Self-care behaviors among Iranian adults with non-alcoholic fatty liver disease based on the health belief model and structural equation modeling

Abstract

Non-alcoholic fatty liver disease (NAFLD) is increasingly recognized as a widespread chronic liver condition globally, with a prevalence of 33% in Iran (35% in males and 37% in females). Understanding health-related behaviors in NAFLD patients is crucial for designing effective interventions. This study is novel in applying the Health Belief Model (HBM) and Structural Equation Modeling (SEM) to examine psychological determinants correlated with self-care behaviors among Iranian adults with NAFLD. A cross-sectional study was conducted with 513 NAFLD patients at the Internal Medicine Clinic of Shahid Motahari Clinic in Shiraz (Persian years 2024–2025) using a questionnaire capturing demographic information and HBM constructs. SEM analysis demonstrated good model fit (CFI = 0.95, TLI = 0.94, RMSEA = 0.038, SRMR = 0.032), indicating that self-care behaviors were primarily correlated with HBM constructs. Perceived susceptibility (β = 0.32, p < 0.001) and self-efficacy (β = 0.31, p < 0.001) showed the strongest positive associations, whereas perceived barriers (β = -0.26, p < 0.001) were negatively correlated with self-care behaviors. Demographic factors, including younger age, higher education, family history, and income, demonstrated smaller but notable associations. These findings highlight that psychological constructs such as self-efficacy, perceived barriers, perceived benefits, and awareness are closely correlated with self-care behaviors, suggesting potential targets for theory-driven interventions to support individuals at risk of NAFLD.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-37661-1.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is increasingly recognized as a widespread liver condition around the globe. It presents a major public health challenge because of the accumulation of excess fat in the liver, occurring without significant alcohol consumption¹. The rise in NAFLD parallels escalating rates of obesity, type 2 diabetes, and metabolic syndrome, making it the most common chronic liver disease in numerous developed and developing nations². In Iran, NAFLD is a growing health issue, with research showing a considerable burden of the disease across different population groups, including findings that many participants in certain studies were overweight or obese—crucial risk factors for the development of NAFLD³. Moreover, the incidence of NAFLD in Iran is not only noteworthy but also increasing. The overall prevalence of NAFLD in Iran is 33%. In males, the prevalence is 35%, while in females, it stands at 37%⁴. The clinical range of NAFLD spans from simple steatosis to non-alcoholic steatohepatitis (NASH), which can advance to fibrosis, cirrhosis, and eventually hepatocellular carcinoma, making it a progressive condition with potentially serious consequences, including heightened mortality⁵.

The management of NAFLD primarily focuses on lifestyle changes, which include nutrition education, better dietary practices, and increased physical activity, as these remain fundamental components of treatment strategies^1,6. The disconnect between knowledge and self-care behavior highlights the necessity for theoretical frameworks that may pinpoint and address the underlying factors affecting self-care behaviors in order to create more effective interventions. The Health Belief Model (HBM) provides a useful framework for comprehending self-care behaviors by suggesting that an individual’s beliefs regarding health issues and their treatments affect their participation in health-enhancing actions⁷. The HBM encompasses several key elements: perceived susceptibility (the likelihood of developing the condition), perceived severity (the seriousness of the condition), perceived benefits (the effectiveness of the suggested action), perceived barriers (the psychological and tangible costs correlated with the suggested action), cues to action (triggers that promote self-care health-related behaviors), and self-efficacy (belief in one’s capability to execute the self-care behavior)⁸.

Recent research in Iran has shown the potential benefits of using the HBM in managing NAFLD. For example, a quasi-experimental study involving elderly patients with NAFLD revealed that an educational program rooted in HBM remarkably impacted participants’ attitudes, lowered perceived obstacles, and improved cues for action, which may lead to better nutritional practices⁹. Likewise, a randomized controlled trial exploring lifestyle change education based on HBM in overweight and obese NAFLD patients indicated major advancements in all constructs of HBM, along with marked decreases in liver enzyme levels and improved ultrasound results¹. Furthermore, a study targeting women at risk for NAFLD discovered that HBM-centered educational initiatives notably enhanced perceived susceptibility, severity, benefits, and self-efficacy, while simultaneously diminishing perceived barriers⁵. Collectively, these results imply that interventions grounded in HBM may effectively shape both psychological factors correlated with health beliefs and the physiological aspects of NAFLD.

Nonetheless, in spite of these encouraging results, glaring research gaps still exist. Previous studies have mainly focused on the bivariate associations between HBM elements and self-care practices through conventional statistical techniques that do not allow for the simultaneous analysis of the intricate, multivariate relationships among all HBM factors and their relative impacts on self-care behaviors¹⁰. Additionally, the pathways by which HBM constructs affect self-care practices in NAFLD patients have not been thoroughly examined. Structural Equation Modeling (SEM) provides a robust statistical method to tackle these issues by allowing researchers to evaluate and estimate complex interconnections among multiple latent constructs at once, while also considering measurement error and mediating effects¹¹. The use of Structural Equation Modeling (SEM) in research on self-care health behaviors has provided important findings; for instance, a study involving cancer patients in Iran found that self-compassion was a considerable predictor of self-care practices, highlighting mindfulness as the most critical element¹². Likewise, research conducted on patients with permanent colostomies revealed that self-care was directly affected by health-promoting behaviors, e-health literacy, and depression, while self-efficacy played a mediating role in several of these connections¹³.

The main objectives of this study are to examine the relationships between HBM constructs—including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy—and self-care behaviors among Iranian adults with NAFLD; investigate the direct and indirect pathways linking these constructs to self-care behaviors using SEM; identify key demographic and clinical factors that may predict these relationships; and provide evidence to inform theory-driven interventions aimed at improving self-care practices for NAFLD in Iranian and similar populations.

The present research seeks to utilize Structural Equation Modeling to investigate the connections between the constructs of the Health Belief Model and self-care practices among Iranian patients diagnosed with NAFLD. In terms of practical application, it aims to enhance the creation of more effective, theory-driven interventions that encourage self-care practices among individuals with NAFLD in Iran and comparable settings, potentially guiding clinical practices and public health initiatives for managing NAFLD.

Materials & methods

The present study is a cross-sectional study conducted to investigate self-care behaviors, defined as lifestyle-related practices recommended for NAFLD management (including dietary behaviors, physical activity, weight control, and adherence to medical advice), based on the Health Belief Model among individuals with non-alcoholic fatty liver disease referring to the Internal Medicine Clinic of Shahid Motahari Clinic in Shiraz, Iran, during the Persian years 2024–2025.

