bluuebunny/crossref_metadata_2025_split huggingface dataset:
064 Higher Resting HRV Enhances Protective Effects of Self-Reported Resilience Against the Impact of Poor Sleep Quality on PTSS
Authors: Huskey Alisa , Taylor Daniel , Friedman Bruce 
Published: May 2021

Abstract

Introduction
Poor sleep quality is a well-established risk for posttraumatic symptoms (PTSS; Casement et al., 2012; Germain et al., 2004; 2005). Conversely, self-reported resilience is a well-established protective factor against PTSS (Mealer et al., 2012; Wrenn et al., 2011) and has also been shown to moderate the negative impacts of stressful life events on sleep quality (Li et al., 2019). Fewer studies have investigated whether autonomic indices of regulatory control moderate the impacts of resilience and sleep quality on PTSS. Resting heart rate variability (HRV) is a widely supported measure of top-down regulation of cognitive, behavioral and autonomic outcomes (Thayer & Ruiz-Padial, 2006). Higher HRV has been linked with increased capacity for stress coping as well as regulation of affect and attention (Bornstein & Suess, 2000). This study investigates whether resting HRV moderates the relationship between resilience, sleep quality, and PTSS.

Methods
Participants completed the PTSD Checklist for DSM-5 criteria (PCL-5), the Pittsburg Sleep Quality Index (PSQI), and the Connor-Davidson Resilience Scale (CD-RISC-10). A resting baseline assessment of electrocardiogram was used to derive resting-state HRV. Participants (N = 42; 86% women; 76% Caucasian) were recruited from college courses and the surrounding community.

Results
Individuals were grouped into higher and lower HRV. In the lower HRV group, resilience was not related to PTSS, but poorer sleep quality predicted higher PTSS severity (B=.79). Specific components of sleep quality that predicted PTSS in the lower HRV group were sleep disturbance (B=.38) and daytime dysfunction (B=.76). In the higher HRV group, greater resilience predicted lower PTSS severity (B=-.63), but sleep quality was not related to PTSS. Sleeping medication was the only component that predicted PTSS in the higher HRV group, such that less sleep medication was related to higher PTSS (B=-.751).

Conclusion
These findings suggest that higher HRV in combination with a self-perception of greater resilience are protective factors against PTSS as well as the effects of poorer sleep quality on PTSS. Resting HRV provides an index of regulatory control that may also be a physiological component of resilience.

Support (if any)
This project was funded by the Dean’s Discovery Fund at Virginia Tech.
082 The Association Between Excessive Daytime Sleepiness and Math Performance in Adolescents
Authors: Scholes Samantha , Rao Akhila , Somerville Gail , Boursier Johanne , Gruber Reut 
Published: May 2021

Abstract

Introduction
Excessive daytime sleepiness (EDS) is defined as an increased tendency to fall asleep in a setting where an individual would be expected to stay awake and alert. Previous studies have found that EDS is associated with poor academic performance but fell short of examining the associations between EDS and achievement in specific academic subjects. Academic performance in mathematics is the most prominent predictor of later success. Findings from studies examining the associations of academic achievement in math and EDS have been inconsistent as some studies have found results indicating the EDS negatively impacts math while others found no such relationship. The aim of this study was to investigate the association between EDS and performance in mathematics in high school students. It was hypothesized that higher level of reported EDS would be associated with lower grades in mathematics.

Methods
116 typically developing adolescents (38 male) between 12–17 years of age (M=13.87, SD=1.38) participated. EDS was measured by the Sleep Disorder Inventory for Students (SDIS); Academic achievement in mathematics was measured by child’s report cards; Sleep was assessed for seven nights using actigraphy and sleep logs.

Results
A hierarchical multiple regression analysis was conducted to examine the associations between parent-reported EDS (independent variable) and their child’s academic achievement in mathematics (dependent variable) while controlling for sleep variables (bedtime, waketime, and duration) and sex. Higher levels of EDS was negatively associated with lower grades in mathematics [F(5,109)= 3.60, p&lt;.01; β= -0.21, p= 0.03].

