bluuebunny/crossref_metadata_2025_split huggingface dataset:
0223 Associations of Pain Frequency and Severity with Excessive and Insufficient Sleep Duration in U.S. Adults
Authors: Szklo-Coxe Mariana , Dunn Christen 
Published: May 2023

Abstract

Introduction
Pain and inadequate sleep duration are prevalent among adults in the United States. Both pain and inadequate sleep have been related to poorer quality of life and adverse health outcomes. However, the relationships of pain frequency and severity, modeled as predictors, to sleep duration have rarely been studied at the population level, particularly in the U.S. We aimed to investigate the relationships of both pain frequency and pain severity to sleep duration in a representative, population-based U.S. sample.

Methods
2018 National Health Interview Survey data were analyzed. Self-reported sleep duration (on average) was categorized as short (6 hours or less), normal (7-9 hours), and long (10+ hours). Self-reported pain frequency (in the last three months) and pain severity (most recent event) were each categorized as “higher” and “lower or no”. Potential confounders/adjustment variables examined included age, sex, weight, chronic health conditions, race/ethnicity, depression, anxiety, and sleep medication use. For adjusted analyses, a multinomial logistic regression was fitted using stepwise selection using a sample of 10,173 U.S. adults. We estimated odds ratios (OR) and 95 percent (%) confidence intervals (CI) for three categories of sleep duration with regard to both pain frequency and severity in an adjusted multinomial model.

Results
In a multinomial model adjusted for age, anxiety, depression, and race/ethnicity, higher pain frequency, versus lower/no, was found to be associated with increased odds of short (OR=1.52, 95% CI, 1.38-1.66) and long (OR=1.85, 95% CI, 1.50-2.28) sleep duration versus normal sleep. In the same adjusted model, higher pain severity, versus lower/none, was found to be associated with an increased odds of short (OR=1.49, 95% CI, 1.33-1.68) and long (OR=1.77, 95% CI, 1.40-2.22) sleep duration versus normal sleep duration.

Conclusion
Higher pain frequency and higher pain severity, versus low or none, were both associated with an increased odds of insufficient (by 1.5-fold) and excessive (by 1.9 and 1.8-fold, respectively) sleep duration (vs. normal) in a large population-based U.S. sample. Results suggest that the prevention of pain, both its frequency and severity, should be considered to improve unhealthy sleep duration among US adults.

Support (if any)
None
0233 Racial/ethnic Residential Segregation - a Component of Structural Racism - and Sleep Health by Neighborhood-level Poverty
Authors: Gaston Symielle , Wilkerson Jesse , MacNell Nat , Jackson W Braxton , Jackson Chandra 
Published: May 2023

Abstract

Introduction
Racial/ethnic residential segregation may contribute to sleep disparities by concentrating poverty and impairing sleep among minoritized groups. Yet, due to racism, equitable access to sleep-promoting resources may not occur in integrated neighborhoods. Further, social/cultural factors within ethnic enclaves could be protective.

Methods
To investigate whether relationships between racial/ethnic residential segregation and sleep vary by census tract-level poverty among US adults, we linked nationally-representative National Health Interview Survey data (2011-2017) to 2012 and 2017 American Community Survey census tract-level data. We used the local Getis-Ord Gi* statistic to categorize residential segregation (high, medium, low [reference]), considering differences between racial/ethnic composition within a census tract to the surrounding county. Survey-weighted, Poisson regressions with robust variance estimation were stratified by race/ethnicity and approximate census tract-level poverty tertile (high [&gt;16.9% of residents below poverty level], medium [&gt;7.9%-16.9%], low [0%-7.9%]) to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for self-reported sleep health measures.

