Clocks & Sleep最新文献

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This study explored whether sleep duration, insomnia, social jetlag, and circadian preference predicted adolescents' risk of anxiety and depression two years later. High school students initially aged 16-17 years were, in 2019 and 2021, invited to a web-based survey assessing sleep patterns, insomnia, circadian preference, anxiety, and depression. Sleep duration, insomnia, circadian preference, depression, and anxiety were assessed using the Munich ChronoType Questionnaire, the Bergen Insomnia Scale, the reduced Morningness-Eveningness Questionnaire, the Patient Health Questionnaire-9, and the Generalized Anxiety-Disorder 7, respectively. Analyses were conducted using logistic regression analyses. The analytic longitudinal sample comprised 1456 students (initial mean age 16.4 years; 61.4% girls). Short school night sleep duration, chronic insomnia, and more severe insomnia symptoms at baseline predicted greater risk of anxiety and depression at follow-up when controlled for anxiety and depression at baseline. Neither free night sleep duration nor social jetlag at baseline were related to the risk of anxiety and depression at follow-up. When circadian preference was investigated continuously, greater morningness at baseline predicted lower risk of anxiety and depression at follow-up. When circadian preference was investigated categorically, evening preference type was associated with higher risk of depression at follow-up than intermediate preference type, while the prospective risk of anxiety and depression otherwise did not differ in relation to circadian preference. The results attest to prospective associations between adolescent sleep problems at baseline and later risk of anxiety and depression.

This study explored the impact of lifestyle habits and sensory processing patterns on sleep quality by analyzing body movements (BMs) during the first and last 3 h of sleep in toddlers. We collected cross-sectional data about sleep-related habits from 58 toddlers using a mobile application. Actigraphy measured BMs during nighttime sleep and 1 h before bedtime, as well as sleep latency, over 8 consecutive days. The Infant/Toddler Sensory Profile was used to assess the toddlers' sensory processing patterns. The participants had a mean age of 22.0 ± 2.0 months. BMs were significantly lower during the first 3 h of sleep. Longer sleep latency was significantly associated with media use and higher activity levels before bedtime. Ending a nap earlier and consuming a substantial breakfast were correlated with lower BMs during the first 3 h of sleep. Auditory and oral sensory scores were positively correlated with BMs during the first 3 h of sleep. However, no specific factors related to lifestyle habits or sensory processing patterns were found to correlate with BMs during the last 3 h of sleep. Lifestyle habits and sensory processing patterns have a significant impact on toddlers' sleep quality, emphasizing the importance of appropriate routines and environments.

Shift workers are at increased risk of insomnia. The standard treatment (cognitive behavioral therapy for insomnia) poses significant challenges for this demographic due to irregular work and sleep schedules. New approaches are still considered insufficient due to high attrition or insufficient effectiveness. Our preliminary study identified sleep-relevant state and trait factors (see secondary outcomes) for incorporation into an innovative manual that addresses sleep in an implicit manner. The objective was to reduce the focus on insomnia and to replace regularity-based interventions. With a sample of 55 insomniacs (67.74% male, mean age 41.62 years), standard and customized treatments were compared using pre-treatment, post-treatment, and three-month follow-up measurements (RCT, self-assessment data). Our linear mixed models revealed the main significant effects of the measurement point for the primary (insomnia severity, sleep quality, sleep onset latency, total sleep time, daytime sleepiness) and the secondary outcomes (selection: anxiety/depression, dysfunctional beliefs, arousal, emotional stability, concern). No main effects of the condition or interaction effects were identified. Non-inferiority and equivalence tests demonstrated that the customized treatment is equivalent to standard therapy, which is a favorable outcome in light of the implicit approach. Consequently, this innovative approach warrants further exploration, incorporating the present results.

It remains unclear how sleep health has developed in the general population after the COVID-19 pandemic. This study aimed to (1) investigate the prevalence of sleep problems and poor sleep quality and (2) explore the associated sociodemographic and health-related factors in South Tyrol, Italy. A cross-sectional, population-based survey was conducted with a stratified probabilistic sample of 4000 adults aged ≥ 18 years. Sleep quality was assessed using the brief version of the Pittsburgh Sleep Quality Index. Descriptive and logistic regression analyses were performed to analyze the data. A total of 2090 adults (53%) completed the survey. Poor sleep quality was reported by 17.8%, with 28.2% of participants reporting insufficient sleep duration (i.e., six hours or less), 12.7% having problems staying asleep (i.e., waking up to 3-4 times a week and unable to fall asleep again), and 8.7% having problems falling asleep (i.e., >30 min). Sleep problems and poor sleep quality were associated with sociodemographic and health-related factors, including gender, age, mother tongue, chronic disease, and sleep hygiene. Notably, Italian-speaking participants reported poorer sleep quality and greater difficulty staying asleep compared to German-speaking participants, highlighting potential sociocultural influences on sleep health. This study contributes to understanding the unique sleep health challenges in a multilingual region, highlighting the role of sociocultural factors in sleep quality differences between language groups.

