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Introduction: Significant interindividual variability in spinal cord stimulation (SCS) outcomes exists. Due to its high cost and risks of complications, criteria to guide patient selection for SCS trials and their outcomes would be helpful. With increased focus on the use of patient-reported outcomes to improve care, we aim to evaluate the National Institute of Health Patient Reported Outcome Measurement Information System measures for an association with successful SCS trials in patients with persistent pain., Methods: Our prospective, observational study enrolled 60 patients with persistent pain who underwent an SCS trial. Patients completed demographic and Patient Reported Outcome Measurement Information System computer adaptive test (PROMIS CAT) assessments to measure self-reported pain interference, depression, anxiety, physical functioning, and sleep disturbance at the time they presented for placement of their trial device., Results: Of the 58 patients who underwent successful electrode placement, 11 had an unsuccessful trial. There were no differences in patient demographics between patients with a successful and an unsuccessful trial. Patients who had a successful SCS trial reported lower pre-trial levels of anxiety, depression, and sleep disturbance and decreased post-trial levels of depression, sleep disturbance, and pain interference., Conclusions: We found that patients with high levels of depression, anxiety, and sleep disturbance using the PROMIS CAT were predictive of unsuccessful trials. In addition, we found that patients with successful SCS trials reported lower levels of these domains on PROMIS CAT administered at the end of the trial., Competing Interests: Competing interests: LB, GLS, JS, and KS have no conflicts to disclose. JHG is a consultant for Saluda Medical, Abbott, and Stratus Medical and the recipient of research support paid to the institution by SPR Therapeutics and Mainstay Medical. NP is an employee of Eli Lilly and Company., (© American Society of Regional Anesthesia & Pain Medicine 2024. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.)

The increasing interruptions of nocturnal sleep with aging must be distinguished from actual sleep disorders. Morbidity and medication have an impact on sleep. The relationship between sleep and morbidity is mutual. Disturbed sleep modifies many clinical symptoms and diseases affect sleep and the ability to sleep. Geriatric syndromes such as falls, depression and dementia are modified by sleep disorders. Geriatric syndromes can be favorably influenced by the treatment of sleep disorders. Adequate diagnostics are important prerequisites. Coincidences of different sleep disorders are frequent. The medical history of patients and a sleep diary form the basis of the diagnostics. Sleep medicine provides further technical examination procedures. Older people should be examined in a sleep laboratory if the results have consequences that are accepted by the patient. This should be clarified in advance., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Sleep problems are common in adults with ADHD and may be bidirectionally associated with ADHD severity and other psychiatric symptoms. We investigated the prevalence of positive screenings for various sleep disorders, and their association with psychiatric comorbidities in a large sample of adults with ADHD from a specialized outpatient clinic., Methods: We included data of 3,691 adult patients diagnosed with ADHD, who had filled out a screener for sleep disorders (Holland Sleep Disorders Questionnaire (HSDQ)) as part of routine diagnostic assessment. The HSDQ screens for the sleep disorders insomnia, parasomnia, hypersomnia, circadian rhythm sleep disorders (CRSD), restless legs syndrome (RLS)/periodic limb movement disorder (PLMD), and sleep-related breathing disorders (SBD). As delayed sleep phase syndrome (DSPS) is very frequent in ADHD, we additionally screened for DSPS. Psychiatric comorbidities were diagnosed through clinical assessment and the Mini International Neuropsychiatric Interview (M.I.N.I.) Plus, which assesses 26 psychiatric disorders following the classification of the DSM-5. All data were retrieved from the electronic patient files., Results: Mean age was 35.4 and 49.4% of the patients were female. About 60% of the adults with ADHD screened positive for any sleep disorder. Highest prevalences were found for symptoms of DSPS (36%), insomnia (30%), and RLS/PLMD (29%). Sleep problems in adults with ADHD were associated with comorbid depression, anxiety, substance use disorder, personality disorder, and post-traumatic stress disorder., Conclusion: Adults with ADHD often report sleep problems, which are associated with specific psychiatric comorbidities. Systematic screening for sleep disorders in adult patients with ADHD can contribute to a better understanding of their complaints and may aid improved and integrated treatment for the sleep and psychiatric problems., 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.

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.)

Sleep and biological rhythms are integral to mood regulation across the lifespan, particularly in bipolar disorder (BD), where alterations in sleep phase, structure, and duration occur in all mood states. These disruptions are linked to poorer quality of life, heightened suicide risk, impaired cognitive function, and increased relapse rates. This review highlights the pathophysiology of sleep disturbances in BD and aims to consolidate understanding and clinical applications of these phenomena. It also summarizes the evolution of sleep and biological rhythms assessment methods, including ecological momentary assessment (EMA) and digital phenotyping. It underscores the importance of recognizing circadian rhythm involvement in mood regulation, suggesting potential therapeutic targets. Future research directions include elucidating circadian clock gene mechanisms, understanding environmental impacts on circadian rhythms, and investigating the bidirectional relationship between sleep disturbances and mood regulation in BD. Standardizing assessment methods and addressing privacy concerns related to EMA technology and digital phenotyping are essential for advancing research. Collaborative efforts are crucial for enhancing clinical applicability and understanding the broader implications of biological rhythms in BD diagnosis and treatment. Overall, recognizing the significance of sleep and biological rhythms in BD offers promise for improved outcomes through targeted interventions and a deeper understanding of the disorder's underlying mechanisms., (© 2024 The Author(s). Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

Purpose: This study aimed to translate and validate the Japanese Sleep Questionnaire for Preschoolers (JSQ-P) into a Thai version and to evaluate factors associated with sleep problems in preschoolers., Methods: The cross-cultural adaptation of JSQ-P into Thai version of JSQ-P (TH-JSQ-P) was performed including forward translation, reconciliation, backward translation, and final approval by the original creator. Children aged 2-6 years from community and clinical settings were enrolled. The TH-JSQ-P was completed by parents of preschool children twice within 10-14 days to assess its reliability and validity. Six validity tests including face validity, content validity, structural validity, construct validity, criterion validity and predictive validity were assessed. Demographic data, Epworth sleepiness scale (ESS), medical and behavior problems, and socioeconomic status were collected for analysis., Results: A total of 2613 participants completed the TH-JSQ-P, with 1207 completing it twice to assess test-retest reliability. The TH-JSQ-P had good internal consistency reliability and test-retest ability with Cronbach's alpha value of 0.898 and intraclass correlation coefficient of 0.814. The content validity was acceptable. The ESS had correlation with sleep problems in almost all subscales except for sleep habit., Conclusions: The TH-JSQ-P is a valid and reliable instrument for evaluating sleep problems in preschool children in Thailand. It has the potential to be extensively utilized to screen for sleep problems, thereby enabling healthcare professionals to identify them early and implement prompt management., 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. Published by Elsevier B.V.)

Objectives: Patients with fibromyalgia syndrome (FM) often suffer from sleep disorders, adversely affecting their prognosis. Active non-pharmacological therapies are considered the mainstay of treatment for FM, but the optimal treatment choice remains contentious. We aimed to compare and rank community-based non-pharmacological interventions for FM with sleep disorder by quantifying information from randomised controlled trials (RCTs)., Methods: Two authors independently selected studies and extracted data. We searched Embase, MEDLINE, PubMed, CNKI, Scopus, Google Scholar, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) from the database inception to June 2022. Network meta-analyses were conducted using a frequency-based method. The study protocol is registered with the Prospective Register of Systematic Reviews (PROSPERO, CRD 42022373704). Eleven RCTs (n=729) were included in the analysis., Results: Mindfulness-based therapy (MBT) (SMD=-0.84 (95% CI: -1.49 to -0.20)) and cognitive behavioural therapy (CBT) (SMD=-0.54 (95% CI: -1.04 to -0.04)) were associated with significantly improved sleep symptoms in a patient with FM compared with usual care., Conclusions: MBT exhibited the highest probability (91.14%) of being the most effective intervention in sleep improvement, followed by CBT (72.39%). MBT exhibited marked advantages over other interventions and is likely to have optimal efficacy in ameliorating sleep disorders.

Lewy body dementia (LBD) is the second most common neurodegenerative disorder after Alzheimer's disease. Cognitive fluctuations, visual hallucinations, parkinsonian signs, and rapid eye movement sleep behavior disorder (RBD) are diagnostic criteria. However, the diverse clinical presentations complicate diagnosis and management, as the disease may begin with psychiatric symptoms, confusion, sleep disturbances, and/or autonomic dysfunction. Its progression often leads to neuropsychiatric and behavioral symptoms that can overshadow cognitive impairments. Diagnosing LBD is crucial to avoid first iatrogenic effects (e.g., neuroleptics), and then facilitate a personalized approach to address cognitive and neuropsychiatric management, while considering comorbidities., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Objective: To construct and verify a risk prediction model for sleep disturbance in elderly patients with hypertension, aiming to offer guidance for sleep management in this demographic., Methods: A cohort of 6,708 elderly hypertensive patients from the NHANES database, spanning 2005 to 2018, met the inclusion criteria and were selected for this study. Participants were randomly assigned to a development group (n = 4,696) and a verification group (n = 2,012) in a 7:3 ratio. The occurrence of sleep disturbance was assessed across the subjects. Independent risk factors for sleep disturbance were analyzed using weighted multivariate logistic regression within the development group. A predictive model for sleep disturbance risk in elderly hypertensive patients was developed and verified using Stata 17.0. The model's predictive accuracy and stability were evaluated using the verification group's data., Results: Of the 6,708 subjects, 2,014 (30.02%) were identified with sleep disturbance, and the weighted prevalence of sleep disturbance among elderly hypertensive patients was 33.283%. Weighted multivariate logistic regression analysis in the development group revealed that six factors were independently associated with sleep disturbance: higher total depression scores, higher education level, asthma, overweight, arthritis, and work restriction (OR > 1 and P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) for the nomogram prediction model was 0.709 in the development group and 0.707 in the verification group, indicating good discrimination ability. Brier scores for the nomogram model were 0.185 in the development group and 0.189 in the verification group, both below 0.25, suggesting good calibration. Decision Curve Analysis (DCA) determined that the nomogram's clinical net benefit was maximized when the threshold probability for sleep disturbance in elderly hypertensive patients was 0.13-0.67 in the development group and 0.14-0.61 in the verification group, highlighting the model's clinical utility., Limitations: This study is not without its limitations, including issues with data collection, the absence of external validation, and the non-extrapolation of results., Conclusion: The prevalence of sleep disturbance among elderly hypertensive patients stands at 33.283%. The nomogram model, based on identified risk factors for sleep disturbance in this population, has demonstrated good predictive efficiency and clinical relevance. It serves as a valuable tool to assist healthcare providers in identifying elderly hypertensive patients at high risk for sleep disturbance., (© 2024. The Author(s).)

Night sweats are a common symptom. There is a lack of a uniform definition and a diagnostic guideline. In this article we propose a structural analysis for all levels of healthcare. First, we need to distinguish night sweats with or without fever. We will then discuss the main differential diagnoses (infection, malignancies, sleeping disorders and medication-related) and emphasize the role of diagnostic clues. A screening for infections, sleeping disorders and a medication review are a must for every patient. Furthermore we will explain the role of PET-CT and bone marrow examination.

Executive summary: This document is a consensus statement of a subcommittee of experienced sleep physicians and scientists, tasked to review the literature and formulate recommendations on the indications, performance, and reporting of sleep studies, to update clinical practice from the 2017 Australasian Sleep Association (ASA) guidelines for sleep studies in adults (Douglas JA, Chai-Coetzer CL, McEvoy D, et al. Guidelines for sleep studies in adults - a position statement of the Australasian Sleep Association. Sleep Med. 2017;36(Suppl 1):S2-S22. doi:10.1016/j.sleep.2017.03.019). This document moves the focus beyond important discussions outlined in the 2017 guidelines, particularly surrounding the sensitivity and specificity of validated questionnaires and home sleep studies. The 2024 guide outlines the performance of the broad range of sleep testing available for the investigations of sleep disorders in adults including indications, strengths, limitations, and reporting standards., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)

Background: Poor memory for treatment is associated with poorer treatment adherence and poorer patient outcomes. The memory support intervention (MSI) was developed to improve patient memory for treatment with the goal of improving patient outcomes. The aim of this study protocol is to conduct a confirmatory efficacy trial to test whether a new, streamlined, and potent version of the MSI improves outcomes for midlife and older adults. This streamlined MSI is comprised of constructive memory supports that will be applied to a broader range of treatment content. The platform for this study is the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C). We will focus on midlife and older adults who are low income and experiencing mobility impairments., Methods: Participants (N = 178) will be randomly allocated to TranS-C + MSI or TranS-C alone. Both intervention arms include eight 50-min weekly sessions. Assessments will be conducted at pre-treatment, post-treatment, 6-, and 12-month follow-up (6FU and 12FU). Aim 1 will compare the effects of TranS-C + MSI versus TranS-C alone on sleep and circadian functioning, daytime functioning, well-being, and patient memory. Aim 2 will test whether patient memory for treatment mediates the relationship between treatment condition and patient outcomes. Aim 3 will evaluate if previously reported poor treatment response subgroups will moderate the relationship between treatment condition and (a) patient memory for treatment and (b) treatment outcome. Exploratory analyses will compare treatment condition on (a) patient adherence, patient-rated treatment credibility, and patient utilization of treatment contents, and (b) provider-rated acceptability, appropriateness, and feasibility., Discussion: This study has the potential to provide evidence for (a) the efficacy of a new simplified version of the MSI for maintaining health, well-being, and functioning, (b) the wider application of the MSI for midlife and older adults and to the treatment of sleep and circadian problems, and (c) the efficacy of the MSI for sub-groups who are likely to benefit from the intervention., Trial Registration: ClinicalTrials.gov NCT05986604. Registered on 2 August 2023., (© 2024. The Author(s).)

Background: Idiopathic Parkinson's disease (IPD) is associated with a spectrum of nonmotor symptoms (NMS) that contribute as much to the burden of the disease as the more obvious motor abnormalities. There is a paucity of literature determining the association between NMS and the severity of IPD, especially in rural eastern parts of India., Aims of Study: This study explores the frequency of NMS in patients with IPD and determines the association between NMS and the severity of IPD., Materials and Methods: A cross-sectional observational study involving 65 serially enrolled IPD patients (diagnosed as per United Kingdom Parkinson's Disease Society Brain Bank Clinical Diagnostic Criteria) was conducted over a period of 18 months. The patients were evaluated for disease severity as per the Hoehn and Yahr scale (original) and were subsequently evaluated with a predesigned and validated Parkinson's Disease Non-Motor Symptoms Questionnaire. Pearson's Chi-squared test for independence of attributes or Fisher's exact test was used for comparison of categorical variables across the groups. Continuous variables were compared using a one-way analysis of variance (ANOVA) test., Results: The most common presenting NMS of IPD was low mood ( n = 61; 93.85%), followed by apathy ( n = 59; 90.77%), impaired concentration ( n = 58; 89.23%), restless leg ( n = 54; 83.08%), and tenesmus ( n = 54; 83.08%). A majority of the neuropsychiatric symptoms, autonomic dysfunctions, sleep abnormalities, gastrointestinal manifestations, and sensory abnormalities individually showed a statistically significant relation with the severity of IPD., Conclusion: Common presenting NMS include neuropsychiatric manifestations, autonomic symptoms, sleep-related symptoms, and gastrointestinal manifestations. Most of the NMS are significantly related to disease severity., (© Journal of the Association of Physicians of India 2024.)

AbstractThere are more than 90 recognized sleep disorders, many of which impair sleep and daytime function and adversely impact heath, well-being, and chronic disease risk. Unfortunately, many sleep disorders are undiagnosed or not managed effectively. This review describes how to identify, evaluate, and treat common sleep disorders.

Background: Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure., Methods: Using three population-based datasets (N = 1813), we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N = 378, N = 334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association., Findings: Sleep quality could predict individual DSS (r = 0.43, R 2  = 0.18, rMSE = 2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R 2  = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS., Interpretation: Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS., Funding: The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia., Competing Interests: Declaration of interests The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Mind-body exercise is a promising non-pharmacological approach for managing sleep disturbance and depression that are prevalent among older adults, but its efficacy remains inconclusive across the studies. This study aimed to systematically evaluate and quantify the overall effectiveness of mind-body exercises on sleep disturbance and depression in older adults., Methods: We searched eight databases to identify relevant articles from their inception to April 2024. Experimental studies that evaluate the effects of mind-body movement therapy on sleep disturbance and depression in older adults were included. The Cochrane Risk of Bias tool version 2.0 was used to appraise included studies. The pairwise meta-analysis was performed through the software Comprehensive Meta-Analysis Version 3.0. Moreover, subgroup analysis was utilized to understand the effect size on each form of mind-body exercise and to determine the source of heterogeneity., Results: A total of 27 studies were eligible and synthesized. The mind-body exercise appears statistically significant in reducing sleep disturbance (SMD = -0.60, 95% CI: 0.76 ∼ 0.44) and depression (SMD = -0.56, 95% CI: 0.75 ∼ 0.36) among older adults. Each form of mind-body exercise, including pilates, yoga, qigong, and tai-chi, effectively decreases sleep disturbance and depression, but there were no significant differences in the effects between these practices., Conclusion: The cumulative evidence concludes that older adults who participated in mind-body exercise programs showed potential improvements in sleep disturbance and depression. This finding may serve as evidence for professionals to use this approach as a non-pharmacological approach to help older adults in the community and long-term care facilities who are having sleep disturbance and depression. More clinical trials are needed for an in-depth meta-analysis that can rank and compare the efficacy of each mind-body exercise practice., Competing Interests: Conflicts of interest No conflicts of interest to declare., (Copyright © 2024. Published by Elsevier B.V.)

Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data for diagnosis and treatment of voice, swallowing, and sleep disorders. Clinicians face challenges in efficiently synthesizing these data for personalized patient care. Artificial intelligence (AI), specifically machine learning and deep learning, offers innovative solutions for processing and interpreting these data, revolutionizing diagnosis and management in these fields, and making care more efficient and effective. In this study, we review recent AI-based innovations in the fields of laryngology, broncho-esophagology, and sleep surgery., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Stroke represents a significant health crisis in the United States, claiming approximately 140,000 lives annually and ranking as the fifth leading cause of death., Objective: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) for 2005 to 2008, this study examines the correlation between various sleep characteristics and both stroke morbidity and all-cause mortality among U.S. adults., Methods: We applied logistic regression, Cox regression, and subgroup analyses to a sample of 7,827 adults aged 18 and older from NHANES 2005-2008. The study focused on six sleep characteristics: duration of sleep, sleep onset latency, snoring frequency, number of awakenings, frequency of leg spasms during sleep, and daytime sleepiness, analyzing their impacts on stroke incidence and mortality rates., Results: Participants had an average age of 45.80 ± 0.45 years, with females accounting for 48.13 % of the sample. Analysis revealed significant associations between sleep duration, onset latency, number of awakenings, leg spasms, and daytime sleepiness with stroke incidence. However, these associations weakened with increasing confounders. Additionally, stroke patients showed a higher likelihood of using sleep aids. The influence of sleep disturbances on stroke appeared more pronounced in females and younger demographics. An association was also noted between the number of awakenings, sleep duration, and stroke mortality rates CONCLUSIONS: The study reinforces the critical role of maintaining healthy sleep patterns in preventing strokes and enhancing stroke prognosis, emphasizing specific sleep disturbances as potential risk factors., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Sleep disturbances are common in chronic liver disease and significantly impact patient outcomes and quality of life. The severity and nature of sleep disturbances vary by liver disease etiology and severity. While there is ongoing research into the association between liver disease and sleep-wake dysfunction, the underlying pathophysiology varies and, in many cases, is poorly understood. Liver disease is associated with alterations in thermoregulation, inflammation, and physical activity, and is associated with disease-specific complications, such as HE, that may directly affect sleep. In this article, we review the relevant pathophysiologic processes, disease-specific sleep-wake disturbances, and clinical management of CLD-associated sleep-wake disturbances., (Copyright © 2024 American Association for the Study of Liver Diseases.)

