bluuebunny/crossref_metadata_2025 huggingface dataset:
224 Digital Media Use and Sleep in College Students during COVID-19 Pandemic
Authors: Li Winnie , Ye Lichuan
Published: May 2021

Introduction

To address the growing sleep deficiency epidemic in college students, more research is needed on recent factors that might affect sleep, such as the digital media use in this young adult population. Furthermore, sleep and the use of digital media can be heavily influenced by the COVID-19 pandemic. The purpose of this study is to examine the use of digital media during the pandemic, and its relationship with sleep disturbance, social isolation, physical and mental health in college students.

Methods

An online survey was sent out to college students enrolled in an urban university. Validated questionnaires including PROMIS (Sleep Disturbance, Global Mental Health, Global Physical Health, Social Isolation), Nighttime Media Usage, and Internet Addition Test were included in the survey. In addition, focus groups were conducted with a subsample of survey respondents to elicit a comprehensive understanding of how digital media use in daily life influences sleep during the COVID-19 pandemic. Data collection was conducted during June to December 2020.

Results

A total of 358 students completed the online survey. Sleep disturbance was significantly related to greater digital media use for recreational purposes two hours before bedtime (62.6±28.1 minutes, r=0.110, p=0.046), and a higher frequency of playing games (r=0.148, p=0.007) and using social media after going to bed (r=0.142, p=0.10). Sleep disturbance was significantly associated with social isolation (r=0.251, p<0.001), poor global physical health (r=-0.186, p<0.001) and mental health (r=-0.376, p<0.001), and lower GPA (r=-0.167, p=0.004). Additionally, seven focus groups were conducted in a total of 32 students, suggesting that the increase in free time from the COVID-19 pandemic led to greater digital media use, compromising sleep duration and quality. With the increase of screen time also came feeling of guilt and anxiety which often led to greater awareness and self-control around media use.

Conclusion

Nighttime digital media use during the challenging pandemic time has a significant impact on poor sleep, which may lead to decreased academic performance, greater social isolation, and poor physical and mental health in college students. Effective interventions targeting digital media use are needed to improve sleep in this population.

Support (if any):
259 Tracking naturalistic sleep over the menstrual cycle with a wearable in healthy young women
Authors: Alzueta Elisabet , de Zambotti Massimiliano , Dulai Teji , Albinni Benedetta , Simon Katharine , Sattari Negin , Zhang Jing , Mednick Sara , Baker Fiona
Published: May 2021

Introduction

A woman’s menstrual cycle is characterized by hormonal changes that might affect sleep and therefore daily functionality. While some studies using self-reports have shown a lower sleep quality in the peri-menstruation phase, objective – in lab – studies have not found significant differences in sleep continuity during the menstrual cycle, but are limited by only a few recordings across the cycle. The aim of this study is to examine changes in sleep during the healthy menstrual cycle using a multi-sensory wearable, allowing continuous, objective, reliable and ecologically valid measurement.

Methods

12 healthy young women (28.14 ± 2.33) were monitored using Oura ring – a sleep and activity tracker – during an entire menstrual cycle. Participants also reported mood, readiness, and sleep quality using a diary. Four phases of the menstrual cycle were compared (menstruation, periovulation, mid-luteal, and late-luteal). Ovulation day was determined using a urinary luteinizing hormone test.

Results

Ovulatory cycles were confirmed by the Oura ring, which showed a significant increase in average nocturnal heart rate and skin temperature during the post-ovulatory luteal phase relative to menstruation and periovulation. Oura ring measures of sleep continuity (Sleep Onset Latency, Wake After Sleep Onset) and self-reported sleep quality did not change across the 4 menstrual phases. We observed a trend for objective sleep duration, which tended to be shorter in the mid-luteal and late-luteal phases. We also observed a small reduction in perceived readiness and mood during these two phases.

Conclusion

Physiological changes (increase in heart rate and body temperature) in the postovulatory phase of the menstrual cycle are detectable with the Oura ring. Sleep features remain quite stable during the healthy, ovulatory menstrual cycle, apart from a trend for slightly shorter sleep duration in the post-ovulatory phases. In comparison to self-reports, which rely on retrospective memory and might be biased by perception and mood, wearable technologies seem to be a sensitive and informative tool to track sleep and physiological changes during the menstrual cycle.

Support (if any)

Supported by RF1AG061355 (Baker/Mednick)
275 Evaluation of Multiple Wearable Sleep-Tracking Devices Tested Under Ad Lib Home Sleep Conditions
Authors: Chinoy Evan , Cuellar Joseph , Jameson Jason , Markwald Rachel
Published: May 2021

Introduction

Consumer wearable sleep-tracking devices are increasingly popular and have performed well versus gold standard sleep measurement techniques (polysomnography and actigraphy) in recent validation studies. However, most validation studies were conducted in laboratories under controlled conditions. We therefore aimed to test the validation performance of multiple consumer wearable sleep-tracking devices under real-world ad lib sleep conditions at home.

Methods

We tested 21 healthy young adults (12 women, 9 men; 29.0±5.0 years, mean±SD) for 7 nights each. Participants slept at home under ad lib sleep conditions, using a set of consumer wearable sleep-tracking devices and completed daily sleep diaries. Consumer wearables included the Fatigue Science Readiband, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Participants also wore the Philips Respironics Actiwatch 2, a research-grade actigraphy watch, for comparison. To assess validity of sleep/wake measures, all devices were compared with the previously-validated Dreem 2 electroencephalography-based headband device. Analyses included agreement of epoch-by-epoch sensitivity (for sleep) and specificity (for wake), and sleep summary comparisons of time-in-bed (TIB) and total sleep time (TST).

