery, four individual breast cancer survivors, a medical school and a research institute, two patient coalitions (one for men with breast cancer and one for young women with breast cancer) and the Journal of the American Medical Association.

The story is quite different if the hashtag #digitalhealth is examined. Another market research company has analysed over 200,000 tweets and almost 30,000 engaged users to identify the top influencers and brands in Twitter discussions using #digitalhealth. The company looked at tweets using this hashtag over a period of four months spanning January to April 2016 and produced a list of the top 100 influencers (based on PageRank analytics that takes into account the number and quality of textual references).

The first four influencers listed (who gained much higher influence scores than any of the 96 others on the list), included Hungarian doctor, genomic scientist, digital health consultant and self-described ‘medical futurist’ Bertalan Mesko, followed by American John Nosta, another digital health consultant who runs his own think tank and is a member of the Google Health Advisory Board. A British health technologist, Alex Butler is next and fourth is American Paul Sonnier, another digital health consultant. These influencers are followed by more representatives of private digital health consulting or technology companies, some tech journalists and a representative from massive American pharmaceutical chain Walgreens. Academics are not well represented in the top 20: only three appear, beginning from number 13 on the list. Practising doctors and individual patients, or organisations for doctors or patients, are scarce.

The most common topics discussed by the top influencers were data (by a long way, accounting for a quarter of the tweets), the Internet of Things and wearable tech. The topics of apps, cancer, artificial intelligence, cybersecurity and telemedicine were the next-most discussed (however, they all received less than 10 per cent of discussion across the tweets).

It is evident from this report that digital health discussions on Twitter (at least those that use #digitalhealth to signify their content) are dominated by commercial and entrepreneurial interests rather than by the experiences of doctors or patients. With the exception of Susannah Fox from the US Department of Health and Human Services, spokespeople from government agencies appear to have little influence in these discussions. This is borne out by the list of top-most influential brands, which are again dominated by commercial enterprises (although the NHS England is included towards the bottom of the top 25).

These data raise some interesting questions for a digital health sociologist. How do voices other than commercial enterprises get heard on Twitter? What makes some conditions or diseases more talked about on Twitter than others? For example, why is breast cancer so prominent — is it because there are far more patient advocates and organisations for patients devoting attention to discussing this , or is it because it is a common form of cancer, or are other factors involved? Why do some practising doctors and medical specialists decide to get involved in Twitter discussions on a particular condition or a digital health technology? How do all the different actors engage with each other –- who pays attention to whom? What kinds of networks are formed between actors from the different groups who are advocates or healthcare providers or developers?

Self-tracking citizenship

An excerpt from Chapter 5 of my new book  The Quantified Self: A Sociology of Self-Tracking.

Nafus and Sherman (2014: 1785) contend that self-tracking is an alternative data practice that is a form of soft resistance to algorithmic authority and to the harvesting of individuals’ personal data. They argue that self-tracking is nothing less than ‘a profoundly different way of knowing what data is, why it is important, who gets to interpret it [sic], and to what ends’. However the issue of gaining access to one’s data remains crucial to questions of data control and use. While a small minority of technically proficient self-trackers are able to devise their own digital technologies for self-tracking and thus exert full control over their personal information, the vast majority must rely on the commercialised products that are available and therefore lose control over where their data are stored and who is able to gain access.

For people who have chronic health conditions, for example, access to their data can be a crucial issue. A debate is continuing over the data that are collected by continuous blood glucose monitoring and whether the patients should have ready access to these data or only their doctors. As one person with diabetes contends on his blog, older self-care blood glucose-monitoring devices produce data that patients can view and act on immediately. Why should the information generated by the newer digitised continuous blood glucose monitors be available only to doctors, who review it some time later, when patients could benefit from seeing their data in real time? A similar issue arises in relation to the information that is collected on heart patients’ defibrillator implants. The data that are conveyed wirelessly to patients’ healthcare professionals cannot be easily accessed by the patients themselves. In jurisdictions such as the United States, the device developers are legally prohibited from allowing patients access to their data (see here).

There is recent evidence that the Quantified Self movement is becoming more interested in facilitating access to personal data for purposes beyond those of individuals. In a post on the Quantified Self website entitled ‘Access matters’, Gary Wolf comments that self-trackers have no legal access to their own data, which they may have collected for years. Nor is there an informal ethical consensus that supports developers in opening their archives to the people who have contributed their information. Wolf and others associated with the Quantified Self movement have begun to campaign for self-trackers to achieve greater access to the personal data that are presently sequestered in the cloud computing archives of developers. They argue for an approach that leads to the aggregation of self-tracked data in ways that will benefit other people than individual self-trackers themselves.

