bluuebunny/crossref_metadata_2025 huggingface dataset:
0144 A Mixed Methods Exploration of Stigma, Discrimination, and Sleep among Individuals on Medication for Opioid Use Disorder
Authors: Nwanaji-Enwerem Uzoji , Sadler Lois , Jeon Sangchoon , Barry Declan , Yaggi H , Redeker Nancy
Published: May 2023

Introduction

Many individuals who suffer from opioid use disorder (OUD) experience symptoms of insomnia. OUD-related stigma and discrimination are major concerns and represent complex, chronic stressors that lead to negative health and social outcomes. However, how OUD-related stigma and discrimination are associated with insomnia among those on Medication for OUD (MOUD) is not known. The purpose of this study is to examine the associations among OUD-related stigma, intersectional discrimination, and insomnia among patients on MOUD.

Methods

This concurrent mixed methods study is a sub-study (N = 120) of the NIH funded Collaboration Linking Opioid Use Disorder and Sleep (CLOUDS) study. Insomnia was measured with the Insomnia Severity Index. OUD-related stigma was assessed with the Brief Opioid Stigma Scale, and intersectional discrimination was assessed with the Intersectional Discrimination Index. Quantitative data were analyzed via Spearman correlations. A sub-sample of 25 participants participated in semi-structured interviews. Qualitative questions explored individuals’ perceptions and experiences of stigma and discrimination and their influence on sleep. Qualitative data were analyzed with thematic analysis.

Results

Hypotheses were partially supported. Spearman correlations revealed a small positive correlation (r = .17, p = .04) between intersectional discrimination and insomnia severity. Correlations between OUD-related stigma and insomnia severity were not statistically significant. Patterns of negative thinking and rumination were described by participants who felt their stigmatizing and discriminatory experiences affected their sleep.

Conclusion

Findings add to the growing literature linking discrimination to sleep deficiency in highly stigmatized populations. With larger diverse samples, future researchers should (1) utilize longitudinal designs to explore underlying pathways by which discrimination and stigma affect sleep and (2) identify intervention approaches that target multiple levels [individual, interpersonal, community, and structural] to decrease the impacts of discrimination and stigma on self-perception and sleep outcomes for those battling OUD.

Support (if any)
0146 Associations Between Mood and Sleep Architecture in Sedentary Office Workers
Authors: Vogan Olivia , Cheruka Caitlin , Egeler Mara , Kubala Andrew , Sanders Rachel , Paley Joshua , Patel Sanjay , Hall Martica , Perera Subashan , Gibbs Bethany , Kline Christopher
Published: May 2023

Introduction

Sleep, and rapid eye movement (REM) sleep in particular, has been suggested to play an important role in mood regulation. Currently, most of this research has come from laboratory settings under constrained conditions; less is known about one’s sleep in their own home. The purpose of this study was to further examine the associations between home-based polysomnography (PSG) sleep parameters and mood disturbance.

Methods

This cross-sectional study used baseline data from 120 inactive adults with desk-based jobs (52.5% female, 43.8±10.7 y) who were enrolled in an ongoing clinical trial. Mood was assessed using the Profile of Mood States (POMS); its 7 subscales (tension, anger, fatigue, depression, esteem-affect, vigor, confusion) were summed (with a constant of 100) to generate a Total Mood Disturbance (TMD) score with a higher score indicating greater TMD. Sleep stages were assessed after a full workday using one night of at-home PSG. Multiple linear regression models examined associations between sleep stages and mood adjusting for age, gender, and whether the assessment occurred pre- or post-COVID-19 emergence.

Results

The mean TMD score was 98.6±18.3. On average, participants slept for 397.4±60.0 minutes with a sleep efficiency of 84.6±9.1%. The mean percentage of time spent in REM sleep was 22.5±6.6%; the mean percentage of time spent in non-rapid eye movement (NREM) sleep, stage 1 (N1), stage 2 (N2), and stage 3 (N3) sleep were 7.8±5.0%, 53.7±10.1%, and 16.0±9.1%, respectively. A greater percentage of REM sleep was associated with lower TMD (β=-0.22, p=0.01). A greater percentage of N1 sleep was also associated with lower TMD (β=-0.23, p=0.01). However, neither percent of N2 nor N3 sleep were associated with TMD (p=0.10 and p=0.12, respectively).

Conclusion

Overall, the findings of this study support current literature that suggests REM sleep plays a significant role in mood regulation. They also suggest, aside from N1 sleep, NREM sleep may not impact mood disturbance. In the future, the effects of sleep architecture on mood disturbance could further be explored through randomized controlled trials.

Support (if any)

This study was funded by National Institutes of Health (NIH) grants R01HL134809 and R01HL147610.
0147 Does Sleep Regularity Link Child Maltreatment to Depressive Symptoms among Incoming First-Year College Students?
Authors: Rojo-Wissar Darlynn , Parade Stephanie , Barker David , Van Reen Eliza , Sharkey Katherine , Carskadon Mary
Published: May 2023

Introduction

Sleep patterns may partially account for associations between early life adversity and poor health outcomes; however, few studies have empirically tested this hypothesis. We examined whether an index of sleep regularity mediated associations between child maltreatment (CM) and depression symptoms among emerging adults undergoing the major life transition of starting college.

