bluuebunny/crossref_metadata_2025_split huggingface dataset:
0324 Localizing Respiratory Events in Short Signal Segment to Improve Respiratory Event Counting
Authors: Chen Ke Wei , Wu Hau Tieng 
Published: April 2024

Abstract

Introduction
When predicting respiratory events over a short segment (60 seconds) as input to a machine learning model, it is common to divide the full night recording into multiple non- overlapping short segments and count segments containing respiratory events. However, underestimate happens when multiple events exist within a segment, which influences the event counting performance.

Methods
The Stanford Technology Analytics and Genomics in Sleep (STAGES) database was used. Recordings from 332 nights were randomly selected for training and 100 nights for testing SpO2, Instantaneous Heart Rate and red-light PPG envelope were inputs to the one-dimensional CNN model to predict the respiratory events. In the first model, consider 60 seconds non-overlapping segments, where a segment is labeled when there is more than one event. In the second model, consider overlapping segments with 50 seconds overlap, where a segment is labeled 1 if there is more than 1 event in the middle 10 seconds. We then compare these two models.

Results
We trained the models with two methods to obtain similar performance with segment prediction Receiver operator curve to have area under curve above 0.9. In the severe apnea testing group, with the non-overlapping method, we can recover 76% of the event, while with the overlapping method, we can recover up to 97% of the event.

Conclusion
By limiting the label window of an input segment, we can provide a more fine-grained event detection.

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0325 Snoring and Obstructive Sleep Apnea Associations Through the Lens of a Smart Bed Platform
Authors: Molina Gary Garcia , Chellamuthu Vidhya , Gorski Brandon , Siyahjani Farzad , Babaeizadeh Saeed , Mushtaq Faisal , Mills Rajasi , McGhee Leah , DeFranco Susan , Aloia Mark 
Published: April 2024

Abstract

Introduction
Snoring and obstructive sleep apnea (OSA) are bidirectionally associated. The majority of OSA patients snore and a substantial percentage of those who snore have OSA. The intensity and the acoustic properties of snoring in apnea patients have been shown to correlate with OSA presence. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring, sleep metrics, demographics, and OSA.

Methods
An IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, sleep quality, mean respiration rate, mean heart rate, and mean heart rate variability. Data were collected May 1st - June 30-2023, and were augmented by survey responses to examine associations between apnea presence, snoring properties, and objective data.

Results
Out of 22048 (12476F/9537M) respondents with mean age 55.8 (SD: 14.1) years, 3163 reported a diagnosis of apnea and 10558 reported no sleep disorder. Snoring was reported by 89.9% and 65.2% of the apnea and healthy sleepers, respectively. Among the respondents who snored, 29.2% had an apnea diagnosis (apnea and snoring are associated with R2=0.02). A mixed model considering age, gender, reported breathing interruption, snore intensity and frequency as predictors and apnea as the dependent variable resulted in a higher R2 = 0.19. The addition of objective data collected by the smart bed increased the R2 = 0.53. A logistic regression model using demographics, snoring properties, and objective metrics as independent and apnea presence as dependent variables, was trained and tested with the data from 80% and 20% of the individuals respectively. This model resulted in 0.91 area under the receiving operator curve, and 84% sensitivity and 90% specificity to detect apnea.

Conclusion
Objective data from a smart bed combined with demographic and snore properties can be used to screen apnea risk. This research is limited by the absence of information about apnea severity.

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0327 Ru-SATED 4.0 Multidimensional Sleep Health Scale and Behavioral Data from Sleep Number Smart Bed Systems
Authors: Conlon Rachel , Wheeler Bradley , Kuzemchak Margaret , Yu Lan , McGhee Leah , DeFranco Susan , Buysse Daniel , Wallace Meredith 
Published: April 2024

Abstract

Introduction
Objective sleep patterns can be measured unobtrusively using instrumented “smart” bed systems, which offer an innovative method to record and evaluate behavioral sleep patterns in people’s own homes and over time. However, little is known about behavioral sleep patterns and their relationships with self-reported multidimensional sleep health. We recently developed the Ru-SATED 4.0 Multidimensional Sleep Health Scale. In the present study, we aimed to (1) characterize Ru-SATED and Sleep Number bed behavioral data, and (2) compare behavioral sleep data with self-reported multidimensional sleep health as an indicator of convergent validity for Ru-SATED.

Methods
We enrolled adults (≥18yo), stratified by age, who participate in the Sleep Science Panel run by the Sleep Number Corporation. The panel consists of research volunteers who use a Sleep Number Smart Bed. Participants completed the Ru-SATED 4.0 scale, a self-report past-month measure of 6 dimensions of sleep health (Regularity, Satisfaction, Alertness, Timing, Efficiency, Duration). We evaluated Ru-SATED total scores and individual dimensions in relation to past-month Sleep Number bed behavioral data (sleep session regularity, timing, efficiency, duration) using Spearman correlations.

