Risk Factor For Sleep Disorders Research Articles

Characteristics and correlated factors of preoperative sleep disorders in cardiac surgical patients

Objective: To investigate the clinical characteristics and correlated factors of preoperative sleep disorders in patients undergoing various types of cardiac surgery. Methods: The data of patients at the Structural Heart Surgery Center of Beijing Anzhen Hospital, Capital Medical University, from April 2023 to February 2024 were retrospectively collected. Patients were categorized into five groups based on cardiac surgical diagnosis: coronary heart disease, valvular disease, large vessel disease, congenital heart disease, and others. Each group was further subdivided into normal sleep (NS) and sleep disorder (SD) groups based on the Pittsburgh Sleep Quality Index (PSQI) scores. Demographic information, cognitive function, psychiatric symptoms, and other relevant data were collected. Clinical characteristics were compared between groups, and factors associated with preoperative sleep disorders were analyzed using multivariate logistic regression. Results: A total of 1 016 patients aged (58.6±12.7) years were included in the study, including 701 males and 315 females. The incidence of SD was 45.6% (463/1 016). Multivariate logistic regression analysis showed that aging was a risk factor for sleep disorders in patients with coronary heart disease (OR=1.050, 95%CI: 1.026-1.077) and valvular disease (OR=1.033, 95%CI: 1.013-1.053). High Self-rating Depression Scale (SDS) score was a risk factor for sleep disorders in patients with valvular disease (OR=1.050, 95%CI: 1.013-1.091). High score on the Montreal Cognitive Assessment (MoCA) subitem-abstraction ability was a protective factor for sleep disorders in patients with coronary heart disease (OR=0.695, 95%CI: 0.490-0.981). Conclusions: The risk factors of preoperative sleep disorders in cardiac surgery patients vary based on the type of cardiac disease. Aging, depression and abstraction ability correlate with sleep disorders in cardiac surgical patients.

High Ambient Temperatures Increase Outpatient Visits for Sleep Disorders in Hefei City, China

Objective: To analyze the impact of ambient temperature on the quantity of outpatient clinic visits for sleep disorders. Methods: Using data from sleep disorder outpatient visits in a large tertiary hospital in Hefei City, a distributional lag nonlinear model combined with a generalized Poisson regression model was used to analyze the relationship between ambient temperature and the number of outpatient visits for sleep disorders. Results: Ambient temperatures above 17.2℃ were found to be connected with a higher prevalence of sleep disorders visits, and that this relationship was most significant on day 8, which lasted for 7 days. For the single-day lagged impact, the maximum relative risk (RR) for moderate heat (75th percentile) was 1.077 (95% CI: 1.015–1.143). The cumulative lag effect was substantially greater than the single-day lag effect, with a maximum relative risk (RR) of 2.609 (95% CI: 1.306–5.212). The longest lag time was 14 days. The RR was similarly greater in women and those over 40. Outpatient visits for men with sleep disorders were not affected by ambient temperature in a statistically significant way. Conclusion: High ambient temperature raises the risk that patients will visit an outpatient facility and serves as a risk factor for sleep disorders. Patients who were 40 years of age or older and women were at vulnerability.

Prevalence and risk factors of sleep disorders in inflammatory bowel disease: a cross-sectional study

BackgroundSleep disorders are one of the major public health problems, which can potentially induce inflammation and exacerbate disease activity, resulting in compromised sleep quality. This study aimed to investigate the prevalence and risk factors associated with sleep disorders among patients with inflammatory bowel disease (IBD).MethodsBetween March 2023 and February 2024, the Pittsburgh Sleep Quality Index was employed to assess sleep quality in both IBD patients and healthy control subjects. Univariate and multivariate analysis were performed to identify the risk factors associated with SD in IBD patients.ResultsOverall, 208 IBD patients [150 Crohn’s disease (CD) and 58 ulcerative colitis (UC)] and 199 healthy individuals were included. Sleep disorders were observed in 59.6% of patients with IBD, with a higher prevalence among females (63.5%) compared to males (56.9%) (P = 0.476). The prevalence of sleep disorders in IBD patients was significantly higher than that found in healthy controls (37.7%) (all P < 0.01). The prevalence of sleep disorders among CD and UC patients was 58% and 63.8%, respectively (P = 0.291). The multivariate analysis revealed that older age (OR, 1.070; 95% CI: 1.035–1.105, P = 0.000), smoking (OR, 2.698; 95% CI: 1.089–6.685, P = 0.032), and depression (OR, 4.779; 95% CI: 1.915–11.928, P = 0.001) were risk factors for sleep disorders in IBD patients. However, higher body mass index (OR, 0.879; 95% CI: 0.790–0.977, P = 0.017) was identified as a protective factor.ConclusionSleep disorders are common among IBD patients regardless of activity levels. Smoking and depression are the major risk factors.

