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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: Appetite. 2022 Jun 11;176:106130. doi: 10.1016/j.appet.2022.106130

Objective and perceived barriers and facilitators of daily fruit and vegetable consumption among under-resourced communities in Central Texas

Kelseanna Hollis-Hansen a,*, Kathryn M Janda b,c,**, Marisa Tiscareño c, Claire Filipowicz a, Alexandra van den Berg b,c
PMCID: PMC9392474  NIHMSID: NIHMS1820787  PMID: 35700839

Abstract

Introduction:

Fruit and vegetable consumption (FVC) continues to be low, particularly among people living in under-resourced communities. Identifying barriers and facilitators of FVC and whether those barriers and facilitators differ for racially and ethnically minoritized people is imperative for developing effective and equitable public health policies and interventions.

Methods:

A baseline cohort of 390 participants from Central Texas communities historically lacking healthy food retailers completed a survey including FVC, 7 psychosocial barriers and facilitators of FVC, distance to a grocery retailer, participation in government assistance programs, and race/ethnicity.

Results:

Not having time to prepare fruits and vegetables was the only significant psychosocial barrier identified (B = −.11, t(390) = 2.04, P = .04), but was not significant after accounting for sociodemographic variables. Significant facilitators of daily FVC were liking F&V (B = .31, t(390) = 6.40, P<.001), participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (B = .14, t(390) = 2.81, P = .005), and Hispanic/Latino(a) ethnicity (B = −.21, t(390) = 4.30, P<.001). The final model accounted for 17% of the variance in daily FVC (R2=.17, F(4, 375) = 7.69, P < .001). Black, white and Other race participants were more likely to report having difficulty using F&V before spoiling than Hispanic/Latino(a) participants (P = .003). White and Other race participants were more likely to report that F&V were hard to prepare (P = .006) and that they didn’t have time to prepare F&V (P = .005).

Discussion:

When designing public health policy and interventions to increase FVC, researchers could prioritize identifying ways to alleviate time constraints, increase F&V liking, and help eligible participants to enroll in WIC. Strategies that reduce the risk of F&V spoiling and make F&V easier to prepare may also benefit some groups.

Keywords: Fruits, Vegetables, Barriers, Facilitators, Community

1. Introduction

Across the United States (US), 10% of adults and 2% of children meet dietary recommendations for vegetable intake and 12.3% of adults and 7% of children meet fruit recommendations (Lee et al., 2022; Centers for Disease Control and Prevention, 2018). While most population subgroups in the US fall short of meeting fruit and vegetable guidelines; food insecurity (Turnbull, Homer, & Ensaff, 2021), lower neighborhood income (Patel et al., 2021), and lower household income (Lee et al., 2022) are sociodemographic factors associated with greater disparities in fruit and vegetable intake. It is well established that consuming fewer fruits and vegetables is associated with malnutrition and other adverse health outcomes, such as cancer (Boeing et al., 2012) and cardiovascular disease (Miller et al., 2022). Alleviating barriers and leveraging facilitators of fruit and vegetable consumption could be one way to reduce disparities in diet-related health outcomes. To effectively do so, it’s important to identify which barriers and facilitators are most salient to community members (Kasprzak, Sauer, Schoonover, Lapp, & Leone, 2020; Singleton et al., 2018).

Research and public health policy have focused attention on physical geographic access to a large food retailer (e.g. Grocery store) as a primary determinant of healthy eating and associated morbidities and mortality (Holzman, 2010; Larson et al., 2009). However, observational research on the association between the proximity to grocery stores and fruit and vegetable intake has been mixed (Aggarwal et al., 2014; Gustat et al., 2015; McGuirt et al., 2018) and experimental and quasi-experimental research have resulted in inverse or null associations (Hollis-Hansen et al., 2019). Researchers have identified reasons for inconsistences in these results, such as variation in geographical information system (GIS) measures and methods (Chen & Kwan, 2015) and challenges assessing the quality, variety, and amount of fruit and vegetables available at stores that are captured by GIS (Haynes-Maslow et al., 2020).

