bluuebunny/crossref_metadata_2025_split huggingface dataset:
0218 Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents
Authors: Schwartz L P , Devine J K , Hursh S R , Mosher E , Schumacher S , Boyle L , Davis J E , Smith M , Fitzgibbons S 
Published: May 2020

Abstract

Introduction
Fatigue and its effects on performance have long been a concern in medicine. Evidence exists that current duty-hour restrictions for resident trainees have a limited impact on physician wellbeing and patient safety, prompting renewed efforts to address this threat. In this study, sleep patterns of general-surgery residents were used to optimize a biomathematical model of performance for use as a tool for fatigue risk management with residents.

Methods
General surgery residents based at a multi-hospital, general surgery residency program were approached for participation in this study. Enrolled residents wore actigraph devices for 8 weeks and completed subjective sleep assessments. Sleep data and shift schedules were then input into the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model to assess predicted cognitive performance. Performance was compared to an “effectiveness” level of 77 (equivalent to a blood-alcohol content of 0.05g/dL). Eight hours of sleep debt was considered “below reservoir criteria”.

Results
Sleep actigraphy data was collected from 22 general surgery residents. Modeling results showed that as shift lengths increased, effectiveness scores generally decreased, and the time spent below criterion (77) increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts included time spent below the reservoir criterion. Adjustments to the sleep prediction were made based on actual sleep, and performance predictions from actual sleep and the adjusted model were significantly correlated (p&lt;.0001).

Conclusion
Despite adherence to national standards limiting work hours, current surgical resident sleep patterns and shift schedules create concerning levels of fatigue. This study illustrates how biomathematical fatigue models can predict resident physician sleep patterns and performance. Modeling represents a novel and important tool for medical educators seeking to create shift schedules that maintain physician preparedness and minimize fatigue risk.

Support
N/A
0232 Impact of Mental Health on 10-Year Trends in Habitual Sleep Duration
Authors: Khader W S , Tubbs A , Fernandez F , Jean-Louis G , Seixas A A , Williams N J , Chakravorty S , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Public health efforts aimed at reducing the decline in habitual sleep duration have not been successful. It is possible that this decline is differentially experienced relative to individuals’ mental health status. This would further support the need to focus on mental health as a strategy for improving sleep in the general population.

Methods
We examined 10 years of the National Health Interview Survey data (N=305,555). During all years, habitual sleep duration, age, sex, race/ethnicity, and height and weight (used to compute body mass index) were recorded in the same way. In addition, depressed mood in the past 30 days was evaluated (coded as none, mild, moderate, or severe). Weighted regression analyses examined sleep duration as an outcome, year and depressed mood as predictors, and sociodemographics as covariates. A year-by-depressed mood interaction was computed, and analyses were stratified by group.

Results
There was a significant year-by-depression interaction on linear change in sleep duration over the 10 year period (p=0.0001). Analyses were then stratified by depressed mood. In adjusted analyses, individuals with no depressed mood lost an average of 0.68 minutes of sleep per year (95%CI -0.82,-0.55; p&lt;0.0001). Among those with mild depression, this was 7% higher, at 0.73 minutes (95%CI -1.13,-0.33; p&lt;0.0001). Among those with moderate depressed mood, this was 154% higher, at 1.73 minutes lost per year (95%CI -2.31,-1.16; p&lt;0.0001). Among those with severe depressed mood, this was 351% higher, at 3.07 minutes per year (95%CI -4.22,-1.92; p&lt;0.0001).

Conclusion
The 10-year linear decline in habitual sleep duration seems to depend on mental health status. Individuals with better mental health lose less sleep over time, relative to those with worse mental health. This highlights the importance of mental health as a possible avenue for improving sleep health in the population.

Support
Dr. Grandner is supported by R01MD011600
0236 Team-Based Athletes Sleep Less Than Individual Athletes, But Do Not Report More Insomnia or Fatigue
Authors: Clay M A , Athey A , Charest J , Auerbach A , Turner R W , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Collegiate student-athletes face challenges balancing academics and athletics, and getting an adequate amount of sleep is one factor that can assist in sustaining an elite level of play. Team-based sports may present with systematically different sets of demands.

