Summary: Progress in the field of insomnia since 2017 necessitated this update of the European Insomnia Guideline. Recommendations for the diagnostic procedure for insomnia and its comorbidities are: clinical interview (encompassing sleep and medical history); the use of sleep questionnaires and diaries (and physical examination and additional measures where indicated) (A). Actigraphy is not recommended for the routine evaluation of insomnia (C), but may be useful for differential‐diagnostic purposes (A). Polysomnography should be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep‐related breathing disorders, etc.), treatment‐resistant insomnia (A) and for other indications (B). Cognitive‐behavioural therapy for insomnia is recommended as the first‐line treatment for chronic insomnia in adults of any age (including patients with comorbidities), either applied in‐person or digitally (A). When cognitive‐behavioural therapy for insomnia is not sufficiently effective, a pharmacological intervention can be offered (A). Benzodiazepines (A), benzodiazepine receptor agonists (A), daridorexant (A) and low‐dose sedating antidepressants (B) can be used for the short‐term treatment of insomnia (≤ 4 weeks). Longer‐term treatment with these substances may be initiated in some cases, considering advantages and disadvantages (B). Orexin receptor antagonists can be used for periods of up to 3 months or longer in some cases (A). Prolonged‐release melatonin can be used for up to 3 months in patients ≥ 55 years (B). Antihistaminergic drugs, antipsychotics, fast‐release melatonin, ramelteon and phytotherapeutics are not recommended for insomnia treatment (A). Light therapy and exercise interventions may be useful as adjunct therapies to cognitive‐behavioural therapy for insomnia (B).

The Epworth Sleepiness Scale (ESS) is one of the most used self-reported instruments to assess sleepiness. Thus, several adaptations into different Languages have been performed worldwide over the years. The scale has produced disparate psychometric properties when applied in different settings. In the current study, our aim was to perform a Reliability Generalization meta-analysis of the Cronbach᾽s alphas of all published studies on ESS, specifically with a psychometric focus. Three reference databases (Scopus, PubMed and Web of Science) were searched since 1991 to October 2022 and all the records on psychometric or validation studies that reported Cronbach's alphas, from clinical and nonclinical groups, were included. In total, data from 46 publications (63 estimates) were extracted, comprising 92,503 participants. Using a Random-Effects Model, the cumulative Cronbach's alpha for the 63 estimates was about 0.82 (CI: 0.798, 0.832) which can be considered as a good measure. However, and as expected, it was observed a high level of heterogeneity (I² = 98.96%). Moderation analyses considering setting, date, continent, risk of bias, sex, age and language were performed in order to account for the heterogeneity. Even so, only the variables study setting and continent were significant, and had little importance in explaining the heterogeneity. The ESS is a reliable tool to measure sleepiness; however, further studies are needed to investigate what variables might explain the observed variability. Moreover, it will be important to include empirical studies beyond psychometric ones.

Summary: The aim of the current study was to conduct a reliability generalisation (RG) meta‐analysis of Cronbachʼs alpha for the Insomnia Severity Index (ISI). A systematic search of three databases (PubMed, Scopus, and Web of Science) from inception to 12 March 2021 was performed. Publications that reported Cronbachʼs alpha for the total ISI score were included. Only psychometric‐focussed studies were considered. Meta‐analysis was carried out using a random‐effects model to derive a pooled estimate of Cronbachʼs alphas. The number of participants in the included publications ranged from 25 to 12,056, with 33 studies (42 estimates) comprising internal consistency coefficients, and a combined sample size of N = 29,688. The age range of the included publications was from 13.4 to 74.3 years. Data extraction implied 33 publications out of 706 found through the database search. Cronbachʼs alphas ranged from 0.65 to 0.92. The majority of the reported coefficients were ≥0.7 and presented a low risk of bias (n = 32). The pooled alpha coefficient was 0.83 (IC [0.81–0.85]; SE = 0.009) with high heterogeneity among the included publications (I² = 97%). Subgroup analyses including moderators such as continent, setting, risk of bias, and age did not affect significantly the overall result. In general, the cumulative estimate of Cronbachʼs alpha for the ISI is good. However, this finding should be interpreted with caution since there is a high heterogeneity level and some of the studies might not have checked the assumptions underlying Cronbach's alphas.

Research shows that nighttime social media is negatively associated with sleep quality and that it might be utilized to cope with aversive psychological states related to cognitive pre‐sleep arousal (i.e., transdiagnostic psychopathology variables, referring to maladaptive repetitive thought), namely Fear of Missing Out (FoMO). The use of nighttime social media to cope with other aversive cognitive states (i.e., worry/rumination), their relationship with FoMO, and these variables' association with sleep are not fully understood. This study explored the relationships between nighttime social media, sleep quality, FoMO, cognitive pre‐sleep arousal, and maladaptive cognitive emotion regulation (i.e., worry/rumination). The present correlational study followed a cross‐sectional design. Participants were 525 university students, ranging in age from 18 to 64 ( M = 22.39, SD = 5.62). Measures of sleep quality, morningness/eveningness, cognitive pre‐sleep arousal, worry, rumination, FoMO, nighttime screen, and social media use were collected online or in pencil‐paper format. Multiple linear regression analyses were performed. Nighttime social media and FoMO were associated with rumination, worry (H1) and cognitive pre‐sleep arousal. Both FoMO and worry predicted higher levels of cognitive pre‐sleep arousal (H2) and nighttime social media use (H3). Nighttime social media use independently predicted poor sleep quality (H4). These results suggest that worry and FoMO may potentially affect sleep quality by increasing cognitive pre‐sleep arousal and nighttime social media. A possible explanation for these findings is that nighttime social media might be used as a strategy to cope with aversive cognitive states. These conclusions may contribute to improving sleep intervention in this population.