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Nearly half of all adults older than 60 years of age report sleep disturbance, as characterised either by reports of insomnia complaints with daytime consequences, dissatisfaction with sleep quality or quantity, or the diagnosis of insomnia disorder. Accumulating evidence shows that sleep disturbance contributes to cognitive decline and might also increase the risk of Alzheimer's disease dementia by increasing β-amyloid burden. That sleep disturbance would be a candidate risk factor for Alzheimer's disease might seem surprising, given that disturbed sleep is usually considered a consequence of Alzheimer's disease. However, a bidirectional relationship between sleep and Alzheimer's disease is supported by advances in our understanding of sleep disturbance-induced increases in systemic inflammation, which can be viewed as an early event in the course of Alzheimer's disease. Inflammation increases β-amyloid burden and is thought to drive Alzheimer's disease pathogenesis. Improved understanding of the mechanisms linking sleep disturbance and Alzheimer's disease risk could facilitate the identification of targets for prevention, given that both sleep disturbance and inflammatory activation might be modifiable risk factors for Alzheimer's disease.

Study Objectives: No systematic review or meta-analysis has yet been conducted to examine the impact of the pandemic on the prevalence of sleep problems among the general population, health care workers, or patients with COVID-19. Therefore, this systematic review was conducted to assess the impact and prevalence of sleep problems among those categories. Methods: American Psychological Association PsycINFO, Cochrane, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EBSCOhost, EMBASE, Google Scholar, MEDLINE, ProQuest Medical, ScienceDirect, Scopus, and Web of Science from November 1, 2019 to July 5, 2020 were used. Additionally, 5 preprints servers (medRxiv.org; preprints.org; psyarxiv.com; arXiv.org; biorxiv.org) were also searched for papers accepted after peer review but not yet published and indexed. There was no language restriction. The random-effect models meta-analysis model was used with the DerSimonian and Laird methodology. Results: Forty-four papers, involving a total of 54,231 participants from 13 countries, were judged relevant and contributed to the systematic review and meta-analysis of sleep problems during COVID-19. The global pooled prevalence rate of sleep problems among all populations was 35.7% (95% confidence interval, 29.4–42.4%). Patients with COVID-19 appeared to be the most affected group, with a pooled rate of 74.8% (95% confidence interval, 28.7–95.6%). Health care workers and the general population had comparative rates of sleep problems, with rates of 36.0% (95% confidence interval, 21.1–54.2%) and 32.3% (95% confidence interval, 25.3–40.2%), respectively. Conclusions: The prevalence of sleep problems during the COVID-19 pandemic is high and affects approximately 40% of people from the general and health care populations. Patients with active COVID-19 appeared to have a higher prevalence rates of sleep problems. Citation: Jahrami H, BaHammam AS, Bragazzi NL, Saif Z, Faris M, Vitiello MV. Sleep problems during the COVID-19 pandemic by population: a systematic review and meta-analysis. J Clin Sleep Med . 2021;17(2):299–313.

The long-term physical and mental sequelae of COVID-19 are a growing public health concern, yet there is considerable uncertainty about their prevalence, persistence and predictors. We conducted a comprehensive, up-to-date meta-analysis of survivors’ health consequences and sequelae for COVID-19. PubMed, Embase and the Cochrane Library were searched through Sep 30th, 2021. Observational studies that reported the prevalence of sequelae of COVID-19 were included. Two reviewers independently undertook the data extraction and quality assessment. Of the 36,625 records identified, a total of 151 studies were included involving 1,285,407 participants from thirty-two countries. At least one sequelae symptom occurred in 50.1% (95% CI 45.4-54.8) of COVID-19 survivors for up to 12 months after infection. The most common investigation findings included abnormalities on lung CT (56.9%, 95% CI 46.2–67.3) and abnormal pulmonary function tests (45.6%, 95% CI 36.3–55.0), followed by generalized symptoms, such as fatigue (28.7%, 95% CI 21.0–37.0), psychiatric symptoms (19.7%, 95% CI 16.1–23.6) mainly depression (18.3%, 95% CI 13.3–23.8) and PTSD (17.9%, 95% CI 11.6–25.3), and neurological symptoms (18.7%, 95% CI 16.2–21.4), such as cognitive deficits (19.7%, 95% CI 8.8–33.4) and memory impairment (17.5%, 95% CI 8.1–29.6). Subgroup analysis showed that participants with a higher risk of long-term sequelae were older, mostly male, living in a high-income country, with more severe status at acute infection. Individuals with severe infection suffered more from PTSD, sleep disturbance, cognitive deficits, concentration impairment, and gustatory dysfunction. Survivors with mild infection had high burden of anxiety and memory impairment after recovery. Our findings suggest that after recovery from acute COVID-19, half of survivors still have a high burden of either physical or mental sequelae up to at least 12 months. It is important to provide urgent and appropriate prevention and intervention management to preclude persistent or emerging long-term sequelae and to promote the physical and psychiatric wellbeing of COVID-19 survivors.

