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

Maryam Saghafi-Asl; Soghra Aliasgharzadeh; Mohammad Asghari-Jafarabadi

doi:10.1371/journal.pone.0228058

Abstract

Background

Overweight and obesity have become a significant public health concern in both developing and developed countries. Due to the health implications of weight-reduction behaviors, it is important 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 as assess the predictive power of the Health Belief Model (HBM) for body mass index (BMI).

Methods

This cross-sectional study was conducted among 336 female students recruited from dormitories of Tabriz University of Medical Sciences, using quota sampling technique. Data were collected by a structured questionnaire in seven parts (including perceived severity, perceived susceptibility, perceived benefit, perceived barrier, cue to action, self-efficacy in dieting and physical activity, and behavioral intention of weight management), based on the HBM. Structural equation modeling (SEM) was conducted to identify the relationship between HBM constructs and behavioral intention of weight management. Linear regression model was performed to test the ability of the HBM to predict students’ BMIs.

Results

Higher level of perceived threats (sum of perceived susceptibility and severity) (β = 0.41, P<0.001), perceived benefits (β = 0.19, P = 0.009), self-efficacy in exercise (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 action, perceived benefits, self-efficacy in exercise, and weight management practices. The fit indices of the SEM model seemed acceptable. The final regression model explained approximately 40% of variance in BMI (P<0.001). Additionally, perceived severity, barrier, and self-efficacy in dietary life were the significant variables to predict students’ BMIs.

Conclusions

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

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

Editor: Berta Schnettler, Universidad de La Frontera, CHILE

Received: August 5, 2019; Accepted: January 6, 2020; Published: February 7, 2020

Copyright: © 2020 Saghafi-Asl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author 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, decision 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 developed and developing countries [1–4]. According to estimates by World Health Organization (WHO) in 2016, there were approximately 1.9 billion overweight adults aged 18 years and above from which at least 650 million were obese [5]. The growing trend in the transition from overweight status to obesity often occurs at ages 18–29 years. Obesity is an important concerns of health care professionals, as it is accompanied by numerous physical and psychological problems including coronary heart disease, diabetes, and several cancers [6–8]. Obesity also imposes enormous financial burdens on both governments and individuals [9]. Several factors contribute to obesity including genetics and behavioral and environmental parameters such as physical activity and dietary behavior [10].

The collegiate period is a critical time for altering physical activity and dietary patterns which lead to weight gain of students [11, 12]. Thus, weight management remains an important health challenge for this population. Several preventive and treatment programs are applied for weight control [13]. However, compliance with weight-loss treatments varies among women for a range of reasons [13, 14]. Previous studies have shown that psychosocial factors such as perceptions about health and obesity, and self-efficacy play important roles in the success of weight loss and maintenance programs [15–17].

To develop effective weight management interventions for college students, it is important to understand 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 change or maintain specific health behaviors [18]. This model assumes that individual involvement in health-related behaviors is determined by understanding six following constructs: Perceived severity (an individual's perception of the seriousness and potential consequences of the condition), Perceived susceptibility (an individual's assessment of their risk of getting a disease or condition), Perceived benefit (an individual's beliefs about whether the recommended behavior will reduce the risk or severity of impact), Perceived barrier (an individual's assessment of the difficulties and cost of adopting behaviors), Cue to action (the internal or external motivations promoting the desired behavior), and Self-efficacy (an individual's belief about their capabilities to successfully perform a new health behavior). These six constructs provide a conceptual framework for designing both long and short-term health behavior interventions (Fig 1) [18, 19].

Download:

Fig 1. Theoretical framework of Health Belief Model applied to behavioral intention of weight management.

https://doi.org/10.1371/journal.pone.0228058.g001

Several studies examined the factors affecting weight control intention through HBM [20–23]. Park et al. examined factors affecting behavior intention of weight reduction among female middle-school students, using HBM [20]. They found that perceived threat (a sum of severity and susceptibility), cues to action, and perceived self-efficacy were significantly associated with behavioral intention of weight reduction for all respondents [20]. McArthur et al. tested the predictive power of HBM (which consisted of perceived susceptibility, perceived severity, perceived benefits, perceived barriers, and cues to action) for body mass index (BMI) among a college student sample [21]. They found significant positive associations between ratings on the perceived susceptibility, perceived barriers, and perceived benefits scales and BMI. Findings also revealed significant inverse associations between ratings on the perceived severity, and external cues to action scales and BMI [21].

To the best of our knowledge, no research has been conducted on the whole HBM constructs for the prediction of weight management among college students. Therefore, the present study aimed to (1) develop and assess the validity and reliability of an HBM-based questionnaire for weight management behavior, (2) explore the effects of all HBM constructs on weight management behaviors among college students. Based on the second objective, we proposed the following hypotheses:

H1: Behavioral intention of weight management 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 relationship between cues to action and behavioral intention of weight management, and (3) determine the predictive power of HBM constructs for the BMI of students.

