Personal and Ubiquitous Computing

, Volume 17, Issue 3, pp 561–570 | Cite as

Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback

  • Daniel Gartenberg
  • Ross Thornton
  • Mortazavi Masood
  • Dustin Pfannenstiel
  • Daniel Taylor
  • Raja Parasuraman
Original Article

Abstract

We describe a mobile health application that collects data relevant to the treatment of insomnia and other sleep-related problems. The application is based on the principles from neuroergonomics, which emphasizes assessment of the brain’s alertness system in everyday, naturalistic environments, and ubiquitous computing. Application benefits include the ability to incorporate both embedded data collection and retrospective manual data input—thus providing the user with a rewarding data access process. The retrospective data input feature was evaluated by comparing an older version of the retrospective editing interface with a newly developed one. The time course of user interactions was precisely measured by exporting time stamps of user interactions using the Google App Engine. We also developed models that closely fit the time course of user interactions using the Goals, Operators, Methods, and Selection rules (GOMS) modeling method. The user data and GOMS models demonstrated that the retrospective sleep tracking feature of the new interface was faster to use but that the retrospective habit tracking feature was slower. Survey results indicated that participants enjoyed using the newly developed interface more than the old interface for the assessment of both sleep and habits. These findings indicate that a mobile application should be designed not only to reduce the amount of time it takes a user to input data, but also to conform to the user’s mental models of its behavior.

Keywords

Capture and access Insomnia Mental models Mobile phones Neuroergonomics Patient diaries Sleep Ubiquitous computing 

References

  1. 1.
    Duh HB, Tan GC, Chen VH (2006) Proceedings of the 8th conference on Human-computer interaction with mobile devices and services. ACM, New York, pp 181–186CrossRefGoogle Scholar
  2. 2.
    Parasuraman R, Rizzo M (2007) Neuroergonomics: the brain at work. Oxford University Press, New YorkGoogle Scholar
  3. 3.
    Wickens CD, Hollands JG (2000) Engineering psychology and human performance, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  4. 4.
    Gazzaniga MS (2009) The cognitive neurosciences, 4th edn. MIT Press, CambridgeGoogle Scholar
  5. 5.
    Parasuraman R (2011) Neuroergonomics: brain, cognition, and performance at work. Curr Dir Psychol Sci 20:181–186CrossRefGoogle Scholar
  6. 6.
    Parasuraman R (2003) Neuroergonomics: research and practice. Theor Issues Ergon Sci 4:5–20CrossRefGoogle Scholar
  7. 7.
    Rizzo M, Robinson, S, Neale V (2007) The brain in the wild: tracking human behavior in naturalistic settings. In: Parasuraman R, Rizzo M (eds) Neuroergonomics: the brain at work. Oxford University Press, New YorkGoogle Scholar
  8. 8.
    Kientz JA (2011) Embedded capture and access: encouraging recording and reviewing of data in the caregiving domain. Pers Ubiquit Comput, 1–13Google Scholar
  9. 9.
    Abowd GD, Mynatt ED (2000) Charting past, present, and future research in ubiquitous computing. ACM Trans Comput Hum Interact 7(1):29–58CrossRefGoogle Scholar
  10. 10.
    Truong KN, Hayes GR (2009) Ubiquitous computing for capture and access. Found Trends Hum Comput Interact 2(2):95–171CrossRefGoogle Scholar
  11. 11.
    Sa M, Carrico L, Antunes P (2007) Ubiquitous psychotherapy. IEEE Pervasive Comput 6:20–27CrossRefGoogle Scholar
  12. 12.
    Taylor DJ, Schmidt-Nowara W, Jessop C, Ahearn JJ (2010) Sleep restriction therapy and hypnotic withdrawal versus sleep hygiene education in hypnotic using patients with insomnia. J Clin Sleep Med 6:169–175Google Scholar
  13. 13.
    Mori C, Bootzin R, Buysse D, Edinger J, Espie C, Lichstein K (2006) Psychological and behavioral treatment of insomnia: update of the recent evidence (1998–2004). Sleep 29(11):1398–1414Google Scholar
  14. 14.
    Purves B, Purves D (2007) Computer based psychotherapy for treatment of depression and anxiety. In: 14th annual IEEE international conference and workshops on the engineering of computer-based systems, 334–338Google Scholar
  15. 15.
    Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR (2003) Patient compliance with paper and electronic diaries. Control Clin Trials 24(2):182–199CrossRefGoogle Scholar
  16. 16.
    Taylor DJ, Lichstein KL, Weinstock J, Sanford S, Temple J (2007) A pilot study of cognitive-behavioral therapy of insomnia in people with mild depression. Behav Ther 38:49–57CrossRefGoogle Scholar
  17. 17.
    Morin CM, Colecchi C, Stone J, Sood R, Brink D (1999) Behavioral and pharmacological therapies for late-life insomnia: a randomized controlled trial. JAMA 281(11):991–999CrossRefGoogle Scholar
  18. 18.
    Jacobs GD, Pace-Schott EF, Stickgold R, Otto MW (2004) Cognitive behavior therapy and pharmacotherapy for insomnia: a randomized controlled trial and direct comparison. Arch Intern Med 164(17):1888–1896CrossRefGoogle Scholar
  19. 19.
    Ritterband LM, Thorndike FP, Gonder-Frederick LA (2009) Efficacy of an Internet-based behavioral intervention for adults with insomnia. Arch Gen Psychiatry 66(7):692–698CrossRefGoogle Scholar
  20. 20.
    Vincent N, Lewycky S (2009) Logging on for better sleep: RCT of the effectiveness of online treatment for insomnia. Sleep 32(6):807–815Google Scholar
  21. 21.
    Morris M, Intille SS, Beaudin JS (2005) Embedded assessment: overcoming barriers to early detection with pervasive computing. In: Gellersen HW, Want R, Schmidt A (eds) Proceedings of pervasive, pp 333–346Google Scholar
  22. 22.
    Siek KA, Connelly KH, Rogers Y (2006) Pride and prejudice:learning how chronically ill people think about food. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ‘06). ACM, New York, pp 947–950Google Scholar
  23. 23.
    Mamykina L, Mynatt ED (2005) Role of community support in coping with chronic diseases: a case study of diabetes support group. HCI International, Las VegasGoogle Scholar
  24. 24.
    Strom L, Pettersson R, Andersson G (2004) Internet-based treatment for insomnia: a controlled evaluation. J Consult Clin Psychol 72(1):113–120CrossRefGoogle Scholar
  25. 25.
    Gartenberg D (November 2010) Sleep and health on the smart phone: Applications towards behavioral treatment for Insomnia. Sleep Review Magazine 12–15Google Scholar
  26. 26.
    Gartenberg D, Parasuraman R (2010) Understanding Brain Arousal and Sleep Quality Using a Neuroergonomic Smart Phone Application. In: Marek T, Karwowski W, Rice V (eds) Advances in Understanding Human Performance, 3rd International Conference on Applied Human Factors and Ergonomics 210–220Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Daniel Gartenberg
    • 1
  • Ross Thornton
    • 1
  • Mortazavi Masood
    • 1
  • Dustin Pfannenstiel
    • 1
  • Daniel Taylor
    • 2
  • Raja Parasuraman
    • 1
  1. 1.George Mason UniversityFairfaxUSA
  2. 2.University of North TexasDentonUSA

Personalised recommendations