Pandemics have become more frequent and more complex during the twenty-first century. Posttraumatic stress disorder (PTSD) following pandemics is a significant public health concern. We sought to provide a reliable estimate of the worldwide prevalence of PTSD after large-scale pandemics as well as associated risk factors, by a systematic review and meta-analysis. We systematically searched the MedLine, Embase, PsycINFO, Web of Science, CNKI, WanFang, medRxiv, and bioRxiv databases to identify studies that were published from the inception up to August 23, 2020, and reported the prevalence of PTSD after pandemics including sudden acute respiratory syndrome (SARS), H1N1, Poliomyelitis, Ebola, Zika, Nipah, Middle Eastern respiratory syndrome coronavirus (MERS-CoV), H5N1, and coronavirus disease 2019 (COVID-19). A total of 88 studies were included in the analysis, with 77 having prevalence information and 70 having risk factors information. The overall pooled prevalence of post-pandemic PTSD across all populations was 22.6% (95% confidence interval (CI): 19.9–25.4%, I2: 99.7%). Healthcare workers had the highest prevalence of PTSD (26.9%; 95% CI: 20.3–33.6%), followed by infected cases (23.8%: 16.6–31.0%), and the general public (19.3%: 15.3–23.2%). However, the heterogeneity of study findings indicates that results should be interpreted cautiously. Risk factors including individual, family, and societal factors, pandemic-related factors, and specific factors in healthcare workers and patients for post-pandemic PTSD were summarized and discussed in this systematic review. Long-term monitoring and early interventions should be implemented to improve post-pandemic mental health and long-term recovery.

Polysomnography (PSG) studies of sleep changes in Alzheimer’s disease (AD) have reported but not fully established the relationship between sleep disturbances and AD. To better detail this relationship, we conducted a systematic review and meta-analysis of reported PSG differences between AD patients and healthy controls. An electronic literature search was conducted in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO inception to Mar 2021. Twenty-eight studies were identified for systematic review, 24 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, and percentage of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, and increases in sleep latency, wake time after sleep onset, number of awakenings, and REM latency in AD compared to controls. Importantly, both decreased SWS and REM were significantly associated with the severity of cognitive impairment in AD patients. Alterations in electroencephalogram (EEG) frequency components and sleep spindles were also observed in AD, although the supporting evidence for these changes was limited. Sleep in AD is compromised with increased measures of wake and decreased TST, SWS, and REM sleep relative to controls. AD-related reductions in SWS and REM sleep correlate with the degree of cognitive impairment. Alterations in sleep EEG frequency components such as sleep spindles may be possible biomarkers with relevance for diagnosing AD although their sensitivity and specificity remain to be clearly delineated. AD-related sleep changes are potential targets for early therapeutic intervention aimed at improving sleep and slowing cognitive decline.

Study Objectives: This study aims to explore the polysomnographically measured sleep and leg movement differences between idiopathic restless legs syndrome (RLS) patients and healthy controls. Methods: An electronic literature search was conducted in EMBASE, MEDLINE, all EBM databases, CINAHL, and PsycINFO. Only observational case-control studies were included in the meta-analysis. The differences in 13 sleep parameters and 23 leg movement parameters between RLS patients and healthy controls were explored. Results: Thirty-eight studies were identified for systematic review, 31 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, stage N2 and rapid eye movement (REM) sleep percentages, and increases in wake time after sleep onset, stage shifts per hour, stage N1 percentage, REM latency, arousal index, and apnea-hypopnea index. Some leg movement parameters, such as periodic limb movement during sleep (PLMS) index, PLMS sequence duration, number of PLMS sequence, and periodicity index, were higher in RLS patients compared with healthy controls. Further, our meta-analysis revealed a higher PLMS index during non-REM sleep compared with that during REM sleep. Conclusions: RLS patients manifest a lightening of sleep, increased sleep fragmentation, and greater sleep-related breathing disruption and limb movements during sleep relative to healthy normal individuals. The distributions of PLMS during a night’s sleep may provide more information to clarify the specific characteristics of leg movements in RLS. PLMS in RLS are concentrated in non-REM sleep. The periodicity index may be a more sensitive and specific marker of RLS than the PLMS index. Citation: Zhang H, Zhang Y, Ren R, et al. Polysomnographic features of idiopathic restless legs syndrome: A systematic review and meta-analysis of 13 sleep parameters and 23 leg movement parameters. J Clin Sleep Med . 2022;18(11):2561–2575.