Methods

First-year college students (N=731; 41% male; 48% Non-Hispanic White, 21% Non-Hispanic Asian, 15% Hispanic all races, 5% Non-Hispanic Black, and 11% Non-Hispanic other races) completed daily sleep diaries (DSDs) for 9 weeks and completed the Childhood Trauma Questionnaire (CTQ) and Center for Epidemiologic Studies Depression Scale (CES-D) following DSD completion. DSD data were used to compute participants’ Sleep Regularity Index (SRI). We used a nonparametric SEM bootstrap approach to examine whether sleep regularity mediated associations between childhood trauma and depressive symptoms, controlling for sex, race/ethnicity, and US vs. international status. Separate models were run for CTQ total score and five CTQ subscale scores: emotional, physical, and sexual abuse, and physical and emotional neglect.

Results

The prevalence of moderate to severe CM was 20.7%, and 40% reported clinically significant depressive symptoms at the end of the assessment period. Significant indirect effects of CTQ total score (estimate=0.02, 95% CI=0.003, 0.036); emotional abuse (0.06 [0.004, 0.107]); physical abuse (0.08 [0.003, 0.164]); sexual abuse (0.06 [0.014, 0.114]); emotional neglect (0.04 [0.002, 0.075]) on greater depression symptoms through lower sleep regularity were observed; and we saw no significant indirect effect of physical neglect (0.04 [-0.015, 0.089]). The percentage of the total effect of CM (i.e., CTQ total score and subscale scores) on greater depressive symptoms accounted for by lower sleep regularity were CTQ total score: 6%; physical abuse: 24%; sexual abuse: 19%; emotional abuse: 7%; and emotional neglect: 5%.

Conclusion

Variable sleep patterns account, in part, for associations between CM and depressive symptoms among first-year college students, with the largest effects observed for physical and sexual abuse. Sleep regularity should be included as a target in student health interventions on college campuses and may help buffer against poor mental health outcomes for students with CM.

Support (if any)

P20GM139743, MH079179, T32HD101392, P20GM139767.
0148 Early Diagnosis of Post-Traumatic Stress Disorder in Nurses based on Physical Behavior and Sleep Actigraphy using Machine Learning
Authors: Garg Ria , Slavish Danica , Taylor Daniel , Ruggero Camilo , Messman Brett , Kelly Kimberly , Dietch Jessica
Published: May 2023

Introduction

Disturbances in sleep/wake patterns and circadian rhythms are common in post-traumatic stress disorder (PTSD). Nurses are one population particularly susceptible to both sleep/circadian disruption and PTSD, given they frequently engage in shift work and experience high occupational stress. However, the specific types of sleep and circadian disruptions in PTSD have not been identified in nurses using validated measures of sleep, limiting potential intervention efforts.

Methods

To address this gap, we developed a machine learning algorithm for calculating risk of PTSD in nurses based on sleep, light, and physical activity patterns derived from actigraphy, in conjunction with inflammatory biomarkers. Participants were 392 nurses (mean age = 39.54, 92% female, 78% White) recruited from two Dallas-area hospitals for a larger longitudinal study on sleep and vaccine response. At baseline, nurses reported on demographics and PTSD symptoms using the PTSD Checklist for DSM-5 (PCL-5). Nurses were categorized as having probable PTSD if they reported scores >31 on the PCL-5. Nurses wore actigraphy devices for two weeks to track sleep/wake, activity counts, and light exposure. In the middle of the two-week period, nurses also had a blood draw to determine inflammatory markers interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), IL-1 beta (IL-1β), and interferon gamma-induced protein 10 (IP-10).

Results

4.7% of nurses met criteria for probable PTSD. Using the actigraphy and inflammatory biomarker data, a Recurrent Neural Network (RNN) was developed to detect PTSD risk. Furthermore the Ridge Classifier was tested using activity counts, light exposure, and sleep/wake data to obtain an accuracy score of 0.97 and an F1 score (ranging between 0 and 1) of 0.96, indicating that the model was able to balance and maximize precision and determine PTSD risk successfully.

Conclusion

Using a novel machine learning algorithm, we show that actigraphy data can be used to accurately and non-invasively detect PTSD risk among nurses. Although additional validation is needed, results tentatively highlight the utility of harnessing real-time data to inform just-in-time adaptive interventions for PTSD among nurses. Given the essential role nurses play in the healthcare system, finding ways to support their well-being is of critical public health importance.

Support (if any)

NIH/NIAID R01AI128359
0150 Increased Prevalence of Clinical Depression in Late Teens with Sleep Complaints
Authors: Lee Ji Hyun , Cho Wonyoung , Cho Marc
Published: May 2023

Introduction

Adolescent mental health is becoming clinical attention since suicide is one of the leading causes of death in late teens. Depression and sleep are interconnected, since depression affects sleep, vice versa. Clinical depression with symptoms of sleep disturbance were interviewed by a board certified psychiatrist following depression inventory. Age, sex matched adolescents were also accessed by depression inventory scales.

Methods

71 adolescence aged from 16-18 ( M:F=1:1) who visited sleep disorders clinic for their complaints of daytime sleepiness, sleep initiation problems, snoring, frequent midnight awakenings. 29% of them diagnosed with narcolepsy, 21% delayed sleep phase syndrome, 19% obstructive sleep apnea syndrome, 15% of them had chronic insomnia. Some of them have multiple sleep disorders associated diagnosis like OSA + narcolepsy, OSA + insomnia, or DSPS + OSA. 21 aged, sex matched high school students were evaluated for their anxiety and depression symptoms.

