bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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 > .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

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

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.
0120 Menstrual Cycle-Related Changes in Sleep-Dependent Emotional Memory Consolidation
Authors: Zhang Jing , Simon Katharine , Shuster Alessandra , Sattari Negin , Alzueta Elisabet , Dulai Teji , de Zambotti Massimiliano , Baker Fiona , Mednick Sara
Published: May 2022

Introduction

Emotional, more than neutral, experiences are preferentially consolidated during sleep. Fluctuating reproductive hormonal levels across the menstrual cycle are associated with changes in sleep features that are implicated in emotional memory consolidation. Yet, the interaction between menstrual cycle phases, sleep, and emotional memory remains unknown. The current study investigates how sleep-dependent emotional memory consolidation changes across the menstrual cycle in both young and midlife women.

Methods

Thirty-one young (MAge=25.32y, SD=5.69y) and 33 midlife (MAge=47.88y, SD=2.86y) women enrolled in the study. Each woman completed four remote visits at different menstrual phases, estimated from self-reports and measured ovulation: menses (low hormone), late-follicular (high estrogen), mid-luteal (high estrogen and progesterone), and late-luteal (falling hormones). During each visit, participants completed the Emotional Picture Task (EPT) via Pavlovia, a web-based testing platform. Participants completed encoding and immediate test in the evening (Test 1) and retested (Test 2) the following morning. During encoding, participants viewed negative and neutral images (selected from the IAPS). During testing, for each image, participants reported if it was old or new, and rated the arousal (low to high) and valence (neutral to negative). We measured dPrime at each test and calculated a difference score for overnight memory improvement. Sleep was recorded using Oura ring, a multi-sensor wearable sleep tracker. Mixed linear models determined the effect of menstrual phase on EPT, with visit as a covariate, and relevant sleep features.

Results

Irrespective of age, menstrual cycle phase (p<0.05) predicted overnight dPrime change score for negative images. Specifically, memory improvement was the highest during mid-luteal, while menses showed the most forgetting. In addition, longer time in bed was associated with less forgetting (p<0.01). These effects were not observed for neutral images (ps>0.05).

Conclusion

Menstrual phases are characterized by specific sex hormone profiles that can interact with both memory and sleep. The current findings indicate that sleep-dependent memory may also be affected by these changes, specifically memories that have negative emotionally content are more resilient to forgetting during the midluteal phase when both estrogen and progesterone are highest. Future studies (e.g., sleep manipulation) are required to reveal the specific sleep features associated with these effects.

Support (If Any)

RF1AG061355 (Baker/Mednick)
0123 The impact of sleep, stress, and environmental context on memory pre- and during the COVID-19 pandemic in the United States
Authors: Silva-Jones Jillian , Smith Anna , Crosswell Alexandra , Gordon Amie , Mendes Wendy Berry , Whitehurst Lauren
Published: May 2022

Introduction

The COVID-19 pandemic has disrupted the lives of many people. The risk and interpersonal cost of infection as well as the public health measures implemented to mitigate the spread likely have psychological costs. Yet, due to the ever-changing nature of the pandemic, psychological impact has been difficult to capture through research efforts. Here, we leveraged an on-going, geographically representative study to examine the relationship among sleep, stress, and memory function before and during the COVID-19 pandemic.

Methods

Participants (N=1958, aged=18+) were enrolled in a 21-day ecological momentary assessment study. All participants provided demographic information, including zipcodes, which were used to identify rural vs urban locale. Participants were instructed to complete up to three daily check-ins during set time windows—morning, afternoon, night--via a phone application. At each morning check-in, participants were asked about sleep duration and quality, and at every check-in, participants reported perceived stress ratings. Participants also completed a paired-associates memory task on Day 2 of the study. For the task, participants were instructed to encode a list of 20 unrelated word and picture pairs. Immediately after encoding, participants were tested on five picture-word pairs. Tests 2 and 3 occurred on a unique set of five words from the initial list three and six days after initial encoding, respectively. Pre- and during COVID assessments were defined as March 2019 to March 2020 and April 2020 to October 2021, respectively.

