Accurate assessment of sleep is vital for both clinical and research purposes, necessitating reliable measurement tools. The present umbrella review synthesizes findings from existing reliability generalization meta-analyses to evaluate the internal consistency and test-retest reliability of widely used sleep assessment scales. Eight moderate-high quality meta-analyses (K = 197 primary studies, N = 336,676 participants) were analyzed, encompassing seven sleep assessment tools targeting insomnia, sleep quality, daytime sleepiness, and maladaptive sleep behaviors. Internal consistency (Cronbach’s α) ranged from 0.73 (Athlete Sleep Behavior Questionnaire) to 0.93 (Anxiety and Preoccupation about Sleep Questionnaire), with most scales demonstrating good to excellent reliability (α > 0.80). Bayesian and classical meta-analyses corroborated these findings, yielding a pooled internal consistency estimate of α = 0.84 (95

Insomnia has been traditionally associated with overactivation/hyperarousal in several domains of functioning. Daytime sleepiness, though a somehow expected phenomenon, has not been clearly demonstrated in most insomnia patients. In this study, we investigated whether a subgroup of self-reported insomnia sufferers with daytime sleepiness, and another subgroup with high levels of alertness/arousal, could be identified. From a previous large database comprising higher education students (n = 2,029), there were selected individuals aged 18-30 years who were full-time students and self-reported themselves as suffering from insomnia. Afterwards, three "insomnia" subgroups (n = 476) were created: 1) "Only Insomnia-Daytime Sleep Propensity"; 2) "Only Insomnia-Daytime Alertness"; and 3) "Neither Criterion". Data showed that in some important domains there are differences between self-reported insomnia individuals. Specifically, "Only Insomnia-Daytime Alertness" group displayed higher insomnia severity, sleep effort and distress about their sleep difficulties compared to "Only Insomnia-Daytime Sleep Propensity" group. Overall, the group which did not exhibit "Neither Criterion" presented significantly less daytime dysfunction, sleep and psychological symptomatology compared to the remaining groups. It seems plausible that arousal and daytime sleepiness may characterize disparate groups of insomnia sufferers. However, more systematic research is needed, mainly relying on objective and neurophysiological measures.

OBJECTIVE:The Glasgow Sleep Effort Scale (GSES) assesses the extent to which individuals engage in conscious, deliberate attempts to control their sleep. The present study aimed to conduct a reliability generalization meta-analysis to estimate the overall internal consistency of the GSES, to examine whether these estimates vary as a function of study and sample characteristics, and to assess the prevalence of reliability induction practices in the literature. METHODS:This systematic review and meta-analysis was preregistered on the Open Science Framework. A search was conducted in PubMed, Scopus, Web of Science, and PsycINFO from inception to November 12, 2025, using the term "Glasgow Sleep Effort Scale". Two reviewers independently screened records using predefined eligibility criteria. Studies reporting Cronbach's alpha for the GSES total score were included. We conducted a random-effects meta-analysis using restricted maximum likelihood estimation. RESULTS:Thirty-four articles contributed 40 independent estimates (total N = 16,022). The pooled estimate was α = 0.81 (95% CI [0.79, 0.82]), indicating good overall internal consistency, although significant heterogeneity was observed (I2 ≈ 85%). Publication year significantly moderated internal consistency estimates, whereas language, mean age, sex, sample type, study design, and methodological quality did not. Reliability induction was observed in nearly 58% of the initially retrieved studies, most commonly due to the omission of reliability coefficients. CONCLUSION:The GSES shows good internal consistency. Its frequent use underscores the need to report sample-specific reliability estimates.

