The dynamics of eating behaviors and eating environment in college students: discrepancies between app-tracked dietary intake and self-perceived food consumption

Introduction

Young adulthood between the ages of 18 to 25 years is a critical period of transition from dependence to independence, both mentally and physically. This period often coincides with the start of college, a time when many students gain weight, also known as the “freshmen 15” phenomenon (1). Data from the most recent National College Health Assessment revealed that only 15% of students ate recommended daily servings of fruits and vegetables (2). During the coronavirus disease 2019 (COVID-19) pandemic, many college students were isolated and forced to eat alone, leading to unhealthy dietary behaviors and increased rates of overweight, obesity and diet-related diseases (3). Establishing healthy eating behaviors in young adulthood is crucial for preventing diet-related chronic diseases and promoting long-term health (4). Thus, it’s important to understand the factors that affect young adults’ dietary behaviors. In this study, we define healthy dietary behaviors as eating patterns that align with recommended calorie intake and nutritional guidelines, including adequate consumption of fruits, vegetables, and balanced macronutrients. Conversely, unhealthy dietary behaviors include overeating, under-eating, or a low intake of nutrient-dense foods—particularly when influenced by social or environmental factors.

A person’s eating behaviors are significantly influenced by one’s eating environment through various psychosocial mechanisms. Understanding modifiable factors that influence one’s eating behaviors can facilitate the design of effective and personalized nutrition interventions. Eating environment encompasses a wide range of factors; two important ones are with whom one eats and where one eats, which are the focus of the current study.

Research has shown that young adults tend to eat more or consume more calories when eating with others compared to eating alone, possibly due to social facilitation effects (5). Conversely, other studies suggest that some young adults tend to eat less in social settings due to impression management (6,7). Studies also indicate that people consume more calories when eating at fast-food restaurants compared to eating at home, likely due to larger portion sizes and higher calorie density of fast-food meals (8,9). Likewise, prior studies suggested that people eat more in dine-in restaurants than at home and that young adults’ dietary behaviors when eating away from home varied by different types of restaurants (10,11). The inconsistencies in the literature on the relationship between the eating environment (i.e., number of people present and types of restaurants) and eating behaviors may be due to different measurement methods (12). Most prior studies on this regard have used food recall surveys whereas some recent studies have utilized mobile apps and ecological momentum assessments to track dietary intake (13,14). Few studies, however, have examined the relationship of eating environment and eating behaviors by comparing data from mobile apps and self-report surveys.

Inconsistencies in the observed relationship between eating environment and eating behaviors may also be influenced by socio-demographic characteristics such as gender. For example, gender differences in social norms of eating suggest that women may feel pressure to eat less or healthier in social settings whereas men may be expected to eat more (15). Prior studies have revealed that women’s and men’s self-reported food consumption aligns with these social norms but sometimes deviates from objective measurement (16-18). Given these gender differences in eating behaviors and social norms, it is critical to stratify analyses by gender when examining the relationship between eating environment and eating behaviors.

Additionally, mood and stress at the time of eating are important social environmental factors that influence one’s eating behaviors. Literature documents that people tend to consume more food when experiencing negative emotions such as depression or anxiety (19). Other studies, however, suggest that people tend to eat more when in a happy mood compared to a neutral or an unhappy mood (20). Stress has been found to be negatively associated with healthy eating, as people tend to indulge in unhealthy eating, especially overeating, when they are under stress (21,22). When studying the relationship between eating environment and eating behaviors, it’s important to control for mood and stress as potential confounders.

The existing literature on the relationship between eating environment and eating behaviors has two notable gaps. First, most studies have been conducted in lab settings or relied on retrospective food recalls while only a small number of studies have used mobile app data to examine these relationships. Even fewer have compared the app-logged dietary intake with self-reported food consumption. Second, most existing studies have used cross-sectional designs with limited observations of eating occasions; longitudinal studies that analyze a sufficient sample of eating occasions in natural settings are rather limited (23,24).

Given the significance of dietary behaviors in young adulthood for lifelong health and the critical need for more rigorous research on the relationship between eating environment and eating behavior, the current study aimed to answer three research questions: (I) how do college students’ eating behaviors vary based on their eating environment, specifically who they eat with and where they eat? (II) What gender differences exist in the relationship between eating environment and eating behaviors? (III) What discrepancies exist between dietary intake logged in a mobile app and self-perceived food consumption reported in surveys? We hypothesized that (I) college students eat more when eating with others and in a sit-down restaurant; (II) there are gender differences in the relationship of eating environment and eating behaviors with women reporting eating less in social or sit-down environment settings; and (III) discrepancies exist in app-tracked dietary intake versus self-reported food consumption as people tend to underreport their food intake in surveys. We present this article in accordance with the STROBE reporting checklist (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-102/rc).

