Showing papers on "Sleep disorder published in 2021"

Sleep problems during the COVID-19 pandemic by population: a systematic review and meta-analysis.

Abstract: 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, healthcare workers, or COVID-19 patients. Therefore, this systematic review was conducted to assess the impact and prevalence of sleep problems among those categories. METHODS: APA 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 01 November 2019 to 05 July 2020. Additionally, four 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 were 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%CI 29.4-42.4%]. COVID-19 patients appeared to be the most affected group, with a pooled rate of 74.8% [95%CI 28.7-95.6%]. Healthcare workers and the general population had comparative rates of sleep problems with rates of 36.0% [95%CI 21.1-54.2%] and 32.3% [95%CI 25.3-40.2%], respectively. CONCLUSIONS: The prevalence of sleep problems during the COVID-19 pandemic is high and approximately affect 40% of people from the general and healthcare populations. COVID-19 active patients appeared to have higher prevalence rates of sleep problems.

The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis

Abstract: Evidence from previous coronavirus outbreaks has shown that infected patients are at risk for developing psychiatric and mental health disorders, such as depression, anxiety, and sleep disturbances. To construct a comprehensive picture of the mental health status in COVID-19 patients, we conducted a systematic review and random-effects meta-analysis to assess the prevalence of depression, anxiety, and sleep disturbances in this population. We searched MEDLINE, EMBASE, PubMed, Web of Science, CINAHL, Wanfang Data, Wangfang Med Online, CNKI, and CQVIP for relevant articles, and we included 31 studies (n = 5153) in our analyses. We found that the pooled prevalence of depression was 45% (95% CI: 37-54%, I2 = 96%), the pooled prevalence of anxiety was 47% (95% CI: 37-57%, I2 = 97%), and the pooled prevalence of sleeping disturbances was 34% (95% CI: 19-50%, I2 = 98%). We did not find any significant differences in the prevalence estimates between different genders; however, the depression and anxiety prevalence estimates varied based on different screening tools. More observational studies assessing the mental wellness of COVID-19 outpatients and COVID-19 patients from countries other than China are needed to further examine the psychological implications of COVID-19 infections.

The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: A systematic review and meta-analysis.

Abstract: The COVID-19 pandemic and its accompanying infection control measures introduced significant disruptions to the routines of many higher education students around the world. It also deprived them of in-person counselling services and social support. These changes have put students at a greater risk of developing mental illness. The objective of this review is to assess the prevalence of depressive symptoms, anxiety symptoms and sleep disturbances in higher education students during the pandemic. A systematic search of English and Chinese databases was conducted current to January 1st, 2021. The quality of included studies was evaluated using a modified Newcastle-Ottawa scale. Prevalence of depressive symptoms, anxiety symptoms and sleep disturbances were pooled using random-effects meta-analysis. Eighty-nine studies (n=1,441,828) were included. The pooled prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances was 34%, 32% and 33%, respectively. The prevalence values differ based on geographical regions, diagnostic criteria, education level, undergraduate year of study, financial situation, living arrangements and gender. Overall, the prevalence of depressive symptoms and anxiety symptoms synthesized in this study was higher compared to pre-pandemic prevalence in similar populations. Evidently, mental health screening and intervention should be a top priority for universities and colleges during the pandemic.

Measuring Subjective Sleep Quality: A Review.

