bluuebunny/crossref_metadata_2025_split huggingface dataset:
314 Alcohol and the Sleep of Elite Athletes
Authors: Shaw Lindsay , Altman Yuval , Eyal Shuli , Baharav Anda 
Published: May 2021

Abstract

Introduction
Extensive evidence indicates that alcohol adversely affects athletic performance, increases the chance of injury, impairs aerobic metabolism, and has detrimental psychological influence. Alcohol consumption was also shown to be significant in young adults, with many elite athletes reporting alcohol intake. Sleep is essential to musculoskeletal recovery, acquisition of new skills, and emotional regulation in athletes. As insufficient sleep has detrimental effects on athletic performances and increases the risk of injury, we aimed to analyze the relation between alcohol consumption and sleep parameters among elite athletes.

Methods
We studied 9,164 nights recorded with the Boost application by 66 adult elite athletes from various sports during October 2019-December 2020. In addition to perceived and measured sleep parameters, nightly data included the previous day reported alcohol consumption and the number of training sessions. For each athlete, the average nightly mean heart rate (HR) in nights with reported alcohol consumption (AY) and without (AN) was calculated, as were the bedtime and the number of training sessions.

Results
In nights with reported alcohol, mean HR was higher (AY: 56.1±7.8 BPM, mean±SD, AN: 52.9±7.3 BPM, p&lt;.05), while bedtime was later (AY: 23:16±70 minutes, AN: 22:52±55 minutes, p&lt;.05) and fewer training sessions were reported in the following day (AY: 0.88±0.59 sessions, AN: 1.1±0.59 sessions, p&lt;.05). No correlation was found between mean HR and bedtime in nights without alcohol consumption (r=0.3). 24% of Saturday night recordings included alcohol consumption reports, with 16% of Friday nights and 12–13% for each of the remaining weeknights.

Conclusion
Adult elite athletes report consuming alcohol, especially during weekends when the probability for a training session the following day is lower. Our data support the well documented physiological effect of alcohol via elevated mean HR values throughout the night. Athletes and coaches should be educated about the impacts of alcohol and insufficient sleep on wellbeing and performance.
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326 Comparison of Psychological and Physiological Features Between Insomnia Patients with Short and Normal Objective Sleep Duration
Authors: Lin Yun-Kai , Yang Chien-Ming , Lee Hsin-Chien , Huang Ya-Chuan , Lin Chia-Yu , Huang Wan-Chun , Kao Yu-Hsuan 
Published: May 2021

Abstract

Introduction
Objective sleep duration has been suggested to be a novel biomarker in the phenotyping of insomnia (Vgontzas & Fernandez-Mendoza, 2013). It was hypothesized that insomnia phenotype with PSG-defined short sleep duration (&lt; 6 hours) is associated with physiological hyperarousal, while the phenotype with normal sleep duration (≥ 6 hours), is associated with anxious-ruminative psychological profile and poor resources for coping with stress. The aim of this study was to assess whether these two insomnia phenotypes differ in terms of self-reported psychological and physiological features.

Methods
A total 45 (mean age=36.6, female=76%) insomnia patients underwent a polysomnographic evaluation, completed several self-rating scales (include ISI, BAI, BDI, FIRST, DBAS, and PSAS), and were split into two groups base on their objective sleep duration (cut-off 6 hours).

Results
When compared to the short sleep group using independent sample t-test, the normal sleep group showed a significant higher mean score on FIRST (t= -2.13; p&lt; .05). The short sleep group showed non-significant trends to have higher mean scores on BDI and BAI. The scores on the other scales showed no significant differences between the two groups. The FIRST score correlated positively with the score of DBAS (r= 0.32; p&lt; .05) and the total (r= 0.36; p&lt; .05) and cognitive-subscale scores (r= 0.41; p&lt; .01) on PSAS. The BAI score correlated positively with the score of the total (r= 0.53; p&lt; .001) and somatic subscale (r= 0.59; p&lt; .001) on PSAS.

