bluuebunny/crossref_metadata_2025_split huggingface dataset:
0934 An Analysis of 5,212 Sleep Medicine Providers’ Online Patient Reviews
Authors: Shaffer Nicholas , Meraj Adeel 
Published: May 2023

Abstract

Introduction
Online provider rating websites are an increasingly popular medium for patients to provide reviews & feedback and obtain information about varying healthcare providers. There have yet to be studies on what factors significantly impact sleep medicine provider ratings. Healthgrades is a popular healthcare provider rating database with over 11 million patient reviews. This study analyzes various dimensions to determine what factors correlate with average sleep medicine provider ratings to improve patient satisfaction.

Methods
Data were collected on 5,212 sleep medicine providers’ online profiles using web data extraction software. Factors such as medical degree, gender, age, and ethnicity were compared to online ratings using Pearson correlation and ANOVA analysis via statistics software.

Results
Variables with positive correlation in provider rating include the presence of a headshot (pc=0.106, p&lt; 0.01), designation of trustworthiness (pc=0.152, p&lt; 0.01), and presence of care philosophy statement (pc=0.105, p&lt; 0.01). The only variable with a negative correlation in provider rating was increasing age (p=-0.109, p&lt; 0.01). A one-way ANOVA revealed that there was a statistically significant difference in mean rating between different healthcare degrees (F(122.271, 5062.570)=[17.955], p=&lt;.001). Tukey’s HSD Test for multiple comparisons found that the mean value of ratings was significantly different between allopathic physicians and sleep medicine dentists (p=0.000, 95% C.I.=[-0.943,0.070]), physician assistants (p=0.000, 95% C.I.=[-1.268,-0.239]), and nurse practitioners (p=0.000, 95% C.I.=[-0.941,-0.188]). There was no statistically significant difference in mean online rating between allopathic physicians and osteopathic physicians (p=0.505), sleep psychologists (p=0.344), or foreign medical graduates (p=0.998), respectively.

Conclusion
This study suggests patients perceive sleep medicine providers’ online profiles with headshots, care philosophy statements, and designation of trustworthiness as contributing factors when rating. Additionally, degree type and age matter when rating online. Patients do not value factors such as ethnicity or gender strongly when rating their sleep medicine provider. Further study is needed to determine factors influencing non-physician providers’ higher ratings when compared to U.S. medical graduates. This study demonstrates the importance of building an online presence for sleep medicine practices and identifying characteristics patients value. The expectation is that this information can be used to improve the quality of care delivered in the field of sleep medicine.

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0935 An Investigation of ICU Ambient Noise Levels as a Potential Source for Poor Sleep Outcomes in a Community Hospital Setting
Authors: Iriana Sidney , Nicola Catalin , Nat Amritpal 
Published: May 2023

Abstract

Introduction
Patients in the intensive care unit (ICU) are subject to multiple necessary diagnostic and therapeutic interventions throughout the course of their treatment that contribute to sleep deprivation. These measures increase ambient noise, impacting patient morbidity and mortality by exacerbating confusion, ICU-related delirium, recovery time, and long term sequelae associated with Post-Intensive Care Syndrome. The World Health Organization suggests critically-ill patients not exceed exposure to sound levels higher than 35-40 decibels (dB). In this study, we measured ICU sound levels in a community hospital setting to support intervention with conservative bundle measures or behavior modification with a visual noise monitoring device.

Methods
This study took place over two months (October-November, 2021) in the critical care unit of St Agnes Medical Center in Fresno, CA. Ambient noise was monitored using a sound decibel meter (Gain Express Holdings, SLM-25) connected to recording software that measured the decibel levels at 1-minute intervals for 24 hours in three areas: two central stations (Station A and B) and one occupied patient room, equidistant to both central stations. The monitor data was exported to Microsoft Excel (2020 version; Microsoft Corp, Redmond, WA, USA) for analysis. Data from each sample was expressed as M±SE (95% confidence interval).

