Skip to main content

Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model

Abstract

Background

Under the outbreak of Coronavirus disease 2019 (COVID-19), a structural equation model was established to determine the causality of important factors that affect Chinese citizens’ COVID-19 prevention behavior.

Methods

The survey in Qingdao covered several communities in 10 districts and used the method of cluster random sampling. The research instrument used in this study is a self-compiled Chinese version of the questionnaire. Of the 1215 questionnaires, 1188 were included in our analysis. We use the rank sum test, which is a non-parametric test, to test the influence of citizens’basic sociodemographic variables on prevention behavior, and the rank correlation test to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis, including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables.

Results

The result showed that the score in the prevention behavior of all citizens is a median of 5 and a quartile spacing of 0.31. The final structural equation model showed that the external support for fighting the epidemic, the demand level of health information, the cognition of (COVID-19) and the negative emotions after the outbreak had direct effects on the COVID-19 prevention behavior, and that negative emotions and information needs served as mediating variables.

Conclusions

The study provided a basis for relevant departments to further adopt epidemic prevention and control strategies.

Peer Review reports

Background

Public health emergency events refer to a sudden outbreak of a major infectious disease epidemic, a group illness of unknown cause, or a major food or occupational poisoning that may or have caused a public health hazard to the whole society [1]. The outbreak of Coronavirus disease 2019 (COVID-19) in December 2019 is a major public health emergency, which poses a major challenge to the health system in China. China has controlled the epidemic to a great extent and has entered a stage of normalized epidemic prevention nowadays, because the Chinese government responded to the COVID-19 epidemic in a highly centralized and efficient way, public epidemic prevention also plays an important role in the overall containment of COVID-19 [2, 3].

The effective behavior prevention intervention must be based on the corresponding theoretical basis [4]. There have been many kinds of research on the influencing factors of health protection behavior, and some generally accepted theories and models have been developed to explain such behavior, such as health belief model [5], planned behavior theory [6], information motivation behavior skill model [7], social cognitive theory [8], etc. Although the mechanism behind these theories is different, the influencing factors for integrating them are individual, psychology, and environment. The 3 factors are taken as the basis and combined with the background of COVID-19, We have chosen the external support for fighting the epidemic, the needs level of health information, the cognition of COVID-19 and the negative emotions after the outbreak to construct a structural equation model for explaining COVID-19 prevention behavior [9].

Analysis Framework and Research Hypothesis

External support

Environmental factors are subjective norms, which means that individual behavior will be affected by the surrounding social environment. Environmental factors include external support in to fight against the epidemic. Sufficient external support can significantly improve people’s confidence and adaptability in responding to the epidemic [10]. Many reports have shown that the performance and effectiveness of the Chinese government and institutions in epidemic prevention and control work brings a great sense of security to the public. Social support of the social cognitive theory is widely used and practiced in individual health behavior change [8]. Social support affects disease control through encouraging healthy behavior and modulating effects by reducing the effects of acute and chronic stress on health and helping patients cope with stress resulting from the disease [11]. Therefore, the hypothesis is put forward:

  • H1: the external support to fight against the epidemic would have a positive impact on the prevention behavior.

  • H2: the external support to fight against the epidemic would have a negative impact on the negative emotions.

Negative emotions

Psychology is the brain’s subjective response to objective reality. Psychological factors include negative emotions after the outbreak. Research has shown that emotional states and behavioral efficiency are on a “U” shaped curve, with appropriate levels of emotion promoting behavior and increasing its efficiency, while when emotional states exceed a certain threshold, they can have a hindering effect on behavior [12]. In the face of a severe epidemic situation, the public may have strong negative stress reactions and adopt irrational behavior, while negative emotions prompt the public to search for more health information [13]. Thus, we hypothesized the following.

  • H3: negative emotions would have a positive impact on information needs.

  • H4: negative emotions would have a negative impact on prevention behavior.

Cognitive and information needs

Individual factors include cognitive and information needs. Cognition refers to people’s understanding and view of things. Human behavior is dominated by consciousness, and cognition will inevitably affect their behavior [14], the more comprehensive the cognition of diseases, the less the risk of negative emotions such as anxiety and depression [15]. The impact of cognitive barriers is mainly negative because they not only give rise to negative reactions such as frustration but also block, limit or hamper information seeking [16], highly cognitive people have rich experience in obtaining information, and are more likely to find health information online. Health information is a key determinant of healthy behavior [7], People seeking disease prevention information showed greater likelihood to perform disease prevention behaviors without intentions to perform health promotion behaviors [16], The survey conducted among Zhihu users who have participated in Q&A related to COVID-19 shows the indirect effects of health information seeking on preventive behavior were greater among those with a high level of health information efficacy, which supports indirect effects of information needs [12]. The hypothesis is:

  • H5: cognitive situation would have a positive impact on prevention behavior.