Results

Sensitivity and specificity, respectively, were as follows: Actiwatch 2 (0.95, 0.36), Readiband (0.93, 0.43), Inspire HR (0.93, 0.45), Ring (0.94, 0.41), and Vantage V Titan (0.96, 0.33). Device average biases, in minutes±SD, for TIB and TST, respectively, were as follows: Actiwatch 2 (N/A, +0.7±42.4), Readiband (+18.2±34.9, +0.4±49.5), Inspire HR (+7.8±35.0, -5.9±44.4), Ring (+9.2±28.0, +4.4±44.5), and Vantage V Titan (+0.2±50.0, -3.2±46.1).

Conclusion

The consumer wearable devices had comparable sleep-tracking performance during real-world ad lib home sleep. Similar to prior studies, the devices all had high sensitivity and low-to-medium specificity, indicating a greater ability to accurately detect sleep than wake. Notably, specificity for most consumer wearables was higher than a research-grade actigraph, indicating potentially greater ability than actigraphy to detect wake. Sleep summary outcomes were similar among the wearables, which accurately tracked TIB and TST on most nights. However, on some nights there was still considerable bias and variability. Overall, preliminary findings indicate that consumer wearables are promising for tracking sleep and wake in real-world home conditions.

Support (if any)

Office of Naval Research, Code 34
293 Predictive utility of a brief scale to identify U.S. Army Soldiers who are genetically vulnerable and resilient to sleep loss
Authors: Mantua Janna , Mickelson Carolyn , Naylor Jacob , Ritland Bradley , Bessey Alexxa , Burke Tina , Sowden Walter , Mckeon Ashlee
Published: May 2021

Introduction

Sleep loss that is inherent to military operations can lead to cognitive errors and potential mission failure. Single Nucleotide Polymorphisms (SNPs) allele variations of several genes (COMT, ADORA2A, TNFa, CLOCK, DAT1) have been linked with inter-individual cognitive resilience to sleep loss through various mechanisms. U.S. Army Soldiers with resilience-related alleles may be better-suited to perform cognitively-arduous duties under conditions of sleep loss than those without these alleles. However, military-wide genetic screening is costly, arduous, and infeasible. This study tested whether a brief survey of subjective resilience to sleep loss (1) can demarcate soldiers with and without resilience-related alleles, and, if so, (2) can predict cognitive performance under conditions of sleep loss.

Methods

Six SNPs from the aforementioned genes were sequenced from 75 male U.S. Army special operations Soldiers (age 25.7±4.1). Psychomotor vigilance, response inhibition, and decision-making were tested after a night of mission-driven total sleep deprivation. The Iowa Resilience to Sleeplessness Test (iREST) Cognitive Subscale, which measures subjective cognitive resilience to sleep loss, was administered after a week of recovery sleep. A receiver operating characteristic (ROC) curve was used to determine whether the iREST Cognitive Subscale can discriminate between gene carriers, and a cutoff score was determined. Cognitive performance after sleep deprivation was compared between those below/above the cutoff score using t-tests or Mann-Whitney U tests.

Results

The iREST discriminated between allele variations for COMT (ROC=.65,SE=.07,p=.03), with an optimal cutoff score of 3.03 out of 5, with 90% sensitivity and 51.4% specificity. Soldiers below the cutoff score had significantly poorer for psychomotor vigilance reaction time (t=-2.39,p=.02), response inhibition errors of commission (U=155.00,W=246.00,p=.04), and decision-making reaction time (t=2.13,p=.04) than Soldiers above the cutoff score.

Conclusion

The iREST Cognitive Subscale can discriminate between those with and without specific vulnerability/resilience-related genotypes. If these findings are replicated, the iREST Cognitive Subscale could be used to help military leaders make decisions about proper personnel placement when sleep loss is unavoidable. This would likely result in increased safety and improved performance during military missions.

Support (if any)

Support for this study came from the Military Operational Medicine Research Program of the United States Army Medical Research and Development Command.
299 Limited Time for Sleep in Night Shift Workers is associated with Risk of Insomnia and Shift Work Disorder
Authors: Lammers-van der Holst Heidi , Zhang Yuan , Barger Laura , Wise John , Murphy Audra , Desnoyers Brian , Qadri Salim , Ronda Joseph , Duffy Jeanne
Published: May 2021

Introduction

Sleep deficiency is a severe problem faced by night shift workers. Approximately one-third of night workers report insomnia during daytime sleep and excessive sleepiness during nighttime work; when severe, these symptoms characterize shift work disorder (SWD). Difficulty sustaining 7 to 9 hours of sleep during the daytime is partly due to a circadian drive for wakefulness. Not much is known, however, about how non-work activities contribute to the inability to obtain recovery sleep. We sought to explore how much time night workers are able to allocate for daytime sleep, and how this relates to insomnia-like symptoms and the likelihood of developing SWD.

Methods

Night shift workers (n=452, 19–69 years old, 54% men) from various occupations who worked at least four night shifts per month completed an online survey. This included questions related to shift duration, hours per workday of non-optional non-work activities, self-rated sleep need, the Insomnia Severity Index (ISI) and a validated 4-item SWD screening questionnaire. For each participant, we calculated the duration of work plus non-optional activities and compared the remaining available time for sleep to their self-described sleep need. Non-parametric Chi-square analyses and Pearson correlations were conducted.

