## Materials and Methods
### Tests Performed on Collected Data  
The research model is carried out through structural equation modelling (SEM) and confirmatory factor analysis (CFA). Since the questionnaire had 125 variables, we applied the variables reduction method using principal component analysis (PCA) in SEM to define the number of components under which the variables could be grouped. Variable reduction and specifying component categories are made through CFA; the determinant was found to be acceptable with a value of 0.01, and KMO (Kaiser-Meyer-Olkin) was found to be acceptable with a value of 0.756, indicating a'moderately good' sampling adequacy for the analysis according to Field [32]. It is worth noting that all KMO values for individual items were above the acceptable limit of 0.50 [32] except for the last component, which was close to 0.50. [PERSON]'s test of sphericity \(\chi^{2}\) (276) = 3379.834, \(p\) < 0.001, indicated that correlations between items are sufficiently significant for PCA. An initial analysis was run to obtain eigenvalues for each component in the data. Thirty-seven components had eigenvalues over [PERSON]'s criterion of 1 and in combination explained 61.08% of the variance. Given the large sample size, and the convergence of the scree plot and [PERSON]'s criterion, seven components were extracted with a value of a factor greater than 0.40; this is the number of components that were retained in the final analysis. The model fit index of CFA is calculated relative to the total sample, normal-BMI category and over-BMI category. All fit indexes were found within the normal range. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Pilot Study** & **Entire Study** \\ \hline
**N** & 50 & 514 \\ \hline
**Gender** & & \\ Male & 50\% & 43.6\% \\ Female & 50\% & 56.4\% \\ \hline
**Religion** & & \\ Christian & 58\% & 64\% \\ Muslim & 40\% & 31\% \\ \hline
**Age** & & \\
19–27 & 18\% & 35.2\% \\
28–36 & 34\% & 23.2\% \\
37–45 & 14\% & 16.9\% \\
46–54 & 18\% & 13.2\% \\ \(>55\) & 16\% & 11.5\% \\ \hline
**Work** & & \\ No & 26.5\% & 26\% \\ Part-time & 12.6\% & 10\% \\ Full-time & 60.9\% & 64\% \\ \hline
**Monthly income** & & \\ \(\leq 500\) USD & 22\% & 31.1\% \\
501–1000 USD & 32\% & 23.2\% \\
1001–1500 USD & 20\% & 20\% \\
1501–2000 USD & 12\% & 12.1\% \\ \(\geq 2001\) USD & 14\% & 13.6\% \\ \hline
**Education level** & & \\ Basic-elementary & 14\% & 9.1\% \\ High school & 1\% & 19.6\% \\ Technical level & 14\% & 12.1\% \\ Bachelor degree & 28\% & 32.5\% \\ Master’s degree & 24\% & 23\% \\ Doctorate & 2\% & 3.7\% \\ \hline
**Home address/work address** & & \\ Mountain/mountain & 4\% & 7.2\% \\ Mountain/city & 16\% & 15.4\% \\ Village/village & 10\% & 13.6\% \\ Village/city & 16\% & 17.7\% \\ City/city & 54\% & 46.1\% \\ \hline
**Civil status** & & \\ Independent single & 16\% & 12.1\% \\ Single with parents & 32\% & 37.2\% \\ Concubine & 2\% & 1\% \\ Married & 42\% & 45.9\% \\
**Divored/widowed** & 8\% & 3.9\% \\ \hline
**Number of children** & & \\
0 & 54\% & 55.1\% \\
1 & 10\% & 8.9\% \\
2 & 8\% & 19.1\% \\
3 & 20\% & 10.3\% \\ \(\geq 4\) & 8\% & 6.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic data of the pilot study (N = 50) and the entire survey (N = 514). Data analysis was carried out by exploring each hypothesis and checking its validity using different statistical tests. Concerning the hypotheses, a cross-sectional study analysis was carried out applying SEM. For this purpose, the extended statistical program of SPSS (Version 22) called AMOS (Version 22, IBM(r), Amonk, NY, USA) was used to test path dependency, and correlations between demographic variables and different components were retrieved from the questionnaire such as medical conditions, food consumption, lifestyle, behaviours toward food, and many other inter-related variables. The questionnaire based on the theoretical background and literature scanning was developed, for which a pilot survey testing its internal consistency was made.