bluuebunny/crossref_metadata_2025_split huggingface dataset:
1111 Do Sleep Disorder Symptom Endorsements Differ Between ADHD Subtypes?
Authors: Seewald M , Muench A , Alio C , Rosenfield B , DiTomasso R , Rostain A , Ramsay J , Klingman K , Perlis M L 
Published: May 2020

Abstract

Introduction
To date, research on differences in sleep complaints between patients with different subtypes of ADHD has been mixed. On balance, the evidence tends towards ADHD-Combined Presentation (ADHD-C) being associated with more severe sleep and sleep-related daytime complaints than ADHD-Primarily Inattentive (ADHD-I). In order to further assess this issue a surveillance study was undertaken in an active ADHD clinic by adding a comprehensive sleep disorders screener (SDS-CL-25) to the clinical intake procedures. These data were used to ascertain whether the two subtypes differ for any of 13 sleep disorders symptoms.

Methods
Subjects (n = 132; 83 male, 49 female, mean age 32.8, age range 18-79), presenting to the clinic for evaluation for ADHD were given the SDS-CL-25. The SDS-CL-25 is a 25-item instrument developed to screen for multiple sleep disorders at one time (problems are endorsed on a Likert-scale; 0 = never and 4 = more than 5x/week). Endorsements greater than 3x/week were counted as positive for the symptom and less than three days per week was considered negative.
Percent per group was compared using Chi Square Analyses. Cumulative morbidity means were also analyzed using t-tests. The subtype, ADHD-I (n=71) and ADHD-C (N=61), was established using EMR records.

Results
No significant differences between patients with ADHD-I and ADHD-C were detected.

Conclusion
The lack of finding in the present analysis may reflect a lack of difference or a failure to detect differences based on the small sample sizes or lack of statistical control for likely confounders (age, sex, illness severity or chronicity, SES status, etc.). Analyses are ongoing.

Support
No support was provided for this abstract.
1117 Associations Between Health Behaviors And Sleep Duration In Women With Depression
Authors: Peprah R , Jenkins D , Donley T , Sexias A , Jean-Louis G 
Published: May 2020

Abstract

Introduction
Sleep duration can have important effects on health. Long and short sleep has been associated with negative health outcomes in women. Depression may aggravate an already impaired sleep quality. This study explored associations between sleep duration and depression in pregnant women.

Methods
We analyzed data for adult women (n=9,372) from the 2017 and 2018 National Health Interview Survey (NHIS), which is a nationally representative study of the US civilian non-institutionalized population. Sleep was categorized by short (≤6 hrs), normal/healthy (7-8 hrs), and long (≥9 hrs) sleep. Using STATA 15.0 for Windows, we report weighted frequencies and Chi-and square tests. Alpha of 0.05 was used for all significance levels.

Results
Of the sample, 81.7% of the women were White, 10.6% were Black and 7.7% were other minorities. The mean age was 51.4±18.3. We found that the proportion of women who reported short sleep increased with age (p&lt;0.000). Current drinkers (37%) had higher numbers of normal sleep than those who were former drinkers or abstainers (p&lt;0.000). With respect to BMI, more obese women were short and sleepers (17% and 4% respectively), but women with normal BMI (19%) were normal sleepers (p&lt;0.000). In short sleepers, more women had trouble falling asleep (13%) and staying asleep (17%), but reported not using medication and never feeling rested. Similar results were found for long sleepers. Higher proportions of normal sleepers reported not have trouble falling asleep (27%), staying asleep (26%), or using medication for sleep (40%) (p&lt;0.000). However, of those who reported normal sleep, greater frequencies of working up feeling rested occurred only 3-6 times in the past week (p&lt;0.000).

Conclusion
In this study, women with depression self-reported more normal sleep duration. This finding is inconsistent with previous research. Whether this association is causal and what pathways explain this association is unknown.

Support
This study was supported by funding from the NIH: R01MD007716, R01HL142066, K01HL135452, and K07AG052685.
1140 Sleep Disturbances, Lifestyle, And Self-Management In Adults With Subarachnoid Hemorrhage
Authors: Byun E , McCurry S , Kwon S , Kim B , Thompson H 
Published: May 2020

Abstract

Introduction
Subarachnoid hemorrhage (SAH) survivors often suffer sleep disturbances. Self-management strategies focusing on lifestyle changes and health-promoting behaviors may improve sleep in SAH survivors. Few studies have examined sleep in SAH survivors, and little is known about sleep management practices used to improve their sleep. The purposes of this study were to: 1) describe the prevalence of sleep disturbances using subjective and objective sleep measures, and 2) explore interest in and engagement with self-management practices to promote sleep health in SAH survivors.

