bluuebunny/crossref_metadata_2025 huggingface dataset:
0269 Associations Between Sleep-Related Impairment and Anxiety Levels Among the Latino/a Population in Florida
Authors: Garchitorena Laurent , Coppello Matthew , Carrasco Mary , Scaramutti Carolina , Palencia Israel , Grandner Michael , Osorio Ricardo , Thawani Sujata , Ramos Alberto , Blanc Judite , Chung Debbie , Jean-Louis Girardin , Seixas Azizi , Briggs Anthony
Published: April 2024

Introduction

In the Hispanic community, sleep impairment and the prevalence of anxiety represent pressing concerns. Sleep quality and patterns can be significantly affected by anxiety-related symptoms and disorders. Conversely, insufficient or disrupted sleep can worsen anxiety symptoms, creating a complex interplay. While chronic sleep disorders affect a substantial portion of the American population, including Hispanics, research addressing these complex issues within this specific demographic remains limited. This study seeks to illuminate the relationship between anxiety and sleep impairment among Hispanics in Florida, aiming to provide valuable insights into these critical health aspects.

Methods

Data were collected from the NIH-funded study DORMIR. DORMIR is a remote health monitoring study designed to identify behavioral, genetic, psychosocial, and environmental factors linked to insufficient sleep and their relationship to cardiovascular health within the Latino/a population. Latino/a adults 18+ years old (N=317, 67.8% female, ages 37.9±15.0 years) completed several surveys including the PROMIS Sleep Related Impairment (SRI) and PROMIS Emotional Distress - Anxiety measures. Linear regression analysis was performed with age, sex, income, and body mass index (BMI) as covariates to determine the impact of anxiety on sleep-related impairment.

Results

The average SRI score was 8.34±3.63, while the mean Emotional Distress - Anxiety score was 18.0±7.46. Anxiety [F (5,311) = 15.6, β1=0.366, p < 0.001], income [F (5,311) = 15.6, β1= - 0.165], p=0.003], and BMI [F (5,311) = 15.6, β1=0.113, p =0.030] all independently predicted sleep-related impairment, while adjusting for covariates. Anxiety levels and BMI scores were positively associated with sleep-related impairment, while income was negatively associated with sleep-related impairment among Latino/a adults.

Conclusion

This study shows that higher levels of anxiety, increased BMI scores, and lower income are linked to increased sleep impairment in Latino/a individuals. Additional research is required to examine whether external factors may act as confounding variables in this correlation. Such investigations would enable the development of tailored solutions aimed at mitigating both poor sleep health and emotional distress. These findings underscore the critical need for targeted interventions to address the impact of poor sleep on the health of the Latino/a population.

Support (if any)

5R01HL152453
0270 Racial/Ethnic Sleep Health Related to Mental Health: Data from the National Sleep Foundation Sleep in America Poll
Authors: Schickner Arden , Kennedy Kathryn , Killgore William , Dzierzewski Joseph , Grandner Michael
Published: April 2024

Introduction

Sleep disparities are well-documented in the population. Rarely are multiple dimensions of sleep experience evaluated on a national level, however. Even more novel is the implication that these differences are partially explained by mental health disparities.

Methods

Data from the Sleep In America Poll included a population-level sample of US adults. N=1042 adults provided complete data. Sleep health metrics included weekday and weekend bedtime and waketime, time in bed, and sleep duration, perceived sleep need, sleep debt (sleep need – duration), and days/week of: feeling well-rested, difficulty falling asleep, difficulty staying asleep, sleep impacting daytime function, daytime sleepiness, and taking sleep medication. Covariates included age, sex, education, household income, and PHQ9 depression score (without the sleep item). Linear regression analyses examined sleep variable as dependent variable and race/ethnicity (Non-Hispanic White, Black/African-American, Hispanic/Latino or Asian/Other) as independent variable, adjusted for covariates. Secondary analyses examined race/ethnicity-by-depression interactions.

Results

In population-weighted, adjusted analyses, compared to non-Hispanic Whites, Blacks/African-Americans reported 0.78hr delayed weekend waketime (95%CI: 0.14,1.43) an additional 0.49 days/week of daytime sleepiness (95%CI: 0.05,0.93), 0.47 fewer days/week taking sleeping medications (95%CI: -0.80,-0.14), 0.41hr perceived sleep need (95%CI: -0.74,-0.10), and 0.51hr weekday less sleep duration (95%CI: -0.87,-0.15). Hispanics/Latinos reported 1.05 days/week difficulty staying asleep (95%CI: -1.42,-0.67) and 0.38 fewer days/week taking sleep medications (95%CI: -0.70,-0.07). Asians/others reported 0.73 fewer days/week with trouble staying asleep (95%CI: -1.26,-0.19) and 0.51 fewer days/week taking sleep medications. Significant group-by-depression interactions were seen for weekday and weekend bedtime, weekday time in bed, days/week well rested, perceived sleep need, weekday sleep duration, and sleep debt. Stratified analyses showed that Blacks/African Americans and Asians/Others especially reported worse sleep health in the presence of higher depression scores, compared to Non-Hispanic Whites.

Conclusion

Several dimensions of sleep health were disproportionately experienced across race/ethnicity groups in the US. In particular, worse sleep health in Blacks/African-Americans and Asians/Others was further impacted by depression symptoms. Future work should further explore the role of mental health disparities in sleep health and mental health interventions may reduce sleep disparities.

