Classification of data and activities in self-quantification systems

by | Apr 3, 2014 | Research Publications

Authors:

Manal Almalki

Dr. Guillermo Lopez-Campos

Dr. Kathleen Gray

Prof. Fernando Martin-Sanchez

Abstract:

Self-quantification may be seen as an emerging paradigm for health data. In recent years the general public has become more health-conscious, due in part to the self-tracking and quantification technologies that enable the non-expert to easily capture and share significant health-related information on a daily basis (Mehta, 2011). This self-tracking of personal health and fitness data has the potential to introduce new research methods in citizen science, and in formal research into personalised medicine and healthcare (Swan, 2009). Such methods capture data in real tasks, natural settings, and in situ, as well as facilitate the measurement of some health and life aspects longitudinally, with an aim of generating healthcare-related hypotheses. However, this field lacks a systematic approach to classifying these data, and making sense of these observational measurements. This paper reports on our data classification model, and how it can be used in data collection, data analysis, data curation and data exchange.

Document:

http://ceur-ws.org/Vol-1149/

References:

  1. Almalki, M, Martin-Sanchez, F & Gray, K 2013, Self-Quantification: The Informatics of Personal Data Management for Health and Fitness, Institute for a Broadband-Enabled Society (IBES), The University of Melbourne, Health and Biomedical Informatics Centre, University of Melbourne, 9780734048318, <http://www.broadband.unimelb.edu.au/resources/white-paper/2013/Self-Quantification.pdf>.

  2. Almalki, M, Martin-Sanchez, F & Gray, K 2013. The Use of Self-Quantification Systems: Big Data Prospects and Challenges. Proceedings of HISA BIG DATA 2013 conference.

  3. Fox, S & Duggan, M 2013, Tracking for Health, Pew Research Center, <http://www.pewinternet.org/Reports/2013/Tracking-for-Health.aspx>.

  4. Happtique 2011, Mobile health has taken the world by storm, viewed 6 Feb 2013, <http://www.happtique.com/wp-content/uploads/HAPP_booklet010212hi.pdf>.

  5. Juniper Research 2013, Mobile Health & Fitness: Monitoring, App-enabled Devices & Cost Savings 2013-2018, <http://www.juniperresearch.com/reports/mobile_health_fitness>.

  6. Madelin, R 2012, The European Directory of Health Apps, PatientView, England, <http://www.patient-view.com/uploads/6/5/7/9/6579846/pv_appdirectory_final_web_300812.pdf>.

  7. Mehta, R 2011, ‘The Self-Quantification Movement–Implications For Health Care Professionals’, SelfCare Journal, vol. 2, no. 3, pp. 87-92.

  8. 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, vol. 6, no. 2, pp. 492-525.

  9. World Health Organization (WHO) 2002, Towards a Common Language for Functioning, Disability and Health CF, Geneva, <www.who.int/classifications/icf/training/icfbeginnersguide.pdf>.