Methods

Overview

This longitudinal observational study was conducted at George Mason University, a large public university in the United States in Spring 2022. A total of 41 students from diverse backgrounds participated, tracking their dietary intake using a mobile app and self-reporting their eating behaviors and eating environment through a daily survey over four consecutive weeks. A total of 3,168 eating occasions were logged. Data were analyzed using multilevel mixed-effect models.

Participants and recruitment

Eligible participations were (I) aged 18 to 25 years and enrolled at the university during the study period; and (II) not in a weight loss program or in eating disorder treatment, including taking weight loss medicine or appetite suppressants, or following a particular dietary plan. Recruitment flyers were posted in campus cafes and on social media platforms. Interested students completed a brief screening survey to determine eligibility. Eligible participants completed an informed consent online with a research assistant available to provide assistance.

Mobile app for logging dietary intake

Participants were asked to use the free mobile app Nutritionix (see Figure S1 for screenshots of the app) to track dietary intake for the following reasons. First, Nutritionix has the largest verified nutrition database with over a million food items, including many branded products and restaurant meals, which makes it easy for users to log food quickly. For example, a user can type part of a brand name, such as “McDonald” or “Starbucks”, and select items from a dropdown menu without typing the full food name. It also has a barcode scanner for quickly logging packaged foods or meals (25). Second, it automatically generates macronutrient info and total calories for each eating occasion and daily total. Third, unlike most dietary tracking apps designed for weight management or dietary restriction, Nutrionix can be used as a “neutral app”, serving solely as a tracking tool without providing feedback based on users’ inputs. And fourth, it has been used frequently in research studies to track dietary intake, including clinical trials with rigorous design, for example, the Nourish Protocol and the DASH-based digital health trial for women with hypertension (26,27). These applications support Nutritionix’s utility to capture real-world dietary behaviors over time.

Data collection procedure

Eligible participants first completed an online baseline survey assessing their general eating habits and other health behaviors. They were then instructed on how to use the Nutritionix app to log everything they ate or drank. Detailed instructions were sent via email and research assistants were available to assist the participants via phone call, text, or email. Participants were reminded to enter all food or drink for every eating occasion and granted “coach” access to the research assistant, enabling the assistant to monitor and download data remotely.

As no existing mobile app could track eating environment, we designed a short survey to document the eating environment for all eating occasions during the day (see Appendix 1). Participants received a Qualtrics survey link via email or text each evening and were asked to complete it before the end of the day. Participants were instructed to use the Nutritionix app and daily survey every day for 4 consecutive weeks. If a participant missed entering data for 2 days, the research assistant sent a reminder. The study protocol was piloted prior with the target population prior to field implementation.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institute of Review Board of George Mason University (No. 1861926). All participants provided written informed consent prior to participation.

Measures

The unit of analysis in this study was eating occasion, defined as any instance when a participant consumed food or drink. The dependent variables were dietary intake logged in the mobile app and perceived food consumption reported in daily survey. Dietary intake measured by total calories was calculated automatically by the Nutritionix app, and the data were downloaded weekly. Perceived food consumption was measured by self-reported responses to the question of “Did you eat more than usual, as usual, or less than usual” in each eating occasion in the daily survey.

The independent variables were eating environment assessed through daily surveys and included (I) time; (II) location; (III) number of companies; (IV) mood; and (V) stress level, at the time of eating. Eating location included home (or dorm), dining hall, inside school building, on-campus fast-food restaurant, off-campus fast-food restaurants, dine-in restaurant, outdoor, work/office, and in-transit (e.g., in bus or driving). For the purpose of data analysis, eating location was grouped into three categories: (I) home; (II) casual dining (fast-food restaurants, work/office, school building, outdoor, and in-transit); and (III) sit-down dining (dining hall, and dine-in restaurant). The number of people present at the time of eating was grouped into four categories: (I) alone; (II) one other person; (III) two other people; and (IV) three or more people. Mood was categorized as happy, neutral and unhappy. Stress level was categorized as not stressed, a little stressed, and very stressed. Eating occasions were categorized as (I) breakfast; (II) morning snack; (III) lunch; (IV) afternoon snack; (V) dinner; and (VI) evening snack.