Some Quantified Self movement-affiliated groups have begun to experiment with ways in which self-tracking can be used for community participation and development. Members of the St Louis Quantified Self meeting group, for example, have worked on developing a context-specific app that allows people to input their moods and identify how certain spatial locations within a community affect emotional responses. They are also developing a Personal Environment Tracker that would allow St Louis citizens to monitor their own environmental impact and that of the community in which they live.

The Quantified Self Lab, the technical arm of the Quantified Self mvement, has also announced that it is becoming involved with citizen science initiatives in collaboration with the US Environmental Protection Agency (see here). It has now joined with the Robert Wood Johnson Foundation, an American philanthropic organisation focused on health issues, to work on improving people’s access to their personal data. Both groups are also collaborating with other partners on the Open Humans Network, which is aimed at facilitating the sharing of people’s details about their health and medical statuses as part of a participatory research initiative. Participants who join in this initiative are asked to upload the data that they have collected on themselves through self-tracking devices as well as any other digitised information about their bodies that they are able to offer for use in research studies. Part of the model that the Open Humans Network has adopted is that researchers agree to return to the participants themselves any new data that emerge from projects that use these participants’ information, and participants decide which of their data they allow others to access.

Beyond the Quantified Self movement, a number of initiatives have developed that incorporate the aggregation of self-tracked data with those of others, as part of projects designed to benefit both the individuals who have collected the data and the broader community. Citizen science, environmental activism, healthy cities and community development projects are examples of these types of communal self-tracking endeavours. These initiatives, sometimes referred to as ‘citizen sensing’ (Gabrys, 2014), are a form of crowdsourcing. They may involve the use of data that individuals collect on their local environs, such as air quality, traffic levels or crime rates, as well as on their own health indicators – or a combination of both. These data may be used in various ways. Sometimes they are simply part of collective projects undertaken at the behest of local agencies, but they may also be used in political efforts to challenge governmental policy and agitate for improved services or planning. The impetus may come from grassroots organisations or from governmental organisations; the latter construe it as a top-down initiative or as an encouragement towards community development.

Self-tracked data here become represented as a tool for promoting personal health and wellbeing at the same time as community and environmental development and sustainability. As these initiatives suggest, part of the ethical practice of self-tracking, at least for some practitioners, may involve the notion of contributing to a wider good as well as collecting data for one’s own purposes. Access to large data sets – rendering these data sets more ‘open’ and accessible to members of the public – becomes a mode of citizenship that is distributed between self, community and physical environment. This idea extends the entrepreneurial and responsible citizen ideal by incorporating expectations that people should not only collect their own, personal information for purposes of self-optimisation but should also contribute it to tailored, aggregated big data that will benefit many others, in a form of personal data philanthropy: self-tracking citizenship, in other words.

References

Gabrys, J. (2014) Programming environments: environmentality and citizen sensing in the smart city. Environment and Planning D: Society and Space, 32 (1), 30-48.

Nafus, D. and Sherman, J. (2014) This one does not go up to 11: the Quantified Self movement as an alternative big data practice. International Journal of Communication, 8 1785-1794.

 

 

 

 

 

Self-tracking practices as knowledge technologies

An edited excerpt from the concluding chapter of my book The Quantified Self: A Sociology of Self-Tracking.

As I have remarked in this book’s chapters, via the mainstream self-tracking devices and software that are available, certain aspects of selfhood and embodiment are selected for monitoring while a plethora of others are inevitably left out, ignored, or not even considered in the first place. Those aspects that are selected become more visible, while others are obscured or neglected through this process. The technologies themselves, including the mobile, wearable and ‘anti-wearable’ sensor-embedded objects and the software that animate them, tend to be the product of a narrow demographic of designers: white, well-paid, heterosexual men living in the Global North. In consequence, the tacit assumptions and norms that underpin the design and affordances of self-tracking technologies are shaped by these people’s decisions, preferences and values. Thus, for example, devices such as Apple Watch initially failed to include a menstrual cycle tracker as part of its built-in features (Eveleth, 2014); sexuality self-tracking apps focus on male sexual performance and competitive displays of prowess (Lupton, 2015); apps that use westernised concepts and images of health and the human body are inappropriate for Aboriginal people living in remote areas of Australia (Christie and Verran, 2014). How people from outside this demographic might engage or not with these technologies and how technologies might be better designed to acknowledge the diversity of socioeconomic advantage, cultures and sexual identities are subjects rarely pondered upon in the world of technology design …

At the same time as self-tracking practices are reductive and selective, they are also productive. They bring into being new knowledges, assemblages, subjectivities and forms of embodiment and social relations. In Chapter 2 I referred to the four types of technology identified by Foucault, which work together to produce knowledges on humans. Acts of reflexive self-monitoring involve all four of these knowledge technologies. Via prosumption, self-trackers generate data on themselves (technologies of production); they manipulate and communicate the symbols, images, discourses and ideas related to their own data and the devices that generate these data (technologies of sign systems); they are involved in strategies that are designed to assist them in participating in certain forms of conduct for specific ends (technologies of power); and all of these practices are overtly and deliberately directed at performing, presenting and improving the self (technologies of the self).

