Next Article in Journal
A Phenomenological Approach to Medication Adherence in Elderly Patients: A Qualitative Study
Previous Article in Journal
Risks Associated with the Use of Intranasal Corticosteroids: An Analysis of Adverse Reactions Reported to EudraVigilance
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 12
Issue 19
10.3390/healthcare12191924
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Individual Characteristics Associated with Fears and Prevention Behaviors Related to Respiratory Infectious Disease among South Korean Adults Using Complex Sample Design

by
Gunsoo Han
and
Jae-Ahm Park
*
Department of Sports and Leisure Studies, College of Humanities, Daegu University, 201 Daegudae-ro, Gyeongsan-si 38453, Republic of Korea
*
Author to whom correspondence should be addressed.
Healthcare 2024, 12(19), 1924; https://doi.org/10.3390/healthcare12191924
Submission received: 8 August 2024 / Revised: 23 September 2024 / Accepted: 24 September 2024 / Published: 25 September 2024
Browse Figure
Versions Notes

Abstract

:
Objectives: This study aimed to identify the relationships among individual characteristics, COVID-19-related fears, and infection-prevention behaviors using data from 228,932 adults (weighted to 43,583,798.71) aged 19 and older who participated in the 2021 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. Methods: The complex sample design analysis using SPSS 20.0 revealed the following. Results: Women had statistically significantly higher COVID-19-related fears and a higher level of preventive behaviors compared to men. During the COVID-19 pandemic, the level of physical activity among men was higher than that of women. Compared to the period before COVID-19, the frequency of smoking and drinking decreased for both men and women, with the reduction being more pronounced in men than in women. The high-exercise group had a lower level of COVID-19-related fears. Self-perceived health status was inversely and significantly related to COVID-19-related fears. The low-exercise group had a lower score for self-perceived health status than the high-exercise group. Conclusions: This study demonstrated that maintaining sufficient physical activity positively influences self-perceived health and reduces anxieties related to COVID-19 infection and fatalities. The results highlight the importance of physical activity and the need to identify strategies that effectively maintain regular exercise while adhering to infection-prevention measures during pandemics.

1. Introduction

1.1. Needs of the Study

In March 2020, COVID-19 was declared a global pandemic by the World Health Organization, and as of November 2023, over 771 million cases and nearly 7 million fatalities have been officially recorded [1,2]. The pandemic profoundly disrupted everyday life and precipitated a significant increase in mental health issues [3,4]. A study involving 113,285 individuals revealed that within the first seven months of the pandemic, the prevalence of depression, anxiety, and stress reached 20%, 35%, and 53%, respectively [5]. An analysis conducted among individuals aged 12–59 in China [6], along with a review of 24 studies [7], revealed that the public expressed concerns about the risk of family members contracting the virus. According to the 2021 Korean Community Health Survey [8], 60.2% of respondents expressed fear of contracting the virus, and 70.2% were worried about its economic impact. In particular, the Korean National Mental Health Survey [9] further indicates that concerns and fears about COVID-19 were highest among those in their 30 s, followed by those in their 40 s, 50 s, teens, and 60 s, in that order. Furthermore, the fear of infection profoundly affected mental health to an extent similar to the immediate threat posed by the virus. Such fears stemmed from the novelty, unpredictability, and perceived lethality of the virus and its potential impacts on personal safety, the welfare of loved ones, and economic situation [9,10]. These worries may have contributed to mental health disorders such as anxiety and depression [10].
Meanwhile, previous studies have indicated that individual characteristics such as gender, income, educational background, physical activity level, and self-perceived health status are related to COVID-19 fear and infection-prevention behaviors [11,12,13,14,15,16,17,18]. Furthermore, research on adolescents [11] and adults [12] demonstrates that physical activity levels significantly predict COVID-19-related anxieties and that individuals who perceive their health favorably are less concerned about COVID-19 than those with negative perceptions of their personal health [13,14]. Individuals with higher income levels tend to engage more frequently in infection-prevention behaviors than those living in low-income households [15,16]. Those with a college education or higher are significantly more likely to engage in infection-prevention behaviors compared to those with lower educational attainment [17]. Females reported wearing masks, washing their hands, and maintaining social distance from others more frequently than males during the COVID-19 pandemic [18].
Although the relationship among individual characteristics, COVID-19 fears, and preventive behaviors are evident, there remains a substantial need for comprehensive research in South Korea, particularly within domestic settings. This study aims to address this gap by analyzing survey data from 228,932 individuals (weighted to 43,583,798.71), conducted by the Korea Disease Control and Prevention Agency [8].

1.2. Theoretical Review

1.2.1. Respiratory Diseases

Since the 2000s, several notable respiratory viruses have emerged, including SARS (SARS-CoV), H1N1 influenza, MERS (MERS-CoV), and COVID-19 [19,20]. SARS originated in China in 2002 and quickly spread globally, resulting in 8096 confirmed cases worldwide, including 4 in South Korea. H1N1 influenza, which first broke out in San Diego, USA, in March 2009, caused more extensive damage than SARS, with 1,632,258 confirmed cases globally and over 700,000 cases in South Korea. MERS began in Saudi Arabia in 2012 and predominantly affected the Middle East and Asia, resulting in 1288 confirmed cases worldwide and 186 cases in South Korea. The most recent respiratory virus, COVID-19, declared a pandemic, first emerged in Wuhan, Hubei Province, China, in December 2019. From 5 January 2020 to 19 May 2024, there were 775,522,404 confirmed cases and 7,049,617 deaths reported globally. During the same period, South Korea reported 34,572,554 confirmed cases and 35,605 deaths. As evidenced, respiratory infectious diseases have not been eradicated and continue to reemerge. Vaccines and antiviral treatments like Tamiflu play crucial roles in managing these infections [21,22]. However, preventive behaviors such as handwashing and mask-wearing have been scientifically proven to significantly curb the spread of infections and reduce fatalities [23,24]. Therefore, understanding how individual characteristics influence infection-prevention behaviors can aid in developing effective strategies for managing future respiratory disease outbreaks.
Meanwhile, in South Korea, research on the impact of COVID-19 on mental health has been continuously conducted [25,26,27,28,29,30,31,32]. However, many studies have focused on stress related to COVID-19 [25,26,27,28]. Although research on anxiety related to COVID-19 has been carried out, these studies are often limited to local data or targeted at specific age groups or genders [29,30,31,32]. Therefore, there is a need for research analyzing the relationship between COVID-19 and anxiety based on data that can represent the South Korean population.

