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. 2022 Dec 28:1–10. Online ahead of print. doi: 10.1007/s10389-022-01808-x

The predictors of COVID-19 preventive health behaviors among adolescents: the role of health belief model and health literacy

Parvaneh Vasli 1,✉, Zahra Shekarian-Asl 1, Mina Zarmehrparirouy 2, Meimanat Hosseini 1
PMCID: PMC9795447  PMID: 36588661

Abstract

Aim

The present study aimed to evaluate the predictive role of the Health Belief Model (HBM) constructs and health literacy (HL) in shaping the coronavirus disease 2019 (COVID-19) preventive health behaviors (PHBs) among adolescents.

Methods

This cross-sectional study was conducted with 503 adolescent girls and boys, randomly selected via cluster sampling. For this purpose, the data were collected online through four research tools, including the demographic-clinical characteristics information questionnaire, the COVID-19 PHB Scale based on HBM, and the Health Literacy Scale for COVID-19. The data analysis was then performed by regression analysis along with the structural equation modeling (SEM), considering the significance level of 0.05.

Results

The regression analysis results demonstrated that following the one-unit increase in the values of self-efficacy and cues to action, the COVID-19 PHBs elevated by 0.063 and 0.078 units, respectively. In addition, the COVID-19 PHBs subsided by 0.018 with the rise in the value of perceived barriers (P < 0.001). According to the path analysis, the direct path from the COVID-19-related HL to the COVID-19 PHBs (B = 0.097, β = 0.087, 95% confidence interval [CI] = 0.005 to 0.189) was significant. Furthermore, the indirect path from the COVID-19-related HL to the COVID-19 PHBs through perceived susceptibility (B = 0.017, β = 0.015, 95% CI = 0.001 to 0.032), perceived barriers (B = 0.029, β = 0.026, 95% CI = 0.004 to 0.055), self-efficacy (B = 0.094, β = 0.084, 95% CI = 0.031 to 0.156), and cues to action (B = 0.153, β = 0.137, 95% CI = 0.092 to 0.215) was significant.

Conclusion

In keeping with the study results, it is essential to take some effective measures to boost the HBM constructs and HL among adolescents to improve their PHBs during pandemics such as COVID-19.

Keywords: Health behavior, Health literacy, Health Belief Model, COVID-19

Introduction

The novel coronavirus (nCoV), declared as the main cause of the coronavirus disease 2019 (COVID-19), leading to severe acute respiratory syndrome (SARS), has recently influenced the world over (Ayaz-Alkaya and Dülger 2022). The outbreak of this infectious virus first occurred in the city of Wuhan, China, in late December 2019, and then spread rapidly to other cities in this country, thereby affecting over 200 nations globally by the end of April 25, 2020 (Chen et al. 2020). The first confirmed case of COVID-19 was reported on February 18, 2020, as stated by Iran’s Ministry of Health and Medical Education (Khademian et al. 2021). Iran and almost all its provinces were among the regions that were extensively impacted by the pandemic (Abdoli 2020).

Given the high infectiousness of COVID-19 as well as no introduction of specific treatments, the main strategy to control the pandemic has been to break the virus transmission chain, exploiting preventive health behaviors (PHBs) by the public (Nakhaeizadeh and Mohammadi 2021). In this way, people of all ages can adopt such behaviors to protect themselves, and then minimize the risk of infection transmission to others (Harvard Health Publishing 2021). Even with the controversy and the wide variety of definitions available, PHBs, with a leading role in controlling the spread of diseases and ensuring public health (Niu et al. 2021), has been described as: “Any behavior that, according to professional medical and scientific standards, prevents disease or disability and/or detect disease at the asymptomatic stage, and is voluntarily undertaken by a person who believes oneself to be healthy” (Langlie 1979).

Being at a significant stage in terms of their viewpoints toward social issues (Campbell et al. 2021), adolescents are an important target group as they are becoming progressively independent and assuming responsibilities for their own health behaviors (Riiser et al. 2020; Yang et al. 2020). By contrast, they do not have self-sufficiency to weigh up the potential risk of diseases, making them take no notice of health advice as well as exposure to self-destructive behaviors that multiply the infection chances (Park and Oh 2021). Therefore, it is of utmost magnitude to identify the factors related to PHBs adopted against COVID-19 in adolescents, and provide effective education accordingly (Fathian-Dastgerdi et al. 2021; Park and Oh 2021).

