BACKGROUND: Unhealthy sleep behaviors are associated with higher risks of osteoporosis (OP), while prospective evidence is limited. This study aimed to prospectively investigate this association, quantify the attributable burden of OP incidence reduction due to unhealthy sleep behaviors, and explore potential modifications by genetic risk factors.
METHODS: This longitudinal cohort study was conducted utilizing data from the UK Biobank, comprising 293,164 participants initially free of OP and with requisite sleep behaviors data at baseline. We followed the participants after recruitment until November 30, 2022, to ascertain incident OP. We assessed the associations of five sleep behaviors including sleep duration, chronotype, insomnia, daytime napping, and morning wake-up difficulties, as well as sleep behavior patterns identified based on the above sleep behaviors, with the risk of OP, using Cox models adjusted for multiple confounders. The analyses were then performed separately among individuals with different OP susceptibility, indexed by standard polygenetic risk scores(PRS) for OP. Our secondary outcome was OP with pathologic fracture. Subgroup and sensitivity analyses were performed. Additionally, attributable risk percent in the exposed population (AR%) and population attributable fraction (PAF) of sleep behaviors were calculated.
RESULTS: Over a median follow-up of 13.7 years, 8253 new-onset OP cases were documented. Unhealthy sleep behaviors, such as long or short sleep duration, insomnia, daytime napping, morning wake-up difficulties, and unhealthy sleep patterns, were associated with elevated risks of OP (HRs ranging from 1.14 to 1.46, all P-value <0.001) compared to healthy sleep behaviors. Similar associations were observed for OP with pathologic fractures. Insomnia exhibited the largest AR% of 39.98 % (95%CI: 36.46, 43.31) and PAF of 33.25 % (95%CI: 30.00, 36.34) among healthy sleep patterns and components. A statistically significant multiplicative interaction was noted between sleep behaviors and OP PRS on OP risk (all P-interaction <0.001).
CONCLUSIONS: Four unhealthy sleep behaviors and sleep behavior patterns were associated to increased OP risk, with insomnia contributing the most to OP incidence, while genetic risk for OP modified this association. These findings underscore the crucial role of adhering to healthy sleep behaviors for effective OP prevention.

UNASSIGNED: Although previous studies of sleep-related behaviors in relation to primary open-angle glaucoma (POAG) have been noted, the causal relationship remains unclear. The purpose of our present study was to investigate the relationships of genetically predicted sleep traits with POAG using a two-sample bidirectional Mendelian randomization (MR) method.
UNASSIGNED: Summary-level data collected from publicly available genome-wide association studies (GWAS) of European decent were applied for the bidirectional MR analysis. After quality control steps, independent single-nucleotide polymorphisms for eight sleep behaviors and POAG were selected as the genetic instruments. The inverse-variance weighted (IVW) approach was adopted as the primary method, which was complemented by a series of sensitivity analyses to assess the robustness of the results by estimating heterogeneity and pleiotropy. Multivariable MR (MVMR) was used to assess the direct effect of sleep traits on POAG, after adjusting for several confounding factors.
UNASSIGNED: Our investigation revealed a positive correlation between genetically predicted ease of getting up in the morning and sleep duration and POAG using the IVW method (odds ratio (OR)=1.78, 95% confidence interval (CI):1.29-2.46, P = 4.33× 10-4; OR = 1.66, 95% CI:1.18-2.34, P = 3.38×10-3, respectively). Other supplementary MR methods also confirmed similar results. Moreover, the MVMR results also revealed that the adverse effects of these two sleep traits on POAG persisted after adjusting for body mass index, smoking, drinking, and education (all P < 0.05). Conversely, the relationships between genetic liability of POAG and different sleep behaviors were not statistically significant in the reverse-direction MR estimate (all P > 0.05).
UNASSIGNED: Our study demonstrated that genetic prediction of getting up easily in the morning or sleep duration were associated with a higher risk of POAG, but not vice versa, in a European population. Further validation and clinical interventions are required to offer potential strategies to prevent and manage POAG.