Haynes-Maslow et al. (2020) have suggested evidence that includes both objective and perceived barriers and facilitators is needed, given that objective and perceived barriers and facilitators are not always in agreement, and may have disparate impacts on fruit and vegetable consumption. There are a number of perceived psychosocial barriers and facilitators that have been identified in the literature, such as the cost of fruit and vegetables (Kern et al., 2017), fruit and vegetable preferences (Kasprzak et al., 2020), time concerns (Livingstone et al., 2020), food literacy (Vidgen & Gallegos, 2014), and concerns over spoilage (Singleton et al., 2018). Assessing both the objective and psychosocial barriers and facilitators in under-resourced communities will help us understand the complexities of fruit and vegetable access and better inform policy and intervention recommendations (Caspi et al., 2012).

Eastern Travis County, Texas, is an area historically deprived of healthy foods and food retail options with high rates of food insecurity, which may put community members at greater risk for adverse health outcomes. The Fresh for Less program is a multi-component multi-strategy initiative undertaken by the City of Austin to introduce alternative food retail operations, such as mobile produce markets, farm stands, and home food delivery into Eastern Travis County as an effort to improve food access and reduce health disparities (Janda et al., 2021). The present study is a cross-sectional analysis of baseline cohort data collected from evaluation participants. This evaluation of baseline data is referred to as FRESH to delineate this analysis from the intervention. The primary aim is to identify which objective and perceived barriers and facilitators are associated with fruit and vegetable consumption among participants.

In addition, researchers have recently identified the need to ensure our understanding of food access and our interventions that aim to increase fruit and vegetable intake are equitable across racial and ethnic groups (Landry et al., 2022; Singleton, Winkler, et al., 2020). For example, Singleton, Kessee, et al. (2020) found that Black farmers’ market incentive shoppers’ attended markets fewer times each month than white and Hispanic/Latino(a) shoppers, had lower odds of consuming fruit each day, and were less likely to report that they went to farmers’ markets to enjoy the event. A key takeaway from these findings is that researchers and community organizers should evaluate their programming and outreach strategies to determine whether they are equitably and effectively serving racially minoritized people. To that end, the secondary aim is to describe barriers and facilitators across racial ethnic groups to identify whether these patterns warrant further research and eventually to determine if some populations in Eastern Travis County could benefit from more support towards policies or interventions that address relevant barriers and facilitators.

2. Methods

2.1. Participants

The parent FRESH-Austin study was a natural experiment consisting of a 3-year evaluation of the City of Austin’s Fresh for Less initiative, and is more comprehensively described in Janda et al., 2021. One component of the FRESH-Austin study was a cohort (N = 400) recruited from Eastern Travis County that completed an annual survey. 130 participants were recruited through random intercept surveys at Fresh for Less retail assets, 185 participants were recruited door-to-door in randomly selected street segments within a 1.5-mile buffer of the Fresh for Less assets, and 85 participants were recruited door-to-door in randomly selected street segments in comparison neighborhoods matched on sociodemographic characteristics using 2017 American Community Survey data (USCB, 2018).

The FRESH-Austin baseline cohort survey was conducted between October 2018–March 2019 and was interviewer-administered in the participants’ preferred language (English or Spanish). Study inclusion criteria consisted of: adults (over the age of 18), identifying as the primary shopper for groceries in the household, speaking Spanish or English, and no plans to move out of the Travis County area for the next few years. Individuals who had a medical condition that would prohibit them from consuming fresh produce were excluded from the study. For this secondary analysis of the FRESH-Austin baseline cohort data, participants missing geographical information that could be used for geocoding the nearest supermarket (N = 7, 1.8%) and participants that did not report race/ethnicity (N = 3, 0.8%) were excluded for a total analytic sample of 390 participants. All participants provided written informed consent upon enrollment in the study and received $25 USD in cash upon completion of the survey. The FRESH-Austin study was approved by the University of Texas Health Science Center at Houston Institutional Review Board (HSC-SPH-18-0233). Participant characteristics are described in Table 1.