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0278 Sexual Dimorphisms in Sleep-disordered Breathing of C57BL/6J Mice
Authors: Kim Lenise , Pham Luu , Polotsky Vsevolod
Published: April 2024

Introduction

Differences in sleep-disordered breathing (SDB) between biological sexes have been extensively reported. Prevalence of SDB is increased in men, but women appear to be more symptomatic at lower SDB severity, exhibiting more daytime sleepiness and insomnia symptoms. However, the mechanisms associated with sex differences in SDB remain unclear. We examined the differences in the sleep architecture and breathing during sleep between male and female mice with the same genetic background. We also analyzed whether ovariectomy abolishes the sex differences in SDB. We hypothesized that differences in sleep and SDB between male and female mice are attributed to different chemoreflex responses to carbon dioxide (CO2).

Methods

Mice were instrumented with EEG/EMG electrodes. Full-polysomnographies were performed in 16 males and 18 females C57BL/6J mice inside a whole-body plethysmography chamber. Poincaré plots of minute ventilation were used to assessed breathing stability during sleep. Mice were exposed to acute episodes of hypercapnia (8% CO2) and hypoxia (10% O2 + 3% CO2) during wakefulness and NREM sleep to measure the hypercapnic ventilatory response (HCVR) and hypoxic ventilatory response (HVR), respectively. Females underwent bilateral ovariectomy and the studies were repeated after 2 weeks.

Results

Sleep fragmentation was 55% increased in female mice compared to males. Unexpectedly, apnea index and breathing instability during sleep were significantly higher in female mice compared to males. HVR was similar between sexes. CO2 sensitivity positively correlated with apnea index (r=0.55, P=0.05). HCVR was significantly augmented in females during wakefulness and NREM sleep, which was attenuated by ovariectomy.

Conclusion

Our findings have shown different phenotypes of SDB between sexes in C57BL/6J mice. SDB in female mice is associated with a hyperarousal state and augmented CO2 sensitivity, which could be related to ovarian hormones.

Support (if any)

American Heart Association Postdoctoral Fellowship Award 828142 (to L.J.K.), NHLBI grant 2R01 HL133100-05, NIH R01 HL128970, and R01 HL13892 (to V.Y.P.), 1K23HL155730 and American Academy of Sleep Medicine Foundation 238-BS-20 (to L.V.P.)
0279 Intermittent Analysis of Obstructive Sleep Apnea Dynamics
Authors: Le Trung , Phat Huynh , Setty Arveity , Sharma Abhay
Published: April 2024

Introduction

The intermittent nature of obstructive sleep apnea's (OSA) pathophysiological processes is characterized by unpredictable fluctuations among OSA endotypes and phenotypes, reflecting the complex dynamics underlying this sleep disorder. It is challenging to character such intermittent behaviors.

Methods

We developed an enhanced version of Koopman spectral analysis, tailored to be robust against noise, a common issue in physiological data, to investigate the nonlinear intermittent behaviors in OSA. Our analysis dissects the complex, nonlinear physiological patterns of Heart Rate Variability (HRV) features, aiming to understand better the cardiorespiratory dynamic nature of OSA and its severity, as indicated by the apnea-hypopnea index (AHI). Utilizing the Apnea-ECG Database from Physionet.org, which includes records from 70 OSA patients with OSA annotation, we validated the model in the ability to characterize the intermittency behavior of the HRV features and the accuracy of using these features to detect OSA.

Results

The results demonstrate that the noise-robust Koopman spectral analysis effectively identifies key dynamical features in the pathophysiology of OSA. It revealed several previously unrecognized patterns and correlations in the data, offering more profound insights into the intermittent dynamics of OSA. The method showed high accuracy in distinguishing critical transitions between different severity levels of OSA.

Conclusion

The study successfully validates the efficacy of the noise-robust Koopman spectral analysis in analyzing complex systems like OSA. This approach provides an effective tool for understanding the intricate dynamics of pathophysiological processes, offering the potential for both diagnostic advancements and a better understanding of the underlying mechanisms of OSA.

Support (if any)

Huynh, P. K., Setty, A. R., Le, T. B., & Le, T. Q. (2023). 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, 13(2), 101-116.
0288 In-vitro Model of the Microcirculation in OSA Based on Patient Profile
Authors: Hetta Ali , Badary Amr , Grosberg Anna , Khayat Rami
Published: April 2024

Introduction

Microcirculatory endothelial dysfunction is a critical marker of subsequent cardiovascular disease in the population. We recently reported a profile of obstructive sleep apnea (OSA) specific gene changes in the human microcirculation. We sought to develop a precise in-vitro model of the microcirculatory effects of OSA using an intermittent hypoxia (IH) exposure that produces similar gene profile changes to that observed in patients with OSA.

