Prevalence Of Sleep Disorders Research Articles

O034 Insomnia Management in Australia: An Overview of Patient, Clinician and Health System Factors

Abstract Introduction Insomnia is the most prevalent sleep disorder in Australia. The recommended treatment is Cognitive Behavioural Therapy for insomnia (CBTi), however most people with insomnia do not access this treatment. Understanding reasons for the discrepancy between clinical guidelines and clinical practice is essential to identify opportunities to increase access to CBTi. This research explores the perspectives of people with insomnia, clinicians, and the health system to provide a comprehensive picture of insomnia management in Australia. Methods A mixed-methods program was undertaken to comprehensively identify facilitators and barriers to CBTi access in Australia. This included two large quantitative surveys of the Australian community and people with insomnia, and semi-structured qualitative interviews with GPs and psychologists. Results Most people with chronic insomnia symptoms reported they did not receive a diagnosis of insomnia, only 37% had discussed sleep with their GP, 9% discussed sleep with a psychologist, and <1% received CBTi. Barriers to effective management of insomnia include low priority of sleep from all perspectives, impaired knowledge and awareness, and inadequate access to care. GPs are under substantial time pressure, other health conditions are often prioritised over insomnia, existing funding mechanisms are not clear, and few psychologists trained to deliver CBTi. Discussion To improve the management of insomnia in Australia efforts are needed to improve clinician and patient recognition of the importance of identifying and managing insomnia. More psychologists and primary care clinicians need to be trained in CBTi delivery, to increase awareness, understanding and access to CBTi in the community.

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders

Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19–82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea–hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland–Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33–0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

Sleep disorders and their relation to injuries among young elite soccer players

Aim: to study the quality of sleep and the relationship between sleep disorders and injury among young elite soccer players.Methods: the study summarizes data from relevant questionnaires obtained through anonymous cohort testing of 236 male football players from leading football academies, two farm teams of leading Russian Premier League clubs and three national youth teams.Results: the average sleep duration in the study participants was 9 hours. The prevalence of sleep disorders was 7 % (PSQI, Pittsburgh Sleep Quality Index) and 10 % (ASBQ, Athlete Sleep Behavior Questionnaire) and among the youngest soccer players, this prevalence of violations was the lowest. Significant or moderate daytime sleepiness was found in 59 % of participants. The risk of serious disorders increased with age. Sleep disorders significantly affected injuries according to the results of the PSQI questionnaire and retrospective collection of information about injuries.Conclusion: the confirmed relationship between injuries and sleep disorders allows us to consider sleep quality improvement as an important component of injury and morbidity prevention among athletes of team sports.

REM Sleep Behavior Disorder and Its Possible Prodromes in General Population: Prevalence, Polysomnography Findings, and Associated Factors.

To evaluate the prevalence of REM sleep behavior disorder (RBD) and its possible prodromal conditions, isolated dream enactment behavior (DEB) and isolated REM without atonia (RWA), in a general population sample, and the factors associated with diagnosis and symptom frequency. From a population-based prospective cohort in Korea, 1,075 participants (age 60.1 ± 7.0 years; range 50-80 years; men 53.7%) completed the RBD screening questionnaire (RBDSQ), a structured telephone interview for the presence and characteristics of repeated DEB, and home polysomnography (PSG). RWA was measured on submentalis EMG, including 30-second epoch-based tonic and phasic activity as well as 3-second mini-epoch-based phasic and any EMG activities. Based on the presence of repeated DEB and any EMG activity of ≥22.3%, we categorized the participants into no RBD, isolated RWA, isolated DEB, and RBD groups. RBD was diagnosed in 20 participants, isolated RWA in 133 participants, and isolated DEB in 48 participants. Sex and DEB frequency-adjusted prevalence of RBD was 1.4% (95% CI 1.0%-1.8%), isolated RWA was 12.5% (95% CI 11.3%-13.6%), and isolated DEB was 3.4% (95% CI 2.7%-4.1%). Total RBDSQ score was higher in the RBD and isolated DEB groups than in the isolated RWA and no RBD group (median 5 [interquartile range (IQR) 4-6] for RBD, median 4 [IQR 3-6] for isolated DEB, median 2 [IQR 1-3] for isolated RWA, and median 2 [IQR 1-4] for no RBD groups, p < 0.001). RBDSQ score of ≥5 had good specificity but poor positive predictive value (PPV) for RBD (specificity 84.1% and PPV 7.7%) and its prodromal conditions (specificity 85.2% and PPV 29.1%). Among the RWA parameters, any EMG activity showed the best association with the RBD and its possible prodromes (area under the curve, 0.917). Three-second mini-epoch-based EMG activity and phasic EMG activity were correlated with the frequency of DEB (standardized Jonckheere-Terpstra statistic [std. J-T static] for trend = 0.488, p < 0.001, and std. J-T static = 3.265, p = 0.001, respectively). This study provides prevalence estimates of RBD and its possible prodromal conditions based on a structured telephone interview and RWA measurement on PSG from the general population.