Methods

Research design and sampling

This cross-sectional study was conducted among Iranian students from dormitories of Tabriz University of Medical Sciences from June to September 2018. It is suggested that the ratio between the sample size and the number of model parameters in the range of 10:1 or even 20:1 seem appropriate [24]. The hypothesised model in this study incorporated 22 parameters. Considering a 16:1 ratio, the sample size was determined to be 352 for the study. In order to allow for potential missing data, the initial sample size was set 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 final sample size in analysis was 366. The subjects were selected through quota sampling method; all dormitories were chosen then in proportion of number of students’ resident in each dormitory and in accordance 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 study.

Measurement tool

The first version of the questionnaire used in measuring HBM variables was derived from Park (2011) and McArthur et al. (2017) [20, 21]. Eighty-nine statements were included and represented 8 perceptional and behavioral categories, as follow: 13 questions on perceived severity consisting of 3 subscales (emotional/mental, health, physical health/ fitness, and social professional); 7 questions on perceived susceptibility consisting of 2 subscales (lifestyle and environmental); 14 questions on perceived barriers consisting of 3 subscales (practical concerns, emotional/ mental health, and awareness); 13 questions on perceived benefits including 3 subscales (emotional/ mental health, physical health/ fitness, and social/ professional); 12 questions on cues to action consisting of 2 subscales (internal and external cues to action); 18 questions on self-efficacy in dieting including 2 subscales (Habits and preferences and Emotional/mental health); 7 questions on self-efficacy in exercise, and 5 questions on behavioral intention of weight management consisting of 2 subscales (dieting and exercising). All statements were rated using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). In order to determine the content validity, ten specialists and professionals (outside the team) in the field of Health and Nutrition were consulted. Then, based on the Lawshe's Table, items with higher values of Content Validity Ratio (CVR) (i.e. higher than 0.62 for 10 people) and Content Validity Index (CVI) (i.e. higher than 0.75) were considered acceptable [25]. CVI and CVR showed satisfactory results for each item (CVI range: 0.78–1.00 and CVR range: 0.80–1.00). 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 alpha, 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 after a two-week interval. Intraclass correlation coefficient (ICC) was computed to evaluate the stability over time. ICC indicated excellent agreement (ICC = 0.86).

Statistical analysis

Data analyses were conducted using STATA version 12. The characteristics and beliefs of the participants were described, using means (SD) and frequencies (percentages), wherever appropriate. Weight groups were divided into three categories: underweight (BMI<18.5 kg/m²), normal weight (18.5≤BMI<25 kg/m²), and overweight (BMI≥25 kg/m2). There were few obese students, who were put into the overweight group. Chi-square tests were applied to analyze categorized variables. The mean differences were determined by Kruskal Wallis test. In the case of significant results, Mann-Whitney U test with Bonferroni correction was used to assess the pair-wise comparisons.

Multiple imputation in expectation–maximization (EM) algorithm method was run to manage missing data [27]. Path analysis was used as a tool for structural equation modeling (SEM) to determine the relationship between HBM constructs and behavioral intention of weight management and recognize direct and indirect influence of independent variables toward dependent variables. The magnitude of the relationship was measured by path coefficients and correlations, as standardized estimates. Goodness of fit indices selected for model evaluation were: normed chi-square (χ2/df, values lower than 5 were accepted); comparative fit index (CFI, values greater than 0.90 were accepted); Tacker Lewis index (TLI, values greater than 0.90 were accepted); standardized root mean squared residual (SRMR, values lower than 0.05 were accepted); and root mean square error of approximation (RMSEA, values lower than 0.08 were accepted) [28, 29].

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

Results

Baseline characteristics

A total of 336 students completed the questionnaires. The mean age of the students was 22.02 (±3.02; range, 18–43) years. Based on self-reported weight and height data, the mean BMI was 22.62 (±3.17; range, 15.63–32.72) kg/m². The baseline characteristics of the participants based on three weight groups are presented in Table 1. The marital status of the students was significantly different among weight groups (P = 0.002). The majority (89.9%) of the students were single.

Download:

Table 1. Baseline characteristics of the study participants.

https://doi.org/10.1371/journal.pone.0228058.t001

There was a significant relationship between family history of obesity and weight status of the student (P = 0.004). Approximately, 68 percent of the participants had at least one obese family member. Nearly half of the students had experience trying to lose weight. This experience differed significantly among weight groups (P<0.001). Most 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 improve their appearance, while about one-thirds did so for health. There were significant differences in “the reasons for weight reduction” among under- and normal-weight and overweight groups (P<0.001). The socioeconomic status of the students was not significantly different among weight groups.

Weight-related beliefs of the participants by weight status

Weight-related beliefs of the students comprising the mean scales and related subscales ratings (SD), and the Cronbach's alpha are presented in Table 2. The mean scores of the 13-item perceived severity of the overweight consequences were 3.26±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 beliefs for the physical health/fitness 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).

