Assessment of the Frequency of Fast Food Consumption of DLSU-LC Grade 12 Students Using Food Delivery Apps
Aleiyah Aubree L. Sandro1*,Anna Joy A. Simon1, Mari Ianne Denise P. Pangilinan1, and Mari-Len C. Hermosa1
1De La Salle University Integrated School
*corresponding author: anna_joy_a_simon@dlsu.edu.ph Shelah R. Alfaro,Research Adviser
1De La Salle University Integrated School
Abstract: The lockdowns implemented to prevent the spread of COVID-19 has increased the utilization of third-party food delivery services (FDS) in order to purchase food. However, the food available in these services are mostly fast-food. Frequent consumption of fast food is often associated with negative health outcomes. This study identified the relationship between the BMI and the frequency of using fast food delivery services of the participants who are in grade 12 students of De La Salle University Laguna Campus. The research conducted has used a quantitative-correlational approach study that gathered primary data through the use of a survey questionnaire. For the data analysis, linear regression analysis and multiple regression analysis were used with a 95% confidence level and an alpha of 0.05. The results of the analyses performed using the linear regression showed that there is a strong relationship between the consumption of fast food and the frequency of using Food Delivery Services (FDS), however, results implied a weak correlation on the BMI of the students. Furthermore, the other factors also revealed to have no correlation with the frequency of using FDS which contradicts previous studies. Future studies similar to this could consider a bigger sample size and explore other locations to get a better understanding of the relationship between the factors.
Keywords:frequency, BMI; fast-food; delivery services
1. INTRODUCTION
1.1. Background of the Study
Countries went on lockdown due to the COVID-19 pandemic (Dunford et al., 2020). This made it difficult for consumers to purchase items like food (Whitten, 2020).
Because of this, Vitali (2020) according to Flores and Castano (2020) stated that an increase in the use of third-party food delivery services such as Grab and FoodPanda was observed; these delivery apps usually have fast-food restaurant icons as choices among consumers. In
addition to this, the study by Bondoc et al. (2019) proved that prior to the pandemic, Filipino young adults were already frequent consumers of fast food.
1.2. Research Objectives
The main purpose of this study was to determine how the grade 12 students of De La Salle University Laguna Campus’ frequency of using food delivery services (FDS) relates to multiple variables, specifically fast food consumption, body mass index (BMI), accessibility, income, and activity level. The specific objectives are the following:
(1) Determine the relationship between the grade 12 students’
BMI and their frequency of using food delivery services; (2) Determine the relationship between the grade 12 students’
consumption of fast food and their frequency of using food delivery services; and (3) Identify how accessibility, income, and activity level may affect the grade 12 students’ frequency of using food delivery services.
1.3. Scope and Delimitations
The study focused on the frequency of using food delivery services and its relation to the students’ BMI, fast food consumption, accessibility, income, and activity level of the Grade 12 students of DLSU-IS of Academic Year 2021-2022. Factors such as age, gender, household income/allowance, BMI, frequency of using these services, and accessibility to fast food outlets were all taken into account. Having that said, other possible factors that may affect the responses of the participants are beyond the scope of this study and were not explored further. Difficulties in collecting responses caused a reduction in statistical power due to constraints with the sample size.
1.4. Significance of the Study
The information that was gathered through this study may be useful in bringing more awareness towards the lifestyle that people live, especially during the pandemic. The current state of the world shows that all nations have been fighting for the health and wellness of their people due to the threat of the COVID-19 virus. According to Poelman et al.
(2021), “diet-related chronic conditions, such as cardiovascular disease, diabetes type 2, and obesity are major risk factors for being hospitalized for COVID-19, severe complications and mortality.” The study may encourage consumers for a lifestyle change regarding their food choices.
1.5. Review of Related Literature
1.5.1 Frequency of Using Food Delivery Services and its Impact on BMI
A large part of the food delivery services users were from the younger generation (Maimaiti et al., 2018;
Poelman et al., 2021). A cross-sectional study made by Keeble et al. (2020) revealed that the frequency of relying on food delivery is not related with weight gain. On the other hand, Maimaiti et al. (2018) claimed that these services promote sedentary lifestyles, leading to higher health risks
such as obesity. Moreover, the results of a cross-sectional study conducted by Poelman et al. (2021) showed that a person’s weight plays a role in employing food delivery services and those with obesity are more likely to use them.
1.5.2 Frequency of Using Food Delivery Services and Fast Food Consumption
The study conducted by Martha et al. (2021) aimed to see the relationship between frequency of ordering food online and consumption of high-risk food such as fast food.
