Can an E-Mail-Delivered CBT for Insomnia Validated in the West Be Effective in the East? A Randomized Controlled Trial

This study examined the effects of an e-mail-delivered cognitive behavioral therapy for insomnia (CBT-I), validated in Western countries, on insomnia severity, anxiety, and depression in young adults with insomnia in Eastern countries, particularly Japan. This prospective parallel-group randomized clinical trial included college students with Insomnia Severity Index (ISI) scores of ten or higher. Participants were recruited via advertising on a university campus and randomized to an e-mail-delivered CBT-I (REFRESH) or self-monitoring (SM) with sleep diaries group. The primary outcomes were insomnia severity, anxiety, and depression; secondary outcomes were sleep hygiene practices, dysfunctional beliefs, sleep reactivity, and pre-sleep arousal. All measurements were assessed before and after the intervention. A total of 48 participants (mean (SD) age, 19.56 (1.86) years; 67% female) were randomized and included in the analysis. The results of the intent-to-treat analysis showed a significant interaction effect for insomnia severity, anxiety, depression, sleep hygiene practice, and pre-sleep arousal. Compared with the SM group, the REFRESH group was more effective in reducing insomnia severity (Hedges’ g = 1.50), anxiety (g = 0.97), and depression (g = 0.61) post-intervention. These findings suggest that an e-mail-delivered CBT-I may be an effective treatment for young adults with elevated insomnia symptoms living in Japan.

Insomnia, Pre-Sleep Arousal, Psychosocial Factors and Changes in Sleep Pattern during the Second Wave Lockdown of the COVID-19 Pandemic in Georgia

Studies performed across the COVID-19 pandemic waves point to the persistent impact of the pandemic on sleep and mental health. We expand these data by examining insomnia, pre-sleep arousal, psychosocial factors, and retrospective changes in sleep pattern during the COVID-19 second wave lockdown period in Georgia. Data were collected through an online survey (n = 1117). The prevalence rate of probable insomnia disorder was 24.2%. Clinically relevant somatic and cognitive pre-sleep arousal was present in 49.8% and 58.0% of participants, and high levels of anxiety, depression and social isolation were found in 47.0%, 37.3%, 47.2% of respondents, respectively. We observed high prevalence rates of worse sleep quality, delayed bedtimes and risetimes, longer sleep latencies, higher awakenings and shorter sleep durations, relative to the pre-pandemic period. COVID-19-infected subjects showed more severe sleep and mental problems. Specific predictors differentially affected insomnia, somatic and cognitive pre-sleep arousal. Depression and COVID-19 infection emerged as vulnerability factors for pre-sleep arousal, which, in turn, wasassociated with a higher predisposition to insomnia disorder. We confirm the strong deteriorating impact of the COVID-19 pandemic on sleep and psychosocial well-being during the second wave lockdown period. The specific association between pre-sleep arousal, insomnia, and psychosocial factors is of clinical relevance for the prevention of severity and persistence of sleep and mental problems across the repeated lockdown/reopening waves. Modulation of pre-sleep arousal may prove beneficial to implement targeted interventions.

Neural Substrates for Regulation of Sleep and General Anesthesia

: General anesthesia has been successfully used in the clinic for over 170 years, but its mechanisms of effect remain unclear. Behaviorally, general anesthesia is similar to sleep in that it produces a reversible transition between wakefulness and the state of being unaware of one’s surroundings. A growing discussion has been imposed regarding the common circuits of sleep and general anesthesia, as an increasing number of sleep-arousal regulatory nuclei are reported to participate in the consciousness shift occurring during general anesthesia. Recently, with progress in research technology, both positive and negative evidence for overlapping neural circuits between sleep and general anesthesia have emerged. This article provides a review of the latest evidence on the neural substrates for sleep and general anesthesia regulation by comparing the roles of pivotal nuclei in sleep and anesthesia.

