bluuebunny/crossref_metadata_2025_split huggingface dataset:
250 AI-Supported Sleep Staging from Activity and Heart Rate
Authors: Chowdhury Samadrita , Song TzuAn , Saxena Richa , Purcell Shaun , Dutta Joyita 
Published: May 2021

Abstract

Introduction
Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM).

Methods
Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase.

Results
Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements.

Conclusion
This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables.

Support (if any)
This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.
251 Agreement and reliability of a new polysomnography sleep staging algorithm against multiple human scorers
Authors: Magalang Ulysses , Keenan Brendan , Staley Bethany , Anderer Peter , Ross Marco , Cerny Andreas , Vasko Raymond , Kuna Samuel , Bakker Jessie 
Published: May 2021

Abstract

Introduction
Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated sleep staging algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual sleep staging, by analyzing a dataset we have previously used to report sleep staging variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC).

Methods
Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Each 30-second epoch was staged manually according to American Academy of Sleep Medicine criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored total sleep time (TST) and time in each sleep stage (N1, N2, N3, rapid eye movement [REM]). We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers.

Results
The participants contributing to the original dataset had a mean (SD) age of 47 (12) years and 80% were male. Auto-scoring showed substantial (ICC=0.60-0.80) or almost perfect (ICC=0.80-1.00) reliability compared to manual-scoring average, with ICCs (95% confidence interval) of 0.976 (0.931, 0.992) for TST, 0.681 (0.291, 0.879) for time in N1, 0.685 (0.299, 0.881) for time in N2, 0.922 (0.791, 0.973) for time in N3, and 0.930 (0.811, 0.976) for time in REM. Similarly, Bland-Altman analyses showed good agreement between methods, with a mean difference (limits of agreement) of only 1.2 (-19.7, 22.0) minutes for TST, 13.0 (-18.2, 44.1) minutes for N1, -13.8 (-65.7, 38.1) minutes for N2, -0.33 (-26.1, 25.5) minutes for N3, and -1.2 (-25.9, 23.5) minutes for REM.

Conclusion
Results support high reliability and good agreement between the auto-scoring algorithm and average human scoring for measurements of sleep durations. Auto-scoring slightly overestimated N1 and underestimated N2, but results for TST, N3 and REM were nearly identical on average. Thus, the auto-scoring algorithm is acceptable for sleep staging when compared against human scorers.

Support (if any)
Philips.
255 Spectrophotometric Properties of Commercial Blue-Blocking Lenses in Sunlight
Authors: Mason Brooke , Tubbs Andrew , Fernandez Fabian-Xosé , Grandner Michael 
Published: May 2021

Abstract

Introduction
Blue-blocking glasses are increasingly used as an intervention for jet-lag and other situations where an individual wishes to promote a “dark” signal despite the presence of ambient light. However, most studies on blue-blockers are done under controlled laboratory settings using emissions generated from electric light sources. The present study evaluated the performance of commercially available blue-blockers under daytime sunlight conditions.

Methods
A calibrated spectroradiometer (Ocean Insight), cosine corrector, optic fiber, and software package were used to measure the absolute irradiance (uW/cm^2/nm) available midday in a standardized location that received direct sunlight. Thirty-one commercially available blue-blockers were individually placed in front of the cosine corrector and intensity was measured and analyzed. Each lens was tested for its ability to block visible light, as well as light within the 440-530nm range. Lenses were evaluated individually and grouped by lens type: red-tinted lenses (RTL), orange-tinted lenses (ORL), orange-tinted lenses with blue reflectivity (OBL), brown-tinted lenses (BTL), yellow-tinted lenses (YTL), and clear lenses with blue reflectivity (RBL).

Results
Across the full spectrum, RTL blocked 66% of the light, OTL blocked 60%, OBL blocked 43%, BTL blocked 56%, YTL blocked 28%, and RBL blocked 20%. When the range was restricted to 440-530nm, RTL blocked 99%, OTL blocked 96%, OBL blocked 90%, BTL blocked 66%, YTL blocked 38%, and RBL blocked 17% of the light. Variation across lens types was significant for the full spectrum (one-way ANOVA, p &lt; 0.0001) as well as the 440-530nm range (one-way ANOVA, p &lt; 0.0001). Individual lenses showed variability in performance, though this variability was smaller than the between-group differences.

