Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Jun 1.
Published in final edited form as: Appetite. 2024 Mar 11;197:107304. doi: 10.1016/j.appet.2024.107304

Examining Measurement Invariance of Appetitive Trait and ARFID Symptom Measures by Food Security Status

Kaoon (Francois) Ban 1, Vivienne M Hazzard 2, Hana F Zickgraf 3,4, Shannon M O’Connor 1
PMCID: PMC11089937  NIHMSID: NIHMS1980965  PMID: 38467192

Abstract

Objective:

Measures assessing appetitive traits (i.e., individual differences in the desire to consume food) and disordered eating have generally been developed in predominantly food-secure populations. The current study aims to test measurement invariance (MI) for a measure of appetitive traits and a measure of Avoidant Restrictive Food Intake Disorder (ARFID) symptomology across food security status.

Method:

Data from a sample of mothers (n=634) and two undergraduate samples (n=945 and n=442) were used to assess MI for the Adult Eating Behavior Questionnaire (AEBQ), which measures appetitive traits, and the Nine Item ARFID Screen (NIAS), which measures ARFID symptomology. Current food security was assessed using the 18-item USDA Household Food Security Survey Module, which was dichotomized into two groups: 1) the ‘food insecure’ group included marginal, low, and very low food security and 2) the ‘food secure’ group included high food security. Overall and multi-group confirmatory factor analyses were conducted separately for each measure in each sample.

Results:

Results demonstrated scalar (i.e., strong) MI for both measures across samples, indicating that these measures performed equivalently across food-secure and food-insecure individuals.

Conclusion:

Findings suggest that differences in appetitive traits by food security status observed in prior research are not artifacts of measurement differences, but instead reflect true differences. Additionally, past mixed results regarding the relationship between food insecurity (FI) and ARFID symptomology are not likely driven by measurement error when using the NIAS.

The United States Department of Agriculture (USDA) defines food insecurity (FI) as the state of having limited or uncertain availability of food needed for healthy and sustainable living (Anderson, 1990). In 2021, approximately 34 million individuals, or 10.2% of U.S. households, reported experiencing FI at some point that year (USDA EDRS - Key Statistics & Graphics, 2023). Adults with FI may be at higher risk for mental health concerns including depression and anxiety (Arenas et al., 2019; Myers, 2020), as well as physical health problems, including hypertension and type 2 diabetes, compared to food-secure individuals (Abdurahman et al., 2019; Weaver et al., 2018) as a result of economic constraints that cyclically interfere with mental and physical health (Weaver et al., 2018). Additionally, individuals experiencing FI may be at greater risk for weight gain and obesity, most notably due to the greater relative accessibility to inexpensive, calorie-dense, and palatable foods (Drewnowski & Darmon, 2005; Leung et al., 2014). In fact, a substantial body of literature has highlighted the association between FI and obesity (Zhou et al., 2023; Mavegam et al., 2023), and more recently its association with disordered eating (DE). Specifically, studies have shown FI to be linked with various DE symptoms and eating disorder (ED) subtypes, including fear of weight gain, weight and shape concerns, and intentional restriction for weight loss, but demonstrates strongest associations with binge eating and inappropriate compensatory behaviors, including self-induced vomiting, laxative misuse, excessive exercise, and skipping meals (Becker et al., 2017; Becker et al., 2019; Christensen et al., 2021; Hazzard et al., 2020; Hazzard et al., 2022a; Hazzard et al., 2022b).

Appetitive traits, which are individual differences in the desire to consume food in response to internal stimuli (e.g., negative affect), external stimuli (e.g., presence of food cues), and various properties of the available food (e.g., familiarity vs. novelty) (French et al., 2012), may be helpful in better understanding DE in the context of FI. These traits encompass several types of eating styles, which can largely be categorized into two domains: 1) ‘food approach’ traits are eating behaviors that involve general appetite and desire to eat and include traits like food responsiveness, emotional overeating, and enjoyment of food, and 2) ‘food avoidance’ traits are eating behaviors that involve movement away from food and include traits like food fussiness/food selectiveness/picky eating, emotional undereating, and satiety responsiveness (Hunot et al., 2016). Both domains have been linked with elevated risk for EDs, such as binge eating disorder (e.g., food approach traits) and avoidant/restrictive food intake disorder (e.g., food avoidance traits) (He et al., 2021). Additionally, food approach traits, in particular, have been associated with greater body mass index (BMI) in adults, which in turn have been linked to DE behaviors (da Luz et al., 2018). Thus, exploration of appetitive traits may inform our understanding of DE.

Despite the proliferation of studies exploring the association between FI and DE in recent years, few studies have explored appetitive traits in the context of FI. O’Connor et al. (2023) examined differences in appetitive traits among four types of mothers: 1) mothers with no FI history, 2) mothers with childhood FI only, 3) mothers with current FI only, and 4) mothers with current and childhood FI. The authors found that mothers who reported current FI not only reported higher levels of DE but also several appetitive traits compared to mothers who reported no history of FI. Specifically, currently food-insecure mothers (without a history of FI in childhood) reported greater levels of food responsiveness, satiety responsiveness, emotional overeating, emotional undereating, and hunger compared to mothers who denied current and childhood FI, which suggests that food-insecure women may express certain appetitive traits more strongly than their food secure peers. Enjoyment of food exhibited a different pattern, with currently food-insecure mothers (without a history of FI) exhibiting less food enjoyment than food-secure mothers. No significant differences in slow eating were observed between mothers with and without a history of FI (O’Connor et al., 2023). The results of this study provided initial evidence for associations between FI and some appetitive traits and subsequently advocated for further investigation of the differences in appetitive traits across FI status (e.g., using longitudinal approaches, accounting for duration or developmental timing of FI exposure, etc.).

Appetitive traits have been implicated in the etiology of Avoidant/Restrictive Food Intake Disorder (ARFID), an ED characterized by food avoidance and/or limited diet variety that results in significant distress and/or impairment. Picky eating is most strongly associated with the selective/neophobic subtype of ARFID, with the other two subtypes characterized by food avoidance due to lack of appetite and fear. A review of picky eating behaviors among children globally suggested that the prevalence of picky eating ranged between 5.6% to 50% (Taylor et al., 2015), and studies have demonstrated its link to the development of ARFID (Thomas et al., 2017; Zickgraf et al., 2019a). Despite the high prevalence of picky eating within the population and the potential for picky eating to develop into more serious problems, picky eating has been largely left out of the recent proliferation of research exploring the association between FI and DE. Importantly, however, theories underpinning picky eating persistence suggest that picky eating behaviors may be elevated in food-insecure populations. Picky eating in children between one and six years of age is viewed as developmentally appropriate, and repeated exposure to unfamiliar foods is needed for children to accept them and, eventually, eat a varied diet (Birch & Marlin, 1982; Hasan et al., 2023; Sullivan & Birch, 1990). However, in the context of FI, parents may not be able to provide exposure to varied foods and/or may be reluctant to offer unfamiliar or non-preferred foods likely to be rejected by their children and potentially wasted (Daniel, 2016). Over time, these parental feeding practices, such as risk aversion in food purchasing or no longer offering a food that has previously been rejected, may lower exposure to food variety and influence the appetitive traits of children by limiting their diets (Daniel, 2016), thus contributing to the persistence of picky eating beyond the developmentally typical window and potentially into adulthood. Thus far, though, the few studies that have sought to examine these associations in children have failed to find a relationship between concurrent childhood FI and childhood food fussiness/pickiness (Berge et al., 2020; Brown et al., 2018).

