bluuebunny/crossref_metadata_2025_split huggingface dataset:
094 Bedtime Hunger Predicts Reduced Sleep Efficiency That Night in Users of a Consumer Sleep Measurement Device
Authors: Dixon Bonnie , Liu Siwei , German J , Nordahl Thomas 
Published: May 2021

Abstract

Introduction
It has long been suspected that bedtime hunger can potentially disturb sleep. The neural circuits that control sleep are now known to receive signals of appetite and energy balance via hypocretin/orexin neurons from the lateral hypothalamus. But there remains need to specifically identify how, and under what conditions, appetite mechanisms affect human sleep. This study documents the relationship between bedtime hunger and subsequent sleep efficiency in users of a consumer sleep measurement device.

Methods
The Zeo headband (sold to the public during 2009–2013) used detected electrical potentials to periodically calculate the most probable stage of sleep or wake. Users uploaded sleep records online and could track nightly conditions — including bedtime hunger — on provided rating scales. De-identified summary data from these nightly records were aggregated into a research registry. We extracted the sleep records with bedtime hunger ratings and analyzed them using multilevel modeling to identify within-person and between-person relationships between bedtime hunger and sleep efficiency. We decomposed bedtime hunger ratings into person-mean hunger and nightly hunger, the difference between each night’s hunger rating and the person’s mean.

Results
4,284 nightly sleep records with a bedtime hunger rating were provided by 183 people (age 19–77, 68% male, mean: 23 records/person). Sleep efficiency was not related to person-mean hunger (p=0.26), but was inversely related to nightly hunger that night (p=0.01). The model predicted a within-person difference in sleep efficiency between nights with high, versus low, nightly hunger that varied across the people in the sample (mean [range]: -2.4 [-12.5-1.6] percentage points) and correlated positively with typical sleep efficiency (r=0.73, p=0.00) and negatively with unexplained variability in sleep efficiency (r=-0.47, p=0.00).

Conclusion
For the people in this dataset, on average, going to bed hungrier than usual predicted reduced sleep efficiency that night. The effect was strongest in people who tend toward low and variable sleep efficiency. This finding strongly suggests that bedtime hunger can indeed disturb sleep, especially in poor sleepers. Further research is needed to determine who is most affected and to understand implications, such as for weight management, eating disorders, food insecurity, or sleep-supporting foods and dietary practices.

Support (if any)
USDA-NIFA-AFRI and NIH-NCATS
096 The Association Between Sleep Regularity Index and Self-Reported Behavioral and Emotional Symptoms in Adolescents
Authors: Grover Eliza , Wong Patricia , Barker David , Gredvig-Ardito Caroline , Carskadon Mary 
Published: May 2021

Abstract

Introduction
Among adolescents, sleep health has been associated with emotional and mood regulation, cognitive functioning, and behavior. Few studies, however, have examined the Sleep Regularity Index (SRI, Phillips et. al, 2017) and its associations with mental health and well-being in this age group. For this study, we examined whether SRI in 15-16-year-old adolescents would predict internalizing and externalizing symptoms as measured by Youth Self-Report (YSR) scores two years later. We hypothesized that a higher baseline sleep regularity would predict lower internalizing and externalizing YSR scores at the 2-year follow-up.

Methods
The sample included 32 adolescents (14 male) ages 15-16yr (mean = 15.6) at baseline and 2 years later (mean age = 17.7). Actigraphy data and YSR scores were collected at baseline, and YSR was examined at follow-up. Participant’s SRIs were calculated using 24-hour actigraphy data scored for sleep and wake. YSR T-scores of 60 or above indicate borderline clinical internalizing (n = 2) and externalizing (n = 4) symptoms at follow-up. We used linear regression modeling to determine whether baseline SRI predicted YSR scores 2 years later. Covariates included sleep start time, sleep duration, sex, and baseline YSR scores.

Results
At baseline, average SRI and YSR scores were not significantly correlated (internalizing: r = 0.10; externalizing: r = 0.24, p’s &gt; 0.1). SRI score at baseline (mean = 80.5 ± 7.4) significantly predicted YSR internalizing scores (mean = 42 ± 9) at the 2-year follow up (t(26) = 2.57, p = 0.016) but not externalizing scores (mean = 44.8 ± 10.3, t(26) = .78, p = 0.44).

Conclusion
We observed that sleep regularity was associated with internalizing symptoms two years later; however, the association was not in the expected direction: higher SRI was correlated with increased YSR internalizing scores at the 2-year follow-up. As most participants were in a healthy range for YSR scores at both assessments, a possible explanation for this finding is that those with higher SRIs have greater self-awareness in assessing their internal feelings. Future work will examine SRI values and YSR in this sample across 6 assessments acquired at 6-month intervals.

