Author(s):

Yli-Kauhaluoma, Sari

Pantzar, Mika

Abstract:

Objective

Self-tracking technologies have created high hopes, even hype, for aiding people to govern their own health risks and promote optimal wellness. High expectations do not, however, necessarily materialize due to connective gaps between personal experiences and self-tracking data. This study examines situations when self-trackers face difficulties in engaging with, and reflecting on, their data with the aim of identifying the specificities and consequences of such connective gaps in self-tracking contexts.

Methods

The study is based on empirical analyses of interviews of inexperienced, experienced and extreme self-trackers (in total 27), who participated in a pilot study aiming at promoting health and wellness.

Results

The study shows that people using self-tracking devices actively search for constant connectivity to their everyday experiences and particularly health and wellness through personal data but often become disappointed. The results suggest that in connective gaps the personal data remains invisible or inaccurate, generating feelings of confusion and doubt in the users of the self-tracking devices. These are alarming symptoms that may lead to indifference when disconnectivity becomes solidified and data ends up becoming dead, providing nothing useful for the users of self-tracking technologies.

Conclusions

High expectations which are put on wearables to advance health and wellness may remain unmaterialised due to connective gaps. This is problematic if individuals are increasingly expected to be active in personal data collection and interpretation regarding their own health and wellness.

Document:

