bluuebunny/crossref_metadata_2025_split huggingface dataset:
0404 Americas Prevalence of Insomnia Disorder in Adults: Estimation Using Currently Available Data
Authors: Benjafield Adam , Kuniyoshi Fatima Sert , Malhotra Atul , Maurer Leonie , Morin Charles , Martin Jennifer , Wickwire Emerson 
Published: April 2024

Abstract

Introduction
Insomnia disorder is a major clinical issue due to its high prevalence and negative health outcomes. While the prevalence of insomnia in developed countries like the United States and Canada is known, its prevalence across the broader Americas region (Northern America, Latin America and the Caribbean) remains unclear. Limited epidemiological studies have been conducted and the majority of the Americas’ population does not have a reliable estimate for insomnia disorder. The purpose of this analysis was to estimate the Americas’ prevalence of adult insomnia disorder using published data.

Methods
Published nation-specific estimates of the general population prevalence of insomnia disorder were applied to current population estimates for all countries in the Americas. We utilized the following criteria to determine which insomnia prevalence estimates to use: 1) studies that reported prevalence stratified by both age and sex together; 2) studies that reported prevalence by age and sex separately, we used the age-based results; 3) studies that reported prevalence by sex but not age; 4) studies that reported only a single prevalence estimate. Expert opinion was used to select the most methodologically rigorous study when &gt;1 potential reference study existed for a nation. For countries lacking a reference study, we applied the estimates from a large, well-conducted study. Population estimates by sex and age (20yrs+) were sourced from the United Nations World Population Prospects 2022.

Results
There were 55 countries defined by the United Nations comprising the Americas of which 3 (5.5%) had a suitable prevalence estimate of insomnia. We estimated an Americas’ adult insomnia disorder prevalence of approximately 123 million adults (16.8%), comprised of 73 million females (19.5%) and 50 million males (14.0%). The nations with the greatest burden of insomnia disorder are the United States (37 million), Brazil (29 million), and Mexico (16 million).

Conclusion
This large burden of disease, ~17% of adults, highlights the importance of comprehensive sleep health initiatives and the need for promotion of sleep health in public health policy. There is a lack of appropriate prevalence studies especially in low- and middle-income countries within the Americas.

Support (if any)
Study funded by ResMed
0408 Predictors of Responders in Sleep Hygiene Education: A Secondary Analysis of Randomized Controlled Trials
Authors: Yeung Wing Fai , Chak Kin Yeung , Wu Min Ru , Pang Lok Yi , Zhang Xuan , Chen Shu Cheng , Ruan Jia Yin 
Published: April 2024

Abstract

Introduction
Few studies have investigated the predictors of the treatment response after attending sleep hygiene education (SHE). We aimed to explore the patient characteristics associated with a response to SHE for insomnia.

Methods
A secondary analysis was conducted to pool the data of two randomized controlled trials for insomnia (Trial registration number ClinicalTrials.gov: #NCT04227587; NCT03623438), in which the subjects in the comparison group had attended two 2-hour sessions (total 4 hours) of small-group based SHE. We examined the association of sociodemographic variables, clinical characteristics, baseline sleep-wake variables, and Sleep Hygiene Practice Scale (SHPS) items to the treatment response. Subjects with a reduction in insomnia severity index (ISI) scores of ≥ 6 points from baseline to week 8 were classified as responders. Factors were compared between responders and non-responders and by univariate and multivariate logistic regression analysis.

Results
A total of 170 subjects with insomnia disorder who had received the SHE program were included and 42 (24.7%) of them were classified as responders. At 8 weeks after baseline, the subjects showed a 2.88 points reduction in ISI total score, 11.5 minutes reduction in sleep onset latency, 10.4 minutes reduction in waketime after sleep onset, 20.3 minutes increase in total sleep time, and 4.42% increase in sleep efficiency. Univariate logistic regression analysis has identified 12 potential predictors for SHE responders, including age, gender, baseline sleep parameters, and SHPS items. In the multivariate logistic regression analysis, age, ever tried exercise for improving sleep, and baseline ISI score remained significant predictors of SHE treatment response (age, adjusted OR 0.88, 95%CI 0.79, 0.97, p = 0.011; ever tried exercise for improving sleep, adjusted OR 11.0, 95%CI 1.16, 104.9, p = 0.037; baseline ISI score, adjusted OR 1.59, 95%CI 1.15, 2.19, p = 0.005).

