bluuebunny/crossref_metadata_2025_split huggingface dataset:
0975 Mediators of the Insomnia-Suicidality Association
Authors: Simmons Zach , Jones Jolynn , Davis Elijah , Sherriff-Clayton Dustin , Jube Eric , Wright Andrew , Cruickshank Jared , Cheney Eric , Mills Andrew , Crawford Olivia , Kay Daniel 
Published: April 2024

Abstract

Introduction
Insomnia severity is a significant risk factor for heightened suicidality, including ideation, attempts, and death by suicide. As a modifiable risk factor, insomnia is a potential target for suicide prevention; however, the models explaining the association between insomnia and suicidality remain unclear. Our model posits that insomnia hinders sleep’s regional recuperative brain functions, contributing to daytime impairments, including emotional dysregulations. Another model links insomnia to suicidality through additional factors including perceived burdensomeness and thwarted belongingness. Each of these potential factors are associated with both insomnia and suicidality. This study aimed to investigate potential psychological mechanisms in the insomnia-suicidality association, using validated measures in a nationally representative sample.

Methods
Through a Qualtrics survey, participants (N=428) completed the Difficulties in Emotion Regulation Scale to assess emotional dysregulation, Interpersonal Needs Questionnaire to measure perceived burdensomeness and thwarted belongingness, Frequency of Suicidal Ideation Inventory to assess suicidality severity, Insomnia Severity Index, and PROMIS–Depression, and -Anxiety short forms. We used regression analyses to investigate the association between insomnia, suicidality severity, and demographic variables. Structural equation modeling was used to determine whether emotional dysregulation, perceived burdensomeness, or thwarted belongingness mediated the insomnia-suicidality severity association, even when accounting for depression.

Results
Insomnia severity was related to greater suicidality (p&lt; 0.001, CI=0.19–0.31). This association was mediated by depression severity (p=0.836, CI=-0.06–0.08). Emotion dysregulation and perceived burdensomeness partially mediated the insomnia-suicidality severity association (β=0.06, p=0.001; β=0.18, p&lt; 0.001), and when accounting for depression severity, fully mediated the insomnia-suicidality severity association (β=0.04, p=0.045; β=0.24, p&lt; 0.001). Thwarted belongingness did not mediate the association before (β=0.02, p=0.142) or after accounting for depression (β=0.01, p=0.412).

Conclusion
This study replicates considerable evidence that insomnia is an important marker and risk factor for suicidality. The mechanisms through which insomnia may confer risk for suicidality include depression, emotion dysregulation, and perceived burdensomeness. Insomnia may be an ideal upstream target for reducing suicidality and its risk factors including depression, emotion dysregulation, and perceived burdensomeness.

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0978 A Spectrum of Altered Non-rapid Eye Movement Sleep in Schizophrenia
Authors: Kozhemiako Nataliia , Jiang Chenguang , Sun Yifan , Guo Zhenglin , Mylonas Dimitrios , Murphy Michael , Zhu Wei , Zhou Zhenhe· , Huang Hailiang , Manoach Dara S ,   GRINS Consortium, Wang Jun , Hall Mei-Hua· , Pan Jen· , Purcell Shaun M · 
Published: April 2024

Abstract

Introduction
Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens.

Methods
Using newly collected high-density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, eLife, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. Sleep EEG preprocessing was conducted using an open-source package called Luna (http://zzz.bwh.harvard.edu/luna/).

Results
We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regime was a greater (albeit still partial) generator of variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated and accelerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG.

Conclusion
We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. That increased NREM heterogeneity was not explained by traditional clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ

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0979 Associations Among Young Adults’ Sleep Quality and Sleepiness, Risk-taking Behaviors, and E-cigarette Attitudes
Authors: Stevens Elise , Lee Donghee , Stevens Hannah , Patterson Freda , Klerman Elizabeth , Rigotti Nancy , Robbins Rebecca 
Published: April 2024

Abstract

Introduction
Young adulthood is both a time of initiation and progression to habitual tobacco use and when risk-taking behaviors (e.g., sensation seeking and impulsivity) peak. Poor sleep quality and greater daytime sleepiness may influence greater risk-taking, which may, in turn, increase susceptibility to use tobacco products. We explored associations among young adults’ sleep quality and sleepiness, risk-taking, and susceptibility and motivation to use electronic cigarettes (e-cigarettes), the most popular tobacco product among this age group.

