bluuebunny/crossref_metadata_2025 huggingface dataset:
0302 One Week of Recovery Sleep is Insufficient to Restore Sustained Attention Performance Following Three Weeks of Chronic Sleep Restriction
Authors: Yuan R K , Zitting K , Vujovic N , Wang W , Buxton O , Williams J S , Czeisler C A , Duffy J F
Published: May 2020

Introduction

Sleep loss negatively impacts many aspects of neurobehavioral performance, including sustained attention and reaction times. However, the time course of recovery from chronic sleep restriction (CSR) is not well understood. To explore this, we assessed the effects of 3 weeks of CSR followed by 1 week of recovery on psychomotor vigilance task (PVT) performance in healthy adults.

Methods

8 healthy adults (27–71; 4f) participated in a 37-day inpatient study. The study consisted of 6 baseline (BL) days with 8–16 h time-in-bed, followed by 3 weeks of CSR (5-5.6h time-in-bed at night), and 1 week of recovery (RC; 8-10h time-in-bed). Sustained attention was assessed by 10-minute visual PVTs administered every 2h starting ~5h after wake (~4/day). Linear and generalized linear mixed models were used to compare average reaction times (RT) and number of lapses, respectively, from the last 3 days of baseline, CSR, and recovery.

Results

Average RT was almost twice as long at the end of CSR compared to baseline (p<0.0001). Moreover, it remained significantly slower than baseline by roughly 173ms, even after 1 week of recovery (p<0.0001). Similarly, there was a threefold increase in the number of lapses at the end of CSR compared to baseline (p<0.0001) which remained elevated after one week of recovery (p<0.0001).

Conclusion

One week of recovery sleep of 8-10 h/night following 3 weeks of chronic sleep restriction was insufficient for full recovery of sustained attention as assessed by PVT reaction time and number of lapses. This suggests that chronic sleep restriction has consequences on neurobehavioral performance that do not fully dissipate within one week.

Support

Study supported by P01AG009975 and conducted in the Brigham and Women’s Hospital Center for Clinical Investigation, part of Harvard Clinical and Translational Science Center supported by UL1TR001102. KMZ supported by a fellowship from the Finnish Cultural Foundation. RKY supported by T32HL007901 and F32HL143893. NV supported by T32HL007901 and F32AG051325.
0320 Psychological Correlates of Morningness/Eveningness in Latinx Pre-Adolescents
Authors: Nguyen-Rodriguez S T , Buxton O M
Published: May 2020

Introduction

Chronotype refers to a preference for morning hours (morningness) vs. evening hours (eveningness) when individuals tend to feel their best (e.g., higher energy levels). People may be classified at either end of this spectrum or along a continuum between these preferences. Among adolescents, eveningness is positively related to depression and anxiety, whereas morningness is negatively related to depression. However, less is known about the relationship of chronotype and psychological health in pre-teens and Latinx youth. The present study explored associations of morningness/eveningness with anxiety symptoms, depressive symptoms, and perceived stress among Latinx pre-adolescents in Southern California.

Methods

A purposive sample of 100 Latinx children, ages 10-12 years old, completed self-report surveys in their homes or a preferred location chosen by the parent. Measures included the Morningness/Eveningness Scale for Children (higher scores indicate morning preference), Revised Child Anxiety and Depression Scale and the Perceived Stress Scale (higher scores indicate higher anxiety, depression and stress, respectively). Associations were tested with Pearson correlations.

Results

The sample was 47% male with a mean±SD age of 10.9±0.8 years. Average score for morningness/eveningness was M=30.2±4.4 (range: 18-41), for anxiety symptoms was M=0.7±0.7 (range: 0-2.8), for depression symptoms was M=0.5±0.4 (range: 0-1.9) and for perceived stress was M=15.2±5.8 (range: 2-30). Greater morningness/eveningness scores, indicating more morningness, were associated with lower scores for anxiety symptoms (r=-.41, p<.001), depressive symptoms (r=-.36, p<.001) and perceived stress (r=-.33, p=.001).

Conclusion

As has been found for adolescents, higher morningness in Latinx pre-teens was related to less frequent anxiety and depression symptoms, as well as lower perceived stress. Youth experience a circadian phase delay during adolescence, shifting their preference toward eveningness, which may exacerbate stressors and negative mental health. Therefore, interventions to promote psychological well-being in pre-adolescents may help prevent worse psychological outcomes in Latinx children as they transition to adolescence.

Support

This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers UL1GM118979, TL4GM118980, and RL5GM118978.
0325 Better Aerobic Fitness is Associated with Distinct Sleep Characteristics in Children and Adolescents - A Pilot Study
Authors: Neikrug A B , Radom-Aizik S , Chen I Y , Stehli A , Lui K K , Chappel-Farley M G , Lim A N , Mander B A , Benca R M
Published: May 2020

Introduction

Aerobic fitness facilitates brain synaptic plasticity, which influences global and local sleep expression. While it is known that sleep patterns/behavior and non-rapid eye movement (NREM) sleep slow wave activity (SWA) tracks brain maturation, little is known about how aerobic fitness and sleep interact during growth and development in children and adolescents. The aim of this pilot study was to characterize relationships among aerobic fitness, measures of global/local sleep expression, and habitual sleep patterns in children and adolescents. We hypothesized that greater aerobic fitness would be associated with better sleep quality, indicated by increased SWA.