Results

Mixed effects binomial regressions revealed that pre-COVID, longer sleep durations were associated with better memory performance (ß=.09, p<.05), and counterintuitively, higher subjective sleep quality was associated with worse memory performance (ß=-.35, p<.001). During COVID, longer nighttime awakenings were associated with poorer memory performance (ß=-.01, p<.05) and living in a rural vs urban environment was associated with poorer memory performance (ß=.48, p<.01). Older age was associated with worse memory performance pre- and during COVID (ß=-.01, p<.01). Stress was not related to memory pre- or during COVID in these models.

Conclusion

Findings support that sleep difficulty before and during the pandemic likely impacts memory function. Additionally, those living in rural U.S. environments may be particularly vulnerable to cognitive changes in the pandemic context.

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0124 Dextroamphetamine biases the brain towards enhanced spatial selective attention compared over working memory
Authors: tselha tenzin , Whitehurst Lauren , Mednick Sara
Published: May 2022

Introduction

There is a growing trend in the non-medical use of prescription psychostimulant (PStim) in healthy adults One of the main reasons of increased usage of PStim is due to their perceived benefits on the cognitive capacity. However, evidences from empirical studies on healthy adults point to an inconclusive answer. There are various factors which could have contributed to these overall mixed findings. These factors range from differences in drug dosages, individual baseline variability and use of different tasks. However, one of the important factors that previous studies have not considered is the presence possible selective bias of PStim towards a specific cognitive domain over others which may lead to its selective enhancement at the cost of others’ degradation.

Methods

To study this, we carried out a double blind, placebo-controlled study, with repeated measures design to investigate the differential influence of a stimulant drug (DEX vs PBO) on the cognitive skills of working memory (WM) and spatial selective attentive in the form multiple object tracking (MOT) across a period of a day. We compared the change in the performance of WM and MOT in DEX vs PBO conditions at 1) pre-drug baseline, 2) 75 minutes post-drug (peak concentration), 3) 12 hours post-drug intake (washout).

Results

First, we found that DEX did not have any overall significant effect on WM performance across a period of day compared to the placebo condition. We also found that MOT performance was rescued by DEX, unlike the placebo condition in which the MOT performance degraded over different testing periods across a wake day period. Importantly, we found that during the peak concentration of DEX in the body MOT performance was significantly superior to that of the WM performance. This superiority of MOT over WM was not present before the drug administration (baseline) and also returned to a level similar to baseline after a gap of 12hours.

Conclusion

Overall, our study findings suggest that DEX has a favorable bias towards MOT compared to WM and selectively enhances its performance when the brain is required to support both of these two cognitive domains concurrently.

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0125 Fluid Intelligence Does Not Mediate Cognitive Throughput Deficits during Total Sleep Deprivation
Authors: Honn Kimberly , Kurinec Courtney , Hinson John , Whitney Paul , Van Dongen Hans
Published: May 2022

Introduction

The Digit Symbol Substitution Test (DSST) has been used in sleep research to measure slowing of cognitive throughput. The task shows large aptitude differences in baseline performance and substantial inter-individual differences in vulnerability to performance deficits during total sleep deprivation (TSD). Fluid intelligence (Gf) is generally positively related to processing speed. However, DSST performance is typically found to be independent of Gf. The possible interaction of sleep loss with Gf on performance remains to be examined.

Methods

N=56 healthy adults (ages 22–37, 29 females) completed a 4-day/3-night in-laboratory study and were randomly assigned (2:1 ratio) to a TSD (n=37) or control (n=19) condition. Sleep opportunities were from 22:00–08:00 on the first (baseline) and last (recovery) night for the TSD group and on all three nights for the control group. Subjects completed a 4min, computerized DSST twice on day 2 (baseline) and twice on day 3 (TSD or control). At baseline, subjects also completed the Shipley Institute of Living Scale (SILS). The abstraction score was used to categorize fluid intelligence (Gf) as relatively high (≥34, n=36) or low (<34, n=20). Mean DSST throughput (number of correct responses in the 4min task) was analyzed using mixed-effects ANOVA with fixed effects for day (2, 3), condition (TSD, control), and their interaction, with and without Gf category (high, low) as a covariate.

