A Review of the Use of the Health Belief Model for Weight Management

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Open Access

Peer-reviewed

Enquiry Article

Factors influencing weight management behavior among college students: An application of the Health Conventionalities Model

• Maryam Saghafi-Asl,
• Soghra Aliasgharzadeh,
• Mohammad Asghari-Jafarabadi

x

• Published: Feb seven, 2020
• https://doi.org/x.1371/journal.pone.0228058

Correction

twenty May 2021: Saghafi-Asl Yard, Aliasgharzadeh Due south, Asghari-Jafarabadi M (2021) Correction: Factors influencing weight management behavior amid college students: An awarding of the Wellness Belief Model. PLOS ONE xvi(v): e0252258. https://doi.org/10.1371/periodical.pone.0252258 View correction

Figures

Abstract

Background

Overweight and obesity have get a significant public wellness business organisation in both developing and developed countries. Due to the health implications of weight-reduction behaviors, it is of import to explore the factors that predict their occurrence. Therefore, the present study was performed to examine factors affecting the behavioral intention of weight management as well every bit assess the predictive power of the Health Belief Model (HBM) for body mass alphabetize (BMI).

Methods

This cross-exclusive written report was conducted among 336 female person students recruited from dormitories of Tabriz University of Medical Sciences, using quota sampling technique. Data were nerveless by a structured questionnaire in seven parts (including perceived severity, perceived susceptibility, perceived benefit, perceived bulwark, cue to action, cocky-efficacy in dieting and physical action, and behavioral intention of weight management), based on the HBM. Structural equation modeling (SEM) was conducted to place the relationship between HBM constructs and behavioral intention of weight direction. Linear regression model was performed to test the power of the HBM to predict students' BMIs.

Results

College level of perceived threats (sum of perceived susceptibility and severity) (β = 0.41, P<0.001), perceived benefits (β = 0.19, P = 0.009), cocky-efficacy in practise (β = 0.17, P = 0.001), and self-efficacy in dieting (β = 0.16, P = 0.025) scales was significantly related to greater behavioral intention of weight management. Moreover, perceived threat mediated the relationships between perceived cue to activeness, perceived benefits, cocky-efficacy in practise, and weight management practices. The fit indices of the SEM model seemed adequate. The final regression model explained approximately 40% of variance in BMI (P<0.001). Additionally, perceived severity, barrier, and cocky-efficacy in dietary life were the significant variables to predict students' BMIs.

Conclusions

These findings propose that health education programs based on the HBM needs to be integrated in preventive wellness programs and health interventions strategies to ensure adherence and well-existence of the participants.

Citation: Saghafi-Asl M, Aliasgharzadeh Southward, Asghari-Jafarabadi Thou (2020) Factors influencing weight management behavior amongst college students: An application of the Health Belief Model. PLoS Ane 15(2): e0228058. https://doi.org/10.1371/journal.pone.0228058

Editor: Berta Schnettler, Universidad de La Frontera, Republic of chile

Received: August 5, 2019; Accepted: January half-dozen, 2020; Published: Feb 7, 2020

Copyright: © 2022 Saghafi-Asl et al. This is an open access article distributed under the terms of the Creative Eatables Attribution License, which permits unrestricted use, distribution, and reproduction in whatsoever medium, provided the original writer and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by the Tabriz University of Medical Sciences to MS-A. The funder had no role in study design, data collection and analysis, determination to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Overweight and obesity have become epidemic rising trends in both adult and developing countries [one–4]. According to estimates by Globe Wellness Organization (WHO) in 2016, there were approximately 1.9 billion overweight adults aged 18 years and above from which at least 650 one thousand thousand were obese [5]. The growing trend in the transition from overweight status to obesity ofttimes occurs at ages 18–29 years. Obesity is an important concerns of wellness care professionals, as it is accompanied past numerous physical and psychological problems including coronary centre disease, diabetes, and several cancers [6–eight]. Obesity also imposes enormous fiscal burdens on both governments and individuals [nine]. Several factors contribute to obesity including genetics and behavioral and environmental parameters such as physical activity and dietary behavior [ten].