Methods

Twenty healthy youth (11-17 years-old, 11 female) were evaluated during summer vacation (no school schedule constraints). Aerobic fitness (VO2peak) was measured using ramp-type progressive cycle ergometry, habitual sleep (i.e., sleep-time consistency and circadian activity patterns) was assessed with 7-day actigraphy, and ad lib sleep was evaluated during overnight polysomnography (PSG) with high-density electroencephalography (hdEEG; 128 channels). Spectral analysis was implemented to quantify SWA (0.5-4.5Hz). Data were analyzed using linear regression analyses and exploratory independent samples t-tests.

Results

Negative correlations were observed between VO2peak and sleep measures including sleep-time consistency (partial r=-0.53, p=0.045) and timing/acrophase of the circadian activity rhythm (partial r=-0.64, p=0.01) while controlling for sex and age. Additionally, after accounting for Tanner stage and sex, data demonstrated significant effects in SWA at frontal derivations (p=0.024) between low and high fitness levels at topographically specific and meaningful EEG derivations, e.g. over frontal cortex.

Conclusion

These results suggest that children and adolescents with greater fitness have less variability in sleep-times (improved sleep consistency), tend to have a more advanced circadian activity phase (i.e., go to sleep earlier), and express greater frontal SWA, supporting the hypothesis that fitness is associated with improved local and global sleep quality. Future research with larger samples is necessary to further evaluate these relationships, and to determine if interventions that improve fitness also improve sleep and related brain plasticity.

Support

NCATS grant #UL1TR001414 & PERC Systems Biology Fund
0332 Effect of Prior Sleep Duration on Distinct Measures of Daytime Cognitive Performance in Late Adolescence
Authors: Campbell I G , Zhang Z Y , Cruz Basilio A , Lawrence-Sidebottom D , Van Dongen H , Feinberg I
Published: May 2020

Introduction

A recent longitudinal study of sleep need changes across adolescence reported how prior sleep duration affects daytime sleepiness and vigilant attention in children ages 10-16 years. In a follow-up study, we extend the age range in a new group of participants and add additional performance tests. Here we report year 1 data on the effect of systematically varied time in bed (TIB) on daytime vigilance, working memory, and decision making.

Methods

Data are for 52 participants aged 15.0-20.4 years (mean±SD: 17.7±1.8 years). Annually, participants keep each of three different TIB schedules: 7h, 8.5h or 10h TIB for 4 consecutive nights. The 4th night is followed by a laboratory day of performance testing. The day includes four 10-minute psychomotor vigilance tests (PVT); a serial position Sternberg working memory task; and an AX continuous performance test with switch (AX-CPTs) measuring cognitive flexibility in decision making.

Results

PVT performance evaluated by the log of the signal to noise ratio (LSNR) improved monotonically with increasing TIB (p<0.0001). TIB also affected serial position Sternberg task accuracy (p=0.008) but not the probe position effect (p=0.66), indicating that TIB did not affect working memory. TIB also affected AX-CPTs accuracy (p<0.0001), but TIB did not significantly affect decision making and cognitive flexibility measures extracted from this task (all p>0.09).

Conclusion

The initial data from this longitudinal study on older adolescents confirm what we observed for younger adolescents. Increasing TIB improves daytime vigilance but does not affect working memory. These initial results also do not indicate a TIB related improvement in decision making. Data from the entire three year longitudinal study will allow us to further investigate relations of performance to prior sleep duration and whether these relations change with age. Results from dose-response studies such as these can help guide sleep duration recommendations.

Support

PHS grant R01 HL116490 supported this work.
0333 Electronic Media Use is Associated with Poor Sleep In 3-6 Year-Old Children
Authors: Wong S , Hartstein L E , LeBourgeois M K
Published: May 2020

Introduction

Recent surveys estimate that electronic media use among young children is increasing and that behavioral sleep problems are prevalent. In this study, we employed assessments of sleep and media use and tested the hypothesis that poor sleeping children would be more likely to engage with media than good sleeping children.

Methods

Participants were 44 children from two different cohorts: (1) Healthy, good sleepers (n=26, 13 males, 4.3±0.4 years) who reportedly obtained ≥10.5 hours per night and had no behavioral sleep problems and (2) Poor sleepers (n=18, 9 males, 5.5±0.7 years) who reportedly obtained chronic insufficient sleep ≤9 hours per night and/or had behavioral sleep problems for ≥6 months. Sleep duration and sleep onset latency (SOL) were quantified through 7 nights of actigraphy and verified with sleep diaries. Media use, defined as any electronic device involving screen time that engages children, was assessed across 2 weekdays and 2 weekend days through a parental media diary. Independent t-tests compared the duration of media use and actigraphy variables between groups.

