bluuebunny/crossref_metadata_2025 huggingface dataset:
0286 Predictive Model of Sleep Slow Oscillations Emergence on the Electrode Manifold
Authors: Alipour Mahmoud , Mednick Sara C , Malerba Paola
Published: April 2024

Introduction

Due to slow oscillations (SO; 0.5-1.5 Hz) known role in cognition and health, brain stimulation techniques targeting SOs in closed loop are being developed as potential clinical interventions, as they hold promise for improving cognitive functions. SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location. Enabling prediction of SO emergence across the electrode manifold could remove the need for online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep.

Methods

SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), with 64 EEG channels. We study SO emergence at progressively more refined time scales. Modeling of SO occurrence was achieved for SOs in slow wave sleep (SWS), or in a combination of stage 2 and SWS (S2&SWS). First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape; representing skewness) in successive cycles.

Results

The analysis reveals an exponential decrease in SO count across sleep cycles, with a decay rate per cycle of 1.49 for SWS and 1.09 for S2&SWS. The SO timing model for SWS also demonstrates an exponential increase in model parameters (μ, λ) across cycles, with increase constants per cycle at 0.38 and 2.65, respectively. These findings imply that personalized model parameters can be estimated building on SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution.

Conclusion

This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of stimulation paradigms.

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0297 Comparing Validation Metrics of Machine Learning Algorithms for Actigraphy Data in Children
Authors: Chen Pin-Wei , Walch Olivia , Cielo Christopher , Jansen Erica , Mitchell Jonathan
Published: April 2024

Introduction

Actigraphy methods are evolving to use machine learning algorithms for sleep health estimation, but most algorithms have been trained on adult data. It is not known if such algorithms can be used for sleep/wake estimation in children. We therefore leveraged machine learning models, trained with adult data for sleep prediction, to determine their validity in a sample of children.

Methods

We enrolled 30 children (14 female, 8-16y) referred for in-lab overnight polysomnography at Children’s Hospital of Philadelphia. Participants wore a GENEActiv device (a 3-axis accelerometer, set at 50 Hz sampling rate) on their non-dominant wrist while completing their overnight sleep test. Machine learning models trained using adult data by Walch et al. were applied to the accelerometer data, and aligned into 30-second epochs against the sleep stages from polysomnography scored by a sleep medicine physician. We used the F1 score, a harmonic average of sensitivity and precision, to rank the algorithms.

Results

Overall, 271.5 hours of polysomnography data were collected with 80% of the epochs scored as sleep. Sleep duration median was 7.0 hours (IQR = 2.0), WASO median was 39 minutes (IQR = 39), and sleep latency median was 48.5 minutes (IQR = 52.5). In rank order, the top average F1 scores were 0.91 (SD=0.04) for k-Nearest Neighbor (kNN), 0.86 (SD=0.05) for Neural Net, 0.83 (SD=0.05) for Logistic Regression, and 0.78 (SD=0.06) for Random Forest. Average sleep duration error was lowest for kNN (12.8 minutes [SD = 44.9]) and highest for Random Forest (-142.1 minutes [SD = 57.5]); the average WASO error was lowest for kNN (9.8 minutes [SD = 45.9]) and the highest for Random Forest (118.0 [SD = 58.9]); whereas the average sleep latency error was lowest for Random Forest (-3.4 minutes [SD = 27.6]) highest for kNN (-32.2 minutes [SD = 34. 8]).

Conclusion

Certain machine learning models trained with adult datasets performed well when applied to pediatric data, with kNN being the most optimal in accuracy. However, validity for sleep latency was the most optimal for Random Forest. Further development can potentially reduce the variation of these predictions with pediatric-data-based machine learning models.

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0301 Investigating the Impact of Daily and Nighttime Noise on Sleep Duration: Insights from an Apple Watch Study
Authors: Cheol-Shin Jong
Published: April 2024

Introduction

Noise exposure is commonly believed to influence sleep quality adversely. This study aims to scrutinize the relationship between both daytime and nighttime noise exposure and sleep duration while considering the influence of sleep deficiency from the previous night. The study aims to explore the correlation between daily and nighttime noise exposure and sleep duration, accounting for the impact of insufficient sleep from the previous night.

Methods

In a 6-week study, 15 university students (5 males and 10 females) wore an Apple Watch to collect data on sleep duration, wake time, time in bed, ambient noise levels, daily step count, and previous night's sleep duration. Linear regression models were applied, adjusting for physical activity and previous sleep deficiency, to analyze the relationship between noise exposure and sleep outcomes.

Results

Contrary to initial hypotheses, the study found no statistically significant correlation between either daily or nighttime noise exposure and the assessed sleep parameters (p>0.05). This was consistent even after accounting for the duration of the previous night's sleep and daily physical activity levels.

Conclusion

This study did not find evidence to support a significant relationship between noise exposure (both daily and nighttime) and sleep duration in university students. These results contribute to the nuanced understanding of sleep dynamics and its environmental influences. Further research, potentially involving larger or more diverse samples and varied methodologies, are encouraged. The study also demonstrates the utility of wearable technology in collecting comprehensive sleep and environmental data.

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0303 Predicting Subjective Sleep Impairment and Disturbance from Wearable Sleep Data
Authors: Heneghan Conor , Yetton Benjamin , McDuff Daniel , Allen Nicholas , Barakat Andrew , Hernandez John , Jiang Allen , Schneider Logan , Nelson Benjamin , Winbush Ari
Published: April 2024

Introduction

Wearables offer scalable, passive and objective measures of sleep, but how well do they capture feelings of sleep disturbance and impairment? We studied a large, diverse group (n=2992 adults) using wearables and compared sleep metrics to self-reported sleep disturbance and impairment.

