Shared EEG correlates between non-REM parasomnia experiences and dreams.

Sleepwalking and related parasomnias result from incomplete awakenings out of non-rapid eye movement sleep. Behavioral episodes can occur without consciousness or recollection, or in relation to dream-like experiences. To understand what accounts for these differences in consciousness and recall, here we recorded parasomnia episodes with high-density electroencephalography (EEG) and interviewed participants immediately afterward about their experiences. Compared to reports of no experience (19%), reports of conscious experience (56%) were preceded by high-amplitude EEG slow waves in anterior cortical regions and activation of posterior cortical regions, similar to previously described EEG correlates of dreaming. Recall of the content of the experience (56%), compared to no recall (25%), was associated with higher EEG activation in the right medial temporal region before movement onset. Our work suggests that the EEG correlates of parasomnia experiences are similar to those reported for dreams and may thus reflect core physiological processes involved in sleep consciousness.

Adolescents' trajectories of depression and anxiety symptoms prior to and during the COVID-19 pandemic and their association with healthy sleep patterns.

The COVID-19 pandemic has seen a rise in anxiety and depression among adolescents. This study aimed to investigate the longitudinal associations between sleep and mental health among a large sample of Australian adolescents and examine whether healthy sleep patterns were protective of mental health in the context of the COVID-19 pandemic. We used three waves of longitudinal control group data from the Health4Life cluster-randomized trial (N = 2781, baseline Mage = 12.6, SD = 0.51; 47% boys and 1.4% 'prefer not to say'). Latent class growth analyses across the 2 years period identified four trajectories of depressive symptoms: low-stable (64.3%), average-increasing (19.2%), high-decreasing (7.1%), moderate-increasing (9.4%), and three anxiety symptom trajectories: low-stable (74.8%), average-increasing (11.6%), high-decreasing (13.6%). We compared the trajectories on sociodemographic and sleep characteristics. Adolescents in low-risk trajectories were more likely to be boys and to report shorter sleep latency and wake after sleep onset, longer sleep duration, less sleepiness, and earlier chronotype. Where mental health improved or worsened, sleep patterns changed in the same direction. The subgroups analyses uncovered two important findings: (1) the majority of adolescents in the sample maintained good mental health and sleep habits (low-stable trajectories), (2) adolescents with worsening mental health also reported worsening sleep patterns and vice versa in the improving mental health trajectories. These distinct patterns of sleep and mental health would not be seen using mean-centred statistical approaches.

Shift schedules and circadian preferences: the association with sleep and mood.

Object: We explored the circadian preferences of non-shift workers (non-SWs) and various types of shift workers (SWs), and the associations of these preferences with sleep and mood. Methods: In total, 4,561 SWs (2,419 women and 2,142 men aged 37.00 ± 9.80 years) and 2,093 non-SWs (1,094 women and 999 men aged 37.80 ± 9.73 years) completed an online survey. Of all SWs, 2,415 (1,079 women and 1,336 men aged 37.77 ± 9.96 years) reported regularly rotating or fixed schedules ("regular SWs"), and 2,146 (1,340 women and 806 men aged 36.12 ± 9.64 years) had irregular schedules ("irregular SWs"). Of the regular SWs, 2,040 had regularly rotating schedules, 212 had fixed evening schedules, and 163 had fixed night schedules. All participants completed the Morningness-Eveningness Questionnaire (MEQ) exploring circadian preferences, the short form of the Center for Epidemiological Studies-Depression Scale (CES-D) evaluating depression, the Insomnia Severity Index (ISI), and the Epworth Sleepiness Scale (ESS). Results: Compared to non-SWs, SWs had lower MEQ scores, i.e., more eveningness, after controlling for age, gender, income, occupation, and weekly work hours (F = 87.97, p < 0.001). Irregular SWs had lower MEQ scores than regular SWs (F = 50.89, p < 0.001). Among regular SWs, the MEQ scores of fixed evening and fixed night SWs were lower than those of regularly rotating SWs (F = 22.42, p < 0.001). An association between the MEQ and ESS scores was apparent in non-SWs (r = -0.85, p < 0.001) but not in SWs (r = 0.001, p = 0.92). Conclusion: SWs exhibited more eveningness than non-SWs; eveningness was particularly prominent in SWs with irregular or fixed evening/night shifts. Eveningness was associated with sleepiness only in non-SWs, but not in SWs.

Analysis of the relationship between shorter sleep duration and wrist fractures: based on NHANES.

