bluuebunny/crossref_metadata_2025 huggingface dataset:
0254 Diagnosis of Obstructive Sleep Apnea using Koopman Spectral Analysis
Authors: Le Trung , Huynh Phat
Published: May 2023

Introduction

Koopman operator theory and the Hankel alternative view of the Koopman (HAVOK) model have been widely used to investigate the chaotic dynamics in complex systems. Although the statistics of intermittent dynamics have been evaluated in the HAVOK model, they are not adequate to characterize intermittent forcing. We proposed the Koopman spectral analysis method to characterize the intermittent transition between apeanic and non-apneaic episodes to predict impending OSA episodes.

Methods

Apnea-ECG Database from Physionet.org was selected to investigate the nonlinear intermittent behaviors of OSA disorder. The data set has 70 records of OSA patients, and the recordings vary from 7 hours to nearly 10 hours each. Each recording consists of a continuous digitized ECG signal and apnea annotations. First, the eigen time-delay coordinates were reconstructed from the heart rate variability (HRV) features. Next, the last eigen time-delay coordinate was modeled as an intermittent forcing component during apneic episodes. Finally, the forcing statistics in OSA were estimated to characterize the intermittency of the pathophysiological process.

Results

The statistics of the forcing in OSA demonstrated the fat-tailed distribution of the intermittent forcing of the underlying OSA pathophysiological process. The pooled means and standard deviations of the burst duration and the inter-burst duration across OSA patients were also calculated to be 1.73±0.26 minutes and 11.71±2.69 minutes. Scalogram amplitude and spectral decomposition of the wavelet transform exhibited various predominant frequencies and dynamics modes associated with apneic events.

Conclusion

We have reported the data-driven HAVOK model to obtain an intermittently forced linear system of chaos for the OSA pathophysiological processes The proposed method has been validated on 70 records of OSA patients from Apnea-ECG database. The forcing signal can predict the intermittent transient events, such as the transition from normal to apneic episodes in OSA case.

Support (if any)

Huynh PK, Setty AR, Le TB, Le TQ. A noise-robust Koopman spectral analysis of an intermittent dynamics method for complex systems: a case study in pathophysiological processes of obstructive sleep apnea. IISE Transactions on Healthcare Systems Engineering. 2022 Nov 1:1-6.
0255 Female Mice Are Less Susceptible to the Effects of Diet-Induced Obesity on Sleep and Sleep-Disordered Breathing
Authors: Kim Lenise , Pho Huy , Pham Luu , Polotsky Vsevolod
Published: May 2023

Introduction

Diet-induced obesity (DIO) in male mice is a well-established model of sleep-disordered breathing (SDB). Male DIO mice manifest hypoventilation, increased apnea severity, and sleep fragmentation. The effects of DIO on breathing during sleep and sleep architecture in females have been insufficiently investigated. We hypothesized that female mice are less susceptible to the detrimental effects of DIO on sleep and SDB compared to males.

Methods

Full-polysomnographies were performed in 15 lean female C57BL/6J and 14 female DIO-C57BL/6J mice using a whole-body plethysmography chamber. Ventilatory response to an arousal was calculated as ratio of the average minute ventilation (VE) during each arousal (2.5s) relative to the baseline VE. Breathing stability was measured using Poincaré plots of VE. Mice were exposed to 8% CO2 to measure the hypercapnic ventilatory response (HCVR). Mice underwent 24-h metabolic studies.

Results

Obesity was associated with hypometabolism, indicated by reduced oxygen consumption (VO2) and CO2 production in DIO mice. VE in 8% CO2 and HCVR were significantly suppressed suggesting a decreased CO2 sensitivity in obese animals. NREM sleep duration was reduced, but REM sleep was preserved. Arousal frequency and VE during NREM and REM sleep were similar between groups. However, apnea severity was 4-fold augmented in lean females compared to DIO mice (14.7±3.9 vs 74.2±11.9 events/h; P< 0.001). In lean mice, the increased frequency of apneas was mainly attributed to sleep fragmentation since the events were predominantly post-arousals. Breathing stability during sleep was significantly increased in lean mice. Ventilatory response to an arousal was 30% increased in lean females compared to DIO group.

Conclusion

Our results suggest potential sex differences in the effects of DIO on breathing during sleep and sleep architecture. DIO in females appears to be less detrimental regarding SDB severity. The mechanisms underlying the augmented SDB severity in lean females are unknown, but an exacerbated CO2 responsiveness during sleep related to sleep fragmentation could play an important role.