  • H6: cognitive situation would have a negative impact on negative emotions.

  • H7: cognitive situation would have a positive impact on information needs.

  • H8: information needs would have a positive impact on prevention behavior.

Based on our research hypothesis, we can get the hypothetical path model as shown in Fig. 1.

Fig. 1
figure 1

Hypothetical path model

In this study, we aimed to explore influence factors and the mesomeric effect between different variables of COVID-19 prevention behavior of Chinese citizens. Also, we further identified the causal relationships among the significant factors affecting their prevention behavior by developing a structural equation model, to provide a basis for the public health prevention of COVID-19. Relevant departments could adopt epidemic prevention and control strategies based on results.

Methods

Participants and data Collection

The target population of our study is the citizens of Qingdao. It was considered along with 5% precision with a two-sided 5% significance level and 95% power. Besides, the dropout rate was estimated at around 10%. Thus, the minimum sample in this research was 1173. The study used the method of cluster random sampling. Several communities in Shinan District, Shibei District, Huangdao District, Laoshan District, Licang District, Chengyang District, Jimo District, Jiaozhou District, Pingdu City and the Laixi City of Qingdao were randomly selected. We followed strict inclusion and exclusion criteria to select participants. Inclusion criteria for this study included: (1) Participants have lived in Qingdao for a long time; (2) Participate in the study voluntarily and fill in the informed consent form; (3) Participants can complete the online questionnaire by themselves or with the help of the online investigator. Exclusion criteria include: (a) Those who are participating in other similar research projects; (b) People who are unwilling to cooperate.

We recruited investigators to conduct multi-center questionnaires collection, all of whom received standardized and unified training. After introducing themselves and clarifying the research objectives, the investigator distributed research questionnaires among the residents of the community online and the residents filled in the questionnaires by themselves through links Write a questionnaire. Informed consent to take part in the study was obtained from each subject by setting relevant questions before conducting the online survey. After collecting the questionnaire, we carried out the logical inspection of the recovered questionnaire, and eliminated the unqualified questionnaire. We also filtered the IP address to avoid filling in the questionnaire repeatedly and the questionnaire whose time is less than 100 s to ensure the quality of the data. 1218 questionnaires were distributed and 1215 were collected between February 4, 2020, and February 13, 2020. The final analysis included 1188 questionnaires.

Research instrument

The research instrument used in this study is a self-compiled Chinese version of the questionnaire, which was designed by 17 experts from public health, psychology, sociology, health science popularization and other fields to hold two questionnaire discussion meetings on January 28 and February 2, 2020, respectively. We rigorously evaluated and modified the questionnaire questions, and finally obtained the questionnaire for use. The contents of the questionnaire consist of three parts, which were designed based on the research purpose and the hot issues of public prevention of COVID-19.

Demographic and sociological characteristics of the participants

This part of the questionnaire includes their gender, age, nation, district / county-level city, highest education background, marital status, family per capita monthly income, occupation, etc.

COVID-19 Prevention behavior

This part associated to the prevention behavior of COVID-19 includes 13 items: (1) Do not contact, purchase and eat wild animals; (2) Refrain from visiting relatives or traveling in the epidemic area; (3) Avoid close contact with people with respiratory disease symptoms; (4) Reduce traveling relatives, friends, and dinner parties; (5) Those who have lived and traveled in the epidemic area within two weeks should be isolated at home by themselves; (6) Prevent going to densely populated public places; (7) Adhere to safe eating habits, such as thoroughly cooking meat and eggs; (8) Maintain indoor cleanliness and open windows frequently for ventilation; (9) Keep hands clean (including washing hands properly); (10) Pay close attention to fever, cough and other symptoms, do a good job in health monitoring; (11) Cover your mouth and nose with cough and sneeze; (12) Take the initiative to wear a mask when suspicious symptoms appear and seek medical attention promptly; and (13) select and wear a mask correctly. A 6-point scale was used for each question to classify protective behaviors according to the diffusion of innovation theory [17] into innovators (early action and persuasion), early adopters (early action and personal evaluation), early public (after action), late public (problematic but action), and laggards (resistance). The scores of each item from “not applicable” (0 points) to “be the first to act and persuade others” (5 points). Each item is scored positively. Finally, the average score of each item is calculated, and the higher the score, the better the participants ' prevention behavior against COVID-19.

