The diverse domains of quantified selves: self-tracking modes and dataveillance

by | Apr 15, 2016 | Research Publications

Author(s):

Lupton, Deborah

Abstract:

The concept of self-tracking has recently begun to emerge in discussions of ways in which people can record specific features of their lives, often using digital technologies, to monitor, evaluate and optimize themselves. There is evidence that the personal data that are generated by the digital surveillance of individuals (dataveillance) are now used by a range of actors and agencies in diverse contexts. This paper examines the ‘function creep’ of self-tracking by outlining five modes that have emerged: private, communal, pushed, imposed and exploited. The analysis draws upon theoretical perspectives on concepts of selfhood, citizenship, dataveillance and the global digital data economy in discussing the wider socio-cultural implications of the emergence and development of these modes of self-tracking.

Document:

https://doi.org/10.1080/03085147.2016.1143726

References:
  1. Amoore, L. & Piotukh, V. (2015). Life beyond big data: Governing with little analytics. Economy and Society, 44(3), 341–366. doi: 10.1080/03085147.2015.1043793 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  2. Andrejevic, M. (2013). Infoglut: How too much information is changing the way we think and know. New York, NY: Routledge. [Crossref], [Google Scholar]
  3. Andrejevic, M. (2014). The big data divide. International Journal of Communication, 8, 1673–1689. [Web of Science ®], [Google Scholar]
  4. Ball, J. (2014). Angry Birds and ‘leaky’ phone apps targeted by NSA and GCHQ for user data. Retrieved from http://www.theguardian.com/world/2014/jan/27/nsa-gchq-smartphone-app-angry-birds-personal-data [Google Scholar]
  5. Banning, M. E. (2016). Shared entanglements – Web 2.0, info-liberalism & digital sharing. Information, Communication & Society, 19(4), 489–503. doi: 10.1080/1369118X.2015.1061573 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  6. Barcena, M. B., Wueest, C. & Lau, H. (2014). How safe is your quantified self? Mountain View, CA: Symantech. [Google Scholar]
  7. Boesel, W. E. (2013). Return of the quantrepreneurs. Cyborgology. Retrieved from http://thesocietypages.org/cyborgology/2013/09/26/return-of-the-quantrepreneurs/ [Google Scholar]
  8. Cheney-Lippold, J. (2011). A new algorithmic identity: Soft biopolitics and the modulation of control. Theory, Culture & Society, 28(6), 164–181. doi: 10.1177/0263276411424420 [Crossref], [Web of Science ®], [Google Scholar]
  9. Choe, E. K., Lee, N. B. & Schraefel, M. (2015). Revealing visualization insights from Quantified-Selfers’ personal data presentations. Computer Graphics and Applications, 35(4), 28–37. doi: 10.1109/MCG.2015.51 [Crossref], [Web of Science ®], [Google Scholar]
  10. Comstock, J. (2014). Johnson & Johnson subsidiary launches self-tracking app. MobiHealthNews. Retrieved from http://mobihealthnews.com/33348/johnson-johnson-subsidiary-launches-self-tracking-app/ [Google Scholar]
  11. 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. [Google Scholar]
  12. Day, S., Lury, C. & Wakeford, N. (2014). Number ecologies: Numbers and numbering practices. Distinktion: Scandinavian Journal of Social Theory, 15(2), 123–154. doi: 10.1080/1600910X.2014.923011 [Taylor & Francis Online], [Google Scholar]
  13. van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. [Crossref], [Google Scholar]
  14. Elliott, A. (2013). Reinvention. London: Routledge. [Google Scholar]
  15. Epstein, D. A., Ping, A., Fogarty, J. & Munson, S. A. (2015). A lived informatics model of personal informatics. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan. [Google Scholar]
