bluuebunny/crossref_metadata_2025_split huggingface dataset:
0165 Association of Circadian Misalignment with Diet and Physical Activity in Adolescents
Authors: Ballester-Navarro Pura , Morales-Ghinaglia Natasha , Calhoun Susan , Liao Jason , Vgontzas Alexandros , Liao Duanping , Bixler Edward , Fernandez-Mendoza Julio 
Published: April 2024

Abstract

Introduction
Circadian rhythms, particularly the sleep-wake cycle, food intake (FI) and physical activity (PA) are reciprocally synchronized. Lipid and carbohydrate metabolism pace depend on circadian stability. Timing, quality and quantity of FI and PA align circadian clocks. Therefore, circadian misalignment, which is highly prevalent in adolescents, is potentially impacting this triad. We examined whether subjective and objective metrics of circadian misalignment are associated with FI and PA in adolescents.

Methods
We assessed 377 adolescents from the Penn State Child Cohort (median 16 years; 46% female; 22% racial/ethnic minority) who had a minimum of 3-nights of at-home actigraphy (ACT) and a 9-h in-lab polysomnography (PSG). ACT-measured sleep midpoint (SM) was calculated as the central point of the sleep period, while sleep regularity (SR) was calculated as the intra-individual standard deviation of the SM. Circadian preference (CP) was measured with the Morningness-Eveningness Questionnaire. Bouts of sedentary behavior (SB) and level of SB and of moderate-to-vigorous PA (MVPA) were also derived from ACT. FI was measured via the Youth/Adolescent Food Frequency Questionnaire. PA and FI were dependent variables. Stepwise linear regression models adjusted for demographics (sex, race/ethnicity, age, BMI percentile), sleep disorders (insomnia, PSG-apnea/hypopnea index) and insufficient sleep (ACT-mean sleep duration, ACT-sleep duration variability).

Results
A later SM was significantly associated with greater intake of carbohydrates (β=.125, p=.022), an association that was attenuated when adjusting for sleep variability (β=.094, p=.102). A later SM was also associated with greater bouts of SB, even when adjusting for all covariables (β=.167, p=.006). Neither SR nor CP were significantly associated with FI or PA in adolescents.

Conclusion
Adolescents with a delayed sleep phase are more likely to consume more carbohydrates and have greater number of periods of sedentary behavior. Circadian misalignment of the sleep-wake cycle, and its associated variability in sleep duration, should be an integral part of interventions targeting poor dietary choices and sedentarism in youth.

Support (if any)
NIH (R01HL136587, UL1TR000127)
0166 Population-Based Estimates of Drowsy Driving Among US Teens: A National Sleep Foundation Study
Authors: Dzierzewski Joseph , Nielson Spencer , Lopos John 
Published: April 2024

Abstract

Introduction
Drowsy driving represents a large public health concern, accounting for a significant proportion of motor vehicle crashes. Among US teenagers, motor vehicle crashes are the second leading cause of death. However, a dearth of research has documented the prevalence and frequency of drowsy driving in teens, as well as beliefs surrounding drowsy driving. The overarching goal of the present study was to document the prevalence, frequency, and perceptions of drowsy driving among teens.

Methods
We conducted a nationally-representative, probability-based survey of 1,124 US individuals aged 13 to 17 years to assess drowsy driving prevalence, frequency, and beliefs. Results have an estimated margin of error of ~4%. Survey respondents reported whether they have ever driven while so tired they had a hard time keeping their eyes open, how often they did so, what kept them from getting the sleep needed to drive alert, and perceived risks associated with drowsy driving. Measures of central tendency and dispersion were used to characterize responses.

Results
Approximately one in six teen drivers reported having driven drowsy. Projected to all US teen drivers, over 400,000 drive drowsy at least once per week. Teen drivers who work for pay were more than twice as likely to have reported drowsy driving than teens who didn’t work for pay. When asked about what prevented them from getting the sleep they needed to drive alert, the majority of teens pointed to work or school schedules. When asked about the risks associated with drowsy driving, 95% of teens said drowsy driving is extremely or very risky. However, when asked about the likelihood of drunk, drugged, distracted, and drowsy driving leading to death or serious injury, drowsy driving was seen as having the lowest risk of death or serious harm.

