Evidence map›Paper›PMID 37224243›Full record

ArticleScience advances2023

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea.

Shinjae Kwon, Hyeon Seok Kim, Kangkyu Kwon, Hodam Kim, Yun Soung Kim, Sung Hoon Lee, Young-Tae Kwon, Jae-Woong Jeong, Lynn Marie Trotti, Audrey Duarte and 1 more

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 3 pooled it
24.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 3 syntheses or guidelines pooled it, 142 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Review
  6. Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026
    Observational
  7. Article
  8. Review
  9. A gradual and sustainable approach to diagnosing sleep and circadian disturbances in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Review
  10. Article
  11. Wrinkle-Adaptive Kirigami Wearables With Anisotropic Deformability for Sleep EEG Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  12. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  13. Article
  14. Article
  15. Review
  16. Article
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2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 2 countries.

Shinjae KwonIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0003-2183-4656
Hyeon Seok KimIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Kangkyu KwonIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0002-4648-4739
Hodam KimIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0003-0439-6236
Yun Soung KimDepartment of Radiology, Icahn School of Medicine at Mount Sinai, BioMedical Engineering and Imaging Institute, New York, NY 10029, USA.ORCID 0000-0002-3892-7560
Sung Hoon LeeIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0003-4082-0786
Young-Tae KwonMetal Powder Department, Korea Institute of Materials Science, Changwon 51508, Republic of Korea.ORCID 0000-0002-4653-1291
Jae-Woong JeongSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.ORCID 0000-0001-7607-5453
Lynn Marie TrottiEmory Sleep Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA 30329, USA.ORCID 0000-0003-2329-6847
Audrey DuarteDepartment of Psychology, University of Texas at Austin, Austin, TX 78712, USA.ORCID 0000-0001-5293-4049
Woon-Hong YeoIEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0002-5526-3882
Georgia Institute of Technology · USEmory University · USKorea Advanced Institute of Science and Technology · KRKorea Institute of Materials Science · KRThe University of Texas at Austin · US

Funding

Developing an imperceptible electroencephalography at-home sleep monitoring system for cognitive and clinical researchR21AG064309 · NIA · UNIVERSITY OF TEXAS AT AUSTIN · PI DUARTE, AUDREY, YEO, WOON-HONG · 2020 to 2020
$421k
NIA NIH HHS R21 AG064309
6 · The paper itself

Abstract

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Indexed as

Sleep Apnea SyndromesSleep QualityBrainHumansPolysomnographySleep

Identifiers

PMID37224243
PMCPMC10208583
OpenAlexW4378070251

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.