Methods

We selected a cell line of immortalized human microvascular endothelial cells (HMEC). Then, we estimated the oxygen tension exposure of the peripheral circulation during OSA assuming a decrease from 120 mmHg in the pulmonary arteries to about 30 mmHg in the microcirculation. During a single episode of apnea, alveolar partial pressure of oxygen (PAO2) and the PaO2 at the level of the pulmonary arteries decrease slightly by about 10%. Therefore, we selected a fluctuation between 30-40 mmHg as baseline (normoxia), and 25-30mmHg to correspond with this decrease during apnea. We used a gas treatment 6-well cell culture chamber (model NOX-E.5-GTC, Noxygen) and monitored the O2 levels in the medium (cell level) and in the gas space with fluorescent oxygen analyzer (Oxylite, Optronix). We selected a 300 second cycle length and an exposure duration of 6-8 hours (72 cycles).

Results

Compared to HMECs in control normoxia, HMECs exposed to the IH protocol showed a profile change similar to that reported in humans with OSA with significant increase in oxidative and inflammatory genes. There was significant upregulation in NADPH subunit NOX4’s from 0.0439 to 0.1570 relative copy number (RCN), p= 0.049. Endothelin (EDN-1) expression increased from 1.0330 to 2.4171 p= 0.002. Superoxide dismutase (SOD-1) increased from 2.1336 to 2.4600 (p= 0.028). Interleukin 8 increased from 0.0891 to 0.2744 (p= 0.020). Interleukin 1-B increased from 0.0193 to 0.0439 (p= 0.048). Additional changes included a decrease in nitric oxide synthase .

Conclusion

The selected IH protocol produced similar gene profile changes in HMECs as those found in patients with OSA before treatment. This model can serve to evaluate the pathways specific to OSA's impact on the microcirculation and to test pharmacological therapies to target OSA-specific vascular disease.

Support (if any)

R 56 127079
0289 Sleep Fragmentation and Estradiol Suppression Effects on Cardiometabolic Health in Women: An Experimental Model
Authors: Rahman Shadab , Grant Leilah , Scheer Frank , Klerman Elizabeth , Crawford Sybil , Sinrod Emily , Wiley Aleta , Kaiser Ursula , Joffe Hadine
Published: April 2024

Introduction

The risk of cardiovascular disease increases during the menopausal transition. In addition to estradiol decline during the transition, there is increased sleep fragmentation due to hot flashes, without reduced total sleep time. We examined the impact of sleep fragmentation and estradiol decline on cardiometabolic outcomes in an experimental menopause model implemented in premenopausal women.

Methods

We studied 38 healthy premenopausal women (mean age [±SD] 29.7±6.7 years) in a 5-night inpatient study under eucaloric conditions, once in the mid-to-late follicular phase (high estradiol) and again after gonadotropin-releasing hormone agonist-induced estradiol suppression in a subset (n=27). Each inpatient study involved 2 nights of unfragmented sleep followed by 3 nights of experimentally sound-induced sleep fragmentation to achieve ~1 h of wake after sleep-onset, without reducing total sleep time. Cardiometabolic endpoints (serum and vitals) were collected while fasted within 1 h of waking each morning. Values were normalized relative to each individual’s baseline (estrogenized, unfragmented sleep) and then compared between conditions using generalized linear mixed models.

Results

Sleep fragmentation across both estradiol states combined significantly lowered self-rated satiety by 13% (p=0.048), and increased hunger by 30%, although this difference did not reach significance (p=0.25), and leptin and acylated ghrelin levels were unchanged (both, p≥0.13). Sleep fragmentation significantly reduced triglycerides by 7% (p< 0.01) and increased heart rate (HR) by 6% (p< 0.01). All other cardiometabolic endpoints were unchanged by sleep fragmentation (p≥0.22). E2 suppression lowered self-rated satiety by 5% and increased hunger by 28%, but neither of these were significant (both, p≥0.28), and significantly lowered leptin levels by 6% (p=0.001), and increased ghrelin by 7% although this did not reach significance (p=0.13). Estradiol suppression across both sleep conditions combined significantly increased triglycerides, total cholesterol and HDL-C by 5% to 8% (all, p< 0.01) but not LDL-C (p=0.18). Estradiol suppression significantly reduced HR by 4% (p< 0.01) and trended to increase diastolic blood pressure by 3% (p=0.06), with no change in systolic blood pressure (p=0.16).

Conclusion

Findings support independent contributions of estradiol decline and sleep fragmentation during the menopause transition toward positive energy balance and adverse changes in cardiovascular disease risk factors.

Support (if any)

NIH R01AG053838
0292 Detecting Sleep Deficiency with Voice Biomarkers and Machine Learning
Authors: Zhang Boyu , Ronda Joseph , Yuan Robin , Duffy Jeanne , Czeisler Charles
Published: April 2024

Introduction

Accurate biomarkers of insufficient sleep have been a central interest of sleep scientists. Given advancements in artificial intelligence, researchers have explored non-invasive digital biomarkers from human voices. In this study, we conducted a within-participant counterbalanced controlled trial of chronic sleep restriction (CSR) and leveraged machine learning to investigate voice biomarkers for detecting sleep deficiency.

