Sleep advances : a journal of the Sleep Research Society最新文献

Study objectives: The aim of this study was to identify heterogeneous insomnia symptom trajectories in a population cohort of adolescents during a developmentally sensitive window (ages 13-17), and to examine the influence of individual, social, and environmental predictors on these different trajectories.

Methods: Insomnia symptoms were assessed in a population-based sample of 6377 adolescents via online questionnaires administered annually at school from year 8 (age 13-14) to year 11 (age 16-17). Measures assessing individual, social, and environmental predictors were administered in year 8 only. We used latent class growth analysis to identify insomnia symptom trajectories and multinomial logistic regression to examine individual, social, and environmental predictors associated with each trajectory class.

Results: We identified four insomnia symptom trajectories: one "low risk" trajectory (low-stable 55.73%), two "elevated risk" trajectories (increasing 27.49% and high-stable 10.95%), and one "remitting" trajectory (high-decreasing 5.83%). Predictors common to elevated risk trajectories (vs low-stable) were female gender, greater internalizing or externalizing symptoms, and greater negative family interactions. Adolescents with high-stable trajectories were also more likely to be gender diverse or diagnosed with ≥1 disabilities, while adolescents with increasing trajectories were more likely to have ≥1 adverse childhood experiences or lower school connectedness. Male gender was the only predictor significantly associated with a remitting trajectory (vs high-stable).

Conclusions: These findings inform future research about the developmental course of insomnia symptoms during adolescence and can guide screening and intervention efforts aimed at improving sleep health for youth at risk of insomnia.

Sleep difficulty is prevalent in aging populations but can be challenging to treat due to the barriers to accessing evidence-based treatments. Further, 30-40 per cent of individuals with insomnia do not benefit from first-line treatments, making it important to consider viable alternatives. This protocol details a trial to investigate the feasibility and efficacy of a digital mindfulness intervention in improving sleep and well-being in older adults. Older adults aged 55 and above (n = 106) recruited into the trial will be randomly allocated to either a sleep hygiene program (n = 53) or a mindfulness intervention program (n = 53). Participants in both programs will engage in 6-week, self-directed, digital programs. They will be assessed for their sleep and well-being via self-report outcomes. Primary (Insomnia Severity Index) and secondary outcomes at the baseline, post-intervention, and 3-month follow-up will be compared in linear mixed models to inform efficacy. Feasibility will be evaluated through attrition and participant feedback on the exit questionnaire. Results may help inform the viability of an online, widely disseminable approach to improving older adult sleep health in the community. (Australian New Zealand Clinical Trials Registry #ACTRN12623000839606).

Study objectives: People living with HIV (PLWH) are at increased risks for chronic inflammation and cognitive impairment. Circadian disruption has been linked to both outcomes in the general populations, but its health relevance in PLWH remains understudied.

Methods: We analyzed data from 87 PLWH in the UK Biobank who had valid wrist actigraphy recordings and no dementia diagnosis. The phase of circadian rest-activity rhythms, a marker that represents the peak activity timing, was extracted using uniform phase empirical mode decomposition. The associations between circadian phase and two outcomes-cognitive performance (reaction time) and systemic inflammation (C-reactive protein [CRP])-were assessed using age- and sex-adjusted linear regressions. To correct for the right skewness, the reaction time and CRP were log-transformed. Additionally, potential outliers in both outcomes were examined and excluded using a 3-SD criterion.

Results: A delayed circadian phase was significantly associated with poorer cognitive performance and elevated CRP levels, with 0.18-0.21 SD increases in the outcomes for each 1-SD delay in circadian phase (both p = .02). To better put this into the context of aging, the effects of 1-SD delayed circadian phase correspond to the effects of approximately 6-7 years of aging. Exploratory analyses showed no significant association between circadian amplitude and either outcome.

Conclusions: In PLWH, delayed circadian phase is associated with modest but significant worse cognitive performance in reaction time and an increase in systemic inflammation. These findings highlight the potential role of circadian function in cognitive and inflammatory outcomes among PLWH and warrant further investigation into interventions targeting circadian alignment in this population.

Study objectives: Research linking children's sleep to cognitive outcomes is inconsistent and has largely focused on one aspect of sleep, such as duration, rather than measuring multiple dimensions of sleep health. We hypothesized that children's sleep health would be positively associated with inhibitory control and cognitive functioning.

Method: We cross-sectionally assessed 1595 participants (ages 7-11) from the Environmental influences on Child Health Outcomes cohort using the NIH Toolbox Cognition Battery, Environmental influences on Child Health Outcomes Sleep Health of Children and Adolescents questionnaire, and Patient Reported Outcome Measurement Information System Sleep Disturbance/Sleep-related Impairment instruments. We created a novel scale measuring sleep health using dichotomous "good-bad" cutoffs for sleep duration, timing, latency, satisfaction, and alertness. We used generalized estimating equations and random forest models to examine associations between sleep health and inhibitory control, working memory, processing speed, cognitive flexibility, episodic memory, reading decoding, and receptive vocabulary.

