Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

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.

Background: Insomnia and depression are highly comorbid and mutually exacerbate clinical trajectories and outcomes. Cognitive behavioral therapy for insomnia (CBT-I) effectively reduces both insomnia and depression severity, and can be delivered digitally. This could substantially increase the accessibility to CBT-I, which could reduce the health disparities related to insomnia; however, the efficacy of digital CBT-I (dCBT-I) across a range of demographic groups has not yet been adequately examined. This randomized placebo-controlled trial examined the efficacy ofdCBT-I in reducing both insomnia and depression across a wide range of demographic groups.Methods: Of 1358 individuals with insomnia randomized, a final sample of 358 were retained in thedCBT-I condition and 300 in the online sleep education condition. Severity of insomnia and depression was examined as a dependent variable. Race, socioeconomic status (SES; household income and education), gender, and age were also tested as independent moderators of treatment effects.Results: ThedCBT-I condition yielded greater reductions in both insomnia and depression severity than sleep education, with significantly higher rates of remission following treatment. Demographic variables (i.e. income, race, sex, age, education) were not significant moderators of the treatment effects, suggesting thatdCBT-I is comparably efficacious across a wide range of demographic groups. Furthermore, while differences in attrition were found based on SES, attrition did not differ between white and black participants.Conclusions: Results provide evidence that the wide dissemination ofdCBT-I may effectively target both insomnia and comorbid depression across a wide spectrum of the population.

Insomnia is a common precursor to depression; yet, the potential for insomnia treatment to prevent depression has not been demonstrated. Cognitive behavioral therapy for insomnia (CBT-I) effectively reduces concurrent symptoms of insomnia and depression and can be delivered digitally (dCBT-I); however, it remains unclear whether treating insomnia leads to sustained reduction and prevention of depression. This randomized controlled trial examined the efficacy of dCBT-I in reducing and preventing depression over a 1-year follow-up period. Patients with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) insomnia disorder were randomly assigned to receive dCBT-I or an attentional control. The follow-up sample included 358 patients in the dCBT-I condition and 300 patients in the online sleep education condition. The primary outcome measure was relative rate ratios for depression at 1-year follow-up. Insomnia responses to treatment were also tested as predictors of incident depression at the 1-year follow-up. At 1-year follow-up, depression severity continued to be significantly lower in the dCBT-I condition relative to control. In addition, the number of individuals who reported no depression at 1-year follow-up was 51% higher in the dCBT-I condition relative to control. In those with minimal to no depression at baseline, the incident rate of moderate-to-severe depression at 1-year follow-up was reduced by half in the dCBT-I condition relative to the control condition. dCBT-I showed robust effects as an intervention that prevents depression. Future research should examine dose–response requirements and further characterize mechanisms of action of dCBT-I for depression prevention. Sleep to Prevent Evolving Affective Disorders; NCT02988375.