Results
We included participants with complete self-report and bed data (N=2,915; age range=19-90y, M age=52.6±16.4y, 54.9% female, 81.3% White/Caucasian, 6.3% Black/African American, 4.9% Hispanic/Latinx, 1.9% Asian, 0.5% American Indian/Alaskan Native, 5.0% Other or Multiple Racial/Ethnic Identities). The average Ru-SATED total score was 15.0±3.5 (range=0.0-24.0; higher scores indicate better multidimensional sleep health). Averages scores on the Ru-SATED individual dimensions ranged from 2.18±1.11 for efficiency to 2.82±1.04 for alertness. The magnitude of correlation between Ru-SATED total scores and bed data variables was strongest for regularity (r=0.26), efficiency (r=0.25), and duration (r=0.12), and was negligible for timing (r=0.01).

Conclusion
Smart bed data collection is a low-burden method for assessing behavioral sleep variables. The Ru-SATED 4.0 scale provides meaningful self-reported multidimensional sleep health data that aligns with objective behavioral measures of sleep. Future analyses will compare individual Ru-SATED dimensions with corresponding objective sleep measures over time as well as measures of satisfaction and alertness to enhance our understanding of multidimensional sleep health across populations.

Support (if any)
This research was supported by an investigator-initiated, industry-sponsored research grant from Sleep Number Corporation (PI: Conlon).
0328 Pandore: A Comprehensive Platform for Advanced Sleep Analysis and Data Management in Healthcare and Research
Authors: Bouchequet Paul , Aloulou Anis , Crosbie Flynn , Chennaoui Mounir , Léger Damien 
Published: April 2024

Abstract

Introduction
The field of sleep medicine is rapidly evolving, with new methodologies and technologies enhancing the accuracy and efficiency of sleep analysis. "Pandore," a novel sleep analysis platform, has been developed to address the growing need for sophisticated data integration and analysis in this domain.

Methods
Pandore is built upon open-source frameworks, ensuring adaptability and accessibility. It integrates various state-of-the-art algorithms for sleep analysis implemented in common sleep software such as Yasa, RSleep, and Luna, and is designed to support the automatic application of these algorithms. The platform also facilitates artificial intelligence training, leveraging its extensive database for machine learning purposes. The data integration pipeline includes rigorous data quality assessments, particularly focusing on polysomnography records. Additionally, Pandore addresses organizational and technical challenges, including updates and maintenance. A unique aspect of this platform is its role-based system, specifically the enhanced role of sleep technologists, allowing them to integrate new methods into their analyses continuously.

Results
The initial deployment of Pandore has successfully integrated a workflow that includes normalization and quantitative analysis of sleep data. To date, the platform houses a database of 1,360 polysomnographies, from which over 500 distinct features per record can be extracted. 24 algorithms (Machine learning or expert systems) from the litterature are automatically applied over the growing database. This rich feature set enables a comprehensive analysis of sleep patterns and disorders.

Conclusion
Pandore development reflects the critical necessity of open-source software in the field of sleep medicine. The platform's design not only serves as a bridge between IT, data science, and sleep medicine but also ensures compliance with legal and regulatory constraints in healthcare data management. The successful deployment of its first version demonstrates its potential as a hub for various research projects or clinical applications and its capability in managing heterogeneous sleep data.

Support (if any)
Direction Générale de l'Armement (DGA)
0329 Cleared for Takeoff: Sleep Leadership Training for Air Traffic Control Instructors
Authors: Ellis Jordan , Burns Rosemary Estevez , Aycock Chase , Wang Xin-Qun , Kalpinski Ryan , Kropp Caley , Katte Kyra , Sollenberger Nathan , Talcott Wayne 
Published: April 2024

Abstract

Introduction
Chronic sleep insufficiency is a serious threat to military readiness in the United States. Many military members develop poor sleep habits early in their career. Career fields such as Air Traffic Control (ATC) are at greater risk for sleep and performance concerns. Sleep health is determined by individual, social, and systemic factors. Thus, a multilevel approach is warranted, including leveraging leaders’ influence to promote sleep health within high-risk communities. The present study examined baseline sleep and sleep leadership behaviors among Air Force ATC training leaders. Sleep leadership is defined as awareness about benefits of good sleep health and promotion of helpful sleep habits among peers/subordinates.