What is particularly intriguing about this expertise is that it both operates at the level of the ‘nonexpert’ (the self-tracker), where it is configured, and is inextricably interbound into the digital data economy and the forms of government regulation of the body politic. The authority of the knowledgeable expert on human life is dispersed among members of the lay public to a greater extent than ever before. However, the shared nature of this authority and expertise also undermines the power that self-trackers possess over their own information. Reflexive self-monitors are able to generate their own truth claims about trackers’ own bodies/selves, but these trackers are increasingly unable to control how these truth claims are used by other actors or what the potential ramifications for their own life chances and opportunities are once these data come under the control of others.

 

Pregnancy apps and gender stereotypes

Pregnant women and those experiencing the early years of motherhood have used online forums for many years to share experiences and seek information. Now there are hundreds of apps that have been designed for similar purposes. As part of an integrated research program looking at apps and other digital media for pregnancy and parenting, I have been researching these apps using several approaches. In a survey of 410 Australian women who were pregnant or who had given birth in the past three years, I found that almost three-quarters had used at least one pregnancy app, while half of the women who already had children reported using a parenting app (see here for an open access report on this survey and here for a journal article about it).

With Gareth Thomas from Cardiff University, I have also conducted a critical analysis of the content of pregnancy apps themselves. This involved analysing all pregnancy-related apps offered in the two major app stores, the Apple App Store and Google Play. We examined the app descriptions, looking for how the developers marketed their apps and what they offered. See here and here for articles that have been published from this analysis.

This study found that the apps designed for pregnant women represent pregnancy as a state in which women must maintain a high degree of vigilance over their own bodies and that of their foetuses. Many apps promoted this level of self-monitoring, often seeking to render the practices aesthetically-pleasing by using beautiful images of foetuses or allowing women to take ‘belfies’ (belly selfies) and share these on social media.

Among the most surprising of our findings were the large numbers of pregnancy-related games designed for entertainment. These include pregnancy pranks such as fake foetal ultrasounds to fool people into thinking someone is pregnant. We also found many games for little girls that are on the market. The encourage girls to give pregnant women ‘make-overs’ so that they will ‘feel more confident’ and look beautiful, ready for the birth. Some even let players perform a caesarean section on the characters, who remain glamorous and serene even on the operating table. The types of messages about pregnancy and childbirth that are promoted to their young female users are troubling.

Other apps are directed at men who are becoming fathers, although there were far fewer of these apps compared with those for pregnant women. We noticed from our analysis of these apps that even though quite a few of them are marketed as being written ‘by men, for men’, they typically portray the father as a bumbling fool, who requires simplistic or jokey information to keep him interested in the impending birth of his child. Men are advised not to stare at attractive women and to constantly reassure their partners that they find them attractive. Foetuses are compared to beer bottles so that men can learn about foetal development in supposedly unthreatening ways.

Our overall finding, therefore, is the highly stereotypical gendered representations of pregnant women and expectant fathers in these apps. Women are encouraged to use apps to achieve the ideal of the self-monitoring ‘good mother’, closely tracking their bodies because they have their foetus’s best interests at heart in every action they take. They are expected to celebrate their pregnancy and changing bodies – there is little room for ambivalence. Their male partners, on the other hand, are assumed to be uninterested and to require nudging to act in a supportive role to their partners.  And little girls are encouraged to accept and perpetuate the ‘yummy mummy’ stereotype in playing the pregnancy games that are marketed to them, and to view caesarean sections as a quick and easy way to give birth.

Living Digital Data research program

People’s encounters and entanglements with the personal digital data that they generate is a new and compelling area of research interest in this age of the ascendancy of digital data. Members of the public are now called upon to engage with a variety of forms of information about themselves and to confront the complexities of how these details are used by others. Personal digital data assemblages are configured as human bodies, digital devices, code, data, time and space come together.

 

Personal digital data assemblages smartart

Personal digital data assemblages

 

Over the past few years I have been researching the social aspects of personal digital data: how people understand and conceptualise these data, how they use their data, what people know about where their personal data go and how their data are used by second and third parties.I have analysed the metaphors that are used to describe digital data, the politics of digital data, the types of data that are collected by apps and self-tracking devices, how people use these software and devices and how personal digital data are materialised, or rendered into visualisations or three-dimensional objects. I have sought to theorise the ontology of personal digital data, drawing particularly sociomaterialism, feminist technoscience, cultural geography and sensory studies. (See My Recent Publications for further details.)