Considering the frequent co-occurrence of major depressive disorder and excessive daytime sleepiness in apneic individuals, this study aimed to explore the relationship between excessive daytime sleepiness and the risk of developing major depressive disorder in this specific subpopulation. Demographic and polysomnographic data were retrospectively extracted from the clinical database of 1849 apneic individuals at the Sleep Unit. Excessive daytime sleepiness was considered present when the Epworth Sleepiness Scale score was >10 and major depressive episodes were diagnosed according to DSM criteria. Logistic regression analyses were performed to assess the risk of major depressive disorder associated with excessive daytime sleepiness in apneic individuals. The prevalence of major depressive disorder was 26.3% in apneic individuals. After controlling for major confounding variables, multivariate logistic regression analyses revealed that apneic individuals with complaints of excessive daytime sleepiness had a higher likelihood of developing major depressive disorder compared to those without complaint of excessive daytime sleepiness. This study highlights the strong association between excessive daytime sleepiness and major depressive disorder in apneic individuals, underlining the importance of systematically assessing and adequately treating excessive daytime sleepiness to better manage depressive symptoms and improve overall treatment outcomes in this specific subpopulation.

Sleep is essential for maintaining both mental and physical well-being. It plays a critical role in the health and development of children. This study investigates sleep patterns and habits of First Nations children, the prevalence of sleep disturbances, and excessive daytime sleepiness (EDS), along with the factors associated with EDS. Our 2024 First Nations Children Sleep Health Study assessed the sleep health of children aged 6 to 17 years living in a First Nation in Canada. Statistical analyses were performed using comparison tests and logistic regression models. A total of 78 children participated; 57.7% were boys. The average age of the participants was 10.49 years (SD = 3.53 years). On school days, children aged 6 to 9 years slept an average of one additional hour, while on weekends, they slept an extra 40 min compared to adolescents aged 10 to 17 years. Only 39.7% of the children (ages 6 to 17) slept alone in a room, with more than 80% of the children sharing a bed every night. Only 30.6% of the children aged 6 to 9 years and 7.2% of the adolescents aged 10 to 17 years adhered to the recommended maximum screen time of 2 h on school days. More than two-thirds of the children reported experiencing sleep disturbances. The prevalence of EDS was 19.7%. After adjusting for age and sex, it was determined that the children who snored loudly and those who did not sleep in their own beds were more likely to experience abnormally high levels of daytime sleepiness. A high proportion of children exceeded the recommended screen time, an important public health issue. Further, identifying sleep patterns among children will facilitate the diagnosis and treatment of disordered sleep.

Trail running has seen a surge in participants, including individuals with disabilities, particularly in ultratrail running (UTRs). Sleep-wake patterns are crucial for optimal performances in UTRs, which present unique physiological and behavioral challenges. This case study evaluated the sleep-wake cycle of a blind trail ultramarathoner (BTR) and his guide (GTR) before, during, and after an 80 km UTR. Two male participants (BTR: 54 years, BMI: 26.1 kg/m²; GTR: 48 years, BMI: 24.2 kg/m²) were assessed using validated questionnaires (MEQ, ESS, ISI, and PSQI) and actigraphy over 35 days. The BTR exhibited a morning chronotype (MEQ = 63), mild insomnia (ISI = 11), poor sleep quality (PSQI = 5), and prolonged sleep latency (>60 min), while the GTR showed an indifferent chronotype (MEQ = 52), good sleep quality (PSQI = 3), and shorter latency (10 min). Post-competition, both athletes experienced an increased total sleep time (TST): the BTR by 17.8% (05:32:00 vs. 04:25:00) and the GTR by 5.5% (07:01:00 vs. 06:39:00). The BTR demonstrated a greater Wakefulness after sleep onset (WASO 01:00:00 vs. 00:49:00) and awakenings (15.4 vs. 6.1). A time series analysis revealed greater variability in the BTR's post-competition sleep efficiency and TST, while the GTR exhibited a greater stability of the circadian phase. These findings highlight the intricate sleep challenges faced by blind athletes, informing strategies to optimize recovery and performance.

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is recognized as a precursor to neurodegenerative diseases. This study aimed to develop predictive models for the timing and subtype of phenoconversion in iRBD. We analyzed comprehensive clinical data from 178 individuals with iRBD over a median follow-up of 3.6 years and applied machine learning models to predict when phenoconversion would occur and whether progression would present with motor- or cognition-first symptoms. During follow-up, 30 patients developed a neurodegenerative disorder, and the extreme gradient boosting survival embeddings-Kaplan neighbors (XGBSE-KN) model demonstrated the best performance for timing (concordance index: 0.823; integrated Brier score: 0.123). Age, antidepressant use, and Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III scores correlated with higher phenoconversion risk, while coffee consumption was protective. For subtype classification, the RandomForestClassifier achieved the highest performance (Matthews correlation coefficient: 0.697), indicating that higher Montreal Cognitive Assessment scores and younger age predicted motor-first progression, whereas longer total sleep time was associated with cognition-first outcomes. These findings highlight the utility of machine learning in guiding prognosis and tailored interventions for iRBD. Future research should include additional biomarkers, extend follow-up, and validate these models in external cohorts to ensure generalizability.

This narrative review explores the intricate relationship between circadian regulation and exercise performance, emphasizing the importance of aligning training strategies with the body's natural physiological fluctuations. The three key mechanisms investigated are temperature, hormonal fluctuations, and diurnal chronotype-an individuals' exhibition of enhanced physical or cognitive performance at specific times of day. Core body temperature variations impact exercise performance, suggesting strategic workout timing and intensity adjustments. Hormonal patterns (i.e., insulin, cortisol, testosterone) influence energy metabolism and muscle growth, informing tailored training plans. Diurnal chronotypes significantly affect performance, advocating for personalized training sessions based on individual preferences and entrained awakening. Integrating circadian mechanisms into training offers strategic advantages, guiding practitioners to design effective, personalized regimens, though we acknowledge relevant challenges and the need for further research.