bluuebunny/crossref_metadata_2025_split huggingface dataset:
235 Age and Chronotype Associated with Sleep Timing Changes During COVID-19-Related Lockdowns in the US
Authors: Bottary Ryan , Fields Eric , Kensinger Elizabeth , Cunningham Tony 
Published: May 2021

Abstract

Introduction
Global lockdowns implemented to reduce spread of the Coronavirus Disease 2019 (COVID-19) have offered unique insight into how sleep patterns change when typical social obligations are significantly reduced. Here, we aimed to replicate findings of sleep timing delays and reduced social jetlag during lockdown using a large, regionally-diverse sample of participants from the United States (US). Further, we conducted exploratory analyses to determine if observed sleep changes were associated with age and self-reported chronotype.

Methods
A sample of 691 US adults (age 18-89) completed the Ultrashort Munich Chronotype Questionnaire twice during the same assessment: once querying retrospective memory for sleep patterns in the 6-weeks prior to February 1, 2020 (Pre-Lockdown) and a second time for sleep patterns in the 6-weeks prior to ~May 20th (Peak-Lockdown in the US). Participants also completed the abbreviated Morningness-Eveningness Questionnaire to assess chronotype. We compared sleep duration (SDur), sleep onset time (SO), sleep end time (SEnd), social jetlag (SJL; difference between work-day and free-day sleep midpoint) and social sleep restriction (SSR; difference between work-day and free-day sleep duration) Pre- to Peak-Lockdown. We conducted exploratory analyses to determine whether Pre- to Peak-Lockdown changes in these sleep metrics were associated with age or chronotype. Main analyses were preregistered with Open Science Framework (https://osf.io/4a3fx).

Results
During the Peak-Lockdown period, participants, on average, reported significantly later SO and SEnd times and significantly reduced SJL and SSR compared with the Pre-Lockdown period. Change in SJL and SSR Pre- to Peak-Lockdown was significantly positively associated with age and chronotype such that SJL and SSR decreased more during lockdown in younger participants and those with an evening chronotype.

Conclusion
Our results support lockdown-associated sleep timing delays and reduced SJL and SSR. Younger age and evening chronotype were associated with greater reductions in SJL and SSR during lockdown. These findings suggest that individuals, particularly young individuals and those with an evening chronotype, experience greatest desynchrony between intrinsic and social sleep timing when conforming to typical pre-pandemic social schedules.

Support (if any)
Harvard Medical School Division of Sleep Medicine T32 HL007901 (RB and TJC); Brandeis University NIH NRSA T32 NS007292 (ECF)
236 The association between recent cannabis use and nightly sleep duration in adults in the USA from 2005-2018
Authors: Diep Calvin , Tian Chenchen , Won Christine , Ladha Karim , Singh Mandeep 
Published: May 2021

Abstract

Introduction
Shifts in medicolegal attitudes towards cannabis, coupled with widespread legalization, have led to North America having the highest prevalence of cannabis use worldwide. Amongst other known physiologic effects, regular cannabis use can cause changes to sleep duration and quality. The purpose of this study was to examine the relationship between recent cannabis use and sleep duration using a nationally representative data set.

Methods
A cross-sectional analysis of adults was undertaken using the National Health and Nutrition Examination Survey (NHANES) data from 2005–2018. Respondents were dichotomized by whether or not they had used cannabis in the past 30 days. The primary outcome was inadequate nightly sleep duration, defined as self-reported sleep duration less than 6 hours per night. Secondary outcomes were related to self-reported issues with sleep. Multiple logistic regression was used to adjust for potential confounders and survey sample weights were considered in the model.

Results
Compared to those with no recent cannabis use (n=18,631), recent users (n=3,135) were more likely to report less than 6 hours of sleep per night (aOR 1.33 95% 1.13–1.57, p&lt;0.001). Recent users were also more likely to report difficulty falling asleep, staying asleep, or sleeping too much in the past two weeks (aOR 1.21, 95% CI: 1.09–1.35, p&lt;0.001), and having ever mentioned these issues to a physician (aOR 1.21, 95% CI: 1.07–1.37, p=0.003). Respondents using cannabis at least 20 of the past 30 days were characterized as heavy users, and were even more likely than moderate users to report insufficient sleep. These results did not significantly differ between years of survey administration.