The collegiate menstruation is a critical fourth dimension for altering physical activity and dietary patterns which pb to weight gain of students [11, 12]. Thus, weight management remains an of import health challenge for this population. Several preventive and treatment programs are applied for weight control [thirteen]. However, compliance with weight-loss treatments varies among women for a range of reasons [thirteen, 14]. Previous studies take shown that psychosocial factors such equally perceptions about wellness and obesity, and self-efficacy play of import roles in the success of weight loss and maintenance programs [15–17].

To develop effective weight management interventions for college students, it is of import to empathise the factors that predict the occurrence of appropriate weight reduction behavior. The Health Belief Model (HBM) is a health-specific social cognitive model that attempts to predict and explain why individuals alter or maintain specific wellness behaviors [18]. This model assumes that private interest in health-related behaviors is determined past understanding 6 post-obit constructs: Perceived severity (an private's perception of the seriousness and potential consequences of the condition), Perceived susceptibility (an individual'due south assessment of their risk of getting a disease or condition), Perceived benefit (an individual'due south beliefs about whether the recommended beliefs will reduce the risk or severity of impact), Perceived barrier (an private's assessment of the difficulties and cost of adopting behaviors), Cue to activeness (the internal or external motivations promoting the desired behavior), and Cocky-efficacy (an private's belief about their capabilities to successfully perform a new health behavior). These vi constructs provide a conceptual framework for designing both long and short-term health behavior interventions (Fig 1) [18, 19].

Several studies examined the factors affecting weight command intention through HBM [20–23]. Park et al. examined factors affecting behavior intention of weight reduction among female heart-school students, using HBM [20]. They constitute that perceived threat (a sum of severity and susceptibility), cues to activity, and perceived cocky-efficacy were significantly associated with behavioral intention of weight reduction for all respondents [20]. McArthur et al. tested the predictive ability of HBM (which consisted of perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action) for trunk mass index (BMI) amid a higher pupil sample [21]. They constitute pregnant positive associations between ratings on the perceived susceptibility, perceived barriers, and perceived benefits scales and BMI. Findings also revealed significant changed associations betwixt ratings on the perceived severity, and external cues to action scales and BMI [21].

To the all-time of our knowledge, no research has been conducted on the whole HBM constructs for the prediction of weight management amidst college students. Therefore, the present study aimed to (one) develop and assess the validity and reliability of an HBM-based questionnaire for weight management behavior, (two) explore the effects of all HBM constructs on weight management behaviors amongst college students. Based on the 2nd objective, we proposed the following hypotheses:

H1: Behavioral intention of weight direction will be positively influenced by perceived threat, perceived benefits, and self-efficacy in dieting and exercise. H2: Perceived barriers will negatively influence behavioral intention of weight management. H3: Perceived threat will mediate human relationship between cues to action and behavioral intention of weight management, and (three) determine the predictive power of HBM constructs for the BMI of students.

Methods

Research design and sampling

This cantankerous-sectional written report was conducted amid Iranian students from dormitories of Tabriz University of Medical Sciences from June to September 2018. Information technology is suggested that the ratio between the sample size and the number of model parameters in the range of x:1 or even xx:i seem appropriate [24]. The hypothesised model in this study incorporated 22 parameters. Because a sixteen:1 ratio, the sample size was adamant to exist 352 for the study. In guild to let for potential missing data, the initial sample size was set up at 380. In the process of sampling, a sample of 380 subjects who agreed to participate was evaluated, 14 of whom given imperfect data in questionnaire were excluded from the study. Therefore, the concluding sample size in analysis was 366. The subjects were selected through quota sampling method; all dormitories were called then in proportion of number of students' resident in each dormitory and in accord with the estimated sample size, a quota was assigned to each one and the convenience sampling from these dormitories was carried out. Data were collected through personal interviews, using a structured questionnaire. Informed consent was obtained from all participants, before the onset of the written report.