Methods
We conducted a cross-sectional study with a convenience sample of 30 SAH survivors recruited from a university hospital. We assessed sleep quality using the Pittsburgh Sleep Quality Index (PSQI), daytime sleepiness using the Epworth Sleepiness Scale (ESS), and objective sleep using wrist actigraphy. We conducted content analysis of semi-structured interviews, with two authors each coding sleep disturbances and self-management practices addressing sleep.

Results
Seventy-three percent of SAH survivors reported poor sleep quality (PSQI &gt; 5) and 27% had daytime sleepiness (ESS &gt; 10). Actigraphy analysis indicated that 41% of SAH survivors slept less than 7 hours or more than 9 hours. Interview content analyses suggested 3 themes and 15 sub-categories: 1) Sleep disturbances (difficulties in falling asleep, wake after sleep onset, daytime sleepiness, too much or insufficient sleep, and poor sleep quality), 2) Sleep management practices (exercise, regular sleep schedule, relaxation, keeping busy and staying active, changing beverage intake, taking supplements, taking medication, recharging energy, and barriers to sleep management), and 3) Healthcare providers (discussing sleep problems with health care providers).

Conclusion
Sleep disturbances are highly prevalent and an urgent need exists to focus on improving sleep in SAH survivors. Developing tailored interventions that incorporate self-management and lifestyle change would be a critical next step to improve sleep and promote health in this at-risk population.

Support
This research was supported by grants from the National Institutes of Health/National Institute of Nursing Research (K23 NR017404), University of Washington Institute of Translational Health Science Translational Research Scholars Program (UL1 TR000423), and University of Washington School of Nursing Research and Intramural Funding Program.
1142 Comprehensive Phenotyping Of Ambulatory Sleep Patterns In Multiple Sclerosis
Authors: Braley T J , Kratz A L , Whibley D , Goldstein C 
Published: May 2020

Abstract

Introduction
The majority of sleep research in persons with multiple sclerosis (PwMS) has been siloed, restricted to evaluation of one or a few sleep measures in isolation. To fully characterize the impact of sleep disturbances in MS, multifaceted phenotyping of sleep is required. The objective of this study was to more comprehensively quantify sleep in PwMS, using a recently developed multi-domain framework of duration, continuity, regularity, sleepiness/alertness, and quality.

Methods
Data were derived from a parent study that examined associations between actigraphy and polysomnography-based measures of sleep and cognitive function in MS. Actigraphy was recorded in n=55 PwMS for 7-12 days (Actiwatch2®, Philips Respironics). Sleep metrics included: duration=mean total sleep time (TST, minutes); continuity=mean wake time after sleep onset (minutes), and regularity=stddev wake-up time (hours). ‘Extreme’ values for continuity/regularity were defined as the most extreme third of the distributions. ‘Extreme’ TST values were defined as the lowest or highest sixth of the distributions. Sleepiness (Epworth Sleepiness Scale score) and sleep quality [Pittsburgh Sleep Quality Index (PSQI) sleep quality item] were dichotomized by accepted cutoffs (&gt;10 and &gt;1, respectively).

Results
Sleep was recorded for a mean of 8.2 days (stddev=0.95). Median (1st, 3rd quartile) values were as follows: duration 459.79 (430.75, 490.60), continuity 37.00 (23.44, 52.57), regularity 1.02 (0.75, 1.32), sleepiness/alertness 8 (4, 12), and sleep quality 1.00 (1.00, 2.00). Extreme values based on data distributions were: short sleep &lt;=426.25 minutes (18%), long sleep &gt;515.5 minutes (16%), poor sleep continuity ≥45 minutes (33%), and poor sleep regularity ≥1.17 hours (33%). Sleepiness and poor sleep quality were present in 36% and 40% respectively. For comparison, in a historical cohort of non-MS patients, the extreme third of sleep regularity was a stddev of 0.75 hours, 13% had ESS of &gt;10, and 16% had poor sleep quality.

Conclusion
In this study of ambulatory sleep patterns in PwMS, we found greater irregularity of sleep-wake timing, and higher prevalence of sleepiness and poor sleep quality than published normative data. Efforts should be made to include these measures in the assessment of sleep-related contributions to MS outcomes.