Conclusion
Due to a confluence of factors, including driving as a newly learned behavior and multiple competing time demands, teenagers find themselves at increased risks for drowsy driving and drowsy driving-related consequences. We found that approximately 1.7 million teenage drivers had driven while so tired they had a hard time keeping their eyes open. Increased attention to this preventable public health concern is needed.

Support (if any)
0170 Insomnia and Impulsivity: A Systematic Review and Meta-analysis
Authors: Yu Shijie , WANG Ran , Li Xiao , Li Shirley Xin , Wing Yun Kwok , Chan Ngan Yin , Chan Joey 
Published: April 2024

Abstract

Introduction
Growing evidence indicates that sleep disturbances are associated with impaired cognitive control, resulting in an increased risk for impulsive behaviors and risk-taking. However, the findings remained mixed and a synthesis of the evidence focusing on insomnia symptoms while including holistic measurements of impulsivity is lacking. This systematic review and meta-analysis aimed to examine whether insomnia symptoms are associated with impulsivity.

Methods
The review was performed following PRISMA guidelines. Four electronic databases, PubMed, Web of Science, Embase, and PsychINFO were systematically searched from inception to December 2022. Studies were included if they reported the correlation between quantitative measures of insomnia symptoms and impulsivity. Excluded studies were case reports, dissertations, reviews, or conference abstracts. Subgroup analyses were performed to determine whether different populations and measures of impulsivity affected the strength of the association. Data were pooled using the random-effects model and heterogeneity was quantified using I2.

Results
Across the 42 eligible studies, involving 17091 subjects (59% female; mean age 33 years), there was a significant positive association between insomnia symptoms and impulsivity (r= 0.16, 95% CI, 0.09- 0.24, p &lt; 0.01). Subgroup analyses showed that impulsivity, as measured by self-report, was positively associated with insomnia symptoms (r = 0.22, 95% CI, 0.15- 0.29, p&lt; 0.01), but this association was not found based on the behavioral measures of impulsivity (r = -0.11, 95% CI, -0.30- 0.09, p = 0.28). Meta-regression suggested that publication year was a significant moderator, with studies published more recently showing smaller effect sizes.

Conclusion
Our findings demonstrate that insomnia symptoms are associated with higher levels of self-reported impulsivity. This points to a crucial next step in clinical practices to target insomnia as a modifiable factor to reduce impulsivity and risk-taking behaviors. Future studies should evaluate this association using samples with a clinical diagnosis of insomnia, with behavioral measures of impulsivity. Furthermore, neuroimaging techniques could be incorporated to unveil the underlying neural mechanisms between impulsivity and insomnia.

Support (if any)
This work was funded by General Research Fund (Ref. 17619123), Research Grants Council, University Grants Committee, Hong Kong SAR, China.
0172 Predictive Relationships Between Baseline and Recovery Sleep and Neurobehavioral Measures During Sleep Deprivation
Authors: Goel Namni , Pasetes Lauren 
Published: April 2024

Abstract

Introduction
We investigated whether baseline sleep measures derived from actigraphy the night before total sleep deprivation (TSD) predicted cognitive performance and subjective sleepiness and fatigue during TSD. We also investigated whether these measures during TSD predicted that night’s recovery sleep.

Methods
Thirty-two adults (ages 27-53;14 females) participated in a five-day experiment under controlled conditions comprised of two baseline 8h time in bed (TIB) nights (B1, B2), approximately 39h of TSD, and two recovery nights of 8-10h TIB (R1=10h, R2=8h). Neurobehavioral measures including the Digit Symbol Substitution Test (DSST), Digit Span (DS), the 10-minute Psychomotor Vigilance Test (PVT), the Karolinska Sleepiness Scale, and the Profile of Mood States Fatigue scale were collected at 0400h, 1130h, and 1730h during TSD. During the second baseline night (B2) and the first recovery night (R1), wrist actigraphy (Actiwatch Spectrum) assessed sleep indices including sleep duration, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), percent sleep, and the timing of sleep onset, offset, and midpoint. Pearson’s correlations determined relationships between B2 sleep and TSD neurobehavioral measures and between TSD neurobehavioral and R1 sleep measures (p&lt; 0.05 was significant).