Results
Among 126,539 participants (mean age±SE=46±0.1 years), non-Hispanic (NH)-Black and Latino adults were most likely to report short sleep (41%) and non-restorative sleep (37%), respectively. Racial/ethnic residential segregation was most common among NH-Black (38%), followed by non-Mexican Latino (34%) and NH-Asian adults (34%), and was least prevalent among NH-White adults (17%) but was not associated with sleep among Mexican, non-Mexican Latino, or NH-Black adults. Among NH-Asian adults, high segregation was marginally associated with a higher prevalence of short sleep (&lt; 7-hours vs. recommended [7-9 hours]) in low-poverty neighborhoods (PR=1.17 [95% CI:0.97-1.42]) but was associated with recommended sleep in medium-poverty neighborhoods (PRshort sleep=0.79 [0.63-0.99]). Among NH-White adults, high residential segregation was associated with more restorative sleep only in high-poverty (PR=1.13 [1.06-1.20]) and low-poverty (PR=1.03 [1.00-1.07]) neighborhoods.

Conclusion
Associations between racial/ethnic residential segregation and sleep health varied by neighborhood-level poverty among NH-Asian and NH-White adults. The lack of differences among most minoritized groups may be related to the close proximity of higher-income and lower-income neighborhoods due to structural racism. Future studies including spatial analyses are warranted. 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.

Support (if any)
0237 Day-to-day Associations Between Nightly Sleep, Sedentary Behavior and Physical Activity in Under-resourced Caregiver-Child Dyads
Authors: Covington Lauren , Brewer Benjamin , Hoopes Elissa , Ji Xiaopeng , Brownlow Janeese , Patterson Freda 
Published: May 2023

Abstract

Introduction
Sleep and physical activity behaviors co-exist on a 24-hour continuum of movement, and both independently contribute to long-term health. Limited research has examined temporal, day-to-day associations between sleep and activity among caregiver-child dyads dwelling in under-resourced homes. We aim to quantify the temporal, bidirectional associations between objectively measured sleep health metrics, sedentary behavior (SB), and moderate-to-vigorous physical activity (MVPA) among caregiver-child dyads in ecologically valid contexts.

Methods
26 families (17 caregiver-child dyads and 7 triads; N=55) from socioeconomically disadvantaged Delaware and Philadelphia communities participated in this micro-longitudinal study. Dyads wore an Actigraph on their wrist for up to 14-days to measure sleep health, SB, and MVPA. Sleep health metrics included nighttime sleep duration (minutes), timing of sleep onset and offset (hours), and sleep efficiency (percentage of time in bed spent asleep). Multi-level models quantified the associations between nightly sleep health with next-day SB and MVPA, and daily SB and MVPA with subsequent nighttime sleep. Models adjusted for age, sex, race, day of week, caregiver education, and primary caregiver status.

Results
At the within-subjects level in both children (46% female, 10.37□2.65 years) and their caregivers (85% female, 40.45□11.82 years), nights with longer-than-average sleep duration were associated with less-than-average next-day SB (p&lt; 0.001). For caregivers, later-than-average sleep onset time was associated with more-than-average next-day SB (p=0.05), while in children, later-than-average sleep offset time was associated with less-than-average next-day SB (p&lt; 0.01). At the within-subjects level in both children and their caregivers, higher-than-average SB was associated with a less-than-average subsequent nighttime sleep duration (p&lt; 0.001); and higher-than-average MVPA was associated with higher-than-average subsequent nighttime sleep efficiency (p&lt; 0.05).

Conclusion
The complex day-to-day associations between sleep, SB, and MVPA in caregiver-child dyads underscores the consideration of the continuum of 24-hour movement behaviors in family-based sleep health interventions. Furthermore, the similarities in associations between children and their caregivers warrants further investigation of the interrelatedness of family health behaviors.