Support
The authors received no external support for this work.
1163 Nickel, Dime or Dollar: Breaking Down Broken Notions of Cost in Sleep Diagnostics
Authors: Jambulingam N , Stretch R , Butz D , Zeidler M 
Published: May 2020

Abstract

Introduction
Home sleep apnea tests (HSATs) are convenient alternatives to in-lab polysomnograms (PSGs) but high non-diagnostic rates limit their utility. A clinical decision support tool (CDST) to triage patients to HSAT versus PSG was developed at the Greater Los Angeles VA Healthcare System (GLA-VAHS). It uses a random forest ensemble to reduce non-diagnostic HSAT rates by 46%. While prior studies have found PSGs to be more profitable than HSATs on a per unit basis, these analyses do not factor in relative profitability over time. Additionally, no prior studies have quantified the financial impact of a CDST in diagnostic sleep testing.

Methods
We performed an analysis of the overall profitability of HSATs and PSGs in 2018-2019 within GLA-VAHS which has 6 PSG beds. Revenue was calculated using 2019 Medicare reimbursement rates. Contribution margin (CM) analysis was used to factor out the high fixed costs of healthcare infrastructure, instead focusing on variable direct costs (VDCs). CM analysis is especially useful when calculated on a per diem basis instead of per study, adjusting for number of tests performed in a given day. CM was calculated by subtracting VDCs from revenue under two simulated conditions: with and without the CDST.

Results
PSGs were 2.5 times more profitable than HSATs on a per unit basis (CM $200/study vs. $81/study). However, on a per day basis, PSGs were only 1.4 times more profitable than HSATs at average nightly occupancy rates of 75% (CM $902/day vs. $646/day). Using the CDST to guide testing, 2.2 times more diagnostic HSATs could be performed per day. As a result, HSATs were 1.3 times more profitable than PSGs on a per day basis with CDST use (CM $1,211/day vs. $902/day).

Conclusion
This analysis demonstrates that implementing a CDST and maximizing utilization of HSATs allow hospitals to better allocate limited sleep lab resources, increase diagnostic throughput and generate higher profits. Analyzing costs using contribution margin avoids erroneous assumptions about profitability and leads to better-informed administrative decisions regarding sleep lab expansion.

Support
1174 Assessing Sleep Disorders in Primary Care: A Provider Survey About the Importance of Sleep Health
Authors: Klingman K J , Morse A , Williams N , Grandner M A , Perlis M L 
Published: May 2020

Abstract

Introduction
Conditions commonly managed by primary care providers (PCPs) such as depression, diabetes, and heart disease, commonly co-occur with sleep disorders. If PCPs could readily identify comorbid sleep disorders in this context, it may provide a pathway to more effective management of both types of disorders. Currently, it is unknown what might encourage or discourage PCPs from routinely screening their patients for sleep disorders.

Methods
PCPs from UPENN and GHS completed surveys regarding sleep health. The 30-item instrument comprised demographic, 14 VAS (0%-100%=strongly disagree-strongly agree), 4 open-ended, 3 yes/no, and 2 multiple-choice questions.

Results
Ninety-nine PCPs responded and were predominately female (61% F, 37%M, 2% other), Caucasian (81%), on-average 45yrs old (25-70) and in primary care for 16yrs (1-43). Fifty-six percent were MDs, 21%DOs, 17%PAs, and 6%NPs. PCPs rated sleep disorders as highly important for cardiopulmonary, mental, and general health (85, 84, & 83%), with no difference (per linear regression, p&gt;0.05) according to system or provider characteristics. PCPs reported high importance for knowing about and diagnosing sleep disorders (88% & 82%) within their practices. Lower comfort levels were reported for discussing (78%) sleep disorders, overseeing/following (62%), diagnosing (60%), or treating (48%) patients. Eighty percent of PCPs stated an efficient sleep disorders screener would be useful for their practice; this perception varied (per logistic regression) according to provider credentials (Wald=0.037) and Hispanic/Latino ethnicity (Wald=0.025). PCPs reported time constraints limit their responsiveness to sleep disorders

Conclusion
A large disparity exists between the importance PCPs place on sleep disorders and their low comfort levels with following, diagnosing, and treating sleep disorders. PCPs endorsed the need to have available an efficient sleep disorders screener to use in their practice.

