Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Apr 29;58(8):1477–1486. doi: 10.1002/eat.24453

Functions of Dietary Restriction and Unique Associations With Mood States Among Young Adults With Food Insecurity: Findings From an Ecological Momentary Assessment Study

Yvette Karvay 1, Dianne Neumark‐Sztainer 2, Natasha L Burke 1, Scott G Engel 3, Stephen A Wonderlich 3, Vivienne M Hazzard 2,4,✉
PMCID: PMC12336751  PMID: 40298206

ABSTRACT

Objective

Limited research has examined the function or consequences of dietary restriction among individuals for whom it may serve differential purposes, such as those with food insecurity. Indeed, food insecurity may uniquely relate to differential functions for engaging in dietary restriction, which may relate to subsequent changes in mood; this study sought to assess these associations.

Method

A subsample of 77 young adults from the Minnesota‐based EAT (Eating and Activity over Time) cohort with food insecurity (M age = 25.2 ± 1.8 years; Women = 72.7%; Black = 18.2%; Latinx = 19.5%; Asian = 18.2%; White = 27.3%) reported on state‐level functions for dietary restriction (financial only, weight‐control only, or both) versus no restriction, and state‐level mood, via ecological momentary assessment (EMA). Demographics‐adjusted linear mixed models were conducted to examine the function of dietary restriction since the last EMA signal and current mood.

Results

Across a 14‐day EMA period, 29.9% of observations involved dietary restriction due to financial functions only, 6.2% involved dietary restriction due to both financial and weight‐control functions, and 3.0% involved restriction for weight‐control functions only. Compared to instances of no dietary restriction, overall negative mood was higher after engaging in dietary restriction for both functions (B = 0.43; p = 0.002), but not for financial (B = 0.12; p = 0.14) or weight‐control (B = 0.30; p = 0.06) functions only.

Discussion

These findings suggest dual‐purpose dietary restriction (i.e., for both financial and weight‐control) is common in young adults with food insecurity and may influence mood; thus, it may be an important intervention target.

Keywords: dietary restriction, disordered eating, ecological momentary assessment, food insecurity, mood

1.

Summary

  • Dietary restriction is common among young adults with food insecurity and often serves financial, weight‐control, or dual purposes.

  • Restricting food for both financial and weight‐control reasons was linked with worse momentary mood states.

  • Results highlight the complex interplay between food insecurity, eating behaviors, and mood.

Recent estimates indicate that on a global scale, between 720 and 811 million individuals are living with food insecurity, defined as limited or uncertain access to adequate, safe, and nutritionally dense foods (Coleman‐Jensen et al. 2021; National Research Council 2006; Unicef 2021). Despite historical assumptions regarding eating disorders (EDs), primarily impacting those with affluence (Bruch 1974; Sonneville and Lipson 2018), burgeoning research suggests that ED pathology is elevated in individuals living with food insecurity (Becker et al. 2017, 2019; Hazzard et al. 2020; Hooper et al. 2020; Kim et al. 2021; Lydecker and Grilo 2019; Rasmusson et al. 2019; Shankar‐Krishnan et al. 2021). Therefore, it is critical to examine facets of our current working models of eating pathology to better grasp their operationalization, validity, and application within and for populations with food insecurity.

At present, the etiology and maintenance of EDs are largely conceptualized in relation to a core ED symptom: dietary restriction. With the exception of Pica (i.e., eating of non‐food or non‐nutritive items), Rumination Disorder (i.e., repeated regurgitation of food) and certain Other Specified Feeding or Eating Disorders (e.g., Night Eating Syndrome), dietary restriction is either considered a diagnostic criterion or conceptualized as a risk factor for the major ED diagnoses: Anorexia Nervosa, Avoidant/Restrictive Food Intake Disorder, Bulimia Nervosa, and Binge‐Eating Disorder (American Psychiatric Association 2022). Several of the leading theoretical models of EDs give great attention to dietary restriction. The transdiagnostic model of EDs suggests that underlying all EDs is a vicious, cyclical pattern of dietary restraint (i.e., the cognitive component of dieting; Schaumberg and Anderson 2016) and restriction (i.e., the behavioral component of dieting; Bottera et al. 2021; Fairburn 2008), fueled by over‐evaluation of body shape and weight, and resulting in binge eating (i.e., eating an excessive amount of food in a short period of time while experiencing a loss of control; American Psychiatric Association 2022), compensatory and non‐compensatory weight‐control behaviors, and/or significant weight loss (Fairburn et al. 2003; Wade et al. 2006). The Tripartite Influence Model similarly considers dietary restriction as it relates to factors such as the internalization of a thin ideal, media, and interpersonal relationships (Shroff and Thompson 2006; Thompson et al. 1999). While dietary restriction receives ample attention as an ED symptom with regard to diagnostic criteria and theoretical models of EDs, it has been conceptualized primarily as being driven by purposeful attempts to control weight or shape.

Notably, current models of EDs and understanding of the core symptom of dietary restriction have developed out of work with primarily affluent, White, and thin populations (Sonneville and Lipson 2018), suggesting an imperative need to better understand dietary restriction for individuals outside of the scope of this stereotype, such as those with food insecurity. For populations with food insecurity, the construct of dietary restriction can manifest entirely differently, and this nuance is important to highlight. In the ED literature, dietary restriction is considered an ED symptom, while in populations with food insecurity, dietary restriction can be an externally imposed circumstance resulting from lack of access to food and nutrient resources. The current text utilizes the term “dietary restriction” in reflection of this duality.

