Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

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

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  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 181–186

    Book  Google Scholar 

  2. Parasuraman R, Rizzo M (2007) Neuroergonomics: the brain at work. Oxford University Press, New York

    Google Scholar 

  3. Wickens CD, Hollands JG (2000) Engineering psychology and human performance, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  4. Gazzaniga MS (2009) The cognitive neurosciences, 4th edn. MIT Press, Cambridge

    Google Scholar 

  5. Parasuraman R (2011) Neuroergonomics: brain, cognition, and performance at work. Curr Dir Psychol Sci 20:181–186

    Article  Google Scholar 

  6. Parasuraman R (2003) Neuroergonomics: research and practice. Theor Issues Ergon Sci 4:5–20

    Article  Google Scholar 

  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 York

  8. Kientz JA (2011) Embedded capture and access: encouraging recording and reviewing of data in the caregiving domain. Pers Ubiquit Comput, 1–13

  9. Abowd GD, Mynatt ED (2000) Charting past, present, and future research in ubiquitous computing. ACM Trans Comput Hum Interact 7(1):29–58

    Article  Google Scholar 

  10. Truong KN, Hayes GR (2009) Ubiquitous computing for capture and access. Found Trends Hum Comput Interact 2(2):95–171

    Article  Google Scholar 

  11. Sa M, Carrico L, Antunes P (2007) Ubiquitous psychotherapy. IEEE Pervasive Comput 6:20–27

    Article  Google Scholar 

  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 6:169–175

    Google Scholar 

  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):1398–1414

    Google Scholar 

  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, 334–338

  15. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR (2003) Patient compliance with paper and electronic diaries. Control Clin Trials 24(2):182–199

    Article  Google Scholar 

  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 38:49–57

    Article  Google Scholar 

  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):991–999

    Article  Google Scholar 

  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):1888–1896

    Article  Google Scholar 

  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):692–698

    Article  Google Scholar 

  20. Vincent N, Lewycky S (2009) Logging on for better sleep: RCT of the effectiveness of online treatment for insomnia. Sleep 32(6):807–815

    Google Scholar 

  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 333–346

  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 947–950

  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 Vegas

  24. Strom L, Pettersson R, Andersson G (2004) Internet-based treatment for insomnia: a controlled evaluation. J Consult Clin Psychol 72(1):113–120

    Article  Google Scholar 

  25. Gartenberg D (November 2010) Sleep and health on the smart phone: Applications towards behavioral treatment for Insomnia. Sleep Review Magazine 12–15

  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 210–220

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel Gartenberg.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gartenberg, D., Thornton, R., Masood, M. et al. Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback. Pers Ubiquit Comput 17, 561–570 (2013). https://doi.org/10.1007/s00779-012-0508-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-012-0508-3

Keywords

Navigation