Results

On average, shift duration was 8.9±1.6 hours, non-optional activities were 3.6±2.9 hours, and sleep need was 7.6±1.6 hours, leaving 15% of shift workers with insufficient free time to obtain the amount of sleep they needed. The percentage of workers at high risk for SWD was significantly greater among those who did not have enough free time for sleep compared to those whose schedules allowed sufficient sleep time (72% vs. 42%; χ2=20.2, p<0.0001). We also found that shift workers with insufficient free time for sleep reported higher insomnia severity (r2=-0.20, p<0.0001).

Conclusion

About 15% of night workers have non-optional activities outside work that limit their time to obtain sufficient sleep, and this contributes to greater insomnia-like symptoms and increased risk for SWD. Future research should focus on understanding what these non-optional activities are and whether they differ between night and day workers. These insights will enable personalized countermeasures to maximize the sleep and health of shift workers.

Support (if any)

Supported by US NIH grant R01-AG044416.
314 Alcohol and the Sleep of Elite Athletes
Authors: Shaw Lindsay , Altman Yuval , Eyal Shuli , Baharav Anda
Published: May 2021

Introduction

Extensive evidence indicates that alcohol adversely affects athletic performance, increases the chance of injury, impairs aerobic metabolism, and has detrimental psychological influence. Alcohol consumption was also shown to be significant in young adults, with many elite athletes reporting alcohol intake. Sleep is essential to musculoskeletal recovery, acquisition of new skills, and emotional regulation in athletes. As insufficient sleep has detrimental effects on athletic performances and increases the risk of injury, we aimed to analyze the relation between alcohol consumption and sleep parameters among elite athletes.

Methods

We studied 9,164 nights recorded with the Boost application by 66 adult elite athletes from various sports during October 2019-December 2020. In addition to perceived and measured sleep parameters, nightly data included the previous day reported alcohol consumption and the number of training sessions. For each athlete, the average nightly mean heart rate (HR) in nights with reported alcohol consumption (AY) and without (AN) was calculated, as were the bedtime and the number of training sessions.

Results

In nights with reported alcohol, mean HR was higher (AY: 56.1±7.8 BPM, mean±SD, AN: 52.9±7.3 BPM, p<.05), while bedtime was later (AY: 23:16±70 minutes, AN: 22:52±55 minutes, p<.05) and fewer training sessions were reported in the following day (AY: 0.88±0.59 sessions, AN: 1.1±0.59 sessions, p<.05). No correlation was found between mean HR and bedtime in nights without alcohol consumption (r=0.3). 24% of Saturday night recordings included alcohol consumption reports, with 16% of Friday nights and 12–13% for each of the remaining weeknights.

Conclusion

Adult elite athletes report consuming alcohol, especially during weekends when the probability for a training session the following day is lower. Our data support the well documented physiological effect of alcohol via elevated mean HR values throughout the night. Athletes and coaches should be educated about the impacts of alcohol and insufficient sleep on wellbeing and performance.

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326 Comparison of Psychological and Physiological Features Between Insomnia Patients with Short and Normal Objective Sleep Duration
Authors: Lin Yun-Kai , Yang Chien-Ming , Lee Hsin-Chien , Huang Ya-Chuan , Lin Chia-Yu , Huang Wan-Chun , Kao Yu-Hsuan
Published: May 2021

Introduction

Objective sleep duration has been suggested to be a novel biomarker in the phenotyping of insomnia (Vgontzas & Fernandez-Mendoza, 2013). It was hypothesized that insomnia phenotype with PSG-defined short sleep duration (< 6 hours) is associated with physiological hyperarousal, while the phenotype with normal sleep duration (≥ 6 hours), is associated with anxious-ruminative psychological profile and poor resources for coping with stress. The aim of this study was to assess whether these two insomnia phenotypes differ in terms of self-reported psychological and physiological features.

Methods

A total 45 (mean age=36.6, female=76%) insomnia patients underwent a polysomnographic evaluation, completed several self-rating scales (include ISI, BAI, BDI, FIRST, DBAS, and PSAS), and were split into two groups base on their objective sleep duration (cut-off 6 hours).

Results

When compared to the short sleep group using independent sample t-test, the normal sleep group showed a significant higher mean score on FIRST (t= -2.13; p< .05). The short sleep group showed non-significant trends to have higher mean scores on BDI and BAI. The scores on the other scales showed no significant differences between the two groups. The FIRST score correlated positively with the score of DBAS (r= 0.32; p< .05) and the total (r= 0.36; p< .05) and cognitive-subscale scores (r= 0.41; p< .01) on PSAS. The BAI score correlated positively with the score of the total (r= 0.53; p< .001) and somatic subscale (r= 0.59; p< .001) on PSAS.

Conclusion

These results support the hypotheses that the normal sleep phenotype tends to be more vulnerable to the impact of stress, especially on sleep, due to pre-sleep cognitive hyperarousal. Since the BAI scale contains more items regarding physiological symptoms of anxiety disorders, the tendency of higher BAI score in short sleep group may indicate that the short sleep phenotype has higher physiological arousal, which supports the phenotyping model proposed by Vgontzas and Fernandez-Mandoza.