Conclusion
EDS was signiﬁcantly associated with report card grades in mathematics in typically developing adolescents. Given the cross-sectional design of the study, it can not identify what led to EDS in this sample of typically developing adolescents. We propose that future studies examine potential causes for adolescents’ EDS, including factors that could impair sleep quality (e.g., undiagnosed sleep disorders) or restrict sleep duration (e.g., life-style choices such as the use of electronic devices at night). This is important as it could help to identify targets for intervention aiming at decreasing EDS and ultimately improving academic performance in mathematics.
Support (if any):
089 Impact of A Single Sleep Education Session on Beliefs and Attitudes Among People with Excessive Daytime Sleepiness: A Pilot Study
Authors: Wills Chloe , Tubbs Andrew , Grandner Michael 
Published: May 2021

Abstract

Introduction
Excessive daytime sleepiness is associated with adverse health outcomes and substantial functional deficits. However, little is known about experiences among these individuals and whether some education about sleep health would be helpful.

Methods
N=28 participants with excessive daytime sleepiness (ESS&gt;=10) but no other major medical problems were recruited from the community. The intervention was a single 2-hour sleep education workshop. Participants were randomized to either the education session or a wait-list control. Change from baseline on Epworth Sleepiness Scale (ESS), Dysfunctional Beliefs About Sleep (DBAS) scale, Fatigue Severity Scale (FSS), Insomnia Severity Index (ISI),pHQ9 depression scale, and GAD7 anxiety scale were evaluated versus controls, adjusted for age, sex, and race/ethnicity. Also,participants were asked whether they agree to a range of beliefs/attitudes about sleep before and after the session (as part of intervention or following wait list). Posttest-Pretest difference scores were compared to determine if any were changed by the session (nominal significance p&lt;0.05).

Results
Compared to the wait-list control, those who received the education session did not show differences in sleepiness (4.2% reduction,p=0.73) or dysfunctional beliefs about sleep (52.1% reduction,p=0.07), but they did demonstrate a 26.5% reduction in fatigue (p=0.01), a 55.2% reduction in insomnia severity (p=0.004), a 59.1% reduction in depression score (p=0.02), and a 54.5% reduction in anxiety score (p=0.04). The single session resulted in increased agreement with the statements, “People with daytime sleepiness should discuss their problems with their doctor or health care provider” (t=-2.3,p=0.03), “I understand the basics of how sleep works” (t=-2.2,p=0.04), “I understand the basics of how sleepiness works” (t=-2.7,p=0.01), “Excessive daytime sleepiness is something that can be improved with psychological treatment” (t=-2.3,p=0.03), and “I know what to do if…” “I have trouble falling asleep” (t=-4.3,p=0.0003), “I have trouble with poor quality sleep” (t=-5.1,p=0.0001), and “I experience daytime sleepiness” (t=-3.0,p=0.007).

Conclusion
A brief sleep health education session improved sleep and mental health in individuals with daytime sleepiness, even if it did not improve sleepiness. Further, the session did likely provide useful educational information.

Support (if any)
This work was supported by Jazz Pharmaceuticals
099 A randomized factorial study to understand the components of behavioral sleep extension
Authors: Baron Kelly , Trela-Hoskins Sarah , Allen Chelsea , Duffecy Jennifer , Lincoln Layla 
Published: May 2021

Abstract

Introduction
Recent studies have demonstrated that behavioral sleep extension can increase sleep duration among short sleepers. However, little is known about the contribution of the intervention components. The goal of this study is to examine the effects of a fitbit and coaching on sleep extension in a behavioral sleep extension intervention.

Methods
Participants included adults aged 25 to 65 years with sleep duration &lt;7 hours who were randomized into one of four groups: self-management, Fitbit, coaching, or Fitbit + coaching. The self-management group did not receive any intervention materials. The other three groups received sleep educational materials emailed weekly. The coaching intervention (5-min telephone call) was delivered weekly for 6 weeks to the coaching and Fitbit+coaching groups to enhance motivation. Assessments were completed at baseline, post intervention (6 weeks), and 12-week follow- up. Participants completed self-report questionnaires and actigraphy at study visits. Results were analyzed using mixed models.

