bluuebunny/crossref_metadata_2025 huggingface dataset:
1078 Sleep Problems In Posttraumatic Stress Disorder (PTSD) In A Nationally Representative Us Sample Before And After Controlling For Comorbid Depression: Results From A Nationally Representative US Sample
Authors: Gupta M A , Vujcic B
Published: May 2020

Introduction

The impact of psychiatric comorbidities on sleep disturbances in PTSD have been studied in the National Comorbidity Survey (NCS)(Leskin GA, 2002) and the NCS-replication (Lauterbach D, 2011) studies. We examined sleep problems in PTSD before and after controlling for comorbid depressive disease, in the National Ambulatory Medical Care Survey(NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) which use a multi-stage probability design to collect nationally representative data on health care visits.

Methods

We examined patient visits(1995-2015) from NAMCS/NHAMCS with a PTSD diagnosis (ICD-9-CM 309.81). Both NAMCS/NHAMCS allow ≥3 reasons for visit(RFV) and ≥3 physician-assigned diagnoses (using ICD9-CM codes). The following variables were created: ‘Insomnia’: ICD9-CM codes 307.41,307.42,780.51,RFV 11351; ‘Sleep Disturbance’(SD): ICD-9CM codes 780.5, 780.50, 780.59; ‘Nightmares’: ICD9-CM code 307.4, RFV 11353; Obstructive sleep apnea (OSA): ICD-CM codes 327.23,780.57, RFV 11355, checklist; and ‘Depression’: ICD9-CM codes 296.2, 296.3, 296.82, 311, 296.20-296.36, 300.4.

Results

There were an estimated 37,262,245±3,203,047 (unweighted count or UWC=3,995; 66.4%±1.8% female;.mean±age: 40.39 ± 0.56 years; ‘Depression’ was comorbid with 37.7%±1.5% cases) PTSD patients visits. All sleep variables accounted for 11.2%±1.2% (UWC=303) of PTSD visits with their individual frequencies as follows: ‘Insomnia’6.5%±1.1%(UWC=153); ‘Nightmares’: 1.9%±0.4%(UWC=74); ‘SD’: 2.0%±0.3%(UWC=67); OSA: 1.3%±0.3%(UWC=27). Logistic regression analysis using PTSD versus all other patient visits as dependent variable revealed the following sleep predictors of PTSD after controlling for age and sex and: (i) before controlling for ‘Depression’: ‘Insomnia’: OR=7.16, (95%CI 4.78-10.73); ‘SD’: OR=4.43(95%CI 2.55-7.71); ‘Nightmares’:OR=104.29 (95%CI56.65-192.02); ‘OSA’: OR=1.71(95%CI 0.94-3.12); and (ii) after controlling for ‘Depression’: ‘Insomnia’: OR=2.88 (95%CI 1.87-4.42); ‘SD’: OR=2.84 (95%CI 1.73-4.67); and ‘Nightmares’: OR=58.33(95%CI 26.39-128.94); and ‘OSA’: OR=2.02(95%CI 1.14-3.57).

Conclusion

In a nationally representative sample, the association of PTSD with insomnia, sleep disturbance and nightmares remained significant, albeit decreased, after controlling for the confounding effect of comorbid depression; however the association of PTSD with OSA emerged only after the effect of depression was controlled for.

Support

None
1101 Daytime Interactions Of Anxiousness, Stressfulness, Sleepiness, And Fatigue In College Students: A Longitudinal Cluster Analysis
Authors: LaJambe C M , Shaffer V N , Brown F M
Published: May 2020

Introduction

Reports of anxiety and stress are increasing among college-age students. Previous findings show bi-directional relationships with nighttime sleep loss and elevated anxiety and stress. Less is known about changes across the day (profiles) in anxiousness and stress, and their relationship to daytime sleepiness and fatigue profiles. The primary objective of this study was to examine these potential interactions.

Methods

Using an ecological momentary assessment design, university undergraduates (N=102, female= 77.5%) made smart phone ratings (0-9 Scale) of anxiousness, stressfulness, fatigue, sleepiness from Monday-Friday, six times per day. Participant were either Low-Anxious (0-1 score: n=34) or High-Anxious (>7 score; n=68) on the GAD-Q-IV Scale, had > 7h sleep per weeknight, and bed- and wake times between 06:30-09:30 and 22:30-02:00, respectively. Mean daytime profile interactions were examined using joint-trajectory longitudinal clustering.

Results

Clustering identified two subtypes, Lo (n=46) and Hi (n=56), which differed primarily in their quadratic curve levels, and in times of highest and lowest ratings. Subtypes differed (p<.001) in overall mean ratings of anxiousness (Lo=1.6±0.9, Hi=3.6±1.1), stressfulness (Lo=2.0±0.9, Hi=4.3±1.1), fatigue (Lo=2.4±1.0, Hi=4.4±1.0), and sleepiness (Lo=2.9±1.0, Hi=4.3±1.1). Subtypes were similar in age (Lo=18.6±0.9, Hi=18.9±1.3), morningness-eveningness BALM (Lo=33.2±4.1, Hi=32.1±5.1) and MEQ (Lo=47.5±6.2, Hi=47.7±6.8) scores, and hours sleep (Lo=8.5±0.8, Hi=8.4±0.9). Notably, more females were Hi subtype (83.9%) than Lo subtype (69.6%). Also, GAD-Q-IV scores differed within subtypes: Hi (80.4% High-Anxiety, 19.6% Low-Anxiety) versus Lo (50.0% for both High- and Low-Anxiety).

