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\"A completely revised and updated second edition of one of the most popular and bestselling parenting books of all time, by America's favorite pediatrician, Dr. Harvey Karp. Harvey Karp, M.D. shares his groundbreaking approach to calming your new baby's crying and transforming your infant into the happiest baby on the block! His highly successful method is based on four revolutionary concepts: 1. Create the Fourth Trimester: How to re-create the womblike atmosphere your newborn baby still yearns for. 2. Find the Calming Reflex: An \"off switch\" all babies are born wtih, which quickly soothes fussing and crying. 3. Use the 5 S's: Five easy methods to turn on your baby's amazing calming reflex. 4. Apply the Cuddle Cure: How to combine the 5 S's to calm even colicky babies.\"-- Provided by publisher.

Abstract Study Objectives: Intraindividual night-to-night sleep duration is often insufficient and variable. Here we report the effects of such chronic variable sleep deficiency on neurobehavioral performance and the ability of state-of-the-art models to predict these changes. Methods: Eight healthy males (mean age ± SD: 23.9 ± 2.4 years) studied at our inpatient intensive physiologic monitoring unit completed an 11-day protocol with a baseline 10-hour sleep opportunity and three cycles of two 3-hour time-in-bed (TIB) and one 10-hour TIB sleep opportunities. Participants received one of three polychromatic white light interventions (200 lux 4100K, 200 or 400 lux 17000K) for 3.5 hours on the morning following the second 3-hour TIB opportunity each cycle. Neurocognitive performance was assessed using the psychomotor vigilance test (PVT) administered every 1–2 hours. PVT data were compared to predictions of five group-average mathematical models that incorporate chronic sleep loss functions. Results: While PVT performance deteriorated cumulatively following each cycle of two 3-hour sleep opportunities, and improved following each 10-hour sleep opportunity, performance declined cumulatively throughout the protocol at a more accelerated rate than predicted by state-of-the-art group-average mathematical models. Subjective sleepiness did not reflect performance. The light interventions had minimal effect. Conclusions: Despite apparent recovery following each extended sleep opportunity, residual performance impairment remained and deteriorated rapidly when rechallenged with subsequent sleep loss. None of the group-average models were capable of predicting both the build-up in impairment and recovery profile of performance observed at the group or individual level, raising concerns regarding their use in real-world settings to predict performance and improve safety.

Abstract The Maintenance of Wakefulness Test (MWT) is a widely accepted objective test used to evaluate daytime somnolence and is commonly used in clinical studies evaluating novel therapeutics for excessive daytime sleepiness. In the latter, sleep onset latency (SOL) is typically the sole MWT endpoint. Here, we explored microsleeps, sleep probability measures derived from automated sleep scoring, and quantitative electroencephalography (qEEG) features as additional MWT biomarkers of daytime sleepiness, using data from a phase 1B trial of the selective orexin receptor 2 agonist danavorexton (TAK-925) in people with narcolepsy type 1 (NT1) or type 2 (NT2). Danavorexton treatment reduced the rate and duration of microsleeps during the MWT in NT1 (days 1 and 7; p ≤ .005) and microsleep rate in NT2 (days 1 and 7; p < .0001). The use of an EEG-sleep-staging − derived measure to determine the probability of wakefulness for each minute revealed a novel metric to track changes in daytime sleepiness, which were consistent with the θ/α ratio, a known biomarker of drowsiness. The slopes of line-fits to both the log-transformed sleepiness score or log-transformed θ/α ratio correlated well to (inverse) MWT SOL for NT1 (R = 0.93 and R = 0.83, respectively) and NT2 (R = 0.97 and R = 0.84, respectively), suggesting that individuals with narcolepsy have increased sleepiness immediately after lights-off. These analyses demonstrate that novel EEG-based biomarkers can augment SOL as predictors of sleepiness and its response to treatment and provide a novel framework for the analysis of wake EEG in hypersomnia disorders. Graphical Abstract Graphical Abstract

Abstract Study Objectives To assess the risks associated with the use of alcohol as a “sleep aid,” we evaluated tolerance development to pre-sleep ethanol’s sedative-hypnotic effects, and subsequent ethanol dose escalation. Methods Volunteers, 21–55 years old, with insomnia in otherwise good medical and psychiatric health and no history of alcoholism or drug abuse participated. In experiment 1 (n = 24) 0.0, 0.3, or 0.6 g/kg (n = 8 per dose) ethanol was administered before sleep and 8-hour nocturnal polysomnograms (NPSGs) were collected. In experiment 2, after six nights pretreatment with ethanol 0.45 g/kg (n = 6) versus placebo (n = 6), choice of pre-sleep ethanol or placebo was assessed over seven choice nights. Results The 0.6 g/kg ethanol dose increased total sleep time and stage 3–4 sleep on night 2, but these effects were lost by night 6 (p < .05). Six nights of ethanol pretreatment produced on the choice nights more self-administered ethanol refills than the placebo pretreatment (p < .03). Conclusions These are the first data to explicitly show the risks associated with the use of alcohol as a “sleep aid” among people with insomnia. Initially, a moderate dose of ethanol improved NPSG sleep, which was lost by night 6. Tolerance was associated with enhanced self-administration of pre-sleep ethanol.

