Differences in circadian phase and weekday/weekend sleep patterns in a sample of middle-aged morning types and evening types

Factors contributing to sleep timing and sleep restriction in daily life include chronotype and less flexibility in times available for sleep on scheduled days versus free days. There is some evidence that these two factors interact, with morning types and evening types reporting similar sleep need, but evening types being more likely to accumulate a sleep debt during the week and to have greater sleep extension on weekend nights. The aim of the present study was to evaluate the independent contributions of circadian phase and weekend-to-weekday variability to sleep timing in daily life. The study included 14 morning types and 14 evening types recruited from a community-based sample of New Zealand adults (mean age 41.1 ± 4.7 years). On days 1–15, the participants followed their usual routines in their own homes and daily sleep start, midpoint and end times were determined by actigraphy and sleep diaries. Days 16–17 involved a 17 h modified constant routine protocol in the laboratory (17:00 to 10:00, <20 lux) with half-hourly saliva samples assayed for melatonin. Mixed model ANCOVAs for repeated measures were used to investigate the independent relationships between sleep start and end times (separate models) and age (30–39 years versus 40–49 years), circadian phase [time of the dim light melatonin onset (DLMO)] and weekday/weekend schedules (Sunday–Thursday nights versus Friday–Saturday nights). As expected on weekdays, evening types had later sleep start times (mean = 23:47 versus 22:37, p < .0001) and end times (mean = 07:14 versus 05:56, p < .0001) than morning types. Similarly on weekend days, evening types had later sleep start times (mean = 00:14 versus 23:07, p = .0032) and end times (mean = 08:56 versus 07:04, p < .0001) than morning types. Evening types also had later DLMO (22:06 versus 20:46, p = .0002) than morning types (mean difference = 80.4 min, SE = 18.6 min). The ANCOVA models found that later sleep start times were associated with later DLMO (p = .0172) and weekend-to-weekday sleep timing variability (p < .0001), after controlling for age, while later sleep end times were associated with later DLMO (p = .0038), younger age (p = .0190) and weekend days (p < .0001). Sleep end times showed stronger association with DLMO (for every 30 min delay in DLMO, estimated mean sleep end time occurred 14.0 min later versus 10.19 min later for sleep start times). Sleep end times also showed greater delays on weekends versus weekdays (estimated mean delay for sleep end time = 84 min, for sleep start time = 28 min). Comparing morning types and evening types, the estimated contributions of the DLMO to the mean observed differences in sleep timing were on weekdays, 39% for sleep start times and 49% for sleep end times; and on weekends, 41% for sleep start times and 34% of sleep end times. We conclude that differences in sleep timing between morning types and evening types were much greater than would be predicted on the basis of the independent contribution of the difference in DLMO on both weekdays and weekend days. The timing of sleep in daily life involves complex interactions between physiological and psychosocial factors, which may be moderated by age in adults aged 30–49 years.     

Evening chronotype,late weekend sleep times and social jetlag as possible causes of sleep curtailment after maintaining perennial DST: ain’t they as black as they are painted?

ABSTRACT

People sleep less in response to setting social clocks earlier relative to the sun clocks. We proposed here a model-based approach for estimating sleep loss as the difference between weekend and weekday risetimes divided on the difference between weekend risetime and weekday bedtime. We compared this approach with a traditional approach to estimating sleep curtailment as the difference in weekly average sleep duration in two conditions. Weekday and weekend sleep times reported for 320 samples provided possibility of testing whether evening types with later weekend sleep times and larger social jetlag differ from morning types with earlier weekend sleep times and smaller social jetlag on amount of sleep lost (1) throughout the week and (2) in response to an advance of weekday wakeups, for instance, after the expected installation of perennial Daylight Saving Time (DST). We found that (1) an amount of sleep lost due to advancing shift of weekday wakeups depends upon neither chronotype nor weekend sleep times nor social jetlag, (2) a very large amount of sleep is usually lost by evening types with later weekend sleep times and larger social jetlag and (3) an essential sleep loss is caused by our usual work/school schedules, even in morning types with early weekend sleep times and small social jetlag. As compared to such permanent sleep losses experienced by any types, an additional loss due to switching from Standard Time (ST) to perennial DST are expected to be relatively small. We also found that the traditional way of calculation of sleep curtailment leads to paradoxical conclusions, such as (1) sleep loss is larger when social jetlag is smaller, not larger, (2) sleep loss is larger when weekend sleep times are earlier, not later, (3) despite 1-h difference between two student samples in weekday wakeups, their sleep losses can be identical.     

