A Randomized Controlled Trial,Non-Nutrition Based mHealth Program: The Potential Impact on Dietary Intake in College Students

Abstract

The aims of this study were to (1) explore this sample’s pre- and post-intervention dietary intake, specifically the macro- and micronutrients, and their eating habits related to location of consumption and use of electronic devices, and (2) compare this sample’s nutritional measures to the current Dietary Guidelines 2020 to 2025. Twenty-eight participants were included in the secondary data analysis. Participants reported a total of 822 items consumed during this study. Most items were consumed at home (n = 629, 76.5%). We found significant differences in the intake of energy, protein, total fat, carbohydrates, total vegetables, total grains, and total meat in different locations. For most of these measures, consumption at home and/or restaurants resulted in a greater magnitude of consumption than at other locations (e.g., car, daycare). Participants reported consuming most of their energy and nutrients while either using electronic devices alone (n = 365, 44.4%) or using no devices (n = 346, 42.1%). Significant differences were found among three measures including energy, total fat, and total fiber. The majority of the macronutrients (total fiber, fruits, vegetables, meat, and dairy) consumed by our sample were under the threshold recommended in the 2020 to 2025 Dietary Guidelines.

Keywords

dietary intake dietary guidelines mobile health intervention college students eating habits

Introduction

It is well established that new college students generally increase body mass throughout the course of the first year, with the average gain reported between 2.6 lbs (Gropper et al., 2009) and 5.5 lbs (Economos et al., 2008). This is accompanied with an increase in the percentage of body fat near 2% (Edmonds et al., 2008; Hajhosseini et al., 2006). The increase in body mass and body fat generally continues to accumulate over 4 years of study (6.6 lbs and 3.6% body fat) and has been suggested to be associated with eating regulation behaviors (Gropper et al., 2012). However, the demographic characteristics of college students have changed since the seminal research on body mass gains in university students was published, with a decreased percentage of White students and an increase in all other minority groups (United States Census Bureau, 2021). Of note, it is unknown how dietary intake of college students compares to the most recent recommended dietary guidelines.

Another factor that may contribute to the student population’s eating habits is smartphones and personal computing devices. In 2011, 48% of university students in the United States owned a smartphone, whereas 93% of college graduates owned devices in 2021 (Pew Research Center, 2021). Thus, it should not be surprising that college students engage with their smartphones during meal times (da Mata Gonçalves et al., 2019; Müller et al., 2015). Even before the invention of the smartphone in 2007, 46% of all meals were eaten in front of the television, and independently snacking with the TV on was associated with greater caloric and fat intake (Gore et al., 2003). The top activities in which smartphone use was conducted as a secondary activity (i.e., participants were engaged in a primary activity while also using their smartphones), include watching TV/videos (14.9%), driving (12.7%), and working (10.7%), followed by eating/drinking (9.3%; Müller et al., 2015). In young adults, total calories ingested was increased by 15% when distractors such as using a smartphone was added to the environment, compared to eating a meal in a distraction-free environment (da Mata Gonçalves et al., 2019). Because smartphones can serve as a distraction, it is worthwhile to explore what effect a focused non-nutrition based mHealth smartphone application has on dietary intake in a cohort of college students.

The location where a person eats can also have an impact on dietary composition. The contribution of fat to the meal is greater when meals are consumed outside of the home (ranges from 41.7 to 46%) compared to at home (ranges from 34.7 to 38.2%; O’dwyer et al., 2005). Eating at home results in increased consumption of grains, and a decrease in fats and sugars compared to eating at work or in restaurants (Bandoni et al., 2013). Adults and adolescents appear to consume more ultra-processed foods at fast food restaurants, and minimally processed foods at home (de Souza et al., 2021). When the location of smartphone use is considered, top locations include the bedroom (45%), living room (29%), and kitchen (16%; Müller et al., 2015). Furthermore, research related to nutrition have been investigated in college students (Barzegari et al., 2011; Cousineau et al., 2004; Franko et al., 2008); however, the combination of location of nutritional intake and smartphone usage has been unexplored in this population.

Our pilot investigation evaluated a mHealth to Optimize Blood Pressure Improvement (MOBILE) Intervention for reducing blood pressure (BP) in college students’ ages 18 to 29 over 28 days, using a two-arm randomized controlled trial. Briefly, we found statistically significant decrease in BP in the intervention group while no significant decrease was found in the control group. With the remarkable reduction of BP (systolic and diastolic BP) in the intervention group after 28 days, we wanted to explore potential dietary habits that may contribute to this finding. Therefore, the aims of this investigation were to (1) explore this sample’s pre- and post-intervention dietary intake, specifically the macro- and micronutrients and their eating habits related to location of consumption and use of electronic devices; and (2) compare this sample’s nutritional measures to the current Dietary Guidelines 2020 to 2025. It is noted for Aim 2 that males and females have different dietary guidelines for some nutritional measures, and hence we provided the sexes separately in the data visualizations for interested readers. These aims will enable the possible explanation of potential domino effect of a non-nutrition based mHealth program, MOBILE intervention, which focuses on BP and its potential impact on dietary intake in a group of college students.

