[The effectiveness of suggestive techniques to improve sleep is enhanced by combining with sound stimulus based on binaural beats]. / Effektivnost' suggestivnykh metodik dlya uluchsheniya sna povyshaetsya pri sochetanii ikh so zvukovym stimulom na osnove binaural'nykh bienii.

OBJECTIVE: To test the hypothesis of the difference between 3 means of sleep latency (SL) during falling asleep: accompanied by audio stimulus embedded with binaural beats (BB); after listening to suggestive body relaxation instructions; accompanied by audio stimulus embedded with BB after listening to suggestive body relaxation instructions (that is the combination of 1 and 2). MATERIAL AND METHODS: For the purpose of the study, a special Android application was developed and installed on the subjects' individual smartphones. The application assumed screen tapping test to control for fall-asleep process. The data of 63 subjects presented with the 3 types of sound stimuli mentioned above in a counterbalanced scheme were analyzed. RESULTS: Statistical analysis confirmed the initial hypothesis about the dependence of LS on the type of sound stimulus (p<0.05). Pairwise SL comparison showed reliable difference between stimuli (3) - 1149±113 s, and (1) - 1469±89 s (p<0.01). SL for the stimulus (2) had an intermediate value of 1269±112 s (difference from (1) at a trend level). CONCLUSION: The use of background sound embedded with BBs enhances the effect of suggestive instructions to improve sleep. But it is the suggestion as a psychotherapeutic technique that is determinant.

The effects of daylight duration on the multiple sleep latency test (MSLT) results: A pilot study.

OBJECTIVE: MSLT results are known to be affected by multiple factors including sleep time, frequency of nighttime arousals, and medications intake. Although being the main synchronizer of sleep and wakefulness, daylight duration effects on MSLT have not been examined. Burlington, Vermont, USA experiences great variations in daylight duration, ranging from 8 h 50 min to 15 h 33 min of daylight. The aim of this study was to test the hypothesis that there would be photoperiod duration effects on MSLTs performed during short daylight (short daylight studies, SDS) vs. long daylight (long daylight studies, LDS) from 2013 to 2023 in our sleep laboratory. METHODS: We identified and analyzed 37 SDS (daylight 530-560 min) and 36 LDS (daylight 903-933 min) from our database. Groups of SDS and LDS results were compared using non-paired student T test, Chi-Square and non-parametric Mann Whitney U Test. RESULTS: Average daylight duration was 15 h 18 ± 14.6 min for LDS and 8 h 57 ± 18 min for SDS. Two groups did not differ in terms of the age, gender, BMI and race of patients studied. Mean total sleep time and sleep efficiency during PSG preceding MSLT, and MSLT mean sleep onset latency did not significantly differ for the two groups. However, SDS MSLT naps had significantly more sleep onset REM periods (SOREMP), and distribution of the number of SOREMP captured during MSLT was different for SDS and LDS groups. Differences of SDS and LDS results did not relate to sleep architecture of the overnight PSG as analysis of sleep and REM latency and relative percentages of N1, N2, REM, and N3 was not significantly different between SDS and LDS. The two groups showed difference in arousal indexes during N1 and REM sleep. CONCLUSIONS: Daylight duration may impact MSLT results and should probably be accounted for in MSLT interpretation. Attention to photoperiod could be considered in MSLT guidelines, if our results are replicated in larger samples.

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.

MEDi-SOL: Multi Ensemble Distribution Model for Estimating Sleep Onset Latency.

Sleep onset latency (SOL) is an important factor relating to the sleep quality of a subject. Therefore, accurate prediction of SOL is useful to identify individuals at risk of sleep disorders and to improve sleep quality. In this study, we estimate SOL distribution and falling asleep function using an electroencephalogram (EEG), which can measure the electric field of brain activity. We proposed a Multi Ensemble Distribution model for estimating Sleep Onset Latency (MEDi-SOL), consisting of a temporal encoder and a time distribution decoder. We evaluated the performance of the proposed model using a public dataset from the Sleep Heart Health Study. We considered four distributions, Normal, log-Normal, Weibull, and log-Logistic, and compared them with a survival model and a regression model. The temporal encoder with the ensemble log-Logistic and log-Normal distribution showed the best and second-best scores in the concordance index (C-index) and mean absolute error (MAE). Our MEDi-SOL, multi ensemble distribution with combining log-Logistic and log-Normal distribution, shows the best score in C-index and MAE, with a fast training time. Furthermore, our model can visualize the process of falling asleep for individual subjects. As a result, a distribution-based ensemble approach with appropriate distribution is more useful than point estimation.