Methods
Data were obtained at the start of the academic semester from N=189 NCAA Division-1 athletes from a wide range of sports. The sample was 46% female. Individuals were classified as playing in a team sport (e.g., football, basketball, baseball, softball, volleyball) or an individual sport (e.g., swimming, track, golf). Sleep-related outcomes included self-reported sleep duration and sleep latency, frequency of sleeping pill use (Never, Rarely, Sometimes, Often), Insomnia Severity Index score, and Fatigue Severity Scale score. Regression analyses were adjusted for age and sex.

Results
In adjusted analyses, team-based athletes reported 22.4 minutes less sleep than individual athletes (95%CI -42.8,-1.9; p&lt;0.05). They also reported 5.6 less minutes of sleep latency (95%CI -10.8,-0.3; p&lt;0.05). More frequent sleeping pill use was also reported (oOR=0.96; 95%CI: 0.26,1.67; p=0.007). They did not report any differences in insomnia or daytime fatigue levels.

Conclusion
These results suggest that even though team-based athletes may not report more sleep complaints or daytime complaints, they may be at increased risk for less sleep and more sleep medication. Further work is needed to identify the sources of these differences to guide interventions.

Support
The REST study was funded by an NCAA Innovations grant.
Dr. Grandner is supported by R01MD011600
0239 Mindfulness Moderates the Relationship Between Lucid Dreaming, Nightmare Distress, and Sleep Quality
Authors: Zendels P , Barngrover S , Peach H 
Published: May 2020

Abstract

Introduction
Nightmares can cause poorer sleep quality. Various mechanisms have been explored as potential treatments for nightmares, including mindfulness practices and lucid dreaming. Presently, little literature has looked at the interaction effects between mindfulness and lucid dreaming to reduce nightmare distress.

Methods
A sample of 275 individuals was recruited from both the United States and Thailand via social media and a student pool of research subjects at UNC Charlotte. Data were recorded on participants’ demographic information, lucid dreaming from the Lucidity and Consciousness in Dreams Scale (Voss et al., 2013), Mindfulness using the Five Facet Mindfulness Questionaire (Baer et al., 2006), Nighmares via the Nightmare Distress Questionaire (Belicki, 1992), and sleep quality using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989). Higher scores on each measure were associated with more lucid dreaming, more mindful behaviors, more severe nightmares, and poorer sleep quality. PROCESS model 8 was run to conduct a moderated mediation analysis (Hayes, 2018). Lucid dreaming was used as the predictor; sleep quality as the outcome variable, nightmare distress as the mediator and mindfulness acted as a moderator on both the direct and indirect pathway of lucid dreaming onto the mediator and outcome.

Results
Mindfulness was a significant predictor at both the mediator and outcome variables. Nightmare distress was a significant predictor of sleep quality. A statistical trend (p=.054) suggests lucid dreaming may have a positive effect on nightmare distress. In the indirect path, lucid dreaming had a positive effect on PSQI scores only for individuals with low mindfulness.

Conclusion
The moderated mediation suggests that individuals with low mindfulness may see a decrease in sleep quality from lucid dreaming. This could be due to lucid dreaming being associated with more severe nightmares. A zero-effect size could not be ruled out of the confidence intervals for individuals of average or high mindfulness, but the data suggest that lucid dreaming alone may not help treat nightmares, but the combination of lucid dreaming and mindfulness therapies could promote lower distress and better sleep quality.

Support
Psychological Sciences departmental funding
0243 Community-Level Daytime Sleepiness and Substance Use: Implications of Sleep Time and Mental Health
Authors: Khader W S , Tubbs A , Fernandez F , Chakravorty S , Hale L , Branas C , Barrett M , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Daytime sleepiness is associated with impaired functioning and well-being. Those with more sleepiness may turn to illicit substances to overcome these problems. The present study examined whether community-level daytime sleepiness is associated with the likelihood of drug use.

