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Background: In pregnancy, epidemiological data have consistently shown strong associations between sleep quality and duration and maternal glycemia. However, other sleep disturbances such as difficulty falling asleep and staying asleep are common in pregnancy. They may contribute to impaired maternal glycemia through sympathetic nervous system activity, systemic inflammation, and hormonal pathways. However, there is little research examining associations between these specific sleep disturbances and maternal glycemia. Objective: This study aimed to investigate the associations of sleep disturbances during mid-pregnancy and mid-pregnancy maternal glycemia and gestational diabetes subtypes. Study Design: This is a secondary data analysis of the Comparison of Two Screening Strategies for Gestational Diabetes trial. Participants (n = 828) self-reported the frequency of sleep disturbances (i.e., trouble falling asleep, trouble staying asleep, waking several times per night, and waking feeling tired or worn out) in mid-pregnancy. Gestational diabetes was diagnosed using either the International Associations of Diabetes and Pregnancy Study Groups or Carpenter-Coustan approach. We defined gestational diabetes subtypes based on the degree of insulin resistance and beta-cell dysfunction. We used multinomial logistic regression to examine associations of sleep disturbances with gestational diabetes status (i.e., normal, mild glycemic dysfunction, and gestational diabetes) and gestational diabetes subtypes (i.e., neither insulin resistance or beta-cell dysfunction, insulin resistance only, beta-cell dysfunction only, and insulin resistance and beta-cell dysfunction). Results: A total of 665 participants (80%) had normal glycemia, 81 (10%) mild hyperglycemia, and 80 (10%) had gestational diabetes. Among participants with gestational diabetes, 62 (78%) had both insulin resistance and beta-cell dysfunction, 15 (19 %) had insulin resistance only, and 3 had beta-cell dysfunction only or neither insulin resistance nor beta-cell dysfunction. Sleep disturbance frequency was not associated with maternal glycemia or gestational diabetes subtypes. Conclusions: Sleep disturbances in mid-pregnancy were not associated with maternal glycemia during mid-pregnancy. Future research should collect data on sleep disturbances at multiple time points in pregnancy and in combination with other sleep disturbances to determine whether sleep plays any role in maternal glycemic control.

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STUDY OBJECTIVES: Structural brain maturation and sleep are complex processes that exhibit significant changes over adolescence and are linked to many physical and mental health outcomes. We investigated whether sleep-gray matter relationships are developmentally invariant (i.e. stable across age) or developmentally specific (i.e. only present during discrete time windows) from late childhood through young adulthood. METHODS: We constructed the Neuroimaging and Pediatric Sleep Databank from eight research studies conducted at the University of Pittsburgh (2009-2020). Participants completed a T1-weighted structural MRI scan (sMRI) and 5-7 days of wrist actigraphy to assess naturalistic sleep. The final analytic sample consisted of 225 participants without current psychiatric diagnoses (9-25 years). We extracted cortical thickness and subcortical volumes from sMRI. Sleep patterns (duration, timing, continuity, regularity) were estimated from wrist actigraphy. Using regularized regression, we examined cross-sectional associations between sMRI measures and sleep patterns, as well as the effects of age, sex, and their interaction with sMRI measures on sleep. RESULTS: Shorter sleep duration, later sleep timing, and poorer sleep continuity were associated with thinner cortex and altered subcortical volumes in diverse brain regions across adolescence. In a discrete subset of regions (e.g. posterior cingulate), thinner cortex was associated with these sleep patterns from late childhood through early-to-mid adolescence but not in late adolescence and young adulthood. CONCLUSIONS: In childhood and adolescence, developmentally invariant and developmentally specific associations exist between sleep patterns and gray matter structure, across brain regions linked to sensory, cognitive, and emotional processes. Sleep intervention during specific developmental periods could potentially promote healthier neurodevelopmental outcomes.

