bluuebunny/crossref_metadata_2025 huggingface dataset:
0164 Move Your Feet and Sleep: A Longitudinal Dynamic Analysis of Physical Activity, Sedentary Behavior, and Insomnia Symptoms
Authors: Nielson Spencer , Boyle Julia , Perlis Michael , Dzierzewski Joseph
Published: May 2023

Introduction

Insomnia symptoms, which include sleep disturbances and daytime dysfunction, are associated with poor physical and mental health. Physical activity has been associated with good sleep while sedentary behavior has been associated with poor sleep. This study investigated whether physical activity and sedentary behavior are associated with insomnia symptoms both generally (i.e., between-persons, mean-level) and dynamically (i.e., within-persons, person-centered).

Methods

Participants (N=775) included adults (Mage=47.3 years,54.6% female) who completed an online longitudinal study investigating sleep and health across the lifespan at three time points (approximately 17-weeks apart). Participants reported: (a) their duration of moderate-to-vigorous physical activity per week (in minutes), (b) the percentage of their day spent sedentary, and (c) their insomnia symptoms via the Insomnia Severity Index which was scored according to traditional standards (i.e., total score) as well as proposed two factor scores: (1) Sleep Disturbance (items 1,2,3) and (2) Daytime Dysfunction (items 4,5,6,7). Multilevel modeling was used to examine the associations among physical activity, sedentary behavior, and insomnia symptoms at both between- and within-persons levels.

Results

No significant associations were observed among total ISI score or the factor scores, and minutes spent in moderate-to-vigorous physical activity (all p’s>.05). Sedentary behavior, however, was significantly associated with total ISI score at both the between-person and within-person levels (β=.02, t=2.71, p=.007; β=0.03, t=2.62, p=.009, respectively). For the Sleep Disturbance factor, between-person sedentary behavior was not a significant predictor while within-person sedentary behavior was associated with higher sleep disturbance scores (β=0.01, t=1.16, p=.245; β=.01, t=2.06, p=.039, respectively). For the Daytime Dysfunction factor, both between-persons and within-person levels of sedentary behavior were associated with increased daytime dysfunction (β=.02, t=3.30, p=.001; β=.01, t=2.53, p=.012).

Conclusion

Sedentary behavior appears to be a more robust predictor of insomnia symptoms than physical activity. Further, the association between sedentary behavior and insomnia symptoms was observed to be dynamic in that when an individual reported being more sedentary than customary, they also reported more insomnia symptoms. Future analyses should consider demographic variables and comorbid conditions to identify subgroups in which these associations are most pronounced.

Support (if any)

National Institute on Aging of the National Institutes of Health under Award Number K23AG049955 (PI: Dzierzewski).
0165 The Relationships between Early Maternal Sleep and Mother-Infant Bonding, and Infant Bedtime Difficulty Later: A Path Analysis
Authors: Jiao Nana , Pituch Keenan , Whisner Corrie , Petrov Megan
Published: May 2023

Introduction

Infant sleep is influenced by biopsychosocial factors. However, the influences of early parental sleep quality and parent-infant bonding on infant sleep development are underexplored. This study aimed to examine the relationships between early maternal sleep quality and mother-infant bonding at 8 weeks postpartum, and infant bedtime difficulty at 3 months postpartum.

Methods

This study is a secondary analysis from the Snuggle Bug/Acurrucadito Study, an observational, longitudinal cohort study investigating the associations between 24-hr sleep-wake patterns, gut microbiome, and weight development among infants. Mothers (n=60, 31.5±5.4y, 66.3% married, 40.8% Hispanic, 41.5% < bachelor’s degree) and their full-term (≥37wk), singleton infants without major complications were recruited from Phoenix, Arizona metropolitan area. At 3 and 8 weeks, and 3 months of infant age, mothers completed the Pittsburgh Sleep Quality Index (PSQI, range:0-21, higher scores indicate worse sleep quality), and reported on infant bedtime difficulty, using a 5-point Likert scale (range:1-5) with higher scores indicating greater bedtime difficulty. Mother-infant bonding was measured by the Postpartum Bonding Questionnaire (PBQ, range:0-125) at 8-week age, with higher scores indicating greater bonding problems. Based on the Transactional Model of Infant Sleep, path analyses were conducted to identify the direct effect of 8-week PSQI scores, and indirect effects of 8-week PBQ total scores and 8-week bedtime difficulty on 3-month bedtime difficulty, using z scores for all variables and the 3-week bedtime difficulty as a covariate.

