bluuebunny/crossref_metadata_2025_split huggingface dataset:
0675 Effects of Circadian Acclimation of Performance, Sleep, 6-sulfatoxymelatonin on Heart Rate & Heart Rate Variability
Authors: Patel Milin , Saissan Kiyan , Mak Natalie , Youngstedt Shawn , Patel Salma 
Published: April 2024

Abstract

Introduction
Jet lag is associated with transmeridian travel across at least two time zones, associated with sleep disturbance as well as daytime dysfunction. The presence of jet lag has been hypothesized to be associated with adverse cardiovascular outcomes; however, objective data are not available. Jet lag can have many negative effects on physical and mental health, mental and physiological performance, and sleep. The objective of this study is to understand the impact of simulated jet lag on heart rate.

Methods
Following a baseline circadian rhythm assessment, participants were placed on a 16 hour wake – 8 hour sleep schedule in which the wake-sleep and light-dark schedule was delayed by 8 hours for 3 days (analogous to traveling 8 time zones west). Participants were randomized to one of 3 treatments administered each of the 3 days of the shifted schedule: (A) placebo control, (B) bright light, and (C) bright light + exercise + melatonin. Participants wore an Extended Wear Holter Monitor for the duration of the 6.5 day laboratory stay. Minimum, average and maximum heart rates as well as heart rate variability were evaluated for each phase in the laboratory.

Results
15 of the 22 patients enrolled wore the Holter monitor and completed the study (86.6% men, mean age 28.4 ± 9.56)— 5 were in the placebo arm, 7 in bright light, 3 in the bright light + exercise + melatonin group. In the placebo group, the average heart rate in beats per minute (± SD) increased in the first ultrashort period when compared to baseline (70 (6) vs. 66 (8), p =0.022). In the bright light group, the average heart rate (± SD) decreased during the shifted schedule when compared to baseline (70 (14) vs. 74 (4), p =0.015). No other significant changes were noted.

Conclusion
The initial induction of jet lag seems to have detrimental effects on heart rate, however the body adjusts thereafter and bright light may be helpful to reduce the impact of jet lag on heart rate.

Support (if any)
Department of Defense, Philips, American Academy of Sleep Medicine Foundation, National Institutes of Health, The University of Arizona.
0678 Genetic Variants Associated with Dim Light Melatonin Onset in a Delayed Sleep-Wake Phase Disorder Cohort
Authors: Brzezynski Jennifer , Johnson Caroline , Smieszek Sandra , Bai Haimeng , Polymeropoulos Christos , Birznieks Gunther , Polymeropoulos Mihael 
Published: April 2024

Abstract

Introduction
We are conducting a double-blind, randomized, clinical study in Delayed Sleep-Wake Phase Disorder (DSWPD) participants with extensive clinical phenotyping. We evaluated screening Dim Light Melatonin Onset (DLMO) assessments in participants with a DSWPD diagnosis to determine the proportion of participants with and without a circadian delay.

Methods
Delayed DLMO is DLMO occurring after or within 60 minutes before desired bedtime, and after 22:00. Each DLMO assessment consisted of eight saliva collections performed at five, four, three, two, and one hour before bedtime, at planned bedtime, and one and three hours after bedtime. DLMO assessments were distributed to participants at Visit 1 (screening) and Visit 3 (treatment) with a questionnaire to record planned and actual collection times. Participants were instructed to wear blue-light blocking glasses. The Morningness-Eveningness Questionnaire (MEQ) was also completed by participants at V1 to evaluate circadian and sleep rhythm patterns. DLMO was defined as the clock time when the melatonin concentration exceeded the mean of three low consecutive values, plus twice the standard deviation of these points. Additionally, a sample for whole genome sequencing was collected.

Results
Forty-seven participants with DSWPD completed the screening DLMO assessment, 38 of which had a DLMO after 22:00 (80.9%). Sub-analyses were conducted on these participants who had delayed DLMO. Within this subset, the average DLMO time was 23:41 and the average MEQ score was significantly lower than those without a circadian delay. Of these 38 participants, 19 had a DLMO time after 00:00. Furthermore, we completed a linear regression analysis on DLMO time in a circadian gene set. The top scoring variant was 3’ UTR rs10181401 in PER2, amongst others detected.