Results
Both central stations had average levels of 44.1dB± 4.7 and 48.0dB± 4.3, with a frequency range of 40-50 dB greater than 50% of the time. Station A had a frequency range of 50-60 dB approximately 11% of the time and Station B at 30% of the time. The occupied patient room had average levels of 48.0dB± 5.4, with frequency of 40-50 dB greater than 50%, and levels 50-60 dB greater than 30% of the time.

Conclusion
ICU’s with higher than recommended ambient sound levels may benefit from appropriate intervention such as visual noise monitoring devices or order-sets with protocols on equipment/staff phone volume, earplugs for patients, and limiting staff conversation to distances away from the bedside.

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0937 Developing a Protocol for Abbreviated Daytime Sleep Studies for Positive Airway Pressure Acclimation
Authors: Palmquist Aaron , El-Ali Mazen 
Published: May 2023

Abstract

Introduction
During the COVID pandemic, there was a decrease in the number of in lab positive airway pressure titration studies as well as in person DME company guided mask fittings due to COVID related restrictions. We wanted to assess the utility of daytime abbreviated sleep studies for PAP acclimation and mask fitting on patient compliance to therapy.

Methods
We assessed patients that were seen consecutively in our sleep lab for abbreviated sleep studies for positive airway pressure acclimation and mask fittings. Compliance rates 30 days prior to encounters were compared to compliance rates over the 30 days after the PAP acclimation and mask fitting took place.

Results
We found that the majority of patients that did undergo an encounter had improvement in their compliance rates. Despite this, we recognized that there was a lack of uniformity in the patient encounter as well as documentation as there was no formal protocol in place for physicians and sleep technicians to follow during these encounters. We sought to developed a new protocol that would allow for more uniform encounters and optimized patient care as well as clear and concise documentation of the encounter.

Conclusion
It is reassuring to see that daytime abbreviated PAP acclimation and mask fitting sessions do seem to improve compliance to therapy, but we recognize that the development of a new protocol is important to determine the clinical significance of this finding. This new protocol will allow us to improve the quality of care delivered to patients during in laboratory daytime abbreviated PAP acclimation and mask fitting sessions. With a new protocol in place we can reduce variables during the encounter and it will hopefully serve as a better platform to analyze how these sessions impact overall patient compliance to PAP therapy.

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0938 Energy Drink Consumption and Motivation related to Sleep and Sleepiness Among College Students: Possible Clinical Implications
Authors: Mastin David , Sanders Madison , Mathis Ashley , Sadiq Adielle , Shuler Molly , Moore Bruce , Peszka Jennifer 
Published: May 2023

Abstract

Introduction
We examined prevalence and motives of energy drink consumption (EDC) and the relationship between EDC and sleep in college students. A literature review revealed most common motives for EDC in the general population include promoting wakefulness, increasing energy, and enhancing alcohol’s effects. Excessive daytime sleepiness has been found the day after EDC and EDC increases sleep latency (persisting up to eight hours). We pursued these questions as well as clinical implications in a college population.

Methods
Recruited from introductory psychology courses, 162 students (age: M=22.06 years, SD=5.87) met course requirements or obtained extra credit for participation. Participants completed demographic information, the Epworth Sleepiness Scale (ESS), the Pittsburgh Sleep Quality Index (PSQI), questions regarding associated features of inadequate sleep, the Sleep Hygiene Index, and questions assessing weekly quantity and motivation for energy drink consumption.

Results
90.6% of participants consumed energy drinks (42% traditional consumer drinks such as RedBull, 77% tea/coffee, 66% soda). The most common motivations were taste (57%), completing projects (43%), staying alert (42%), preventing sleepiness (41%), and staying productive (37%). More EDC was significantly related to greater daytime dysfunction (PSQI-7) (r(156)=.158, p=.024), more maladaptive sleep hygiene (r(158)=.206, p=.005), poor sleep quality (PSQI-Global) r(155)=.154, p=.027), shorter sleep duration (r(155)=-.161, p=.022), and associated features of poor sleep (daytime sleepiness: r(158)=.280, p&lt;.001, worry: r(158)=.139, p=.04, and reduced attention: r(158)=.174, p=.014). Logistic regression models showed for each one additional energy drink per day, increasing odds of sleepiness (OR=1.71, CI=1.21-2.40) poor sleep quality (OR=1.63, CI=1.04-2.55), and short sleep duration (OR=1.56, CI=1.14-2.13).