Statistical analysis

The data from all surveys and Nutritionix app were cleaned and linked by participant ID. Once the clean dataset was ready, the following analytical steps were taken to answer the above research questions. First, frequency analyses with Chi-square tests for categorical variables and mean with standard deviations for continuous variables were performed to understand the distribution of key variables. Second, bivariate analyses were employed to determine Pearson’s coefficients and test the hypotheses on whether there was a positive relationship between total calorie intake and eating environment (i.e., number of eating companies, location of eating, and mood at the time of eating). Third, to account for repeated measures in longitudinal design, mixed-effect regression models were used to examine the independent relationship between outcome variable (calorie intake) and eating environment while controlling for potential confounders, including demographic factors, gender (based on self-report), meal types, mood, and stress. Fourth, mixed-effect logistic regression models were used to assess the relationship of perceived food consumption (i.e., more or less than usual) and eating environment while controlling for potential confounders. In all mixed-effect models, individual effects were modeled as random. We also performed a stratified analysis on gender groups to examine whether the relationships between eating environment and eating behavior differed between male and female students.

Results

Participant characteristics

A total of 45 participants signed up for the study and 4 dropped out in the first week without providing any data. All remaining 41 participants completed the 4-week study, logging 3,168 eating occasions. As shown in Table 1, out of 41 participants, 56% were female, 20% were Black or African American, 32% Hispanic or Latino, and 34% Asian or Pacific Islanders. Approximately 51% were freshmen, 12% were sophomores, 27% were juniors, and the remaining were seniors or graduate students. Thirty-nine percent (39%) of participants were in the healthy weight body mass index (BMI) category (BMI 18.5–24.9 kg/m²), 24% were in the overweight category (BMI 25.0–29.9 kg/m²) and 37% were in the obesity category (BMI ≥30.0 kg/m²). A majority of eating occasions (60%) were logged as eating alone, 19% involved one companion, and 21% involved two or more companions. In terms of eating locations, 54% were at home or in a dorm, 28% at casual dining locations such as offices/schools, outdoors, school cafes, and 18% at formal dining places such as dining halls and dine-in restaurants. Participants reported that on 59% of eating occasions, they were happy, 32% were neutral, and only 9% were unhappy. They also reported that on 30% of eating occasions, they were not stressed, 36% a little stressed, and 35% were very stressed.

Research question 1: relationship between eating environment and eating behaviors

Total energy intake

Table 2 depicts the relationship between total energy intake (logged in Nutritionix) and number of eating companions. Calorie intake increased when participants ate with two or more companions compared to eating alone (β=37.17, 95% CI: −0.04 to 74.37). Among males, the increase was more pronounced (β=70.29, 95% CI: 15.81 to 124.78). Among females, the increase was not significant (β=35.84, 95% CI: −13.38 to 85.05).

Table 3 reports the relationship between total energy intake (logged in Nutrionix app) and eating location. Formal dining locations (e.g., dining halls or dine-in restaurants) were associated with significantly higher calorie intake compared to eating at home (β=155.97, 95% CI: 114.89 to 197.05). The increase was particularly strong among females (β=215.67, 95% CI: 156.97 to 274.36) compared to males (β=99.57, 95% CI: 44.80 to 154.34). Casual dining locations (e.g., fast-food restaurants, school buildings, or outdoor settings) showed no significant difference in calorie intake compared to eating at home.

Perceived food consumption

Table 4 displays the relationship of perceived food consumption (reported in daily survey) and the number of eating companions. Participants were more likely to report eating less than usual when eating with two or more companions [odds ratio (OR) =1.48, 95% CI: 1.18 to 1.84]. This effect was observed for both genders, with males showing an OR of 1.67 (95% CI: 1.10 to 2.53) and females showing an OR of 1.48 (95% CI: 1.13 to 1.95). Eating with one companion did not significantly influence the likelihood of perceived food consumption compared to eating alone.

Table 5 reports the relationship between the perceived food consumption (reported in daily survey) and the eating location. Participants were more likely to report “eating more than usual” at casual settings compared to eating at home (OR =1.32, 95% CI: 1.06 to 1.63). This effect was particularly strong among males (OR =1.84, 95% CI: 1.28 to 2.65), while it was not significant among females. Formal dining locations were associated with a lower likelihood of reporting eating more than usual (OR =0.76, 95% CI: 0.56 to 1.01). Among females, the likelihood of reporting eating more than usual at formal dining settings was significantly lower (OR =0.57, 95% CI: 0.37 to 0.87), despite their higher calorie intake in these settings.

