Associations of COVID-19 risk perception, eHealth literacy, and protective behaviors among Chinese college students following vaccination: a cross-sectional study

Background

COVID-19 is renowned for causing an infectious pneumonia which broke out in the end of 2019^[1]. Subsequently, the COVID-19 epidemic upgraded into a pandemic in Jan, 2020, and it was declared a public health emergency of international concern by WHO. In March of that year, WHO had declared COVID-19 to be a pandemic. COVID-19 is characterized as a highly infectious and strongly pathogenic condition which posed serious threats to global health. As of June 25th, 2021, there have been 179.69 million cases of COVID-19 diagnosed globally, of which about 3.90 million have died^[2]. College students, a group of well-educated young people, are characterized by active social contacts and intense cross-regional mobility; thus, they are at a higher risk of being infected with COVID-19 compared with other groups and are among those who should be vaccinated. During the outbreak of COVID-19, 92.7% of Chinese college students were highly in favor of epidemic containment strategies and showed high adherence to them. Specifically, well over three quarters of college students performed well in frequent hand washing or hand hygiene (86.9%) and in wearing face masks (over 90%). They also avoided going out in public and hosting gatherings. The female students and those enrolled in post-graduate studies were inclined to take preventive measures^[3]; however, it is uncertain how they will behave and respond to regular COVID-19 prevention and control at this phase after receiving COVID-19 vaccination and the differences among individuals with varying socio-demographic characteristics also require further analysis.

Risk perception is defined as the individual feelings and recognition to exterior objective risk which drive vital decisions on the behaviors^[4]. During the period of COVID-19, the risk perception for citizens was highly associated with their individual preventive measures against COVID-19. Savadori et al.^[5] found that the risk perception exerted positive effects on preventive measures upon infection prevention for citizens. Specifically, those who felt anxiety towards COVID-19 were more inclined to engage in washing hands, wearing face masks and socially distancing from others. Additionally, those who perceived themselves to be highly susceptible were inclined to reduce social contact^[5]. Alegria et al.^[6] identified positive correlation between frequency of washing hands with self-perceived effectiveness of washing hands and epidemic risk perception based on the Common-Sense Model of Self-Regulation (CSM-SR). Chinese college students were associated with a relatively high risk perception towards COVID-19 in the study of Ding Y et al.^[7]., especially in female students and non-medical students 92.5% of them believed that those in good health condition may be infected with COVID-19, and 85.1% of the college students were concerned about members of their family becoming infected. Those with a higher level of risk perception were better informed with regards to COVID-19, as well as carrying out more preventive actions^[7]. The pandemic has been contained effectively as the research work continues on COVID-19, disease diagnosis and treatment go into standardization along with the regular prevention and control measures being placed into effect. At present, China has entered the phase of regular prevention and control, with the government offering free vaccines for COVID-19 for all of its citizens. The national vaccination plan has observed great progress due to the diligence of the government and its personnel combined with the active cooperation of Chinese citizens; however, sporadic cases have emerged in several regions in recent days. Under these circumstances, we sought to identify the risk perception of COVID-19 among Chinese college students after receiving vaccination. We also sought to confirm the association between risk perception and preventive actions on infection prevention post vaccination.

During the time of COVID-19, home quarantine and limiting in person social contact were recommended and enforced globally. Public outings and social contact were confined on a large scale; thus, the internet and social media have become the main channels for citizens to grasp dynamic information and maintain social connections with one another. However, misinformation with regard to COVID-19 shows up frequently on the internet. eHealth literacy plays a critical role in rapidly targeting high-quality information from the confusing online environment that is flooded with mixed messages, as well as in making correct decisions for practices. eHealth literacy is a fundamental skill from which individuals can benefit from eHealth services.