Measurement tool

The first version of the questionnaire used in measuring HBM variables was derived from Park (2011) and McArthur et al. (2017) [twenty, 21]. 80-nine statements were included and represented eight perceptional and behavioral categories, as follow: 13 questions on perceived severity consisting of 3 subscales (emotional/mental, health, physical wellness/ fettle, and social professional); vii questions on perceived susceptibility consisting of ii subscales (lifestyle and environmental); 14 questions on perceived barriers consisting of 3 subscales (practical concerns, emotional/ mental wellness, and sensation); 13 questions on perceived benefits including three subscales (emotional/ mental health, concrete wellness/ fitness, and social/ professional person); 12 questions on cues to action consisting of 2 subscales (internal and external cues to action); xviii questions on self-efficacy in dieting including two subscales (Habits and preferences and Emotional/mental health); 7 questions on self-efficacy in practise, and 5 questions on behavioral intention of weight management consisting of ii subscales (dieting and exercising). All statements were rated using a five-point Likert scale ranging from ane (strongly disagree) to 5 (strongly concur). In order to determine the content validity, ten specialists and professionals (outside the squad) in the field of Health and Diet were consulted. And so, based on the Lawshe's Table, items with higher values of Content Validity Ratio (CVR) (i.due east. college than 0.62 for ten people) and Content Validity Alphabetize (CVI) (i.e. higher than 0.75) were considered acceptable [25]. CVI and CVR showed satisfactory results for each item (CVI range: 0.78–ane.00 and CVR range: 0.80–i.00). Reliability was calculated using internal consistency (Cronbach's Alpha). Alpha coefficients equal to or higher than 0.70 were considered satisfactory [26]. The overall reliability of the instrument based on the Cronbach's blastoff, was 0.92. To assess the test-retest reliability of the questionnaire, a subgroup of 30 randomly selected students were asked to repeat the survey afterwards a ii-calendar week interval. Intraclass correlation coefficient (ICC) was computed to evaluate the stability over time. ICC indicated splendid agreement (ICC = 0.86).

Statistical analysis

Data analyses were conducted using STATA version 12. The characteristics and behavior of the participants were described, using means (SD) and frequencies (percentages), wherever advisable. Weight groups were divided into three categories: underweight (BMI<18.five kg/chiliadⁱⁱ), normal weight (xviii.five≤BMI<25 kg/m²), and overweight (BMI≥25 kg/m2). In that location were few obese students, who were put into the overweight group. Chi-foursquare tests were practical to analyze categorized variables. The hateful differences were determined past Kruskal Wallis test. In the example of significant results, Mann-Whitney U test with Bonferroni correction was used to appraise the pair-wise comparisons.

Multiple imputation in expectation–maximization (EM) algorithm method was run to manage missing data [27]. Path assay was used as a tool for structural equation modeling (SEM) to determine the human relationship betwixt HBM constructs and behavioral intention of weight management and recognize direct and indirect influence of independent variables toward dependent variables. The magnitude of the human relationship was measured by path coefficients and correlations, equally standardized estimates. Goodness of fit indices selected for model evaluation were: normed chi-square (χ2/df, values lower than five were accustomed); comparative fit alphabetize (CFI, values greater than 0.90 were accepted); Tacker Lewis index (TLI, values greater than 0.ninety were accepted); standardized root hateful squared remainder (SRMR, values lower than 0.05 were accustomed); and root mean square error of approximation (RMSEA, values lower than 0.08 were accepted) [28, 29].

A hierarchical linear regression assay was performed to approximate the relationships between HBM scales, demographic characteristic, and BMI. P-Values less than 0.05 were considered as statistically pregnant.

Results

Baseline characteristics

A full of 336 students completed the questionnaires. The mean age of the students was 22.02 (±3.02; range, eighteen–43) years. Based on cocky-reported weight and acme data, the mean BMI was 22.62 (±3.17; range, 15.63–32.72) kg/m^two. The baseline characteristics of the participants based on three weight groups are presented in Table 1. The marital status of the students was significantly unlike amongst weight groups (P = 0.002). The majority (89.9%) of the students were single.

In that location was a meaning relationship between family history of obesity and weight condition of the pupil (P = 0.004). Approximately, 68 percent of the participants had at to the lowest degree one obese family unit member. Virtually one-half of the students had experience trying to lose weight. This experience differed significantly among weight groups (P<0.001). Near of the students controlled their diet and exercised to lose their weight. More than half of the students responded that they attempted to manage their weight to ameliorate their appearance, while almost one-thirds did so for wellness. There were pregnant differences in "the reasons for weight reduction" among under- and normal-weight and overweight groups (P<0.001). The socioeconomic condition of the students was non significantly dissimilar amidst weight groups.

