1932

Abstract

Over the past decade, data-intensive logics and practices have come to affect domains of contemporary life ranging from marketing and policy making to entertainment and education; at every turn, there is evidence of “datafication” or the conversion of qualitative aspects of life into quantified data. The datafication of health unfolds on a number of different scales and registers, including data-driven medical research and public health infrastructures, clinical health care, and self-care practices. For the purposes of this review, we focus mainly on the latter two domains, examining how scholars in anthropology, sociology, science and technology studies, and media and communication studies have begun to explore the datafication of clinical and self-care practices. We identify the dominant themes and questions, methodological approaches, and analytical resources of this emerging literature, parsing these under three headings: datafied power, living with data, and data–human mediations We conclude by urging scholars to pay closer attention to how datafication is unfolding on the “other side” of various digital divides (e.g., financial, technological, geographic), to experiment with applied forms of research and data activism, and to probe links to areas of datafication that are not explicitly related to health.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anthro-102116-041244
2017-10-23
2024-10-08
Loading full text...

Full text loading...

/deliver/fulltext/anthro/46/1/annurev-anthro-102116-041244.html?itemId=/content/journals/10.1146/annurev-anthro-102116-041244&mimeType=html&fmt=ahah

Literature Cited

  1. Ajana B. 2017. Digital health and the biopolitics of the Quantified Self. Digit. Health 3:1–18 [Google Scholar]
  2. Al Dahdah M, Du Loû AD, Méadel C. 2015. Mobile health and maternal care: a winning combination for healthcare in the developing world?. Health Policy Technol 4:225–31 [Google Scholar]
  3. Anderson C. 2008. The end of theory: The data deluge makes the scientific method obsolete. Wired June 23. http://www.wired.com/science/discoveries/magazine/16-07/pb_theory [Google Scholar]
  4. Andrejevic M. 2014. The big data divide. Int. J. Commun. 8:1673–89 [Google Scholar]
  5. Andrejevic M, Burdon M. 2015. Defining the sensor society. Telev. New Media 16:19–36 [Google Scholar]
  6. Armstrong D. 1995. The rise of surveillance medicine. Sociol. Health Illn. 17:393–404 [Google Scholar]
  7. Arora P. 2016. Bottom of the data pyramid: big data and the Global South. Int. J. Commun. 10:1681–99 [Google Scholar]
  8. Baack S. 2015. Datafication and empowerment: how the open data movement re-articulates notions of democracy, participation, and journalism. Big Data Soc 2: https://doi.org/10.1177/2053951715594634 [Crossref] [Google Scholar]
  9. Barta K, Neff G. 2016. Technologies for sharing: lessons from Quantified Self about the political economy of platforms. Inf. Commun. Soc. 19:518–31 [Google Scholar]
  10. Beer D. 2009. Power through the algorithm? Participatory web cultures and the technological unconscious. New Media Soc 11:985–1002 [Google Scholar]
  11. Bell G. 2015. The secret life of big data. See Boellstorff & Maurer 2015 7–26
  12. Berg M. 2017. Making sense of sensors: self-tracking and the temporalities of wellbeing. Digit. Health 3:1–11 [Google Scholar]
  13. Berson J. 2015. Computable Bodies: Instrumented Life and the Human Somatic Niche London: Bloomsbury [Google Scholar]
  14. Boellstorff T. 2015. Making big data, in theory. See Boellstorff & Maurer 2015 87–108
  15. Boellstorff T, Maurer B. 2015. Data, Now Bigger and Better! Chicago: Prickly Paradigm Press [Google Scholar]
