Sleep Arousal Research Articles

Sleep apnea and atrial fibrillation: the spell of Groundhog Day.

Sleep apnea and atrial fibrillation: the spell of Groundhog Day.

DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable parameters. The input to the network is a full-length multichannel polysomnographic recording signal. The network has been designed and optimized to efficiently predict nonapnea sleep arousals on held-out test data at a 5 ms resolution level, while not compromising the prediction accuracy. When compared to DeepSleep, the obtained experimental results in terms of gross area under the precision–recall curve (AUPRC) and gross area under the receiver operating characteristic curve (AUROC) suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable.

The Parasomnias and Sleep Related Movement Disorders—A Look Back at Six Decades of Scientific Studies

The objective of this article is to provide a comprehensive personal survey of all the major parasomnias with coverage of their clinical presentation, investigation, physiopathogenesis and treatment. These include the four major members of the slow-wave sleep arousal parasomnias which are enuresis nocturna (bedwetting), somnambulism (sleepwalking), sleep terrors (pavor nocturnus in children, incubus attacks in adults) and confusional arousals (sleep drunkenness). Other parasomnias covered are sleep-related aggression, hypnagogic and hypnopompic terrifying hallucinations, REM sleep terrifying dreams, nocturnal anxiety attacks, sleep paralysis, sleep talking (somniloquy), sexsomnia, REM sleep behavior disorder (RBD), nocturnal paroxysmal dystonia, sleep starts (hypnic jerks), jactatio capitis nocturna (head and total body rocking), periodic limb movement disorder (PLMs), hypnagogic foot tremor, restless leg syndrome (Ekbom syndrome), exploding head syndrome, excessive fragmentary myoclonus, nocturnal cramps, and sleep-related epileptic seizures. There is interest in the possibility of relationships between sleep/wake states and creativity.

Exclusion of EEG-based arousals in wake epochs of polysomnography leads to underestimation of the arousal index.

There is an internal contradiction in current American Academy of Sleep Medicine standards for arousal index (AI) calculation in polysomnography: Arousals in sleep and wake epochs are counted, but only sleep time is used in the denominator. This study aimed to investigate the impact of including arousals scored in wake epochs on the AI. We compared AIs including (AIinc) vs excluding (AIexc) awake-epoch arousals from 100 consecutive polysomnograms conducted for investigation of possible obstructive sleep apnea. To determine the AI that most closely approximated "truth," AIinc and AIexc were compared to an AI calculated from continuous sleep analysis (AIcont) in a 20-polysomnogram subgroup of patients. The median (interquartile range) increase in AIinc was 5.2 events/h (3.5-8.1) vs AIexc (AIinc = 28.0 events/h [18.4-38.9] vs AIexc = 22.9 events/ h [13.1-31.3]), equating to an increase of 25.3% (15.6-40.8). As the AI increased, the difference increased (P < .001), with decreasing sleep efficiency and an increasing apnea-hypopnea index as the strongest predictors of the difference between AIexc and AIinc. The absolute AIexc-AIcont difference (7.7 events/h [5.1-13.6]) was significantly greater than the AIinc-AIcont difference (1.2 events/h [0.6-5.7]; z = -3.099; P = .002). There was a notable increase in AI when we included wake-epoch arousals, particularly in patients with more severe obstructive sleep apnea or reduced sleep efficiency. However, the AI including wake-epoch arousals best matched the "true" continuous sleep-scoring AI. Our study informs clinical and research practice, highlights epoch scoring pitfalls, and supports the current American Academy of Sleep Medicine standard arousal reporting approach for future standards. Wilson DL, Tolson J, Churchward TJ, Melehan K, O'Donoghue FJ, Ruehland WR. Exclusion of EEG-based arousals in wake epochs of polysomnography leads to underestimation of the arousal index. J Clin Sleep Med. 2022;18(5):1385-1393.

Brain reactivity to emotion persists in NREM sleep and is associated with individual dream recall.