Persistence of the Effects of the COVID-19 Lockdown on Sleep: A Longitudinal Study

The effects of the COVID-19 pandemic on sleep have been widely documented, but longitudinal evaluations during different phases of the “COVID-19 era” are needed to disentangle the specific consequences of the r145estrictive measures on sleep variables. The aim of this study was to assess the immediate effect of the lockdown’s end on sleep and sleep-related dimensions in an Italian sample, also considering the stress and depressive symptoms. We used an online survey to longitudinally collect data on sociodemographic, environmental, clinical, sleep, and sleep-related variables in two time points: during and immediately after the lockdown. The final sample included 102 participants. The large prevalence of poor sleep quality, clinically relevant pre-sleep arousal, and depressive symptoms, as well as poor sleep quality and pre-sleep arousal score observed during the lockdown, remained stable after its end. On the other hand, the prevalence of moderate-to-severe event-related stress and intrusive symptom scores exhibited a drastic reduction after the end of home confinement. Both bedtime and rise time were anticipated after the lockdown, while sleep quality exhibited only a trend of post-lockdown sleep disturbance reduction. Our findings point to a reduced stress level (specific for the intrusive symptomatology) after the end of the lockdown and persistence of sleep problems, suggesting two non-mutually exclusive hypotheses: (a) the strict restrictive measures are not the main cause of sleep problems during the pandemic and (b) home confinement induces long-lasting effects on sleep observable after its end, and a longer period of time might be needed to observe an improvement.

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

REM Sleep Fragmentation in Patients With Short-Term Insomnia Is Associated With Higher BDI Scores

Objective: To observe the changes in sleep characteristics and BDI scores in patients with short-term insomnia disorder (SID) using a longitudinal observational study.Methods: Fifty-four patients who met the criteria for SID of the International Classification of Sleep Disorders, third edition, were recruited. Depression levels were assessed using the Beck depression inventory (BDI) at enrollment and after 3 months of follow-up, respectively. Sleep characteristics were assessed by polysomnography.Results: After 3 months of follow-up, the group was divided into SID with increased BDI score (BDI >15) and SID with normal BDI score (BDI ≤ 15) according to the total BDI score of the second assessment. The differences in rapid eye movement (REM) sleep latency, REM sleep arousal index, and NREM sleep arousal index between the two groups were statistically significant. The total BDI score was positively correlated with REM and NREM sleep arousal index and negatively correlated with REM sleep latency, which were analyzed by Pearson correlation coefficient. Multiple linear regression was used to construct a regression model to predict the risk of depression in which the prediction accuracy reached 83.7%.Conclusion: REM sleep fragmentation is closely associated with future depressive status in patients with SID and is expected to become an index of estimating depression risk.

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

The Acute Effects of Aerobic Exercise on Nocturnal and Pre-Sleep Arousal in Patients with Unipolar Depression: Preplanned Secondary Analysis of a Randomized Controlled Trial

Unipolar depression is associated with insomnia and autonomic arousal. The aim of this study was to quantify the effect of a single bout of aerobic exercise on nocturnal heart rate variability and pre-sleep arousal in patients with depression. This study was designed as a two-arm, parallel-group, randomized, outcome assessor-blinded, controlled, superiority trial. Patients with a primary diagnosis of unipolar depression aged 18–65 years were included. The intervention consisted of a single 30 min moderate-intensity aerobic exercise bout. The control group sat and read for 30 min. The primary outcome of interest was RMSSD during the sleep period assessed with polysomnography. Secondary outcomes were additional heart rate variability outcomes during the sleep and pre-sleep period as well as subjective pre-sleep arousal. A total of 92 patients were randomized to either the exercise (N = 46) or the control (N = 46) group. Intent-to-treat analysis ANCOVA of follow-up sleep period RMSSD, adjusted for baseline levels and minimization factors, did not detect a significant effect of the allocation (β = 0.12, p = 0.94). There was no evidence for significant differences between both groups in any other heart rate variability measure nor in measures of cognitive or somatic pre-sleep arousal. As this is the first trial of its kind in this population, the findings need to be confirmed in further studies. Patients with depression should be encouraged to exercise regularly in order to profit from the known benefits on sleep and depressive symptoms, which are supported by extensive literature.