Results

Patients with sleep complaints had higher level of emotional distress compared with healthy adolescents without sleep complaints. Among sleep disorders, patients with narcolepsy had higher level of depression. These explains why children or adolescents with narcolepsy are initially misdiagnosed with depression in clinical setting. Comorbid depression and narcolepsy should be differentiated in clinical field.

Conclusion

In adolescents, patients with sleep disorders especially with narcolepsy have higher level of depression. Misdiagnosis of narcolepsy as depression may delay narcolepsy diagnosis.

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0154 Perceived Stress Partially Mediates the Association Between Adverse Childhood Experiences and Sleep Quality in Young Adults
Authors: Flores Mark , Schwager Laura , Moon Chooza , Thomas Emily , Jenkins Nathaniel
Published: May 2023

Introduction

Growing evidence suggests that Adverse Childhood Experiences (ACEs) are associated with poor sleep in young adulthood. It has also been proposed that ACEs may cause stress sensitization, and perceived stress has been linked with poor sleep. We hypothesized that the relation between ACE exposure and poor sleep is mediated by perceived stress.

Methods

One-hundred and twenty-five (86% Female; mean±SD, age = 22±3 y) young adults between the ages of 18 and 30 years completed the 10-item ACE questionnaire, the Perceived Stress Scale, and the Pittsburgh Sleep Quality Index to quantify ACE exposure, perceived stress (PS), and global sleep quality (GSQ), respectively. Zero-order correlations were used to examine relations among ACE exposure, PS, and GSQ. Multiple linear regression analyses were used to examine the effect of ACE exposure on PS and GSQ after adjustment for sex, age, Race, and Ethnicity. Mediation analysis was then performed using the Bootstrapped Bias-Corrected Percentile Confidence Interval method, and the variance accounted for (VAF) was calculated as the ratio of the indirect effect to total effect β-coefficients.

Results

ACEs were highly prevalent (69% with ≥1 ACE; 30% with ≥4 ACEs) and variable (range = 0 – 10 ACEs). ACE exposure was associated with greater PS (r=0.40; p< 0.0001) and worse GSQ (r=0.31; p< 0.0001). Higher PS was associated with worse GSQ (r=0.31; p=0.0005). After adjustment, ACE exposure remained a significant predictor of both GSQ (β=0.29; p=0.002) and PS (β=0.39; p< 0.0001). PS significantly, partially mediated the association between ACE exposure and GSQ (indirect effect standardized β-estimate = 0.035 [95% CI = 0.013 – 0.069]; VAF = 29%).

Conclusion

ACE exposure is associated with greater perceived stress and poorer sleep quality among young adults. Perceived stress partially mediated the association of ACE exposure with sleep quality. Studies confirming the directionality and causality of these associations (including longitudinal mediation), and subsequently whether stress reduction interventions can promote improvements in sleep among individuals with ACEs are needed.

Support (if any)

Research reported in this publication was supported in part by the NCATS of the NIH (UL1TR002537) and by the Injury Prevention Research Center through the CDC (R49 CE003095).
0156 Personality traits and chronicity of insomnia among soldiers
Authors: Barilla Holly , Miller Katherine , Gehrman Philip
Published: May 2023

Introduction

Within the behavioral model of insomnia, predisposing factors can increase vulnerability to developing problems sleep. One predisposing factor is personality characteristics and past research has identified associations between traits such as rumination/worry and perfectionism with insomnia. Past studies have not considered the chronicity of insomnia and its association with personality characteristics. The goal of this study was to examine the association between chronicity of insomnia and a wide range of personality traits to identify a risk profile for chronic sleep disturbance.

Methods

Data were acquired from the New Soldier Study(NSS) components of the Army Study to Assess Risk and Resilience in Service members(Army STARRS), which collected survey data from soldiers newly recruited to the military(N=38,348; 20.9 (3.5) years old; 82.8% male). The data captures information regarding soldiers sleep, personal characteristics and mental health symptoms. Participants reported on lifetime history of insomnia with a frequency of at least 3 nights/week as well as the number of years they have experienced insomnia. Items also assessed a range of personality traits. A stepwise selection model was used to identify a profile of personality traits associated with chronicity of insomnia.

Results

Lifetime history of insomnia was very high at 88.7%, with a mean(SD) duration of insomnia mean (SD)=3.0(2.4) years. The selection model identified three personality traits significantly associated with duration of insomnia: feeling empty inside, the need to rely more heavily on friends for emotional support, and being scared of things most people are (all p< 0.05).

Conclusion

These results demonstrate that, among new Army soldiers, those who have experienced chronic problems with insomnia endorse feeling empty inside leading to them stating either they are not scared of most things (increase in risk taking) or need to rely heavily on others for emotional support. Consistent with previous research, these data suggest that emotional instability is linked to poorer sleep quality.

Support (if any)

This publication is based on public use data from Army STARRS(Inter-university Consortium for Political and Social Research, University of Michigan-http://doi.org/10.3886/ICPSR35197-v1), funded by U.S. NIMH-U01MH087981. KEM’s time was supported by the U.S Department of Veterans Affairs, Veterans Health Administration (Clinical Science Research and Development Service – IK2 CX001874).
0157 Physiological responses to acute psychosocial stress in adolescents with insomnia
Authors: Yuksel Dilara , Khajehpiri Boshra , Forouzanfar Mohamad , Kiss Orsolya , Prouty Devin , Arra Nicole , Volpe Laila , Baker Fiona C , de Zambotti Massimiliano
Published: May 2023

Introduction

Insomnia often develops during adolescence, with girls being at greater risk than boys. Alterations in response to psychosocial stress have been linked to the etiology of the disorder, but it remains unclear whether the reactivity of the major stress systems (hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS)) in response to stress is altered in adolescents with insomnia.