Digital CBTI hubs as a treatment augmentation strategy in military clinics: study protocol for a pragmatic randomized clinical trial

BackgroundChronic insomnia is the most prevalent sleep disorder among military service members, and it compromises readiness, performance, and physical and mental health. Cognitive behavioral treatment for insomnia (CBTI) is the standard of care for the treatment of insomnia recommended by the VA/DoD Clinical Practice Guideline, the American Academy of Sleep Medicine, and the American College of Physicians. CBTI is highly effective but has limited scalability. It is often unavailable in clinical settings where service members receive sleep care. Digital technologies offer unique opportunities to scale and broaden the geographic reach of CBTI services and support increased patient access and engagement in behavioral sleep care. This study aims to evaluate the impact and acceptability of digital CBTI hubs to augment military treatment facilities’ capabilities in behavioral sleep medicine.MethodsThis is a multi-site, non-inferiority randomized clinical trial designed to compare the effects of in-person (face-to-face or virtual) insomnia care as usual at three military sleep clinics versus CBTI delivered remotely and asynchronously through digital CBTI hubs. Digital CBTI hubs are led by licensed, certified clinicians who use NOCTEM’s® evidence-based clinical decision support platform COAST™ (Clinician Operated Assistive Sleep Technology). Changes in insomnia severity and daytime symptoms of depression and anxiety will be compared at baseline, at 6–8 weeks, and at 3-month follow-up. Patient satisfaction with insomnia care as usual versus digital CBTI hubs will also be examined. We hypothesize that digital CBTI hubs will be non-inferior to insomnia care as usual for improvements in insomnia and daytime symptoms as well as patient satisfaction with insomnia care.DiscussionDigital technology has a high potential to scale CBTI accessibility and delivery options required to meet the insomnia care needs of military service members. Digital CBTI hubs using COAST offer a novel approach to broaden service members’ access to CBTI and to serve as an augmentation strategy for existing sleep services at military treatment facilities. The pragmatic approach leveraging technology in this trial has the potential to rapidly inform clinical practice within the Defense Health Agency as well as other healthcare systems.Trial registrationClinicalTrials.gov NCT05490550. Registered on 14 July 2023.

SAT611 Prevalence Of Comorbidities Among Treated And Untreated Adults With Suspected Growth Hormone Deficiency

