bluuebunny/crossref_metadata_2025_split huggingface dataset:
0271 Patterned Planar Bed Surface Vibration Is Effective in Shortening Time to Onset of Sleep (As Good as a Sleeping Pill?)
Authors: Bentley Jacob , Dunn Megan , Mohler Brandon , Blotter Jonathan , Manwaring Kim 
Published: May 2023

Abstract

Introduction
70 million Americans suffer from poor sleep, 42 million have a chronic sleep disorder, and an estimated 64% report regularly taking a pharmacological sleep aid regularly with well-known risks. A link between vibration and sleep was first observed during investigations of highway hypnosis. We developed a prototype bed producing sub-audible, tactilely perceptible traveling-wave vibration where noninvasive sleep monitors could objectively measure sleep.

Methods
A bed was outfitted with low-frequency effectors (LFEs) and phasers to create infrasonic, low-frequency vibration that traversed the mattress in patterns. Amplitude was adjusted to comfort using a bedside dial. Sleep was monitored over four sequential weeks in a single 22-year-old healthy male subject with 3 devices each employing a unique mechanism: the OuraRing (actigraphy), Google NestHub (GHz radar chip), and SleepScore (ultrasound). No vibration was used during weeks 1 and 4 to establish a baseline/return to baseline. Week 2 vibration was a highly periodic waveform (25 Hz, migrating at 1 Hz), while week 3 vibration was an irregular, chaotic waveform. Objective data was collected from the 3 devices to plot total sleep duration and latency of onset.

Results
1. Strong agreement was seen among the 3 different sensors 2. Patterned, highly periodic, low-frequency vibration shortened sleep latency by nearly 50% consistently for one week (from 15 to 8 minutes) 3. Chaotic, irregular vibration of week 3 also shortened latency, but less so (from 15 to 12 minutes) 4. Baseline and repeat baseline were statistically nearly identical 5. Sleep duration was not impacted by vibration (mean 7 hours 45 minutes)

Conclusion
The primary mechanism of improved latency with vibration may be somatic/mental distraction. The subject reported that vibration diverted away from pre-occupying thoughts and was highly pleasant. These observations were reflected in nearly 50% reduction of latency in week 2 and more modest reduction in week 3. Patterned planar vibration appears as effective as prescribed sleep medications, suggesting it may be a useful non-pharmacologic sleep adjunct.

Support (if any)
Jacob Bentley is the recipient of scholarship support for this research from the American Medical Association and AASM.
0272 BINAURAL BEATS AT SLOW FREQUENCIES LESS THAN 1 HZ MAY INDUCE SLOW-WAVE SLEEP FASTER
Authors: Fan Zhiwei , Zhu Yunyao , Suzuki Chihiro , Suzuki Yoko , Watanabe Yumi , Endo Junki , Watanabe Takahiro , Abe Takashi 
Published: May 2023

Abstract

Introduction
Binaural beats are an auditory phenomenon when two sounds with a certain frequency difference are transmitted simultaneously to each ear, one perceives a beat with a frequency exactly equal to that frequency difference. Previous studies have shown that binaural beats synchronize neural oscillations and induce related brain states and behaviors. One previous study showed that listening to binaural beats at 1 Hz during naps increased the duration of slow-wave sleep. However, the effect of binaural beats in the slow frequency range (&lt; 1 Hz) on sleep is poorly understood.

Methods
We hypothesized that 0.25 Hz-binaural beats could synchronize slow neural oscillations and thus enhance slow-wave sleep by shortening its latency and lengthening its duration. Twelve healthy subjects (six females, 25.3 ± 2.6 years) were included in this study. The experiment consisted of four 90-minute afternoon naps, including a sham condition (no acoustic stimulation) and three binaural-beat conditions (0 Hz, 0.25 Hz, 1 Hz) with a 250 Hz carrier tone. Participants were not informed of these conditions. Auditory stimuli were delivered through headphones and continued throughout the 90-minute nap.

Results
The results showed that the binaural-beat condition at 0.25 Hz had shorter latencies for both N2 and N3 than the sham stimulus condition.

Conclusion
This study suggests that binaural beats at slow frequencies, such as 0.25 Hz, may induce slow-wave sleep faster in healthy individuals.

Support (if any)
KYOCERA Corporation, Japan, AMED (Grants No. JP21zf0127005), and World Premier International Research Center Initiative (WPI), MEXT, Japan.
0274 Comparing Sleep Architecture in Apple Watch Series 7 with SleepImage Rings
Authors: Meghpara Sanket , Niraula Rijesh , Awad Dana , DelRosso Lourdes , Keenan Lynn , Van Gundy Karl 
Published: May 2023

Abstract

Introduction
The awareness of the importance of healthy sleep is increasing in the community, hence many devices were developed in the last decade to help recognize sleep architecture. Studies that assessed the matching between sleep rings and iOS systems in apple watches are limited. We aimed to show that the Apple watch can be a useful tool in evaluating sleep architecture for many sleep disorders by comparing it with sleepimage rings.