Abstract: Sleep quality is an important clinical construct since it is increasingly common for people to complain about poor sleep quality and its impact on daytime functioning. Moreover, poor sleep quality can be an important symptom of many sleep and medical disorders. However, objective measures of sleep quality, such as polysomnography, are not readily available to most clinicians in their daily routine, and are expensive, time-consuming, and impractical for epidemiological and research studies., Several self-report questionnaires have, however, been developed. The present review aims to address their psychometric properties, construct validity, and factorial structure while presenting, comparing, and discussing the measurement properties of these sleep quality questionnaires. A systematic literature search, from 2008 to 2020, was performed using the electronic databases PubMed and Scopus, with predefined search terms. In total, 49 articles were analyzed from the 5734 articles found. The psychometric properties and factor structure of the following are reported: Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Mini-Sleep Questionnaire (MSQ), Jenkins Sleep Scale (JSS), Leeds Sleep Evaluation Questionnaire (LSEQ), SLEEP-50 Questionnaire, and Epworth Sleepiness Scale (ESS). As the most frequently used subjective measurement of sleep quality, the PSQI reported good internal reliability and validity; however, different factorial structures were found in a variety of samples, casting doubt on the usefulness of total score in detecting poor and good sleepers. The sleep disorder scales (AIS, ISI, MSQ, JSS, LSEQ and SLEEP-50) reported good psychometric properties; nevertheless, AIS and ISI reported a variety of factorial models whereas LSEQ and SLEEP-50 appeared to be less useful for epidemiological and research settings due to the length of the questionnaires and their scoring. The MSQ and JSS seemed to be inexpensive and easy to administer, complete, and score, but further validation studies are needed. Finally, the ESS had good internal consistency and construct validity, while the main challenges were in its factorial structure, known-group difference and estimation of reliable cut-offs. Overall, the self-report questionnaires assessing sleep quality from different perspectives have good psychometric properties, with high internal consistency and test-retest reliability, as well as convergent/divergent validity with sleep, psychological, and socio-demographic variables. However, a clear definition of the factor model underlying the tools is recommended and reliable cut-off values should be indicated in order for clinicians to discriminate poor and good sleepers.

Oxidative Stress and Inflammation Biomarker Expression in Obstructive Sleep Apnea Patients.

Abstract: Obstructive Sleep Apnea Syndrome (OSAS) is a respiratory sleep disorder characterised by repeated episodes of partial or complete obstruction of the upper airway during the night. This obstruction usually occurs with a reduction (hypopnea) or complete cessation (apnea) of the airflow in the upper airways with the persistence of thoracic-diaphragmatic respiratory movements. During the hypopnea/apnea events, poor alveolar ventilation reduces the oxygen saturation in the arterial blood (SaO2) and a gradual increase in the partial arterial pressure of carbon dioxide (PaCO2). The direct consequence of the intermittent hypoxia is an oxidative imbalance, with reactive oxygen species production and the inflammatory cascade’s activation with pro and anti-inflammatory cytokines growth. Tumour necrosis factors, inflammatory cytokines (IL2, IL4, IL6), lipid peroxidation, and cell-free DNA have been found to increase in OSAS patients. However, even though different risk-related markers have been described and analysed in the literature, it has not yet been clarified whether specified inflammatory bio-markers better correlates with OSAS diagnosis and its clinical evolution/comorbidities. We perform a scientific literature review to discuss inflammatory and oxidative stress biomarkers currently tested in OSAS patients and their correlation with the disease’s severity and treatment.

RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

Abstract: Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods demonstrated better performance than human sleep experts on specific datasets, they remain largely unused in sleep clinics. The main reason is that each sleep clinic uses a specific PSG montage that most automatic approaches cannot handle out-of-the-box. Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics. To address these issues, we introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages. We trained and evaluated this model in a leave-one-out-dataset fashion on a large corpus of 8 heterogeneous sleep staging datasets to make it robust to demographic changes. When evaluated on an unseen dataset, RobustSleepNet reaches 97% of the F1 of a model explicitly trained on this dataset. Hence, RobustSleepNet unlocks the possibility to perform high-quality out-of-the-box automatic sleep staging with any clinical setup. We further show that finetuning RobustSleepNet, using a part of the unseen dataset, increases the F1 by 2% when compared to a model trained specifically for this dataset. Therefore, finetuning might be used to reach a state-of-the-art level of performance on a specific population.

Google Trends reveals increases in internet searches for insomnia during the 2019 coronavirus disease (COVID-19) global pandemic

Abstract: Study Objectives:The 2019 coronavirus disease (COVID-19) has become a global health and economic crisis. Recent evidence from small samples suggest that it has increased mood and sleep disturbances...