Support (if any)
AA13252 (NIH)
098 Effects of Metameric Display-Light on Alertness, Vigilance and Melatonin
Authors: Schöllhorn Isabel , Stefani Oliver , Spitschan Manuel , Lucas Robert , Cajochen Christian 
Published: May 2021

Abstract

Introduction
Light emitted from visual displays can acutely increase alertness, improve cognitive performance and suppress melatonin in the evening. Here we tested the influence of different melanopic irradiance levels emitted by a metameric display setting on alertness, vigilance and salivary melatonin levels.

Methods
In an ongoing study, 37 healthy, male participants have so far completed a 2-week study protocol. Volunteers were assigned to one of four luminance groups which differed in brightness levels (27 cd/m2 - 280 cd/m2). Illuminance ranged between 7 and 85 lx. Within the four groups each volunteer was exposed to a low melanopic (LM) and a high melanopic condition (HM). The LM and HM differed in melanopic irradiance (ca. 3-fold change), but matched in terms of cone excitation (metamers). Before, during and after the light exposure, volunteers performed a psychomotor vigilance task (PVT). Subjective alertness and melatonin levels were continuously measured in half-hourly intervals throughout scheduled wakefulness in the 17-h in lab study.

Results
Preliminary analysis yielded an overall alerting response in the HM vs. the LM condition (p&lt;0.05) concomitant with a trend of reduced melatonin levels in HM vs. LM (p=0.08). So far, we could not observe a difference in PVT performance for HM and LM (Reaction time responses between 100 and 500 ms). Since we are still lacking statistical power in the ongoing study, we cannot yet satisfactorily interpret interaction effects between melanopic condition and brightness.

Conclusion
Our data indicate that rather low brightness levels of high melanopic display light impacts alertness and melatonin levels in the evening. Thus, metameric low melanopic display light may be a promising method to attenuate activating properties of evening light on circadian physiology without affecting visual appearance.

Support (if any)
This project is funded by the Swiss National Science Foundation (SNSF).
101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test
Authors: Deering Sean , Stepnowsky Carl 
Published: May 2021

Abstract

Introduction
The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings.

Methods
We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged.

Results
A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality.

Conclusion
Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed.

Support (if any)
This analysis was supported by the VA San Diego Healthcare System Research Service.
110 Physiological correlates of the Epworth Sleepiness Scale reveal different dimensions of daytime sleepiness
Authors: Lok Renske , Zeitzer Jamie 
Published: May 2021

Abstract

Introduction
The Epworth Sleepiness Scale (ESS) is used as a clinical tool for determining excessive daytime sleepiness. However, the behavior and biology that underlie ESS scores remain to be elucidated. The main objective of this analysis is to determine objective behavioral and physiologic correlates of the ESS. Secondarily, we examine the relationship of the ESS to parallel subjective and objective endpoints that could represent measures of daytime sleepiness.

Methods
Using two separate machine learning algorithms, Random Forest and Lasso, we determined the association between ESS scores and 55 sleep and medical variables in individuals who participated in the Sleep Heart Health Study (N=2105). These variables include self-reported sleep characteristics (e.g., habitual sleep length and latency, frequency of not getting enough sleep), polysomnographic sleep measures from a single night, medication use, and mental and physical health status. Additional analyses were conducted on data stratified by age and gender. To investigate the relationship between ESS and other measures of daytime sleepiness, cross-correlation analysis was conducted on the ESS and five variables that could analog daytime sleepiness (feeling unrested, nap duration and frequency, sleep latency, frequency of not getting enough sleep).

Results
Analysis of the main dataset resulted in low explained variance (7.15 - 10.0%), with self-reported frequency of not getting enough sleep as most important predictor (10.3–13.9% of the model variance). Stratification by neither age nor gender significantly improved explained variance. Habitual sleep length was not an important predictor in any model. Cross-correlational analysis revealed low correlation of other daytime sleepiness measures to ESS score.