Conclusion
These results support the hypotheses that the normal sleep phenotype tends to be more vulnerable to the impact of stress, especially on sleep, due to pre-sleep cognitive hyperarousal. Since the BAI scale contains more items regarding physiological symptoms of anxiety disorders, the tendency of higher BAI score in short sleep group may indicate that the short sleep phenotype has higher physiological arousal, which supports the phenotyping model proposed by Vgontzas and Fernandez-Mandoza.
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341 Acute effects of seltorexant, a selective orexin-2 antagonist (JNJ- 42847922), on driving after bedtime administration
Authors: Brooks S , Zuiker R G J A , Jacobs G E , Kezic I , Savitz A , Van Der Ark P D , Van Gerven J M A 
Published: May 2021

Abstract

Introduction
Seltorexant (JNJ-42847922), a potent and selective antagonist of the human orexin-2 receptor, is being developed for the treatment of major depressive disorder. Seltorexant also has sleep-promoting properties. Investigating the effects of sleep-promoting medications on driving is important because some of these agents (e.g. GABAA receptor agonists) may be associated with increased risk of motor vehicle accidents. We evaluated the effect of seltorexant on driving after forced awakening at night, using a validated driving simulator.

Methods
This double-blind, placebo and active-controlled, randomized, 3-way cross-over study was conducted in 18 male and 18 female healthy subjects. All subjects received seltorexant 40 mg, zolpidem 10 mg, or placebo 15 minutes before bedtime. Eighteen subjects were awakened at 2- and 6-hours post-dose, and the other 18 at 4- and 8-hours post-dose. At those timepoints, pharmacokinetics, objective (standard deviation of the lateral position [SDLP]) and subjective effects (using Perceived Driving Quality and Effort Scales) on driving ability, postural stability and subjective sleepiness were assessed.

Results
For seltorexant, the SDLP difference from placebo (95% confidence interval) at 2-, 4-, 6- and 8-hours post-dose was 3.9 cm (1.26, 6.60), 0.9 cm (-1.08, 2.92), 1.1 cm (-0.42, 2.63), and 0.6 cm (-2.75, 1.55), respectively vs. 9.6 cm (6.97, 12.38), 6.6 cm (3.53, 9.60), 4.7 cm (1.46, 7.85), and 1.3cm (-1.16, 3.80), respectively for zolpidem. The difference from placebo was significant at 2-hours after taking seltorexant, while the difference from placebo was significant at 2, 4 and 6-hours after zolpidem. Subjective driving quality was decreased for both drugs at all time points and driving effort was increased up to 4-hours post-dose for both medications. Subjective sleepiness showed a significant increase compared to placebo 2- and 4-hours after administration of either drug. Postural stability was decreased up to 2-hours after administration of seltorexant, and up to 4-hours after administration of zolpidem.

Conclusion
Compared to zolpidem, objective effects on driving performance were more transient after seltorexant administration and largely normalized by 4–6 hours post-dose.

Support (if any)
This work was sponsored by Janssen R&D.
363 The Apnea and Insomnia Research (AIR) Trial: An Interim Report
Authors: Edinger Jack , Edinger Jack , Manber Rachel 
Published: May 2021

Abstract

Introduction
Many sleep apnea patients suffer from comorbid insomnia disorder. Although cognitive behavioral insomnia therapy (CBTI) is recommended as the first line insomnia treatment for such patients, access to trained providers of this treatment remains limited. The current study is testing he efficacy of an online CBTI among CPAP treated sleep apnea patient with comorbid insomnia.

Methods
Patients enrolled in this trial complete baseline measures and then are randomized to either an online version of Cognitive Behavioral Insomnia Therapy (CBTI) or no additional treatment beyond their CPAP therapy (CTRL). After 8 weeks of treatment all patients are reassessed. The current report considers changes in scores on the ISI and Epworth Sleepiness Scale (ESS) as well as average minutes of nightly CPAP use from pre-treatment to the end of the initial 8 weeks of online treatment relative to the no treatment CTRL. The sample for this report included the first 276 participants enrolled in this trial (mean age = 56.5±12.5 yrs; 58.7% females).

Results
Those receiving online CBTI showed greater reductions in their ISI scores from baseline to the end of the initial 8-week treatment phase than did those in the CTRL group (p = .0001). Average ISI score improvements among those receiving online CBTI moved patients from moderately severe insomnia to mild insomnia symptoms. In contrast, no differences were noted between the online CBTI and CTRL groups in regard to pre- to post-treatments changes on the ESS (p= .2541) scores or amount of CPAP use (p = .4383).

