PDF herunterladen
Methods Inf Med 2017; 56(01): 46-54
DOI: 10.3414/ME15-02-0007
Wearable Therapy
Schattauer GmbH

Refining the Concepts of Self-quantification Needed for Health Self-management[*]

A Thematic Literature Review
Manal Almalki
1   Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Victoria, Australia
,
Kathleen Gray
1   Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Victoria, Australia
,
Fernando J. Martin-Sanchez
1   Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Victoria, Australia
2   Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA
› InstitutsangabenFunding for this project was provided by Melbourne Networked Society Institute (MNSI), the University of Melbourne.
Weitere Informationen

Publikationsverlauf

received: 19. Oktober 2015

accepted in revised form: 27. April 2016

Publikationsdatum:
22. Januar 2018 (online)

    Lizenzen und Reprints

Summary

Background: Questions like ‘How is your health? How are you feeling? How have you been?’ now can be answered in a different way due to innovative health self-quantification apps and devices. These apps and devices generate data that enable individuals to be informed and more responsible about their own health.

Objectives: The aim of this paper is to review studies on health SQ, firstly, exploring the concepts that are associated with the users’ interaction with and around data for managing health; and secondly, the potential benefits and challenges that are associated with the use of such data to maintain or promote health, as well as their impact on the users’ certainty or confidence in taking effective actions upon such data.

Methods: To answer these questions, we conducted a comprehensive literature review to build our study sample. We searched a number of electronic bibliographic databases including Scopus, Web of Science, Medline, and Google Scholar. Thematic analysis was conducted for each study to find all the themes that are related to our research aims.

Results: In the reviewed literature, conceptualisation of health SQ is messy and inconsistent. Personal tracking, personal analytics, personal experimentation, and personal health activation are different concepts within the practice of health SQ; thus, a new definition and structure is proposed to set out boundaries between them. Using the data that are generated by SQS for managing health has many advantages but also poses many challenges.

Conclusions: Inconsistency in conceptualisation of health SQ – as well as the challenges that users experience in health self-management – reveal the need for frameworks that can describe the users’ health SQ practice in a holistic and consistent manner. Our ongoing work toward developing these frameworks will help researchers in this domain to gain better understanding of this practice, and will enable more systematic investigations which are needed to improve the use of SQS and their data in health self-management.

Keywords

Health - self-management - self-experimenta – tion - self-quantification - quantified self

* Supplementary material published on our website https://doi.org/10.3414/ME15-02-0007


 