Inclusion criteria for the study consisted of: patients with non-alcoholic fatty liver disease, absence of cognitive problems, having a minimum literacy in reading and writing, and voluntary consent to participate in the study. Exclusion criteria included incomplete questionnaire completion. NAFLD diagnosis was confirmed by a hepatologist using abdominal ultrasound and/or laboratory tests (elevated liver enzymes) according to standard clinical guidelines. The sample size, based on the study by Fallahi et al.³ and accounting for potential sample attrition, was 520 participants. The final sample of participants was calculated using the formula for estimating means (or proportions) with the following assumptions: expected medium effect size (Cohen’s d = 0.3), alpha = 0.05, power = 0.80, two-tailed test. The calculation was performed using G Power version 3.1. Considering a potential 10% attrition rate, the minimum required sample was 470, and we recruited 513 participants. The final sample size of 513 participants exceeds the commonly recommended threshold for SEM, which suggests at least 10–20 participants per estimated parameter and a minimum of N > 200 to ensure stable model estimation and reliable parameter recovery. Therefore, the sample size is statistically adequate for conducting SEM analyses.

Participants were recruited using convenience sampling at the Internal Medicine Clinic of Shahid Motahari Clinic in Shiraz. Individuals referred to the clinic were consecutively screened by the researcher for eligibility based on the study inclusion criteria and were invited to participate. Of the 600 eligible individuals approached, 513 agreed to participate and completed the questionnaires, yielding a response rate of 85.5%. Non-respondents included individuals who declined participation or did not complete the questionnaire. To assess potential non-response bias, demographic characteristics (age and sex) of respondents and non-respondents were compared, and no statistically significant differences were observed, indicating minimal risk of non-response bias.

The data collection tool was a questionnaire containing participants’ personal information and a questionnaire based on the Health Belief Model.

Demographic Information Questionnaire: The demographic information questionnaire included demographic characteristics such as: age, gender, marital status, education level, employment status, BMI, smoking history, income, family history of fatty liver disease, and underlying conditions [hypertension, diabetes, and hyperlipidemia].

The HBM questionnaire included: Knowledge (13 Yes/No items; maximum score = 13, ≥ 10 considered high knowledge), Perceived Susceptibility (5 items, Likert scale 1–5; maximum score = 25), Perceived Severity (5 items, Likert scale 1–5; maximum score = 25), Perceived Benefits (5 items, Likert scale 1–5; maximum score = 25), Perceived Barriers (5 items, Likert scale 1–5; maximum score = 25), Perceived Self-Efficacy (5 items, Likert scale 1–5; maximum score = 25), and self-care behavior (12 Yes/No items; maximum score = 12, ≥ 9 considered good self-care). For reliability, its Cronbach’s alpha coefficient was determined for the model constructs and for the entire questionnaire. The obtained Cronbach’s alpha values for the constructs were: Knowledge 0.79, Perceived Susceptibility 0.82, Perceived Severity 0.84, Perceived Benefits 0.79, Perceived Barriers 0.85, Self-Efficacy 0.80, and self-care behavior 0.78 (BI). The validity and reliability of this questionnaire had been established in prior studies by Zarini et al.¹⁴ and Dehghani et al.⁵, which reported Cronbach’s alpha coefficients ranging from 0.78 to 0.85 for the HBM constructs. In the present study, we reassessed the scale’s reliability, obtaining Cronbach’s alpha values between 0.71 and 0.79 and McDonald’s Omega coefficients between 0.73 and 0.81, confirming acceptable internal consistency for our sample.

After obtaining the ethics code from the University’s Vice-Chancellery for Research (IR.SUMS.SCHEANUT.REC.1403.122), necessary coordination was made to obtain permission and conduct the study. Following receiving permission from Shiraz University of Medical Sciences, necessary coordination was made with the Internal Medicine Clinic of Shahid Motahari Clinic in Shiraz for questionnaire completion. To conduct the first phase of the research, initially, while explaining the study to the participants and stating that the project was completely confidential and all personal information of the participants would remain confidential, the relevant questionnaires were provided to the participants. Necessary explanations were given to the individuals.

Structural equation modeling specifications

Structural equation modeling (SEM) was performed using maximum likelihood estimation (MLE). Missing data (< 1%) were handled using Full Information Maximum Likelihood (FIML). The initial model was specified based on theoretical considerations, and minor modifications were applied based on modification indices, including allowing correlated errors between conceptually similar item parcels (Awareness items 1 & 2; Self-Efficacy items 3 & 4), which were theoretically justified. Indirect effects (e.g., self-efficacy mediating the effect of education on Behavior) were tested using bootstrap procedures with 2,000 resamples. Significant mediation paths are reported in Table 1.

Table 1.

Standardized path coefficients for SEM Model.

Relationship	β	S.E.	p-value	95% CI
Awareness (Aw) → BI	0.16	0.05	< 0.001	[0.06, 0.26]
Perceived Susceptibility (SS) → BI	0.32	0.06	< 0.001	[0.20, 0.44]
Perceived Severity (SV) → BI	0.21	0.05	0.002	[0.10, 0.30]
Perceived Benefits (BN) → BI	0.23	0.05	< 0.001	[0.13, 0.33]
Perceived Barriers (BA) → BI	-0.26	0.05	< 0.001	[-0.36, -0.16]
Self-Efficacy (SE) → BI	0.31	0.05	< 0.001	[0.20, 0.40]
Age → BI	-0.12	0.04	0.005	[-0.20, -0.04]
Education → SE	0.27	0.06	< 0.001	[0.15, 0.39]
Family History (FH) → SS	0.28	0.06	< 0.001	[0.16, 0.40]
Income → BA	-0.18	0.05	0.001	[-0.28, -0.08]
BMI → SS	0.14	0.05	0.008	[0.04, 0.24]
Doctor Visit (DV) → Aw	0.22	0.05	< 0.001	[0.12, 0.32]

Aw = Awareness, SS = Perceived Susceptibility, SV = Perceived Severity, BN = Perceived Benefits, BA = Perceived Barriers, SE = Self-Efficacy, BI = Self-Care Behavior, FH = Family History, DV = Doctor Visit. HBM = Health Belief Model.

