Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Stressors of Sleep Tracking: Instrument Development and Validation

  • Conference paper
  • First Online:
Disruptive Innovation in a Digitally Connected Healthy World (I3E 2024)

Abstract

The adverse effects of sleep tracking have attracted interest in both practice and research. However, there is limited quantitative research measuring the relationship between the stressors of sleep tracking and its adverse outcomes, such as health anxiety. This paper develops and tests a measurement instrument for stressors related to sleep tracking. We introduce and validate three new stressor constructs: data-perception discrepancy, the pursuit of perfect data, and vague guidance, and four stressors adapted from prior literature: complexity, invasion, inaccuracy, and unreliability. We test our instrument with data from 324 sleep-tracking users. The results show that invasion, unreliability, pursuit of perfect data, and vague guidance have positive effects on health anxiety.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Grandner, M.A.: Sleep, health, and society. Sleep Med. Clin. 12(2), 1–22 (2017)

    Article  Google Scholar 

  2. de Zambotti, M., Cellini, N., Goldstone, A., Colrain, I.M., Baker, F.C.: Wearable sleep technology in clinical and research settings. Med. Sci. Sports Exerc. 51(7), 1538–1557 (2019)

    Article  Google Scholar 

  3. Precedence Research: Sleep tech devices market size to hit USD 113.16 bn by 2033. https://www.precedenceresearch.com/sleep-tech-devices-market. Accessed 09 Apr 2024

  4. Feng, S., Mäntymäki, M., Salmela, H.: Affordances of sleep-tracking: insights from smart ring users. In: Papagiannidis, S., Alamanos, E., Gupta, S., Dwivedi, Y.K., Mäntymäki, M., Pappas, I.O. (eds.) The Role of Digital Technologies in Shaping the Post-Pandemic World. I3E 2022. LNCS, vol. 13454, pp. 343–355. Springer, Cham (2022)

    Google Scholar 

  5. Kuosmanen, E., et al.: How does sleep tracking influence your life? experiences from a longitudinal field study with a wearable ring. In: The Proceedings of the ACM on Human-Computer Interaction, vol. 6(MHCI), pp. 1–19 (2022)

    Google Scholar 

  6. Karasneh, R.A., Al-Azzam, S.I., Alzoubi, K.H., Hawamdeh, S., Jarab, A.S., Nusair, M.B.: Smartphone applications for sleep tracking: rating and perceptions about behavioral change among users. Sleep Sci. 15(01), 65–73 (2022)

    Google Scholar 

  7. Quante, M., Khandpur, N., Kontos, E.Z., Bakker, J.P., Owens, J.A., Redline, S.: A qualitative assessment of the acceptability of smartphone applications for improving sleep behaviors in low-income and minority adolescents. Behav. Sleep Med. 17(5), 573–585 (2019)

    Article  Google Scholar 

  8. Wang, J., Hugh, J., Yang, Y.C.: Mobile sensor-based community gaming for improving vocational students’ sleep and academic outcomes. Comput. Educ. 151, 103812 (2020)

    Article  Google Scholar 

  9. Zhang, S., Schaub, F., Feng, Y., Sadeh, N.: “It only tells me how I slept, not how to fix it”: exploring sleep behaviors and opportunities for sleep technology. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds.) Information in Contemporary Society. iConference 2019. LNCS, vol 11420, pp. 754–766. Springer, Cham (2019)

    Google Scholar 

  10. Feng, S., Mäntymäki, M., Salmela, H.: Sleep tracking as a stressor: experiences from smart ring users. In: European Conference on Information Systems (2023)

    Google Scholar 

  11. Baron, K.G., Abbott, S., Jao, N., Manalo, N., Mullen, R.: Orthosomnia: are some patients taking the quantified self too far? J. Clin. Sleep Med. 13(2), 351–354 (2017)

    Article  Google Scholar 

  12. Kim, E., Dimsdale, J.E.: The effect of psychosocial stress on sleep: a review of polysomnographic evidence. Behav. Sleep Med. 5(4), 256–278 (2007)

    Article  Google Scholar 

  13. Staner, L.: Sleep and anxiety disorders. Dialogues Clin. Neurosci. 5(3), 249–258 (2003)

    Article  Google Scholar 

  14. Rosman, L., Gehi, A., Lampert, R.: When smartwatches contribute to health anxiety in patients with atrial fibrillation. Cardiovasc. Digit. Heal. J. 1(1), 9–10 (2020)

    Article  Google Scholar 

  15. Asmundson, G.J.G., Abramowitz, J.S., Richter, A.A., Whedon, M.: Health anxiety: current perspectives and future directions. Curr. Psychiatry Rep. 12, 306–312 (2010)

    Article  Google Scholar 

  16. Turel, O., et al.: Panel report: the dark side of the digitization of the individual. Internet Res. 29(2), 274–288 (2019)

    Article  Google Scholar 

  17. Rieder, A., Vuckic, S., Schache, K., Jung, R.: Technostress from persuasion: wearable users’ stressors, strains, and coping. In: International Conference on Information Systems (2020)

    Google Scholar 

  18. Hoogstraten, M.L.: Technostressed out by your tracker? The effects of wearing an activity tracker and use of the app. Radboud university (2018)

    Google Scholar 

  19. Koeske, G.F., Koeske, R.D.: A preliminary test of a stress-strain-outcome model for reconceptualizing the burnout phenomenon. J. Soc. Serv. Res. 17(3–4), 107–135 (1993)

    Article  Google Scholar 

  20. Ayyagari, R., Grover, V., Purvis, R.: Technostress: technological antecedents and implications. MIS Q. 35, 831–858 (2011)

    Article  Google Scholar 

  21. Tarafdar, M., Cooper, C.L., Stich, J.F.: The technostress trifecta-techno eustress, techno distress and design: theoretical directions and an agenda for research. Inf. Syst. J. 29(1), 6–42 (2019)

    Article  Google Scholar 

  22. Lazarus, R.S., Folkman, S.: Stress, Appraisal, and Coping. Springer, New York (1984)

    Google Scholar 

  23. Lazarus, R.S.: Psychological Stress and the Coping Process. McGraw-Hill, New York (1966)

    Google Scholar 

  24. Brod, C.: Technostress: The Human Cost of the Computer Revolution. Addison-Wesley, Reading (1984)

    Google Scholar 

  25. Liu, W., Ploderer, B., Hoang, T.: In bed with technology: challenges and opportunities for sleep tracking. In: The Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, pp. 142–151. ACM, New York (2015)

    Google Scholar 

  26. Jakowski, S., Stork, M.: Effects of sleep self-monitoring via app on subjective sleep markers in student athletes. Somnologie. 26(4), 244–251 (2022)

    Article  Google Scholar 

  27. Liang, Z., Ploderer, B.: Sleep tracking in the real world: a qualitative study into barriers for improving sleep. In: Australian Computer-Human Interaction Conference, pp. 537–541. ACM, New York (2016)

    Google Scholar 

  28. Ravichandran, R., Sien, S.-W., Patel, S.N., Kientz, J.A., Pina, L.R.: Making sense of sleep sensors: how sleep sensing technologies support and undermine sleep health. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 6864–6875. ACM, New York (2017)

    Google Scholar 

  29. Liang, Z., Ploderer, B.: How does fitbit measure brainwaves: a qualitative study into the credibility of sleep-tracking technologies. Proc. ACM Interact. Mobile Wearable Ubiq. Technol. 4(1), 1–29 (2020). https://doi.org/10.1145/3380994

    Article  Google Scholar 

  30. Behne, A., Teuteberg, F.: A healthy lifestyle and the adverse impact of its digitalization: the dark side of using ehealth technologies. In: 15th International Conference on Wirtschaftsinformatik (2020)

    Google Scholar 

  31. Andersen, T.O., Langstrup, H., Lomborg, S.: Experiences with wearable activity data during self-care by chronic heart patients: qualitative study. J. Med. Internet Res. 22, e15873 (2020)

    Article  Google Scholar 

  32. te Poel, F., Baumgartner, S.E., Hartmann, T., Tanis, M.: The curious case of cyberchondria: a longitudinal study on the reciprocal relationship between health anxiety and online health information seeking. J. Anxiety Disord. 43, 32–40 (2016)

    Article  Google Scholar 

  33. White, R.W., Horvitz, E.: Cyberchondria: studies of the escalation of medical concerns in web search. ACM Trans. Inf. Syst. 27, 23 (2009)

    Article  Google Scholar 

  34. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 192–222 (1991)

    Article  Google Scholar 

  35. Maier, C., Laumer, S., Weinert, C., Weitzel, T.: The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. Inf. Syst. J. 25(3), 275–308 (2015)

    Article  Google Scholar 

  36. Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)

    Article  Google Scholar 

  37. Fischer, T., Reuter, M., Riedl, R.: The digital stressors scale: development and validation of a new survey instrument to measure digital stress perceptions in the workplace context. Front. Psychol. 12, 1–18 (2021)

    Article  Google Scholar 

  38. Salkovskis, P.M., Rimes, K.A., Warwick, H.M.C., Clark, D.M.: The health anxiety inventory: development and validation of scales for the measurement of health anxiety and hypochondriasis. Psychol. Med. 32(5), 843–853 (2002)

    Article  Google Scholar 

  39. Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011)

    Article  Google Scholar 

  40. Chin, W.W.: The partial least squares approach to structural equation modeling. In: Modern for Business Research, pp. 295–336 (1998)

    Google Scholar 

  41. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)

    Article  Google Scholar 

  42. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135 (2015)

    Article  Google Scholar 

  43. Chae, J.: Online cancer information seeking increases cancer worry. Comput. Human Behav. 52, 144–150 (2015)

    Article  Google Scholar 

  44. Lagoe, C., Atkin, D.: Health anxiety in the digital age: an exploration of psychological determinants of online health information seeking. Comput. Human Behav. 52, 484–491 (2015)

    Article  Google Scholar 

  45. Califf, C.B., Sarker, S.S.: The bright and dark sides of technostress: a mixed-methods study involving healthcare IT. MIS Q. 44(2), 809–856 (2020)

    Article  Google Scholar 

  46. Conboy, E., Flood, C., Power, A.: Exploring health anxiety and dependence in healthy adult users of m-Health apps and wearables. In: Cyberpsychology and Society, pp. 34–46. Routledge, London (2018)

    Google Scholar 

  47. Attig, C., Franke, T.: I track, therefore I walk – exploring the motivational costs of wearing activity trackers in actual users. Int. J. Hum. Comput. Stud. 127, 211–224 (2019)

    Article  Google Scholar 

  48. Lupton, D.: Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit. Public Health 23(4), 393–403 (2013)

    Article  Google Scholar 

  49. Meng, F., Guo, X., Peng, Z., Ye, Q., Lai, K.H.: Trust and elderly users’ continuance intention regarding mobile health services: the contingent role of health and technology anxieties. Inf. Technol. People 35(1), 259–280 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matti Mäntymäki .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Feng, S., Mäntymäki, M. (2024). Stressors of Sleep Tracking: Instrument Development and Validation. In: van de Wetering, R., et al. Disruptive Innovation in a Digitally Connected Healthy World. I3E 2024. Lecture Notes in Computer Science, vol 14907. Springer, Cham. https://doi.org/10.1007/978-3-031-72234-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72234-9_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72233-2

  • Online ISBN: 978-3-031-72234-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics