Diagnosis Of Sleep Disorders Research Articles

Ayurvedic Insights into Nidra: Understanding Physiological Aspects

Ahara, Nidra and Brahmacharya plays significant role in preventing major diseases affecting both physical and mental well-being. Nidra stands out as one of the prominent factors to be examined in the contemporary era, constituting a vital component of a healthy lifestyle. Ayurveda defines Nidra (sleep) as the physiological state of rest for the Sharira (body), Mana (mind) and Indriya (sense organs). Lack of Nidra, results in conditions like primary insomnia or Anidra and is associated with the onset of various clinical diseases. Over time, it progress into chronic disorders such as secondary or tertiary insomnia. Aim: To introduce and underscore the concept of Nidra and its physiological impact on the human body. Materials and Method: The literature review was compiled from available Ayurvedic literatures and various relevant articles related to Nidra and sleep. Conclusion: Ayurveda has given more importance to Nidra (sleep) by considering it as one among three pillars of life. The Samhitas provide an in-depth exploration of Nidra, presenting its classification and physiology. Nidra (sleep) is not just a phenomenon to ignore but it has great impact in our body and the study of sleep phenomenon is important for the early diagnosis of sleep disorders.

Association between sleep quality and blood pressure control among hypertensive patients at a rural tertiary hospital in Southern Nigeria: a cross-sectional study

ObjectivesRestorative sleep is critical in preventing hypertension and other chronic diseases. Limited research has explored the relationship between sleep quality and hypertension in Africa. This study investigated the association between...

From trenches to technology: a narrative review of sleep medicine in the military.

Thomas CL, Carr K, Yang F, etal. From trenches to technology: a narrative review of sleep medicine in the military. J Clin Sleep Med. 2024;20(6):973-981.

Sleep disorders and cancer incidence: examining duration and severity of diagnosis among veterans.

Sleep disruption affects biological processes that facilitate carcinogenesis. This retrospective cohort study used de-identified data from the Veterans Administration (VA) electronic medical record system to test the hypothesis that patients with diagnosed sleep disorders had an increased risk of prostate, breast, colorectal, or other cancers (1999-2010, N=663,869). This study builds upon existing evidence by examining whether patients with more severe or longer-duration diagnoses were at a greater risk of these cancers relative to those with a less severe or shorter duration sleep disorder. Incident cancer cases were identified in the VA Tumor Registry and sleep disorders were defined by International Classification of Sleep Disorder codes. Analyses were performed using extended Cox regression with sleep disorder diagnosis as a time-varying covariate. Sleep disorders were present among 56,055 eligible patients (8% of the study population); sleep apnea (46%) and insomnia (40%) were the most common diagnoses. There were 18,181 cancer diagnoses (41% prostate, 12% colorectal, 1% female breast, 46% other). The hazard ratio (HR) for a cancer diagnosis was 1.45 (95% confidence interval [CI]: 1.37, 1.54) among those with any sleep disorder, after adjustment for age, sex, state of residence, and marital status. Risks increased with increasing sleep disorder duration (short [<1-2 years] HR: 1.04 [CI: 1.03-1.06], medium [>2-5 years] 1.23 [1.16-1.32]; long [>5-12 years] 1.52 [1.34-1.73]). Risks also increased with increasing sleep disorder severity using cumulative sleep disorder treatments as a surrogate exposure; African Americans with more severe disorders had greater risks relative to those with fewer treatments and other race groups. Results among patients with only sleep apnea, insomnia, or another sleep disorder were similar to those for all sleep disorders combined. The findings are consistent with other studies indicating that sleep disruption is a cancer risk factor. Optimal sleep and appropriate sleep disorder management are modifiable risk factors that may facilitate cancer prevention.