Conclusion
Recent cannabis use was associated with inadequate nightly sleep duration in adults and demonstrates a dose-dependent relationship. Although this relationship is complex and our findings cannot suggest directionality, they highlight the need to further characterize the sleep health of regular cannabis users in the general population. This is especially prudent as cannabinoids are becoming widely accepted for recreational use and increasingly prescribed as medical therapy.
Support (if any):
238 Adolescent Sleep Variability, Social Jetlag, and Mental Health during COVID-19: Findings from a Large Nationwide Study
Authors: Wong Patricia , Wolfson Amy , Honaker Sarah , Owens Judith , Wahlstrom Kyla , Saletin Jared , Seixas Azizi , Meltzer Lisa , Carskadon Mary 
Published: May 2021

Abstract

Introduction
Adolescents are vulnerable to short, insufficient sleep stemming from a combined preference for late bedtimes and early school start times, and also circadian disruptions from frequent shifts in sleep schedules (i.e., social jetlag). These sleep disruptions are associated with poor mental health. The COVID-19 pandemic has impacted education nationwide, including changes in instructional formats and school schedules. With data from the Nationwide Education and Sleep in TEens During COVID (NESTED) study, we examined whether sleep variability and social jetlag (SJL) during the pandemic associate with mental health.

Methods
Analyses included online survey data from 4767 students (grades 6-12, 46% female, 36% non-White, 87% high school). For each weekday, participants identified if they attended school in person (IP), online-scheduled synchronous classes (O/S), online-no scheduled classes (asynchronous, O/A), or no school. Students reported bedtimes (BT) and wake times (WT) for each instructional format and for weekends/no school days. Sleep opportunity (SlpOpp) was calculated from BT and WT. Weekday night-to-night SlpOpp variability was calculated with mean square successive differences. SJL was calculated as the difference between the average sleep midpoint on free days (O/A, no school, weekends) versus scheduled days (IP, O/S). Participants also completed the PROMIS Pediatric Anxiety and Depressive Symptoms Short Form. Data were analyzed with hierarchical linear regressions controlling for average SlpOpp, gender, and school-level (middle vs high school).

Results
Mean reported symptoms of anxiety (60.0 ±9.1; 14%≧70) and depression (63.4 ±10.2; 22%≧70) fell in the at-risk range. Shorter average SlpOpp (mean=8.3±1.2hrs) was correlated with higher anxiety (r=-.10) and depression (r=-.11; p’s&lt;.001) T-scores. Greater SlpOpp variability was associated with higher anxiety (B=.71 [95%CI=.41-1.01, p&lt;.001) and depression (B=.67 [.33-1.00], p&lt;.001) T-scores. Greater SJL (mean=1.8±1.2hrs; 94% showed a delay in midpoint) was associated with higher anxiety (B=.36 [.12-.60], p&lt;.001) and depression (B=.77 [.50-1.03], p&lt;.001) T-scores.

Conclusion
In the context of system-wide education changes during COVID-19, students on average reported at-risk levels of anxiety and depression symptoms which were associated with greater variability in sleep opportunity across school days and greater social jetlag. Our findings suggest educators and policymakers should consider these sleep-mental health associations when developing instructional formats and school schedules during and post-pandemic.

Support (if any)
T32MH019927(P.W.)
245 Sleep and cortical thickness are influenced by surgical menopause
Authors: Gravelsins Laura , Gervais Nicole , Brown Alana , Nicoll Gina , Ramana Shreeyaa , Li Alisa , Almey Anne , Reuben Rebekah , Karkaby Laurice , Perovic Mateja , Einstein Gillian 
Published: May 2021

Abstract

Introduction
Early loss of 17β-estradiol (E2), as experienced by women with bilateral salpingo-oophorectomy (BSO; removal of ovaries and fallopian tubes), is associated with increased prevalence of sleep disorders and greater Alzheimer’s disease (AD) risk. In older adults, poor sleep heightens AD risk; hypoxia increases markers for incipient AD, including circulating Aβ, and is linked to prefrontal cortical thinning. Thus, we wondered: 1) if this at-risk population of middle-aged women with BSO had sleep hypoxia, measured by oxygen desaturation, and 2) whether this related to decreased prefrontal cortical thickness in women taking and not taking estradiol therapy (ET).