Conclusion
Whereas online CBTI does not seem to reduce daytime sleepiness or improve CPAP adherence among patients with comorbid sleep apnea and insomnia, it appears to be an effective intervention for reducing insomnia severity for this patient group.

Support (if any)
National Heart. Lung and Blood Institute Grant # 1R01HL130559-01A1
380 The role of self-esteem and sleep on inflammatory markers in young adults
Authors: Squyres Emily , Slavish Danica , Graham-Engeland Jennifer 
Published: May 2021

Abstract

Introduction
Disturbed sleep is common among young adults and is associated with poorer health and developmental outcomes. A large percentage of young adults also struggle with low self-esteem. Together, disturbed sleep and low self-esteem may deplete coping resources, heighten to reactivity to stress, and increase disease risk. Yet no studies to our knowledge have examined interactions between self-esteem and sleep on biomarkers of health among young adults.

Methods
To address this gap, we investigated associations between sleep quality, self-esteem, and two inflammatory markers, C-reactive protein (CRP) and interleukin-6 (IL-6), in a sample of 60 young adults (mean age 25.3 ± 4.0 years old, 53% female, 83% White). Participants completed a baseline survey to assess self-esteem (Rosenberg Self-Esteem Questionnaire) and sleep quality (Pittsburgh Sleep Quality Index [PSQI] sleep quality item), followed by 14 days of self-reported sleep disturbances each morning (PROMISTM sleep disturbances short-form; averaged across the 14 days). A plasma blood draw was then collected to assess CRP and IL-6 approximately one week after the end of the daily portion.

Results
Lower self-esteem (b = -0.04, 95%CI [-0.06,-0.01], p = 0.015) and lower sleep quality were each associated with higher CRP (b = -0.34, 95%CI [-0.62, -0.07], p = 0.015), but not IL-6. Greater daily sleep disturbances were marginally associated with higher CRP (b = 0.37, 95%CI [-0.06,-0.79], p = 0.088]. Interactions between self-esteem and either sleep quality or sleep disturbances did not predict CRP or IL-6.

Conclusion
Our results suggest low self-esteem and poorer sleep are each associated with higher levels of inflammation but may not interact to exacerbate risk. It is possible low self-esteem and poor sleep each lead to negative emotions or engagement in risky behaviors (e.g., substance use, sedentary behavior) that impact levels of inflammatory markers. Overall, our results highlight the importance of assessing both sleep and personality traits in relation to biomarkers of health among young adults.

Support (if any)
American Psychological Association Dissertation Research Award
383 Cortical thickness moderates association between sleep architecture and pain in adults with fibromyalgia
Authors: Nair Neetu , Curtis Ashley , Stephens Riley , Hayse Braden , Studdard Paul , Craggs Jason , McCrae Christina S 
Published: May 2021

Abstract

Introduction
Fibromyalgia is associated with chronic widespread pain and insomnia. While sleep and pain are mutually influential, how the cortical thickness (CT) of pain-related brain regions influences that relationship (i.e., sleep architecture and evening/morning pain) is unknown. This study examined how the CT of two regions involved in pain processing - the anterior cingulate cortex (ACC) and insula - influenced the link between sleep architecture and evening vs next morning pain in women with fibromyalgia.

Methods
Thirty adults with fibromyalgia and insomnia (Mage=57.2, SD=13.1) completed overnight polysomnography and daily diaries (14 days) tracking sleep and pain [0–100 (most intense). Pain discrepancy (PD) is the average difference between nighttime and next morning pain (evening - morning). Participants underwent MRI, and FreeSurfer (v5.1.0) estimated CT. Multiple regressions examined CT’s influence on the sleep stage [%stage1, %stage2, %stage3, %rapid eye movement (REM)] and PD relationship (controlling for age and education). For significant CT moderation, significance of simple slopes at different CT levels were examined: high (1 SD above), average, and low (1 SD below).

