bluuebunny/crossref_metadata_2025_split huggingface dataset:
127 Cognitive Throughput, Behavioral Attention, and Sleepiness Show Robust Relationships During Sleep Loss but Not During Recovery
Authors: Brieva Tess , Antler Caroline , Yamazaki Erika , Casale Courtney , Goel Namni 
Published: May 2021

Abstract

Introduction
The Digit Symbol Substitution Task (DSST) is a frequently used measure to determine cognitive throughput responses to sleep loss. However, the specific time course of relationships between cognitive throughput and behavioral attention [using the 10-minute Psychomotor Vigilance Test (PVT10)] and subjective sleepiness [using the Karolinska Sleepiness Scale (KSS)] across sleep loss and recovery remains unknown yet is critical for assessing whether tasks involving learning and those without learning track each other. Repeated measures correlation (rmcorr) examined within-individual associations between measures of these tests throughout a highly controlled sleep deprivation study.

Methods
Forty-one healthy adults (ages 21-49;mean ± SD, 33.9 ± 8.9y;18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the DSST, the KSS, and the PVT10, was administered every 2h during wakefulness. Rmcorr analyses compared DSST [number correct], KSS score, and PVT10 performance [lapses (reaction time [RT] &gt;500ms) and 1/RT (response speed)] by examining correlations by day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: r=0.1: small; r=0.3: moderate; r=0.5: large.

Results
During SR and TSD, correlations were significant, ranging from moderate to large, with the strongest correlation occurring during TSD. By contrast, baseline and recovery correlations were not significant or were small for DSST relative to PVT10 lapses, PVT10 response speed, or KSS scores. Additionally, all three pairs showed moderate to large correlations by time point across the entire study.

Conclusion
The various test measure relationships were consistently strong at specific times of day throughout the study. In addition, the associations between cognitive throughput and behavioral attention and sleepiness were strongest during sleep loss, particularly during TSD, suggesting that these measures are most acutely attuned to neurobehavioral changes resulting from sleep loss. The lack of a significant relationship at baseline and at recovery may be due to the learning effect reported for the DSST that is not present for the PVT10 or KSS.

Support (if any)
ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488
129 Greater NREM Sleep Rebound in Response to Experimental Sleep Disturbance Associated with Higher Inflammatory Resolution in Humans
Authors: Engert Larissa , Dubourdeau Marc , Dang Rammy , Mullington Janet , Haack Monika 
Published: May 2021

Abstract

Introduction
Sleep disturbances deteriorate immune function by not only affecting pro-inflammatory pathways, but also inflammatory resolution pathways, which actively terminate inflammation. It is assumed that slow wave sleep (SWS) amount and slow wave activity (SWA) convey the immune-supportive functions of sleep. We investigated whether changes in SWS induced by experimental sleep disturbance followed by recovery sleep predict changes in inflammatory resolution mediators.

Methods
The randomized controlled within-subjects trial (N=24, 20-42 years, 12 women) consisted of two 19-day in-hospital protocols (experimental sleep disturbance/control). After three nights of baseline sleep (8h/night), participants in the experimental sleep disturbance condition were exposed to three cycles of three nights of disturbed sleep (delayed sleep-onset, hourly sleep disruption, advanced sleep-offset) followed by one night of 8h-recovery sleep. The protocol ended with three nights of recovery sleep. In the control condition, participants had uninterrupted sleep (8h/night). Sleep (PSG) and resolvin lipid mediators in plasma (1100h, LC-MS/MS) were assessed at baseline, during the last cycle of sleep disturbance, and during/after the first and third night of final recovery sleep. Data were analyzed using generalized linear mixed models and Pearson/Spearman correlations.

Results
As expected, SWS amount decreased during experimental sleep disturbance and increased during the first recovery sleep night (p&lt;.001). Similarly, resolvin (Rv) D2 and RvD3 decreased during sleep disturbance and RvD2 increased with subsequent recovery sleep (p&lt;.001). The SWS response did not correlate with the resolvin response to sleep disturbance or to recovery sleep. However, the NREM sleep response correlated with the resolvin response during the third recovery sleep night, i.e., a greater NREM response was associated with a greater RvD2 and RvD3 response (r=.68, p=.002; r=.58, p=.012). In contrast, a greater REM sleep response was associated with a lower resolvin response (r=−.63, p=.005; r=−.66, p=.003).

