Journal of Sleep Research最新文献

Patients suffering from Nonorganic Insomnia (NI) are burdened by significant subjective daytime impairments which contribute to the reduction of quality of life for the patients and lead to greater healthcare utilization and increased indirect costs. Besides being central to diagnosis, operationalized criteria for daytime symptoms are lacking and data remain heterogeneous, especially regarding objectively measurable deficits. This study examines daytime performance in 329 NI patients through neuropsychological testing as well as self-report questionnaires, and correlates the results with polysomnographic data. In the main analysis, neuropsychological data, normalized for age and health status, displayed no impairment of vigilance or alertness-contrary to what is often assumed for NI and what would typically be expected in cases of sleepiness. For secondary analyses, neuropsychological data was then correlated with self-report and polysomnographic measures, and comparisons between NI patients with and without comorbidities were conducted. NI patients displayed a positive correlation of performance with nocturnal arousal markers, predominantly during REM sleep and a slightly diminished capability to increase focus in the phasic compared to tonic alertness paradigm. In summary, the current study in a well characterized large sample of NI patients with state-of-the-art measures of the most sensitive markers for sleep related daytime impairment found no evidence for diminished general vigilance or alertness due to sleep loss. The results help to understand conflicting evidence on neurocognitive deficits in insomnia by distinguishing between alertness or vigilance deficits and subtle changes in neurocognitive processing that might be better interpreted in line with underlying hyperarousal and anxiety.

We conducted a cross-sectional analysis of the baseline survey of participants aged > 30 years enrolled in the Mwanza HIV&CVD Cohort in Tanzania. Our primary objective was to examine the association between HIV status and sleep apnea (SA). Secondary objectives were (1) to examine the association between HIV status and excessive daytime sleepiness (EDS) and (2) to identify risk factors associated with SA and with EDS. The cohort enrolled 500 people living with HIV (PLWH) and 500 people without HIV (PWoH) in 2021-2023. Participants completed overnight oximetry; SA was defined as an oxygen desaturation index (ODI) of ≥ 5 events/h. EDS was defined as an Epworth Sleepiness Scale score (ESS) of ≥ 11. The median age was 46 and 43 in PLWH and PWoH, respectively. The prevalence of sleep apnea was similar between PLWH and PWoH (17% and 19%, respectively; adjusted odds ratio (aOR) = 0.84, 95% confidence interval (CI) = 0.60-1.17). In contrast, the prevalence of EDS (ESS score ≥ 11) was higher in PWoH (21%) than in PLWH (13%) (aOR = 0.58, 95% CI = 0.41-0.83). In a multivariable model, factors associated with SA were older age, alcohol use, higher BMI category, hypertension and depression. Both objectively measured SA and subjectively reported EDS are common in Tanzanian adults. SA was strongly associated with overweight or obesity, suggesting that the prevalence of SA will grow with projected increases in age and obesity rates in Tanzania.

Driver fatigue poses a severe risk to road safety, contributing to approximately 20% of fatal accidents worldwide. While EEG signals are the gold standard for detecting fatigue, existing methods struggle to capture the complex spatiotemporal patterns in EEG data. We propose MSDA-Net, a multiscale spatiotemporal dual-attention network that integrates multiscale CNNs, GRUs and dual-attention mechanisms to dynamically prioritise spatial channels and temporal segments, which are critical for fatigue detection. The model processes EEG data through three blocks: a multidimensional signal encoding block, which transforms raw signals into 4D differential entropy features; a multiscale spatial attention block, which extracts local and global spatial patterns; and a temporal modelling block, featuring a GRU and temporal attention. Finally, a fully connected layer and sigmoid activation are used to classify fatigue states. Evaluated on the SEED-VIG dataset, MSDA-Net achieves state-of-the-art performance, significantly outperforming existing methods. This study can provide new insights into brain fatigue research and play a significant role in advancing the field's development.

