Prevalence Of Poor Sleep Quality Research Articles

Sleep quality and its correlates among undergraduate students of a medical school in North Karnataka, India

Background: Poor sleep quality is a global health concern and is fast becoming one of the silent epidemics. Sleep quality among medical students is of particular interest as they are considered high risk groups for developing sleep related issues because of their demanding schedules. This study aims to estimate the magnitude of poor sleep and its correlates among undergraduate medical students of a medical school in Belgaum, India. Methods: An institutional based descriptive cross-sectional study was conducted among 293 undergraduate medical students using a self-administered questionnaire. Sleep quality was assessed using Pittsburgh sleep quality index (PSQI). The potential determinants of quality of sleep were identified using logistic regression. A p<0.05 was considered statistically significant and adjusted odds ratio with 95%CI was used to present the strength of association. Results: The prevalence of poor sleep quality among medical students was 69.3%. Factors like gender, female students (AOR=2.381, 95%CI=1.359-4.172), being overweight or obese (AOR=2.499, 95%CI=1.297-4.817) were associated with poor sleep quality. Students with good sleep hygiene (AOR=0.415, 95%CI=0.231-0.745) and who did not use technology during bedtime (AOR=0.38, 95%CI=0.168-0.878) were less likely of having poor sleep quality at p<0.05. Conclusions: Prevalence of poor sleep quality among substantial proportion of medical students in the current study is alarming. The already existing personal and professional development programme committee can conduct routine screening to assess sleep quality among students and focus on programmes to improve their sleep hygiene.

Poor sleep quality and associated factors among healthcare professionals at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia

BackgroundPoor sleep quality is linked to physiological dysfunction, which increases the risk of obesity, cardiovascular disease, cognitive impairment, and other medical conditions. Despite the known health risks of sleep disturbances, literature is still scant regarding sleep quality and its associated factors among healthcare professionals in Ethiopia. Therefore, this study aimed to determine the prevalence of poor sleep quality and its associated factors among healthcare professionals at the University of Gondar Comprehensive Specialized Hospital.MethodsAn institution-based cross-sectional study was conducted at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. A total of 418 healthcare professionals participated in the study. The study participants were chosen using the stratified random sampling method. Data were collected using a structured, self-administered questionnaire. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality of participants. Bi-variable and multivariable logistic regression models were used. p ≤ 0.05 was used to declare statistically significant variables.ResultsThe mean age of the study participants was 30.7 years (SD ± 6.3). The overall prevalence of poor sleep quality was 58.9% [95% CI (54.2, 63.6%)]. Being female [adjusted odds ratio (AOR) = 1.9, 95% CI (1.2, 2.9)], being a shift worker [AOR = 5.7, 95% CI (2.3, 14.3), not performing regular exercise [AOR = 2.08 (1.2–3.6)], being a khat chewer [AOR = 3.1, 95% CI (1.2, 7.6)], and having depressive symptoms [AOR = 2.6, 95% CI (1.3, 6.8)] were significantly associated with higher odds of having poor sleep quality.ConclusionThe prevalence of poor sleep quality among healthcare professionals was found to be high. As a result, we recommend that healthcare providers at the University of Gondar Comprehensive Specialized Hospital focus on early regular screening for sleep disturbances and pay special attention to shift work schedules and behaviors such as khat chewing, exercise, and depressive symptoms.

Poor sleep quality and associated factors among adult chronic kidney disease patients

Poor sleep quality and associated factors among adult chronic kidney disease patients

0405 Prevalence of Poor Sleep Quality and Its Association with Smartphone Use in Singapore

0405 Prevalence of Poor Sleep Quality and Its Association with Smartphone Use in Singapore

Role of social support in the relationship between resilience and sleep quality among cancer patients.