Methods
Sleep and percent oxygen desaturation (SPO2%) were measured via at-home polysomnography (TEMEC). Prefrontal cortical thickness was obtained from T1-weighted structural scans using the CIVET pipeline. We recruited middle-age women with BSO, some of whom were taking ET (BSO+ET; n=15), and some not (BSO; n=15). We compared their sleep and cortical thickness with that of age and education-matched premenopausal controls (AMC; n=18).

Results
Women with BSO (BSO, BSO+ET) had lower minimum SPO2% values than AMC, and thinner right medial orbitofrontal (rmOF) cortices. There was a trend for women with BSO to have lower average SPO2% than AMC. Analyses separating groups based on ET therapy status (BSO vs BSO+ET vs AMC) revealed only trending differences between groups, such that women with BSO tended to have lower minimum SPO2% and thinner rmOF cortices than AMC.

Conclusion
These preliminary results suggest early loss of E2 due to BSO may drive greater drops in SPO2% in middle-age women, and may be related to reduced prefrontal cortical thickness. This study is the first to show hypoxia in women with BSO.

Support (if any)
Ontario Graduate Scholarship Award (to LG), Alzheimer’s Association Research Fellowship (co-sponsored by Brain Canada Foundation; AARF-17-504715 to NJG), Alzheimer’s Society Canada Postdoctoral Fellowship (to AA), Canadian Institutes of Health Research (CIHR) Masters Award (to LG, AB, and RR), Wilfred and Joyce Posluns Chair in Women’s Brain Health and Aging (from the Posluns Family Foundation, CIHR, Ontario Brain Institute, and Alzheimer Society of Canada; WJP-150643 to GE)
246 Maintenance of Circadian/Daily Activity Patterns and Cognitive Resilience to Alzheimer’s Pathology in Late Life
Authors: Li Peng , Gao Lei , Yu Lei , Fan Wengqing , Lim Andrew , Buchman Aron , Schneider Julie , Bennett David , Hu Kun 
Published: May 2021

Abstract

Introduction
Many individuals remain free from dementia despite substantial plaque and tangle deposits, the hallmarks of Alzheimer’s disease (AD). Understanding of this cognitive resilience is poor. Evidence suggests that circadian disturbances predict increased risk of incident AD, and that circadian regulation deteriorates as clinical AD progresses. We hypothesize that circadian robustness protects against dementia, and the effect is stronger in individuals with AD pathology than those without.

Methods
We studied 575 deceased participants (age at death: 91.1□6.2; female: 414) in the Rush Memory and Aging Project who underwent brain autopsy at death, had clinical diagnostic opinion of dementia and motor activity assessments with actigraphy of ~10 days before death. Using actigraphy proximate to death, we calculated four circadian metrics: amplitude, acrophase, interdaily stability, and intradaily variability. Logistic regressions, stratified by postmortem pathologic AD diagnosis, were used to examine associations of circadian metrics with odds of dementia, and separately cognitive impairment (CI, including both dementia and mild cognitive impairment [MCI]), adjusting for age at death, sex, education, and time-lag between actigraphy and death.

Results
Based on postmortem assessment, 378 participants met the NIA-Reagan criteria for high/intermediate likelihood AD (AD group), including 197 clinically diagnosed with dementia, 86 MCI, and 85 cognitively intact. Non-AD group consisted of the remaining 197 participants, including 36 with clinical dementia, 47 MCI, and 114 cognitively intact. In the AD group, greater amplitude, greater interdaily stability, and lower intradaily variability were associated with lower odds of CI and dementia, i.e., odds ratios [OR] for CI corresponding to 1-SD changes were 0.54 (95% CI: 0.40-0.71), 0.70 (0.54-0.91), and 0.63 (0.47-0.84), and were 0.46 (0.35-0.60), 0.55 (0.43-0.70), and 0.61 (0.48-0.78) for dementia. In the non-AD group, only amplitude was associated with the odds of CI or dementia, i.e., the ORs corresponding to 1-SD increase was 0.61 (0.42-0.88) and 0.50 (0.31-0.82), respectively.

Conclusion
Better preserved circadian function, as characterized by more pronounced, more stable and less fragmented rest-activity rhythms, links to lower risk of CI or dementia in older people, especially those with pathological AD.

Support (if any)
NIH RF1AG064312, RF1AG059867, R01AG017917, R01AG56352; and the BrightFocus Foundation A2020886S.
248 Behavioural Biometrics: Using Smartphone Keyboard Activity as a Proxy for Rest-Activity Patterns
Authors: Druijff-van de Woestijne Gerrieke , McConchie Hannah , de Kort Yvonne , Licitra Giovanni , Zhang Chao , Overeem Sebastiaan , Smolders Karin 
Published: May 2021

Abstract

Introduction
Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose a fully unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. This study investigated whether keyboard activities from habitual smartphone use are reliable estimates of rest and activity timing compared to daily self-reports within healthy participants.

Methods
First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours, and filled out the Consensus Sleep Diary for one week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers.

Results
Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged 0.74 - 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged 0.60 - 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated.

Conclusion
Implementing this monitoring method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly useful in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders.
Support (if any):
253 Better and faster automatic sleep staging with artificial intelligence
Authors: Grassi Massimiliano , Caldirola Daniela , Daccò Silvia , Perna Giampaolo , Defillo Archie 
Published: May 2021

Abstract

Introduction
Sleep staging of polysomnography (PSG) is a time-consuming task, it requires significant training, and significant variability among scorers is expected. A new software (MEBsleep by Medibio Limited) was developed to automatically perform sleep scoring based on machine learning algorithms. This study aimed to perform an extensive investigation of its agreement with expert sleep technicians.

Methods
Forty polysomnography recordings of patients that were referred for sleep evaluation to three sleep clinics were retrospectively collected. Three experienced technicians independently staged the recording complying with the scoring rules of the American Academy of Sleep Medicine guidelines. Positive Percent Agreement (PPA), Positive Predictive Value (PPV), and other agreement statistics between the automatic and manual staging, among the staging performed by the three technicians, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and statistical significance of the differences.

Results
Automatic staging took less than two minutes per PSG on a consumer laptop. The automatic staging resulted for the most comparable (PPA difference of N1, N3, and REM; PPV difference of N1, N2, N3, and REM) or statistically significantly more in agreement with the technicians’ staging than the between-technician agreement (PPA difference of N2: 3.90%, 95% bootstrap CI 1.79%-6.01%; PPV difference of Wake: 1.16%, 95% bootstrap CI 0.64%/1.67%), with the sole exception of a partial reduction in the positive percent agreement of the Wake stage (PPA difference of Wake -7.04%, 95% bootstrap CI -10.40%/-3.85%). The automatic staging also demonstrated very high accuracy in an indirect comparison with other similar software.

Conclusion
Given these promising results, the use of this software may support sleep clinicians by improving efficiency in sleep scoring.
Support (if any):
258 Blue Blockers’ Ability to Block Circadian-Active Light Emitted from a Tablet
Authors: Bobadilla Vanessa , Mason Brooke , Tubbs Andrew , Fernandez Fabian-Xosé , Killgore William , Grandner Michael 
Published: May 2021

Abstract

Introduction
Short-wavelength light emitted from electronic devices in the evening can harm circadian health by suppressing endogenous melatonin and phase-delaying the timing of the wake-sleep cycle. Blue-blocking glasses are one possible intervention to reduce this exposure. The present study evaluated the differential ability of commercially available blue-blockers to filter out the blue range of visible-spectrum light emitted by a common electronic device.

