bluuebunny/crossref_metadata_2025_split huggingface dataset:
0064 Youth Sleep-Wake Experience in Juvenile Justice Facilities: A Descriptive Analysis
Authors: Woodard Kelsey , Adornetti Julianna , Nogales Josefina Munoz , Foster Mea , Leask Lauren , McGee Ryann , Carlucci Marianna , Crowley Stephanie , Wolfson Amy 
Published: May 2022

Abstract

Introduction
Adolescents are susceptible to sleep loss due to biological and environmental factors such as delayed circadian timing and schedule demands. Few studies have examined sleep-wake patterns for adolescents residing in juvenile justice facilities. The current study assessed youth’s self-reported sleep-wake schedules, sleep environment perceptions, and sleep quality.

Methods
Participants were recruited from 11 juvenile services detention and treatment facilities in Maryland. For seven consecutive mornings, youth completed a sleep-wake diary reporting their bed/wake times, sleep onset, and type of (nocturnal) light exposure. Youth wore digital wristwatches to accurately depict their sleep-wake schedules. Sleep quality and wake difficulty were rated on a scale from 1-10 (1=very poor/easy to 10=very good/hard, respectively).

Results
Participants (N= 64) were 13-19 years old (M= 16.7, SD= 1.3 years) and 85.9% male. Racial backgrounds: 61% Black, 18% White, 8% Multiracial, and 13% Other. Youth-reported bedtimes (M= 21:04, SD= :50) were about 50 minutes earlier than their sleep onset times (M= 21:52, SD= 1:02) while wake times (M= 6:41, SD= :46) were about 20 minutes earlier than the time youth reported leaving their bed (M= 7:00, SD= :44). Youth disclosed waking up throughout the night (M= 1.7, SD= 9) for an average 16.8 minutes (SD= 14.9). Multiple diary-responses (58%) noted “partial or overhead” lights were on in youth’s sleeping areas; 23.4% wrote in “other” types of light sources, most of which were blue lights (63%). Average sleep quality (M= 5.7, SD= 2.1) and difficulty waking up ratings (M= 5.4, SD= 2.2) indicate mediocre sleep.

Conclusion
Findings summarize youth’s sleep-wake experience while residing in a juvenile justice facility. Reported bedtimes are earlier than sleep onset times which increases the likelihood for conditioned insomnia. Circadian dysregulation of sleep behavior can develop from frequent night awakenings and light exposure, particularly, blue light. Ultimately, these findings will help develop facility-wide interventions, improving the youth’s sleep-wake schedules and other environmental influences.

Support (If Any)
This research is made possible by the American Academy of Sleep Medicine (AASM) Foundation Award #22-CS-19 and Department of Juvenile Services’ supportive collaboration.
0069 Trajectories of Sleep Characteristics in Black and White Women During the First Year Postpartum
Authors: Kishman Erin , Sparks Joshua , Youngstedt Shawn , Wang Xuewen 
Published: May 2022

Abstract

Introduction
In the postpartum period, many women experience sleep deficiency due to caring for their infant. Racial disparities exist in sleep characteristics in the adult population, with Black adults having shorter total sleep time (TST) and worse sleep efficiency (SE) than White adults. However, few studies have investigated sleep changes in postpartum Black and White women. The purpose of this study was to examine trajectories of sleep characteristics from 6-8 weeks to 12 months postpartum in Black and White women.

Methods
Black (n=48) and White (n=86) women who gave birth to a singleton infant at ≥37 weeks gestation, wore an Actiwatch Spectrum Plus (Phillips Respironics, Inc) at 6-8 weeks, 4, 6, 9, and 12 months postpartum. Participants were instructed to wear the monitor, complete a sleep diary, and to maintain their normal daily activities over 7 days. Daily time in bed (TIB), TST, SE, and wake after sleep onset (WASO) were determined.