Results

As expected, DSST throughput was significantly reduced by TSD (day by condition interaction: F[1,166]=27.99, p<0.001). Adding Gf had no effect on the day by condition interaction, and Gf category was not significant as a covariate (F[1,166]=1.67, p=0.20).

Conclusion

Our results indicate that the Gf measure from the SILS does not capture the aspects of cognition that are influenced by TSD and that lead to a decline in DSST throughput. This is consistent with findings that while the DSST has high sensitivity for cognitive dysfunction in clinical settings, it has low specificity in identifying components of cognition responsible for dysfunction.

Support (If Any)

ONR N00014-13-1-0302 and PRMRP W81XWH-20-1-0442.
0137 Phase response curve for effects of school start times on students’ diurnal learning rhythm
Authors: Yeo Sing Chen , Yabuki Hana , Charoenthammanon Rachel , Gooley Joshua
Published: May 2022

Introduction

School start times impose constraints on sleep-wake behaviour that can result in social jet lag. Here, we used university archived datasets (1) to test whether social jet lag was associated with lower grades, and (2) to assess the phase resetting effects of school start times on students’ diurnal learning behaviour.

Methods

Social jet lag was estimated in 33,645 university students by measuring the phase shift in their Learning Management System (LMS) login rhythm on school days relative to non-school days (LMS social jet lag). ANCOVA was used to test the association between LMS social jet lag and grade point average, adjusting for demographic variables. We constructed a phase response curve by plotting phase shifts on school days (LMS social jet lag) against the initial phase when students’ first class of the day took place. The initial phase was expressed relative to students’ LMS login rhythm on non-school days (LMS chronotype).

Results

Students with greater LMS social jet lag had a lower grade point average (ANCOVA: F9,32269 = 44.8, P < 0.001). Social jet lag was larger in students with a later LMS login rhythm on non-school days (later chronotype) and for earlier school start times. The phase response curve revealed that the direction and magnitude of social jet lag were strongly dependent on the phase of students’ diurnal rhythm when their first class of the day took place. Phase shifts of up to 12 h were observed when school start times occurred out of phase with students’ diurnal rhythm.

Conclusion

School start times have a profound impact on students’ diurnal behaviour. Students whose diurnal patterns of LMS logins were similar on both school days and non-school days obtained better grades than their peers with LMS social jet lag. Universities can potentially improve learning by scheduling classes at times that are better aligned with students’ diurnal learning rhythm.

Support (If Any)

Data storage and management were supported by the NUS Office of the Senior Deputy President & Provost and ALSET. The work was funded by the Ministry of Education, Singapore (MOE2019-T2-2-074) and the National Research Foundation, Singapore (NRF2016-SOL002-001).
0146 Sevoflurane preconditioning promotes sleep reintegration from lipopolysaccharide induced shattered sleep
Authors: Nemoto Tsuyoshi , Irukayama-Tomobe Yoko , Hirose Yuki , Takahashi Satoshi , Takahashi Genki , Tanaka Hiromu , Yanagisawa Masashi , Kanbayashi Takashi
Published: May 2022

Introduction

Anesthesia and sleep partially share neural circuits, yet little is known regarding the effect of anesthetics on sleep. Anesthetic agents exhibit a variety of biological effects concomitant with anesthesia. Sevoflurane, one of the wildly used volatile anesthetics, is known to increase the survival of sepsis model mice. Systemic inflammation like sepsis increases the inflammatory mediators in the brain affecting sleep dynamics with increase in NREM and decrease in REM. We hypothesized that sevoflurane preconditioning positively impacts disturbed sleep caused by systemic inflammation.