Respiratory Analysis with Electrocardiogram Data: Evaluation of Pan-Tompkins Algorithm and Cubic Curve Interpolation Method

Advancements in bioinstrumentation have facilitated the easier monitoring of biometric signals such as electrocardiogram (ECG) and respiration. This development is particularly crucial for the diagnosis and management of various conditions like stress and sleep disorders. Two commonly used features in heart rate variability (HRV) analysis derived from ECG data are standard deviation and serial correlation coefficients of R-R intervals (the time durations between heartbeats). The former utilizes the fundamental components of QRS complexes, while the latter is designed to extract relationships between respiration and heart rate. In the proposed methodology, R-R wave detection is performed on processed ECG data using the Pan-Tompkins algorithm, and the respiration duration for each R-R interval from respiration data is selected. Additionally, missing respiration data for selected R-R intervals is interpolated based on the interpolation method. The results of this study are compared with the standard interpolation and cubic spline interpolation models to assess the effectiveness of the proposed method and its ability to capture temporal fluctuations. Since standard interpolation fails to accurately detect respiration data from R-R intervals and cannot precisely handle missing R-R intervals in short samples, cubic spline interpolation is recommended as a replacement and its results are presented. The obtained results provide insights into the effectiveness and application of the Pan-Tompkins algorithm, FFT (fast fourier transform) implementation, and cubic spline interpolation in the selection of respiration and R-wave features. According to the findings of the study, in the analysis conducted on 2-second samples with a 1000 Hz sampling frequency created from each participant's respiratory data set, missing respiratory data were successfully reconstructed from the R-R intervals of the EKG data using standard and cubic curve interpolation methods. Upon examination of RMSE (Root mean square error) values, it was observed that for 30% of the participants, as RMSE values increased, completion counts for standard interpolation increased, while completion counts for cubic curve interpolation decreased. Conversely, when RMSE values decreased, 60% of the participants showed a decrease in completion counts for standard interpolation and an increase in completion counts for cubic curve interpolation. A 10% participant group was identified where there was no apparent relationship between RMSE values and interpolation method. This indicates that in 90% of the participants, there is a linear relationship between the study's interpolation method, RMSE values, and completion counts for missing R-R intervals.

Impact of obstructive sleep apnea on patients'’ quality of life

Abstract Obstructive sleep apnea (OSA) is a very commonly spread disease, not only in Bulgaria, but all over the world. One of the reasons for this is that patients suffering from OSA find it difficult to share their problem with their treating doctor (sleep doctor or ENT specialist). Doctor-patient communication is extremely important when gaining the patient'’s trust, for the early detection and diagnosis of obstructive sleep apnea. Sleep disorders lead to a decrease in the quality of life, due to frequent anxiety, fatigue, depressive states, reduced cognitive and motor functions, lack of energy, headaches and sleepiness throughout the day. Improving the quality of life of patients is a primary goal of the health care system, which is conveyed in patient care aimed at reducing or eliminating the effects of diseases and their associated negative psychological and social effects. Prevention, screening and effective communication with the patient are extremely important for a successful diagnosis and treatment of sleep disorders.

Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults.

Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

Initial validation of the new browser-based application DREEP for diagnosis of common chronic sleep disorders

Abstract Background Traditionally, individuals with chronically disordered sleep are referred to a sleep specialist, who would usually arrange diagnostic investigations including polygraphy and/or polysomnography (PSG). Dr. Sleep (DREEP) is a browser-based application developed for diagnosing common chronic sleep disorders such as obstructive sleep apnea (OSA), periodic leg movements (PLMD), and insomnia in adults. The DREEP algorithm is based on disease-specific questions adapted from validated sleep questionnaires. This study evaluated the preliminary performance of DREEP for the diagnosis and severity assessment of sleep disorders in adults with chronically disordered sleep. Methods Sixty-four individuals admitted to the sleep laboratory of the Department of Sleep Medicine at the University of Essen, Germany, were recruited. Medical sleep history was taken by a sleep specialist. Each participant underwent full-night PSG and used the DREEP application on a tablet in the evening of admission or the morning after diagnostic sleep testing. Results DREEP predicted OSA with a sensitivity of 97%, but specificity was low at 25% (it had a tendency to overestimate OSA). DREEP had sensitivity of 79.2% and specificity of 80% for detecting PLMD/restless legs syndrome (RLS). The application showed the best performance for detection of insomnia (sensitivity 90%, specificity 83%), and successfully detected one individual with clinically confirmed narcolepsy. Conclusion This initial validation study has shown that the browser-based application DREEP is adequately able to assess risk and improve the pre-test probability for prescribing further tests in individuals with common chronic sleep disorders, including OSA, PLMD/RLS, and insomnia. In addition, DREEP was able to correctly identify most individuals without these conditions despite a tendency to overestimate. In a follow-up study, the promising clinical accuracy of DREEP needs to be substantiated by adding a healthy control group.