Methods
Data were pulled from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 community adults (age 22–60). Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Use of different substances was assessed with the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). The present analyses examined use within the past month of alcohol, tobacco, cannabis, cocaine, amphetamines, inhalants, sedatives, hallucinogens, and illicit opioids. A separate item assessed caffeine. Ordinal logistic regression analyzed ESS score as a predictor of frequency of substance use adjusted for age, sex, education, and race/ethnicity. Additional models included habitual sleep duration and score on the PHQ9 depression scale.

Results
In sociodemographically-adjusted analyses, ESS score was associated with an increased risk of using tobacco (OR=1.04, p=0.015), cannabis (OR=1.04, p=0.014), cocaine (OR=1.07, p=0.009), amphetamines (OR=1.06, p=0.025), inhalants (OR=1.13, p=0.002), sedatives (OR=1.07, p=0.003), hallucinogens (OR=1.12, p=0.001), and opioids (OR=1.12, p=0.0001). Controlling for sleep duration did not significantly affect these relationships, while controlling for depression made every association non-significant except hallucinogens (OR=1.09, p=0.040).

Conclusion
Daytime sleepiness was associated with increased use of nearly all drug categories, but not alcohol or caffeine. Public consumption of alcohol and caffeine might be sufficiently common that the presence of their use cannot be adequately associated with sleepiness. Moreover, the increased frequency of drug use with sleepiness is not linked to sleep deprivation but may reflect emotional distress.

Support
This work was supported by a grant from Jazz Pharmaceuticals
Dr. Grandner is supported by R01MD011600
0273 Residual Sleep Difficulties During Reset Operations Predict Greater Post-Deployment Mental Health Difficulties in U.S. Soldiers: A Cross-Lagged Analysis
Authors: So C J , Alfano C A , Riviere L A , Quartana P J 
Published: May 2020

Abstract

Introduction
Military service is associated with a number of occupational stressors, including non-conducive sleeping environments, shift schedules, and extended deployments overseas. Service members who undergo combat deployments are at increased risk for mental health and sleep difficulties. Bidirectional associations between sleep and mental health difficulties are routinely observed, but the directional association of these difficulties from one deployment to the next has not been addressed. The purpose of this study was to examine whether residual sleep problems or mental health difficulties after a 12-month period of reset operations following an initial deployment were associated with changes in sleep and mental health following a subsequent deployment.

Methods
Data from 74 U.S. Soldiers were case-matched across three time points. Participants were assessed 6 months (T1) and 12 months (T2) following an initial deployment. Participants were then assessed 3 months (T3) following a subsequent deployment. Symptoms of PTSD, anxiety, depression, and sleep difficulties were assessed at all three time points.

Results
Cross-lagged hierarchical regression models revealed that residual sleep difficulties across the time points uniquely predicted later changes in PTSD and anxiety symptoms, but not depressive symptoms, following a subsequent deployment. Conversely, residual mental health difficulties were not unique predictors of later changes in sleep difficulties.

Conclusion
These findings suggest that higher levels of residual sleep difficulties 12 months following a prior deployment are associated with larger increases in mental health problems following a subsequent deployment. Moreover, and importantly, the converse association was not supported. Residual mental health difficulties prior to deployment were not associated with changes in sleep difficulties. These data provide a viable target for intervention during reset operations to mitigate mental health difficulties associated with combat deployments. They might also help inform return-to-duty decisions.

Support
N/A.
0278 Changes in Sleep Architecture During Long-Duration Spaceflight
Authors: Piltch O , Flynn-Evans E , Stickgold R 
Published: May 2020

Abstract

Introduction
Previous projects have shown that astronauts sleep significantly worse in mission than on Earth. However, it is unclear how sleep architecture is influenced by microgravity. Such information could inform our understanding of the adaptive mechanisms NREM and REM sleep on Earth. We investigated how sleep architecture is affected during spaceflight relative to on Earth.

Methods
Sleep architecture was assessed using the Nightcap monitor before (pre-flight, n=113 nights), during (in-flight, n=68 night), and after (post-flight, n=61 nights) missions aboard the Mir space station for four cosmonauts and one astronaut. We compared hand-scored REM/NREM/wake staging in/post-flight to a pre-flight baseline using mixed-effects regression to account for subject variability. We also used mixed-effects modeling to assess changes over time in different phases of the mission.