Repeated polysomnography and multiple sleep latency test in narcolepsy type 1 and other hypersomnolence disorders.

BACKGROUND: The diagnosis of narcolepsy is based on clinical information, combined with polysomnography (PSG) and the Multiple Sleep Latency Test (MSLT). PSG and the MSLT are moderately reliable at diagnosing narcolepsy type 1 (NT1) but unreliable for diagnosing narcolepsy type 2 (NT2). This is a problem, especially given the increased risk of a false-positive MSLT in the context of circadian misalignment or sleep deprivation, both of which commonly occur in the general population. AIM: We aimed to clarify the accuracy of PSG/MSLT testing in diagnosing NT1 versus controls without sleep disorders. Repeatability and reliability of PSG/MSLT testing and temporal changes in clinical findings of patients with NT1 versus patients with hypersomnolence with normal hypocretin-1 were compared. METHOD: 84 patients with NT1 and 100 patients with non-NT1-hypersomnolence disorders, all with congruent cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) levels, were included. Twenty-five of the 84 NT1 patients and all the hypersomnolence disorder patients underwent a follow-up evaluation consisting of clinical assessment, PSG, and a modified MSLT. An additional 68 controls with no sleep disorders were assessed at baseline. CONCLUSION: Confirming results from previous studies, we found that PSG and our modified MSLT accurately and reliably diagnosed hypocretin-deficient NT1 (accuracy = 0.88, reliability = 0.80). Patients with NT1 had stable clinical and electrophysiological presentations over time that suggested a stable phenotype. In contrast, the PSG/MSLT results of patients with hypersomnolence, and normal CSF-hcrt-1 had poor reliability (0.32) and low repeatability.

Muscle atonia index during multiple sleep latency test: A possible marker to differentiate narcolepsy from other hypersomnias.

OBJECTIVE: The complexity and delay of the diagnosis of narcolepsy require several diagnostic tests and invasive procedures such as lumbar puncture. Our study aimed to determine the changes in muscle tone (atonia index, AI) at different levels of vigilance during the entire multiple sleep latency test (MSLT) and each nap in people with narcolepsy type 1 (NT1) and 2 (NT2) compared with other hypersomnias and its potential diagnostic value. METHODS: Twenty-nine patients with NT1 (11 M 18F, mean age 34.9 years, SD 16.8) and sixteen with NT2 (10 M 6F, mean age 39 years, SD 11.8) and 20 controls with other hypersomnias (10 M, 10F mean age 45.1 years, SD 15.1) were recruited. AI was evaluated at different levels of vigilance (Wake and REM sleep) in each nap and throughout the MSLT of each group. The validity of AI in identifying patients with narcolepsy (NT1 and NT2) was analyzed using receiver operating characteristic (ROC) curves. RESULTS: AI during wakefulness (WAI) was significantly higher in the narcolepsy groups (NT1 and NT2 p < 0.001) compared to the hypersomniac group. AI during REM sleep (RAI) (p = 0.03) and WAI in nap with sudden onsets of REM sleep periods (SOREMP) (p = 0.001) were lower in NT1 than in NT2. The ROC curves showed high AUC values for WAI (NT1 0.88; Best Cut-off > 0.57, Sensitivity 79.3 % Specificity 90 %; NT2 0.89 Best Cut-off > 0.67 Sensitivity 87.5 % Specificity 95 %; NT1 and NT2 0.88 Best Cut-off > 0.57 Sensitivity 82.2 % Specificity 90 %) in discriminating subjects suffering from other hypersomnias. RAI and WAI in nap with SOREMP showed a poor AUC value (RAI AUC: 0.7 Best cutoff 0.7 Sensitivity 50 % Specificity 87.5 %; WAI in nap before SOREMP AUC: 0.66, Best cut-off < 0.82 sensitivity 61.9 % and specificity 67.35 %) differentiating NT1 and NT2. CONCLUSIONS: WAI may represent an encouraging electrophysiological marker of narcolepsy and suggests a vulnerable tendency to dissociative wake / sleep dysregulation lacking in other forms of hypersomnia. SIGNIFICANCE: AI during wakefulness may help distinguish between narcolepsy and other hypersomnias.

