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. Author manuscript; available in PMC: 2023 Oct 2.
Published in final edited form as: Eat Behav. 2023 Jun 28;50:101776. doi: 10.1016/j.eatbeh.2023.101776

The relation of food insecurity to eating disorder characteristics and treatment-seeking among adult respondents to the National Eating Disorders Association online screen

Agatha A Laboe a,1,*, Laura D’Adamo a,b,1, Anne Claire Grammer a, Claire G McGinnis a, Genevieve M Davison a, Katherine N Balantekin c, Andrea K Graham d, Lauren Smolar e, C Barr Taylor f,g, Denise E Wilfley a, Ellen E Fitzsimmons-Craft a
PMCID: PMC10542957  NIHMSID: NIHMS1931378  PMID: 37390519

Abstract

Background:

Food insecurity (FI), characterized by limited or uncertain access to adequate food, has been associated with eating disorders (EDs). This study explored whether FI was associated with ED behaviors, ED diagnosis, current treatment status, and treatment-seeking intentions among adults who completed an online ED screen.

Methods:

Respondents to the National Eating Disorders Association online screening tool self-reported demographics, FI, height and weight, past 3-month ED behaviors, and current treatment status. Respondents were also asked an optional question about treatment-seeking intentions. Hierarchical regressions evaluated relations between FI and ED behaviors, treatment status, and treatment-seeking intentions. Logistic regressions explored differences in probable ED diagnosis by FI status.

Results:

Of 8714 respondents, 25 % screened at risk for FI. FI was associated with greater binge eating (R2Change = 0.006), laxative use (R2Change = 0.001), and presence of dietary restriction (R2Change = 0.001, OR: 1.32) (ps < .05). Having FI was associated with greater odds of screening positive for a probable ED or as high risk for an ED (ps < .05). FI was not associated with current treatment status or treatment-seeking intentions (ps > .05).

Conclusions:

Findings add to existing literature supporting a relation between FI and EDs. Implications include a need to disseminate EDs screening and treatment resources to populations affected by FI and to tailor treatments to account for barriers caused by FI.

Keywords: Eating disorders, Food insecurity, Mental health screening, Treatment-seeking

1. Introduction

Eating disorders (EDs) are serious psychiatric illnesses with adverse psychological and physical health effects and the second highest mortality rate of any mental illness (Chesney et al., 2014). Food insecurity (FI), characterized by limited or uncertain access to adequate food (USDA, 2020), is experienced by approximately 10 % of the U.S. population and is a key contributor to poor health outcomes (Gundersen & Ziliak, 2015; USDA, 2020). FI has been cross-sectionally associated with greater frequency of disordered eating behaviors (e.g., binge eating and compensatory weight control behaviors) as well as increased likelihood of having bulimia nervosa (BN) and binge-eating disorder (BED), even after controlling for related demographic variables including age, gender, race, income, and education (Hazzard et al., 2020; Lydecker & Grilo, 2019; Rasmusson et al., 2019). This relation may occur due to the “feast or famine” cycle, through which food intake increases during periods of relative food abundance and decreases during periods of food scarcity (Dinour et al., 2007). For instance, monthly U.S. Supplemental Nutrition Assistance Program benefits are often redeemed soon after their receipt and exhausted prior to the end of the month, leading to periods of dietary restriction until the next month’s benefits are received (Dinour et al., 2007; Middlemass et al., 2021). Indeed, elevated dietary restriction has been documented in individuals with FI. Additionally, adults with FI deliberately restrict food intake to make food last longer or to prioritize availability of food to other family members (Middlemass et al., 2021). Importantly, food restriction, whether or not it is driven by shape or weight concerns, is associated with increased preoccupation with food and binge eating (Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022; Keys et al., 1950).

