Prevalence Of Poor Sleep Quality Research Articles

What are the sleep characteristics among early kidney transplant recipients? An objective and subjective measurement from China.

To evaluate the sleep quality by self-reported questionnaires and polysomnography (PSG) among early kidney transplant recipients (KTRs) and to further explore their correlation. This was a prospective and cross-sectional analysis of the sleep characteristics among early kidney transplant recipients through an objective and subjective measurement. Patients with end stage renal disease on the transplant waiting list and after kidney transplantation were from a major organ transplantation center in Southern China (n = 83). Objective outcomes: PSG, noise and light. Subjective outcomes: demographic and clinical questionnaires, self-reported pain and Richards Campbell sleep questionnaire (RCSQ). After agreement with the informed consent, participants first completed demographic and clinical questionnaires, then worn the PSG within 5-10 days after kidney transplantation. Both noise, light and self-reported pain were monitored during sleep. After completion of PSG, the RCSQs were filled out next morning. A total of 298 patients were recruited and 83 participants were finally analyzed. The total RCSQ mean score was 51.0±18.9mm. The prevalence of poor sleep quality among early KTRs was 45.1%. Most of PSG characteristics were significantly correlated with their corresponding RCSQ items. And the total RCSQ scores were significantly correlated with the number of awakenings, the N2 percentage and the total sleep time (r = 0.79, 0.47 and 0.40, P<0.05) respectively. Noise was a statistically significant factor affecting the subjective sleep quality. The sleep quality in early KTRs measured by both PSG and RCSQ exhibits consistency with each other. Sleep disruption always remains a substantial problem and is affected by self-reported noise among early KTRs. The RCSQ is easily applicable and interpretable so that it can be used for future daily clinical practice.

Association between older subjective age and poor sleep quality: a population-based study

ABSTRACT Objective To examine the association of subjective age (SA) with sleep quality in an adult population. Methods In the Korean Sleep and Headache Study, 2,349 participants (49.2% men; 48.1 ± 16.4 years old) were interviewed face-to-face using structured questionnaires between September and December 2018. SA was assessed by asking participants their perceived age in years and then compared with their chronological age (CA). Participants were assigned to three groups: feeling younger, feeling their age, and feeling older. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Association between SA and sleep quality was analyzed with multiple linear regression controlling for demographics, psychosocial, and sleep characteristics. Results The group feeling older (n = 404, 17.2%; men, 58.2%; age, 46.5 ± 16.2 years) had worse sleep quality than the groups feeling younger and feeling their age (PSQI score, 4.3 ± 2.7, 3.8 ± 2.4, 3.4 ± 2.1, respectively, p <.001; prevalence of poor sleep quality, 29.0%, 18.4%, 13.5% respectively, p <.001). The association between SA and the PSQI score remained significant after adjusting for confounders (β = 1.05, 95% confidence interval 0.26, 1.83; p <.001). Stratified analyses by sex and CA showed that the association between SA and the PSQI score was significant only in women and in middle-aged and older group (aged 50–79), suggesting that sex and CA modified the association. Conclusion Age perception was associated with self-reported sleep quality, independent of CA. SA may be a useful marker that complements the conventional assessment of subjective sleep quality.

Sleep Quality and Its Determinants Among Type 2 Diabetes Patients with Comorbid Metabolic Syndrome.

The prevalence of poor sleep quality in patients with diabetes was higher than the general population. This study aimed to explore risk factors for not only poor sleep quality, but also long sleep latency, short sleep duration and low sleep efficiency, in type 2 diabetes patients (T2DM) with comorbid metabolic syndrome (MS). A total of 281 patients aged 18-75 years were enrolled from Ningbo First Hospital during October 2021 to March 2022. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Sleep latency, sleep duration and sleep efficiency were obtained by a response to the questionnaire. Descriptive, independent two-sample t-test, Chi-square test and multiple logistic regression were conducted using SPSS Version 28. The prevalence of poor sleep quality in T2DM with comorbid MS patients was 59.10%. The factors significantly associated with poor sleep quality were depression symptoms (OR = 3.10, 95% CI: 1.38 to 6.96, P = 0.006), poor quality of life (OR = 2.49, 95% CI: 1.24 to 4.99, P = 0.010), and age (OR = 1.07, 95% CI: 1.04 to 1.10, P < 0.001). The factor significantly associated with long sleep latency was depression symptoms (OR = 2.19, 95% CI: 1.15 to 4.16, P = 0.017). The factors significantly related to short sleep duration were depression symptoms (OR = 2.56, 95% CI: 1.31 to 5.00, P = 0.006) and age (OR = 1.05, 95% CI: 1.02 to 1.08, P = 0.002). The factor significantly related to short sleep efficiency was age (OR = 1.03, 95% CI: 1.01 to 1.06, P = 0.019). This study found that depression symptoms, together with poor quality of life, and increasing age were associated with poor sleep quality. Symptoms of depression were related to long sleep latency and short sleep duration. The increasing age was associated with short sleep duration and low sleep efficiency.