Results
Right rostral ACC (rRACC) CT moderated the relationship between %stage2 (B=-1.41, SE=0.6, p=.03, R^2=0.14), %stage3 (B=2.10, SE=0.97, p=.04, R^2 =0.13), %REM (B=2.35, SE=1.12, p=.04, R^2=0.13) and PD. Greater %stage2 was associated with higher morning vs evening pain at high rRACC thickness (B=-0.67, SE=0.22, p=0.005). Greater %stage3 (B=0.97, SE=0.38, p=0.02) and %REM (B=0.92, SE=0.42, p=0.04) were associated with lower morning pain vs evening pain at high, but not average or low rRACC thickness. Left insula CT moderated the association between increased %stage3 and lower morning vs evening pain (B=2.91, SE=1.18, p=.02, R^2=0.34) at the average and high, but not low thickness levels (B=0.36, SE=0.17, p=0.05; B=1.41, SE=0.48, p=0.008 respectively).

Conclusion
High cortical thickness in the rRACC and average to high L-insula cortical thickness moderated the association between restorative sleep (%stage3, REM) and lower morning vs evening pain. Future studies examining the role of CT in pain-related brain regions on the association of restorative sleep with overnight pain processing are warranted.

Support (if any)
National Institute of Nursing Research (NR017168; PI: McCrae). Clinical trial NCT02001077 Sleep and Pain Interventions (SPIN2), University of Missouri (PI: McCrae).
393 Comparison of Oxygenation Abnormalities Between Obstructive Sleep Apnea and Central Sleep Apnea
Authors: Thanaviratananich Sikawat , Cheng Hao , Pino Maria , Sundar Krishna 
Published: May 2021

Abstract

Introduction
The apnea-hypopnea index (AHI) is used as a generic index to quantify both central sleep apnea (CSA) and obstructive sleep apnea (OSA) syndromes. Patterns of oxygenation abnormalities seen in CSA and OSA may be key to understanding differing clinical impacts of these disorders. Oxygen desaturation and resaturation slopes and durations in OSA and CSA were compared between OSA and CSA patients.

Methods
Polysomnographic data of patients aged 18 years or older with diagnosis of OSA and CSA, at University of Iowa Hospitals and Clinics, were analyzed and demographic data were collected. Oximetric changes during hypopneas and apneas were studied for desaturation/resaturation durations and desaturation/resaturation slopes. Desaturation and resaturation slopes were calculated as rate of change in oxygen saturation (ΔSpO2/Δtime). Comparison of hypoxemia-based parameters between patients with OSA and CSA was performed using unpaired t-test.

Results
32 patients with OSA with median AHI of 15.4 (IQR 5.1 to 30.55) and median ODI of 15.47 (IQR 9.50 to 29.33) were compared to 15 patients with CSA with a median AHI of 20.4 (IQR 12.6 to 47.8) and median ODI of 27.56 (IQR 17.99 to 29.57). The mean number of desaturation and resaturation events was not significantly different between patients with OSA and CSA (OSA - 106.81±87.93; CSA - 130.67±76.88 with a p-value 0.1472). 4/15 CSA patients had Cheyne-Stokes breathing, 2/15 had treatment emergent central sleep apnea, 1/15 had methadone-associated CSA and for 8/15, no etiologies for CSA were found. Mean desaturation durations was significantly longer in OSA (20.84 s ± 5.67) compared to CSA (15.94 s ± 4.54) (p=0.0053) and consequently the desaturation slopes were steeper in CSA than OSA (-0.35%/sec ±0.180 vs. -0.243 ± 0.073; p=0.0064). The resaturation duration was not significantly longer in OSA (9.76 s ± 2.02) than CSA (9.057 s ± 2.17) (p=0.2857). Differences between desaturation duration and slopes between CSA and OSA persisted during REM and NREM sleep, and in supine sleep.

Conclusion
As compared to OSA, patients with CSA have different patterns of desaturations and resaturations with lesser hypoxic burden with CSA. This may have implications on the clinical outcomes seen between these two disorders.
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397 Health Insurance Status in Subjects at High Risk for Obstructive Sleep Apnea
Authors: Abbasi Adnan , Thatipelli Sameepya 
Published: May 2021

Abstract

Introduction
Undiagnosed obstructive sleep apnea (OSA) is a major public health problem. Undiagnosed OSA can result in decreased productivity due to absenteeism, increased risk of comorbidities (cardiovascular disease, diabetes, and depression), and increased motor vehicle as well as workplace accidents. Lack of health insurance coverage can lead to undiagnosed and therefore untreated OSA. The objective of this study is to evaluate health insurance status in subjects at high-risk for OSA.