Results

Poor sleeping children on average had longer SOL (28.6±17.9 vs. 17.3±8.66 minutes, t=-2.5, p<0.05) and shorter sleep duration (589.6±37.5 vs. 627.4±27.4 minutes, t=3.7, p<0.01) compared to good sleeping children. Additionally, average daily media use (125.1±88.5 vs. 66.5±48.3 minutes, t=-2.6, p<0.05), evening media use (22.0±21.3 vs. 4.2±10.4 minutes, t=-3.3, p<0.01), and weekend media use (154.4±105.9 vs. 79.8±55.6 minutes, t=-2.7, p<0.05) duration was higher in poor than good sleepers.

Conclusion

Our findings indicate that media use duration and timing likely play an important role in early childhood sleep health. Young children who use more evening media are more likely to take longer to fall asleep and have shorter sleep duration overall. Time displacement (time spent using media instead of sleeping), psychological stimulation, and the effects of screen light on circadian timing are potential mechanisms underlying these associations.

Support

NIH R01-MH086566 and R21-MH110765 to MKL
0342 Self-Silencing within Intimate Relationships, Sleep, and Subclinical Atherosclerosis in Midlife Women
Authors: Jakubowski K P , Chang Y , Barinas-Mitchell E , Matthews K A , Maki P M , Thurston R C
Published: May 2020

Introduction

Social relationships are important for health. In some relationships, women learn to self-silence, or to inhibit self-expression to avoid conflict or loss. Self-silencing is associated with reported psychiatric and physical symptoms, but no studies have examined whether self-silencing is related to worse sleep or cardiovascular (CV) health. We tested relationships of self-silencing to sleep and carotid plaque in midlife women; secondary analyses examined whether sleep mediated or moderated relationships between self-silencing and plaque.

Methods

In an ongoing community-based study of nonsmoking women, 304 women aged 40-60 were assessed at baseline; 157 of these women have been assessed 5 years later. At baseline, women reported on self-expression in their current/last intimate relationship via the Silencing the Self Scale. At both visits, women provided self-reports (demographics, medical history, CESD depression, PSQI sleep quality), physical measures, actigraphy (total sleep time [TST], wake after sleep onset [WASO], and efficiency), and carotid artery ultrasound to quantify plaque. Relationships of self-silencing and subscales to sleep (subjective and actigraphic sleep at baseline and averaged across visits) and carotid plaque (0, 1, ≥2) were tested in linear regression and multinomial regression models, respectively, adjusted for demographic and health indices, including depressive symptoms and snoring.

Results

At baseline, women (72% White) were on average 54 years old; 44% reported poor sleep quality, 46% had plaque (24% score ≥2), and average TST, WASO, and efficiency were 6.2 hrs, 46 min, and 84%, respectively. At baseline, self-silencing (particularly the tendency to judge oneself by external standards) was related to worse sleep quality (p=.001), but better actigraphic WASO (p=.02) and efficiency (p=.02). Self-silencing was related to worse average sleep quality across visits (p=.001). Self-silencing related to higher odds of baseline plaque ≥2 [OR(95% CI)=1.14 (1.02,1.28), p=.02], yet sleep did not explain or moderate this relationship.

Conclusion

Self-silencing was associated with worse subjective, but better actigraphic sleep at baseline, and with poorer sleep quality over 5 years. Self-silencing related to carotid atherosclerosis, yet sleep did not appear to impact this relationship. Emotional expression is relevant to midlife women’s sleep and CV health.

Support

R01HL105647, K24123565 (RCT); RF1AG053504 (RCT & PM); T32MH018269 (KPJ)
0348 Sleep EEG-Based Brain Age Index is Reduced Under Continuous Positive Airway Pressure Treatment
Authors: Sun H , Dunham K , Cunningham L , Ni Y , Westover M , Thomas R
Published: May 2020

Introduction

Continuous positive airway pressure (CPAP) is a treatment for apnea. With long-term CPAP, changes in electroencephalogram (EEG) include increased delta power (1 - 4Hz) and sigma power (11 - 15Hz, spindle). However, the short-term EEG response to CPAP in a split-night study is less quantified. We recently developed a “brain age” model using sleep EEG features. The brain age index (BAI) is defined as the difference between chronological age and brain age (BA - CA). Here we first quantify how BAI changes during CPAP in the same patient, and then investigate how much brain age features during the diagnostic part can predict the reduction in apnea-hypopnea index (AHI) during CPAP.

Methods

The dataset consisted of 160 subjects. The average age was 59 years with 53% male, 24% female and 23% unknown. We extracted 480 features including band powers, and then computed the BAIs for both diagnostic and CPAP parts. To predict the reduction in AHI during CPAP, we fit a Bayesian regression model using the brain age features, demographics, and sleep parameters during the diagnostic part, and assessed the feature importance using dominance analysis.

Results

The BAI from the diagnostic part is significantly reduced compared to BAI during CPAP for the same subject (paired t-test, p < 0.01). The diagnostic part has an average BAI 2.24 years; and the CPAP part -4.75 years. The brain age features that are increased during CPAP include sigma powers in N2 and N3. The prediction of AHI reduction has Pearson’s correlation 0.85. The features predictive of reduced AHI are the diagnostic AHI (explained variance 69%), followed by high/low waveforms during N2 (e.g. K-complex, measured by kurtosis) (8.6%), delta power during REM (4.5%) and N1 (2%). The feature predictive of increased AHI is frontal alpha power during quiet awake (2.6%).

