Abstract
Background:
Fast-food advertising (FFA) is a potential contributor to obesity. Few studies have examined the relationship between FFA exposure and body mass index (BMI) among young adults. Furthermore, these studies have rarely examined ethnic differences in the relationship between FFA exposure and BMI, specifically across Asian American/Pacific Islander (AAPI) subgroups.
Objective:
This study aimed to investigate ethnic differences in the association between FFA exposure and BMI in a sample of predominantly AAPI young adults.
Methods:
Cross-sectional data were collected in 2018 from 2622 young adult college students (ages 18–25 years; 54% women) on O‘ahu, Hawai‘i. FFA exposure was assessed using a cued-recall measure. Multiple regression and analysis of covariance were used to analyze the data.
Results:
A significant association was found between higher FFA exposure and higher BMI (p < 0.05; 2-tailed) in the entire sample, adjusting for ethnicity, other demographic variables, and levels of physical activity. However, when examined by ethnic group, the association between FFA exposure and BMI was not statistically significant. A statistically significant main effect of ethnicity on BMI was found. Native Hawaiian/other Pacific Islanders (NHPI) reported the highest mean BMI [27.07 (SD ± 7.74) kg/m2] compared with the other four ethnic groups (p < 0.001). The effect of ethnicity on FFA exposure was not found to be statistically significant.
Conclusion:
FFA exposure appears to adversely influence BMI in a population of predominantly AAPI young adults. Although we did not find ethnic differences in FFA exposure or in the association between FFA exposure and BMI, the current data make a case for similar future investigation with larger subgroup sample sizes. Regulations that curtail FFA exposure among young adults may be needed.
Keywords: Fast-food, Advertising, Obesity, Body mass index, Ethnic groups
1. Introduction
Obesity, which is defined as accumulated, excessive body fat, may increase an individual’s risk for diabetes, heart disease, and certain cancers [1]. In the United States (US), among adults 20 years of age and over, the prevalence of obesity increased from 39.6% in 2015–2016 to 42.5% in 2018 [2]. In Hawai‘i, the prevalence of obesity differs by race/ethnicity. For example, 47.3% of Native Hawaiian/other Pacific Islanders (NHPI) adults experience obesity compared with 19.6% Whites, and 15.6% among Asian-Americans [3,4]. NHPI represent the indigenous people of the islands of Hawai‘i and the Pacific such as the US-Affiliated Pacific Islands (USAPI) (e.g., Guam, American Samoa, the Federated States of Micronesia).
Obesity may result from different risk factors including behavior, environment, genetics, diseases, and drugs [5]. Fast-food advertising (FFA) exposure is considered to be an important risk factor of obesity, especially among young people, as FFA exposure is known to promote high fat/salt/sugar (HFSS) food consumption [6]. Previous studies have shown an association between increased FFA exposure and higher body mass index (BMI) [7-10], across different advertising platforms, including television (TV) [8], radio [11], and the internet [12]. However, most of this evidence comes from research among children. Currently, limited evidence exists supporting a relationship between FFA exposure and obesity among young adults (ages 18–25 years) [7, 12-14]. Young adults are at the life stage where they experience increased personal autonomy, new environments and social networks; such as when they transition from high school to college [15]. These changes may increase their risk for experiencing obesity and being a potential target population for FFA [16,17].
Moreover, ethnic differences in the association between FFA exposure and BMI have been rarely studied [7,10]. Studying ethnic differences in FFA exposure, BMI, and the relationship between FFA exposure and BMI is important mainly for two reasons. First, certain ethnic groups within the US, such as NHPI, have a higher prevalence of obesity. Second, racial/ethnic minority populations have been found to be targeted specifically by FFAs [18].
Importantly, studies have rarely researched the relationship between FFA exposure and obesity across Asian American/Pacific Islander (AAPI) subgroups which has left a gap in the literature. Given the disparities in prevalence of obesity across AAPI subgroups, with the NHPI at markedly higher risk, it is important to examine whether FFA exposure affects NHPI young adults more strongly than others. Given the evidence that socially disadvantaged ethnic minorities may be specifically targeted by FFAs [18], NHPI are more likely to be exposed to FFA compared with other AAPI subgroups. As indigenous people, NHPI have experienced health and socioeconomic inequities for most of modern history [19].
