eHealth Literacy of Chinese Residents During the Coronavirus Disease 2019 Pandemic: A Cross-sectional Survey.

The Coronavirus Disease 2019 (COVID-19) pandemic has become a leading societal concern. eHealth literacy is important in the prevention and control of this pandemic. The purpose of this study is to identify eHealth literacy of Chinese residents about the COVID-19 pandemic and factors influencing eHealth literacy. A total of 15 694 individuals clicked on the link to the questionnaire, and 15 000 agreed to participate and completed the questionnaire for a response rate of 95.58%. Descriptive statistics, χ2 test, and logistic regression analysis were conducted to analyze participants' level of eHealth literacy about COVID-19 and its influencing factors. The results showed 52.2% of participants had relatively lower eHealth literacy regarding COVID-19 (eHealth literacy score ≤ 48). The scores of the information judgment dimension (3.09 ± 0.71) and information utilization dimension (3.18 ± 0.67) of the eHealth literacy scale were relatively lower. The logistics regression showed that sex, age, education level, level of uncertainty, having people around the respondent diagnosed with COVID-19, relationship with family, and relationship with others were associated to eHealth literacy (χ2 = 969.135, P < .001). The public's eHealth literacy about COVID-19 needs to be improved, especially the ability to judge and utilize online information. Close collaboration among global health agencies, governments, healthcare institutions, and media is needed to provide reliable online information to the public. Interventions to improve eHealth literacy should take into account and accentuate the importance of sex, age, educational background, level of uncertainty, exposure to disease, and social support.

Trajectories and predictors of vicarious traumatization in Chinese college students during the COVID-19 pandemic: A longitudinal study.

Objectives: This longitudinal study aimed to identify the trajectories and the predictors among sociodemographic and psychosocial variables at baseline of vicarious traumatization (VT) in Chinese college students during the COVID-19 pandemic. Materials and methods: A total of 544 Chinese college students enrolled in a public University in central China, majored in Clinical Medicine, Nursing, Musicology, Physics, etc., participated in this longitudinal study lasting 19 months. Three-wave (wave 1: February 2020; wave 2: November 2020; wave 3: September 2021) of data were collected. Resourcefulness Scale and the 10-item Kessler scale (K10) were only assessed in the first-wave survey, and the Event Scale-Revised (IES-R) was repeatedly measured in all three-wave surveys. A link to an online survey created by Questionnaire Star (https://www.wjx.cn/) was sent to the students to collect data. The Growth mixture modeling (GMM) and multiple logistic regression were used to identify the trajectories of VT and predictors for the distinct trajectories. Results: The incidence of VT at each wave varied from 9.9% at wave 1, 4.0% at wave 2, to 2.6% at wave 3. Three trajectories of VT were the medium-level escalating group (3.0%), medium-level maintaining group (32.3%), and the low-level descending group (64.7%). Seniors (OR = 1.575, 95% CI: 1.059-2.341; OR = 1.161, 95% CI: 1.043-1.293) and those with poor mental health status (OR = 1.101, 95% CI: 1.030-1.177; OR = 1.083, 95% CI: 1.060-1.106) at baseline were more likely to be classified into the medium-level escalating group and medium-level maintaining group, respectively. Additionally, females (OR = 3.601, 95% CI: 1.311-9.887) were more likely to be included in the medium-level escalating group. Conclusion: Targeted psychological interventions are urgently needed for students vulnerable to VT. Further studies with more representative samples, longer period of follow-up, and predictors based on scientific theoretical framework, are needed to update the findings.

Associations between depressive symptoms and quality of life among residents of Wuhan, China during the later stage of the COVID-19 pandemic: A network analysis.

BACKGROUND: Various populations have experienced significant increases in depression and decreased quality of life (QOL) during the coronavirus disease 2019 (COVID-19) pandemic. This network analysis study was designed to elucidate interconnections between particular depressive symptoms and different aspects of QOL and identify the most clinically important symptoms in this network among adults in Wuhan China, the initial epicenter of the COVID-19 pandemic. METHODS: This cross-sectional, convenience-sampling study (N = 2459) was conducted between May 25 to June 18, 2020, after the lockdown policy had been lifted in Wuhan. Depressive symptoms and QOL were measured with the Patient Health Questionnaire-9 (PHQ-9) and first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. A network structure was constructed from the extended Bayesian Information Criterion (EBIC) model. Network centrality strength and bridge strength were evaluated along with the stability of the derived network model. RESULTS: Loss of energy (DEP-4) and Guilt feelings (DEP-6) were the two central symptoms with the highest strength as well as the two most prominent bridge symptoms connecting the clusters of depression and quality of life (QOL) in tandem with the two nodes from the QOL cluster. Network structure and bridge strengths remained stable after randomly dropping 75 % of the sample. CONCLUSION: Interventions targeting "Loss of energy" and "Guilt feelings" should be evaluated as strategies for reducing depressive symptoms and promoting improved QOL in COVID-19-affected populations.

Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study.

Background: The 2019 novel coronavirus (COVID-19)-related depression symptoms of healthcare workers have received worldwide recognition. Although many studies identified risk exposures associated with depression symptoms among healthcare workers, few have focused on a predictive model using machine learning methods. As a society, governments, and organizations are concerned about the need for immediate interventions and alert systems for healthcare workers who are mentally at-risk. This study aims to develop and validate machine learning-based models for predicting depression symptoms using survey data collected during the COVID-19 outbreak in China. Method: Surveys were conducted of 2,574 healthcare workers in hospitals designated to care for COVID-19 patients between 20 January and 11 February 2020. The patient health questionnaire (PHQ)-9 was used to measure the depression symptoms and quantify the severity, a score of ≥5 on the PHQ-9 represented depression symptoms positive, respectively. Four machine learning approaches were trained (75% of data) and tested (25% of data). Cross-validation with 100 repetitions was applied to the training dataset for hyperparameter tuning. Finally, all models were compared to evaluate their predictive performances and screening utility: decision tree, logistics regression with least absolute shrinkage and selection operator (LASSO), random forest, and gradient-boosting tree. Results: Important risk predictors identified and ranked by the machine learning models were highly consistent: self-perceived health status factors always occupied the top five most important predictors, followed by worried about infection, working on the frontline, a very high level of uncertainty, having received any form of psychological support material and having COVID-19-like symptoms. The area under the curve [95% CI] of machine learning models were as follows: LASSO model, 0.824 [0.792-0.856]; random forest, 0.828 [0.797-0.859]; gradient-boosting tree, 0.829 [0.798-0.861]; and decision tree, 0.785 [0.752-0.819]. The calibration plot indicated that the LASSO model, random forest, and gradient-boosting tree fit the data well. Decision curve analysis showed that all models obtained net benefits for predicting depression symptoms. Conclusions: This study shows that machine learning prediction models are suitable for making predictions about mentally at-risk healthcare workers predictions in a public health emergency setting. The application of multidimensional machine learning models could support hospitals' and healthcare workers' decision-making on possible psychological interventions and proper mental health management.

Electronic health literacy among older adults in the context of the COVID-19 pandemic: A mixed-methods study.

AIM: This study aimed to investigate eHealth literacy about coronavirus disease 2019 (COVID-19) among older adults during the pandemic. BACKGROUND: The COVID-19 pandemic promoted the development of online health care. Higher demand for accessing information from the Internet was seen. METHODS: This was a sequential explanatory mixed-method study, involving a survey of older adults to explore the status and influencing factors of eHealth literacy regarding COVID-19. Semi-structured interviews were used to understand experiences and challenges regarding information retrieval, judgment and utilization. RESULTS: A total of 337 older adults participated in the online questionnaire survey. Overall, older adults had slightly higher scores on eHealth literacy during the COVID-19 pandemic. Participants' location in the past month and current health issues were associated with eHealth literacy. Qualitative data were collected from nine older adults and included that some older adults retrieved health-related information during the pandemic. However, those who used non-smartphones described difficulties in information retrieval. A glut of misinformation has resulted in an 'infodemic', which has not only increased the difficulty of judging information but also posed challenges in information utilization for older adults. CONCLUSION: Improving older adults' eHealth literacy is essential in promoting an improved response to major public health events and in providing better health care for this group in the future. It is essential that government health agencies and health care providers provide evidence-based health information via social media platforms. Further efforts are needed to combine aspects of traditional and online health care services and provide reliable and updated online information and resources for older adults. IMPLICATIONS FOR NURSING MANAGEMENT: Providing evidence to eHealth literacy improvement and health management of older adults in the context of public health events.

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic.

Background: Symptoms of depression and pain often overlap, and they negatively influence the prognosis and treatment outcome of both conditions. However, the comorbidity of depression and pain has not been examined using network analysis, especially in the context of a pandemic. Thus, we mapped out the network connectivity among the symptoms of depression and pain in Wuhan residents in China during the late stage of the COVID-19 pandemic. Methods: This cross-sectional study was conducted from May 25, 2020 to June 18, 2020 in Wuhan, China. Participants' depressive and pain symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ9) and a pain numeric rating scale (NRS), respectively. Network analyses were performed. Results: In total, 2,598 participants completed all assessments. PHQ4 (fatigue) in the depression community showed the highest strength value, followed by PHQ6 (worthlessness) and PHQ2 (depressed or sad mood). PHQ4 (fatigue) was also the most key bridge symptom liking depression and pain, followed by PHQ3 (sleep difficulties). There were no significant differences in network global strength (females: 4.36 vs. males: 4.29; S = 0.075, P = 0.427), network structure-distribution of edge weights (M = 0.12, P = 0.541), and individual edge weights between male and female participants. Conclusion: Depressive and pain symptoms showed strong cross-association with each other. "Fatigue" was the strongest central and bridge symptom in the network model, while "sleep difficulties" was the second strongest bridge symptom. Targeting treatment of both fatigue and sleep problems may help improve depressive and pain symptoms in those affected.

When health care workers became patients with COVID-19: A qualitative study.

AIMS: This study aimed to describe the experiences of nurses and other health care workers who were infected with coronavirus disease 2019. METHODS: An empirical phenomenological approach was used. Sixteen participants were recruited in Wuhan using purposive and snowball sampling. Semistructured, in-depth interviews were conducted by telephone in February 2020. Interviews were transcribed verbatim and analysed following Colaizzi's method. RESULTS: Two themes emerged: (1) Intense emotional distress since becoming infected. Participants were fearful of spreading the virus to family and overwhelmed by a lack of information, experienced uncertainty and worried about treatment, felt lonely during isolation and reported moral distress about inadequate health care staffing. (2) Coping strategies were needed. They tried their best to address negative psychological reactions using their professional knowledge and gaining support from others and community resources. CONCLUSIONS: Preparedness for catastrophic events and providing timely and accurate information are major considerations in government policy development, related to pandemics and adequacy of health care personnel. Mental health resources and support, both short- and long-term should be anticipated for health care providers to alleviate their fear and anxiety.

Changes in Nursing Students' Career Choices Following the COVID-19 Pandemic in China.

Background: Health professionals including nurses have experienced heavy workload and great physical and mental health challenges during the coronavirus disease 19 (COVID-19) pandemic, which may affect nursing students' career choices. This study examined the changes in nursing students' career choices after the onset of the COVID-19 pandemic in China. Methods: This study was conducted in five University nursing schools in China between September 14, 2020 and October 7, 2020. Career choices before and after the COVID-19 pandemic were collected and analyzed. Results: In total, 1,070 nursing students participated in the study. The reported choice of nursing as future career increased from 50.9% [95% confidence interval (CI): 47.9-53.9%] before the COVID-19 pandemic to 62.7% (95%CI: 59.8-65.6%) after the onset of COVID-19 pandemic. Students who chose nursing as their future career following the COVID-19 outbreak had less severe depression and anxiety compared to those who did not choose nursing, but the associations of depression and anxiety with career choice disappeared in multivariable analyses. Binary logistic regression analysis revealed that male gender [odds ratio (OR) = 0.68, 95% CI: 0.50-0.91], rural residence (OR = 1.53, 95%CI: 1.17-2.00), fourth year students (OR = 0.50, 95%CI: 0.35-0.72), negative experiences during the COVID-19 pandemic (OR = 0.66, 95%CI: 0.47-0.92), and good health (OR = 4.6, 95%CI: 1.78-11.87) were significantly associated with the choice of nursing as future career after the onset of the COVID-19 pandemic. Conclusions: The COVID-19 pandemic appeared to have a positive influence on the career choice of nursing among Chinese nursing students.

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic.

Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic. Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample. Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms. Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established. Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.

Mental health status and quality of life in close contacts of COVID-19 patients in the post-COVID-19 era: a comparative study.

Close contacts of those with COVID-19 (CC) may experience distress and long-lasting mental health effects. However, the mental health status and quality of life (QOL) in CC have not been adequately examined. This study examined the mental health status and QOL in CC during the post-COVID-19 period. This cross-sectional study comprised 1169 CC and 1290 who were non-close contacts (non-CC). Demographic data were collected; depression, fatigue, post-traumatic stress symptoms (PTSS) and QOL were assessed using the Patient Health Questionnaire - 9 items (PHQ-9), fatigue numeric rating scale, Post-Traumatic Stress Disorder Checklist - 17 items (PCL-17), and the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. Analysis of covariance was used to compare depressive symptoms, QOL, fatigue, and PTSS between the CC and non-CC groups. Multiple logistic regression analyses were performed to determine the independent correlates for depression, fatigue, PTSS, and QOL in the CC group. Compared to the non-CC group, the CC group reported significantly more severe depression (F(1, 2458) = 5.58, p = 0.018) and fatigue (F(1, 2458) = 9.22, p = 0.002) in the post-COVID-19 period. No significant differences in PTSS and QOL between the CC and non-CC groups were found (F(1, 2458) = 2.93, p = 0.087 for PTSS; F(1, 2458) = 3.45, p = 0.064 for QOL). In the CC group, younger age, financial loss due to COVID-19, and perception of poor or fair health status were significantly associated with depression and fatigue, while frequent use of mass media was significantly associated with fatigue. In conclusion, close contacts of COVID-19 patients experienced high levels of depression and fatigue in the post-COVID-19 period. Due to the negative effects of depression and fatigue on daily functioning, early detection and timely interventions should be provided to this neglected population.

Prevalence of problematic Internet use and its association with quality of life among undergraduate nursing students in the later stage of COVID-19 pandemic era in China.

BACKGROUND AND OBJECTIVES: The prevalence of problematic Internet use (PIU) in the post-COVID-19 pandemic era is not known. This cross-sectional study aimed to determine the prevalence of PIU among baccalaureate nursing students (hereafter: nursing students) in the post-COVID-19 era. METHODS: A total of 1070 nursing students were consecutively invited to participate in this study from the nursing schools of five universities. PIU and quality of life (QOL) were assessed using the Internet Addiction Test (IAT) and the World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF), respectively. t Tests, χ2 , tests, and Kruskal-Wallis tests were used to compare basic demographic and clinical characteristics between participants with and without PIU. Binary logistic regression analysis was used to examine independent correlates. RESULTS: The prevalence of PIU was 23.3% (95% confidence interval [CI]: 20.7%-25.8%). Multiple logistic regression analysis revealed that second- (p = .024) and third-year (p = .012) students were more likely to suffer from PIU compared with first year students. Students with more severe depressive (p = .014) and anxiety symptoms (p = .011) were independently and significantly associated with more severe PIU. After controlling for covariates, nursing students with PIU had a lower overall QOL score (p = .002). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Problematic Internet use (PIU) was common among nursing students in the post-COVID-19 era. Considering the negative impact of PIU on QOL and academic performance, regular screening should be conducted and effective interventions implemented for nursing students with PIU. This was the first study on the prevalence of PIU among nursing students in the post-COVID-19 era. The findings of this study could help health professionals and education authorities to understand the patterns of PIU and its influence on QOL among nursing students and to allocate health resources and develop effective measures to reduce the risk of PIU in this population.

Nurses' experiences of providing care to patients with COVID-19 in the ICU in Wuhan: a descriptive phenomenological research.

OBJECTIVES: This phenomenological study aimed to examine intensive care unit (ICU) nurses' experiences of caring for patients with COVID-19, and understand better their everyday experiences of patient' management in the ICU. DESIGN: A descriptive phenomenological research design was used. Individual interviews were conducted. The data were transcribed verbatim and analysed using Colaizzi's seven-step framework. SETTING: An ICU with 16 beds in a tertiary hospital in Wuhan, China. PARTICIPANTS: Nurses who had more than 1 year of experience and had provided care to patients with COVID-19 in ICU for more than 1 week were identified as participants. A total of 13 nurses were interviewed. RESULTS: An analysis of these significant statements yielded four distinct stages of feelings, thereby revealing the essence of this phenomenon. Worry about being infected and infecting family members was present across in all four stages. The themes associated with the four stages were as follows: initial contradictory feelings, quick adaption to the 'new working environment' in the first 1-2 weeks in the ICU, desperation after adaption, holding on and survive. CONCLUSIONS: The nurses reported distinct experiences of providing care to patients with COVID-19 in ICUs. Interventions, such as providing information about the disease, simulation training, emotional support and follow-up care, are needed to help nurses manage patients with COVID-19 and maintain nurses' health.

Network analysis of anxiety and depressive symptoms among nursing students during the COVID-19 pandemic.

BACKGROUND: The 2019 coronavirus disease (COVID-19) pandemic has impacted the mental health and well-being of medical personnel, including nursing students. Network analysis provides a deeper characterization of symptom-symptom interactions in mental disorders. The aim of this study was to elucidate characteristics of anxiety and depressive symptom networks of Chinese nursing students during the COVID-19 pandemic. METHOD: A total of 932 nursing students were included. Anxiety and depressive symptom were measured using the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. RESULTS: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood had the highest centrality values. Three bridge symptoms (Depressed mood, Nervousness, and Anhedonia) were also identified. Neither gender nor region of residence was associated with network global strength, distribution of edge weights or individual edge weights. LIMITATIONS: Data were collected in a cross-sectional study design, therefore, causal relations and dynamic changes between anxiety and depressive symptoms over time could not be inferred. Generalizability of findings may be limited to Chinese nursing students during a particular phase of the current pandemic. CONCLUSIONS: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood constituted central symptoms maintaining the anxiety-depression network structure of Chinese nursing students during the pandemic. Timely, systemic multi-level interventions targeting central symptoms and bridge symptoms may be effective in alleviating co-occurring experiences of anxiety and depression in this population.

The prevalence of fatigue among Chinese nursing students in post-COVID-19 era.

BACKGROUND: Due to the COVID-19 outbreak, all teaching activities in nursing schools were suspended in China, and many nursing students were summoned to work in hospitals to compensate for the shortage of manpower. This study examined the prevalence of fatigue and its association with quality of life (QOL) among nursing students during the post-COVID-19 era in China. METHODS: This was a multicenter, cross-sectional study. Nursing students in five Chinese universities were invited to participate. Fatigue, depressive and anxiety symptoms, pain and QOL were measured using standardized instruments. RESULTS: A total of 1,070 nursing students participated. The prevalence of fatigue was 67.3% (95% CI [64.4-70.0]). Multiple logistic regression analysis revealed that male gender (P = 0.003, OR = 1.73, 95% CI [1.20-2.49]), and being a senior nursing student (second year: OR = 2.20, 95% CI [1.46-3.33], P < 0.001; third year: OR = 3.53, 95% CI [2.31-5.41], P < 0.001; and fourth year OR = 3.59, 95% CI [2.39-5.40], P < 0.001) were significantly associated with more severe fatigue. In addition, moderate economic loss during the COVID-19 pandemic (OR = 1.48, 95% CI [1.08-3.33], P < 0.015; compared to low loss), participants with more severe depressive (OR = 1.48, 95% CI [1.22-1.78], P < 0.001) and anxiety symptoms (OR = 1.12, 95% CI [1.05-1.20], P = 0.001), and more severe pain (OR = 1.67, 95%CI [1.46-1.91], P < 0.001) were significantly associated with reported more severe fatigue. After controlling for covariates, nursing students with fatigue had a lower overall QOL score compared to those without (F (1, 1070) = 31.4, P < 0.001). CONCLUSION: Fatigue was common among nursing students in the post-COVID-19 era. Considering the negative impact of fatigue on QOL and daily functioning, routine physical and mental health screening should be conducted for nursing students. Effective stress-reduction measures should be enforced to assist this subpopulation to combat fatigue and restore optimal health.

Mental Health Status of the General Public, Frontline, and Non-frontline Healthcare Providers in the Early Stage of COVID-19.

Background: The outbreak of COVID-19 occurred in 2020 which resulted in high levels of psychological stress in both the general public and healthcare providers. Purpose: The study aimed to address the mental health status of people in China in the early stage of the COVID-19 outbreak, and to identify differences among the general public, frontline, and non-frontline healthcare providers. Method: A cross-sectional study was used to identify the mental health status of the general public and healthcare providers between Jan 29 and Feb 11, 2020. Data were collected using an online survey from a convenience sample. The instruments used included: Patient Health Questionnaire, Generalized Anxiety Disorder scale, Insomnia Severity Index, and Impact of Event Scale-Revised. Descriptive statistics were used to describe the data. Kruskal-Wallis H tests were performed to assess differences in measurements among the three groups; P < 0.05 (two-sided) was considered to be statistically significant. Results: Results showed that a majority of participants experienced post-traumatic stress (68.8%), depression (46.1%), anxiety (39.8%), and insomnia (31.4%). Significant changes in the mental health status of frontline providers was found as compared to those of the other groups (P < 0.001). Interestingly, the scores of the general public were significantly higher than those of the non-frontline healthcare providers (P < 0.001). Conclusion: These findings provide information to evaluate outbreak associated psychological stress for the general public and healthcare providers, and assist in providing professional support and actionable guidance to ease psychological stress and improve mental health.

Relationship between eHealth literacy and psychological status during COVID-19 pandemic: A survey of Chinese residents.

AIMS: To investigate the eHealth literacy and the psychological status of Chinese residents during the COVID-19 pandemic and explore their interrelationship. BACKGROUND: The COVID-19 outbreak has placed intense psychological pressure on community residents. Their psychological status may be affected by eHealth literacy due to home isolation during this rampant pandemic. METHODS: This is a Web-based cross-sectional survey conducted on the JD Health platform, which resulted in 15,000 respondents having participated in this survey. The eHealth Literacy Questionnaire (EHLQ), Patient Health Questionnaire-9 (PHQ-9), Insomnia Severity Index (ISI) and Impact of Event Scale-Revised (IES-R) were used. The Pearson correlation was used to analyse the relationship between eHealth literacy and depression, insomnia and post-traumatic stress disorder. RESULTS: The score of eHealth literacy was 48.88 ± 8.46, and 11.4%, 6.8% and 20.1% of respondents experienced moderate to severe depression, insomnia and post-traumatic stress disorder. eHealth literacy negatively correlated with depression (r = -0.331), insomnia (r = -0.366) and post-traumatic stress disorder (r = -0.320). CONCLUSION: eHealth literacy is closely related to psychological status. Improving eHealth literacy may contribute to maintaining good psychological well-being. IMPLICATIONS FOR NURSING MANAGEMENT: It is necessary to strengthen the education of primary health care providers to enhance their ability to help community residents effectively use eHealth information.