Burden of Sleep Disturbance During COVID-19 Pandemic: A Systematic Review

Abstract: Coronavirus disease 2019 (COVID-19) pandemic may exert adverse impacts on sleep among populations, which may raise awareness of the burden of sleep disturbance, and the demand of intervention strategies for different populations. We aimed to summarize the current evidence for the impacts of COVID-19 on sleep in patients with COVID-19, healthcare workers (HWs), and the general population. We searched PubMed and Embase for studies on the prevalence of sleep disturbance. Totally, 86 studies were included in the review, including 16 studies for COVID-19 patients, 34 studies for HWs, and 36 studies for the general population. The prevalence of sleep disturbance was 33.3%-84.7%, and 29.5-40% in hospitalized COVID-19 patients and discharged COVID-19 survivors, respectively. Physiologic and psychological traumatic effects of the infection may interact with environmental factors to increase the risk of sleep disturbance in COVID-19 patients. The prevalence of sleep disturbance was 18.4-84.7% in HWs, and the contributors mainly included high workloads and shift work, occupation-related factors, and psychological factors. The prevalence of sleep disturbance was 17.65-81% in the general population. Physiologic and social-psychological factors contributed to sleep disturbance of the general population during COVID-19 pandemic. In summary, the sleep disturbance was highly prevalent during COVID-19 pandemic. Specific health strategies should be implemented to tackle sleep disturbance.

Sleep quality: An evolutionary concept analysis.

Abstract: AIM To clarify the meaning of the concept sleep quality. BACKGROUND Sleep loss and sleep quality are global health concerns. Poor sleep quality has significant adverse health outcomes. A clarification of the term is necessary to inform patients and healthcare providers, promote consistent theoretical and operational definitions in research, and develop prevention and treatment strategies. DESIGN Concept analysis. DATA SOURCES Scientific literature from electronic databases (CINAHL, PsycINFO, PubMED, Web of Science, and JSTOR) and definitions from online dictionaries. REVIEW METHODS Rodgers' Evolutionary method was applied to guide the concept analysis to identify and determine the attributes, antecedents, and consequences. RESULTS Sleep quality is defined as an individual's self-satisfaction with all aspects of the sleep experience. Sleep quality has four attributes: sleep efficiency, sleep latency, sleep duration, and wake after sleep onset. Antecedents include physiological (e.g., age, circadian rhythm, body mass index, NREM, REM), psychological (e.g., stress, anxiety, depression), and environmental factors (e.g., room temperature, television/device use), and family/social commitments. Good sleep quality has positive effects such as feeling rested, normal reflexes, and positive relationships. Poor sleep quality consequences include fatigue, irritability, daytime dysfunction, slowed responses, and increased caffeine/alcohol intake. CONCLUSIONS Sleep quality is essential, and poor sleep quality contributes to disease and poor health outcomes. Given the extensive consequences of poor sleep quality, nurses and clinicians are vital in instructing the importance of good sleep.

Role of interleukin 8 in depression and other psychiatric disorders.

Abstract: Low grade neuroinflammation has been suggested as one of the underlying mechanisms of many psychiatric diseases as well as cognitive disorders. Interleukin 8 (IL-8), a proinflammatory cytokine produced by many cell types including macrophage and microglia, mainly functions as a neutrophil chemoattractant in the bloodstream. IL-8 is also found in the brain, where it is released from microglia in response to proinflammatory stimuli. In this review, we highlight studies focusing on the role of IL-8 in psychiatric diseases such as major depression, bipolar disorder, schizophrenia, sleep disorder, autism spectrum disorder, anxiety disorders and dementia. Increased peripheral IL-8 levels have been reported in these diseases, particularly in schizophrenic disorder, bipolar disorder, obstructive sleep apnea and autism spectrum disorder. The literature on IL-8 and major depression is inconsistent. IL-8 has been found to be a factor associated with schizophrenic prognosis and therapeutic response, and may affect a wide range of symptomatology. Considering that the exact role of immune alterations is still under research, the success of immune-based therapies in psychiatric diseases is limited for the time being.

WiFi-Sleep: Sleep Stage Monitoring Using Commodity Wi-Fi Devices

Abstract: Sleep monitoring is essential to people’s health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi-based sensing, this article proposes a low-cost and nonintrusive sleep monitoring system using commodity Wi-Fi devices, namely, WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.

Suicidal ideation and suicide attempts: associations with sleep duration, insomnia, and inflammation.