A study on factors related to sleep disorders and serum BDNF expression levels in patients with primary Sjӧgren's syndrome.

We aimed to assess the sleep quality of patients with primary Sjögren's syndrome (pSS) and the associated factors. Moreover, Preliminary exploration of the clinical significance of serum brain-derived neurotrophic factor (BDNF) in pSS patients with sleep disorders. A self-report survey was administered to 111 pSS patients and 40 healthy individuals using the Pittsburgh Sleep Quality Index (PSQI) for sleep quality. General clinical information,the sleep quality and mental conditions were collected using on-site questionnaires and various scales. 40 healthy controls from the health examination center of the same hospital, who were age and sex matched. Detection of serum BDNF levels by ELISA method . Independent samples t tests, Chi-square analysis, logistic regression were used to analyze these data. Patients with pSS had higher scores on the PSQI than the healthy individuals. Abnormal sweating, high PHQ-9 and ESSPRI scores were independent risk factors for sleep disorders. pSS patients had lower serum BDNF than the healthy individuals, The area under the curve (AUC) of predicting sleep disorder in pSS patients using detection of serum BDNF level was 0.8470, and the sensitivity and specificity were 0.951 and 0.727, which were superior to PHQ-9 and GAD-7. Compared with the healthy individuals, pSS patients had a higher prevalence of sleep disorders and lower serum BNDF. Serum BDNF level demonstrated greater predictive advantage for sleep disorder in pSS patients.

The association of mixed multi-metal exposure with sleep duration and self-reported sleep disorder: A subgroup analysis from the National Health and Nutrition Examination Survey (NHANES)

Sleep disorders significantly affect sleep duration and constitute a major public health issue. However, the relationship between metal exposure and sleep is not fully elucidated. This study utilized publicly available data from the National Health and Nutrition Examination Survey (NHANES) to measure blood concentrations of seven metals—copper (Cu), zinc (Zn), selenium (Se), manganese (Mn), mercury (Hg), cadmium (Cd), and lead (Pb)—in a cohort of 4263 American adults. The relationship between metal exposure and self-reported sleep duration and sleep disorder risk was analyzed using single exposure models like logistic and linear regression and mixedexposure models such as weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR). The results indicated an absence of statistically significant findings in the single exposure model. In contrast, the mixed exposure model revealed a positive correlation between selenium levels and the risk of sleep disorders across the entire population. A “U-shaped” association was identified between copper levels and the risk of sleep disorders in males, females, and individuals aged 60 and above. Moreover, a positive trend was observed between manganese levels and the risk of sleep disorders in individuals aged 60 and above. Additionally, elevated concentrations of metal mixtures were significantly associated with reduced sleep duration among females. Sensitivity analyses corroborated these findings. In conclusion, within the context of metal mixtures, selenium may be a risk factor for sleep disorders in the general population. Manganese may be a unique risk factor in older adults. Copper levels have a “U" shaped link to sleep disorder risk in specific population subgroups. Finally, the accumulation of blood metal mixtures in females, mainly due to lead and mercury, may reduce sleep duration. Further research is necessary to validate these findings.

Pain, obesity, adenosine salvage disruption, and smoking behavior mediate the effect of gut microbiota on sleep disorders: results from network Mendelian randomization and 16S rDNA sequencing.

