ArticleInternational Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare2025
Detecting Sleep Disruptions in Adolescents Using Context-Sensitive Ecological Momentary Assessment: A Feasibility Study.
Article in International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Feasibility and Utility of Multimodal Micro Ecological Momentary Assessment on a Smartwatch.Proceedings of the SIGCHI conference on human factors in computing systems. CHI ConferenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Adolescents are recommended to sleep at least 8-10 h per day. Inadequate sleep in adolescents is detrimental to their overall wellbeing and is linked to poor academic performance. Identifying causes of poor sleep in the sleep environment can help researchers and adolescents determine what changes need to be made to improve sleep quality. However, in-situ sleep monitoring is challenging because measurements cannot interfere with sleep, and people are poor at remembering what happens during the night. We report on the feasibility testing of an in-situ sleep monitoring application that uses passive sensing to drive context-sensitive ecological momentary assessments (EMAs) to help participants recall sleep disruptions when they wake up in the morning. Participants answered over 80% of EMAs delivered during the feasibility study and could recall meaningful reasons for over 40% of noise and motion events when they answered context-sensitive questions presented in the morning EMA. We discuss some challenges and future opportunities in sleep disruption detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.