Abstract: Background Sleep disturbance has been consistently identified as an independent contributor to suicide risk. Inflammation has emerged as a potential mechanism linked to both sleep disturbance and suicide risk. This study tested associations between sleep duration, insomnia, and inflammation on suicidal ideation (SI) and history of a suicide attempt (SA). Methods Participants included 2329 adults with current or remitted depression and/or anxiety enrolled in the Netherlands Study of Depression and Anxiety. Sleep duration, insomnia, past week SI, and SA were assessed with self-report measures. Plasma levels of C-reactive protein, interleukin-6, and tumor necrosis factor-α were obtained. Results Short sleep duration (⩽6 h) compared to normal sleep duration (7–9 h) was associated with reporting a prior SA, adjusting for covariates [adjusted odds ratio (AOR) 1.68, 95% CI 1.13–2.51]. A higher likelihood of SI during the past week was observed for participants with long sleep duration (⩾10 h) compared to normal sleep duration (AOR 2.22, 95% CI 1.02–4.82), more insomnia symptoms (AOR 1.44, 95% CI 1.14–1.83), and higher IL-6 (AOR 1.31, 95% CI 1.02–1.68). Mediation analyses indicated that the association between long sleep duration and SI was partially explained by IL-6 (AOR 1.02, 95% CI 1.00–1.05). Conclusions These findings from a large sample of adults with depression and/or anxiety provide evidence that both short and long sleep duration, insomnia symptoms, and IL-6 are associated with the indicators of suicide risk. Furthermore, the association between long sleep duration and SI may operate through IL-6.

Testing a mindfulness meditation mobile app for the treatment of sleep-related symptoms in adults with sleep disturbance: A randomized controlled trial.

Abstract: The objective of this randomized controlled trial was to test whether a commercially available, mindfulness meditation mobile app, (i.e., Calm app), was effective in reducing fatigue (primary outcome), pre-sleep arousal, and daytime sleepiness (secondary outcomes) in adults with sleep disturbance (Insomnia Severity Index Score >10) as compared to a wait-list control group. Associations between the use of the Calm app (i.e., adherence to the intervention) and changes in sleep quality was also explored in the intervention group only. Adults with sleep disturbance were recruited (N = 640). Eligible and consenting participants (N = 263) were randomly assigned to the intervention (n = 124) or a wait-list control (n = 139) group. Intervention participants were asked to meditate using the Calm app ≥10 minutes/day for eight weeks. Fatigue, daytime sleepiness, and pre-sleep arousal were assessed at baseline, mid- (4-weeks) and post-intervention (8-weeks) in both groups, whereas sleep quality was evaluated only in the intervention group. Findings from intent-to-treat analyses suggest the use of the Calm app for eight weeks significantly decreased daytime fatigue (p = .018) as well as daytime sleepiness (p = .003) and cognitive (p = .005) and somatic (p < .001) pre-sleep arousal as compared to the wait-list control group. Within the intervention group, use of the Calm app was associated with improvements in sleep quality (p < .001). This randomized controlled trial demonstrates that the Calm app can be used to treat fatigue, daytime sleepiness, and pre-sleep arousal in adults with sleep disturbance. Given that the Calm app is affordable and widely accessible, these data have implications for community level dissemination of a mobile app to improve sleep-related symptoms associated with sleep disturbance. Trial registration: ClinicalTrials.gov NCT04045275.

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Abstract: Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

Sleep classification from wrist-worn accelerometer data using random forests

Abstract: Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.

Sleep disorders in people with type 2 diabetes and associated health outcomes: a review of the literature

