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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Appetite. 2021 Jun 14;166:105466. doi: 10.1016/j.appet.2021.105466

Food insecurity, food-related characteristics and behaviors, and fruit and vegetable intake in mobile market customers

Melissa L Horning 1, Bonnie Alver 2, Leah Porter 3, Sophia Lenarz-Coy 4, Nipa Kamdar 5
PMCID: PMC8445326  NIHMSID: NIHMS1721617  PMID: 34139297

Abstract

Mobile markets (MM) bring affordable, quality, healthy foods to high-need, low-food access communities. However, little is known about food insecurity of MM customers. This manuscript evaluates food insecurity prevalence in MM customers and assesses associations between food insecurity and MM use, food-related characteristics and behaviors, and fruit and vegetable (FV) intake. Customers (N=302) completed cross-sectional surveys in summer 2019 that assessed: food security, food availability, cooking attitude, self-efficacy for healthy cooking, self-efficacy for cooking and eating FV, social connectedness, and FV intake. Descriptive and multivariate analyses were used to describe and assess associations with food insecurity and FV intake. Results show most MM customers were food insecure (85%). In logistic regression models adjusted for sociodemographic characteristics, long-term MM use (OR=0.77, CI=0.60–0.997), access to affordable, quality foods (OR=0.81, CI=0.71–0.93), and self-efficacy for both cooking healthy foods (OR=0.88, CI=0.80–0.97) and cooking and eating FV (OR=0.90, CI=0.82–0.98) were associated with lower odds of food insecurity; negative cooking attitudes (OR=1.12, CI=1.02–1.24) were associated with higher odds of food insecurity. Being food insecure (β= −1.37, SE=0.43, p<0.01) was associated with poorer FV intake; this association attenuated slightly (β= −1.22, SE=0.43, p<0.01) when length of MM use was added to the general linear model, which was also associated with higher fruit and vegetable intake (β=0.26, SE=0.10, p=0.01). Results suggest the MM reaches customers experiencing high levels of food insecurity and long-term MM use is associated with lower food insecurity and higher FV intake. Relationships between food insecurity and several food characteristics/behaviors provide insight for potential targets for wrap-around interventions to address food insecurity among customers. Findings suggest longitudinal evaluation of the MM’s impact on food security and other food- related characteristics/behaviors is warranted.

Keywords: mobile markets, food insecurity, self-efficacy, food access, fruit and vegetable intake

1.1. Introduction

As a negative social determinant of health (Healthy People 2030, n.d.), food insecurity is defined as the worry about running out of food or actually running out of food (Coleman-Jensen et al., 2018). Those at increased risk for food insecurity include individuals who are low income (United States Department of Agriculture [USDA], 2019) or who identify as Black (USDA, 2019), Hispanic (USDA, 2019), Native American/American Indian (Blue Bird Jernigan et al., 2017; Gundersen, 2008; Jernigan et al., 2017), and/or Native Hawaiian/Pacific Islander (Long et al., 2020; Stupplebeen, 2019). Additionally, while 12% of Americans in 2019 experienced food insecurity (Coleman-Jensen et al., 2019), this national aggregate does not capture other disparate food insecurity rates faced by many Americans. For example prior to the COVID-19 pandemic, 69% of public housing residents experienced food insecurity (Kirkpatrick & Tarasuk, 2011), as did 25% of households with a working adult who has a disability (Coleman-Jensen & Nord, 2013), 29% of those with low incomes (Coleman-Jensen et al., 2019), 16–28% of households with children headed by a single adult (Coleman-Jensen et al., 2019), and 14% of families with young children (Coleman-Jensen et al., 2019). Therefore, innovative interventions to address food insecurity disparities are warranted.