Sleepiness is one of the possible diurnal consequences of insomnia. However, this construct demands appropriate measures to cover its different dimensions. Previous studies have shown that daytime sleepiness perception is likely the most relevant sleepiness facet of insomnia. The purpose of the current study was to enhance the psychometric properties of the Daytime Sleepiness Perception Scale (DSPS-4), which is a brief self-report instrument consisting of four items aimed at evaluating daytime sleepiness perception, particularly in insomnia. A sample composed of 537 Portuguese higher education students (75

Since its publication in 1989, the Pittsburgh sleep quality index (PSQI) has become one of the most widely used self-report instruments in sleep medicine. Despite its enduring popularity, its extensive adoption has been accompanied by conceptual and methodological concerns regarding its psychometric foundations and the construct it purports to measure.This review offers a critical appraisal of the PSQI nearly four decades after its introduction and outlines directions for its contemporary use. Five interrelated issues are examined: (i) the absence of a clearly specified construct model of “sleep quality”; (ii) the frequent use of the PSQI as a proxy for insomnia or sleep disturbance; (iii) the interpretive fragility of the conventional global score cutoff (>5); (iv) heterogeneity in component scoring logic and instability of factorial structure; and (v) the redundancy and recall vulnerability of retrospective sleep continuity estimates.Drawing on the shift from “sleep quality” toward multidimensional sleep health, we propose reframing PSQI outputs within a theoretically grounded sleep health perspective. Accordingly, alternative strategies for scoring, reporting, and interpretation are discussed to preserve the value of this legacy instrument while improving its conceptual alignment with contemporary models of sleep assessment.

The heterogeneity of insomnia presentations has long challenged research and clinical practice, motivating efforts to identify reliable disorder phenotypes. Person-centered, data-driven approaches such as latent class analysis (LCA) have provided new insights, suggesting that insomnia subtypes may differ not only in nocturnal symptoms but also in perceived impact and daytime distress. Despite this progress, LCA solutions often remain confined to the original datasets, limiting replication and applied use. To address this gap, we developed the insomnia-LCA classifier, an open-source web application that assigns new Insomnia Severity Index (ISI) response profiles to one of four subtypes identified in a previously published LCA of Italian university students: no insomnia (NI), subthreshold insomnia (SI), high insomnia risk (HI), and predominant daytime symptoms (DS). Using the original model's class priors and item-level conditional response probabilities, the app computes posterior class probabilities from user-entered ISI responses, individually or in batch mode. Outputs include class probabilities and modal assignment, ISI total and subscale scores, and a visual comparison between the individual profile and subtype mean patterns. Reclassification of the original dataset showed near-perfect agreement with the latent class model (accuracy = 0.999; Cohen's kappa = 0.999), and synthetic profiles behaved as expected. The insomnia-LCA classifier provides a practical, reproducible tool for deploying and testing LCA-derived phenotypes in clinical research.

Non-linear associations between sleep duration and health outcomes are well-documented, but the role of circadian timing in these relationships remains scantly explored. This study examined linear and non-linear associations between self-reported sleep duration and mental and physical health-related quality of life in Italian university students, accounting for the moderating effect of bedtime. A total of 1,234 students (mean age 23.3 ± 2.5 years; 87.3

BACKGROUND:Insomnia and its association with mental health problems are prevalent in young populations. While person-centered statistical methods have identified insomnia phenotypes using a wide range of variables, the potential of common screening tools like the Insomnia Severity Index (ISI) for subtyping insomnia is underexplored. This study aimed to investigate insomnia subtypes in university students using ISI items. METHODS:In a cross-sectional online survey, 1,234 Italian university students (mean age: 23.3 ± 2.4 years) completed the ISI, the Pittsburgh Sleep Quality Index, the Depression Anxiety Stress Scale, and the Short Form-12 health survey. Latent class analysis (LCA) was performed using ISI items as indicators, and class differences in sleep quality, psychological distress, and health-related quality of life were assessed. RESULTS:A four-class solution was identified: "no insomnia" (NI; 31.4%) with no significant sleep complaints; "high insomnia risk" (HI; 17.7%) showing severe nighttime and daytime symptoms; "subthreshold insomnia" (SI; 37.0%) characterized by moderate nighttime symptoms and sleep dissatisfaction; and "predominant daytime symptoms" (DS; 13.9%) featuring pronounced daytime dysfunction without major nighttime issues. The HI group exhibited the worst sleep quality and highest psychological distress. NI had the best overall outcomes, with SI and DS in intermediate positions. DS had worse mental well-being, higher daytime dysfunction, and more psychological distress compared to SI. CONCLUSION:LCA identified four insomnia subtypes based on ISI scores, delineating a continuum from no insomnia to high risk, with one subtype marked primarily by daytime impairments. These findings could guide tailored interventions for different clinical presentations.

