2458-PUB: Prevalence and Correlates of Sleep Disorders in Greek Patients with Type 2 Diabetes: Comparison of an Urban and a Semiurban Population

The impact of hospitalization on sleep in late-life is underexplored. The current study examined patterns of sleep quality before, during, and following hospitalization, investigated predictors of sleep quality patterns, and examined predictors of classification discordance between two suggested clinical cutoffs used to demarcate poor/good sleep. This study included older adults (n = 163; mean age 79.7 ± 6.9 years, 31% female) undergoing inpatient post-acute rehabilitation. Upon admission to inpatient post-acute rehabilitation, patients completed the Pittsburgh Sleep Quality Index (PSQI) retrospectively regarding their sleep prior to hospitalization. They subsequently completed the PSQI at discharge, and 3 months, 6 months, 9 months, and 1 year post discharge. Patient demographic and clinical characteristics (pain, depression, cognition, comorbidity) were collected upon admission. Using latent class analysis methods, older adults could be classified into (1) Consistently Good Sleepers and (2) Chronically Poor Sleepers based on patterns of self-reported sleep quality pre-illness, during, and up to 1 year following inpatient rehabilitation. This pattern was maintained regardless of the clinical cutoff employed (> 5 or > 8). Logistic regression analyses indicated that higher pain and depressive symptoms were consistently associated with an increased likelihood of being classified as a chronic poor sleeper. While there was substantial classification discordance based on clinical cutoff employed, no significant predictors of this discordance emerged. Clinicians should exercise caution in assessing sleep quality in inpatient settings. Alterations in the cutoffs employed may result in discordant clinical classifications of older adults. Pain and depression warrant detailed considerations when working with older adults on inpatient units when poor sleep is a concern.

Poor sleep quality, often resulting from poor sleep hygiene, is common among medical students. Educational interventions aimed at improving sleep knowledge are beneficial for sleep quality in healthy populations. However, sleep education is often given minimal attention in medical school curriculums. The aim of the study was to explore whether a short educational intervention could improve sleep knowledge, and consequently sleep quality, among medical students. We recruited preclinical- and clinical-stage medical students during the 2017-2018 academic year. Students completed a demographic survey, the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Assessment of Sleep Knowledge in Medical Education (ASKME) questionnaire. Students then attended a lecture on the physiology and importance of sleep. To assess the efficacy of the intervention, questionnaires were repeated 4 months thereafter. A total of 87 students (31 preclinical) with a mean age of 25.86 years (standard deviation [SD], 3.33), 51 of whom were women, participated in the study. At baseline, students had poor sleep quality with a PSQI mean score of 5.9 (SD, 2.37), without significant sleepiness, and a mean ESS score of 8.86 (SD, 4.32). The mean ASKME scores were consistent with poor sleep knowledge at 11.87 (SD, 4.32). After the intervention, the mean ASKME results improved to 14.15 (SD, 4.5; P < .001), whereas sleep quality did not. The effect was similar in preclinical and clinical medical students. Sleep knowledge was inadequate among medical students, who also experienced poor sleep quality. A short educational intervention improved sleep knowledge but was insufficient at improving sleep quality. Further studies are needed to determine which interventions may provide benefit in both sleep knowledge and sleep quality.

Article AbstractBecause this piece does not have an abstract, we have provided for your benefit the first 3 sentences of the full text.To the Editor: Sleep disturbances commonly co-occur with mental disorders and often are sufficiently severe to warrant targeted treatment. Left untreated, sleep disturbances may exacerbate comorbid conditions and complicate recovery. Moreover, sleep disturbances are considered modifiable risk factors for onset and relapse of mental disorders.