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

ABSTRACT

The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here, we performed a retrospective EHR study based on a cohort of 3,652 individuals with self-reported sleep behaviors documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m²) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was 1 hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p < 10^?12). Sleep duration varied considerably as a function of age and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q < 0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n = 555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p < 0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.     

Midpoint of sleep on school days is associated with depression among adolescents

Depression is a serious and prevalent disease among adolescents. Identifying possible factors involved with its genesis and presentation is an important task for researchers and clinical practitioners. The individual’s chronotype and social jetlag have been associated with depression in different populations. However, information on this is lacking among adolescents. The objective of this cross-sectional study was to examine the relationship between chronotype (midpoint of sleep) and social jetlag with the presence of depression symptoms in young students. We assessed 351 students aged 12–21?years old. They answered a questionnaire on demographic characteristics, the Munich Chronotype Questionnaire (MCTQ) and the Beck Depression Inventory (BDI). Demographic characteristics (age, sex and classes’ schedule) and circadian rhythmic variables for school and free days (sunlight exposure, sleep duration, midpoint of sleep and social jetlag) were taken as factors and the presence of at least mild depression symptoms as outcome. In univariate analysis, girls (χ²?=?5.01, p?≤?0.05) and evening students (χ²?=?6.63, p?≤?0.05) were more frequently present among the depressed. Also, the depression group was significantly delayed for both midpoints of sleep during school (t?=?2.84, p?≤?0.01) and free days (t?=?2.20, p?≤?0.05). The two groups did not differ in relation to their social jetlag hours (t?=??0.68, p?=?0.501) neither subjects with two or more hours of social jetlag were more frequent among the depressed (χ²?=?1.00, p?=?0.317). In multivariate analysis, the model that best explained our outcome (R²?=?0.058, F?=?2.318, p?≤?0.05) included sex (β?=??0.12, p?≤?0.05) and the midpoint of sleep on school days (β?=??0.21, p?≤?0.001) as significant predictor variables. A sleep phase delay (later midpoints of sleep for school and free days) was associated with higher levels of depression. However, we were not able to detect similar relationship with the social jetlag hours. This could be attributed to the fact that our sample showed a smaller amount of social jetlag, possibly because even during free days a social routine, this time parents’ rules, limited the observation from what could be a natural tendency to sleep later and over. Yet, even when considering the group with more social jetlag, we did not find an association. Perhaps, this variable will only manifest its effect if it is maintained for longer periods throughout life. Additionally, when considering all the variables together, the midpoint of sleep on school days was pointed as the predictor of greatest weight for depression, together with the factor sex. Young girls, possibly earlier types, who are required to study in the evening have more chances of presenting depression symptoms. This study explicit some peculiar characteristics of the assessment of chronobiological variables in the young, such as the presence of an imposed social routine also during free days. Therefore, the expression of chronotype under the influence of the weekly social schedule (midpoint of sleep on school days) could be a more useful marker to measure the stress produced from the mismatch between external and inner rhythms rather than social jetlag. This also reinforces the importance of reconsidering the weekly routine imposed on young people.     

Sleep pattern is associated with adipokine levels and nutritional markers in resident physicians

