Midpoint Of Sleep Research Articles

Association Between Chrononutrition Patterns and Multidimensional Sleep Health

Background/Objectives: Sleep health has been associated with diet quality, but the relationship between chrononutrition patterns and multidimensional sleep health is unclear. This study identifies chrononutrition patterns among U.S. adults and examines their associations with multidimensional sleep health. Methods: This cross-sectional analysis used data from the 2017–2020 National Health and Nutrition Examination Survey. Chrononutrition behaviors were assessed using two 24 h dietary recalls. Latent profile analysis was used to identify chrononutrition profiles. Multivariable survey regression models determined the associations between chrononutrition patterns and sleep health dimensions. Results: The sample included 5228 subjects with a median age of 49 years. Of the sample, 52% of the participants were female, and 65% were White. In adjusted models, each additional hour between wake time and first instance of eating was associated with a 19% increase in the odds of poor timing (sleep midpoint < 2:00 a.m. or >4:00 a.m.; 95% CI: 1.07–1.33) and a 21% increase in poor duration (<7 or >9 h/night; 95% CI: 1.09–1.33). Each additional hour between last eating and bedtime was associated with 9% higher odds of poor duration (95% CI: 1.03–1.16). A one-hour longer eating window was associated with 10% lower odds of poor timing (95% CI: 0.84–0.98). We identified five chrononutrition profiles: Typical Eating (reference), Early Finished Eating, Later Heavy Eating, Extended Window Eating, and Restricted Window Eating. The Later Heavy Eating profile exhibited 96% higher odds of poor timing (95% CI: 1.09–3.51) and the Restricted Window Eating profile had 94% higher odds of poor duration (95% CI: 1.10–3.43). Conclusions: These findings highlight the importance of unique chrononutrition patterns in relation to multidimensional sleep health. We provide a framework for future studies to identify personalized chrononutrition interventions and their role in improving sleep health.

Sleep timing and the prevalence of hypertension in middle-aged and older populations: the sleep heart health study

ObjectivesSleep characteristics such as duration, continuity, and irregularity are associated with the risk of hypertension. This study aimed to investigate the association between sleep timing (including bedtime, wake-up time, and sleep midpoint) and the prevalence of hypertension.MethodsParticipants were selected from the Sleep Heart Health Study (n = 5504). Bedtime and wake-up times were assessed using sleep habit questionnaires. The sleep midpoint was calculated as the halfway point between the bedtime and wake-up time. Restricted cubic splines and logistic regression analyses were performed to explore the association between sleep timing and hypertension.ResultsA significant nonlinear association was observed between bedtime (Poverall<0.001; Pnonlinear<0.001), wake-up time (Poverall=0.024; Pnonlinear=0.076), sleep midpoint (Poverall=0.002; Pnonlinear=0.005), and the prevalence of hypertension after adjusting for potential confounders. Multivariable logistic regression showed that both late (> 12:00AM and 23:01PM to 12:00AM) and early (≤ 22:00PM) bedtimes were associated with an increased risk of hypertension compared to bedtimes between 22:01PM and 23:00PM. In addition, individuals with late (> 7:00AM) and early (≤ 5:00AM) wake-up times had a higher prevalence of hypertension than those with wake-up times ranging between 5:01AM and 6:00AM. Delaying the sleep midpoint (> 3:00AM) was also associated with an increased risk of hypertension. Furthermore, no significant interaction effect was found in the subgroup analyses stratified by age, sex, or apnea-hypopnea index.ConclusionsOur findings identified a nonlinear association between sleep timing and hypertension. Individuals with both early and late sleep timing had a high prevalence of hypertension.