Evidently, picky eating is prevalent in adulthood, with approximately a third of community dwelling adults self-identifying as being picky to some degree (Kauer et al., 2015; Zickgraf et al., 2016; Zickgraf & Ellis, 2018). Picky eaters of all ages report a similar dietary pattern, characterized by high intake of processed carbohydrates and proteins and low intake of nutrient rich foods such as fruits and vegetables and unprocessed animal proteins (e.g., meat and fish; Dial et al., 2021; Ellis et al., 2018; Nansel et al., 2018; Pesch et al., 2020; Zickgraf & Schepps, 2016). Studies demonstrating a relationship between FI and picky eating among adult populations have been sparse and somewhat inconclusive. In an observational, qualitative study of 63 food-insecure adults, Cummer et al. (2020) found that the participants showed preferences for high intake of calorie-dense foods and low consumption of vegetables. These preferences may suggest a disposition towards picky eating, however, adults experiencing FI may make these food choices for reasons other than food pickiness. Higher calorie-dense foods are more accessible and satiable while fresh vegetables are easily perishable and may require more time to prepare; Cummer et al., 2020). The insurance hypothesis posits that humans are evolutionary prone to storing fat as a buffer against subsequent lack of food supply (Nettle et al., 2017). O’Connor et al. (2023) found contrasting results as mothers with current FI reported similar levels of picky eating on the AEBQ compared to mothers with no FI history. Further, results suggest that experiencing FI in childhood may be associated with less picky eating in adulthood, as mothers who experienced FI in childhood (but denied current FI in adulthood) reported significantly less picky eating than mothers currently experiencing FI in adulthood (but who denied FI in childhood) (O’Connor et al., 2023). Nevertheless, given evidence that adults with FI tend to eat diets high in processed foods and low in perishable fresh foods and fruits/vegetables, their endorsement of food selectivity-related behaviors on self-report instruments might be influenced by the similarity of this diet to the prototypical diet of selective eaters, thus contributing to differential measurement of the trait.

Currently, no research on other ARFID subtypes (i.e., fear and appetite) and FI have been conducted. Importantly, the fear subtype of ARFID appears less theoretically linked to FI, however, the appetite subtype of ARFID involves behaviors like meal skipping and prioritizing other activities overeating, which may have different implications for food-insecure populations. For example, severe FI may lead to appetitive disturbances secondary to delayed gastric motility/intestinal transit (i.e., movement of food throughout the body), which can emerge in the context of chronic food restriction (e.g., Norris et al., 2016; Sato & Fukudo, 2015). Given the possible associations between FI and ARFID subtypes, measures of ARFID symptoms need to be validated for FI populations so that future research can accurately explore the relationship between ARFID subtypes and FI.

Because existing DE measures have largely been developed in convenient samples with an overrepresentation of female, White, and/or middle-class participants, it is unclear whether these measures operate in the same way in individuals outside this group, and they may be driving some of the inconsistencies found in the literature (Machado et al., 2018; Machado et al., 2020; Stice et al., 2004). Indeed, recently there has been calls for psychometric validation of assessment measures in food-insecure populations (Christensen et al., 2022), and thus far, a few studies have examined the appropriateness of ED measures in food-insecure populations (O’Connor et al., 2022; Richson et al., 2023). It is of particular importance to consider eating-related constructs for individuals with FI because their eating behaviors may differ from those of the general population due to reasons outside of typical ED etiology. For example, O’Connor et al. (2022) demonstrated differential item functioning (DIF) for two items on the Eating Disorder Diagnostic Scales (EDDS): 1) eating large amounts without physical hunger and 2) negative emotions about overeating in a food-insecure population. Food-insecure individuals with higher levels of DE were less likely to report eating large amounts of food without physical hunger, suggesting that eating large amounts of food in the absence of physical hunger may be viewed as less pathological in food-insecure populations given their general limited access to food. Additionally, food insecure individuals with higher levels of DE were less likely to report negative emotions about overeating, which may reflect on its potential normative, if not adaptive, behavior to eat large amounts of food when food is available. These results suggest the importance of exploring how measures of eating behaviors function within food-insecure populations to ensure that mean differences in constructs are accurately interpreted.

In contrast, O’Connor et al. (2022) did not find DIF for the 7-item abbreviated Eating Disorder Examination Questionnaire (EDE-Q), and similarly, Richson et al. (2023) found no evidence of practically significant DIF for the SCOFF (i.e., an acronym of five questions addressing the core features of anorexia nervosa and bulimia nervosa), a commonly used screening measure for ED. These findings suggest that both the abbreviated EDE-Q and the SCOFF may be appropriate screening measures for ED pathology among individuals with FI. Based on available evidence, it is unclear whether measures of ARFID symptoms and appetitive traits operate in the same way across the food security spectrum, and thus, calls for additional exploration.

The aim of the present study was to test for measurement Invariance (MI; i.e., equivalence of a construct across groups) in a secondary analysis of an appetitive trait measure (i.e., Adult Eating Behavior Questionnaire (AEBQ)) and an ARFID symptom measure (i.e., Nine Item ARFID Screen (NIAS)) in two samples: mothers and undergraduate students. These two populations are both at relatively high risk for FI. FI has been reported to be as high as 12.5% for all U.S. households with children compared to the 10% national prevalence (USDA EDRS - Key Statistics & Graphics, 2023). Importantly, parents often attempt to buffer the effects of FI from their children, and mothers are more likely to be involved with food purchasing, preparation, and feeding children, leading to mothers experiencing more severe impacts of FI than other family members (Knowles et al., 2016; McIntyre et al., 2003; Middlemass et al., 2021; Radimer et al., 1992; Rose & Oliveira, 1997). Additionally, a review of FI prevalence among college students in the U.S. indicated that about 41% of these young adults experienced FI (Nikolaus et al., 2020). In addition to puberty, emerging adulthood is one of the bimodal time periods in which eating disorders tend to onset (Volpe et al., 2016). Thus, food-insecure young adults are hallmarked as one of the most vulnerable populations to experience DE symptomatology (Darling et al., 2017). Given the findings from O’Connor et al. (2022) and Richson et al. (2023) which found minimal or no clinically significant DIF across mothers and college students with and without FI on the EDDS/EDEQ and SCOFF, respectively, we hypothesized that both the AEBQ and NIAS questionnaires would demonstrate MI in both high-risk samples (i.e., mothers of young children and undergraduate students).

