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Objective/background: Insomnia is common in children with autism spectrum disorder (ASD). We recently developed and validated the 21-item Pediatric Autism Insomnia Rating Scale (PAIRS). This report explores the associations and agreements between actigraphy and PAIRS., Participants Methods: Children with ASD, with and without sleep problems, were assessed with a battery of parent-rated and clinician measures (N = 134). In a subset (n = 70), a wrist-worn actigraph measured sleep for five consecutive nights. Parents completed logs for scoring sleep intervals. Spearman correlations evaluated associations with the PAIRS and actigraphy indices (sleep onset latency = SOL, wake after sleep onset = WASO, total sleep time = TST, sleep efficiency = SE%). Agreements on "poor sleepers" based on PAIRS total score (≥33) and conventional thresholds for TST and SE% were evaluated with Cohen's Kappa and McNemar's test., Results: Actigraphy data were averaged over 4.64 ± 0.68 nights in 70 children (mean age = 7.3 ± 2.9, 74.3 % male). There were no significant correlations between PAIRS and any actigraphy indices. On TST, 48.6 % (n = 34) and on SE% 52.9 % (n = 37) were classified as "poor sleepers" compared to 32.9 % (n = 23) on PAIRS (kappa = 0.11 for TST and 0.27 for SE%). P-values on McNemar's Chi square test for PAIRS with TST and with SE% were 0.072 and 0.011, respectfully., Conclusions: These results suggest that actigraphy and PAIRS do not agree. Actigraphy TST captures movement and an estimate of specific sleep parameters. PAIRS is a broader measure that incorporates sleep disturbance and sleep-related impairment., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Dr. Scahill has served as a consultant to Yamo Pharmaceuticals and Cogstate. He receives book royalties from Oxford, Guilford and American Psychological Association and license fees from Yamo, Roche and Abbvie. Dr. Lecavalier receives book royalties from Oxford and license fees from Yamo, Roche and Abbvie. Dr. Johnson receives book royalties from Oxford and American Psychological Association. The authors have no other financial interests to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: COVID-19 infection has resulted in a high prevalence of a post-infectious syndrome, known as post-acute sequelae of SARS-CoV-2 (PASC) or "Long COVID". PASC is a heterogeneous disease with a high prevalence of sleep disturbances, varying from an insomnia disorder to excessive daytime sleepiness., Methods: Patients seen in the Covid Survivorship Program at the Beth Israel Deaconess Medical Center Boston, USA, were screened for sleep disorders as part of a comprehensive multi-system evaluation. Those who screened positive were referred for a comprehensive sleep evaluation in a dedicated COVID-19-Sleep clinic, followed by diagnostic sleep testing and treatment. This report summarizes patients who completed an American Academy of Sleep Medicine (AASM) accredited facility-based diagnostic evaluation. International Classification of Sleep Disorders 3rd Edition-Revised criteria were met for all diagnoses., Results: In 42 patients with PASC, five categories of sleep disorder syndromes were observed following a sleep clinic evaluation, including obstructive sleep apnea, chronic insomnia disorder, primary hypersomnia, REM behavior disorder (RBD), and new onset circadian phase delay. Seven patients met criteria for idiopathic hypersomnia, and two had narcolepsy type 2. RBD patients were infected in three different waves; circadian disturbance patients were all infected in the winter wave of 2020/21, and the primary hypersomnolence group occurred during all waves, predominantly the initial wave of 2020. A peculiar form of insomnia was a persistent loss of sleep regularity., Conclusions: Specific sleep symptoms/syndromes are reported in this select group of patients with PASC/Long Covid. As new onset sleep complaints are prevalent in PASC, we recommend a complete clinical and investigative sleep evaluation for persistent severe sleep symptoms following COVID-19 infection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: This study aimed to validate the Korean version of the sleep hygiene practice scale (SHPS-K) and determine its effectiveness in screening poor sleepers with insomnia., Methods: Online survey was conducted using translated SHPS in Korean, the Korean versions of the Pittsburgh Sleep Quality Index (PSQI-K), Insomnia Severity Index (ISI-K), and Epworth Sleepiness Scale (KESS) in a non-clinical population. The internal consistency and test-retest reliability of the SHPS-K were assessed using Cronbach's alpha and intraclass correlation coefficients (ICC), respectively. Construct validity was evaluated using correlation analyses with other questionnaires and confirmatory factor analysis. We determined the cutoff values that could identify poor sleepers with insomnia symptoms (PSQI-K > 5 and ISI-K ≥ 15) using receiver operating characteristic analysis., Results: A total of 484 participants (242 women, mean age of 43.8 years) were enrolled. The average SHPS-K score was 71.2, with no significant sex differences. Women had poorer sleep scheduling and timing behaviors, and men had poorer eating and drinking behaviors. Good internal consistency (Cronbach's alpha = 0.88) and test-retest reliability (ICC = 0.80) were observed. The SHPS-K was positively correlated with the PSQI-K ( r  = 0.55), ISI-K ( r  = 0.54), and KESS ( r  = 0.42). A cutoff value of 73 identified poor sleepers with insomnia (area under the curve = 0.828)., Conclusions: The SHPS-K is a reliable instrument for evaluating sleep hygiene in non-clinical Korean populations.

Background: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment., Objective: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population., Methods: We included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data., Results: The best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F 1 -score=0.49 and Beck Depression Inventory: AUC=0.78; F 1 -score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F 1 -score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F 1 -score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F 1 -score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30)., Conclusions: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems., (©Rachid Riad, Martin Denais, Marc de Gennes, Adrien Lesage, Vincent Oustric, Xuan Nga Cao, Stéphane Mouchabac, Alexis Bourla. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.10.2024.)

Background: Sleep problems are common in young people. Yet brief screening measures to identify those most in need of an intervention are lacking. This study investigated the potential of the two-item Sleep Condition Indicator (SCI-02) for screening insomnia symptoms in children and adolescents. We sought to establish whether there are distinct subgroups with different sleep profiles and whether subgroup membership varied with gender and school year group., Methods: Students (school years 5-13; typical age 9-18 years) in England completed the OxWell Student Survey in 2021. Sleep measures included: SCI-02, sleep onset latency (SOL), sleep duration, daytime sleepiness, and worry disrupting sleep. Latent profile analysis and multinomial logistic regression explored sleep profiles and predicted class membership., Results: In total, 29,304 participants answered sleep items. Of these, 95% provided binary gender (n = 27,802, 55% female) for analyses. Five sleep profiles emerged. The profiles, labelled "good", "moderate", or "poor" sleepers, vary by sleep quality - which includes time taken to fall asleep (SOL), amount of sleep (sleep duration), and the disruption of sleep due to worry. The profiles are then further differentiated by high levels of daytime sleepiness - labelled "sleepy". "Good Sleepers" (18,355, 66%), "Moderate Sleepers" (4825, 17.4%), "Moderate Sleepy Sleepers" (1250, 4.5%), "Poor Sleepers" (1037, 3.7%) and "Poor Sleepy Sleepers" (2335, 8.4%). Probable insomnia rates (SCI-02 ≤ 2) were high in both poor sleeper profiles (70-80%) compared with other profiles (0%) and the sample overall (9%). Compared with "Good Sleepers", all other profiles were mostly female. Daytime sleepiness - the defining characteristic of the sleepy sleeper profiles - was more common in secondary school participants than primary school., Conclusions: The SCI-02 is an efficient, two-question measure to screen for potential sleep problems in young people. Sleep disruption was high: one in ten were experiencing poor sleep. Females and adolescents appeared more vulnerable to poor sleep and daytime sleepiness. The SCI-02 has the potential for use in school and community contexts to identify children and adolescents who may benefit from support managing their sleep., (© 2024. The Author(s).)