  16. Foucault, M. (1988). Technologies of the self. In L. Martin, H. Gutman, & P. Hutton (Eds.), Technologies of the self: A seminar with Michel Foucault (pp. 16–49). London: Tavistock. [Google Scholar]
  17. Fox, S. & Duggan, M. (2013). Tracking for health. Washington, DC: Pew Research Center. [Google Scholar]
  18. Gabrys, J. (2014). Programming environments: Environmentality and citizen sensing in the smart city. Environment and Planning D: Society and Space, 32(1), 30–48. doi: 10.1068/d16812 [Crossref], [Web of Science ®], [Google Scholar]
  19. de Groot, M. (2014). Quantified self, quantified us, quantified other. Quantified Self Institute. Retrieved from http://www.qsinstitute.org/?p=2048 [Google Scholar]
  20. Huckvale, K., Prieto, J., Tilney, M., Benghozi, P.-J. & Car, J. (2015). Unaddressed privacy risks in accredited health and wellness apps: A cross-sectional systematic assessment. BMC Medicine, 13(1). [Crossref], [Web of Science ®], [Google Scholar]
  21. John, N. (2013). Sharing and Web 2.0: The emergence of a keyword. New Media & Society, 15(2), 167–182. doi: 10.1177/1461444812450684 [Crossref], [Web of Science ®], [Google Scholar]
  22. Kamel Boulos, M. & Al-Shorbaji, N. (2014). On the internet of things, smart cities and the WHO Healthy Cities. International Journal of Health Geographics, 13(1), 10. doi: 10.1186/1476-072X-13-10 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  23. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage. [Crossref], [Google Scholar]
  24. Kitchin, R. & Lauriault, T. (2014). Towards critical data studies: Charting and unpacking data assemblages and their work. Social Science Research Network. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112 [Google Scholar]
  25. Lash, S. (2007). Power after hegemony: Cultural studies in mutation? Theory, Culture & Society, 24(3), 55–78. doi: 10.1177/0263276407075956 [Crossref], [Web of Science ®], [Google Scholar]
  26. Li, I., Dey, A. K. & Forlizzi, J. (2011). Understanding my data, myself: Supporting self-reflection with ubicomp technologies. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China. [Google Scholar]
  27. Li, J. (2015). A privacy preservation model for health-related social networking sites. Journal of Medical Internet Research, 17(7), e168. doi: 10.2196/jmir.3973 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  28. Lohr, S. (2014). Unblinking eyes track employees. The New York Times. Retrieved from http://www.nytimes.com/2014/06/22/technology/workplace-surveillance-sees-good-and-bad.html?module=Search&mabReward=relbias%3Ar&_r=1 [Google Scholar]
  29. Lupton, D. (2012). M-health and health promotion: The digital cyborg and surveillance society. Social Theory & Health, 10(3), 229–244. doi: 10.1057/sth.2012.6 [Crossref], [Web of Science ®], [Google Scholar]
  30. Lupton, D. (2013a). The digitally engaged patient: Self-monitoring and self-care in the digital health era. Social Theory & Health, 11(3), 256–270. doi: 10.1057/sth.2013.10 [Crossref], [Web of Science ®], [Google Scholar]
  31. Lupton, D. (2013b). Quantifying the body: Monitoring and measuring health in the age of mHealth technologies. Critical Public Health, 23(4), 393–403. doi: 10.1080/09581596.2013.794931 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  32. Lupton, D. (2013c). Understanding the human machine. IEEE Technology & Society Magazine, 32(4), 25–30. doi: 10.1109/MTS.2013.2286431 [Crossref], [Web of Science ®], [Google Scholar]
  33. 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), 856–869. doi: 10.1111/1467-9566.12109 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  34. Lupton, D. (2014b). Critical perspectives on digital health technologies. Sociology Compass, 8(12), 1344–1359. doi: 10.1111/soc4.12226 [Crossref], [Google Scholar]
