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

      , , , ,
      Chronobiology International
      Informa UK Limited

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          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).

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          The Rise of Consumer Health Wearables: Promises and Barriers

          Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.
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            Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography--a systematic review.

            Sleep disturbance influences human health. To examine sleep patterns, it is advisable to utilize valid subjective and objective measures. Laboratory-based polysomnography (PSG) is deemed the gold standard to measure sleep objectively, but is impractical for long-term and home utilization (e.g. resource-demanding, difficult to use). Hence, alternative devices have been developed. This study aimed to review the literature systematically, providing an overview of available objective sleep measures in non-laboratory settings as an alternative to PSG. To identify relevant articles, a specific search strategy was run in EMBASE, PubMed, CINAHL, PsycInfo and Compendex (Engineering Village 2). In addition, reference lists of retrieved articles were screened and experts within this research field were contacted. Two researchers, using specified in/exclusion criteria, screened identified citations independently in three stages: on title, abstract and full text. Data from included articles were extracted and inserted into summarizing tables outlining the results. Of the 2217 electronically identified citations, 35 studies met the inclusion criteria. Additional searches revealed eight papers. Psychometric characteristics of nine different objective measures of sleep pattern alternatives to PSG [(bed) actigraphy, observation, bed sensors, eyelid movement- and non-invasive arm sensors, a sleep switch and a remote device] were evaluated. Actigraphy is used widely and has been validated in several populations. Alternative devices to measure sleep patterns are becoming available, but most remain at prototype stage and are validated insufficiently. Future research should concentrate on the development and further validation of non-invasive, inexpensive and user-friendly sleep measures for non-laboratory settings. © 2010 European Sleep Research Society.
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              Sleep, Health, and Society.

              Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures. Insufficient sleep duration and sleep disorders such as insomnia and sleep apnea are highly prevalent in the US population. This article outlines some of these downstream factors, including cardiovascular and metabolic disease risk, neurocognitive dysfunction, and mortality, as well as societal factors such as age, sex, race/ethnicity, and socioeconomics. This review also discusses societal factors related to sleep, such as globalization, health disparities, public policy, public safety, and changing patterns of use of technology.
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                Author and article information

                Journal
                Chronobiology International
                Chronobiology International
                Informa UK Limited
                0742-0528
                1525-6073
                December 13 2017
                December 13 2017
                :
                :
                : 1-12
                Article
                10.1080/07420528.2017.1413578
                29235907
                9b6162e1-4842-4c1f-a1b2-0dcdb7508769
                © 2017
                History

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