A negative attitude toward sleep has greatly affected sleep habits. In addition to contributing to physical and metabolic disorders, poor sleep quality may cause emotional disturbances. This study aimed to measure sleep behavior and factors contributing to poor sleep quality in the Madinah region, Saudi Arabia. We also assessed whether the use of sleeping aids improved peoples sleep. Three hundred and ninety-nine adults in the Madinah region of Saudi Arabia participated in this cross-sectional study. Three data domains were collected using an online questionnaire between 30 January and 26 April 2022. In the first domain, the characteristics of participants were discussed. In the second domain, questions about sleep behavior were asked. In the third domain, we examined the types, frequency, and impact of sleep aid use. Out of the 399 participants, 154 (38.59%) reported sleep problems. A total of 64.94% of the 154 participants blamed stress as the leading cause of their sleep disorders, and 74.68% of those with sleep problems reported reduced productivity. Among those who reported having sleep problems, 46.10% used sleep aids, with Panadol night (antihistamine) being the most used, 49.30%, followed by Melatonin at 39.44%. Sleep quality improved by 67.6% among those who used sleep aids. A total of 71.8% of the participants think it is not safe to use sleep aids in the long term. Our findings suggest that sleep problems are a prevalent concern in Madinah, Saudi Arabia, and even though the use of sleep aids improved sleep quality, it should be considered an emerging and important public health objective in Saudi Arabia. Further studies are needed to evaluate sleep quality and the level of sleep aid usage among other Saudi Arabian regions.

UNASSIGNED: Vitamin D has been known to be associated with asthma, particularly in children, while the evidence among adults is limited and inconclusive. This study aimed to investigate the association between serum, vitamin D concentrations, and the incidence of adult-onset asthma and also the modified effect caused by sleep patterns and genetic risks.
UNASSIGNED: A prospective cohort study with 307,872 participants aged between 37 and 73 years was conducted based on the UK Biobank, with a median follow-up of 12 years. The Cox proportional hazard model was applied to evaluate the association between vitamin D status and incident adult-onset asthma, and the modified effect was investigated by conducting stratified analysis according to sleep pattern score and genetic risk score, and subgroup analyses were performed by sex, age, BMI, and smoking status as well.
UNASSIGNED: Individuals with optimal vitamin D concentration were associated with 11.1% reduced risk of incident asthma compared to those participants with deficient vitamin D (HR = 0.889; 95% CI: 0.820-0.964; p = 0.005). Moreover, stratification analysis demonstrated that the protective effect of vitamin D on asthma risk was modified by sleep patterns or genetic susceptibility, with the strongest protective effect being observed in the subpopulation with a moderate sleep pattern (HR = 0.883; 95% CI: 0.797-0.977; p = 0.016) and a moderate genetic risk (HR = 0.817; 95% CI: 0.711-0.938; p = 0.004). In subgroup analyses, the protective effect of optimal vitamin D levels was only significant among men, individuals younger than 60 years of age, overweight individuals, and current or previous smokers.
UNASSIGNED: Increased serum vitamin D levels were associated with a lower risk of incident adult-onset asthma, and this association was modified by sleep patterns and genetic predisposition to some extent.

