bluuebunny/crossref_metadata_2025_split huggingface dataset:
0257 Maladaptive Coping Mediates Associations Between Everyday Racial/Ethnic Discrimination and Insomnia Symptoms
Authors: Singh Rupsha , Gaston Symielle , Sandler Dale , Jackson Chandra 
Published: April 2024

Abstract

Introduction
Structural and interpersonal racism contribute to sleep disparities, likely due in part to heightened arousal/vigilance associated with the chronic stress caused by racial/ethnic discrimination. While working to address structural inequities is essential, it is important to identify how individuals cope – whether healthy/adaptive or maladaptive – with exposure to racial/ethnic discrimination to inform intervention targets since myriad coping strategies may either drive or buffer the effects of racial/ethnic discrimination on insomnia symptoms. Our study is the first to investigate whether associations between everyday racial/ethnic discrimination (e.g., treated as less intelligent/worthy/honest than others) and insomnia symptoms are mediated by coping strategies.

Methods
We used data collected over two follow-up periods from 29,137 women (n=903 Latina, n=1,702 non-Hispanic (NH)-Black, and n=26,532 NH-White) enrolled from 2003-2009 (mean±SD age=54.9±8.5 years) in the Sister Study cohort. In 2008-2012, participants self-reported everyday racial/ethnic discrimination experiences (ever vs. never) and healthy/adaptive (e.g., using emotional support) and maladaptive (e.g., behavioral disengagement) coping strategies (‘not at all=1’ to ‘a lot=4’). Self-reported insomnia symptoms (yes vs. no to either difficulty falling or staying asleep) were obtained between 2017-2019. Mediation analysis was performed using PROCESS macro adjusting for sociodemographic/health (e.g., age, sleep medication use) characteristics to provide bias-corrected bootstrapped standard errors and confidence intervals for total, direct, and indirect effects.

Results
Overall, 9% of women reported everyday racial/ethnic discrimination, and prevalence varied by racial/ethnic group (79% among NH-Black, 31% Latina, and 4% NH-White). All racial/ethnic groups reported similar healthy/adaptive (mean±SD overall score=16.2±2.4) and maladaptive (mean±SD overall score=7.1±1.6) coping styles. The prevalence of insomnia symptoms was also similar across racial/ethnic groups (39%-44%). Maladaptive coping fully mediated/explained the relationship between everyday racial/ethnic discrimination and insomnia symptoms for NH-Black (βdirect effect=0.02, 95% CI [-0.229, 0.271]; βindirect effect=0.03, 95% CI [0.006, 0.062]) and partially mediated/explained for NH-White (βdirect effect=0.24, 95% CI [0.107, 0.367]; βindirect effect=0.04, 95% CI [0.025, 0.048]) women, but healthy/adaptive coping did not mediate/explain this relationship across racial/ethnic groups.

Conclusion
Maladaptive coping strategies explain relationships between racial/ethnic discrimination and insomnia symptoms among NH-Black and NH-White women, underscoring the need for interventions that address maladaptive coping strategies to mitigate the impact of racial/ethnic discrimination on sleep disturbances.

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0260 Skin Tone - A Marker for Bias Known as Colorism - In Relation to Sleep Health Among African American Women
Authors: Ogbenna Bethany , Gaston Symielle , Harmon Quaker , Baird Donna , Jackson Chandra 
Published: April 2024

Abstract

Introduction
Colorism – or skin tone bias – likely impacts sleep health through activation of stress pathways in psychological response to trauma from colorist experiences and/or through differential access to health-promoting resources due to fewer educational/economic opportunities granted to darker- compared to lighter-skinned individuals. However, investigations of skin tone in relation to sleep are sparse.

