Associations between longitudinal changes in sleep stages and risk of cognitive decline in older menWang, Qianwen; Stone, Katie L; Lu, Zhengan; Tian, Shanshan; Zheng, Yongbo; Zhao, Bingxin; Bao, Yanping; Shi, Le; Lu, Lin
doi: 10.1093/sleep/zsae125pmid: 38829819
Study ObjectivesTo investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men.MethodsThis study included 978 community-dwelling older men who participated in the first (2003–2005) and second (2009–2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up.ResultsMen with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the modified mini-mental state examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline.ConclusionsOur results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.
Australasian Sleep Association 2024 guidelines for sleep studies in adultsEllender, Claire M; Ruehland, Warren R; Duce, Brett; Joyce, Rosemarie; Worsnop, Christopher; Mercer, Jeremy; Naughton, Matthew; Hukins, Craig A; Wheatley, John; Cunnington, David
doi: 10.1093/sleep/zsae107pmid: 38721674
Executive summary: This document is a consensus statement of a subcommittee of experienced sleep physicians and scientists, tasked to review the literature and formulate recommendations on the indications, performance, and reporting of sleep studies, to update clinical practice from the 2017 Australasian Sleep Association (ASA) guidelines for sleep studies in adults (Douglas JA, Chai-Coetzer CL, McEvoy D, et al. Guidelines for sleep studies in adults - a position statement of the Australasian Sleep Association. Sleep Med. 2017;36(Suppl 1):S2–S22. doi:10.1016/j.sleep.2017.03.019). This document moves the focus beyond important discussions outlined in the 2017 guidelines, particularly surrounding the sensitivity and specificity of validated questionnaires and home sleep studies. The 2024 guide outlines the performance of the broad range of sleep testing available for the investigations of sleep disorders in adults including indications, strengths, limitations, and reporting standards.
When to sleep and consume caffeine to boost alertnessVital-Lopez, Francisco G; Doty, Tracy J; Reifman, Jaques
doi: 10.1093/sleep/zsae133pmid: 38877981
Study ObjectivesSleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to suboptimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day.MethodsWe combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm’s recommendations against that achieved by following the U.S. Army caffeine guidelines.ResultsCompared to the alertness-impairment levels in the original studies, the algorithm’s recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines.ConclusionsWe provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.
Development of a mindfulness-based intervention for narcolepsy: a feasibility studyMundt, Jennifer M; Zee, Phyllis C; Schuiling, Matthew D; Hakenjos, Alec J; Victorson, David E; Fox, Rina S; Dawson, Spencer C; Rogers, Ann E; Ong, Jason C
doi: 10.1093/sleep/zsae137pmid: 38895897
Study ObjectivesMindfulness-based interventions (MBI) have been shown to improve psychosocial functioning in medical populations but have not been studied in narcolepsy. This study examined the feasibility and acceptability of an MBI that was adapted for narcolepsy, including three variations in program length.MethodsAdults with narcolepsy (N = 60) were randomized to MBI groups of varying durations: brief (4 weeks), standard (8 weeks), or extended (12 weeks). Participants completed assessments at baseline, 4, 8, and 12 weeks. To assess feasibility and acceptability, primary outcomes included attendance, meditation practice, and data completeness. Additionally, participants completed measures of mindfulness, self-compassion, mood, sleep, psychosocial functioning, and cognition. An effect size of Cohen’s d ≥ 0.5 was used as the prespecified benchmark for a minimal clinically important difference (MCID).ResultsThe attendance, meditation, and data completeness benchmarks were met by 71.7%, 61.7%, and 78.3% of participants, respectively. Higher proportions of the brief and extended groups met these benchmarks compared to the standard group. All groups met the MCID for mindfulness, self-compassion, self-efficacy for managing emotions, positive psychosocial impact, global mental health, and fatigue. Standard and extended groups met the MCID for anxiety and depression, and extended groups met the MCID for additional measures including social and cognitive functioning, daytime sleepiness, hypersomnia symptoms, and hypersomnia-related functioning.ConclusionsResults suggest that the remote delivery and data collection methods are feasible to employ in future clinical trials, and it appears that the extended MBI provides the most favorable clinical impact while maintaining attendance and engagement in meditation practice.Clinical Trial RegistrationAwareness and Self-Compassion Enhancing Narcolepsy Treatment (ASCENT), NCT04306952, https://clinicaltrials.gov/ct2/show/NCT04306952
The interplay between insomnia symptoms and Alzheimer’s disease across three main brain networksElberse, Jorik D; Saberi, Amin; Ahmadi, Reihaneh; Changizi, Monir; Bi, Hanwen; Hoffstaedter, Felix; Mander, Bryce A; Eickhoff, Simon B; Tahmasian, Masoud; Alzheimer’s Disease Neuroimaging Initiative,
doi: 10.1093/sleep/zsae145pmid: 38934787
Study ObjectivesInsomnia symptoms are prevalent along the trajectory of Alzheimer’s disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network, and central executive network (CEN).MethodsWe selected 320 participants from the ADNI database and divided them by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores.ResultsInsomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterized by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were nonsignificant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD.ConclusionsWe conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.