bluuebunny/crossref_metadata_2025 huggingface dataset:
0236 Psychometric Validation of the Ru-SATED 4.0 Multidimensional Sleep Health Scale in a Representative U.S. Sample
Authors: Conlon Rachel , Yu Lan , Tapia Amanda , Wallace Meredith , Buysse Daniel
Published: April 2024

Introduction

The construct of multidimensional sleep health is increasingly used to examine the impact of sleep on health. However, psychometric validation of self-report sleep health measures is limited. We developed the Ru-SATED 4.0 Multidimensional Sleep Health Scale to evaluate 6 sleep health dimensions (Regularity, Satisfaction, Alertness, Timing, Efficiency, Duration), including several alternative questions within dimensions. We assessed the comprehensibility and psychometric properties of Ru-SATED, yielding a finalized, valid measure of multidimensional sleep health among adults.

Methods

We conducted cognitive interviews (n=23) and lexile analysis. We then administered Ru-SATED and other self-reported sleep health measures via YouGov.com (n=2,000; stratified by age, sex assigned at birth, and racial/ethnic identity, representative of the U.S. population). We used factor analysis to identify the best combination of 6 items (1 for each dimension), then calibrated these 6 items using item response theory (IRT) Graded Response Model and explored the associations with legacy measures to establish Ru-SATED’s construct validity.

Results

Cognitive interviews evaluated 8 items across dimensions of sleep health (including 2 alternate items for alertness and 2 for duration) and confirmed the comprehensibility of the items/response options. The final set of 6 items had excellent model fit (RMSEA=0.08, SRMR=0.03, CFI=0.97, TLI=0.95) from factor analysis. In IRT calibration results, the location parameters ranged from -3.41 to 2.41 and the discrimination parameter ranged from 0.58 to 2.54, indicating the unique contributions of each item to the overall theta scores. Ru-SATED theta scores derived from IRT calibration had a mean of -0.01±0.83. Ru-SATED scores demonstrated acceptable convergent validity (|rs|=0.30 to 0.54) with other sleep scales (PSQI, PROMIS Sleep Disturbance, PROMIS Sleep-Related Impairment, Epworth Sleepiness Scale), and test-retest reliability (r=0.66) one month later. Ru-SATED scores significantly differed among those with (n=441, 22%) and without sleep diagnoses (n=1,558, 78%; Cohen’s d=0.95, 95% CI=0.84 to 1.06), indicating acceptable known-groups validity.

Conclusion

These findings support the psychometric validity of Ru-SATED 4.0 with an advantage of IRT-derived theta scores. Ru-SATED is a brief, easy-to-complete self-report measure of 6 dimensions of sleep health that can be easily scored and interpreted in clinical and research settings.

Support (if any)

Supported by an investigator-initiated, industry-sponsored research grant from Sleep Number Corporation (PI: Conlon).
0237 Association Between Healthy Sleep Time and Brain Health in US Adolescents-The Role of Social Determinants of Health
Authors: De Moraes Augusto César , Nascimento-Ferreira Marcus Vinícius , Hunt Ethan , III Harold Kohl
Published: April 2024

Introduction

Recent studies highlight the importance of healthy sleep for adolescent brain development and memory. However, factors like night awakenings and social disparities can affect sleep quality, crucial for cognitive growth. Understanding these influences is key to addressing their impact on children's cognitive development. This study aimed to: I) Examine the relationship between healthy sleep and executive brain development in US adolescents, focusing on the mediating role of nightly awakenings; II) Explore how social determinants of health (SDoH) influence this relationship.

Methods

We analyzed cross-sectional data from Year 2 (2018–2020) of the ABCD Study (n = 5,742) focusing on Fitbit Charge 2 device-measured average sleep hours per night. Sleep was classified per American Heart Association guidelines, with a scoring system based on optimal sleep durations. Wake after sleep onset (WASO) was used as a mediator and brain health was assessed using MRI measurements of cortical and gray matter volume. The Child Opportunity Index (COI) was used to assess the potential effect modification of SDoH, categorized as very low, low, moderate, high, or very high.

Results

In our study of 5,064 adolescents, averaging 11.9 years old, the typical sleep duration was 7.4 hours per day, and the average nightly awakenings was 36.9 minutes daily. We found that those with healthier sleep patterns had a larger total whole brain cortical volume (β = 440.2; 95% CI, 371.3 to 509.1). SDoH significantly buffers this association, particularly in the moderate to very high COI categories. Notably, WASO did not mediate this relationship. However, in the very low COI group, longer healthy sleep was inversely related to gray matter volume (β = -20.09; 95% CI, -38.18 to -2.00), with WASO mediating 5.1% of this association.

Conclusion

Adequate healthy sleep time correlates positively with brain-healthy development (brain cortical volume), and this association varies with socio-economic factors. In adolescents with very low Child Opportunity Index scores, healthy sleep time is inversely associated with gray matter volume, with wake-up during the night as an important mediator. These findings suggest the need for targeted strategies to enhance sleep and brain health among socio-economically disadvantaged adolescents.