Download:

Table 2. The students’ average score of weight-related beliefs.

https://doi.org/10.1371/journal.pone.0228058.t002

The mean score of the total perceived susceptibility of obesity risk was 3.46±0.76 for all respondents. Students in the underweight group had the highest score (3.64±0.66); however, there were no significant differences among the three groups.

The mean score of the 14-item 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 group showed the strongest perceived barrier (3.60±0.73); followed by students in the normal weight (2.81±0.64), and underweight (2.39±0.59) group. Beliefs from the emotional/mental health subscale received higher rating than other ones.

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

The mean 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), but there were no significant differences among the three groups. The mean rating of external and internal cues to action were not different 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 (3.81±0.42); followed by students in the normal-weight (3.27±0.58) and overweight group (2.82±0.67).

The mean rating on the self-efficacy in exercise was 3.27±0.79 for all respondents. Students in the normal-weight group had the highest score (3.39±0.71) and indicated significant differences in comparison to those in the overweight group (P≤0.001). But these two groups showed no significant difference, compared to the underweight group.

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 mean score of the total behavioral intention of weight management and the two subscales did not demonstrate significant differences among the three groups.

Path models

Effects of the final model of HBM constructs on weight management behaviors are displayed in Fig 2. This model was identified given the good 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 directly affected behavioral intention of weight management. Higher level of aforementioned scales was significantly related to greater behavioral intention of weight management. Moreover, cues to action, perceived benefits and self-efficacy in exercise indirectly affected behavioral intention of weight management through the impact of perceived threats. Tables 3 shows total, direct, 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.

Download:

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

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

https://doi.org/10.1371/journal.pone.0228058.g002

Download:

Table 3. The total, direct and indirect effect of Health Belief constructs on behavioral intention of weight management.

https://doi.org/10.1371/journal.pone.0228058.t003

HBM as a predictor for BMI

Table 4 presents findings from the two-step hierarchical regression analysis constructed to test the ability of HBM and some of the general characteristics to predict the BMIs of college students. The models were constructed from data 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 self-efficacy in exercise 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 = 1.18, P<0.001), and perceived severity (β = -1.17, P<0.001) seemed to be the most important among these seven variables. Findings also revealed significant positive associations between ratings on the perceived barriers and BMI. In model two, those demographic variables that had a significant correlation with BMI were added to model 1. The inclusion of age and marital status increased the R², and explained 40% of the variance in BMI (P<0.001).

Download:

Table 4. Hierarchical multiple regression analysis for predicting body mass index.

https://doi.org/10.1371/journal.pone.0228058.t004

Discussion

The present study was conducted to investigate the factors influencing behavioral intention by applying HBM and estimate the relationships between several belief scales and the BMIs of students. Weight loss is usually less successful, despite applying various weight-loss programs, available to the public; once succeeded, the maintenance as well as long-term weight-loss program compliance rates are usually low [30]. Therefore, the identification of psychological predictors of weight management could contribute to improv treatment efficacy [15–17].

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

The current finding showed that the most common weight management methods among students were exercise and dieting. This result is consistent with those of other studies that examined weight-loss practices among university students [31, 32]. Nearly, 55% of the students responded that they attempted to control their weight for a better appearance. The current findings are in line with those of other studies which have indicated that keeping up appearance was the main reason for managing body weight among university students [31]. The socioeconomic conditions of the participants were not related to their weight status. Previous studies have reported contradictory results regarding the association between socioeconomic status 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 exercise, but 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 have serious health complications. Some previous studies have shown that obese people have less perceived self-efficacy in relation to eating and exercise than non-obese groups [37–39]. Participants’ perceived self-efficacy reflects the confidence in their capacity to perform a new health behavior. A person with a higher level of confidence will more likely engaged in a specific healthy eating behavior to improve health. In this regard, it has been reported that obese Americans are more likely to 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 practical obstacles hamper students to refrain from unhealthy eating behaviors or calorie-dense foods. Moreover, underweight and normal-weight students gave higher, but not significant ratings to perceived susceptibility beliefs than overweight students. Unlike previous studies, the current results suggested that these groups of students may not consider themselves susceptible enough to being overweight to take further action. 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’s own weight and inadequate information about the consequences of obesity could lessen the perceived threat of being obese. Students in underweight and normal-weight groups showed the strongest beliefs about the emotional/mental benefits of adopting healthy eating and physical activity habits. Differences did not reach the significance level in other subscales of perceived benefit. These results are inconsistent with prior research [42, 43]. Such findings suggest that anticipation of the favorable outcomes of adopting healthy eating habits and engaging in regular physical activity can encourage participants to manage their weight.