Background:Student-athletes face challenges balancing academic and athletic commitments, often leading to dysfunctional sleep patterns. Social jetlag - a misalignment between biological and social clocks - has emerged as a potential contributor to mental and physical strain. The current study is aimed at determining the social jetlag frequency and its associations with other sleep and health variables among student-athletes in higher education. Methods:Sixty-seven European Portuguese student-athletes (mean age = 21.4 years) were evaluated for chronotype, social jetlag, sleep effort, daytime sleepiness, psychological distress, and perceived academic and athletic performance. Results:Social jetlag was computed using a corrected midpoint of sleep approach (SJLsc). Most participants (62.7 %) reported moderate social jetlag (1-2 h), and only 6 % of the sample presented high social jetlag (>2 h). Higher social jetlag was negatively correlated with academic performance and positively associated with evening chronotypes. Although group differences across social jetlag levels and chronotypes were not statistically significant, there is a trend suggesting morning types experience lower misalignment. Compared to normative data derived from Portuguese samples, participants showed higher sleep effort and lower perceived daytime sleepiness. Conclusions:The prevalence of significant social jetlag among student-athletes was low, suggesting that participation in sports may serve as a protective factor, possibly due to greater health awareness and monitoring. However, further studies with larger samples are needed.

OBJECTIVES/BACKGROUND:Identifying subtypes or phenotypes of insomnia remains a remarkable challenge. Study 1 aimed to examine how two diagnosis-derived subtypes of Insomnia Disorder (ID) - ID alone and ID comorbid with mental disorders (anxiety or depression) - may be differentiated by sleep and non-sleep variables. The primary objective of Study 2 was to identify distinct psychological profiles of insomnia. METHODS:A sample of 208 patients with ID (Mean age=48.73±13.56 years; 60.6% women) was assessed through a semi-structured interview based on ICSD-3-TR/DSM-5 diagnostic criteria. Participants completed self-reported instruments measuring sleep-related variables (insomnia severity, somatic/emotional and cognitive sleep activation, dysfunctional sleep beliefs regarding consequences/helplessness, and medication/hopelessness) and psychological variables (perfectionism and emotional disturbance). Additionally, they filled in a sleep diary for at least seven days. RESULTS:Multivariate and univariate analyses of covariance, controlling for sex and age, revealed that patients with ID comorbid exhibited higher scores on sleep-related variables, except for insomnia severity, as well as increased socially prescribed perfectionism and emotional disturbance, than those with ID alone. Using Latent Profile Analysis, four well-defined psychological profiles of insomnia were identified: (i) standalone insomnia (18.3%); (ii) major insomnia with an overactive and worried mind (22.1%); (iii) major insomnia with emotional disturbance (21.6%); and (iv) insomnia with perfectionism (38.0%). CONCLUSION:Differences across insomnia subtypes are better explained by emotional/somatic and cognitive activation (hyperarousal), dysfunctional sleep beliefs, perfectionism and emotional disturbance, rather than by insomnia severity, sleep features or daytime sleepiness. These sleep-related and psychological factors enable the identification of distinct psychological profiles of insomnia, which might guide more tailored interventions.

Introduction Poor sleep quality and insomnia are major public health concerns, often associated with mental health problems and particularly prevalent among young adults. While the relationship between insomnia and psychological distress is well-documented, the symptom-level interactions underlying these associations remain largely unexplored. Psychometric network analysis, a method for assessing large-scale interactions among sets of variables and identifying influential nodes within symptom networks, was employed in this study to investigate the relationships between insomnia symptoms, depression, anxiety, and stress in a cohort of Italian university students, focusing on differences between good and poor sleepers. Methods Participants (n = 1,234, mean age: 23.3 ± 2.4 years) were classified as good sleepers (GS; n = 406) or poor sleepers (PS; n = 808) based on the Pittsburgh Sleep Quality Index. Gaussian Graphical Models were used to estimate network structures for each group, with insomnia symptoms (Insomnia Severity Index items) and subscale scores from the 21-item Depression Anxiety Stress Scale and the 10-item Perceived Stress Scale as nodes. Results The PS network showed greater density (26/55 vs. 19/55) and more connections linking insomnia and distress symptoms. Expected influence, a measure of node centrality, identified worry about sleep, difficulty maintaining sleep, and stress symptoms as the most central nodes in both groups, while anxiety symptoms were more influential in the PS network, underscoring their role in the interplay between poor sleep and distress. Conclusion These findings highlight the transdiagnostic nature of insomnia and support the utility of network analysis in identifying key symptoms that may serve as targets for interventions addressing both sleep and psychological problems.