The objective of this study is to investigate the indirect causalities between gut microbiota and sleep disorders. In stage 1, we utilized 196 gut microbiota as the exposure factor and conducted a two-sample univariable Mendelian randomization (MR) analysis on five sleep disorders: insomnia, excessive daytime sleepiness (EDS), sleep-wake rhythm disorders (SWRD), obstructive sleep apnea (OSA), and isolated REM sleep behavior disorder (iRBD). In stage 2, we validated the MR findings by comparing fecal microbiota abundance between patients and healthy controls through 16S rDNA sequencing. In stage 3, we explored the indirect pathways by which the microbiota affects sleep, using 205 gut microbiota metabolic pathways and 9 common risk factors for sleep disorders as candidate mediators in a network MR analysis. In stage 1, the univariable MR analysis identified 14 microbiota potentially influencing five different sleep disorders. In stage 2, the results from our observational study validated four of these associations. In stage 3, the network MR analysis revealed that the Negativicutes class and Selenomonadales order might worsen insomnia by increasing pain [mediation: 12.43% (95% CI: 0.47, 24.39%)]. Oxalobacter could raise EDS by disrupting adenosine reuptake [25.39% (1.84, 48.95%)]. Allisonella may elevate OSA risk via obesity promotion [36.88% (17.23, 56.54%)], while the Eubacterium xylanophilum group may lower OSA risk by decreasing smoking behavior [7.70% (0.66, 14.74%)]. Triangulation of evidence from the MR and observational study revealed indirect causal relationships between the microbiota and sleep disorders, offering fresh perspectives on how gut microbiota modulate sleep.

Research on sleep disorders and related risk factors among healthcare workers from Fujian province supporting Hubei province during the COVID-19 pandemic.

To explore the impact of COVID-19 on the sleep of healthcare workers from Fujian Province supporting Hubei Province and its related risk factors. A cross-sectional, anonymous, self-reported online questionnaire survey was conducted among all participants. The questionnaire consisted of five parts: sociodemographic characteristics and COVID-19 epidemic-related factors, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Morningness-Eveningness Questionnaire-5 (MEQ-5), and 12-item General Health Questionnaire (GHQ-12). Among 552 participants, 203 (36.8%) had a PSQI score > 7, indicating the presence of sleep disorders. Logistic regression analysis revealed that sleep disorders were independently associated with a history of previously diagnosed sleep disorders (OR 6.074, 95% CI 2.626-14.049, P < 0.001), rotating night shifts > 3 times per week (OR 3.089, 95% CI 1.650-5.781, P < 0.001), using electronic devices before sleep >1 h (OR 1.685, 95% CI 1.131-2.511, P = 0.010), concern about contracting COVID-19 (OR 1.116, 95% CI 1.034-1.204, P = 0.005), perception of societal support for supporting healthcare workers in Hubei (OR 0.861,95% CI 0.744-0.998, P = 0.047) (OR 0.861, 95% CI 0.744-0.998, P = 0.047), non-medical staff (OR 0.257, 95% CI 0.067-0.987, P = 0.048), ESS score (OR 1.068, 95% CI 1.018-1.121, P = 0.007), and GHQ-12 score (OR 1.511, 95% CI 1.281-1.782, P < 0.001). Sleep disorders were highly prevalent among healthcare workers from Fujian Province supporting Hubei Province during the COVID-19 pandemic. Risk factors for sleep disorders included a history of previously diagnosed sleep disorders, rotating night shifts > 3 times per week, using electronic devices before sleep >1 h, excessive concern about contracting COVID-19, and poorer psychological health. Higher perceived societal support and understanding of support for healthcare workers supporting Hubei were associated with a reduced risk of sleep disorders, as was being non-medical staff. Providing more sleep hygiene education and psychological health services for frontline healthcare workers is necessary.

Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic

BackgroundThe prevalence of sleep disorders among medical students was high during the COVID-19 pandemic. However, fewer studies have been conducted on sleep disorders among medical students after the COVID-19 pandemic. This study investigated the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19.MethodsA total of 1,194 Chinese medical students were included in this study from 9th to 12th July 2023. We used the Self-administered Chinese scale to collect the demographic characteristics. In addition, we used the Chinese versions of the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) to assess subjects’ depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors influencing sleep disorders. The receiver operating characteristic (ROC) curve was utilized to assess the predictive value of relevant variables for sleep disorders.ResultsWe found the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR = 1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR = 1.908, 95% CI 1.264 to 2.880).ConclusionThe prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic levels and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is essential.

Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction

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

Risk Factors for Sleep Disorders in Patients with Chronic Pain: A Meta-Analysis

Background: Chronic pain and sleep disorders frequently co-occur, exacerbating each other in a vicious cycle. This meta-analysis aimed to identify and quantify risk factors associated with sleep disorders in individuals experiencing chronic pain. Methods: A systematic search of PubMed, Embase, Web of Science, and Cochrane Library databases was conducted from January 2018 to June 2024. Studies assessing sleep disorders (insomnia, obstructive sleep apnea, restless legs syndrome) in adults with chronic pain (non-cancer pain lasting &gt;3 months) were included. Data on demographics, pain characteristics, sleep measures, and potential risk factors were extracted. Meta-analyses were performed using random-effects models to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs). Results: Twenty-seven studies (n = 12,453 participants) met the inclusion criteria. Chronic pain significantly increased the odds of having any sleep disorder (OR 2.83, 95% CI 2.19-3.65). Specific risk factors identified included: Female gender: OR 1.41 (95% CI 1.18-1.67); Higher pain intensity: OR 1.15 per 1-unit increase on a 0-10 scale (95% CI 1.08-1.23); Longer pain duration: OR 1.04 per year (95% CI 1.01-1.07); Presence of depression or anxiety: OR 2.32 (95% CI 1.85-2.91); Use of opioid medications: OR 1.58 (95% CI 1.23-2.04). Conclusion: Chronic pain is a substantial risk factor for sleep disorders. Gender, pain intensity, duration, comorbid mental health conditions, and opioid use emerged as modifiable risk factors. Targeted interventions addressing these factors may improve sleep outcomes in individuals with chronic pain.

Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.

Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. The study aimed to compare how different model metrics rank the importance of various covariates. A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.

Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model.

This study aimed to investigate the risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model. A cross-sectional survey was conducted in a hospital in Zhejiang province, China from January 1, 2020, to November 31, 2022 among patients undergoing hemodialysis. Dietary intake was assessed applying a Food Frequency Questionnaire. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index. Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis was using a nomogram model. This study included 201 patients and 87 individuals (43.3%, 87/201) exhibited sleep disorders. The average age of included patients was 51.1 ± 9.0 years, with males accounting for 55.7% (112/201). Results from nomogram model exhibited that potential risk factors for sleep disorders in patients undergoing hemodialysis included female, advanced age, increased creatinine and alanine aminotransferase levels, as well as higher red meat consumption. Inversely, protective factors against sleep disorders in these patients included higher consumption of poultry, fish, vegetables, and dietary fiber. The C-index demonstrated a high level of discriminative ability (0.922). This study found that age, sex, and dietary factors were associated with sleep disorders in hemodialysis patients. Hemodialysis patients with sleep disorders require urgent dietary guidance.

Associations between sleep quality and coping style among front-line nurses: a cross-sectional study in China

Abstract Aims Good sleep is crucial for healthcare professionals, especially nurses, to prevent errors and injuries that may occur during caregiving. This study investigated the sleep quality and coping styles of nurses during the post-pandemic period, as well as related factors. Subject and methods An online observational cross-sectional study involving 1022 nursing staff was conducted. Participants completed questionnaires on job characteristics, the Pittsburgh Sleep Quality Index (PSQI) and Simplified Coping Style Questionnaire (SCSQ). Pearson’s correlation and multiple logistic regression analyses confirmed interactions between these factors. Results A total of 1022 nurses completed the survey, of whom 90.90% (n = 929) were female, and 87.38% were over 25 years old. The PSQI score was 10.51(2.23), and 91.1% (n = 931) of the nurses had sleep disturbances. In this study, we found that PSQI was associated with SCSQ. Multivariate analysis showed that age (36–40 years), frequency of night shifts (9–11 times/month, ≥ 12 times/month), and less humanistic care were risk factors for sleep disorders among night shift nurses (P &lt; 0.05), whereas frequency of exercise (1 time/week), non-attendance of training and learning after night shifts, and SCSQ positivity were protective factors for sleep disorders (P &lt; 0.05). Conclusions Sleep quality of medical staff, especially nurses, was affected by shift work during the post-pandemic era. Designing the best strategy for employees who are more likely to have sleep disorders, such as experienced nurses, will support them through sleep hygiene education and strengthen the humanistic solicitude of nurses.

Fatigue, depression, and sleep disorders are more prevalent in patients with metabolic-associated fatty liver diseases.

