Risk Factors For Sleep Disturbances Research Articles

Construction and verification of the prediction model for risk of sleep disturbance in elderly patients with hypertension: a cross-sectional survey based on NHANES database from 2005 to 2018

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

The risk and protective factors on the mental health of healthcare workers during the lockdown period due to covid-19 pandemic

This study aimed to analyze the impact of the lockdown period due to COVID-19 pandemic on the mental health status of healthcare workers and identify the related risk factors of psychosomatic distress. We conducted an online questionnaire survey to investigate the general demographic characteristics, perceived stress level, adult attachment style (AAS), family cohesion and adaptability, social support, sleep state, emotional state, and physical health of healthcare workers during the lockdown period due to the pandemic in 2022. We compared the mental health status between doctors and nurses, and further analyzed the factors influencing sleep, emotions, physical symptoms, and severe psychosomatic distress separately. For factors that showed statistical significance in the univariate analysis, forward stepwise regression was used for logistic regression analysis to identify risk factors for the corresponding issues. A total of 622 healthcare workers participated in the survey. Among the participants, 121 (19.5%) reported sleep problems, 209 (33.6%) had negative emotional states, and 147 (23.6%) reported physical health problems. There were 48 (7.7%) healthcare workers with severe psychosomatic distress. Compared to the group of nurses, the group of doctors exhibit a higher prevalence of emotional issues, physical health problems and psychosomatic distress. Perceived stress was identified as a risk factor for sleep disturbance, while living with others during quarantine and family adaptability were identified as protective factors. Higher educational background and perceived stress were identified as risk factors for negative emotion, while subjective support was identified as a protective factor. Perceived stress and coming from a rural area were also identified as risk factors for physical health. Overall, for the comparison between the no psychosomatic distress and severe psychosomatic distress groups, perceived stress was identified as a risk factor for severe psychosomatic distress, while subjective support was identified as a protective factor. Healthcare workers’ potential mental and physical health problems are related to their educational background, family cohesion and adaptability, perceived stress and social support. This makes it clearer on how to deal with and prevent adverse consequences when facing stressful situations.

Prevalence and correlates of sleep disturbance among adolescents in the eastern seaboard of China

BackgroundSleep disturbances are serious public health issues that warrant increased attention, especially in adolescents. The aim of this study was to investigate the prevalence and factors associated with sleep disorders among urban adolescents in China.MethodsThis study utilized an online survey to assess the demographic characteristics and mental health status of secondary school students in Lianyungang City. The Patient Health Questionnaire-9 (PHQ-9) was used to evaluate sleep disturbances in adolescents. The seven-item Generalized Anxiety Disorder (GAD-7) assessed anxiety symptoms, and the Perceived Social Support Scale (PSSS) was used to measure perceived social support.ResultsAmong 3443 adolescents, the prevalence of sleep disorders were 10.8%, with significantly higher proportions of sleep disorders (13.7% VS 8.3%, P < 0.001) among female adolescents when compared to males. Binary regression analysis revealed that anxiety symptoms (OR = 1.305, 95% CI: 1.269–1.342, P < 0.001) was risk factor for sleep disturbances, and significant other support (OR = 0.944, 95% CI: 0.896–0.994, P = 0.028) and good annual household income (OR = 0.616, 95% CI: 0.394–0.963, P = 0.034) were protective factors. Furthermore, multinomial logistic regression analysis showed that age, sex, and anxiety symptoms were associated with an elevated risk of experiencing more frequent sleep disturbances (all P < 0.05).ConclusionsWe have found that 10.8% of adolescents experience sleep disorders, and it is evident that various factors can influence healthy sleeping. These results underscore the significance of addressing these factors to enhance sleep health among this population.

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

A Psychological Study of the Impact of Social Media Addiction on Sleep Patterns

This study presents findings from a comprehensive psychological investigation aimed at exploring the impact of social media addiction on sleep patterns among young adults. The research utilized a mixed-methods approach, incorporating quantitative surveys, objective sleep assessments, and qualitative interviews to provide a holistic understanding of the complex relationship between social media use and sleep disturbances. Eightyparticipants aged 18-35 were recruited from diverse demographic backgrounds and assessed for social media addiction using validated scales such as the Bergen Social Media Addiction Scale (BSMAS). Sleep patterns were evaluated using self-report measures (e.g., Pittsburgh Sleep Quality Index) and objective measures such as actigraphy. Psychological factors including anxiety, depression, stress, and self-esteem were also assessed. Results revealed a significant association between social media addiction and disrupted sleep patterns, with higher levels of social media addiction correlating with poorer sleep quality and decreased sleep duration. Qualitative analysis of interviews provided insights into the psychological mechanisms underlying this relationship, highlighting factors such as fear of missing out (FOMO), heightened cognitive arousal, and maladaptive coping strategies. The findings underscore the importance of addressing social media addiction as a potential risk factor for sleep disturbances and suggest the need for targeted interventions to promote healthier digital habits and improve sleep hygiene among young adults.