Results
Trajectories of TIB, TST, SE and WASO were not different between Black and White women from 6-8 weeks to 12 months postpartum. However, Black women had shorter TIB and TST, and lower sleep efficiency (p&lt;0.001 for all). WASO was similar between Black and White women. For the entire sample, TIB significantly decreased from 470±74 (mean±SD) minutes at 6-8 weeks to 459±54 minutes at 12 months (p=0.0038). TST significantly increased from 347±86 minutes at 6-8 weeks to 369±70 minutes at 4 months (p=0.0085) but did not change at the later timepoints. SE increased from 80±8% at 6-8 weeks to 83±7% at 6 months (p=0.0034) but did not change at the later timepoints. WASO decreased from 54±24 minutes at 6-8 weeks to 46±21 minutes at 4 months (p&lt;0.0001) but did not change at later timepoints.

Conclusion
In the first year postpartum, Black and White women had similar trajectories for sleep characteristics, but Black women had shorter TIB and TST and lower SE than White women. TIB and WASO decreased while TST and SE increased over time. The first 4 to 6 months show the greatest changes.

Support (If Any)
NIH Grant R21MD012740
0073 A Mexican Spanish Version of the Sleep Disorders Symptom Checklist
Authors: Begay Tommy , Valencia Dora , Granados Karla , Delgadillo Marcos , Ghani Sadia , Molina Patricia , Alfonso-Miller Pamela , Perlis Michael , Klingman Karen , Wills Chloe , Grandner Michael 
Published: May 2022

Abstract

Introduction
The Sleep Disorders Symptom Checklist (SDSCL-25) is a brief assessment of patient-reported symptoms that suggest risk for a wide range of sleep disorders. To ensure the validity of study subject responses, the SDSCL-25 was translated into Spanish by a bilingual research study team. It was then administered to study subjects in their preferred language.

Methods
Data were collected from a sample of N=100 individuals of Mexican Descent in Nogales, Arizona, US-Mexico border. The SDSCL-25 is a screening tool that assesses the presence of a wide range of symptoms for sleep disorders. To translate the measure into Spanish, the following procedure was followed: (1) a bilingual study team member performed an initial translation; (2) a bilingual community member edited the translation; (3) a certified medical translator edited the revision; (4) a focus group of N=5 bilingual community members made contextual edits; (5) a back-translation was performed; (6) an additional bilingual focus group examined the final version for compatibility; and (7) the medical translator certified the accuracy of the final version. T-tests examined differences between those who completed the measure in Spanish vs English.

Results
Of the N=100 survey respondents, N=42 completed the SDSCL-25 in Spanish. Those who took the measure in Spanish showed no differences in frequency of reports of delayed sleep phase (p=0.28), snoring (p=0.85), morning dry mouth (p=0.87), choking/gasping (p=0.09), uncomfortable sensations in legs (p=0.25), urge to move legs (p=0.09), cataplexy (p=0.09), sleep paralysis (p=0.12), sleepwalking (p=0.08), or bruxism (p=0.13). Respondents in Spanish reported lower frequency of insufficient sleep (p=0.01), variability in bedtime (p=0.02), difficulty falling asleep (p=0.002), difficulty staying asleep (p=0.01), early morning awakenings (p=0.02), daytime tiredness/fatigue (p=0.006), phase advance (p=0.0003), daytime sleepiness (p=0.03), loud snoring (0.002), breathing pauses (p=0.049), frequent awakenings (p=0.0007), hypnogogic/pompic hallucinations (p=0.003), nightmares (p=0.047), panic awakenings (p=0.01), and overall sleep disturbance (p=0.043).

Conclusion
The present study describes a Spanish translation of the SDSCL-25. For those that took the Spanish version, there were lower reported frequencies of insufficient sleep, variability in bedtime, insomnia symptoms, daytime sleepiness and tiredness/fatigue, phase advance, sleep apnea symptoms, frequent awakenings, hypnogogic/pompic hallucinations, nightmares, panic awakenings, and overall sleep disturbance.

Support (If Any)
0102 Performance Evaluation of a 24-hour Sleep-Wake State Classifier Derived from Research-Grade Actigraphy
Authors: Roberts Daniel M , Schade Margeaux M , Chang Anne-Marie , Honavar Vasant , Gartenberg Daniel , Buxton Orfeu M 
Published: May 2022

Abstract

Introduction
Wrist-worn research-grade actigraphy devices are commonly used to identify sleep and wakefulness in freely-living people. However, common existing algorithms were developed primarily to classify sleep-wake within a defined in-bed period with PSG, and exhibit relatively high sensitivity (accuracy on sleep epochs) but relatively low specificity (accuracy on wake epochs). This classification imbalance results in the algorithms performing poorly when attempting to classify data that does not have a predefined sleep period, such as over a 24-hour interval. Here, we develop a 24-hour actigraphy classifier to overcome limitations in specificity (accuracy on wake epochs), which typically afflict in-bed focused algorithms.