Influence factors of COVID-19 Prevention behavior

The third part of the questionnaire was designed based on the analysis framework, including four dimensions: external support, cognitive situation, information needs and negative emotions.

External support

It is a scale in which participants rate the extent to which the medical and health system, frontline medical personnel, news media, government headquarters, transportation departments, and netizens have played an active role in the fight against the COVID-19 epidemic. The lowest score of 0 means “Feeling the lowest level of support for the epidemic prevention and control of this group”, and the highest score of 10 means “Feeling the highest level of support for the epidemic prevention and control of this group”. Each item is scored positively. The final calculation results in an average of the evaluation scores for each sector. The higher the score, the higher the level of external support.

Cognitive Situation

The cognitive situation contains 10 items. (1) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is sensitive to ultraviolet light and heat; (2) only alcohol can effectively inactivate SARS-CoV-2; (3) the source of infection is mainly symptomatic patients; (4) the sources of infection are all people returning from Wuhan; (5) COVID-19 is mainly transmitted by respiratory droplets. (6) COVID-19 is not transmitted by contact; (7) people of all ages are generally susceptible; (8) the home isolation period for suspected infected persons is 14 days; (9) if infected, there will be symptoms; and (10) the symptoms of COVID-19 are similar to those of influenza. Scores were calculated based on the correctness of the answers to the 10 questions. Each question was scored 1 point for a correct answer and 0 points for an incorrect or unclear answer. Accumulate the total scores of the 10 questions. The higher the score, the higher the respondent’s recognition of COVID-19.

Information needs

The information needs consist of nine items. (1) scientific understanding of COVID-19; (2) epidemiological characteristics of COVID-19; (3) methods of disinfection of COVID-19; (4) knowledge of disease prevention; (5) distinction between COVID-19 and other respiratory diseases such as influenza; (6) clinical manifestations after infection; (7) severity of COVID-19; (8) treatment of COVID-19; and (9) vaccine accessibility. Each item was scored positively using a 5-point scale ranging from not at all necessary (1 point) to very necessary (5 points). Finally, the mean score for each item was calculated. Finally, the mean score for each item was calculated. The higher the score, the greater the information needs of the participants.

Negative emotions

Considering the special psychology of residents under the epidemic such as anxiety, fear, depression, compulsion, and suspicion [18, 19], negative emotions were referred to the Generalized Anxiety Scale (GAD-7) [20], Patient Health Questionnaire (PHQ-9) [21], and Symptom Self-Rating Scale (SCL-90) [22], containing six items as follows: (1) lack of energy or interest in doing things; (2) feeling depressed, frustrated, or hopeless; (3) repeatedly washing hands and scrubbing things, but always feel that they are not clean enough; (4) feel more nervous than before in places where people gather; (5) often suspect that they or their family members have been infected; and (6) often worry about the impact on their future lives. A 5-point scale was used for each item, ranging from “not at all” (1 point) to “almost every day” (5 points). Each item was scored positively. The final calculation was the average of the evaluation scores for each sector. The higher the score, the higher the level of negative emotions of the participants.

Reliability and validity test of the Questionnaire

The content validity of the questionnaire was confirmed using quantitative validity methods. Cronbach’s α was used to measure the internal reliability of the item. External support for fighting the epidemic (α = 0.835, 95%CI 0.820–0.849), demand level of health information (α = 0.959, 95%CI 0.955–0.962), COVID-19 prevention behavior (α = 0.941, 95%CI 0.936–0.946), and cognition of COVID-19 (α = 0.604, 95%CI 0.569–0.638), negative emotions (α = 0.795, 95%CI 0.777–0.813). The reliability of the questionnaire was within the acceptable range. The internal consistency Cronbach’s α coefficient was above 0.60, Kmo value is 0.917 > 0.6. Through Bartlett spherical test, the cumulative variance interpretation rate is 55.221%, which was consistent with our structural framework and had good structural validity. In conclusion, the questionnaire design was reasonable and the evaluation of reliability and validity was good.

Statistical analysis

IBM SPSS Statistics 22.0 was used for descriptive analysis of the general characteristics of the citizens. The normality of the data of prevention behavior was tested before analysis, and the S-W(Shapiro-Wilk) test results and histogram were both expressed as skewed distribution (p < 0.001), so the prevention behavior was expressed by the median ± quartile interval. Residual tests showed insignificant linear trends between the variables and the data did not meet the requirement for chi-squared residuals, making this study unsuitable using linear regression for correlation testing [23, 24]. We use the rank sum test in the nonparametric test (Wilcoxon Mann-Whitney test for two-sample data and Kruskal Wallis test for multi-sample data) to test the influence of citizens’ basic sociodemographic variables on prevention behavior, and use the rank correlation test(Spearman correlation) to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis with the generalised least squares (GLS) method for parameter estimation [25, 26], including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables.

