bluuebunny/crossref_metadata_2025_split huggingface dataset:
0168 Subjective and Objective Markers of Sleepiness in Drivers
Authors: Thieux Marine , Guyon Aurore , Herbillon Vania , Merle Lydie , Lachaux Jean-Philippe , Plancoulaine Sabine , Seugnet Laurent , Franco Patricia 
Published: May 2022

Abstract

Introduction
Sleepiness is associated with a decrease in cognitive abilities with effects comparable to those of alcohol. It remains one of the main causes of fatal road accidents. Various tools are available to assess sleepiness subjectively and objectively, such as questionnaires and multiple sleep latency test. However, the former are subject to intra- and inter-individual variability, and the latter is only feasible in a sleep laboratory. The main objective of this study was to explore new potential markers (biological, neurocognitive) to assess sleepiness in drivers. In this perspective, the easy-to-use BLAST attention test allows the evaluation of micro-fluctuations in vigilance. In addition, salivary markers are good physiological markers, easily accessible and non-invasive to collect. Recent studies have suggested that salivary a-amylase and oxalate may be biomarkers of sleep pressure.

Methods
185 drivers (median age 44 years, 72% male, 15% obese) were included during a break at a highway service area, in the morning, while on the road for vacation. Questionnaires on sleepiness, sleep the day before departure, an attention test and two salivary samples (alpha-amylase and oxalate) were taken. Associations between subjective and objective measures of sleepiness, sleep characteristics and salivary concentrations were tested using regression models adjusted for confounding factors.

Results
The night before departure, 57% of drivers reduced their sleep time and more than ¼ slept 5 hours or less. The higher the number of miles to drive, the shorter the sleep time. 16% of the drivers complained of poor sleep quality and difficulty falling asleep. At the time of the test, 46% of the drivers felt drowsy. Poor sleep quality or difficulty falling asleep the night before departure was associated with increased sleepiness as assessed by the Stanford Sleepiness Scale and decreased attentional ability as assessed by the BLAST. No association between salivary samples and sleepiness was observed.

Conclusion
Sleep characteristics on the day before departure were associated with sleepiness and attentional performance. The Stanford Sleepiness Scale and the BLAST could be used by individual drivers in a self-evaluation context.

Support (If Any)
This study was funded by the Vinci Foundation.
0189 High Levels of Sleep Disturbance across Early Childhood Increases Cardiometabolic Disease Risk Index in Early Adolescence: Longitudinal Sleep Analysis Using the HOME Study
Authors: Duraccio Kara , Xu Yingying , Beebe Dean , Lanphear Bruce , Chen Aimin , Braun Joseph , Cecil Kim , Yolton Kimberly 
Published: May 2022

Abstract

Introduction
Sleep is a predictor of cardiometabolic disease (CMD) risk, and new evidence links early childhood sleep to later CMD risk. This study examines the impact of early childhood sleep duration, bedtime timing, and sleep disturbance on a CMD risk score in early adolescence.

Methods
Within the Health Outcomes and Measures of Environment (HOME) Study, a prospective pregnancy and birth cohort study, we assessed sleep patterns among 346 children using the Children’s Sleep Habits Questionnaire from ages 2 to 8 years. We calculated cardiometabolic risk scores ate age 12 for 183 of these children from visceral adiposity area, blood pressure, fasting serum triglyceride, high density lipoprotein, leptin, and adiponectin concentrations. We used a group-based semi-parametric mixture model to identify distinct trajectories in sleep duration, bedtime timing, and sleep disturbance for the entire sample. We then examined the associations between sleep trajectories and CMD risk score using general linear models for children with a CMD risk score, using both an unadjusted model (no covariates) and an adjusted model (adjusting for child pubertal stage, child sex, duration of breastfeeding, household income, and maternal education).

Results
Three sleep trajectories emerged for bedtime timing (late timing, medium timing, and early timing) and for sleep disturbance (high, medium, and low), and two for sleep duration (high and low). In the unadjusted model, we found significant differences in CMD risk scores by trajectories of sleep disturbance. Children in the ‘high’ trajectory had higher CMD risk scores (Least Square Mean=1.51; 95% CI: 0.39, 2.64) than those in the ‘low’ trajectory (Least Square Mean =-0.51; 95% CI: -1.16, 0.15; p=.002) and ‘medium’ trajectory (Least Square Mean=-0.15; 95% CI: -1.14, 0.85; p=.03). These findings only approached significance after adjusting for covariates. No significant differences in CMD risk were observed for bedtime timing or total sleep time trajectories in the unadjusted or adjusted models.

