bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.

Support (if any)
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

Abstract

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&lt; 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

Abstract

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 &lt; 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.

Support (if any)
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

Abstract

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&lt;.01) and higher levels of pre-sleep cognitive and somatic arousal (p&lt;.01), beta-band activity in non-rapid eye movement stage 1 (p&lt;.05), stage 2 (p&lt;.05), and rapid-eye movement stage (p&lt;.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&lt;.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

Abstract

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

Abstract

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 &lt; 15 AHI or &gt;50% reduction from baseline AHI and AHI is &lt; 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