Results

At 8 weeks postpartum, the means of PSQI, PBQ, and bedtime difficulty were 5.8±2.6, 5.3±4.7, and 2.4±1.0, respectively. Bedtime difficulty at 3 months was 2.0±1.0. After adjusting for the 3-week bedtime difficulty, the 8-week maternal sleep quality was not directly associated with 8-week (β=.005, 95%CI [-0.31, 0.32]) or 3-month infant bedtime difficulty (β=-0.14, 95%CI [-0.43, 0.14]), whereas 8-week mother-infant bonding significantly mediated the relationship between 8-week maternal sleep quality and 3-month infant bedtime difficulty (β=0.1, 95%CI [0.02, 0.22]).

Conclusion

This study highlighted that early maternal sleep may disrupt early mother-infant bonding, which, in turn, may increase infant bedtime difficulty later in development. Future research should investigate how best to support healthy family sleep and bonding relationships.

Support (if any)

NIH/NHLBI R01HL147931
0169 Variability in Sleep and Eating are Associated with Subclinical Atherosclerosis in Early Adulthood
Authors: Hoopes Elissa , Witman Melissa , D'Agata Michele , Edwards David , Robson Shannon , Keiser Thomas , Malone Susan , Patterson Freda
Published: May 2023

Introduction

Experimental studies show that sleeping and eating at times that do not align with one’s endogenous biological timing (‘circadian misalignment’) promotes a physiological environment that favors cardiovascular disease (CVD) development. Day-to-day variability in sleep and eating are considered common causes of circadian misalignment in the general population. While emerging evidence suggests that variability in sleep and eating behaviors predicts overt CVD in middle-aged and older adults, the extent to which these behaviors relate to biomarkers of CVD development in subclinical stages has not been fully elucidated.

Methods

Adults without chronic health conditions or sleep disorders completed 14 days of concurrent sleep and dietary assessments via continuous wrist actigraphy and time-stamped photo-assisted diet records, respectively. Variability in sleep duration, sleep midpoint, eating onset/offset (clock time of first and last caloric consumption, respectively), and caloric midpoint (clock time at which 50% of total daily calories are consumed) were quantified for each variable, operationalized as the standard deviation (SD) across the monitoring period. Multivariable regression models determined the cross-sectional association between metrics of sleep and eating variability with carotid intima-media thickness (CIMT), an established biomarker of subclinical atherosclerosis, measured at end-diastole via B-mode ultrasonography. Models adjusted for age, sex, resting systolic blood pressure, sleep duration, and total daily caloric intake.

Results

Participants (N=61) were 29.1±7.1 years, 66% female, with a blood pressure of 113±9/70±7 mmHg. Variability in sleep midpoint (p=0.19), eating onset (p=0.19), and eating offset (p=0.40) were not associated with CIMT. Alternatively, each 60-minute increase in sleep duration SD was associated with a 0.05±0.02 mm greater CIMT (p=0.005), while each 60-minute increase in caloric midpoint SD was associated with a 0.04±0.01 mm greater CIMT (p=0.009). Sleep duration SD (p=0.02) and caloric midpoint SD (p=0.04) remained associated with CIMT in an exploratory adjusted model that included both variables.

Conclusion

Metrics of sleep and eating variability are associated with subclinical atherosclerosis in free-living adults. These data support a growing body of literature indicating that encouraging regularity in nightly sleep duration and the timing of daily caloric consumption may represent behavioral strategies to mitigate risk for overt CVD.