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STUDY OBJECTIVES: Sleep quantity and continuity vary across the lifespan. Actigraphy is a reliable and widely used behavioral measure of sleep in research and personal health monitoring. This meta-analysis provides a novel examination of whether age (in years) is associated with actigraphy-assessed sleep across the lifespan. METHODS: A systematic search of PubMed, Embase.com, Cochrane CENTRAL, and PsycINFO using "actigraphy" and "sleep" terms provided 7079 titles/abstracts; studies of individuals with known psychiatric or medical comorbidities were excluded. Ninety-one articles (N = 23 365) provided data for six meta-analyses examining sleep duration (k = 89), sleep efficiency (k = 58), bedtime (k = 19) and waketime (k = 9) for individuals ages 6-21, and bedtime (k = 7) and waketime (k = 7) for individuals ages 22 and older. RESULTS: At older ages, sleep duration was shorter (r = -0.12) and sleep efficiency was lower (r = -0.05). Older age was associated with later bedtime (r = 0.37) and wake-up time (r = 0.24) from ages 6-21, whereas older age was associated with earlier bedtime (r = -0.66) and wake-up time (r = -0.59) for ages 22 and above. The strength of these associations was modified by study continent, but not by any other moderator. CONCLUSIONS: Age was negatively associated with actigraphy-assessed sleep duration and efficiency, but the effects were small in magnitude. On the other hand, large associations were observed between age and sleep timing, despite a smaller literature and the absence of analyzable data for ages 30-60. Changes in sleep timing, rather than changes in sleep duration or continuity, may better characterize the effects of age on human sleep.

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STUDY OBJECTIVES: Neighborhood disadvantage is associated with poor sleep, which may contribute to and exacerbate racial and socioeconomic health disparities. Most prior work has been cross-sectional and thus it has not been possible to estimate causal effects. METHODS: We leveraged a natural experiment opportunity in two low-income, predominantly African American Pittsburgh, PA neighborhoods, following a randomly selected cohort of households (n = 676) between 2013 and 2016. One of the neighborhoods received substantial public and private investments (housing, commercial) over the study period, while the other socio-demographically similar neighborhood received far fewer investments. Primary analyses used a difference-in-difference analysis based on neighborhood, to examine changes in actigraphy-assessed sleep duration, efficiency, and wakefulness after sleep onset (WASO), and self-reported sleep quality. Secondary analyses examined whether residents' proximity to investments, regardless of neighborhood, was associated with changes in sleep outcomes. RESULTS: Resident sleep worsened over time in both neighborhoods with no significant differences among residents between the two neighborhoods. Secondary analyses, including covariate adjustment and propensity score weighting to improve comparability, indicated that regardless of neighborhood, those who lived in closer proximity to investments (<0.1 mile) were significantly less likely to experience decreases in sleep duration, efficiency, and quality, or increases in WASO, compared to those who lived farther away. CONCLUSIONS: While we did not observe sleep differences among residents between neighborhoods, living closer to a neighborhood investment was associated with better sleep outcomes. Findings have relevance for public health and policy efforts focused on investing in historically disinvested neighborhoods.

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The multidimensional sleep health framework emphasizes that sleep can be characterized across several domains, with implications for developing novel sleep treatments and improved prediction and health screening. However, empirical evidence regarding the domains and representative measures that exist in actigraphy-assessed sleep is lacking. We aimed to establish these domains and representative measures in older adults by examining the factor structure of 28 actigraphy-derived sleep measures from 2,841 older men from the Osteoporotic Fractures in Men Sleep Study and, separately, from 2,719 older women from the Study of Osteoporotic Fractures. Measures included means and standard deviations of actigraphy summary measures and estimates from extended cosine models of the raw actigraphy data. Exploratory factor analyses revealed the same five factors in both sexes: Timing (e.g. mean midpoint from sleep onset to wake-up), Efficiency (e.g. mean sleep efficiency), Duration (e.g. mean minutes from sleep onset to wake-up), Sleepiness/Wakefulness (e.g. mean minutes napping and amplitude of rhythm), and Regularity (e.g. standard deviation of the midpoint). Within each sex, confirmatory factor analyses confirmed the one-factor structure of each factor and the entire five-factor structure (Comparative Fit Index and Tucker-Lewis Index ≥ 0.95; Root Mean Square Error of Approximation 0.08-0.38). Correlation magnitudes among factors ranged from 0.01 to 0.34. These findings demonstrate the validity of conceptualizing actigraphy sleep as multidimensional, provide a framework for selecting sleep health domains and representative measures, and suggest targets for behavioral interventions. Similar analyses should be performed with additional measures of rhythmicity, other age ranges, and more racially/ethnically diverse samples.

