Variability In Sleep Patterns Research Articles

Sleep Variability, Eating Timing Variability, and Carotid Intima-Media Thickness in Early Adulthood.

Background Day-to-day variability in sleep patterns and eating timing may disrupt circadian rhythms and has been linked with various adverse cardiometabolic outcomes. However, the extent to which variability in sleep patterns and eating timing relate to atherosclerotic development in subclinical stages remains unclear. Methods and Results Generally healthy adults (N=62, 29.3±7.3 years, 66% female) completed 14 days of sleep and dietary assessments via wrist accelerometry and photo-assisted diet records, respectively. Variability in sleep duration, sleep onset, eating onset (time of first caloric consumption), eating offset (time of last caloric consumption), and caloric midpoint (time at which 50% of total daily calories are consumed) were operationalized as the SD across 14 days for each variable. Separate regression models evaluated the cross-sectional associations between sleep and eating variability metrics with end-diastolic carotid intima-media thickness (CIMT) measured via ultrasonography. Models adjusted for age, sex, systolic blood pressure, sleep duration, and total energy intake. Each 60-minute increase in sleep duration SD and sleep onset SD were associated with a 0.049±0.016 mm (P=0.003) and 0.048±0.017 mm (P=0.007) greater CIMT, respectively. Variability in eating onset and offset were not associated with CIMT; however, each 60-minute increase in caloric midpoint SD was associated with a 0.033±0.015 mm greater CIMT (P=0.029). Exploratory post hoc analyses suggested that sleep duration SD and sleep onset SD were stronger correlates of CIMT than caloric midpoint SD. Conclusions Variability in sleep patterns and eating timing are positively associated with clinically relevant increases in CIMT, a biomarker of subclinical atherosclerosis, in early adulthood.

Objective and Subjective Sleep Patterns in Adults With Maturity-Onset Diabetes of the Young (MODY).

To examine sleep patterns in adults with maturity-onset diabetes of the young (MODY). Adults with glucokinase (GCK)-MODY and transcription factor (TF)-related MODY (HNF1A, HNF1B, HNF4A) were recruited (n = 24; age 46.0 years, 79% women, BMI 24.7 kg/m2) from The University of Chicago's Monogenic Diabetes Registry. Sleep patterns were assessed by 2-week wrist actigraphy (total 315 nights), one night of a home sleep apnea test, and validated surveys. Overall, compared with established criteria, 29% of participants had sleep latency ≥15 min, 38% had sleep efficiency ≤85%, 46% had wake after sleep onset >40 min, all indicating poor objective sleep quality. Among all participants, 54% had a sleep duration below the recommended minimum of 7 h, 88% reported poor sleep quality, 58% had obstructive sleep apnea, and 71% reported insomnia. Compared with GCK-MODY, participants with TF-related MODY had poorer objective sleep quality and increased night-to-night variability in sleep patterns. Sleep disturbances appear to be common in adults with MODY despite absent traditional risk factors for sleep disorders. Future research investigating the sleep-diabetes relationship is warranted in this population.

Impact of Sleep Pattern of Pharmacy College Students on Academic Performance.

Disrupted sleep pattern has shown to impact the mental health of medical sciences students. However, few studies assessed its association with academic performance, and the impact of other factors among pharmacy students. This study aimed to evaluate the relationship between different sleep pattern variables and academic performance among pharmacy students. A cross-sectional study using self-administered survey was distributed to all students at a large pharmacy college in Saudi Arabia between November 2020 and February 2021. The students were asked to answer based on their sleep pattern and academic performance in the previous year of 2019-2020 (before COVID-19 lockdown). 237 students completed the survey. Shorter sleeps before exams and taking medicines for insomnia were significantly associated with failing at least one course (adjOR = 0.78 and 3.68; 95% CI 0.63-0.97 and 1.18-11.49; P = 0.028 and 0.025, respectively). As such, for each extra hour of sleep/night on exam nights, there is a 0.22 lower odd of failing at least one course, and taking insomnia medications increases the odds of failing by 3.68 times. Fewer sleep hours were significantly correlated with tiredness upon wake up, daytime sleepiness, and in-class sleepiness (r s = - 0.24, - 0.38, and - 0.24, respectively; P ≤ 0.001 for all correlations). Disrupted sleep pattern negatively impacted the academic performance of pharmacy students. Getting sufficient quality sleep, especially before exams, maybe associated with better performance and lower odds of course failures. College administrators should encourage good sleep hygiene to their students and pair the recommendations with evidence on the impact of sleep on academic performance.