Conclusion
In this cohort, parent-reported sleep disturbance in early childhood was associated with more adverse cardiometabolic profiles in early adolescence. Our findings suggest that trials to reduce CMD risk via sleep interventions – which have been conducted in adolescents and adults – may be implemented too late.

Support (If Any)
National Institute of Environmental Health Sciences grants PO1 ES11261, R01 ES014575, R01 ES020349, R01 ES027224, R01 ES025214.
0203 Rise and shine! The efficacy of a multimodal environmental alarm system on mitigating the effects of sleep inertia
Authors: Campanella Carolina , Byun Kunjoon , Senerat Araliya , Li Linhao , Zhang Rongpeng , Aristizabal Sara , Porter Paige , Bauer Brent 
Published: May 2022

Abstract

Introduction
Sleep inertia is a temporary period of reduced alertness and impaired physical and cognitive performance that immediately follows waking. Sleep inertia can have devastating consequences necessitating an intervention to successfully mitigate symptoms. Previous work has demonstrated modest benefits for individual environmental interventions which manipulate either lighting, sound, or temperature. The current study sought to expand on previous work and measure the impact of a multimodal intervention that collectively manipulated light, sound, and ambient temperature on vigilance, mood, and sleepiness.

Methods
37 adults (M=27.13 years, 19 F) who self-reported taking longer than 30 minutes to wake up for 60% of their work week slept in the lab for four nights. They were woken up each morning with either a traditional alarm sound or the multimodal intervention (two control nights and two intervention nights, counterbalanced across participants). Feelings of sleep inertia were measured each morning through completion of the Psychomotor Vigilance Test and ratings of sleepiness and mood at five different time-points (5, 15, 30, 60, and 90 min after wake).

Results
While there was little impact of the intervention on all outcome measures, there were differential impacts depending on a person’s chronotype and the length of the lighting exposure during the intervention condition. Moderate evening-types who received a shorter lighting exposure (£15 min) demonstrated more vigilance lapses (p = 0.04) relative to the control condition whereas intermediate-types demonstrated better response speed (p &lt; 0.005) and fewer lapses (p = 0.002). Conversely, moderate evening-types who experienced a longer light exposure (&gt;15 min) during the intervention exhibited fewer false alarms over time (p = 0.03). Participants who received a longer light exposure also reported marginally lower negative affect the longer they were awake (p = 0.06).

Conclusion
Collectively, the results suggest that the length of the environmental intervention may play a role in mitigating feelings of sleep inertia, particularly for groups who may exhibit stronger feelings of sleep inertia including evening-types. Results may help inform the efficacy of “smart alarms” that activate based on entering light sleep. Future studies should measure this impact using additional measures of cognitive performance.

Support (If Any)
0206 Rest-Activity Rhythms are Associated with Sleep Characteristics and Cognitive Functions in People with Heart Failure Over 6 Months
Authors: Jeon Sangchoon , Conley Samantha , O'Connell Meghan , Yang Raymond , Redeker Nancy 
Published: May 2022

Abstract

Introduction
People with Heart failure (HF) often suffer from sleep deprivation and poor cognitive function. The purpose of this study was to examine the extent to which repeatedly measured rest-activity rhythms (RARs) measured with wrist actigraphy predict sleep characteristics and cognitive function in people with HF.

Methods
We measured insomnia severity (ISI), sleep quality (Pittsburgh sleep quality index: PSQI), sleepiness (Epworth sleepiness scale: ESS), psychomotor vigilance (Psychomotor vigilance test: PVT), and quality of life (Euroqol 5D) among people with HF patients who participated in a randomized controlled trial of cognitive behavioral therapy for insomnia vs. HF self-management education at baseline, 3-, and 6-months post-intervention. We performed cosinor analysis with 24-hour rest-activity counts obtained with 7 days of wrist actigraphy at each time point and calculated the circadian quotient, which represents the strength of RARs. We used the Generalized Linear Mixed Model with random intercepts to examine the association between the circadian quotient, sleep characteristics, cognitive function, and quality of life after adjusting for time-group interactions over 6 months. Statistical significance for standardized coefficients was accepted at 5% type I error.

