Personal and Ubiquitous Computing

, Volume 19, Issue 1, pp 91–102 | Cite as

Developing a model for understanding patient collection of observations of daily living: a qualitative meta-synthesis of the Project HealthDesign program

  • Deborah J. Cohen
  • Sara R. Keller
  • Gillian R. Hayes
  • David A. Dorr
  • Joan S. Ash
  • Dean F. Sittig
Original Article


We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting observations of daily living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients’ collection of ODL data: usability, illness experience, relevance of ODL, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.


Observations of daily living (ODL) Mobile health tracking Behavior change Patient/provider communication Smartphones User burden User motivation 


  1. 1.
    Verbrugge LM (1980) Health diaries. Med Care 18(1):73–95CrossRefGoogle Scholar
  2. 2.
    Marceau LD et al (2007) Electronic diaries as a tool to improve pain management: is there any evidence? Pain Med 8(Suppl 3):S101–S109CrossRefMathSciNetGoogle Scholar
  3. 3.
    Bolger N, Davis A, Rafaeli E (2003) Diary methods: capturing life as it is lived. Annu Rev Psychol 54(1):579–616CrossRefGoogle Scholar
  4. 4.
    Robert Wood Johnson Foundation (2009) The power and potential of personal health records. In: Chapter 3: Observations of daily living 2009. Accessed August 6 2013
  5. 5.
    Brennan PF, Downs S, Casper G (2010) Project HealthDesign: rethinking the power and potential of personal health records. J Biomed Inform 43(5):S3–S5CrossRefGoogle Scholar
  6. 6.
    Coffman A et al (2010) Observations of daily living. University of California Berkeley School of Information. Accessed 7 August 2013
  7. 7.
    Burke LE et al (2012) Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med 43(1):20–26CrossRefMathSciNetGoogle Scholar
  8. 8.
    Wilcox AB et al (2012) Research data collection methods: from paper to tablet computers. Med Care 50:S68–S73CrossRefGoogle Scholar
  9. 9.
    Fox S, Duggan M (2012) Mobile health 2012. Pew Research Center’s Internet and American Life Project, Washington, DCGoogle Scholar
  10. 10.
    Li I (2011) Personal informatics and context: using context to reveal factors that affect behavior. In: School of Computer Science, Carnegie Mellon University: Pittsburgh. p. 178Google Scholar
  11. 11.
    Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of the CHI 2010, Atlanta, Georgia, USA, April 10–15, 2010Google Scholar
  12. 12.
    Swan M (2009) 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 6(2):492–525CrossRefGoogle Scholar
  13. 13.
    Swan M (2012) Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J Pers Med 2(3):93–118CrossRefMathSciNetGoogle Scholar
  14. 14.
    Bassett D, Cureton AL, Ainsworth BE (2000) Measurement of daily walking distance-questionnaire versus pedometer. Med Sci Sports Exerc 32(5):1018–1023CrossRefGoogle Scholar
  15. 15.
    Schneider PL, Crouter SE, Bassett DR (2004) Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc 36(2):331–335CrossRefGoogle Scholar
  16. 16.
    Nike (2013) Nike plus. Accessed 8 Jul 2013
  17. 17.
    Archer N et al (2011) Personal health records: a scoping review. J Am Med Inform Assoc 18(4):515–522CrossRefMathSciNetGoogle Scholar
  18. 18.
    Chomutare T et al (2011) Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res 13(3):e65CrossRefGoogle Scholar
  19. 19.
    Fonda SJ et al (2010) Evolution of a web-based, prototype personal health application for diabetes self-management. J Biomed Inform 43(5 Suppl):S17–S21CrossRefGoogle Scholar
  20. 20.
    Massoudi BL et al (2010) A web-based intervention to support increased physical activity among at-risk adults. J Biomed Inform 43(5 Suppl):S41–S45CrossRefGoogle Scholar
  21. 21.
    Rabin C, Bock B (2011) Desired features of smartphone applications promoting physical activity. Telemed J E Health 17(10):801–803CrossRefGoogle Scholar
  22. 22.
    Abroms LC et al (2011) iPhone apps for smoking cessation: a content analysis. Am J Prev Med 40(3):279–285CrossRefGoogle Scholar
  23. 23.
    Proudfoot J et al (2010) Community attitudes to the appropriation of mobile phones for monitoring and managing depression, anxiety, and stress. J Med Internet Res 12(5):e64CrossRefGoogle Scholar
  24. 24.
    Luxton DD et al (2011) mHealth for mental health: integrating smartphone technology in behavioral healthcare. Prof Psychol Res Pr 42(6):505CrossRefGoogle Scholar
  25. 25.
    Greenhalgh T et al (2004) Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q 82(4):581–629CrossRefGoogle Scholar
  26. 26.
    Greenhalgh T, Russell J, Swinglehurst D (2005) Narrative methods in quality improvement research. Qual Saf Health Care 14(6):443–449CrossRefGoogle Scholar
  27. 27.
    Greenhalgh T et al (2009) Tensions and paradoxes in electronic patient record research: a systematic literature review using the meta-narrative method. Milbank Q 87(4):729–788CrossRefGoogle Scholar
  28. 28.
    Pawson R, Tilley N (1997) Realistic Evaluation. Sage, LondonGoogle Scholar
