key: cord-0697281-qbffv5g4 authors: Zhou, Min; Long, Piao; Kong, Nan; Campy, Kathryn S. title: Characterizing Wuhan residents’ mask-wearing intention at early stages of the COVID-19 pandemic date: 2020-12-25 journal: Patient Educ Couns DOI: 10.1016/j.pec.2020.12.020 sha: c56505fac21fdd6d67eb8b1bfa42f7359f200c7e doc_id: 697281 cord_uid: qbffv5g4 OBJECTIVE: As an effective measure to prevent the COVID-19 pandemic, wearing mask is widely recommended in countries around the world. This study aims to identify factors that explain the behavioral intention of Wuhan City urban residents to wear masks. METHODS: A theoretical model was extended on UTAUT by incorporating the feature on residents having relevant knowledge and sufficient awareness on the pandemic. During early stages of the COVID-19 outbreak, an online survey was conducted in Wuhan City and 728 valid samples were collected from 35 communities. Structural equations modeling and bootstrapping were applied. RESULTS: Sample data present acceptable reliability and validity. Performance expectancy, effort expectancy, social influence, and knowledge about COVID-19 have positive effects on behavioral intention. Facilitating condition, knowledge, and behavioral intention have significant effects on use behavior. Gender, age, education, income, and current marital status are significant moderators in the theoretical model. CONCLUSION: Having relevant knowledge on the pandemic, together with performance expectancy, effort expectancy, social influence, and facilitating condition, affects behavioral intention and usage behavior of Wuhan residents to wear masks at early stages of the COVID-19 pandemic. Subgroups have different psychological mechanisms based on their demographic characteristics. PRACTICE IMPLICATIONS: Health policy makers should focus on enhancing residents’ knowledge on infectious disease and their awareness of the risk mitigation, and develop personalized measures for different subgroups. According to Coronavirus disease (COVID-19) pandemic reports released by the World Health Organization, there had been more than 61.87 million confirmed cases and more than 1.45 million confirmed deaths worldwide by 29 November, 2020 (WHO). Fastspreading COVID-19 emerged as a global pandemic. Countries around the world have adopted various measures to control the pandemic. President Trump announced that the United States was in a state of emergency and suspended immigration into US for 60 days. China [1] , Italy [2] , and Iran [3] implemented city lockdown and community quarantine (total stay-at-home) policies. Poland introduced border sanitary control on the fifth day after reporting the first laboratory confirmed case of COVID-19 [4] . Due to the absence of specific treatments and efficacious vaccines against COVID-19, personal precautions are necessary. A mathematical modeling study based on the cumulative number of confirmed cases and cumulative deaths estimated that the initial basic reproduction number (R 0 ) of COVID-19 was 5.32, which is significantly higher than the initial R 0 value of SARS (i.e., 2.90) [5] . The virus typically causes respiratory and gastrointestinal diseases in humans and animals, and it can be transmitted through aerosols and direct/indirect contact, as well as during the handling of medical cases and laboratory samples [6] . Non-pharmaceutical interventions, e.g. wearing facemasks or washing hands, are effective measures to block the epidemics of COVID-19 and many other respiratory infectious diseases [7] . Wearing a mask can significantly reduce the exposure of air pollutants into the body through the respiratory route, and this measure is an easy and effective protection method. Therefore, health departments in most Asian countries recommend that residents wear masks in public during the COVID-19 outbreak [3, 6, 7] . In particular, China has many megacities with large populations, such as 11.21 million in Wuhan. Due to the huge number of infections and presence of asymptomatic patients, it is necessary for people in large cities to wear masks in public settings and when around people who don't live in the same household, especially when other social distancing measures are difficult to maintain. At early stages of this pandemic, Wuhan (the Chinese city with the most severe outbreak of COVID-19) municipal government and health experts repeatedly urged the public to wear masks. However, urban residents did not have much motivation to wear masks [8] . Many Wuhan residents appeared without wearing masksin public places, such as subways, airports, and railway stations. This is a period of rapid increase in new infected cases in Wuhan. On February 6, the Wuhan government strictly implemented the policy of "residents must wear masks" and mandatory quarantine measures. Residents' willingness to wear masks reversed significantly, which led to masks out-of-stock and skyrocketing price in the Chinese market. Then the number of new infected cases showed a turning point on February 