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. Author manuscript; available in PMC: 2024 Dec 1.
Published in final edited form as: Appetite. 2023 Sep 22;191:107062. doi: 10.1016/j.appet.2023.107062

Associations among Eating Behaviors, Food Security Status, and Dietary Intake during Pregnancy

Lenka H Shriver 1, Sally G Eagleton 2, Mali Hosseinzadeh 3, Cheryl Buehler 4, Laurie Wideman 5, Esther M Leerkes 6
PMCID: PMC10957504  NIHMSID: NIHMS1935380  PMID: 37742786

Abstract

Dietary intake of certain food groups and/or nutrients during pregnancy has been associated with maternal and infant pregnancy-related outcomes. Few studies have examined how behavioral and environmental factors interact to influence prenatal diet. We examined associations between eating behaviors (dietary restraint, emotional eating, external eating) and food security status regarding dietary intake of selected nutrients/food groups during pregnancy. Participants (N = 299; 29% Non-Hispanic Black; 16% ≤ high school education; 21% food insecure) completed validated questionnaires to assess estimated daily intake of food groups/nutrients during pregnancy [e.g., added sugars from sugar-sweetened beverages (SSBs), % of energy from fat, fruit and vegetable (FV) intake] via National Cancer Institute Dietary Screener Questionnaires); eating behaviors (Dutch Eating Behavior Questionnaire); and food security status (6-item USDA Food security Module). Separate hierarchical multiple regressions for each dietary outcome were conducted controlling for maternal age, education, income-to-needs, race/ethnicity, pre-pregnancy BMI, and gestational diabetes. A significant interaction was found between dietary restraint and food security status on added sugar intake from SSBs (β = −0.15, p = 0.02). The negative association between restraint and added sugar from SSBs was stronger among food insecure participants (β = −0.47, p < 0.001 vs. β = −0.15, p = 0.03). Higher external eating (β = 0.21, p < 0.01) and lower restraint (β = −0.13, p = 0.03) were associated with higher % of energy from fat and living in a food insecure household (β = −0.15, p = 0.01) was associated with lower FV intake. Understanding dietary intake during pregnancy requires consideration of the broader context in which eating behaviors occur.

Keywords: pregnancy, prenatal diet, eating behaviors, food security

1. Introduction

1.1. The Role of Prenatal Diet in Maternal and Infant Health

Diet has been shown to both directly and indirectly influence pregnancy-related outcomes, including a healthy gestational weight gain (Angali, Shahri, & Borazjani, 2020; Hirko, Comstock, Strakovsky, & Kerver, 2020; Murphy, Stettler, Smith, & Reiss, 2014). Although overall diet quality has not been linked to better outcomes across all studies (Shin, Bianchi, Chung, Weatherspoon, & Song, 2014), certain food groups and/or nutrients appear to be protective, whereas others have adverse effects on maternal and/or infant outcomes (Angali et al., 2020; Hirko et al., 2020; Scott & Aubuchon-Endsley, 2021; Shin et al., 2014). A diet rich in fruit and vegetables (FV) is associated with a lower risk of cardiovascular disease, diabetes, and cancer and it also plays an important role in weight management across the lifespan (Aune et al., 2017). Among pregnant women, greater FV consumption has been correlated with better weight management (Hirko et al., 2020). In a study of 500 pregnant women, Angali et al. (2020) found that a “vegetable-fruit-protein” dietary pattern was associated with lower risk of hyperglycemia whereas a “high-fat-fast food” dietary pattern was associated with greater risk of hyperglycemia and excessive weight gain (Angali et al., 2020). Prenatal diet may also have adverse effects on maternal psychological health and infant weight outcomes (Avalos et al., 2020; Crume et al., 2016; Scott & Aubuchon-Endsley, 2021; Shapiro et al., 2016). Overall, there is substantial evidence that healthy dietary intake during pregnancy is a key prevention strategy for optimizing pregnancy-related health outcomes for both the mother and the infant.

1.2. Associations Among Eating Behaviors, Dietary Intake, & Food Security

Dietary intake is influenced not only by biological factors, but also a variety of psychological and socioenvironmental factors (Leng et al., 2017). Restrained (sometimes referred to as dietary restraint), external, and emotional eating represent three broad eating styles that influence daily food intake, with one behavior typically more dominant than the others in any given individual (van Strien, Frijters, Bergers, & Defares, 1986). Emotional eating refers to using food in response to emotional triggers, most often when faced with negative emotions, such as sadness, anger, and/or irritability (Arnow, Kenardy, & Agras Stewart W., 1995; Strien, Schippers, & Cox, 1995). Previous studies show that emotional eating tends to be higher among individuals with overweight and obesity compared to their healthy weight counterparts (Guerrini-Usubini et al., 2023). External eating is defined as eating in response to external food cues, such as the sight, smell, or taste of food, regardless of internal feelings of hunger and satiety (Strien et al., 1995; van Strien et al., 1986). In previous research, higher levels of both emotional and external eating behaviors have been associated with a variety of negative nutrition-related outcomes, including increased intake of foods that can exacerbate weight gain, such as fast food and snack foods high in fat and/or sugar (Anschutz, Van Strien, Van De Ven, & Engels, 2009; Benbaibeche, Saidi, Bounihi, & Koceir, 2023; Camilleri et al., 2014; Elran Barak et al., 2021; Fuente González et al., 2022; Konttinen, Van Strien, Männistö, Jousilahti, & Haukkala, 2019; Zhang et al., 2020).