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices.

Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability. The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling. More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism). Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Sleep variability over a 2-week period is associated with restfulness and intrinsic limbic network connectivity in adolescents.

Sleep duration and intraindividual variability in sleep duration undergo substantial changes in adolescence and impact brain and behavioral functioning. Although experimental work has linked acute sleep deprivation to heightened limbic responding and reduced regulatory control, there is limited understanding of how variability in sleep patterns might interact with sleep duration to influence adolescent functioning. This is important for optimal balancing of length and consistency of sleep. Here, we investigated how objective indices of sleep duration and variability relate to stress, restfulness, and intrinsic limbic network functioning in adolescents. A sample of 101 adolescents ages 14-18 reported their stressors, after which they wore wrist actigraph watches to monitor their sleep and rated their restfulness every morning over a 2-week period. They also completed a resting-state fMRI scan. Adolescents reporting more stress experienced shorter sleep duration and greater sleep variability over the 2-week period. Longer nightly sleep duration was linked to feeling more rested the next morning, but this effect was reduced in adolescents with high cumulative sleep variability. Sleep variability showed both linear and quadratic effects on limbic connectivity: adolescents with high sleep variability exhibited more connectivity within the limbic network and less connectivity between the limbic and frontoparietal networks than their peers, effects which became stronger once variability exceeded an hour. Results suggest that cumulative sleep variability is related to stress and limbic network connectivity and shows interactive effects with sleep duration, highlighting the importance of balancing length and consistency of sleep for optimal functioning in adolescence.

Chronicity of sleep restriction during Army basic military training

ObjectivesTo investigate: (i) the chronicity and phasic variability of sleep patterns and restriction in recruits during basic military training (BMT); and (ii) identify subjective sleep quality in young adult recruits prior to entry into BMT. DesignProspective observational study. MethodsSleep was monitored using wrist-worn actigraphy in Army recruits (n = 57, 18–43 y) throughout 12-weeks of BMT. The Pittsburgh Sleep Quality Index (PSQI) was completed in the first week of training to provide a subjective estimate of pre-BMT sleep patterns. A mixed-effects model was used to compare week-to-week and training phase (Orientation, Development, Field, Drill) differences for rates of sub-optimal sleep (6–7 h), sleep restriction (≤6 h), and actigraphy recorded sleep measures. ResultsSleep duration was 06:24 ± 00:18h (mean ± SD) during BMT with all recruits experiencing sub-optimal sleep and 42% (n = 24) were sleep restricted for ≥2 consecutive weeks. During Field, sleep duration (06:06 ± 00:36h) and efficiency (71 ± 6%; p < 0.01) were reduced by 15–18 min (minimum - maximum) and 7–8% respectively; whereas, sleep latency (30 ± 15 min), wake after sleep onset (121 ± 23 min), sleep fragmentation index (41 ± 4%) and average awakening length (6.5 ± 1.6 min) were greater than non-Field phases (p < 0.01) by 16–18 min, 28–33 min, 8–10% and 2.5–3 min respectively. Pre-BMT global PSQI score was 5 ± 3, sleep duration and efficiency were 7.4 ± 1.3 h and 88 ± 9% respectively. Sleep schedule was highly variable at pre-BMT (bedtime: 22:34 ± 7:46 h; wake time: 6:59 ± 1:42 h) unlike BMT (2200–0600 h). ConclusionsThe chronicity of sub-optimal sleep and sleep restriction is substantial during BMT and increased training demands exacerbate sleep disruption. Exploration of sleep strategies (e.g. napping, night-time routine) are required to mitigate sleep-associated performance detriments and maladaptive outcomes during BMT.