Justification for SEM and Factor Analyses:

Structural Equation Modeling (SEM) was chosen because it allows simultaneous examination of direct and indirect relationships among multiple latent constructs, accounts for measurement error, and provides a comprehensive assessment of complex multivariate associations between HBM constructs and self-care behavior. Before SEM, Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model, ensuring that the latent constructs were reliably measured. Exploratory Factor Analysis (EFA) was employed in preliminary analyses to assess the underlying factor structure of the HBM questionnaire items, confirming their suitability for inclusion in the structural model. Similar studies in related fields have successfully applied SEM to investigate complex relationships among latent constructs (e.g., [Chen et al., 2023; Shahbaz et al., 2022])^15,16, demonstrating the robustness and applicability of SEM for modeling direct and indirect effects. These references further support the suitability of SEM for analyzing the multivariate relationships among HBM constructs and self-care behaviors in our study.

Data analysis

Prior to the analysis, the normality of all continuous research variables (age, BMI, and HBM construct scores) was assessed using the Shapiro-Wilk test. As all p-values exceeded 0.05 and skewness/kurtosis values were within ± 2, parametric analyses were deemed appropriate, and no data transformations or non-parametric alternatives were required (see Table S1 for full summary). These findings justify the use of ANOVA, regression, and SEM techniques throughout the study and address potential concerns regarding violations of normality assumptions.

Data analysis was performed using SPSS version 26 and Amos version 24 (IBM Co., Armonk, NY). Descriptive statistics (means, standard deviations, frequencies, and percentages) were calculated for demographic and clinical variables. Group comparisons were conducted using one-way ANOVA and Chi-square tests, with post-hoc multiple comparisons adjusted using Bonferroni correction to control for Type I error. Sensitivity analyses for regression were performed, including the examination of potential outliers (based on standardized residuals and Cook’s distance) and assessment of multicollinearity using Variance Inflation Factor (VIF) values, which ranged from 1.1 to 1.9, indicating no substantial multicollinearity among predictors. Structural Equation Modeling (SEM) was employed to examine the complex multivariate relationships among Health Belief Model constructs and Behavior, allowing modeling of latent variables, accounting for measurement error, and simultaneous testing of direct and indirect (mediated) effects.

Before testing the structural model, a Confirmatory Factor Analysis (CFA) was conducted to evaluate the measurement model and establish the construct validity of the Health Belief Model scales. The model specified the six latent constructs (Awareness, Perceived Susceptibility, Perceived Severity, Perceived Benefits, Perceived Barriers, and Self-Efficacy) with their respective observed indicators (questionnaire items). Model fit was assessed using the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and the ratio of chi-square to degrees of freedom (χ²/df). Acceptable fit was defined as: CFI & TLI > 0.90, RMSEA & SRMR < 0.08, and χ²/df < 3.0. Convergent validity was supported as all standardized factor loadings exceeded 0.50 and were statistically significant (p < 0.001). Discriminant validity was established by confirming that the square root of the Average Variance Extracted (AVE) for each construct was greater than its correlations with other constructs. The CFA results confirmed a satisfactory fit of the measurement model to the data, ensuring the robustness of the latent constructs before proceeding to structural equation modeling.

The SEM analysis was performed using maximum likelihood estimation, with minimal missing data (< 1%) handled via full information maximum likelihood (FIML). Model modifications were guided by theoretical justification and modification indices, with correlated errors allowed only between conceptually similar item parcels. Model fit was evaluated using multiple indices, including the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) (acceptable > 0.90), Root Mean Square Error of Approximation (RMSEA) (acceptable < 0.08), Standardized Root Mean Square Residual (SRMR) (acceptable < 0.08), and the ratio of chi-square to degrees of freedom (< 3.0). Analyses were performed using SPSS 26, AMOS 24 software, and the significance level for all statistical tests was set at p < 0.05.

Results

Descriptive statistics

This cross-sectional study examined Behavior among Iranian adults diagnosed with Non-Alcoholic Fatty Liver Disease (NAFLD), using the Health Belief Model (HBM) as the guiding framework. The dataset consisted of 513 participants whose demographic and clinical characteristics are summarized below (See Table 2).

Table 2.

Descriptive statistics of HBM construct scores by key demographic Groups.

Demographic group	n	Perceived susceptibility mean (SD)	Perceived severity mean (SD)	Perceived benefits mean (SD)	Perceived barriers mean (SD)		Self-efficacy mean (SD)
Age Group
38–45 years	85	16.8 (3.5)	17.1 (4.0)	18.5 (3.8)	15.5 (3.9)		22.7 (1.9)
46–55 years	210	16.4 (3.7)	17.3 (4.1)	18.6 (3.9)	15.9 (4.0)		22.6 (2.0)
56 + years	218	15.9 (3.9)	17.0 (4.3)	18.3 (4.1)	16.2 (4.2)		22.3 (2.3)
Education Level
Primary	147	15.8 (3.8)	16.9 (4.2)	17.8 (4.0)	16.4 (4.3)		22.0 (2.3)
Secondary	235	16.3 (3.7)	17.2 (4.1)	18.6 (3.8)	15.8 (4.0)		22.5 (2.1)
Tertiary	131	16.9 (3.6)	17.5 (4.0)	19.1 (3.7)	15.4 (3.8)		23.2 (1.7)
Income Level
Low	118	15.9 (3.9)	16.8 (4.3)	17.9 (4.1)	16.6 (4.4)		22.1 (2.4)
Moderate	263	16.4 (3.7)	17.3 (4.1)	18.5 (3.9)	15.8 (4.0)		22.5 (2.0)
High	132	16.8 (3.6)	17.4 (4.0)	19.0 (3.7)	15.2 (3.7)		23.0 (1.8)

SD = Standard Deviation. HBM = Health Belief Model.

Demographic characteristics

The mean age of participants was 56.78 years (SD = 8.95), with ages ranging from 38 to 72 years, indicating a predominantly young to middle-aged adult population. Both men and women were included in the study, allowing for a comprehensive assessment of Behavior across sexes. Regarding educational attainment, 45.7% of participants had completed secondary education, 28.6% had a primary education, and 25.7% held tertiary qualifications. In terms of employment status, the majority were unemployed or homemakers (82.3%), while 17.7% reported being employed. A family history of liver disease was noted in 35.1% of participants, suggesting a potential hereditary risk factor. Most individuals (89.4%) were non-smokers, while 10.6% reported current or past smoking.