The positive association between FI and eating pathology has been well-documented cross-sectionally (Becker et al., 2017; Becker et al., 2019; Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022; Hooper et al., 2020; Kim et al., 2021; Zickgraf et al., 2022). Further, prospective data demonstrated that severe FI at baseline significantly predicted greater prevalence of binge eating at 5 years in a population-based sample of teens and young adults (Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022). However, prior research exploring which ED diagnoses are most prevalent among individuals with FI has been limited. Most research establishing links between FI to BN and BED did not include individuals with restrictive ED presentations (e.g., anorexia nervosa) in their samples. Only one study using nationally representative data from 2001 to 2003 examined the full spectrum of ED diagnostic presentations and found a relation to bulimic-spectrum EDs (i.e., BN and BED) only (Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022). However, this study used DSM-IV criteria for EDs, suggesting that research reflecting DSM-5 diagnostic updates is needed. Given the strong association between FI and other demographics (e.g., income) (Walker et al., 2021), previous research indicates that it is important to examine the influence of FI on EDs (e.g., Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022).

It is also plausible that FI is associated with likelihood of treatment-seeking behavior and treatment status, such that those with FI are less likely to seek or be in treatment for EDs. However, no study to our knowledge has assessed these associations. Early detection of EDs is critical for a positive prognosis (LeGrange & Loeb, 2007), yet <20 % of individuals with EDs ever receive treatment (Kazdin et al., 2017). Documented rates of treatment-seeking and treatment uptake are even lower among certain underserved groups with EDs, including racial/ethnic minorities and individuals with lower income (Ali et al., 2017; Grammer et al., 2022; Sonneville & Lipson, 2018). Barriers to mental health treatment, including high cost, lack of availability and accessibility, and low perceived need (Ali et al., 2017; Eisenberg et al., 2009; Eisenberg et al., 2011) may be particularly salient for individuals experiencing FI, preventing those with FI from seeking treatment. Furthermore, participating in evidence-based treatment for EDs, which requires making significant changes to eating habits, may be particularly challenging in the context of FI. A recent qualitative study of patient perceptions of the relation between FI and ED treatment found that FI precluded individuals from adhering to treatment recommendations (Frayn et al., 2022). Specifically, individuals with FI reported difficulty adhering to recommendations to eat regularly and circumvent dietary restraint when food was not consistently available (Frayn et al., 2022). Thus, current FI may interfere with treatment, potentially reducing willingness to engage in treatment.

This study aimed to replicate and extend prior findings by exploring whether FI is associated with ED behaviors (i.e., binge eating, compensatory weight control behaviors, dietary restriction) in a sample of adults. We used data from an online ED screening tool that is completed by ~200,000 respondents per year (Fitzsimmons-Craft, Balantekin, et al., 2019; Fitzsimmons-Craft, Firebaugh, et al., 2019) via a partnership with the National Eating Disorders Association (NEDA). Data from a mass ED screen have not yet been used to examine EDs between individuals with and without FI. We hypothesized that FI would be associated with greater ED behaviors and that respondents with FI would have lower rates of treatment-seeking and treatment receipt compared to those without FI. Guided by prior research, we also hypothesized that respondents with FI would more frequently have a probable diagnosis of BN and BED; given the dearth of literature on FI and ED diagnoses outside of the binge-spectrum, we did not formulate hypotheses regarding the association of FI to other ED diagnoses.

2. Methods

2.1. Participants and procedures

This study used data from NEDA’s online ED screening tool collected from July 18, 2022 (when a FI screener was added to the NEDA screen), through September 30, 2022 (when data were downloaded to be used for the current study). No specific recruitment strategies were conducted to target those who may be experiencing FI. Users accessed the confidential screen through NEDA’s website (https://www.nationaleatingdisorders.org/screening-tool). This tool assesses demographics, ED psychopathology, probable ED diagnosis, ED treatment status, and intentions to seek treatment among those who screen positive for a probable ED. After completing the screen, respondents were provided feedback on their probable ED diagnosis, referral information, and optional questions about their future treatment-seeking intentions. We received Institutional Review Board approval from Washington University in St. Louis to analyze the de-identified NEDA screen data.

2.2. Measures

2.2.1. Demographics

Participants self-identified their gender, age, race, ethnicity, and annual household income.