  16. Bolnick DA, Fullwiley D, Duster T, Cooper RS, Fujimura JH. et al. 2007. The science and business of genetic ancestry testing. Science 318:399–400 [Google Scholar]
  17. Bowker GC. 2014. The theory/data thing. Int. J. Commun. 8:1795–99 [Google Scholar]
  18. boyd d, Crawford K. 2012. Critical questions for big data: provocations for a cultural, technological and scholarly phenomenon. Inf. Commun. Soc. 15:662–79 [Google Scholar]
  19. Bucher T. 2017. The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Inf. Commun. Soc. 20:30–44 [Google Scholar]
  20. Cheney-Lippold J. 2011. A new algorithmic identity: soft biopolitics and the modulation of control. Theory Cult. Soc. 28:164–81 [Google Scholar]
  21. Chib A. 2013. The promise and peril of mHealth in developing countries. Mobile Media Commun 1:69–75 [Google Scholar]
  22. Chigona W, Nyemba-Mudenda M, Simphiwe Metfula A. 2013. A review on mHealth research in developing countries. J. Community Inform. 9:3 http://ci-journal.net/index.php/ciej/article/view/941/1011 [Google Scholar]
  23. Christophersen M, Mørck P, Langhoff TO, Bjørn P. 2015. Unforeseen challenges: adopting wearable health data tracking devices to reduce health insurance costs in organizations. International Conference on Universal Access in Human-Computer Interaction M Antona, C Stephanidis 288–99 Berlin: Springer Int. [Google Scholar]
  24. Clarke AE, Shim JK, Mamo L, Fosket JR, Fishman JR. 2003. Biomedicalization: technoscientific transformations of health, illness, and U.S. biomedicine. Am. Sociol. Rev. 68:161–94 [Google Scholar]
  25. Couldry N, Powell A. 2014. Big data from the bottom up. Big Data Soc 1:1–5 [Google Scholar]
  26. Crawford K, Gray ML, Miltner K. 2014. Critiquing big data: politics, ethics, epistemology. Int. J. Commun. 8:1663–72 [Google Scholar]
  27. Crawford K, Lingel J, Karppi T. 2015. Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device. Eur. J. Cult. Stud. 18:479–96 [Google Scholar]
  28. Day C, Lury S. 2016. Biosensing: tracking persons. See Nafus 2016 43–66
  29. Deleuze G. 1992. Postscript on the society of control. October 59:3–8 [Google Scholar]
  30. Delfanti A. 2013. Biohackers: The Politics of Open Science London: Pluto Press [Google Scholar]
  31. Delfanti A, Iaconesi S. 2016. Open source cancer: brain scans and the rituality of biodigital data sharing. The Participatory Condition in the Digital Age D Barney, G Coleman, C Ross, J Sterne, T Tembeck 123–43 Minneapolis: Univ. Minn. Press [Google Scholar]
  32. Depper A, Howe PD. 2017. Are we fit yet? English adolescent girls’ experiences of health and fitness apps. Health Sociol. Rev. 26:98–112 [Google Scholar]
  33. Didžiokaitė G, Saukko P, Greiffenhagen C. 2017. The mundane experience of everyday calorie trackers: beyond the metaphor of Quantified Self. New Media Soc https://doi.org/10.1177/1461444817698478 [Crossref] [Google Scholar]
  34. Dourish P. 2016. Algorithms and their others: algorithmic culture in context. Big Data Soc 3:1–11 [Google Scholar]
  35. Duclos V. 2016. The map and the territory: an ethnographic study of the low utilization of a global eHealth network. J. Inf. Technol. 31:334–46 [Google Scholar]
  36. Dudhwala F. 2017. Doing the self: an ethnographic analysis of the ‘Quantified Self.’ PhD Thesis Manag. Stud., Said Bus. Sch., Univ. Oxford [Google Scholar]
  37. Ellerbrok A. 2011. Playful biometrics: controversial technology through the lens of play. Sociol. Q. 52:528–47 [Google Scholar]
  38. Elsden C, Kirk DS, Durrant AC. 2016. A quantified past: toward design for remembering with personal informatics. Hum.-Comput. Interact. 31:518–57 [Google Scholar]