Methods

Healthy young adults completed a 32-day in-patient protocol. The protocol included 5 days of baseline 8-hour time-in-bed (TIB) followed by 5 days of CSR (5-hour TIB), during which their light exposure, activity, and diets were controlled. Every 4 hours during waking episodes, a voice measurement (VM) was administered via computer. During each VM, the participant read 10 sentences at their habitual volume, pitch, and pace. The sentences were phonetically balanced and tailored for professional speech quality assessment. Each VM used different sentences to prevent memorization. An omnidirectional microphone with an adjustable stand was used. 85 common acoustic features, such as fundamental frequency, formants, and mel-frequency cepstral coefficients, were extracted from each VM. Additionally, 11 features from the Cepstral Spectral Index of Dysphonia were extracted, forming a 96-dimensional vector. Within each participant, the vectors were z-scored to remove personal vocal traits. After dimension reduction via principal component analysis, under a leave-one-out procedure, we trained a support vector machine (SVM) to classify recordings into those taken during CSR or baseline. We tested the SVM on one unseen participant every iteration.

Results

After excluding low-quality data, we analyzed 196 VMs (100 during CSR and 96 during baseline) from 5 participants, including 1 non-native English speaker. The SVM reached a sensitivity of 0.74 (95% CI: 0.71-0.78), a specificity of 0.71% (95% CI: 0.68-0.75), and an area under the ROC curve of 0.76 (95% CI: 0.73-0.80) in classifying CSR vs. baseline conditions.

Conclusion

These preliminary findings reveal that vocal characteristics may represent a target for non-invasive, objective sleep deficiency biomarkers. Additional studies in both controlled conditions and field settings should be carried out to explore whether features of the human voice can be developed for non-intrusive monitoring of sleep status for sleep disorders patients, fitness-for-duty evaluations, and other applications in ambulatory settings.

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0294 Performance of Commercial Wearable Devices for Tracking Irregular Sleep Schedules
Authors: Chinoy Evan , Cuellar Joseph , Gordy Prayag , Jameson Jason , Markwald Rachel
Published: April 2024

Introduction

Many new popular commercial wearable devices perform well at tracking sleep/wake patterns, often equaling or exceeding the sleep-tracking performance of research-grade actigraphy. However, commercial wearables have not been subjected to extensive testing under irregular sleep schedules, such as split sleep and daytime sleep/naps, common in shift-working and operational populations. Therefore, to continue uncovering their real-world strengths/limitations, we evaluated the sleep-tracking performance of commercial wearables in people with irregular sleep schedules.

Methods

Participants were 12 healthy young adults (6 men, 6 women; 28.3±4.8 years, mean±SD) with habitual irregular sleep schedules, often due to shift work, sleeping or napping during daytime hours at least twice weekly. They completed sleep diaries and used actigraphy (Philips Respironics Actiwatch 2) and a set of wearables for 1 week under unrestricted home sleep conditions. Wearables included the Garmin Fenix 7 (GF) and Oura Rings (OR; generations 2 and 3). Device performance was compared with an electroencephalography headband (Dreem 3; Beacon Biosignals) and sleep diaries. Analyses included epoch-by-epoch agreement (sensitivity [for sleep] and specificity [for wake]) and summary bias comparisons for time in bed (TIB) and total sleep time (TST).

Results

Device sensitivity and specificity, respectively, were Actiwatch (0.92, 0.44), GF (0.96, 0.35), and OR (Gen-2: 0.92, 0.64; Gen-3: 0.93, 0.60). Device mean biases for TIB and TST were low for Actiwatch and both ORs but were higher and with significant proportional bias for GF. This appeared to be driven by GF combining multiple sleep episodes into single long episodes, which produced very long sleep recordings on those days when split sleep occurred.

Conclusion

Overall device sensitivity was high and specificity was low to medium, similar to previously known performance limitations of actigraphy and commercial wearables. However, OR had ~20% higher specificity than in our previous testing, suggesting improved wake-tracking performance in newer OR models/algorithms. Like previous GF models we tested, GF again underperformed in specificity and summary TST tracking. This underperformance raises concerns for real-world uses of GF with its current algorithm, especially regarding the finding that extreme summary bias values sometimes occurred when GF combined multiple sleep episodes/naps during split sleep schedules.

Support (if any)

Office of Naval Research, Code 34
0295 Influence of Sleep Regularity, Chronotype, and Sleep Duration on Daytime Sleepiness Caused by Sleep Disorders
Authors: Chellamuthu Vidhya , Gorski Brandon , Mills Rajasi , McGhee Leah , DeFranco Susan , Aloia Mark , Molina Gary Garcia
Published: April 2024

Introduction

Excessive daytime sleepiness is characterized by the difficulty of staying awake during daytime. The goal of this research is to examine the role of regularity, sleep duration, and chronotype on the association between sleep disorders and daytime sleepiness. To do this, we leveraged the Sleep Number platform to acquire longitudinal objective sleep with survey data from a single point in time.

Methods

An IRB-approved survey, which included questions about diagnosed sleep disorders and daytime sleepiness, was presented to a cohort of Sleep Number customers in the June 12 to 26, 2023 period. Objective data including sleep duration, bedtime, restful sleep duration, mean breathing rate, and heart rate collected between May 1 and June 30, 2023 were used along with survey responses. Individual sleep regularity and chronotype were quantified using objective data. Regularity was categorized into regular or irregular if bedtime variability was lower or greater than 60 minutes. Chronotype was categorized into early, intermediate, or late if mean sleep onset time was before 10 pm, between 10 and 11:59 pm, or after 12 am. Odd-ratios (OR) were used to quantify the influence of regularity, chronotype, and sleep duration on daytime sleepiness moderated by sleep disorders.

