Literature DB >> 32048595

Validation of sleep measurement in a multisensor consumer grade wearable device in healthy young adults.

Jennifer C Kanady1,2, Leslie Ruoff1, Laura D Straus1,2, Jonathan Varbel1, Thomas Metzler1, Anne Richards1,2, Sabra S Inslicht1,2, Aoife O'Donovan1,2, Jennifer Hlavin1, Thomas C Neylan1,2,3.   

Abstract

STUDY
OBJECTIVES: Our objective was to examine the ability of a consumer-grade wearable device (Basis B1) with accelerometer and heart rate technology to assess sleep patterns compared with polysomnography (PSG) and research-grade actigraphy in healthy adults.
METHODS: Eighteen adults underwent consecutive nights of sleep monitoring using Basis B1, actigraphy, and PSG; 40 nights were used in analyses. Discrepancies in gross sleep parameters and epoch-by-epoch agreements in sleep/wake classification were assessed.
RESULTS: Basis B1 accuracy was 54.20 ± 8.20%, sensitivity was 98.90 ± 2.70%, and specificity was 8.10 ± 15.00%. Accuracy, sensitivity, and specificity for distinguishing between the different sleep stages were 60-72%, 48-62%, and 57-86%, respectively. Pearson correlations demonstrated strong associations between Basis B1 and PSG estimates of sleep onset latency and total sleep time; moderate associations for sleep efficiency, duration of light sleep, and duration of rapid eye movement sleep; and a weak association for duration of deep sleep. Basis B1 significantly overestimates total sleep time, sleep efficiency, and duration of light sleep and significantly underestimates wake after sleep onset and duration of deep sleep.
CONCLUSIONS: Basis B1 demonstrated utility for estimates of gross sleep parameters and performed similarly to actigraphy for estimates of total sleep time. Basis B1 specificity was poor, and Basis B1 is not useful for the assessment of wake. Basis B1 accuracy for sleep stages was better than chance but is not a suitable replacement for PSG assessment. Despite low cost, ease of use, and attractiveness for patients, consumer devices are not yet accurate or reliable enough to guide treatment decision making in clinical settings.
© 2020 American Academy of Sleep Medicine.

Entities:  

Keywords:  actigraphy; consumer wearable; photoplethysmography; polysomnogrrahy; sleep tracker; validation

Year:  2020        PMID: 32048595      PMCID: PMC7849656          DOI: 10.5664/jcsm.8362

Source DB:  PubMed          Journal:  J Clin Sleep Med        ISSN: 1550-9389            Impact factor:   4.062


  23 in total

1.  Actigraphic assessment of a polysomnographic-recorded nap: a validation study.

Authors:  Jennifer C Kanady; Sean P A Drummond; Sara C Mednick
Journal:  J Sleep Res       Date:  2011-03       Impact factor: 3.981

2.  The SBSM Guide to Actigraphy Monitoring: Clinical and Research Applications.

Authors:  Sonia Ancoli-Israel; Jennifer L Martin; Terri Blackwell; Luis Buenaver; Lianqi Liu; Lisa J Meltzer; Avi Sadeh; Adam P Spira; Daniel J Taylor
Journal:  Behav Sleep Med       Date:  2015       Impact factor: 2.964

3.  Ability of the Multisensory Jawbone UP3 to Quantify and Classify Sleep in Patients With Suspected Central Disorders of Hypersomnolence: A Comparison Against Polysomnography and Actigraphy.

Authors:  Jesse D Cook; Michael L Prairie; David T Plante
Journal:  J Clin Sleep Med       Date:  2018-05-15       Impact factor: 4.062

Review 4.  Consumer Sleep Technologies: A Review of the Landscape.

Authors:  Ping-Ru T Ko; Julie A Kientz; Eun Kyoung Choe; Matthew Kay; Carol A Landis; Nathaniel F Watson
Journal:  J Clin Sleep Med       Date:  2015-12-15       Impact factor: 4.062

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

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Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

Review 6.  Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep.

Authors:  Kelly Glazer Baron; Jennifer Duffecy; Mark A Berendsen; Ivy Cheung Mason; Emily G Lattie; Natalie C Manalo
Journal:  Sleep Med Rev       Date:  2017-12-20       Impact factor: 11.609

7.  Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations.

Authors:  Xuan Kai Lee; Nicholas I Y N Chee; Ju Lynn Ong; Teck Boon Teo; Elaine van Rijn; June C Lo; Michael W L Chee
Journal:  J Clin Sleep Med       Date:  2019-09-15       Impact factor: 4.062

8.  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

9.  Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers.

Authors:  Seung-Gul Kang; Jae Myeong Kang; Kwang-Pil Ko; Seon-Cheol Park; Sara Mariani; Jia Weng
Journal:  J Psychosom Res       Date:  2017-03-23       Impact factor: 3.006

10.  Evaluation of a consumer fitness-tracking device to assess sleep in adults.

Authors:  Massimiliano de Zambotti; Stephanie Claudatos; Sarah Inkelis; Ian M Colrain; Fiona C Baker
Journal:  Chronobiol Int       Date:  2015       Impact factor: 2.877

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  1 in total

1.  Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market.

Authors:  Jaime K Devine; Lindsay P Schwartz; Steven R Hursh
Journal:  Sensors (Basel)       Date:  2022-04-02       Impact factor: 3.576

  1 in total

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