Methods

Participants in the Digital Wellbeing Study wore one of several wearable devices (Fitbit) capable of tracking objective sleep metrics such as Total Sleep Time (TST), bedtime, wake-up time, estimated sleep stage durations etc. over a 4-week period under normal free-living conditions. Self-reported sleep questionnaires were administered at enrollment and at study-end. The wearable-derived objective sleep metrics were summarized across the 7 days prior to administration of the study-end PROMIS Sleep Disturbance (SD) and Sleep-Related Impairment (SI) Short Form surveys. Stepwise Ordinary Least Squares (OLS) regressions were then used to test the predictive power of the objective sleep metrics to estimate the raw survey scores (and compared to predictions from a baseline model of age and sex only).

Results

Sleep variables of TST, resting heart rate, and the variability in total sleep time and restlessness improved both SI and SD above the baseline model. Minutes-in-deep-sleep improved the SI model fit, while longest wake length and total wake minutes improved SD fit. REM percent and normalized nightly heart rate did not improve model fit. The final model combining the set of objective sleep metrics explained 12.9% of the variance of SI (+4.2% over baseline), and 8.4% of the variance of SD (+6.0% over baseline). The most predictive single sleep metric was the variability in total sleep time for SI, and total sleep time for SD.

Conclusion

Previous studies using polysomnography (PSG) or actigraphy derived objective sleep metrics found only modest predictive power of self-reported sleep satisfaction measures (e.g., correlations of 0.3-0.46). This study showed that longitudinal free-living sleep data from wearables provided a similar moderate level of prediction for two self-reported sleep questionnaires. Composite metrics that include measures of sleep variability may provide some additional insight into sleep-related impairment.

Support (if any)

This research was funded by Google Inc.
0310 Multi-center Evaluation of a User Operable EEG Based Automated Sleep Monitor
Authors: Savietto Silvia Frati , Guillot Antoine , Harris Mason , Pathmanathan Jay , Bertaina-Anglade Valérie , Viardot Geoffrey , Hill Derek , Arnal Pierrick , Donoghue Jacob
Published: April 2024

Introduction

Sleep pathology is increasingly being recognized in many neuropsychiatric conditions, including those not traditionally associated with sleep medicine. Improved therapeutic strategies will benefit from EEG-based sleep analytics, but EEG is limited by cost and availability. Here we evaluate the performance and usability of a user operated dry-EEG electrode-based sleep monitoring headband (the Dreem 3S, D3S), which is intended to provide detailed brain EEG activity with automated sleep staging and longitudinal recording ability from the home environment.

Methods

Two prospective studies included 60 subjects who underwent either one overnight in-lab traditional polysomnography (PSG) while concomitantly wearing the D3S, or three nights at home with the D3S. At home, subjects self-applied the D3S. Device usability and automated sleep staging performance were evaluated.

Results

Overall agreement between D3S and PSG scoring was 85.6% across sleep stages. Intra Class Correlation (ICC) for Total Sleep Time (TST), Sleep Efficiency (SE), Latency to Persistent Sleep (LPS), and Wake After Sleep Onset (WASO) exceeded 90%, indicating excellent agreement between D3S and PSG. ICC ranged from 65% to 86% for automated machine learning interpretation of time spent in N1, N2, N3, and REM, comparable or superior to other devices and individual human performance. Usability evaluation demonstrated safe and comfortable use of the device in the home setting, with a System Usability Score (SUS) score of ≥ 68. Subjects were able to operate and record 2-3 nights of high-quality data without additional support or technologist intervention. Furthermore, 96.6% of each record was deemed to be of sufficient EEG signal quality to enable manual expert review.

Conclusion

The Dreem 3S provided accurate sleep-staging and sleep metrics from EEG data acquired in conjunction with in-lab PSG or multiple nights in the home setting. Results were comparable to in-lab PSG results. D3S therefore offers objective EEG-based sleep investigation that is not feasible with traditional PSG, such as longitudinal data acquisition in a patient’s home environment without need for expert technologist applications. Such technology enables expansion of EEG-based sleep analysis for novel conditions.

Support (if any)

This work was supported by Beacon Biosignals, which is a private company with ownership of the D3S.
0311 Artificial Intelligence (AI) to Predict Arousals in Home Sleep Testing (HST) Without Electroencephalography (EEG)
Authors: Jonsson Sigurdur , Zahawi Hrafnkell , Arnardottir Eydis , Erlingsson Ernir , Hlynsson Hlynur , Finnsson Eysteinn , Montazeri Kristofer , Agustsson Jon
Published: April 2024

Introduction

The American Academy of Sleep Medicine recommends scoring hypopneas terminating in either oxygen desaturation or arousals. A limitation of HST, when EEGs are unavailable, is not scoring hypopneas that terminate in arousals. This lowers the average apnea-hypopnea index (AHI) in HST studies and disproportionately affects patients who predominantly have hypopneas that terminate in arousals.