  • References

  • 1 Almalki M, Gray K, Martin-Sanchez F. The use of self-quantification systems for personal health information: big data management activities and prospects. Health Inf Sci Syst. 2015; 3 (Suppl. 01) S1. doi: 10.1186/2047-2501-3-S1-S1.
    CrossrefPubMedGoogle Scholar
  • 2 Statista. 2015 Number of apps available in leading app stores as of July 2015. Available from: http://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores.
    PubMedGoogle Scholar
  • 3 Accenture Digital Consumer Tech Survey. 2014 Racing toward a complete digital lifestyle: Digital consumers crave more. Available from: http://www.accenture.com/us-en/Pages/insight-digital-consumer-tech-survey-2014.aspx.
    PubMedGoogle Scholar
  • 4 Research2guidance. 2013 The market for mobile health sensors will grow to $5.6 Billion by 2017. Available from: http://research2guidance.com/the-market-for-mobile-health-sensors-will-grow-to-5-6-billion-by-2017.
    PubMedGoogle Scholar
  • 5 FDA Food and Drug Administration. 2015 General wellness: Policy for low risk devices – draft guidance for industry and food and drug administration staff. Available from: http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM429674.pdf.
    PubMedGoogle Scholar
  • 6 Lupton D, Jutel A. ‘It’s like having a physician in your pocket!’ A critical analysis of self-diagnosis smartphone apps. Soc Sci Med. 2015; 133: 128-135. doi: 10.1016/j.socscimed.2015.04.004.
    CrossrefPubMedGoogle Scholar
  • 7 Lupton D. You are your data: self-tracking practices and concepts of data. Selke S. Life-logging. Springer, Forthcoming; 2014. Available from: http://ssrn.com/abstract=2534211.
    Google Scholar
  • 8 Lupton D. ‘Understanding the human machine’: an analysis of the quantified self as a cultural phenomenon. IEEE Technology and Society Magazine. 2013; 32 (04) 25-30. doi: 10.1109/mts.2013. 2286431.
    PubMedGoogle Scholar
  • 9 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 (02) 492-525. doi: 10.3390/ijerph6020492.
    CrossrefPubMedGoogle Scholar
  • 10 Lee VR. What’s happening in the „Quantified Self” movement?. Proc. of International Conference of the Learning Sciences, ICLS. 2014; 2 (January): 1032-1036. Available from: http://digitalcommons.usu.edu/itls_facpub/491.
    PubMedGoogle Scholar
  • 11 Wikipedia. Quantified Self. 2015 Available from: http://en.wikipedia.org/wiki/Quantified_Self.
    PubMed
  • 12 QS QuantifiedSelf meetup. 2015 Available from: http://quantified-self.meetup.com.
    PubMed
  • 13 Quantified Self. 2015 Conference. Available from: http://qs15.quantifiedself.com.
    PubMed
  • 14 Quantified Self Guide. 2015 Available from: http://quantifiedself.com/guide.
    PubMed
  • 15 ISB – Institute for Systems Biology. 2015 100K Wellness. Available from: https://www.systemsbiology.org/research/100k-wellness-project.
    PubMedGoogle Scholar
  • 16 Lupton D. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Critical Public Health. 2013; 23 (04) 393-403. doi: 10.1080/09581596.2013.794931.
    CrossrefPubMedGoogle Scholar
  • 17 Lupton D. M-health and health promotion: the digital cyborg and surveillance society. Social Theory and Health. 2012; 10 (03) 229-244. doi: 10.1057/sth.2012.6.
    PubMedGoogle Scholar
  • 18 Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Journal of Big Data. 2013; 1 (02) 85-99. doi: 10.1089/big.2012.0002.
    PubMedGoogle Scholar
  • 19 Swan M. Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. Journal of Personalized Medicine. 2012; 2 (03) 93-118. doi: 10.3390/jpm2030093.
    CrossrefPubMedGoogle Scholar
  • 20 Swan M. Next-generation personal genomic studies: extending social intelligence genomics to cognitive performance genomics in quantified creativity and thinking fast and slow. 2013 AAAI Spring Symposium Series (Data Driven Wellness). 2013
    PubMedGoogle Scholar
  • 21 Li I, Dey A, Forlizzi J. A stage-based model of personal informatics systems. Proceedings of the SIG-CHI Conference on Human Factors in Computing Systems. Atlanta, Georgia, USA: 1753409: ACM; 2010: 557-566.
    Google Scholar
  • 22 Li I, Medynskiy Y, Froehlich J, Larsen J. Personal informatics in practice: improving quality of life through data. CHI ‘12 Extended Abstracts on Human Factors in Computing Systems. Austin, Texas, USA: 2212724: ACM; 2012: 2799-2802. doi: 10.1145/2212776.2212724.
    Google Scholar