Shift work and long hours of work are common in medical training and have been associated with a higher propensity for developing nutritional problems and obesity. Changes in leptin and ghrelin concentrations – two hormones that contribute importantly to the central regulation of food intake – are poorly described in this population. The aim of this study was to identify possible negative associations between sleep patterns, nutritional status and serum levels of adipokines. The study included 72 resident physicians (52 women and 20 men) who underwent the following assessments: nutritional assessment (3-day dietary recall evaluated by the Adapted Healthy Eating Index), anthropometric variables, fasting metabolism, physical activity level, sleep quality and sleepiness. Resident physicians with poor sleep quality reported greater weight gain after the beginning of residency (5.1 and 3.0?kg, respectively; p?=?0.01) and higher frequency of abnormal waist circumference (44.2 and 17.6%, respectively; p?=?0.04) than those with better sleep quality. Mean ghrelin concentration was greater in volunteers with poor sleep quality (64.6?±?67.8 and 26.2?±?25.0?pg/mL, respectively; p?=?0.04). Women identified as having excessive daytime sleepiness had lower levels of leptin (9.57?±?10.4?ng/mL versus 16.49?±?11.4?ng/mL, respectively; p?=?0.03) than those without excessive sleepiness. Furthermore, correlations were found between hours of additional work per week and: intake of cereals, bread and pasta (r?=?0.22, p?=?0.01); intake of servings of fruits (r?=??0.20; p?=?0.02) and beans (r?=??0.21; p?=?0.01); and global score for Adapted Healthy Eating Index (r?=??0.23; p?=?0.008; Table 3). The sleep quality total score correlated with servings of beans (r?=??0.22; p?=?0.01) and servings of oils (r?=?0.23; p?=?0.008). Significant correlations were found between mean of time of sleep and servings of cereals, bread and pasta (r?=?0.20; p?=?0.02), servings of meat (r?=??0.29; p?=?0.02) and cholesterol levels (r?=?0.27; p?=?0.03). These observations indicate that sleep patterns and long working hours of resident physicians are negatively associated with biological markers related to central food control, the lipid profile, cholesterol levels and eating healthy foods. These factors may predispose these shift workers to become overweight and develop metabolic disorders.     

Exposure to screens of digital media devices,sleep, and concentration abilities in a sample of Israel adults

A major consequence of the invasion of digital media devices with screens equipped with light-emitting diode (LED) into bedrooms exposes the users to ongoing short wavelength (SWL) lighting during the evening and at night when under natural conditions, long wavelength are dominant. Results of several studies reveal a negative physiological, behavioral, and functional outcome of the exposure to SWL artificial light at night (ALAN) from digital media screens. The aims of our study are to assess the relationships between digital media usage, sleep patterns, subjective sleepiness, and attention abilities in adult Israeli citizens compared with Israeli adolescents. We recruited 280 adult participants using convenience sample method, 49% males and 51% females with an age range of 18–82. The participants filled out self-reporting novel and original questionnaires as follows: demographic, general health evaluation, sleep habits, and difficulties by the Pittsburgh Sleep Quality Index (PSQI) and the Karolinska Sleepiness Scale (KSS), prevalence, and usage patterns of digital media devices. Smartphones are the most used digital media device in the evening and after bedtime (the time one gets to sleep in bed). Israeli adults used smartphones for 30 min and TV for about 15 min after bedtime. We noted that excessive exposure to these devices at nighttime was associated with longer sleep latency (r = 0.192, p < 0.01) and decreased sleep hours (r = − 0.143, p < 0.05). Moreover, we found a negative correlation between attention abilities in the morning and the usage time of digital media at nighttime (r = − 0.155, p < 0.01). Exposure to digital screens at evening and nighttime was positively correlated with subjective sleepiness on the KSS (r = 0.135, p < 0.05, and r = 0.261, p < 0.01). To the best of our knowledge, this study is the first to explore the association between digital media screens usage, sleep, and concentration abilities in the Israeli adult.

     

The midpoint of sleep on working days: A measure for chronodisruption and its association to individuals’ well-being

There is consistent evidence suggesting a relationship between individuals’ sleep–wake rhythms and well-being. The indiscriminate demands from daily working routines, which do not respect this individual physiological rhythm, might be mediating this phenomenon. The aim of the present study was to evaluate the relationship between the characteristics of sleep routines during working days and psychological well-being. This was a cross-sectional study on 825 individuals from rural communities from southern Brazil. The study protocol included a questionnaire on demographic characteristics, working routines, health complaints, and habits; the Munich Chronotype Questionnaire for sleep–wake rhythm and; the WHO-Five well-being index. Since sex has been shown to affect sleep circadian rhythm and well-being, analysis was performed on men and women separately. In the proposed hierarchical regression models, different factors contributed to well-being according to sex. Among men, sleep–wake and work-related variables did not predict well-being scores. Among women, later midpoints of sleep on working days (B?=??1.243, SE B?=?0.315, β?=??0.220), working more days per week (B?=??1.507, SE B?=?0.494, β?=?0.150), having longer working journeys (B?=??0.293, SE B?=?0.105, β?=??0.166), earlier working journey midpoints (B?=?0.465, SE B?=?0.222, β?=?0.115), and being exposed to less sunlight (B?=?0.140, SE B?=?0.064, β?=?0.103) predicted worse well-being. For the subgroup of women with days free from work, we have found a correlation between later midpoints of sleep during the week with worse well-being (Pearson’s r?=??0.159, p?=?0.045) while the same relationship was not significantly observed with the midpoint of sleep on non-working days (Pearson’s r?=??0.153, p?=?0.054). Considering WHO-Five as categorical, based on proposed clinical cut-offs, among women working 7-d/week, those with worst well-being (WHO-Five?<?13) had the latest midpoint of sleep (F?=?4.514, p?=?0.012). Thus, the midpoint of sleep on working days represents the interaction between individuals’ sleep–wake behavior and working routines. It plays an important role as a stress factor and may be a useful alternative variable related to chronodisruption.     