Support (if any)

UTHealth Houston NIH
0239 The Relationship Between Asthma and Sleep in Adolescents from the Future of Families and Child Wellbeing Study
Authors: Lewis Sarah , Master Lindsay , Hale Lauren , Chang Anne-Marie
Published: April 2024

Introduction

Asthma has been linked to adverse sleep outcomes in adolescents but few studies have evaluated whether there are differences in the sleep health of adolescents with asthma from various racial/ethnic backgrounds. We aimed to test the association of asthma with sleep in a large, diverse, national sample of adolescents and to examine potential associations between race/ethnicity and sleep in those with asthma.

Methods

Data from 3307 adolescents were collected at the age-15 wave from the Future of Families and Child Wellbeing Study, a longitudinal birth cohort. Parent-reported asthma diagnosis and self-reported sleep measures, including bedtime, sleep duration, and number of nights/week with trouble falling asleep and staying asleep, were collected. Objective sleep measures, collected via actigraphy (n=784), included sleep onset, 24-hour sleep duration, sleep maintenance efficiency, and wake after sleep onset (WASO). Separate linear regression models were used to test the association between asthma and sleep outcomes in the entire sample, and to test the association between race/ethnicity and sleep outcomes in those with asthma (n=193-820). Models adjusted for age, sex, race/ethnicity, family structure, primary caregiver’s education, and household income to poverty ratio.

Results

Asthma diagnosis was significantly associated with more nights/week with trouble falling asleep in the entire sample (p<.001). Of those with asthma, 51.6% were African American, 25.1% were Hispanic/Latino, 11.8% were in the other category, and 11.5% were white. In those with asthma, race was significantly associated with multiple sleep outcomes. African American adolescents had an actigraphic sleep onset that was 1 hour earlier (p<.05) and reported sleeping 28.2 minutes less (p<.01) than white adolescents. Additionally, Hispanic/Latino adolescents had 9 more minutes of actigraphic WASO (p<.05) than white adolescents.

Conclusion

Overall we show that asthma is associated with more trouble falling asleep than those without asthma. Among those with asthma, we found shorter self-reported sleep duration and, unexpectedly, earlier sleep onset among African Americans and more WASO among Hispanic/Latino adolescents compared to White adolescents. Further studies should continue to explore the relationship between asthma and sleep by examining differences in asthma-related exposures that may underlie these sleep disparities.

Support (if any)

R25-HL147668, R01HD073352, R01HD36916, R01HD39135, R01HD40421
0242 Self-reported Short Sleep in Overnight Workers and Impoverished Individuals
Authors: Scott Jared
Published: April 2024

Introduction

Overnight shift work and rotating shift work have been consistently shown to have negative impacts on worker sleep health and therefore worker health overall. Factors associated socio-economic status (SES) have been shown to affect sleep in various ways. This study aims to identify the interaction between SES and night shift work and their impact on worker sleep outcomes.

Methods

Data for 9,212 workers ages 18 and older were acquired from the National Health and Nutrition Examination Survey (NHANES). Workers were divided into groups for overnight, evening, daytime, and rotating shift work. Using poverty income ratios (PIR) SES groups of poverty (PIR≤1), below median (PIR>1, ≤3) and non-poverty (PIR>3) were also formed. Membership within these groups as well as interaction terms between shift work and SES were regressed on short sleep (< 7 hours), extreme short sleep (≤5 hours), and unhealthy sleep (≤5 or >9 hours).

Results

A significant interaction was found between overnight shift work and short sleep, extreme short sleep, and unhealthy sleep (p< 0.001for each). The interactions seen were strongest for unhealthy sleep as opposed to short or extreme short sleep. Those in the below median SES group had the largest likelihood of poor sleep under all three classifications (p< 0.02 for all). African American individuals were also more likely to have unhealthy sleep with p< 0.001 for all classifications of poor sleep while being Hispanic had no significant impact on sleep. Surprisingly, interaction terms between overnight shift work and SES were not shown to be statistically significant.

Conclusion

Consistent with previous findings, overnight shift work is predictive of shorter sleep; however, overnight shift work was most strongly predictive of unhealthy sleep. This may be an indication that overnight shift may also lead to consistent oversleeping in some individuals. SES does not seem to have any compounding effects with overnight shift work. Future research should look at how overnight shift work may be leading to consistent oversleeping as well as why SES does not impact overnight shift effects.

Support (if any)
0244 Time Poverty as a Potential Pathway of Sleep Disparities Among Four Disability Populations
Authors: Garrison Michelle
Published: April 2024

Introduction

Past research has found that increased sleep disparities in populations experiencing disability, and this has often been attributed to increases in pain, stress, and comorbid conditions. However, it is also possible that the excess time burdens imposed by life with disability may also play a mediating role, especially in those with other risk factors for time poverty.

Methods

The Behavioral Risk Factor Surveillance System (BRFSS) is an annual cross-sectional survey sampled to be representative of the non-institutionalized US adult population. Using the 2022 data for 18-65 year-olds, we conducted survey weighted linear regression models to evaluate the impact of four disability categories (deafness, blindness, mobility impairment, and difficulties with concentrating, remembering, or making decisions) on sleep duration in cumulative models 1) adjusting for age category and sex, 2) adding poor health and financial stress, and 3) stratifying by risk for time poverty (risk index based on employment, children in the home, caregiving load, poor access to reliable transportation, and rural location).

