bluuebunny/crossref_metadata_2025_split huggingface dataset:
228 The Association of Prior Mental Health Conditions with COVID-19-related Sleep Changes
Authors: Kasraeian Kimiya , Petrov Megan 
Published: May 2021

Abstract

Introduction
Among persons with mental health conditions, the impact of the COVID-19 pandemic on sleep health is underexplored. The present study investigated whether sleep changes related to the COVID-19 pandemic differed among individuals with mood and/or anxiety disorders compared to individuals without these disorders.

Methods
A 25-minute online survey, distributed globally to adults aged &gt;18y through social media advertising from 5/28/2020-7/10/2020, examined the association of mental health diagnoses with COVID-19 related sleep changes. Participants reported prior history of mood and anxiety disorders, and pre-COVID-19 and current sleep patterns including bedtime, wake time, total sleep time (TST), sleep efficiency (SE:[TST/time in bed*100%], and nightmare frequency/wk. ANOVA models comparing mental health disorder groups (no diagnoses, mood disorders, anxiety disorders, mood and anxiety disorders) on mean differences in sleep changes were conducted.

Results
Among 1,048 participants, 71.5% reported no prior mood and/or anxiety disorders9.3% reported anxiety disorders only, 4.3% reported prior mood disorders only, and 14.9% reported both mood and anxiety disorders. There were significant group differences in total sleep time (F (3,670)=4.6, p=0.003) and sleep efficiency (F (3,670) =2.8, p=0.038) such that individuals with both mood and anxiety disorders experienced greater decreases in total sleep time (Mean Difference: 39.0min, SE=13.0) and sleep efficiency (Mean Difference=3.8%, SE=1.6) compared to individuals without any mood or anxiety disorders. In addition, the model for nightmare frequency per week was significant (F(3,654)=5.6, p=0.001) such that individuals with both anxiety and mood disorders (Mean Difference=1.1, SE=0.4) and individuals with mood disorders only (Mean Difference=1.1, SE=0.4) reported greater increases in nightmare frequency compared to participants without any mood or anxiety disorders. There were no group differences in bedtime and wake time.

Conclusion
Among a global sample, COVID-19 pandemic-related sleep health significantly worsened among individuals with prior mood and anxiety disorders relative to individuals without these disorders.
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229 Sleep-wake behavior, meal timings and digital media duration of Indians during COVID-19 Lockdown
Authors: Pande Babita , Sinha Meenakshi , Sinha Ramanjan 
Published: May 2021

Abstract

Introduction
Lockdown and stay home order has been imposed on people in many countries including India to prevent the community transmission of COVID-19 pandemic. However this social restriction led to disturbed daily routine and lifestyle behaviour that is needed to be attended for proper therapeutic management of overall health during such crisis. The impact of lockdown on the most apparent behavioral changes viz. sleep-wake behaviour, major meal timings, and digital screen duration of Indians were investigated. In addition the effects of gender and age were explored.

Methods
After seeking permission from Ethical Institution, an online questionnaire based survey was circulated within India in the first week of May, 2020 for which total 1511 male and female (age ≥18 years) subjects participated. The sleep-wake behavior observed were sleep-wake timings, sleep duration, mid sleep time (MST) as function of lockdown, and social (lockdown) jetlag (SJL = MST before lockdown-MST during lockdown).

Results
The sleep onset-wakeup and meal times were significantly delayed during lockdown, which was more pronounced in younger age group. The sleep duration increased, specifically in young individuals during lockdown. Females showed more delayed sleep onset-waking times and first meal timing with longer sleep duration during lockdown. Increased digital media duration was observed in all age groups, primarily in males. The younger age group and specifically female reported higher SJL and delayed MST. A positive association was obtained between sleep duration & first meal time, and SJL & major meal timings/screen duration, and a significant negative relationship of sleep duration and SJL with age.

Conclusion
The study shows delayed sleep-wake schedule, meal timings and increased digital media duration among Indians during COVID-19 lockdown compared to before lockdown. Also, gender and age emerged as important mediating factors for this alteration. The pandemic has given opportunity to sleep more and compensate for the sleep. In spite of that, the higher social jetlag in young age group and female showed the compromised sleep and maladaption with societal timing. These findings have applied implications in sleep health during longer social isolation conditions and for proper therapeutic management.

Support (if any)
No
239 Changes in Children’s Schoolyear and Summer Sleep during the COVID-19 Pandemic
Authors: Burkart Sarah , Parker Hannah , Von Klinggraeff Lauren , Hunt Ethan , Jones Alexis , Dugger Roddrick , Reesor-Oyer Layton , Beets Michael , Weaver R Glenn , Armstrong Bridget 
Published: May 2021

Abstract

Introduction
In spring 2020, elementary schools closed to minimize the spread of COVID-19. Questionnaire data suggest children’s sleep was impacted during the pandemic, yet device-based data (i.e. accelerometry) on this topic is lacking. The purpose of this study was to examine children’s sleep during the COVID-19 pandemic (i.e. spring and summer 2020) compared to previous data collected from the same children during each of the two previous years (spring and summer 2018 and 2019).