Abstract Study Objectives Post hoc analysis to evaluate the effect of daridorexant on sleep architecture in people with insomnia, focusing on features associated with hyperarousal. Methods We studied sleep architecture in adults with chronic insomnia disorder from two randomized phase 3 clinical studies (Clinicaltrials.gov: NCT03545191 and NCT03575104) investigating 3 months of daridorexant treatment (placebo, daridorexant 25 mg, daridorexant 50 mg). We analyzed sleep–wake transition probabilities, EEG spectra, and sleep spindle properties including density, dispersion, and slow oscillation phase coupling. The wake EEG similarity index (WESI) was determined using a machine learning algorithm analyzing the spectral profile of the EEG. Results At month 3, daridorexant 50 mg decreased wake-to-wake transition probabilities (p < .05) and increased the probability of transitions from wake-to-N1 (p < .05), N2 (p < .05), and REM sleep (p < .05), as well as from N1-to-N2 (p < .05) compared to baseline and placebo. Daridorexant 50 mg decreased relative beta power during wake (p = .011) and N1 (p < .001) compared to baseline and placebo. During the wake, relative alpha power decreased (p < .001) and relative delta power increased (p < .001) compared to placebo. Daridorexant did not alter EEG spectra bands in N2, N3, and REM stages or in sleep spindle activity. Daridorexant decreased the WESI score during wake compared to baseline (p = .004). Effects with 50 mg were consistent between months 1 and 3 and less pronounced with 25 mg. Conclusions Daridorexant reduced EEG features associated with hyperarousal as indicated by reduced wake-to-wake transition probabilities and enhanced spectral features associated with drowsiness and sleep during wake and N1. Clinical Trials ClinicalTrials.gov NCT03545191: study to assess the efficacy and safety of ACT-541468 (daridorexant) in adult and elderly participants with insomnia disorder. URL: Study Details | study to assess the efficacy and safety of ACT-541468 (daridorexant) in adult and elderly participants with insomnia disorder | ClinicalTrials.gov ClinicalTrials.gov NCT03575104: study to assess the efficacy and safety of ACT-541468 (daridorexant) in adult and elderly participants who experience difficulties sleeping. URL: study details | study to assess the efficacy and safety of ACT-541468 (daridorexant) in adult and elderly participants who experience difficulties sleeping | ClinicalTrials.gov Graphical Abstract Graphical Abstract

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.

Abstract Study Objectives To investigate the cumulative effect of five consecutive nights of partial sleep deprivation (PSD) on a panel of cerebrospinal fluid (CSF) biomarkers in healthy adults. Methods A randomized, cross-over study conducted at the University of Gothenburg. The participants (N = 13) were healthy adults (20–40 years of age) with a normal sleeping pattern. The participants underwent a baseline sleep period consisting of five nights with 8 hr spent in bed. A subsequent period with PSD consisted of five nights of maximum 4 hr of sleep per night. Four participants were also subjected to a prolonged period of PSD consisting of eight nights with 4 hr of sleep per night. Sleep was monitored by means of observation, actigraphy, and continuous polysomnographic recordings. CSF samples were collected by routine lumbar puncture after each period. CSF biomarkers included the 38, 40, and 42 amino acid–long Aβ isoforms, total-τ, phospho-τ, orexin, monoamine metabolites (3-methoxy-4-hydroxyphenylglycol, homovanillinic acid, and 5-hydroxyindoleacetic acid), neuron-derived biomarkers (neurofilament light, neuron-specific enolase, and fatty acid–binding protein), and astro- and microglia-derived biomarkers (glial fibrillary acidic protein, S-100B, and YKL-40). Results PSD was associated with a 27 per cent increase in CSF orexin concentrations (p = 0.001). No PSD-related changes in CSF biomarkers for amyloid build-up in the brain, Alzheimer’s disease (AD)-type neurodegeneration, or astroglial activation were observed. PSD led to a shortening of time spent in all sleep stages except slow-wave sleep (SWS). Conclusion Five to eight consecutive nights of PSD, with preserved SWS, increased CSF orexin but had no effect on CSF biomarkers for amyloid deposition, neuronal injury, and astroglial activation.

