bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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&gt;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

Abstract

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&gt;0.05). On drinking nights, participants went to bed later (p&lt; 0.05) but woke up at similar times compared to non-drinking nights, resulting in significantly shorter sleep durations on drinking nights (p&lt; 0.05). There were no effects of alcohol use on subjective sleep quality or daytime sleepiness (ps&gt;0.05) or on next-day nocturnal sleep variables (ps&gt;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&gt;0.05). There were also no differences in actigraphy-derived daytime sleep variables between drinkers and non-drinkers (ps&gt;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&gt;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

Abstract

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=&gt;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

Abstract

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.