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Abstract Study Objectives Individual circadian type is a ubiquitous trait defining sleep, with eveningness often associated with poorer sleep and mental health than morningness. However, it is unknown whether COVID-19 pandemic has differentially affected sleep and mental health depending on the circadian type. Here, the differences in sleep and mental health between circadian types are examined globally before and during the COVID-19 pandemic. Methods The sample collected between May and August 2020 across 12 countries/regions consisted of 19 267 adults with information on their circadian type. Statistical analyses were performed by using Complex Sample procedures, stratified by country and weighted by the number of inhabitants in the country/area of interest and by the relative number of responders in that country/area. Results Evening-types had poorer mental health, well-being, and quality of life or health than other circadian types during the pandemic. Sleep–wake schedules were delayed especially on working days, and evening-types reported an increase in sleep duration. Sleep problems increased in all circadian types, but especially among evening-types, moderated by financial suffering and confinement. Intermediate-types were less vulnerable to sleep changes, although morningness protected from most sleep problems. These findings were confirmed after adjusting for age, sex, duration of the confinement, or socio-economic status during the pandemic. Conclusions These findings indicate an alarming increase in sleep and mental health problems, especially among evening-types as compared to other circadian types during the pandemic.

Internet gaming disorder (IGD) has been associated with impairments in executive functioning, particularly inattention and impulsivity. Sleep quality has separately been linked to both gaming behavior and cognitive performance, yet its role as a mediating factor in this relationship is underexplored. This study aimed to determine whether sleep quality mediates the relationship between IGD symptoms and executive dysfunction in adolescents, specifically focusing on the domains of inattention and hyperactivity or impulsivity. A reverse mediation model was also tested to explore the bidirectional nature of these relationships. A representative sample of 1000 adolescents (539/1000, 53.9% males), aged between 12 and 17 years (mean 14.52, SD 1.64), completed validated self-report measures of IGD symptoms, executive dysfunction, and sleep quality. Structural equation modeling was used to test direct and indirect effects with age and gender included as covariates. Of the sample, 2.4% (24/1000) met criteria for IGD (875/1000, 87.5% males), and 22.6% (226/1000) met criteria for chronic sleep reduction. Among those with IGD, 54.2% (542/1000) also experienced chronic sleep reduction. In model A (IGD → Sleep → Executive Dysfunction), IGD symptoms were associated with poorer sleep quality (a=0.32, 95% CI 0.19-0.44), which in turn were associated with greater executive dysfunction (b=0.05, 95% CI 0.01-0.10). The indirect effect was significant (a×b=0.02, 95% CI 0.01-0.04), and sleep quality was a partial mediator. In the reverse model (model B), executive dysfunction was associated with poorer sleep quality (a=0.15, 95% CI 0.06-0.25), which subsequently was associated with higher IGD symptoms (b=0.11, 95% CI 0.07-0.16); indirect effect a×b=0.02, 95% CI 0.01-0.04. Simple slope analysis showed that IGD symptoms were associated only with executive dysfunction at average or poor levels of sleep quality. At higher levels of sleep quality, this relationship was no longer significant. The results of this study suggest that sleep quality may be an important intermediary mechanism by which IGD might contribute to executive dysfunction and provide a basis for the development and implementation of strategies that target sleep issues in IGD. Prospective longitudinal research is needed to examine the directionality of the relationships between IGD, sleep quality, and executive dysfunction longitudinally.

