Dimensions Of Sleep Health Research Articles

Association Between Chrononutrition Patterns and Multidimensional Sleep Health.

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. 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. 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). 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 Health Composite and Diabetes Symptom Burden in Young Adults With Type 1 Diabetes.

Multiple individual sleep health dimensions (satisfaction, regularity, and duration) are associated with diabetes symptoms, precursors to micro-and macrovascular complications, among young adults with type 1 diabetes mellitus (T1DM). Nearly half of young adults with T1DM develop vascular complications; however, modifiable contributors of diabetes symptoms, including sleep health, have been understudied. This cross-sectional quantitative descriptive study involved the completion of multiple validated self-report questionnaires and the collection of raw continuous glucose monitor and diary data over a 14-day period. The sleep health composite score was calculated by summing the number of dimensions with "good" sleep health. Chronotype was estimated with the 19-item Morningness-Eveningness Questionnaire. Multiple linear regression analyses were conducted to evaluate the associations between the independent variables (sleep health composite and chronotype) and diabetes symptom burden. Covariates, including age, race, sex at birth, T1D duration, continuous subcutaneous insulin infusion use, and hemoglobin A1C (HbA1C), were considered to determine their contribution to these relationships. One hundred nineteen young adults with T1DM who were aged 18 to 26 years were included in this study from 2 cohorts. Higher sleep health composite scores were associated with a lower overall diabetes symptom burden, even after adjusting for covariates in the linear regression models. Initially, a later chronotype was linked to a higher diabetes symptom burden, but this association became insignificant after accounting for HbA1C levels. Improving multiple dimensions of sleep health may alleviate the diabetes symptom burden among young adults with T1DM.

Mental health, substance use, and a composite of sleep health in adults, 2018 Ohio behavioral risk factor surveillance system

ObjectivesVarious factors impact sleep health including mental health and substance use. Mental health issues and substance use continue to rise in the United States. Yet, the association between mental health, substance use and sleep health in adults remains unclear. MethodsWe used multivariable linear regression models to examine the associations between mental health (poor mental health days in the past 30 days) and substance use (marijuana, tobacco, alcohol) with sleep health (individual dimensions of sleep: alertness, sleep efficiency, duration, and sleep health composite score) in 4333 participants from the 2018 Ohio Behavioral Risk Factor Surveillance System Survey. ResultsBetter mental health was associated with higher alertness, higher sleep efficiency, longer sleep duration and a higher sleep health composite score even after controlling for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p < .001). Higher marijuana and tobacco use were associated with lower individual sleep health dimensions (marijuana with sleep efficiency and duration and tobacco use with lower efficiency) and a lower sleep health composite score even after controlling for covariates for tobacco use (p < .001). Contrary to the hypothesis, higher alcohol use was associated with higher alertness and a higher sleep health composite score (p < .001), however after adjusting for covariates these associations were no longer significant. ConclusionsThe implications of these trends on sleep health are important to address as mental health and substance use are modifiable targets to consider when addressing sleep health.

Correlates of multidimensional sleep in premenopausal women: The BioCycle study

PurposeTo identify sleep dimensions (characteristics) that co-occur in premenopausal women. The second aim was to examine associations between multiple dimensions of sleep and a set of demographic, lifestyle, and health correlates. The overarching goal was to uncover patterns of poor-sleep correlates that might inform interventions to improve sleep health of women in this age group. MethodsThe BioCycle Study included 259 healthy women aged 18–44y recruited between 2005 and 2007 from Western New York. Participants reported sleep data through daily diaries and questionnaires that were used to create five sleep health dimensions (duration, variability, timing, latency, and continuity). We used multivariate analysis – canonical correlation methods – to identify links among dimensions of sleep health and patterns of demographic, psychological, and occupational correlates. ResultsTwo distinct combinations of sleep dimensions were identified. The first - primarily determined by low variability in nightly sleep duration, low variability in bedtime (timing), greater nocturnal awakening, and less sleep onset latency – was distinguished from the second – primarily determined by sleep duration.The first combination of sleep dimensions was associated with older age and higher parity, fewer depressive symptoms, and higher stress level. The second combination of sleep dimensions was associated with perception of longer sleep duration as optimal, lower parity, not engaging in shift work, older age, lower stress level, higher prevalence of depressive symptoms, and White race. ConclusionAmong premenopausal women, we demonstrated distinct patterns of sleep dimensions that co-occur and vary by demographic, health, and lifestyle correlates. These findings shed light on the correlates of sleep health vulnerabilities among young women.

