bluuebunny/crossref_metadata_2025_split huggingface dataset:
650 The Association between Sleep and Psychological Distress among New York Healthcare Workers During the COVID-19 Pandemic
Authors: Diaz Franchesca , Bramley Sean , Venner Hadiah , Shaw Kaitlin , McMurry Cara , Cornelius Talea , Schwartz Joseph , Shechter Ari , Abdalla Marwah 
Published: May 2021

Abstract

Introduction
Healthcare workers (HCWs) treating patients with COVID-19 report high rates of acute stress, depressive and anxiety symptoms. We examined whether sleep disturbances were associated with psychological distress in New York City (NYC) HCWs during the initial peak of COVID-19 in-patient admissions (April 9 - May 11, 2020).

Methods
HCWs (physicians, nurses, and advanced practice providers) completed a web-based survey which screened for acute stress (4-item Primary Care PTSD screen), depressive symptoms (Patient Health Questionnaire-2), and anxiety (2-item Generalized Anxiety Disorder scale). Past week insomnia symptoms were assessed with a modified item from the Insomnia Severity Index (5-point Likert Scale: none, mild, moderate, severe, very severe). Insomnia was defined as having “moderate, severe, or very severe” symptoms. Short sleep (SS) was defined as self-reported sleep duration &lt;6 hours per day. Poisson regression analyses predicting psychological distress from SS and, separately insomnia. adjusting for age, gender, race/ethnicity, clinical setting (COVID-focused or not COVID-focused), physician vs. non-physician status, and redeployment status, were performed.

Results
Data included 813 HCWs (80.6% female, 59.0% White, 75.6% worked in a COVID-focused setting). Mean sleep duration was 5.79 ± 1.22 hours/night. The prevalence of SS and insomnia were 38.8% and 72.8%; the prevalence of acute stress, depressive symptoms, and anxiety were 57.9%, 33.8% and 48.2%, respectively. Having SS, vs. not was associated with acute stress (adjusted prevalence ratio [PR]: 1.21, 95% CI: 1.07, 1.31), depressive symptoms (PR: 1.65, 95% CI: 1.35, 2.02), and anxiety (PR: 1.51, 95% CI: 1.30, 1.74). Presence of insomnia symptoms vs. “none or mild” was associated with acute stress (PR: 1.92, 95% CI: 1.57, 2.34), depressive symptoms (PR: 3.13, 95% CI: 2.16, 4.52), and anxiety (PR: 2.40, 95% CI: 1.86, 3.11).

Conclusion
Among NYC HCWs, sleep disturbances, including SS and insomnia symptoms during COVID-19 are common. In our study, SS and insomnia were associated with acute stress, depressive symptoms, and anxiety in HCWs, however further research on whether a bidirectional relationship exists between sleep and psychological distress during the COVID19 pandemic are still needed.
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652 Association Between Poor Sleep and Lymphocyte Subsets In Chronic HIV Infection
Authors: Borker Priya , Macatangay Bernard , Punjabi Naresh , Rinaldo Charles , Wolinsky Steven , Hyong-Jin Cho Joshua , McKay Heather , Patel Sanjay 
Published: May 2021

Abstract

Introduction
Low CD4+ T lymphocyte counts and CD4+/CD8+ lymphocyte ratios predict mortality and cardiovascular risk among people living with HIV (PLWH). Whether polysomnographic (PSG) sleep measures impact T lymphocyte subset counts among PLWH is unknown. We sought to evaluate the association between lymphocyte subsets and PSG-derived sleep measures in a cohort with HIV seropositive men.

Methods
We analyzed data from HIV seropositive men who have sex with men participating in the Multicenter AIDS Cohort Study on antiretroviral therapy for &gt;1 year with undetectable (&lt;500 copies/mL) plasma HIV-1 RNA who underwent a sleep evaluation with home polysomnography. The following seven sleep parameters were examined: total sleep time (TST), sleep efficiency, sleep stage (N1, N2, N3, and REM) duration, and apnea-hypopnea index. Multivariable linear regression models adjusted for age and body mass index were used to assess whether sleep measures were associated with CD4+ T cell count, CD8+ T cell count, or CD4+/CD8+ ratio.