Conclusion

The average BAI is reduced during CPAP. BAI provides a novel view of the acute response to CPAP in sleep EEG. Future study with more CPAP failure patients has the potential of predicting CPAP failure.

Support

MBW is supported by Glenn Foundation for Medical Research. RJT is supported by Category I AASM Foundation.
0351 Sleep Continuity, Sleep-Related Daytime Dysfunction, and Problem Endorsement: Do These Vary Concordantly by Age?
Authors: Boyle J T , Rosenfield B , DiTomasso R A , Vargas I , Grandner M , Perlis M L
Published: May 2020

Introduction

It is well documented that sleep continuity (i.e., SC [ability to initiate and/or maintain sleep]) worsens with age. It is unclear whether problem endorsement and/or daytime dysfunction show similar age-related trends. Accordingly, an analysis was undertaken to assess whether initial, middle, and/or late insomnia all exhibit age related change and whether problem endorsement and/or daytime dysfunction show comparable age-related changes.

Methods

The study utilized a cross-sectional group design in an archival/community dataset (www.sleeplessinphilly.com). This dataset (N=932) was comprised of adults between 18 and 89 years of age with self-reported sleep complaints. Participants were categorized as: Young Adults (18-29 years); Adults (30-44 years); Middle Age Adults (45-64 years); and Older Adults (65-89 years). Age groups were matched to the Older Adults group (n=233) by sex, race, and BMI. ANOVAs with Bonferroni corrections (alpha = .001), and contingency analyses were performed to assess for age group differences.

Results

It was found that, as expected, SC worsens with age but that this was limited to middle and late insomnia. Further, problem endorsement increased with age (except for SL) but sleep-related daytime dysfunction did not (except for concentration issues).

Conclusion

These results have several implications. Methodologically speaking, when evaluating the effects and/or correlates of SC, it may be wise to concomitantly assay “is this a problem for you” and “does this affect your daytime function”, as SC can occur without perceived daytime consequences, especially in older adults. Conceptually speaking, the observed discordance requires further exploration. In the past, it has been argued that sleep need is reduced in older adults. While this is a reasonable hypothesis (no need, no functional consequence), it remains to be demonstrated that older adults require less sleep.

Support
0354 Age-Related Spectral Changes in NREM And REM Sleep in Mice are Global and Not Local
Authors: Dube J , Lina J , Soltani S , Chauvette S , Bukhtiyarova O , Carrier J , Timofeev I
Published: May 2020

Introduction

Brain topography modulates age-related changes in the human sleep electroencephalogram, which are linked with differences in integrity of specific cortical areas and may reflect local changes in sleep homeostasis. In mice, there is conflicting evidence regarding the topography of age-related changes for NREM and REM sleep. To disambiguate this issue, we investigated in mice the topography of age-related spectral differences for REM and NREM sleep.

Methods

LFP electrodes were implanted in 5 cortical areas and in the hippocampus of 17 C57/BL6 mice (8 young and 9 old, mean age = 7.5 and 16 months). Mice LFPs were recorded for a week and states of vigilance were semi-automatically detected in light and dark periods (12h-12h). Spectral analysis was run on 4s windows. Values were averaged for each electrode and in each period of the light/dark cycle in REM/NREM sleep for slow delta (0.25-2Hz), delta (2-4Hz), theta (4-8Hz), sigma (10-16Hz) and ripples (150-200Hz). Mixed models were computed separately for REM and NREM in dark and light period, with age as group factor and electrode and frequency as repeated factors.

Results

Two-way interactions were found between age and frequency and between electrode and frequency, for NREM and REM in dark and light periods. Each frequency band, except ripples, showed a topographical signature in NREM and REM (e.g. higher power in anterior compared to posterior areas for delta band in NREM sleep). These relative patterns did not change in older mice, but global changes occurred on all electrodes: in older mice, delta power was globally higher in NREM and REM sleep whereas sigma power was lower in REM sleep.

Conclusion

Age-related changes in spectral power of sleeping mice do not vary according to brain topography as in humans. Sleep deprivation studies are needed to investigate whether age is associated with global changes in sleep homeostasis in mice.

Support

This work has been supported by the Quebec Fonds de Recherche Nature et Technologies (FQRNT).
0357 CPAP Adherence is Lower in Minority Neighborhoods
Authors: Carmona E T , Nouraie S M , Bakker J P , Stitt C J , Aloia M S , Patel S R
Published: May 2020

Introduction

The effectiveness of continuous positive airway pressure (CPAP) in treating obstructive sleep apnea (OSA) is limited by adherence. Small, single-center studies have reported CPAP adherence is lower in racial minorities suggesting disparities in OSA care. We used nationally representative data to assess racial differences in CPAP adherence at a neighborhood level.

Methods

Telemonitoring data were obtained from a therapy database maintained by a CPAP manufacturer. Usage over the first 90 days in patients initiated on CPAP between 11/01/2015 and 10/31/2018 who had at least one usage session, age 18-90 years, and valid U.S. zip code were mapped to a zip code tabulation area (ZCTA). Age- and sex-adjusted CPAP usage was calculated for each ZCTA with greater than 10 CPAP users. Ecologic analyses were performed to model the association of the proportion of blacks and Hispanics in each ZCTA (obtained from the 2013-2017 American Community Survey) on CPAP usage controlling for proportion of adults with bachelor’s degree and proportion of adults with household income below the poverty line.