Background: Adequate sleep and rest are essential for patient recovery; however, lack of sleep has become a common problem faced by Chinese patients during hospital stays. Reduced sleep is often associated with a higher risk of disease progression and is strongly associated with increased hospital stay. However, there is no specific tool in China to assess short-term insomnia caused by hospitalization. This study aimed to translate the Hospital-acquired Insomnia Scale (HAIS) into Chinese, test its applicability to Chinese inpatients through reliability and validity indicators, and investigate the potential influencing factors of hospital-acquired insomnia., Methods: Psychometric analysis from a sample of 679 hospitalized patients to whom the HAIS questionnaire was applied. The structural validity was assessed by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and the content validity of the scale was assessed using the content validity index. Cronbach's alpha coefficient, split-half reliability and test-retest reliability were calculated to evaluate the internal consistency of the scale. Multiple stepwise linear regression analysis was conducted to determine the potential correlates of hospital-acquired insomnia., Results: EFA supported a four-factor structure with factor loadings for all dimensions greater than 0.40. CFA showed good indicators of model fit. The content validity index of the scale was 0.94. the Cronbach's alpha of the scale was 0.915, the split-half reliability coefficient was 0.819, and the retest reliability was 0.844. Gender, age, total hours of sleep during the night, medical insurance, length of hospital stay, perceived stress level, and perceptions about sleep explained 46.2% of the variance in hospital-acquired insomnia., Conclusion: The Chinese version of HAIS has good psychometric characteristics and is an effective instrument for evaluating hospital-acquired insomnia. In addition, hospital-acquired insomnia is more common in women, of younger age, less than 5 h of sleep a night, without medical insurance, stressed, and patients with more misconceptions about sleep., (© 2024. The Author(s).)

Background: Sleep distrurbances involves daytime preoccupation about sleep, which plays a critical role in perpetuating sleep disturbances. Recent cognitive models highlight the importance of daytime processes, like processing sleep-related information and interpretations during waking hours, in influencing sleep quality and quantity. The Sleep Preoccupation Scale (SPS) quantifies this daytime cognitive activity related to sleep. This study aimed to translate and validate an Arabic version of the SPS for assessing sleep preoccupation among Arabic speakers., Methods: 523 Arabic speakers (mean age 23.6 years, 75% female) from four countries completed the Arabic SPS alongside the Athens Insomnia Scale, Generalized Anxiety Disorder-7 Scale, and the Anxiety and Preoccupation about Sleep Questionnaire (APSQ). The sample repeated the SPS after two weeks for test-retest reliability. Confirmatory factor analysis evaluated the SPS's two-factor structure. Internal consistency, item response theory, and convergent validity with the other scales were examined., Results: Confirmatory factor analysis supported the two-factor model's fit. Measurement invariance results suggest that the SPS exhibits a similar basic factor structure across both insomnia and non-insomnia groups. The Arabic SPS demonstrated good internal consistency (Cronbach's α = 0.92, McDonald's ω = 0.92) and two-way mixed effects, consistency, single rater/measurement intraclass correlation coefficient (ICC) test-retest reliability (ICC = 0.95). Most items exhibited satisfactory item response theory fit statistics and discrimination. The SPS total score showed strong positive correlations with insomnia severity (r = 0.48, p < 0.001), generalized anxiety (r = 0.57, p < 0.001), and sleep-related anxiety/preoccupation (r = 0.79, p < 0.001), demonstrating convergent validity., Conclusion: The Arabic version of the SPS demonstrated good psychometric properties and validity, supporting its use for assessing sleep preoccupation among Arabic speakers. This culturally-adapted version enhances sleep assessment capabilities for improving insomnia understanding and treatment within Arabic-speaking populations., (© 2024. The Author(s).)

Insomnia was diagnosed by analyzing sleep stages obtained during polysomnography (PSG) recording. The state-of-the-art insomnia detection models that used physiological signals in PSG were successful in classification. However, the sleep stages of unbalanced data in small-time intervals were fed for classification in previous studies. This can be avoided by analyzing the insomnia detection structure in different frequency bands with artificially generated data from the existing one at the preprocessing and post-processing stages. Hence, the paper proposes a double-layered augmentation model using Modified Conventional Signal Augmentation (MCSA) and a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic signals from raw EEG and synthetic data from extracted features, respectively, in creating training data. The presented work is independent of sleep stage scoring and provides double-layered data protection with the utility of augmentation methods. It is ideally suited for real-time detection using a single-channel EEG provides better mobility and comfort while recording. The work analyzes each augmentation layer's performance individually, and better accuracy was observed when merging both. It also evaluates the augmentation performance in various frequency bands, which are decomposed using discrete wavelet transform, and observed that the alpha band contributes more to detection. The classification is performed using Decision Tree (DT), Ensembled Bagged Decision Tree (EBDT), Gradient Boosting (GB), Random Forest (RF), and Stacking classifier (SC), attaining the highest classification accuracy of 94% using RF with a greater Area Under Curve (AUC) value of 0.97 compared to the existing works and is best suited for small datasets., (© 2024. The Author(s).)

Objectives: (1) Assess the prevalence of postoperative insomnia; (2) identify the risk factors for postoperative insomnia before exposure to surgery; (3) explore the impact of postoperative insomnia on rehabilitation., Methods: A study was conducted with 132 participants aged ≥ 65 undergoing spine interbody fusion. We collected the basic demographic data, Numeric Rating Scales (NRS), Pittsburgh Sleep Quality Index (PSQI), Geriatric Depression Scale (GDS), and Beck Anxiety Inventory (BAI). We measured Quality of Recovery 40 (QoR-40), GDS, BAI, NRS, and PSQI on the first and third nights post-surgery, followed by QoR-40 and NRS assessments two weeks after surgery., Results: The cases of postoperative insomnia on the first and third nights and after two weeks were 81 (61.36%), 72 (54.55%), and 64 (48.48%), respectively, and the type of insomnia was not significantly different (P = 0.138). Sleep efficiency on the first night was 49.96% ± 23.51. On the first night of postoperative insomnia, 54 (66.67%) cases were depression or anxiety, and the PSQI was higher in this group than in the group without anxiety or depression (P < 0.001). PSQI, GDS, and the time of surgery were related factors for postoperative insomnia (PPSQI < 0.001, PGDS = 0.008, and PTime = 0.040). Postoperative rehabilitation showed differences between the insomnia and non-insomnia groups (P < 0.001)., Conclusions: The prevalence of postoperative insomnia in the elderly was high, and postoperative insomnia had a significant correlation with postoperative rehabilitation. Interventions that target risk factors may reduce the prevalence of postoperative insomnia and warrant further research., Clinical Trial Registration: Multivariate analysis of postoperative insomnia in elderly patients with spinal surgery and its correlation with postoperative rehabilitation ( https://www.chictr.org.cn/bin/project/edit?pid=170201 ; #ChiCTR2200059827)., (© 2024. The Author(s).)

Purpose: Insomnia is an important sleep disorder in older persons. Conceptual analysis studies on this nursing diagnosis have been developed, but the diagnostic accuracy has not been verified. This study aimed to verify the diagnostic accuracy of the nursing diagnosis of Insomnia (00095) in older adults in a community center in Brazil., Methods: A validation study for diagnostic accuracy of the defining characteristics of the nursing diagnosis Insomnia. Data were collected through telephone interviews with 90 participants. Latent class analysis was used to verify the sensitivity and specificity of the defining characteristics. Poisson regression was used to assess the prevalence of the association of factors related to the nursing diagnosis of insomnia., Findings: A 47.42% prevalence of insomnia in the sample was identified. Defining characteristics such as expresses dissatisfaction with sleep and nonrestorative sleep-wake cycle showed the best accuracy values for insomnia, with a specificity of 0.92 (0.78-1.00) and 0.89 (0.74-1.00), respectively. Related factors such as stressors and frequent naps during the day were more likely to develop insomnia in individuals., Conclusions: This study indicates diagnostic accuracy of nursing diagnosis of insomnia in community-dwelling older people from a community center. The findings highlighted the importance of the defining characteristics of nursing diagnosis insomnia and its main related factors contributing to accurate diagnostic identification., Implications for Nursing Practice: This study can contribute to providing objective clinical indicators of insomnia in the older population to guide nurses in early diagnostic confirmation for the selection of health interventions. In addition, it can be a consistent contribution to theoretical and conceptual reviews of this diagnosis., (© 2023 NANDA International, Inc.)