Support (if any)

American Heart Association (#831488); University of Delaware Research Fund-SI Award
0171 Assessment of Student Sleep and Well-Being During a Stress Inoculation Training at The U.S. Navy Surface Warfare Officers School
Authors: Dotson Elizabeth , Shattuck Nita , Matsangas Panagiotis , Lawrence-Sidebottom Darian
Published: May 2023

Introduction

Military operations frequently take place in complex, volatile, and ambiguous environments, where important information may be uncertain. These circumstances are often highly stressful, and fatiguing due to sustained operations, shift work, sleep loss, and the taxing nature of the work. As a crucial part of their preparation for leading U.S. Navy sailors, prospective executive officers (PXOs) in the Surface Warfare community receive training at the Surface Warfare Schools Command (SWSC). At the request of SWSC, the NPS research team was asked to assess the stress loads of SWO students during their expanded 10-week training course, which included the implementation of a stress inoculation training (SIT) program.

Methods

SWOs (N=50) attending the Prospective Executive Officer (PXO) Course at SWSC participated in a quasi-experimental longitudinal study. They completed questionnaires three times during the PXO course: at the beginning of the study, at the mid-point, and at the end of the course. Standardized tools were used to assess perceived stress levels (Perceived Stress Scale – PSS), depression symptoms (Patient Health Questionnaire – PHQ-8), anxiety (General Anxiety Disorder questionnaire – GAD-7), and mood (Profile of Mood States – POMS). Sleep attributes were assessed by wearable devices (ŌURA rings).

Results

Overall, the scores of SWOs on the PSS, PHQ-8, GAD-7, and POMS were lower or equivalent to scores reported for the general population. However, reported levels of perceived stress increased significantly from the beginning to the end of the PXO course (average PSS=9 vs 12). However, PHQ-8, GAD-7, and POMS scores remained stable across the three time points. On average, PXOs received 6.6 ± 0.65 hours of sleep per night on weeknights at SWSC, with about one additional hour of sleep on weekends.

Conclusion

These findings indicate that, while SWOs experienced an increase in perceived stress during the PXO course, their overall well-being is not substantively different from the general population. Participants appear to use weekends for additional restorative sleep, obtaining adequate amounts of sleep on weekends. We did not observe evidence of impairments in mental or physical wellness of SWOs attending the PXO course at SWSC.

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0177 Quantifying the duration and timing of pilot sleep during layovers
Authors: Rempe Michael , Rasmussen Ian , Gregory Kevin , Hilditch Cassie , Flynn-Evans Erin , Lamp Amanda
Published: May 2023

Introduction

Pilots flying long range (8-16 h) and ultra long range (16+ h) flights crossing several time zones must decide when and how much to sleep during layover between the outbound and return flight. Some pilots choose to sleep on home base time (HBT), while others attempt to sleep on local layover time. We sought broad patterns in the timing and amount of pilot sleep during layovers.

Methods

Using actigraphy, verified by self-report diaries, we collected sleep/wake data on 229 pilots (flying 460 total trips) from nine routes ranging from 10 to 17 hours in scheduled duration. All trips originated in the United States and seven were westbound routes, and two were eastbound routes. Scheduled layover durations ranged from 24 to 56 hours. We collected continuous sleep data three days before the trip, during both flights, during layover, and at least three days after the trip.

Results

Across all routes, pilots slept an average of 7 hours 58 minutes per 24 hours of layover (± 1 hour 53 minutes SD). Neither the amount nor timing of layover sleep depended on any of the following variables: 1) time (relative to home base) that the layover began; 2) direction of travel (westbound vs eastbound); 3) crew role (flying vs relief); and 4) total amount of sleep during the outbound flight. On layovers lasting 48 hours or longer, the second 24 hours showed more pilots sleeping during local night compared to the first 24 hours. Interindividual differences in the timing and amount of layover sleep were very large (larger than group differences between routes, between crews, and between flight directions.) On average, on layover days pilots obtained 17 minutes less sleep compared to pre-trip days and 32 minutes less sleep compared to post-trip days.

Conclusion

There are many complex factors that contribute to the timing and amount of layover sleep. We found that interindividual differences are larger than the variables that are constrained by the operational environment, which suggests that the timing and duration of layover sleep may be impacted more by individual traits rather than environmental or operational factors.