  29. 29.
    Pawson R et al (2005) Realist review—a new method of systematic review designed for complex policy interventions. J Health Serv Res Policy 10(Suppl 1):21–34CrossRefGoogle Scholar
  30. 30.
    Sandelowski M (2008) Reading, writing and systematic review. J Adv Nurs 64(1):104–110CrossRefGoogle Scholar
  31. 31.
    Voils C et al (2008) Making sense of qualitative and quantitative findings in mixed research synthesis studies. Field Methods 20(1):3–25CrossRefGoogle Scholar
  32. 32.
    Borkan J (1999) Immersion/crystallization. In: Crabtree B, Miller W (eds) Doing qualitative research. Sage, Thousand Oaks, pp 179–194Google Scholar
  33. 33.
    Miller W, Crabtree B (1992) Primary care research: a multimethod typology and qualitative roadmap. In: Crabtree B, Miller W (eds) Doing qualitative research. Sage, Newbury Park, pp 3–28Google Scholar
  34. 34.
    Miller W, Crabtree B (1994) Clinical Research. In: Denzin N, Lincoln Y (eds) Handbook of qualitative research. Sage, Thousand Oaks, pp 340–352Google Scholar
  35. 35.
    Miller W, Crabtree B (1994) Qualitative analysis: how to begin making sense. Fam Pract Res J 14(3):289–297Google Scholar
  36. 36.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340Google Scholar
  37. 37.
    Chadia A, Maloney-Krichmar D, Preece J (2004) User-centered design. In: Bainbridge W (ed) Encyclopedia of human-computer interaction. Sage, Thousand OaksGoogle Scholar
  38. 38.
    Mainwaring SD, Chang MF, Anderson K (2004) Infrastructures and their discontents: implications for ubicomp. UbiComp 2004: Ubiquitous Computing. Springer, Berlin Heidelberg, pp 418–432CrossRefGoogle Scholar
  39. 39.
    Lee, M (2012) Evaluation infrastructure headaches. Project HealthDesign Blog Mar 6, 2012. Accessed 5 Jun 2012
  40. 40.
    Sabee CM et al (2011) Five clicks, five minutes: providing a voice for youth with obesity and depression with a mobile health platform. Paper presented at the international conference on communication and healthcare annual meeting, Chicago, IL, USA, Oct 16–19, 2011Google Scholar
  41. 41.
    Patel SN et al (2006) Farther than you may think: an empirical investigation of the proximity of users to their mobile phones. UbiComp 2006: Ubiquitous Computing. Springer, Berlin Heidelberg, pp 123–140CrossRefGoogle Scholar
  42. 42.
    Lee ML (2012) Task-based embedded assessment of functional abilities for aging in place. Dissertation, Carnegie Mellon UniversityGoogle Scholar
  43. 43.
    Prochaska JO, Velicer WF (1997) The transtheoretical model of health behavior change. Am J Health Promot 12(1):38–48CrossRefGoogle Scholar
  44. 44.
    Dennison L et al (2013) Opportunities and challenges for smartphone applications in supporting health behavior change: Qualitative Study. J Med Internet Res 15(4)Google Scholar
  45. 45.
    Kim K (2011) Is theory-based design practical? Project HealthDesign Blog Nov 10, 2011. Accessed 10 May 2012
  46. 46.
    Weiser M (2009) The computer for the 21st century. Sci Am 265(3):94–104CrossRefMathSciNetGoogle Scholar
  47. 47.
    Truong KN, Hayes GR (2009) Ubiquitous computing for capture and access. Found Trends Hum Comput Interact 2(2):95–171CrossRefGoogle Scholar
  48. 48.
    Abowd GD, Mynatt ED (2000) Charting past, present, and future research in ubiquitous computing. ACM Trans Comput-Hum Interact (TOCHI) 7(1):29–58CrossRefGoogle Scholar
  49. 49.
    Brandt J, Weiss N, Klemmer SR (2007) txt 4 l8r: lowering the burden for diary studies under mobile conditions. In: CHI’07 extended abstracts on Human factors in computing systems. ACMGoogle Scholar
  50. 50.
    Hong JI et al (2004) Privacy risk models for designing privacy-sensitive ubiquitous computing systems. In: Proceedings of the 5th conference on designing interactive systems: processes, practices, methods, and techniques. 2004. ACM, 91–100Google Scholar
  51. 51.
    Hayes GR, Abowd GD (2006) Tensions in designing capture technologies for an evidence-based care community. In: Proceedings of the CHI 2006, New York, NYGoogle Scholar
  52. 52.
    Short L, Saindon E (1998) Telehomecare rewards and risks. Caring 17(10):36Google Scholar
  53. 53.
    Roback K, Herzog A (2003) Home informatics in healthcare: assessment guidelines to keep up quality of care and avoid adverse effects. Technol Health Care 11(3):195–206Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Deborah J. Cohen
    • 1
  • Sara R. Keller
    • 1
  • Gillian R. Hayes
    • 2
  • David A. Dorr
    • 3
  • Joan S. Ash
    • 3
  • Dean F. Sittig
    • 4
  1. 1.Department of Family MedicineOregon Health & Science UniversityPortlandUSA
  2. 2.Department of Informatics, Donald Bren School of Information and Computer SciencesUniversity of California, IrvineIrvineUSA
  3. 3.Department of Medical Informatics and Clinical EpidemiologyOregon Health & Science UniversityPortlandUSA
  4. 4.UT - Memorial Hermann Center for Healthcare Quality and SafetyUniversity of Texas School of Biomedical Informatics at HoustonHoustonUSA

Personalised recommendations