  39. Eveleth R. 2014. How self-tracking apps exclude women. The Atlantic Dec. 15. https://www.theatlantic.com/technology/archive/2014/12/how-self-tracking-apps-exclude-women/383673/ [Google Scholar]
  40. Fiore-Gartland B, Neff D. 2015. Communication, mediation, and the expectations of data: data valences across health and wellness communities. Int. J. Commun. 9:1466–84 [Google Scholar]
  41. Fishman JR, McGowan ML. 2014. Will personalized medicine transform our selves?. Genetics as Social Practice: Transdisciplinary Views on Science and Culture G Werner-Felmayer, S Schicktanz, B Prainsack 29–42 Farnham, UK: Ashgate Press [Google Scholar]
  42. Fotopoulou A, O'Riordan K. 2016. Training to self-care: fitness tracking, biopedagogy and the healthy consumer. Health Sociol. Rev. 26:54–68 [Google Scholar]
  43. Foucault M. 1973. Birth of the Clinic: An Archaeology of Medical Perception New York: Pantheon Books [Google Scholar]
  44. Foucault M. 1977. Discipline and Punish: The Birth of the Prison New York: Pantheon Books [Google Scholar]
  45. Foucault M. 1986. The History of Sexuality 3 The Care of the Self New York: Pantheon Books [Google Scholar]
  46. Foucault M. 1988. Technologies of the self. Technologies of the Self: A Seminar with Michel Foucault LH Martin, H Gutman, PH Hutton 16–49 Amherst: Univ. Mass. Press [Google Scholar]
  47. Foucault M. 1991. Governmentality, transl. R. Braidotti. The Foucault Effect: Studies in Governmentality G Burchell, C Gordon, P Miller 87–104 Chicago: Univ. Chicago Press [Google Scholar]
  48. Foucault M. 1997. Society Must Be Defended: Lectures at the Collège de France, 1975–1976 New York: St. Martin's Press [Google Scholar]
  49. Gabrys J. 2014. Programming environments: environmentality and citizen sensing in the smart city. Environ. Plan. D: Soc. Space 32:30–48 [Google Scholar]
  50. Gandy OH Jr.. 1993. The Panoptic Sort: A Political Economy of Personal Information Boulder, CO: Westview Press [Google Scholar]
  51. Gillespie T. 2014. The relevance of algorithms. Media Technologies: Essays on Communication, Materiality, and Society T Gillespie, PJ Boczkowski, KA Foot 167–94 Cambridge, MA: MIT Press [Google Scholar]
  52. Gilmore JN. 2016. Everywear: the quantified self and wearable fitness technologies. New Media Soc 18:2524–39 [Google Scholar]
  53. Gitelman L. 2013. Raw Data Is an Oxymoron Cambridge, MA: MIT Press [Google Scholar]
  54. Goetz T. 2010. The Decision Tree: Taking Control of Your Health in the New Era of Personalized Medicine New York: Rodale [Google Scholar]
  55. Greenfield L. 2016. Deep data: notes on the n of 1. See Nafus 2016 123–46
  56. Gregg M. 2018. Counterproductive: A Brief History of Time Management Durham, NC: Duke Univ. Press In press [Google Scholar]
  57. Gregory J, Bowker GC. 2016. The data citizen, the quantified self, and personal genomics. See Nafus 2016 211–22
  58. Haggerty KD, Ericson RV. 2000. The surveillant assemblage. Br. J. Sociol. 51:605–22 [Google Scholar]
  59. Hampshire K, Porter G, Mariwah S, Munthali A, Robson E. et al. 2017. Who bears the cost of ‘informal mhealth’? Health-workers’ mobile phone practices and associated political-moral economies of care in Ghana and Malawi. Health Policy Plan 32:34–42 [Google Scholar]
  60. Harris A, Kelly SE, Wyatt S. 2014. Autobiologies on YouTube: narratives of direct-to-consumer genetic testing. New Genet. Soc. 33:60–78 [Google Scholar]
  61. Harris A, Wyatt S, Kelly SE. 2013. The gift of spit (and the obligation to return it): how consumers of online genetic testing services participate in research. Inf. Commun. Soc. 16:236–57 [Google Scholar]
  62. Hogle LF. 2016. Data-intensive resourcing in healthcare. BioSocieties 11:372–93 [Google Scholar]
