bluuebunny/crossref_metadata_2025_split huggingface dataset:
0406 Daytime Sleepiness in The Community: Implications for Sleep, Circadian, and Physical Health
Authors: Grandner M A , Tubbs A , Jean-Louis G , Seixas A , Hale L , Branas C , Killgore W D , Wills C C 
Published: May 2020

Abstract

Introduction
Daytime sleepiness impacts performance and well-being. The present study used validated measures to explore associations of community-level daytime sleepiness with sleep health, preferred sleep phase, physical inactivity, and overall health.

Methods
Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22-60 from the community. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Outcomes of interest included the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), endorsement of a preference for an advanced or delayed sleep phase on the Sleep Disorders Symptom Check List (SDSCL), STOP-BANG sleep apnea questionnaire score, sedentary time assessed with the International Physical Activity Questionnaire (IPAQ), and the global health item on the SF-12, operationalized as excellent/good or fair/poor health. Through regression analyses, we assessed whether daytime sleepiness was independently associated with several sleep, circadian and physical health outcomes, adjusting for habitual sleep duration and sociodemographic factors like age, sex, education, and race/ethnicity.

Results
Our adjusted models indicate that daytime sleepiness was associated with insomnia (B=0.57; 95%CI: 0.50, 0.65; p&lt;0.0001), sleep quality (B=0.34; 95%CI: 0.29, 0.39; p&lt;0.0001), advanced sleep phase (OR=1.06; 95%CI: 1.03, 1.09; p&lt;0.0001), delayed sleep phase (OR=1.05; 95%CI: 1.02, 1.07; p=0.0003), STOP-BANG score (B=0.08; 95%CI: 0.07, 0.10; p&lt;0.0001), sedentary minutes (B=6.12; 95%CI: 2.77, 9.47; p=0.0004), and overall poor health (OR=1.10; 95%CI: 1.07, 1.13; p&lt;0.0001). After additional adjustment for habitual sleep duration, all relationships were maintained.

Conclusion
Daytime sleepiness is associated with more severe insomnia, preference for advanced or delayed sleep timing, worse sleep quality, and greater risk of sleep apnea. Moreover, daytime sleepiness was associated with greater sedentary time and worse overall health. Since these relationships are independent of sleep duration, they likely do not reflect an effect of sleep deprivation.

Support
This work was supported by a grant from Jazz Pharmaceuticals. The SHADES study was funded by R21ES022931. Dr. Grandner is supported by R01MD011600.
0412 Parents Sleep Longer When School is Out for the Summer: Associations Among Parenthood, Gender, and Season
Authors: Ruder M , Rus H M , Raj A , Gahan L , O’Mullane B , Danoff-Burg S , Weaver M , Raymann R 
Published: May 2020

Abstract

Introduction
Seasonal effects in sleep are often attributed to day length; however, change in obligatory daily activities might also have an impact on sleep behavior. Longitudinal measurement using consumer sleep technology enables the observation of patterns in sleep behavior in the home environment. We analyzed the impact of parenthood and gender on total sleep time (TST) over the summer break period using data collected in the home.

Methods
Sleep data were collected using the SleepScore mobile application from October 2018 through October 2019, with the summer break period defined as June 25th - August 5th. U.S. age and gender matched samples of parents and non-parents were selected using Mahalanobis distance from a pool of users more likely to have school-aged children. The final samples included n=345 parents (38.7 +/- 4.5 years) and n=345 non-parents (37.8 +/- 4.7 years); both groups were 46% female. Only weeknights (n=34,323) were analyzed to maximize impact of school schedule. Linear regression and independent t-tests were used to analyze main and interaction effects for gender, parenthood, and summer break.

Results
Male gender, parenthood, and summer break were associated with decreased sleep duration (ps &lt; .01). However, during summer break, parents exhibited an increase in TST, with mothers (+5.6 mins) having a greater increase than fathers (+1.1 mins). In contrast, adults without children showed a decrease in TST during summer break, with males having a greater reduction (-8.8 mins) than females (-6.5 mins).

Conclusion
These results suggest that parental status may play a part in seasonal sleep patterns. Contrary to the typical trend of shorter TST during summer, being a parent is associated with longer TST during summer break, with a greater increase for females. This change may be attributed to parents following a less rigid schedule when their children are not in school.

