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. 2025 Feb 18;51:103010. doi: 10.1016/j.pmedr.2025.103010

The relationship between food insecurity and emotional eating during the coronavirus pandemic in northern California

Andreza SB Souza a, Marcelle M Dougan b, Giselle AP Pignotti a,⁎
PMCID: PMC11891732  PMID: 40065755

Abstract

Objectives

This cross-sectional study examined the association between food insecurity and emotional eating and explored correlations between emotional eating and dietary behavior in the U.S during the pandemic.

Methods

Participants (N = 515, 77.1 % female, 44.1 ± 14.7 years old) completed a validated online survey between August and November of 2020 including questions about food security and eating behaviors. A multivariable linear regression model was used to evaluate the association between food insecurity and emotional eating. Pearson correlations were calculated to assess the relationship between emotional eating and dietary behavior

Results

Overall, 31.3 % of the participants experienced food insecurity. Food insecurity was associated with higher emotional eating in the crude model (beta = 0.33; 95 % confidence level [CL]: 0.15, 0.52), but not in the fully adjusted regression model (beta = 0.15; 95 % CL: −0.06, 0.37). Intakes of sugary snacks (r = 0.16, p < 0.01) and salty snacks (r = 0.14, p < 0.01) were weakly positively correlated with higher emotional eating scores, while the intakes of vegetables (r = −0.13, p < 0.01) and alcohol (r = −0.09, p = 0.03) were negatively correlated with emotional eating

Conclusions

In this study, emotional eating was not associated with food insecurity and was weakly positively correlated with the intake of energy-dense foods. The relationship between food insecurity and emotional eating is complex and not yet clearly defined. Examination of longitudinal associations between emotional eating, food insecurity, and energy-dense foods is warranted.

Keywords: Food access, Food security, Diet intake, Emotional eating, COVID-19

Highlights

  • •

    Nearly one third of participants experienced food insecurity.

  • •

    Food insecurity was associated with higher emotional eating in the crude model.

  • •

    However, food insecurity was not associated with emotional eating in the adjusted model.

  • •

    Emotional eating was weakly positively correlated with the intake of energy-dense foods.

1. Introduction

Food insecurity is described as the lack of regular access to sufficient, nutritious, and safe foods necessary to maintain a healthy and balanced diet (Food Agriculture Organization, 2008). Research suggests that food insecurity differs across individual sociodemographic factors, disproportionately affecting low-income populations, females, individuals with lower education, and people of color (Fitzpatrick et al., 2021; Pruitt et al., 2016). Recently, the coronavirus disease (COVID-19) pandemic has profoundly impacted food insecurity due to supply chain disruption and an economic crisis. In the U.S., unemployment rates reached 13 % nationwide and 16.1 % statewide during the pandemic, limiting access to food and exacerbating health and social disparities related to food security status (Niles et al., 2020a; U.S. Bureau of Labor Statistics, 2021; Wolfson and Leung, 2020). In addition, studies conducted in the U.S. during the early pandemic stage suggested that food insecurity has risen above pre-pandemic levels (Fitzpatrick et al., 2021; Niles et al., 2020a; Wolfson and Leung, 2020).

Studies have shown that a food-insecure environment is associated with mental health outcomes such as depression, anxiety, and stress and could lead to the disruption of eating patterns (Hazzard et al., 2020; López-Cepero et al., 2020a; Pourmotabbed et al., 2020). Food insecurity may influence eating behavior as some individuals could use food to cope with the distress caused by food insufficiency (Keenan et al., 2021). The pandemic has been a stressful event that has aggravated food insecurity and generated anxiety due to health risks and the economic crisis (Rahman et al., 2021; Wolfson et al., 2021). In such a stressful environment, a dysfunctional eating pattern, such as emotional eating, could be triggered (Puhl et al., 2020). In addition, there is evidence showing the association between emotional eating with symptoms of depression and anxiety, as well as perceived stress (Camilleri et al., 2014; Carpio-Arias et al., 2022; Kaner et al., 2023). Emotional eating is a maladaptive eating behavior in which an individual consumes food in response to negative emotions, such as stress, rather than hunger cues (van Strien et al., 2013). A recent study conducted during the pandemic concluded that individuals who reported higher levels of perceived stress had a higher risk of being emotional eaters (Carpio-Arias et al., 2022). Furthermore, some studies suggest that food-insecure individuals have higher odds of emotional eating (Frank et al., 2023; López-Cepero et al., 2020a; Sharpe et al., 2016).

