bluuebunny/crossref_metadata_2025_split huggingface dataset:
0270 EEG Slow Wave Coherence is Related to PSG-Derived Measures of Sleep Fragmentation and Cognition in the WashU BASE Cohort
Authors: Milman Noah , Reynolds Christina , Balba Nadir , Ju Yo-El , Lim Miranda 
Published: May 2022

Abstract

Introduction
There exists a bidirectional relationship between sleep disruption and neuropathology in Alzheimer’s disease (AD). Electroencephalogram (EEG) during polysomnography (PSG) provides an opportunity to examine stereotyped, coordinated brain activity. Specifically, slow wave activity (SWA), a defining feature of non-REM sleep, is aberrant in AD, and disruption of SWA in healthy adults is related to increased amyloid-beta levels. Coherence of slow waves across different cortical regions may serve as a metric of brain network coordination, sensitive to earliest AD pathology. We explored slow wave coherence during sleep in relation to sleep macrostructure and cognitive performance in the Biomarkers of Alzheimer’s Disease in Sleep and EEG(BASE) cohort.

Methods
EEG was collected during an attended overnight PSG from the BASE cohort (n=79, average age = 70.8 ± 4.4 years), approximately 20% of whom had Clinical Dementia Rating (CDR) of 0.5-1. A custom slow wave peak detector was implemented in MATLAB to across 6 EEG leads (C3, C4, F3, F4, O1, and O2), and slow wavecoherence was calculated as the proportion of sleep with slow waves occurring within 100ms in all 6 leads.

Results
EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM. Slow wave coherence in N2/N3 was negatively correlated with measures of sleep fragmentation (wake after sleep onset Spearman r = -0.3, p 0.008; number of awakenings r = -0.39, p 0.0004), and positively correlated with sleep efficiency (r = 0.28, p = 0.013). Slow wave coherence in N2/N3 was correlated with overall cognition as measured by MoCA adjusted z-score (r = 0.2, p 0.087) and executive function as measured by Trailmaking B adjusted z-score (r = 0.25, p 0.038).

Conclusion
Slow wave coherence was strongly correlated with measures of sleep quality. Reduced slow wave coherence was associated with poorer cognitive executive function. EEG slow wave coherence is a novel, promising approach to measure coordinated brain network function that is sensitive to early cognitive dysfunction in AD.

Support (If Any)
NIH R01 AG059507, ARCS Foundation Scholar
0307 Is Poor Sleep Associated with Use of Multiple Benzodiazepine Receptor Agonists in Older Veterans?
Authors: Ghadimi Sara , Alessi Cathy , Kelly Monica , Martin Jennifer , Moore Alison , Grinberg Austin , Zeidler Michelle , Dzierzewski Joseph , Mitchell Michael , Guzman Andrew , Armendariz Jessica , Badr Safwan , Fung Constance 
Published: May 2022

Abstract

Introduction
Benzodiazepine receptor agonists (BZAs) are often prescribed for insomnia in older adults. Polypharmacy increases risk of adverse events in this population in general, particularly when medications within the same class are prescribed. During an ongoing hypnotic deprescribing trial, we explored use of more than one BZA (multi-BZA use) and self-reported sleep quality among older adults.

Methods
Telephone surveys were performed for recruitment to an ongoing BZA deprescribing trial. Participants aged ≥ 55 years who were prescribed at least one BZA (i.e., alprazolam, clonazepam, lorazepam, temazepam, or zolpidem) from a Department of Veterans Affairs pharmacy in Southern California were asked about their use of each BZA over the past 3 months for sleep (yes/no). Multi-BZA use was defined as using &gt; 1 different BZA over the past 3 months. Self-reported sleep items included duration of sleep problems (&lt;3, 3-12, or &gt;12 months), sleep quality (very or fairly good, fairly or very bad), and sleep efficiency (mean total sleep time over time in bed). Analyses compared sleep variables between multi-BZA and non-multi-BZA users (Fisher’s Exact or t-tests).

Results
Among participants (N=359), 152 (42.3%) reported using zolpidem, 41 (11.4%) lorazepam, 39 (10.9%) alprazolam, 31 (8.6%) clonazepam, and 29 (8.1%) temazepam during the past 3 months. 35 (9.8%) participants reported taking more than one of these drugs. 93.9% reported their sleep problems were present for ≥3 months. 68.3% of participants reported their sleep was fairly/very bad, and mean sleep efficiency was 67.9 (SD 18.5). There were no significant differences between multi-BZA versus non-multi-BZA users in duration of sleep complaints (Fisher’s Exact=1.0; p=.842), sleep quality (Fisher’s Exact=0.70; p=0.56) or sleep efficiency (p=0.91).