Metabolic risk factors and psychosocial problems independently explain poor sleep quality and obstructive sleep apnea symptoms among adults in urban India.

To determine if metabolic risk factors are associated with poor sleep quality and obstructive sleep apnea-like symptoms (OSA symptoms) independent of psychosocial problems and demographic and lifestyle factors in older Indian adults. We analyzed baseline data from adults (≥ 50years) from a population-based cohort, the LoCARPoN study, in India. Variables were grouped as (a) demographic and lifestyle factors such as smoking, alcohol use, and physical activity; (b) psychosocial problems including symptoms of depression, anxiety, and perceived stress; and (c) metabolic risk factors including glycated hemoglobin, high-density lipoprotein, low-density lipoprotein, total cholesterol, body mass index, and hypertension. Variables were examined as predictors of poor sleep quality and OSA symptoms. Groups of variables were added stepwise to a logistic regression. Variance explained by nested models was quantified using McFadden's pseudo R2, and change wasformally tested with the log-likelihood ratio test. Among 7505 adults, the prevalence of poor sleep quality was 16.9% (95% CI: 16.0, 17.7), and OSA symptoms were present in 7.0% (95% CI: 6.4, 7.6). Psychosocial problems had a strong independent association with both poor sleep quality (pseudo R2 increased from 0.10 to 0.15, p < 0.001) and more OSA symptoms (pseudo R2 increased from 0.08 to 0.10, p < 0.001). Metabolic risk factors had a modest independent association with sleep quality (pseudo R2 increased from 0.14 to 0.15, p < 0.01), but a strong association with OSA symptoms (pseudo R2 increased from 0.08 to 0.10, p < 0.001). Psychosocial and metabolic risk factors were independently associated with sleep quality and OSA symptoms. This fact implied that OSA symptoms may affect both mental health and physical health. Our findings have public health implications because the number and proportion of the elderly in India is increasing, while the prevalence of metabolic risk factors and psychosocial problems is high already. These facts have the potential to exacerbate not only the burden of sleep disorders and OSA symptoms but also associated cardiovascular and neurologic sequelae, further stretching the Indian health-care system.

Sleep quality and associated factors among type 2 Dm patients and non-Dm individuals in Bahir Dar governmental hospitals: comparative cross-sectional study

BackgroundMultiple factors may contribute to sleep disruption among individuals with type 2 diabetes mellites. Sleep disruption among individuals with type 2 diabetes mellites is frequently associated with long-term damage, dysfunction, and failure of different organs. Nevertheless, literature in this regard is scanty in Ethiopia. Therefore, this study aimed to assess and compare the prevalence of poor sleep quality and associated factors among type 2 diabetes mellites patients and non-diabetes individuals in Bahir Dar governmental hospitals.MethodsComparative cross-sectional study was employed among 292 individuals with type 2 diabetes mellites and 291 non-diabetic individuals in Bahir Dar governmental hospitals from March 01- to April-01. A two-stage cluster sampling method was employed to select participants. Pittsburgh sleeps quality index was used for assessing sleep quality. For analysis, descriptive statistics and binary logistic regression models were used.ResultThe prevalence of poor sleep was 50.7% (95% CI; 44.9–56.2) and 31.8% (95% CI 26.5–37.5) among individuals with type 2 diabetes melilites and non-diabetic, respectively. Among the overall participants, type 2 diabetes melilites patients were also significantly associated with poor sleep quality than non-diabetic individuals (AOR = 1.89; 95% CI; 1.19–2.87). Comorbidity, duration of DM > 10 years, Poor glycaemic control, depression, low physical activity, and poor social support were factors significantly associated with poor sleep quality among individuals with type 2 diabetes melilites. Among non-diabetic individuals, low physical activity, poor social support, depression, and age group (> 50 years) were factors significantly associated with poor sleep quality.ConclusionIn this study, poor sleep among individuals with type 2 diabetes melilites was higher than in non-diabetes individuals.