Perioperative Sleep Quality Disturbances in Total Joint Arthroplasty is Multifactorial

BackgroundSleep quality following arthroplasty procedures is important for patient recovery and satisfaction, but remains poorly understood. The purpose of this study was to report risk factors for sleep disturbances in the perioperative period in patients undergoing primary total joint arthroplasty procedures. MethodsSleep surveys were prospectively collected on 751 consecutive patients undergoing total joint arthroplasty at our institution between June 2019 and February 2021 at their preoperative and postoperative visits (2 and 6 weeks). Data were collected on patient demographics, opioid use (preoperatively and postoperatively) as well as tobacco and alcohol use, and specific medical diagnosis that may influence sleep patterns (ie, depression). Statistical analyses were performed using the Student’s t-tests and 1-way analysis of variances. ResultsFor both total hip and total knee patients, worse sleep patterns preoperatively were found in patients who used opioids prior to surgery (P < .001), were current smokers (P < .001), and were aged less than 65 years (P < .001). Postoperative persistent opioid use (more than 3 months) was seen in patients who had worse reported sleep quality preoperatively (P < .001). In comparison to total hip arthroplasty, patients who underwent total knee arthroplasty were more likely to report less sleep in the postoperative period. Patients who were current smokers (compared to nonsmokers or previous smokers) (P = .014) had worse sleep quality at all time points that persisted at 6 weeks, although these differences were seen more in total hip patients than in total knee patients (P = .006 versus P = .059). ConclusionsSleep quality disturbances around the time of surgery appear to be multifactorial. Level of EvidenceTherapeutic Level III.

Prevalence and influencing factors of sleep disturbance among medical students under the COVID-19 pandemic.

Medical students may feel severe psychological stress during COVID-19, which might impair their ability to sleep. This research aimed to look at the risk factors for sleep disturbance and the prevalence of sleep disturbance among medical students. 538 medical students in total were recruited for this research. Anxiety, depression, and sleep disturbance were assessed using the Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and Pittsburgh Sleep Quality Index (PSQI). To evaluate the possible risk variables, we computed descriptive statistics for each assessment item and ran univariate and logistic regression analyses. Medical students had a 63.6% prevalence of sleep disturbance (n = 342). According to logistic regression, introverted students are 1.77 times more likely than extroverted students to have sleep disturbance (OR = 1.77, 95% CI 1.08-2.91). Medical students with depression had a 5.6-times higher risk of sleep disturbance than those without depression (OR = 5.60, 95% CI 3.43-9.15). Additionally, medical students with anxiety were 3.95 times more likely than those without anxiety to have sleep disturbance (OR = 3.95, 95% CI 2.04-7.64). According to this research, the COVID-19 pandemic caused significant sleep disturbance among medical students. Additionally, among medical students, introversion, anxiety, and depression were risk factors for sleep disturbance.

Risk prediction of sleep disturbance in clinical nurses: a nomogram and artificial neural network model

BackgroundSleep disturbance occur among nurses at a high incidence.AimTo develop a Nomogram and a Artificial Neural Network (ANN) model to predict sleep disturbance in clinical nurses.MethodsA total of 434 clinical nurses participated in the questionnaire, a cross-sectional study conducted from August 2021 to June 2022.They were randomly distributed in a 7:3 ratio between training and validation cohorts.Nomogram and ANN model were developed using predictors of sleep disturbance identified by univariate and multivariate analyses in the training cohort; The 1000 bootstrap resampling and receiver operating characteristic curve (ROC) were used to evaluate the predictive accuracy in the training and validation cohorts.ResultsSleep disturbance was found in 180 of 304 nurses(59.2%) in the training cohort and 80 of 130 nurses (61.5%) in the validation cohort.Age, chronic diseases, anxiety, depression, burnout, and fatigue were identified as risk factors for sleep disturbance. The calibration curves of the two models are well-fitted. The sensitivity and specificity (95% CI) of the models were calculated, resulting in sensitivity of 83.9%(77.5–88.8%)and 88.8% (79.2–94.4%) and specificity of83.1% (75.0–89.0%) and 74.0% (59.4–84.9%) for the training and validation cohorts, respectively.ConclusionsThe sleep disturbance risk prediction models constructed in this study have good consistency and prediction efficiency, and can effectively predict the occurrence of sleep disturbance in clinical nurses.