Methods
We assessed associations between skin tone and sleep dimensions cross-sectionally (2010-2012) and longitudinally (until 2014-2018) among a cohort of 1,674 Black women aged 23-35 years residing in Detroit, MI at enrollment (2010 – 2012). Skin tone was measured objectively using a digital skin reflectance instrument and categorized as light [29.3–57.6] (25%), medium [57.7-72.4] (50%), and dark [72.5-106.1] (25%) brown. Self-reported sleep dimensions were dichotomized (yes vs no) as: short sleep duration (&lt; 7 hours), non-restorative sleep (waking up feeling well-rested &lt; 4 days/week), insomnia symptoms (difficulty falling or staying asleep 10+ days/month), and diagnosis of sleep apnea. Adjusting for age, we used Poisson regression with robust variance to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) and applied generalized estimating equations to determine risk ratios (RRs) and 95% CIs between skin tone and each sleep dimension, separately.

Results
At baseline, mean age was 28.7±3.4 years. Women with darker skin had the highest prevalence of short sleep (61.6% vs. 59.5% [medium] and 55.7% [light]) and comparable prevalence of non-restorative sleep (37.9% vs. 37.8% [medium]) and 37.2% [light] and sleep apnea (6% vs. 5.3% [medium] and 4.5% [light]). Women with light skin had the highest prevalence of insomnia symptoms (21.1% vs. 17.7% [medium] and 15.6% [dark]). Women with dark vs. light skin had a marginally higher prevalence and risk of short sleep (PR=1.04 [95% CI:1.00-1.08] and RR=1.07 [95% CI:0.99-1.16]) and a lower prevalence of insomnia symptoms (PR=0.95 [95% CI:0.91-0.99]). Skin tone was not associated with the remaining sleep dimensions.

Conclusion
Sleep disturbances were prevalent, and dark- compared to light-brown skinned participants had a higher prevalence of short sleep duration but a lower prevalence of insomnia symptoms. Associations between dark skin tone and short sleep duration persisted over time. Future studies should identify structural and psychosocial contributors to inform interventions.

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0261 The Social Determinants of Sleep in Emerging Adulthood
Authors: Joshi Crishnaa , Ebenezer Abisha , Taylor Ronald D 
Published: April 2024

Abstract

Introduction
Emerging adults, especially Black college students, are at risk for poor sleep outcomes. Race and family support are crucial factors linked to sleep, yet lifestyle and health factors are frequently investigated in this population. We examined sleep quality as a mediator of the association between race-related stress and depression at varying levels of housing security and kinship support.

Methods
Black students attending a large predominantly White institute (n = 93, 86.02% female, Mage = 21.7 years, 41.9% low SES) reported on sleep quality, race-related experiences, family factors, and depression. A moderated mediation model was tested using methods established by Baron and Kenny (1986). Participants’ sleep was assessed using the Pittsburgh Sleep Quality Index (Buysse et al., 1989). 86% of participants reported poor sleep based on the recommended cutoff (5 points) established by Buysse (1989), with a higher score indicating poorer sleep quality.

Results
Sleep partially mediated the association between race-related stress and depression. A significant association between race-related stress and depression was found in a simple mediation analysis (b=0.25, p&lt; 0.001), however, the relationship between race-related stress and depression was non-significant (b=0.114, p=0.07) after introducing sleep as a mediator. Sleep was a significant predictor of depression (b=1.69, p&lt; 0.001), and its inclusion significantly improved the model, explaining an additional 25 percent of the variance in depression (R^2=0.25, p&lt; 0.001). Associations between race-related stress and sleep quality varied by the level of kinship support and housing security. The association of race-related stress with depression through sleep is significant for participants with low kinship support (b = .220, p &lt; 0.001), but not for those with high kinship support (b=.056, p= .364). Similarly, the association of race-related stress with depression through sleep was significant for participants experiencing housing insecurity (b=.175, p &lt; .001), but not for those experiencing housing security (b=.07, p = .211).

Conclusion
The conditional effects of kinship support and housing security emphasize the crucial role of social support in mitigating the adverse effects of racial stress on sleep. Our findings demonstrate the interplay of race, sleep, and adjustment, suggesting the importance of tailored interventions for promoting the well-being of Black college students.

