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. Author manuscript; available in PMC: 2026 Apr 19.
Published in final edited form as: Autism Res. 2025 Apr 19;18(7):1381–1388. doi: 10.1002/aur.70039

Screening for avoidant/restrictive food intake disorder symptoms among autistic adults: Measurement invariance with a comparison general sample

Courtney E Breiner 1, Goldie A McQuaid 2, Gregory L Wallace 3, Hana F Zickgraf 4
PMCID: PMC12353346  NIHMSID: NIHMS2076163  PMID: 40251946

Abstract

Background.

Research suggests a higher prevalence of avoidant/restrictive food intake disorder (ARFID) in autistic people across the lifespan compared to the general population. However, ARFID symptoms in autistic people may be misattributed to core autistic traits and gastrointestinal symptoms that often co-occur with autism. This diagnostic overshadowing could lead to the under-recognition and under-treatment of modifiable symptoms of psychopathology in autistic people. Validating ARFID symptom measures in this population is essential to screening for ARFID and tracking treatment outcomes in this population.

Methods.

Multigroup confirmatory factor analysis was used to evaluate the equivalence of the factor structure, factor loadings, and item intercepts of the Nine Item ARFID Screen (NIAS) between a sample of autistic adults (n=248) who self-disclosed their diagnosis and a comparison general sample (n=398).

Results.

There was support for strong measurement invariance (configural, metric, and scalar) on the NIAS. Autistic adults scored significantly higher on each of the three subscales: selective eating (d=.54), appetite impairment (d=.27), and fear-driven avoidance of eating (d=.37).

Conclusions.

The NIAS is a valid instrument for measuring ARFID symptomatology in autistic adults. Autistic adults experience elevated symptomatology across all three ARFID eating restrictions. Future research should address whether evidence-based ARFID treatments are efficacious for autistic adults or need to be modified.

Introduction

Avoidant/restrictive food intake disorder (ARFID) is an eating disorder characterized by weight, nutritional, and/or psychosocial impairment caused by restrictive eating attributed to one or more of three mechanisms: food selectivity/neophobia/avoidance based on the sensory properties of food, poor appetite/limited interest in eating, and/or fear of aversive consequences of eating (APA, 2022). Although the current diagnostic criteria for ARFID allow for the possibility of other drivers of restrictive eating leading to the impairment necessary for the diagnosis, there is an emerging consensus among clinical researchers in support of a new, mechanistic definition of ARFID based on the presence of specific affective and physiological negative reinforcers of food avoidance (Attia & Walsh, 2025; Bourne et al., 2025; Karvay, 2025; Sharp & Pederson, 2025; Zickgraf et al., 2025). The current literature, based on chart reviews, prospective diagnosis, and person-centered analysis, suggests that these three eating restrictions are sufficient to categorize the motivations for food restriction in all—or nearly all—clinically-referred patients with ARFID (e.g., Kambanis et al., 2024; Sanchez-Cerezo, 2025; Richson et al., 2025). The patterns of restrictive eating commonly co-occur in clinical samples (e.g., Norris et al., 2019; Reilly et al., 2019) and when measured dimensionally are moderately correlated (Thomas et al., 2017; Zickgraf & Ellis, 2018). While co-occurrence among presentations is common, many ARFID patients experience symptoms attributable to a single eating restriction (Richmond et al., 2023; Zickgraf et al., 2019).

ARFID appears to be more common in autistic individuals than in the general population (e.g., Bourne et al., 2022). In clinical eating disorder settings, 13-28% of ARFID patients have co-occurring autism, a higher proportion compared to those with other eating disorder diagnoses (e.g., Niceley et al., 2014; Norris et al., 2021; Watts et al., 2023). 30% of children newly diagnosed with autism at one large center had clinically significant symptoms of ARFID identified during the evaluation (Kuschner et al., 2023). Less is known about the prevalence of ARFID in autistic adults, but one study in a large cohort estimated that approximately 20% were at high risk for ARFID based on their self-reported symptoms (Koomar et al., 2021).

In both autistic and general population samples, trait-like sensory sensitivity and cognitive/behavioral rigidity have been shown to contribute to the intensity of selective eating (Nimbley et al., 2022; Richson et al., 2024; Williams et al., 2023; Watts et al., 2023; Zickgraf et al., 2020). Because these contributing features are core symptoms of autism (APA, 2022), selective eating behavior and any associated impairment may be attributed to autism rather than to ARFID (e.g., Lazaro & Ponde, 2017). However, ARFID is the appropriate diagnosis if selective eating behavior leads to significant nutritional or caloric deficits or psychosocial impairment (APA, 2022). This phenomenon, known as diagnostic overshadowing, leads to under-recognition, underdiagnosis, and undertreatment of mental and behavioral health problems in autistic people (Rosen et al., 2018).