Abstract MP05: Night-to-night Variability In Sleep Duration Is Associated With Higher Energy Intake And Poorer Diet Quality In The Multi-Ethnic Study Of Atherosclerosis

Introduction: Variability in sleep patterns is an emerging risk factor for metabolic and cardiovascular diseases (CVD). Poor diet likely contributes to these relations, given the intricate link between sleep and diet, both of which are important determinants of cardiometabolic health. However, associations of irregular sleep with energy intake and consumption of a Mediterranean diet pattern, key markers of diet quantity and quality predictive of CVD risk, have not been evaluated. Hypothesis: Greater night-to-night variability in sleep duration will be associated with higher energy intake and poorer diet quality, defined as lower alternate Mediterranean (aMed) diet scores (range: 0-9). Methods: This was a cross-sectional analysis of 2,007 men and women (age: 68.6±9.2 y, 61% racial/ethnic minority) from the Multi-Ethnic Study of Atherosclerosis Sleep Ancillary Study. The standard deviation (SD) of sleep duration, derived from 1-wk of wrist actigraphy, was used as a measure of sleep duration variability. Diet, including daily energy intake, aMed score, and consumption of aMed components, was ascertained from a food frequency questionnaire. Multivariable linear regression models adjusted for age, race/ethnicity, sex, study site, marital status, and education, evaluated associations of sleep duration variability with diet. Sensitivity analyses, also adjusted for sociodemographic factors, were conducted to evaluate relations of weekday sleep duration SD, a potentially more representative measure of habitual sleep, with diet. Results: Mean sleep duration variability was 84±48 min. Greater sleep duration variability was associated with higher energy intake and lower aMed score; each 30-min increase in sleep duration SD related to 23±11 more kcal/d (P=0.04) and 0.06±0.02 lower aMed score (P=0.02). Higher sleep duration SD (per 30 min increase) was also associated with lower intakes of whole grains (β=-0.01±0.01, P&lt;0.05), nuts/seeds (β=-0.01±0.01, P=0.02), and monounsaturated to saturated fat ratio (β=-0.02±0.01, P=0.02). Results of sensitivity analyses showed that each 30-min increase in weekday sleep duration SD was associated with 28±11 kcal higher energy intake (P=0.01) and 0.06±0.02 lower aMed score (P=0.02) as well as 0.02±0.01 and 0.01±0.01 fewer servings (per 1000 kcal) of whole grains and seeds/nuts, respectively (P&lt;0.01). Conclusions: We provide novel initial evidence that irregular sleep duration, particularly during weekdays, may contribute to overconsumption and poor diet quality. Further investigation into a possible mechanistic role of diet in the association of sleep variability with cardiometabolic disease risk is warranted. Longitudinal or experimental data are needed to elucidate a potential complex interplay between sleep duration variability, diet, and other health and lifestyle factors, including physical activity, obesity, and depression.

193 Sleep Variability and Affect Dynamics Among College Students during COVID-19 Pandemic

IntroductionSleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently mood-related disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic.MethodsCollege student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state.ResultsMultilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p<.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps< .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity).ConclusionSleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities.Support (if any):

SLEEP PATTERNS AND CARDIOVASCULAR HEALTH IN CHINESE CHILDREN AND ADOLESCENTS

Objective: Despite accumulating evidence showing that sleep patterns are correlated with cardiometabolic risks, their relations to cardiovascular health (CVH) in children and adolescents remains inconclusive. Therefore, we aimed to investigate the associations of sleep duration and bedtime with CVH among Chinese children and adolescents. Design and method: Data were obtained from a population-based cross-sectional study, with 7,220 Chinese children and adolescents aged 6–18 years were included for analysis. The scores and levels of behavioral (smoking, body mass index, physical activity, and diet), biological (total cholesterol, blood pressure, and glucose), and global CVH were evaluated according to the American Heart Association's recommendation. Information on sleep duration and timing were collected by questionnaires. Subjects with sufficient sleep duration (&gt; = 9 hours in individuals aged 6–12y or &gt;8 hours in those over 13y) and early bedtime (22:00 before) were defined as having optimal sleep patterns. Generalized additive models and logistic regressions were performed to estimate the relationship between CVH and sleep patterns. Results: For boys, positive dose-response relations were found between sleep duration and all three CVH scores, but negative associations were observed for bedtime. Compared with boys with optimal sleep patterns, those with insufficient sleep duration and late bedtime had lower odds of ideal biological CVH (OR = 0.680, 95% CI: 0.525–0.881, p = 0.004) and ideal global CVH (OR = 0.681, 95% CI: 0.525–0.883, p = 0.004). Though similar results were also showed in girls, no statistically significant association was noted between sleep pattern variables (duration and timing) and any CVH scores or levels. Conclusions: Sufficient sleep duration and early bedtime are associated with desirable cardiovascular health in children and adolescents, especially for boys. However, causal interpretation cannot be made and associations might be bidirectional.

Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions.

Rapidly accumulating data from mobile assessments are facilitating our ability to track patterns of emotions, behaviors, biologic rhythms, and their contextual influences in real time. These approaches have been widely applied to study the core features, traits, changes in states, and the impact of treatments in bipolar disorder (BD). This paper reviews recent evidence on the application of both passive and active mobile technologies to gain insight into the role of the circadian system and patterns of sleep and motor activity in people with BD. Findings of more than two dozen studies converge in demonstrating a broad range of sleep disturbances, particularly longer duration and variability of sleep patterns, lower average and greater variability of motor activity, and a shift to later peak activity and sleep midpoint, indicative of greater evening orientation among people with BD. The strong associations across the domains tapped by real-time monitoring suggest that future research should shift focus on sleep, physical/motor activity, or circadian patterns to identify common biologic pathways that influence their interrelations. The development of novel data-driven functional analytic tools has enabled the derivation of individualized multilevel dynamic representations of rhythms of multiple homeostatic regulatory systems. These multimodal tools can inform clinical research through identifying heterogeneity of the manifestations of BD and provide more objective indices of treatment response in real-world settings. Collaborative efforts with common protocols for the application of multimodal sensor technology will facilitate our ability to gain deeper insight into mechanisms and multisystem dynamics, as well as environmental, physiologic, and genetic correlates of BD.

Sleep Regularity and Cardiometabolic Heath: Is Variability in Sleep Patterns a Risk Factor for Excess Adiposity and Glycemic Dysregulation?

Night-to-night variability in sleep patterns leads to circadian disruption and, consequently, could increase cardiometabolic risk. The purpose of this review is to summarize findings from studies published between 2015 and 2020 examining various measures of night-to-night variability in sleep in relation to metabolic syndrome (MetS), type 2 diabetes (T2D), and their risk factors. We illustrate a potential causal pathway between irregular sleep patterns and T2D, highlighting knowledge gaps along the way. Across different measures of sleep variability, irregular sleep patterns were associated with poorer cardiometabolic outcomes. Higher standard deviations (SD) across nights of sleep duration and onset or midpoint of sleep were associated with increased odds of having MetS and clusters of metabolic abnormalities as well as greater adiposity and poorer glycemic control. Conversely, greater regularity of rest-activity patterns related to lower risk for T2D. Social jetlag was associated with glycemic dysregulation, adiposity, T2D, and MetS. These associations are often observed in both metabolically healthy and unhealthy individuals; both higher SD of sleep duration and social jetlag relate to poorer glucose regulation in individuals with diabetes. There is consistent evidence of associations of sleep variability with increased risk for adiposity, glucose dysregulation, T2D, and MetS. Although experimental evidence is needed to determine causation, there is support to recommend stabilizing sleep patterns for cardiometabolic risk prevention.

Variability in Sleep Patterns: an Emerging Risk Factor for Hypertension.

In this review, we summarize recent epidemiological data (2014-2019) that examine the association of sleep variability with blood pressure (BP), discuss potential underlying mechanisms, and highlight future research directions. Higher standard deviations of sleep duration and sleep-onset timing were not related to BP. However, a higher Sleep Regularity Index score was associated with lower odds of hypertension. Studies on social jetlag, a prevalent form of sleep variability, reported null associations. In contrast, lower interdaily stability in circadian rest-activity rhythms, a measure of invariability in sleep-wake cycles between days and synchronization to light and dark cycles, was associated with higher BP and greater hypertension odds, particularly among non-shift workers. Sleep variability is consistently associated with risk factors for hypertension. Evidence on sleep variability and BP is limited and varies depending on the measure used to characterize day-to-day variability in sleep. Studies that identify and utilize a standard definition of sleep variability, incorporate a 24-h ambulatory BP monitoring, and ensure coinciding timing of sleep and BP measurements are necessary to disentangle these relationships.

Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach.

BackgroundThe current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.PurposeTo investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters.Participants and methodsWe used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses.ResultsOur experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant.ConclusionPersonalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.