Results
Participants (n= 286) had a mean age of 55.2 ± 11.3 years, 52.8% had sleep apnea and mean CD4+ count was 728 ± 306 cells/mm3. None of the sleep measures were associated with CD4+ counts but longer TST and REM duration were associated with lower CD8+ counts and higher CD4+/CD8+ ratio. In adjusted analyses, every one hour increase in TST was associated with a 35 ± 18 cells/mm3 lower CD8+ count (p=0.049) and 6.3% elevation in CD4+/CD8+ ratio (p=0.006) while every hour increase in REM was associated with 123 ± 50 cells/mm3 lower CD8+ count (p=0.01) and 20% elevation in CD4+/CD8+ ratio (p=0.003).

Conclusion
In PLWH, longer total sleep time and REM sleep duration are associated with protective CD4+/CD8+ ratios due to lower CD8+ cell count. Further research is needed to assess if longer sleep duration is associated with decreased inflammatory markers.

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American Thoracic Society Academic Sleep Pulmonary Integrated Research/Clinical (ASPIRE) Fellowship
659 Optimizing Telehealth technology in Sleep Medicine at a VA Medical center during the COVID19 pandemic
Authors: Konikkara John , Kabir Arif , David Robert , Donovan Lucas , Palen Brian 
Published: May 2021

Abstract

Introduction
The COVID19 pandemic poses an unprecedented challenge for healthcare delivery. Optimization of resources and safety of patients and healthcare workers is crucial. The aim of this study is to analyze impact on resource utilization at VA Maryland Healthcare System (VAMHCS). At the pandemic onset, we deployed a mail-in store-and-forward technology (SFT) for carrying out home sleep apnea testing (HSAT), using WatchpatDirect® Peripheral Arterial Tonometry-based testing. This led to a substantial decrease in reliance on care in the community (CITC) or out-of-VA care, resulting in fiscal savings. Interpretation of studies is done by VA physicians using a cloud-based network, resulting in improved workforce optimization and continuity of care.

Methods
We compared CITC expenditures for sleep studies in financial year (FY) 2019 (October 2018 to September 2019) with that of FY2020 in the VA Support Service Center Capital Assets (VSSC) database.

Results
In FY2019, VAMHCS conducted 402 polysomnograms, 805 HSATs and referred 64 patients to CITC, including 5 HSATs, with CITC costs of $102,388. CITC referral initiated by primary care providers often resulted in clinic visit and polysomnography (PSG). In FY2020, VAMHCS conducted 436 PSGs, 986 HSATs and referred 10 patients to CITC, including 3 HSATs, costing $6,780; a decrease of $95,608 compared to FY2019. The ratio of VA to CITC studies was 18:1 in FY2019, compared to 142:1 in FY2020. VAMHCS conducted 166 HSATs between 3-15-2020 and 6-30-2020, while the sleep lab was closed due to COVID surge, at a cost of $160 per study. According to VSSC data, an average of $1,005 is spent per patient (including a third-party administrator fee of $200) when utilizing CITC. Approximately 80% of CITC referrals underwent PSG. If all studies were done using CITC, it would have cost an additional $ 140,270. The cost would have been $546, 855 if all studies done between 3-15-2020 and 9-30-2020 were done through CITC.

Conclusion
The use of SFT during the pandemic resulted in VAMHCS relying less on CITC, leading to a decrease in expenditure, administrative burden and turnaround time. SFT minimizes the risk of infection transmission because PAT probes are disposable and obviates patient visits to the hospital.
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666 Sleep Architecture in the Intensive Care Unit As Revealed via Breathing and Heart Rate Variability
Authors: Ganglberger Wolfgang , Velpula Krishnamurthy Parimala , Bucklin Abigail , Tesh Ryan , Da Silva Cardoso Madalena , Sun Haoqi , Adra Noor , Leone Michael , Qader Quadri Syed Abdul , Ye Elissa , Kuller David , Thompson Taylor , Akeju Oluwaseun , Thomas Robert , Westover Michael 
Published: May 2021

Abstract

Introduction
Sleep in the intensive care unit (ICU) is difficult to measure by conventional polysomnography. We investigated the feasibility of assessing sleep state from readily available ICU signals: heart rate variability (HRV) from electrocardiography and breathing from a wearable respiratory band. We compared findings with an age and sex matched sleep laboratory group.