Conclusion

Despite apparent adequate nightly sleep, elevated anxiousness and stressfulness may be accompanied by increased daytime fatigue and sleepiness in college students. Furthermore, standardized clinical tests designed to identify levels of anxiety and stress may not align with daily experiences of those states. Better understanding of concomitant daytime profiles of anxiousness, stress, sleepiness, and fatigue could facilitate treatment development and reduce the burden on student counseling centers.

Support

None
1127 Utility of Quantitative EEG During Sleep as a Potential Biomarker of Lewy Body Disease Progression
Authors: Matar E , Ehgoetz Martens K A , Grunstein R R , D’Rozario A , Lewis S J
Published: May 2020

Introduction

Sleep disturbances are common among patients with Lewy body disorders. Idiopathic REM sleep behavior disorder (iRBD) has been identified as a prodromal Lewy body condition with a significantly increased risk of conversion to either Parkinson’s disease (PD) or Dementia with Lewy Bodies (DLB). Pathological involvement of thalamic and brainstem structures involved in sleep regulation has been reported in these disorders, especially in later stages. We hypothesized that progression along the Lewy body disease spectrum may be associated with unique changes in spindle density and EEG power spectra during sleep reflecting involvement of these deep brain structures.

Methods

A cross-sectional design was used. 9 polysomnography confirmed iRBD, 18 early PD, 23 DLB and 13 controls underwent overnight polysomnography, neurological and neuropsychological assessment. Power spectrum analysis during NREM and REM sleep was undertaken using a previously validated quantitative EEG algorithm and compared between groups. Following artefact and outlier removal, results were analysed using the Cz derivation. Groups were statistically compared with a non-parametric Jonckheree-Terpstra test for ordered alternatives, controlling for age and sex.

Results

We found a significant and ordered reduction in power in the spindle frequency band (12-15 Hz) in NREM sleep across the Lewy body disease spectrum compared to controls (Controls > iRBD > early PD > DLB; TJT = 521.00, z = -2.902. p<0.001). In REM sleep we found a shift in power to slower frequencies with increased power in the theta (4.5-8 Hz) band in order of disease severity (DLB > early PD > iRBD > Controls; TJT = 950.00, z = 2.253. p=0.024). No differences were found across the other frequency bands in NREM or REM sleep.

Conclusion

There is a significant and progressive reduction in spindle density and corresponding slowing in REM sleep frequencies during sleep with clinical Lewy body staging. Thus, such measures have the potential to be useful biomarkers of progression towards Lewy body dementia from prodromal stages.

Support

This work was supported by a NHMRC Dementia Team Grant (#1095127), the NHMRC Postgraduate Scholarship and the University of Sydney Research Excellence Initiative 2020 grant.
1221 Flumazenil for the treatment of hypersomnolence and fatigue in a patient with Ehlers-Danlos syndrome with multiple cardiac comorbidities
Authors: Pepito Donna Lea , Markun Leslie , Sampat Ajay
Published: May 2020

Introduction

Ehlers-Danlos syndrome (EDS) is a genetically inherited connective tissue disorder which has a high prevalence of sleep conditions, including obstructive sleep apnea, insomnia, fatigue, and hypersomnia., Chronic fatigue is an important factor in the impaired quality of life in patients with EDS. Successful treatment of fatigue and hypersomnia with traditional wake-promoting medications and stimulants is limited, due to the high prevalence of postural tachycardia, orthostatic intolerance, and other cardiac conditions in these patients. We present a case of EDS with underlying cardiac comorbidities, hypersomnia and fatigue who had significant improvement in excessive daytime sleepiness after treatment with flumazenil.

Report of Case

A 19 yoF with PMH of Ehlers-Danlos syndrome, postural tachycardia syndrome (POTS), autonomic instability and well-controlled depression presented with symptoms of fatigue and excessive daytime sleepiness (ESS of 17/24) despite obtaining 10-15 hours of sleep each day. Polysomnogram followed by MSLT was notable for borderline excessive sleepiness without other abnormalities (PSG: AHI 3/hr, SpO2 nadir 92%; MSLT: 0 SOREMS, mean sleep latency 10 minutes). Prior autonomic and cardiac work-up revealed POTS (maximum HR 180 bpm), orthostatic intolerance and aortic root dilatation. Physical exam and previous laboratory work up for fatigue were unremarkable. A trial of flumazenil 6 mg lozenge every 4-6 hours, as needed for sleepiness, was initiated. On her subsequent visit 6 months later, patient reported 50% improvement in symptoms of fatigue and sleepiness, with decrease in ESS from 17 to 5. No adverse effects to flumazenil were reported.

Conclusion

Flumazenil is a gamma-aminobutyric acid (GABA)-A receptor antagonist that has been previously documented to provide sustained clinical benefit in treatment-refractory hypersomnolence. This case report highlights a successful alternative treatment option for hypersomnolence in patients with EDS and cardiac comorbidities, in which traditional wake-promoting agents and stimulants may be contraindicated.
1229 Management of Severe Sleep Related Disordered Breathing in a Pediatric Case of Vagal Nerve Stimulation
Authors: Ryba-White Benjamin , Molero Helena , Irfan Muna
Published: May 2020

Introduction

Vagal nerve stimulator (VNS) can be an effective treatment for refractory epilepsy but can lead to sleep related disordered breathing in the form of obstructive and central sleep apnea. We describe a pediatric case with VNS developing severe obstructive sleep apnea (OSA) and the course of management.