The current study was designed to examine the association between FFA exposure and BMI in a sample composed predominantly of AAPI young adults. We examined the association between FFA exposure and BMI in the entire sample, adjusting for ethnicity, other demographic variables (e.g., age, sex, household income), and physical activity. We then examined the association between FFA exposure and BMI across the following ethnic groups: East Asian (e.g., Japanese, Chinese, Korean), Filipino, NHPI, and White. Participants of Japanese, Chinese, and Korean descent were combined into a single category because these groups have been observed to show a similar risk profile, in terms of obesity as well as obesity-related health outcomes [20,21]. We also examined the ethnic differences in the association between FFA exposure and BMI. We hypothesized that compared with the other ethnic groups, NHPI would show higher FFA exposure, higher BMI, and a stronger relationship between FFA exposure and BMI, after adjusting for demographic variables and levels of physical activity.
2. Material and methods
2.1. Design
The present study is a cross-sectional analysis of FFA exposure and BMI among college students in Hawai‘i. Participants were recruited in 2018 at two four-year universities and four two-year colleges belonging to a single university system located on the island of O‘ahu in Hawai‘i. The survey was administered online via Inquisit Web. All students ages 18–25 years were invited via email to complete a screener survey that was sent at random. The link to the screener survey was accompanied by an invitation text that described the study as addressing marketing and young adult health behaviors. Sixty percent of the invited students completed the screener survey. To ensure a representative sample of students ages 18–25 years, participants were screened for age, sex, and smoking status. Smoking status was one of the criteria used to obtain a representative sample of young adults as one of the main purposes of the parent study was to study tobacco product marketing exposure among current, former (including experimenters), and never cigarette smokers. Invited students were given on average two weeks to respond and were sent reminders up to three times. Data were collected from 2622 participants, of whom 2344 were included in the current analyses to facilitate clear-cut ethnic comparisons. Participants (n = 278) categorized as other than Asian, Filipino, NHPI or White were excluded from analysis.
2.2. Measures
Self-reported demographic data were collected, including participants’ age, sex, ethnicity, and socioeconomic status (SES). Other self-reported data were BMI and physical activity. Participants were asked to select which ethnicity they most identified with, from a list of ethnicities common in Hawai‘i and the US (i.e., NHPI, Japanese, Chinese, Korean, Filipino, and White) [22]. For analysis purposes, Native Hawaiian and other Pacific Islander groups were combined into the “Native Hawaiian/other Pacific Islander” (NHPI) category. In addition, Japanese, Chinese, and Korean groups were combined into the “Asian” category. The “Other” category was comprised of African American, Hispanic, and any ethnicity not specified [20]. Socioeconomic status was assessed by the annual parental income (7-point scale; “$0 - $39,999” to “Over $60,000”) [20]. Physical activity level was assessed based on three items regarding days of doing three types of exercise in the past seven days (i.e. moderate intensity aerobic exercise, vigorous intensity aerobic exercise, resistance training exercise). A single physical activity variable was created by calculating the average of the three physical activity variables collected.
2.2.1. Cued-recall fast-food advertising exposure
A cued-recall assessment of FFA exposure was conducted. Participants were provided three still frames that were captured from TV advertisements pertaining to fast-food. Each image was digitally modified to remove the logo or identifying descriptors. The fast-food advertisements were randomly selected from a pool of advertisements that were being aired at the time (2018) on TV in Hawai‘i. The advertisements represented the following brands: Kentucky Fried Chicken (KFC), McDonald’s, and Pizza Hut.
