bluuebunny/crossref_metadata_2025_split huggingface dataset:
0078 Assessing Sleep Health in Children Recently Adopted from Foster Care
Authors: Cifre Anthony , Kim Jinu , Alfano Candice 
Published: May 2022

Abstract

Introduction
An estimated 450,000 children were in the U.S. foster care system in 2019. Early adversity including maltreatment and/or neglect renders this vulnerable population at high risk for negative outcomes, both physical and psychological. Burgeoning evidence suggests that children in foster care develop high rates of sleep disruption. Sleep disruption is associated with negative life outcomes including heart disease, diabetes, and psychiatric disorders. However, there is still a lack of knowledge examining sleep health in children adopted from foster care.

Methods
Sleep quality and problems were examined among a sample of children adopted from foster care (n = 234) within the past two years, ages 4 to 11 years (M = 5.94, SD = 1.97). Caregivers across the US were invited to complete an anonymous Qualtrics survey via private Facebook groups for foster families. The Child Sleep Habits Questionnaire (CSHQ) was completed by foster parents to assess child sleep problems (e.g., nightmares, bedwetting, snoring, etc.) and overall sleep quality. Caregivers endorsed sleep problems and frequency using responses “Never,” “Sometimes,” and “Usually; sleep quality was rated using a Likert scale from 1 = Very Poor to 10 = Excellent.

Results
Paired-samples t-test compared first month (i.e., when first arrived in the home) and current child sleep quality based on caregiver-report. Sleep quality showed significant improvement since arrival in the foster home; t(233) = 12.98, p &lt; .001, d = 0.85. However, on the CSHQ, 99% of the sample scored above the clinical cutoff for this measure indicating elevated sleep problems.

Conclusion
Children adopted from foster care show some improvement in sleep quality after achieving permanency. However, these data suggest this population continues to experience clinical levels of sleep problems even after being adopted. Our results suggest a need for specialized intervention services targeting sleep health among children recently adopted from foster care.

Support (If Any)
None
0087 Feasibility of examining component-specific effects of yogic breathing on self-report sleep metrics: A three-arm pilot RCT
Authors: Vazquez Michael , Buraks Olivia , Haack Monika , Mullington Janet , Yang Huan , Goldstein Michael 
Published: May 2022

Abstract

Introduction
Mind-body interventions (MBIs) continue to receive widespread interest for improving sleep disturbances. This study investigated the feasibility of using an automated electronic survey system in REDCap in the context of a fully remote clinical trial study to produce detailed measures of participant adherence, daily sleep quality, and associations with physiological outcomes captured by wearable devices.

Methods
Eighteen healthy participants (age 18-30 yrs, 12 female) were randomized to one of three 8-week long interventions: slow-paced breathing (SPB, N=5, 24.6 ± 2.1 years, 4 female), mindfulness (M, N=6, 23.7 ± 3.7 years, 4 female), or yogic breathing (SPB+M, N=7, 24.3 ± 3.1 years). Participants completed two weeks of daily sleep logs along with the Pittsburgh Sleep Quality Index (PSQI) prior to a virtual laboratory visit, which consisted of a 60-min intervention-specific training, including a 20-min guided practice, and subsequent tasks including experimental stress induction. Participants were then instructed to repeat their assigned intervention practice daily, selecting either the same or a similar guided audio as their initial training. After an initial video check-in appointment, participants received regular visual feedback of their data and completed weekly check-ins with the study team to improve adherence. At the end of the intervention period, participants again completed daily sleep logs and the PSQI, in addition to other outcome measures and a virtual laboratory visit. Data were analyzed using linear mixed models.

Results
Sleep log adherence was over 90% in all three groups. The groups were successfully distinguishable based on HRV-derived breathing and mindfulness ratings. For the SBP+M group only, there was a trend of reduced sleep onset latency (SOL, p=.093) and a significant increase in sleep efficiency (SE, p=.025). There were no significant changes in PSQI or other sleep log measures. More detailed analysis of timecourse across these measures is ongoing.

Conclusion
These findings support feasibility for a fully remote, semi-automated clinical trial study assessing component-specific effects of these MBIs on sleep in generally healthy young adults. Research evaluating MBIs for sleep in both clinical and nonclinical populations would benefit from similar study designs to examine intervention-specific components while increasing both scalability and quality control.