Conclusion
Data analyses indicate that ESS scores are not well explained by habitual or polysomnographic sleep values, or a variety of other biomedical characteristics. This suggests that there are different, potentially orthogonal dimensions of the concept of “daytime sleepiness” that may be driven by different aspects of sleep physiology. Caution should be used when considering the ESS as a clinical measure given that the physiologic correlates still remain to be elucidated.
Support (if any):
111 Artificial Light-Triggered Sleep Deprivation May Lead to Allodynia in Rodent Model
Authors: Kanegi Skyler , Akopian Armen 
Published: May 2021

Abstract

Introduction
The combination of artificial light and lack of exposure to natural light can delay the circadian clock, dysregulate the circadian cycle, and decrease alertness upon waking. This effect has been especially significant during the COVID-19 pandemic, where overexposure to artificial light at improper hours has contributed to increased rates of clinical insomnia. Artificial light may also contribute to concomitant neurological conditions such as primary headache, but the mechanisms by which light triggers sleep deprivation-induced headache are not well-understood.

Methods
To measure pain sensitivity, we habituated 13 wild-type male mice to von Frey filaments applied to the periorbital area until there was no response to 0.6g stimulus. We then applied 5 lux of continuous dim light to mice during their usual 12-hour dark cycle. The 12-hour light cycle remained unchanged with 200 lux continuous light. Three groups of mice experienced the dim light stimulus for one, three, or five consecutive days. Ambulation and rest activity were measured using SOF-812 Activity Monitor machines. After the experiment concluded, we waited 24 hours and measured mechanical threshold using von Frey filaments at 1, 3, 5, 8, and every 3 days subsequently until mice no longer responded to 0.6g stimulus.

Results
Artificial light triggered changes in circadian behavior including increased number of rest periods during 12-hour dark (dim light) cycle and shortened sleep duration during 12-hour light cycle. Following the artificial light stimulus, there was a significant decrease in mechanical threshold (P&lt;0.05), representing allodynia. The one-day group displayed one day of significant allodynia. The three-day group displayed three days of significant allodynia. The five-day group displayed five days of significant allodynia.

Conclusion
Artificial light may trigger circadian dysregulation, and the duration of artificial light exposure seemed to be directly correlated to the duration of allodynia up to one week after the stimulus was removed. We will repeat these experiments and analyze CNS and PNS tissue samples to understand the underlying physiological and biochemical bases of how artificial light triggers sleep deprivation-induced headache. This knowledge could increase our understanding of the pathophysiology and comorbidity of sleep deprivation and headache.

Support (if any)
Funding was received from the National Institute of Health (NS104200).
114 Stability of Gut Microbiome Alpha Diversity During Combined Sleep Restriction and Circadian Misalignment
Authors: Withrow Dana , Gonzalez Antonio , Sprecher Kate , Depner Christopher , Burke Tina , Fleshner Monika , Lowry Christopher , Turek Fred , Vitaterna Martha , Dorrestein Pieter , Knight Rob , Wright Kenneth 
Published: May 2021

Abstract

Introduction
Disturbed gut microbiome diversity has been associated with poor health outcomes and various disease states. We investigated the impact of combined sleep restriction (3h time in bed [TIB] sleep opportunities per day) and circadian misalignment (daytime sleep and nighttime wakefulness) on gut microbiome alpha diversity in healthy young individuals in a controlled laboratory setting.

Methods
Twenty healthy adults (8 female), mean age (±SD) 25.65(±4.2) completed a 39-day protocol consisting of two laboratory visits lasting 4 days each. Two weeks of ambulatory monitoring prior to laboratory visits confirmed ~8h habitual sleep duration per night. Participants consumed energy-balanced diets, identical within participants, 2 days before and during the laboratory visits. The laboratory visits consisted of sleep opportunities as follows: night 1 (8h TIB), night 2 (3h TIB), day 3 (3h TIB) and day 4 (3h TIB). Fecal microbiome samples were obtained at baseline between day 1 and 2, and during sleep and circadian disruption (between day 3 and 4). Alpha diversity measures were calculated using Pielou’s evenness, Faith’s phylogenetic diversity and number of observed OTUs.

Results
Linear mixed models with subject as a random factor and visit as a fixed factor were performed to assess whether any alpha diversity measures changed during sleep and circadian disruption compared to baseline. Alpha diversity did not change significantly between baseline and sleep and circadian disruption (all p &gt; 0.57). Additionally, intraclass correlation coefficients (ICCs) were calculated at baseline and during sleep and circadian disruption to determine if alpha diversity measures showed trait-like stability at both time points. ICCs were substantial to almost perfect (ICC 0.64–0.84) at baseline and substantial (ICC 0.70–0.80) during sleep and circadian disruption.

Conclusion
Four days of combined sleep restriction and circadian misalignment does not appear to alter alpha diversity of gut microbiota species in healthy adults. Further, substantial to almost perfect intraclass correlation coefficients suggest alpha diversity of the human microbiome is stable during combined sleep and circadian perturbation and that examination at the level of microbiota community composition and functional outcomes are needed.