Methods
As part of a clinical trial, 102 adult non-ventilated patients in three ICUs in the Massachusetts General Hospital wore a respiratory band. Both heart rate variability (RR-intervals) from ECG, and breathing (respiratory effort waveforms) data for up to seven days per patient were obtained. 220 age- and sex-matched subjects from a sleep lab cohort who wore the same respiratory effort band and ECG were selected for comparison. We staged sleep from the HRV and breathing data using previously published deep neural network models. We defined discordant sleep epochs as those where HRV- and breathing-based models disagreed. Agreement was computed for the following pairs: (R,R),(N1,N1),(N2,N2),(N3,N3),(N1,W),(N1,N2),(N2,N3).

Results
Demographics: Mean(STD) age: ICU 68(9), sleeplab 68(9); BMI: ICU 27(6), sleeplab 31(6); ICU 40% female, sleeplab 44% female; race: ICU%:Sleeplab% 90:69 White, 5:4 Black, 2:7 Asian. 34% of ICU-subjects were in a medical ICU, 66% in a surgical ICU. Mean total sleep duration in the ICU was 8.9 hours (4.5h concordant, 4.4h discordant sleep). We observed increased amounts of discordant sleep in the ICU compared with the sleeplab cohort (4.4h vs. 1h, p&lt;0.01). We found different REM sleep distributions (p&lt;0.01) with reduced median (10% vs. 20%) but elevated 90% quantile (45% vs. 26%), elevated N1(%) (41% vs. 26%, p&lt;0.05), reduced N2(%) (19 vs. 44, p&lt;0.01), and reduced N2+N3(%) (34 vs. 59, p&lt;0.05). We further observed higher mean respiratory rate (17.4 vs. 15.9 breaths per minute, p&lt;0.01), lower inter-breath-intervals (3.9 vs. 4.7 seconds per breath, p&lt;0.01), and more breathing variability than in sleeplab AHI&lt;5 group but less than in AHI&gt;15 group.

Conclusion
HRV and respiratory-based measures can assess sleep in the ICU. The findings of increased discordant sleep in the ICU might stem from limitations of the models, fundamental changes in sleep biology during critical illness, pharmaceutical drugs, sleep fragmentation, and/or associated pathology in the ICU.
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668 Utilizing Risk Score Assessment to Maximize Sleep Research Participant Safety During the COVID-19 Pandemic
Authors: Zamora Tania , Stepnowsky Carl , Deering Sean 
Published: May 2021

Abstract

Introduction
Research study recruitment has been profoundly affected by the COVID-19 pandemic, demonstrated by significant delays or pauses. Various guidelines pertaining to in-person visits have applied to research. Some call for exclusion of participants that the CDC has labeled “at increased risk”.1 For obstructive sleep apnea (OSA) studies, these guidelines have caused a sharp decrease in the number of new participants. This decrease is due to high rates of OSA comorbidities including obesity and diabetes. New evidence-based risk scores have been developed using individual- and community-level factors. The use of more refined COVID-19 risk scores can help protect patient safety while allowing research to continue.

Methods
The risk score assessment used for this study (COVID-19 Mortality Risk Calculator; Johns Hopkins University, Baltimore, MD)2 is evidence-based and uses a set of risk factors and community-level pandemic dynamics in the state of residence.3,4 It was compared to the list of CDC medical conditions that are considered to put an individual “at increased risk.” Both measures were calculated retrospectively on current participants to determine how many could safely attend in-person visits based on each risk assessment method.

Results
Sample characteristics of the 110 participants were: mean age: 49.5±13.7(24–76); mean BMI: 32.3±5.3(20.9–46.1); mean AHI: 24.3±21.4(5.1–110). Mortality Risk Calculator scores were: 91(82.7%) close to/lower than average [Level 1]; 12(10.9%) moderately elevated; 6(5.5%) substantially elevated; 1(0.9%) high; and 0(0%) very high [Level 5]. Using CDC guidance, 63 (57.3%) had at least one at-risk condition and 47 (42.7%) had 0. Using only Level 1 of the Risk Calculator would allow an additional 28 (25%) participants to attend in-person visits; using Levels 1 and 2 would allow an additional 40 (37%) participants.

Conclusion
Policies based on CDC at-risk conditions resulted in higher levels of participant exclusion in research during the COVID-19 pandemic than use of an evidence-based Mortality Risk Calculator. This analysis shows that researchers can use risk-adjusted scores to make informed decisions about study participation that balances both participant safety and research study progress.

