![]() If found to be sensitive and specific enough, BST would answer the need for a low cost and easy-to-use sleep monitoring method for consumer, clinical and scientific use. ![]() Additionally, we analyzed whether BST could be used to detect sleep stages. BST recordings were compared to PSG, and the parameters total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO) and sleep efficiency (SE) were investigated. In this study, we investigated the accuracy of the BST to measure objective parameters of sleep and to differentiate sleep stages. 18 It should be noted, however, that these validation studies have been performed as a part of product development or by persons with corporate interests, and therefore an independent validation study is warranted. BST seems to be a promising tool, as it is based on a previously validated principle, and its demonstrated ability to measure heart rate 11 and respiration 17 has been found accurate, and it has undergone a single subject validation study. An automated algorithm then transforms these aggregated sleep measures into an easy-to-read graph, compares it to previous nights, and provides information about sleep parameters in a cloud service. 15, 16 BST transmits body, respiratory and heart (ballistocardiograph) movement data via a Bluetooth connection to a commercially designed app to calculate sleep parameters. BST relies on a 3-channel movement detection method, originally designed for the Static Charge Sensitive Bed. One of the recent commercially available home sleep monitoring devices is the Beddit Sleep Tracker (BST), a thin strip sensor placed under the mattress or mattress topper. The preliminary research findings have shown promise for this multidata approach, 14 but further studies are still required. 7 One such novel device is the ŌURA ring, which in addition to actigraph data, collects heartbeat variation, heart rate and other variables from the finger to evaluate sleep parameters. 2, 4, 13 Recently, multidata approaches have gained ground and actigraphy devices have been used aside other data sources, such as the peripheral arterial blood flow tone. 12 Actigraphs have problems in detecting wakefulness, and as a result, overestimate both total sleep time and sleep efficiency. 2, 4, 11, 12 Actigraphy studies have provided mixed results, yet are currently used, for example, as an adjunct measure in sleep apnea monitoring. The latter, movement measurement devices, include increasingly popular actigraphs, which are light wearable devices, usually connected to wrist, ankle or hip that provide information via an accelometer. Of the former, the most promising EEG methods (Zeo headband with 74 % PSG agreement 3 and the Nightcap with 93 % PSG agreement 10) have unfortunately been discontinued and are no longer commercially available. 1, 5, 7 – 9 Most of the sleep measures rely on either electroencephalography (EEG) or movement (cardiac, respiratory or body movements) data. Thus far, however, only a few affordable home sleep monitoring devices have been validated against PSG. The current gold standard, polysomnography (PSG), is labor-intensive and costly in terms of time and resources, and as such often unfeasible for clinical or research settings. 1 – 5 Additionally, there is an urgent need in both clinical and research settings for a cost-effective, portable and reliable measuring device for studying sleep 6 in home settings. Recent rise of interest in consumer health monitoring has spurred the development of mobile devices designed to measure objective sleep parameters.
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