Support
N/A
0413 The Influence of Community Environment Exposure and Individual Health Behavior on Insufficient Sleep
Authors: Shin J , Grigsby-Toussaint D 
Published: May 2020

Abstract

Introduction
Short sleep duration is associated with the risk of various chronic diseases, and it has been hypothesized to influence health behaviors and environmental exposure such as green space and noise. However, little studies have explored this relationship, especially with the consideration of the integrated environment information. The purpose of this study is to examine the influence of environmental exposure and individuals’ health behavior on sleep duration.

Methods
We examined aggregate national-level datasets regarding health information, noise, and green space. Sleep, health behavior, and socio-demographic variables were derived from 500 cities data, and the unit of analysis was the prevalence of each variable in the census tract. The insufficient sleep was calculated by the percentage of the population who had less than 7 hours of sleep in the census block. Spatial analysis was performed for green space and noise measurement; both values were calculated within the community activity space, which is a combined area with the census urban area and census place to examine the potential activity space. Multiple linear regression analysis was performed to model the relationship.

Results
The final sample was 29,610 census tracts from 497 cities, and the average median age of each census tract was 36.02±7.085. 36.64% of insufficient sleep was reported from the entire sample. The model (R2=0.804) indicate that green space (β=-.053, p&lt;.001) and natural noise (β=-.029, p&lt;.001) has negatively associated with insufficient sleep; while, smoker (β=.374, p&lt;.001), binge drinker (β=.105, p&lt;.001), no leisure physical activity time (β=.111, p&lt;.001), and artificial noise (β=.034, p&lt;.001) has positive association with insufficient sleep.

Conclusion
The results indicate that a better environment source in the community mitigates the insufficient sleep population as well as individual health behavior. Further studies are needed, however, to fully disentangle the association between sleep duration and sleep quality associated with other environmental exposure.

Support
N/A
0414 Chronotype and Sleep Among Ovarian Cancer Survivors Participating in a Lifestyle Intervention
Authors: Crane T E , Skiba M B , Donzella S , Thomson C A , Parthasarathy S 
Published: May 2020

Abstract

Introduction
Chronotype is defined as an individual’s propensity to sleep at a specific time in a 24-hour cycle with late chronotype associated with poorer health outcomes including cancer. The role of chronotype on lifestyle behaviors remains relatively undefined in ovarian cancer. The Lifestyle Intervention for oVarian cancer Enhanced Survival study is testing whether 1205 women randomized to a diet and physical activity intervention for 24-months will have longer progression-free survival versus attention control. Here we determine the frequency and predictors of late versus early and mid chronotypes in disease-free ovarian cancer survivors.

Methods
894 ovarian cancer survivors with baseline measures were included in analyses. Chronotypes were determined using self-reported time to bed (early- &lt; 9 pm; mid- ≥ 9 pm - ≤12 am; late- &gt;12 am) captured through the Pittsburgh Sleep Quality Index. Demographic, diet and physical activity data were captured with validated questionnaires and BMI measured in clinic. Descriptive statistics and logistic regression, adjusted for smoking status and race, were performed.

Results
12.4% of women were late chronotype with significant differences between chronotypes observed for race, smoking history, sleep duration, and physical activity (p &lt; 0.05). Late chronotype reported fewer hours of sleep per night (6.54 ± 1.51hrs) compared to mid (7.10± 1.31hrs) and early (7.74 ± 1.30hrs) chronotype. Blacks had higher odds of being late chronotype, OR 4.28 (95% CI 2.16-8.46). Late chronotype were more likely to report a history of smoking and lower recreational activity and had a higher mean BMI of 29.1± 6.0 kg/m2 compared to mid and early chronotype 27.8± 6.2 kg/m2 and 27.4± 5.4kg/m2, respectively. No significant differences were observed for sleep or diet quality, age, education or employment status.

Conclusion
Results of this analysis are consistent with other community-based population studies with regard to chronotype and race. Ovarian cancer is aggressive and late chronotype are more likely to have other risk factors that elevate risk of recurrence (obesity, tobacco use and inactivity. Six-month data are being analyzed by treatment arm and will provide important insights as to the role of sleep phase and lifestyle behaviors in this vulnerable population.

