3
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      COVID-19 and Sleep in Medical Staff: Reflections, Clinical Evidences, and Perspectives

      review-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose of the review

          There is evidence that, before the coronavirus pandemic 2019 (COVID-19), healthcare workers did not experience good sleep quality with relevant consequences on health. By contrast, little is known about the sleep quality of medical staff during the COVID-19 pandemic. In this review, we aimed to contribute with a review of the literature, sharing our clinical experience supported by actigraphic evaluation and by proposing future strategies.

          Recent findings

          Sleep disorders, in particular insomnia, have been commonly reported in frontline medical workers, in hospitals during the COVID-19 pandemic and are often accompanied by depressive and anxiety symptoms. Sleep quality, however, has been mainly assessed by the use of self-reported measures, thus limiting clinical usefulness.

          Summary

          Poor sleep quality among the medical staff is prevalent, and our experience supports that this has increased during the COVID-19 pandemic. A longitudinal investigation assessing whether and for how long sleep remains altered in medical staff could be of interest to evaluate the temporal effect of the pandemic on health.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: found
          • Article: not found

          The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus

          In December, 2019, a novel coronavirus outbreak of pneumonia emerged in Wuhan, Hubei province, China, 1 and has subsequently garnered attention around the world. 2 In the fight against the 2019 novel coronavirus (2019-nCoV), medical workers in Wuhan have been facing enormous pressure, including a high risk of infection and inadequate protection from contamination, overwork, frustration, discrimination, isolation, patients with negative emotions, a lack of contact with their families, and exhaustion. The severe situation is causing mental health problems such as stress, anxiety, depressive symptoms, insomnia, denial, anger, and fear. These mental health problems not only affect the medical workers' attention, understanding, and decision making ability, which might hinder the fight against 2019-nCoV, but could also have a lasting effect on their overall wellbeing. Protecting the mental health of these medical workers is thus important for control of the epidemic and their own long-term health. The local government of Wuhan has implemented policies to address these mental health problems. Medical staff infected with 2019-nCoV while at work will be identified as having work-related injuries. 3 As of Jan 25, 2020, 1230 medical workers have been sent from other provinces to Wuhan to care for patients who are infected and those with suspected infection, strengthen logistics support, and help reduce the pressure on health-care personnel. 4 Most general hospitals in Wuhan have established a shift system to allow front-line medical workers to rest and to take turns in high-pressured roles. Online platforms with medical advice have been provided to share information on how to decrease the risk of transmission between the patients in medical settings, which aims to eventually reduce the pressure on medical workers. Psychological intervention teams have been set up by the RenMin Hospital of Wuhan University and Mental Health Center of Wuhan, which comprise four groups of health-care staff. Firstly, the psychosocial response team (composed of managers and press officers in the hospitals) coordinates the management team's work and publicity tasks. Secondly, the psychological intervention technical support team (composed of senior psychological intervention experts) is responsible for formulating psychological intervention materials and rules, and providing technical guidance and supervision. Thirdly, the psychological intervention medical team, who are mainly psychiatrists, participates in clinical psychological intervention for health-care workers and patients. Lastly, the psychological assistance hotline teams (composed of volunteers who have received psychological assistance training in dealing with the 2019-nCoV epidemic) provide telephone guidance to help deal with mental health problems. Hundreds of medical workers are receiving these interventions, with good response, and their provision is expanding to more people and hospitals. Understanding the mental health response after a public health emergency might help medical workers and communities prepare for a population's response to a disaster. 5 On Jan 27, 2020, the National Health Commission of China published a national guideline of psychological crisis intervention for 2019-nCoV. 4 This publication marks the first time that guidance to provide multifaceted psychological protection of the mental health of medical workers has been initiated in China. The experiences from this public health emergency should inform the efficiency and quality of future crisis intervention of the Chinese Government and authorities around the world.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Effects of Social Support on Sleep Quality of Medical Staff Treating Patients with Coronavirus Disease 2019 (COVID-19) in January and February 2020 in China