Conclusion
These data suggest that during recovery from sleep disturbance, NREM rather than REM sleep promotes inflammatory resolution, thereby acting as the sleep state that protects against low-grade systemic inflammation, which has been frequently observed as a consequence of sleep disturbances. Analysis whether SWA is related to inflammatory resolution is in progress.

Support (if any)
NIH/NINDS R01-NS091177; NIH/NCRR UL1-RR02758, M01-RR01032; German Research Foundation (DFG) EN1291/1-1.
140 Short and Long Sleep Duration, Poor Sleep Quality, and Later Sleep are Associated with Lower Odds of Adolescents Eating Breakfast
Authors: Mathew Gina , Reichenberger David , Buxton Orfeu , Hale Lauren , Chang Anne-Marie 
Published: May 2021

Abstract

Introduction
Sleep health is linked to dietary choices, and skipping breakfast is associated with risk of negative health outcomes in adolescents. However, there is a lack of research on whether dimensions of sleep at night predict adolescents eating breakfast the next day. We investigated within- and between-person associations of multiple aspects of sleep with adolescent breakfast consumption.

Methods
Data were collected from a subset of the age 15 wave of the Fragile Families and Child Wellbeing Study (n=590). Teens wore an actigraphy device and completed daily surveys for ~1 week (mean=5.9 days). Surveys assessed subjective sleep quality (low–high) and consumption of breakfast (no/yes). Mixed models assessed whether actigraphy-measured sleep timing (onset, midpoint, and offset) and subjective sleep quality predicted odds of breakfast consumption in both within- and between-person models. A curvilinear association between sleep duration and breakfast consumption was also assessed. Irregularity of sleep duration and timing were tested as additional predictors, calculated as SD per person (in between-person models only). Analyses included random intercepts for participants and covariates: school day, boredom, loneliness, happiness, depressive symptoms, sex, race/ethnicity, body mass index, and household income.

Results
Within-person analyses revealed a significant curvilinear association between sleep duration and breakfast consumption, such that on nights when teens slept shorter or longer than their average, they had lower odds of eating breakfast the next day (p=.005). Additionally, on nights when teens had a later sleep midpoint or offset than their usual, they tended to skip breakfast the next day (both p&lt;.05). Between-person models showed that teens who on average had later sleep timing (onset, midpoint, and offset) and who reported lower sleep quality had lower odds of eating breakfast (all p&lt;.04). Lastly, teens with greater irregularity of sleep duration and sleep timing (midpoint and offset) had lower odds of eating breakfast (all p&lt;.009).

Conclusion
Findings indicate that multiple dimensions of adolescent sleep health, including long and short sleep duration, later sleep timing, and poorer sleep quality, are associated with lower odds of eating breakfast. These sleep and dietary behaviors in adolescence may consequently impact future metabolic health.

Support (if any)
R01HD073352
144 Temporal and Bidirectional Associations between Objectively Measured Physical Activity and Sleep in Preschoolers
Authors: Laurent Christine St , Holmes Jennifer , Andre Chloe , Spencer Rebecca 
Published: May 2021

Abstract

Introduction
Physical activity (PA) and sleep contribute to overall health in early childhood. To explore the interactive relationships of these behaviors in older children and adults, previous studies have examined temporal between- and within-person associations through micro-longitudinal designs. However, such analyses have not been conducted in early childhood, when behaviors are guided by adult caregivers. The purpose of this analysis was to examine temporal and bidirectional associations between SED, PA, and sleep in preschool children.