Conclusion
These initial data indicate that, on average, participants with DSWPD that completed the screening DLMO assessment had delayed DLMO. Further analyses show that half of this subset had significantly delayed DLMO (00:00 or later). This study is currently ongoing and blinded. Further data will be analyzed as more participants enroll. ‘Phase typing’ will be important in further understanding the underlying pathophysiology and in the treatment selection for patients with DSWPD.

Support (if any)
This work was supported by Vanda Pharmaceuticals Inc.
0689 Automated Detection of Isolated REM Sleep Behaviour Disorder Using Computer Vision
Authors: Abdelfattah Mohamed , Sum-Ping Oliver , Galati Joanna , Marwaha Salonee , Alahi Alexandre , During Emmanuel 
Published: April 2024

Abstract

Introduction
Isolated Rapid Eye Movement (REM) Sleep Behavior Disorder (iRBD) is a marker of neurodegeneration and is currently diagnosed based on REM sleep without atonia (RSWA) and a history of dream enactment. However, even for sleep experts, scoring RSWA can be challenging and incidental RBD cases are easily missed. Previous research focused on the automatic identification of iRBD using 3D video analysis. This study aims at developing a machine learning classifier to detect iRBD using only conventional 2D cameras and evaluating its performance on an expanded cohort.

Methods
We used 2D video data from in-lab video-polysomnography recorded at the Stanford Sleep Center in 78 patients with definite iRBD per ICSD-3 TR criteria and 109 without RBD (41 OSA [AHI &gt; 15], 37 RLS/PLM [PLMi&gt;15], 9 non-RBD parasomnias, 5 insomnia, 2 narcolepsy, 39 with normal sleep). An automatic computer vision algorithm, i.e. optical flow, was applied on the recorded videos to detect periods of movements in REM sleep. Movement periods were divided based on their duration from very brief (0.1s) to extended (&gt;30s) motor behaviors, 1 second of immobility separating each period. Features were movement frequency and proportion of movement periods in REM sleep. Different combinations of these features fed a logistic regression classifier, which was trained and tested in the 10-fold cross-validation scheme.

Results
On average, 58.13 and 42.53 movement periods were detected during REM sleep in cases and controls, respectively. The best performance was achieved using short movements (0.1-2s) with sensitivity of 0.921, specificity of 0.674, accuracy of 0.835 and F1 score of 0.847. With medium (2-15s) and long (15-300s) movements, accuracy was 0.845 and 0.672, sensitivity 0.743 and 0.147, specificity 0.901 and 1.0, and F1 score 0.808 and 0.256, respectively. Adding features of gender, AHI, and PLM index to short movements increased accuracy to 0.886.

Conclusion
One-night video data from conventional video-polysomnography can detect iRDB using machine learning techniques. Automated video analysis of REM sleep could augment our current ability to screen and diagnose RBD in the sleep laboratory setting.

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0701 Perceived Exercise Response Heterogeneity in Adults with Restless Legs Syndrome
Authors: Cederberg Katie , Sikes E Morghen , Mejia Giorgio Ricciardiello , Mignot Emmanuel 
Published: April 2024

Abstract

Introduction
Restless legs syndrome (RLS) is a prevalent, sensorimotor sleep disorder that is temporarily relieved by movement. There is evidence of inter-individual variation in the response to exercise (i.e., response heterogeneity). We examined factors that may account for inter-individual differences in perceived response to exercise as a mode of treatment for RLS symptoms.

Methods
Participants (N=528) completed a mixed-methods survey including items assessing RLS outcomes, physical activity levels, and personal experiences with exercise. Participants were classified as “perceived responders” (i.e., exercise impacts symptoms) and “perceived non-responders” (i.e., exercise does not impact symptoms). Perceived responders were further classified as “positive responders” (i.e., exercise improves symptoms) or “negative responders” (i.e., exercise exacerbates symptoms). In this pilot study, an XGboost classifier was used to predict responsiveness to exercise based on clinical and demographic characteristics. Two binary models were implemented, one for non-responders vs responders (M1) and the other for positive responders vs. negative responders (M2). Each model datasets were split into training and test sets.