  63. Hull G, Pasquale F. 2017. Toward a critical theory of corporate wellness. BioSocieties In press [Google Scholar]
  64. Iliadis A, Russo F. 2016. Critical data studies: an introduction. Big Data Soc July–Dec.:1–7 [Google Scholar]
  65. Jethani S. 2015. Mediating the body: technology, politics and epistemologies of self. Commun. Politics Cult. 473:34–43 [Google Scholar]
  66. Kahn J. 2014. Privatizing biomedical citizenship: risk, duty, and potential in the circle of pharmaceutical life. Minn. J. Law Sci. Technol. 15: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2464253 [Google Scholar]
  67. Kennedy H, Poell T, van Dijck J. 2015. Introduction: data and agency. Big Data Soc 2: https://doi.org/10.1177/2053951715621569 [Crossref] [Google Scholar]
  68. Kitchin R. 2014. Big data, new epistemologies and paradigm shifts. Big Data Soc 1: https://doi.org/10.1177/2053951714528481 [Crossref] [Google Scholar]
  69. Koopman C. 2014. Michel Foucault's critical empiricism today: concepts and analytics in the critique of biopower and infopower. Foucault Now: Current Perspectives in Foucault Studies JD Faubion 88–111 Cambridge, UK: Polity Press [Google Scholar]
  70. Kragh-Furbo M, Mackenzie A, Mort M, Roberts C. 2016. Do biosensors biomedicalize? Sites of negotiation in DNA-based biosensing data practices. See Nafus 2016 5–26
  71. Lash S. 2007. Power after hegemony: cultural studies in mutation?. Theory Cult. Soc. 24:55–78 [Google Scholar]
  72. Latour B. 2005. Reassembling the Social: An Introduction to Actor-Network-Theory Oxford, UK: Oxford Univ. Press [Google Scholar]
  73. Lazar A, Koehler C, Tanenbaum J, Nguyen DH. 2015. Why we use and abandon smart devices. Proc. ACM Int. Joint Conf. on Pervasive Ubiquitous Comput., Osaka, Jpn.635–46 New York: ACM [Google Scholar]
  74. Lock MM. 1993. Encounters with Aging: Mythologies of Menopause in Japan and North America Berkeley: Univ. Calif. Press [Google Scholar]
  75. Lomborg S, Frandsen K. 2016. Self-tracking as communication. Inf. Commun. Soc. 19:1015–27 [Google Scholar]
  76. Lucivero F, Prainsack B. 2015. The lifestylisation of healthcare? ‘Consumer genomics’ and mobile health as technologies for healthy lifestyle. Appl. Transl. Genom. 4:44–49 [Google Scholar]
  77. Lupton D. 2012. M-health and health promotion: the digital cyborg and surveillance society. Soc. Theory Health 10:229–44 [Google Scholar]
  78. Lupton D. 2013a. The digitally engaged patient: self-monitoring and self-care in the digital health era. Soc. Theory Health 11:256–70 [Google Scholar]
  79. Lupton D. 2013b. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit. Public Health 23:393–403 [Google Scholar]
  80. Lupton D. 2015. Quantified sex: a critical analysis of sexual and reproductive self-tracking using apps. Cult. Health Sex. 17:440–53 [Google Scholar]
  81. Lupton D. 2016a. Digital companion species and eating data: implications for theorising digital data–human assemblages. Big Data Soc 3: https://doi.org/10.1177/2053951715619947 [Crossref] [Google Scholar]
  82. Lupton D. 2016b. The Quantified Self: A Sociology of Self-Tracking Cambridge, UK: Polity Press [Google Scholar]
  83. Lupton D. 2017. How does health feel? Towards research on the affective atmospheres of digital health. Digit. Health 3: https://doi.org/10.1177/2055207617701276 [Crossref] [Google Scholar]
  84. Lupton D, Jutel A. 2015. ‘It's like having a physician in your pocket!’ A critical analysis of self-diagnosis smartphone apps. Soc. Sci. Med. 133:128–35 [Google Scholar]
  85. Lyon D. 2002. Everyday surveillance: personal data and social classifications. Inf. Commun. Soc. 5:242–57 [Google Scholar]