Methods

We report on a deep neural network, Nox BodySleep 2.0 experimental prototype, that predicts arousals and sleep stages using non-EEG signals. The model uses abdomen and thorax respiratory inductance plethysmography (RIP) and activity signals. The model outputs arousal events; and Wake, rapid-eye-movement (REM), and non-REM sleep epochs. It was trained on ~3200 PSG sleep studies from the United States, Europe, and Asia, and validated using 2,407 PSGs from clinical sleep labs in the United States. The performance was measured using epoch-based sensitivity, specificity, and accuracy for scoring arousals. Furthermore, the clinical performance was validated by the sensitivity, specificity, and accuracy for AHI severity classification for the diagnostic cutoff thresholds of AHI ≥ 5 and AHI ≥ 15.

Results

The model sensitivity, specificity, and accuracy was 65%, 85%, 80% for arousal scoring. For AHI ≥ 5, the sensitivity, specificity, and accuracy were 95%, 88%, 94%, respectively. For AHI ≥ 15, the results were 86%, 97%, 92%. When the sleep studies were scored as HST without using the model, the corresponding results for AHI ≥ 5 were 75%, 95%, 77%, and for AHI ≥ 15, 60%, 99%, 81%. When the studies were scored as PSG, the results for AHI ≥ 5 were 96%, 95%, 96%, while for AHI ≥ 15, they were 89%, 98%, 96%.

Conclusion

The model is a promising method of providing conclusive results from HST sleep studies. The model-based AHI is closer to the AHI from a PSG than when using HST. The AI model was trained on a large and diverse dataset and validated on a clinical population from the United States. Predicting arousals in HST studies without EEG improves patients' access to conclusive sleep apnea testing, and may improve health equity and the operation of sleep clinics.

Support (if any)

None
0341 Navigating Sleep: Perceptions of Graduate Health Professions Students
Authors: Hood-Abernethy Amanda , Santurri Laura , Stith Elizabeth , Lee-Iannotti Joyce
Published: April 2024

Introduction

Sleep health is a term used to describe the characteristics of individuals that influence how much sleep they get and how well they sleep throughout the night. Postsecondary students are at a greater risk of poor sleep health due to the demands and changes in college. The purpose of this basic interpretative qualitative study was to understand how being a graduate health profession student influenced sleep practices and habits in Doctor of Physical Therapy (DPT) and Occupational Therapy Doctorate (OTD) students: DPT and OTD students enrolled in traditional (face-to-face) programs were recruited for this study.

Methods

Using the purposeful sampling approach, the researcher recruited eleven participants who met the study’s inclusion criteria. Participants were interviewed about their sleep experiences in a private setting through Zoom. Data analysis was performed through a review of the transcripts. Identifying themes explained participants' experiences, and interpretations were shared with participants as part of the analysis process

Results

Four themes emerged: factors influencing sleep health, consequences of poor sleep, sleep education, and proposed student support.

Conclusion

The sleep experiences of DPT and OTD students identified the need for sleep education and prioritization in graduate health science programs. Knowledge about sleep, specifically sleep hygiene, could assist graduate health science students in their role as clinicians.

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0350 Pathways: Exploring the Relationship Between Shift Work and the Gut Microbiome
Authors: Holzhausen Elizabeth , Clark Kevin , Coffman Maria , Stieneker Zachary , Lim Joseph , Kraemer Gabby , Haslett Charlotte , Patterson William B , Broussard Josiane , Rowe Rachel , Melanson Edward , Wright Kenneth , Alderete Tanya L
Published: April 2024

Introduction

Shift work is associated with sleep and circadian disruption, which may have adverse effects on the gut microbiome. However, little is known about the relationship between shift work and the gut microbiome in free-living humans. In this study, we aimed to investigate whether shift work detrimentally impacts the gut microbiome.

Methods

Day- and night-shift workers were recruited from clinical settings. Mann-Whitney tests were used to compare alpha-diversity (Inverse Simpson), beta-diversity (Bray-Curtis dissimilarity), and taxa relative abundance between groups. Percent differences are reported for taxa that differed based on Mann-Whitney tests (P< 0.05). Because these analyses are preliminary, unadjusted p-values are presented.

Results

Six night- (83% female, age 29 ± 2.5) and six day-shift (50% female, age 33 ± 6.8) workers were included. Mean alpha-diversity was 9.0 among day-shift workers and 8.1 among night-shift workers (P=0.64). Shift work explained 8.5% and 9.7% of variability in genera and species beta-diversity, respectively (P=0.50, 0.38). Night-shift workers had 102% higher mean relative abundance of Intestinimonas (P=0.04), while day-shift workers had 99% higher relative abundance of Mogibacterium (P=0.01). Day-shift workers had 79% higher mean relative abundance of Mogibacterium diversum (P=0.02), while night-shift workers had 87% higher relative abundance of Streptococcus suis (P=0.04) and 108% higher relative abundance of Treponema succinifaciens (P=0.02).

Conclusion

This study provides preliminary evidence that shift work may be associated with alterations to the gut microbiome. For example, we observed differences in the abundance of Intestinimonas and Mogibacterium. Intestinimonas is a butyrate-producing microbe, with anti-inflammatory effects, while Mogibacterium is thought to be deleterious to gut health. These associations are in the opposite direction than we expected, perhaps because participants were from a healthy population and this is a compensatory mechanism, though further exploration is needed. Future analyses will examine the functional potential of the gut microbiota in day- vs. night-shift workers.