Results

The analytic sample included 153,040 adults, with 24.4% endorsing at least one of the four disabilities, with prevalence ranging from 4.4% (deaf or hard of hearing) to 14.0% (difficulty with concentrating, remembering, or making decisions). All four disability categories were associated with decreased sleep duration (such as a 20.2 minute deficit, 95%CI 16.7-23.7 in those with mobility impairment), which remained significant after adjusting for poor health and financial stress. The sleep deficits associated with disability categories were consistently about twice as large among those in the top decile of the time poverty risk index as in the rest of the sample.

Conclusion

All disability categories measured by BRFSS are associated with sleep disparities, and these are exacerbated among those with other risk factors for time poverty, consistent with the time burden hypothesis, as those with multiple risk factors for time poverty would have less flexibility to allocate additional time without sacrificing sleep. Especially given that those experiencing disabilities are under-represented in much of the broader sleep literature, as are those with time poverty, sleep disparities in these populations are a crucial area for more robust exploration.

Support (if any)
0245 Treatment for Obstructive Sleep Apnea by Area Socioeconomic Deprivation in Six Million Adults
Authors: Greider Montana , Fernandez Chris , Rusk Sam , Nygate Yoav , Richardson Dana , Hutchinson Brian , Bartholow Tim , Watson Nathaniel , Wickwire Emerson
Published: April 2024

Introduction

Area socioeconomic deprivation, as measured by the Area Deprivation Index (ADI), is associated with numerous adverse health and economic outcomes (e.g., cardiovascular events, hospital readmissions, Alzheimer’s disease). This composite score is based on 17 health disparities indicators (e.g., income, education, employment, housing quality) used to rank relative disadvantage across communities, and is a widely utilized key social determinant of health and a validated marker of health risk. The purpose of this study is to determine the association between the ADI and OSA treatment initiation.

Methods

Our data source was the All-Payer Claims Database (APCD) for the Wisconsin Health Information Organization (WHIO). The APCD includes claims data (e.g., healthcare visits, procedures, pharmacy information) from health insurers, employers, and Medicaid. Inclusion criteria included continuous enrollment coverage for a minimum of 12-months prior to the date of OSA diagnosis (based on ICD code G47.33) and a diagnostic sleep test (based on CPT codes). ADI was measured at state and national levels. OSA treatment initiation was defined using Healthcare-Common-Procedure Coding System (HCPCS) codes. Rates of OSA treatment initiation were compared between individuals with OSA living in the highest and lowest ADI quantiles (e.g., the areas of greatest and least socioeconomic deprivation) using ordinary least squares (OLS) regression analysis to evaluate the directionality and significance of their association.

Results

Of N=6,026,463 participants in the overall sample, N=154,821 underwent OSA diagnostic testing, and N=43,601 were subsequently diagnosed with OSA. OSA treatment initiation was significantly, negatively associated with area socioeconomic deprivation based on National-ADI (Slope: -0.0011, p< 0.0050) and State-ADI (Slope: -0.0016, p< 0.0050). The highest rates of OSA treatment were observed in areas of greatest socioeconomic advantage (>70% in ADIs 15-25). Conversely the lowest OSA treatment initiation rates were observed in areas of greatest socioeconomic disadvantage (< 50% in ADIs 90-100), reflecting a 20% difference in the likelihood of OSA treatment between the highest and lowest levels of socioeconomic disparity.

Conclusion

Area socioeconomic deprivation measured by ADI is associated with significantly reduced OSA treatment initiation. Future research should seek to increase access to OSA care in areas of socioeconomic disadvantage to reduce sleep health disparities and achieve health equity.

Support (if any)
0247 Impact of Area Socioeconomic Deprivation and Demographic Variables on Machine Learning Models for OSA Treatment
Authors: Greider Montana , Fernandez Chris , Rusk Sam , Nygate Yoav , Richardson Dana , Hutchinson Brian , Bartholow Tim , Arguelles Jessica , Klimper Matthew , Hwang Dennis , Watson Nathaniel , Wickwire Emerson
Published: April 2024

Introduction

Ensuring equitable performance of sleep-related machine learning (ML) models is vital for public health and health equity. This research sought to determine the impact of sociodemographic and health disparities factors on the performance and characteristics of ML models designed to predict OSA treatment initiation.

Methods

Our data source was the All-Payer Claims Database (APCD) for the Wisconsin Health Information Organization (WHIO). Inclusion criteria included continuous insurance coverage for >12 months prior and >30 months after OSA diagnosis (defined by OCD code G47.33), and having undergone OSA diagnostic testing (defined by CPT codes). OSA treatment was defined based on durable medical equipment charges for PAP machines and supplies. Sociodemographic variables were extracted from APCD and included race, gender, age, and area socioeconomic deprivation (Area Deprivation Index; ADI). The ADI is a validated marker of health risk based on 17 health disparities factors ranking relative disadvantage across communities. Random Forest ML models were trained to predict OSA treatment initiation using sociodemographic variables and a medication history including 39,712 unique medications codified across 94 medication categories.