The development of alcohol use disorder (AUD) involves binge or heavy drinking to high levels of intoxication that leads to compulsive intake, the loss of control in limiting intake, and a negative emotional state when alcohol is removed. This cascade of events occurs over an extended period within a three-stage cycle: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. These three heuristic stages map onto the dysregulation of functional domains of incentive salience/habits, negative emotional states, and executive function, mediated by the basal ganglia, extended amygdala, and frontal cortex, respectively. Sleep disturbances, alterations of sleep architecture, and the development of insomnia are ubiquitous in AUD and also map onto the three stages of the addiction cycle. During the binge/intoxication stage, alcohol intoxication leads to a faster sleep onset, but sleep quality is poor relative to nights when no alcohol is consumed. The reduction of sleep onset latency and increase in wakefulness later in the night may be related to the acute effects of alcohol on GABAergic systems that are associated with sleep regulation and the effects on brain incentive salience systems, such as dopamine. During the withdrawal/negative affect stage, there is a decrease in slow-wave sleep and some limited recovery in REM sleep when individuals with AUD stop drinking. Limited recovery of sleep disturbances is seen in AUD within the first 30 days of abstinence. The effects of withdrawal on sleep may be related to the loss of alcohol as a positive allosteric modulator of GABAA receptors, a decrease in dopamine function, and the overactivation of stress neuromodulators, including hypocretin/orexin, norepinephrine, corticotropin-releasing factor, and cytokines. During the preoccupation/anticipation stage, individuals with AUD who are abstinent long-term present persistent sleep disturbances, including a longer latency to fall asleep, more time awake during the night, a decrease in slow-wave sleep, decreases in delta electroencephalogram power and evoked delta activity, and an increase in REM sleep. Glutamatergic system dysregulation that is observed in AUD is a likely substrate for some of these persistent sleep disturbances. Sleep pathology contributes to AUD pathology, and vice versa, possibly as a feed-forward drive to an unrecognized allostatic load that drives the addiction process.

Study Objectives: Sleep restriction alters food intake, but less is known about how dietary patterns affect sleep. Current goals were to determine whether: (1) sleep is different after consumption of a controlled diet vs. an ad libitum diet, and (2) dietary intake during ad libitum feeding is related to nocturnal sleep. Methods: Twenty-six normal weight adults (30–45 y), habitually sleeping 7-9 h/night, participated in a randomized-crossover inpatient study with 2 phases of 5 nights: short (4 h in bed) or habitual (9 h in bed) sleep. Only data from the habitual sleep phase were used for the present analyses. During the first 4 days, participants consumed a controlled diet; on day 5, food intake was self-selected. Linear regression was used to determine relations between daytime food intake and nighttime sleep on day 5. Results: Sleep duration did not differ after 3 days of controlled feeding vs. a day of ad libitum intake. However, sleep after ad libitum eating had less slow wave sleep (SWS, P = 0.0430) and longer onset latency (P = 0.0085). Greater fiber intake predicted less stage 1 (P = 0.0198) and more SWS (P = 0.0286). Percent of energy from saturated fat predicted less SWS (P = 0.0422). Higher percent of energy from sugar and other carbohydrates not considered sugar or fiber was associated with arousals (P = 0.0320 and 0.0481, respectively). Conclusions: Low fiber and high saturated fat and sugar intake is associated with lighter, less restorative sleep with more arousals. Diet could be useful in the management of sleep disorders but this needs to be tested. Clinical Trial Registration: http://www.clinicaltrials.gov , #NCT00935402. Citation: St-Onge MP, Roberts A, Shechter A, Choudhury AR. Fiber and saturated fat are associated with sleep arousals and slow wave sleep. J Clin Sleep Med 2016;12(1):19–24.

Slow-wave sleep (SWS) is important for overall health since it affects many physiological processes including cardio-metabolic function. Sleep and autonomic nervous system (ANS) activity are closely coupled at anatomical and physiological levels. Sleep-related changes in autonomic function are likely the main pathway through which SWS affects many systems within the body. There are characteristic changes in ANS activity across sleep stages. Notably, in non-rapid eye-movement sleep, the progression into SWS is characterized by increased parasympathetic activity, an important measure of cardiovascular health. Experimental manipulations that enhance slow-wave activity (SWA, 0.5–4 Hz) can improve sleep-mediated memory and immune function. However, effects of SWA enhancement on autonomic regulation have not been investigated. Here, we employed an adaptive algorithm to deliver 50 ms sounds phase-locked to slow-waves, with regular pauses in stimulation (~5 s ON/~5 s OFF), in healthy young adults. We sought to determine whether acoustic enhancement of SWA altered parasympathetic activity during SWS assessed with heart rate variability (HRV), and evening-to-morning changes in HRV, plasma cortisol, and blood pressure. Stimulation, compared with a sham condition, increased SWA during ON versus OFF intervals. This ON/OFF SWA enhancement was associated with a reduction in evening-to-morning change of cortisol levels and indices of sympathetic activity. Furthermore, the enhancement of SWA in ON intervals during sleep cycles 2–3 was accompanied by an increase in parasympathetic activity (high-frequency, HRV). Together these findings suggest that acoustic enhancement of SWA has a positive effect on autonomic function in sleep. Approaches to strengthen brain–heart interaction during sleep could have important implications for cardiovascular health.