bluuebunny/crossref_metadata_2025_split huggingface dataset:
0665 Does Exercise Have an Effect on Daytime Sleepiness?
Authors: Patel Anshum , Cheung Joseph 
Published: April 2024

Abstract

Introduction
Regular exercise improves sleep quality by reducing sleep onset latency, extending total sleep duration, and enhancing sleep efficiency, which may alleviate daytime sleepiness. However, the specific impact of exercise on daytime sleepiness remains unclear, with conflicting findings in the literature. We analyzed existing interventional studies which used Epworth Sleepiness Scale (ESS) to measure changes in degree of daytime sleepiness before and after an exercise regimen, allowing us to explore the relationship between exercise and daytime sleepiness.

Methods
Following PRISMA guidelines, a systematic search was conducted in electronic database of PubMed and Google Scholar from 1991 to the present. Thorough screening and inclusion criteria were applied to identify relevant studies which included ESS assessment before and after an exercise intervention. Also assessed sleep quality by Pittsburgh Sleep Quality Index (PSQI).

Results
Seven studies met all the inclusion criteria and were included in this study. Exercise exhibited a significantly beneficial impact on sleepiness measured by ESS in four out of seven studies we included in review while two studies showed a marginal reduction in ESS without statistical significance, and one study indicated no change. Additionally, exercise showed an improvement in sleep quality in four out of five studies that included PSQI. Variability in results was noted, influenced by factors such as exercise type, intensity, duration, and timing, as well as individual adherence. Additionally, exercise interventions improved sleep quality, as suggested by reduced scores in PSQI which may indirectly improve daytime sleepiness.

Conclusion
This analysis of existing interventional studies on exercise and daytime sleepiness suggests exercise can be beneficial for reducing daytime sleepiness and improving sleep quality. While the exact mechanism remains elusive, several factors contribute to the impact of both acute and chronic exercise on daytime sleepiness such as improved sleep quality, regulation of circadian rhythm, neurotransmitters release, stress reduction, increased energy levels, and weight reduction ultimately alleviating daytime sleepiness. Future research will be essential for addressing gaps on how exercise regimen and the underlying mechanisms may prove efficacious on improving daytime sleepiness.

Support (if any)
Mayo Clinic Florica Research Accelerator for Clinicians Engaged in Research Program
0667 Narcolepsy & Idiopathic Hypersomnia Patient Journey, Clinical Features and Treatments in a Real-World US Population
Authors: Teltsch Dana Y , Bui Bonnie , Anderson Amy , Crawford Stephen , Duhig Amy , Tetali Srini , Diessner Brandon , Veeranki Phani , Pyatkevich Yelena 
Published: April 2024

Abstract

Introduction
Narcolepsy and idiopathic hypersomnia (IH) are central disorders of hypersomnolence. People with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and IH endure long diagnostic delays (up to 15 years), have many comorbidities, and suffer considerable, poorly recognized disease burden. Administrative health data present an opportunity to characterize these patients, their diagnosis, and treatment journey in real-world clinical practice.

Methods
This retrospective cohort study included patients aged ≥2 years with (non-diagnostic) medical claims for narcolepsy or IH between January 01, 2014 and December 31, 2021 in Optum’s Market Clarity integrated claims and EHR database. Patients with ≥24 months continuous enrollment prior to last narcolepsy/IH diagnosis (index date) were classified into NT1; NT2; IH; NT2/IH cohorts. Patient journeys, including diagnostic evaluations and treatments, were described using all enrolled time, while comorbidities were described during a 12-month baseline period.

Results
Overall, 51,548 patients (median age: 45 years [IQR 32–58]; female: 64%) were included with a median pre-index observation period of 52 months: 7,742 (15%) NT1; 31,132 (60%) NT2; 12,287 (24%) IH; and 387 (1%) NT2/IH. Cardiovascular disease (52%), depression (39%), anxiety disorders (38%), obesity (32%) and sleep apnea (40%) were similarly common across cohorts during the 12 months pre-index. Substance-abuse disorders were reported among 18%–19% of narcolepsy and 13%–14% of IH cohorts. Most patients (81%) had a consistent diagnosis (NT1;NT2;IH) throughout the study period, but 9,608 patients had diagnosis switches, most commonly NT2-&gt;NT1 (n=3,355), NT1-&gt;NT2 (n=2,656), NT2-&gt;IH (n=1,187), IH-&gt;NT2 (n=859) or NT1-&gt;IH (n=205). Among patients with these most common switches (n=8,262), 68% had no record of sleep tests between the switches and 38% had no sleep test recorded during all observed time. Common treatments included SSRI/SNRI (57%), stimulants (41%), and wake-promoting agents (39%); 15% of NT1 patients received oxybate and 2.6% pitolisant. Among patients without a diagnosis switch (n=41,940), 26% did not receive any NT/IH medication after diagnosis.