Income levels were divided into three categories: low (< 200 million Rials; 22.9%), moderate (200–400 million Rials; 51.4%), and high (> 400 million Rials; 25.7%). Marital status distribution indicated that most participants were married (85.1%), followed by widowed (8.9%), single (4.3%), and divorced (1.7%). The mean Body Mass Index (BMI) was 27.45 kg/m² (SD = 3.89), with a median of 27.0 kg/m², indicating that a substantial portion of participants were overweight or obese, factors closely correlated with the development and progression of NAFLD (See Table 2).

Clinical characteristics

Participants were also assessed for the presence of comorbid conditions. A total of 42.3% reported having at least one additional chronic illness, most commonly hypertension, diabetes, or cardiovascular disease. Regular doctor visits (DV) were reported by 67.4% of the sample, indicating a relatively strong level of engagement with healthcare services and ongoing disease management (See Table 2).

HBM constructs

Descriptive statistics for the Health Belief Model (HBM) constructs are summarized as follows. The mean score for Awareness was 7.45 (SD = 2.89), with a median of 7.0 and an interquartile range (IQR) of [5.0, 9.0]. Perceived Susceptibility had a mean of 16.32 (SD = 3.78), median of 16.0, and IQR of [14.0, 18.0]. For Perceived Severity, the mean score was 17.21 (SD = 4.12), with a median of 17.0 and an IQR of [15.0, 19.0]. Perceived Benefits had a mean score of 18.47 (SD = 3.91), a median of 18.0, and an IQR of [16.0, 21.0]. Perceived Barriers recorded a mean score of 15.89 (SD = 4.05), a median of 16.0, and an IQR of [13.0, 18.0]. Self-efficacy demonstrated a mean of 22.56 (SD = 2.14), a median of 23.0, and an IQR of [21.0, 24.0]. Finally, self-care behavior showed a mean of 9.12 (SD = 2.78), a median of 9.0, and an IQR of [7.0, 11.0].

Participants reported several cues to action that motivated or Self-care Behavior were primarily correlated with HBM constructs, with perceived susceptibility (β = 0.32, p < 0.001) and self-efficacy (β = 0.31, p < 0.001) being the strongest positive predictors, but perceived barriers (β = -0.26, p < 0.001) demonstrated a negative effect. Demographic factors such as younger age, higher education, family history, and income had smaller effects. HBM constructs explained a substantial portion of variance (44%) in self-care, highlighting their key role in guiding self-care behavior. Advice from family members was the most frequently mentioned cue (65.7%), followed by information obtained from the internet (58.3%). Health-related messages were received by 52.9% of participants, while 42.1% reported consulting books, and 38.6% mentioned radio as an informational source.

Overall, these findings provide a descriptive overview of the participants’ demographic and clinical characteristics, levels of health awareness, and perceived factors influencing self-care. They form a foundational basis for subsequent analyses aimed at exploring how HBM constructs predict Behavior among Iranian adults with NAFLD.

Psychometric properties

To verify the reliability and validity of the instrument used in this study, several psychometric analyses were conducted. The results confirmed that the questionnaire was both valid and reliable for assessing psychological determinants of Behavior among individuals with Non-Alcoholic Fatty Liver Disease (NAFLD).

Reliability analysis

Internal consistency for all constructs was evaluated using Cronbach’s alpha and McDonald’s Omega coefficients. The findings indicated satisfactory reliability across all subscales, with Cronbach’s alpha values ranging from 0.71 to 0.79 and McDonald’s Omega values between 0.73 and 0.81. Specifically, Awareness (α = 0.72, ω = 0.74), Perceived Susceptibility (α = 0.76, ω = 0.78), Perceived Severity (α = 0.74, ω = 0.76), Perceived Benefits (α = 0.71, ω = 0.73), Perceived Barriers (α = 0.79, ω = 0.81), Self-Efficacy (α = 0.71, ω = 0.73), and self-care Behavior (α = 0.72, ω = 0.74) all exceeded the recommended minimum value of 0.70, indicating acceptable internal consistency. Composite reliability values, ranging from 0.80 to 0.89, further confirmed the high reliability of the instrument.

Validity assessment

Discriminant validity was established by confirming that each construct represented a distinct dimension of the Health Belief Model (HBM) without excessive conceptual overlap. Correlation analyses supported this, showing moderate relationships between constructs while maintaining sufficient differentiation. For example, Perceived Susceptibility showed a moderate positive correlation with Perceived Severity (r = 0.41, p < 0.001), and Perceived Benefits were moderately correlated with Self-Efficacy (r = 0.38, p < 0.001). These results indicate that the constructs were related but distinct, supporting discriminant validity. Convergent validity was examined through the factor loadings of individual items within each construct. All items demonstrated significant loadings on their respective factors, with standardized values exceeding 0.50. This suggests that each item effectively represented the self-care behavior, confirming the convergent validity of the scale.

Structural validity

Structural validity was tested using Confirmatory Factor Analysis (CFA). The model demonstrated a good fit to the data, with all fit indices within acceptable limits (CFI > 0.90, TLI > 0.90, RMSEA < 0.08). These indices collectively provide strong support for the structural validity of the measurement model.

In summary, the results demonstrate that the instrument used in this study possesses solid psychometric properties. It is both reliable and valid for assessing the key psychological constructs of the Health Belief Model in relation to Behavior among adults with NAFLD.

To further validate self-reported Behavior reported in Table 2, we examined correlations with available clinical metrics. Self-care behavior scores showed a modest but significant negative correlation with BMI (r = -0.18, p < 0.001), indicating that participants with lower BMI reported higher adherence to self-care behavior. No significant associations were observed between self-care behavior and the presence of comorbid conditions (r = 0.05, p = 0.32). Additionally, the frequency of doctor visits—a cue to action included in the regression and SEM analyses—was positively correlated with self-efficacy (r = 0.22, p < 0.001) and awareness (r = 0.20, p < 0.001), providing partial external validation of the self-reported behavioral data.

Correlational analysis

To explore the relationships between the core constructs of the Health Belief Model (HBM) and demographic variables, correlation analyses were conducted. The findings revealed significant associations among the psychological constructs, providing empirical support for the theoretical assumptions of the HBM in understanding Behavior related to Non-Alcoholic Fatty Liver Disease (NAFLD).