1.2.2. The Effect of Gender on COVID-19-Related Fears and Infection Prevention Behaviors

Previous studies have demonstrated that factors such as gender, economic activity, and occupation significantly influence concerns about COVID-19 and engagement in preventive behaviors [15,16,17,18,33]. Bronfman et al. [18] found that women are significantly more likely than men to engage in infection-prevention behaviors, such as wearing masks, washing hands upon returning home, and maintaining social distance. Women also exhibited a higher tendency to avoid going out and attending social events compared to men. The authors suggest that worry and fear about COVID-19 are crucial drivers of these infection-preventive behaviors. Differences in vaccine willingness have also been observed based on gender and educational background [33]. For instance, in France, Germany, Russia, and Sweden, women were significantly more likely to accept a vaccine than men. Moreover, previous studies [34,35,36,37,38] indicate that women experience higher levels of worry and fear about the risk of infection and the spread of respiratory infectious diseases.

1.2.3. The Effect of Income and Educational Background on COVID-19 Infection Prevention Behaviors

Research by Chen and Chen [15], which analyzed 1591 people aged 16 to 71 in China, suggests that individuals with higher income levels tend to engage more frequently in infection-prevention behaviors and exhibit more positive attitudes, greater knowledge, and better information appraisal. Leung et al. [17] analyzed 1115 Chinese adults aged over 18 and found people with higher education levels adopted more thorough precautionary measures to prevent infection during the SARS outbreak in Hong Kong. Individuals with a college education or higher are significantly more likely to engage in infection-prevention behaviors compared to those with lower educational attainment. Conversely, Raifman and Raifman [16] report that individuals living in low-income households face a higher risk of contracting COVID-19 compared to their higher-income counterparts.

1.2.4. The Effect of Physical Activity on COVID-19-Related Fears

A clear link has been established between physical activity and fears related to COVID-19. A study involving adolescents [11] found that physical activity levels significantly influenced COVID-19-associated anxieties, a finding was supported by a study on adults [12]. Additionally, a cross-sectional study by Maugeri et al. [39] showed that individuals who sustained physical activity levels during the COVID-19 lockdown experienced lower anxiety and fear about the pandemic than those who reduced their physical activity levels.

1.2.5. The Relationship between Self-Perceived Health Status and COVID-19-Related Fears

Self-perceived health status has been shown to be inversely associated with COVID-19-related anxieties [13,14]. According to a recent study [13,14], individuals who perceive their health positively are less fearful of COVID-19 than those who view their health negatively. Moreover, recent evidence suggests that self-perceived health status might alleviate negative psychological impacts linked to COVID-19 [13,14,40].

1.2.6. The Relationship between Subjective Health and Physical Activity

Maintaining an adequate level of physical activity is crucial for both physical and mental health [41]. Physical activities encompass structured exercises and sports, as well as walking or climbing stairs [41]. These activities enhance measurable health indicators, including self-perceived health status [42], which is a significant factor in psychological healthcare [43]. Research on adults and seniors has demonstrated that physical activity enhances subjective perception of health [42]. Elderly individuals who regularly engage in physical activity report significantly better self-perceived health statuses than those who are inactive [42]. Physical activity can improve self-reported health status through several mechanisms, such as increasing physical capabilities, reducing chronic disease symptoms, enhancing mood, elevating self-esteem, and promoting social interaction [42,43,44,45,46,47,48]. Furthermore, in longitudinal studies, physically active adults reported better self-assessed health than their inactive counterparts [48].

1.2.7. The Effect of COVID-19 on Lifestyle Changes

COVID-19 has been found to impact lifestyle factors such as smoking and alcohol consumption [49,50,51,52]. During the lockdown, changes in tobacco consumption were observed among current smokers, with 26.7% reporting an increase, 18.6% a decrease, and 54.7% maintaining their usual consumption levels. Similarly, among those who drink alcohol, 10.7% reported consuming more, 24.4% noted a reduction, and 64.8% experienced no change in their drinking habits [49].
Based on the theoretical review above, this study was undertaken to analyze the individual characteristics (including gender, physical activity level, self-perceived health status, engagement in economic activity, occupation, smoking, and drinking) associated with fears and infection-prevention behaviors regarding respiratory infectious disease among South Korean adults during COVID-19.

2. Materials and Methods

2.1. Samples

This study used data from the 2021 Community Health Survey conducted by the Korea Disease Control and Prevention Agency [8]. The survey was conducted based on the Regional Public Health Act in Korea [8]. The target population consisted of adults aged 19 and older. The sampling frame was created by linking the resident registration population data from the Ministry of the Interior and Safety of Korea with housing data from the Ministry of Land, Infrastructure and Transport of Korea, providing a comprehensive list of the entire population. The first stage of sampling involved selecting locations proportionally to the number of households by housing type within each town/village ensuring that the probability of selection was proportional to the household size. The second stage of sampling entailed identifying the number of households in the selected town/villages and selecting households using a systematic sampling method.

2.2. Weights

Since the sample for the 2021 Community Health Survey was extracted under a complex sampling design rather than a simple random sampling, the weights, stratification variables, and cluster variables must be considered when estimating means and variances [8]. The weight factors were calculated and distributed by the Korea Disease Control and Prevention Agency [8], and this study utilized these weight factors accordingly. The Korea Disease Control and Prevention Agency [8] calculated the weights as follows. The weights were divided into household weights and individual weights. Household weights were calculated by considering the household sampling rate, the rate of eligible households, and the household proportions by housing type. The individual weight is a household weight adjusted for the individual response rate.