            Background Coronavirus disease 2019 (COVID-19), formerly known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 2019 novel coronavirus (2019-nCoV), was first identified in December 2019 in Wuhan City, China. Structural equation modeling (SEM) is a multivariate analysis method to determine the structural relationship between measured variables. This observational study aimed to use SEM to determine the effects of social support on sleep quality and function of medical staff who treated patients with COVID-19 in January and February 2020 in Wuhan, China. Material/Methods A one-month cross-sectional observational study included 180 medical staff who treated patients with COVID-19 infection. Levels of anxiety, self-efficacy, stress, sleep quality, and social support were measured using the and the Self-Rating Anxiety Scale (SAS), the General Self-Efficacy Scale (GSES), the Stanford Acute Stress Reaction (SASR) questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Social Support Rate Scale (SSRS), respectively. Pearson’s correlation analysis and SEM identified the interactions between these factors. Results Levels of social support for medical staff were significantly associated with self-efficacy and sleep quality and negatively associated with the degree of anxiety and stress. Levels of anxiety were significantly associated with the levels of stress, which negatively impacted self-efficacy and sleep quality. Anxiety, stress, and self-efficacy were mediating variables associated with social support and sleep quality. Conclusions SEM showed that medical staff in China who were treating patients with COVID-19 infection during January and February 2020 had levels of anxiety, stress, and self-efficacy that were dependent on sleep quality and social support.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Prevalence of Poor Sleep Quality in Nursing Staff: A Meta-Analysis of Observational Studies

              Objective: Poor sleep quality is common in nursing staff. This meta-analysis aimed to examine the pooled prevalence of poor sleep quality in nursing staff. Methods: A systematic search in PubMed, EMBASE, PsycINFO, and Web of Science databases was performed. Studies that reported sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) were synthesized using a random-effects model. Results: Fifty-three studies were analyzed. The pooled prevalence of poor sleep quality was 61.0% (95% CI: 55.8-66.1%). The pooled total PSQI score was 7.13 ± 0.18 (95% CI: 6.78-7.50). The pooled component scores were 1.47 ± 0.20 (95% CI of mean score: 1.08-1.85) in sleep latency, 0.91 ± 0.15 (95% CI of mean score: 0.61-1.21) in sleep duration, 1.59 ± 0.13 (95% CI of mean score: 1.35-1.84) in overall sleep disturbances, 0.33 ± 0.18 (95% CI of mean score: 0-0.67) in sleeping medication, 1.21 ± 1.20 (95% CI of mean score: 0.83-1.60) in daytime dysfunction, 1.39 ± 0.14 (95% CI of mean score: 1.11-1.67) in subjective sleep quality, and 0.66 ± 0.11 (95% CI of mean score: 0.44-0.87) in habitual sleep efficiency. Subgroup and meta-regression analyses found that PSQI cutoff values, mean age, body mass index (BMI), sample size, study quality, and work experience moderated the prevalence of poor sleep quality. Conclusions: Poor sleep quality appears to be common in nursing staff. Considering its negative impact on health, effective measures should be taken to improve poor sleep quality in this population. Longitudinal studies should be conducted to examine the contributing factors of nurses' poor sleep quality.
                Bookmark

                Author and article information

                Contributors
                ferinistrambi.luigi@hsr.it
                Journal
                Curr Treat Options Neurol
                Curr Treat Options Neurol
                Current Treatment Options in Neurology
                Springer US (New York )
                1092-8480
                1534-3138
                6 August 2020
                2020
                : 22
                : 10
                : 29
                Affiliations
                [1 ]GRID grid.15496.3f, Vita-Salute San Raffaele University, ; Milan, Italy
                [2 ]GRID grid.18887.3e, ISNI 0000000417581884, Sleep Disorders Center, Division of Neuroscience, , IRCCS San Raffaele Hospital, ; Milan, Italy
                [3 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Institute of Molecular Bioimaging and Physiology, , National Research Council, ; Catanzaro, Italy
                Author information
                http://orcid.org/0000-0003-2867-5424
                Article
                642
                10.1007/s11940-020-00642-4
                7406692
                32834711
                265100dc-f148-4ded-bf66-0ac3752244d3
                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Categories
                Reflections from the COVID Pandemic (A Iranzo and M Rosenfeld, Section Editors)
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                Neurology
                covid-19 pandemic,sleep quality,actigraphy,healthcare workers/medical staff
                Neurology
                covid-19 pandemic, sleep quality, actigraphy, healthcare workers/medical staff

                Comments

                Comment on this article