Food insecurity has been linked to negative outcomes including: poor dietary quality (Bruening et al., 2012; Hanson & Connor, 2014; Leung & Tester, 2018; Nguyen et al., 2015), obesity (Arenas et al., 2018; Moradi et al., 2019), and numerous health conditions such as hypertension/heart disease (Gregory & Coleman-Jensen, 2017; Seligman et al., 2010; Vercammen et al., 2019), type 2 diabetes (Abdurahman et al., 2019; Berkowitz et al., 2013; Seligman et al., 2010, 2012), dyslipidemia (Arenas et al., 2018; Berkowitz et al., 2013; Seligman et al., 2010; Shin et al., 2015), asthma (Gregory & Coleman-Jensen, 2017; Gundersen & Ziliak, 2015; Mangini et al., 2015; Nagata, Palar, Gooding, Garber, Bibbins-Domingo, et al., 2019), and poor mental health (Arenas et al., 2018; Brostow et al., 2019; Leung et al., 2014; Martin et al., 2016; Nagata, Palar, Gooding, Garber, Whittle, et al., 2019). Moreover, food insecurity is associated with greater health care utilization (Berkowitz, Seligman, et al., 2018), contributes to an excess of $77 billion dollars of health care spending each year (Berkowitz, Basu, et al., 2018), and is associated with being in the top 10% of health care expenditures (Berkowitz, Seligman, et al., 2018). Thus, food insecurity is a serious public health concern.

Growing in popularity, mobile food markets are ‘grocery stores on wheels’ that aim to bring affordable, quality, healthy foods to high-need, low-food access communities. As such, mobile markets have the potential to influence food insecurity and dietary intake. Recent research on mobile produce markets from two large-scale cluster randomized trials is promising; both trials indicated improvements in fruit and vegetable intake by as much as ½ to 1 serving per day (Gans et al., 2018; Leone et al., 2018). These findings are consistent with several smaller scale mobile market research studies that have also found improvements in access, purchase or dietary intake (AbuSabha et al., 2011; Gorham et al., 2015; Horning et al., 2020; Hsiao et al., 2018; Tester et al., 2012; Zepeda et al., 2014). However, while studies have posited that mobile markets may reduce food insecurity (Best & Johnson, 2016; Raciti & Reardon, 2017; Robinson et al., 2016), most mobile market research has not evaluated the food security status of customers. To date, only one mobile market study has assessed customer food security and found 38% were food insecure (Gary-Webb et al., 2018). Assessing food insecurity rates of mobile market customers is essential to understand who mobile markets reach and the context around customer purchases.

The Social Cognitive Theory (Bandura, 2012) suggests many personal (e.g., cooking attitudes), behavioral (e.g., cooking self-efficacy), and environmental (e.g., perceived food access) characteristics can simultaneously influence proximal and tertiary outcomes such as food insecurity and fruit and vegetable intake. Thus, it is important to assess associations between characteristics and behaviors that could intensify or mitigate food insecurity among mobile market shoppers. If risk or protective characteristics/behaviors are found, then similar to successful interventions hosted with food pantries, mobile food markets could serve as a conduit for additional interventions to address customer needs related to food insecurity and diet quality (An et al., 2019). Therefore, aligned with the Social Cognitive Theory, evaluating how food insecurity associates with mobile market shopping, food-related characteristics and behaviors (e.g., perceived food access, cooking related attitudes and efficacy, community social connectedness), and fruit and vegetable intake can inform intervention work within mobile market service delivery models.

This manuscript aims to describe the prevalence of food insecurity and associations between food insecurity, mobile market shopping, food-related characteristics and behaviors, and fruit and vegetable intake of mobile market customers.

2.1. Methods

The mobile market being evaluated within this manuscript is the Twin Cities Mobile Market (Horning et al., 2020; Horning & Porter, 2020; The Food Group, 2021). The Twin Cities Mobile Market operates from a city bus that was converted into a grocery store with shelving, refrigeration, and freezers. The Twin Cities Mobile Market has regular stops in Minneapolis and Saint Paul Minnesota communities that experience low incomes and/or low food access. This full-service mobile market provides customers with over 200 healthy food options from all food groups including: fresh fruits and vegetables, dairy items (e.g., milk, cheese), grains, proteins (e.g., eggs, ground beef, turkey, chicken, fish, beans), and dry goods (e.g., seasonings, spices), which can be purchased with SNAP/EBT, cash, or debit/credit. To increase affordability, the Twin Cities Mobile Market prices foods at approximately 10% below supermarket prices and participates in the state-funded and -legislated Market Bucks program (17.1017 Good Food Access Program: 2016 Minnesota Statutes, 2016), which provides customers who purchase with SNAP dollars a match (up to $10) for a future purchase of fruit and vegetables. This cross-sectional study is part of a larger study using a repeated cross-sectional research design aimed at evaluating the impact of shifting mobile market service from biweekly to weekly for mobile market stops not currently receiving weekly mobile market service.