Support (If Any)
Pilot Research Grant, Osher Center for Integrative Medicine of Harvard Medical School and Brigham & Women’s Hospital; National Institutes of Health (5T32HL007901-22)
0093 Efficacy of a Chatbot-Based Sleep Intervention on Sleep Quality Improvement among Young Adults
Authors: Oh Yoo Jung , Zhang Jingwen , Ji Xiaopeng , Liao Wang , Feng Bo 
Published: May 2022

Abstract

Introduction
Insufficient sleep duration and poor sleep quality are prevalent in adolescents and young adults. Artificial-intelligent agents such as chatbots have been integrated into digital health interventions to support symptom management and promote health behaviors. This pilot study tested the efficacy of a sleep advice chatbot in young adults with self-reported sleep disturbances.

Methods
Young adults aged 18 to 28, who scored 55 and above (t-score) on the PROMIS Sleep Disturbance (Short Form) were enrolled into the study. Using a 2 x 2 research design, participants were randomly assigned into four groups: chatbot 1 (sleep advice), chatbot 2 (sleep advice & emotional support), human 1(sleep advice), human 2 (sleep advice & emotional support). Sleep advice chatbots were developed using Google’s Dialogflow. After the conversation, participants rated the effectiveness of the sleep advice on a 5-point Likert scale. Participants completed the PROMIS Sleep Disturbance seven days after the study.

Results
Of 293 participants who were enrolled, mean age was 19.75 (SD = 1.57) and 85.1% were females. Average sleep disturbance scores measured before and after the study was 25.47 (SD = 4.91) and 20.97 (SD = 5.55), showing a significant decrease in sleep disturbance (p &lt; .001). Mean advice effectiveness was 3.96 (SD = 1.10), with human advice provider receiving better scores (p &lt; 0.05). Controlling for age and sex, two-way ANOVA analysis showed a significant interaction between advice giver type (chatbot vs. human) and advice type (sleep advice vs. sleep & emotional support) on alleviating sleep disturbances [F(1, 262) = 4.07, p = .045, partial η2 = .015]. Specifically, participants who received sleep advice from a chatbot had greater decreases in sleep disturbances (Mdiff = -4.69, SD = 3.65) than from a human (Mdiff = -3.78, SD = 5.92). On the other hand, with emotional support, advice given by humans was more effective (Mdiff = -5.56, SD = 5.86) than chatbots (Meandiff = -3.44, SD = 6.31).

Conclusion
The results of this study suggest the potential efficacy of a chatbot-based sleep advice intervention among young adults. Future research can consider using a combination of chatbot and human expert-based sleep interventions.

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0096 Sleep Apnea Diagnosis using Tracheal Signals and Oximetry
Authors: Ghahjaverestan Nasim Montazeri , Aguiar Cristiano , Hummel Richard , Fan Wei , Yu Jackson , Bradley T Douglas 
Published: May 2022

Abstract

Introduction
Diagnosing sleep apnea requires detection of apneas and hypopneas during sleep either via in-laboratory overnight polysomnography (PSG) or portable in-home sleep apnea testing (HSAT). While PSG is the optimal method, it is expensive, inconvenient and often inaccessible for patients. Although HSATs are more convenient and less expensive than PSG, they are not as accurate and have relatively high failure rates because of the nature of the sensors used to measure respiratory variables. We have developed a HSAT, the Patch, that is unique in that it is very simple and reliable because it measures respiratory variables using tracheal motion and sound combined with oximetry to detect respiratory events. In this study we tested the ability of the Patch to detect respiratory events versus simultaneous PSG.

Methods
Participants were adults with a suspected sleep disorder referred to the sleep laboratory at Toronto Rehabilitation Institute for PSG. Simultaneous to the PSG, the Patch, consisting of a module containing a microphone and a 3-D accelerometer that was affixed to participants’ suprasternal notch, and a finger oximeter. After filtering the tracheal signals, the envelope of the tracheal motions in the cranial and postero-anterior directions and tracheal sound envelope were extracted. Along with tracheal features, the amplitude and slopes of oxygen desaturations were also extracted and, were fed into a supervised deep neural network model to detect apneas and hypopneas. The total number of detected events was divided by total estimated sleep time to estimate the apnea-hypopnea index (AHI). The performance of the model in diagnosing sleep apnea was evaluated by sensitivity (AHI≥15) and specificity (AHI&lt;15). The relationship between the estimated AHI and PSG-based AHI was quantified using Pearson correlation.

