bluuebunny/crossref_metadata_2025_split huggingface dataset:
0075 Food Insecurity is Associated with Sleep Duration and Sleep Disorders Symptoms in a Community Adult Sample
Authors: Craig Chloe , Martinez Suzanna , Hale Lauren , Branas Charles , Killgore William , Wills Chloe , Grandner Michael 
Published: May 2022

Abstract

Introduction
Food insecurity is an issue of socioeconomic disadvantage and is increasingly recognized as a key risk factor psychosocial stress and metabolic health. This study examined relationships with aspects of sleep health including sleep duration, quality, continuity, and control, and sleep disorders symptoms.

Methods
This cross-sectional study used data from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of working-age adults ages 22-60 (N=1003). Food insecurity was assessed using items regarding: worry that food would run out, food would not last, and inability to afford healthy meals. Each item was scored 0-2 and total scores ranged from 0-6. Sleep-related variables included habitual sleep duration, Insomnia Severity Index, Brief Index of Sleep Control, Epworth Sleepiness Scale, self-reported habitual sleep latency and wake after sleep onset (WASO), nightmares, restless legs symptoms, STOP-BANG, and statements indicating that medical health and/or stress interfere with sleep. Covariates included age, sex, race/ethnicity, education, insomnia, and shiftwork.

Results
Greater food insecurity was associated with an increased likelihood of being a very short (&lt;5h) sleeper (RRR=1.23, 95%CI[1.08,1.04) or short (5-6h) sleeper (RRR=1.15, 95%CI[1.05.1.25]), as well as having mild (RRR=1.28, 95%CI[1.16,1.41]) or moderate/severe insomnia (RRR=1.38, 95%CI[1.24,1.54]). Those with higher food insecurity perceived less control over sleep (B=-0.11, 95%CI[-0.14,-0.07]), more sleepiness (B=0.40, 95%CI[0.23,0.57]), longer sleep latency (B=2.24, 95%CI[1.09,3.38]), more WASO (B=1.80, 95%CI[0.09,3.51]), and more nightmares sometimes (RRR=1.13, 95%CI[1.04,1.23]) or often (RRR=1.23, 95%CI[1.07,1.40]). Participants were more likely to report uncomfortable sensations in their legs at night and needing to move legs to alleviate discomfort &gt;3 times/week (RRR=1.33, 95%CI[1.17,1.51] and RRR=1.27, 95%CI[1.11,1.45], respectively). They also experienced a higher STOP-BANG score (B=0.09, 95%CI[0.05,0.13]) and were more likely to either agree or strongly agree that medical symptoms (RRR=1.30, 95%CI[1.15,2.47]l and RRR=1.34, 95%CI[1.15,1.57], respectively) and stress (RRR=1.88, 95%CI[1.31,2.69]l and RRR=2.16, 95%CI[1.51,3.10], respectively). Post-hoc analyses showed that most of these relationships were consistent across all 3 components of food insecurity.

Conclusion
Food insecurity was associated with worse sleep health, including risk for insufficient sleep, insomnia, sleep apnea, restless legs, and nightmares. Future research should investigate if efforts to reduce food insecurity can help improve sleep health and other associated outcomes.

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0085 SleepInceptionNet: A Deep Learning Algorithm for Real-Time Sleep Stages Scoring Using Single-channel EEG
Authors: Haghayegh Shahab , Hu Kun , Stone Katie , Redline Susan , Schernhammer Eva 
Published: May 2022

Abstract

Introduction
Most of the current automatic polysomnography sleep staging methods use multi-signal data and require a sequence of preceding and following epochs to score the stage of a specific epoch, which may not be desirable for analysis in real-time and/or in free-living conditions. We developed a deep learning-based sleep staging algorithm, namely SleepInceptionNet, that is designed to score each epoch using only single-channel electroencephalogram (EEG) data within that specific epoch.

