bluuebunny/crossref_metadata_2025_split huggingface dataset:
0174 Patterns of sleep habits and their impact on nighttime sleep quality in college students
Authors: Zhong Jie , Katigbak Carina , Gregas Matthew , Ye Lichuan 
Published: May 2023

Abstract

Introduction
Poor sleep quality is common in college student. Unhealthy sleep habits (e.g., delayed bedtime, inconsistent sleep routine between schooldays and weekends) could lead to poor sleep quality, which is associated with poor academic performance, mental and physical health. However, little is known about the patterns of sleep habits in terms of timing and consistency and their impact on nighttime sleep quality in college students.

Methods
This was a cross-sectional anonymous web-based survey to examine sleep in college students. The Classification and Regression Tree (C&RT) was used to identify distinct patterns based on sleep habits including bedtime and wake-up time (usual, schooldays, and weekends), and consistency of bedtime, wake-up time, and duration between schooldays and weekends. The response variable was nighttime sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) global score. The imputed PSQI global score was used to minimize missing data.

Results
A total of 439 students participated in this survey, with a response rate at 22%. Four distinct clusters were identified. Both Cluster 1 (37.8%) and Cluster 2 (10.9%) included participants who reported a difference in sleep duration between schooldays and weekends &lt; 1.75 hours, with Cluster 1 reporting a usual bedtime before 1:45AM while Cluster 2 after 1:45AM. Both Cluster 3 (3.4%) and Cluster 4 (47.8%) included participants who reported a difference in sleep duration between schooldays and weekends ≥ 1.75 hours, with Cluster 3 reporting school-day wake-up time before 7AM while Cluster 4 after 7AM. Among the four clusters, participants in Cluster 3 (late riser with inconsistent schooldays-weekends duration) had the worst sleep quality (8.95±3.43), followed by Cluster 2 (late sleeper with consistent schooldays-weekends duration, 6.98±2.79) and Cluster 4 (late riser with inconsistent schooldays-weekends duration, 6.83±2.87). Cluster 1 (“early” sleepers with consistent schooldays-weekends duration) had the best sleep quality among all (5.61±2.54).

Conclusion
Distinct patterns of sleep timing and consistency can have significant impact on sleep quality, with some habits such as extremely late bedtime and inconsistent sleep duration between schooldays and weekends had the most negative impact. Tailoring interventions targeting these unhealthy sleep habits are needed in promoting sleep in this population.

Support (if any)
None
0176 Predictors of sleep duration among university students in Japan: Cross-cultural insights for developing sleep health interventions
Authors: Eakman Aaron , Kawakatsu Yuki , Takahata Miki , Muraoka Shuhei , Kudo Daisuke , Satake Shinji , Sato Toshiaki 
Published: May 2023

Abstract

Introduction
Inadequate sleep among university students, especially in Japan, contributes to low academic performance and mental health challenges. Sleep health, including adequate sleep duration and quality, may serve a protective role in promoting performance and full participation in daily life. We sought to identify factors explaining sleep duration in Japanese college students as potential targets for culturally sensitive sleep health promoting intervention.

Methods
Cross-sectional descriptive study of university students in Japan in 2021; N = 362 completed validated Japanese versions of the Pittsburg Sleep Quality Index (PSQI), Morningness and Eveningness Questionnaire, Sleep Hygiene Practice Scale, and Dysfunctional Beliefs and Attitudes about Sleep. Indicators of sleep and PSQI subscales (except sleep duration), personality, depression, anxiety, time use, and participation quality were included in multivariate logistic regression modeling to explain less healthy (6 hours or less) versus healthier (more than 6 hours) sleep duration.

Results
Students were 20.0 (SD = 1.3) years, 86.4% female with a mean sleep duration of 400 (SD = 67) minutes. The mean PSQI score was 4.8 (SD = 2.5) indicating good sleep quality, though 41.4% of the sample reported sleeping 6 hours or less and these persons had lower sleep quality (PSQI; d = 1.20) compared to longer sleepers. Logistic regression explained category assignment (Chi-square = 68.978, df = 8, p &lt;.001; Nagelkerke R-squared = .234). Longer sleepers were more likely to report greater sleep efficiency, less frequent sleep disturbances, lesser daytime dysfunction, shorter commuting times, a morning-like chronotype, greater participation quality, and a less agreeable personality type. Interestingly, longer sleepers reported less effective sleep hygiene practices.

