bluuebunny/crossref_metadata_2025 huggingface dataset:
1095 Adaptation of SIESTA (Sleep for Inpatients: Empowering Staff to Act) Protocol for Acute Stroke Rehabilitation
Authors: Siddiqui Sehar , Ramos Bianca , Ram Kavita , Billeh Lauren , Sunderrajan Aashna , Sindorf Jacob , Barnes Alyah , Masangkay Alyssa , Martinez Maylyn , Morris Linda , Prabhakaran Shyam , O’Brien Megan , Prokup Sara , Jayaraman Arun , Arora Vineet
Published: April 2024

Introduction

Improving sleep for patients with stroke is vital due to the importance of sleep for neuroplasticity and functional recovery. While sleep interventions have been successful in reducing nocturnal disruptions for hospitalized patients, few interventions have been tested for patients with stroke in an acute rehabilitation setting. We aimed to adapt the successful hospital protocol SIESTA (Sleep for Inpatients: Empowering Staff to Act) to an acute stroke rehabilitation setting.

Methods

Surveys and focus groups with nurses caring for patients with stroke on inpatient floors at Shirley Ryan AbilityLab informed the development of the adapted protocol, SIESTA-Rehab. The protocol included educational video modules for nurses regarding sleep hygiene and sleep apnea screening, sleep enhancing measures (eye masks, quiet signs), a nurse badge card with a checklist reinforcing protocol components, and advocacy for patient sleep at shift handoff. Two nurse champions were recruited to monitor protocol implementation. Periodic re-education was conducted to account for nurse turnover. Ongoing fidelity interviews using the Consolidated Framework for Implementation Research (CFIR) framework assessed intervention perceptions.

Results

We initially trained 27 (93%) nurses using the training module. Fidelity interviews of over 20 nurses in the years post-implementation demonstrated over 57% of nurses recalled sleep masks and “cluster care” as part of the protocol. Moreover, more than 75% of nurses considered the protocol to be “straightforward” and “part of the workflow.” Nurses perceived the protocol positively, noting it “could be really beneficial for patients and their long-term health,” and have incorporated behavioral changes into their routine since being educated on sleep hygiene. Nurses also reported understanding the importance of sleep for stroke patients, stating sleep “helps in their recovery and feeling well rested for the day so that they can participate in therapy” and understood “how [they] affect that in [their] roles” as nurses.

Conclusion

The SIESTA protocol can be successfully adapted to an acute rehabilitation setting for stroke patients, as evidenced by positive staff responses to sleep-promoting interventions, ease of protocol incorporation, and high staff compliance. Ongoing work is investigating patient perceptions and outcomes following implementation of this sleep protocol.

Support (if any)
1098 Hypoglossal Nerve Stimulation Program Analysis and Financial Impact over 6 Months
Authors: Richardson Stephen , Hope Christopher
Published: April 2024

Introduction

Inspire hypoglossal nerve stimulation (HNS) is being increasingly utilized as an alternative treatment for moderate to severe obstructive sleep apnea in select patients who fail positive airway pressure therapy. There is substantial cost and effort in developing an HNS program. We report on a programmatic and financial analysis in a non-academic, community-based sleep center 2 years into its HNS program.

Methods

A retrospective review of all clinic visits was performed for a 6-month period from January 2022 through June 2022. Visits were reviewed as being referred for, or discussing HNS as a treatment option, for inclusion in the analysis. Included visits were categorized as new or follow up visit, and data was extracted including referring provider specialty, insurer, and if the visit resulted in ordering of a sleep study, referral to ENT, if the patient qualified for HNS, and billing for interrogation/programming of the stimulator. Financial impact was estimated by multiplying the average reimbursement rate for associated CPT codes by the number of CPT's generated.

Results

During this study period, our HNS program generated 215 patient visits, which accounted for 9% of total clinical volume. 81 were new evaluations and 134 were follow-ups. Of the new patients, 77 were referred for interest in HNS, most commonly from primary care or self-referred (33% each). Forty-four polysomnograms and 18 home sleep apnea tests were performed related to these visits. A total of 93 HNS programming/interrogation services were performed. The total revenue generated across all services was estimated at $83,276.

Conclusion

This observational program analysis revealed that offering HNS services generated a significant number of clinic visits, sleep studies ordered, and device interrogation services, accounting for increased clinical and study revenue. Marketing appears to have generated a significant number of self-referrals. Future research could include further analysis over a longer period of time and at other sleep clinic settings in an effort to make the results more generalizable.

Support (if any)

None.
0110 Insomnia Symptom Prevalence by Foster Care History from Adolescence to Adulthood
Authors: Rojo-Wissar Darlynn , Vieira Alyssa , Rech Megan , Alfano Candice , Parade Stephanie
Published: April 2024

Introduction

Known associations between stress and sleep suggest that early-life adversity faced by children who spend time in foster care (FC) confers vulnerability to sleep disturbances, though research in this population is scarce. We utilized data from a nationally representative longitudinal study to examine insomnia symptom prevalence by FC history from adolescence to adulthood.