Results
Ninety-nine participants (42 females, age: 48±16 years, body mass index: 29.2±5.2 kg/m2, and AHI: 15.8±19.4 events/hour) completed the study. We found that the Patch had 88.6% sensitivity and 89.1% specificity for diagnosing sleep apnea. Strong agreement was observed between the estimated and reference AHI values (r = 0.92, p &lt; 0.001).

Conclusion
The Patch is a novel, robust and convenient portable device that provides an accurate means of detecting and quantifying sleep apnea. It has the potential to provide reliable home-based sleep apnea monitoring.

Support (If Any)
Funded by Bresotec Inc.
0101 Performance of a Multisensor Ring to Evaluate Sleep At-Home Relative to PSG and Actigraphy: Importance of Generalized Versus Personalized Scoring
Authors: Hutchison Stephen , Grandner Michael , Bromberg Zohar , Morrell Zoe , Graf Arnulf , Freckleton Dustin 
Published: May 2022

Abstract

Introduction
Multisensor sleep wearable devices have demonstrated utility for research and relative accuracy for discerning sleep-wake patterns at home and in the laboratory. Additional sensors and more complex scoring algorithms may improve the ability of wearables to assess sleep health.

Methods
Thirty-six healthy adults completed assessment while wearing the experimental device (Happy Ring), as well as Philips Actiwatch, Fitbit, Oura, and Whoop devices. Evaluations at home were conducted using the Dreem headband as an at-home polysomnography reference. The experimental Happy Ring device includes accelerometry, photoplethysmography, electrodermal activity, and skin temperature. Epoch-by-epoch analyses compared the Happy Ring to home polysomnography, as well as other sleep-tracking wearable devices. Scoring was accomplished using two machine-learning-derived algorithms: a “generalized” algorithm, similar to that used in other devices, which was static and applied to all users, and a “personalized” algorithm where parameters are personalized, dynamic, and change based on data collected across different parts of the night of sleep.

Results
Compared to home polysomnography, the Happy generalized algorithm demonstrated good sensitivity (94%) and specificity (67%), and the Happy personalized algorithm also performed well (93% and 75%, respectively). Other devices demonstrated good sensitivity, ranging from 91% (Whoop) to 96% (Oura). However, specificity was more variable, ranging from 41% (Actiwatch) to 60% (Fitbit). Overall accuracy using the Happy Ring was 91% for generalized and 92% for personalized algorithms, compared to 92% for Oura, 89% for Whoop, 89% for Fitbit, and 89% for Actiwatch. Regarding sleep stages, accuracy for the Happy Ring was 66%, 83%, and 78% for light, deep, and REM sleep, respectively, for the generalized algorithm. For the personalized algorithm accuracy was 78%, 92%, and 95%, for light, deep and REM sleep, respectively. Post-hoc analyses showed that the Happy personalized algorithm demonstrated better specificity than all other modalities (p&lt;0.001). Kappa scores were 0.42 for generalized and 0.60 for personalized, compared to 0.45 for Oura, 0.47 for Whoop, and 0.48 for Fitbit.

Conclusion
The multisensory Happy ring demonstrated good sensitivity and specificity for the detection of sleep at home. The personalized approach outperformed all others, representing a potential innovation for improving detection accuracy.

Support (If Any)
Dr. Grandner is supported by R01DA051321 and R01MD011600. This work was supported by Happy Health, Inc.
0106 Unitization Improves Memory for Associations during Sleep Deprivation
Authors: Kurinec Courtney , Whitney Paul , Hinson John , Satterfield Brieann , Honn Kimberly , Van Dongen Hans 
Published: May 2022

Abstract

Introduction
Total sleep deprivation (TSD) impairs binding, i.e., the ability to form new associations. Unitization – when separate memory items are learned as a single unit (e.g., combining two words into a novel compound word) – reduces the need for binding. Unitization mitigates impaired memory for associations in amnesiacs, but whether it offsets binding problems from TSD is unknown.