Purpose: Insomnia is a prevalent sleep disorder among patients undergoing hemodialysis for chronic kidney disease. This study aimed to translate the sleep condition indicator (SCI), an insomnia screening tool based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), into a traditional Chinese version (SCI-TC) and evaluate the reliability and validity of this version for patients undergoing hemodialysis., Methods: This cross-sectional study conducted from November 2022 to June 2023 involved 200 patients on hemodialysis (mean age, 65.56 years; 61.5% men). Participants completed a series of questionnaires, with insomnia diagnosed according to DSM-5 criteria as the gold standard. A receiver operating characteristic (ROC) curve analysis was conducted to examine the sensitivity and specificity of the SCI-TC., Results: According to the DSM-5 criteria, 38% of the participants had insomnia. Cronbach's alpha for the SCI-TC was 0.92. The SCI-TC exhibited a good fit as a two-factor model, and its scores were significantly associated with those of the traditional Chinese versions of the Insomnia Severity Index, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, EuroQol 5-Dimensions scale, and EuroQol Visual Analogue Scale (r =  - 0.94, - 0.53, - 0.38, 0.27, and 0.30, respectively; all p < 0.05). The ROC curve analysis revealed an optimal cutoff of 16 points, with the sensitivity, specificity, and area under curve of 88.2%, 84.7%, and 0.91(95% confidence interval, 0.87-0.95), respectively., Conclusion: The SCI-TC demonstrates robust reliability and validity in detecting insomnia among patients undergoing hemodialysis. These findings suggest that health-care providers should considering using the SCI as an easy-to-use tool for the timely detection of insomnia in this population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: Insomnia is a common psychiatric disorder that has oxidative and degenerative effects on the brain. It is thought that the brain's processes affect the retina through their synaptic connections. However, the effects of sleep disorders on the retina and choroid are not fully understood. We aimed to investigate the impact of insomnia on retinal nerve fiber layer (RNFL), central foveal thickness, retinal layers, and choroidal thickness., Methods: The right eye of 16 healthy controls and 15 patients with insomnia complaints for 3 months, no history of psychiatric drug use, and an Insomnia Severity Index (ISI) score of 15 or higher were included in the study. The retinal layers and RNFL analyses were performed using optical coherence tomography (OCT), and choroidal layers were analyzed using enhanced depth imaging OCT., Results: Nasal and temporal ganglion cell complex thicknesses were significantly lower in patients with insomnia compared to the controls (97 μm vs. 111 μm P = 0.004; 94 μm vs. 105 μm P = 0.012, respectively). A significant negative correlation was detected between the ISI score and global RNFL thickness (rho, P = 0.03) Additionally, pachychoroid-like vascular structures were observed in choroidal images., Conclusion: These changes in the retina and the choroid layers due to insomnia may be precursors to retinal degenerative conditions, such as age-related macular degeneration that may occur in the future. Multicenter studies including more patients are needed to demonstrate the importance of quality sleep for eye health., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Objectives: Examine psychometric properties of the Insomnia Severity Index (ISI) in a sample of nurses., Method: In a sample of day shift nurses ( N  = 289), a confirmatory factor analysis (CFA), convergent and discriminant validity analyses, and a test-retest reliability analysis were performed., Results: CFA showed that a two-factor model provided the best fit. The ISI had moderate to poor convergent validity with sleep diary parameters, and moderate convergent validity with the Sleep Condition Indicator ( r  = -.66), Pittsburgh Sleep Quality Index ( r  = .66), and PROMIS Sleep-Related Impairment measure ( r  = .67). The ISI demonstrated good discriminant validity with the measures Composite Scale of Morningness ( r  = -.27), Nightmares Disorder Index ( r  = .25), PTSD Checklist for DSM-5 (sleep items removed; r  = .32), and Perceived Stress Scale ( r  = .43). The ISI had weaker discriminant validity with the PHQ-9 ( r  = .69) and Generalized Anxiety Disorder Screener ( r  = .51). The ISI demonstrated a good test-retest reliability (ICCs = .74-.88)., Conclusions: The ISI is a psychometrically strong measure for the assessment of insomnia severity in day shift nurses. Overlap with psychological symptoms, primarily anxiety and depression, suggests caution while interpreting these constructs.

Objectives: Perfectionism is an important factor in insomnia development and maintenance. Previous studies exploring the relationship between perfectionism and insomnia have predominantly relied on self-reported sleep measures. Therefore, this study sought to assess whether actigraphy-measured sleep parameters were associated with perfectionism., Methods: Sixty adults (85% females, mean age 30.18 ± 11.01 years) were sampled from the Australian general population. Actigraphy-derived objective sleep measures, subjective sleep diary measures, the Frost Multidimensional Perfectionism Scale (FMPS), Hewitt-Flett Multidimensional Perfectionism Scale (HFMPS) and Depression, Anxiety and Stress Scale 21 (DASS-21) were collected., Results: High perfectionism levels were associated with poor sleep, but these relationships differed between objective and subjective measures. Perfectionism via FMPS total score and subscales of Concern over Mistakes, Doubts about Actions, Personal Standards and Self-oriented Perfectionism correlated with subjective sleep onset latency and sleep efficiency with moderate effects ( r  = .26 to .88). In contrast, perfectionism via HFMPS total score and subscales of Socially Prescribed Perfectionism and Parental Expectations predicted objective sleep onset latency and sleep efficiency. Additionally, stress mediated the relationships between objective sleep efficiency and Concern over Mistakes and Doubts about Actions., Conclusions: Perfectionism demonstrated stronger associations with subjective than objective sleep measures. Higher Parental Expectations and Socially Prescribed Perfectionism may increase one's vulnerability to objectively measured poor sleep. Therefore, perfectionism may be important in preventing and treating insomnia.

Psychometric properties of the Insomnia Severity Index (ISI) were analyzed in U.S. college samples. ISI items and total score with sleep and psychosocial questionnaires were examined in Experiment I. ISI diagnostic accuracy in a clinical sample with and without insomnia was assessed in Experiment II. ISI test-retest validity, confirmatory factor analysis (CFA), and item response theory via graded response model (GRM) were assessed in Experiment III. Results indicated analogous ISI and sleep diary items showed moderate correlations (r 1  = .40; r 2  = .45). The ISI total had weak to strong correlations with other indicators of sleep-related disturbance (rs = .25-.62). The ISI had weak to moderate correlations with psychosocial measures commonly associated with insomnia (rs = .10-.57). The diagnostic accuracy of the ISI was very high (area under the curve [AUC] = .999). Sensitivity and specificity were maximized at a cutoff score ≥ 8. The ISI demonstrated good test-retest reliability (ICC = .87). CFA revealed a three-factor model for two study samples and GRM indicated better ability of the ISI to assess moderate (Sample III) and moderate to high (Sample I) levels of insomnia severity. The ISI demonstrated good psychometric properties and appears generally valid for screening insomnia disorder and assessing insomnia severity in college students. Overlap with psychological symptoms suggests caution while interpreting these constructs independently., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Wilson's disease (WD) is a rare autosomal recessive disease that causes impaired copper circulation and excretion. The initial manifestations of WD vary clinically, which makes early diagnosis very difficult. Sleep disorders have been described as common symptoms of WD, but the initial manifestations are in rare cases., Case Report: This study aims to present a patient with acute insomnia as the initial manifestation of WD. Cranial magnetic resonance imaging showed extensive lesions in the bilateral putamen and caudate nucleus, pressure area of corpus callosum, midbrain, and pons. Interestingly, rare but characteristic signs of WD, such as "face of the giant panda," were shown in this case. WD diagnosis was further established by decreased ceruloplasmin level and ATP7B (adenosine-triphosphatase copper transporting beta polypeptide) gene mutations., Conclusions: We describe acute insomnia as the initial manifestation of WD in a 21-year-old male patient. Timely diagnosis allows for early copper-eliminating pharmacotherapy, which is of high prognostic importance, as the patient may be more responsive to treatment at this point., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: This study aimed to evaluate the Turkish validity and reliability of the Adolescent Insomnia Questionnaire., Methods: The study was carried out with 265 adolescents. Data were collected with the Adolescent Insomnia Questionnaire and the Cleveland Adolescent Sleepiness Questionnaire. Exploratory factor analysis and confirmatory factor analysis were used to analyze the construct validity of Adolescent Insomnia Questionnaire. The scale reliability was tested using test-retest, Cronbach's α test, Pearson correlation analysis, and inter-item correlation analysis., Results: The Cronbach's α coefficients were found to be above 0.80 for all sub-dimensions and the total scale. Correlations between Adolescent Insomnia Questionnaire and Cleveland Adolescent Sleepiness Questionnaire scores were positively highly significant. The test-retest correlation analysis of Adolescent Insomnia Questionnaire was 0.675. The results of confirmatory factor analysis were χ2/df=2.861, comparative fit index=0.966, incremental fit index=0.966, Tucker-Lewis index=0.956, normed fit index=0.949, root-mean-square error of approximation=0.084. The suitability of the data for exploratory factor analysis was evaluated with Bartlett's test of sphericity (p<0.05), and the sample adequacy was evaluated with the Kaiser-Meyer-Olkin test (0.77)., Conclusion: The Adolescent Insomnia Questionnaire Turkish version is a valid and reliable tool for measuring insomnia in adolescents aged 11-18 years. Adolescent Insomnia Questionnaire is a brief, practical, self-reported, age-appropriate, easily applicable, valid, and reliable tool in Turkish. This is the first Turkish validity and reliability study of Adolescent Insomnia Questionnaire.