Conclusion
Short sleep was common and associated with poor sleep quality. Longer sleepers were more likely to report optimal sleep health indicators and greater participation quality (i.e. engagement in meaningful activities). Counter-intuitive sleep hygiene findings indicate potential cultural variation in sleep timing, pre-sleep arousal, and environment regulation as targets for intervention. Sleep health promotion intervention should consider personality, chronotype preference and time use (e.g. meaningful activities, commuting) as factors promoting or inhibiting sufficient sleep duration and quality in university students.

Support (if any)
Fulbright U.S. Scholar Award to Japan
0185 Circadian Rhythm of Rock Climbing Specific Finger Strength
Authors: Yao Zhixing , Smith Andrew , Vaughn Bradley 
Published: May 2023

Abstract

Introduction
Circadian rhythm influences various aspects of athletic performance; however, the sport specific effects in rock climbing are not well studied. This study aimed to evaluate circadian changes in finger strength in rock climbers. This may have important implications for timing of training and challenging climbing goals.

Methods
Participants completed two rounds of finger strength testing on the same day (morning 7-9 am and evening 5-7 pm) consisting of maximal pulls on a polyurethane training block (18 mm flat edge) anchored to a dynamometer (TindeqTM). Participants were cued to keep 4 fingers on without using the thumb and given two attempts on each hand with the highest load recorded each session. Demographic data, climbing experience level, sleep quality (Promis Sleep short form.), and chronotype (Morningness-Eveningness questionnaire) were recorded for secondary outcome analysis. Standard descriptive statistics and paired t-tests were performed to compare the morning and afternoon findings.

Results
10 participants completed finger strength testing. There was a significant increase in mean maximum finger strength from morning (M = 35.2 kg, SD = 14.8) to afternoon (M = 40.1 kg, SD = 16.1), t(9) = 4.1, p = .003, with a large effect size (Cohen’s d = 1.3). The mean increase was 5.0 kg with 95% confidence interval of 2.2 kg to 7.7 kg. The morning to afternoon increase in maximal strength remained significant when subdivided by sex (males n = 6, p = .04, 95% CI [0.4kg, 10.0] kg, and females n = 4, p = 0.48, 95% CI [0.1, 9.1] kg). This study lacked sufficient power to detect differences related to other secondary measures.

Conclusion
Maximum finger strength in rock climbers was greater in the afternoon compared to morning suggesting a circadian rhythm influence. Rock climbers needing peak finger strength may benefit from timing hard climbs in the afternoon rather than early morning. Further study is needed to determine if this finding is seen in larger groups or impacted by sleep quality and chronotype.

Support (if any)
Training space was supported by Triangle Rock Club, Durham NC.
0187 Hypnograms for 365 Nights Predicts Subjective Sleep Quality in Healthy Adults: Results from the Ultra Long-Term Sleep (ULTS) Study
Authors: Ahrens Esben , Hemmsen Martin C , Duun-Henriksen Jonas , Taptiklis Nick , Cormack Francesca , Djurhuus Bjarki , Homøe Preben , Kjær Troels W , Jennum Poul 
Published: May 2023

Abstract

Introduction
Sleep quality is fundamental to our somatic and mental health. However, the relationship between subjective sleep quality and sleep architecture remains poorly understood. New wearable or minimally invasive technologies facilitate the recording of electroencephalography (EEG) with lower spatial resolution than standard EEG but much greater longitudinal dispersion. This enables investigation of day-to-day variation in sleep measured directly with EEG. This study will compare EEG-derived sleep parameters with covariates such as sustained attention and subjective sleep quality.

Methods
Twenty-five healthy adults were implanted with a two-channel subcutaneous EEG (sqEEG) lead. Twenty subjects completed the 1-year protocol (average 32±13 years of age). Their sqEEG signals were recorded each night for 1 year alongside a morning 3-minute Psychomotor Vigilance Task (PVT) and self-reported sleep quality, which included Karolinska Sleepiness Scale (KSS). A deep learning model, U-Sleep, was fine-tuned on sqEEG with synchronized gold standard polysomnography used as ground truth. Hypnograms and sleep parameters were thus automatically calculated.