Prediction of risk factors of sleep disturbance in patients undergoing total hip arthroplasty.

The purpose of this study was to assess sleep quality in patients undergoing total hip arthroplasty (THA) from preoperatively to 12weeks postoperatively and to establish a risk predictor for postoperative sleep disturbance to enable early care and intervention. A self-designed data collection form was used. Patients were assessed preoperatively and at 5 postoperative time points using visual analog scale (VAS) for pain, sleep quality and neuropsychological status with the following assessment tools: the Chinese versions of the Pittsburgh Sleep Quality Index (CPSQI), the Epworth Sleepiness Scale (CESS), the Zung Self-Rating Anxiety Scale (ZSAS) and the Epidemiological Studies Depression Scale (CESD). Univariate and multivariate logistic regression analysis was used for the identification of risk factors for postoperative sleep disturbance. The receiver operating characteristic (ROC) curve was plotted to evaluate the regression model. Of the 290 eligible patients, 193 (133 women) were included in the study. There was a 60.6% prevalence of preoperative sleep disturbance. The CPSQI score increased significantly at 2weeks postoperatively compared to preoperative baseline, but appeared to decrease at 4weeks postoperatively. Multivariate logistic regression analysis showed that pain (VAS score: OR = 1.202 [95% CI = 1.002-1.446, P < 0.05]), daytime sleepiness (CESS score: OR = 1.134 [95% CI = 1.015-1.267, P < 0.05]) and anxiety (ZSAS score: OR = 1.396 [95% CI = 1.184-1.645, P < 0.001]) were risk factors associated with postoperative sleep disturbance at 2weeks. The ROC curve showed that the AUC was 0.762, the sensitivity was 83.19% and the specificity was 64.86%. Postoperative sleep disturbance is highly prevalent in the first 2weeks after THA. The risk prediction model constructed according to the above factors has good discriminant ability for the risk prediction of sleep disturbance after THA. The use of this risk prediction model can improve the recognition of patients and medical providers and has good ability to guide clinical nursing observation and early screening of sleep disturbance after THA.

Alterations in Sleep Cycle among Nonhospitalized COVID-19-Affected Population during the Pandemic in Karnataka State—A Web-Based Study

Abstract Objectives The psychological and physiological distress and social isolation could adversely affect sleep. As sleep disturbances may persist and deteriorate health, it is important to assess prevalence and related factors of sleep disturbances among the residents of Karnataka state during the coronavirus disease 2019 (COVID-19) infection. Methods In this study, 250 responders across the state of Karnataka accessed the survey voluntarily during September 2021 and January 2022. Pittsburgh Sleep Quality Index, Insomnia Severity Index scales, features that are related to sleep quality, and some hypothesized risk factors of sleep disturbances in context of COVID-19 infection are employed to format a self-reported questionnaire. Results In this study, females 27(19.1%) experienced more sleep disturbances than males 9 (8.3%). The study revealed the participants who received both the doses of vaccination (126; 54.3%) had better sleep. The participants who reported themselves to be stressed (182; 72.8%) had more disturbances in sleep. A total number of 45 participants (18.4%) experienced difficulty in breathing and out of all the responders 11 (47.8%) were actually worried about their current sleep problem. Conclusion COVID-19 infection and changes imposed during the pandemic have led to a surge in individuals reporting sleep problems across the globe. The findings raise the need to screen for worsening sleep patterns to create more awareness about the secondary consequences of COVID-19 infection.

Prevalence of sleep disturbance in patients with cervical radiculopathy and an analysis of risk factors: a cross-sectional study.