Support (if any)

United Airlines
0179 Mathematical Models to Predict Sleep Latency and Sleep Duration
Authors: Reifman Jaques , Vital-Lopez Francisco , Balkin Thomas
Published: May 2023

Introduction

The demands of a 24/7 society prevent millions of shift workers around the world from obtaining regular nightly sleep, negatively affecting their health, safety, and productivity. To mitigate this problem, workers should maximize the benefits of their atypical sleep opportunities. This requires assessing the ability of an individual to initiate and maintain sleep at the intended time, given their recent sleep history. Here, we present two validated mathematical models, one for predicting sleep latency and a second for predicting sleep duration, as decision aids for planning effective sleep schedules that maximize restorative sleep.

Methods

As an extension of the two-process model of sleep regulation, the proposed models predict sleep latency and sleep duration by accounting for the nonlinear interactions between homeostatic sleep pressure and the circadian rhythm. We estimated the models’ parameter values for a population of young, healthy adults using experimental data from two studies and validated the models by comparing their predictions with experimental measurements from 22 different studies, involving a variety of sleep conditions spanning the entire circadian cycle and a total of 317 unique subjects.

Results

Comparison of the sleep-latency and sleep-duration models’ predictions with the experimental data showed that the models yielded acceptable average prediction errors: 4.0 minutes for sleep latency and 0.8 hours for sleep duration. Importantly, we identified conditions under which small shifts in bedtime may result in disproportionately large differences in sleep duration--knowledge that may be applied to improve productivity, safety, and sustainability in civilian and military operations.

Conclusion

By extending the capabilities of existing predictive fatigue-management tools, the mathematical models described here allow users to determine the most opportune times to sleep. This information can be used to optimize sleep efficiency, allowing shift workers to maximize the benefits of restorative sleep.

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0181 A relationship between academic performance and nighttime sleep in Japanese university students
Authors: Sato Toshihiko
Published: May 2023

Introduction

Sleep loss can cause various kinds of behavioral and psychological problems. It has been reported that sleep loss may even influence academic performance. To examine the relationship between nighttime sleep and academic performance, we conducted a questionnaire study targeting university students majoring in social sciences.

Methods

To recruit undergraduates for participation, we selected a few psychology-related classes at a university in Nagano Prefecture, Japan, and went to the classroom to ask the students to respond to a questionnaire that dealt with nighttime sleep and grade point average (GPA) as a measure of their academic performance. We approached 116 students, of whom 109 consented to participate and completed the questionnaire. The mean age of the final sample was 20.11 years old.

Results

The participants were divided into six groups: three groups based on grade and two based on sex. A two-factor analysis of variance (ANOVA) indicated that students with worse grades reported significantly shorter sleep durations than those with intermediate and better grades. It is also suggested that the male students with better grades tended to report delayed sleep schedules, that is, a later bedtime and waking time, compared to the two other male groups, while the females with better grades did not.

Conclusion

The finding strongly suggests that sleep duration may affect the academic performance of undergraduates. Sufficient sleep may result in a better academic performance and might improve the quality of our lives. In this regard, the results may be consistent with a previous study targeted high school students (Wolfson & Carskadon, 1998). In sleep hygiene education, the students should be taught to have enough sleep in every night. In addition, the finding also suggets that the Japanese male students with better grades may have a tendency to stay up late to study more, while the female with better grades may not.

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0190 Propensity for Sleep Inertia during Sleep and Its Manifestation After Awakening in a Dynamic Biomathematical Fatigue Model
Authors: McCauley Mark , McCauley Peter , Kalachev Leonid , Van Dongen Hans
Published: May 2023

Introduction

Sleep inertia, the transient period of cognitive impairment experienced immediately after awakening, is of concern in on-call and around-the-clock operational settings and merits development of a predictive tool to manage the associated fatigue risk. Recent work on our previously published biomathematical fatigue model revealed that predictive information about sleep inertia can be gained from tracking the propensity for sleep inertia across time spent asleep. In this approach, a neurobiological process with relatively fast dynamics (in the order of several minutes) interacts with the much slower dynamics of circadian, homeostatic, and allostatic sleep/wake regulation. Here we refine our modeling approach by mathematically dissociating the magnitude of sleep inertia from its dissipation rate.