Moreover, while dietary restriction is considered both a symptom and a maintainer of eating pathology (e.g., leading to downstream consequences of binge eating and compensatory behaviors; Fairburn et al. 2003), it has been operationalized primarily as a weight‐control behavior. For individuals with food insecurity, dietary restriction is reflective of experiencing significant barriers to accessing food and attempts at surviving with limited available resources. Therefore, for individuals with food insecurity, dietary restriction may not reflect the traditional ED symptom conceptualization, nor should dietary restriction instances due to financial restraints be pathologized. Nonetheless, individuals with food insecurity still exist within societies (particularly in Eurocentric countries) that put emphasis on dietary restriction for weight‐control reasons, especially aligned with an idealization of thinness (Thompson et al. 1999). It is plausible that individuals with food insecurity, who engage in dietary restriction due to resource scarcity, experience heightened risk of further engaging in dietary restriction in an ED pathology manner. Therefore, dietary restriction may serve dual functions for this population: financial and/or weight‐control.

Assessing dietary restriction “for any reason” in populations with food insecurity to determine the unique reasons for such restriction is needed (Middlemass et al. 2021) as individuals with food insecurity may attempt to restrict food for unique financial functions, including: to increase food available for family members, to make food last longer, and to prioritize other necessities over food (Middlemass et al. 2021). However, differential functions (e.g., financial and/or weight‐control) for engaging in dietary restriction for populations with food insecurity have yet to be teased apart and examined. Understanding the factors contributing to dietary restriction in either (or both) of these instances (financial and weight‐control) is needed to better understand the ways in which those with food insecurity may be at a heightened risk for the development of EDs.

Along with dietary restriction, negative mood contributes to ED symptoms (Fairburn et al. 2003). However, unlike dietary restriction, mood is not considered a core disordered eating symptom, nor is it embedded in the cycle of disordered eating symptoms. Rather, mood is entangled in the engagement of disordered eating symptoms (De Young et al. 2019; Fairburn et al. 2003). Nonetheless, how mood intersects with aspects of disordered eating such as dietary restriction for individuals with food insecurity—a population experiencing multiple concurrent stressors that may negatively impact mood—has received limited attention. Moreover, the differential functions for engaging in dietary restriction among individuals with food insecurity may uniquely relate to subsequent changes in mood states.

The present study examined the potential associations between functions of dietary restriction and mood states, utilizing an ecological momentary assessment study design. The first aim of this study was to examine the functions for engaging in dietary restriction in a population of young adults living with food insecurity: for weight control, financial, or both functions. The second aim was to assess the associations between the differential functions of dietary restriction (for weight‐control, financial, or both) and subsequent mood states in this population.

2. Method

2.1. Participants

The current study included a subsample of participants from the larger, population‐based Project EAT (Eating and Activity over Time) cohort (Larson et al. 2020; Larson et al. 2013; Neumark‐Sztainer et al. 2012). The original, larger EAT cohort was initially recruited as middle and high school students in the 2009–2010 academic year from 20 public middle and high schools in the urban area of Minneapolis‐St. Paul, Minnesota, USA. These participants completed follow‐up surveys over the past decade, including a follow‐up survey in 2020 during the outbreak of the COVID‐19 pandemic.

Participants were recruited based on endorsement of past‐year household food insecurity during the 2020 survey (Larson et al. 2021). Interested participants were screened for eligibility using the 18‐item U.S. Household Food Security Survey Module with a 30‐day reference period (Carlson et al. 1999). Those experiencing past‐month household food insecurity (i.e., with ≥ 3 affirmative responses, corresponding to either low or very low household food security) were eligible to participate. Participants were only excluded from participating if they did not indicate past‐month household food insecurity. This study received institutional review board approval at both The University of Minnesota and Sanford Health; all participants provided informed consent.

The current sample includes 77 young adults (M age = 25.2 years, SD = 1.8, range: 22–29) living with food insecurity. Participants largely self‐identified as women (72.7%), had children under 18 years living in the household (53.2%), and were racially and ethnically diverse (18.2% Black/African American, 19.5% Hispanic/Latinx, 18.2% Asian/Asian American, 27.3% White, 16.9% mixed/other race/ethnicity). Individuals had a mean Body Mass Index (BMI) of 30.8 kg/m2 (SD = 8.8, range: 16.5–58.2). Though not recruited based on engagement in disordered eating, participants endorsed various forms of disordered eating behaviors over the past year (32.5% experienced binge eating, 22.1% utilized diet pills to control their weight, 5.2% vomited to control their weight, 10.4% utilized laxatives to control their weight, and 3.9% utilized diuretics to control their weight) on a baseline survey.

2.2. Procedure

Participants completed a baseline questionnaire for the present study and then participated in a 14‐day ecological momentary assessment (EMA) protocol. They received five EMA surveys each day, each administered on a signal‐contingent basis (i.e., participants were signaled by a text message or email to complete a survey at five time points throughout the day—subsequently referred to as signaled assessments). The beginning‐of‐day signaled assessment, administered at the same time each day, included questions regarding the participant's food availability, general eating behaviors, and sleep quality from the previous day/night. The other four signaled assessments were administered on a semi‐random schedule over the span of a 10‐h period, such that participants would receive a signal within each of four 2.5‐h windows. These four signaled assessments asked about dietary restriction and mood states, 1 h to respond before the survey expired. From the time a signal was sent, the average time to the beginning of the response was 11.7 ± 14.6 min. Data were collected between November 2020 and May 2021. Participants were compensated after the end of the 14‐day EMA protocol with gift cards in the amount of $40 for completing the baseline questionnaire plus $3 per EMA signal they responded to (for a total amount of up to $265). Further details regarding the protocol used in this study, including information on the scope of baseline questionnaires, can be found in Hazzard et al. (2023).

2.3. Measures

2.3.1. Functions of Dietary Restriction

Participants were asked four times each day about engagement in dietary restriction for financial and for weight‐control functions. Participants were asked three separate questions assessing dietary restriction related to financial functions: “Since my last survey, I have [(1) cut the size of my meals or skipped meals/(2) eaten less than I felt I should/(3) been hungry but not eaten] because there wasn't enough money for food.” Responses were recorded on a 6‐point Likert‐scale (strongly disagree, disagree, somewhat disagree, somewhat agree, agree, strongly agree). These financial restriction questions have shown strong internal consistency within the current sample (McDonald's ωwithin‐person = 0.91, McDonald's ωbetween‐person = 0.99; Hazzard et al. 2023). Participants who indicated any level of agreement (i.e., selecting “somewhat agree”, “agree”, or “strongly agree”) on any of the three financial reason questions at a given signaled assessment were considered to have engaged in dietary restriction for financial functions at that signaled assessment.