BackgroundGastroesophageal reflux disease (GERD) often coexists with anxiety, depression, and poor sleep quality, which worsen symptom perception and reduce quality of life. While each factor has been linked to GERD, their combined effects have rarely been examined, especially in sleep clinic populations. This study aimed to assess the individual and combined associations of anxiety, depression, and sleep quality with probable GERD in patients at a tertiary hospital sleep center.MethodsThis retrospective cross-sectional study examined patient data from the Sleep Center of National Cheng Kung University Hospital collected between July 2020 and July 2021. All included patients completed the Gastroesophageal Reflux Disease Questionnaire (GERDQ), Pittsburgh Sleep Quality Index (PSQI), and Hospital Anxiety and Depression Scale (HADS). Multivariable logistic regression was used to determine associations between elevated HADS-A (anxiety), HADS-D (depression), PSQI scores, and probable GERD (GERDQ score ≥ 9), adjusting for relevant confounders.ResultsAmong 877 patients, 93 (10.6%) had probable GERD. Elevated HADS-A (adjusted odds ratio [aOR]: 2.18, 95% confidence interval [CI]: 1.40–3.39, p < 0.001), HADS-D (aOR: 2.10, 95% CI: 1.34–3.29, p = 0.001), and PSQI (aOR: 3.32, 95% CI: 2.10–5.27, p < 0.001) scores were significantly associated with probable GERD. Patients with both high HADS-A and PSQI scores had a stronger association (aOR: 4.74, 95% CI: 2.62–8.60), and those with both high HADS-D and PSQI scores had the greatest odds (aOR: 5.09, 95% CI: 2.72–9.41).ConclusionResults reaffirm that anxiety, depression, and poor sleep quality are significantly associated with probable GERD. However, given the cross-sectional design, causal relationships cannot be established. The combined use of HADS and PSQI may enhance GERD risk identification, suggesting that individuals with psychological and/or sleep disturbances should consider gastroenterological evaluation.

Does Sleep Quality Affect Temporomandibular Joint With Degenerative Joint Changes?

Background: Approximately 40% of the population reports sleep problems such as poor quality sleep and insufficient sleep duration. Physical activity (PA) can help improve sleep, but data on whether PA intensity or duration is most strongly associated with sleep are lacking. In addition, given that sedentary behaviour (e.g., TV, computer use) is distinct from physical inactivity, the association between sedentary behaviour and sleep in young adults needs to be characterized. Objective: To describe the relationships between sleep quality and sleep duration and (1) frequency and duration of light, moderate, and vigorous PA, and (2) different types of sedentary behaviours (TV, computer, reading) in young adults. Methods: Self-report data for 658 participants were from the 22nd wave of the Nicotine Dependence in Teens (NDIT) cohort study (mean age=24.0 years, 46% male [300 of 658]). PA measures assessed frequency (number of days) and minutes of light, moderate and vigorous PA in the past week. Sedentary measures assessed number of hours spent reading, watching TV, and using the computer per day. Sleep measures included (1) the Pittsburgh Sleep Quality Index (PSQI) which assessed seven dimensions of sleep (daytime dysfunction, disturbances, duration, efficiency, latency, quality, use of sleeping medications), (2) general sleep quality, and (3) sleep duration in the past month. General sleep quality and sleep duration were two separate additional measures distinct from similar PSQI items (r=0.73 between general sleep quality and PSQI score; r=0.69 between sleep duration and PSQI score). Data were analyzed using multiple linear regression. Due to evidence of non-normality the PSQI score was log-transformed. Results: Controlling for age, sex, and maternal education, each additional day of light or vigorous PA was associated with 3 minutes less sleep per night (p&lt;0.05). Each additional 10 minutes of moderate PA was associated with greater general sleep quality (β=0.004, p=0.04). TV was associated with a poorer PSQI score (β=0.01, p&lt;0.05) and each additional hour of reading was associated with 2 minutes less sleep per night (p=0.04). Computer use was associated with a poorer PSQI score (β=0.02, p=0.005) and poorer sleep quality (β=-0.02, p=0.05). Results were similar when sedentary and PA measures were included in the same model. The inclusion of body mass index, self-rated mental and general health, and stress did not affect the results and were omitted from the final models. Conclusion: PA and sedentary behaviours are independently associated with sleep duration and quality. Sedentary behaviours are associated with poorer sleep duration and quality. In contrast, PA frequency may decrease sleep duration while PA duration may improve sleep quality. Clinicians who treat sleep problems in young adults may need to take PA and sedentary behavior into account in treatment plans.