2.2.2. BMI

Respondents’ self-reported height and weight was used to calculate BMI (kg/m2).

2.2.3. ED behaviors and probable diagnosis

The Stanford-Washington University Eating Disorder Screen (SWED; Graham et al., 2019) was used to evaluate ED behaviors. These included frequency of binge eating and compensatory weight control behaviors (i. e., fasting, vomiting, laxative/diuretic use, excessive exercise) over the past three months, as well as current presence of regular dietary restriction (<1200 kcal/day).

Probable ED diagnoses were also assessed with the SWED. Participants were categorized into one of the following diagnostic categories based on their responses: (a) anorexia nervosa (AN); (b) clinical/subclinical BN; (c) clinical/subclinical BED; (d) purging disorder (PD); (e) unspecified feeding or eating disorder (UFED); (f) avoidant/restrictive food intake disorder (ARFID); (g) at risk for an ED; or (h) no risk for an ED. The SWED has been used with individuals of various gender identities and ages (Fitzsimmons-Craft, Balantekin, et al., 2019; Fitzsimmons-Craft, Firebaugh, et al., 2019), and demonstrates specificity and sensitivity for identifying most ED diagnoses (Graham et al., 2019).

2.2.4. FI

To assess FI, respondents completed the Hunger Vital Sign questionnaire (Hager et al., 2010), a two-question screening tool based on the US Household Food Security Scale. This tool identifies individuals as being at risk for FI if they choose “often true” or “sometimes true,” rather than “never true,” for one or both of the following statements: “Within the past 12 months, I worried whether my food would run out before I got money to buy more” and “Within the past 12 months the food I bought just didn’t last and I didn’t have money to get more.” This questionnaire has demonstrated adequate sensitivity (96.7 %) and specificity (86.2 %) relative to the 18-item Household Food Security Survey Module (HFSSM; Gattu et al., 2019) and has been used in previous research assessing relations between FI and disordered eating (Linsenmeyer et al., 2021; Zickgraf et al., 2022). As a screening tool for FI, the Hunger Vital Sign questionnaire measures FI risk. We use FI as a shorthand for FI risk as follows.

2.2.5. Current ED treatment status and treatment-seeking intentions

All respondents self-reported whether they were currently in treatment for an ED at the time of the NEDA screen. Possible responses were: “Yes,” “No,” and “Not currently, but I have been in the past.” After completion of the initial screen, respondents who screened as having a probable ED diagnosis were shown feedback and referral information, followed by an optional question evaluating their intention to seek treatment: “Do you intend to seek professional help and/or take any steps to address these concerns?” (response options: “Definitely not,” “Probably not,” “Probably,” and “Definitely”).

2.3. Statistical analyses

Analyses were conducted using R version 4.1.3. Of those who completed the screen, 3864 were excluded because they reported being under 18 years of age, and 2315 were excluded because they were outside of the U.S. 15 respondents were excluded from analyses for the following reasons: (1) self-reported ED behaviors that were above implausible values (i.e., frequency of binge eating, fasting, laxative/diuretic use, exercise, and vomiting >500 over the course of 3 months); (2) biologically implausible weights for adults (i.e. <60 lbs); (3) biologically implausible heights for adults (i.e., < 48 in or >84 in); (4) and/or did not screen for a probable ED. To generate a probable ED diagnosis, respondents were required to have provided data on all ED screen items, including height and weight. If these data were missing, a probable ED diagnosis was not assigned. The final analytic sample included 8714 U.S. adults.

FI was coded as 0 if responses on the Hunger Vital Sign indicated no FI and 1 if responses indicated FI as described above. Descriptive statistics for demographics, ED behaviors (i.e., presence of dietary restriction, frequency of binge eating, frequency of compensatory weight control behaviors), probable ED diagnosis, treatment-seeking, treatment status, and FI were calculated. Preliminary analyses explored associations between demographics and outcome variables, as well as demographic differences between participants with and without FI.