Research question 2: gender differences in the relationship of eating environment and eating behaviors

Gender differences were observed in how eating companions influenced calorie intake and perceived food consumption. When eating with two or more companions, males showed a significant increase in calorie intake (β=70.29, 95% CI: 15.81 to 124.78), while the increase among females was not statistically significant. Both genders were more likely to perceive that they ate less than usual when eating with two or more companions, but the effect was stronger for males (OR =1.67, 95% CI: 1.10 to 2.53) compared to females (OR =1.48, 95% CI: 1.13 to 1.95). These findings suggest that males are more influenced by the social facilitation of eating, consuming more calories in group settings, yet are more likely to underreport their perceived intake.

Gender differences were also evident in the context of eating locations. In formal dining settings, females consumed significantly more calories (β=215.67, 95% CI: 156.97 to 274.36) compared to males (β=99.57, 95% CI: 44.80 to 154.34). Despite this, females were less likely to report eating more than usual in formal dining settings (OR =0.57, 95% CI: 0.37 to 0.87), whereas males showed no significant change in their perception. In casual dining settings, males were more likely to report eating more than usual (OR =1.84, 95% CI: 1.28 to 2.65), while females showed no significant change in perception. These results highlight a disconnect for females in formal dining environments, where their higher calorie intake does not align with their perceived food consumption, in contrast to males, whose perceptions more closely align with their intake.

Research question 3: discrepancy between app-logged dietary intake and self-reported food consumption

The study revealed notable discrepancies between app-logged dietary intake and self-reported food consumption, shaped by the number of eating companions and eating locations. Eating with two or more companions significantly increased calorie intake (37.17 more calories overall), especially for males, yet participants were more likely to report eating less than usual in these social settings (OR =1.48). In formal dining settings, calorie intake was the highest (155.97 more calories overall), but participants, particularly females, were less likely to report eating more than usual (OR =0.57). Conversely, casual dining locations did not result in significant increases in calorie intake but prompted participants, especially males, to report eating more than usual (OR =1.84). These findings indicate a mismatch between actual dietary intake and self-perceived food consumption, influenced by social and environmental contexts.

Gender differences were prominent in these discrepancies. Males consumed significantly more calories when eating with two or more companions (70.29 more calories) compared to females (35.84 more calories) but were more likely to report eating less than usual in these settings (OR =1.67 for males vs. OR =1.48 for females). In formal dining settings, females consumed significantly more calories than males (215.67 vs. 99.57 more calories), yet they were less likely to report eating more than usual (OR =0.57), while males showed no change in perception. Conversely, males were more likely to overreport eating more than usual in casual dining settings (OR =1.84), despite no significant increase in calorie intake. These findings highlight gender-specific reporting biases, with males tending to overreport and females underreporting their consumption in specific eating environments.

Other notable findings on the effects of mood and stress on eating behaviors

The study also identified notable findings related to mood and stress in relation to eating behaviors. Happy moods were associated with significantly higher calorie intake (β=50.60, 95% CI: 15.80 to 85.40), with males exhibiting a stronger effect (β=138.29, 95% CI: 84.90 to 191.68) compared to females (β=29.82, 95% CI: −17.06 to 76.71). Despite this, females were less likely to report eating more in happy moods (OR =0.37, 95% CI: 0.28 to 0.50), highlighting a notable discrepancy between their actual and perceived consumption during positive emotional states. Unhappy moods and stress did not significantly increase calorie intake for either gender. However, under very stressful conditions, females were significantly less likely to report eating less than usual (OR =0.53, 95% CI: 0.38 to 0.76), in other words, they would eat more, whereas males showed no significant changes in perceived consumption during stress. These findings underscore gender-specific differences in how mood and stress influence both objective dietary intake and self-reported food consumption.

Discussion

Our data revealed a significant relationship between eating behaviors and eating environment among college students. Specifically, using the Nutritionix app to track their dietary intake and total calorie count, we found that participants had a higher calorie intake when eating with two or more companions compared to eating alone; they also consumed more calories when eating in a formal dining setting than in a casual setting or at home. These results corroborate prior studies on the relationship between eating behaviors and eating environment, which suggested that the number of people at the time of eating and location of eating influence how much is consumed (28). Earlier studies also indicated that young adults tend to eat more when they believe others are eating more and eat less when they believe others are eating less (17).