Following Institutional Review Board approval, mobile market customers completed surveys during mobile market stops in June and July of 2019. To be eligible, participants had to be interested in completing the survey, 18 years of age or older, a mobile market customer (i.e., individuals had to report having shopped at the mobile market at least one or more times) and able to read/speak in English, Hmong, or Somali. Surveys were offered to anyone who was approaching or exiting the mobile market, and those interested were screened for eligibility. If eligible, participants were given a consent form to review, the opportunity to ask questions about the study, and a paper/pen survey. Participants implied their consent for research participation by completing and turning in the survey; completion of the survey was voluntary and did not affect ability to shop within the market. The survey took approximately 15–30 minutes, and those completing the survey received a $10 mobile market gift card.

2.1.1. Response Rate

Research staff visited 22 of the 25 mobile market stops on at least two different days during mobile market service for survey administration. Staff conducted surveys at one additional mobile market stop for one day only because it was being discontinued due to low service levels. The remaining two other stops were not surveyed to avoid survey fatigue (the stops had been previously surveyed in March 2019 for a different project). At each stop visited, research staff used a tracking log to record the number of: surveys completed, people who screened ineligible, and people not interested in participating. Staff also recorded the number of people who reported previously completing the survey. These data were collected to allow for calculation of the survey response rate.

Of the 541 individuals screened for eligibility, 302 individuals completed the survey, resulting in a response rate of 56%. Reasons for exclusion included disinterest in taking the survey (n=120), under 18 years of age (n=8), not mobile market shoppers (n=49), and did not speak English, Hmong, or Somali (n=36). The remaining individuals (n=26) could not participate for other reasons (e.g., no time, survey was too long, no glasses, etc.).

2.1.2. Measures

Participants self-reported demographic characteristics including age, gender (male, female, transgender, or fill-in blank), receipt of economic assistance (e.g., reported yes to receiving income qualified programs such as: SNAP, Women Infants and Children [WIC], Minnesota Family Investment Program [MFIP], Medical Assistance), annual household income in categories, number of people supported by income, and race/ethnicity (see Table 1).

Table 1.

Mobile market participant 627 characteristics (N = 302)

n %
Age
 18–24 years old 9 3%
 25–34 years old 19 6%
 35–44 years old 22 7%
 45–54 years old 43 14%
 55–64 years old 115 38%
 65 years or older
 (missing n=6, 2%) 		88 29%
Gender
 Male 105 35%
 Female 189 64%
 Transgender/filled in a blank
 (missing n=5, 2%) 		3 1%
Economic Assistance Use
 Yes 235 79%
 No
 (missing n=6, 2%) 		61 21%
Income level
 Less than $10,000 122 46%
 $10,000 to under $15,000 82 31%
 $15,000 to under $25,000 43 16%
 $25,000 to under $35,000 7 2%
 $35,000 to under $50,000 7 2%
 $50,000 to under $100,000
 (missing n=36, 12%) 		5 2%
Number of People Income Supports
 1 219 78%
 2 31 11%
 3 18 6%
 4 5 2%
 5 or more
 (missing n=21, 7%) 		8 3%
Race/ethnicity
 Native America/Alaskan Native 16 6%
 Asian, including Southeast Asian 12 4%
 Pacific Islander 1 0%
 Black or African American 99 35%
 African native, including Oromo, Somali, Ethiopian, etc. 9 3%
 Hispanic/Latino(a) 13 5%
 White or Caucasian 110 39%
 Another race or ethnic group (Specified) 10 4%
 More than one racial ethnic background 14 5%
 Missing 18 6%
Food Insecurity
 Food insecure in the last 12 months 253 85%
 Food secure (not food insecure) in the last 12 months
 (missing n=4, 1%) 		45 15%
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Note. % may not add up to 100 due to rounding.