Abstract: Sleep disorders are linked to development of type 2 diabetes and increase the risk of developing diabetes complications. Treating sleep disorders might therefore play an important role in the prevention of diabetes progression. However, the detection and treatment of sleep disorders are not part of standardised care for people with type 2 diabetes. To highlight the importance of sleep disorders in people with type 2 diabetes, we provide a review of the literature on the prevalence of sleep disorders in type 2 diabetes and the association between sleep disorders and health outcomes, such as glycaemic control, microvascular and macrovascular complications, depression, mortality and quality of life. Additionally, we examine the extent to which treating sleep disorders in people with type 2 diabetes improves these health outcomes. We performed a literature search in PubMed from inception until January 2021, using search terms for sleep disorders, type 2 diabetes, prevalence, treatment and health outcomes. Both observational and experimental studies were included in the review. We found that insomnia (39% [95% CI 34, 44]), obstructive sleep apnoea (55–86%) and restless legs syndrome (8–45%) were more prevalent in people with type 2 diabetes, compared with the general population. No studies reported prevalence rates for circadian rhythm sleep–wake disorders, central disorders of hypersomnolence or parasomnias. Additionally, several cross-sectional and prospective studies showed that sleep disorders negatively affect health outcomes in at least one diabetes domain, especially glycaemic control. For example, insomnia is associated with increased HbA1c levels (2.51 mmol/mol [95% CI 1.1, 4.4]; 0.23% [95% CI 0.1, 0.4]). Finally, randomised controlled trials that investigate the effect of treating sleep disorders in people with type 2 diabetes are scarce, based on a small number of participants and sometimes inconclusive. Conventional therapies such as weight loss, sleep education and cognitive behavioural therapy seem to be effective in improving sleep and health outcomes in people with type 2 diabetes. We conclude that sleep disorders are highly prevalent in people with type 2 diabetes, negatively affecting health outcomes. Since treatment of the sleep disorder could prevent diabetes progression, efforts should be made to diagnose and treat sleep disorders in type 2 diabetes in order to ultimately improve health and therefore quality of life.

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Abstract: Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

Social Distancing in Chronic Migraine during the COVID-19 Outbreak: Results from a Multicenter Observational Study.

Abstract: Background: The restrictions taken to control the rapid spread of COVID-19 resulted in a sudden, unprecedented change in people’s lifestyle, leading to negative consequences on general health. This study aimed to estimate the impact of such changes on migraine severity during 2020 March–May lockdown. Methods: Patients affected by migraine with or without aura, diagnosed by expert physicians, completed a detailed interview comprehensive of: assessment of migraine characteristics; measure of physical activity (PA) levels; measure of the intake frequency of main Italian foods; the Insomnia Severity Index (ISI) questionnaire investigating sleep disorders. Results: We included 261 patients with a mean age of 44.5 ± 12.3 years. During social distancing, 72 patients (28%) reported a headache worsening, 86 (33%) an improvement, and 103 (39%) a stable headache frequency. A significant decrease of the PA levels during COVID-19 quarantine in the whole study sample was observed (median total metabolic equivalent task (METs) decreased from 1170 to 510; p < 0.001). Additionally, a significant difference was reported on median ISI scores (from 7 to 8; p < 0.001), which were increased in patients who presented a stable or worsening headache. Conclusions: Our study confirmed that the restrictions taken during the pandemic have affected the practice of PA levels and sleep quality in migraine. Hence, PA and sleep quality should be assessed to find strategies for an improvement in quality of life.

Examining sleep deficiency and disturbance and their risk for incident dementia and all-cause mortality in older adults across 5 years in the United States.

Abstract: Background: Sleep disturbance and deficiency are common among older adults and have been linked with dementia and all-cause mortality Using nationally representative data, we examine the relationship between sleep disturbance and deficiency and their risk for incident dementia and all-cause mortality among older adults Methods: The National Health and Aging Trends Study (NHATS) is a nationally-representative longitudinal study of Medicare beneficiaries in the US age 65 and older Surveys that assessed sleep disturbance and duration were administered at baseline We examined the relationship between sleep disturbance and deficiency and incident dementia and all-cause mortality over the following 5 years using Cox proportional hazards modeling, controlling for confounders Results: Among the sample (n = 2,812), very short sleep duration (≤5 hours: HR = 204, 95% CI: 126 - 333) and sleep latency (>30 minutes: HR = 145, 95% CI: 103 - 203) were associated with incident dementia in adjusted Cox models Difficulty maintaining alertness (“Some Days”: HR = 149, 95% CI: 113 - 194 and “Most/Every Day”: HR = 165, 95% CI: 117 - 232), napping (“Some days”: HR = 138, 95% CI: 103 - 185; “Most/Every Day”: HR = 173, 95% CI: 129 - 232), sleep quality (“Poor/Very Poor”: HR = 175, 95% CI: 117 - 261), and very short sleep duration (≤5 hours: HR = 238, 95% CI: 144 - 392) were associated with all-cause mortality in adjusted Cox models Conclusions: Addressing sleep disturbance and deficiency may have a positive impact on risk for incident dementia and all-cause mortality among older adults