Methods

We conducted a prospective, randomized laboratory investigation in C57BL/6J mice. A mouse model of lipopolysaccharide (LPS)-induced systemic inflammation was employed to investigate the effects of sevoflurane on sleep recovery. We evaluated symptoms recovery through electroencephalography/electromyography (EEG/EMG) and histological studies. The mice were exposed to 2% sevoflurane before and after peritoneal injection of LPS. The EEG and EMG were recorded for 24 h after the procedure. Brain tissue was harvested after the sevoflurane/LPS procedure and was immunostained using individual antibodies against choline acetyltransferase (ChAT) and Fos.[YI1] We quantitatively analyzed the ChAT-positive and ChAT/Fos double-positive cells in the pedunculopontine tegmental nucleus and laterodorsal tegmental nucleus (PPTg/LDTg).

Results

Compared to control mice, mice preconditioned with sevoflurane but not post-conditioned showed a significant recuperation in rapid eye movement (REM) sleep and waking time during EEG recording following the LPS challenge. They also demonstrated shorter REM latency and restored theta power, indicating an early recovery from LPS-altered sleep. The bouts of REM episodes were retained with sevoflurane preconditioning. The number of ChAT/Fos double-positive cells in the PPTg/LDTg decreased by LPS challenge. However, group with sevoflurane preconditioning followed by LPS challenge showed higher number of activated neurons, restoring physiological degree of activation.

Conclusion

Compared to control mice, mice preconditioned with sevoflurane but not post-conditioned showed a significant recuperation in rapid eye movement (REM) sleep and waking time during EEG recording following the LPS challenge. They also demonstrated shorter REM latency and restored theta power, indicating an early recovery from LPS-altered sleep. The bouts of REM episodes were retained with sevoflurane preconditioning. The number of ChAT/Fos double-positive cells in the PPTg/LDTg decreased by LPS challenge. However, group with sevoflurane preconditioning followed by LPS challenge showed higher number of activated neurons, restoring physiological degree of activation.

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0160 Sleep and high body mass index in adolescents: Ethnicity and socioeconomic status as moderators
Authors: Ji Xiaopeng , Covington Lauren , Brownlow Janeese
Published: May 2022

Introduction

Sleep deficiency and obesity disproportionately affect racial/ethnic minorities and those with lower socioeconomic status (SES). Considerable research has linked sleep deficiency to overweight/obesity. Less clear is the interactive effects of sleep duration (SD) and social determinants (i.e., SES, race/ethnicity) on weight status in adolescents. This study examined the role of race/ethnicity and SES as moderators of associations between SD and overweight/obesity in a nationally representative adolescent sample.

Methods

Using the National Survey of Children’s Health 2017-2018 dataset, we included adolescents (10-17 y.o.) with available SD and Body Mass Index (BMI) data (n=24,337). Parents reported children’s SD and sleep regularity. Adolescents with a BMI ≥85th percentile were classified as overweight/obese. We used a stepwise approach to identify SES factors and covariates to include in the model. Accounting for complex survey design, as well as sleep regularity and selected covariates (i.e., age, sex, smoking, exercise, and depression, diabetes), logistic regression (STATA 16.0) estimated the interaction between SD and selected social determinants (i.e., race/ethnicity, family income, primary caregiver education, neighborhood condition) in adolescents.

Results

Every hour increase in SD was associated with a 7% decrease in the odds of high BMI (OR=0.93, p=0.03) regardless of race/ethnicity and SES. There were significant interactions between SD and social determinants. Compared with the White (OR=0.88, p<0.001), the association between longer sleep and lower odds of high BMI was weakened and even reversed in Hispanic adolescents (OR=1.20, p=0.02). Similarly, family income below 100% FPL (versus 300% or above) (OR=1.19, p=0.02) and primary caregiver having education below high school (versus high school or above) (OR=1.15, p=0.03) also attenuated the associations. Poor neighborhood condition was not a moderator but independently associated with high BMI (OR=1.57, p=0.02).