Methods
Four datasets scored via either PSG or direct observation of daytime wakefulness were combined (n=52 participants of mean age 49.8yrs, age range 19 - 86; 52% male; 221 total days/nights). Actigraphy (counts) and PSG (RPSGT-staged epochs) were temporally aligned. A model was trained to transform a time-series actigraphy counts to a time series of sleep-wake classifications, using the TensorFlow library for Python. 5-fold cross-validation was used to train and evaluate the model. Classification performance was compared to the output of the Spectrum device (Philips-Respironics) using the Oakley algorithm with a wake threshold of ‘medium’.

Results
The developed classifier was compared to the Spectrum classifications across the 24-hour intervals. The developed classifier had higher accuracy (95.4% vs. 76.8%), higher specificity (95.9% vs. 68.9%) and higher balanced-accuracy (95.2% vs. 81.6%) relative to the Spectrum classifications, each assessed via paired-sample t-test. Sensitivity did not statistically differ (94.5% vs. 94.4%).

Conclusion
The model trained and evaluated on 24-hour data outperformed the existing algorithm output in terms of overall accuracy, specificity, and balanced accuracy, while sensitivity did not significantly differ. A model trained on 24-hour data may be more appropriate for analyses of freely living people, or older populations where napping is more common. Developing an accurate 24-hour sleep/wake classifier fosters new opportunities to evaluate sleep patterns in the absence of self-reports or assumptions about time in bed.

Support (If Any)
UL1TR002014, NSF#1622766, R43/44-AG056250
0103 Feasibility of examining component-specific effects of yogic breathing on heart rate variability during sleep: A three-arm pilot RCT
Authors: Goldstein Michael , Ma Yan , Vazquez Michael , Buraks Olivia , Haack Monika , Mullington Janet , Yang Huan 
Published: May 2022

Abstract

Introduction
Wearable devices and mind-body interventions (MBIs) continue to receive widespread interest as tools for improving sleep. This study investigated the feasibility of using an automated electronic survey system and wearable heart rate (HR) monitor in the context of a fully remote clinical trial study to produce detailed measures of participant adherence, daily sleep quality, and associations with physiological outcomes captured by wearable devices.

Methods
Eighteen healthy participants (age 18-30yrs, 12 female) were randomized to one of three 8-week long interventions: slow-paced breathing (SPB, N=5, 24.6 ± 2.1 years, 4 female), mindfulness (M, N=6, 23.7 ± 3.7 years, 4 female), or yogic breathing (SPB+M, N=7, 24.3 ± 3.1 years). Participants completed two weeks of daily sleep logs prior to a virtual laboratory visit, consisting of a 60-min intervention-specific training with 20-min guided practice, and subsequent tasks including experimental stress induction. Participants started a 24-hour HR recording using a Polar H10 chest strap on the night prior. Then, participants were instructed to repeat their assigned intervention practice daily, using a guided audio similar to their initial training, while concurrently recording HR data and completing a detailed practice log. HR interbeat interval data were examined with spectral analysis using full spectrograms for inspection of timecourse and frequency-specific patterns in both the nocturnal recordings and daily practice sessions.

Results
Participants completed an average of 75% of daily practice sessions across the 8-week intervention period (SPB: 77%, M: 65%, SPB+M: 77%). An automated procedure was developed to analyze and visualize the timecourse of HRV-derived breathing patterns in the 754 completed practice sessions and 36 nocturnal recordings. The three groups were then successfully distinguishable based on breathing rates and mindfulness questionnaires. Nocturnal HR recordings demonstrated visually identifiable patterns of interindividual variability and intraindividual consistency. Statistical analysis is ongoing to further characterize these patterns.

Conclusion
These findings support feasibility for a fully remote, semi-automated clinical trial study assessing component-specific effects of these MBIs on sleep, including detailed spectral analysis of high-quality HR data. Future studies would benefit from examining scalability of this type of study design with wearable physiology in both clinical and nonclinical populations.