To examine whether FI explained variance in the outcome variables, hierarchical linear regressions and hierarchical logistic regressions with FDR corrections for multiple comparisons were conducted. To preserve power and aid interpretation of results, treatment-seeking intentions and treatment status were treated as binary variables (Yes/No) in logistic regressions. In Step 1, relevant demographics were entered into the models. Demographic variables that did not improve model fit were not included. In Step 2, FI was added to assess whether it uniquely explained additional variance in the outcome variables. Diagnostic tests were used to ensure absence of multicollinearity among predictor variables. Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC) values evaluated superior model fit. Multinomial logistic regressions explored whether odds of each probable ED diagnosis/risk category differed between respondents with and without FI.

3. Results

3.1. Descriptive statistics

Table 1 presents demographic characteristics of the sample. Of 8714 adults who completed the NEDA screen, 2226 (25.5 %) were at risk for FI. 80 % of participants were white, 85 % were female, and 70 % were between the ages of 18 and 35. 3.09 % of respondents reported currently being in treatment for an ED. Among those who screened positive for probable EDs (n = 7517), respondents reported an average of 17.3 binge episodes, 3.08 laxative/diuretic use episodes, 7.13 fasting episodes, 4.56 vomiting episodes, and 7.09 compensatory exercise episodes in the past 3 months. 129 respondents with probable EDs answered the optional treatment-seeking intentions question after receiving the results of their screen and referral information. Of these 129 individuals, 55.8 % reported intentions to seek treatment for an ED. See Table 2 for descriptive statistics of key variables. See Table 2 for descriptive statistics of key variables. Presence of FI was significantly associated with younger age, non-binary or self-described gender, lower income, racial minority status, Hispanic/Latino origin, and higher BMI.

Table 1.

Participant characteristics.

Age 18–24 25–34 35–44 45–54 55–64 65–74 75+
n (%) 2987 (39.7 %) 2265 (30.1 %) 1103 (14.8 %) 654 (8.7 %) 343 (4.6 %) 136 (1.8 %) 28 (0.4 %)
Gender Female Male Non-binary Prefer not to say Prefer to self-describe
n (%) 6363 (84.7 %) 745 (9.9 %) 282 (3.8 %) 91 (1.2 %) 30 (0.4 %)
Race White Black African American Asian Native Hawaiian/Pacific Islander American Indian/Alaska Native Multiracial Other
n (%) 5998 (80.1 %) 319 (4.3 %) 333 (4.4 %) 31 (0.4 %) 70 (0.9 %) 360 (4.8 %) 377 (5.0 %)
Income <$20,000 $20,000–39,000 $40,000–59,999 $60,000–79,999 $80,000–99,999 $100,00–149,999 $150,000+
n (%) 1088 (14.8 %) 1140 (15.5 %) 1147 (15.6 %) 962 (13.1 %) 762 (10.4 %) 1166 (15.9 %) 1082 (14.7)
Ethnicity Hispanic/Latino Non-Hispanic/Latino
n (%) 963 (12.8 %) 6540 (87.2 %)
BMI
M (SD) 28.7 (9.59)
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Table 2.

Descriptive statistics for key variables.

M SD
Past 3-month binge eating 17.3 28.2
Past 3-month compensatory behaviors
 Laxative/diuretic use 3.08 13.1
 Fasting 7.13 17.12
 Vomiting 4.56 18.3
 Compensatory exercise 7.09 18.7
n %
Current dietary restriction 3860 51.4 %
FI 2226 25.5 %
Current treatment status
 Yes 232 3.09 %
 No 6574 87.5 %
 No, but I have in the past 711 9.46 %
Treatment-seeking intentions
 Definitely 23 17.8 %
 Probably 49 38.0 %
 Probably not 47 36.4 %
 Definitely not 10 7.8 %
ED diagnosis/risk category
 Anorexia nervosa 404 4.64 %
 Clinical/subclinical bulimia nervosa 2636 30.3 %
 Clinical/subclinical binge eating disorder 1275 14.6 %
 Purging disorder 179 2.0 %
 Unspecified feeding or ED 2635 302 %
 Avoidant/restrictive food intake disorder 388 4.4 %
 At risk for an ED 836 9.59 %
 Not at risk for an ED 361 4.14 %
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Note: Treatment-seeking intentions were reported by a subset of the overall sample (n = 129).

