Development of a nomogram model for predicting the risk of insomnia in nurses who underwent the Long- COVID

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Methods General demographic information was obtained, and assessments of sleep quality, burnout, and stress were performed in a single center in May 2023. Three hundred and ninety-eight nurses were recruited. The Lasso regression technique was employed to screen for potential factors contributing to insomnia. A prognostic nomogram was constructed and evaluated by receiver operating characteristic curves and calibration curves. Results Fifty-four percent of nurses complained of insomnia in this study. Eleven variables were independently associated with sleep patterns, including family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout. The R-squared value was 0.4642 and the area under curve was 0.8661. The derived nomogram showed that neurological sequela, stress, job burnout, sleep time before infection, and previous sleep problems also made the most substantial contributions to predicting sleep patterns. The calibration curves for predicting insomnia showed significant agreement between the nomogram models and actual observations. Conclusion The present study established a nomogram prediction model of insomnia for nurses diagnosed with Long-COVID, which is helpful for the early clinical identification of high-risk individuals with insomnia. Long-COVID insomnia nurses nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As of December 2022, it no longer seems necessary to repeatedly outline how and to what extent the COVID-19 pandemic has plagued humanity in the past year, and it is accepted that this virus is responsible for morbidity and mortality levels for which there are few precedents in recent history [ 1 – 3 ] . A growing number of studies have reported a set of neurological complications associated with COVID-19 [ 4 ] and significant psychopathological symptoms related to intense distress, some of which have long-term effects, including neurological symptoms and sleep disturbance [ 5 ] . Marshall [ 6 ] termed it “Long-COVID” which may incorporate the following symptoms: chest tightness, chest pain, breathlessness, cough, sleep disturbance, and dizziness, with symptoms persisting beyond 3 or 12 weeks [ 7 ] . Clinical studies have reported increased sleep-related problems, including insomnia, due to Long-COVID. A meta-analysis [ 8 ] detected a prevalence rate of 42% for insomnia among medical workers, which was higher than the 18–31% prevalence rate identified in the general population for the same period. Another meta-analysis reported a prevalence of 43% for sleep disturbance among nurses during the pandemic [ 9 ] . Insomnia is a serious clinical disorder that frequently goes undiagnosed, and can result in a wide range of negative outcomes. Insufficient sleep or poor sleep quality due to insomnia leads to fatigue, which can create a potentially hazardous environment for patients [ 10 ] and have negative effects on physical health outcomes in medical workers. Lenzer reported a three-fold increase in patient deaths from preventable events when poor sleep duration [ 11 ] . Moreover, numerous observational studies also reported that healthcare workers who have poor sleep quantity and quality could not function to the best of their ability and could make attention-related errors that not only compromised a patient’s care, but also put themselves in danger [ 12 – 14 ] . For example, studies have shown an increased incidence of self-inflicted needle-stick injuries when nurses were tired. Insomnia disorder also places a serious burden on the economy. A study. reported that, in the U.S., direct medical costs and indirect costs (i.e., absenteeism, disability, and lost productivity) associated with insomnia disorder are estimated to be between $ 28.1 billion and $ 216.6 billion, respectively [ 15 ] . Improving the sleep quality of nurses, especially those suffering from Long COVID, has become an important social issue that needs to be addressed. Several studies have reported sociodemographic factors associated with sleep quality among medical staff, including age [ 16 ] , marital status [ 17 ] , and educational level [ 18 ] . Emerging from prolonged emotional and interpersonal stress [ 19 ] , burnout has been recognized as an increasing hazard with a high prevalence rate among medical workers during the COVID-19 pandemic in many countries [ 20 ] . Previous research among this population has acknowledged the association between insomnia and burnout [ 21 ] , and preliminary results with regards to causality suggest that burnout may increase the likelihood of poor sleep quality. During the initial phases of the epidemic's normalization, a prevalent infectious condition emerged, prompting the need to investigate potential sleep disorders among nurses operating within this particular context. Despite the ubiquity of the infection, there is a lack of comprehensive studies examining the determinants of sleep disturbances among the nurses during this period. However, it is conceivable that similar wide-scale infections might arise in the future, underscoring the importance of elucidating these influencing factors and establishing predictive frameworks. In the present study, we screened for potential factors associated with insomnia and aimed to construct a nomogram model to recognize high risk groups. Method Study design This study was performed at Ningbo Medical Center LiHuiLi Hospital Zhejiang province China. The inclusion criteria were as follows: ① Licensed nurses and ② nurses who had received a COVID-19 diagnosis and the resulting pathological condition persisted beyond 3 or 12 weeks [ 7 ] . Nurses were asked to complete an online questionnaire survey. Basic demographic information, and assessments of sleep quality, burnout, and stress overload were collected. Data were extracted simultaneously by two reviewers in duplicate and compiled into a pre-prepared data collection form, with any discrepancies being resolved in consultation with the senior reviewer. Data were collected across the following domains. The study was approved by the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital (2023-C-119). We explained the study to all participants and obtained their informed consents. Measurement Demographic information survey The survey focused on demographic information including age gender, family structure, family relationship, education, years of work, overtime, marriage, night shift frequency, technical qualification, relaxion time, department, recovery time, sequela, sleep duration, previous sleep problems, execise, department rotation, attitude towards COVID-19. Sleep quality assessment The Insomnia Severity Index (ISI) includes seven items and was used to assess the nature, severity, and impact of insomnia [ 22 ] . The reliability of the Chinese version of the scale is 0.65–0.92 [ 23 ] . Participants reflected on their experiences over the past month by considering aspects such as sleep onset issues, sleep maintenance problems, waking up too early, dissatisfaction with sleep, how sleeping difficulties impact daytime functioning, whether others notice the sleep problems, and the distress caused by these issues. Each item was rated according to a five-point Likert scale, with scores ranging from 0 (no problem) to 4 (very severe). All participants were divided into four subgroups according to the total score, ranging from 0 to 28: Sleep 0 indicated no insomnia (0–7), Sleep 1 indicated sub-threshold insomnia (8–14), Sleep 3 indicated moderate insomnia (15–21), and Sleep 4 indicated severe insomnia (22–28). Participants with scores greater than 7 were considered to be experiencing insomnia. Burnout assessment The Chinese Maslach Burnout Inventory (CMBI), which assesses the degree of employed burnout of workers, was revised by Li et al. [ 24 ] based on the MBI [ 25 ] questionnaire developed by Maslach et al., and is suitable for the Chinese cultural context. It includes three dimensions: emotional exhaustion, personality disintegration, and decreased sense of achievement, with a total of 15 items scored according to a seven-point Likert scale. Based on the diagnostic criteria for occupational burnout and the scores of this scale, employed job burnout of workers was divided into four levels according to the critical values obtained from the study (emotional exhaustion score ≥ 25 points, personality disintegration score ≥ 11 points, and achievement reduction score ≥ 16 points): none, mild, moderate, and severe. The test's internal consistency was considered high, with respective coefficients of 0.95, 0.93, and 0.96 for the three dimensions [ 26 ] . We obtained consent from the CMBI authorsee. Stress overload assessment The stress levels of medical staff were assessed by the Stress Overload Scale (SOS) which was developed by Amirkhan [ 27 ] . The scale consisted of 22 items, organized into two subscales: Event Load and Personal Vulnerability. Participants were requested to rate each item according to a five-point scale ranging from 1 (never) to 5 (always). The total score was the sum of all responses and ranges between 22 and 110. Higher scores indicated greater stress overload. The Chinese version of the SOS was validated to be a reliable and valid instrument with a Cronbach’s coefficient of 0.936, item content validity index (CVI) of 0.86, and CVI for each dimension ranging from 0.80 to 0.86 [ 28 ] . Statistical analyses Statistical analyses were mostly conducted using R Statistical Software version 4.2.1 ( http://www.R-project.org ). The chi-square test, correlation analysis, and the crosstab function of statistical software version 20.0 (SPSS, Inc., Somers, NY, USA) were used to describe the association between variables. The Lasso regression technique was adopted to select the most informative features (i.e., family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout ) from the dataset using the glmnet Package (version 4.1-6). Meanwhile, one-hot encoding was employed to process the data, facilitating an investigation of potential associations between different classes of variables and each class of sleep after Lasso regression. The nomogram was established using the “rms” package (version 6.4-1). The receiver operating characteristic curve (ROC) and area under curve were calculated to predict the performance of the established nomogram model, and a calibration curve (1000 times bootstrap resampling) to test the calibration power. A two-tailed p-value of ≤ 0.05 was considered statistically significant. Results Participant Characteristics The characteristics of the participants are summarized in Table 1 . A total of 398 nurses were enrolled in this study, of whom 390 (98%) were female. Two hundred and fifteen nurses complained of insomnia, with a prevalence of 54%. Nurses who participated in the survey had different levels of stress (score 54.536 ± 16.275) and job burnout (score 57.424 ± 15.338). Lasso regression The Lasso regression analysis involved the selection of 22 categorical variables. After some coefficients were set to zero (dummy variables in Table 2 ), 11 variables, including amily, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnoutt were independently associated with sleep. The results of the Lasso regression after one-hot encoding were as follows: sequela (no nervous system) had the highest weight (-0.27), followed by sleep duration 3 (w=-0.221), previous sleep problems 0 (w=-0.182), stress 2 (w = 0.128), job burnout 1 (w=-0.092), and stress 0 (w=-0.076). The R 2 value turned out to be 0.464. The AUC of the Lasso regression was 0.866. Factors associated with insomnia among nurses The correlation factors associated with insomnia among nurses are shown in Fig. 1 . According to the correlation analysis, the strongest positive correlations were observed between sleep and the sequela nervous system (r = 0.52). Other negative factors for sleep included stress (r = 0.39), job burnout (r = 0.34), previous sleep problems (r = 0.37), years of work (r = 0.10), relaxion time (r = 0.18), respiratory sequela (r = 0.14), circulatory sequela (r = 0.16), and other sequela (r = 0.09). Moreover, a negative correlation was detected between sleep and the independent variables attitudes towards COVID-19 (r=-0.17) and sleep duration (r=-0.43). Construction and verification of nomogram To further analyze the prognostic values of risk factors, we established the nomogram model which incorporated all significant factors in the Lasso regression (Fig. 2 ). All the prediction parameters have corresponding accurate values in the nomogram model. Add these values and put them in the total score scale to calculate the risk of insomnia. The ROC curves of the nomogram model showed acceptable values in predicting different degrees of insomnia: Sleep 0 (AUC = 0.892), Sleep 1 (AUC = 0.772), Sleep 2 (AUC = 0.865) and Sleep 3 (AUC = 0.974) (Fig. 3 ). In addition, calibration curves showed acceptable anticipated and actually observed probabilities of insomnia. (Fig. 4 ) Discussion In the present research, the first study was carried out to investigate the prevalence of sleep disorders among nurses with Long-COVID. Nurses are at a high risk of insomnia, especially when they have been diagnosed with Long-COVID. Fifty-four percent of nurses complained of insomnia, including 1.80% (7/398) with severe sleep disorder and 11.5% (46/398) with moderate sleep disorder. Interestingly, 98% of the participants were female. A meta-analysis [ 29 ] which included 401 studies, representing 458,754 participants across 58 countries, reported that women working in high risk units and those providing direct care had significantly higher odds for insomnia. This finding was consistent with the results of the current study. Our study also found that, among nurses who suffered from Long-COVID, only 26.5% had no Long-COVID symptoms. However, 42.1% of the nurses reported nervous system symptoms, which showed the strongest negative correlation with sleep outcomes and insomnia in this population. Pulmonary dysfunction leading to poor oxygenation of the brain may explain encephalopathy and sleep disorders in COVID-19 patients [ 30 ] . A retrospective study found that COVID-19 infection caused neurological injury and neurogenic diseases, such as fatigue (58%), headache (44%) [ 31 ] , and attention disorder (27%) [ 32 ] . Though there are several hypotheses reported in the literature, but a unifying pathophysiological mechanism of many of these disorders remains unclear. Cough and dyspnea were the most commonly reported pulmonary sequelae [ 33 – 35 ] , and these symptoms were also observed in the current study. Cough and dyspnea could interrupt the continuous sleep state, which had a greater impact on sleep quality. Our results highlighted that respiratory complications were an independent risk factor affecting sleep and were positively correlated with sleep status, which was consistent with the Aytac et al [ 36 ] study. The Lasso regression analysis found that no stress (w=-0.076) was a protective factor for nurses. On the contrary, stress was a risk factor for insomnia. It is widely accepted that higher levels of stress in medical workers directly and significantly reduce their self-efficacy and sleep quality [ 37 ] . Previous studies have found enormous psychological burdens and psychological barriers among medical staff working in high-stress and high-risk epidemic environments [ 38 ] . Stress has the potential to augment the activity of excitatory neural pathways, including the sympathetic nervous system, leading to a persistent state of heightened physiological arousal [ 39 ] . This elevated state of arousal may consequently have a detrimental impact on sleep quality, incite inflammatory responses, and potentially disrupt the normal functioning of the nervous system. Blume et al. [ 40 ] found that a significant increase in stress and burden shortened sleep time and reduces sleep quality. Our results revealed that nurses who experienced Long-COVID had levels of stress (26.4%) and occupational burnout (98.2%) that were higher compared to the study reported by Xiao et al [ 41 ] , which examined stress (41.3%) and burnout (43.6%) among medical staff. As the main force in the fight against the epidemic, medical staff face significant burdens and may be more prone to physical and mental problems than the general public. Moreover, the majority of the participants were female, and studies have highlighted an association between stress and burnout in women [ 29 ] , who are also at a significantly higher risk than men. Family structure was another factor in the nomogram, and the extended family structure can be regarded as a positive factor for sleep outcomes, perhaps due to the additional support that the extended family can provide. It was found that increased social support corresponded to a concomitant reduction in anxiety and stress levels among medical personnel. This paradigm may further clarify observations that medical practitioners within extended familial contexts experience a lower incidence of sleep-related issues [ 41 ] Although previous studies have reported a variety of independent risk factors for insomnia, there is no efficient system that could be helpful in predicting diagnoses of insomnia in nurses who had suffered from the Long-COVID. We constructed a novel, easy-to-use prediction model that incorporated 11 key parameters (i.e., family, years of work, work breaks, respiratory system sequela, nervous system sequela, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout) and established a prediction nomogram and scores. The current study had notable limitations that should be acknowledged. First, the lack of involvement of multiple medical centers could limit the wider applicability of the findings. Having a more diverse sample from various centers would enhance the credibility and relevance of the outcomes. In addition, the research sample was not sex balanced, which could impact the accuracy of the model. Indeed, 98% of the participants in this study were female, which is consistent with the proportion of female in nurses in the China [ 42 ] . To enhance the accuracy of the findings, future research should aim to ensure greater gender balance in the samples studied and focus on incorporating a broader range of healthcare facilities. Second, despite the broad spectrum of parameters analyzed and the acceptable performance of the score, we recognize that additional potentially relevant variables such as pharmacological treatments merit future consideration. Conclusion This study combined 11 independent variables and established a nomogram model that predicted insomnia in nurses with Long-COVID, so as to provide a potential clinical tool for risk assessment. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital (2023-C-119). Written informed consent was obtained from all the participants prior to the enrollment of this study. The patient's statement of consent is not applicable to this study. Consent for publication All authors approved the final manuscript and the submission to this journal.This article does not applicable to statements of patient consent to publication. Availability of data and materials The datasets generated during and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request. Competing interests The authors have no conflicts of interest to declare. Funding This work was funded by the General Projects of Zhejiang Provincial Department of Education (2020PJ152). Authors' contributions Tingting Cai, Lili Wang and Yufei Chen was involved in the analysis, interpretation of the data and drafting of the manuscript and gave final approval of the manuscript. Lingxiao Ye, Feng Zhang and Jiaran Shi made substantial contributions to the data collection conception and data analysis, and revision of the manuscript. 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Supplementary Files Table1.xlsx Table2.xlsx Cite Share Download PDF Status: Published Journal Publication published 03 Aug, 2024 Read the published version in BMC Nursing → Version 1 posted Editorial decision: Revision requested 23 Apr, 2024 Reviews received at journal 22 Apr, 2024 Reviews received at journal 22 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers agreed at journal 03 Apr, 2024 Reviewers invited by journal 10 Mar, 2024 Editor invited by journal 10 Mar, 2024 Editor assigned by journal 07 Mar, 2024 Submission checks completed at journal 07 Mar, 2024 First submitted to journal 26 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3899333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277854590,"identity":"bd41bb6f-1bca-41f9-9c45-cd4c4affca88","order_by":0,"name":"Lingxiao Ye","email":"","orcid":"","institution":"Nursing Department, Li Huili Hospital, Ningbo Meical Center","correspondingAuthor":false,"prefix":"","firstName":"Lingxiao","middleName":"","lastName":"Ye","suffix":""},{"id":277854591,"identity":"bac6060f-089b-4661-bdf3-3cd201c18dd8","order_by":1,"name":"Feng Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhang","suffix":""},{"id":277854592,"identity":"081eabf7-999a-45d4-89ca-1d46acd20956","order_by":2,"name":"Lili Wang","email":"","orcid":"","institution":"Nursing Department, Li Huili Hospital, Ningbo Meical Center","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Wang","suffix":""},{"id":277854593,"identity":"57ac298a-9d22-4d6c-8836-fa3b1f135cbc","order_by":3,"name":"Yufei Chen","email":"","orcid":"","institution":"Nursing Department, Li Huili Hospital, Ningbo Meical Center","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Chen","suffix":""},{"id":277854594,"identity":"0f0f6d2e-16d3-4c13-b515-c0f81acc6b10","order_by":4,"name":"Jiaran Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCcYGgwQQg5mx8cEHAxs5ErSwMx82nFGQZkyEFhiDny1NmufD4USCOuRnNzcUPKi5J2/OzGMgbWPAnMDAfvjoBnxaDO4cBDrsWLHhzmYeA+McA7Y8Bp60tBt4tUgkArWwJTBuOMxjkJxjwFPMIMFjhleL/AyQln8J9iAthy1AJhDSwnADqCWxLSFxw2G2xGYGAwPCWgzAWvoSkjccZj7M2GOQYMxGyC/yM9KfGf74lmC74fzB9h8//vyX42c/fAy/wxgY2AxQuQSUgwDzAyIUjYJRMApGwUgGADt5S1BwRZ0wAAAAAElFTkSuQmCC","orcid":"","institution":"Lihuili Hospital Facilitated to Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Jiaran","middleName":"","lastName":"Shi","suffix":""},{"id":277854595,"identity":"4c7b691d-b20e-4a24-a299-c05774472912","order_by":5,"name":"Tingting Cai","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2024-01-26 07:44:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3899333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3899333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-024-02212-4","type":"published","date":"2024-08-03T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52452846,"identity":"53016e86-be8e-4e4a-b5c7-3d2b6d1b3d09","added_by":"auto","created_at":"2024-03-11 19:17:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151655,"visible":true,"origin":"","legend":"\u003cp\u003eIndividualized predictive nomogram model to predict the risk of insomnia in nurses. Eleven variables were selected based on the results of the Lasso analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/3af7f928507f5a8f97deae78.png"},{"id":52452850,"identity":"13388590-759e-4c32-ae12-290acdb549d6","added_by":"auto","created_at":"2024-03-11 19:17:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":256021,"visible":true,"origin":"","legend":"\u003cp\u003eThe R package “rms” was utilized to build the prediction model of the post-coronavirus insomnia nomogram. The factors with significantly statistical differences were showed by asterisks. Points are assigned for each risk factor by drawing a line upward from the corresponding values to the ‘point’ line. The total points are the sum of the points obtained by the four risk factors, and are plotted on the ‘total points’ line. The first row is taken as observation data; that is all risk factor points calculated to 552 risk scores corresponding to the risk of 1.114.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/3bc9b54a4919a4c664210d60.png"},{"id":52452851,"identity":"3d7c4ccd-a325-4da9-9f86-e49b3dd28e2c","added_by":"auto","created_at":"2024-03-11 19:17:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the nomogram model to predict the quality of sleep among nurses with insomnia.\u003c/strong\u003e The ROC curve of the models, X-axis: specificity, Y-axis: sensitivity. The AUC of the models: Sleep 0 (0.892), Sleep 1(0.772), Sleep 2 (0.865), Sleep 3 (0.974). This figure was drawn using R software version 4.2.1 (http://www.R-project.org).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/6bb889770a4fdc2812019f0f.png"},{"id":52452841,"identity":"395a76b3-a311-436e-889b-aafa6b985173","added_by":"auto","created_at":"2024-03-11 19:17:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the nomogram model to predict insomnia in nurses.\u003c/strong\u003e The x-axis represents the predicted probability (sleep≥2), and they-axis represents the actual probability. The 45-degree thick dotted line represents perfect prediction. The thin dotted line represents the entire cohort (n=398), and the solid line is bias-corrected by bootstrapping (B=1000 repetitions), displaying the observed performance of the nomogram.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/6d95a21acb9ba7fc311a3d55.png"},{"id":61793676,"identity":"42c6cb0a-3e7c-4788-8e85-537455716d43","added_by":"auto","created_at":"2024-08-05 16:14:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/7a627b26-c543-49e7-8de6-0b2fd4d0b1fb.pdf"},{"id":52452909,"identity":"51932a25-cd18-4492-aad9-83f2c78fe7ac","added_by":"auto","created_at":"2024-03-11 19:18:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12709,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/125e9904be2d119019acd74d.xlsx"},{"id":52452845,"identity":"368e5792-9f83-4aa1-9244-f348408700c7","added_by":"auto","created_at":"2024-03-11 19:17:57","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10169,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3899333/v1/25b013b40b16d19131bcde1b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a nomogram model for predicting the risk of insomnia in nurses who underwent the Long- COVID","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs of December 2022, it no longer seems necessary to repeatedly outline how and to what extent the COVID-19 pandemic has plagued humanity in the past year, and it is accepted that this virus is responsible for morbidity and mortality levels for which there are few precedents in recent history\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. A growing number of studies have reported a set of neurological complications associated with COVID-19\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and significant psychopathological symptoms related to intense distress, some of which have long-term effects, including neurological symptoms and sleep disturbance\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Marshall\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e termed it \u0026ldquo;Long-COVID\u0026rdquo; which may incorporate the following symptoms: chest tightness, chest pain, breathlessness, cough, sleep disturbance, and dizziness, with symptoms persisting beyond 3 or 12 weeks\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Clinical studies have reported increased sleep-related problems, including insomnia, due to Long-COVID. A meta-analysis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e detected a prevalence rate of 42% for insomnia among medical workers, which was higher than the 18\u0026ndash;31% prevalence rate identified in the general population for the same period. Another meta-analysis reported a prevalence of 43% for sleep disturbance among nurses during the pandemic\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Insomnia is a serious clinical disorder that frequently goes undiagnosed, and can result in a wide range of negative outcomes.\u003c/p\u003e \u003cp\u003eInsufficient sleep or poor sleep quality due to insomnia leads to fatigue, which can create a potentially hazardous environment for patients\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and have negative effects on physical health outcomes in medical workers. Lenzer reported a three-fold increase in patient deaths from preventable events when poor sleep duration\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Moreover, numerous observational studies also reported that healthcare workers who have poor sleep quantity and quality could not function to the best of their ability and could make attention-related errors that not only compromised a patient\u0026rsquo;s care, but also put themselves in danger\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. For example, studies have shown an increased incidence of self-inflicted needle-stick injuries when nurses were tired. Insomnia disorder also places a serious burden on the economy. A study. reported that, in the U.S., direct medical costs and indirect costs (i.e., absenteeism, disability, and lost productivity) associated with insomnia disorder are estimated to be between \u003cspan\u003e$\u003c/span\u003e28.1\u0026nbsp;billion and \u003cspan\u003e$\u003c/span\u003e216.6\u0026nbsp;billion, respectively\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Improving the sleep quality of nurses, especially those suffering from Long COVID, has become an important social issue that needs to be addressed.\u003c/p\u003e \u003cp\u003eSeveral studies have reported sociodemographic factors associated with sleep quality among medical staff, including age\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, marital status\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and educational level\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Emerging from prolonged emotional and interpersonal stress\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, burnout has been recognized as an increasing hazard with a high prevalence rate among medical workers during the COVID-19 pandemic in many countries\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Previous research among this population has acknowledged the association between insomnia and burnout\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, and preliminary results with regards to causality suggest that burnout may increase the likelihood of poor sleep quality.\u003c/p\u003e \u003cp\u003eDuring the initial phases of the epidemic's normalization, a prevalent infectious condition emerged, prompting the need to investigate potential sleep disorders among nurses operating within this particular context. Despite the ubiquity of the infection, there is a lack of comprehensive studies examining the determinants of sleep disturbances among the nurses during this period. However, it is conceivable that similar wide-scale infections might arise in the future, underscoring the importance of elucidating these influencing factors and establishing predictive frameworks.\u003c/p\u003e \u003cp\u003eIn the present study, we screened for potential factors associated with insomnia and aimed to construct a nomogram model to recognize high risk groups.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was performed at Ningbo Medical Center LiHuiLi Hospital Zhejiang province China. The inclusion criteria were as follows: ① Licensed nurses and ② nurses who had received a COVID-19 diagnosis and the resulting pathological condition persisted beyond 3 or 12 weeks\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Nurses were asked to complete an online questionnaire survey. Basic demographic information, and assessments of sleep quality, burnout, and stress overload were collected.\u003c/p\u003e \u003cp\u003eData were extracted simultaneously by two reviewers in duplicate and compiled into a pre-prepared data collection form, with any discrepancies being resolved in consultation with the senior reviewer. Data were collected across the following domains. The study was approved by the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital (2023-C-119). We explained the study to all participants and obtained their informed consents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eDemographic information survey\u003c/h2\u003e \u003cp\u003eThe survey focused on demographic information including age gender, family structure, family relationship, education, years of work, overtime, marriage, night shift frequency, technical qualification, relaxion time, department, recovery time, sequela, sleep duration, previous sleep problems, execise, department rotation, attitude towards COVID-19.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSleep quality assessment\u003c/h2\u003e \u003cp\u003eThe Insomnia Severity Index (ISI) includes seven items and was used to assess the nature, severity, and impact of insomnia\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The reliability of the Chinese version of the scale is 0.65\u0026ndash;0.92\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Participants reflected on their experiences over the past month by considering aspects such as sleep onset issues, sleep maintenance problems, waking up too early, dissatisfaction with sleep, how sleeping difficulties impact daytime functioning, whether others notice the sleep problems, and the distress caused by these issues. Each item was rated according to a five-point Likert scale, with scores ranging from 0 (no problem) to 4 (very severe). All participants were divided into four subgroups according to the total score, ranging from 0 to 28: Sleep 0 indicated no insomnia (0\u0026ndash;7), Sleep 1 indicated sub-threshold insomnia (8\u0026ndash;14), Sleep 3 indicated moderate insomnia (15\u0026ndash;21), and Sleep 4 indicated severe insomnia (22\u0026ndash;28). Participants with scores greater than 7 were considered to be experiencing insomnia.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBurnout assessment\u003c/h2\u003e \u003cp\u003eThe Chinese Maslach Burnout Inventory (CMBI), which assesses the degree of employed burnout of workers, was revised by Li et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e based on the MBI\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e questionnaire developed by Maslach et al., and is suitable for the Chinese cultural context. It includes three dimensions: emotional exhaustion, personality disintegration, and decreased sense of achievement, with a total of 15 items scored according to a seven-point Likert scale. Based on the diagnostic criteria for occupational burnout and the scores of this scale, employed job burnout of workers was divided into four levels according to the critical values obtained from the study (emotional exhaustion score\u0026thinsp;\u0026ge;\u0026thinsp;25 points, personality disintegration score\u0026thinsp;\u0026ge;\u0026thinsp;11 points, and achievement reduction score\u0026thinsp;\u0026ge;\u0026thinsp;16 points): none, mild, moderate, and severe. The test's internal consistency was considered high, with respective coefficients of 0.95, 0.93, and 0.96 for the three dimensions\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. We obtained consent from the CMBI authorsee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStress overload assessment\u003c/h2\u003e \u003cp\u003eThe stress levels of medical staff were assessed by the Stress Overload Scale (SOS) which was developed by Amirkhan\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The scale consisted of 22 items, organized into two subscales: Event Load and Personal Vulnerability. Participants were requested to rate each item according to a five-point scale ranging from 1 (never) to 5 (always). The total score was the sum of all responses and ranges between 22 and 110. Higher scores indicated greater stress overload. The Chinese version of the SOS was validated to be a reliable and valid instrument with a Cronbach\u0026rsquo;s coefficient of 0.936, item content validity index (CVI) of 0.86, and CVI for each dimension ranging from 0.80 to 0.86\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were mostly conducted using R Statistical Software version 4.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The chi-square test, correlation analysis, and the crosstab function of statistical software version 20.0 (SPSS, Inc., Somers, NY, USA) were used to describe the association between variables. The Lasso regression technique was adopted to select the most informative features (i.e., family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout ) from the dataset using the glmnet Package (version 4.1-6). Meanwhile, one-hot encoding was employed to process the data, facilitating an investigation of potential associations between different classes of variables and each class of sleep after Lasso regression. The nomogram was established using the \u0026ldquo;rms\u0026rdquo; package (version 6.4-1). The receiver operating characteristic curve (ROC) and area under curve were calculated to predict the performance of the established nomogram model, and a calibration curve (1000 times bootstrap resampling) to test the calibration power. A two-tailed p-value of \u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eThe characteristics of the participants are summarized in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. A total of 398 nurses were enrolled in this study, of whom 390 (98%) were female. Two hundred and fifteen nurses complained of insomnia, with a prevalence of 54%. Nurses who participated in the survey had different levels of stress (score 54.536\u0026thinsp;\u0026plusmn;\u0026thinsp;16.275) and job burnout (score 57.424\u0026thinsp;\u0026plusmn;\u0026thinsp;15.338).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLasso regression\u003c/h2\u003e \u003cp\u003eThe Lasso regression analysis involved the selection of 22 categorical variables. After some coefficients were set to zero (dummy variables in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e), 11 variables, including amily, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnoutt were independently associated with sleep. The results of the Lasso regression after one-hot encoding were as follows: sequela (no nervous system) had the highest weight (-0.27), followed by sleep duration 3 (w=-0.221), previous sleep problems 0 (w=-0.182), stress 2 (w\u0026thinsp;=\u0026thinsp;0.128), job burnout 1 (w=-0.092), and stress 0 (w=-0.076). The R\u003csup\u003e2\u003c/sup\u003e value turned out to be 0.464. The AUC of the Lasso regression was 0.866.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with insomnia among nurses\u003c/h2\u003e \u003cp\u003eThe correlation factors associated with insomnia among nurses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the correlation analysis, the strongest positive correlations were observed between sleep and the sequela nervous system (r\u0026thinsp;=\u0026thinsp;0.52). Other negative factors for sleep included stress (r\u0026thinsp;=\u0026thinsp;0.39), job burnout (r\u0026thinsp;=\u0026thinsp;0.34), previous sleep problems (r\u0026thinsp;=\u0026thinsp;0.37), years of work (r\u0026thinsp;=\u0026thinsp;0.10), relaxion time (r\u0026thinsp;=\u0026thinsp;0.18), respiratory sequela (r\u0026thinsp;=\u0026thinsp;0.14), circulatory sequela (r\u0026thinsp;=\u0026thinsp;0.16), and other sequela (r\u0026thinsp;=\u0026thinsp;0.09). Moreover, a negative correlation was detected between sleep and the independent variables attitudes towards COVID-19 (r=-0.17) and sleep duration (r=-0.43).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and verification of nomogram\u003c/h2\u003e \u003cp\u003eTo further analyze the prognostic values of risk factors, we established the nomogram model which incorporated all significant factors in the Lasso regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All the prediction parameters have corresponding accurate values in the nomogram model. Add these values and put them in the total score scale to calculate the risk of insomnia. The ROC curves of the nomogram model showed acceptable values in predicting different degrees of insomnia: Sleep 0 (AUC\u0026thinsp;=\u0026thinsp;0.892), Sleep 1 (AUC\u0026thinsp;=\u0026thinsp;0.772), Sleep 2 (AUC\u0026thinsp;=\u0026thinsp;0.865) and Sleep 3 (AUC\u0026thinsp;=\u0026thinsp;0.974) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, calibration curves showed acceptable anticipated and actually observed probabilities of insomnia. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present research, the first study was carried out to investigate the prevalence of sleep disorders among nurses with Long-COVID. Nurses are at a high risk of insomnia, especially when they have been diagnosed with Long-COVID. Fifty-four percent of nurses complained of insomnia, including 1.80% (7/398) with severe sleep disorder and 11.5% (46/398) with moderate sleep disorder. Interestingly, 98% of the participants were female. A meta-analysis\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e which included 401 studies, representing 458,754 participants across 58 countries, reported that women working in high risk units and those providing direct care had significantly higher odds for insomnia. This finding was consistent with the results of the current study.\u003c/p\u003e \u003cp\u003eOur study also found that, among nurses who suffered from Long-COVID, only 26.5% had no Long-COVID symptoms. However, 42.1% of the nurses reported nervous system symptoms, which showed the strongest negative correlation with sleep outcomes and insomnia in this population. Pulmonary dysfunction leading to poor oxygenation of the brain may explain encephalopathy and sleep disorders in COVID-19 patients\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. A retrospective study found that COVID-19 infection caused neurological injury and neurogenic diseases, such as fatigue (58%), headache (44%)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, and attention disorder (27%)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Though there are several hypotheses reported in the literature, but a unifying pathophysiological mechanism of many of these disorders remains unclear.\u003c/p\u003e \u003cp\u003eCough and dyspnea were the most commonly reported pulmonary sequelae\u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, and these symptoms were also observed in the current study. Cough and dyspnea could interrupt the continuous sleep state, which had a greater impact on sleep quality. Our results highlighted that respiratory complications were an independent risk factor affecting sleep and were positively correlated with sleep status, which was consistent with the Aytac et al\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e study.\u003c/p\u003e \u003cp\u003eThe Lasso regression analysis found that no stress (w=-0.076) was a protective factor for nurses. On the contrary, stress was a risk factor for insomnia. It is widely accepted that higher levels of stress in medical workers directly and significantly reduce their self-efficacy and sleep quality\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Previous studies have found enormous psychological burdens and psychological barriers among medical staff working in high-stress and high-risk epidemic environments\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Stress has the potential to augment the activity of excitatory neural pathways, including the sympathetic nervous system, leading to a persistent state of heightened physiological arousal\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. This elevated state of arousal may consequently have a detrimental impact on sleep quality, incite inflammatory responses, and potentially disrupt the normal functioning of the nervous system. Blume et al.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003efound that a significant increase in stress and burden shortened sleep time and reduces sleep quality.\u003c/p\u003e \u003cp\u003eOur results revealed that nurses who experienced Long-COVID had levels of stress (26.4%) and occupational burnout (98.2%) that were higher compared to the study reported by Xiao et al\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, which examined stress (41.3%) and burnout (43.6%) among medical staff. As the main force in the fight against the epidemic, medical staff face significant burdens and may be more prone to physical and mental problems than the general public. Moreover, the majority of the participants were female, and studies have highlighted an association between stress and burnout in women\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, who are also at a significantly higher risk than men.\u003c/p\u003e \u003cp\u003eFamily structure was another factor in the nomogram, and the extended family structure can be regarded as a positive factor for sleep outcomes, perhaps due to the additional support that the extended family can provide. It was found that increased social support corresponded to a concomitant reduction in anxiety and stress levels among medical personnel. This paradigm may further clarify observations that medical practitioners within extended familial contexts experience a lower incidence of sleep-related issues\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough previous studies have reported a variety of independent risk factors for insomnia, there is no efficient system that could be helpful in predicting diagnoses of insomnia in nurses who had suffered from the Long-COVID. We constructed a novel, easy-to-use prediction model that incorporated 11 key parameters (i.e., family, years of work, work breaks, respiratory system sequela, nervous system sequela, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout) and established a prediction nomogram and scores.\u003c/p\u003e \u003cp\u003eThe current study had notable limitations that should be acknowledged. First, the lack of involvement of multiple medical centers could limit the wider applicability of the findings. Having a more diverse sample from various centers would enhance the credibility and relevance of the outcomes. In addition, the research sample was not sex balanced, which could impact the accuracy of the model. Indeed, 98% of the participants in this study were female, which is consistent with the proportion of female in nurses in the China\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. To enhance the accuracy of the findings, future research should aim to ensure greater gender balance in the samples studied and focus on incorporating a broader range of healthcare facilities. Second, despite the broad spectrum of parameters analyzed and the acceptable performance of the score, we recognize that additional potentially relevant variables such as pharmacological treatments merit future consideration.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study combined 11 independent variables and established a nomogram model that predicted insomnia in nurses with Long-COVID, so as to provide a potential clinical tool for risk assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Ningbo Medical Center LiHuiLi Hospital (2023-C-119).\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all the participants prior to the enrollment of this study. The patient\u0026apos;s statement of consent is not applicable to this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.This article does not applicable to statements of patient consent to publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the General Projects of Zhejiang Provincial Department of Education (2020PJ152).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTingting\u0026nbsp;Cai,\u0026nbsp;Lili\u0026nbsp;Wang\u0026nbsp;and Yufei Chen\u0026nbsp;was\u0026nbsp;involved\u0026nbsp;in\u0026nbsp;the analysis,\u0026nbsp;interpretation\u0026nbsp;of\u0026nbsp;the\u0026nbsp;data\u0026nbsp;and\u0026nbsp;drafting\u0026nbsp;of\u0026nbsp;the\u0026nbsp;manuscript\u0026nbsp;and\u0026nbsp;gave\u0026nbsp;final approval\u0026nbsp;of\u0026nbsp;the\u0026nbsp;manuscript.\u0026nbsp;Lingxiao\u0026nbsp;Ye,\u0026nbsp;Feng\u0026nbsp;Zhang\u0026nbsp;and Jiaran Shi\u0026nbsp;made\u0026nbsp;substantial contributions\u0026nbsp;to\u0026nbsp;the\u0026nbsp;data\u0026nbsp;collection\u0026nbsp;conception\u0026nbsp;and\u0026nbsp;data\u0026nbsp;analysis,\u0026nbsp;and\u0026nbsp;revision\u0026nbsp;of\u0026nbsp;the manuscript.\u0026nbsp;Every\u0026nbsp;author\u0026nbsp;fully\u0026nbsp;participated\u0026nbsp;in\u0026nbsp;this\u0026nbsp;work\u0026nbsp;and\u0026nbsp;assumes\u0026nbsp;public responsibility\u0026nbsp;for\u0026nbsp;the\u0026nbsp;relevant\u0026nbsp;part\u0026nbsp;of\u0026nbsp;the\u0026nbsp;content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the General Research Project of Zhejiang Provincial Department of Education (Y202043652).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaud, D.; Qi, X.; Nielsen-Saines, K.; et al. Real estimates of mortality following COVID-19 infection. Lancet Infect. Dis. 2020, 20, 773.\u003c/li\u003e\n\u003cli\u003eWang, Y, Kala, M.P, Jafar, T.H. Factors associated with psychological distress during the coronavirus disease 2019 (COVID-19) pandemic on the predominantly general population: A systematic review and meta-analysis. PLoS ONE 2020, 15, e0244630.\u003c/li\u003e\n\u003cli\u003eRuan, Q, Yang, K, Wang, W, et al. Correction to: Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020, 46, 1294\u0026ndash;1297.\u003c/li\u003e\n\u003cli\u003eAhmad, I. Rathore, F.A. Neurological manifestations and complications of COVID-19: A literature review. J. Clin. Neurosci. 2020, 77, 8\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eMazza, C.; Ricci, E.; Biondi, S.; et al. 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Prevalence of stress, depression, anxiety and sleep disturbance among nurses during the COVID-19 pandemic: A systematic review and meta-analysis [J]. J Psychosom Res, 2021, 141: 110343. \u003c/li\u003e\n\u003cli\u003eGhalichi L, Pournik O, Ghaffari M, et al. Sleep quality among health care workers. Arch Iran Med 2013;16:100\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eLenzer J. Doctors underwent \u0026ldquo;extreme sleep deprivation\u0026rdquo; in studies of effect on patient deaths. BMJ 2015;351, h6295.\u003c/li\u003e\n\u003cli\u003eParker RS, Parker P. The impact of sleep deprivation in military surgical teams: a systematic review. J R Army Med Corps 2017;163:158\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eKhajuria A, Khajuria A. Effect of pharmacological enhancement on cognitive and clinical psychomotor performance of sleep-deprived doctors. Int J Surg 2013;11:1143\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eSanches I, Teixeira F, dos Santos JM, et al. Effects of acute sleep deprivation resulting from night shift work on young doctors. Acta Med Port 2015;28:457\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eWickwire EM, Shaya FT, Scharf SM. Health economics of insomnia treatments: The return on investment for a good night\u0026apos;s sleep. Sleep Med Rev. 2016; 30:72-82.\u003c/li\u003e\n\u003cli\u003eGARG M, MARALAKUNTE M, GARG S, et al. The Conundrum of \u0026apos;Long-COVID-19\u0026apos;: A Narrative Review [J]. Int J Gen Med, 2021, 14: 2491-506.\u003c/li\u003e\n\u003cli\u003eCARF\u0026Igrave; A, BERNABEI R, LANDI F. Persistent Symptoms in Patients After Acute COVID-19 [J]. Jama, 2020, 324(6): 603-5.\u003c/li\u003e\n\u003cli\u003eXIONG Q, XU M, LI J, et al. Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-centre longitudinal study [J]. Clin Microbiol Infect, 2021, 27(1): 89-95.\u003c/li\u003e\n\u003cli\u003eMASLACH C, LEITER M P. Understanding the burnout experience: recent research and its implications for psychiatry [J]. World Psychiatry, 2016, 15(2): 103-11.\u003c/li\u003e\n\u003cli\u003eJONES A M, CLARK J S, MOHAMMAD R A. Burnout and secondary traumatic stress in health-system pharmacists during the COVID-19 pandemic [J]. Am J Health Syst Pharm, 2021, 78(9): 818-24.\u003c/li\u003e\n\u003cli\u003eLATIF I, HUGHES A T, BENDALL R C. Positive and negative affect mediate the influences of a maladaptive emotion regulation strategy on sleep quality [J]. Frontiers in Psychiatry, 2019, 10: 628.\u003c/li\u003e\n\u003cli\u003eFERNANDEZ-MENDOZA J, RODRIGUEZ-MU\u0026ntilde;OZ A, VELA-BUENO A, et al. The Spanish version of the Insomnia Severity Index: a confirmatory factor analysis [J]. Sleep Med, 2012, 13(2): 207-10.\u003c/li\u003e\n\u003cli\u003eCerri LQ, Justo MC, Clemente V, Gomes AA, Pereira AS, Marques DR. Insomnia Severity Index: A reliability generalisation meta-analysis. J Sleep Res. 2023 Aug;32(4):e13835. \u003c/li\u003e\n\u003cli\u003eLi Yongxin, Wu Mingzheng. Structural research on job burnout [J]. Psychological Science, 2005, (02): 454-457.\u003c/li\u003e\n\u003cli\u003eSCHAUFELI W B. Maslach burnout inventory-general survey (MBI-GS) [J]. Maslach burnout inventory manual, 1996.\u003c/li\u003e\n\u003cli\u003eHe WB. The impact of nurses\u0026apos; perception of high-performance work systems on their sense of occupational burnout: a mediating role based on emotional regulation self-efficacy [D] Gansu: Lanzhou University, 2020.\u003c/li\u003e\n\u003cli\u003eAMIRKHAN J H. Stress overload: a new approach to the assessment of stress [J]. Am J Community Psychol, 2012, 49(1-2): 55-71.\u003c/li\u003e\n\u003cli\u003eXI S, LEILEI G. Reliability and validity of the stress overload scale in Chinese nurses [J]. Chinese J Nurs, 2014, 49: 1264-8.\u003c/li\u003e\n\u003cli\u003eLee BEC, Ling M, Boyd L, et al. The prevalence of probable mental health disorders among hospital healthcare workers during COVID-19: A systematic review and meta-analysis. J Affect Disord. 2023 Jun 1;330:329-345.\u003c/li\u003e\n\u003cli\u003eAhmad SJ, Feigen CM, Vazquez JP, et al. Neurological Sequelae of COVID-19. J Integr Neurosci. 2022 Apr 6;21(3):77. \u003c/li\u003e\n\u003cli\u003eXiao H, Zhang Y, Kong D, Li S, Yang N. 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. Med Sci Monit. 2020;26:e923549.\u003c/li\u003e\n\u003cli\u003eKUNZWEILER K, VOIGT K, KUGLER J, et al. Factors influencing sleep quality among nursing staff: Results of a cross sectional study [J]. Appl Nurs Res, 2016, 32: 241-4.\u003c/li\u003e\n\u003cli\u003eGARG M, MARALAKUNTE M, GARG S, et al. The Conundrum of \u0026apos;Long-COVID-19\u0026apos;: A Narrative Review [J]. Int J Gen Med, 2021, 14: 2491-506.\u003c/li\u003e\n\u003cli\u003eCARF\u0026Igrave; A, BERNABEI R, LANDI F. Persistent Symptoms in Patients After Acute COVID-19 [J]. Jama, 2020, 324(6): 603-5.\u003c/li\u003e\n\u003cli\u003eXIONG Q, XU M, LI J, et al. Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-centre longitudinal study [J]. Clin Microbiol Infect, 2021, 27(1): 89-95.\u003c/li\u003e\n\u003cli\u003eAytac S O, Kilic S P, Ovayolu N. Effect of inhaler drug education on fatigue, dyspnea severity, and respiratory function tests in patients with COPD[J]. Patient education and counseling, 2020, 103(4): 709-716.\u003c/li\u003e\n\u003cli\u003eXIAO H, ZHANG Y, KONG D, et al. 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 [J]. Med Sci Monit, 2020, 26: e923549.\u003c/li\u003e\n\u003cli\u003eKISELY S, WARREN N, MCMAHON L, et al. Occurrence, prevention, and management of the psychological effects of emerging virus outbreaks on healthcare workers: rapid review and meta-analysis [J]. BMJ, 2020, 369: m1642.\u003c/li\u003e\n\u003cli\u003eYARIBEYGI H, PANAHI Y, SAHRAEI H, et al. The impact of stress on body function: A review [J]. EXCLI journal, 2017, 16: 1057.\u003c/li\u003e\n\u003cli\u003eBLUME C, SCHMIDT M H, CAJOCHEN C. Effects of the COVID-19 lockdown on human sleep and rest-activity rhythms [J]. Curr Biol, 2020, 30(14): R795-r7.\u003c/li\u003e\n\u003cli\u003eXIAO H, ZHANG Y, KONG D, et al. Social Capital and Sleep Quality in Individuals Who Self-Isolated for 14 Days During the Coronavirus Disease 2019 (COVID-19) Outbreak in January 2020 in China [J]. Med Sci Monit, 2020, 26: e923921.\u003c/li\u003e\n\u003cli\u003eThe National Health Commission. As of the end of last year, the number of nurses in China has exceeded 5 million. (2022-05-11) [2023-04-10]. http:// www.gov.cn/xinwen/2022-05/13/content-5690090.htm.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Long-COVID, insomnia, nurses, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3899333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3899333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the prevalence of insomnia among nurses diagnosed with Long-COVID, analyze the potential risk factors, and establish a nomogram prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGeneral demographic information was obtained, and assessments of sleep quality, burnout, and stress were performed in a single center in May 2023. Three hundred and ninety-eight nurses were recruited. The Lasso regression technique was employed to screen for potential factors contributing to insomnia. A prognostic nomogram was constructed and evaluated by receiver operating characteristic curves and calibration curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifty-four percent of nurses complained of insomnia in this study. Eleven variables were independently associated with sleep patterns, including family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout. The R-squared value was 0.4642 and the area under curve was 0.8661. The derived nomogram showed that neurological sequela, stress, job burnout, sleep time before infection, and previous sleep problems also made the most substantial contributions to predicting sleep patterns. The calibration curves for predicting insomnia showed significant agreement between the nomogram models and actual observations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study established a nomogram prediction model of insomnia for nurses diagnosed with Long-COVID, which is helpful for the early clinical identification of high-risk individuals with insomnia.\u003c/p\u003e","manuscriptTitle":"Development of a nomogram model for predicting the risk of insomnia in nurses who underwent the Long- COVID","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:17:47","doi":"10.21203/rs.3.rs-3899333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-23T05:52:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-22T12:37:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-22T07:02:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0b8bdb20-4b0d-4b12-9a6f-9ae6855095ae","date":"2024-04-05T09:55:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1d857b7a-bae9-4192-9b9a-197beffce670","date":"2024-04-03T11:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-10T08:53:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-10T08:17:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-07T13:51:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-07T13:50:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2024-01-26T07:35:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb562ed8-41b2-43fb-bca2-a57a700fe1bd","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:05:45+00:00","versionOfRecord":{"articleIdentity":"rs-3899333","link":"https://doi.org/10.1186/s12912-024-02212-4","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2024-08-03 15:58:04","publishedOnDateReadable":"August 3rd, 2024"},"versionCreatedAt":"2024-03-11 19:17:47","video":"","vorDoi":"10.1186/s12912-024-02212-4","vorDoiUrl":"https://doi.org/10.1186/s12912-024-02212-4","workflowStages":[]},"version":"v1","identity":"rs-3899333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3899333","identity":"rs-3899333","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}