Sleep Health and Anxiety Symptoms in Midlife Women: The Study of Women's Health Across the Nation (SWAN).

To investigate the associations between anxiety symptoms in midlife women and sleep features later in life, the aim is to test the hypothesis that poor sleep, as measured by each of six individual dimensions (4 objective actigraphy measures, 2 self-reports) of sleep health, is associated with higher levels of anxiety symptoms in midlife women. The participants in this longitudinal analysis included women from the SWAN Sleep I Study, a subcohort of the community-dwelling midlife women participating in the core Study of Women's Health Across the Nation (SWAN), which was initiated in 1996. Of the 370 participants enrolled in the Sleep Study, 270 were included in the analytic sample, and 100 who did not meet the inclusion criteria were excluded. Baseline measures of six dimensions of multidimensional sleep health (actigraphy measures: efficiency, duration, mid-sleep timing, regularity; self-report measures: alertness, satisfaction) were obtained between 2003 and 2005, corresponding to SWAN core annual/biennial assessments 5-8. Associations of each dimension with self-reported anxiety symptoms (Generalized Anxiety Disorder - 7-item scale; GAD-7), collected during visits 12 (2009-2011), 13 (2011-2013), and 15 (2015-2017), were examined using mixed models. The GAD-7 outcome was measured both continuously and as a categorical variable due to its skewed distribution. No statistically significant associations were found between any of the six baseline sleep health dimensions and the GAD-7 score after adjustment for covariates. The reasons for the lack of support for our hypothesis, despite previous evidence supporting an association between sleep and anxiety, are unclear. There is considerable overlap between anxiety and sleep symptoms, which may complicate the interpretation of our the findings. Thus, the failure to identify associations is likely multifactorial, and more studies with shorter follow-up intervals are warranted to better understand these relationships.

Bedtime Regularity and Sleep Sufficiency in Children With Tourette Syndrome

BackgroundDespite research demonstrating sleep disturbance in children with Tourette syndrome (TS), few studies have examined bedtime regularity and sleep sufficiency, two important sleep health dimensions. Therefore, this study examined bedtime regularity and sleep sufficiency in children with TS relative to matched healthy control subjects, and its associated demographic, clinical, and behavioral factors. MethodsParticipants were 384 parents or caregivers of children aged three to 17 years, including 192 with current TS and 192 matched healthy control subjects drawn from the 2020-2021 cycle of the National Survey of Children's Health. Parents completed questions assessing demographic (i.e., age, race, sex), clinical (i.e., attention-deficit/hyperactivity disorder [ADHD], autism spectrum disorder, anxiety, depression, tic severity, behavioral or conduct problems, ADHD medication, health condition-related impairment), and behavioral (i.e., screen time) characteristics. Mann-Whitney U test and chi-square test of independence were performed to compare groups on bedtime regularity and sleep sufficiency, respectively. Ordinal regression and binary logistic regression without and with backward elimination were performed to evaluate indicators of bedtime regularity and sleep sufficiency, respectively, in children with TS. ResultsChildren with current TS had significantly poorer bedtime regularity, but not sleep sufficiency, relative to matched healthy control subjects. In children with TS, anxiety and two or more hours of daily screen time were associated with higher likelihood of poor bedtime regularity. Autism was associated with lower likelihood of insufficient sleep, and depression was associated with increased likelihood of insufficient sleep. ConclusionsFindings put forth screen time, anxiety, and depression as intervention targets to optimize sleep health in children with TS.

The Association of Multidimensional Sleep Health With HbA1c and Depressive Symptoms in African American Adults With Type 2 Diabetes.

