bluuebunny/crossref_metadata_2025_split huggingface dataset:
0929 The Social Determinants of Health and Sleep Health: A Latent Class Analysis
Authors: Narcisse Marie-Rachelle , Moise Rhoda , Locklear Clarence , Turner Arlener D , Grandner Michael , Seixas Azizi , Jean-Louis Girardin 
Published: May 2023

Abstract

Introduction
Social determinants of health (SDOH) have been linked to well-being, quality-of-life and health disparities. We aim to investigate, 1) To classify adults based on their SDOH characteristics, and 2) To examine association between SDOH classes and sleep health.

Methods
This study used 2020 National Health Interview Survey data (n=31,568). SDOH was captured by health insurance, well-visits, delayed medical care, neighborhood walkability, social support, food insecurity, food stamp, poverty, and education. Sleep health was captured by: 1) Sleep quantity (hours): very short (&lt; 5), short (5-6), normal (7-8), and long (≥9); 2) Sleep quality: trouble falling asleep or staying asleep; 3) Sleep medication, 4) Feeling well-rested, and 5) Fatigue. Latent class analysis generated a 3-class model with a gradient on all SDOH characteristics. Generalized structural equation modeling was used to examine associations between adverse’ SDOH classes and sleep health, with Class 1 (lowest probability of adverse’ SDOH), as reference category.

Results
Adults in Class 3 were 36% more likely to have short sleep than normal sleep. Classes 2 and 3 were 16% and 24% more likely to have trouble falling asleep on “some days”; 26% and 40% more likely on “most days”; and 28% and 77% more likely every day than never. Classes 2 and 3 were 22% and 23% more likely to have trouble staying asleep on “some days”; 36% and 35% more likely on “most days”; and 48% and 77% more likely every day than never. Class 2 was 19% more likely to take sleep medications on “some days” and 25% more likely every day than never. Classes 2 and 3 were 26% and 86% more likely to never feel well-rested; 30% and 60% more likely to feel well-rested on “some days” than every day. Classes 2 and 3 were 17% and 49% more likely to feel fatigued on “some days”; 43% and 99% more likely on “most days”; 55% and twice as likely to feel fatigued every day than never.

Conclusion
Adverse SDOH are associated with worsened unhealthy sleep. Further research is needed for the implementation of interventions to improve sleep health among marginalized groups more impacted by adverse SDOH.

Support (if any)
G1-5524901
0930 What explains high self-reports of poor sleep quality in Native Hawaiian/Pacific Islanders?
Authors: Locke Brian , Situmeang Josiah , Anderson Nicholas , Sundar Krishna 
Published: May 2023

Abstract

Introduction
High rates of poor sleep quality have been reported among patients of Native Hawaiian/Pacific Islander (NHPI) race. Unrestful sleep might be attributable to short sleep times or to elevated rates of obstructive sleep apnea among NHPIs. We aimed to evaluate factors associated with self-reported poor sleep quality among NHPIs in a nationwide, representative community sample.

Methods
Using the 2014 National Health Interview Survey (NHIS), we used logistic regression to model the odds of individuals reporting that they did not awake feeling rested at least two out of the prior seven days. NHPIs from the NHPI-NHIS and non-NHPIs from the general sample were compared. Age, sex, BMI, smoking status, report of heavy drinking, employment status, marital status, and reported average hours of sleep time were evaluated in terms of their effects on self-reported sleep quality.

Results
Data from 36,697 non-NHPI and 2,590 NHPI individuals was available for analysis. There were significant differences in age, BMI, marital and current employment status between NHPIs vs. non-NHPIs. NHPI patients reported less sleep (mean 6.86±1.41 hours) in contrast to non-NHPIs (mean 7.12±1.42 hours) (P&lt; 0.001). NHPIs were more likely to report two or more days awakening not feeling rested (Odds Ratio (OR) 1.22, 95% confidence interval (95CI) 1.12–1.32) as compared to non-NHPIs. After adjusting for demographics, BMI, alcohol, smoking, and marital and employment status, the relationship persisted (OR 1.17, 95CI 1.07-1.28). When additionally controlling for sleep time, which was strongly associated with the likelihood of awakening unrested (OR 3.1 95CI 2.9-3.2 for six or fewer average hours of sleep), there was no significant relationship between race and likelihood of awakening unrested (OR 1.02, 95CI 0.93-1.13). Findings were similar when evaluating different thresholds of unrestful nights per week.