Methods
In an online survey, young adults recruited from social media who did not use e-cigarettes in the past 30 days (N=75) completed questions about their sleep quality (Pittsburgh Sleep Quality Index [PSQI], higher scores = greater sleep problems) and sleepiness (Epworth Sleepiness Scale [ESS], higher scores = greater daytime sleepiness), risk-taking (sensation seeking and impulsivity), susceptibility to use e-cigarettes, and motivations to avoid e-cigarettes in the future. Using a series of PROCESS mediation models, we examined the association between sleep (quality and sleepiness) and susceptibility and motivation to use e-cigarettes when mediated by risk-taking.

Results
Participants identified as predominantly female (77%), non-Hispanic White (60%), and were on average 23 years old (range: 18 to 26 years). Higher PSQI scores were significantly associated with greater sensation seeking (p = 0.02). Greater sensation seeking was associated with greater susceptibility to use e-cigarettes (p = 0.03). The indirect effects were significant (95% CI [0.002, 0.064]) while the direct effect between PSQI and susceptibility was not. Similarly, higher ESS scores were significantly associated with greater impulsivity (p = 0.01). Greater impulsivity was significantly associated with lower motivations to avoid e-cigarettes (p = 0.01). The indirect effects were significant (95% CI [-0.100, -0.004]) while the direct effect between ESS and motivation was not. No other associations were significant.

Conclusion
We provide preliminary evidence on how worse sleep is indirectly associated with increased susceptibility/motivation to use e-cigarettes through risk-taking. This complex association between sleep health and e-cigarette susceptibility should be further examined.

Support (if any)
This work was supported by the National Institute on Drug Abuse at the National Institutes of Health (NIH, Grant # R00DA046563 Supplement).
0981 Gender Has Inversed Roles in a Model Linking Lifetime Substance Use and Sleep
Authors: Silva Vinicius , Berro Laís , Galduróz José , Tufik Sergio , Andersen Monica 
Published: April 2024

Abstract

Introduction
The knowledge about the relationship between sleep and substance use constantly undergoes change. Many questions still remain, including how substance use initiation correlates to sleep parameters. In order to expand this body of knowledge, this study aimed at evaluating how lifetime substance use predicts changes in objective and subjective sleep parameters.

Methods
Volunteers from the EPISONO 2007, a large-scale epidemiological study assessing sleep in a representative sample from the city of São Paulo, Brazil, filled in questionnaires evaluating their general health, and underwent a full-night polysomnographic exam. For this analysis the first question of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), asking if the individual ever used any of the listed substances, was considered. Age and scores in the Beck Depression and Anxiety Indexes were included as covariates. Gender was also added as an independent variable.

Results
From the total 1,042 participants, 876(388 men and 488 women) were included. Volunteers reported use of cannabis(n=181), cocaine(n=79), inhalants(n=69), amphetamines(n=54), sedatives(n=31), alcohol(n=802) and tobacco(n=538). Lifetime use of cannabis, cocaine and sedatives directly correlated to alterations in sleep latency, REM sleep onset latency, sleep efficiency and scores in the Epworth Sleepiness Scale. Gender significantly interacted with substance use, with women who consumed cocaine and cannabis and men who never did presenting increased total sleep time when compared to their counterpart groups (men who ever consumed these substances and women who never did).

Conclusion
We found not only an effect of the lifetime consumption of popular drugs, such as cocaine and cannabis, but also identified an important role for gender, which seems to have inversed roles in males and females. These results provide an intriguing lead to unravelling a network in which gender, sleep and substance use are present, and advances in this area will be a major step in the investigation of the importance of sleep in the psychopharmacology scenario.

Support (if any)
Our studies are supported by grants from the Associacão Fundo de Incentivo à Pesquisa (AFIP) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP – grant #2020/13467-8 to MLA). MLA is a Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) fellowship recipient.
0984 Sleep, Anxiety, Depression, and Demand for Wearables in Adults with Autism Spectrum Disorder versus Controls
Authors: Reed Derek , Devine Jaime , Choynowski Jake , Hursh Steven 
Published: April 2024

Abstract

Introduction
Sleep difficulties are common in Autism Spectrum Disorder (ASD) populations. While much literature exists on sleep difficulties in ASD children, there is relatively scant research on adults. ASD is frequently co-morbid with symptoms of anxiety or depression, which are independently related to sleep problems. Personalized feedback from wearables may appeal to ASD adults looking to improve their sleep. This study examined sleep quality and severity of anxiety/depression in ASD adults with comorbid anxiety/depression and matched controls and establish demand for information about sleep from wearables in each group.