Results
The analysis included 162 participants with HF and insomnia (Insomnia severity index &gt;7) who completed actigraph monitoring for at least 7 days at baseline There was no significant change in the mean circadian quotient (Mean=0.78, SD=0.16) over 6 months. After adjusting for significant intervention effects, a greater circadian quotient was statistically associated with lower insomnia severity (-0.11±0.05), sleepiness (-0.12±0.05), sleep quality (-0.15±0.05), longer sleep duration (0.33±0.04) and better sleep efficiency (0.13±0.05). The circadian quotient was positively associated with cognitive function measured by fewer PVT lapses (-0.11±0.05) and quality of life (0.12±0.05).

Conclusion
In addition to the significant intervention effects for insomnia, HF patients may benefited from strengthening RAR to improve sleep characteristics, cognitive function, and quality of life. Further research to assess the contributions of RAR in people who received the intervention for insomnia and the HF self-education separately is recommended.

Support (If Any)
R01NR016191 and P20NR014126
0208 Daytime alertness quantification and modelling: results from a large observational study
Authors: Barr Shawn , Garcia-Molina Gary 
Published: May 2022

Abstract

Introduction
Subjective alertness variations throughout the day can be characterized using the two-process model (TPM) of sleep regulation, which combines sleep homeostasis and the circadian rhythm to derive a theoretical daytime alertness curve. The TPM has been adopted to model the effect of sleep deprivation on memory, circadian misalignment, temperature regulation, and brain function; however, despite its broad influence, evidence supporting the TPM-derived alertness comes largely from small-scale, controlled studies. Here, we show that a similar three-parameter alertness measure can scale to a large study sample under real-world conditions.

Methods
Subjective alertness was voluntarily rated on a scale from 1 to 10 by Sleep Number smart bed users (N=22 499) through the SleepIQ app. Three age groups (18–40, 41–65, and 66–90 years) were analyzed. A 3-parameter version of the TPM-derived alertness curve was fit to the self-rated alertness responses using nonlinear least-squares fitting.

Results
A total of 65 528 sleep sessions were gathered over 95 days and analyzed. Overall, subjective alertness followed a similar trend to that reported in published literature: mean hourly alertness increased in the morning, dipped slightly in the afternoon, increased during the evening, and dropped again during the night. In contrast to previous studies, mean alertness ratings only changed by approximately 1 unit from low to high, and a greater increase in alertness occurred from afternoon to evening. Age-group analyses found that youngest sleepers’ mean daily alertness was more stable throughout the day, and the amplitude of alertness variation decreased with age. These experimental results showed high agreement with model prediction (R2=0.96, P&lt;0.001).

Conclusion
Overall, our results were similar to previous reports, with the exception of a small absolute change over the course of the day (about 1 unit) and an evening peak in alertness that was more pronounced in our data. These results show that the TPM-derived alertness can effectively predict daily alertness trends in a large sample under real-world conditions.

Support (If Any)
This study was funded by Sleep Number Corporation. Medical writing support, provided by Rachel C. Brown, PhD, from Oxford PharmaGenesis Inc., Newtown, PA, USA, was funded by Sleep Number Corporation.
0242 Intraindividual Variability in Sleep Duration Blunts Response to Academic Stressors
Authors: Do Alexander , Gao Chenlu , Scullin Michael 
Published: May 2022

Abstract

Introduction
Cross-sectional studies indicate that greater intra-individual variability (IIV) in sleep is correlated with poorer cognitive-emotional outcomes. Yet, the causal direction of these relationships is unclear. Therefore, we conducted an experimental study to compare cognitive and stress outcomes following random assignment to normal sleep, sleep restriction, and irregular/IIV sleep conditions.