Association between chronotype, social jetlag, sleep quality, and academic burnout among nursing students: A cross-sectional study

ABSTRACT Academic burnout in nursing students is a serious problem worldwide that results in emotional exhaustion, cynicism and low professional efficacy. The aim of the current study was to examine the association between sleep quality and circadian rhythm indicators and academic burnout among Iranian nursing students. This cross-sectional study was conducted on 325 undergraduate nursing students studying at Qazvin University of Medical Sciences between November and February 2023. Data collection was carried out using self-report questionnaires including demographic characteristics, The Pittsburgh Sleep Quality Index (PSQI), reduced-Morningness-Eveningness Questionnaire (r-MEQ), and the Maslach Burnout Inventory-Student Survey (MBI-SS). Social jetlag (SJL) was defined as the absolute value of the difference between the midpoint of sleep on free days (MSF) and the midpoint of sleep on workdays (MSW). Data were analyzed using univariate and multivariate linear regression models. About 31.4% of participants had evening chronotype and almost half of them suffered from poor sleep quality. About 47.7% of the participants had SJL ≥ 2 h. In the multivariate linear regression model, students with evening chronotype compared to morning chronotype experienced more academic burnout in emotional exhaustion, cynicism and professional efficacy. PSQI score was only correlated with emotional exhaustion and cynicism subscales. Increased SJL was a predictor on all three subscales of academic burnout. The findings suggested that evening chronotype, poor sleep quality, and high SJL were associated with increased likelihood of academic burnout in nursing students. Assessment of circadian preference and sleep quality is beneficial for timely identification and prevention of academic burnout in nursing students.

Are Adolescent Athletes Sleeping Enough? An Observational Study of Sleep Parameters during Schooldays and Holidays.

Background: Inconsistent sleep schedules, frequent awakening after sleep onset (WASO), and decreased sleep efficiency (SE) are common issues among adolescent team sports athletes. Moreover, research indicates that sleep problems are enhanced across schooldays. The aim of the present study was to assess sleep patterns of adolescent athletes and compare sleep parameters between schooldays and holidays. Methods: The chronotype and sleep quality of twelve adolescent basketball players (mean age: 15.58 ± 0.67 years) were assessed. Objective sleep parameters were then analyzed using actigraphy over a 12-day period, which included six days during the school period and six days during holidays. Results: Data showed that total sleep time (TST), SE, and WASO (382.48 min, 81.81%, and 66.70 min, respectively) did not meet international recommendations for sleep quantity and quality. During school weekdays, time in bed (TIB), TST, and SE significantly decreased compared to weekends (p < 0.001, d = -1.49; p < 0.001, d = -1.64; and p = 0.01, d = -0.89, respectively). On weekdays, TIB, TST, and WASO were significantly lower on schooldays compared to holidays (p < 0.001, d = -1.83; p < 0.01, d = -1.01; and p = 0.02, d = -0.77, respectively). While no significant difference was observed in social jetlag, the mid-point of sleep was significantly later on holiday weekdays compared to school weekdays (p < 0.05, d = 0.65). Conclusions: Adolescent athletes experience insufficient sleep, especially on school weekdays, which is partially improved during weekends and holidays. Although sleep duration was longer during holidays, our results suggest that adolescent athletes' sleep was more fragmented. Consequently, it remains crucial to implement strategies to enhance their sleep health (e.g., napping).

Combined effects of sleep timing and nighttime sleep duration on non-alcoholic fatty liver disease

BackgroundWhile short sleep duration is linked to higher risk of non-alcoholic fatty liver disease (NAFLD), the combined effects of sleep timing and sleep duration on NAFLD are less explored. MethodsIn this cross-sectional study of 39,471 participants from Beijing-Tianjin-Hebei region of China, self-reported sleep information and ultrasonography-diagnosed NAFLD were obtained from Jan 2018 to Jan 2020. Sleep timing was categorized based on sleep midpoint: early-type (before 2:00 AM), intermediate-type (2:00–2:30 AM), and late-type (after 2:30 AM). We used multivariable logistic regression to explore the relationship between sleep timing, duration, and NAFLD. We analyzed sleep midpoint and duration categorically and continuously, and conducted stratification analyses by age, sex, body mass index, hypertension, diabetes, and dyslipidemia. ResultsIntermediate-type (OR: 1.15, 95% confidence interval: 1.05–1.26) and late-type sleep timing (OR: 1.08, 1.00–1.16) were associated with higher NAFLD risk compared to early-type. Additionally, longer sleep duration was linked to lower risk (OR: 0.92, 0.90–0.95 per hour increase). Notably, intermediate to late-type sleepers with normal sleep duration (7 to <8 h) exhibited a 20% higher NAFLD risk compared to early-type sleepers with the same duration (OR: 1.20, 1.04–1.39). The increased NAFLD risk associated with intermediate to late sleep timing was particularly evident in men, hypertension, and prediabetes or diabetes participants. ConclusionsIntermediate to late sleep timing, even with normal sleep duration, is associated with increased NAFLD risk. These findings underscore the importance of considering both sleep timing and sleep duration for NAFLD prevention, especially in men and individuals with cardiometabolic conditions.