The high prevalence of insomnia in cancer patients leads to a significant reduction in the quality of life of those affected. A detailed record of symptoms therefore plays an essential role for further course of treatment. Which screening instruments enable identification of cancer patients with insomnia is the subject of this single-arm nonrandomized study. During the data collection period, cancer patients meeting the following criteria: self-reported tiredness and/or trouble falling or staying asleep or sleeping too much in an electronic patient-reported outcome measurement were enrolled. For further analysis, focus was placed on the Patient Health Questionnaire Depression Scale (PHQ-8), the Minimal Documentation System (MIDOS 2 ) and the Insomnia Severity Index (ISI). Frequency, correlation, and variance analyses were conducted to identify likely predictors of insomnia. Our findings indicate a closer correlation between the screening question pertaining to sleep disorders and the ISI, compared to the question on tiredness and the ISI. The initial recording of sleep-related parameters plays an essential role for cancer patients in order to identify and treat modifiable factors as promptly as possible. For an initial assessment, we recommend asking about trouble falling or staying asleep or sleeping too much., (© 2024. The Author(s).)

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz&#95;Cz, Cz&#95;Oz, and C4&#95;M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

Objective: Two similar but distinct versions of prolonged grief disorder (PGD) have recently been included in the International Classification of Diseases eleventh edition (ICD-11) and the Diagnostic and Statistical Manual of Mental Disorders - fifth edition, Text-Revision ( DSM -5-TR). This study provides a criterion validity test of both new criteria sets of PGD, by examining concurrent and longitudinal associations of ICD-11 and DSM -5-TR prolonged grief symptoms with quality of life (QOL)., Methods: Bereaved adults completed a survey assessing ICD-11 and DSM -5-TR prolonged grief symptoms, depressive symptoms, insomnia symptoms and QOL at baseline and 6-month follow-up., Results: Both ICD-11 and DSM -5-TR prolonged grief symptoms related negatively to QOL concurrently, while controlling for insomnia and depressive symptoms. ICD-11 prolonged grief symptoms, but not DSM -5-TR prolonged grief symptoms, predicted QOL at 6-month follow-up, while controlling for baseline QOL and insomnia and depression symptoms., Conclusions: Results provide consistent evidence for the criterion validity of ICD-11 PGD, but mixed evidence for the criterion validity of DSM -5-TR PGD. Study results can help guide attempts to optimize and harmonize future PGD criteria., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Background: Sleep effort refers to the cognitive and behavioral exertion involved in initiating and maintaining sleep. High sleep effort is increasingly recognized as perpetuating insomnia and poor sleep quality. Validated sleep effort scales enable the quantification of this construct in clinical and research settings. However, no Arabic version has been available. This study translated and validated the Glasgow Sleep Effort Scale (GSES) into Arabic to assess sleep effort in Arabic-speaking populations., Method: The GSES was translated into Arabic using the forward-backward translation approach. This involved an initial Arabic translation from the source followed by a back translation into English to ensure accuracy. A total of 369 participants were recruited to assess the psychometric properties of the Arabic version of the GSES, specifically its reliability and validity. The reliability analysis included Cronbach's α, McDonald's ω, and test-retest reliability. Validity was examined using confirmatory factor analysis (CFA) to evaluate the unidimensionality of the scale and assess model fit. Convergent validity was also assessed through correlation analysis with the Athens Insomnia Scale (AIS) and the Generalized Anxiety Disorder-7 Scale (GAD-7)., Results: The Cronbach's α reliability coefficient and McDonald's ω for the scale were found to be 0.87 (95% CI: 0.85-0.89). The test-retest reliability was 0.95 (95% CI: 0.93 - 0.97) after two weeks. The one-factor model showed an acceptable fit, with a CFI of 0.96, a TLI of 0.94, and an SRMR of 0.04. Invariance analysis revealed that male and female participants conceptualized and responded to the GSES items similarly, without differences in factor loadings or scale characteristics between the sexes. The Arabic version of the GSES has good convergent validity, as shown by the significant correlation between the AIS and the GSES (r = 0.72, p < 0.001). Similarly, the GAD-7 score was significantly correlated with the GSES score (r = 0.77, p < 0.001)., Conclusion: This is the first study in which the GSES was validated in Arabic. This allows the scale to reliably gauge sleep effort among Arabic speakers, providing new clinical and research opportunities to understand how maladaptive sleep effort may contribute to insomnia and suboptimal sleep in this demographic population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder., Methods: We collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost)., Results: Based on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination ( R 2 = 0.910 ) for the ISI score prediction task and the accuracy of 0.965, precision of 0.841, and recall of 0.838 for the multiclass-classification task, outperforming four previous versions of the shortened ISI., Conclusion: As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objectives: Greater sleep disturbances on average are a risk factor for impaired mental health. Recent research has shown that more intraindividual variability (i.e., inconsistency) in sleep (hereafter called "sleep intraindividual variability") may also be uniquely related to mental health, even above the influence of mean sleep patterns averaged across days. The current study examined associations between sleep intraindividual variability and symptoms of anxiety, depression, and insomnia across different facets of sleep intraindividual variability (sleep duration, efficiency, and timing) and sleep measurement types (sleep diary and actigraphy)., Methods: We pooled eight datasets (N = 3053 participants) that assessed repeated measures of sleep diary- and/or actigraphy-determined sleep across multiple days, as well as one-time measures of mental health or sleep disorder symptoms (i.e., anxiety, depression, and insomnia). Multilevel regression analyses were conducted to examine associations between sleep intraindividual variability and mental health or sleep disorder symptoms., Results: Greater diary- and actigraphy-determined sleep duration intraindividual variability was associated with more depression symptoms (diary: b=0.02, p < .001; actigraphy: b=0.03, p = .006) and more insomnia symptoms (diary: b=0.02, p < .001; actigraphy: b=0.02, p < .001). Greater diary-determined sleep efficiency intraindividual variability was associated with fewer anxiety symptoms (b=-0.23, p = .019) and fewer insomnia symptoms (b=-0.15, p < .001). Greater diary- and actigraphy-determined sleep midpoint intraindividual variability was associated with more insomnia symptoms (diary: b=0.41, p = .044; actigraphy: b=0.66, p = .021)., Conclusions: More inconsistent sleep duration and sleep timing may be a correlate of poorer mental health. Future experimental work should examine whether stabilizing sleep patterns can improve mental health outcomes., Competing Interests: Declaration of conflicts of interest None., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Sleep is important for our survival. Research suggests that getting 'good' sleep is a problem for a proportion of adolescents. The paper advocates for holistic treatment of sleep disorders incorporating expertise from a multidisciplinary team. Much of the assessment and treatment of sleep disorders in adolescents comes from research within adult populations, therefore, there is a need for further research to be completed within the adolescent population to ensure there is a robust evidence base for assessment and treatment of sleep disorders., Competing Interests: Competing interests: None., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The Sleep Condition Indicator (SCI), an insomnia measurement tool based on the updated Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria with sound psychometric properties when applied in various populations, was evaluated here among healthcare students longitudinally, to demonstrate its measurement properties and invariance in this particularly high-risk population., Methods: Healthcare students of a Chinese university were recruited into this two-wave longitudinal study, completing the simplified Chinese version of the SCI (SCI-SC), Chinese Regularity, Satisfaction, Alertness, Timing, Efficiency, Duration (RU&#95;SATED-C) scale, Chinese Patient Health Questionnaire-4 (PHQ-4-C), and sociodemographic variables questionnaire (Q-SV) between September and November 2022. Structural validity, measurement invariance (MI), convergent and discriminant validity, internal consistency, and test-retest reliability of the SCI-SC were examined. Subgroups of gender, age, home location, part-time job, physical exercise, and stress-coping strategy were surveyed twice to test cross-sectional and longitudinal MI., Results: We identified 343 valid responses (62.9% female, mean age = 19.650 ± 1.414 years) with a time interval of seven days. The two-factor structure was considered satisfactory (comparative fit index = 0.953-0.989, Tucker-Lewis index = 0.931-0.984, root means square error of approximation = 0.040-0.092, standardized root mean square residual = 0.039-0.054), which mostly endorsed strict invariance except for part-time job subgroups, hence establishing longitudinal invariance. The SCI-SC presented acceptable convergent validity with the RU&#95;SATED-C scale (r ≥ 0.500), discriminant validity with the PHQ-4-C (0.300 ≤ r < 0.500), internal consistency (Cronbach's alpha = 0.811-0.835, McDonald's omega = 0.805-0.832), and test-retest reliability (intraclass correlation coefficient = 0.829)., Conclusion: The SCI-SC is an appropriate screening instrument available for assessing insomnia symptoms among healthcare students, and the promising measurement properties provide additional evidence about validity and reliability for detecting insomnia in healthcare students., (© 2024. The Author(s).)