Conclusion
Lower age, higher baseline ISI score, and ever-tried exercise for improving sleep were shown to predict the response of SHE. Our findings inform individuals who have insomnia to select the treatment for chronic insomnia in the future.

Support (if any)
The RCTs were supported by the Research Grants Council, General Research Fund (NCT04227587, Project no.: 15100419), and Health and Medical Research Fund (NCT03623438, Project no.: 19200171).
0409 The Impact of Fragrance on the Quality of Sleep
Authors: Seiger Anja , Spahn Lukas , Rosenblum Lisa , Penzel Thomas , Lederer Katharina , Salanitro Matthew , Fietze Ingo 
Published: April 2024

Abstract

Introduction
The utilization of scents in sleep medicine is gaining increased attention due to their demonstrated ability to enhance sleep quality. This study employed a randomized double-blinded crossover trial to investigate the effects of continuous nightly exposure to a synthetic jasmine scent, combined with lavender oil and passionflower herb, on both subjective and objective measures of sleep quality. With a focus on individuals characterized by delayed sleep onset and frequent disturbances, this research aimed to assess the potential benefits of aromatherapy in addressing sleep-related issues.

Methods
Thirty sensitive sleepers, falling within the Insomnia Severity Index (ISI) range of 7-14 and experiencing 2-3 sleep disturbances per week, participated in the study. The experiment was conducted over four nights in a sleep laboratory, with the initial night serving as an acclimation (baseline) period. The subsequent nights involved randomized exposure to different conditions: a placebo rose scent, a high-dose of jasmine scent, or a low-dose relative to essential oils. Participants were monitored, and standardized questionnaires were administered in both morning and evening sessions. The scent application, specifically on T-shirts, was assessed for tolerance.

Results
Comparisons to baseline revealed a significant improvement in both sleep quality and total sleep time when using scents. Notably, both high and low doses of jasmine demonstrated a reduction in wake after sleep onset. The application of scent on T-shirts was well-tolerated by all participants. These results collectively suggest a positive impact of jasmine scent on sleep parameters.

Conclusion
In conclusion, the findings of this study highlight the potential of aromatherapy, particularly the use of synthetic jasmine scent, in positively influencing sleep quality. The reduction in wake after sleep onset indicates a promising avenue for addressing sleep disturbances. Further exploration into factors such as dosage, application method, and duration of scent exposure is warranted to refine our understanding and optimize the therapeutic use of scents in sleep medicine.

Support (if any)
This study received support from Marome GmbH, which supplied the scents.
0413 Exploring the Efficacy of Cannabinol in Sleep Enhancement: A Breakthrough in Non-Intoxicating Solutions
Authors: Lindquist Alleh 
Published: April 2024

Abstract

Introduction
Sixty percent of Americans report getting less than the recommended seven hours of nightly sleep. Poor sleep is a public health concern linked to many health issues, including inflammatory conditions, obesity, heart disease and reduced quality of life. As consumers seek natural, safe alternatives for better sleep, the introduction of a non-intoxicating hemp-derived cannabinoid solution presents an attractive option.

Methods
Conducting the first-ever double-blind, randomized, placebo-controlled trial, an independent clinical research company examined the effects of TruCBN, FloraWorks proprietary ultra pure CBN on sleep patterns. The trial involved over 1,000 participants at least 21 years old from across the US, comparing the outcomes of CBN to melatonin and a placebo. There were three CBN groups, one melatonin group and one placebo group, as follows: CBN groups: 25 mg, 50 mg, 100 mg Melatonin: 4 mg Control: placebo

Results
- The group taking 50 mg of CBN showed significant sleep improvements compared to placebo, and most of the group experienced a clinically important improvement. - The group taking 4 mg melatonin also showed significant sleep improvements compared to the placebo. - The group taking 50 mg of CBN had slightly better sleep improvements compared to 4 mg of melatonin. - The groups taking 25 mg and 100 mg of CBN had marginally significantly better sleep improvements compared to placebo. - All three tested doses of CBN are statistically equivalent to one another, and statistically equivalent to 4 mg melatonin, for improving sleep quality. All three doses of CBN show a Minimal Clinically Important Difference (MCID) with respect to placebo. - All side effects were mild or moderate. There were no significant differences in the frequency of reported side effects between any dose of CBN or melatonin compared to placebo.