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Background: Sleep apnea is a prevalent sleep disorder, especially in males and older ages. The common diagnostic methods, including polysomnography (PSG), are expensive, difficult to perform, and time-consuming. Numerous studies are focusing on developing easy-to-perform methods based on artificial intelligence (AI) for the early diagnosis of sleep apnea. This systematic review aimed to gather current methods based on the convolutional neural network (CNN) for the diagnosis of sleep apnea. Methods: Three international electronic databases (PubMed, Web of Science [WoS], and Scopus) were searched from 2010 to October 2023. All studies that have developed CNN-based methods for the diagnosis of sleep apnea and have accomplished the performance tests were included. Finally, the characteristics of the studies were extracted and summarized. Results: A total of 36 studies were included in this systematic review. Various physiological signals have been proposed to detect sleep apnea, including electrocardiogram (ECG), blood oxygen saturation (SpO2), sound signals, respiratory signals, electroencephalogram (EEG), and nasal airflow. Electrocardiogram was the most frequently used signal in the studies, followed by SpO2. The highest reported accuracy was achieved by SpO2 or ECG-based methods and with a one-dimensional CNN (1D-CNN) classifier. Using multiple signals did not necessarily increase the performance of test results. Conclusions: Diagnostic methods based on CNN can be used only as screening tools or home diagnosis of sleep apnea. These methods are easy to perform and can only reduce the diagnostic costs and waiting time for a sleep study in special scenarios. Nevertheless, PSG is still the gold standard for the diagnosis of sleep disorders.

Association of Sleep Characteristics with Tinnitus and Hearing Loss.

The impact of poor sleep on tinnitus has been mainly attributed to central processes. There is an association between sleep disorders and hearing loss, but whether hearing levels mediate the association between sleep disorders and tinnitus is unknown. This study investigates the association between sleep characteristics, tinnitus, and hearing loss. Cross-sectional. National Health and Nutrition Examination Survey (NHANES). Study cohort includes 9693 adults (≥20 years) from the NHANES 2005 to 2018 who completed audiometric testing and questionnaires on tinnitus and sleep characteristics. Multivariable regression analyses were performed to quantify associations between sleep characteristics, tinnitus, and hearing loss. In this cohort, 29% (95% confidence interval [CI]: 28%-31%) reported trouble sleeping and 9% (95% CI: 8%-10%) reported being diagnosed with sleep disorders. Negative sleep characteristics (less hours of sleep, diagnosis of a sleep disorder, trouble sleeping, or OSA symptoms) were not associated with audiometry-measured hearing loss in multivariable models adjusted for demographics and comorbidities but were significantly associated with bothersome tinnitus. This association remained significant without substantial attenuation in multivariable models additionally adjusting for hearing levels: sleeping <8 h/day (vs≥8) (odds ratio [OR]: 1.28 [95% CI: 1.08-1.52]), trouble sleeping (OR: 1.78 [95% CI: 1.45-2.19]), diagnosis of sleep disorders (OR: 1.57 [95% CI: 1.14-2.15]), and report of OSA symptoms (OR: 1.42 [95% CI: 1.08-1.88]). Negative sleep characteristics were associated with tinnitus while there was no clinically meaningful association between sleep and hearing loss. Our findings suggest that the relationship between poor sleep and tinnitus is likely contributed by central processes without a major role of mediation via the peripheral auditory system.

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.

Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.

Psychiatric comorbidity in Danish patients with narcolepsy type 1, narcolepsy type 2, and idiopathic hypersomnia: a case-control study.