The present study aimed to investigate the effect of resilience on sleep quality and explore the role of social support between resilience and sleep quality in cancer patients. A multicenter and cross-sectional study was conducted in China from May to November 2021.A total of 202 cancer patients were recruited to complete the questionnaires composed of demographic information, Pittsburg Sleep Quality Index (PSQI), Resilience Scale-14 (RS-14), and Multidimensions Scale of Perceived Social Support (MSPSS). The associations between resilience, social support, and sleep quality were explored through hierarchical regression analysis. The prevalence of poor sleep quality was 50% among cancer patients. Resilience, social support, and the interaction between resilience and social support were all found to be significantly associated with sleep quality. Results of simple slope analysis indicated that the association between resilience and sleep quality were gradually decreased with the increasing social support levels (1 SD below the mean, B=-0.225, β=-0.551, P<0.001), mean social support (B=-0.147, β=-0.353, P<0.001) and high social support (1 SD above the mean, B=-0.065, β=-0.156, P<0.001). Additionally, social support mediated the effect of resilience on sleep quality among cancer patients. Poor sleep quality has been common in cancer patients. Social support could mediate and alleviate the relationship between resilience and sleep quality among cancer patients. Besides providing sufficient social support, interventions based on resilience should be applied to address sleep problems in cancer patients.

Poor Sleep Quality in Jamaican Adults With Sickle Cell Disease: Prevalence, Risk Factors, and Association With Quality of Life

Poor sleep and chronic illnesses have a bidirectional relationship where presence of one can worsen the other. Sickle cell disease (SCD) is associated with significant morbidity and early mortality. In this study, we examine sleep quality, its predictors, and its association with quality of life in Jamaican adults with SCD. This cross-sectional study evaluated 177 well adult SCD patients for sleep quality using The Pittsburgh Sleep Quality Index (PSQI) and quality of life using the Adult Sickle Cell Quality of Life Measurement Information System (ASCQ-Me). Multiple linear regression models examined the predictors of poor sleep quality. The mean global PSQI score was 6.9 (SD 4.2) with 56.5% having poor sleep quality. Women had significantly worse scores for sleep efficiency (p 0.005), sleep latency (p 0.03) and higher use of sleeping medications (p 0.02). Those overweight/obese had significantly worse subjective sleep quality (p 0.001) and sleep efficiency (p 0.05). In multivariate regression analysis, overweight individuals had poorer sleep quality (OR: 2.9; 95% C.I.: 1.07, 7.88) than those with normal weight whereas those unemployed and looking for a job had lower prevalence of poor sleep quality (OR 0.2; 95% C.I.: 0.05, 0.77) compared to employed individuals. Participants with good sleep quality had significantly better functioning in all 5 domains of the ASCQ-Me. In conclusion, persons with SCD who are overweight or obese are at increased risk of poor sleep which can negatively affect quality of life. Patient populations and healthcare providers will need to manage the emerging burden of overweight/obesity.

Prevalence and related factors of sleep quality among Chinese undergraduates in Jiangsu Province: multiple models' analysis.

In China, a significant number of undergraduates are experiencing poor sleep quality. This study was designed to investigate the prevalence of poor sleep quality and identify associated factors among undergraduates in Jiangsu Province, China. A total of 8,457 participants were collected in 2022 using whole-group convenience sampling. The factors studied included basic demographics, family and social support, personal lifestyles, physical and mental health, mobile phone addiction index (MPAI), and the Connor-Davidson resilience scale (CD-RISC). The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Four models, including weighted multiple linear regression, binary logistic regression, weighted linear mixed model, and logistic regression with random effects, were applied to identify associated factors for sleep quality. Of the 8,457 participants analyzed, 26.64% (2,253) were classified into the poor sleep quality group with a PSQI score >7. No significant relationship was found between sleep quality and gender, native place, economic level of family, physical exercise, dormitory light, dormitory hygiene, and amativeness matter. Risk factors for sleep quality identified by the four models included lower CD-RISC, higher MPAI, fourth grade or above, smoking, drinking, greater academic pressure, greater employment pressure, roommate sleeping late, noisy dormitory, poorer physical health status, poorer mental health status, and psychological counseling. These findings provide valuable insights for university administrators, enabling them to better understand the risk factors associated with poor sleep quality in undergraduates. By identifying these factors, administrators can provide targeted intervention measures and counseling programs to improve students' sleep quality.