The Relationship Between Breakfast Skipping,Chronotype, and Glycemic Control in Type 2 Diabetes

Breakfast skipping is associated with obesity and an increased risk of type 2 diabetes. Later chronotypes, individuals who have a preference for later bed and wake times, often skip breakfast. The aim of the study was to explore the relationships among breakfast skipping, chronotype, and glycemic control in type 2 diabetes patients. We collected sleep timing and 24-h dietary recall from 194 non-shift-working type 2 diabetes patients who were being followed in outpatient clinics. Mid-sleep time on free days (MSF) was used as an indicator of chronotype. Hemoglobin A1C (HbA_1C) values were obtained from medical records. Hierarchical linear regression analyses controlling for demographic, sleep, and dietary variables were computed to determine whether breakfast skipping was associated with HbA_1C. Additional regression analyses were performed to test if this association was mediated by chronotype. There were 22 participants (11.3%) who self-reported missing breakfast. Breakfast skippers had significantly higher HbA_1C levels, higher body mass indices (BMI), and later MSF than breakfast eaters. Breakfast skipping was significantly associated with higher HbA_1C values (B?=?0.108, p?=?0.01), even after adjusting for age, sex, race, BMI, number of diabetes complications, insulin use, depressive symptoms, perceived sleep debt, and percentage of daily caloric intake at dinner. The relationship between breakfast skipping and HbA_1C was partially mediated by chronotype. In summary, breakfast skipping is associated with a later chronotype. Later chronotype and breakfast skipping both contribute to poorer glycemic control, as indicated by higher HbA_1C levels. Future studies are needed to confirm these findings and determine whether behavioral interventions targeting breakfast eating or sleep timing may improve glycemic control in patients with type 2 diabetes.     

Changes in growth and sleep across school nights,weekends and a winter holiday period in two Australian schools

Studies suggest that there may be an association between sleep and growth; however, the relationship is not well understood. Changes in biology and external factors such as school schedule heavily impact the sleep of adolescents, during a critical phase for growth. This study assessed the changes in sleep across school days, weekends and school holidays, while also measuring height and weight changes, and self-reported alterations in food intake and physical activity. The impact of morningness–eveningness (M-E) on height change and weight gain was also investigated. In a sample of 63 adolescents (mean age = 13.13, SD = 0.33, 31 males) from two independent schools in South Australia, height and weight were measured weekly for 4 weeks prior to the school holidays and 4 weeks after the school holidays. Participants also completed a Morningness/Eveningness Scale and 7-day sleep, diet and physical activity diaries prior to, during and after the school holidays. Participants at one school had earlier wake times during the weekends than participants attending the other school, leading to a significantly shorter sleep duration on weekends for those participants. Regardless of school, sleep was significantly later and longer during the holidays (p < 0.001) and those with a stronger morning preference fell asleep (F_18,36 = 3.4, p = 0.001) and woke (F_18,44 = 2.0, p = 0.027) earlier than evening types. Growth rate was lower during the holiday weeks. For those attending the school with limited sleep in opportunities, growth after the holidays was lower for those with greater evening preference, whereas for those at the other school, growth was greater for those with greater evening preference. The increase in average weight from pre- to post-holidays was greater for those attending the school with limited opportunities to sleep longer. Participants reported greater food intake during the holidays compared to school days and greater physical activity levels on weekends compared to school days, and school days compared to holidays. Results suggest that time of day preference may impact growth, with evening types who cannot sleep in growing at a slower rate than evening types who can or morning types. This may be related to sleep restriction. Despite sleep being both later and longer during the school holidays, participants’ growth slowed during the holiday period. It is possible that this may be a reflection of other behavioural changes in the holidays (increased food intake and reduced physical activity), as sleep timing during the school period was related to growth.     