Support (if any)

American Heart Association Postdoctoral Fellowship Award 828142, NHLBI grant NIH R01s HL128970, HL133100, and HL13892, American Academy of Sleep Medicine Foundation 238-BS-20, and American Thoracic Society Unrestricted Award.
0256 Modeling Individual Sleep Needs from Wearable-Device Data
Authors: Shah Palak , Braun Rosemary
Published: May 2023

Introduction

There are currently thought to be two processes that interact to regulate one’s sleep/wake cycle: a sleep homeostat, which depends on the amount of time you have been awake; and the circadian rhythm, an internal 24-hour molecular clock present in nearly every cell of the body. Both processes differ between individuals, whether that’s from a decision to pull an all-nighter or from a genetic inclination towards being an “early bird” or a “night owl”. This project focuses on building a model that can predict how sleep disruptions impact an individual’s sleep cycle, and the ability to recover from these disruptions.

Methods

A “two-process” model with the capability to simulate a theoretical sleep-wake cycle with disruptions was implemented in Python, and is being tuned using machine learning (ML) on actigraphy data. Actigraphy measures motion using a noninvasive accelerometer embedded into a wearable-device (e.g. FitBit), allowing for the study of sleep activity patterns in a real-world setting. Data was collected from approximately 20,000 individuals, who reside in 121 different countries. ML algorithm was trained and tested on synthetic data generated from the “two-process” model, and then applied to the actigraphy dataset to evaluate prediction accuracy.

Results

Our model has shown that recovery from sleep disruptions can be modeled mathematically, and can be individualized using actigraphy data. The mathematical model was able to successfully simulate the “two-process” interaction, and, by sweeping parameter values, those that yielded a single sleep bout of 8-9 hours/night were identified. ML algorithm tuning with actigraphy data is ongoing.

Conclusion

Our results suggest that it is possible to predict the sleep patterns of individuals despite disruptions in their schedules from sleep disorders, travel, work, or social and family commitments. Future research should focus on optimization of sleep in individuals using the informational output of our model, and extending it to incorporate the behavioral and/or clinical changes that have proven to improve an individual’s sleep. By incorporating this additional set of data, the resulting model can not only predict the impact of disruptions, but also provide recommendations for prevention and recovery based on an individual’s input about their lifestyle and their sleep.

Support (if any)
0258 Altered sleep regulation in a mouse model of Parkinson’s disease
Authors: Summa Keith , Jiang Peng , Gonzalez-Rodriguez Patricia , Surmeier James , Vitaterna Martha , Turek Fred
Published: May 2023

Introduction

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by pathognomonic motor abnormalities that lead to significant disability and morbidity in advanced stages. PD is also associated with sleep abnormalities such as increased fragmentation, insomnia, and excessive daytime sleepiness. Little is known of the underlying mechanisms, in part due to the limited ability of existing mouse models to adequately replicate human PD patient sleep phenotypes. Here we characterize sleep-wake behavior in an innovative genetic mouse model of PD (MCI-Park) targeting a mitochondrial complex I gene (Ndufs2), which experiences progressive dopaminergic neuron loss and displays motor phenotypes resembling those seen in human PD.

Methods

Undisturbed sleep-wake behavior during 12 hr light: 12 hr dark cycles was evaluated in MCI-Park mice during prodromal stages (6-8 weeks of age) and later parkinsonian stages (14-18 weeks of age) as well as in age-matched wildtype littermate controls. Electroencephalographic (EEG) and electromyographic (EMG) recording electrodes were surgically implanted using sterile technique in male and female MCI-Park and wildtype mice. After one week of recovery and acclimation to recording chambers, EEG and EMG were recorded continuously and sleep phenotypes were analyzed.

Results

In prodromal stages, MCI-Park mice exhibited similar overall sleep state amounts compared to wildtype mice, however there were significantly more state shifts as well as longer bouts of wakefulness and shorter bouts of non-REM (NREM) sleep. As mice progressed to parkinsonian stages, these fragmentation measures worsened. In addition, parkinsonian MCI-Park mice experienced a significant increase in wakefulness, a significant reduction in NREM sleep amount, and alterations in REM sleep, with pronounced reductions in REM sleep amount and number of REM bouts. Power spectral analysis revealed consistent decreases in theta2 (also known as alpha) power across all sleep-wake states in MCI-Park mice.

Conclusion

These results indicate that mice harboring a genetic defect causing selective disruption of dopaminergic neurons exhibit progressive sleep abnormalities that resemble sleep disturbances in PD patients. This model thus offers an opportunity to evaluate mechanisms underlying and therapeutic strategies targeting altered sleep in PD patients.

Support (if any)

This work was supported by the Department of Defense award number W81XWH-21-0582 (F.W.T. and D.J.S.).
0259 Associations between exercise, sleep, and mood during pregnancy
Authors: McCarthy Traci , Velez Sarah , Spaeth Andrea
Published: May 2023

Introduction

Sleep and mood disturbances are common complaints among women during pregnancy. Exercise is a non-pharmacological intervention that may help improve sleep and mood; however the relationship between these variables in pregnancy is unclear. The purpose of the current study is to determine if meeting exercise guidelines during pregnancy associates with improved sleep and mood.