In addition to potential overshadowing of selective eating ARFID symptoms, other traits and co-occurring medical conditions common in autistic individuals could contribute to overshadowing of appetite and fear presentations of ARFID. Many autistic people experience sensory under-responsivity (not only sensory over-responsivity), which is associated with low awareness of internal states like hunger and satiety (e.g., Adams et al., 2022; Trevesian et al., 2021). Autistic people experience gastrointestinal (GI) symptoms and conditions at higher rates than their neurotypical peers (Leader et al., 2022; McElhanon et al., 2014). GI disease contributes to diagnostic overshadowing of disordered eating in the general population, leading to low rates of identification of ARFID and other eating disorders in patients seeking GI-focused treatments despite a high prevalence of ARFID in this group (Burton Murray et al., 2020). In short, the ARFID diagnosis involves three behavioral phenotypes of avoidant/restrictive eating that are each associated with mechanisms or risk factors (i.e., atypical sensory processing, cognitive rigidity, GI symptoms) that are present in the general population but more prevalent and/or pronounced in autistic individuals. This implies that not only might ARFID be more common in autistic people, but also that it may be overlooked or overshadowed in this group.

To meaningfully compare dimensional ARFID presentations in autistic people to general population findings, existing instruments must measure them with equivalent fidelity and on the same scale in both populations. There is preliminary evidence for the factorial validity of the NIAS in autistic people; Koomar and colleagues (2021) conducted exploratory factor analysis in a large sample of autistic adolescents and young adults and their parents, replicating the three-factor structure of the measure.

The aim of the present study was to establish the measurement invariance (MI) of the NIAS between autistic adults and adults from a general community sample. MI is the assumption, underlying group comparisons on self-reported symptoms, that an instrument has the same latent structure in both groups (configural invariance), that the items on a scale measure the latent construct with equal precision (metric invariance), and that members of different groups are equally likely to endorse items when equated on their level of the latent trait being measured (scalar invariance; e.g., Putnick & Bornstein, 2016). Because core autistic traits (sensory issues, rigid/repetitive behaviors) and commonly co-occurring conditions (GI symptoms) contribute to the severity of the ARFID eating restrictions, it is possible that autistic adults themselves may attribute these restrictions to autism rather than to disordered eating (e.g., Koomar et al., 2021). To ensure that comparisons of ARFID symptomatology in autistic adults and adults from the general population are valid and interpretable, it must be established that measurement of these symptoms is equally valid and on the same scale across populations.

Methods

All study protocol were approved by the local Institutional Review Board. Participants signed informed consent prior to completing study measures.

Measures

The Nine item ARFID screen (NIAS; Zickgraf & Ellis, 2018) assesses the three ARFID eating restrictions with three items each, answered on a 0-5 Likert-type scale. Scores are summed within each factor, resulting in possible scores of 0-15 for dimensional picky eating, appetite, and fear ARFID scores; higher scores indicate more eating challenges. The three-factor structure of the NIAS has been widely validated in eating disorder and general population samples (e.g., Billman Miller et al., 2022; Burton Murray et al., 2021; He et al., 2021; Koomar et al., 2021). In the current study, subscale internal consistency was good: .86-.90 (General), .83-.88 (Autistic).

Participants & procedures

Table 1 presents demographic characteristics of the autistic and general samples.

Table 1.

Participant demographic characteristics for autistic and general population adults.

Autistic
n=248		 General
n=389
Age, years
 Mean (SD) 31.19 (5.60) 37.42(12.84)
 Range 20.58-41.33 18-74
Sex designated at birth, n (%)
 Female 168 (67.74%)
 Male 80 (32.26%)
Gender Identity
 Gender diverse 38 (15.32%) 20 (5.1%)
 Cisgender 210 (84.68%) 384 (93.6%)
 Woman 241 (61.95%)
 Man 129 (33.16%)
 Nonbinary 19 (4.89%)
Race and Ethnicity, n (%)
 Race*
 Asian 3 (1.21%) 13 (3.4%)
 Black/African-American 5 (2.02%) 41 (10.5%)
 More than one race 23 (9.27%) 34 (8.7%)
 Native American/Alaska Native 2 (0.81%) 0
 White 211 (85.08%) 270 (69.4%)
 Other 3 (1.21%) 4 (1.0%)
Missing 1 (0.40%) 2 (0.5%)
 Ethnicity
 Hispanic 22 (8.87%) 25 (6.4%)
 Not Hispanic 223 (89.92%) 364 (93.6%)
 Unknown 3 (1.21%)
Educational Attainment
 Less than bachelor’s degree 163 (65.73%) 197 (50.64%)
 Bachelor’s degree or higher 85 (34.27%) 192 (49.36%)
AQ28
 Mean (SD) 82.43 (11.65) i
 Range 47-108
NIAS Subscale Scores
 Picky Eating, Mean (SD) 6.84 (4.56) 4.57 (3.82)
 Appetite, Mean (SD) 4.68 (4.13) 3.64 (3.51)
 Fear, Mean (SD) 3.83 (4.27) 2.42 (3.38)
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Autistic adults were significantly younger than neurotypical adults (Permutation-based t-test with 10k Monte-Carlo permutations t(635)=−7.22, p<.00001).