Results

Our analysis included 13,118 ZCTAs averaging data over 737,274 patients. In adjusted analyses, each 10% increase in the proportion of blacks and Hispanics was associated with a 0.12 (95% CI 0.11-0.12) hour and 0.14 (95% CI 0.14-0.15) hour decrease in nightly CPAP use, respectively. Mean usage in ZCTAs with <1%, 1-2.5%, 2.5-10%, 10-25%, and 25-100% blacks were 4.96, 4.81, 4.67, 4.56, and 4.14 hours respectively (p<0.001). Mean usage in ZCTAs with <1%, 1-2.5%, 2.5-10%, 10-25%, and 25-100% Hispanics were 4.87, 4.86, 4.75, 4.50, and 4.10 hours respectively (p<0.001).

Conclusion

CPAP adherence is lower in neighborhoods with higher proportions of black and Hispanic residents independent of differences in education or poverty. These differences lead to lower likelihood of meeting insurance coverage requirements for CPAP therapy, potentially exacerbating sleep health disparities.

Support

Philips Respironics, NIH R25HL130600 and K24HL127307.
0375 Culturally Consistent Diet Among Individuals of Mexican Descent at the Us-Mexico Border is Associated with Sleep Duration and Quality
Authors: Ghani S , Delgadillo M E , Killgore W D , Wills C C , Grandner M A
Published: May 2020

Introduction

Previous studies have shown that people who consume culturally consistent foods have improved cardiometabolic profiles. Few studies have examined whether this finding extends to sleep health.

Methods

Data were collected from N=100 adults (age 18-60, 53% female) of Mexican descent in the city of Nogales, AZ (66% not born in the US, 33% 1st-generation). Surveys were presented in English or Spanish. Acculturation was assessed with the Acculturation Scale for Mexican-Americans (ARSMA-II), which has separate scales for Mexican and Anglo acculturation (subscale range 0-4). A supplemental ARSMA item asks how often “My family cooks Mexican foods.” Responses were coded as either frequent or infrequent. Insomnia was assessed with the Insomnia Severity Index (ISI), Sleepiness with the Epworth Sleepiness Scale (ESS), Sleep quality with the Pittsburgh Sleep Quality Index (PSQI), and Sleep duration and sleep medication use with PSQI items. Regression analyses examined these outcomes relative to whether individuals frequently consumed Mexican foods. Covariates included age, sex, and acculturation scores. Parental education level was also included, as an indicator of childhood socioeconomic status and since food culture likely involves parents.

Results

Regular consumption of Mexican foods was associated with 1.41 more hours of sleep, on average (95%CI 0.19,2.62, p<0.05). It was also associated with a decreased likelihood of snoring (oOR=0.25; 95%CI 0.07,0.93; p<0.05). No differences were seen for PSQI, ISI, or ESS score.

Conclusion

Individuals of Mexican descent at the US-Mexico border who regularly consume culturally consistent food report overall more sleep and less snoring. Previous studies show that Mexican acculturation may be associated with improved sleep sufficiency; it is possible that this reflects an overall healthier lifestyle that also includes a culturally consistent diet. Further studies would be beneficial to help determine the role acculturation plays in sleep and diet and how it effects cardiometabolic risk.

Support

Dr. Grandner is supported by R01MD011600. This work was supported by a UAHS grant.
0388 Predictors of Incident Reduced Sleep Efficiency in Community-Dwelling Older Women
Authors: Vo T , Blackwell T , Kats A , Langsetmo L , Taylor B , Schousboe J , Redline S , Stone K , Smagula S , Chu H , Rodriguez R , Schommer J , Carlson A , Ensrud K
Published: May 2020

Introduction

There is a paucity of longitudinal studies with sleep efficiency (SE) as an outcome measure. Our objective was to examine potential risk factors for incident reduced SE among community-dwelling women in late life.

Methods

We studied 700 women (mean age 82.5 [SD=3.0] years) with a SE ≥70% at the Year 16 (2002-04) visit of the Study of Osteoporotic Fractures with a follow-up measure of SE at the Year 20 (2006-08) visit. SE (percentage of time sleeping while in bed) at both visits was measured using a wrist actigraph with data collected for an average of four 24-hour periods. Women were classified as having incident reduced SE if they had SE <70% at Year 20. Logistic regression was used to estimate the associations between potential risk factors (demographics, lifestyle, use of medications, self-reported medical conditions, functional impairment, frailty, mental and physical health) at Year 16 and reduced SE at Year 20. The association of each candidate risk factor with reduced SE at Year 20 was examined in models adjusted for age, clinical site and continuous SE at Year 16. Candidate risk factors with Benjamin Hochberg false-discovery rate q-values <0.10 were included in a final multivariate model.

