From Quantified Self to Quality of Life

  • Katarzyna WacEmail author
Part of the Health Informatics book series (HI)


“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.


Human-computer interaction Mobile health Tracking and self-management systems Ubiquitous computing and sensors Physiologic modeling and disease processes 



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).


  1. Adair JG. The Hawthorne effect: a reconsideration of the methodological artifact. J Appl Psychol. 1984;69(2):334–45. CrossRefGoogle Scholar
  2. 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. Google Scholar
  3. Boillat, T., Lienhard, K., & Legner, C. (2015). Entering the World of individual routines: the affordances of mobile applications. Proceedings ICIS 2015.
  4. 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. CrossRefPubMedGoogle Scholar
  5. Choe, E. K. (2014). Designing self-monitoring technology to promote data capture and reflection. Retrieved from
  6. 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. Scholar
  7. Choe EK, Lee B, Schraefel M. Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Comput Graph Appl. 2015;35(4):28–37. CrossRefGoogle Scholar
  8. Ciman M, Wac K. Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Trans Affect Comput. 2016:1–1.
  9. Cortez NG, Cohen IG, Kesselheim AS. FDA regulation of mobile health technologies. N Engl J Med. 2014;371(4):372–9. CrossRefPubMedGoogle Scholar
  10. Da Silva JP, Pereira AMS. Perceived spirituality, mindfulness and quality of life in psychiatric patients. J Relig Health. 2017;56(1):130–40. CrossRefPubMedGoogle Scholar
  11. Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. 2015;33(5):456–61. CrossRefPubMedGoogle Scholar
  12. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Feldman MS, Pentland BT. Reconceptualizing organizational routines as a source of flexibility and change. Adm Sci Q. 2003;48(1):94. CrossRefGoogle Scholar
  14. Fox, S. (2013). .The self-tracking data explosion Google Scholar
  15. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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. CrossRefPubMedGoogle Scholar
  18. Higgins JP. Smartphone applications for patients’ health and fitness. Am J Med. 2016;129(1):11–9. CrossRefPubMedGoogle Scholar
  19. Kanade T. Quality of Life Technology. Proc IEEE. 2012;100(8):2394–6. CrossRefGoogle Scholar
  20. 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. Google Scholar
  21. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Lupton D. The diverse domains of quantified selves: self-tracking modes and dataveillance. Econ Soc. 2016;45(April):1–22. Google Scholar
  23. McKee KJ, Kostela J, Dahlberg L. Five years from now. Res Aging. 2015;37(1):18–40. CrossRefPubMedGoogle Scholar
  24. Minerva, R., & Crespi, N. (2017). Technological evolution of the ICT sector. SpringerNew York53–87. Scholar
  25. Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015;313(5):459–60. CrossRefPubMedGoogle Scholar
  26. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Poushtr, J. (2016). Smartphone ownership and internet usage continues to climb in emerging economies.
  29. Roberts S. The reception of my self-experimentation. J Bus Res. 2012;65(7):1060–6. CrossRefGoogle Scholar
  30. 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. CrossRefGoogle Scholar
  31. Schaller RR. Moore’s law: past, present and future. IEEE Spectr. 1997;34(6):52–9. CrossRefGoogle Scholar
  32. Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520(7549):609–11. CrossRefPubMedGoogle Scholar
  33. Shapiro AK. Semantics of the placebo. Psychiatr Q. 1968;42(4):653–95. CrossRefPubMedGoogle Scholar
  34. 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. CrossRefPubMedGoogle Scholar
  35. 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. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013;1(2):85–99. CrossRefPubMedGoogle Scholar
  37. Wac K. Smartphone as a personal, pervasive health informatics services platform: literature review. Yearb Med Inform. 2012;7(1):83–93.PubMedGoogle Scholar
  38. 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.Google Scholar
  39. 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. CrossRefGoogle Scholar
  40. 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. Google Scholar
  41. 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. CrossRefPubMedGoogle Scholar
  42. 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.
  43. Wolf G, Kelly K. Quantified self: self knowledge through numbers. 2014. website, Visited April 207

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science, University of CopenhagenCopenhagenDenmark
  2. 2.Quality of Life Technologies Lab, University of GenevaGenevaSwitzerland

Personalised recommendations