Results

The responder count was 22,082 (9530M/12479F). Men were 56.6 (SD 13.9) and Women 54.9 (SD 13.8) years-old on average. Daytime sleepiness was significantly associated with the presence of untreated sleep disorders (insomnia, apnea, and RLS). Individuals being treated for apnea and insomnia demonstrated significantly less daytime sleepiness. Healthy individuals showed a significant association between early chronotype and longer sleep duration with reduced daytime sleepiness. Individuals with apnea who receive treatment showed a significant association between longer sleep and reduced daytime sleepiness. In the case of insomnia, regularity, chronotype or sleep duration do not moderate any significant relationship with daytime sleepiness. For RLS longer sleep duration leads to lower daytime sleepiness.

Conclusion

Daytime sleepiness is significantly associated with any untreated sleep disorders. Regularity, chronotype and sleep duration had limited influence on daytime sleepiness caused by sleep disorders. Healthy individuals had lower daytime sleepiness if they had an earlier chronotype and longer sleep duration.

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0296 The Relationship Between Sleep Metrics, Age and Sex in a Large Cohort Study
Authors: Patterson Matthew
Published: April 2024

Introduction

The aims of the study are to investigate the relationship between sleep metrics, age and sex in a large cohort study

Methods

Wrist acceleration data (GT9x, ActiGraph) was collected from 10,175 subjects in the National Health and Nutrition Examination Survey (NHANES) 2013-2014 for up to 8 days. Data from the tri-axial accelerometer was sampled at 80hz and was processed using the sleep analysis pipeline from ActiGraph. The sleep analysis pipeline first consisted of wear detection, followed by total sleep opportunity detection and then finally sleep / wake classification within the total sleep opportunity. The total sleep opportunity algorithm detects one sleep opportunity per 24-hour cycle (in noon-to-noon windows) and allows for up to 60 minutes of continuous sleep disruption within the sleep opportunity. The following sleep metrics were calculated in each total sleep opportunity using the DACNN sleep / wake classification algorithm: total sleep time (TST), wake after sleep onset (WASO) and sleep efficiency (SE). These sleep metrics were combined with age and gender data from the study to perform a Pearson product correlation (r).

Results

For females in the study (n=3790), TST had a Pearson r of -0.285, WASO had a Pearson r of 0.149 and SE had a Pearson r of 0.058. For males in the study (n=3529), TST had a Pearson r of -0.300 versus age, WASO had a Pearson r of 0.192 versus age and SE had a Pearson r of -0.005 versus age.

Conclusion

Sleep measures showed low correlation to age in both the male and female cohorts. This is contrary to the information that sleep quality declines as individual ages. Perhaps the participants in this cohort were healthy enough that decline in sleep metrics was not observed.

Support (if any)

This research was funded by ActiGraph L.L.C.
0299 An Examination of Discrepancies in Sleep Device Data Captured by Sleep Ring, Fitbit, AndActigraphy
Authors: Moise Rhoda , Oliveira Bruno , Blanc Judite , Rogers April , Seixas Azizi , Jean-Louis Girardin
Published: April 2024

Introduction

Wearable devices have improved access to sleep data by empowering patients with greater control over their information. However, the choice of the best wearable for sleep measurement remains uncertain. This study investigated variations in sleep data collected by Sleep Ring, Fitbit, and Actigraphy in a diverse US group of Black adults.

Methods

Wearables descriptive statistics were calculated for sleep efficiency (% by time in bed/ time sleep), REM duration (in minutes), awakening (count), and total sleep duration (in minutes) where data was available. One-sample t-tests were conducted for sleep efficiency (Sleep Ring and Actigraphy), REM duration (Sleep Ring and Fitbit), and awakening (Fitbit and Actigraphy). ANOVA was conducted for total sleep duration (Sleep Ring, Fitbit, Actigraphy). Significance was specified as α=0.05.

Results

Participants (Sleep Ring N=199, Fitbit N=147, Actigraphy N=202) were predominantly female (71%, Mage=48.9) compared to male (28%, Mage=50.2). Using the Insomnia Severity Index, 35% were insignificant, 37% met subthreshold, 23% exhibited moderate severity, and 5% reported severe insomnia. Sleep efficiency for Sleep Ring (M=77%, SD=10.4; t(198)=104.6, p<.001) was significantly different than Actigraphy (M=93%, SD=3.3; t(201)= 397.7, p=<.001). REM duration for Sleep Ring (M=62 minutes, SD=29; t(198)=30.0, p<.001) was also significantly different Fitbit (M=70 minutes, SD=26; t(142)= 31.6, p<.001). Additionally, there was a significant difference in awakening count in Fitbit (M=15 times, SD 7; t(146)=26.6, p<.001) compared to Actigraphy (M=9 times, SD=4; t(201)= 28.7, p<.001). ANOVA indicated an effect of sex on sleep duration for all devices: Sleep Ring [M=368 minutes, SD=106; F(1,197)= 5.4,p=.021], Fitbit [M=305, SD=89; F(1,145)=13.4,p<.001] and Actigraphy [M=374 minutes, SD=163; F(1,198)=4.9,p=.027).