Diabetic Complications Associated with Poor Sleep Quality in Type 2 Diabetes

Objective To investigate the relation between sleep quality and mental health of secondary insomnia of type 2 diabetes mellitus and to provide a new treatment for this disease.Methods Ninety patients suffering from secondary insomnia of type 2 diabetes mellitus were evaluated and analyzed with Pittsburgh Sleep Quality Index(PSQI)and Symptom Checklist 90(SCL-90).Results The mean total PSQI score was 14.13 with a standard deviation of 3.38 in the sampled patients.According to the total score PSQI≥11 as a standard of poor sleep quality,poor sleep quality was present by 83.33%of the subjects with no significant difference between males and females(75 cases:male 22,female 53).According to the single item score PSQI≥2 as,a positive case,poor sleep quality was present by 95.56%(86 cases),insufficiency of sleep time was present by 42.22%(38 cases),low sleeping efficiency was present by 53.33%(48 cases).All these patients had sleep disturbance.There were 21 patients(21.33%)taking drugs for insomnia,81patients(90%)with long sleep latency and 78 patients (86.67%)had daytime dysfunction.The correlation coemcient between total PSQI score and total SCL-90 score was r=0.400(P＜0.01).All the factors were significantly related to total PSQI score,except phobic factor.The correlation coemcient between somatization and total PSQI score was r=0.400(P＜0.01).The correlation coemcient between sleep disturbance and somatization was r=0.458(P＜0.01).Through multiple stepwise regression analysis we found that somatization was the major factor affecting sleep disturbance.Conclusion Sleep quality of patients suffering from secondary insomnia of type 2 diabetes mellitus had a closely relationship with psychological factors especially somatization of SCL-90.Psychological treatment for improving somatization symptoms should be considered. Key words: Secondary insomnia of type 2 diabetes mellitus; Sleep quality; Psychological health

Poor sleep quality, mood disorders, and vitamin D deficiency are common in stroke. We investigated the association between serum vitamin D levels and pre-stroke sleep quality and the occurrence of poststroke depression (PSD) and poststroke anxiety (PSA) in acute ischemic stroke (AIS). This prospective cross-sectional study included hospitalized patients withAIS and age- and sex-matched controls. Vitamin D levels were measured within 24h of admission. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) at admission. The severity of depression and anxiety symptoms was evaluated according to Beck Depression Inventory and Beck Anxiety Inventory scores, respectively, within 72h after admission. Comparing 214AIS patients with 103controls, patients had significantly higher scores of Beck Depression Inventory, Beck Anxiety Inventory, and PSQI and lower vitamin D levels (p < 0.001). Among AIS patients, Beck Depression Inventory (p = 0.004) and Beck Anxiety Inventory (p = 0.018) scores were significantly higher in bad sleepers (PSQI score ≥ 6) than in good sleepers (PSQI score ≤ 5). Correlation analysis revealed negative correlations between serum vitamin D levels and Beck Depression Inventory (r = - 0.234; p < 0.001), Beck Anxiety Inventory (r = - 0.135; p = 0.016), and PSQI (r = - 0.218; p < 0.001) scores. Decreased serum vitamin D levels at admission are associated with a high risk for PSD and PSA in patients with poor pre-stroke sleep quality during the early stages of AIS.

