Since the beginning of the COVID-19 pandemic, older adults have been found to be a highly vulnerable group, with a higher prevalence of severe cases and negative outcomes. Research has focused on the reasons why older adults are at greater risk; Sleep-related factors have been suggested as one possible explanation for this. An individual’s sleep pattern undergoes significant changes over the course of their life. In older adults a specific sleep profile can be observed, one characterized by advanced sleep timing, a morningness preference, longer sleep-onset latency, shorter overall sleep duration, increased sleep fragmentation, reduced slow-wave sleep and, increased wake time after sleep onset. Additionally, an increased prevalence of sleep disorders can be observed, such as obstructive sleep apnea and insomnia. Previous research has already linked sleep disorders (especially sleep apnea) with COVID-19, but few studies have focused specifically on the older population. We believe that the intrinsic sleep patterns of older adults, and the prevalence of sleep disorders in this population, may be important factors that could explain why they are at a greater risk of negative COVID-19 outcomes. In this review, we discuss the relationship between sleep and COVID-19 among older adults, focusing on three different aspects: (1) Sleep-related issues that might increase the likelihood of getting infected by SARS-COV-2; (2) Sleep disturbances that might increase the predisposition to worse COVID-19 prognosis and outcomes; and (3) COVID-19-related aspects affecting community-dwelling older adults, such as social isolation, quarantine, and home confinement, among others, that might impact sleep.

Chronic insomnia disorder (simplified in this document as insomnia) is an increasingly common clinical condition in society and a frequent complaint at the offices of different areas of health practice (particularly Medicine and Psychology). This scenario has been accompanied by a significant evolution in treatment, as well as challenges in approaching patients in an appropriately way. This clinical guideline, coordinated by the Brazilian Sleep Association and the Brazilian Association of Sleep Medicine and counting on the active participation of various specialists in the area, encompasses an update on the diagnosis and treatment of insomnia in adults. To this end, it followed a structured methodology. Topics of interest related to diagnosis were written based on theoretical framework, evidence in the literature, and professional experience. As for the topics related to the treatment of insomnia, a series of questions were developed based on the PICO acronym (P – Patient, problem, or population; I – Intervention; C – Comparison, control, or comparator; O – Outcome). The work groups defined the eligible options within each of these parameters. Regarding pharmacological interventions, only the ones currently available in Brazil or possibly becoming available in the upcoming years were considered eligible. Systematic reviews were conducted to help prepare the texts and define the level of evidence for each intervention. The final result is an objective and practical document providing recommendations with the best scientific support available to professionals involved in the management of insomnia.

Summary: There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep‐disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta‐analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full‐text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea–hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch‐by‐epoch and event‐by‐event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta‐analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta‐analysis will be performed for continuous outcomes using the DerSimonian and Laird random‐effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.