Gender-specific link between sleep quality and body composition components: a cross-sectional study on the elderly

Sleep duration has been associated with overweight/obesity. Since sleep quality and body composition alter during aging, we conducted this study to determine if sleep quality is linked to body composition components in elderly people. This is a cross-sectional study conducted on 305 Iranian community-dwelling elderly aged ≥ 65 years. Sleep quality and body composition components were evaluated using Pittsburgh sleep quality index and bioelectric impedance analysis, respectively. The association of sleep quality and body composition components was examined using linear regression analysis. The prevalence of poor sleep quality and overweight/obesity was 48.9% and 54.4% in men and 77.0% and 79.3% in women, respectively. Women had significantly higher scores in most PSQI items than men, indicating their worse sleep quality compared to men. Women also had significantly higher body mass index (BMI), body fat percentage, and visceral adipose tissue and lower skeletal muscle and fat-free mass percentages than men. In the adjusted regression model, men showed positive associations between the third tertile of poor sleep quality and BMI (B = 1.35; 95% CI 0.08–2.61) and waist circumference (B = 4.14; 95% CI 0.39–7.89), but they did not demonstrate an association between sleep quality and body composition components. In the adjusted regression model for women, there were positive associations for BMI (B = 1.21; 95% CI 0.34–2.07), waist circumference (B = 2.95; 95% CI 0.99–4.91), body fat percentage (B = 2.75; 95% CI 1.06–4.45), and visceral adipose tissue (B = 7.80; 95% CI 1.73–13.87); also there were negative associations for skeletal muscle (B =  − 1.40; 95% CI − 2.39 – – 0.41) and fat-free mass (B =  − 2.76; 95% CI − 4.46 – –1.07) percentages. Except for waist circumference, other variables differed between men and women (P < 0.001). Weight management, prevention of muscle wasting, and improvement of sleep quality should be considered in a consortium when designing healthcare strategies for the elderly.

Associations between neighbourhood safety, social cohesion, sleep quality and sleep duration among older adults in India: Findings from the Study on Global Aging and Adult Health (WHO-SAGE), 2015.

Most studies on later-life health in India focus on families, with far less attention given to the health repercussions of neighbourhood conditions among older Indians. We address this limitation in existing research by examining the associations between perceptions of neighbourhood safety and social cohesion and sleep duration and sleep quality among older adults in India. Data come from the Study on Global Aging and Adult Health (WHO-SAGE), India 2015 wave 2, with a sample of 7118 adults aged 50 years and above. Sleep quality and duration were assessed using subjective responses. Multivariable logistic and linear regression analyses were employed to test the research hypotheses. Prevalence of poor sleep quality was higher among older adults living in unsafe neighbourhoods (4.46%) than peers residing in safe neighbourhoods (3.52%), and it was also higher among those living in neighbourhoods with poor social cohesion (5.31%) than counterparts who lived in socially cohesive communities (3.10%). Older adults in neighbourhoods with poor social cohesion had higher odds of reporting compromised sleep quality (adjusted odds ratio 1.75, CI: 1.22-2.51) than those living in socially cohesive neighbourhoods. Moreover, compared to those who perceived they were living in safe neighbourhoods, their peers who perceived their neighbourhoods as unsafe reported shorter sleep duration, with a negative beta coefficient of -0.27 (CI: -0.45 to -0.085). That perceived unsafety and poor social cohesion within one's neighbourhood are associated with compromised sleep reflects the significance of making neighbourhoods safer and more integrated for later-life sleep health. In addition to micro-level strategies (e.g., balanced nutrition and physical activity), efforts to improve sleep health should optimise macro-level opportunities, such as rehabilitating and revitalising neighbourhoods, which may alleviate sleep disturbances and improve sleep outcomes among older adults.

Demographic correlates, lifestyle habits, and health effects of poor sleep quality in Indian adolescent girls living in a second-tier city

Background: Insufficient sleep, poor sleep quality, and sleepiness are common problems related to learning, memory, and school performance. During puberty, sleep habits change earlier among girls in parallel to their earlier pubertal maturity. The changes occur during adolescence in response to maturational changes and increased autonomy and independence. Aims and Objectives: This study aims to estimate the prevalence of poor sleep quality among adolescent girls in a second-tier city. Materials and Methods: Cross-sectional observational study conducted in 150 school-going adolescent girls from three different schools in a second-tier city. The study was conducted using the Pittsburgh Sleep Quality Index scale for assessment of the quality of sleep and an investigator designed pro forma to analyze various lifestyle habits. Kutcher Adolescent Depression Scale 6 was used to address the depressive symptoms. Results: Prevalence of poor sleep quality was 9.2%. Pre-bed screen time of &gt;30 min was found in 53% of girls with poor sleep quality. Overweight girls also have poor sleep quality. Good grades (&gt;90% marks in examinations) were associated with poor sleep quality. About 8.3% of girls with poor sleep quality have one or more symptoms pertaining to depression. Conclusion: The pre-bed screen time of more than 30 min in the form of any electronic screens leads to poor sleep quality and poor sleep quality is significantly associated with being overweight. Poor sleep quality is also associated with some symptoms of depression. These all are observed in the girls living in a tier two city, awareness among adolescent girls must be spread to decrease the screen time and they should also be encouraged for better sleep hygiene.