Availability of a simple self-report sleep questionnaire for 9- to 12-year-old children

Self-report questionnaires about sleep habits are useful for population-based studies because of their low cost. However, there is no valid and reliable self-report sleep questionnaire for elementary school-aged children. The aim of this study was to examine the availability of a simple self-report sleep questionnaire for 9- to 12-year-old children. Participants were 58 children aged 9–12 years from one elementary school in a rural area of Japan. Participants wore an accelerometer for 10 consecutive days and completed the sleep questionnaire twice. Sleep measures included bedtime, wake time, and assumed and actual sleep duration on weekdays and weekends. The data obtained from the accelerometer and sleep/wake scoring software were used to assess criterion validity. Pearson correlation coefficients and Bland-Altman plots were used to evaluate the relationships between objective and self-reported sleep measures. Test-retest reliability was evaluated using intraclass correlation coefficients. The correlations between the objective and questionnaire measures were moderate to high (r = 0.45 to 0.90) and significant, except girls’ wake time, assumed sleep duration, and actual sleep duration on weekends. The Bland-Altman plots indicated that bedtime and wake time obtained from the questionnaire were underestimated for both weekdays and weekends. Test-retest reliability of the questionnaire was high, with intraclass coefficients ranging from 0.71 to 0.99. Although caution should be exercised when evaluating sleep duration on weekends, this simple self-reported sleep questionnaire is a useful tool for assessing sleep habits in 9- to 12-year-old children, particularly in school-based and large-scale epidemiological studies.

     

Delaying and Extending Sleep During Weekends: Sleep Recovery or Circadian Effect?

There is a well-known tendency to delay and prolong our sleep during weekends (Saturday and Sunday), with an advance and reduction of sleep during workdays (Monday to Friday). The objective of this work was to determine if the changes of sleep during weekends are produced by a partial sleep deprivation or a lack of entraining of circadian rhythms to an advanced phase, during workdays. The subjects were 52 undergraduate female students, mean age = 17.5 years, SD = 1.32. All students attended school following a regular schedule, from Monday to Friday. Two groups of students were studied: one attended school from 07:00 to 12:00 h (morning group, n = 30); the other attended school from 14:00 to 18:00 (afternoon group, n = 22). None of the students worked or was engaged in other activity with a fixed schedule. All kept a sleep-wake diary for 2 weeks, in which they recorded their bedtimes, wakeup times, and sleep-onset latencies. The morning group delayed 47.4 min [t(29) = 4.72, p < 0.0001] and prolonged their sleep 118.2 min [t(29) = 9.4, p < 0.0001] during weekends. Although the afternoon group had the opportunity to maintain a delayed phase and a long sleep time throughout the week, they delayed their bedtime by 24 min [t(21) = 2.99, p < 0.01] during weekends, without changing their sleep duration. The findings suggest that the prolonged sleep during weekends is due to reduction of sleep during workdays, whereas the delay of bedtime seems to be associated with a tendency of the human circadian system to maintain a delayed phase     

Association of depressive symptoms with habitual sleep duration and sleep timing in junior high school students

ABSTRACT

This study aims to investigate independent associations of habitual sleep durations and sleep timings on weekdays and weekends with depressive symptoms in adolescents who have classes in the morning. We studied grade 7–9 students (942 males and 940 females, aged 12–15 years), who had classes in the morning, at public junior high schools in Japan in a cross-sectional design. The students answered a self-report questionnaire, which covers habitual sleep durations, bedtimes and wake-up times on weekdays and weekends, and depressive symptoms. The Short Mood and Feelings Questionnaire (SMFQ) was used to determine the level of depressive symptoms. The relationship between the variables on sleep habits and the SMFQ score were studied using multivariate linear regression and generalized additive models (GAM), controlling for sex, age and school. Multivariate linear regression analysis revealed that sleep duration on weekdays and relative mid-sleep time on weekdays (i.e. mid-sleep time on weekdays – mid-sleep time on weekends) were independently significantly (p < .001) associated with the SMFQ score. GAM analysis also revealed that sleep duration on weekdays (a reverse J-shaped relationship) and the relative mid-sleep time on weekdays (a negative monotonic/linear relationship) were independently significantly (p < .001) associated with the SMFQ score. These associations were confirmed in both males and females when they were analyzed separately. These results suggest that sleep duration on weekdays and the relative mid-sleep time on weekdays may be independently associated with the level of depressive symptoms in junior high school students who have classes in the morning. These findings may have important implications for the development of novel strategies for preventing mental health problems in adolescents.     