Restrained eating was originally defined as a chronic restriction of food intake to control weight and is often marked by repeated attempts to lose weight via dietary restriction followed by bouts of overeating (Heatherton, Polivy, & Herman, 1991; Herman & Mack, 1975). Previous studies suggest that dietary restraint is associated with an increased risk of obesity and disordered eating (Bryant, Rehman, Pepper, & Walters, 2019; Cornelis et al., 2014; Lopez- Cepero, Frisard, Lemon, & Rosal, 2018). However, more recent measures of restrained eating have been introduced, including the Dutch Eating Behavior Questionnaire (DEBQ), that characterize it differently and identify individuals who are considered “successful restrainers,” meaning they are able to restrict intake without subsequent overeating (J. Polivy, Herman, & Mills, 2023). Thus, a new line of inquiry has emerged suggesting that restrained eating is associated with positive nutrition-related outcomes for some individuals, such as lower energy intakes, greater vegetable consumption and less frequent unhealthy snacking (Adriaanse, de Ridder, & Evers, 2011; Aguirre, Kuster, & Koehler, 2017; Fuente González et al., 2022; Olea López & Johnson, 2016; Yong et al., 2021). However, the nature of the associations between restrained eating and various nutrition-related outcomes remains unclear, largely due to the use of different conceptualizations measures of restrained eating across studies, and differences in the main outcomes and/or sample characteristics across studies (J. Polivy et al., 2023).

Only a few studies have examined restrained eating among pregnant women, with several investigations finding that a higher level of restraint was associated with a greater gestational weight gain (Heery, Wall, Kelleher, & Mcauliffe, 2016; Mumford, Siega-Riz, Herring, & Evenson, 2008). Although very little research exists on restrained eating and diet in this unique population (Laraia, Vinikoor-Imler, & Siega-Riz, 2015; Nansel et al., 2020), a recent study of pregnant women utilizing the DEBQ found that restrained eating was associated with a higher overall diet quality (Nansel et al., 2020). Given the complex nature of restrained eating and the scarcity of research with pregnant women, further research is warranted to better understand whether and what role dietary restraint plays in diet quality during pregnancy (Kosmas, Wildes, Graham, & O’Connor, 2023).

Food insecurity is defined as “a lack of consistent, dependable access to enough food for active, healthy living” (Coleman-Jensen, Rabbitt, Gregory, & Singh, 2017). Previous research shows that food insecurity is associated with both dietary intake and eating behaviors (Augusto, De Abreu Rodrigues, Domingos, & Salles-Costa, 2020; Bastian et al., 2022; Gonzalez-Nahm, Østbye, Hoyo, Kravitz, & Benjamin-Neelon, 2022; Laraia, Epel, & Siega-Riz, 2013). Leung et al. (2014) found that individuals living in food insecure households consumed more sugar-sweetened beverages (SSBs) and salty snacks, fewer vegetables, and had a lower overall diet quality compared to their food secure counterparts (Leung, Epel, Ritchie, Crawford, & Laraia, 2014). Though research in this area is limited among pregnant women, some studies have shown that food insecure women consume fewer FV than food secure women during pregnancy (Gonzalez-Nahm et al., 2022; Nunnery, Labban, & Dharod, 2017).

Previous research has demonstrated that food insecurity can influence eating behaviors and thus might influence associations between eating behaviors and dietary outcomes (Bastian et al., 2022; Kosmas et al., 2023; Laraia, Vinikoor-Imler, & Siega-Riz, 2015). For example, some individuals may engage in greater dietary restraint to manage food supply in the face of food insecurity (Middlemass et al., 2021). In a large study of pregnant women, Laraia et al. (2013) found that higher restraint was associated with a greater risk of excessive weight gain in the context of food insecurity, indicating that women experiencing food insecurity who engage in restrained eating may struggle more to achieve the recommended gestational weight gain. Food insecure individuals also commonly report higher levels of stress, anxiety, and depressive symptoms due to uncertainty about when and whether they will have enough food to feed themselves and their family members (Augusto et al., 2020). The associated uncertainty and negative emotions may increase their vulnerability and/or tendency to engage in emotional or external eating. Because disinhibited eating behaviors, such as external and emotional eating, typically involve consuming highly palatable, energy-dense foods, these behaviors may ultimately lead to greater consumption of sugar, fat, and overall energy intake over time (Augusto et al., 2020; Ivers & Cullen, 2011; Laraia et al., 2015; Stinson et al., 2018).

1.3. The Present Study

Pregnancy is a unique period marked by significant physiological, hormonal, and emotional changes (Altazan et al., 2019). Pregnant women experiencing food insecurity may be particularly vulnerable to consuming a lower quality diet due to a lack of consistent, healthy food availability and greater vulnerability to disinhibited eating behaviors (Augusto et al., 2020; Bastian et al., 2022). Thus, the main purpose of this study was to examine associations among eating behaviors, food security status, and dietary outcomes (i.e., estimated daily intake of total added sugars, added sugars from SSBs, FVs, and percentage energy from fat) in a community sample of pregnant women. We hypothesized that disinhibited eating behaviors (i.e., external eating and emotional eating) would be uniquely associated with a greater consumption of total added sugars, added sugars from SSBs, and percentage energy from fat, as well as with lower FV intake. We also hypothesized that higher restrained eating would be associated with lower consumption of total added sugars, added sugars from SSBs, and percentage energy from fat, as well as higher FV intake. Finally, we hypothesized that food security status would moderate associations between eating behaviors and dietary outcomes such that the positive associations between disinhibited eating behaviors and poor dietary outcomes (e.g., greater consumption of added sugar) and the negative association between restrained eating and poor dietary outcomes would be stronger among food insecure compared to food secure women.