Results

Sociodemographic characteristics and Rank Sum Test results

The characteristics of the 1188 participants in the study are presented in Table 1. The number of female citizens was more than twice the number of males. Most of the citizens were married (74.16%). Around 63.64% of them never drink wine. The majority of the citizens (90.74%) reported no religion. The scores in the prevention behavior of all citizens varied from 0 to 5, with a median of 5 and a quartile spacing of 0.31.

Table 1 Results of Descriptive and Rank Sum Test

Implementation of Prevention actions

More than 75% of the citizens had been able to take early prevention actions and persuade others, including choosing and wearing masks correctly, coughing and sneezing to cover their mouths and noses, keeping the room clean, frequently opening windows and ventilating, avoiding going to densely populated public places, etc., but there are still a few people (about 1.0%) who resist taking prevention actions. Further analysis of the proportion of innovators in various behaviors revealed that more than 83.1% of innovators adopted short-term preventive behaviors such as avoiding densely populated public places, reducing family visits and gatherings, and avoiding visiting relatives or traveling to infected areas. For those who make the right choice and wear masks, cough, sneeze, cover their mouths and noses, health monitoring, and other prevention behavior that can form good habits, the proportion was below 82.7% (Table 2).

Table 2 Implementation of prevention actions

Correlation among research variables

The correlation matrix among the research variables is presented in Table 3. Information needs positively correlated with prevention behavior (r = 0.312, p < 0.001). Cognitive situation showed a positive correlation with prevention behavior ( = 0.246, p < 0.001), and information needs (r = 0.230, p < 0.001 ), but negatively correlated with negative emotions (r = -0.123, p < 0.001). Negative emotions showed negative correlation with prevention behavior (r = -0.157, p < 0.001), and cognitive situation (r = -0.208, p < 0.001).

Table 3 Correlation matrix of research variables

Test of study models

The fit indices of the hypothesis model did not satisfy all fit criteria (hypothesis model: χ2/df = 49.325, NFI = 0.806, IFI = 0.809, TLI=-0.979, CFI = 0.802, RMSEA = 0.202), which means the hypothetical model might be overqualified with unnecessary paths among the variables.

According to the path coefficients of the model test results in Table 4, among the 8 hypotheses in the hypothetical model(Fig. 2), 7 (H1, H2, H3, H4, H5, H7 and H8) were confirmed to have statistically significant direct effects. H6, the relationship between the cognitive situation and prevention behavior was not statistically significant and was rejected. Moreover, we find the relationship between the cognitive situation and prevention behavior was not statistically significant external support.

Based on the fit indices of the hypothetical model and test of the hypotheses, it was necessary to revise the model. The paths with no significant statistical effect were eliminated from the hypothetical model. Finally, all of the fit indices of the model were satisfied with the conservative criterion(final model: χ2/df = 1.521, NFI = 0.994, IFI = 0.998, TLI = 0.979, CFI = 0.998, RMSEA = 0.021).

The final structural equation model of this study showed that external support, information needs, cognitive situation, and negative emotions had direct effects on prevention behavior, while negative emotions and information needs were used as intermediary variables (Fig. 3). The path coefficients of the model test are shown in Table 4.

We found that based on the model shown in Fig. 3, no matter whether any path was deleted or added, the modified model coefficients were not as good as the model. Therefore, we believe that the model shown in Fig. 2 is optimal.

Fig. 2
figure 2

The model before adjustment

Fig. 3
figure 3

The adjusted model (Standardized Path Estimates)

Table 4 Path coefficients of the model test

Discussion

Our study indicated that most citizens had been able to take early prevention actions and persuade others, which may be related to the fact that our research sample population is mainly the young and the middle-aged, who are more likely to get and accept more new information [27]. However, the adoption rate of short-term prevention actions was higher than that of long-term ones. It showed that the current epidemic prevention and control propaganda had been in place, and health education should be maintained to make the prevention behavior a living habit of citizens, especially promoting people to vaccinate against COVID-19, as one of the most effective early prevention methods, to increase the vaccination rate [28, 29].

Results of demographic variables indicated that gender, occupation, and drinking were found to be significantly difference in prevention behavior (p < 0.01), and age, educational background, and marital status had statistically difference in prevention behavior (p < 0.05). The implementation rate of men, aged from 26 to 30 years old, those who have drinking habits and those who are professional technicians (excluding medical personnel) was lower, which might be caused by (1) Women are more likely than men to perceive the risk of disease and take prevention actions; (2) People aged 26 to 30 need to find a job or complete the graduation project, and the epidemic has a greater impact on their lives; (3) People have healthier living habits or a medical background may take the healthy behavior more accurate and timely. Therefore, we should give correct guidance to different groups and behavior.