The waking brain efficiently detects emotional signals to promote survival. However, emotion detection during sleep is poorly understood and may be influenced by individual sleep characteristics or neural reactivity. Notably, dream recall frequency has been associated with stimulus reactivity during sleep, with enhanced stimulus-driven responses in high vs. low recallers. Using electroencephalography (EEG), we characterized the neural responses of healthy individuals to emotional, neutral voices, and control stimuli, both during wakefulness and NREM sleep. Then, we tested how these responses varied with individual dream recall frequency. Event-related potentials (ERPs) differed for emotional vs. neutral voices, both in wakefulness and NREM. Likewise, EEG arousals (sleep perturbations) increased selectively after the emotional voices, indicating emotion reactivity. Interestingly, sleep ERP amplitude and arousals after emotional voices increased linearly with participants’ dream recall frequency. Similar correlations with dream recall were observed for beta and sigma responses, but not for theta. In contrast, dream recall correlations were absent for neutral or control stimuli. Our results reveal that brain reactivity to affective salience is preserved during NREM and is selectively associated to individual memory for dreams. Our findings also suggest that emotion-specific reactivity during sleep, and not generalized alertness, may contribute to the encoding/retrieval of dreams.

Heterogeneity in the links between sleep arousals, amyloid-β, and cognition.

BACKGROUNDTight relationships between sleep quality, cognition, and amyloid-β (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD) neuropathology, have been shown. Sleep arousals become more prevalent with aging and are considered to reflect poorer sleep quality. However, heterogeneity in arousals has been suggested while their associations with Aβ and cognition are not established.METHODSWe recorded undisturbed night-time sleep with EEG in 101 healthy individuals aged 50–70 years, devoid of cognitive and sleep disorders. We classified spontaneous arousals according to their association with muscular tone increase (M+/M–) and sleep stage transition (T+/T–). We assessed cortical Aβ burden over earliest affected regions via PET imaging and assessed cognition via neuropsychological testing.RESULTSArousal types differed in their oscillatory composition in θ (4–8 Hz) and β (16–30 Hz) EEG bands. Furthermore, T+M– arousals, interrupting sleep continuity, were positively linked to Aβ burden (P = 0.0053, R²β* = 0.08). By contrast, more prevalent T–M+ arousals, upholding sleep continuity, were associated with lower Aβ burden (P = 0.0003, R²β* = 0.13), and better cognition, particularly over the attentional domain (P < 0.05, R²β* ≥ 0.04).CONCLUSIONContrasting with what is commonly accepted, we provide empirical evidence that arousals are diverse and differently associated with early AD-related neuropathology and cognition. This suggests that sleep arousals, and their coalescence with other brain oscillations during sleep, may actively contribute to the beneficial functions of sleep and constitute markers of favorable brain and cognitive health trajectories.TRIAL REGISTRATIONEudraCT 2016-001436-35.FUNDINGFRS-FNRS Belgium (FRSM 3.4516.11), Actions de Recherche Concertées Fédération Wallonie-Bruxelles (SLEEPDEM 17/27-09), ULiège, and European Regional Development Fund (Radiomed Project).

Relationship of overweight and obesity to insomnia severity, sleep quality, and insomnia improvement in a clinically referred pediatric sample.

Children with overweight or obesity are more likely to experience sleep disorders, although the role of weight in pediatric insomnia treatment has not been examined. The current study examined the relationships of high body mass with pretreatment insomnia severity and global sleep problems and the potential moderating impact of weight on changes in insomnia severity following insomnia treatment. Participants included 1,133 youth ages 2-18 years clinically referred for insomnia treatment. The Pediatric Insomnia Severity Index was collected at the initial assessment and throughout treatment as part of routine clinical care. Treatment status was coded as no treatment, early termination, and completed treatment. Secondary measures of global sleep problems at the initial assessment included the Adolescent Sleep Wake Scale, Adolescent Sleep Hygiene Scale, and Children's Sleep Habits Questionnaire. Medical chart review of visits within ± 3 months of baseline was used to obtain age-adjusted and sex-adjusted body mass index Z-score. Among adolescents, regression analyses found that higher body mass index Z-score modestly predicted baseline insomnia severity (P = .021) and worse sleep hygiene (P < .001). For children, higher body mass index Z-score was modestly associated with baseline total sleep problems (P = .006) but not insomnia severity (P = .792). Across ages, body mass index Z-score predicted neither treatment status nor insomnia improvement (P > .05). Findings were similar in categorical analyses comparing patients with overweight/obesity to healthy weight. Although there is evidence that children of higher body mass present for insomnia treatment with greater sleep concerns, body mass does not predict treatment completion or insomnia improvement. Data suggest insomnia treatment is effective irrespective of weight status. Duraccio KM, Simmons DM, Beebe DW, Byars KC. Relationship of overweight and obesity to insomnia severity, sleep quality, and insomnia improvement in a clinically referred pediatric sample. J Clin Sleep Med. 2022;18(4):1083-1091.