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STUDY OBJECTIVES: For most women, the menopause is accompanied by hot flashes and sleep problems. Although hot flashes reportedly wake women from sleep, in the few studies that have used objective measures of both sleep and hot flashes, links between hot flashes and nocturnal awakening have been inconsistent. In a well-characterized cohort of midlife women, we examined the association between objectively assessed hot flashes and actigraphically defined wake from sleep. We hypothesized that wake episodes would be more likely during an objective hot flash relative to minutes without a hot flash. METHODS: Peri- and postmenopausal midlife women underwent simultaneous objective measurement of hot flashes (sternal skin conductance) and sleep (actigraphy) over 24 hours in the home. The likelihood of waking in the minutes during the hot flash relative to the minutes preceding the hot flash was compared using generalized estimating equations. RESULTS: We studied 168 women with at least one objective nocturnal hot flash and actigraphy data. Actigraphy-assessed wake episodes were concurrent with 78% of the objective hot flashes. We found an increased likelihood of wake in the minutes during the objective hot flash (0 to +5 min: OR [95% CI] = 5.31 (4.46 to 6.33); p < .0001) relative to the minutes preceding it (-10 to -1 min). The increased likelihood of wake occurred irrespective of whether the women reported the objective hot flash. CONCLUSION: Among these women who underwent objective measurement of sleep and hot flashes, nocturnal wakefulness was observed with the majority of hot flashes.

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STUDY OBJECTIVES: Emerging evidence supports a multidimensional perspective of sleep in the context of health. The sleep health model, and composite sleep health score, are increasingly used in research. However, specific cutoff values that differentiate "good" from "poor" sleep, have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for sleep health dimensions and examined the relationship between sleep health and cardiometabolic morbidity. METHODS: Participants from two independent Biomarker Studies in the MIDUS II (N = 432, 39.8% male, age = 56.92 ± 11.45) and MIDUS Refresher (N = 268, 43.7% male, age = 51.68 ± 12.70) cohorts completed a 1-week study where sleep was assessed with daily diaries and wrist actigraphy. Self-reported physician diagnoses, medication use, and blood values were used to calculate total cardiometabolic morbidity. Receiver operating characteristic (ROC) curves were generated in the MIDUS II cohort for each sleep health dimension to determine cutoff values. Using derived cutoff values, logistic regression was used to examine the relationship between sleep health scores and cardiometabolic morbidity in the MIDUS Refresher cohort, controlling for traditional risk factors. RESULTS: Empirically derived sleep health cutoff values aligned reasonably well to cutoff values previously published in the sleep health literature and remained robust across physical and mental health outcomes. Better sleep health was significantly associated with a lower odds of cardiometabolic morbidity (OR [95% CI] = 0.901 [0.814-0.997], p = .044). CONCLUSIONS: These results contribute to the ongoing development of the sleep health model and add to the emerging research supporting a multidimensional perspective of sleep and health.