Methods

Participants

Sample 1: Mothers

Sample 1 consisted of 634 cis-gender mothers (mean age=34.77, SD=7.40) recruited to participate in an online study of the “impact of food availability on eating and feeding behaviors” via Amazon’s Mechanical Turk (MTurk). The current study is a secondary analysis. The primary research questions centered on exploring associations between childhood and current FI and adulthood DE (O’Connor et al., 2023) and exploring associations between household FI and environmental risk factors for DE within the home (e.g., parent feeding practices, weight/shape communication, frequency of family meals, etc.) (O’Connor et al., submitted). Notably, approximately half the sample reported experiencing FI during childhood with the other half denying childhood FI. Other inclusion criteria included having at least one child between the ages of 6–11 years and residing in the United States. Study procedures were approved by the University of Chicago’s Institutional Review Board. Participants provided informed consent online prior to starting the survey. Completion of the survey took an average of 28.49 (SD = 22.31) minutes and participants were paid $5.

We collected our sample using CloudResearch, a third-party website that interfaces with MTurk that is known to improve data quality through additional vetting of MTurk participants (Hauser et al., 2022). We also required MTurk worker qualifications including a Human Intelligence Task (HIT) approval rate of greater than 95% (number of approved HITs that a worker has completed) and the number of HITs approved greater than 100 (number of HITs that a worker has successfully completed since registering with MTurk) in order to collect data from MTurk workers with a strong history of successful HIT completions. Before data cleaning, we collected information from 805 mothers. To further ensure data quality, six quality checks were administered throughout the study. These quality checks asked participants to select the response that does not make semantic sense (e.g., “Planes yell on the dream”) from a set of four syntactically correct sentences (“Boats are sailing on the lake”). The average survey completion time did not differ by the number of quality checks missed with one exception. We then explored the internal consistency of well-validated, reliable measures (that include reverse-scored items; e.g., AEBQ (Hunot et al., 2016)) by the number of missed quality checks. Internal consistency decreased when including individuals who missed four or more quality checks. Thus, individuals who missed at least four of the six quality checks (n=171) were excluded, resulting in our final analytic sample of 634.

Samples 2 and 3: Undergraduate Student Samples

Samples 2 and 3 were student samples recruited from two public universities in the United States. All study procedures were approved by the respective university’s Institution Review Board. For both samples, informed consent was obtained online at the start of the online survey. Eligibility criteria included being 18 years or older, enrolled as a student at the university where the data was being collected, and being currently enrolled in a psychology course that required research participation credit. Before data cleaning for Sample 2, we collected 963 responses. Quality check questions were included after recruiting 394 participants, and thus, 569 participants received eight quality check questions. Of these, 11 participants were excluded for getting less than 50% of the quality check questions correct. Additionally, another seven participants were excluded for responding with an illogical age response (e.g., <18). Sample 2 consisted of 945 undergraduate students (mean age = 19.80, SD = 2.95) recruited to participate in research studies via a psychology student subject pool. Students were compensated with credits for their psychology course. Before data cleaning for Sample 3, we collected 627 responses. Of these, 166 were excluded for completing the surveys more than once (the first response was retained), finishing in less than 10 minutes or failing one or more of six embedded bot checks, which were the same questions used to detect bots and careless responses in Sample 1. Sample 3 consisted of 442 undergraduate students (mean age =19.83, SD =3.47) also recruited to participate in research via a psychology student subject pool and compensated with credits for their psychology course.

Measures

Food Security Status

Current food security was assessed by self-report using the 18-item USDA Household Food Security Survey Module (Bickel, Nord, & Hamilton, 2006). Participants were asked to reflect on their eating patterns and level of anxiety about accessing food over the past 12 months. A total score ranging from 0 to 18 was calculated from the sum of affirmative responses. A total score of 0 indicated high food security, 1–2 indicated marginal food security, 3–7 indicated low food security, and 8–18 indicated very low food security (Bickel et al., 2006). For the current study, individuals within the marginal food security, low food security, and very low food security groups were considered “food insecure”, whereas those in the high food security group were considered “food secure”.

Appetitive Traits

The AEBQ (Hunot et al., 2016) was used to assess appetitive traits in adults. This self-report questionnaire consisted of 35 items rated on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree”. The scale yielded eight traits: food responsiveness, emotional overeating, enjoyment of food, emotional undereating, food fussiness, slowness in eating, hunger, and satiety responsiveness. The AEBQ has previously demonstrated good factor structure and test-retest reliability, and internal consistency in the general population (Hunot et al., 2016). Cronbach’s αs demonstrated acceptable to excellent internal consistency within the present samples (Sample 1: αs =0.71– 0.91; Sample 2: αs=0.69–0.89; see Table 1).

Table 1.

Descriptive Statistics by Sample

Sample 1 Mothers n=634 Sample 2 University #1 n=945 Sample 3 University #2 n=442
USDA Household FI
High Food Security 203 684 299
Marginal Food Security 37 127 56
Low Food Security 117 103 60
Very Low Food Security 277 31 27
AEBQ
Food Responsiveness 2.18 (0.91) 2.14 (0.78) -
 Range (0–4) α= .73 α =.69
Emotional Overeating 1.89 (1.17) 1.59 (1.06) -
 Range (0–4) α = .91 α =.89
Emotional Undereating 2.05 (1.15) 2.11 (1.04) -
 Range (0–4) α = .91 α =.89
Satiety Responsiveness 1.98 (0.96) 2.05 (0.89) -
 Range (0–4) α = .78 α =.78
Hunger 1.99 (0.90) 2.09 (0.77) -
 Range (0–4) α =.76 α =.68
Food Fussiness 1.40 (0.84) 1.57 (0.95) -
 Range (0–4) α = .75 α = .88
Slow Eating 1.89 (0.92) 1.81 (1.01) -
 Range (0–4) α = .71 α =.85
Food Enjoyment 2.98 (0.74) 3.11 (0.76) -
 Range (0–4) α = .77 α =.84
NIAS
Picky Eating 6.07 (4.41) - 5.46 (4.47)
 Range (0–15) α = .87 α =.89
Appetite 5.62 (4.29) - 4.69 (4.04)
 Range (0–15) α =.86
Fear 4.59 (4.55) - 2.66 (3.59)
 Range (0–15) α =.91 α =.92
Age 34.75 (7.40) 19.80 (2.95) 19.83 (3.47)
Open in a new tab

ARFID symptomatology

The NIAS (Zickgraf & Ellis, 2018) is a 9-item self-report measure that assesses the three primary ARFID subtypes. The questionnaire is comprised of three subscales: 1) picky eating, which assesses avoidance of food based on unfamiliarity or aversion to its sensory properties, 2) appetite, which assesses lack of interest in eating, and 3) fear, which assesses fear of aversive consequences of eating. Participants respond to each item on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree”. Each subscale consists of three items and is scored on a scale from 0–15 with higher scores indicating higher levels of each restrictive eating pattern. The NIAS has demonstrated good to excellent internal consistency in past studies (e.g., Cronbach’s alpha = 0.87–0.93; Zickgraf & Ellis, 2018) and within the current study’s samples (Sample 1: alpha= 0.86–0.91; Sample 3: alpha= 0.86–0.92; see Table 1).