Support
NCT00719303; NCI R01CA186700-01A1
0416 Poor Sleep Quality Predicts Serum Markers of Neurodegeneration and Cognitive Deficits in Warriors with Mild Traumatic Brain Injury
Authors: Werner K , Shahim P , Gill J , Nakase-Richardson R , Kenney K 
Published: May 2020

Abstract

Introduction
Increasing evidence links neurodegeneration to traumatic brain injury (TBI), and a separate body of literature links neurodegeneration to sleep dysfunction, implicating increased toxin production and decreased glymphatic clearance. Sleep disorders affect 50% of TBI patients, yet the sleep-neurodegeneration connection in these patients remains unexplored. We hypothesized that warfighters with TBI and sleep dysfunction would have increased neuronal injury, revealing potential mechanistic underpinnings for TBI outcomes. We measured plasma biomarkers, cognitive function and sleep surveys for correlation analysis.

Methods
In a retrospective cross-sectional study of warfighters (n=113 chronic mild TBI patients), the Pittsburgh sleep quality index (PSQI) was compared with amyloid β42 (Aβ42), neurofilament light (NFL), tau, and phospho-tau (threonine 181) isolated from plasma and exosomes. Executive function was tested with the categorical fluency test. Exosomes were precipitated from plasma. Proteins were measured with the Single Molecule Array (Quanterix). Linear models were adjusted for age, ApoE, and number of TBIs.

Results
Poor sleepers with TBI (PSQI&gt;8) had elevated NFL compared to good sleepers in plasma (p=0.007) and exosomes (p=0.00017), and PSQI directly correlated with NFL (plasma: Beta=0.23, p=0.0079; exosomes: Beta=2.19, p=0.0013) stronger than any other marker of neurodegeneration. Poor sleepers also showed higher obstructive sleep apnea (OSA) risk compared to good sleepers by STOP-BANG scores (3.6, SD=1.6 vs 2.8, SD=1.74; p=0.0014) as well as decreased categorical fluency (20.7, SD=4.1) (18.3, SD=4.6, p=.0067). Plasma tau and Aβ42 also correlated with PSQI (Beta=0.64, p=0.028, and Beta=0.40, p=0.049 respectively).

Conclusion
This is the first reported data correlating markers of neuronal injury and cognitive deficits with sleep complaints and OSA risk in patients with TBI - possibly identifying treatable pathophysiological mediators of TBI neurodegeneration. Limitations include a small sample size, lack of objective sleep measures, and inability to establish directionality due to cross-sectional design. Prospective trials will be required to further explore our proposed hypothesis. If confirmed, these findings would call for targeting sleep disorders in the TBI population to mitigate risk of neurodegeneration.

Support
This work was supported by grant funding from: Department of Defense, Chronic Effects of Neurotrauma Consortium (CENC) Award W81XWH-13-2-0095 and Department of Veterans Affairs CENC Award I01 CX001135.
0421 Decreased Actigraphic Daytime Activity is Associated with Lower Memory Performance in Cognitively-Unimpaired Individuals with Autosomal Dominant Alzheimer’s Disease
Authors: Pardilla-Delgado E , Ramirez Gomez L , Baena A Y , Montes M I , Bocanegra Y , Martinez J E , Lopera F , Quiroz Y T 
Published: May 2020

Abstract

Introduction
Alzheimer’s disease (AD) impacts brain regions that control circadian regulation systems such as wakefulness and daytime physical activity. Recent evidence shows that AD pathology is damaging for wake-promoting neurons. Whether early changes in wakefulness and daytime activity occur during asymptomatic stages of familial AD (fAD) remains unknown. In this study, we aimed to investigate whether daytime activity differs between cognitively-unimpaired carriers of early-onset fAD and age-matched non-carrier family members. Further, we examined the associations between daytime activity and memory performance.

Methods
A total of 25 members of the large Colombian kindred with the Presenilin1 (PSEN1) E280A mutation were included in the study (9 mutation carriers and 16 non-carriers, mean age=38.2). PSEN1 mutation carriers develop dementia before the age of 50. All subjects underwent wrist actigraphy for 7-14 days to measure daytime activity (average activity per minute and per epoch), and completed the CERAD Word List Learning and the Free and Cued Selective Reminding Test (FCSRT).

Results
Compared to non-carriers, mutation carriers had less average daytime activity (Mann-Whitney U Test p=.04). Higher average daytime activity was associated with better memory recall in both the CERAD word list delayed recall (r=.47, p=.05) and the FCRST delayed total recall (r=.53, p=.02). No associations with age were observed.