The study was conducted on people aged 18 to 45 years old which included both students and those who have jobs. The study found that the most frequently purchased food item through online food delivery apps is fast food. Similarly, Stephens et al. (2020) stated that fast food was the most frequently ordered type of food in general and has been carried on to food delivery apps since consumers also frequently purchase it through these apps.
1.5.3 Additional Factors Affecting Frequency of Using Food Delivery Services
The studies done by Liu et al. (2020) and Van Rongen et al. (2020) share the idea that the presence of fast food establishments may have an effect on fast food consumption. However, only Liu et al. (2020) was able to find a direct relationship between fast food consumption and exposure. However, the studies were done before lockdowns were imposed, so they did not take into account the difficulty of accessing fast food establishments during a lockdown.
Furthermore, it is worth noting that there has been an increase in the usage of third-party food delivery services (Flores &
Castano, 2020). Because of this, it would be worth studying how the accessibility of fast food establishments relates to people’s usage of food delivery apps to obtain fast food items.
Aside from accessibility, other factors have also been noted to affect the purchasing of fast food and the use of food delivery apps. Bondoc et al. (2021) stated that young adult Filipinos have depended on cheap and easily accessible food such as fast food even before the pandemic began. In their study, they were able to identify lifestyle, age, weight, height, and biological sex as the factors that affect young adults’ choice of purchasing fast food. In addition to that, another study also suggested studying how the intention to use food delivery apps is affected by factors such as income,
age, education, and gender (Muangmee, 2021).
2. METHODOLOGY
2.1. MaterialsThe materials that were used for data gathering were the following: Google Forms and Food delivery apps such as Grab and FoodPanda. To sort and analyze the data that were gathered Microsoft Excel and Google Sheets were used while Pearson R Correlation table and BMI Chart (Asian) were used to interpret the sorted data.
2.2. Procedure
The researchers conducted the study in three phases: Creation of Survey Questionnaire, Distribution of Questionnaire and Collection of Data, and Sorting and Interpreting Collected Data. The survey questionnaire was first created in Google Docs with some parts being adapted from the IPAQ Self-Administered Short Form. Once the questionnaire was finalized, it was transferred to Google Docs which was also utilized in collecting responses. An email was sent to the participants who consented to participate every week but after quite some time, participants were contacted in another platform: Messenger. After three months of the survey link being active, the survey was closed and stopped collecting responses. The summary of the responses was downloaded from Google Docs to Google Sheets and Microsoft Excel where the data were sorted and interpreted using the XLMiner Analysis ToolPak.
2.4. Research Design
The study is a quantitative, correlational study which used convenience and purposive sampling in collecting primary data.
2.5. Data Analysis Strategy
The data analysis was divided into three phases, the first being the computation of the BMI of each respondent using the BMI metric system formula: weight in kilograms divided by the square of the height in meters. The second phase involved computing the accessibility of each of the respondents from the different fast food restaurants where the formula for geographical accessibility was used; the
geographical accessibility is equal to the summation of the distance of all the locations divided by the number of locations. The lower the value obtained from solving was, the more accessible it is (Rodrigue, 2020).The third phase was where statistical analysis tools were used to interpret the data collected. To analyze the effects of BMI and consumption of fast food to the frequency of using Food Delivery Services (FDS), linear regression was utilized while multiple regression was used to analyze the effects of other factors such as the income, activity level, and accessibility to the frequency of using FDS. Google Sheets and Microsoft Excel were used to generate the graphs and tables needed for the analysis.
3. RESULTS AND DISCUSSION
After closing the survey link, a total of 82 responses were collected. Out of the 82 students who responded in the survey, 58 had a measuring tape and a weighing scale; the participants needed to have these items to proceed to the next part of the survey. After further data validation, only 56 of the 58 responses were used in the data analysis. For the data analysis, linear regression analysis and multiple regression analysis were used with a 95% confidence level and an alpha of 0.05.
Table 1
Summary of the results of the linear regression analysis.
x r r2 sig F remarks
Consumption 0.709 0.503 0.000 strong positive BMI 0.291 0.085 0.0291 weak positive Income 0.191 0.002 0.7735 no correlation Accessibility 0.001 0.000 0.9931 no correlation Activity Level 0.257 0.066 0.1414 no correlation Table 1 presents the solved linear regression of the relationship between the frequency of using FDS as the dependent variable and the different factors that affect the frequency of using FDS as the independent variable.
3.1 Fast Food Consumption and Frequency of Using FDS
The second row of table 1 shows that there is a positive relationship between the fast food consumption and frequency of using FDS. This observation was further strengthened by the computed r and r2values which are 0.709 and 0.503 respectively. The r value or the correlation coefficient, indicates the strength and direction of the relationship while the r2 value in the regression is the percentage of how fit the regression model is. This indicates that there is a strong positive relationship; this result accounts for 50.31% of the data. As previously mentioned by Poelman et al. (2021), frequent consumption of fast food may lead to consumers getting chronic conditions such as diabetes.