Report of Case

A 10-year-old boy with history of cerebral palsy, medically intractable epilepsy secondary to Lennox-Gastaut syndrome, and noted improvement in seizure frequency with VNS placement, presented to the pediatric sleep clinic with concern for sleep disordered breathing in the setting of snoring and witnessed gasping at night. The polysomnogram (PSG) showed severe OSA with apnea/hypopnea index (AHI) of 21.8 events/hour and associated hypoxemia. He subsequently underwent adenotonsillectomy. Post procedural PSG demonstrated persistent severe OSA. The obstructive events had rhythmicity temporally coincident with the VNS cycling. Subsequent PSG, after turning VNS off, showed a reduction in AHI to 11 events/hour without hypoxemia. Positive airway pressure (PAP) was initiated during this PSG, however titration was rendered unsuccessful due to VNS activation. A final titration PSG was performed with optimal resolution of OSA with VNS turned off. Since the patient did not have nocturnal seizures, it was recommended to turn the VNS off at nighttime with CPAP use. Patient was noted to have less sleep disruptions at night and more alertness during the day.

Conclusion

This case poses a challenging situation where patient’s epilepsy responded to VNS but the device contributed to OSA and affected PAP titration. One strategy to improve OSA in such cases is to change VNS parameters at the risk of changing stimulation settings effective for seizure control. Turning VNS off during sleep provided a simpler solution while ensuring effective treatment. Thus, clinicians should be vigilant about concomitant OSA treatment in the setting of vagal nerve stimulation, which can improve quality of life.
0138 Behaviorally Assessed Sleep Duration and Oxidative Stress
Authors: Decker A , Cribbet M
Published: May 2020

Introduction

Sleep may promote health by acting as an antioxidant, thereby increasing the body’s resistance to oxidative stress. Lipid peroxidation, a marker of oxidative stress, is one of the key early events in the development of atherosclerosis, the pathologic condition that underlies cardiovascular disease (CVD). Short sleep duration is prevalent in the general population and associated with CVD risk. However, the mechanisms linking short sleep to CVD are not well understood.

Methods

To test the hypothesis that short sleep duration would be associated with higher levels of oxidative stress, we conducted secondary data analysis on a diverse sample of participants (N= 81; Mage = 30.1(SD=10.9); 57% Male; 27% African American) from Pittsburgh, Pennsylvania. Participants wore actigraphs on their non-dominant wrists for 7 days to collect rest and activity data, which were used to derive average sleep duration. Participants provided urine samples and self-reported demographic data, along with daily mood, alcohol use, and physical activity. Oxidative stress was quantified as urinary concentrations of 15-F2t-isoprostane (15-F2-IsoP). Levels of 15-F2-IsoP were measured with a competitive enzyme linked-immunosorbent assay (ELISA) in duplicate. Urinary 15-F2-IsoP was adjusted by urinary creatinine to create an index of oxidative stress that took into account a measure of antioxidant defenses.

Results

Sleep duration was inversely correlated with oxidative stress (b= -.24, p=.03). In multiple regression analyses that controlled for age, sex, race, body mass index, alcohol use, physical activity, and depressed mood, sleep duration remained significantly negatively associated with oxidative stress, β = -.16, t = -2.33, p = .02, R2= .20.

Conclusion

These findings are important because they suggest that a relationship exists between short sleep and increased oxidative damage to lipids. Increasing our understanding of this relationship could motivate new CVD prevention strategies that target the inhibition of oxidative stress through sleep interventions.

Support

This study was supported by a grant from the National Center for Complementary and Integrative Health (AT006694); a grant from the National Institute of Allergy and Infectious Diseases (R01 AI066367); grants from the National Institutes of Health (UL1 RR024153 and UL1 RT000005); and by the John D. and Catherine T. MacArthur Foundation.
0241 Habitual Daytime Sleepiness and the Timing of Use of Alcohol, Tobacco, and Caffeine
Authors: Tubbs A , Hale L , Branas C , Killgore W D , Wills C C , Grandner M A
Published: May 2020

Introduction

Alcohol, caffeine, and tobacco are frequently used in the community, and the timing of use may impact daytime sleepiness. The present analysis examined relationships between daytime sleepiness and timing of alcohol, tobacco, and caffeine use in a real-world sample.

Methods

Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22–60 from the community. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Participants were asked if they had ever used caffeine, alcohol and tobacco. If they reported using a substance, they were then asked which times of day they were likely to use the substance: 5AM-8AM, 8AM-11AM, 11AM-2PM, 2PM-5PM, 5PM-8PM, 8PM-11PM, 11PM-2AM, and 2AM-5AM. Logistic regression analyses examined the relationship between ESS score and likelihood of use of substances at each time.

Results

ESS score was associated with increased odds of ever using alcohol (OR: 1.05, 95% CI: 1.01 to 1.09) or tobacco (OR: 1.04, 95% CI: 1.01 to 1.07). ESS score was associated with an increased likelihood of drinking alcohol in the morning (5AM-8AM, OR: 1.13) and night (11PM-5AM, OR: 1.05). Sleepiness was also associated with increased likelihood of tobacco use in the afternoon (11AM-2PM, OR 1.04) and night (11PM-2AM, OR 1.05). Finally, ESS score was associated with increased likelihood of caffeine use during the midday and afternoon (11AM-5PM, OR: 1.04).