Abstract Disclosure: A.R. Hoffman: Consulting Fee; Self; Ascendis. S. Raveendran: Employee; Self; Ascendis Pharma. J. Manjelievskaia: Employee; Self; Veradigm. Research Investigator; Self; Veradigm. A. Komirenko: Employee; Self; Ascendis Pharma. M. Bonafede: Employee; Self; Veradigm. Research Investigator; Self; Veradigm. Stock Owner; Self; Veradigm. I. Winer: Employee; Self; Veradigm. Research Investigator; Self; Veradigm. J. Cheng: Employee; Self; Veradigm. Research Investigator; Self; Veradigm. P. Miner: Employee; Self; Ascendis Pharma. A.R. Smith: Employee; Self; Ascendis Pharma. Introduction: Adult growth hormone deficiency (AGHD) is associated with an increased risk of morbidity and mortality, yet is known to be underdiagnosed and undertreated. Untreated or undertreated patients with AGHD can experience reduced quality of life, adverse changes in metabolic and cardiovascular (CV) function, and increased risk of other comorbidities. This study captured and described suspected AGHD cases based on a previously published algorithm in order to characterize the treatment rate and clinical burden of this disease. Methods: Using the Veradigm Network EHR database linked to administrative claims, this retrospective cohort study selected adult patients with suspected AGHD between 1/1/2017 and 12/31/2021 based on: hypopituitarism diagnosis or ≥1 diagnosis for related conditions, ≥3 pituitary hormone deficiencies, ≥3 replacement therapies with pituitary hormones or vasopressin, or ≥1 prescription for GH. Index date was based on the earliest qualifying event. Baseline demographics and clinical characteristics were assessed throughout the 12-month baseline period. Receipt of GH treatment was assessed throughout the 12-month baseline and 12-month follow-up periods. Results: Of the 51,588 patients with suspected AGHD, 59% were female, median age was 51 years, and 59% were White. About 3.2% had evidence of GH therapy during the study period. Among the 1,659 GH-treated patients, 40% were treated during both baseline and follow-up. Median age among treated patients was 48 years and 53% were male. At baseline, the top clinical comorbidities among the overall cohort were CV disease (52%), hyperlipidemia (47%), endocrine-related conditions (45%), mental health conditions (43%), and sleep disorders (29%). Treated adults had higher proportions of endocrine-related conditions (66% vs 44%), the most prevalent of which were hypopituitarism (38% vs 6%), hypothyroidism (37% vs 29%), and hypogonadism (27% vs 17%) compared to untreated. Higher proportions of CV disease (52% vs 36%), hyperlipidemia (47% vs 36%), obesity (35% vs 20%), diabetes mellitus (27% vs 15%), and liver disease (14% vs 11%) were observed in the untreated vs treated cohort. Prevalence of depression, anxiety, and sleep disorders was observed in about a third of both cohorts (treated and untreated). Conclusions: Most adults with suspected GHD remain untreated. Fewer than 4% of suspected AGHD patients in our study were treated with GH. Substantially higher proportions of clinical conditions related to metabolic and CV function were observed among the untreated cohort supporting the need for improved diagnosis and management of this disease. Presentation: Saturday, June 17, 2023

605 Description of sleep breathing disorder prevalence, risk factors, and treatment in adults with cystic fibrosis

605 Description of sleep breathing disorder prevalence, risk factors, and treatment in adults with cystic fibrosis

Efficacy and Safety of Prolonged-Release Melatonin for Primary Insomnia in Elderly Patients

Objective: Insomnia, a prevalent sleep disorder affecting the elderly, necessitates effective and safe treatment options. This study explores the potential of the prolonged-release melatonin (Circadin&lt;sup&gt;®&lt;/sup&gt;) addressing insomnia in elderly patients.Methods: The 8-week prospective observational study involved 115 participants, assessing sleep parameters using the Pittsburgh Sleep Quality Index (PSQI) and WHO-5 Well-being Index. The prolonged-release melatonin (2 mg) was administered nightly, and assessments were conducted at baseline, 4 weeks, and 8 weeks.Results: Both per protocol and last observation carried forward (LOCF) analyses consistently revealed significant improvements in sleep latency, total sleep time, sleep efficiency, and overall well-being. Notably, sleep latency decreased after 4 and 8 weeks, while total sleep time and sleep efficiency increased, reflecting longer and more restful sleep. The WHO-5 Well-being Index exhibited noticeable enhancement. Adverse events, including dizziness and heartburn, were manageable. Despite a high dropout rate, this drug’s potential as an efficacious and safe treatment option for elderly insomnia patients was evident, aligned with prior research.Conclusion: The prolonged-release formulation’s resemblance to the natural circadian rhythm of melatonin release offers advantages over conventional medications. This study contributes to understanding the prolonged-release melatonin’s promise as a valuable therapeutic alternative, encouraging further investigation into its longterm effects and optimal implementation.

