ArticleComputers in biology and medicine2019
Sleep quality prediction in caregivers using physiological signals.
Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Effect of Superficial Heat-Cold Application on the Sleep Quality of Patients With Restless Leg Syndrome: A Systematic Review and Meta-Analysis.Nursing open · 2024Pooled it
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- A gradual and sustainable approach to diagnosing sleep and circadian disturbances in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Unobtrusive inference of diurnal rhythms from smartphone data.NPJ digital medicine · 2025Review
- The Impact of Domain Shift on Predicting Perceived Sleep Quality from Wearables.Sensors (Basel, Switzerland) · 2025Article
- Stress in action wearables database: A database of noninvasive wearable monitors with systematic technical, reliability, validity, and usability information.Behavior research methods · 2025Article
- Unobtrusive inference of circadian rhythms from smartphone data.medRxiv : the preprint server for health sciences · 2025Article
- Wearable sensors for monitoring caregivers of people with dementia: a scoping review.European geriatric medicine · 2025Article
- Predicting Sleep Quality in Family Caregivers of Dementia Patients From Diverse Populations Using Wearable Sensor Data.Computers, informatics, nursing : CIN · 2025Article
- A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices.Scientific data · 2022Article
- Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health.Frontiers in psychiatry · 2022Article
- Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements.Sensors (Basel, Switzerland) · 2020Article
- Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia.Alzheimer's & dementia (New York, N. Y.) · 2020Article
- Analysis of Sleep Quality in People With Dementia: A Preliminary Study.Gerontology & geriatric medicineArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.
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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.