Support (if any)
This project was supported in part by Department of Veteran Affairs VA HSRD IIR 16–277, VA RRD D2651-R, and VA San Diego Healthcare System Research Service.
701 Daily Associations Between Adolescent Sleep and Mental Health During the COVID-19 Pandemic
Authors: Palmer Cara , Oosterhoff Benjamin 
Published: May 2021

Abstract

Introduction
Sleep disturbances after a trauma forecast mental health difficulties such as post-traumatic stress symptoms (PTSS). COVID-19 has resulted in numerous stressors for youth (e.g., school closures, social isolation) that directly disrupt sleep and may have negative mental health consequences. However, investigations of sleep and adverse events in youth have primarily been limited to months or years after a trauma has occurred. How adolescent sleep patterns and mental health intersect during an ongoing adverse experience is unknown.

Methods
Adolescents from the United States (N = 503; 80.9% female; 13–18 years) participated in an online daily diary study within 3 weeks after COVID-19 was declared a national emergency. Participants completed one week of twice daily reports (5406 observations) on their nightly sleep (sleep timing, sleep quality, sleep onset difficulties, and nightmares) and daytime mental health symptoms (COVID-19 PTSS, positive affect, negative affect, and loneliness).

Results
Mixed models adjusting for age, sex, and socioeconomic status indicated that cyclical, bidirectional effects emerged, with daytime mental health symptoms predicting same-night sleep disturbances, and sleep disturbances predicting next-day mental health. Greater PTSS predicted sleep onset difficulties (Estimate = .02, SE = .004, t = 5.56 p &lt; .001) and sleep onset difficulties predicted next-day PTSS (Estimate = .35, SE = .17, t = 2.04, p &lt; .05). Greater daytime negative affect predicted greater nightmares (Estimate = .07, SE = .01, t = 4.97, p &lt; .001), and nightmares predicted marginally more next-day negative affect (Estimate = .07, SE = .04, t = 1.66, p = .09). PTSS (Estimate = .01, SE = .003, t = 3.47, p &lt; .001) and negative affect (Estimate = .05, SE = .03, t = 2.06, p &lt; .05) both predicted poorer sleep quality the following night.

Conclusion
Findings suggest that mental health symptoms during the onset of the COVID-19 pandemic are associated with increased sleep disturbances. In turn, these sleep disturbances predict increased mental health symptoms. Overall, results provide a greater understanding of youth sleep patterns during COVID-19 and how sleep disturbances may inhibit resilience in the context of an ongoing stressor.

Support (if any)
Center for American Indian and Rural Health Equity
702 Patient Characteristics & Positive Airway Pressure Therapy Compliance During NYC’s 2020 COVID-19 Pandemic Stay-at-Home Orders
Authors: Nair Sunil , Rodriguez Alcibiades 
Published: May 2021

Abstract

Introduction
During the first surge of the COVID-19 pandemic in New York City, as New Yorkers were enjoined by authorities to stay-at-home, patient encounters were transitioned from office visits to telemedicine consultations. It was thought that increased stress during the pandemic would worsen rates of insomnia, and concerns regarding use of positive airway pressure (PAP) therapy during a respiratory pandemic would affect compliance. We sought to describe telemedicine success rates, the distribution of sleep problems evaluated remotely by telemedicine, and PAP compliance in our patient population.

Methods
Telemedicine encounters from March 16th through May 31st, 2020, were reviewed for show-rates, patient characteristics and clinician impressions, and were compared to administrative data from the 2.5 months prior to the stay-at-home period (the “control period”). PAP compliance was analyzed for forty randomly selected patients established on PAP therapy between January 1st and October 1st, 2019, and whose machines transmitted data to a digital management system between October 1st and December 31st, 2019. Compliance reports and AHI were compared between the stay-at-home- and control- periods for this randomly selected group of patients.

Results
The telemedicine show rate was 89.37% (compared to a 91.91% in-office show-rate during the control period); all these encounters were successfully completed. Sleep apnea was the predominant diagnosis during the stay-at-home period (44.33% of encounters), followed by insomnia (23.16%). Insomnia complaints were significantly higher during the stay-at-home period compared to the control period (23.16% versus 14.6% of encounters; p &lt; .05), and among new- versus follow-up- patients (41.76% versus 35.99%). PAP compliance and AHI, however, were not statistically different for forty randomly selected patients between the control and stay-at-home periods.