ABSTRACT Purpose This study aims to compare subjective and objective sleep quality in patients with obstructive sleep apnea (OSA), given the high prevalence of this sleep disorder that can affect sleep quality. Method This research enrolled 195 individuals diagnosed with OSA, with an Apnea Hypopnea Index (AHI) of 5 or higher based on polysomnography. Participants completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire for subjective sleep quality. Objective sleep quality derived from sleep efficiency reported in overnight polysomnography. Findings An analysis of sleep efficiency showed that 12.8% of people had poor‐quality sleep. The PSQI was also used to measure subjective sleep quality, and 64.1% of respondents reported having poor sleep quality. No significant correlation was observed between sleep efficiency and PSQI scores. Obesity has a negative correlation (ρ = −0.168, p = 0.019) with sleep efficiency, highlighting the effect of BMI on sleep fragmentation. Male sex was linked to a lower risk of poor objective sleep quality, according to logistic regression analysis (adjusted OR = 0.314, 95% CI = 0.113–0.872). Frequent use of sleeping pills was linked to a lower probability of experiencing subjectively poor sleep quality (adjusted OR = 0.077, 95% CI = 0.024–0.243). Conclusion This study highlights that a significant portion of OSA patients have poor sleep quality, subjectively. Although sleep efficiency is an important objective metrics, its lack of correlation with subjective sleep quality in this population highlights the complexity of assessing sleep health and the need for comprehensive evaluation tools in these patients. This study compares subjective (Pittsburgh Sleep Quality Index) and objective (polysomnography‐derived sleep efficiency) sleep quality in 195 OSA patients. Results reveal significant discordance between the two measures, with 64.1% reporting poor subjective sleep quality while only 12.8% exhibited poor objective sleep efficiency, highlighting the need for comprehensive sleep health evaluations in OSA patients.

This study aimed to explore the association between sleep quality and its components and both dimensions of health-related quality of life (HRQoL) in a sample of young adults. The sample comprised 337 participants with a mean age of 19.6 y (SD = 2.22). Sleep quality and HRQoL were measured through the Pittsburgh Sleep Quality Index and the SF-12, respectively. Regression analyses were used to investigate the association between sleep quality and HRQoL. Our results confirm the significant association between sleep quality and both physical (p = 0.015; β = −0.138; R2 = 0.07) and mental (p < 0.001; β = −0.348; R2 = 0.22) HRQoL in the adjusted models. However, our results also highlight the differential association between sleep quality and mental and physical HRQoL. Whereas all the sleep quality components (except sleep latency; p = 0.349) were significantly associated with mental HRQoL (p < 0.05), just two subscales (subjective sleep quality; p = 0.021; β = −0.143 and sleep disturbances p = 0.002; β = −0.165) showed a significant association. This study showed that there is a stronger association between sleep quality and mental health than sleep quality and physical health in young adults.

To investigate the relationship between dry eye and sleep quality in a large community-based Chinese population. A total of 3,070 participants aged 18-80 were recruited from a community-based study in Hangzhou, China during 2016-2017. Sleep quality was evaluated using the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI), and dry eye was evaluated using the Ocular Surface Disease Index (OSDI) questionnaire. Multivariable linear regression and logistic regression models were used to investigate the associations, adjusting for age, smoking, drinking, season, and other potential confounders. Overall, CPSQI score and sleep dysfunction were significantly associated with mild, moderate, and severe dry eye (ORs for CPSQI score: 1.07, 1.13, 1.14, all p < 0.001; for sleep dysfunction: 1.31, 1.73, 1.66, all p < 0.05). Furthermore, worse OSDI score was presented in participants with worse CPSQI score or sleep dysfunction (CPSQI score > 7) (β: 0.13, 0.54; all p < 0.001). In addition, six of the seven components of CPSQI showed significant associations with dry eye (all p < 0.001), except for the component of sleep medication use. Moreover, we observed significant associations of dry eye in all three subscales of OSDI with CPSQI score and sleep dysfunction. Our large, community-based study showed a strong association between poor sleep quality and an increased severity of dry eye, suggesting that preventing either one of the discomforts might alleviate the other.

ObjectiveTo investigate the prevalence and associated factors of poor sleep quality among community-dwelling elderly population in a rural area of Northern China.MethodsWe conducted a cross-sectional survey in August–December 2014 and recruited 2195 participants who were aged 65 years or older and living in Yanlou Town of Yanggu County in western Shandong Province, China. Data on demographics, health-related behaviors, and clinical conditions were collected through structured interviews. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality and patterns. Poor sleep quality was defined as a PSQI score > 7. We employed multiple logistic models to relate poor sleep quality to various factors.ResultsThe overall prevalence rates of poor sleep quality were 33.8% in the total sample, 39.2% in women and 26.3% in men (P < 0.01). The most common abnormal sleep domains were prolonged sleep latency (39.7%), decreased sleep duration (31.0%), and reduced habitual sleep efficiency (28.8%). Multiple logistic regression analyses revealed that poor sleep quality was significantly associated with female sex (OR = 1.76, 95% CI 1.46–2.12) and clinical comorbidities such as hypertension (OR = 1.28, 95% CI 1.06–1.54), coronary heart disease (OR = 1.60, 95% CI 1.27–2.00), and chronic obstructive pulmonary disease (OR = 1.82, 95% CI 1.34–2.49).ConclusionsThe sleep disorders were highly prevalent among the elderly in rural China. Modifiable risk factors such as cardiometabolic risk factors and disorders were associated with poor sleep quality, which might be potential targets for interventions to improve sleep quality in elderly population.