Methods
Participants were recruited through Prolific; 100 ASD adults with anxiety/depression and 100 matched controls with anxiety/depression completed the survey. Participants completed the Pittsburgh Sleep Quality Index (PSQI), General Anxiety Scale (GAD-7), Center for Epidemiologic Studies Depression Scale (CESD-10), and a task to establish demand for wearables that provide sleep or blood oxygenation information. Student’s t-tests and Cohen’s d examined between-group differences. Pearson's chi-squared test (χ²) examined differences between expected and observed frequencies of GAD-7 categories and poor sleepers (PSQI ≥ 5) between groups.

Results
Participants in the ASD group had higher anxiety (t=3.42, d=0.47, p=0.001) and depression (t=3.23, d=.45, p=0.001) than controls. Bad sleepers had higher anxiety (t=5.20, d=0.99, p&lt; 0.001) and depression (t=5.17, d=0.98, p&lt; 0.001) than good sleepers. Individuals in the autistic group had higher frequencies of moderate or severe anxiety than expected (χ²=12.90, p=0.05); control participants had lower frequencies of poor sleepers than expected (χ²=3.19, p=0.07). Both groups demonstrated more robust demand for sleep data than blood oxygenation data. ASD participants had more robust demand than matched controls for any wearable data.

Conclusion
ASD participants may have a higher burden of mental health and sleep issues relative to controls. The effect sizes suggest that self-report sleep quality was more strongly related to self-report depression and anxiety than autism diagnosis status. The demand task offers a novel framework to examine interest in potential sleep solutions in adult populations with co-morbid mental health diagnoses. ASD participants may have a greater interest in data from wearables; next steps will explore whether feedback from wearables can improve sleep in an adult ASD population.

Support (if any)
NA
0985 Comparison of Various Sleep Measurement Devices in Psychiatric Patients
Authors: Kawai Keita , Iwamoto Kunihiro , Miyata Seiko , Okada Ippei , Fujishiro Hiroshige , Ando Motoo , Noda Akiko , Ozaki Norio , Ikeda Masashi 
Published: April 2024

Abstract

Introduction
Sleep disturbances are key symptoms in psychiatric disorders. Despite the development of various methods to assess sleep patterns and their quality, the concordance between these methods in those with psychiatric disorders is unclear. In particular, it is not well understood how comorbid symptoms of depression and obstructive sleep apnea (OSA), which are common in psychiatric disorders, relate to the concordance of sleep measurements.

Methods
In this study, 62 individuals with psychiatric disorders from Nagoya University Hospital were included as participants. Their sleep was monitored using several

methods
polysomnography (PSG), a portable EEG device called Zmachine, Fitbit, and sleep logs. The morning after the sleep measurement with these objective sleep devices for a single night, participants estimated their sleep duration subjectively. To evaluate sleep parameters, Intraclass Correlation Coefficients (ICCs) were used. This research was carried out following the approval of the Ethics Review Committees at Nagoya University Hospital, and written consent was acquired from all participants.

Results
PSG and Zmachine demonstrated moderate concordance in Total Sleep Time (TST: ICC=0.46), Wake After Sleep Onset (WASO: ICC=0.39), and Sleep Efficiency (SE: ICC=0.40). However, there was a lack of agreement between PSG and Zmachine parameters with Fitbit, and between Fitbit and sleep logs. Sleep logs showed minimal correlation with PSG and Zmachine for TST and WASO, and a slight agreement for SE with Zmachine. When participants with severe OSA were excluded, ICCs for TST, WASO, and SE between PSG and sleep logs improved marginally (TST: ICC=0.27; WASO: ICC=0.38; SE: ICC=0.22). Excluding participants with severe depression, the TST ICC for PSG and sleep logs were slightly elevated (TST: ICC=0.24). However, even when participants with severe depression and OSA were excluded, ICCs between Fitbit and other sleep measurement methods did not elevate.

Conclusion
Our results indicated that Fitbit has a discrepancy when comparing data from PSG and sleep logs in individuals with psychiatric disorders. Although the growing adoption of Fitbit for sleep measurement, it is essential to acknowledge its weakness in the assessment of individuals with psychiatric disorders.

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0986 Inter-Individual Differences in Sleep Architecture of Patients Receiving Methadone for Opioid Use Disorder
Authors: Gardiner Poppy , Finlay Myles , Erwin Julie , Hansen Devon , Quock Raymond , Wilson Marian , Layton Matthew , Van Dongen Hans 
Published: April 2024

Abstract

Introduction
Methadone treatment for opioid use disorder (OUD) reduces drug cravings and promotes abstinence, but sleep disturbances can negatively impact OUD recovery outcomes. Irregular sleep architecture and respiratory disturbance in outpatients administered methadone as medication for OUD (MOUD) are common, but inter-individual differences may be substantial. We investigated the magnitude and stability of inter-individual differences in sleep architecture and respiratory measures in outpatients in their first 90 days of MOUD treatment.