In the present study, the constructed SEM provides a better understanding of the mechanism through which psychosocial factors affect weight management behavior. The results of path analysis indicated that perceived variables including threat and self-efficacy in dieting, have a significant direct effect and perceived benefits and self-efficacy in exercise have significant direct and indirect effects on predicting weight management behavior. Higher levels of the mentioned perceptions further predicted a higher chance of executing behavioral intention of weight management. 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 health behaviors [42–44]. Bishop et al. reported that perception of threat and self-efficacy account for a considerable amount of the variance in the performance of patient safety practices [44]. When the rate of self-efficacy or person’s confidence in their ability to perform a specific behavior was high, the probability of incorporating health behavior changes was also increased. O'Connell et al. found that dieting benefits were the most powerful predictor of dieting behavior, especially for obese adolescents [43]. In a study by Kang et al., perceived benefits was the most important predictor of intention to control obesity among female students [42]. This result 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 physical activity could not significantly affect behavior intention of weight management. This result was consistent 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 study, the perceived barriers were increased among students living in dormitories due to problems such as lack of time, insufficient knowledge, and insufficient skills in preparing healthy food [48, 49]; thus this component failed to justify the behavioral intention of weight management.

In the present research, perceived threat mediated the relationship between cues to action and behavioral intention of weight management. This suggests that external and internal cues would arouse a person’s perceived threat of the risk of obesity by influencing perceived seriousness, susceptibility, or both which led the students to weight management behavior. For example, the person believes that others judge her unfairly, owing to her weight or an obese family member or a friend, and a serious health problem developed 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 40% 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 behavior had lower BMIs. This is compatible with previous results showing that obese women scored significantly less than the non-obese on self-efficacy in relation to food [37]. The significant inverse 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 being obese (e. g., chronic disease, mental health problems, difficulties in social relationship) had lower BMIs. The significant positive associations between the ratings of the perceived barriers scales and students’ BMIs suggested that participants who regarded difficulties (e. g., lack of time, knowledge, and motivation) and cost of performing behaviors had higher BMIs.

There were several worth noting limitations in the design and performance of this study. The main limitation was the cross-sectional, non-experimental design, which provides only a glimpse of the population at a specific point of time. In addition, only 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 prone to control eating habits and weight. Also, the anthropometric data was collected through self-reporting and data was collected through personal interviews that could lead to bias in the results. Future studies are needed to use HBM to identify the associations between weight-related beliefs of diverse samples and their weight management 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 management 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 different perceptions about obesity and weight reduction behavior by weight status. These results suggest that to ensure the adherence and success of the participants in health intervention, it is essential to design and implement health education programs along 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 college students.

Supporting information

S1 File. Questionnaire in English.

https://doi.org/10.1371/journal.pone.0228058.s001

(PDF)

S2 File. Questionnaire in Persian.

https://doi.org/10.1371/journal.pone.0228058.s002

(PDF)

Acknowledgments

The authors would like to thank Dr. Hossein Karimzadeh, Assistant Professor, Department of Urban Planning of Tabriz University for his assistance with data analysis, and also the participants who involved in this study. Also, the authors would like to acknowledge the support of this work by Student Research Committee of Tabriz University of Medical Sciences.

