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[...]the present study has focussed on assessing these changes in the sleeping patterns and providing the insight to prescribe interventions that may help to optimize sleep continuity and minimize sleep disruption. For group 2, mean subjective sleep quality score for pre-COVID period was 0.88 ± 0.55 while for COVID period score was 2.06 ± 0.7. [...]for group 3, mean subjective sleep quality score for pre-COVID period was 0.91 ± 0.58 while for COVID period score was 2.21 ± 0.74. For group 2, mean sleep latency score for pre-COVID period was 0.79 ± 0.65 while for COVID period score was 2.42 ± 0.66. [...]for group 3, mean sleep latency score for pre-COVID period was 0.85 ± 0.51 while for COVID period score was 2.3 ± 0.68. For group 2, mean sleep duration score for preCOVID period was 0.79 ± 0.55 while for COVID period score was 2.18 ± 0.64. [...]for group 3, mean sleep duration score for pre-COVID period was 0.76 ± 0.61 while for COVID period score was 2.12 ± 0.74.

The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people's sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep. This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period. We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables. The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (β=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (β=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (β=.15, 95% CI 0.05-0.27; P=.006) and MS (β=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (β=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (β=-.15, 95% CI -0.27 to -0.05; P<.001). Engaging in PA later in the day was associated with longer TST (β=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (β=-.01, 95% CI -0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (β=-.26, 95% CI -0.29 to -0.23; P<.001), earlier MS (β=-.29, 95% CI -0.33 to -0.26; P<.001), and reduced variability in TST (β=-.16, 95% CI -0.23 to -0.09; P<.001). Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce.

Study Objectives: This study aimed to (1) provide data on normal sleep patterns in Chinese preschool children, (2) identify cross-cultural differences of sleep patterns among children from China and other countries, (3) estimate the prevalence of sleep duration not meeting the optimal amount, and (4) characterize delayed weekend sleep pattern. Methods: A population-based sample of 1,610 children aged 3–6 years was recruited from 10 cities across China. Parents completed questions about their child's sleep patterns adapted from the Children's Sleep Habits Questionnaire (CSHQ). Results: The mean bedtime was 9:31 PM, wake time was 7:27 AM, nighttime sleep duration was 9 hours 30 minutes, daytime sleep duration was 1 hour 31 minutes, and total sleep duration was 11 hours 2 minutes. The children had a shorter nighttime sleep duration but longer daytime naps, resulting in no differences in total sleep duration compared with counterparts predominantly in the west. Of the children, 85.3% met the recommended amount of sleep of 10 to 13 hours, and 10.8% slept fewer than 10 hours. The prevalence of sleep less than 10 hours was higher in older children and children from eastern China. Children went to bed and woke up more than 30 minutes later on weekends than weekdays, accounting for 40.1% and 50%, respectively. Children in western China showed longer delay than children in eastern China ( P < .05). Conclusions: Age- and region-specific variability of sleep patterns are reported as well as insufficient sleep and delayed weekend sleep pattern in Chinese preschool children. The cross-cultural difference of sleep patterns was in temporal placement rather than sleep duration. Citation: Wu R, Wang GH, Zhu H, Jiang F, Jiang CL. Sleep patterns in Chinese preschool children: a population-based study. J Clin Sleep Med. 2018;14(4):533–540.

Despite the widespread use of actigraphy in pediatric sleep studies, there are currently no age-related normative data. To systematically review the literature, calculate pooled mean estimates of actigraphy-derived pediatric nighttime sleep variables and to examine the magnitude of change with age. A systematic search was performed across eight databases of studies that included at least one actigraphy sleep variable from healthy children aged 0-18 years. Data suitable for meta-analysis were confined to ages 3-18 years with seven actigraphy variables analyzed using random effects meta-analysis and meta-regression performed using age as a covariate. In total, 1334 articles did not meet inclusion criteria; 87 had data suitable for review and 79 were suitable for meta-analysis. Pooled mean estimates for overnight sleep duration declined from 9.68 hours (3-5 years age band) to 8.98, 8.85, 8.05, and 7.4 for age bands 6-8, 9-11, 12-14, and 15-18 years, respectively. For continuous data, the best-fit (R2 = 0.74) equation for hours over the 0-18 years age range was 9.02 - 1.04 × [(age/10)^2 - 0.83]. There was a significant curvilinear association between both sleep onset and offset with age (p < .001). Sleep latency was stable at 19.4 min per night. There were significant differences among the older age groups between weekday and weekend/nonschool days (18 studies). Total sleep time in 15-18 years old was 56 min longer, and sleep onset and offset almost 1 and 2 hours later, respectively, on weekend or nonschool days. These normative values have potential application to assist the interpretation of actigraphy measures from nighttime recordings across the pediatric age range, and aid future research.