Support (If Any)
Pilot Research Grant, Osher Center for Integrative Medicine of Harvard Medical School and Brigham & Women’s Hospital; National Institutes of Health (5T32HL007901-22)
0105 Sleep timing and consistency are associated with the standardised test performance of Icelandic adolescents
Authors: Stefansdottir Runa , Brychta Robert , Rognvaldsdottir Vaka , Johannsson Erlingur , Kong Chen 
Published: May 2022

Abstract

Introduction
Sleep has been shown to affect cognitive function in laboratory studies; however, its association to the academic performance of adolescents has largely been demonstrated using self-reported measures. Studies with objective measures of both sleep and academic performance are limited. The aim of the present study was to determine whether the free-living sleep quantity, quality, and timing of 15-year-old adolescents measured with wrist actiography are associated with their scores on national standardised examinations as an objective measure of academic achievement.

Methods
We measured sleep with wrist actiography for 1 week in 253 (150 girls) Icelandic adolescents with a mean (SD) age of 15.9 (0.3) years. Multiple linear regression was used to assess associations between sleep parameters and combined standardised examination scores in mathematics, English, and Icelandic obtained from the Icelandic Directorate of Education.

Results
We found that students went to bed at 00:49 hours (± 51.8 min) and slept for a mean (SD) of 6.6 (0.7) hr/night, with a median (interquartile range) night-to-night variation in sleep duration of 1.2 (0.7) hr and an efficiency of 88.1 (5.3)%. Combined analyses adjusted for sex, demonstrated that both bedtime and night-tonight variability in total sleep time were negatively associated with the average score across all topics. Sex-specific associations did not indicate clear differences between boys and girls.

Conclusion
These findings suggest that, in addition to appropriate sleep duration, public health guidance should also highlight the importance of early and consistent sleep schedules to academic achievement for both boys and girls.

Support (If Any)
National Institute of Diabetes and Digestive and Kidney Diseases intramural research program, Grant/Award Number: Z01 DK071013 and Z01 DK071014; Icelandic Centre for Research, Grant/ Award Number: 152509-051
0111 Effects of Sleep on Working Memory
Authors: Chen Pin-Chun , Zhang Jing , Tambini Arielle , Mednick Sara 
Published: May 2022

Abstract

Introduction
Improvements in working memory (WM) are associated with increased vagal autonomic activity during sleep. Sex hormones, which fluctuate across a menstrual cycle, influence sleep, autonomic activity, and cognitive performance. Given this complex interaction, we examined whether the relation between WM improvement and autonomic activity across a night of sleep was modulated by menstrual phase.

Methods
Twenty-five healthy female participants with natural, regular menstrual cycles (age = 28.14 ± 4.41 years) were enrolled. We employed a within-subject design to investigate the role of menstrual phase on autonomic activity and sleep-dependent working memory improvement. All participants completed two in-lab visits, with one visit during their low hormones phase (LH: 0 to +2 days from the start of menses) and one visit during their high hormone phase (HH: +1 to +4 days from the start of ovulation). We measured WM with the Operation-Span Task at 9PM and 8AM. Participants’ overnight sleep was monitored with EEG and ECG. We measured parasympathetic activity using the high-frequency heart rate variability (hfHRV) and the root-mean-square of successive differences between normal heartbeats (RMSSD). We used linear-mixed effect models and Pearson’s r.

Results
No differences in WM were found between menstrual phases (all ps &gt; .223). Interestingly, however, HRV during NREM positively correlated with WM improvement in LH (hfHRV: r = .329, p = .093; RMSSD: r = .327, p = .096), but not during HH (hfHRV: r = -.197, p = .355; RMSSD: r = -.179, p = .402). The differences between correlations were significant (hfHRV: p = .038; RMSSD: p = .045).

Conclusion
Prior meta-analysis revealed greater vagal autonomic activity during LH (menses), compared with other phases. Though we didn’t replicate this finding, we did show a significantly stronger relation between vagal autonomic activity and overnight WM improvement during this low hormone phase. Our results suggest that menstrual phase shifts the reliance of sleep-dependent WM improvement to vagal autonomic mechanisms.

