Objective: The aim of the study was to compare the comorbidities and sleep patterns most com- monly associated with each gender in obstructive sleep apnea (OSA). Methods: This was a cross- sectional study of obese individuals with OSA. The polysomnographies were carried out in a sleep laboratory environment, using a 15-channel polysomnography setup. Airflow was measured using a nasal pressure cannula/thermistor combination. A standard handbook was used for interpreta- tion of PSG findings. Results: A total of 284 subjects were included in the study, (147 females). The mean age, body mass index and neck circumference were similar between females and males (p = 0.9579, p < 0.0001, and p < 0.0001, respectively). On polysomnography, females exhibited longer latency to REM sleep (146.50 ± 85.93 vs. 122.3 ± 68.28, p = 0.0210) and a higher percentage of delta sleep (10.09 ± 7.48 vs. 7.55 ± 6.57, p = 0.0037); males had more frequent microarousals (38.37 ± 27.44 vs. 28.07 ± 21.23, p = 0.0017) and a higher AHI score (30.56 ± 27.52 vs. 17.31 ± 21.23, p < 0.0001). The comorbidities most commonly associated with female gender were diabetes (29% vs. 9.49%, p = 0.0132), hypothyroidism (20% vs. 2.19%, p < 0.0001), and depression (81.63% vs. 51.22%, p < 0.0001). Male gender was associated with myocardial infarction (6.57% vs. 1.38%, p = 0.0245) and alcohol intake (33.88% vs. 11.34%, p < 0.0001). Obese males with OSA have a larger neck circumference and higher AHI and arousal indices than females. Conclusions: There are gender differences both in the sleep patterns and in the comorbidities of patients with OSA. Men had a larger neck circumference, higher apnea and sleep fragmentation scores, were more likely to con- sume alcohol, and were more likely to have a history of myocardial infarction than women.
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Abstract: Attention-deficit/hyperactivity disorder (ADHD) is often associated with comorbid sleep disturbances. Sleep disturbances may be a risk factor for development of the disorder, a symptom of the disorder, or a comorbid condition affected by a similar psychopathology. Various studies have examined the impact of sleep deprivation on the presence/exacerbation of ADHD symptomology, as well as longitudinal and concurrent associations between different sleep disturbances and ADHD, yet the notion of sleep disturbances as a predecessor to ADHD remains unclear. As such, this review examines the evidence for sleep disturbances as a risk factor for the development of ADHD, as well as the mechanisms underlying the association between sleep patterns and ADHD. Additionally, clinical implications regarding the comorbid nature of sleep disturbances and ADHD will be considered.
Sleep is an imperative physiological aspect that maintains circadian rhythm and hence a healthy life. It is a prominence of unconsciousness in which the brain is more responsive to internal than external stimuli. 1 Sleep pattern is dictated by the body to specify the sleep and wake timings aligned by natural daylight and night cycles. A healthy sleep pattern is branded by individual satisfaction, speci ﬁ c sleep timing, suf ﬁ cient duration, high ef ﬁ ciency, and continuous alertness throughout waking hours. 2 Sleep/wakefulness rhythm in humans depends upon a balance between homeostatic sleep duration, tendency, need, and an internal circadian rhythm that altogether determine the ideal timing of a ﬁ ttingly structured and restorative sleep occurrence inhibiting the various bodily functions associated with being awake. 3,4 Generally, sleep patterns are classi ﬁ ed into three categories. Monophasic sleep pattern is de ﬁ ned as sleeping once per day at nighttime only. 5 Its
Abstract: Psychological disorders, particularly mood disorders, such as unipolar depression, are often accompanied by comorbid sleep disturbances, such as insomnia, restless sleep, and restricted sleep duration. The nature of the relationship between unipolar depression and these sleep disturbances remains unclear, as sleep disturbance may be a risk factor for development, an initial manifestation of the disorder, or a comorbid condition affected by similar mechanisms. Various studies have examined the impact of sleep deprivation on the presence of (or exacerbation of) depressive symptoms, and have examined longitudinal and concurrent associations between different sleep disturbances and unipolar depression. This review examines the evidence for sleep disturbances as a risk factor for the development and presence of depres- sion, as well as examining common underlying mechanisms. Clinical implications pertaining to the comorbid nature of various sleep patterns and depression are considered.