Methods
Wake (activity counts/min and percent time in SED, light PA [LPA], and moderate-to-vigorous PA [MVPA]) and overnight sleep (sleep duration, sleep efficiency [SE], mid-sleep point [MSP]) were assessed via wrist-based actigraphy (mean = 10.4 days and 9.8 nights) and recorded as repeated (daily) measures. Multilevel models with lagged effects and AR(1) error covariance structure were used to examine the temporal associations between wake and sleep measures and adjusted for age, sex, socioeconomic status, and nap frequency.

Results
With PA measures as predictors, between-person associations were positive between activity counts and SE (p=0.004), SED and SE (p=0.004), LPA and sleep duration (p=0.005), and negative between LPA and MSP (p=0.039) and MVPA and SE (p=0.003). Within-person associations were positive between activity counts and sleep duration (p=0.010), activity counts and SE (p=0.018), MVPA and sleep duration (p=0.003), MVPA and SE (p=0.004), and negative between SED and SE (p=0.034) and LPA and sleep duration (p=0.045). With sleep measures as predictors, associations were positive between sleep duration and LPA (p&lt;0.001) and SE and SED (p=0.008), and negative between MSP and LPA (p=0.009), SE and activity counts (p=0.001), and SE and MVPA (p=0.003). Within-person associations were positive between SE and activity counts (p=0.001) and SE and MVPA (p=0.001), and negative between sleep duration and LPA (p=0.001) and SE and SED (p=0.012).

Conclusion
Generally, days with higher levels of activity or sleep were not associated with greater subsequent sleep or PA. Conversely, when participants obtained greater PA or sleep compared to their individual average, some beneficial associations were evident. These findings demonstrate some evidence of temporal associations between PA and sleep, although the bidirectional nature was not conclusive.

Support (if any)
NIH R01 HL111695
145 Aerobic Fitness Is Associated with Advanced Circadian Rhythms in Adolescents
Authors: Neikrug Ariel , Radom-Aizik Shlomit , Chen Ivy , Stehli Annamarie , Lui Kitty , Chappel-Farley Miranda , Lim Alexandra , Mander Bryce , Benca Ruth 
Published: May 2021

Abstract

Introduction
Aerobic fitness facilitates brain synaptic plasticity, which influences global and local sleep expression. While it is known that sleep patterns/behavior and non-rapid eye movement (NREM) sleep slow wave activity (SWA) tracks brain maturation, little is known about how aerobic fitness and sleep interact during development in youth. The aim of this pilot was to characterize relationships among aerobic fitness, measures of global/local sleep expression, and habitual sleep patterns in children and adolescents. We hypothesized that greater aerobic fitness would be associated with better sleep quality, indicated by increased SWA.

Methods
20 adolescents (mean age=14.6±2.3 years old, range 11-17, 11 females) were evaluated for AF (peak VO2 assessed by ramp-type progressive cycle ergometry in the laboratory), habitual sleep duration and efficiency (continuous 7-14 day actigraphy with sleep diary), and topographic patterns of spectral power in slow wave, theta, and sleep spindle frequency ranges in non-rapid eye movement (NREM) sleep using overnight polysomnography with high-density electroencephalography (hdEEG, 128 channels).

Results
Significant relationships were observed between peak VO2 and habitual bedtime (r=-0.604, p=0.013) and wake-up time (r=-0.644, p=0.007), with greater fitness associated with an earlier sleep schedule (going to bed and waking up earlier). Peak VO2 was a significant predictor of slow oscillations (0.5-1Hz, p=0.018) and theta activity (4.5-7.5Hz, p=0.002) over anterior frontal and central derivations (p&lt;0.001 and p=0.001, respectively) after adjusting for sex and pubertal development stage. Similar associations were detected for fast sleep spindle activity (13-16Hz, p=0.006), which was greater over temporo-parietal derivations.

Conclusion
Greater AF was associated with earlier habitual sleep times and with enhanced expression of developmentally-relevant sleep oscillations during NREM sleep. These data suggest that AF may 1) minimize the behavioral sleep delay commonly seen during adolescence, and 2) impact topographically-specific features of sleep physiology known to mechanistically support neuroplasticity and cognitive processes which are dependent on prefrontal cortex and hippocampal function in adolescents and adults.