Data Analysis

Confirmatory Factor Analyses

Overall confirmatory factor analyses (CFAs) and evaluation of MI via multi-group CFAs were conducted using Mplus 8.6. Given there were five or more response categories for items on each measure, robust maximum likelihood was used. Significant Kolmogorov-Smirnov tests (Kolmogorov-Smirnov statistics ranging from 0.168 – 0.325; all p’s < 0.001) and visual analysis of histograms and Q-Q plots indicated that data were non-normal, however, not severely non-normal (Rosellini & Brown, 2021).

Overall and multi-group CFAs were conducted separately for each measure in each full sample. Model fit was assessed using the following fit indices: comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root-mean square residual (SRMR). Good model fit was indicated by values ≥ 0.95 for CFI, ≤ 0.06 for RMSEA, and ≤ 0.08 for SRMR (Hu & Bentler, 1999). Values of 0.90 or higher for CFI, up to 0.10 for RMSEA, and up to 0.10 for SRMR indicate acceptable but mediocre model fit (Bentler, 1990; Browne & Cudeck, 1993; Hu & Bentler, 1995; MacCallum et al., 1996; Schermelleh-Engel & Müller, 2003). Considering that cut-offs for model fit indices are not advised to be used with absolute rigidity due to a wide variety of factors which can influence these indices (Brown, 2015), a two-index combination approach has been recommend, such that SRMR should be considered alongside another index such as CFI or RMSEA (Hu & Bentler, 1999; Worthington & Whittaker, 2006). Models were thus deemed to have an acceptable fit if SRMR and at least one of the other two fit indices examined suggested an acceptable fit.

Each model that demonstrated acceptable fit in overall CFA underwent multi-group CFA to assess configural, metric, and scalar MI across food security status. Models testing configural invariance allowed for all factor loadings and item intercepts to vary across groups. Models assessing metric (i.e., weak) invariance constrained the factor loadings to be equal across groups, whereas the models assessing scalar (i.e., strong) invariance constrained factor loadings and item intercepts across food security status. Nested models (i.e., metric compared to configural; scalar compared to metric) were compared using the differences in CFI, RMSEA, and SRMR, as well as the Satorra-Bentler scaled χ2 difference test (Satorra & Bentler, 2010). Changes in CFI, RMSEA, and SRMR were given stronger consideration than the χ2 difference test given χ2 is sensitive to sample size (Putnick & Bornstein, 2016). MI across food security status was not supported if changes between nested models indicated worse model fit as indicated by changes greater than 0.010 for CFI, 0.015 for RMSEA, and 0.030 for SRMR for metric invariance or 0.015 for SRMR for scalar invariance (Chen, 2007; Cheung & Rensvold, 2002). The focus of MI testing via multi-group CFA was to assess whether model fit became substantially worse with increasing model constraints, regardless of what fit indices were observed in the overall CFA conducted initially.

Results

Descriptive Analyses

Table 1 provides descriptive statistics for the three samples used within this study. Sample 1 was economically diverse with 18.0% reporting a household income < $30,000, 40.6% between $30,000 and $59,999, 23.1% between $60,000 and $89,999, and 18.3% ≥ $90,000. Participants primarily identified as non-Hispanic (82.0%) and 1.3% preferred not to provide their ethnicity. For race, majority of participants identified as White (77.4%), followed by participants who identified as Black (18.3%), Asian (3.8%), American Indian/Alaskan Native (3.2%), Native Hawaiian/Pacific Islander (1.1%), and those who preferred not to provide their race (2.4%).

Approximately, 77.9% of Sample 2 identified as cis-gender female, with 19.6% identifying as cis-gender male, 0.4% as transgender male, 0.1% as transgender female, and 2% as non-binary. Approximately, 36.5% of Sample 2 identified as Hispanic with 58.0% identifying as non-Hispanic and 5.4% preferring to not disclose their ethnic identity. Approximately 58.4% of Sample 2 identified as White, with 16.2% identifying as Black, 6.1% as Asian, 0.5% as American Indian/Alaskan Native, 0.6% Native Hawaiian/Pacific Islander, 11.5% indicated “other”, and 6.3% preferred to not disclose their racial identity.

Sample 3 was predominately cis-gender female (73.1%) with 25.8% identifying as cis-gender male, 0.7% as transgender female, and 0.4% as transgender male. Approximately 62.7% of Sample 3 identified as White and 94.1% as non-Hispanic, 22.0% as Black, 1.6% as East Asian, 3.1% as Southeast Asian, 3.4% as Native American, 0.2% as Pacific Islander, 4.3% as multi-racial, and 2.8% reporting an “other” race or preferred to not disclose their race.

AEBQ

As discussed, good model fit was indicated by values ≥ 0.95 for CFI, ≤ 0.06 for RMSEA, and ≤ 0.08 for SRMR (Hu & Bentler, 1999) whereas ≥ 0.90 for CFI, ≤ 0.10 for RMSEA, and ≤ 0.10 for SRMR indicate acceptable but mediocre model fit (Bentler, 1990; Browne & Cudeck, 1993; Hu & Bentler, 1995; MacCallum et al., 1996; Schermelleh-Engel & Müller, 2003). Overall CFAs indicated acceptable model fit for the eight-factor AEBQ in both the full sample of mothers (i.e., Sample 1; CFI = 0.853, RMSEA [90% CI] = 0.060 [0.057, 0.064], SRMR = 0.097) and the full sample of undergraduate students (i.e., Sample 2; CFI = 0.895, RMSEA [90% CI] = 0.052 [0.049, 0.054], SRMR = 0.058). Results of multi-group CFAs (Table 2) demonstrated MI across food security status, as changes between nested models (i.e., metric compared to configural; scalar compared to metric) for the mothers sample were 0.006 and 0.007 for CFI (i.e., CFI < 0.010), 0.001 for RMSEA (i.e., RMSEA <0.015), and 0.001 and 0.009 for SRMR (i.e., SRMR <0.030 for metric invariance and SRMR <0.015 for scalar invariance), and for the undergraduate student sample were 0.000 and 0.001 for CFI (i.e., CFI < 0.010), 0.001 for RMSEA (i.e., RMSEA <0.015), and 0.000 and 0.001 for SRMR (i.e., SRMR <0.030 for metric invariance and SRMR <0.015 for scalar invariance). Configural, metric, and scalar MI was supported across food security groups in both samples.