Results

Among the 700 eligible women, 62 (8.9%) developed incident reduced SE between the Year 16 and Year 20 visits. After adjusting for age, site and baseline SE, antidepressant use [OR=3.06; 95% CI: 1.50-6.25], benzodiazepine use [OR=2.97; 95% CI: 1.30-6.80] and the presence of hypertension [OR=2.83; 95% CI: 1.47-5.45] at Year 16 were independently associated with a higher odds of having reduced SE at follow-up.

Conclusion

These findings suggest that antidepressant use, benzodiazepine use and hypertension are risk factors or markers for the development of reduced sleep efficiency in older women. Future studies are warranted to examine the underlying mechanisms for these associations.

Support

The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576.
0394 Differential Role of Sleep Problems on Depression and Suicide in Community Adolescents
Authors: Kim J , Vander Stoep A , McCauley E
Published: May 2020

Introduction

Sleep changes during adolescence, including “eveningness” or a preference for staying up late, decreased sleep hours, increased daytime sleepiness and irregular sleep patterns, can contribute to adolescent sleep disturbances, such as insomnia, daytime fatigue, and other sleep problems. The goals of the study were; 1) to examine the proportion of adolescents who experienced each type of sleep disturbances based on six sleep problems; and 2) to examine the association between six types of sleep disturbances and concurrent depression, suicide ideation, and suicide attempt at 12th grade.

Methods

Using the data from the Developmental Pathways Project (DPP), a community-based study in adolescence, total 425 students were included. Sequential logistic regression analyses were performed to examine the association between each sleep item and depression, suicide ideation, and lifetime history of suicide attempt.

Results

The most frequently reported sleep problems were ‘overtired without good reason (49.9%)’, ‘sleeps less than most kids (46.8%)’, and ‘sleep more than most kids (46.7%)’. After adjusting for depressive symptoms at baseline, 12th grade reports of ‘overtired without good reason’ (OR = 1.63, 95% CI = 1.22 - 2.17), ‘sleep less’ (OR = 2.03, 95% CI = 1.51 - 2.74), ‘trouble sleeping’ (OR = 1.50 95% CI = 1.10-2.06, p < .05), ‘nightmare’ (OR = 1.51, 95% CI = 1.12-2.02, p < .01) were significantly associated with depression. For suicide ideation (SI) and suicide attempt, ‘nightmare’ (OR = 1.68, 95% CI = 1.15 - 2.48; OR = 2.43 95% CI = 1.30 - 4.53, respectively) was significantly and positively associated with SI and having history of suicide attempt.

Conclusion

‘Nightmare’ has the strongest association with depression, suicide ideation, and suicide attempt. To disentangle the mechanism of the association between nightmares and mental health issues, longitudinal studies examining causal or bidirectional relationships of the mechanism are warranted.

Support

National Institutes of Mental Health and Drug Abuse, Nesholm Family Foundation, Seattle Children’s Hospital Outcomes Research Steering Committee, Loeb Family Foundation, Seattle Children’s Research Institute, University of Washington Office of the Provost, AETNA Foundation.
0397 Neighborhood Physical and Social Environments and Sleep Among Children and Adolescents: A Systematic Review
Authors: Mayne S L , Mitchell J A , Virudachalam S , Williamson A A
Published: May 2020

Introduction

Understanding salient environmental determinants of pediatric sleep is essential for informing interventions and public health initiatives. Emerging evidence suggests the neighborhood environment can impact pediatric sleep. We are conducting a systematic review of studies examining associations of neighborhood physical and social environments with sleep among children and adolescents.

Methods

We searched 6 databases (MEDLINE, PubMed, EMBASE, SCOPUS, Web of Science, PsychInfo) using search terms related to sleep, neighborhood environment, and pediatric populations to identify English-language articles with an abstract. We identified and screened 2,581 abstracts. Inclusion criteria included 1) assessing associations of ≥1 neighborhood-level factor with ≥1 sleep outcome and 2) including participants ≤18 years. We excluded review articles, protocols, qualitative and non-human studies. In total, 134 full-text articles were independently reviewed by 2 reviewers each to confirm eligibility. One reviewer abstracted preliminary data from included studies. Next steps include independent data abstraction by two reviewers using a standardized form, synthesis of results, and assessment of study quality according to the study design, sleep assessment method, sampling strategy, and control for confounding.

Results

Sixty-one articles met inclusion criteria. Fourteen articles included children aged 0-5 years, 38 included children aged 6-12 years, and 36 included adolescents aged 13-18 years (25 included multiple age groups). Twenty-two studies (36%) used objective sleep assessment methods (e.g. actigraphy). Seven studies (11%) examined sleep apnea/snoring. The most common neighborhood-level factors were safety/crime/community violence (n=28) and socioeconomic status (n=25), with fewer studies examining other exposures like noise (n=7) and social cohesion (n=4). Results on key associations and study quality are forthcoming.

Conclusion

A growing body of epidemiological data has emerged in recent years to provide insight into how the neighborhood environment can impact pediatric sleep. Preliminary results suggest few studies have examined associations of the built environment with sleep, with most studies focusing on school-aged children and adolescents.

