bluuebunny/crossref_metadata_2025_split huggingface dataset:
1074 The Relationship Between Blood Glucose and Sleep Consistency in Surgeons: Results from a Pilot Study
Authors: Leahy Nicholas , Kang Ellison , Marquardt Matthew , Orr Morgan , Emerson Angela , Hagen Josh , Quatman Carmen 
Published: April 2024

Abstract

Introduction
Both sleep and blood glucose are vital, interconnected health metrics. These parameters are yet to be explored in surgeons, who often suffer from poor sleep hygiene and thus risk poor glucose handling. This study examines the interplay between surgeons’ sleep consistency and fasting blood glucose levels on both operative and non-operative days.

Methods
Ten surgeons were recruited from a large midwestern academic center’s Department of Orthopedics. Surgeons were given commercially available Oura rings to track sleep and continuous glucose monitors to measure minute-by-minute blood glucose levels over a 14-day period. Fasting glucose levels were calculated by taking the average glucose levels over the 75-to-15-minute period before waketime. Unpaired T-tests were used to analyze the data, and statistical significance was considered for p-values &lt; 0.05.

Results
Seven residents and three faculty surgeons were a part of this pilot study’s cohort, four of whom were female and six were male. Of the 140 total nights in the study, 88 (62.9%) had both glucose and sleep data fully recorded. In 22 of these, fasting glucose levels exceeded 100 mg/dL, six of which were operating days. When surgeons went to bed more than an hour earlier or later than usual before operating, their fasting glucose levels the next morning increased to 95.9 mg/dL compared to 85.7 mg/dL when they went to bed closer to their regular bedtime before operating (p = 0.128). Additionally, there was a statistically significant difference (p=0.012) in fasting glucose levels in days surgeons woke up more than an hour earlier or later from their average waketime, resulting in an increase from 84.0 to 98.8 mg/dL in these days, although whether they were operating was not significant.

Conclusion
The strong association between poor wake time regularity and elevated fasting glucose adds to the current body of work demonstrating the importance of sleep regularity—in particular wake time regularity—on overall health. Impaired glucose handling due to poor sleep habits in this population can have consequences ranging from daily fatigue to increased intra-operative mistakes. In future studies, intra-operative glucose levels could be correlated with sleep metrics to better correlate glucose handling in this novel population.

Support (if any)
1075 The Impact of Operating on the Sleep Consistency of Surgeons: Results from a Pilot Study
Authors: Kang Ellison , Marquardt Matthew , Leahy Nicholas , Orr Morgan , Emerson Angela , Hagen Josh , Quatman Carmen 
Published: April 2024

Abstract

Introduction
It is well known that sleep is essential for health, but not as much attention is given towards aspects of sleep like sleep timing or regularity, both of which are important characteristics of sleep quality. In medicine, surgeries may not always be planned. As a result, many surgeons suffer from poor sleep consistency. This study explores surgeons’ sleep habits and compares sleep regularity on operating days to non-operating days.

Methods
Ten surgeons were recruited from a large midwestern academic center’s Department of Orthopedics. Each surgeon was given an Oura ring for a 14-day period to track their sleep. After the 14-day study period, overall average bedtimes and wake times, and bedtime and wake time regularity were compared for operating and non-operating days. Regularity was calculated by taking the absolute change in bedtime or wake time compared to the previous day. Paired T-tests were used to analyze the data; statistical significance was considered for p-values ≤0.05.

Results
Of the 10 surgeons recruited, 7 were residents and 3 were faculty; 4 were female, and 6 were male. Through the 14-day period, a total of 95 (67.9%) of the possible 140 nights were recorded, 41 (43.2%) of which were OR days, and 54 (56.8%) were non-operating days. Average bedtimes (p = 0.185) and regularity (p =0.731) between operating and non-operating days showed no statistically significant difference. However, both average wake-up time (p = 0.024) and regularity (p = 0.006) illustrated statistically significant changes. Furthermore, when comparing the number of days wake-up times changed by more than 1 hour on OR days to non-OR days, a significant difference was appreciated (p = 0.016), while that of bedtime regularity remained insignificant (p = 0.082).

Conclusion
The significant changes in wake-up times support the idea that many surgeons have poor sleep regularity. While doctors and research stress the importance of maintaining a robust sleeping schedule to their patients, the structure of our healthcare system complicates surgeon’s ability to maintain regular sleep themselves. Future studies should explore how poor sleep consistency impacts the physiological processes of surgeons and other healthcare workers.