Support (if any):
341 Acute effects of seltorexant, a selective orexin-2 antagonist (JNJ- 42847922), on driving after bedtime administration
Authors: Brooks S , Zuiker R G J A , Jacobs G E , Kezic I , Savitz A , Van Der Ark P D , Van Gerven J M A
Published: May 2021

Introduction

Seltorexant (JNJ-42847922), a potent and selective antagonist of the human orexin-2 receptor, is being developed for the treatment of major depressive disorder. Seltorexant also has sleep-promoting properties. Investigating the effects of sleep-promoting medications on driving is important because some of these agents (e.g. GABAA receptor agonists) may be associated with increased risk of motor vehicle accidents. We evaluated the effect of seltorexant on driving after forced awakening at night, using a validated driving simulator.

Methods

This double-blind, placebo and active-controlled, randomized, 3-way cross-over study was conducted in 18 male and 18 female healthy subjects. All subjects received seltorexant 40 mg, zolpidem 10 mg, or placebo 15 minutes before bedtime. Eighteen subjects were awakened at 2- and 6-hours post-dose, and the other 18 at 4- and 8-hours post-dose. At those timepoints, pharmacokinetics, objective (standard deviation of the lateral position [SDLP]) and subjective effects (using Perceived Driving Quality and Effort Scales) on driving ability, postural stability and subjective sleepiness were assessed.

Results

For seltorexant, the SDLP difference from placebo (95% confidence interval) at 2-, 4-, 6- and 8-hours post-dose was 3.9 cm (1.26, 6.60), 0.9 cm (-1.08, 2.92), 1.1 cm (-0.42, 2.63), and 0.6 cm (-2.75, 1.55), respectively vs. 9.6 cm (6.97, 12.38), 6.6 cm (3.53, 9.60), 4.7 cm (1.46, 7.85), and 1.3cm (-1.16, 3.80), respectively for zolpidem. The difference from placebo was significant at 2-hours after taking seltorexant, while the difference from placebo was significant at 2, 4 and 6-hours after zolpidem. Subjective driving quality was decreased for both drugs at all time points and driving effort was increased up to 4-hours post-dose for both medications. Subjective sleepiness showed a significant increase compared to placebo 2- and 4-hours after administration of either drug. Postural stability was decreased up to 2-hours after administration of seltorexant, and up to 4-hours after administration of zolpidem.

Conclusion

Compared to zolpidem, objective effects on driving performance were more transient after seltorexant administration and largely normalized by 4–6 hours post-dose.

Support (if any)

This work was sponsored by Janssen R&D.
363 The Apnea and Insomnia Research (AIR) Trial: An Interim Report
Authors: Edinger Jack , Edinger Jack , Manber Rachel
Published: May 2021

Introduction

Many sleep apnea patients suffer from comorbid insomnia disorder. Although cognitive behavioral insomnia therapy (CBTI) is recommended as the first line insomnia treatment for such patients, access to trained providers of this treatment remains limited. The current study is testing he efficacy of an online CBTI among CPAP treated sleep apnea patient with comorbid insomnia.

Methods

Patients enrolled in this trial complete baseline measures and then are randomized to either an online version of Cognitive Behavioral Insomnia Therapy (CBTI) or no additional treatment beyond their CPAP therapy (CTRL). After 8 weeks of treatment all patients are reassessed. The current report considers changes in scores on the ISI and Epworth Sleepiness Scale (ESS) as well as average minutes of nightly CPAP use from pre-treatment to the end of the initial 8 weeks of online treatment relative to the no treatment CTRL. The sample for this report included the first 276 participants enrolled in this trial (mean age = 56.5±12.5 yrs; 58.7% females).

Results

Those receiving online CBTI showed greater reductions in their ISI scores from baseline to the end of the initial 8-week treatment phase than did those in the CTRL group (p = .0001). Average ISI score improvements among those receiving online CBTI moved patients from moderately severe insomnia to mild insomnia symptoms. In contrast, no differences were noted between the online CBTI and CTRL groups in regard to pre- to post-treatments changes on the ESS (p= .2541) scores or amount of CPAP use (p = .4383).

Conclusion

Whereas online CBTI does not seem to reduce daytime sleepiness or improve CPAP adherence among patients with comorbid sleep apnea and insomnia, it appears to be an effective intervention for reducing insomnia severity for this patient group.

Support (if any)

National Heart. Lung and Blood Institute Grant # 1R01HL130559-01A1
380 The role of self-esteem and sleep on inflammatory markers in young adults
Authors: Squyres Emily , Slavish Danica , Graham-Engeland Jennifer
Published: May 2021

Introduction

Disturbed sleep is common among young adults and is associated with poorer health and developmental outcomes. A large percentage of young adults also struggle with low self-esteem. Together, disturbed sleep and low self-esteem may deplete coping resources, heighten to reactivity to stress, and increase disease risk. Yet no studies to our knowledge have examined interactions between self-esteem and sleep on biomarkers of health among young adults.

Methods

To address this gap, we investigated associations between sleep quality, self-esteem, and two inflammatory markers, C-reactive protein (CRP) and interleukin-6 (IL-6), in a sample of 60 young adults (mean age 25.3 ± 4.0 years old, 53% female, 83% White). Participants completed a baseline survey to assess self-esteem (Rosenberg Self-Esteem Questionnaire) and sleep quality (Pittsburgh Sleep Quality Index [PSQI] sleep quality item), followed by 14 days of self-reported sleep disturbances each morning (PROMISTM sleep disturbances short-form; averaged across the 14 days). A plasma blood draw was then collected to assess CRP and IL-6 approximately one week after the end of the daily portion.