Association between discrimination and poor sleep health outcomes among 2192 South Korean gay, lesbian, and bisexual adults: A nationwide cross-sectional survey

ObjectiveThe study aimed to assess the prevalence of poor sleep health outcomes and examine the associations between experiences of discrimination and the sleep health outcomes among lesbian, gay, and bisexual (LGB) adults in Korea. Design & settingThe study used data from Rainbow Connection Project I, a nationwide cross-sectional survey of Korean LGB adults, conducted via online in 2016. MeasurementsParticipants were asked about experiences of anti-LGB discrimination and discrimination based on other characteristics (ie, gender, age, place of origin, nationality/race, religion, appearance including height and weight, disability status, and other) in the past 12 months. Using the responses, the participants were categorized into 4 groups: those who experienced (1) none, (2) only anti-LGB discrimination, (3) only other types of discrimination, and (4) both anti-LGB and other types of discrimination. Past-week sleep quality, unrestful sleep, and long sleep latency were included as sleep health outcomes. ResultsOf the 2192 participants, 740 (33.8%) had poor sleep quality, 1211 (55.2%) had unrestful sleep, and 681 (31.1%) had long sleep latency in the past week. Participants who experienced both anti-LGB and other types of discrimination had 1.65 times (95% confidence interval [CI] = 1.38-1.98), 1.30 times (95% CI = 1.16-1.45), and 1.58 times (95% CI = 1.31-1.90) higher prevalence of poor sleep quality, unrestful sleep, and long sleep latency, respectively, compared to those without any experiences of discrimination. ConclusionsExperiencing discrimination may deprive Korean LGB adults of good quality sleep. Interventions that seek to prevent discrimination are needed to promote sleep health among Korean LGB individuals.

Prevalence and Predictors of Sleep Disturbance, Anxiety and Depression among Patients with Chronic Respiratory Diseases.

Background: Poor sleep quality, depression, and anxiety are common comorbidities among individuals with chronic respiratory diseases (CRDs). However, there has been no work to estimate their prevalence and assess their associations among the CRDs population in Saudi Arabia. Methods: A cross-sectional study was conducted in primary healthcare centers and included a total of 390 patients. Structured self-administered questionnaires were completed that included the Sleep Quality Pittsburgh Sleep Quality Index (PSQI) and Hospital Anxiety and Depression Score (HADS). Multiple linear regression analyses were performed to assess the associations between patients’ characteristics and sleep disturbance, anxiety and depression. Results: Poor sleep quality, depression, and anxiety affect 75%, 49.2%, and 36.4% of the study participants, respectively. The PSQI was significantly correlated with anxiety (r = 0.30) and depression (r = 0.16). Furthermore, a significant correlation was found between anxiety and depression (r = 0.44). The predictors of poor sleep quality were age, gender, and family history of CRDs, education level and anxiety and these variables accounted for 0.19% of the variance in PSQI. Variables that independently predicted an increased level of depression were age, gender, marital status, family history of CRDs, diagnosis, previous hospital admission, the presence of comorbidities, dyspnea last month and anxiety. On the other hand, the variables that independently predicted an increased level of anxiety were age, BMI, family history of CRDs, previous hospital admission, the presence of comorbidities, dyspnea last month and depression. Conclusion: Healthcare providers managing patients with CRDs should be alert to the high prevalence of poor sleep quality, depression, and anxiety. Appropriate interventions to reduce the prevalence should be developed and timely applied.

Risk factors for the prevalence of poor sleep quality in lecturers during COVID-19 pandemic in Ethiopia: an institution-based cross-sectional study

ObjectiveThis study was conducted to assess the prevalence and risk factors of poor sleep quality (SQ) among the academic staff at the University of Gondar, Northwestern Ethiopia.DesignAn institution-based cross-sectional study...