Results: Sleep health did not have statistically significant associations with any aspect of cognitive functioning. Notably, over 75 per cent of our sample had good sleep health.

Conclusions: This study assessed sleep health as a multi-faceted construct, distinguishing between "good" and "poor" sleep health across several domains. The absence of statistically significant associations between sleep health and cognitive functioning suggests children's cognitive functioning may not be cross-sectionally related to multidimensional sleep health measures. Experimentally manipulating key sleep domains such as duration or timing (as done in prior research) may be more robust. Future research might benefit from examining the cumulative impact of poor sleep health over time.

People frequently dream of recent experiences, which may reflect the consolidation of memories in the sleeping brain. Many studies demonstrate that experimentally introducing new learning prior to sleep can induce dreams of recently encoded memories. Here, for the first time, we tested whether activating a remote memory just before sleep can similarly induce participants to dream about the remote past. Participants (N = 34) completed an Autobiographical Emotional Memory Task (AEMT), in which they recalled and wrote about an emotionally negative remote memory prior to a daytime nap. In a control condition, participants instead wrote about designing a new college course. As hypothesized, the AEMT induced emotionally negative dreams related to the remote memory activated before sleep. Specifically, according to judge ratings, dreams incorporated content from the remote memory recalled during the AEMT to a greater degree than they incorporated content from the control task. While participants themselves did not perceive their dreams as more strongly related to the AEMT memory than the control task, they did rate their dreams as more emotionally negative following the AEMT. This shows it is possible to experimentally induce dreams of a specific remote memory by activating it before sleep. These findings are discussed in light of the hypothesis that dream content might be influenced by the reconsolidation of recently reactivated remote memory.

Frequent sleep stage transitions and abnormal sleep stage distribution are features of sleep disorders, including insomnia, sleep apnea, and narcolepsy, and have been associated with altered affective processing, including mood disorders. Research on the role of sleep stage transitions is nascent, and mixed operationalizations abound. In this comparative methods study, we overview the ways that prior research has operationalized sleep stage transitions, propose guidelines for four types of metrics, and compare the relevance of each for different analytic purposes. We then discuss three definitional and methodological "hard problems" for research on sleep stage transitions: bias due to scoring discrepancies, low temporal resolution, and opposing definitions of transitions. We discuss the pros and cons of several solutions that use machine learning (ML) scoring algorithms, with examples derived from the Stanford Sleep and Affect polysomnography dataset scored with validated ML algorithms (Stanford STAGES, U-Sleep, and YASA), and conclude with a call to return to descriptive physiological studies to shift the current framing of sleep stage transitions away from categorical state changes. This intends to lay the foundation for further insight into the role of sleep stage transitions in affective function and in clinical dysfunction.

Study objectives: Sleep disruptions are associated with adverse mental and physical health outcomes. Individuals with post-acute sequelae of COVID-19 (PASC) commonly report worsened sleep. This study examined sleep quality and efficiency and their associations with neuropsychiatric symptoms and fatigue in non-hospitalized individuals with PASC.

Methods: Sixty-one participants (73.8 percent female; - _age = 45.4) who reported being infected with COVID-19 ≥ 2 months before enrollment, non-hospitalized, and experiencing ≥3 symptoms since infection were eligible. The Pittsburgh Sleep Quality Index was used to measure self-reported sleep quality, and the Fitbit Charge-4 to assess sleep efficiency. Participants completed the Beck Anxiety Index, Beck Depression Index, Post-Traumatic Stress Disorder-Checklist Civilian Version, and the Fatigue Severity Scale. We conducted multivariable linear regressions to examine associations controlling for age, sex, time since first COVID-19 infection, pre-COVID sleep disorders, and sleep aids.

Results: Pittsburgh Sleep Quality Index scores were not associated with objective sleep efficiency. Nearly 97 percent of PASC participants reported poor sleep quality, 85 percent indicated that sleep difficulties interfered with their daily functioning, and 93.9 percent achieved optimal sleep efficiency. Higher Beck Depression Index scores were linked to worse sleep quality, while Beck Anxiety Index, Post-Traumatic Stress Disorder-Checklist Civilian Version, and Fatigue Severity Scale scores were not. However, Beck Anxiety Index and Fatigue Severity Scale scores were related to distinct Pittsburgh Sleep Quality Index components. None were associated with sleep efficiency.

Conclusion: Individuals with PASC experience significant sleep difficulties impacting daily functioning. Although they showed adequate sleep efficiency, most participants perceived their sleep as inefficient, which correlated with worse depressive symptoms. Therefore, sleep is a modifiable factor that could enhance the quality of life for patients with PASC.