Few studies have distinguished overall sleeping time between TIB and actual sleep durations  or weekday and weekend sleep time. Although the difference between TIB and sleep duration is obvious, the two terms have been easily confused with one another in previous studies with subjective sleep measures. More impor- tantly, their potential different health implications indicate that it might be worth investigating the two sleep times separately in epi- demiologic studies. Little is known about how each of these sleeping times vary by sociodemographic factors and how they may be linked to sleep quality. Our study aimed to provide a subjective proﬁle of the sleep patterns in a British population, with a particular emphasis on the following questions. (1) How much do we sleep every night? (2) How is sleep duration different from the time we spend in bed? (3) How is weekend sleep different from weekday sleep? (4) How do these times vary by sociodemographic factors? (5) How does re- ported sleep quantity relate to sleep quality or sleep difﬁculties?
This study was initially designed to obtain a sample of ⬃ 1000 participants, with ⬃ 500 seventh-graders and ⬃ 500 tenth-graders. We restricted our participants to seventh-graders and 10th-graders, to monitor them for 2 years before graduation. After we obtained permission from the principals of the target schools before initiation of the study, we randomly selected 16 classes of seventh- graders and 10 classes of 10th-graders, on the basis of the class sizes of the 6 target schools. All of the students in the target classes were recruited as potential participants. Self-administered paper-and-pencil questionnaires were used to collect data on adolescent and parent sleep, through the Adolescent Health Questionnaire (AHQ) and Parent and Family Questionnaire (PFQ) developed especially for this study. Adolescents were asked to com- plete the AHQ within 1.5 hours in the classroom setting, during school days. Students who returned the AHQ were asked to bring the PFQ to their parents after school. One of the parents (either the mother or the father, as chosen by the family) was invited to fill out the PFQ. The PFQ asked about that parent’s sleep patterns and the history of sleep problems for both parents and was com- pleted by the parent within 2 weeks. This procedure for data collection was approved by the research ethics com- mittee of Shandong University and target school princi- pals. Students and parents were invited to participate in the survey and their participation was voluntary, with- out any penalties for nonparticipation. This is the most commonly used procedure to conduct school-based sur- veys in China. 4,19
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METHODS: We studied 219 children who were enrolled in an asthma intervention trial and were exposed regularly to SHS. Serum cotinine levels were used to measure exposure to tobacco smoke, and sleep patterns were assessed through parent reports using the Children’s Sleep Habits Questionnaire. Covariates in adjusted analyses included gender, age, race, maternal marital status, education, and income, prenatal tobacco exposure, maternal depression, Home Observation for Measurement of the Environment total score, household density, asthma severity, and use of asthma medications.
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this study can be summarized as follows: (1) for all children in the United States and China, bedtime was increasingly delayed and sleep duration decreased with increasing age; (2) compared with the same- aged US children, Chinese children went to bed half an hour later, woke up half an hour earlier, and slept 1 hour less; (3) Chinese children were rated signifi- cantly higher than the US children on almost all CSHQ scales; (4) difficulty falling asleep, being afraid of sleeping in the dark, sleep talking, restless sleep, and teeth grinding during sleep and daytime sleepiness were common sleep problems for both groups of children, although most sleep problems were more frequent in Chinese children than in the US children; (5) there was a relationship between shorter sleep duration and difficulty falling asleep, struggling at bedtime, and trouble sleeping away from home for the US children, whereas going to bed at different times and having a fear of sleeping alone were related to shorter sleep duration in the Chinese sample; and (6) short sleep duration was a main predictor of daytime sleepiness for Chinese children, whereas restless sleep and snoring during sleep were main predictors for the US children.
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The management remedies stated above along with relaxation techniques, biofeedback, cognitive therapy would definitely help a woman achieve better sleep. The management should target both the physical pain and the sleep to achieve better results. Studies have only found out the relationship of sleep with different diseases in women but have not proved disturbed sleep to be a cause for any disease. Gender specificities occur in terms of sleep disorders and there are genetics and sexual hormones that affect sleep. 
statistical approach in performing multilevel models testing lead–lag associations. Lastly, and most importantly, no con- clusion can be made about “stress-related” increases over the course of the study, since no proper control condition was implemented. The authors only have comparison data from pre- and postsurgery time points but not from time points around the time of surgery. Whereas for obvious reasons no real comparable event in the healthy control group could be performed, future studies might apply a “natural stressor” (eg, regular medical examination) as a comparison condition. Accordingly, without any information on how a “normal” child would respond to a specific event, no conclusion can be drawn on the specificity of any potential responses to OFC surgery. However, it was most important to us to show that both sleep and cortisol secretion returned to baseline 5 days after OFC surgery and that, accordingly, adverse long-term effects of OFC surgery are unlikely.