Methods

This study is a secondary analysis using the randomized controlled trial MOBILE intervention data (Tran et al., 2022). Institutional Review Board approval (IRB# 1565271) was obtained prior to implementing the MOBILE intervention. The inclusion and exclusion criteria, recruitment, enrollment, and measurements, and details of the intervention are reported elsewhere (Tran et al., 2022). Briefly, full-time college students were recruited, and the study used mHealth technology to modify behaviors and encourage healthy habits. The outcome focused on lowering BP, including measuring the students’ hypertension knowledge, nutrition intake, and daily motivation. This secondary analysis more closely evaluates this population’s nutrition values related to their eating habits (i.e., location and use of electronic devices). All subjects (intervention and control groups) received information on BP, complete anthropometric measurements (i.e., height, body mass), and completed various questionnaires including the sociodemographic information and automated Self-Administered Recall System (ASA-24) Dietary Assessment Tool 24 hour’s recall.

The ASA-24 Dietary Assessment Tool was used to measure participants’ 24 hours dietary and sodium intakes and has been reported to be a valid instrument (Subar et al., 2012). The ASA24 is a web application which enables the collection of self-administered dietary recalls thus utilizing technology to overcome some of the limitations of traditional assessment methodologies. The participants were asked to complete a 24-hour recall using the ASA24 at the educational session and exit meeting. The ASA24 uses the food codes, portion sizes and other such information from the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (Subar et al., 2012). The development, design and presentation of this online system has been outlined and described in full detail elsewhere (Subar et al., 2012).

Data Analysis

Data Set—Aim 1: Location and Activity of Consumed Items

Based on pre- and post-ASA24 dietary assessment, this data set consisted of granular measures of food item consumption from each participant as a function of where they consumed their food and the potential activities they were involved in while eating. Hence the unit of measurement for this analysis was the individual food item, not the individual subject. Because the original data contained several categories with small cell sizes, data were collapsed into a reduced subset for analysis. There were originally 13 response categories for location (i.e., home; fast food restaurant; other restaurant; cafeteria; bar or tavern; work—not in cafeteria; car; sports or entertainment venue; school—cafeteria; school—not in cafeteria; child care or daycare; don’t know; or someplace else). These were recategorized into four categories for summary and analysis (i.e., home, restaurant, work or school, or other). Similarly, data for location originally had eight response categories (i.e., watching tv, using a computer, using a mobile phone or tablet, watching tv and using computer and using mobile phone or tablet, watching tv and using a computer, watching tv and using a mobile phone or tablet, using a computer and using a mobile phone or tablet, or none of these). These were recategorized into four categories for summary and analysis (i.e., watching tv only, using electronic device only, television and electronic device together, or none). Data for the newly created categories were summarized with standard descriptive statistics for each of 11 outcome measures, namely: (1) energy (kcal), (2) protein (g), (3) total fat (g), (4) carbohydrate (g), (5) total sugars (g), (6) total fiber (g), (7) total fruits (cup eq.), (8) total vegetables (cup eq.), (9) total grains (oz. eq.), (10) total meat (oz. eq.), and (11) total dairy (cup eq.). These outcome measures were chosen to accommodate comparisons to standard American Diabetes Association (ADA) guidelines (described below). Data did not meet the distributional assumptions for traditional comparisons; therefore, for each of the 11 measures, the four location categories and four activity categories were each compared using nonparametric Kruskal–Wallis tests with a Bonferroni post hoc comparison if significant differences were found.

Data Set—Aim 2: Overall Nutritional Measures for Subjects

Cumulative nutritional data (e.g., total calories, total fat, etc.) for each individual intervention or control subject were utilized in a second analysis. Descriptive statistics for the four groups (i.e. Intervention group Times 1 and 2 and control groups Times 1 and 2) were calculated among the same 11 outcome measures described for the location and activity data. Again, nonparametric measures were utilized owing to data not meeting distributional assumptions. Within-group (Time 1 vs. Time 2) comparisons were tested using the Wilcoxon signed-ranks test. Further, between-subjects comparisons to compare intervention versus control groups and to assess the Time × Group interaction were completed using a rank-based repeated-measures analysis of variance. Finally, to compare the 11 outcome measures among each subject at each time period against the ADA recommended nutritional guidelines, we developed error-bar graphs showing the mean and 95% confidence interval. This allowed us to examine the 11 outcome measures separately for males and females since they often have different recommended intakes; further, this allowed us to visualize, in a single graph, multiple groups simultaneously compared to the ADA recommendations.