Results

Lower self-esteem (b = -0.04, 95%CI [-0.06,-0.01], p = 0.015) and lower sleep quality were each associated with higher CRP (b = -0.34, 95%CI [-0.62, -0.07], p = 0.015), but not IL-6. Greater daily sleep disturbances were marginally associated with higher CRP (b = 0.37, 95%CI [-0.06,-0.79], p = 0.088]. Interactions between self-esteem and either sleep quality or sleep disturbances did not predict CRP or IL-6.

Conclusion

Our results suggest low self-esteem and poorer sleep are each associated with higher levels of inflammation but may not interact to exacerbate risk. It is possible low self-esteem and poor sleep each lead to negative emotions or engagement in risky behaviors (e.g., substance use, sedentary behavior) that impact levels of inflammatory markers. Overall, our results highlight the importance of assessing both sleep and personality traits in relation to biomarkers of health among young adults.

Support (if any)

American Psychological Association Dissertation Research Award
383 Cortical thickness moderates association between sleep architecture and pain in adults with fibromyalgia
Authors: Nair Neetu , Curtis Ashley , Stephens Riley , Hayse Braden , Studdard Paul , Craggs Jason , McCrae Christina S
Published: May 2021

Introduction

Fibromyalgia is associated with chronic widespread pain and insomnia. While sleep and pain are mutually influential, how the cortical thickness (CT) of pain-related brain regions influences that relationship (i.e., sleep architecture and evening/morning pain) is unknown. This study examined how the CT of two regions involved in pain processing - the anterior cingulate cortex (ACC) and insula - influenced the link between sleep architecture and evening vs next morning pain in women with fibromyalgia.

Methods

Thirty adults with fibromyalgia and insomnia (Mage=57.2, SD=13.1) completed overnight polysomnography and daily diaries (14 days) tracking sleep and pain [0–100 (most intense). Pain discrepancy (PD) is the average difference between nighttime and next morning pain (evening - morning). Participants underwent MRI, and FreeSurfer (v5.1.0) estimated CT. Multiple regressions examined CT’s influence on the sleep stage [%stage1, %stage2, %stage3, %rapid eye movement (REM)] and PD relationship (controlling for age and education). For significant CT moderation, significance of simple slopes at different CT levels were examined: high (1 SD above), average, and low (1 SD below).

Results

Right rostral ACC (rRACC) CT moderated the relationship between %stage2 (B=-1.41, SE=0.6, p=.03, R^2=0.14), %stage3 (B=2.10, SE=0.97, p=.04, R^2 =0.13), %REM (B=2.35, SE=1.12, p=.04, R^2=0.13) and PD. Greater %stage2 was associated with higher morning vs evening pain at high rRACC thickness (B=-0.67, SE=0.22, p=0.005). Greater %stage3 (B=0.97, SE=0.38, p=0.02) and %REM (B=0.92, SE=0.42, p=0.04) were associated with lower morning pain vs evening pain at high, but not average or low rRACC thickness. Left insula CT moderated the association between increased %stage3 and lower morning vs evening pain (B=2.91, SE=1.18, p=.02, R^2=0.34) at the average and high, but not low thickness levels (B=0.36, SE=0.17, p=0.05; B=1.41, SE=0.48, p=0.008 respectively).

Conclusion

High cortical thickness in the rRACC and average to high L-insula cortical thickness moderated the association between restorative sleep (%stage3, REM) and lower morning vs evening pain. Future studies examining the role of CT in pain-related brain regions on the association of restorative sleep with overnight pain processing are warranted.

Support (if any)

National Institute of Nursing Research (NR017168; PI: McCrae). Clinical trial NCT02001077 Sleep and Pain Interventions (SPIN2), University of Missouri (PI: McCrae).
393 Comparison of Oxygenation Abnormalities Between Obstructive Sleep Apnea and Central Sleep Apnea
Authors: Thanaviratananich Sikawat , Cheng Hao , Pino Maria , Sundar Krishna
Published: May 2021

Introduction

The apnea-hypopnea index (AHI) is used as a generic index to quantify both central sleep apnea (CSA) and obstructive sleep apnea (OSA) syndromes. Patterns of oxygenation abnormalities seen in CSA and OSA may be key to understanding differing clinical impacts of these disorders. Oxygen desaturation and resaturation slopes and durations in OSA and CSA were compared between OSA and CSA patients.

Methods

Polysomnographic data of patients aged 18 years or older with diagnosis of OSA and CSA, at University of Iowa Hospitals and Clinics, were analyzed and demographic data were collected. Oximetric changes during hypopneas and apneas were studied for desaturation/resaturation durations and desaturation/resaturation slopes. Desaturation and resaturation slopes were calculated as rate of change in oxygen saturation (ΔSpO2/Δtime). Comparison of hypoxemia-based parameters between patients with OSA and CSA was performed using unpaired t-test.