Conclusion
NHPIs are more likely to awake feeling unrested than non-NHPIs. The higher rate of unrestful sleep is associated with higher reports of short (6 hours or less) sleep time. Therefore, future research to improve sleep quality in NHPIs should also investigate the cultural and social factors leading to short sleep time.

Support (if any)
BWL: NIH Ruth L. Kirschstein National Research Service Award 5T32HL105321 and the ATS ASPIRE Fellowship
0933 Sleep Specialists in Federally Qualified Health Centers: A Gap in Sleep Health Care Potentially Contributing to Sleep Disparities
Authors: Singh Rupsha , Juarez Paul , Redline Susan , Nápoles Anna , Jackson Chandra 
Published: May 2023

Abstract

Introduction
Poor sleep is associated with adverse cardiometabolic health outcomes and increased risk of mortality. Although primary care providers recognize the importance of sleep, they report low comfort levels in diagnosing, treating, and managing sleep disorders; lack of clinical knowledge and skills is the most common barrier to recognizing insomnia in family practice. Furthermore, minoritized racial/ethnic groups are disproportionately affected by poor sleep health, likely due in part to structural and other forms of racism that result in limited access to sleep healthcare. Health Resources & Services Administration (HRSA) has a variety of awards that support scholarships and loan repayment programs at colleges and universities to meet critical workforce shortages and diversify the health professions. We assessed the distribution of sleep specialists among clinicians who participated in a HRSA grant program because they tend to work for health care organizations that serve persons from lower socioeconomic and diverse racial-ethnic backgrounds.

Methods
Publicly available data were obtained from the HRSA data warehouse. Data were available for 44,199 clinicians (49% MD/DO and 16% Nurse Practitioner) who participated in a HRSA grant program between 2011 and 2021. We report data for those clinicians who endorsed sleep medicine as their subspecialty.

Results
Of the 44,199 clinicians, 27 (0.06%) physicians trained in a medical subspecialty of sleep medicine, and the majority (52%) were in Michigan. The remainder of sleep medicine specialists completed their program in Pennsylvania (33%), Ohio (7%), Minnesota (4%), and California (4%). Only the participants in the Children's Hospital Graduate Medical Education program designated their subspecialty as sleep medicine. None of the sleep medicine specialists currently practice in Health Professional Shortage Areas (HPSAs) or Medically Underserved Areas/Populations (MUA/P), and 1 physician currently practices in a rural area.

Conclusion
A marked paucity of trained sleep specialists in HRSA grant programs and an absence of such practitioners in HSPAs underscores the disparities in access to sleep specialty care. Initiatives are needed to incentivize sleep specialists to serve in under-resourced communities as well as to implement programs training primary care practitioners working in these areas to deliver sleep health care, supported by sleep specialists.

Support (if any)
Divisions of Intramural Research: NIMHD/NIEHS, NIH
0936 Characteristics and predictors of prescription sleep medication use in a national sample of Veterans with chronic pain
Authors: Li Y , Chen Claire , Donovan Lucas , Zeliadt Steven , Chen Jessica 
Published: May 2023

Abstract

Introduction
Insomnia and other sleep problems commonly occur in patients with chronic pain. While behavioral approaches are recommended first-line treatments for conditions such as insomnia, patients with sleep complaints often receive sleep medications. Thus, we aim to understand patterns of prescription sleep medication use in Veterans with chronic pain receiving care at the Veterans Health Administration (VHA).

Methods
We reviewed electronic medical records to identify patients with moderate/severe chronic pain receiving care at a VA hospital between FY2019—2021. Multivariate logistic regressions examined associations between 14 patient demographic and clinical variables on sleep medication prescription. Short-term prescriptions associated with upcoming procedures were excluded.