Conclusion

Differences among wearable devices exist. Actigraphy may overestimate sleep efficiency, while Sleep Ring may provide poor estimates. REM duration is similar in Sleep Ring and Fitbit but more variable in Sleep Ring. Sleep duration ranked highest in Actigraphy, but also exhibited the highest variance/deviation, followed by Sleep Ring and Fitbit. Fitbit is more sensitive in detecting awakenings compared to Actigraphy. Future research should exploresleep health parameters across wearable devices to ensure accuracy in Blacks, considering discrepancies in metrics potentially due to differences in skin tone and the impact of refractory lights

Support (if any)

NIH R25HL10-5444; R01HL142066 and R01AG067523.
0300 Interim Analysis from the Oura Validation Study: Examining Sleep Staging from Oura Rings Worn Simultaneously
Authors: Robbins Rebecca , Sullivan Jason , Gilmore Katherine , Shaw Samantha , Benz Abigail , Weaver Matthew , Barger Laura , Quan Stuart , Czeisler Charles , Duffy Jeanne
Published: April 2024

Introduction

Sleep tracking using consumer devices is increasingly prevalent in the population. Given this interest, it is essential that data produced by these monitors is reliable and accurate. In a single night study with the primary aim of validating the Oura Ring in comparison to polysomnography (PSG), participants wore Oura Rings on their right and left hand. This interim analysis explores the correlation between the rings with respect to total sleep time (TST) and sleep staging.

Methods

Participants (n=20) underwent single-night PSG in the Brigham and Women’s Hospital Center for Clinical Investigation. Each participant wore two Gen3 Oura Rings, one on each index finger, both running the Oura Sleep Staging Algorithm 2.0. We computed within-subject nightly totals for TST and sleep stages (light sleep; deep sleep; and Rapid Eye Movement, REM sleep) as detected by each ring. Then, we computed pairwise correlation coefficients for sleep and sleep staging data between the rings.

Results

Participants were 75% female and 25% male; 20% Asian, 10% Black/African American, and 60% White; 15% were Hispanic. Average age was 30.7±7.8 years. In-lab bedtimes ranged from 9:15 pm–1:40 am and waketimes from 5:15 am–9:40 am. On the Beck Anxiety index, most participants (95%) demonstrated minimal anxiety. No participants met criteria for insomnia (Athens Insomnia Scale), sleep apnea (Berlin Sleep Questionnaire), or Restless Legs Syndrome. On the Pittsburgh Sleep Quality Index, most participants reported good sleep quality (90%). Correlation between the rings was 0.90 (p< 0.001) for TST, 0.85 (p< 0.001) for wake. 0.83 (p< 0.001) for light sleep, 0.86 (p< 0.001) for deep sleep, and 0.86 (p< 0.001) for REM sleep.

Conclusion

We observed a high correlation between TST and sleep staging data from Oura Rings worn on the left and right hands simultaneously, similar to those observed between Registered Polysomnographic Technologists. We plan to examine how Oura sleep staging relates to PSG, and whether agreement is associated with hand dominance.

Support (if any)

This project was funded by Ouraring Inc. and the studies were carried out at the BWH CCI, with support from Harvard Catalyst, The Harvard Clinical and Translational Science Center (NIH UL1TR002541). Additional support from the NHLBI (K01HL150339).
0305 Can We Rely on Wearable Sleep-Tracker Devices for Fatigue Management?
Authors: Reifman Jaques , Priezjev Nikolei , Vital-Lopez Francisco
Published: April 2024

Introduction

The importance of sleep in daily life and advancements in our ability to measure bio-signals have led to an unprecedented growth in the use of commercial wearable sleep-tracker devices. However, sleep parameters estimated from these tracker devices often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment.

Methods

Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the well-validated Unified Model of Performance, we compared the temporal variation of alertness for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night, or an irregular sleep schedule each night, for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 ms.

Results

On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 ms, suggesting that most devices yield acceptable sleep-measurement errors. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion.

Conclusion

We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

Support (if any)

This work was sponsored by the Military Operational Medicine Program Area Directorate of the U.S. Army Medical Research and Development Command (USAMRDC), Fort Detrick, MD. The Henry M. Jackson Foundation was supported by the USAMRDC under Contract No. W81XWH20C0031.
0306 Improving Accessibility and Minimizing Errors: Reliable Sleep Stage Classification Using Wearable Sensors
Authors: Schabus Manuel , Baron Sebastian , Topalidis Pavlos , Hinterberger Alexandra , Eigl Esther-Sevil , Heib Dominik
Published: April 2024

Introduction

The widespread use of wearable devices has led to the emerging trend to quantify sleep. However, concerns arise due to the potential inaccuracies in these devices, which even can result in adverse daytime consequences. Here, we present an automatized, low-cost, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch classification in four classes (Wake, Light [N1 + N2], Deep, REM) and solely relies on inter-beat-interval (IBI) HRV data.

Methods

We trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 manually sleep-staged full-night (PSG) recordings and tested the MCNN for classification accuracy using two affordable consumer wearables: an optical heart rate sensor using photoplethysmography (PPG-VS) and an ECG breast belt (H10). Our most recent study tests the model on 136 self-reported poor sleepers equipped with PSG and these wearables.

