Diagnosis Of Sleep Disorders Research Articles

Life After EBPs: Characterizing Subsequent Engagement in Evidence-Based Psychotherapy After Completion of an Initial Trauma-Focused EBP in a National Sample of VA Patients.

Many Veterans who complete prolonged exposure (PE) or cognitive processing therapy (CPT) report residual symptoms, but it is unclear how to best address the mental health needs of these individuals. Examining patterns of mental health service utilization following completion of these two treatments may provide insight into how to best serve this group of individuals. In a large cohort of Veterans (N = 12,514) who sought treatment in the Veterans Health Administration during Fiscal Years 2015-2019, logistic regression models were used to assess the odds of initiating an additional course of trauma-focused (i.e., PE or CPT) or depression-focused psychotherapy in the year following completion of PE or CPT based on demographic, psychiatric, and treatment effectiveness-related variables. Approximately 9% of Veterans engaged in either trauma-(6%) or depression-(3%) related psychotherapy in the year following discharge from PE or CPT. Factors associated with increased odds of trauma-focused treatment initiation included having a sleep disorder diagnosis (OR = 1.23), a substance use disorder diagnosis (OR = 1.27), or experiencing military sexual trauma (OR = 1.64). Factors associated with increased odds of depression-focused treatment initiation included having a depression diagnosis (OR = 2.02). This study suggests that certain subgroups of Veterans who engage in PE or CPT (e.g., Veterans with comorbid sleep or substance use problems) are more likely to seek additional evidence-based treatment and may require augmentations to maximize clinical benefits, either during the initial course of treatment or subsequent to PTSD treatment.

Sleep disorders associated with cranial radiation - a systematic review.

Radiation is standard-of-care treatment for primary brain tumors but may have profound effects on sleep that have not yet been fully characterized. This systematic review aims to further our understanding of radiation therapy on risk of development of sleep disorders in patients with primary brain tumors (PBTs), as well as potential opportunities for prevention and treatment. A systematic search of PubMed, Embase, and Web of Science was performed (last Jan 2024) with predefined inclusion (PBT patients, radiation therapy, somnolence/circadian disruption) and exclusion (reviews/abstracts/cases/chapters, non-PBT cancer, lack of radiation) criteria, yielding 267 papers initially and 38 studies included. Data extraction and analysis (descriptive statistics, individual study summary) focused on incidence of sleep disturbances, radiation types/doses, and pharmacologic interventions. Risk of bias assessment was conducted with Effective Public Health Practice Project's Quality Assessment Tool for Quantitative Studies. The included 38 studies (n=2948 patients) demonstrated high incidence of sleep disturbances in patients with primary brain tumors throughout radiation therapy, but primarily from the end of radiation to 6 months after. Sleep symptoms were associated with radiation (dose-dependent), and pharmacotherapies were helpful in patients with formal sleep disorder diagnoses. Terminology and incidence reporting of sleep symptoms are inconsistent, and many studies had high risk of bias. This systematic review highlights the ongoing challenges with sleep symptoms/disorders related to cranial irradiation treatment in the primary brain tumor population. Further investigations on the interconnectedness of sleep disturbance constructs and possible pharmacotherapies to alleviate symptoms are warranted.

Bidirectional association of sleep disorders with chronic kidney disease: a systematic review and meta-analysis.

Published studies have suggested a link between chronic kidney disease (CKD) and sleep disorders, although the exact nature of this association has not been uniformly described. Clarifying this relationship may facilitate evidence-based interventions that address the interplay between these disease entities. Such interventions could prevent obstructive sleep apnea (OSA) from worsening CKD and improve the quality of life for CKD patients by reducing the risk of developing OSA. Therefore, the objective of this meta-analysis is to assess the bidirectional association between sleep disorders and CKD. Following a PROSPERO-registered protocol, three blinded reviewers conducted a systematic review of the Medline/PubMed, Embase, Cochrane Library and Cumulative Index of Nursing and Allied Health (CINAHL) databases for observational studies pertaining to the relationship between sleep disorders and CKD. A meta-analysis was conducted in risk ratios (RRs). From 63 studies (26 777 524 patients), OSA [RR 1.68; 95% confidence interval (CI) 1.45 to 1.93], albuminuria (RR 1.54; 95% CI 1.18 to 1.99), restless leg syndrome (RLS) (RR 1.88; 95% CI 1.48 to 2.38) and insomnia (RR 1.24; 95% CI 1.01 to 1.54) were significantly associated with CKD. There was a significant association between OSA (RR 1.77; 95% CI 1.56 to 2.01) with incident CKD. There was a significant association of OSA (RR 1.74; 95% CI 1.55 to 1.96), RLS (RR 1.73; 95% CI 1.32 to 2.25) and insomnia (RR 1.14; 95% CI 1.03 to 1.27) in patients with CKD compared with healthy controls. CKD was also significantly associated with incident OSA (RR 1.60; 95% CI 1.35 to 1.89). The bidirectional associations of obstructive sleep apnea with CKD remained consistent across different stages of CKD, modes of diagnosis of sleep disorder and geographical region. A bidirectional association was observed between CKD and obstructive sleep apnea, RLS and insomnia. The treatment of sleep disorders may reduce the risk of CKD, and vice versa.

