“You Don’t Own a FitBit, the FitBit Owns You”: A Taxonomy of Privacy Attitudes in the Context of Self-Quantification

by | Jan 1, 2022 | Research Publications

Author(s):

  • Svetlana Smirnova

Abstract:

Self-tracking—the process of self-quantification for health and wellbeing via mobile applications and wearable devices—increasingly contributes to shaping individuals’ self-understanding, and delivering health and wellness services. At the same time, continuous collection of fine-grained health data presents new challenges for informational privacy. This chapter focuses on examining the attitudes of self-quantifiers to privacy. Empirically, the chapter draws on a dataset consisting of month-long diaries and interviews with 45 self-quantifiers. The findings show that attitudes to informational privacy in the context of tracking fall into three categories: low privacy concerns, due to self-tracking being viewed as a trade-off for services; mixed privacy concerns that stem from limited recognition of the value of personal data and its accumulation; and, active concern and resistance. While the level of concern varies, informational privacy of self-tracked data matters to self-quantifiers across the spectrum. At the same time, it is hard for participants to verbalize what precisely they are concerned about. A concept of “unease” most accurately describes users” views on privacy, which leads to a new direction in studying informational privacy. Given that self-tracking relies heavily on communication infrastructure to generate mediated health insight, scholars of communication and health are uniquely positioned to evaluate these practices of self-quantification.

Documentation:

https://doi.org/10.1007/978-981-16-4290-6_13

References:
  1. Ancker, J. S., Witteman, H. O., Hafeez, B., Provencher, T., Van de Graaf, M., & Wei, E. (2015). “You get reminded you’re a sick person”: Personal data tracking and patients with multiple chronic conditions. Journal of Medical Internet Research, 17(8), e202.  https://doi.org/10.2196/jmir.4209.
  2. Austen, K. (2015). The trouble with wearables. Nature, 525(7567), 22–24.  https://doi.org/10.1038/525022a.CrossRefGoogle Scholar
  3. Balebako, R., Marsh, A., Lin, J., Hong, J. I., & Cranor, C. F. (2014, April 27). The privacy and security behaviors of smartphone app developers. In Network and Distributed System Security (NDSS) Symposium. San Diego, CA. https://www.ndss-symposium.org/wp-content/uploads/2017/09/01_2-paper.pdf.
  4. Charitsis, V. (2019). Survival of the (data) fit: Self-surveillance, corporate wellness, and the platformization of healthcare. Surveillance & Society, 17(1/2), 139–144.  https://doi.org/10.24908/ss.v17i1/2.12942.
  5. Ching, K., & Singh, M. (2016). Wearable technology devices security and privacy vulnerability analysis. International Journal of Network Security and Its Applications, 8(3), 19–30.  https://doi.org/10.5121/ijnsa.2016.8302.CrossRefGoogle Scholar
  6. Colonna, L. (2019). Legal and regulatory challenges to utilizing lifelogging technologies for the frail and sick. International Journal of Law and Information Technology, 27(1), 50–74.  https://doi.org/10.1093/ijlit/eay018.CrossRefGoogle Scholar
  7. Costa Figueiredo, M., Caldeira, C., Reynolds, T., Victory, S., Zheng, K., & Chen, Y. (2017). Self-tracking for fertility care: Collaborative support for a highly personalized problem. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 1–21.  https://doi.org/10.1145/3134671.
  8. Cusack, B., Antony, B., Ward, G., & Mody, S. (2017). Assessment of security vulnerabilities in wearable devices. In The Proceedings of 15th Australian Information Security Management Conference. Perth, Western Australia.  https://doi.org/10.4225/75/5a84e6c295b44.
  9. Cyr, B., Horn, W., Miao, D., & Specter, M. (2014). Security analysis of wearable fitness devices (Fitbit). Retrieved January 27, 2021, from https://courses.csail.mit.edu/6.857/2014/files/17-cyrbritt-webbhorn-specter-dmiao-hacking-fitbit.pdf.
  10. Daly, A. (2015). The law and ethics of ‘self-quantified’ health information: An Australian perspective. International Data Privacy Law, 5(2), 144–155.  https://doi.org/10.1093/idpl/ipv001.CrossRefGoogle Scholar
  11. Dow Schüll, N. (2016). Data for life: Wearable technology and the design of self-care. BioSocieties, 11(3), 317–333.  https://doi.org/10.1057/biosoc.2015.47.
  12. Goodyear, V. A., Kerner, C., & Quennerstedt, M. (2019). Young people’s uses of wearable healthy lifestyle technologies; surveillance, self-surveillance and resistance. Sport, Education and Society, 24(3), 212–225.  https://doi.org/10.1080/13573322.2017.1375907.CrossRefGoogle Scholar
  13. Gorm, N., & Shklovski, I. (2016). Steps, choices and moral accounting: Observations from a step-counting campaign in the workplace. In CSCW ‘16: 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. New York.  https://doi.org/10.1145/2818048.2819944.
  14. Goyal, R., Dragoni, N., & Spognardi, A. (2016). Mind the tracker you wear: A security analysis of wearable health trackers. In SAC ‘16: Proceedings of the 31st Annual ACM Symposium on Applied Computing (pp. 131–136). Pisa, Italy.  https://doi.org/10.1145/2851613.2851685.
  15. Grundy, Q., Chiu, K., Held, F., Continella, A., Bero, L., & Holz, R. (2019). Data sharing practices of medicines related apps and the mobile ecosystem: Traffic, content, and network analysis. BMJ, 364, 1920.  https://doi.org/10.1136/bmj.l920.CrossRefGoogle Scholar
  16. Hilts, H., Parsons, C., & knocked, J. (2016). Every step you fake: A comparative analysis of fitness tracker and privacy. Open Effect. Retrieved January 27, 2021, from https://openeffect.ca/reports/Every_Step_You_Fake.pdf.
  17. Hutton, L., Price, B. A., Kelly, R., McCormick, C., Bandara, A. K., Hatzakis, T., Meadows, M., & Nuseibeh, B. (2018). Assessing the privacy of mhealth apps for self-tracking: Heuristic evaluation approach. JMIR mHealth and uHealth, 6(10), e185.  https://doi.org/10.2196/mhealth.9217.
  18. IMS Institute for Health Informatics. (2015). Patient adoption of mHealth: Use, evidence, and remaining barriers to the mainstream acceptance. Danbury, CT: IMS Institute for Health Informatics. Retrieved from https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/patient-adoption-of-mhealth.pdf.
  19. IQVIA Institute for Human Data Science. (2017). The growing value of digital health: Evidence and impact on human health and the healthcare system. https://www.iqvia.com/insights/the-iqvia-institute/reports/the-growing-value-of-digital-health.