Table 1.

Participant characteristics.

Variable N Percent Mean (SD, Range)
Age 43.9 (13.7, 20–90)
Gender
Male 112 28.7%
Female 278 71.3%
Household Size 3.5 (1.9, 1–11)
Race/Ethnicity
Hispanic 211 54.1%
Black 40 10.3%
White 126 32.3%
Other 13 3.3%
2017 Gross Income*
Under $45,000 201 52.9%
$45,000 or greater 179 47.1%
Education Level*
Less than high school 48 12.4%
High school or GED 82 21.1%
Some college 82 21.1%
College or more 176 45.4%
Language spoken at home
Only or mostly English 230 59.0%
Both English and Spanish equally 51 13.1%
Only or mostly Spanish 106 27.2%
Mostly other language 3 0.8%
Food Assistance Utilization in Last 12 Months
SNAP 70 18.0%
WIC 36 9.2%
Food bank or pantry 47 12.5%
Fruit and Vegetable Consumption (Cups/day) 3.6 (1.6, 0–8)
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Note: Total N = 390, except for income (N = 380) as 10 participants did not provide income data and level of education (N = 388) as 2 participants did not provide education data; SNAP = Supplemental Nutrition Assistance Program; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

2.2. Measures

2.2.1. Sociodemographic measures

The survey included self-reported sociodemographic information for race/ethnicity, food assistance utilization in the past 12 months (including receipt of Supplemental Nutrition Assistance Program [SNAP] benefits, receipt of Special Supplemental Nutrition Program for Women, Infants, and Children [WIC] benefits and food bank or pantry usage), 2017 gross income, and other factors described at length in Janda et al., 2021. 2017 gross income was a categorical variable with 7 response options ($15,000 and below, $15,001-$25,000, $25,001-$35,000, $35,001–45,000, $45,001-$55,000, $55,001–65,000, $65,001 or greater) dichotomized to under $45,000 USD per year and $45,000 or greater USD per year due to small cell sizes for some racial/ethnic groups. $45,000 USD was selected as the cutoff based on the distribution of the data. Food assistance variables, such as SNAP, WIC, or food pantry receipt within the past 12-months were categorical variables with yes/no response options. Race/ethnicity was a categorical variable that was dummy coded into 4 groups to improve statistical power: Hispanic/Latino(a) (Group 1, n = 211), Black (Group 2, n = 40), White (Group 3, n = 126) and “Other” (Group 4, n = 13) which encompassed Asian (n = 6), American Indian/Alaskan Native (n = 2), Middle Eastern (n = 1), and Native Hawaiian/Pacific Islander (n = 4) participants.

2.2.2. Daily fruit and vegetable consumption.

Fruit and vegetable consumption were measured using a modified Block Food Frequency questionnaire that was previously adapted for a study among SNAP recipients with a predominantly Hispanic/Latino(a) sample in Central Texas (Sanjeevi et al., 2017). The fruits and vegetables included in the modified Block FFQ were lettuce, dark leafy greens, broccoli or cauliflower, carrots, tomatoes, avocadoes, sweet potatoes, potatoes (not sweet), cabbage, peppers, corn, zucchini or other squash, onions, apples, citrus, bananas, berries, grapes, melon, and respondents could list up to four additional fruits and vegetables consumed beyond those listed. For each type of produce, respondents were asked about the frequency they ate that fruit/vegetable (how many times a week or month). For each type of produce consumed, they were then asked the amount usually consumed, either in pieces or cups. These quantities were then standardized into cups. Fruit and vegetable consumption were aggregated to determine total fruit and vegetable consumption in cups per day. This modified food frequency questionnaire, titled the FRESH FFQ, was subsequently validated using 24-h dietary recalls (Jovanovic et al., 2021). The validation study found acceptable levels of agreement between the FRESH FFQ and 24-h dietary recalls, thus validating this instrument for measuring fruit and vegetable consumption among the FRESH sample.