Methods

N=280 healthy young adults (ages 21–49y) each participated in one of four laboratory studies of total sleep deprivation, sustained sleep restriction, and/or daytime napping. At 2–4 hour intervals while awake, participants performed a Psychomotor Vigilance Test (PVT), assessing number of lapses (RT>500ms), and rated their sleepiness on the Karolinska Sleepiness Scale (KSS). Sleep periods were recorded polysomnographically. Data were divided into a calibration set used to estimate two new model parameters capturing sleep inertia, and a validation set used to independently verify model goodness-of-fit. Sleep inertia was modeled with two ordinary differential equations – one during wakefulness to track manifest impairment, and one during sleep to track the propensity for sleep inertia upon awakening. Two parameters were fit, one modulating sleep inertia magnitude and one providing a dynamic time constant.

Results

Based on the calibration data set, the sleep inertia dissipation half-life was found to be 0.43h±0.07h. Based on the validation data set, the goodness-of-fit root-mean-square-error was 2.56 for PVT and 1.05 for KSS, indicating moderate-to-high predictive accuracy. As an emergent property of the model, the propensity of sleep inertia first increased and then decreased over sleep time, peaking 1–2h into the sleep period.

Conclusion

A two-parameter expansion of our biomathematical fatigue model captured the transient effect of sleep inertia accurately for sleep deprivation, sleep restriction, and daytime napping scenarios, and revealed a novel, emerging dynamic of sleep inertia propensity during sleep.

Support (if any)

Federal Express Corporation and WSU HPC
0193 Sleep profiles of drinking and non-drinking college students
Authors: Spaeth Andrea , Piersol Kelsey , Pawlak Anthony , Buckman Jennifer
Published: May 2023

Introduction

Alcohol use and insufficient sleep are prevalent in American college students and studies suggest that these behaviors are reciprocally interrelated. However, there is limited information about the relationship between sleep and alcohol use in college students without sleep or alcohol disorders. This study aimed to characterize the relationship between sleep and alcohol use in healthy college students by considering the effects of using alcohol on same-day nocturnal sleep, next-day nocturnal sleep, and napping.

Methods

Thirty-one college students (n=12 consumed alcohol during the study, n=19 did not) completed twice-daily online diary reports and wore a wrist-actigraphy device across two weeks. Linear mixed models examined person-specific (e.g., drinker or non-drinker) and event-specific (e.g., drinking or non-drinking night; school or free night) effects of alcohol use on subjective and objective sleep outcomes.

Results

When averaged across all nights, sleep behavior did not differ between drinkers and non-drinkers (ps>0.05). On drinking nights, participants went to bed later (p< 0.05) but woke up at similar times compared to non-drinking nights, resulting in significantly shorter sleep durations on drinking nights (p< 0.05). There were no effects of alcohol use on subjective sleep quality or daytime sleepiness (ps>0.05) or on next-day nocturnal sleep variables (ps>0.05). Daytime sleep (i.e., napping) was observed in 25 (81%) participants. The proportion of nappers and number of naps per participant did not significantly differ by drinking status (ps>0.05). There were also no differences in actigraphy-derived daytime sleep variables between drinkers and non-drinkers (ps>0.05). Among drinkers who napped (n=9), naps were taken on 27% of days following drinking nights and on 17% of days following non-drinking nights (p>0.05).

Conclusion

This study bolsters prior reports that insufficient nocturnal sleep and daytime napping are common in college by validating self-reported sleep behaviors with objective actigraphy data. Efforts to convey the importance of proper sleep are still needed in the college context. Future research should examine consequences of layering acute bouts of excessive alcohol consumption onto insufficient nocturnal sleep.

Support (if any)

This work was supported by the NIH NIAAA grant R01AA028286 to JB and AS.
0196 The relationship between brain functional connectivity and objective and subjective assessments of chronic habitual sleep status
Authors: Khalsa Sakh , Mayhew Stephen , Bagary Manny , Bagshaw Andrew
Published: May 2023

Introduction

Sleep is a multiscale phenomenon. It has subjective and objective components often not highly correlated. Given the importance of brain networks for cognition and evidence that sleep deprivation and variability in habitual sleep patterns affects Functional brain Connectivity (FC), we would expect brain networks would be differentially related to subjective and objective markers of sleep. As well as region specific studies, investigating the effect of habitual sleep measures across whole network interactions is important. This study investigates whether overall FC from 3 Intrinsically Connected Networks (ICN); Default mode(DMN), Salience(SN) and Central executive(CEN) network is differentially related to subjective and objective sleep measures from sleep quality questionnaire, sleep diary data and actigraphy metrics.