Conclusion
EDC, including coffee/tea, is widespread among college students and is most frequently motivated by liking the taste and promoting productivity/alertness. The active ingredient most frequently associated with energy drinks, caffeine, is viewed as relatively innocuous. Causality is unclear, however, these data suggest frequent EDC in college students is associated with sleep problems and may oppose motivations for use (e.g., one consumes ED to combat sleepiness and/or improve efficiency and instead finds attention and sleep impaired). Education and clinical guidance could help users understand associated risks and that changes in even one drink per day could lead to clinically important improvements.

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0940 Reductions in Use of Sleep Aids Following an App-Based Sleep Coaching Platform
Authors: Gorovoy Suzanne , Grandner Michael 
Published: May 2023

Abstract

Introduction
Use of sleep supplements and other sleep aids is increasing. Yet, many of these are not supported strategies for ameliorating sleep problems. Providing educational and behavioral strategies may reduce reliance on these substances.

Methods
Data were obtained from N=93 individuals who participated in the 12-week Sleep Reset program. The Sleep Reset program (Simple Habit, Inc.) includes ongoing text-based communication with coaches trained to support sleep behavior change, and a mobile app that includes a sleep diary, educational modules, mindfulness exercises, and progress tracking. Potential users are screened prior to entry and if they demonstrate a contraindicated medical or psychiatric problem, they were not admitted to the coaching program. If they demonstrate a likely sleep disorder (including insomnia disorder) they are referred for additional support. The coaching communications included information about basic sleep and circadian principles (e.g., sleep hygiene, behavioral scheduling, light exposure, relaxation) and techniques based on CBTI (e.g., stimulus control). Participants were asked if they used any “sleep aids” (which could include medications or melatonin/supplements) daily while in the program. Type of substance was not assessed. Data were binned by week, dichotomized as yes/no regarding any use in that week.

Results
Of the N=93 participants, N=46 (49%) indicated that they used “sleep aids” at least once in the first week. Among Sleep Reset participants who reported using sleep aids at least once in their first week in the program, use was reduced to N=13 (28.9%) by week 12. This difference was statistically significant (t(44)=10.4, p&lt; 0.0005). Reductions from week 1 were already seen in week 2 (57.8%; t(44)=5.7, p&lt; 0.0005). Another reduction was seen between weeks 3 and 4 (51.1% to 37.8%, t(44)=1.8, p=0.042), and a trend was seen between weeks 9 and 10 (35.6% to 26.7%, t(44)=1.7, p=0.052).

Conclusion
In an app-led sleep coaching program, those who used sleep aids in week 1 reduced use by 71.1% by the end of the 12-week period, with the largest reductions in the first week. Future controlled studies are needed to validate whether a sleep coaching program can reduce use of sleep aid such as supplements that may not be helpful.

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0944 Telemedicine (TM) to provide outpatient care for sleep disorders in complex neurological patients in an academic Medical Center
Authors: Tallavajhula Sudha 
Published: May 2023

Abstract

Introduction
Since the widespread adoption of telemedicine (TM) as a reimbursable option for the delivery of healthcare, (particularly clinic visits), the field of sleep medicine has been a beneficiary. In complex neurological patients, it was hypothesized that physical and cognitive constraints would limit the use of TM. This analysis was undertaken to evaluate patient care strategies in an academic neurology department based in a large metropolitan city

Methods
2964 adult patients who opted for neurology clinic visits via video TM in the last 6 months were included from electronic medical record review at the University of Texas Health Sciences Center in Houston. Of these, 557 were seen in sleep medicine specialty. A successful visit was defined as a simultaneous connection of at least 2 parties for a minimum duration of time defined by institutional guidelines. Patients were analyzed in age subgroups (&lt; 30, 31-50, 51-70 and &gt;70 years of age) as well as race (Asian, Black, Hispanic, White and Others). Neurological diagnoses in these patients included stroke, epilepsy, movement disorders, cognitive disorders, neuromuscular conditions and less commonly multiple sclerosis