To examine the difference in psychiatric comorbidity of Danish patients with Narcolepsy type 1 (NT1), Narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH). Polysomnography (PSG), Multiple Sleep Latency Test (MSLT), and lumbar puncture were performed on 505 patients referred to a sleep clinic for diagnostic evaluation of hypersomnia. Diagnosis, clinical characteristics, electrophysiologic data, and cerebrospinal fluid hypocretin-1 (Csf-Hcrt-1) results were retrieved. Subsequently, the patients were identified in the Danish national health registers to collect information on psychiatric diagnoses and psychotropic medication use 10 years before the sleep disorder diagnosis. The prevalence of psychiatric comorbidities per hypersomnia group was compared to a 1:4 general population control group matched on age, gender, and educational level. A diagnosis of NT2 and IH was significantly associated with total psychiatric comorbidity compared to the matched controls but not NT1 (NT1: OR = 1.5; NT2: OR = 6.1; IH: OR = 5.2). NT1 was not significantly associated with any psychiatric disorder. NT2 was significantly associated with schizophrenia spectrum disorders (OR = 8.5), mood disorders (OR = 6.7), neurotic disorders (OR = 3.8), personality disorders (OR = 3.1), and behavioral and emotional disorders (OR = 4.3). IH was significantly associated with schizophrenia spectrum disorders (OR = 3.3), mood disorders (OR = 5.9), neurotic disorders (OR = 3.0), and behavioral and emotional disorders (OR = 4.0). NT2 and IH had a close relationship to psychiatric disorders before diagnosis of their sleep disorder, while NT1 did not. This supports previous studies finding higher rates of psychiatric illness in patients with hypersomnia; however, it highlights the similarity between NT2 and IH. We believe this link to psychiatric disorders could play a role in the pathophysiology. Future studies evaluating the relation between hypersomnias of central origin and psychiatric diseases should include hypersomnia subclassifications to further the understanding of the differences in these disorders.

AFSleepNet: Attention-based multi-view feature fusion framework for pediatric sleep staging.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional raw polysomnograms or two-dimensional spectrograms, which omit critical details due to single-view processing. This shortcoming is particularly apparent in pediatric sleep staging, where the lack of a specialized network fails to meet the needs of precision medicine. Therefore, we introduce AFSleepNet, a novel attention-based multi-view feature fusion network tailored for pediatric sleep analysis. The model utilizes multimodal data (EEG, EOG, EMG), combining one-dimensional convolutional neural networks to extract time-invariant features and bidirectional-long-short-term memory to learn the transition rules among sleep stages, as well as employing short-time Fourier transform to generate two-dimensional spectral maps. This network employs a fusion method with self-attention mechanism and innovative pre-training strategy. This strategy can maintain the feature extraction capabilities of AFSleepNet from different views, enhancing the robustness of the multi-view model while effectively preventing model overfitting, thereby achieving efficient and accurate automatic sleep stage analysis. A "leave-one-subject-out" cross-validation on CHAT and clinical datasets demonstrated the excellent performance of AFSleepNet, with mean accuracies of 87.5% and 88.1%, respectively. Superiority over existing methods improves the accuracy and reliability of pediatric sleep staging.

Advanced EEG-Based Classification of Periodic Leg Movements and Bruxism Using Complexity and Entropy Features

Periodic leg movements (PLM) and bruxism are prevalent sleep disorders that significantly impact sleep quality and overall health. Accurate and timely detection of these disorders is pivotal for effective treatment and improved patient well-being. In this research paper, we present a novel approach for the identification of Periodic Leg Movements (PLM) and bruxism. Our methodology leverages sophisticated complexity and entropy features extracted from electroencephalogram (EEG) signals recorded across various sleep stages. The implementation involves a diverse set of artificial intelligence (AI) classifiers, encompassing K-nearest neighbours (KNN), artificial neural networks, support vector machines (SVM) with radial basis function, SVM with polynomial kernel, and SVM with sigmoid kernel. These classifiers are employed to categorize EEG signals into three distinct classes: PLM, bruxism, or normal sleep patterns. To underscore the efficacy and superiority of our proposed approach, we conduct a thorough comparison with existing publications in the field. Our approach consistently outperforms previous methods and attains remarkable accuracies of 98.74% for PLM and 94.74% for bruxism, particularly when utilizing the SVM Sigmoid classifier. Furthermore, our study highlights the potential of EEG signals in elucidating alterations in brain activity associated with PLM and bruxism, underscoring the reliability of complexity and entropy features as robust biomarkers for these sleep disorders. Our research significantly advances the contemporary pinnacle in EEG-based detection of Periodic Leg Movements (PLM) and bruxism signifies a significant advancement, holding potential for impactful clinical applications in the realm of sleep disorder diagnosis and treatment.