3.2. Hierarchical multiple regressions

Results of hierarchical multiple regressions exploring the relations between FI and ED behaviors are shown in Table 3. To examine binge eating frequency, a model including age as a covariate had superior model fit (BIC: 79,738; AIC: 79,668, X2(1) = 0.00, p < .01). To examine compensatory behavior frequency, the best-fitting models included age and gender (BIC: 66,134; AIC: 66,034, X2(1) = 0.00, p < .001). To examine likelihood of dietary restriction, the best-fitting model included gender, age, and income (BIC: 11,237; AIC: 11,103, X2(1) = 0.00, p < .001).

Table 3.

Hierarchical multiple regression analyses exploring the relation of FI to ED behaviors.

Step Binge eating Laxative/diuretic use Exercise Fasting Vomiting Dietary restriction
F R 2 b F R 2 b F R 2 b F R 2 b F R 2 b OR R 2 b
1: Covariates 29.6 0.024 3.96 .010 4.20 0.010 8.85 0.011 2.50 0.003 .089
 Age
  25–34 4.7 −0.1 −1.3 −0.4 0.6 −0.6
  35–44 8.9 1.6 −1.4 −2.1 0.2 −0.8
  45–54 10.7 1.3 −2.5 −3.3 −0.2 −1.0
  55–64 11.7 −0.2 −4.4 −5.5 −1.6 −1.3
  65–74 8.9 0.9 −4.6 −4.8 −2.8 −1.5
  75–84 12.5 −1.0 −2.5 −5.9 1.3 −1.1
  85+ −9.9 0.5 −8.6 −7.9 −2.1 0.2
 Gender
  Male −1.5 −1.2 −2.2 −1.6 −0.9
  Non-binary −0.5 −2.2 0.1 −0.8 0.4
  Self-described −1.8 1.1 1.6 7.1 1.4
  Prefer not to say −1.0 1.8 2.1 −2.9 0.0
 Income
  $20 k–39,999 0.1
  $40 k–59,999 −0.1
  $60 k–79,999 −0.1
  $80 k–99,999 −0.3
  $100 k–149,999 −0.3
  $150 k+ −0.3
2: FI 33.2 .030 4.9 4.55 .010 0.99 3.95 0.010 −0.45 14.1 0.020 3.4 4.29 0.006 2.07 1.12 .090 0.12
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Note: The models examining binge eating included age as a covariate. The models examining laxative use, exercise use, fasting, and vomiting included age and gender as covariates. The model examining dietary restriction included gender, age, and income as covariates. R2 in the logistic models refers to Nagelkerke R2. The reference group was 18–24 for age, female for gender, and <$20 k for income. Cohen’s f2 was calculated as a measure of effect size for linear models as follows: 0.001 for binge eating, 0.002 for laxative/diuretic use, 0.000 for exercise, 0.000 for fasting, and 0.000 for vomiting. f2 ≥ 0.02 = small effect size, f2 ≥ 0.15 = medium effect size, f2 ≥0.35 = large effect size. Bolded statistics indicate significance at p < .05.

Results of hierarchical multiple regressions exploring the relations between FI and treatment-seeking and treatment status are shown in Table 4. To examine treatment-seeking intentions, a model including age and income as covariates had superior model fit (BIC: 228; AIC: 190, X2(1) = 0.92, p < .001). To examine treatment status, the best-fitting model included age and income (BIC: 2300; AIC: 2272, X2(1) = 0.94, p = .076).

Table 4.

Hierarchical multiple regression analyses exploring the relation of FI to treatment-seeking and treatment status.