Participants were also asked to “mark all that apply” to several statements assessing engagement in behaviors to “try to lose weight or keep from gaining weight” since the last survey they received. If a participant selected “ate as little as possible”, they were considered to have engaged in dietary restriction for weight‐control functions at that signaled assessment. Previous work has highlighted this item as most reflective of end‐of‐day measures of restrictive eating (Fitzsimmons‐Craft et al. 2015).

As described above, responses were dichotomized at each time point as representing engagement (or lack thereof) in dietary restriction for financial functions or for weight‐control functions. These dichotomous variables were utilized to create an overall functions of dietary restriction variable with the following four possible levels at a given signaled assessment: (1) no dietary restriction, (2) dietary restriction for financial function only, (3) dietary restriction for weight‐control function only, and (4) dietary restriction for both financial and weight‐control functions. This variable was utilized in subsequent analyses.

2.3.2. Mood States

Participants were asked four times each day about their mood symptoms with The Immediate Mood Scalar (IMS)‐12, a 12‐item measure designed to assess momentary mood symptoms associated with anxiety and depression (Nahum et al. 2017). Participants were asked 12 separate questions assessing state‐level fear, pessimism, worry, worthlessness, apathy, restlessness, guilt, anxiousness, numbness, tension, withdrawal, and hopelessness. Each symptom was presented as a 7‐point Likert‐type scale, allowing participants to select the option that best represented their current mood state (“rate how you feel right now”). For example, when asked to report on their state‐level fear, participants were presented the following response options: fearful, somewhat fearful, slightly fearful, not fearful or fearless, slightly fearless, somewhat fearless, fearless.

The overall IMS score was obtained by averaging across all items of the measure. Analyses examined the overall score to capture associations with global negative mood and responses for each individual item to capture associations of distinct constructs of negative emotionality. In the current sample, the IMS showed excellent internal consistency at the within‐ and between‐person levels (McDonald's ωwithin‐person = 0.91, McDonald's ωbetween‐person = 0.99) as estimated via multilevel confirmatory factor analysis with robust maximum likelihood estimation using the approach described by Geldhof et al. (2014), conducted in Mplus version 8.11.

2.3.3. Demographic and Anthropometric Information

Participants self‐reported their date of birth, race, ethnicity, gender identity, presence of children under 18 years in the household, and height and weight, which were used to calculate body mass index (BMI; kg/m2).

2.4. Statistical Analysis

All analyses described hereafter were conducted in SPSS Version 29. To address the first aim of this study (i.e., to examine functions for engaging in dietary restriction for young adults with food insecurity), frequency statistics were computed to describe endorsement of functions of engaging in dietary restriction across the 14‐day EMA protocol. Frequencies were examined at the observation level (i.e., average observations endorsed per participant) and the participant level (i.e., endorsement at any point during the 14‐day protocol).

To address the second aim (i.e., to assess associations between differential functions of dietary restriction and mood states), the four‐level dietary restriction variable was examined as a categorical independent fixed effect variable in a linear mixed model nested within participants with a random intercept term predicting the overall IMS score and a random slope for the dietary restriction variable, adjusting for age, gender, race/ethnicity, presence of children under 18 years in the household, and BMI as fixed effects to control for potential confounding. Restriction and mood assessed at the same time point were utilized in this analysis, given the nature of dietary restriction being assessed “since the last survey” and mood being assessed at the present state‐level. This analysis was repeated for each of the 12 individual IMS items (i.e., fear, pessimism, worry, worthlessness, apathy, restlessness, guilt, anxiousness, numbness, tension, withdrawal, and hopelessness), replacing item‐level responses for the overall IMS score as the dependent variable. Model residuals were inspected visually and found to approximate normality. Random slopes were not included in the models due to no a priori expectations that fixed effects of dietary restriction functions on mood states would vary by participant. Given the relatively large number of tests conducted, significance thresholds for the item‐level analyses were corrected for multiple comparisons using False Discovery Rate (FDR) procedures (Benjamini and Hochberg 1995) with a FDR of Q = 0.10. To investigate the extent to which negative mood states prior to dietary restriction might help explain findings, sensitivity analyses additionally adjusting for negative mood states at the prior within‐day signal were conducted.

3. Results

Across 77 participants, a total of 3481 observations were included in the analyses. Each participant contributed an average of 45.2 ± 9.8 observations across the 14‐day EMA period. Survey compliance across the four daily time points was 80.7%.

3.1. Functions of Engaging in Dietary Restriction Among Young Adults Living With Food Insecurity

Table 1 presents frequency distributions of the functions for engaging in dietary restriction at the observation level and the participant level. Across the 14‐day EMA period, 29.9% of observations involved dietary restriction due to financial functions only, 6.2% involved dietary restriction due to both financial and weight‐control functions, and 3.0% involved restriction for weight‐control functions only. During the 14‐day EMA study, 77.9% of participants endorsed dietary restriction for only financial functions at least once, 32.5% endorsed dietary restriction for both financial and weight‐control functions at least once, and 29.9% endorsed dietary restriction for only weight‐control functions at least once.

TABLE 1.

Endorsement of functions of engaging in dietary restriction across the 14‐day EMA protocol.

% (#) of observations across all participants % (n) of participants ever endorsing
No dietary restriction 60.9 (2119) 93.5 (72)
Dietary restriction due to: Financial functions only 29.9 (1042) 77.9 (60)
Dietary restriction due to: Weight functions only 3.0 (105) 29.9 (23)
Dietary restriction due to: Both functions 6.2 (215) 32.5 (25)
Open in a new tab

Note: Participant percentages in the right column add to > 100% because participants could have endorsed as many as all four scenarios at different assessments across the EMA period.

