Determinants of Dietary Behaviors Among University Students: A Theory-based Approach

The purpose of this cross-sectional study was to explore factors that influence intentions to adopt healthy dietary behaviors among university students. Five hundred twenty-five university students participated in this study. A questionnaire that included items on intention to adopt healthy eating behaviors, exposure to nutrition, media, parental and peer modeling, barriers, attitudes, self-efficacy, and outcome expectations was administered via Qualtrics. The findings of this study underscore influences of media, self-efficacy, and modeling on healthy eating behaviors among university students. The findings also emphasize the role of nutrition exposure in influencing self-efficacy and attitudes toward intentions to healthy eating behaviors.

Bong Nguyen, PhD

Department of Biomedical and Health Informatics

School of Medicine

University of Missouri–Kansas City

Kansas City, MO

Ana Florencia MoyedaCarabaza, PhD, RD, LMNT

EZ Nutrition Consulting P.C. Columbus, NE

Xu Li, PhD

Assistant Professor

School of Health and Consumer Sciences

College of Education & Human Sciences

South Dakota State University

Brookings, SD

Faith Bala, PhD

Department of Nutritional Sciences

College of Human Sciences

Texas Tech University

Lubbock, TX

Poor dietary habits among university students, such as increased consumption of fast foods, frequently skipping meals, and insufficient consumption of fruit and vegetables, have been reported consistently (Al-Nakeeb et al., 2015; Alsunni & Badar, 2015; Mithra et al., 2018). As a result of these poor dietary habits, university students are likely to experience weight gain (Vadeboncoeur et al., 2015), which could contribute to a higher risk of chronic diseases such as cardiovascular disease, type-2 diabetes, and metabolic syndrome later in life (Ha & Caine-Bish, 2009).

A number of studies have reported factors that influence dietary habits among university students, including knowledge and perception about nutritional benefit (Kabir et al., 2018), self-discipline and time constraints to cook/prepare meals (Greaney et al., 2009), stress levels (Marquis et al., 2019; Supa et al., 2014), social support from family and friends in encouraging healthy eating behaviors (Deliens et al., 2014; Sogari et al., 2018), costs (LaCaille et al., 2011), and the availability of and access to food options (Cluskey & Grobe, 2009; Greaney et al., 2009). However, all of these exploratory studies were either qualitative studies using focus group discussions (Deliens et al., 2014; Greaney et al., 2009; Kabir et al., 2018; LaCaille et al., 2011; Sogari et al., 2018) or did not include students from various disciplines or students with more university experience (older students), which could contribute to wider range of experiences and opinions (Cluskey & Grobe, 2009; Supa et al., 2014).

Phrashiah Githinji, PhD Institute of Advancing Health through Agriculture

Texas A&M AgriLife Research Dallas, TX

Mary W. Murimi, PhD, RD Professor Department of Nutritional Sciences College of Human Sciences

Texas Tech University

Lubbock, TX

(mary.murimi@ttu.edu)

Acknowledgment. The authors would like to acknowledge all the university students who participated in this study. The study was not funded in whole or in parts by any research grant.

In addition to all those factors, the internet and social media have become critical resources for nutrition-related information; indeed, a majority of adults between the ages of 18–29 years use the internet and social networks to look for health and wellness information (Frimming et al., 2011). Researchers have indicated that these sources could influence college students’ eating behaviors. For instance, Hawkins et al. (2020) found that college students who felt their social circles “approved” of eating junk food consumed more junk foods, and those who thought their friends ate a healthy diet ate more portions of fruit and vegetables.

The Theory of Planned Behavior (TPB) has been used in understanding and predicting healthy dietary behaviors in young adults and university students (Jung & Bice, 2019; Menozzi et al., 2015). TPB postulates that an individual’s future behavior can be predicted by intention to perform the behavior, and that intention is directly influenced by attitude, subjective norm, and perceived behavioral control (Ajzen & Madden, 1986). Self-efficacy is a term that has sometimes been used to define perceived behavioral control. It has been shown to be the most important predictor of both behavior and intention, and researchers have suggested that adding self-efficacy to the TPB may enhance the theory’s predictive utility (Fila & Smith, 2006). This study used the TPB model, which incorporates the constructs of attitudes, subjective norms, and perceived behavioral control as self-efficacy and barriers, to investigate healthy eating behaviors in university students.