Sleep disturbance is identified as a prominent concern in cancer patients with detrimental effect on health outcome, which accompanies a decline in functional status, reduces quality of life, and even accelerates deterioration of disease. Therefore, in order to design safe and effective therapy, and improve the quality of life in cancer patients, it is necessary to seek the optimal measures of sleep quality evaluation, which include the objective assessments (e.g., polysomnography [PSG], the bispectral index [BIS], actigraphy) and subjective assessments (e.g., Pittsburgh Sleep Quality Index [PSQI], Insomnia Severity Index [ISI], Epworth Sleepiness Scale [ESS], Consensus Sleep Diary [CSD]) and understand the status of sleep quality in cancer patients, especially patients with cancers in the breast, lung, head and neck, ovaries, and uterus. This review summarizes the common methods used to measure sleep quality and compares the sensitivity, specificity, and practicability of these methods. In addition, the status of sleep disturbance in patients with cancer is analyzed.

Background 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. Methods 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. Results 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). Limitations 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. Conclusion 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.

When entering the university setting, poor sleep quality is reportedly prevalent among students and has been linked to a range of adverse health outcomes, including reduced academic performance. Moreover, determinants of sleep quality are not yet fully understood. This study was designed to (1) assess the prevalence of poor sleep quality and (2) identify determinants of sleep quality in German university students. In total, 1,684 undergraduate and graduate students (50.6% female, mean age 22.87 ± 3.15 years) from multiple academic disciplines completed a cross-sectional online survey assessing socio-demographic, health, and study-related indicators and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). In our sample, 820 (48.7%) met the PSQI cut-off score (>5) for poor sleep quality. Multiple regression analysis showed that older age, being a business student, lower subjective social status, poorer self-rated health, stress, exhaustion, and poor academic performance significantly predicted poor sleep quality. Our findings document a high prevalence of poor sleep quality among university students and suggest that business students, especially, might be exposed to a greater risk for poor sleep quality. Furthermore, the results of this study are valuable for academic staff to develop tailored interventions to promote healthy sleep-in university students.

Over 40% of Taiwanese College students experience sleep problems that not only impair their quality of life but also contribute to psychosomatic disorders. Of all the factors affecting the sleep quality, internet surfing is among one of the most prevalent. Female college students are more vulnerable to internet-associated sleep disorders than their male counterparts. Therefore, this study aims to investigate (1) the relationship between internet addiction and sleep quality, and (2) whether significant variations in sleep quality exist among students with different degrees of internet use. This structured questionnaire-based cross-sectional study enrolled students from a technical institute in southern Taiwan. The questionnaire collected information on the following three aspects: (1) demography, (2) sleep quality with Pittsburgh Sleep Quality Index (PSQI), and (3) severity of internet addiction using a 20-item Internet Addiction Test (IAT). Multiple regression analysis was performed to examine the correlation between PSQI and IAT scores among the participants. Logistic analysis was used to determine the significance of association between PSQI and IAT scores. In total, 503 female students were recruited (mean age 17.05 ± 1.34). After controlling for age, body mass index, smoking and drinking habits, religion, and habitual use of smartphone before sleep, internet addiction was found to be significantly associated with subjective sleep quality, sleep latency, sleep duration, sleep disturbance, use of sleep medication, and daytime dysfunction. Worse quality of sleep as reflected by PSQI was noted in students with moderate and severe degrees of internet addiction compared to those with mild or no internet addiction. Logistic regression analysis of the association between scores on IAT and sleep quality, demonstrated significant correlations between quality of sleep and total IAT scores (odds ratio = 1.05:1.03 ∼ 1.06, < 0.01). The results of this study demonstrated significant negative association between the degree of internet addiction and sleep quality, providing reference for educational institutes to minimize adverse effects associated with internet use and improve students' sleep quality.