Table 2.

Measurement invariance of the AEBQ by food security status

Invariance Model χ2 (df) CFI RMSEA (90% CI) SRMR Δχ2 (Δdf) ΔCFI ΔRMSEA ΔSRMR Invariance?
In mothers sample (n = 203 with high food security vs. n = 431 with marginal, low, or very low food security)
Configural 2,291.36 (1,064)*** .858 .060 (.057, .064) .096 -- -- -- -- Yes
Metric 2,370.81 (1,091)*** .852 .061 (.057, .064) .105 81.28 (27)*** .006 .001 .009 Yes
Scalar 2,458.51 (1,118)*** .845 .062 (.058, .065) .106 91.86 (27)*** .007 .001 .001 Yes
In undergraduate student sample (n = 684 with high food security vs. n = 261 with marginal, low, or very low food security)
Configural 2,563.49 (1,064)*** .888 .055 (.052, .057) .063 -- -- -- -- Yes
Metric 2,587.77 (1,091)*** .888 .054 (.051, .057) .064 20.96 (27) .000 .001 .001 Yes
Scalar 2,626.16 (1,118)*** .887 .053 (.051, .056) .064 34.90 (27) .001 .001 .000 Yes
Open in a new tab

Note. AEBQ = Adult Eating Behavior Questionnaire; CFI = comparative fit index; RMSEA = root mean square error of approximation; CI = confidence interval; SRMR = standardized root-mean square residual. Measurement invariance across food security status supported if changes between nested models are < 0.010 for CFI, < 0.015 for RMSEA, < 0.030 for SRMR for metric invariance, and < 0.015 for SRMR for scalar invariance.

***

p < .001.

NIAS

Overall CFAs indicated good or acceptable model fit for the three-factor NIAS in both the full sample of mothers (i.e., Sample 1; CFI = 0.986, RMSEA [90% CI] = 0.047 [0.032, 0.063], SRMR = 0.021) and the full sample of undergraduate student (i.e., Sample 3; CFI = 0.974, RMSEA [90% CI] =0.062 [0.044, 0.080], SRMR = 0.037). Results of multi-group CFAs (Table 3) demonstrated MI across food security status, as changes between nested models (i.e., metric compared to configural; scalar compared to metric) for the mothers sample were 0.001 for CFI (i.e., CFI < 0.010), 0.002 and 0.003 for RMSEA (i.e., RMSEA <0.015), and 0.000 and 0.003 for SRMR (i.e., SRMR <0.030 for metric invariance and SRMR <0.015 for scalar invariance), and for the undergraduate student sample were 0.002 and 0.003 for CFI (i.e., CFI < 0.010), 0.000 and 0.006 for RMSEA (i.e., RMSEA <0.015), and 0.001 and 0.002 for SRMR (i.e., SRMR <0.030 for metric invariance and SRMR <0.015 for scalar invariance). Configural, metric, and scalar MI was supported across food security groups in both samples.

Table 3.

Measurement invariance of the NIAS by food security status

Invariance Model χ2 (df) CFI RMSEA (90% CI) SRMR Δχ2 (Δdf) ΔCFI ΔRMSEA ASRMR Invariance?
In mothers sample (n = 203 with high food security vs. n = 431 with marginal, low, or very low food security)
Configural 92.39 (48)*** .980 .054 (.037, .070) .027 -- -- -- -- Yes
Metric 97.70 (54)*** .981 .051 (.034, .066) .030 3.60 (6) .001 .003 .003 Yes
Scalar 105.79 (60)*** .980 .049 (.033, .064) .030 7.06 (6) .001 .002 .000 Yes
In undergraduate student sample (n = 299 with high food security vs. n = 143 with marginal, low, or very low food security)
Configural 90.34 (48)*** .974 .063 (.043, .083) .042 -- -- -- -- Yes
Metric 92.55 (54)*** .976 .057 (.036, .076) .043 2.26 (6) .002 .006 .001 Yes
Scalar 103.20 (60)*** .973 .057 (.038, .075) .045 10.75 (6) .003 .000 .002 Yes
Open in a new tab

Note. NIAS = Nine Item Avoidant/Restrictive Food Intake Disorder (ARFID) Screen; CFI = comparative fit index; RMSEA = root mean square error of approximation; CI = confidence interval; SRMR = standardized root-mean square residual. Measurement invariance across food security status supported if changes between nested models are < 0.010 for CFI, < 0.015 for RMSEA, < 0.030 for SRMR for metric invariance, and < 0.015 for SRMR for scalar invariance.

***

p < .001.

Discussion

The present study tested for MI for two commonly used measurements of appetitive traits and ARFID symptomology in food insecure vs. food secure participants recruited from two populations at high risk for experiencing FI (i.e., mothers and undergraduate students). Consistent results emerged across our two samples, both the AEBQ and NIAS demonstrated strong (i.e., scalar) MI, suggesting that the constructs measured by these questionnaires had equivalence across food-secure and food-insecure individuals. The results of this study are similar to previous findings (O’Connor et al., 2022; Richson et al., 2023) that found no clinically significant DIF across college students with and without FI for two ED screening measurements (i.e., brief EDE-Q and SCOFF) and demonstrate the appropriateness of using the AEBQ and NIAS to assess appetitive traits and ARFID symptomology in two high-risk and diverse populations experiencing FI.

These results suggest that differences in appetitive traits between individuals with and without FI demonstrated in prior research are not an artifact of measurement differences, and instead reflect true differences in appetitive traits across food security status (e.g., O’Connor et al., 2023). The findings of O’Connor et al. (2023), which suggest higher mean levels of certain appetitive traits in food insecure relative to food secure women, and the findings of the current study, which validate the use of appetitive trait measures across food security status, suggest a need for more comprehensive longitudinal studies of differences in appetitive traits across food security status using these validated measures. More nuanced longitudinal studies examining how appetitive traits may precede or exist concurrently with DE behaviors are required to understand the temporal relationship between appetitive traits and DE in the context of FI. Although there is a lack of research on the relationship between FI and ARFID symptomatology, the current study supports the validity of the NIAS, a common measure of ARFID symptoms, in research on ARFID and FI. In particular, researchers should consider examining the impact of FI at different developmental periods as picky eating tends to remit with exposure to food over time. Picky eating that persists beyond the developmental period where it is typical may be a risk factor, prodrome, or symptom of ARFID (Breiner et al., under review).