Results
Participants averaged an hour less sleep in space (5.4 ± 0.66) compared to pre-flight (6.6 ± 0.70; p &lt; .0001) and spent significantly more time awake in bed, leading to a 20.8% reduction in sleep efficiency. Sleep architecture was also affected by spaceflight: percentages of time in bed for NREM and REM decreased significantly by 9.9% and 26.6% respectively. REM latency nearly doubled during spaceflight to 88 ± 3 minutes. All metrics were stable across the in-flight phase, with the exception of an increase in sleep latency (β: 0.47; p = 0.0009) and a decrease in time in bed (β = 0.85; p &lt; .0001).

Conclusion
These data substantiate previous findings focused on sleep continuity in microgravity. A variety of metrics demonstrate worse sleep in space. NREM and REM time significantly decreased alongside an increase in wakefulness, but the relative proportion of these stages also changed significantly: REM sleep suffered more than NREM in spaceflight conditions. These longitudinal data add value to our nebulous understanding of how sleep functions in microgravity.

Support
Mary Gordon Roberts Fellowship, NAS 9-19406, NIMH #MH-48,832, The MacArthur Foundation Mind-Body Network, and Healthdyne Technologies
0283 Differential Impact of Chronotype on Positive and Negative Affect Following Sleep Restriction
Authors: Cox R C , Olatunji B O 
Published: May 2020

Abstract

Introduction
Previous research indicates sleep deprivation significantly decreases positive affect, while negative affect is often unaffected (Reddy et al., 2017; Schwarz et al., 2019). However, it is unclear whether individual difference factors predict a differential emotional response to sleep loss. Though one study found chronotype predicts decreased positive affect following sleep loss in adolescents (Dagys et al., 2012), the effect of chronotype on emotional response to sleep loss in adults remains unknown.

Methods
The present study addressed this limitation by examining whether chronotype predicts changes in positive and negative affect following partial sleep restriction. Healthy sleeping adults (N=113; Insomnia Severity Index&lt;8) completed the Morningness-Eveningness Questionnaire (Horne & Ostberg, 1976) on day 1, and were instructed to sleep normally on night 1. On the evening of day 2, participants completed the Positive and Negative Affect Schedule (PANAS; MacKinnon et al., 1999) to sample baseline daily emotions. On night 2, sleep was restricted to 4-8am. On day 3, participants completed the PANAS post-sleep restriction. Compliance was verified with actigraphy (n=22 excluded for noncompliance), and response to the sleep restriction procedure was checked with the Stanford Sleepiness Scale (Hoddes, Dement, & Zarcone, 1972; n=18 excluded for nonresponse).

Results
Results suggest a significant decrease in positive affect from pre-sleep restriction (M=15.63, SD=4.09) to post-sleep restriction (M=11.08, SD=3.93), t(71)=10.33, p&lt;.01, and a trend decrease in negative affect from pre-sleep restriction (M=7.68, SD=3.62) to post-sleep restriction (M=6.90, SD=2.41), t(71)=1.60, p=07. Chronotype was significantly associated with post-sleep restriction negative affect, controlling for baseline negative affect (β=-.42, p&lt;.01), such that eveningness was associated with increased negative affect following sleep restriction. In contrast, chronotype was unrelated to post-sleep restriction positive affect.

Conclusion
These findings indicate that evening types may be more likely to experience emotions implicated in anxiety-related disorders following sleep loss, such as fear and nervousness.

Support
This work was supported by the National Institute of Mental Health of the National Institutes of Health [F31MH113271] and a Graduate Student Summer Research Award from Vanderbilt University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
0292 Baseline Pro-Inflammatory Cytokine and Cortisol Levels Differentially Predict Mood Disturbance and Working Memory Deficits Induced by Chronic Sleep Restriction
Authors: Jones C W , Kaizi-Lutu M , Mange A , Basner M , Dinges D F 
Published: May 2020

Abstract

Introduction
Sleep restriction disturbs mood, impairs neurocognitive performance, and elevates proinflammatory cytokine levels. However, whether basal inflammation influences mood and neurocognitive performance across sleep restriction is unknown. This study examines whether baseline IL-6, cortisol, and TNF-α levels predict the deterioration of mood and working memory performance across sleep restriction.

