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      The promotion of sleep wellness: Resilience as a protective factor

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          Abstract

          Objectives:

          To evaluate the association between resilience, sleep quality, and health.

          Methods:

          This cross-sectional study included 190 patients (Mean age = 51, SD = 15.57) recruited from the Johns Hopkins Center for Sleep and Wellness. Patients completed a modified version of the brief resilience scale (BRS) to assess characteristics of resilience and questions to assess aspects of mental health, physical health, sleep quality, and daytime functioning.

          Results:

          Participants’ average score on the BRS was 4.67 ( SD = 1.32, range = 1.17–7), reflecting a high level of resilience. There was a significant gender difference in resilience levels for men (Mean = 5.04, SD = 1.14) and women (Mean = 4.30, SD = 1.38), such that men reported significantly higher levels of resilience compared to women ( t (188) = 4.02, p < 0.001) [lower levels of resilience were significantly associated with higher levels of (current) fatigue and tiredness after adjusting for demographic, physical, and mental covariates. In those reporting between one and three mental health symptoms, high levels of resilience minimized the negative influence that these symptoms had on sleep quality. This minimizing effect was no longer evident in those experiencing >3 mental health symptoms, who also reported significantly higher symptoms of fatigue despite their high resilience scores.

          Conclusions:

          This study emphasizes how resilience may affect the relationship between mental health and sleep quality in sleep patients. Resilience may further our understanding of the inter-relationships between sleep and the manifestation of physical health symptoms, a relationship that will likely heighten in relevance during personal and global crisis. An awareness of this interaction could be used as a proactive prevention and treatment strategy. In other words, incorporating methods to evaluate resilience in patients with mental illnesses regularly can be useful for predicting the potential manifestation and severity of sleep disturbance. Therefore, strategies that focus on promoting resilience could improve health and wellness.

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          Most cited references28

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          The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research

          Daniel J. Buysse, Charles Reynolds III, Timothy H Monk … (1989)
          Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
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            An interactive web-based dashboard to track COVID-19 in real time

            Ensheng Dong, Hongru Du, Lauren Gardner (2020)
            In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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              A new method for measuring daytime sleepiness: the Epworth sleepiness scale.

              Murray Johns (1991)
              The development and use of a new scale, the Epworth sleepiness scale (ESS), is described. This is a simple, self-administered questionnaire which is shown to provide a measurement of the subject's general level of daytime sleepiness. One hundred and eighty adults answered the ESS, including 30 normal men and women as controls and 150 patients with a range of sleep disorders. They rated the chances that they would doze off or fall asleep when in eight different situations commonly encountered in daily life. Total ESS scores significantly distinguished normal subjects from patients in various diagnostic groups including obstructive sleep apnea syndrome, narcolepsy and idiopathic hypersomnia. ESS scores were significantly correlated with sleep latency measured during the multiple sleep latency test and during overnight polysomnography. In patients with obstructive sleep apnea syndrome ESS scores were significantly correlated with the respiratory disturbance index and the minimum SaO2 recorded overnight. ESS scores of patients who simply snored did not differ from controls.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9918451286706676
                Journal ID (pubmed-jr-id): 51881
                Journal ID (nlm-ta): Front Sleep
                Journal ID (iso-abbrev): Front Sleep
                Title: Frontiers in sleep
                ISSN (Electronic): 2813-2890
                Publication date Nihms-submitted: 28 June 2023
                Publication date (Print): 2023
                Publication date (Electronic): 11 April 2023
                Publication date PMC-release: 07 July 2023
                Volume: 2
                Electronic Location Identifier: 1133347
                Affiliations
                [1 ]Human Development and Family Studies, The Pennsylvania State University, University Park, PA, United States
                [2 ]Department of Neurology, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
                [3 ]Johns Hopkins Carey Business School, Baltimore, MD, United States
                Author notes

                Author contributions

                RS, CG, and BG contributed to conception and design of the study. AA and AG conducted and reviewed the statistical analyses. AA, CG, RS, and AG wrote and/or made major edits to the final submitted manuscript. BG, IA, and EI wrote initial drafts of manuscript sections. All authors contributed to manuscript revision, read, and approved the submitted version. All authors have seen and approved this manuscript.

                [* ] CORRESPONDENCE: Alexa C. Allan, aca5399@ 123456psu.edu
                Article
                Manuscript ID: NIHMS1913208
                DOI: 10.3389/frsle.2023.1133347
                PMC ID: 10327647
                SO-VID: 2fcd2e0f-fd27-4b7f-b1b3-b91796190165
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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                Categories
                Subject: Article

                Keywords: resilience,sleep disturbance,mental health,physical health,wellness
                Data availability:
                Keywords: resilience, sleep disturbance, mental health, physical health, wellness

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