I am bringing these research questions together under a program that I have named ‘Living Digital Data’. This title builds on my conceptualisation of digital data as ‘lively’ in a number of ways.

 

 

Lively data smartart

Lively Data

 

 

The first element of the vitality of digital data relates to the ways in which they are generated and what happens thereafter. The personal digital information that is constantly generated contributes to data assemblages that are heterogeneous and dynamic, their character changing as more data points are added and others removed. Digital data may be described as having their own social lives as they circulate in the digital data economy and are purposed and repurposed. Second, digital data constitute forms of knowledge about human (and nonhuman) life itself and hence possess another type of vitality. Third, personal digital data have impacts on people’s lives, shaping the decisions and actions that people make and those that other people make about them.  The profiles constructed from these data can influence decisions about the opportunities people have to travel, access employment, credit or insurance, the people that they meet on online dating sites, the knowledges that they hold about themselves and their bodies and those of intimate others. Finally, personal digital data are forms of livelihoods, contributing to the commodification and capitalisation of information. Indeed, they may be described as a form of biocapital, which possesses many forms of value beyond the personal: for research, commercial, security, managerial and governmental agencies.

This approach recognises the entanglements of personal digital data assemblages with human action. Not only are personal digital data assemblages partly comprised of information about human action, but their materialisations are also the products of human action, and these materialisations can influence future human action.

Rather than refer to data literacy or data management skills, I take up the term ‘data sense’ to encapsulate a broader meaning of ‘data sense’ that includes human senses (sight, sound, touch, smell and taste) and how these are part of people’s responses to data and also acknowledges the role played by digital sensors in the act of ‘sense-making’; or coming to terms with digital data.

 

Data sense smartart

Data Sense

 

Interesting HCI research on self-tracking: a reading list

In a recent blog post, I published a reading list of critical social research into self-tracking. And in another recent post, I discussed what I saw as the intersections of digital sociology and human-computer interaction (HCI) research. I argued that researchers in each approach should pay attention to what the others are doing, as there are many shared interests.

In this post, I present a reading list of what I (as sociologist) have chosen to designate as ‘interesting’ HCI research on the same phenomenon. This is based on what I consider ‘interesting’ – studies that go beyond design or technical features of self-tracking technologies to address how people use them and incorporate the data into their everyday lives.

There is a wealth of HCI research on self-tracking. HCI researchers have published earlier and more often on self-tracking compared to sociologists and other social researchers. This is largely due to their publishing conventions, in which peer-reviewed conference papers have the status of journal articles, and allow people to publish their research much more quickly. Probably because they are located within the world of digital technology design, HCI researchers devoted their attention much earlier than social scientists to what was initially (and sometimes still) called ‘lifelogging’ and how digital devices were used by practitioners.

Some of the research below takes an explicitly critical or reflective approach to self-tracking – although I have found that this is quite rare in HCI, where the ‘persuasive computing’ approach dominates in research on this topic. A few articles report on speculative design approaches or ways of materialising data that are innovative. Others simply offer some interesting material on how and why people are engaging in self-tracking.

Barrass S. (2016) Diagnosing blood pressure with Acoustic Sonification singing bowls. International Journal of Human-Computer Studies 85: 68-71.

Choe EK, Lee NB, Lee B, Pratt W and Kientz JA. (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI ’14). Toronto: ACM Press, 1143-1152.

Choe EK, Lee NB and Schraefel M. (2015) Revealing visualization insights from Quantified-Selfers’ personal data presentations. Computer Graphics and Applications 35: 28-37.

Cuttone A, Petersen MK and Larsen JE. (2014) Four data visualization heuristics to facilitate reflection in personal informatics. Universal Access in Human-Computer Interaction. Design for All and Accessibility Practice. Heraklion: Springer, 541-552.

Doherty AR, Caprani N, Conaire CÓ, Kalnikaite V, Gurrin C, Smeaton AF and O’Connor NE. (2011) Passively recognising human activities through lifelogging. Computers in Human Behavior 27: 1948-1958.

Doherty AR, Pauly-Takacs K, Caprani N, Gurrin C, Moulin CJA, O’Connor NE and Smeaton AF. (2012) Experiences of aiding autobiographical memory using the SenseCam. Human–Computer Interaction 27: 151-174.

Elsden C, Kirk D, Selby M and Speed C. (2015) Beyond personal informatics: designing for experiences with data. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing System (CHI ’15). Seoul: ACM Press, 2341-2344.