Methods

Participants were from the National Longitudinal Study of Adolescent to Adult Health (N=6,954, representing 21,059,854), 1.5% had a history of FC involvement (N=123, representing 318,515), 50% were female, 65% were non-Hispanic (NH) white, 15% were NH Black, and 10% reported a resident parent receiving public assistance at Wave I (W1). Participants were in grades 7-12 during W1, grades 8-12 in Wave II (W2), ages 24-32 in Wave IV (W4), and ages 33-43 in Wave V (W5). Participants were considered positive for insomnia symptoms if they reported trouble falling asleep or staying asleep ≥ “3 times per week”, “often or every day”, or “almost every day or every day”. Design-based analyses were conducted, including use of sampling weights. Poisson regression was used to estimate whether relative risk for insomnia differed at each wave by FC history, and prevalence estimates were estimated at each wave, adjusting for demographic characteristics.

Results

The relative risk for insomnia symptoms was significantly greater among those with a FC history at W1 (p=.039), W2 (p=.001), and W4 (p=.000), but not W5 (p=.840). Insomnia symptom prevalences among those with versus without FC history were: 20% versus 11% (W1), 20% versus 8% (W2), 51% versus 27% (W4), and 31% versus 30% (W5).

Conclusion

Among individuals in the US, those with a FC history experience substantially higher risk for significant insomnia symptoms from adolescence to ages 24-32, but this difference disappears around ages 33-43. Addressing sleep issues in the FC population is of critical public health importance. Future research should examine how sleep disturbances contribute to poor health outcomes in the FC population.

Support (if any)

This research uses data from Add Health, funded by grant NICHD:P01 HD31921, NIA:U01 AG071448 and U01AG071450. DMR was supported by NIMH:5T32MH014592-39; the Sleep Research Society Foundation, NICHD:T32HD101392. DMR & SP received support from NIGMS:P20GM139767.
1106 Patient-Centric Design: Incorporating Patient Input into a Clinical Study of Idiopathic Hypersomnia and Narcolepsy
Authors: Nichols Deborah , Barker Emily , Akerman Sarah , Steininger Teresa , Macfadden Wayne
Published: April 2024

Introduction

Jazz DUET (Develop hypersomnia Understanding by Evaluating low-sodium oxybate Treatment; NCT05875974) is a phase 4, prospective, multicenter, single-arm, open-label interventional study that comprehensively evaluates low-sodium oxybate effects on sleep architecture and daytime/nighttime symptoms in participants with idiopathic hypersomnia or narcolepsy. Multiple assessments, including overnight polysomnography (PSG), are needed to generate robust, relevant data, but these assessments may impose a burden on study participants. A patient advisory board was convened to understand patient perspectives and assess opportunities to incorporate patient feedback into the study protocol.

Methods

Advisors completed a premeeting survey, then attended a 3-hour advisory board meeting with the study sponsor. Five main topics were discussed: feasibility of oxybate washout for participants entering the study on treatment, burden of assessments, relevance of specific symptom evaluation to participants, value of reporting individualized data back to participants, and burden of overnight visits.

Results

The advisory board included 2 people with idiopathic hypersomnia and 4 people with narcolepsy. All 6 advisors had experience with patient advocacy, and at least 1 had been a clinical trial participant; these experiences may help give voice to a broader patient community. Premeeting survey responses were reviewed and discussed during the meeting. Based on advisor input, the final study protocol incorporated several points, including support for participants undergoing oxybate washout (eg, transportation, childcare, and meal service, as needed), additional breaks between assessments, moving assessments from evening to morning to reduce participant burden, measuring fatigue separately from sleepiness, and suggestions to make participants more comfortable for overnight visits. Advisors noted the high value of reporting individualized data back to study participants—particularly PSG data, with specific interest in number of awakenings, duration of rapid eye movement sleep, and duration of slow-wave sleep. In addition to these changes, study materials were developed to help participants prepare for overnight visits, and a checklist of items to bring to these visits was created.

Conclusion

The final DUET study design incorporated patient-centric elements recommended by a patient advisory board. Implementation of these elements is anticipated to reduce participant burden, improve participant experience, enhance recruitment and retention, and facilitate collection of meaningful and comprehensive data.

Support (if any)

Jazz Pharmaceuticals
1107 Facilitating Accurate Sleep Diary Data Collection and Processing Based on Common Participant Data Entry Errors
Authors: Tully Isabelle , Suh Sooyeon , Dietch Jessica , Manber Rachel
Published: April 2024

Introduction

Sleep diaries are the gold-standard measure of self-reported sleep parameters; however, data utility is contingent upon accurate data entry and clarity of standardized diary terminology/calculations between participants and researchers. Informed by the RESTING study, an effectiveness-implementation RCT of cognitive-behavioral therapy for insomnia, sleep diary dataset, the current study elucidates common participant sleep diary entry errors to inform standardizable research strategies for minimizing post hoc inaccuracies.

Methods

RESTING Study participants (N=245) with insomnia disorder completed Consensus Sleep Diaries through REDCap at baseline. Embedded survey data validation prompted participants to check unexpected values (e.g., calculated TIB >10 hours). Researchers reviewed diaries weekly, correcting immediately apparent errors. Post hoc data cleaning scripts flagged sleep diaries with potential inaccuracies based on: (1) 9 hours< Calculated Total Sleep Time (TST)< 4.5 hours, (2) Difference between calculated and self-reported TST >2 hours, (3) 100%< Sleep Efficiency< 45%, (4) Difference between lights out (LO) and bedtime (BT)< 0, or (5) Difference between rise time (RT) and wake time (WT)< 0. Researchers examined internal consistency within flagged diaries and across all entries by a participant.