Conclusion
Our results suggest that cognitively-unimpaired mutation carriers have reduced daytime activity, years before the onset of dementia. Reduced daytime activity in carriers is also associated with lower memory performance. Our preliminary findings add to the growing evidence that circadian dysfunction is present in early AD, and may play an important role in subsequent memory impairment. Future research with large samples is needed to further examine sleep and circadian dysfunction in asymptomatic individuals at genetic risk for AD.

Support
NIA 5R01AG054671-03 to YTQ
0423 Older Age Modifies the Association Between Combined Sleep Disordered Breathing and Sleep Duration with Neurocognitive Decline in Hispanic/Latino Adults
Authors: Kaur S , Tarraf W , Wu B , Daviglus M , Shah N , Sotres-Alvarez D , Gallo L , Wohlgemuth W , Redline S , Gonzales H , Ramos A 
Published: May 2020

Abstract

Introduction
We aimed to determine if age or sex modifies associations between sleep-disordered breathing (SDB), sleep duration and severe phenotypes of combined SDB/sleep duration with 7-year neurocognitive change (NC) in a diverse sample of U.S. Hispanic/Latinos.

Methods
We analyzed data of 5,235 adults 50-80 years of age from SOL-INCA, an ancillary to the Hispanic Community Health Study/Study of Latinos that determines the risk factors for NC. The main outcome was NC after a mean follow-up of 7-years on measures of memory (SEVLT sum and SEVLT recall), language (word fluency), processing speed (DSS) and a cognitive impairment screener. We evaluated the effect of baseline SDB (AHI ≥ 15), sleepiness (Epworth Sleepiness Scale, ESS ≥ 10), self-reported sleep duration (i.e. &lt;6 hours, 6-9 hours, ≥ 9 hours), age and sex on NC. Survey linear regression models with interaction terms were used to examine the relationship between SDB, sleep duration, combinations of SDB and sleep duration phenotypes and NC. Depression, vascular risk, sleep medication, and study site were entered into all models as covariates.

Results
Overall, the mean age was 56.0 years, 54.8% females, 62.2% completed high school, 17.3% had SDB, 6.6% had short sleep,and 14.8% had long sleep. Sleep duration and SDB were not associated with NC. There was a significant interaction between agexSDB+sleep duration on delayed recall (F10,599= 2.40, p=0.01) and processing speed (F10,597= 2.55, p=0.01). Combined SDB + short sleep was associated with decline in processing speed (β=-0.6, 95% CI= [-1.2, -0.1], and combined SDB+long sleep was associated with decline in verbal memory (β=-0.9, 95% CI=[-1.7, -0.2] in adults aged ≥ 65 years. There was no association in participants aged &lt;65 years and no sex differences.

Conclusion
Age, but not sex, modified the association between SDB and sleep duration with decline on processing speed and verbal memory. Sleep interventions tailored for older adults may be useful in slowing or preventing neurocognitive decline.

Support
This work is supported by National Institute on Aging (R01AG048642, RF1AG054548, R01AG061022, and R21AG056952).
0431 Two-Way Communication Between Dreamers and Experimenters
Authors: Konkoly K R , Paller K A 
Published: May 2020

Abstract

Introduction
Dreams are emblematic of human sleep, but they have yet to be adequately explained. In part, this is due to the limited options available for peering into dream experiences. Mapping neural measures onto dreams is problematic when those dreams are recounted after waking. Retrospective dream reports are subject to distortion and rapid forgetting.

Methods
Here, we describe a method to overcome these obstacles through two-way communication between dreamers and experimenters. To demonstrate proof-of-concept, we presented softly spoken math problems to participants during lucid REM sleep, and they provided answers using covert physiological signals such as eye movements. We confirmed REM sleep using standard polysomnographic methods.

Results
Thus far, 3 out of 8 participants who had lucid dreams correctly answered problems during REM sleep.

Conclusion
Results document that sleeping individuals can have sufficient abilities for veridical perceptual analysis, maintaining information, computing simple answers using working memory, and expressing volitional replies. Dreamers can thus be capable of interacting and exchanging information with other individuals. In this way, the mental content experienced by the dreamer can be interrogated to characterize the phenomenological experiences and cognitive abilities of dreaming.