Eveningness, sleep patterns, daytime functioning, and quality of life in Israeli adolescents

The purpose of the study was to assess the relationships between eveningness, sleep patterns, measures of daytime functioning, i.e., sleepiness, sleep problem behaviors, and depressed mood, and quality of life (QOL) in young Israeli adolescents. A cross-sectional survey was performed in urban and rural middle schools in Northern Israel. Participants were 470 eighth and ninth grade middle school students (14?±?0.8 yrs of age) in the normative school system. Students completed the modified School Sleep Habits Survey (SSHS) and Pediatric Quality of Life Inventory Short Form, assessing six subscales of physical, emotional, social, school performance, and psychosocial functioning, plus an addition generated total score. During weekdays and weekends, evening types went to bed later, their sleep latency was longer, their wake-up time was later, and their sleep duration was shorter than intermediate and morning types. Evening types exhibited more sleep problem behaviors, sleepiness, depressed mood, and lower QOL compared to intermediate and morning types. Based on the regression model, sleepiness, sleep-problem behaviors, and depressed mood were the variables most strongly associated with QOL, followed by morning-evening preference, weekday sleep duration, and weekend sleep latency. This study is the first to assess QOL in normative, healthy adolescents and to demonstrate strong associations between morning-evening preference and QOL. These findings enhance the need to identify young individuals with an evening preference, and to be aware of the characteristics and manifestations of the evening chronotype on daytime and nighttime behaviors in adolescence.     

Chronotype,bed timing and total sleep time in seniors

Many older adults (seniors) experience problems with getting enough sleep. Because of the link between sleep and circadian rhythms, changes in bedtime lead to changes in the amount of sleep obtained. Although primarily determined genetically, chronotype changes with advancing age towards a more morning-type (M-type) orientation. In a 2006 study, we have found a linear relationship, by which the earlier a senior’s bedtime, the more sleep she/he will obtain. The aim of this study was to see whether this relationship differs for M-type seniors, as compared to seniors outside the M-type category. Retired seniors (n?=?954, 535?M, 410F, 65?years+, mean age 74.4?years) taking part in a telephone interview were divided into M-types and Other types (O-types) using the Composite Scale of Morningness (CSM). The relationship between bedtime and Total Sleep Time (TST), and between rise-time and TST, was tested using linear regression separately for M-types and O-types. For each participant, habitual bedtime, rise-time and total Sleep Time (TST) [after removing time spent in unwanted wakefulness] were obtained using a telephone version of the Sleep Timing Questionnaire (STQ). Both chronotype groups showed a significant linear relationship between bedtime and TST (p?<?0.001); with earlier bedtimes leading to more TST (M-type 5.6?min; O-type 4.4?min per 10?min change [slope difference p?=?0.05]); and an opposite relationship between rise-time and TST with earlier rise-times leading to less TST (M-type 6.7?min; O-type 4.2?min per 10?min change [slope difference p?=?0.001]). M-types retired to bed 56?min earlier (p?<?0.001), awoke 93?min earlier (p?<?0.001) and obtained 23?min less TST (p?<?0.001) than O-types. In conclusion, both chronotypes showed TST to be related in a linear way to bedtime and rise-time; the overall shorter TST in M-types was due to them rising 93?min earlier, but only retiring to bed 56?min earlier than O-types; as well as having a steeper rise-time versus TST relationship.     