Methods

Functional Magnetic Resonance Imaging data acquired from 32 healthy adults (right handed, 14 female, age 23-59 years). Regions of interest (ROI) representing nodes of the ICN created from separate data of 55 subjects (28 male, age 25±4yrs). Seed-based FC analysis performed using in-house MATLAB code. Average FC across all paired connections in each ICN calculated to define overall mean FC. Two-way multiple measures factorial ANOVA split-plot analysis performed with a 2x3x6 design to determine any interaction of 6 sleep measures (Pittsburgh sleep quality index, subjective sleep efficiency, diary total sleep time, wake after sleep onset, total sleep time, sleep efficiency) with the FC of the ICN. Pairwise comparisons for significant interactions and univariate analysis on the linearly independent pairwise comparisons among the estimated marginal means performed to confirm significant interactions between ICN FC and sleep measures.

Results

No significant interactions for any of the sleep metrics tested, subjective or quantitative (P=>0.05). Except subjective sleep efficiency (SSE) which demonstrated a significant within subject interaction of overall network FC (F(2,30) = 3.682, P = 0.033, n2=0.109) for CEN.

Conclusion

Significant findings of SSE in relation to CEN demonstrate increased FC of the CEN in relation to higher SSE and lower CEN FC in subjects with low SSE. Overall findings suggests FC changes within ICN in relation to chronic habitual sleep status metrics (subjective or quantitative) don’t involve whole network FC, therefore we believe them to be region specific.

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0220 SLEEP DEPRIVATION ENHANCES THETA FLUCTUATIONS IN TIME PERCEPTION
Authors: Fan Zhiwei , Abe Takashi
Published: May 2023

Introduction

Sleep deprivation can impair time perception. In a sleep-deprived state, time tends to be overestimated. In addition, sleep deprivation can affect circadian fluctuations in time perception. Time perception may also fluctuate at rhythms other than circadian rhythms, such as theta frequency. Previous studies have shown that perceived time can fluctuate at theta frequencies and that such fluctuations are a function of the cue-to-target interval for the response. However, whether sleep deprivation affects theta fluctuations in time perception is unknown.

Methods

In this study, we compared a sleep deprivation condition with a normal sleep condition that served as an experimental control. In both conditions, participants (N=24, 13 females, mean age 25.6±3.5 years) were asked to complete a task to generate 3 seconds of time by pressing a key before and after sleep or sleep deprivation time (23:00 to 8:00). The interval between the clue (cueing that a response signal will appear) and the response signal (requiring 3 seconds to be generated as soon as possible) was designed to range from 0.25 to 1.05 seconds in steps of 1/30th of a second.

Results

As a result, similar to previous studies, an overestimation of time was observed in the case of sleep deprivation. In addition, stronger fluctuations in time perception were found at low theta frequencies (4.4 to 4.8 Hz) in the case of sleep deprivation.

Conclusion

This study suggests that cognitive abilities such as time perception fluctuate at different rhythms, and sleep deprivation may affect these fluctuations. These fluctuations may be associated with instability of brain states and functions (e.g., attention) due to sleep deprivation. Further studies are needed to elucidate the neurophysiological mechanisms.

Support (if any)

JSPS Foreign Researcher Scholarship, JSPS KAKENHI (Grants No. JP17K19639), AMED (Grants No. JP21zf0127005), and World Premier International Research Center Initiative (WPI), MEXT, Japan.
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

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

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 [>16.9% of residents below poverty level], medium [>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 (< 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.

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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

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< 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< 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< 0.001); and higher-than-average MVPA was associated with higher-than-average subsequent nighttime sleep efficiency (p< 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

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.

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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

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 > 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< 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< 0.01), while REM ambiguity was only slightly increased (p< 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< 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.

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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

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.

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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

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 < .001. Average self-reported duration (7.05 hours) was significantly longer than actigraphy average (M = 6.65 hours), t(55) = 3.27, p < .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.

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