  • 23 Li I. Personal informatics & context: Using context to reveal factors that affect behavior. Journal of Ambient Intelligence and Smart Environments. 2012; 4 (01) 71-72. doi: 10.3233/ais-2011-0130.
    PubMedGoogle Scholar
  • 24 Wolfram S. 2012 The personal analytics of my life 2012. Available from: http://blog.stephenwol-fram.com/2012/03/the-personal-analytics-of-my-life.
    PubMedGoogle Scholar
  • 25 Roberts S. The reception of my self-experimentation. Journal of Business Research. 2012; 65 (07) 1060-1066. doi: 10.1016/j.jbusres.2011.02.014.
    CrossrefPubMedGoogle Scholar
  • 26 Roberts S. The unreasonable effectiveness of my self-experimentation. Medical Hypotheses. 2010; 75 (06) 482-489. doi: 10.1016/j.mehy.2010.04.030.
    CrossrefPubMedGoogle Scholar
  • 27 Martin-Sanchez F, Lopez-Campos G, Gray K. Biomedical informatics methods for personalized medicine and participatory health. Sarkar IN. Methods in Biomedical Informatics.. Oxford: Academic Press; 2013: 347-394. doi: 10.1016/B978-0-12-401678-1.00011-7.
    Google Scholar
  • 28 Fiore-Silfvast B, Neff G. What we talk about when we talk data: valences and the social performance of multiple metrics in digital health. Ethnographic Praxis in Industry Conference Proceedings. 2013; 2013 (01) 74-87. doi: 10.1111/j.1559-8918.2013. 00007.x.
    CrossrefPubMedGoogle Scholar
  • 29 Fox S, Duggan M. 2013 Tracking for health. Available from: http://www.pewinternet.org/Reports/2013/Tracking-for-Health.aspx.
    PubMedGoogle Scholar
  • 30 Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009; 151 (04) 264-9. doi: 10.1136/bmj.b2535.
    CrossrefPubMedGoogle Scholar
  • 31 Almalki M, Gray K, Martin-Sanchez F. Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical studies. J Med Internet Res. 2016; 18 (05) e131. doi: 10.2196/jmir.5000.
    CrossrefPubMedGoogle Scholar
  • 32 Elo S, Kyngas H. The qualitative content analysis process. J Adv Nurs. 2008; 62 (01) 107-115. doi: 10.1111/j.1365-2648.2007.04569.x.
    CrossrefPubMedGoogle Scholar
  • 33 Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3 (02) 77-101.
    CrossrefPubMedGoogle Scholar
  • 34 Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods. 2006; 5 (01) 1-11.
    PubMedGoogle Scholar
  • 35 Rooksby J, Rost M, Morrison A, Chalmers MC. Personal tracking as lived informatics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Toronto, Ontario, Canada: 2557039: ACM; 2014: 1163-1172. doi: 10.1145/2556288.2557039.
    Google Scholar
  • 36 Packer HS, Buzogany G, Smith DA, Dragan L, Kleek MV, Shadbolt NR. The editable self: a workbench for personal activity data. CHI ‘14 Extended Abstracts on Human Factors in Computing Systems. Toronto, Ontario, Canada: 2581283: ACM; 2014: 2185-90. doi: 10.1145/2559206.2581283.
    Google Scholar
  • 37 Lee VR, Drake J. Digital physical activity data collection and use by endurance runners and distance cyclists. Technology, Knowledge and Learning. 2013; 18 1-2 39-63. doi: 10.1007/s10758-013-9203-3.
    CrossrefPubMedGoogle Scholar
  • 38 Lee VR, Drake J. Quantified recess: design of an activity for elementary students involving analyses of their own movement data. Proceedings of the 12th International Conference on Interaction Design and Children. New York, New York: 2485822: ACM; 2013: 273-276. doi: 10.1145/2485760 .2485822.
    Google Scholar
  • 39 Gimpe H, Nißen M, Görlitz R. Quantifying the quantified self: a study on the motivations of patients to track their own health. 2013 International Conference on Information Systems ICIS. Milan, Italy: Association for Information Systems (AIS); 2013
    Google Scholar
  • 40 De Maeyer C, Jacobs A. Sleeping with technology-designing for personal health. 2013 AAAI Spring Symposium (Shikakeology: Designing Triggers for Behavior Change) 2013; SS-13-06: 11-16.
    PubMedGoogle Scholar
  • 41 Kim J. A qualitative analysis of user experiences with a self-tracker for activity, sleep, and diet. Interact J Med Res. 2014; 3 (01) e8. doi: 10.2196/ijmr.2878.
    CrossrefPubMedGoogle Scholar
  • 42 Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You get reminded you’re a sick person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res. 2015; 17 (08) e202. doi: 10.2196/jmir.4209.
    CrossrefPubMedGoogle Scholar
  • 43 Choe EK, Lee NB, Lee B, Pratt W, Kientz JA. Understanding quantified-selfers’ practices in collecting and exploring personal data. Proceedings of the 32nd annual ACM conference on Human factors in computing systems. Toronto, Ontario, Canada: 2557372: ACM; 2014: 1143-1152. doi: 10.1145/2556288.2557372.
    Google Scholar
  • 44 Chang J. Self-tracking for distinguishing evidence-based protocols in optimizing human performance and treating chronic illness. 2012 AAAI Spring Symposium Series (Self-Tracking and Collective Intelligence for Personal Wellness). 2012
    PubMedGoogle Scholar
  • 45 Papi E, Belsi A, McGregor AH. A knee monitoring device and the preferences of patients living with osteoarthritis: a qualitative study. BMJ Open. 2015; 5 (09) e007980. doi: 10.1136/bmjopen-2015-007980.
    CrossrefPubMedGoogle Scholar
  • 46 Oh J, Lee U. Exploring UX issues in quantified self technologies. 2015 8th International Conference on Mobile Computing and Ubiquitous Networking, ICMU 2015. 2015 doi: 10.1109/ICMU.2015.7061028.
    PubMedGoogle Scholar
  • 47 Dontje ML, De Groot M, Lengton RR, Van Der Schans CP, Krijnen WP. Measuring steps with the Fitbit activity tracker: an inter-device reliability study. J Med Eng Technol. 2015; 39 (05) 286-290. doi: 10.3109/03091902.2015.
    CrossrefPubMedGoogle Scholar
  • 48 Kim J. Analysis of health consumers’ behavior using self-tracker for activity, sleep, and diet. Telemed J E Health. 2014; 20 (06) 552-558. doi: 10.1089/tmj.2013.0282.
    CrossrefPubMedGoogle Scholar
  • 49 Shih PC, Han K, Poole ES, Rosson MB, Carroll JM. Use and adoption challenges of wearable activity trackers. iConference 2015 Proceedings: iSchools. 2015 Available from: http://hdl.handle.net/2142/73649.
    PubMedGoogle Scholar
  • 50 Guo F, Li Y, Kankanhalli MS, Brown MS. An evaluation of wearable activity monitoring devices. Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia. Barcelona, Spain: 2512882: ACM; 2013: 31-34. doi: 10.1145/2509352.2512882.
    Google Scholar
  • 51 Doyle J, Walsh L, Sassu A, McDonagh T. Designing a wellness self-management tool for older adults: results from a field trial of Your Wellness. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. Oldenburg, Germany: 2686912: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering); 2014: 134-141. doi: 10.4108/icst.pervasivehealth.2014 .254950.
    Google Scholar
  • 52 Pickard KT, Swan M. Big desire to share big health data: a shift in consumer attitudes toward personal health information. 2014 AAAI Spring Symposium Series (Association for the Advancement of Artificial Intelligence). 2014 doi: 10.13140/2.1 .2107.2960.
    PubMedGoogle Scholar
  • 53 Choe EK, Lee B, Schraefel MC. Characterizing visualization insights from quantified selfers’ personal data presentations. IEEE Computer Graphics and Applications. 2015; 35 (04) 28-37. doi: 10.1109/MCG.2015.51.
    CrossrefPubMedGoogle Scholar
  • 54 Punnoose B, Gray K. Comparative evaluation of two systems for integrating biometric data from self-quantification. Yin X, Ho K, Zeng D, Aickelin U, Zhou R, Wang H. Health Information Science. Lecture Notes in Computer Science.. Vol. 9085. New York: Springer International Publishing; 2015: 195-201. doi: 10.1007/978-3-319 -19156-0_20.
    Google Scholar
  • 55 Whooley M, Ploderer B, Gray K. On the integration of self-tracking data amongst Quantified Self members. Proceedings of the 28th International BCS Human Computer Interaction Conference on HCI 2014. Southport, UK: 2742958: BCS; 2014: 151-160. doi: 10.14236/ewic/hci 2014.16.
    Google Scholar
  • 56 Liang Z, Martell MAC. Framing self-quantification for individual-level preventive health care. Proceedings of the 8th International Conference on Health Informatics HEALTHINF 2015; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies BIOSTEC 2015. 2015. Scitepress Digital Library; doi: 10.5220/0005202503360343.
    Google Scholar
  • 57 Ivanov A, Sharman R, Rao HR. Exploring factors impacting sharing health-tracking records. Health Policy and Technology. 2015; 4 (03) 263-276. doi: 10.1016/j .hlpt.2015.04.008.
    CrossrefPubMedGoogle Scholar
  • 58 Almalki M, Martin-Sanchez F, Gray K. Quantifying the activities of self-quantifiers: management of data, time and health. MEDINFO 2015: EHealth-enabled Health: Proceedings of the 15th World Congress on Health and Biomedical Informatics. 2015 IOS Press.
    PubMedGoogle Scholar
  • 59 Almalki M, Gray K, Martin-Sanchez F. Classification of data and activities in self-quantification systems. Proceedings of HISA Big Data in Bio-medicine and Healthcare 2014 Conference. 2014
    PubMedGoogle Scholar