bluuebunny/crossref_metadata_2025 huggingface dataset:
0320 Foundational Transformers with Linear Probing for Sleep Stage Classification Using Time Series Sleep Study Data
Authors: Fox Benjamin , Wickramaratne Sajila , Parekh Ankit , Nadkarni Girish
Published: April 2024

Introduction

Sleep disorders and deprivation disrupt people's daily activities, mental health, and longevity and are related to widespread conditions. Currently, sleep disorders are diagnosed via polysomnography (PSG), where electrophysiological data is collected and manually annotated by a clinician. State of the art machine learning (ML) models, such as the transformer, are particularly well-suited for modeling timeseries PSG data. Specifically, self-supervised models with linear probing could assist with any relevant sleep predictive task including automating sleep stage classification, which would save clinicians time, reduce variability in manual scoring, and help scale to treat more people.

Methods

Using a self-supervised learning approach and the transformer architecture, we trained a self-supervised ML model that inputs seven PSG channels of length three hours including electroencephalogram, electrooculogram, electromyography, electrocardiography, oxygen saturation, and thoracic and abdomen respiratory rate using the Sleep Heart Health Study database (1995-1998). The model architecture uses the transformer’s attention mechanism to learn long range dependencies between intervals of sleep and a convolutional layer to learn relationships among channels. The model learns representations of PSG data through masked reconstruction with a mean squared error loss function. The representations are used as input into a deep neural network that is trained via linear probing (without adjusting the weights of the transformer model) to classify sleep stages.

Results

5,794 sleep studies from the Sleep Heart Health Study with at least three hours of relevant PSG sleep channel and hypnogram data are included in training the self-supervised and linear probing models. Area under the receiver operator characters curve for sleep stage classification are 0.960 [0.960-0.961], 0.848 [0.846-0.849], 0.906 [0.906-0.907], 0.968 [0.967-0.968], and 0.931 [0.930-0.932] for wake, stage 1, stage 2, stage 3, and REM, respectively. Hyperparameter tuning, class weighting, and dataset cleaning will be performed to increase classification results.

Conclusion

A self-supervised training approach using the transformer architecture with linear probing was utilized to learn multichannel PSG data representations. These representations were used as input into a downstream model to classify sleep stages accurately. Future work should be done to examine the capabilities of the self-supervised model representations for other predictive sleep tasks.

Support (if any)

NIH K25HL151912, NIH R01HL171813, NIH R21HL165320
0321 Luna: Merging Open-source Tools, Data and Models for Sleep Signal Analyses
Authors: Purcell Shaun M ·
Published: April 2024

Introduction

Data-sharing is increasingly recognized as a critical driver of discovery and reproducibility in modern biomedical science. To realize the promise of data-sharing, important corollaries are the need for open tools, standards and models to work with and make sense of those data. Here we outline a new software framework (Luna), which is tightly coupled to data in the National Sleep Research Resource (NSRR) and supports predictive models based on these and other datasets.

Methods

Luna is well-documented, multi-platform open-source software package that supports a variety of analyses of sleep macro- and micro-architecture and is being integrated into the NHLBI BioData Catalyst ecosystem. Web-based Moonlight & Moonbeam components support the direct access, interactive viewing and analysis of NSRR data. We are also developing modular and extensible model-based prediction capabilities in Luna, to predict outcomes such as biological age directly from raw sleep (EEG) signals.

Results

We will present specific results from three exemplar applications of Luna: 1) a sleep stager and biological/brain age prediction model tailored to pediatric contexts, trained on over 5000 individuals, 2) risk models for schizophrenia from high-density sleep EEG data, and 3) sleep-based biomarkers of cognitive aging in older adults. As well demonstrating the performance of these models, we will discuss challenges to validity and interpretation more generally.

Conclusion

The power of open sleep data is greatly augmented by robust tools, standards and models to facilitate working with those data. Greater community-wide sharing and transparency of tools and methods is an important step to greater reproducibility and rigor in sleep science.

Support (if any)

NIH/NIA 1R01AG070867; NIH/NHLBI 5R01HL146339; NIH/NHLBI 75N92019C00011.
0323 Is Snoring Associated with Lower Sleep Quality? If Yes, Does the Association Depend on Sleep Apnea?
Authors: Molina Gary Garcia , Chellamuthu Vidhya , Gorski Brandon , Mills Rajasi , McGhee Leah , DeFranco Susan , Aloia Mark
Published: April 2024

Introduction

Snoring, as a common manifestation of sleep apnea, has often been used as a surrogate parameter for apnea. Significant associations were found between snoring and daytime sleepiness and cardiovascular conditions. 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 and sleep quality depending on the presence of treated or untreated apnea.