Conclusion
In a large real-world US population, sleep diagnostic evaluations were underutilized and many patients were untreated. A small proportion received narcolepsy-specific medications. These results indicate an unmet need for effective diagnostic and treatment strategies for patients with narcolepsy/IH.

Support (if any)
Funded by Takeda
0668 Narcolepsy Disorders Explainability in EEG via Spectral Band Cluster Prevalence
Authors: Fernandez Chris , Rusk Sam , Nygate Yoav , Vanasse Tom , Sprague Matt , Wodnicki Jan , Glattard Nick , Wickwire Emerson , Watson Nathaniel , Van Veen Barry 
Published: April 2024

Abstract

Introduction
AI models have previously demonstrated clinically promising performance for detecting Narcolepsy Type-1 (NT1) versus clinical control patients in overnight polysomnography (PSG), while explainability of AI detection for complex disorders remains an unsolved challenge. Seeking to increase explanatory power of AI results, we introduce a novel analysis method, Spectral-Band Cluster-Prevalence (SBCP), for clustering and categorizing PSG without AI/ML techniques or sleep scoring measures. We demonstrate the method for explainability of EEG comparisons evaluating Narcolepsy versus clinical control groups.

Methods
Our data source was retrospective EEG/EOG recordings from N=78 PSG participants including n=54 Narcolepsy Type-1 diagnosed patients (based on MSLT findings and patient-reported Cataplexy) with n=24 clinical controls. EEG channels were excluded based on artifact, normalized to maximum voltage, EOGs normalized to in-channel voltage, then extracted into 10-second segments. Signal features were extracted for each segment: EEG delta (1-4), theta (4-8), alpha (8-12), beta (12-30) spectral band-powers and EOG broadband-powers. Feature EEG band-powers were projected into 3-dimensional subspace, where optimal parameters for Gaussian Mixture Model (GMM) were identified to allow overlapping EEG states. Cluster quality measures Silhouette, Davies-Bouldin, Akaike-Information-Criterion were evaluated to determine the optimal number of components (i.e. unique EEG states) required by the GMM to maximize explainability based on global optima in cluster quality values. Dwell Fraction was estimated by assigning components to each 10-second EEG segment, and reported for comparison between NT1 and clinical controls.

Results
The global optima GMM identified n=3 unique components as the optimal number of components for describing 10-second segments of EEG/EOG in terms of explainability and predictability of between-group differences for NT1-vs-controls. The n=3 components GMM showed the highest cluster quality scores in Silhouette (0.23), DB (2.30), and AIC (-7,318,399). Components were characterized by differences in spectral and broadband-power distributions. Dwell Fraction, the percent of sleep-time in each component, revealed statistically significant differences associated with Narcolepsy (Component-1: NT1&lt; Normals, Component-2: NT1&gt; Normals) in Mann-Whitney-U and t-test results. ROC-AUCs were calculated for classifying NT1-vs-Normals, based only on percentage of time spent in each component (Component-1: 0.71, Component-2: 0.78).

Conclusion
We demonstrate novel analytic methods for explainability, SBCP, with potential applications to Narcolepsy disorder-specific EEG biomarkers and AI understandability.

Support (if any)
0671 Racial and Ethnic Characteristics in Adults Living with Narcolepsy
Authors: Romero Ronald Gavidia , Kokinda Sussan , Chung Steve , Pader Enrique Tobias , Uy Roma Balbin , Devarajan Anusha , Kramer Joshua , Gallup Jennifer 
Published: April 2024

Abstract

Introduction
Demographics and polysomnographic characteristics (PC) stratified by race and ethnicity in narcolepsy type 1 and 2 (NC1-2) have been occasionally examined. Despite differences in definitions, most studies in NC1-2 have used race and ethnicity terms interchangeably. This study in adults living with narcolepsy (ALWNC) examined demographics and PC by race and ethnicity separately.