Results

Results indicate promising outcomes with high epoch-by-epoch accuracy for ECG (accuracy: 86.3%, κ = 0.79), the H10 ECG (84.4%, κ = 0.76), and the PPG-VS wearable (84.2%, κ = 0.75). The HRV-based model exhibits substantial correlations and agreement with PSG on key sleep parameters, e.g., for Sleep Onset Latency (ρ = 0.82, p < 0.001), Sleep Efficiency (ρ = 0.84, p < 0.001), Total Sleep Time (ρ = 0.94, p < 0.001) and REM sleep (ρ = 0.78, p < 0.001). Notably, the model accurately classifies sleep also among users on heart-affecting or psychoactive medication; a critical aspect for potential clinical use. A first RCTs using this algorithm in our App “Nukkuaa” tested effects of i) daily objective sleep feedback using such sensors in combination with ii) tailored sleep training for insomnia patients using digital cognitive behavioural therapy (dCBT-I) and revealed subjective as well as objective improvements.

Conclusion

The present validation underscores the potential of automatic sleep stage classification algorithms based on cost-effective wearables and paves the way for scientific and clinical applications which strive for continuous and reliable sleep measurements including epoch-by-epoch sleep stage information as known from traditional PSG.

Support (if any)

The study was supported by the county of Salzburg (20102-F2002176-FPR) and the FWF/Austrian Science Fund (W 1233-B) for P. Topalidis.
0308 Non-Contact Sleep Monitoring with a Depth Camera: Morphology Similarity Between a Touchless Flow Signal and RIPflow
Authors: Addison Paul , Antunes Andre , Montgomery Dean , Smit Philip , Borg Ulf , Brewer Lara , Farney Robert , Sundar Krishna
Published: April 2024

Introduction

We have developed a non-intrusive sleep monitoring technology based on data from a commercially available depth sensing camera [Intel RealSenseTM D415, Intel, Santa Clara, CA]. This non-contact, or touchless, technology allows continuous respiratory monitoring without attaching probes to the patient. We have noticed a strikingly similar morphology between the touchless flow signal, NCMflow, derived from the depth camera and the RIPflow signal derived from the chest and abdomen respiratory inductance plethysmography (RIP) bands. Here we report on a preliminary quantitative assessment of this morphological similarity between the two signals.

Methods

For this proof-of-principle study, we analyzed a patient data set acquired continuously during a 9-hour sleep study at the Sleep | Wake Center at the University of Utah, where informed consent was given. Depth data was acquired with the depth camera placed approximately 1.1m above the torso of the patient. A patient with severe sleep apnea (AHI of 74/hr) was selected to compare NCMflow and RIPflow, and we examined both the concordance and the Pearson coefficient, which are measures of the mutual trending and linear correlation between the two signals respectively. The concordance was examined in two distinct timescales (1 and 2 seconds), which allowed for intra-breath morphology analysis, using a data exclusion zone of 10% to account for background signal noise. Both concordance and Pearson coefficient of linear correlation were calculated over the full 9 hours of the sleep study (including sleep and wake periods).

Results

The concordance for timescales of 1 and 2 seconds were 0.93 and 0.95 respectively, (where complete concordance would equal 1.00 and correspond to perfect co-trending). The corresponding Pearson R value was 0.90, (out of a maximum achievable value of 1.00 corresponding to perfect linear correlation between the signals).

Conclusion

The high values of the concordances and R confirm the excellent match we observe visually between the touchless flow signal, NCMflow, and the RIPflow signal. Our findings strongly support the potential for non-contact continuous monitoring of respiratory disturbances during sleep based on a depth camera system. Future work will aim to determine the viability of developing a touchless sleep scoring methodology.

Support (if any)

The work was funded by Medtronic.
0313 Analysis of Objective and Subjective Sleep Metrics and Smartphone Usage Patterns
Authors: Heneghan Conor , McDuff Daniel , Winbush Ari , Allen Nicholas , Hernandez John , Jiang Allen , Barakat Andrew , Schneider Logan , Nelson Benjamin , Yetton Ben
Published: April 2024

Introduction

The Digital Wellbeing Study is an IRB approved joint study between the University of Oregon and Google to investigate how smartphone usage interacts with objective and subjective parameters of well-being such as sleep, exercise and stress. The study recruited a demographically diverse population who each wore a smartwatch and installed a smartphone app linked to the study. Participants completed demographic and health questionnaires including the PROMIS Sleep Disturbance (SD) Short Form. Aims of the study included (a) whether objective sleep duration was correlated with smartphone use, and (b) whether smartphone usage could predict the subjective self-reported sleep instrument.

Methods

There was sufficient data from 7,499 users to conduct a population modeling analysis. An Ordinary Least Squares linear model was used as a predictor of each subject’s average total sleep time (TST) and their SD t-score. The inputs to the model included demographics, and population z-scored activity measures (steps, sedentary time, time driving, time at work, home and other locations, phone screen time, frequency of phone unlocks) over seven days prior to the survey.

Results

The activity measures and baseline demographics could only explain a small amount of the overall variance in TST and SD (R^2=0.04 for TST and R^2=0.05 for SD). Phone screen time was a statistically significant predictor of both TST (-8.19 mins, p< 0.001) and self-reported sleep disruption (0.611 t-score units, p< 0.001). The number of phone unlocks was a predictor of variability in TST (-3.33 mins, p< 0.001) suggesting that longer session times are correlated with greater TST variability. The effects are minimal (e.g., a subject who has one standard deviation greater phone screen time than average would be predicted to only see a 2% reduction in TST, and a 0.6% increase in perceived sleep disturbance). Time driving and step count were also minor predictors of SD and TST.