2. Methods

2.1. Study Design, Participants and Procedures

Data presented in this cross-sectional study came from the Infant Growth and Development Study (iGrow). iGrow is a longitudinal study examining early life risk factors for obesity in which we followed participants from the third trimester of pregnancy to child age of 2 years (Leerkes, Buehler, Calkins, Shriver, & Wideman, 2020). Recruitment occurred in an urban area of North Carolina from January 2019 to April 2022, with a break mandated by COVID related safety concerns from March 2020 to November 2020. Participants were recruited via childbirth education classes (in person and virtual) provided at three locations, via targeted emails sent to listservs and human resources offices of large local employers (e.g., local school system and hospital), referral from enrolled participants, paid advertisements in print and online media, flyers with QR codes posted in local stores, parks and other public locations likely to draw families, and social media outlets. The inclusion criteria for iGrow were as follows: 1) maternal age of 18 years or older; 2) expecting a singleton birth; 3) being fluent in oral and written English; and 4) planning to remain in the region for at least 3 years. Of the 542 who initially agreed to participate, 3 became ineligible because they gave birth prior to completing the prenatal measures, 240 enrolled via phone but never completed the written consent and/or measures, and 299 women were consented, enrolled, and completed the prenatal wave. Data analyzed in the current study came from the prenatal wave and included the full sample of participants (N = 299). All of the participants were pregnant individuals and thus, we refer to participants as “pregnant women” and/or “mothers” from here on. Prior to a lab visit during the third trimester of pregnancy, participants provided written consent and completed questionnaires online via Qualtrics. Participants received $50 at the prenatal lab visit and were compensated for transportation if desired. The study was approved by the university’s Institutional Review Board (protocol #18-0198).

2.2. Study Measures and Variables

Sociodemographic Characteristics.

Participants reported their age, race/ethnicity, educational attainment, household income, household composition, and additional demographic characteristics prenatally. An income-to-needs ratio was calculated by dividing total annual household income by its corresponding poverty threshold based on the number of household members and the year that income was reported. The threshold values used in the current study were published by the U.S. Census Reports in the Poverty Thresholds for 2018 and 2019 (United States Census, 2022).

Anthropometric Variables.

Pre-pregnancy BMI (kg/m2) was calculated using participants’ height, which was measured in duplicate by trained research staff during the prenatal lab visit, and self-reported pre-pregnancy weight. Though kept as a continuous measure in the analyses, for descriptive purposes pre-pregnancy BMI was used to categorize participants as having: 1) underweight (BMI < 18.5 kg/m2); 2) normal weight (BMI = 18.5–24.9 kg/m2); 3) overweight (BMI = 25.0–29.9 kg/m2); and 4) obesity (BMI ≥ 30.0 kg/m2) (Centers for Disease Control and Prevention, 2022; National Heart & National Institute of Diabetes, 1998).

Dietary Intake.

Dietary intake was assessed using the Dietary Screener Questionnaire (DSQ). The DSQ is a 26-item self-report screener that was developed by the National Cancer Institute (NCI). The DSQ screener is comprised of questions about intake of 19 food-group components over the past month used to predict estimated daily intakes of fruits, vegetables, fruits and vegetables together, dairy, whole grains, added sugars, added sugars from SSBs, calcium, and dietary fiber. Frequency of consumption for each food and beverage item is assessed on a 9-point (coded never to 2-3 times per day) and on an 11-point scale (coded never to 6 or more times per day), respectively. The DSQ estimates of mean intakes have been shown to agree closely with dietary intakes estimated from 24-h dietary recalls in a large nationally representative sample of individuals participating in the National Health and Nutrition Examination Survey (NHANES) 2009-2010, (Thompson et al., 2017). Dietary fat intake was estimated using the NCI’s Percentage Energy from Fat Screener (Thompson et al., 2007, 2008). The measure includes 15 food items, with responses ranging from 1 = never to 6 = almost always/always. For both screeners, scoring algorithms were created by regressing the frequency responses of relevant foods and sex- and age-specific portion size information on dietary intake from 24-h dietary recalls using the NCI usual intake method (National Cancer Institute, 2021a, 2021b). The publicly available algorithms were used to calculate estimated daily intake of total added sugars (tsp/day), added sugars from sugar sweetened beverages (SSBs) (tsp/day), fruit and vegetable (FV) intake (including legumes and excluding French fries) (cups/day), and the percentage of daily energy (% kcal/day) consumed from fat (National Cancer Institute, 2018; Thompson, Midthune, Kahle, & Dodd, 2017; Thompson et al., 2007, 2008). These measures have been used in previous research with adults, including studies with pregnant women (Mahabamunuge et al., 2021; Scott & Aubuchon-Endsley, 2021).

Eating Behaviors.