Relationships between daytime sleepiness and sleep quality,duration, and phase among school-aged children: a cross-sectional survey

A cross-sectional survey was conducted to simultaneously evaluate sleep quality, duration, and phase in school-aged children and correlations between each dimension of sleep and daytime sleepiness were comprehensively examined. A cross-sectional survey was conducted with school-aged children enrolled in four public elementary schools in Joetsu city, Niigata prefecture in Japan (n = 1683). Among the collected responses (n = 1290), 1134 valid responses (547 boys and 587 girls) were analyzed (valid response rate was 87.90%). Data on daytime sleepiness, sleep quality (problems in sleeping at night), sleep duration (the average sleeping time during a week), and sleep phase (sleep timing: bedtime and rising time on weekdays, and sleep regularity: differences in bedtime and rising time between on weekdays and weekends) were collected. The results of multivariate logistic regression analysis indicated that the following dimensions were significantly correlated with daytime sleepiness: the decline in sleep quality [adjusted odds ratio (AOR) = 2.62, 95% confidence interval (CI) = 1.71–4.00], bedtime after 21:30 on weekdays (AOR = 1.58, 95% CI = 1.15–2.18), bedtime delay on weekends, compared to weekdays (AOR = 1.75, 95% CI = 1.27–2.41), and bedtime advance on weekends, compared to weekdays (AOR = 3.33, 95% CI = 1.78–6.20). Sleep dimensions that significantly affected daytime sleepiness in school-aged children are sleep quality, bedtime-timing, and regularity of bedtime. It is important to detect problems in night sleep and establish treatments, as well as to provide support for early bedding on weekdays and for a regular bedtime both on weekdays and on weekends to prevent daytime sleepiness in school-aged children.

     

Differences in sleep and circadian preference between Eastern and Western German adolescents

Only a few studies focus on comparisons to reveal differences in sleep and circadian preferences in adolescents. This study used the same instrument to compare adolescents in Eastern and Western Germany. In all, 674 pupils between 11 and 16 yrs participated. The questionnaire asked questions about wake times and bed times (on weekend and weekdays), and the Composite Scale of Morningness (CSM) was completed to assess diurnal preferences. Locality (East/West Germany) had no effect on rise and bed times during the week and on sleep length on the weekend. Western pupils rose later on weekends and went to bed later on weekends. While sleep length on weekdays was shorter in West Germany, weekend oversleep was longer and misalignment was higher. Diurnal preferences (CSM scores) suggested a higher eveningness in West Germany. As interaction effects were insignificant, changes throughout adolescence seem similar in East and West Germany. These data suggest that given similar school start times, Western German pupils are at higher risk because they are later chronotypes. The finding of earlier rise and bed times of the East German pupils is consistent with the hypothesis that sunlight acts as the entrainment for the biological clock of adolescents, as sunrise is earlier in Eastern Germany.     

The Influence of Study Schedules and Work on the Sleep–Wake Cycle of College Students

College students usually exhibit an irregular sleep-wake cycle characterized by great phase delays on weekends and short sleep length on weekdays. As the temporal organization of social activities is an important synchronizer of human biological rhythms, we investigated the role played by study's schedules and work on the sleep-wake cycle. Three groups of female college students were investigated: (1) no-job morning group, (2) no-job evening group, (3) job evening group. The volunteers answered a sleep questionnaire in the classroom. The effects of day of the week and group on the sleep schedules and sleep length were analyzed by a two way ANOVA for repeated measures. The three groups showed delays in the wake up time on weekends. No-job evening and morning groups also delayed bedtime, but the job evening group slept at the same time on weekdays as on weekends. Sleep length increased on weekends for morning group and job evening group, whereas the no-job evening group maintained the amount of sleep from weekdays to weekends. This survey showed that the tendency of phase delay on weekends was differently expressed according to study's schedules and work.     

Associations between nocturnal urinary 6-sulfatoxymelatonin,obstructive sleep apnea severity and glycemic control in type 2 diabetes