To determine the prevalence and risk factors for depression, sleep disturbances, and exhaustion in MAFLD patients. Two hundred twenty-four consecutive patients with MAFLD attending the outpatient clinic from April to October 2023; were subjected to clinical evaluation, laboratory testing including non-invasive laboratory markers, fibroscan (measuring steatosis and fibrosis), and different quantitative and qualitative fatigue scores. A control group including 342 patients without MAFLD was taken. The prevalence of fatigue, depression, and sleeping disorders in the MAFLD group was 67.8%, 75%, 62.5% vs 21%, 16.4%, and 19.5% in the control group respectively ( P = <0.001, P = <0.001 and P = <0.001). MAFLD with fatigue was significantly associated with the presence and severity of steatosis and fibrosis by fibroscan ( P = <0.0001). By univariate and multivariate analysis: age, BMI, waist circumference, T2DM, hypertension, steatosis, fibrosis, and Fib-4 were considered risk factors for fatigue in the MAFLD group. The age, high social level, diabetes, hypertension, steatosis, fibrosis, and fib-4 were considered, by univariate and multivariate analysis, independent risk factors for depression in the MAFLD group. age, BMI, waist circumference, diabetes, hypertension, steatosis, fibrosis, and fib-4 were independent risk factors for sleep disorders in MAFLD. Fatigue, sleeping disorders, and depression are more prevalent in MAFLD patients than in the general population. The lower health utility scores in patients with MAFLD are associated with more advanced stages of the disease.

The Causal Association Between Blood Lead and Sleep Disorders: Evidence from National Health and Nutrition Examination Survey and Mendelian Randomization Analysis

BackgroundPoor sleep quality is a global public health concern. This study aimed to identify the risk factors for sleep disorders and clarify their causal effects.MethodsData were obtained from the National Health and Nutrition Examination Survey (NHANES) and Mendelian randomization (MR)-Base databases. Baseline characteristics of individuals with and without sleep disorders were compared. A multivariate logistic regression analysis was performed to calculate the effects of each variable on sleep disorders. Causal effects of blood lead levels and hypertension on sleep disorders were assessed using MR analysis.ResultsIn total, 3660 individuals were enrolled in the study. The prevalence of self-reported sleep disorders was 26.21%. Serum lead level, serum mercury level, serum retinol level, prevalence of hypertension, and daily vigorous work duration were significantly higher for those in the sleep disorders group than the control group. After adjusting for various covariates, the effects of serum lead and hypertension on sleep disorders were stable from logistic regression models 1–4. MR analysis showed that blood lead levels were causally related to the risk of sleep disorders (odds ratio (OR) = 1.09, 95% confidence interval (CI) 1.01–1.17, P = 0.030). There was no causal link between elevated blood pressure and sleep disorders (OR = 0.99, 95% CI 0.94–1.04, P = 0.757). Goodness-of-fit tests and sensitivity analyses were used to verify the reliability of the results.ConclusionsBlood lead is positively and causally associated with an increased risk of sleep disorders. These findings provide a novel perspective regarding sleep protection. Taking effective measures to reduce lead exposure may significantly improve sleep health.

Relationship between relative fat mass and low-carbohydrate diet scores and sleep disorders in United States: a real-world cross-sectional study.

To investigate the relationship between relative fat mass (RFM) and low-carbohydrate diet (LCD) scores and sleep disorders in the U.S. population. Data were collected from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2014. A total of 5,394 respondents participated in the study. Univariate and multivariate linear regression analyses were used to investigate the relationship between RFM and LCD scores, and univariate and multivariate logistic regression analyses were used to investigate the relationship between RFM and LCD scores and sleep disorders. Restricted cubic spline (RCS) analyses were conducted to test for nonlinear associations between RFM and LCD scores and sleep disorders. A total of 5,394 participants were included in the statistical analysis, including 5,080 healthy participants and 314 with sleep disorders. Univariate and multivariate linear regression showed a bivariate positive correlation between RFM and LCD scores (p < 0.05), and logistic regression analysis showed a significant positive correlation between RFM (95% CI: 1.02-1.07, p = 0.005) LCD scores (95% CI: 1.00-1.03, p = 0.044) and sleep disturbances. Subgroup analyses showed robust effects of RFM and LCD score on sleep disorders. RFM was positively and bi-directionally associated with LCD scores, both of which resulted as risk factors for sleep disorders. This study emphasizes that an LCD and lowering RFM can prevent and ameliorate the risk of sleep disorders.

Possible association between Toxoplasma gondii infection and sleep disorders: A systematic review.