Results
Enrollment and data collection were ended prematurely due to the COVID-19 pandemic. Participants included 32 adults (self-management n=8, coaching n=11, Fitbit n=11, and Fitbit+coaching n=8). Fitbit+coaching group increased hours of sleep by 0.62 h hours more (95% CI: 0.04, 1.20; p=0.047) than the self-management group between their first and second visit. Coaching and the Fitbit groups showed estimated improvements over the self-management group as well: 0.54 h and 0.39 h, respectively, though their differences were not found to be statistically significant (p=0.081 and p=0.20, respectively). At the 12-week follow-up visit, there were no statistically significant differences between groups but the Fitbit+coaching group did maintain their sleep improvement.

Conclusion
These results suggest that sleep extension intervention components may affect the pattern of sleep changes, but more research is needed to refine and explore changes in sleep with behavioral interventions.

Support (if any)
R01NR018891
104 The influence of self-reported distractions on the Psychomotor Vigilance Task (PVT) in laboratory and field environments
Authors: Bathurst Nicholas , Gregory Kevin , Arsintescu Lucia , Costedoat Gregory , Evans Erin 
Published: May 2021

Abstract

Introduction
The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments.

Methods
We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3282 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall.

Results
Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 275.7ms; 1=285.0ms; 2=304.0ms; 3=322.9ms; &gt;4=408.6ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=286.4ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; &gt;4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; &gt;4=354.4ms). These findings were similar for other PVT outcomes.

Conclusion
Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time.

Support (if any)
NASA Airspace Operations and Safety Program, System-Wide Safety Project
105 Sleep Behaviors Are Differentially Associated with Eating Behavior Characteristics Based on Sex
Authors: Barragan Rocio , Zuraikat Faris , Tam Victoria , Scaccia Samantha , Cochran Justin , Li Si , Cheng Bin , St-Onge Marie-Pierre 
Published: May 2021

Abstract

Introduction
Poor sleep health is a key determinant of obesity risk, largely explained by overconsumption of energy. Eating behavior characteristics are predictive of energy intake and weight change and may link sleep with risk factors for obesity. However, the relationships between sleep and dimensions of eating behavior, and potential individual differences in these relations, are not well characterized. Elucidating these relations may aid in the development of targeted strategies to mitigate obesity risk. Therefore, we aimed to 1) evaluate whether associations of sleep were related with eating behavior characteristics, 2) explore if these associations differed by sex.

Methods
This was a cross-sectional analysis of 179 adults aged 20–73 y (68.7% women; 64.8% with BMI≥25 kg/m2; 27.4% minority). Sleep was assessed over 2 wk using wrist actigraphy; eating behavior characteristics (dietary restraint, disinhibition and hunger) were measured with the Three-Factor Eating Questionnaire. Linear regression models were used to evaluate associations of sleep with eating behavior characteristics, adjusting for confounding variables. In separate models, sex was added as an interaction term and analyses were stratified when interactions were significant (p&lt;0.10).

Results
Variable (sleep midpoint standard deviation &gt;60 min) vs. stable sleep timing was associated with greater tendency towards hunger (β=0.84 ± 0.39, p=0.03). When evaluated on the continuous scale, lower sleep efficiency (β=-0.13 ± 0.05; p=0.01), longer wake after sleep onset (β=0.03 ± 0.01; p=0.01) and higher sleep fragmentation index (β=0.074 ± 0.036; p=0.041) were associated with higher dietary restraint. Sex influenced associations of sleep efficiency, sleep onset latency, and sleep fragmentation index with hunger. In men, but not women, lower sleep efficiency (β=-0.15 ± 0.05; p&lt;0.01), longer sleep onset latency (β=0.17 ± 0.07; p=0.02) and higher sleep fragmentation index (β=0.11 ± 0.04; p&lt;0.01) were associated with greater hunger.