Previous research has shown that many patients with musculoskeletal diseases suffer from sleep disturbances. However, the sleep quality of patients with cervical radiculopathy (CR) has yet to be fully investigated. This study aims to investigate the prevalence and status of sleep disturbances in patients with CR and identify the mechanisms and risk factors associated with this condition. We conducted a cross-sectional study of patients diagnosed with CR. The Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) were used to evaluate the sleep quality of patients and determine whether patients experience sleep disturbances. In addition, we analyzed the clinical and radiological risk factors of sleep disturbance and determined the key risk factors related to sleep disturbance by multivariable analysis. According to specific inclusion and exclusion criteria, 186 patients with CR were finally included, with a mean age of 59.1 ± 14.3years, of which 113 (60.8%) were female. Sleep disturbance was defined as a PSQI score ≥ 6 and was identified in 56.5% of patients (105/186). By performing multivariate analysis, we determined that sleep disturbance was closely related to the female gender, a higher Beck Depression Inventory (BDI) score, a reduction in cervical mobility, an increase in C2-C7 sagittal vertical axis (C2-C7 SVA), and severe asymmetry of the paravertebral muscle at C5 and C6 levels. In this study, we identified the high prevalence and potential high-risk factors of sleep disturbance in patients with CR. Clinicians should closely evaluate and monitor such patients and consider appropriate treatment strategies. IV.

TRPV1 is a risk factor for sleep disturbance in patients with gastro-oesophageal reflux disease: a case control study

Background/aims Gastro-oesophageal reflux disease (GORD) is a chronic high-morbidity disease with a bidirectional relationship with sleep disturbance (SD) that may occur via the transient receptor potential vanilloid type 1 receptor (TRPV1) in the oesophageal mucosa. Yet the related mechanism was still unclear, the aim of this study is to investigate whether TRPV1 is associated with the presence of SD in GORD patients. Methods A case-control study was performed. After the screening, A total of 88 subjects were assigned to GORD without sleep disturbance (GORD + NOSD, n = 28), GORD comorbid sleep disturbance (GORD + SD, n = 30) and matched healthy controls (n = 30). Mucosal tissue was obtained from the participants by digestive endoscopy, the levels of TRPV1 expressed in the oesophageal mucosa were detected via RT-qPCR and western blot in different groups, and the correlation between GORD and SD were also analysed. Results In this study, we found that the Gastroesophageal Reflux Disease Diagnostic Questionnaire (GerdQ) scores was positively correlated with Pittsburgh Sleep Quality Index (PSQI) scores but negatively correlated with total sleep time (TST). We also found that the level of TRPV1 expressed in the oesophageal mucosa of GORD + SD was significantly higher than GORD + NOSD patients, and they were all higher than healthy controls. Conclusion The current study suggested a closer link exists between GORD and sleep disturbance, and TRPV1 in oesophageal mucosa may be a crucial factor affecting sleep in GORD patients.

Impacts of COVID-19 Pandemic on Sleep Quality Evaluated by Wrist Actigraphy: A Systematic Review.

COVID-19 has probably contributed as a risk factor for sleep disturbance. Actigraphy has been used to evaluate sleep complaints in self-isolated populations and frontline doctors during the COVID-19 pandemic. This systematic review aims to summarize the impact of the COVID-19 pandemic on sleep through wrist actigraphy, estimating sleep latency, total sleep time, awakening-after-sleep onset, and sleep efficiency. Searches were conducted of observational studies on the PubMed, Embase, Scopus, Web of Science, and PEDro databases from 1 December 2019 to 31 December 2022. Ninety articles were found, and given the eligibility criteria, fifteen were selected. Six studies were classified by the National Health and Medical Research Council as evidence level IV, two studies as level III-3, and seven studies as level III-2. According to the ACROBAT-NRSI instrument, three studies were classified as having a "serious" risk of bias, two as having "critical" risk, four as having "moderate" risk, and six as having "low" risk. In the selected publications, various populations were evaluated via actigraphy during the COVID-19 pandemic, with reports of "poor" sleep quality. Actigraphy may be a relevant tool to assess individual day-night rhythms and provide recommendations under enduring pandemic conditions. Moreover, as actigraphy presents objective data for sleep evaluations, it is suggested that this method be used in similar pandemics and that actigraphy be included as part of the sleep hygiene strategy.

The association between sedentary behavior, exercise, and sleep disturbance: A mediation analysis of inflammatory biomarkers.