We also observe differences in dietary intake tracked by the mobile app and the perceived food consumption reported in daily surveys, as participants reported they ate “less than usual” when eating with two or more companions or eating in a formal dining setting. The discrepancy between calorie intake logged in a mobile app and perceived food consumption reported in daily surveys may reflect a lack of awareness or disconnection between students’ actual food intake and their perception of it (29). It may also suggest social modeling or impression management among young adults as they attempt to adhere to social norms of eating less (7,13). Further research is needed to explore the factors underlying this complex relationship between eating behaviors and eating environment among college students.

Significant gender differences were observed in how participants’ eating behaviors changed in different eating settings. Specifically, mobile app data revealed that male students ate more when eating with two or more companions or in a formal dining setting. In contrast, female students were more likely to report they ate less than usual when eating with two or more companions or in a formal dining setting in the daily surveys. These gender differences reflect the differences in social norms of eating patterns and in perceptions of food in social eating (7,15). Understanding these gender differences can inform the design of gender-specific nutrition interventions for college students.

This study has also identified other factors associated with eating behaviors, including BMI, mood and stress level, which further suggest the complexity of eating behaviors (20). As young adulthood is a critical life stage to establish eating habits, understanding the various factors associated with eating behaviors at individual, interpersonal and environmental levels can inform personalized nutrition intervention for the target population.

Our study had two notable strengths that contributed to its overall quality. First, we used primary data collection to examine the relationship between eating environment and dietary intake. We designed a daily survey to capture detailed information on eating environment for each occasion. Second, participants provided data on their eating behaviors and eating environment from over 3,100 eating occasions over 4 weeks. Such a longitudinal design with a large number of data points enabled us to examine the independent relationship between eating behaviors and eating environment while controlling for potential confounders. We were also able to examine gender differences and account for other influential factors, such as mood and stress level.

This study has a few limitations. First, although participants used a mobile app with an advanced food database to track their dietary intake and used a daily survey to document their perceived food consumption and eating environment, underreporting or overreporting of dietary intake may have occurred, either intentionally or unintentionally. As digital technology advances, future studies can benefit from unintrusive wearable sensors or mobile devices to track dietary intake and eating environment objectively. Additionally, future studies could examine how engagement with mobile dietary tracking tools evolves over time and whether this influences both actual intake and perceived consumption. Second, we focused on calorie intake as the primary measure of eating behavior; other important indicators of healthy eating, such as fruit and vegetable intake, were not examined in the study. Future research should include different aspects of healthy eating when studying how environment influences eating behaviors. Third, we focused on two factors of eating environment: the number of companions and type of eating location. Other important factors of eating environment such as atmosphere of eating location, availability of healthy food options, or even temporal variables like date, season, or weather, may also influence one’s eating behaviors. While we have included mood and stress in the models, more research is needed to examine how other environmental factors affect eating behaviors and the interrelationships between these factors. Fourth, other important covariates, such as nutrition knowledge, that might have influenced eating behaviors, were not measured. Future studies should include standardized measures of nutrition knowledge to explore its potential moderating effects on dietary reporting and eating behaviors. Fifth, we had a small sample of 41 students from a large public university. Though our participants represented a diverse race and age distribution; they were slightly more overweight than average American college students, possibly because overweight students were more motivated to join a dietary study. The findings from this study might not be generalizable to all college students or young adults in the US. Finally, data collection took place in Spring 2022, not long after the COVID-19 pandemic, and many participants still chose to eat alone or at home, leading to a predominance of “eating at home or in a dorm” and “eating alone” responses, which might not represent typical college students’ eating patterns prior to or after the pandemic.

Conclusions

This longitudinal study, utilizing a diverse sample of college students using a mobile app and daily survey, provides valuable insights into the relationship between eating behaviors and eating environment. The findings show that young adults tend to eat more when eating with more than companion or in a formal dining setting, as tracked by a mobile app, even though they perceived otherwise. Other significant factors associated with eating behaviors included gender, race, BMI, mood, and stress level. These findings highlight the complexity of eating behaviors, demonstrating how individual, interpersonal, and environmental factors interact to influence dietary intake. Future studies should consider using wearable sensors or other technologies for more accurate and passive measurement of dietary intake and eating environment. We advocate for more research on the relationship of eating behaviors and eating environment to inform personalized nutrition interventions to improve healthy eating among college students and young adults.

Acknowledgments

We thank all participants in the study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-102/rc

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-102/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-102/prf

Funding: The study was supported by the George Mason University College of Public Health Pilot Grant (PI: Y.A.H.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-102/coif). Y.A.H. reports the study was supported by the George Mason University College of Public Health Pilot Grant. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institute of Review Board of George Mason University (No. 1861926). All participants provided written informed consent prior to participation.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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