References

1. Bhurosy T, Jeewon R. Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? The Scientific World Journal. 2014;2014.
- View Article
- Google Scholar
2. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007–2008 to 2015–2016. Jama. 2018;319(16):1723–5. pmid:29570750
- View Article
- PubMed/NCBI
- Google Scholar
3. Marques A, Peralta M, Naia A, Loureiro N, de Matos MG. Prevalence of adult overweight and obesity in 20 European countries, 2014. The European Journal of Public Health. 2017;28(2):295–300.
- View Article
- Google Scholar
4. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The lancet. 2014;384(9945):766–81.
- View Article
- Google Scholar
5. World Health Organization. Obesity and overweight. Available online: http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. (accepted on Nov 2.
6. Frech A. Healthy behavior trajectories between adolescence and young adulthood. Advances in life course research. 2012;17(2):59–68. pmid:22745923
- View Article
- PubMed/NCBI
- Google Scholar
7. Vella-Zarb RA, Elgar FJ. The ‘freshman 5’: a meta-analysis of weight gain in the freshman year of college. Journal of American College Health. 2009;58(2):161–6. pmid:19892653
- View Article
- PubMed/NCBI
- Google Scholar
8. Kearns K, Dee A, Fitzgerald AP, Doherty E, Perry IJ. Chronic disease burden associated with overweight and obesity in Ireland: the effects of a small BMI reduction at population level. BMC public health. 2014;14(1):143.
- View Article
- Google Scholar
9. Tremmel M, Gerdtham U-G, Nilsson PM, Saha S. Economic burden of obesity: a systematic literature review. International journal of environmental research and public health. 2017;14(4):435.
- View Article
- Google Scholar
10. Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics. 2015;33(7):673–89. pmid:25471927
- View Article
- PubMed/NCBI
- Google Scholar
11. Karabulut US, Romero Z, Conatser P, Karabulut M. Assessing Overweight/Obesity, Dietary Habits, and Physical Activity in Hispanic College Students. Exercise Medicine. 2018;2.
- View Article
- Google Scholar
12. Cheng L, Liu Z. Differences of food intakes with gender, the year in college and body mass index among university students in Beijing, China. British Food Journal. 2019.
- View Article
- Google Scholar
13. Heymsfield S, Aronne LJ, Eneli I, Kumar RB, Michalsky M, Walker E, et al. Clinical Perspectives on Obesity Treatment: Challenges, Gaps, and Promising Opportunities. NAM Perspectives. 2018.
- View Article
- Google Scholar
14. Burgess E, Hassmén P, Pumpa KL. Determinants of adherence to lifestyle intervention in adults with obesity: a systematic review. Clinical obesity. 2017;7(3):123–35. pmid:28296261
- View Article
- PubMed/NCBI
- Google Scholar
15. Lazzeretti L, Rotella F, Pala L, Rotella CM. Assessment of psychological predictors of weight loss: How and what for? World journal of psychiatry. 2015;5(1):56. pmid:25815255
- View Article
- PubMed/NCBI
- Google Scholar
16. Faghri P, Simon J, Huedo-Medina T, Gorin A. Effects of self-efficacy on health behavior and body weight. Journal of Obesity & Weight Loss Therapy. 2016;6(6).
- View Article
- Google Scholar
17. Hays LM, Finch EA, Saha C, Marrero DG, Ackermann RT. Effect of self-efficacy on weight loss: a psychosocial analysis of a community-based adaptation of the diabetes prevention program lifestyle intervention. Diabetes Spectrum. 2014;27(4):270–5. pmid:25647049
- View Article
- PubMed/NCBI
- Google Scholar
18. Laranjo L. Social media and health behavior change. Participatory Health Through Social Media: Elsevier; 2016. p. 83–111.
19. Glanz K, Rimer BK, Viswanath K. Health behavior and health education: theory, research, and practice: John Wiley & Sons; 2008.
20. Park D-Y. Utilizing the Health Belief Model to predicting female middle school students' behavioral intention of weight reduction by weight status. Nutrition research and practice. 2011;5(4):337–48. pmid:21994529
- View Article
- PubMed/NCBI
- Google Scholar
21. McArthur LH, Riggs A, Uribe F, Spaulding TJ. Health Belief Model Offers Opportunities for Designing Weight Management Interventions for College Students. Journal of nutrition education and behavior. 2018;50(5):485–93. pmid:29097024
- View Article
- PubMed/NCBI
- Google Scholar
22. James DC, Pobee JW, Brown L, Joshi G. Using the health belief model to develop culturally appropriate weight-management materials for African-American women. Journal of the Academy of Nutrition and Dietetics. 2012;112(5):664–70. pmid:22709771
- View Article
- PubMed/NCBI
- Google Scholar
23. Zhu DQ, Norman IJ, While AE. Nurses’ self-efficacy and practices relating to weight management of adult patients: a path analysis. International Journal of Behavioral Nutrition and Physical Activity. 2013;10(1):131.
- View Article
- Google Scholar
24. Kline RB. Principles and practice of structural equation modeling: Guilford publications; 2015.
25. Lawshe CH. A quantitative approach to content validity 1. Personnel psychology. 1975;28(4):563–75.
- View Article
- Google Scholar
26. Cronbach LJ. Coefficient alpha and the internal structure of tests. psychometrika. 1951;16(3):297–334.
- View Article
- Google Scholar
27. Allison PD. Missing data techniques for structural equation modeling. Journal of abnormal psychology. 2003;112(4):545. pmid:14674868
- View Article
- PubMed/NCBI
- Google Scholar
28. Lt Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1–55.
- View Article
- Google Scholar
29. Hooper D, Coughlan J, Mullen M. Structural equation modelling: Guidelines for determining model fit. Articles. 2008:2.
- View Article
- Google Scholar
30. Stubbs J, Whybrow S, Teixeira P, Blundell J, Lawton C, Westenhoefer J, et al. Problems in identifying predictors and correlates of weight loss and maintenance: implications for weight control therapies based on behaviour change. Obesity reviews. 2011;12(9):688–708. pmid:21535362
- View Article
- PubMed/NCBI
- Google Scholar
31. Méndez-Hernández P, Dosamantes-Carrasco D, Lamure M, López-Loyo P, Hernández-Palafox C, Pineda-Pérez D, et al. Weight-loss practices among university students in Mexico. International journal of public health. 2010;55(3):221–5. pmid:20013143
- View Article
- PubMed/NCBI
- Google Scholar
32. Senekal M, Lasker GL, van Velden L, Laubscher R, Temple NJ. Weight-loss strategies of South African female university students and comparison of weight management-related characteristics between dieters and non-dieters. BMC public health. 2016;16(1):918.
- View Article
- Google Scholar
33. Morgenstern M, Sargent JD, Hanewinkel R. Relation between socioeconomic status and body mass index: evidence of an indirect path via television use. Archives of pediatrics & adolescent medicine. 2009;163(8):731–8.
- View Article
- Google Scholar
34. Wronka I, Suliga E, Pawlińska-Chmara R. Socioeconomic determinants of underweight and overweight in female Polish students in 2009. Anthropologischer Anzeiger. 2012:85–96.
- View Article
- Google Scholar
35. Pengpid S, Peltzer K. Prevalence of overweight and underweight and its associated factors among male and female university students in Thailand. Homo. 2015;66(2):176–86. pmid:25697745
- View Article
- PubMed/NCBI
- Google Scholar
36. Basto‐Abreu A, Barrientos‐Gutiérrez T, Zepeda‐Tello R, Camacho V, Gimeno Ruiz de Porras D, Hernández‐Ávila M. The Relationship of Socioeconomic Status with Body Mass Index Depends on the Socioeconomic Measure Used. Obesity. 2018;26(1):176–84. pmid:29152913
- View Article
- PubMed/NCBI
- Google Scholar
37. Richman R, Loughnan G, Droulers A, Steinbeck K, Caterson I. Self-efficacy in relation to eating behaviour among obese and non-obese women. International Journal of Obesity. 2001;25(6):907. pmid:11439307
- View Article
- PubMed/NCBI
- Google Scholar
38. Stutts WC. Physical activity determinants in adults: perceived benefits, barriers, and self efficacy. Aaohn Journal. 2002;50(11):499–507. pmid:12465206
- View Article
- PubMed/NCBI
- Google Scholar
39. Emery CF, Olson K, Lee V, Habash DL, Nasar JL, Bodine A. Home environment and psychosocial predictors of obesity status among community-residing men and women. International journal of obesity. 2015;39(9):1401. pmid:25916909
- View Article
- PubMed/NCBI
- Google Scholar
40. NEW INSIGHTS INTO AMERICANS’ PERCEPTIONS AND MISPERCEPTIONS OF OBESITY TREATMENTS, AND THE STRUGGLES MANY FACE [internet]. 2016 October [cited 22 Nov 2018]. Available from: http://www.norc.org/PDFs/ASMBS%20Obesity/Issue%20Brief%20B_ASMBS%20NORC%20Obesity%20Poll.pdf.
41. Moore SE, Harris C, Wimberly Y. Perception of weight and threat to health. Journal of the national medical association. 2010;102(2):119. pmid:20191924
- View Article
- PubMed/NCBI
- Google Scholar
42. Kang Y, Sohn M, Jin K, Kim H, Ohr H, Shin S. Factors influencing weight control behavior and intention of obese children and adolescents. Korean Journal of Preventive Medicine. 1998;31(2):199–214.
- View Article
- Google Scholar
43. O'Connell JK, Price JH, Roberts SM, Jurs SG, McKinley R. Utilizing the health belief model to predict dieting and exercising behavior of obese and nonobese adolescents. Health education quarterly. 1985;12(4):343–51. pmid:4077546
- View Article
- PubMed/NCBI
- Google Scholar
44. Bishop AC, Baker GR, Boyle TA, MacKinnon NJ. Using the H ealth B elief M odel to explain patient involvement in patient safety. Health Expectations. 2015;18(6):3019–33. pmid:25303173
- View Article
- PubMed/NCBI
- Google Scholar
45. Garcia K, Mann T. From ‘I wish’to ‘I will’: Social-cognitive predictors of behavioral intentions. Journal of Health Psychology. 2003;8(3):347–60. pmid:14670213
- View Article
- PubMed/NCBI
- Google Scholar
46. Huang H-T, Kuo Y-M, Wang S-R, Wang C-F, Tsai C-H. Structural factors affecting health examination behavioral intention. International journal of environmental research and public health. 2016;13(4):395. pmid:27043606
- View Article
- PubMed/NCBI
- Google Scholar
47. Deshpande S, Basil MD, Basil DZ. Factors influencing healthy eating habits among college students: An application of the health belief model. Health marketing quarterly. 2009;26(2):145–64. pmid:19408181
- View Article
- PubMed/NCBI
- Google Scholar
48. Papadaki A, Hondros G, Scott JA, Kapsokefalou M. Eating habits of university students living at, or away from home in Greece. Appetite. 2007;49(1):169–76. pmid:17368642
- View Article
- PubMed/NCBI
- Google Scholar
49. Lupi S, Bagordo F, Stefanati A, Grassi T, Piccinni L, Bergamini M, et al. Assessment of lifestyle and eating habits among undergraduate students in northern Italy. Annali dell'Istituto superiore di sanita. 2015;51:154–61. pmid:26156187
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Bhurosy T, Jeewon R. Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? The Scientific World Journal. 2014;2014.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007–2008 to 2015–2016. Jama. 2018;319(16):1723–5. pmid:29570750
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref3] 3. Marques A, Peralta M, Naia A, Loureiro N, de Matos MG. Prevalence of adult overweight and obesity in 20 European countries, 2014. The European Journal of Public Health. 2017;28(2):295–300.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref4] 4. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The lancet. 2014;384(9945):766–81.
View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref5] 5. World Health Organization. Obesity and overweight. Available online: http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. (accepted on Nov 2.