Introduction: The electronic collection of patient data is used for a large variety of conditions. The boundaries of its use in initial diagnostics and the extent to which it can replace specialist contact, however, remain vague. Our newly developed virtual diagnostic process for common sleep disorders involves digital questionnaire administration, neurophysiological diagnostic studies, and virtual consultation with a sleep specialist. In this study, we evaluated patients' virtual diagnostic process experiences as part of a full evaluation of this process., Methods: Patients who completed the virtual diagnostic process participated in online semi-structured interviews based on the following criteria: age ≥ 18 years, no obvious cognitive deficits, and access to the internet. Patients who did not complete the virtual diagnostic process were contacted by telephone and given the opportunity to explain why., Results: Of the 24 patients included, 14 completed the virtual diagnostic process. Most participants understood the concept of the virtual diagnostic process, could navigate the process, and felt that the combined use of a questionnaire and virtual consultation was meaningful. Although participants could provide information as free text, the simplified closed questions, required for diagnostic classification, triggered feelings of insecurity and the inability to sufficiently describe symptoms, thereby evoking concern. All patients deemed the complementary personal contact important., Discussion: The findings demonstrate that the virtual diagnostic process is feasible and highly accepted by most patients. However, (virtual) personal (telephone or video) contact is mandatory to address patients' concerns. The virtual diagnostic process application evaluated here likely represents the outer limit of the use of electronic data collection in virtual diagnostic procedures., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: The diagnostic workup for assessment of sleep disorders commonly involves overnight testing to assess sleep patterns and pathological events. So far, little is known about preferences for provision of home sleep tests to patients with sleep disorders. This study aims to close this gap by eliciting preferences for home sleep testing using a discrete choice experiment (DCE)., Methods: A DCE with seven attributes of at-home sleep testing and three levels per attribute was developed using a fractional factorial design. Patients with and without previous sleep testing experience were recruited from two large sleep centers in Germany. Coefficients for attribute levels were calculated using a conditional logit model to estimate their influence on choice decisions and calculate the relative importance of each attribute., Results: 305 patients (54.5 ± 13,1 years, 65.3% male) were enrolled, and 288 surveys with complete data included for analysis. Attributes with greatest relevance were Waiting time to discuss sleep study results; Waiting time to conduct sleep study, and Sleep quality during measurement. Of lowest importance was Diagnostic accuracy of sleep study, followed by Effort to apply sleep study device. Significant heterogeneity in choice behavior was found, including differences by gender, willingness-to-pay for sleep studies, and previous experience with sleep studies. Preferred location for conducting sleep testing was at-home in 50.7% and in-lab in 46.9%., Conclusions: Preferences and relative importance of home sleep test attributes vary among different subgroups. Considering those preferences can be important for clinicians and policymakers when designing care pathways and planning of testing policies for sleep disorders., (© 2024. The Author(s).)

This systematic review aims to identify the sleep parameters of Olympic athletes and the instruments used to assess and monitor the sleep of these athletes. The search was conducted until February 2023 and was performed in PubMed, Web of Science, and Scopus databases. This systematic review has included studies that investigated at least one of the following sleep parameters: total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SE), awakenings after sleep onset (WASO), quality of sleep, daytime sleepiness, and chronotype; the participants were Olympic athletes. The search returned a total of 280 studies. After screening based on exclusion and inclusion criteria, 11 studies were included. The main results demonstrate that Olympic athletes have TST of 06:10 h, SE of 84%, SOL of 28 min, and WASO of 49 min. The most predominant chronotype is indifferent; over half of the athletes have poor sleep quality and complaints. Furthermore, actigraphy was the most used method to assess sleep. It is concluded that Olympic athletes have TST, SE, and WASO poor than the recommended values. In addition, sleep complaints and poor sleep quality were also observed. Among the objective sleep assessment methods, actigraphy was the method most frequently used in this population., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Study Objectives: To construct and evaluate the inter-rater reliability of the Structured Clinical Interview for DSM-5-TR Sleep Disorders - Kid (SCISD-Kid)., Method: The SCISD-Kid was modeled on the adult SCISD-R and accounted for pediatric developmental and sociocultural factors. Fifty sleep-disturbed children ( M age  = 11.9, SD  = 2.9) and 50 caregivers responded to the final SCISD-Kid. Video recordings were double-scored to evaluate inter-rater reliability., Results: The final SCISD-Kid contained approximately 90 questions. Eight of the nine covered disorders had prevalence rates sufficient for analyses for both samples (i.e., k  > 2). Inter-rater reliability was examined using Cohen kappa coefficients (κ); reliability estimates ranged from excellent to good. For youth, restless legs syndrome yielded the lowest reliability (.48), while nightmare disorder, narcolepsy, and NREM sleep arousal disorder - sleepwalking type showed the highest reliability (1.00). Across caregivers, NREM sleep arousal disorder - sleep terror type (.49) and hypersomnolence (.54) had the lowest reliability. In contrast, circadian rhythm - delayed sleep phase type, nightmare disorder, and NREM sleep arousal disorder - sleepwalking type showed the highest reliability (1.00)., Conclusions: The SCISD-Kid is a promising tool for screening sleep disorders. It showed good to excellent reliability across both samples. Next steps for validation will be discussed.

Paranoid ideation is a transdiagnostic construct that is associated with social impairment and often occurs in psychotic spectrum disorders. Little research has examined how paranoid ideation is related to social behaviors that underlie social impairment and may ultimately lead to social rejection. It is important to consider that negative symptoms and sleep problems also contribute to social impairment. No research has assessed the unique and combined influence of paranoid ideation, negative symptoms, and sleep problems on social impairment. Therefore, the current study examined how paranoid ideation, negative symptoms, and sleep problems contribute to poorer social skills and social rejection in a transdiagnostic sample of persons with psychosis and community members (N = 112). Assessments included diagnostic and symptom interviews, questionnaires, behavioral ratings of social skill and facial displays of affect, and naive observer reactions utilizing thin-slice methodology. Greater paranoid ideation, negative symptoms, and sleep problems were each related to poorer social skill and more negative reactions from observers. When considered in path analyses, negative symptoms were associated with observer reports of less willingness to interact with participants through poorer social skill. These findings demonstrate the symptom correlates of social rejection and how interpersonal behavior may contribute to social exclusion., Competing Interests: Declaration of competing interest The authors have no conflicts of interest with respect to the authorship or the publication of this article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Study Objectives: A growing body of literature suggests that deep brain stimulation to treat motor symptoms of Parkinson's disease may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of deep brain stimulation on sleep macro-architecture during the first nights of stimulation., Methods: Polysomnograms were recorded for 3 consecutive nights in 14 patients with advanced Parkinson's disease (10 male, 4 female; age: 53-74 years), with intermittent, unilateral subthalamic nucleus deep brain stimulation on the final night or 2. Sleep scoring was determined manually by a consensus of 4 experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset and on each stage of sleep (rapid eye movement and non-rapid eye movement stages 1, 2, 3)., Results: Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with Parkinson's disease. No statistically significant difference was observed due to stimulation., Conclusions: These objective measures suggest that 1 night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture., Clinical Trial Registration: Registry: ClinicalTrials.gov; Name: Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551; Identifier: NCT04620551., Citation: Das R, Gliske SV, West LC, et al. Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study. J Clin Sleep Med . 2024;20(9):1489-1496., (© 2024 American Academy of Sleep Medicine.)