3.2. Associations Between Functions of Dietary Restriction and Mood States

Random effects demonstrated significant variability in both intercepts (variance estimate for overall negative mood model = 1.19, p < 0.001) and dietary restriction function slopes (variance estimate for overall negative mood model = 0.14, p < 0.001) across participants. Fixed effects estimates demonstrated that significantly higher overall negative mood was reported after engaging in dietary restriction for both financial and weight‐control functions (B = 0.43; 95% CI: 0.16, 0.69; p = 0.002). In contrast, no significant differences emerged after engaging in dietary restriction for only financial functions (B = 0.12; 95% CI: −0.04, 0.29; p = 0.14) or for only weight‐control functions (B = 0.30; 95% CI: −0.02, 0.61; p = 0.06), as compared to not reporting any dietary restriction for overall negative mood. Some differences emerged for specific facets of negative mood (see Table 2). Restlessness was significantly higher after engaging in dietary restriction for only weight‐control functions. Participants reported feeling significantly more guilty, worthless, hopeless, numb, withdrawn, pessimistic, anxious, and tense after engaging in dietary restriction for both functions.

TABLE 2.

Associations between functions of engaging in dietary restriction and specific facets of negative mood.

Depressed mood state facets, B (95% CI)
Guilty Worthless Hopeless Apathetic Numb Withdrawn Pessimistic
Dietary restriction due to:
Financial functions only 0.09 (−0.10, 0.28) 0.13 (−0.08, 0.34) 0.05 (−0.16, 0.25) 0.13 (−0.06, 0.33) 0.15 (−0.04, 0.34) 0.22 (0.02, 0.42)* 0.13 (−0.06, 0.31)
Weight‐control functions only 0.24 (−0.13, 0.61) 0.25 (−0.15, 0.65) 0.32 (−0.09, 0.72) 0.16 (−0.24, 0.57) 0.26 (−0.13, 0.66) 0.18 (−0.23, 0.59) 0.36 (−0.02, 0.75)
Both functions 0.55 (0.24, 0.86) *** 0.49 (0.16, 0.83) ** 0.41 (0.06, 0.75) * 0.35 (0.02, 0.68)* 0.52 (0.20, 0.84) ** 0.56 (0.22, 0.89) ** 0.40 (0.08, 0.72) *
Anxious Mood State Facets, B (95% CI)
Worried Fearful Anxious Tense Restless
Dietary restriction due to:
Financial functions only 0.11 (−0.10, 0.32) 0.14 (−0.05, 0.33) 0.08 (−0.14, 0.31) 0.17 (−0.03, 0.37) 0.07 (−0.16, 0.29)
Weight‐control functions only 0.14 (−0.29, 0.56) 0.28 (−0.09, 0.66) 0.28 (−0.17, 0.73) 0.19 (−0.23, 0.62) 0.67 (0.21, 1.12) **
Both functions 0.37 (0.02, 0.72)* 0.31 (−0.003, 0.62) 0.62 (0.24, 0.99) ** 0.50 (0.15, 0.48) ** 0.37 (−0.01, 0.75)
Open in a new tab

Note: Instances of no dietary restriction serve as the reference group. Associations come from separate linear mixed models in which each mood state facet was regressed on the dietary restriction function variable, adjusting for participant age, gender, race/ethnicity, presence of children under 18 years in the household, and body mass index fixed effects. Random effects demonstrated significant variability in both intercepts (variance estimates ranged from 1.17 to 1.63, all p's < 0.001) and dietary restriction function slopes (variance estimates ranged from 0.15 to 0.22, all p's < 0.001) across participants. Bolded values retained statistical significance after applying Benjamini‐Hochberg FDR procedures.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

In sensitivity analyses additionally adjusting for negative mood state at the time point prior to assessment of dietary restriction, dietary restriction for both weight‐control and financial functions remained significantly associated with higher overall negative mood (B = 0.26; 95% CI: 0.05, 0.48; p = 0.02) and with feeling guilty, withdrawn, and tense (Table S1) as compared to instances of no dietary restriction, though the strength of most of these associations was attenuated. No significant associations were observed in these sensitivity analyses for dietary restriction for only financial functions or for only weight‐control functions.

4. Discussion

Food insecurity is a phenomenon driven by a host of systemic, structural, and dynamic factors (i.e., food production and supply, climate/environmental disasters, economic stability, urbanization, structural racialization, etc.; Elsheikh and Barhoum 2013; Hadley et al. 2023; Tacoli 2019). Within the past decade, a link between food insecurity and disordered eating has become evident (Becker et al. 2017, 2019; Hazzard et al. 2020). However, there is a dearth of literature examining the complexity of this relationship, perhaps driven by assumptions that individuals experiencing food insecurity may only engage in disordered eating as it relates to financial constraints. Relatedly, the present study examined the nuance that exists for individuals living with food insecurity engaging in disordered eating behavior; specifically, it assessed the functions (financial, weight‐control, or both) of momentary engagement in dietary restriction for individuals with food insecurity. At some point during the two‐week protocol, almost one‐third of participants engaged in dietary restriction simultaneously for both weight‐control and financial functions. Moreover, this study also examined how engagement in dietary restriction for different functions (financial, weight‐control, or both) related to negative emotional states. Depressed or anxious mood states were generally only experienced after instances of dietary restriction for both weight‐control and financial functions. This suggests that for individuals with food insecurity, engagement in dietary restriction is nuanced and especially distressing when engaged in for both financial and weight‐control reasons.