Participants were asked if they recognized the product being advertised and if they could name the product, if they had seen the advertisement before, if they recognized the advertisement itself, and whether they could name the brand that the advertisement represented. A score of zero was assigned if the participant could not recognize and name the product, had not seen the advertisement before, and could not recognize the advertisement and name the brand. A score of one was assigned if a participant reported having seen the advertisement before. An additional score of one was assigned if the participant could name the product correctly. Additional two points were assigned if the participant named the brand correctly. Thus, for each image stimulus, the total score ranged from 0 to 4. As in a previous study by Pokhrel et al. [21], for the purpose of analysis, a FFA cued-recall index was created by calculating the average scores across the three advertisements.
2.2.2. Body mass index
Body mass index was calculated using self-reported weight and height by dividing weight in kilograms by height in meters squared (BMI = kg/m2). The World Health Organization (WHO) classification system for BMI was used to categorize participants with BMI < 18.5 kg/m2, 18.5–24.99 kg/m2, 25–29.99 kg/m2, and ≥ 30 kg/m2 as underweight, normal weight, overweight, and obese, respectively [23].
2.3. Statistical analysis
Data analysis was performed with Statistical Analysis System (SAS) (Version 9.3). Ethnic differences in FFA exposure and BMI were tested using general linear model (GLM), adjusting for age, sex, household income, and physical activity. Post-hoc pairwise comparison of means among ethnic groups was conducted using Tukey’s Honestly Significant Difference (HSD) test, which controls for Type-I experiment-wise error rate. The association between FFA exposure and BMI was also tested using GLM. To test for ethnic differences in the association between FFA exposure and BMI, an interaction analysis was conducted using steps outlined by Aiken and West (1991) [24]. First, all continuous variables were centered on their means. Next, the interaction analysis was conducted in GLM by entering the “ethnicity X FFA exposure” term (with White as the reference group) along with age, sex, income, physical activity, FFA exposure, and ethnicity. Finally, regardless of the results of the interaction analysis, we tested the association between FFA exposure and BMI for each of the four ethnic groups.
3. Results
3.1. Demographic characteristics of study participants
Descriptive data for the study population (n = 2622) are displayed in Table 1. The mean age of participants was 21.2 (SD ± 2.2) years. More than half (54.4%) of the study participants were female. The sample represented the ethnic diversity of Hawai‘i. The majority (26%) identified as Asian, and White was the second largest ethnic group (24.3%), followed by NHPI (21.2%). Over half (56.6%) of the students had an annual parental income of less than $80,000. The mean BMI was 24.7 (SD ± 6.3) kg/m2. According to the WHO BMI classifications, more than a third of the participants were within the overweight (23.5%) or obese (14.6%) categories. Most of the participants (74.4%) engaged in moderate intensity aerobic exercise 0–3 days in the past 7 days.
Table 1.
Descriptive characteristics of study participants (n = 2622) in the sample of college students in Hawai‘i.
| Mean ± Standard Deviation or Frequency (%) |
|
|---|---|
| Age, years | 21.2 ± 2.2 |
| Sex | |
| Female | 1423 (54.4) |
| Male | 1194 (45.6) |
| Ethnicity | |
| Asian | 681 (26.0) |
| White | 636 (24.3) |
| Native Hawaiian/other Pacific Islander | 556 (21.2) |
| Filipino | 470 (17.9) |
| Parental Income per annum, $ (n = 2583) | |
| 0–39,999 | 596 (23.1) |
| 40,000–79,999 | 866 (33.5) |
| 80,000–119,999 | 649 (25.1) |
| 120,000–159,999 | 254 (9.8) |
| 160,000 or over | 218 (8.5) |
| Body Mass Index, kg/m2 (n = 2564) | 24.7 ± 6.3 |
| Body Mass Index Classification (n = 2564) | |
| Underweight, < 18.5 kg/m2 | 199 (7.8) |