The interdisciplinary field of Sleep Medicine-time for dentists to sink their teeth into it!

Sleep Medicine has evolved into a highly multidisciplinary field over the last few decades, involving respiratory physicians, neurologists, cardiologists, ENT surgeons, psychiatrists and psychologists to name a few. It is within this highly multidisciplinary context that we have seen an increasing role for dentists in the recognition, diagnosis and management of select sleep disturbances and disorders. Over the last couple of decades, this growing role for dentists has seen the informal emergence of a new interdisciplinary field of Dental Sleep Medicine-a field that bridges medicine and dentistry as it pertains to the diagnosis and management of sleep disorders. This article describes this new field in terms of its history, the evolving scope of practice for dentists, the implications for education and training, and the importance of multidisciplinary care that optimises outcomes for patients.

T2 MRI visible Perivascular Spaces in Parkinson`s Disease: clinical significance and association with polysomnography measured sleep.

Poor sleep quality might contribute to the risk and progression of neurodegenerative disorders via deficient cerebral waste clearance functions during sleep. In this retrospective cross-sectional study, we explore the link between enlarged perivascular spaces (PVS), a putative marker of sleep-dependent glymphatic clearance, with sleep quality and motor symptoms in Parkinson`s disease (PD) patients. T2-weighted MRI images of 20 patients and 17 healthy control subjects were estimated visually for PVS in the basal ganglia (BG) and centrum semiovale (CSO). The patient group additionally underwent a single-night polysomnography. Readouts included polsyomnographic sleep features and slow-wave activity (SWA), a quantitative EEG marker of sleep depth. Associations between PVS counts, PD symptoms (MDS-UPDRS scores) and sleep parameters were evaluated using correlation and regression analyses. Intra- and inter-rater reproducibility was assessed with weighted Cohen`s kappa coefficient. BG and CSO PVS counts in both patients and controls did not differ significantly between groups. In patients, PVS in both brain regions were negatively associated with SWA (1-2Hz) (BG: r(15)=-0.58, padj=0.015 and CSO: r(15)=-0.6, padj=0.015). Basal ganglia PVS counts were positively associated with motor symptoms of daily living (IRR=1.05, CI [1.01, 1.09], p=0.007, padj=0.026) and antidepressant use (IRR=1.37, CI [1.05, 1.80], p=0.021, padj=0.043) after controlling for age. Centrum Semiovale PVS counts in patients were positively associated with a diagnosis of REM sleep behaviour disorder (IRR=1.39, CI [1.06 , 1.84]), p=0.018, padj=0.11). These results add evidence that sleep deterioration may play a role in impairing glymphatic clearance via altered perivascular function, potentially contributing to disease severity in PD patients.

Modeling the two-dimensional variations in EEG signals to analyze the impact of music on sleep states

Variations in music intensity and pitch can significantly impact sleep and alleviate insomnia through relaxing music. Traditional EEG analysis methods struggle with capturing temporal features and managing time-series data complexity. To address these challenges, we propose an improved Temporal Network model integrated with a Temporal Self-Attention (TSA) mechanism. This model enhances time-step feature capture in EEG signals, transforming one-dimensional EEG data into two-dimensional tensors to capture multi-scale temporal features. Experiments on the DEAP dataset demonstrate significant performance improvements, with 84.26% accuracy, 90.06% precision, and 97.82% recall, outperforming current models. Our model effectively analyzes the impact of relaxing music on different sleep stages, providing insights for improving sleep quality. Future work will optimize the model structure, incorporate additional deep learning techniques, and expand the scope to include multiple physiological signals, aiming to enhance the clinical value for sleep disorder diagnosis and personalized music therapy.

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep assessment and sleep disorder diagnosis. However, traditional sleep stage classification relies on manual work by sleep experts, which is time-consuming and labor-intensive. Faced with this obstacle, computer-aided diagnosis (CAD) has the potential to become an intelligent assistant tool for sleep experts, aiding doctors in the assessment and decision-making process. In fact, in recent years, CAD supported by artificial intelligence, especially deep learning (DL) techniques, has been widely applied in SSC. DL offers higher accuracy and lower costs, making a significant impact. In this paper, we will systematically review SSC research based on DL methods (DL-SSC). We explores DL-SSC from several important perspectives, including signal and data representation, data preprocessing, deep learning models, and performance evaluation. Specifically, this paper addresses three main questions: (1) What signals can DL-SSC use? (2) What are the various methods to represent these signals? (3) What are the effective DL models? Through addressing on these questions, this paper will provide a comprehensive overview of DL-SSC.