PHBs and the health belief model (HBM)

As PHBs can significantly contribute to controlling the spread of diseases and ensuring public health, they are widely investigated during public health crises (Niu et al. 2021). So far, comprehensive theories have been developed to elucidate the determinants of PHBs in individuals, including the HBM (Rosenstock et al. 1988), comprising a set of constructs to predict the way people seek to prevent or control diseases. The model constructs accordingly consist of perceived susceptibility (that is, one’s perception of the chances of developing a disease), perceived severity (viz. a person’s perception of the disease consequences), perceived benefits (i.e., the belief in the effectiveness of a recommended action to reduce a risk or its severity), perceived barriers (means, one’s opinions on tangible costs), cues to action (referring to the strategy utilized to activate the readiness of an individual to interact with certain behaviors), and self-efficacy (explicitly, the self-confidence to function) (Osta et al. 2018).

From this perspective, the HBM aims to determine why individuals tend to engage or not engage in certain health behaviors, and further explains and predicts their adoption (Alsulaiman and Rentner 2021). The given model, introduced by Rosenstock (1974), may thus help illuminate the adoption of the COVID-19 PHBs (Rabin and Dutra 2021). In this regard, the first research hypothesis was addressed as follows:

  • H1: The HBM constructs can predict the adoption of the COVID-19 PHBs in adolescents.

PHBs and health literacy (HL)

Today, health care is characterized by some intricacies (Erdoğan and Araman 2017). Given the growing demands for complex health information processing in the modern society, HL is becoming vital to help individuals assume responsibilities for their own health behaviors, as well as those related to their family members, and the entire society (Niu et al. 2021). As far as this, previous research has demonstrated that low HL significantly diminishes individuals’ abilities to adopt and practice appropriate PHBs (Berkman et al. 2011; Sun et al. 2013). Over recent years, the concept of HL has been further regarded as an effective factor in the adoption of such behaviors in individuals (Kim and Oh 2021). Based on the systematic review and qualitative synthesis fulfilled by Liu et al. (2020), personal HL means “The degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others.”

It is thus essential to shed light on the role of HL in the adoption of PHBs by high-risk populations during COVID-19 (Paakkari and Okan 2020). As HL can facilitate the distinction between reliable and inaccurate information about this pandemic, and above all help with the search for the sources of health information and services, it enables individuals to make informed health decisions, and involve more in healthy and protective behaviors in the course of this infectious disease (Okan et al. 2020). Individuals’ adherence to PHBs also depends largely on the outcomes of their assessment of the risks and benefits of such behaviors, along with their abilities to process information about pandemics (Abdel-Latif 2020; Matterne et al. 2021).

Since HL potentially influences the HBM constructs, and it is exploited as a factor moderating the HBM, it was considered as the variable shaping the HBM in this study. This conceptual framework was thus a useful tool for changing and interpreting the ways in which HL could form the desired behaviors (Panahi et al. 2021). In this regard, the second research hypothesis was put forward as follows:

  • H2: The COVID-19-related HL can affect the COVID-19 PHBs in adolescents through the HBM constructs.

Literature review

A growing number of studies have to date examined public involvement, particularly by adolescents, in the COVID-19 PHBs, the HBM, and HL (Niu et al. 2021). In this line, Park and Oh (2021) reflected on the factors associated with the COVID-19 PHBs among South Korean adolescents, applying the Theory of Planned Behavior (TPB) and the HBM, and reported that adherence to the COVID-19 PHBs in this age group could be directly or indirectly correlated with some constructs of both models. One other survey of the factors linked with the COVID-19 PHBs in adolescents, based on the HBM, had observed that the given model could be the predictor of the COVID-19 PHBs (Fathian-Dastgerdi et al. 2021). Explaining adolescents’ health information sources, knowledge, HL, health protective measures, and health-related quality of life at the early stages of the COVID-19 pandemic in Norway, it was further revealed that HL was significantly related to hand-washing knowledge and behavior (Riiser et al. 2020). One other study, aimed at the evaluation of COVID-19 awareness, the adoption of the COVID-19 PHBs, and the outcomes of the COVID-19 lockdowns on mental health status, socioeconomic disorders, and engagement in unhealthy behaviors in Uganda also confirmed that the primary COVID-19 preventive measures were at low levels, even though the knowledge level of COVID-19 prevention among adolescent boys and young men, aged 10–24, was high (Matovu et al. 2021).