Bipolar disorder is characterized by chronotype instability: A longitudinal investigation of circadian typology and mood

BackgroundChronotype is associated with circadian rhythmicity, a core etiological factor underlying bipolar disorder (BD). Given converging evidence linking late chronotype with poor mental health, the goal of the present study was to examine chronotype (in)stability and its relation to mood symptoms over time. MethodsParticipants with BD I (n = 271), BD II (n = 88), and healthy controls (n = 217) were included (follow-upM=10 years, Range=5–15) from the Prechter Longitudinal Study. Chronotype category and midpoint of sleep, corrected for weekend sleep-debt (MSFsc), were measured with the Munich Chronotype Questionnaire administered every 12 months alongside clinician-rated mood and medication usage. Self-reported mood was measured bi-monthly. Mixed effects models tested whether mood was associated with (in)stability of chronotype category and MSFsc covarying for age, sex, age, and medication. ResultsCompared to HC, individuals with BD self-reported having a later chronotype that significantly fluctuated over time. Individuals with BDI showed significantly less stability in MSFsc than HC. Anticonvulsant use was associated with more stability in MSFsc whereas antidepressant use was associated with less stability in MSFsc. ConclusionsIn a large longitudinal cohort, individuals with BD displayed significant instability in circadian typology. Psychopharmacology in BD may have differential impacts on circadian timing that is important to monitor.

Chrononutrition, eating behaviour, and metabolic health among obese patients elected for bariatric surgery

ABSTRACT There is evidence of the impact of chrononutrition on weight loss and metabolic control. However, the precise chrononutrition behaviours that promote these benefits are not fully described, and there are doubts if chrononutrition may be related to other eating behaviour features. The main aim was to evaluate the associations between chrononutrition and eating behaviour, and their relationships with anthropometric and biochemical parameters among obese patients elected for bariatric surgery. Eighty participants (76.3% females, mean age = 45 years, mean BMI = 41.6 kg/m2) attending bariatric surgery consultations at Centro Hospitalar Universitário de São João (Porto, Portugal) were assessed regarding chrononutrition (Chrononutrition Profile – Questionnaire) and eating behaviour (Three-Factor Eating Questionnaire – R21 and General Eating Self-Efficacy Scale). Height, weight, waist circumference, and biochemical values (total, HDL and LDL cholesterol, triglycerides and glycated haemoglobin) were collected. Eating window midpoint was positively correlated with uncontrolled eating and negatively with eating self-efficacy. Sleep duration and midpoint on free days negatively correlated with eating self-efficacy, mainly due to later waking times, supporting that predominantly later energy consumption may negatively impact eating behaviour.

The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder

BackgroundChanges in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity. MethodsData were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393). ResultsIncreased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %. LimitationsLimitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected. ConclusionOur study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions.