Methods

Women (n=31, 32.5±4.3y) with healthy pregnancies were enrolled in the study during their third trimester (28-32 weeks gestation). Participants completed the Pregnancy Physical Activity Questionnaire (PPAQ), Pittsburgh Sleep Quality Index (PSQI), and PROMIS mood questionnaires and then wore a wrist actigraph and completed a daily sleep diary for two weeks. Participants who reported meeting exercise guidelines (>150 minutes moderate-to-vigorous physical activity [MVPA]/week, EX, n=14, pre-pregnancy BMI 23.7±0.9) were compared to those who reported not meeting these guidelines (< 90 minutes/week, SED, n=17, pre-pregnancy BMI 23.8±0.9). Multivariate ANOVAs were conducted to compare groups on outcome measures and correlations were conducted between exercise, sleep and mood variables.

Results

Exercise guideline groups did not differ in subjective sleep quality (PSQI Global, p=0.23), objective measures of sleep behavior (sleep duration, efficiency wake after sleep onset, and fragmentation, ps>0.10) or in self-reported anger, anxiety, depression or positive affect (ps>0.25). Greater total daily activity associated with improved sleep efficiency (p=0.07) and less wake after sleep onset (p=0.07) but did not associate with mood outcomes. Less wake after sleep onset and sleep fragmentation were associated with higher positive affect (ps< 0.05) and less depressive mood (ps< 0.07).

Conclusion

Meeting exercise guidelines (150 minutes MVPA/week) did not associate with improved sleep or mood in the third trimester of pregnancy. However, greater daily physical activity associated with improved sleep, and improved sleep associated with better mood. Women are particularly interested in non-pharmacological treatment options during pregnancy and these preliminary results support the development of behavioral interventions aimed at increasing daily physical activity in this population.

Support (if any)

American College of Sports Medicine Foundation Research Grant awarded to TM.
0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders
Authors: O'Hora Kathleen , Saletin Jared , Kushan-Wells Leila , Bearden Carrie
Published: May 2023

Introduction

There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS).

Methods

Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode.

Results

All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in < 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute).

Conclusion

This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders.

Support (if any)

RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award
0264 Untangling Correlations Between Positive Affect and Light and Activity Data Using Deep Learning
Authors: Wheeler Bradley , Hasler Brant , Wallace Meredith
Published: May 2023

Introduction

Research has found activity and light data continuously captured via wearable devices predict psychological outcomes. Understanding which sources are most predictive could suggest which biological or environmental factors most impact outcomes. Existing work has largely relied on summary measures of these sources. This may overgeneralize important nuances in the sources that are correlated to the outcomes. We utilized deep learning to predict daily affected based on raw actigraphy and light data to determine which sources are most predictive of daytime positive affect.

Methods

We modeled the daily mean (DMPA) and standard deviation (DSDPA) of positive affect using actigraphy and light data from a cohort of 172 adolescents who completed an ecological momentary protocol for 7-8 days. Positive affect was measured via PANAS-SF and administered via smartphone 5-6 times daily. The outcomes are predicted using the actigraphy and light data during the environmental night (time between astronomical twilight and dawn) prior to each outcome. Convolutional neural networks are used to predict the outcomes using all permutations of actigraphy, white light, and red-green-blue (RGB) light data.

Results

The mean (standard deviation) of the DMPA and DSDPA outcomes were 6.53(1.60) and 1.28(1.15) respectively. Using white and RGB light we could predict DMPA and DSDPA with a root mean squared error (RMSE) of 1.59 and 1.08 respectively (i.e., within 0.99 and 0.93 deviations respectively).

Conclusion

Our work indicates that white and RGB light best predict positive affect. This may indicate that light-based environmental night factors have a larger impact on daily affect than activity-based factors. Most results were within a few hundredths of a deviation from each other, and the channels that best predicted affect differed depending on whether DMPA or DSDPA was modeled. Our methods provide a way to predict affect from raw data and suggest which environmental and biological data have clinical implications towards affect. Important next steps include using time periods related to individual’s dim light melatonin onset and comparing findings from deep learning to those based on summary measures.

Support (if any)

CARRS Pilot Grant (Wheeler), R01-AA025626 (Hasler), R01-DA044143 (Hasler), RF1-AG056311-04 (Wallace)
0266 An mHealth intervention to deliver personalized messages to increase adherence to OSA care among transportation workers
Authors: Robbins Rebecca , Quan Stuart , Troxel Andrea , DiClemente Ralph , Barger Laura , Rapoport David , Viswanath Kasisomayajula , Jean-Louis Girardin , Czeisler Charles
Published: May 2023

Introduction

Transportation workers are at risk for obstructive sleep apnea (OSA), yet OSA is commonly undiagnosed in this population. In a two-phase study, we design and evaluate a tailored, mobile health (mHealth) intervention aimed at achieving OSA awareness, including changes in OSA beliefs, among operators of large highway maintenance vehicles.