IJCM_177A: A study of sleep quality and associated factors amongst type II diabetes mellitus patients in a rural hospital in Goa

Background: Patients with type 2 Diabetes Mellitus (DM) often experience a reduced quality of life, primarily attributed to a multitude of clinical effects. Among these effects, sleep disturbances present a significant challenge, as individuals with type 2 DM frequently encounter difficulties in maintaining healthy sleep patterns due to physiological imbalances. However, there is limited published research on this issue in Goa prompting the initiation of this study. Objectives: 1. To estimate the proportion of poor sleep quality among type II diabetes mellitus patients attending OPD of a rural hospital in Goa 2. To study the factors associated with poor sleep quality among type II diabetes mellitus patients attending OPD of a rural hospital in Goa Methodology: A Cross sectional study was conducted among type II diabetes mellitus patients attending OPD of a rural hospital in Goa. A semi structured questionnaire based on Pittsburgh Sleep Quality Index (PSQI) was used to collect data. At a prevalence of poor sleep quality among type II diabetes mellitus patients 12%,95%confidence interval,5% absolute error the sample size was 200.Systematic random sampling was employed and interviews were conducted for data collection. Statistical analysis was done using SPSS software, with results presented as means, frequencies, percentages, and associations assessed through chi-square test. Results: In this study of 200 subjects, the majority were above 55 years old (63%) (p=0.04), and males made up 69.5%(p=0.03) of the population. About 35% had education up to the secondary level, and 54.5% were unemployed. A significant proportion of subjects were married (88.5%) (p=&lt;.05). A significant proportion of subjects had diabetes (21.5%) (p=&lt;0.001) for over 10 years. Half of the subjects (48%) were overweight, while 23% were obese.120 subjects (60%) had a PSQI score ≥5 i.e. they were poor sleepers while 80(40%) had a PSQI score &lt; 5 i.e. they were good sleepers. Conclusion: In this study, a high proportion of type 2 diabetes mellitus patients were found to have poor quality of sleep. Further, subjects with prolonged duration of diabetes mellitus and other comorbidities had poorer quality of sleep. Thus, its concluded that sleep quality is affected in patients with diabetes mellitus.

Sleep Pattern and Diabetes: Finding from a Cross-Sectional Study in Iraq

Aim: Diabetes is a seriously debilitating disease with deleterious complications associated with physical and psychological aspects of diabetic patients. Sleep quality in diabetics is affected by various factors such as the duration of the disease and its complications. The study aimed to investigate the prevalence of poor sleep quality among type 2 diabetic patients and it is relation to glycemic control. Methods: Comparative cross-sectional study to investigate the prevalence of sleep disturbances among type 2 diabetes mellitus patients was conducted. Face to face interview was used to obtain participant’s information by using a validated questionnaire. The value of fasting blood sugar or random blood sugar and HbA1c were also obtained. Results: Among 380 type 2 diabetes mellitus, 76.8% had poor sleep quality. No significant correlation was found between age, body-mass index, duration of diabetes, and duration of comorbid disease. Sleep showed a significant association with HbA1c, gender, educational level, employment and monthly income. Conclusion: Sleep disorder is a common problem among diabetic patients, in this study about two third of patients with type 2 diabetes mellitus had poor sleep quality. Bad sleep has a negative impaction on glycemic control so it is important to focus on sleep quality and try to improve it to maintain accepted glycemic control and reduce the risk of diabetic complications. Keywords: type 2 diabetes, sleep, glycemic control, sleep quality

