Abstract
Objectives:
In health emergencies such as pandemics, nurses are on the front lines, thus increasing their risk of psychological distress. The mental health of nursing students may also deteriorate as a result of changes in learning and clinical practice environments. We measured the psychological effects of the COVID-19 pandemic and electronic health (eHealth) literacy among nursing students and identified associated factors.
Methods:
We used a cross-sectional design to analyze students studying at 2 nursing schools in the United States and Türkiye (N = 887 nursing students). We used the Fear of COVID-19 Scale (range, 7-35) and the Coronavirus Anxiety Scale (range, 5-20) to measure fear and anxiety of the COVID-19 pandemic, and we used the Electronic Health Literacy Scale (range, 8-40) to measure eHealth literacy among students from April through June 2022. We conducted 1-way multivariate analysis of variance (F) to examine the relationships among variables, with P ≤ .05 considered as significant.
Results:
Students had mean scores of 30.7 for eHealth literacy, 14.1 for Fear of COVID-19 Scale, and 6.2 for Coronavirus Anxiety Scale. Scores for eHealth literacy varied according to the students’ school, academic level, and employment but were generally high. Sex (Wilks λ = 0.952; F = 14.787; P < .001) and the frequency of following news related to COVID-19 (Wilks λ = 0.927; F = 11.424; P < .001) influenced COVID-19–related fear and anxiety. eHealth literacy and fear of COVID-19 differed significantly by students’ vaccine dose (λ = 0.983; F = 5.081; P = .002).
Conclusions:
Increasing the level of eHealth literacy can contribute to reducing the psychological effects of health emergencies, such as the COVID-19 pandemic, among nursing students.
Keywords: anxiety, eHealth literacy, fear, mental health, nursing student
The COVID-19 pandemic has affected people’s mental health. 1 Changes in daily routine and educational processes as a result of the COVID-19 pandemic, such as prolonged home isolation, online learning, and social distancing, have led to mental health problems among young adults (aged 18-24 y). 2 In a systematic review and meta-analysis of studies of college students, the global rates of anxiety, depression, and stress in the first year of the COVID-19 pandemic were 29%, 37%, and 23%, respectively. 3 In the second and third years of the pandemic, moderate to extremely severe levels of stress, anxiety, and depression were observed among university students.4,5
Along with increased mental health problems among college students as a result of the COVID-19 pandemic, rapid and continuous changes in health information, resulting in increased uncertainty, also occurred. During the spread of COVID-19, an “infodemic” arose, 6 described as the overabundance of information, whether true or false, leading to confusion and ultimately distrust of governments and public health responses. 7 Despite some advantages in the use of online health-related searches, the uncontrollable quality and credibility of online information may have resulted in individuals who are overly anxious or stressed about their health being at risk for “cyberchondria.” 8 Health service administrators recognized the need for digitalization of health services during the COVID-19 pandemic and included approaches such as telehealth, webpages containing health information, social media, and smartphone applications in to health care services. 9 Therefore, electronic health (eHealth) literacy has become an important topic in the fight against the infodemic.
eHealth literacy is “the ability to search, find, understand, appraise, and evaluate health information from electronic sources and apply the gained knowledge to solve a health problem.” 10 Although reports have shown that people often used the internet and social media to access information about the COVID-19 pandemic,11 -13 people may have also accessed false information, resulting in increased fear, health uncertainty, and anxiety. 6 Searching for health-related knowledge compulsively and excessively because of health anxiety can cause cyberchondria.14,15 Thus, understanding the potential relationship between eHealth literacy and increased COVID-19–related fear and anxiety is important. 12
Nursing education has also been affected by the COVID-19 pandemic, and problematic internet use has also been shown to be high among nursing students.16,17 Nursing students use the internet to find information on health care education and clinical practice settings for patient care.18,19 In health care emergencies, such as pandemics, having good eHealth literacy among nursing students is essential for clinical practice, lifelong learning, and support for training activities. 20
Studies have aimed to understand the psychological effects of COVID-19 and eHealth literacy.6,21 -23 Studies revealed a relationship between eHealth literacy and anxiety2,23,24 and fear. 25 A positive correlation was also reported between anxiety and fear of COVID-19.26,27 However, little is known about the associations between individual characteristics and eHealth literacy, COVID-19–related fear, and anxiety among nursing students. Improving eHealth literacy among nursing students may help students to access accurate eHealth information on COVID-19, thus reducing psychological problems.
The aim of our study was to compare the psychological effects of COVID-19 and eHealth literacy among nursing students. We hypothesized that individual characteristics and factors related to COVID-19 might affect eHealth literacy and COVID-19–related fear and anxiety among nursing students.