Results

3,382 days of sleep diary data were submitted at baseline (98% of expected). During active data collection, researchers corrected errors within 61 (1.8%) unique diaries, completed by 37 participants. Of 503 (14.9%) distinct sleep diaries (among 173 participants) flagged by post hoc scripts, 173 (5.1%) contained at least one error. Examining internal consistency across diary dates when reviewing flagged entries revealed 20 additional diaries with errors, among 2 participants. In sum, 348 errors were made among 119 participants. The most common errors were: confusion between WT and RT (n=150), confusion between BT and LO (n=45), inclusion of multiple sleep parameters within self-reported “wake after sleep onset” (n=29), unambiguous typos (n=29), and AM/PM mix-ups (n=28).

Conclusion

Despite training participants on sleep diary completion and utilizing survey data validation, 5.7% of diaries contained errors corrected post hoc. This highlights the importance of standardizing use of post hoc data cleaning scripts. Common errors identified can inform best practices for embedded sleep diary data validation and development of sensitive, precise protocols for reviewing diaries throughout data collection.

Support (if any)

1R01AG057500
1108 Prior-night Sleep Predicts Next-day Symptoms over Ten Days Among Military Personnel with Sleep Problems
Authors: Collen Jacob , Capaldi Vincent , Zhao Zhiwei , Thomas Connie , Williams Scott , Assefa Samson , Chen Shuo , Albrecht Jennifer , Wickwire Emerson
Published: April 2024

Introduction

Insufficient and disturbed sleep are highly prevalent and associated with adverse consequences in the U.S. military. The purpose of this study was to employ remote monitoring/ecological momentary assessment methods to test the hypothesis that prior-night sleep is associated with next-day subjective symptoms among military personnel.

Methods

Participants (N=270) with sleep problems were recruited from two military treatment facilities and completed an intensive ten-day remote monitoring assessment. Prior night sleep was measured using standardized sleep diaries and a commercial wearable sleep tracker (Fitbit Inspire 2). Next-day symptoms were measured using twice-daily symptom surveys (i.e., 20 surveys over ten days). Individual items assessed subjective cognition, energy level, and mood using an “I feel tired” format and scored from 0-4. To determine the lagged impact of prior-night sleep on next-day symptoms, a total of 40 mixed models (MMs) were developed. Individual prior-night sleep diary (total sleep time [TST], sleep efficiency [SE], and sleep quality [QUAL]) and Fitbit (TST, SE) parameters were entered as independent variables, and individual daytime symptoms (feeling alert, clear-headed, refreshed, fatigued, happy, sad, stressed, and relaxed) were entered as dependent variables. All models controlled for age and sex. The Benjamini-Hochberg procedure for correcting false discovery rate (BH-FDR) method was used to correct for multiple comparisons.

Results

Lagged MM analyses revealed that all prior-night sleep diary variables were significantly associated with next-day symptoms (all ps< 0.001 with df=2081) over ten days. Specifically, prior-night sleep diary parameters (TST, SE, QUAL) were positively associated with next-day feeling alert, clear-headed, refreshed, happy, and relaxed; and negatively associated with next-day feeling fatigued, sad, and stressed. Prior-night Fitbit sleep parameters were significantly associated with most next-day symptoms (largest p=0.023 with df=1956). Specifically, Fitbit TST and SE were positively associated with next-day feeling clear-headed, refreshed, happy, and relaxed and negatively associated with next-day fatigue. Fitbit TST was negatively associated with next-day stress.

Conclusion

Among military personnel with sleep problems, prior-night sleep predicts next-day subjective cognition, energy level, and mood over ten days. Future research should employ remote monitoring approaches to predict treatment response and deliver personalized care to improve sleep and daytime symptoms.

Support (if any)

DoD W81XWH1990006 (via Medical Technology Enterprise Consortium)
1109 The Impact of Sleep Duration and Its Variability on the Human Phenome: Analysis of All of Us Wearables Data
Authors: Manetta Mason , Mazzotti Diego
Published: April 2024

Introduction

Epidemiological evidence indicates that short, long and variable sleep duration are associated with cardiometabolic outcomes. However, due to the ubiquitous role of sleep, one would expect to see effects on a broad range of human diseases. Using electronic health records (EHR) and wearable sleep data from the All of Us Research Program, we conducted a cross-sectional analysis of sleep duration and variability with the risk of over 3530 chronic conditions across the human phenome.

Methods

All of Us participants (≥18 years) with available EHR data and at least 90 days of continuous monitoring using Fitbit devices under a ‘bring your own device’ model were included. Average (mean) and variability (standard deviation) of sleep duration were derived based on device-generated data. Chronic conditions were determined based on mapping of International Classification of Disease 10 codes available in the EHR to phecodes, an ontology representing clinically meaningful concepts. Associations between sleep traits and phecodes were assessed using multiple test corrected logistic regression adjusted for age, sex, and race.