Maternal eating behaviors were assessed using a modified version of the Dutch Eating Behavior Questionnaire (DEBQ) (van Strien et al., 1986). The DEBQ is a 33-item measure that assesses emotional eating (13 items; α = .86; e.g., “Do you have a desire to eat when you are depressed or discouraged”), external eating (10 items; α = .83; e.g., “If food tastes good to you, do you want to eat more than normal”), and restrained eating (10 items; α = .92; e.g., “Do you take into account your weight with what you eat”). The response options ranged from 1 = never to 5 = very often and a mean score was calculated for each subscale, with a higher score indicating a higher level of that particular eating behavior. Because pregnant women are expected to gain weight, one item that was related to weight gain was modified and participants were asked to think about the time prior to pregnancy when answering this question. DEBQ has been previously validated in studies with adults and also utilized to quantify eating behaviors in pregnant women (Blau, Orloff, Flammer, Slatch, & Hormes, 2018; Burton, J. Smit, & J. Lightowler, 2007; Shloim, Hetherington, Rudolf, & Feltbower, 2015).

Food Security Status.

Household food security status was assessed using the 6-item Short Form Household Food Security Module from the United States Department of Agriculture (USDA) (Blumberg, Bialostosky, Hamilton, & Briefel, 1999). Rather than asking about food insecurity during the past 12 months, the survey was modified for the current study to assess food insecurity experienced during the current pregnancy. Response options were dichotomized (1 = yes; 0 = no) and summed to create a total score ranging from 0 to 6, which had acceptable internal consistency (Cronbach’s α = .89). Participants with 2 or more affirmative responses were classified as food insecure (Blumberg et al., 1999).

2.3. Statistical Analyses

Preliminary analyses were conducted in SPSS Statistics (Version 29, IBM Chicago IL). We used univariate statistics to describe the sample, t-tests for continuous variables, and Chi-squared tests for categorical variables to examine differences in sample characteristics and key study variables by food security status. Pearson correlations were calculated to examine bivariate associations between covariates and key study variables. Missing data on primary variables in our sample were minimal [dietary intake variables (n = 2); food security status (n = 2); eating behaviors (n = 10)]. Primary analyses were conducted in MPLUS (Muthén & Muthén, 2012) using full information maximum likelihood to handle missing data, which minimizes estimation bias (Acock, 2005). Hierarchical, multiple regression models were used to examine cross-sectional associations between eating behaviors (independent variable) and food security status (hypothesized moderator) for each dietary outcome (dependent variable) during pregnancy, controlling for participant age, educational attainment, race/ethnicity, pre-pregnancy BMI, gestational diabetes diagnosis, and household income-to-needs ratio. An interaction term (grand-mean centered before multiplication) between each eating behavior and food security status was included in models to test the hypothesis that the association between eating behaviors and dietary outcomes would differ according to food security status. Significant interactions were probed by calculating simple slopes for each level of the moderator (i.e., food secure vs. food insecure). Separate analytic models were conducted for each dietary outcome. The alpha level for statistical significance was set at p < 0.05.

3. Results

Table 1 displays sample characteristics and descriptive statistics, for the total sample and by food security status. On average, participants were 29.71 (5.48) years old. Approximately half of participants self-identified as non-Hispanic Black (29%) or Hispanic/multiracial/other (19%), 35% of participants reported attending some college or less, and the sample’s median annual household income was $62,500. More than half of the sample had overweight or obesity (61%) and 21% of participants reported being food insecure during pregnancy. On average, food insecure participants were younger (p < 0.001) and had a lower income-to-needs ratio (p < 0.001). A greater proportion of food insecure participants self-identified as non-Hispanic Black (53%, p < 0.001) and reported a high school education or less (38%, p < 0.001). In addition, predicted daily intake of total added sugars (p = 0.03), added sugars from SSBs (p = 0.01), and percentage of energy from fat (p = 0.01) was higher among food insecure compared to food secure participants whereas estimated daily intake of FVs was lower (p < 0.01).

Table 1.

Sample Characteristics and Descriptive Statistics by Food Security Status

Total Sample
(N = 299)		 Food Secure1
(N = 236)		 Food Insecure1
(N = 61)
Participant characteristics N M (SD)/% N M (SD)/% N M (SD)/% p-value
Age, years 297 29.71 (5.48) 235 30.29 (5.52) 60 27.42 (4.80) < 0.001
Income-to-needs ratio 290 3.49 (2.96) 231 4.00 (3.01) 59 1.51 (1.68) < 0.001
Pre-pregnancy weight status2 0.16
   Underweight 8 2.7 4 1.7% 4 6.6%
   Normal weight 108 36.1 89 37.7% 19 31.1%
   Overweight 83 27.8 67 28.4% 16 26.2%
   Obese 100 33.4 76 32.2% 22 36.1%
Gestational diabetes3 0.94
   Yes 31 10.4 24 10.2% 6 9.8%
   No 268 89.6 212 89.8% 55 90.2%
Race/ethnicity < 0.001
   Non-Hispanic White 157 52.5% 141 59.7% 16 26.2%
   Non-Hispanic Black 86 28.8% 54 22.9% 32 52.5%
   Hispanic/Other/Multiracial 56 18.7% 41 17.4% 13 21.3%
Educational attainment < 0.001
   ≤High school diploma/GED 47 15.9% 29 12.3% 18 38.3%
   Some college 57 19.3% 37 15.7% 20 33.3%
   2-year college degree 24 8.1% 18 7.7% 6 10.0%
   4-year college degree 74 25.1% 65 27.7% 9 15.0%
   Post graduate work/degree 93 31.5% 86 36.6% 7 11.7%
Eating behaviors 289 233 56
   Restrained eating 2.19 (0.78) 2.23 (0.77) 2.05 (0.83) 0.13
   Emotional eating 2.17 (0.92) 2.18 (0.94) 2.13 (0.84) 0.68
   External eating 2.96 (0.61) 2.98 (0.61) 2.86 (0.60) 0.19
Dietary intake outcomes 297 236 61
   Total added sugars, tsp/day 15.89 (4.68) 15.53 (4.27) 17.28 (5.85) 0.03
   Added sugars from SSBs, tsp/day 6.57 (3.66) 6.18 (3.12) 8.07 (5.03) 0.01
   Percentage energy from fat (% kcal/day) 32.96 (4.26) 32.61 (4.13) 34.34 (4.50) 0.01
   Fruit and vegetables, cups/day 2.49 (0.69) 2.55 (0.70) 2.25 (0.60) < 0.01
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1