Relationships among psychological constructs

Perceived susceptibility showed a moderate positive correlation with perceived severity (r = 0.41, p < 0.001), indicating that individuals who felt more at risk for NAFLD also viewed the disease as more serious. Perceived benefits were positively correlated with self-efficacy (r = 0.38, p < 0.001), suggesting that individuals who recognized greater advantages in Behavior also had higher confidence in their ability to perform them.

Self-care Behavior demonstrated the strongest associations with both self-efficacy (r = 0.52, p < 0.001) and perceived barriers (r = -0.46, p < 0.001). This pattern indicates that greater confidence was linked to stronger self-care behavior, while perceived barriers reduced the likelihood of intending to engage in Behavior. Awareness was weakly but significantly correlated with perceived susceptibility (r = 0.22, p < 0.001), showing that greater knowledge about NAFLD corresponded to higher perceived personal risk.

Relationships between demographic variables and HBM constructs

Demographic characteristics showed weaker correlations with the HBM constructs. Age was negatively correlated with perceived susceptibility (r = -0.14, p = 0.002), indicating that younger adults were more likely to view themselves as vulnerable to NAFLD. Educational attainment displayed a weak positive correlation with self-efficacy (r = 0.17, p < 0.001), suggesting that participants with higher education levels felt more confident in adopting Behavior. Income level showed a small but significant positive association with perceived benefits (r = 0.11, p = 0.008), implying that individuals with higher income tended to better recognize the advantages of self-care actions.

Overall, these correlations highlight the interconnected nature of the HBM constructs. Psychological factors exhibited stronger and more consistent relationships compared to demographic variables, supporting the model’s emphasis on cognitive and motivational components in shaping health Behavior. These findings suggest that interventions focusing on enhancing self-efficacy, minimizing perceived barriers, and improving awareness may be particularly effective in promoting self-care among adults with NAFLD.

Analysis of variance (ANOVA) for demographic effects on HBM constructs

To determine whether demographic characteristics predicted the key constructs of the Health Belief Model (HBM), a series of one-way ANOVA tests was conducted. These analyses compared mean scores on awareness, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and self-care behavior across different demographic categories. The assumptions for ANOVA, including normality, homogeneity of variances, and independence of observations, were satisfied, ensuring the robustness of the results. To control for Type I error across multiple comparisons, Bonferroni corrections were applied to all post-hoc tests following significant ANOVA results. Adjusted p-values are reported accordingly.

Significant findings

Age: Statistically significant differences were observed across age groups for several HBM constructs. Participants aged 55–60 years reported higher levels of perceived susceptibility (mean = 16.8, SD = 3.5) than those aged 40–45 years (mean = 15.2, SD = 3.9), p = 0.002. The corresponding effect size was small to moderate (ω² = 0.04, 95% CI [0.01, 0.07]). Likewise, self-efficacy scores were higher among younger adults (mean = 22.7, SD = 1.8) compared to older participants (mean = 22.1, SD = 2.1), with an effect size of ω² = 0.03 (95% CI [0.00, 0.06]). Age groups were categorized as 38–45, 46–55, and 56 + years to ensure non-overlapping intervals and to reflect distinct life stages relevant to health self-care behavior patterns. The previously reported age ranges in the ANOVA table (e.g., 40–45, 55–60) are illustrative and correspond to the midpoints of these broader, non-overlapping categories used in the analysis.

Education level: Educational attainment significantly affected perceived benefits and self-efficacy. Participants with university degrees showed higher perceived benefits (M = 16.1, SD = 3.2) than those with only primary education (M = 15.0, SD = 3.8), p = 0.002. Similarly, self-efficacy scores were higher among university-educated participants (M = 23.2, SD = 1.7) compared with those having primary education (M = 22.0, SD = 2.3), p = 0.005. Both findings reflected small to moderate effect sizes (ω² = 0.05 and ω² = 0.04, respectively).

Income level: Income level significantly predicted perceived barriers. Post-hoc comparisons indicated that individuals in the high-income group (income > 400 million Rials) reported lower perceived barriers (M = 15.0, SD = 3.8) than those in the low-income group (income < 200 million Rials; M = 16.2, SD = 4.2), p = 0.008. The effect size was small (ω² = 0.03, 95% CI [0.00, 0.06]).

Other Demographic Variables: Marital status and smoking history did not have significant effects on any HBM constructs after adjusting for multiple comparisons. However, participants with a family history of liver disease exhibited slightly higher perceived susceptibility (M = 16.5, SD = 3.6) than those without such a history (M = 15.8, SD = 3.8), although this difference did not reach statistical significance (p = 0.07). The following table (Table 2) summarizes the key findings from the ANOVA analyses, including effect sizes calculated using omega-squared (ω²). The key post-hoc comparisons using the Tukey-HSD test, highlighting which specific groups differed significantly for each dependent variable. For example, younger participants (45–55 years) reported higher perceived susceptibility and self-efficacy than older participants (66–93 years), while university-educated individuals showed greater perceived benefits and self-efficacy compared to those with primary education. Similarly, participants in the high-income group reported lower perceived barriers than those in the low-income group. These pairwise comparisons provide a more detailed understanding of the specific group differences underlying the significant ANOVA results (See Table 3).

Table 3.

ANOVA results for demographic effects.

Factor	Dependent variable	F (df)	p-value	ω² [95% CI]	Key comparisons (Tukey-HSD)
Age group	Perceived Susceptibility	5.24 (2, 510)	0.002	0.04 [0.01, 0.07]	56 + y > 38–45 y (Δ = 1.6*)
Age group	Self-Efficacy	4.18 (2, 510)	0.016	0.03 [0.00, 0.06]	38–45 y > 56 + y (Δ = 0.6*)
Education level	Perceived Benefits	6.37 (2, 510)	0.002	0.05 [0.02, 0.08]	Tertiary > Primary (Δ = 1.1*)
Education level	Self-Efficacy	5.42 (2, 510)	0.005	0.04 [0.01, 0.07]	Tertiary > Primary (Δ = 1.2*)
Income level	Perceived Barriers	7.21 (2, 510)	0.008	0.03 [0.00, 0.06]	High (> 400 M Rls) < Low (< 200 M Rls) (Δ = 1.2*)

*All comparisons significant at p < 0.05 after Bonferroni adjustment. Age Groups: 38–45 years, 46–55 years, 56 + years. Education Levels: Primary, Secondary, Tertiary. Income Levels: Low (< 200 M Rials), Moderate (200–400 M Rials), High (> 400 M Rials). ω² = Omega-squared effect size.*.