In our SEM analysis, the external support against the epidemic not only directly had a positive impact on the protection behavior but also took negative emotions and information needs as the intermediary to have a positive impact. external support is crucial to the citizen, helps to promote awareness of the government and the confidence of the medical scientific research institutions in China, have to cope with and overcome the outbreak of the epidemic, because the previous study has found the public measures the government’s handling ability, responsible attitude, communication sincerity, and other factors under the risk situation to form a positive expectation that the government can be relied on, and then take preventive actions [15], Researchers have proved that the external support against the epidemic, especially the support, publicity and guidance from the government, can reduce the risk of negative emotions [9, 30]and increases the degree of information needs [31]. Misinformation on social media will fuel people’s panic regarding the COVID-19 [32, 33]. It is suggested that we should timely convey scientific and positive information and knowledge to the masses looking for the public by using social media [34, 35] and pay attention to the epidemic early warning, and improve the public’s confidence in fighting against the epidemic. At the same time, medical institutions could provide adequate medical protection to the public by vigorously developing new technologies such as telemedicine and health information technology, which are also an important way to provide external support to the public [36, 37].

According to the results of path analysis, the cognitive status had a positive impact on the prevention behavior directly, and it also had a positive impact on the negative emotions as the intermediary. In our study, it was found that there was no significant positive effect of cognitive status on information needs, which may be because people with high cognitive status have learned enough health information and the degree of their needs decreased. Previous studies have indicated that the cognition of diseases is the precondition for an individual to do health behavior [38]. In total, the more comprehensive the public’s cognition of COVID-19, the better the awareness of preventive measures, the better their psychological state, and the more actively they will respond to the changes brought by the epidemic situation and take preventive and control measures, which is consistent with the research of Su’s [39], so after the emergent public health events should be handled in time, strengthen the education of health knowledge and psychological counseling, to improve the cognitive level of the citizens [40].

COVID-19 pandemic was indicated to have a profound and long lasting impact on people’s negative emotions, especially on children and adolescents [41, 42]. The results of SEM analysis indicated that negative emotions not only had a negative impact on the prevention behavior directly but also had a positive impact through the intermediary of information needs. In the face of severe epidemic situations, it is normal to have some stress reactions, but a strong negative stress reaction will lead the public to take irrational actions. For example, the panic buying of Shuanghuanglian Liquid during the epidemic of COVID-19 suggests that psychological counseling should be strengthened during the prevention and control of the epidemic. Public health emergency in the public’s psychological stress reaction stems from a lack of information and misunderstanding [43], but psychological stress reaction can urge citizens to access information, understand the preventive measures,take prevention actions [13], tips on health communication should follow the principle of risk assessment front, avoid causing misunderstanding and public panic.

According to the results of SEM analysis, information needs had a direct positive impact on the protection behavior, and the effect value was the largest. In the outbreak of large-scale epidemic diseases, the public has urgent information needs. Previous studies have shown that meeting the public’s information needs are shown to be associated with better self-efficacy and health-promoting behavior [44]. Therefore, it is necessary to carry out scientific and effective information dissemination, publicity, and education with different groups of people, meet the public’s information needs and guide the public’s cognition and rational prevention and control behavior in time.

Also, we found that there is a difference between the significance of the results of the path analysis and the significance of the rank correlation test. We considered that it may be because the path analysis is a comprehensive analysis involving multiple variables, while the rank correlation only analyzes the linear correlation between two variables. So there may be significant causal paths between certain variables, but their correlation is not significant. So our result is reasonable. However, the results need to be accepted and applied with caution, and further research will be conducted to determine the accuracy of the findings.

The causes and solutions of “p-value inflation”, a statistical problem that is often overlooked, are also worth discussing. We consider that there may be a discrepancy between the significance of the statistical results (p-value) and the actual results in the rank sum test, rank correlation test and path analysis used in this study, which may be due to several reasons: (1) Limitations of the sampling data. (2) The influence of latent variables. (3) Relatively small differences in the means of different groups. (4) Inadequate construction of the model and limited variables, etc. To try to avoid such errors, the results should be further adjusted during data analysis using appropriate statistical methods to ensure the accuracy of the results. For multiple comparisons of variance either the Bonferroni or Holm method can be chosen [45]. The Bayesian analysis can also be used to provide more accurate parameter estimates for the available data [46].