Methods
Ninety college students (mean age=19.16, SD=0.98; 77.78% female) completed an Organic Chemistry virtual lecture during session 1. Then, for the next five days, participants were randomly assigned to the following conditions: normal sleep (8h in bed every night), sleep restriction (6h in bed every night), or IIV sleep (nightly oscillation between 6.5h and 9.5h in bed, with mean of 8h/night). Adherence was confirmed using actigraphy. On the sixth day (session 2), participants took a test on the Organic Chemistry lecture (retention measure) and then completed a new lecture and test (acquisition measure). Participants reported their stress levels across the lectures and tests.

Results
ANCOVA tests showed that sleep condition did not affect retention (F=0.50, p=.611) or acquisition of Organic Chemistry knowledge (F=1.33, p=.275; adjusted for Grade Point Average). Interestingly, participants in the IIV condition reported lower stress levels throughout session 2 than students in the normal sleep condition, adjusting for baseline stress levels (p=.025). Furthermore, when collapsing across conditions, correlation analyses confirmed that greater IIV in TST predicted lower stress throughout session 2, after adjusting for mean TST and baseline stress level (r=-.33, p=.014). This “blunted” stress response following nightly sleep fluctuations was in contrast to the heightened stress levels that were associated with shorter mean TST (r=-.22, p=.051).

Conclusion
Brief sleep variability and mild sleep restriction had minimal impact on laboratory measures of STEM knowledge retention and acquisition. However, greater sleep IIV blunted reactivity to academic stressors, which may reflect altered hypothalamic–pituitary–adrenal axis functions and have implications for student metacognition and course persistence. Future research should investigate the chronic effect of maintaining irregular sleep patterns and whether improving regularity of sleep across longer time intervals promotes cognitive and emotional functioning.

Support (If Any)
American Psychological Association, APAGS, Psi Chi, National Science Foundation (1920730, 1943323)
0297 Exploring sex and strain differences in mouse models of sleep disruption
Authors: Harper Kathryn , Nikolova Viktoriya , Conrad Monika , Moy Sheryl 
Published: May 2022

Abstract

Introduction
Sleep abnormalities are a common feature of neurodevelopmental disorders (NDDs), and can have severe consequences for the affected individual and their families. Although mouse models can be used to investigate the mechanistic basis for social deficits and other symptoms of NDDs, recapitulating sleep phenotypes in mice has proven to be difficult. In this study, we determined whether a sleep-disruption challenge could reveal or exacerbate sex and strain differences in sleep patterns, using two well-validated mouse models of autism-like behavior; the BALB/cByJ and C58/J inbred strains.

Methods
Mice were placed in a piezoelectric sleep system for 3 days to evaluate baseline sleep rhythms. Mice were then exposed to a series of novel environments, including open field boxes and activity wheel cages, for 3 hours during the morning period of the light cycle. Sleep rebound was determined by comparing percent time spent sleeping and average sleep bout length in the three 6-hr intervals before and after the sleep disruption. Subjects were males and females of the C57BL/6J, BALB/cByJ and C58/J strains.

Results
At baseline, the C57BL/6J and BALB/cByJ females showed significantly reduced percent sleep, in comparison to males. During rebound from sleep disruption, BALB/cByJ mice had higher percent time and longer sleep bouts than C57BL/6J, suggesting a greater vulnerability to the effects of sleep disruption. Additionally, female C58/J mice lacked the highly robust increase in percent sleep seen in C57BL/6J in the early night interval after sleep disruption.

Conclusion
Overall, the data provide evidence that this sleep disturbance procedure can induce a sleep rebound effect, which could be helpful in revealing strain and sex differences in NDD mouse models.

Support (If Any)
0315 Quantifying the Temporal Relationship Between Self-report Sleep Quality and Cognition in Older Adults
Authors: Tapia Amanda , Yu Lan , Lim Andrew , Barnes Lisa , Hall Martica , Butters Meryl , Buysse Daniel , Wallace Meredith 
Published: May 2022

Abstract

Introduction
Poor sleep is a promising modifiable risk factor for impaired cognition in older adults. However, the relationship between sleep and cognition is likely bi-directional, and few studies have examined these temporal associations. We seek to investigate the temporal relationships between self-report sleep quality and global cognition.