Support (if any)

Anschutz CCTSI Pilot Grant Award, CTSA Grant ULI TR002535, Transdisciplinary Training Grant in Sleep and Circadian Research (T32 HD007014), Office of Naval Research MURI grant N00014-15-1-2809.
0360 Obstructive Sleep Apnea and Risk of Insomnia Among Diverse Patient Populations
Authors: Lyu Xiru , Attarian Hrayr , Sankari Abdulghani , Romero Ronald Gavidia , Kaleba Erin , Shedden Kerby , Dunietz Galit Levi
Published: April 2024

Introduction

Concurrent obstructive sleep apnea (OSA) and insomnia have been reported in cross-sectional studies suggesting that 50% of adults with OSA experienced insomnia symptoms, while 43% of adults with insomnia symptoms had OSA. However, prospective studies reporting associations between these two sleep disorders are uncommon. Therefore, this study sought to examine associations between OSA on subsequent insomnia diagnosis among a diverse cohort of adult women and men.

Methods

We utilized electronic health records of 1,971,279 patients age 40+y who obtained care at a large US-based medical center. Patients were stratified into three groups based on self-reported race/ethnicity: Whites, Blacks and Arab-Americans. To create the Arab American group, we implemented a name-based algorithm that has been developed and validated to identify Arab ethnicity. Diagnosis of OSA and insomnia based on ICD-10 codes, as well as age at diagnosis were extracted from medical records. Robust (modified) Poisson regression models, adjusting for sex, race/ethnicity, current age, smoking behavior and age at diagnosis for patients with OSA, were utilized to study the association between OSA and insomnia diagnosis. Effect modification by race/ethnicity was also examined.

Results

The mean age of patients was 66y and over half were women (54%). The cohort was predominately White (87%) and 3% were Arab-Americans. The prevalence of OSA was 4.8%, 5.1% and 3.5% for Whites, Blacks and Arab-Americans, respectively. Overall, patients with OSA were more likely to have insomnia than those without OSA (RR = 6.9, 95% CI: 6.3, 7.5). In addition, we found statistically significant evidence for racial/ethnic differences in the association (p-values for interaction < 0.05). Further racial/ethnic-stratified analysis revealed that, the risk of insomnia for OSA patients increased by 11-fold among Arab-Americans (95% CI: 6.5, 19.2), 7-fold among Whites (95% CI: 6.2, 7.5), and 5-fold among Blacks (95% CI: 3.8, 6.9).

Conclusion

In a large and diverse sample of patients, we found significant positive associations between OSA and insomnia, which were differential among ethnic and racial groups. Arab-Americans with OSA were the most vulnerable to insomnia. These findings suggest ethnic and racial disparities in sleep disorders among Arab-Americans.

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0364 The Role of Hyperarousal in Sleep-wake State Discrepancy in Youths with Insomnia
Authors: WANG Ran , Cheung Forrest Tin Wai , Li Xiao , Chan Ngan Yin , Chan Joey , Wing Yun Kwok , Li Shirley Xin
Published: April 2024

Introduction

Sleep-wake state discrepancy, characterized by a difference in objectively and subjectively evaluated sleep, is common among individuals with insomnia. While it was hypothesized that hyperarousal might play a role, there has been limited research to examine self-reported pre-sleep arousal and objectively measured cortical arousal in youth insomnia. The current study aimed to explore the potential differences in subjective and objective hyperarousal between youths with insomnia and healthy sleepers, and to examine whether hyperarousal mediated the association between sleep-wake state discrepancy and insomnia severity.

Methods

Sixty-six youths with DSM-5 insomnia disorder (age:20.03±2.31; female: 62.12%) and 40 healthy controls (age:19.52±2.25; female: 65%) were included. Participants completed a 7-day prospective sleep diary and actigraphy monitoring, a single-night in-lab polysomnography assessment, and self-reported questionnaires including Insomnia Severity Index for the measure of insomnia severity and Pre-sleep Arousal Scale (PSAS) for the measure of cognitive and somatic hyperarousal. The discrepancy sleep indices were computed by subtracting the data from 7-day actigraphy and sleep diary, where a positive value indicated a subjective underestimation.

Results

There was a significant group difference in sleep-wake state discrepancy in sleep onset latency (13.71 minutes overestimation in the insomnia group vs. 7.44 minutes underestimation in the control group). Moreover, relative to the healthy control group, the insomnia group showed significantly more severe insomnia (p<.01) and higher levels of pre-sleep cognitive and somatic arousal (p<.01), beta-band activity in non-rapid eye movement stage 1 (p<.05), stage 2 (p<.05), and rapid-eye movement stage (p<.05). Subjectively reported pre-sleep cognitive and somatic arousal, but not objective arousal indices, were found to mediate the association between sleep-wake state discrepancy in sleep onset latency and insomnia severity (ab=-0.39, p<.01 ).

Conclusion

Both subjectively and objectively measured hyperarousal is present in youth insomnia. The mediating effect of subjective pre-sleep arousal on the association between sleep-wake state discrepancy and insomnia severity warrants further neuroimaging research on its underlying mechanism. Future studies should also consider exploring the effects of insomnia treatment on self-perceived sleep-related arousal and sleep-wake state discrepancy in youth.

Support (if any)

This work was funded by General Research Fund (Ref. 17613820 and 17613321), Research Grants Council, University Grants Committee, Hong Kong SAR, China
0366 Insomnia in State Medicaid Administrative Claims: Missed Opportunity for Implementation and Dissemination?
Authors: Cheng Philip , Clack Sarah , Bayoneto Alec , Treger Marleigh , Fellman-Couture Cynthia , Kalmbach David , Drake Christopher
Published: April 2024

Introduction

It is well-established that insomnia is more prevalent in under-resourced communities, which is further exacerbated by the limited access to treatment. To address this health need, essential health care services are made available by the government to those without the financial resources to purchase them (e.g., Medicaid). However, it is unclear if services for insomnia are available and accessible even within these programs. This study aimed to estimate the prevalence and patterns of diagnosis of insomnia among recipients of a state Medicaid program.