Results

A total of 28 participants’ data were used in the final analysis (14 participants in the intervention group and 14 participants in the control group), consisting of 42.9% females and 57.1% males. The average age of our sample is 22.8 (standard deviation = 2.84; range = 19–29). Race and ethnicity were distributed as followed: 50.0% of participants were White, 28.6% Asian, 17.9% mixed or other, and 3.6% Black with approximately 39.3% Hispanic or Latino. Figures and tables can be found in Supplemental Materials.

Data Set—Aim 1: Location and Activity of Consumed Items

Participants reported a total of 822 items consumed during this study. Most items were consumed at home (n = 629, 76.5%). Significant differences were found among the measures for several locations, including energy, protein, total fat, carbohydrates, total fiber, total vegetables, total grains, and total meat (all p < .05). For most of these measures, consumption at home and/or restaurants resulted in greater magnitude of consumption than at other locations.

Participants reported consuming most of their energy and nutrients while either using electronic devices alone (n = 365, 44.4%) or, surprisingly, using no devices (n = 346, 42.1%). Significant differences were found among three measures including energy, total fat, and total fiber; those reporting consuming foods while watching TV exceeded both electronic devices and none for energy and total fat, and TV exceeded none for total fiber.

Data Set—Aim 2: Overall Nutritional Measures for Subjects

There were no significant differences when examining control versus intervention subjects for each of the 11 outcome variables, nor for temporal comparisons. Notably among all graphs were the relatively large error bars, suggesting substantial variability among subjects. The following outcomes were noted: (1) total fiber—all subjects were below recommendations, with both control males and females significantly lower than recommended; (2) total fruits—all subjects were significantly below recommended guidelines (3) total vegetables—all subjects were below recommendation, with control males at both time periods and intervention/control females post-intervention significantly below recommended intake; (4) total meat—all subjects were significantly below recommended guide-lines; and (5) total dairy—only intervention males at baseline and post-intervention met guidelines, with all other groups significantly below recommendation.

Discussion

The purpose of this investigation was to (1) explore this sample’s pre- and post-intervention dietary intake, specifically the macro- and micronutrients and their eating habits related to location of consumption and use of electronic devices, and (2) compare this sample’s nutritional measures to the current Dietary Guidelines 2020 to 2025. Given our significant findings in BP reduction in the intervention group, but not in the control group, we were surprised that our nutritional evaluation found no statistical significance in most dietary habits between the two groups. To answer our own question, our non-nutrition based mHealth program focusing on BP did not appear to improve dietary intake patterns.

Aim 1: Location and Activity of Consumed Items

This study found that the majority of college students reported eating most food items at home. Based on this population demographic, eating at home instead of other locations such as restaurants was unexpected given this population reports rarely staying home (U.S. Bureau of Labor Statistics, 2022). Studies have shown that young adults and college students spend most of their time at school attending classes, studying, and going to work (Horne, 2000; U.S. Bureau of Labor Statistics, 2022). A potential explanation to this phenomenon could be the financial burden of eating outside of the home, where costs have increased at least by 8.7% (USDA, 2022). Additionally, it could be that students were more likely to report their dietary intake data while at home, potentially skewing their results. Nevertheless, eating at home has better benefits for these students compared to eating at restaurants or elsewhere (Sogari et al., 2018). Based on our results, students who eat at home consume more macronutrients.

Our study also found that the distribution of using and not using electronic devices are almost evenly distributed in this population (44.4 vs. 42.1%, with the remaining 13.5% including TV). Given the climatic change of electronic devices and usages, we would have expected a larger number of participants to use their electronic devices more frequently while eating compared to those who do not. There were no clear explanations or indications in our study to explain this finding. Even TV usage was not highly reported in our findings. Studies have shown that using electronic devices while eating is a distractor that resulted in more calories and poor nutrient consumption because participants are not focusing on internal state cues and interceptive signals while eating (i.e., hunger and fullness; Robinson et al., 2013; Spence et al., 2019). This may be a positive finding for our population regarding eating habits.