Nonrestorative sleep and its association with insomnia severity, sleep debt, and social jetlag in adults: Variations in relevant factors among age groups

ObjectiveNonrestorative sleep (NRS) is unrefreshing sleep that is harmful to mental and physical health. Although few studies have reported the relationship between NRS and sleep problems, none have evaluated this relationship by age group. This study aimed to determine whether NRS could be associated with insomnia severity, sleep debt, and social jetlag and whether this relationship would vary with age. MethodsThe 1592 participants were allocated into age groups: 18–25 years (n = 317), 26–40 years (n = 405), 41–65 years (n = 454), and >65 years (n = 416). The study used Restorative Sleep Questionnaire to assess NRS, Athens Insomnia Scale to assess insomnia severity, Sleep Debt Index to assess sleep debt, and the absolute difference in midpoints of sleep between weekdays and days off to assess social jetlag. ResultsLogistic regression analysis was performed to determine the association between NRS and sleep problems. In the 18–25-year group, insomnia severity, sleep debt of ≥2 h, and social jetlag of ≥2 h; in the 26–40-year group, insomnia severity and sleep debt of ≥2 h; in the 41–65-year group, insomnia severity and sleep debt of ≥2 h; and in the >65-year group, insomnia severity were significantly associated with NRS. ConclusionNRS was associated with insomnia severity in all age groups, with sleep debt in the young and working-age groups and social jetlag in the young age group. Different approaches to improving the NRS are required for different age groups.

Sleep–wake changes and incident depressive symptoms in midlife women

Our study aimed to investigate the relationship between sleep–wake changes and depressive symptoms events among midlife women. We enrolled 1579 women aged 44–56 years who had no clinically relevant depressive symptoms at baseline. Depressive symptoms were assessed at each visit using the Center for Epidemiologic Studies Depression scale. At the third and fourth follow-up visits, women reported their sleep habits. The sleep midpoint was defined as the time to fall asleep plus one-half of the sleep duration. Sleep–wake changes were determined by the difference in the midpoint of sleep between the third and fourth visits, which were 1 year apart. The median follow-up time was 7 years (range 1–7 years). Cox proportional hazard models were fitted to calculate hazard ratios and 95% confidence intervals for the incidence of depressive symptoms associated with sleep–wake changes. After adjusting for potential confounding factors, the hazard ratio (95% confidence interval) of depressive symptoms for severe sleep midpoint changes was 1.51 (1.12, 2.05) compared with mild sleep midpoint changes. This relationship remained statistically significant and changed little when additionally controlling for sleep duration, sleep quality, insomnia symptoms, use of sleep medications, use of nervous medications, glucose, insulin, lipids, dietary energy intake, and C-reactive protein. Our findings indicate that exposure to long-term severe sleep–wake changes increases the risk of depressive symptoms in midlife women.

Device-estimated sleep metrics do not mediate the relation between race and blood pressure dipping in young black and white women.

Short, disturbed, and irregular sleep may contribute to blunted nocturnal blood pressure (BP) dipping, a predictor of cardiovascular disease. Black women (BLW) demonstrate less BP dipping and poorer sleep health than White women (WHW). However, it remains unclear whether device-estimated sleep health metrics mediate the relation between race and BP dipping in young women. We hypothesized that the relation between race and BP dipping would be partly mediated by sleep health metrics of sleep duration, sleep efficiency, and sleep regularity. Participants (20 BLW, 17 WHW) were 18-29 years old, normotensive, nonobese, and without evidence of sleep disorders. Systolic and diastolic BP dipping were derived from 24-h ambulatory BP monitoring. Habitual sleep duration and sleep efficiency were estimated via 14 days of wrist actigraphy. Sleep duration regularity was calculated as the standard deviation (SD) of nightly sleep duration (SDSD). Sleep timing regularity metrics were calculated as the SD of sleep onset and sleep midpoint (SMSD). Mediation analysis tested the mediating effect of each sleep metric on the relation between race and BP dipping. BLW experienced less systolic (P=.02) and diastolic (P=.01) BP dipping. Sleep duration (P=.14) was not different between groups. BLW had lower sleep efficiency (P<.01) and higher SDSD (P=.02), sleep onset SD (P<.01) and SMSD (P=.01). No sleep metrics mediated the relation between race and BP dipping (all indirect effects P>.38). In conclusion, mediation pathways of sleep health metrics do not explain racial differences in nocturnal BP dipping between young BLW and WHW.

Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities.

Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the "Staff Hours" app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data's nested structure. Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P<.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P<.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night's midpoint of sleep (β=-0.263, P<.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA's activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies.

Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial.

Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention). This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2). Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization). In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001). This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study.

This study aimed to investigate the relationships between adiposity and circadian rhythm and compare the measurement of circadian rhythm using both actigraphy and a smartphone app that tracks human-smartphone interactions. We hypothesized that the app-based measurement may provide more comprehensive information, including light-sensitive melatonin secretion and social rhythm, and have stronger correlations with adiposity indicators. We enrolled a total of 78 participants (mean age 41.5, SD 9.9 years; 46/78, 59% women) from both an obesity outpatient clinic and a workplace health promotion program. All participants (n=29 with obesity, n=16 overweight, and n=33 controls) were required to wear a wrist actigraphy device and install the Rhythm app for a minimum of 4 weeks, contributing to a total of 2182 person-days of data collection. The Rhythm app estimates sleep and circadian rhythm indicators by tracking human-smartphone interactions, which correspond to actigraphy. We examined the correlations between adiposity indices and sleep and circadian rhythm indicators, including sleep time, chronotype, and regularity of circadian rhythm, while controlling for physical activity level, age, and gender. Sleep onset and wake time measurements did not differ significantly between the app and actigraphy; however, wake after sleep onset was longer (13.5, SD 19.5 minutes) with the app, resulting in a longer actigraphy-measured total sleep time (TST) of 20.2 (SD 66.7) minutes. The obesity group had a significantly longer TST with both methods. App-measured circadian rhythm indicators were significantly lower than their actigraphy-measured counterparts. The obesity group had significantly lower interdaily stability (IS) than the control group with both methods. The multivariable-adjusted model revealed a negative correlation between BMI and app-measured IS (P=.007). Body fat percentage (BF%) and visceral adipose tissue area (VAT) showed significant correlations with both app-measured IS and actigraphy-measured IS. The app-measured midpoint of sleep showed a positive correlation with both BF% and VAT. Actigraphy-measured TST exhibited a positive correlation with BMI, VAT, and BF%, while no significant correlation was found between app-measured TST and either BMI, VAT, or BF%. Our findings suggest that IS is strongly correlated with various adiposity indicators. Further exploration of the role of circadian rhythm, particularly measured through human-smartphone interactions, in obesity prevention could be warranted.

Sleep Problems and Health Outcomes Among Urban American Indian and Alaska Native Adolescents

Adolescent sleep problems are prevalent, particularly among racial and ethnic minority groups, and can increase morbidity. Despite the numerous strengths of their racial and ethnic group, urban American Indian and Alaska Native adolescents face significant health disparities but are rarely included in health research. Understanding how sleep problems are associated with health outcomes among American Indian and Alaska Native adolescents may elucidate novel targets for interventions to promote health equity. To assess whether baseline sleep problems are associated with changes in behavioral and cardiometabolic health outcomes among urban American Indian and Alaska Native adolescents 2 years later. American Indian and Alaska Native adolescents were recruited via flyers and community events for an observational cohort study in California. Baseline assessments were conducted among 142 adolescents from March 1, 2018, to March 31, 2020, and follow-ups were conducted among 114 adolescents from December 1, 2020, to June 30, 2022. Baseline actigraphy-assessed sleep duration and efficiency and self-reported sleep disturbances and social jet lag (absolute value of the difference in sleep midpoint on weekends vs weekdays; indicator of circadian misalignment). Main outcome measures included self-reported depression (measured using the Patient Health Questionnaire), anxiety (measured using the Generalized Anxiety Disorder 7-item scale), past year alcohol and cannabis use, body mass index, systolic blood pressure (SBP) and diastolic blood pressure (DBP), waist circumference, and glycosylated hemoglobin (HbA1c). Analyses examined whether baseline sleep was associated with health outcomes at follow-up, controlling for age, sex, and baseline outcome measures. The baseline sample included 142 urban American Indian and Alaska Native adolescents (mean [SD] age, 14.0 [1.4] years; 84 girls [59%]), 80% of whom (n = 114; mean [SD] age, 14.1 [1.3] years; 71 girls [62%]) completed follow-ups. Linear or logistic regressions showed significant negative associations between shorter sleep duration and depression (β = -1.21 [95% CI, -2.19 to -0.24]), anxiety (β = -0.89 [95% CI, -1.76 to -0.03]), DBP (β = -2.03 [95% CI, -3.79 to -0.28]), and HbA1c level (β = -0.15 [95% CI, -0.26 to -0.04]) and likelihood of alcohol (odds ratio [OR], 0.57 [95% CI, 0.36-0.91]) and cannabis use (full week: OR, 0.59 [95% CI, 0.35-0.99]) at follow-up. Greater social jet lag was associated with significantly higher SBP (β = 0.06 [95% CI, 0.01-0.11]) at follow-up. This cohort study found significant associations between poor sleep and adverse changes in health outcomes. Findings highlight the importance of developing culturally responsive interventions that target sleep as a key modifiable risk factor to improve the health of American Indian and Alaska Native adolescents.