Methods
Participants were 44 ATC instructors and military training leaders who completed an anonymous pre-training survey and then received an hour-long sleep leadership intervention. The baseline survey included the Split Week Self-Assessment of Sleep Survey as well as the Sleep Leadership Questionnaire, a self-report instrument measuring how often leaders engage in behaviors to promote healthy sleep among students. Participants then rated satisfaction and relevancy of the training and provided qualitative feedback on action steps they were considering

Results
Summary statistics indicated that leaders’ total sleep time (TST), on average, was 6.04 hours (SD=1.63, IQR=1.87) on weekdays and 7.06 hours (SD=2.44, IQR=2.81) on weekends. Ninety-one percent reported they regularly (i.e., “sometimes”/“often”/“always”) instruct students to get adequate sleep, but only 55% reported they inform Airmen about healthy sleeping habits (Fisher’s exact p=0.036) Leaders agreed the training was interesting (endorsed by 77%) and relevant (endorsed by 68%), and 64% reported the training would help them as a leader. Qualitative data showed that many leaders identified specific action steps they planned to implement with ATC students.

Conclusion
Results suggested leaders tell students to prioritize sleep, but rarely provide specific behavioral tips for improving sleep or model optimal sleep behavior. Leaders noted the training was relevant to their role and identified concrete steps to promote sleep among their students. ATC leaders are highly respected by their students, and targeting sleep leadership appears to be a feasible and acceptable solution within a multi-level approach for improving sleep among high-risk military career fields

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0330 Caffeine Consumption Aboard the International Space Station
Authors: Jansen Rachel , Flynn-Evans Erin , Braun Alisa , Smith Scott , Zwart Sara , Glaros Zachary 
Published: April 2024

Abstract

Introduction
Caffeine is the most widely used performance-enhancing drug on Earth and astronaut crews have access to liquid coffee. While caffeine is a potent countermeasure to improve alertness and performance, it can also interfere with sleep. This can lead to performance deficits on the following day, driving a cycle of caffeine use to counter the effects of caffeine-induced sleep disruption. Caffeine availability may be limited during future Artemis and/or Mars missions. Therefore, we aimed to characterize how caffeine is used aboard the International Space Station (ISS) to better inform future spaceflight operations.

Methods
Daily caffeine consumption was determined from food and beverage intake tracking for 25 astronauts (11F) on 6-month International Space Station (ISS) missions. Data were recorded using the ISS Food Intake Tracker iPad App or other detailed recording technique. On average, crew recorded dietary intake on 179.7±68.6 (mean±SD) days inflight.

Results
Astronauts consumed 97±92 mg caffeine per day. All astronauts (25/25) in this study consumed caffeine at some point during flight, though the frequency, amounts, and regularity differed across crewmembers. Approximately a quarter of crewmembers were regular caffeine consumers (6/25), i.e., they ingested at least 90 mg caffeine (equivalent to one cup of coffee) on 90% of inflight days.

Conclusion
Our findings demonstrate that caffeine is regularly consumed by astronauts, though consumption patterns vary across individuals. Future analyses will involve evaluating these data in light of sleep and performance outcomes. This information will provide us with an understanding of how caffeine is being used inflight to help guide future countermeasure development.

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0331 Opportunities to Improve Sleep and Circadian Health in Active Duty Service Members
Authors: Leslie Emily , Willing Maegan Paxton , Riggs David , Schmied Emily 
Published: April 2024

Abstract

Introduction
Insufficient sleep and circadian disruption have both psychological and physiological consequences that impact the health and safety of military service members, yet research shows only about one-third of service members meet recommended sleep duration guidelines. To address this issue, interventions must be developed that are tailored to meet the specific needs of this community. Although substantial research has identified the adverse impacts of poor sleep, limited research has examined the underlying contextual factors that contribute to it between service branches or in different operational settings. Thus, the objective of this qualitative study was to identify factors that impede sleep health among active duty Marines.

Methods
Four focus groups were conducted with 23 active duty Enlisted Marines (87% male; 13% female). The semi-structured focus group guide assessed factors that impact sleep in Marines. Interview transcripts were analyzed using applied thematic analysis by two independent coders.

Results
All focus group participants reported low satisfaction with their sleep quality or duration. The most commonly reported barriers to sleep were work schedules. Many participants reported regularly working long and/or varied hours as part of their standard schedule, and others described unpredictable schedules due to frequently being kept at work late to complete unfinished tasks or for punitive reasons. Participants also described how Marine Corps culture influenced their attitudes towards sleep and sleep-related behavior — even when acknowledging the adverse effects of insufficient sleep on performance and mental health, participants described forgoing it for the sake of the mission, e.g., “It's very important to keep in mind that mission accomplishment is more important than, like, a night of sleep.”

Conclusion
Poor sleep and circadian health were viewed by study participants as inherent to military service. However, the most salient barriers reported by Marines are potentially modifiable through scheduling interventions and interventions designed to reduce sleep stigma and promote healthy sleep hygiene.