Conclusion
Beyond illuminating CBN's efficacy in enhancing sleep, this research positions it as a formidable player in the pursuit of superior sleep aids, disrupting the conventional over-the-counter sleep aid market. The discovery that CBN is at least as effective as melatonin for improving sleep is particularly promising, especially given the side effects of melatonin and the growing concerns surrounding children consuming melatonin beyond recommended limits.

Support (if any)
0416 The Effect of Doxepin 3 Mg on Sleep Latency: Results from a Meta-analysis of Two Phase 3 Trials
Authors: Fisher Matt , Franks Billy , Araga Mako , Rawal Kapil , Kyle Michael , Krystal Andrew 
Published: April 2024

Abstract

Introduction
Doxepin 3 and 6 mg are FDA-approved treatments for insomnia characterized by sleep maintenance symptoms. A large, randomized, placebo-controlled trial has confirmed the sleep latency effects of doxepin 6 mg in a phase advance model of healthy adults. The antihistaminic effects of doxepin 3 mg may also lead to sleep latency benefits in adults with insomnia symptoms.

Methods
Two randomized, double-blind, parallel group, placebo-controlled phase 3 trials involving doxepin 3 mg in subjects with chronic insomnia were used to assess the sleep latency effects on night 1/day 2. Meta-analysis methods were used to assess latency to sleep onset (LSO) and latency to persistent sleep (LPS), each on a log scale, for subjects with evaluable data in the intent to treat analysis set. Subgroup analyses were performed for both LSO and LPS for subjects above and at or below the median baseline values. Fixed and random effects models were performed using generic inverse variance methods.

Results
The analysis included a total of 460 subjects. Among these subjects, 68.5% were female, 65.2% were White and 20.0% were African American. The mean age was 58.5 years (median 65 years). The baseline median LSO was 55 minutes and median LPS was 35 minutes. We observed limited evidence for heterogeneity (all p-values &gt; 0.2) and therefore present fixed effects estimates only. Doxepin 3 mg improved LSO by 12% (RR=0.88; 95% CI [0.73 to 1.05]) and LPS by 22% (RR=0.78; 95% CI [0.64 to 0.94]) when compared to placebo. There was a non-statistically significant 6% improvement in LSO observed among subjects above the median (RR=0.94) and a 15% non-statistically significant improvement among subjects below the median (RR=0.85). A statistically significant 29% improvement in LPS was observed for subjects above the median (RR=0.71; 95% CI [0.56 to 0.91]) and a non-statistically significant 6% improvement for those below the median (RR=0.94). All subgroup data reflect results compared to placebo.

Conclusion
Doxepin 3 mg may be an effective option to treat sleep latency in adults with insomnia symptoms, especially those with a long latency to persistent sleep.

Support (if any)
This work was supported by Haleon and Currax.
0417 The Effect of CBT-I Combines with Craving Management Strategies on Hypnotic Discontinuation
Authors: Yu Tsai-Ching , Yang Chien-Ming , Huang Ya-Chuan , Wu Yu-Ting , Huang Chih-Shan , Sung Jia-Ying 
Published: April 2024

Abstract

Introduction
Although the use of hypnotics is recommended for short-term management, long-term use is quite common in clinical practice. Gradual tapering, either alone or in combination with cognitive-behavioral therapy for insomnia (CBT-I), has been shown to facilitate the discontinuation of hypnotics. However, some patients are unsuccessful with these strategies. Previous studies suggest that individuals having difficulty discontinuing hypnotic use may develop a craving for these drugs, and such craving is a significant barrier in discontinuing substances like alcohol or drugs. The present study aims to examine whether combining CBT-I with craving management strategies can enhance the success rate of discontinuing hypnotics.