2.3. Procedure

The survey period was from 16 August 2021 to 31 October 2021. Trained surveyors conducted one-on-one interviews (electronic surveys) by visiting the households selected for the sample. The data collected using tablet PCs were transmitted in real-time to a central server. The survey gathered responses from 229,242 adults aged 19 and older. However, this study excluded 310 surveys from the analysis due to non-responses or unclear answers among the analyzed factors of the study (Figure 1), resulting in a final sample size of 228,932. Applying the sampling weight factors calculated and distributed by the Korea Disease Control and Prevention Agency [8], the interpreted data represented 43,583,798.71 individuals, as indicated in Table 1. The table also includes unweighted and weighted frequencies and percentages.

2.4. Instruments

The 2021 Community Health Survey [8] posed questions that addressed gender, physical activity levels, economic activity, occupation, vaccination status, smoking, drinking, self-perceived health status, COVID-19-related fears and COVID-19 infection-prevention behaviors. Physical activity levels were assessed using three questions targeting high-intensity (In the past week, on how many days did you engage in vigorous physical activities for at least 10 min that made you feel much more fatigued or out of breath than usual?), moderate (In the past week, on how many days did you engage in moderate physical activities for at least 10 min that made you feel slightly more fatigued or slightly out of breath than usual?), and low-intensity (In the past week, on how many days did you walk for at least 10 min at a time?) activity levels. Each question was scored on a 7-point scale (1 = 1 day, 2 = 2 days, 3 = 3 days, 4 = 4 days, 5 = 5 days, 6 = 6 days, 7 = 7 days), and the average value was used to determine physical activity levels. A higher score indicates a higher level of physical activity. Respondents were subsequently allocated to low or high-exercise groups about the median value.
The measure of economic activity asked whether the respondent was currently engaged in economic activity, with the answer options being “yes” or “no”. The occupation categories consist of 10 groups, including manager, professional, clerical worker, service worker, sales worker, agricultural, fishery worker, technician, plant worker, elementary worker, and military personnel.
The question regarding vaccination status asked whether the respondent had received a COVID-19 vaccination up to that point, with responses categorized as either “Yes” or “No”. Questions about drinking and smoking asked respondents about changes in the frequency of their drinking and smoking habits after COVID-19, with the response options being “decreased”, “stayed the same” and “increased.
Self-perceived health status was determined using responses to a single question (How would you rate your overall health?) on overall self-perceived health status, rated on a 5-point scale (1 = very good, 2 = good, 3 = average, 4 = poor, 5 = very poor). After applying inverse coding, a higher score indicates a higher level of self-perceived health status.
COVID-19-related fears were assessed using three questions that addressed fears of infection (I am worried about getting infected with COVID-19), blame from others (I am worried that if I get infected with COVID-19, I might be blamed or face negative reactions from those around me), and economic impacts (I am worried that the COVID-19 pandemic may cause economic harm to me and my family, including losing jobs or facing difficulties in finding employment). These questions were evaluated using a 5-point scale (1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, 5 = strongly disagree). After applying inverse coding, a higher score indicates a greater level of fear. COVID-19 preventive behaviors were assessed using three questions asking respondents whether they adhered to indoor mask-wearing, outdoor mask-wearing, and social distancing over the past week. The responses were categorized into “highly comply”, “comply” and “never”. The analyzed factors of the study are presented in Figure 1.

2.5. Data Analysis

Weighting methods are essential when analyzing raw data from sources, such as the Community Health Survey of Korea, to ensure sample data accurately reflect entire populations [53]. In this study, we used a complex sample design, incorporating weight, stratification, and cluster variables. The analyses included a complex sample design chi-square test, complex sample design t-test, and complex sample design simple regression analysis, all conducted using SPSS 20.0.

3. Results

3.1. Differences According to Gender

The complex sample design chi-square analysis results showed that, compared to the period before COVID-19, the frequency of smoking and drinking decreased for both men and women. Specifically, the analysis revealed that the reduction in smoking and drinking was more pronounced in men than in women. However, statistical significance was observed only in the change in drinking. In addition, compared to the period before COVID-19, physical activity levels decreased for both men and women, with the reduction being more pronounced in women than in men (Table 2).
The results of the complex sample design t-test indicated that men have a statistically significantly higher self-perceived health status compared to women (Table 3).
The results of the complex sample design t-test indicated that women have statistically significantly higher COVID-19-related fears including fear of infection, fear of blame from others, and fear of economic impacts compared to men (Table 4).
The results of the complex sample design of the t-test indicated that women have statistically significantly higher levels of infection-prevention behaviors including vaccination, wearing a mask and social distancing compared to men (Table 5).

3.2. Differences According to Economic Activity

The complex sample design chi-square analysis results showed that there is a difference in vaccination status, wearing a mask outdoors, and social distancing depending on engagement in economic activity. People who are engaged in economic activities showed a higher vaccination rate compared to those who are not. In terms of wearing a mask outdoors, it was found that people engaged in economic activities had a lower rate of “highly comply” compared to those who were not. However, when combining “highly comply” and “comply”, the rate was found to be identical at 99.7% for both groups. In terms of social distancing, people engaged in economic activities had higher rates of “highly comply” and “comply” compared to those who were not (Table 6).
The complex sample design chi-square test revealed differences in vaccination rates by occupation. The occupation with the highest vaccination rate was found to be military personnel, while sales workers had the lowest vaccination rate. However, all occupational groups showed a high vaccination rate of over 70%. Similarly, in terms of wearing a mask both indoors and outdoors, and practicing social distancing, military personnel exhibited the highest rate of “highly comply” (Table 7).

3.3. Differences According to Physical Activity Level

Complex sample t-test analysis revealed that the low-exercise group had a lower score for self-perceived health status than the high-exercise group (Table 8).
In terms of COVID-19-related fear, the high-exercise group had a lower level of COVID-19-related fears, including fears of infection, blame from others, and economic impact, than the low-exercise group. All differences were statistically significant (Table 9).

3.4. Differences According to Self-Perceived Health Status

Complex sample design simple regression analysis showed that self-perceived health status was inversely and significantly related to COVID-19-related fears including infection, blame from others, and economic impacts (Table 10).