  35. Lupton, D. (2015a). Data assemblages, sentient schools and digitised health and physical education (response to Gard). Sport, Education and Society, 20(1), 122–132. doi: 10.1080/13573322.2014.962496 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  36. Lupton, D. (2015b). Personal data practices in the age of lively data. Social Science Research Network. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2636709 [Google Scholar]
  37. Madden, M. & Rainie, L. (2015). Americans’ attitudes about privacy, security and surveillance. Retrieved from http://www.pewinternet.org/files/2015/05/Privacy-and-Security-Attitudes-5.19.15_FINAL.pdf [Google Scholar]
  38. Mann, S. (2013). Steve Mann: My ‘augmediated’ life. IEEE Spectrum. Retrieved from http://spectrum.ieee.org/geek-life/profiles/steve-mann-my-augmediated-life [Google Scholar]
  39. Mann, S. & Ferenbok, J. (2013). New media and the power politics of sousveillance in a surveillance-dominated world. Surveillance & Society, 11(1/2), 18–34. [Crossref], [Google Scholar]
  40. Marwick, A. (2012). The public domain: Social surveillance in everyday life. Surveillance & Society, 9(4), 378–393. [Crossref], [Google Scholar]
  41. McKenna, E., Richardson, I. & Thomson, M. (2012). Smart meter data: Balancing consumer privacy concerns with legitimate applications. Energy Policy, 41(C), 807–814. doi: 10.1016/j.enpol.2011.11.049 [Crossref], [Web of Science ®], [Google Scholar]
  42. Michael, M. & Lupton, D. (2016). Toward a manifesto for the ‘public understanding of big data’. Public Understanding of Science, 25(1), 104–116. [Google Scholar]
  43. Moore, P. & Robinson, A. (2015). The quantified self: What counts in the neoliberal workplace. New Media & Society, earlyview online. doi:10.1177/1461444815604328. [Crossref], [Google Scholar]
  44. MyLifeBits. (2015) Retrieved January 5, 2015, from http://research.microsoft.com/en-us/projects/mylifebits/ [Google Scholar]
  45. Nafus, D. & 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. [Web of Science ®], [Google Scholar]
  46. NAIC. (2014). Usage-based insurance and telematics. National Association of Insurance Commissioners and the Center for Insurance Policy and Research. Retrieved from http://www.naic.org/cipr_topics/topic_usage_based_insurance.htm [Google Scholar]
  47. Nielsen, C. (2014). Tech-styles: Are consumers really interested in wearing tech on their sleeves? Retrieved from Nielsen Newswire website: http://www.nielsen.com/us/en/newswire/2014/tech-styles-are-consumers-really-interested-in-wearing-tech-on-their-sleeves.html [Google Scholar]
  48. Olson, P. (2014). Wearable tech is plugging into health insurance. Forbes. Retrieved from http://www.forbes.com/sites/parmyolson/2014/06/19/wearable-tech-health-insurance/ [Google Scholar]
  49. Pew Research Center. (2014). Public perceptions of privacy and security in the post-Snowdon era. Pew Research Internet Project. Retrieved from http://www.pewinternet.org/2014/11/12/public-privacy-perceptions/# [Google Scholar]
  50. Pugliese, J. (2010). Biometrics: Bodies, technologies, biopolitics. London: Routledge. [Google Scholar]
  51. Purpura, S., Schwanda, V., Williams, K., Stubler, W. & 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, Canada. [Google Scholar]
  52. Quantified Self. (2015) Retrieved February 5, 2015, from http://quantifiedself.com/ [Google Scholar]
  53. Quantified Self guide to self-tracking tools. (2015) Retrieved April 18, 2015, from http://quantifiedself.com/guide/tools?sort=reviews&pg=1 [Google Scholar]
  54. Raley, R. (2013). Dataveillance and countervailance. In L. Gitelman (Ed.), “Raw data” is an oxymoron (pp. 121–145). Cambridge, MA: MIT Press. [Google Scholar]
  55. Ramirez, E. (2013). How can we get more meaning out of our data? Retrieved from: http://quantifiedself.com/2013/08/how-can-we-get-more-meaning-out-of-our-data/ [Google Scholar]