Methods
Polysomnography data of 883 participants (937,975 thirty-second epochs) in the Multi-Ethnic Study of Atherosclerosis (MESA) obtained from the National Sleep Research Resource (NSRR) were randomly divided into a separate training/validation set of 194 participants and a test set of 689 participants. Each 30-second raw central EEG channel signal was transformed to time-frequency domain images using continuous wavelet transform method. Sleep stage in each epoch was obtained using SleepInceptionNet, in which the InceptionV3 convolutional neural network structure was trained and tuned on the time-frequency images of the training set to classify each epoch into one of the five stages of Wake, N1, N2, N3, or rapid eye movement (REM) sleep.

Results
Compared to the ground truth manually scored polysomnography sleep stages, SleepInceptionNet achieved an overall kappa agreement of 0.690 and overall weighted accuracy of 0.897. The model showed accuracy (mean±SD across the test set participants) of 0.940±0.067 in detecting Wake, 0.883±0.047 in detecting N1, 0.845±0.055 in detecting N2, 0.939±0.038 in detecting N3, and 0.930±0.038 in detecting REM epochs, in reference to manually scored polysomnography.

Conclusion
SleepInceptionNet showed a high agreement with manually scored polysomnography in epoch-by-epoch classification of sleep stages. This study demonstrates the viability of real-time, accurate sleep staging using a single-channel EEG, which could have a variety of applications such as delivery of on-demand interventions during specific sleep stages in free-living conditions.

Support (If Any)
MESA Sleep Ancillary study was funded by NIH-NHLBI Association of Sleep Disorders with Cardiovascular Health Across Ethnic Groups (RO1 HL098433). MESA is supported by NHLBI (HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01- HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169) and NCATS (UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420). NSRR was supported by NHLBI (R24 HL114473, 75N92019R002).
0088 Remote clinical research operations during COVID-19: Lessons learned and recommendations
Authors: Adornetti Julianna , Wade Christine , Deeley Maura , Eldringhoff Hannah , Jones Rachell , Mantua Janna , Collen Jacob , Wickwire Emerson 
Published: May 2022

Abstract

Introduction
As a result of the global COVID-19 pandemic, there have been significant challenges conducting clinical sleep research. Participant recruitment has been a particular challenge due to federal safety guidelines and institutional directives. The purpose of this project is to describe the adaptation of an in-person study protocol (of sleep problems among military service members and their families) to an entirely remote approach.

Methods
Prior to COVID-19, planned research methods included in-person recruitment, enrollment, and study support; approved by the Institutional Review Board (IRB) of Walter Reed National Military Medical Center (WRNMMC), Fort Belvoir Community Hospital (FBCH), and the University of Maryland, Baltimore (UMB). As COVID-19 restrictions increased, the research team adapted to a fully remote approach (via phone/email) and developed a “research call center” to replace in-clinic recruitment/enrollment. Detailed operating procedures were standardized, including shipping study materials (wearable device) via FedEx. Following enrollment, participants completed multiple assessments, sleep diaries 2x/day over 10 days, and a post-monitoring satisfaction survey.

Results
Thirty-five participants between the ages of 18-75 years (M= 46 years, SD= 15.8) were successfully recruited from the Internal Medicine clinic and Sleep Disorders Center at WRNMMC. Following data collection, the research team debriefed and developed recommendations to execute a successful remote study protocol. Three key operational domains were identified: research team, remote procedures, and data management. Recommendations included 1) prioritizing consistent communication, mutual support, and personal wellbeing among the research team, 2) advancing recruitment by establishing and refining preferred recruitment pathways, and 3) providing critical attention to remote data management—allocating responsibilities to regulate the evolving changes of multiple data sources. In addition, partnering closely with IRB personnel was invaluable to refine procedures and maintain regulatory compliance.

Conclusion
Despite challenges associated with the on-going pandemic, researchers can conduct high-quality clinical research by transitioning to a fully remote study approach. These recommendations can help guide investigative teams to transition from in-person protocols to remote approaches, thus advancing the perpetuation of research activities through a pandemic.