4. Discussion

This study aimed to identify the individual characteristics associated with COVID-19 fears and infection-prevention behaviors among South Koreans. First, this study found a statistically significant difference in COVID-19-related fears and infection-prevention behaviors according to gender. Our findings align with previous research [18,33,34,35,36,37,38]. For instance, as compared to males, females showed significantly higher levels of COVID-19-related fears, including concerns about infection, blame from others, and economic impacts. Studies of similar respiratory infectious diseases, such as SARS-CoV-2, have also indicated that women experience higher levels of worry about the risk of infection and the spread of disease compared to men [34,35,36,37,38]. In addition, this study found that women were significantly more likely than men to participate in infection-prevention behaviors, including wearing masks, practicing social distancing, and getting vaccinated. Bronfman, Repetto, Cordón, and Castañeda [18] also observed that females more often reported wearing masks, washing their hands, keeping social distancing, and abstaining from attending social events. Previous studies [36] propose that female’s prominent caregiving roles contribute to their elevated levels of concern. Moreover, this increased anxiety among women is associated with the fear of transmitting infections to those close to them, which further underscores their proactive involvement in preventive behaviors [24] also suggested that worry and fear about COVID-19 play a crucial role in driving these infection preventive behaviors. In terms of vaccination, this study supports Lazarus et al. [33]’s finding, which indicates females in France, Germany, Russia, and Sweden were significantly more likely to accept a vaccination compared to males. Since Lazarus et al. [33]’s study also found that age might affect the willingness to accept vaccination, diverse follow-up studies are necessary to identify an individual’s vaccination willingness by region, gender, and age.
Secondly, this study found that a positive self-perceived health status is associated with less COVID-19-related fear, which aligns with previous studies [13,14,54,55]. According to Goodwin et al.’s study [54], a correlation exists between diminished self-perceived health status and increased anxiety about health, which can exacerbate fears associated with infectious diseases. Rubin et al. [55] found that the perception of subjective health status may influence fear of health threats, such as infectious diseases. Individuals who regard their health as suboptimal might perceive themselves as more susceptible to illnesses and, consequently, experience higher levels of fear. Research conducted during the H1N1 influenza pandemic demonstrated that individuals with lower self-perceived health status reported greater fear of infection [44]. Szabo, Ábel, and Boros [13] examined whether COVID-19-related fears differed among groups with high, average, or low self-perceived health status levels. Their results showed that individuals with average or low self-perceived health status expressed greater fears about COVID-19 and were more pessimistic regarding the termination of the pandemic compared to those with high self-perceived health status [13]. According to a study performed on adults 18 years and older, with an average age of 45.16 years [14], self-perceived health status was significantly and inversely correlated with COVID-19-related fear.
Thirdly, COVID-19-related fears are inversely related to physical activity levels, which concurs with findings from prior research [11,12,39,55]. The latest studies [11,12,39,56] have addressed the relationship between COVID-19-related mental health and physical activity. According to a study in 2020 by Matias et al. [56], engagement in physical activities such as yoga and tai chi during the COVID-19 pandemic positively influenced mental health, significantly reducing COVID-19-associated stress and anxiety. A cross-sectional study conducted in 2020 showed that individuals who maintained their physical activity levels during the COVID-19 lockdown experienced lower levels of anxiety and pandemic-related fear than those who decreased their physical activity levels [39]. In 2020, Alsalhe et al. [12] concluded that physical activity levels were inversely related to COVID-19 fears in a study of adults aged 18 and older. Furthermore, a recent study conducted in 2021 found that higher physical activity levels were significantly associated with lower COVID-19 fears [11]. This study was performed on German adolescents. Although we focused on adults, our findings are similar to those for adolescents.
Lastly, levels of physical activity are associated with increased self-perceived health status, which aligns with previous studies [44,45,48,57,58]. A prior study conducted in 2011 analyzed college students and reported that those perceiving their health status as “very healthy” had the highest proportion of individuals who exercised regularly [44]. Furthermore, a study involving 12 women in their 20s who engaged in trekking exercises twice a week for 10 weeks reported a significant increase in self-perceived health status levels [45]. According to Bize, Johnson, and Plotnikoff [47], physical activities can improve physical capabilities, alleviate the symptoms of chronic conditions, boost mood, increase self-esteem, and foster social connections. Moreover, they emphasized that regular physical activity could lead to a heightened sense of control over one’s health, which may, in turn, positively influence self-perceived health status. A study conducted in 2001 also indicated that adults aged 20 and older who regularly participated in exercise had higher self-perceived health status levels than those who did not [57]. According to Kim [58], engaging in sports for leisure purposes positively influences self-perceived health status. This study included a diverse range of ages, from adolescents to adults over 60.
Meanwhile, this study has the following limitations. First, the subjects of this study were limited to adults aged 19 and older. Second, it did not analyze differences based on certain demographic variables such as age and residential area (urban or rural). Third, it did not include information on whether the subjects were actually infected with COVID-19. Fourth, comparing this study’s results with global findings requires caution, especially without a clear direct link between them. Fifth, this study used a complex sample design focused on population-level inference. Therefore, when interpreting the results, it is important to consider findings from similar studies that used simple random sampling.

5. Conclusions

In summary, this study aims to update the understanding of individual characteristics affecting fears and infection-prevention behaviors related to respiratory infectious diseases. This study holds several significant contributions. The subjects are Korean, and the data are based on a national survey, providing valuable insights specific to this population. The results of this study offer the potential to integrate and analyze research data from different regions and races worldwide. Additionally, as COVID-19 is the most recent global pandemic, this study builds on previous research on SARS and other past pandemics, providing foundational data for updated research. Furthermore, this study demonstrated that maintaining sufficient physical activity positively influences self-perceived health and reduces anxieties related to COVID-19 infection and fatalities. This emphasizes the importance of identifying strategies that effectively sustain physical activities and exercise while adhering to infection-prevention measures during pandemics.

Author Contributions

Conceptualization, J.-A.P. and G.H.; methodology, J.-A.P.; formal analysis, J.-A.P. and G.H.; writing—original draft preparation, J.-A.P. and G.H.; writing—review and editing, G.H.; visualization, J.-A.P. and G.H.; supervision, J.-A.P. and G.H.; project administration, J.-A.P.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Daegu University Research Grant 2022.