Step Treatment-seeking Treatment status
OR R 2 b OR R 2 b
1: Covariates 0.247 0.020
 Age
  25–34 0.9 −0.1
  35–44 1.9 −0.0
  45–54 0.8 −0.0
  55–64 0.1 −0.2
  65–74 −0.4 0.1
  75–84 − 0.2
  85+ − −10.2
 Income
  $20 k–39,999 −2.1 0.1
  $40 k–59,999 −0.6 0.1
  $60 k–79,999 −1.5 −0.4
  $80 k–99,999 −1.2 −0.1
  $100 k–149,999 −1.3 −0.1
  $150 k+ −2.4 0.1
2: FI 1.27 0.249 0.2 0.97 0.020 −0.1
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Note: The models included age and income as covariates. R2 in the logistic models refers to Nagelkerke R2. The reference group was 18–24 for age, female for gender, and <$20 k for income. Bolded statistics indicate significance at p < .05. The treatment-seeking model was conducted in a subsample of 129 participants with probable EDs who completed optional questions on treatment-seeking; this subsample did not contain any respondents aged 75 or older.

3.2.1. Binge eating frequency

At Step 1, demographics explained 2 % of the variance in binge eating frequency, with a small effect size; R2 = 0.024, F(7,8478) = 2996.6, p < .001. Adding FI to the model at Step 2 significantly improved the model. FI contributed to a greater number of binge eating episodes in the past 3 months, with a small effect size (M episodes with FI = 19.5 vs. M episodes without FI = 16.5); R2 Change = 0.006, F Change(88477) = 33.2, b = 4.9, p < .001.

3.2.2. Compensatory weight control behavior frequency

At Step 1, demographics explained 1 % of the variance in frequency of laxative/diuretic use, with a small effect size; R2 = 0.010, F(11,8464) = 3.96, p < .001. Adding FI at Step 2 uniquely contributed to an increase in laxative/diuretic use, with a small effect size (M episodes with FI = 3.64 vs. M episodes in without FI = 2.87); R2 Change = 0.001, F Change (12,8463) = 4.55, b = 0.99, p < .01. Neither demographics nor FI had significant associations with frequency of compensatory exercise, fasting, or vomiting (ps > .05).

3.2.3. Presence of dietary restriction

At Step 1, demographics explained a significant proportion of the variance in presence of dietary restriction; Nagelkerke R2 = 0.089, p < .001. Adding FI at Step 2 significantly improved the model and contributed to an increase in the likelihood of dietary restriction (endorsing restriction with FI = 51.5 % vs. endorsing restriction without FI = 44.4 %); Nagelkerke R2 Change = 0.001, Wald’s X2 = 20.1, OR: 1.32, 95 % CI: 0.16–0.40, p < .001.

3.2.4. Treatment-seeking intentions and treatment status

At Step 1, demographics explained a significant proportion of the variance in presence of treatment-seeking intentions; Nagelkerke R2 = 0.247, p < .001. Adding FI at Step 2 did not significantly improve the model; Nagelkerke R2 Change = 0.000, Wald’s X2 = 0.011, OR: 1.05, 95 % CI: −1.00–1.09, p > .05. Neither demographics nor FI had significant associations with treatment status (p > .05).

3.3. Multinomial logistic regressions

Results of multinomial logistic regressions exploring whether probable ED diagnosis differed by FI are shown in Table 5.

Table 5.

Multinomial logistic regression evaluating the association between FI and ED diagnosis/risk category, controlling for demographics.

Independent variable At risk for an ED AN Clinical/subclinical BN Clinical/subclinical BED PD UFED ARFID
FI OR b OR b OR b OR b OR b OR b OR b
1.59 0.5 2.18 0.8 2.79 1.0 2.46 0.9 2.07 0.7 2.03 0.7 2.06 0.7
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Note: Analyses adjusted for age, gender, race, income, and ethnicity. Abbreviations: ED = eating disorder; FI = food insecurity; AN = anorexia nervosa; BN = subclinical/clinical bulimia nervosa; BED = subclinical/clinical binge eating disorder; PD = purging disorder; UFED = unspecified feeding or eating disorder; ARFID = avoidant/restrictive food intake disorder. The reference group was respondents at low risk for an ED. Bolded statistics indicate significance at p < .05.