[ref6] 6. Frech A. Healthy behavior trajectories between adolescence and young adulthood. Advances in life course research. 2012;17(2):59–68. pmid:22745923
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref7] 7. Vella-Zarb RA, Elgar FJ. The ‘freshman 5’: a meta-analysis of weight gain in the freshman year of college. Journal of American College Health. 2009;58(2):161–6. pmid:19892653
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref8] 8. Kearns K, Dee A, Fitzgerald AP, Doherty E, Perry IJ. Chronic disease burden associated with overweight and obesity in Ireland: the effects of a small BMI reduction at population level. BMC public health. 2014;14(1):143.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref9] 9. Tremmel M, Gerdtham U-G, Nilsson PM, Saha S. Economic burden of obesity: a systematic literature review. International journal of environmental research and public health. 2017;14(4):435.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref10] 10. Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics. 2015;33(7):673–89. pmid:25471927
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref11] 11. Karabulut US, Romero Z, Conatser P, Karabulut M. Assessing Overweight/Obesity, Dietary Habits, and Physical Activity in Hispanic College Students. Exercise Medicine. 2018;2.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref12] 12. Cheng L, Liu Z. Differences of food intakes with gender, the year in college and body mass index among university students in Beijing, China. British Food Journal. 2019.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref13] 13. Heymsfield S, Aronne LJ, Eneli I, Kumar RB, Michalsky M, Walker E, et al. Clinical Perspectives on Obesity Treatment: Challenges, Gaps, and Promising Opportunities. NAM Perspectives. 2018.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref14] 14. Burgess E, Hassmén P, Pumpa KL. Determinants of adherence to lifestyle intervention in adults with obesity: a systematic review. Clinical obesity. 2017;7(3):123–35. pmid:28296261
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref15] 15. Lazzeretti L, Rotella F, Pala L, Rotella CM. Assessment of psychological predictors of weight loss: How and what for? World journal of psychiatry. 2015;5(1):56. pmid:25815255
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref16] 16. Faghri P, Simon J, Huedo-Medina T, Gorin A. Effects of self-efficacy on health behavior and body weight. Journal of Obesity & Weight Loss Therapy. 2016;6(6).
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref17] 17. Hays LM, Finch EA, Saha C, Marrero DG, Ackermann RT. Effect of self-efficacy on weight loss: a psychosocial analysis of a community-based adaptation of the diabetes prevention program lifestyle intervention. Diabetes Spectrum. 2014;27(4):270–5. pmid:25647049
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref18] 18. Laranjo L. Social media and health behavior change. Participatory Health Through Social Media: Elsevier; 2016. p. 83–111.