Methods

We accessed Medicaid administrative data through the state data warehouse to identify beneficiaries who had a service between 11/1/2019 and 10/31/2023 with a diagnosis code for sleep problems. We calculated the prevalence of sleep-related diagnoses out of the total number of beneficiaries with enrollment during the study period.

Results

Of 3.7 million beneficiaries, 310,092 had at least one service during the study period with a sleep problem diagnosis code, for a prevalence of 8.25%. Their most common sleep problem diagnosis codes were G4700-INSOMNIA, UNSPECIFIED (5.65%), F5101-PRIMARY INSOMNIA (1.14%), G479-SLEEP DISORDER, UNSPECIFIED (0.82%), and F5102-ADJUSTMENT INSOMNIA (0.27%). Among beneficiaries with at least one sleep problem diagnosis code, 55.10% had diagnosis codes in the past 6 months indicating a significant mental health condition, substance use disorder, or neurologic condition; and 6.80% had a diagnosis of shift work disorder, free running type and/or restless leg syndrome in the past 48 months.

Conclusion

Other health care administrative datasets have estimated the prevalence of insomnia diagnoses at ~30-35%; in comparison, these findings suggest that insomnia may be severely underdiagnosed and treated among Medicaid beneficiaries. This is a significant missed opportunity for implementation and dissemination of insomnia treatment for low-income adults with Medicaid coverage.

Support (if any)

Support for this study was provided from the National Heart Lung and Blood Institute R01HL159180 awarded to Dr. Philip Cheng.
0368 Prevalence of Insomnia in Patients with Hypoglossal Nerve Stimulation (HNS) at a Tertiary Care Sleep Center
Authors: Vuong Jocelyn , Oster Joel , Grover Aarti
Published: April 2024

Introduction

Insomnia is a sleep disorder that may be under diagnosed and under treated. We reviewed insomnia type in patients who underwent implantation of Hypoglossal Nerve Stimulation (HNS). Hypoglossal nerve stimulation (HNS) is an efficacious option for treating obstructive sleep apnea although there is evolving literature regarding coincidental prevalence of insomnia in this select group of patients.

Methods

A retrospective, single-center, cohort study used medical records from Sept 2017- March 2023 of 144 participants (28% Female, n=40) with HNS. Analysis included insomnia diagnosis date, comorbidities, and the insomnia type. Effective OSA mitigation was defined by < 15 AHI or >50% reduction from baseline AHI and AHI is < 20 with HNS device. HNS adherence and device usage was reviewed at 90 days and 180 days. HNS compliance was assessed using SleepSync™ software.

Results

Of the 144 participants, 30 adults had insomnia (21%). Of the type of insomnia, Psychophysiological (33%, n=10), Sleep onset (27%, n=8), and Sleep maintenance (17%, n=5) were the most frequently diagnosed. Psychophysiological insomnia was unmasked in 33% of the patient after HNS implantation while sleep onset insomnia was reported prior to HNS implantation in 24% of the patients. In the psychophysiological group, 3 patients tolerated HNS after treatment of insomnia although five patients were lost to follow up and two patients had their HNS explanted. The group with psychophysiological insomnia had an increase of pauses on their HNS device at 180 days compared to at 90 days.

Conclusion

Of the 144 participants, 30 adults had insomnia (21%). Of the type of insomnia, Psychophysiological (33%, n=10), Sleep onset (27%, n=8), and Sleep maintenance (17%, n=5) were the most frequently diagnosed. Psychophysiological insomnia was unmasked in 33% of the patient after HNS implantation while sleep onset insomnia was reported prior to HNS implantation in 24% of the patients. In the psychophysiological group, 3 patients tolerated HNS after treatment of insomnia although five patients were lost to follow up and two patients had their HNS explanted. The group with psychophysiological insomnia had an increase of pauses on their HNS device at 180 days compared to at 90 days.

Support (if any)

None
0372 Quetiapine Effect on Sleep, Breathing, and Next Day Performance in People with OSA and Difficulty Maintaining Sleep
Authors: Fauska Cricket , Bastiampillai Tarun , Rawson Georgina , Toson Barbara , Vakulin Andrew , Adams Robert , Wittert Gary , Loffler Kelly , Eckert Danny
Published: April 2024

Introduction

Quetiapine is commonly prescribed "off-label" to people with insomnia symptoms. People with undiagnosed obstructive sleep apnea (OSA) frequently report insomnia symptoms, particularly difficulties in maintaining sleep. In 2020, 10.6 million prescriptions were dispensed for quetiapine in the United States. Yet, there is limited information regarding the effects of quetiapine on breathing, sleep, and next-day performance in people with OSA.

Methods

We performed a double-blind, randomized, placebo-controlled, cross-over study (NCT05303935) in 15 people with OSA who also reported difficulty maintaining sleep. Participants were studied overnight via polysomnography on two separate occasions ~1 week apart and received either 50mg of quetiapine or a placebo (order randomised) just prior to sleep. 10-minute psychomotor vigilance and 30-minute AusEd driving simulator tests were performed each morning.

