An international expert group (European Sleep Foundation Think Tank) convened in 2022 to discuss the state of the evidence in the domain of hypersomnolence. The expert group considered the current state of knowledge based on the most relevant recent publications, discussed the current challenges in the field and identified future priorities. The purpose of this white paper is to summarize the definition, diagnosis, and pathophysiology of hypersomnolence, the epidemiology, phenotype, and management of hypersomnolence in obstructive sleep apnea and in neurological and psychiatric disorders, and the impact of hypersomnolence on daily activities, workability and health-related quality of life. The key results of the discussion were that: a) hypersomnolence is both prevalent and heterogeneous in its manifestations in a wide variety of pathological conditions encompassing obstructive sleep apnea and neurological and psychiatric disorders; and b) while multiple pathophysiological pathways are potentially involved in hypersomnolence, knowledge of the specific causal factors in individual patients remains undefined, and the specific factors responsible for excessive daytime sleepiness vs. excessive need for sleep remain largely unclear. The clinical implications of these results are the occurrence of important limitations to the development of personalized approaches to diagnosis, prognosis, and management of hypersomnolence, which is essential considering the high societal and personal costs of hypersomnolence, and its substantial adverse impact on quality of life. Research priorities should address these limitations with improved quantification of hypersomnolence and with an evidence base on the costs and benefit of hypersomnolence management in patients with respiratory, neurologic, and psychiatric disorders.

This study examined the predictive effect of day-to-day variations in sleep on self-reported social motivation and social activity in 126 parents of young children with sleep problems. Controlling for other sleep factors, worse subjective sleep quality predicted less morning sociability and social motivation throughout the day. Unexpectedly, longer sleep duration predicted less social activity the following day. Sleepiness at wakeup predicted morning sociability, but not social motivation or activity throughout the day. This highlights the importance of good sleep quality for motivation to socialize, but also the complexity of investigating the relationship between sleep and social measures in daily life.

Pressing the snooze button is a common way to start the day, but little is known about this behaviour. Through two studies we determined predictors and effects of snoozing. In Study 1 (n = 1732) respondents described their waking habits, confirming that snoozing is widespread, especially in younger individuals and later chronotypes. Morning drowsiness and shorter sleep were also more common for those who snooze. Study 2 was a within-subjects laboratory study (with polysomnography) on habitual snoozers (n = 31), showing that 30 min of snoozing improved or did not affect performance on cognitive tests directly upon rising compared to an abrupt awakening. Bayes factors indicate varying strengths of this evidence. Snoozing resulted in about 6 min of lost sleep, while preventing awakenings from slow-wave sleep (N3). There were no clear effects of snoozing on the cortisol awakening response, morning sleepiness, mood, or overnight sleep architecture. A brief snooze period may thus help alleviate sleep inertia, without substantially disturbing sleep, for late chronotypes and those with morning drowsiness.

Introduction: The social effects of insufficient sleep represent a relatively new area of study in sleep research. For instance, the influence of sleep on verbal communication, particularly communicative perspective-taking and adaptation based on audience, is underexplored. Furthermore, studies on how sleep affects speaking patterns are limited, although slowed speech has been indicated as an effect of sleep loss. Given the importance of effective communication in all areas of life, understanding the harmful effects of insufficient sleep on communication is vital. The present study investigated whether sleep restriction affects speech speed and the ability to adjust one's speech depending on the listener.

Method: In an experimental cross-over study, 273 participants described nine abstract figures separately to a child and an adult, both of whom were depicted as photos on a computer screen. This task was completed under two conditions: sleep-saturated (nine in bed hours/night for two nights) and sleep-restricted (four hours in bed/night for two nights). The descriptions were analysed for number of words spoken per minute, average number of words spoken per figure described, and average word length.