Conclusion
Objective measures of sleep were associated with eating behaviors previously linked with obesity and its risk factors. Moreover, we provide evidence of sex-specific associations between poor sleep and tendency towards hunger. Our results suggest that, particularly in men, differences in eating behavior traits may underlie susceptibility to overeating in response to poor sleep.

Support (if any)
Supported by NIH grants R01HL128226 and R01HL142648 and AHA grant 16SFRN27950012 (PI: St-Onge).
106 Self-Reported Sleep and Resilience
Authors: Persich Michelle , Cloonan Sara , Grandner Michael , Killgore William 
Published: May 2021

Abstract

Introduction
Psychological resilience is the ability to withstand setbacks, adapt positively to challenges, and bounce back from the adversities of life. While the construct of resilience is broadly understood, the specific individual factors that contribute to the ability to be resilient and persevere in the face of difficulties remain poorly understood. We recently showed that psychological resilience during the COVID-19 pandemic was associated with a number of factors, including fewer complaints of insomnia, and others have suggested that sleep is an important contributor. We therefore tested the hypothesis that sleep quality and acute sleep quantity would combine to predict measures of psychological resilience and perseverance (i.e. “grit”).

Methods
We asked 447 adults (18–40 yrs; 72% female) to report the number of hours of sleep obtained the night before their assessment session (SLEEP), and complete several questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Connor-Davidson Resilience Scale (CD-RISC), Bartone Dispositional Resilience Scale (Hardiness), and the Grit Scale. Sleep metrics were used to predict resilience, hardiness, and grit using multiple linear regression.

Results
For resilience, PSQI (β=-.201, p&lt;.00003) and SLEEP (β=.155, p&lt;.001) each contributed uniquely to prediction of CD-RISC (R2=.08, p&lt;.00001). Hardiness was also predicted (R2=.08, p&lt;.00001) by a combination of PSQI (β=-.218, p&lt;.00001) and SLEEP (β=.128, p=.007). Interestingly, worse sleep quality over the past month on the PSQI (β=.13, p=.008) in combination with more SLEEP the night before the assessment (β=.137, p=.005) each contributed uniquely to higher Grit (i.e., perseverance; R2=.03, p=.003).

Conclusion
Self-reported sleep quality and quantity were both independently associated with greater self-reported resilience, hardiness, and grit. While better sleep quality and more sleep the night before testing each uniquely predicted greater resilience and hardiness, a different pattern emerged for Grit. The combination of lower quality sleep over the past month followed by greater recent sleep duration was associated with increased perseverance. Whereas sleep quality appears to be more important for general resilience/hardiness, recent sleep time appears more important for the subjective perception of perseverance. Because these data are purely self-report and cross sectional, future work will need to determine the longitudinal effects on behavior.
Support (if any):
107 Sleep Quality and Duration Are Associated with Greater Trait Emotional Intelligence
Authors: Persich Michelle , Cloonan Sara , Grandner Michael , Killgore William 
Published: May 2021

Abstract

Introduction
Lack of sleep has been associated with altered connectivity between the emotion-regulating regions of the medial prefrontal cortex and the emotionally reactive structure of the amygdala. This altered brain function following insufficient sleep is believed to impair a range of emotional perception and regulation capacities. Our prior research has also shown that two nights of total sleep deprivation led to significant declines in Trait Emotional Intelligence (TEI) and other aspects of emotional processing and perception. Nonetheless, the extent to which general sleep quality and acute sleep duration may contribute to everyday TEI outside of laboratory conditions remains unknown. We hypothesized that poorer sleep quality and the amount of sleep obtained the night before assessment would both predict TEI scores.

Methods
447 adults (18–40 yrs; 72% female) completed the Pittsburgh Sleep Quality Index (PSQI) and a question about the number of hours of sleep obtained the night before the assessment (SLEEP), as well as the Petrides Trait Emotional Intelligence Questionnaire (TEIQue). Sleep metrics were used to predict Total TEI, and the four TEIQue factors (Wellbeing, Self-Control, Emotionality, and Sociability) using multiple linear regression.