Insomnia disorder is the most common sleep disorder. It not only increases the risk of multiple somatic and mental disorders, but also carries a heavy social health economic burden. Currently, there is a lack of diagnosis and treatment standards for insomnia disorder in primary medical institutions at home and abroad. To this end, the Chinese Sleep Research Society organized domestic sleep medical experts in sleep medicine to develop this consensus based on the latest research on insomnia disorder. The current consensus elaborates on the pathophysiological mechanism, epidemiology, diagnosis, screening assessment, prevention and control measures and basic management, and has established a total of 15 recommendations. These recommendations aim to provide comprehensive and standardized references and guidelines for the diagnosis and treatment of insomnia disorder.

Background: Hyperarousal plays an essential role in the initiation and maintenance of insomnia, highlighting the need for a tool that measure the hyperarousal state during the early course of insomnia. Pre-sleep Arousal Scale (PSAS) is a self-report questionnaire to evaluate subjective pre-sleep arousal of insomnia. The aim of this study was to examine the psychometric properties of the PSAS among patients with acute insomnia disorder (AID) using Rasch analysis., Methods: Totally 170 patients with AID from 31 public hospitals in China were recruited and completed the test. The psychometric properties of the PSAS were tested using Rasch analysis by Winsteps v5.4.1.0, including unidimensionality, local item independence, item fit, category diagnostics, reliability, item-person maps and differential item functioning (DIF) by age and gender., Results: According to Rasch analysis, somatic and cognitive subscales were unidimensional and basically demonstrated good item-fit statistics. 4-point Likert scale may be more appropriate for PSAS. All items have a good reliability and separation. No gender and age bias were detected for the scale. However, the person arousal level is not well matched to item difficulty., Conclusion: The present study further reveals appropriate psychometric properties of the PSAS in patients with AID and provides suggestions for refinements and supplements to the PSAS., Competing Interests: Declaration of competing interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Insomnia disorder is a subjective complaint of sleep dissatisfaction including both night-time and daytime symptoms. Currently there are three commonly used diagnostic manuals each with their own set of criteria, which is often credited for the wide range in insomnia prevalence reported by population-based studies, especially those with self-reported insomnia. However, there are limited studies directly comparing different criteria and little is known about associations with health outcomes. Thus, the aim of this study was to compare the most commonly used diagnostic criteria for insomnia from the literature and to explore the associations with a range of physical and mental health outcomes. We used data from 21,083 women and men from the seventh survey of the population-based Tromsø Study which included adults aged 40-99 years. A revised version of the Bergen Insomnia Scale was used to define insomnia based on the 4th (revised) and 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR and DSM5), the 10th edition of the International Classification of Diseases (ICD-10), and the 3rd edition of the International Classification of Sleep Disorders (ICSD-3). We found the following prevalence of insomnia: DSM-IV-TR 23.6 %, DSM5 8.5 %, ICD-10 9.9 % and ICSD-3 20.0 %. When looking at each symptom, we found over half the participants classified as having insomnia using the DSM-IV-TR and ICSD-3 criteria did not report having impaired daytime functioning at least three days per week. Overall, participants with DSM5 and ICD-10 insomnia appeared to have worse health profiles, based on a higher percentage meeting the cut-off for possible anxiety or depression, reporting a psychological problem or chronic pain, and using antidepressants, painkillers or sleeping pills. However logistic regression models showed largely the same health factors had the same association with the odds for being classified as having insomnia disorder from each set of criteria. Overall, this study suggests that insomnia prevalence may be overestimated if daytime symptoms are not adequately included in accordance with current guidelines., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) is a widely used self-report instrument for identifying sleep-related cognition. However, its length can be cumbersome in clinical practice. This study aims to develop a data-driven shortened version of the DBAS-16 that efficiently predicts the DBAS-16 total score among the general population., Methods: We collected 1000 responses to the DBAS-16 from the general population through three separate surveys, each focusing on different aspects of insomnia severity and related factors. Using Exploratory Factor Analysis (EFA) on the survey responses, we grouped DBAS-16 items based on response pattern similarities. The most representative item from each group, showing the highest regression performance with eXtreme Gradient Boosting (XGBoost) in predicting the DBAS-16 total score, was selected to create a shortened version of the DBAS-16., Results: Through EFA and XGBoost, we categorized the DBAS-16 items into six distinct groups. Selecting one item from each group, based on the highest coefficient of determination R 2 values in predicting the DBAS-16 total score. After measuring the R 2 values for all possible combinations of six items, items 4, 5, 7, 11, 13, and 15 were chosen, exhibiting the highest R 2 value. Based on these six items, we developed the DBAS-6, a data-driven shortened version of the DBAS-16. The DBAS-6 exhibited outstanding predictive ability, achieving the highest R 2 value of 0.90 for predicting the DBAS-16 total score, surpassing that of a previously developed shortened version. Notably, the DBAS-6 efficiently encapsulates the core aspects of the DBAS-16 and demonstrates robust predictive power over heterogeneous test data samples with distinct statistical characteristics from the training data., Conclusion: With its concise format and high predictive accuracy, the DBAS-6 offers a practical tool for assessing dysfunctional beliefs about sleep in clinical settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jaekyung Kim reports financial support was provided by National Research Foundation of Korea. Jaekyung Kim reports financial support was provided by Institute for Basic Science. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: There is limited understanding regarding prospective associations of insomnia symptoms and trajectories with functional disability. We aimed to investigate the associations of insomnia symptoms and trajectories with functional disability., Method: A total of 13 197 participants were eligible from the Health and Retirement Study. Insomnia symptoms included non-restorative sleep, difficulty initiating sleep, early morning awakening, and difficulty maintaining sleep. We also identified four distinct trajectories of insomnia symptoms: low, decreasing, increasing, and high insomnia symptoms. Functional status was assessed through activities of daily living (ADL) and instrumental activities of daily living (IADL)., Results: Participants experiencing one (HR, 1.21; 95% CI, 1.13-1.29), two (HR, 1.43; 95% CI, 1.29-1.57), or three to four (HR, 1.41; 95% CI, 1.25-1.60) insomnia symptoms had a higher risk of ADL disability than asymptomatic respondents. Similarly, participants with one or more insomnia symptoms had a higher risk of IADL disability. Furthermore, using the trajectory with low insomnia symptoms as the reference, decreasing insomnia symptoms (HR, 1.22; 95% CI, 1.12-1.34), increasing insomnia symptoms (HR, 1.21; 95% CI, 1.05-1.41), and high insomnia symptoms (HR, 1.36; 95% CI, 1.18-1.56) were all associated with an increased risk of ADL disability., Conclusion: Both a single measurement and dynamic trajectory of insomnia symptoms are associated with the onset of ADL disability. Increased awareness and management of insomnia symptoms may contribute to the prevention of functional disability occurrence., (© 2024. The Author(s).)