Support
No funding was received for this study.
1177 Cost Effectiveness Of Diagnostic Approaches To Sleep Apnea Evaluation During Inpatient Rehabilitation For Moderate To Severe TBI
Authors: Nakase-Richardson R , Dismuke-Greer C , Jeanne H , Drashser-Phillips L , Schwartz D , Calero K , Bogner J , Whyte J , Almeida E , Ketchum J , Magalang U 
Published: May 2020

Abstract

Introduction
Recent work has highlighted prevalent obstructive sleep apnea (OSA) after traumatic brain injury (TBI) when patients are vulnerable to disruption of neural repair. The recently completed clinical trial comparing screening and diagnostic tools for OSA during inpatient rehabilitation provided the opportunity to conduct economic modeling of phased approaches using actual trial findings to address one perspective (the payor) on the value of phased testing.

Methods
A cost-effectiveness analysis of four phased approaches to OSA diagnosis including initial utilization of portable sleep monitoring [HSAT] regardless of pre-test probability, determination of pre-test probability using two prediction models [STOPBANG, MAPI], and initial assessment using Level 1 polysomnography was conducted. The analyses were modeled assuming all participants were considered high risk thus a negative screen or portable diagnostic test would result in a participant being referred for Level 1 polysomnography. The cost aversion used in analyses were derived from a recent white paper on the economic modeling of untreated OSA. Trial data from 214 participants were used in analyses (mean age 44 [SD 18], 82% male, 75% white, with primarily motor-vehicle related injury [44%] and falls [33%] with a sample mean emergency department Glasgow Coma Scale of 8 (SD 5).

Results
At AHI ≥15 (33.6%), the prediction models (STOPBANG [-$5,291], MAPI [-$5,262]) resulted in greater cost savings and effectiveness relative to the HSAT approach (-$5,210) and initial use of Level 1 PSG (-$5,011). Sensitivity analyses at AHI ≥5 (70.1%) revealed the initial use of HSAT (-$6,322.85) relative to the prediction models (MAPI [-$6,249.71], STOPBANG [-$6,237) and initial assessment with Level 1 PSG (-$5,977) resulted in greater savings and cost effectiveness.

Conclusion
The high rates of sleep apnea after TBI highlight the importance of accurate diagnosis and treatment of this comorbid disorder. However, financial and practical barriers exist to obtaining an earlier diagnosis during inpatient rehabilitation hospitalization. Diagnostic cost savings are demonstrated across all phased approaches and OSA severity levels with the most cost-effective approach varying by incidence of OSA.

Support
PCORI (CER-1511-33005), GDHS (W91YTZ-13-C-0015; HT0014-19-C-0004)) for DVBIC, NIDILRR (NSDC Grant # 90DPTB00070, #90DP0084, 90DPTB0013-01-00, 90DPTB0008, 90DPT80004-02).
1201 Performance Evaluation Of A Novel Contactless Breathing Monitor And Machine Learning Algorithm For Sleep Stage Classification In A Healthy Population
Authors: Lauteslager T , Kampakis S , Williams A J , Maslik M , Siddiqui F 
Published: May 2020

Abstract

Introduction
Although polysomnography (PSG) remains the gold standard for sleep assessment in a lab setting, non-EEG signals such as respiration and motion are directly affected by sleep stages and can be used for sleep stage prediction. Importantly, these signals can be obtained in a low-cost and unobtrusive manner, allowing for large scale and longitudinal data collection in a home environment. The Circadia C100 System (FDA 510(k) clearance expected Q1 2020) is a novel ‘nearable’ device that uses radar for contactless monitoring of respiration and motion. The current study aims to validate the performance of the associated sleep analysis algorithm.

Methods
A total of 41 nights of sleep data were recorded from 33 healthy participants using the device, alongside PSG. Data were recorded both in a sleep lab and home environment. PSG data were scored by RPSGT-certified technicians. Respiration and movement features were extracted, and machine learning algorithms were developed to perform sleep stage classification and predict sleep metrics. Algorithms were trained and validated on PSG data using cross-validation.

Results
An epoch-by-epoch true positive rate of 56.2%, 79.4%, 55.5% and 72.6% was found for ‘Wake’, ‘REM’, ‘Light’ and ‘Deep’ respectively. No statistical differences in performance were found between home-recorded and lab-recorded contactless data. Mean absolute error of total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) was 13.2 minutes, 11.3 minutes and 3%, respectively. The contactless monitor was found to outperform both medical grade and clinical grade actigraphy based devices: The Philips Actiwatch Spectrum Plus and the Fitbit Alta HR.

