ArticleInternational journal of environmental research and public health2022
Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods.
Article in International journal of environmental research and public health, 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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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
7 citing papers in PubMed, 19 citations in OpenAlex.
- Tai Chi exercise improves sleep quality in older adults with mild insomnia by enhancing slow-wave activity during deep sleep: a 12-week randomized controlled trial.Frontiers in physiology · 2026Article
- Predictors of Poor Sleep Quality in Heart Failure Patients: A Cross-sectional Multivariable Analysis of Clinical, Demographic, and Psychosocial Factors.Journal of research in health sciences · 2025Article
- Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.BMC public health · 2025Article
- What goes on when the lights go off? Using machine learning techniques to characterize a child's settling down period.Frontiers in network physiology · 2025Article
- WACSAW: An adaptive, statistical method to classify movement into sleep and wakefulness states.PloS one · 2025Article
- Knowledge, attitude, and practice toward sleep disorders and sleep hygiene among perimenopausal women.Scientific reports · 2024Article
- Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people's productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80-86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.
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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.