2.2.3. Potential perceived barriers and facilitators.

Items measuring potential perceived barriers and facilitators were also included in the FRESH survey. Perceived barriers included questions on issues with having/using fruits and vegetables every day and were adapted from the Food and Attitudes Behavior Survey (Singleton et al., 2018). Questions were adapted to have dichotomous, yes/no answer options, to minimize survey burden of participants. Barriers included prompts such as “Fruits and vegetables are hard to prepare” “It’s hard to use fruits and vegetables before spoiling” “My family doesn’t eat fruits and vegetables” “Fruits and vegetables are too expensive” “I don’t have time to prepare fruits and vegetables” “I don’t know how to prepare fruits and vegetables” and “My family doesn’t like the taste of fruits and vegetables” (Singleton et al., 2018). As a facilitator, “I like to eat fruits and vegetables” was adapted from work by De Bourdeaudhuij and colleagues (De Bourdeaudhuij et al., 2005).

2.2.4. Distance to the nearest supermarket.

The distance to the nearest supermarket was calculated by measuring the distance in miles from the participants reported home address to the nearest grocery store using ArcGIS (version 10.7.1). The food environment was geocoded using data from the City of Austin’s Food Environment Analysis (FEA), which collected data in 2018, the same year as the Baseline survey, and all participant home addresses were geocoded (City of Austin, 2019). Distance to the closest supermarket was calculated in miles using a street-network distance between the participant’s home address and the nearest supermarket using the ArcGIS closest facility tool.

2.3. Data analysis

Descriptive statistics were calculated using means for continuous variables and frequencies for categorical variables. A multivariate linear regression model was prespecified to examine the association between hypothesized barriers and facilitators and average daily fruit and vegetable consumption (cups per day). Step 1 included all perceived psychosocial barriers and facilitators to fruit and vegetable intake described in the 2.2.2 measures section above. Step 2 introduced an objective barrier or facilitator, the distance to a supermarket from the participants home. Lastly, Step 3 added sociodemographic variables that may promote fruit and vegetable consumption: Race/ethnicity, participation in WIC within the past year, participation in SNAP within the past year, and using a food bank or pantry. The dependent variable was cups of fruits and vegetables consumed per day. Plots showed that the distribution of residuals did not violate assumptions of normality, suggesting that multivariate linear regression was an appropriate statistical technique.

To describe barriers and facilitators by race and ethnicity, a Pearson’s chi-squared test was planned. However, due to small or zero observed cell counts for some variables, 2-sided Fisher’s exact tests were used (Ruxton & Neuhäuser, 2010).

3. Results

3.1. Participant characteristics

The total sample included for this analysis was N = 390, and participant characteristics are presented in Table 1. The sample, similar to the parent study, was predominantly female (71.3%), Hispanic/Latino(a) (54.1%), had a 2017 gross income under $45,000 (52.3%), and 40.3% spoke mostly Spanish or spoke English and Spanish equally at home. Additionally, the average age of participants was 44 years old, and the average household had 3.5 members. Education status varied across the sample, but a majority of participants had less than a college degree (54.6%). In terms of food assistance utilization in the prior 12 months, the most commonly reported food assistance program utilized was SNAP (18.0%), followed by food pantries/banks (12.1%), and WIC (9.2%).

3.2. Step 1: psychosocial barriers and facilitators predicting fruit and vegetable consumption

Psychosocial barriers and facilitators of fruit and vegetable consumption alone predicted 10% of the variance in daily fruit and vegetable consumption (R2 = .10, F(8, 380) = 6.59, p < .001). In Step 1, not having enough time to prepare fruits and vegetables was associated with consumption of fewer fruits and vegetables per day (B = −.11, t(390) = 2.04, p = .04) and the participant liking to consume fruits and vegetables daily was associated with greater fruit and vegetable consumption (B = .30, t(390) = 5.90, p < .001). Finding fruits and vegetables hard to prepare (B = −.06, t(390) = 1.10, p = .27), hard to use before spoiling (B = −.08, t(390) = 1.57, p = .12), expensive (B = .06, t(390) = 1.23, p = .22), not knowing how to prepare (B = .02, t(390) = 0.30, p = .76), family not liking the taste (B = .08, t(390) = 1.54, p = .13), and family not eating (B = −.05, t(390) = 0.84, p = .40) were not statistically significant predictors of fruit and vegetable consumption among the study participants.

