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Nightmares are often associated with psychiatric disorders and acute stress. This study explores how the COVID‐19 pandemic may have influenced the content of nightmares. A sample of N = 419 US adults completed online surveys about sleep and COVID‐19 experiences. Participants were asked about the degree to which they agreed with statements linking greater general stress, worse overall sleep and more middle‐of‐the‐night insomnia with the COVID‐19 pandemic. They were also asked if, during the pandemic, they experienced nightmares related to various themes. Logistic regression analyses examined each nightmare content as outcome and increased stress, worse sleep and more middle‐of‐the‐night insomnia as predictors, adjusted for age, sex and race/ethnicity. Those who reported greater general COVID‐related stress were more likely to have nightmares about confinement, failure, helplessness, anxiety, war, separation, totalitarianism, sickness, death, COVID and an apocalypse. Those who reported worsened sleep were more likely to have nightmares about confinement, oppression, failure, helplessness, disaster, anxiety, evil forces, war, domestic abuse, separation, totalitarianism, sickness, death, COVID and an apocalypse. Those who reported worsened middle‐of‐the‐night insomnia were more likely to have nightmares about confinement, oppression, failure, helplessness, disaster, anxiety, war, domestic abuse, separation, totalitarianism, sickness, death, COVID and an apocalypse. These results suggest that increased pandemic‐related stress may induce negatively‐toned dreams of specific themes. Future investigation might determine whether (and when) this symptom indicates an emotion regulation mechanism at play, or the failure of such a mechanism. [ABSTRACT FROM AUTHOR]

Summary: Female menstrual health and its relationship with sleep is an understudied subject. The aim of this investigation was to determine the association between the two in a community sample. Data were obtained from n = 579 menstruating females who participated in the Sleep and Health Activity, Diet, Environment, and Socialization (SHADES) study, a community‐based sample of adults aged 22–60 years. Participants were asked, "How regular is your period?", with response choices of "very regular", "mostly regular", "fairly regular" and "not regular". They were also asked, "How much bleeding do you usually experience during your period?" Response choices were: "very heavy", "heavy", "medium", "light" or "very light". These were evaluated as ordinal outcomes. Sleep‐related predictors included sleep duration (in hr; ≤ 6 [short], 7–9 [normal] and ≥ 9 [long]), Insomnia Severity Index score, Pittsburgh Sleep Quality Index score, Epworth Sleepiness Scale score and Fatigue Severity Scale score. Covariates included age, education, income, race/ethnicity and body mass index. Short sleep duration was associated with heavier bleeding (odds ratio = 1.46, p = 0.026) and greater cycle irregularity (odds ratio = 1.44, p = 0.031) as compared with normal sleep. Higher Pittsburgh Sleep Quality Index score was associated with greater cycle irregularity (odds ratio = 1.05, p = 0.022). Higher Fatigue Severity Scale score was associated with heavier bleeding (odds ratio = 1.02, p = 0.003) and greater cycle irregularity (odds ratio = 1.02, p = 0.008). Long sleep, Insomnia Severity Index and Epworth Sleepiness Scale were not associated with either outcome. These results demonstrate an association between short sleep duration, poor sleep quality, fatigue, stress and depression with heavier bleeding and menstrual cycle irregularity, highlighting the need for further studies to improve treatment options. [ABSTRACT FROM AUTHOR]

Summary: Chronic sleep restriction and obesity are two major public health concerns. This study investigated how chronic sleep restriction changes implicit attitudes towards low‐ and high‐calorie foods. In a randomized, counterbalanced cross‐over design, 17 participants (eight females, nine males) underwent two laboratory testing sessions where they were either sleep‐restricted for 3 weeks (i.e. underwent three weekly cycles of 5 nights of 4 h of sleep followed by 2 nights of 8 h of sleep opportunity) or received 3 weeks of control sleep (i.e. 8 h of sleep opportunity per night for 3 weeks). There was evidence for a significant sleep condition x sex interaction (F(1, 20) = 4.60, P = 0.04). After chronic sleep restriction, men showed a trend towards a significant decrease in their implicit attitudes favouring low‐calorie foods (P = 0.08), whereas women did not show a significant change (P = 0.16). Men may be at increased risk of weight gain when sleep‐deprived due to a reduced bias towards low‐calorie foods. [ABSTRACT FROM AUTHOR]

Summary It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep‐loss phenotype are not scalable or involve effort‐dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline‐sleep phase followed by a sleep‐loss challenge. After classifying subjects into sleep‐loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow‐wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not‐resilient and vulnerable versus not‐vulnerable) to predict the probability of sleep‐loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not‐resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not‐vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep‐loss phenotype. [ABSTRACT FROM AUTHOR]