Methods
N=124 healthy participants (52% female; n=64), 22–45 years of age, had valid protein, mood, and working memory data. The study included two baseline nights (8h time in bed [TIB]) followed by five nights of 4h TIB. Venous blood was collected on the second baseline day and IL-6, cortisol, and TNF-α levels were measured via commercially available ELISA assays. The profile of mood states (POMS) and the digit span test (DST) were completed every 2h during wakefulness and daily averages were computed. Mixed-effects multi-level models, adjusted for baseline, evaluated the main effect of IL-6, cortisol, and TNF-α levels on the POMS and DST independently and examined the trajectory of POMS and DST by the interaction of protein levels and day of sleep restriction.

Results
At baseline, IL-6, cortisol, and TNF-α levels were not associated with POMS or DST. There was a main effect of IL-6, but not cortisol or TNF-α, levels on mood disturbance (β=3.811; P=0.015); IL-6 levels did not predict the trajectory of mood across sleep restriction (β=0.187; P=0.57). Higher baseline cortisol levels predicted increasing mood disturbance across days (β=-1.329; P&lt;0.0001). Higher baseline TNF-α levels predicted degrading DST performance across days (β=-0.313; P=0.020). Higher IL-6 (β=-0.246; P=0.010) and lower cortisol (β=0.185; P=0.037) levels also predicted degrading DST performance across days.

Conclusion
The study findings suggest that basal inflammatory cytokine and cortisol levels are implicated in the individual risk of mood disturbance and working memory deficits resulting from chronic sleep restriction and highlight the need to consider biological processes and phenotypes together.

Support
This work was supported by National Institute of Health NIH R01NR004281 and National Space and Biomedical Research Institute NSRBI NCC 5–98.
0300 The Circadian Timing of Sleep Affects the Rate of Accumulation of Neurobehavioral Impairment Across Days of Sleep Restriction
Authors: McCauley M E , Van Dongen H , Banks S , Dinges D F 
Published: May 2020

Abstract

Introduction
Chronic restriction of nighttime sleep to less than ~8h/day leads to build-up of neurobehavioral impairment across days. Although it is known that sleep loss effects depend on the circadian timing of sleep, it is not known how the timing of restricted sleep influences the accumulation of neurobehavioral impairment over days. Here we studied the accumulation of impairment across days of restricted sleep placed in the morning or afternoon.

Methods
N=71 healthy young adults (39% female; ages 21-45y, mean±SD: 27.9±6.6y) completed a 14-day laboratory study. After two baseline days with nighttime sleep (8h TIB: 23:30-07:30), subjects were randomized to 10 consecutive days of A) morning sleep at 4h, 6h, or 8h TIB ending at 11:30 each day (n=18, 8, 8, respectively), or B) afternoon sleep at 4h, 6h, or 8h TIB ending at 19:30 each day (n=13, 17, 7, respectively). Subjects were tested on the 10min psychomotor vigilance test (PVT) every ~2 hours during scheduled wakefulness. Daily averages for PVT lapses (RTs&gt;500ms) observed between 2h and 14h after awakening were analyzed with non-linear mixed-effects regression to investigate differences in the neurobehavioral impairment build-up rate between sleep restriction conditions.

Results
Afternoon sleep conditions showed a significant sleep dose-response effect (p&lt;0.001), with the fastest accrual of PVT performance deficits across days in the 4h condition, and slow-to-negligible accumulation (p=0.36) of PVT performance deficits in the 8h condition. However, morning sleep resulted in no significant sleep dose-response effect (p=0.96). All 3 morning sleep doses displayed negligible (p≥0.12) accumulation of impairment across days.

Conclusion
In this sample of young adults, sleep dosages ending in the morning (at 11:30) appear to provide considerable protection against cumulative performance deficits from sleep restricted to 4h-6h/day over 10 days, suggesting that the afternoon circadian promotion of wakefulness can sustain behavioral alertness even over multiple days of repeated sleep restriction.

Support
NIH grants R01-NR04281 and M01-RR00040