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, mean respiration rate, mean heart rate, mean heart rate variability, and sleep quality. The latter considers a demographic dependent aggregation of sleep duration, restful sleep and cardiorespiratory activity. Data were collected May 1st - June 30-2023, and were augmented by survey responses. Four groups were defined for analysis HNS: non-snorers reporting no sleep disorder, HES: snorers reporting no sleep disorders, TAS: snorers with treated apnea, and UAS: snorers with untreated apnea. Analysis of variance (ANOVA) was performed to identify any significant objective data differences between the groups. A second analysis focused on identifying the specific metric showing significant differences.

Results

The demographic responder composition per group was HNS 2454 (1717F/734M) individuals aged 53.9 (SD: 15.0) years-old, HES: 4702 (2626F/2069M) individuals aged 55.1 (SD: 13.5) years-old, TAS: 1784 (679F/1103M) individuals aged 59.7 (SD: 12.0) years-old, and UAS: 134 (59F/75M) individuals aged 59.4 (SD: 10.9) years-old. The ANOVA revealed significant objective data differences between groups. Only sleep quality showed significant group differences. Quality decreased significantly with respect to the HNS reference. Healthy snorers had a 0.96% lower quality, snorers with treated apnea had 1.8% lower quality, and snorers with untreated apnea had 3.9% lower quality.

Conclusion

Compared to healthy non-snorers, sleep quality quantification by a smart bed suggests a gradual decrease for healthy snorers, snorers with treated apnea, and snorers with untreated apnea.

Support (if any)
0324 Localizing Respiratory Events in Short Signal Segment to Improve Respiratory Event Counting
Authors: Chen Ke Wei , Wu Hau Tieng
Published: April 2024

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.

Support (if any)
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

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.

Support (if any)
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

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

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

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

Support (if any)
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

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.

Support (if any)
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

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.

Support (if any)
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

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 < .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

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< 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< 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.
0336 Workday Rhythms and Bedtime Behaviors: The Role of Work Timing and Duration in Bedtime Procrastination
Authors: Carlson Steven , Williams Paula
Published: April 2024

Introduction

Bedtime procrastination, or bedtime delays in the absence of external obligations, is a common behavior that undermines multiple dimensions of sleep health. Most research investigating the factors which underly bedtime procrastination centers on cognitive processes (i.e., self-regulation, rumination, worry). However, individuals may also intentionally delay their bedtime to provide themselves with personal time to wind down at the end of a late work day. The current study sought to evaluate whether work timing, duration, and time spent winding down at the end of the day were associated with bedtime procrastination in a daily diary investigation of young adults’ sleep behaviors.

Methods

117 young adult participants (Mage = 25.2, SD = 8.3) completed 14-days of experience sampling assessment, including self-report measures of daily work schedules and morning ratings of prior-night bedtime procrastination and wind down time. Participants also completed the morningness-eveningness questionnaire as a measure of chronotype. Multilevel models were constructed to identify the contributions of work timing (start and end time), work duration, and end-of-day wind down duration in daily bedtime procrastination.

Results

Across days, average later work start time (B = 0.50, p = 0.017) and end time (B = 0.39, p = 0.043) were associated with higher average bedtime procrastination. However, there were no significant associations observed at the level of daily observations. Work duration and self-reported wind down duration at night were not associated with either average or daily bedtime procrastination. Secondary analyses revealed that, when chronotype was included in the model, the association between work timing and bedtime procrastination was reduced to non-significance.

Conclusion

Individuals who had later average work timing were more likely to habitually procrastinate their bedtime. However, the within-person association was not significant; daily changes in work schedule were not associated with same-day variation in bedtime procrastination, indicating that this may not be a mechanism for sleep delay behavior. Further, findings suggest that shared variance with evening chronotype may partially explain the observed association between work timing and bedtime procrastination. Future research should distinguish between preferences for doing activities in the evening and circadian timing in the development of bedtime procrastination.

Support (if any)
0337 Impact of Sleep Schedule Changes on the International Space Station
Authors: Jansen Rachel , Flynn-Evans Erin , Glaros Zachary
Published: April 2024

Introduction

Astronaut crews have historically averaged six hours of sleep per night. In recent years, ISS crew have had more stable schedules, with consistent bed and wake times. Despite these improvements, there remain uncontrollable operational events that require crewmembers to shift their sleep. It is unclear what impact these acute schedule changes have on sleep and performance outcomes. The aims of this analysis are to characterize approaches to sleep shifting and to determine the impact on sleep outcomes and crew alertness and performance.