BACKGROUND: Wrist fracture is one of the common limb fractures. Its incidence rate increases with age and osteoporosis. Nowadays, Sleep health is increasingly valued, but the relationship between wrist fractures and sleep time is not yet clear. METHODS: Data in this study were collected and screened from the NHANES from 2005 to 2010 and 2013 to 2014. The variables were extracted from interviews and compared between the wrist fractures and the sleep duration. The data was analyzed by weighted multivariate logistic regression. RESULTS: After excluding individuals who were not eligible and had invalid data, we finally identified 1835 participants for inclusion in this study. We found a negative association between the sleep duration and the fractured of the wrist (OR = 1.027,95% CI (1.027, 1.028), P < 0.00001). CONCLUSION: This study demons that the association between the sleep duration and the fractures of the wrist is significant. Our findings provide a better understanding of the relationship between sleep duration and wrist fractures. This study may help us reducing the incidence of wrist fractures in the population based on healthy sleep management in the future, and improve the quality of life of middle-aged and elderly patients. Provide evidence for clinical patients to manage healthy sleep.

Stressors in Atopic Dermatitis.

As with other inflammatory skin disorders, atopic dermatitis has a tendency to cause stress and also be exacerbated by it. Patients with atopic dermatitis have several disease-associated stressors, some of which include physical discomfort due to itching and altered appearance due to flare-ups. These stressors have been shown to effect patients psychosocially by altering sleep patterns, decreasing self-esteem, and interfering with interpersonal relationships. In combination with its direct effect on patients, atopic dermatitis also causes stress for parents and caregivers. Studies suggest that atopic dermatitis is strongly correlated with co-sleeping habits, which can negatively impact the health and mood of parents or caregivers. It has also been reported to interfere with the formation of a strong mother-child relationship. In order to optimize treatment for patients with atopic dermatitis, it is important to note the impact that it has on quality of life. By implementing patient counseling, sleep-targeted therapies, and the use of quality of life (QoL) indices, atopic dermatitis patients and caregivers have the potential to experience greater satisfaction with treatment.

Impact of increased protein intake in older adults: a 12-week double-blind randomised controlled trial.

BACKGROUND: Emerging evidence suggests health-promoting properties of increased protein intake. There is increased interest in plant protein but a dearth of information in relation to its impact on muscle function. The objective of the present work was to examine the impact of intake of different types of proteins on muscle functional parameters including handgrip strength, biomarkers of metabolic health, sleep quality and quality of life in a group of older adults. METHODS: Healthy men and women aged 50 years and older entered a double-blinded, randomised, controlled nutritional intervention study with three parallel arms: high plant protein, high dairy protein and low protein. Participants consumed once daily a ready-to-mix shake (containing 20 g of protein in high protein groups) for 12 weeks. Changes in handgrip and leg strength, body composition, metabolic health, quality of life and sleep quality were analysed by linear mixed models in an intention-to-treat approach. RESULTS: Eligible participants (n = 171) were randomly assigned to the groups (plant: n = 60, dairy: n = 56, low protein: n = 55) and 141 completed the study. Handgrip strength increased after the intervention (Ptime = 0.038), with no significant difference between the groups. There was no significant difference between groups for any other health outcomes. CONCLUSIONS: In a population of older adults, increasing protein intake by 20 g daily for 12 weeks (whether plant-based or dairy-based) did not result in significant differences in muscle function, body composition, metabolic health, sleep quality or quality of life, compared with the low protein group.

Delineating memory reactivation in sleep with verbal and non-verbal retrieval cues.

Sleep supports memory consolidation via the reactivation of newly formed memory traces. One way to investigate memory reactivation in sleep is by exposing the sleeping brain to auditory retrieval cues; a paradigm known as targeted memory reactivation. To what extent the acoustic properties of memory cues influence the effectiveness of targeted memory reactivation, however, has received limited attention. We addressed this question by exploring how verbal and non-verbal memory cues affect oscillatory activity linked to memory reactivation in sleep. Fifty-one healthy male adults learned to associate visual stimuli with spoken words (verbal cues) and environmental sounds (non-verbal cues). Subsets of the verbal and non-verbal memory cues were then replayed during sleep. The voice of the verbal cues was either matched or mismatched to learning. Memory cues (relative to unheard control cues) prompted an increase in theta/alpha and spindle power, which have been heavily implicated in sleep-associated memory processing. Moreover, verbal memory cues were associated with a stronger increase in spindle power than non-verbal memory cues. There were no significant differences between the matched and mismatched verbal cues. Our findings suggest that verbal memory cues may be most effective for triggering memory reactivation in sleep, as indicated by an amplified spindle response.