t-tests and chi-squared tests were used to compare differences by food security status for continuous and categorical variables, respectively.

2

Weight status [underweight (BMI = <18.5 kg/m2; normal weight (BMI = 18.5-24.9 kg/m2); overweight (BMI = 25.0-29.9 kg/m2); obese (BMI = ≥ 30 kg/m2) (CDC, 2022).

3

Gestational diabetes diagnosis (Yes=1, No=0).

There were several statistically significant correlations between covariates and dietary outcomes. Participant age was negatively correlated with added sugar intake from SSBs (r = −0.18, p < 0.01). Participant education and household income-to-needs were both negatively correlated with total added sugar intake in (education: r = −0.16, p < 0.01; income-to-needs: r = −0.17, p < 0.01), added sugar intake from SSBs (education: r = −0.24, p < 0.001; income-to-needs: r = −0.22, p < 0.001), and percentage energy intake from fat (education: r = −0.15, p < 0.01; income-to-needs: r = −0.21, p < 0.001), and positively correlated with FV intake (education: r = 0.16, p < 0.01; income-to-needs: r = 0.15, p < 0.01). Pre-pregnancy BMI was negatively correlated with FV intake (r = −0.17, p < 0.01) and positively correlated with percentage energy intake from fat (r = 0.20, p < 0.001). Compared to other racial-ethnic groups, non-Hispanic White participants tended to consume less added sugar from SSBs (M = 6.14 tsp/day, SD = 3.30 vs. M = 7.05 tsp/day, SD = 3.99, t (295) = 2.15, p = 0.03) and had a lower percentage energy intake from fat (M = 32.39% of kcal/day, SD = 3.89 vs. M = 33.60% of kcal/day, SD = 4.57, t (295) = 2.46, p = 0.01). Compared to other racial-ethnic groups, non-Hispanic Black participants tended to have a higher percentage energy intake from fat (M = 33.89% of kcal/day, SD = 4.81 vs. 32.58% of kcal/day, SD = 3.96, t (295) = −2.42, p = 0.02).

Hierarchical regression analyses were conducted to examine the main effects of eating behaviors and food security status on each dietary outcome (block 1) and to examine whether associations between eating behaviors and dietary outcomes during pregnancy varied by food security status (block 2, Table 2). There were main effects for gestational diabetes and restrained eating on total added sugar intake. Having a gestational diabetes diagnosis (β = −0.13, SE = 0.06, p = 0.03) and having higher restraint (β = −0.21, SE = 0.06, p < 0.01) were uniquely associated with lower added sugar intake. Higher restraint was also associated with lower added sugar intake from SSBs (β = −0.23, SE =0.06, p < 0.001). Additionally, experiencing food insecurity during pregnancy (β = 0.15, SE = 0.06, p = 0.01) and higher pre-pregnancy BMI (β = 0.14, SE = 0.06, p = 0.02) were associated with higher added sugar intake from SSBs whereas a gestational diabetes diagnosis was associated with lower added sugar intake from SSBs (β = −0.13, SE = 0.06, p = 0.02). There was an interaction between restrained eating and food security status on added sugar intake from SSBs (β = −0.15, SE = 0.06, p = 0.02) (Figure 1). Specifically, the association between higher restraint and lower added sugar intake from SSBs was stronger among food insecure (β = −0.47, SE = 0.11, p < 0.001) compared to food secure participants (β = −0.15, SE = 0.07, p = 0.03). Each one unit increase in dietary restraint was associated with a 2.21 tsp/day decrease in added sugars from SSBs among food insecure participants and a 0.69 tsp/day decrease in added sugars from SSBs among food secure participants.

Table 2.

Hierarchical Regression Models Predicting Estimated Daily Dietary Intake of Selected Food Groups and/or Nutrients from Eating Behaviors and Food Security Status during Pregnancy (N=299)

