Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography-Reference-Cited by-同舟云学术

Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: A comparison against polysomnography

Published:2018-11-08 Issue:4 Volume:28 Page:e12789
ISSN:0962-1105
Container-title:Journal of Sleep Research
language:en
Short-container-title:J Sleep Res

Author:

Cook Jesse D.¹²^ORCID,Eftekari Sahand C.¹,Dallmann Erika¹,Sippy Megan¹,Plante David T.¹

Affiliation:

1. Department of Psychiatry; School of Medicine and Public Health; University of Wisconsin-Madison; Madison Wisconsin

2. Department of Psychology; University of Wisconsin-Madison; Madison Wisconsin

Funder

American Sleep Medicine Foundation

Publisher

Wiley

Subject

Behavioral Neuroscience,Cognitive Neuroscience,General Medicine

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/jsr.12789/fullpdf

Reference26 articles.

1. Measurement in medicine: the analysis of method comparison studies;Altman;The Statistician,1983

2. American Academy of Sleep Medicine 2014 ICSD-3 - International classification of sleep disorders, 3rd ed Darien, IL American Academy of Sleep Medicine

3. Nocturnal rapid eye movement sleep latency for identifying patients with narcolepsy/hypocretin deficiency;Andlauer;JAMA Neurology,2013

4. Berry , R.B. Brooks , R. Gamaldo , C.E. Harding , S.M. Marcus , C.L. Vaughn , B.V. 2014 The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications, Version 2.1 Darien, Illinois

5. What does one sleep-onset REM period-during either nocturnal polysomnography or multiple sleep latency test-mean in differential diagnosis of central hypersomnias?;Bozluolcay;Journal of Clinical Neurophysiology,2015

Cited by 46 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction;Computational and Structural Biotechnology Journal;2024-12

2. Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints;Journal of Sleep Research;2024-06-14

3. The role of comfort, personality, and intention in smartwatch usage during sleep;Humanities and Social Sciences Communications;2024-05-30

4. Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data;eBioMedicine;2024-05

5. The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study: feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy;SLEEP;2024-03-29