Elsden C, Kirk DS and Durrant AC. (2015) A quantified past: toward design for remembering with personal informatics. Human–Computer Interaction online first: 1-40.

Epstein D, Cordeiro F, Bales E, Fogarty J and Munson S. (2014) Taming data complexity in lifelogs: exploring visual cuts of personal informatics data. Proceedings of the 2014 Conference on Designing Interactive Systems (DIS ’14). Vancouver: ACM Press, 667-676.

Epstein DA, Ping A, Fogarty J and Munson SA. (2015) A lived informatics model of personal informatics. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15). Osaka: ACM Press, 731-742.

Fan C, Forlizzi J and Dey A. (2012) A spark of activity: exploring informative art as visualization for physical activity. Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Ubicomp ’12). Pittsburgh: ACM Press, 81-84.

Gaver WW, Bowers J, Boehner K, Boucher A, Cameron DWT, Hauenstein M, Jarvis N and Pennington S. (2013) Indoor weather stations: investigating a ludic approach to environmental HCI through batch prototyping. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13). Paris: ACM Press, 3451-3460.

Grönvall E and Verdezoto N. (2013) Beyond self-monitoring: Understanding non-functional aspects of home-based healthcare technology. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’13). Zurich: ACM Press, 587-596.

Hoyle R, Templeman R, Anthony D, Crandall D and Kapadia A. (2015) Sensitive lifelogs: a privacy analysis of photos from wearable cameras. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’ 15). ACM Press, 1645-1648.

Huang D, Tory M, Aseniero BA, Bartram L, Bateman S, Carpendale S, Tang A and Woodbury R. (2015) Personal visualization and personal visual analytics. Visualization and Computer Graphics 21: 420-433.

Kalnikaite V, Sellen A, Whittaker S and Kirk D. (2010) Now let me see where I was: understanding how lifelogs mediate memory. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10). Atlanta: ACM Press, 2045-2054.

Khot R, Hjorth L and Mueller FF. (2014) Understanding physical activity through 3D printed material artifacts. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). Toronto: ACM Press, 3835-3844.

Khot R, Lee J, Munz H, Aggarwal D and Mueller F. (2014) Tastybeats: making mocktails with heartbeats. Designing Interactive Futures. Vancouver: ACM Press, 467-470.

Khot RA, Pennings R and Mueller FF. (2015) EdiPulse: supporting physical activity with chocolate printed messages. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’15). Seoul: ACM Press, 1391-1396.

Khovanskaya V, Baumer EP, Cosley D, Voida S and Gay G. (2013) Everybody knows what you’re doing: a critical design approach to personal informatics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13). Paris: ACM Press, 3403-3412.

Lawson S, Kirman B, Linehan C, Feltwell T and Hopkins L. (2015) Problematising upstream technology through speculative design: the case of quantified cats and dogs. Proceedings of the SIGCHI Conference on Human Factors in Computer Systems (CHI ’15). ACM Press, 2663-2672.

Lazar A, Koehler C, Tanenbaum J and Nguyen DH. (2015) Why we use and abandon smart devices. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’15). Osaka: ACM Press, 635-646.

Lee M-H, Cha S and Nam T-J. (2015) Patina engraver: visualizing activity logs as patina in fashionable trackers. Proceedings of the SIGCHI Conference on Human Factors in Computing System (CHI ’15). Seoul: ACM Press, 1173-1182.

Li I, Dey AK and Forlizzi J. (2011) Understanding my data, myself: supporting self-reflection with ubicomp technologies. Proceedings of the International Conference on Ubiquitous Computing (Ubicomp ’11). Beijing: ACM, 405-414.

Liu W, Ploderer B and Hoang T. (2015) In bed with technology: challenges and opportunities for sleep tracking. Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction (OzCHI ’15). ACM Press, 142-151.

Mathur A, Van den Broeck M, Vanderhulst G, Mashhadi A and Kawsar F. (2015) Tiny habits in the giant enterprise: understanding the dynamics of a quantified workplace. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp ’15). Osaka: ACM Press, 577-588.

Nissen B and Bowers J. (2015) Data-Things: digital fabrication situated within participatory data translation activities. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). Seoul: ACM Press, 2467-2476.

Oh J and Lee U. (2015) Exploring UX issues in Quantified Self technologies. Eighth International Conference on Mobile Computing and Ubiquitous Networking. Hakodate, Japan: IEEE, 53-59.

Ploderer B, Smith W, Howard S, Pearce J and Borland R. (2012) Things you don’t want to know about yourself: ambivalence about tracking and sharing personal information for behaviour change. Proceedings of the 24th Australian Computer-Human Interaction Conference (OzCHI ’12). Melbourne: ACM Press, 489-492.