Support (if any)
NCATS grant #UL1TR001414 & PERC Systems Biology Fund
146 Better Aerobic Fitness Is Associated with Distinct Sleep Characteristics in Adolescents
Authors: Neikrug Ariel , Radom-Aizik Shlomit , Chen Ivy , Stehli Annamarie , Lui Kitty , Chappel-Farley Miranda , Lim Alexandra , Mander Bryce , Benca Ruth 
Published: May 2021

Abstract

Introduction
Aerobic fitness facilitates brain synaptic plasticity, which influences global and local sleep expression. While it is known that sleep patterns/behavior and non-rapid eye movement (NREM) sleep slow wave activity (SWA) tracks brain maturation, little is known about how aerobic fitness and sleep interact during development in youth. The aim of this pilot was to characterize relationships among aerobic fitness, measures of global/local sleep expression, and habitual sleep patterns in children and adolescents. We hypothesized that greater aerobic fitness would be associated with better sleep quality, indicated by increased SWA.

Methods
20 adolescents (mean age=14.6±2.3 years old, range 11-17, 11 females) were evaluated for AF (peak VO2 assessed by ramp-type progressive cycle ergometry in the laboratory), habitual sleep duration and efficiency (continuous 7-14 day actigraphy with sleep diary), and topographic patterns of spectral power in slow wave, theta, and sleep spindle frequency ranges in non-rapid eye movement (NREM) sleep using overnight polysomnography with high-density electroencephalography (hdEEG, 128 channels).

Results
Significant relationships were observed between peak VO2 and habitual bedtime (r=-0.604, p=0.013) and wake-up time (r=-0.644, p=0.007), with greater fitness associated with an earlier sleep schedule (going to bed and waking up earlier). Peak VO2 was a significant predictor of slow oscillations (0.5-1Hz, p=0.018) and theta activity (4.5-7.5Hz, p=0.002) over anterior frontal and central derivations (p&lt;0.001 and p=0.001, respectively) after adjusting for sex and pubertal development stage. Similar associations were detected for fast sleep spindle activity (13-16Hz, p=0.006), which was greater over temporo-parietal derivations.

Conclusion
Greater AF was associated with earlier habitual sleep times and with enhanced expression of developmentally-relevant sleep oscillations during NREM sleep. These data suggest that AF may 1) minimize the behavioral sleep delay commonly seen during adolescence, and 2) impact topographically-specific features of sleep physiology known to mechanistically support neuroplasticity and cognitive processes which are dependent on prefrontal cortex and hippocampal function in adolescents and adults.

Support (if any)
NCATS grant #UL1TR001414 & PERC Systems Biology Fund
147 Within-Family Dynamics Influencing Parent and Child Sleep Quality and Nighttime Activities
Authors: Hanley Brigid , Gorovoy Suzanne , Chamberlain Stihlyn , Bhushan Bharat , Ghani Sadia , Killgore William D S , Wills Chloe , Grandner Michael 
Published: May 2021

Abstract

Introduction
Increasing efforts have focused on child sleep due to its benefits to academics and physical/mental health. Less understood are the within-family dynamics that drive sleep-related behavior.

Methods
Data were obtained from the 2014 National Sleep Foundation Sleep in America Poll (N=1102 parent/child dyads). Variables including age, gender, sleep quality (Excellent/Good/Fair/Poor), evening activities including reading, TV-watching, and online browsing/gaming (Never/Rarely/Sometimes/Often) were reported for parent and child. Ordinal logistic regressions examined child nighttime activity as outcome and nighttime activities (entered simultaneously in the model, adjusted for each other) as independent variables, adjusted for parent and child age, sex, and sleep quality.