Methods

In Phase 1, we recruited highway maintenance vehicle operators employed at worksites in the US northeast for focus groups to examine OSA beliefs and barriers. Results of the Phase 1 focus groups were used to inform the tailored intervention. In Phase 2, we evaluated the tailored approach, “Sleep Strong, Live Long,” in a mini-RCT. Worksites were randomized to intervention (Sleep Strong, Live Long) or control. Employees at both worksites completed baseline questionnaires and follow-up questionnaires 2-months post-baseline. Employees at the intervention worksite received a 30 minute in-person education session, access to an interactive website including video success stories, then follow-up text messages over 2 months as part of the Sleep Strong, Live Long program. The control condition received the intervention after completing follow-up surveys. We compared responses on the follow-up surveys between intervention and control conditions using linear regression with baseline responses as covariates.

Results

Phase 1 themes included common sleep complaints, inconsistent work schedules, and misconceptions about OSA (n=45). 75% of Phase 1 participants reported resistance to OSA behavior change. Phase 2 mini-RCT participants (n= 38) were on average 43.4 (sd=12.1) years old. Participants were 86.8% male and 67.6% reported an annual household income of $50,000 or more. Exposure to the intervention was associated with an improvement in OSA literacy (b=0.14, p=0.037), but not OSA self-efficacy (b=0.12, p=0.330) or OSA readiness to change (b=0.76, p=0.336).

Conclusion

We provide preliminary support for improving OSA literacy among a sample of transportation workers. Future research may evaluate the tailored program in a larger sample of workers and with a longer follow-up period to consider whether OSA literacy and self-efficacy mediate actual OSA behaviors, such as changes in OSA evaluation and treatment.

Support (if any)

This work was supported by the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health (NIH, Grant # K01HL150339).
0268 Mathematical recursive model presenting a formula to optimize apnea-hypopnea index based on sleep stage and sleep position
Authors: Im SangHyuk , Lee Chang-Hoon , Im KyoungBin
Published: May 2023

Introduction

Despite apnea-hypopnea index (AHI) being the gold standard in diagnosing obstructive sleep apnea, AHI can be further optimized if AHI is differentiated according to sleep stages/positions, then integrated with weighted time. The primary aim of this study is to demonstrate the mathematical modeling process from which ‘projected AHI’ (pAHI) can be calculated.

Methods

With the hypothesis of the nth projected AHI, expressed as AHI_n, being the gold standard and AHI_0 being test results, using AHI of 5 as cutoffs, subjects were grouped into a confusion matrix of TN_n, FP_n, FN_n and TP_n. Through recursive optimization, information obtained from AHI_0 and AHI_n was used to calculate AHI_(n + 1). The coefficients of the equation to calculate AHI_(n + 1) consisted of A_(n + 1), B_(n + 1), C_(n + 1), and D_(n + 1), each representing the % of total time of each of the four types of sleep (REM supine, REM non-supine, NREM supine, NREM non-supine) out of the people in the groups TN_n and FP_n. We tested a closed polysomnographic dataset of 337 subjects to determine the point at which all four coefficients would stay constant, producing the ideal pAHI of the dataset.

Results

Each of the four coefficients of the equation converged at n = 3. At n = 3, A_3 = 0.090, B_3 = 0.087, C_3 = 0.433, and D_3 = 0.390. The patients in the groups TN_2 and FP_2 turned out to be the same patients that were in the groups TN_3 and FP_3, explaining why each of the four coefficients of the equation converged at n = 3, and continuously onwards. As a result, it logically follows that for all n > 2, the formulas of AHI_n were determined to be equal in this closed dataset.

Conclusion

Ideal pAHI is not a fixed formula. Instead, a recursive optimization model can generate an ideal formula in a big data set when each coefficient of the equation converges. In addition, the recursive optimization model used in our pAHI can be particularly useful in machine learning as new data sets are prospectively added to the existing data.

Support (if any)

n/a
0271 Patterned Planar Bed Surface Vibration Is Effective in Shortening Time to Onset of Sleep (As Good as a Sleeping Pill?)
Authors: Bentley Jacob , Dunn Megan , Mohler Brandon , Blotter Jonathan , Manwaring Kim
Published: May 2023

Introduction

70 million Americans suffer from poor sleep, 42 million have a chronic sleep disorder, and an estimated 64% report regularly taking a pharmacological sleep aid regularly with well-known risks. A link between vibration and sleep was first observed during investigations of highway hypnosis. We developed a prototype bed producing sub-audible, tactilely perceptible traveling-wave vibration where noninvasive sleep monitors could objectively measure sleep.