Methods

Crewmembers (n = 20) on the ISS were provided with actiwatches that they wore before flight, inflight, and immediately upon return. They were scheduled for 8.5 hours of sleep per day, between 2130-0600, unless operational events took place during the sleep episode. We characterized the impact of two types of sleep shifts, including splitting the daily sleep opportunity in two (“split sleep”) and shifting the entire sleep opportunity later, following the operational event.

Results

Ten crewmembers had 15 episodes of split sleep while inflight. These periods surrounded seven separate operational events that interfered with their nominal sleep window. The first sleep opportunity typically occurred before the disrupting event, while the second took place the following morning. Sleep duration was shorter (M1=2.60(0.80) vs. M2=4.11(1.51)) and efficiency lower (M1=74.72(13.94) vs. M2=82.50(8.99)) during the first sleep opportunity. When aggregating the two periods, the sleep opportunity lasted 7.08(1.06) hours. Thirteen individuals had 28 phase delayed sleep episodes following 11 disruptive events. These single sleep episodes were 5.99(1.05) hours long, sleep efficiency averaged 84.31(7.71), and the sleep opportunity was 6.85(0.67) hours. On remaining nights, crewmembers averaged 6.63(0.49) hours of sleep with a sleep efficiency of 82.51(3.41) and sleep opportunity of 7.45(0.51).

Conclusion

Scheduling astronauts for off-nominal operations came with some costs. When crew split their sleep, they slept longer than the average, while a delay reduced sleep. Crew had shorter sleep opportunities when forced to sleep outside of the nominal window, leading to improved sleep efficiencies. Further study is needed to determine the best scheduling approaches to accommodate uncontrollable operational events.

Support (if any)
0338 Consolidated Work Schedule: The Impact on Firefighters’ Sleep and Health
Authors: Billings Joel
Published: April 2024

Introduction

Career fire departments in the US typically operate on a variety of 24-hour shift schedules, the most common being 24-hours-on and 48-hours-off (24/48). Recently, more departments have expressed interest in consolidated work schedules, specifically the 48-hours-on and 96-hours-off (48/96), to attract new recruitments, reduce commute frequencies, and improve firefighter morale. While some firefighters may be able to rest when not otherwise responding to emergencies and other tasks, it is uncertain how consolidated work schedules impact sleep and health. Therefore, the objective of this research is to assess sleep and health outcomes before and after firefighters transitioned to a consolidated work schedule.

Methods

A Southcentral US fire department (n=24) participated in this longitudinal study. Baseline assessment was conducted while firefighters operated on the 24/48 and post assessment was conducted six months after transitioning to the 48/96. Total sleep time (TST) was assessed for 18-days each round using wrist actigraphy and the Emergency Services Sleep Diary. At the beginning of each round, participants completed a questionnaire that included the Insomnia Severity Index (ISI), Beck Depression Inventory II (BDI-II), Beck Anxiety Invitatory (BAI), and Multidimensional Assessment of Fatigue (MAF).

Results

The results of paired t-tests indicated statistically significance difference means for ISI scores [t(23) = 2.83,p< 0.01] and BDI-II scores [t(23) = 4.49,p< 0.001], suggesting insomnia and depression scores improved during the 48/96. Mean ISI scores before and after shift schedule transition are 7.4(4.2) and 5.5(3.5), respectively. The mean BDI-II scores before and after are 5.3(4.8) and 3.3(4.4). No statistically significant differences were found in TST, BAI, and MAF scores.

Conclusion

Firefighters reported an improvement in insomnia and depression six months after transitioning to the 48/96 consolidated work schedule. Since sleep, anxiety, and fatigue were not statistically different, the results suggest that the 48/96 may provide firefighters an opportunity to develop a more consistent sleep pattern due to fewer early awakenings to commute to work. However, further research is needed to continue assessing these relationships along with exploring how differences in workload (e.g., emergency calls) may influence these outcomes.