The Interplay Between Sleep Disorders and Depression Examining the Influence of Insomnia and Daytime Sleepiness on Mental Health

Introduction: Depression is one of the most common mental disorders, characterized by a wide range of symptoms, including sleep disturbances. Sleep, as a key component of mental health, plays a significant role in the development and treatment of depression. This study examines the impact of sleep, particularly insomnia and excessive sleepiness, on depressive disorders. Objective: The aim of this study is to analyze the relationship between sleep quality and the occurrence and progression of depression, with particular attention to the influence of antidepressant treatment on sleep quality. Materials and Methods: A literature review covering studies on the relationship between sleep disorders and depression, as well as the effectiveness of various therapeutic methods for treating these disorders, found on the PubMed and Google Scholar platforms. Conclusions: Sleep quality has a significant impact on the onset and severity of depressive symptoms. Appropriate treatment of sleep disorders can significantly improve the effectiveness of depression treatment and, in some cases, even prevent it. Early diagnosis and treatment of sleep disorders may be a key component in the prevention and treatment of depression.

Overnight Sleep Staging Using Chest-Worn Accelerometry.

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

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.

Sleep stages detection based on analysis and optimisation of non-linear brain signal parameters

The analysis and detection of sleep stages continue to preoccupy researchers, particularly bioinformaticians and neurologists aiming to understand various aspects and functioning of the brain during sleep cycles. This understanding is crucial for developing systems capable of early diagnosis of sleep disorders and adapting these systems for use in operating rooms to monitor the brain activity of patients under sedation or anesthesia. In this study, we apply a set of methods to process and decompose the single-channel EEG neural signal into different brain waves such as beta, alpha, theta, and delta. We then focus on the analysis and optimization of the extracted non-linear features using Principal Component Analysis (PCA) to improve the performance and robustness of the classification of six sleep stages. Classifier validation is performed using the cross-validation method. The results show that when the dimensions of the combined non-linear features are reduced, the RF-PCA model achieves an accuracy of 95.2%, outperforming other models such as MLP, SVM, DT, and GB, as well as recent studies. These findings validate the effectiveness of the system, indicating its potential for embedded implementation in practical applications for sleep disorder diagnosis and monitoring.

Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.

Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.

Epigenetics and aging: relevance for sleep medicine.

Sleep disorders encompass a wide range of conditions with substantial individual variability. Epigenetics, the study of heritable changes beyond DNA sequence, offers a promising avenue for personalized medicine in this field. There is great potential of epigenetic markers for sleep disorder diagnosis and the development of epigenetic drugs for targeted treatment. Epigenetic age acceleration, a marker of biological aging, is linked to sleep disorders and comorbidities. Very importantly, this acceleration may be reversible with effective treatment. While the underlying mechanisms and assessment of clinical utility require further investigation, the potential of epigenetics in sleep medicine is recognized. Future research focused on closing knowledge gaps and clinical validation is crucial to translate these findings into practical applications, paving the way for more effective and personalized management of sleep disorders.

The association of depression and sleep disorders in patients with type 1 diabetes in Taiwan.

The association between depression and sleep disorders in patients with type 1 diabetes mellitus (T1DM) in Taiwan is underexplored. We used a nationwide population-based dataset to evaluate the association of T1DM with these conditions in Taiwan from 2001 to 2019. Patients with T1DM were identified as cases, and 2 control groups were used for comparison: patients with type 2 diabetes mellitus (T2DM) and nondiabetic patients. Age, sex, date of diagnosis, and multiple comorbidities were included and matched using propensity score matching between cases and controls. The primary outcome of this study was to identify new occurrences of the first diagnosis of depression or sleep disorders. After matching, this study included 27,029 T1DM cases, 54,058 T2DM controls, and 108,116 nondiabetic controls. Patients with T1DM exhibited a 1.55-fold higher risk of developing depression (hazard ratio [HR] 1.55, 95% confidence intervals [CI] 1.48-1.61) and a 1.41-fold higher risk of experiencing sleep disorders (HR 1.41, 95% CI 1.37-1.46) compared to nondiabetic controls. Similarly, patients with T2DM displayed elevated risks of both depression (HR 1.39, 95% CI 1.34-1.43) and sleep disorders (HR 1.40, 95% CI 1.37-1.44) relative to non-diabetic controls. When comparing the T1DM and T2DM groups, T1DM patients demonstrated a slightly higher risk of depression (HR 1.11, 95% CI 1.07-1.16) but no significant difference in the risk of sleep disorders compared to T2DM patients. These results were consistent regardless of different ages or sexes. This study demonstrates a significant association between diabetes mellitus and the risk of depression and sleep disorders in a large cohort of Taiwanese patients.