Movements and behaviors during spontaneous arousals in healthy young adults: an intermediary stage between wakefulness and sleep?

BackgroundArousals are common, sudden and transient elevations of the vigilance level during normal sleep, but arousal-associated behaviors have not yet been studied. ObjectiveWe aimed to describe the duration as well as motor and autonomic patterns associated with arousals across sleep stages in normal subjects. MethodsThe spontaneous arousals of 25 healthy young adults were randomly analyzed on polysomnography with body- and face-oriented video cameras. The duration of the heart rate response as well as the frequency, amplitude, speed, body segment and semiology of associated movements were measured. ResultsAmong 624 arousals (258 in N2, 140 in N3 and 226 in REM sleep), REM sleep arousals had the shortest duration, and N3 arousals were associated with greater heart rate acceleration. Movements and behaviors (mostly involving the head and neck, then the upper limbs, with rare eyes opening and no turning in bed) were frequent during arousals (69.4% during N2 sleep, 89.3% during N3 and 93.8% during REM sleep). Arousals more frequently included ample, prolonged and whole-body movements during N3 sleep and fast movements and facial expressions during REM sleep. During N2 arousals, chewing was the most prevalent behavior. Some movements resembled orientation and comfort behaviors (flexing/rotating the neck and trunk, scratching, pulling the sheets, rubbing the nose, yawning, smiling, frowning and speaking), whereas others resembled sleep-associated automatisms (swallowing, chewing). ConclusionIn contrast with previous assumptions, most arousals are associated with movements. The type of movements suggests that arousal is an intermediary state between wakefulness and sleep.

Using Actigraphy and Heart Rate Variability (HRV) to Assess Sleep Quality and Sleep Arousal of Three App-Based Interventions: Sleep Music, Sleepcasts, and Guided Mindfulness

Using Actigraphy and Heart Rate Variability (HRV) to Assess Sleep Quality and Sleep Arousal of Three App-Based Interventions: Sleep Music, Sleepcasts, and Guided Mindfulness

Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population.

Dementia is associated with sleep disorders. However, the relationship between dementia and sleep arousal remains unclear. This study explored the associations among sleep parameters, arousal responses, and risk of mild cognitive impairment (MCI). Participants with the chief complaints of memory problems and sleep disorders, from the sleep center database of Taipei Medical University Shuang-Ho Hospital, were screened, and the parameters related to the Cognitive Abilities Screening Instrument, Clinical Dementia Rating, and polysomnography were determined. All examinations were conducted within 6 months and without a particular order. The participants were divided into those without cognitive impairment (Clinical Dementia Rating = 0) and those with MCI (Clinical Dementia Rating = 0.5). Mean comparison, linear regression models, and logistic regression models were employed to investigate the associations among obtained variables. This study included 31 participants without MCI and 37 with MCI (17 with amnestic MCI, 20 with multidomain MCI). Patients with MCI had significantly higher mean values of the spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage than those without MCI. An increased risk of MCI was significantly associated with increased spontaneous arousal index and spontaneous arousal index in the non-rapid eye movement stage with various adjustments. Significant associations between the Cognitive Abilities Screening Instrument scores and the oximetry parameters and sleep disorder indexes were observed. Repetitive respiratory events with hypoxia were associated with cognitive dysfunction. Spontaneous arousal, especially in non-rapid eye movement sleep, was related to the risk of MCI. However, additional longitudinal studies are required to confirm their causality. Tsai C-Y, Hsu W-H, Lin Y-T, etal. Associations among sleep-disordered breathing, arousal response, and risk of mild cognitive impairment in a northern Taiwan population. J Clin Sleep Med. 2022;18(4): 1003-1012.

The negative health effects of having a combination of snoring and insomnia.