Results

A total of 8,256 participants with available Fitbit data were included. Participants were 89% White, 72% female, 72% college educated and had median (IQR) age of 51 (36.6-52.0) years, BMI 28.3 (24.4-33.0) kg/m2, and 1,566 (860-2307) days with sleep measurements. The median average nightly sleep duration was 6.34 (5.67-6.91) hours and the median nightly sleep variability was 1.63 (1.33-1.95) hours. In adjusted analyses, participants with greater sleep duration had significantly lower odds of obesity (OR; 95%CI = 0.85; 0.80-0.91, obstructive sleep apnea (0.74; 0.74-0.88), type 2 diabetes (0.68, CI 0.58-0.80), and essential hypertension (0.86; 0.81-0.92). Conversely, participants with greater sleep duration variability had higher odds of major depressive disorder (2.15; 1.84-2.51), anxiety disorder (1.75; 1.51-2.03), gastroesophageal reflux disease (1.61; 1.39-1.86), and abnormal electrocardiogram (1.80; 1.47-2.19).

Conclusion

Our findings suggest important associations between sleep duration patterns and chronic conditions, emphasizing the importance of sleep health in disease prevention and management. Important limitations include analysis of a selected population with access to wearable devices, which may not represent the general population enrolled in All of Us.

Support (if any)

All of Us Research Scholars Program
0111 Nighttime Vagal Nerve Stimulation Interacts with Sleep to Predict Next-day Affective States
Authors: Silva-Jones Jillian , Subramoniam Anjana , Crosswell Alexandra , Whitehurst Lauren
Published: April 2024

Introduction

Sleep and the autonomic nervous system (ANS) independently modulate emotional experiences and interact bidirectionally - changes in ANS modulate sleep and stages of sleep are associated with distinct autonomic profiles. Changes to ANS via transcutaneous vagal nerve stimulation (tVNS) can improve emotion recognition and mood, however, the role of sleep has not been considered. We used a 14-day within-subject, sham-controlled study to examine the impact of nightly tVNS administration on sleep and morning affect.

Methods

Participants(N=20participants, aged=18+) received seven continuous days of non-invasive, active or sham (counterbalanced) tVNS via the left cymba concha between 9PM-11PM each night across two weeks in naturalistic sleeping conditions. Participants completed morning and nightly affect diaries and wore an actigraph watch to track sleep/wake behavior (N=136observations). Affect was aggregated into high-arousal/negative (distressed, irritable), low-arousal/negative (nervous, on edge), high-arousal/positive (alert, attentive, happy), and low-arousal/positive (content, safe). Actigraphy determined total sleep duration, wake minutes in bed, sleep onset latency(SOL), and sleep continuity or wake after sleep onset(WASO).

Results

Linear mixed effects models examined how night affect, stimulation condition, and sleep behavior predicted next morning affect. A three-way-interaction emerged: low-arousal/negative affect pre-sleep predicted low-arousal/negative affect post-sleep on nights when people experienced longer SOLs and active stimulation (ß=-.40, p=.002). Importantly, on nights with shorter SOL, active stimulation led to reductions in low-arousal/negative affect the next morning (ß=-.63, p=.02). Another three-way-interaction emerged such that low-arousal/negative affect, active stimulation, and WASO at night predicted low-arousal/negative affect in the morning. Nights with less WASO and sham stimulation predicted mornings with low-arousal/negative affect (ß=1.26, p=.03). Also, a three-way interaction suggested that nights with high-arousal/negative affect, more WASO, and sham stimulation, also led to mornings with high-arousal/negative affect. However, nights with high WASO and active stimulation led to mornings with the highest amount of high-arousal/negative affect (ß=.89, p=.003). Lastly, low-arousal/positive affect at night led to morning increases in the same emotions, but active stimulation blunted this effect (ß=-.58, p=.04).

Conclusion

Findings suggest that sleep and tVNS are influential in mitigating or intensifying the impact of nighttime affective states on morning affect, particularly for negative emotions. TVNS’s impact on noradrenergic signaling may account for these findings.

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1110 Utilizing Consumer Wearables to Empower and Engage Patients Prescribed non-PAP Obstructive Sleep Apnea Therapy
Authors: Chohan Moeed , Mak Selene , Ash Garrett , Ghadimi Sara , Der-Mcleod Erin , Naeem Saadia , Moore Virginia , Zeidler Michelle , Fung Constance H
Published: April 2024

Introduction

Increased adoption of consumer wearables has resulted in abundant patient-generated health data (PGHD). However, guidance on incorporation of metrics from such devices in the management of sleep disorders is lacking. We aimed to develop a PGHD program, using continuous oxygen saturation (SpO2) monitoring from a consumer wearable as a prototype. By implementing SpO2 data and patient survey outcomes in reports, we engaged patients prescribed non-PAP therapy for obstructive sleep apnea (OSA) and promoted shared decision making to enhance therapy.

Methods

In an ongoing quality improvement project at a VA medical center, we conducted three plan-do-study-act (PDSA) cycles. Patients share SpO2 data collected with a clinic-issued Fitbit via a third-party platform (Fitabase), OSA patient-reported outcome (PRO) measures and their nightly use of non-PAP therapy via Qualtrics. We created a report that juxtaposes SpO2 distribution, PRO measures, and therapy compliance and then send it to the patient for review. At cycle end, patients meet with a sleep clinician to discuss the reports, OSA therapy, and program feedback.