Background: Obstructive sleep apnoea (OSA) and insomnia are the two most common sleep disorders and are frequent reasons for presentation in Australian general practice., Objective: This article describes the development, content and suggested uses of the online sleep health primary care clinical resource, which provides general practitioners and other primary healthcare professionals with evidence-based information on the aetiology, assessment, management, referral and ongoing care for OSA and chronic insomnia., Discussion: The Royal Australian College of General Practitioners-accepted clinical resource for the management of OSA and chronic insomnia in primary care was developed by the Australian National Centre for Sleep Health Services Research. The resource is designed to be used during consultations (eg following the steps in assessment and management and the use of online questionnaires for the assessment of OSA [Epworth Sleepiness Scale/OSA50/STOP-Bang] and insomnia [Sleep Condition Indicator/and Insomnia Severity Index]) and as an education/training tool (eg evidence on the role of continuous positive airway pressure/mandibular advancement splints for management of OSA and brief behavioural therapy for insomnia/cognitive behavioural therapy for insomnia for the management of insomnia).

To develop and validate the Insomnia in Response to Sports-related Stress Test (IRSST) questionnaire, a new specific instrument with the goal of sensitively measuring vulnerability to sport-specific stressful situations among elite athletes. Five hundred and thirty-one competitive elite athletes (mean age = 17.6 ± 4.4 years) completed the Ford Insomnia Response to Stress Test (FIRST) questionnaire and the IRSST, a six-item questionnaire developed to assess the level of sleep disturbance in response to the commonly experienced sport-specific stressful situations. A development and validation process including substantive, structural, and external stages was used in the present study. One eigenvalue of the exploratory factor analyses was greater than 1.0 (i.e., 2.91, 48.52% of explained variance) whereas the scree test provided evidence for a one-factor solution, with all the six items achieving a loading of 0.40 or higher on the factor. Cronbach alpha was 0.77 and provided evidence for the reliability of the IRSST score. The correlation between IRSST and FIRST scores was 0.47 (p < 0.001, moderate effect size). These results provide strong evidence for construct validity, indicating that the IRSST is a promising scale for assessing the likelihood of sleep disruption due to sports-related stressful situations. The results of reliability and correlational analyses provided further evidence of the promising psychometric properties of the IRSST. We believe that the IRSST could provide to the sport and sleep science communities a sleep screening tool for use in this unique population., (© 2024 The Authors. European Journal of Sport Science published by Wiley‐VCH GmbH on behalf of European College of Sport Science.)

Aims: As people age, sleep stages and characteristics transition over time, but sleep deficits can profoundly impact health and cognitive functioning. Chronic sleep deprivation is linked to impaired attention and productivity, weakened immunity, increased risk of cardiovascular disease, obesity, and mental health disorders. Insomnia, obstructive sleep apnea syndrome, hormonal changes, nocturia, neurological disorders, and life events interfere with sleep patterns and some are linked to lower urinary tract symptoms (LUTS). This NOPIA symposium on Lifelong LUTS aimed to analyze the literature on associations between sleep and LUTS, generate ideas for future research, and explore whether there is support for the concept of lifelong LUTS in relation to changes in sleep throughout the lifespan., Methods: An international panel of experts took part in an online meeting addressing the role of lifelong LUTS in relationship to sleep and the brain organized by the NOPIA research group. The manuscript summarizes existing literature, hypotheses, future research ideas, and clinical recommendations., Results: Insomnia, sleep fragmentation, hyperarousal, and sensory processing disorders emerged as potential factors in the relationship between sleep and LUTS. Insomnia is often a persistent factor and may have been the initial symptom; however, it is often unrecognized and/or unaddressed in healthcare settings. By recognizing insomnia as a primary driver of various health issues, including nocturia, transitional care aims to address root causes and underlying problems earlier to initiate appropriate treatment., Conclusions: A multidisciplinary approach with collaboration between healthcare professionals from various disciplines, such as urology, sleep medicine, gynecology, pediatrics, and geriatrics, is needed and should include validated measurements such as the insomnia severity index and sleep and voiding diaries. Ensuring ongoing follow-up and monitoring through transitional care is crucial for individuals with persistent sleep problems and LUTS, allowing issues that arise or fluctuate over the lifespan to be addressed., (© 2024 Wiley Periodicals LLC.)

Sleep significantly impacts health. Insomnia, characterized by difficulty with sleep onset, maintenance, and subsequent daytime symptoms, is increasingly prevalent and increases the risk of other medical comorbidities. The pathophysiology involves hyperarousal during non-REM sleep and altered sleep homeostasis. The 3P model explains the development and persistence of insomnia. Assessment is primarily clinical and based on appropriate history while distinguishing from other sleep disorders. "Somnomics" suggests a personalized approach to management. Cognitive behavioral therapy for insomnia is the first-line treatment in addition to other nonpharmacological strategies. Medications are a secondary option with weak supporting evidence., Competing Interests: Disclosure No disclosure., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: In spite of the major role of early maladaptive schemas in vulnerability to various psychological disorders, studies about the relationship between early maladaptive schemas and insomnia disorder are scarce. Hence, the aim of the current study was to explore the contribution of early maladaptive schemas in insomnia severity by comparing a sample of patients with chronic insomnia and good sleepers., Methods: Patients with chronic insomnia and good sleepers were evaluated using Young Schema Questionnaire-Short Form (YSQ-SF), Depression Anxiety and Stress Scale (DASS-21), and Insomnia Severity Index (ISI)., Results: The study enrolled 117 patients with chronic insomnia and 76 good sleepers. All early maladaptive schemas (EMSs) except for enmeshment showed significant correlations with insomnia severity. After controlling for depression/anxiety symptoms, logistic regression analysis showed that the EMSs including emotional deprivation, vulnerability to harm, and subjugation schemas were significantly associated with insomnia severity., Conclusions: These preliminary findings suggest that EMSs may constitute a vulnerability factor for developing insomnia. Early maladaptive schemas may require attention in the existing treatments of insomnia., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers., Methods: A panel of experts developed an algorithm for diagnosing and treating chronic insomnia disorder, based on current guidelines and experience in clinical practice. An online survey was conducted with primary care physicians from France, Germany, Italy, Spain, and the United Kingdom, who treat chronic insomnia patients, between January and February 2023. A sub-sample of participants provided open-ended feedback on the algorithm and gave suggestions for improvements., Results: Overall, 106 primary care physicians completed the survey. Half (52%, 55/106) reported they did not regularly screen for insomnia and half (51%, 54/106) felt they did not have enough time to address patients' needs in relation to insomnia or trouble sleeping. The majority (87%,92/106) agreed the algorithm would help diagnose chronic insomnia patients and 82% (87/106) agreed the algorithm would help improve their clinical practice in relation to managing chronic insomnia. Suggestions for improvements were making the algorithm easier to read and use., Conclusion: The algorithm developed for, and tested by, primary care physicians to diagnose and treat chronic insomnia disorder may offer significant benefits to providers and their patients through ensuring standardization of insomnia diagnosis and management., (© 2024. The Author(s).)