Results
Subjective sleep quality measured by KSS revealed a moderate negative correlation with rapid-eye-movement (REM) duration (r=-0.31, 95% CI=(-0.31, -0.31)), and total sleep time (TST) (r=-0.31, 95% CI=(-0.31, -0.31)). There was a moderate correlation between KSS and mean PVT reaction time (r=0.21, 95% CI=(0.21, 0.22)). There was a low negative correlation between PVT and TST (r=-0.1). Preliminary results indicate a moderate correlation between sleep parameters and subjective sleep quality. The correlations with PVT were lower, which suggests that 3-minute PVT is not sensitive to TST in normal sleep. However, the correlation between PVT and KSS suggests that PVT does predict subjective sleep quality, but to a smaller degree than standard sleep parameters.

Conclusion
Measuring day-to-day variation in high-quality EEG-based sleep recordings has the potential of creating a new branch in sleep medicine. Patients can be evaluated not only by findings in a single recording but the stability and variation of all findings can be analyzed. Preliminary results suggest that subjective sleep quality can be predicted directly from sqEEG and potentially be explained by behavioral factors in a subsequent cause-effect analysis.

Support (if any)
The project is supported by Innovation Fund Denmark, UNEEG medical, and T&W Engineering.
0188 OBJECTIVE AND SUBJECTIVE FEATURES OF SLEEP AND BEHAVIOR IN ADULT SURVIVORS OF PEDIATRIC HODGKIN LYMPHOMA
Authors: Navarrete Miguel , Mulrooney Daniel , Flerlage Jamie , Hudson Melissa , Mandrell Belinda , Krull Kevin 
Published: May 2023

Abstract

Introduction
Adult survivors of pediatric Hodgkin Lymphoma (HL) report poor sleep quality and excessive fatigue, often associated with obstructive sleep apnea (OSA). However, little is known about sleep dynamics in HL survivors without sleep disorders. In this exploratory study we evaluated quantitative and qualitative sleep of HL survivors without OSA and examined associations with cancer treatment and functional outcomes.

Methods
Adult participants completed two consecutive nights of in-home polysomnography (PSG) with at least 4 hours of recorded sleep. For those participants with a sleep efficiency &gt;85% and without OSA (HL survivors N=39, mean[SD] age=35[7.8] years; community controls N=33, age=29[7.4] years) PSG’s were scored following AASM guidelines. Standardized surveys assessed subjective sleep quality (PSQI), fatigue (FACIT-F) and quality-of-life (SF-36). Group differences were evaluated using ANCOVA adjusting for age. Multivariable linear regression was used to evaluate associations between sleep and SF36 measures. Kendall correlations were computed for objective and subjective sleep and chest radiation dosimetry.

Results
No significative differences between groups were found for standard objective PSG variables (sleep stages, latency, efficiency, etc.). Compared to controls, survivors had higher mean heart rate (HR) during sleep (p&lt; 0.003), higher fatigue (p&lt; 0.004), and lower sleep quality (p=0.005). Among HL survivors, poor PSQI ratings were associated with higher HR (τ = 0.36, p=0.001), lower sleep time (τ = -0.26, p=0.019) and increased fatigue (τ = -0.47, p&lt; 0.001). Higher radiation dosimetry was associated with lower PSG sleep efficiency (τ = -0.23, p=0.042). Among survivors and community controls, mean HR during sleep (p=0.049) and lower sleep efficiency by PSG (p=0.032) were associated with poorer SF36 physical health (p&lt; 0.001).

Conclusion
HL Survivors demonstrate elevated mean HR during sleep, which is associated with patient reported functional limitations. Interventions to lower mean HR during sleep may have the potential to improve sleep quality, physical function, and overall quality of life.

Support (if any)
NCI - NCI NIH: 1R01CA215405, T32CA225590
0189 Particulate Matter 2.5: Examining How Its Exposure Affects Perceived Sleep Quality and Electroencephalography Based Sleep Metrics
Authors: Byun Kunjoon , Tang Mengjia , Liu Qingyang , Mazurek Kevin , Pantelic Jovan 
Published: May 2023

Abstract

Introduction
Individuals are exposed to various air pollutants every day, both when they are inside and when they are outside. Poor air quality can negatively impact an individual’s sleep quality, especially when the individual has been exposed to Particulate Matter 2.5 (PM2.5) which can adversely affect human health due to its ability to penetrate into a human body easily.