Prevalence and associated factors of poor sleep quality among Chinese retirees: A multicenter cross-sectional study

BackgroundSleep problems are common among older adults worldwide. The present study aims to determine the prevalence of poor sleep quality and its independent factors among retired people in health check-ups population in China. MethodIn this multicenter, cross-sectional survey in 2017, a group of retired people was invited to participate in an online survey of health status, and their data, including socio-demographic information, lifestyle, and medical characteristics, were recorded. Binary logistic regression was used to analyze the independent factors responsible for poor sleep quality. ResultsData from 17,408 responders who met the inclusion criteria were analyzed; among them, 53.04 % (95 % CI = 52 %–54 %) reported poor sleep quality. Binary logistic regression showed that poor sleep quality was associated with a number of factors, including being female (OR = 1.42, 95 % CI = 1.32–1.53), being single (OR = 1.35, 95 % CI = 1.18–1.54), non-smoker (OR = 1.12, 95 % CI = 1.03–1.22), physical inactivity (OR = 1.14, 95 % CI = 1.05–1.23), poor self-rated health status (OR = 1.69, 95 % CI = 1.43–2.00), long-term medication use (OR = 1.05, 95 % CI = 1.07–1.23), chronic pain (OR = 1.33, 95 % CI = 1.22–1.45), comorbidity (OR = 1.16, 95 % CI = 1.07–1.25), and depressive symptoms (mild depression: OR = 2.14, 95 % CI = 1.96–2.34; moderate depression: OR = 4.00, 95 % CI = 3.49–4.58, moderately severe depression: OR = 4.15, 95 % CI = 3.47–4.97, severe depression: OR = 4.27, 95 % CI = 2.93–6.22); while age (OR = 0.99, 95 % CI = 0.99–1.00) was negatively related to poor sleep quality. ConclusionThe prevalence of poor sleep quality in the studied population is relatively high (53.04 %). Sleep problems are common among Chinese retirees, especially older females, and have a great impact on their quality of life. People living with depression, chronic diseases, and chronic pain were at a higher risk of developing sleep disorders. Therefore, it is critical to formulate effective management strategies for Chinese retirees with poor sleep quality in the context of healthy aging.

Factors Associated with Poor Sleep among Young People in Mauritius: A Survey-Based Study

Background: Poor sleep quality in young people is a global concern. This study was conducted to explore sleep quality and its contributory factors in young people of Mauritius. Methods: This cross-sectional study comprised 202 participants aged between 14 to 29 years. The Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS) and the Adolescent Sleep Hygiene Scale (ASHS) were used to measure sleep quality, daytime sleepiness, and sleep hygiene respectively. Chi-squared tests were conducted to evaluate relationships between the collected variables. Multivariate logistic regression was used to identify associated factors with poor sleep quality. Results: The mean global PSQI was 4.81 (95%CI: 4.4, 5.22). Prevalence of poor sleep quality (global PSQI score &gt; 5) was 30.7%. The male gender reported better sleep (p=0.008). Sleep quality was significantly associated with longer sleep duration (p&lt;0.0001), pre-bedtime relaxing activities (p=0.01), and daytime physical exercise (p=0.001). In contrast, alcohol consumption after 18:00 (p&lt;0.0001), tobacco smoking after 18:00 (p&lt;0.0001), pre-bedtime awakening activities (p=0.001), and poor sleep environment (p&lt;0.0001) negatively influenced sleep quality. Multivariate logistic regression confirmed that female gender and sleep environment factors were associated with higher likelihood of poor sleep quality, while participants with longer duration of sleep (&gt;7 hours), cognitive/emotional and sleep stability factors were found to be less likely to have poor sleep quality. Conclusion: This study showed that the prevalence of poor sleep in young people in Mauritius was 30.7%. Female gender and sleep environment factors were main contributory factors. These initial results can guide further research on sleep quality

Development and validation of mathematical nomogram for predicting the risk of poor sleep quality among medical students.