A dual fusion recognition model for sleep posture based on air mattress pressure detection.

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body's backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

Bidirectional Associations Between Loneliness, Emotional Support, and Sleep in Daily Life.

OBJECTIVE: Evidence suggests a link between positive social relationship perceptions and improved sleep (e.g., quality, efficiency) across the life span. Less work has probed the directionality of these relationships. Here, we report findings from the first study to examine bidirectional between- and within-person associations between loneliness and emotional support with daily life measures of sleep. METHODS: Participants were 389 healthy adults aged 40 to 64 years (61% female) who completed hourly surveys assessing loneliness and perceptions of emotional support over the course of 4 days. Measures of actigraphy-assessed sleep and nightly sleep quality were also assessed for 7 to 10 days. RESULTS: Individuals with lower average daily loneliness showed higher sleep quality and efficiency than individuals with higher loneliness (r = -0.19, p < .001; r = -0.14, p = .008, respectively), and greater average emotional support was likewise linked with better sleep quality (r = 0.18, p < .001). Controlling for neuroticism attenuated the effects of average loneliness on sleep. Within-person analyses showed unexpected bidirectional effects. Specifically, days in which people felt relatively lonelier were followed by nights with greater sleep efficiency (γ = 1.08, p = .015), and nights when people reported relatively poorer sleep quality were followed by days with greater emotional support (γ = -0.04, p = .013). These unexpected findings are probed in exploratory analyses. CONCLUSIONS: Individuals with higher loneliness and lower emotional support report poorer sleep quality and efficiency, on average. Day-to-day fluctuations in perceptions of social relationships may affect the following night's sleep, and vice versa.

The Association of Multidimensional Sleep Health With HbA1c and Depressive Symptoms in African American Adults With Type 2 Diabetes.

OBJECTIVE: Sleep is important for diabetes-related health outcomes. Using a multidimensional sleep health framework, we examined the association of individual sleep health dimensions and a composite sleep health score with hemoglobin A1c (HbA1c) and depressive symptoms among African American adults with type 2 diabetes. METHODS: Participants (N = 257; mean age = 62.5 years) were recruited through local churches. Wrist-worn actigraphy and sleep questionnaire data assessed multidimensional sleep health using the RuSATED framework (regularity, satisfaction, alertness, timing, efficiency, duration). Individual sleep dimensions were dichotomized into poor or good sleep health and summed into a composite score. HbA1c was assessed using the DCA Vantage™ Analyzer or A1CNow® Self Check. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9). Regression models examined the association of individual sleep dimensions and composite sleep health with HbA1c and depressive symptoms. RESULTS: Higher composite sleep health scores were associated with a lower likelihood of having greater than minimal depressive symptoms (PHQ-9 ≥ 5) (odds ratio [OR] = 0.578, 95% confidence interval [CI] = 0.461-0.725). Several individual sleep dimensions, including irregularity (OR = 1.013, CI = 1.005-1.021), poor satisfaction (OR = 3.130, CI = 2.095-4.678), and lower alertness (OR = 1.866, CI = 1.230-2.833) were associated with a greater likelihood of having depressive symptoms. Neither composite sleep health scores nor individual sleep dimensions were associated with HbA1c. CONCLUSIONS: Better multidimensional sleep health is associated with lower depressive symptoms among African American adults with type 2 diabetes. Longitudinal research is needed to determine the causal association between multidimensional sleep health and depressive symptoms in this population. TRIAL REGISTRY: ClinicalTrials.gov identifier NCT04282395.

Prospective Bidirectional Relationship Between Sleep Duration and Posttraumatic Stress Disorder Symptoms After Suspected Acute Coronary Syndrome.