Results

Of N=6,026,463 subjects in the ACPD, n=154,821 underwent OSA diagnostic testing, and n=43,601 were diagnosed with OSA. 10-Fold Cross-Validation training-testing was applied to estimate sensitivity-specificity of ML models for predicting treatment initiation. Receiver operating characteristic curve area-under-the-curve (ROC-AUC) analyses were used to compare relative differences in predictive power of each variable. In ROC-AUC analysis of individual variables, the power for predicting OSA treatment were observed in relative rank order by age (0.568), race (0.547), ADI national-level (0.545), ADI state-level (0.544), gender (0.524), and medication history (0.516). In ROC-AUC analysis of combination variables, the highest ML model performance was observed in the combination of only three-variables (age-gender-race, 0.600), while the combination of all-variables showed an ROC-AUC value of (0.594), and the resulting performance difference was not statistically significant based on comparison of the ROC-AUC measures.

Conclusion

Demographic and health disparity factors may play an important role in future development of predictive AI/ML models. Population sleep health data represents an important resource to identify and bridge care gaps, reduce sleep health disparities, and achieve health equity.

Support (if any)
0251 Racial Residential Segregation and Outdoor Artificial Light at Night: Potential Contributors to Sleep Disparities
Authors: Gaston Symielle , Wilkerson Jesse , MacNell Nathaniel , Jackson W Braxton , Jackson Chandra
Published: April 2024

Introduction

Racial/ethnic residential segregation (RRS) likely contributes to sleep health disparities. For instance, nighttime lighting for surveillance and policing efforts that vary by neighborhood racial composition likely contributes to differences in exposure to artificial light at night (ALAN), which disrupts melatonin production and sleep. Yet, the RRS-ALAN-sleep pathway is understudied.

Methods

To investigate RRS and ALAN as mediators of racial/ethnic differences in sleep among US adults, we geographically linked National Health Interview Survey (2011-2017) data to 2012 American Community Survey 5-year population estimates and outdoor ALAN data from the 2010-2011 US Defense Meteorological Satellite Program’s Operational Linescan System. The local Getis-Ord Gi* was used to categorize RRS (low [z-score< 0], medium [0<=z-score<=1.96], high [z-score>1.96]). ALAN was divided into quintiles. Short sleep duration (< 7-hours) and poor sleep quality were self-reported. We used adjusted, survey-weighted robust Poisson regression to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs); tested for effect modification by race/ethnicity; and assessed mediation using the Sobel’s test.

Results

Among 126,539 adults (mean±SE age=46±0.1 years), RRS prevalence was 35% low, 43% medium, and 22% high. RRS and poor sleep were higher among minoritized adults (non-Hispanic Black (NHB), Mexican Latino, non-Mexican Latino, and Asian); ALAN was higher among non-Hispanic White (NHW) adults. High RRS was associated with a lower prevalence of ALAN (Q5 vs. Q1: PR=0.59 [95% CI:0.44-0.80]), short sleep (PR=0.94 [0.91-0.97]), and poor sleep quality (e.g., PR-trouble falling asleep =0.94 [0.90-0.98]), with no differences by race/ethnicity. ALAN was not associated with short sleep (sleep quality data unavailable). RRS explained differences in short sleep between both NHB and Mexican vs. NHW adults (e.g., NHB: PR-total effect=1.30 [1.26-1.33]; PR-direct effect=1.39 [1.34-1.45]; PR-indirect effect=0.93 [0.89-0.98]) before but not after adjustment for ALAN (PR-indirect effect=0.97 [0.87-1.08]).

Conclusion

RRS was associated with lower ALAN and non-poor sleep. RRS attenuated sleep duration disparities among NHB and Mexican adults only prior to adjustment for ALAN. RRS and ALAN may be contrasting modifiable contributors to sleep disparities among US adults.

Support (if any)

The findings/conclusions in this research are those of the authors and do not necessarily represent the views of the Research Data Center, National Center for Health Statistics, or CDC.
0252 Association Between Sleep Environment and Insomnia Among Hispanics in Florida
Authors: Palencia-Lamela Israel , Coppello Matthew , Carasco Mary , Garchitorena Laurent , Grandner Michael A , Chung Debbie , Jean-Louis Girardin , Seixas Azizi
Published: April 2024

Introduction

Insomnia prevalence among Hispanics is a significant public health concern, underscoring the need to uncover novel predictors of this sleep disorder. While prior studies have highlighted the high incidence of insomnia within this demographic, there is an urgent need to explore less-explored factors contributing to its development. Specifically, the impact of the sleep environment on insomnia symptoms in the Hispanic population. This study aims to bridge this gap by investigating the relationship between sleep environment quality and insomnia severity among Hispanics in Florida, offering fresh insights into the predictors of insomnia within this group.

Methods

Analyses include data from a Hispanic population in Florida (N= 221), ages 18-87 years that were enrolled in the NIH-funded heart health study, DORMIR. Participants completed several surveys on behavioral, clinical, social, and environmental determinants of sleep. For the current study, we measured insomnia symptoms via the Insomnia Severity Index (ISI) and the self-perceived quality of sleep environment was measured via the Assessment of Sleep Environment (ASE) survey. ISI measures presence and severity of insomnia symptoms and ASE asks respondents to rate their physical sleep environment by answering questions about the amount of light, temperature, and noise among other factors. Greater ISI scores indicate insomnia severity and greater ASE scores indicate worst sleep environment. To test whether sleep environment is associated with insomnia symptoms, we performed descriptive and inferential statistical analyses to characterize the sample as well as to determine the association between sleep environment and insomnia symptoms.