Reduced nocturnal secretion of melatonin, a pineal hormone under circadian control, and obstructive sleep apnea have been both identified as risk factors for the development of type 2 diabetes mellitus. Whether they interact to impact glycemic control in patients with existing type 2 diabetes is not known. Therefore, this study explores the relationships between obstructive sleep apnea, melatonin and glycemic control in type 2 diabetes. As diabetic retinopathy may affect melatonin secretion, we also explore the relationship between retinopathy, melatonin and glycemic control. Fifty-six non-shift workers with type 2 diabetes, who were not using beta-blockers, participated. Most recent hemoglobin A1c (HbA1c) levels and the results of ophthalmologic examinations were obtained from medical records. Obstructive sleep apnea was diagnosed using an ambulatory device. Sleep duration and fragmentation were recorded by 7-day wrist actigraphy. The urinary 6-sulfatoxymelatonin/creatinine ratio, an indicator of nocturnal melatonin secretion, was measured in an overnight urine sample. Mediation analyses were applied to explore whether low nocturnal urinary 6-sulfatoxymelatonin/creatinine ratio could be a causal link between increasing obstructive sleep apnea severity [as measured by an Apnea Hypopnea Index (AHI)] and poorer glycemic control, and between the presence of retinopathy and glycemic control. AHI and HbA1c were log-scale (ln) transformed. Obstructive sleep apnea was found in 76.8%, and 25.5% had diabetic retinopathy. The median (interquartile range) of urinary 6-sulfatoxymelatonin/creatinine ratio was 12.3 (6.0, 20.1) ng/mg. Higher lnHbA1c significantly correlated with lower 6-sulfatoxymelatonin/creatinine ratio (p = 0.04) but was not directly associated with OSA severity. More severe obstructive sleep apnea (lnAHI, p = 0.01), longer diabetes duration (p = 0.02), retinopathy (p = 0.01) and insulin use (p = 0.03) correlated with lower urinary 6-sulfatoxymelatonin/creatinine ratio, while habitual sleep duration and fragmentation did not. A mediation analysis revealed that lnAHI negatively correlated with urinary 6-sulfatoxymelatonin/creatinine ratio (coefficient = ?2.413, p = 0.03), and urinary 6-sulfatoxymelatonin/creatinine negatively associated with lnHbA1c (coefficient = ?0.005, p = 0.02), after adjusting for covariates. Mediation analysis indicated that the effect of lnAHI on lnHbA1c was indirectly mediated by urinary 6-sulfatoxymelatonin/creatinine ratio (B = 0.013, 95% CI: 0.0006, 0.0505). In addition, having retinopathy was significantly associated with reduced nocturnal urinary 6-sulfatoxymelatonin/creatinine ratio, and an increase in HbA1c by 1.013% of its original value (B = ?0.013, 95% CI: ?0.038, ?0.005). In conclusion, the presence and severity of obstructive sleep apnea as well as the presence of diabetic retinopathy were associated with lower nocturnal melatonin secretion, with an indirect adverse effect on glycemic control. Intervention studies are needed to determine whether melatonin supplementation may be beneficial in type 2 diabetes patients with obstructive sleep apnea.     

Effect of exposure to evening light on sleep initiation in the elderly: A longitudinal analysis for repeated measurements in home settings

Epidemiologic data have demonstrated associations of sleep-onset insomnia with a variety of diseases, including depression, dementia, diabetes and cardiovascular diseases. Sleep initiation is controlled by the suprachiasmatic nucleus of the hypothalamus and endogenous melatonin, both of which are influenced by environmental light. Exposure to evening light is hypothesized to cause circadian phase delay and melatonin suppression before bedtime, resulting in circadian misalignment and sleep-onset insomnia; however, whether exposure to evening light disturbs sleep initiation in home settings remains unclear. In this longitudinal analysis of 192 elderly individuals (mean age: 69.9 years), we measured evening light exposure and sleep-onset latency for 4 days using a wrist actigraph incorporating a light meter and an accelerometer. Mixed-effect linear regression analysis for repeated measurements was used to evaluate the effect of evening light exposure on subsequent sleep-onset latency. The median intensity of evening light exposure and the median sleep-onset latency were 27.3?lux (interquartile range, 17.9–43.4) and 17?min (interquartile range, 7–33), respectively. Univariate models showed significant associations between sleep-onset latency and age, gender, daytime physical activity, in-bed time, day length and average intensity of evening and nighttime light exposures. In a multivariate model, log-transformed average intensity of evening light exposure was significantly associated with log-transformed sleep-onset latency independent of the former potential confounding factors (regression coefficient, 0.133; 95% CI, 0.020–0.247; p?=?0.021). Day length and nighttime light exposure were also significantly associated with log-transformed sleep-onset latency (p?=?0.001 and p?<?0.001, respectively). In conclusion, exposure to evening light in home setting prolongs subsequent sleep-onset latency in the elderly.