[ref19] 19. Glanz K, Rimer BK, Viswanath K. Health behavior and health education: theory, research, and practice: John Wiley & Sons; 2008.

[ref20] 20. Park D-Y. Utilizing the Health Belief Model to predicting female middle school students' behavioral intention of weight reduction by weight status. Nutrition research and practice. 2011;5(4):337–48. pmid:21994529
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref21] 21. McArthur LH, Riggs A, Uribe F, Spaulding TJ. Health Belief Model Offers Opportunities for Designing Weight Management Interventions for College Students. Journal of nutrition education and behavior. 2018;50(5):485–93. pmid:29097024
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref22] 22. James DC, Pobee JW, Brown L, Joshi G. Using the health belief model to develop culturally appropriate weight-management materials for African-American women. Journal of the Academy of Nutrition and Dietetics. 2012;112(5):664–70. pmid:22709771
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref23] 23. Zhu DQ, Norman IJ, While AE. Nurses’ self-efficacy and practices relating to weight management of adult patients: a path analysis. International Journal of Behavioral Nutrition and Physical Activity. 2013;10(1):131.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref24] 24. Kline RB. Principles and practice of structural equation modeling: Guilford publications; 2015.

[ref25] 25. Lawshe CH. A quantitative approach to content validity 1. Personnel psychology. 1975;28(4):563–75.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref26] 26. Cronbach LJ. Coefficient alpha and the internal structure of tests. psychometrika. 1951;16(3):297–334.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref27] 27. Allison PD. Missing data techniques for structural equation modeling. Journal of abnormal psychology. 2003;112(4):545. pmid:14674868
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref28] 28. Lt Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1–55.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref29] 29. Hooper D, Coughlan J, Mullen M. Structural equation modelling: Guidelines for determining model fit. Articles. 2008:2.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref30] 30. Stubbs J, Whybrow S, Teixeira P, Blundell J, Lawton C, Westenhoefer J, et al. Problems in identifying predictors and correlates of weight loss and maintenance: implications for weight control therapies based on behaviour change. Obesity reviews. 2011;12(9):688–708. pmid:21535362
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref31] 31. Méndez-Hernández P, Dosamantes-Carrasco D, Lamure M, López-Loyo P, Hernández-Palafox C, Pineda-Pérez D, et al. Weight-loss practices among university students in Mexico. International journal of public health. 2010;55(3):221–5. pmid:20013143
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref32] 32. Senekal M, Lasker GL, van Velden L, Laubscher R, Temple NJ. Weight-loss strategies of South African female university students and comparison of weight management-related characteristics between dieters and non-dieters. BMC public health. 2016;16(1):918.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref33] 33. Morgenstern M, Sargent JD, Hanewinkel R. Relation between socioeconomic status and body mass index: evidence of an indirect path via television use. Archives of pediatrics & adolescent medicine. 2009;163(8):731–8.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref34] 34. Wronka I, Suliga E, Pawlińska-Chmara R. Socioeconomic determinants of underweight and overweight in female Polish students in 2009. Anthropologischer Anzeiger. 2012:85–96.
View Article
Google Scholar