Methods
Study Design and Participants
We used a cross-sectional design and followed STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. 28 We estimated a sample size based on the eHealth literacy variable from a previous study 19 of at least 131 participants, using a medium effect size of 0.176, α = .05, and power level (1 − β) of 0.95 with 7 predictors. We included students who (1) were aged ≥18 years, (2) had an email address or used WhatsApp communication, and (3) agreed to participate in the research. We excluded students who had an audiovisual impairment or any psychiatric disease.
We contacted all students at 2 nursing schools (1 in Ankara, Türkiye, and 1 in St. Cloud, Minnesota) by email or through social media to contribute to our study. Students at both nursing schools acquire simple computer skills during their computer and informatics courses. We obtained student emails from the nursing schools. Because the number of students in the Turkish nursing school was approximately 6 times greater than the number of students in the US nursing school, we recruited Turkish students at a ratio of 1:6.
Instruments
We collected demographic data by using a personal information form, which included closed-ended questions on age, sex, academic level, monthly household income, having a chronic disease, being vaccinated against COVID-19, and daily time spent on the internet. We used the Electronic Health Literacy Scale to determine eHealth literacy and the Fear of COVID-19 Scale and the Coronavirus Anxiety Scale to determine fear and anxiety of the COVID-19 pandemic.29 -31
Norman and Skinner 10 developed the Electronic Health Literacy Scale in 2006. Tamer Gencer 29 performed the Turkish adaptation study of the scale and determined a Cronbach α (reliability coefficient) of .92. For the Electronic Health Literacy Scale, we calculated results from student responses to 8 items on a 5-point Likert-type scale (from 1 = strongly disagree to 5 = strongly agree), with scores ranging from 8 to 40. A high score indicated high perceived skills at using information technology. 29 We found the scale to have a Cronbach α = .92.
Ahorsu et al 32 developed the Fear of COVID-19 Scale in 2020. Satici et al 30 performed the Turkish adaptation study and determined a Cronbach α of .86. We calculated the results from the students’ responses to 7 items in a single dimension on a 5-point Likert-type scale (from 1 = strongly disagree to 5 = strongly agree), 30 with scores ranging from 7 to 35. A high score indicated a high level of fear of COVID-19. We found the scale to have a Cronbach α = .87.
Lee 33 developed the Coronavirus Anxiety Scale in 2020 to identify possible cases of dysfunctional anxiety related to the COVID-19 pandemic. Biçer et al 31 performed the Turkish adaptation study and determined a Cronbach α of .83. We calculated results from students’ responses to 5 items in a single dimension on a 5-point Likert-type scale (from 0 = not at all to 4 = nearly every day during the past 2 weeks), with scores ranging from 5 to 20. We found the scale to have a Cronbach α = .86.
Data Collection
We created the online survey by using Google Forms. We collected surveys from April through June 2022. We shared an online survey link with US and Turkish students through email and WhatsApp, respectively. Before students started the survey, we provided students with information on the study and asked for their online consent by selecting a button to agree or not agree to participate in the study. Students who agreed could access the survey instruments. We added a student identification number to the survey to prevent repeated participation. We sent online reminders twice a week to increase student participation. We determined a completion time for all instruments in the survey of approximately 15 minutes.
Data Analysis
We used IBM SPSS Statistics for Windows version 28.0 (IBM Corp) for data analysis. We reported descriptive statistics as numbers, percentages, and means ± SDs. We evaluated normality distribution of data by kurtosis and skewness values ±2. We made comparisons between 2 independent variables (school location and all individual and COVID-19–related characteristics) versus fear of COVID-19, anxiety of COVID-19, and eHealth literacy scores by using 2-way multiple analysis of variance (MANOVA). Because variables did not interact, we used 1-way MANOVA to compare the influence of independent variables on eHealth literacy, COVID-19–related fear, and COVID-19–related anxiety. We used MANOVA because it is statistically more efficient than ANOVA in discovering important factors, can reduce the type I error rate, and can identify effects smaller than those found with ANOVA. Because ANOVA could determine only whether groups differed along a single dimension, we used MANOVA for its power to detect whether groups differed along a combination of dimensions.34,35 Therefore, we performed MANOVA with follow-up discriminant analyses. We checked normality, linearity, univariate outliers, multivariate outliers, homogeneity of variance–covariance matrices, and multicollinearity, among the assumptions required to run 1-way MANOVA, and detected no serious violations. We considered P ≤ .05 to be significant.
Ethical Consideration
We conducted research at Turkish and US nursing schools, respectively. The University Ethics Commission (code: 2022-199) of Türkiye approved the Turkish research. The St. Cloud State University Nursing Department Institutional Review Board approved the US research. We obtained institutional permission from both nursing schools. We informed students that they had the right to leave the study at any time and that the results of the study would not affect their course grades.