Methods
A calibrated spectroradiometer (Ocean Insight), cosine corrector, optic fiber, and software package were used to measure the absolute irradiance (uW/cm^2/nm) emitted from a commercially-available computer tablet (iPad) displaying a blank white screen in a closeted dark room. Thirty-one commercially-available blue-blockers were individually placed between the cosine corrector and the tablet. At a standardized distance and angle, the resulting intensity profile was measured and analyzed. Each lens was evaluated individually relative to the light source and then evaluated across subtypes, including red-tinted lenses (RTL), orange-tinted lenses (OTL), orange-tinted lenses with blue reflectivity (OBL), brown-tinted lenses (BTL), yellow-tinted lenses (YTL), and clear reflective blue lenses (RBL).

Results
There was significant variation in tablet-generated light-blocking across the full spectrum (one-way ANOVA, p &lt; 0.0001) and for the 440-530nm range in particular (one-way ANOVA, p &lt; 0.0001). RTL blocked 99%, OTL blocked 81%, OBL blocked 75%, BTL blocked 83%, YTL blocked 33%, and RBL blocked 17% of broadspectrum light (380-780nm). In the 440nm-530nm range, RTL, OTL, and OBL blocked 100% of the emission, while BTL blocked 81%, YTL blocked 47%, and RBL blocked 18% of it.

Conclusion
When using a popular tablet device, RTL, OTL and OBL blocked the most circadian photosensitive parts of the light exposure, indicating they can best preserve the timing of endogenous melatonin secretion in the presence of tablet light at night. By contrast, RBL demonstrated very little efficacy.

Support (if any)
R01MD011600, R01DA051321
261 Accelerometer-Derived Sleep and Circadian Domains and Sociodemographic Correlates in the UK Biobank
Authors: Gehrman Philip , Malone Susan , Patterson Freda , Mitchell Jonathan , Mazzotti Diego 
Published: May 2021

Abstract

Introduction
Sleep health encompasses sleep regularity, duration, timing, efficiency and satisfaction. Accelerometry is an established method to estimate sleep in ecologically valid contexts, capturing key characteristic of rest-activity patterns, and facilitating population sleep health research. While hundreds of traits can be generated from open-source algorithms applied to raw acceleration data, the lack of clarity around their meaningful use beyond conventional measures limit the ability of these data to systematically inform evidence-based practices promoting sleep health. Here, we propose a method to identify key sleep and circadian domains, using data reduction methods for hundreds of accelerometer-derived traits to inform population-based sleep heath research. We also aimed to validate our findings by assessing whether the identified domains captured known sociodemographic associations.

Methods
We analyzed sociodemographic and raw triaxial accelerometer data recorded for 7 days from 79,876 adults (mean age 56.3±2.1 years, 56.3% women) participating in the UK Biobank. Standardized data processing using the open-source package GGIR (v1.7-1) resulted in the generation of 107 sleep and circadian traits. Variable clustering was used to identify key sleep and circadian domains, pertinent to sleep health, representing interpretable biological constructs minimizing correlation with other domains. Associations between identified domains and sociodemographic factors were evaluated using general linear models, and clinically significant differences were determined by standardized mean differences (SMD) ≥0.3.

Results
We identified 25 sleep and circadian domains explaining ≥80% of the variability of all 107 included traits. Domains capturing measures of variability tended to cluster together. The most clinically significant associations with sociodemographic characteristics were: women (vs. men) had higher sleep efficiency and lower accumulation of diurnal sleep periods; older (vs. younger) individuals had earlier most active starting time, lower acceleration amplitude and lower number of nocturnal sleep periods; and shift (vs. non-shift) workers had higher variability in sleep timing on weekends.

Conclusion
We demonstrate that variable clustering on accelerometer-derived data can identify meaningful sleep and circadian domains. In addition, identified domains captured known sociodemographic associations commonly observed in the sleep and circadian literature, suggesting that they could be relevant to inform public health practices that promote sleep health.

Support (if any)
NHLBI 5R01HL143790-02(PG); NIMHHD R01MD012734(FP); NIDA R01DA051321(FP); NIH/NHLBI K01HL123612(JM)