Methods
This cross-sectional study at a large academic center identified ALWNC through detailed chart review. Adults aged ≥18 years with NC1-2 diagnosis were included. We stratified participants based on self-reported race (Asian, Black, White, Others) and ethnicity (Hispanic, non-Hispanic, and Others). Descriptive statistics were obtained per racial and ethnic groups. Linear regression models adjusted for age and body mass index (BMI) at diagnosis, gender, NC1-2, and race or ethnicity (accordingly) were utilized to examine associations between race, ethnicity, and mean sleep latency (MSL) and number of sleep onset REM periods (SOREMPs).

Results
We identified 250 participants; 58% had NC2, 70.8 % were females, mean age and BMI at diagnosis were 29.1±12.9 years and 26.0±6.3 Kg/m2, respectively. Asian, Black, White, and Other races were 8, 28, 194, and 20 participants, respectively. Hispanic, non-Hispanic, and Other ethnicities were 14, 225, and 11 participants, respectively. Blacks had significantly shorter MSL and greater number of SOREMPs than Asians, Whites, and Other races (2.7±2.1 vs. 3.8±1.1, 4.8±2.4, 4.7±2.4 minutes, and 3.3±1.3 vs. 2.4±0.9, 2.5±1.3, 2.0±1.3 SOREMPs, respectively). Other ethnicities had shorter MSL and greater number of SOREMPs than Hispanics and non-Hispanics (3.3±2.9 vs. 4.6±2.4, 4.5±2.4 minutes, and 3.1±1.2 vs. 2.2±1.5, 2.5±1.3 SOREMPs, respectively); albeit, statistically insignificant. Adjusted analyses showed associations between Black race and MSL (β=-1.7, 95% CI [-2.9, -0.6] minutes), and between Black, Other races, and number of SOREMPs (β=0.7, 95% CI [0.1,1.3]; β=-0.9, 95% CI [-1.8, -0.1] SOREMPs, respectively). No associations were observed between ethnicity and MSL or number of SOREMPs.

Conclusion
This study highlighted racial differences in PC among ALWNC. Blacks had shorter MSL and greater number of SOREMPs, while Other races had lesser number of SOREMPs. In addition, this report suggested a trend toward ethnic differences in NC PC.

Support (if any)
0676 The Effects of Circadian Acclimation of Performance, Sleep, and 6-sulfatoxymelatonin on QTc and QT Variability
Authors: Saissan Kiyan , Patel Milin , Mak Natalie , Youngstedt Shawn , Patel Salma 
Published: April 2024

Abstract

Introduction
Jet lag is a circadian rhythm disorder whose effects on QTc and QT variability are unknown. QTc and QT variability are markers of ventricular repolarization associated with ventricular arrhythmias including torsade de pointes and overall mortality. The objective of this study is to understand the impact of simulated jet lag on QTc and QT variability.

Methods
Following a baseline circadian rhythm assessment, participants were placed on a 16 hour wake - 8 hour sleep schedule in which the wake-sleep and light-dark schedule was delayed by 8 hours for 3 days (analogous to traveling 8 time zones west). Participants were randomized to one of 3 treatments administered each of the 3 days of the shifted schedule: (A) placebo control, (B) bright light, and (C) bright light + exercise + melatonin. Participants wore an Extended Wear Holter Monitor for the duration of the 6.5 day laboratory stay. Bazette’s correction was used to correct the QT interval for heart rate. The standard deviation of QTc was used as a marker of QT variability.

Results
15 of the 22 patients completed the study with the Holter monitor in place (86.6% men, mean age 28.4 ± 9.56). In the bright light group, the average QTc in milliseconds (± SD) declined in the in the first ultrashort sleep/wake schedule (406 (19), p=0.04), the shifted light dark and sleep wake schedule (404(23), p=0.053) and second ultrashort period (396 (26), p=0.015) when compared with baseline (410(24)). In the bright light + exercise + melatonin group the average QTc (± SD) increased during the shifted schedule when compared to baseline (403 (40) vs. 410 (40), p =0.02). No other significant changes were noted.

Conclusion
There may be effects of jet lag and its treatment on QTc length though larger studies are needed to further explore this relationship.

