Abstract
“Know Thyself” is a motto leading the Quantified Self (QS) movement, which at first originated as a “hobby project” driven by self-discovery, and is now being leveraged in wellness and healthcare. QS practitioners rely on the wealth of digital data originating from wearables, applications, and self-reports that enable them to assess diverse domains of their daily life. That includes their physical state (e.g., mobility, steps), psychological state (e.g., mood), social interactions (e.g., a number of Facebook “likes”) and environmental context they are in (e.g., pollution). The World Health Organization (WHO) recognizes these four QS domains as contributing to individual’s Quality of Life (QoL), with health spanning across all the four domains. The collected QS data enables an individual’s state and behavioral patterns to be assessed through these different QoL domains, based on which individualized feedback can be provided, in turn enabling to improve the individual’s state and QoL. The evidence of causality between QS and QoL is still being established, as only data from limited cases and domains exist so far. In this chapter, we discuss the state of this evidence via a semi-systematic review of the exemplary QS practices documented in 609 QS practitioners’ talks and a review of the 438 latest available personal wearable technologies enabling QS. We discuss the challenges and opportunities for the QS to become an integral part of the future of healthcare and QoL-driven solutions. Some of the opportunities include using QS technologies as different types of affordances supporting the goal-oriented actions by the individual, in turn improving their QoL.
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C. de Looper, “Runkeeper is the latest mobile app to run afoul of privacy advocates”, available from http://www.digitaltrends.com/mobile/runkeeper-user-tracking/, May 2016.
References
Adair JG. The Hawthorne effect: a reconsideration of the methodological artifact. J Appl Psychol. 1984;69(2):334–45. http://doi.org/10.1037/0021-9010.69.2.334
Bergland A, Meaas I, Debesay J, Brovold T, Jacobsen EL, Antypas K, Bye A. Associations of social networks with quality of life, health and physical functioning. Eur J Phys. 2016;18(2):78–88. http://doi.org/10.3109/21679169.2015.1115554
Boillat, T., Lienhard, K., & Legner, C. (2015). Entering the World of individual routines: the affordances of mobile applications. Proceedings ICIS 2015. http://aisel.aisnet.org/icis2015/proceedings/ISstrategy/14.
Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625. http://doi.org/10.1001/jama.2014.17841
Choe, E. K. (2014). Designing self-monitoring technology to promote data capture and reflection. Retrieved from https://digital.lib.washington.edu/researchworks/handle/1773/26199
Choe EK, Lee NB, Lee B, Pratt W, Kientz JA, Choe EK, et al. Understanding quantified-selfers’ practices in collecting and exploring personal data. In:Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ‘14. New York: ACM Press; 2014. p. 1143–52. http://doi.org/10.1145/2556288.2557372.
Choe EK, Lee B, Schraefel M. Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Comput Graph Appl. 2015;35(4):28–37. http://doi.org/10.1109/MCG.2015.51
Ciman M, Wac K. Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Trans Affect Comput. 2016:1–1. http://doi.org/10.1109/TAFFC.2016.2592504
Cortez NG, Cohen IG, Kesselheim AS. FDA regulation of mobile health technologies. N Engl J Med. 2014;371(4):372–9. http://doi.org/10.1056/NEJMhle1403384
Da Silva JP, Pereira AMS. Perceived spirituality, mindfulness and quality of life in psychiatric patients. J Relig Health. 2017;56(1):130–40. http://doi.org/10.1007/s10943-016-0186-y
Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. 2015;33(5):456–61. http://doi.org/10.1038/nbt.3222
Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11. http://doi.org/10.2196/jmir.7.1.e11
Feldman MS, Pentland BT. Reconceptualizing organizational routines as a source of flexibility and change. Adm Sci Q. 2003;48(1):94. http://doi.org/10.2307/3556620
Fox, S. (2013). .The self-tracking data explosion
Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 2013a;10(1):e1001362. http://doi.org/10.1371/journal.pmed.1001362
Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 2013b;10(1):e1001363. http://doi.org/10.1371/journal.pmed.1001363
Goyal S, Morita P, Lewis GF, Yu C, Seto E, Cafazzo JA. The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Can J Diabetes. 2016;40(1):95–104. http://doi.org/10.1016/j.jcjd.2015.06.007
Higgins JP. Smartphone applications for patients’ health and fitness. Am J Med. 2016;129(1):11–9. http://doi.org/10.1016/j.amjmed.2015.05.038
Kanade T. Quality of Life Technology. Proc IEEE. 2012;100(8):2394–6. http://doi.org/10.1109/JPROC.2012.2200555
Leibenger D, Möllers F, Petrlic A, Petrlic R, Sorge C. Privacy challenges in the quantified self movement – an EU perspective. Proc Privacy Enhanc Technol. 2016;2016(4):315–34. http://doi.org/10.1515/popets-2016-0042
Lobelo F, Kelli HM, Tejedor SC, McConnell MV, Martin SS, Welk GJ. The wild wild west: A framework to integrate mhealth software applications and wearables to support physical activity assessment, counseling and interventions for cardiovascular disease risk reduction. Prog Cardiovasc Dis. 2016;58(6):584–94. http://doi.org/10.1016/j.pcad.2016.02.007
Lupton D. The diverse domains of quantified selves: self-tracking modes and dataveillance. Econ Soc. 2016;45(April):1–22. http://doi.org/10.1080/03085147.2016.1143726
McKee KJ, Kostela J, Dahlberg L. Five years from now. Res Aging. 2015;37(1):18–40. http://doi.org/10.1177/0164027513520329
Minerva, R., & Crespi, N. (2017). Technological evolution of the ICT sector. SpringerNew York53–87. http://doi.org/10.1007/978-3-319-33995-5_3.
Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015;313(5):459–60. http://doi.org/10.1001/jama.2014.14781
Pepys, S. (1660). Diary of samuel pepys. https://books.google.com/books?hl=en&lr=&id=Y35KAAAAYAAJ&oi=fnd&pg=PA1&ots=0IaL7LkXbd&sig=oNa59Jp7sOq2Cx_IGhcMa7AaZQo#v=onepage&q&f=false.
Petit A, Cambon L. Exploratory study of the implications of research on the use of smart connected devices for prevention: a scoping review. BMC Public Health. 2016;16:552. http://doi.org/10.1186/s12889-016-3225-4
Piwek L, Ellis DA, Andrews S, Joinson A, Yang B, Rhee S, et al. The rise of consumer health wearables: promises and barriers. PLoS Med. 2016;13(2):e1001953. http://doi.org/10.1371/journal.pmed.1001953
Poushtr, J. (2016). Smartphone ownership and internet usage continues to climb in emerging economies. http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage--continues-to-climb-in-emerging-economies/#fn-35095-2.
Roberts S. The reception of my self-experimentation. J Bus Res. 2012;65(7):1060–6. http://doi.org/10.1016/j.jbusres.2011.02.014
Rueger SY, Malecki CK, Pyun Y, Aycock C, Coyle S. A Meta-analytic review of the association between perceived social support and depression in childhood and adolescence. Psychol Bullet. 2016;42(10):1017–67. http://doi.org/10.1037/bul0000058
Schaller RR. Moore’s law: past, present and future. IEEE Spectr. 1997;34(6):52–9. http://doi.org/10.1109/6.591665
Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520(7549):609–11. http://doi.org/10.1038/520609a
Schulz, R. (2012). Quality of life technology handbook. https://books.google.dk/books/about/Quality_of_Life_Technology_Handbook.html?id=nrE7LfUiO5oC&pgis=1.
Shapiro AK. Semantics of the placebo. Psychiatr Q. 1968;42(4):653–95. http://www.ncbi.nlm.nih.gov/pubmed/4891851
Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture. 2014;40(1):11–9. http://doi.org/10.1016/j.gaitpost.2014.03.189
Steinhubl SR, Muse ED, Topol EJ, Barrett PM, Komatireddy R, Haaser S, et al. The emerging field of mobile health. Sci Transl Med. 2015;7(283):283rv3. http://doi.org/10.1126/scitranslmed.aaa3487
Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013;1(2):85–99. http://doi.org/10.1089/big.2012.0002
Wac K. Smartphone as a personal, pervasive health informatics services platform: literature review. Yearb Med Inform. 2012;7(1):83–93.
Wac K. Beat-by-beat getting fit : leveraging pervasive self-tracking of heart rate in self-management of health. Stanford: Association for the Advancement of Artificial Intelligence; 2014.
Wac K, Tsiourti C. Ambulatory assessment of affect: survey of sensor systems for monitoring of autonomic nervous systems activation in emotion. IEEE Trans Affect Comput. 2014;5(3):251–72. http://doi.org/10.1109/TAFFC.2014.2332157
Wac K, Fiordelli M, Gustarini M, Rivas H. Quality of life technologies: experiences from the field and key research challenges. IEEE Internet Comput. 2015;99:1. http://doi.org/10.1109/MIC.2015.52
Wedgeworth M, LaRocca MA, Chaplin WF, Scogin F. The role of interpersonal sensitivity, social support, and quality of life in rural older adults. Geriatr Nurs. 2016;38(1):22–6. http://doi.org/10.1016/j.gerinurse.2016.07.001
WHO. The World Health Organization quality of life assessment (WHOQOL): position paper from the World Health Organization. Soc Sci Med, 1995;41(10):1403–1409. http://doi.org/10.1016/0277-9536(95)00112-K.
Wolf G, Kelly K. Quantified self: self knowledge through numbers. 2014. http://quantifiedself.com website, Visited April 207
Acknowledgments
This research is supported by the Swiss NSF MIQmodel (157003), AAL ANIMATE (6-071) and CoME (7-127) projects, and COST actions (1303, 1304). I appreciate the help of the QoL team members and collaborators with getting the data required for this chapter (especially Alexandre De Masi) and for overall feedback (especially Thomas Boillat).
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Wac, K. (2018). From Quantified Self to Quality of Life. In: Rivas, H., Wac, K. (eds) Digital Health. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-61446-5_7
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