It has been documented that food insecurity and emotional eating are associated with adverse nutrition-related conditions and can affect dietary intake and diet quality (Leung et al., 2014; Lopez-Cepero et al., 2018). Emotional eating has been linked to the overconsumption of energy-dense foods high in calories, fat, and sugar (Camilleri et al., 2014). This suggests that these palatable foods are especially consumed in response to negative emotions as they are considered “comforting” foods and may play a role in emotion regulation. Excessive intake of energy-dense foods can result in a positive energy balance and fat accumulation, leading to the development of diet-sensitive comorbidities (Fuente González et al., 2022). In fact, emotional eating has been associated with greater odds of obesity, diabetes, and hypertension (Lopez-Cepero et al., 2018). Similarly, food insecurity has been related to lower diet quality and a higher prevalence of chronic diseases (Leung et al., 2014; Nagata et al., 2019). Considering that these chronic conditions are known as risk factors for COVID-19, addressing the relationship between food insecurity and emotional eating behavior becomes even more important.

To date, there are limited studies assessing the association between food insecurity and emotional eating during the pandemic (Jackson et al., 2022; Keenan et al., 2021). Results from this research may help identify strategies to improve health-related outcomes in vulnerable populations. Thus, the purpose of the present study was to assess the relationship between food insecurity and emotional eating during the pandemic among residents of Northern California. A secondary objective was to analyze the relationship between emotional eating and dietary behavior during the pandemic.

2. Methods

2.1. Study design and participants

In this cross-sectional study, data were collected through a self-administered online survey offered in English and Spanish via the Qualtrics platform from August to November 2020. Eligibility criteria included being 18 years or older and residing in the San Francisco Bay Area, California, United States, since at least January 1st of 2020. Participants were recruited through social media posts (Instagram, Twitter, NextDoor, LinkedIn, Facebook, Craiglist, and Yelp Talk) and community partner outreach. The survey was also advertised on El Tecolote's, a free bilingual newspaper based in San Francisco. Participants were excluded from the study if they did not live in one of the following Northern California counties: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Santa Cruz, Solano, and Sonoma. A total of 1457 responses were initially collected, of which 515 provided complete information on food insecurity and emotional eating, and were included in this analysis. Assuming, a population mean emotional eating score of 3.0 and a standard deviation of 1.0, a sample size of at least 284 is needed to estimate a 95 % confidence level with 5 % margin of error. Sample size calculations were performed using OpenEpi for continuous variables (OpenEpi: Open Source Epidemiologic Statistics for Public Health, Version 3.01).

2.2. Data collection

We used the validated National Food Access and COVID Research Team (NFACT) questionnaire version 2.1 (Niles et al., 2020b), containing questions about food security, eating behaviors, and demographics during the pandemic. The survey was adapted to include questions related to diet and risk factors, such as the presence of chronic diseases, and anthropometrics measures, such as weight and height. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the San José State University Institutional Review Board (Protocol Tracking Number: 20162). Participants provided written consent prior to completing the survey and were given a choice to participate in a raffle to win a $20 gift card.

2.2.1. Demographics and covariates

Sociodemographic variables included age, gender, race/ethnicity, education level, income, household composition (with/without children), employment disruption, and presence of chronic conditions. Body mass index (BMI) was calculated as the ratio of weight to the square of height (kg/m2) using self-reported weight (pounds and height (feet and inches). The participants were categorized as normal weight (18.5 to <25 kg/m2), overweight (≥25 to <30 kg/m2), or obese (≥30 kg/m2). These variables were included as they were hypothesized to influence emotional eating behaviors and function as possible confounders. Employment disruption was measured based on participants' reports of job loss, furlough, reduced hours, or income. The chronic conditions selected were based on the likelihood of developing severe diseases from contracting the coronavirus and included asthma, cancer, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, hypertension, neurologic conditions, other chronic lung diseases, serious heart conditions, type 1 and 2 diabetes, and other diseases that might compromise the immune system (Center for Disease Control and Prevention, 2025).

