|
 
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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 5
| Issue : 4 | Page : 186-192 |
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Do perceived barriers, benefits, and severity have effect on mask-wearing habits during the coronavirus disease-2019 pandemic?
Raheleh Soltani, Mohsen Shamsi, Atefe Moradi
Department of Health Education and Health Promotion, School of Health, Arak University of Medical sciences, Arak, Iran
| Date of Submission | 12-Apr-2022 |
| Date of Decision | 28-Sep-2022 |
| Date of Acceptance | 19-Oct-2022 |
| Date of Web Publication | 22-Nov-2022 |
Correspondence Address:
Mohsen Shamsi
Department of Health Education and Health Promotion, School of Health, Arak University of Medical Sciences, Arak
Iran
 Source of Support: None, Conflict of Interest: None  | 6 |
DOI: 10.4103/shb.shb_52_22
Introduction: The centers for disease prevention and control advise wearing a cloth face covering in public to prevent the spread of the coronavirus disease 2019, especially in situations when maintaining social distancing is challenging. As a result, the current study sought to identify the factors influencing mask behavior using constructs from the health belief model (HBM). Methods: This cross-sectional study was conducted on 311 participants who were referred to the Health Centers of Arak, Iran, from November 2021 to December 2021. The participants were selected through multi-stage stratified random sampling. Data were collected using a questionnaire and consisted of sociodemographic data, mask-wearing behavior, and structures of HBM regarding mask wearing. Results: The participants' mean (standard deviation) age was 37.9 (12) years (ranging from 18–81). The rate of “always” wearing a face mask was 57.9%. Multiple regression analysis revealed that mask-wearing behavior was associated with demographic variables (age and gender), perceived severity (β = 0.17, P < 0.001), perceived benefits (β = 0.24, P < 0.001), and self-efficacy (β = 0.35, P < 0.001). The HBM constructs explained 46% of the variance of mask-wearing behavior (F [9,301] = 30, R = 0.68, [P < 0.001]). Conclusion: According to the findings of this study, HBM constructs can be treated as a predictor of mask wearing. Based on this predictor (self-efficacy, perceived severity, and benefits), effective interventions and healthy messages can be designed to improve mask-wearing behavior.
Keywords: Coronavirus disease-2019, health belief model, mask wearing, prevention behaviors
How to cite this article:
Soltani R, Shamsi M, Moradi A. Do perceived barriers, benefits, and severity have effect on mask-wearing habits during the coronavirus disease-2019 pandemic?. Asian J Soc Health Behav 2022;5:186-92 |
How to cite this URL:
Soltani R, Shamsi M, Moradi A. Do perceived barriers, benefits, and severity have effect on mask-wearing habits during the coronavirus disease-2019 pandemic?. Asian J Soc Health Behav [serial online] 2022 [cited 2023 Dec 2];5:186-92. Available from: http://www.healthandbehavior.com/text.asp?2022/5/4/186/361712 |
| Introduction | |  |
Coronavirus disease-2019 (COVID-19), as a novel disease and global challenge, is one of the leading public health emergencies and concerns.[1] A new contagious disease was reported in December 2019, and it was called COVID-19 by the World Health Organization (WHO) on January 30, 2020.[2] According to the WHO's most recent weekly report on March 8, 2022, approximately 433 million people were infected with COVID-19, with 5.9 million deaths reported globally. The number of new COVID-19 cases was reported to be over 10 million, and new deaths were over 52,000 in the six WHO regions during a week.[3] The overall risk of the new variants of coronavirus, such as Omicron, is a concern for nations and health-care settings.[3] It generally spreads from person to person via respiratory droplets, sneezing, coughing, shouting, or singing. Similarly, poor ventilation in indoor environments increases the risk of airborne transmission.[4] Covering the nose and mouth with a mask reduces the spray of droplets.[5] WHO has issued several guidelines regarding the prevention and surveillance of COVID-19. Mask wearing and social distancing are two nonpharmaceutical, effective methods to prevent virus transmission. Health-care providers have recommended mask wearing as one of the easy and simple strategies to prevent COVID-19, and guidelines for using the mask have been provided by the WHO to communities.[6],[7] A meta-analysis study reported that mask wearing reduces the risk of respiratory infection by 80% among both health-care workers and nonhealth-care workers.[8] According to the literature, mask wearing is the most effective and significant issue in the fight against the spread of COVID-19 infection, and it is highly recommended.[5],[9],[10]