Results

Our population comprised 41% male (n=64) and 59% female (n=157). The mean ASE score of our total sample was 22.4±7.00 and the mean ISI score was 8.82±5.29. The regression model indicated that ASE was independently associated with ISI (F(1,219=6.09, B-0.12, p=0.0114), after controlling for sociodemographic, behavioral, and clinical variables.

Conclusion

Our results indicate that poorer sleep environments are associated with greater insomnia symptoms. These findings can be used to increase awareness of the benefits of monitoring the quality of sleep environments and associated risk of experiencing insomnia. These findings also elucidate the importance of limiting nighttime disturbances to improve quality of sleep.

Support (if any)

5R01HL152453
0253 Sleep Disturbances and Short Sleep Duration Among Rural and Non-Rural Populations
Authors: Leete Jacqueline , Graves Janessa
Published: April 2024

Introduction

Rural populations experience a higher prevalence of insomnia and insufficient sleep compared to urban populations. The purpose of this study was to compare self-reported sleep disturbances and objective sleep duration measured using consumer-wearable devices among individuals living in rural versus non-rural communities.

Methods

Participants in this analysis were from the National Institutes of Health (NIH) All of Us (AOU) Research Program, a diverse US health database. We assessed rurality using an item on a self-report questionnaire measuring geographic isolation, dichotomized as rural or non-rural. Sleep disturbance symptoms were measured using item 3 on the Patient Health Questionaire-9, which assesses trouble falling or staying asleep or sleeping too much in the past 2 weeks. Objective total sleep time (TST) was measured using Fitbit devices. Participants with < 14 days of wearable data were excluded from TST analyses.

Results

Chi-squared analysis revealed that AOU participants living in rural communities were more likely to report sleep disturbance symptoms at least several nights per week (75.4%, n=1,552, ◻2(1) =165.6, p<.001) compared to individuals living in non-rural communities (59.2%, n=59,778). Linear regression analysis revealed that rurality was significantly associated with increased self-reported sleep disturbances (rural M=1.44, SD=1.12; non-rural M=.92, SD=.97; b=.424, SE=.025, F(3,59812)=1187, p<.001, adjusted R2=.056) and decreased objective average TST (rural M=351 min, SD=73, n=356; non-rural M=363 min, SD=75, n=11,760; b=-15.769, SE=3.980, F(3,12112)=75.93, p<.001, adjusted R2=.018), when controlling for age and sex.

Conclusion

Our findings align with previous research that individuals in rural communities typically experience poorer sleep than those living in non-rural communities. These results suggest a need for targeted sleep interventions for individuals living in geographically isolated areas of the US. Moreover, these findings imply that future research examining geographic sleep health disparities is warranted.

Support (if any)

The AOU Research Program is supported by the NIH and the contributions of its participants.
0254 Acculturative Stress Is Associated with Sleep-Related Impairment Among a Sample of Rural and Urban Latinos/as
Authors: Carrasco Mary , Coppello Matthew , Garchitorena Laurent , Scaramutti Carolina , Palencia Israel , Grandner Michael , Osorio Ricardo , Thawani Sujata , Ramos Alberto , Blanc Judite , Chung Debbie , Jean-Louis Girardin , Seixas Azizi
Published: April 2024

Introduction

Latinos/as in the United States face distinctive sociocultural stressors rooted in their marginalized status, immigration experiences, and challenges adapting to a new culture. These stressors, including acculturative stress, contribute to elevated stress levels that can have detrimental effects on sleep health and may engender sleep-related impairment. While the association between stress and sleep difficulties is well-documented, the impact of acculturative stress on Latinos/as sleep health remains an understudied area. This study aims to illuminate the relationship between acculturative stress and sleep, highlighting a critical aspect of sleep health within this demographic.

Methods

This work utilized data from DORMIR, an NIH-funded study investigating multi-level determinants of sleep and heart health outcomes among urban and rural Latinos/as. Participants in New York and Florida completed surveys capturing biological, clinical, behavioral, psychosocial, and environmental determinants of health, including the Sleep-Related Impairment (SRI) and the Multidimensional Acculturation Stress Inventory (MASI) measures. We hypothesized that acculturative stress will be associated with sleep impairment outcomes and differentially across two locations. Linear regression analyses were performed to determine if acculturative stress predicted sleep-related impairments differentially in different geographical regions.

Results

Florida’s sample (n=241, 35.6±14.7 years, 71% female) differed from New York’s sample (n=112, 33.6±12.6 years, 61% female). For Florida participants, the average SRI score was 8.32±3.53 and the MASI score was 76.7±7.56. For New York participants, the average SRI was 9.35±3.50 and the MASI score was 87.6±25.1. Acculturative stress was found to be significantly associated with sleep-related impairment in Florida [F(1,239) = 8.95, p=0.003] and New York [F(1,110) = 11.8, p=0.008]. Regression analysis indicates greater acculturative stress predicted higher sleep related impairment in Florida (β1=0.08) and New York (β1=0.04), albeit a greater magnitude was observed in Florida.

Conclusion

Higher levels of acculturative stress predicted greater sleep-related impairments among Latinos/as in Florida and New York. With the recent influx of immigrants at the U.S southern border, this study illustrates the importance of addressing unique stressors like acculturative stress and its effect on sleep health among these populations. Further studies are needed focusing on reducing acculturation stress as a meaningful intervention for improving sleep health among Latinos/as in the United States.