Results
Of the 1,706,214 moderate/severe patients with chronic pain (88% male, 66% White), 14% had comorbid diagnosis of insomnia, 30% sleep disordered breathing, and 19% were prescribed sleep medications. Trazodone (40%), melatonin (23%), and benzodiazepines (14%) were most commonly prescribed. Although 14% of patients without insomnia or sleep apnea diagnostic codes were prescribed medication, patients with insomnia were four times more likely to use sleep medication (odds ratio (OR) = 4.05, 95% CI [4.01-4.09]). Strongest predictors of medication use were insomnia (adjusted OR = 2.84, 95% CI [2.81-2.86]), PTSD (adjusted OR = 2.77, 95% CI [2.75-2.79]), depression (adjusted OR = 1.89, 95% CI [1.87-1.91]), bipolar disorder (adjusted OR = 1.84, 95% CI [1.81-1.87]), and ongoing opioid use (adjusted OR = 1.82, 95% CI [1.80-1.84]). Prevalence of medication use was similar across gender, age, and racial/ethnic groups.

Conclusion
Sleep medication use is common in Veterans with chronic pain, with highest rates among patients with insomnia diagnoses. Additionally, a large number of patients without a sleep disorder diagnosis also receive medications. Findings highlight need to connect chronic pain patients with behavioral sleep interventions. Notably, we observed a wide variety of medications without strong patterns across demographic characteristics.

Support (if any)
This work was conducted as a non-research quality improvement effort supported by Quality Enhancement Research Initiative (PEC 13-001) and the VA Office of Patient Centered Care and Cultural Transformation. YIL is supported by a VA MIRT Fellowship. JAC is supported by a VA HSR&D CDA (IK2 HX002866). LMD is supported by a VA HSR&D CDA (CDA 18-187).
0941 Sleep Apnea Testing in Hospitalized Patients: Are Patients Following Up?
Authors: Lam Nhi , Singh Monica , Lastra Alejandra 
Published: May 2023

Abstract

Introduction
Obstructive sleep apnea (OSA) can often be recognized in an inpatient setting where patients are continuously monitored. Although an unattended portable sleep study can successfully diagnose OSA in many inpatients, allowing for earlier intervention, a loss to follow up can compromise effective treatment. This study aims to determine the rate of hospital follow-up in patients who are diagnosed with OSA on a portable inpatient sleep study.

Methods
We included adult inpatients who underwent portable sleep studies from 2013 to 2022. This is a retrospective study design. Individual charts were reviewed to determine demographics including age, gender, and race. Co-morbidities and length of stay were recorded. Sleep study data analyzed included total recording time, total estimated sleep time, overall apnea-hypopnea index (AHI) and nadir oxygen saturation. Primary outcome was percentage of patients with AHI 5/h with scheduled follow up visit. Secondary outcome was percentage of patients with AHI 5/h completed outpatient follow-up visit.

Results
104 patients were included for analysis. Majority were middle aged (55.2 SD15.9 years-old), black (n=71, 68.2%) and male gender (n=64, 62%). A minority identified as Hispanic (n=7, 6.7%). The total number of sleep studies with AHI 5/h was 95 (91.3%) and the mean AHI was in the severe range (42.8 SD31.4/h) with significant associated oxygen desaturations (mean nadir oxygen saturation 77.1 SD10.6%). Primary outcome: the number of patients with a positive sleep study for OSA (AHI 5/h) who had scheduled for follow-up at discharge was 57 (60%). Secondary outcome: 54.4% of patients with positive sleep study and scheduled follow-up completed the outpatient visit.

Conclusion
The percentage of patients lost to follow-up is high despite diagnosing OSA successfully with an inpatient portable sleep study. Close to half of the patients had no scheduled follow-up, and the “no show” rate remained high (close to 50%) in those who had a sleep medicine appointment. Future studies should explore effective interventions to improve the follow-up rate and overall adherence to treatment.

Support (if any)
0943 Sleep Medicine Tweet-by-Tweet, an Electronic Platform for Collaborative Medical Education
Authors: Liu Ran , Thomas Robert , Heckman Eric , Mak Michael 
Published: May 2023

Abstract

Introduction
Twitter is a novel and accessible platform for the dissemination of medical education, and it is used by many medical practitioners (1). Many physicians have used Twitter as a means of meeting continuing medical education needs. This can manifest as Twitter-based Journal Clubs, curated conference data and webinars (2).