BackgroundDespite the increasing prevalence of poor sleep quality among medical students, only few studies have identified the factors associated with it sing methods from epidemiological surveys. Predicting poor sleep quality is critical for ensuring medical Students’ good physical and mental health. The aim of this study was to develop a comprehensive visual predictive nomogram for predicting the risk of poor sleep quality in medical students.MethodsWe investigated medical Students’ association with poor sleep quality at JiTang College of North China University of Science and Technology through a cross-sectional study. A total of 5,140 medical students were randomized into a training cohort (75%) and a validation cohort (25%). Univariate and multivariate logistic regression models were used to explore the factors associated with poor sleep quality. A nomogram was constructed to predict the individual risk of poor sleep quality among the medical students studied.Results31.9% of medical students in the study reported poor sleep quality. We performed multivariate logistic analysis and obtained the final model, which confirmed the risk and protective factors of poor sleep quality (p < 0.05). Protective factors included the absence of physical discomfort (OR = 0.638, 95% CI: 0.546–0.745). Risk factors included current drinking (OR = 0.638, 95% CI: 0.546∼0.745), heavy study stress (OR = 2.753, 95% CI: 1.456∼5.631), very heavy study stress (OR = 3.182, 95% CI: 1.606∼6.760), depressive symptoms (OR = 4.305, 95% CI: 3.581∼5.180), and anxiety symptoms (OR = 1.808, 95% CI: 1.497∼2.183). The area under the ROC curve for the training set is 0.776 and the area under the ROC curve for the validation set is 0.770, which indicates that our model has good stability and prediction accuracy. Decision curve analysis and calibration curves demonstrate the clinical usefulness of the predictive nomograms.ConclusionOur nomogram helps predict the risk of poor sleep quality among medical students. The nomogram used includes the five factors of drinking, study stress, recent physical discomfort, depressive symptoms, and anxiety symptoms. The model has good performance and can be used for further research on and the management of the sleep quality of medical students.

Associations of cooking fuel types and daily cooking duration with sleep quality in rural adults: Effect modification of kitchen ventilation

BackgroundAssociations of combined different cooking fuel types and cooking duration with sleep quality were scarcely reported. This study aimed to explore the associations of exposure to household air pollution (HAP) from different cooking fuel types and cooking duration with sleep quality and examine the effect modification of kitchen ventilation on these associations. MethodsA total of 28,135 eligible participants were obtained from the Henan rural cohort study. Sleep quality was assessed by the Pittsburgh sleep quality index (PSQI), and participants with a PSQI score > 5 were identified as having poor sleep quality. A questionnaire was used to collect the cooking fuel types, cooking frequency, cooking duration, and kitchen ventilation. The average daily cooking duration was calculated as the cooking frequency per week multiplied by the cooking duration for each meal divided by 7. ResultsBoth domestic fuel use and cooking duration were positively related to poor sleep quality. The solid fuel users with a cooking duration ≥1.5 h/day had a higher prevalence of poor sleep quality (OR: 1.390, 95% CI: 1.233, 1.567), relative to those who never cooked. Notably, the estimated OR and 95% CI for poor sleep quality of clean fuel users with a cooking duration <1.5 h/day plus mechanical or natural ventilation versus those who never cooked was 1.05 (0.933, 1.187) or 1.306 (1.163, 1.466) and these associations were modified by kitchen ventilation. ConclusionPositive associations of combined different cooking fuel types and cooking duration with the prevalence of poor sleep quality were attenuated by kitchen ventilation, implying that installing mechanical ventilation may be an effective measure to improve sleep quality in low- and middle-income countries. These results need to be confirmed by future studies, which measure HAP more accurately using personal monitoring devices or behavior diaries.

Insomnia in cancer patients - A survey of health-care professionals' views and current practice in Romania.

Changes in the quality of sleep are reported often in cancer patients and have a major impact on general health. However, insomnia tends to be insufficiently evaluated and managed. The aim of the study was to analyze the perception of clinical staff working in oncology units regarding patients' sleep disorders. Furthermore, we assessed the prevalence of insomnia in this category of professionals. The study was cross sectional and used a descriptive and correlational design. Clinical personnel working in oncology departments were invited to complete a questionnaire regarding sleep problems in patients, and optionally, the Pittsburgh sleep quality index questionnaire was administered. Correlations between age, profession, experience, and their responses were statistically analyzed. We gathered 101 responses, with 63.4% coming from doctors. With one exception, all professionals observed changes in sleep patterns in patients, but less than a third were actively looking for these problems and only three health professionals used questionnaires. No significant differences in answers were noted based on age, profession, or experience. Regarding professionals, 45.6% of them had impaired sleep based on the Pittsburgh questionnaire results. Oncology staff are aware of the existence and impact of sleep problems, but active assessment for sleep problems is low. There is a relative high prevalence of poor sleep quality among oncology staff. In order to improve the well-being of patients, the quality of sleep should be documented as part of the care plan. The existence of guidelines is desirable.