Support (if any)
1077 Home Transcutaneous Carbon Dioxide Tracking for Optimizing Care of Chronic Respiratory Failure
Authors: Hiranrattana Anunya , Campbell Kimberly , Holzer Rena Schrier , Anaka Kufre , Ramos Amy , Thomas Robert , Ahn Anjali , Heckman Eric , Chang Jacqueline 
Published: April 2024

Abstract

Introduction
Hypoventilation is a key component of respiratory failure, yet both in the clinical sleep laboratory and in the home ventilation care pathway, oximetry is often only measured. Thus, the degree of hypercapnia remains undocumented. Optimal home ventilation should reasonably target both oxygenation and ventilation. The Sleep Respiratory Failure program at the Beth Israel Deaconess, Boston, is a dedicated service managing nocturnal respiratory failure. In collaboration with a home care company, home ventilation tracking program was established.

Methods
Home testing/tracking of transcutaneous carbon dioxide (TcCO2) using the Sentec device was performed by Regional Home Care, a local DME company during 2021-2023. A respiratory therapist went to the patient’s home and set up the device (pulse oximetry and TcCO2). A report was then sent to the ordering sleep medicine practitioner. TcCO2 tracking was not routine, and ordered only when there was concern for inadequate therapy.

Results
We included 45 TcCO2 testing (with 34 complete data) from 37 subjects, mean age 61.2 years old (31-84, SD 15.1), mean BMI 33.9 (16.8-58.5, SD 10), female 60% vs male 40% (mean age 63.6 and 57.7, respectively). COPD, OHS and CHF were the key diseases in 8, 5 and 10 subjects and out of those, 9 subjects had 2 combined diseases. 51.5% of subjects were on a ventilator, 30.3% on CPAP, 6% on BiPAP. 9.1% on O2 NC and 3% on room air with 30.3% on both PAP therapy and O2. Overall, average minimal saturation was 77.9% (51-92, SD 11.5) and average % of time with TcCO2 above 50 mmHg (Tover50P) was 30% (0-100, SD 38.2). Male and female subjects demonstrated Tover50P of 41.7% (95% CI 14.2-61.9) and 23.6 (95% CI 8.4-38.9), respectively.

Conclusion
Home TcCO2 is feasible and informative regarding the state of ventilation in patients with chronic respiratory failure. While oximetry is useful, knowing the actual TcCO2 allows critical decision making, including adjustment of ventilator settings or planning a sleep laboratory titration. It was revealing that residual hypoxia and hypercapnia were very common in this high risk group, despite substantial management effort including a dedicated respiratory failure clinic.

Support (if any)
Institute for Personalized Sleep Health, Beth Israel Deaconess, Boston
1079 Robust Automated Sleep Staging Using Only EEG Signals
Authors: Chan Alexander , Cakir Ahmet , Josephs David , Kleinschmidt Dave , Pathmanathan Jay , Donoghue Jacob 
Published: April 2024

Abstract

Introduction
Automated algorithms for assisting sleep technologists and clinicians in the staging of sleep have the potential to significantly speed the scoring of PSGs, reduce inter-rater variability, and improve the reliability of diagnosis of sleep disorders. Here, we describe an automated sleep staging machine learning algorithm that scores PSG using only EEG signals.

Methods
We describe a convolutional neural network-based sleep staging algorithm, SleepStageML™, that was trained on a database of &gt;19,000 polysomnography recordings from a heterogeneous patient population within the Beacon Clinico-PSG Database. The algorithm was evaluated on 100 held-out recordings from 5 clinical institutions across 11 sites, comprising a highly diverse population representative of patients evaluated in sleep clinics. Each recording was independently staged by 3 registered polysomnographic technologists to generate consensus ground-truth sleep staging for each recording.