Results

32 patients with OSA with median AHI of 15.4 (IQR 5.1 to 30.55) and median ODI of 15.47 (IQR 9.50 to 29.33) were compared to 15 patients with CSA with a median AHI of 20.4 (IQR 12.6 to 47.8) and median ODI of 27.56 (IQR 17.99 to 29.57). The mean number of desaturation and resaturation events was not significantly different between patients with OSA and CSA (OSA - 106.81±87.93; CSA - 130.67±76.88 with a p-value 0.1472). 4/15 CSA patients had Cheyne-Stokes breathing, 2/15 had treatment emergent central sleep apnea, 1/15 had methadone-associated CSA and for 8/15, no etiologies for CSA were found. Mean desaturation durations was significantly longer in OSA (20.84 s ± 5.67) compared to CSA (15.94 s ± 4.54) (p=0.0053) and consequently the desaturation slopes were steeper in CSA than OSA (-0.35%/sec ±0.180 vs. -0.243 ± 0.073; p=0.0064). The resaturation duration was not significantly longer in OSA (9.76 s ± 2.02) than CSA (9.057 s ± 2.17) (p=0.2857). Differences between desaturation duration and slopes between CSA and OSA persisted during REM and NREM sleep, and in supine sleep.

Conclusion

As compared to OSA, patients with CSA have different patterns of desaturations and resaturations with lesser hypoxic burden with CSA. This may have implications on the clinical outcomes seen between these two disorders.

Support (if any):
397 Health Insurance Status in Subjects at High Risk for Obstructive Sleep Apnea
Authors: Abbasi Adnan , Thatipelli Sameepya
Published: May 2021

Introduction

Undiagnosed obstructive sleep apnea (OSA) is a major public health problem. Undiagnosed OSA can result in decreased productivity due to absenteeism, increased risk of comorbidities (cardiovascular disease, diabetes, and depression), and increased motor vehicle as well as workplace accidents. Lack of health insurance coverage can lead to undiagnosed and therefore untreated OSA. The objective of this study is to evaluate health insurance status in subjects at high-risk for OSA.

Methods

This is a cross-sectional, population-based study of adults 18 years and older who participated in the 2017–2018 National Health and Nutrition Examination Survey (NHANES). A modified STOP-Bang score was used to calculate OSA risk. This score included all the variables from the standard STOP-Bang questionnaire, except neck circumference, since it was not reported in the NHANES survey. Subjects were divided into two groups: those at low-risk for OSA with a modified STOP-Bang score of ≤ 3 and those at high-risk for OSA with a modified STOP-Bang score of >4.

Results

A total of 4,847 adult subjects were included, which represented 223,385,241 of the U.S. non-institutionalized population. Using the modified STOP-Bang score cutoff of >4, 20.9% of the sample were classified as high-risk for OSA, while 79.1% were classified as low-risk for OSA. 90% of the high-risk OSA group and 85.1% of the low-risk OSA group reported having health insurance. Sociodemographic data will also be analyzed and included.

Conclusion

Approximately 10% of subjects who are at high-risk for OSA reported not having health insurance. This represents over 4.6 million Americans in the non-institutionalized population. Health insurance can improve access to health care. Timely diagnosis and treatment of OSA not only can reduce morbidity and mortality, but can also reduce health care costs.

Support (if any)

CDC for NHANES Data.
407 Explanatory analysis of polysomnography for the identification of sleep apnea hypopnea events using deep learning neural network
Authors: Yook Soonhyun , Gupte Chaitanya , Han Zhixian , Joo Eun Yeon , Park Hea Ree , Kim Hosung
Published: May 2021

Introduction

Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed.

Methods

We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea.

Results

SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%).

Conclusion

Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome.

Support (if any)

This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).
408 Validation of claim based algorithms for sleep apnea using ICD codes
Authors: Sultana Rizwana , Lam Elizabeth , Hsu Enshuo , Gurski Erin , Sharma Gulshan
Published: May 2021

Introduction

Obstructive sleep apnea (OSA) is a common condition characterized by repeated episodes of partial or complete obstruction of the respiratory passages during sleep. According to recent studies prevalence of obstructive sleep apnea ranges between 9–38%. OSA is associated with increased all-cause mortality particularly associated with cardiac diseases. In order to provide representation of larger population estimates, administrative data using ICD codes have been utilized. Accurate identification of sleep apnea is important for research related to health care utilization and health outcomes. Our aim is to validate an algorithm for identification of patients with obstructive sleep apnea using ICD 10 codes seen at UTMB.

Methods

Patient medical records were collected from University of Texas Medical Branch EHR system. We included patients who visited from 6/1/2015 to 5/31/2018 in pulmonary or primary care clinics who had any sleep disorder diagnostic codes (ICD-10: G47.30, G47.31, G47.33, G47.34, G47.36, G47.20, G47.10, G47.39, G47.8, G47.9, F51.13, F51.09, R06.89, J96.90, R40.0, F51.9, R06.83, R06.3, G47.63, G47.39, Z86.69). Two algorithms were created. First algorithm included patient with sleep diagnostic codes used at 2 separate office visits. Second algorithm included patients with sleep diagnostic codes and evidence of sleep study. The performance of most used codes was calculated individually.

Results

1200 patients were identified with ICD codes used during two office visits. According to the first algorithm with only ICD codes 75% of patients had sleep apnea. Upon addition of evidence of sleep apnea with ICD codes the % of patients with sleep apnea increased to 95.44. Among most used ICD codes, G47.30 had 86.47% patients with sleep apnea according to first algorithm and 96.01% with second algorithm. The percentages for G47.33 was 80.86% and 96.4%, for G47.10, 78.05% and 87.67%, for R40.0 78.91% and 90.63% respectively.

Conclusion

In conclusion, claim based algorithms for sleep apnea diagnostic codes showed good test positive percentages overall, but algorithm with ICD 10 codes with sleep study performed better in identifying patients with sleep apnea than ICD-9-CM codes alone. Similarly, the individual performance of most used ICD codes was highly improved when evidence of sleep study was present.