Support (If Any)
UCI MIND/WAM 02-2020
0112 Classification of reconstructed depth profiles shows Global and non-Global slow oscillations differentiate in the hippocampus and thalamus
Authors: Seok SangCheol , Mednick Sara , Malerba Paola 
Published: May 2022

Abstract

Introduction
Sleep slow oscillations (SOs, 0.5-1.5 Hz) can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects.Sleep slow oscillations (SOs, 0.5-1.5 Hz) can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects.

Methods
32 volunteers (18 females) slept in the lab with polysomnography including 24 head EEG channels; their sleep was scored according to AASM criteria. SOs were algorithmically detected at each channel and classified as Global or non-Global using our method (Malerba et al., 2019). The depth profile of each SO was reconstructed with current source estimation (in Brainstorm followed by sLORETA), with a standardized head model including 17 regions. Each depth profile was embedded in a matrix averaging current by region and in three 200ms-long time bins: before, during and after the SO trough. Thirty classifiers were applied to this dataset, leveraging Matlab’s supervised learning application. We compared accuracy within and across subjects and identified best-performing algorithms across dataset size. We then used univariate feature selection to quantify the relevance of each region-time pair to successful classification.

Results
Global/non-Global SOs current depth profiles have higher variance across subjects, and accuracy improves when data is sampled between rather than within individuals. Ensemble subspace methods reached highest accuracy (98.5%). Feature selectivity identified cortical, hippocampal, and thalamic currents at the trough of the SO as the most relevant for Global/non-Global SO classifications.

Conclusion
We introduce an analytical framework enabling the study of SO depth profiles, including their time evolution, as matrices. The predominant differentiation of Global/non-Global SOs in cortical, hippocampal, and thalamic currents supports the potential functional difference of these SO types.

Support (If Any)
NIH grant (R01 AG046646) to S.C.M.
0115 Discrepancies Between Actual and Ideal Bedtime are Associated with Cognitive Performance
Authors: Hagen Erika , Olaithe Michelle , Ravelo Laurel , Peppard Paul 
Published: May 2022

Abstract

Introduction
Experimental studies have shown that acute sleep-circadian misalignment can lead to cognitive deficits. Additionally, chronic jetlag among pilots and long-term exposure to shiftwork are associated with worse cognitive performance. This study investigated whether small daily discrepancies in actual versus perceived ideal bedtime are associated with cognitive performance.

Methods
A subset of Wisconsin Sleep Cohort study participants (N=750; 58% male; mean[range] age 59 [38-78] years) participated in a neurocognitive test battery, provided a 6-night sleep diary, and completed questionnaires at the same study visit. The neurocognitive assessment included the Controlled Oral Word Association Test, Trails Making Test Part B, Grooved Pegboard Test, Auditory Verbal Learning Test, Digit Cancellation, and Symbol Digit Modalities Test. Weekday bedtimes from the sleep diary were averaged to represent “actual” bedtime. “Ideal” bedtime was represented by the answer to the question from the Horne-Ostberg Morningness-Eveningness Questionnaire: If you were free of any schedule and could go to bed at any time you wanted, what time would that be? Bedtime mismatch was represented as (ideal bedtime – actual bedtime). Cognitive performance was regressed on bedtime mismatch (separate models for each cognitive test) in linear regression models, adjusting for age, sex, BMI, sedative and stimulant use, average total sleep time, actual bedtime, smoking, caffeine consumption, and education level.

Results
There was a significant association between bedtime mismatch and performance on both the Grooved Pegboard (p=0.02) and Symbol Digit Modalities (p=0.02) tests, and a borderline significant association (p=0.09) with performance on the Oral Word test. On average, participants that had actual bedtimes that were later than ideal bedtimes demonstrated worse cognitive performance, while actual bedtimes that were earlier than ideal bedtimes were associated with better performance. No associations were found for the other 3 cognitive tests.

Conclusion
Going to bed at a time that is later than perceived ideal bedtime may be associated with cognitive deficits, even after accounting for actual bedtime and total sleep time.

Support (If Any)
This work was supported by the National Heart, Lung, and Blood Institute (R01HL62252), National Institute on Aging (R01AG058680) and the National Center for Research Resources (1UL1RR025011) at the US National Institutes of Health.