Conclusion
We found 1 in 10 older adults prescribed a BZA for sleep reported multi-BZA use over the past 3 months. Multi-BZA use was unrelated to duration of sleep complaints, sleep quality or sleep efficiency. Whether the use was simultaneous or staggered, these findings are concerning, given the elimination half-life of most of the BZAs and that polypharmacy, especially within medication class, may increase risk of adverse events (e.g., falls). Further research is needed to explore factors contributing to multi-BZA use.

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NIA R01AG057929, VA IIR 17-234
0309 A multidisciplinary initiative for screening and treating sleep disorders in menopause: experience in a tertiary center
Authors: Garza-Marichalar Jose , Chávez-Luévanos Beatriz , Hernández-Sánchez Elisa , Ferrari-Aquino Ananké , Garza-Leal Jose , Castillo-Saenz Lorena , Sánchez-Gutiérrez Paola , Parra-Mendoza Itzia 
Published: May 2022

Abstract

Introduction
Menopause is a time of adaptation and transition to a new biological situation that implies the loss of reproductive function. The onset of menopause coincides with the onset of other symptoms related to hypertension, weight gain, hormonal changes, osteoporosis, and vascular disease. These hormonal changes could be responsible for some sleep disorders. It is well documented that the prevalence of sleep disorders is higher in post-menopause, regardless of other factors such as aging.

Methods
As part of routine care for patients in a gynecology outpatient clinic, all patients with menopause were screened for sleep disorders using the following clinical tools: the Pittsburgh Sleep Quality Index (PSQI), the Patient Health Questionnaire (PHQ-9), the STOP-BANG Questionnaire, and the Epworth Scale.A full clinical history was also taken, focusing on sleep habits and risk factors that could contribute to sleep difficulties.Patients that presented with any sleep disorders were assessed and continued their treatment depending on these results.

Results
Between August 2021 and December 2021, a total of 60 patients going through menopause that attended the gynecology outpatient clinic for other reasons were screened for sleep disorders.Using the PSQI we identified 51 patients with low quality sleep (defined as a score of 5 or higher using this tool).Depression was also identified in a total of 43 patients, none of which were receiving treatment. All these patients also reported low-quality sleep and other problems such as hypersomnia, which in turn was identified in 15 patients using the Epworth Scale.Thirty-five patients were identified as having at least 3 risks factors for obstructive sleep apnea with the STOP-BANG questionnaire and were ordered a polysomnography.

Conclusion
Sleep disorders are highly prevalent in menopause. With this initiative, we present our experience and results incorporating routine screening for these disorders in a clinical setting outside of a sleep clinic.Integrating this information can be helpful for working in multidisciplinary teams aimed at reducing chronic diseases and mortality in these patients. This is a multidisciplinary approach that will continue in our institution.

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0336 Sleep Time and Risk Taking Behaviour in Night Shift-Workers
Authors: Ravi Anushree , Bryans Helena , Ruprich Melissa , Burgeess Helen , Roth Thomas , Drake Christopher , Cheng Philip 
Published: May 2022

Abstract

Introduction
Night shift-workers are integral in a multitude of industries. Many night shift-workers experience sleep loss, which may impact their risk taking behaviour.This study aimed to identify the correlation between total sleep time (TST) and shift-workers' engagement in risk-taking behavior.

Methods
53 night shift-workers participated in the experiment. Polysomnography was conducted to determine the TST of shift-workers prior to completing a task that measured risk taking behaviour. This task simulated a driving scenario in which participants must decide whether or not to proceed at a yellow traffic light. Participants were not informed of the duration of the yellow light, which varied unpredictably in duration. The task consisted of 16 trials, and participants earned points that translated to a cash incentive (25 points = 25 cents). Participants either earned or lost 25 points depending on the success of each trial. Failure was defined as running a red light. Those who proceeded at every trial were categorized as insensitive to risk.

Results
The data revealed that those insensitive to risk had a significantly lower TST value.Those insensitive to risk had a lower mean sleep time (374 minutes, SD=75) compared to those sensitive to risk (415 minutes, SD= 42), p =.01 (Cohen’s d=0.67).