Results
A total of 1160 nursing students at the 2 schools were invited to complete the survey (990 Turkish students and 170 US students). Among the invited nursing students, 170 did not reply to the online invitation. Among the remaining 990 students, 81 declined the invitation and 22 answered the survey incompletely. The final sample comprised 887 nursing students (761 Turkish students and 126 US students). The participation rate was 90% (887 of 990 students).
Characteristics of Nursing Students
The mean (SD) age of the students was 21.2 (2.1) years. Among the Turkish students, 85.3% (649 of 761) were female and 24.7% (188 of 761) were third-year students. Among US students, 83.3% (105 of 126) were female and 26.2% (33 of 126) were third-year students (Table 1).
Table 1.
Individual and COVID-19–related characteristics among nursing students surveyed at nursing schools in the United States and Türkiye, 2022 a
| Characteristic | No. (%) of nursing students | ||
|---|---|---|---|
| Turkish nursing school (n = 761) | US nursing school (n = 126) | Total (N = 887) | |
| Age group, y | |||
| 18-21 | 506 (66.5) | 57 (45.2) | 563 (63.5) |
| 22-38 | 255 (33.5) | 69 (54.8) | 324 (36.5) |
| Sex | |||
| Female | 649 (85.3) | 105 (83.3) | 754 (85.0) |
| Male | 112 (14.7) | 21 (16.7) | 133 (15.0) |
| Academic year | |||
| First | 183 (24.0) | 13 (10.3) | 196 (22.1) |
| Second | 186 (24.4) | 23 (18.3) | 209 (23.6) |
| Third | 188 (24.7) | 33 (26.2) | 221 (24.9) |
| Fourth | 204 (26.8) | 57 (45.2) | 261 (29.4) |
| Employment | |||
| Yes | 48 (6.3) | 96 (76.2) | 144 (16.2) |
| No | 713 (93.7) | 30 (23.8) | 743 (83.8) |
| Monthly household income | |||
| Less than expenditures | 273 (35.9) | 39 (31.0) | 312 (35.2) |
| Equal to expenditures | 443 (58.2) | 51 (40.5) | 494 (55.7) |
| More than expenditures | 45 (5.9) | 36 (28.6) | 81 (9.1) |
| Presence of chronic disease | |||
| Yes | 71 (9.3) | 20 (15.9) | 91 (10.3) |
| No | 690 (90.7) | 106 (84.1) | 796 (89.7) |
| Medicine use | |||
| Yes | 77 (10.1) | 55 (43.7) | 132 (14.9) |
| No | 684 (89.9) | 71 (56.3) | 755 (85.1) |
| Had COVID-19 | |||
| Yes | 271 (35.6) | 75 (59.5) | 346 (39.0) |
| No | 490 (64.4) | 51 (40.5) | 541 (61.0) |
| Had COVID-19 vaccine | |||
| Yes | 746 (98.0) | 121 (96.0) | 867 (97.7) |
| No | 15 (2.0) | 5 (4.0) | 20 (2.3) |
| No. of COVID-19 vaccine doses | |||
| 2 | 413 (54.2) | 68 (54.0) | 481 (54.2) |
| 3 | 333 (43.8) | 58 (46.0) | 391 (44.1) |
| Not having vaccine | 15 (2.0) | 0 | 15 (1.6) |
| Obeyed COVID-19 precautions | |||
| Yes | 735 (96.6) | 100 (79.4) | 835 (94.1) |
| No | 26 (3.4) | 26 (20.6) | 52 (5.9) |
| Living arrangement | |||
| In a dormitory | 480 (63.1) | 14 (11.1) | 494 (55.7) |
| With family | 243 (31.9) | 70 (55.6) | 313 (35.3) |
| With friends | 38 (5.0) | 42 (33.3) | 80 (9.0) |
| Frequency of following news related to COVID-19 | |||
| Daily | 559 (73.5) | 22 (17.5) | 581 (65.5) |
| A couple of times a week | 98 (12.8) | 61 (48.4) | 159 (17.9) |
| Not following | 104 (13.7) | 43 (34.1) | 147 (16.6) |
| Hours spent on the internet per day | |||
| ≤2 | 57 (7.5) | 20 (15.9) | 77 (8.7) |
| 3-4 | 248 (32.6) | 24 (19.0) | 272 (30.7) |
| 5-6 | 285 (37.5) | 42 (33.3) | 327 (36.9) |
| ≥7 | 171 (22.5) | 40 (31.7) | 211 (23.8) |
Respondents were students at nursing schools in Ankara, Türkiye, and St. Cloud, Minnesota.