Conclusion

At a population level, average activity measures from wearables and smartphones such as steps, smartphone usage time, sedentary activity etc. are limited predictors of objective sleep metrics such as Total Sleep Time, and subjective sleep metrics such as the PROMIS Sleep Disturbance t-score.

Support (if any)

This research was funded by Google Inc.
0314 Design of a Continuous Melatonin Monitoring System: A Needs Assessment for Direct and Indirect Users
Authors: McCormack Cassandra , Garrison Michelle , Caldwell Barrett
Published: April 2024

Introduction

Circadian misalignment can interact bidirectionally with existing chronic conditions, especially those affecting the physical-cognitive boundary, in a manner causing progressive degradation of the chronic condition and circadian health. Current techniques for detecting circadian misalignment involve salivary, serum, or urinary melatonin testing or applying machine learning to accelerometry and heart rate data from wearable devices, and have considerable limitations affecting accessibility, generalizability, and real-time intervention. A continuous, patient-facing melatonin monitoring device (CMM) offers an alternative detection method while also supporting patients in performing real-time self-care tasks to improve their circadian alignment, sleep health, and wider health outcomes. Systematic design processes emphasize the importance of identifying end-user needs to derive specific design system requirements.

Methods

Three relevant frameworks were to support a process for stakeholder needs assessment to improve circadian / sleep management. Diffusion of innovations addresses factors that impact the adoption of an idea or product through social networks. Health literacy framework references patient knowledge and beliefs that influence health behaviors and outcomes. The chronic care model considers community and health system factors affecting interactions between patients and provider teams. These frameworks were adapted to a sleep health context and used to develop the NAP-CART (Needs Assessment Protocol for a Circadian Assessment and Realignment Tool).

Results

The NAP-CART involves performing semi-structured interviews with direct users (patients) and indirect users (sleep researchers and clinicians), with 15-20 subjects of each. Patient interviews using interface drafts will focus on health literacy specific to circadian alignment and melatonin, user interaction preferences for both short-term and sustained use, including broader issues of health management. Inclusion of indirect users will inform device use for research or clinical settings, but also provider interactions with patients specific to melatonin data, as patients are unlikely to have had previous interactions with melatonin data. Indirect user interviews will also consider the prospective role of CMM in research, clinical care, and decision-making support.

Conclusion

User-centered needs assessments for NAP-CART go beyond technical design of the CMM physical sensor. Incorporating end-user needs, previous experiences, and preferences into the systematic development of specific, measurable design requirements facilitates both initial adoption and sustained use for patients and clinicians.

Support (if any)
0315 Exploring AHI Variability Across Nights: Insights from Wearable Technology in Patients with No or Mild OSA
Authors: Link Brittany , Tsai Chih-Wei , Palomo Juan , Leung Lydia , Cheung Cynthia , Chiang Ambrose
Published: April 2024

Introduction

Night-to-night variability (NtNV) of obstructive sleep apnea (OSA) severity is a widely recognized phenomenon in sleep testing. Studies suggested that 20% of patients with mild-to-moderate OSA may be misdiagnosed or misclassified on the first night. Multiple-night assessments utilizing wearables may present a convenient approach to enhance the reliability of OSA estimation. The Belun Sleep System (BLS-100, a.k.a., Belun Ring) is an FDA-cleared, deep-learning-powered home sleep apnea testing system. This analysis aims to assess the NtNV of Belun apnea-hypopnea index (bAHI, events/h) in subjects with no or mild OSA.

Methods

Participants were recruited from a dental school orthodontic clinic for multiple-night Belun Ring (BR) testing. The bAHI was derived for each night, and a reference bAHI was computed based on the mean from all nights. Linear mixed-effects (LME) models were employed to evaluate differences between night-specific bAHI (1st, 2nd, 3rd night, etc) and the reference bAHI.

Results

Thirty-six patients underwent BR testing, with 30 completing ≥3 nights with ≥60 mins/night. Analysis was conducted on 28 subjects with no or mild OSA: age 31.7; 73% female; BMI 24.6; average 6.5 nights (3-10); mean total sleep time 340.0 mins; average AHI 6.2; 46% no OSA and 54% mild OSA. No significant differences were observed between the first night bAHI and the reference bAHI (6.8 vs. 6.2, P=0.362). This trend held true for subgroups with no OSA (3.9 vs. 3.6, P=0.712) and mild OSA (9.3 vs. 8.5, P=0.716). The first night study misclassified 4 (14%) subjects, including 3 (10%) non-OSAs as mild OSA and 1 (4%) mild as moderate OSA (κ=0.72, P< 0.001). While the number of assessment nights was non-significant in NtNV (LME, P=0.618), a drop in misclassification from 14% to 4% after 4-night (κ=0.92, P< 0.001) suggested that a 4-night evaluation may offer a more dependable bAHI assessment.

Conclusion

This research marks the first exploration into the AHI NtNV utilizing wearables in subjects with no or mild OSA. No significant bAHI differences were observed across the nights. A 4-night assessment may provide a more reliable OSA estimation within this particular population. Further investigation into AHI NtNV utilizing BR in moderate-to-severe OSA is warranted.

Support (if any)