While a majority of dietary restriction instances in the present study were identified as related to financial functions, at some point during the study, about one‐third of participants engaged in dietary restriction for both financial and weight‐control functions. In some ways, this finding provides evidence in favor of moving away from traditional assessment of dietary restriction as imposed by the ED field (i.e., related to shape and weight concerns) for this population. There are multiple functions for which an individual may engage in dietary restriction, and therefore the restriction may most benefit from assessment under the umbrella “for any reason” (Middlemass et al. 2021). However, our results suggest that there are distinct and intersecting reasons for why individuals may engage in dietary restriction; teasing out these functions may best serve the field in understanding dietary restriction's relationship to emotional distress. Once again, this is not intended to pathologize engagement in dietary restriction related to financial functions, but rather to ensure that the construct of restriction is adequately assessed, especially for individuals experiencing food insecurity. When dietary restriction is assumed to only occur in relation to weight‐control functions, treatment providers and researchers may inadvertently pathologize this externally imposed circumstance by utilizing treatment approaches aimed distinctly at treating dietary restriction for weight‐control functions rather than considering treatment approaches that may support an individual with food insecurity on broader systemic and structural levels (as described in Treatment Considerations below).

Although the Tripartite Influence Model (Shroff and Thompson 2006; Thompson et al. 1999) was originally developed with majority White woman populations, our findings provide support for the model's utility in food insecure populations. More recently, this model has had relatively strong empirical support across multiple marginalized populations (Burke et al. 2021; Hazzard et al. 2019; Yamamiya et al. 2008). Living within a society that values thinness and stigmatizes fatness across a multitude of domains (e.g., media, education, employment, health care, and so forth; Puhl and Heuer 2010) likely predisposes individuals to the basic building blocks of disordered eating behaviors. Those living with food insecurity are not sheltered from this; rather, as the results of this study suggest, those with food insecurity are dually impacted by financial difficulties and sociocultural body size ideals.

The duality of experiencing financial difficulties and living within a society that idolizes a thin ideal likely poses an increased risk of emotional distress, as both experiences have been linked to emotional distress (Hawkins et al. 2004; Myers 2020). Interestingly, in the present study, compared to instances where dietary restriction was not endorsed, overall negative mood was higher after individuals engaged in dietary restraint for both financial and weight‐control functions; this same result largely did not replicate within instances when individuals engaged in dietary restraint for financial or weight‐control functions only. Moreover, only in instances where engagement in dietary restriction for both financial and weight‐control functions was endorsed did individuals experience increased guilt, worthlessness, hopelessness, numbness, feelings of withdrawal, pessimism, anxiety, and tension. While engagement in dietary restriction for any reason may be assumed to relate to emotional distress, this result suggests that the unique intersection of dieting due to lack of food along with controlling one's weight is particularly distressing for individuals.

Nonetheless, one association did emerge for a single function for engaging in dietary restriction. During instances when individuals engaged in restriction for weight‐control functions, restlessness was elevated. As research has not previously teased apart these constructs, the rationale for this association is largely speculative. For example, it is plausible that the self‐imposed nature of restriction for weight control may lead to internal tension because it goes against a deeply ingrained biological drive to eat. Alternatively, given the relatively high prevalence of binge eating in this sample—not unexpected in a sample with food insecurity (Abene et al. 2023)—it is plausible that these instances of dietary restriction were entangled in the restriction‐binge cycle, increasing participants' sense of restlessness. Nevertheless, these associations attenuated and became nonsignificant after accounting for prior‐survey negative mood states. Overarchingly, more research is needed to understand these particular associations.

The findings of this study provide important insight into the experience of dietary restriction for individuals with food insecurity. Largely, they suggest that dietary restriction is not a one‐size‐fits‐all experience for individuals with food insecurity, but rather a unique, nuanced behavior related to both financial and weight‐related functions. Nonetheless, future work may consider and assess a multitude of additional reasons by which individuals with and without food insecurity may engage in dietary restriction, especially related to mood.

4.1. Treatment Considerations

The findings of this study suggest that models for understanding and treating eating pathology may need reviewing and restructuring to better support individuals experiencing food insecurity. Indeed, it may be particularly important to attend to how individuals with food insecurity interact with and relate to food, as such interactions may symbolize and embody more nuance than is traditionally considered in gold standard ED treatments.

On a global treatment level, ED centers and individual practitioners can greatly support their food‐insecure patients by directly finding and partnering with local food pantries; such partnerships would enhance patients' abilities to obtain food resources during and after treatment. For centers that are able to provide food to patients for meal exposures or meal support, prioritizing enrolling patients with food insecurity in these groups may additionally act as a buffer while patients are in treatment. Integrating social work professionals to determine supports relevant to food‐insecure populations during and after treatment would also be valuable.

ED providers should also enhance their intake evaluations to comprehensively assess their patients' socioeconomic status and access to food resources. Understanding the role of financial resources in a patient's life would undoubtedly meaningfully inform case conceptualization; for example, as most ED treatments require engagement in “regular eating,” understanding a patient's ability to obtain food resources to regularly eat is critical to treatment success. Furthermore, paying attention to how dietary restriction is discussed with this population during treatment is important. Often, reinforcement strategies are employed during ED treatment to combat the utilization of dietary restriction as it pertains to shape and weight control only; these strategies may be invalidating when an individual is simultaneously restricting for both weight control and financial functions.

Similarly, adapting how treatment tools are discussed with patients is an additional way in which patients with food insecurity could be better served. For example, in treatments such as CBT‐E that utilize daily food monitoring logs, there is the opportunity to reframe certain monitoring categories such as “context and comments” to act as a space for patients to provide details regarding their food access status during each eating instance they log. Overarchingly, ED interventions are consistently aimed at considering each patients' unique presentation, even when interventions are manualized—it is recommended that providers consider the systemic concerns that their patients with food insecurity present with, especially in how this entangles with their ED presentations.

4.2. Strengths, Limitations, and Future Directions

The relationship between food insecurity and disordered eating has only come into the ED field's greater awareness within the past decade. Therefore, a notable strength of the current study is the examination of food insecurity related constructs in an exclusive sample of individuals experiencing current food insecurity. Moreover, participants were racially and ethnically diverse, which contributes to filling the literature gap about individuals experiencing disordered eating with marginalized racial and ethnic identities. Further, this is among the first studies to assess food insecurity and related constructs on a momentary basis, allowing for a better, ecologically valid grasp on the daily experiences of living with food insecurity.