Methods
I have created a Sleep Medicine Medical Education Twitter account @SleepyNeuroDoc to share complex cases in all areas of sleep medicine, including sleep-disordered breathing, movement disorders in sleep, circadian rhythm disorders and nocturnal epilepsy. I share notable images of polysomnogram outputs, home sleep apnea tests, compliance data, neuro-imaging, electroencephalogram, cardiopulmonary coupling and more. This digital education platform allows rapid circulation of unique cases and promotes in-depth scholarly discussion, with no geographical limit. Polls are conducted for complex topics to facilitate knowledge exchange and consumer engagement. This educational twitter is followed by the entire spectrum of professions within the sleep medicine care team, including physicians, allied health, and researchers. To date, there are 40 cases posted.

Results
We conducted online questionnaires with consumers of this Twitter account, and the results so far indicate greater practitioner comfort with management of various sleep medicine conditions. Some consumers report having changed their approach to practice.

Conclusion
Our work suggests that this unique use of a social medical platform is beneficial for continuing medical education and knowledge exchange in the field of Sleep Medicine. References: 1. Chretien KC, Azar J, Kind T. Physicians on Twitter. JAMA. 2011;305(6):566-568. 2. Thamman R, Gulati M, Narang A, et al. Twitter-based learning for continuing medical education? Eur Heart J. 2020;41(46):4376-4379.

Support (if any)
0945 A Robust Hybrid algorithm for automatic respiratory events scoring in adults
Authors: Kim Myeong Seok , Ha Tae Kyoung , Lee Ho Dong , Lee Young Jun 
Published: May 2023

Abstract

Introduction
Even though Polysomnography has many advantages, manual scoring is a labor intensive and time consuming. Therefore, automatic scoring system using a deep learning algorithm has been developed and commercialized for clinical sites. However, it is challenging to explain the results of respiratory events and to measure a precise event’s duration. To overcome these challenges, a new hybrid algorithm was proposed to explain why this event was categorized and provided the accurate duration of respiratory events.

Methods
In order to classify respiratory events - OSA, MSA, CSA, O-hypopnea, and C-hypopnea, the proposed algorithm consists of two algorithms – deep learning and post processing algorithms. Deep learning algorithm was used to detect candidates of respiratory events and post processing algorithm was used to determine the final classification and to measure duration of events. Deep learning model consisted of five layers: a perceptron layer, three consecutive layers of long short-term memory (LSTM) cells, and two more perceptron layers. A total of 1,000 case PSG data were enrolled to develop this algorithm. To classify respiratory events from the results provided by the deep learning algorithm, auto-correlation, peak detection, de-noise filtering and transition detection were used to obtain respiratory rate and measure signal amplitude. Using these algorithm’s results, amplitude and time of baseline breathing and respiratory events were calculated and classified based on the AASM’s guidelines.

Results
To evaluate the performance of proposed algorithm, 30 PSG data were selected and compared with the results of deep learning algorithm. The test dataset consisted of thirty subjects with 15 OSA, 5 PLMS, 2 insomnia and 8 healthy. The kappa value of deep learning algorithms showed 0.77 (0.759, 0.78) and that of proposed algorithm was 0.838 (0.832, 0.843). They showed better agreement by 0.068 than that of deep learning algorithm. In addition, the proposed algorithm provided the classification results such as OSA, MSA, CSA, O-hypopnea and C-hypopnea and explanations of the reason of event detection.

Conclusion
The proposed algorithm showed better agreement and versatile classified results. This algorithm will be adopted for new automatic sleep scoring solution and this solution will be very helpful to use in real clinical sites.