The purpose of this theory-driven study was to explore factors that influence intentions to adopt healthy dietary behaviors among university students, namely, previous nutrition exposure, media, parental and peer modeling, barriers, attitude, self-efficacy, and outcome expectations.

The purpose of this theory-driven study was to explore factors that influence intentions to adopt healthy dietary behaviors among university students, namely, previous nutrition exposure, media, parental and peer modeling, barriers, attitude, selfefficacy, and outcome expectations.

Method

Participants and Recruitment

This study targeted university students aged 18 to 36 years from a university located in northwestern Texas (Texas Tech University).

The age group was chosen because it covers more than 90% of the student population at the university, including students in both undergraduate and graduate programs. The study used a convenience online sampling method. The sample size was set at 385 at 95% confidence level with 95% desired accuracy (N = (Zscore)2 * StdDev*(1-StdDev) / (margin of error)2 = ((1.96)2 x .5(.5)) / (.05)2 = 385) (Qualtrics.com). Participants were recruited through campus announcements sent weekly via institutional email from November 2016 to March 2017. All interested participants were invited to follow a Qualtrics link included in the announcement to complete an online questionnaire. Agreeing to complete the survey was taken as consent to participate in the study, and respondents who completed the questionnaire were included in the analysis. All study procedures were approved by the University Review Board: IRB2016-362.

A total of 525 university students completed the questionnaire. Respondents represented various academic programs offered by the university such as engineering, agriculture, biology, and management. A majority of the participants were women (69.6%) and Caucasian (54.4%). Most of the participants (80%) reported that they had not taken any nutrition education programs. Social media and the internet were reported as the main influencers of food choices by a majority of the participants (73.9%) (Table 1).

Instrument and Data Collection

The online questionnaire, which was adapted from validated and reliability-tested questionnaires (Sallis et al., 1987; Sheeshka & Mackinnon, 1993), consisted of 102 items including demographic characteristics, previous nutrition exposure (referred to the participants who had previously taken any nutrition courses in college), and seven constructs derived from the TPB, including media, modeling, perceived behavioral control (barriers, self-efficacy), attitudes (attitude and outcome expectations) and intentions toward healthy eating behaviors.

Media

This 12-item scale measured the degree to which people listen to nutrition information promoted

Table 1. Demographic Characteristics of the University Students in This Study (N = 525)

Note: Previous nutrition exposure refers to if the participants had previously taken any nutrition courses in college by the media. In the original scale, media referred to newspaper, magazine, and TV. Due to the rise of digital media, we added the internet, social media, and mobile apps to our study. Responses ranged from strongly disagree (coded as 1) to strongly agree (coded as 7). Some items in the scale were reverse coded. An overall measure of Media was created by calculating the mean score of the 12 items. Higher scores reflected higher attention to media when promoting healthy eating behaviors. Samples of the statements used in this scale were the following: “information from different media sources suggest that people should buy foods with bran” and “commercials showing fresh fruit and vegetables make those foods appealing to me.” Internal consistency reliability was acceptable (Cronbach’s a = .69).

Modeling

A Likert scale with 23 items was used to measure if friends and family had a positive influence on healthy eating behaviors by providing their support to follow a healthy diet. Responses ranged from never (1) to very often (7). Some items of the scale were reverse coded. An overall measure of Modeling was created by calculating the mean score of the 23 items. Higher scores reflected greater support from family and friends on healthy eating behaviors. An example of the items used in this scale is “my friends encouraged me not to eat high-salt foods when I was tempted to do so.”

Cronbach’s alpha in this sample was 0.73, suggesting high internal consistency reliability.

Barriers

This construct was measured using a 7-point Likert scale with 15 items. Three items assessed cost, three items measured taste, three items evaluated time, and the last six items assessed availability as barriers to healthy eating behaviors. Response options ranged from strongly disagree (1) to strongly agree (7), with higher scores reflecting higher barriers. Some items of the scale were reverse coded. An overall measure of Barriers was created by calculating the mean score of the 15 items. An example of the statements used in this scale is “It takes a lot of time to prepare nutritious meals.” The scale showed high internal consistency reliability (Cronbach’s a = 0.85).