Sleep is important for diabetes-related health outcomes. Using a multidimensional sleep health framework, we examined the association of individual sleep health dimensions and a composite sleep health score with hemoglobin A1c (HbA1c) and depressive symptoms among African American adults with type 2 diabetes. Participants (N = 257; mean age = 62.5 years) were recruited through local churches. Wrist-worn actigraphy and sleep questionnaire data assessed multidimensional sleep health using the RuSATED framework (regularity, satisfaction, alertness, timing, efficiency, duration). Individual sleep dimensions were dichotomized into poor or good sleep health and summed into a composite score. HbA1c was assessed using the DCA Vantage™ Analyzer or A1CNow® Self Check. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9). Regression models examined the association of individual sleep dimensions and composite sleep health with HbA1c and depressive symptoms. Higher composite sleep health scores were associated with a lower likelihood of having greater than minimal depressive symptoms (PHQ-9 ≥ 5) (odds ratio [OR] = 0.578, 95% confidence interval [CI] = 0.461-0.725). Several individual sleep dimensions, including irregularity (OR = 1.013, CI = 1.005-1.021), poor satisfaction (OR = 3.130, CI = 2.095-4.678), and lower alertness (OR = 1.866, CI = 1.230-2.833) were associated with a greater likelihood of having depressive symptoms. Neither composite sleep health scores nor individual sleep dimensions were associated with HbA1c. Better multidimensional sleep health is associated with lower depressive symptoms among African American adults with type 2 diabetes. Longitudinal research is needed to determine the causal association between multidimensional sleep health and depressive symptoms in this population. ClinicalTrials.gov identifier NCT04282395.

Sleep Patterns and Traditional Cardiovascular Health Metrics: Joint Impact on Major Adverse Cardiovascular Events in a Prospective Cohort Study.

This study examines the association between traditional cardiovascular health (CVH) metrics and major adverse cardiovascular events (MACE) incidence in individuals with diverse sleep patterns. We analyzed data from 208 621 participants initially free of cardiovascular disease (CVD) in the UK Biobank study. Sleep patterns were assessed using scores for chronotype, duration, insomnia, snoring, and daytime dozing. Traditional CVH scores were derived from the Life's Simple 7 metrics. Cox proportional hazards multivariate regression assessed associations between distinct combinations of CVH and sleep scores and MACE, including nonfatal myocardial infarction, nonfatal stroke, and CVD mortality. Over a mean follow-up of 12.73 years, 9253 participants experienced incident MACE. Individuals with both a healthy sleep pattern and ideal CVH levels had the lowest MACE risk compared with those with a poor sleep pattern and poor CVH levels (hazard ratio, 0.306 [95% CI, 0.257-0.365]; P<0.001). Elevated CVH scores were associated with a reduced risk of MACE across different sleep patterns. Similar trends were observed for individual MACE components, heart failure, and all-cause mortality. These findings remained robust in sensitivity analyses and across various subgroups. In individuals without known CVD, maintaining a favorable sleep pattern and achieving optimal CVH levels, as measured by traditional metrics, were associated with the lowest MACE risk. Enhanced CVH significantly reduced CVD risk, even in individuals with a poor sleep pattern. These results emphasize the importance of considering multiple dimensions of sleep health alongside CVH to mitigate CVD risk. URL: https://www.ukbiobank.ac.uk; Unique identifier: 91090.

Exposure to maternal experiences of IPV in early childhood and sleep health in adolescence

BackgroundHalf of US women will experience intimate partner violence (IPV), and nearly all children in families experiencing such violence will be exposed. The negative effects of IPV exposure on children's mental and physical health are well-documented; however, less is known about effects on children's sleep health, a key marker of well-being. ObjectiveThis study examines the associations of early childhood exposure to maternal experiences of IPV with sleep health in adolescence, focusing on multiple dimensions of sleep health and differences by child sex. Participants and settingWe rely on data from the Future of Families and Child Wellbeing Study, a population-based study following 5000 children in large US cities, from birth to age 15. Analyses are based on 2687 adolescents interviewed at the year-15 follow-up. MethodsEarly childhood exposure to IPV is based on mothers' reports of her experiences of IPV from the child's birth to age 5. Adolescent sleep health is measured using 6 items approximating the BEARS sleep problem scale, at age 15. We estimate multivariate linear regression models to examine associations of early childhood IPV exposure with adolescent sleep health and stratify models by child sex. ResultsEarly childhood IPV exposure is associated with worse overall sleep health (β = 0.08; p < 0.001) and multiple dimensions of poor sleep health in adolescence. Associations are notably stronger among girls than boys. ConclusionsThe strong and lasting associations of early childhood exposure to IPV with adolescents' sleep health, particularly among girls, has important consequences for addressing adolescent health disparities.