Among all students, the mean (SD) scores were 30.7 (5.0) for eHealth literacy, 14.1 (5.1) for COVID-19–related fear, and 6.2 (2.1) for COVID-19–related anxiety. We found a negative association between eHealth literacy and COVID-19–related fear (r = −0.166; P < .001) and between eHealth literacy and COVID-19–related anxiety (r = −0.106; P = .002) and a positive association between COVID-19–related fear and COVID-19–related anxiety (r = 0.526; P < .001).
eHealth Literacy, COVID-19–Related Fear, and COVID-19–Related Anxiety
Results of 2-way MANOVA showed that the interaction effects between school location and all individual and COVID-19–related characteristics were not significant (P > .05).
Results of 1-way MANOVA revealed significant differences according to scale scores (eHealth literacy, COVID-19–related fear, and COVID-19–related anxiety) between groups (Table 2). Discriminant analysis after MANOVA found significant differences between Turkish and US school groups in eHealth literacy (r = 0.953), anxiety (r = 0.223), and fear (r = −0.084); between students in their first and second academic year versus their third and fourth academic year in eHealth literacy (r = 0.987), anxiety (r = −0.196), and fear (r = −0.228); between female and male students in eHealth literacy (r = −0.230), anxiety (r = 0.360), and fear (r = 0.983); between students who were employed versus not employed in eHealth literacy (r = 0.898), anxiety (r = −0.163), and fear (r = 0.532); between students who had versus did not have a chronic disease in eHealth literacy (r = 0.344), anxiety (r = 0.915), and fear (r = 0.342); between students who had versus did not have COVID-19 in eHealth literacy (r = 0.345), anxiety (r = 0.304), and fear (r = −0.641); in number of COVID-19 vaccine doses (3 vs 2 doses) in eHealth literacy (r = 0.597), anxiety (r = −0.213), and fear (r = 0.505); among students who followed versus did not follow COVID-19 precautions in eHealth literacy (r = −0.014), anxiety (r = −0.023), and fear (r = 0.851); in type of living arrangement (in a dormitory, with family, with friends) in eHealth literacy (r = 0.839), anxiety (r = −0.210), and fear (r = −0.656); and in frequency of following COVID-19 news (daily, several times a week, not following) in eHealth literacy (r = −0.245), anxiety (r = 0.840), and fear (r = 0.990).
Table 2.
One-way MANOVA results according to individual and COVID-19–related characteristics among nursing students surveyed at nursing schools in the United States and Türkiye, 2022 a
| Characteristic | COVID-19–related fear b | COVID-19–related anxiety c | eHealth literacy d | Wilks λ | F | P |
|---|---|---|---|---|---|---|
| Nursing school location | ||||||
| Türkiye | 14.1 (5.0) | 6.1 (2.1) | 30.4 (4.9) | |||
| United States | 13.9 (5.3) | 6.4 (2.0) | 32.8 (5.3) | 0.969 | 9.453 | <.001 |
| Sex | ||||||
| Female | 14.5 (5.0) | 6.2 (2.1) | 30.6 (4.8) | |||
| Male | 11.5 (4.7) | 5.8 (1.9) | 31.3 (6.0) | 0.952 | 14.787 | <.001 |
| Academic year | ||||||
| First and second | 14.4 (5.3) | 6.3 (2.2) | 29.5 (5.4) | |||
| Third and fourth | 13.8 (4.9) | 6.1 (2.0) | 31.7 (4.4) | 0.950 | 15.619 | <.001 |
| Employment | ||||||
| Yes | 13.5 (5.4) | 6.1 (1.8) | 31.8 (5.6) | |||
| No | 14.2 (5.0) | 6.2 (2.1) | 30.5 (4.9) | 0.989 | 3.141 | .03 |
| Income | ||||||
| Less than expenditures | 14.0 (5.1) | 6.3 (2.2) | 30.4 (5.2) | |||
| Equal to expenditures | 14.3 (5.0) | 6.1 (2.1) | 30.8 (4.9) | 0.989 | 1.633 | .13 |
| More than expenditures | 13.0 (5.2) | 6.0 (1.7) | 31.2 (5.0) | |||
| Presence of chronic disease | ||||||
| Yes | 14.6 (4.9) | 6.7 (5.5) | 31.2 (4.6) | 0.991 | 2.773 | .04 |
| No | 14.0 (5.1) | 6.1 (2.0) | 30.7 (5.1) | |||
| Medication use | ||||||
| Yes | 14.1 (4.9) | 6.3 (2.2) | 31.4 (5.3) | 0.995 | 1.515 | .21 |
| No | 14.1 (5.1) | 6.1 (2.0) | 30.6 (5.0) | |||
| Had COVID-19 | ||||||
| Yes | 13.5 (5.0) | 6.3 (2.3) | 31.0 (5.3) | 0.982 | 5.412 | .001 |
| No | 14.4 (5.1) | 6.1 (1.9) | 30.5 (4.8) | |||
| No. of COVID-19 vaccine doses | ||||||
| 3 | 14.5 (5.2) | 6.1 (2.0) | 31.2 (5.0) | 0.983 | 5.081 | .002 |
| 2 | 13.8 (4.9) | 6.2 (2.1) | 30.4 (5.1) | |||
| Obeyed COVID-19 precautions | ||||||
| Yes | 14.3 (5.0) | 6.2 (2.1) | 30.7 (4.9) | 0.975 | 7.606 | <.001 |