Results
Greater SE at baseline was significantly associated with less subjective sleepiness during TSD (r=−0.415;r2=0.172), and a later sleep onset (r=−0.409;r2=0.167) and a later midpoint (r=−0.376; r2=0.142) were significantly correlated with less subjective fatigue during TSD. Higher DSST scores during TSD were significantly associated with shorter duration, greater SOL, and a later sleep onset during recovery (r:−0.473-0.547; r2:0.172-0.299). Similarly, higher DS scores significantly correlated with later sleep onset (r=0.370; r2=0.137). Fewer PVT lapses during TSD were significantly associated with shorter duration, higher SE, lower WASO, higher percent sleep, and an earlier sleep offset during recovery (r:−0.417-0.489; r2:0.136-0.239).

Conclusion
Our novel findings demonstrated that measures of sleep continuity and timing the night before TSD predicted resilience to subjective sleepiness and fatigue, but not cognitive performance during TSD. By contrast, cognitive but not subjective performance resilience during TSD predicted that night’s recovery sleep duration, continuity, and timing. Thus, actigraphic measures uniquely predict subjective responses to TSD and reflect cognitive performance responses during sleep loss.

Support (if any)
NASA grants NNX14AN49G and 80NSSC20K0243 (NG)
0173 Baseline Actigraphic Sleep Measures Predict Cardiovascular Responses to Sleep Deprivation and Psychological Stress
Authors: Pasetes Lauren , Rosendahl-Garcia Kathleen , Goel Namni 
Published: April 2024

Abstract

Introduction
We determined whether baseline sleep measures the night before total sleep deprivation (TSD) predicted cardiovascular (CV) responses to TSD and to TSD and psychological stress.

Methods
We conducted a five-day experiment under controlled conditions in thirty-two healthy adults (ages 27-53; 14 females). During this experiment, CV measures were collected via echocardiography or blood pressure monitor at four assessment time points: 1) after two baseline 8h time in bed (TIB) nights (B1, B2); 2) in the morning of TSD (TSD AM; after 25h of TSD); 3) in the evening of TSD following a modified Trier Social Stress Test, which induced psychological stress (TSD PM; after 34h of TSD); and 4) after two recovery nights of 8-10h TIB. Seated stroke volume (SV), heart rate (HR), cardiac index (CI), left ventricular ejection time (LVET), systemic vascular resistance index (SVRI), mean arterial pressure (MAP), and systolic and diastolic blood pressure (SBP and DBP) were collected. Wrist actigraphy (Actiwatch Spectrum) assessed sleep indices during the second baseline night (B2) including sleep duration, sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), percent sleep, and the timing of sleep onset, offset, and midpoint. Pearson’s correlations assessed relationships between B2 sleep metrics and TSD AM and TSD PM CV responses (p≤0.05 was significant).

Results
Higher WASO during baseline was significantly associated with lower SV (r=−0.374; r2=0.140) and higher SVRI (r=0.358; r2=0.128) during the TSD evening. By contrast, there were inverse relationships for percent sleep and SE during baseline, whereby these metrics were significantly associated with higher SV and lower SVRI during the TSD evening (r:−0.398-0.387; r2:0.126-0.159). In addition, a later sleep offset during baseline was significantly associated with higher MAP, SBP, and DBP during the morning and the evening of TSD (r:0.391-0.453; r2:0.153-0.205).

Conclusion
Our novel results found that actigraphic sleep metrics the night before TSD predicted CV responses in healthy adults, particularly during TSD and psychological stress in the evening. Thus, WASO, percent sleep, SE, and sleep offset timing during fully rested conditions are possible predictors and biomarkers for assessing the adverse cardiovascular responses to TSD and psychological stress.

Support (if any)
NASA grants NNX14AN49G and 80NSSC20K0243 (NG)
0178 Cumulative Effects of Mild Sleep Restriction on Lapses of Attention
Authors: Barley Blake , Scullin Michael 
Published: April 2024

Abstract

Introduction
Sleep deprivation leads to detriments in performance, including alertness and vigilance. Less is known, however, about the cognitive impact of mild sleep restriction, including whether negative outcomes are time-dependent and cumulative. We tested the impact of sleep restriction on sustained attention in a multi-week, crossover experiment in a controlled laboratory setting.