Objectives: Although common, sleep disorders often remain undiagnosed in psychiatric patients. A screening instrument, like the Holland Sleep Disorders Questionnaire (HSDQ) could improve this. Previous work indicated a 6-factor structure for the HSDQ, but this hasn't been investigated in psychiatric patients., Methods: HSDQ data was collected in a psychiatric-outpatient sample (n = 1082) and general-population sample (n = 2089). Internal reliability of the HSDQ was investigated and Confirmatory Factor Analyses (CFA) were used to compare 1-, 6-, and second-order 6-factor models in both samples. Next, multigroup-CFA was used to investigate measurement invariance., Results: Except for one subscale, internal reliability was acceptable in both samples. The 6-factor structure model fitted best in both samples and investigation of measurement invariance showed evidence for equality of the overall factor structure (configural invariance). Addition of equality constraints on factor loadings (metric invariance) and item thresholds (scalar invariance) showed good fit for all fit statistics, except for one. Exploratory analyses identified three items for metric and three different items for scalar invariance explaining this non-invariance., Conclusion: The HSDQ has a 6-factor structure in psychiatric patients, which is comparable to the general population. However, due to the observed non-invariance, users should be cautious with comparing HSDQ scores between psychiatric and general populations., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

The perinatal period is a critical phase in a woman's life characterized by significant physical, emotional, and societal changes. Sleep disorders such as insomnia, restless legs syndrome, obstructive sleep apnea, and poor sleep quality have been observed to increase in prevalence during the perinatal period. Given the harmful impact of sleep disturbances on the health of both mother and newborn, it is crucial to diagnose and treat them promptly. There is a paucity of literature on sleep problems during the perinatal period. This narrative review aimed to summarize the existing evidence and provide suggestions for promptly identifying and managing these disorders., (© 2024 International Federation of Gynecology and Obstetrics.)

Neurodegenerative diseases are characterised by neuronal loss and abnormal deposition of pathological proteins in the nervous system. Among the most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease and transmissible spongiform encephalopathies (TSEs). Sleep and circadian rhythm disturbances are one of the most common symptoms in patients with neurodegenerative diseases. Currently, one of the main objectives in the study of TSEs is to try to establish an early diagnosis, as clinical signs do not appear until the damage to the central nervous system is very advanced, which prevents any therapeutic approach. In this paper, we provide the first description of sleep disturbance caused by classical scrapie in clinical and preclinical sheep using polysomnography compared to healthy controls. Fifteen sheep classified into three groups, clinical, preclinical and negative control, were analysed. The results show a decrease in total sleep time as the disease progresses, with significant changes between control, clinical and pre-clinical animals. The results also show an increase in sleep fragmentation in clinical animals compared to preclinical and control animals. In addition, sheep with clinical scrapie show a total loss of Rapid Eye Movement sleep (REM) and alterations in Non Rapid Eyes Movement sleep (NREM) compared to control sheep, demonstrating more shallow sleep. Although further research is needed, these results suggest that prion diseases also produce sleep disturbances in animals and that polysomnography could be a diagnostic tool of interest in clinical and preclinical cases of prion diseases.

Sleep disorders are common, and largely undiagnosed in early-career workers. The combination of sleep disorders and shift work has implications for mental health, workplace safety, and productivity. Early identification and management of sleep disorders is likely to be beneficial to workers, employers and the community more broadly. We assessed the feasibility and acceptability of a tailored sleep disorder screening and management pathway for individuals with future shift work requirements. Paramedic students were invited to complete an online sleep health survey, which included validated sleep disorder screening questionnaires for insomnia, obstructive sleep apnea and restless legs syndrome. Participants were able to express interest in participating in a sleep monitoring and management study. Participants at risk for a sleep disorder were identified, contacted by the study physician (RJA), notified of their sleep disorder screening results and provided with information regarding management options. Feasibility of the screening and management pathways were determined by completion of the 12 week follow-up, and ability to engage with health services for diagnostic testing or treatment. Acceptability of these pathways was assessed with a semi-structured interview on completion of the study at 12 weeks. Screening was completed in thirty participants (mean age 22.5 ± 6.7, 63% female), 17 of whom were 'at-risk' for a sleep disorder and offered a management pathway. All participants engaged with the study physician (RJA), with 16 completing the study (94% completion rate). Three participants with excessive daytime sleepiness received feedback from the study physician (RJA) and no further care required. Of the remaining 14 participants, 11 (78%) engaged with health services after speaking with the study physician (RJA). Those who engaged with diagnostic and management services reported that a structured pathway with online screening was convenient and easy to follow. Facilitating screening and management of sleep disorders in students with future shift work requirements is both feasible and acceptable. These findings can inform the development of a preventive strategy for sleep disorders and ideally, a health services feasibility trial for future shift workers., (© 2024. The Author(s).)