Conclusion
Under daylight conditions, red and orange lenses (RTL, OTL, and OBL) blocked at least 90% of the light in the 440-530nm range. Notably, RBL lenses restricted the most short-wavelength light as a proportion of the total light blocked. These data suggest that RTL, OTL, and OBL are effective at blocking the most circadian photosensitive components of daylight at the cost of reducing total illumination.

Support (if any)
R01MD011600, R01DA051321
269 Sleep Enhancement Technology: A Survey of Apps
Authors: Stekl Emily , Klosterman Grace , Simonelli Guido , Collen Jacob , Doty Tracy Jill 
Published: May 2021

Abstract

Introduction
The ever-evolving market for sleep technologies far outpaces the ability of providers to understand and counsel patients about developments in this area. Although significant literature has validated the performance of sleep tracking technologies, there is little evidence regarding sleep enhancement technologies. Our study systematically surveys currently available commercial sleep enhancement smartphone applications to empower both providers and patients alike.

Methods
We systematically searched the App Store (Apple) and Google Play Store (Android) in the US on 26 MAY 2020 using the keyword “sleep.” This survey is inclusive of all smartphone applications found.

Results
We identified 342 apps: 70.2% were found on Android (N=240) and 29.8% on Apple (N=102). Ninety-five percent of apps offer a free version. The majority of sleep apps are intended for use during wake (65.8% exclusively during wake; 28.7% during both wake and sleep), with only 5.6% intended to be used during sleep alone. Most apps purport to enhance rather than measure sleep (78.7% versus 1.8%). The vast majority of apps claim to enhance sleep via reductions in sleep latency (65.8%). Reduced sleep latency is primarily achieved using a combination of non-verbal auditory stimuli such as nature sounds (84.4%), artificial stimuli (64.5%), and instrumental music (77.1%).

Conclusion
Interestingly, most sleep apps are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency. Given the multitude of available sleep enhancement apps, many of which are free to try, these should be considered a reasonable strategy for providers and consumers to consider for empowering patients to improve sleep!

Support (if any)
Department of Defense Military Operational Medicine Research Program (MOMRP)
287 East? I Thought You Said Weast! The Influence of Travel on College Football Team Performance
Authors: Kealaiki-Sales Micah , Pradhan Sean 
Published: May 2021

Abstract

Introduction
Previous research in professional basketball and baseball has shown that traveling up to three hours westward can hamper performance due to circadian disadvantages. However, findings in the context of collegiate football are conflicting, as some prior studies have reported negative effects on scoring during either eastward or westward travel. The current study extends the literature by investigating the impact of travel on both offensive and defensive team performance within National Collegiate Athletic Association (NCAA) Division I college football.

Methods
Following the NCAA’s introduction of the College Football Playoff in 2014, data from 1,909 away games from 64 “Power Five” conference teams played during the 2014 to 2019 regular seasons were collected from the publicly available sports database, Sports-Reference. For the purposes of our analyses, we excluded all games played at neutral sites. We examined the effects of the direction of travel away from the college’s home city and time of game day on visiting team performance, specifically game outcomes, points scored, points allowed, completion percentages, penalties, fumbles, interceptions, and total turnovers forced and committed, controlling for both visiting and home team conference, day of game, and team rankings.

Results
Teams playing in the afternoon allowed significantly more points (OR = 1.05, p &lt; .001) and forced more opponent turnovers than those playing in the evening (OR = 1.14, p = .05). Teams traveling eastward threw significantly more interceptions than those traveling in the same time zone (OR = 1.48, p = .004). A significant interaction between direction of travel and time of day was detected for points allowed (χ2 = 12.30, p = .02), and a marginal interaction was present for points scored (χ2 = 8.42, p = .08). Several other marginal differences were also identified for points scored, interceptions, and team turnovers (OR &gt; 1.03, p &lt; .10).

Conclusion
Findings from our study offer evidence for the influence of circadian factors on team points allowed, interceptions, and opponent turnovers forced. Specifically, travel in varying directions and the time of day when a game is played can impact team performance during away games within college football.

Support (if any)
None
294 Impacts of travel and time zone differences in the National Hockey League (NHL)
Authors: Charest Jonathan , Samuels Charles , Bastien Célyne , Lawson Doug , Grandner Michael 
Published: May 2021

Abstract

Introduction
Elite athletes are at risk of poor sleep which can be exacerbated by frequent travel. The present exploratory study investigated the impact of travel on the winning percentage, number of goals scored in the 3rd period and the number of penalties in the 3rd period over the 2013–2020 seasons in the National Hockey League (NHL).