Neurexan Prescription Is Associated with Lower Risk of Sleep Disorder Recurrence and Depression Prevalence as Compared to Z-Drugs and Benzodiazepines: A Retrospective Database Analysis in Germany.

Real-world evidence on the association between natural medicinal products and the recurrence of sleep disorders is currently limited, particularly when compared to the evidence reported for prescription hypnotics. In a retrospective cohort analysis, we investigated patients with sleep disorders prescribed either the natural medicinal product Neurexan (Nx4), benzodiazepines, or nonbenzodiazepines (Z-drugs) using the IQVIA Disease Analyzer database, which encompasses electronic medical records nationwide in Germany. A 1:1 matching procedure based on age, sex, prevalent depression, anxiety or adjustment disorder, and the number of medical consultations in the past 12 months resulted in four cohorts: patients prescribed Nx4 were matched with those prescribed Z-drugs (two cohorts with 8594 matched patients each), and another cohort of patients prescribed Nx4 were matched with those prescribed benzodiazepines (7779 matched pairs). Results from multivariable-adjusted Cox regression models demonstrated that Nx4 was associated with a significantly lower risk of recurrent sleep disorder diagnosis within 30-365 days after prescription compared to both Z-drugs (HR = 0.65, 95%CI = 0.60-0.70, p < 0.001) and benzodiazepines (HR = 0.85, 95%CI = 0.79-0.93, p < 0.001). Additionally, Nx4 was associated with a lower prevalence of depression compared to Z-drugs (HR = 0.90, 95%CI = 0.83-0.98, p = 0.020) and benzodiazepines (HR = 0.89, 95%CI = 0.82-0.97, p = 0.009). These findings suggest an association between Nx4 and improved sleep and mental health outcomes. However, due to inherent limitations in the study design, the causality of this relationship cannot be stated.

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep.

Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

Sleep disturbances and associated factors in patients with Parkinson's disease

AimsThis study aimed to describe clinical characteristics and sleep quality of Parkinson’s Diseases (PD) patients and identify associated factors with sleep quality. MethodsA cross-sectional study was conducted at the National Geriatric Hospital, Hanoi, Vietnam, from December 2022 to April 2023. A total of 130 Parkinson's disease patients undergoing treatment at the hospital were invited. Demographic and clinical characteristics were obtained. The diagnosis of sleep disorders was based on the standards outlined in the DSM-V. A multivariate logistic regression model was employed. Results90.9 % experienced sleep disorder, with the significant types including insomnia (76.2 %) and restless legs syndrome (56.2 %). The majority of patients suffered two (33.1 %) and one kind of sleep disorder (32.3 %). Most patients experienced sleep disorders after diagnosis of PD (80.0 %). Only having shoulder and neck pain was positively associated with a likelihood of having sleep disturbances (OR=4.87, 95 %CI=1.18–20.15). ConclusionThis study found a high rate of sleep disorders among PD patients in our sample. Shoulder and neck pain was found to be associated with a risk of sleep disorders. Pain management should be performed to improve the sleep quality of PD patients.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

Diagnosis and management of common sleep disorders during the perinatal period.

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.

Patient accessibility and utilization of behavioral sleep medicine referrals in an academic center.

Behavioral sleep medicine (BSM) is a subspecialty that combines behavioral psychology and sleep medicine specialties. The objective of this study was to analyze referral patterns to a BSM clinic. The three specific aims were: (1) describe factors that predict referral acceptance, (2) identify barriers to attending initial appointment, and (3) describe variables associated with the number of visits attended. Retrospective chart reviews were conducted as part of a quality improvement project by this study team's clinical setting. Adults over 21 years of age who were referred to a behavioral sleep medicine clinic in an urban Midwestern academic healthcare system between 2014-2019 were included in this study. Sleep medicine was the main referral source for BSM patients (74.2%), followed by internal medicine (9.3%) and neurology/psychiatry (7.3%). Thirty-eight percent of patients did not schedule an appointment after a referral for BSM was initiated. Younger age, longer distance from clinic, commercial insurance and out of network insurance were all significantly greater for non-schedulers. Eighty-three percent of patients did attend the initial intake session with BSM providers. Older age was associated with lower likelihood of not attending scheduled BSM appointments. Patient characteristics of older age, closer distance from clinic, and in network insurance coverage were found to significantly increase the likelihood of BSM scheduling, while younger age, Black race and not getting a primary sleep disorder diagnosis (versus a diagnosis of Insomnia Disorder) and shorter days from referral to appointment were associated with an increased likelihood of not attending the scheduled BSM treatment engagement.