Methods
We examined how PM2.5 exposure impacts perceived sleep quality and sleep metrics. 11 individuals (7 Female, 4 Male) with an average age 34.18±10.24 years participated where each participant lived in a simulated one-bedroom apartment unit located in Rochester, MN for 20 nights in total over 4 weeks. Participants wore an EEG-based sleep headband (Dreem) to measure sleep metrics and completed surveys to assess perceived sleep quality and fatigue level each weekday after they woke up. Air quality including PM2.5 was monitored in the units from sensors and from the participants outside the units using a portable research grade air quality monitor paired with GPS. Total PM2.5 exposure was calculated each day by multiplying the PM2.5 concentration by 6 liter/min of inhalation rate and duration (min). We excluded nights with less than 5 hours of sleep duration. One participant who reported poor sleep quality over 70% of the time was also excluded.

Results
Participants had an average of 58.2μg±63.8 PM2.5 exposure during a day until they went to bed and 0.8μg±2.6 PM2.5 exposure during sleep. Because there was minimal night PM2.5 exposure, only the day exposure was correlated to sleep measures. There was no significant effect of PM2.5 exposure on sleep efficiency, sleep onset latency, wake after sleep onset, and total sleep time as measured from the Dreem headband. There was a modest but significant effect of PM2.5 on perceived sleep quality, as participants rated poorer sleep quality (r(141)=.17, p=.042) and higher fatigue levels (r(141)=.26, p=.002) the next morning.

Conclusion
These findings suggest acute PM2.5 exposure levels were not high enough to affect sleep metrics but did affect one’s perceived sleep. Future studies should examine longer period to assess whether there is a chronic effect of PM2.5 exposure on sleep.

Support (if any)
0194 Sleep Tracker: How Accurate Does It Need To Be?
Authors: Reifman Jaques , Vital-Lopez Francisco , Priezjev Nikolai 
Published: May 2023

Abstract

Introduction
Commercial sleep tracker devices offer the ability to measure an individual’s sleep patterns, providing a portable and low-cost option to collect sleep-wake data to enhance fatigue management and minimize alertness impairment. While multiple studies have reported that sleep parameters estimated with these devices differ from gold-standard polysomnography (PSG), the extent to which these differences are acceptable is not known. Here, we provide a method to quantitatively assess whether sleep-duration errors generated by sleep trackers hinder our ability to assess alertness impairment.

Methods
To obtain representative statistics of sleep-duration errors, we performed a literature review and identified 14 studies that compared estimations of sleep parameters between 18 unique commercial sleep trackers and PSG. Then, we used the well-validated Unified Model of Performance (UMP), a mathematical model that predicts alertness impairment as a function of sleep history, to quantitatively assess the effect of sleep-duration errors on alertness predictions. For this purpose, we performed 20,000 simulations in which an individual slept 8 hour per night for 30 consecutive days. Each day, we randomly sampled sleep-duration errors using the obtained representative statistics and computed daily alertness-prediction errors. We repeated these simulations for 5 and 9 hours of sleep per night. We assumed that sleep-duration errors that lead to alertness-prediction errors of ≤30 ms (i.e., the within-subject variability under well-rested conditions) are not a concern.

Results
Based on the 14 studies, sleep trackers yielded sleep-duration errors with a mean of 22 minutes and a standard deviation of 44 minutes. Using these statistics to sample daily errors, we found that, at the end of the 30 days of UMP simulations, these sleep-duration errors led to a mean alertness-prediction error of ~20 ms, with ~75% of the errors ≤30 ms. We also found that, for each additional minute of sleep-duration error, the prediction error increased by ~1 ms. These results were consistent when the hours of sleep per night varied from 5 to 9 hours.

Conclusion
On average, sleep trackers lead to alertness-prediction errors that are consistently smaller than the within-subject variability of alertness impairment, suggesting that they are an alternative approach for measuring sleep duration.

Support (if any)
0198 Effects of a 1-hour nightly weeklong sleep extension pilot study in undergraduates on health
Authors: Mathew Gina , Nahmod Nicole , Master Lindsay , Reichenberger David , Rosinger Asher , Chang Anne-Marie 
Published: May 2023

Abstract

Introduction
Sleep restriction impairs health, but less is known about the potential health benefits of extending sleep in undergraduate students with habitual short sleep. This study examined the effects of a 1-week, 1-hour per night sleep extension on cardiometabolic health, hydration status, sedentary behavior, and physical activity in undergraduate students.