Norman et al.^[8]defined eHealth literacy as the ability to search, find, understand, and produce critical analyses of targeted health information from online health resources, followed by making the correct decision in order to address health problems; it makes up an integral driving factor behind individual health behaviors^{[9, 10]}. Do BN et al.^[11] discovered that during the time of COVID-19, higher eHealth literacy was closely associated with increased adherence to actions on infection prevention and control (IPC) and maintenance of a better life style. Nevertheless, frequent exposure to social media also has its downside, which may lead to an individual’s reluctance to obtain or even refusing to obtain the COVID-19 vaccine. This may be the result of online spreading of false information about the COVID-19 vaccines^[12]. Still, because individuals with higher eHealth literacy perform better in critically evaluating online information, their decision-making is less likely to be influenced by misinformation. Li X et al.^[13] have confirmed a positive moderating effect of eHealth literacy in between social media use and preventative actions. Therefore, higher eHealth literacy is strongly correlated to an increased adherence to infection preventive actions. Li S et al.^[14] found that Chinese college students showed a relatively high eHealth literacy during the time of COVID-19, and higher eHealth literacy predicted better preventive behavior at a significant level. However, the eHealth literacy for Chinese college students under the phase of regular prevention and control remains to be seen. In addition, how eHealth literacy is associated with individuals’ preventive actions after receiving COVID-19 vaccines requires further investigation and analysis.

Based on the aforementioned research questions, the present study sought to: gain knowledge of the preventive actions on infection defense, risk perception of COVID-19 and the eHealth literacy among Chinese college students, as well as discussing links between preventive actions with risk perception and eHealth literacy. This study presented not only as a basis for implementing measures of COVID-19 prevention and control among Chinese college students, but also, as a reference for public health management on COVID-19 prevention and control among Chinese college students from the perspective of risk perception and eHealth literacy.

Methods

Study design and population

This cross-sectional study with onsite survey methods was conducted at a comprehensive university in Changsha city in the province of Hunan in China. The university, as one of key universities and colleges in China, offers 106 degree programs at the bachelor’s level and enrolls more than 50,000 full-time students. The questionnaire QR code, downloaded from the online platform (Questionnaire Star, URL: https://www.wjx.cn/), was distributed as a paper questionnaire. College students were invited to scan the QR code on the campus from June 10th to 15th of 2021. An informed consent form was initially signed online by all participates. Before participants filled in the questionnaire, the introduction, i.e., research background, purpose, rules of anonymity and confidentiality and precautions would be explained to them. The inclusion criteria for participants consisted of: a. college students over 18; b. Chinese students from the target university; c. completion of the COVID-19 vaccination; and d. consent to participate in the survey. A total of 6312 college students responded with 6282 responses having met the inclusion criteria (1 person was under 18 and 29 people who did not consent to this survey). We declared that the minimum time for completing the survey was 90 seconds, and 641 invalid responses were excluded for the time limit. In total, 5641 valid responses were included in the data analysis (effective response rate: 89.37%).

Variables and measurement

Socio-demographic information included gender, age, major, education level, health condition, self-perception of susceptibility and suspicion of suffering from post-vaccination reactions.

COVID-19 risk perception was evaluated by the public health emergency risk perception scale (the PHERPS) compiled by Zhiying Shen et al.^[15] in 2020. The public health emergency in this survey was defined as COVID-19. The PHERPS included 9 items within 3 domains of dread risk perception (3 items), severe risk perception (3 items) and unknown risk perception (3 items). Each item was scored on a 5-point Likert scale from strongly disagree to strongly agree. The total score was the sum of 9 items with a range of 9-45. The higher the score, the higher the level of risk perception. (Cronbach α=0.84)

eHealth literacy adopted the eHealth Literacy scale (eHEALS) developed by Norman et al. ^[16] in 2006. The Chinese version of eHEALS was developed from the English version and translated into Chinese by two graduate students who held an IELTS certificate firstly. Afterwards, the back-translation was performed by a professor who had previously studied abroad to ensure the accuracy of the translation. The eHEALS included 8 items with one domain. Each item was scored on a 5-point Likert scale from strongly disagree to strongly agree with a total score ranged 8-40. The higher the score, the higher the level of eHealth literacy is. (Cronbach α=0.96)