Support (If Any)
This research was supported by an investigator-initiated research award from the Department of Defense (via the Medical Technology Enterprise Consortium) to the University of Maryland, Baltimore (PI: EMW).
0090 Performance of a Multisensor Ring to Evaluate Sleep: In-Lab Evaluation Relative to PSG and Actigraphy: Importance of Generalized Versus Personalized Scoring
Authors: Grandner Michael , Hutchison Stephen , Bromberg Zohar , Morrell Zoe , Graf Arnulf , Freckleton Dustin 
Published: May 2022

Abstract

Introduction
Multisensor sleep wearable devices have demonstrated utility for research and relative accuracy for discerning sleep-wake patterns at home and in the laboratory. Additional sensors and more complex scoring algorithms may improve the ability of wearables to assess sleep health.

Methods
Thirty-six healthy adults completed assessment while wearing the experimental device (Happy Ring), as well as Philips Actiwatch, Fitbit, Oura, and Whoop devices. Evaluations occurred in the laboratory (Alice 6 polysomnogram). The Happy Ring includes sensors for accelerometry, photoplethysmography, electrodermal activity, and skin temperature. Epoch-by-epoch analyses compared the Happy Ring to lab polysomnography, as well as other sleep-tracking devices. Scoring was accomplished using two machine-learning-derived algorithms: a “generalized” algorithm which was static and applied to all users (like those used for other devices) and a “personalized” algorithm where parameters are personalized, dynamic, and change based on data collected across different parts of the night of sleep.

Results
Compared to in-lab polysomnography, the generalized algorithm using data from the Happy Ring demonstrated good sensitivity (94%) and specificity (70%). The personalized algorithm also performed well with good sensitivity (93%) and specificity (83%). Other devices also demonstrated good sensitivity, ranging from 89% (Fitbit) to 94% (Actiwatch); specificity however, was more variable, ranging from 19% (Actiwatch) to 54% (Whoop). Overall accuracy was 91% for generalized and 92% for personalized, compared to 88% for Oura, 86% for Whoop, 84% for Fitbit, and 85% for Actiwatch. Measurement of sleep stage accuracy was 67%, 85%, and 85% for light, deep, and REM sleep, respectively, for the Happy generalized algorithm. For the Happy personalized algorithm, accuracy for sleep stages were 81%, 95%, and 92%, for light, deep and REM sleep, respectively. Post-hoc analyses showed that the Happy personalized algorithm demonstrated better specificity than all other modalities (p&lt;0.001). Kappa scores were 0.45 for generalized and 0.68 for personalized, compared to 0.32 for the Oura Ring, 0.32 for Whoop Strap, and 0.37 for Fitbit wristband.

Conclusion
The multisensory Happy ring demonstrated good sensitivity and specificity for the detection of sleep in the laboratory. The personalized approach outperformed all others, representing a potential innovation for improving detection accuracy.

Support (If Any)
Dr. Grandner is supported by R01DA051321 and R01MD011600. This work was supported by Happy Health, Inc.
0095 Sleep Enhancement Technology in 2021: An Updated Survey of Apps
Authors: Stekl Emily , Klosterman Grace , Simonelli Guido , Collen Jacob , Doty Tracy 
Published: May 2022

Abstract

Introduction
Commercially available smartphone apps that claim to improve sleep quantity and/or quality represent an ever-evolving and fast-growing market. Although a large body of work has validated the performance of sleep tracking technologies, there is little information regarding potential sleep enhancement technologies. Our study systematically surveyed currently available commercial sleep enhancement smartphone apps to provide details to inform both providers and patients alike, in addition to the healthy consumer market.

Methods
We systematically searched the Google Play Store (Android) on 30 JUN 2021 and the App Store (Apple) on 30 JUN 2021 and 26 JUL 2021 in the US using the keyword “sleep.” The Android search was conducted via the Google Play Store website. The Apple search was conducted via third-party websites linked to the App Store due to restrictions on searching the App Store online. This survey was conducted using Google Chrome web browsers and is inclusive of all smartphone applications found.