Nonetheless, this study is not without its limitations. It focused on the temporal relationships between dietary restriction and mood, irrespective of time‐of‐day or time of month. Future work should explore the possibility of time‐of‐day effects related to dietary restriction and mood, and end‐of‐the‐month financial effects. This study was conducted during the COVID‐19 pandemic, an unprecedented time that likely altered the daily experiences of individuals both with and without food insecurity, potentially impacting the mood states examined in this study. This study also did not account for all potential factors that might influence both eating behaviors and mood (e.g., sleep). Further, negative mood states were exclusively focused on; future researchers may consider examining positive mood states in relation to these constructs. While the measures of mood states and dietary restriction for financial reasons utilized in this study were psychometrically strong, dietary restriction for weight‐control reasons was assessed with a single item that has only been validated in a sample of individuals with anorexia nervosa (Fitzsimmons‐Craft et al. 2015). Assessment of dietary restriction for financial reasons was more robust than that of assessment of restriction for weight‐control reasons. Future work may consider further teasing apart functions for dietary restriction in similar frequency (i.e., duration of restriction) and quantity (i.e., amount eaten during meals/snacks) scales. Moreover, functions of dietary restriction are not limited to financial or weight‐control reasons; functions such as health (e.g., restriction of food due to allergies) or religion (e.g., fasting) are also possible. To balance the time burden that each EMA signal posed on participants, fewer questions were presented throughout the day; future research may wish to assess functions of dietary restriction for reasons other than financial and weight‐control. Additionally, we did not disaggregate the categorical dietary restriction functions variable into within‐ and between‐person effects due to the logistical challenges of calculating person mean‐centered and grand mean‐centered values for a categorical variable.

4.3. Conclusions

The current study provides evidence for the nuanced experience of disordered eating for individuals living with food insecurity. Findings suggest that individuals with food insecurity may and may not engage in dietary restriction—this engagement is dynamic and shifts across and over days. Moreover, when food insecure individuals engage in dietary restriction, it may be for financial functions, weight‐control functions, or both financial and weight‐control functions simultaneously. For those instances when individuals engage in restriction for both financial and weight‐control functions, there is an increase in the negative mood states that individuals experience. This suggests that the relationship between food insecurity and disordered eating is complex and deserves more thorough investigation to adequately address on a public health level and within the scope of intervention.

Author Contributions

Yvette Karvay: conceptualization, writing – original draft, writing – review and editing. Dianne Neumark‐Sztainer: methodology, writing – original draft, writing – review and editing. Natasha L. Burke: conceptualization, writing – original draft, writing – review and editing. Scott G. Engel: writing – review and editing. Stephen A. Wonderlich: writing – review and editing. Vivienne M. Hazzard: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, writing – original draft, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

TABLE S1. Associations between functions of engaging in dietary restriction and specific facets of negative mood.

EAT-58-1477-s001.docx (20.2KB, docx)

Acknowledgments

Data collection for the study was supported by Grant Numbers R01HL127077 and R35HL139853 from the National Heart, Lung, and Blood Institute (PI: Neumark‐Sztainer). The authors' time was funded by Grant Number T32MH082761 (PI: Peterson) from the National Institute of Mental Health, Grant Number K99HD108200 (PI: Hazzard) from the National Institute of Child Health and Human Development, Grant Number R01DK112487 (PIs: Engel/Wonderlich) from the National Institute of Diabetes and Digestive and Kidney Diseases, Grant Number P20GM134969 from the National Institute of General Medical Science (PI: Wonderlich) and a Graduate School of Arts and Sciences Research Fellowship from Fordham University (Karvay). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, the National Institute of Mental Health, the National Institute of Child Health and Human Development, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Science, the National Institutes of Health or Fordham University. We would also like to acknowledge the late Dr. Ross D. Crosby for his contributions to the design of this study and his patient mentorship of Vivienne M. Hazzard in analyzing ecological momentary assessment data.

Karvay, Y. , Neumark‐Sztainer D., Burke N. L., Engel S. G., Wonderlich S. A., and Hazzard V. M.. 2025. “Functions of Dietary Restriction and Unique Associations With Mood States Among Young Adults With Food Insecurity: Findings From an Ecological Momentary Assessment Study.” International Journal of Eating Disorders 58, no. 8: 1477–1486. 10.1002/eat.24453.

Action Editor: Ruth Striegel Weissman

Funding: This work was supported by National Institute of General Medical Sciences, P20GM134969, National Institute of Child Health and Human Development, K99HD108200, National Heart, Lung, and Blood Institute, R01HL127077, R35HL139853, National Institute of Mental Health, T32MH082761, Graduate School of Arts and Sciences, Fordham University, Research Fellowship, National Institute of Diabetes and Digestive and Kidney Diseases, R01DK112487.

Data Availability Statement

Investigators interested in utilizing the dataset used in the current study should contact the corresponding author.