Methods
We conducted home sleep studies on 1 participant for 7 nights using SleepImage rings and Apple Watch Series 7 with sleep health simultaneously. The participant is a healthy 32 year old male. N1, N2 and N3 stages of sleep were clumped together as NREM in the apple watch for comparative data. Data collected included total sleep time (TST), time in bed (TIB), time in REM, and time in NREM. Data was analyzed with IBM SPSS v. 29.0.0 using descriptive statistics. Mann Whitney test for comparison with p&lt; 0.05 considered significant.

Results
Data in minutes included mean TST with watch 408.7, standard deviation (SD) 44.3 and with ring 404.57, SD 47.1 (p=0.902). Total NREM sleep with watch 330.1, SD 32.8 and with ring 337.8, SD 42.3 (p=0.620). Total REM with watch 72.7 SD 19.5 and with ring 66.7 SD 13.1 (p=0.535). Total TIB with watch 475.3 SD 20.8 and with ring 421.4 SD 52.8 (p= 0.017).

Conclusion
We found no statistically significant difference between the sleep ring and apple watch in estimating TST, REM-sleep and non-REM sleep. The only measurement that was significantly different is the time in bed. This report suggests that Apple Watch can be used as an adjunctive tool for sleep architecture for treating sleep disorders.

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0280 Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach
Authors: Hu Xiangyu , Yang Hongpeng , White James , Nelakuditi Srihari , Ghosal Rahul , Tong Yan , Weaver Glenn , Finnegan Olivia 
Published: May 2023

Abstract

Introduction
Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms.

Methods
38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model.

Results
LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz.

Conclusion
Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices.

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0281 An Assessment of the Agreement Between Sleep Diary and Actigraphy in Firefighters
Authors: McGoldrick-Ruth Logan , Marmis Ryan , Kelly Monica , Haynes Patricia 
Published: May 2023

Abstract

Introduction
Sleep disruption is a common complaint for firefighters, largely thought to emanate from work and personal demands that prohibit recovery sleep. These disruptions increase the risk of sleep disorders, cardiovascular disease, cancer, and other disorders. Given the significance of sleep on health and the high frequency of sleep disruption in the fire service, we sought to examine the concordance between actigraphic and daily diary sleep assessments. The purpose of this analysis was to contribute information on the use of actigraphy and sleep diaries in shift workers individuals prone to insufficient sleep.

Methods
The sleep of 60 firefighters (n = 329 observations) working 24-hour shifts was assessed on a 6-day recovery period. Objective sleep was assessed via the Actiwatch-2, a research-grade, wrist-worn actigraph that measures motor activity in 30 sec intervals. Subjective sleep was assessed via the research consensus daily sleep diary, a self-reported measure collected upon awakening. Major sleep indices compared were: sleep onset latency (SOL), total sleep time (TST), sleep efficiency (SE), and wake time after sleep onset (WASO).

Results
Repeated effects Bland-Altman analysis using a mixed effects technique (measurement = fixed effect; participant = random effect) found that firefighters underestimated WASO (M = 32 min) and overestimated SE (M = 10%) at a level greater than the a priori clinical significance thresholds set by the American Academy of Sleep Medicine for insomnia. The limits of agreement for all sleep indices were very broad. For instance, 95% of the differences between self-report and actigraphic TST fell within a 4.7h range. The majority of the variability could be attributed to within-subject sources of variability versus between-subject sources of variability.

Conclusion
In firefighters, actigraphy and sleep diaries showed substantial disagreement in major sleep indices, with a systematic underestimation of WASO and overestimation of SE. The wide range of differences suggest that daily assessments of SOL, WASO, SE, or TST in on-call shift workers should be compared against a feasible gold-standard. Further research is needed to understand within-subject factors (e.g., daily differences) that predict a discrepancy between actigraphic and self-reported sleep in different working populations.

