Journal of Sleep Research最新文献

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.

Sleep profiles, including chronotype, sleep duration and sleep-wake time, may affect glycaemic outcomes. However, their associations with plasma glycaemic outcomes and 24-h interstitial glucose levels in individuals with prediabetes remains ambiguous. This study aimed to examine the association between sleep profiles and glycaemic outcomes in adults with prediabetes. Chronotype was assessed using the Malay-translated Munich Chronotype Questionnaire. Glycaemic outcomes, including fasting plasma glucose (FPG), 2-h postprandial glucose (2hPPG), glycated haemoglobin (HbA1c) levels and 24-h glucose profiles derived from seven-day continuous glucose monitoring (CGM). Generalised linear models and generalised estimating equations were used and adjusting for potential confounders. A total of 120 participants (mean age: 54 ± 15 years) were categorised as morning (18.4%), intermediate (60.8%) or evening (20.8%) chronotypes. Evening chronotypes demonstrated greater weekday-weekend discrepancy in awake time (0.7 h [±1.1 h]), indicating higher social jet lag. Each additional hour of jet lag in awake time was associated with a 0.28 mmol/L reduction in 2hPPG levels (95% CI: -0.49, -0.08), reflecting compensatory catch-up sleep on weekends. Longer sleep time was positively associated more time spent within the target glucose range (3.9-7.8 mmol/L) (β: 0.58, 95% CI: 0.06, 1.10), while evening chronotype showed higher 24-h mean glucose levels (β: 0.65 mmol/L, 95% CI: 0.22, 1.12). Evening chronotype and shorter sleep duration were associated with adverse glycaemic outcomes, while the unexpected inverse association between awake-time jet lag and 2hPPG may reflect short-term catch-up sleep rather than a protective effect. These findings highlight the importance of addressing sleep regularity in lifestyle interventions for prediabetes management.

This longitudinal study of Arctic residents investigates how physical activity and sleep, known to be affected by extreme Arctic photoperiods, are linked to blood lipid profiles and thus cardiovascular disease risk in this particularly vulnerable population. We analyzed parametric and non-parametric actigraphy data of physical activity (PA) and sleep measures across three distinct photoperiods (winter solstice, spring equinox, and summer solstice) in a cohort of Arctic residents. Multiple regression models, adjusted for photoperiod, were employed to determine independent associations of PA and sleep parameters with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Robust circadian rhythms in PA were significantly associated with improved lipid profiles. Specifically, lower intra-daily variability IV (β = 0.423, p < 0.001) and higher inter-daily stability IS (β = -0.263, p = 0.019) of PA, indicating stable and predictable daily activity patterns, were associated with lower TG and TG/HDL-C ratios. A greater amplitude of PA rhythms was also associated with lower TG (β = -0.345, p = 0.002). An earlier timing of daily activity (acrophase) was associated with higher TC and LDL-C (β = -0.492, p < 0.001), while earlier wake times (β = -0.309, p = 0.009) and higher sleep efficiency (β = 0.237, p = 0.011) were associated with higher HDL-C. These findings highlight the relevance of circadian rhythmicity in dyslipidemia management, particularly in extreme photoperiods, suggesting that lifestyle interventions promoting stable daily activity and sleep patterns may be associated with improved metabolic health and reduced cardiovascular risk.

Sleep reactivity-an individual's susceptibility to sleep disruptions due to stress-has been linked to increased insomnia risk. Investigating how sleep reactivity moderates the 'stress → pre-sleep arousal → sleep' pathway may help mitigate sleep disturbances and enhance treatment outcomes. In the present study, full-time university students without sleep disorders completed the Ford Insomnia Response to Stress Test (FIRST), which assesses sleep reactivity. From 264 students, 30 students with the lowest and 30 with the highest FIRST scores were selected for further study. They provided daily actigraphy, Pre-sleep Arousal Scale ratings, pre-sleep heart rate (via an ŌURA ring), and perceived stress scores over 2 weeks. Multilevel moderated mediation analyses were conducted using 800 nights of data to examine within- and between-individual associations. At the within-individual level, days with higher-than-usual perceived stress were associated with reduced total sleep time and increased sleep onset latency (p's < 0.05). These effects were mediated by heightened pre-sleep cognitive arousal (p's < 0.05) but not moderated by the FIRST group. In contrast, between-individual analyses revealed a significant moderation by the FIRST group (p < 0.05). High sleep-reactive individuals reported significantly greater average levels of perceived stress and pre-sleep cognitive arousal, leading to prolonged wakefulness after sleep onset (b = 0.123, Monte Carlo confidence interval [MCCI] = 0.006-0.292), compared to low-reactive sleepers. Overall, on a day-to-day basis, both groups showed increased pre-sleep cognitive arousal and sleep disruptions in response to elevated daily stress. However, between individuals, high sleep reactivity significantly amplified the effect of pre-sleep cognitive (but not physiological) arousal, leading to more pronounced sleep disturbances compared to low-reactive sleepers.

PSG, a cornerstone diagnostic instrument in sleep medicine, is recommended for the diagnosis of numerous sleep disorders and might be used as a benchmark for evaluating therapeutical effectiveness. However, PSG has its limitations, and its usefulness in the future warrants reappraisal. First, it is a complex test that requires highly-trained personnel to correctly place the electrodes, monitor the patient, and manually analyse the data. This constitutes a significant economic burden for both society and healthcare systems. PSG also presents few technical limitations: variability of data between nights and variable reliability of scoring between readers. The results given to patients are also limited to the macrostructure of sleep, risking the loss of important information that escapes detection when relying solely on polysomnographic evaluation based on macro sleep stages. The current sleep scoring guidelines raise some doubts about their ability to capture the dynamic and complex nature of human sleep in both clinical and physiological contexts. On the other hand, advanced PSG analysis can provide key information for diagnosis particularly through the microstructural analysis of NREM oscillatory pattern, characterisation of spindles and slow waves, eye movement density, spectrum analysis with hypnodensity and sleep propensity with odd ratio products (ORP). These elements provide a better understanding of the differences between insomnia and poor sleep perception. Furthermore, these methods take into account the dynamics of sleep states, going beyond the mere distinction of sleep into macro stages, which poorly reflects the dynamic nature of sleep itself and including in the assessment of sleep function all the complex associations that sleep itself has with autonomic and cardiorespiratory variables. These insights will transform the role of technicians and clinicians in PSG analysis, with a shift towards training in digital data analysis and algorithms to better inform patients about their PSG results.