Study Objectives: The 3P and 4P models represent illness severity over the course of insomnia disorder. The 3P model suggests that illness severity is worst during acute onset. The 4P model suggests that illness severity crescendos with chronicity. The present analysis from an archival dataset assesses illness severity with new onset illness (i.e. from good sleep [GS] to acute insomnia [AI] to chronic insomnia [CI]). Illness severity is quantified in terms of total wake time (TWT)., Methods: GSs (N = 934) were followed up to 1 year with digital sleep diaries, and classified as GS, AI, or CI. Data for CIs were anchored to the first of 14 days with insomnia so that day-to-day TWT was represented prior to and following AI onset. A similar graphic (+/-acute onset) was constructed for number of days per week with insomnia. GS data were temporally matched to CI data. Segmented linear mixed regression models were applied to examine the change in slopes in the AI-to-CI period compared to GS-to-AI period., Results: Twenty-three individuals transitioned to AI and then CI. Average TWT rose during the first 2 weeks of AI onset (b = 1.8, SE = 0.57, p = 0.001) and was then stable for 3 months (b = -0.02, SE = 0.04, p = 0.53). Average number of affected days was stable from AI to CI (b = 0.0005, SE = 0.002, p = 0.81). That is, while there was week-to-week variability in the number of days affected, no linear trend was evident., Conclusions: In our sample of CIs, primarily with middle insomnia, the average severity and number of affected days were worst with the onset of AI (worst is first) and stable thereafter., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Insomnia and eveningness are common and often comorbid conditions in youths. While cognitive behavioural therapy for insomnia (CBT-I) has been suggested as a promising intervention, it remains unclear whether it is sufficient to also address circadian issues in youths. In addition, despite that light has been shown to be effective in phase-shifting one's circadian rhythm, there has been limited data on the effects of bright light therapy and its combination with CBT-I on sleep and circadian outcomes in youths. The current protocol outlines a randomised controlled trial that examines the efficacy of CBT-I and CBT-I plus bright light therapy (BLT) in reducing insomnia severity, improving mood symptoms and daytime functioning (e.g. sleepiness, fatigue, cognitive function), and improving subjective and objective sleep and circadian measures compared to a waitlist control group., Methods: We will carry out a randomised controlled trial (RCT) with 150 youths aged 12-24 who meet the criteria of insomnia and eveningness. Participants will be randomised into one of three groups: CBT-I with bright light therapy, CBT-I with placebo light, and waitlist control. Six sessions of CBT-I will be delivered in a group format, while participants will be currently asked to use a portable light device for 30 min daily immediately after awakening throughout the intervention period for bright light therapy. The CBT-I with light therapy group will receive bright constant green light (506 lx) while the CBT-I with placebo light group will receive the modified light device with the LEDs emitting less than 10 lx. All participants will be assessed at baseline and post-treatment, while the two active treatment groups will be additionally followed up at 1 month and 6 months post-intervention. The primary outcome will be insomnia severity, as measured by the Insomnia Severity Index. Secondary outcomes include self-reported mood, circadian, daytime functioning, and quality of life measures, as well as sleep parameters derived from actigraphy and sleep diary and neurocognitive assessments. Objective measures of the circadian phase using dim-light melatonin onset assessment and sleep parameters using polysomnography will also be included as the secondary outcomes., Discussion: This study will be the first RCT to directly compare the effects of CBT-I and BLT in youths with insomnia and eveningness. Findings from the study will provide evidence to inform the clinical management of insomnia problems and eveningness in youths., Trial Registration: ClinicalTrials.gov NCT04256915. Registered on 5 February 2020., (© 2024. The Author(s).)

Objective: To test the validity and reliability of the Sleep Health Index (SHI) in a Chinese clinical sample, and thereby provide more evidence for the assessment of sleep health in future research and clinical practice., Methods: This study used a cross-sectional design. A convenient sample of 265 participants with spinal degenerative diseases was recruited from outpatient clinics. The SHI, Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Patient Health Questionnaire-9 (PHQ-9), Visual Analogue Scale (VAS), and EuroQoL 5-Dimension 5-Level (EQ-5D-5L) were administered via REDCap. Structural, concurrent, convergent, known-group validity, internal consistency, and test-retest reliability were evaluated., Results: Confirmatory factor analysis confirmed a 3-factor structure (sleep duration, sleep quality, and disordered sleep). The overall SHI score had a high correlation with PSQI and ISI (r = -0.62 and -0.70, respectively) as well as a moderate correlation with PHQ-9 (r = -0.50, p<0.001). The overall SHI was significantly associated with VAS, ESS, and EQ-5D-5L (r = -0.15 to -0.23, p<0.05). Participants with pain had a lower score on the sleep quality sub-index than those without (p<0.001). Those with chronic diseases had a significantly lower score on the sleep duration sub-index than those without (p<0.05). Those with depression, poor sleep quality, and insomnia had lower scores on the overall scale and the three sub-indices than those without (p<0.05). The overall SHI showed acceptable internal consistency (Cronbach's α = 0.74) and test-retest reliability (intraclass correlation coefficient = 0.73)., Conclusions: The Chinese version of SHI showed good validity and acceptable reliability and could be used to assess sleep health among clinical populations., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Study Objectives: We evaluated the accuracy of ChatGPT in addressing insomnia-related queries for patient education and assessed ChatGPT's ability to provide varied responses based on differing prompting scenarios., Methods: Four identical sets of 20 insomnia-related queries were posed to ChatGPT. Each set differed by the context in which ChatGPT was prompted: no prompt, patient-centered, physician-centered, and with references and statistics. Responses were reviewed by 2 academic sleep surgeons, 1 academic sleep medicine physician, and 2 sleep medicine fellows across 4 domains: clinical accuracy, prompt adherence, referencing, and statistical precision, using a binary grading system. Flesch-Kincaid grade-level scores were calculated to estimate the grade level of the responses, with statistical differences between prompts analyzed via analysis of variance and Tukey's test. Interrater reliability was calculated using Fleiss's kappa., Results: The study revealed significant variations in the Flesch-Kincaid grade-level scores across 4 prompts: unprompted (13.2 ± 2.2), patient-centered (8.1 ± 1.9), physician-centered (15.4 ± 2.8), and with references and statistics (17.3 ± 2.3, P < .001). Despite poor Fleiss kappa scores, indicating low interrater reliability for clinical accuracy and relevance, all evaluators agreed that the majority of ChatGPT's responses were clinically accurate, with the highest variability on Form 4. The responses were also uniformly relevant to the given prompts (100% agreement). Eighty percent of the references ChatGPT cited were verified as both real and relevant, and only 25% of cited statistics were corroborated within referenced articles., Conclusions: ChatGPT can be used to generate clinically accurate responses to insomnia-related inquiries., Citation: Alapati R, Campbell D, Molin N, et al. Evaluating insomnia queries from an artificial intelligence chatbot for patient education. J Clin Sleep Med . 2024;20(4):583-594., (© 2024 American Academy of Sleep Medicine.)

