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Study Objectives: By March 2020, COVID-19 forced much of the world to stay at home to reduce the spread of the disease. Whereas some health care workers transitioned to working from home, many continued to report to work in person as essential employees. We sought to explore changes in sleep, health, work, and mood in health care workers during the stay-at-home orders., Methods: We developed a cross-sectional online survey administered to health care workers. The survey assessed changes in sleep, work, screen time, media exposure, diet, exercise, substance use, and mood. The survey data were collected between March 28, 2020, and April 29, 2020., Results: A total of 834 of 936 individuals completed the entire survey. Respondents were from 41 US states. Mood after the stay-at-home orders worsened, and screen time and substance use increased. Total sleep time shortened in those continuing to work in person (P < .001), whereas it was unchanged in those working from home (P = .73). Those working from home went to bed later, woke up later, and worked fewer hours. Reduced total sleep time and increased screen time before bed were associated with worse mood and screen time. Longer sleep time was associated with better mood., Conclusions: Health care workers' mood worsened regardless of whether work was in person or remote, although total sleep time was shorter for those working in person. Those working from home may have shifted their sleep time to be more in line with their endogenous circadian phase. Peer or other support services may be indicated to address sleep, mood, and health behaviors among health care workers during these unprecedented times., (© 2021 American Academy of Sleep Medicine.)

None: Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associated events during polysomnography (PSG). This position statement outlines the potential opportunities and limitations of integrating artificial intelligence (AI) into the practice of sleep medicine. Additionally, although the most apparent and immediate application of AI in our field is the assisted scoring of PSG, we propose potential clinical use cases that transcend the sleep laboratory and are expected to deepen our understanding of sleep disorders, improve patient-centered sleep care, augment day-to-day clinical operations, and increase our knowledge of the role of sleep in health at a population level., (© 2020 American Academy of Sleep Medicine.)

Abstract: Consumer sleep technologies (CSTs) are widespread applications and devices that purport to measure and even improve sleep. Sleep clinicians may frequently encounter CST in practice and, despite lack of validation against gold standard polysomnography, familiarity with these devices has become a patient expectation. This American Academy of Sleep Medicine position statement details the disadvantages and potential benefits of CSTs and provides guidance when approaching patient-generated health data from CSTs in a clinical setting. Given the lack of validation and United States Food and Drug Administration (FDA) clearance, CSTs cannot be utilized for the diagnosis and/or treatment of sleep disorders at this time. However, CSTs may be utilized to enhance the patient-clinician interaction when presented in the context of an appropriate clinical evaluation. The ubiquitous nature of CSTs may further sleep research and practice. However, future validation, access to raw data and algorithms, and FDA oversight are needed., (© 2018 American Academy of Sleep Medicine.)