For educational attainment,, a greater proportion of neurotypical relative to autistic adults had obtained a Bachelor’s degree or higher (X2(1)=14.02, p=.0002).

Autistic sample

Autistic adults were recruited via Simons Powering Autism Research (SPARK; The SPARK Consortium, 2018) Research Match service to be part of an online study to understand eating behaviors and factors influencing these behaviors in autistic adults (RM0086Wallace). Recruited individuals had taken part in a previous study on adult outcomes in autism (RM0045Wallace1839) 1 year, 3 months earlier (see McQuaid et al., 2022). All autistic adults self-disclosed a professional, community-based autism spectrum disorder (ASD) diagnosis. Consistent with these self-disclosed diagnoses, 93.25% scored >65 on a self-report screening measure of autistic traits that has demonstrated good internal consistency in autistic adult samples (alpha=.77-.86; Hoekstra et al., 2011). This sample included “independent” autistic adults, who are ≥18 years of age, do not have a court-appointed legal guardian, and therefore provide informed consent themselves. No participants reported a current or past diagnosis of intellectual disability. Out of 261 participants who began the study, nine did not complete the NIAS and were therefore excluded from analyses. An additional four participants were missing ≥20% of scores on one or more of the NIAS subscales and were thus excluded from analyses here, resulting in a final sample of 248 participants.

General population sample

The general population sample was recruited via Prolific Academic, a participant platform designed for online research studies. Data from participants on Prolific Academic is of comparable or higher quality compared to data collected from Cloud Research, Mechanical Turk, Qualtrics Panel services, and undergraduate subject pools (Douglas et al., 2023; Peer et al., 2021). Data were collected for a separate study on emotional eating (Hooper et al., 2025). These participants were also used as a reference group for a separate paper on the measurement invariance of the NIAS in patients with GI diseases (Hooper et al., 2023). The study was described to participants as being about “emotions and how they affect food choice and eating behavior.” Data were collected from 423 participants, of whom 31 (7.1%) were excluded from the final sample for completing surveys in less than 25 minutes or failing or skipping one or more of six attention/comprehension-check questions, resulting in a final sample of 389 participants.

Data analysis

Measurement invariance was evaluated using a multigroup confirmatory factor analysis (CFA) approach (Hirschfield & Von Brachel, 2016). The fit of a CFA model is typically evaluated using effect sizes of model fit/misfit, with ≥0.95 for CFI, ≤0.06 for RMSEA, and ≤0.08 for SRMR indicating good model fit (Hu & Bentler, 1999). In multigroup CFA, fit of models with more parameters constrained to be equal between the two groups is iteratively compared to evaluate configural invariance (factor structure), metric invariance (factor loadings), and scalar invariance (item intercepts). Decrement in fit with the addition of each set of constraints is interpreted as indicating significant model differences when ΔRMSEA>.015, ΔCFI>.01, and ΔSRMR>.03 for metric invariance and >.015 for scalar invariance (Chen, 2007; Cheung & Rensvold, 2002). All CFAs were conducted using a robust WLSMV estimator (Li et al., 2016; Yilmaz et al., 2019). Analyses were conducted using the R “lavaan” package (Roseel, 2012).

Results

The three-factor model of the NIAS was supported in the full, combined sample: RMSEA=.027 [.017, .036], CFI=1.0, SRMR=.028. At each stage of model comparison (configural, metric, scalar), decrements in all goodness of fit indices were <.01 and ranged from .0-.004 (see Table 2). The results suggest that the NIAS items measure their intended latent construct with equal fidelity and on the same response scale in autistic adults compared to a sample from the general population.

Table 2.