SLEEP QUALITY AND RELATED FACTORS IN INDIVIDUALS AGED 20 AND OVER APPLYING TO PRIMARY HEALTH CARE CENTERS IN MANISA

ABSTRACT&#x0D; Objective: This cross-sectional study aimed to evaluate the prevalence of poor sleep quality and to identify related factors among adults. Methods: This study was among adults aged ≥20 years registered at Primary Health Care Center in August-September 2022. Primary outcome was sleep quality, which was assessed by Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes were sociodemographic, lifestyle, health characteristics, sleep-related factors, anxiety-depression. Chi-square test for categorical variables was used in univariate analysis, logistic regression models in multivariate analysis was used. SPSS version 23.0 was used for analysis, type-1 error accepted as 0.05. Results: Participant’s average age was 40.98±13.68 years. 34.6% of participants had poor sleep quality. 12.7% of them had abnormal level anxiety while 8.5% had abnormal level depression. Between total PSQI score and anxiety-depression, there was a moderate positive correlation (r=.375,p

Factors Associated With Poor Sleep Quality Among Primary Healthcare Workers During the SARS-CoV-2 Pandemic.

Sleep is one of the most important activities for health and the processes related to the central nervous system. Healthcare workers commonly present alterations in the sleep-wake cycle due to complex work schedules because 24-hour attention to the population is required in public health institutions. The increase in care needs caused by the COVID-19 pandemic causedchanges in work schedules; as in Mexico, the number of patients requiring consultation in all public health units increased. Chronic partial sleep deprivation (< 7 hours of sleep in the 24-hour cycle) is the most frequent sleep alteration in Mexican health workers. However, it has not been explored whether work modifications due to the pandemic had an impact on the sleep quality of workers. We aimed to describe the prevalence of poor sleep quality and the associated factors in workers (clinical and non-clinical) of a primary care medical unit. We conducted an analytical and cross-sectional studyduring November and December 2022.We used the following tools for studying clinical and non-clinical staff working at a family medicine primary care unit: Pittsburgh Sleep Quality Index, Hamilton Anxiety Scale, Beck Depression Inventory, Maslach Burnout Inventory, and Graffar-Méndez-Castellanos socioeconomic level scale, as well as a data collection sheet and a survey of workers' knowledge, attitudes, fears, and needs regarding COVID-19. A total of 233 workers were surveyed. The prevalence of poor sleep quality was 56.7%. A higher score on the Beck Depression Inventory (OR: 1.21, CI 95%: 1.13-1.29), being a doctor (OR: 3.48, CI 95%: 1.5-8.01), and frequent alcohol consumption (OR: 2.4, CI 95%: 1.13-5.2) were identified as risk factors for poor sleep quality. A lower score in the depersonalization dimension of the Maslach Burnout Inventory (OR: 0.5, CI 95%: 0.26-0.99) was identified as a protective factor for poor sleep quality. During the pandemic, the stress of health workers increaseddue to work alterations that were necessary to treat the greatest number of patients, so theirquality of sleep decreased. Unfortunately, the mental health of healthcare workers is often under-assessed in many institutions. Thus, it is relevant to identify risk factors for alterations (especially those of sleep), since by identifying the target population, comprehensive interventions can be carried out, which can reduce the prevalence of burnout, anxiety, and depression, but if not addressed, the alterations can lead to inadequate care for users of health units.

Assessment of sleep disturbance in patients with Wilson’s disease

BackgroundWilson’s disease (WD) is frequently manifested with anxiety, depression and sleep disturbance; this investigation aimed to elucidate these manifestations and identify the influencing factors of sleep disturbance.MethodsSleep disturbance, anxiety and depression were compared in 42 WD and 40 age- and gender-matched healthy individuals. 27 individuals indicated a neurological form of the disease (NV), and 15 had a non-neurological variant (NNV).ResultsThis investigation revealed that the Parkinson’s disease sleep scale (PDSS) score of WD individuals was lower, whereas their Epworth Sleepiness Scale (ESS), Pittsburgh sleep quality index (PSQI), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores were higher than the healthy individuals (p < 0.05). Furthermore, the WD subjects had markedly increased prevalence of poor sleep quality, anxiety, and depression than healthy individuals (p < 0.05). Subgroup analysis showed that NV subjects had significantly higher scores on the UWDRS, PSQI, HAMA, and HAMD scales than those in the NV group, as well as higher rates of EDS, anxiety, and depression (p < 0.05). In patients with sleep disturbance, we identified UWDRS, neurological variant, and depression as associated factors. The linear regression model demonstrated depression as the dominant risk factor.ConclusionsDepression is highly correlated with and is a determinant of sleep disturbance in WD patients.

