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).

Insomnia disorder is very common in adolescents; it is particularly manifest in older adolescents and girls, with a prevalence comparable to that of other major psychiatric disorders (e.g., depressive disorders). However, insomnia disorder in adolescence is poorly characterized, under-recognized, under-diagnosed, and under-treated, and the reason for the female preponderance for insomnia that emerges after puberty is largely unknown. Insomnia disorder goes beyond an individual complaint of poor sleep or a sleep state misperception, and there is emerging evidence supporting the association of insomnia symptoms in adolescents with alterations in several bio-systems including functional cortical alterations and systemic inflammation. Insomnia disorder is associated with depression and other psychiatric disorders, and is an independent risk factor for suicidality and substance use in adolescents, raising the possibility that treating insomnia symptoms in early adolescence may reduce risk for these adverse outcomes. Cognitive behavioral treatments have proven efficacy for adolescent insomnia and online methods seem to offer promising cost-effective options. Current evidence indicates that insomnia in adolescence is an independent entity that warrants attention as a public health concern in its own right.

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.

The “International Biomarkers Workshop on Wearables in Sleep and Circadian Science” was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: (1) characterize the term “wearable” for use in sleep and circadian science and identify relevant sleep and circadian metrics for wearables to measure; (2) assess the current use of wearables in sleep and circadian science; (3) identify current barriers for applying wearables to sleep and circadian science; and (4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms “wearables,” “nearables,” and “ingestibles.” Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.

Objective/Background. Toevaluate the performance of a multi-sensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants. Forty-one healthy adolescents and young adults (13 females; Age: 17.2±2.4y). Methods. Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results. Summary variables for sleep onset latency (SOL), total sleep time (TST) and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p<.001). Compared with PSG, ŌURA ring underestimated PSG N3 (~20 min) and overestimated PSG REM (~17 min) (p<.05). PSG-ŌURA differences for TST and WASO lay within the ≤30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively. From EBE analysis, ŌURA ring had a 96% sensitivity to detect sleep,and agreement of 65%, 51%, and 61%, in detecting “light sleep” (N1+N2), “deep sleep” (N3),and REM sleep, respectively. Specificity in detecting wake was 48%. Similarly to PSG-N3 (p<.001), “deep sleep” detected with the ŌURA ring was negatively correlated with advancing age (p=.001). ŌURA ring correctly categorized 90.9%, 81.3%, and 92.9% into PSG-defined TST ranges of <6h, 6–7h, >7h, respectively. Conclusions. Multi-sensor sleep trackers, such as the ŌURA ringhave the potential for detecting outcomes beyond binary sleep/wake using sources of informationin additionto motion. While these first results could be viewed as promising, future development and validation is needed.

To validate measures of sleep and heart rate (HR) during sleep generated by a commercially-available activity tracker against those derived from polysomnography (PSG) in healthy adolescents. Sleep data were concurrently recorded using FitbitChargeHR™ and PSG, including electrocardiography (ECG), during an overnight laboratory sleep recording in 32 healthy adolescents (15 females; Age, mean±SD: 17.3±2.5 years). Sleep and HR measures were compared between FitbitChargeHR™ and PSG using paired t-tests and Bland-Altman plots. Epoch-by-epoch analysis showed that FitbitChargeHR™ had high overall accuracy (91%), high sensitivity (97%) in detecting sleep, and poor specificity (42%) in detecting wake on a min-to-min basis. On average, FitbitChargeHR™ significantly but negligibly overestimated total sleep time by 8min and sleep efficiency by 1.8%, and underestimated wake after sleep onset by 5.6min (p<0.05). Within FitbitChargeHR™ epochs of sleep, the average HR was 59.3±7.5 bpm, which was significantly but negligibly lower than that calculated from ECG (60.2±7.6 bpm, p<0.001), with no change in mean discrepancies throughout the night. FitbitChargeHR™ showed good agreement with PSG and ECG in measuring sleep and HR during sleep, supporting its use in assessing sleep and cardiac function in healthy adolescents. Further validation is needed to assess its reliability over prolonged periods of time in ecological settings and in clinical populations.

Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.

A substantial number of women experience sleep difficulties in the approach to menopause and beyond, with 26% experiencing severe symptoms that impact daytime functioning, qualifying them for a diagnosis of insomnia. Here, we review both self-report and polysomnographic evidence for sleep difficulties in the context of the menopausal transition, considering severity of sleep complaints and links between hot flashes (HFs) and depression with poor sleep. Longitudinal population-based studies show that sleep difficulties are uniquely linked with menopausal stage and changes in follicle-stimulating hormone and estradiol, over and above the effects of age. A major contributor to sleep complaints in the context of the menopausal transition is HFs, and many, although not all, HFs are linked with polysomnographic-defined awakenings, with HF-associated wake time contributing significantly to overall wakefulness after sleep onset. Some sleep complaints may be comorbid with depressive disorders or attributed to sleep-related breathing or movement disorders, which increase in prevalence especially after menopause, and for some women, menopause, age, and environmental/behavioral factors may interact to disrupt sleep. Considering the unique and multifactorial basis for sleep difficulties in women transitioning menopause, we describe clinical assessment approaches and management options, including combination treatments, ranging from cognitive behavioral therapy for insomnia to hormonal and nonhormonal pharmacological options. Emerging studies suggest that the impact of severe insomnia symptoms could extend beyond immediate health care usage and quality of life issues to long-term mental and physical health, if left untreated in midlife women. Appropriate treatment, therefore, has immediate benefit as well as advantages for maintaining optimal health in the postmenopausal years.