0374 Dimensions of Sleep Health and Mood Disorders in Middle Eastern/North African Women

Abstract Introduction Insomnia symptoms are a common sleep disturbance during midlife. However, emerging evidence suggests that midlife women also experience increased risk of obstructive sleep apnea and short sleep duration. Cohorts of women in midlife have mostly included white women, despite suspected sleep health disparities among women from underrepresented racial and ethnic backgrounds. To address this gap, we conducted a pilot study that examined sleep health among women from Middle Eastern/North African (MENA) ancestry. Methods This pilot study enrolled 77 women aged 40-60y from MENA ancestry, recruited mostly from Michigan. Women provided demographic, lifestyle, sleep and health information through interviews with structured and validated questionnaires, including the Pittsburgh Sleep Quality Index (PSQI) and Patient Health Questionnaire (PHQ-9). Risk for obstructive sleep apnea (OSA) was evaluated using the STOP questionnaire+BMI and a subset of the women was tested with Watch-PAT, a one-night home sleep apnea testing device (OSA diagnosis, apnea-hypopnea index pAHI3%&amp;gt;5/h). The menopause rating scale was used to collect data on psychological and physiological symptoms. Results Mean age of women was 48y±5 years and mean BMI was 27.6±5 kg/cm2. Anxiety, irritability, and depression symptoms were identified in 33%, 25% and 31% of women. Half reported trouble falling asleep within 30 minutes, and 73% experienced nocturnal and early morning awakenings three or more times per week. Most women (75%) were aware of adequate sleep recommendations, but 54% reported short sleep duration, &amp;lt; 6 hours, and 30% reported &amp;lt; 5 hours of sleep per night. A STOP+BMI score ≥3 was observed in 20% of women. Twenty women were tested for OSA and 75% had it (mean pAHI3%=12.6/h). Moderate and severe OSA was apparent among 20% and 10% of the 20 women. Concurrent OSA and insomnia symptoms were observed among 40% of women. The odds ratio of insomnia were 9-fold and 3-fold for women with depression and anxiety, respectively. Conclusion We found a high burden of poor sleep and sleep disorders among midlife women from MENA ancestry. Moreover, poor sleep was associated with symptoms of depression and anxiety, raising the possibility that efforts to improve sleep health could benefit the mental health of these women as well. Support (if any)

Interaction between sleep duration and trouble sleeping on depressive symptoms among U.S. adults, NHANES 2015-2018

BackgroundThis study aims to examine the associations and interaction effects of sleep duration and trouble sleeping on depressive symptoms among U.S. adults. MethodsNational Health and Nutrition Examination Survey (NHANES) data from 2015 to 2018 were analyzed (N = 10,044). Trouble sleeping and sleep duration were self-reported. Sleep duration was defined as short (≤6 h) or long (≥9 h), compared with normal (>6 and < 9 h). Depressive symptoms were determined by the Patient Health Questionnaire-9 score ≥ 10. Both multiplicative interaction and additive interaction were reported. ResultsThere was a significant positive additive interaction between short sleep duration and trouble sleeping on depressive symptoms in the fully adjusted model (Relative excess risk due to interaction, RERIOR = 4.42, 95 % CI: 1.12, 7.73), with 43 % of the association with depressive symptoms attributed to the interaction (attributable proportion of interaction, AP = 0.43, 95 % CI: 0.22, 0.64). Similarly, a significant positive additive interaction between long sleep duration and trouble sleeping on depressive symptoms was found (RERIOR = 4.17, 95 % CI: 0.96, 7.38), with 41 % of the association with depressive symptoms attributed to the interaction (AP = 0.41, 95 % CI: 0.21, 0.60). No multiplicative interaction was detected between short or long sleep duration and trouble sleeping. LimitationsThe cross-sectional design limits the ability to draw causal inferences. ConclusionsFindings suggest that different aspects of sleep health interact synergistically, accounting for a substantial portion of the association with depressive symptoms. This underscores the importance of simultaneously considering multiple dimensions of sleep health in relation to depressive symptoms.