Methods
Sixty-six young adults (Mage = 20.03; 53.0% female; 51.5% non-white) completed seven nights of in-laboratory testing, spread across 3-weeks. On the first week, participants completed a polysomnography adaptation night and baseline assessments including the psychomotor vigilance task (PVT) in the evening (9:00PM) and in the morning (7:30AM) with buffer time to prevent sleep inertia following initial awakening. During the following week, participants completed either three nights of sleep restriction (5 hours of sleep/night) or three nights of normal sleep (8 hours of sleep/night), with PVT testing each morning and evening. After a four-night washout period, participants returned to complete the other sleep condition (order counterbalanced). Research assistants who administered the PVT were kept blinded to the participant’s sleep condition.

Results
Relative to normal sleep, three nights of sleep restriction significantly worsened PVT mean reaction times (RTs; M = 361 ms versus M = 390 ms; t(65) = 4.38, p &lt; .001, d = .54) and number of lapses (M= 3.78 versus M = 5.80; t(65) = 5.00, p &lt; .001, d = .62]. There were no main effects of time of day or interactions with sleep condition (ps&gt;.05). However, study day significantly interacted with sleep condition (F(2, 128) = 3.42, p = .036, η2 = .051) such that the negative impact of sleep restriction on lapses worsened across the week (night 1: Mdiff = 1.59, night 2: Mdiff = 0.88; night 3: Mdiff = 3.26).

Conclusion
Mild sleep restriction quickly produces detriments in sustained attention, with effects being cumulative across repeated days of sleep loss.

Support (if any)
This study was supported by the National Science Foundation (1920730).
0179 Cardiovascular Responses to Sleep Deprivation and Psychological Stress Predict Actigraphic Sleep During Recovery
Authors: Pasetes Lauren , Rosendahl-Garcia Kathleen , Goel Namni 
Published: April 2024

Abstract

Introduction
We assessed whether cardiovascular (CV) responses to total sleep deprivation (TSD) and to TSD and psychological stress predicted sleep measures during that night’s recovery.

Methods
Thirty-two healthy adults (ages 27-53; 14 females) participated in a five-day experiment under controlled conditions. During this experiment, CV measures were collected via blood pressure monitor or echocardiography at four assessment time points: 1) after two baseline 8h time in bed (TIB) nights; 2) in the morning of TSD (TSD AM; after 25h of TSD); 3) in the evening of TSD following a modified Trier Social Stress Test, which induced psychological stress (TSD PM; after 34h of TSD); and 4) after two recovery nights (R1=10h TIB; R2=8h TIB). Seated heart rate (HR), cardiac index (CI), stroke volume (SV), systemic vascular resistance index (SVRI), left ventricular ejection time (LVET), systolic and diastolic blood pressure (SBP and DBP), and mean arterial pressure (MAP) were collected. Wrist actigraphy (Actiwatch Spectrum) assessed sleep measures during the first recovery night (R1) including the timing of sleep onset, offset, and midpoint, sleep duration, wake after sleep onset (WASO), sleep efficiency (SE), percent sleep, and sleep onset latency (SOL). Pearson’s correlations assessed relationships between TSD AM and TSD PM CV responses and R1 sleep indices (p&lt; 0.05 was significant).

Results
Longer LVET in the morning and in the evening of TSD were significantly associated with longer sleep duration and later sleep offset during recovery (r: 0.386-0.471; r2: 0.149-0.222). In addition, longer LVET in the evening of TSD was significantly associated with higher WASO during recovery (r=0.385; r2=0.148). By contrast, lower HR in the morning (r=−0.449; r2=0.202) and in the evening (r=−0.499; r2=0.249) of TSD were significantly related to a later sleep offset during recovery.

Conclusion
To the best of our knowledge, this is the first demonstration that CV responses to TSD and to TSD and psychological stress, are reflected in the duration, quality, and timing of actigraphic sleep during the immediate recovery night in healthy adults. Overall, LVET and HR during sleep deprivation may be unique biomarkers for determining sleep responses during recovery that night.

Support (if any)
NASA grants NNX14AN49G and 80NSSC20K0243 (NG)
0193 The Effect of Sleep Deprivation on Pain Perception in Awake Rats
Authors: Lim Sung Chul 
Published: April 2024

Abstract

Introduction
This study was conducted to determine how sleep deprivation affects headache perception and brain structure in an animal model of headache.