Conclusion

Adolescence may be a sensitive period that sets the stage for the interaction between sleep and social risk factors on overweight/obesity. Increasing sleep duration is associated with decreased risk of overweight/obesity, but the protective role is dampened in Hispanic adolescents and those with the low SES. Our findings suggest that sleep-related prevention and intervention efforts should target at-risk populations who experience health disparities.

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0161 Strategies for Enhancing Recruitment of Adults for Randomized Controlled Trial Among Heterogenous Sample of Cancer Survivors with Insomnia Amid the Covid Pandemic
Authors: Loomis Dianne , Tyrpak Donna , Larkin Karen , Kwon Misol , Foltz-Ramos Kelly , Mclaughlin Pamela , Gaughan Mary Rose , Dickerson Suzanne , Dean Grace
Published: May 2022

Introduction

Challenges associated with recruiting participants in a longitudinal research study have been recognized yet remain a major barrier for researchers. The current study details strategies used in recruiting a heterogenous sample of cancer survivors with insomnia from multiple clinical sites, referral sources and outreach.

Methods

Enrollment goals were 158 participants over 3 years (June 2019 to May 2022). Recruitment strategies included 1) face-to-face (FTF) recruitment at hospital clinics; 2) posting recruitment flyers in clinical settings; 3) completion of insomnia screening instrument at community clinic sites; 4) research registries; 5) institutional social media outreach; 6) community events; 7) PI interview and request for study volunteers in local newspaper; and 8) ongoing engagement and communication with recruited participants.

Results

108 of 158 participants have been recruited and completed baseline surveys; 9 participants dropped out. To date, 42 of 49 (85.7%) participants have completed the 12-month study. June 2019 through December 2019 FTF recruitment occurred, where 104 were eligible and 32 (30.76%) were enrolled. Due to changes in study personnel and the COVID pandemic restricting access to in-person recruitment and enrollment, the study pivoted to develop protocols for electronic consent and enrollment using video conferencing. In addition, research databases, institutional social media, community events and local newspaper were utilized, where 76 of 239 (31.79%) interested participants enrolled. The most effective recruitment strategies included on-site FTF recruitment (57.9%) and local newspaper interview (13.88%). The local newspaper interview was the most cost-effective considering personnel costs associated with FTF recruitment.

Conclusion

Despite the onset of the COVID pandemic during the recruitment phase, we were able to pivot and employ innovative techniques to meet our targeted enrollment goal for the projected study deadline. FTF recruitment, perceived value by clinic staff in benefitting cancer survivors, and participants’ acceptance of video-conferencing were significant contributors. The importance of building and maintaining relationships with providers and nurses in local clinical sites cannot be underestimated.

Support (If Any)

This research was funded by R01 NR018215 (Dean) by the National Institute of Nursing Research.
0166 The Role of Vitamin B12 Supplementation in the Association between Depression Symptoms and Daytime Sleepiness
Authors: Klettner Annelise , Perez Elliottnell
Published: May 2022

Introduction

A substantial body of literature has demonstrated an association between depression and sleep disturbances. While depressive symptoms are strongly associated with limited sleep duration and quality, high depressive symptomology has been also linked to symptoms of hypersomnolence. Vitamin B12 supplementation is associated with reduced risk of depression and the enhancement of pharmacological treatment for depression, but less is known about the role of vitamin B12 on sleep disturbances associated with depressive symptoms. Thus, our current study examined vitamin B12 intake as a moderator of depression and daytime sleepiness in a national sample of adults.

Methods

The sample consisted of 5,553 adults who completed the 2017-18 National Health and Nutrition Examination Survey (M age=49.8, SD=18.6; 51.7% female). Participants reported on prescription and nonprescription dietary supplement use in the last 30 days, sleep habits and disorders adapted from the Munich Chronotype Questionnaire, and completed the Patient Health Questionnaire-9 for depression screening. All measures were administered by trained interviewers. A moderation analysis was performed using R statistical programming. The analysis controlled for age, gender, ethnicity, sleep duration, and other B-complex vitamins (e.g., B1, B2, B3, and B6).