Results
We identified 550 apps: 59.5% on Android (N=327) and 40.5% on Apple (N=223). Ninety-four percent of apps offered a free version. The majority of sleep apps were intended for use during wake (72.7% exclusively during wake; 25.1% during both wake and sleep), with only 2.2% intended to be used during sleep alone. Most apps purport to enhance rather than measure sleep (87.8% versus 0.5%). The vast majority of apps claim to enhance sleep via reductions in sleep latency (92.9%). Reduced sleep latency is primarily achieved using auditory stimuli (74.5%).

Conclusion
Most current 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 evidence that supports sleep latency as an important target for sleep promoting interventions and the multitude of available sleep enhancement apps across both Android and Apple platforms, sleep apps could be considered a possible strategy for patients and consumers to improve their sleep, although validation of these apps is required.

Support (If Any)
Department of Defense Military Operational Medicine Research Program (MOMRP)
0140 Operationalizing Sleep for the Military: Advancing an Infrastructure for Fatigue Risk Management with Wearable Technology
Authors: Markwald Rachel , Jameson Jason , Chinoy Evan , Kubala Andrew , Roma Pete , Casachahua John , Russell Dale 
Published: May 2022

Abstract

Introduction
A recent investigation on fatigue within United States (US) Naval Surface Force by the Government Accountability Office recommended that the US Navy incorporate methods to empirically monitor and manage fatigue-related threats. In response, a feasibility effort was undertaken to assess two commercial wearable devices previously evaluated against polysomnography by our group for potential use within a novel fatigue monitoring system onboard US warships.

Methods
501 Sailors from three warships (415 men, 85 women, 1 response missing; 29.1±7.4 years, mean±SD) were included in analyses. During multi-day missions at sea, participants were instructed to continuously wear a wrist-worn Fatigue Science Readiband and an Oura Ring. Participants met with the research team daily to sync wearable devices and complete self-ratings of sleep and fatigue on a tablet. While returning to port, participants completed a user experience questionnaire that included 5-point scales for device comfort and interference with daily activities (larger values indicate greater comfort or interference).

Results
There were no overall differences in total sleep time (TST) and sleep efficiency (SE) between devices (TST: Ring=352.23±87.1, Readiband=353.89±97.79; p=0.45, d=-0.01; SE: Ring=84.59±7.08, Readiband=84.20±10.24; p=0.06, d=0.04). There were 9 days for TST and 4 days for SE (of 66 total days) that resulted in effect sizes &gt;0.50 between devices (for &gt;0.20: TST, 39/66; SE, 29/66). Preliminary analyses between TST and self-reported exhaustion and inability to function suggest predictive relationships with the wearables (Ring: r=-0.20; Readiband: r=-0.21), with no relationship with SE across devices (both r&lt;0.01). Average comfort ratings for Ring (3.74) exceeded Readiband (3.59), but this difference was small (p=0.04, d=0.12). Interference with daily activities was also similar between devices (Ring=2.10, Readiband=2.20; p=0.14, d=0.08).

Conclusion
Both commercial wearables performed similarly for their TST and SE assessments, and TST was associated with self-rated fatigue within this operational environment. While the ring form factor was selected more for comfort than the wrist form factor, the difference was small. Collectively, findings indicate that both devices show promise for operational use within fatigue monitoring systems. Further testing is being conducted to better understand the strengths and limitations of wearable devices for monitoring sleep in warship environments.

Support (If Any)
Military Operational Medicine Research Program
0148 Associations Between Subjective and Objective Sleep Outcomes and Nightly Pain Changes in a Chronic Pain Sample
Authors: McGovney Kevin , Curtis Ashley , McCrae Christina 
Published: May 2022

Abstract

Introduction
Patients with chronic pain often experience poor sleep. Research demonstrating the bidirectional nature of the pain/sleep association has focused on interindividual patterns based on between person averages, leaving acute, intraindividual patterns based on night-to-night fluctuations understudied. Because both pain and sleep fluctuate considerably, better understanding of those fluctuations may provide important insight into the pain/sleep relationship. Here we examine inter- and intraindividual associations between subjective and objective sleep outcomes and nightly pain changes in individuals with chronic pain and sleep complaints.