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0273 Photovoice for Leveraging Integrative Medicine Amongst Black Adults to Improve Sleep Health
Authors: Moise Rhoda , Chery Maurice , Wyrick MyKayla , Moise Imelda , Seixas Azizi , Jean-Louis Girardin 
Published: April 2024

Abstract

Introduction
Average adults are recommended to have 7-8 hours of sleep. However insufficient sleep (IS; &lt; 7 hours/nightly) is associated with increased risk of chronic diseases such as cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). Integrative medicine, a burgeoning area of research and practice, leverages both modern and traditional approaches to improve health. Despite integrative medicine’s recognition as a tool to improve sleep and related outcomes, there is a gap in literature in addressing its impact among blacks, who experience a disproportionate burden of IS and chronic disease. This qualitative study aimed to examine current integrative medicine practices to overcome IS and overall health in black communities.

Methods
Using photovoice methodology, a qualitative tool which applies community-engaged principles to produce culturally informed results through interviews and digital media, consented participants were recruited from Miami, Florida and 1) instructed to capture images over one week that communicated their integrative practices to improve sleep and overall health on their mobile device; 2) interviewed using individual, semi-structured procedures to add “voice” to the “photos” they captured for ~20 minutes; and 3) invited to participate in follow-up focus groups for refined discussion and data triangulation for ~1.5 hours. Both individual and focus group interviews were conducted over Zoom with recordings transcribed for formal content analysis using Nvivo software.

Results
The sample included N=25 diverse US blacks (M=37, SD=13, range 21-57). Approximately a quarter of the sample were unemployed (N=7) and majority were women (N=21). Results highlighted five themes of integrative medicine practice including: (1) natural wellness (sleep supplements, comfort beverages, aromatherapy, herbalism, outdoors); (2) self-care (self- maintenance, physical activity, spatial comfort); (3) leisure (pet support, play); (4) mental stimulation (mindfulness, reading); and (5) spiritual wellness (faith-based practices). Study results elucidate the heterogeneity of diverse US blacks regarding sociocultural knowledge, beliefs, and behaviors.

Conclusion
Addressing IS in black communities requires a comprehensive strategy that integrates cultural sensitivity, family and community dynamics, education, mental health support, and informed policymaking. Future studies should consider how sleep health literacy, stress appraisal, and coping strategies may vary by race/ethnicity for tailored intervention.

Support (if any)
National Institutes of Health (NIH) R25HL-10-5444 and R01HL142066-06.
0281 Mortality and Cardiovascular Changes in a Murine Model of OSA: Aging and 22 Months of Intermittent Hypoxia
Authors: Badran Mohammad , Puech Clementine , Khalyfa Abdelnaby , Gozal David 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is a prevalent condition marked by intermittent hypoxia (IH), which significantly elevates the risk of cardiovascular disease (CVD) and overall mortality. Furthermore, aging is a widely acknowledged risk factor for the onset and progression of CVD. In this pioneering study, we aim to elucidate the interplay between 22 months of IH exposures and age-related factors by delineating their combined influence on a spectrum of cardiovascular parameters.

Methods
Male C57Bl/6J mice (n=19-23) were exposed to IH (cycles of FiO2 21% 90 s-6% 90s or room air (RA; 21%) for 12 hours/day during the light period) for 22 months. Mean arterial blood pressure (MBP) was assessed using tail-cuff method, while aortic peak velocity (indicator of systolic function), E/A ratio (indicator of diastolic function), coronary flow velocity reserve (CFVR), and pulse wave velocity (PWV) were assessed using Doppler Flow Velocity System. Then, left anterior descending coronary arteries were excised, mounted on wire myographs, and used to obtain vascular relaxation dose-response curves to acetylcholine (ACh).

Results
The mortality rate in IH-exposed mice was 26% versus 0% in RA-exposed mice. Compared to young (8 weeks old) RA mice, MBP was elevated in old RA mice (p &lt; 0.05) and further elevated in surviving IH mice (p &lt; 0.0001). Similarly, diastolic function was impaired in old RA mice (p &lt; 0.001) and further compromised in old IH mice (p &lt; 0.0001). Moreover, systolic function was preserved in old RA mice but impaired in old IH mice vs. old and young RA (p &lt; 0.0001). Similarly, CFVR was only impaired in old IH mice (p &lt; 0.0001). Interestingly, PWV was higher in old RA vs. young RA (p &lt; 0.001), but significantly lower in old IH mice vs. young and old RA (p &lt; 0.001). Coronary maximal ACh-induced vasodilation was significantly impaired in both old RA (52 ± 9%) and IH-exposed mice (47 ± 9%) when compared to young RA mice (86 ± 4%, p &lt; 0.0001).