Results

Participant demographics: 7 women, 8 men, ([Mean±SD] age 61 ± 10 years, BMI 28 ± 4 kg/m2). Quetiapine reduced the apnea/hypopnea index (primary outcome) versus placebo (20±12 vs. 27±16 events/h, p=0.02), arousal index (25±9 vs. 32±16 arousals/h, p=0.02), and increased sleep efficiency (87±9 vs. 80±11, p< 0.01) without worsening hypoxemia (e.g., mean overnight SpO2 94.5±1.5 vs. 94.7±1.2 %, p=0.42). However, next morning vigilance (e.g., median reaction time 382±84 vs. 336±48ms, p=0.02) and driving simulator performance (e.g., steering deviation 95±54 vs. 73±39, p=0.02) were impaired with quetiapine versus placebo.

Conclusion

Consistent with a hypnotic effect, a single night low dose of quetiapine reduces OSA severity as measured via the apnea/hypopnea index and increases sleep efficiency without worsening overnight hypoxemia. However, there is evidence of next day impairment in vigilance and driving simulator performance

Support (if any)

This study was funded by a National Health and Medical Research Investigator Grant (PI Eckert 1196261).
0376 The Hyperarousal Subtype of Insomnia Disorder Is Associated with Depressive Symptoms
Authors: Lyu Dongbin , Qian Ruiyi , Wang Yang , Ma Huajian , Yuan Chengmei
Published: April 2024

Introduction

Both the hyperarousal state and depressive symptoms are associated with insomnia disorder (ID). However, it is unknown whether the characteristics of hyperarousal and depressive symptoms vary across ID subtypes.

Methods

This study (NCT05985512) included 136 participants aged 18 to 65, diagnosed with ID by DSM-5 criteria. We employed psychological assessments of depression using Hamilton Depression Rating Scale (HAMD-17) and one-night polysomnography (PSG) to collect psychological and electroencephalographic (EEG) characteristics including macro sleep structures and spectral features including peak frequency (frequency of the local maximum peak of mean power). Uniform Manifold Approximation and Projection (UMAP) and Variational Bayesian Gaussian Mixture Model (VB-GMM) were applied for data dimensionality reduction and cluster analysis. Dunn’s test were conducted to compare depressive and EEG features across subtypes. Significance was set at P< 0.05.

Results

VB-GMM clustering identified three subtypes of ID: (i) Light Sleep Subtype: This group exhibited moderate HAMD-17 scores and the least amount of N3 sleep (Dunn's test p < 0.001). (ii) Least Depression Subtype: Participants reported the lowest levels of depression and had the most N3 sleep (Dunn's test p < 0.05) with the lowest alpha and beta EEG peak frequencies during NREM sleep (Dunn's test p < 0.05). (iii) Hyperarousal with Depression Subtype: This subtype had the highest level of depressive symptoms (HAMD-17 scores, Dunn's test p < 0.001). Although the third subtype's N3 sleep was more than that in Subtype 1 (Dunn's test p < 0.001), the higher alpha and beta EEG peak frequencies (Dunn's test p < 0.05) during NREM sleep suggested a state of hyperarousal.

Conclusion

The subtype of ID with the highest level of depressive symptoms is associated with cortical hyperarousal. Future research is needed to study the causal relationship between insomnia and depression under the hyperarousal model.

Support (if any)

National Natural Science Foundation of China (81971261); Shanghai Shenkang Development Center (SHDC2020CR4074); Shanghai Science and Technology Innovation Action Plan (20Y11906600)
0384 Multicomponent Behavioral Sleep Intervention for Insomnia in Older Adults with Mild Cognitive Impairment
Authors: McPhillips Miranda , Li Junxin , Petrovsky Darina , Brewster Glenna , Coni Jani , Aryal Subhash , Gooneratne Nalaka , Hodgson Nancy
Published: April 2024

Introduction

Insomnia is prevalent in older adults with mild cognitive impairment. Current treatment options are limited due to the nature of cognitive impairment and a paucity of providers. We sought to determine the preliminary effects of a multicomponent behavioral sleep intervention (MBSI) compared to a sleep education control condition on sleep latency, wake after sleep onset, total sleep time and efficiency.

Methods

Participants were enrolled and randomized in a 1:1 ratio to intervention or control in this pilot randomized controlled trial. Inclusion criteria were: self-reported and diary based evidence of insomnia (latency ≥ 30 minutes or wake after sleep onset ≥ 60 minutes) and Modified Telephone Interview for Cognitive Status scores between 28-36. The intervention was delivered virtually via tablet and consisted of sleep hygiene education, meaningful activity in the daytime and relaxation therapy in the evening for 4 weeks. The active control received sleep hygiene education only. Data collection occurred at baseline and immediately post intervention. Sleep variables were averaged over one week at each time point from daily sleep diaries. We used non-parametric Kruskal-Wallis tests to compare intervention and control post-intervention.

Results

We screened 180 potential participants and enrolled 27 older adults with mild cognitive impairment. Our sample was balanced on all key variables, had a mean age of 65.63 ± 6.39, was primarily (81.5%) female, and mixed between white (51.9%), Black (44.4%). Retention rate was high at almost 90%. At baseline, average latency was 46.5 (27.3) minutes for control and 41.4 (33.10) for intervention group. Post intervention, average latency for control group was 47.0 (34.6) and 28.3 (31.8) for intervention (p=0.076) Standardized mean difference (SMD) was 0.56. Average efficiency for control group was 65% (11%) and 70% (12%) for intervention at baseline. Post intervention, efficiency was 68% (14%) for control group and 78% (12%) for intervention (p=0.044). SMD was 0.76. There were no significant differences between groups for wake after sleep onset or total sleep time.