The association between sleep behaviors and gout risk remains uncertain. We aimed to evaluate the relationship of sleep patterns based on a combination of five major sleep behaviors with the risk of new-onset gout, and to explore whether genetic risks of gout may modify this association in the general population.
403,630 participants without gout at baseline in UK Biobank were included. A healthy sleep score was created by combining five major sleep behaviors, including chronotype, sleep duration, insomnia, snoring, and daytime sleepiness. Genetic risk score for gout was calculated based on 13 single nucleotide polymorphisms (SNPs) with independently significant genome-wide association with gout. The primary outcome was new-onset gout.
During a median follow-up duration of 12.0 years, 4270 (1.1%) participants developed new-onset gout. Compared to participants with poor sleep patterns (0 ≤ healthy sleep score ≤ 1), those with healthy sleep patterns (4 ≤ healthy sleep score ≤ 5) had a significantly lower risk of new-onset gout (HR, 0.79; 95% CI: 0.70-0.91). Moreover, the significantly lower risk of new-onset gout associated with healthy sleep patterns were mainly found in those with low (HR, 0.68; 95%CI: 0.53-0.88), or intermediate genetic risks of gout (HR, 0.78; 95%CI: 0.62-0.99), but not in participants with high genetic risks of gout (HR, 0.95; 95%CI: 0.77-1.17) (P for interaction =0.043).
Among the general population, a healthy sleep pattern was associated with a significant lower of new-onset gout risk, especially in those with lower genetic risks of gout.

Sleep hygiene practices may hinder university athletes from obtaining quality sleep to support health and performance. We sought to provide a comprehensive evaluation of sleep quality and behaviors in varsity athletes using validated sleep questionnaires: the Athlete Sleep Screening Questionnaire (ASSQ) and the Athlete Sleep Behavior Questionnaire (ASBQ). Sixty-four (n = 64) athletes participated (54% female; 71% Caucasian). The mean age was 20.3 ± 1.7 years and the mean BMI was 23.3 ± 3.3. Fifty-one percent met the threshold for adequate sleep (7+ h) and 54% reported being somewhat/very satisfied with sleep quality. Global scores for ASSQ Sleep Difficulty and ASBQ sleep behaviors were significantly correlated (r = 0.31; p = 0.014) and not significantly different across age, academic year, or residence. According to the ASSQ, 11% and 24% were classified as having severe or moderate sleep problems, respectively. The ASBQ categorized 62% as having \"poor\" sleep behaviors. Notable sleep-influencing factors included a high frequency of emotional/cognitive processing of sport-performance issues (46.9%), frequent use of light-emitting devices before bed (90%), training after 7 pm (65%), and the use of sleep medication (19%). Half of the university athletes did not meet the thresholds for adequate sleep, and some may require a referral for clinical sleep issues. The majority of these athletes\' sleep behaviors do not promote adequate sleep. The ASSQ shows utility to assess gradations in clinical sleep difficulty; the ASBQ could be used in concert with the ASSQ to discern \"cognitive and physiological arousal\" targets for use in educational workshops designed to promote optimal sleep hygiene in university athletes.

Poor sleep is associated with chronic health conditions among older adults. As substance use rates increase in this population, age-related physiological and cognitive declines may exacerbate its detrimental consequences, including sleep problems. We analyzed cross-sectional associations between sleep patterns, smoking, and alcohol use using baseline data from 30,097 community-dwelling Canadian adults aged 45-85 years from the Canadian Longitudinal Study on Aging. Insomnia symptoms (difficulties falling/staying asleep), sleep duration (short:<6h; long:>8h), and sleep satisfaction(dissatisfied/neutral/satisfied) were measured. Smoking and alcohol-use frequency (past 12 months), average daily amount (past 30 days), and binge drinking (past 12 months) were self-reported, and associations were examined using modified Poisson regression. Approximately 23% of participants had insomnia symptoms, and 26% reported sleep dissatisfaction. 91% of participants were current non-smokers, whereas 7% reported smoking daily. Over 50% drank ≤ 2 drinks daily, and 3% reported binge drinking. There was a higher adjusted prevalence of insomnia among daily smokers (PR = 1.10, 95% CI = 1.00-1.21) and binge drinkers (PR = 1.21, 95% CI = 1.02-1.43). Odds of short sleep duration were lower among regular drinkers (COR = 0.71, 95% CI = 0.56-0.90) and higher among daily smokers (COR = 1.19, 95% CI = 1.01-1.40). Heavy and frequent smoking and alcohol use are associated with both insomnia symptoms and sleep dissatisfaction, but not consistently with sleep duration. Further longitudinal investigation of this relationship in aging populations is needed in clinical and public health settings to infer the extent of causality and design effective public health interventions in this vulnerable population.