Weight-related beliefs of the participants by weight status

Weight-related beliefs of the students comprising the hateful scales and related subscales ratings (SD), and the Cronbach's alpha are presented in Table two. The hateful scores of the thirteen-detail perceived severity of the overweight consequences were iii.26±0.76 for all respondents that showed significant differences among the three groups (P≤0.001). Students in the underweight group showed the highest mean score for perceived severity (3.84±0.57). The behavior for the physical health/fettle subscale received higher ratings than the other severity subscales (3.44±0.85). Underweight and normal weight students rated the emotional/mental health subscale higher than overweight students (P≤0.001). The mean score of physical health/fitness and social/professional subscales showed significant differences among the three groups (P≤0.001).

The mean score of the total perceived susceptibility of obesity hazard was 3.46±0.76 for all respondents. Students in the underweight group had the highest score (three.64±0.66); however, there were no meaning differences among the three groups.

The mean score of the 14-detail perceived barriers to adopting healthy eating and physical activity habits were 2.94±0.75 for all respondents that showed significant differences among the three groups (P≤0.001). In addition, students in overweight grouping showed the strongest perceived bulwark (3.sixty±0.73); followed past students in the normal weight (2.81±0.64), and underweight (2.39±0.59) group. Beliefs from the emotional/mental health subscale received college rating than other ones.

The hateful score of the 13-detail perceived benefits to adopting healthy eating and physical activity habits were 3.73±0.67 for all respondents. In that location were no meaning differences in mean rating on full scale among the three groups. The Emotional/mental health subscale construct received higher rating than other ones.

The hateful score of the perceived cues to action for weight management was 3.49±0.70 for all respondents. Normal-weight students had the highest score (3.54±0.65), simply in that location were no significant differences among the three groups. The hateful rating of external and internal cues to action were non dissimilar among the study groups.

The mean rating on the self-efficacy in dieting was 3.22±0.64 for all respondents that showed significant differences among three groups (P≤0.001). As, students in the underweight group showed the strongest belief about their self-efficacy in dieting (three.81±0.42); followed by students in the normal-weight (three.27±0.58) and overweight group (2.82±0.67).

The hateful rating on the self-efficacy in practice was 3.27±0.79 for all respondents. Students in the normal-weight grouping had the highest score (three.39±0.71) and indicated significant differences in comparison to those in the overweight grouping (P≤0.001). But these ii groups showed no significant difference, compared to the underweight grouping.

The mean rating on behavioral intention of weight management was 3.07±0.78 for all respondents. The result showed that students intended to manage their weight by exercising rather than dieting. The hateful score of the total behavioral intention of weight management and the two subscales did non demonstrate significant differences among the three groups.

Path models

Effects of the last model of HBM constructs on weight management behaviors are displayed in Fig 2. This model was identified given the proficient fit indices (χ2/df = 2.68, CFI = 0.99, TLI = 0.95, RMSEA = 0.07, SRMR = 0.02) for the all students sample. The model indicated that perceived threats, perceived barriers, perceived benefits, self-efficacy in dieting and self-efficacy in exercise direct affected behavioral intention of weight management. Higher level of same scales was significantly related to greater behavioral intention of weight management. Moreover, cues to activity, perceived benefits and self-efficacy in exercise indirectly affected behavioral intention of weight management through the touch of perceived threats. Tables 3 shows total, directly, and indirect effects of HBM constructs on weight management behavior. Perceived threats and perceived benefits were the greatest predictor of weight loss behaviors with a total correlation coefficient of 0.40 and 0.35, respectively. All of these associations were significant, except for the association of perceived barriers and behavioral intention of weight management.

Fig 2. Effects of Health Belief Model constructs on behavioral intention of weight management.

Path coefficients were shown above. *Meaning at 0.05 level. χ2/df = 2.68, CFI = 0.99, TLI = 0.95, RMSEA = 0.07, SRMR = 0.02.

https://doi.org/ten.1371/journal.pone.0228058.g002

HBM every bit a predictor for BMI

Table four presents findings from the two-step hierarchical regression analysis synthetic to test the ability of HBM and some of the general characteristics to predict the BMIs of college students. The models were constructed from information provided by all students who responded to the whole HBM scale. When perceived severity, perceived susceptibility, perceived benefits, perceived barriers, cues to action, and self-efficacy in dieting and cocky-efficacy in practice were regressed against BMI, the model was highly significant (P<0.001). The first model explained approximately 34% of the variance of the students' BMIs. Self-efficacy in dieting and perceived severity had an inverse significant association with BMI. Self-efficacy in dieting (β = -1.63, P<0.001), perceived barriers (β = 1.eighteen, P<0.001), and perceived severity (β = -1.17, P<0.001) seemed to be the well-nigh important amid these seven variables. Findings too revealed significant positive associations between ratings on the perceived barriers and BMI. In model 2, those demographic variables that had a significant correlation with BMI were added to model 1. The inclusion of age and marital status increased the R^two, and explained twoscore% of the variance in BMI (P<0.001).