Outcome: Total added sugars Block 1 Block 2
Main effects β (SE) p-value β (SE) p-value
Age 0.01 (0.06) 0.99 −0.02 (0.07) 0.80
Education −0.08 (0.08) 0.29 −0.08 (0.08) 0.34
Income-to-needs ratio −0.07 (0.07) 0.35 −0.08 (0.07) 0.30
Pre-pregnancy BMI 0.08 (0.06) 0.17 0.08 (0.06) 0.17
Gestational diabetes1 −0.13 (0.06) 0.03 −0.13 (0.06) 0.03
Non-Hispanic white2 0.11 (0.08) 0.19 0.11 (0.08) 0.19
Non-Hispanic black3 0.02 (0.08) 0.80 0.02 (0.08) 0.82
Food security status4 0.12 (0.06) 0.05 −0.08 (0.38) 0.84
Restrained eating −0.21 (0.06) <0.01 −0.15 (0.07) 0.03
Emotional eating 0.03 (0.08) 0.66 0.03 (0.08) 0.71
External eating 0.14 (0.07) 0.06 0.12 (0.08) 0.14
Interaction terms
Restrained Eating X food security status -- -- −0.11 (0.07) 0.10
Emotional eating X food security status -- -- −0.02 (0.08) 0.83
External eating X food security status -- -- 0.18 (0.38) 0.63
Outcome: Added sugars from SSBs Block 1 Block 2
Main effects
Age −0.05 (0.06) 0.42 −0.07 (0.06) 0.24
Education −0.09 (0.08) 0.26 −0.08 (0.08) 0.32
Income-to-needs ratio −0.05 (0.07) 0.52 −0.06 (0.07) 0.42
Pre-pregnancy BMI 0.14 (0.06) 0.02 0.14 (0.06) 0.02
Gestational diabetes1 −0.13 (0.06) 0.02 −0.12 (0.06) 0.04
Non-Hispanic white2 −0.01 (0.08) 0.93 −0.01 (0.08) 0.88
Non-Hispanic black3 −0.06 (0.08) 0.40 −0.07 (0.08) 0.34
Food security status4 0.15 (0.06) 0.01 0.19 (0.37) 0.60
Restrained eating −0.23 (0.06) <0.001 −0.16 (0.07) 0.02
Emotional eating −0.03 (0.08) 0.71 −0.04 (0.08) 0.62
External eating 0.09 (0.07) 0.19 0.10 (0.08) 0.20
Interaction terms
Restrained eating X food security status -- -- −0.15 (0.06) 0.02
Emotional eating X food security status -- -- −0.00 (0.08) 0.99
External eating X food security status -- -- −0.06 (0.37) 0.87
Outcome: Fruit and vegetable intake Block 1 Block 2
Main effects
Age 0.03 (0.07) 0.62 0.04 (0.07) 0.57
Education 0.10 (0.08) 0.21 0.10 (0.08) 0.20
Income-to-needs ratio −0.03 (0.08) 0.73 −0.02 (0.08) 0.78
Pre-pregnancy BMI −0.16 (0.06) 0.01 −0.15 (0.06) 0.01
Gestational diabetes1 −0.00 (0.06) 0.96 −0.01 (0.06) 0.93
Non-Hispanic white2 0.06 (0.08) 0.44 0.05 (0.08) 0.52
Non-Hispanic black3 0.08 (0.08) 0.29 0.08 (0.08) 0.33
Food security status4 −0.15 (0.06) 0.01 −0.09 (0.39) 0.82
Restrained eating 0.05 (0.06) 0.47 0.00 (0.07) 0.98
Emotional eating −0.07 (0.08) 0.37 −0.05 (0.09) 0.55
External eating −0.03 (0.08) 0.66 −0.02 (0.08) 0.77
Interaction terms
Restrained eating X food security status -- -- 0.08 (0.07) 0.23
Emotional eating X food security status -- -- −0.04 (0.09) 0.64
External eating X food security status −0.06 (0.39) 0.89
Outcome: Percentage energy from fat Block 1 Block 2
Main effects
Age −0.01 (0.06) 0.87 −0.00 (0.06) 0.96
Education −0.02 (0.08) 0.83 −0.02 (0.08) 0.84
Income-to-needs ratio −0.09 (0.07) 0.20 −0.09 (0.07) 0.24
Pre-pregnancy BMI 0.18 (0.06) <0.01 0.18 (0.06) <0.01
Gestational diabetes1 −0.04 (0.06) 0.48 −0.04 (0.06) 0.48
Non-Hispanic white2 −0.01 (0.08) 0.90 −0.02 (0.08) 0.76
Non-Hispanic black3 0.08 (0.08) 0.30 0.07 (0.08) 0.37
Food security status4 0.11 (0.06) 0.07 0.37 (0.37) 0.32
Restrained eating −0.13 (0.06) 0.03 −0.18 (0.07) <0.01
Emotional eating 0.07 (0.08) 0.37 0.08 (0.08) 0.29
External eating 0.21 (0.07) <0.01 0.24 (0.08) <0.01
Interaction terms
Restrained eating X food security status -- -- 0.09 (0.06) 0.15
Emotional eating X food security status -- -- −0.03 (0.08) 0.69
External eating X food security status -- -- −0.26 (0.37) 0.49
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1

Gestational diabetes diagnosis (Yes=1 vs. No=0).

2

Non-Hispanic white (non-Hispanic white=1 vs. all other=0);

3

Non-Hispanic black (non-Hispanic black=1 vs. all other=0).

4

Households were classified as food insecure if 2 or more of the 6 items from the Household Food Security Short Form were answered affirmatively and food secure if less than 2 of the 6 items were answered affirmatively. Note: Bold font indicates statistically significant effects at p < 0.05.

Figure 1.

Figure 1.

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Interaction between Restrained Eating and Food Security Status on Predicted Intake of Added Sugars from Sugar-sweetened Beverages (SSBs) (N=299).

Note: Adjusted for participant age, education, race/ethnicity, pre-pregnancy BMI, gestational diabetes, and household income-to-needs ratio.