2.2.2. Assessment of food security

Food security was measured with the United States Department of Agriculture Household Food Security Survey Module: Six-Item Short Form (USDA, 2012), which was adapted to ask about food insecurity “since the coronavirus outbreak.” The start of the coronavirus outbreak was defined as March 11, 2020, when the World Health Organization declared COVID-19 a pandemic. As per guidelines, the food security score was calculated by summing up the affirmative responses to the six questions in the module. The score ranged from 0 to 6, with higher scores indicating greater food insecurity. The total score was categorized as high or marginal food security (0–1), low food security (2–4), and very low food security (5–6). In this study, respondents classified as low food security and very low security were combined to describe food insecurity.

2.2.3. Assessment of emotional eating

Emotional eating was assessed using an adapted version of the Eating for Physical Rather Than Emotional Reasons subscale of the Intuitive Eating Scale-2 (Tylka and Kroon Van Diest, 2013). The emotional eating subscale measured eating in response to negative emotions (e.g., anxious, depressed, sad, lonely, stressed out, bored, restless, angry, frustrated) and consisted of six questions rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). One of the questions (eating for comfort) was reverse scored. Then, the total emotional eating score was calculated by adding responses to all items and dividing it by six to create a mean score. Higher scores indicated greater levels of emotional eating. The Cronbach α coefficient of this subscale was 0.89, suggesting good internal consistency.

2.2.4. Assessment of eating behaviors

Participants completed questions about their dietary intake of certain groups of foods. This study specifically evaluated the consumption of sugary and salty snacks, alcohol, fruits, and vegetables, as the intake of these foods has been associated positively or negatively with emotional eaters.

Intakes of sugary snacks, salty snacks, and alcohol were measured using adapted questions from the National Health and Nutrition Examination Survey Food Frequency Questionnaire (“Dietary Screener Questionnaires DSQ in the NHANES 2009-10: DSQ, n.d.). For example, to assess the consumption of sugary snacks, participants were asked, “How often did you eat sugary snacks (such as cookies, chocolate, candy, ice cream, pastries)? Include homemade and ready-to-eat items”. There were nine possible frequency categories for the items evaluated, ranging from “never” to “two or more times per day,” and the responses were converted to daily frequencies.

The survey used adapted questions from the National Cancer Institute's two-item cup fruit and vegetable screener to assess fruit and vegetable consumption (Yaroch et al., 2012). For instance, to evaluate fruit intake, respondents were asked the following question “About how many cups of fruit (including 100% pure fruit juice) do you eat or drink each day?” Serving sizes were exemplified in the questionnaire, and seven responses were possible, ranging from “none” to “4 cups or more”. A similar question was used to assess vegetable intake.

2.3. Statistical analysis

Descriptive statistics included frequencies and percentages for categorical variables and means and standard deviations for continuous variables. Sample sociodemographic characteristics and emotional eating scores were contrasted by food security status using the independent t-test for continuous variables and the chi-square test for categorical variables. Bivariate relationships between emotional eating (dependent variable) and independent variables (food insecurity and covariates) were assessed using a separate logistic regression model for each predictor. To avoid missing important confounders, those variables that were significant in the bivariate model at a p-value of <0.2 were included in the fully adjusted model. A multivariable linear regression model was used to evaluate the association between food insecurity and emotional eating adjusted for covariates (age, gender, income, employment disruption, and BMI). Person correlations were calculated to examine the relationship between emotional eating and dietary behavior. Statistical analyses were performed using SAS software (SAS On Demand for Academics: User's Guide, 2014), and a p-value of <0.05 indicated statistical significance.