Methods

Forty-seven post-pubertal adolescents (age range: 16-20 years, 28 female) with (N=16; insomnia group) and without (N=31; control group) insomnia symptomatology underwent two randomized nights of polysomnographic (PSG) laboratory recordings. Participants underwent an experimental pre-bedtime stress anticipation protocol (stress night) and a control night during which they engaged in neutral activities before bedtime. The morning after the stress night, participants underwent the Trier Social Stress Test (TSST), a standardized protocol that requires participants to complete verbal and arithmetic tasks in front of observers. On both nights, heart rate (HR) was measured across the night and cortisol was collected upon awakening and 30 minutes later to calculate cortisol awakening responses (CAR). Stress perception, salivary cortisol (HPA activity), and HR were measured during the TSST.

Results

There were no differences in nocturnal HR recovery or in the CAR on the stress night compared with the control night in either group. Morning pre-TSST stress levels did not differ between groups. Both groups exhibited significant increases in HR in response to the TSST (p< 0.05). HR was higher in girls compared to boys in the control group at baseline (p< 0.05). Potential group differences in HPA responses to the TSST were inconclusive due to the apparent confounding effect of the CAR on cortisol responses to stress: many participants did not show a cortisol response to the task and larger CARs were associated with an attenuated cortisol response.

Conclusion

There were no significant differences in HR responses to anticipation of experimental stress during the night or in response to the morning TSST between adolescents with and without insomnia. Further work is needed to examine whether altered responses to stress may emerge if insomnia becomes chronic.

Support (if any)

This study was supported by the National Heart, Lung and Blood Institute (NHLBI) grant R01HL139652(MdZ).
0159 Sleep Disturbance, Media Consumption, and Post Traumatic Stress following Hurricane Harvey
Authors: Kim Jinu , Vieira Alyssa , Myers Annika , Alfano Candice
Published: May 2023

Introduction

Experiencing one-time traumatic events such as a hurricane is known to produce sleep disturbances that may contribute to the development and maintenance of PTSD symptoms (Mellman, 2019). Additionally, greater exposure to media coverage during and following such events has been shown to contribute to elevated post-traumatic stress (PTS; Shin & Sommers, 2021). However, whether media consumption might moderate the relationship between sleep disturbances and PTS symptoms has not been examined. We examined these relationships in a sample of college students following Hurricane Harvey, an epic storm that hit Texas in 2017 and received non-stop media coverage for an extended period after the storm.

Methods

Participants were undergraduate students (N=260, M age=21.32) at the University of Houston during Hurricane Harvey. Students answered online surveys 6-8 months following the hurricane that measured sleep quality and PTS symptoms over the last month, and media use both during and immediately following the hurricane. Sleep was assessed using the Pittsburg Sleep Quality Index (PSQI) and PTS symptoms were assessed using the PTSD Checklist for DSM-5 with sleep questions removed.

Results

Results indicated that active media consumption post hurricane was significantly correlated with PTS symptoms (r=.21, p<.001). Regression analyses showed sleep quality significantly predicted PTS symptoms (R2 change=.133, p<.001). However, contrary to expectation, this relationship was not moderated by active media consumption after Hurricane Harvey (R2 change=.19, p=.495).

Conclusion

Previous research has found both sleep disturbance and greater exposure to media coverage of traumatic events are associated with greater symptoms of PTS. Although media exposure to trauma may impact sleep quality, our findings suggest these factors to exert independent effects on PTS symptoms. Better understanding of the role of media consumption in the aftermath of trauma is needed to understand these relationships.

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0160 Sleep quality affects the heart rate signal when facing the different stress sources
Authors: Chiu Amy , Tou Shao Wen , Liu Yu Ting , Chang Ya Ju
Published: May 2023

Introduction

Stressful conditions can impact our judgement and increase the risk of adverse events such as workplace accidents, falls, and automobile collisions, among others. Sleep quality has also been shown to influence stress responses and heart rate variability (HRV) has proved to be a useful indicator of stress in a variety of scenarios. Therefore, wearable devices such as heart rate monitors may be able to reduce adverse events by measuring stress responses. Among subjects reporting different sleep quality, we measured heart rate variability (HRV) before and after different stressors to understand whether these stressful events could be detected by a wearable device.

Methods

Twenty-four subjects (male = 10; female = 14) with no known health conditions participated in this study. Subjects were divided into good sleep quality (N = 12) and bad sleep quality (N = 12) groups based on their Pittsburgh Sleep Quality Index (PSQI) score. Each subject engaged in a physical stress and mental stress on separate days and HRV was assessed before and after each stress intervention. T-tests were used to assess the change in HRV from pre- to post stress condition for each group.

Results

In the good sleep quality group we found several statistically significant differences across several HRV frequency-domains before and after physical stress (Low frequency: p = 0.0292; High frequency: p = 0.0287; Low frequency/ High frequency ratio: p = 0.0245) and mental stress (Low frequency: p = 0.0394; High frequency: p = 0.0387; Low frequency/ High frequency ratio: p = 0.0373).

Conclusion

Our findings indicate that sleep quality may influence the HRV response to different stressors. This suggests that HRV measured via wearable device may be used to provide warning under stressful conditions that could help prevent accidents and other adverse events.

