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Paper Number
2339
Paper Type
Completed
Description
Advances in digital health allow us to take an active part in monitoring and improving our sleep quality. Both, objectively recorded and subjectively perceived sleep quality impacts our general health and well-being. This research shows how these two dimensions of sleep quality can be captured with smartwatches and digital symptom trackers. We contribute to the gap in the literature on how recorded values from wearables and user-generated content from mobile applications can elevate each other. Analysing the recorded and re- ported sleep quality in a longitudinal sleep study (n=45) shows differences in how partic- ipants perceive their sleep. We address this need for personalization, by creating clusters of participants with a similar perception of sleep using unsupervised machine learning. Analysing these clusters provides us with a more wholesome understanding of their sleep quality and raises awareness for the uniqueness of individuals in digital health.
Recommended Citation
Biedebach, Luka; Óskarsdóttir, María; Arnardottir, Erna Sif; and Islind, Anna Sigríður, "Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning" (2023). ICIS 2023 Proceedings. 20.
https://aisel.aisnet.org/icis2023/ishealthcare/ishealthcare/20
Two Sides of the Same Pillow: Unfolding the Relationship between Objective and Subjective Sleep Quality with Unsupervised Learning
Advances in digital health allow us to take an active part in monitoring and improving our sleep quality. Both, objectively recorded and subjectively perceived sleep quality impacts our general health and well-being. This research shows how these two dimensions of sleep quality can be captured with smartwatches and digital symptom trackers. We contribute to the gap in the literature on how recorded values from wearables and user-generated content from mobile applications can elevate each other. Analysing the recorded and re- ported sleep quality in a longitudinal sleep study (n=45) shows differences in how partic- ipants perceive their sleep. We address this need for personalization, by creating clusters of participants with a similar perception of sleep using unsupervised machine learning. Analysing these clusters provides us with a more wholesome understanding of their sleep quality and raises awareness for the uniqueness of individuals in digital health.
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16-HealthCare