A Qualitative Analysis of User Experiences With a Self-Tracker for Activity, Sleep, and Diet

by | Mar 4, 2014 | Research Publications

Author(s):

Jeongeun Kim

Abstract:

Background: The recent increase in chronic diseases and an aging population warrant the necessity of health self-management. As small electronic devices that track one’s activity, sleep, and diet, called self-trackers, are being widely distributed, it is prudent to investigate the user experience and the effectiveness of these devices, and use the information toward engineering better devices that would result in increased efficiency and usability.

Objective: The aim of this study was to abstract the constructs that constitute the user experiences of the self-tracker for activity, sleep, and diet. Additionally, we aimed to develop and verify the Health Information Technology Acceptance Model-II (HITAM-II) through a qualitative data analysis approach.

Methods: The study group consisted of 18 female college students who participated in an in-depth interview after completing a 3-month study of utilizing a self-tracker designed to monitor activity, sleep, and diet. The steps followed in the analysis were: (1) extraction of constructs from theoretical frameworks, (2) extraction of constructs from interview data using a qualitative methodology, and (3) abstraction of constructs and modeling of the HITAM-II.

Results: The constructs that constitute the HITAM-II are information technology factors, personal factors, social factors, attitude, behavioral intention, and behavior. These constructs are further divided into subconstructs to additionally support the HITAM-II.

Conclusions: The HITAM-II was found to successfully describe the health consumer’s attitude, behavioral intention, and behavior from another perspective. The result serves as the basis for a unique understanding of the user experiences of HIT.

Documentation:

https://doi.org/10.2196/ijmr.2878

References:

  1. Carmichael A. H+ Magazine. Self tracking: The quantified life is worth living   URL: http://hplusmagazine.com/2010/02/08/self-tracking-quantified-life-worth-living/ [accessed 2014-02-20] [WebCite Cache]
  2. Pescovitz D. Good-solutions for living well and doing good. Learn in boing boing on good   URL: http://www.good.is/posts/the-quantified-self-you-are-your-data [accessed 2014-02-19] [WebCite Cache]
  3. Wicks P, Vaughan TE, Massagli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat Biotechnol 2011 May;29(5):411-414. [CrossRef] [Medline]
  4. Eysenbach G. Medicine 2.0: Social networking, collaboration, participation, apomediation, and openness. Medicine 2008 [FREE Full text] [CrossRef]
  5. Chosun B. Provocative prophecy by the 21st century Edison (in Korean). Humankind will never die by the year 2045   URL: http://biz.chosun.com/site/data/html_dir/2013/07/19/2013071901553.html [accessed 2013-07-22] [WebCite Cache]
  6. Fitbit. A family of advanced products that track your activity and sleep   URL: http://www.fitbit.com/ [accessed 2014-02-22] [WebCite Cache]
  7. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 1989 Sep;13(3):319 [FREE Full text] [CrossRef]
  8. Rogers EM. Diffusion of innovations. In: Diffusion of innovations, 5th Edition. New York: Free Press; 2003.
  9. IBM Institute for Business Value. 2011 Mar. The future of connected health devices   URL: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?infotype=PM&subtype=XB&htmlfid=GBE03398USEN [WebCite Cache]
  10. Kim J, Park HA. Development of a health information technology acceptance model using consumers’ health behavior intention. J Med Internet Res 2012;14(5):e133 [FREE Full text] [CrossRef] [Medline]
  11. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 2000 Feb;46(2):186-204. [CrossRef]
  12. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly 2003 Sep;27(3) [FREE Full text]
  13. Moore GC, Benbasat I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 1991 Sep;2(3):192-222 [FREE Full text] [CrossRef]
  14. Flight RL, D’Souza G, Allaway AW. Characteristics-based innovation adoption: scale and model validation. Journal of Product & Brand Management 2011;20(5):343-355. [CrossRef]
  15. Risjord M, Moloney M, Dunbar S. Methodological triangulation in nursing research. Philosophy of the Social Sciences 2001 Mar 01;31(1):40-59. [CrossRef]