The present study has several strengths, including using large, demographically distinct samples to explore MI for two validated appetitive trait measures in two high-risk groups for FI. To date, no study has validated two widely used appetitive trait measures (i.e., the AEBQ and NIAS) across food security status using large diverse samples, which allows for adequate generalizability to these high-risk subpopulations (i.e., college students and mothers). However, there are some limitations that warrant careful consideration. First, as this study used data consisting of mothers and undergraduate students, the results may not be generalizable to other at-risk subpopulations experiencing FI, including veterans, minoritized racial-ethnic groups, and individuals with chronic medical conditions. However, as previously noted, mothers and undergraduate students are at high risk for FI and findings were consistent across the two samples. Thus, it is necessary to explore how appetitive trait measurements perform in these subpopulations. Second, the sample size for exploring ARFID symptoms in the undergraduate student sample was smaller (n=299 with high food security and n=143 with at least some FI) than the sample size for exploring AEBQ in the undergraduate student sample. Notably, however, results were consistent across this smaller undergraduate sample and mother sample. Third, this study was conducted in the context of a U.S. population, and therefore, it is unclear if these results would generalize to less developed countries where FI is associated with underweight. Lastly, our samples were either primarily (i.e., both undergraduate samples) or entirely (i.e., sample of mothers) female; thus, future research is needed to test for MI by food security status in males and gender-minority individuals.

In conclusion, results from our study suggested that the AEBQ and the NIAS may be appropriately used to assess appetitive traits and ARFID symptoms in undergraduate students and in mothers experiencing FI. Both questionnaires demonstrated equivalent assessment of measured constructs across food-secure and food-insecure populations, indicating that mean scores on each measure can be accurately compared across groups. Future directions include examining whether AEBQ and NIAS demonstrate MI by food security status in other populations, including different developmental groups and subpopulations of varying diversity (e.g., veterans, racial-ethnic groups, individuals with comorbidities).

Funding:

Drs. Zickgraf & O’Connor were supported by the National Institute of Mental Health under award number T32 MH082761. Dr. Hazzard’s time was funded by Grant Number K99HD108200 (PI: Hazzard) from the National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Mental Health or the National Institute of Child Health and Human Development. Undergraduate data collection by Dr. Zickgraf was supported by startup funding from the University of South Alabama.

Footnotes

Conflicts of interest/Competing interests: Authors have no conflicts to declare.

Ethical Statement

All study procedures were approved by the Institutional Review Board at each institution where data was collected. All participants provided online informed consent prior to completion of the study.

The mother sample (MTurk) was approved by the University of Chicago’s Institutional Review Board (IRB19–0900). Sample 2 (Undergraduate Student Sample) was approved by Montclair State University’s Institutional Review Board (IRB-FY20–21-2064). Sample 3 (University Student Sample) was approved by the University of South Alabama’s Institutional Review Board (IRB-1696513).

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.

