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      Wearable Sleep Technology in Clinical and Research Settings :

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          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 bio-signals for determining, for example, heart rate and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large datasets (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.

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          Most cited references72

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          AASM Scoring Manual Updates for 2017 (Version 2.4)

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            Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients.

            Comparison of polysomnography (PSG)-derived sleep parameters (total sleep time, sleep efficiency, and number of awakenings) to those derived from actigraphy and subjective questionnaires. Actigraphy is commonly used to assist sleep specialists in the diagnosis of various sleep and circadian-rhythm disorders. However, few validation studies incorporate large sample sizes, typical sleep clinic patients, or comparisons with subjective reports of sleep parameters. Clinical series with 100 consecutive sleep-disordered patients (69 men, 31 women, mean age of 49+/-14.7 years) at a tertiary sleep disorders center. Sensitivity, specificity, and accuracy measures were obtained from epoch-by-epoch comparison of PSG and actigraphic data. Subjective sleep parameter data were derived from questionnaires given to subjects in the morning following their recording night. We found that total sleep time and sleep efficiency did not significantly differ between PSG data and the combined data obtained from actigraphy and subjective reports. Using a high-threshold (low-wake-sensitivity) actigraphic algorithm, the number of awakenings was not significantly different from those detected by PSG. We recommend the use of subjective data as an adjunct to actigraphic data in estimating total sleep time and sleep efficiency in sleep-disordered patients, especially those with disorders of excessive somnolence.
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              Is Open Access

              Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0

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                Author and article information

                Journal
                Medicine & Science in Sports & Exercise
                Medicine & Science in Sports & Exercise
                Ovid Technologies (Wolters Kluwer Health)
                0195-9131
                2019
                July 2019
                : 51
                : 7
                : 1538-1557
                Article
                10.1249/MSS.0000000000001947
                6579636
                30789439
                23d225cc-b1d7-4c08-b3da-c045d0ed1105
                © 2019
                History

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