  86. Lyon D. 2014. Surveillance, Snowden, and big data: capacities, consequences, critique. Big Data Soc 1:1–12 [Google Scholar]
  87. Mackenzie A. 2005. The performativity of code: software and cultures of circulation. Theory Cult. Soc. 22:71–92 [Google Scholar]
  88. Martin E. 2007. Taking the measure of moods and motivations. Bipolar Expeditions: Mania and Depression in American Culture Princeton, NJ: Princeton Univ. Press [Google Scholar]
  89. Maturo A, Setiffi F. 2016. The gamification of risk: how health apps foster self-confidence and why this is not enough. Health Risk Soc 17:477–94 [Google Scholar]
  90. Mayer-Schönberger V, Cukier K. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think Boston: Houghton Mifflin Harcourt [Google Scholar]
  91. McGowan M, Choudhury S, Juengst E, Lambrix M, Settersten R, Fishman J. 2017. ‘Let's pull these technologies out of the ivory tower’: the politics, ethos, and ironies of participant-driven genomic research. BioSocieties https://doi.org/10.1057/s41292-017-0043-6 [Crossref] [Google Scholar]
  92. McQuillan D. 2016. Algorithmic paranoia and the convivial alternative. Big Data Soc 3: https://doi.org/10.1177/2053951716671340 [Crossref] [Google Scholar]
  93. Mehta R, Nafus D. 2016. Atlas of Caregiving Pilot study report March, Atlas of Caregiving. http://atlasofcaregiving.com/wp-content/uploads/2016/03/Study_Report.pdf [Google Scholar]
  94. Milan S, van der Velden L. 2016. The alternative epistemologies of data activism. Digit. Cult. Soc. 2:57–74 [Google Scholar]
  95. Millington B. 2016. ‘Quantify the invisible’: notes toward a future of posture. Crit. Public Health 26:405–17 [Google Scholar]
  96. Miscione G. 2007. Telemedicine in the Upper Amazon: interplay with local health care practices. MIS Q 31:403–25 [Google Scholar]
  97. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. 2016. The ethics of algorithms: mapping the debate. Big Data Soc 3: https://doi.org/10.1177/2053951716679679 [Crossref] [Google Scholar]
  98. Mol A, Law J. 2004. Embodied action, enacted bodies: the example of hypoglycaemia. Body Soc 10:43–62 [Google Scholar]
  99. Moore P, Robinson A. 2016. The quantified self: what counts in the neoliberal workplace. New Media Soc 8:2774–94 [Google Scholar]
  100. Morozov E. 2014. To Save Everything, Click Here: The Folly of Technological Solutionism New York: PublicAffairs [Google Scholar]
  101. Mort M, Finch T, May C. 2009. Making and unmaking telepatients: identity and governance in new health technologies. Sci. Technol. Hum. Values 34:9–33 [Google Scholar]
  102. Nafus D. 2014. Stuck data, dead data, and disloyal data: the stops and starts in making numbers into social practices. Distinktion: Scand. J. Soc. Theory 15:208–22 [Google Scholar]
  103. Nafus D. 2016. Quantified: Biosensing Technologies in Everyday Life Cambridge, MA: MIT Press [Google Scholar]
  104. Nafus D, Neff G. 2016. Self-Tracking Cambridge, MA: MIT Press [Google Scholar]
  105. Nafus D, Sherman J. 2014. This one does not go up to 11: The Quantified Self movement as an alternative big data practice. Int. J. Commun. 8:1784–94 [Google Scholar]
  106. Neff G. 2013. Why big data won't cure us. Big Data 1:117–23 [Google Scholar]
  107. Nelson A. 2016. The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome Boston: Beacon Press [Google Scholar]
  108. Nissenbaum H, Patterson H. 2016. Biosensing in context: health privacy in a connected world. See Nafus 2016 79–100
  109. Niva M. 2017. Online weight-loss services and a calculative practice of slimming. Health 21:409–24 [Google Scholar]
  110. O'Neill C. 2017. Taylorism, the European science of work, and the quantified self at work. Sci. Technol. Hum. Values 42:4600–21 [Google Scholar]