References

  1. Abdurahman AA, Chaka EE, Nedjat S, Dorosty AR, & Majdzadeh R (2019). The association of household food insecurity with the risk of type 2 diabetes mellitus in adults: a systematic review and meta-analysis. European journal of nutrition, 58(4), 1341–1350. 10.1007/s00394-018-1705-2 [DOI] [PubMed] [Google Scholar]
  2. Anderson SA (1990). Core indicators of nutritional state for difficult-to-sample populations. The Journal of nutrition, 120 Suppl 11, 1559–6000. 10.1093/jn/120.suppl_11.1555 [DOI] [PubMed] [Google Scholar]
  3. Arenas DJ, Thomas A, Wang J, & DeLisser HM (2019). A Systematic Review and Meta-analysis of Depression, Anxiety, and Sleep Disorders in US Adults with Food Insecurity. Journal of general internal medicine, 34(12), 2874–2882. 10.1007/s11606-019-05202-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashcroft J, Semmler C, Carnell S, Van Jaarsveld CHM, & Wardle J (2008). Continuity and stability of eating behaviour traits in children. European journal of clinical nutrition, 62(8), 985–990. [DOI] [PubMed] [Google Scholar]
  5. Becker CB, Middlemass K, Taylor B, Johnson C, & Gomez F (2017). Food insecurity and eating disorder pathology. International journal of eating disorders, 50, 1031–1040. 10.1002/eat.22735 [DOI] [PubMed] [Google Scholar]
  6. Becker CB, Middlemass KM, Gomez F, & Martinez-Abrego A (2019). Eating disorder pathology among individuals living with food insecurity: A replication study. Clinical psychological science, 7, 1144–1158. 10.1177/2167702619851811 [DOI] [Google Scholar]
  7. Bentler PM (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238–246. 10.1037/0033-2909.107.2.238 [DOI] [PubMed] [Google Scholar]
  8. Berge JM, Fertig AR, Trofholz A, Neumark-Sztainer D, Rogers E, & Loth K (2020). Associations between parental stress, parent feeding practices, and child eating behaviors within the context of food insecurity. Preventive medicine reports, 19, 101146. 10.1016/j.pmedr.2020.101146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bickel G, Nord M & Hamilton W (2006). US household food security survey module: Three-stage design with screeners. Washington, DC; US Department of Agriculture, Economic Research Service. [Google Scholar]
  10. Birch LL, & Marlin DW (1982). I don’t like it; I never tried it: effects of exposure on two-year-old children’s food preferences. Appetite, 3(4), 353–360. 10.1016/s0195-6663(82)80053-6 [DOI] [PubMed] [Google Scholar]
  11. Browne MW, & Cudeck R (1992). Alternative Ways of Assessing Model Fit. Sociological methods & research, 21(2), 230–258. 10.1177/0049124192021002005 [DOI] [Google Scholar]
  12. Brown CL, Perrin EM, Peterson KE, Brophy Herb HE, Horodynski MA, Contreras D, Miller AL, Appugliese DP, Ball SC, & Lumeng JC (2018). Association of Picky Eating With Weight Status and Dietary Quality Among Low-Income Preschoolers. Academic pediatrics, 18(3), 334–341. 10.1016/j.acap.2017.08.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brown TA (2015). Confirmatory factor analysis for applied research, Second Edition. Guilford Press. [Google Scholar]
  14. Chen FF (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling, 14, 464–504. 10.1080/10705510701301834 [DOI] [Google Scholar]
  15. Cheung GW, & Rensvold RB (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural equation modeling, 9(2), 233–255. 10.1207/S15328007SEM0902_5 [DOI] [Google Scholar]
  16. Chmielewsi M, & Kucker SC (2020). An MTurk crisis? Shifts in data quality and the impact on study results. Social psychological and personality science, 11, 464–473. [Google Scholar]
  17. Christensen KA, et al. (2021). Food insecurity associated with elevated eating disorder symptoms, impairment, and eating disorder diagnoses in an American University student sample before and during the beginning of the COVID-19 pandemic. The International journal of eating disorders, 54, 1213–1223. 10.1002/eat.23517 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Christensen KA, Hazzard VM, Richson BN, & Hagan KE (2022). Measures Used with Populations with Food Insecurity: A Call for Increased Psychometric Validation. Journal of the academy of nutrition and dietetics, 122(10), 1833–1836. 10.1016/j.jand.2022.05.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Cummer E, Loyola Amador C, Montez K, Skelton JA, Ramirez B, Best S, Zimmer R, & Palakshappa D (2020). What a city eats: Examining the dietary preferences of families living in communities at high risk for food insecurity. Journal of clinical and translational science, 5(1), e55. 10.1017/cts.2020.549 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. da Luz FQ, Hay P, Touyz S, & Sainsbury A (2018). Obesity with Comorbid Eating Disorders: Associated Health Risks and Treatment Approaches. Nutrients, 10(7), 829. 10.3390/nu10070829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Daniel C (2016). Economic constraints on taste formation and the true cost of healthy eating. Social science & medicine (1982), 148, 34–41. 10.1016/j.socscimed.2015.11.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Darling KE, Fahrenkamp AJ, Wilson SM, D’Auria AL, & Sato AF (2017). Physical and mental health outcomes associated with prior food insecurity among young adults. Journal of health psychology, 22(5), 572–581. 10.1177/1359105315609087 [DOI] [PubMed] [Google Scholar]
  23. Dial LA, Jordan A, Emley E, Angoff HD, Varga AV, & Musher-Eizenman DR (2021). Consequences of picky eating in college students. Journal of nutrition education and behavior, 53(10), 822–831. [DOI] [PubMed] [Google Scholar]
  24. Drewnowski A, & Darmon N (2005). Food choices and diet costs: an economic analysis. The Journal of nutrition, 135(4), 900–904. 10.1093/jn/135.4.900 [DOI] [PubMed] [Google Scholar]
  25. Ellis JM, Galloway AT, Zickgraf HF, & Whited MC (2018). Picky eating and fruit and vegetable consumption in college students. Eating behaviors, 30, 5–8. 10.1016/j.eatbeh.2018.05.001 [DOI] [PubMed] [Google Scholar]
  26. USDA ERS - Key Statistics & Graphics. (2023, June 20). Retrieved from https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/key-statistics-graphics/
  27. French SA, Epstein LH, Jeffery RW, Blundell JE, & Wardle J (2012). Eating behavior dimensions. Associations with energy intake and body weight. A review. Appetite, 59(2), 541–549. 10.1016/j.appet.2012.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hasan F, Nguyen AV, Reynolds AR, You W, Zoellner J, Nguyen AJ, Swift D, & Kranz S (2023). Preschool- and childcare center-based interventions to increase fruit and vegetable intake in preschool children in the United States: a systematic review of effectiveness and behavior change techniques. The international journal of behavioral nutrition and physical activity, 20(1), 66. 10.1186/s12966-023-01472-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hauser DJ, Moss AJ, Rosenzweig C, Jaffe SN, Robinson J, & Litman L (2023). Evaluating CloudResearch’s Approved Group as a solution for problematic data quality on MTurk. Behavior research methods, 55(8), 3953–3964. 10.3758/s13428-022-01999-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hazzard VM, Barry MR, Leung CW, Sonneville KR, Wonderlich SA, & Crosby RD (2022a). Food insecurity and its associations with bulimic-spectrum eating disorders, mood disorders, and anxiety disorders in a nationally representative sample of U.S. adults. Social psychiatry and psychiatric epidemiology, 57(7), 1483–1490. 10.1007/s00127-021-02126-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hazzard VM, Hooper L, Larson N, Loth KA, Wall MM, & Neumark-Sztainer D (2022b). Associations between severe food insecurity and disordered eating behaviors from adolescence to young adulthood: Findings from a 10-year longitudinal study. Preventive medicine, 154, 106895. 10.1016/j.ypmed.2021.106895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hazzard VM, Loth KA, Hooper L, & Becker CB (2020). Food insecurity and eating disorders: A review of emerging evidence. Current psychiatry reports, 22, 74. 10.1007/s11920-020-01200-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. He J, Sun S, Zickgraf HF, Ellis JM, & Fan X (2021). Assessing appetitive traits among Chinese young adults using the adult eating behavior questionnaire: Factor structure, gender invariance and latent mean differences, and associations with BMI. Assessment, 28, 877–889. 10.1177/1073191119864642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hu LT, & Bentler PM (1995). Evaluating model fit. In Hoyle RH (Ed.), Structural equation modeling: Concepts, issues and application (pp. 77–99). Thousand Oaks, CA: Sage. [Google Scholar]