Poor sleep and insomnia are pervasive public health concerns, with insomnia ranking as the second most prevalent mental health disorder in the general population. Moreover, insomnia is a significant predictor of subsequent depression and is frequently co-occurring with other psychological difficulties. While both in-person and digital cognitive-behavioural treatments have proven effective as first-line strategies for managing insomnia, their accessibility remains limited, with several barriers preventing dissemination, particularly, among at-risk or hard-to-reach populations such as adolescents, university students and minority groups. Single-session interventions (SSIs) for mental health have recently emerged as a strategic asset and treatment approach, grounded in specific theoretical tenets and assumptions. The efficacy and feasibility of SSIs have been well documented, with systematic evidence highlighting their flexibility, applicability and cost-effectiveness across a wide range of clinical and subclinical conditions. However, their full implementation in the treatment of insomnia remains limited. This brief review aims to illustrate the alignment between the SSI framework and the needs of psychological treatments for insomnia and to summarise current evidence on single-session, one-shot interventions for insomnia. Notably, several one-shot interventions based on cognitive-behavioural therapy for insomnia have been tested in clinical trials with promising results, though their integration with the broader SSI approach appears partial. Further research is warranted to develop consensus-based in-person and digital SSIs for insomnia and to assess their feasibility and effectiveness within a stepped-care framework.

Purpose: The Pre-Sleep Arousal Scale (PSAS) is a self-report tool for assessing cognitive and somatic arousal before sleep. While the English version is well-validated, research on translations is limited. This meta-analysis examines PSAS translations' internal consistency and reliability. Methods: We conducted a comprehensive literature search using multiple databases to identify studies that reported the reliability of the PSAS. We used a random-effects meta-analysis model to pool estimates of internal consistency and test-retest reliability and explored potential moderators using subgroup analyses and meta- regression. To ensure transparency, we registered the study protocol, utilized the PRISMA checklist, and made all study code and data available on the Open Science Framework. Results: Systematic review yielded a total 27 studies (reported in 25 publications) with 9354 participants employing eleven language versions of the PSAS. Meta-analysis showed good internal consistency for the total PSAS (0.88, 95%CI 0.86-0.90) as well as the cognitive (alpha = 0.89, 95%CI 0.88-0.90) and somatic (alpha = 0.80, 95% CI 0.77-0.83) subscales. The PSAS also displayed excellent test-retest reliability for the total scale (r = 0.87, 95% CI 0.84-0.90), cognitive subscale (alpha = 0.80, 95%CI 0.77-0.84) and somatic subscale (alpha = 0.70, 95%CI 0.67-0.74). Participant characteristics (age and sex) did not significantly affect results. Conclusions: This meta-analysis shows that the PSAS is a reliable tool for detecting pre-sleep arousal based on its high internal consistency and test-retest reliability. The PSAS is useful across languages but quality translation appears to be crucial. Recommendations are offered for future adaptations and clinical use.

Journal Article Accepted manuscript Searching for cognitive behavioral therapy for insomnia on Google: An infodemiological perspective Get access Daniel Ruivo Marques Daniel Ruivo Marques University of Aveiro, Department of Education and Psychology, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalCINEICC - Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Faculty of Psychology and Educational Sciences, University of Coimbra, Portugal Corresponding author: Daniel Ruivo Marques, PhD, University of Aveiro, Department of Education and Psychology, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal, Phone: +351 234 372 428, E-mail: drmarques@ua.pt Search for other works by this author on: Oxford Academic Google Scholar Sleep, zsae144, https://doi.org/10.1093/sleep/zsae144 Published: 26 June 2024 Article history Received: 06 June 2024 Published: 26 June 2024