Conclusion

Preliminary evidence supports testing MBSI on a larger scale to improve insomnia symptoms in older adults with mild cognitive impairment and to determine if these effects are sustained over time.

Support (if any)

K23NR018487
0391 Effects of Brief Behavioral Treatment for Insomnia on Insomnia and Sleep Quality in Cancer Survivors
Authors: Dickerson Suzanne , Kwon Misol , Zhu Jingtao , Wilding Gregory E
Published: April 2024

Introduction

Insomnia is prevalent in more than 50% of cancer survivors, occurring during and after their treatment trajectory, potentially leading to subsequent insomnia disorder. Brief Behavioral Treatment for Insomnia (BBTI) aims to assist cancer survivors to self-manage their behaviors to improve their sleep to reduce insomnia symptom burden. This study determined efficacy of BBTI versus a healthy eating program attention control on insomnia severity index (ISI) and sleep quality measures overtime, in a heterogeneous sample of cancer survivors.

Methods

A sample of 132 eligible cancer survivors were recruited and randomized into BBTI (experimental) and control groups. All participants completed baseline demographics and sleep-related surveys (e.g., ISI, Pittsburgh Sleep Quality Index [PSQI]) at: baseline, 1-month, 3-month, and 12-month post intervention. Statistical analysis of ISI and sleep quality at each time point proceeded using fitted ANCOVA models with independent variables including randomized treatment assignment, participant cancer type, the interaction between treatment assignment and cancer type, and baseline outcome level. Tests for treatment effect were performed using an exact permutation testing approach with a two-sided 0.05 nominal significance level.

Results

Statistically significant group differences (p-values < 0.05) were observed at all time points for both ISI and sleep quality. Mean differences (95% CI) in ISI (BBTI minus control) were estimated to be -2.2(-4.1, 0.4), -2.1(-4.2, 0.1), and -2.3(-4.4, 0.1) at 1-, 3-, and 12-months, respectively. For sleep quality, the estimated mean differences were -1.4(-2.6, 0.2), -1.8(-3.0, 0.5), and -1.8(-3.3, 0.3), respectively. When adjusting for covariates known to be predictive of outcome, ISI results were robust, with treatment group difference remaining statistically significant except for when adjusting for treatment related surgery where results became borderline non-significant.

Conclusion

BBTI was significantly effective in reducing insomnia severity and improving sleep quality overtime compared to control among cancer survivors with insomnia symptoms. Use of BBTI in cancer survivors can help bridge the gap in addressing insomnia symptoms in survivorship settings.

Support (if any)

NIH/NINR R01NR018215 (Dean/Dickerson), ClinicalTrials-NCT03810365
0393 Insomnia with Short Sleep Duration Is Associated with Hypertension and Endothelial Dysfunction in Young Adults
Authors: Fernandez-Mendoza Julio , Gao Zhaohui , Atha Raegan , Calhoun Susan , Liao Jason , Vgontzas Alexandros , Liao Duanping , Bixler Edward
Published: April 2024

Introduction

Prior studies have examined the association between insomnia with short sleep duration (ISSD) with hypertension in middle-aged adults. However, no study to date has examined its association with elevated blood pressure (eBP) and flow-mediated dilation (FMD) in young adults.

Methods

We studied 270 young adults (median 25y, 53% female, 24% racial/ethnic minority) from the Penn State Child Cohort who underwent a 9-hour polysomnography (PSG) recording and Doppler ultrasound to asses FMD. The average of three consecutive BP readings was used to calculate resting mean arterial pressure (MAP) in the evening and morning. eBP was defined as systolic BP ≥120 mmHg, diastolic BP ≥80 mmHg, hypertension diagnosis, and/or antihypertensive medication use. Insomnia symptoms were defined as difficulties initiating or maintaining sleep, insomnia diagnosis or complaint, and/or sleep medication use. PSG-measured short sleep duration was defined by the median of the sample (i.e., < 7-h), identifying normal sleep duration (NSD), short sleep duration (SSD), insomnia with normal sleep duration (INSD) and ISSD. Multivariate general linear models tested mean differences in MAP and FMD across the four groups adjusting for sex, race/ethnicity, age, waist circumference, sleep apnea, cardiometabolic disorders, substance and medication use. A logistic regression model examined the association of the four groups with the presence of eBP, while accounting for the same covariables.

Results

Compared to NSD (84.6±1.2) or INSD (85.7±0.8), ISSD showed significantly higher evening MAP levels (88.0±0.8; p=0.022 and p=0.044, respectively), a finding replicated by morning MAP levels. Neither INSD (p=0.466) nor SSD (86.9±1.3; p=0.190) showed significantly elevated MAP levels compared to NSD. Similarly, ISSD (9.4±0.5; p=0.012), but not INSD (10.3±0.5; p=0.170) or SSD (10.3±0.7; p=0.257), showed significantly lower FMD levels compared to NSD (11.4±0.6). The odds of eBP were significantly increased in ISSD (OR=2.5, 95%CI=1.0-6.2; p=0.044), but not in INSD (OR=1.7, 95%CI=0.7-4.3; p=0.244) or SSD (OR=1.4, 95%CI=0.5-4.1; p=0.580), compared to NSD.