Two related lifestyle behaviors associated with sleep disturbance are sedentary behavior and physical exercise participation. We aimed to use a population-based study to disentangle the relationships between sedentary behavior, exercise, and sleep disturbance based on blood-cell-based inflammatory biomarkers. A total of 22,599 participants from the National Health and Nutrition Examination Survey (NHANES) were included in the analyses. Sleep disturbance was assessed according to the NHANES questionnaire. Exercise participation ansd sedentary behavior were evaluated by the global physical activity questionnaire. The inflammatory biomarkers in the examination were white blood cell (WBC) count, neutrophil count (NEU), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII). A complex multistage sampling design and weighted multivariable logistic regression were applied for further analysis. Mediation models were constructed to figure out the mediating role of inflammatory biomarkers. The weighted prevalence of sleep disturbance was 24.17%. Sedentary behavior and exercise were associated with sleep disturbance after full adjustment [for sedentary behavior, OR (95% CI): 1.261 (1.154, 1.377); for exercise, OR (95% CI): 0.849 (0.757, 0.953)]. In severe sedentary behavior groups, the mitigation effect of exercise on sleep disturbance was observed [OR (95% CI): 0.687 (0.551, 0.857)]. For the mechanism, strong associations were detected between inflammatory biomarkers and sleep disturbance. Mediation analysis showed that WBC, NEU, NLR, and SII mediated the statistical association between sedentary behavior and sleep disturbance with proportions (%) of 2.09, 2.27, 1.76, and 0.82, respectively. Our data suggested that sedentary behavior was a risk factor for sleep disturbance. Blood-cell-based inflammatory biomarkers were an easily accessible and cost-effective strategy for identifying sleep disturbance and also significantly mediated the association between sedentary behavior and sleep disturbance. Exercise was proved to be effective in severe sedentary behavior groups to improve sleep disturbance symptoms, while the internal mechanism needed further exploration.

SLEEP QUALITY ASSESSMENT IN ADOLESCENTS WITH TYPE 1 DIABETES MELLITUS

Background and Aims: Diabetes has been reported to be a risk factor for sleep disturbances in adolescent age groups. Although the impact of type1 diabetes mellitus (T1DM) on sleep patterns of children is well recognized, data about its prevalence is scant. This study aims to determine the quality of sleep among adolescents with T1DM and factors inuencing it. Methods: This is a cross-sectional study collected data from 30 adolescent diagnosed with T1DM at least 6 months earlier. They presented to Paediatric endocrine OPD from May 2021 to May 2022. Subjects were asked to complete the Pittsburgh Sleep Quality Index (PSQI) 19-item self-report questionnaire. Children with T1DM being under treatment for sleep disorder were excluded. The combined score of the 7 components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efciency, sleep disturbances, use of sleeping medication, and daytime dysfunction) was used for analysis and the score ranged from 0 to 21. Atotal score &gt;5 was considered as “poor sleep quality”. The quality of sleep was correlated with drug, dietary compliance and metabolic control. p-value &lt;0.05 was considered signicant. Results: The mean age of study population was 14.3±1.7 years. 18 were boys and 12 were girls. Total 8 patients showed poor sleep quality (PQSI score &gt;5). No signicant relationship was found between sleep quality, duration of diabetes and HbA1c levels in the diabetes group (P= 0.59, P= 0.41, respectively). The total sleep time of boys (8.4 ± 1.2 h) was longer than girls (7.6 ± 1.7 h), but the overall total sleep time per night did not differ signicantly between boys and girls (P= 0.053). Conclusion: The present study shows that diabetes control may affect sleep quality in adolescents. Optimal diabetes management can positively affect sleep patterns in these adolescents. Further larger studies are needed to validate our results

Clinical Characteristics of HIV-Infected Patients With Sleep Disturbance

Objective: This study aims to examine clinical characteristics and to identify possible risk factors of sleep disturbances in people living with HIV (PLWH).Methods: All research data of patients who were first diagnosed with HIV/AIDS from January 1, 2012 to December 31, 2021 and complained of sleep disturbance at least once were retrospectively reviewed by the Severance Clinical Research Analysis Portal (SCRAP) service of Severance hospital. The presence of sleep disturbance was evaluated based on whether insomnia disorder diagnosis code was included or whether insomnia medication was prescribed. The patients were divided into either the group with sleep disturbance within 3 months (SDW3) and the group with after 3 months (SDA3). All data were reported using descriptive statistics.Results: Of the 674 patients diagnosed with HIV during the period, 56 patients experienced sleep disturbances at least once and approximately 50% of patients have experienced sleep disturbance in the first 3 months after HIV diagnosis. CD4+ cell count at the time of first onset of sleep disturbance was significantly lower (p=0.03) and HIV viral load at the time of first onset of sleep disturbance was significantly higher (p&lt;0.001) in SDW3 group. SDW3 patients showed higher rates of opportunistic infections compared to SDA3 patients.Conclusion: The current study suggests that further investigation of the underlying pathophysiology of sleep disturbance and association with immunological changes for early diagnosis and treatment of sleep disturbance in PLWH.