Attitude

A 7-point Likert scale was used to measure positive attitudes toward healthy eating to reduce risk of chronic diseases. This scale contained six items, with responses ranging from strongly disagree (1) to strongly agree (7). Some items of the scale were reverse coded. An overall measure of Attitude was created by calculating the mean score of the six items. Higher scores indicated greater importance placed on future health. An example of the items used in this scale is “it is very important to lower my risk of having disease in old age.” The

Cronbach’s alpha was 0.86, suggesting high internal consistency reliability.

Self-efficacy

A 23-item scale was used to measure perceived ability to follow healthy eating practices under various situations. In this section, participants were asked to rate their confidence in performing different healthy eating behaviors using a scale ranging from not at all confident (1) to extremely confident (7). An overall measure of Self-efficacy was created by calculating the mean score of the 23 items. Some sample items are “I am able to choose fruit instead of donuts or pastries most of the time, at coffee breaks” and “I can always resist buying candy bars, chips, and cookies from vending machines.” A reliability coefficient of 0.89 indicated high internal consistency for this scale.

Outcome Expectations

This construct was measured by having participants rate their likelihood of possible consequences of healthy eating practices (physical appearance, body weight, and health consequences such as heart diseases, cancer, and gastrointestinal disorders). This scale consisted of 14 items and used a 7-point scale ranging from not at all likely (1) to extremely likely (7). An overall measure of Outcome Expectations was created by calculating the mean score of the 14 items. An example of an item used in this scale is “Eating fewer high-fat foods (e.g., fried foods, fatty meals, rich desserts, and butter/margarine/oil) help to protect me from heart disease.” The scale had high internal consistency reliability (Cronbach’s a = 0.96).

Intentions

Intentions to adopt eight healthy eating practices within the next 6 weeks were measured. The eight healthy eating practices were the following: selecting smaller portions of meat or poultry; choosing baked potatoes instead of French french fries; selecting whole grain products instead of refined grains; reducing the consumption of butter or margarine; incorporating legumes in salads, soups, and entrees; consuming at least three servings of vegetables per day; preferring home-made hamburgers rather than fast-food hamburgers; and choosing skim milk instead of whole milk. An overall measure of Intentions was created by calculating the mean score of the eight items. Cronbach’s a = 0.81. The questionnaire was administered via a secure website (Qualtrics, Provo, Utah). Before the questionnaire was administered to the study sample, it was pre-tested for clarity, length, and format using a group of 10 students enrolled in the spring 2016 semester. The questionnaire was then revised for some wording problems and survey layout. A screening question was asked to ensure the survey takers were college students aged 18 to 36 years.

Statistical Analyses

Data analysis was performed using SPSS version 25.0. The internal consistency of each construct was examined using Cronbach’s alpha. Mann-Whitney U tests were performed to compare differences of variables (media, modeling, attitudes, outcome expectations, self-efficacy, barriers, and intentions to healthy eating behaviors) by gender and previous nutrition exposure.

Multiple linear regression models, predicting intentions to adopt healthy eating behaviors, were estimated. In the regression model, independent variables were media, modeling, barriers, attitudes, self-efficacy, and outcome expectations. Gender and ethnicity were adjusted in the regression model. The estimated variance inflation factors (VIF) for the independent variables ranged from 1.04 to 1.65, suggesting that multi-collinearity was not a problem in these analyses.

Results

Female participants had significantly higher intention to adopt healthy eating behaviors (p < 0.001) and were significantly influenced by media—more than their male counterparts (p = 0.011) (Table 2). In addition, female participants scored significantly higher on self-efficacy (p = 0.001) and outcome expectations (p = 0.007) compared to male participants. Participants who had taken nutrition classes had significantly higher scores on attitudes and self-efficacy (p = 0.02 and 0.001, respectively) (Table 2).