A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information.

Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model. Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17-70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model. Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR). An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.

Socioeconomic factors, sleep timing and duration, and amygdala resting-state functional connectivity in children.

Reduced sleep health has been consistently linked with increased negative emotion in children. While sleep characteristics have been associated with neural function in adults and adolescents, much less is known about these associations in children while considering socioeconomic context. In this study, we examined the associations among socioeconomic factors, sleep duration and timing, and resting-state functional connectivity (rsFC) of the amygdala in children. Participants were typically-developing 5- to 9-year-olds from socioeconomically diverse families (61% female; N = 94). Parents reported on children's weekday and weekend bedtimes and wake-up times, which were used to compute sleep duration and midpoint. Analyses focused on amygdala-anterior cingulate cortex (ACC) connectivity followed by amygdala-whole brain connectivity. Lower family income-to-needs ratio and parental education were significantly associated with later weekday and weekend sleep timing and shorter weekday sleep duration. Shorter weekday sleep duration was associated with decreased amygdala-ACC and amygdala-insula connectivity. Later weekend sleep midpoint was associated with decreased amygdala-paracingulate cortex and amygdala-postcentral gyrus connectivity. Socioeconomic factors were indirectly associated with connectivity in these circuits via sleep duration and timing. These results suggest that socioeconomic disadvantage may interfere with both sleep duration and timing, in turn possibly altering amygdala connectivity in emotion processing and regulation circuits in children. Effective strategies supporting family economic conditions may have benefits for sleep health and brain development in children.

Chronotype and depression in adolescence: Results from a UK birth cohort study

AbstractBackgroundResearch has established a bidirectional association between sleep disturbances and depression in both adults and youth, as well as links between depression and circadian rhythms and chronotype, predominantly in adult populations. However, the link between chronotype and depression in the general adolescent population, independently of poor sleep and prior mental health problems, remains unclear.MethodsThis study investigated the association between time‐to‐sleep (TTS) and depressive symptoms in middle adolescence (age 14 years) using data from a large, nationally representative birth cohort from the UK. The relationship between TTS and self‐reported number of depressive symptoms was adjusted for individual, family, and neighborhood characteristics, including sleep quality, earlier mental health, diet and family meal routines, body‐mass index, screen time, physical activity, chronic illness, special educational needs, peer victimization, socioeconomic status, maternal mental health, area safety and the built environment (air pollution).ResultsAn “evening” chronotype was positively associated with depressive symptoms, and biological sex moderated this association—with eveningness being more strongly related to depressive symptoms in females. TTS inconsistency between non‐school and school nights was associated with depressive symptoms and sleeping later on non‐school nights predicted fewer depressive symptoms. The results were robust to further sensitivity analyses that used the sleep midpoint on non‐school nights and controlled for sleep duration.LimitationsThis was a correlational study. The independent and dependent variables were self‐reported, and there was no clinical screening for sleep disorders. The TTS variables were provided in crude hour slots.ConclusionsA robust association was found between evening chronotype and depressive symptoms in middle adolescence, even after adjustment for a wide range of confounders. Eveningess and depressive symptoms were more strongly associated in females.

