Literature DB >> 29235907

A validation study of Fitbit Charge 2™ compared with polysomnography in adults.

Massimiliano de Zambotti1, Aimee Goldstone1, Stephanie Claudatos1, Ian M Colrain1, Fiona C Baker1.   

Abstract

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19-61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep ("light sleep"), 0.49 accuracy in detecting N3 sleep ("deep sleep"), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland-Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM-REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

Entities:  

Keywords:  Wearables; actigraphy; multisensory; validation

Mesh:

Year:  2017        PMID: 29235907     DOI: 10.1080/07420528.2017.1413578

Source DB:  PubMed          Journal:  Chronobiol Int        ISSN: 0742-0528            Impact factor:   2.877


  88 in total

1.  Predictive Analytics and the Return of "Research" Information to Participants.

Authors:  Shengzhi Wang; Ellen E Lee; Benjamin Zywicki; Ho-Cheol Kim; Dilip Jeste; Camille Nebeker
Journal:  Appl Hum Factors Ergon Conf       Date:  2020-07-10

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

3.  Deviations from normal bedtimes are associated with short-term increases in resting heart rate.

Authors:  Louis Faust; Keith Feldman; Stephen M Mattingly; David Hachen; Nitesh V Chawla
Journal:  NPJ Digit Med       Date:  2020-03-23

4.  Self-monitoring diabetes with multiple mobile health devices.

Authors:  Ryan J Shaw; Q Yang; A Barnes; D Hatch; M J Crowley; A Vorderstrasse; J Vaughn; A Diane; A A Lewinski; M Jiang; J Stevenson; D Steinberg
Journal:  J Am Med Inform Assoc       Date:  2020-05-01       Impact factor: 4.497

Review 5.  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

6.  Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography.

Authors:  Daniel M Roberts; Margeaux M Schade; Gina M Mathew; Daniel Gartenberg; Orfeu M Buxton
Journal:  Sleep       Date:  2020-07-13       Impact factor: 5.849

7.  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

8.  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

9.  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

10.  Changes in children's sleep and physical activity during a 1-week versus a 3-week break from school: a natural experiment.

Authors:  R Glenn Weaver; Michael W Beets; Michelle Perry; Ethan Hunt; Keith Brazendale; Lindsay Decker; Gabrielle Turner-McGrievy; Russell Pate; Shawn D Youngstedt; Brian E Saelens; Alberto Maydeu-Olivares
Journal:  Sleep       Date:  2019-01-01       Impact factor: 5.849

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.