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0333 Sleep Patterns Throughout Mountain Warfare Training in U.S. Marines Measured Using a Consumer Wearable
Authors: LaGoy Alice , Cuellar Joseph , Smith Max , Weller Rebecca , McClintock Rebecca , Duong Tony , Dunn Timothy , Jones Douglas , Markwald Rachel 
Published: April 2024

Abstract

Introduction
Poor acclimatization, high training demands, and sleeping conditions in the field can compromise the sleep of military personnel completing mountain warfare training. As chronic poor sleep can impact performance and recovery, individual differences in sleep may relate to differences in resilience during prolonged training. We characterized sleep patterns of Marines completing a mountain warfare training exercise (MTX) using a consumer wearable and explored relationships between sleep patterns and stress during training.

Methods
Marines (n = 59, aged 22±4 years [mean ± SD]) wore a consumer wearable (Oura Ring, Gen 2) during a 4-week MTX conducted in the fall (n = 17) or winter (n = 42). Participants also completed the Patient-Reported Outcomes Measurement Information System Sleep Disturbance scale (PROMIS; short form version 8b) and Stress Impact Scale (SIS) at three time points during each MTX (start, mid, end). Wearable-derived sleep outcome measures were averaged for each training phase and included total sleep time (TST), standard deviation of sleep midpoint, and wake after sleep onset (WASO). Longitudinal k-means clustering was used to identify different sleep patterns. Cluster by training phase (3×3) ANOVA were performed to explore relationships between sleep patterns and stress.

Results
Three clusters were identified. Cluster A (n = 31; 8 fall, 23 winter) had longer TST (6.8±0.7h) and low WASO (1.2±0.4h) but high PROMIS (high sleep disturbance; 56.2±5.2). Cluster B (n = 16; 6 fall, 10 winter) had short sleep (6.3±1.0h) but low WASO (1.4±0.5h) and low PROMIS (43.4±6.2). Cluster C (n = 12; 3 fall, 9 winter) had short sleep (5.9±0.8h), high WASO (1.9±0.5h), and high PROMIS (59.7±6.6). There was a significant effect of cluster on SIS (p &lt; .001); cluster B (low sleep disruption group) reported higher scores (better stress management) than clusters A and C.

Conclusion
Three distinct sleep patterns were identified that varied in sleep duration with self-reported (PROMIS) and wearable-derived sleep disturbance metrics (WASO). Importantly, the low sleep disturbance cluster (Cluster B) reported managing stress better throughout MTX. Personnel with less disturbed sleep may cope better with prolonged training.

Support (if any)
Office of Naval Research (work unit no. 62417).
0335 Evaluating the Role of Circadian Rhythm Dysregulation in Alcohol Use Disorder (AUD) Relapse Probability
Authors: Hughes Alexandria , Barb Jennifer , Wallen Gwenyth 
Published: April 2024

Abstract

Introduction
Identifying factors predictive of relapse in patients with alcohol use disorder (AUD) is essential to learning how to effectively treat this disorder. Our group previously found that following inpatient treatment, AUD patients with lower sleep regularity were more likely to relapse. The aim of this study is to discern whether circadian rhythm dysregulation is predictive of relapse.

Methods
A cohort of patients who received inpatient treatment for AUD (n=126) wore Philip Respironics actiwatches following discharge for four weeks. A subset of patients additionally wore the watches during prior discharge from inpatient treatment (n=24). Relapse status was assigned if a patient consumed any alcohol during the outpatient period of data collection. We calculated circadian rhythm nonparametric statistics including interdaily stability (IS) and intradaily variability (IV) for the inpatient and outpatient periods. Linear and logistic generalized mixed models were fit in R to estimate the effect of the inpatient-outpatient transition on circadian behavior, and of weekly circadian parameters on relapse probability. All analyses account for within-patient repeated measurements.

Results
Final sample size included 103 patients after actigraphy data filters were applied. Patients were 48.6±11.3 years of age and 32% female. Among patients with at least 4 days of inpatient data, IV was on average 0.068 units lower during the first week post-discharge (SE=0.018, F[1,29]=14.92, p&lt; 0.001). By contrast, IS did not substantially change following discharge. Twenty-six patients (25.2%) experienced a relapse event. Relapse at any time during the study was associated with a mean 0.03 unit decrease in IS (SE=0.014, F[1,101]=4.29, p&lt; 0.05), whereas relapse was not associated with a significant shift in IV.

Conclusion
Our preliminary analyses detected a change in circadian behavior (IV) at discharge, which may be an effect of scheduled activities that cease upon discharge. General alignment to a 24-h cycle, as captured by IS, was not disrupted by discharge. However, IS but not IV exhibited an association with relapse. Additional research on this topic will include clinical patient covariates and time to determine if IV and/or IS are predictive of later relapse and time until relapse.

Support (if any)
This research was supported by the National Institutes of Health Intramural Research Program.