  35. Hu LT, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling, 6, 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  36. Hunot C, et al. (2016). Appetitive traits and relationships with BMI in adults: Development of the adult eating behaviour questionnaire. Appetite, 105, 356–363. 10.1016/j.appet.2016.05.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kauer J, Pelchat ML, Rozin P, & Zickgraf HF (2015). Adult picky eating. Phenomenology, taste sensitivity, and psychological correlates. Appetite, 90, 219–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Knowles M, Rabinowich J, Ettinger de Cuba S, Cutts DB, & Chilton M (2016). “Do you wanna breathe or eat?”: parent perspectives on child health consequences of food insecurity, trade-offs, and toxic stress. Maternal and child health journal, 20, 25–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Leung CW, Epel ES, Ritchie LD, Crawford PB, & Laraia BA (2014). Food insecurity is inversely associated with diet quality of lower-income adults. Journal of the academy of nutrition and dietetics, 114(12), 1943–53.e2. 10.1016/j.jand.2014.06.353 [DOI] [PubMed] [Google Scholar]
  40. Machado PPP, Grilo CM, & Crosby RD (2018). Replication of a Modified Factor Structure for the Eating Disorder Examination-Questionnaire: Extension to Clinical Eating Disorder and Non-clinical Samples in Portugal. European eating disorders review: the journal of the eating disorders association, 26(1), 75–80. 10.1002/erv.2569 [DOI] [PubMed] [Google Scholar]
  41. MacCallum RC, Browne MW, & Sugawara HM (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130–149. 10.1037/1082-989X.1.2.130 [DOI] [Google Scholar]
  42. Machado PPP, Grilo CM, Rodrigues TF, Vaz AR, & Crosby RD (2020). Eating Disorder Examination - Questionnaire short forms: A comparison. The international journal of eating disorders, 53(6), 937–944. 10.1002/eat.23275 [DOI] [PubMed] [Google Scholar]
  43. Mavegam Tango Assoumou BO, Coughenour C, Godbole A, & McDonough I (2023). Senior food insecurity in the USA: a systematic literature review. Public health nutrition, 26(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. McIntyre L, Glanville NT, Raine KD, Dayle JB, Anderson B, & Battaglia N (2003). Do low-income lone mothers compromise their nutrition to feed their children?. Cmaj, 168(6), 686–691. [PMC free article] [PubMed] [Google Scholar]
  45. Middlemass KM, Cruz J, Gamboa A, Johnson C, Taylor B, Gomez F, & Becker CB (2021). Food insecurity & dietary restraint in a diverse urban population. Eating disorders, 29(6), 616–629. [DOI] [PubMed] [Google Scholar]
  46. Myers CA (2020). Food Insecurity and Psychological Distress: a Review of the Recent Literature. Current nutrition reports, 9(2), 107–118. 10.1007/s13668-020-00309-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Nansel TR, Lipsky LM, Haynie DL, Eisenberg MH, Dempster K, & Liu A (2018). Picky eaters improved diet quality in a randomized behavioral intervention trial in youth with type 1 diabetes. Journal of the academy of nutrition and dietetics, 118(2), 308–316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Nettle D, Andrews C, & Bateson M (2017). Food insecurity as a driver of obesity in humans: The insurance hypothesis. The Behavioral and brain sciences, 40, e105. 10.1017/S0140525X16000947 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Nikolaus CJ, An R, Ellison B, & Nickols-Richardson SM (2020). Food Insecurity among College Students in the United States: A Scoping Review. Advances in nutrition (Bethesda, Md.), 11(2), 327–348. 10.1093/advances/nmz111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Norris ML, Harrison ME, Isserlin L, Robinson A, Feder S, & Sampson M (2016). Gastrointestinal complications associated with anorexia nervosa: A systematic review. International journal of eating disorders, 49(3), 216–237. [DOI] [PubMed] [Google Scholar]
  51. O’Connor SM, Hazzard VM, & Zickgraf HF (2022). Exploring differential item functioning on eating disorder measures by food security status. Eating and weight disorders: EWD, 27(4), 1449–1455. 10.1007/s40519-021-01289-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. O’Connor SM, Zickgraf HF, Hazzard VM, Haller LL, & Wildes JE (2023). Exploring differences in disordered eating and related appetitive traits among women based on childhood and current food security status. Eating behaviors, 49, 101729. 10.1016/j.eatbeh.2023.101729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pesch MH, Bauer KW, Christoph MJ, Larson N, & Neumark-Sztainer D (2020). Young adult nutrition and weight correlates of picky eating during childhood. Public health nutrition, 23(6), 987–995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Putnick DL, & Bornstein MH (2016). Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. Developmental review: DR, 41, 71–90. 10.1016/j.dr.2016.06.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Radimer KL, Olson CM, Greene JC, Campbell CC, & Habicht JP (1992). Understanding hunger and developing indicators to assess it in women and children. Journal of nutrition education, 24(1), 36S–44S. [Google Scholar]
  56. Rose D, & Oliveira V (1997). Nutrient intakes of individuals from food-insufficient households in the United States. American journal of public health, 87(12), 1956–1961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rosellini AJ, & Brown TA (2021). Developing and Validating Clinical Questionnaires. Annual review of clinical psychology, 17, 55–81. 10.1146/annurev-clinpsy-081219-115343 [DOI] [PubMed] [Google Scholar]
  58. Sato Y, & Fukudo S (2015). Gastrointestinal symptoms and disorders in patients with eating disorders. Clinical journal of gastroenterology, 8, 255–263. [DOI] [PubMed] [Google Scholar]
  59. Satorra A, & Bentler PM (2010). Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic. Psychometrika, 75(2), 243–248. 10.1007/s11336-009-9135-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Schermelleh-Engel K, & Müller H (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research, 8, 28–74. [Google Scholar]
  61. Stice E, Fisher M, & Martinez E (2004). Eating disorder diagnostic scale: additional evidence of reliability and validity. Psychological assessment, 16(1), 60–71. 10.1037/1040-3590.16.1.60 [DOI] [PubMed] [Google Scholar]
  62. Sullivan SA, & Birch LL (1990). Pass the sugar, pass the salt: Experience dictates preference. Developmental psychology, 26(4), 546–551. 10.1037/0012-1649.26.4.546 [DOI] [Google Scholar]
  63. Taylor CM, Wernimont SM, Northstone K, & Emmett PM (2015). Picky/fussy eating in children: Review of definitions, assessment, prevalence and dietary intakes. Appetite, 95, 349–359. 10.1016/j.appet.2015.07.026 [DOI] [PubMed] [Google Scholar]
  64. Thomas JJ, Lawson EA, Micali N, Misra M, Deckersbach T, & Eddy KT (2017). Avoidant/restrictive food intake disorder: A three-dimensional model of neurobiology with implications for etiology and treatment. Current psychiatry reports, 19, 54. 10.1007/s11920-017-0795-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Volpe U, Tortorella A, Manchia M, Monteleone AM, Albert U, & Monteleone P (2016). Eating disorders: What age at onset?. Psychiatry research, 238, 225–227. 10.1016/j.psychres.2016.02.048 [DOI] [PubMed] [Google Scholar]
  66. Weaver LJ, & Fasel CB (2018). A Systematic Review of the Literature on the Relationships between Chronic Diseases and Food Insecurity. Food and nutrition sciences, 09, 519–541. [Google Scholar]
  67. Worthington RL, & Whittaker TA (2006). Scale development research: A content analysis and recommendations for best practices. The counseling psychologist, 34(6), 806–838. 10.1177/0011000006288127 [DOI] [Google Scholar]
  68. Zickgraf HF, & Ellis JM (2018). Initial validation of the Nine Item Avoidant/Restrictive Food Intake disorder screen (NIAS): A measure of three restrictive eating patterns. Appetite, 123, 32–42. 10.1016/j.appet.2017.11.111 [DOI] [PubMed] [Google Scholar]
  69. Zickgraf HF, Franklin ME, & Rozin P (2016). Adult picky eaters with symptoms of avoidant/restrictive food intake disorder: comparable distress and comorbidity but different eating behaviors compared to those with disordered eating symptoms. Journal of eating disorders, 4, 26. 10.1186/s40337-016-0110-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zickgraf HF, & Schepps K (2016). Fruit and vegetable intake and dietary variety in adult picky eaters. Food quality and preference, 54, 39–50. [Google Scholar]
  71. Zickgraf HF, Lane‐Loney S, Essayli JH, & Ornstein RM (2019a). Further support for diagnostically meaningful ARFID symptom presentations in an adolescent medicine partial hospitalization program. International journal of eating disorders, 52(4), 402–409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Zickgraf HF, Murray HB, Kratz HE, & Franklin ME (2019b). Characteristics of outpatients diagnosed with the selective/neophobic presentation of avoidant/restrictive food intake disorder. The international journal of eating disorders, 52, 367–377. 10.1002/eat.23013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Zhou C, Miao H, Zhao Y, & Wan X (2023). Food insecurity increases the risk of overweight and chronic diseases in adolescents: a systematic review and meta-analysis. Food science and human wellness, 12(6), 1937–1947. [Google Scholar]

RESOURCES