Results
The automated algorithm achieved human-level sleep staging performance with per-stage positive-percent-agreements (PPA) of 89% for W, 65% for N1, 81% for N2, 90% for N3, and 92% for R. Negative-percent-agreements (NPA) were 98% for W, 95% for N1, 95% for N2, 94% for N3, and 98% for R. The algorithm achieved a macro-average F1-score of 0.77. In addition, the algorithm’s median absolute error in estimating total sleep time (TST) was 12 minutes, wake after sleep onset (WASO) was 7 minutes, latency to persistent sleep (LPS) was 2 minutes, and REM latency was 1 minute. The algorithm was also able to stage recordings with the minimum number of AASM recommended EEG electrodes without a statistically significant reduction in performance. The sleep staging model was able to generate sleep stages for all 100 recordings within 26 minutes on a computer with an available GPU.

Conclusion
We demonstrate a model trained on large amounts of highly diverse PSG data that is capable of automatically staging EEG channel data from the PSG to achieve performance matching human experts. An automated staging algorithm that operates on EEG alone has the potential to rapidly provide accurate diagnostic information in a variety of neuropsychiatric conditions with a reduced testing burden, and greatly accelerate the development of novel therapies.

Support (if any)
0108 Sleep Deprivation Impairs the Ability to Reappraise Negative Situations
Authors: Campbell Rebecca , Nguyen Annamarie , Gournay L Riley , Vargas Ivan , Williams Parker , Assar Arash , Leen-Feldner Ellen 
Published: April 2024

Abstract

Introduction
Sleep and emotion regulation are linked components in the development and maintenance of mental health disorders. Reappraisal is a commonly used emotion regulation strategy that may be impacted by sleep loss. Importantly, the literature to date primarily focuses on sleep-restricted adolescence when discussing the relation between sleep loss and reappraisal.

Methods
This study aimed to examine the relation between sleep deprivation and emotion regulation by asking 76 undergraduate students (39 men, Mage = 19.14; SD = 1.26) to reappraise a series of vignettes translated from the Script-Based Reappraisal Test (SBRT) before and after a randomly assigned night of sleep deprivation or normal sleep. Participants completed Self-Assessment Manikin (SAM) ratings of valence and arousal after each script presentation. At-home sleep deprivation was confirmed with hourly check-ins via Qualtrics and a subset of 10 participants in the sleep deprivation group also wore actigraphy.

Results
Factorial ANOVAS with repeated measures were conducted to examine within-subjects effects, between-subjects effects, and interactions. With regards to valence, sleep-deprived participants struggled to reappraise as indicated by a significant effect of time (F(1, 74) = 11.23, p = 0.001), condition (F(1, 74) = 6.55, p = 0.012), and significant interaction (F(1, 74) = 13.83, p &lt; 0.001). Pairwise contrasts demonstrate that sleep-deprived participants at post-manipulation reported more negative valence compared to their baseline (t(148) = 4.19, p &lt; 0.001) and the control group at post-manipulation (t(148) = 4.27, p &lt; 0.001). Additionally, there was a decrease in arousal, as indicated by a significant effect of time (F(1, 74) = 18.93, p &lt; 0.001), condition (F(1, 74) = 5.08, p = 0.027), and significant interaction (F(1, 74) = 8.31, p = 0.005). Pairwise contrasts demonstrate that sleep-deprived participants at post-manipulation reported less arousal compared to their baseline (t(148) = 3.80, p = 0.001) and the control group at post-manipulation (t(148) = 3.43, p = 0.004).

Conclusion
This study expands the current literature on sleep loss and reappraisal. Findings may help inform mental health providers when working with patients that may struggle with emotion regulation concepts such as reappraisal.

Support (if any)
Internally funded Howells Student Research Grant (Grant Number: 2110.01S)
1081 A Randomized Controlled Trial of ISLEEP: Impact on Inpatient Disruption and Sleep
Authors: Sunderrajan Aashna , Cursio John , Martinez Maylyn , Orlov Nicola , Mason Noah , Byron Maxx , Press Valerie , Knutson Kristen , Mokhlesi Babak , Meltzer David , Arora Vineet 
Published: April 2024

Abstract

Introduction
Hospitals often contribute to acute sleep deprivation in patients through modifiable disruptions. Although staff-directed interventions have proven beneficial, research exploring direct patient education and empowerment in reducing disruptions and enhancing inpatient sleep is lacking.