¿Qué es el test de latencia múltiple de sueño y cuál es su uso en pediatría? / What is the multiple sleep latency test and what is its use in pediatrics?

La prueba de latencia múltiple del sueño nos permite evaluar objetivamente las variaciones normales y patológicas en la somnolencia y el estado de alerta. Es una prueba que evalúa qué tan rápido una persona se duerme en condiciones estandarizadas que facilitan el sueño, y se repite a intervalos de 2 horas durante todo el día. Es el estándar para documentar el inicio del sueño REM (SOREMP), que es un síntoma de narcolepsia y en la somnolencia idiopática podría ser útil. Su uso está ampliamente descrito en adultos, pero la prueba no es tan común en niños. En esta revisión, se analizan los valores en adultos y niños, y su utilidad, a partir de la historia de la prueba.

The multiple sleep latency test allows us to objectively assess normal and pathological variations in sleepiness and alertness. It is a test that assesses how quickly a person falls asleep under standardized conditions that facilitate sleep and is repeated at 2-h intervals throughout the day. is the standard for documenting sleep onset REM (SOREMP), which is a symptom of Narcolepsy and idiopathic sleepiness could be useful. Its use is widely described in adults, but the test is not so common in children. In this review, we analyze the values in adults and children, and their usefulness, based on from the history of the test.

Differential characteristics of repeated polysomnography and multiple sleep latency test parameters in narcolepsy type 1 and type 2 patients: a longitudinal retrospective study.

PURPOSE: Narcolepsy is a chronic disorder and its phenotype is dichotomized into narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2). The clinical course and pathophysiological mechanisms of these two clinical entities and their differences are not adequately defined. This study aimed to explore the differential longitudinal patterns of polysomnography (PSG) and multiple sleep latency test (MSLT) in NT1 and NT2. METHODS: In this retrospective study demographic characteristics, PSG, and MSLT parameters at baseline and follow-up were compared between NT1 and NT2 patients. Patients with both follow-up MSLT and PSG were selected for sub-group analysis. Baseline and follow-up MSLT and PSG parameters were compared. RESULTS: Of 55 patients with narcolepsy, mean follow-up periods were 7.4 ± 3.5 years for NT1 and 5.5 ± 2.9 for NT2. Demographic data showed increased body mass index and prevalence of sleep paralysis in NT1. Baseline PSG characteristics between NT1 and NT2 showed decreased sleep latency (p = 0.016) and REM latency (p = 0.046) in NT1 group when compared with NT2. Nocturnal SOREMP on PSG was more prevalent in NT1 (p = 0.017), and half of NT2 patients with nocturnal SOREMP on PSG changed their diagnoses to NT1. On follow-up PSG, NT1 displayed reductions in sleep stage N2 (p = 0.006) and N3 (p = 0.048), while wake after sleep onset (WASO) (p = 0.023) and apnea-hypopnea index (AHI) (p = 0.007) were significantly increased. CONCLUSION: Differential MSLT and PSG characteristics of NT1 and NT2 in at baseline and follow-up indicate that NT1 and NT2 are distinct disease phenotypes, and that they present with a contrasting course of disease.

Effects of propofol on sleep architecture and sleep-wake systems in rats.