Results

Depressive symptoms were significantly associated with greater daytime sleepiness (b=.06, p <.001). Furthermore, there was a significant interaction between depressive symptoms and vitamin B12 consumption (b=-.002, p=.003, R2=.12). Higher vitamin B12 consumption buffered the relationship between depressive symptoms and daytime sleepiness. In contrast, a stronger positive relationship between depressive symptoms and daytime sleepiness was observed among participants with lower Vitamin B12 consumption. Thus, the findings suggest that consuming vitamin B12 may be beneficial for counteracting daytime sleepiness associated with depression.

Conclusion

Findings from the current study suggest that vitamin B12 supplementation provides a small, but significant buffering effect on the relationship between depressive symptoms and daytime sleepiness. Although existing pharmacological and behavioral interventions for sleep and depression are clinically effective, vitamin B12 intake may be an additional modifiable behavior that could increase prognosis of treatment. Given the very modest interaction effect, further

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0175 Sleep characteristics in older adults and the risk of Alzheimer’s Disease mortality: The NIH-AARP Diet and Health Study
Authors: Schneider Aaron , Moon Chooza , Xiao Qian
Published: May 2022

Introduction

Alzheimer’s Disease (AD) is the most common form of dementia (1) and the 6th leading cause of death in the U.S. (2). Additionally, it is estimated that currently 5 million individuals are living with AD in the U.S. and this number is expected to triple by 2050 (3). Decades of research has identified various risk factors for AD and a wide array of cardiometabolic risk factors, such as hypertension, type 2 diabetes, metabolic syndrome and obesity (4, 5, 6, 7). Several behavioral factors have also been suggested to play a protective role against AD, including Mediterranean-type diet (8), physical activity (9), and cognitively stimulating activities (10, 11). More recently, growing evidence has pointed sleep deficiency as a modifiable risk factor for AD. (12).

Methods

The purpose of our study was to determine if sleep and napping were associated with AD mortality. We used data from the NIH-AARP Diet and Health Study. Sleep duration and napping were self-reported and AD death were ascertained via linkage to the National Death Index. Of 566,398 members (aged 50-71) who completed baseline questionnaire in 1995-1996, 337,373 participants were included (195,656 men and 137,018 women).

Results

Long sleep and napping were both associated with increased AD mortality. Specifically, 9+ hours of sleep was associated with 50% increase (Hazard Ratio 1.50, 95% CI (1.17-1.92)) in AD mortality when compared 7-8 hours, while napping for 1+ hours was associated with 29% increase (1.29 (1.08, 1.55)) when compared to no napping. Results appeared to be stronger in men and remained after removing AD deaths within first 5 years after baseline.

Conclusion

The results of this study showed that 9+ hours sleep duration was associated with a 50% increase in AD mortality risk when compared to people who slept between 7 and 8 hours, particularly in men. Additional longitudinal studies that follow middle-aged adults through death and include objective measures of sleep and AD related biomarkers are needed to further elucidate the mechanisms contributing to cognitive decline and AD death. Such studies would be useful for informing interventions designed to reduce risk of AD and AD-related death.

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0177 Relationship Between Sleep Architecture and Age by Gender in Brazil: Baependi Heart Study
Authors: Taporoski Tâmara , Beijamini Felipe , Alexandria Shaina , Zumpf Katelyn , von Schantz Malcolm , Pereria Alexandre , Knutson Kristen
Published: May 2022

Introduction

Sleep stage duration has been associated with age. However, few studies have examined sleep stages across adulthood in both men and women. The objective of this analysis was to describe sleep architecture across age by gender in a large cohort of Brazilian adults.

Methods

This ancillary study added polysomnography (PSG) recordings to the Baependi Heart study, a prospective family-based cohort of Brazilian adults. Preliminary analyses used data from 812 participants (517 women). Sleep was staged following standard criteria. Generalized linear models were used to assess associations between age (cubic polynomial) and sleep outcomes in analyses stratified by gender.