Results
Sleep medicine providers comprise of 2/53 (3.7%) of the department’s clinical providers, but accounted for 18.7% of TM visits. 97% (182/186) of new patients and 95% (354/371) of established patients had successful TM visits. Among general neurology patients, these numbers were 89% and 88% respectively. There was a statistically significant difference between successful visits in the sleep medicine and overall neurology patient groups (p value 0.0003 for new, 0.0001 for established patients). Among sleep medicine patients, percentage failures were 2.9, 3.3, 4.1 and 3.9 among the respective age groups. Percentage failures across the respective racial groups were 0, 6.1, 4.8, 4.2 and 1.9. Reasons cited for unsuccessful visits included camera, microphone and general hardware failures. Surprisingly network failure was not reported

Conclusion
The success of telemedicine clinic visits for the management of sleep disorders is not impacted by physical and cognitive impairment in patients with complex neurological conditions. Further analysis to include success of sleep diagnostics and treatment of sleep disorders (start-to-finish pathway), via telemedicine in this complex population is underway

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None
0948 End-to-End utility of the Cardiopulmonary Coupling Sleep Spectrogram in Sleep Medicine
Authors: Barber Cameron , Chopra Sahil , Holzer Rena , Campbell Kimberly , Heckman Eric , Ahn Anjali , Thomas Robert 
Published: May 2023

Abstract

Introduction
Technology has improved the diagnosis and therapeutic tracking of sleep disorders, but remains fragmented. For example, the laboratory polysomnogram and home sleep apnea test is disconnected from the data available from therapy devices. Remote patient monitoring has focused more on apnea metrics rather than sleep quality. There is some value to a device or system which can be used for diagnosis, risk stratification, and tracking of clinical outcomes across different sleep disorders.

Methods
A method which offers the potential of end-to-end utility in sleep medicine is the cardiopulmonary coupling (CPC) spectrogram. Originally an ECG-base device, the current form is that of a Ring oximeter (finger) with an integrated activity monitor. The oximeter data is processed via Bluetooth in a smartphone, and then transferred to a Cloud-based analysis (www.sleepimage.com). The CPC analysis integrates heart rate variability and ECG or photoplethysmography-derived respiration. Outputs include a Sleep Quality Index and percentage of stable sleep (high frequency coupling which covaries with delta power on the electroencephalogram and is associated with blood pressure dipping), unstable sleep (low frequency coupling), an apnea-hypopnea index, oxygen desaturation index, heart rate profile across the night (dipping or non-dipping heart rate), total sleep time and sleep efficiency. Data from two clinical systems were used: Empower Sleep (an Internet-based “digital sleep clinic”) and Sleep Continuum (a sleep tracking service offered by Neurocare, Inc, in the Boston area).

Results
Data from over 500 patients across a range of sleep disorders show end-to-end utility of the SleepImage system. These include diagnosis of sleep apnea including multi-night recording, sleep apnea phenotyping (high loop gain and sleep fragmentation), remote patient monitoring during CPAP or oral appliance use, supportive information for diagnosis of hypersomnia, tracking medication effects (sodium oxybate, sedative-hypnotics, acetazolamide), sleep-wake instability in mood disorders and Long-Covid, impaired sleep quality in chronic fatigue syndrome and restless legs syndrome, off-label therapies for central sleep apnea (oxygen, acetazolamide, buspirone), and sleep fragmentation in Long-Covid and parasomnia. Examples from each of these clinical use cases will be shown.

Conclusion
The CPC-based SleepImage Ring system can provide complete diagnostic, phenotyping, and tracking functions.