There is limited research into treatment for sleep dis- turbance in MPS III. Given the abnormalities in diurnal melatonin concentration, exogenous melatonin is often the pharmacological treatment of choice, with some level of effectiveness reported by parents in approxi- mately 70 % of individuals with MPS III . In this study , 60 % of families also reported some degree of success with behavioural strategies although only 37 % of the overall sample had tried a behavioural interven- tion. There is therefore undoubtedly scope for further research into the acceptability and effectiveness of be- havioural intervention for sleep in MPS III. Finding ef- fective treatment is critical given that sleep disturbance has a major impact on individuals with MPS III and their families. Parents report disrupted sleep patterns for the whole family , and both parents and clinicians have described associations between sleep difficulties and increased daytime challenging behaviour in children with MPS III [31,34]. Sleep disturbance places extra strain on parents who are already coming to terms with their child’s diagnosis and its emotional, social and fi- nancial burdens. Previous research has indicated that parents of children with MPS III experience clinically significant levels of depression and anxiety [39, 40]. There are, therefore, significant clinical implications for families if an increased understanding of sleep and circa- dian rhythmicity in MPS III results in tailored, effective interventions.
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The Insomnia Severity Index is composed of seven items assessing recent problems with sleep onset, sleep maintenance, early morning awakening, and satisfac- tion with sleep patterns. In addition, the ISI estimates perceived impairment due to insomnia by subjects. Pa- tients rate each item on a 0-4 scale and a total score ≥ 8 is considered as insomnia (sadeghniiat et al., 2008). We used Berlin questionnaire to screen sleep breathing disorder. The BQ includes eight items about snoring, daytime somnolence and history of hypertension. The patients were categorized as being at low risk or high risk of having sleep apnea (Amra et al., 2011).
around the world would be seen as a resource for future work in what is a relatively new field in pediatrics. To that end, original research articles were solicited both from the IPSE Task Force mem- bers and from the international pediatric sleep com- munity. The final articles presented in this supple- ment represent a broad range of cultures, age groups, and subtopics and include several cross-cul- tural comparison studies (LeBourgeois et al and Liu et al), a longitudinal study conducted over a 10-year period (Jenni et al), and a comprehensive review of the literature (Jenni and O’Connor). They explore a number of the most important cultural issues in the pediatric sleep field including cosleeping (Fukumizu et al and Valentin), adolescent sleep patterns (Lebourgeois et al and Yang et al), and napping (Crosby et al) and together comprise a body of work that is both unique and exciting. It is the hope of the task force that the scientific work presented here will provide inspiration and an impetus for more cross- cultural pediatric sleep research and collaboration in the future.
Results: Adult ADHD patients scored higher on total scores (t=8.75, p<0.001), eating patterns (t=2.55, p<0.001), sleep patterns- social rhythm (t=3.41, p=0.001) and activity levels (t=3.0, p<0.001) in regarding to biologic rhythms. In the BRIAN subdomains; activity levels (t=4.59, p<0.001) and sleep-eating patterns (t=3.62, p<0.001) were also significantly different between the two groups. Conclusions: Our findings suggested that there was a significant difference between ADHD patients and healthy controls in most dimensions of biorhythm.
Research is underway to identify the key causes of accelerated summer weight gain so effective policies and interventions can be designed. A model by Bara- nowski and colleagues suggests that seasonal differ- ences in physical activity, diet, screen media use, and sleep patterns are likely contributors . Few studies have explored school year and summer differences in these factors, and existing findings are mixed. A within-subjects analysis of elementary school children in Massachusetts showed lower fruit and vegetable consumption in the summer, lower engagement in moderate-to-vigorous physical activity (MVPA), and higher engagement in sedentary time than in the school year . Another within-subjects analysis of elementary-aged African American children from low- income households in the Southeastern United States found no significant differences in MVPA but more sedentary time and less light-intensity physical activity (PA) in the summer . Children consumed both fruits and sweets/desserts more frequently during the summer, and slept about 14 min longer. Differences in screen time were more striking: children engaged in 2 more hours of screen time in the summer than the school year. Larger within-subjects studies are needed to identify meaningful differences in obeso- genic behaviors from school year to summer among children in the United States. An analysis of National Health and Nutrition Examination Survey (NHANES) data showed poorer dietary patterns and higher levels of physical activity in the summer compared to the school year , though different children were sur- veyed at each time. Another study from Massachu- setts reported high levels of sedentary and light activity and poor dietary patterns in summer, though no school year comparison was available . Several studies also show summertime losses in physical fit- ness in school-age youth [12–15], suggesting that lack of physical activity or increased sedentary time may play a role. However, a study comparing school year and summer total energy expenditure in youth at risk for obesity found no significant difference .