Limitations

Our study has several limitations. First, our results only reflected the situations in Qingdao city, and might not represent the whole situation in China. Second, due to the pandemic restriction, collecting data by interviewing participants in-person was not feasible, our survey was an online survey. For those who did not use the Internet or smartphones, some of them were missing, which may cause the sample can’t represent the entire population. Third, Various influencing factors of COVID-19 prevention behavior of the structural model were not comprehensive, the representative was limited. Fourth, our study only investigated the prevention behavior of the subjects against COVID-19 in a limited period and did not conduct a longitudinal comparison of people’s prevention behavior against COVID-19 at different stages of the epidemic. Finally, the correlation coefficients between some of the variables in the results are small, which is a very prominent shortcoming of this study. It indicates that although there are indeed correlations between these variables (significant correlations), the correlations between them is relatively weak. Therefore, we consider that the findings of this study are of limited significance and need to be viewed and applied with caution.

Further research will also be conducted to determine the accuracy of the results in order to improve the reliability of the study findings. We could explore more factors that may influence COVID-19 prevention behavior among general Chinese citizens. In addition, further prospective studies should be conducted to evaluate the relationship between various influencing factors and COVID-19 prevention behavior. Under suitable conditions, we could implement a face-to-face questionnaire survey, which can more effectively prove the research results than online surveys.

Conclusions

Our findings underscore individual, environmental, and psychological factors interact and influence each other. It is to be noted that most of the effective factors of COVID-19 prevention behavior, the external support for fighting the epidemic, the demand level of health information, the cognition of COVID-19, and the negative emotions after the outbreak can be adjusted and modified by some interventions. We also recommend that the government should release information in a timely and effective manner, the mainstream media should actively provide a communication platform for the government and the public, and report the truth in a timely and accurate manner to meet the information needs of the public, and the relevant departments should actively carry out health education and psychological intervention activities when sudden public health events occur. The implementation of systematic public health strategies, practices and interventions can be used as an effective model for current and future management of public health emergencies, especially for the COVID-19.

Availability and data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

References

  1. Zhou Z, Wang J, et al. The knowledge, attitude, and behavior about public health emergencies and the response capacity of primary care medical staffs of Guangdong Province China. Bmc Health Serv Res. 2012;12(1):1–9.

    Article  Google Scholar 

  2. Lin X, Ian N, Rocha, Shen X. Challenges and Strategies in Controlling COVID-19 in Mainland China: Lessons for Future Public Health Emergencies. J Sicial Health. 2021;4(2):57–61.

    Google Scholar 

  3. Xu W, Wu J, Cao L. COVID-19 pandemic in China: Context, experience and lessons. Health Policy Technol. 2020;9(4):639–648.

    Article  Google Scholar 

  4. Kaljee L, Genberg B, Minh T, et al. Alcohol use and HIV risk behavior among rural adolescents in Khanh Hoa province Viet Nam. Health Educ Res. 2005;21(1):71–80.

    Article  Google Scholar 

  5. Rosenstock IM. Historical origins of the health belief model. Health Educ Monographs. 1974;2(4):328–35.

    Article  Google Scholar 

  6. Ajzen I. The theory of planned behavior. Organization Behav Hum Decision Proc. 1991;50(2):179–211.

    Article  Google Scholar 

  7. Fisher JD, Fisher WA, Misovich SJ, et al. Changing AIDS risk behavior: Effects of an intervention emphasizing AIDS risk reduction information, motivation, and behavioral skills in a college student population. Health Psychol. 1996;15(2):114–23.

    CAS  Article  Google Scholar 

  8. Bandura A. Toward a psychology of human agency: Pathways and reflections. Perspect Psychol Sci. 2018;13(2):130–6.

    Article  Google Scholar 

  9. Roma P, Monaro M, Muzi L, et al. How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms. Int J Environ Res Public Health. 2020;17(19):7252.

    CAS  Article  Google Scholar 

  10. Albott Cristina Sophia, et al. Battle Buddies: Rapid Deployment of a Psychological Resilience Intervention for Health Care Workers During the COVID-19 Pandemic. Anesthesia Analgesia. 2020;131(1):43–54.

    CAS  Article  Google Scholar 

  11. Jeihooni AliKhani, Hidarnia Alireza, Kaveh Mohammad, et al. Application of the health belief model and social cognitive theory for osteoporosis preventive nutritional behavior in a sample of Iranian women. Iran J Nurs Midwifery Res. 2016;21(2):131–41.

    Article  Google Scholar 

  12. Junting M, Park H, Choi J. Health Information Seeking on Social Q&A Sites and Preventive Behavior: Focusing on Coronavirus Infection-19. J Digital Contents Soc. 2021;22(6):959–67.