References

  1. Abene, J. A. , Tong J., Minuk J., Lindenfeldar G., Chen Y., and Chao A. M.. 2023. “Food Insecurity and Binge Eating: A Systematic Review and Meta‐Analysis.” International Journal of Eating Disorders 56, no. 7: 1301–1322. 10.1002/eat.23956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychiatric Association . 2022. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Publishing. 10.1176/appi.books.9780890425787. [DOI] [Google Scholar]
  3. Becker, C. B. , Middlemass K., Taylor B., Johnson C., and Gomez F.. 2017. “Food Insecurity and Eating Disorder Pathology.” International Journal of Eating Disorders 50, no. 9: 1031–1040. 10.1002/eat.22735. [DOI] [PubMed] [Google Scholar]
  4. Becker, C. B. , Middlemass K. M., Gomez F., and Martinez‐Abrego A.. 2019. “Eating Disorder Pathology Among Individuals Living With Food Insecurity: A Replication Study.” Clinical Psychological Science 7, no. 5: 1144–1158. 10.1177/2167702619851811. [DOI] [Google Scholar]
  5. Benjamini, Y. , and Hochberg Y.. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B: Methodological 57, no. 1: 289–300. 10.1111/j.2517-6161.1995.tb02031.x. [DOI] [Google Scholar]
  6. Bottera, A. R. , Kambanis P. E., and De Young K. P.. 2021. “Persistence: A Key Factor in Understanding the Circumstances Under Which Dietary Restraint Predicts Restriction of Caloric Intake.” Eating Behaviors 43: 101563. 10.1016/j.eatbeh.2021.101563. [DOI] [PubMed] [Google Scholar]
  7. Bruch, H. 1974. Eating Disorders. Obesity, Anorexia Nervosa, and the Person Within. Routledge & Kegan Paul. [Google Scholar]
  8. Burke, N. L. , Schaefer L. M., Karvay Y. G., et al. 2021. “Does the Tripartite Influence Model of Body Image and Eating Pathology Function Similarly Across Racial/Ethnic Groups of White, Black, Latina, and Asian Women?” Eating Behaviors 42: 101519. 10.1016/j.eatbeh.2021.101519. [DOI] [PubMed] [Google Scholar]
  9. Carlson, S. J. , Andrews M. S., and Bickel G. W.. 1999. “Measuring Food Insecurity and Hunger in the United States: Development of a National Benchmark Measure and Prevalence Estimates.” Journal of Nutrition 129, no. 2: 510S–516S. 10.1093/jn/129.2.510s. [DOI] [PubMed] [Google Scholar]
  10. Coleman‐Jensen, A. , Rabbitt M. P., Gregory C., and Singh A.. 2021. Household Food Security in the United States in 2020. Department of Agriculture, Economic Research Service, ERR‐298. [Google Scholar]
  11. De Young, K. P. , Zander M., Towne T., Della Longa N. M., Hovrud L., and Murtha‐Berg E.. 2019. “The Relation of Dietary Restraint and Affect With Food Choice and the Experience of Guilt After Eating.” Current Psychology 38, no. 1: 92–99. 10.1007/s12144-017-9592-x. [DOI] [Google Scholar]
  12. Elsheikh, E. , and Barhoum N.. 2013. “Structural Racialization and Food Insecurity in the United States.” In A Report to the UN Human Rights Committee on the International Covenant on Civil and Political Rights, vol. 20, 20–26. Othering and Belonging Institute. [Google Scholar]
  13. Fairburn, C. G. 2008. Cognitive Behavior Therapy and Eating Disorders. Guilford Press. [Google Scholar]
  14. Fairburn, C. G. , Cooper Z., and Shafran R.. 2003. “Cognitive Behaviour Therapy for Eating Disorders: A “Transdiagnostic” Theory and Treatment.” Behaviour Research and Therapy 41, no. 5: 509–528. [DOI] [PubMed] [Google Scholar]
  15. Fitzsimmons‐Craft, E. E. , Accurso E. C., Ciao A. C., et al. 2015. “Restrictive Eating in Anorexia Nervosa: Examining Maintenance and Consequences in the Natural Environment.” International Journal of Eating Disorders 48, no. 7: 923–931. 10.1002/eat.22439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Geldhof, G. J. , Preacher K. J., and Zyphur M. J.. 2014. “Reliability Estimation in a Multilevel Confirmatory Factor Analysis Framework.” Psychological Methods 19, no. 1: 72–91. 10.1037/a0032138. [DOI] [PubMed] [Google Scholar]
  17. Hadley, K. , Wheat S., Rogers H. H., et al. 2023. “Mechanisms Underlying Food Insecurity in the Aftermath of Climate‐Related Shocks: A Systematic Review.” Lancet Planetary Health 7, no. 3: e242–e250. 10.1016/S2542-5196(23)00003-7. [DOI] [PubMed] [Google Scholar]
  18. Hawkins, N. , Richards P. S., Granley H. M., and Stein D. M.. 2004. “The Impact of Exposure to the Thin‐Ideal Media Image on Women.” Eating Disorders 12, no. 1: 35–50. 10.1080/10640260490267751. [DOI] [PubMed] [Google Scholar]
  19. Hazzard, V. M. , Loth K. A., Crosby R. D., et al. 2023. “Relative Food Abundance Predicts Greater Binge‐Eating Symptoms in Subsequent Hours Among Young Adults Experiencing Food Insecurity: Support for the “Feast‐Or‐Famine” Cycle Hypothesis From an Ecological Momentary Assessment Study.” Appetite 180: 106316. 10.1016/j.appet.2022.106316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hazzard, V. M. , Loth K. A., Hooper L., and Becker C. B.. 2020. “Food Insecurity and Eating Disorders: A Review of Emerging Evidence.” Current Psychiatry Reports 22, no. 12: 74. 10.1007/s11920-020-01200-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hazzard, V. M. , Schaefer L. M., Schaumberg K., et al. 2019. “Testing the Tripartite Influence Model Among Heterosexual, Bisexual, and Lesbian Women.” Body Image 30: 145–149. 10.1016/j.bodyim.2019.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hooper, L. , Telke S., Larson N., Mason S. M., and Neumark‐Sztainer D.. 2020. “Household Food Insecurity: Associations With Disordered Eating Behaviours and Overweight in a Population‐Based Sample of Adolescents.” Public Health Nutrition 23: 3126–3135. 