2.1.2.1. Food Security Status.

The validated two-item Food Security Vital Sign (Children’sHealthWatch, 2018; Hager et al., 2010) was used to assess food security (see Table 2 for psychometrics, example item, response options). The two items were summed and collapsed into two categories: those who were food secure (0; those with scores of 0) and those who were food insecure (1; those with scores of 1 or more; Children’sHealthWatch, 2018; Hager et al., 2010).

Table 2.

Measures Used to Assess Food-Related Characteristics and Behaviors of Mobile Market Shoppers

Measure (Psychometrics, if previously validated) Source Example item No. items Response options Present study
 % missing
Alpha Mean SD Range
Food Security Vital Sign (sensitivity=97%, specificity=83%, validated) (Children’sHealthWatch, 2018; Hager et al. 2010) Within the past month we worried whether our food would run out before we got money to buy more. 2 3 pointa NA NAb NAb 0–1 0%
Access to Affordable and Quality Foods (α=0.79–0.93) (Campbell et al., 2007) Please select how much you agree with the following statement: I don’t buy many fruits because they cost too much. 4 4 pointc α=0.83 11.0 2.8 4–16 0%
Negative Cooking Attitude (α=0.85, validated) (Condrasky et al., 2011) Indicate the degree to which you agree or disagree with each statement: I do not like to cook because it takes too much time. 4 5 pointd α=0.91 9.7 4.1 4–20 1%
Self-efficacy of Healthy Cooking (α=0.85) (Beshara et al., 2010) How likely are you to prepare a healthy meal after a tiring day? 4 5 pointe α=0.82 11.6 3.6 4–20 1%
Self-efficacy for Cooking and Eating Fruits and Vegetables (α=0.90, validated) (Condrasky et al., 2011) Indicate the extent to which you feel confident about performing each of the following activities: Eating fruits and vegetables at every meal every day. 4 5 pointf α=0.85 12.7 3.4 4–20 1%
Social Connectedness (α=0.92, validated) (Petersnn et al., 2008) Indicate the degree to which you agree or disagree with each statement: I can get what I need in 8 5 pointd α=0.90 27.0 7.7 8–40 1%
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Note.

a

The food security vital sign items were summed and collapsed into two categories: those who were food secure (0) and those who were food insecure (1). Items for the rest of the measures were coded and summed such that a higher score indicates a greater presence of the trait (e.g., higher access to affordable and quality foods, more negative cooking attitude, higher self-efficacy, etc).

a

Never True to Always True.

b

Frequency (percent) can be found on Table 1.

c

Strongly Disagree to Strongly Agree (items were reverse coded such that a higher score indicates higher access to quality and affordable foods).

d

Strongly Disagree to Strongly Agree.

e

Not at All Likely to Very Likely.

f

Not at All Confident to Extremely Confident

2.1.2.2. Length of mobile market shopping.

Mobile market length of use was measured by the item “How long have you been shopping at the mobile market?” The five responses were: (1) less than 3 months, (2) over 3 months but less than 6 months, (3) over 6 months but less than 1 year, (4) over 1 year but less than 2 years, (5) or over 2 years.

2.1.2.3. Food-related characteristics and behaviors.

The following food-related characteristics and behaviors of participants were measured: access to affordable and quality foods (Campbell et al., 2007), negative cooking attitude (Condrasky et al., 2011), self-efficacy of healthy cooking (Beshara et al., 2010), self-efficacy for cooking and eating fruits and vegetables (Condrasky et al., 2011), and social connectedness (Peterson et al., 2008). See Table 2 for measure psychometrics, example items, response options, validation, and levels of missing data. Items for these measures were coded and summed such that a higher score indicates a greater presence of the trait (e.g., higher access to affordable and quality foods and higher self-efficacy).

2.1.2.4. Fruit and vegetable intake.

Fruit and vegetable intake was measured with two items (Coyne et al., 2005), which while less precise than other dietary measures (National Cancer Institute Division of Cancer Control and Population Sciences, 2000, 2016a, 2016b; Townsend & Kaiser, 2007) have been found to successfully rank individuals based on overall fruit and vegetable intake in population-based surveys (Coyne et al., 2005). The items were: “Thinking back over the past week, how many servings of fruit did you usually eat on a typical day?” and “Thinking back over the past week, how many servings of vegetables did you usually eat on a typical day?” Responses options were: zero servings (score 0), less than 1 serving (0.5), 1 serving (1), 2 servings (2), 3 servings (3), 4 servings (4) or 5 or more servings, (5). Serving sizes were defined as: “A serving is a half-cup of fruit or 100% fruit juice, or a medium piece of fruit” and “A serving is a half-cup of cooked vegetables or one cup of raw vegetables.” Item scores were summed to calculate fruit and vegetable intake in servings.