Conclusion

Greater sleepiness is associated with use of alcohol in the morning and at night, and with use of tobacco in the afternoon and at night. Finally, increased sleepiness was associated with caffeine use during the latter part of the workday. Some of these use patterns may be a cause of sleepiness (e.g., morning alcohol use or nighttime smoking) and some a consequence (e.g., daytime caffeine and tobacco use). More research on the impact of real-world sleepiness on real-world substance use is warranted.

Support

This work was supported by a grant from Jazz Pharmaceuticals

Dr. Grandner is supported by R01MD011600The SHADES study was funded by R21ES022931
0281 Sleep Deprivation Impairs Cooperative Behavior Selectively: Evidence from Prisoner’s and Chicken Dilemmas
Authors: Ma N , Lin Y , Mo L
Published: May 2020

Introduction

Cooperation, the cornerstone of human interaction, has attracted much attention since it was indispensable in the contemporary world. However, little research has been done on whether sleep deprivation altered human cooperative behavior. In the present work, we investigated cooperation and sleep deprivation directly, aiming to evaluate the influences of acute sleep deprivation on cooperation with two classical social dilemmas, the Prisoner’s dilemma (PD) and the chicken dilemma (CD).

Methods

All participants (N=24) were required to come for the experiments twice, one time for normal sleep condition, the other time for sleep deprivation condition, with a counter-balanced sequence. In the following afternoon, participants completed the psychomotor vigilance task (PVT) and two social dilemmas tasks, as well as the Karolinska Sleepiness Scale (KSS), the Risk Orientation Questionnaire (ROQ) and the Positive and Negative Affect Schedule (PANAS).

Results

Our results demonstrated that sleep deprivation significantly impaired cooperative behaviors in the CD but not in the PD. In addition, this detrimental effect was not related with the alteration in the risk-seeking, objective alertness, subjective sleepiness, and mood.

Conclusion

The current findings revealed that sleep deprivation impairs human cooperative behaviors selectively. However, the underlying mechanism remains to further explore with neuroimaging studies and focus on the mediating role of general trust and sensitivity of loss.

Support

This research was supported by the National Natural Science Foundation of China (31500906), Guangdong Natural Science Funding - General Program (2019A1515012182).
0371 Educational Disparities in U.S. Elementary School Children are Related to Sleep Duration and Bedtimes
Authors: Woods A D , Morgan P L , Jiao J L , Buxton O M
Published: May 2020

Introduction

Sleep is vital for healthy development in children. Suboptimal sleep health may play an under-appreciated role in educational achievement gaps among vulnerable student populations. Students of color, students from economically disadvantaged homes, and students with disabilities are especially likely to experience poor sleep. Sleep deficiency could interfere with school functioning, including academic achievement.

Methods

Longitudinal data from the nationally-representative Early Childhood Longitudinal Study (ECLS-K: 2011) included ~12,000 students. We tested the hypothesis that parent-reported child sleep duration (typical hours per night) was associated with academic achievement trajectories (3rd-5th grade). We further tested the extent to which this relation is linked to parent-reported bedtimes in kindergarten. Preregistered analyses (osf.io) used structural equation path modeling, stratified by racial/ethnic group (White, Black, Hispanic, Asian), disability status, and socioeconomic status (SES) tertiles. Students were assessed using psychometrically-validated standardized academic achievement tests.

Results

Children with later kindergarten bedtimes had shorter sleep duration across 3rd-5th grade. Children with shorter sleep duration also had poorer achievement in 3rd grade. The path by which sleep associates with achievement differed by vulnerable subgroups. Among children from average- or high-SES families, earlier bedtimes were related to higher reading achievement growth across 3rd-5th grade, but not among children from low-SES families. For children with disabilities, longer sleep duration was significantly and positively associated with growth in reading achievement across 3rd-5th grade, but this relation among children with disabilities was not evident within racial or ethnic groups or SES strata.

Conclusion

Sleep duration, a modifiable behavioral factor, may be a promising target of intervention in families for promoting healthy childhood sleep health behaviors. Results provide evidence that age-appropriate bedtimes and adequate sleep duration could be promoted among vulnerable populations including students with disabilities. For instance, although most screening instruments do not currently evaluate sleep or sleep disorders, assessing and treating disability or behavioral difficulties could include such evaluations for clinical and parent consideration.

Support

Penn State Center for Educational Disparities Research
0385 Risk Assessment of Sleep Disorder Comorbidity Across Active Duty Army Installations from Military Medical Databases
Authors: Brager A J , Hosamane N , Capaldi V , Simonelli G
Published: May 2020

Introduction

The impact of sleep disorders on active duty Soldiers’ medical readiness is clinically significant. Sleep disorders present high comorbidity with disease states directly impacting medical readiness, ranging from musculoskeletal injury (MSK-I), obesity, and drug dependence. The current study performed a risk assessment of sleep disorder comorbidity with MSK-I, obesity, and drug dependence across active duty United States Army installations.

Methods

Health incidences (percent active duty per installation) were queried from the Office of the Surgeon General Health of the Force (HoF) report, specifically for Fiscal Year (FY) 2017 (n = 471,000; 85.5% male, > 70% between 18 -34). Nonparametric ranked tests identified active duty Army installations at low risk (green; < 25% percentile relative to mean rank), moderate risk (amber; 25% - 50%), and high risk (red; > 75% percentile). Linear regressions determined extent of comorbidity of sleep disorders with MSK-I, obesity, and drug dependence (tobacco use and substance abuse).