Previous studies have shown that the synchronization of electroencephalogram (EEG) signals is found during propofol-induced general anesthesia, which is similar to that of slow-wave sleep (SWS). However, a complete understanding is lacking in terms of the characteristics of EEG changes in rats after propofol administration and whether propofol acts through natural sleep circuits. Here, we examined the characteristics of EEG patterns induced by intraperitoneal injection of propofol in rats. We found that high (10 mg/kg) and medium (5 mg/kg) doses of propofol induced a cortical EEG of low-frequency, high-amplitude activity with rare electromyographic activity and markedly shortened sleep latency. The high dose of propofol increased deep slow-wave sleep (SWS2) to 4 h, as well as the number of large SWS2 bouts (>480 s), their mean duration and the peak of the power density curve in the delta range of 0.75-3.25 Hz. After the medium dose of propofol, the total number of wakefulness, light slow-wave sleep (SWS1) and SWS2 episodes increased, whereas the mean duration of wakefulness decreased. The high dose of propofol significantly increased c-fos expression in the ventrolateral preoptic nucleus (VLPO) sleep center and decreased the number of c-fos-immunoreactive neurons in wake-related systems including the tuberomammillary nucleus (TMN), perifornical nucleus (PeF), lateral hypothalamic nucleus (LH), ventrolateral periaqueductal gray (vPAG) and supramammillary region (SuM). These results indicated that the high dose of propofol produced high-quality sleep by increasing SWS2, whereas the medium dose produced fragmented and low-quality sleep by disrupting the continuity of wakefulness. Furthermore, sleep-promoting effects of propofol are correlated with activation of the VLPO cluster and inhibition of the TMN, PeF, LH, vPAG and SuM.

Effects of physical activity on sleep problems in breast cancer survivors: a meta-analysis.

PURPOSE: To investigate the possible role of physical activity (PA) on sleep disturbance in breast cancer patients. METHODS: Literature in PubMed, Embase, and the Cochrane Library was systematically searched until January 30, 2020. Randomized controlled trials that focused on the role of PA interventions on sleep disturbance were selected. The main outcome measures included the global Pittsburgh Sleep Quality Index (PSQI) score and PSQI subscales. Subgroup analysis was performed based on the study area and intervention time. The stability and authenticity of the results were measured by sensitivity analysis and publication bias analysis, respectively. RESULTS: Six articles were included in this meta-analysis. There were no significant differences in global PSQI scores between the PA intervention group and the usual care group (P = 0.057). As for PSQI subscales, PA intervention could improve sleep quality (weighted mean difference = 0.22; 95% confidence interval 0.04-0.40; P = 0.018). There were no significant differences in sleep duration, sleep medication, sleep latency, habitual sleep efficiency, and daytime dysfunction between the two groups (all P > 0.05). CONCLUSION: PA serves as an effective intervention to improve sleep quality.

Napping in high-performance athletes: Sleepiness or sleepability?

Daytime napping is a common practice in high-performance athletes, and is widely assumed to reflect sleepiness arising from sports-related sleep debt. The possibility that athlete naps may also be indicative of 'sleepability', a capacity to nap on demand that is only weakly related to homeostatic sleep pressure, has not previously been tested. The present study compared daytime sleep latencies in high-performance athletes and non-athlete controls using a single nap opportunity model. Elite (n = 10), and sub-elite (n = 10) athletes, and non-athlete controls (n = 10) attended the laboratory for a first adaption trial, and a subsequent experimental trial. Subjective sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) at 14:00, 14:30 and immediately prior to a 20-minute nap opportunity at 15:00. Sleep latencies were measured using polysomnography, and defined as the time from lights out to the first epoch of any stage of sleep (N1, N2, N3, REM). In unadjusted comparisons with non-athlete controls, elite athletes showed significantly shorter sleep latencies in both the adaptation (p < 0.05) and experimental trials (p < 0.05). These significant differences were maintained in models controlling for pre-trial KSS scores and pre-trial total sleep time (all p < 0.05). Sleep latency scores for sub-elite athletes showed similar trends, but were more labile. These results are consistent with a conclusion that, among elite athletes, napping behaviour can reflect sleepability and may not necessarily result from nocturnal sleep disruption and daytime sleepiness.