| Normal weight, 18.5–24.9 kg/m2 | 1386 (54.1) |
| Overweight, 25–29.9 kg/m2 | 604 (23.5) |
| Obese, ≥ 30 kg/m2 | 375 (14.6) |
| Days of doing moderate intensity aerobic exercise in the past 7 days (n = 2575) | |
| 0 | 740 (28.7) |
| 1–3 | 1175 (45.7) |
| 4–6 | 542 (21.0) |
| 7 | 118 (4.6) |
| Days of doing vigorous intensity aerobic exercise in the past 7 days (n = 2577) | |
| 0 | 1129 (43.8) |
| 1–3 | 1055 (40.9) |
| 4–6 | 342 (13.3) |
| 7 | 51 (2.0) |
| Days of doing resistance training exercise in the past 7 days (n = 2579) | |
| 0 | 1359 (52.7) |
| 1–3 | 768 (29.8) |
| 4–6 | 403 (15.6) |
| 7 | 49 (1.9) |
3.2. Ethnic differences in body mass index and fast-food advertising exposure
Table 2 displays the mean BMI, based on self-reported height and weight, for the four ethnic categories. The omnibus effect of ethnicity on BMI was statistically significant, F (3, 2231) = 29.51, p < 0.001 (see Table 2). NHPIs reported the highest mean BMI compared with the other four ethnic categories. Pairwise comparisons of means among ethnic groups indicated that the difference in mean BMI was statistically significant between NHPIs and the other four ethnic groups. The Filipino group showed higher mean BMI compared with Asian and White groups. Asian and White groups did not significantly differ in mean BMI. With age, sex, physical activity, and household income controlled for, ethnicity accounted for 8.4% of the variance in BMI. Relative to White and adjusting for age, sex, household income, and physical activity, Asian ethnicity predicted lower BMI (B = −0.03, SE = 0.01, p = 0.02), and Filipino (B = 0.04, SE = 0.01, p = 0.005) and NHPI (B = 0.10, SE = 0.01, p < 0.0001) ethnicity predicted higher BMI.
Table 2.
Ethnic differences in body mass index (BMI) and fast-food advertising (FFA) exposure (n = 2344).
| BMI (mean ± SD) | P-value | FFA Exposure score (mean ± SD) | P-value | |||
|---|---|---|---|---|---|---|
| NHPI | (n = 539) | 27.27 ± 7.45a | (n = 548) | 0.66 ± 1.01d | ||
| Filipino | (n = 460) | 25.15 ± 5.53b | (n = 466) | 0.57 ± 0.93d | ||
| Asian | (n = 664) | 23.54 ± 4.90c | (n = 667) | 0.62 ± 1.02d | ||
| White | (n = 629) | 24.12 ± 5.00c | (n = 629) | 0.53 ± 0.87d | ||
| Model F (DF) | 29.51 (2231) | < 0.001 | 1.44 (2267) | 0.18 |
General Linear Model controlled for age, sex, parental income, and physical activity. Same-letter subscript in the columns indicates no statistically significant difference between means at alpha = 0.05 between ethnic groups [multiple comparison procedure conducted using Tukey’s Honestly Significant Difference test, which controls for the type I experiment-wise error rate].
Body Mass Index (BMI)
Fast-Food Advertising (FFA)
Native Hawaiian/other Pacific Islander (NHPI)
Table 2 shows the mean FFA exposure by ethnicity. The omnibus effect of ethnicity on FFA exposure was not statistically significant F (3, 2267) = 1.44, p = 0.18. Although Fisher’s Least Significant Difference (LSD) test showed a significant difference in mean FFA exposure between NHPI and White, Tukey HSD test did not find any difference in FFA exposure among ethnic groups (see Table 2). The NHPI ethnicity, relative to White and adjusting for age, sex, household income, and physical activity, was found to be a marginally significant predictor of FFA exposure (B = 0.06, SE = 0.03, p = 0.05).
3.3. Association between fast-food advertising exposure and body mass index
Table 3 shows the results of the analysis testing the association between FFA exposure and BMI, adjusting for age, sex, ethnicity, household income, and physical activity. A statistically significant association between higher FFA exposure and higher BMI was found (p = 0.01). A one unit increase in FFA exposure was associated with a 0.23 unit increase in BMI. In addition, being female, Asian (relative to White), and belonging to a higher income household were found to be inversely associated with BMI. There was no statistically significant association between physical activity level and BMI. Overall, the independent variables examined explained 8.9% of the variance in BMI.