Methods
Twenty-one patients with insomnia disorder, who had been taking hypnotics for over three months, were randomly assigned to a CBT-I group (N=11) or a CBT-I+CM (craving management) group (N=10). Participants in both groups received eight sessions of CBT-I along with a progressive tapering plan. Those in the CBT-I+CM group received additional craving management strategies. Outcome variables included the average number of pills taken per day, the Insomnia Severity Index (ISI), and the Hypnotic Urge Scale (HUS), assessed at baseline and at the end of the program.

Results
ANOVA results indicated a significant reduction in the number of pills taken daily at the end of the program for both groups (CBT-I: 1.96 to 0.24; CBT-I+CM: 1.17 to 0.03; F=61.59, p&lt;.001) and in the HUS scores (CBT-I: 97.00 to 72.55; CBT-I+CM: 105.89 to 63.67; F=25.73, p&lt;.001). Unexpectedly, in the ISI scores, only the CBT-I+CM group showed significant improvement at the end of the program (17.5 to 7.2; t=6.46, p&lt;.001), while the CBT-I group did not show significant improvement (13.36 to 11.55; t=1.20, p=0.25). No significant differences were found between the CBT-I and the CBT-I+CM group in all outcome variables.

Conclusion
The results suggest that CBT-I, whether alone or combined with craving management strategies, can facilitate the tapering of hypnotics and the reduction of craving for them. The inclusion of craving management strategies proved beneficial in improving symptoms of insomnia and, thus, could be integrated into clinical practice. Future research should investigate the long-term outcomes of these strategies in managing hypnotic discontinuation, with follow-up evaluations.

Support (if any)
0418 A Day-by-Day Prospective Analysis of Stress and Hypnotic Craving During Hypnotic Tapering in Insomnia
Authors: Wu Yu-Ting , Yang Chien-Ming , Huang Ya-Chuan , Yu Tsai-Ching , Huang Chih-Shan , Sung Jia-Ying 
Published: April 2024

Abstract

Introduction
Craving plays a crucial role in the persistence and relapse of substance use. While hypnotic usage may differ from that of addictive substances, previous research has indicated that long-term use of hypnotics is associated with a craving for hypnotics among patients with insomnia. Stress, which is commonly linked to sleep disturbances, has also been shown to enhance cravings for substance use. The current study aims to investigate the relationship between stress and craving for hypnotics using day-to-day assessments.

Methods
Twenty-seven participants with insomnia disorder (age = 41.66 ± 12.88; Male:Female = 14:13), who had been taking hypnotics for more than three months, were recruited for a two-week study. During the first week, they were permitted to take their usual dosage of hypnotics, while in the second week, their dosage was reduced by 25%. Each night before bed, participants recorded the level of stress they experienced and their craving for hypnotics, both on a scale of 1 to 9. Data were collected through responses to questions delivered via a social media app (Line). Mixed Effects Models were utilized to examine the relationship between stress and craving, separating out between-subject and within-subject associations.

Results
The findings revealed no significant associations between stress and craving at the between-subject level for either week (β = -0.08, p = 0.77; β = 0.07, p = 0.80). However, at the within-subject level, there was a significant association between stress and craving during the usual dosage week (β = 0.22, p &lt; .05), but not during the reduced dosage week (β = 0.14, p = 0.17).

Conclusion
Our study indicates that there is no significant correlation between stress and craving across the participants. Conversely, within individuals, heightened stress was correlated with increased craving when participants were allowed to take their usual hypnotic dosage. These results underscore the importance of taking stress into consideration when assisting insomnia patients in managing their cravings for hypnotics.

Support (if any)
National Science and Technology Council
0425 Detecting Insomnia and Predicting Difficulty Falling Asleep: Machine Learning of Heart Rate Data During Sleep Onset
Authors: Ma Yan , Wayne Peter , Yu Chen-Hsiang , Yeh Gloria 
Published: April 2024

Abstract

Introduction
Insomnia is highly prevalent and difficulty falling asleep is the most common complaint. The transition from wakefulness to sleep is characterized by a shift from sympathetic to parasympathetic regulation, which can be assessed by heart rate dynamics. Using a machine learning approach, we aimed to explore whether heart rate dynamics during sleep onset can indicate the presence of insomnia.