The effect of evening cycling at different intensities on sleep in healthy young adults with intermediate chronobiological phenotype: A randomized, cross-over trial.

This study investigated the effect of various cycling intensities on sleep-related parameters in healthy young adults with intermediate chronobiological phenotype. Ten recreationally trained male volunteers underwent an evening i) moderate-intensity continuous training (MICT; 45 min at 70% Wmax), ii) high-intensity interval training (HIIT; 10 × 1 min at 90% Wmax), iii) sprint interval training (SIT; 6 × 20 sec at 140% Wmax) or iv) a non-exercise (CON) trial in randomized, counter-balanced and crossover order. At baseline, somatometric data, maximum oxygen uptake and chronotype were evaluated. Sleep-related indices and daily activity were recorded by a multi-sensor activity monitor. Total sleep time was longer after SIT compared to CON and MICT (p < 0.05). Sleep efficiency was higher in SIT than in CON (p < 0.05). Sleep onset latency did not differ among trials. Wake after sleep onset was decreased after SIT compared to CON (p= 0.049). No differences were found for bedtime among trials. Wake time was earlier in the MICT trial compared to CON (p = 0.026). Evening cycling exercise -independently of intensity- did not impair sleep of individuals with intermediate chronobiological phenotype. Furthermore, a single SIT session improved sleep quantity and continuation of individuals with this specific chronotype.

The brain network organization during sleep onset after deprivation.

OBJECTIVE: Aim of the present study is to investigate the alterations of brain networks derived from EEG analysis in pre- and post-sleep onset conditions after 40 h of sleep deprivation (SD) compared to sleep onset after normal sleep in 39 healthy subjects. METHODS: Functional connectivity analysis was made on electroencelographic (EEG) cortical sources of current density and small world (SW) index was evaluated in the EEG frequency bands (delta, theta, alpha, sigma and beta). RESULTS: Comparing pre- vs. post-sleep onset conditions after a night of SD a significant decrease of SW in delta and theta bands in post-sleep onset condition was found together with an increase of SW in sigma band. Comparing pre-sleep onset after sleep SD versus pre-sleep onset after a night of normal sleep a decreased of SW index in beta band in pre-sleep onset in SD compared to pre-sleep onset in normal sleep was evidenced. CONCLUSIONS: Brain functional network architecture is influenced by the SD in different ways. Brain networks topology during wake resting state needs to be further explored to reveal SD-related changes in order to prevent possible negative effects of SD on behaviour and brain function during wakefulness. SIGNIFICANCE: The SW modulations as revealed by the current study could be used as an index of an altered balance between brain integration and segregation processes after SD.

EEG and behavioural correlates of mild sleep deprivation and vigilance.

OBJECTIVE: The current study investigated the behavioral, cognitive, and electrophysiological impact of mild (only a few hours) and acute (one night) sleep loss via simultaneously recorded behavioural and physiological measures of vigilance. METHODS: Participants (N = 23) came into the lab for two testing days where their brain activity and vigilance were recorded and assessed. The night before the testing session, participants either slept from 12am to 9am (Normally Rested), or from 1am to 6am (Sleep Restriction). RESULTS: Vigilance was reduced and sleepiness was increased in the Sleep Restricted vs. Normally Rested condition, and this was exacerbated over the course of performing the vigilance task. As well, sleep restriction resulted in more intense alpha bursts. Lastly, EEG spectral power differed in Sleep Restricted vs. Normally Rested conditions as sleep onset progressed, particularly for frequencies reflecting arousal (e.g., delta, alpha, beta). CONCLUSIONS: The findings of this study suggest that only one night of mild sleep loss significantly increases sleepiness and, importantly, reduces vigilance. In addition, this sleep loss has a clear impact on the physiology of the brain in ways that reflect reduced arousal. SIGNIFICANCE: Understanding the neural correlates and cognitive processes associated with loss of sleep may lead to important advancements in identifying and preventing deleterious or potentially dangerous, sleep-related lapses in vigilance.