Prevalence of poor sleep quality among physicians and nurses in a tertiary health care center

ObjectivesSleep quality among tertiary healthcare professionals in KSA has not been well studied. Therefore, in this study, we aimed to assess sleep quality among physicians and nurses in a tertiary care center in Jeddah City and to identify the associated factors. MethodsIn this quantitative, analytical, cross-sectional study, an online, self-administered questionnaire was distributed to all physicians and nurses working at King Abdulaziz University Hospital (KAUH). A total of 395 healthcare professionals participated in this study. The questionnaire included the participants’ demographic characteristics and Pittsburgh Sleep Quality Index (PSQI) values. ResultsThe mean age and body mass index of the participating healthcare professionals were 37.74 ± 10.35 years and 26.32 ± 4.97 kg/m2, respectively. Most participants were women (70.4%) and expatriates (55.4%). The prevalence of poor sleep quality was high: 70.4% of the participants had a PSQI score >5. Several factors, such as female sex (adjusted odds ratio (AOR) = 2.03; 95% confidence interval (CI) = 1.11−3.74), shift work (AOR = 1.87; 95% CI = 1.01−3.45), physical inactivity (AOR = 2.43; 95% CI = 1.01−5.85), and current smoking (AOR = 4.64; 95% CI = 1.68−12.80), were associated with poor sleep quality among healthcare professionals. ConclusionsOur findings are consistent with those from previous studies indicating high prevalence of poor sleep quality among healthcare professionals. Furthermore, female sex, shift work, smoking, and physical inactivity were identified as risk factors for poor sleep quality.

The prevalence of poor sleep quality in the general population in China: a meta-analysis of epidemiological studies.

The high prevalence of poor sleep quality (PSQ) in the general population leads to negative health outcomes. Since estimates of PSQ prevalence in the Chinese general population vary widely, this meta-analysis aimed to refine these estimates and to identify moderating factors. A comprehensive literature search was undertaken in both international (PubMed, PsycINFO, Web of Science, and EMBASE) and Chinese (Wanfang, and the China National Knowledge Infrastructure databases) databases from inception to 23 November 2023. Studies were required to have used standard scales such as the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). The pooled prevalence of PSQ and 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup and meta-regression analyses were performed to identify sources of heterogeneity. In 32 studies with a combined 376,824 participants, the pooled prevalence of PSQ was 19.0% (95% CI 15.8-22.8%; range 6.6-43.6%). Across 22 studies that reported PSQI data, the pooled mean score was 4.32 (95%CI 3.82-4.81; SD = 0.502). The pooled mean sleep duration across 8 studies was 7.62 (95% CI 7.23-8.00; SD = 0.194) hours. Subgroup analyses showed that lower education (Q = 4.12, P = 0.042), living in less developed regions (Q = 60.28, P < 0.001), and lower PSQI cutoff values (Q = 9.80, P = 0.007) were significantly associated with PSQ. Meta-regression analyses showed that study quality was inversely associated with estimated PSQ prevalence (β = - 0.442, P = 0.004). Although measures such as subgroup and meta-regression analyses were performed, substantial heterogeneity remained. Information related to sleep quality, such as comorbid physical diseases or psychiatric disorders, substance use, occupational types, and employment status, were not reported in most studies. One in five people in the general population of China may have PSQ and people with lower education or living in western regions may be more susceptible.

Sleep traits and associated factors among people living with HIV/AIDS in Iran: a two-step clustering analysis