Support (if any)
0946 CIRCUL: VALIDATION OF A SMART RING FORM OXIMETER IN INDIVIDUALS WITH DARK SKIN PIGMENT
Authors: Cruz Miguel Meira e , Chen Enguo , Zhou Young , Shu Dengui , Zhou Congcong , Kryger Meir 
Published: May 2023

Abstract

Introduction
The potential impact of skin pigmentation on the accuracy of oximetric assessment has long been known to be a clinically relevant matter. During the COVID pandemic it became apparent that oximeters may underestimate oxyhemoglobin saturation in darkly pigmented individuals. Validation data for SaO2 is not generally available for the many consumer wearable oximeter devices. Circul® (Bodimetrics Corp, Los Angeles, CA), a wearable oximeter with a form factor of a ring, measures several variables for cardiorespiratory assessment (SpO2, movement, heart rate). We aimed to evaluate the accuracy this device in the measurement of SaO2 compared to a simultaneous values obtained by measurement of SaO2 by arterial blood gases.

Methods
24 subjects (8 Black, 16 Non-Black) participated in the validation of the Circul device. In an operating suite they had catheters inserted into their radial arteries. They were administered nitrogen rich air which made them hypoxic in steps to an SaO2 of 70%. Arterial blood was sampled at various levels of hypoxia and simultaneous readings were obtained from the Circul device. We compared SaO2 obtained by Circul vs measurement of ABGs by a medical grade blood gas analyzer.

Results
There was excellent correlation between SaO2 measured by the ring oximeter and ABGs in both Black (y = 1.0174x - 1.573; R² = 0.9414) and Non-Black (y = 1.0209x - 2.5607; R² = 0.9207) subjects. No significant differences were found in comparing the intercept and slopes of the regressions. At ABG of 70% and 100% the SaO2 measured by the ring was calculated to be 69.6% and 100.2% for the Black subjects and 68.9% and 99.5% for the non-Black subjects.

Conclusion
Results from this study confirmed that Circul oximetry accuracy seems to be independent of the skin tone.

Support (if any)
0952 A Long Winter’s Sleep: What Does Big Data From Consumer Sleep Technology Tell Us?
Authors: Gahan Luke , Wilson Samantha , Watson Nathaniel F , Gottlieb Elie 
Published: May 2023

Abstract

Introduction
Light is the most powerful zeitgeber influencing sleep-wake patterns, and winter’s shorter days have profound effects on mood, energy, and metabolic function. Here, we leverage big data from consumer sleep technology to examine the relationship between objectively measured sleep in winter versus summer months in the United States.

Methods
The dataset included 21,101 users residing in the US across 1,255,518 nights (age range: 17-90, mean age: 46.6 ± 16.7 years, 59% female). Data were extracted from 01 January 2019 until 31 August 2022. Seasons were defined according to the Northern Hemisphere’s meteorological calendar and included winter (December - February) and summer (June - August). USA public holidays were mapped and removed from the data. The relationships between objectively measured sleep and winter versus summer were examined using linear regression models, controlling for age and gender.

Results
A shift to later average monthly wake times were observed in both summer and winter, peaking in summer. Similarly, bedtimes shift later in summer and winter, yet the shift in summer was greater than winter (winter vs summer: waketime: 07:06 vs 07:07, ß = -0.46, SE = 0.023, p&lt; 0.001, bedtime: 23:17 vs 23:23, ß = 0.12, SE = 0.006, p&lt; 0.001). Although bedtime and wake times delay during the winter and summer seasons, measures of sleep health including sleep duration, sleep efficiency, and SleepScore peak in winter and slowly decline to their lowest point during the summer months (winter vs. summer: SleepScore 78.4 vs 77.6, ß = -0.89, SE = 0.17, p&lt; 0.001; sleep duration 359.6 min vs 352.8 min, ß = -4.8, SE = 1.02, p&lt; 0.001; sleep efficiency 77.5 vs 76.6, ß = 0.86, SE = 0.038, p&lt; 0.001).

Conclusion
Bedtime and wake time delays were greater in summer than winter, likely contributing to small improvements to sleep quality, duration, and efficiency in winter compared to summer. These findings are generally supported by previous research examining sleep-wake patterns in locations with extreme seasonal differences, such as Northern Norway, where, despite midnight sun and polar nights, researchers have found little variation in sleep-wake timing.

Support (if any)
SleepScore Labs