Study Objectives Multisensor wearable consumer devices allowing the collection of multiple data sources, such as heart rate and motion, for the evaluation of sleep in the home environment, are increasingly ubiquitous. However, the validity of such devices for sleep assessment has not been directly compared to alternatives such as wrist actigraphy or polysomnography (PSG). Methods Eight participants each completed four nights in a sleep laboratory, equipped with PSG and several wearable devices. Registered polysomnographic technologist-scored PSG served as ground truth for sleep–wake state. Wearable devices providing sleep–wake classification data were compared to PSG at both an epoch-by-epoch and night level. Data from multisensor wearables (Apple Watch and Oura Ring) were compared to data available from electrocardiography and a triaxial wrist actigraph to evaluate the quality and utility of heart rate and motion data. Machine learning methods were used to train and test sleep–wake classifiers, using data from consumer wearables. The quality of classifications derived from devices was compared. Results For epoch-by-epoch sleep–wake performance, research devices ranged in d′ between 1.771 and 1.874, with sensitivity between 0.912 and 0.982, and specificity between 0.366 and 0.647. Data from multisensor wearables were strongly correlated at an epoch-by-epoch level with reference data sources. Classifiers developed from the multisensor wearable data ranged in d′ between 1.827 and 2.347, with sensitivity between 0.883 and 0.977, and specificity between 0.407 and 0.821. Conclusions Data from multisensor consumer wearables are strongly correlated with reference devices at the epoch level and can be used to develop epoch-by-epoch models of sleep–wake rivaling existing research devices.

Study Objectives: To assess the sleep detection and staging validity of a non-contact, commercially available bedside bio-motion sensing device (S+, ResMed) and evaluate the impact of algorithm updates. Methods: Polysomnography data from 27 healthy adult participants was compared epoch-by-epoch to synchronized data that were recorded and staged by actigraphy and S+. An update to the S+ algorithm (common in the rapidly evolving commercial sleep tracker industry) permitted comparison of the original (S+V1) and updated (S+V2) versions. Results: Sleep detection accuracy by S+V1 (93.3%), S+V2 (93.8%), and actigraphy (96.0%) was high; wake detection accuracy by each (69.6%, 73.1%, and 47.9%, respectively) was low. Higher overall S+ specificity, compared to actigraphy, was driven by higher accuracy in detecting wake before sleep onset (WBSO), which differed between S+V2 (90.4%) and actigraphy (46.5%). Stage detection accuracy by the S+ did not exceed 67.6% (for stage N2 sleep, by S+V2) for any stage. Performance is compared to previously established variance in polysomnography scored by humans: a performance standard which commercial devices should ideally strive to reach. Conclusions: Similar limitations in detecting wake after sleep onset (WASO) were found for the S+ as have been previously reported for actigraphy and other commercial sleep tracking devices. S+ WBSO detection was higher than actigraphy, and S+V2 algorithm further improved WASO accuracy. Researchers and clinicians should remain aware of the potential for algorithm updates to impact validity.

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Supplementary key words triglycerides • nutrition • lipolysis and fatty acid metabolism • diet and dietary lipids • fatty acid • insulin resistance • inflammation • hormones • glucose According to the Centers for Disease Control and Prevention, one in three US adults sleeps fewer than 7 h per night, increasing their risk of obesity and for risk of developing CVD, type 2 diabetes, and earlier mortality, among other comorbidities (1-4). The mechanisms by which chronic insufficient sleep increases cardiometabolic disease risk are poorly understood, but results from carefully controlled laboratory studies demonstrate that sleep restriction simultaneously increases orexigenic hormonal signaling and impairs glucose metabolic functioning (5, 6). Furthermore, there is mounting evidence that adipocyte insulin sensitivity and function are impaired by sleep restriction resulting in aberrantly elevated overnight and early morning NEFAs (7-10). Adipocytes are a key integrator of systemic metabolism, absorbing and storing excess energy postprandially and releasing stored fatty acids as needed to meet the energy requirements of the body (11). Adipocytes respond to the postprandial increase in insulin by suppressing intracellular TG lipolysis and by increasing extracellular lipolysis by transporting LPL from intracellular vesicles to the surface Abstract Chronic sleep restriction, or inadequate sleep, is associated with increased risk of cardiometabolic disease. Laboratory studies demonstrate that sleep restriction causes impaired whole-body insulin sensitivity and glucose disposal. Evidence suggests that inadequate sleep also impairs adipose tissue insulin sensitivity and the NEFA rebound during intravenous glucose tolerance tests, yet no studies have examined the effects of sleep restriction on high-fat meal lipemia. We assessed the effect of 5 h time in bed (TIB) per night for four consecutive nights on postprandial lipemia following a standardized high-fat dinner (HFD). Furthermore, we assessed whether one night of recovery sleep (10 h TIB) was sufficient to restore postprandial metabolism to baseline. We found that postprandial triglyceride (TG) area under the curve was suppressed by sleep restriction (P = 0.01), but returned to baseline values following one night of recovery. Sleep restriction decreased NEFAs throughout the HFD (P = 0.02) and NEFAs remained suppressed in the recovery condition (P = 0.04). Sleep restriction also decreased participant-reported fullness or satiety (P = 0.03), and decreased postprandial interleukin-6 (P < 0.01). Our findings indicate that four nights of 5 h TIB per night impair postprandial lipemia and that one night of recovery sleep may be adequate for recovery of TG metabolism, but not for markers of adipocyte function.