OBJECTIVE: Sleep disturbance is a "hallmark" symptom of posttraumatic stress disorder (PTSD). Poor sleep (including short sleep) after combat-related trauma can also predict subsequent PTSD. Less is known about the association between sleep duration and PTSD symptoms when PTSD is induced by acute coronary syndrome (ACS). We examined the bidirectional relationship between sleep duration and PTSD symptoms over the year after hospital evaluation for ACS. METHODS: Participants were enrolled in this observational study after emergency department evaluation for ACS. Sleep duration ("During the past month, how many hours of actual sleep did you get at night?") and cardiac event or hospitalization-induced PTSD symptoms (PTSD Checklist) were assessed at 1, 6, and 12 months after hospital discharge. Cross-lagged path analysis was used to model the effects of sleep duration and PTSD symptoms on each other. Covariates included age, sex, race/ethnicity, cardiac severity, baseline depression symptoms, and early acute stress disorder symptoms. RESULTS: The sample included 1145 participants; 16% screened positive for probable PTSD (PTSD Checklist score ≥33). Mean sleep duration across time points was 6.1 hours. Higher PTSD symptoms predicted shorter sleep duration at the next time point (i.e., 1-6 and 6-12 months; B = -0.14 hours/10-point difference, SE = 0.03, p < .001). Shorter sleep duration was associated with higher PTSD symptoms at the next time point (B = -0.25 points/hour, SE = 0.12, p = .04). CONCLUSIONS: Short sleep duration and PTSD symptoms are mutually reinforcing across the first year after ACS evaluation. Findings suggest that sleep, PTSD symptoms, and their relationship should be considered in the post-ACS period.

Children and adolescents hospitalized with chronic conditions: sleep patterns, resilience and quality of life.

OBJECTIVE: To evaluate the sleep pattern of children and adolescents with chronic conditions during hospitalization and correlate it with resilience, quality of life, clinical and sociodemographic data. METHOD: Quantitative, descriptive and cross-sectional study. Data collection took place between May 2022 and January 2023, with children and adolescents with chronic conditions from two hospitals in Rio de Janeiro. The instruments used were the Actigraph, Sandra Prince-Embury's Resilience Scale for Children and Adolescents and the Pediatric Quality of Life Inventory. Data analysis involved descriptive statistics and correlation tests. RESULTS: 40 hospitalized children and adolescents between the ages of nine and 18 took part. The results showed compromised sleep, especially in terms of duration and time awake after sleep onset. Quality of life scores were low and resilience levels were classified as medium to high. Correlations were found between resilience and sleep. In addition, sleep was influenced by diagnosis and treatment. CONCLUSION: Children and adolescents hospitalized with chronic conditions experience significant sleep disturbances and have a low quality of life, but have satisfactory levels of resilience.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Sleeping more than 8 h: a silent factor contributing to decreased muscle mass in Chinese community-dwelling older adults.

BACKGROUND: Muscle mass loss is an age-related process that can be exacerbated by lifestyle, environmental and other factors, but can be mitigated by good sleep. The objective of this study was to investigate the correlation between varying time lags of sleep duration and the decline in muscle mass among individuals aged 60 years or older by using real-world health monitoring data obtained from wearable devices and smart home health monitoring devices. METHODS: This study included 86,037 observations from 2,869 participants in the Mobile Support System database. Missing data were supplemented by multiple imputation. The investigation utilized generalized estimating equations and restricted cubic spline curve to examine the relationship between sleep duration and low muscle mass. Various lag structures, including 0, 1, 2, 0-1, 0-2, and 1-2 months, were fitted, and the interaction effect of observation time with sleep duration was estimated for each lag structure. Additionally, subgroup analyses were conducted. The models were adjusted for various covariates, including gender, age, body mass index, footsteps, smoking status, drinking status, marital status, number of chronic diseases, number of medications, diabetes mellitus, hyperlipidemia, coronary artery disease, respiratory disease, and musculoskeletal disease and an interaction term between time and sleep duration. RESULTS: The results of the generalized estimating equation showed a significant correlation (p < 0.001) between sleep duration of 8 h or more and low muscle mass in older adults, using 6-7 h of sleep as a reference. This effect was seen over time and prolonged sleep accumulated over multiple months had a greater effect on muscle mass loss than a single month. The effect of long sleep duration on muscle mass loss was significantly greater in females than in males and greater in the over-75 than in the under-75 age group. Restricted cubic spline plots showed a non-linear relationship between sleep duration and low muscle mass (p < 0.001). CONCLUSIONS: This study found an association between sustained nighttime sleep of more than eight hours and decreased muscle mass in older adults, especially older women.

Correspondence to "association of sleep patterns and cardiovascular disease risk is modified by glucose tolerance status".

After reading the article written by Wang et al., we have encountered several concerns that may compromise the credibility of the article. There are some factors, such as changes in sleep patterns, glucose tolerance status, and the use of hypnotics, which may interfere with the research results. Additionally, the design of the sleep pattern could lead to biased outcomes. Therefore, we are writing this letter to recommend that further research should take these concerns into consideration.