2.1.3. Data Analysis

Analyses were performed in SAS 9.4. Statistical significance was set at alpha=0.05. Descriptive statistics were run for all variables and Cronbach’s alphas were used to assess internal validity of scales. To determine which demographic characteristics to adjust the multivariate models for, bivariate associations were run between both dependent variables (food insecurity and fruit and vegetable intake) and potentially confounding demographic characteristics (i.e., age, race/ethnicity, gender, and economic assistance receipt); demographic characteristics that were significantly associated with food insecurity or fruit and vegetable intake were adjusted for in multivariate models.

Three categories of gender were collected and reported on Table 1; however, only 3 individuals identified as gender non-binary, which would have been too small of a category for analysis, and thus these individuals were not included in analysis. Additionally, owing to small numbers in some race/ethnicity categories, the covariate of race/ethnicity was collapsed into a three-category variable: (0) White, (1) Black or African American, and (2) Indigenous, Persons of Color, and Hispanic (i.e., American Indian/Native American, Asian, Pacific Islander, African Native, Hispanic/Latino(a), Another race or ethnic group (specified), or More than one racial/ethnic background). Finally, most variables had less than 5% of missing data (see Table 1 and 2) and all available data were used in each statistical model.

2.1.3.1. Logistic Regression Analyses.

Adjusted for economic assistance receipt, gender, and race/ethnicity, separate logistic regression models were run to assess associations between the dependent variable (food insecurity) and each independent variable (length of mobile market use and each food-related characteristic/behavior).

2.1.3.2. General Linear Models (GLM).

Also adjusted for economic assistance receipt, gender, and race/ethnicity, a GLM was used to assess the association between fruit and vegetable intake (dependent variable) and food security (independent variable). Then, this model was rerun with the additional independent variable of length of mobile market use to assess how long-term mobile market use was associated with fruit and vegetable intake.

3.1. Results

Scales demonstrated appropriate internal consistency with alpha’s over 0.70; Table 1 contains alpha values, means, standard deviations, and range of scales within the sample.

Briefly, the majority of mobile market customers were over age 55 (67%), identified as female (64%), reported receiving at least one form of economic assistance like SNAP, WIC, free or reduced-price school lunch (79%), reported incomes below $15,000/year (69%), and identified as being from a racially or ethnically diverse background (61%). Table 2 shows full sociodemographic data. Most customers also reported experiencing food insecurity in the past 12 months (85%) and over half reported using the mobile market for over one year (56%).

In logistic regression models adjusted for economic assistance receipt, race/ethnicity, and gender, length of mobile market use, self-efficacy for cooking and eating fruits and vegetables, self-efficacy for healthy cooking, and access to affordable and quality foods were associated with significantly lower odds of food insecurity. Negative cooking attitudes were associated with higher odds of food insecurity (Table 3).

Table 3.

Logistic regression models (adjusted for economic assistance use, gender,a race/ethnicityb) assessing associations between mobile market use or food-related characteristics/behaviors and odds of food insecurity

Logistic Models Independent variables Odds of being food insecure 95% Confidence Interval
Model 1 Social Connectedness 1.02 0.97–1.07
Model 2 Self-efficacy for cooking and eating fruits and vegetables 0.90 0.82–0.98
Model 3 Self-efficacy for healthy cooking 0.88 0.80–0.97
Model 4 Negative cooking attitude 1.12 1.02–1.24
Model 5 Access to affordable and quality foods 0.81 0.71–0.93
Model 6 Length of mobile market use 0.77 0.60–0.997
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Note.

a

Three categories of gender were collected; however, only 3 individuals identified as gender non-binary, which would have been too small of a category for analysis and thus these individuals were not included in analysis.

b

Owing to small numbers in some racial/ethnic categories, the covariate of race/ethnicity was collapsed into a three-category variable: (0) White, (1) Black or African American, and (2) Indigenous, Persons of Color, and Hispanic (i.e., American Indian/Native American, Asian, Pacific Islander, African Native, Hispanic/Latino(a), Another race or ethnic group (specified), or More than one racial/ethnic background).