Results

Eleven patients aged 30 to 85 have enrolled (3-5 per cycle). In Cycle 1 (7 days), we developed orientation procedures and patient-facing materials and refined the process for merging the SpO2, PRO, and patient-reported therapy use into a graphical and textual report. A majority of patients (67%, n=6 of 9) indicated motivation to retrial PAP therapy after seeing low SpO2 values. We developed a standardized script to clearly explain the report and data for our patients. In Cycle 2 (14 days), we tested the standardized script and troubleshot barriers to accessing data that emerged— ie unexplained gaps in SpO2 values and device sync issues. In Cycle 3 (28 days), we revised the graphs to reduce production time. Eight patients completed the post program survey. When asked if they would recommend the program (scale 1-10; 10 best), the average score was 9.25.

Conclusion

We developed a consumer wearable based program to engage patients in OSA treatment. Incorporating oxygen data from wearables may promote self-management and participation in shared decision making, which ultimately may improve health outcomes of OSA.

Support (if any)

VA Office of Connected Care
1111 Association Between Technology Use and Sleep: Examination of Population Data from National Sleep Foundation
Authors: Sparks Christopher , Carlson Steven , Dzierzewski Joseph , Baron Kelly
Published: April 2024

Introduction

Evening technology use is common and may be associated with poorer sleep health. The purpose of this study was to evaluate rates of technology before bedtime in a national sample and examine the association between technology use and sleep quality and duration.

Methods

We evaluated sleep and technology use among a nationally representative sample of US adults collected by the National Sleep Foundation. Participants reported their use of four classes of technology in the hour before bedtime (i.e., A) TV, smart TV, game consol, B) smartphones, C) computers, laptops, tablets, D) other devices) and where they used these devices (i.e., in the bedroom, outside of the bedroom, both in and outside the bedroom). Participants also reported their average sleep duration over the past week on both weekdays and weekends. Sleep quality was rated on a 5-point scale using a single item from the Sleep Health Index. We used weighted analyses to evaluate descriptives, as well as associations between technology use, location of technology use, and sleep duration and quality using t-tests and regression analysis while controlling for age and gender.

Results

Data were available for 1,009 participants (Mage = 48.2, SD = 18.0, 51.3% women). Results indicated that 90% of participants used electronic devices within one hour of bedtime, with 76.6% used electronic devices in the bedroom. The most frequently used devices before bed were TVs (71.3% of the sample and among TV watchers 49.7% used in the bedroom) and smartphones (70.6% of the sample and among smartphone users, 79.1% used in the bedroom) Individuals who used smartphones before bed had poorer sleep quality compared to those who did not use a smartphone (t(1007) = -2.19, p = 0.029). There were no other significant associations between number of devices, specific device use, or device use location with sleep duration or quality.

Conclusion

Technology use before bed and in the bedroom is a common occurrence, and smartphone use in the hour before bed was associated with poorer sleep quality. Given the widespread use of smartphones before bed, reducing use in the hour before bed may have implications for improving sleep health.

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1112 Performance Evaluation of a Multisensor Ring During a Clinical Multiple Sleep Latency Test
Authors: Amadei Michael , Freckleton Dustin , Kennedy Kathryn E R , Grandner Michael
Published: April 2024

Introduction

Commercially-available wearable devices are increasingly used to detect sleep-wake patterns in free-living conditions. These devices, however, are infrequently assessed relative to their ability to detect naps. The present study evaluated whether a commercially-available wearable device was able to detect nap onset during a clinical Multiple Sleep Latency Test (MSLT).

Methods

Participants referred for a clinical MSLT wore a commercially-available multisensor ring device (Happy Ring; Happy Health) during their in-lab assessment at one of two AASM-accredited sleep centers. Each sleep lab followed AASM guidelines for administering the MSLT and scoring the polysomnogram for sleep onset. Due to potential differences in scoring across sites, a third reviewer (INSERT CREDENTIALS HERE) blindly scored all records, also according to AASM guidelines.

Results

Internal sleep lab scoring somewhat differed from the independent scorer, with 90% agreement regarding the proportion of naps that were clinically significant; there were 2 cases where only the sleep lab reported a clinically significant MSLT and 3 cases where only the independent rater reported a clinically significant MSLT. The ring achieved 86% diagnostic accuracy relative to the lab scorer and 84% relative to the independent scorer. The mean difference between the lab and independent scorer for sleep latency was 142 seconds, with N=30 records that achieved agreement of < 150 seconds. Of these 30 records, the Happy Ring achieved 96.7% accuracy (only 1 case of disagreement). The mean difference between the lab scorer and the ring was 152 seconds.

Conclusion

The Happy Ring shows promise in its ability to detect sleep onset during an MSLT. Future work could be used to optimize wearables for being able to predict who will test positive in an MSLT, thus reducing costs, wait times, and patient burden.