Given that individuals’ PHBs have been fully examined within the context of public health crises (Niu et al. 2021) and understanding the factors closely associated with such behaviors is of utmost importance (Chang et al. 2020), recognizing health beliefs and HL, as the significant motives for public health promotion, have been critical topics in scientific research (Erdoğan and Araman 2017). Against this background, the present study aimed to evaluate the predictive role of the HBM constructs and HL in shaping the COVID-19 PHBs among adolescents.

Methods

Design

This cross-sectional study, with a descriptive-analytical approach, investigated the predictive role of the HBM and the COVID-19-related HL in the COVID-19 PHBs among adolescents in Tehran, the capital city of Iran.

Participants and setting

The adolescent boys and girls, enrolled in public high schools in the city of Tehran, as the eligible cases, were included in this study, using random cluster sampling.

The minimum sample size was thus estimated at 503 individuals, based on the following equation as well as the study by Fathian-Dastgerdi et al. (2021), α = 0.05, β = 0.0, and r = 0.2.

n≥z1-α/2+z1-β0.5×ln1+r/1-r2+3

The inclusion criteria for this purpose were access to the internet and Shad software (as the national software to provide virtual education for students during the COVID-19 pandemic in Iran), self-reported good mental health status, and willingness to take part in the study. Assuming that the socioeconomic level could be an effective factor in shaping health behaviors and lifestyles, cluster sampling was performed randomly. In so doing, 22 education districts in the city of Tehran were first divided into four categories, including the north, south, west, and east. Then, one out of four education districts was randomly selected. Afterward, as per the required sample size, several public high schools for girls and boys were randomly chosen from the selected districts, and entered into the study. In total, 16 schools for boys and 16 schools for girls were selected from the education districts no. 3, 12, 14, and 16. Ultimately, almost all male and female students enrolled in the 10th and 11th grades from each of the selected high schools were included in the study.

Measures

Demographic-clinical characteristics information questionnaire

This questionnaire, implemented to collect the participants’ demographic and clinical characteristics information, contained 12 items, including age, grade, gender, parental education, parental occupation, family income adequacy, family size, the history of COVID-19 over the past year, the history of COVID-19 in family members over the past year, and underlying diseases.

COVID-19 PHBs scale based on HBM

This researcher-made tool was designed in accordance with the study by Shahnazi et al. (2020), Fathian-Dastgerdi et al. (2021), and World Health Organization (2021a, b), in two general sections, namely the HBM constructs and the COVID-19 PHBs to measure beliefs about the COVID-19 prevention and control (i.e., the HBM constructs) and the COVID-19 PHBs. The HBM constructs consisted of 38 items, of which the items of the first section were in six constructs, viz. perceived susceptibility (3 items), perceived severity (5 items), perceived benefits (5 items), perceived barriers (9 items), and self-efficacy (11 items), based on a five-point Likert-type scale from “strongly disagree” (score 1) to “strongly agree” (score 5). In addition, the items related to cues to action (5 items), had the minimum and maximum scores of 5 and 25, respectively, as regards a five-point Likert-type scale from “never” (score 1) to “always” (score 5). The minimum and maximum scores of the first section of the COVID-19 PHB Scale were 38 and 190, respectively. The second section, measuring the COVID-19 PHBs, also contained 15 items with a five-point Likert-type scale from “never” (score 1) to “always” (score 5), based on the latest PHB guide developed by the WHO against COVID-19 (World Health Organization 2020, 2021a, b). The minimum and maximum comparison scores were equal to 15 and 75, respectively.

Health literacy scale for COVID-19 (HLS-COVID-19)

The HLS-COVID-19 was administered to measure the COVID-19-related HL. The initial form of this scale was developed by Okan et al. (2020), with reference to the shortened form of the European Health Literacy Survey Questionnaire (HLS-EU-Q), comprising 16 items, and then extended to 22 items as its psychometric properties were evaluated. The construct validity and reliability of this tool had already been confirmed. Upon examining its psychometric properties, the HLS-COVID-19 in the present study reduced to 18 items.

Face validity, qualitative/quantitative content validity (viz. content validity ratio [CVR] and content validity index [CVI]), and reliability were further examined to meet the requirements for the psychometric evaluation of the COVID-19 PHB Scale based on the HBM. To this end, the tool was submitted to a panel of experts, consisting of 10 faculty members of the School of Nursing and Midwifery, affiliated to Shahid Beheshti University of Medical Sciences, Tehran, Iran, and they were asked to analyze each item in terms of its fluency and transparency and the scale with regard to the CVR of each item as needed. They accordingly rated each item based on being “necessary” (score 3), “useful but unnecessary” (score 2), and “unnecessary” (score 1). Then, the CVR was calculated via the following formula:

CVR=ne-n/2/n/2

wherein “ne” represents the number of experts confirming the necessity of each item and “n” shows the total number of experts. The scores given to each item were further compared with reference to the Lawshe Table. For a panel of 10 experts, the minimum value for each item was better to be above 0.62 (Ayre and Scally 2014). The lowest CVR among those for all questionnaire items was 0.8. As a result, they were retained.