In GLM analyses adjusted for economic assistance receipt, race/ethnicity, and gender, food insecurity was significantly associated with lower fruit and vegetable intake (β= −1.37, SE=0.43, p<0.01). This association was attenuated slightly (β= −1.22, SE=0.43, p<0.01) when the covariate of length of mobile market use was added to the model. Moreover, length of mobile market use was also significantly and independently associated with higher fruit and vegetable intake (β=0.26, SE=0.10, p=0.01).

4.1. Discussion

This study investigated the prevalence and food-related characteristics and behaviors associated with food insecurity in mobile market customers. Overall, our findings demonstrate the mobile market is reaching a diverse and low-income population with 85% of mobile market customers experiencing food insecurity. These findings indicate the mobile market is reaching the individuals it aims to serve with its affordable, healthy grocery options. However, in comparison to local and national food insecurity rates (10–12% as measured with Household Food Security Surveys; Coleman-Jensen et al., 2019) and the only other mobile market study that has assessed food insecurity rates (38% as measured with a non-validated measure; Gary-Webb et al., 2018) our mobile market customers experience disproportionately higher levels of food insecurity. This data showcases the complex context in which customers are making food purchasing decisions.

It should be noted that food insecurity is often measured differently, and rates are not always directly comparable. For our brief customer intercept survey, we selected to use the short, validated two-item food security vital sign (Children’sHealthWatch, 2018; Hager et al., 2010) over one of the longer Household Food Security Surveys of 6, 10 or 18 items (United States Department of Agriculture, 2012). Although the two-item food security vital sign does not allow for assessing level of food insecurity, it has high sensitivity (97%) and specificity (83%) to the 18-item Household Food Security Survey, the gold standard measure. Yet, this specificity does suggest that 17% of customers who screened positive for food insecurity on our survey would have been classified as food secure by the Household Food Security Survey. This information indicates the food insecurity prevalence of our sample of mobile market customers may be closer to 70% (rather than 85%), which remains critically high. Measurement of food security using a validated measure is essential to having confidence in findings of food insecurity prevalence in specific populations like mobile market customers.

Given the nearly universal experience of food insecurity among our customers, our findings suggest mobile markets are reaching communities with needs and may be an important location for additional intervention work to reduce disparities. Our findings indicate (that when adjusting for potentially confounding demographic characteristics), greater access to quality and affordable foods, self-efficacy for cooking healthy meals and self-efficacy for healthy cooking and eating fruits and vegetables were independently associated with lower odds of food insecurity. Furthermore, negative cooking attitudes were associated with higher odds of food insecurity. Long-term mobile market use was also significantly associated with lower odds of food insecurity. Given these findings, at the local level, future interventions could consider targeting these areas in an effort to reduce food insecurity-related disparities of mobile market customers. Such interventions might include increasing partnerships and outreach through focused community classes (e.g., Cooking Matters with SNAP-Ed) and/or fruit and vegetable prescription programs from local providers (Aiyer et al., 2019; Ridberg et al., 2019).

Consistent with evidence on associations between food insecurity and lower dietary quality (Bruening et al., 2012; Hanson & Connor, 2014; Leung & Tester, 2018; Nguyen et al., 2015), our study findings indicated that food insecurity was associated with lower fruit and vegetable intake. However, when length of mobile market use was added into the model, long-term mobile market use was significantly associated with higher fruit and vegetable intake. This finding is aligned with robust previous research on mobile produce markets that have demonstrated higher fruit and vegetable intake outcomes (Gans et al., 2018; Leone et al., 2018). Moreover, our results also indicated that the relationship between food insecurity and fruit and vegetable intake was attenuated when length of mobile market use was included in the model. While a previous study did not find a change in food security after mobile market implementation (Gary-Webb et al., 2018), our cross-sectional findings indicate promising associations between mobile market use, food insecurity, and fruit and vegetable intake. Therefore, further research with more robust study designs (e.g., randomized trials, longitudinal cohort studies) is critical to determine temporality and causality of whether long-term mobile market use facilitates fruit and vegetable intake while ameliorating food insecurity.