Methods
The effect of sleep deprivation on mechanical pain threshold was assessed in two groups of animals: (i) NSD-C group and (ii) SD group. The VFMF thresholds were assessed every day during 96 hours of sleep deprivation. After 96 hours sleep deprivation, the brain of each groups were analyzed with immunohistochemistry staining. The effect of sleep deprivation in supradural capsicin infusion were assessed in two groups. The VFMF threshold was performed during sleep deprivation and 4 weeks of recovery phase.

Results
1) The effect of sleep deprivation on mechanical pain threshold. In comparison between SD and NSD-C group, the significant difference appeared after 1 day of sleep deprivation and lasted 4 days. 2) The effect of sleep deprivation on FOS reactivity. In hyptothalamus, the number of Fos-positive cells in PoHT increased significantly in SD group compared NSD-C group. In PAG, the number of Fos-positive cells in VLPAG increased significantly in SD group compared NSD-C group. The number of Fos-positive cells of supf C in TCC increased significantly in SD group compared NSD-C group. 3) The effect of sleep deprivation on mechanical pain threshold in supradural capsicin infusion. SD-Capsicin showed the tendency of lower VFMF threshold compared to NSD-Capsicin, which reach to statistical significant 3rd days of capsicin infusion period. During the recovery phase, the reduced VFMF threshold of NSD-Capsicin group was persisted 1 week after sleep deprivation and returned to baseline thereafter. In SD-Capsicin group, the reduced VFMF threshold was persisted 3 week after sleep deprivation.

Conclusion
In this study, pain lasted for 1 week after 4 days of continuous infusion of capsicin without sleep deprivation, but lasted for more than 3 weeks when combined with sleep deprivation, confirming the possibility that sleep deprivation contributes to the chronicity of headache. The chronicity and persistence of headache is associated with the centralization of pain. The findings of this study that sleep deprivation results in changes in TNC activity, which plays an important role in centralization, support this possibility.

Support (if any)
0194 Impact of Sleep Deprivation Combined with Alcohol or Oxycodone on Sleep Architecture in Healthy Young Adults
Authors: Chowdhury Madihah , Finlay Myles , Layton Matthew , Van Dongen Hans , Stroer Claire , Mollicone Daniel , Hansen Devon 
Published: April 2024

Abstract

Introduction
Alcohol and opioids disturb sleep, but the impact of substance use on recovery sleep after sleep deprivation is largely unknown. Here we piloted a study protocol to assess the combined impact of total sleep deprivation (TSD) and substance administration on recovery sleep.

Methods
N=6 healthy normal sleepers (ages 28.2±5.6y; 4 males) completed two 24h laboratory study sessions. During each session, which began at 15:00, participants were kept awake for 15h until 06:00 the next day. They were then given an 8h recovery sleep opportunity (06:00–14:00) and went home. During the second study session, participants were randomly assigned to receive alcohol (n=3; peak BAC of 0.043±0.008% at 00:55, decayed to zero by 06:00) or an opioid (n=3; 10mg oxycodone administered at 22:30). Recovery sleep was recorded polysomnographically and scored using AASM criteria; analyses focused on total sleep time (TST), sleep efficiency, sleep latency, sleep stages N1–N3 and REM, and latency to each of the sleep stages.

Results
Session 1 (pre-substance) TST was considerably shorter in the opioid group (282±28min) than the alcohol group (403±24min). Therefore, we analyzed sleep variables for the two groups separately, using mixed-effects ANOVA with a fixed effect for study session (TSD vs. TSD+drug) and a random effect over subjects on the intercept. For the opioid group, latency to N3 sleep was significantly longer by 10±2min after opioid administration compared to TSD alone (F=33.8, p=0.028). There were no significant effects of TSD+alcohol compared to TSD alone.

Conclusion
Opioid administration during TSD delayed N3 onset, but no other effects of opioid or alcohol administration were seen during recovery sleep. Our sample was small, but the within-subjects study design provided considerable statistical power – substance effects on neurobehavioral performance during TSD (reported elsewhere) were readily detectable. However, BAC at the onset of recovery sleep had decayed to zero, and elevated homeostatic sleep pressure from TSD may have negated any residual substance effects other than the opioid-induced delay in N3. Investigating the impact of substances on recovery sleep after TSD, as well as post-recovery neurobehavioral functioning, is important for safety and health in today’s sleep-deprived society.

Support (if any)
National Safety Council