Support (if any):
409 Dynamic Phenotype Learning: A Novel Machine Learning Approach To Develop And Discover New OSA Sub-Types
Authors: Fernandez Chris , Rusk Sam , Nygate Yoav , Glattard Nick , Arguelles Jessica , Shi Jiaxiao , Hwang Dennis
Published: May 2021

Introduction

Current approach to processing polysomnography is labor intensive and produces metrics that are poor at identifying obstructive sleep apnea (OSA) phenotypes necessary to enhance personalized care. We describe our approach to utilize Dynamic Phenotype Learning (DPL) as an innovative machine learning technique to identify OSA subtypes that can better predict clinical risk and success with therapies.

Methods

This study is a collaboration between Kaiser Permanente Southern California (KPSC), a large integrated health system, and EnsoData Research, which specializes in applied A.I. analysis of physiologic waveforms. KPSC sleep medicine compiled a database of N=5,368–234,250 subjects that include Types I, II, III, or IV sleep study data, daily PAP data, patient reported data, and comprehensive electronic health record information, with present research applications to study the relationship between OSA and PAP adherence with cardiovascular outcomes, health economic impacts, novel coronavirus (COVID-19) outcomes, and predictive PAP adherence and OSA severity clinical decision tools. DPL is a machine learning method for studying known and new biomarkers and care-pathway indices, including personalized screening, diagnostic, treatment, adherence, and outcomes predictors, that can be rooted in physiologic data. DPL processes waveform signal data without scoring, annotations, or expert synthesis, by applying a novel machine learning mechanism that blurs supervised and unsupervised deep learning paradigms, to find relationships between physiome dynamics expressed in waveforms and phenotypes and endotypes of interest.

Results

We demonstrate DPL method with an illustrative study on known indices, to explain its ability to (1) lift theoretical-empirical predictive accuracy ceilings and (b) reduce several sources of bias and variance. We show DPL exceeds the ROC-AUC and PRC-AUC of equivalent deep learning models in N=30,000 Report-Demographic (ODI, PLMSI, Weight), Scoring (REM, OSA), and Waveform (EEG, PPG) datasets respectively to predict AHI, TST, Brain Age, and OSA-Insomnia. We present our current collaboration advancing DPL to identify specific phenotypes that better predict: (a) cardiovascular risk; (b) neurocognitive outcomes; (c) response to PAP and alternative therapies.

Conclusion

DPL methods are being applied to large and comprehensive patient dataset to identify phenotypic indices and biomarkers with potential to take us beyond the AHI, and uncover relationships between OSA sub-types, treatments, and health outcomes.

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417 Transgender Hormone Therapy and Sleep-Disordered Breathing
Authors: Gavidia Ronald , Dunietz Galit Levi , Matlen Lisa , Hershner Shelley , Stroumsa Daphna , Kaplish Neeraj , O’Brien Louise
Published: May 2021

Introduction

Sex hormones may affect human respiration during wakefulness and sleep. Testosterone has been associated with increased obstructive respiratory events contributing to sleep-disordered breathing (SDB) in men, whereas a protective effect against SDB has been attributed to estrogen in women. These associations, primarily observed in cisgender populations, have been rarely examined in transgender individuals on hormone replacement therapy (HRT). The present study investigated associations between HRT and SDB in transgender adults.

Methods

A chart review of medical records from transgender patients was conducted in a large academic sleep medicine center. Individuals were included if they were at least 18 years old, had one or more sleep complaints, and SDB testing results available. Participants were then stratified by affirmed gender (transmasculine and transfeminine) and by HRT status. We used descriptive statistics procedures to examine differences between gender and HRT groups. Associations between HRT and the apnea-hypopnea index (AHI) were estimated with age-adjusted linear regression models.

Results

Of the 194 individuals identified, 89 satisfied the inclusion criteria. Nearly half of participants were transmasculine (52%). The mean age was 38±13 years, and mean body mass index was 34.7±9.0 Kg/m2. Approximately 60% of participants were on HRT at the time of SDB evaluation. Transmasculine people who were prescribed testosterone had a significantly increased AHI and lower oxygen nadir in comparison to transmasculine individuals not on testosterone (AHI 36.8±37.8/hour vs.15.3±16.6/hour, p=0.01; oxygen nadir 83.4±8.3% vs. 89.1±2.4%, p=0.001). In contrast, differences between transfeminine people with and without feminizing HRT (androgen blocker + estrogen) were not statistically significant (AHI 21.4±27.7/hour vs. 27.7±26.0/hour, p=0.45; oxygen nadir 86.5±6.7% vs. 84.1±7.7%, p=0.29). Linear regression models adjusted for age found an association between HRT and AHI for transmasculine (β=16.7, 95% CI 2.7, 30.8), but not for transfeminine participants (β=-2.5, 95% CI -17.9, 12.9).

Conclusion

These findings suggest differential associations between HRT and AHI among transgender individuals, with transmasculine on testosterone having a significant increase in AHI. Prospective studies with large sample sizes are warranted to evaluate these associations.

Support (if any)

Dr. Gavidia’s work was supported by an NIH/NINDS T32-NS007222 grant
422 Influence of Sex-Specific Differences in Inpatient Sleep Testing Approach for Diagnosis of Obstructive Sleep Apnea
Authors: Orbea Cinthya Pena , McIntire Cody , Wang Lu , Mehra Reena
Published: May 2021

Introduction

Increased attention has been focused on sex-specific differences in approaches to diagnostic testing for obstructive sleep apnea (OSA) given differences in hypoxia, arousal thresholds and sleep state dependent influences, but with sparse data available for inpatient testing. We postulate that women are more likely to have a lesser degree of sleep apnea on inpatient home sleep apnea testing (HSAT) versus polysomnography (PSG).