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In a second round of analyses, we ana- lyzed the weight-sleep-biomarker level interrelationships from a sleep per- spective. All children were regrouped on the basis of their sleep patterns af- ter we standardized TST with adjust- ment for age (ie, created TST z scores for our sample), thus controlling for potential age ﬂuctuations, to deter- mine a cutoff value for TST at which health is at risk (eg, analogous to a BMI z score). We used 3 cutoff values that is, 1 SD, 1.5 SDs, and 2 SDs above and below the mean sleep duration for weekdays and weekends. Accordingly, 9 sleep pattern groups could be de- ﬁned (Supplemental Fig 3 and Supple- mental Table 9).
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value is significant which was correlated Nao Nishinoue et al; shown the cores regarding pre and post follow up. In our study we mainly assessed the sleep patterns (figure: 2) of the subjects by using PSQI scale where we found the sleep latency, sleep duration and number of awakenings during sleep. We found that majority of the patients were having less hours of sleep i.e; up to less than 6 hours which was correlated with the study conducted by Matthias J.Müller et al.,  Sleep patterns were also assessed after sleep hygiene education (figure: 3) which shows that duration of sleep was increased, which was correlated with the study conducted by Franklin C.
Main calculations: first, correlations were computed between sleep patterns (PSQI: subjective sleep quality, sleep duration, sleep latency, sleep efficacy, sleep disturbances, sleep medication, daytime dysfunction; overall score), driv- ing behavior (MDBQ: overall score), general health (GHQ:2 overall score), aggression (overall score), and reaction times (auditory, visual, and visual–auditory stimuli). Second, t-tests for unrelated samples were carried out with the PSQI cutoff score of less than five points (no sleep disturbances) or five and higher points (sleep disturbances) as independent variables and driving behavior, general health, aggression, and reaction time as dependent variables. Third, a multiple regression analysis (stepwise; backward) was performed with the MDBQ total score as a dependent variable, and subjective sleep, general health, and aggression as predictors. Fourth, a path analysis was executed, again with the MDBQ total score as a dependent variable; we tested the direct effects of sleep on driving behavior and indirect effects via general health. The nominal level of statistical significance was set at alpha < 0.05. Statistical computations were performed with SPSS ®
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We used Poisson regression with robust variance estima- tion  to calculate adjusted prevalence ratios (PRs) and 95% confidence intervals (CIs) to compare MetS preva- lence between women with poor sleep to women with recommended sleep (yes vs. no for short sleep, inconsist- ent weekly sleep patterns, sleep debt, frequent napping, insomnia symptoms, difficulty falling asleep, or difficulty staying asleep) for premenopausal and postmenopausal women, separately. A two-sided p-value of 0.05 was used to determine statistical significance in all models. Based on directed acyclic graphs  and the prior literature, all adjusted models included age, educational attainment, annual household income, smoking status, alcohol con- sumption, healthy eating index score, log-transformed metabolic equivalents, hormone replacement therapy use, clinical depression/bipolar disorder, and sleep medication use. Models for sleep debt were addition- ally adjusted for consistent weekly sleep patterns. Mod- els including all participants were additionally adjusted for race/ethnicity. We determined statistical significance for a menopausal status-by-sleep characteristic interac- tion term in models that included all participants. Within each menopausal status category, we also included a sleep parameter-by-race/ethnicity interaction term and then stratified models by race/ethnicity. All analyses were performed using SAS software, version 9.4 of the SAS System for Windows (Cary, NC).
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suring in this regard). Second, the data are cross-sec- tional and as such, causality cannot be assumed. Finally, although we used validated outcomes measures, the sleep measures were based on subjective parent report. More objective measures of sleep may have resulted in less impact of sleep on child outcomes. Nonetheless, parent report is an established marker of problematic child sleep patterns, and there are strong indications of the reliability of parent reporting. Parents who report a sleep problem in younger children also report more fre- quent and longer night wakings and greater delay in sleep onset than do parents who report that their child has no sleep problems. 28 Parents who report a child sleep
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