The questionnaire of protective behaviors was developed by the research team based on the COVID-19 advice for the public after getting vaccinated^[17]. It included 7 items within seven aspects, i.e., social distancing, mask-wearing, handwashing, sneeze protection, going-out limit, ventilating and traveling limit. The score adopted a Likert 5 rating from never (1 point) to always (5 points). The item 2 was scored in reverse and the others were positive scores. The total score was the sum of the 7 items with a range of 7-35. (Cronbachα=0.73) The above instruments appeared in Multimedia Appendix1.

Data Analysis

SPSS 25.0 (IBM Corporation) was used in data analysis. For descriptive statistics, categorical variables were presented as N (%), including gender, age groups, major, education level, health condition, self-perception of susceptibility and suspicion of suffering from post-vaccination reactions. Continuous variables were presented as Mean±Standard deviation (M±SD), including age, the level of COVID-19 risk perception, e-health literacy and protective behaviors after COVID-19 vaccination. Univariate analysis was performed using student t test or one way ANOVA. A multiple linear regression was employed to test the determinant factors affecting protective behaviors. Variance inflation factor (VIF) was used to examine the multicollinearity among the variables. Statistical testing was bilateral with statistical significance at P<0.05.

Results

Socio-demographic Profiles

The results showed that among 5641 investigated college students, male students accounted for 59.01% with the average age being (21.39±2.75) years. The majority of this population were non-medical students (95.87%) and undergraduate students (73.11%), with 91.83% of them in very good or pretty good health condition. A small proportion (13.76%) thought they would likely or most likely be infected with COVID-19 after getting vaccinated. Besides, more than 1 in 10 (10.35%) college students had ever suspected to suffer from post-vaccination reactions after the COVID-19 vaccination (Table 1).

Table 1 Socio-demographic profiles and univariate analysis for protective behaviors after vaccination (N=5641)

Variables	n(%)	The score for protective behaviors （M±SD）	*t/F*	P value
Gender			-2.52	0.012
Male	3329（59.01）	25.95±4.07
Female	2312（40.99）	26.21±3.83
Age（year）			16.48	＜0.001
18-20	2507（44.44）	25.86±3.91
21-23	2071（36.71）	25.98±3.96
≥24	1063（18.84）	26.68±4.09
Major			1.26	0.207
Medical	233（4.13）	26.38±3.91
Non-medical	5408（95.87）	26.04±3.98
Education			12.65	<0.001
Undergraduate	4124（73.11）	25.90±3.93
Post-graduate	1220（21.63）	26.44±4.12
PHD	297（5.27）	26.68±3.82
Health condition			80.61	<0.001
Very good	2530（44.85）	26.73±4.19
Pretty good	2650（46.98）	25.66±3.68
in General level, pretty poor, very poor	461（8.17）	24.64±3.70
Self-perception of the possibility of being infected with COVID-19 after vaccination			13.50	<0.001
Impossible	838（14.86）	26.69±4.44
Not likely	4027（71.39）	25.98±3.87
Likely, most likely	776（13.76）	25.76±3.88
Suspicion to suffer from post-vaccination reactions after vaccination			-3.545	<0.001
Yes	584（10.35）	25.51±3.96
No	5057（89.65）	26.12±3.97

Characteristics of COVID-19 risk perception, eHealth Literacy, and Protective Behaviors

The mean score for COVID-19 risk perception was (36.82±5.43) at a pretty good level. The domains of COVID-19 risk perception with declining mean scores were dread risk perception (12.93±1.96), severe risk perception (12.93±2.19) and unknown risk perception (10.96±2.61). The eHealth literacy was at a relatively high level (30.68±7.16). Additionally, the level of protective behaviors for Chinese college students was in the middle (26.06±3.97). The majority (60.96%) of college students always or often avoided going out into a crowd. Over two thirds (70.74%) always or often cancelled non-work or work-related trips. The protective behaviors for some college students was insufficient. Nearly one-third (30.42%) of the students always or often failed to wear a mask while going out. A fair portion of college students (29.25%) failed to maintain at least one meter of distance from others in social settings (Table 2).