Conclusion

ISSD, but not INSD, is associated with hypertension and endothelial dysfunction in young adults. Clinical trials should examine whether improving insomnia symptoms and lengthening objective sleep duration in this phenotype may lead to favorable cardiovascular outcomes.

Support (if any)

NIH (R01HL136587, R01MH118308, UL1TR00127)
0401 Effects of a 4-week Vinyasa Yoga Intervention on Sleep and Cardiovascular Health in Adults with Insomnia Symptoms
Authors: Cheruka Caitlin , Sherman Sally , Quinn Tyler , Giordano Elizabeth , Lebegern Sophie , Lehrer Matthew , Buysse Daniel , Kline Christopher
Published: April 2024

Introduction

Insomnia is associated with an increased risk for cardiovascular disease. Yoga may have the potential to improve both sleep and cardiovascular health. However, there is limited evidence of how vinyasa yoga (VY), a form of yoga that links breath with movement and exhibits a greater energy expenditure than most other styles, impacts sleep and cardiovascular function. The purpose was to examine the effects of a 4-week VY intervention on sleep and cardiovascular health in adults with insomnia symptoms.

Methods

33 insufficiently active adults (84.8% female; 78.8% White; age=34.9±10.6 y; body mass index=28.9 kg/m2) with at least mild insomnia symptoms (Insomnia Severity Index [ISI]≥10) were randomized to a 4-week VY intervention (n=17) or non-active control (CON; n=16). VY practiced 3x/week following pre-recorded 60-minute videos and CON maintained their current lifestyle. Resting daytime measures of blood pressure, heart rate (HR) and heart rate variability (HRV) were obtained at baseline and post-intervention. Sleep was measured using the ISI, 7 nights of actigraphy, and a sleep diary. On one night during the sleep assessment, participants wore a chest HR monitor overnight to assess HRV. HRV was standardized for sleep duration, with root mean square of successive differences (RMSSD) the primary HRV outcome. Analyses compared changes in daytime cardiovascular health, sleep, and nocturnal HRV from pre- to post-intervention between groups using linear mixed models and Cohen’s d effect sizes.

Results

The change in ISI score from baseline to post-intervention did not differ between VY (15.18±0.96 to 9.93±1.00) and CON (16.63±0.98 to 13.87±0.98) (p=0.11) despite a medium-sized between-group effect size (d=-0.70) favoring VY. Among highly adherent participants, VY reduced ISI scores to a greater extent than CON (p=0.002). Changes in nocturnal RMSSD HRV and daytime resting cardiovascular measures were not statistically significant between groups (each p≥0.30). However, daytime resting HR decreased for VY (72.41±2.92 to 69.50±2.60 bpm) and increased for CON (70.47±2.54 to 72.93±2.60 bpm) (p=0.047).

Conclusion

Insomnia symptom reductions may be most apparent among those who consistently practice VY. VY had minimal impact on acute indications of cardiovascular health, suggesting changes may not occur with an intervention that is relatively short duration and/or low frequency.

Support (if any)
0412 Long-Term Treatment Outcomes for CBT-I in Black Americans: 3 & 6-Months Pilot Study Results
Authors: Thompson Michelle , Vancliff Camya , Seewald Mark , Muench Alexandria , Gallop Erin-Leigh , Scott Sadeaqua , Boyle Julia , Grandner Michael A , Jean-Louis Girardin , Posner Donn , Jemmott John , Perlis Michael
Published: April 2024

Introduction

In the US, Black Americans (BA) are disproportionately impacted by sleep continuity disturbance (SCD; i.e. insomnia) and insufficient sleep duration (ISD; i.e., < 6hrs). Simultaneously, treating SCD and ISD may improve health and reduce sleep disparities experienced by BA. However, Increased risk for, and severity of, SCD and ISD in BA suggests that BA may respond differently to cognitive behavior therapy for insomnia (CBT-I). The primary reason is that standard CBT-I ranges from six to eight sessions (i.e., directly addresses SCD) but does not increase total sleep time and therefore may not adequately address ISD during acute treatment. A pilot study was conducted to assess the acceptability of CBT-I to treat insomnia in BA, and address whether increasing sessions of CBT-I (i.e., > standard 8) will effectively address ISD. This study evaluates insomnia severity and sleepiness outcomes at 3- and 6- months post CBT-I treatment follow-ups.

Methods

Twelve BA adults (83% female, Mage 46.0±13.9yrs) were recruited from the Philadelphia area to receive CBT-I treatment by a master CBT-I therapist via telehealth for up to 16 sessions. Participants completed daily measures of sleep continuity (sleep diaries) and weekly measures of insomnia severity. Follow-up assessments were conducted at 3 and 6 months. Descriptive statistics are provided to show change in insomnia severity and sleepiness from baseline to post, baseline to 3-months and baseline to 6 months among the sample. Analyses were conducted using SPSSv27.

Results

Overall, on average, insomnia severity was reduced by 58% (effect size [ES]=1.3) at post, 61% (ES=1.8) at 3 months, and 67% at 6 months (ES=1.9). For sleepiness, on average, severity was reduced by 18% (ES=.2) at post, increased to 38% (ES=.7) at 3 months, and reduced to 28% (ES=.4) at 6 months.

Conclusion

Results from this pilot study indicate that standard CBT-I may produce robust effects. Reductions in insomnia severity and sleepiness remained durable at 6 months follow-up. Future studies with larger sample sizes are needed to explore further the use of CBT-I to treat sleep health problems in BA.

Support (if any)

K24AG055602; T32HL166609-02