Results

Mean rank comparisons for sleep disorders vs. injury index (p=0.499), obesity (p=0.306), tobacco use (p=0.378), and substance abuse (p=0.591) did not differ for each installation. Further, there was a high degree of co-morbidity for mean percentage of diagnosed sleep disorder with injury index (p<0.001; r2 = 0.517), obesity (p<0.001; r2 = 0.963), tobacco use (p<0.001; r2 = 0.928), and substance abuse (p<0.001; r2 = 0.968).

Conclusion

In general, large infantry and artillery training units located in the Southeastern United States were “in the red” for not meeting medical readiness standards. A few exceptions include Virginia-Maryland triangle, a heavily populated area. These data demonstrate strong geographical influences on health risk comorbidity in active duty Soldiers comparable to civilian sectors.

Support

Military Operational Medicine Research Program
0432 A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models
Authors: Svetnik V , Wang T , Xu Y , Hansen B J , Fox S V
Published: May 2020

Introduction

Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. It typically involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual identification, or scoring, of the sleep-wake states from these recordings is time-consuming. We sought to develop software for automated sleep-wake scoring capable of processing large databases of multi-channel signal recordings in a range of animal species.

Methods

We used a large historical database of signal recordings and scores in non-human primates, dogs, mice, and rats, to develop a deep Convolutional Neural Network (CNN) classification algorithm for automatically scoring sleep-wake states. We compared the performance of the CNN algorithm with that of a widely used Machine Learning algorithm, Random Forest (RF).

Results

In non-human primates and dogs, CNN accuracy in sleep-wake scoring of data was significantly higher than RF accuracy: 0.75 versus 0.66 for non-human primates and 0.73 versus 0.64 for dogs. In rodents, the difference between CNN and RF was smaller: 0.83 versus 0.81 for mice and 0.78 versus 0.77 for rats. The variability of CNN accuracy was lower than that of RF for non-human primates, dogs and mice, but similar for rats.

Conclusion

We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents.

Support

Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
0458 Smoking and Caffeine Consumption Differ Between Insomnia Phenotypes Based on Objective Sleep Duration
Authors: Vgontzas A N , Fernandez-Mendoza J , Puzino K , Calhoun S L , Krishnamurthy V B , Basta M , Bixler E O
Published: May 2020

Introduction

The insomnia with short sleep phenotype (ISS), in contrast to the normal sleep phenotype (INS), is characterized by physiological hyperarousal including activation of the stress system and cardiometabolic morbidity. The aim of this study was to assess whether these two insomnia phenotypes differ in terms of the use of two common stimulants (i.e., caffeine and nicotine).

Methods

Data from the Penn State Adult Cohort (N=1741) was used in this study (52.2% women, 48.8±13.6 years). A 1-night, 8-hour, polysomnography (PSG) was used to classify subjects into normal (≥6h) and short (<6h) sleep duration groups. Self-reported sleep difficulty was defined based on three levels of severity as normal sleep (n=1022), poor sleep (n=520) and insomnia (n=199). Self-reported heavy caffeine use was defined as ≥3 cups daily and heavy smoking as ≥20 cigarettes daily. Multinomial logistic regression analyses were conducted adjusting for covariates such as age, gender, and race.

Results

Compared to normal sleepers, ISS (OR=0.55, 95% CI=0.31-0.97, p=0.04), but not INS (OR=0.92, 95% CI=0.52-1.64, p=0.77), was associated with significantly less heavy caffeine use. In contrast, INS (OR=2.20, 95% CI=1.10-4.40, p=0.03), but not ISS (OR=0.95, 95% CI=0.41-2.17, p=0.90), was associated with significantly more heavy smoking.

Conclusion

These results indicate that the use of common stimulants (i.e., smoking cigarettes and drinking caffeine) is higher in the INS phenotype than the ISS phenotype. Individuals with the ISS phenotype may be using less caffeine and tobacco to avoid further stimulation of the already hyperaroused physiologic system, which may result in worsening of their insomnia. In the INS phenotype, changes in health behaviors should be an important part of a multidimensional approach to treatment.

Support

American Heart Association (14SDG19830018), National Institutes of Health (R01HL51931, R01HL40916)
0594 Can a Deep Convolutional Neural Network Extract Diagnostic Information on Obstructive Sleep Apnea from Images?
Authors: Tsuiki S , Nagaoka T , Fukuda T , Sakamoto Y , Almeida F R , Nakayama H , Inoue Y , Enno H
Published: May 2020

Introduction

Lateral cephalometric radiography is a simple way to provide craniofacial soft/hard tissue profiles specific for patients with obstructive sleep apnea (OSA) and may thus offer diagnostic information on the disease. We hypothesized that a machine learning technology, a deep convolutional neural network (DCNN), could make it possible to detect OSA based solely on lateral cephalometric radiographs without the need for either large amounts of subjective/laboratory data or skilled analyses.

Methods

In this diagnostic study, a DCNN was developed (n=1,258) and tested (n=131) using data from 1,389 lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n=867; apnea hypopnea index >30/hour) or non-OSA (n=522; apnea hypopnea index < 5) at a single center for sleep disorders from March, 2006 to February, 2017. Three kinds of data sets were prepared by changing the area of interest using a single image; original image without any modification (Full Image), image containing a facial profile, upper airway, craniofacial soft/hard tissues, and image containing part of the occipital region (upper left corner of the image; Head Only). A radiologist and an orthodontist also performed a manual cephalometric analysis of the Full Image for comparison. Observers were blinded to the patient groupings. Data analysis was performed from April, 2018 to August, 2019. When the predictive score obtained from the DCNN analysis exceeded the threshold (0.50), the patient was judged to have OSA. The primary outcome was diagnostic accuracy in terms of area under the receiver-operating characteristic curve.