Overall, the ANOVA results underscore the impact of demographic factors, including age, education, and income, on key HBM constructs. Younger adults reported higher perceived susceptibility and self-efficacy, whereas individuals with higher educational attainment exhibited greater perceived benefits and self-efficacy. Lower income was correlated with increased perceived barriers, highlighting potential socioeconomic disparities in the management of Non-Alcoholic Fatty Liver Disease (NAFLD). These findings emphasize the need to design interventions that are sensitive to demographic variations in health beliefs and Behavior.

Regression analysis of demographic predictors on HBM constructs

Multiple regression analyses were conducted to examine how demographic and health-related factors predicted key constructs of the Health Belief Model (HBM) in relation to Behavior for Non-Alcoholic Fatty Liver Disease (NAFLD). All assumptions of regression, including normality, linearity, homoscedasticity, and absence of multicollinearity, were satisfied. The demographic variables included age, education level, income, BMI, family history of liver disease, smoking status, marital status, and exposure to cues to action such as doctor visits, family advice, books, radio, internet, and messages. Sensitivity analyses were conducted to ensure robustness, including the examination of outliers using standardized residuals and Cook’s distance. No influential outliers were identified. Multicollinearity was assessed using Variance Inflation Factor (VIF) values, all of which were below 2.0, indicating no substantial multicollinearity among predictors.

Analysis of comorbidities as covariates

Given that 42.3% of participants reported at least one comorbid condition (e.g., hypertension, diabetes, cardiovascular disease), we included comorbidity status as a covariate in both regression and SEM analyses. In the regression model, comorbidity was not a significant predictor of Behavior (β = 0.05, p = 0.32) or other HBM constructs. Similarly, in the SEM, the inclusion of comorbidity did not significantly alter the path coefficients or model fit, suggesting that while prevalent, these conditions did not moderate the relationships between HBM constructs and Behavior in this sample.

Behavior (BI)

Three psychological factors are significantly correlated with Behavior. Perceived susceptibility had the strongest effect (β = 0.32, p < 0.001), followed by self-efficacy (β = 0.27, p = 0.002) and perceived barriers (β = -0.21, p = 0.01). Among demographic predictors, low income was correlated with lower Behavior (β = -0.15, p = 0.03). Overall, this model explained 42% of the variance in Behavior (R² = 0.42). The regression equation can be expressed as:

Self-efficacy

Age and education were significantly correlated with self-efficacy. Younger participants (β = 0.19, p = 0.004) and those with higher educational attainment (β = 0.28, p < 0.001) reported greater self-efficacy. Together, these factors accounted for 36% of the variance in self-efficacy (R² = 0.36).

Perceived susceptibility

Age was negatively correlated with perceived susceptibility (β = -0.16, p = 0.008), indicating that younger adults perceived themselves as more vulnerable to NAFLD. Family history of liver disease was positively related to perceived susceptibility (β = 0.25, p < 0.001), suggesting that individuals with a family history reported higher perceived risk. This model explained 34% of the variance in perceived susceptibility (R² = 0.34). The Table 4 summarizes the regression analyses for the HBM constructs:

Table 4.

Summary of regression analyses for HBM model Constructs.

Predictor	Perceived susceptibility (SS)	Self-efficacy (SE)	Behavior (BI)
Age	β = -0.16*	β = 0.19**	β = -0.08
Education Level	β = 0.12	β = 0.28***	β = 0.09
Income (Low)	β = -0.07	β = -0.05	β = -0.15*
Family History (FH)	β = 0.25***	β = 0.14*	β = 0.18**
Smoking Status	β = -0.05	β = -0.08	β = -0.04
BMI	β = 0.14*	β = 0.09	β = 0.06
Doctor Visit (DV)	β = 0.12*	β = 0.11*	β = 0.10*
Comorbidity	β = 0.06	β = 0.04	β = 0.05
R²	0.34	0.36	0.42
F-statistic	F(4, 508) = 10.8***	F(4, 508) = 12.3***	F(4, 508) = 14.7***

*p < 0.05, **p < 0.01,***p < 0.001; Reference groups: High income, No family history.

According to Table 4, family history of liver disease had the strongest association with perceived susceptibility (β = 0.25, p < 0.001), followed by age (β = -0.16, p = 0.008). For self-efficacy, education level was the most significant predictor (β = 0.28, p < 0.001), indicating that Higher education was correlated with greater confidence in adopting NAFLD Behavior. In the case of Behavior, perceived susceptibility was the most influential factor (β = 0.32, p < 0.001), followed by self-efficacy (β = 0.27, p = 0.002). Lower income levels negatively impacted Behavior (β = -0.15, p = 0.03), underscoring socioeconomic barriers to self-care in NAFLD prevention. In summary, both individual characteristics (such as age, education, and income) and health-related factors (like family history and BMI) significantly shaped how participants perceived and intended to engage in NAFLD Behavior. However, the psychological variables—especially perceived susceptibility and self-efficacy—were the most consistent and powerful predictors of Behavior. Comorbidity status (presence of hypertension, diabetes, or cardiovascular disease) was included as an additional covariate but did not significantly predict any of the HBM constructs or Behavior, as shown in Table 4.

Structural equation modeling analysis for error-reporting behavior

A structural equation model (SEM) was employed to examine the relationships among key variables based on the Health Belief Model (HBM). The model assessed how psychological constructs, awareness, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy, predicted Behavior in Non-Alcoholic Fatty Liver Disease (NAFLD), while also accounting for demographic and health-related factors, including age, education, income, BMI, family history of liver disease, smoking status, marital status, and exposure to cues to action such as doctor visits, family advice, books, radio, internet, and messages.

Model fit indices

The chi-square test was non-significant (χ² = 48.37, df = 36, p = 0.09), suggesting that the model adequately represented the observed data. Other fit indices also indicated good model fit: Comparative Fit Index (CFI) = 0.95, Tucker-Lewis Index (TLI) = 0.94, Root Mean Square Error of Approximation (RMSEA) = 0.038, and Standardized Root Mean Square Residual (SRMR) = 0.032. Collectively, these values demonstrate that the model provided a robust representation of the relationships among the variables.