Support (if any)

Happy Health
1115 Nano-material Based Brain Computer Interface (BCI) Sleep Monitoring Device and Its Application
Authors: Sun Yu
Published: April 2024

Introduction

PSG (Polysomnography) as a traditional instrument for multi-modal monitoring device, has been considered as golden standard for sleep related vital sign monitoring for many decades. But it is sometimes considered burdensome due to its wet EEG electrodes cap, its time-consuming wearing, and it is not suitable for tele-health and at-home monitoring purpose, for chronic disease management especially for elderly senior people.

Methods

Given this, a nano-electronic based sleep monitoring patch device has been developed to overcome these obstacles. The device implements metallic nano-particle electrodes to increase data acquisition efficiency, and well-developed hydrogel to increase skin biocompatibility and gas permeability. Also, the device integrates EEG chip design and three-electrode based setup, to avoid traditional 8-channel or 16-channel EEG headset and dramatically increase the convenience of usage. In terms of software, the device uses artificial intelligence deep learning network to train sleep staging algorithm and implement advanced data processing technology to differentiate EEG, EOG and EMG from the raw data acquired by the device in the forehead.

Results

In this way, the device can also detect characteristic waveform such as slow waveform, kappa complex and spindle etc. All these factors result in 85-95% accuracy of sleep staging percentages, as compared to PSG golden standard, which is dramatic.

Conclusion

The device has passed Class II medical NMPA approval in China and it is getting approval in US FDA very soon. It is believed that this device can be used not only in traditional sleep center and hospital all over the world, but can it also be used in a variety of different departments such as psychiatry, ICU and neurology, as well as rehab centers, nursery, and many other circumstances and locations. It is believed it is beneficial not only for doctors, but for researchers as well.

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1119 Validation of a Portable Sleep Electroencephalography Device in Good Sleepers and People with Sleep Apnea
Authors: Lanthier Malika , Foti Micheal-Christopher , Higginson Caitlin , Tavakoli Paniz , Oksit Defne , Ray Laura , Fogel Stuart , Robillard Rebecca
Published: April 2024

Introduction

Sleep wearable restricted to accelerometry or heart rate monitoring have proved to be helpful to delineate sleep-wake profiles outside of the laboratory environment, but have limited accuracy and only provide indirect estimations of sleep and wake states. This study aimed to assess the validity of a novel electroencephalography headband for ambulatory sleep monitoring as compared to standard polysomnography in good sleepers and individuals with sleep apnea.

Methods

Forty-seven adult males and females from the community took part to this study. This includes a preliminary sample of eight individuals with sleep apnea detected through level 1 polysomnography. All participants underwent one night of in-laboratory sleep recording with the portable EEG headband (MUSE-S, Interaxon) and simultaneous standard polysomnography (Embla N7000/RemLogic, Natus). The Muse-S headband is a commercially available consumer headband with 7 EEG sensors: 2 on the forehead, 2 behind the ears, and 3 reference sensors. MUSE-S data was scored using an automated sleep staging algorithm. Polysomnography data was scored by an independent registered technologist who was blinded to the MUSE-S algorithm-based scoring.

Results

In the overall sample, the accuracy of the Muse-S relative to standard polysomnography ranged between 88% and 96% across all sleep stages, with a sensitivity of 79% to 92%, and a specificity of 90% to 99%. Cohen’s Kappa for all sleep stages combined was 0.76 (CI:0.75-0.76). Analyses per sleep stages showed that Cohen’s Kappa scores were in the fair agreement range for NREM 1 sleep (K=0.41, CI:0.39-0.43), increased to the substantial agreement range for both NREM 2 (K=0.75, CI:0.74-0.75) and NREM3 sleep (K=0.77, CI:0.76-0.77), and further increased to the near perfect agreement range for REM sleep (K=0.85, CI:0.85-0.86) and wake (K=0.84, CI: 0.83-0.84). Similar results were obtained in the subgroup with sleep apnea (overall K= 0.87, CI:0.85-0.88; NREM 1 K=0.33, CI:0.28-0.38; NREM 2 K=0.72, CI:0.70-0.73; NREM3 K=0.81, CI:0.79-0.79; REM K=0.80, CI:0.78-0.82) and wake (K=0.86, CI:0.85-0.88).

Conclusion

Portable EEG-based sleep monitoring with the MUSE-S shows good validity for sleep macroarchitecture variables relative to standard polysomnography. Fair to near perfect concordance was observed across sleep stages in a diverse sample of good sleepers and people with sleep disorders.

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1121 Multi-sensor Fusion Approach for Sleep Stage Analysis: A Non-contact Sensor Study
Authors: Kim Doyoon , Chen Hao , Lee Joonhyun , An Yuhyun , Chang Hao Hsuan , Kim Seho , Park Jonghyun , Yang Seungman , Bae Hyoeun , Joo Eunyeon
Published: April 2024

Introduction

There has been a growing interest in utilizing non-contact sensors such as photoplethysmogram (PPG), ultra-wideband (UWB), millimeter-wave (mmWave) or piezoelectric sensors to measure physiological signals, which are then processed through machine learning for sleep stage analysis. In this study, we conducted an analysis of sleep stages by leveraging data collected from multiple sensors on the subjects and employing machine learning algorithms to combine and interpret the information.

