Author(s):

  • Sjöklint, Mimmi

Abstract:

The advancement of information technology, online accessibility and wearable computing is fostering a new playground for users to engage with quantified data sets. On one hand, the online user is continuously yet passively exposed to different types of quantified data in online interfaces and mobile apps. On the other hand, the user may actively and knowingly be gathering quantified data through ubiquitous sensory devices, such as wearable technology, e.g. the Jawbone UP and Fitbit. In both instances, the user is exposed to versions of self-quantified measures, namely the aggregation and transformation of personally attributed activity into quantified data. This study approaches the adoption of wearables by looking at active and passive self-quantification online and explores how it may influence and support the user’s cognitive processes and subsequent decision-making process.

Document:

https://doi.org/10.1145/2641248.2642737

References:
  1. Ariely, D., and Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219–224.
  2. Bawden D. and Robinson L. (2009). The dark side of information: overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35 (2), 180–191.
  3. Bottles, K. (2012). Will the Quantified Self Movement Take Off in Health Care?. Physician Executive, 38(5), 74–75.
  4. Deterding, S., Dixon, D., Khaled, R. and Nacke, L. (2011). From game design elements to gamefulness: defining gamification. In Proceedings of the 15th International Academic MindTrek Conference. 9–15.
  5. Economist (2012). The Quantified Self: Counting every moment. The Economist: Technology Quarterly. Available from: <http://www.economist.com/node/21548493>. {Accessed 30/04/14}.
  6. Eyseneck, M. W. (2012). Fundamentals of Cognition. Psychology Press.
  7. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. The American Economic Review, 93(5), 1449–1475.
  8. Koroleva, K., Stimac, V., Krasnova, H., and Kunze, D. (2011). I like it because I (‘m) like you — Measuring User Attitudes Towards Information on Facebook. In proceedings of ICIS 2011,
  9. Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 557–566). ACM.
  10. Morgnan, D. L. (1988). Focus Groups as Qualitative Research. Sage Publications.
  11. Park, S., and Jayaraman, S. (2003). Enhancing the quality of life through wearable technology. Engineering in Medicine and Biology Magazine, IEEE,22(3), 41–48.
  12. Pritchard, M. P., Havitz M. E., and Howard, D. R. (1999). Analyzing the commitment-loyalty link in service contexts. Journal of the Academy of Marketing Science, 27 (3), 333–348.
  13. Ritchie, J. and Lewis, J. (2003). Qualitative Research Practice: A Guide for Social Science Students and Researchers. London: Sage Publications.
  14. Sunstein, C. (2012, in press). The Storrs Lectures: Behavioral Economics and Paternalism. Yale Law Journal. Chicago.
  15. Swan, M. (2009). Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. International Journal of Environmental Research and Public Health, 6(2), 492–525.
  16. Swan, M. (2013). The quantified self: fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85–99.
  17. Quantified Self, (2012). Available from: <http://quantifiedself.com>. {Accessed 1/05/14}.
  18. Wolf, G., (2010). The data-driven life. The New York Times. Available from: <http://goo.gl/UXZLk>. {Accessed 30/04/14}.

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