Methods

A bed was outfitted with low-frequency effectors (LFEs) and phasers to create infrasonic, low-frequency vibration that traversed the mattress in patterns. Amplitude was adjusted to comfort using a bedside dial. Sleep was monitored over four sequential weeks in a single 22-year-old healthy male subject with 3 devices each employing a unique mechanism: the OuraRing (actigraphy), Google NestHub (GHz radar chip), and SleepScore (ultrasound). No vibration was used during weeks 1 and 4 to establish a baseline/return to baseline. Week 2 vibration was a highly periodic waveform (25 Hz, migrating at 1 Hz), while week 3 vibration was an irregular, chaotic waveform. Objective data was collected from the 3 devices to plot total sleep duration and latency of onset.

Results

1. Strong agreement was seen among the 3 different sensors 2. Patterned, highly periodic, low-frequency vibration shortened sleep latency by nearly 50% consistently for one week (from 15 to 8 minutes) 3. Chaotic, irregular vibration of week 3 also shortened latency, but less so (from 15 to 12 minutes) 4. Baseline and repeat baseline were statistically nearly identical 5. Sleep duration was not impacted by vibration (mean 7 hours 45 minutes)

Conclusion

The primary mechanism of improved latency with vibration may be somatic/mental distraction. The subject reported that vibration diverted away from pre-occupying thoughts and was highly pleasant. These observations were reflected in nearly 50% reduction of latency in week 2 and more modest reduction in week 3. Patterned planar vibration appears as effective as prescribed sleep medications, suggesting it may be a useful non-pharmacologic sleep adjunct.

Support (if any)

Jacob Bentley is the recipient of scholarship support for this research from the American Medical Association and AASM.
0272 BINAURAL BEATS AT SLOW FREQUENCIES LESS THAN 1 HZ MAY INDUCE SLOW-WAVE SLEEP FASTER
Authors: Fan Zhiwei , Zhu Yunyao , Suzuki Chihiro , Suzuki Yoko , Watanabe Yumi , Endo Junki , Watanabe Takahiro , Abe Takashi
Published: May 2023

Introduction

Binaural beats are an auditory phenomenon when two sounds with a certain frequency difference are transmitted simultaneously to each ear, one perceives a beat with a frequency exactly equal to that frequency difference. Previous studies have shown that binaural beats synchronize neural oscillations and induce related brain states and behaviors. One previous study showed that listening to binaural beats at 1 Hz during naps increased the duration of slow-wave sleep. However, the effect of binaural beats in the slow frequency range (< 1 Hz) on sleep is poorly understood.

Methods

We hypothesized that 0.25 Hz-binaural beats could synchronize slow neural oscillations and thus enhance slow-wave sleep by shortening its latency and lengthening its duration. Twelve healthy subjects (six females, 25.3 ± 2.6 years) were included in this study. The experiment consisted of four 90-minute afternoon naps, including a sham condition (no acoustic stimulation) and three binaural-beat conditions (0 Hz, 0.25 Hz, 1 Hz) with a 250 Hz carrier tone. Participants were not informed of these conditions. Auditory stimuli were delivered through headphones and continued throughout the 90-minute nap.

Results

The results showed that the binaural-beat condition at 0.25 Hz had shorter latencies for both N2 and N3 than the sham stimulus condition.

Conclusion

This study suggests that binaural beats at slow frequencies, such as 0.25 Hz, may induce slow-wave sleep faster in healthy individuals.

Support (if any)

KYOCERA Corporation, Japan, AMED (Grants No. JP21zf0127005), and World Premier International Research Center Initiative (WPI), MEXT, Japan.
0274 Comparing Sleep Architecture in Apple Watch Series 7 with SleepImage Rings
Authors: Meghpara Sanket , Niraula Rijesh , Awad Dana , DelRosso Lourdes , Keenan Lynn , Van Gundy Karl
Published: May 2023

Introduction

The awareness of the importance of healthy sleep is increasing in the community, hence many devices were developed in the last decade to help recognize sleep architecture. Studies that assessed the matching between sleep rings and iOS systems in apple watches are limited. We aimed to show that the Apple watch can be a useful tool in evaluating sleep architecture for many sleep disorders by comparing it with sleepimage rings.

Methods

We conducted home sleep studies on 1 participant for 7 nights using SleepImage rings and Apple Watch Series 7 with sleep health simultaneously. The participant is a healthy 32 year old male. N1, N2 and N3 stages of sleep were clumped together as NREM in the apple watch for comparative data. Data collected included total sleep time (TST), time in bed (TIB), time in REM, and time in NREM. Data was analyzed with IBM SPSS v. 29.0.0 using descriptive statistics. Mann Whitney test for comparison with p< 0.05 considered significant.

Results

Data in minutes included mean TST with watch 408.7, standard deviation (SD) 44.3 and with ring 404.57, SD 47.1 (p=0.902). Total NREM sleep with watch 330.1, SD 32.8 and with ring 337.8, SD 42.3 (p=0.620). Total REM with watch 72.7 SD 19.5 and with ring 66.7 SD 13.1 (p=0.535). Total TIB with watch 475.3 SD 20.8 and with ring 421.4 SD 52.8 (p= 0.017).