Conclusion
Sleep time is associated with risk-taking behavior in night shift-workers. This study offers insight into the amplification of risk taking behaviours within night shift-workers posed by sleep loss, possibly leading to errors and injuries in the workplace. This association may also suggest that implementation of measures to increase sleep for night shift-workers could decrease risk-taking behaviours.

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K23HL133186
0351 Towards Interpreting Consumer Sleep Data: Distributions of Sleep Scores
Authors: Raymann Roy , Nayak Nyayabrata , Watson Nathaniel , Gahan Luke , Gottlieb Elie 
Published: May 2022

Abstract

Introduction
With the rise of sleep measurement technology becoming widely available to the public, it has become apparent that traditional sleep metrics might not be best suited for a lay audience. Most consumer industry has started including a metric that would capture sleep quality, although the exact calculations of these scores remain proprietary. These novel outcome metrics require a set of reference values in order to become interpretable. Here, we provide reference values for the parameters SleepScore, BodyScore and MindScore as included in the SleepScore Labs non-contact radiofrequency sleep measurement devices.

Methods
SleepScore is a sleep quality metric that includes objectively measured total sleep time (TST), sleep onset latency (SOL) and sleep stage durations, normalized for aged and sex, using reference values of Ohayon et al (2004), ranging from 0-100. BodyScore reflects the normalized amount of deep sleep, whereas MindScore reflects the normalized amount of REM, ranging from 0-100. Data from 40,862 S+ and Max users between 18 and 98 years old were used to calculate distribution statistics.

Results
The average age of users was 53±15 years old. Individual scores of SleepScore, BodyScore and MindScore ranged from 0-100 and their distribution was left-skewed. SleepScore averaged 81±11, with the first quartile (Q1) at 73, median at 81 and third quartile (Q3) at 88, and a mode of 89. BodyScore averaged 81±10 with Q1 at 73, median at 80 and Q3 at 86, and a mode of 84. MindScore averaged 78±10 with Q1 at 72, median at 79 and Q3 at 84, and a mode of 83. Despite being algorithmically normalized for age, average SleepScore increased from 70 to 88 across the age range, BodyScore increased from 71 to 89, and MindScore increased from 75 to 81.

Conclusion
SleepScores, BodyScores and MindScores presented to the average consumer will mostly show them a number in the low 70 to high 80 range. This distribution was intentionally created as being left-skewed to prevent triggering anxiety that may contribute to orthosomnia. Despite the intent to create a normalized score that would not be impacted by age, the data show an increase of scores by age.

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0364 GOOD Sleep Care: Geospatial Optimization of Distant Sleep Care in the VA
Authors: Atwood Charles , Smith Conor , Totten Annette , Boudreau Eilis , Sarmiento Kathleen , Hahn Zachary , Folmer Robert , Tock Jamie , Hotchkiss John 
Published: May 2022

Abstract

Introduction
Obstructive Sleep Apnea (OSA) is very prevalent in the Veterans Administrations (VA) clinical population, affecting about 24% of Veterans. Sleep medicine services in VA suffers from uneven distribution, which especially affects Veterans in rural populations, where care is often not available except by long distance travel. Travel burden is increasingly recognized as a burden on Veteran care that can be ameliorated by telemedicine. Little is known about travel burden and few attempts have been made to quantify it. We have attempted to quantify travel burden for rural Veterans using data from VA's Office of Rural Health supported Enterprise Wide Initiative for Telesleep Medicine. In this project we focus on travel from a Veteran's home to a facility-based sleep clinic visit (traditional face-face visit) vs. how much travel would be avoided if visits were performed using video-based telemedicine with the Veteran at his/her residence.

Methods
Purpose built geocoding software (validated in prior studies) was used to quantify the driving distance from the Veterans home zipcode to the zipcode of the facility where the visits would were performed. The data were taken from the VA's national corporate data warehouse (CDW). Over 4 million sleep encounters were coded. CPT4 codes were used to define the clinic visit encounters from 2016 through 2020. We set the travel range where telemedicine is reasonable based on distance at greater than 40 miles from the VA medical center which is how VA currently defines "distant travel".

Results
No. of Encounters (thous) 2016, 557; 2017, 623; 2018, 678; 2019, 667; 2020, 420Miles ObsTravel (mil) 2016, 38; 2017, 42; 2018, 45; 2019, 43; 2020, 27 Miles Travel if telemed used (mil) 2016, 25; 2017, 27; 2018, 29; 2019, 27: 2020, 17Difference (mil miles) 2016, 13; 2017, 15; 2018, 16; 2019, 16 2020, 10Between 10 and 16 million miles could be saved each year for sleep medicine visits if all travel &gt; 40 miles was converted to video home visits.