Adolescent sleep patterns are associated with the selection of positive and negative emotional situations

Poor sleep in youth is a risk factor for experiencing increased negative emotions and decreased positive emotions, which can contribute to the development of later emotional disorders. Understanding of specific processes that produce sleep-related alterations in emotion is limited, although preliminary studies suggest changes in the ability to appropriately regulate or control emotions as one mechanism. The current study builds on this research by examining the relationship between adolescent sleep patterns and a previously unexplored emotion regulation strategy: situation selection. Situation selection strategies are implemented prior to the onset of an emotional experience via decisions to approach rewarding/positive situations or avoid unwanted/negative situations. Fifty-four healthy adolescents (ages 13-17 years) completed 1 week of actigraphy and assessments of situation selection using: (a) trait-based questionnaires; (b) daily reports; and (c) an experimental lab task where participants were given the option to watch various emotional video clips of their choice. Greater variability in sleep timing was associated with less avoidance of negative emotional situations, and a longer sleep-onset latency was associated with more avoidance of negative emotional situations. Greater variability in nightly sleep patterns was also associated with decreased tendencies to select positive emotional situations as assessed by trait questionnaires, daily reports, and the lab-based task, but only for boys. These findings add to a growing body of research on sleep and emotional experience, and provide further support for the importance of intra-individual variability of sleep patterns in youth.

Relationships between Sleep Patterns, Health Risk Behaviors, and Health Outcomes among School-Based Population of Adolescents: A Panel Analysis of the Korean Children and Youth Panel Survey

Sleep patterns among adolescents are related to health outcomes and health risk behaviors. This study aimed to describe sleep patterns of Korean adolescents and to find the association between sleep patterns and health risk behaviors and health outcomes. Using the junior high school students’ panel data (n = 2351, 12–15 years old) from the Korean Children and Youth Panel Survey, this study described the sleep patterns operationalized as rising time, bedtime, and sleep duration both on weekdays and weekends. The relationships of sleep patterns with health outcomes and/or health risk behaviors were tested using mixed effect linear regression for continuous health variables and using mixed effect logit regression for binary health variables. Obesity status, the number of chronic symptoms, self-rated health status, smoking, and alcohol consumption were associated with rising time on weekdays after controlling for gender, living area, and housing type. The same set of variables except for the number of chronic symptoms were associated with bedtime during the weekdays. Sleep duration during the weekdays was associated with obesity status, smoking, and alcohol consumption. Similar patterns of association between sleep pattern variables during the weekends and health-related outcome variables were found, but were less obvious than those for weekdays. Significant relationships between sleep patterns and various health-related variables were found among adolescents in Korea. The results from this study indicate that helping adolescents change their sleeping times as necessary to ensure adequate sleep should be considered important in diminishing health risk behaviors and promoting positive health outcomes.

Sleep Patterns in Chinese Preschool Children: A Population-Based Study.

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. 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). 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). 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.

P232 Attention deficit hyperactivity disorder in children; clinical and polysomnographic study

Objectives Attention deficit hyperactivity disorder (ADHD) is a common pediatric neurodevelopmental disorder. The core symptoms include a functionally significant level of inattentiveness and distractibility. The aim of the present study is to detect the abnormalities in clinical andpolysomnography (PSG) studies in children with ADHD. It aims at finding the relation between ADHD symptoms severity and results of sleep studies, and exploring the correlations between them. Methods The study included 40 ADHD patients and 40 healthy control children. All patients and controls were subjected to history taking, clinical examination, psychaitric assessment, and then to electrophysiological studies including overnight long-term electroencephalography (EEG) and PSG. Results: EEG results showed increased theta activity, central sharp waves and generalized spikes, sharp and slow waves. PSG studies revealed statistically significant increased nocturnal movements and greater night-to-night variability in sleep patterns among children with ADHD, with otherwise inconsistent differences in sleep architecture between children with and without ADHD. Discussion: Some of our results came in agreement with other simulating researches, however, video monitoring may be used in future studies to detect any subtle events. Conclusion ADHD is frequently associated with sleep-disordered breathing and limb movement disorder. Significance EEG abnormalities and sleep abnormalities can play a role in the etiology or even accentuation of ADHD. Aiming at their treatment can ameliorate the symptoms of the disease.