Conclusion
Prolonged exposure to IH simulating OSA results in elevated mortality rates and imposes substantial cardiovascular disturbances in surviving mice when contrasted with normal aging.

Support (if any)
NIH grant R01-HL166617
0284 Disrupted NREM Sleep Physiology Linked to 22q11.2 Deletion Syndrome
Authors: O'Hora Kathleen , Schleifer Charles , Xu Jennifer , Fung Hoki , Kushan-Wells Leila , Saletin Jared , Bearden Carrie 
Published: April 2024

Abstract

Introduction
A 22q11.2 deletion (22qDel), is a recurrent copy number variant with profound impacts on neurodevelopment. Disruptions in non-rapid eye movement (NREM) sleep neurophysiology have been observed across idiopathic psychiatric disorders. However, it is unknown whether NREM disruptions exist in 22qDel carriers, who have elevated risk of developing psychiatric disorders. Here, we test the hypothesis that 22qDel carriers will exhibit disrupted NREM sleep neurophysiology across the power spectrum.

Methods
22qDel carriers (n=12, Mage=20.42, 13-28 years, 58.3% males) and TD controls (n=11; Mage=19.18, 13-23 years, 27.3% males) completed multiple nights (89 nights total; 1-6 nights per subject; median=3 nights) of sleep EEG recordings with a wearable headband (Dreem 3). 30-second epochs were excluded for visual artifact or high 20-40Hz power. Relative power in the F7-O2 channel during NREM sleep was calculated using multitaper spectral estimation. Differences in relative power between 22qDel carriers and TD controls were tested across 0.5-20Hz using linear mixed-effects models controlling for age and sex. False Discovery Rate (FDR) was used to correct for multiple comparisons.

Results
There was increased NREM power in slow delta frequencies (0.76-0.85 Hz; q&lt; 0.05) and theta/alpha frequencies (5.95-9.73 Hz; q&lt; 0.05) in 22qDel carriers, relative to TD controls. There were increases in the alpha/sigma range (9.73-12.63 Hz) in 22qDel carriers, but these results did not survive FDR correction (q&lt; 0.10).

Conclusion
These findings suggest that neural mechanisms of NREM sleep regulation, particularly homeostatic sleep regulation, may be dysregulated in 22qDel carriers. Next steps will involve examining group differences in specific features of NREM sleep physiology (e.g., slow waves).

Support (if any)
R01MH085953; UCLA Center for Autism Research Pilot Grant; Autism Speaks Predoctoral Fellowship #13530
0286 Predictive Model of Sleep Slow Oscillations Emergence on the Electrode Manifold
Authors: Alipour Mahmoud , Mednick Sara C , Malerba Paola 
Published: April 2024

Abstract

Introduction
Due to slow oscillations (SO; 0.5-1.5 Hz) known role in cognition and health, brain stimulation techniques targeting SOs in closed loop are being developed as potential clinical interventions, as they hold promise for improving cognitive functions. SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location. Enabling prediction of SO emergence across the electrode manifold could remove the need for online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep.

Methods
SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), with 64 EEG channels. We study SO emergence at progressively more refined time scales. Modeling of SO occurrence was achieved for SOs in slow wave sleep (SWS), or in a combination of stage 2 and SWS (S2&SWS). First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape; representing skewness) in successive cycles.

Results
The analysis reveals an exponential decrease in SO count across sleep cycles, with a decay rate per cycle of 1.49 for SWS and 1.09 for S2&SWS. The SO timing model for SWS also demonstrates an exponential increase in model parameters (μ, λ) across cycles, with increase constants per cycle at 0.38 and 2.65, respectively. These findings imply that personalized model parameters can be estimated building on SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution.