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0166 Effects of Evening Pre-Sleep Alcohol on Next-Day Psychomotor Vigilance (PVT) Performance
Authors: Carskadon Mary , Gredvig-Ardito Caroline , Barker David H , Coston Cosette , McGeary John
Published: May 2023

Introduction

Few reports have examined how pre-sleep alcohol affects performance on the next day given adequate sleep opportunity on a preferred schedule. Our goal is to identify whether alcohol intake targeting .08g% 1 hr before sleep of ~8 h will affect PVT performance on subsequent days, with the hypothesis that morning performance will be negatively affected.

Methods

25 adults (13F; age range 22-57 y) completed the full study, including at-home sleep on a fixed schedule of 8-9 hr and two 3-night in-lab PSG sessions spaced ≥3 days apart. Sessions were counterbalanced for alcoholic or nonalcoholic control beverage administered in 3 drinks over 45 minutes ending 1 hr before bedtime. Alcohol was targeted to .08g%. The 10-min PVT (Ambulatory Monitoring, Inc.) was given each night (PM-Lab) 1.5 hr before drinking and in the morning after a light breakfast (AM-Lab) 50 min after waking. PVT was performed at home/work twice each day within 1.5-h windows starting 3h after waking (AM-Home) and 7.5h after waking (PM-Home). Two reaction time (RT) variables were assessed: RRT (1/RT), FRT (10% fastest RT). Linear mixed effects models were used to account for nesting of assessments within alcohol/control condition and days.

Results

RRTs showed slowed RT (meanRT±SE) following alcohol: AM-Lab F=7.234, p=.008, alcohol RT=246±9.6 msec, control RT=234±9.6 msec; AM-Home F=17.936, p<.001, alcohol RT=254±9.5 msec, control RT=242±9.6 msec; but not PM-Home or PM-Lab tests. FRT slowed for AM-Lab F=9.391, p=.003, alcohol FRT=195±4.3 msec, control FRT=201±4.3 msec; AM-Home F=17.251, p=.001, alcohol FRT= 202±4.4 msec, control FRT=194±4.4 msec; PM-Home F=3.379, p=.068 (trend), alcohol FR=200±4.8 msec, control FRT=196±4.8 msec, but not for PM-Lab PVTs. PM-Home FRT also had a significant day order effect: F=3.087, p=.0496, Day 1 FRT=197±5.0 msec, Day 2 FRT=202±5.0 msec, Day 3 FRT=195±5.0 msec.

Conclusion

We found statistically significant slowing of psychomotor vigilance RT on days after pre-sleep alcohol lasting up to 3-4.5h after waking in the morning (with a trend up to 7.5-9h post waking). PVT lapses (not shown) were similarly affected by alcohol. Thus, even with sufficient sleep opportunity, RT is impaired following 3 consecutive nights of alcohol drinking.

Support (if any)

R01AA025593
0170 When to Sleep and Work to Optimize Alertness
Authors: Reifman Jaques , Vital-Lopez Francisco , Doty Tracy Jill
Published: May 2023

Introduction

Currently, millions of individuals work outside of the conventional 9-to-5 work shift and are often required to sleep at times that are not conducive to maximizing restorative sleep. This insufficient sleep leads to health and cognitive performance impairments that compromise workers’ safety and productivity. This can be mitigated by sleeping at the time of day that maximizes the restorative benefits of sleep and working at favorable hours, within their particular opportunity windows. Here, we present an optimization algorithm to identify sleep and work schedules that maximize alertness during work hours, while reducing impairment during non-working hours to the greatest extent possible.

Methods

We developed an optimization algorithm that searches through a large number of possible sleep and work schedules to identify the one that maximizes alertness during work times, while meeting user-provided constraints, such as the periods during which the user has flexible opportunities to sleep and work. To generate the possible sleep and work schedules, we used the well-validated Unified Model of Performance (UMP), including its ability to predict sleep latency and sleep duration. To assess the algorithm, we simulated four experimental studies, where we compared the effectiveness of the sleep schedules obtained by the algorithm against those of the sleep schedules used in the studies. In addition, we tested the algorithm’s ability to simultaneously optimize sleep and work schedules in one of the studies.

Results

Using the same amount of sleep as in the studies, the algorithm proposed sleep schedules that optimally allocated sleep, reducing alertness impairment during work hours by an average of 29%. Similarly, by simultaneously optimizing sleep and work schedules during a recovery period following a chronic sleep-restriction challenge, the algorithm’s solutions resulted in recovery to baseline alertness levels two days earlier than the conventional 9-to-5 work schedule.

Conclusion

Our work provides the first quantitative tool to optimize sleep and work schedules and complements other existing fatigue-management tools to provide a more comprehensive set of countermeasure strategies to mitigate alertness impairment due to limited sleep.

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0173 Improving Well-being by Changing the Shiftwork Schedules at U.S. Navy Watchfloors
Authors: Lawrence-Sidebottom Darian , Dotson Elizabeth , Matsangas Panagiotis , Shattuck Nita
Published: May 2023

Introduction

Watchfloors provide around-the-clock information, intelligence, and technical support to the U.S. military. Active-duty service members (ADSMs) working on these 24/7 watchfloors stand watches to include night shifts and/or frequent shift rotations. Tailoring shiftwork schedules to optimize individual sleep patterns may reduce the negative effects of shiftwork. The purpose of this study was to test whether a new shiftwork schedule would improve ADSM well-being and sleep.