Support
Mind Science Foundation, National Science Foundation
0438 Automatic Nighttime Agitation and Sleep Disruption Detection Using a Wearable Ankle Device and Machine Learning
Authors: Kumar R , Feltch C , Richards K , Morrison J , Rangel A , Janney R , Shayesteh S , Allen R , Banerjee N 
Published: May 2020

Abstract

Introduction
Nighttime agitation behavior such as wandering and restlessness during awake and sleep in people with Alzheimer’s disease (AD) is expensive to manage and adversely affects sleep. Nighttime agitation is mostly noted by subjective caregiver reports. An automated process for this assessment would improve clinical management. Here we report on the RestEaZeTM system that uses an ankle band and machine learning to automatically classify sleep status and nighttime agitation behaviors in older adults with AD.

Methods
We collected data on 7 adults (mean: 81 years, SD: 10.6) with AD. They wore the RestEaZeTM ankle band with a 3-axis accelerometer, a 3-axis gyroscope, and three textile capacitive sensors. A trained Research Assistant (RA) continuously observed for wandering, restlessness, wake, and sleep between 5pm and 7am using the Cohen Mansfield Agitation Inventory (CMAI). We merged, and band-pass filtered the data and divided it into 10-second non-overlapping windows. CMAI labels and time-series features (scaled using StandardScaler) extracted from the RestEaZeTM data were used to train a Random Forest binary classifier. The significant features were extracted based on the impact on the p-value for the classifier. We used the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and performed 5-fold cross-validation with a 67-33 train-test split.

Results
We report the sensitivity, specificity, accuracy, and Area-under-the Curve (AUC) for the ROC curve for the classifiers: (1) Sleep/Awake: sensitivity=0.95, specificity=0.87, accuracy=0.92, AUC=0.97; (2) Wandering/Non-Wandering: sensitivity=0.85, specificity=0.99, accuracy=0.98, AUC=0.99; and (3) Restless/Non-Restless: sensitivity=0.84, specificity=0.84, accuracy=0.84, AUC=0.92. The significant features were related to the intensity of movements.

Conclusion
Our preliminary results show the feasibility of using RestEaZeTM for quantitatively measuring nighttime agitation. These can provide clinically useful objective measures of agitation that can be automatically transmitted to clinical or research records with minimal staff time requirements.

Support
The authors acknowledge the funding support from the National Institute on Aging under award R01AG051588 and Arbor Pharmaceuticals for support for Horizant and the matching placebo.
0442 Experimenting Automatic Sleep Analysis Application in a Clinical Context
Authors: Bouchequet P , Leger D , Lebrun M , Elbaz M 
Published: May 2020

Abstract

Introduction
Multiplication of publications describing groundbreaking automatic sleep analysis processes and algorithms push for real-life experimentation in clinical context, outside of controlled research environments.

Methods
Various automatic sleep analysis processes from the literature were implemented and orchestrated in a streamlined workflow. Artificial Intelligence algorithms using regular statistical learning or deep learning were re-trained on our own data after repeating the ad-hoc pre-processing steps described in the corresponding articles. For this, we used polysomnographic records previously taped in our clinic, subject to adequate legal authorizations and agreements: 500 nights from single patients with various pathologies. Those trained models were then applied to newly recorded polysomnographies through a platform developed and hosted on premise. For each polysomnography, a standardized and automatized report were generated and transmitted to the clinician in charge of the analysis. This report contains algorithms outputs, including automatic staging and related statistics such as hypnodensity, quantitative electroencephalography (EEG) analysis, spindles detection and automatic diagnosis. Aggregated record statistics are displayed next to our database statistics for benchmarking purposes.

Results
For sleep staging, we not only reproduced the results of the selected literature but obtained better metrics: a 0.76 Kappa agreement vs 0.69 in the literature. This may be due to our larger training database or the quality of physiologic signals in our data. Clinicians showed interest in the automatic staging part of the analysis. They noticed algorithm errors are mostly focused on ambiguous epochs, just like visual scoring. However, they found help into automated output and explanatory variables (hypnodensity) to score those ambiguous epochs.

Conclusion
Automatic sleep analysis algorithms used as decision helping tools shows real potential and should be generalized, as long as underlying processes are published and understood by users and clinicians.

Support
Banque Publique d’Investissement.