Study objectives: This secondary data analysis study was designed to evaluate demographic and socioeconomic characteristics that may increase the magnitude of the impact of experimentally induced shortened sleep on suboptimal eating behaviors in adolescence.

Methods: Sixty-four adolescents completed a two-phase crossover study comparing five nights of restricted sleep to five nights of healthy sleep with adherence determined via accelerometry and with order randomized. Participants completed a 24-h dietary recall on the final day of each condition. We conducted repeated-measure t-tests to examine the main effect of experimental condition on dietary outcomes and general linear models to test the moderating impact of sex, age, race, income, and weight class on these relationships.

Results: There was no significant main effect of sleep condition on any dietary outcome. However, we found that males in the restricted sleep condition ate more added sugar, more carbohydrates, and fewer fruits than when in healthy sleep. Furthermore, younger adolescents consumed more carbohydrates, sugar, and added sugar when sleep restricted, compared to sleep extension. Lastly, adolescents from lower-income households consumed fewer vegetables when sleep restricted compared to when sleep was healthy, while-contrary to our hypothesis-participants from higher-income households consumed more vegetables in the restricted sleep condition relative to healthy sleep.

Conclusions: While no significant main effects of sleep duration on any dietary outcome were observed, this study provides preliminary evidence that restricted sleep can increase unhealthy eating habits, especially for males, younger adolescents, and adolescents from low-income households, informing obesity prevention and intervention efforts.

Study objectives: Dim light melatonin onset (DLMO) is the best-established marker of central circadian phase and may contribute to unraveling the role of the circadian system in obesity. This study evaluated DLMO among individuals with obesity using a home-based assessment and explored its clinical correlates and procedural variations.

Method: Fifty-eight women (mean [SD] age 40.9 [7.8] years) and body mass index (41.4 [6.6] kg/m²) completed a home-based DLMO assessment, measures of sleep quality, diurnal preference, and cardiometabolic parameters. Procedural variations we explored included individualized versus standardized DLMO thresholds, 7 versus 3 days assessment of sleep onset timing (SOT), as well as diary-based, actigraphy-based, or a "combined" method to calculate SOT, and hourly versus half-hourly saliva sample data points. Correlation coefficients and univariate ANOVA models were used for statistical analysis. Bland-Altman plots were used to inform agreement between methods.

Results: DLMO was detected in 98.2% and 89.6% of participants using an individualized or a standardized threshold, respectively. DLMO correlated with SOT but not with body mass index, cardiometabolic parameters, sleep quality, or diurnal preference. A later SOT and a larger phase angle of entrainment (DLMO-SOT) correlated with younger age and with eveningness. Most procedural alternatives showed good agreement with the original methods.

Conclusions: Home-based assessment yielded a high rate of detectable DLMO in women with obesity. Diurnal preference was not correlated with central circadian phase, suggesting that other factors (e.g. behavioral, sociodemographic) may be relevant in chronotype assessment in this population. We offer implications for future research including procedural variations to consider.

Study objectives: Conduct a multidimensional analysis of sleep perception, objective sleep, and neuropsychiatric wellbeing in individuals with subacute concussion compared to controls.

Methods: Thirty-one recently concussed individuals completed the Pittsburgh Sleep Quality Index, Insomnia Severity Index, and Patient-Report Outcomes Measurement Information System measures of depression, anxiety, stress, and cognitive function. Concussion symptom severity scores (Sports Concussion Assessment Tool) were obtained from participants' health records. Sleep parameters were derived from at least 7 days of monitoring with the Emfit QS device (total sleep time [TST], time in bed, sleep onset latency, sleep efficiency, wake after sleep onset). Data were compared to 19 controls using parametric or non-parametric tests for independence (α = 0.05). Pearson correlations and linear mixed models assessed relationships between data modalities.

Results: Concussed individuals reported worse sleep and had lower sleep efficiency, longer time in bed, and greater sleep onset latency than controls (p < .05). Patient-Report Outcomes Measurement Information System symptom scores moderated these relationships at significant or near-significant levels. Controls demonstrated agreement between reported and measured sleep (for TST: r = 0.52; p = .023) and a positive relationship between sleep dissatisfaction and wake after sleep onset (p < .05). These relationships were not observed in individuals with concussion. Moreover, individuals with greater discrepancy between reported and measured sleep scored higher on Sports Concussion Assessment Tool concussion symptom inventories (β _TSTdisc = 9.5/h; p _beta = .007; p _model = .022; Total R ² = 0.34).

Conclusions: Individuals with subacute concussion exhibited worse self-reported and objective sleep than controls, but showed discrepancy between reported and measured sleep characteristics that correlated with concussion severity at diagnosis. Future assessment of sleep discrepancy may improve understanding of post-concussive sleep disturbance.