| No | 11.3 (5.0) | 6.2 (2.2) | 30.8 (6.6) | |||
| Living arrangement | ||||||
| In a dormitory | 14.5 (5.1) | 6.1 (2.1) | 30.4 (5.0) | |||
| With family | 13.5 (5.1) | 6.2 (2.1) | 30.9 (5.2) | 0.978 | 3.249 | .004 |
| With friends | 13.8 (4.9) | 6.1 (1.8) | 32.0 (4.3) | |||
| Frequency of following news related to COVID-19 | ||||||
| Daily | 15.0 (5.1) | 6.3 (2.2) | 30.5 (4.8) | |||
| A couple of times a week | 13.1 (4.6) | 6.1 (1.8) | 30.9 (5.3) | 0.927 | 11.424 | <.001 |
| Not following | 11.5 (4.3) | 5.5 (1.5) | 31.4 (5.7) | |||
| Hours spent on the internet per day | ||||||
| ≤2 | 13.9 (5.3) | 6.4 (2.7) | 31.9 (4.9) | |||
| 3-4 | 14.0 (5.0) | 6.0 (1.9) | 30.8 (5.1) | 0.988 | 1.738 | .11 |
| 5-6 | 14.2 (5.1) | 6.2 (2.0) | 30.5 (5.0) | |||
Abbreviations: eHealth, electronic health; F, 1-way multiple analysis of variance (MANOVA).
Respondents were students at nursing schools in Ankara, Türkiye, and St. Cloud, Minnesota. Results are shown as mean (SD).
The Fear of COVID-19 Scale was used to determine COVID-19–related fear. Scores ranged from 7 (low level of fear) to 35 (high level of fear). 30
The Coronavirus Anxiety Scale was used to determine COVID-19–related anxiety. Scores ranged from 5 (low level of anxiety) to 20 (high level of anxiety). 31
The Electronic Health Literacy Scale was used to determine eHealth literacy. Scores ranged from 8 (low eHealth literacy skills) to 40 (high eHealth literacy skills). 29
Discussion
This analysis revealed that among our nursing student cohort, school location, sex, academic year, presence of chronic illness, having COVID-19, number of COVID-19 vaccine doses, obeying COVID-19 precautions, and frequency of following news related to COVID-19 affected their eHealth literacy, COVID-19–related fear, and COVID-19–related anxiety.
The psychological effects of COVID-19 on nursing students have increased because of social isolation, uncertainty, online learning challenges, and fear of being infected during the pandemic.26,36 Our results showed a moderate level of COVID-19–related fear and low level of COVID-19–related anxiety among nursing students, and other studies demonstrated that nursing students experienced COVID-19–related fear generally at a moderate level6,17,37 and low to severe levels of anxiety during the pandemic.6,37 Fear of contracting an illness may lead to psychological problems and harmful coping mechanisms. 38 Although fear and anxiety levels among students may have increased because of the rapid transmission of COVID-19, other factors may have included lack of a definitive treatment, news about high morbidity and mortality, and the increasing infodemic.
The COVID-19 pandemic resulted in an immense amount of information published on the internet, leading to an infodemic. 17 Therefore, having a proficient health literacy level has become essential for nursing students. 39 Similar to our study, in which most nursing students had high eHealth literacy levels, other studies also found high eHealth literacy among students,40 -42 although some studies41,43 found eHealth literacy levels among nursing students that were lower than shown in our study. The high level of eHealth literacy among students could be explained by advances in information and communication technologies and the parallel increased experience of using the internet among students. Online activities, such as attending online courses, doing online homework, and increasing online communication opportunities because of distance education during the COVID-19 pandemic, may have contributed to increased skills in digital device use and, thus, increased eHealth literacy levels among students.
The finding of higher eHealth literacy levels among US nursing students than among Turkish nursing students can be explained by differences in the level of development between the 2 countries. Developed countries generally have increased industrialization, higher standards of living, more technological and educational opportunities, and more robust economic growth than developing countries do, which could have contributed to more advanced digital literacy and eHealth literacy skills in the US nursing students than in the Turkish nursing students.