Results
There were no significant differences in demographic or clinical characteristics between perceived responders (n=450) and perceived non-responders (n=76). Perceived positive responders (n=172) reported significantly lower BMI, overall RLS severity, and had a lower proportion of people with periodic limb movements than perceived negative responders (n=72). M1 classifier resulted in weighted F1 score, sensitivity, and specificity of 0.92, 0.92 and 1 for training and 0.83, 0.8, 0 for testing, respectively. Whereas the M2 classifier resulted in a weighted F1 score, sensitivity, and specificity of 0.9, 0.90, 0.95 for training and 0.6, 0.79, 0 for testing, respectively. XGBoost feature importance ranked RLS pregnancy incidence, RLS time onset (morning, afternoon), total RLS severity score, physical activity level, BMI, age, and sedentary time as most important.

Conclusion
The present study reveals factors that correlate with individual differences in response to exercise for people with RLS. If confirmed, such individual differences and specific attributes may be useful to optimize individual treatment plans that include exercise as a recommendation.

Support (if any)
This work was supported, in part, by the National Heart, lung, and Blood institute [T32HL110952].
0703 Catch-up Weekend Sleep Duration Is Associated with Abnormal Behavior During Sleep in Adolescents
Authors: Yang Kwang Ik , Park Jongkyu , Kim Jee Hyun , Kim Daeyoung , Hong Seung Bong 
Published: April 2024

Abstract

Introduction
Studies focusing on the association between sleep habit and parasomnia in adolescents are relatively scarce compared to other age group. Our study aimed to investigate the association between sleep duration and abnormal behavior during sleep (ABS) in Korean adolescents.

Methods
In this cross-sectional study, we evaluated 25,789 adolescents between 12 and 18 years of age (mean 15.76 ± 1.73 years; male 48.51%). Global Sleep Assessment Questionnaire and self-report questionnaires were used to assess ABS, sleep habits including catch-up weekend (CUW) sleep duration, and various socio-behavioral factors as covariates.

Results
The prevalence of ABS was 3.39% (n = 873). The analyses of multivariate logistic regression of the adjusted OR for ABS were significantly associated with male (OR, 1.304; 95% CI, 1.111 - 1.532), usually/always snoring (OR, 3.452; 95% CI, 2.800 - 4.257), usually/always witnessed sleep apnea (OR, 5.494; 95% CI, 4.143 - 7.286), weekday sleep duration (OR, 0.850; 95% CI, 0.797 - 0.90), weekend sleep duration (OR, 1.041; 95% CI, 1.004 - 1.080), CUW sleep duration (OR, 1.063; 95% CI, 1.026 - 1.101), perceived insufficient sleep (OR, 1.299; 95% CI, 1.092 - 1.545), increased internet addiction (OR, 1.031; 95% CI, 1.024 - 1.038), often coffee consumption (OR, 1.857; 95% CI, 1.443 - 2.390), often alcohol consumption (OR, 2.377; 95% CI, 1.292 - 4.374), sleeping with a doll or pets (OR, 1.60; 95% CI, 1.351 - 1.895), and keeping TV or radio during sleep (OR, 1.325; 95% CI, 1.084 - 1.619).

Conclusion
Short weekday sleep duration and CUW sleep duration are associated with abnormal behavior during sleep in Korean adolescents.

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0713 Variability of Software Scored PLM's
Authors: Rosenberg Carl , Gandotra Kamal 
Published: April 2024

Abstract

Introduction
Periodic leg movements have been a source of debate as to their clinical significance. Interpretation varies between no clinical significance (Mahowald MW 2001) to a relationship to Heart disease ( Skomo R 2009). Routine technician scoring of PLM’s is rare outside of academic institutions. Insurances, including Medicare and Medicaid, will not approve a repeat study only for evaluation of PLM’s. The following brief study was initiated to review automated software scoring of periodic leg movements based on one sleep study per a patient (Respironics Alice G3).

Methods
Data was obtained from 56 sequential sleep studies with ~240 min of sleep (225 – 434, median 330). “PSG” represents all studies without titration. There were more females (17) than males (8), Ages (24 – 70, median 49.5). This data set is small and not a normal distribution. A technician scored minutes of sleep and AHI, (.4-96.7, median 5.4) while PLM's were autoscored (G3).