Background Sleep quality and depression represent significant public health concerns with complex bidirectional relationships. Despite extensive research in specific populations, comprehensive studies examining their association with general populations remain limited, particularly in developing regions. This study aimed to assess the prevalence of depression and poor sleep quality among the general population of Perambalur, Tamil Nadu, examine their interrelationship, and identify associated sociodemographic and behavioral factors influencing this relationship. Additionally, the study sought to analyze the impact of depression severity on sleep quality parameters and investigate potential risk factors affecting both conditions. Methodology A cross-sectional analytical study was conducted among 650 participants from Perambalur district, Tamil Nadu, India. Data collection involved face-to-face interviews using a structured questionnaire comprising sociodemographic profiles, the Pittsburgh Sleep Quality Index (PSQI) for sleep quality assessment, and the Patient Health Questionnaire-9 (PHQ-9) for depression evaluation. The questionnaire underwent forward-backward translation and pilot testing. Sleep quality was categorized using PSQI scores (>5 indicating poor sleep), while depression severity was classified as minimal (0-4), mild (5-9), moderate (10-14), moderately severe (15-19), and severe (20-27). Data analysis employed descriptive statistics, chi-square tests, and multivariable logistic regression, with p<0.05 considered statistically significant. Results The study population, with a mean age of 35.71±14.70 years, comprised 381 (58.6%) females and 269 (41.4%) males. The depression analysis revealed that 385 (59.2%) participants had minimal depression, 171 (26.3%) had mild depression, 62 (9.5%) had moderate depression, 27 (4.2%) had moderately severe depression, and five (0.8%) had severe depression. Poor sleep quality was reported by 142 (21.8%) participants. Sleep-related parameters showed 100 (15.4%) participants experiencing difficulty initiating sleep, 102 (15.7%) reporting midnight awakenings, and 34 (5.2%) using self-medication. Logistic regression identified self-medication (adjusted odds ratio (AOR)=8.45, 95% CI: 3.12-22.86) and moderately severe/severe depression (AOR=7.92, 95% CI: 3.45-18.19) as the strongest predictors of poor sleep quality. PSQI scores demonstrated progressive deterioration across depression severity levels, increasing from 3.2±1.8 in minimal to 11.3±3.2 in severe depression. A strong positive correlation was observed between PSQI and PHQ-9 scores (r=0.65, p<0.001). Conclusion The study establishes significant associations between depression severity and sleep quality, highlighting the need for integrated healthcare approaches. The identified sociodemographic risk factors and high prevalence of self-medication underscore the importance of targeted interventions and improved access to professional healthcare services, particularly focusing on vulnerable populations in both urban and rural settings.

The association between PSQI score and hypertension in a Chinese rural population: the Henan Rural Cohort Study

Migraine is a primary headache disorder that affects more than 1 billion individuals globally and imposes a significant disability burden on society. Although migraine patients commonly experience poor sleep quality, the relationship between migraine and sleep is not yet fully understood. This study therefore aimed to determine the association between sleep quality and migraine severity. A comparative cross-sectional study was conducted with 1,399 participants across all regions of Saudi Arabia from August to October 2023 using standardized questionnaires. Participants were categorized into patients with migraine and non-migraine patients, according to the International Headache Society (IHS) criteria. This study utilized The Migraine Disability Assessment Scale (MIDAS) and Pittsburgh Sleep Quality Index (PSQI) to evaluate migraine severity and sleep quality, respectively. The prevalence of migraine was 25%, while poor sleep quality was evident in 42.4% of the patients. No significant difference in PSQI scores was observed between patients with migraine and non-migraine patients (p = 0.821). Migraine patients with poor sleep quality showed significantly higher MIDAS scores than those with good sleep quality (10.37 vs. 6.58; p = 0.002), while patients with migraine with higher levels of disability had higher PSQI scores than those with lower levels of disability, although the difference was not statistically significance (7.61 vs. 6.81, p = 0.053). A significant positive correlation was found between the PSQI and MIDAS scores (r = 0.179, p < 0.001). MIDAS was also significantly positively correlated with the following PSQI components: subjective sleep quality (p = 0.047), sleep latency (p < 0.001), sleep disturbance (p < 0.001), and daytime dysfunction (p < 0.001). These findings suggest a notable correlation between poor sleep quality and increased migraine severity, emphasizing the importance of addressing sleep disturbance as a potential strategy to mitigate migraine severity and improve patient outcomes.