As students advance through their academic years, their ability to access, understand, and interpret information also advances. 39 Similar to several previous studies,40,43 in our study, eHealth literacy levels were higher among the third- and fourth-year versus first- and second-year nursing students. Distance education, especially during the COVID-19 pandemic, could have resulted in increased length of internet use and, thus, increased use of online health information sources among students. Having nursing students with excellent eHealth literacy levels is essential to helping educate future patients who are searching for health-related information online.
Our 1-way MANOVA results showed that COVID-19–related fear was high among nursing students who did not have COVID-19, who had 3 doses of COVID-19 vaccine, and who followed COVID-19 precautions, whereas COVID-19–related anxiety was higher among nursing students who did not have versus had a chronic disease. We suggest that nursing students who were afraid of contracting COVID-19 considered all protective measures such as face masks, social distancing, hygiene, and vaccination to protect themselves from COVID-19. Unlike some studies, 37 we found that nursing students who followed the news about COVID-19 every day had high levels of COVID-19–related fear and anxiety. Exposure to more information about COVID-19, whether true or false, and uncertainty and ambiguity about the future may have increased anxiety and fear.
Similar to our finding of higher COVID-19–related fear and anxiety levels among female students than among male students, a meta-analysis showed that COVID-19 negatively affected female populations more than male populations. 44 Additional studies showed that COVID-19–related fear17,38 and COVID-19–related anxiety 36 were higher among female nursing students than among male nursing students. This finding may be related to biological factors, including hormonal influences and reproductive cycles in females. 44
Limitations
Our study had several limitations. First, our study was conducted at only 2 nursing schools; thus, the findings cannot be generalized to all nursing schools. Second, because of our study’s correlational design, exact causal relationships cannot be described. Third, although levels of COVID-19–related anxiety and fear among nursing students were measured with scales specific to COVID-19, responses may have been affected by other variables, such as COVID-19 pandemic measures (wearing a face mask, social distancing, following quarantine guidelines, and practicing hand hygiene), socioeconomic level, and employment status.
Conclusions
The present study may contribute to designing interventions by considering the influencing factors that may increase eHealth literacy levels and reduce the mental health issues of nursing students. Increasing the eHealth literacy level of students during health emergencies, such as the COVID-19 pandemic, can make important contributions, especially in reducing mental health symptoms among students. Future studies are needed to investigate the effects of interventions to increase eHealth literacy levels and to enhance psychological resilience among nursing students.
Footnotes
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Sultan Ayaz-Alkaya, PhD 
https://orcid.org/0000-0003-4745-5478
References
- 1. Cangussu LR, Barros IR, Botelho Filho CA, Sampaio Filho JD, Lopes MR. COVID-19 and health literacy: the yell of a silent epidemic amidst the pandemic. Rev Assoc Med Bras (1992). 2020;66(suppl 2):31-33. doi: 10.1590/1806-9282.66.S2.31 [DOI] [PubMed] [Google Scholar]
- 2. Fang Y, Ji B, Liu Y, et al. The prevalence of psychological stress in student populations during the COVID-19 epidemic: a systematic review and meta-analysis. Sci Rep. 2022;12(1):12118. doi: 10.1038/s41598-022-16328-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Wang C, Wen W, Zhang H, et al. Anxiety, depression, and stress prevalence among college students during the COVID-19 pandemic: a systematic review and meta-analysis. J Am Coll Health. 2023;71(7):2123-2130. doi: 10.1080/07448481.2021.1960849 [DOI] [PubMed] [Google Scholar]
- 4. Kavvadas D, Kavvada A, Karachrysafi S, et al. Stress, anxiety and depression prevalence among Greek University students during COVID-19 pandemic: a two-year survey. J Clin Med. 2022;11(15):4263. doi: 10.3390/jcm11154263 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Kavvadas D, Kavvada A, Karachrysafi S, Papaliagkas V, Chatzidimitriou M, Papamitsou T. Stress, anxiety, and depression levels among university students: three years from the beginning of the pandemic. Clin Pract. 2023;13(3):596-609. doi: 10.3390/clinpract13030054 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Tran HTT, Nguyen MH, Pham TTM, et al. Predictors of eHealth literacy and its associations with preventive behaviors, fear of COVID-19, anxiety, and depression among undergraduate nursing students: a cross-sectional survey. Int J Environ Res Public Health. 2022;19(7):3766. doi: 10.3390/ijerph19073766 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. World Health Organization. Infodemic. 2023. Accessed December 2, 2024. https://www.who.int/health-topics/infodemic#tab=tab_1
- 8. Zhu X, Zheng T, Ding L, Zhang X. Exploring associations between eHealth literacy, cyberchondria, online health information seeking and sleep quality among university students: a cross-section study. Heliyon. 2023;9(6):e17521. doi: 10.1016/j.heliyon.2023.e17521 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Jackson DN, Trivedi N, Baur C. Re-prioritizing digital health and health literacy in Healthy People 2030 to affect health equity. Health Commun. 2021;36(10):1155-1162. doi: 10.1080/10410236.2020.1748828 [DOI] [PubMed] [Google Scholar]
- 10. Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. 2006;8(4):e27. doi: 10.2196/jmir.8.4.e27 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Tanaka J, Kuroda H, Igawa N, Sakurai T, Ohnishi M. Perceived eHealth literacy and learning experiences among Japanese undergraduate nursing students: a cross-sectional study. Comput Inform Nurs. 2020;38(4):198-203. doi: 10.1097/CIN.0000000000000611 [DOI] [PubMed] [Google Scholar]
- 12. Yang BX, Xia L, Huang R, et al. Relationship between eHealth literacy and psychological status during COVID-19 pandemic: a survey of Chinese residents. J Nurs Manag. 2021;29(4):805-812. doi: 10.1111/jonm.13221 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kiraly O, Potenza MN, Stein DJ, et al. Preventing problematic internet use during the COVID-19 pandemic: consensus guidance. Compr Psychiatry. 2020;100:152180. doi: 10.1016/j.comppsych.2020.152180 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Khazaal Y, Chatton A, Rochat L, et al. Compulsive health-related internet use and cyberchondria. Eur Addict Res. 2021;27(1):58-66. doi: 10.1159/000510922 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Zheng H, Sin SCJ, Kim HK, Theng YL. Cyberchondria: a systematic review. Internet Res. 2021;31(2):677-698. doi: 10.1108/INTR-03-2020-0148 [DOI] [Google Scholar]
- 16. Cai H, Xi HT, Zhu Q, et al. 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. Am J Addict. 2021;30(6):585-592. doi: 10.1111/ajad.13216 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Oducado RM, Tuppal C, Estoque H, et al. Internet use, eHealth literacy and fear of COVID-19 among nursing students in the Philippines. Int J Educ Res Innov. 2021;15:487-502. doi: 10.46661/ijeri.5520 [DOI] [Google Scholar]
- 18. Park H, Lee E. Self-reported eHealth literacy among undergraduate nursing students in South Korea: a pilot study. Nurse Educ Today. 2015;35(2):408-413. doi: 10.1016/j.nedt.2014.10.022 [DOI] [PubMed] [Google Scholar]
- 19. Sinan O, Ayaz-Alkaya S, Akca A. Predictors of eHealth literacy levels among nursing students: a descriptive and correlational study. Nurse Educ Pract. 2023;68:103592. doi: 10.1016/j.nepr.2023.103592 [DOI] [PubMed] [Google Scholar]
- 20. Haruna H, Hu X. International trends in designing electronic health information literacy for health sciences students: a systematic review of the literature. J Acad Librar. 2018;44(2):300-312. doi: 10.1016/j.acalib.2017.12.004 [DOI] [Google Scholar]
- 21. Fakhari A, Shalchi B, Rahimi VA, et al. Mental health literacy and COVID-19 related stress: the mediating role of healthy lifestyle in Tabriz. Heliyon. 2023;9(7):e18152. doi: 10.1016/j.heliyon.2023.e18152 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Kwon M, Oh J. The relationship between depression, anxiety, e-health literacy, and health-promoting behavior in nursing students during COVID-19. Medicine (Baltimore). 2023;102(6):e32809. doi: 10.1097/MD.0000000000032809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Akingbade O, Adeleye K, Fadodun OA, et al. eHealth literacy was associated with anxiety and depression during the COVID-19 pandemic in Nigeria: a cross-sectional study. Front Public Health. 2023;11:1194908. doi: 10.3389/fpubh.2023.1194908 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Lee JJ, Poon CY, O’Connor S, et al. Associations of eHealth literacy and knowledge with preventive behaviours and psychological distress during the COVID-19 pandemic: a population-based online survey. BMJ Open. 2023;13(12):e069514. doi: 10.1136/bmjopen-2022-069514 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Aydan S, Donar GB, Yeşilaydın G, Kartal N. Impact of e-health literacy and cyberchondria severity on fear of COVID-19 in Turkish society. Hacettepe Sağlık İdaresi Dergisi. 2023;26(2):495-510. [Google Scholar]