Results
Worse sleep quality in parents was associated with worse sleep quality in their children. Moreover, increased likelihood of child television-watching at night was not associated with parental sleep quality, but it was associated with child sleep quality, with “Fair” and “Poor” sleepers more likely to watch TV (Fair: oOR=1.7,p=0.018; Poor: oOR=8.0,p=0.001). Child television-watching was not associated with likelihood of parental reading, but it was associated with likelihood of parental online browsing/gaming (Rarely oOR=1.7,p=0.001; Sometimes oOR=2.3,p&lt;0.0005; Often oOR=1.9,p=0.004) and parental TV-watching (Rarely oOR=2.6,p&lt;0.0005; Sometimes oOR=5.4,p&lt;0.0005; Often oOR=13.3,p&lt;0.0005). Child online browsing/gaming was also not associated with parental sleep quality but it was associated with child sleep quality (Fair oOR=2.3,p=0.001; Poor oOR=4.8,p=0.009) and parental reading (Rarely oOR=1.5,p=0.04; Often oOR=1.6,p=0.03), TV-watching (Rarely oOR=2.3,p=0.004; Sometimes oOR=2.8,p&lt;0.0005; Often oOR=4.6,p&lt;0.0005) and online browsing/gaming (Rarely oOR=2.8,p&lt;0.0005; Sometimes oOR=5.0,p&lt;0.0005; Often oOR=7.8,p&lt;0.0005). Child reading was not associated with parent or child sleep quality or parental online browsing/gaming, but it was related to parental TV-watching (Sometimes oOR=1.45,p=0.04; Often oOR=1.6,p=0.02) and reading (Rarely oOR=2.4,p&lt;0.0005; Sometimes oOR=4.4,p&lt;0.0005; Often oOR=6.9,p&lt;0.0005).

Conclusion
Children who do not sleep well have parents who do not sleep well. Further, parents who read are more likely to have children who read, and parents on screens are more likely to have children on screens. Interventions targeted to parents may lead to better sleep habits in children.
Support (if any):
161 Sleep, Emotion, and Physical Activity in Older Adults Who Engage in Resonant Breathing Biofeedback
Authors: Nasseri Noor , Lagman Hanna , Simon Katharine , Zhang Jing , Mednick Sara 
Published: May 2021

Abstract

Introduction
Resonant breathing (RB) biofeedback increases rhythmic heart-respiration coherence patterns and has been associated with improved emotional wellbeing, physiological health, and sleep quality (Lehrer et al, 2000). Sleep quality declines with age, which leads to emotion dysregulation, cognitive impairment, and poor physical health (Crowley, 2011). However, limited research has investigated the sleep characteristics of older adults who practice RB-biofeedback. Therefore, our study investigates this population’s sleep characteristics, emotional stability, and physical health.

Methods
Thirty-one healthy participants (24 Female; M=54.68 years, SD=9.74) who self-identified as RB-biofeedback experts completed a series of online questionnaires assessing history, frequency, and duration of practice, sleep (habits and quality), physical activity (frequency, duration, and intensity), and mood (depression symptoms). They also reported their typical coherence level achieved, which is a numerical composite value associated with the heart rhythm’s uniform sine-wave pattern at approximately .1HZ (McCraty et al., 2010).

Results
Using bivariate correlations, we found that poor sleep quality was positively correlated with stress (r = .954, p = .001), poor sleep hygiene (r = .591, p &lt; .001), severe sleepiness (r = .518, p = .003), emotion dysregulation (r = .511, p = .004), depressive symptoms (r = .089, p &lt; .001), and negatively correlated with subjective happiness (r = .511, p &lt; .003). Severe sleepiness was negatively correlated with older adults’ enhanced physical fitness (r = .612, p &lt; .001), and poor sleep hygiene was positively correlated with depressive symptoms (r = .503, p = .004). We found no significant correlations between coherence level, mood, physical activity, or sleep measures.

Conclusion
We found significant associations between healthy sleep habits and emotional wellbeing. Those with better sleep quality and more positive sleep habits also had fewer depression symptoms. Moreover, those categorized as more athletic reported lower levels of severe sleepiness, suggesting that physical activity may be a protective factor for sleep in older adults. We did not find a relation between coherence level and sleep, or physical activity. These null results may be due to the high expertise level of the subject sample. Future studies should compare results to older adults who do not practice RB-biofeedback.

Support (if any)
Undergraduate Research Opportunity Program