There were main effects for food security status and pre-pregnancy BMI on FV intake such that food insecure participants reported lower FV intake (β = −0.15, SE = 0.06, p = 0.01) and a higher pre-pregnancy BMI was associated with lower FV intake (β = −0.16, SE = 0.06, p = 0.01). There were main effects for pre-pregnancy BMI, restrained eating, and external eating on percentage energy intake from fat. A higher pre-pregnancy BMI was associated with a higher percentage energy intake from fat (β = 0.18, SE = 0.06, p < 0.01); higher restrained eating was associated with a lower percentage energy intake from fat (β = −0.13, SE = 0.06, p = 0.03); and higher external eating was associated with a higher percentage energy intake from fat (β = 0.21, SE = 0.07, p < 0.01). Across all dietary outcomes, there were no main or interaction effects observed for emotional eating.

4. Discussion

The current study examined associations between pregnant women’s eating behaviors several dietary outcomes and tested food security status as a potential moderator. Our findings advance the current understanding of factors that influence prenatal dietary intake by demonstrating that restrained and external eating, but not emotional eating, were each uniquely associated with dietary intake during pregnancy. Because unhealthy dietary intake during pregnancy is associated with adverse maternal and infant health outcomes (Driscoll & Gregory, 2020; Ferrara, 2007; Moses, Morris, Petocz, San Gil, & Garg, 2011), the poor dietary intake found in our sample is concerning. The mean reported added sugar intake far exceeded the maximum of 6 teaspoons per day that is currently recommended by the American Heart Association for adult females (Johnson et al., 2009). The dietary fat intake was within the Dietary Recommended Intakes of 20-35% of total energy, with food insecure participants nearly exceeding the upper limit (Institute of Medicine (US), 2000). Pregnant women in our sample were also significantly below the recommended amounts of cups for FVs per day recommended by the Dietary Guidelines for Americans (U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2020). Unfortunately, our results are consistent with findings of other studies with pregnant women where excessive amounts of added sugars and/or dietary fat and inadequate amounts of FV have been reported (Casas, Barquero, & Estruch, 2020; Francis, Zhang, Witrick, & Chen, 2019; Hirko et al., 2020; Shin et al., 2014).

Our hypothesis that restrained eating would be associated with positive dietary outcomes was supported. Consistent with previous literature (Most et al., 2019), the current study showed that pregnant women who engaged in greater restraint consumed a lower amount of total added sugars and had a lower estimated percentage energy intake from fat. A previous study of 187 Latina women found a similar trend where maternal dietary restraint was linked to more healthful food choices (Contento, Zybert, & Williams, 2005). Furthermore, our finding that the negative association between restrained eating and added sugar intake from SSBs was stronger among food insecure compared to food secure participants is novel and enhances the current understanding of the role of dietary restraint on SSB consumption in the context of food insecurity. Specifically, at lower levels of restraint, food insecure women consumed approximately 3 tsp/day more of added sugar from SSBs than food secure pregnant women. However, at higher levels of restraint, pregnant women consumed a similar amount of added sugar from SSBs regardless of food security status. These finding might suggest that restrained eating, measured by a research tool that defines this eating behavior as a consistent restricted intake without bouts of overeating (J. Polivy, et al. 2023) might be a more salient factor for reducing added sugar intake from SSBs among pregnant food insecure compared to food secure individuals. However, it is important to note that added sugar intake from SSBs was relatively high in our total sample and was higher among food insecure compared to food secure pregnant women. Overall, our results must be interpreted with caution as the direction of these associations cannot be determined in this cross-sectional study.

The significant interaction between restrained eating and food security status on added sugar intake from SSBs in our sample could be explained, at least in part, by different experiences with the broader food environment (Nunnery et al., 2017). For example, many communities with a high proportion of food insecure households are designated as food deserts, defined as residential areas with no supermarket or large grocery store within a 1-mile radius (Ploeg, Breneman, & Farrigan, 2009). Consequently, individuals experiencing food insecurity make daily food choices in a more obesogenic food environment and thus, might consume more added sugars from SSB, as shown in the current study. Consequently, food insecure individuals might have to exert a greater degree of restraint to lower SSB intake compared to food secure individuals. Previous studies suggest that a certain type and degree of dietary restraint might, in fact, support better dietary intake and weight maintenance and our study provides preliminary evidence that achieving a lower added sugar intake might be a greater challenge for pregnant women living in food insecure households. Other research also suggests that dietary restraint might be used by some individuals to conserve food due to limited resources or during times of scarcity, which could increase the risk of disordered eating and other health outcomes such as weight cycling (i.e., cycles of weight loss and regain) (Middlemass et al., 2021). As noted earlier, the nature of the associations between restrained eating and nutrition-related outcomes has not been clarified in the existing literature. Although focused on a different nutrition-related outcome, our results contrast with findings from a study by Laraia et al. (2013) in which restraint was associated with a greater risk of excessive gestational weight gain among food insecure pregnant women. However, it is critical to point out that Laraia et al. utilized a measure of restrained eating that primarily assessed history of dieting and weight fluctuations rather than more consistent and/or “flexible” restraint that is captured by other measures, such as the DEBQ, that was utilized in the current study (J. Polivy, et al. 2023) Thus, future longitudinal studies are warranted to clarify the nature and direction of these associations across different populations, including pregnant individuals in the context of food insecurity (Polivy, Herman, & Mills, 2020).