Recognition of Sleep Disorders using IoT-Based Wearables and Neutrosophic Data Analytics

In the dynamic landscape of healthcare technology, the amalgamation of Internet of Things (IoT) systems and Neutrosophic Data Analytics has heralded a paradigm shift. This study delves deep into this transformative synergy by presenting an innovative IoT-based wearable system design for the recognition of sleep disorders. Our meticulously crafted multilayer cellular system seamlessly integrates IoT devices, data acquisition, cloud computing, and machine learning to unlock a wealth of insights into sleep patterns, their anomalies, and the presence of sleep disorders. Through fair and rigorous experimental comparisons, we unveil the prowess of Long Short-Term Memory (LSTM) within the machine learning realm, showcasing its superior performance over baseline models. The results affirm LSTM's ability to detect sleep disorders with remarkable accuracy, precision, and recall, revolutionizing sleep medicine and healthcare practices. This research, at the crossroads of innovation and healthcare, not only illuminates the path to advanced sleep disorder diagnosis but also heralds a new era of personalized healthcare interventions and remote monitoring solutions. As we navigate the realm of IoT and data-driven healthcare, our findings hold the promise of improving the quality of life for countless individuals, reaffirming the pivotal role of technology in safeguarding one of the most fundamental aspects of human well-being – a peaceful and restorative night's sleep.

MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model

Background and ObjectiveSleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. MethodsA novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). ResultsBased on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. ConclusionThe results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.

Sexual minorities and sleep health: How does discrimination and stigma influence a sleep disorder diagnosis?

Sleep difficulties are associated with many physical and mental health problems, such as diabetes, hypertension, and depressive symptoms. Sleep can be affected by stressors such as discrimination and stigma, which are disproportionately experienced by sexual and gender minority (SGM)individuals. This research assesses how different characteristics, including discrimination and stigma, sociodemographic characteristics, community connectedness, self-rated good health, and outness levels, influence a sleep disorder diagnosis among SGMs. We analyzed data from wave 3 of Generations: A Study of the Life and Health of LGB People in a Changing Society. We used bivariate analysis to document the distribution of key variables across SGM status. Next, we conducted logistic regression analyses to measure how each variable influences the likelihood of an SGM individual being diagnosed with a sleep disorder considering experiences of discrimination and stigma. We found that feeling stigma was statistically significant in predicting a sleep disorder diagnosis among SGM individuals. However, after controlling for sociodemographic characteristics, community connectedness, self-rated good health, and outness levels, SGM individuals only had increased odds of a sleep disorder diagnosis if they reported higher scores on the Internalized Homophobia Scale. Variation in having a sleep disorder diagnosis differs based on SGM status and could not be explained away with the inclusion of all covariates. Our findings support the need for more research to compare health outcomes between different sexual and gender identities, rather than comparing them with their nonminority counterparts.