An adapted transdiagnostic sleep and circadian intervention for adults with excess weight and suboptimal sleep health: pilot study results.

This single-arm, mixed-methods, pilot study examined the feasibility and preliminary efficacy of an adapted version of the transdiagnostic intervention for sleep and circadian dysfunction (TranS-C) on multidimensional sleep health (MDSH) in a sample of adults with excess weight and suboptimal sleep health. Participants received up to eight, weekly, remotely delivered, tailored TranS-C sessions. At pre- and post-intervention, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and 7 days of Fitbit data were used to evaluate changes in sleep dimensions (regularity, alertness, timing, satisfaction, duration, and efficiency) and the composite MDSH score. Study feasibility examined recruitment, data collection, and intervention engagement (completion of core TranS-C sessions). Acceptability of the intervention was assessed with semi-structured interviews, which were analyzed using thematic analysis. From 85 referrals, 11 individuals were eligible, and 10 completed the study. All intervention participants completed the measures needed to calculate their composite MDSH score and completed the core intervention sessions. Themes from interviews support the intervention's remote delivery approach, applicability of the information provided, and impact on self-reported health. The intervention resulted in a large improvement in the mean composite MDSH score (Cohen's d = 1.17). Small-to-large effects were also observed for individual sleep health dimensions except for timing. Adapted TranS-C is acceptable for adults with excess weight and suboptimal sleep health and may be effective at improving short-term MDSH. With changes to recruitment methods, a larger study is feasible. Limitations include the small sample size and the lack of a control condition.

Abstract 19123: Associations Between Multiple Dimensions of Poor Sleep and Life’s Essential 8 Cardiovascular Health Score During the Menopausal Transition: Findings From the AHA Research Goes Red Weight Study

Introduction: The sleep health metric in AHA Life’s Essential 8 (LE8), a measurement of cardiovascular health (CVH), consists only of sleep duration. Associations of other dimensions of sleep health including sleep quality with the LE8 CVH and its individual components have not yet been evaluated. Methods: Participants were 291 women (n=98 pre-, n=94 peri-, n=99 post-menopausal, mean age=50 ± 3.2yr) between 45-55y from the AHA Research Goes Red Weight Study, which utilized Verily’s online platform. Standardized questionnaires included the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Berlin questionnaire for risk of obstructive sleep apnea (OSA), and a 1-wk NIH ASA 24-Hour Dietary Assessment. The LE8 CVH and component scores were computed. Cross-sectional associations between sleep and overall CVH scores and individual metrics were tested using multivariable logistic regression models adjusted for age, race/ethnicity, education, employment, insurance, and menopausal status. Results: Half of women slept &lt;7 hours/night, 79% reported poor sleep quality (PSQI≥5), one-third had high-risk OSA phenotype, 51% reported insomnia symptoms (ISI≥8), and 12% had evening chronotype. Compared to pre-menopausal women (67%), significantly more post-(83%) and peri-menopausal (86%) women reported poor sleep quality (p&lt;0.01). Women with poor sleep quality had 3-fold higher odds of poor overall CVH score (p=.04). Women with high-risk OSA phenotype and evening chronotype also had ~3-fold higher risk of poor overall CVH scores (OR=3.58, CI=1.79-7.18, and OR=3.33, CI=1.45-7.69, respectively). Poor sleep quality was associated with greater odds of low score on the diet component (OR=2.97, CI=1.55-5.69). Insomnia symptoms were associated with poor score on the BMI component (OR=1.92, CI=1.15-3.21). High-risk OSA phenotype was associated with greater odds of poor scores on the blood pressure, blood glucose, and BMI components (OR=2.3, CI=1.2-4.3; OR=3.2, CI=1.5-7.1, and OR=11.6, CI=6.2-21.6). Conclusions: Multiple aspects of sleep other than duration were associated with overall LE8 CVH score, and individual metrics, among women across menopause. Findings support potential incorporation of sleep quality into CVH constructs.