Methods

mmWave and UWB sensors were placed within approximately 500mm around the subject, and Piezo sensors were positioned inside the subject's mattress. Concurrently, physiological signals from those sensors and from polysomnography (PSG) were obtained. . Key indicators for machine learning were extracted from the sensor data collected from each sensor. Convolutional neural network (CNN) and Transformer machine learning methods were employed to derive sleep stages for each sensor. the sleep stages obtained from each sensor were used as indicators, combined to estimate the final sleep stage. The results were then compared and analyzed against PSG parameters to estimate the accuracy of the algorithm.

Results

A total of 340 participants (244 males, mean age 44.95± 12.40 y) were enrolled in this study. Data from 200 individuals were utilized to train sleep estimation algorithms for each sensor, while data from 110 individuals were employed to train algorithms that combine results from each sensor to determine sleep. Additional 30 datasets were used to validate these algorithms. The sleep stage accuracy (Accuracy = True epoch/ Total epoch) relative to PSG: mmWave (72.9%), UWB (75.5%), and Piezo (72.4%). Cohen’s Kappa values were mmWave (0.55), UWB (0.62), and Piezo (0.53). The fusion algorithm based on each sensor demonstrated an accuracy of 79.8% and a Cohen’s Kappa value of 0.69 compared to PSG.

Conclusion

In contrast to previous research relying on single-sensor predictions, our approach harnessed the diverse strengths of different sensor types, conducting a comprehensive analysis of sleep stages by combining data collected from multiple sensors.

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1122 A Deep Learning Model for Inferring Sleep Stage from a Flexible Wireless Dual Sensor Wearable System Without EEG
Authors: Zhang Andrew , Li Chunlin , Tang Yuzhi , He-Mo Alex , Ghahjaverestan Nasim Montazeri , Goubran Maged , Lim Andrew
Published: April 2024

Introduction

In-lab polysomnography (PSG) is costly and difficult to scale due to a need for specialized personnel for data acquisition and annotation. Numerous novel wearable devices without electroencephalography (EEG) have been developed to improve scalability of data acquisition. However, validated automated approaches to data annotation, including sleep staging are needed. Here, we apply deep learning approaches to the problem of sleep staging using data from the ANNE One (Sibel Health, Evanston, IL), a minimally intrusive flexible wireless dual sensor system measuring chest electrocardiography (ECG), triaxial accelerometry, and temperature, and finger photoplethysmography (PPG).

Methods

We obtained wearable sensor recordings from 281 adults undergoing concurrent clinical polysomnography at a tertiary care sleep lab. PSG recordings were scored according to AASM criteria. PSG and wearable sensor data were automatically aligned using their ECG signals with alignment confirmed by visual inspection. We trained a neural-network model to predict both 3-class (Wake, NREM, REM) and 2-class (Wake, Sleep) sleep stage classifications using a randomly selected 85% of the recordings and tested the model on the remaining recordings. We applied the model to ambulatory wearable sensor recordings from 233 older adults at risk for dementia. Our neural-network employed a convolutional-encoder and autoregressive-decoder architecture. In addition to time domain signals, we also engineered frequency domain features as well as selected scalar and metadata features as input to our model to improve performance. Ensembling of model variants was performed.

Results

Our approach achieved a 2-class macro-F1 of 0.718 with a sensitivity of 0.760 and specificity of 0.763 and a 3-class macro-F1 of 0.585 (wake precision 0.564 accuracy 0.745; NREM precision 0.886 accuracy 0.634; REM precision 0.258 accuracy 0.671). Our feature engineering and training techniques offered a 9% performance improvement from the time domain signals only baseline given the same neural network architecture, while ensembling different model variants offered a further 4% performance improvement.

Conclusion

A deep learning model can infer sleep stage from an EEG-less flexible wireless system and can be successfully applied to data from older community-dwelling adults at high risk for dementia.

Support (if any)

The Centre for Aging and Brain Health Innovation, Canadian Institutes of Health Research, National Institute on Aging
1123 Circadian Phase Assessments from Clinical Patients Using an At-Home Self-Collection Saliva Kit
Authors: Okun Michele , Schwartz Chris , Granger Steve
Published: April 2024

Introduction

The dim light melatonin onset (DLMO) is the gold-standard for estimating sleep timing and is recognized as a reliable diagnostic tool for circadian rhythm sleep-wake disorders (CRSWD). Its assessment may help optimize treatment of various sleep disorders. The DLMO is usually assessed in a research laboratory or clinic, which presents significant barriers. Thus, there is a need to further evaluate the ecological validity of self-directed collection at home. Salimetrics, Inc. recently introduced a circadian phase assessment kit that allows for self-collection of saliva at home accompanied by laboratory assessed DLMOs. This is a preliminary report of a limited dataset identifying normal (typical) and abnormal (atypical) melatonin profiles.

Methods

Sleep clinic patients (N = 197) utilized one of four different protocols that varied in the number and timing of saliva collection based on their provider’s suggestion. Despite different protocols, the actual sampling protocol was performed under the same guidance for all assessments. A typical profile was determined as an expected onset (estimated ~2.5-1.5 hours before bedtime) with a baseline of < 10 pg/ml and a sleep onset of >10 pg/ml. An atypical profile fell outside these parameters. Routine medication usage was also analyzed to determine if there was a correlation between melatonin modulating medications and resulting profiles. Only sex and age were recorded as the participants collected samples as part of the clinical care protocol.