Institutional Review Board Statement

The raw data used in this study were obtained from the Korea Disease Control and Prevention Agency. The official statement from the Korea Disease Control and Prevention Agency indicates that this study is “excluded from review as it does not fall under human subject research in accordance with Article 2, Paragraph 2 of the Enforcement Regulations of the Bioethics and Safety Act of the Republic of Korea”.

Informed Consent Statement

All procedures related to participant consent and other relevant aspects were conducted by the Korea Disease Control and Prevention Agency in accordance with the applicable laws of the Republic of Korea.

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from [Korea Disease Control and Prevention Agency] and are available [at https://chs.kdca.go.kr/chs/index.do (accessed on 20 September 2024)] with the permission of [Korea Disease Control and Prevention Agency].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ministry of Health and Welfare. Available online: https://dportal.kdca.go.kr/pot/index.do (accessed on 3 March 2024).
  2. World Health Organization. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public?adgroupsurvey={adgroupsurvey}&gad_source=1&gclid=CjwKCAjwrcKxBhBMEiwAIVF8rET2ezQkKv5CUzYvmW21qvbw7RQtd6lIUjOMT8lECjcBl2RnAUvCshoCg_0QAvD_BwE (accessed on 5 March 2024).
  3. Rajkumar, R.P. COVID-19 and mental health: A review of the existing literature. Asian J. Psychiatry 2020, 52, 102066. [Google Scholar] [CrossRef] [PubMed]
  4. Fitzpatrick, K.M.; Harris, C.; Drawve, G. Fear of COVID-19 and the mental health consequences in America. Psychol. Trauma 2020, 12, S17. [Google Scholar] [CrossRef] [PubMed]
  5. Lakhan, R.; Agrawal, A.; Sharma, M. Prevalence of depression, anxiety, and stress during COVID-19 pandemic. J. Neurosci. Rural Pract. 2020, 11, 519–525. [Google Scholar] [CrossRef] [PubMed]
  6. Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; Ho, C.S.; Ho, R.C. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health 2020, 17, 1729. [Google Scholar] [CrossRef] [PubMed]
  7. Brooks, S.K.; Webster, R.K.; Smith, L.E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G.J. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet 2020, 395, 912–920. [Google Scholar] [CrossRef]
  8. Korea Disease Control and Prevention Agency. 2021 Community Health Survey; Korea Disease Control and Prevention Agency: Seoul, Republic of Korea, 2022.
  9. Ministry of Health and Welfare of the Republic of Korea. Available online: http://kstss.kr/wp-content/uploads/2020/10/KSTSS10-03-COVID-19-%E1%84%80%E1%85%AE%E1%86%A8%E1%84%86%E1%85%B5%E1%86%AB%E1%84%8C%E1%85%A5%E1%86%BC%E1%84%89%E1%85%B5%E1%86%AB%E1%84%80%E1%85%A5%E1%86%AB%E1%84%80%E1%85%A1%E1%86%BC%E1%84%89%E1%85%B5%E1%86%AF%E1%84%90%E1%85%A2%E1%84%8C%E1%85%A9%E1%84%89%E1%85%A1_3%E1%84%8E%E1%85%A19%E1%84%8B%E1%85%AF%E1%86%AF.pdf (accessed on 13 September 2024).
  10. Pakpour, A.H.; Griffiths, M.D. The fear of COVID-19 and its role in preventive behaviors. J. Concurr. Disord. 2020, 2, 58–63. [Google Scholar] [CrossRef]
  11. Bösselmann, V.; Amatriain-Fernández, S.; Gronwald, T.; Murillo-Rodríguez, E.; Machado, S.; Budde, H. Physical activity, boredom and fear of COVID-19 among adolescents in Germany. Front. Psychol. 2021, 12, 624206. [Google Scholar] [CrossRef]
  12. Alsalhe, T.A.; Aljaloud, S.O.; Chalghaf, N.; Guelmami, N.; Alhazza, D.W.; Azaiez, F.; Bragazzi, N.L. Moderation effect of physical activity on the relationship between fear of COVID-19 and general distress: A pilot case study in Arabic countries. Front. Psychol. 2020, 11, 570085. [Google Scholar] [CrossRef]
  13. Szabo, A.; Ábel, K.; Boros, S. Attitudes toward COVID-19 and stress levels in Hungary: Effects of age, perceived health status, and gender. Psychol. Trauma Theory Res. Pract. Policy 2020, 12, 572–575. [Google Scholar] [CrossRef]
  14. Inbar, L.; Shinan-Altman, S. Emotional reactions and subjective health status during the COVID-19 pandemic in Israel: The mediating role of perceived susceptibility. Psychol. Health Med. 2021, 26, 75–84. [Google Scholar] [CrossRef]
  15. Chen, X.; Chen, H. Differences in preventive behaviors of COVID-19 between urban and rural residents: Lessons learned from a cross-sectional study in China. Int. J. Environ. Res. Public Health 2020, 17, 4437. [Google Scholar] [CrossRef] [PubMed]
  16. Raifman, M.A.; Raifman, J.R. Disparities in the population at risk of severe illness from COVID-19 by race/ethnicity and income. Am. J. Prev. Med. 2020, 59, 137–139. [Google Scholar] [CrossRef] [PubMed]
  17. Leung, G.; Lam, T.; Ho, L.; Ho, S.; Chan, B.; Wong, I.; Hedley, A.J. The impact of community psychological responses on outbreak control for severe acute respiratory syndrome in Hong Kong. J. Epidemiol. Community Health 2003, 57, 857–863. [Google Scholar] [CrossRef] [PubMed]
  18. Bronfman, N.; Repetto, P.; Cordón, P.; Castañeda, J.; Cisternas, P. Gender differences on psychosocial factors affecting COVID-19 preventive behaviors. Sustainability 2021, 13, 6148. [Google Scholar] [CrossRef]
  19. National Museum of Korean Contemporary History. Viruses that have lived alongside humanity. In History Empathy; National Museum of Korean Contemporary History: Seoul, Republic of Korea, 2020; Volume 28, pp. 08–10. [Google Scholar]