Actigraphy-estimated sleep and 24-hour activity rhythms and the risk of dementia.

INTRODUCTION: We investigated and compared associations of objective estimates of sleep and 24-hour activity rhythms using actigraphy with risk of dementia. METHODS: We included 1322 non-demented participants from the prospective, population-based Rotterdam Study cohort with valid actigraphy data (mean age 66 ± 8 years, 53% women), and followed them for up to 11.2 years to determine incident dementia. RESULTS: During follow-up, 60 individuals developed dementia, of which 49 had Alzheimer's disease (AD). Poor sleep as indicated by longer sleep latency, wake after sleep onset, and time in bed and lower sleep efficiency, as well as an earlier "lights out" time, were associated with increased risk of dementia, especially AD. We found no associations of 24-hour activity rhythms with dementia risk. DISCUSSION: Poor sleep, but not 24-hour activity rhythm disturbance, is associated with increased risk of dementia. Actigraphy-estimated nighttime wakefulness may be further targeted in etiologic or risk prediction studies.

Analysis of Psychological and Sleep Status and Exercise Rehabilitation of Front-Line Clinical Staff in the Fight Against COVID-19 in China.

BACKGROUND The aim of this study was to understand the changes in psychological factors and sleep status of front-line medical staff in the fight against COVID-19 and provide evidence of exercise interventions to relieve psychological stress and improve sleep status for medical staff. MATERIAL AND METHODS A survey study was conducted among 120 front-line medical staff in the fight against COVID-19, of which 60 medical staff worked at the designated hospital (experimental group) and 60 medical staff worked at the non-designated hospital (control group). The Symptom Checklist 90 (SCL-90), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and PTSD Checklist-Civilian Version (PCL-C) were used to assess mental status. Sleep status was assessed using the Pittsburgh Sleep Quality Index (PSQI). RESULTS SCL-90 scores of somatization, depression, anxiety, and terror were higher than normal in front-line medical staff at the designated hospital. The SAS (45.89±1.117), SDS (50.13±1.813), and PCL-C (50.13±1.813) scores in the experimental group were higher than the normal control group, and were significantly different from those in the control group on SDS and PCL-C scales (P<0.05). The total average PSQI of the experimental group was 16.07±3.761, indicating that the sleep quality was poor. Among them, participants with moderate insomnia reached 61.67%, and participants with severe insomnia reached 26.67%. CONCLUSIONS There are psychological symptoms and sleep symptoms in front-line medical staff who participate in the fight against COVID-19, and they affect each other. Hospitals should improve emergency management measures, strengthen psychological counseling for clinical front-line medical staff, strengthen exercise intervention, and improve their sleep quality and mental health.

Normal sleep development in infants: findings from two large birth cohorts.

OBJECTIVE: Sleep difficulties are highly prevalent and often persistent in young children, but sometimes parents are worried about sleep symptoms that belong to the normative range rather than to actual disturbances. Therefore, the aim of this study was to describe the normative development of sleep at the ages of 3, 6, 8, 12, 18 and 24 months in healthy children. METHODS: The present study is based on two birth cohorts that comprise representative samples of families recruited systematically during pregnancy. In the CHILD-SLEEP cohort, the sample sizes were 1427 at three, 1301 at eight, 1163 at 18, and 950 at 24 months. In the Finnbrain cohort, the sample sizes were 2002 at six months and 1693 at 12 months. Healthy term-born children were eligible for this study. To assess the infants' sleep duration and sleep quality, the Brief Infant Sleep Questionnaire was used in both cohorts and additionally the Infant Sleep Questionnaire in the CHILD-SLEEP cohort. The distributions of the study variables were reported using standard parameters. RESULTS: We found that sleep quality is highly variable particularly during the first two years of life, but this variability decreased markedly towards the second year. First, sleep latency decreased by the age of six months, while night-time sleep began to consolidate during the second year. However, parent-reported sleeping problems were common during the entire study period. CONCLUSION: As many families struggle with infants' sleeping problems, the reference values reported in this article can be valuable tools in various clinical settings to define clinically significant deviances in the sleep development and to identify individuals benefitting from counselling and clinical interventions.