Results

The sensitivity/specificity was 0.87/0.82 for Full Image, 0.88/0.75 for Main Region, 0.71/0.63 for Head Only, and 0.54/0.80 for the manual analysis. The area under the curve was the highest for Main Region (0.92): 0.89 for Full Image, 0.70 for Head Only, and 0.75 for the manual analysis.

Conclusion

A DCNN identified individuals with OSA with high accuracy. This is a useful approach that does not require any laborious analyses in a primary care setting or in remote areas where an initial specialized OSA diagnosis is not feasible.

Support

This study was supported in part by the Japan Society for the Promotion of Science (grant numbers 17K11793, 19K10236).
0607 Clinical Phenotypes of OSA in Diverse Hispanics/Latinos: Results from the Hispanic Community Health Study/Study of Latinos
Authors: Gonzalez K T , Tarraf W , Wallace D M , Stickel A , Schneiderman N , Redline S , Patel S R , Gallo L C , Mossavar-Rahmani Y , Daviglus M , Zee P C , Talavera G A , Sotres-Alvarez D , Gonzalez H M , Ramos A R
Published: May 2020

Introduction

Recent work on US non-Latino Whites and Europeans from clinical samples used obstructive sleep apnea (OSA) symptoms to generate OSA phenotypes for individuals with moderate-severe OSA and proposed between 3-5 clusters. Validating these clusters in a diverse Hispanic/Latino community-based population with different biopsychosocial characteristics is crucial for early OSA identification and more personalized treatment.

Methods

This work is based on baseline data from The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). HCHS/SOL is a prospective cohort study designed using a multisite (Bronx, NY, Chicago, IL, Miami, FL, San Diego, CA) multistage probability sample. The subpopulation of interest included adults 18-74 years (unweighted n=1,623) meeting criteria for moderate-severe OSA symptoms (≥15 Apnea-Hypopnea index (AHI) events per hour). We performed latent class analysis (LCA) using 15 common OSA symptoms to identify phenotype clusters.

Results

Average age was 52.4 ± 13.9 years and 34.1% were female. Mean AHI was 33.8 ± 22.5 events per hour. Fit statistics and clinical significance suggested that a three-class solution provided best fit to the data. The symptom profiles were consistent with (1) a Minimally Symptomatic group (46.8%), (2) a Disturbed Sleep group (38.1%), and (3) a Daytime Sleepiness group (15.1%). Validation analyses using alternative hierarchical and partitioning algorithms also suggested support for a three-class solution.

Conclusion

Sleep apnea phenotypes among diverse Hispanics/Latinos were consistent with recent findings from the Sleep Apnea Global Interdisciplinary Consortium. However, we found notable differences in the prevalence of these clusters relative to Whites. This suggests that other biopsychosocial factors may be contributing to OSA phenotypes among Hispanics/Latinos. Identification of OSA phenotypes in Hispanics/Latinos could inform better sleep interventions and therapeutics and help better align public health resources.

Support

5R01AG048642-05; R21AG056952; R21HL140437.
0636 Using AI To Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions
Authors: Hevener W , Beine B , Woodruff J , Munafo D , Fernandez C , Rusk S , Nygate Y , Glattard N , Piper D , Sheedy C , Simpson M , Turkington F , Shokoueinejad M
Published: May 2020

Introduction

Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence.

Methods

We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions.

Results

The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups.

Conclusion

The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence.

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0643 Daytime Intra-Oral Neuromuscular Stimulation Therapy on Patients With Mild Obstructive Sleep Apnoea
Authors: Kotecha B
Published: May 2020

Introduction

Upper airway muscle stimulation can improve pharyngeal muscle tone and thus could be considered for treatment of obstructive sleep apnoea (OSA). We present a novel device SnooZeal® that uses daytime awake neuromuscular electrical stimulation (NEMS) as an application to induce oropharyngeal muscular training. This study demonstrates objective and subjective usefulness of SnooZeal® in improving Mild OSA.

Methods

Prospective cohort study of 40 patients with Mild OSA (Apnoea-Hypopnea Index of 5-15/hour) was conducted. Objective respiratory parameters were recorded with two consecutive WatchPat sleep studies before and after treatment with the SnooZeal® device. The device was used by the patients for 20 minutes every day for 6 weeks. Furthermore, visual analogue scale for the snoring was completed by the partner as was the Epworth Sleepiness Score (ESS) as secondary outcome measures over the duration of the study.

Results

A statistically significant improvement was demonstrated in this cohort with mean AHI (Apnoea-Hypopnea Index) dropping from 9.91/hour prior to treatment to 5.2/hour following the treatment. The Oxygen desaturation index also reduced from a mean of 7.9 to 4.7 and similarly, there was also a statistically significant drop in the ESS from 8.86 to 5.43.Objective reduction in snoring was also demonstrated on the sleep studies in 95% of patients with an average reduction of 48% which was reflected subjectively by 40%reduction as recorded on visual analogue scale by partners.

Conclusion

SnooZeal® device study in this cohort of mild OSA patients demonstrates encouraging results. It offers the advantage of not requiring a device in situ whilst asleep which many patients find annoying and intolerable and thus a useful treatment option in patients with snoring and mild OSA. The device improves the muscle tone of the tongue and hence the upper airway dimensions during sleep.

