ArticleSleep2023
Contactless and longitudinal monitoring of nocturnal sleep and daytime naps in older men and women: a digital health technology evaluation study.
Article in Sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Night-to-night variability in respiratory outcomes from home sleep testing in children with suspected sleep-disordered breathing: a retrospective analysis using real-world data.Sleep & breathing = Schlaf & Atmung · 2026Article
- Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection at Home in a Healthy Population: Prospective Study in Free-Living Conditions.JMIR human factors · 2026Article
- Review
- The untapped potential of ballistographic technology in behavioural sleep medicine.NPJ digital medicine · 2026Article
- Continuous At-Home Monitoring of Nighttime Bed Behavior in Frontotemporal Dementia.Neurology open access · 2026Article
- Contactless longitudinal monitoring in the home characterizes aging and Alzheimer's disease-related night-time behavior and physiology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Sensor based sleep patterns and reported sleep quality in breast cancer patients undergoing neoadjuvant chemotherapy.Scientific reports · 2025Article
- Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary.Sleep · 2025Article
- Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints.Journal of sleep research · 2025Observational
- A narrative review on in-flight use of consumer sleep technologies for aviation research.Sleep advances : a journal of the Sleep Research Society · 2025Review
- Investigation of Sleep Disorders and Related Influencing Factors Among the Elderly in Southeast Coastal Regions of China: A Cross-Sectional Survey Analysis.Clinical interventions in aging · 2025Article
- Within- and between-individual associations between sleep and cognition in older community-dwelling individuals.Frontiers in aging · 2025Article
- Assessment of sleep patterns in dementia and general population cohorts using passive in-home monitoring technologies.Communications medicine · 2024Article
- P-tau217 and other blood biomarkers of dementia: variation with time of day.Translational psychiatry · 2024Article
- Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.JMIR mHealth and uHealth · 2024Article
- A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community.Clocks & sleep · 2024Article
- Effect of closed-loop vibration stimulation on sleep quality for poor sleepers.Frontiers in neuroscience · 2024Article
- Method to determine whether sleep phenotypes are driven by endogenous circadian rhythms or environmental light by combining longitudinal data and personalised mathematical models.PLoS computational biology · 2023Article
- Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study.JMIR mHealth and uHealth · 2023Article
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7 authors.
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Abstract
STUDY
objectivesTo compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults.
methodsWe collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD).
resultsCompared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary.
conclusionsCSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.
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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.