Discussion

The present written report was conducted to investigate the factors influencing behavioral intention by applying HBM and judge the relationships between several conventionalities scales and the BMIs of students. Weight loss is usually less successful, despite applying various weight-loss programs, bachelor to the public; once succeeded, the maintenance every bit well equally long-term weight-loss plan compliance rates are ordinarily depression [30]. Therefore, the identification of psychological predictors of weight management could contribute to improv treatment efficacy [15–17].

The nowadays results showed that students with different weight statuses had dissimilar perceptions about obesity and weight reduction behavior. The synthetic SEM in this study supported the theoretical framework, indicating that health beliefs can directly and indirectly predict student'south behavior intention for weight management. In improver, the HBM scales partially predicted the students' BMIs.

The electric current finding showed that the about common weight direction methods among students were do and dieting. This result is consistent with those of other studies that examined weight-loss practices among university students [31, 32]. Near, 55% of the students responded that they attempted to control their weight for a improve advent. The current findings are in line with those of other studies which have indicated that keeping up advent was the main reason for managing trunk weight among university students [31]. The socioeconomic conditions of the participants were not related to their weight condition. Previous studies have reported contradictory results regarding the association between socioeconomic condition and BMI [20, 33–35]. The lack of standard methods for categorizing SES might be the main reason for this contradiction [36].

Overweight students in comparison with other groups showed lower ratings on perceived severity and self-efficacy in dieting and self-efficacy in do, only higher ratings on all subscales of perceived barriers to adopting healthy eating and physical activity habits. The higher ratings on the severity belief scale given by underweight and normal-weight students may have motivated them to manage their weight, since individuals make changes if they perceive that their current status could take serious health complications. Some previous studies have shown that obese people accept less perceived self-efficacy in relation to eating and exercise than not-obese groups [37–39]. Participants' perceived cocky-efficacy reflects the confidence in their capacity to perform a new health behavior. A person with a higher level of confidence volition more likely engaged in a specific healthy eating beliefs to ameliorate wellness. In this regard, information technology has been reported that obese Americans are more likely to proper name several barriers to weight-loss behaviors, compared with non-obese individuals [40]. The results demonstrated that emotional/mental factors, unawareness of healthy food choices, and applied obstacles hamper students to refrain from unhealthy eating behaviors or calorie-dense foods. Moreover, underweight and normal-weight students gave college, simply not meaning ratings to perceived susceptibility behavior than overweight students. Dissimilar previous studies, the electric current results suggested that these groups of students may not consider themselves susceptible enough to existence overweight to take further activeness. Moore et al. reported that African American normal-weight women reported the same perceived threat of obesity-related diseases as overweight women [41]. In fact, an inappropriate perception of one'south own weight and inadequate information about the consequences of obesity could lessen the perceived threat of existence obese. Students in underweight and normal-weight groups showed the strongest beliefs about the emotional/mental benefits of adopting healthy eating and concrete action habits. Differences did non achieve the significance level in other subscales of perceived do good. These results are inconsistent with prior inquiry [42, 43]. Such findings propose that anticipation of the favorable outcomes of adopting healthy eating habits and engaging in regular physical activeness tin can encourage participants to manage their weight.