The opinions raised by the panel of experts were also classified into two relevant (scores 1 and 2) and irrelevant (scores 3 and 4) groups. Dividing the number of experts, who considered the item as relevant by the total number of experts, the CVI value was thus calculated exclusively for each item. The items scored over 79%, between 70% and 79%, and below 70% were accordingly regarded as the suitable ones, the items that needed to be reviewed, and the cases that were removed, respectively (Vasli 2018). The scale level-CVI was additionally obtained from the average item level-CVI of 0.92.

As the HLS-COVID-19 was implemented in Persian in this study for the first time, it was translated and then its psychometric properties were evaluated at four stages, including translation, backward translation, pre-test and psychological interview, and presentation of the final version upon acquiring written permission from the designers. Thus, at the first stage, the scale was translated from English into Persian by a fluent translator in both languages. It was subsequently translated in a backward manner by a translator in both languages, and the words in the original English and the English back-translated versions were compared with regard to the conceptual similarities by two translators familiar with English. No disagreement was observed between the translations at this stage, and the Persian version was approved (Vasli 2018). Other psychometric properties were further measured like that for the COVID-19 Scale based on the HBM. The CVR of four items was thus less than 0.62, and removed from the scale, but that was above 0.82 for the rest of the study items. Therefore, the number of items reduced from 22 to 18. Moreover, the scale level-CVI value was 0.9 once the changes in the scale were applied.

In addition to benefiting from the opinions of the panel of experts to check the face validity for both research tools, the questionnaires were given to three students not in the research samples, and their opinions were utilized.

The reliability of both scales was determined by test-retest reliability and internal consistency. The intraclass correlation coefficient (ICC) for the data obtained from 22 students outside the research samples, meeting the inclusion criteria with a two-week interval, also ranged from 0.736 for perceived sensitivity to 0.933 for perceived severity. Perceived sensitivity with moderate reliability (ICC = 0.736) and the highest ICC were further related to perceived severity with excellent reliability (ICC = 0.933). The ICC values less than 0.5 correspondingly indicated poor reliability, the scores between 0.5 and 0.75 showed moderate reliability, the values from 0.75 to 0.9 represented good reliability, and the scores greater than 0.9 denoted excellent reliability (Jafarpoor et al. 2020).

To evaluate the reliability of both tools in terms of internal consistency, Cronbach’s alpha coefficient was calculated for the data obtained from 40 students not included in the research samples, but meeting the inclusion criteria. Given that the minimum acceptable reliability coefficient was 0.70 (Jafarpoor et al. 2020) and both scales had Cronbach’s alpha coefficients above 0.9, their reliability was established.

Data collection

Upon making coordination and receiving permission from the selected high school principals, the research tools were distributed online among the students through the Shad software. Then, the completed questionnaires were collected to analyze the data by the same software.

Data analysis

The demographic and clinical characteristics of the adolescents were shown as frequency (percentage). The assumptions, including normality distribution, missing data, outliers, and variable collinearity, were further investigated. The normality of data was then analyzed based on histograms as well as skewness and kurtosis values (Kim 2013). The outliers were also detected with reference to the constant score of 3.29, and the missing data were replaced with the mean values (Kwak and Kim 2017). Bivariate correlations, using the Spearman correlation coefficient, were then calculated to assess multicollinearity (r > 0.90) among the variables (Kline 2015).

In addition, the multiple linear regression model was performed to determine the relationship between the HBM constructs and the COVID-19 PHBs. The structural equation modeling (SEM) was then implemented to determine the direct, indirect, and total effects of the independent variable (i.e., the COVID-19-related HL) on the dependent one (viz. the COVID-19 PHBs) in the presence of six mediators (namely, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action). Likewise, the maximum likelihood parameter estimate with robust Chi-square test statistic (i.e., MLR) estimator was used to evaluate the path model. The goodness-of-fit (GoF) statistics were comparative fit index (CFI), root-mean-square error of approximation (RMSEA), Tucker–Lewis index (TLI), and standardized root-mean-square residual (SRMR). The values of >0.95 for CFI and TLI<0.05 for RMSEA and SRMR represented a good fit (Bartholomew et al. 2008). The data analysis was performed using the SPSS (ver. 26.0) and Mplus 6 (path model) software packages.