Conclusion

We found no statistically significant difference between the sleep ring and apple watch in estimating TST, REM-sleep and non-REM sleep. The only measurement that was significantly different is the time in bed. This report suggests that Apple Watch can be used as an adjunctive tool for sleep architecture for treating sleep disorders.

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0280 Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach
Authors: Hu Xiangyu , Yang Hongpeng , White James , Nelakuditi Srihari , Ghosal Rahul , Tong Yan , Weaver Glenn , Finnegan Olivia
Published: May 2023

Introduction

Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms.

Methods

38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model.

Results

LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz.

Conclusion

Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices.

Support (if any)
0281 An Assessment of the Agreement Between Sleep Diary and Actigraphy in Firefighters
Authors: McGoldrick-Ruth Logan , Marmis Ryan , Kelly Monica , Haynes Patricia
Published: May 2023

Introduction

Sleep disruption is a common complaint for firefighters, largely thought to emanate from work and personal demands that prohibit recovery sleep. These disruptions increase the risk of sleep disorders, cardiovascular disease, cancer, and other disorders. Given the significance of sleep on health and the high frequency of sleep disruption in the fire service, we sought to examine the concordance between actigraphic and daily diary sleep assessments. The purpose of this analysis was to contribute information on the use of actigraphy and sleep diaries in shift workers individuals prone to insufficient sleep.

Methods

The sleep of 60 firefighters (n = 329 observations) working 24-hour shifts was assessed on a 6-day recovery period. Objective sleep was assessed via the Actiwatch-2, a research-grade, wrist-worn actigraph that measures motor activity in 30 sec intervals. Subjective sleep was assessed via the research consensus daily sleep diary, a self-reported measure collected upon awakening. Major sleep indices compared were: sleep onset latency (SOL), total sleep time (TST), sleep efficiency (SE), and wake time after sleep onset (WASO).

Results

Repeated effects Bland-Altman analysis using a mixed effects technique (measurement = fixed effect; participant = random effect) found that firefighters underestimated WASO (M = 32 min) and overestimated SE (M = 10%) at a level greater than the a priori clinical significance thresholds set by the American Academy of Sleep Medicine for insomnia. The limits of agreement for all sleep indices were very broad. For instance, 95% of the differences between self-report and actigraphic TST fell within a 4.7h range. The majority of the variability could be attributed to within-subject sources of variability versus between-subject sources of variability.

Conclusion

In firefighters, actigraphy and sleep diaries showed substantial disagreement in major sleep indices, with a systematic underestimation of WASO and overestimation of SE. The wide range of differences suggest that daily assessments of SOL, WASO, SE, or TST in on-call shift workers should be compared against a feasible gold-standard. Further research is needed to understand within-subject factors (e.g., daily differences) that predict a discrepancy between actigraphic and self-reported sleep in different working populations.

Support (if any)

#1R01HL117995-01A1
0282 Behavioral–social rhythms and metabolic syndrome prevalence in retired night shift and day workers
Authors: Tracy Eunjin , Chin Brian , Lehrer H Matthew , Hasler Brant , Thomas Mark , Smagula Stephen , Kimutis Sarah , Hall Martica , Buysse Daniel
Published: May 2023

Introduction

Disrupted sleep and circadian rhythms, such as those occurring during night shift work, are established cardiovascular disease (CVD) risk factors like hypertension, obesity, and insulin resistance. However, we do not know whether the effects of shift work on CVD risk factors persist into retirement. Conversely, regularity in behavioral-social rhythms – the systematic patterning or timing of daily routines like work/housework, sleeping, eating and social interactions – may keep endogenous circadian rhythms synchronized to the 24-hour day and reduce CVD risk. This study examined: 1) whether behavioral–social rhythms are associated with a major CVD risk factor, prevalent metabolic syndrome, in retired night shift workers and day workers; and 2) whether past night shift work exposure moderates the associations between behavioral–social rhythm and metabolic syndrome prevalence.

Methods

Participants were 71 retired night shift workers (39 women (54.9%), 32 men (45.1%)) and 83 retired day workers (46 women (55.4%), 37 men (44.6%)), with mean age 68.4 (5.4) years. Logistic regression models were used to examine associations between behavioral–social rhythms and metabolic syndrome prevalence. Independent variables included Social Rhythm Metric-5 (SRM5; a 5-item scale to quantify rhythms of daily life) score and actigraphy rest-activity rhythm inter-daily stability (IS; rhythm stability across days) and intra-daily variability (IV; rhythm fragmentation within days). The dependent variable was metabolic syndrome prevalence.

Results

More regular behavioral-social rhythms were associated with lower odds of having metabolic syndrome (SRM5: OR=0.57, 95%CI [0.35, 0.88]; IV (Higher score means lower regularity): OR=2.05, 95%CI [1.23, 3.44]). In addition, more regular behavioral-social rhythms were associated with lower odds of having two individual components of metabolic syndrome , body mass index (SRM5: OR=0.56, 95%CI [0.37, 0.85]; IV: OR=2.05, 95%CI [1.30, 3.23]) and high-density lipoprotein cholesterol level (SRM5: OR=0.49, 95%CI [0.30, 0.80]; IV: OR=1.78, 95%CI [1.06, 2.98]). Shift work exposure did not moderate the association between behavioral–social rhythms and metabolic syndrome prevalence.