  20. Infectious Disease Portal. Available online: https://ncv.kdca.go.kr/pot/cv/trend/dmstc/selectMntrgSttus.do (accessed on 13 September 2024).
  21. U.S. Food and Drug Administration. Available online: https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/covid-19-vaccines (accessed on 13 September 2024).
  22. U.S. Food and Drug Administration. Available online: https://www.fda.gov/drugs/postmarket-drug-safety-information-patients-and-providers/tamiflu-oseltamivir-phosphate-information (accessed on 13 September 2024).
  23. Aravindakshan, A.; Boehnke, J.; Gholami, E.; Nayak, A. The impact of mask-wearing in mitigating the spread of COVID-19 during the early phases of the pandemic. PLoS Glob. Public Health 2022, 2, e0000954. [Google Scholar] [CrossRef]
  24. Beale, S.; Johnson, A.M.; Zambon, M.; Flu Watch Group; Hayward, A.C.; Fragaszy, E.B. Hand hygiene practices and the risk of human coronavirus infections in a UK community cohort. Wellcome Open Res. 2021, 5, 98. [Google Scholar] [CrossRef]
  25. Jin, M.J.; Sung, M.A.; Son, S.H.; Yoo, J.E.; Jang, Y.E. Changes in Family Life and Relationships during the COVID-19 pandemic and their associations with perceived stress. Fam. Environ. Res. 2020, 58, 447–461. [Google Scholar]
  26. Kim, E.H.; Park, S.Y.; Lee, Y.J.; Park, H. Development and initial validation of the COVID stress scale for Korean people. Korean J. Couns. 2021, 22, 141–163. [Google Scholar]
  27. Song, J.H. The effect of COVID-19-related stress of those who work in the food service industry on their job satisfaction and customer orientation. Food Serv. Ind. J. 2021, 17, 165–178. [Google Scholar]
  28. Lee, H.J.; Lee, H.Y. COVID-19 Stress: Is the level of COVID-19 stress same for everybody? Segmentation approach based on COVID-19 stress level. Korea Logist. Rev. 2020, 30, 75–87. [Google Scholar] [CrossRef]
  29. Park, H.E.; Jung, N.H.; Cho, H.A. Survey of Korean college students’ anxiety and depression in COVID-19. J. Res. Methodol. 2021, 6, 255–278. [Google Scholar] [CrossRef]
  30. Lee, R.H. Factors related to COVID-19 anxiety and stress: Focusing on demographic characteristics and neighborhood satisfaction. J. Korea Contents Assoc. 2021, 21, 71–80. [Google Scholar]
  31. Park, J.S.; Kim, H.Y.; Lee, S.J. COVID-19 related anxiety among young women and its influence on government and community trust: The mediating effect between corona blue and corona red. Korean Soc. Public Adm. 2021, 32, 95–117. [Google Scholar]
  32. Na, R.G.; Bae, S.Y. The Impact of depression from COVID-19 and career anxiety on career indecision among college students majoring in hospitality and tourism. J. Tour. Leis. Res. 2021, 33, 275–291. [Google Scholar]
  33. Lazarus, J.V.; Wyka, K.; Rauh, L.; Rabin, K.; Ratzan, S.; Gostin, L.O.; El-Mohandes, A. Hesitant or not? The association of age, gender, and education with potential acceptance of a COVID-19 vaccine: A country-level analysis. In Vaccine Communication in a Pandemic; Routledge: Abingdon, UK, 2020; Volume 25, pp. 799–807. [Google Scholar]
  34. Barber, S.J.; Kim, H. COVID-19 worries and behavior changes in older and younger men and women. J. Gerontol. Ser. B 2021, 76, e17–e23. [Google Scholar] [CrossRef]
  35. Niño, M.; Harris, C.; Drawve, G.; Fitzpatrick, K.M. Race and ethnicity, gender, and age on perceived threats and fear of COVID-19: Evidence from two national data sources. SSM Popul. Health 2021, 13, 100717. [Google Scholar] [CrossRef]
  36. Ahmad, M.; Iram, K.; Jabeen, G. Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China. Environ. Res. 2020, 190, 109995. [Google Scholar] [CrossRef]
  37. Reznik, A.; Gritsenko, V.; Konstantinov, V.; Khamenka, N.; Isralowitz, R. COVID-19 fear in Eastern Europe: Validation of the fear of COVID-19 scale. Int. J. Ment. Health Addict. 2021, 19, 1903–1908. [Google Scholar] [CrossRef]
  38. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.; Gill, H.; Phan, L.; Chen-Li, D.; Iacobucci, M.; Ho, R.; Majeed, A. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020, 277, 55–64. [Google Scholar] [CrossRef]
  39. Maugeri, G.; Castrogiovanni, P.; Battaglia, G.; Pippi, R.; D’Agata, V.; Palma, A.; Rosa, M.D.; Musumeci, G. The impact of physical activity on psychological health during COVID-19 pandemic in Italy. Heliyon 2020, 6, e04315. [Google Scholar] [CrossRef]
  40. Buneviciene, I.; Bunevicius, R.; Bagdonas, S.; Bunevicius, A. The impact of pre-existing conditions and perceived health status on mental health during the COVID-19 pandemic. J. Public Health 2022, 44, e88–e95. [Google Scholar] [CrossRef] [PubMed]
  41. U.S. Department of Health and Human Services. Available online: http://www.health.gov/paguidelines (accessed on 5 January 2024).
  42. Kim, N.J. The Effect of Regular Exercise on the Subjective Health and Social Activity of the Elderly. Korean J. Phys. Educ. 2000, 39, 149–158. [Google Scholar]
  43. Ocampo, J.M. Self-rated Health: Importance of Use in Elderly Adults. Colomb. Med. 2020, 41, 275–289. [Google Scholar] [CrossRef]