Methods
Patients were randomly assigned to either the I-SLEEP or standard care arm. Patients in the I-SLEEP arm received a 5-minute video emphasizing sleep hygiene and empowering them to advocate for reduced sleep disruptions. Both groups received a sleep kit. The Potential Sleep Disruptions Questionnaire measured patient-reported disruptions (vitals, tests, medications) and wrist actigraphy measured nightly sleep duration in minutes. Mixed-effects models, controlling for study day, age, gender, race, BMI, apnea risk, comorbidities (COPD or asthma, diabetes, chronic heart failure, end-stage renal disease), and subject random effects tested the effectiveness of I-SLEEP on outcomes.

Results
A total of 175 patients were randomized (95 I-SLEEP patients for 265 nights; 80 standard patients for 174 nights). Both groups were similar with respect to baseline characteristics. In unadjusted analyses, I-SLEEP patients reported fewer disruptions from vitals (63% vs. 74%), medications (49% vs. 60%), and tests (57% vs. 69%) compared to standard patients (p=0.02 for all). In adjusted analyses, I-SLEEP patients reported significantly lower odds of disruptions due to medications (OR 0.50 [0.27, 0.94], p=0.03) and tests (OR 0.44 [0.21, 0.91], p=0.026). In unadjusted analyses, although sleep duration was greater on I-SLEEP nights compared to standard nights (334 minutes vs. 309 minutes), this difference was not statistically significant (p=0.16). Adjusted analyses confirmed these results. However, an interaction between I-SLEEP and study night revealed that patients in I-SLEEP gained an additional 15 minutes of sleep per night during the intervention, although this difference was just shy of significance (p=0.06).

Conclusion
I-SLEEP is linked to decreased patient-reported sleep disruptions from medications and tests, and a trend towards greater objective inpatient sleep for each day in the intervention. Given the importance of improving sleep in hospitalized patients, I-SLEEP emerges as a patient-centered intervention with the potential to empower patients in advocating for improved sleep quality in the hospital.

Support (if any)
1K24HL136859-05
1087 Recognizing Sleep Disorders in the US Military Health System: Differences Between On-base and Private Sector Care
Authors: Capaldi Vincent , Williams Scott , Thomas Connie , Herrin Jeph , Hong Allison , Funk Wendy , Collen Jacob , Stryckman Benoit , Albrecht Jennifer , Wickwire Emerson 
Published: April 2024

Abstract

Introduction
Sleep disorders such as insomnia and obstructive sleep apnea (OSA) are common and costly in the US Military Health System (MHS). Further, demand for sleep specialty care greatly exceeds available supply. Many MHS beneficiaries are referred off-base to the TRICARE network to access private sector care. The purpose of this study was to compare military, demographic, and clinical characteristics between MHS beneficiaries newly diagnosed with either insomnia or OSA on-base (direct care) and off-base (private sector care).

Methods
We identified MHS beneficiaries with a first diagnosis of insomnia or OSA between years 2016-2021 within the Military Data Repository (MDR). This large data repository includes encounter, procedure, medication, and durable medical equipment information for Active Duty personnel, dependents, National Guard, and Reserves. Demographic and military information was obtained from the MDR. Sleep disorders and medical and psychiatric comorbidities were defined based on ICD-10 diagnostic codes. Determination of direct vs private sector care was based on an indicator in the MDR.

Results
235,823 MHS beneficiaries were diagnosed with either insomnia (n=143,877) or OSA (n=91,946). Of beneficiaries with insomnia, the majority (n=119,923, 83.4%) were diagnosed in direct care. Of beneficiaries with OSA, the majority (n=51,014, 55.5%) were diagnosed in private sector care. Between-groups differences (direct vs private sector care) were observed in service branch (standard mean difference [SMD]=0.46) and beneficiary category (i.e., Active Duty, dependent, National Guard, or Reserve; SMD=0.37), with most Active Duty (n=131,321, 81.6%) and dependent (n=26,987, 53.5%) beneficiaries being treated via direct care. No between-groups differences were observed in medical and psychiatric comorbidities. In terms of comorbid sleep disorders, of beneficiaries diagnosed with hypersomnia (n=12,403), the majority (n=10,055, 81.1%) were diagnosed in private sector care.

Conclusion
Differences in service branch and beneficiary category were observed between individuals diagnosed with insomnia or OSA in direct and private sector care. Insomnia was more likely to be diagnosed in direct care, and OSA was more likely to be diagnosed in private sector care. Future research should examine health and economic outcomes associated with direct and private sector sleep care and identify opportunities (e.g., telehealth) to bring sleep care back into the MHS.