Methods
Data from away and home games from the 2013–2020 seasons in the NHL were included in this study. The outcomes were based on winning percentage with additional covariates including home and away games; timing of the game (afternoon/17:30 or earlier; evening/18:00 or later; number of time zones travelled (one, two or three); direction of the travel (eastward or westward); length of the game (regular, overtime or shootout). Additionally, data exclusively from the 3rd period were assessed for the number of penalties received and the number of goals scored for and against. Data were analyzed with logistic regressions to evaluate the effects of the aforementioned variables on winning percentage for both eastern and western conference teams.

Results
Regardless of the length of the game, results indicated no difference between eastern and western teams on winning percentage. However, there was a significant impact of home-ice on winning percentage for both conferences (p&lt;0.001). In addition, there was no difference on the winning percentage based on the travel direction and the number of time zones crossed (p = 0.747) or the time of the day (p=0.991). Moreover, visiting teams received significantly more 3rd period penalties than home teams (p&lt;0.001), regardless of travel and while travelling within the same time zone compared to eastward travel (p&lt;0.001) but not westward travel (p=0.078). Finally, there was an increased risk of being scored against when team travelled three time zones (p=0.03), regardless of the direction.

Conclusion
This 7-year investigation of data from the NHL demonstrates an unexplored aspect of the impact that travel and circadian factors may have on emotion regulation and performance. Translational application of this knowledge to enhance general public health and performance would be warranted.
Support (if any):
307 Sleep and occupational wellbeing in active duty U.S Army Soldiers
Authors: Pirner Maddison , Doyle Scott , Mantua Janna , Bessey Alexxa , Naylor Jacob , Ritland Bradley , Sowden Walter , Burke Tina , Mckeon Ashlee 
Published: May 2021

Abstract

Introduction
Insufficient sleep is ubiquitous among active duty service members in operational settings. Although insufficient sleep has been linked to poor cognitive, psychological, and physiological outcomes in military populations, little research has investigated the impact of insufficient sleep on Soldier occupational wellbeing. This study examined the longitudinal association between sleep quality and occupational functioning in a population of active duty U.S. Army Soldiers.

Methods
Sixty male Soldiers (age 25.41±3.74 years) participated. Sleep quality and occupational outcomes were assessed four weeks apart (before and after an annual training mission). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Occupational outcome measures included the Emotional Exhaustion Scale, Walter Reed Functional Impairment Short Scale, Role Overload Scale, and Perceived Stress Scale. Linear regressions assessed the prediction of PSQI Global Score on occupational outcome scores. Student’s t-tests compared occupational outcomes between “good” and “poor” sleepers (PSQI Global Score &gt; 5 = poor sleeper).

Results
Poorer sleep quality at baseline broadly predicted poor occupational outcomes post-training. Specifically, higher PSQI Global Scores predicted higher emotional exhaustion (B = 1.6, p &lt; 0.001, R2 = 0.25), functional impairment (B = 0.29, p &lt; 0.03, R2 = 0.14), role overload (B = 28, p &lt; 0.008, R2 = 0.12), and perceived stress (B = 0.34, p &lt; 0.004, R2 = 0.2). Furthermore, occupational outcome scores were significantly higher in poor sleepers than good sleepers: emotional exhaustion: (t(58) = -4.18, p &lt; .001); functional impairment: (t(59) = -3.68, p = .001); role overload (t(58) = -3.20, p = .002); and perceived stress (t(58) = -2.43, p = .02).

Conclusion
This study identified a longitudinal relationship between sleep quality and occupational outcomes, suggesting that service members with poor sleep may be at risk for experiencing poor workplace wellbeing. Given the association between service member wellbeing and likelihood to re-enlist, insufficient sleep may negatively impact Soldier attrition. Future studies should aim to augment sleep quality and track occupational outcomes in this population.

Support (if any)
This work was funded by the Military Operational Medicine Research Program of the United States Army Medical Research and Development Command.
308 Sleep Quality Predicts Discrepancy between Objective and Self-Ratings of Performance in Cadets during U.S Army ROTC Advanced Camp
Authors: Klosterman Grace , Mickelson Carolyn , Choynowski Jake , Sowden Walter , Mantua Janna , Mckeon Ashlee 
Published: May 2021

Abstract

Introduction
U.S. Army Reserve Officer Training Corps (ROTC) Advanced Camp (AC) is a month-long capstone course that evaluates Cadet leadership. Although the relationship between sleep and objective performance is well established, less is known about how sleep may impact self-perception of performance, especially in the military context. This study examined the impact of habitual sleep on self-expected and objective AC performance.