Results: Using mixed linear models, we found that sleep restriction significantly reduced speech speed by about 3.52 words per minute (p = 0.003) and decreased the number of words used per figure by 2.20 (p < 0.001), with no difference in word length (p = 0.261). Participants adapted their speech depending on whether the listener was a child or an adult by using fewer words per figure (1.71 fewer words, p < 0.001) and shorter words (0.02 fewer letters, p = 0.011) when speaking to a child. There was no difference in how quickly they spoke to a child compared to an adult (p = 0.136). Additionally, no interaction effects were observed between sleep condition and whether the listener was an adult or a child (all p > 0.570).

Conclusion: Although sleep restriction led to slowed speech, it did not significantly impair the tendency to tailor one's speech to different listeners. These findings suggest a resilience in the social cognitive processes involved in tailoring speech to different audiences following insufficient sleep.

Fluid intelligence is seen as a beneficial attribute, protecting against stress and ill-health. Whether intelligence provides resilience to the cognitive effects of insufficient sleep was tested in the current pre-registered experimental study. Participants (N = 182) completed the Raven's test (measuring fluid intelligence) and a normal night of sleep or a night of total sleep deprivation. Sleepiness and four cognitive tests were completed at 22:30 hours (baseline), and the following day after sleep manipulation. At baseline, higher fluid intelligence was associated with faster and more accurate arithmetic calculations, and better episodic memory, but not with spatial working memory, simple attention or sleepiness. Those with higher fluid intelligence were more, not less, impacted by sleep deprivation, evident for arithmetic ability, episodic memory and spatial working memory. We need to establish a more nuanced picture of the benefits of intelligence, where intelligence is not related to cognitive advantages in all situations.

Identification of sick conspecifics allows for avoidance of infectious threats, and is therefore an important behavioral defense against diseases. Here, we investigated if humans can identify sick individuals solely from biological motion and posture (using point-light displays). Additionally, we sought to determine which movements and sickness parameters would predict such detection. We collected video clips and derived point-light displays (one stride presented in a loop) of sick walkers (injected with lipopolysaccharide at 2.0 ng/kg body weight) and the same walkers when healthy (injected with saline). We then presented these displays to two groups, one group classified each walker as sick or healthy (study 1, n = 106), and the other group scored the walkers’ health on a visual analogue scale (study 2, n = 106). The raters were able to identify sick individuals above chance, and rated sick walkers as having worse health, both from observing video clips and point-light displays. Furthermore, both sickness detection and worse apparent health were predicted by inflammation-induced increase in rigidity and slower walking, but not other cues. Altogether, these findings indicate that biological motion can serve as a sickness cue, possibly allowing humans to identify sick conspecifics from a distance, and thereby allowing for disease avoidance.

Polysomnography (PSG) is the gold standard for recording sleep. However, the standard PSG systems are bulky, expensive, and often confined to lab environments. These systems are also time-consuming in electrode placement and sleep scoring. Such limitations render standard PSG systems less suitable for large-scale or longitudinal studies of sleep. Recent advances in electronics and artificial intelligence enabled ‘wearable’ PSG systems. Here, we present a study aimed at validating the performance of ZMax, a widely-used wearable PSG that includes frontal electroencephalography (EEG) and actigraphy but no submental electromyography (EMG). We analyzed 135 nights with simultaneous ZMax and standard PSG recordings amounting to over 900 hours from four different datasets, and evaluated the performance of the headband’s proprietary automatic sleep scoring (ZLab) alongside our open-source algorithm (DreamentoScorer) in comparison with human sleep scoring. ZLab and DreamentoScorer compared to human scorers with moderate and substantial agreement and Cohen’s kappa scores of 59.61% and 72.18%, respectively. We further analyzed the competence of these algorithms in determining sleep assessment metrics, as well as shedding more lights on the bandpower computation, and morphological analysis of sleep microstructural features between ZMax and standard PSG. Relative bandpower computed by ZMax implied an error of 5.5% (delta), 4.5% (theta), 1.6% (alpha), 0.5% (sigma), 0.8% (beta), and 0.2% (gamma), compared to standard PSG. In addition, the microstructural features detected in ZMax did not represent exactly the same characteristics as in standard PSG. Besides similarities and discrepancies between ZMax and standard PSG, we measured and discussed the technology acceptance rate, feasibility of data collection with ZMax, and highlighted essential factors for utilizing ZMax as a reliable tool for both monitoring and modulating sleep.