Behavioral disorders in children and adolescents with atopic dermatitis

ObjectivesTo assess the prevalence and pattern of behavioral problems in children and adolescents with atopic dermatitis (AD) and to study their associations with clinical data and severity. MethodsThis was a single-center, cross-sectional study of patients (6–17 years) with AD. Assessment of competencies and syndrome scale scores of behavioral problems was performed by applying the Child Behavior Checklist 6–18 (CBCL 6–18) and AD severity using the Eczema Area Severity Index (EASI) score. ResultsOf the 100 patients with AD, 56% were male, with a mean age of 11±3 years, and 43% had moderate/severe AD. Borderline or abnormal values were found in 75% of the patients for total social competence, 57% for internalization, 27% for externalization, and 18% for aggressive behavior. A higher prevalence of aggressive behavior (27.9% vs. 10.5%; p = 0.02) and sleep disorders (32.6% vs. 15.8%; p = 0.04) was observed in patients with moderate/severe AD than in those with mild AD. Children with current or previous use of immunosuppressants/immunobiological tests had a lower frequency of normal social competence (53% vs. 83%, p = 0.012). Regarding the critical questions, 8% responded affirmatively to suicidal ideation. ConclusionA high prevalence of behavioral problems was observed among children and adolescents with AD, with a predominance of internalizing profiles, mainly anxiety and depression. Children with moderate/severe AD have a higher prevalence of aggressive behaviors and sleep disorders. These findings highlight the importance of multidisciplinary teams, including mental health professionals, in caring for patients with AD.

Prevalence of Sleep Disorders Among the General Population of the Jazan Region of Southwest Saudi Arabia.

Sleep disorders impose a substantial burden on the global population, leading to an array of health complications. Understanding their prevalence and associated risk factors is crucial to formulating effective interventions. This study aimed to determine the prevalence and associated risk factors of sleep disorders among residents of the Jazan region of Saudi Arabia. This cross-sectional study conducted an online survey from December 2022 to March 2023. The sample comprised 670 respondents aged 18 years and older residing in Jazan. Demographic data, lifestyle habits, sleep patterns, and sleep disorder symptoms were assessed. The participants were predominantly women (62.2%), with an average age of 30.99 years and a normal body mass index. The analyses revealed that 28.8% of the respondents reported experiencing sleep disorders, and 13.4% particularly had obstructive sleep apnea. Nearly half of the participants reported having primary insomnia, excessive daytime sleepiness, and restless leg syndrome. Significant associations were found between sleep disorders and older age (p = 0.012), obesity (p = 0.043), short or thin neck (p = 0.034), smoking (p = 0.003), caffeine use (p = 0.001), existing health conditions (p = 0.001), medication use (p = 0.013), lack of daytime naps (p = 0.043), and frequent nighttime awakenings to urinate (p = 0.001). The most common self-reported reasons for nightly awakenings were urination, anxiety or stress, discomfort or noise, and health conditions. The findings suggest a high prevalence of sleep disorders among adults in Jazan, Saudi Arabia. Various demographic, lifestyle, and health-related factors are linked to these disorders. Therefore, targeted sleep health education and interventions could be instrumental in tackling this significant public health issue.

The association between wealth and sleep medication use in a nationally-representative sample of older Medicare beneficiaries.

Sleep disorders are common among older adults, leading to high prevalence of over-the-counter and prescription sleep medication use. Socioeconomically disadvantaged individuals have higher prevalence of sleep disorders. Frequent use of sleep medications can increase the risk of falls. Little is known about the association between wealth and sleep medication use in older adults. We conducted a cross-sectional analysis using a nationwide sample of 7603 Medicare beneficiaries (65+ years) from Round 1 (2011) of the National Health and Aging Trends Study. We measured self-reported wealth as the sum of assets (retirement savings, stocks/bonds, checking/savings accounts, business assets, and home value) minus liabilities (mortgage, credit card, and medical debt). Self-reported sleep medication use in the past month was categorized as frequent (5-7 nights/week), sometimes (1-4 nights/week), or never (0 night/week). We estimated differences in the prevalence of sleep medication use by quintiles of wealth using crude and adjusted binomial regression models. Individuals missing sleep medication information were excluded. Median wealth was $152 582 (IQR: $24 023-412 992). Sixteen percent reported frequent sleep medication use, 15% reported some use, and 70% reported no use. Frequent sleep medication use was more common in lower wealth quintiles (lowest: 20%, highest: 12%). Alternatively, some use was more common in higher wealth quintiles (lowest: 11%, highest: 18%). Results were similar after adjustment for demographic factors, anxiety, depression, and sleep disorders. In this study, less wealthy older adults had higher prevalence of frequent sleep medication use. This may lead to dependency or increased fall risk in this vulnerable population.