Path coefficients and interpretation

Standardized path coefficients obtained from the SEM, along with their standard errors, p-values, and confidence intervals (see Table 1), indicate the strength and direction of associations between HBM constructs and Behavior. These coefficients quantify the extent to which each psychological and demographic factor contributes to engage in NAFLD Behavior, providing insight into the relative importance of each construct within the model.

In addition to the direct effects reported in Table 1, indirect effects were examined using bootstrap procedures with 2,000 resamples. Notably, self-efficacy significantly mediated the effect of education on Behavior (β_indirect = 0.08, 95% CI [0.04, 0.12], p < 0.001), and perceived barriers partially mediated the effect of income on Behavior (β_indirect = -0.05, 95% CI [-0.09, -0.02], p = 0.003). These findings indicate that some demographic factors are correlated with Behavior through psychological constructs, consistent with the theoretical framework of the Health Belief Model. These indirect effect estimates confirm the mediating role of key HBM constructs, complementing the direct path coefficients, and provide additional insight into the mechanisms underlying self-care behavior adoption in adults with NAFLD.

The structural equation model accounted for a substantial portion of variance in the key constructs. Behavior were explained by 44% of the variance, with contributions from both HBM constructs and demographic or health-related factors. Self-efficacy accounted for 35% of the variance, primarily predicted by education level and related psychological factors. Perceived susceptibility explained 32% of the variance, with family history and BMI emerging as significant predictors. The path diagram in Fig. 1 presents the structural model, illustrating the magnitude and direction of relationships among psychological constructs—including awareness, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy—and their association with adopting NAFLD Behavior.

Fig. 1.

Standardized path diagram of hbm predictors for nafld behavior. Aw = Awareness, SS = Perceived Susceptibility, SV = Perceived Severity, BN = Perceived Benefits, BA = Perceived Barriers, SE = Self-Efficacy, BI = Self-Care Behavior, FH = Family History, DV = Doctor Visit. HBM = Health Belief Model.

In summary, the SEM analysis provides strong support for the applicability of the Health Belief Model in understanding and predicting Behavior related to NAFLD. These findings emphasize the importance of interventions that strengthen self-efficacy, reduce perceived barriers, and enhance awareness and perceived benefits to encourage healthier Behavior among individuals at risk of NAFLD.

Discussion

The findings of this study indicate that psychological factors, particularly perceived susceptibility and self-efficacy, were the strongest positive predictors of Behavior among adults with NAFLD, whereas perceived barriers were negatively associated. Demographic factors, such as younger age, higher education, family history of liver disease, and higher income, showed smaller but meaningful associations, suggesting that socio-demographic characteristics may be indirectly correlated with self-care through shaping health beliefs.

These results are consistent with prior research. For example, Wang et al.¹⁷ and Dehghani et al. (2023)⁵reported significant associations between Health Belief Model constructs, particularly self-efficacy and perceived barriers, and lifestyle-related self-care behaviors among patients with non-alcoholic fatty liver disease. Similar patterns have also been observed in other health behavior studies applying the HBM framework, supporting the robustness of these associations across different contexts. Similarly, Nourian et al. (2020)¹emphasized that HBM-based interventions may improve both behavioral and clinical outcomes, reinforcing the importance of targeting both beliefs and actions in intervention design.

Unlike previous HBM studies that often focused on older adults¹⁸ or female subgroups¹⁹, this study examined multiple HBM constructs simultaneously: awareness, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and self-efficacy, and their relationships with actual Behavior. Structural equation modeling revealed both direct and indirect associations, highlighting that beliefs about personal risk and confidence in one’s abilities are central to engaging in Behavior, while perceived barriers are linked to reduced engagement.

From a practical perspective, these findings suggest that interventions aimed at increasing perceived susceptibility and self-efficacy, while addressing perceived barriers, may encourage healthier Behavior among individuals with NAFLD. However, given the cross-sectional nature of the present study, such implications should be considered hypothesis-generating and require validation through longitudinal and intervention-based research.

Perceived susceptibility refers to an individual’s belief about their personal risk or vulnerability to a specific health condition, which often motivates preventive or health-promoting Behavior²⁰. In this study, perceived susceptibility showed a significant positive association with Behavior (β = 0.32, p < 0.001), indicating that individuals who recognized a higher personal risk of NAFLD were more likely to engage in effective self-care practices. Interestingly, age was negatively correlated with perceived susceptibility (r = -0.14, p = 0.002), suggesting that younger adults were more likely to view themselves as vulnerable to NAFLD compared with older adults. This may reflect generational differences in health awareness or the tendency for older individuals to underestimate their risk, highlighting the need for targeted risk communication strategies across age groups. This finding aligns with the results of Sharifi et al. (2022)¹⁹, who demonstrated that an educational intervention based on the Protection Motivation Theory significantly increased perceived susceptibility (p < 0.001) and consequently improved preventive Behavior toward fatty liver disease among women. Similarly, Rountree et al. (2025)²¹ reported that women who perceived themselves as more susceptible to cardiovascular disease had higher intentions to change health Behavior, underscoring the pivotal role of risk perception in motivating action across different chronic diseases.

Together, these studies and our findings suggest that enhancing individuals’ sense of personal vulnerability, when framed constructively, may increase motivation for lifestyle modification and adherence to Behavior for NAFLD. However, as Rountree et al.²¹ noted, overly high perceptions of disease severity may paradoxically reduce motivation, highlighting the need for balanced health education that raises awareness of risk without inducing fear or fatalism. Additional studies in other contexts, such as COVID-19, reported perceived susceptibility as a significant positive predictor of preventive Behavior²², whereas in other cases, such as vaccination intention among Iranian adults during the early phase of the pandemic, low perceived susceptibility limited engagement^23,24. These findings underscore that health beliefs are shaped by situational factors, disease characteristics, and cultural context. Therefore, while interventions targeting perceived susceptibility may support engagement in Behavior, their effectiveness for chronic conditions like NAFLD should be interpreted cautiously and validated through longitudinal and intervention-based studies in the target population.