  111. Oudshoorn N. 2011. Telecare Technologies and the Transformation of Healthcare Houndmills, UK: Palgrave Macmillan [Google Scholar]
  112. Oxlund B. 2012. Living by numbers: the dynamic interplay of asymptotic conditions and low cost measurement technologies in the cases of two women in the Danish provinces. Suom. Antropol. 37:42–56 [Google Scholar]
  113. Pantzar M, Ruckenstein M. 2017. Living the metrics: self-tracking and situated objectivity. Digit. Health. http://journals.sagepub.com/doi/full/10.1177/2055207617712590 [Google Scholar]
  114. Pantzar M, Ruckenstein M, Mustonen V. 2017. Social rhythms of the heart. Health Sociol. Rev. 26:22–37 [Google Scholar]
  115. Peake B. 2015. Decolonizing design anthropology with Tinn. Blog. CASTAC.org. April 21. http://blog.castac.org/2015/04/designing-tinn [Google Scholar]
  116. Pink S, Fors V. 2017. Being in a mediated world: self-tracking and the mind-body-environment. Cult. Geogr. https://doi.org/10.1177/1474474016684127 [Crossref] [Google Scholar]
  117. Piras EM, Miele F. 2017. Clinical self-tracking and monitoring technologies: negotiations in the ICT-mediated patient-provider relationship. Health Sociol. Rev. 26:38–53 [Google Scholar]
  118. Potts T. 2010. Life hacking and everyday rhythm. Geographies of Rhythm: Nature, Place, Mobilities and Bodies T Edensor 33–44 Farnham, UK: Ashgate [Google Scholar]
  119. Purpura S, Schwanda V, Williams K, Stubler W, Sengers P. 2011. Fit4Life: the design of a persuasive technology promoting healthy behavior and ideal weight. Proc. SIGCHI Conf. Hum. Factors Comput. Syst., Vancouver, BC423–32 New York: ACM [Google Scholar]
  120. Pybus J, Coté M, Blanke T. 2015. Hacking the social life of big data. Big Data Soc 2: https://doi.org/10.1177/2053951715616649 [Crossref] [Google Scholar]
  121. Rabinow P. 1992. Artificiality and enlightenment: from sociobiology to biosociality. Essays on the Anthropology of Reason91–111 Princeton, NJ: Princeton Univ. Press [Google Scholar]
  122. Rabinow P, Rose N. 2006. Biopower today. BioSocieties 1:195–217 [Google Scholar]
  123. Raley R. 2013. Dataveillance and counterveillance. See Gitelman 2013 121–46
  124. Rapp R. 2016. Big data, small kids: medico-scientific, familial and advocacy visions of human brains. BioSocieties 11:296–316 [Google Scholar]
  125. Rich E, Miah A. 2014. Understanding digital health as public pedagogy: a critical framework. Societies 4:296–315 [Google Scholar]
  126. Rich E, Miah A. 2017. Mobile, wearable and ingestible health technologies: towards a critical research agenda. Health Sociol. Rev. 26:84–97 [Google Scholar]
  127. Ruckenstein M. 2014. Visualized and interacted life: personal analytics and engagements with data doubles. Societies 4:68–84 [Google Scholar]
  128. Ruckenstein M. 2015. Uncovering everyday rhythms and patterns: food tracking and new forms of visibility and temporality in health care. Techno-Anthropology in Health Informatics: Methodologies for Improving Human-Technology Relations L Botin, P Bertelsen, C Nøhr 28–40 Amsterdam: IOS Press [Google Scholar]
  129. Ruckenstein M. 2016. The politics of self-tracking reconsidered Presented at Annu. Meet. 4S, Aug. Barcelona: [Google Scholar]
  130. Ruckenstein M. 2017. Keeping data alive: talking DTC genetic testing. Inf. Commun. Soc. 20:1024–39 [Google Scholar]
  131. Ruckenstein M, Pantzar M. 2015. Datafied life: techno-anthropology as a site for exploration and experimentation. Techné: Res. Philos. Technol. 19:191–210 [Google Scholar]
  132. Ruppert E. 2011. Population objects: interpassive subjects. Sociology 45:218–33 [Google Scholar]