3.3.1. Differences in presence of a probable ED diagnosis by FI status

FI was significantly associated with greater odds of screening positive for probable AN, clinical/subclinical BN, clinical/subclinical BED, PD, UFED, ARFID, and at risk for an ED, controlling for age, gender, race, income, and ethnicity (ORs: 1.59–2.79; ps < .05).

4. Discussion

This study examined the associations between FI, ED behaviors, probable ED diagnosis, treatment status, and treatment-seeking intentions in a sample of adult NEDA screen respondents. The prevalence of FI was 25 %, a rate higher than that of a previous study using the same FI screening tool with youth and young adults (21 %) (Linsenmeyer et al., 2021). It was also higher than the overall proportion of the U.S. population with FI and previously reported rates in nationally-representative samples of U.S. adults (USDA, 2022; Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022). However, the Hunger Vital Sign assesses risk for FI rather than FI status, and thus is not as precise as gold-standard assessment measures (Hager et al., 2010), which may partly explain the high FI rate. The high prevalence could also be related to the accessibility of the NEDA online screen, which may result in greater use by underserved populations, such as those with FI. Given that NEDA screen respondents voluntarily complete the screen to learn about their ED symptoms and receive resources, this also suggests that FI is prevalent among individuals seeking ED treatment resources and that incorporating assessment of FI into mass ED screens is warranted.

We found significant, albeit small, associations between FI and presence of regular dietary restriction, greater frequency of binge eating, and greater frequency of laxative use. The finding that FI was associated with presence of dietary restriction is consistent with several previous studies (e.g., Becker et al., 2017; Becker et al., 2019). Of note, the item assessing dietary restriction on the NEDA screen explicitly asks if restriction was employed to control shape/weight, which is appropriate as DSM-5 ED diagnoses are made based on restriction and other ED behaviors due to shape/weight concerns. This question may also be particularly appropriate for the present study, as pressure for thinness may be particularly salient for white women (Frederick et al., 2022), and this study utilized a predominantly white, female sample. However, it is also important to note that in a study with a more racially diverse sample, individuals with FI reported restriction for reasons other than weight and shape concerns (e.g., to ensure that other family members have sufficient food) (Middlemass et al., 2021). As such, disentangling motivations for dietary restriction and their respective associations to ED pathology among individuals with FI is an important future direction.

We also found that FI was independently associated with greater frequency of binge eating, replicating findings from prior cross-sectional studies (Becker et al., 2017; Becker et al., 2019; Bruening et al., 2012; Larson et al., 2020; Zickgraf et al., 2019). One theory aimed to explain this association posits that fluctuations in food availability influence cycles of restriction and binge eating, such that following dietary restriction, individuals with FI are more likely to engage in binge eating when food is abundantly available (Keys et al., 1950). Furthermore, a study using ecological momentary assessment found that relative food abundance was associated with subsequent binge eating among young adults with FI (Hazzard et al., 2023). Another possible explanation is that FI is associated with several variables related to disordered eating, including disrupted sleep and stress (Allison et al., 2016; Billings et al., 2020), which may play a role in the relation between FI and binge eating. We also found that FI was independently associated with greater laxative use, mirroring past research in a population-based sample of adolescents (Hooper et al., 2020) and among adult food pantry clients (Becker et al., 2017; Becker et al., 2019). However, in contrast to previous work, FI was not associated with compensatory exercise or fasting (Becker et al., 2017; Becker et al., 2019). It is possible that the previous study’s sample of food pantry clients had more severe FI or ED symptoms compared to the current study sample, which may explain inconsistent findings regarding compensatory behaviors (Becker et al., 2017, Becker et al., 2019). FI was also not associated with vomiting in this study, adding to mixed results in the literature (Becker et al., 2017; Becker et al., 2019; Hooper et al., 2020).