Methods
Twelve healthy undergraduate students (83% female; mean±SD age 20.2±1.5; range 18-23 years) obtaining fewer than 7 hours of average nightly sleep completed a two-week study. The first week consisted of sleeping typically ("Habitual"), followed by a peer-delivered intervention to extend time in bed by ≥1 hour/night during the second week ("Extension"). Sleep and physical activity actigraphy were collected throughout. Following each week, participants completed in-person cardiometabolic assessments, including a standardized meal response procedure and blood pressure measurement. Blood samples for measurement of glucose and insulin were collected before, during, and following a standardized breakfast. Urine samples yielded markers of hydration (specific gravity and osmolality). Mixed-effects regression models with random intercepts for individuals compared outcomes after the Extension week to Habitual outcomes.

Results
Compared to Habitual sleep, average sleep duration across the Extension week increased (b±SEM, +42.6±.3 minutes; p=.005). After the breakfast meal, glucose area under the curve (AUC; −26.5±10.2 mg/dL*h; p=.025) and time to return to baseline levels (−83.0±46.4 minutes; p=.031) decreased following Extension. There was no change in insulin AUC. Systolic blood pressure decreased (−6.6±2.8 mmHg; p=.037) after Extension. Average time spent in sedentary behavior (−44.3±15.7 minutes; p=.018) and percentage of wake spent in moderate-to-vigorous physical activity (−1%±.4%; p=.030) decreased across the Extension week. Participants who increased average sleep duration ≥15 minutes (n=9) were better hydrated according to urine osmolality (−187.0±68.4 mOsm/kg; p=.026) and specific gravity (−.01±.002 g/mL; p=.012) and had lowered odds of dehydration according to urine osmolality (≥800 mOsm/kg; −67%; OR=.03; p=.035).

Conclusion
This pilot study provided evidence that sleep extension may improve cardiometabolic health and hydration and reduce both sedentary behavior and moderate-to-vigorous physical activity. Future research in randomized controlled trials should examine if such findings extrapolate to larger samples and older populations.

Support (if any)
CRC/CTSI: UL1-TR002014; pilot grant to AMC from the Department of Biobehavioral Health, Pennsylvania State University
0204 Associations of Reallocating Sedentary or Activity Time to Sleep on Body Weight in Postpartum Women
Authors: Kishman Erin , Youngstedt Shawn , Wang Xuewen 
Published: May 2023

Abstract

Introduction
In the postpartum period, decreased sleep time is associated with increased postpartum weight retention. Increasing sleep time in the postpartum period may be beneficial; however, increasing total sleep time will reduce the time available for light physical activity (LPA), moderate to vigorous activity (MVPA), or sedentary behavior (SED). Therefore, the purpose of this study was to examine the associations of reallocating time in SED, MVPA, and LPA to sleep with body weight in postpartum women.

Methods
Women (n=130) who gave birth to a singleton infant at ≥37 weeks gestation participated in the study. At 6-8 weeks, 6 months, and 12 months postpartum wrist actigraphy was used to measure total sleep time and hip actigraphy was used to measure SED, LPA, and MVPA. Women were instructed to wear the monitors for 7 days and maintain their normal daily activities. Weight was measured at each timepoint using a calibrated scale. Isotemporal substitution models were preformed to examine the association of reallocating time to sleep from SED, LPA, and MVPA on body weight.

Results
At 6-8 weeks, reallocating ten minutes of SED or MVPA to sleep was associated with a -0.54 kg (p=0.0223) and -3.33 kg (p=0.0091) change in body weight, respectively. At 6 months, reallocating ten minutes of SED or MVPA to sleep was associated with a -0.931 kg (p=0.0069) and -2.97 kg (p=0.0434) change in body weight, respectively. At 12 months, reallocating ten minutes of SED to sleep was associated with a -1.21 kg (p=0.0166) change in body weight. Reallocating time from MVPA to sleep was not associated with changes in body weight at 12 months. Reallocating time from LPA to sleep was not associated with changes in body weight at any timepoint.

Conclusion
Throughout the first year postpartum, reallocating time from SED to sleep was associated with decreases in body weight. Surprisingly, reallocating time from MVPA to sleep was also associated with a decrease in body weight at 6-8 weeks and 6 months postpartum. These results suggest the relative importance of the behaviors comprising the 24-hour day to body weight may be changing over the first year postpartum.

Support (if any)
NIH Grant R21MD012740