The regression results showed that the independent variables significantly predicted 53% of the variability in intention to healthy eating behaviors among the university students in this study (F (8,523) = 95.7, p < 0.001, R2 = 0.53). The correlation coefficients indicated that significant predictors of intention to adopt healthy eating behaviors

Note: a significant difference by gender based on 95% CI b significant difference by previous nutrition education exposure based on 95% CI included self-efficacy (b = 0.23, p < 0.001), media (b = 0.26, p < 0.001), and modeling from parents and peers (b = 0.05, p = 0.03) (Table 3). The results did not show any significant effects of the barrier construct, analyzed as a group or divided into taste, cost, time, and availability (p > 0.05), in predicting intentions to adopt healthy eating behaviors.

Discussion

The purpose of this theory-driven study was to explore factors, including previous nutrition exposure, media, parental and peer modeling, barriers, attitude, self-efficacy, and outcome expectations that influence intentions to adopt healthy dietary behaviors among university students. The results indicated that media, parental and peer modeling, and self-efficacy directly predicted intentions to adopt healthy dietary behaviors, and previous nutrition exposure significantly influenced selfefficacy and attitudes.

Most of the participants in this study reported social media and the internet as the main sources of nutrition information and that media was a significant predictor of the intention to adopt healthy eating behaviors. Findings from previous studies align with these results. For example, in a qualitative study using focus groups (Deliens et al., 2014), the participants reported being influenced by media and advertising in making healthy food choices. Similarly, a study conducted by Amore et al. (2019) on barriers and enablers of healthy eating among college students reported that college students regarded social media as a source of health promotion, allowing them to shape their perception of normalized behavior, and modeling behavior seen in social media, or using it as reinforcement of healthy behaviors. Moreover, findings of this study revealed that female participants were influenced by media more than their male counterparts were. This finding is consistent with previous findings by Barcaccia et al. (2018) and Fernandez and Pritchard (2012) who found that females were significantly more influenced by the media in terms of dysfunctional eating behaviors and drive for thinness than males were. Media should be used as a platform for nutrition interventions and programs to college students, especially female students, to share reliable nutrition education information and facilitate the interaction between students and nutrition educators in order to set goals and provide tailored information.

In this study, students reported that modeling from family and friends influenced their intentions to adopt healthy eating behaviors. Previous studies also found that parental and friend modeling helped to promote healthy eating behaviors in college students. Sogari et al. (2018) found that parents had a crucial role, both positive and negative, in shaping the concept of healthy eating. A study by Grumbine et al. (2011) on factors influencing college student’s choices of milk and soda showed that family consumption of milk was directly associated with college students’ milk consumption. Other studies found support and encouragement from friends when making a lifestyle change to be important for performing the desired behavior (Amore et al., 2019; Deliens et al., 2014; Kabir et al., 2018).

Self-efficacy was found to be an important predictor in determining intention to adopt healthy eating behaviors among university students in this study. This finding confirms the results of Cox et al. (2017) who found that self-efficacy was a significant predictor of students’ intake of fruit and vegetables, grains, milk, and meat.

Previous nutrition exposure did not show significant influence on intentions to adopt healthy eating behaviors; yet it was significantly associated with positive attitudes and self-efficacy in making healthy food choices. This finding suggests that previous nutrition exposure might be important for developing self-efficacy and positive attitudes, which were important factors in predicting intentions and nutrition behaviors as shown in this study and previous studies of college students (Deshpande et al., 2009; Garcia & Mann, 2003). In this study, the barrier construct related to taste, time, cost, and availability was not a significant predictor of intention to adopt healthy eating behaviors. Deshpande et al. (2009) also found no significant effects of price, taste, ease of preparation, and convenience on the likelihood of college students to eat a healthy diet. The large sample size (525 participants) was a strength in this study. Moreover, the study population included university students of all study years and disciplines, which contributed to a wider range of experiences and opinions.

This study had some limitations. First, data were self-reported, which may introduce memory or social desirability bias. Second, the participants were recruited from one university campus in the United States using a convenience sampling method; therefore the findings may not be generalizable to the rest of the population. In addition, a full understanding of the intention to make healthy dietary choices was limited by the fact that we did not assess actual food intake. Despite its limitations, this study provides unique and important insights for future studies of college students, an important population that is at a key crossroads in their lives.