Psychotic disorders as well as psychosis proneness in the general population have been associated with perceptual instability, suggesting weakened predictive processing. Sleep disturbances play a prominent role in psychosis and schizophrenia, but it is unclear whether perceptual stability diminishes with sleep deprivation, and whether the effects of sleep deprivation differ as a function of psychosis proneness. In the current study, we aimed to clarify this matter. In this preregistered study, 146 participants successfully completed an intermittent version of the random dot kinematogram (RDK) task and the 21-item Peters Delusion Inventory (PDI-21) to assess perceptual stability and psychosis proneness, respectively. Participants were randomized to sleep either as normal (8 to 9 h in bed) (n = 72; Mage = 24.7, SD = 6.2, 41 women) or to stay awake through the night (n = 74; Mage = 24.8, SD = 5.1, 44 women). Sleep deprivation resulted in diminished perceptual stability, as well as in decreases in perceptual stability over the course of the task. However, we did not observe any association between perceptual stability and PDI-21 scores, nor a tendency for individuals with higher PDI-21 scores to be more vulnerable to sleep-deprivation-induced decreases in perceptual stability. The present study suggests a compromised predictive processing system in the brain after sleep deprivation, but variation in psychosis trait is not related to greater vulnerability to sleep deprivation in our dataset. Further studies in risk groups and patients with psychosis are needed to evaluate whether sleep loss plays a role in the occurrence of objectively measured perceptual-related clinical symptoms.

Methods: In two studies, raters watched video recordings and point-light displays (i.e. dots depicting the body joints) of walking individuals who were either experimentally sick (after injection with lipopolysaccharide at 2.0 ng/kg bw) or healthy (after a placebo injection). In study 1, 106 raters classified each walking individual as either sick or healthy. In study 2, 106 other raters graded health of the stimuli on a visual analogue scale. We assessed the predicting effect on sickness detection (study 1) and apparent health (study 2) of walking parameters (objective measures of stride length, width, time, as well as knee angle, arm angle, and head angle) and well-known sickness responses (Sickness Questionnaire score, pain intensity, body temperature, and interleukin-6 concentration).

Results: In study 1, shorter steps was the only predictor of the detection of sick individuals from video recordings (β=0.712(0.257), p=0.02). In the point-light displays, slower, wider, stiffer and shorter steps, all predicted a better sickness detection (β=0.0003(0.0001)-0.415(0.126), p<0.05).

In study 2, lipopolysaccharide-induced slower, shorter and stiffer steps (B=5.214(1.888)-6.385(2.083), p<0.01), as well as higher interleukin-6 concentrations (B=0.051(0.020), p=0.01), predicted worse health ratings of sick individuals in the video recordings. In the point-light displays, lipopolysaccharide-induced slower, shorter and stiffer steps, and more head tilting, predicted worse health ratings of sick individuals (B=4.185(1.892)-6.701(2.092), p<0.05).

Conclusions: The results imply that specific changes in walking parameters may aid in sickness detection, possibly regulating approach-avoidance behaviors towards sick peers.

Sleep deprivation has in several studies been found to increase anxiety. However, the extent to which this anxiogenic effect depends on one's underlying trait anxiety has not previously been determined. Using two separate sleep-loss experiments, the current research investigated whether trait anxiety (STAI-T) moderates the increase in state anxiety (STAI-S) following one night of total sleep loss (study 1, N = 182, age 25.3 ± 6.5, 103 women) and two nights of partial sleep restriction (study 2, N = 67, age 26.5 ± 7.4, 38 women). Both studies showed the expected anxiogenic effect of sleep loss, and a clear relationship between trait anxiety and state anxiety. However, the anxiogenic effect of sleep loss was not moderated by trait anxiety, as there was an equal impact regardless of trait anxiety level. These findings indicate that, although sleep loss is related to general anxiety as well as anxiety disorders, for a non-clinical sample the anxiogenic effect of short-term sleep loss is not affected by baseline levels of anxiety.