Support

This work was supported by: the Children’s Hospital of Philadelphia’s Possibilities Project (SLM); Sleep Research Society Foundation and K23HD094905 (AAW); NIH/NHLBI K01HL123612 (JAM)
0435 Autoscoring of Sleep and Associated Events Versus a Reference Scorer Competing with Three Additional Manual Scorings: A Clinical Validation Study
Authors: Anderer P , Ross M , Cerny A , Moreau A
Published: May 2020

Introduction

Manual scoring of polysomnographic (PSG) data is a time-consuming and tedious process with noticeable inter-rater variability. Autoscoring may overcome these limitations if it delivers valid results. The goal of this study was to validate a comprehensive autoscoring system in a clinically representative population.

Methods

The validation data consisted of 97 PSGs in patients with sleep-related breathing disorder, scored manually by a reference scorer and three further experts. The Somnolyzer autoscoring system combined pattern recognition for events such as spindles, k-complexes, slow-waves, eye-movements, apneas, hypopneas, desaturations and leg movements with an artificial intelligence classifier consisting of a bidirectional long short-term memory recurrent neural network (RNN) architecture. Intra-class correlation coefficients (ICC) for absolute agreement were determined for the commonly used metrics in sleep medicine to compare both, the three human expert scorings and the autoscoring versus the reference scoring.

Results

ICC coefficients for autoscoring and the three manual scorings versus the reference scoring were for sleep efficiency: .95, .83, .91, .93; N1(%): .71, .44, .39, .56; N2(%): .87, .63, .55, .45; N3(%): .80, .62, .44, .32; R(%): .92, .89, .91, .88; arousal index: .88, .81, .22, .78; PLMI: .97, .88, .86, .91; AHI: .91, .89, .87, .78; OA: .94, .89, .91, .90; CA: .96, .96, .96, .82; MA: .93, .77, .43, .41. Thus, the ICCs between autoscoring and the reference scoring were equal or higher than the ICCs between any of the three manual scorings and the reference scoring for all endpoints.

Conclusion

Autoscoring of PSGs based on artificial intelligence outperformed even the best of three human expert scorers. Since the autoscoring performs pattern recognition in real-time, the final autoscoring results are available immediately after the end of the recording.

Support

All authors are employees of Philips
0436 Deep Learning for Scoring Sleep Based on Signals Available in Home Sleep Apnea Test Studies: Cardiorespiratory Sleep Staging
Authors: Anderer P , Ross M , Cerny A , Radha M , Fonseca P
Published: May 2020

Introduction

Typically, neurological signals are not recorded in home sleep apnea testing (HSAT) and thus standard sleep scoring is not applicable. The respiratory event index is calculated using total recording time rather than total sleep time (TST) resulting in a risk of underestimating sleep apnea severity. The objective of the study was to evaluate if artificial intelligence approaches can provide sleep scoring based on cardiorespiratory signals (CReSS) with reasonable accuracy.

Methods

Supervised deep learning for scoring sleep was trained with 472 and tested in 116 polysomnographies (PSG), scored independently by two experts and by a consensus scorer. The resulting bidirectional long short-term memory recurrent neural network (RNN) was integrated in the Somnolyzer system and validated in 97 PSGs of patients with obstructive sleep apnea (OSA) which had been scored independently by four human experts. Cohen’s kappa agreement for four stages (W, L: N1+N2, D: N3, R) was determined as compared to a consensus scoring.

Results

Epoch-by-epoch comparison between CReSS autoscoring and manual consensus scoring resulted in Cohen’s kappa of 0.68 (W: 0.74, L: 0.63, D: 0.54, R: 0.79). The intra-class correlation coefficient (ICC) between TST derived from CReSS and from neurological scoring was 0.86 (95%-CI: 0.79-0.90), while the ICC between subjective TST from sleep questionnaire and the objective TST was only 0.65 (95%-CI: 0.45-0.77). REM-related OSA had a prevalence of 16% and was detected with an accuracy of 95%.

Conclusion

With a kappa of 0.68, the cardiorespiratory-based RNN classifier is far above previously published values and reflects a substantial agreement with the manual consensus scoring in patients with sleep-disordered breathing. Thus, applying CReSS allows a more accurate determination of the OSA-severity and even a detection of REM related OSA in HSAT studies.

Support

All authors are employees of Philips
0439 Nonlinear Dynamics Forecasting for Personalize Prognosis of Obstructive Sleep Apnea Onsets
Authors: Le T
Published: May 2020

Introduction

The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes.

Methods

This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea)

Results

The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R2) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively.

Conclusion

The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies.

Support

N/A
0440 Validation Study of Neural Network Algorithm for Automated Sleep Stage Scoring: StageNet
Authors: Choi J , Moon J
Published: May 2020

Introduction

Polysomnography (level 1) is an important test for evaluating sleep status or disorder. In general, measured raw data of polysomnography is manually scored by sleep expert. However, manual scoring is a time-consuming and labor-intensive work. The purpose of this study was to verify the accuracy of automated sleep stage scoring based on the neural network algorithm compared to the manual sleep stage scoring.

Methods

A total 604 polysomnography data set of subjects (Male: Female = 409: 195) aged 19 to 60 years were finally included in the study. The performance of proposed model was evaluated with kappa and bootstrapped point-estimated of median percent agreement with 95% percentile bootstrap confidence interval with R=1000. The proposed model is trained with 484 data set and validated with 48 data set. For test, 72 data set are randomly selected.