https://pubmed.ncbi.nlm.nih.gov/29942640/

References:
1.Lupton D. Self-tracking modes: Reflexive self-monitoring and data practices, https://ssrn.com/abstract=2483549 (2014, accessed 17 November, 2017).
Google Scholar
2.Pantzar, M, Ruckenstein, M. The heart of everyday analytics: Emotional, material and practical extensions in self-tracking market. Consump Mark Cult 2015; 18: 92–109.
Google Scholar | Crossref | ISI
3.Swan, M . Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw 2012; 1: 217–253.
Google Scholar | Crossref
4.Wolf G. The data-driven life, http://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html (28 April 2010, accessed 13 February 2018).
Google Scholar
5.Kelly K. Self-tracking? You will, http://kk.org/thetechnium/self-tracking-y/ (25 March 2011, accessed 14 February 2018).
Google Scholar
6.Wyatt, S, Harris, A, Adams, S Illness online: Self-reported data and questions of trust in medical and social research. Theory Cult Soc 2013; 30: 131–150.
Google Scholar | SAGE Journals | ISI
7.Hood, L, Friend, SH. 2011. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011; 8: 184–187.
Google Scholar | Crossref | Medline | ISI
8.Brown, N . 2003. Hope against hype – accountability in biopasts, presents and futures. Sci Stud 2003; 16: 3–21.
Google Scholar
9.Lury, C . Going live: Towards an amphibious sociology. Sociol Rev 2012; 60: 184–197.
Google Scholar | SAGE Journals | ISI
10.Kolb, DG . Exploring the metaphor of connectivity: Attributes, dimensions and duality. Organ Stud 2008; 29: 127–144.
Google Scholar | SAGE Journals | ISI
11.Kolb, DG, Caza, A, Collins, PD. 2012. States of connectivity: New questions and new directions. Organ Stud 2012; 33: 267–273.
Google Scholar | SAGE Journals | ISI
12.Dery, K, Kolb, D, MacCormick, J. 2014. Working with connective flow: How smartphone use is evolving in practice. Eur J Inf Syst 2014; 23: 559–670.
Google Scholar | Crossref
13.Symon, G, Pritchard, K. Performing the responsive and committed employee through the sociomaterial mangle of connection. Organ Stud 2015; 36: 241–263.
Google Scholar | SAGE Journals | ISI
14.Kolb, DG, Collins, PD, Lind, EA. Requisite connectivity: Finding flow in a not-so-flat world. Organ Dyn 2008; 37: 181–189.
Google Scholar | Crossref | ISI
15.Wajcman, J, Rose, E. Constant connectivity: Rethinking interruptions at work. Organ Stud 2011; 32: 941–961.
Google Scholar | SAGE Journals | ISI
16.Castells, M, Fernández-Ardèvol, M, Qiu, JL Mobile communication and society: A global perspective, Cambridge, MA: The MIT Press, 2007.
Google Scholar
17.Mayer-Schönberger, V, Cukier, K. Big data: A revolution that will transform how we live, work and think, Boston: Houghton Mifflin Harcourt, 2013.
Google Scholar
18.Van Dijck, J . Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveill Soc 2014; 12: 197–208.
Google Scholar | Crossref
19.Lynch, R, Cohn, S. In the loop: Practices of self-monitoring from accounts by trial participants. Health 2016; 20: 523–538.
Google Scholar | SAGE Journals | ISI
20.Nafus, D . Stuck data, dead data, and disloyal data: the stops and starts in making numbers into social practices. Distinktion: Scand. J. Soc. Theory 2014; 15: 208–222.
Google Scholar | Crossref
21.Borup, M, Brown, N, Konrad, K The sociology of expectations in science and technology. Technol Anal Strateg 2006; 18: 285–298.
Google Scholar | Crossref | ISI
22.Lunde, M, Røpke, I, Heiskanen, E. Smart grid: Hope or hype? Energ Effic 2016; 9: 545–562.
Google Scholar | Crossref
23.Alvial-Palavicino, C . The future as practice. A framework to understand anticipation in science and technology. TECNOSCIENZA, Italian Journal of Science & Technology Studies 2015; 6: 135–172.
Google Scholar
24.Konrad, K . The social dynamics of expectations: The interaction of collective and actor-specific expectations on electronic commerce and interactive television. Technol Anal Strateg 2006; 18: 429–444.
Google Scholar | Crossref | ISI
25.Pantzar, M, Shove, E. Understanding innovation in practice: A discussion of the production and re-production of Nordic Walking. Technol Anal Strateg 2010; 22: 447–461.
Google Scholar | Crossref | ISI
26.Wolf G. Know thyself: Tracking every facet of life, from sleep to mood to pain, 24/7/365, https://www.wired.com/2009/06/lbnp-knowthyself/ (2009, accessed 17 November, 2017).
Google Scholar
27.Swan, M . Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health 2009; 6: 492–525.
Google Scholar | Crossref | Medline | ISI
28.Swan, M . Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J Pers Med 2012; 2: 93–118.
Google Scholar | Crossref | Medline
29.Scitovsky, T . The joyless economy, New York: Oxford University Press, 1976.
Google Scholar
30.Douglas, M, Isherwood, B. The world of goods: Towards an anthropology of consumerism, London: Routledge, 1996.
Google Scholar
31.Pantzar, M . Domestication of everyday life technology: Dynamic views on the social histories of artifacts. Design Issues 1997; 13: 52–65.
Google Scholar | Crossref | ISI
32.Shove, E, Pantzar, M, Watson, M. The dynamics of social practice: Everyday life and how it changes, London: Sage, 2012.
Google Scholar | Crossref
33.Rooksby J, Rost M, Morrison A, et al. Personal tracking as lived informatics. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, Toronto, Canada, 26 April–1 May 2014, pp.1163–1172. New York: ACM.
Google Scholar
34.Li I, Dey AK and Forlizzi J. Understanding my data, myself: Supporting self-reflection with Ubicomp technologies. In: Proc. of the 13th Int. Conf. on Ubiquitous Computing, Beijing, China, 17-21 September 2011, pp.405–414. New York: ACM.
Google Scholar
35.Lupton, D . The quantified self, Cambridge: Polity Press, 2016.
Google Scholar
36.Preuveneers D and Berbers Y. Mobile phones assisting with health self-care: A diabetes case study. In: Proc. of the 10th Int. Conf. on Human Computer Interaction with Mobile Devices and Services, Amsterdam, the Netherlands, 2–5 September 2008, pp.177–186. New York: ACM.
Google Scholar
37.Davis J. The qualified self, https://thesocietypages.org/cyborgology/2013/03/13/the-qualified-self/ (13 March 2013, accessed 16 February 2018).
Google Scholar
38.Lupton D. You are your data: Self-tracking practices and concepts of data, https://ssrn.com/abstract=2534211 (2014, accessed 17 November 2017).
Google Scholar
39.Rapp, A, Cena, F. Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data. Int J Hum Comput Stud 2016; 94: 1–17.
Google Scholar | Crossref | ISI
40.Graham, S, Thrift, N. Out of order: Understanding repair and maintenance. Theory Cult Soc 2007; 24: 1–25.
Google Scholar | SAGE Journals | ISI
41.Hargittai, E, Hinnant, A. Digital inequality: Differences in young adults’ use of the Internet. Communic Res 2008; 35: 602–621.
Google Scholar | SAGE Journals | ISI
42.Baum, F, Newman, L, Biedrzycki, K. Vicious cycles: Digital technologies and determinants of health in Australia. Health Promot Int 2014; 29: 349–360.
Google Scholar | Crossref | Medline | ISI
43.Choe EK, Lee NB, Lee B, et al. Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, Toronto, Canada, 26 April–1 May 2014, pp.1143–1152. New York: ACM.
Google Scholar
44.Ledger D and McCaffrey D. Inside wearables: How the science of human behavior change offers the secret to long-term engagement, https://blog.endeavour.partners/inside-wearable-how-the-science-of-human-behavior-change-offers-the-secret-to-long-term-engagement-a15b3c7d4cf3 (2014, accessed 17 November, 2017).
Google Scholar
45.Lazar A, Koehler C, Tanenbaum J, et al. Why we use and abandon smart devices. In: Proc. of the 2015 ACM Int. Joint Conf. on Pervasive Ubiquitous Computing, Osaka, Japan, 7–11 September 2015, pp.635–646. New York: ACM.
Google Scholar
46.Withings. Withings Activité Pop activity tracker – sleep analyzer installation and operating instructions, https://images-eu.ssl-images-amazon.com/images/I/91IVGccIuLL.pdf (2015, accessed 28 February 2018).
Google Scholar
47.Zöllner M, Zapf A, Truong ND, et al. Okinesio – the development of open hardware for quantified self. In: Proc. of the 3rd Int. Workshop on Sensor-based Activity Recognition and Interaction, Rostock, Germany, 23–24 June 2016, paper no. 3, pp.1–5. New York: ACM.
Google Scholar
48.Eriksson, P, Kovalainen, A. Qualitative methods in business research, London: Sage, 2008.
Google Scholar | Crossref
49.Pantzar, M, Ruckenstein, M. Living the metrics: Self-tracking and situated objectivity. Digit Health 2017; 3: 1–10.
Google Scholar | SAGE Journals
50.Locke, K, Golden-Biddle, K, Feldman, MS. Making doubt generative. Rethinking the role of doubt in the research process. Organ Sci 2008; 19: 907–918.
Google Scholar | Crossref | ISI
51.Paasonen, S . As networks fail: Affect, technology, and the notion of the use. Telev New Media 2015; 16: 701–716.
Google Scholar | SAGE Journals | ISI
52.van Dijck, J . ‘You have one identity’: Performing the self on Facebook and LinkedIn. Media Cult Soc 2013; 35: 199–215.
Google Scholar | SAGE Journals | ISI
53.Fenn, J, Raskino, M. Mastering the hype cycle: How to choose the right innovation at the right time, Boston: Harvard Business School Press, 2008.
Google Scholar