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0702 Phrenic Nerve Stimulation Improves Oxygenation And Quality Of Life In Patients With Central Sleep Apnea And History Of Cerebrovascular Accidents
Authors: Schwartz A R , Khayat R , Sundar K M , Germany R , McKane S , Costanzo M
Published: May 2020

Introduction

Central sleep apnea (CSA) is a recognized complication of cerebrovascular accidents (CVA), although CSA treatment in this setting is of uncertain benefit. A recent randomized clinical trial showed that transvenous phrenic nerve stimulation (TPNS, remedē system, Respicardia Inc.) treats CSA. The clinical impact of TPNS on CSA outcomes in the subgroup of patients with prior CVA was studied.

Methods

Six subjects with predominantly CSA and history of CVA >6 months prior to enrollment in the remedē System Pivotal Trial were analyzed. Patients underwent attended full-night polysomnography prior to TPNS implantation and 6, 12 and 18 months on TPNS therapy. Apnea-hypopnea index (AHI) and sleep metrics were evaluated. The Epworth Sleepiness Scale (ESS) and Patient Global Assessment (PGA) questionnaire were completed at 12 months. Treated patients and former controls (therapy was turned on after 6 months) with 18 months of TPNS therapy were pooled for analysis based on months of therapy.

Results

Apnea-hypopnea index decreased from a median of 47 events/hour [interquartile range: 23, 71] at baseline to 15 [4,24], 17 [6,48], and 12 [8,27] at 6, 12, and 18 months. Comparable improvements were also seen in oxygen desaturation index (4%) and arousal index. Central apnea index decreased from 30 [21,61] to ≤3/hour at each follow up. Compared to baseline, ESS decreased by 5 [-10,-3] and 4 [-8,-4] points at 6 and 12 months, while moderate or markedly improved overall health per the PGA was reported by 4/6 and 3/6 patients, respectively. No patient reported recurrent CVA or transient ischemic attack.

Conclusion

Transvenous phrenic nerve stimulation improved sleep, daytime somnolence and quality of life in patients with CSA and prior CVA. Transvenous phrenic nerve stimulation is a novel therapy that may be an option for treating patients with CSA and prior CVA.

Support

Respicardia and NIH R01 HL 144859
0751 Epworth Sleepiness Scale Test-Retest Reliability Analysis In Solriamfetol Studies
Authors: Rosenberg R , Babson K , Menno D , Morris S , Baladi M , Hyman D , Black J
Published: May 2020

Introduction

The Epworth Sleepiness Scale (ESS) measures excessive daytime sleepiness. This analysis examined test-retest reliability of ESS scores in participants with narcolepsy or obstructive sleep apnea (OSA) in solriamfetol studies.

Methods

Intraclass correlation coefficient (ICC) estimates and 95% confidence intervals (CIs) for ESS scores from two 12-week, placebo-controlled trials (1 narcolepsy; 1 OSA), and one long-term open-label extension (OLE) trial (narcolepsy or OSA) were calculated separately for each trial, based on assessments (at time-point pairs) when scores were expected to be stable (at weeks 4 and 8, 8 and 12, and 4 and 12 in the 12-week trials, and weeks 14 and 26/27, 26/27 and 39/40, and 14 and 39/40 in the OLE). ICCs were analyzed for the overall population in each trial and by treatment and adherence to primary OSA therapy. An ICC >0.7 has been recommended as a quality criterion for acceptable test-retest reliability.

Results

In the 12-week narcolepsy trial, ICCs (95% CI) were 0.83 (0.79, 0.87) for weeks 4 and 8 (n=199), 0.87 (0.83, 0.90) for weeks 8 and 12 (n=196), and 0.81 (0.76, 0.85) for weeks 4 and 12 (n=196). In the 12-week OSA trial, ICCs (95% CI) were 0.74 (0.69, 0.78) for weeks 4 and 8 (n=416), 0.80 (0.76, 0.83) for weeks 8 and 12 (n=405), and 0.74 (0.69, 0.78) for weeks 4 and 12 (n=405). In the OLE trial, ICCs (95% CI) were 0.82 (0.79, 0.85) for weeks 14 and 27/26 (n=495), 0.85 (0.82, 0.87) for weeks 27/26 and 40/39 (n=463), and 0.78 (0.74, 0.81) for weeks 14 and 40/39 (n=463). Treatment (solriamfetol combined/placebo) or adherence to primary OSA therapy did not impact reliability.

Conclusion

In 3 large clinical trials of participants with narcolepsy or OSA, the ESS demonstrated an acceptable level of test-retest reliability.

Support

Jazz Pharmaceuticals
0765 Reliability And Validity Study Of The Chinese Version Of Narcolepsy Severity Scale For Adult Patients With Narcolepsy Type 1
Authors: Li C , Zhang C , Han F
Published: May 2020

Introduction

Narcolepsy is a chronic sleep disorder that can affects significantly patient functioning, involving social, work, and affective life. At present, many drugs have been developed to treat narcolepsy efficiently. But there is no Chinese version of Narcolepsy Severity Scale (NSS) available yet, for that the aim of this study is to translate the NSS into Chinese and evaluate reliability and validity of the NSS in adult patients with narcolepsy type 1 (NT1).

Methods

NSS was translated according to the standard procedures of double-back translation and cross-cultural adaptation steps. The NSS was administered to 62 adult patients (42 males, 20 females; mean age 34 years; range 19 to 67 years) with NT1 from April 2019 to December 2019. The validity of the scale was assessed by the exploratory factor analysis, discriminant validity and convergent validity. The reliability was assessed by the Cronbach’s α coefficient and test-retest reliability.