    Article  Google Scholar 

  13. Muse Kate McManus, Freda Leung, Christie Meghreblian, Ben, Williams J, Mark G. Cyberchondriasis: fact or fiction? A preliminary examination of the relationship between health anxiety and searching for health information on the Internet. J Anxiety Disord. 2012;26(1):189–96.

    CAS  Article  Google Scholar 

  14. Leila Ghahremani Reza, Faryabi. Mohammad Hossein Kaveh.Effect of health education based on the protection motivation theory on malaria preventive behavior in rural households of Kerman, Iran. J Anxiety Disord. 2014;5(4):463–71.

    Google Scholar 

  15. Alessandra Pokrajac-Bulian. Ambrosi-Randić Neala. Illness perception in overweight and obese patients with cardiovascular diseases. Eat Weight Disord. 2020;25(1):69–78.

    Article  Google Scholar 

  16. Savolainen R. Cognitive barriers to information seeking: A conceptual analysis. J Inf Sci. 2015;41(5):613–623.

    Article  Google Scholar 

  17. Rogers EM. Diffusion of preventive innovations. Addict Behav. 2002;27(6):989–993.

    Article  Google Scholar 

  18. Zhang YY, Bao XQ, Yan JX, Miao HL, Guo C. Anxiety and Depression in Chinese Students During the COVID-19 Pandemic: A Meta-Analysis. Front Public Health. 2021;9

  19. Jia SZ, Zhao YZ, Liu JQ, et al. Study of Mental Health Status of the Resident Physicians in China During the COVID-19 Pandemic. Front Psychol. 2022;13:764638.

    Article  Google Scholar 

  20. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–1097.

    Article  Google Scholar 

  21. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA. 1999;282(18):1737–1744.

    CAS  PubMed  Google Scholar 

  22. Derogatis LR, Lipman RS, Covi L. SCL-90: an outpatient psychiatric rating scale–preliminary report. Psychopharmacol Bull. 1973;9(1):13–28.

    CAS  PubMed  Google Scholar 

  23. Casson RJ, Farmer LD. Understanding and checking the assumptions of linear regression: a primer for medical researchers. Clin Exp Ophthalmol. 2014;42(6):590–596.

    Article  Google Scholar 

  24. Schmidt AF, Finan C. Linear regression and the normality assumption. J Clin Epidemiol. 2018;98:146–151.

    Article  Google Scholar 

  25. Bagos PG, Nikolopoulos GK. Generalized least squares for assessing trends in cumulative meta-analysis with applications in genetic epidemiology. J Clin Epidemiol. 2009;62(10):1037–1044.

    Article  Google Scholar 

  26. Berrington A, Cox DR, Generalized least squares for the synthesis of correlated information. Biostatistics. 2003;4(3);423–431.

  27. Adella Halim D, Kurniawan A, Agung FH, et al. Understanding of Young People About COVID-19 During Early Outbreak in Indonesia. Asia Pac J Public Health. 2020;32(6–7):363–365.

    Article  Google Scholar 

  28. Al-Amer R, Maneze D, Everett B, et al. COVID-19 vaccination intention in the first year of the pandemic: A systematic review [published online ahead of print, 2021 Jul 6]. J Clin Nurs. 2021;10.1111.

  29. Liu T, He Z, Huang J, et al. A Comparison of Vaccine Hesitancy of COVID-19 Vaccination in China and the United States. Vaccines (Basel). 2021;9(6):649.

    CAS  Article  Google Scholar 

  30. Rousseau D, Sitkin S, Burt R, et al. Not So Different After All: A Cross-Discipline View of Trust. Acad Manag Rev. 1998;23(3):393–404.

    Article  Google Scholar 

  31. Tzelepis F, Paul CL, Sanson-Fisher RW, et al. Unmet supportive care needs of hematological cancer survivors: rural versus urban residents. Ann Hematol. 2018;97(7):1283–92.

    CAS  Article  Google Scholar 

  32. Luo Y, Yao L, Zhou L, et al. Factors influencing health behaviours during the coronavirus disease 2019 outbreak in China: an extended information-motivation-behaviour skills model. Public Health. 2020;185:298–305.

    CAS  Article  Google Scholar 

  33. Pu G, Jin L, Xiao H, et al. Systematic evaluation of COVID-19 related Internet health rumors during the breaking out period of COVID-19 in China. Health Promot Perspect. 2021;11(3):288–298.