Purpura S, Schwanda V, Williams K, Stubler W and Sengers P. (2011) Fit4life: the design of a persuasive technology promoting healthy behavior and ideal weight. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Vancouver: ACM, 423-432.

Rooksby J, Rost M, Morrison A and Chalmers MC. (2014) Personal tracking as lived informatics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Toronto: ACM, 1163-1172.

Snow S, Buys L, Roe P and Brereton M. (2013) Curiosity to cupboard: self reported disengagement with energy use feedback over time. Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration. Adelaide: ACM, 245-254.

Stusak S. (2015) Exploring the potential of physical visualizations. Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction (TEI ’15). Stanford, CA: ACM Press, 437-440.

Stusak S, Tabard A, Sauka F, Khot RA and Butz A. (2014) Activity sculptures: exploring the impact of physical visualizations on running activity. IEEE Transactions on Visualization and Computer Graphics 20: 2201-2210.

Whooley M, Ploderer B and Gray K. (2014) On the integration of self-tracking data amongst Quantified Self members. Proceedings of the 28th International BCS Human Computer Interaction Conference (HCI ’14). Southport, UK: BCS, 151-160.

Williams K. (2013) The weight of things lost: self-knowledge and personal informatics. Personal Informatics.  <http://www.personalinformatics.org/docs/chi2013/williams.pdf&gt; accessed 12 May 2014.

Call for abstracts for themed issue on body weight and digital media

I am editing a themed issue for Fat Studies: An Interdisciplinary Journal of Body Weight and Society on the topic of body weight and digital media. Fat Studies is the first academic journal that critically examines theory, research, practices, and programs related to body weight and appearance.

If you are interested in contributing to this themed issue, please send me an article title and an abstract of 200-250 words outlining what you would propose to cover by 29 February 2016. Final submissions should be no longer than 7,000 words, including the abstract, all notes and references. Please email to deborah.lupton@canberra.edu.au

In keeping with the journal’s emphasis on ‘body weight and society’, the themed issue will include contributions that address the following and related topics from a critical sociocultural perspective:

  • representations of body weight and size in the digital news media (and also how readers may comment on news reports online)
  • apps and wearable devices for weight control, physical fitness and energy expenditure
  • selfies and body size
  • the discussion and portrayal of such issues as weight loss, body size, fat activism, thinspo, fitspo, pro-ana, pro-mia and fat pornography and erotica in blogs, social media platforms and other websites
  • big data and body weight

If your abstract is accepted, the following deadlines apply:

  • Full papers by 31 May 2016
  • Revised final versions by 30 August 2016

 

My new book: The Quantified Self

My new book The Quantified Self: A Sociology of Self-Tracking is due out with Polity Press this April. The publishers are offering a 20% discount for six months (from 18 January 2016 to 31 July 2016) if it is ordered via their website. Please use the code PY703 when you order to receive the discount.

Here is a PDF of the Introduction: Lupton 2016 Introduction to The Quantified Self.

Table of Contents

Acknowledgements

Introduction

1          ‘Know Thyself’: Self-tracking Practices and Technologies

2          ‘New Hybrid Beings’: Theoretical Perspectives

3          ‘An Optimal Human Being’: the Body and Self in Self-Tracking Cultures

4          ‘You are Your Data’: Personal Data Meanings, Practices and Materialisations

5          ‘Data’s Capacity for Betrayal’: Personal Data Politics

Conclusion

References

Index

 

POLITY-Lupton-Quantified Self Visuals-AUG24-3 (1)

 

My 2015 publications

Here are my publications that came out in 2015.

Book

  • Lupton, D. (2015) Digital Sociology. London: Routledge.

Book chapters

  • Lupton, D. (2015) Digital sociology. In Germov, J. and Poole, M. (eds), Public Sociology: An Introduction to Australian Society, 3rd St Leonards: Allen & Unwin.
  • Lupton, D. (2015) Donna Haraway: the digital cyborg assemblage and the new digital health technologies. In Collyer, F. (ed), The Palgrave Handbook of Social Theory in Health, Illness and Medicine. Houndmills: Palgrave Macmillan.

Peer-reviewed journal articles

Report

  • Lupton, D. and Pedersen, S. (2015) ‘What is Happening with Your Body and Your Baby’: Australian Women’s Use of Pregnancy and Parenting Apps. Available here.

Critical social research on self-tracking: a reading list

Self-tracking has recently become a new area of fascination for critical social researchers. A body of literature has now been established of research that has sought to investigate the social, cultural and political dimensions of self-tracking, nearly all of which has come out in the last few years. This literature complements an established literature in human-computer interaction research (HCI), first into lifelogging and then into self-tracking (or personal informatics/analytics, as HCI researchers often call it).