Conclusion

Behavioral–social rhythms are related to metabolic syndrome prevalence in retired adults regardless of prior shift work exposure. Older retired workers may benefit from interventions that promote behavioral–social rhythm regularity.

Support (if any)

This research was supported by the NIH grants AG047139, HL082610, MH019986, and AG07517
0283 Firefighters’ Sleep Changes After Switching Shift Schedules: A Longitudinal Study
Authors: Billings Joel
Published: May 2023

Introduction

In the past few years, fire departments in the US have been switching from 24 hours on / 48 hours off (24/48) shift schedule to the 48 hours on / 96 hours off (48/96) shift schedule to increase recruitment and reduce commute frequency for members living outside the jurisdiction. While firefighters at work may be able to rest and sleep when not otherwise busy, it is not a guarantee and concerns on health and safety understandably exist when subject to 48 hours on shift. Yet, it is unclear how switching schedules influence firefighters’ sleep. Therefore, the objective of this research is to assess sleep before and after firefighters transitioned from the 24/48 to the 48/96 shift schedule.

Methods

A pre-experimental, within-subjects, longitudinal sleep study was implemented with a Southcentral US fire department (n=24). Daily sleep included total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO), and was assessed using wrist actigraphy and the Emergency Services Sleep Diary. Participants completed three, 18-day rounds of data collection: baseline assessment was conducted on the 24/48 and post assessments were conducted after three and six months following the transition to the 48/96.

Results

The results of separate one-way repeated measures ANOVA indicated a statistically significant difference in TST (F(2,46) = 4.58, p< 0.05), SE (F(2,46) = 7.97, p< 0.01), SL (F(2,46) = 6.88, p< 0.05), and WASO (F(2,46) = 5.58, p< 0.05) as firefighters transitioned schedules. Post-hoc analysis identified statistically significant pairs between the 24/48 and the three-month post assessment for each sleep parameter: TST (6.4h vs 6.8h), SL (85% vs 89%), SL (8.7min vs 4.9min), and WASO (58min vs 45min).

Conclusion

Firefighters reported an improvement in sleep three months after transitioning to the 48/96. The results suggest that improved sleep on the 48/96 may be related to consolidating work shifts, which reduces the frequency of experiencing advance sleep offset on commute days and allows a greater recovery period at home. While further research is needed to assess how health and performance outcomes are related to firefighter schedules, the findings suggest that the pattern of work and non-workshifts influences sleep.

Support (if any)
0285 Sleep health in veteran population with and without mild traumatic brain injury
Authors: Cho Yeilim , Peskind Elaine , Iliff Jeffrey
Published: May 2023

Introduction

Sleep duration is frequently observed in military and Veteran populations. Some of this effect may result from exposure to traumatic brain injury (TBI) during military service. However, other features of combat deployment, circadian disruption, and chronic stress may contribute to poor sleep independent of the effects of TBI. In this study we tested whether combat deployment with or without TBI exposure was associated with poor sleep compared to civilian controls.

Methods

In a prospective study of Operation Enduring Freedom/Iraqi Freedom/ New Dawn Veterans with repeated blast mild TBI (mTBI) conducted at the VA Puget Sound Healthcare System, self-reported sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Age-matched civilians (CONciv) non-TBI deployed Veterans (CONdep) were included in the study for comparison. The outcomes of interest included the PSQI sum score and its key components scored as 0-3. One way ANOVA and paired comparison with Bonferroni adjustment for each component were performed .

Results

A total of 104 mTBI, 36 CONdep and 15 CONciv participants were included. Poor overall sleep quality (PSQI sum>5) was most frequently reported by mTBI participants, followed by CONdep and CONciv participants (80 % vs. 50 vs. 11, p< 0.0001). Across virtually every sleep feature, sleep impairment was most pronounced in mTBI participants followed by CONdep: PSQI sum (mTBI VS. CONdep vs. CONvic= 11.6 vs. 6.5 vs. 3, p< 0.0001), sleep duration (5.5 hrs vs. 6.6 vs. 7.1, p< 0.0001), sleep onset insomnia (2.1 vs. 1.3 vs. 0.3, p< 0.0001) , sleep maintenance insomnia (2.5 vs. 1.4 vs. 0.7, p< 0.0001), daytime sleepiness (1.3 vs. 0.6 vs. 0.4, p< 0.0001), daytime function (1.8 vs. 0.9 vs. 0.3, p< 0.0001) and sleep disturbance by pain (1.5 vs. 0.7 vs. 0.1, p< 0.0001). Both mTBI and CONdep groups had significantly worse sleep quality than the CONciv group.