Conclusion
This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of stimulation paradigms.

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0297 Comparing Validation Metrics of Machine Learning Algorithms for Actigraphy Data in Children
Authors: Chen Pin-Wei , Walch Olivia , Cielo Christopher , Jansen Erica , Mitchell Jonathan 
Published: April 2024

Abstract

Introduction
Actigraphy methods are evolving to use machine learning algorithms for sleep health estimation, but most algorithms have been trained on adult data. It is not known if such algorithms can be used for sleep/wake estimation in children. We therefore leveraged machine learning models, trained with adult data for sleep prediction, to determine their validity in a sample of children.

Methods
We enrolled 30 children (14 female, 8-16y) referred for in-lab overnight polysomnography at Children’s Hospital of Philadelphia. Participants wore a GENEActiv device (a 3-axis accelerometer, set at 50 Hz sampling rate) on their non-dominant wrist while completing their overnight sleep test. Machine learning models trained using adult data by Walch et al. were applied to the accelerometer data, and aligned into 30-second epochs against the sleep stages from polysomnography scored by a sleep medicine physician. We used the F1 score, a harmonic average of sensitivity and precision, to rank the algorithms.

Results
Overall, 271.5 hours of polysomnography data were collected with 80% of the epochs scored as sleep. Sleep duration median was 7.0 hours (IQR = 2.0), WASO median was 39 minutes (IQR = 39), and sleep latency median was 48.5 minutes (IQR = 52.5). In rank order, the top average F1 scores were 0.91 (SD=0.04) for k-Nearest Neighbor (kNN), 0.86 (SD=0.05) for Neural Net, 0.83 (SD=0.05) for Logistic Regression, and 0.78 (SD=0.06) for Random Forest. Average sleep duration error was lowest for kNN (12.8 minutes [SD = 44.9]) and highest for Random Forest (-142.1 minutes [SD = 57.5]); the average WASO error was lowest for kNN (9.8 minutes [SD = 45.9]) and the highest for Random Forest (118.0 [SD = 58.9]); whereas the average sleep latency error was lowest for Random Forest (-3.4 minutes [SD = 27.6]) highest for kNN (-32.2 minutes [SD = 34. 8]).

Conclusion
Certain machine learning models trained with adult datasets performed well when applied to pediatric data, with kNN being the most optimal in accuracy. However, validity for sleep latency was the most optimal for Random Forest. Further development can potentially reduce the variation of these predictions with pediatric-data-based machine learning models.

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0301 Investigating the Impact of Daily and Nighttime Noise on Sleep Duration: Insights from an Apple Watch Study
Authors: Cheol-Shin Jong 
Published: April 2024

Abstract

Introduction
Noise exposure is commonly believed to influence sleep quality adversely. This study aims to scrutinize the relationship between both daytime and nighttime noise exposure and sleep duration while considering the influence of sleep deficiency from the previous night. The study aims to explore the correlation between daily and nighttime noise exposure and sleep duration, accounting for the impact of insufficient sleep from the previous night.

Methods
In a 6-week study, 15 university students (5 males and 10 females) wore an Apple Watch to collect data on sleep duration, wake time, time in bed, ambient noise levels, daily step count, and previous night's sleep duration. Linear regression models were applied, adjusting for physical activity and previous sleep deficiency, to analyze the relationship between noise exposure and sleep outcomes.

Results
Contrary to initial hypotheses, the study found no statistically significant correlation between either daily or nighttime noise exposure and the assessed sleep parameters (p&gt;0.05). This was consistent even after accounting for the duration of the previous night's sleep and daily physical activity levels.

Conclusion
This study did not find evidence to support a significant relationship between noise exposure (both daily and nighttime) and sleep duration in university students. These results contribute to the nuanced understanding of sleep dynamics and its environmental influences. Further research, potentially involving larger or more diverse samples and varied methodologies, are encouraged. The study also demonstrates the utility of wearable technology in collecting comprehensive sleep and environmental data.

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