Methods

ADSMs (N=79; 83.5% males, 91.2% enlisted, 19 to 54 years of age) participated in a two-phase quasi-experimental longitudinal study to assess well-being and sleep before and after the implementation of a new shiftwork schedule. Both schedules involved three rotating shifts: day shift (0730-1530), evening shift (1320-2330), and night shift (2330-0730). The old schedule had 5 successive days on-shift (5-on/4-off) and the new schedule had 4 successive days on-shift (4-on/4-off). Shifts rotated after every off period. Standardized questionnaires were administered while participants were on the old schedule (study start) and ~5 months after switching to the new schedule. The questionnaires included the Pittsburg Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Epworth Sleepiness Scale (ESS), and the Profile of Mood States (POMS). Sleep was monitored using wearable devices (ŌURA rings).

Results

POMS total mood disturbance scores were lower (i.e., better) on the new schedule (p< 0.001), as were several POMS sub-scale scores including tension (p< 0.001), depression (p< 0.001), anger (p=0.014), confusion (p< 0.001), and vigor (p=0.029). There was a statistical trend for a decrease in ISI scores from the old schedule to the new schedule (p=0.067). Sleep duration, PSQI, and ESS scores did not differ between schedules (all p>0.05).

Conclusion

Implementation of a new shiftwork schedule that was tailored to worker sleep patterns was associated with decreased mood disturbance, improvement in negative mood states (e.g., depression and tension), and reduced insomnia symptom severity. Also, the research team received several anecdotal reports of high levels of worker satisfaction with the new schedule. This study demonstrates the positive real-world effects of using basic sleep and circadian methods to tailor shiftwork schedules to improve the well-being of military personnel.

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0178 2B-Alert App: Personalized Caffeine Recommendations for Optimal Alertness
Authors: Reifman Jaques , Doty Tracy Jill , Anlap Ian , Killgore William , Vital-Lopez Francisco
Published: May 2023

Introduction

Sleep loss causes millions of individuals around the world to carry out daily activities with impaired alertness, affecting injury risk and productivity. If properly consumed, caffeine can safely and effectively mitigate the effects of sleep loss on alertness. However, to be effective, caffeine should be consumed at the right time and in the correct amount, depending on sleep history, work schedule and, importantly, sensitivity to sleep loss. Here, we present the 2B-Alert app, a unique smartphone application with capabilities to learn an individual’s trait-like response to sleep loss and to provide personalized caffeine recommendations to optimize alertness.

Methods

We conducted a prospective 62-h total sleep deprivation study to validate the capabilities of 2B-Alert. Throughout the challenge, 21 participants used the app to measure their alertness via the psychomotor vigilance test (PVT). The app used the PVT data collected during the first 36 h of wakefulness to learn each participant’s sleep-loss response and provided on-the-fly personalized caffeine recommendations (from 0 to 800 mg), so that each participant would sustain alertness at a pre-specified target level (mean PVT response time of 270 ms) during a 6-h period starting at 44 h into the challenge. To assess the effectiveness of the personalized recommendations, we computed the amount of PVT data during the 6-h period that fell within the target alertness level +/-2 times the within-subject variability of alertness impairment (i.e., +/-60 ms).

Results

We observed a wide range of responses to sleep loss, with some participants displaying high levels of resilience and others high levels of vulnerability. Accordingly, 2B-Alert recommended no caffeine to five participants, 100-400 mg to 11 participants, and 500-800 mg to five participants. Regardless of their sleep-loss response and the consumed amount of caffeine, participants sustained the target alertness level ~80% of the time during the 6-h period.

Conclusion

2B-Alert automatically learns an individual’s trait-like response to sleep loss and provides personalized caffeine recommendations in real time so that individuals achieve a desired alertness level regardless of their susceptibility to sleep loss.

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0180 The Impact of Fatigue on Patient Care and Risk of Post-Shift Driving Collisions among 12-Hour Day and Night Shift Nurses
Authors: James Lois , James Stephen
Published: May 2023

Introduction

Evidence across industries links work-related fatigue with errors, accidents, and adverse long-term health outcomes. Within the healthcare profession, 12-hour day and night shifts have been widely adopted for nurses, despite concerns about fatigue from these shifts affecting nursing performance and safety. The purpose of this study was to determine the impact of 12-hour day-vs-night-shift-accumulated fatigue on patient care and driving safety.

Methods

This between-groups, repeated-measures quasi-experiment was conducted in the Washington State University (WSU) College of Nursing and Sleep and Performance Research Center. 44 nurses working 12-hour day shifts and 49 nurses working 12-hour night shifts were recruited from Providence Medical Center. Participants reported to WSU for testing on two separate occasions—once immediately following their 3rd consecutive 12-hour shift and once on their 3rd consecutive day (72 hours) off work. Nurses' sleep was monitored using wrist actigraphy, and were tested for patient care using LAERDAL medical simulation, and driving safety using an immersive driving simulator.

Results

We found that both day and night shift nurses were significantly more sleep restricted and subjectively sleepy following three consecutive shifts. Night shift nurses were significantly more impaired on patient care skills, in particular the communication domain, compared to day shift nurses (who were largely able to maintain performance across all tests). Furthermore, night shift participants had significantly greater lane deviation during the post shift drive home, a key indicator of collision risk, demonstrating impaired driving safety.