Few studies investigated the relationship between toxoplasmosis and sleep disorders. Sleep disorders can lead to suicidal behavior and traffic accidents. Therefore, the purpose of this study is to collect information and investigate the possible relationship between Toxoplasma gondii (T. gondii) and sleep disorders. To achieve the goal of the present study, five English databases (PubMed, ScienceDirect, Web of Science, Scopus, and ProQuest) were systematically searched for related studies from their inception until October 18, 2022. The obtained articles were screened based on the title, abstract and full text. Then, the quality of the papers investigating the relationship between toxoplasmosis and sleep disorders was evaluated, and finally, the data from the relevant studies were extracted in a Microsoft Excel data sheet. Eight articles (4 case-control and 4 cross-sectional studies) were entered in this systematic review containing 926 patients with sleep disorders and 1877 people without sleep disorders in casecontrol studies, out of which 212 (22.89%) and 392 (20.88%) individuals were positive for anti-T. gondii IgG antibody using different serological methods. Also, 2885 people with sleep disorders were investigated for anti-T. gondii IgG antibody in cross-sectional studies, out of which 1559 (54.03%) cases were positive. The results of this study suggest that T. gondii infection may be a risk factor for sleep disorders. However, the number of related studies is small, and there are contradictions in the findings of these articles. Therefore, further studies are necessary to clarify the possible association between T. gondii infections and sleep disorders.

Analysis of sleep for the American population: Result from NHANES database

ObjectivesTo assess the contemporary prevalence and decade-long trends of sleep duration, sleep disorders and trouble sleeping among adults in the United States, as well as their risk factors, from 2005 to 2018. Materials and methodsWe used National Health and Nutrition Examination Survey data to calculate the sleep duration and weighted prevalence of sleep disorders and trouble sleeping in adults aged 20 years or older. Sleep duration, sleep disorders and trouble sleeping were assessed by questionnaire. ResultsA total of 27,399 people were included in the survey on sleep duration, with a weighted percentage of normal sleep (7–8 h/night) of 56.33 % (95 % CI, 53.06–59.60 %) and a weighted percentage of short sleep (5–6 h/night) of 31.73 %. In stratified descriptions, participants aged 40–49 years were more likely to sleep less than five hours, while women aged 80 years and older were more likely to sleep longer and blacks were more likely to sleep shorter. A total of 27,406 participants were included in the survey for sleep disorders. The weighted proportion of the population with sleep disorders was 8.44 % (95 % CI, 7.79–9.8 %). Independent risk factors for sleep disorders were being 40–69 years old, being white, having a high education level, smoking, having hypertension, diabetes, heart disease, and BMI ≥ 25. From 2005 to 2014, the prevalence of sleep disorders increased year by year, from 7.44 % in 2005–2006 to 10.40 % in 2013–2014 (P for Trend<0.001). A total of 38,165 participants were included in the survey on trouble sleeping. The weighted proportion of the population with troubled sleeping was 27.30 % (25.70–28.90 %). Independent risk factors for troubled sleeping were being 30–79 years old, being white, having a high education level, smoking, drinking, having hypertension, diabetes, heart disease and BMI ≥ 25. From 2005 to 2018, the prevalence of trouble sleeping increased annually, from 24.44 % in 2005–2006 to 30.58 % in 2017–2018 (P for trend<0.001). ConclusionAdults in the United States are likely to have abnormal sleep durations, and the prevalence of sleep disorders and troubled sleeping is on the rise.

Assessment of Lumbar Spinal Stenosis as a Risk Factor for Development of Sleep Disorder: The Locomotive Syndrome and Health Outcome in Aizu Cohort Study (LOHAS).

Insomnia has been reported to coexist with various musculoskeletal disorders. Although lumbar spinal stenosis (LSS) is the most frequently operated on spinal disease, the causal relationship between LSS and development of sleep disorders remains unclear due to lack of longitudinal studies. This study aimed to determine whether LSS was a risk factor for developing new sleep disorders, primarily insomnia, using a prospective cohort of community residents. This study was a prospective cohort study. Participants aged ≥65 years from the "Locomotive Syndrome and Health Outcomes in Aizu Cohort Study (LOHAS)" conducted in 2008 formed our study population. LSS was diagnosed using the self-administered, self-reported history questionnaire, a validated diagnostic support tool for LSS. Sleep disorder was investigated using a questionnaire during the 2-year follow-up. The impact of LSS on sleep disorder onset was analyzed after adjusting for potential confounders, such as age, sex, obesity, hypertension, diabetes, depression, and smoking habits, using propensity score matching. Of the 489 participants who were followed up for two years, 38 (7.8%) had newly developed a sleep disorder in 2010. After adjusting for confounding factors, a comparison of 133 participants each in the control and LSS groups showed significantly higher frequency of new-onset sleep disorders (19 [14.3%] in the LSS group versus 6 [4.5%] in the control group). LSS was found to be an independent risk factor for sleep disorders.

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.