Insomnia and snoring are common sleep disorders. The aim was to investigate the association of having a combination of insomnia symptoms and snoring with comorbidity and daytime sleepiness. The study population comprised 25,901 participants (16-75 years, 54.4% women) from 4 Swedish cities, who answered a postal questionnaire that contained questions on snoring, insomnia symptoms (difficulties initiating and/or maintaining sleep and/or early morning awakening), smoking, educational level, and respiratory and nonrespiratory disorders. Snoring was reported by 4,221 (16.2%), while 9,872 (38.1%) reported ≥ 1 insomnia symptom. A total of 2,150 (8.3%) participants reported both insomnia symptoms and snoring. The association with hypertension (adjusted odds ratio [OR], 1.4; 95% confidence interval [CI], 1.2-1.6), chronic obstructive pulmonary disease (adjusted OR, 1.8; 95% CI, 1.3-2.4), asthma (adjusted OR, 1.9; 95% CI, 1.6-2.3), daytime sleepiness (adjusted OR, 7.9; 95% CI, 7.1-8.8), and the use of hypnotics (adjusted OR, 7.5; 95% CI, 6.1-9.1) was highest for the group with both insomnia symptoms and snoring. Participants with both snoring and insomnia run an increased risk of hypertension, chronic obstructive pulmonary disease, asthma, daytime sleepiness, and use of hypnotics. It is important to consider snoring in patients seeking medical assistance for insomnia and, vice versa, in patients with snoring inquiring about insomnia. Hägg SA, Ilieva E, Ljunggren M, etal. The negative health effects of having a combination of snoring and insomnia. J Clin Sleep Med. 2022;18(4):973-981.

Sleep Instability Correlates with Attentional Impairment in Boys with Attention Deficit Hyperactivity Disorder.

Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involving the association of attentional instability with sleep instability, i.e., sleep stage transitions and arousals. Twelve ADHD and 15 healthy control (HC) boys aged between 8 and 12 years old underwent polysomnography recording and attentional testing. The microarousal index, the number of awakenings, and the number of stage shifts between stages 1, 2, 3, 4 and REM sleep throughout the night were computed as sleep stability parameters. Attentional functioning was assessed using the Continuous Performance Test-II. We found significantly higher sleep instability in ADHD compared to HC. Sleep arousals and stage transitions (micro arousal index, stage 4/3 and 2/4 transitions) in ADHD significantly correlated with lower attentional scores. No association whatsoever was found between sleep instability and attentional functioning in HC. The results show that sleep instability is associated with lower attentional performance in boys with ADHD, but not in HC. This could be compatible with a model according to which attention and sleep stability share a common neural substrate in ADHD.

A Novel Electroencephalogram-derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk.

Rationale: Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance. Objectives: This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics. Methods: Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5,804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an approximately 11-year follow-up period. Results: Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratio, 1.32 [95% confidence interval, 1.14-1.50]), after adjusting for total sleep time and other clinical and lifestyle-related covariates, including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5 hours to 4.25 hours. Conventional measures of sleep quality, including wake after sleep onset and arousal index, were not predictive of all-cause mortality. Conclusions: Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health, and safety outcomes has considerable potential to provide unique neurophysiological insight.

27.2 COGNITIVE-BEHAVIORAL INTERVENTION FOR INSOMNIA

Insomnia is a sleep disturbance characterized by prolonged sleep onset, frequent awakenings and arousals, reduced sleep efficiency, and related daytime impairment, occurring at least 3 nights a week for a period of 3 months. The prevalence of insomnia increases with the progression of puberty and therefore increases significantly in adolescence, reaching a reported prevalence of 23.8%. CBT for insomnia (CBT-I) is the first-line treatment for insomnia. The objectives of this presentation are to briefly describe when and for whom this treatment is indicated, the strong evidence for this treatment, and its behavioral and cognitive components.

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.

The Role of Reproductive Hormones in Sex Differences in Sleep Homeostasis and Arousal Response in Mice.

There are various sex differences in sleep/wake behaviors in mice. However, it is unclear whether there are sex differences in sleep homeostasis and arousal responses and whether gonadal hormones are involved in these sex differences. Here, we examined sleep/wake behaviors under baseline condition, after sleep deprivation by gentle handling, and arousal responses to repeated cage changes in male and female C57BL/6 mice that are hormonally intact, gonadectomized, or gonadectomized with hormone supplementation. Compared to males, females had longer wake time, shorter non-rapid eye movement sleep (NREMS) time, and longer rapid eye movement sleep (REMS) episodes. After sleep deprivation, males showed an increase in NREMS delta power, NREMS time, and REMS time, but females showed a smaller increase. Females and males showed similar arousal responses. Gonadectomy had only a modest effect on homeostatic sleep regulation in males but enhanced it in females. Gonadectomy weakened arousal response in males and females. With hormone replacement, baseline sleep in gonadectomized females was similar to that of intact females, and baseline sleep in gonadectomized males was close to that of intact males. Gonadal hormone supplementation restored arousal response in males but not in females. These results indicate that male and female mice differ in their baseline sleep–wake behavior, homeostatic sleep regulation, and arousal responses to external stimuli, which are differentially affected by reproductive hormones.