3. Results

Sample sociodemographic characteristics, overall and by food security status, are presented in Table 1. Overall, the sample (n = 515) had a mean age of 44.1 (±14.7) and was predominantly female (77.1 %) and white (42.5 %). A large share of respondents had a bachelor's degree or higher (61.7 %) and had an annual income of $35,000–$150,000 (45.6 %). About half of the study participants were classified as obese or overweight (51.3 %), and 23.3 % reported having at least one chronic condition (Table 1).

Table 1.

Descriptive characteristics of northern California adults during the pandemic (2020) by food security status.

Overall
 Food Secure
 Food Insecure
 p-valuea
n = 515 n = 354 (68.7) n = 161 (31.3)
Age, m ± sd 44.1 (14.7) 45.9 (14.9) 40.0 (13.5) <0.01
Gender, n (%) 0.01
 Female 397 (77.1) 270 (76.27) 127 (78.9)
 Male 76 (14.8) 61 (17.23) 15 (9.3)
 Unknown 42 (8.1) 23 (6.49) 19 (11.8)
Race/Ethnicity, n(%) <0.01
 Asian 82 (15.9) 62 (17.5) 20 (12.4)
 Hispanic 122 (23.7) 61 (17.2) 61 (37.9)
 White 219 (42.5) 174 (49.1) 45 (27.9)
 Other 92 (17.8) 57 (16.1) 35(21.7)
BMI category, n(%) <0.01
 Normal weight (18.5 to <25 kg/m2) 203 (39.4) 159 (44.9) 44 (27.3)
 Overweight (≥25 to <30 kg/m2) 120 (23.3) 89 (25.1) 31 (19.2)
 Obese (≥30 kg/m2) 144 (28.0) 79 (22.3) 65 (40.4)
 Unknown 48 (9.3) 27 (7.6) 21(13.0)
Education, n(%) <0.01
 Associate or less 166 (32.2) 72 (20.3) 94 (58.4)
 Bachelor's 135 (26.2) 104 (29.4) 31 (19.2)
 Advanced degree 183 (35.5) 161 (45.5) 22 (13.7)
 Unknown 31 (6.0) 17 (4.8) 14 (8.7)
Income, n(%) <0.01
 <$35,000 93 (18.1) 30 (8.5) 63 (39.1)
 $35,000–$150,000 235 (45.6) 155 (43.8) 80 (49.7)
 >$150,000 133 (25.8) 132 (37.3) 1 (0.6)
 Unknown 54 (10.5) 37 (10.4) 17 (10.5)
Household composition, n(%) <0.01
 with children 199 (38.6) 119 (33.6) 80 (49.7)
 without children 103 (20.0) 83 (23.4) 20 (12.4)
 Unknown 213 (41.4) 152 (42.9) 61 (37.9)
Employment disruption, n(%) <0.01
 No job loss 384 (74.6) 304 (85.9) 80 (49.7)
 Job loss 131 (25.4) 50 (14.1) 81 (50.3)
Presence of chronic condition, n(%) <0.01
 None 228 (44.3) 181 (51.1) 47 (29.2)
 One chronic condition 120 (23.3) 77 (21.7) 43 (26.7)
 Two or more chronic conditions 88 (17.1) 49 (13.8) 39 (24.2)
 Unknown 79 (15.3) 47 (13.3) 32 (19.9)
Emotional Eatingb (1–5), m ± sd 3.1 (1.0) 3.0 (1.0) 3.4 (1.0) <0.01
Fruit & Vegetable intake (cups/day), m ± sd 3.1 (1.8) 3.4 (1.7) 2.5 (1.8) <0.01
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Abbreviations: BMI - body mass index; m - mean; sd - standard deviation.

a

Independent t-test for continuous variables and Chi-square for categorical variables.

b

Emotional eating was assessed with an adapted version of the Eating for Physical Rather Than Emotional Reasons subscale of the Intuitive Eating Scale-2 (Tylka and Kroon Van Diest, 2013).

Approximately one-third of the sample (31.3 %) experienced food insecurity. A larger proportion of food-insecure individuals was Hispanic, female, had a lower education level, lived with children, and reported experiencing job disruption after the pandemic. A significant association was observed between BMI categories by food security status, with a higher percentage of food-secure individuals classified as normal weight (44.9 %) and food-insecure participants more likely to have obesity (40.7 %, p < 0.01). Compared with individuals who were food secure, respondents experiencing food insecurity had higher emotional eating scores (3.4 vs. 3.0, p < 0.01) and had a lower intake of fruits and vegetables (2.5 vs. 3.4 cups, p < 0.01) (Table 1).