[106] View Article

[107] Google Scholar

[ref35] 35. Pengpid S, Peltzer K. Prevalence of overweight and underweight and its associated factors among male and female university students in Thailand. Homo. 2015;66(2):176–86. pmid:25697745
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref36] 36. Basto‐Abreu A, Barrientos‐Gutiérrez T, Zepeda‐Tello R, Camacho V, Gimeno Ruiz de Porras D, Hernández‐Ávila M. The Relationship of Socioeconomic Status with Body Mass Index Depends on the Socioeconomic Measure Used. Obesity. 2018;26(1):176–84. pmid:29152913
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref37] 37. Richman R, Loughnan G, Droulers A, Steinbeck K, Caterson I. Self-efficacy in relation to eating behaviour among obese and non-obese women. International Journal of Obesity. 2001;25(6):907. pmid:11439307
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref38] 38. Stutts WC. Physical activity determinants in adults: perceived benefits, barriers, and self efficacy. Aaohn Journal. 2002;50(11):499–507. pmid:12465206
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref39] 39. Emery CF, Olson K, Lee V, Habash DL, Nasar JL, Bodine A. Home environment and psychosocial predictors of obesity status among community-residing men and women. International journal of obesity. 2015;39(9):1401. pmid:25916909
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref40] 40. NEW INSIGHTS INTO AMERICANS’ PERCEPTIONS AND MISPERCEPTIONS OF OBESITY TREATMENTS, AND THE STRUGGLES MANY FACE [internet]. 2016 October [cited 22 Nov 2018]. Available from: http://www.norc.org/PDFs/ASMBS%20Obesity/Issue%20Brief%20B_ASMBS%20NORC%20Obesity%20Poll.pdf.

[ref41] 41. Moore SE, Harris C, Wimberly Y. Perception of weight and threat to health. Journal of the national medical association. 2010;102(2):119. pmid:20191924
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref42] 42. Kang Y, Sohn M, Jin K, Kim H, Ohr H, Shin S. Factors influencing weight control behavior and intention of obese children and adolescents. Korean Journal of Preventive Medicine. 1998;31(2):199–214.
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref43] 43. O'Connell JK, Price JH, Roberts SM, Jurs SG, McKinley R. Utilizing the health belief model to predict dieting and exercising behavior of obese and nonobese adolescents. Health education quarterly. 1985;12(4):343–51. pmid:4077546
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref44] 44. Bishop AC, Baker GR, Boyle TA, MacKinnon NJ. Using the H ealth B elief M odel to explain patient involvement in patient safety. Health Expectations. 2015;18(6):3019–33. pmid:25303173
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref45] 45. Garcia K, Mann T. From ‘I wish’to ‘I will’: Social-cognitive predictors of behavioral intentions. Journal of Health Psychology. 2003;8(3):347–60. pmid:14670213
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref46] 46. Huang H-T, Kuo Y-M, Wang S-R, Wang C-F, Tsai C-H. Structural factors affecting health examination behavioral intention. International journal of environmental research and public health. 2016;13(4):395. pmid:27043606
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref47] 47. Deshpande S, Basil MD, Basil DZ. Factors influencing healthy eating habits among college students: An application of the health belief model. Health marketing quarterly. 2009;26(2):145–64. pmid:19408181
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref48] 48. Papadaki A, Hondros G, Scott JA, Kapsokefalou M. Eating habits of university students living at, or away from home in Greece. Appetite. 2007;49(1):169–76. pmid:17368642
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref49] 49. Lupi S, Bagordo F, Stefanati A, Grassi T, Piccinni L, Bergamini M, et al. Assessment of lifestyle and eating habits among undergraduate students in northern Italy. Annali dell'Istituto superiore di sanita. 2015;51:154–61. pmid:26156187
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

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

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

Correction

Figures

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Research design and sampling

Measurement tool

Statistical analysis

Results

Baseline characteristics

Weight-related beliefs of the participants by weight status

Path models

HBM as a predictor for BMI

Discussion

Conclusions

Supporting information

S1 File. Questionnaire in English.

S2 File. Questionnaire in Persian.

Acknowledgments

References