Prevalence and risk factors of sleep disturbance in adults with underlying health conditions during the ongoing COVID-19 pandemic.

To determine the prevalence of sleep disturbance during the coronavirus disease 2019 (COVID-19) pandemic among US adults who are more vulnerable to complications because of age and co-morbid conditions, and to identify associated sociodemographic and psychosocial factors. Cross-sectional survey linked to 3 active clinical trials and 2 cohort studies, conducted between 11/30/2020 and 3/3/2021. Five academic internal medicine practices and 2 federally qualified health centers. A total of 715 adults ages 23 to 91 years living with one or more chronic conditions. A fifth (20%) of participants reported poor sleep. Black adults were twice as likely to report poor sleep compared to Whites. Self-reported poor physical function (51%), stress (42%), depression (28%), and anxiety (36%) were also common and all significantly associated with poor sleep. Age ≥70 years and having been vaccinated for COVID-19 were protective against poor sleep. Sex, education, income, alcohol use, and employment status were not significantly associated with sleep quality. In this diverse sample of adults with chronic conditions, by race, ethnicity, and socioeconomic status, disparities in sleep health amid the ongoing pandemic were apparent. Worse physical function and mental health were associated with poor sleep and should be considered targets for health system interventions to prevent the many subsequent consequences of disturbed sleep on health outcomes. Measurements: self-reported sleep quality, physical function, stress, depression, and anxiety.

Who reports insufficient and disturbed sleep? Results from a representative population-based health survey in Hong Kong

ObjectivesTo highlight the prevalence of sleep problems and identify associated risk factors among a representative sample recruited from the general population of Hong Kong.Design, setting and participantsParticipants included 12 022...

An Update on Prevalence, Assessment, and Risk Factors for Sleep Disturbances in Patients with Advanced Cancer-Implications for Health Care Providers and Clinical Research.

Simple SummaryThis review focuses on sleep in patients with advanced cancer. Cancer patients experience multiple symptoms and they receive concomitant medications. These are all factors that may affect sleep. In this paper, we present recommendations on sleep assessment in patients with advanced cancer and highlight cancer-related factors that may contribute to insomnia. Sleep is an essential aspect of health-related quality of life; therefore, it is important for health care providers to focus on sleep to improve patient care.Patients with advanced cancer experience multiple symptoms, with fluctuating intensity and severity during the disease. They use several medications, including opioids, which may affect sleep. Sleep disturbance is common in cancer patients, decreases the tolerability of other symptoms, and impairs quality of life. Despite its high prevalence and negative impact, poor sleep quality often remains unrecognized and undertreated. Given that sleep is an essential aspect of health-related quality of life, it is important to extend both the knowledge base and awareness among health care providers in this field to improve patient care. In this narrative review, we provide recommendations on sleep assessment in patients with advanced cancer and highlight cancer-related factors that contribute to insomnia. We also present direct implications for health care providers working in palliative care and for future research.

Risk factors and prediction model of sleep disturbance in patients with maintenance hemodialysis: A single center study.

ObjectivesThis study aimed to explore the risk factors and develop a prediction model of sleep disturbance in maintenance hemodialysis (MHD) patients.MethodsIn this study, 193 MHD patients were enrolled and sleep quality was assessed by Pittsburgh Sleep Quality Index. Binary logistic regression analysis was used to explore the risk factors for sleep disturbance in MHD patients, including demographic, clinical and laboratory parameters, and that a prediction model was developed on the basis of risk factors by two-way stepwise regression. The final prediction model is displayed by nomogram and verified internally by bootstrap resampling procedure.ResultsThe prevalence of sleep disturbance and severe sleep disturbance in MHD patients was 63.73 and 26.42%, respectively. Independent risk factors for sleep disturbance in MHD patients included higher 0.1*age (OR = 1.476, 95% CI: 1.103–1.975, P = 0.009), lower albumin (OR = 0.863, 95% CI: 0.771–0.965, P = 0.010), and lower 10*calcium levels (OR = 0.747, 95% CI: 0.615–0.907, P = 0.003). In addition, higher 0.1*age, lower albumin levels, and anxiety were independently associated with severe sleep disturbance in MHD patients. A risk prediction model of sleep disturbance in MHD patients showed that the concordance index after calibration is 0.736, and the calibration curve is approximately distributed along the reference line.ConclusionsOlder age, lower albumin and calcium levels are higher risk factors of sleep disturbance in MHD, and the prediction model for the assessment of sleep disturbance in MHD patients has excellent discrimination and calibration.