  44. Kwak, H.K.; Lee, M.Y.; Kim, M.J. Comparisons of body image perception, health related lifestyle and dietary behavior based on the self-rated health of university students in Seoul. Korean J. Community Nutr. 2011, 16, 672–682. [Google Scholar] [CrossRef]
  45. Kang, J.Y.; Ji, Y.S.; Kim, M.H.; Kim, Y.P. Effects of exercise-participated rate on self-perceived health status and depression degrees in silver town elderly. Korean J. Health Promot. 2009, 9, 114–121. [Google Scholar]
  46. Lee, H.Y.; Yoon, T.H.; Joo, S.B. Effects of forest trekking exercise on body composition, maximum strength and VAS of perceived health in female university student. Korean J. Sport 2018, 16, 561–568. [Google Scholar]
  47. Bize, R.; Johnson, J.A.; Plotnikoff, R.C. Physical activity level and health-related quality of life in the general adult population: A systematic review. Prev. Med. 2007, 45, 401–415. [Google Scholar] [CrossRef]
  48. Pucci, G.; Reis, R.S.; Rech, C.R.; Hallal, P.C. Quality of life and physical activity among adults: Population-based study in Brazilian adults. Qual. Life Res. 2012, 21, 1537–1543. [Google Scholar] [CrossRef]
  49. Castañeda-Babarro, A.; Arbillaga-Etxarri, A.; Gutiérrez-Santamaría, B.; Coca, A. Physical Activity Change during COVID-19 Confinement. Int. J. Environ. Res. Public Health 2020, 17, 6878. [Google Scholar] [CrossRef]
  50. Mayo, X.; Liguori, G.; Soler, E.I.; Copeland, R.J.; Emeterio, I.C.S.; Lowe, A.; Del Villar, F.; Jimenez, A. The active living gender’s gap challenge: 2013–2017 Eurobarometers physical inactivity data show constant higher prevalence in women with no progress towards global reduction goals. BMC Public Health 2019, 19, 1677. [Google Scholar] [CrossRef]
  51. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob. Health 2019, 7, e36. [Google Scholar] [CrossRef] [PubMed]
  52. Mielke, G.I.; Da Silva, I.C.M.; Kolbe-Alexander, T.; Brown, W.J. Shifting the physical inactivity curve worldwide by closing the gender gap. Sports Med. 2018, 48, 481–489. [Google Scholar] [CrossRef] [PubMed]
  53. Heo, S.Y.; Chung, Y. Effect of complex sample design on Pearson test statistic for homogeneity. J. Korean Data Inf. Sci. Soc. 2012, 23, 757–764. [Google Scholar]
  54. Goodwin, R.; Gaines, S.O., Jr.; Myers, L.; Neto, F. Initial psychological responses to Swine flu. Int. J. Behav. Med. 2011, 18, 88–92. [Google Scholar] [CrossRef]
  55. Rubin, G.J.; Amlôt, R.; Page, L.; Wessely, S. Public perceptions, anxiety, and behaviour change in relation to the Swine flu outbreak: Cross-sectional telephone survey. BMJ 2009, 339, b2651. [Google Scholar] [CrossRef]
  56. Matias, T.; Dominski, F.H.; Marks, D.F. Human needs in COVID-19 isolation. J. Health Psychol. 2020, 25, 871–882. [Google Scholar] [CrossRef]
  57. Kim, N.J. A Study on the effect between health problems and exercise of women working. Korean J. Phys. Educ. 2001, 40, 125–137. [Google Scholar]
  58. Kim, Y.R. The relationship among types of leisure activity, subjective health status and the happy life. Korean J. Sport Sci. 2009, 20, 553–566. [Google Scholar]
Healthcare 12 01924 g001
Figure 1. The analyzed factors of the study.
Figure 1. The analyzed factors of the study.
Healthcare 12 01924 g001
Table 1. Demographic information (N = 43,583,798.71).
Table 1. Demographic information (N = 43,583,798.71).
TypeWeightedUnweighted
N%N
Male21,607,261.5249.6%104,352
Female21,976,537.1950.4%124,580
Age19–297,250,736.9116.6%24,679
30–396,760,819.2915.5%25,716
40–498,202,946.7118.8%35,509
50–598,586,211.2719.7%43,027
60–696,728,597.8815.4%47,457
70–794,112,996.159.4%33,981
80 or older1,941,490.474.5%18,563
Educational Background *Elementary school3,727,487.248.6%35,905
Middle school3,334,616.727.7%24,744
High school12,663,486.1929.1%66,300
2-year/3-year college6,092,725.5714.0%25,964
4-year university14,322,020.6732.9%54,635
Graduate school or higher2,413,120.985.5%9085
No formal education987,309.532.3%11,944
Others21,213.690.0%222
Engagement in Economic Activity *Yes27,732,897.8963.6%143,344
No15,848,981.1836.4%85,580
Physical Activity LevelLow20,845,572.9047.8%115,323
High22,738,225.8052.2%113,609
* Respondents who refused to answer or answered “don’t know” were excluded.
Table 2. Differences of change in smoking, drinking and physical activity level according to gender after the COVID-19 pandemic.
Table 2. Differences of change in smoking, drinking and physical activity level according to gender after the COVID-19 pandemic.
GenderSmokingDrinkingPhysical Activity Level
DecreasedSameIncreasedDecreasedSameIncreasedDecreasedSameIncreased
Male20.1%68.9%11.0%47.0%45.0%8.0%42.8%48.8%8.4%
Female19.8%67.9%12.3%42.3%48.8%8.9%49.5%41.8%8.7%
Total20.1%68.8%11.1%45.0%46.6%8.4%46.2%45.3%8.6%
χ22.92796.684 ***343.179 ***
*** = p < 0.001.
Table 3. Differences in self-perceived health status by gender.
Table 3. Differences in self-perceived health status by gender.
VariableParameterWeightedt
NMStandard Error
GenderMale21,607,261.523.480.00345.522 ***
Female21,976,537.193.290.003
*** = p < 0.001.
Table 4. Differences in COVID-19-related fears by gender.
Table 4. Differences in COVID-19-related fears by gender.
Fear of Infection
VariableParameterNMStandard Errort
GenderMale21,607,261.523.540.004−64.541 ***
Female21,976,537.193.890.003
Fear of Blame from Others
VariableParameterNMStandard Errort