Methods
68 children (age = 9.9±1.2 years, 56% Black, 53% male) previously recruited for an observational cohort study wore a Fitbit Charge 2 on their wrist during the spring and summer from 2018-2020 (i.e. six 30-day measurement periods). We used multilevel mixed models to examine how children’s sleep patterns changed during the pandemic accounting for previous trajectory (i.e. 2018 to 2019). Models included age, sex, and race as covariates.

Results
Children had an average of 84 nights of sleep data across all six 30-day measurement periods. In the spring of the pandemic, children slept 24.6 minutes more (95%CI = 11.6, 37.5) compared to previous springs. During the pandemic summer, they slept 40.0 minutes more (95%CI = 24.6, 58.5) compared to previous summers. Sleep midpoint was 117.1 minutes later (95%CI = 103.6, 130.6) in the spring during the pandemic and 46.0 minutes later (95% CI = 26.9, 65.2) in the summer during the pandemic compared to previous years. Sleep efficiency improved slightly by 1.3% (95% CI = 0.7, 1.9) and 3.6% (95% CI = 2.7, 4.5) in spring and summer, respectively, during the pandemic compared to previous years.

Conclusion
During the COVID-19 pandemic, children slept longer after accounting for previous developmental trends. Notably, the shift in sleep timing during the pandemic was nearly two hours later in the spring compared to previous years, potentially due to the lack of structure usually provided by school. Later sleep timing is independently associated with poor health behaviors (e.g., nutrition, physical activity, screen time). Future studies should examine if these changes in sleep persist over time and have potential long-term effects on children’s health.

Support (if any)
R21HD095164 (PI Weaver) & UofSC COVID-19 Research Initiative Grant (PI Armstrong)
240 Habitual Sleep Changes Following COVID-19 Outbreak
Authors: Eyal Shuli , Altman Yuval , Baharav Anda 
Published: May 2021

Abstract

Introduction
The ongoing COVID-19 pandemic emerges as one of the most impressive and strenuous events of the century, with unthinkably huge global effects. We aimed at analyzing if and how does the pandemic affect sleep and related behaviors.

Methods
We reviewed 48,047 nights recorded in the US with the Sleeprate application by 3,381 users during a period of 2 years, December 2018-November 2020. Nightly data included perceived and measured sleep parameters. In addition, users reported their perceived daytime stress and sleepiness. We analyzed the monthly variability of the studied parameters and compared their values during the COVID-19 period of March-November 2020 (CP) with those in the previous corresponding period in 2019, the pre-COVID-19 period (PCP).

Results
Starting March 2020, wake-up time (WUT) was significantly delayed relative to PCP. WUT in April 2020 was the latest (8:06AM±2:12hours, mean±SD, p&lt;.000), being an hour later than in April 2019. This delay started to diminish in June 2020, reaching 7:27AM±2:10 hours by November 2020, which was not significantly higher than during PCP. Bedtime (BT) exhibited similar behavior, yet it returned to PCP times faster. Delayed BT and WUT on weekends were observed during CP as well as PCP. No consistent differences in sleep duration or sleep efficiency were detected between CP and PCP. Subjective sleep satisfaction was higher in CP relative to PCP. However, daytime sleepiness and daytime stress were also higher during most of CP compared to PCP.

Conclusion
Our data, based on digital in-app sleep diaries coupled with perceived sleep parameters, demonstrate the pandemic’s effects on sleep behavior in the US. Users in this study adapted to the new circumstances with delayed sleep schedule, while not reducing the sleep opportunity. The higher sleep satisfaction may be connected to later sleep schedules, allowing a wake-up time that fits better human biological clocks. The reported increased stress and sleepiness further portray the uncertainty and turbulence characterizing the pandemic’s effects on populations life during the pandemic. As good sleep is linked with immune response efficacy, higher quality of life, and improved mood, the importance of sleep must not be overlooked, especially during the pandemic.
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247 Automated sleep staging using wrist-worn device and deep neural networks
Authors: Sridhar Niranjan , Ghoreyshi Atiyeh , Myers Lance , Owens Zachary 
Published: May 2021

Abstract

Introduction
Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations.