3.3. Step 2: distance to the closest supermarket

The distance from the participants home to the nearest grocery store did not improve the variance accounted for in the model and was not significantly associated with total fruit and vegetable consumption in this sample (B = .08, t(390) = 1.66, p = .10).

3.4. Step 3: sociodemographic factors, participation in food relief programs and ethnicity

The final model accounted for 17% of the variance in fruit and vegetable consumption among study participants (R2 = .17, F(13, 375) = 7.69, p < .001). In Step 3, statistically significant predictors of fruit and vegetable consumption were liking to consume fruits and vegetables daily (B = .31, t(389) = 6.39, p < .001), participation in WIC (B = .14, t(389) = 2.81, p = .005), and Hispanic/Latino(a) ethnicity (B = −.21, t(389) = 4.30, p < .001). Time was no longer a statistically significant predictor in Step 3 (B = −.09, t(389) = 1.67, p = .10). Participation in SNAP (B = −.07, t(389) = 1.22, p = .22) and utilizing a food book or pantry (B = −.05, t(389) = 0.87, p = .39) were not significantly associated with fruit and vegetable consumption. Similar to the previous 2 steps, none of the other psychosocial variables or distance to the grocery store were significant or approaching significance in the final model. All correlations between variables can be found in Table 2 and the full models can be found in Table 3 .

Table 2.

Zero-order correlations between study variables of interest.

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
Likes to Eat Vegetables Daily (1) –
Difficulty Preparing Fruits and Vegetables (2) −.055 –
Difficulty Using Before Spoiling (3) −.068 .201 *** –
Family Doesn’t Eat Fruits and Vegetables (4) −.296 *** .042 .163 ** –
Fruits and Vegetables Too Expensive (5) −.004 .104 * .156 ** .093 –
Don’t Have Time to Prepare (6) .010 .175 ** .280 *** .122 * .164 ** –
Don’t Know How to Prepare (7) −.016 .133 ** .163 ** .149 ** .111 * .222 *** –
Family Doesn’t Like the Taste (8) −.204 *** .176 *** .196 *** .414 *** .107 * .164 ** .087 –
Distance to Supermarket (9) −.029 −.014 .022 .007 −.025 −.049 −.045 .006 –
Ethnicity (10) .056 .156 ** .182 *** −.034 −.053 .168 ** .007 .073 −.046 –
Use of Food Bank/Pantry (11) .012 −.006 −.036 −.033 .005 −.117 * −.002 .019 −.057 −.127 * –
Participation in SNAP (12) .013 .024 −.057 .041 .069 −.117 * −.034 −.001 −.015 −.145 ** .422 ***
Participation in WIC (13) −.014 .057 .030 .097 .042 .013 .105 * .083 .068 −.175 *** .208 *** .336 ***
Cups of Fruits and Vegetables Per Day (14) .303 *** −.085 −.120 * −.120 * .031 −.113 * −.026 −.031 .072 −.229 *** .002 .011 .146 **
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SNAP = Supplemental Nutrition Assistance Program; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children

*

= p < .05,

**

= p < .01,

***

= p < .001.

Table 3.

Standardized regression estimates from stepwise multiple regression models predicting fruit and vegetable consumption.