"The worst thing is lying in bed thinking 'I want a cigarette'" a qualitative exploration of smoker's and ex-smoker's perceptions of sleep during a quit attempt and the use of cognitive behavioural therapy for insomnia to aid cessation.

Smokers report poorer sleep quality than non-smokers and sleep quality deteriorates further during cessation, increasing risk of smoking relapse. Despite the use of cognitive behavioural therapy for insomnia (CBT-I) to aid quit attempts emerging in the area, little is known about smokers and ex smoker's experiences of sleep during a quit attempt or their perceptions of CBT-I. This study addresses this gap by exploring smoker's and ex-smoker's experiences of the link between smoking and sleep and how this may change as a function of smoking/smoking abstinence. It also explores views of traditional CBT-I components (i.e., perceived feasibility, effectiveness, barriers of use). We conducted semi-structured interviews with current and recently quit smokers (n = 17) between January and September 2022. The framework method was used for analysis. Four themes addressing research questions were described. These included: 1) A viscous cycle; poor sleep quality and negative psychological state during cessation; 2) Perceived engagement and effectiveness; the importance of feasibility, experience, value, identity and psychological state in assessing CBT-I as a cessation tool; 3) Striking a balance; tailoring CBT-I to reduce psychological overload in a time of lifestyle transition; and 4) Personalisation and digital delivery helping overcome psychological barriers during cessation. The analysis suggested during quit attempts smokers experienced a range of sleep problems that could increase risk of relapse due to a negative impact on psychological state. It also revealed participants thought that CBT-I is something they would use during a quit attempt but suggested changes and additions that would improve engagement and be better tailored to quitting smokers. Key additions included the integration of smoking-based cognitive restructuring, starting the intervention prior to a quit attempt, and the need for personalisation and tailoring.

The 24-h movement behaviours composition is associated with parasympathetic modulation in preschoolers.

AIM: To investigate the associations between 24-h movement behaviours and heart rate variability (HRV) in preschool children. METHODS: A total of 123 preschoolers (4.52 ± 0.25 years old; 62 girls) were assessed for physical activity (PA) and sedentary behaviour (SB) using an accelerometer (Actigraph WGT3x). Sleep duration (SD) was parent-reported. Children were laid down in a supine position for 10 min to assess HRV data. The R-R intervals recorded during the last 5 min of this period were analysed. We conducted compositional analysis in R studio, and the significance level was 95%. All ethical procedures were followed, and the study had the approval of the local ethical board. RESULTS: When considered as a composition, adjusted for age, body mass index and sex, the 24-h movement composition (PA, SB and SD) significantly predicted better parasympathetic modulation (Root mean square of the successive differences [RMSSD] [p = 0.04; r2 = 0.13]), but not high frequency (HF) (nu) (p = 0.51, r2 = 0.01), low frequency (nu) (p = 0.52, r2 = 0.02),or standard deviation (standard deviation of normal-to-normal intervals) (p = 0.55, r2 = 0.02), respectively. CONCLUSION: These results suggest the 24-h movement composition predicted the RMSSD time-domain index related to parasympathetic activity.

Association Between Sleep Quality and Deep Learning-Based Sleep Onset Latency Distribution Using an Electroencephalogram.

To evaluate sleep quality, it is necessary to monitor overnight sleep duration. However, sleep monitoring typically requires more than 7 hours, which can be inefficient in termxs of data size and analysis. Therefore, we proposed to develop a deep learning-based model using a 30 sec sleep electroencephalogram (EEG) early in the sleep cycle to predict sleep onset latency (SOL) distribution and explore associations with sleep quality (SQ). We propose a deep learning model composed of a structure that decomposes and restores the signal in epoch units and a structure that predicts the SOL distribution. We used the Sleep Heart Health Study public dataset, which includes a large number of study subjects, to estimate and evaluate the proposed model. The proposed model estimated the SOL distribution and divided it into four clusters. The advantage of the proposed model is that it shows the process of falling asleep for individual participants as a probability graph over time. Furthermore, we compared the baseline of good SQ and SOL and showed that less than 10 minutes SOL correlated better with good SQ. Moreover, it was the most suitable sleep feature that could be predicted using early EEG, compared with the total sleep time, sleep efficiency, and actual sleep time. Our study showed the feasibility of estimating SOL distribution using deep learning with an early EEG and showed that SOL distribution within 10 minutes was associated with good SQ.