Barrington and White (2016) share similar sentiments as they stated that frequent consumption of fast food could cause obesity, cancers, and even death among consumers which is why this study wanted to learn and understand how fast food consumption and frequency of usage of FDS related to one another.
3.2 BMI and Frequency of Using Food Delivery Services
Similar to the results of the relationship between fast food consumption and frequency of using FDS in 3.1, the third row of table 1 shows a positive relationship between the BMI and frequency of using FDS. The r value of 0.291 indicates that there is a weak positive relationship while the r2 of 0.085 shows that the result only accounts 8.5% of the data;
however, the computed significance F is 0.0291 which is lower than the alpha which proves that there is no correlation.
This weak correlation is similar to the results that Keeble et al. (2020) found between the frequency of relying on food delivery and weight in which they found no relationship.
3.3 Other Factors and Frequency of Using Food Delivery Services
Using linear regression analysis, the researchers evaluated other factors such as average monthly income, accessibility, and activity level as potential factors that may affect the participants’ frequency of using food delivery services. With that said, due to the varying nature of the responses, the sample size for each factor also differed with each other.
3.3.1 Income and Frequency of Using Food Delivery Services
To identify how average monthly household income affects the frequency of using FDS, a rank regression analysis was used; the items were ranked from 1 to 6 in an ascending order where: 1 - Under P40,000; 2 - P40,000 to P59,999; 3 - P60,000 to P99,999; 4 - P100,000 to P249,999;
5 - P250,000 to P499,999; 6 - P500,000 and over. Based on 56 valid responses, the analysis resulted in an r2 value of 0.002 and a significant F value of 0.7735; this could be seen in the fourth row of table 1. Since the significance F value is higher than the alpha value of 0.05, this indicates that there is no significant relationship between the two factors. The linear regression model neither explains nor predicts the frequency of using food delivery services based on the average monthly household income.
3.3.2 Accessibility to Fast Food Restaurants and Frequency of Using FDS
From the 56 validated responses, the analysis resulted in an r2value of 0.00 and a significance F value of 0.9931 as seen in the fifth row of table 1. With a 0.05 alpha value, this indicates that the null hypothesis should be accepted and that the two factors are not related to each other;
thus, the frequency of using food delivery services is not affected by accessibility. This renders the linear regression model to be ineffectual. People from the younger generation are frequent users of the said delivery apps (Maimati et al., 2018; Poelman et al., 2021). Other studies found that one factor that may affect the consumers’ choice for purchasing from these apps is the distance of the restaurants presented in the app (Liu et al., 2020; Van Rongen et al., 2020). Most of the restaurants seen in these apps are fast food restaurants (Thamaraiselvan et al., 2019). However, the findings from this study suggest otherwise which is similar to Le et al. 's (2016) study which also did not find a strong association between proximity of restaurants and the BMI of their participants.
3.3.3 Activity Level and Frequency of Using Food Delivery Services
The model of linear regression between frequency of using food delivery apps and activity level has an r2value of 0.0663 as shown in the sixth row of table 1. This means
that only 6.63% of the data can be explained by the linear regression model and the equation y = 1.273030953 + 0.00006044179473x. An r value of 0.2576 signifies that there is a positive weak linear relationship. However, given that the significance F value of 0.1414 is greater than the alpha which is 0.05, the null hypothesis would not be rejected. This concludes that there is no significant relationship between the activity level and the frequency of using food delivery services, and the linear regression model will not be accepted.
4. CONCLUSIONS
Due to the pandemic, an increase in the usage of food delivery services has been observed. This study determined the relationship between the frequency of using FDS and the BMI of the DLSU-LC Grade 12 students, along with the other factors such as accessibility, income, and activity level. Data analyses revealed a strong relationship between the consumption of fast food and the frequency of using FDS, however, results implied a weak correlation on the BMI of the students. Furthermore, the other aforementioned factors also revealed to have no correlation with the frequency of using FDS which contradicts the findings of previous studies. Future research may validate these findings with a bigger sample size whose participants are from a different university or area; since this study was done on students from a private school, the results that could be gathered from a more rural area may manifest different results which is why studying different areas and cultures may contribute more to this topic. Further studies may show different results or validate what the researchers have found with this study. In addition to this, future studies could also consider the specific food ordered as a potential area for further exploration to better understand the effects of food delivery services on the people’s health.
5. ACKNOWLEDGMENTS
The researchers would like to thank Ms. Shelah Alfaro and Dr. Glena Fe Yapchulay-Alcabasa for guiding the researchers throughout this study. They would also like to thank the participants for contributing to this study.
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