Results

Participants’ characteristics

In total, 503 adolescents with the mean age of 17.5±1.6 were recruited in the present study, of whom 51.1% of the cases were male, and 55.3% of the individuals were in the 10th grade in high school. Most fathers (41.2%) and mothers (43.5%) had high school diplomas. The highest percentages of fathers were self-employed (49.3%) and employees (41.5%). In addition, 83.9% of mothers were homemakers. Most of the participants had adequate income (70.6%). Moreover, 16.1% of adolescents and 30.2% of their families had the history of being infected with COVID-19. The demographic and clinical characteristics information of the participants is illustrated in Table 1.

Table 1.

Demographic and clinical characteristics of adolescents

Characteristics Number Percent
Education grade in high school
   10th 278 55.3
   11th 225 44.7
Gender
   Female 246 48.9
   Male 257 51.1
Father education
   Illiterate and elementary 67 13.3
   Junior high school 94 18.7
   High school and diploma 207 41.2
   University 135 26.8
Mother education
   Illiterate and elementary 77 15.3
   Junior high school 79 15.7
   High school and diploma 219 43.5
   University 128 25.4
Father`s occupation
   Unemployed 16 3.2
   Self-employed 248 49.3
   Employee 209 41.5
   Retired 30 6
Mother`s occupation
   Housewife 242 83.9
   Employed 76 15.1
   Retired 5 1
Family income adequacy
   Yes 355 70.6
   No 148 29.4
Family members number
   2 15 3
   3 84 16.7
   4 246 48.9
   5 or more 158 31.4
History of COVID-19 infection
   Yes 81 16.1
   No 422 83.9
History of family members COVID-19 infection
   Yes 152 30.2
   No 351 69.8
Living with one or more chronic illness (cardiac, thyroid, kidney diseases; diabetes; asthma)
   Yes 65 12.9
   No 438 87.1
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Correlations between study variables

Table 2 shows the bivariate analysis results. The COVID-19 PHBs were significantly associated with perceived susceptibility (r = 0.166, P < 0.001), perceived severity (r = 0.198, P < 0.001), perceived benefits (r = 0.383, P < 0.001), perceived barriers (r = –0.418, P < 0.001), self-efficacy (r = 0.706, P < 0.001), cues to action (r = 0.664, P < 0.001), and the COVID-19-related HL (r = 0.336, P < 0.001). The COVID-19-related HL was also significantly associated with perceived susceptibility (r = 0.197, P < 0.001), perceived severity (r = 0.134, P < 0.001), perceived benefits (r = 0.282, P < 0.001), perceived barriers (r = –0.353, P < 0.001), self-efficacy (r = 0.348, P < 0.001), and cues to action (r = 0.350, P < 0.001). In addition, all correlation coefficients ranged from 0.002 to 0.706, suggesting the absence of multicollinearity among the study variables.

Table 2.

Bivariate correlation coefficients among study variables

Variables (1) (2) (3) (4) (5) (6) (7) (8)
COVID-19 PHBs (1) 1 – – – – – – –
Perceived susceptibility (2) 0.166** 1 – – – – – –
Perceived severity (3) 0.198** 0.376** 1 – – – – –
Perceived benefit (4) 0.383** 0.331** 0.447** 1 – – – –
Perceived barrier (5) –0.418** –0.165** 0.017 –0.213** 1 – – –
Self-efficacy (6) 0.706** 0.239** 0.344** 0.577** –0.387** 1 – –
Cues to action (7) 0.664** 0.215** 0.281** 0.497** –0.400** 0.696** 1 –
COVID-19-related HL (8) 0.336** 0.197** 0.134** 0.282** –0.353** 0.348** 0.350** 1
Grade (9) 0.002 –0.064 –0.067 –0.103* 0.040 –0.080 –0.050 –0.122**
Gender (10) –0.149** 0.141** 0.129** 0.073 0.015 –0.060 –0.030 –0.015
Father education (11) –0.010 0.146** –0.010 0.005 0.020 0.050 –0.002 0.070
Mother education (12) 0.032 0.129** –0.002 0.020 –0.050 0.070 0.020 0.133**
Family income adequacy (13) –0.050 –0.030 0.060 –0.034 0.168** –0.104* –0.111* –0.203**
History of family members COVID-19 infection (14) –0.100* 0.124** 0.070 –0.040 0.080 –0.080 –0.050 –0.100*
Living with one or more chronic illness (15) –0.060 –0.040 0.020 –0.050 0.060 –0.116** –0.209** –0.100*
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HBM, health belief model; PHBs, preventive health behaviors; HL, health literacy