Methods
This is a cross-sectional, population-based study of adults 18 years and older who participated in the 2017–2018 National Health and Nutrition Examination Survey (NHANES). A modified STOP-Bang score was used to calculate OSA risk. This score included all the variables from the standard STOP-Bang questionnaire, except neck circumference, since it was not reported in the NHANES survey. Subjects were divided into two groups: those at low-risk for OSA with a modified STOP-Bang score of ≤ 3 and those at high-risk for OSA with a modified STOP-Bang score of &gt;4.

Results
A total of 4,847 adult subjects were included, which represented 223,385,241 of the U.S. non-institutionalized population. Using the modified STOP-Bang score cutoff of &gt;4, 20.9% of the sample were classified as high-risk for OSA, while 79.1% were classified as low-risk for OSA. 90% of the high-risk OSA group and 85.1% of the low-risk OSA group reported having health insurance. Sociodemographic data will also be analyzed and included.

Conclusion
Approximately 10% of subjects who are at high-risk for OSA reported not having health insurance. This represents over 4.6 million Americans in the non-institutionalized population. Health insurance can improve access to health care. Timely diagnosis and treatment of OSA not only can reduce morbidity and mortality, but can also reduce health care costs.

Support (if any)
CDC for NHANES Data.
407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network
Authors: Yook Soonhyun , Gupte Chaitanya , Han Zhixian , Joo Eun Yeon , Park Hea Ree , Kim Hosung 
Published: May 2021

Abstract

Introduction
Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed.

Methods
We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea.

Results
SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%).

Conclusion
Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome.

Support (if any)
This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).
408 Validation of claim based algorithms for sleep apnea using ICD codes
Authors: Sultana Rizwana , Lam Elizabeth , Hsu Enshuo , Gurski Erin , Sharma Gulshan 
Published: May 2021

Abstract

Introduction
Obstructive sleep apnea (OSA) is a common condition characterized by repeated episodes of partial or complete obstruction of the respiratory passages during sleep. According to recent studies prevalence of obstructive sleep apnea ranges between 9–38%. OSA is associated with increased all-cause mortality particularly associated with cardiac diseases. In order to provide representation of larger population estimates, administrative data using ICD codes have been utilized. Accurate identification of sleep apnea is important for research related to health care utilization and health outcomes. Our aim is to validate an algorithm for identification of patients with obstructive sleep apnea using ICD 10 codes seen at UTMB.

Methods
Patient medical records were collected from University of Texas Medical Branch EHR system. We included patients who visited from 6/1/2015 to 5/31/2018 in pulmonary or primary care clinics who had any sleep disorder diagnostic codes (ICD-10: G47.30, G47.31, G47.33, G47.34, G47.36, G47.20, G47.10, G47.39, G47.8, G47.9, F51.13, F51.09, R06.89, J96.90, R40.0, F51.9, R06.83, R06.3, G47.63, G47.39, Z86.69). Two algorithms were created. First algorithm included patient with sleep diagnostic codes used at 2 separate office visits. Second algorithm included patients with sleep diagnostic codes and evidence of sleep study. The performance of most used codes was calculated individually.

Results
1200 patients were identified with ICD codes used during two office visits. According to the first algorithm with only ICD codes 75% of patients had sleep apnea. Upon addition of evidence of sleep apnea with ICD codes the % of patients with sleep apnea increased to 95.44. Among most used ICD codes, G47.30 had 86.47% patients with sleep apnea according to first algorithm and 96.01% with second algorithm. The percentages for G47.33 was 80.86% and 96.4%, for G47.10, 78.05% and 87.67%, for R40.0 78.91% and 90.63% respectively.

Conclusion
In conclusion, claim based algorithms for sleep apnea diagnostic codes showed good test positive percentages overall, but algorithm with ICD 10 codes with sleep study performed better in identifying patients with sleep apnea than ICD-9-CM codes alone. Similarly, the individual performance of most used ICD codes was highly improved when evidence of sleep study was present.
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