Methods
Our analytic sample includes 1,610 participants from the Memory and Aging Project and Minority Aging Research Study without cognitive impairment at the initial visit (41% black, 77% female, mean[min,max] age = 77[54,100] years). Participants have cognition and sleep quality measured at an initial visit and up to 14 years of annual follow-up (median 6 years). Sleep quality was measured using a modified 10-item Pittsburgh Sleep Quality Index score (higher scores indicating worse quality) and standardized; global cognition was a composite z-score computed from an average of 19 cognitive tests. We used linear mixed effects models to quantify the concurrent and prospective (1-year) relationships of sleep quality and global cognition. Quadratic terms were also tested to allow for a potentially U-shaped relationship.

Results
When examining same-year associations with cognition as the outcome, sleep quality and cognition exhibit a negative quadratic association (linear term BL[p] = 0.01[0.021]; quadratic term BQ[p] = -0.01[0.051]), indicating that both better- and worse-than-average sleep quality are associated with lower cognition. Regarding 1-year associations, both better- and worse-than average sleep quality predict worse next-year global cognition (BL[p] = 0.01[0.008], BQ[p] = -0.01[0.033]). In contrast, better-than-average cognition predicts worse next-year sleep quality (B L[p] = 0.05[p=0.005]; BQ[p] = -0.01[0.650]) with a stronger association in this direction.

Conclusion
Understanding the temporal association between sleep and cognition has important implications for screening and development of novel treatments and interventions. The finding that both better and worse sleep quality are associated with worse cognition may reflect an underreporting of poor sleep symptoms in older adults with worsened cognition. Future work will examine these associations considering specific domains of self-report sleep (e.g., timing, efficiency, duration) and cognitive function (memory and perception), consider mechanisms relating sleep and cognition, and use objective measures of sleep (e.g., actigraphy).

Support (If Any)
RF1AG056331 (PI: Wallace), R01AG17917, R01AG22018
0352 Compliance to Sleep Recommendations: A Big Data Analysis In Users of a Consumer Sleep Technology
Authors: Raymann Roy , Watson Nathaniel , Gahan Luke , Gottlieb Elie 
Published: May 2022

Abstract

Introduction
The National Sleep Foundation has published sleep time duration (Hirshkowitz et al., 2015) and sleep quality (Ohayon et al., 2017) recommendations across the lifespan based on expert panel input. These recommendations offer sleep guidance to millions of individuals. Many individuals are using commercially available sleep tracking devices to measure their sleep. We analyzed the data of two non-contact radiofrequency sleep measurement devices (SleepScore Max (SleepScore Labs) and S+ (ResMed), both validated against PSG) to determine how well the users of these devices are sleeping.

Methods
Total Sleep Time (TST), sleep latency (SL) and sleep efficiency (SE) data of 40,892 users between 15 and 98 years old were used in this analysis, covering 5,513,369 nights of data, and averages per user were calculated. In alignment with the aforementioned papers, the data were split in 3 age groups; Young Adults (18-25), Adults (26-64) and Older Adults (&gt;=65), and the criteria as listed were used, classifying the sleep indicators in 3 bins; Appropriate/Recommended, Uncertain/ May be Recommended, and Inappropriate/Not Recommended. Proportion of users meeting the Appropriate/Recommended, Uncertain/May be Appropriate and Inappropriate/Unlikely were calculated for each age group.

Results
In the Young Adults group most users had an Appropriate SL (SL&lt;= 30 min; 79.7 %), an Appropriate SE (SE&gt;=85%; 53.6 %), but only 30.0% had a Recommended TST (7 to 9 h.). In the Adult group, larger proportions of users met on average the Appropriate measures for SL (SL&lt;= 30 min; 84.8%), SE (SE&gt;= 85%; 58.4%), but only 27.3% slept on average the Recommended hours (TST 7 to 9 h). In the Older Adults group, the average SL and SE was considered Appropriate for most elderly users (SL&lt;30min; 84.3: SE&gt;=85%; 59.6%), but only 28.2% slept the Recommended amount (TST 7 to 8 h).

Conclusion
30% or less users slept on average the recommended amount, whereas slightly over half of the users showed the recommended sleep efficiency, and at least 79.7% fell on average asleep within 30 minutes. These results show that sleep improvement campaigns need to focus on extending the sleep duration and sleep hygiene to improve SE.

Support (If Any)