Risk factors for post-traumatic stress disorder in nurses from the regional medical alliance during the COVID-19 epidemic: A prospective cross-sectional study

Mental health issues among nursing professionals have been increasingly reported during the coronavirus disease (COVID-19) pandemic. However, there is a paucity of research on post-traumatic stress disorder (PTSD) among nurses working in Medical Alliances. In this study, we aimed to investigate the risk factors associated with PTSD in the Regional Medical Alliance (MA) in Shantou (China) during the COVID-19 pandemic. A total of 1286 nurses from four MA hospitals participated in the study from February to March 2020. Our findings revealed that the incidences of PTSD, depression, anxiety, and sleep disorders among nurses from MA were 15.6%, 35.5%, 18.3%, and 36.4%, respectively. Moreover, PTSD was positively correlated with depression, anxiety, and sleep disorders. In addition, the results of logistic regression analysis showed that working in a tertiary hospital, older age, more severe depression, more severe anxiety, and prevalent sleep disorders were independent risk factors for PTSD among nurses. Therefore, mental health interventions targeting high-risk nurses in MA with an incidence of PTSD are urgently needed.

Associations between Milk Intake and Sleep Disorders in Chinese Adults: A Cross-Sectional Study.

We aimed to examine the association of milk intake with sleep disorders and their specific indicators. The current study included 768 adults aged 28-95 from Wenling, China. Milk intake was assessed using a food frequency questionnaire with ten food items, while sleep disorders were measured using the Pittsburgh Sleep Quality Index (PSQI), with higher scores indicating poorer sleep. The participants were divided into two groups according to the average intake of milk per week: rare intake (≤62.5 mL/week) and regular intake (>62.5 mL/week). Primary measurements were multivariate-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for the prevalence of sleep disorders concerning regular milk intake compared with rare intake. In secondary analyses, linear regression analyses were performed to assess the effects of milk intake on sleep disorders and their specific dimensions. Regular intake of milk did not have a significant association with sleep disorders compared with rare intake (adjusted OR: 0.72, 95%; CI: 0.51, 1.03), but this association was found to be pronounced with sleep disturbances (OR: 0.49, 95%; CI: 0.28, 0.87). Increased intake of milk was significantly associated with the lower scores of PSQI for sleep quality (β: -0.045, 95%; CI: -0.083, -0.007) and sleep disturbances (β: -0.059, 95%; CI: -0.090, -0.029), respectively. When stratified by age and gender, the benefits of milk intake for sleep disorders and sleep disturbances were more significant in older adults (≥65) and men than in younger persons and women. In summary, regular milk intake benefits sleep quality, which may contribute to nutritional psychiatric support for prevention against sleep disorders.

Multiscale entropy analysis of single lead ECG and ECG derived respiration for AI based prediction of sleep apnea events

Sleep apnea (SA) is a prevalent sleep disorder that affects a significant portion of the adult population. The proposed method involves coarse graining a signal at different scales, using the popular multiscale entropy algorithms to detect apnea and normal events from single lead Electrocardiography (ECG), Instantaneous Heart Rate and ECG derived respiration. The idea of nonlinear dynamical systems analysis for feature extraction along with machine learning approach, the authors present here a moderately accurate sleep apnea detection model with an accuracy ranging from 70% to 100% based on two different probabilistic thresholds of 50 % and 70% with reduction in false positive rate from 28% to 14 % for applications in development of AI based IoT connected smart wearable devices. The two thresholding modes offer a choice to do a trade-off between high training accuracy with high false positive rate and low accuracy with low false positive rate. The performance metrics of each model have been reported and choice is made for best suitable model with prospectives of implementation for diagnostic purposes. The effectiveness of the proposed method in accurate detection of sleep apnea requires further validation of the models with out of distribution dataset.

Redefining Comorbid Insomnia and Sleep Apnea: The Association of Sleep Breathing Impairment and Insomnia with Incident Diabetes.

