Literature DB >> 30789439

Wearable Sleep Technology in Clinical and Research Settings.

Massimiliano de Zambotti1, Nicola Cellini2, Aimée Goldstone1, Ian M Colrain1,3, Fiona C Baker1,4.   

Abstract

: The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.

Entities:  

Mesh:

Year:  2019        PMID: 30789439      PMCID: PMC6579636          DOI: 10.1249/MSS.0000000000001947

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  92 in total

1.  Wearable Devices for Precision Medicine and Health State Monitoring.

Authors:  In Cheol Jeong; David Bychkov; Peter C Searson
Journal:  IEEE Trans Biomed Eng       Date:  2019-05       Impact factor: 4.538

Review 2.  Wearables and the medical revolution.

Authors:  Jessilyn Dunn; Ryan Runge; Michael Snyder
Journal:  Per Med       Date:  2018-09-27       Impact factor: 2.512

3.  High agreement but low kappa: I. The problems of two paradoxes.

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Journal:  J Clin Epidemiol       Date:  1990       Impact factor: 6.437

4.  Direct comparison of two new actigraphs and polysomnography in children and adolescents.

Authors:  Lisa J Meltzer; Colleen M Walsh; Joel Traylor; Anna M L Westin
Journal:  Sleep       Date:  2012-01-01       Impact factor: 5.849

5.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

Review 6.  Sleep apps: what role do they play in clinical medicine?

Authors:  Christopher P Lorenz; Adrian J Williams
Journal:  Curr Opin Pulm Med       Date:  2017-11       Impact factor: 3.155

7.  A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

Authors:  Stanislas Chambon; Mathieu N Galtier; Pierrick J Arnal; Gilles Wainrib; Alexandre Gramfort
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-04       Impact factor: 3.802

8.  Bias, prevalence and kappa.

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Journal:  J Clin Epidemiol       Date:  1993-05       Impact factor: 6.437

9.  Movement toward a novel activity monitoring device.

Authors:  Hawley E Montgomery-Downs; Salvatore P Insana; Jonathan A Bond
Journal:  Sleep Breath       Date:  2011-10-06       Impact factor: 2.816

10.  The validity of Actiwatch2 and SenseWear armband compared against polysomnography at different ambient temperature conditions.

Authors:  Mirim Shin; Paul Swan; Chin Moi Chow
Journal:  Sleep Sci       Date:  2015-03-03
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  57 in total

1.  Consumer Sleep Technologies, Clinical Guidelines, and Evidence-Based Medicine: This is Not a Zero-Sum Game.

Authors:  Nathaniel F Watson; Colin Lawlor; Roy J E M Raymann
Journal:  J Clin Sleep Med       Date:  2019-05-15       Impact factor: 4.062

2.  Investigating the within-person relationships between activity levels and sleep duration using Fitbit data.

Authors:  Yue Liao; Michael C Robertson; Andrea Winne; Ivan H C Wu; Thuan A Le; Diwakar D Balachandran; Karen M Basen-Engquist
Journal:  Transl Behav Med       Date:  2021-03-16       Impact factor: 3.046

Review 3.  Sensors Capabilities, Performance, and Use of Consumer Sleep Technology.

Authors:  Massimiliano de Zambotti; Nicola Cellini; Luca Menghini; Michela Sarlo; Fiona C Baker
Journal:  Sleep Med Clin       Date:  2020-01-03

4.  A standardized framework for testing the performance of sleep-tracking technology: step-by-step guidelines and open-source code.

Authors:  Luca Menghini; Nicola Cellini; Aimee Goldstone; Fiona C Baker; Massimiliano de Zambotti
Journal:  Sleep       Date:  2021-02-12       Impact factor: 5.849

5.  Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions.

Authors:  Christopher M Depner; Philip C Cheng; Jaime K Devine; Seema Khosla; Massimiliano de Zambotti; Rébecca Robillard; Andrew Vakulin; Sean P A Drummond
Journal:  Sleep       Date:  2020-02-13       Impact factor: 5.849

6.  Field-based Measurement of Sleep: Agreement between Six Commercial Activity Monitors and a Validated Accelerometer.

Authors:  Andrew G Kubala; Bethany Barone Gibbs; Daniel J Buysse; Sanjay R Patel; Martica H Hall; Christopher E Kline
Journal:  Behav Sleep Med       Date:  2019-08-27       Impact factor: 2.964

7.  Feasible but Not Yet Efficacious: A Scoping Review of Wearable Activity Monitors in Interventions Targeting Physical Activity, Sedentary Behavior, and Sleep.

Authors:  Maan Isabella Cajita; Christopher E Kline; Lora E Burke; Evelyn G Bigini; Christopher C Imes
Journal:  Curr Epidemiol Rep       Date:  2020-01-28

8.  Performance of seven consumer sleep-tracking devices compared with polysomnography.

Authors:  Evan D Chinoy; Joseph A Cuellar; Kirbie E Huwa; Jason T Jameson; Catherine H Watson; Sara C Bessman; Dale A Hirsch; Adam D Cooper; Sean P A Drummond; Rachel R Markwald
Journal:  Sleep       Date:  2021-05-14       Impact factor: 5.849

9.  Evaluations of Commercial Sleep Technologies for Objective Monitoring During Routine Sleeping Conditions.

Authors:  Jason D Stone; Lauren E Rentz; Jillian Forsey; Jad Ramadan; Rachel R Markwald; Victor S Finomore; Scott M Galster; Ali Rezai; Joshua A Hagen
Journal:  Nat Sci Sleep       Date:  2020-10-27

Review 10.  Sleep Disorders in Children With Autism Spectrum Disorder: Insights From Animal Models, Especially Non-human Primate Model.

Authors:  Shufei Feng; Haoyu Huang; Na Wang; Yuanyuan Wei; Yun Liu; Dongdong Qin
Journal:  Front Behav Neurosci       Date:  2021-05-20       Impact factor: 3.558

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