Identification of the risk factors for insomnia in nurses with long COVID-19

Abstract

Abstract Purpose To investigate the prevalence of insomnia among nurses with long COVID-19, analyze the potential risk factors and establish a nomogram model. Methods Nurses in Ningbo, China, were recruited for this study. General demographic information and insomnia, burnout, and stress assessment scores were collected through a face-to face questionnaire survey administered at a single center from March to May 2023. We used LASSO regression to identify potential factors contributing to insomnia. Then, a nomogram was plotted based on the model chosen to visualize the results and evaluated by receiver operating characteristic curves and calibration curves. Results A total of 437 nurses were recruited. 54% of the nurses had insomnia according to the Insomnia Severity Index (ISI) score. Eleven variables, including family structure, years of work experience, relaxation time, respiratory system sequelae, nervous system sequelae, others sequelae, attitudes toward COVID-19, sleep duration before infection, previous sleep problems, stress, and job burnout, were independently associated with insomnia. The R-squared value was 0.464, and the area under the curve was 0.866. The derived nomogram showed that neurological sequelae, stress, job burnout, sleep duration before infection, and previous sleep problems contributed the most to insomnia. The calibration curves showed significant agreement between the nomogram models and actual observations. Conclusion This study focused on insomnia among nurses with long COVID-19 and identified eleven risk factors related to nurses’ insomnia. A nomogram model was established to illustrate and visualize these factors, which will be instrumental in future research for identifying nurses with insomnia amid pandemic normalization and may increase awareness of the health status of healthcare workers with long COVID-19.

Keywords

• Long COVID-19
• Insomnia
• Nurses
• Nomogram