Support (if any)

R01HL152453
0255 Associations Between Coping with Discrimination, Sleep, and Mental Health Among Black Sexual Minority Men
Authors: Dong Lu , Bogart Laura , Klein David , Mutchler Matt , Lawrence Sean , Pantalone David
Published: April 2024

Introduction

Black sexual minority men (SMM) suffer disproportionately from health inequities, including high rates of HIV, as well as poor mental and physical health. In addition to intersectional stigma and discrimination, sleep health, an understudied factor, may contribute to these health inequities. We examined the associations between coping with discrimination, sleep disturbances, and mental and behavioral health outcomes among Black SMM.

Methods

We recruited 191 Black SMM participants both in person at community-based events and organizations and online. Sleep disturbance, depression, anxiety, alcohol use, and quality of life were measured using PROMIS-Sleep Disturbance, Physical Health Questionnaire, Generalized Anxiety Disorder, Alcohol Use Disorders Identification Test, and PROMIS global health items. Experience of and coping with discrimination were measured using validated questionnaires. Regression and mediation analysis were conducted, controlling for age.

Results

One-quarter of the sample reported elevated levels of sleep disturbance. Experiences of racial discrimination were marginally associated with greater sleep disturbances (b = 0.59, p = 0.07). Effective coping (e.g., getting emotional support, thinking about steps to take) (b = -2.62, p = 0.03) and higher coping self-efficacy (b = -2.00, p < 0.001) were associated with fewer sleep disturbances. Less effective coping (e.g., self-blame, denial) was associated with more sleep disturbances (b = 4.70, p = 0.009). Greater sleep disturbance was associated with higher ratings of depression (b = 0.04, p < 0.001), anxiety (b = 0.03, p < 0.001) and lower ratings of quality of life (b = -0.04, p < 0.001), and marginally with greater alcohol use disturbances (b = 0.12, p = 0.06). Indirect paths via sleep disturbance were significant for the effects of effective coping or ineffective coping on depression, anxiety, alcohol use (for ineffective coping only), and quality of life.

Conclusion

Effective coping strategies appeared to mitigate sleep disturbances and other health challenges, underscoring the need for culturally congruent interventions that address both sleep and mental health to improve overall wellbeing in this population. Additionally, it is imperative to actively address intersectional stigma and discrimination. Mediation analysis was based on cross-sectional data and should be interpreted with caution.

Support (if any)

This work is supported by NIMHD R01MD014722 (LMB)
0256 Discrimination's Relationship with Sleep Environments Assessment in the Sunshine State vs the Empire State of Mind
Authors: Blanc Judite , Oliveira Bruno , Moise Rhoda , Rogers April , Elvariste Stessie , Locklear Clarence , Seixas Azizi , Jean-Louis Girardin
Published: April 2024

Introduction

This study investigates the relationship between everyday discrimination and objective and subjective assessment of sleep environments in a sample of Florida and New York residents.

Methods

Our sample included 745 Black and African American individuals participating in NIH-funded community-based sleep studies conducted between January 2020 and November 2023 in the New York City or Tri-State area and South Florida. Data encompassed demographics, zip code-based noise levels, overall air quality (IQAir), seven-day sleep-wake activities (Sleep Image Ring, and Actigraphy), Everyday Discrimination Scale (EDDS), and responses to the 13-item Assessment of Sleep Environment (ASE), measuring subjective experiences of environmental factors. Correlation analyses explored associations among continuous variables, ASE, noise scores, air quality, sleep-wake activities, and discrimination scores. Stratified multilinear regression analysis by state (Florida vs. NY), controlling for age and sex, examined relationships between sociodemographic factors, air quality, discrimination experiences, and ASE.

Results

Of the 745 participants, 57% were female (424), with a mean age of 47.8 years. A total of 27% (201) resided in New York, while 72.8% (542) were from South Florida. Participants in Florida were exposed to higher levels of Noise (t = 6.097, df = 513, p < 0.001), daily discrimination (t = -2.036, df = 741, p = 0.021), and perceived negative stimuli (t = 5.512, df = 741, p < 0.001) in their physical sleep environment compared to those in New York. No significant or strong association was found between objectively measured sleep health components and ASE or discrimination. Controlling for sex, age, and noise levels, among Florida residents, air quality (Beta = -0.332, p < 0.001) and discrimination (Beta = 0.331, p < 0.001) were the strongest independent predictors of ASE. In contrast, in NY participants, age (Beta = -0.274, p < 0.001) and air quality (Beta = 0.451, p = 0.049) were the strongest independent predictors of ASE.

Conclusion

Our analysis unveiled distinct patterns in exposure to discrimination and sleep environment assessment between Florida and New York, underscoring the importance of addressing the impact of discrimination on sleep and environmental assessment in policy and health outcomes.

Support (if any)

ESSENTIALS; R01HL142066-06; MOSAIC; R01AG067523-05.
0258 Intersectional Discrimination: An Approach to Understanding Insomnia Symptoms in Minoritized Populations
Authors: Lane Rashon , Varma Prerna , Barger Laura , Howard Mark ` , Rajaratnam Shantha M W , Czeisler Charles
Published: April 2024

Introduction

Minoritized groups in the United States experience discrimination that impacts their physical and mental well-being. People with multiple minoritized identities, such as being a racial/ethnic minority or sexual minority, often experience worsened health outcomes due to the intersectional nature of discrimination. The insidious nature of structural racism, classism, and genderism serves as a root cause of chronic disease disparities, including insomnia. This study aims to test the association of intersectional discrimination and symptoms of insomnia among minoritized populations with multiple marginalized identities.