16 . While some studies do not agree that healthy urban residents wear masks when in public [9, 10] , Wuhan's data suggest the necessity of implementing the policy in this city. There is an urgent need for research on the willingness to wear masks, and particularly, among Wuhan's urban residents at early stages of the pandemic, which appears to have rarely been the focal population of previous research in this field. In this paper, we address this research gap with the development of a comprehensive theoretical model. We use the model to characterize the impact mechanism of willingness to wear masks among urban residents during the early period of the pandemic, and the influencing psychological and demographic factors. The theoretical framework of our study was expanded on the Unified Theory of Acceptance and Use of Technology (UTAUT) [11, 12] . UTAUT developed by integrating elements from eight models, i.e. Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of planned behavior (TPB), Combined TAM and TPB(C-TAM-TPB), Model of PC utilization (MPCU), Innovation diffusion theory (IDT), and Social cognitive theory (SCT) [11] . Given that UTAUT explains up to 70% of the variance in behavioral intentions, it is widely used to explore individual acceptance to medical technology, device and service, including Canadian patients' intention to use online postings of ED wait times [13] , physicians' adoption of electronic health record in the healthcare system of Bangladesh [14] , health information technology adoption in Thailand's community health centers [15] , elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare in Australia [16] , and wearable technology acceptance in healthcare [17] . Previous studies demonstrate the general applicability of UTAUT model in the field of individual health behavior, and the reliability and validity of constructs in the theoretical model have been extensively verified [13, 18, 19] . In view of the significant influence of knowledge on the behavioral intention of individuals in the context of acceptance of health technologies, we incorporated Knowledge about COVID-19 as a new construct in the extended theoretical model. Then, an extended UTAUT model ( Fig. 1 ) was developed to explain Wuhan city residents' intention and use behavior of wearing masks in early stages of the pandemic. UTAUT suggests three constructs acting as direct determinants of behavioral intention, namely performance expectancy, effort expectancy, and social influence. Performance expectancy is defined as the degree to which an individual believes that using the system (masks) will help him or her to attain gains in job performance. It was represented as perceived usefulness, usefulness and extrinsic motivation, usefulness and relative advantage [11] . The performance expectancy construct in a UTAUT model is the strongest predictor to individual behavioral intentions [20, 21] . Effort expectancy is defined as the degree of ease associated with the use of the system (masks). It was developed by perceived ease of use in TAM, complexity in MPCU, and ease of use in IDT [11] . Empirical studies show that effort expectancy has a significant impact on behavioral willingness and the relationship is moderated by gender, age, and experience, especially at early stages [17, 22, 23] . Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system. It was represented as a subjective norm in TRA, TAM2, TPB/DTPB and C-TAM-TPB, social factors in MPCU, and image in IDT [11] . Previous research confirmed the significant impact of social influence on behavioral intentions. It was moderated by gender, age, voluntariness and experience, and the effect would be stronger for women, particularly older women [24, 25] . Based on the previous research and the current context, we developed the following three hypotheses: H2. Effort expectancy will have a positive influence on urban residents' behavioral intention of wearing masks. H3. Social influence will have a positive influence on urban residents' behavioral intention of wearing masks. Facilitating conditions is defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system. It captures concepts embodied by perceived behavioral control (TPB/DTPB, C-TAM-TPB), facilitating conditions (MPCU), and compatibility (IDT) [11] . Under the UTAUT theoretical framework, facilitating conditions and behavioral intention influence user acceptance and use behavior. It is confirmed by empirical evidence that facilitating conditions has a significant influence on use behavior but does not significantly affect behavioral intention [14, 15] . Consistent with the theoretical model of behavioral intention discussed in previous studies, we assume that behavioral intention will have a significant positive impact on the behavior of urban residents wearing masks [11] . Then, the following two hypotheses were proposed: H4. Facilitating conditions will have a positive influence on urban residents' use behavior of wearing masks. H7. Behavioral intention will have a positive influence on urban residents' use behavior of wearing masks. Furthermore, previous studies found that the knowledge level of individuals has a general positive or negative intention to using healthcare technology, device, and service, such as HPV vaccination [26] [27] [28] , electronic health record (EHR) [14] , and face masks [29, 30] . On this basis, we add knowledge about COVID-19 into our research model as a direct determinant to behavioral intention and use behavior. This construct (Knowledge about COVID-19) is defined as the degree to which an individual has relevant knowledge about the COVID-19 pandemic, including incubation period, difference with seasonable flu, preventive measures, and case fatality rate. We added the following two hypotheses: H5. Knowledge about COVID-19 will have a positive influence on urban residents' behavioral intention of wearing masks. H6. Knowledge about COVID-19 will have a positive influence on urban residents' use behavior of wearing masks. Previous studies suggested that direct effects of determinants in the UTAUT model are moderated by demographic variables, i.e. age, gender, experience, educational level, health insurance, ethnicity/race, and voluntariness of use [11] [12] [13] 15, 19] . Based on the current research context, we adjusted demographic variables as control factors. First, given that our study adds the construct of use behavior into the research model, difference on the knowledge about the infectious disease was considered. Second, at early stages of the COVID-19 pandemic, wearing masks was voluntary among Wuhan's urban residents. Therefore, we removed the two control variables, experience and voluntariness of use. Third, we considered characterizing the demographic variables of different segments and incorporating them into the research model, including education, annual household income before tax, and current marital status. These five demographic variables will mediate the relationship between the constructs in the research model. The questionnaire consists of two parts. The first part (socioeconomic information) includes five choice questions on gender, age, education, annual household income before tax, and current marital status. The second part (behavioral intentions scale) includes questions (variables) to measure respondents' behavioral intentions, and all measurement items were derived from previous studies and scored on a seven-point Likert scales. The questionnaire was pretested by a pilot survey, and confirmatory factor analysis (CFA) was applied to the validated sample data. Items with standardized factor loading less than 0.5 were deleted, and some language expressions were revised. Table 1 lists the detailed items of each construct and their sources. It is a necessary premise that the sample size meets the statistical requirements. The sample size has been extensively discussed in previous studies, but it is difficult to reach a broad consensus. Even if the sample size is small, the structural equation model can implement meaningful tests [31] . Generally, N = 100 is considered the smallest acceptable sample size [32, 33] . Some researchers believe that a larger sample size is required for structural equation model analysis, for example, N = 200 [34] . Simulation studies show that for normally distributed variables and no missing data, the reasonable sample size of the simple CFA model is about N = 150 [35] [36] [37] . The ratio (N: q) of the sample size (N) to the number of free variables (q) in the model is a widely adopted rule. The rule of thumb is at least N/q = 5 [38] ; for complexity models, N/q should be 10-20 [32, 39] . Considering the above studies and the number of variables in currently studied (25) , we set the valid sample size required for this study to be over 500. The survey was conducted in the center area of Wuhan city from January 1, 2020 (Wuhan Municipal Government announced the discovery of unexplained pneumonia) to January 22, 2020 (Wuhan closed all transportation channels in January 23). Wuhan is located on the Yangtze River in central China, with a total population of 14 million. This questionnaire survey was implemented with the assistance of a local community government. According to the last two digits of residents' ID number, 860 participants were randomly selected from 35 communities in seven districts of Wuhan, namely Jiangan, Jianghan, Qiaokou, Hanyang, Wuchang, Qingshan and Hongshan. A written informed consent was signed before the investigation. An online shopping coupon was offered to the participants who successfully submitted their responses. We used Windows SPSS 22.0 as descriptive statistics, and compared the quantitative proportion, average score, cumulative percentage and other indicators in different subgroups. One-way analysis of variance (one-way ANOVA), also known as F-test, was used to verify whether the mean of subgroup samples is different in data variation among each construct. For example, we placed "Performance Expectancy" on the dependent variable list, select "Gender" as an independent variable, and use the post-hoc multiple comparison method to implement the homogeneity test of variance. We used the confirmatory factor analysis (CFA) to assess the reliability and validity of the measurement model. Reliability refers to the internal consistency and stability of the measurement results, including item reliability, construct reliability and discriminant validity. Content validity refers to the degree to which the measurement items can accurately represent the domain of constructs, or the usefulness of the measurements. In the current study, all items were selected and revised from the wellestablished measurements in previous studies, then the content validity was assured. The measurement reliability was tested by factor loadings with recommended threshold set at above 0.5 [40] . Scale reliability was assessed with Cronbach's alpha and composite reliability (CR) with both thresholds set at 0.7. The discriminant validity can be verified by that all square root of the AVE are greater than the off-diagonal values in the corresponding rows and columns. Given that the data were collected by a single method, it is necessary to test the common method deviation (CMB) in confirmatory factor analysis [41, 42] . The Harman's single-factor test method was adopted in current study, and the largest initial eigenvalue should not exceed the acceptable threshold of 50%. To evaluate the fitness of the theoretical model and survey data, we referred to common guidelines as following indicators and recommended values: CHI/DF < 3; NFI > 0.9; IFI > 0.9; RFI > 0.9; TLI > 0.9; CFI > 0.9; GFI > 0.9; AGFI > 0.9; P-value<0.05; RMSEA < 0.08 [43] . To verify the theoretical hypotheses proposed in the current study, structural equation modeling (SEM), maximum likelihood estimates and Bootstrapping methods were adopted. We used Amos 24.0 to transform the theoretical framework into a structural equation model, and the hypotheses were tested by factor and path analysis. Regarding the estimates of regression weights, p < 0.05 is the acceptable significant level. The moderating effects of demographic variables have been confirmed in previous studies about health behavior, such as health information technologies and systems [15, 18] , mHealth [19] and wearable technology in healthcare [17] . The moderating effects of the five demographic variables in current study was tested by regression analysis based on unstandardized coefficients between the comparison groups. The different qualities between the two groups of variances do not affect the moderating effects evaluation [44] . For the five demographic variables, two groups with the largest difference were taken as the comparison groups: Gender (Male, Female), Age (Under 20, Over 65), Education (High school education or below, Bachelor degree and above), Income (Below 60,000 Yuan, More than 180,000 Yuan), Current marital status (Married, Unmarried). Then we calculated the unstandardized slope, based on the path loading coefficient (b) and standard error (SE) of comparison groups on relationship paths. If the absolute value of Z is greater than 1.96, it means that the moderating effects of control variable on this path is valid with the 0.05 confidence level [45, 46] . We collected 728 valid samples from 860 respondents (84.7% completion rate) from Wuhan city residents, and samples that included missing data in the present study were eliminated. The sample size is in accordance with the structural equation analysis requirements [35] [36] [37] . As shown in The results of one-way ANOVA suggest that gender is a significant factor on two constructs, and females had a higher average score than males on behavioral intention (4.29 vs 4.47, p < 0.05), while males had a higher average score than females on use behavior (4.95 vs 4.63, p < 0.001). Young residents (under 20) have lower scores than older people (65+ years old) in two constructs, including social influence (4.72 vs 4.89, p < 0.05) and behavioral intention (4.17 vs 4.67, p < 0.01). It shows that young people are less affected by the opinions from others, and their willingness to wear masks is lower than that of the elderly. The subgroup with lowest annual household income before tax (under $ 10,000) had a significantly higher score in on use behavior than the highest income subgroup (5.08 vs 4.51, p < 0.01). Marital status is also an important variable on two constructs, and the unmarried subgroup scores higher, including knowledge about COVID-19 (3.63 vs 3.88, p < 0.01) and use behavior (4.61 vs 5.20, p < 0.001). This implies that unmarried people have more knowledge about the new crown pneumonia virus and are more active in wearing masks. Education did not show significant difference among all constructs. The Harman's single-factor test method was adopted in current study for detecting Common Method Bias, and the results showed that the largest initial eigenvalue is 31.6% (