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.

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.

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.

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 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.

Sleep-wake cycle of elite athletes prior to the Rio 2016 Olympic Games / Ciclo de vigilia-sueño de atletas de élite antes de los Juegos Olímpicos de Río 2016 / Ciclo de vigília-sono de atletas de elite antes dos Jogos Olímpicos Rio 2016

OBJECTIVE: To analyze chronotype, duration and quality of sleep among elite athletes, to compare differences in sleep variables between sex, and to compare differences between athletes of individual and team sports. METHOD: The sample included 70 Brazilian elite athletes of both sex (male=37; female=33) with a mean age 23.0 ± 4.0 years old. To measure sleep-wake cycle, athletes wore an actigraph on the wrist for 10 days. Moreover, athletes answered the chronotype questionnaire of Horne and Östberg. RESULTS: The most athletes are intermediate-type (n=55, 78.6%), with a mean of 07h:18min of sleep per night. The athletes demonstrated higher sleep fragmentation (39.26 ± 23.66 minutes) and higher sleep latency (30.88 ± 16.19 minutes) during pre-competition training days. Additionally, the athletes of individual sports demonstrated more fragmentation (p < 0.001) and less sleep efficiency (p < 0.001) compared athletes of team sports. However, there was no significant difference in all sleep variables between the male and female sex. CONCLUSION: The overall elite athletes presented poor sleep quality during the training periods prior to the Rio 2016 Olympic Games, and individual athletes showed higher fragmentation and poorer sleep efficiency compared to team athletes

OBJETIVO: Analizar el cronotipo, la duración y la calidad del sueño entre los atletas de élite, comparar las diferencias en las variables de sueño entre los sexos y comparar las diferencias entre los atletas de los deportes individuales y de equipo. MÉTODO: La muestra incluyó a 70 atletas de élite brasileños de ambos sexos (hombre = 37; mujer = 33) con una edad media de 23.0 ± 4.0 años. Para medir lo ciclo de vigilia-sueño, los atletas usaron un actígrafo en la muñeca durante 10 días. Además, los atletas respondieron el cuestionario cronotipo de Horne y Östberg. RESULTADOS: La mayoría de los atletas son de tipo intermedio (n = 55, 78.6%), con una media de 07h: 18min de sueño por noche. Los atletas demostraron una mayor fragmentación del sueño (39.26 ± 23.66 minutos) y una mayor latencia del sueño (30.88 ± 16.19 minutos) durante los días de entrenamiento previo a la competencia. Además, los atletas de deportes individuales demostraron más fragmentación (p <0.001) y menos eficiencia del sueño (p <0.001) en comparación con los atletas de deportes de equipo. Sin embargo, no hubo diferencias significativas en todas las variables de sueño entre el sexo masculino y el femenino. CONCLUSIÓN: Los atletas de élite en general presentaron mala calidad del sueño durante los períodos de entrenamiento previos a los Juegos Olímpicos de Río 2016, y los atletas individuales mostraron una mayor fragmentación y una menor eficiencia del sueño en comparación con los atletas del equipo

OBJETIVO: Analisar o cronotipo, a duração e a qualidade do sono de atletas de elite, comparar as diferenças nas variáveis do sono entre os sexos e as diferenças entre atletas de esportes individuais e coletivos. MÉTODO: A amostra incluiu 70 atletas de elite brasileiros de ambos os sexos (masculino = 37; feminino = 33) com idade média de 23.0 ± 4.0 anos. Para mensurar o ciclo vigília-sono, os atletas usaram um actígrafo no punho por 10 dias. Além disso, os atletas responderam ao questionário de cronotipo de Horne e Östberg. RESULTADOS: A maioria dos atletas é do tipo intermediário (n = 55, 78.6%), com média de 07h:18min de sono por noite. Os atletas demonstraram maior fragmentação do sono (39.26 ± 23.66 minutos) e maior latência do sono (30.88 ± 16.19 minutos) durante os dias de treinamento pré-competição. Além disso, os atletas de esportes individuais demonstraram maior fragmentação (p <0.001) e menor eficiência do sono (p <0.001) em comparação aos atletas de esportes coletivos. No entanto, não houve diferença significativa em todas as variáveis de sono entre os sexos masculino e feminino. CONCLUSÃO: Os atletas de elite em geral apresentaram baixa qualidade do sono durante os períodos de treinamento antes dos Jogos Olímpicos Rio 2016, e os atletas de esportes individuais apresentaram maior fragmentação e menor eficiência do sono em comparação aos atletas de esportivos coletivos

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.

Nocturnal heart rate variability may be useful for determining the efficacy of mandibular advancement devices for obstructive sleep apnea.

A mandibular advancement device (MAD) is recommended as an alternative therapy for obstructive sleep apnea (OSA), which effectively reduces the collapsibility of the upper airway during sleep by advancing the mandible. However, the effects of MAD therapy on cardiac autonomic modulation remain unclear. We evaluated the effects of MAD on nocturnal heart rate variability (HRV) in OSA. Anthropometric data, questionnaire results, and HRV parameters (evaluated using time domain and frequency domain methods) of 58 adult patients with OSA treated with MAD therapy were retrospectively reviewed. All patients underwent polysomnography at baseline and 3-month follow-up. The average normal-to-normal (NN) interval, standard deviation of the NN interval, low-frequency power in normalized units (LFnu), and high-frequency power in normalized units (HFnu) showed significant changes with MAD therapy. Based on the criteria for success (decrease in the apnea-hypopnea index by >50% and value <20/h), 34 and 24 patients were classified into the response and nonresponse groups, respectively. No differences in baseline characteristics were detected between groups, except for higher body mass index and lower minimal oxygen saturation in the nonresponse group. A subgroup analysis indicated that the average NN interval and HFnu significantly increased, and that Total power (TP), very low frequency, low frequency(LF), low frequency/high frequency and LFnu significantly decreased compared to baseline in the response group; however, no HRV changes were found in the nonresponse group. After adjusting for age, sex, and body mass index, the response group showed significant changes from baseline in TP and LF compared to the nonresponse group. Therefore, HRV may be useful for determining the efficacy of MAD therapy in OSA.

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.

Is time elapsed between cannabis use and sleep start time associated with sleep continuity? An experience sampling method.

BACKGROUND: A substantial proportion of people using cannabis report using it to improve sleep. Yet, little research exists on the associations between the timing of cannabis use and sleep. This study examines the time elapsed between cannabis use and sleep start time and its association with two of the main indicators of sleep continuity: (1) sleep onset latency (SOL) and (2) number of awakenings (NOA) throughout the night. METHODS: Each morning, for 7 consecutive days, daily cannabis users (n = 54) reported on the timing of previous night's cannabis use and sleep indicators on their smartphones. Mixed effects models examined the relations of within- and between-subjects' time elapsed between previous night cannabis use and sleep start time, with (1) SOL and (2) NOA. RESULTS: Within subjects, shorter time elapsed between cannabis use and sleep start time was associated with shorter SOL (ß = 0.519, p = 0.010), but not NOA (ß = -0.030, p = 0.535). Furthermore, between individuals, the time gap between the previous night cannabis use and sleep start time was not associated with SOL or NOA (p > 0.05). CONCLUSIONS: It is possible that cannabis use proximal to bedtime is associated with shorted sleep onset latency but not nighttime awakenings. Cannabis users should be informed about both the potential sleep aid effects of cannabis and its limitations. Pending further evidence of the effects of cannabis on sleep, cannabis users experiencing sleep problems should be provided with evidence-based alternatives to improve sleep, e.g., pharmacological and behavioral treatments.

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.