Results
There was significant variability in the software generated PLMI, Range 765 (4 –769) median 75.5. • There wasn’t a useful correlation between AGE and PLMI (r2 = .019); thus, of little predictive value in an individual patient’s single test. • The PLMI was not predicted nor differentiated by gender. • There wasn’t a useful correlation between PLMI being related to AHI (r2 = .006), also of little predictive value in an individual patient’s single test.

Conclusion
Most clinicians can rely only on one sleep study, one measure, of PLMI. This brief study suggests that PLM’s scored by software varies greatly when obtained from a single study. Software scored PLMI results must be used with caution in making diagnoses such as PLMD or criteria for RLS. A clinician might have to reject the reported PLMI result.

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0715 NREMembrance of Things Past: Obstructive Sleep Apnea Impacts Sleep Neurophysiology and Memory Retention in Aging
Authors: Dave Abhishek , Lui Kitty K , Sprecher Kate E , Berisha Destiny , Sattari Negin , Chappel-Farley Miranda G , Chen Ivy Y , Riedner Brady , Bendlin Barbara , Mander Bryce , Benca Ruth 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is associated with sleep disturbances, memory impairment, and dementia risk in older adults. The impact of OSA on sleep neurophysiology and related memory function remains unclear. We examined associations among OSA severity, non-rapid eye movement (NREM) sleep expression, and overnight memory retention in cognitively intact older adults at risk for Alzheimer’s disease (AD).

Methods
Overnight clinical polysomnography (PSG) with 256-channel electroencephalography (EEG) was conducted in 81 cognitively intact older adults (aged 61.68◻5.99 years, 49 female, Apnea/hypopnea Index, AHI: 7.61◻10.64 ), with word-pairs task (WPT) encoding and cued recall occurring prior to and following sleep. AHI, respiratory disturbance index (RDI), and oxygen desaturation index (ODI) were used as measures of OSA severity and cube-root transformed to normality. EEG data underwent preprocessing, artifact rejection, and segmentation into concatenated NREM epochs, and were then processed using multitaper spectral analysis with 11 tapers. Behavioral data, obtained as overnight proportion change in retention of word-pairs, were also cube-root transformed to enhance normality. Topographical correlations were then computed to examine associations between NREM sleep features and OSA severity, using 5000 permutations of threshold-free cluster enhancement (TFCE) for multiple comparisons correction. Multiple linear regression modeling was leveraged to examine whether spatiospectral NREM clusters associated with OSA severity were in turn predictive of overnight memory retention when adjusting for age and sex.

Results
Global, TFCE-significant (p&lt; 0.05 corrected) negative correlations were seen between delta power (1-4.5Hz) and AHI and RDI, theta power (4.5-7.5Hz) and AHI and RDI, alpha power (7.5-11Hz) and RDI, and slow sigma power (11-13Hz) and ODI . Regression models (adjusted for age and sex) using cluster averages of spectral power from prior correlations to predict WPT performance further revealed significant effects for theta power (AHI cluster: B=0.077, p=0.007; RDI cluster: B=0.076, p=0.008), alpha power (RDI cluster: B=0.107, p=0.010), and for slow sigma power (ODI cluster: B=0.203, p=0.007), but not for delta power (both clusters: p&gt;0.33).

Conclusion
These findings indicate that OSA is associated with globally disrupted NREM sleep expression, and that reduced theta, alpha, and spindle activity is further associated with impaired overnight episodic memory retention.

Support (if any)
NIH grants R56AG052698, P50AG033514, F31AG048732, K01AG068353.
0721 Association Between Subjective-Objective Sleep Time Discrepancy and Mortality in Older Men With/Without Insomnia
Authors: Utusumi Tomohiro , Yoshiike Takuya , Aritake-Okada Sayaka , Matsui Kentaro , Nagao Kentaro , Saitoh Kaori , Otsuki Rei , Suzuki Masahiro , Kuriyama Kenichi 
Published: April 2024

Abstract

Introduction
Subjective sleep duration often differs from objective measurements. Our prior research indicates that overestimating sleep duration may be a risk factor for all-cause mortality in older men. Conversely, those with insomnia, which is an another mortality risk factor, typically underestimate their sleep duration. This study investigates how both underestimation and overestimation of sleep duration impact all-cause mortality in community-dwelling older men (aged ≥ 65) comparing with and without insomnia symptoms.

Methods
We conducted a secondary analysis using data from the Osteoporotic Fractures in Men Sleep Study, involving 2,200 participants who underwent in-home polysomnography and completed surveys assessing their subjective sleep duration, without using sleep-affecting medication (such as antidepressants, hypnotics or benzodiazepines). Insomnia was defined by a Pittsburgh Sleep Quality Index (PSQI) score over 5 (N = 816). We examined the relationship between subjective-objective sleep duration discrepancy index (SODI) and mortality using Cox regression. SODI, the ratio of subjective to objective sleep duration, was analyzed categorically, comparing its lowest and highest quartiles to the interquartile range within each group. The study was approved by the Ethics Committee of the National Center of Neurology and Psychiatry.

Results
During follow-up periods (mean: 10.6 years for insomniacs, 11.2 years for non-insomniacs), 504 (61.8%) insomniacs and 774 (55.9%) non-insomniacs died. In the insomnia group, both the lowest (adjusted Hazard Ratio [HR], 1.31; 95% Confidence Interval [CI], 1.02–1.68) and the highest (adjusted HR, 1.31; 95% CI, 1.03–1.66) SODI quartiles were associated with increased mortality compared to the interquartile range. Conversely, in the non-insomnia group, only the highest SODI quartile showed a significant increase in mortality (adjusted HR, 1.30; 95% CI, 1.07–1.59), with the lowest quartile showing no significant mortality association (adjusted HR, 0.94; 95% CI, 0.78–1.14).

Conclusion
Results demonstrate the significant role of subjective-objective sleep duration discrepancy in long-term health outcomes in both the insomnia and the non-insomnia group. Further physiological studies may elucidate the mechanisms of subjective-objective sleep duration discrepancy on mortality.

Support (if any)
This study received a grant-in-aid for scientific research from the Ministry of Health, Labor and Welfare, Government of Japan (grant numbers #19FA1009 and #21FA1002).
0726 Longitudinal Changes in 24-h Sleep-wake Patterns and All-cause Mortality in Older Women
Authors: Leng Yue , Milton Sasha , Cavailles Clemence , Ancoli-Israel Sonia , Ensrud Kristine , Stone Katie 
Published: April 2024

Abstract

Introduction
Sleep-wake patterns generally worsen with advancing age. However, little is known about changes in sleep and circadian patterns in very old adults, and how these changes may be associated with future mortality risk. We examined the association between changes in 24-hour sleep-wake patterns and risk of all-cause mortality in community-dwelling older women.

Methods
We studied 702 women (mean[SD] age = 82.9[2.7] years) who had nighttime sleep (total sleep time [TST]; sleep efficiency; and wake after sleep onset), napping (duration and frequency) and 24-hour rest-activity rhythm (acrophase, amplitude, mesor, and pseudo-F [regularity]) parameters assessed using 4-night/5-day actigraphy at baseline (2002-04) and again 5 years later (2007-08), after which all-cause mortality information was collected using death certificates. The 5-year changes in all parameters were calculated and included in a hierarchical clustering on principal components analysis to identify patterns of sleep changes. We used Cox proportional hazards models to evaluate the longitudinal associations between patterns of sleep changes and all-cause mortality.

Results
During a mean follow-up time of 2.1 years, 90 (12.8%) women died. We identified three patterns of sleep changes: 159 (22.6%) women with Pattern 1 (increased nighttime and daytime sleep and reduced circadian rhythmicity), 233 (33.2%) with Pattern 2 (decreased nighttime sleep quality and duration and reduced circadian rhythmicity), and 310 (44.2%) with Pattern 3 (stable sleep or slight improvement). After adjustment for age, education, body mass index, diabetes, hypertension, heart attack, antidepressant use, and baseline cognition, women with Pattern 1 [HR (95% CI) = 2.23 (1.25,4.01)] or Pattern 2 [HR (95% CI) = 2.26 (1.35,3.79)] had more than twice the mortality risk compared to women with Pattern 3. When individual sleep change parameters were examined, the association with mortality risk was particularly strong for increased TST, napping duration, and reduced circadian amplitude and regularity.

Conclusion
Among women in their 80s, those with increased nighttime and daytime sleep duration, decreased nighttime sleep quality or reduced circadian rhythmicity over 5 years had double the risk of all-cause mortality compared to those with stable sleep. Sleep changes in late life may be important marker or risk factor for adverse aging outcomes.

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