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.

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.

The effect of sleep restriction therapy for insomnia on sleep pressure and arousal: a randomized controlled mechanistic trial.

Sleep restriction therapy (SRT) effectively treats insomnia but mechanisms are poorly understood. Theoretical models suggest that potentiation of sleep pressure and reduction of arousal are key mechanisms of action. To our knowledge, this has never been directly tested. We designed a randomized controlled trial with embedded mechanistic measurement to investigate if SRT causally modifies multidimensional assessments of sleep pressure and arousal. Participants aged 25-55 who met DSM-5 diagnostic criteria for insomnia disorder were randomized to four weeks of SRT or time in bed regularization (TBR), a control intervention that involves prescription of a regular but not reduced time in bed. Sleep pressure was assessed through daily diary appraisal of morning and evening sleepiness, weekly Epworth sleepiness scale (ESS) scores, psychomotor vigilance, and non-rapid eye movement (NREM) delta power (0.75-4.5 Hz) from ambulatory polysomnographic recordings. Arousal was assessed through daily diary appraisal of cognitive arousal, the pre-sleep arousal scale (PSAS), and NREM beta power (15-32 Hz). Outcomes were assessed at baseline (2-week period prior to randomization), during the intervention phase (1-4 weeks post-randomization), and at 12-week follow-up. We performed intention-to-treat analyses using linear mixed models. For continuous daily measures, the treatment period was split into early (weeks 1-2) and late (weeks 3-4) treatment. Fifty-six participants (39 females, mean age = 40.78 ± 9.08) were assigned to SRT (n = 27) or TBR (n = 29). The SRT group showed enhanced sleep pressure relative to TBR, reflected in (1) enhanced sleepiness in the evening during early (d = 1.17) and late treatment (d = 0.92), and in the morning during early treatment (d = 0.47); (2) higher daytime sleepiness on the ESS at weeks-1 and -2 (d = 0.54, d = 0.45); and (3) reduced psychomotor vigilance at week-1 (d = 0.34). The SRT group also showed reduced arousal relative to TBR, reflected in lower levels of daily-monitored cognitive arousal during early treatment (d = 0.53) and decreased PSAS total score at week-4 and week-12 (ds ≥ 0.39). Power spectral analysis of all night NREM sleep revealed an increase in relative, but not absolute, EEG delta power at week-1 and week-4 (ds ≥ 0.52) and a decrease of relative EEG beta power at week-4 (d = 0.11). For the first time, we show that SRT increases sleep pressure and decreases arousal during acute implementation, providing support for mechanism-of-action.

Sleep Disturbances During the Menopausal Transition: The Role of Sleep Reactivity and Arousal Predisposition

ABSTRACT Background Sleep disturbances are common during the menopausal transition and several factors can contribute to this increased incidence. This study examined the association between sleep reactivity, arousal predisposition, sleep disturbances, and menopause. Methods Data for this study were derived from a longitudinal, population-based study on the natural history of insomnia. A total of 873 women (40–60 years) were divided into two groups according to their menopausal status at baseline: reproductive (n = 408) and postmenopausal (n = 465). Participants were evaluated annually throughout the five-year follow-up period. Four questionnaires were used to examine sleep quality, insomnia severity, sleep reactivity, and arousal predisposition. The data were analyzed using two approaches: cross-sectional with a multivariate analysis and binary regression, and longitudinal with a linear mixed models using menopausal groups (3) x time (5) design. Results Cross-sectional analyses showed that postmenopausal women reported significantly more severe insomnia and poorer sleep quality than reproductive women. Sleep reactivity and arousal predisposition were significant predictors of sleep disturbances. Longitudinal analyses revealed increased sleep disturbances in the two years before and after the menopausal transition. Sleep reactivity and arousal predisposition did not moderate the temporal relationship between menopausal transition and sleep disturbances. Conclusion More sleep disturbances were reported during the menopausal transition, but those difficulties were not explained by sleep reactivity and arousal predisposition. These results suggest the involvement of other psychophysiological factors in the development of sleep disturbances during the menopause.