Conclusion

This study provides objective evidence of the impact of shift work-related fatigue on patient and nurse safety, allowing us to make concrete recommendations regarding safe shift-scheduling for day and nightshift nurses—information that may help keep nurses safer and improve patient care.

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0184 Can Subjective Sleepiness Pre-flight Predict the Use of Controlled Rest on the Flight Deck?
Authors: Hilditch Cassie , Arsintescu Lucia , Pradhan Sean , Gregory Kevin , Flynn-Evans Erin
Published: May 2023

Introduction

Pilot sleepiness in flight continues to be an issue in aviation due to irregular and extended working hours. In some regions of the world, pilots are allowed to use an in-flight countermeasure called controlled rest, which involves taking a short nap on the flight deck. The use of this countermeasure in practice is understudied. We aimed to investigate whether subjective sleepiness ratings taken pre-flight are predictive of controlled rest use.

Methods

Data from 94 long (> 6 hours flight duration), non-augmented (two flight crewmembers) flights were analyzed (n = 30 pilots). Mixed-effects logistic regression was used to assess the likelihood of controlled rest based on a Karolinska Sleepiness Scale rating (KSS) taken at pre-flight.

Results

Controlled rest was taken on 62.77% (n = 59) of the analyzed flights. Pilots with higher KSS ratings (i.e., subjectively sleepier) pre-flight were more likely to take controlled rest during the subsequent flight (b ± SE: 1.03 ± 0.33; OR = 2.81, 95% CI [1.47, 5.37]; p = .002; R2 (Marginal): .28, R2 (Conditional): .51). That is, for every one-unit increase in KSS ratings, there was a near three-fold increase in the odds of taking controlled rest in flight.

Conclusion

Our results suggest that subjective sleepiness prior to a flight may be predictive of the decision to take controlled rest later in the flight. Further research is needed to understand the influence of other scheduling factors and individual differences.

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NASA Airspace Operations and Safety Program, System-Wide Safety Project.
0186 Continuous Theta Burst Transcranial Magnetic Stimulation and Signal Detection Performance on a Military Go/No-Go Task
Authors: Hildebrand Lindsey , Jankowski Samantha , Henderson-Arredondo Kymberly , Trapani Christopher , Patel Salma , Chen Yu-Chin , Chou Ying-Hui , Dailey Natalie , Killgore William
Published: May 2023

Introduction

Repetitive transcranial Magnetic Stimulation (rTMS) is an emerging non-pharmacological treatment method utilized to treat multiple clinical diagnoses to include anxiety, depression, and now insomnia. As part of a larger project, we showed that a single rTMS stimulation with continuous theta burst stimulation (cTBS) to the default mode network (DMN) was associated with improved sleep. However, there is little known about potential effects on attention and vigilance because of cTBS to the DMN. We hypothesized that active cTBS would have no significant impact on signal detection tasks post-TMS administration.

Methods

Twenty adults with clinical insomnia completed this study (Mage=26.90, SD=6.56). Participants completed two overnight visits in total; one where they received active cTBS and one where they received sham cTBS over an easily accessible node of the DMN (left angular gyrus). We analyzed signal detection performance utilizing a Go-No-Go (GNG) task. Three variables were calculated as a result: hit rate, correct rejection rate, and false alarm rate. Higher scores on hit rate and correct rejection rate indicate higher correct responses. Higher scores on false alarm rate indicates a higher number of incorrect responses. We conducted a repeated measures ANOVA with factors of condition, administration, and reversal.

Results

There was no main effect of condition on GNG hit rate (F(1,17)=1.365, p=.259, ηp2 =.074), GNG false alarm rate (F(1,17)=.210, p=.091, ηp2 =.244), or GNG correct rejection rate (F(1, 17)=1.677, p=.213, ηp2 =.090). We also found there were no significant interaction effects of TMS condition on performance on reversal conditions for GNG hit rate (F(1,17)=0.45, p=.834, ηp2 =.003), GNG false alarm rate (F(1,17)=1.572, p=.227, ηp2 =.085), or GNG correct rejection rate (F(1, 17)=1.510, p=.236, ηp2 =.082). Posthoc pairwise comparisons demonstrated that participants did not significantly differ on GNG hit rate, false alarm rate, or correct rejection rate between their active cTBS and sham cTBS session (p>.05).

Conclusion

Active cTBS was not significantly associated with any decline in response inhibition or signal detection. It is helpful for future recipients to know they are not adversely affected in attention or vigilance after receiving cTBS rTMS of the DMN for treatment of insomnia.

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W81XWH2010173
0192 Sleep Hygiene and Self-Efficacy for Sleep Hygiene among Good and Poor Sleepers: A Secondary Analysis
Authors: Gipson Christine , Dickerson Suzanne
Published: May 2023

Introduction

College students are at risk for sleep disturbances that may influence academic performance and cognitive function. Poor sleep hygiene and sleep quality over time results in inadequate sleep and daytime sleepiness, which, if not addressed, eventually leads to sleep deprivation and adverse health effects. The current study aims to identify if sleep hygiene practices and self-efficacy relate to poor and good sleepers categorized according to the Pittsburg Sleep Quality Index (PSQI) Score > or < 5) among a sample of college students.

Methods

Secondary analysis of baseline data from college student participants aged 18-26 from an intervention study was used. All participants completed demographic, sleep hygiene, and sleep quality assessments. (e.g., Sleep hygiene index [SHI], Self-efficacy for sleep hygiene inventory [SHESHI], and PSQI). Descriptive statistics were calculated for demographic, sleep quality, and sleep hygiene variables.