Multidimensional Sleep Health and Long-Term Cognitive Decline in Community-Dwelling Older Men.

Specific sleep characteristics have been associated with cognitive decline, Alzheimer's disease, and related dementias; however, studies examining the association between multidimensional sleep (a more comprehensive integration of sleep parameters) and cognitive decline are lacking. Among 2,811 older men without dementia, those with none, 1-2, and 3-5 "poor" self-reported sleep health dimensions had an adjusted 10-year change score of global cognition (3MS) of 2.9, 4.0 and 3.5 points (p-trend = 0.05), and in executive function (Trails B) completion time of 36.7, 42.7, and 46.7 seconds (p-trend < 0.01), respectively. In conclusion, a multidimensional measure of sleep health was associated with greater cognitive decline.

Menstrual disturbances and its association with sleep disturbances: a systematic review

BackgroundMenstrual disturbances harm women’s health, and general well-being. As growing evidence highlights the relationship between sleep and menstrual disturbances, it is imperative to comprehensively examine the association between sleep and menstrual disturbance considering the multiple dimensions of sleep. This systematic review aims to identify the association between sleep and menstrual disturbances by evaluating using Buysse’s sleep health framework.MethodsA comprehensive search of the literature was conducted in PubMed, EMBASE, psychINFO, and CINAHL to identify publications describing any types of menstrual disturbances, and their associations with sleep published between January 1, 1988 to June 2, 2022. Quality assessment was conducted using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. The findings were iteratively evaluated menstrual disturbances and their association with sleep using Buysse’s sleep health framework. This framework understands sleep as multidimensional concept and provides a holistic framing of sleep including Satisfaction, Alertness during waking hours, Timing of sleep, Efficiency, and Sleep duration. Menstrual disturbances were grouped into three categories: premenstrual syndrome, dysmenorrhea, and abnormal menstrual cycle/heavy bleeding during periods.ResultsThirty-five studies were reviewed to examine the association between sleep and menstrual disturbances. Premenstrual syndrome and dysmenorrhea were associated with sleep disturbances in sleep health domains of Satisfaction (e.g., poor sleep quality), Alertness during waking hours (e.g., daytime sleepiness), Efficiency (e.g., difficulty initiating/maintaining sleep), and Duration (e.g., short sleep duration). Abnormal menstrual cycle and heavy bleeding during the period were related to Satisfaction, Efficiency, and Duration. There were no studies which investigated the timing of sleep.Conclusions/ImplicationsSleep disturbances within most dimensions of the sleep health framework negatively impact on menstrual disturbances. Future research should longitudinally examine the effects of sleep disturbances in all dimensions of sleep health with the additional objective sleep measure on menstrual disturbances. This review gives insight in that it can be recommended to provide interventions for improving sleep disturbances in women with menstrual disturbance.

Early emergence of racial and ethnic differences in sleep health among toddlers living in low-income families

ObjectivesEmerging evidence suggests racial and ethnic and socioeconomic differences in children’s sleep health, yet few have examined these differences among very young children. The purpose of this study is to identify potential racial, ethnic, and sociodeomographic factors associated with multiple dimensions of sleep health in toddlers living in very low-income families. ParticipantsSample included 110 racially and ethnically diverse dyads with toddlers aged 12-15 months living in low-income families. MethodsActigraph data (9 days and nights), caregiver completed sleep diaries, Brief Infant Sleep Questionnaire—extended, and caregiver-reported socioeconomic characteristics were collected. ResultsToddlers’ average sleep duration (10.25 hours; SD = 0.76) was less than the age-based recommendations. There were significant race and ethnic differences in toddler’s actigraph-measured bedtime (p < .001) and variability in bedtimes (p = .004). Non-LatinX White toddlers had earlier bedtimes and less variability than Black and LatinX children. These between-group differences remained statistically significant after controlling for measured socioeconomic variables (p’s < 0.001). Within racial and ethnic group differences in bedtime and bedtime variability by education, employment, and marital status were identified with medium to large effect sizes. ConclusionsRacial and ethnic between-group differences in sleep occurred as early as 12 months of age and were not explained by sociodemographic variables (eg, income-to-needs, education, housing). Further research is necessary to determine structural and contextual factors that explain the racial and ethnic differences in sleep health in early childhood. Identifying these factors may inform the development of socially and culturally tailored interventions to reduce sleep health disparities.