Multiple Regression
 Multiple Regression
 Multiple Regression
Step 1 Step 2 Step 3
Predictor B SE P B SE P B SE P
Problems
Hard to prepare −.055 .051 .272 −.054 .051 .278 −.032 .050 .519
Hard to use before spoiling −.081 .052 .118 −.085 .052 .102 −.055 .051 .278
Family doesn’t eat −.046 .055 .401 −.047 .054 .397 −.069 .053 .201
Too expensive .061 .049 .221 .062 .049 .208 .043 .048 .374
Don’t have time to prepare −.106 .052 .042 −.102 .052 .050 −.085 .051 .097
Don’t know how to prepare .015 .050 .762 .018 .050 .715 −.005 .048 .912
Family doesn’t like taste .084 .055 .125 .083 .055 .126 .091 .053 .086
F&V preferences
I like to eat F&V every day .299 .050 <.001 .301 .050 <.001 .314 .049 <.001
Distance to supermarket .080 .048 .099 .056 .047 .231
Food Assistance
Food bank or pantry 2018 −.045 .052 .388
SNAP 2018 −.066 .054 .223
WIC 2018 .143 .051 .005
Ethnicity −.213 .050 <.001
Adjusted R2 0.103 *** 0.107 0.166 ***
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SNAP = Supplemental Nutrition Assistance Program; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

*

= p < .05,

**

= p < .01,

***

= p < .001.

3.5. Barriers and facilitators by race and ethnicity

Other race (15%) and white (9%) participants were more likely to report that fruits and vegetables were hard to prepare than Black (0%) or Hispanic/Latino(a) (2%) participants (p = .006). Many participants reported that fruits and vegetables were hard to use before spoiling; however, Black (40%), Other Race (54%), and white (44%) participants were more likely to report it than Hispanic/Latino(a) participants (27%) (p = .003). Other race (30%) and white (19%) participants were more likely to report not having time to prepare fruits and vegetables than Black (8%) or Hispanic/Latino(a) (9%) participants (p = .005). Black (23%) and Hispanic/Latino(a) (15%) participants were more likely to report using a food bank or pantry (p = .004) and participating in SNAP (p = .005) and WIC (p < .001) within the past year, though this is likely due to income disparities reported among these groups in our sample. For example, of the 380 participants that reported annual household income, 54% of Black participants (N = 20), 71% of Hispanic/Latino(a) participants (N = 145), and 43% of Other race participants (N = 6) reported an income below $45,000 USD within the past year while 76% of white participants (N = 94) reported an annual household income above $45,000 USD. Distributions by race and ethnicity and Fisher’s Exact Tests p-values can be found in Table 4.

Table 4.

Barriers and facilitators of fruit and vegetable consumption and annual household income by race and ethnicity.

Barriers and Facilitators Hispanic/Latino(a)
 Black
 Other Race
 White
 P
No
 Yes
 No
 Yes
 No
 Yes
 No
 Yes
N (%) N (%) N (%) N (%) N (%) N (%) N (%) N (%)
Barriers
Hard to prepare 206 (98) 5 (2) 40 (100) 0 (0) 11 (85) 2 (15) 115 (91) 11 (9) .006
Hard to use before spoiling 155 (74) 56 (26) 24 (60) 16 (40) 6 (46) 7 (54) 71 (56) 55 (44) .003
Family doesn’t eat 197 (93) 14 (7) 36 (90) 4 (10) 13 (100) 0 (0) 119 (94) 7 (6) .660
Too expensive 182 (86) 29 (14) 36 (90) 4 (10) 13 (100) 0 (0) 111 (88) 15 (12) .622
No time to prepare 193 (92) 18 (8) 37 (93) 3 (7) 9 (69) 4 (31) 102 (81) 24 (19) .005
Don’t know how to prepare 202 (96) 9 (4) 39 (98) 1 (2) 13 (100) 0 (0) 119 (94) 7 (6) .867
Family doesn’t like the taste 198 (94) 13 (6) 37 (93) 3 (7) 9 (69) 4 (31) 118 (94) 8 (6) .038
Facilitators
Use of a food pantry 180 (85) 31 (15) 31 (78) 9 (22) 12 (92) 1 (8) 120 (95) 6 (5) .004
SNAP participation 164 (78) 47 (22) 30 (75) 10 (25) 11 (85) 2 (15) 115 (91) 11 (9) .005
WIC participation 182 (86) 29 (14) 36 (90) 4 (10) 12 (92) 1 (8) 124 (98) 2 (2) <.001
< $ 45K ≥ $ 45K < $ 45K ≥ $ 45K < $ 45K ≥ $ 45K < $ 45K ≥ $ 45K P
Annual household income 145 (71) 60 (29) 20 (54) 17 (46) 6 (43) 8 (57) 30 (24) 94 (76) <.001
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SNAP = Supplemental Nutrition Assistance Program; WIC = Special Supplemental Nutrition Program for Women, Infants, and Children. < $45K = Annual household income is less than $45,000 United States Dollars. ≥ $45K = Annual household income is greater than or equal to $45,000 United States Dollars. P-value derived from 2-sided Fisher’s exact test to correct for low cell count in some groups.