Support (if any)
#1R01HL117995-01A1
0282 Behavioral–social rhythms and metabolic syndrome prevalence in retired night shift and day workers
Authors: Tracy Eunjin , Chin Brian , Lehrer H Matthew , Hasler Brant , Thomas Mark , Smagula Stephen , Kimutis Sarah , Hall Martica , Buysse Daniel 
Published: May 2023

Abstract

Introduction
Disrupted sleep and circadian rhythms, such as those occurring during night shift work, are established cardiovascular disease (CVD) risk factors like hypertension, obesity, and insulin resistance. However, we do not know whether the effects of shift work on CVD risk factors persist into retirement. Conversely, regularity in behavioral-social rhythms – the systematic patterning or timing of daily routines like work/housework, sleeping, eating and social interactions – may keep endogenous circadian rhythms synchronized to the 24-hour day and reduce CVD risk. This study examined: 1) whether behavioral–social rhythms are associated with a major CVD risk factor, prevalent metabolic syndrome, in retired night shift workers and day workers; and 2) whether past night shift work exposure moderates the associations between behavioral–social rhythm and metabolic syndrome prevalence.

Methods
Participants were 71 retired night shift workers (39 women (54.9%), 32 men (45.1%)) and 83 retired day workers (46 women (55.4%), 37 men (44.6%)), with mean age 68.4 (5.4) years. Logistic regression models were used to examine associations between behavioral–social rhythms and metabolic syndrome prevalence. Independent variables included Social Rhythm Metric-5 (SRM5; a 5-item scale to quantify rhythms of daily life) score and actigraphy rest-activity rhythm inter-daily stability (IS; rhythm stability across days) and intra-daily variability (IV; rhythm fragmentation within days). The dependent variable was metabolic syndrome prevalence.

Results
More regular behavioral-social rhythms were associated with lower odds of having metabolic syndrome (SRM5: OR=0.57, 95%CI [0.35, 0.88]; IV (Higher score means lower regularity): OR=2.05, 95%CI [1.23, 3.44]). In addition, more regular behavioral-social rhythms were associated with lower odds of having two individual components of metabolic syndrome , body mass index (SRM5: OR=0.56, 95%CI [0.37, 0.85]; IV: OR=2.05, 95%CI [1.30, 3.23]) and high-density lipoprotein cholesterol level (SRM5: OR=0.49, 95%CI [0.30, 0.80]; IV: OR=1.78, 95%CI [1.06, 2.98]). Shift work exposure did not moderate the association between behavioral–social rhythms and metabolic syndrome prevalence.

Conclusion
Behavioral–social rhythms are related to metabolic syndrome prevalence in retired adults regardless of prior shift work exposure. Older retired workers may benefit from interventions that promote behavioral–social rhythm regularity.

Support (if any)
This research was supported by the NIH grants AG047139, HL082610, MH019986, and AG07517
0283 Firefighters’ Sleep Changes After Switching Shift Schedules: A Longitudinal Study
Authors: Billings Joel 
Published: May 2023

Abstract

Introduction
In the past few years, fire departments in the US have been switching from 24 hours on / 48 hours off (24/48) shift schedule to the 48 hours on / 96 hours off (48/96) shift schedule to increase recruitment and reduce commute frequency for members living outside the jurisdiction. While firefighters at work may be able to rest and sleep when not otherwise busy, it is not a guarantee and concerns on health and safety understandably exist when subject to 48 hours on shift. Yet, it is unclear how switching schedules influence firefighters’ sleep. Therefore, the objective of this research is to assess sleep before and after firefighters transitioned from the 24/48 to the 48/96 shift schedule.

Methods
A pre-experimental, within-subjects, longitudinal sleep study was implemented with a Southcentral US fire department (n=24). Daily sleep included total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO), and was assessed using wrist actigraphy and the Emergency Services Sleep Diary. Participants completed three, 18-day rounds of data collection: baseline assessment was conducted on the 24/48 and post assessments were conducted after three and six months following the transition to the 48/96.

Results
The results of separate one-way repeated measures ANOVA indicated a statistically significant difference in TST (F(2,46) = 4.58, p&lt; 0.05), SE (F(2,46) = 7.97, p&lt; 0.01), SL (F(2,46) = 6.88, p&lt; 0.05), and WASO (F(2,46) = 5.58, p&lt; 0.05) as firefighters transitioned schedules. Post-hoc analysis identified statistically significant pairs between the 24/48 and the three-month post assessment for each sleep parameter: TST (6.4h vs 6.8h), SL (85% vs 89%), SL (8.7min vs 4.9min), and WASO (58min vs 45min).

Conclusion
Firefighters reported an improvement in sleep three months after transitioning to the 48/96. The results suggest that improved sleep on the 48/96 may be related to consolidating work shifts, which reduces the frequency of experiencing advance sleep offset on commute days and allows a greater recovery period at home. While further research is needed to assess how health and performance outcomes are related to firefighter schedules, the findings suggest that the pattern of work and non-workshifts influences sleep.