Support (if any)

The development of the research reported herein was supported by FEMA Fire Prevention & Safety Grant, EMW-2022-FP-00464.
0343 Sleep Knowledge, Information, and Values Test: A New Measure to Examine College Students
Authors: Reynolds Alexandria , Scullin Michael , Oliver Michael , Walther Sam , Hughes Joshua , Holladay Cathryn
Published: April 2024

Introduction

College students are notorious for inconsistent sleep schedules and poor sleep hygiene, which can influence mental health and academic success. Many intervention programs seek to improve college student sleep quality by educating them on the importance of sleep and healthy sleep behaviors; however, it remains an open question whether poor sleep behaviors are due to a lack of knowledge about sleep. To inform this issue, we developed a test of sleep knowledge and compared outcomes to standard scales of sleep duration and quality.

Methods

Participants (N=130, 21 male) included full-time undergraduate students (mean age=19.65, SD=1.54 years) who completed a series of online questionnaires examining sleep quality and duration (Pittsburgh Sleep Quality Index, PSQI), sleep hygiene (Sleep Hygiene Index, SHI), and a new measure of sleep knowledge (Sleep Knowledge, Information, and Values Test, SKIV). The SKIV is a self-reported questionnaire that includes questions about weekday and weekend sleep duration, sleep quality, confidence and source of sleep health knowledge, perceived importance of sleep education in different populations, and basic sleep hygiene and sleep health questions.

Results

Average sleep duration (PSQI) was 7.00 hours per night (SD=1.36) and sleep quality was poor (PSQI M=6.86, SD=3.33). Participants scored an average 13.47 out of 20 points (SD=3.59) on multiple choice and multi-answer questions about general sleep knowledge (SKIV). SKIV sleep knowledge test scores and SKIV ratings of importance of sleep education were positively correlated with SHI scores (M=21.53, SD=7.56; r=.309, p<.001 and r=.267, p=.002), but not PSQI global sleep quality (r=-.025, p=.783; r=.005, p=.954) or PSQI sleep duration (r=.065, p=.463; r=.071, p=.423). SHI scores were significantly correlated with PSQI sleep quality (r=.467, p<.001).

Conclusion

College students with greater sleep knowledge reported engaging in better sleep hygiene practices, but still showed short duration or poor quality sleep. Therefore, future interventions should go beyond sleep education to changing environmental features (e.g., noise and light in dormitories) and institutional practices (early class start times, midnight homework deadlines) that hinder sleep health in students.

Support (if any)

None.
0344 Convergent and Divergent Validity of a Sleep Health Literacy Measure
Authors: Feldman Emily , Slavish Danica
Published: April 2024

Introduction

The Sleep Health Literacy (SHL) scale, grounded in the integrated model of health literacy, consists of two subscales: “Sleep Health Communication” and “Sleep Health Knowledge.” This study aims to explore the validity of the SHL scale by examining its relationships with scales measuring health literacy, sleep, and non-related constructs. Further, this study sought to conduct confirmatory factor analysis to evaluate the reliability of the SHL measure.

Methods

253 students were recruited from the undergraduate SONA psychology pool (M age = 20.3, 53% white, 75% women). Participants completed the Dysfunctional Beliefs about Sleep Scale, Sleep Practices and Attitudes Questionnaire (SPAQ), Sleep Beliefs Scale (SBS), Sleep Hygiene Index (SHI), Insomnia Severity Index (ISI), All Aspects of Health Literacy Scale (AAHL), Alcohol Use Disorder Identification Test (AUDIT), and General Anxiety Disorder 7 Item (GAD-7) Scale as well as the Sleep Health Literacy (SHL) Scale.

Results

The SHL part 1 was significantly associated with part 2 scores (r = .25, p < .00), the sleep hygiene index (r = .21, p < .002), sleep beliefs scale total correct (r = .28, p < .00), AAHL total score (r = -.27, p < .00), AAHL communicative subscale (r = -.39, p < .00), functional (r = .15, p = .04) and AAHL critical subscale (r = -.25, p < .00). Scores on part 2 of the SHL were correlated with items correct on the SBS (r = .53, p < .00) and AAHL functional subscale (r = .15, p = .04). Confirmatory factor analysis (CFA) was performed on two factors and showed a model with reasonable fit (χ² = 144.986, df = 43, p < 0.001, RMSEA = 0.103, CFI = 0.833, GFI = 0.891, AGFI = 0.832).

Conclusion

This study examined the Sleep Health Literacy (SHL) Scale, a tool designed to assess an individual’s ability to communicate and comprehend sleep-related concepts. The scale demonstrated significant associations with related constructs, including sleep hygiene, sleep beliefs, and health literacy. These findings contribute to the SHL scale’s potential for assessing sleep health literacy and offer useful insights into areas of refinement.