Measurement invariance fit statistics and change in goodness of fit

Chi-square
(df)		 RMSEA ΔRMSEA CFI ΔCFI SRMR ΔSRMR
Baseline 17.50 (24)
p = .84		 .027 - 1.0 - .028 -
Configural 23.43 (48)
p = .99		 .023 −.004 1.0 0 .028 0
Metric 27.66 (54)
p = .99		 .022 −.001 1.0 0 .030 .002
Scalar 36.07 (60)
p = .99		 .028 .006 1.0 0 .034 .004
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Given the finding of measurement invariance, it is possible to interpret comparisons of observed subscale scores between autistic adults and adults from the general population. See Table 1 for mean scores comparisons between the samples. Autistic adults scored significantly higher on each NIAS subscale, with small-moderate effect sizes (picky eating d=.54, appetite d=.27, fear d=.37).

Discussion

The current study aimed to provide empirical support for the validity of the NIAS in autistic adults. The three-factor structure of the NIAS was replicated with measurement invariance across autistic and general population adults. The NIAS measures the same three latent constructs—distinct ARFID eating restrictions—in autistic adults. Autistic adults are equally likely to endorse each item on the NIAS as general population adults with comparable symptom severity, which provides support that findings of elevated ARFID symptoms in the current autistic sample and in Koomar and colleagues’ sample (2022) are a real clinical phenomenon and not a measurement artifact. Importantly, there is a paucity of literature regarding ARFID symptoms in autistic adults, with recent systematic reviews focusing solely on selective eating in children and adolescents (Baraskewich et al., 2021; Rodrigues et al., 2023). The validation of the NIAS in autistic adults is crucial to the assessment and therefore treatment of ARFID in this group, particularly given the high rates of ARFID symptoms in the current sample.

The NIAS is intended to be used as a screening tool, as it does not capture all diagnostic criteria, for identifying the possible presence of ARFID and a measure of the severity of core ARFID eating restrictions, although emerging evidence suggests that it performs somewhat better as a severity measure than as a screener (Ortiz et al., 2024). The differential diagnosis of eating disorders is complex and should never be based on responses to a single self-report instrument. Because the NIAS does not include questions that rule out the contribution of weight/shape concerns to restriction, it should be co-administered with a measure of weight/shape disordered eating (e.g., Burton Murray et al., 2021). The validation of an ARFID measure in an autistic population adds a new tool to help clinicians to make a differential diagnosis between ARFID and anorexia nervosa, which is often diagnosed in autistic individuals despite having lower weight/shape concerns and higher levels of sensory sensitivities than non-autistic individuals with anorexia nervosa (Brede et al., 2024; Brede et al., 2020), and to differentiate ARFID eating restrictions from the impact of autism or co-occurring medical conditions. Although the finding of measurement invariance in the current sample suggests that NIAS scores in autistic adults are not inflated by autistic traits and third variables unrelated to disordered eating, this possibility should always be considered in the actual differential diagnosis of ARFID. When considering whether an individual meets criteria for ARFID, another eating disorder, or a non-eating disorder diagnosis, the clinician should evaluate all potential drivers of restriction. If symptoms of non-eating psychopathology (e.g., trauma, obsessive compulsive disorder, psychosis), autism, or a medical condition best account for impairing restriction, a diagnosis of unspecified feeding or eating disorder (UFED) may be appropriate (e.g., Zickgraf et al., 2025).

Taken together, MI results and group mean comparisons on NIAS subscales further support the hypothesis that autistic adults are at elevated risk for ARFID symptoms, which are not best understood as manifestations of autistic traits and co-occurring medical conditions, but disordered eating symptoms potentially modifiable with effective behavioral treatment. Understanding ARFID as a diagnosis separate from autism is vital in autistic individuals receiving appropriate treatment. Limited research on eating behaviors in autistic adults is primarily qualitative in nature and indicates a perception that selective eating is a behavior to be managed rather than overcome (Kinnaird et al., 2019). Similarly, previous research on anxiety in autistic individuals considered anxiety to be part of autism symptoms, when in fact, a modified version of the Anxiety Disorders Interview Schedule was able to differentiate anxiety symptoms from overlapping and ambiguous behaviors and experiences attributable to autism, allowing for more precise conceptualizations of disorders (Kerns et al., 2017). The current results suggest that ARFID eating restrictions that cause impairment in autistic adults would benefit from targeted treatment aimed at reducing their severity. This interpretation is consistent with a recent longitudinal analysis from the Generation R cohort, which found no evidence for reciprocal relationships between autistic traits and disordered eating at the within-individual level, suggesting that childhood eating problems are mechanistically distinct from autism (Harris et al., 2024). Currently, treatment for ARFID in autistic individuals is focused primarily on toddlers and children with high support needs through behavioral feeding therapy programs (e.g., Sharp et al., 2017), although emerging research has begun to explore the extension of evidence-based eating disorder treatments to ARFID in autistic children (Burton et al., 2021). Further research is needed in autistic adult samples to understand whether and how existing eating disorder treatments should be modified for this population.