Examining the Variability of Sleep Patterns during Treatment for Chronic Insomnia: Application of a Location-Scale Mixed Model.

The purpose of this study was to introduce a novel statistical technique called the location-scale mixed model that can be used to analyze the mean level and intra-individual variability (IIV) using longitudinal sleep data. We applied the location-scale mixed model to examine changes from baseline in sleep efficiency on data collected from 54 participants with chronic insomnia who were randomized to an 8-week Mindfulness-Based Stress Reduction (MBSR; n = 19), an 8-week Mindfulness-Based Therapy for Insomnia (MBTI; n = 19), or an 8-week self-monitoring control (SM; n = 16). Sleep efficiency was derived from daily sleep diaries collected at baseline (days 1-7), early treatment (days 8-21), late treatment (days 22-63), and post week (days 64-70). The behavioral components (sleep restriction, stimulus control) were delivered during late treatment in MBTI. For MBSR and MBTI, the pre-to-post change in mean levels of sleep efficiency were significantly larger than the change in mean levels for the SM control, but the change in IIV was not significantly different. During early and late treatment, MBSR showed a larger increase in mean levels of sleep efficiency and a larger decrease in IIV relative to the SM control. At late treatment, MBTI had a larger increase in the mean level of sleep efficiency compared to SM, but the IIV was not significantly different. The location-scale mixed model provides a two-dimensional analysis on the mean and IIV using longitudinal sleep diary data with the potential to reveal insights into treatment mechanisms and outcomes.

Effects Of Sleep-wake Patterns On Field Testing In Female High School Field Hockey Players

Sleep times and patterns are related to sport performance. Research has determined sleep patterns contribute to differences in laboratory maximal effort treadmill tests, but little research has been done on maximal effort testing in the field. Further, research is lacking on whether sprint and acceleration times are correlated to sleep patterns. PURPOSE: The purpose of this study is to determine if a relationship exists between field performance testing and sleep patterns in female high school field hockey players. METHODS: Within the first week of pre-season training female high school field hockey players (n=15, age=15.47±1.06) completed a demographic survey, Pittsburg Sleepiness Quality Index (PSQI) scale, and a seven day sleep-wake journal using an online survey platform (Qualtrics, Provo, UT). Sleep latency, overall sleep quality, and duration of sleep were three PSQI measures chosen for analysis. The onset of sleep time and concentration were two measures used from the sleep-wake journal for analysis. A beep test and six 40m sprints with 10m splits were conducted within the first week of pre-season training, with 48 hours between each test. Beep test was a 20m multistage test that involved running in accordance to a pre-recording of beeps. Field tests were conducted on a level grass field under research supervision. An average of six 40m sprints and 10m splits were taken for analysis. RESULTS: PSQI revealed no subjects suffered from sleep disorders. A Pearson correlation was conducted between performance and sleep pattern variables. A positive strong correlation exists between 40m average sprint time and 10m average sprint time (r= 0.896, p≤0.05). A negative correlation exists between onset of sleep and total sleep time (r= -0.598, p≤0.01). There were no correlations between performance and sleep pattern variables. CONCLUSIONS: These results indicate maximal effort field testing does not correlate with sleep patterns in female high school field hockey players. Supported by a Research and Faculty Development grant.

What Role Does Sleep Play in Weight Gain in the First Semester of University?

We hypothesized that shorter sleep durations and greater variability in sleep patterns are associated with weight gain in the first semester of university. Students (N = 132) completed daily sleep diaries for 9 weeks, completed the MEQ (chronotype) and CES-D (depressed mood) at week 9, and self-reported weight/height (weeks 1 & 9). Mean and variability scores were calculated for sleep duration (TST, TSTv), bedtime (BT, BTv), and wake time (WT, WTv). An initial hierarchical regression evaluated (block 1) sex, ethnicity; (block 2) depressed mood, chronotype; (block 3) TST; (block 4) BT, WT; and (block 5; R2 change = 0.09, p = 0.005) TSTv, BTv, WTv with weight change. A sex-by-TSTv interaction was found. A final model showed that ethnicity, TST, TSTv, and BTv accounted for 31% of the variance in weight change for males; TSTv was the most significant contributor (R2 change = 0.21, p < 0.001). Daily variability in sleep duration contributes to males' weight gain. Further investigation needs to examine sex-specific outcomes for sleep and weight.