Methods
N=23 healthy adults (ages 19-35, 8 women) participated in an ongoing, double-blind, 4-day/3-night in-laboratory study with a 10h baseline sleep opportunity, 38h TSD, and a 10h recovery sleep opportunity. During TSD, participants were randomized to four administrations of caffeine (200mg), modafinil (alternating between 200mg and 0mg), or placebo at 4h intervals beginning at 01:00. They completed a unitization task at 14:45 on day 2 (baseline, session 1), day 3 (TSD, session 2), and day 4 (recovery, session 3). The task began with a study phase where participants studied 60 pairs of words that were presented individually (e.g., “penny” and “tower”) or as new, unitized words (e.g., “pennytower”) (50% each). Afterward, in the test phase, participants indicated whether 60 presented pairs of individual words were old (presented together at study) or new (recombined into new pairs) (50% each).

Results
Repeated-measures ANOVA revealed significant effects of study pair type (individual or unitized), session (1–3), and their interaction (p&lt;0.05). Performance did not differ by pair type in session 1 (p=0.46), and performance for pairs of individual words did not change across sessions (p=0.34). However, performance on unitized word pairs improved across sessions (p=0.003), and unitized word pairs were recognized better than individual word pairs in sessions 2 and 3 (p&lt;0.05).

Conclusion
Across sessions, participants benefitted from practice on unitized word pairs, such that performance improved even during TSD. Although potentially partly attributable to drug condition (to which investigators are still blinded), no such practice effect was seen for word pairs studied individually. Whether this dissociation implies that unitization bypasses the need for binding and thus lessens the impact of TSD requires further investigation. Regardless, unitization may mitigate performance impairment from sleep loss in settings that require forming novel associations, such as eyewitness identifications.

Support (If Any)
USAMRDC W81XWH-18-1-0100 and CDMRP W81XWH-20-1-0442.
0113 Daily Patterns of Sleep and Metacognition in College Students
Authors: Costa Amy , McGovney Kevin , Liu Karina , McCrae Christina , Curtis Ashley 
Published: May 2022

Abstract

Introduction
Sleep problems have been associated with subjective cognitive complaints (a type of metacognition) in older adults, but little research has examined if this same relationship exists in younger adults. Additionally, college students have high intra-individual variability with their sleep, making day-to-day sleep an understudied parameter of interest in this population. Considering that metacognition has been associated with academic outcomes (e.g., GPA) in college students, it is important to understand how daily sleep patterns might impact metacognition. The present study examined how intra-individual variability in sleep is associated with metacognitive ratings in college students.

Methods
College students (N=81, Mage=18.8, SD = 1.1, 64 females) completed seven days of sleep diaries reporting total sleep time (TST), sleep onset latency (SOL), number of nighttime awakenings, and wake after sleep onset (WASO). Students also provided morning metacognitive ratings regarding the perceived quality of mental functioning from very poor (0) to very good (100). Multilevel modeling analyses tested whether intraindividual variability in daily sleep variables was associated with daily metacognitive ratings, after controlling for interindividual sleep patterns, age, sex, sleep medication usage, and anxiety symptoms (via the Hospital Anxiety and Depression Scale).

Results
Daily TST was associated with metacognitive ratings (B=2.35, p=0.003), in that those who slept less than their typical average reported worse metacognitive ratings. Similarly, daily number of nighttime awakenings (B=-1.92, p=0.02) and WASO (B=-0.13, p=0.009) were also associated with metacognitive ratings, in that those with more awakenings and greater WASO than their typical average reported worse metacognitive ratings.

Conclusion
Findings suggest that deviation in typical daily sleep patterns (shorter TST, a greater number of nighttime awakenings, greater WASO) may impact daily metacognitive ratings in college students. Similar patterns are not observed at the average/interindividual level, prompting the need for future studies to examine daily sleep in college students. These findings point to the need for research examining whether sleep interventions (e.g., Cognitive Behavioral Therapy for Insomnia) for college students who experience sleep variability, could improve metacognition, which in turn could improve academic outcomes.

Support (If Any)
0116 Slow oscillations promote long range effective communication: the key for memory consolidation in a broken down network
Authors: Niknazar Hamid , Malerba Paola , Mednick Sara 
Published: May 2022

Abstract

Introduction
The relation between slow oscillations (SOs, &lt;1Hz) during non-rapid eye movement (NREM) sleep and systems-level memory consolidation is one of the most robust findings in cognitive neuroscience. However, NREM is a brain state seemingly unfavorable to systems consolidation because a hallmark characteristic of this state is a breakdown in connectivity and reduction in synaptic plasticity with increasing depth of sleep. Our study addresses this apparent paradox and how SOs orchestrate neural communication.

Methods
We employed generalized partial directed coherence to estimate directional causal information flow between EEG channels across the electrode manifold during SO and non-SO periods. We examined the magnitude of causal information flow over the phase of SOs and found two peaks of flow preceding and following the trough of the SO. We categorized source-sink pairs of flows into three groups based on distance between source and sink of information flow. All the peak flows in each group were averaged and we tested relation between averaged magnitude of the flow and overnight episodic memory improvement using correlation test.

Results
The results reveal that NREM generally (non-SO periods) and during the SO trough show dampened neural communication. Causal communication during non-rapid eye movement sleep peaks during specific phases of the SO ( before and after SO trough), but only across long distances. Correlation test results showed that episodic memory improvement was predicted by peaks of information flow with longest distances between sinks and sources, and not by any other phase of the SO or non-SO period.

Conclusion
This work introduces a non-invasive approach to examine information processing during sleep, a behavioral stage whose function, until now, has been understood only at a delay. The findings represent a conceptual leap in understanding how slow oscillations unlock memory consolidation in a broken down network which is by promoting long range effective communication. This research will promote further investigations of understanding how brain oscillations alone and in nested rhythms promote network communication, as well as to investigate how these properties vary and predict patterns of deficits in clinical populations and aging humans.

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0118 Performance on a Computerized Threat Elimination Task in an Animated Environment during Total Sleep Deprivation
Authors: Moslener Emily , Honn Kimberly 
Published: May 2022

Abstract

Introduction
Military and law enforcement operators must make split-second decisions on whether to shoot during confrontations. Quick responses are crucial when force is necessary, but accurate decision-making is also imperative. Often, decisions are made while fatigued, which could impair speed and/or accuracy. Furthermore, the reliability of background information may impact performance. We investigated performance on a computerized shooting task during a total sleep deprivation (TSD) study.

Methods
N=86 healthy adults (39 males, age 21-38) completed a 4-day/3-night in-laboratory study, randomly assigned to a TSD (n=56) or control (n=28) condition. A custom task was administered after 32h or 8h of wakefulness (TSD and control groups). Participants were to shoot enemy robots (press spacebar) and not shoot friendly robots (no response) within 500ms of each robot being revealed inside shipping crates (1-5s inter-trial-interval). The task introduction described which crates would contain enemies, but the intel’s accuracy varied across four phases: 100% (20 trials), 80% (120 trials), and 20% (40 trials), then irrelevant in a new environment (60 trials). Reaction time (RT) and accuracy (hits and false alarms (FAs)) were analyzed using 2x4 mixed-effects ANOVAs to determine the effects of condition, phase, and their interaction.

Results
There was a significant effect of phase on RT (p&lt;0.001); in both conditions, participants reacted faster in phase 1 than all other phases. However, there was no effect of condition (p=0.20) or phase-condition interaction (p=0.080) on RT. There were significant effects of condition on hits (p&lt;0.001) and FAs (p=0.004); TSD had fewer hits and more FAs than the control group. There was an effect of phase on hits (p=0.045), with fewer hits in phase 1, and a condition-phase interaction (p=0.026) showing that the TSD group experienced less improvement in hits. For FAs, there was no effect of phase (p=0.86) or phase-condition interaction (p=0.86).

Conclusion
The results suggest a speed/accuracy tradeoff during TSD, where relative to the control group, RTs remained equivalent, but accuracy was worse. In both groups, the RT slowing from phase 1 to subsequent phases, suggests that participants initially used the intel to facilitate quicker decision-making, but disregarded it once it was not completely reliable.

Support (If Any)
CDMRP grants W81XWH-16-1-0319 and W81XWH-20-1-0442