Study Objectives: This study aims to explore the polysomnographically measured sleep differences between patients with multiple sclerosis (MS) and healthy control patients. Methods: An electronic literature search was conducted in EMBASE, MEDLINE, all EBM databases, CINAHL, and PsycINFO from inception to March 2022. A random-effects model was applied to explore the pooled effect sizes of polysomnographic differences between patients with MS and control patients. Results: Thirteen studies were identified for meta-analysis. The meta-analyses revealed significant reductions in stage N2 sleep and sleep efficiency and increases in wake time after sleep onset, the periodic limb movement index, and the periodic limb movement arousal index in patients with MS compared with control patients. Meta-regression analyses showed that some of the heterogeneity was explained by age and daytime sleepiness of patients with MS. Conclusions: Our study showed that polysomnographic abnormalities are present in MS. Our findings also underscore the need for a comprehensive polysomnographic assessment of sleep changes in patients with MS. Furthermore, the effects of age and daytime sleepiness in patients with MS on sleep changes should also be carefully considered and closely monitored in the management of MS. Citation: Zhang Y, Ren R, Yang L, et al. Sleep in multiple sclerosis: A systematic review and meta-analysis of polysomnographic findings. J Clin Sleep Med. 2023;19(2):253–265.

Wrist actigraphy has been used to assess sleep in older adult populations for nearly half a century. Over the years, the continuous raw activity data derived from actigraphy has been used for the characterization of factors beyond sleep/wake such as physical activity patterns and circadian rhythms. Behavioral activity rhythms (BAR) are useful to describe individual daily behavioral patterns beyond sleep and wake, which represent important and meaningful clinical outcomes. This paper reviews common rhythmometric approaches and summarizes the available data from the use of these different approaches in older adult populations. We further consider a new approach developed in our laboratory designed to provide graphical characterization of BAR for the observed behavioral phenomenon of activity patterns across time. We illustrate the application of this new approach using actigraphy data collected from a well-characterized sample of older adults (age 60+) with osteoarthritis (OA) pain and insomnia. Generalized additive models (GAM) were implemented to fit smoothed nonlinear curves to log-transformed aggregated actigraphy-derived activity measurements. This approach demonstrated an overall strong model fit (R2 = 0.82, SD = 0.09) and was able to provide meaningful outcome measures allowing for graphical and parameterized characterization of the observed activity patterns within this sample.

Background Insomnia and urinary incontinence (UI) are both diseases burdening older adults. However, the association between them has not been well elucidated. The purpose of this study is to assess the correlation between insomnia symptoms and UI in a large community‐dwelling sample of older Indian adults. Methods Data were from Wave 1 (2017–2018) of the Longitudinal Ageing Study of India (LASI). Male and female participants aged ≥ 60 years who provided complete information on insomnia symptoms, UI, stress UI (SUI), and covariates were included. Insomnia symptoms were identified by a report of: trouble falling asleep, waking up at night, or waking too early, ≥ 5 times/week. UI was defined by self-reported diagnosis. SUI was identified by self-report of involuntary urine leakage when sneezing, coughing, laughing, or lifting weights. Multivariable logistic regression analyses evaluated the associations between insomnia symptoms and UI and SUI. Stratified linear regression evaluated for interactions in prespecified subgroups. Results Twenty-six thousand eight hundred twenty-one LASI participants met entry criteria. 2979 (11.11%) reported insomnia symptoms, 976 (3.64%) UI, and 2726 (10.16%) SUI. After full adjustment, insomnia symptoms were associated with both UI and SUI among males (OR 1.53; 95%CI 1.20–1.96 and OR 1.51; 95%CI 1.25–1.83) and females (OR 1.53; 95% CI 1.21–1.92 and OR 1.50; 95% CI 1.31–1.73). A significant interaction effect by age was observed between insomnia symptoms and SUI among both males ( p  = 0.048) and females ( p  = 0.042). Conclusions Insomnia symptoms were associated with UI and with SUI in both male and female older Indian adults. Further prospective study is called for to better characterize these associations and to explore underlying mechanisms.

Aims: To examine the 24-h hyperarousal hypothesis of insomnia using electroencephalographic (EEG) spectral analysis of overnight polysomnography (PSG) and daytime multiple sleep latency tests (MSLTs). Methods: Standard PSG and MSLT were recorded in 31 chronic insomniacs (CIs) (21 females, mean age 36.19) and in 21 normal controls (NCs) (18 females, mean age 34.76). EEG spectral analyses were conducted and relative power was obtained for each sleep stage during PSG and each session during MSLTs. Subsequently, CIs were subdivided based on sleep efficiency (SE < or ≥ 85%) or mean sleep latency (MSL) of MSLT (< or ≥ 15min), and beta power was compared among NCs and CIs subgroups. General liner regression analyses of beta power and PSG parameters were conducted. Results: CIs had significantly greater beta power in nighttime W, N1, N2, NREM and in total overnight and in MSLT sessions compared with NCs. CIs with lower PSG-SE or longer MSLT-MSL showed higher beta power at nighttime. Compared with NCs, increased beta power was limited to CIs with lower PSG-SE or longer MSLT-MSL during MSLT sessions. In all subjects, total daytime beta was positively correlated to total overnight beta and MSL, total overnight beta was negatively related to SE. In CIs, total daytime beta and total overnight beta were positively correlated. Conclusions: Our results support the hypothesis of 24-h cortical hyperarousal in insomnia. We conclude that 24-h cortical hyperarousal is clearly present in insomnia and is greater in insomnia with objective findings.