To investigate sleep patterns in the camel by combining behavioral and polysomnography (PSG) methods.
A noninvasive PSG study was conducted over four nights on four animals. Additionally, video recordings were used to monitor the sleep behaviors associated with different vigilance states.
During the night, short periods of sporadic sleep-like behavior corresponding to a specific posture, sternal recumbency (SR) with the head lying down on the ground, were observed. The PSG results showed rapid shifts between five vigilance states, including wakefulness, drowsiness, rapid eye movement (REM) sleep, non-REM (NREM) sleep, and rumination. The camels typically slept only 1.7 hours per night, subdivided into 0.5 hours of REM sleep and 1.2 hours of NREM sleep. Camels spent most of the night being awake (2.3 hours), ruminating (2.4 hours), or drowsing (1.9 hours). Various combinations of transitions between the different vigilance states were observed, with a notable transition into REM sleep directly from drowsiness (9%) or wakefulness (4%). Behavioral postures were found to correlate with PSG vigilance states, thereby allowing a reliable prediction of the sleep stage based on SR and the head position (erected, motionless, or lying down on the ground). Notably, 100% of REM sleep occurred during the Head Lying Down-SR posture.
The camel is a diurnal species with a polyphasic sleep pattern at night. The best correlation between PSG and ethogram data indicates that sleep duration can be predicted by the behavioral method, provided that drowsiness is considered a part of sleep.

To examine the impact of a sleep course on sleep-related behaviors, mood, and anxiety in college students.
Participants were 145 students enrolled in either the sleep course (n = 70) or a psychology course (n = 75); data were collected in September 2014, November 2014, and February 2015.
Sleep characteristics and symptoms of depression and anxiety were assessed using validated questionnaires and sleep logs. Linear, logistic and proportional odds regression models were used to test course effects.
In November, sleep course students reported significant differences in sleep hygiene (SHI; p < .001), perceived sleep latency (PSQI; p < .05), and circadian sleep phase (MEQ; p < .05), compared to controls. In February, the sleep course students maintained most of the aforementioned gains and reported fewer symptoms of depression (CES-D; p = .05) and anxiety (BAI; p < .05).
These positive preliminary results indicate that focused education has the potential to improve sleep among college students.

OBJECTIVE: To quantify sources of night-to-night variability.
METHODS: This project was conducted in 285 middle-aged African American, Caucasian, and Chinese women from the Study of Women\'s Health Across the Nation (SWAN) Sleep Study living in Chicago, the Detroit area, Oakland, and Pittsburgh. The study used 3 repeated nights of in-home polysomnography (PSG) measures. Night 1 data included assessment of sleep staging, sleep apnea, and periodic limb movements, while Nights 2 and 3 focused on sleep staging.
RESULTS: Mean total sleep time (TST) increased substantially from 365 minutes on Night 1 to 391 minutes and 380 minutes, respectively, on Nights 2 and 3. Mean percent sleep efficiency (SE%) for the 3 nights were 83%, 85%, and 85%, respectively. Night 1 sleep values were significantly different than Nights 2 and 3 measures except for S2 (%), S1 (min), and Delta (S3+4)%. Nights 2 and 3 differences in variability were negligible. Obesity, past smoking, and financial strain measures were associated with greater Night 1 vs. Night 2 or Night 3 differences. We concluded that there was significant Night 1 vs. Nights 2 and 3 variability and, though relatively modest, it was sufficient to bias estimates of association. Additionally, personal characteristics including smoking, obesity, and financial strain increased night-to-night variability.
CONCLUSIONS: This reports adds new information about between and within person sources of variation with in-home PSG and identifies elements that are essential in the design and planning of future sleep studies of multi-ethnic groups in social and physiological transition states such as the menopause.