Support (if any)
0345 The Moderating Effect of Screen Time on the Relationship Between Physical Activity and Sleep in Young Athletes
Authors: turpin chloé , Caron Jean-François , Pétrin Rachel , Forest Geneviève
Published: April 2024

Introduction

Studies have emphasized the significance of sleep in performance and well-being of young athletes. Physical activity has been shown to improve various aspects of sleep in adolescents, including sleep quality. Yet, excessive screen time has been found to have a negative impact on sleep among adolescents, possibly dampening the beneficial effects of physical activity. Hence, our research aimed to investigate the moderating effect of screen time on the relationship between physical activity and sleep quality in young athletes.

Methods

211 young elite athletes (M=14.9±1.6 years old; 60.4% females) completed online questionnaires, including the Pittsburgh Sleep Quality Index (PSQI) and a homemade sports and lifestyle habits questionnaire. A moderation analysis was conducted using PROCESS 4.2 to examine the moderating effect of screen time (the average number of hours per day spent on screens) on the relationship between physical activity (the average number of training hours per week) and sleep quality (subscale #1 of the PSQI). Age, BMI, and sex were added as covariates, since they were correlated with the physical activity and sleep quality variables.

Results

The moderation model was significant (F(6,203)=8.14, p<.001) and accounted for 16.2% of the variance. Results indicate a significant main effect of screen time on sleep quality (b=.294, p=.004) and a significant interaction of screen time and physical activity on sleep quality (b=.005, p=.021). Physical activity was associated with sleep quality when screen time was at one SD below the mean (b=-.025, p<.018) but not at the mean (b=-.006, p<.433) nor above the mean (b=.014, p<.236). The simple plot analysis revealed that when young athletes had low screen time, more physical activity was related to better sleep quality, while lower training hours were associated with poorer sleep quality. However, in athletes with high and average screen time, their level of physical activity was not related to their sleep quality.

Conclusion

This study highlights the possible mitigating effect of screen time on the potential beneficial association between physical activity and sleep in young athletes. This underscores the importance of promoting healthy lifestyle habits and appropriate sleep hygiene among athletes, who are also a population at greater risk of sleep disturbances.

Support (if any)
0347 Sleep Behaviour and Shift Work Tolerance in New Paramedics Beginning Shift Work
Authors: Harris Rachael , Drummond Sean P A , Meadley Ben , Rajaratnam Shantha M W , Williams Brett , Smith Karen , Bowles Kelly-Ann , Nguyen Elle , Dobbie Megan L , Wolkow Alexander P
Published: April 2024

Introduction

In experienced shift workers, researchers have evaluated levels of shift work tolerance (SWT) and sleep behaviours. SWT refers to not experiencing negative consequences associated with shift work, whereas sleep behaviours, describe strategies workers may use to cope with shift work. Neither sleep behaviours nor SWT have been longitudinally investigated in new emergency personnel. The current study aimed to track changes in sleep behaviour during the first 12-months of shift work, and secondly to explore whether changes in sleep behaviour were associated with SWT after 12-months of work.

Methods

One-hundred and five recruit paramedics were investigated at baseline (before shift work), and after 6- and 12-months of shift work. Paramedics’ sleep and mental health were evaluated at each timepoint via questionnaires. Participants also completed a 14-day shift work (via work diaries) and sleep (via sleep diaries and actigraphy) monitoring period at each timepoint. Sleep opportunity (SO), regularity, and behaviours in relation to nightshift were investigated. SWT at 12-months was categorised based on symptoms of depression, anxiety, insomnia, and sleep quality and efficiency on rest days.

Results

Linear mixed models found SO increased on day shifts and rest days, and sleep regularity declined from baseline to 6- and 12-months of shift work. There were no changes in SO on rest days, day shifts, or nightshifts between 6- and 12-months of shift work. Latent profile analyses identified levels of SWT as high (n=52), medium (n=27), and low (n=9) after 12-months of shift work. These profiles were primarily distinguished by levels of insomnia, depression, and anxiety. Increasingly irregular sleep between 6- and 12-months of shift work, and prioritising major sleep episodes (i.e., ≥3hrs) rather than naps at 6-months predicted classification as high SWT.

Conclusion

Our findings suggest after 12-months of shift work, different levels of SWT exist in paramedics, distinguished by their severity of insomnia, depression, and anxiety symptoms. Though sleep behaviours across the whole cohort did not change between 6- and 12-months, those whose sleep became less regular were more likely to have high SWT. Likewise, prioritising major sleep episodes at 6-months was associated with better SWT after 12-months of shift work.

Support (if any)