Methods
Adults with chronic pain and sleep complaints (n=169, Mage=52, SD= 12, 95% female) completed 14 days of actigraphy and sleep diaries each morning and evening. Evening diaries recorded pain and sleep medication use (yes/no), and evening pain intensity (0-none, 100-most intense). Actigraphy and morning diaries recorded sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and morning pain intensity (0-none, 100-most intense). A nightly pain difference score (morning – evening) was calculated, where positive values indicate worse morning pain relative to previous evening pain. Multi-level models examined the inter- and intraindividual associations between sleep (subjective and objective SOL, WASO, TST) and nightly pain difference scores. Analyses controlled for age, sleep, and pain medication use.

Results
Greater interindividual subjective WASO was associated (B=0.06, SE=0.03, p=0.02) with greater interindividual pain difference scores. Other interindividual sleep outcomes (subjective and objective) were not significantly associated with interindividual pain difference scores. There were no associations between sleep outcomes (subjective and objective) and pain difference scores at the intraindividual level.

Conclusion
Findings show sleep and pain were not linked at the daily, intraindividual level. However, on average, greater WASO was linked with worse morning relative to evening pain. Thus, although a single night of poor sleep may not impact pain, the buildup of fragmented sleep over time may interfere with restorative properties of sleep and exacerbate morning pain. Future work should investigate mechanisms underlying sleep fragmentation (e.g., sleep architecture, physiological arousal) and how such factors relate to nightly pain changes.

Support (If Any)
NIH/NIAMS R01AR055160, ClinicalTrials-NCT02001077, PI McCrae.
0174 The Odds Ratio Product as a Marker of Sleep Homeostasis
Authors: Toutant Darion , Younes Magdy , Ng Marcus 
Published: May 2022

Abstract

Introduction
Classic EEG markers of sleep homeostasis (process S) usually study the level and amount of slow wave activity (SWA). In recent years, the odds ratio product (ORP) has been described as a novel quantitative measure of sleep depth (and conversely, arousability), which is derived from a four-digit permutation of the power in four respective frequency bands. Therefore, we sought to determine whether the ORP can also be used as a novel quantitative marker of process S.

Methods
Utilizing C3/C4 electrodes with TP9/TP10 reference, ORP was calculated from 25 nights in 15 patients. We used ORP from REM and NREM1-3 segments/bouts in each person’s night(s) with varying number of data points per REM segment(s) in each night(s). Artifacts and wake segments were discarded from analysis. Average ORP is calculated for an individual bout per night per patient. Generalized Estimating Equation (GEE) modelling used GEEQBOX MATLAB toolbox to identify change in sleep depth over bouts. Resultant beta (β) values represented change in nightly sleep depth over sequential NREM/REM segments per patient. Significance was p≤0.000833.

Results
From 15 people in the epilepsy monitoring unit, 3 did not have epilepsy. In NREM, the average ORP for C3/C4 is 1.26/1.19 (n=146307/125709). Average number of nightly NREM segments for C3/C4 are 4.64/4.9 (n=1261.27/1282.74 average ORP values per nightly segment). In REM, the average ORP for C3/C4 is 1.44/1.44 (n=47629/43528). Average number of nightly REM segments for C3/C4 are 4.36/4.38 (n=418.68/453.33 average ORP values per nightly segment). Signifying the change in sleep depth the beta values from C3/C4 were consistently positive: REM (epilepsy 0.033/0.018, non-epilepsy 0.096/0.053), NREM (epilepsy 0.047/0.053, non-epilepsy 0.026/0.064).

Conclusion
As a marker of sleep homeostasis, SWA decreases across a period of sleep. Consistently positive beta values derived from ORP in both REM and NREM are comparable because they demonstrate shallower sleep depth (i.e. greater arousability) across segments over nights as sleep debt is repaid. Also as expected, REM has a higher ORP than NREM. Furthermore, our findings of positive beta values are robust against sleep stage and pathology (i.e. epilepsy). Therefore, these findings may represent a novel quantitative marker of sleep homeostasis that includes REM.

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