I am currently working on an article that is a comprehensive review of both literatures, in the attempt to outline what each can contribute to understanding self-tracking as an ethos and a practice, and its wider sociocultural implications. Here is a reading list of the work from critical social researchers that I am aware of. I will publish a similar list of interesting HCI research in a forthcoming post.

Albrechtslund, A, and P Lauritsen, (2013) Spaces of everyday surveillance: Unfolding an analytical concept of participation, Geoforum, 49: 310-16.

Allen, AL, (2008) Dredging up the past: Lifelogging, memory, and surveillance, The University of Chicago Law Review, 75 (1): 47-74.

Barta, K, and G Neff, (2015) Technologies for sharing: lessons from Quantified Self about the political economy of platforms, Information, Communication & Society, online first.

Bode, M, and DB Kristensen,  (2016) The digital doppelgänger within. In Assembling Consumption: Researching actors, networks and markets, edited by R. Canniford and D. Badje. London: Routledge, pp.

Bossewitch, J, and A Sinnreich, (2013) The end of forgetting: Strategic agency beyond the panopticon, New Media & Society, 15 (2): 224-42.

Copelton, D, (2010) Output that counts: pedometers, sociability and the contested terrain of older adult fitness walking, Sociology of Health & Illness, 32 (2): 304-18.

Crawford, K, J Lingel, and T Karppi, (2015) Our metrics, ourselves: A hundred years of self-tracking from the weight scale to the wrist wearable device, European Journal of Cultural Studies, 18 (4-5): 479-96.

Daly, A, (2015) The law and ethics of ‘self quantified’ health information: an Australian perspective, International Data Privacy Law, online first.

Dodge, M, and R Kitchin, (2007) ‘Outlines of a world coming into existence’: pervasive computing and the ethics of forgetting, Environment and Planning B: Planning & Design, 34 (3): 431-45.

Drew, DL, and JM Gore, (2014) Measuring up? The discursive construction of student subjectivities in the Global Children’s Challenge™, Sport, Education and Society, online first.

Fiore-Gartland, B, and G Neff, (2015) Communication, mediation, and the expectations of data: data valences across health and wellness communities, International Journal of Communication, 9: 1466-84.

Fox, NJ, (2015) Personal health technologies, micropolitics and resistance: a new materialist analysis, Health:, online first.

Gardner, P, and B Jenkins, (2015) Bodily intra-actions with biometric devices, Body & Society, online first.

Gerlitz, C, and C Lury, (2014) Social media and self-evaluating assemblages: on numbers, orderings and values, Distinktion: Scandinavian Journal of Social Theory, 15 (2): 174-88.

Gilmore, JN, (2015) Everywear: The quantified self and wearable fitness technologies, New Media & Society, online first.

Jethani, S, (2015) Mediating the body: Technology, politics and epistemologies of self, Communication, Politics & Culture, 47 (3): 34-43.

Klauser, FR, and A Albrechtslund, (2014) From self-tracking to smart urban infrastructures: towards an interdisciplinary research agenda on Big Data, Surveillance & Society, 12 (2): 273-86.

Lomborg, S, and K Frandsen, (2015) Self-tracking as communication, Information, Communication & Society: 1-13.

Lupton, D, (2012) M-health and health promotion: the digital cyborg and surveillance society, Social Theory & Health, 10 (3): 229-44.

Lupton, D, (2013a) The digitally engaged patient: self-monitoring and self-care in the digital health era, Social Theory & Health, 11 (3): 256-70.

Lupton, D, (2013b) Quantifying the body: monitoring and measuring health in the age of mHealth technologies, Critical Public Health, 23 (4): 393-403.

Lupton, D, (2013c) Understanding the human machine, IEEE Technology & Society Magazine, 32 (4): 25-30.

Lupton, D, (2014a) The commodification of patient opinion: the digital patient experience economy in the age of big data, Sociology of Health & Illness, 36 (6): 856-69.

Lupton, D, (2014b) Self-tracking cultures: towards a sociology of personal informatics. In Proceedings of the 26th Australian Computer-Human Interaction Conference (OzCHI ’14). Sydney: ACM Press.

Lupton, D. (2014c) Self-tracking modes: reflexive self-monitoring and data practices. Social Science Research Networkhttp://ssrn.com/abstract=2483549  (accessed 27 August 2014).

Lupton, D. (2014d) You are your data: self-tracking practices and concepts of data. Social Science Research Networkhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=2534211  (accessed 12 December 2015).

Lupton, D, (2015a) Data assemblages, sentient schools and digitised health and physical education (response to Gard), Sport, Education and Society, 20 (1): 122-32.