Results

Age ranged between 18 and 88 years. Mean age was 50.0 (SD = 13.3) for women and 49.9 (SD = 14.9) for men. Expected means for women at age 50 were 6.35 hours total sleep time (TST) (95% CI: 6.22, 6.48), 213.5 minutes in N2 (95% CI: 207.2, 219.9), and 75.6 minutes (95% CI: 71.9, 79.3) in REM. These were similar to expected means for men at age 50, which were 6.3 hours TST (95% CI: 6.12, 6.48), 212.9 minutes in N2 (95% CI: 204.0, 221.8), and 78.3 minutes (95% CI: 73.7, 82.8) in REM. Expected N3 at 50 years was higher in women (44.7 minutes i, 95% CI: 44.0, 45.5) than men (30.2 minutes,95% CI: 29.4, 31.1) and WASO duration was lower for women (62.6 minutes; 95% CI: 61.7, 63.5) than for men (69.8 minutes; 95% CI: 68.5, 71.1).Non-linear relationships with age were demonstrated for N3, REM, and WASO. For example, in women there appears to be a steeper decline in N3 between approximately ages 20-40 years, a plateau until approximately 55-60 years and then another decline. Men also exhibit a steeper decline at younger ages followed by a more gradual decline that begins around 35-40 years. By contrast, TST showed a stable, linear decline with age in men and women.

Conclusion

Preliminary analyses suggest that the relationship between age and some sleep outcomes differ by gender. Future analyses on the full sample will consider splines for age to investigate further these non-linear relationships.

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1R01HL141881
0180 Sex Differences in the Association between Mood and Actigraphic Sleep Variability in Adolescents
Authors: Mathew Gina , Reichenberger David , Master Lindsay , Buxton Orfeu , Chang Anne-Marie , Hale Lauren
Published: May 2022

Introduction

Poor mental health in adolescents has been linked to more variable sleep. However, most studies use self-reported measures of sleep. There is also a lack of research on sex differences in these associations, which is important given that poor emotional health is more common in female than male adolescents. The current study examined whether sleep variability measured through actigraphy was associated differently with self-reported mood in female versus male adolescents in between-person analyses.

Methods

Data were collected from a micro-longitudinal substudy of the age 15 wave of the Fragile Families and Child Wellbeing Study. Adolescents wore a wrist-actigraphy device and completed daily surveys for approximately one week (mean=5.6 actigraphy days; n range: 577-604 depending on the mood variable) where they reported levels of loneliness, happiness, excitement, and anger on a five-point Likert scale (0, not at all, to 4, extremely). Separate mixed models assessed whether sex moderated the association between mean mood and actigraphy-measured sleep variability measures including residual standard deviation (riSD) of sleep duration, onset, and offset; sleep regularity index (SRI), and social jetlag (|midpoint of sleep on school nights – midpoint of sleep on free nights|; n for social jetlag ranged from 363-376 depending on the mood variable).

Results

In both sexes, greater average happiness (β=−.11, p=.009) and excitement (β=−.11, p=006) were associated with less variability in sleep duration, and greater average excitement was associated with higher SRI (β=.13, p=.001). Male adolescents reporting greater average happiness and excitement had less variability (riSD) in sleep onset (happiness β=−.15, p=.008; excitement β=−.14, p=.016) and lower levels of social jetlag (happiness β=−.25, p<.001; excitement β=−.22, p=.005), but these associations were null in female adolescents (all p>.20). Neither loneliness nor anger were associated with sleep variability measures in either sex (all p>.15).

Conclusion

Greater positive moods are associated with lower sleep variability, which may protect mental and physical health. Boosting positive mood in adolescents may assist in normalizing sleep schedules, and decreasing sleep variability may aid in the maintenance of positive emotional health, particularly for males. Future research may use a within-participant design to examine the direction of these associations.

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R01HD073352