Table 2 The status of protective behaviors for Chinese college students after vaccination

Protective behaviors	M±SD	Frequency [ n(%)]
Protective behaviors	M±SD	Never	Seldom	Sometimes	Often	Always
Total score	26.06±3.97	-	-	-	-	-
Keep at least 1 meter away from others in social situations	3.08±1.00	249(4.41)	1401(24.84)	2102（37.26）	1431（25.37）	458（8.12）
Do not wear a mask when going out	3.06±1.03	468（8.30）	1457（25.83）	2000（35.45）	1403（24.87）	313（5.55）
Wash hands promptly after returning to your residence	3.80±0.96	50（0.89）	531（9.41）	1391（24.66）	2203（39.05）	1466（25.99）
Cover any cough or sneeze in your bent elbow	4.30±0.83	21（0.37）	182（3.23）	692（12.27）	1956（34.67）	2790（49.46）
Avoid going to the crowded	3.74±0.90	44（0.78）	384（6.81）	1774（31.45）	2230（39.53）	1209（21.43）
Open windows for ventilation to maintain air circulation	4.12±0.77	13（0.23）	114（2.02）	925（16.40）	2696（47.79）	1893（33.56）
Cancel non-work or work-related trips	3.95±0.92	53（0.94）	308（5.46）	1290（22.87）	2189（38.81）	1801（31.93）

The factors which influence COVID-19 Protective Behaviors

As shown in Table1, the univariate analysis indicated that the female college students, those above 24 years of age, those with a higher education level, those in superior health, those with a lower self-perception of susceptibility, and those never suspected to suffer from post-vaccination reactions were more likely to perform better in the personal protection (P<0.05).

Multiple linear regression analysis with stepwise method (α in=0.05，α out=0.10) was conducted with the score of protective behaviors after vaccination as dependent variable and the variables with statistical significance in univariate analysis, three domains of risk perception and eHealth literacy as independent variables. Variable assignments were as follows. They were gender (1=male; 2=female), age (1=18~20 y; 2=21~23y; 3=≥24y), education (1=undergraduate; 2=post-graduate; 3=PHD), health condition (1=very good; 2=pretty good; 3=in General level, pretty poor, very poor), self-perception of susceptibility (1=Impossible; 2=Not likely; 3=Likely, most likely) and suspicion to suffer from post-vaccination reactions (1=yes; 2=no). VIF ranged 1.02-1.80, indicating there was no multicollinearity among selected independent variables. Results (Table 3) showed that nine determinant factors, i.e., gender, age, health condition, self-perception of susceptibility, suspicion of suffering from post-vaccination reactions, dread risk perception, severe risk perception, unknown risk perception and eHealth Literacy, were reserved accounting for 14.2% of variation in predicting the level of protective behaviors.

Table 3 Multivariate linear regression analysis on the protective behaviors after COVID-19 vaccination for Chinese college students (N=5641)

Variables	Partial regression coefficient	Standard error	Standardized partial regression coefficient	t	P Value	*95%CI*
Constant	16.124	0.593	-	27.204	<0.001	(14.962,17.286)
Gender	0.528	0.102	0.065	5.196	<0.001	(0.329,0.727)
Age（year）	0.370	0.066	0.070	5.626	<0.001	(0.241,0.499)
Health condition	-0.750	0.081	-0.119	-9.246	<0.001	(-0.910,-0.591)
Self-perception of susceptibility to COVID-19	-0.190	0.095	-0.026	-2.000	0.046	(-0.376,-0.004)
Suspicion to suffer from post-vaccination reactions after the COVID-19 vaccination,	0.326	0.162	0.025	2.007	0.045	(0.008,0.645)
Dread risk perception	0.381	0.032	0.188	11.917	<0.001	(0.319,0.444)
Severe risk perception	0.099	0.030	0.055	3.289	0.001	(0.040,0.158)
Unknown risk perception	-0.047	0.021	-0.031	-2.217	0.027	(-0.089,-0.005)
eHealth Literacy	0.125	0.007	0.225	17.874	<0.001	(0.111,0.139)

R²=0.142，F=103.586，P<0.001.