Support (if any)
Department of Defense, Philips, American Academy of Sleep Medicine Foundation, National Institutes of Health, The University of Arizona.
0679 Sleep Reactivity in Shift Workers: In Association with Sleep Disturbance, Mood Symptoms, and Quality of Life
Authors: Kim Hanseul , Lee Kyung Hwa , Shin Jiyoon , Seo Won Woo , Jeon Jeong Eun , Lee Ha Young , Yoo Jin Hyuk , Kim Sun-Young , Kim Seog Ju , Lee Yu Jin 
Published: April 2024

Abstract

Introduction
Shift work is associated with sleep disturbance, mood problems, and quality of life. Sleep reactivity, a trait-like characteristic reflecting how stress disrupts sleep, is related to insomnia, depression, and shift-work disorder. We aim to investigate the relationship between sleep reactivity and shift work, exploring the associations between sleep reactivity and sleep disturbance, mood symptoms, or quality of life in shift workers.

Methods
Seventy shift workers (SW, 55 females, 30.47±5.90 years old) and 54 healthy controls (HC, 36 females, 31.76±6.59 years old) participated in the current study. All participants completed self-reported measurements, including the Ford Insomnia Response to Stress Test (FIRST), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Morningness-Eveningness Questionnaire (MEQ), and the World Health Organization Quality of Life (WHOQOL). We compared scores on self-reported measurements between SW and HC. Subsequently, we examined the interaction effect between FIRST and shift work. Within SW, we used multiple linear regression to investigate the relationship between FIRST and scores on self-reported questionnaires, adjusting for age, sex, shift work duration, and chronotype. We used SPSS 22 for statistical analysis.

Results
SW showed higher scores on BDI (t=2.97, p=0.004) and ISI (t=3.04, p=0.003) than HC. In SW, the high sleep reactivity (FIRST≥18) subgroup showed higher scores on ESS, PSQI, ISI, BDI, BAI, and lower WHOQOL compared to the low sleep reactivity (FIRST&lt; 18) subgroup. We found a similar pattern, but only PSQI, BDI, and BAI showed significant differences in HC. Two-way ANOVA revealed an interaction effect between FIRST and shift work on WHOQOL (F=5.636, p=0.020). Multiple regression analysis found that high sleep reactivity was associated with high ISI (p=0.002), BDI (p=0.004), BAI (p=0.020), and low WHOQOL (p&lt; 0.001) in SW.

Conclusion
Our findings suggest that sleep reactivity could be key in shift workers’ quality of life. In addition, current results indicate that high sleep reactivity in shift workers can be associated with increased sleep disturbance, depressed mood, and decreased quality of life. This suggests that sleep reactivity may serve as a predictive factor for shift work tolerance.

Support (if any)
National Research Foundation (No. 2016M3C7A1904338 and No. NRF-2022R1A2C1008209)
0680 Body Clock and Aging: Relationships of Circadian Rhythm with Sleep Quality, Mental Health and Geriatric Conditions
Authors: Adirim Zachary , Pan Yulu , Doyle Margaret , Knauert Melissa , Yaggi Henry , Miner Brienne 
Published: April 2024

Abstract

Introduction
Circadian rhythm is associated with sleep quality and mental health. Research to date has been primarily focussed on adolescent and general adult populations, with little inquiry in older adults, whom have relatively earlier circadian phases. We sought to clarify correlates of circadian function with sleep quality and mental health, in addition to geriatric conditions to further explore these relationships in an older adult population.

Methods
We undertook a cross-sectional study of 67 community-dwelling older adults (≥60yrs) with self-reported sleep complaints. Circadian rhythm was assessed with 7-day actigraphy, to derive sleep midpoint and variables of circadian strength (interdaily stability (IS), intradaily variability (IV), amplitude, and MESOR), calculated using non-parametric or cosinor analysis. Assessments evaluated outcome measures of subjective sleep quality (Insomnia Severity Index [ISI], Pittsburgh Sleep Quality Index [PSQI], Epworth Sleepiness Scale [ESS]), objective sleep physiology (nocturnal polysomnography parameters), mental health (prior psychiatric diagnosis [depression, bipolar anxiety, PTSD], Patient Health Questionnaire of depressive symptoms [PHQ8], Generalized Anxiety Disorder Questionnaire [GAD2]), geriatric conditions (age, frailty [Fried phenotype], cognition with Montreal Cognitive Assessment [MOCA], falls (≥1 in past year), multimorbidity [≥3 chronic conditions], and polypharmacy [≥5 medications]). Pearson correlation coefficients were employed to investigate relationships.