Despite the importance of this issue, the studies indicate the use of a mask is inadequate,[11],[12] and some individuals are against mask wearing.[13],[14] Wearing a mask has been reported to range from low (<25%) to high (>75%) in 38 countries worldwide.[15] In a cross-sectional study conducted by Barile et al., the rate of “always” and “never” mask wearing was 23% and 13%, respectively.[16] Similarly, it was reported as 46.4% and 36% in Nigeria and Bangladesh, respectively.[17] A population-based study in Iran by Honarvar et al. showed that “always” wearing a mask was 29.8% among individuals.[13]
Studies emphasize the need to identify the preventive behaviors of COVID-19 as well as the factors affecting it in different communities.[18],[19],[20] When we want to know the impact of various factors on the adoption of healthy behavior, such as why people do not wear masks or why they do not follow COVID-19 prevention behaviors, we will need to use behavioral study theory to explain better and understand it. One of the patterns for identifying factors related to behavior is the health belief model (HBM).[19],[21],[22] The HBM was developed in the early 1950s by Rosenstock et al. at the United States Public Health Service. It aimed to demonstrate how people's beliefs can affect their healthy behavior and the adoption of disease prevention strategies.[22]
The studies recommend that the HBM can be applied to understand adherence to COVID-19 prevention practices.[19],[23] There are very few theory-based studies on mask-wearing behaviors based on our knowledge of health-care settings. Therefore, the present study was designed to determine how the constructs of HBM could predict mask-wearing behavior during the COVID-19 pandemic.
| Methods | | |
Study design and participants
This cross-sectional study was conducted on 311 participants who were referred to the health centers of Arak for routine preventive Health Care from November 2021 to December 2021. Arak, the center of Markazi Province, is located in the central part of Iran. The eligibility criteria for inclusion were willingness to participate in the study and not suffering from particular mental and emotional diseases. Data were collected through a self-administered questionnaire, and completing each questionnaire took nearly 10 min. The Strengthening of the Reporting of Observational Epidemiological Studies was applied to prepare this manuscript.
Sample size estimation
To calculate the sample size, based on a previous study,[13] the prevalence of face mask wearing was considered as 29.8%, with a type one error of 0.05 (α = 0.05) and maximum acceptable error of 0.05 (d = 0.05), the required sample size was estimated as 320 participants using the following formula: n = z2p (1 − p)/d2.
Sampling procedure
The participants were selected by multi-stage stratified random sampling. First, the city was divided into three regions according to socioeconomic status, and then two health centers (providing health services) were randomly selected from each region. At each health center, eligible participants were randomly selected according to the proportion of individuals referred to each health center. Finally, 311 people were enrolled across six different health services.
Outcome measures
Data were collected through researcher-made questionnaires consisting of three parts: sociodemographic data, mask wearing as a COVID-19 preventive behavior, and structures of HBM regarding face mask use. In part one, the demographic characteristics included gender (female/male), age, educational level (primary school, secondary school, high school, diploma, and academic education), employment and marital status, and self-rated economic status (poor, average, or good). Part two measured the participant's COVID-19 preventive behaviors through a single item. For example, the frequency of mask use was asked, and the given score ranged from 1 (never) to 5 (always). In part three, the HBM constructs related to mask-wearing behavior were assessed through 11 items derived from the available literature and guidelines for HBM measures.[12],[19] The items are presented in [Table 1]. | Table 1: Distribution of responses to questions based on health belief model construct towards mask-wearing habit of the study participants (n=311)
Click here to view |
The HBM-based questions included perceived susceptibility (1 item), perceived severity (1 item), perceived benefit (4 items), perceived barrier (4 items), and self-efficacy (single item). Responses were scored from 1 (strongly disagree) to 5 (strongly agree).
The single-item self-efficacy score ranged from 1 (not at all confident) to 10 (very confident), and a higher score indicated higher self-efficacy.
The validity of the content was confirmed by the expert panel of ten academicians (health education and health promotion experts and health care providers). The mean content validity ratio and content validity index were calculated at 0.72 and 0.83, respectively. The total reliability (Cronbach's alpha) for the scale was 0.71. The test–retest coefficient (2-week interval for 20 participants) was 0.84 (P = 0.001).
Ethical consideration
The Ethical Committee of the Arak University of Medical Science, Iran, approved the study protocol (ID number- IR.ARAKMU.REC.1399.312). The purpose of the study was explained to the participants, and then, written consent was obtained from participants who volunteered to enter this study.
Statistical analysis
SPSS statistical software version 16 (SPSS, Inc., Chicago, IL, USA) was used to analyze the data. Descriptive statistics were performed to explore the data (mean and standard deviation [SD] for quantitative variables and frequencies for categorical variables). One-way ANOVA, independent samples t-test, and multiple linear regression were performed to compare the scores of mask use habits in categorical variables. For all data, the significance level of α was considered 0.05.
| Results | | |
The mean (SD) age of the participants was 37.9 (12) years. The mean (SD) mask-wearing scores in the female and male participants were 4.5 (2.1) and 4.1 (2), respectively, and a significant difference was observed based on the independent t-test (P < 0.001). According to the results, there was a substantial difference between occupation and economic status regarding mask use (P < 0.05). Other demographic characteristics are presented in [Table 2]. | Table 2: Distribution of sociodemographic characteristics and its relationship with mask-wearing habit of the study participants (n=311)
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In this study, 66.9% of the participants agreed with “I consider myself to be at risk of coronavirus” in perceived susceptibility, 69.5% of them agreed with “COVID-19 is dangerous and can lead to general health problems” in perceived severity, and 36.1% agreed with “difficult to breathe when wearing a facemask” in perceived barrier [Table 1].
According to the study's findings, there was a significant and direct relationship between mask-wearing behavior and perceived susceptibility, perceived severity, perceived benefits, and perceived barriers (P < 0.001). Moreover, an important inverse relationship was observed between the perceived barrier and mask use [R = −0.349, P < 0.001, [Table 3]]. | Table 3: Pearson's correlation coefficients between health belief model construct and mask-wearing habit (n=311)
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Multiple regression indicated that the demographic variables (age and gender), perceived severity (β = 0.17, P < 0.001), perceived benefits (β = 0.24, P < 0.001) and self-efficacy (β = 0.35, P < 0.001) were associated with participants' mask-wearing behavior. The HBM constructs explained 46% of the variance in mask-wearing behavior [Table 4]. | Table 4: Regression analysis of predictor factors and participants' mask-wearing habit (n=311)
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| Discussion | | |
To our knowledge, few documents have been published about mask-wearing behavior using HBM constructs in health-care setting-based studies. This study showed that HBM theory significantly predicts mask-wearing behavior and explained 46% of the variance. We found apparent effects of HBM constructs on participants' mask-wearing habits. These results are consistent with some previous studies in this regard. In the Karimy et al. study, HBM explained 27%, and in the Khazaee-Pool et al. study, HBM demonstrated 26% of the variance of COVID-19 preventive behaviors.[24],[25] In the study by Fathian-Dastgerdi et al., HBM constructs explained 42% of the variance of preventive behaviors.[26] This issue is significant for health-care providers in primary health-care settings to design appropriate educational intervention programs based on HBM constructs regarding healthy behaviors such as the mask-wearing habit.
The results of this study indicated that the rate of “always wearing a face mask” was 57.9%. This finding confirms several previous studies in Iran that the rate of “always wearing a face mask” was 45.6% in the Southwest of Iran[18] and 46% in Kermanshah.[27] A population-based study by Honarvar et al.[13] showed that mask-wearing was 29.8% among Iranian adults. Taylor and Asmundson also found that 84% of the participants (age ≥18 years) wore masks.[12] In the study conducted by Barile et al., the proportion of “always wearing masks” and “never wearing masks” was 23% and 13%, respectively, in the United States.[16] Based on this study's findings, the face mask wear rate was not desirable. Because of unfavorable mask use, developing appropriate programs to promote healthy behavior regarding COVID-19, as well as wearing a mask, seems necessary.
This study displayed that mask-wearing behavior was positively associated with HBM components, and self-efficacy (R = 0.589) was a significant predictor. This finding is consistent with Barile et al.'s study, which found that self-efficacy is one of the predictors of face mask wearing.[16] This finding also confirms the study by Tadesse et al. in Addis Ababa, Ethiopia, which showed 47.6% of the individuals had low self-efficacy toward COVID-19 preventive behaviors and was associated with preventive behaviors of COVID-19.[28] In a study by Lin et al., preventive behaviors of COVID-19 were associated with both action and maintenance of self-efficacy among Iranian residents.[29]
Self-efficacy is a crucial predictor for enhancing healthy behaviors. It is defined as the level of trust and confidence in overcoming barriers to healthy behavior.[19] Improving individuals' self-efficacy toward COVID-19 preventive behaviors could be helpful in this regard. According to this study, the mask-wearing habit was positively associated with perceived benefits (R = 0. 520). This finding is consistent with Shahnazi et al.,[19] Khatatbeh et al.[30] and Mirzaei et al.[31] In a study by Larebo and Abame, most respondents had a positive attitude toward face mask use.[14] Another similar study by Fisher et al. showed that cloth face covering usage was associated with outcome expectations, and 83% (confidence interval: 78.28–87.76) of them believed that mask usage could help to control COVID-19 in their community.[32]
In this study, the habit of wearing a mask was positively associated with participants' perceived severity (R = 0. 361) of COVID-19 disease. A study in Hong Kong showed the perceived severity was positively related to mask-wearing habits.[33] Furthermore, Barile et al.[16] reported the same finding. This finding was supported by Sinicrope et al., who discovered a link between the perceived severity of getting COVID-19 disease and willingness to wear a mask.[20] Perceived severity is another construct of HBM, based on which participants believe that the disease is very dangerous and can lead to general health problems.
According to our findings, there was no significant relationship between participants' perceived susceptibility to COVID-19 disease and mask-wearing habits, which was confirmed by the findings of several previous studies.[16],[28],[19],[31] In this study, 66.9% of participants chose the “strongly agree” or “agree” option regarding the possibility of getting COVID-19. Still, there was no significant relationship between it and the mask-wearing behavior in multiple regressions.
In line with some previous studies,[33],[34] we found there was no significant relationship between perceived barriers and mask-wearing behavior. However, it is not in line with the findings of studies by Mirzaei et al.,[31] Tadesse et al.,[28] and Wong et al.,[35] which reported that perceived barriers were associated with adherence to COVID-19 preventive behaviors. This difference is probably due to the study design, sample size, and sampling method. Moreover, it can be due to the difference in instruments (scale) and the number of COVID-19 preventive behaviors. Furthermore, in the studies,[28],[23],[31] all aspects of COVID-19 preventive behaviors were considered, but in our survey, just the mask-wearing habit was considered.
According to this study, the rate of wearing a face mask by women was significantly higher than that of men, confirming the results of other studies by Barile et al.,[16] Rahimi et al.[18] in Iran, and Sinicrope et al.[20] in the Midwestern Community. A video-based observational study on the Bangladeshi population demonstrated that women's mask use was higher than that of men (53.3% vs. 34.5%).[36] This point could be due to women's highly engaging attention to healthy behaviors. Health educational activities and appropriate policies seem necessary for men to enhance healthy behaviors regarding COVID-19.
The current study found a link between age (age-increasing) and mask-wearing habits, consistent with previous research.[18],[20],[37] In a cross-sectional survey by Rader et al., the “always wearing a mask” rate was 33% in individuals aged between 18 and 24 years and 48% in individuals aged 65 and up.[38] This may be due to the higher risk perception of morbidity and death because of COVID-19 infection in higher age groups and their concern.[18]
Limitations
There were some limitations to this study. This was a cross-sectional study, and the self-reported questionnaire could be a subject of recall bias. Despite the limitations, this study's results can provide evidence to design effective interventions in health-care settings for mask-wearing habits.
| Conclusion | | |
According to the findings of this study, HBM constructs can be treated as an essential predictor of participants' mask-wearing habits. Effective interventions and healthy messages could be designed based on this predictor (self-efficacy, perceived, severity, and benefits) to help improve mask-wearing behavior.
Acknowledgments
The authors appreciate the Education Deputy of Arak University of Medical Sciences for their financial support, the participants for their involvement in completing the questionnaires, and the staff members of health centers for their cooperation.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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