Methods
We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM. The largest probability is chosen as the network’s class prediction and used to form the hypnogram. We used the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis Study (MESA) and Physionet Computing in Cardiology (CinC) dataset (over 10000 nights) for training and evaluation. Then we deployed the algorithm on PPG based heart rate measured by a wrist-worn device worn by subjects in a free-living setting.

Results
On the held out test SHHS dataset (800 nights, 561 subjects), the overall 4-class staging accuracy was 77% and Cohen’s kappa was 0.66. On the CinC dataset (993 nights, 993 subjects), the overall 4 class accuracy was 72% and Cohen’s kappa was 0.55. The study on free-living subjects is underway and these novel results will be collated and presented upon completion.

Conclusion
We hope these results build more trust in automated heart rate based sleep staging and encourage further research into its clinical application in screening and diagnosis of sleep disorders. Low cost, high efficacy devices which can be used in longitudinal studies can lead to breakthroughs in clinical applications of sleep staging for early diagnosis of chronic conditions and novel treatment endpoints.

Support (if any)
We recently published the training/testing of the algorithm as well a population level analysis showing differences in predicted sleep stages between disease cohorts. The article was published in NPJ Digital Medicine in Aug 2020. The study on free living subjects is currently underway and these new results will be presented at the sleep conference. Preliminary results indicate high concordance with our published results.
249 Overnight heart rate variability depends on age, gender, and day of the week: a field observation using a smart bed platform
Authors: Barr Shawn , Sohn Kwanghyun , Garcia Gary 
Published: May 2021

Abstract

Introduction
Heart rate variability (HRV) is commonly used to assess the activity of the autonomic nervous system (ANS). ANS function changes, reflected in HRV, result from factors including lifestyle, aging, cardiorespiratory illnesses, sleep state, and physiological stress. Despite broad interest in HRV, few studies have established normative overnight HRV values for a large population. To better understand population level HRV changes, ecologically-valid, overnight sleep SDNN (standard deviation of all normal heartbeat intervals, lower HRV is reflected by lower SDNN) values have been analyzed for a large sample of Sleep Number 360 smart bed users.

Methods
Overnight SDNN values were obtained over the course of 18.2M sleep sessions from 379,225 sleepers (48 ± 14.7 sessions/user). 50.9 percent of sleepers were female. The age was normally distributed with mean ± SD of 52.8 ± 12.7 years (range 21 to 84). Heartbeat intervals used to compute SDNN were extracted from a ballistocardiogram (BCG). BCG-based HRV estimation during sleep has previously been validated against ECG-based HRV with an R-square of 0.5.

Results
Using a Generalized Linear Model, significant cross-sectional associations with SDNN were observed for three variables of interest: age, gender, and day-of-the-week. For sleepers under 50, SDNN declined at a rate of about 2.1 ms/year, then leveled off for sleepers aged 50-65, and increased slightly thereafter. Women under 50 displayed lower, more slowly declining, SDNN values than men, but this trend reversed for sleepers over 50. Throughout the week, SDNN values followed a U-shaped (women) or L-shaped (men) pattern, where values were highest during the weekend and lowest at mid-week.

Conclusion
Using a smart bed to unobtrusively measure overnight SDNN values for a large set of sleepers in an ecologically valid environment, reveals significant effects of age, gender, and day of the week on overnight SDNN.
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256 Spectrophotometric Properties of 31 Different Commercially Available Blue Blocking Glasses Under Electric Room Lighting
Authors: Rupple Destiny , Mason Brooke , Tubbs Andrew , Fernandez Fabian-Xosé , Grandner Michael 
Published: May 2021

Abstract

Introduction
Blue blocking glasses are often marketed to promote relaxation, sleep, and circadian health by attenuating melatonin-suppressing light exposure. But these glasses represent a wide range of tint and other lens properties. Further, the utility of these glasses under ecologically valid indoor conditions (where light is typically generated from overhead broadspectrum fluorescent lamps) is still unclear, especially across various products.

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 overhead fluorescent lighting in a closeted dark room. Thirty-one commercially available blue blockers were individually placed between the cosine corrector and the luminaire, at a standardized distance and angle, where intensity was measured and analyzed. Each lens was evaluated individually relative to the light source under identical conditions. Then, lenses were collapsed by type into the following groups: 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 light-blocking across lens types (one-way ANOVA, p &lt; 0.0001). On average, RTL and BTL restricted 59% of the visible light measured from 380-780nm. OTL blocked 47% of the light in this range, while OBL blocked 29%. Both YTL and RBL blocked 14% of the exposure. When narrowing the range of light to 440-530nm (the part of the spectrum most likely to produce a response from melanopsin-expressing retinal ganglion cells), we estimated the following performance: the RTL and OTL blocked close to 100% of the light, OBL blocked 98%, BTL blocked 80%, YTL blocked 33%, and RBL blocked 15%. These differences were statistically significant (one-way ANOVA, p &lt; 0.0001). Individual lenses performed variably within groups, but these differences were small.