Relative to respondents who did not screen to be at risk for an ED, respondents who screened positive for probable AN, clinical/subclinical BN, clinical/subclinical BED, PD, UFED, ARFID, and at risk for an ED had greater odds of having FI. This finding adds to previous literature which has supported a relationship between FI and a diagnosis of BN (Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022; Lydecker & Grilo, 2019) and BED (Lydecker & Grilo, 2019; Rasmusson et al., 2019). Furthermore, this finding was the first to support a relationship between FI and restrictive ED diagnoses and suggests that additional investigation of these associations is warranted.

There were no significant effects of FI on treatment status and treatment-seeking intentions. However, these results should be interpreted with caution due to low rates of respondents who reported being in treatment or intending to seek treatment in this sample. Furthermore, this may in part reflect the racial of participants and cultural norms that may make it easier for white individuals to be aware of ED resources. Research should further investigate treatment variables among larger samples of individuals with FI, particularly in light of work demonstrating that certain underserved groups have lower treatment-seeking and uptake (Grammer et al., 2022).

This study was the second largest to examine the relation between FI and EDs (second to Hazzard, Barry, et al., 2022; Hazzard, Hooper, et al., 2022). Given that most studies in this area used small samples, replication of previous results in large samples is key. Additionally, using an online screen allowed us to reach a population with high ED pathology, which allowed us to explore the relation between FI and EDs within a sample in which ED symptomatology was highly prevalent. This study also had limitations. Although adding FI to the models improved model fit, FI explained only a small proportion of unique variance in the outcomes of this study. On a population level, the contribution to ED symptoms that FI added may be significant; however, findings suggest that the link between FI and ED symptoms is likely also driven by related sociodemographic factors. The self-selecting nature of the NEDA online screen may hinder our ability to generalize findings to broader populations. Additionally, only a small subsample of respondents with probable EDs completed the optional treatment-seeking question, which may have limited our ability to detect effects on treatment-seeking, and as such, our results should be interpreted with caution and replicated in a larger sample. It is also possible that the optional nature of the treatment-seeking question led to a self-selecting sample, and thus, findings may not generalize to the broader population. Furthermore, the diversity of the sample is limited as it is composed of predominantly white, cisgender women, and as such, the results of this study may not reflect the experiences of other racial groups and those with a different gender identity who are experiencing FI. Finally, as we only included demographic covariates that improved model fit, we did not thoroughly explore demographic disparities in the relation between FI and EDs. Additional research is needed to investigate how demographics and FI may interact to predict ED symptoms.

5. Conclusions

Results indicate an urgent need to expand detection and treatment of EDs in individuals experiencing FI. Future efforts should disseminate screening and treatment resources such as the NEDA online screen to populations affected by FI to improve detection and subsequent treatment provision. Work is also needed to tailor evidence-based treatments to account for key factors related to symptomatology (e.g., dietary restriction to make food last longer) and barriers to treatment engagement (e.g., challenges with buying foods to eat regularly) that may affect individuals with FI.

Role of funding sources

This work was supported by NIH grants K08 MH120341, K01 DK120778, K01 DK116925, F31 HL158000, F31 HD106750, and T32 HL130357.

Footnotes

CRediT authorship contribution statement

Ms. Laboe, Ms. D’Adamo, and Dr. Fitzsimmons-Craft conceptualized the design of the study. Ms. Laboe, Ms. D’Adamo, and Ms. McGinnis wrote the original draft of the manuscript. Drs. Fitzsimmons-Craft, Taylor, Balantekin, Graham, and Wilfley participated in data collection. Ms. D’Adamo conducted formal data analysis. All authors reviewed and edited the manuscript.

Declaration of competing interest

Dr. Fitzsimmons-Craft receives royalties from UpToDate and is on the Clinical Advisory Board for Beanbag Health.

Data availability

Data will be made available on request.

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Associated Data

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

Data Availability Statement

Data will be made available on request.

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