Support (if any)
University of Delaware General University Research Grant, School of Nursing SEED funding, and the Centers of Biomedical Research Excellence (COBRE) in cardiovascular health (P20GM113125).
0267 Development and validation of low-level Transcranial Electrical Stimulation to enhance slow oscillations during human NREM sleep
Authors: Lim Miranda , Reynolds Christina , Milman Noah , Elliott Jonathan , Tinsley Carolyn , Levendovzsky Swati , Iliff Jeffrey , Luu Phan , Kernagis Dawn , Tucker Don 
Published: May 2023

Abstract

Introduction
Sleep disruption, and in particular decreased NREM stage N3 sleep, has been implicated in cognition and pathogenesis of Alzheimer's Disease (AD). Several recent findings suggest that enhancing N3 sleep may mitigate impairments due to chronic sleep disruption. However, approaches such as transcranial electrical stimulation (TES) have been limited by discomfort, awakenings, and lack of blinding. In response to the need to develop low-level TES, our team first identified the source of slow oscillations (SOs) of N3 sleep using high-density EEG array modeling (Sourcerer®), which localized within anterior limbic brain structures. Next, we showed that low-level TES targeted to this region induced and enhanced SOs during N3 sleep in the laboratory. More recently, we developed a portable TES device with the minimum deployable set of stimulation electrodes targeting the anterior limbic SO-generating regions: Wireless Interface Sensor Pod (WISP).

Methods
Thirteen healthy adults (m = 42 years) participated in three all-night sleep EEG recordings where they received low level (0.5 mA) TES designed to target anterior limbic SO-generating regions and a sham stimulation (placebo). TES was initiated after 4 minutes of naturally occurring NREM N2 sleep and included five TES blocks of five minutes each separated by one minute rest periods. Subjects were blinded to stimulation and sham conditions, and outcomes included feasibility, acceptability, and total time in each sleep stage. A convolutional neural network (CNN) was developed, trained, and tested on manually scored EEG sleep staging for automated sleep scoring.

Results
Subjects rated that the WISP device was highly feasible and acceptable. Subjects were adequately blinded to sham versus stimulation. When compared to the sham session, limbic-targeted TES significantly increased the duration of N3 sleep and spectral power in the 0.5-1 Hz frequency band. Finally, our CNN was able to detect sleep stages with good accuracy (84%, kappa 0.77).

Conclusion
These results suggest that low-level portable TES using a minimum deployable set of electrodes, when specifically targeting anterior limbic sites, is feasible, acceptable, maintains blinding, and increases deep (N3) sleep. The WISP device is a promising method to both assess and manipulate sleep in the home setting.

Support (if any)
DoD MOMRP MTEC-21-06-MPAI-129
0273 Advanced assessment of sleep derived from hypnodensity
Authors: Anderer Peter , Ross Marco , Cerny Andreas , Shaw Edmund , Fonseca Pedro 
Published: May 2023

Abstract

Introduction
In a recent analysis of PSG studies with six, nine and twelve human scorers, we showed that sleep stage ambiguity is the rule rather than the exception, and that sleep stage probabilities (SSPs) calculated with artificial intelligence provide an excellent estimate of this ambiguity. Here we investigate clinical and demographic factors driving this ambiguity and evaluate potential benefits of using hypnodensity in addition to the traditional hypnogram for sleep assessment.

Methods
SSPs were determined in 195 healthy subjects aged 20–95 years and in 49 apnea patients aged 29–73 years (AHI: 5.8–105.5 events/hour). In addition to the recommended sleep parameters, we derived the percentage of ambiguous sleep stages (SSP ≤ 0.95), the mean amount of ambiguity per sleep stage (1-SSP), sleep stage continuity (absolute difference in SSP between two epochs) and sleep stage stability (two adjacent epochs belonging to the same sleep stage with SSP &gt; 0.95 each) as well as NREM sleep depth (weighted average of NREM SSPs).

Results
The percentages of ambiguous NREM epochs (±SD) increased significantly with age (Pearson’s r=0.72; p&lt; 0.01) from 40±9% in young healthy subjects (20-39 years, n=61) to 50±9% in middle-aged healthy subjects (40-59 years, n=59) and to 62±11% in older healthy subjects (≥ 60 years, n=75). The percentages of ambiguous REM epochs did not change significantly across age (r=0.1). Apnea patients showed an increased NREM ambiguity (77±15% compared to 53±10% in age- and sex-matched healthy controls; p&lt; 0.01), while REM ambiguity was only slightly increased (p&lt; 0.05). Furthermore, sleep stage continuity, stability, and NREM sleep depth decrease significantly with age and are significantly lower in apnea patients than in healthy controls (p&lt; 0.01).