GenderMale21,607,261.523.700.004−47.021 ***
Female21,976,537.193.970.004
Fear of Economic Impacts
VariableParameterNMStandard Errort
GenderMale21,607,261.523.820.004−28.382 ***
Female21,976,537.193.980.004
*** = p < 0.001.
Table 5. Differences in compliance with infection-prevention behaviors according to gender.
Table 5. Differences in compliance with infection-prevention behaviors according to gender.
GenderVaccinationWearing a Mask IndoorsWearing a Mask OutdoorsSocial Distancing
YesNoHighly ComplyComplyNeverHighly ComplyComplyNeverHighly ComplyComplyNever
Male76.1%23.9%90.9%8.9%0.2%89.9%9.6%0.4%71.6%25.8%2.7%
Female78.5%21.5%93.1%6.8%0.1%92.4%7.4%0.2%74.1%23.6%2.3%
Total77.3%22.7%92.0%7.8%0.1%91.2%8.5%0.3%72.9%24.7%2.5%
χ2141.747 ***113.783 ***179.612 ***57.620 ***
*** = p < 0.001.
Table 6. Differences of compliance with infection-prevention behaviors according to engagement in economic activity.
Table 6. Differences of compliance with infection-prevention behaviors according to engagement in economic activity.
Engagement in Economic ActivityVaccinationWearing a Mask IndoorsWearing a Mask OutdoorsKeeping Social Distancing
YesNoHighly ComplyComplyNeverHighly ComplyComplyNeverHighly ComplyComplyNever
Yes77.722.392.07.90.191.18.60.372.924.72.4
No76.723.392.17.80.291.68.10.372.624.62.9
Total77.322.792.07.80.191.28.50.372.924.72.5
χ217.427 ***1.6314.777 **10.226 ***
*** = p < 0.001, ** = p < 0.01.
Table 7. Differences in compliance with infection-prevention behaviors according to occupation.
Table 7. Differences in compliance with infection-prevention behaviors according to occupation.
OccupationVaccinationWearing a Mask IndoorsWearing a Mask OutdoorsKeeping Social Distancing
YesNoHighly ComplyComplyNeverHighly ComplyComplyNeverHighly ComplyComplyNever
Manager83.0%17.0%92.6%7.3%0.1%92.2%7.6%0.3%75.2%22.0%2.8%
Professional79.3%20.7%93.7%6.2%0.1%93.2%6.6%0.2%74.6%22.5%2.9%
Clerical worker73.9%26.1%92.8%7.1%0.1%92.5%7.2%0.2%73.3%24.3%2.4%
Service worker79.0%21.0%92.7%7.1%0.1%92.2%7.6%0.2%74.0%23.5%2.4%
Sales worker73.0%27.0%91.6%8.1%%0.3%91.0%8.7%0.3%70.1%26.6%3.3%
Agricultural, fishery worker88.8%11.2%88.1%11.6%0.3%83.5%15.2%1.3%67.7%29.7%2.6%
Technician73.4%26.6%90.8%9.0%0.2%89.7%9.7%0.6%69.3%27.9%2.8%
Plant worker75.3%24.7%91.4%8.4%0.1%90.6%9.1%0.3%69.1%27.6%3.3%
Elementary worker81.5%18.5%91.1%8.8%0.2%89.8%9.9%0.4%69.7%27.6%2.7%
Military personnel96.4%3.6%94.5%5.5%0.0%94.4%5.5%0.1%80.2%18.2%1.6%
Total77.7%22.3%92.2%7.7%0.0%91.3%8.4%0.3%72.1%25.2%2.8%
χ298.530 ***13.008 ***34.496 ***15.282 ***
*** = p < 0.001.
Table 8. Differences in self-perceived health status by physical activity level.
Table 8. Differences in self-perceived health status by physical activity level.
VariableParameterWeightedt
NMStandard Error
Physical
Activity		Low20,845,572.913.26980.0034649.725 ***
High22,738,225.803.49950.00330
*** = p < 0.001.
Table 9. Differences in COVID-19-related fears by physical activity level.
Table 9. Differences in COVID-19-related fears by physical activity level.
Fear of Infection
VariableParameterNMStandard Errort
Physical
Activity		Low20,845,572.913.75480.0041911.515 ***
High22,738,225.803.68780.00444
Fear of Blame from Others
VariableParameterNMStandard Errort
Physical
Activity		Low20,845,572.913.85770.004434.871 ***
High22,738,225.803.82810.00462
Fear of Economic Impacts
VariableParameterNMStandard Errort
Physical
Activity		Low20,845,572.913.93430.004688.413 ***
High22,738,225.803.88140.00487
*** = p < 0.001.
Table 10. The effect of self-perceived health status on COVID-19-related fears.
Table 10. The effect of self-perceived health status on COVID-19-related fears.
COVID-19 Infection According to Subjective Health
Dependent VariableIndependent VariableEstimateStandard ErrortF
Fear of infectionIntercept4.0770.012326.441 ***830.699 ***
Subjective health−0.1050.004−28.822 ***
COVID-19 Blame according to Subjective Health
Dependent VariableIndependent VariableEstimateStandard ErrortF
Fear of blame from othersIntercept4.0840.013320.579 ***359.296 ***
Subjective health−0.0710.004−18.955 ***
COVID-19 Economic according to Subjective Health
Dependent VariableIndependent VariableEstimateStandard ErrortF
Fear of economic impactsIntercept4.2720.013336.073 ***825.310 ***
Subjective health−0.1080.004−28.728 ***
*** = p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Han, G.; Park, J.-A. Individual Characteristics Associated with Fears and Prevention Behaviors Related to Respiratory Infectious Disease among South Korean Adults Using Complex Sample Design. Healthcare 2024, 12, 1924. https://doi.org/10.3390/healthcare12191924

AMA Style

Han G, Park J-A. Individual Characteristics Associated with Fears and Prevention Behaviors Related to Respiratory Infectious Disease among South Korean Adults Using Complex Sample Design. Healthcare. 2024; 12(19):1924. https://doi.org/10.3390/healthcare12191924

Chicago/Turabian Style

Han, Gunsoo, and Jae-Ahm Park. 2024. "Individual Characteristics Associated with Fears and Prevention Behaviors Related to Respiratory Infectious Disease among South Korean Adults Using Complex Sample Design" Healthcare 12, no. 19: 1924. https://doi.org/10.3390/healthcare12191924

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop