Stressors of Sleep Tracking: Instrument Development and Validation
: Feng, Shan; Mäntymäki, Matti
: van de Wetering, Rogier; Helms, Remko; Roelens, Ben; Bagheri, Samaneh; Dwivedi, Yogesh K.; Pappas, Ilias O.; Mäntymäki, Matti
: IFIP Conference e-Business, e-Services, and e-Society
Publisher: Springer Science and Business Media Deutschland GmbH
: 2024
: Lecture Notes in Computer Science
: Disruptive Innovation in a Digitally Connected Healthy World: 23rd IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2024, Heerlen, The Netherlands, September 11–13, 2024, Proceedings
: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
: 14907
: 344
: 357
: 978-3-031-72233-2
: 978-3-031-72234-9
: 0302-9743
: 1611-3349
DOI: https://doi.org/10.1007/978-3-031-72234-9_29
: https://doi.org/10.1007/978-3-031-72234-9_29
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.