- 26. Kuru Alici N, Ozturk Copur E. Anxiety and fear of COVID-19 among nursing students during the COVID-19 pandemic: a descriptive correlation study. Perspect Psychiatr Care. 2022;58(1):141-148. doi: 10.1111/ppc.12851 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Curuk GN, Ozgul E, Karadag S. The effect of COVID-19 on fear, anxiety, and sleep in nursing students. Ir J Med Sci. 2023;192(6):3125-3131. doi: 10.1007/s11845-023-03308-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. von Elm E, Altman DG, Egger M, Pocock SJ, GØtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147(8):573-577. doi: 10.7326/0003-4819-147-8-200710160-00010 [DOI] [PubMed] [Google Scholar]
- 29. Tamer Gencer Z. Analysis of validity and reliability of Norman and Skinner’s e-Health scale literacy for cultural adaptation. Istanbul Univ Fac Comm J. 2017;52(1):131-145. doi: 10.17064/iuifd333165 [DOI] [Google Scholar]
- 30. Satici B, Gocet-Tekin E, Deniz ME, Satici SA. Adaptation of the Fear of COVID-19 Scale: its association with psychological distress and life satisfaction in Turkey. Int J Ment Health Addict. 2021;19(6):1980-1988. doi: 10.1007/s11469-020-00294-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Biçer I, Çakmak C, Demir H, Kurt ME. Coronavirus Anxiety Scale Short Form: Turkish validity and reliability study. Anatol Clin. 2020;25(1):216-225. doi: 10.21673/anadoluklin.731092 [DOI] [Google Scholar]
- 32. Ahorsu DK, Lin CY, Imani V, Saffari M, Griffiths MD, Pakpour AH. The Fear of COVID-19 Scale: development and initial validation. Int J Ment Health Addict. 2022;20(3):1537-1545. doi: 10.1007/s11469-020-00270-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Lee SA. Coronavirus Anxiety Scale: a brief mental health screener for COVID-19 related anxiety. Death Stud. 2020;44(7):393-401. doi: 10.1080/07481187.2020.1748481 [DOI] [PubMed] [Google Scholar]
- 34. Liu X. Traditional methods of longitudinal data analysis. In: Methods and Applications of Longitudinal Data Analysis. Elsevier; 2016:19-59. [Google Scholar]
- 35. Tabachnick BG, Fidell LS, Ullman JB. Using Multivariate Statistics. 7th ed. Pearson; 2019. [Google Scholar]
- 36. Savitsky B, Findling Y, Ereli A, Hendel T. Anxiety and coping strategies among nursing students during the COVID-19 pandemic. Nurse Educ Pract. 2020;46:102809. doi: 10.1016/j.nepr.2020.102809 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Kaya Ç, Bilik Ö. Investigation of the effect of nursing students’ coronavirus fear on their coronavirus anxiety, learning and study approaches. Gevher Nesibe J Med Health Sci. 2022;7(20):99-110. doi: 10.5281/zenodo.7133490 [DOI] [Google Scholar]
- 38. Alsolais A, Alquwez N, Alotaibi KA, et al. Risk perceptions, fear, depression, anxiety, stress and coping among Saudi nursing students during the COVID-19 pandemic. J Ment Health. 2021;30(2):194-201. doi: 10.1080/09638237.2021.1922636 [DOI] [PubMed] [Google Scholar]
- 39. Ayaz-Alkaya S, Terzi H. Investigation of health literacy and affecting factors of nursing students. Nurse Educ Pract. 2019;34:31-35. doi: 10.1016/j.nepr.2018.10.009 [DOI] [PubMed] [Google Scholar]
- 40. Holt KA, Overgaard D, Engel LV, Kayser L. Health literacy, digital literacy and eHealth literacy in Danish nursing students at entry and graduate level: a cross sectional study. BMC Nurs. 2020;19:22. doi: 10.1186/s12912-020-00418-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Turan N, Guven Ozdemir N, Culha Y, Ozdemir Aydin G, Kaya H, Asti T. The effect of undergraduate nursing students’ e-Health literacy on healthy lifestyle behaviour. Glob Health Promot. 2021;28(3):6-13. doi: 10.1177/1757975920960442 [DOI] [PubMed] [Google Scholar]
- 42. Kim KA, Hyun MS, De Gagne JC, Ahn JA. A cross-sectional study of nursing students’ eHealth literacy and COVID-19 preventive behaviours. Nurs Open. 2023;10(2):544-551. doi: 10.1002/nop2.1320 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Shiferaw KB, Mehari EA, Eshete T. eHealth literacy and internet use among undergraduate nursing students in a resource limited country: a cross-sectional study. Inform Med Unlocked. 2020;18:100273. doi: 10.1016/j.imu.2019.100273 [DOI] [Google Scholar]
- 44. Metin A, Erbicer ES, Sen S, Cetinkaya A. Gender and COVID-19 related fear and anxiety: a meta-analysis. J Affect Disord. 2022;310:384-395. doi: 10.1016/j.jad.2022.05.036 [DOI] [PMC free article] [PubMed] [Google Scholar]