Little is known about the relationship between sleep quality trajectories during pregnancy and preterm birth. To address this issue, we conducted a longitudinal assessment of maternal sleep quality to examine the relationship between sleep quality trajectories across all trimesters and the risk of preterm birth. A prospective birth cohort study was conducted in China, and a total of 15,042 women who had singleton births were included, including 647 who subsequently developed preterm birth. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) scale at each study visit (8-14, 22-27 and 32-37 weeks of gestation). Preterm birth was defined as birth less than 37 weeks of gestation. Latent class trajectory models were applied to identify different sleep quality trajectories, and multivariate logistic regression models were applied to examine the associations between the determined trajectories and preterm birth. This study identified three distinct sleep quality trajectories: stable good group, stable poor group and increasing poor group. After adjusting for covariates, the odds ratio for preterm birth in the increasing poor group was 1.32 (95% confidence interval: 1.05-1.66) compared to the stable good group. Positive associations of the increasing poor group with preterm birth were exhibited only among women aged less than 30 years, those with normal BMI, or mothers of female infants. Our findings revealed that an increasing poor sleep quality trajectory was associated with an increased risk of preterm birth and emphasised the imperative to identify the high-risk groups as a priority target for intervention and treatment.

The optimal timing of sleep's influence on motor skill consolidation following observational sequence learning remains largely undefined. This study investigated how the interval between visual pattern observation and subsequent night-time sleep impacts skill retention. Forty female university students (mean age = 24.35 ± 1.25 years) were assigned to a wake control group (Noon-Night, observing at 12 PM) and three experimental groups undergoing overnight sleep with varying observation-to-sleep intervals: Noon-Morning (12 PM observation), Evening-Morning (6 PM observation) and Night-Morning (11:30 PM observation). Performance was assessed via overall reaction times on sequential trials (procedural task performance) and reaction time difference scores (sequence learning). Results of independent t-test on difference scores revealed no significant difference in sequence learning between the Noon-Morning and Noon-Night groups. The mixed ANOVA with repeated measures indicated that participants generally improved their sequence learning from training to retention. There were also significant overall differences in sequence learning among the groups, with the Night-Morning group exhibiting better performance. However, the interaction between group and test phase was not significant. Results on reaction time revealed the Noon-Morning group's reaction time was significantly faster than the Noon-Night group's, confirming a positive effect of night-time sleep on ASRTT performance. Mixed ANOVA indicated a significant interaction effect; both the Noon-Morning and Night-Morning groups demonstrated significantly better retention than the Evening-Morning group. These findings suggest a nonlinear, inverted-U relationship between the observation-sleep interval and procedural task performance, where optimal consolidation occurs with either very short or relatively long pre-sleep intervals.

Psychosocial Safety Climate (PSC) is a key organisational factor that reflects management's commitment to protecting employees' psychological health and safety. Although PSC is known to influence work stress and mental health, its association with sleep health remains underexplored. We analysed cross-sectional data from 4827 daytime wage workers enrolled in the 5th wave of the Korean Work, Sleep, and Health Study (KWSHS). PSC was measured using a validated four-item scale and categorised into low, moderate, and high-risk. Sleep quality and insomnia symptoms were assessed using the Korean versions of the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI), respectively. Linear and logistic regression models were used to examine associations between PSC levels and sleep health outcomes, adjusting for age, sex, and occupation. Lower PSC was significantly associated with poorer sleep quality (β = -0.05, p < 0.001) and more severe insomnia symptoms (β = -0.05, p < 0.001). Compared with the low-risk PSC group, the high-risk group showed increased odds of poor sleep quality (OR = 1.22, 95% CI: 1.04-1.43). Subgroup analysis revealed that older adults (≥ 60 years), highly educated workers, full-time employees, and non-unionised workers were particularly vulnerable to poor sleep health in low PSC environments. PSC plays a protective role in sleep health, and its absence may exacerbate the adverse effects of work-related stressors. Enhancing PSC at the organisational level may improve sleep health, particularly for vulnerable worker populations.