Methods
We used data from a multi-center cohort study (Sleep Heart Health Study) and included 1835 subjects (age 40-90 years, mean age 62.2±11.2 years, 50.8% male) with overnight polysomnography-based sleep studies and validated sleep scorings. Heart rate data during sleep onset was analyzed using heart rate variability time domain measures (NN, SDNN, RMSSD, pNN20) and a nonlinear measure (multiscale entropy). These measures along with basic demographic features (age, sex and BMI) were used as features in machine learning. We ran four commonly used machine learning approaches, including support vector machines (SVMs), decision tree, random forest, and K-nearest neighbors (KNN), with 80% data for training and 20% for testing.

Results
Based on the mean of test accuracy of five iterations for each model, the highest accuracy for detecting insomnia was achieved by random forest (mean accuracy of 0.902), followed by SVM (mean accuracy 0.894), decision tree (mean accuracy 0.892), and KNN (mean accuracy 0.891). When using the same features to predict self-reported difficulty falling asleep during the study night, SVM achieved the highest accuracy (0.68) while the accuracy of the other approaches were slightly lower.

Conclusion
Machine learning models may effectively predict insomnia using demographic features and heart rate data during sleep onset, but unbiased analysis is needed for the next step. With wearable technology, easily accessible heart/pulse rate data may provide valuable detection of physiological alteration during sleep onset and offer an evaluation approach for the efficacy of interventions targeting sleep onset difficulties.

Support (if any)
This research project was made possible by a grant from the American Academy of Sleep Medicine Foundation (PI: Yan Ma). We acknowledge the National Sleep Research Resource as the resource through which the Data/Datasets were obtained. PW and GY were supported by NIH K24 AT009282 and K24 AT009465 respectively.
0426 Graph Convolutional Network (GCN)-Based Prediction of Brain Age in Individuals with Insomnia
Authors: Bae Heewon , Jo Hyunjin , Joo Eun Yeon , Kim Hosung , Park Gilsoon 
Published: April 2024

Abstract

Introduction
The impact of insomnia on brain aging and cognitive function remain undisclosed. The Brain Age Index has arisen as a robust biomarker for assessing an individual’s brain health, quantifying the deviations from normative aging trajectories. This study investigates accelerated brain aging in insomnia suffers compared to non-suffers using the Brain Age Index, exploring its correlation with the neurocognitive function.

Methods
Forty chronic insomnia patients (31 females, mean age 51.2 years) and 80 healthy controls (57 females, mean age 52.0 years) were analyzed. Graphical convolutional networks (GCNs) constructed regional brain age prediction models, integrating cortical morphology and topology data for comprehensive graph structure. Cortical thickness and gray-/white matter intensity ratio mapped on the cortical surface mesh were input to the GCNs. Ten brain network regions (sensorimotor, frontoparietal, dorsal attention, ventral attention, default mode, salience, language, auditory, visual, and limbic) were employed in the analysis of regional brain ages, utilizing automated anatomical labeling. Each model was trained on data from 6,563 healthy controls, with five-fold cross-validation indicating approximately three years of mean absolute error. These models computed the Brain Age Index for 80 healthy sleepers and 40 insomnia individuals. Linear regression models, adjusted for age, sex, and Beck Depression Inventory, scrutinized group disparities in regional Brain Age Indices. An additional analysis explored the association Brain Age Index and neuropsychological test utilizing linear regression analysis.

Results
While the global Brain Age Index revealed no significant group differences, notable regional Brain Age Index distinctions emerged in insomnia subjects. The regional Brain Age Index were significantly elevated in three distinct brain network regions—limbic, frontoparietal, and language (in descending significance order)—compared to normal subjects. The Brain Age Index within the language network exhibited a discernible negative correlation with neuropsychological deficits in attention and visuospatial domains.

Conclusion
This pioneering study validates neuroimaging-driven brain age in chronic insomnia, unveiling accelerated brain aging in distinct regions. Such revelations signify the potential impact of insomnia on the brain aging trajectory independently of chronological age. The potential association between accelerated brain aging in the language network and cognitive impairments has the potential to elucidate the intricate interplay between brain aging patterns and cognitive performance.

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