Conclusions and Implications

The results of this study underscore the importance of media influence, self-efficacy, and parental and peer modeling as direct predictors of healthy eating behaviors among university students. These findings should be considered when developing tailored nutrition intervention programs aiming to improve university students’ eating behaviors, such as use of media as a platform and the involvement of parents, friends, and peers in the nutrition interventions.

The results of this study also emphasize the role of nutrition exposure in influencing self-efficacy and attitudes. This finding suggests the need for nutrition education programs and that they should focus primarily on improving self-efficacy and behavior skills as pertaining to healthy foods and eating behaviors.

References

Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453–474. doi: 10.1016/0022-1031(86)90045-4

Al-Nakeeb, Y., Lyons, M., Dodd, L., & Al-Nuaim, A. (2015). An investigation into the lifestyle, health habits and risk factors of young adults. International Journal of Environmental Research and Public Health, 12(4), 4380–4394. doi: 10.3390/ijerph120404380

Alsunni, A. A., & Badar, A. (2015). Fruit and vegetable consumption and its determinants among Saudi university students. Journal of Taibah University Medical Sciences, 10(2), 201–207. doi: 10.1016/j.jtumed.2014.11.003

Amore, L., Buchthal, O. V., & Banna, J. C. (2019). Identifying perceived barriers and enablers of healthy eating in college students in Hawai’i: A qualitative study using focus groups. BMC Nutrition, 5(16). doi: 10.1186/ s40795-019-0280-0

Barcaccia, B., Balestrini, V., Saliani, A. M., Baiocco, R., Mancini, F., & Schneider, B. H. (2018). Dysfunctional eating behaviors, anxiety, and depression in Italian boys and girls: The role of mass media. Brazilian Journal of Psychiatry, 40(1), 71–77. doi: 10.1590/1516-4446-2016-2200

Cluskey, M., & Grobe, D. (2009). College weight gain and behavior transitions: Male and female differences. Journal of the American Dietetic Association, 109(2), 325–329. doi: 10.1016/j.jada.2008.10.045

Cox, V. M. L., Blotnicky, K., & Rossiter, M. (2017). University students’ eating behaviors: Implications for the Social Cognitive Theory. International Journal of Health Sciences, 5(4), 25–37. doi: 10.15640/ijhs.v5n4a3

Deliens, T., Clarys, P., De Bourdeaudhuij, I., & Deforche, B. (2014). Determinants of eating behavior in university students: A qualitative study using focus group discussions. BMC Public Health, 14, 53. doi: 10.1186/1471-2458-14-53

Deshpande, S., Basil, M. D., & Basil, D. Z. (2009). Factors influencing healthy eating habits among college students: An application of the Health Belief Model. Health Marketing Quarterly, 26(2), 145–164. doi: 10.1080/07359680802619834

Fernandez, S., & Pritchard, M. (2012). Relationships between self-esteem, media influence and drive for thinness. Eating Behaviors, 13(4), 321–325. doi: 10.1016/j. eatbeh.2012.05.004

Fila, S. A., & Smith, C. (2006). Applying the Theory of Planned Behavior to healthy eating behaviors in urban Native American youth. International Journal of Behavioral Nutrition and Physical Activity, 3(11). doi: 10.1186/1479-5868-3-11

Frimming, R., Polsgrove, M., & Bower, G. (2011). Evaluation of a health and fitness social media experience. American Journal of Health Education, 42(4), 222–227. doi: 10. 1080/19325037.2011.10599191

Garcia, K., & Mann, T. (2003). From “I Wish” to “I Will”: Social-cognitive predictors of behavioral intentions. Journal of Health Psychology, 8(3), 347–360. doi: 10.1177/13591053030083005

Greaney, M. L., Less, F. D., White, A. A., Dayton, S. F., Riebe, D., Blissmer, B., Shoff, S., Walsh, J. R., & Greene, G. W. (2009). College students’ barriers and enablers for healthful weight management: A qualitative study. Journal of Nutrition Education and Behavior, 41(4), 281–286. doi: 10.1016/j.jneb.2008.04.354

Grumbine, R. M. E., Collins, N., Randall, G. K., & Bradley University. (2011). Beverage consumption of college students: Factors that influence their choices. Undergraduate Research Journal for the Human Sciences, 10, 8–18.