Conclusion
Current results are encouraging and suggest that the contactless monitor could be used for long-term sleep assessment and continuous evaluation of sleep therapy outcomes. Further clinical validation work is ongoing in subjects diagnosed with sleep disorders such as obstructive sleep apnea.

Support
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1204 Analyzing User Journey Data In Digital Health: Predicting Dropout From A Digital CBT-I Intervention
Authors: Bremer V , Chow P , Funk B , Thorndike F , Ritterband L 
Published: May 2020

Abstract

Introduction
Intervention dropout is an important factor for the evaluation and implementation of digital therapeutics, including in insomnia. Large amounts of individualized data (logins, questionnaires, EMA data) in these interventions can combine to create user journeys - the data generated by the path an individual takes to navigate the digital therapeutic. User journeys can provide insight about how likely users are to drop out of an intervention on an individual level and lead to increased prediction performance. Thus, the goal of this study is to provide a step-by-step guide for the analysis of user journeys and utilize this guide to predict intervention dropout, illustrated with an example from a data in a RCT of digital therapeutic for chronic insomnia, for which outcomes have previously been published.

Methods
Analysis of user journeys includes data transformation, feature engineering, and statistical model analysis, using machine learning techniques. A framework is established to leverage user journeys to predict various behaviors. For this study, the framework was applied to predict dropouts of 151 participants from a fully automated web-based program (SHUTi) that delivered cognitive behavioral therapy for insomnia. For this task, support vector machines, logistic regression with regularization, and boosted decision trees were applied at different points in 9-week intervention. These techniques were evaluated based on their predictive performance.

Results
After model evaluation, a decision tree ensemble achieved AUC values ranging between 0.6-0.9 based on application of machine earning techniques. Various handcrafted and theory-driven features (e.g., time to complete certain intervention steps, time to get out of bed after arising, and days since last system interaction contributed to prediction performance.

Conclusion
Results indicate that utilizing a user journey framework and analysis can predict intervention dropout. Further, handcrafted theory-driven features can increase prediction performance. This prediction of dropout could lead to an enhanced clinical decision-making in digital therapeutics.

Support
The original study evaluating the efficacy of this intervention has been reported elsewhere and was funded by grant R01 MH86758 from the National Institute of Mental Health.
1208 Sleep Stage Prediction And Sleep Disordered Breathing Detection Using Raw Actigraphy And Photoplethysmography From Wearable Consumer Device
Authors: Olsen M , Sorensen H , Jennum P , Mignot E 
Published: May 2020

Abstract

Introduction
Wearable, multisensory consumer devices that estimate sleep are prevalent and hold great potential. Most validated actigraphic prediction studies of sleep stages (SS) have only used low resolution (30 sec) data and the Cole-Kripke algorithm. Other algorithms are often proprietary and not accessible or validated. We present an automatic, data-driven deep learning algorithm that process raw actigraphy (ACC) and photoplethysmography (PPG) using a low-cost consumer device at high (25Hz) and low resolution to predict SS and to detect sleep disordered breathing (SDB) events.

Methods
Our automatic, data-driven algorithm is a deep neural network trained and evaluated to predict SS and SDB events on 236 recordings of ACC data from a wrist-worn accelerometer and PPG data from the overlapping PSG. The network was tested on raw ACC and PPG data, which was collected at 25 Hz using the HUAMI Arc2 wristband from 39 participants that underwent a nocturnal polysomnography (PSG).

Results
Overall accuracy (Acc), recall (Re), specificity (Sp), and kappa (κ) per subject on the test dataset the prediction of wake, NREM, REM was Acc=76.6%, Re=72.4%, Sp=78.0%, kappa=0.42. On average, we found a 7 % higher performance using the raw sensor data as input instead of processed, low resolution inputs. PPG was especially useful for REM detection. The network assigned 55.6% of patients to the correct SDB severity group when using an apnea-hypopnea index above 15.

Conclusion
Current results show that SS prediction is significantly improved when using the raw sensor data; it indicates that the system holds promise as a potential pervasive monitoring device for patients with chronic sleep disorders. In contrast the system did not show potential as a sleep apnea screening tool. Additional studies are ongoing to examine the effects of pathology such as sleep apnea and periodic leg movement on SS prediction.

Support
Technical University of Denmark; University of Copenhagen, Copenhagen Center for Health Technology, Klarman Family Foundation.