Background: It is estimated that half of Canadians have insufficient sleep, which over time is associated with poor physical and mental health. Currently, the only publicly funded option for the diagnosis of sleep disorders in Ontario is an in-person overnight sleep study, performed in a hospital or independent health facility (known as a level 1 polysomnography). Level 2 polysomnography has been proposed as an alternative that can be conducted at home for the diagnosis of suspected sleep disorders, if considered to have sufficient diagnostic accuracy. We conducted a health technology assessment of level 2 polysomnography for the diagnosis of suspected sleep disorders in adults and children, which included an evaluation of the test performance, cost-effectiveness, and budget impact of publicly funding level 2 polysomnography, and the experiences, preferences, and values of people with suspected sleep disorders., Methods: We performed a systematic literature search of the clinical evidence to identify diagnostic accuracy, test failures and subjective measures of patient preferences. We assessed the risk of bias of each included study (using the Quality Assessment of Diagnostic Accuracy Studies [QUADAS-2] tool) and the quality of the body of evidence (according to Grading of Recommendations Assessment, Development, and Evaluation [GRADE] Working Group criteria). We performed a systematic literature search of economic evidence and conducted a primary economic evaluation and budget impact analysis to determine the cost-effectiveness and additional costs of publicly funding level 2 polysomnography for adults and children with suspected sleep disorders in Ontario. To contextualize the potential value of using level 2 polysomnography, we spoke with people with sleep disorders., Results: We included 10 studies that reported on diagnostic accuracy and found level 2 polysomnography had sensitivity ranging between 0.76-1.0 and specificity ranging between 0.40-1.0 (GRADE: Moderate to Very low) when compared with level 1 polysomnography. Studies reported test failure rates from 0% to 20%, with errors present in both level 1 and level 2 tests conducted (GRADE: Very low). As well, some of these studies reported patients were found to have mixed opinions about their experiences, with more people preferring their experience with level 2 testing at home and having better quality of sleep compared with when they underwent level 1 testing (GRADE not conducted).Our primary economic evaluation showed that for adults with suspected sleep disorders, the new diagnostic pathway with level 2 polysomnography was equally effective (outcome: confirmed diagnosis at the end of the pathway) as the current practice diagnostic pathway with level 1 polysomnography. With the assumption of a lower technical fee for level 2 polysomnography, the new diagnostic pathway with level 2 polysomnography was less costly than the current practice diagnostic pathway (a saving of $27 per person with a wide 95% credible interval [95% CrI, -$137 to $121]), indicating that the results are highly uncertain. For children, a new diagnostic pathway with level 2 polysomnography was associated with additional costs (mean, $9.70; 95% CrI, -$125 to $190), and similarly, this estimate was highly uncertain.We estimated that the budget impact of publicly funding level 2 polysomnography for adults is uncertain and could range from savings of $22 million to additional costs of $43 million. Publicly funding a diagnostic pathway with level 2 polysomnography for children could result in additional costs of about $0.005 million over the next 5 years.People with whom we spoke reported that their sleep disorder negatively impacted their day-to-day lives, mental health, social and family relationships, and work. Participants who had experience with in-clinic (level 1) polysomnography described negative experiences they had at the clinic. Most people said they would prefer at-home (level 2) polysomnography over in-clinic (level 1) polysomnography, citing comfort and convenience as the main reasons; however, some people who have physical limitations preferred level 1 (in-clinic) polysomnography because they needed assistance to set up the equipment., Conclusions: Level 2 polysomnography may have good test performance for adults and children, with adequate diagnostic accuracy, compared with level 1 polysomnography. The economic analyses showed that level 2 polysomnography for adults with suspected sleep disorders could be potentially cost saving but there is high uncertainty in the cost-effectiveness results. Given very limited information, the cost-effectiveness of this technology is also highly uncertain for children and young adults with suspected sleep disorders. The budget impact of publicly funding level 2 polysomnography for adults could range from savings of $22 million to additional costs of $43 million. Publicly funding level 2 polysomnography in children would require additional costs of about $0.005 million over the next 5 years. A clearer understanding of uptake of the technology, test costs, and the implementation pathway for adopting the technology is needed to improve the certainty of the cost-effectiveness and budget impact estimates. People with sleep disorders highlighted how important getting a diagnosis had been in order to be able to seek proper treatment for their sleep disorder and improve their lives. For many people with suspected sleep disorders, undergoing a sleep study at home would be a more comfortable and convenient option than undergoing a sleep study in clinic., (Copyright © King's Printer for Ontario, 2024.)

Purpose: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defecation. The condition is classified as a disorder of the gut-brain interaction (DGBI), and patients with IBS commonly experience psychological distress. The present study focuses on this distress, defined from reports of fatigue, anxiety, depression, sleep disturbances, and performance on cognitive tests. The aim was to investigate the joint contribution of these features of psychological distress in predicting IBS versus healthy controls (HCs) and to disentangle clinically meaningful subgroups of IBS patients., Methods: IBS patients ( n = 49 ) and HCs ( n = 28 ) completed the Chalder Fatigue Scale (CFQ), the Hamilton Anxiety and Depression Scale (HADS), and the Bergen Insomnia Scale (BIS), and performed tests of memory function and attention from the Repeatable Battery Assessing Neuropsychological Symptoms (RBANS). An initial exploratory data analysis was followed by supervised (Random Forest) and unsupervised (K-means) classification procedures., Results: The explorative data analysis showed that the group of IBS patients obtained significantly more severe scores than HCs on all included measures, with the strongest pairwise correlation between fatigue and a quality measure of sleep disturbances. The supervised classification model correctly predicted belongings to the IBS group in 80% of the cases in a test set of unseen data. Two methods for calculating feature importance in the test set gave mental and physical fatigue and anxiety the strongest weights. An unsupervised procedure with K = 3 showed that one cluster contained 24% of the patients and all but two HCs. In the two other clusters, their IBS members were overall more impaired, with the following differences. One of the two clusters showed more severe cognitive problems and anxiety symptoms than the other, which experienced more severe problems related to the quality of sleep and fatigue. The three clusters were not different on a severity measure of IBS and age., Conclusion: The results showed that psychological distress is an integral component of IBS symptomatology. The study should inspire future longitudinal studies to further dissect clinical patterns of IBS to improve the assessment and personalized treatment for this and other patient groups defined as disorders of the gut-brain interaction. The project is registered at https://classic., Clinicaltrials: gov/ct2/show/NCT04296552 20/05/2019., (© 2024. The Author(s).)

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Abstract ., Background: There is growing evidence that understanding the role of sleep disturbance in bipolar disorder (BD) and major depressive disorder (MDD) is helpful when studying the high heterogeneity of patients across psychiatric disorders., Objective: The present study was designed to investigate the transdiagnostic role of sleep disturbance measured by polysomnography (PSG) in differentiating from MDD with BD., Methods: A total of 256 patients with MDD and 107 first-episode and never medicated patients with BD using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria were recruited. All patients completed 1 night of PSG recording, and the changes in objective sleep structure parameters were determined by PSG analysis., Results: We showed that patients with MDD had statistically longer rapid eye movement (REM) latency, a higher percentage of stage N2 sleep, and lower percentages of stage N3 sleep and REM sleep than those with BD after controlling for confounding factors (all P < .05). Moreover, using the logistic regression analysis, we identified that REM latency was associated with BD diagnosis among the PSG sleep features. The cutoff value for PSG characteristics to differentiate BD from MDD was 261 in REM latency (sensitivity: 41.4% and specificity: 84.1%)., Conclusions: Our findings suggest that PSG-measured sleep abnormalities, such as reduced REM latency, may be a diagnostic differentiating factor between MDD and BD, indicating their roles in identifying homogeneous transdiagnostic subtypes across psychiatric disorders., (© Copyright 2024 Physicians Postgraduate Press, Inc.)