Ha, E. J., & Caine-Bish, N. (2009). Effect of nutrition intervention using a general nutrition course for promoting fruit and vegetable consumption among college students. Journal of Nutrition Education and Behavior, 41(2), 103–109. doi: 10.1016/j.jneb.2008.07.001

Hawkins, L. K., Farrow, C., & Thomas, J. M. (2020). Do perceived norms of social media users’ eating habits and preferences predict our own food consumption and BMI? Appetite, 149, 104611. doi: 10.1016/j.appet.2020.104611

Jung, S. E., & Bice, C. (2019). The role of self-identity in predicting college students’ intention to consume fruits and vegetables. Journal of Nutrition Education and Behavior, 51(2), 173–181. doi: 10.1016/j.jneb.2018.07.015

Kabir, A., Miah, S., & Islam, A. (2018). Factors influencing eating behavior and dietary intake among resident students in a public university in Bangladesh: A qualitative study. PLoS ONE, 13(6), e0198801. doi: 10.1371/jour nal.pone.0198801

LaCaille, L. J., Dauner, K. N., Krambeer, R. J., & Pedersen, J. (2011). Psychosocial and environmental determinants of eating behaviors, physical activity, and weight change among college students: A qualitative analysis. Journal of American College Health, 59(6), 531–538. doi: 10.1080/07448481.2010.523855

Marquis, M., Talbot, A., Sabourin, A., & Riopel, C. (2019). Exploring the environmental, personal and behavioural factors as determinants for university students’ food behavior. International Journal of Consumer Studies, 43(1), 113–122. doi: 10.1111/ijcs.12490

Menozzi, D., Sogari, G., & Mora, C. (2015). Explaining vegetable consumption among young adults: An application of the Theory of Planned Behavior. Nutrients, 7(9), 7633–7650. doi: 10.3390/nu7095357

Mithra, P., Unnikrishnan, B., Thapar, R., Kumar, N., Hegde, S., Kamat, A. M., Kulkarni, V., Holla, R., Darshan, B. B., Tanuj, K., Guddattu, V., & Kumar, A. (2018). Snacking behaviour and its determinants among college-going students in Coastal South India. Journal of Nutrition and Metabolism, 2018, ID 6785741. doi: 10.1155/2018/6785741

Qualtrics.com. (2021, March 24). Determining sample size: How to make sure you get the correct sample size https:// www.qualtrics.com/experience-management/research/ determine-sample-size/

Sallis, J. F., Grossman, R. M., Pinski, R. B., Patterson, T. L., & Nader, P. R. (1987). The development of scales to measure social support for diet and exercise behaviors. Preventive Medicine, 16(6), 825–836. doi: 10.1016/0091-7435(87)90022-3

Sheeshka, J. D. W. D., & Mackinnon, N. J. (1993). Social Cognitive Theory as a framework to explain intentions to practice healthy eating behaviors. Journal of Applied Social Psychology, 23(19), 1547–1573. doi: 10.1111/ j.1559-1816.1993.tb01047.x

Sogari, G., Velez-Argumedo, C., Gómez, M. I., & Mora, C. (2018). College students and eating habits: A study using an ecological model for healthy behavior. Nutrients, 10(12), 1823. doi: 10.3390/nu10121823

Supa, P., Karl, P., & Erkin, M. M. (2014). Prevalence of health risk behaviors and their associated factors among university students in Kyrgyzstan. International Journal of Adolescent Medicine and Health, 26(2), 175–185. doi: 10.1515/ijamh-2013-0516

Vadeboncoeur, C., Townsend, N., & Foster, C. (2015). A meta-analysis of weight gain in first year university students: Is freshman 15 a myth? BMC Obesity, 2, 22. doi: 10.1186/s40608-015-0051-7