Table 3.
Association between fast-food advertising (FFA) exposure and body mass index (BMI) (n = 2344).
| Variables | Unstandardized Coefficients | P-value | |
|---|---|---|---|
| B | Std. Error | ||
| FFA Exposure | 0.23 | 0.09 | 0.01 |
| Asian | −0.72 | 0.33 | 0.03 |
| NHPI | 2.89 | 0.34 | < 0.0001 |
| Filipino | 0.93 | 0.36 | 0.01 |
| Age | 0.37 | 0.06 | < 0.0001 |
| Female Sex | −0.63 | 0.24 | 0.009 |
| Household Income | −0.21 | 0.05 | < 0.0001 |
| Physical Activity | −0.08 | 0.07 | 0.25 |
Ethnicity variable was coded with White as the reference group.
Fast-Food Advertising (FFA)
Native Hawaiian/other Pacific Islander (NHPI)
3.4. Ethnic differences in association between fast-food advertising exposure and body mass index
Interaction analysis did not find a statistically significant moderation by ethnicity on the effects of FFA exposure on BMI, adjusting for age, sex, household income, and physical activity. Table 4 shows the results of the interaction analysis. The model with interaction terms was statistically significant overall, F (11, 2212) = 19.92, p < 0.0001 and explained 9.0% variance in BMI. When analyzed separately by ethnic group, the association between FFA exposure and BMI was not statistically significant for any group, adjusting for age, sex, income, and physical activity.
Table 4.
The effects of interaction between ethnicity and fast-food advertising (FFA) exposure on body mass index (BMI) (n = 2344).
| Variables | Unstandardized Coefficients | P-value | |
|---|---|---|---|
| B | Std. Error | ||
| FFA Exposure | 0.49 | 0.16 | 0.01 |
| Asian | −0.44 | 0.38 | 0.25 |
| Filipino | 1.07 | 0.42 | 0.01 |
| NHPI | 2.98 | 0.40 | < 0.0001 |
| Asian*FFA Exposure | −0.49 | 0.34 | 0.16 |
| Filipino*FFA Exposure | −0.27 | 0.39 | 0.48 |
| NHPI*FFA Exposure | −0.19 | 0.36 | 0.59 |
| Age | 0.37 | 0.05 | < 0.0001 |
| Female Sex | −0.62 | 0.24 | 0.01 |
| Household Income | −0.21 | 0.05 | < 0.0001 |
| Physical Activity | −0.08 | 0.07 | 0.26 |
Ethnicity variable was coded with White as the reference group.
Fast-Food Advertising (FFA)
Native Hawaiian/other Pacific Islander (NHPI)
4. Discussion
Consistent with the existing literature, a positive association between FFA exposure and BMI was found in the current sample of predominantly AAPI young adults. Previous studies had found such an association among children [8,9,25]. Few studies have been conducted among young adults [7], and even fewer among AAPI young adults. These existing studies differed in results where several found a relationship between FFA exposure and BMI [8-10], while others did not [14]. The current study adds to the limited research on the relationship between FFA exposure and BMI among this age group and in the AAPI population.
No statistically significant association was found between FFA exposure and BMI for NHPI ethnicity, or for the other ethnic groups. This may have been because of the small size of the ethnic sub-samples. The present study adjusted for SES (assessed by annual parental income), but education level was not included as an independent moderator since all participants were at the college education level, which suggests that the expected observation of an association between ethnicity and FFA exposure may have been negated. The ethnic differences in obesity that are typically observed in the general US population are often SES-driven [26,27], so the lack of varying education levels and of SES diversity in the study sample may explain why these ethnic differences in the association between FFA exposure and BMI were not observed in this study.
However, for all ethnic groups together, the direction of the association was as expected—a positive association between FFA exposure and BMI. Few studies have specifically examined the moderating role of ethnicity in this relationship, so this adds to the limited body of evidence which remains inconclusive [7]. Albeit this study did not find any ethnic differences in the association between FFA exposure and BMI, further research is still needed to understand the role played by ethnicity among the association between these variables.