Support (if any)
The Institute for Personalized Sleep Health, BIDMC, Empower Sleep
0949 Real-time acoustic apnea event detector by training a deep learning model with home noise added data
Authors: Le Linh , Kim Daewoo , Cho Eunsung , Jang Hyeryung , Reyes Roben Delos , Kim Hyunggug , Lee Dongheon , Yoon In-Young , Hong Joonki , Kim Jeong-Whun 
Published: May 2023

Abstract

Introduction
For diagnosis and management of Obstructive Sleep Apnea (OSA), long-term multi-night monitoring is crucial. Convenient detection of OSA at home is required for this purpose. Using sound recorded by smartphone can provide a convenient way to detect OSA. In this study, we present a sound-based OSA detection deep learning model that can detect OSA in real-time even in a home environment where various noises exist. The model is trained with home noise simulated sound to be robust for detecting home noises.

Methods
Two types of data were used for training and testing. The first type was sleep breathing sound data collected at the hospital while patients underwent a PSG. It included 1,154 and 297 nights recorded by a PSG microphone and a smartphone, respectively. We split them into 150 nights of smartphone data for testing and the rest for training. The second type was home noise data, which included 22,500 noises that might occur in a residential environment. The proposed acoustic apnea event detector inputs Mel spectrograms of sleep breathing sounds and outputs OSA event classes for each epoch (APNEA, HYPOPNEA, or NO-EVENT). The home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed by epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).

Results
Our model achieved 86 % epoch-by-epoch agreement (0.75 in macro F1) for 3-class event detection task. The model had an accuracy of 92% for NO-EVENT, 84% for APNEA, and 51% for HYPOPNEA. Most misclassifications were made for HYPOPNEA, with 15% and 34% of HYPOPNEA being wrongly predicted as APNEA and NO-EVENT, respectively. The sensitivity and specificity of OSA severity classification (AHI ≥ 15) were 0.85 and 0.84, respectively.

Conclusion
Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multi-night monitoring and real-time diagnostic technologies in the home environment.

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0950 Sound-based Sleep Staging at Home Using Smartphone via Deep Learning
Authors: Tran Hai , Hong Jung Kyung , Jang Hyeryung , Jung Jinhwan , Kim Jongmok , Hong Joonki , Lee Minji , Kim Jeong-Whun , Kushida Clete , Lee Dongheon , Kim Daewoo , Yoon In-Young 
Published: May 2023

Abstract

Introduction
Daily sleep tracking at home is growing in demand as more and more people are aware of the significance of sleep. The objective of this study is to propose a sound-based sleep staging model based on deep learning that works well in home environments with recorded audio data from general smartphones.

Methods
Three different audio datasets were used. A labeled hospital dataset (PSG and audio, N=812) and an unlabeled home dataset (audio only, N=829) were used for training. A limited number of labeled sound data from home (PSG and audio, N=45) were used for testing. Our proposed HomeSleepNet has three components: (1) supervised learning using the labeled hospital data that trains the model to make correct predictions in hospital environments; (2) unsupervised domain adaptation (UDA), which used both the labeled hospital data and unlabeled home data, and transferred the sleep staging power from hospital domain to home domain by adversarial training; (3) unsupervised data augmentation for consistency training (UDC), which augmented hospital data by adding home noise and trained the model to make consistent predictions on original and augmented data. After all training, HomeSleepNet is expected to make robust sleep staging despite the home noise presence.

Results
HomeSleepNet achieved 76.2% accuracy on the sleep staging task in home environments for the 3-stage classification case (Wake, NREM, REM). Specifically, it correctly predicted 63.4% of wake, 83.6% of NREM sleep, and 64.9% of REM sleep. The contributions of UDA and UDC were demonstrated by the following results. The accuracy of the model trained without both was 69.2%. Either addition of UDA or UDC training to the model improved the performance, with increased accuracy of 69.3% for UDA and 73.5% for UDC. As expected, using both UDA and UDC (i.e., HomeSleepNet) achieved the best performance, with a 7% increase in accuracy compared to the model trained without both components.

Conclusion
To the best of our knowledge, this is the first sound-based sleep staging study conducted in home environments. Moreover, the sounds were recorded by commercial smartphones and not through professional devices. Our proposed model introduced a reliable and convenient method for daily sleep tracking at home.

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