Strengths of our study include tracking the survey response rate and use of many strong validated measures of food insecurity and food-related characteristics and behaviors. Our study also has several limitations. Namely, our cross-sectional survey does not provide insight on market impact over time or the directionality of associations. Additionally, because a large portion of our study sample was food insecure, it should have been more challenging to detect differences (Hsieh, 1989). Future studies evaluating use and impact of mobile markets should consider designs that will allow for greater distribution of food security among participants. The sample size of 302 mobile market participants and multicollinearity between several factors prevented us from testing a single logistic regression model loaded with the six characteristics/behaviors while adjusting for economic assistance use, gender, and race/ethnicity. This limitation may overstate the significance of findings by presenting the separate models individually warranting further investigation in larger samples. Because the surveys were completed outside, weather may have affected day-to-day, stop-by-stop participation. In addition, some stops received more than two days of surveys as demand for surveys was higher than could be accommodated in two scheduled survey days; thus, participants from these sites may be over-representative. Mobile market shopping frequency was measured (e.g., weekly, 2–3 times a month) with the surveys; however, because some mobile market stops received weekly mobile market service and others received biweekly service at the time of survey administration, interpretation of this shopping frequency variable is not clear, and thus, was not used in the present study limiting analyses possible. Finally, drawing from key food pantry/bank literature, there are other broader determinants that have been linked to food insecurity for individuals visiting food pantries/banks that were not specifically measured in this study. These factors include: participating in national programs like SNAP, School Lunch and Breakfast Programs, and WIC (Engelhard & Hake, 2020), trading off expenses like housing and medical bills with food (Gundersen et al., 2017), having financial management skills (Gundersen & Garasky, 2012), utilization of coping strategies (e.g., buying the least expensive foods, asking for help from others; Gundersen et al., 2017), and lower levels of family chaos, household disruptions, and meal planning (Fiese et al., 2016). Future mobile market research should evaluate these factors, which may also be important determinants of food insecurity within mobile market customers.

5.1. Conclusion

Mobile market customers experience high levels of food insecurity. Food insecurity was associated with risk and protective food-related characteristics/behaviors. These characteristics/behaviors could be important targets for future community-centered interventions with mobile market customers that may help alleviate food insecurity. Moreover, long-term mobile market use was associated with lower odds of food insecurity and higher fruit and vegetable intake, which suggests mobile markets may play a role in addressing food insecurity-related disparities. These findings warrant further robust, longitudinal evaluation. Future mobile market interventions and research should continue to address the complexities of multiple factors that impact food security.

Acknowledgements

We are grateful to all Mobile Market customers for their time completing the surveys. We would also like to acknowledge the volunteer research interns who collected data at the Mobile Stops including Abdirahman Hassan, Allison Krueger, Kelly Kruse, Brian Lee, Anna Ribbens, Mackenzie Schara, and Kari Swanson.

Funding

This research was funded by a National Institute for Food and Agriculture grant of the United States Department of Agriculture (NIFA2017-0275; Project Director: Porter, Subcontract-PI of the Research: Horning). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture. Additionally, the data collected with this grant used REDCap Software, which was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences, grant UL1TR002494. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences. Additionally, the views expressed in this article do not represent the views of the U.S. Department of Veterans Affairs or the United States government.

Footnotes

The research protocols used for the study described in this manuscript were reviewed and approved by the Institutional Review Board at the University of Minnesota. The research was conducted in accordance with the Declaration of Helsinki.

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Contributor Information

Melissa L Horning, School of Nursing, University of Minnesota, 5-140 Weaver Densford Hall, 308 Harvard St SE, Minneapolis, MN 55414.

Bonnie Alver, School of Nursing, University of Minnesota, 5-140 Weaver Densford Hall, 308 Harvard St SE, Minneapolis, MN 55414.

Leah Porter, Twin Cities Mobile Market.

Sophia Lenarz-Coy, The Food Group, 8501 54th Ave. N, New Hope, MN 55428.

Nipa Kamdar, VA Health Services Research and Development, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX..

Data Availability

The author has full access to all the data reported within the manuscript.

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