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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.

Study Objectives: A single-item sleep quality scale (SQS) was developed as a simple and practical sleep quality assessment and psychometrically evaluated. Methods: SQS measurement characteristics were evaluated using the Pittsburgh Sleep Quality Index (PSQI) and morning questionnaire-insomnia (MQI) according to prespecified analysis plans in separate clinical studies of patients with insomnia and depression. Patients with insomnia (n = 70) received 4 weeks' usual care with an FDA-approved hypnotic agent; patients with depression (n = 651) received 8 weeks' active or experimental therapy. Results: Concurrent criterion validity (correlation with measures of a similar construct) was demonstrated by strong (inverse) correlations between the SQS and MQI (week 1 Pearson correlation −.76) and PSQI (week 8 Goodman-Kruskal correlation −.92) sleep quality items in populations with insomnia and depression, respectively. In patients with depression, stronger correlations between the SQS and PSQI core sleep quality components versus other items supported convergent/divergent construct validity (similarity/dissimilarity to related/unrelated measures). Known-groups validity was evidenced by decreasing mean SQS scores across those who sleep normally, those borderline to having sleep problems, and those with problems sleeping. Test-retest reliability (intraclass correlation coefficient) was .62 during a 4-week period of sleep stability in patients with insomnia and .74 in stable patients with depression (1 week). Effect sizes (standardized response means) for change from baseline were 1.32 (week 1) and .67 (week 8) in populations with insomnia and depression, respectively. Mean SQS changes from baseline to week 8 convergently decreased across groups of patients with depression categorized by level of PSQI sleep quality improvement. Conclusions: The SQS possesses favorable measurement characteristics relative to lengthier or more frequently administered sleep questionnaires in patients with insomnia and depression. Clinical Trial Registration: Registry: ClincalTrials.gov, Title: Treatment of Patients With Major Depressive Disorder With MK0869, Identifier: NCT00034983, URL: https://clinicaltrials.gov/ct2/show/NCT00034983 Citation: Snyder E, Cai B, DeMuro C, Morrison MF, Ball W. A new single-item sleep quality scale: results of psychometric evaluation in patients with chronic primary insomnia and depression. J Clin Sleep Med. 2018;14(11):1849–1857.

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.

Improving sleep is critical for optimizing short-term and long-term health. Although in-person meditation training has been shown to impact sleep positively, there is a gap in our understanding of whether apps that teach self-guided meditation are also effective. This study aims to test whether Headspace (Headspace, Inc) improves sleep quality, tiredness, sleep duration, and sleep efficiency. Staff employees (N=135; mean age 38.1, SD 10.9; 75.0% female; 59.3% non-Hispanic White; 27.1% Hispanic) from a university in California's San Joaquin Valley participated in the study. Participants were randomized to complete 10 minutes of daily meditation via the Headspace app for 8 weeks or waitlist control. Sleep assessments were taken for 4 consecutive days at baseline, and then for 4-day bursts at 2, 5, and 8 weeks after randomization. Sleep quality and subjective sleep duration were assessed each morning with a sleep diary, tiredness was assessed throughout the day using ecological momentary assessment, and objective sleep duration and efficiency were measured using a Fitbit Charge 2. Both subjective and objective sleep outcomes improved. For subjective sleep outcomes, multilevel modeling revealed that those in the Headspace condition, compared to the control group, reported better sleep quality at sessions 2 (β=0.48, SE=0.12; P<.001), 5 (β=0.91, SE=0.13; P<.001), and 8 (β=0.69, SE=0.15; P<.001) compared to baseline, and a decrease in tiredness at session 5 (β=-0.58, SE=0.19; P=.001) compared to baseline, but not at sessions 2 or 8. For objective sleep outcomes, those in the Headspace condition compared to the control group had longer sleep durations at session 5 (β=23.96, SE=12.19; P=.04) compared to baseline, but not at sessions 2 or 8. There were no significant effects for sleep efficiency. This study continues adding to the ever-developing field of mobile health apps by demonstrating that Headspace can positively impact sleep quality, tiredness, and duration.

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.

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.

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.

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.

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.

Young and middle-aged adults are vulnerable to poor sleep quality. eHealth literacy, defined as the ability to effectively access and use digital health information, has been linked to improved health behaviors and may promote better sleep outcomes. However, its relationship with sleep quality remains unclear, especially across age groups. Age-related disparities in eHealth literacy may contribute to a digital health divide in sleep outcomes. This study aimed to examine the relationship between eHealth literacy and sleep quality among adults aged 18 to 59 years in Shanghai, China, as well as explore age-stratified effects. A cross-sectional study was conducted between October and December 2022 in 3 districts of Shanghai, with 7 community health service centers randomly selected. Participants were recruited through convenience sampling to complete an online survey. eHealth literacy was assessed using the eHealth Literacy Scale, and sleep quality was measured using the Pittsburgh Sleep Quality Index. Covariates included sociodemographic characteristics, health status, and health behaviors. Logistic regression models were applied to examine the relationship between eHealth literacy and sleep quality, with stratified analyses conducted by age (emerging adults [18-29 years], established adults [30-45 years], and middle-aged adults [46-59 years]). A total of 1810 participants completed the survey. The prevalence of poor sleep quality was 37.9% (686/1810). Participants with eHealth literacy scores in the 25th to 75th percentile range (odds ratio [OR] 1.594, 95% CI 1.216-2.089, P<.001) and below the 25th percentile (OR 1.584, 95% CI 1.149-2.182, P=.005) had a significantly higher likelihood of reporting poor sleep quality compared to those with scores above the 75th percentile. Age-stratified analysis indicated that this association was significant only among emerging adults (OR 2.491, 95% CI 1.133-5.479, P=.02 for scores between the 25th and 75th percentiles; OR 2.975, 95% CI 1.230-7.195, P=.02 for scores below the 25th percentile) and established adults (OR 1.439, 95% CI 1.001-2.067, P=.049 for scores between the 25th and 75th percentiles). This study found that eHealth literacy was associated with sleep quality among younger participants but not middle-aged ones, highlighting the digital divide in sleep health. These findings suggest that enhancing eHealth literacy may serve as an effective strategy for improving sleep outcomes. However, to ensure equitable health outcomes, interventions should be tailored to address the age-specific needs and varying levels of digital access across different groups.