SLEEP DISORDERS AND RISK OF MID- TO LATE-LIFE SUICIDE ATTEMPT

Abstract Sleep is a compelling target for suicide prevention in later life, as 50% of older adults experience sleep difficulties. The primary objective of the current study was to evaluate whether sleep disorders increase risk of late-life suicide attempt and death by suicide. We examined a nationally based cohort of over 5 million US veterans 50 years and older, who used Department of Veterans Affairs (VA) health services. Veterans with a sleep diagnosis (i.e., sleep apnea, insomnia, hypersomnia, and others) at baseline (October 1, 2011, to September 30, 2013) or earlier (October 1, 2007, to September 30, 2011) were propensity matched 1:1 with patients without these diagnoses based on demographic characteristics and Charlson Comorbidity Index (Total N=1,990,362; 995,181 each group). Secondary analyses examined psychiatric disorders as potential moderators. Fully adjusted models (additionally adjusted for psychiatric disorders) demonstrated that patients with sleep disorders had a 23% increased risk of any suicide attempt (HR 1.23, 95% CI [1.20-1.27]), while risk mitigated for lethality of attempt (HR 0.98, 95% CI [0.92-1.03]). Depression, PTSD, and generalized anxiety disorder were all approximately twice as prevalent among the sleep disorder group. Among veterans with a psychiatric disorder, any sleep disorder diagnosis was associated with a 28% increased risk for suicide attempt, which attenuated among those without a psychiatric diagnosis. In conclusion, sleep disorder diagnoses conferred increased risk for suicide attempt, especially in older adults with psychiatric disorders. These findings suggest that treatment of sleep disorders may mitigate risk of suicide attempts in older vulnerable veterans.

Sleep quality of adult psychiatric outpatients at Chris Hani Baragwanath Academic Hospital.

Sleep disorders are increasingly prevalent among the general population and individuals with mental disorders. However, little research has focused on the sleep quality of psychiatric patients beyond depression, despite its relevance in diagnostic criteria. This study aimed to assess overall sleep quality in psychiatric outpatients and to assess for an association with socio-demographic variables. This study took place at the adult psychiatric outpatient department of Chris Hani Baragwanath Academic Hospital. A cross-sectional study design was employed to evaluate overall sleep quality using the self-administered Pittsburgh Sleep Quality Index (PSQI), a validated tool. The PSQI yields a global score ranging from 0 to 21, with scores of 5 or greater indicating poor sleep quality. Eligibility was determined through structured clinical interviews and data obtained from participant records. Poor sleep quality was found in 50% of participants. Sleep quality did not differ significantly based on sex or age. Subscale analysis revealed reduced sleep duration and efficiency, nocturnal disturbances and daytime dysfunction. Additionally, 38% of participants required pharmacological intervention for sleep issues, despite lacking a diagnosis of primary or comorbid sleep disorders. Half of the psychiatric outpatients experienced poor sleep quality, irrespective of socio-demographic factors, psychiatric diagnosis, symptom remission or medication type. This study highlights the importance of addressing sleep disturbances as comorbid conditions in psychiatric patients. Comprehensive evaluation and management of sleep quality can lead to improved patient outcomes and quality of life.

Diagnosis and management of sleep disorders in shift workers, with patient informed solutions to improve health services research and practice

BackgroundThe combination of shift work and an unmanaged sleep disorder carries health and safety risks. Yet, diagnosis rates for sleep disorders are low in shift workers. The aim of this study was to understand the experience of sleep disorder diagnosis and treatment in shift workers, and consider patient informed solutions to improve access to health services. MethodsSemi-structured interviews were conducted with 16 Australian shift workers with a diagnosed sleep disorder. Patient journey mapping and reflexive thematic analysis were used to understand diagnosis and management experiences. ResultsThere were highly variable experiences with diagnosis and management, often taking >5 years to seek help from a health care provider (HCP) after noticing symptoms of a sleep disorder. Three themes were constructed, including ‘the cause of the problem’, ‘prioritising work’, and ‘(dis)satisfaction and (dis)connection’. Extent of patient and HCP awareness of sleep disorders, and a prevailing attitude of shift work being ‘the problem’ impacted diagnosis, as did organisational needs (including rostering, which had both positive and negative influences on help seeking). Relationships with HCPs were important, and living on non-standard time was both a barrier and an enabler to sleep disorder care. Participants identified the need for education and awareness, prompts and easy access to screening and referral pathways, and tailored models of care. ConclusionEducation and awareness initiatives for shift workers, their employers and HCPs, together with development of a model of care for shift workers with sleep disorders may address some of the unique barriers to diagnosis and management.