Results

The proposed model showed good concordance rates in stage W (94%), N1 (83.9%), N2 (89%), N3 (92%) and R (93%) between automated neural network algorithm and manual scoring. The average kappa value was 0.85. In bootstrap method, high overall agreements of automated neural network algorithm were found in stage W (98%), N1 (94%), N2 (92%), N3 (99%), R (98%) and total (96%).

Conclusion

Automated sleep stage scoring using proposed model - StageNet may be a reliable method for sleep stage classification.

Support
0449 Characterizing Spindle Activity as a Time-Frequency Phenomenon
Authors: Dimitrov T S , He M , Prerau M J
Published: May 2020

Introduction

Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records.

Methods

We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method.

Results

Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles.

Conclusion

These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology.

Support

National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
0451 Fully Automatic Detection of Sleep Disordered Breathing Events
Authors: Thybo J , Olesen A N , Olsen M , Leary E , Arnal P , Sørensen H B , Jennum P , Mignot E
Published: May 2020

Introduction

Evaluation of sleep apnea involves manual annotation of Polysomnography (PSG) file, a time-consuming process subject to interscorer variations. The DOSED algorithm has been shown to be helpful in detecting Central Sleep Apnea (CSA), Obstructive Sleep Apnea (OSA), and Hypopnea when merged into a single event type. This work uses a modified version of DOSED capable of detecting each event type separately.

Methods

The network consists of 3 blocks of 1D convolutional layers followed by 6 blocks of 2D convolutional layers. The network has 2 classification layers, one determines the probability of each class, and the other determines the start and duration time of the event with the highest probability. Four channels from nasal and mouth airflow and position of abdomen and thorax are used as input to the model. The model was trained using 2800 PSG from 4 different cohorts (MESA, MROS, SSC, WSC) and tested on 70 PSG, which have been scored by six technicians (Stanford, U Penn, St Louis).

Results

On an event by event basis, model F1-scores versus a weighted consensus score based on 6 technicians were 0.60 for OSA, 0.43 for CSA, and 0.34 for Hypopnea. Average F1-scores for the 6 technicians were 0.48 (std 0.04) for OSA, 0.29 (std 0.145) for CSA, and 0.54 (std 0.183) for Hypopnea, indicating that the model functions better on an event-by-event basis than an average technician. Correlations between indices/hr for central apnea, obstructive apnea, and hypopnea indicate excellent correlations for apneas, but poor correlation for hypopnea. We are now adding the snoring channel to explore if predictions can be improved.

Conclusion

The result shows that deep learning-based models can detect respiratory events with an accuracy similar to technicians. The poor agreement between technicians from different universities indicates that we need better definitions of hypopnea.

Support
0453 Longitudinal Relationship Between Insomnia and Work Productivity in Japanese City Government Employees
Authors: Kadotani H , Ito K , Matsuda A , Nishikawa K , Sumi Y , Matsuo M
Published: May 2020

Introduction

“Presenteeism” refers to the decrease in productivity in employees who are present but not functioning at full capacity due to illness or other medical conditions. It is reported that the cost of presenteeism to businesses is 10 times higher than absenteeism (away from work due to illness or disability). Relative presenteeism is a ratio of actual performance to the performance of most workers at the same job. We analyzed effects of insomnia and depression two years before on presenteeism in a Japanese working population.

Methods

Questionnaire survey was conducted as a part of a cohort study named “Night in Japan Home Sleep Monitoring Study (NinJaSleep Study)” in 2016 and 2018. Participants were the city government employees in a rural city in Shiga prefecture, Japan. Presenteeism, insomnia and depression were analyzed by WHO-HPQ (Health and Work Performance Questionnaire), ISI (insomnia severity index) and PHQ-9 (Patient Depression Questionnaire), respectively. Pearson correlation coefficient analyses were performed to determine the strength of the association between two variables. Logistic regression was performed to estimate the odds ratio (OR) and 95% confidence interval (CI) of poor relative presenteeism (the lowest tertile of the relative presenteeism scores) after 2-year follow up.

Results

1143 subjects (participation rate: 61.7%, 36.7% male, 44.5±11.4 years, BMI: 22.3±3.30) participated in both 2016 and 2018. Participants with poor productivity (poor relative presenteeism) in 2018 was significantly associated with ISI in 2016 (OR: 1.050, 95%CI: 1.010-1.090, p=0.013) but not with PHQ-9 in 2016 (OR: 1.008, 95%CI: 0.972-1.045, p=0.664) after adjusting for age, gender and BMI. Positive correlation was found between the total score of ISI and item 3 of PHQ-9 which asks insomnia or hypersomnia symptom (r=0.6122, P<0.0001).

Conclusion

Insomnia may be an independent risk factor for poor presenteeism. ISI may be useful to predict poor productivity in the future.

Support

Supported in part by a research grant from Investigator-Initiated Studies Program of Merck Sharp & Dohme Corp. / MSD K.K. The opinions expressed in this paper are those of the authors and do not necessarily represent those of Merck Sharp & Dohme Corp. / MSD K.K.”