Results
Circadian rhythm markers (lower amplitude, MESOR) were associated with daytime sleepiness (r=0.25-0.29, p values &lt; 0.05). Later sleep midpoint was correlated with reduced total sleep time on PSG (r=0.36, p&lt; 0.05), and lower ISI severity (r=0.29, p&lt; 0.05). Measures of circadian weakness (lower IS, higher IV) were associated with mental health diagnoses (r=0.25-0.30; p values &lt; 0.05), though no significant associations were seen with psychiatric symptoms. Earlier sleep midpoint was correlated with increasing age (r=0.41, p&lt; 0.05). Parameters of circadian weakness (low IS, high IV, low amplitude) were associated with lower cognitive scores (r=0.32-0.44, p values &lt; 0.05) and multimorbidity (r=0.30-0.36, p values &lt; 0.05).

Conclusion
Our data strengthens links between circadian rhythm characteristics with sleep quality and mental health conditions, demonstrating generalizability to older adult populations. Markers of weaker circadian rhythm were associated with cognitive dysfunction and multimorbidity. Future research should examine the association of circadian markers with incident geriatric illness longitudinally.

Support (if any)
NIA (K76AG0749505, P30AG021342)
0682 Circadian Rest/activity Rhythms Are Associated with Daytime Symptoms Among Older Adults with Insomnia
Authors: Jones Ashley , Pfaeffle Claire , Zhao Zhiwei , Verceles Avelino , Johnston Christine , Chen Shuo , Rogers Valerie , Wilkens Kristine , Spira Adam , Buysse Daniel , Wickwire Emerson 
Published: April 2024

Abstract

Introduction
We investigated the association between circadian rest/activity rhythms (RARs) and daytime insomnia symptoms among older adults with insomnia. We hypothesized that: 1) RARs differ between older adults with insomnia and healthy sleepers; and 2) among older adults with insomnia, RARs are associated with daytime insomnia symptoms.

Methods
Older adults with insomnia (n=30) and healthy sleepers (n=33) were recruited from a tertiary academic medical center. Nonparametric RARs were derived from actigraphy data over 14 days: relative amplitude (RA), interdaily stability (IS), intradaily variability (IV), maximum ten-hour activity (M10) and minimum five-hour activity (L5). Secondary parametric RARs included amplitude, acrophase, and midline statistics of rhythm (MESOR). Daytime function was evaluated via the Daytime Insomnia Symptoms Scale (administered via smartphone 4x/day, i.e., 56 assessments per participant) and validated research questionnaires (administered once). Between-groups differences (insomnia vs healthy sleep) were evaluated using t-tests. Among individuals with insomnia, a series of 104 linear regression models examined the association between RARs and daytime symptoms. The Benjamini-Hochberg procedure for correcting false discovery rate (BH-FDR) was used to correct for multiple comparisons.

Results
Relative to healthy sleepers, older adults with insomnia demonstrated significantly lower RA (p=0.028,t=-2.28) and significantly higher L5 (p=0.018,t=2.48). Among older adults with insomnia, IS was positively associated with positive mood (p=0.013, t =2.67) and negatively associated with pre-sleep somatic arousal (p=0.01, t=-3.06). IV was negatively associated with DISS subscales including Alert Cognition (p=0.001, t=-3.64) and Positive Mood (p&lt; 0.001, t=-4.15) and positively associated with Negative Mood (p&lt; 0.001, t=4.74), and Sleepiness/fatigue (p=0.005, t=3.11), as well as validated questionnaires measuring depression (p=0.02, t=2.47), negative mood (p&lt; 0.01, t=3.43), and pre-sleep somatic arousal (p=0.01, t=3.77). M10 was negatively associated with depression (p&lt; 0.0,1 t=-3.29) and anxiety (p=0.01 t=-2.9). Of parametric RARs, amplitude was negatively associated with negative mood (p=0.005, t=-3.08) and depression (p=0.01, t=-2.75), and positively associated with positive mood (p=0.010, t=2.77). MESOR was negatively associated with depression (p=0.02, t=-2.48) and anxiety (p=0.01, t=-2.81).

Conclusion
Among older adults with insomnia, RARs were associated with daytime insomnia symptoms. Future research should examine how to optimize circadian components of therapeutic interventions in insomnia clinical care.

Support (if any)
Merck Investigator Studies Program (#59170).