0230 Sleep Duration Associated with Hospital Utilization in the US: Revisiting Andersen's Behavioral Model of Health Service Use

Abstract Introduction One in three Americans is sleep deprived, sleep disturbances are associated with greater health service utilization and costs. This study examined factors associated with Emergency Department visits (ED) and overnight admission hospitalization (OA) in a large representative sample of adults aged ≥18 in the US. Methods Guided by Andersen's Behavioral Model of Health Service Use, zero-inflated negative binomial and multivariable logistic regressions were used to examine associations between predisposing, enabling, and health needs factors and health service utilization (α=0.05). Results ED visits significantly increased with age, female sex, Black race, and less than high school education (15%, 36%, and 85%, respectively). Enabling factors analysis found that those who were unemployed, had problems paying medical bills, lived under the federal poverty line, and did not have private insurance were more likely to have ED visits. ED visits also increased by health needs including adults reporting poor/fair health, increased number of chronic conditions, and very short or long sleep. Regarding OA, significant risk predisposing factors included female sex and foreign-born nativity. Factors for OA found adults who were unemployed and had with problems paying bills were more likely to be hospitalized. Adults with Medicaid, Medicare, and other insurance were more likely (70%, 34%, 44%, respectively) to be hospitalized. Of the health needs factors examined, OA odds increased for those with less than excellent health status, without a usual source of care, and an increased number of chronic conditions. Adults with very short or short sleep were also more likely to be hospitalized (48% and 22%, respectively). Conclusion This is the first-time sleep duration is examined as a health needs component of Andersen's Behavioral Model of Health Service Use. When determining predictors associated with hospital utilization, sleep duration should be considered a key factor as it significantly increases the odds of ED visits and overnight hospital admissions. Future studies should investigate the influence of other dimensions of sleep health and sleep disorders. Support (if any) Programs to Increase Diversity among individuals engaged in Health-Related Research (PRIDE) training grant from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute (NHLBI): R25HL-10-5444 (PIs: G Jean-Louis, OG Ogedegbe).

Exposure to indoor light at night in relation to multiple dimensions of sleep health: findings from the Sister Study.

To examine the association between light at night (LAN) and multiple sleep health dimensions. Among 47 765 Sister Study participants, indoor LAN (TV on in the room, light(s) on in room, light from outside the room, nightlight, no light) and sleep dimensions were self-reported at baseline (2003-2009). We used Poisson regression with robust variance to estimate adjusted prevalence ratios (PR) and 95% confidence intervals (CI) for the cross-sectional associations between LAN and short sleep duration (<7 hours/night), insomnia symptoms (difficulty falling or staying asleep), frequent napping (≥3 naps/week), inconsistent sleep/wake time (differed day-to-day and week-to-week), sleep debt (≥2 hours between longest and shortest duration), recent sleep medication use, and a cumulative poor sleep score (≥3 poor sleep dimensions). Population-attributable risks (PARs) were determined for any light exposure vs. none by race/ethnicity. Compared to sleeping with no light in the bedroom, sleeping with a TV on was associated with a higher prevalence of most dimensions of poor sleep (e.g. short sleep duration: PR = 1.38, 95% CI: 1.32 to 1.45; inconsistent sleep/wake time: PR = 1.55, 95% CI: 1.44 to 1.66; sleep debt: PR = 1.36, 95% CI: 1.29 to 1.44; poor sleep score: PR = 1.58, 95% CI: 1.48-1.68). PARs tended to be higher for non-Hispanic black women compared to non-Hispanic white women. Sleeping with a TV on was associated with poor sleep health among US women, and non-Hispanic black women may be disproportionately burdened.

Sleep health dimensions and shift work as longitudinal predictors of cognitive performance in the UK Biobank cohort.