Background: Poor sleep is an unrecognized problem among cancer survivors that affects quality of life. However, screening for sleep disorders is not routine in cancer care. To fill this gap, a self-paced online training program was designed for RNs to screen patients for sleep disturbance and provide brief intervention or referral for treatment (Sleep-SBIRT)., Method: A three-phase evaluation pilot study included the following steps: (a) develop an online training program with in situ simulation; (b) implement the program with RNs at a comprehensive cancer center; and (c) evaluate module and quiz completion rates and focus group thematic analysis., Results: Of the 22 RNs participating, 17 completed online modules and in situ simulation. The RNs were satisfied ( M = 4.74/5, SD = 0.42) and self-confident ( M = 4.45/5, SD = 0.45) with the learning. Focus group themes were learning new knowledge, learning online effectively, applying learning to in situ simulation, and intending to implement., Conclusion: The RNs gained knowledge applying Sleep-SBIRT, but future larger studies are warranted. [ J Contin Educ Nurs. 2024;55(8):387-392.] .

Introduction: In older adults, where sleep disturbances and cognitive impairment are common, mounting evidence suggests a potential connection between sleep and cognitive function, highlighting the significance of utilizing sleep as a biomarker for early detection of cognitive impairment to improve clinical outcomes in a noninvasive, cost-effective manner., Areas Covered: This review describes the relationship between sleep and cognitive function in older adults, encompassing both subjective and objective measures of sleep quality, duration, architecture, and sleep-disordered breathing. The authors consider the directionality of the associations observed in prospective and cross-sectional studies, exploring whether sleep disturbances precede cognitive decline or vice versa. Furthermore, they discuss the potential bidirectional relationships between sleep and Alzheimer's disease (AD) risks in older adults while also examining the neurodegenerative pathways of this relationship., Expert Opinion: Routine sleep monitoring in primary care settings has the potential to bolster early detection and treatment of sleep disturbance, and by extension, reduce the risk of dementia. Improving sleep assessment tools, such as wearables, provide scalable alternatives to traditional methods like polysomnography, potentially enabling widespread monitoring of sleep characteristics. Standardized measurement and inclusive participant recruitment are needed to enhance generalizability, while longitudinal studies are essential to understand the interaction between sleep and AD pathology.

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep-wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep-wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period.

Background: One of the challenges in bipolar disorder (BD) lies in early detection of the illness and its recurrences, to improve prognosis. Sleep disturbances (SD) have been proposed as reliable predictive markers of conversion. While preliminary studies have explored the relationship between SD and the onset of mood episodes, the results remain heterogeneous and a few have specifically examined patients' perception of prodromal symptoms and their progression until the episode occurs. Identifying prodromes represents a crucial clinical challenge, as it enables early intervention, thereby reducing the severity of BD. Therefore, the objective of this study is to better characterize and evaluate the progressive nature of SD as prodromal symptoms of mood episodes, and patients' perception of it., Methods: Patients diagnosed with BD, either hospitalized or seeking treatment for a (hypo)manic or depressive episode benefited from standardized questionnaires, structured interviews, and self-report questionnaires to evaluate SD prior to the current episode, as well as sociodemographic and clinical information., Results: Out of the 41 patients included, 59% spontaneously reported SD prior to the episode, appearing 90 days before depression and 35 days before mania (pre-indexed/spontaneous reports: 51.22% insomnia complaints, 4.88% hypersomnolence complaints, 7.32% parasomnias, 2.44% sleep movements). After inquiry about specific SD, the percentage of patients reporting prodromal SD increased significantly to 83%, appearing 210 days before depression and 112.5 days before mania (post-indexed reports: 75.61% presented with insomnia complaints appearing 150 days before depression and 20 days before mania, 46.34% had hypersomnolence complaints appearing 60 days before depression, 43.9% had parasomnias appearing 210 days before depression and 22.5 days before mania, 36.59% had sleep movements appearing 120 days before depression and 150 days before mania). Of note, bruxism appeared in 35% of patients before mania, and restless legs syndrome in 20% of patients before depression., Conclusion: This study highlights the very high prevalence of SD prior to a mood episode in patients with BD with differences between depressive and manic episodes. The more systematic screening of sleep alterations of the prodromal phase improved the recognition and characterization of different symptoms onset by patients. This underscores the need for precise questioning regarding sleep patterns in patients, to better identify the moment of transition toward a mood episode, referred to as "Chronos syndrome". The study emphasizes the importance of educating patients about the disorder and its sleep prodromal symptoms to facilitate early intervention and prevent recurrences., (© 2024 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Background: Sleep disorders (SDs) are among many co-morbid medical conditions that affect children with autism spectrum disorder (ASD). Raising awareness and improving the standard of care for children diagnosed with ASD may result from identifying SDs among them. This study aims to evaluate patterns of SDs among Sudanese children diagnosed with ASD., Method: Using the Childhood Sleep Habit Questionnaire (CSHQ) to gather data on sleep disorders and SPSS version 26.0 for data analysis, a descriptive cross-sectional study was carried out in the five main autistic centres in Khartoum state covering all registered patients with ASD between April and June 2022. Ninety-two children diagnosed with ASD were enrolled in this study after the purpose of the research was explained and consent was obtained from their guardians. A p-value < 0.05 was considered to indicate statistical significance., Results: The mean age was 6.90 (± 2.6) years with a boys-to-girls ratio of 2.17:1. The prevalence of SDs (at least one sleep condition almost daily) was 95.65%. Sleep onset 71 (77.2%), limit setting 32 (32.6%), resistant onset to sleep 48 (52.2%), and combined 52 (56.5%) insomnia affected the majority of children. Additionally, there were significant associations between sex and Limit-setting insomnia, advanced sleep phase disorder, and narcolepsy type 2 (P values = 0.033, 0.009, and 0.037, respectively). Additionally, there was a significant association between age and sleep-related breathing disorders-snoring (p value = 0.031)., Conclusion: The frequency of SDs is significant among children diagnosed with ASD from Sudan, and certain SDs are associated with age and sex. Subsequent studies are required to develop national guidelines for the prevalence, presentation, screening, and treatment of SDs in children diagnosed with ASD., (© 2024. The Author(s).)

Purpose: Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders., Methods: Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted., Results: A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance., Conclusions: We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice., (© 2024. The Author(s).)

Idiopathic hypersomnia(IH) is a chronic central disorders of hypersomnolence that manifests as excessive daytime sleepiness occurring despite normal or prolonged sleep time. Due to the individual heterogeneity of disease, the high overlap of clinical, poor repeatability of polysomnography monitoring results and the lack of clear disease biomarkers, clinical diagnosis and differential diagnosis are still difficult. This article summarizes the update of diagnostic criteria, clinical manifestations, diagnosis and treatment strategies of IH, in order to receive attention, increase the recognition rate of clinical diagnosis, reduce the misdiagnosis rate and missed diagnosis rate.