4.1. Strengths and limitations
Strengths of this study include the relatively large sample size (n = 2622) and the anonymous nature of the survey, which may have encouraged more candid responses from participants. The cross-sectional nature of the study limits the inferences that can be made from the results, as in previous studies [10,14], in which the directionality of the relationship between FFA exposure and BMI cannot be inferred. Participants’ FFA exposure during childhood and how this may affect BMI over time could not be accounted for as well.
A main limitation of the present study is the use of BMI as an outcome measure and proxy for obesity. Although BMI is a convenient and inexpensive measure of body fat, the measure may be inaccurate in terms of representing actual excess body fat. Several demographic variables such as age, sex, ethnicity, and muscle mass may influence the relationship between BMI and body fat [28]. In addition, BMI does not distinguish among body fat, muscle or bone mass, and does not take into consideration distribution of body fat [29]. Future studies should include a direct measure of BMI, and waist circumference or waist to height ratio, as these have been shown to be superior to self-reported BMI in assessing obesity [30].
The survey used was primarily designed to explore the effects of e-cigarette marketing on young adults’ knowledge, attitudes, and behaviors which represents another limitation. The questions used did not have all the tools to conduct extensive assessments of diet (e.g., a validated dietary recall questionnaire) and physical activity. Additional data on physical activity, frequency of visits to fast-food restaurants and a food frequency questionnaire would add value and predictive power. Data regarding the participants’ living arrangements (i.e., if the participants were living at home with their families or not) may be an additional confounding variable because those living away from home may be at higher risk to develop unhealthy dietary habits [15,31,32]. Also, we used parental income as a proxy for young adults’ SES which may be limited in validity, as for young adults, parental income is just one of the several indicators of SES.
This study only used FFA aired on TV, while it has been shown that there is variation in the FFA mediums—including online and via social media [33]. This would be important to consider while selecting the advertisements to create the cued-recall measure. Self-selection bias was a limitation to the recruitment phase of this study as well. The initial email invitation for the screener survey may have only been completed by young adults interested in health.
Nevertheless, despite the limitations, the findings of this study may assist in promoting future research related to FFA exposure and BMI across ethnic groups in young adult populations.
4.2. Future research and recommendations
This study highlights the need for making policies and regulations that protect young adults, such as college students, from FFA. Current FFA regulations focus primarily on children [34]. A positive association was found between FFA exposure and BMI in this sample of college students, ages 18–25 years. If government and voluntary regulations (such as the ones protecting children) [33,35] were put in place to protect this young adult population from FFA, this may affect the role of FFA as a potential contributor to obesity in this age group. Conducting further research on this age group and their exposure to FFA may help fill the gap in the literature on this topic; which may also provide evidence to support the implementation of strategies and policy options to reduce the exposure of FFA to young adults [7,12,13].
5. Conclusion
This study is one of the first to explore ethnic differences in the association between FFA exposure and BMI in a sample of predominantly AAPI young adults. A significant association between FFA exposure and BMI was found for the total sample, which supports that FFA impacts not only children but young adults as well. Although no statistically significant difference in the association between FFA exposure and BMI was found across ethnic groups, the direction of the non-significant association was positive.
Acknowledgments
This research was supported by grants from the US National Cancer Institute: R01CA202277, R01CA228905, U54CA143727.
Abbreviations:
- FFA
fast-food advertising
- BMI
body mass index
- AAPI
Asian American/Pacific Islander
- NHPI
Native Hawaiian/other Pacific Islander
- US
United States
- USAPI
US-Affiliated Pacific Islands
- HFSS
high fat/salt/sugar
- TV
television
- SES
socioeconomic status
- KFC
Kentucky Fried Chicken
- WHO
World Health Organization
- SAS
Statistical Analysis System
- GLM
general linear model
- Tukey HSD
Tukey honestly significant difference
- SD
standard deviation
- LSD
Fisher’s least significant difference
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