Discussion

The results showed that the protective behaviors after COVID-19 vaccination for Chinese college students were at the middle level and the majority of college students maintained good protective habits. Dread risk perception, severe risk perception and eHealth literacy would positively predict the protective behaviors, but unknown risk perception had a negative predictive effect. Female college students, those who were older, in good health condition, with a lower self-perception of susceptibility, and those never suspected to suffer from post-vaccination reactions were inclined to have a higher level of protective behaviors. In the case of the sporadic diagnoses which occurred in many cities, the results indicated a sufficient level of protection awareness among Chinese students post vaccination. The COVID-19 risk perception was at a high level which was strongly correlated with the enforcement of strict measures to control the epidemic. Overall, our findings emphasized the importance of risk perception and eHealth literacy, and prepares policymakers and health managers to develop the necessary prevention policies and target education measures.

The results of this study suggested that female college students, those who were older, in good health condition, with less self-perception of susceptibility, those never suspected to suffer from post-vaccination reactions were more likely to perform better in COVID-19 protection. This was confirmed via several former studies. Ferdous et al.^[18] and Li X et al.^[19] found that the female, elder residents in good health condition will take precautions more frequently, which may be attributed to a higher level of cognition, a more precautious attitude towards COVID-19 as well as better compliance of the IPC guidance for those residents^{[20, 21]}. A previous study^[18]showed that those residents with a high level of education would perform better in protective behaviors, and education showed a statistically significant association with the level of protective behaviors in this study during the univariate analysis. This was similar to the results of Olaimat et al.^[22]- that is, undergraduates performed worse in protective behaviors compared with graduate students. However, it was insignificant in the multivariate linear regression. This may be related to the characteristics of education among college students - that is, with more advanced age, higher levels of education were more prevalent and the education factor was adjusted in the regression analysis. A small proportion of those who were thought to likely or most likely be infected with COVID-19 after being vaccinated or suspected to suffer from post-vaccination reactions performed significantly worse in protective behaviors, which may be due to their negative attitudes toward COVID-19 as well as lack of trust in the vaccine’s efficacy for these college students^[23].Wang et al.^[24]pointed out that lower self-perception of susceptibility and good protective behaviors were two important protective factors for good mental health during the COVID-19 outbreak. Mo PKH et al.^[25]found that much greater concerns and self-perceived susceptibility towards COVID-19 were the risk factors for anxiety and depression. This suggested that more attention should be given to assessing the psychological state to prevent the rising of psychological health issues.