Rationale: Obstructive sleep apnea (OSA) is a prevalent sleep disorder that is frequently comorbid with insomnia and often accompanied by metabolic diseases such as type 2 diabetes. Although the apnea-hypopnea index (AHI) is currently the diagnostic criterion for gauging the severity of OSA, the AHI has not consistently predicted incident diabetes. Objectives: To test whether a combined insomnia-OSA (COMISA) phenotype based on comorbid insomnia and sleep breathing impairment index (COMISA-SBII) predicts incident diabetes and to compare the association with an AHI definition of COMISA (COMISA-AHI) in the MrOS (Osteoporotic Fractures in Men) study. Methods: The study samples came from participants in the MrOS sleep study without diabetes at their baseline examination. The SBII was derived as the product of the duration of each respiratory event (apnea and hypopnea) and the accompanying desaturation area from baseline unattended polysomnography. A subgroup of individuals classified as having comorbid insomnia (difficulties falling asleep, waking up in the middle of the night and/or early morning awakenings >15 times per month, and daytime impairments) and sleep breathing impairment (greater than 50th percentile of SBII) were identified at baseline. The primary outcome was incident diabetes during the follow-up visits. Cox proportional models were built to assess the adjusted hazard ratios of COMISA-AHI and COMISA-SBII. Prediction model performances of incident diabetes were compared across different models. Results: A total of 2,365 men (mean age, 76 yr) without diabetes at baseline were included. During a median follow-up of 10.0 years, diabetes developed in 181. After adjusting for demographic characteristics, comorbidities, and behavioral risk factors, participants with COMISA-SBII had a higher risk of incident diabetes (hazard ratio, 1.82; 95% confidence interval, 1.15-2.89) than those without sleep disorders (those with an SBII ⩽13.17 and no insomnia). The result remained significant in the risk competing model. Compared with COMISA-AHI, the addition of COMISA-SBII to a crude model with established risk factors significantly improved the predictive value of incident diabetes. Conclusions: COMISA-SBII, but not COMISA-AHI, predicted incident diabetes after accounting for multiple covariates in a cohort of older men. A comorbid insomnia phenotype based on SBII plus insomnia symptoms may be an important clinical subtype.

Sleep Disorders Associated with Neurodegenerative Diseases.

Sleep disturbances are common in various neurological pathologies, including amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), hereditary ataxias, Huntington's disease (HD), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB). This article reviews the prevalence and characteristics of sleep disorders in these conditions, highlighting their impact on patients' quality of life and disease progression. Sleep-related breathing disorders, insomnia, restless legs syndrome (RLS), periodic limb movement syndrome (PLMS), and rapid eye movement sleep behavior disorder (RBD) are among the common sleep disturbances reported. Both pharmacological and non-pharmacological interventions play crucial roles in managing sleep disturbances and enhancing overall patient care.

Association of age and night flight duration with sleep disorders among Chinese airline pilots.

Night flights might aggravate sleep disorders among aging airline pilots, posing a threat to flight safety. In this study, we assess the prevalence of sleep disorders as well as the combined effects of night flight duration and aging on sleep disorders. A cross-sectional study was conducted between July and December, 2021. Participants were recruited from a commercial airline. Sleep disorders were evaluated using the Pittsburgh Sleep Quality Index (PSQI). The interaction effect of night flight duration and age on sleep disorders and their correlates were examined using logistic regression models. In total, 1,208 male airline pilots were included in the study, with a median age of 34 (interquartile range [IQR]: 29-39) years. The overall prevalence of sleep disorders was 42.6%. The multivariate logistic regression identified an interaction between night flight duration and age on sleep disorders (adjusted odds ratio [aOR] of the interaction term was 5.85 95% CI: 2.23-15.34 for age ≥ 45 years; 1.96 95% CI:1.01-3.81 for the age group 30-44 years). Longer night flight duration (aOR: 4.55; 95%CI: 1.82-11.38) and body mass index (BMI) ≥28.0 kg/m2 (aOR: 0.16; 95% CI: 0.03-0.91) were significantly associated with sleep disorders in participants aged ≥45 years. Hyperuricemia (aOR: 1.54; 95% CI: 1.09-2.16) and regular exercise (aOR: 0.23; 95% CI: 0.08-0.70) were significantly associated with sleep disorders in the 30-44 years age group. The mean monthly night flight duration and aging had a synergistic effect on airline pilots' sleep disorders, implying an aging and work-related mechanistic pathogenesis of sleep disorders in airline pilots that requires additional exploration and intervention.

Prevalence of Sleep Disorders Among Patients With Type 2 Diabetes Mellitus at Primary Healthcare Centers in the South Region of Abha City.