Support (if any)
DoD HT94022210006.
0109 A Gender Disparity in the Experience of Fear in Dreams
Authors: Wells Mallory , Baber Garrett , Hamilton Nancy 
Published: April 2024

Abstract

Introduction
Determining whether gender differences exist in dream experiences is crucial for understanding potential disparities in offline emotional processing. Women tend to experience more nightmares (i.e., dreams characterized by fear) than men, which could indicate a failure of overnight fear extinction. Yet, there is mixed evidence as to whether gender differences in dream emotions exist. To resolve this ambiguity, our study employed a novel natural language processing (NLP) tool on a large dataset to explore potential gender disparities in the experience of fear in dreams.

Methods
Data utilized in this study were sourced from Cunningham et al.'s (2020) sleep and mental health daily survey conducted at Boston College, involving 667 participants (Female = 569, Male = 98). Dream reports and sleep efficiency calculations were surveyed across several days and self-reported gender, age, and neuroticism scores were surveyed once. We applied a deep learning NLP model to dream reports to estimate a regression value representing fear intensity from the text. Mean values per participant were computed for dream fear, dream report word count, and sleep efficiency. We fitted a general linear regression model to test whether mean dream fear could be predicted using binary self-reported gender while controlling for mean dream report word count, mean sleep efficiency, neuroticism, and age.

Results
Female identity was significantly associated with higher mean dream fear independent of mean sleep efficiency, mean dream report word count, trait neuroticism, and age, β = -0.03, SE = 0.01, 95% CI: [-0.05, -0.01], p &lt; .001. All variables except for age were significantly related to mean dream fear.

Conclusion
We found that those who experienced greater fear in dreams were more likely to be female identifying, after controlling for other variables. Mean sleep efficiency, mean dream report word count, and neuroticism were significantly related to higher mean dream fear. Word count may be related to dream fear due to a bias in the NLP model. Future research should investigate differences in dream fear among other gender identities, collect a more representative sample, and include measures of stress and trauma as possible explanatory variables of gender disparities in dream fear.

Support (if any)
1091 "If You Sleep, You Have Missed It": Perceptions of Sleep Among Long-distance Drivers in Ile-Ife, Nigeria
Authors: Komolafe Deborah , Olorunmoteni Oluwatosin , Afolabi Hannah , Akinwale Oluwatobiloba , Idowu Ahmed , Sanusi Ahmed , Akanji Jamiu , Edu Benita , Eziyi Josephine , Amusa Yemisi , Komolafe Morenikeji 
Published: April 2024

Abstract

Introduction
Road Traffic Accidents (RTAs) are a major cause of mortality and disability in developing countries. Previous studies suggest that one-third of road traffic accidents are due to drowsy driving. The frequency and causes of drowsy driving in Nigeria are unknown as there is limited data and publication on the subject. Hence, this study was carried out as part of an awareness program for the 2023 World Sleep Day to explore the perceptions and determinants of poor sleep among long-distance drivers in Ile-Ife.

Methods
A cross-sectional qualitative study was conducted with a focus group conducted among 12 leaders of drivers in Ile-Ife. Ethical approval and consent were obtained beforehand. Participants were asked about their perception of sleep with a pre-developed interview guide. Their responses were recorded and transcribed verbatim, and ATLAS.ti 22.3 was used to manage the data. Thematic analysis was done, and sample quotes were documented.

Results
All respondents were male aged 45 to 52 years. The drivers described their sleep as not sufficient as they leave for work early and return late. A recurrent response was; “sleep is very important, but for people like us, sleep is not sufficient.” Four factors were identified that affect sleep quality: financial debt, responsibilities, drinking alcohol for recreation, and distance traveled. Participants reported that they work longer hours to be able to pay for the hired buses they drive, alongside responsibilities such as payment of children’s school fees, as described; “Had it been that one has a basic salary; one can sleep conveniently till 9 am.” The drivers also added that they prefer to drink alcohol and have fun after work, which affects their sleep. Additionally, drivers who travel intra-state return early and sleep more.

Conclusion
Commercial drivers are at an increased risk for drowsy driving and, consequently, are at risk for RTAs. This study highlights the multifactorial and socioeconomic challenges that influence sleep quality among long-distance drivers in South West Nigeria. Further studies are required, and educational and economic measures are recommended to be implemented to reduce this risk.

Support (if any)