Results

Findings indicate 22% typical profiles vs. 78% atypical profiles. With regards to medication use: 26% of typical profiles vs 19% of atypical profiles had no medications listed, 53% vs 68% used a melatonin modulator, respectively, 14% vs 54% used a psychiatric medication, 21% vs 25% used 2+ psychiatric medications, and 9% vs 14% used a melatonin supplement within 10 days of assessment.

Conclusion

These data corroborate earlier investigations by Burgess and colleagues and suggest that the use of a self-directed, at-home DLMO assessment is feasible and accurate. It further suggests that medication use, or an underlying psychiatric disorder can modulate melatonin secretion exaggerating the CRSWD. Data indicates that this diagnostic tool can accurately identify many clinical sleep patients with an existing CRSWD or significant sleep wake misalignment.

Support (if any)

Salimetrics
1125 Use of a Pillow Designed to Help Users Feel Cool Improves Objective Sleep Quality and Perceived Sleep
Authors: Carmon Kiara , Danoff-Burg Sharon , Rus Holly , Weaver Morgan , Upadhyaya Devanshi , Ledesma Duvia Lara
Published: April 2024

Introduction

Comfort and temperature regulation play important roles in sleep quality. This study compared sleep on a pillow designed to help the sleeper feel cool to participants’ prior sleep on their original pillow.

Methods

Healthy adults (71% female; ages 24-72, mean age=42) who reported difficulties with sleeping hot or night sweats and frequent awakenings or time spent awake at night, participated in a 9-week field study, using a pre-post intervention design. During the 4-week baseline period, participants used their regular pillow at home. During the 5-week intervention period, following a 1-week adjustment period, participants used Sleepy’s SUB-0 Triple Cooling Pillow. The pillow is made with materials designed to help regulate temperature and draw heat away from the body. Sleep was measured objectively using a PSG-validated, non-contact biomotion device, SleepScore Max, every night and by daily and pre-post self-report. Multilevel regression and paired t-tests were used to test for statistical significance.

Results

There were 1,175 nights of tracked sleep across 29 participants. Compared to baseline, objective sleep measurements showed increased REM, indicated by 3 metrics: REM in minutes (9% increase, p=.001), REM as a proportion of the night (13% relative increase, p<.001), and MindScore (4% increase, p=001). There also was improvement in SleepScore, a measure of overall sleep quality (3% increase, p=.005). Self-report measures revealed that the pillow felt cooler and more comfortable compared to the original pillow, and participants perceived that they had less intense night sweats, a reduction in the experience of sleeping hot, and increases in sleep quality, feeling more rested in the morning, and satisfaction with sleep (all ps <.001).

Conclusion

Objectively measured sleep quality and perceived sleep improved when using the cooling pillow compared to the original pillow. Self-report results indicated that the intervention was perceived as comfortable and cool. These perceptions likely are what led to the key sleep improvements.

Support (if any)

Mattress Firm INC.
1126 In-Person Expert Mattress Matcher Process Is Associated with Improved Objective Deep Sleep and Perceived Sleep
Authors: Danoff-Burg Sharon , Rus Holly , Weaver Morgan , Carmon Kiara , Mertz Catalina , Ledesma Duvia Lara
Published: April 2024

Introduction

Unsubstantiated claims about the potential benefits of various mattress types can make it challenging for consumers to choose a mattress that fits their personalized needs. Services that provide information and recommendations regarding mattress selection may help solve this problem. However, more evidence and research are necessary to understand such services' potential to support better sleep. This study examined the sleep of research participants using mattresses selected during an in-store matching process compared to using their original mattresses.

Methods

Healthy adults (75% female; ages 27-73, mean=46) participated in a 10-week field study, using a pre-post intervention design. The intervention consisted of being guided in person through the Mattress Firm Mattress Matcher process. During the 4-week baseline period, participants used their regular mattress at home. After being matched at a store near them with a new mattress, participants slept on that mattress for 6 weeks, including a 2-week adjustment period. Sleep was measured objectively using SleepScore Max every night and by daily and pre-post self-report. Multilevel regression and paired t-tests were used to test for statistical significance.

Results

There were over 1,189 nights of tracked sleep across 29 participants. Objective sleep measurements showed that deep sleep improved when using the matched mattress compared to baseline, indicated by 3 metrics: deep sleep in minutes (5% increase, p=.003), deep sleep as a proportion of the night (4% relative increase; p=.030), and BodyScore (1% increase; p=.022). Improvements also were found for a variety of self-reported sleep outcomes, including feeling better able to sleep through the night without tossing and turning, waking up feeling refreshed and well-rested, feeling satisfied with sleep more often, and perceiving better overall sleep quality (all ps<.001). Additionally, compared to baseline, the matched mattress felt more comfortable, pressure relieving, supportive, and cooler (all ps<.001).

Conclusion

Objectively measured deep sleep and numerous aspects of self-reported sleep improved among healthy adults using a mattress selected via an expert, in-person mattress matcher process. The results suggest that this process can support sleep by helping individuals find a mattress that provides an optimal fit for their personal needs.

Support (if any)

Mattress Firm INC