Sleep最新文献

Study objectives: To identify sleep multi-trajectories in children from age 1 to 5.5 years and their early correlates.

Methods: We collected early family, maternal, and child characteristics, including children's nighttime sleep duration (NSD) and daytime sleep duration (DSD), night waking (NW), and sleep-onset difficulties (SOD), by parental phone interviews at age 2 months and 1-, 2-, 3.5-, and 5.5 years. Group-based multi-trajectory modeling identified sleep multi-trajectory groups. Multinomial logistic regression assessed associations with early factors.

Results: We identified five distinct sleep multi-trajectory groups for NSD, DSD, NW, and SOD in 9273 included children. The "Good sleepers" (31.6%) and "Long sleepers" (31.0%) groups had low NW and SOD prevalence and shorter NSD but longer DSD in "Good sleepers" than in "Long sleepers." The "Good sleepers but few SOD" group (10.3%) had long NSD and DSD but a SOD peak at age 3.5 years; the "Improving NW and SOD" group (9.6%) showed short but rapidly increasing NSD to a plateau and high but decreasing NW and SOD; the "Persistent NW and SOD" group (17.5%) had persistent high NW and SOD. Maternal depression during pregnancy and sleep habits at age 1 (e.g. parental presence or feeding to fall asleep, sleeping at least part of the night away from own bed) were common risk factors associated with the most disordered sleep multi-trajectory groups.

Conclusions: We identified distinct sleep multi-trajectory groups and early life-associated factors in preschoolers. Most of the factors associated with the most sleep-disordered multi-trajectory groups are likely modifiable and provide clues for early prevention interventions.

Phase-amplitude coupling (PAC) across frequency might be associated with the long-range synchronization of brain networks, facilitating the spatiotemporal integration of multiple cell assemblies for information transmission during inhibitory control. However, sleep problems may affect these cortical information transmissions based on cross-frequency PAC, especially when humans work in environments of social isolation. This study aimed to evaluate changes in the theta-beta/gamma PAC of task-related electroencephalography (EEG) for humans with insufficient sleep. Here, we monitored the EEG signals of 60 healthy volunteers and 18 soldiers in the normal environment, performing a Go/Nogo task. Soldiers also participated in the same test in isolated cabins. These measures demonstrated theta-beta PACs between the frontal and central-parietal, and robust theta-gamma PACs between the frontal and occipital cortex. Unfortunately, these PACs significantly decreased when humans experienced insufficient sleep, which was positively correlated with the behavioral performance of inhibitory control. The evaluation of theta-beta/gamma PAC of Go/Nogo task-related EEG is necessary to help understand the different influences of sleep problems in humans.

Study objectives: Despite the negative impact of poor sleep on mental health, evidence-based insomnia management guidelines have not been translated into routine mental healthcare. Here, we evaluate a state-wide knowledge translation effort to disseminate sleep and insomnia education to graduate psychology programs online using the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) evaluation framework.

Methods: Using a non-randomized waitlist control design, graduate psychology students attended a validated 6-hour online sleep education workshop delivered live as part of their graduate psychology program in Victoria, Australia. Sleep knowledge, attitudes, and practice assessments were conducted pre- and post-program, with long-term feedback collected at 12 months.

Results: Seven out of ten graduate psychology programs adopted the workshop (adoption rate = 70%). The workshop reached 313 graduate students, with a research participation rate of 81%. The workshop was effective at improving students' sleep knowledge and self-efficacy to manage sleep disturbances using cognitive behavioral therapy for insomnia (CBT-I), compared to the waitlist control with medium-to-large effect sizes (all p < .001). Implementation feedback was positive, with 96% of students rating the workshop as very good-to-excellent. Twelve-month maintenance data demonstrated that 83% of students had used the sleep knowledge/skills learned in the workshop in their clinical practice. However, more practical training is required to achieve CBT-I competency.

Conclusions: Online sleep education workshops can be scaled to deliver cost-effective foundational sleep training to graduate psychology students. This workshop will accelerate the translation of insomnia management guidelines into psychology practice to improve sleep and mental health outcomes nationwide.

Study objectives: Sleep-related adverse effects during acute treatment with antidepressants undermine adherence and impede remission. We aimed to address subtypes of sleep-related adverse effects and depict the relationship between dose and sleep-related adverse events.

Methods: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science for double-blind randomized controlled trials of depression published before April 30th, 2023. Eligible studies reporting sleep-related adverse effects during short-term monotherapy were included. The odds ratios (ORs) for sleep-related adverse effects were addressed with network meta-analysis. A Bayesian approach was used to depict the dose-effect relationship. Heterogeneity among studies was assessed using the τ2 and I2 statistics. Sensitivity analyses were performed without studies featuring high risk of bias.

Results: Studies with 64 696 patients were examined from 216 trials. Compared to placebo, 13 antidepressants showed higher ORs for somnolence, of which fluvoxamine (OR = 6.32; 95% CI: 3.56 to 11.21) ranked the top. Eleven had higher risks for insomnia, reboxetine ranked the top (OR = 3.47; 95% CI: 2.77 to 4.36). The dose-effect relationships curves between somnolence or insomnia and dose included linear shape, inverted U-shape, and other shapes. There was no significant heterogeneity among individual studies. The quality of evidence for results in network meta-analyses was rated as very low to moderate by Grading of Recommendations Assessment, Development, and Evaluation.

Conclusions: Most antidepressants had higher risks for insomnia or somnolence than placebo. The diverse relationship curves between somnolence or insomnia and dose of antidepressants can guide clinicians to adjust the doses. These findings suggest clinicians pay more attention to sleep-related adverse effects during acute treatment with antidepressants.

Study objectives: To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults.

Methods: We collected 7-14 days of data at home from 35 older adults (age: 65-83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD).

Results: Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (-2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary.

Conclusions: CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings.

Study objectives: This study investigated alterations in resting-state functional connectivity (RSFC) and hyperarousal biomarkers in patients with chronic insomnia disorder (CID), compared with good sleepers (GS). We also examined the relationships between altered RSFC and hyperarousal biomarkers.

Methods: Fifty patients with CID and 52 GS completed self-reporting questionnaires, and then underwent polysomnography and resting-state functional magnetic resonance imaging. We analyzed RSFC in the amygdala (AMG) and anterior insula (aINS), which are core regions of the salience network that are likely to be involved in hyperarousal. We also analyzed electroencephalography (EEG) relative beta power and heart rate variability (HRV) parameters (e.g. low and high frequency) during sleep. We then tested between-group differences in the RSFC and hyperarousal biomarkers; we examined correlations of RSFC with EEG beta power and HRV.

Results: Compared with GS, patients with CID showed more negative RSFC between the right amygdala (R.AMG) and left supramarginal gyrus (L.SMG), but less positive RSFC between the left aINS and bilateral lateral prefrontal cortex. The R.AMG-L.SMG RSFC was negatively correlated with EEG beta power in central regions (C3: r = -0.336, p = 0.012; C4: r = -0.314, p = 0.024).

Conclusions: Decreased RSFC between the R.AMG and L.SMG in patients with insomnia may reflect the difficulty in cortical top-down regulation of the AMG, indicating daytime hyperarousal. Individuals who experience hyperarousal during the daytime may also exhibit cortical hyperarousal during sleep, as indicated by increased EEG beta power.