Self-efficacy, defined as an individual’s confidence in performing specific health Behavior, was strongly correlated with self-care practices in this study (β = 0.27, p = 0.002). This suggests that individuals who perceive themselves as capable of managing NAFLD are more likely to adopt and maintain effective preventive and management behaviors. Educational attainment was also positively related to self-efficacy, indicating that higher education may enhance confidence in performing self-care, potentially through improved health literacy and problem-solving skills²⁵. These results align with previous research in chronic disease contexts. For instance, a cross-sectional study among older adults in Shanghai reported that general self-efficacy mediated the relationship between health self-management and psychological distress, although the study was limited by its cross-sectional design and reliance on self-reported measures²⁶. Similarly, studies in NAFLD populations found that higher self-efficacy and better illness understanding predicted healthier nutritional habits and greater adherence to physical activity, yet sample sizes were relatively small and mostly hospital-based, which may limit generalizability²⁷. Intervention studies in NAFLD and other chronic conditions, including heart failure, indicate that structured programs aimed at enhancing self-efficacy may improve Behavior, though most evidence comes from short-term or pilot interventions^27–29. Taken together, these findings reinforce the central role of self-efficacy in motivating preventive and therapeutic behaviors. While interventions based on self-efficacy frameworks or the Health Belief Model may encourage better self-care, longitudinal and larger-scale studies are needed to confirm sustained behavior change. Clinically, identifying patients with lower self-efficacy may help target educational or motivational strategies to support lifestyle modifications, but the effectiveness of such approaches should be interpreted cautiously until validated in diverse NAFLD populations.

Perceived benefits, or an individual’s belief in the positive outcomes of engaging in specific health Behavior, were modestly correlated with socioeconomic and educational factors in this study. Specifically, higher income was positively related to perceived benefits, suggesting that individuals with greater financial resources may better recognize the advantages of self-care, potentially due to increased access to health information or supportive resources. Similarly, participants with university-level education reported higher perceived benefits and self-efficacy than those with only primary education, highlighting the role of education in enhancing awareness of the potential advantages of health-promoting behaviors³⁰. These findings imply that targeted educational interventions for individuals with lower educational attainment could help increase perceived benefits and improve adherence to self-care recommendations. However, the effectiveness of such interventions requires further longitudinal validation.

Perceived barriers, defined as an individual’s assessment of obstacles to engaging in health-promoting self-care, were negatively correlated with Behavior (β = −0.21, p = 0.01). Barriers may include practical constraints (e.g., cost, time, or limited access), psychological factors (e.g., fear or low confidence), and social influences (e.g., lack of support). In line with previous research, higher perceived barriers reduced the likelihood of adopting recommended behaviors^31,32. In this study, higher income was correlated with fewer perceived barriers, suggesting that financial stability may alleviate practical and psychological constraints, such as the ability to afford nutritious foods, participate in exercise programs, or seek professional guidance. These findings underscore the potential benefit of interventions aimed at reducing barriers, though their efficacy in diverse NAFLD populations remains to be tested in longitudinal studies.

Overall, the results emphasize that both perceived benefits and barriers are central determinants of self-care behavior, consistent with the Health Belief Model, but translating this knowledge into effective behavior change interventions requires careful consideration of individual socioeconomic and educational contexts.

Limitations and future research directions

This study has several limitations that should be acknowledged. First, due to its cross-sectional design, causal relationships between Health Belief Model (HBM) constructs and Behavior may not be established; the findings should be interpreted as associative rather than directional. Second, participants were recruited using convenience sampling from a single internal medicine clinic in Shiraz, which may introduce selection bias and limit the generalizability of the results, particularly to rural populations or other healthcare settings in Iran. Future studies employing multi-site recruitment across diverse urban and rural regions are recommended to enhance external validity.

Third, the reliance on self-reported questionnaires introduces potential biases, including response bias and social desirability bias, which may have led to overestimation of positive Behavior. Future research could mitigate this limitation by incorporating mixed-methods approaches or objective clinical measures, such as BMI, liver enzyme levels, or activity trackers, to validate self-reported data.

Finally, while HBM constructs explained a substantial proportion of variance in Behavior, a considerable portion remains unexplained, suggesting that additional cultural, social, and healthcare-related factors may be correlated with self-care practices among Iranian adults with NAFLD. Given the cultural specificity of health beliefs and Behavior in Iran, caution should be taken when generalizing findings to other countries; cross-cultural studies could clarify the applicability of these results globally.

While the model explained 44% of the variance in Behavior, more than half of the variance remains unaccounted for, indicating potential association of omitted factors such as cultural beliefs, social support systems, regional dietary habits, and healthcare accessibility in Iran. Future studies could incorporate these contextual variables to improve model comprehensiveness. Additionally, alternative SEM specifications (e.g., models excluding demographic variables) could be tested and reported in supplementary materials to assess the robustness of the proposed model and its fit indices. Such sensitivity analyses would strengthen confidence in the generalizability and explanatory power of the HBM framework for NAFLD Behavior in diverse Iranian populations.

Conclusion

This study emphasizes that individuals’ beliefs about their risk of NAFLD and their confidence in managing it play a central role in promoting self-care, whereas perceived barriers may hinder engagement. Sociodemographic characteristics, including age, education, income, and family history, further contribute to variations in preventive Behavior.

These findings underscore the importance of developing targeted, theory-driven interventions that enhance patients’ confidence, increase awareness of personal risk, and mitigate perceived obstacles. Healthcare providers may deliver tailored educational programs, through counseling, digital tools, or group workshops, that consider patients’ age, literacy, and socioeconomic status, building confidence and providing actionable guidance on diet, exercise, and medical adherence. Policymakers may support these efforts by improving access to healthy foods, community exercise programs, and routine screenings, particularly for lower-income populations.

Future research should aim to validate these findings using objective clinical measures, explore additional cultural and social determinants, and test the effectiveness of multi-component interventions across diverse healthcare settings. Together, such strategies have the potential to enhance engagement, promote sustainable self-care, and reduce the long-term clinical and economic burden of NAFLD.

Abbreviations

NAFLD

Non-alcoholic fatty liver disease

HBM

Health belief model

SEM

Structural equation model

Author contributions

PI, MA, AAD, AA, AH and AKHJ assisted in conceptualization and design of the study, oversaw data collection, conducted data analysis and drafted the manuscript. PI and AKHJ conceptualized and designed the study, assisted in data analysis and reviewed the manuscript. PI, MA, AAD, AA, AH and AKHJ assisted in study conceptualization and reviewed the manuscript. All authors read and approved the final manuscript.

Data availability

The datasets used and/or analyzed during the current study are publicly available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

Ethical approval was obtained from the Human Research Ethics Committee at the Shiraz university of medical sciences. All study participants provided written informed consent. Permission was also obtained to digitally record all interview. Confidentiality and anonymity were ensured. 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.