In the nowadays report, the synthetic SEM provides a better understanding of the machinery through which psychosocial factors affect weight direction behavior. The results of path assay indicated that perceived variables including threat and cocky-efficacy in dieting, have a significant direct effect and perceived benefits and cocky-efficacy in practice accept significant direct and indirect effects on predicting weight direction beliefs. Higher levels of the mentioned perceptions further predicted a higher chance of executing behavioral intention of weight direction. Perceived threat exerted the greatest influence on behavioral intention of weight management in all respondents, followed by perceived benefit. These results are in agreement with those that suggest perceived benefits, threat, and self-efficacy as strong predictors of some wellness behaviors [42–44]. Bishop et al. reported that perception of threat and self-efficacy account for a considerable corporeality of the variance in the performance of patient prophylactic practices [44]. When the rate of self-efficacy or person's confidence in their ability to perform a specific beliefs was high, the probability of incorporating wellness behavior changes was as well increased. O'Connell et al. plant that dieting benefits were the nigh powerful predictor of dieting behavior, peculiarly for obese adolescents [43]. In a study by Kang et al., perceived benefits was the well-nigh important predictor of intention to command obesity among female students [42]. This outcome suggests that if patients are aware of the benefits of managing weight by dieting and exercise, they might become involved in the programs.

The results showed that perceived barriers to eating healthy foods and to undertaking regular concrete activity could not significantly impact behavior intention of weight management. This result was consequent with the results of some [20, 45], but not other [46, 47] studies which have reported that a higher perception of the difficulties and cost of performing behaviors are negatively related to a lower likelihood of performing the positive health behaviors. In the present written report, the perceived barriers were increased among students living in dormitories due to problems such as lack of time, bereft knowledge, and insufficient skills in preparing salubrious nutrient [48, 49]; thus this component failed to justify the behavioral intention of weight management.

In the nowadays research, perceived threat mediated the relationship between cues to activeness and behavioral intention of weight management. This suggests that external and internal cues would arouse a person's perceived threat of the hazard of obesity past influencing perceived seriousness, susceptibility, or both which led the students to weight management beliefs. For example, the person believes that others gauge her unfairly, owing to her weight or an obese family member or a friend, and a serious health problem adult from being obese.

In both regression analysis models, perceived severity, perceived barriers, and self-efficacy in dieting were the significant variables in predicting the BMIs of all respondents. Self-efficacy in dieting seemed to be the most significant parameter among the three variables. The final model, in which the demographic variables were added, explained approximately xl% of the variance of students' BMIs. The results of the current study showed that students who assumed themselves to be confident in their ability to perform the beliefs had lower BMIs. This is compatible with previous results showing that obese women scored significantly less than the not-obese on cocky-efficacy in relation to food [37]. The significant changed association between perceived severity and students' BMIs in both regression models proposed that students who noticed the possible negative physiological, psychological, and social consequences of beingness obese (e. g., chronic affliction, mental health problems, difficulties in social relationship) had lower BMIs. The meaning positive associations between the ratings of the perceived barriers scales and students' BMIs suggested that participants who regarded difficulties (due east. g., lack of fourth dimension, knowledge, and motivation) and toll of performing behaviors had higher BMIs.

There were several worth noting limitations in the design and performance of this report. The chief limitation was the cross-sectional, non-experimental design, which provides merely a glimpse of the population at a specific betoken of fourth dimension. In improver, just dormitory students of medical sciences were included herein, which confines the generalizability of the findings to all college students. Moreover, all the subjects were females, that are more decumbent to control eating habits and weight. As well, the anthropometric information was collected through self-reporting and data was nerveless through personal interviews that could lead to bias in the results. Hereafter studies are needed to use HBM to identify the associations between weight-related beliefs of various samples and their weight direction behaviors. In addition, it would be worthwhile to expand interventional studies to investigate the effect of HBM-based educational programs on weight management in college students or other populations.

Conclusions

The significant variables in predicting behavioral intention of weight direction were perceived threat, perceived benefits, self-efficacy in dieting and self-efficacy in exercise, and cues to action. In addition, it was reported that students have unlike perceptions about obesity and weight reduction behavior by weight status. These results advise that to ensure the adherence and success of the participants in health intervention, it is essential to pattern and implement health education programs forth with dietary approaches. Such programs should emphasize the negative outcomes of obesity, benefits of adopting a healthy lifestyle, increase of self-efficacy in dieting and physical activity, and internal and external stimuli for higher students.

Supporting information

Acknowledgments

The authors would like to thank Dr. Hossein Karimzadeh, Assistant Professor, Department of Urban Planning of Tabriz University for his assist with data assay, and also the participants who involved in this study. Also, the authors would like to acknowledge the back up of this work by Pupil Inquiry Committee of Tabriz Academy of Medical Sciences.

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Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0228058

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