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0285 Sleep health in veteran population with and without mild traumatic brain injury
Authors: Cho Yeilim , Peskind Elaine , Iliff Jeffrey 
Published: May 2023

Abstract

Introduction
Sleep duration is frequently observed in military and Veteran populations. Some of this effect may result from exposure to traumatic brain injury (TBI) during military service. However, other features of combat deployment, circadian disruption, and chronic stress may contribute to poor sleep independent of the effects of TBI. In this study we tested whether combat deployment with or without TBI exposure was associated with poor sleep compared to civilian controls.

Methods
In a prospective study of Operation Enduring Freedom/Iraqi Freedom/ New Dawn Veterans with repeated blast mild TBI (mTBI) conducted at the VA Puget Sound Healthcare System, self-reported sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Age-matched civilians (CONciv) non-TBI deployed Veterans (CONdep) were included in the study for comparison. The outcomes of interest included the PSQI sum score and its key components scored as 0-3. One way ANOVA and paired comparison with Bonferroni adjustment for each component were performed .

Results
A total of 104 mTBI, 36 CONdep and 15 CONciv participants were included. Poor overall sleep quality (PSQI sum&gt;5) was most frequently reported by mTBI participants, followed by CONdep and CONciv participants (80 % vs. 50 vs. 11, p&lt; 0.0001). Across virtually every sleep feature, sleep impairment was most pronounced in mTBI participants followed by CONdep: PSQI sum (mTBI VS. CONdep vs. CONvic= 11.6 vs. 6.5 vs. 3, p&lt; 0.0001), sleep duration (5.5 hrs vs. 6.6 vs. 7.1, p&lt; 0.0001), sleep onset insomnia (2.1 vs. 1.3 vs. 0.3, p&lt; 0.0001) , sleep maintenance insomnia (2.5 vs. 1.4 vs. 0.7, p&lt; 0.0001), daytime sleepiness (1.3 vs. 0.6 vs. 0.4, p&lt; 0.0001), daytime function (1.8 vs. 0.9 vs. 0.3, p&lt; 0.0001) and sleep disturbance by pain (1.5 vs. 0.7 vs. 0.1, p&lt; 0.0001). Both mTBI and CONdep groups had significantly worse sleep quality than the CONciv group.

Conclusion
Veterans with repetitive mTBI exhibited overall poor sleep quality and impaired sleep in multiple sleep domains. Deployed Veterans without TBI, but exhibited similar, but less severe sleep disruption. Future studies should explore how poor sleep quality may be related to the long-term effects of TBI and military deployment.

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0288 Multidimensional Sleep Health and Cognitive Function in Retired Night Shift Workers and Retired Day Workers
Authors: Lehrer H Matthew , Butters Meryl , Buysse Daniel , Hall Martica 
Published: May 2023

Abstract

Introduction
Night shift work is associated with compromised cognitive function, and with chronic exposure, may place shift workers at elevated risk for dementia. Given that former night shift workers experienced prior long-term sleep and circadian rhythm disruption, their sleep in retirement—after returning to a daytime schedule—may be particularly salient for cognitive function. This study examined associations between multidimensional sleep health and neurocognitive function among retired night shift workers and retired day workers.

Methods
Participants (N = 61; mean age: 67.9 +/- 4.7 years; 61% females; 13% non-Hispanic White) were 31 RDW and 30 RNSW equated on age, sex, race/ethnicity, premorbid IQ, and years in retirement. Multidimensional sleep health was quantified using wrist actigraphy measures of sleep efficiency, timing, duration, and regularity, and diary measures of alertness and satisfaction. Each component was dichotomized and summed to create a composite score (0-6); higher values indicated better sleep health. Participants completed a neurocognitive battery assessing six cognitive domains (language, visuospatial ability, attention, immediate and delayed memory, executive function) and self-reported cognitive function. Linear regression models tested associations of multidimensional sleep health and its individual components with cognitive domains in each group, adjusting for age, sex, and education.

Results
Retired night shift workers and retired day workers did not differ on multidimensional sleep health or its individual components. Among retired night shift workers, poorer multidimensional sleep health was associated with poorer self-reported global cognitive function (beta = 0.51, p = .018), and more irregular sleep timing was associated with poorer self-reported memory (beta = 0.41, p = .054). Multidimensional sleep health and its individual components were unrelated to cognitive function in retired day workers.

Conclusion
Retired night shift workers may be cognitively vulnerable to poor sleep health in retirement, possibly due to a history of long-term sleep and circadian rhythm disruption while working. Given that self-reported cognitive difficulties often precede more objective cognitive changes, future research should determine whether associations of sleep health with cognitive function are found in more objective cognitive measures as retired night shift workers age.

Support (if any)
R01AG047139, R01AG047139-S1, K01AG075171