Conclusion
Travel burden in this analysis, defined by miles driven to and from a sleep medicine encounter, could be dramatically reduced if telemedicine visits were used for sleep apnea evaluation and management. This reduction in travel equates to a reduction in automobile related use-costs, lower carbon footprint, fewer automobile crashes with injury and death. Assuming that clinical effectiveness of home telemedicine is comparable to traditional visits, these data support the vigorous adoption of telemedicine for sleep medicine services.

Support (If Any)
VA Office of Rural Health; Measurement QUERI, San Francisco VA Medical
0379 At-home Sample Collection for Identification of Alterations in HPA-axis Activity and Cortisol Area Under the Curve
Authors: Kreitzberg Daniel , Gavin Kathleen , Daumeyer Natalie , Seixas Azizi , Bauer Timothy 
Published: May 2022

Abstract

Introduction
Sleep problems are associated with alterations in diurnal cortisol patterns, reflective of altered hypothalamic-pituitary-axis (HPA) activity. At-home collection of dried samples for measurement of urinary free cortisol are useful for assessment of diurnal rhythms because they do not impose the stress or sleep disruption associated with inpatient studies. The purpose of this analysis was to assess urinary cortisol responses among a large real-world dataset of at-home collection kit users and the potential for utility in identifying alterations in HPA-axis activity.

Methods
This retrospective analysis evaluated data from 3,966 individuals who used a Sleep and Stress Test (Everlywell, Inc.) between September 2017 and July 2021. Four dried urine spot samples were collected according to habitual sleep patterns: upon waking (T1), two hours after waking (T2), prior to the evening meal (T3), and at bedtime (T4). Urinary free cortisol normalized to creatinine, indicative of HPA-axis activity was assessed (area under the curve (AUC), wake to bedtime diurnal cortisol slope (DCS), mid-morning (T2) and bedtime (T4) cortisol levels). Sample reference ranges were established from two standard deviations above and below the log-transformed mean at each time point, values were reverted to original units for reporting.

Results
The sample was predominantly female (n=2,832, 71%). Mean age was 42.8 (sd=12.0) years. Seventy-eight individuals had mid-morning (T2) cortisol levels that were higher than the sample reference range (7.0-170.7 ug/g Cr), and 88 individuals below. Bedtime (T4) cortisol levels were elevated in 145 individuals (reference range 1.1-25.0 ug/g Cr). Total AUC for cortisol was high in 93 and low in 81 of individuals. The slope of the diurnal cortisol response was flattened in 20.6% (n=816) of the sample.

Conclusion
This real-world study demonstrates that at-home dried urine sample collection can be used to characterize normal and atypical HPA-axis activity. Convenient assessment of alterations in diurnal cortisol patterns are critical in identifying and understanding the mechanisms of HPA-axis activity common in sleep disruptions/disorders. Future studies utilizing this methodology may prove useful in identifying subpopulations with altered HPA-axis activity, sleep issues, and potential relationships to cardiometabolic health.

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0385 Children, Adolescents, and Their providers: the Narcolepsy Assessment Partnership (CATNAP™) Pediatric Narcolepsy Registry: Baseline Demographics
Authors: Macfadden Wayne , Leary Eileen , Gwadry-Sridhar Femida , Owens Judith 
Published: May 2022

Abstract

Introduction
Limited information is available on the natural history, presentation, and management of pediatric narcolepsy. CATNAP™ is a retrospective/prospective, longitudinal, multicenter, web-based pediatric narcolepsy registry (NCT04899947). The primary objectives are to improve understanding of the natural history of pediatric narcolepsy, characterize symptom presentation and diagnosis, and understand treatment practices and outcomes.

Methods
Eligible children/adolescents (&lt;18 years) had confirmed narcolepsy. Using web-based portals, patients, caregivers, and clinicians completed an initial survey on sociodemographic characteristics; diagnostic, medical, and treatment history; comorbidities; and disease progression.