Bivariate analyses were carried out to evaluate the association between food security status and sociodemographic covariates on emotional eating scores during the pandemic. Emotional eating was positively associated with food insecurity in the crude model (beta = 0.33; 95 % confidence level [CL]: 0.15, 0.52). However, after adjusting for gender, age, body mass index category, income, and employment disruption this association was no longer significant (Table 2).

Table 2.

Regression analysis on the association between emotional eating and food insecurity of northern California adults during the pandemic in 2020.

Crude
 Adjusteda
Parameter estimate Lower 95 % CL Upper 95 % CL Parameter estimate Lower 95 % CL Upper 95 % CL
Food insecurityb 0.33 0.15 0.52 0.15 −0.06 0.37
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Abbreviations: CL - confidence level.

a

Adjusted for: gender, age, body mass index category, income, and employment disruption.

b

Food insecurity was measured with the United States Department of Agriculture Household Food Security Survey Module: Six-Item Short Form (USDA, 2012).

In secondary analyses, the relationship between eating behaviors and emotional eating scores during the pandemic was evaluated. Correlational analysis indicated a small and positive association between the intake of sugary snacks (r = 0.16, p < 0.01) and salty snacks (r = 0.14, p < 0.01) with higher emotional eating scores, while the intake of vegetables (r = −0.13, p < 0.01) and alcohol (r = −0.09, p = 0.03) were weakly and negatively correlated with emotional eating behavior. No correlation was observed between the consumption of fruits and emotional eating scores (Table 3).

Table 3.

Correlation between emotional eating and dietary behavior among northern California adults during the pandemic (2020).

M SD Range (min-max) r p-value
Fruit (cups/day) 1.4 0.9 0–4 −0.06 0.14
Vegetable (cups/day) 1.7 1.1 0–4 −0.14 <0.01
Salty snacks (times/day) 0.4 0.5 0–2 0.14 <0.01
Sugary snacks (times/day) 0.5 0.5 0–2 0.16 <0.01
Alcohol (times/day) 0.2 0.3 0–2 −0.09 0.03
Open in a new tab

Pearson Correlation Test.

Abbreviations: M – mean; SD – standard deviation.

4. Discussion

In this cross-sectional study, we found that food insecurity was not associated with emotional eating after adjusting for covariates, which is inconsistent with the literature (Frank et al., 2023; Jackson et al., 2022; López-Cepero et al., 2020a). The race/ethnicity of the participants may have influenced the results. We included a diverse sample with 42 % of whites, while most of the studies that found an association between food insecurity and emotional eating were predominantly Latins or African Americans (López-Cepero et al., 2020a; Sharpe et al., 2016). Additionally, as suggested by Myles et al. ((Myles et al., 2016), the absence of questions exploring food availability/hunger in the emotional eating subscale could be a reason for the lack of association. Another possible explanation is that mental health issues were exacerbated during the COVID-19 pandemic and may have been a confounding factor considering the known relationship between mental health outcomes and emotional eating (Camilleri et al., 2014; Carpio-Arias et al., 2022; Kaner et al., 2023). We did not collect data about mental health and were therefore unable to take it into consideration in the analyses. Future research could explore the interplay between food insecurity, emotional eating, and mental health.

In our study, food insecurity prevalence was considerable, with about one-third of participants (31.3 %) experiencing food insecurity since the coronavirus outbreak. A national survey of US adults at the beginning of the pandemic found a food insecurity estimate of 38 % among the respondents (Fitzpatrick et al., 2021). Similarly, research in the state of Vermont indicated a 24.8 % pandemic-related food insecurity rate among the participants, which represented a 32.3 % increase in household food insecurity since COVID-19 (Niles et al., 2020a). We found that individuals who are Hispanic, female, with lower education levels, and live with children had a higher prevalence of food insecurity compared with their counterparts. These findings are consistent with pre- and post-pandemic literature, suggesting that these sociodemographic disparities related to food security status remained and were magnified during COVID-19 (Pruitt et al., 2016; Wolfson and Leung, 2020).