Results should be interpreted considering several limitations. Because participants in the general population sample were not asked to self-report autism diagnoses or respond to autism screening questionnaires, we cannot characterize this as a neurotypical sample. However, the aim was to evaluate the MI of the NIAS for autistic adults against a general population sample typical of those used to develop and validate the NIAS and its translations, and these studies also did not exclude autistic individuals or describe the autistic symptoms of their samples (e.g., Zickgraf & Ellis, 2018). A related limitation is that professional autism diagnoses were self-reported by the autism sample. Both samples were majority women. Although recent research suggests that the male to female ratio is smaller in autistic adults compared to children (approximately 3:1, Posserud et al., 2021), the assigned female predominance in the autistic sample (2:1) makes the results potentially less generalizable to the autistic adult population. Additionally, the results only apply to autistic adults who could complete self-report measures: future studies should investigate MI of the youth self-report version of the NIAS (Billman Miller et al., 2022) and the parent report version for caregivers of autistic individuals of all ages with higher support needs. The current study examined the factor structure of the NIAS; further work should examine other measures of ARFID, such as the Pica, ARFID, Rumination Disorder Interview (Bryant-Waugh et al., 2022) which measures both dimensional severity of the three ARFID eating restrictions and impairment criteria for the ARFID diagnosis, and which may better discriminate ARFID from non-disordered eating than the NIAS (Ortiz et al., 2024). The criterion-related validity of the NIAS still needs to be evaluated in autistic samples, and the suggested cut-scores for screening, which thus far have only been evaluated in tertiary eating disorder treatment settings (i.e., Billman Miller et al., 2022; Burton Murray et al., 2021), need further refinement and testing before we would recommend using them to screen for ARFID in the general or autistic population. Although the NIAS should be co-administered with a measure of weight/shape disordered eating when assessing restrictive eating presentations, to our knowledge no weight/shape eating disorder self-report measures have been validated in this population (Longhurst et al., 2024). This may limit the NIAS’s clinical utility.

Finally, although there is evidence that the three eating restrictions measured by the NIAS adequately capture the psychopathology of ARFID in clinically-referred samples (e.g., Sanchez-Cerezo et al., 2025; Kambanis et al., 2024; Norris et al., 2018), the three drivers given as examples in the DSM-5 have influenced the development of ARFID assessment instruments including the NIAS, the clinical variables collected in chart reviews and naturalistic cohort studies, and patterns of referral to eating disorder treatment. Future research should continue to explore the mechanistic drivers of restrictive eating beyond those currently recognized in the DSM-5—selectivity, poor appetite, fear of aversive consequences, and weight/shape concerns—particularly in samples from populations who may be under-represented in the current eating disorder literature, including autistic adults (e.g., Longhurst et al., 2024; Halbeisen et al., 2022).

Despite these limitations, the study has multiple strengths, including the inclusion of an autistic adult population and the comparison of raw mean NIAS scores only after establishing MI. Our results underscore the importance of continued research on ARFID in adults, particularly autistic adults who may otherwise be overlooked due to the overlaps between ARFID symptoms and autism symptoms.

Acknowledgements:

We wish to express our gratitude to the general sample participants and the autistic adults in SPARK, as well as the SPARK clinical sites, and SPARK staff. We appreciate obtaining access to recruit participants through SPARK Research Match on SFARI Base.

Funding:

C.E.B. is supported by NIH T32MH096679. SPARK sample: This research was supported by start-up funds from The George Washington University to G.L.W. General sample: This research was supported by startup funds from the University of South Alabama to H.F.Z.

Data Availability Statement:

The general population data that support the findings of this study are available from the senior author [H.F.Z.] upon reasonable request. For the SPARK data, qualified researchers approved by SPARK can obtain the broader demographic and phenotypic data described in this study by applying through SFARI Base at https://base.sfari.org/

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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

The general population data that support the findings of this study are available from the senior author [H.F.Z.] upon reasonable request. For the SPARK data, qualified researchers approved by SPARK can obtain the broader demographic and phenotypic data described in this study by applying through SFARI Base at https://base.sfari.org/

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