Results
Patient/caregiver-reported interim baseline data are included (N=25 patients; mean±SD age: 15.6±2.9 years; 52.0% female; 60.0% White; 64.0% narcolepsy type 1 [NT1]; 28.0% narcolepsy type 2; mean±SD age at narcolepsy diagnosis: 11.0±4.0 years). At narcolepsy diagnosis, the percentages of participants who were &lt;10, 10 to 15, and ≥16 years of age were 36.0%, 52.0%, and 12.0%, respectively. Symptoms at first diagnosis included excessive daytime sleepiness (EDS; 96.0%), cataplexy (64.0%), disrupted sleep (64.0%), vivid dreams (52.0%), and nightmares (40.0%). Comorbid psychiatric disorders were present in 36.0% of participants and included anxiety disorder (20.0%), depression (16.0%), attention-deficit/hyperactivity disorder (ADHD; 12.0%), and panic attacks (8.0%). Misdiagnosis of narcolepsy was reported in 32.0% of participants; alternative diagnoses included anxiety disorder, ADHD, sleep apnea (all 8.0%), and obsessive-compulsive disorder (4.0%). Physician specialties that confirmed narcolepsy diagnosis included neurologists and pediatricians (each 24.0%), pulmonologists (16.0%), pediatric neurologists (12.0%), general practitioners/internists (8.0%), and endocrinologists (4.0%). In participants with NT1 (n=16), warning symptoms for cataplexy were reported by 43.8% and included a sense that cataplexy was imminent without physical symptoms, a sense that time had somewhat suspended, fear/fright, and a feeling of warmness (all 6.3%). At diagnosis, the number of cataplexy episodes per day in order of frequency was 2 (37.5%), 3 (25.0%), 4 (18.8%), and 1 (12.5%). Current medications for narcolepsy included stimulants (60.0%), wake-promoting agents (40.0%), sodium oxybate (32.0%), serotonin-norepinephrine reuptake inhibitors (16.0%), and selective serotonin reuptake inhibitors (8.0%).

Conclusion
Interim baseline data from CATNAP provide valuable information on the experience and management of pediatric narcolepsy that will facilitate education of patients and caregivers, inform clinical decision-making, and potentially improve treatment strategies.

Support (If Any)
Jazz Pharmaceuticals.
0387 Weight Changes During Treatment With Lower-Sodium Oxybate in a Phase 3 Clinical Study in Patients With Idiopathic Hypersomnia
Authors: Dauvilliers Yves , Chandler Patricia , Hickey Luke , Chen Abby , Steininger Teresa , Foldvary-Schaefer Nancy 
Published: May 2022

Abstract

Introduction
Treatment with sodium oxybate (SXB) has been associated with weight loss in patients with narcolepsy. Lower-sodium oxybate (LXB) contains the same active moiety as SXB, with 92% less sodium, and is approved in the United States for the treatment of idiopathic hypersomnia in adults. This analysis assessed weight changes during LXB treatment in a phase 3 clinical study (NCT03533114).

Methods
Participants 18–75 years of age with idiopathic hypersomnia (treatment naive or taking an alerting agent [with or without SXB] at study entry) began LXB treatment in a 10- to 14-week, open-label, optimized treatment and titration period. After a 2-week stable-dose period (SDP) on their optimized dose of LXB, participants were randomized (1:1) to LXB or placebo for a 2-week, double-blind, randomized withdrawal period, followed by a 24-week open-label extension (OLE).

Results
Study participants (N=154) had a mean (SD) age of 40.3 (13.7) years; baseline mean (SD) weight was 76.9 (18.6) kg, and baseline mean (SD) body mass index (BMI) was 27.1 (5.9) kg/m2. At baseline, 1.3% (2/154) of participants were underweight (BMI &lt;18.5 kg/m2), 40.3% (62/154) of participants had a normal weight (BMI 18.5 to &lt;25 kg/m2), 33.8% (52/154) were overweight (BMI 25 to &lt;30 kg/m2), and 24.7% (38/154) were obese (BMI ≥30 kg/m2). At the end of the SDP, 28.7% (31/108) of participants had weight loss ≥5%. Mean (SD) change in weight at the end of the SDP (n=108) was −2.5 (4.1) kg. Mean (SD) decreases in weight at the end of SDP were numerically greater in participants with higher baseline BMI (normal baseline BMI, −1.8 [3.0] kg; overweight baseline BMI, −2.8 [3.1] kg; obese baseline BMI, −3.2 [5.9] kg).

Conclusion
In this phase 3 clinical trial, adults with idiopathic hypersomnia treated with LXB experienced weight loss, including weight loss ≥5% in 28.7% of participants. Mean weight loss was greater in participants with a higher baseline BMI.

Support (If Any)
Jazz Pharmaceuticals.