Methods
577 Cadets (age 22.22 ± 2.74; 74.36% male) completed the Pittsburgh Sleep Quality Index (PSQI) at baseline to measure subjective sleep quality (Global; higher scores indicate poorer sleep quality) and total sleep time (TST) in the month before training. Self-expected AC performance was captured by asking Cadets to estimate what their final performance score would be and objective performance was determined from summary scores from Instructors. Performance discrepancy was calculated as the difference between Cadet’s expected and objective scores. Regression models assessed the predictive utility of habitual TST and Global on performance.

Results
Ordinal regressions showed that as Global increased, expected AC score also decreased with an OR of .684 (95% CI, -.694 to -.064), Wald χ2(1) = 5.56, p = .018. Further, Global independently predicted performance discrepancies, where the odds of a difference existing between a Cadet’s self-expected and their objective performance was .895 less likely for those with increasing Global (p = .028). Together TST and Global predicted discrepancy magnitude between Cadet self-expected and objective performance, F(2, 349) = 2.99, p = .05, with Global as a independent predictor p &lt; .05. Independent findings related to TST were varied and warrant further testing.

Conclusion
Cadets with poorer sleep quality prior to AC self-expected to perform worse and had discrepancies between their self-expected and objective performance when compared to those with higher sleep quality. TST enhanced the predictive power of Global when predicting magnitude of performance discrepancy. Therefore future research examining Global, while accounting for TST, is warranted to better understand how sleep may influence self-expectations of military performance.

Support (if any)
Support for this study came from the Military Operational Medicine Research Program of the United States Army Medical search and Development Command.
332 Non-REM EEG Spectral Power at Baseline and After Total Sleep Deprivation in Individuals with Sleep-Onset Insomnia
Authors: Finlay Myles , Hansen Devon , Skeiky Lillian , Van Dongen Hans 
Published: May 2021

Abstract

Introduction
The baseline non-REM sleep EEG of individuals with insomnia has been found to display increased spectral power at frequencies &gt;14Hz, which may reflect hyperarousal. There is some evidence in this population of reduced slow wave activity after total sleep deprivation (TSD), potentially indicating altered sleep homeostasis. We investigated non-REM sleep EEG spectra at baseline and after TSD in individuals with sleep-onset insomnia.

Methods
10 individuals with sleep-onset insomnia and 5 healthy controls (ages 22-40y, 11 females) completed a 5-day laboratory study with an adaptation night, baseline night, assignment to 38h TSD (n=5 insomnia, n=5 control) or equivalent non-TSD control (n=5 insomnia), and recovery night. Sleep periods were 10h (22:00-08:00) with digital polysomnography (250Hz; Nihon Kohden). Following artifact rejection, 5s subepochs of the non-REM (stages N2, N3) sleep EEG (C3-M2 derivation) in baseline and recovery nights were subjected to spectral analysis. Spectra (0.2Hz bins) were averaged over subepochs in 30s epochs. Repeated-measures ANOVA compared baseline spectra between insomnia and controls, and baseline-recovery difference spectra between TSD insomnia, non-TSD insomnia, and TSD controls.

Results
Average non-REM sleep amount was 5.9 at baseline, increasing by 1.1h after TSD, with no differences between groups (p≥0.20). At baseline, the insomnia group showed increased power in theta/alpha (~4–12Hz), reaching significance in the lower spindle range, compared to controls (p&lt;0.05). As anticipated, no differences emerged between baseline and recovery nights in the non-TSD insomnia group. However, the TSD insomnia group showed increased delta (~1–3Hz) and theta/alpha (~6–10Hz) power (p&lt;0.05) during recovery. Healthy controls showed expected power increases in delta and lower spindle range, and decreases in upper spindle range (~14–15Hz), after TSD (p&lt;0.05).

Conclusion
Compared to healthy controls, individuals with sleep-onset insomnia showed increased non-REM sleep EEG power in the theta/alpha bands and low spindle frequency range, with further significant increases in theta/alpha in addition to delta power following TSD, despite small sample size. The increase in delta power following TSD was equivalent to that in healthy controls, suggesting no sleep homeostasis abnormality. Whether the elevated theta/alpha power may be related to hyperarousal is unclear.

Support (if any)
ONR grant N00014-13-C-0063