Conclusion
Most stay-at-home period telemedicine encounters were successful. Sleep apnea, then insomnia, were common findings among new and follow-up patients during the stay-at-home period, and insomnia rates increased during the pandemic. PAP compliance and AHI were similar in a random cohort when compared between the stay-at-home and control periods.
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710 Association of Social Isolation, Perceived Stress, and CPAP Use in Sleep Medicine Patients during the COVID-19 Pandemic
Authors: Nowakowski Sara , Arentson-Lantz Emily , Debian Ahmad , Kokanda Manasa , Shaib Fidaa 
Published: May 2021

Abstract

Introduction
Due to the COVID-19 pandemic, many individuals are likely experiencing increased stress and social isolation. This study aimed to examine the effect of perceived stress and social isolation on self-reported continuous positive airway pressure (CPAP) use and treatment adherence among sleep medicine clinic patients during the pandemic.

Methods
Between June-November 2020, 81 sleep medicine clinic patients (54.8±15.9y, 44% male, 69% Caucasian) completed an online survey that included self-reported changes in CPAP use and using CPAP as advised; and PROMIS Social Isolation and Perceived Stress Scale (PSS). CPAP measures were categorized based on reported changes during the pandemic. Stepwise logistic regression was performed using SAS to determine if Social Isolation and PSS predicted change in CPAP measures.

Results
Among participants, 53% reported using CPAP. Out of those, 61% reported change, 16% reported no change, and 23% reported they do not know if there is a change in using CPAP as advised during the pandemic. Social Isolation predicted an increase in odds of CPAP use by a factor of 1.15 (p=0.024). PSS predicted a decrease in odds of using CPAP therapy as advised by a factor of 0.86 (p=0.049).

Conclusion
Increases in perceived stress predicted lower odds of utilizing CPAP as advised. Increases in self-reported social isolation predicted greater odds of CPAP use in sleep medicine clinic patients during the COVID-19 pandemic. Addressing stressors/coping and social isolation/support as part of routine clinical care in sleep medicine clinic patients is advised.

Support (if any)
This work is supported by National Institutes of Health Grant # R01NR018342 (PI: Nowakowski) and by the Department of Veteran Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413).
715 Insomnia as a Risk for PTSD During the COVID-19 Pandemic
Authors: Killgore William , Cloonan Sara , Taylor Emily , Grandner Michael , Dailey Natalie 
Published: May 2021

Abstract

Introduction
The COVID-19 pandemic and associated attempts to curb its spread have led to a significant increase in mental health issues. Evidence suggests that sleep provides a protective resilience against the adverse effects of stress. Moreover, sleep disruption is often considered the “hallmark symptom” of posttraumatic stress disorder (PTSD). Here we hypothesized that insomnia would increase during the first six months of the pandemic, and that higher insomnia would be associated with elevated rates of PTSD.

Methods
A total of 6,190 adults ranging in age from 18 to 84 years (53.6% female), completed an online cross-sectional survey at one of six time points between April and September 2020 (~1,000 per administration). Instruments included the Insomnia Severity Index (ISI), Primary Care PTSD Checklist (PC-PTSD), and the PTSD Checklist-5 (PCL-5). Standard clinical cutoffs were used for ISI (≥10) and PCL-5 (≥38). Data were analyzed with analysis of variance, chi-square contingency tables, and bivariate correlations.

Results
Over the first six-months of the pandemic, PTSD increased with each passing month on both the PC-PTSD (p=.001) and PCL-5 (p&lt;.0001). Similarly, ISI scores increased month-by-month (p&lt;.0002). Insomnia scores were highly correlated with PCL-5 PTSD scores (r=.62, p&lt;.0001), even when sleep items on the scale were excluded (r=.60, p&lt;.0001). Finally, the rate of PTSD remained below 5% across all months for those without insomnia, but among those with insomnia, the prevalence of PTSD increased between May (26% positive) and September (40% positive), representing an increase of 56% over the data collection period (interaction p=.0004).

Conclusion
Both insomnia and PTSD have increased dramatically over the first six months of the COVID-19 pandemic. Moreover, insomnia appears to be highly linked with the emergence of PTSD during this time. While it is not possible to make causal attributions from these cross-sectional findings, the steadily increasing rates of PTSD over time only among those with insomnia, raise the possibility that sleep disruption could act as a diathesis for the development of PTSD symptoms in response to the pandemic. Addressing insomnia during the pandemic may be an important aspect of maintaining psychological resilience in the populace.
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