  133. Salmela-Leppänen T, Valtonen A, Lupton D. 2018. Affective relationships with an intimate research device. Qual. Inq In press [Google Scholar]
  134. Schüll ND. 2016a. Data for life: wearable technology and the design of self-care. BioSocieties 11:317–33 [Google Scholar]
  135. Schüll ND. 2016b. Tracking. Experience: Culture, Cognition, and the Common Sense CA Jones, D Mather, R Uchill 195–203 Cambridge, MA: MIT Press [Google Scholar]
  136. Schüll ND. 2017. Our metrics, ourselves. Public Books Jan. http://www.publicbooks.org/our-metrics-ourselves/ [Google Scholar]
  137. Sharon T. 2015. Healthy citizenship beyond autonomy and discipline: tactical engagements with genetic testing. BioSocieties 10:295–316 [Google Scholar]
  138. Sharon T. 2016. The Googlization of health research: from disruptive innovation to disruptive ethics. Personal. Med. 13:563–74 [Google Scholar]
  139. Sharon T. 2017. Self-tracking for health and the Quantified Self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philos. Technol. 30:93–121 [Google Scholar]
  140. Sharon T, Zandbergen D. 2016. From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media Soc https://doi.org/10.1177/1461444816636090 [Crossref] [Google Scholar]
  141. Sherman J. 2016. Data in the age of digital reproduction: reading the quantified self through Walter Benjamin. See Nafus 2016 27–42
  142. Smith GJD. 2016. Surveillance, data and embodiment: on the work of being watched. Body Soc 22:108–39 [Google Scholar]
  143. Smith GJD, Vonthethoff B. 2017. Health by numbers: exploring the practice and experience of datafied health. Health Sociol. Rev. 26:6–21 [Google Scholar]
  144. Taylor L, Broeders D. 2015. In the name of Development: Power, profit and the datafication of the global South. Geoforum 64:229–37 [Google Scholar]
  145. Till C. 2014. Exercise as labour: Quantified Self and the transformation of exercise into labour. Societies 4:446–62 [Google Scholar]
  146. Till C. 2017. Commercialising bodies: the new corporate health ethic of philanthrocapitalism. Quantified Lives and Vital Data R Lynch, C Farrington Basingstoke, UK: Palgrave-Macmillan In press [Google Scholar]
  147. Topol EJ. 2012. The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care New York: Basic Books [Google Scholar]
  148. Van Den Eede Y. 2015. Tracing the tracker: a postphenomenological inquiry into self-tracking technologies. Postphenomenological Investigations: Essays on Human-Technology Relations R Rosenberger, P-P Verbeek 143–58 Lanham, MD: Lexington Books [Google Scholar]
  149. Van Dijck J. 2014. Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveill. Soc. 12:197–208 [Google Scholar]
  150. Van Dijck J, Poell T. 2016. Understanding the promises and premises of online health platforms. Big Data Soc 3: https://doi.org/10.1177/2053951716654173 [Crossref] [Google Scholar]
  151. Viseu A, Suchman L. 2010. Wearable augmentations: imaginaries of the informed body. Technologized Images, Technologized Bodies J Edwards, P Harvey, P Wade 161–84 New York: Berghahn Books [Google Scholar]
  152. Watson S. 2014. Stepping down: rethinking the fitness tracker. The Atlantic Sept 25. https://www.theatlantic.com/technology/archive/2014/09/hacking-the-fitness-tracker-to-move-less-not-more/380742/ [Google Scholar]
  153. Whitson JR. 2013. Gaming the quantified self. Surveill. Soc. 11:163–76 [Google Scholar]
  154. Williamson B. 2015. Algorithmic skin: health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education. Sport Educ. Soc. 20:133–51 [Google Scholar]
/content/journals/10.1146/annurev-anthro-102116-041244
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error