Impact of Lockdown On College Students Sleep Quality, Physical Activity and Eating Attitudes in South India—A Cross-Sectional Observational Study

The COVID-19 pandemic has forced the public health authorities to impose a lockdown as an epidemiological containment strategy. Due to COVID-19, the world is facing an unprecedented challenge that has changed people's lifestyles. This study aimed to evaluate how the COVID-19 pandemic lockdown affected students in Tamilnadu in terms of changes to their eating habits, sleeping patterns, and level of physical activity. A cross-sectional study was conducted using a random representative sample of 263 undergraduate students (mean age 19.63+1.86 years, males 33.46%) from Tamilnadu voluntarily filling out an online questionnaire. The Pittsburgh Sleep Quality Index (PSQI) and The International Physical Activity Questionnaires, Eating Attitude Test (EAT-26) were used to assess sleep quality, Physical Activity, and Eating Attitude disorder, respectively. The prevalence of poor sleep quality among participants was 43.3% (males: 44.32% and females: 42.86%). The mean score of the participants' EAT-26 score was found to be 10.92 ± 8.63. Students' high body weight and BMI risk factors for eating disorders (2=9.68 p=0.02, 2=9.59 p=0.02). 46% of students did not do any physical activity. There is no significant correlation between sleep and Eating attitude scores (p=0.21). 66.16% of participants experienced Psychological stress due to lock down. Findings from this study indicate that a lockdown period due to COVID-19 negatively impacted young undergraduate adults' physical activity levels and sleep quality. Finally, body weight, BMI, monthly family income, and poor physical activity with excess eating during the COVID 19 lockdown were the common exposures that are significantly associated with a higher risk of developing sleep disturbances and eating disorders. Several efficient strategies, programs, and coordinated efforts must be rigorously executed to encourage a seamless transition between school and daily life.

Study to determine prevalence of poor sleep quality and its correlation with sleep hygiene practices among medical students

Background: Sleep is essential to optimize physical, emotional, and cognitive functioning of the body. Sleep quality is a critical dimension of both physical and mental health. Poor sleep quality among medical students may have both academic and health consequences. Aims and Objectives: The objectives of this study were to assess the subjective sleep quality and determine the prevalence of poor sleep quality among medical students. The study also aimed to evaluate their sleep hygiene practices and determine its correlation with sleep quality. Materials and Methods: A cross-sectional study was done in 120 medical students after receiving Institutional Ethical Committee approval and informed consent from participants. They were subjected to self-administered questionnaires to document socio-demographic details and to assess sleep quality using Pittsburgh Sleep Quality Index (PSQI) and sleep hygiene practices using Sleep Hygiene Index (SHI). The data were subjected to statistical analysis. Results: The study showed that the prevalence of poor sleep quality among medical students was 49.16%. The mean±SD global score of PQSI was 5.5±2.3. Chi-square test showed that there was no statistically significant difference in sleep quality between males and females. Ninety (75%) students had higher values of SHI (&gt;16). Pearson’s correlation showed the positive correlation between higher global scores of PSQI with higher SHI scores to be statistically significant (P&lt;0.01). Conclusion: There is prevalence of poor sleep quality among medical students, which is significantly associated with inadequate sleep hygiene. This needs to be addressed by promoting optimal sleep hygiene practices.

Evaluating the impact of a sleep health education and a personalised smartphone application on sleep, productivity and healthcare utilisation among employees: results of a randomised clinical trial

ObjectivesWe evaluated an online Sleep Health and Wellness (SHAW) programme paired with dayzz, a personalised sleep training programme deployed via smartphone application (dayzz app) that promotes healthy sleep and treatment...

Sleep quality and associated factors among pregnant women attending antenatal care unit at Gondar, Ethiopia: a cross-sectional study

ObjectiveThis study aimed to determine the prevalence and associated factors of poor sleep quality among pregnant women in Ethiopia.MethodInstitutional based cross-sectional study.SettingUniversity of Gondar Comprehensive Specialized Hospital, Gondar, Ethiopia.ParticipantsA total...