Results

The sample (n=119) included 15 Freshmen, 30 Sophomores, 52 Juniors, and 21 Seniors, of which 26 were male and 93 female college students. The total sample mean age=20.6 y +2.0, range: 18-26; 82.4% were White, 55% worked part-time, and 69.8% considered themselves night owls. Data were examined to determine if poor and good sleepers differed across key demographic variables. There were no statistically significant differences between the groups. The sample comprised 47.0 % (n=56) poor sleepers and 52.9% (n=63) good sleepers. A Mann-Whitney U test was run to determine if there were differences in self-efficacy scores and sleep hygiene practices between poor and good sleepers. Mean rank self-efficacy scores for poor sleepers (44.15) were significantly lower than for good sleepers (74.09), U=876, z = -4.725, p <.001. Mean rank sleep hygiene index scores for poor sleepers (76.05) were significantly higher than good sleepers (45.73) U=865, z = -4.793, p <.001.

Conclusion

Preliminary descriptive findings highlight significant characteristics of sleep hygiene efficacy in college students with good and poor sleep. All surveys were subjective measures. Future use of an objective measure of actual sleep time and sleep health index would add to an understanding of sleep issues in college students.

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N/A
0195 The association of electronic device use before bedtime on objective sleep quality measured with Odds Ratio Product
Authors: Lambing Kari , Bender Amy
Published: May 2023

Introduction

Previous research has shown mixed results when it comes to electronic device use and the impact on sleep quality. The use of electronic devices before bedtime can impact sleep quality through engaging content and the impact of blue light on melatonin secretion. In the present study, we assessed the relationship between electronic device use and sleep under real-world conditions.

Methods

Nineteen participants (age 41.1±10.9; 9 females) recorded their sleep with in-home PSG using the Cerebra Sleep System for 18 to 31 nights (21.5±3.4). Sleep quality was measured using odds ratio product (ORP) derived from spectral analysis of the frontal EEG channels during wake, NREM and REM sleep. Participants completed a nighttime questionnaire within one hour of bedtime where they were asked “Did you do any of these activities today” and indicated “social media or surfed the internet in the last hour before bedtime”. The Karolinska Sleepiness Scale (KSS) was also completed during the before bedtime test battery.

Results

Across the 19 participants, 17 participants reported using social media or surfing the internet within an hour before bedtime for at least one night. Participants reported usage for 9.6 ± 8.2 nights on average (n=165) but no usage for 11.7 ± 7.9 nights on average (n=210). On nights that internet or social media usage before bedtime was reported, participants had lower levels of evening sleepiness on the Karolinska Sleepiness Scale than on nights where no usage was reported (p <.001). On these nights, participants also had higher ORPNREM (p <.001), higher ORPREM (p<.001), higher ORPTRT (p<.001), higher ORP-9 (p<.001) which is an indication of poorer sleep depth after an EEG arousal.

Conclusion

On the nights when electronic devices were used before bedtime, participants experienced less sleepiness and had worse objective sleep quality during the night. These results indicate the utility of ORP to be sensitive enough to detect changes in sleep quality after electronic device use.

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0197 To delay perchance to sleep: The daily association between pre-sleep arousal and bedtime procrastination
Authors: Carlson Steven , Johnson Kimberley , Williams Paula
Published: May 2023

Introduction

Bedtime procrastination, a behavior associated with poor sleep health and wellbeing, refers to voluntary delays of bedtime. Although bedtime procrastination is often defined as a needless or mindless delay, bedtime procrastinators often report delaying their bedtime strategically to regulate sleep-interfering processes. One such process is pre-sleep arousal, an important vulnerability for and perpetuating factor in insomnia. Preliminary evidence supports this association, as some bedtime procrastinators report delaying bedtime to avoid anxiety-producing bedtime routines. However, this association has not been directly examined. To further investigate strategic delay in bedtime procrastination, the current study examined the association between pre-sleep arousal and bedtime procrastination using a daily diary design.

Methods

390 young adult participants (Mage = 24.4, SD = 6.5) completed 14-days of experience sampling procedures, including completing self-report measures of nightly pre-sleep arousal and morning ratings of prior-night bedtime procrastination. Associations between pre-sleep arousal and bedtime procrastination were estimated with multilevel models using centering procedures which disaggregated between- and within-person covariance. Gender and age were entered into each model as covariates.

Results

Pre-sleep arousal was associated with increased bedtime procrastination both within- (B = 0.01, p < 0.001) and between-persons (B = 0.02, p < 0.001). Additionally, cognitive pre-sleep arousal was more strongly associated with bedtime procrastination (Bwithin = 0.014, p < 0.001; Bbetween = 0.032, p < 0.001) than somatic arousal (Bwithin = 0.007, p = 0.165; Bbetween = 0.038, p < 0.001).

Conclusion

The results suggest that pre-sleep arousal is a putative mechanism for bedtime procrastination. Cognitive arousal, including worry and rumination, may be particularly relevant for the development of this behavior. This finding supports the notion that bedtime procrastination may occur as a (maladaptive) strategy to regulate pre-sleep arousal. Alternatively, findings may suggest that bedtime procrastinators engage in arousing pre-sleep activities. Regardless, the association between bedtime procrastination and pre-sleep arousal suggests that bedtime procrastination is relevant in the development and/or maintenance of sleep-related disorders. Treatment of bedtime procrastination may be enhanced by considering the role this behavior has in regulating pre-sleep arousal.

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