4. Discussion

In our analysis of 390 participants in Eastern Travis County, Texas, not having time to prepare fruits and vegetables was a barrier associated with lower daily fruit and vegetable consumption. Liking to eat fruits and vegetables as well as participation in WIC were associated with higher daily fruit and vegetable consumption. Other hypothesized predictors, such as the distance to a supermarket and other psychosocial barriers were not associated with fruit and vegetable consumption. However, certain psychosocial barriers were more likely to be reported by some racial/ethnic groups, which may warrant attention in future studies.

Liking to eat fruits and vegetables was the strongest predictor of daily fruit and vegetable consumption, which suggests that efforts to increase preferences and palatability of fruit and vegetables, such as cooking demonstrations (Metcalfe et al., 2022), taste tests (Gans et al., 2018), and repeated exposure (Appleton et al., 2016) may be important components to incorporate into multi-level strategies. Although concern over vegetable spoiling was not a significant predictor of daily fruit and vegetable consumption in the regression analysis, a third of the study population identified it as a barrier and previous literature found spoilage was a barrier to consumption among participants with lower-income (Chen & Gazmararian, 2014). Therefore, researchers and community organizers could consider providing education on how to clean, store, and prepare fresh fruits and vegetables to extend freshness or when to supplement fresh produce with low-sodium and low-sugar canned and frozen alternatives.

Participation in WIC was significantly associated with fruit and vegetable consumption in our analysis, which mirrors findings from previous studies looking at how government assistance programs influence eating behaviors (Zhang et al., 2020). The City of Austin estimated that only 35% of the population eligible for WIC enrolled in the program in Travis County as of September 2019 (City of Austin, 2020), which is significantly lower than the national average of 57% over a similar time period (USDA, 2021). Future research could explore reasons for these gaps and identify innovative strategies to recruit and retain WIC eligible families. Nutrition practitioners, health care providers, community-based organizations, and government entities could prioritize known strategies for enrolling and retaining those who are eligible to receive WIC benefits. For example, state and local governments can streamline WIC enrollment with technologic advances, provide WIC appointments online or by phone, and inform all families receiving other government benefits that they may qualify for WIC (Neuberger, 2020). Nutrition practitioners and health care providers can screen clients for food insecurity, help eligible clients enroll in WIC, and educate clients on how and where to use WIC benefits (Cullen et al., 2021). Community-based organizations that work with under-resourced populations can similarly promote WIC, explain eligibility guidelines, and provide enrollment assistance.

Hispanic/Latino(a) participants (N = 211) were more likely than other racial/ethnic groups to report consuming fruits and vegetables in our study. The median was below the recommended daily value of 5 servings per day at 3.9 cups per day, but higher than the statewide average reported in the most recent Behavioral Risk Factor Surveillance Survey, where researchers found that across all racial/ethnic groups, Texans had a median fruit and vegetable consumption of 2.6 cups per day (Lee et al., 2022). In large population studies, researchers have found that Hispanic/Latino(a) populations consume the same or more fruits and vegetables than non-Hispanic white and Black populations (Lee et al., 2022; Sawyer-Morris et al., 2021), though results have been reported to vary by origin, nativity, and acculturation (Siega-Riz et al., 2014; Di Noia et al., 2015). Hispanic/Latino(a) populations continue to have a higher prevalence of type-2 diabetes (Cheng et al., 2019) and liver and stomach cancer (Miller et al., 2021), and therefore should continue to be prioritized in dietary interventions that promote fruit and vegetable consumption.

This study has limitations. The cross-sectional nature of the data limits the interpretation of the findings and does not suggest causality. Future research should look at how barriers and facilitators may change over time with the presence or absence of food assistance programs and other lifestyle changes that may make facilitators more meaningful or barriers more burdensome during certain life events. Additionally, barriers and facilitators might be different if fruit and vegetable consumption are studied as separate variables. For example, preparation of vegetables might be more challenging or time consuming than fruit and previous research has shown that people might like the taste of fruit more than vegetables (Lee et al., 2022). Future research could separate fruit and vegetable consumption to identify whether barriers and facilitators are food group specific.

Another limitation is the smaller sample size of Black participants (N = 40, 9.5% of sample) and participants that identified as “Other” race (N = 14, 3.6% of sample). To address this statistically, we corrected for small cell counts using 2-sided Fisher’s Exact Tests (Ruxton & Neuhäuser, 2010). While these subgroup sample sizes are reflective of the demographic make-up of Travis County as a whole where the Black population accounts for 8.3% of the total population (USCB, 2022), the sample sizes may make the findings less generalizable to the US or other countries. Similarly, nativity and acculturation were not explicitly measured in this survey, which may be an important variable to consider when assessing Hispanic/Latino(a) fruit and vegetable consumption. Future research could explore potential barriers and facilitators by race and ethnicity with larger samples and include more detailed questions on nativity, origin, and acculturation.

Multicomponent interventions may be the most effective strategy for alleviating barriers and implementing facilitators to fruit and vegetable consumption (Appleton et al., 2016). However, formative work should be done to determine which components are most effective and desired by the community with a focus on ensuring those components are equitably benefiting racially and ethnically minoritized people. Multilevel approaches, such as the ‘Live Well, Viva Bien’ intervention designed by researchers at the Rudd Center have shown promising results, with the intervention group (N = 837) increasing fruit and vegetable consumption by 0.44 cups and the control group (N = 760) decreasing intake by 0.08 cups from baseline to 1-year follow-up (Gans et al., 2018). As mentioned the Fresh for Less initiative is a multicomponent intervention employing food retail and marketing strategies to increase access to healthy food in Eastern Travis County, Texas. In this cross-sectional analysis of baseline cohort data, we found that liking to eat fruits and vegetables, participating in WIC, and Hispanic/Latino(a) ethnicity were associated with greater fruit and vegetable consumption. Findings will be shared and updated when data collection is complete to assess whether results change when analyzed longitudinally.

FUNDING

The Parent study was funded by the Foundation for Food and Agriculture Research, 560815–42731 (Sustainable Food Center, Subcontract – UTHealth School of Public Health). Dr. Janda’s time was funded by a National Cancer Institute grant awarded to the University of Texas Health Science Center at Houston School of Public Health Cancer Education and Career Development Program (T32-CA057712).

Ethical statement

The FRESH-Austin study was approved by the University of Texas Health Science Center at Houston Institutional Review Board (HSC-SPH-18-0233). All participants provided written informed consent upon enrollment in the study. Acknowledgements Dr. Janda’s time was funded by a National Cancer Institute grant awarded to the University of Texas Health Science Center at Houston School of Public Health Cancer Education and Career Development Program (T32-CA057712).

Footnotes

5.

Data statement

De-identified data is available upon request to Dr. Kathryn Janda at kathryn.m.janda@uth.tmc.edu.

Declaration of competing interest

The authors have no conflicts of interest to declare.

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