Shorter night-time sleep duration and later sleep timing from infancy to adolescence.

Here, we (a) examined the trajectories of night-time sleep duration, bedtime and midpoint of night-time sleep (MPS) from infancy to adolescence, and (b) explored perinatal risk factors for persistent poor sleep health. This study used data from 12,962 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent or self-reported night-time sleep duration, bedtime and wake-up time were collected from questionnaires at 6, 18 and 30 months, and at 3.5, 4-5, 5-6, 6-7, 9, 11 and 15-16 years. Child's sex, birth weight, gestational age, health and temperament, together with mother's family adversity index (FAI), age at birth, prenatal socioeconomic status and postnatal anxiety and depression, were included as risk factors for persistent poor sleep health. Latent class growth analyses were applied first to detect trajectories of night-time sleep duration, bedtime and MPS, and we then applied logistic regressions for the longitudinal associations between risk factors and persistent poor sleep health domains. We obtained four trajectories for each of the three sleep domains. In particular, we identified a trajectory characterized by persistent shorter sleep, a trajectory of persistent later bedtime and a trajectory of persistent later MPS. Two risk factors were associated with the three poor sleep health domains: higher FAI with increased risk of persistent shorter sleep (OR = 1.20, 95% CI = 1.11-1.30, p < .001), persistent later bedtime (OR = 1.28, 95% CI = 1.19-1.39, p < .001) and persistent later MPS (OR = 1.30, 95% CI = 1.22-1.38, p < .001); and higher maternal socioeconomic status with reduced risk of persistent shorter sleep (OR = 0.99, 95% CI = 0.98-1.00, p = .048), persistent later bedtime (OR = 0.98, 95% CI = 0.97-0.99, p < .001) and persistent later MPS (OR = 0.99, 95% CI = 0.98-0.99, p < .001). We detected trajectories of persistent poor sleep health (i.e. shorter sleep duration, later bedtime and later MPS) from infancy to adolescence, and specific perinatal risk factors linked to persistent poor sleep health domains.

Irregular sleep is linked to poorer mental health: A pooled analysis of eight studies

ObjectivesGreater sleep disturbances on average are a risk factor for impaired mental health. Recent research has shown that more intraindividual variability (i.e., inconsistency) in sleep (hereafter called “sleep intraindividual variability”) may also be uniquely related to mental health, even above the influence of mean sleep patterns averaged across days. The current study examined associations between sleep intraindividual variability and symptoms of anxiety, depression, and insomnia across different facets of sleep intraindividual variability (sleep duration, efficiency, and timing) and sleep measurement types (sleep diary and actigraphy). MethodsWe pooled eight datasets (N = 3053 participants) that assessed repeated measures of sleep diary- and/or actigraphy-determined sleep across multiple days, as well as one-time measures of mental health or sleep disorder symptoms (i.e., anxiety, depression, and insomnia). Multilevel regression analyses were conducted to examine associations between sleep intraindividual variability and mental health or sleep disorder symptoms. ResultsGreater diary- and actigraphy-determined sleep duration intraindividual variability was associated with more depression symptoms (diary: b=0.02, p < .001; actigraphy: b=0.03, p = .006) and more insomnia symptoms (diary: b=0.02, p < .001; actigraphy: b=0.02, p < .001). Greater diary-determined sleep efficiency intraindividual variability was associated with fewer anxiety symptoms (b=−0.23, p = .019) and fewer insomnia symptoms (b=−0.15, p < .001). Greater diary- and actigraphy-determined sleep midpoint intraindividual variability was associated with more insomnia symptoms (diary: b=0.41, p = .044; actigraphy: b=0.66, p = .021). ConclusionsMore inconsistent sleep duration and sleep timing may be a correlate of poorer mental health. Future experimental work should examine whether stabilizing sleep patterns can improve mental health outcomes.