Lupton, D. (2015b) Lively data, social fitness and biovalue: the intersections of health self-tracking and social media. Social Science Research Networkhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=2666324  (accessed 13 November 2015).

Lupton, D. (2015c) Personal data practices in the age of lively data. Social Science Research Networkhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=2636709  (accessed 15 August 2015).

Lupton, D, (2015d) Quantified sex: a critical analysis of sexual and reproductive self-tracking using apps, Culture, Health & Sexuality, 17 (4): 440-53.

Lupton, D, (2016a) The diverse domains of quantified selves: self-tracking modes and dataveillance, Economy and Society, in press.

Lupton, D, (2016b) The Quantified Self: A Sociology of Self-Tracking Cultures. Cambridge: Polity Press.

Lynch, R, and S Cohn, (2015) In the loop: Practices of self-monitoring from accounts by trial participants, Health:, online first.

Millington, B, (2015) ‘Quantify the Invisible’: notes toward a future of posture, Critical Public Health, online first.

Moore, P, and A Robinson, (2015) The quantified self: What counts in the neoliberal workplace, New Media & Society, online first.

Nafus, D, (2013) The data economy of biosensors. In Sensor Technologies:  Healthcare, Wellness and Environmental Applications, edited by M. McGrath and C. N. Scanaill: Springer.

Nafus, D, (2014) Stuck data, dead data, and disloyal data: the stops and starts in making numbers into social practices, Distinktion: Scandinavian Journal of Social Theory, 15 (2): 208-22.

Nafus, D, and J Sherman, (2014) This one does not go up to 11: the Quantified Self movement as an alternative big data practice, International Journal of Communication, 8: 1785-94.

Neff, G, and D Nafus, (2016) Self-Tracking. Cambridge, MA: The MIT Press.

Niva, M, (2015) Online weight-loss services and a calculative practice of slimming, Health:, online first.

Pantzar, M, and M Ruckenstein, (2015) The heart of everyday analytics: emotional, material and practical extensions in self-tracking market, Consumption Markets & Culture, 18 (1): 92-109.

Reigeluth, TB, (2014) Why data is not enough: digital traces as control of self and self-control, Surveillance & Society, 12 (2): 243-54.

Rettberg, JW, (2014) Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves. Basingstoke: Palgrave Macmillan.

Ruckenstein, M, (2014) Visualized and interacted life: personal analytics and engagements with data doubles, Societies, 4 (1): 68-84.

Ruckenstein, M,  (2015) Uncovering everyday rhythms and patterns: food tracking and new forms of visibility and temporality in health care. In Techno-Anthropology in Health Informatics, edited by L. Botin, C. Nohr and P. Bertelsen. Amsterdam: IOS Press, pp. 28-40.

Ruckenstein, M, and M Pantzar, (2015a) Beyond the Quantified Self: thematic exploration of a dataistic paradigm, New Media & Society, online first.

Ruckenstein, M, and M Pantzar, (2015b) Datafied life: techno-anthropology as a site for exploration and experimentation, Techne, 19 (2): 191-210.

Sellen, AJ, and S Whittaker, (2010) Beyond total capture: a constructive critique of lifelogging, Communications of the ACM, 53 (5): 70-77.

Smith, WR, (2015) Communication, sportsmanship, and negotiating ethical conduct on the digital playing field, Communication & Sport, earlyview online.

Stragier, J, T Evens, and P Mechant, (2015) Broadcast yourself: an exploratory study of sharing physical activity on social networking sites, Media International Australia, 155 (1): 120-29.

Thomas, GM, and D Lupton, (2015) Threats and thrills: pregnancy apps, risk and consumption, Health, Risk & Society, online first.

Till, C, (2014) Exercise as labour: Quantified Self and the transformation of exercise into labour, Societies, 4 (3): 446-62.

Van Den Eede, Y,  (2015) Tracing the tracker: a postphenomenological inquiry into self-tracking technologies. In Postphenomenological Investigations: Essays on Human–Technology Relations, edited by R. Rosenberger and P.-P. Verbeek. Lanham, Maryland: Lexington Books, pp. 143-58.

Whitson, J, (2013) Gaming the quantified self, Surveillance & Society, 11 (1/2): 163-76.

Wilkinson, J, C Roberts, and M Mort, (2015) Ovulation monitoring and reproductive heterosex: living the conceptive imperative?, Culture, Health & Sexuality, 17 (4): 454-69.

Williamson, B, (2015) Algorithmic skin: health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education, Sport, Education and Society, 20 (1): 133-51.

Yang, Y, (2014) Saving the Quantified Self: How we come to know ourselves now, Boom: A Journal of California, 4 (4): 80-87.