Methods

In August 2022, US adults aged >18 years completed internet-based surveys. Demographic quota sampling was used to make the sample representative of the US 2020 population by age, sex, and race/ethnicity. Respondents answered questions assessing intersectional discrimination using the intersectional discrimination index (InDI). The InDi captures measures of three forms of discrimination: anticipated, everyday, and major. Symptoms of insomnia were measured via the Sleep Condition Indicator. Descriptive and logistic regression analyses were performed.

Results

Among respondents (n= 4,966), insomnia symptoms were more prevalent among women (21.3%) vs men (14.0%), among those who identified as multi-racial or other (24.5%) vs other racial and ethnic groups (Asian/Pacific Islander, 10.4%; non-Hispanic Black 12.1%; Hispanic 17.9%; and non-Hispanic White, 18.5%. Across all three subscales of the InDI (e.g., anticipated, every day, and major discrimination), intersectional discrimination was a better predictor for insomnia than those without InDi. Across race-by-gender interactions, individuals who are both racial/ethnic and gender minorities did not endorse higher symptoms of insomnia. Individuals who are other/multi race and a sexual minoritized group (e.g. non-heterosexual) OR= 1.7 (C.I. 0.68-2.02) and experienced discrimination were likely to endorse symptoms of insomnia.

Conclusion

In this study individuals who reported any form of discrimination were more likely to report symptoms of insomnia. While race or sex alone (except among Asian American/Pacific Islanders) are not predictors of insomnia, experiences of discrimination do predict insomnia. Individuals who are sexual minorities and of other racial minority groups might endorse high symptoms of insomnia. This study suggests that more research is needed to understand the unique role of how discrimination might increase insomnia for persons who sit at the margins of oppression.

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0259 Sleep Duration Associated with Perceived Racial Discrimination: Data from 25 US States
Authors: Rasmussen Elizabeth , Valencia Dora , Killgore William , Grandner Michael
Published: April 2024

Introduction

Previous studies have shown sleep duration associated with racism, but few studies have examined population-level data and how these relationships differentiate across race/ethnicity groups.

Methods

Data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS) was used. The BRFSS is a state-based population survey conducted annually by the CDC. In 2022, 25 states collected data on perceived discrimination (N=144.267), which was assessed with the question: “Within the past 12 months, do you feel that in general you were treated worse than, the same as, or better than people of other races?” As well as the same question regarding treatment at work and when seeking healthcare, and whether individuals experienced physical symptoms due to race-based treatment. Response options were Same (reference), Better, or Worse. Habitual sleep duration was categorized as <=4h, 5h, 6h, 7h (reference), 8h, 9h, and 10+h. Sleep-by-race/ethnicity interactions were explored. Models were adjusted for age, sex, education, and depression, and were population-weighted.

Results

Decreased likelihood of perceived better treatment was associated with <=4h, 5h, 6h, and 8h, increased likelihood of worse treatment was associated with <=4h, 5h, and 6h, and decreased likelihood was associated with 8h and 9h. Sleep-by-race/ethnicity interaction was significant (p< 0.0001). In stratified analyses, among Non-Hispanic Whites, insufficient and excessive sleep were both associated with perceived racism. Among Blacks/African-Americans, longer sleep duration was associated with perceived better treatment and insufficient sleep was associated with worse treatment. Among Hispanics/Latinos, 9h sleep was associated with perceived better treatment, but insufficient sleep was associated with worse treatment. Among Asians, worse treatment was associated with 10+h only. Among American Indians/Alaskan Natives, worse treatment was associated with all categories of insufficient sleep. Similar sleep-by-race/ethnicity interactions were found for treatment in work (p< 0.0001) and healthcare settings (p< 0.0001). Although no sleep-by-race/ethnicity interaction was found, physical symptoms due to treatment based on race was associated with <=4h (OR=2.28), 5h (OR=1.93), and 6h (OR=1.27).

Conclusion

Insufficient (and in some cases excessive) sleep duration is associated with increased perceived discrimination across groups and settings, though groups differ in terms of degree of impact. Insufficient sleep is also associated with increased physical symptoms due to racism.

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0263 Food Insecurity After Job Loss Is Associated with Lower Multidimensional Sleep Health over Time
Authors: Miller Hannah , Fnu Nupur , Haynes Patricia
Published: April 2024

Introduction

About 35 million people live in food insecure households creating substantial public health burden. While prior research supports the relationship between food insecurity and disturbed/curtailed sleep, most studies have employed cross-sectional designs and relied on surveys. This project sought to examine the longitudinal relationship between food insecurity and multidimensional sleep health in a group of individuals highly vulnerable to financial strain, participants with a recent history of involuntary job loss.

Methods

The ADAPT study is an observational, longitudinal study that assessed the negative health effects of involuntary job loss over the course of approximately 18 months (6 visits). A multidimensional sleep health composite (Ru-SATED) was calculated using raw actigraphy data, a daily sleep diary, and a sleep interview. Participants were classified as food insecure if they reported a score of 2-6 on the Household Food Security Scale within the first six months of job loss (first 2 visits).