Repeated afternoon sleep recordings indicate first-night-effect-like adaptation process in family dogs.

The importance of dogs (Canis familiaris) in sleep research is primarily based on their comparability with humans. In spite of numerous differences, dogs' comparable sleep pattern, as well as several phenotypic similarities on both the behavioural and neural levels, make this species a most feasible model in many respects. Our aim was to investigate whether the so-called first-night effect, which in humans manifests as a marked macrostructure difference between the first and second sleep occasions, can be observed in family dogs. We used a non-invasive polysomnographic method to monitor and compare the characteristics of dogs' (N = 24) 3-hr-long afternoon naps on three occasions at the same location. We analysed how sleep macrostructure variables differed between the first, second and third occasions, considering also the effects of potential confounding variables such as the dogs' age and sleeping habits. Our findings indicate that first-night effect is present in dogs' sleep architecture, although its specifics somewhat deviate from the pattern observed in humans. Sleep macrostructure differences were mostly found between occasions 1 and 3; dogs slept more, had less wake after the first drowsiness episode, and reached drowsiness sleep earlier on occasion 3. Dogs, which had been reported to sleep rarely not at home, had an earlier non-rapid eye movement sleep, a shorter rapid eye movement sleep latency, and spent more time in rapid eye movement sleep on occasion 3, compared with occasion 1. Extending prior dog sleep data, these results help increase the validity of further sleep electroencephalography investigations in dogs.

A systematic review of the accuracy of sleep wearable devices for estimating sleep onset.

The accurate estimation of sleep onset is required for many purposes, including the administration of a behavioural treatment for insomnia called Intensive Sleep Retraining, facilitating power naps, and conducting objective daytime sleepiness tests. Specialised equipment and trained individuals are presently required to administer these applications in the laboratory: a costly and impractical procedure which limits their utility in practice. A wearable device could be used to administer these applications outside the laboratory, increasing accessibility. This systematic review aimed to identify practical wearable devices that accurately estimate sleep onset. The search strategy identified seventy-one articles which compared estimations of sleep onset latency from wearable devices against polysomnography (PSG). Actigraphy devices produced average estimations of sleep onset latency that were often not significantly different from PSG, but there was large inter-individual variability depending on participant characteristics. As expected, electroencephalography-based devices produced more accurate and less variable estimates. Devices that measured behavioural aspects of sleep onset consistently overestimated PSG-determined sleep onset latency, but to a comparatively low degree. This sleep measurement method could be deployed in a simple wearable device to accurately estimate sleep onset and administer Intensive Sleep Retraining, power naps, and objective daytime sleepiness tests outside the laboratory setting.

Pre-sleep cognitive activity in adults: A systematic review.

This systematic review focuses on three themes: 1) the nature of pre-sleep cognitive activity in good sleepers and individuals with insomnia, 2) the links between measures of pre-sleep cognitive activity and sleep onset latency (SOL) or insomnia, and 3) the effect of manipulating pre-sleep cognitive activity on SOL or insomnia. Regarding the first theme, mentation reports have been collected in a sleep laboratory, with an ambulatory monitoring device, or using a voice-activated tape-recorder. Normal transition to sleep is characterized by sensorial imagery, deactivation of higher cognitive processes, and hallucinations. Moreover, pre-sleep thoughts in individuals with insomnia frequently relate to planning or problem-solving, and are more unpleasant than in good sleepers. Regarding the second theme, twelve questionnaires and three interviews were identified. Insomnia is associated with more thoughts interfering with sleep, counterfactual processing, worries, maladaptive thought control strategies, covert monitoring, and cognitive arousal. Regarding the third theme, several strategies have been tested: mental imagery, hypnosis, paradoxical intention, articulatory suppression, ordinary suppression, and distraction. Their effect is either beneficial, negligible, or detrimental. Future research should focus on the mechanisms through which some forms of cognitive activity affect sleep onset latency.