Emotional eating did not predict any of the dietary outcomes in our sample of pregnant women. This finding was unexpected given that emotional eating is common among individuals with excessive adiposity (Guerrini-Usubini et al., 2023) and the majority of our participants had overweight or obesity prior to pregnancy. The level of emotional eating in our sample was relatively low but similar to previous research, including a study of pregnant women where only 7% were classified as emotional eaters (van der Wijden et al., 2014; van Strien, Herman, & Verheijden, 2009). Furthermore, emotional eating did not differ by food security status, suggesting that our food insecure participants might have been relatively resilient to using food as a response to stress or negative emotions. Recent research suggests that the types of emotions that might stimulate emotional eating is broader than previously thought and that emotional eating does not always result in negative outcomes, including poor diet quality (Braden, Musher-Eizenman, Watford, & Emley, 2018). Our findings highlight the need to assess emotional eating in response to different emotions and for including assessments of perceived stress and/or physiological stress in future studies examining eating behaviors among pregnant women.

Our finding that external eating was associated with a higher percentage energy from fat is consistent with previous studies examining dietary patterns. A study by Anschutz et al. (2009) found that external eating tends to be associated with consuming a greater amount of energy as well as more high-fat foods and fewer nutrient dense foods due to the contextual nature of external eating (Anschutz et al., 2009). For example, external eating is often triggered in situations such as eating out and social gatherings making it might be more difficult to control the fat content (i.e., high-fat meat) compared to the sugar content of the foods consumed (i.e., choosing diet soda vs. regular soda) in social situations (van Strien et al., 2009). Previous research found that positive dietary changes are possible during pregnancy; yet might be less likely among pregnant women with overweight or obesity prior to pregnancy compared to those (Bennett et al., 2018; Hui et al., 2014). Given that in our sample a higher pre-pregnancy BMI was associated with greater percentage of energy consumed from fat, our findings support this notion from previous research.

Food insecurity was associated with a higher percentage of energy consumed from fat and lower FV consumption in our sample, which is consistent with previous research (Leung et al., 2014; Nunnery et al., 2017). Because low-income food insecure families often live in food deserts, availability of nutrient dense, low-fat foods, such as FV, is often limited whereas more affordable high-fat foods are more accessible (Berg et al., 2022). More than 20% of our sample reported some level of food insecurity. Given the established associations between food insecurity and poor nutrition outcomes (Bhattacharya, Currie, & Haider, 2004; Stinson et al., 2018), our findings related to food insecurity are concerning. In recent years, nationally representative data have indicated that Black pregnant women living in food insecure households represent a high-risk group in terms of poor pregnancy health outcomes (Minhas et al., 2020; Petersen et al., 2019). In the current study, over half of the food insecure participants self-identified as Non-Hispanic Black, which further confirms the clear racial disparities that exist in food security status among pregnant women in the U.S.

4.1. Strengths and Limitations

The present study has several strengths that should be noted. First, our study examines the associations between eating behaviors and dietary outcomes among pregnant women, a unique population that undergoes significant physiological, emotional, and hormonal changes that influence food choices and thus affect several maternal and infant health outcomes. Therefore, the current study fills an important gap in the current literature. Second, our data come from a relatively diverse sample in terms of socioeconomic status and racial-ethnic identity, with more than half of participants identifying as non-White. Third, more than 20% of the participants reported experiencing food insecurity, despite a wide range of educational attainment, pointing to the recent changes in the landscape of food insecurity in the U.S. (Morales, Morales, & Beltran, 2021).

The study also has several limitations that should be noted. Our findings come from an urban and suburban population of pregnant women and cannot be generalized to other populations. Second, our analyses are descriptive in nature. Given the utility of the findings from this cross-sectional study, however, we plan to examine the associations between eating behaviors and food insecurity and dietary outcomes using longitudinal data from the iGrow study in the future. Lastly, we relied on maternal report of eating behaviors and dietary intake and were not able to utilize more detailed assessments of dietary intake, such as 24-hour recalls, due to the high burden of data collection on participants. However, the NCI screeners represent widely utilized and validated measures of dietary intakes and have been used in recent studies with pregnant women (Hirko et al., 2020).

4.2. Conclusion

Dietary intake during pregnancy influences not only weight gain, but also many other physical and mental health outcomes of mothers and their offspring (Scott & Aubuchon-Endsley, 2021). Although previous studies have examined prenatal diet (Hartman et al., 2017), less is known about how eating behaviors and food security influence diet during pregnancy. Based on the results of the current study, restrained eating appears to be associated with lower intakes of added sugars and % energy from fat among pregnant women, especially among those who are faced with food insecurity. It is also clear that both external eating and food insecurity are associated with a less healthy diet, reflected by lower FV consumption and higher fat intake. Interestingly, emotional eating was not associated with intake of any of the dietary outcomes examined in our study. However, further research examining eating behaviors among pregnant women while utilizing advanced measures of dietary intake and eating behaviors and using longitudinal designs are warranted in the literature.

Acknowledgements

We would like to thank the participating families for their time and effort as part of this study. We would also like to thank our research team, led by Megan Chandler, for their dedication to data collection efforts.

Funding

This study was supported by R01HD093662 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health. Postdoctoral fellowship (T32-HD07376) through the Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill funded Sally Eagleton.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declarations of Interest

None

Conflict of Interests Statement

The authors have no conflicts of interest to declare.

Ethics Statement

This research was conducted in accordance with the ethical principles for research with human participants stated by the American Psychological Association. The study protocol (#18-0198) was submitted to full review and was approved by the UNC Greensboro Internal Review Board.

Data and Code Availability

Data may be requested from the first author.

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