    Article  Google Scholar 

  34. Oberiri Destiny Apuke, Bahiyah Omar, et al. Fake news and COVID-19: modelling the predictors of fake news sharing among social media users. Telematics Informatics. 2021;56.

  35. Venegas-Vera AV, Colbert GB, Lerma EV. Positive and negative impact of social media in the COVID-19 era. Rev Cardiovasc Med. 2020;21(4):561–564.

    Article  Google Scholar 

  36. Bahl S, Singh RP, Javaid M, et al. Significance of Health Information Technology (HIT) in Context to COVID-19 Pandemic: Potential Roles and Challenges. J Industrial Integration Manag. 2020;5(4):427–40.

    Article  Google Scholar 

  37. Bahl S, Javaid M, Bagha AK, et al. Telemedicine Technologies for Confronting COVID-19 Pandemic: A Review. J Industrial Integration Manag. 2020;5(4):547–61.

    Article  Google Scholar 

  38. Zhang M, Zhou M, Tang F, Wang Y, Nie H, Zhang L, You G. Knowledge, attitude, and practice regarding COVID-19 among healthcare workers in Henan, China. J Hospital Infect. 2020; Available online, April 9, 2020.

  39. Mao-ling Su. Effect of cognitive intervention on relieving anxiety and depression of patients with a brain tumor and their families [In Chinese]. Chin J Health Psychol. 2020;28(01):86–90.

    Google Scholar 

  40. Mingyu W, Shizhong L, Yibo W. Overview of emergency management and disaster medicine in the context of COVID-19. J Emerg Manag Disaster Commun. 2020;1(1):89–94.

  41. Angelina S, Kurniawan A, Agung FH, et al. Adolescents’ mental health status and influential factors amid the Coronavirus Disease pandemic. Clin Epidemiol Glob Health. 2021;12:100903.

    Article  Google Scholar 

  42. O’Connor RC, Wetherall K, Cleare S, et al. Mental health and well-being during the COVID-19 pandemic: longitudinal analyses of adults in the UK COVID-19 Mental Health & Wellbeing study [published online ahead of print, 2020 Oct 21]. Br J Psychiatry. 2020;1–8.

  43. Clauw DJ, Engel CC Jr, Aronowitz R, et al. Unexplained symptoms after terrorism and war: an expert consensus statement. J Occup Environ Med. 2003;45(10):1040–8.

    Article  Google Scholar 

  44. Stewart DE, Abbey SE, Shnek ZM. et al. Gender differences in health information needs and decisional preferences in patients recovering from an acute ischemic coronary event.  Psychosomatic Med. 2004;66(1):42–48.

  45. Aickin M, Gensler H. Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am J Public Health. 1996;86(5):726–728.

    CAS  Article  Google Scholar 

  46. Greenland S, Hofman A. Multiple comparisons controversies are about context and costs, not frequentism versus Bayesianism. Eur J Epidemiol. 2019;34:801–8.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors have approved the submitted version (and any substantially modified version that involves the author’s contribution to the study); have agreed to take personal responsibility for the authors’ contributions and ensure that issues relating to the accuracy or completeness of any part of the work, even those in which the authors were not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Funding

Publication of this article was co-sponsored by Springer Nature and BMC. The authors disclosed receipt of the following financial support for the research of this article: The research was supported by Qingdao Key Health Discipline Development Fund (Grand: 2020B047).

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization, Yi-bo Wu and Fei Qi; methodology, Yun-shan Li; software, Yi-bo Wu; validation, Yun-shan Li, Rui Wang and Yu-qian Deng; formal analysis, Yun-shan Li; investigation, Rui Wang; Fei Qi, Yi-bo Wu; data curation, Xiao-rong Jia, Shan-peng Li; writing—original draft preparation, Yun-shan Li, Rui Wang, Yu-qian Deng; writing—review and editing, Xin-ying Sun, Li-ping Zhao; visualization, Xiao-rong Jia; supervision, Xin-ying Sun; project administration, Fei Qi, Yi-bo Wu. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fei Qi or Yi-bo Wu.

Ethics declarations

Ethics approval and consent to participate

All participants participated in the study voluntarily and filled out the informed consent Ethics approval (Related Files 1). The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Key Research Base of Philosophy and Social Sciences in Shaanxi Province, Health Culture Research Center of Shaanxi (JKWH-2020-08) (Related Files 2).

Consent for publication

Not applicable.

Competing interests

The authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, Ys., Wang, R., Deng, Yq. et al. Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model. BMC Public Health 22, 1098 (2022). https://doi.org/10.1186/s12889-022-13514-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-13514-0

Keywords

  • COVID-19
  • Coronavirus disease 2019
  • Prevention behavior
  • The structural equation model