Results

Three common factors were extracted and 15 items explained 57.4% of the total variance. Cronbach’s α coefficient for total scale was 0.767 and Cronbach’s α for three dimensions ranged from 0.729 to 0.787. Scores were significant difference between treated and untreated group in dependent samples (p=0.036), but no differences in the independent samples (p>0.05). The NSS had good correlations with Epworth Sleepiness Scale (r=0.302, p=0.017) and Insomnia Severity Index (r=0.526, p=0.000). The NSS showed good test-retest reliability (r=0.72, p=0.029).

Conclusion

The Chinese version of NSS was proved to be valid and reliable and can be used to evaluate the severity and consequences of symptoms in Chinese adult patients with NT1.

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0832 Physical Exercise Improves Sleep and Muscle Function in Sarcopenic Patients: A Randomized Controlled Trial
Authors: De Sá Souza H , Piovezan R D , Chagas Miranda R E , Silva B M , Tufik S , Poyares D , D’Almeida V
Published: May 2020

Introduction

Sarcopenia is a multifactorial condition that, like sleep debt, affects the elderly and is related to metabolic, endocrine, inflammatory alterations and risk to mortality. Resistance training (RT), in turn, can improve both factors. Aim: investigate the effects of 12-week RT on sleep and muscle function in the sarcopenic elderly.

Methods

28 sarcopenic elderly were equally distributed in 2 groups at random: CTL: who participated in weekly lifestyle change lectures or; RT: who did the progressive load RT. Sleep was assessed by polysomnography, actigraphy and questionnaires. Isokinetic and isometric of peak torque (PT) of skeletal muscle, anabolic and catabolic hormones, pro and anti-inflammatory cytokines concentrations were also evaluated. For intention to treat analysis (Δ) the generalized linear/non-linear for absolute variables or Wilcoxon rank-sum (Mann-Whitney) test. Data are expressed as mean±standard deviation or median, minimum and maximum values and difference witch p<0.05.

Results

The RT reduced the time to sleep onset (16.09±15.21) compared to CTL (29.98±22.57) group after the intervention. The Δ shows that RT had more N3 sleep (median:0.90, min:-13.40, max: 25.00) than CTL (median:-3.35, min:-15.20, max:19.10). The RT increases TTS (median:57.55, min:-204.75, max:220.91 vs median:-9.63, min:-120.98, max:185.57) and improved self-reported sleep quality (median: -1.50, min: -9.00, max: 4.00 vs median: 0.50, min: -3.00, max: 6.00) and sleep efficiency (median: 9.50, min: -15.00, max: 34.00 vs 0.00, min: -28.00, max: 18.00). For all muscle function parameters (extension and flexion knee in isokinetic or isometric PT) RT had higher values compared to CTL group after 12 weeks of intervention (p<0.05). IL-1ra concentrations were higher in RT (median: 0.04, min: -0.02, max: 0.36) vs CTL (median:-0.01, min:-0.12, max:0.07).

Conclusion

Progressive load resistance training improves sleep parameters associated to muscle recovery in elderly people with sarcopenia, along with positive changes in physical performance.

Support

Associação Fundo de Incentivo à Pesquisa (AFIP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
0964 Out Like a Light: Preliminary Results of Parent-Child Dyad Use of an Audio-Based Mobile Application Aiding Bedtime Routine and Sleep Health
Authors: Chung A , Chanko N , Blanc J , Donley T , Robbins R , Brotman L , Jean-Louis G
Published: May 2020

Introduction

Adequate sleep is essential for a child’s growth and development. However, a growing number of children are experiencing trouble falling asleep. Smartphone audio-based mobile applications with soothing melodies and calming nighttime stories may improve sleep onset. Our study examined the efficacy of Moshi Twilight, an app designed to improve sleep onset, among children ages 3-8 years old using a parent-child dyadic approach.

Methods

Our within-subjects pre-post study design focused on healthy children studied over 10 days, spanning 3 weeknights and 2 weekend nights. During the baseline (Days 1-5) and exposure (Days 6-10) conditions the Child Sleep Health Questionnaire was used to measure children’s sleep behavior. The PROMIS and Pittsburgh Sleep Quality Index were used to assess parents’ sleep quality. Parents exposed their child to 1 story per night (15-20 minutes) during the exposured condition. Statistical analysis was based on paired t-tests, independent t-tests, and correlations.

Results

On average, participating parents were 37 (SD +9.6) year-old mothers. The sample was: 60% Black; 20% White, 20% other race/ethnicity. On average, children were 4 (SD + 0.78) years old and 50% male. Paired t-tests showed significant differences in children’s sleep onset within 20 minutes (t=2.582, 95% CI 0.116, 2.634, p= 0.036). Significant correlations were noted for children’s bedtime consistency (r = -0.755, p = 0.030), falling asleep in own bed (r = 0.735, p=0.015) and sleep duration (r = -0.715, p=0.046,) Significant correlations and paired t-test in parents’ sleep onset were also found (r = 0.744, p = 0.014); (mean= -1.2, t= -3.674, 95% CI -1.939, -0.461, p=0.005)

Conclusion

Our results showed that the audio-based sleep app, Moshi Twilight, might be useful in improving sleep health among both children and parents. This could be included in enhance bedtime routine among preschool-aged children.

Support

Bezos Grant and Community Service Plan grant.