Conclusion

Veterans with repetitive mTBI exhibited overall poor sleep quality and impaired sleep in multiple sleep domains. Deployed Veterans without TBI, but exhibited similar, but less severe sleep disruption. Future studies should explore how poor sleep quality may be related to the long-term effects of TBI and military deployment.

Support (if any)
0288 Multidimensional Sleep Health and Cognitive Function in Retired Night Shift Workers and Retired Day Workers
Authors: Lehrer H Matthew , Butters Meryl , Buysse Daniel , Hall Martica
Published: May 2023

Introduction

Night shift work is associated with compromised cognitive function, and with chronic exposure, may place shift workers at elevated risk for dementia. Given that former night shift workers experienced prior long-term sleep and circadian rhythm disruption, their sleep in retirement—after returning to a daytime schedule—may be particularly salient for cognitive function. This study examined associations between multidimensional sleep health and neurocognitive function among retired night shift workers and retired day workers.

Methods

Participants (N = 61; mean age: 67.9 +/- 4.7 years; 61% females; 13% non-Hispanic White) were 31 RDW and 30 RNSW equated on age, sex, race/ethnicity, premorbid IQ, and years in retirement. Multidimensional sleep health was quantified using wrist actigraphy measures of sleep efficiency, timing, duration, and regularity, and diary measures of alertness and satisfaction. Each component was dichotomized and summed to create a composite score (0-6); higher values indicated better sleep health. Participants completed a neurocognitive battery assessing six cognitive domains (language, visuospatial ability, attention, immediate and delayed memory, executive function) and self-reported cognitive function. Linear regression models tested associations of multidimensional sleep health and its individual components with cognitive domains in each group, adjusting for age, sex, and education.

Results

Retired night shift workers and retired day workers did not differ on multidimensional sleep health or its individual components. Among retired night shift workers, poorer multidimensional sleep health was associated with poorer self-reported global cognitive function (beta = 0.51, p = .018), and more irregular sleep timing was associated with poorer self-reported memory (beta = 0.41, p = .054). Multidimensional sleep health and its individual components were unrelated to cognitive function in retired day workers.

Conclusion

Retired night shift workers may be cognitively vulnerable to poor sleep health in retirement, possibly due to a history of long-term sleep and circadian rhythm disruption while working. Given that self-reported cognitive difficulties often precede more objective cognitive changes, future research should determine whether associations of sleep health with cognitive function are found in more objective cognitive measures as retired night shift workers age.

Support (if any)

R01AG047139, R01AG047139-S1, K01AG075171
0289 A PICTURE ON THE SLEEP OF THE PORTUGUESE HEALTH SYSTEM: PRELIMINARY FINDINGS OF A WORK IN PROGRESS
Authors: Beires Teresa , Borges Maria Inês , Paixão Carla , Cruz Miguel Meira e
Published: May 2023

Introduction

Sleep difficulties are commonly referred by health care workers that can affect quality of life and productivity, both in patients and healthy individuals. This study firstly aimed to evaluate sleep quality in distinct groups of health care workers as well as the impact of shift work and health related habits on the sleep of such professionals.

Methods

This prospective, cross sectional, observational study involved health care workers (Physicians, Residents, Nurse Staff, Psychologists and Therapeutic and Diagnostic Technicians - TDT) from the Portuguese Health System (public or private) aged from 25 and 65 years old. A General Health Survey was followed by Standard validated sleep questionnaires for assessment of sleep quality (Pittsburgh Sleep Quality Index - PSQI), insomnia (Insomnia Severity Index - ISI), risk of obstructive sleep apnea (STOP-BANG), and excessive daytime sleepiness (Epworth Sleepiness Scale - ESS).

Results

From the pretended pool of 425 health care workers to whom questionnaires where sent, 160 (131: 81.9% females) responded (37.7% of response rate). From those, 30% of those professionals were Residents, 30.6% were TDT, 21.9% Physicians, 10.6% Nurses, and 6.9% Psychologists. 46.5% assumed itself with a morning-type profile whereas 39.5% were intermedium and 14% evening-type persons. From the global sample, 62.5% had poor sleep quality with 37.5% in the insomnia threshold whereas 14.4% presented with either moderate or severe forms of insomnia. Regarding the risk for sleep disordered breathing, 6.5% where at high risk for Obstructive Sleep Apnea - OSA, and 15.6% complained from mild to moderately forms of excessive sleepiness (ESS>10). In this sample, psychologists achieved the better sleep quality scores while nurses had the poorest scores for the same parameter.

Conclusion

This preliminary findings on the sleep of the portuguese health care workers, suggest that such professionals experience a poor sleep quality in general with a prevalence of major sleep disorders similar to the general population. Although the majority seemed to denie a significant degree of daytime sleepiness, those within a shift work regimen are likely to perform worse.

Support (if any)