Methods

The Cleveland Clinic Sleep Laboratory registry was queried for inpatient sleep testing (HSAT or PSG conducted over the last 15 years. Demographics, comorbidities, and sleep study (Nihon Kohden®) data were collated. Logistic regression was used to examine sleep study type predictive of OSA at various severity thresholds (apnea hypopnea index (AHI, 3 or 4% hypopnea rule)>5,>15 and >30 and hypoxia (11% (median) time spent with SaO2<90%) adjusted for age, race and body mass index and comorbidities (hypertension, coronary artery disease, arrhythmias, heart failure, diabetes, stroke, chronic obstructive pulmonary disease, mood disorders, respiratory failure and epilepsy with a sex interaction term) (OR, 95%CI presented).

Results

The analytic sample was comprised of 639 patients: age:55.8±16.3 years, 45% female, 73% Caucasian, BMI:37.5 ± 13.3kg/m2, 74% had OSA and 51% HSAT. Men had higher AHI:16.2 [5.9, 42.3] vs 8.2 [2.9, 20.7]p<0.001, higher arousal index:33.1[18.9,.54] vs 25.3 [15.6, 39.2]p=0.003. Women had higher BMI:40.2 ±14.7, vs 36 ±11.7kg/m2,p<0.001. Unlike AHI>5, at AHI>15, men had lower odds of OSA: OR=0.51:0.32–0.80,p=0.004 for HSAT versus PSG compared to women: OR=1.03:0.61–1.72,p=0.92; interaction p-value=0.046. Men had lower odds of OSA (AHI >30): OR=0.57(0.35,0.92,p=0.022) in HSAT vs PSG; albeit sex-interaction was not statistically significant. Men versus women had 2-versus 3-fold higher hypoxia ie. OR=2.04:1.22–3.41,p=0.006 in men undergoing HSAT versus PSG with strength of association higher in women: OR=3.03:1.68–5.46,p=0.001, interaction p-value=0.32

Conclusion

We unexpectedly observe sex-specific differences in inpatient sleep testing such that men had an overall lower odds of detection of moderate to severe and OSA and nocturnal hypoxia relative to women with HSAT versus PSG. Future investigation focused on concurrent inpatient PSG and HSAT should verify these sex-specific findings and clarify potential biophysiologic rationale

Support (if any)

Transformative Neuroscience Research Development Program: Multimodal Neurocardiorespiratory Physiologic Sleep Signal Repository Transformative Resource Facilitating Transdisciplinary Research Opportunities
423 Evaluation of Electronic Medical Record Artificial Intelligence Screening Tools for Undiagnosed OSA
Authors: Rusk Sam , Nygate Yoav , Turkington Fred , Fernandez Chris , Glattard Nick , Watson Nathaniel
Published: May 2021

Introduction

The STOP-Bang is a concise, simple and widely adopted obstructive sleep apnea (OSA) screening tool. However, it has limited predictive ability and is susceptible to subjective reporting bias. Artificial Intelligence (AI) methodologies can be utilized together with existing data in electronic medical records (EMRs) to create new screening tools to increase diagnostic sensitivity and facilitate discovery of preclinical OSA phenotypes.

Methods

The study comprised two independent retrospective sleep study datasets: 1) Type III HSATS (N=5583) and, 2) Type I polysomnograms (N=1037). Each contained raw sleep study waveforms, manually scored sleep events (respiratory, arousal, sleep staging), and standard report indices (apnea-hypopnea index; AHI, arousal index). Additionally, the first dataset contained 90 EMR based metadata variables and the second dataset contained 54 EMR based metadata variables. Three random forest models were trained to detect OSA diagnostic thresholds (AHI> 5, AHI>15, and AHI>30) over three different screening models: STOP-Bang, P-Bang (blood-pressure, BMI, age, neck-size, gender), and Common Clinical Data Set (CCDS)-OSA (all metadata variables simulating EMR CCDS standard).

Results

CCDS-OSA ROC-AUC exceeded STOP-Bang and P-Bang for both sleep study collections, resulting in AHI>15 ROC-AUC values of 0.73 and 0.71 (CCDS-OSA) compared to AHI>15 ROC-AUC values of 0.68 and 0.69 (STOP-Bang). Additionally, we analyzed the Gini feature importance ranking of the trained CCDS-OSA model to evaluate which variables showed highest predictive value of OSA. The ranking revealed the top 5 features were the five physiologic based STOP-Bang parameters, followed by EMR based physiologic measurements such as HDL, triglycerides, systolic BP, and disease conditions such as diabetes, hypertension, and depression.

Conclusion

This study shows that while STOP-Bang contains data critical to OSA screening, a variety of other EMR-based parameters can improve performance of OSA detection. AI-based EMR screening can provide a critical tool for more systematic and accurate screening of undiagnosed sleep apnea. Nationwide standards facilitating patient EMR data interoperable health information exchange, particularly the United States Core Data for Interoperability (USCDI CCDS), holds promise to foster broad clinical and research opportunities. Resulting data sharing will allow application of AI screening tools at the population health scale with ubiquitous, existing EMR data to improve population sleep health.

Support (if any):