ArticleFrontiers in sleep2022
Pilot study of personalized sleep-coaching messages to promote healthy sleeping behaviors.
Article in Frontiers in sleep, 2022. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Active nudging towards digital well-being: reducing excessive screen time on mobile phones and potential improvement for sleep quality.Frontiers in psychiatry · 2025Article
- App-Supported Sleep Coaching: Implications for Sleep Duration and Sleep Quality.Frontiers in sleep · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Assess a program of sleep schedule recommendations and behavior change "nudges" algorithmically selected using passively collected, longitudinal sleep data. Improvements were primarily measured by sleep schedule adherence and changes in sleep health (quantified by the RU_SATED framework). Methods: This study used a convenience sample of self-screening volunteers, responding to recruitment emails. Sleep data was gathered with a commercial under-mattress sensor through three phases: baseline passive data collection-2 weeks; intervention-4 weeks; maintenance monitoring passive data collection-8 weeks. The intervention included sleep schedule recommendations and SMS "nudges," based on rules and recommendations derived from the extant literature. A daily sleep-health score (based on RU_SATED) was derived from passively collected sleep data and daily self-reports of alertness and sleep quality. Results: Twenty-six participants (34.92 ± 10.08-years-old; 20M:6F) had adequate data for analysis. The main findings were: (1) Adherence-defined as a wake time (WT) within 30 min of the recommendation-rates did not differ significantly between the three study phases. However, there was a general decline in adherence over the course of the study, with adherence rates dropping by about 1.5%/week in a pattern of progressive delay of WTs. (2) Linear mixed models (LMMs) of individual sleep metrics did not demonstrate a significant change over the course of the intervention, possibly due to widely varying, yet relatively healthy, sleep patterns at baseline. (3) Comparatively, the composite, sleep-health (RU_SATED) score demonstrated general improvement over the intervention period, in association with higher rates of WT adherence. Conclusions: While, in general, adherence to a sleep schedule and individual dimensions of sleep health did not demonstrate meaningful improvements during the intervention phase, those individuals that were more consistently meeting the wake-time schedule recommendation had associated improvements in their overall sleep-health. As such, this pilot study demonstrates the feasibility and potential efficacy among more adherent individuals of implementing a sleep wellness coaching framework using passively collected sleep data and a rule-based coaching infrastructure.
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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.