10.1017/S1368980020000464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kim, B. H. , Ranzenhofer L., Stadterman J., Karvay Y. G., and Burke N. L.. 2021. “Food Insecurity and Eating Pathology in Adolescents.” International Journal of Environmental Research and Public Health 18, no. 17: 9155. https://www.mdpi.com/1660‐4601/18/17/9155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Larson, N. , Laska M. N., and Neumark‐Sztainer D.. 2020. “Food Insecurity, Diet Quality, Home Food Availability, and Health Risk Behaviors Among Emerging Adults: Findings from the EAT 2010–2018 Study.” American Journal of Public Health 110, no. 9: 1422–1428. 10.2105/AJPH.2020.305783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Larson, N. I. , Wall M. M., Story M. T., and Neumark‐Sztainer D. R.. 2013. “Home/Family, Peer, School, and Neighborhood Correlates of Obesity in Adolescents.” Obesity 21, no. 9: 1858–1869. 10.1002/oby.20360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Larson, N. , Slaughter‐Acey J., Alexander T., Berge J., Harnack L., and Neumark‐Sztainer D.. 2021. “Emerging Adults’ Intersecting Experiences of Food Insecurity, Unsafe Neighbourhoods and Discrimination During the Coronavirus Disease 2019 (COVID‐19) Outbreak.” Public Health Nutrition 24, no. 3: 519–530. 10.1017/S136898002000422X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lydecker, J. A. , and Grilo C. M.. 2019. “Food Insecurity and Bulimia Nervosa in the United States.” International Journal of Eating Disorders 52, no. 6: 735–739. 10.1002/eat.23074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Middlemass, K. M. , Cruz J., Gamboa A., et al. 2021. “Food Insecurity & Dietary Restraint in a Diverse Urban Population.” Eating Disorders 29, no. 6: 616–629. 10.1080/10640266.2020.1723343. [DOI] [PubMed] [Google Scholar]
  29. Myers, C. A. 2020. “Food Insecurity and Psychological Distress: A Review of the Recent Literature.” Current Nutrition Reports 9, no. 2: 107–118. 10.1007/s13668-020-00309-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Nahum, M. , Van Vleet T. M., Sohal V. S., et al. 2017. “Immediate Mood Scaler: Tracking Symptoms of Depression and Anxiety Using a Novel Mobile Mood Scale.” JMIR mHealth and uHealth 5, no. 4: e44. 10.2196/mhealth.6544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. National Research Council . 2006. Food Insecurity and Hunger in the United States: An Assessment of the Measure. National Academies Press. 10.17226/11578. [DOI] [Google Scholar]
  32. Neumark‐Sztainer, D. , Wall M. M., Larson N., et al. 2012. “Secular Trends in Weight Status and Weight‐Related Attitudes and Behaviors in Adolescents from 1999 to 2010.” Preventive Medicine 54, no. 1: 77–81. 10.1016/j.ypmed.2011.10.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Puhl, R. M. , and Heuer C. A.. 2010. “Obesity Stigma: Important Considerations for Public Health.” American Journal of Public Health 100, no. 6: 1019–1028. 10.2105/AJPH.2009.159491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rasmusson, G. , Lydecker J. A., Coffino J. A., White M. A., and Grilo C. M.. 2019. “Household Food Insecurity Is Associated With Binge‐Eating Disorder and Obesity.” International Journal of Eating Disorders 52, no. 1: 28–35. 10.1002/eat.22990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Schaumberg, K. , and Anderson D.. 2016. “Dietary Restraint and Weight Loss as Risk Factors for Eating Pathology.” Eating Behaviors 23: 97–103. [DOI] [PubMed] [Google Scholar]
  36. Shankar‐Krishnan, N. , Fornieles Deu A., and Sánchez‐Carracedo D.. 2021. “Associations Between Food Insecurity and Psychological Wellbeing, Body Image, Disordered Eating and Dietary Habits: Evidence From Spanish Adolescents.” Child Indicators Research 14, no. 1: 163–183. 10.1007/s12187-020-09751-7. [DOI] [Google Scholar]
  37. Shroff, H. , and Thompson J. K.. 2006. “The Tripartite Influence Model of Body Image and Eating Disturbance: A Replication With Adolescent Girls.” Body Image 3, no. 1: 17–23. 10.1016/j.bodyim.2005.10.004. [DOI] [PubMed] [Google Scholar]
  38. Sonneville, K. , and Lipson S.. 2018. “Disparities in Eating Disorder Diagnosis and Treatment According to Weight Status, Race/Ethnicity, Socioeconomic Background, and Sex Among College Students.” International Journal of Eating Disorders 51, no. 6: 518–526. [DOI] [PubMed] [Google Scholar]
  39. Tacoli, C. 2019. “Editorial: The Urbanization of Food Insecurity and Malnutrition.” Environment and Urbanization 31, no. 2: 371–374. 10.1177/0956247819867255. [DOI] [Google Scholar]
  40. Thompson, J. K. , Heinberg L. J., Altabe M., and Tantleff‐Dunn S.. 1999. Exacting Beauty: Theory, Assessment, and Treatment of Body Image Disturbance. American Psychological Association. [Google Scholar]
  41. UNICEF . 2021. ““The State of Food Security and Nutrition in the World 2021.”,” https://www.fao.org/3/cb4474en/online/cb4474en.html.
  42. Wade, T. D. , Bergin J. L., Martin N. G., Gillespie N. A., and Fairburn C. G.. 2006. “A Transdiagnostic Approach to Understanding Eating Disorders.” Journal of Nervous and Mental Disease 194, no. 7: 510–517. [DOI] [PubMed] [Google Scholar]
  43. Yamamiya, Y. , Shroff H., and Thompson J. K.. 2008. “The Tripartite Influence Model of Body Image and Eating Disturbance: A Replication With a Japanese Sample.” International Journal of Eating Disorders 41, no. 1: 88–91. 10.1002/eat.20444. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

TABLE S1. Associations between functions of engaging in dietary restriction and specific facets of negative mood.

EAT-58-1477-s001.docx (20.2KB, docx)

Data Availability Statement

Investigators interested in utilizing the dataset used in the current study should contact the corresponding author.

Articles from The International Journal of Eating Disorders are provided here courtesy of Wiley

RESOURCES