Association between sleep quality and internet addiction in high school students

Internet addiction and poor sleep quality are two types of common mental health disorders among high school students. This study was conducted to determine the prevalence of poor sleep quality and its association with internet addiction in high school students. A cross-sectional study was conducted on 820 high school students in Dong Hoa town, Phu Yen province in April 2021. The students were requested to fill out a structured questionnaire. internet addiction was evaluated by the short version Internet Addiction Test (s-IAT). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. The prevalence of poor sleep quality was 33.5% among high school students. The proportion of students with internet addiction evaluated by the s-IAT was 21%. The association between internet addiction and poor sleep quality remained statistically significant with p&lt;0,001 (OR=2.98, 95% CI: 2.05-4.34) after adjusting for potential confounding variables. The proportions of internet addiction and poor sleep quality were high and there was an association between them. Students should not use the internet after 9 pm and should spend at least 8 hours each night on sleep.

Association between sleep quality, migraine and migraine burden.

BackgroundThe relationship between sleep and migraine is well known to be bidirectional. However, few studies have systematically assessed the association between sleep quality and the risk of developing migraine, and its gender and age differences are unclear. And there is currently limited evidence on the associations between sleep quality and migraine-related burdens.ObjectiveThe objectives of this study were to: (1) explore the association between sleep quality and the risk of developing migraine, and its gender and age differences; (2) investigate the associations between sleep quality and the total pain burden, severity, disability, headache impact, quality of life, anxiety, and depression of migraine patients.MethodsThis study consecutively enrolled 134 migraine patients and 70 sex- and age-matched healthy control subjects. Sleep quality was assessed through the Pittsburgh Sleep Quality Index (PSQI). Logistic regression and linear regression analyses were used to explore the associations between sleep quality with the risk of developing migraine and the migraine-related burdens.ResultsThe prevalence of poor sleep quality in migraine patients was significantly higher than that in subjects without migraine (P < 0.001). After adjusting for various confounding factors, the risk of migraine with poor sleep quality remained 3.981 times that of those with good sleep quality. The subgroup analysis showed that there were significant additive interactions between poor sleep quality and the risk of migraine in gender, age, and education level (P for interaction < 0.05), and the stronger correlations were found in females, populations with ages more than 35 years old, and with lower education levels. In addition, multivariate linear regression analysis showed that poor sleep quality was significantly and independently associated with the total pain burden, severity, headache impact, quality of life, anxiety, and depression in migraine patients (P trend < 0.05).ConclusionPoor sleep quality was significantly independently associated with an increased risk of developing migraine and the migraine-related burdens. Strengthening PSQI assessment is valuable for the early prevention and treatment of migraine patients.

Prevalence and related factors of poor sleep quality in patients with pre-dialysis chronic kidney disease

Sleep disturbances in patients with chronic kidney disease (CKD) are related to decreased quality of life and increased health-related risks. There is insufficient data about actual prevalence and related factors of poor sleepers in this group. In this study, we aimed to investigate prevalence and related risk factors of self-reported poor sleep quality in patients with pre-dialysis CKD. In this cross-sectional study, 259 pre-dialysis CKD patients (median age 56 years; range, 19-85) were included. Demographical, clinical and laboratory correlates were recorded. Body mass index (BMI) was calculated. Estimated glomerular filtration rate (eGFR) was calculated by the Modification of Diet in Renal Disease (MDRD) formula. Sleep quality was assessed by Pittsburgh Sleep Quality Index (PSQI), a self-rated questionnaire. Depression was evaluated using the Beck Depression Inventory (BDI). Median eGFR was 27.6 ml/min/1.73 m2 (range, 9-56). Of the 259 patients, 110 (42.5%) were poor sleepers with global PSQI score >5. The univariate correlation analysis revealed that global PSQI score was positively correlated with age, BMI, waist circumferences (WC), hip circumferences (HC), serum phosphorus and triglyceride levels, systolic blood pressure (BP), pulse pressure and BDI score, and negatively correlated with male gender and hemoglobin level. Logistic regression analysis, showed that HC, systolic BP, and BDI scores were independently associated with poor sleep quality (p = 0.001, p = 0.020 and p < 0.001, respectively). Prevalence of poor sleep quality in our pre-dialysis CKD patients was 42.5%. Systolic BP, depression and HC, all of these are potentially correctable factors, were associated with poor sleep quality independently.