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

Individuals with mild cognitive impairment (MCI) demonstrate cognitive decline without major functional impairment and are at increased risk for developing Alzheimer's disease and related dementias (ADRD). Sleep and biobehavioral rhythm disturbances (disruptions in 24-h oscillations in physiology and behavior, including rest-activity patterns and mealtimes) are more than twice as common among patients with MCI than cognitively intact older adults. Importantly, the consequences of sleep and biobehavioral rhythm disruption in MCI extend beyond the patient, also profoundly affecting the spouse/partner. However, scant research has investigated sleep and biobehavioral rhythms that may contribute to the health and cognitive functioning of individuals with MCI and their partners. The current paper presents the rationale and methods for the Couples Healthy Aging and Rhythms (CHARMS) study, a longitudinal cohort study of sleep and biobehavioral rhythms among older couples in which one partner evidences cognitive decline but is independent in daily functioning. The targeted enrollment goal will consist of 185 couples who meet study eligibility criteria including that one partner shows evidence of cognitive decline but reports being independent in daily activities. This is a longitudinal observational study that includes a baseline assessment, a seven-day at-home naturalistic study protocol, and a two-year follow-up to examine change over time. Findings from this study will advance the understanding of the daily and longitudinal relationships between the individual and couple-level processes in sleep and biobehavioral rhythms that influence the progression of cognitive decline in a population at increased risk for developing ADRD.

Study objectives: Sleep health is essential for wellbeing, yet few studies examine sociodemographic differences among young adults using objective sleep measures. We examined sociodemographic differences in actigraphic sleep health among a national, diverse sample of several hundred young adults.

Methods: Data from the Young Adult Sleep Sub-Study were collected during year 22 (mean age = 22.1 ± 0.3 years; range 21.6-24.4) of the Future of Families and Child Wellbeing Study (n = 442), a national, diverse sample of US-based young adults. Participants wore wrist-actigraphs for ~2 weeks. Multivariable linear regression models assessed whether sex, race, Hispanic/Latino ethnicity, education level, and employment status (simultaneously adjusted) were associated with dimensions of actigraphic sleep health.

Results: In adjusted models, women had longer total sleep time (TST), earlier sleep onset, and less weekend night catchup sleep than men. Black young adults had shorter TST, later sleep onset, more variability in nighttime TST, in sleep onset, and in sleep maintenance efficiency, and lower sleep regularity index (SRI) than White young adults. Young adults with a lower education level had greater variability in nighttime TST and in sleep timing and lower SRI than those with a higher education level. Young adults who were unemployed had later sleep timing, lower SRI, and less weekend night catchup sleep than those employed full-time.

Conclusions: Male sex, Black race, lower education level, and unemployment are associated with poorer actigraphic sleep health among this sample of young adults. Strategies to improve young adult sleep health should specifically prioritize men, marginalized populations, and those of lower socioeconomic status.

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.