Objective: To assess the use of cannabis as a symptom management strategy for patients with fibromyalgia., Patients and Methods: An electronic, cross-sectional survey was conducted among patients diagnosed with fibromyalgia and treated in Integrative Medicine & Health at Mayo Clinic, Rochester, Minnesota. The survey was constructed with the Symptom Management Theory tool and was sent anonymously via web-based software to patients with a diagnosis of fibromyalgia., Results: Of 5234 patients with fibromyalgia sent the online survey, 1336 (25.5%) responded and met the inclusion criteria. Survey respondents had a median age of 48 (Q1-Q3: 37.5-58.0) years, and most identified as female. Nearly half of respondents (49.5%, n=661) reported cannabis use since their fibromyalgia diagnosis. The most common symptoms for which respondents reported using cannabis were pain (98.9%, n=654); fatigue (96.2%; n=636); stress, anxiety, or depression (93.9%; n=621); and insomnia (93.6%; n=619). Improvement in pain symptoms with cannabis use was reported by 82.0% (n=536). Most cannabis-using respondents reported that cannabis also improved symptoms of stress, anxiety, and depression and of insomnia., Conclusion: Considering that cannabis is a popular choice among patients for managing fibromyalgia symptoms, clinicians should have adequate knowledge of cannabis when discussing therapeutic options for fibromyalgia with their patients., (Copyright © 2024 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Background: The Jenkins Sleep Scale is a widely used self-report questionnaire that assesses sleep quality and disturbances. This study aimed to translate the scale into Arabic and evaluate its psychometric properties in an Arabic-speaking population., Methods: The Jenkins Sleep Scale was translated into Arabic using forward and backward translation procedures. The Arabic version was administered to a convenience sample of 420 adults along with the Pittsburgh Sleep Quality Index (PSQI) and Athens Insomnia Scale (AIS) for validation purposes. Reliability was examined using Cronbach's alpha and McDonald's omega coefficients. Confirmatory factor analysis (CFA) was also conducted to test the unidimensional factor structure. Convergent validity was assessed using correlations with PSQI and AIS scores., Results: The Cronbach's alpha and McDonald's omega values for the Arabic Jenkins Sleep Scale were 0.74 and 0.75, respectively, indicating good internal consistency. The 2-week and 4-week test-retest intraclass correlation coefficients were both 0.94 (p < 0.001), indicating excellent test-retest reliability. The CFA results confirmed the unidimensional factor structure (CFI = 0.99, TLI = 0.96, RMSEA = 0.08). The measurement model had an equivalent factor structure, loadings, intercepts, and residuals across sex, age, and marital status. Significant positive correlations were found between the Arabic Jenkins scale score and the PSQI (r = 0.80, p < 0.001) and AIS (r = 0.74, p < 0.001), supporting convergent validity., Conclusion: The Arabic version of the Jenkins Sleep Scale demonstrated good psychometric properties. The findings support its use as a valid and reliable measure for evaluating sleep quality and disturbances among Arabic-speaking populations., (© 2024. The Author(s).)

Individuals with delayed sleep phase disorder (DSPD) are unable to naturally fall asleep and awake at conventional times; for this reason, DSPD is often mistaken for insomnia. However, unlike many patients with insomnia, those with DSPD struggle to get up at appropriate times. DSPD is associated with school refusal, academic difficulties, and lower employment rate. DSPD in youth has prevalence as high as 16%, and is often comorbid with other psychiatric disorders. Treatments include appropriate light exposure during the day, melatonin use, developing an evening routine that minimizes arousal-increasing activities, and gradually shifting sleep-wake times toward more functional ones., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Competing Interests: D. Léger déclare avoir participé à des interventions ponctuelles pour Idorsia, BioSerenity, Vanda Pharmaceuticals, iSommeil, Sanofi, et avoir été pris en charge, à l’occasion de déplacements pour congrès, par Linde, SOS Oxygène et VitalAire. V. Pitron déclare n’avoir aucun lien d’intérêts.

PATHOPHYSIOLOGICAL HYPOTHESES AND DIAGNOSIS OF INSOMNIA DISORDER. All pathophysiological models place hyperarousal as a central process in the mechanisms of insomnia. These models differ, however, in terms of the importance and role of the variables explaining this hyperarousal. Behavioral and cognitive models describe self-maintenance behaviors and dysfunctional thoughts, such as worries and concerns about sleep and the consequences of insomnia. Alterations in cognitive functions related to hyperarousal in perceptual and memory processes can explain these behaviors and thoughts. Neurobiological models show instability in the sleepwake balance, with orexin possibly involved, but this remains to be confirmed. The diagnosis of insomnia must consider the semiology related to the mechanisms of insomnia, as well as co-morbidities., Competing Interests: J.-A. Micoulaud-Franchi, L. Boileau et C. Quiles déclarent n’avoir aucun lien d’intérêts. J. Coelho déclare avoir été pris en charge à l’occasion de déplacement pour congrès par SOS Oxygène. A. Geoff roy déclare avoir participé à des interventions ponctuelles pour Apneal, Biocodex, Idorsia, Janssen-Cilag, Jazz Pharmaceuticals, Myndblue, Posos, ResilEyes, Withings, Lundbeck, MySommeil et avoir été pris en charge par les mêmes entreprises à l’occasion de déplacements pour congrès.

Competing Interests: D. Léger déclare avoir participé à des interventions ponctuelles pour Idorsia, BioSerenity, Vanda Pharmaceuticals, iSommeil, Sanofi et avoir été pris en charge, à l’occasion de déplacements pour congrès, par Linde, SOS Oxygène et VitalAire . J . Taïeb déclare n’avoir aucun lien d’intérêts.

Competing Interests: V. Pitron et B. Faraut déclarent n’avoir aucun lien d’intérêts. D. Léger déclare avoir participé à des interventions ponctuelles pour Idorsia, BioSerenity, Vanda Pharmaceuticals, iSommeil, Sanofi et avoir été pris en charge, à l’occasion de déplacements pour congrès, par Linde, SOS Oxygène et VitalAire.

INSOMNIA AND PSYCHIATRIC DISORDERS. Insomnia is frequent in psychiatric disorders. In particular, insomnia can be a risk factor, as well as a comorbid condition, or a symptom and an early sign of psychiatric disorders. Insomnia may emerge during any stage of illness. It includes prodromal, first episode, acute, recurrence, and even remission stages, thereby being associated with a worse course of illness. Insomnia increased symptom severity, relapses or recurrences, and increased suicidal risk. Thus, insomnia is an important modifiable risk factor to prevent psychiatric disorders and/or achieve and maintain remission. Thereby insomnia evaluation and management should be a priority in psychiatric cares. Indeed, it has been demonstrated that targeting insomnia can not only improve insomnia itself but also have a positive impact on the trajectory of psychiatric disorders., Competing Interests: J. Maruani déclare avoir participé à des interventions ponctuelles pour Biocodex et Idorsia et avoir été prise en charge, à l’occasion de déplacements pour congrès, par Biocodex. P. A. Geoffroy déclare avoir participé à des interventions ponctuelles pour Apneal, Biocodex, Idorsia, Janssen-Cilag, Jazz Pharmaceuticals, Myndblue, Posos, ResilEyes, Withings, Lundbeck, MySommeil et avoir été pris en charge par les mêmes entreprises à l’occasion de déplacements pour congrès.

Objective: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue., Methods: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis., Results: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643)., Conclusions: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts., Competing Interests: Declaration of competing interest None. The authors have no competing interests to report., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To determine differences in insomnia, depression and anxiety between ENT patients with benign and malignant conditions prior to and after an urgent suspicion of cancer appointment., Methods: Out-patients with urgent suspicion of cancer completed three psychometric questionnaires prior to their appointment and at two to four weeks post-diagnosis., Results: There was no significant difference in questionnaire scores between malignant and benign patients prior to the patients' appointments ( p > 0.05 for all questionnaires). In benign patients, there was significant improvement in scores for all questionnaires ( p < 0.01) and in malignant patients there was significant worsening of scores for all questionnaires ( p < 0.01) at follow-up appointments., Conclusion: Prior to appointments, patients with benign and malignant conditions experienced similar levels of insomnia, depression and anxiety. Following diagnosis, cancer patients had significantly poorer scores, indicating worsening of these symptoms. In patients with benign diagnoses, all questionnaire scores improved, indicating resolution of their symptoms and possible association between the appointment and their baseline scores.