Conclusion
Artificial intelligence-based autoscoring shows ambiguity in 40% of NREM epochs in young healthy subjects, increasing to 77% in apnea patients. Assigning a single sleep stage to each epoch and presenting sleep architecture as a traditional hypnogram may be misleading, especially for older subjects and patients with sleep-disordered breathing. A hypnodensity chart representing sleep stage probabilities reflects this sleep staging ambiguity, provides all the information contained in a hypnogram, and offers insights into sleep ambiguity, continuity, stability, and sleep depth.

Support (if any)
0275 Deep Learning-based Sleep Detection using Torso Patch Vital Signs Improves Sleep-Wake Detection over Wrist Actigraphy
Authors: Pipke Matt , Alla Srilakshmi , Sekaric Jadranka , Richards Dylan , Gendy Maged , Abbott Sabra , Grimaldi Daniela , Reid Kathryn , Zee Phyllis 
Published: May 2023

Abstract

Introduction
Motion-based wearable sensors, typically on wrist, have long been used for free-living sleep detection and quantification. However, it is hard to differentiate sleep from sedentary awake time by immobility alone. Vital signs, like heart rate and respiration rate, can greatly enhance determination of wake-sleep state, and are easily monitored with newer wearable sensors. Deep learning techniques are particularly adept at learning labeled physiological states. By combining movement plus vital signs in a deep neural network algorithm, improved sleep detection, fragmentation and sleep staging should be possible compared to activity alone. We report on performance of a deep learning sleep detection and REM/NREM algorithm providing 24-hour evaluation with high specificity using data from a torso-wearable patch sensor as compared to polysomnography (PSG).

Methods
Twenty-six healthy adults (mean age 53.7 years, 81% female) contributed 150 nights of PSG during laboratory visits, during which participants simultaneously wore a multi-day skin-adherent patch with continuous single-lead ECG and 3-axis accelerometer streams, as well as a wrist activity monitor. A pre-trained deep neural network algorithm generated epoch-level Wake/REM/NREM classification (Sleep equals REM plus NREM) using vital signs and movement derived from the patch sensor ECG and accelerometer waveforms and was then compared to expert human staging of PSGs. The wrist actigraphy sleep-wake determinations (Actiware) were also compared to PSG.

Results
Data includes 900 hours sleeping and 139 hours awake, of which 195 hours of sleep were in REM state. Using patch data, the deep neural net algorithm achieved 92% sensitivity and 85% specificity to detect sleep as compared to PSG; REM was detected with 85% sensitivity and 97% specificity. By comparison, the wrist motion-based algorithm only exhibited 33% specificity and 95% sensitivity, essentially overcalling immobile wake as sleep.

Conclusion
Sleep evaluation in free-living environments with wearable sensors can be greatly improved over conventional motion-based wrist sensors by leveraging continuous vital signs. Deep learning-trained neural network algorithms are particularly effective for use with such data, as demonstrated with this algorithm.

Support (if any)
R01 HL140580 and P01 AG011412
0277 Improving the Validity of Self-Reported Sleep Duration: Experimental Comparisons of Question Format with Actigraphy
Authors: Zeller Camryn , Tagler Michael 
Published: May 2023

Abstract

Introduction
Accurate measurement of sleep is a challenge for researchers and practitioners. Objective measures (i.e., polysomnography, actigraphy) are expensive and time-consuming. Thus, the practical approach is self-report – with the cost of reduced validity. The purpose of this actigraphy ­experiment was to compare the validity of differently ­formatted self-reported sleep duration measures. It was hypothesized that question formats which encourage duration reporting to the nearest 15 minutes would lead to improved validity.