The long-term effects of sleep health and shift work on cognitive performance are unclear. In addition, research has been limited by small sample sizes and short follow-up periods. We conducted one of the largest examinations of the longitudinal influence of sleep health dimensions and shift work on cognitive performance in people of middle and old age using data from the UK Biobank. The hypothesis was that poor sleep health and shift work would predict lower cognitive performance. Self-reported sleep duration, daytime sleepiness, insomnia symptoms, chronotype and shift work status were assessed as predictors at baseline. Cognitive performance was operationalised by a touchscreen test battery at follow-up between 7.4 ± 2.2 and 9.0 ± 0.9 years after baseline assessment, depending on the specific task. Models were performed for each cognitive domain including relevant confounders (e.g., depression). The alpha level was set at p<0.01 for all analyses. The study sample comprised 9,394 participants for the reasoning task, 30,072 for the reaction time task, 30,236 for the visual memory task, 2,019 for the numeric memory task and 9,476 for the prospective memory task. Shift work without night shifts (ß= -2.0x10- 1 ± 6.5x10- 2, p=0.002) and with night shifts (ß= -1.9x10- 1 ± 7.2x10- 2, p=0.010) predicted a significantly reduced performance in the reasoning task. Short sleep duration (ß= -2.4x10- 1 ± 7.9x10- 2, p=0.003) and shift work without night shifts (ß= -3.9x10- 1 ± 1.2x10- 1, p=0.002) predicted a significantly lower performance in the task probing prospective memory. Our results suggest that, after controlling for confounding variables, shift work and short sleep duration are important predictors for cognitive performance in people of middle and old age. Further work is required to examine causal mechanisms of the observed associations.

Abstract P460: A Behavioral Intervention to Improve Health Sleep Among Adults With Excess Weight and Suboptimal Sleep Health: Preliminary Results of a Feasibility Study

Introduction: Suboptimal sleep health is highly prevalent and multiple dimensions of poor sleep have been identified as obesity predictors. Prior sleep interventions have not demonstrated a convincing effect on reducing obesity but this may be due to focusing on a single sleep dimension. The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) is an efficacious, individually tailored behavioral intervention aimed at improving multiple dimensions of sleep. However, TranS-C has not been tested in adults with excess weight and suboptimal sleep health. Purpose: This single group, pre-test, post-test pilot study conducted in adults with excess weight and suboptimal sleep health examined the feasibility and impact of a remotely delivered, 8-week TranS-C intervention on a composite sleep health score and individual sleep health dimensions. Methods: The six dimensions of sleep addressed in TranS-C were regularity, satisfaction, alertness, timing, efficiency, and duration. Pre- and post-intervention individual sleep dimensions were assessed using validated questionnaires and Fitbit-derived accelerometry data, with values dichotomized into ‘good’ or ‘poor’ for each sleep dimension. A composite sleep health score was calculated as the count of the ‘good’ individual dimensions (possible range 0-6). Effect sizes were calculated as standardized within-subject mean differences ( d ) to describe the intervention effect on the composite score and individual dimensions. We conducted post-intervention semi-structured interviews and used thematic analysis to analyze the qualitative data. Results: Participants (N = 7) were mostly female (71.4%) and white (71.4%) with a mean age of 41.0 ± 17.2 years and mean body mass index of 33.2 ± 3.8 kg/m 2 . Mean composite sleep health score pre-TranS-C was 3.6 ± 1.3 (range 2-5) compared to a mean post-TranS-C composite sleep health score of 4.7 ± 0.8 (range 4-6) ( d = 1.27). Improvements were seen in all individual dimensions except timing with effect sizes ranging from 0.38 to 1.46. During the semi-structured interviews, participants stated that they found the intervention to be informative, liked receiving the sessions remotely, and thought it improved their sleep. However, participants mentioned that some of the modules were not very helpful because information was repetitive or the information did not apply to them, and they suggested that the intervention be delivered in fewer sessions. Conclusions: TranS-C was acceptable for participants with overweight or obesity and suboptimal sleep health and had a large, positive effect on the composite sleep health score. Future refinement of the intervention will include reducing the number of sessions by identifying the most relevant modules and eliminating repetitive information. Continued refinement of the intervention will focus on the content addressing the timing dimension of sleep.