Background Type 2 diabetes is a chronic condition that affects the way the body processes blood sugar (glucose). This issue is of considerable importance in the field of public health, as it has a global impact on a substantial number of individuals. The primary emphasis in the management of type 2 diabetes is centered around achieving glycemic control, implementing lifestyle adjustments, and employing pharmaceutical therapies as preventive measures or for the purpose of managing problems that may arise as a result of the disease. Aim This research aimed to investigate the prevalence of sleep-belated issues among individuals diagnosed with type 2 diabetes. Methodology A total of 230 participants with type 2 diabetes patients of primary healthcare in Abha city whose age is ≥18 years were included in the study. The data collection process involved the distribution of a self-administered questionnaire that assessed various aspects of sleep disturbances, including difficulties in falling asleep, waking up during the night, excessive daytime sleepiness, and restless legs or leg muscle cramps. The questionnaire also collected demographic information and data on potential risk factors such as alcohol consumption, caffeine consumption, and smoking/tobacco product use. Data analysis was conducted using chi-square tests and significance levels were set at p < 0.05. Results The findings revealed a prevalence of sleep disturbances among individuals with type 2 diabetes. Difficulties in falling asleep and waking up during the night were reported by a substantial proportion of participants, and a notable number experienced excessive daytime sleepiness. Restless legs or leg muscle cramps that interrupted sleep were experienced occasionally by 16.5% and frequently by 8.7% of the participants. The study also found a significant association between the presence of sleep problems and lower sleep quality ratings. However, no significant associations were found between sleep disturbances and the duration of type 2 diabetes or the examined risk factors. Conclusion The findings from this study emphasize the detrimental effects of sleep disturbances on sleep quality and suggest that improving sleep quality can positively influence the overall health and well-being of individuals with type 2 diabetes.

The effect of glycemic control on sleep quality in type 2 diabetes mellitus

Objectives: There are publications showing sleep quality is impacted in type 2 diabetes mellitus (T2DM) cases. In our study, we planned to assess the prevalence of sleep disorder in these patients compared to society, and investigate whether poor glycemic regulation and increased body mass index (BMI) caused disruption of sleep quality or not. Methods: Sleep quality was compared between patients followed in our clinic with T2DM (n = 534) for minimum 5 years and a control group (n = 269). Assessment was performed for whether increased glycated haemoglobin (HbA1c) and increased BMI caused an increase in Pittsburgh Sleep Quality Index (PSQI) score or not. Cases with any comorbid disease or drug use affecting sleep quality were excluded from the study. Results: T2DM patients had higher PSQI points compared to the control group. A statistically significant, very low-level positive correlation was identified between BMI measurements and PSQI scores (as BMI increased, PSQI increased). A statistically significant, very low-level positive correlation was identified between HbA1c measurements and PSQI scores (as HbA1c increased, PSQI increased). HbA1c measurements of those in the good sleep quality group were significantly lower compared to those in the moderate sleep quality and poor sleep quality groups. The BMI measurements in the poor sleep quality group were significantly higher than those in the good sleep quality group. Conclusions: The sleep quality of T2DM cases was worse compared to the control group, while the increase in HbA1c level further disrupted sleep quality. The increase in BMI is another factor disrupting sleep quality in diabetic patients.

Association between exposure to wind turbines and sleep disorders: A systematic review and meta-analysis.

To date, there is scarce evidence on the association between sleep disorders and noise generated by wind turbines. We searched six relevant electronic databases from the inception to May 2023 for relevant articles. The methodological quality of the included articles was evaluated using the US National Institutes of Health tool. Fifteen articles met the inclusion criteria. The overall prevalence of sleep disorders among residents close to wind turbines was 34% (95% Confidence Interval, 0.22-0.47). Univariate meta-regressions for distance and sound power level showed that at higher distance the prevalence of sleep disorders decreases (p=0.010) and with a higher sound power level the prevalence increases (p=0.037). Furthermore, this systematic review and meta-analysis highlighted that the overall quality of current research on this topic is poor, and the methods to measure the results are often based on subjective assessments and not validated questionnaires. In conclusion, our preliminary findings suggest that there may be a possible relation between exposure to wind turbines and sleep disorders, although no conclusions can be drawn in terms of causality due to the nature of the retrieved data and the poor quality of current evidence. Future studies should adopt a longitudinal design and focus on objective measurements, supported by validated subjective methods such as questionnaires.