  20. Kantar Media. (2014). Health and fitness wearable: Tech market set to double to 13.1m users in 2015 [Press release]. https://uk.kantar.com/consumer/leisure/2014/uk-health-and-fitness-wearable-tech-market-stats/
  21. Katuska, J. (2018). Wearing down HIPAA: How wearable technologies erode privacy protections. Journal of Corporation Law, 44(2), 385–401.Google Scholar
  22. La Porta, L. (2019, July 22). iOS App permissions—Are your apps asking too much? Retrieved July 12, 2020, from https://www.wandera.com/ios-app-permissions/.
  23. Langley, M. (2015). Hide your health: Addressing the new privacy problem of consumer wearables. The Georgetown Law Journal, 103(6), 1641.Google Scholar
  24. Lupton, D. (2016). The quantified self: A sociology of self-tracking. Polity.Google Scholar
  25. Lupton, D., & Michael, M. (2017). ‘Depends on who’s got the data’: Public understandings of personal digital dataveillance. Surveillance & Society, 15(2), 254–268.  https://doi.org/10.24908/ss.v15i2.6332.
  26. Moore, P., & Robinson, A. (2015). The quantified self: What counts in the neoliberal workplace. New Media & Society, 18(11), 2774–2792.  https://doi.org/10.1177/1461444815604328.CrossRefGoogle Scholar
  27. Motti, V. G., & Caine, K. (2015). Users’ privacy concerns about wearables—Impact of form factor, sensors and type of data collected. In M. Brenner, N. Christin, B. Johnson, & K. Rohloff (Eds.), Financial cryptography and data security. FC 2015 International Workshops, BITCOIN, WAHC, and Wearable (pp. 231–244). Springer.Google Scholar
  28. Nafus, D., & Neff, G. (2016). Self-tracking. MIT Press.Google Scholar
  29. Newman, T., & Kreick, J. (2015). The impact of HIPAA (and other federal law) on wearable technology. SMU Science & Technology Law Review, 18(4), 429.Google Scholar
  30. Patterson, H. (2013, September 28). Contextual expectations of privacy in self-generated health information flows. In TPRC 41: The 41st Research Conference on Communication, Information and Internet Policy. New York.  https://doi.org/10.2139/ssrn.2242144.
  31. Privacy Right Clearinghouse. (2013). Mobile health and fitness apps: What are the privacy risks? Retrieved January 27, 2021, from https://privacyrights.org/consumer-guides/mobile-health-and-fitness-apps-what-are-privacy-risks.
  32. Sensor Tower. (2020). Health & fitness app adoption up record 47% so far in Q2 2020. Sensor Tower Blog: Store Intelligence. Retrieved January 27, 2021, from https://sensortower.com/blog/health-and-fitness-app-record-download-growth.
  33. Solove, D., & Citron, D. (2018). Risk and anxiety: A theory of data-breach harms. Texas Law Review, 96(4), 737–786.Google Scholar
  34. Solove, D. J. (2006). A taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477–564.  https://doi.org/10.2307/40041279.CrossRefGoogle Scholar
  35. Spiller, K., Ball, K., Bandara, A., Meadows, M., Mccormick, C., Nuseibeh, B., & Price, B. A. (2018). Data privacy: Users’ thoughts on quantified self personal data. In B. Ajana (Ed.), Self-tracking: Empirical and philosophical investigations (pp. 111–124). Palgrave Macmillan.CrossRefGoogle Scholar
  36. Statista. (2020). Wearable technology—Statistics & facts. Statista. Retrieved July 13, 2020, from https://www.statista.com/topics/1556/wearable-technology/.
  37. Till, C. (2014). Exercise as labour: Quantified self and the transformation of exercise into labour. Societies, 4(3), 446–462.  https://doi.org/10.3390/soc4030446.CrossRefGoogle Scholar
  38. Turow, J., Hennessy, M., & Draper, N. (2015). The trade off fallacy: How marketers are misrepresenting American consumers and opening them up to exploitation. Annberg School for Communication, University of Pennsylvania. https://www.asc.upenn.edu/sites/default/files/TradeoffFallacy_1.pdf.
  39. Vitak, J., Liao, Y., Kumar, P., Zimmer, M., & Kritikos, K. (2018). Privacy attitudes and data valuation among fitness tracker users. In iConference 2018: Transforming Digital Worlds. Springer.Google Scholar
  40. Urban, M. (2017). ‘This really takes it out of you!’ The senses and emotions in digital health practices of the elderly. Digital Health, 3, 2055207617701778.  https://doi.org/10.1177/2055207617701778.CrossRefGoogle Scholar
  41. Ward, C., Ellis, D., D’Ambrosio, L. A., & Coughlin, J. F. (2018). Digital breadcrumbs: A lack of data privacy and what people are doing about it. In 20th International Conference, HCI International 2018, Human-Computer Interaction. Theories, Methods, and Human Issues. Las Vegas, NV.Google Scholar
  42. Wissinger, E. (2017). Wearable tech, bodies, and gender. Sociology Compass, 11(11), E12514.  https://doi.org/10.1111/soc4.12514.CrossRefGoogle Scholar
  43. Zimmer, M., Kumar, P., Vitak, J., Liao, Y., & Chamberlain Kritikos, K. C. (2018). ‘There’s nothing really they can do with this information’: Unpacking how users manage privacy boundaries for personal fitness information. Information, Communication & Society, 23(7), 1020–1037.  https://doi.org/10.1080/1369118X.2018.1543442.