Results
Both PSQI (β=-.275, p&lt;.00001) and SLEEP (β=.130, p&lt;.00001) each contributed uniquely to prediction of Total TEI (R2=.11, p&lt;.00001). Wellbeing was also predicted by a combination of PSQI (β=-.272, p&lt;.00001) and SLEEP (β=.129, p=.006). In contrast, for Self-Control, only PSQI was significantly related (β=-.296, p&lt;.00001). Both PSQI (β=-.131, p&lt;.007) and SLEEP (β=.103, p&lt;.034) each contributed to Emotionality. Finally, both PSQI (β=-.126, p&lt;.010) and SLEEP (β=.107, p&lt;.028) each contributed to Sociability.

Conclusion
Greater total TEI was uniquely predicted by a linear combination of 1) better general sleep quality over the past month and 2) greater quantity of sleep the night before the assessment session. The same pattern held for all scale factors, except Self-Control, which was only related to sleep quality over the past month. Although limited by the cross-sectional nature of the data, these findings support prior work suggesting that lack of sleep alters normal emotional processing and further suggests that both long-term sleep quality and acute restriction of sleep can affect trait-like emotional domains.
Support (if any):
109 Chemogenetic silencing of corticotropin releasing factor neurons in the paraventricular nucleus: the effect on post-stress sleep
Authors: Gvilia Irma , Kumar Sunil , McGinty Dennis , Szymusiak Ronald 
Published: May 2021

Abstract

Introduction
We have previously shown that pharmacological elevation of corticotropin releasing factor (CRF) signaling in the brain results in exacerbation of sleep disturbances evoked by the exposure of rats to an acute stressor, the dirty cage of a male rat. In the present study we (1) assessed wake-sleep behavior of mice after the exposure to the dirty cage stress paradigm, and (2) examined the effect of chemogenetic silencing of CRF neurons in the hypothalamic paraventricular nucleus (PVN) on sleep occurring following the exposure to this stressor.

Methods
First, a group of mice (n=12) was implanted with EEG/EMG electrodes. In two weeks, post-surgery, six mice were transferred to dirty cages of male rats and recorded for 24 hours. Control mice were transferred to clean cages. In the second study, a group of CRF-ires-cre mice (n=8) received bilateral injections of AAV-hSyn-DIO-hM4Di-mCherry targeting the PVN. The other group of CRF-ires-cre mice (n=8) was injected AAV-hSyn-DIO-mCherry (control vector). All mice were implanted with EEG/EMG electrodes. Dirty cage experiments were started following a 4-week postsurgical period to allow gene recombination and expression. Mice were subjected to intraperitoneal (IP) administration of clozapine-n-oxide (CNO; 3 mg/kg) at ZT1, placed into dirty cages, and recorded for post-stress sleep.

Results: Results
In mice expressing hM4Di inhibitory DREADDs (designer receptors activated by designer drugs) versus mice injected with control AAV, IP CNO (3 mg/kg) resulted in a significant decrease of post-stress sleep onset latency, decrease of time spent in wakefulness (first hour, 74±5.3 vs. 89±11.0, second hour, 37.2±10.3% vs. 81.3±9.3%; third hour, 40.1±3.3% vs. 47.1±14.3%; fourth hour, 44.4±6.0 vs. 55.5±9.9), and increase in non-rapid eye movement (NREM) sleep time (26.0±5.4% vs. 11.0±11.1%; 62.8%±9.8 vs. 18.7 ± 9.6%; 59.9±3.2% vs. 52.9±14.5%; 55.6±6.2 vs. 44.5±10.0). The hM4Di expressing mice exhibited longer episodes of NREM sleep, compared to mice injected with control AAV (first hour, 133.3±80.1sec vs. 21±1.7sec; second hour, 43256±83.4sec vs. 73.5±44.1sec; third hour, 459.2±139.8sec vs. 139±80.6sec; fourth hour, 233.1±82.6sec vs. 190±72.3sec).

Conclusion
Chemogenetic silencing of CRF neurons in the PVN attenuates acute stress-induced sleep disturbance in mice.

Support (if any)
Supported by Department of Veterans Affairs Merit Review Grant # BX00155605 and SRNSF (Georgia) grant FR-18-12533