Support (if any)
Office of Naval Research MURI (N00014- 15-1-2809), NIH/NCATS (UL1TR002535), NIH T32 HL149646, CU Undergraduate Research Opportunities.
117 Comparison of Various Methods to Differentiate Resilience and Vulnerability to Sleep Loss Using Self-Rated Measures
Authors: Casale Courtney , Yamazaki Erika , Brieva Tess , Antler Caroline , Goel Namni 
Published: May 2021

Abstract

Introduction
There are robust, trait-like individual differences in subjective perceptions in response to sleep restriction (SR) and total sleep deprivation (TSD). How to best define neurobehavioral resilience and vulnerability to sleep loss remains an open question. We compared multiple approaches and cutoff thresholds for defining resilience and vulnerability using scores on the Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor (POMS-F and POMS-V) subscales.

Methods
Forty-one adults (33.9±8.9y;18 females) participated in a 13-night experiment (two baseline nights [10h-12h time in bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The KSS, POMS-F, and POMS-V were administered every 2h during wakefulness. Resilience and vulnerability were defined by the following: average score during SR1-5, average change from baseline to SR1-5, and variance during SR1-5. Resilient and vulnerable groups were defined by the following cutoffs: the top and bottom 12.5%, 20%, 25%, 33%, 50%, and +/-1 standard deviation. Bias-corrected and accelerated bootstrapped t-tests compared the scores of resilient and vulnerable groups during baseline and across SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group (tau=0.4:moderate,0.7:strong).

Results
Resilient and vulnerable groups for POMS-F, as defined by all three approaches, significantly differed in their scores at all cutoffs during SR. However, only raw score and change from baseline approaches defined significantly different resilient and vulnerable groups during SR for KSS, and only raw score and variance approaches defined significantly different groups during SR for POMS-V. Notably, raw scores at baseline significantly differed between resilient and vulnerable groups for all measures. Correlations revealed moderate to strong associations between all three approaches at all cutoffs for POMS-F, between raw score and change from baseline approaches for KSS, and between raw score and variance approaches for POMS-V.

Conclusion
Defining resilience and vulnerability on self-rated measures by change from baseline was comparable to using raw score for KSS and POMS-F, whereas defining these groups by variance was comparable for POMS-F and POMS-V. Differences across methods may be due to the differential impact of SR on these various distinct subjective states.

Support (if any)
ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488
118 Relationships Between Perceptions of Subjective States Differ by Sleep Loss and During Recovery in Healthy Adults
Authors: Casale Courtney , Brieva Tess , Yamazaki Erika , Antler Caroline , Goel Namni 
Published: May 2021

Abstract

Introduction
The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor subscales (POMS-F and POMS-V) are commonly used to assess subjective sleepiness, fatigue and vigor in response to sleep loss. However, the detailed time course of relationships between these measures across sleep loss and recovery remains unknown yet is critical for assessing varying changes in perception of subjective states. Repeated measures correlation (rmcorr) examined within-individual association between the measures throughout a highly controlled sleep deprivation study.

Methods
Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the KSS, POMS-F, and POMS-V, was administered every 2h during wakefulness. Rmcorr compared KSS, POMS-F, and POMS-V scores by examining correlations by study day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr cutoffs were as follows: r=0.1:small, 0.3:moderate, 0.5:large.

Results
KSS and POMS-F maintained positive correlations throughout the study, whereas POMS-F and POMS-V and KSS and POMS-V were inversely correlated. All correlations were significant except those for POMS-F and POMS-V across recovery day 1 and KSS and POMS-F across recovery day 4. All measure pairs showed moderate to large correlations across baseline and SR1-5, but only small to moderate correlations across recovery. KSS and POMS-F and KSS and POMS-V showed moderate to large correlations across TSD; however, POMS-F and POMS-V only showed a small correlation. All three pairs showed consistent moderate (POMS-F and POMS-V) or large (KSS and POMS-F, KSS and POMS-V [moderate at 2000h]) correlations when analyzed by time point across the study.

Conclusion
Overall, the strength of relationships between KSS, POMS-F, and POMS-V scores varied as a function of type of sleep loss (SR or TSD) and by fully rested states, but not by time of day. This demonstrates the importance of determining perceptions of sleepiness, fatigue, and vigor in relation to each other, especially during recovery for all three constructs.

Support (if any)
ONR Award No. N00014-11-1-0361; NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488