Cognitive performance is significantly affected by sleep, but mild chronic sleep deprivation in daily life remains difficult to measure. Laboratory-enforced sleep restriction may not fully replicate real-life conditions. This study investigates whether Social Jet Lag (SJL), an indicator of misalignment between biological and social time, can used as a proxy for mild chronic sleep deprivation and its impact on cognitive function. Participants leading typical social lives were selected based on their SJL scores, and cognitive performance was assessed using an online experiment incorporating a Psychomotor Vigilance Task (PVT) and a Sustained Attention to Response Task (SART) at different times (post-wake and pre-sleep) and across multiple days (Sunday, Monday, and Friday). Generalised linear modelling (GLM) revealed that SJL was consistently the most explanatory factor for cognitive performance, while test timing also had a significant impact. Cognitive performance impairments due to SJL remained stable across days, suggesting a stable influence of sleep timing irregularity. Additionally, SJL was associated with increased false-positive rates in the SART, indicating reduced response inhibition ability. While SJL proved to be a useful measure compared to average sleep duration and the Sleep Regularity Index (SRI), its effectiveness may be specific to populations following structured work schedules.

Despite the high prevalence of insomnia among U.S. military veterans, there is limited population-based longitudinal data on its incidence and predictors. Data were analysed from a population-based cohort of 2232 U.S. veterans without insomnia symptoms at baseline who were followed across two assessments over 3 years. The aim was to identify the incidence and baseline predictors associated with incident insomnia, defined as an Insomnia Severity Index score ≥ 15. During the 3-year follow-up period, 128 veterans (weighted 6.0%, 95% confidence interval = 4.8%-7.5%) developed insomnia. Baseline neuroticism emerged as the strongest predictor of insomnia, followed by attention problems, younger age and childhood physical abuse. Together, these factors accounted for 11.1%-29.9% of the explained variance. The predicted probability of incident insomnia increased progressively with the accumulation of these risk factors: 2.4% with none, 7.3% with one, 20.2% with two and 45.0% with three. In conclusion, in this population-based study of U.S. veterans, neuroticism, attentional difficulties, younger age and childhood physical abuse emerged as key predictors of incident insomnia. Results highlight the cumulative impact of these risk factors and underscore the importance of incorporating personality, trauma history and health assessments into clinical care to identify veterans at greatest risk and guide targeted prevention strategies for insomnia.

Wind farm noise (WFN) exposure effects on sleep remain poorly understood. This study compared the probability of electroencephalographically (EEG) defined arousal from established sleep following WFN versus road traffic noise (RTN) onset. Sixty-eight adults were studied in a sleep laboratory on one night with repeated 20-s WFN and RTN exposures. Following ≥ 2 min of established sleep and ≥ 20-s between noise exposures, pre-recorded WFN or RTN samples were reproduced at sound pressure levels (SPLs) of 30, 40, and 50 dBA in random order. The primary outcome was the probability of EEG-defined arousal events (> 3 s EEG shifts to faster frequencies) following the onset of each noise exposure. Awakening responses (> 15 s EEG frequency shifts) were also evaluated. Noise type, SPL, and sleep stage effects on arousal and awakening response probabilities were evaluated using mixed effects logistic regression analyses. Of 68 participants, 62 (mean ± SD aged 49 ± 20 years, 35 females) had sufficient replicates of noise exposure data for analysis. Arousal response probabilities were low, particularly in deep sleep, but showed a significant noise type-by-SPL interaction (χ² = 13, p = 0.001), with marginally but significantly lower WFN compared to RTN arousal probabilities at 40 dBA (mean [95% CI]: 2.1 [1.5, 2.9] vs. 3.2 [2.4, 4.2]%, p = 0.016) and 50 dBA (5.0 [4.0, 6.2] vs. 8.6 [6.9, 10.6]%, p < 0.001). Awakenings were infrequent (< 4% at 50 dBA) but showed similar effects. These findings show that acute WFN onset is marginally less sleep disruptive than road traffic noise events of equivalent SPL ≥ 40 dBA.