Conclusion
Focusing on the portion of the visible spectrum most likely to suppress melatonin secretion, RTL and OTL blocked exposure the best, followed by OBL, BTL, YTL, and (lastly) RBL.

Support (if any)
R01MD011600, R01DA051321
257 How Much Blue Do Blue-Blockers Block if Blue-Blockers Do Block Blue?
Authors: Mason Brooke , Tubbs Andrew , Killgore William , Fernandez Fabian-Xosé , Grandner Michael 
Published: May 2021

Abstract

Introduction
Short-wavelength light (440-530nm) can suppress endogenous melatonin secretion from the pineal gland. This has been observed in realworld settings when people use electronic media at night that emits light from this part of the visible spectrum. Blue-blocking glasses are a possible intervention to reduce blue light exposure. The present study evaluated the ability of commercially available blue-blockers to block blue light emitted by LEDs.

Methods
A calibrated spectroradiometer (Ocean Insight), cosine corrector, optic fiber, and software package were used to measure the absolute irradiance (uW/cm^2/nm) generated from a blue light source (Phillips Go Lite Blu) in an otherwise completely dark room. Thirty-one different commercially-available blue-blockers were individually placed between the cosine corrector and the light source at a standardized distance, and then intensity was measured and analyzed. Lenses were evaluated with regards to the amount of blue light they suppressed both individually and grouped by lens tint: 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 lenses with blue reflectivity (RBL).

Results
RTL blocked 100% of the short-wavelength light, while OTL and OBL blocked 99%, BTL blocked 66%, YTL blocked 38%, and RBL blocked 11% of it. This represented a statistically significant between-group difference (one-way ANOVA, &lt; 0.0001). Within groups, there was variability in performance among individual lenses, though this variability was small compared to the between-group differences.

Conclusion
The RTL, OTL, and OBL block light best capable of suppressing melatonin secretion at night (440-530 nm); with slightly less efficacy, BTL and YTL also restricted much of the light exposure. Lastly, RBL were not effective at curtailing short-wavelength light. Those looking to optimize blue-blocking capabilities should use RTL, OTL, and OBL, rather than other lens types.
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262 Comparing Two Methods for Scoring Wrist Actigraphy to Polysomnography for Estimating Total Sleep Time in Healthy Men
Authors: Tussey Emma , Rynders Corey , Swanson Christine 
Published: May 2021

Abstract

Introduction
This analysis assessed whether manually setting rest (i.e., time in bed) intervals prior to using a proprietary software package (Actiware, version 6.09) to analyze wrist actigraphy data improved estimates of total sleep time (TST) compared to polysomnography (PSG).

Methods
The Phillips Actiwatch 2 and PSG (reference method) were used to calculate TST on two separate nights in twelve men (age=28.3 ± 5.7). Participants had an 8-hour sleep opportunity on night one and a 5-hour sleep opportunity and on night two. Estimates of TST from actigraphy data were calculated using two scoring methods. For scoring method 1, we allowed the software to automatically choose rest intervals and then applied a proprietary algorithm to calculate TST. For scoring method 2, we manually entered rest intervals using a published decision tree that incorporates activity, light, event marker, and sleep diary data. After the rest intervals were set in method 2, the proprietary algorithm was applied to calculate TST. Mean bias and limits of agreement (LOA) from Bland-Altman plots compared TST derived from both actigraphy scoring methods to PSG estimates.

Results
On night 1 (n=8) TST measured by PSG was 398.4 ± 40.6 minutes, compared to 395.5 ± 70.9 minutes using actigraphy scoring method 1 and 396 ± 44.5 minutes using scoring method 2. Mean bias was similar when comparing both scoring methods to PSG, but the LOA were wider in method 1 compared to method 2 (method 1 vs. PSG: -2.9 [-110.4, 104.7]; method 2 vs. PSG: -2.4 [-66.5, 61.7]; minutes). On night 2 (n=12) TST determined by PSG was 283.3 ± 11.2 minutes, compared to 302.1 ± 84.4 minutes using actigraphy scoring method 1 and 273.1 ± 14.5 minutes using scoring method 2. Again, LOA for TST estimated by actigraphy scoring method 1 were wider compared to scoring method number 2 (method 1 vs. PSG: 18.8 [-136.9, 174.6]; method 2 vs. PSG: -10.2 [-35.1, 14.8]).

Conclusion
These data demonstrate that applying a decision tree to manually set time in bed intervals prior to running analyses in the software results in better agreement when estimating TST from wrist actigraphy compared to PSG.

Support (if any)
UL1RR025780, K23AR070275.