This study supports evidence from previous observations that food-insecure respondents were more likely to have obesity (Bruening et al., 2012; Franklin et al., 2012; López-Cepero et al., 2020b). Studies suggest that this association could be explained by the fluctuation in food availability, as food insecure individuals would overconsume food when it is readily available. Also, a high intake of less expensive energy-dense foods by food-insecure households may play a role in this relationship (Bruening et al., 2012; Crawford and Webb, 2011). Furthermore, food insecurity as a stressor could increase eating to cope and indirectly promote weight gain (Keenan et al., 2021).

In agreement with previous research, this study showed that food-insecure respondents had significantly higher emotional eating scores than food-secure individuals (Frank et al., 2023; López-Cepero et al., 2020a; Sharpe et al., 2016). Food insecurity has been cross-sectionally associated with greater levels of overall eating disorders pathology. It is suggested that the feast-or-famine cycle experienced by food-insecure individuals could be a risk factor for disordered eating behaviors (Hazzard et al., 2020). Moreover, COVID-19-related food insecurity has been indirectly associated with stress-related eating behaviors, implying that difficulties accessing food could affect food intake (Keenan et al., 2021).

This study also sought to explore the relationship between emotional eating and dietary behavior during the pandemic. Our results suggest that emotional eating was positively related to the consumption of energy-dense snacks and negatively correlated with the intake of vegetables and alcohol, although the strength of these correlations was weak. Emotional eating has been associated with increased consumption of energy-dense foods (Camilleri et al., 2014; Fuente González et al., 2022; Lopez-Cepero et al., 2019). It has been suggested that the palatability of sugary-fatty foods may improve mood and reduce stress responses, and that eating could distract from negative emotions (Macht, 2008). Considering that the pandemic was a stressor, emotional eaters may have turned to energy-dense foods to alleviate negative emotions during the outbreak. This is concerning as dietary energy density has been positively linked to markers for chronic diseases, such as insulin insensitivity (Vernarelli et al., 2015).

We also examined the overall consumption of fruits and vegetables by food security status. Aligned with studies conducted during the pandemic, we found that food-insecure participants consumed fewer fruits and vegetables than food-secure respondents (Coulthard et al., 2021; Litton and Beavers, 2021). Healthier dietary patterns that include fruits and vegetables are more expensive (Rao et al., 2013), which may explain why food-insecure households experiencing economic hardship consume less fresh produce. Furthermore, pandemic-related factors, such as job disruption, food shortages, and shopping restrictions, may have further contributed to a decrease in the intake of fruits and vegetables (Litton and Beavers, 2021).

5. Conclusions

Our results suggest that the relationship between food insecurity, emotional eating, and weight status is more complex and not yet clearly defined. We also found a small positive correlation between emotional eating and the intake of energy-dense snacks and observed a lower consumption of fruits and vegetables among food-insecure persons during the pandemic. More research is needed to further investigate the longitudinal relationship between emotional eating and food insecurity to identify potential targets for interventions and prevent nutrition-related comorbidities.

Financial support

This work was supported by the Circle of Friends Molly and Gene Rauen Endowed Research Assistance Award.

CRediT authorship contribution statement

Andreza S.B. Souza: Writing – original draft, Methodology, Conceptualization. Marcelle M. Dougan: Writing – review & editing, Methodology, Formal analysis, Conceptualization. Giselle A.P. Pignotti: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We wish to thank those who assisted in the dissemination of the survey and the study participants for their time. We also thank the funding from Circle of Friends Molly and Gene Rauen Endowed Research Assistance Award to help support this research. This research is conducted as part of the National Food Access and COVID Research Team (NFACT). NFACT is a national collaboration of researchers committed to rigorous, comparative, and timely food access research during the time of COVID-19. To learn more visit: www.nfactresearch.org.

Data availability

The authors do not have permission to share data.

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