Prolonged-release melatonin in Parkinson's disease patients with a poor sleep quality: A randomized trial

The relationship between Pittsburgh Sleep Quality Index (PSQI) score and survival of dialysis patients has not been well studied. The aim of this study was to explore the association between PSQI score and all-cause mortality in dialysis patients. Fifty-one hemodialysis and 58 peritoneal dialysis patients were enrolled in this study. PSQI score > 5 and ≤ 5 indicated "poor sleepers" and "good sleepers", respectively. The primary outcome was all-cause mortality. Kaplan-Meier survival curve and Cox proportional hazards regression analysis were performed. The median PSQI score was 7.0 (4.0-10.0). Sixty-seven (61.5%) patients had poor sleep quality (SQ). Compared with good sleepers, poor sleepers had significantly lower levels of hemoglobin [74.0 (61.0, 85.0) vs. 78.0 (68.0, 97.0), P = 0.03] and serum bicarbonate (18.0 ± 4.5 vs. 20.0 ± 3.7, P = 0.022). The follow-up time was 69.1 ± 29.9months. By multivariate Cox proportional hazards analysis, PSQI total score was the independent risk factor of all-cause mortality [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.05-1.36, P = 0.007]. Restricted cubic spline (RCS) analysis showed that 7 was the cutoff value at which the effect of PSQI score on mortality changed. Patients with a PSQI score > 7 had a 2.96-fold increased risk of all-cause mortality (HR 2.96, 95% CI 1.15-7.61, P = 0.025). PSQI score can be used as a predictor of all-cause mortality in dialysis patients, and those with PSQI > 7 were associated with increased odds of mortality.

Background:Children with depression frequently experience sleep disorders, which may significantly impact their cognitive function and quality of life. Investigating the relationship between sleep quality, cognitive performance, and quality of life in this population is essential for developing targeted interventions.Methods:From February 2022 to January 2024, 78 children diagnosed with depression at the Hunan Children's Hospital were assessed using the 17-item Hamilton Rating Scale for Depression (HAMD-17). Based on their HAMD-17 scores, participants were categorized into mild, moderate, and severe depression groups, with 26 children in each group. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), cognitive function was assessed via the Wisconsin Card Sorting Test (WCST), and quality of life was measured using the 36-item Short Form Health Survey (SF-36). Correlations between PSQI, WCST, and SF-36 scores were analyzed for all groups.Results:Compared to the control group, the depression group of children with depression had significantly higher levels of depression and significantly lower levels of quality of life, sleep quality, and cognitive function (p < 0.05). Further analysis showed that sleep quality in children with depression worsened with increasing severity of depression, as evidenced by a gradual increase in PSQI scores (p < 0.05). Cognitive function assessment (WCST scores) revealed that with increasing depression severity, the number of classifications completed by children decreased, while the total number of errors, perseverative errors, and non-perseverative errors all significantly increased (p < 0.001). Quality of life assessment (SF-36 scores) showed that increasing depressive symptoms significantly affected the quality of life of children, with an overall significant decrease in scores (p < 0.05). Correlation analysis further revealed that cognitive function was closely related to sleep quality in children with depression. Specifically, the number of classifications completed was significantly negatively correlated with PSQI scores (r = –0.5534, p < 0.0001), while the total number of errors, perseverative errors, and non-perseverative errors were all significantly positively correlated with PSQI scores (r = 0.6769, 0.6988, and 0.6937, respectively, all p < 0.0001). In addition, four dimensions of quality of life (social function, physical function, role function, and cognitive function) were all significantly negatively correlated with sleep quality (r = –0.6866, –0.5309, –0.5823, –0.5698, respectively, all p < 0.0001).Conclusion:Poor sleep quality in children with depression is positively correlated with poor cognitive function and poor quality of life. Routine evaluation of sleep disturbances in this population can provide critical insights for timely intervention and management.