Results

With robust control for socioeconomic status, food insecurity was associated with worse multidimensional sleep health across all timepoints (Estimate = -.43, SE = .18, 95% CI [-0.78, -0.08]). Food insecurity was associated with poor sleep quality (sleep diary; p < .01), worse sleep efficiency (actigraphy; p < .01), and later sleep timing (actigraphy; p < .05). Food insecurity was not associated with sleep duration (actigraphy) or sleep regularity (actigraphy).

Conclusion

Results suggest that individuals experiencing food insecurity six months after job loss had worse multidimensional sleep health that sustained over time. These findings extend the negative impact of food insecurity on self-reported sleep indices to multidimensional sleep health that incorporates subjective, objective, and interview measures. Future research is needed to assess whether increased accessibility to food after job loss could improve sleep health along with other health risks in this vulnerable population.

Support (if any)

This work is supported by the US National Institute of Health, National Heart, Lung, and Blood Institute (NHLBI,1R01HL117995-01A1).
0266 Sleep Attitudes in Adolescents: Demographic Differences and Associations with Sleep Health
Authors: Alvarado Giovanni , Palmer Cara
Published: April 2024

Introduction

Between 70-90% of teenagers in the United States sleep fewer than the minimum recommendation of 8 hours per night, which can result in a number of downstream physical and psychological consequences. Prior studies show there are individual differences in how adults prioritize and think about the benefits of adequate sleep, which in turn predicts sleep behaviors and sleep health. However, research is lacking on sleep attitudes in adolescents and how these attitudes may relate to sleep behaviors.

Methods

Participants included 649 adolescents from across the United States (ages 13-18 years; 79.7% female; 45.2% white) who completed an online survey to assess sociodemographics, sleep attitudes using the Charlotte Attitudes Towards Sleep Scale, and their sleep using the Pittsburgh Sleep Quality Index, Sleep Timing Questionnaire, and the Adolescent Sleep Hygiene Scale.

Results

Non-white participants were more likely to report more positive sleep attitudes compared to white participants (t(414) = -2.18, p = .03). A series of regression models were conducted to examine whether sleep attitudes predicted sleep, while adjusting for sociodemographics (age, gender, race/ethnicity, SES). Adolescents who reported more positive attitudes towards sleep also reported longer sleep duration (β = -.47, SE = .06, p < .001), better sleep quality (β = -.36, SE = .2, p < .001 ), shorter sleep onset latency (β = -.22, SE = .06, p < .001 ), earlier weekday (β = -.33, SE = 364.67, p < .001 ) and weekend (β = -.33, SE = 348.87, p < .001) bedtimes, and better sleep hygiene (β = .26, SE = .03, p < .001). There were no significant differences in sleep attitudes based on race, ethnicity, gender, or SES when adjusting for other demographic characteristics.

Conclusion

This study fills an important gap in the research on adolescent sleep by examining how sleep attitudes may contribute to sleep health. Prior research on health attitudes suggest that attitudes are modifiable, and findings from this study advance research on adolescent sleep and suggests that sleep attitudes may be an important target to minimize sleep health difficulties when developing and adapting sleep interventions.

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0267 ROTC Cadets’ Sleep Quality Varies as a Function of Prior Stressful Experiences
Authors: Krslovic Kristian , Huang Kathleen , Paulus Erin , Jones Rachell , Sowden Walter
Published: April 2024

Introduction

Sleep difficulties and stress are common among U.S. Army Soldiers – especially among those who have been exposed to traumatic events. In the short term, elevated stress can reduce sleep quality, alter circadian rhythms, and disrupt normal metabolism and endocrine functions. Because ROTC cadets are new to the Army, their level of exposure to operationally stressful experiences is relatively low. Understanding the extent to which stress impacts sleep at this point in their military careers can help ROTC cadets be better prepared for later exposure to stressors in military operational contexts. In the present study, the impact of stress on sleep quality of ROTC cadets was investigated.

Methods

Participants included 519 ROTC cadets at the annual Cadet Summer Training Advanced camp with a mean age of 22.24 ± 2.788 years (M ± SD). Participants completed the Pittsburgh Sleep Quality Index (PSQI), with scores ranging from “1” (good) to “4” (bad). They also completed the Post Traumatic Stress Disorder Checklist – Military Version; PCL-4, which measures prior exposure to stressful life events. This version of the PCL includes only four questions, with responses indicated on a 30-point rating scale, with “1” connoting less stressful and “30” connoting more stressful life events.

Results

Cadets’ self-reported sleep quality scores averaged 2.1 ± .618 (M ± SD). Cadets reported an average PCL-4 score of 5.58 ± 2.606 (M ± SD). Quality of sleep and stressful life events were significantly negatively correlated, r = -.303, p < .01.

Conclusion

Greater prior exposure to stressful life events is associated with poorer subjective sleep quality. Cadet training is intense and stressful. Early identification of those cadets who are especially susceptible to stress-induced sleep disturbance is a necessary first step toward development of targeted interventions to improve the performance, health, and likelihood of success of such individuals during military training.

Support (if any)

Support for this study came from the Military Operational Medicine Research Program (MOMRP) of the United States Army Medical search and Development Command (USAMRDC).