Methods
N=6 adults (42.5±10.4y, four females) enrolled in local MOUD programs participated in this research study. The daily methadone dose varied between participants (75.8±23.2mg, range 50-120mg). All participants reported past use of multiple substances; four reported current mental health disorders; and four reported chronic pain. As part of the study, participants underwent two consecutive nights of cardiorespiratory PSG, with lights off at 21:49 on average (range 21:17-22:16) on both nights. Sleep stages and apnea-hypopnea index (AHI) were analyzed for systematic inter-individual differences using variance components analysis. The stability of inter-individual differences was quantified with the intraclass correlation coefficient (ICC, ranging from 0 to 1 for negligible to complete stability).

Results
On night one, mean±SD was 441.9±21.1min for total sleep time (TST), 9.0±5.2min for sleep latency (SL), 35.3±22.4min for wakefulness after sleep onset (WASO), 28.8±26.6min for N1, 220.8±58.6min for N2, 114.0±77.2min for N3, and 78.3±65.6min for REM. AHI scores were 16.4±9.1, indicating moderate sleep apnea on average. Compared to similarly aged healthy adults, N1 was low, and TST, WASO, N2, REM, and AHI were high. Stability was significant and substantial for N1, N2, REM, and AHI (ICC≥0.68, F≥5.19, P&lt; 0.05).

Conclusion
Despite small sample size, we found substantial, stable inter-individual differences in N1, N2, REM, and AHI, whereas TST, SL, WASO, and N3 did not reach statistical significance. Varying methadone doses, prior substance use, physical and mental health, chronic pain, and (epi)genetic predisposition may partially account for these inter-individual differences. Our PSG data were collected in-laboratory, not naturalistically; results should be interpreted accordingly. Given the substantial, stable inter-individual differences in sleep architecture, a personalized approach to sleep management may be crucial to help improve OUD recovery outcomes.

Support (if any)
WSU ORAP and HSCL
0988 Evaluation of Sleep Disorders in College Student Athletes
Authors: Emert Sarah , Kim Kelly , Huskey Alisa , Taylor Daniel 
Published: April 2024

Abstract

Introduction
College student athletes experience unique factors in addition to those of being a traditional college student that may exacerbate sleep difficulties and/or mental health difficulties which may in turn negatively affect sleep (e.g., competition pressures, training, travel demands). Previous research has found that mental health problems are common in this population, however, less attention has been given to the evaluation of sleep disorders. This study provides an evaluation of sleep disorder questionnaires and comparison with a structured clinical sleep disorders interview (SCISD-R).

Methods
College student athletes (N = 114) from two Universities completed self-report questionnaires and participated in a clinical interview. Average total scores were calculated for the Epworth Sleepiness Scale (ESS), Nightmare Disorders Index (NDI), Reduced Composite Scale of Morningness (rCSM), Sleep Condition Indicator (CSI), Shiftwork Disorder Index (SWDI), STOP-Bang Questionnaire, PROMIS Sleep Disturbance B (SD) and Sleep Related Impairment (SRI), and International RLS Rating Scale (IRLS-RS). Diagnostic criteria were calculated for the SCISD-R, NDI, SCI, and SWDI. Endorsement of Insomnia Identity (“I am an Insomniac”) was obtained.

Results
Averages and standard deviations are as follows: ESS = 6.75±3.16; NDI = 2.83±3.38; rCSM = 20.42±2.92; SCI = 24.87±5.92; SWDI = 3.11±2.03; STOP-Bang = 1.28±1.06; PROMIS SD = 18.05±6.35; PROMIS SRI = 17.37±6.46; IRLS-RS = 2.70±4.62. Diagnostic criteria for assessments and clinical interview with shared symptoms are as follows: Nightmare disorder: 4 participants met criteria on the NDI and 3 met criteria on the SCISD-R. Insomnia disorder: 1 participant met criteria on the SCI and 10 met criteria on the SCISD-R. Circadian disorder - Shiftwork type: 11 participants met criteria for on the SWDI and 1 met criteria on the SCISD-R. Additionally on the SCISD-R, 5 participants met for hypersomnolence, 5 met for possible obstructive sleep apnea, and 1 met for possible narcolepsy. 6 participants endorsed an insomnia identity.

Conclusion
College student athletes endorse symptoms across a variety of sleep disorders. Differences in the number of participants who met criteria based on assessment measures versus the structured clinical interview indicate that alternate cutoff scores may be warranted for this population.

Support (if any)
Pacific-12 Conference Student-Athlete Health and Well-Being Initiative Grant