The results of this study showed relatively high levels of dread risk perception and severe risk perception with a relatively low level of unknown risk perception. Unknown risk perception was primarily associated with the level of cognition with regards to COVID-19 and the accuracy of detection and diagnosis^[15], for which the results determined the development of COVID-19. During the pandemic, as a result of the low level of cognition and the lack of effective detection and diagnosis methods, the level of unknown risk perception for the college students was quite high. With the establishment of the global COVID-19 IPC guidance, the epidemic was gradually brought under control, the vaccination strategy was carried on methodically, and the level of unknown risk perception for the college students was improved side by side. The results also suggested that different domains of risk perception had various effects on protective behaviors - that is, dread risk perception and severe risk perception had positive effects in contrast to unknown risk perception which played a negative role. This appeared to be inconsistent with previous studies. In the perspective of dread to evaluate the risk perception, Taghrir et al.^[26]found a negative relationship between protective behaviors and risk perception for medical students. Xu H et al.^[27]found that dread risk perception had a positive effect on COVID-19 protective behaviors. However, Quandt et al.^[28] revealed that with high levels of dread risk perception of COVID-19, the actual protective behaviors may be poor when the self-efficacy level was low. Jahangiry et al.^[29] indicated that either dread control (protective behaviors) or risk control (non-protective behaviors) may be adopted when the level of risk perception was high. This may be closely associated with the attitude towards COVID-19, the level of self-efficacy and response efficacy. Xie K et al.^[30] also found the mediating effect of safety climate between risk perception and social distance, and the lower the climate of safety that exists, the stronger the positive correlation between them. Thus, there was not a simple linear relationship between risk perception and health behaviors, and multiple factors (i.e., socio-demographics, psychological factors, self-efficacy, safety climate, etc.) made it complex. The findings of this study suggested that the relationship between risk perception and protective behavior should be considered to balance for COVID-19 risk management - that is, mobilizing positive factors to promote protective behaviors ^[31] and avoiding excessive risk perception may deter from the negative effects such as poor mental health^[32].

Compared with health literacy, eHealth literacy may play a crucial role during the COVID-19 outbreak^[33]. Do BN et al.^[11]compared the effects of health literacy and eHealth literacy on the compliance with COVID-19 infection control measures of healthcare workers and found that the latter had a more positive effect. The results of this study also showed eHealth literacy positively predicted the protective behaviors of college students post vaccination. This corresponded to previous findings^[34] and emphasized the importance of promoting eHealth literacy and the necessity of the related educational programs in epidemic prevention and control. However, the positive effects seemed to be weaker in this study (β=0.125) than those in the COVID-19 global pandemic^{[11, 35]}. There may be two reasons for this. One was relatively stable protective habits were developed after the epidemic and the other was likely the more significant effect of protective attitude and self-efficacy on health behaviors when the epidemic was under control in China. Previous studies indicated that multiple factors affected the eHealth literacy. Shi Y et al.^[36] divided the influencing factors into individual-level factors (age, gender, education, economy, frequency of Internet use and trust in online health resources), interpersonal-level factors (marital status, family caregivers and the experience of studying looking for health information) and social/community level factors (language and cultural barriers) based on socio-ecological model with the systematic review methods. Levin-Zamir et al.^[37]indicated that eHealth literacy was affected by the complexity of the network system. When the accessibility and usability of electronic resources were good, that is, the complexity of the network system decreased, the public eHealth literacy would be greatly positively affected. Thus, the comprehensive factors should be considered to improve the level of eHealth literacy from multiple perspectives. Besides, although the eHealth literacy reported by college students was at high levels in this study, the ability of actual information application may not be optimistic. Kim et al. ^[38] found that people with a greater ability to seek out information and judgments had a lower accuracy rate when answering actual questions. This indicated a gap between the subjective self-reporting and objective application. Neter et al.^[39] confirmed a weak correlation (r=0.34) between subjective and objective eHealth literacy, and proposed that different evaluation tools should be used for evaluation independently.

This study may have the following limitations: firstly, data was drawn from one comprehensive university and thus the generalizability of this study was limited. Therefore, further multi-center or nationwide investigations were recommended to generalize the findings. Secondly, the data quality with the online questionnaire may have declined, as the limit of investigators resulting in the impossibility of oversight throughout when the participants completed the questionnaires. The complete time was used to control the data quality. Also, this study failed to include college students with lower than a bachelor’s degree and the education levels were intended to include all. Finally, self-reported eHEALS was used to assess the level of eHealth literacy in this study and it might differ from the objective levels to some extent. It is recommended to further explore the associations between subjective and objective eHealth literacy.

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Associations of COVID-19 risk perception, eHealth literacy, and protective behaviors among Chinese college students following vaccination: a cross-sectional study

Abstract

Background

Methods

Results

Discussion

Conclusions

Declarations

Abbreviations

References