Methods
Adults (n=56, 73.2% female, 80.4% white, mean age = 21.3 years, SD = 4.18) were recruited through university email, a psychology participant pool, and word of mouth. Sleep was recorded via actigraphy for 7 days. Participants were randomly assigned to respond via computer to one of three versions of the Pittsburgh Sleep Quality Index (Buysse et al., 1989). Specifically, they reported hours of sleep per night in one of the following formats: 1) Open-ended response with standard instructions 2) Open-ended with modified instructions to “round to the nearest 15-minutes”, or 3) Standard instructions with drop-down menus to select hours and minutes (15-minute increments).

Results
Self-reported and actigraphy sleep duration were strongly correlated, r = .63, p &lt; .001. Average self-reported duration (7.05 hours) was significantly longer than actigraphy average (M = 6.65 hours), t(55) = 3.27, p &lt; .01, d = 0.44. There were no significant differences across the three self-reported sleep duration formats, F(2, 53) = 0.02, p = .98, η2 = .00. As expected, the open-ended standard format had the weakest correlation with actigraphy (r = .47), whereas the open-ended with modified instructions (r = .74) and drop-down menu (r = .68) versions were better predictors of actigraphy duration. The effect sizes for the improvement (q = .44, .32) were medium in magnitude (Cohen, 1969).

Conclusion
The results suggest that researchers and practitioners should consider the format of self-report sleep duration questions. We found formats that encourage participants the reporting of more precise estimates to more strongly predict actigraphy. Additional data is needed to replicate these findings, and for a better understanding of the factors that influence the magnitude of agreement between objective and subjectively reported sleep duration.

Support (if any)
0279 Sleep Hygiene for Sleep Health in the General Population: What Does Data From Consumer Sleep Technology Tell Us?
Authors: Gottlieb Elie , Gahan Luke , Wilson Samantha , Watson Nathaniel F , Raymann Roy 
Published: May 2023

Abstract

Introduction
Despite being used and widely recommended since the 1970s, few studies have examined whether adherence to sleep hygiene practices affect objectively measured sleep in non-clinical populations. While individual components of sleep hygiene such as limiting caffeine and alcohol consumption are clearly related to sleep by plausible physiological and psychosocial mechanisms, the real-world evidence of overall sleep hygiene practices on sleep is surprisingly inconsistent. Here, we examined the association between self-reported sleep hygiene practices and objectively measured sleep in a general population.

Methods
Responses to a survey on sleep hygiene were used and matched with objective sleep data, resulting in data from 720 users (mean age: 52.5 ± 15.9, 63.4% female). Objective sleep data across 92,808 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact sonar-based method to capture sleep-related metrics and self-reported lifestyle. Self-reported sleep hygiene practices were assessed with 13-items on a 5-point scale ranging from “Never” to “Always”. Descriptive statistics and linear regressions were used for the analysis, controlling for age and gender.

Results
Overall, the top three most frequented poor sleep hygiene practices were going to bed at different times (29.7%), overthinking/worrying in bed (24.0%), and waking at different times (22.7%). Linear regressions revealed a significant negative association between composite sleep hygiene scores and objective sleep measures, whereby poorer sleep hygiene was associated with significant reductions in total sleep time (ß=-0.89, SE=0.33, p&lt; 0.01), REM duration (ß=-0.24, SE=0.10, p&lt; 0.05), and SleepScore (ß=-0.15, SE=0.05, p&lt; 0.01), an objective sleep quality metric. No significant associations were observed between individual sleep hygiene factors and objectively measured sleep.

Conclusion
While we could not identify a relationship between individual hygiene factors and sleep, poorer aggregated sleep hygiene scores were associated with poorer objectively measured sleep. Thus, sleep health may not be defined by one single behavior, but rather by the sum of its parts. Future work should examine the efficacy of personalized sleep hygiene factors in sub-clinical populations, where targeted sleep hygiene education may be preferred given it is more intuitive and less burdensome than other behavioral interventions.

Support (if any)
SleepScore Labs