Aim 2: Overall Nutritional Measures for Subjects

The majority of the macronutrients consumed by our sample was under the threshold recommended in the Dietary Guidelines. Our sample exceeded the recommended intake for sugar, but we did not find statistical significance in total sugar consumption (Marriott et al., 2019; Rosinger et al., 2017). This finding aligns with the Dietary Guidelines of 2020 to 2025 and the National College Health Assessment (NCHA) that found most adults exceed recommended limits for added sugars as a result of eating foods and drinking beverages higher in added sugars (Marriott et al., 2019; U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2020). Furthermore, a total of 70.1% of college students consume more than one sugar-sweetened beverage per day (American College Health Association, 2023). Regarding fiber consumption, our sample also was below the recommended consumption in both males and females, which aligned with national data (Farvid et al., 2016). More than 90% of women and 97% of men do not meet the recommended intakes for dietary fiber (Farvid et al., 2016). Unfortunately, our sample’s fruits and vegetables consumption was also well below the recommended guidelines. It is reported that approximately 80 to 90% of the U.S. population does not meet recommendations for the consumption of fruits and vegetables (Sogari et al., 2018). Over 60% of all fruit intake comes from fresh, canned, frozen, or dried or juice form (Huang et al., 1994). Based on the NCHA reports, a total of 16.5% of college students consumed three or more serving of fruits per day and 28.4% servings of vegetables (American College Health Association, 2023). It is alarming that for the most part, the U.S. population does not meet the recommended intake for any vegetable subgroup (Mello Rodrigues et al., 2019). As expected, dietary fiber consumption normally aligns with intake patterns of fruits, vegetables, and whole grains, which are underconsumed by 85% of adults (Bernardo et al., 2017), which is similar to the national data of dietary consumption. However, it is alarming, albeit not completely surprising, to discover that college students are already demonstrating unhealthy eating habits by not meeting dietary guidelines; further, there is little evidence toward improvement in this regard. The implications of these findings suggest the need for the development of dietary interventions to address appropriate eating habits such as location of consumption and effect of electronic device usages to optimize consumption (e.g., healthier food choices, avoid overeating) in college students.

Limitations

There are several limitations that should be noted. This study is a secondary analysis of cross-sectional data; therefore, generalizability is limited, specifically the findings were from a small sample of ethnically diverse college students at a single site. However, the results of this analysis of data from a pilot grant are hypothesis generating and can be useful for developing additional studies to create interventions on dietary habits of college students. Additionally, recall bias may exist as data was obtained from a self-reported questionnaire. However, given that the ASA 24 is a valid, reliable, and extensive instrument from the MOBILE trial intervention (Tran et al., 2022), it also provided valuable information.

Conclusions

Our findings suggest that the location of consumption and electronic device usage seem to affect dietary intake in college students. Future research can focus on encouraging eating at home as well as limiting electronic device usage during mealtime. Our findings, though preliminary, provides interesting results that can be used to power larger studies to investigate the overall dietary habits of college students, and importantly, this can facilitate the development of a dietary intervention program in college students.

Supplemental Material

sj-docx-1-cnr-10.1177_10547738231197864 – Supplemental material for A Randomized Controlled Trial, Non-Nutrition Based mHealth Program: The Potential Impact on Dietary Intake in College Students

Supplemental material, sj-docx-1-cnr-10.1177_10547738231197864 for A Randomized Controlled Trial, Non-Nutrition Based mHealth Program: The Potential Impact on Dietary Intake in College Students by Dieu-My T. Tran, Chad L. Cross and James W. Navalta in Clinical Nursing Research

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: NIH Grant 2U54GM104944 MW CTR-IN Pilot Grant to D.M.T.

Ethical Conduct of Research

IRB was obtained and approved by UNLV (IRB# 1565271).

ORCID iD

Dieu-My T. Tran

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Dr. Dieu-My Tran is a Tenured Associate Professor at the University of Nevada, Las Vegas. Dr. Tran’s research focuses on cardiovascular risk factors among young adults and college students. Dr. Tran’s work identifies and measures risk factors related to cardiovascular disease among college students and includes designing, developing, and implementing a cardiovascular risk reduction program to decrease cardiovascular disease mortality and morbidity as this young population ages.

Dr. Chad Cross is trained as a multidisciplinary scientist and holds advanced degrees in statistics, the life sciences, and the social sciences. He applies his quantitative expertise broadly in research related to both human and animal medicine utilizing a One Health Perspective. Dr. Cross has particular expertise in quantitative ecology, applied biostatistics, medical and veterinary entomology, disease ecology, and the ecology and epidemiology of parasites and vector-borne diseases.

Dr. James Navalta is an Associate Professor at the University of Nevada, Las Vegas, Department of Kinesiology and Nutrition Sciences. Dr.Navalta’s research focuses on the physiological response to outdoor exercise and the validity and reliability of wearable technology.

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