Sleep is essential for the cognitive, emotional, and physical development of children. Common sleep problems occur in 20–30% of children and are often resolved by improved sleep hygiene. Sleep disorders are more severe conditions, e.g., insomnia, obstructive sleep apnea, and circadian rhythm disturbances. If left untreated, these can have significant long-term consequences, including impaired cognitive function, increased risk of psychiatric conditions like anxiety and depression, and higher risk of obesity and cardiovascular diseases. To prevent these complications, timely recognition and management is essential. In this paper, we address the medical perspectives of common sleep disturbances in children, focusing on their diagnosis and treatment. Sleep hygiene education, behavioral interventions, and ambient adaptations are first-line interventions for managing all sleep disturbances in children. In cases where behavioral approaches are insufficient, other (non-)pharmacological options are discussed, with a focus on their efficacy and safety in children. Conclusions : Finally, potential long-term consequences and directions for future research are discussed that may improve sleep-related health and well-being.

In this narrative review, we examined what level of research evidence is available that shift workers' sleep-wake disturbances can be minimized through ergonomie shift scheduling. We classified the pertinent studies conducted on real shift workers in field conditions by the type of shift system and study design (ie, whether the shift systems were modified or not— \"treatment\" versus \"no treatment\"). The results of the observational studies in which no changes to the shift system were made (ie, no treatment) showed that, irrespective of the shift system, night and early-morning shifts and quick returns are associated with short sleep and increases in sleepiness. The same is true for very long shifts (> 16 hours) and extremely long weekly working hours (> 55 hours). For all categories of shift systems, there were a lack of controlled intervention studies, limiting the possibility to provide solution-focused recommendations for shift scheduling. Most of the controlled intervention studies had been conducted on workers under regular 3-shift systems. These studies suggested that a change from slowly backward-rotating shifts to rapidly forward-rotating shifts is advantageous for alertness and, to some degree, sleep. We also found that a change from an 8-to 12-hour shift system does not necessarily result in impairments in the sleep-wake pattern. The level of research evidence was affected by many of the studies' frequent methodological limitations in measuring sleep and sleepiness. In all, to have reliable and solution-focused recommendations for shift scheduling, methodologically sound controlled intervention studies are required in different categories of shift systems.

Background Little is known about the effects of maintaining healthy sleep patterns on frailty transitions. Methods Based on 23,847 Chinese adults aged 30–79 in a prospective cohort study, we examined the associations between sleep patterns and frailty transitions. Healthy sleep patterns included sleep duration at 7 or 8 h/d, without insomnia disorder, and no snoring. Participants who persisted with a healthy sleep pattern in both surveys were defined as maintaining a healthy sleep pattern and scored one point. We used 27 phenotypes to construct a frailty index and defined three statuses: robust, prefrail, and frail. Frailty transitions were defined as the change of frailty status between the 2 surveys: improved, worsened, and remained. Log-binomial regression was used to calculate the prevalence ratio (PR) to assess the effect of sleep patterns on frailty transitions. Results During a median follow-up of 8.0 years among 23,847 adults, 45.5% of robust participants, and 10.8% of prefrail participants worsened their frailty status, while 18.6% of prefrail participants improved. Among robust participants at baseline, individuals who maintained sleep duration of 7 or 8 h/ds, without insomnia disorder, and no-snoring were less likely to worsen their frailty status; the corresponding PRs (95% CIs) were 0.92 (0.89–0.96), 0.76 (0.74–0.77), and 0.85 (0.82–0.88), respectively. Similar results were observed among prefrail participants maintaining healthy sleep patterns. Maintaining healthy sleep duration and without snoring, also raised the probability of improving the frailty status; the corresponding PRs were 1.09 (1.00–1.18) and 1.42 (1.31–1.54), respectively. Besides, a dose-response relationship was observed between constantly healthy sleep scores and the risk of frailty transitions ( P for trend < 0.001). Conclusions Maintaining a comprehensive healthy sleep pattern was positively associated with a lower risk of worsening frailty status and a higher probability of improving frailty status among Chinese adults.