Background: Patient-focused approaches to capturing day-to-day variability in sleep disturbance are needed to properly evaluate the sleep benefits of new treatments. Such approaches rely on patient-reported outcome (PRO) measures validated in the target patient population., Methods: Using atopic dermatitis (AD) as an example of a disease in which sleep is commonly disturbed, we developed a strategy for measuring sleep disturbance in AD trials. In developing this strategy, we conducted a targeted literature review and held concept elicitation interviews with adolescents and adults with AD. We subsequently identified potentially suitable PRO measures and cognitively debriefed them. Finally, we evaluated their psychometric properties using data from phase 2b (NCT03100344) and phase 3 (NCT03985943 and NCT03989349) clinical trials., Results: The literature review confirmed that sleep disturbance is a key impact of AD but failed to identify validated PRO measures for assessing fluctuations in sleep disturbance. Subsequent concept elicitation interviews confirmed the multidimensional nature of sleep disturbance in AD and supported use of a single-item measure to assess overall sleep disturbance severity, complemented by a diary to capture individual components of sleep disturbance. The single-item sleep disturbance numerical rating scale (SD NRS) and multi-item Subject Sleep Diary (SSD)-an AD-adapted version of the Consensus Sleep Diary-were identified as potentially suitable PRO measures. Cognitive debriefing of the SD NRS and SSD demonstrated their content validity and their understandability to patients. Psychometric analyses based on AD trial data showed that the SD NRS is a well-defined, reliable, and fit-for-purpose measure of sleep disturbance in adults with AD. Furthermore, the SD NRS correlated with many SSD sleep parameters, suggesting that most concepts from the SSD can be covered using the SD NRS., Conclusions: Using these findings, we developed an approach for measuring sleep disturbance in AD trials. Subject to further research, the same approach could also be applied to future trials of other skin diseases where itch causes sleep disturbance., (© 2024. The Author(s).)

Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people with various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as input. We compared this algorithm with models trained on publicly available datasets, and evaluated these models using our clinical dataset, particularly with regard to the effects of different sleep disorders. In an effort to evaluate clinical relevance, we designed a metric based on the error of the predicted arousal index. Our models achieve an area under the precision recall curve (AUPRC) of up to 0.83 and F1 scores of up to 0.81. The model trained on our data showed no age or gender bias and no significant negative effect regarding sleep disorders on model performance compared to healthy sleep. In contrast, models trained on public datasets showed a small to moderate negative effect (calculated using Cohen's d) of sleep disorders on model performance. Therefore, we conclude that state-of-the-art arousal detection on our clinical data is possible with our model architecture. Thus, our results support the general recommendation to use a clinical dataset for training if the model is to be applied to clinical data., (© 2024. The Author(s).)

Background and Aims: Sleep disorders are bidirectionally linked with eating behaviors and glucose metabolism, which could be clinically relevant in type 1 diabetes (T1D). We investigated the relationship between dietary habits and sleep quality in individuals with T1D on insulin pumps and continuous glucose monitoring (CGM)., Methods and Results: In a cross-sectional study, dietary habits (7-day food diary, EPIC questionnaire) and sleep quality (Pittsburgh Sleep Quality Index questionnaire) were assessed in 59 men and 58 women with T1D, aged 19-79 years, using CGM and insulin pump. Differences in dietary habits and blood glucose after dinner (6 h) between participants differing in sleep quality, sleep duration, and sleep onset latency were evaluated. Bad Sleepers (n = 81) were twice as prevalent as Good Sleepers (n = 36) and had a significantly higher intake of fat than Good Sleepers (dinner: 30.7 ± 10.7 vs. 24.0 ± 10.5 g, p = 0.004). Short sleepers had a significantly higher usual intake (g/1000 kcal) of coffee and tea (90.4 ± 71.7 vs. 62.0 ± 35.6), alcoholic (47.8 ± 51.1 vs. 28.9 ± 31.5) and carbonated beverages (21.8 ± 38.1 vs. 9.3 ± 17.2) (p < 0.05 for all) than Long Sleepers. Long Sleep Onset Latency was associated with a significantly higher fat intake at dinner (41.8 ± 7.4 vs. 38.1 ± 9.1 % total energy, p = 0.029) than Short Sleep Onset Latency. No significant differences in post-dinner blood glucose levels were detected between participants with good or bad sleep quality., Conclusion: Sleep disruption is common in T1D and is associated with unhealthy dietary choices, especially at dinner, independently of post-dinner blood glucose control., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: Sleep disorders impact at least 10 % of children, pose risks to overall wellbeing, and are key targets of preventive interventions. The objectives of this study were to describe the prevalence of pediatric sleep disorder diagnoses across sociodemographic characteristics and co-occurring conditions, and to explore potential sociodemographic disparities., Methods: Cross-sectional analysis of 12,394,902 children (0-17 years; 50.9 % Medicaid-insured) in the 2017 MarketScan database. Prevalence was assessed utilizing ICD-10 codes, with multivariate logistic regressions examining disparities (insurance coverage; race and ethnicity in Medicaid-insured) for diagnoses in ≥0.10 % of children., Results: The prevalence of sleep disorder diagnoses was 2.36 %. The most common diagnoses were obstructive sleep disordered breathing (oSDB, 1.17 %), unspecified sleep disorders (0.64 %), insomnia (0.52 %), and other SDB (0.10 %), with <0.10 % for all other diagnoses. Insomnia and parasomnias diagnoses were much lower than diagnostic estimates. Sleep diagnoses were more prevalent in Medicaid versus commercially insured youth, 2-5-year-olds, and in children with co-occurring medical, neurodevelopmental, or behavioral health conditions. Girls and boys were generally equally likely to be diagnosed with any sleep disorder. In Medicaid-insured children, white children were more likely to have any sleep diagnosis compared to all other racial and ethnic groups. Black/African American children were more likely than white children to have oSDB., Conclusions: Compared to diagnostic estimates, claims data suggest sleep disorders are under-diagnosed, with notable sociodemographic disparities. Findings suggest a need for clinical resources to identify and address sleep disorders and to understand biases potentially driving disparities, given that sleep is a modifiable determinant of child wellbeing., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ariel A. Williamson reports financial support was provided by National Institute of Child Health and Human Development. Jodi A. Mindell reports a relationship with Johnson & Johnson Services Inc, a subsidiary of Kenvue, that includes: consulting or advisory and funding grants. Corresponding author Ariel A. Williamson is an editorial board member for Sleep Health, the official journal of the National Sleep Foundation. - 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.)