  56. Rodden, T. A., Fischer, J. E., Pantidi, N., Bachour, K. & Moran, S. (2013). At home with agents: Exploring attitudes towards future smart energy infrastructures. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France. [Google Scholar]
  57. Rooksby, J., Rost, M., Morrison, A. & Chalmers, M. C. (2014). Personal tracking as lived informatics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Canada. [Google Scholar]
  58. Rose, N. (1990). Governing the soul: The shaping of the private self. London: Routledge. [Google Scholar]
  59. Rose, N. (2007). Molecular biopolitics, somatic ethics and the spirit of biocapital. Social Theory & Health, 5(1), 3–29. doi: 10.1057/palgrave.sth.8700084 [Crossref], [Google Scholar]
  60. Rosenblat, A., Kneese, T. & boyd, d. (2014). Workplace surveillance. Data & Society Research Institute Working Paper. Retrieved from http://www.datasociety.net/pubs/fow/WorkplaceSurveillance.pdf [Google Scholar]
  61. Rosenblat, A., Wikelius, K., boyd, d., Gangadharan, S. P. & Yu, C. (2014). Data & civil rights: Health primer. Data & Society Research Institute. Retrieved from http://www.datacivilrights.org/pubs/2014-1030/Health.pdf [Google Scholar]
  62. Ruckenstein, M. & Pantzar, M. (2015). Beyond the quantified self: Thematic exploration of a dataistic paradigm. New Media & Society, earlyview online. doi:10.1177/1461444815609081. [Crossref], [Web of Science ®], [Google Scholar]
  63. Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64–82. doi: 10.1080/17439884.2014.921628 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  64. Strathern, M. (2000). New accountabilities: Anthropological studies in audit, ethics and the academy. In M. Strathern (Ed.), Audit cultures: Anthropological studies in accountability, ethics and the academy (pp. 1–18). London: Routledge. [Crossref], [Google Scholar]
  65. Sunder Rajan, K. (2012). Introduction: The capitalization of life and the liveliness of capital. In K. Sunder Rajan (Ed.), Lively capital: Biotechnologies, ethics, and governance in global markets (pp. 1–41). Durham, NC: Duke University Press. [Crossref], [Google Scholar]
  66. The Wellcome Trust. (2013). Summary report of qualitative research into public attitudes to personal data and linking personal data. n.p.: The Wellcome Trust. [Google Scholar]
  67. Thrift, N. (2005). Knowing capitalism. London: Sage. [Google Scholar]
  68. Walgreens. (2014). Walgreens rewards healthy activities through first community pharmacy program to include behavior change training based on Dr BJ Fogg’s methodology. Walgreens. Retrieved from http://news.walgreens.com/article_display.cfm?article_id=5883 [Google Scholar]
  69. Whitson, J. (2013). Gaming the quantified self. Surveillance & Society, 11(1/2), 163–176. [Crossref], [Google Scholar]
  70. Wicks, P. & Chiauzzi, E. (2015). ‘Trust but verify’: Five approaches to ensure safe medical apps. BMC Medicine, 13(1), 205. doi: 10.1186/s12916-015-0451-z [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
  71. Williamson, B. (2015a). Algorithmic skin: Health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education. Sport, Education and Society, 20(1), 133–151. doi: 10.1080/13573322.2014.962494 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  72. Williamson, B. (2015b). Governing software: Networks, databases and algorithmic power in the digital governance of public education. Learning, Media and Technology, 40(1), 83–105. doi: 10.1080/17439884.2014.924527 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
  73. Zamosky, L. (2014). Digital health tools are a growing part of workplace wellness programs. iHealthBeat. Retrieved from http://www.ihealthbeat.org/insight/2014/digital-health-tools-are-a-growing-part-of-workplace-wellness-programs [Google Scholar]