Study Objectives: To compare the secular trends of sleep/wake patterns in school-aged children in Hong Kong and Shanghai, two major metropolitan cities in China with two different policies that school start time was delayed in Shanghai, but advanced in Hong Kong in 10 years’ time. Methods: Participants were from two waves of cross-sectional school-based surveys of children aged 6 to 11 years. In Shanghai, 4,339 and 13,795 children participated in the 2005 and 2014 surveys, respectively. In Hong Kong, 6,231 and 4,585 children participated in the 2003 and 2012 surveys, respectively. Parents reported their children’s bedtime and wakeup time, and thus sleep duration, short sleep (≤ 9 hours) and weekend oversleep (difference in sleep duration between weekday and weekend > 2 hours) were determined. Results: Hong Kong children had later bedtime and wakeup time and slept consistently less than their Shanghai counterparts at both survey time points. The shorter sleep duration was particularly marked during weekdays. Over the interval period, weekday sleep duration significantly decreased from 9.2 to 8.9 hours as wakeup time became earlier for Hong Kong children, but increased from 9.4 to 9.6 hours as wakeup time became later for children in Shanghai. Children from both cities slept longer on the weekends. Prevalence of weekend oversleep significantly increased in Hong Kong children, but no interval change was found in Shanghai children. Conclusions: The findings indicate subcultural differences in sleep/wake patterns in Shanghai and Hong Kong school-aged children. In particular, sleep duration had increased for Shanghai children, but decreased for Hong Kong children over 10 years. The benefits and barriers of delaying school start time for optimizing sleep health in school-aged children should be further explored. Citation: Wang G, Zhang J, Lam SP, Li SX, Jiang Y, Sun W, Chan NY, Kong APS, Zhang Y, Li S, Li AM, Jiang F, Shen X, Wing YK. Ten-year secular trends in sleep/wake patterns in Shanghai and Hong Kong school-aged children: a tale of two cities. J Clin Sleep Med. 2019;15(10):1495–1502.

Background: To evaluate the association of sleep factors (sleep duration, self-reported trouble sleeping, diagnosed sleep disorder) and combined sleep behaviors with the risk of hypertension. Methods: We analyzed 12,166 adults aged 30–79 years who participated in the 2007–2014 National Health and Nutrition Examination Survey. Sleep duration, self-reported trouble sleeping and sleep disorders were collected using a standardized questionnaire. We included three sleep factors (sleep duration, self-reported trouble sleeping and sleep disorder) to generate an overall sleep score, ranging from 0 to 3. We then defined the sleep pattern as “healthy sleep pattern” (overall sleep score = 3), “intermediate sleep pattern” (overall sleep score = 2), and “poor sleep pattern” (0 ≤ overall sleep score ≤ 1) based on the overall sleep score. The definition of hypertension was based on self-reported antihypertensive medication use or biological measurement (systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg). We used weighted logistic regression models to investigate the associations between sleep and hypertension. Results: The overall prevalence of hypertension was 37.8%. A short sleep duration (OR = 1.20, 95% CI: 1.08 to 1.33, p = 0.001), self-reported trouble sleeping (OR = 1.45, 95% CI: 1.28 to 1.65, p < 0.001) and sleep disorder (OR = 1.33, 95% CI: 1.07 to 1.66, p = 0.012) were related to the risk of hypertension. Poor sleep patterns were closely correlated with the risk of hypertension (OR = 1.90, 95% CI: 1.62 to 2.24). Conclusions: Participants with poor sleep patterns were associated with an increased risk for hypertension.

Background and Aims: During the month of Ramadan, Moslems refrain from drinking and eating between sunrise and sunset. This review aimed to analyze the effects of Ramadan fasting on physiological and behavioral variables in healthy subjects. Methods: Articles included in this paper were taken from Medline, three international congresses on health and Ramadan, and in several cases from local journals. Results: Ramadan fasting did not dramatically affect the metabolism of lipids, carbohydrates and proteins, or the daily mean of hormonal serum levels. An increase in serum urea and uric acid was frequently reported and this could be attributed to dehydration during this month. Some changes, such as the increase of HDL and apoprotein A1, and the decrease in LDL, could be beneficial for the cardiovascular system. However, the chronobiological studies have shown that Ramadan fasting affects the circadian distribution of body temperature, cortisol, melatonin and glycemia. The amplitude of most of these rhythms decreased and the acrophase shifted. Nocturnal sleep, daytime alertness and psychomotor performance were decreased. Conclusion: The major changes during Ramadan fasting are chronobiological and behavioral. They could be responsible for the high incidence of road traffic accidents and the reduction of working hours during the month of Ramadan.