Sleep plays an essential role in improving the quality of life of people living with HIV (PLWH); however, sleep traits in this population are not well studied. This study aims to evaluate the sleep traits and related associated factors among PLWH in Iran. A nationwide cross-sectional study was conducted with 1185 PLWH who attended Voluntary Counseling and Testing centers in 15 provinces in Iran between April 2021 and March 2022. The Berlin Obstructive Sleep Apnea questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index were used. A two-step clustering method was employed to identify the number of sleep clusters in PLWH. Prevalence of poor sleep quality, sleepiness and insomnia were 49.6%, 21.15% and 42.7% respectively. Three sleep trait clusters were identified: I. minor sleep problems (45.6%); II. Snoring & sleep apnea (27.8%), and III. poor sleep quality and insomnia (26.7%). Age (Odds Ratio (OR) 1.033, 95% Confidence Interval (CI) 1.017–1.050), academic education (OR 0.542, 95% CI 0.294–0.998) and HIV duration were associated with being in Snoring & sleep apnea cluster, while age (OR = 1.027, 95% CI 1.009–1.040) was associated with being in Poor sleep quality and insomnia cluster. PLWH with depression had higher odds of being in Poor sleep quality and insomnia cluster, and those with anxiety had higher odds of being in Snoring & sleep apnea cluster and Poor sleep quality and insomnia cluster. A significant proportion of PLWH have poor sleep quality, sleepiness, and insomnia. The identification of three distinct sleep trait clusters underscores the need for increased attention and tailored interventions to address the specific sleep issues experienced by PLWH.

Sleep quality and its association with anxiety, stress, sleep-related beliefs and attitudes and postural control in young adults: a cross-sectional study.

Adequate sleep is essential for young adults, as this age group primarily consists of students, job seekers, and working people constantly managing a hectic lifestyle. Poor quality of sleep, which is essential for physical and mental well-being in the short and long term, can result in impaired overall health. However, there exists a gap in the literature regarding the factors affecting sleep among young adults. Hence, the objective of this study was to find the prevalence of sleep-related parameters (sleep health, hygiene, and sleep-related beliefs and attitudes) and potential risk factors for sleep quality and to investigate the association between sleep and postural control in young adults. This prospective, cross-sectional, analytical study included 181 young adults, 113 (62.43 %) females, and 68 (37.57 %) males, with a mean age of 23.82±2.88 years. Sleep quality, health, beliefs, hygiene, stress, anxiety, and depression were assessed using questionnaires. Postural control was assessed using center of pressure (COP) indices. The overall prevalence of poor sleep quality was 79.01 % among young adults. Multiple linear regression showed that predictors explained 44 % of sleep quality variance (adjusted R square=0.44, f (12,168) = 20.91, p<0.05). Poor sleep-related beliefs and attitudes (95 % CI [0.037, 0.679], p=0.029), higher perceived stress (95 % CI [0.005, 0.219], p=0.039), higher anxiety severity (95 % CI [0.108, 0.526], p=0.003), and poor COP stability index A/P (95 % CI[4.986, 11.248], p=0.00) were found to be significant predictors of poor sleep quality. High anxiety and stress, and poor dysfunctional beliefs and attitudes about sleep are predictors of poor sleep quality in young adults. These factors may significantly affect sleep quality and the experience of restful sleep among young adults. Also, poor sleep quality is associated with reduced postural control (decreased stability in the A/P direction). These findings are crucial for improving young adults' overall health and well-being, as poor sleep is highly prevalent among this age group.

Prevalence of Poor Sleep Quality and its Association with Lifestyle Habits, Competition-Based Activities, and Psychological Distress in Japanese Student-Athletes During the COVID-19 Pandemic.

Objectives  The present study clarified the prevalence of poor sleep quality and its relation to lifestyle habits, competitive-based activities, and psychological distress among Japanese student-athletes in the initial pandemic period (2020) and 1 year later (2021). Methods  In the present study, student-athletes were defined as individuals belonging to university athletic clubs. The data of two cross-sectional surveys (2020: n  = 961 and 2021: n  = 711) were collected from student-athletes in 6 universities in Japan. First, the prevalence of poor sleep quality (Pittsburgh sleep quality index score > 5) was investigated. Relationships between poor sleep quality and lifestyle habits, competition-based activities, and psychological distress were then explored using multivariate logistic regression analysis adjusted for age, sex, and body mass index. Results  The prevalence of poor sleep quality was 33.6% in 2020 and 36.6% in 2021. Poor sleep quality in 2020 was related to late bedtime, taking supplements before bed, part-time job (no late night), stressors of expectations and pressure from others, and psychological distress, whereas that in 2021 was related to early wake-up time, skipping breakfast, taking caffeinated drinks before bed, use of smartphone/cellphone after lights out, stressors of motivation loss, and psychological distress. Conclusions  In both 2020 and 2021, one-third of student-athletes had poor sleep quality and psychological distress was its common risk factor. Lifestyle habits and competition stressors associated with poor sleep quality were pandemic-specific in 2020, but similar to the prepandemic period in 2021.