ArticleFrontiers in psychiatry2022
Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- The associations between pre-sleep smartphone use, negative life events and depressive symptoms among Chinese undergraduate students.BMC public health · 2026Article
- Article
- Uncovering the symptom relationship among sleep quality, anxiety, and depression in Chinese patients with breast cancer: multidimensional data validation using PSQI versus actigraphy.Journal of cancer survivorship : research and practice · 2026Article
- Night-to-Night Sleep Variability Modulates Subjective Sleep Perception in Chronic Insomnia: A Multilevel Analysis.Nature and science of sleep · 2026Article
- Relationship Between Short Video Addiction Tendency and Depression Among Rural Older Adults: Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Real-time monitoring to predict depressive symptoms: study protocol.Frontiers in psychiatry · 2024Article
- Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
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Abstract
Objectives: Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability. Materials and methods: The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling. Results: More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism). Conclusion: Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.
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