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Study Objectives: Neighborhood disadvantage has been linked to poor sleep. However, the extant research has primarily focused on self-reported assessments of sleep and neighborhood characteristics. The current study examines the association between objective and perceived neighborhood characteristics and actigraphy-assessed sleep duration, efficiency, and wakefulness after sleep onset (WASO) in an urban sample of African American adults. Methods: We examined data from predominantly African American adults (n = 788, mean age 55 years; 77% female) living in two low-income neighborhoods. Perceived neighborhood characteristics included safety, social cohesion, and satisfaction with one's neighborhood as a place to live. Objective neighborhood conditions included walkability, disorder, street lighting, and crime levels. Sleep duration, efficiency, and WASO were measured via 7 days of wrist-worn actigraphy. Analyses estimated each of the sleep outcomes as a function of perceived and objective neighborhood characteristics. Individual-level sociodemographics, body mass index, and psychological distress were included as covariates. Results: Greater perceived safety was associated with higher sleep efficiency and shorter WASO. Greater neighborhood disorder and street lighting were associated with poorer sleep efficiency and longer WASO and greater likelihood of short sleep duration (<7 versus 7-9 hr as referent). Higher levels of crime were associated with poorer sleep efficiency and longer WASO, but these associations were only evident in one of the neighborhoods. Conclusions: Both how residents perceive their neighborhood and their exposure to objectively measured neighborhood disorder, lighting, and crime have implications for sleep continuity. These findings suggest that neighborhood conditions may contribute to disparities in sleep health.

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Study Objectives: To develop and evaluate the measurement properties of child-report and parent-proxy versions of the Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Sleep Disturbance and Sleep-Related Impairment item banks. Methods: A national sample of 1104 children (8-17 years old) and 1477 parents of children 5-17 years old was recruited from an internet panel to evaluate the psychometric properties of 43 sleep health items. A convenience sample of children and parents recruited from a pediatric sleep clinic was obtained to provide evidence of the measures' validity; polysomnography data were collected from a subgroup of these children. Results: Factor analyses suggested two dimensions: sleep disturbance and daytime sleep-related impairment. The final item banks included 15 items for Sleep Disturbance and 13 for Sleep-Related Impairment. Items were calibrated using the graded response model from item-response theory. Of the 28 items, 16 are included in the parallel PROMIS adult sleep health measures. Reliability of the measures exceeded 0.90. Validity was supported by correlations with existing measures of pediatric sleep health and higher sleep disturbance and sleep-related impairment scores for children with sleep problems and those with chronic and neurodevelopmental disorders. The sleep health measures were not correlated with results from polysomnography. Conclusions: The PROMIS Pediatric Sleep Disturbance and Sleep-Related Impairment item banks provide subjective assessments of child's difficulty falling and staying asleep as well as daytime sleepiness and its impact on functioning. They may prove useful in the future for clinical research and practice. Future research should evaluate their responsiveness to clinical change in diverse patient populations.

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Study Objectives: Sleep is multidimensional, with domains including duration, timing, continuity, regularity, rhythmicity, quality, and sleepiness/alertness. Individual sleep characteristics representing these domains are known to predict health outcomes. However, most studies consider sleep characteristics in isolation, resulting in an incomplete understanding of which sleep characteristics are the strongest predictors of health outcomes. We applied three multivariable approaches to robustly determine which sleep characteristics increase mortality risk in the osteoporotic fractures in men sleep study. Methods: In total, 2,887 men (mean 76.3 years) completed relevant assessments and were followed for up to 11 years. One actigraphy or self-reported sleep characteristic was selected to represent each of seven sleep domains. Multivariable Cox models, survival trees, and random survival forests were applied to determine which sleep characteristics increase mortality risk. Results: Rhythmicity (actigraphy pseudo-F statistic) and continuity (actigraphy minutes awake after sleep onset) were the most robust sleep predictors across models. In a multivariable Cox model, lower rhythmicity (hazard ratio, HR [95%CI] =1.12 [1.04, 1.22]) and lower continuity (1.16 [1.08, 1.24]) were the strongest sleep predictors. In the random survival forest, rhythmicity and continuity were the most important individual sleep characteristics (ranked as the sixth and eighth most important among 43 possible sleep and non-sleep predictors); moreover, the predictive importance of all sleep information considered simultaneously followed only age, cognition, and cardiovascular disease. Conclusions: Research within a multidimensional sleep health framework can jumpstart future research on causal pathways linking sleep and health, new interventions that target specific sleep health profiles, and improved sleep screening for adverse health outcomes.

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