* P < 0.05; **P < 0.001

Relationship between HBM constructs and COVID-19 PHBs

Table 3 presents the results of the MLR of the HBM constructs on the COVID-19 PHBs. Perceived barriers, self-efficacy, and cues to action were accordingly acknowledged as the significant predictors of the COVID-19 PHBs. For the one-unit increase in the values of self-efficacy and cues to action, the COVID-19 PHBs elevated by 0.063 and 0.078 units, respectively (P < 0.001). However, the COVID-19 PHBs decreased by 0.018 following the rise in the perceived barriers (P < 0.001).

Table 3.

The multiple linear regression model for relationship between HBM constructs and COVID-19 PHBs

Predictors B Standard error β 95% CI for B P-value Adjusted R2 Durbin-Watson
Lower limit Upper limit
Perceived susceptibility 0.010 0.010 0.022 –0.015 0.031 0.500 0.606 1.926
Perceived severity –0.010 0.011 –0.030 –0.032 0.012 0.389
Perceived benefit –0.020 0.012 –0.061 –0.044 0.004 0.111
Perceived barrier –0.018 0.004 –0.135 –0.027 –0.010 0.000***
Self-efficacy 0.063 0.006 0.480 0.052 0.075 0.000***
Cues to action 0.078 0.010 0.343 0.059 0.096 0.000***
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B= Unstandardized coefficient; β=Standardized coefficient;

Dependent variables: COVID-19 PHBs

Independent variables: HBM constructs (perceived susceptibility, perceived severity, perceived benefit, perceived barrier, cues to action)

Covariates controlled in model (grade, gender, father education, mother education, family income adequacy, history of family members COVID-19 infection, living with one or more chronic illness)

HBM, health belief model; PHBs, preventive health behaviors.

***P < 0.001

Path analysis results

To evaluate the effect of the COVID-19-related HL on the COVID-19 PHBs in the presence of six mediators, the path model was performed. The main objective was to assess whether the relationship between the COVID-19-related HL and the COVID-19 PHBs was mediated by perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and cues to action (Fig. 1, Table 4). The direct path from the COVID-19-related HL to the COVID-19 PHBs (B = 0.097, β = 0.087, 95% CI = 0.005 to 0.189) was also significant (path C). In addition, the COVID-19-related HL was significantly associated with perceived susceptibility (B = 0.039, β = 0.147, 95% CI = 0.017 to 0.061), perceived severity (B = 0.033, β = 0.108, 95% CI = 0.005 to 0.062), perceived barriers (B =–0.181, β = –0.248, 95% CI = –0.248 to –0.113), self-efficacy (B = 0.087, β = 0.115, 95% CI = 0.031 to 0.142), and cues to action (B = 0.123, β = 0.279, 95% CI = 0.082 to 0.163), whereas the direct path from the COVID-19-related HL to perceived benefits (B = –0.011, β = –0.034, 95% CI = –0.048 to 0.026) was not significant (path A). The direct effects of perceived susceptibility (B = 0.431, β = 0.103, 95% CI = 0.138 to 0.724), perceived benefits (B = 2.326, β = 0.652, 95% CI = 1.670 to 2.982), perceived barriers (B = –0.163, β = –0.107, 95%CI = –0.314 to –0.013), self-efficacy (B = 1.082, β = 0.732, 95% CI = 0.854 to 1.311), and cues to action (B = 1.253, β=0.493, 95% CI = 0.896 to 1.609) were significant, whereas perceived severity (B = 0.241, β= 0.067, 95% CI = –0.121 to 0.603) was not significantly associated with the COVID-19 PHBs (B path). The indirect path from the COVID-19-related HL to the COVID-19 PHBs through perceived susceptibility (B = 0.017, β = 0.015, 95% CI=0.001 to 0.032), perceived barriers (B = 0.029, β = 0.026, 95% CI = 0.004 to 0.055), self-efficacy (B = 0.094, β = 0.084, 95% CI = 0.031 to 0.156), and cues to action (B = 0.153, β = 0.137, 95% CI = 0.092 to 0.215) was also significant. The indirect path from the COVID-19-related HL to the COVID-19 PHBs via perceived severity (B = 0.008, β = 0.007, 95% CI = –0.006 to 0.022) and perceived benefits (B = –0.025, β = –0.022, 95% CI = –0.064 to 0.114) was not significant (Table 4). The total effect of the COVID-19-related HL on the COVID-19 PHBs (B = 0.268, β = 0.240, 95% CI = 0.141 to 0.395) was significant. The path model also met the GoF indices (namely, RMSEA = 0.036, CFI = 0.981, TLI = 0.967, SRMR = 0.037). The parameter estimations are shown in Table 4 and Fig. 1.

Fig. 1.

Fig. 1

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The path model of the mediators in relation to the COVID-19 health literacy and preventive health behaviors (PHBs). Standardized estimates are labeled. Non-significant coefficients are shown with dotted lines. Covariates (grade, gender, mother education, family income adequacy, history of family members COVID-19 infection, living with one or more chronic illness) were controlled in the model. Total effect = 0.240*** Model fit: RMSEA = 0.036, CFI = 0.981, TLI = 0.967, SRMR = 0.037

Table 4.

Path model for COVID-19-related health literacy as the predictor, six parallel mediators (perceived susceptibility, perceived severity, perceived benefit, perceived barrier, self-efficacy, cues to action), and preventive health behaviors (outcome)

Path B (SE) β 95% confidence interval for B P-value
Lower Upper
Direct effect
   a1 0.039 (0.011) 0.147 0.017 0.061 0.001**
   a2 0.033 (0.015) 0.108 0.005 0.062 0.023*
   a3 0.011(0.020) –0.034 –0.048 0.026 0.571
   a4 0.181 (0.034) –0.248 –0.248 –0.113 0.000***
   a5 0.087 (0.028) 0.115 0.031 0.142 0.002**
   a6 0.123 (0.021) 0.279 0.082 0.163 0.000***
   b1 0.431 (0.149) 0.103 0.138 0.724 0.004**
   b2 0.241 (0.185) 0.067 –0.121 0.603 0.192
   b3 2.326 (0.335) 0.652 1.670 2.982 0.000***
   b4 –0.163 (0.077) –0.107 – 0.314 –0.013 0.034*
   b5 1.082 (0.116) 0.732 0.854 1.311 0.000***
   b6 1.253 (0.182) 0.493 0.896 1.609 0.000***
   C 0.097 (0.047) 0.087 0.005 0.189 0.040*
Indirect effect
   a1b1 0.017 (0.008) 0.015 0.001 0.032 0.032*
   a2b2 0.008 (0.007) 0.007 –0.006 0.022 0.246
   a3b3 –0.025 (0.045) –0.022 –0.064 0.114 0.581
   a4b4 0.029 (0.013) 0.026 0.004 0.055 0.025*
   a5b5 0.094 (0.032) 0.084 0.031 0.156 0.003**
   a6b6 0.153 (0.032) 0.137 0.092 0.215 0.000***
   Total effect 0.268 (0.065) 0.240 0.141 0.395 0.000***
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B, unstandardized estimate; SE, standard error; β, standardized estimate.

a1 direct path from health literacy to perceived susceptibility; a2 direct path from health literacy to perceived severity; a3 direct path from health literacy to perceived benefit; a4 direct path from health literacy to perceived barrier; a5 direct path from health literacy to self-efficacy; a6 direct path from health literacy to cues to action; b1 direct path from perceived susceptibility to PHBs; b2 direct path from perceived severity to PHBs; b3 direct path from perceived benefit to PHBs; b4 direct path from perceived barrier to PHBs; b5 direct path from self-efficacy to PHBs; b6 direct path from cues to action to PHBs; a1b1 indirect path from health literacy to PHBs via perceived susceptibility; a2b2 indirect path from health literacy to PHBs via perceived severity; a3b3 indirect path from health literacy to PHBs via perceived benefit; a4b4 indirect path from health literacy to PHBs via perceived barrier; a5b5 indirect path from health literacy to PHBs via self-efficacy; a6b6 indirect path from health literacy to PHBs via cues to action.

Covariates (grade, gender, mother education, family income adequacy, history of family members COVID-19 infection, living with one or more chronic illness) were controlled in the model.

*P < 0.05; **P < 0.01; ***P < 0.001

Discussion

This study aimed to evaluate the predictive role of the HBM constructs and HL in the adoption of the COVID-19 PHBs by adolescents, wherein the regression analysis outcomes showed that the HBM constructs, including perceived barriers, self-efficacy, and cues to action could predict the adoption of such behaviors. The path model also revealed that the COVID-19-related HL, both directly and indirectly, could mediate four HBM constructs, viz. perceived susceptibility, perceived barriers, self-efficacy, and cues to action, during the adoption of the COVID-19 PHBs by adolescents. Considering these results, both research hypotheses were partially confirmed.

In line with the results of the first research hypothesis, proving that the HBM constructs could affect the adoption of the COVID-19 PHBs, other surveys had sometimes obtained similar or different outcomes. According to Fathian-Dastgerdi et al. (2021), all HBM constructs could be the predictors of the COVID-19 PHBs in adolescents, but self-efficacy was the strongest one. In the same survey in South Korea, the results further demonstrated that adolescents’ adherence to the COVID-19 PHBs had been associated with two HBM constructs, including perceived susceptibility and perceived severity (Park and Oh 2021). The results of another study on the Iranian adult population correspondingly indicated that three HBM constructs, i.e., perceived barriers, perceived benefits, and self-efficacy, could be the key determinants of the COVID-19 PHBs (Mirzaei et al. 2021). In their study, Karimy et al. (2021) also found that perceived barriers, perceived benefits, and cues to action were associated with the COVID-19 PHBs in adults.

Similar to the partial confirmation of the second research hypothesis, that is, the COVID-19-related HL could be both directly and indirectly mediated by four HBM constructs, including perceived susceptibility, perceived barriers, self-efficacy, and cues to action, and affect the adoption of the COVID-19 PHBs in adolescents, the results of one survey had further suggested that HL, cues to action, and self-efficacy could predict health-promoting behaviors in women by simultaneously considering HL and the HBM constructs as the independent variables in a regression model (Ghorbani-Dehbalaei et al. 2021). In a cohort case, in consonance with the results of this study, Kale et al. (2015) found that low HL was independently associated with several health beliefs in patients with chronic obstructive pulmonary disease, which could predict adherence to relevant disease management behaviors. In another study, relatively different results had been obtained, indicating that HL had not directly affected the adoption of the COVID-19 PHBs in Chinese vulnerable populations, but mediated by some variables, i.e., the HBM constructs, such as benefits, barriers, self-efficacy, and trust in media sources, wherein the adoption of the COVID-19 PHBs varied (Niu et al. 2021).

Conclusion

This study revealed that three HBM constructs, including perceived barriers, self-efficacy, and cues to action could predict the COVID-19 PHBs in adolescents. Likewise, the COVID-19-related HL could be directly and indirectly mediated by four HBM constructs, viz. perceived susceptibility, perceived barriers, self-efficacy, and cues to action. The COVID-19-related HL was also effective in the adoption of the COVID-19 PHBs by adolescents. This study was thus unique as the first attempt to investigate the direct effect of HL on the COVID-19 PHBs through the HBM constructs in adolescents during the pandemic.

Based on the study results, indicating the importance of the HBM in the adoption of PHBs during COVID-19 among the adolescent population, it was suggested to take some effective measures to boost HL and the HBM constructs. These measures could be focused on improving adolescents’ HL and knowledge of the importance of the disease control through the adoption of PHBs during the pandemic, recognizing the barriers and benefits of PHBs, and obtaining information from valid sources and mass media. It was also necessary to take measures to augment adolescents’ self-efficacy by augmenting their self-confidence and exploiting problem-solving methods.

Ultimately, it was recommended to perform further research on the effect of some interventions on the COVID-19 PHBs through HL- and HBM-based enhancement. The main limitation facing this study was the use of self-reporting tools and online information acquisition, which might have affected the results. Although this study was conducted in Iran, its results could be generalized to other countries and cultures.

Acknowledgments

The authors hereby express their gratitude to all the adolescents who participated in this study.

Author contributions

All authors have seen and approved the manuscript and contributed significantly to the study. PV and ZS-A conceptualized the study; ZS-A acquired the data; PV and MZ analyzed and interpreted the data; PV, MZ drafted the manuscript; MH critically reviewed the manuscript and approved the final manuscript.

Declarations

Ethical statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (code of ethics IR.SBMU.PHARMACY.1399.320).

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Footnotes

Publisher’s note

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

Contributor Information

Parvaneh Vasli, Email: p-vasli@sbmu.ac.ir.

Zahra Shekarian-Asl, Email: zshekar203@gmail.com.

Mina Zarmehrparirouy, Email: zarmehrmina@gmail.com.

Meimanat Hosseini, Email: meimanathosseini@yahoo.com.

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