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. Author manuscript; available in PMC: 2025 Nov 1.
Published in final edited form as: Eur Eat Disord Rev. 2024 Jun 10;32(6):1105–1116. doi: 10.1002/erv.3116

Momentary Mechanisms of Binge-Eating Symptoms using Ecological Momentary Assessment: The Moderating Role of Food Addiction

Shirley S Li 1,4, Rachel E Kalan 2,3, Alexandro Smith 2, Tyler B Mason 4,*, Kathryn E Smith 2,*
PMCID: PMC11449669  NIHMSID: NIHMS2006437  PMID: 38857200

Abstract

Objective:

Food addiction (FA) shows phenotypic and diagnostic overlap with eating disorders characterized by binge eating, though it is unknown how momentary processes driving binge-eating symptoms differ by FA. The present study examined the possible moderating influence of FA severity on momentary mechanisms underlying binge-eating symptomatology using ecological momentary assessment (EMA).

Method:

Adults (N=49, mean age=34.9±12.1, cis-gender female=77.1%) who met criteria for FA and/or binge-eating disorder completed baseline measures including the Yale Food Addiction Scale (YFAS) followed by a 10-day EMA protocol. Generalized linear mixed models assessed main effects of YFAS, momentary antecedents (affect, impulsivity, food cue exposure, appetite, and eating expectancies) and two-way interactions between YFAS and within-person antecedents.

Results:

FA severity moderated momentary associations between food cue exposure and subsequent binge-eating symptoms: the association was stronger among participants with lower but not higher YFAS scores. No other interactions were significant.

Conclusions:

Some functional associations underlying binge-eating symptoms vary based on individuals’ level of FA symptoms. Future research to further understand how observed associations may differ amongst diverse populations and over course of illness may also inform future prevention and interventions.

Keywords: binge eating, food addiction, eating disorders, ecological momentary assessment

1. Introduction

Food addiction (FA) conceptualizes the mechanisms underlying compulsive overeating as being similar to those of substance use disorders or other behavioral addictions (Schulte et al., 2016). In particular, evidence supporting FA has shown that highly palatable, ultra-processed foods high in fat and sugar may activate neural responses similar to that of drugs of abuse, including substance cravings, tolerance, and withdrawal (Gearhardt et al., 2014). Thus far these symptoms have typically been assessed by the Yale Food Addiction Scale (YFAS) (Gearhardt et al., 2016), a self-report questionnaire that provides a “diagnostic” threshold and dimensional measure of FA severity. This scale was selected to encompass the wide spectrum of symptoms and experiences covered in FA, including distress and impairment, and thereby measuring a range of psychosocial functional impacts. This is distinct, for example, from binge eating measures which focus only on assessing the frequency and severity of loss of control and overeating behaviors. However, there remains controversy surrounding the validity of FA given that FA and binge-eating symptomatology are both marked by loss of control over consumption, continued excess use despite negative consequences, and repeated, failed attempts to cut down on consumption (Pursey et al., 2014). Mechanistically, both syndromes involve reward dysfunction, emotion regulation, cravings, and impulsivity. Furthermore, it is not clear if and how FA and binge-eating symptomatology differ with respect to momentary mechanisms and functional relationships between contextual triggers and eating behavior. Such information is imperative to understand further the extent to which FA diverges from eating disorders characterized by binge eating, particularly bulimia nervosa (BN) and binge-eating disorder (BED).

Ecological momentary assessment (EMA) has been a particularly useful methodological approach to elucidate functional relationships between contextual triggers preceding binge eating and underlying reinforcement processes that maintain eating disorder behaviors (Schaefer et al., 2020). EMA is the collection of data from participants in their natural environment over short time intervals (e.g., five times per day over two weeks), facilitating the examination of temporal patterns between participants’ experiences and behaviors, often through utilization of brief assessments such as single-item measures (Song et al., 2023). Real-time data collection in the natural environment decreases recall bias and enhances the ecological validity of findings.

Thus far, evidence from EMA studies has implicated affect, impulsivity, food-related cognitions (e.g., eating expectancies and preoccupation with food), external eating (i.e., tendencies to eat in response to external food cues - e.g., sight and smell of food), and food craving as antecedents of binge-eating symptoms (i.e., loss of control eating and overeating) (Haedt-Matt & Keel, 2010). In support of affect regulation theories of binge eating, individuals with eating disorders have shown patterns of increasing negative affect (NA) and decreasing positive affect (PA) preceding binge eating, followed by a decrease in NA and increase in PA post-binge episodes (Wonderlich et al., 2021; Schaefer et al., 2020). In addition, facets of impulsivity have been consistently linked to EMA-measured binge eating (Smith et al., 2020a; Goldschmidt et al., 2019), though less research has examined momentary self-reported impulsivity as a predictor of binge-eating symptoms (Leenaerts et al., 2023). Another EMA study found that momentary food cue exposure (i.e., presence of palatable foods) was positively associated with overeating among individuals with higher body mass index (BMI), but the assessment of eating behavior did not include loss of control (Thomas et al., 2011). Other EMA research showed that momentary food craving was associated with subsequent binge eating (Schaefer et al., 2023; Wonderlich et al., 2017). One EMA study found higher momentary craving intensity predicted increased high-calorie snack consumption among adults without eating disorders (Richard et al., 2017). This study also found snack-related thoughts were related to greater snack consumption, though another EMA study of adults with binge eating did not show an association between momentary preoccupation with food and binge eating (Mason et al., 2019). In addition, an association has been found between hunger and cravings for snack-type foods while general food cravings were consistent with hunger cravings around lunch and dinner time (Richard et al., 2017; Reichenberger et al., 2018). Of note, the term craving can be described as eagerness and urge to eat (Kikuchi et al., 2015). Studies have also used a 4-item “appetite” subscale, aggregating preoccupation, eagerness to eat, urge to eat, and hunger, with good factor structure and psychometric properties (Kikuchi et al., 2015; MacIntyre et al., 2021). With respect to eating expectancies (i.e., the expectation that eating will improve mood), EMA studies of adults with obesity and binge-eating symptoms have found associations between momentary eating expectancies and subsequent binge eating, though these effects have depended on other momentary factors (e.g., restraint, negative affect, attention bias) (Pearson et al., 2018; Smith et al., 2020b).

Despite the phenotypic overlap between FA and binge-eating symptoms, it is not clear whether momentary antecedents of binge eating differ in FA, and no EMA study has assessed these processes among individuals with and without FA. The current study used EMA to explore how individual differences in FA severity may moderate momentary (i.e., within-person) mechanisms underlying binge-eating symptoms, lending insight into potential maintenance and reinforcement processes that could differ by FA. We examined whether FA severity moderated momentary associations between hypothesized antecedents (i.e., negative and positive affect, impulsivity, food cue exposure, appetite, and eating expectancies) and subsequent binge-eating symptoms. It was hypothesized that higher within-subjects levels of each antecedent would predict greater binge-eating symptoms at the subsequent EMA time point. The moderating role of FA severity was exploratory.

2. Methods

2.1. Participants

Fifty enrolled participants were recruited from prior studies and online platforms (i.e., ResearchMatch), of which 49 completed the EMA protocol and were included in analyses. Inclusion criteria were as follows: (1) meeting criteria for BED and/or FA (as determined by the Eating Disorder Diagnostic Scale for DSM-5 [EDDS DSM-5] and Yale Food Addiction Scale 2.0 [YFAS 2.0]), (2) between the ages of 18 and 64, (3) able to read and speak English, (4) own and regularly use a smartphone to complete the EMA protocol, and (5) reside in the United States. Exclusion criteria were as follows: (1) a body mass index (BMI) less than 18.5 kg/m2, (2) an intellectual disability, (3) symptoms of psychosis, (4) severe cognitive impairment, (5) they were currently pregnant or breastfeeding, (6) they had inpatient or partial hospitalization in the past four weeks, or (7) had previous bariatric surgery.

2.2. Procedure

Eligibility criteria was assessed via an online screening questionnaire, after which eligible participants were asked to complete an e-consent process. Next, enrolled participants completed self-report questionnaires administered via REDCap followed by a 10-day EMA protocol administered on an Android or IOS smartphone using the LifeData platform. During the protocol, up to 5 daily semi-random signal contingent prompts were administered. If no response was received, up to 5 reminders were delivered. Participants were requested to initiate surveys after eating episodes, in addition to the opportunity to record any missed episodes at signal contingent prompts. Participants received up to $65 for completing all study components ($15 for completing the baseline surveys, $35 for completing EMA, and $15 for completing 80% or more of the semi-random EMA prompts). All initial 50 enrolled participants completed the e-consent process.

2.3. Measures

2.3.1. Demographics and BMI

The online questionnaires included demographic and anthropometric information including age, sex, gender-identity, race/ethnicity, and marital status (see Table 1). Self-reported weight and height were used to calculate BMI (kg/m2).

Table 1.

Descriptive information

Age M SD
Age 34.9 12.1
Gender Identity n %
Cisgender Female 37 77.1
Cisgender Male 6 12.2
Transgender Male 1 2
Gender Questioning/Non-Conforming 3 6.1
Female at Birth w/o Identifying Gender Identity 1 2
Race and ethnicity n %
Non-Hispanic White 27 55.1
Hispanic 7 14.3
Black/African American 7 14.3
Asian 5 10.2
Multi-racial 3 6.1
BMI M SD
BMI 33.7 8.1
Overlap between YFAS and EDDS diagnoses n %
Food addiction diagnosis with/without EDDS diagnoses 39 79.6
 Co-occurring bulimia nervosa 25 51
 Co-occurring sub-threshold bulimia nervosa 3 6.1
 Co-occurring atypical anorexia 2 4.1
 Co-occurring night eating syndrome 2 4.1
 Co-occurring binge eating disorder 5 10.2
 No EDDS diagnosis 2 4.1
Binge eating disorder without food addiction diagnosis 10 20.4
EDDS probable diagnoses n %
Bulimia nervosa 25 51
Binge eating disorder 15 30.6
Sub-threshold bulimia nervosa 3 6.1
Night eating syndrome 2 4.1
Atypical anorexia nervosa 2 4.1
No eating disorder diagnosis 2 4.1
EMA variables M SD
Negative affect 1.74 0.81
Positive affect 2.15 0.79
Impulsivity 1.83 1.08
Food cue exposure 2.23 1.11
Eating expectancy 2.71 1.24
Appetite 36.82 25.96
 Eagerness and urge to eat 37.4 28.18
 Preoccupation with thoughts of food 36.44 29.60
 Hunger 36.03 28.38
Baseline questionnaire M SD
YFAS total 6.88 3.32
Binge eating symptoms 2.44 1.07
Overeating
 “To what extent do you feel that you overate?” 2.20 1.216
Loss of Control
 “To what extent did you feel a sense of loss of control over eating?” 2.21 1.257
 “To what extent did you feel driven to eat?” 3.02 1.254
 “To what extent did you feel you could not stop eating?” 2.34 1.263
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Note. YFAS=Yale Food Addiction Scale 2.0 total score; EDDS=Eating Disorder Diagnostic Scale for DSM-5; EMA=ecological momentary assessment. EMA variables were averaged across the EMA protocol.

2.3.2. Yale Food Addiction Scale 2.0 (YFAS 2.0) (Gearhardt et al., 2016)

The YFAS 2.0 was used for screening and primary analyses. During screening, participants completed the 35-item YFAS 2.0 which assessed 11 FA symptoms, each examined by 2–3 items with 2 additional items for clinically significant impairments and distress. Items were converted to a binary form of 0 or 1, corresponding to cutoff thresholds for the 11 FA symptoms. These values were summed to create a total score reflective of FA severity. To meet criteria for FA, participants must endorse clinically significant impairment in addition to at least 2 of the 11 symptoms. For the primary analyses, the sum score was used to represent FA severity. The internal consistency of the YFAS 2.0 symptom count measure was adequate in the present study (Cronbach’s α=0.85). To ensure YFAS scores are not simply capturing increased propensity to binge eat, we ran exploratory analyses between YFAS 2.0 and the Eating Pathology Symptom Inventory (EPSI) Binge Eating subscale (Forbush et al., 2013a, Forbush et al., 2013b), which measures binge-eating symptom severity. The analysis showed a weak association between YFAS 2.0 symptom count and EPSI binge eating subscale scores (r=0.25), thereby indicating that YFAS scores are capturing a construct beyond simply binge eating severity.

2.3.3. Eating Disorder Diagnostic Scale for DSM-5 (EDDS DSM-5) (Stice et al., 2000).

In this study, the EDDS DSM-5 was used as a screening tool to assess criteria for BED, assess frequency of binge eating and compensatory behaviors, and characterize the diagnostic composition of the participant sample. Of note, the screening questionnaire administered included the EDDS updated for the DSM-5 (https://www.ori.org/sticemeasures), a self-report measure that identifies probable DSM-5 eating disorder diagnoses as well as a composite score.

2.3.4. EMA

At signal-contingent prompts, surveys assessed momentary affect state and severity, impulsivity, food cue exposure, appetite, and expectancy that eating would alleviate negative affect (i.e., eating expectancies).

Affect was assessed by 10 items from the Positive and Negative Affect Schedule Short Form (Thompson et al., 2007), with the addition of stress and guilt as it was found to be particularly relevant for disordered eating behavior (Berg et al., 2013) for a total of 12 items. For each item, participants rated severity of affect experienced on a Likert scale ranging from “not at all” (1) to “extremely” (5). Ratings were averaged to create composite negative and positive affect scores at each EMA signal. The multilevel reliability was adequate for negative affect (within-subjects omega=0.78, between-subjects omega=0.94) and positive affect (within-subjects omega=0.73, between-subjects omega=0.93).

Momentary impulsivity was assessed by the item showing the highest factor loading on the modulate subscale of the State Difficulties in Emotion Regulation Scale (DERS) (Lavender et al., 2016): “I am having difficulty controlling my behaviors”. It was rated on a scale from “not at all” (1) to “extremely” (5).

Food cue exposure was assessed by 1 item pertaining to presence of food: “Since the last assessment, how often have you encountered good tasting, high-calorie foods or drinks?” (Thomas et al. 2011), rated from “not at all” (1) to “frequently” (4).

Appetite was evaluated by four items from the momentary appetite scale (Kikuchi et al., 2015) measuring eagerness to eat, urge to eat, preoccupation with thoughts of food, and hunger. Items were rated on scales from “none” (0) to “most intense” (100). Given high inter-item correlation in the sample, scores were averaged at each signal to create a momentary appetite measure, which showed high internal consistency (within-subjects omega=0.90, between-subjects omega=0.96).

Momentary eating expectancy was measured by the item “Right now, eating would make me feel better” (Pearson et al., 2018), and rated on a scale from “strongly disagree” (1) to “strongly agree” (5). Evaluation of eating expectancy measures suggest the convergent validity of EMA regulation of eating expectancy items (Mason et al., 2023a).

Binge-eating behaviors were measured during eating episode recordings. Participants who reported recent eating episodes at event- or signal-contingent surveys were requested to complete 1 item assessing degree of overeating (“To what extent do you feel that you overate?”) and 3 items assessing loss of control eating (“To what extent did you feel [1]... a sense of loss of control over eating? [2]… driven to eat? [3]…you could not stop eating?”) (Smith et al., 2020a). Each item was rated on a 5-point scale ranging from “not at all” (1) to “extremely” (5). Loss of control ratings were averaged at each eating episode. The binge-eating symptoms scale had adequate internal consistency (within-subjects omega=0.84, between-subjects omega=0.94) and has been shown to hold construct validity (Mason et al., 2023b).

2.3.5. Statistical Analyses

Descriptive statistics were examined for all measures. Analyses used generalized linear mixed models (GLMMs) to examine main effects of the six independent variables (momentary affect (negative and positive), impulsivity, food cue exposure, appetite, and expectancy that eating would alleviate negative affect (i.e., eating expectancies), in addition to two-way interactions between FA and within-person antecedent variables, as predictors of subsequent binge-eating symptoms measured at the next EMA signals. Main effects of independent variables were separated into within- and between-person components; within-person effects were person-mean centered (PMC) and therefore reflected momentary changes relative to an individual’s average level, whereas between-person effects were grand-mean centered (GMC) and therefore reflected how an individual’s average level compares to the overall sample average. Predictor variables were lagged within day but not across day. GLMMs specified a random intercept and an AR1 covariance structure to target binge-eating symptoms with a gamma distribution. Covariates included BMI, compensatory behavior frequency as measured by the EDDS, and binge frequency as measured by the EDDS. We corrected for multiple comparisons using the Benjamini-Hochberg procedure. All data analysis was performed on SPSS.

3. Results

3.1. Demographics and Descriptive Analyses

Demographics and descriptives are reported in Table 1. Participants were on average 34.9±12.1 years old (range: 20–64) and most identified as cisgender female (77.1%) and non-Hispanic White (55.1%). Mean BMI was calculated at 33.7±8.1 kg/m2 (range: 18.6–53.7), and the majority of participants were met BMI criteria for obesity (61.2%) or overweight (28.6%), with a minority (10.2%) with a BMI within the healthy range (Centers for Disease Control and Prevention, 2022).

Participants completed 2344 EMA surveys, of which 1891 were signal-contingent prompts and 453 were event-contingent prompts. There was an average EMA compliance of 74.6%. There were 1,358 eating episodes (453 event-contingent and 905 reported at signal-contingent prompts). A total of 49 participants were included in the analysis. GLMM results are shown in Table 2. FA was a significant predictor of binge eating across all models, such that higher FA severity predicted more severe binge-eating symptoms during EMA.

Table 2.

Generalized linear mixed models examining antecedents and association with binge eating symptoms at next EMA signal

Binge Eating
B SE t p
Intercept 0.861 0.2017 4.27 <.001
BMI −0.011 0.0057 −1.972 0.049
Comp 0.002 0.0041 0.596 0.551
Binge Freq 0.005 0.0092 0.564 0.573
YFAS 0.042 0.0136 3.061 0.002
Negative affect (Between person) 0.046 0.069 0.669 0.504
Negative affect (Within person) −0.013 0.0511 −0.245 0.806
YFAS x Negative affect 0.006 0.0071 0.817 0.414
B SE t p
Intercept 0.902 0.1982 4.553 <.001
BMI −0.014 0.0053 −2.66 0.008
Comp 0.002 0.004 0.449 0.653
Binge Freq 0.008 0.0087 0.944 0.346
YFAS 0.049 0.0134 3.621 <.001
Positive affect (Between person) −0.072 0.0729 −0.983 0.326
Positive affect (Within person) 0.003 0.043 0.08 0.936
YFAS x Positive affect −0.002 0.0059 −0.355 0.723
B SE t p
Intercept 0.863 0.169 5.11 <.001
BMI −0.008 0.0046 −1.628 0.104
Comp 0.003 0.0034 0.81 0.418
Binge Freq 0.002 0.0074 0.301 0.763
YFAS 0.024 0.012 1.977 0.048
Impulsivity (Between person) 0.216 0.0526 4.107 <.001
Impulsivity (Within person) 0.113 0.0306 3.698 <.001*
YFAS x Impulsivity −0.008 0.004 −2.108 0.035†
B SE t p
Intercept 0.692 0.2067 3.347 <.001
BMI −0.007 0.0056 −1.193 0.233
Comp 0.003 0.0038 0.733 0.464
Binge Freq 0.014 0.0087 1.621 0.105
YFAS 0.031 0.0133 2.358 0.019
Food cue exposure (Between person) 0.174 0.0717 2.428 0.015
Food cue exposure (Within person) 0.103 0.0266 3.893 <.001
YFAS x Food cue exposure −0.012 0.0037 −3.216 0.001
B SE t p
Intercept 0.783 0.2036 3.844 <.001
BMI −0.009 0.0057 −1.562 0.119
Comp 0.003 0.004 0.678 0.498
Binge Freq 0.007 0.0085 0.77 0.441
YFAS 0.038 0.0134 2.859 0.004
Appetite (Between person) 0.005 0.003 1.53 0.126
Appetite (Within person) 0.003 0.0012 2.184 0.029‡
YFAS x Appetite <.001 0.0002 −1.293 0.196
B SE t p
Intercept 0.879 0.1993 4.408 <.001
BMI −0.013 0.0052 −2.5 0.013
Comp 0.002 0.0042 0.419 0.675
Binge Freq 0.007 0.0088 0.849 0.396
YFAS 0.046 0.0133 3.494 <.001
Eating expectancy (Between person) −0.013 0.0612 −0.219 0.826
Eating expectancy (Within person) 0.055 0.027 2.048 0.041§
YFAS x Eating expectancy −0.006 0.0034 −1.664 0.097
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Note. YFAS=Yale Food Addiction Scale 2.0 total score; EMA=ecological momentary assessment; BMI=body mass index; Comp=compensatory behavior frequency as measured by the Eating Disorder Diagnostic Scale; Binge Freq=binge frequency as measured by the Eating Disorder Diagnostic Scale. Bold values reflect significant effects.

*

=corrected p=0.072 after alpha correction

†

=corrected p=0.063 after alpha correction

‡

=corrected p=0.058 after alpha correction

§

=corrected p=0.067 after alpha correction

3.2. Antecedents as Predictors of Binge Eating Symptoms

3.2.1. Food Cue Exposure

Food cue exposure was positively associated with binge-eating symptoms for both between- (B=0.17, SE=0.07, p=0.015) and within-person effects (B=0.10, SE=0.03, p<0.001). The between-person effect suggests that participants with higher food cue exposure reported increased binge-eating symptoms across EMA. The within-person effect suggests that at moments when individuals endorsed greater food cue exposure compared to their usual level, they reported increased binge-eating symptoms at the next signal. FA interacted with within-person food cue exposure (B=−0.01, SE<0.01, p=0.001) to predict binge-eating symptoms. As shown in Figure 1, the relationship between momentary food cue exposure and subsequent binge-eating symptoms was stronger among those with lower, but not higher YFAS scores.

Figure 1.

Figure 1.

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Interaction between the Yale Food Addiction Scale (YFAS) symptom count score and momentary exposure to palatable food cues and predicting subsequent binge-eating symptoms measured via ecological momentary assessment (EMA). Momentary food cue exposure reflected within-person (i.e., person-mean centered) effect. High/low values reflect ± 1 SD above or below the sample mean (YFAS) or individual mean (food cue exposure).

Note. Yale Food Addiction Scale 2.0 total scores (YFAS) interacted with momentary food cue exposure to predict subsequent binge-eating symptoms. Momentary food cue exposure reflected within-person (i.e., person-mean centered) effect. High/low values reflect ± 1 SD above or below the sample mean (YFAS) or individual mean (food cue exposure).

3.2.2. Impulsivity

The within-person effect of impulsivity and the interaction of within-person impulsivity and YFAS scores were not significant predictors of binge-eating symptoms after correction for multiple comparisons. The significant between-person effect (B=0.22, SE=0.05, p<0.001) suggests that participants with higher impulsivity reported increased binge-eating symptoms across EMA.

3.2.3. Appetite and Eating Expectancy

After alpha corrections, within-person effects of appetite and eating expectancies were not significant predictors of binge-eating symptoms, nor were their interactions with YFAS scores. Between-person and interaction effects of appetite and eating expectancy were nonsignificant.

3.2.4. Negative and Positive Affect

No significant within- or between-person effects were observed for negative affect and positive affect, nor were there significant interactions with YFAS.

3.2.5. Covariates and BMI

When assessing covariates, both compensatory behaviors and binge frequency were not significantly associated with binge-eating symptoms in any model. Of note, BMI was found to be significantly associated with binge-eating symptoms across models of eating expectancy, negative affect, and positive affect (p-values range: 0.008–0.049).

4. Discussions

The present study examined the moderating role of FA severity on momentary mechanisms underlying binge-eating symptoms using EMA. Higher FA severity was associated with greater EMA binge-eating symptoms, and FA severity moderated momentary associations between one antecedent, food cue exposure, and subsequent binge-eating symptoms. Momentary appetite, eating expectancies, impulsivity and affect, either positive or negative, were not statistically significant predictors of subsequent binge-eating symptoms in our study. While not the primary focus of this study, individual differences in impulsivity and BMI were also linked to binge-eating symptoms.

Moderation effects revealed diminished associations between food cue exposure among individuals with higher FA severity even after alpha correction. It is possible that binge-eating symptoms among those with higher FA severity are more habitual in nature and less influenced by fluctuations in external (i.e., food cue exposure) factors. In addition, it has been suggested that binge eating can become more compulsive over time (Pearson et al., 2015). Thus, FA may represent a more compulsive and habitual form of eating pathology regardless of exposure or environment. It is also notable that the measure of food cue exposure in this study referred to “[encountering] good tasting, high-calorie foods or drinks”, which implies a conscious awareness of exposure by the respondent. This assumes participants are conscious of the external factors in their environment, but it is possible that participants may lack this awareness, leading to inaccuracies in attempts to measure this antecedent. It is also important to note our relatively small effect sizes.

Impulsivity was found to only be significant for the between-person effect, indicating those with higher impulsivity also reported more severe binge-eating symptoms across EMA. This largely aligns with prior literature showing impulsive traits are consistently linked to binge-eating symptoms (e.g., Lavender & Mitchell, 2015).

Contrary to expectations, momentary appetite and eating expectancy were not significant after multiple comparison correction. While other studies have found these variables to be related to binge-eating symptoms (Richard et al., 2017; Schaefer et al., 2023), these effects may have been too small to detect in the present sample.

Likewise, neither NA nor PA were significant predictors of subsequent binge-eating symptoms. This is contradictory to some previous studies that suggest an affect regulation function of binge-eating symptoms (Schaefer et al., 2020), yet consistent with others finding no association which used a similar analytic approach (Heron et al., 2014; Mason et al., 2019). Difference across studies, including our study, may be due in part to the measurement of affect, which were composite measures of NA and PA items. In comparison, prior studies have highlighted the importance of individual facets of affect, such as guilt, fear, loneliness, and irritability, amongst others (Berg et al., 2013). The timing of EMA surveys may have also played a role. The intervals between EMA signals may have been too long to detect the fluctuating and fleeting nature of affect fluctuations preceding binge-eating symptoms. Additionally, differences in analytical approach (e.g., single time points vs. slope or trajectory-based approaches) can be factors influencing observed results. This indicates a need to examine the potential utility of more complex models, such as the interactions between within-person factors (Pearson et al., 2018; Smith et al., 2020a; Smith et al., 2020b).

Limitations of this study include the relatively modest sample size at the between-person level that may not be representative of the general population with respect to demographics and BMI. Continued research is needed with larger and more diverse samples, as well as clinical samples and comparisons between diagnostic groups. However, comparisons of subgroups (e.g., those with and without FA diagnosis) was not feasible in this study given the heterogeneity in eating disorder diagnoses (e.g., overlap of diagnoses where most individuals with FA also had BN). Diagnostic heterogeneity within our sample poses concerns about robustness of the findings as prior research has shown differences in the role of affect and impulsivity in binge eating between eating disorder diagnostic groups (Wonderlich et al., 2021; Schaefer et al., 2020; Smith et al., 2020a; Goldschmidt et al., 2019). Because most participants with FA met criteria for full- or sub-threshold BN rather than BED, we included compensatory behaviors frequency as a covariate in models. This reduces concerns about the high prevalence of BN in our sample potentially impacting findings. Additionally, considering the controversies surrounding overlap between FA and binge-eating symptomatology, future larger-scale research with multiple moderators, rather than only FA, may help better explain the unique distinction between FA and other related constructs such as binge-eating symptomatology and/or severity.

Although EMA data was valuable in offering real-time data collection and large numbers of data points, an additional limitation relates to use of brief and single-item measures to assess constructs in EMA. While single-item measures are integral for managing assessment burden and increasing efficiency (i.e., maximize participant compliance) in EMA and have been shown to have adequate concurrent and predictive validity, this may introduce potential for measurement error (Song et al., 2023; Allen et al., 2022). Utilization of other standardized instruments for our measures may be a consideration for future non-EMA studies. Data were also self-reported, and it would be helpful to integrate objective measures of food consumption and food cue exposure, for example. It is important to also note that this is an exploratory study, and results should be interpreted with caution. Further study and replication in larger and more diverse samples should be conducted prior to any conclusive inferences.

Despite these limitations, findings offered insight regarding how FA moderates momentary processes driving binge-eating symptoms. Given that mechanisms of binge eating may change over the course of illness (e.g., Bodell & Racine, 2023; Pearson et al., 2015), moving forward it will also be important to conduct longitudinal studies with multiple waves of EMA to understand how the observed associations may differ by stage of illness. For example, food cue exposure may be a more potent predictor of binge eating during the initial onset of this behavior; however, over time as individuals may develop FA and progress to more chronic and habitual symptoms, the initial triggers may become less predictive of binge eating. Ultimately, further understanding of maintenance mechanisms underlying binge eating, and how they are similar to or different in FA, may inform future prevention and interventions.

Highlights.

  • Ecological momentary assessment was used to assess the extent to which food addiction severity moderated associations between momentary antecedents and binge-eating symptoms.

  • Higher food addiction severity attenuated the momentary association food cue exposure and binge-eating symptoms.

  • Food addiction severity did not moderate associations between other antecedents (affect, impulsivity, appetite, and eating expectancies) and binge-eating symptoms.

Funding:

This research was supported by grants K23DK128568 and K01DK124435 from the National Institutes of Health (National Institute of Diabetes and Digestive and Kidney Diseases). The funder had no role in this study.

Footnotes

Ethics approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study received approval from the Institutional Review Board at the University of Southern California.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The authors have no relevant financial or non-financial interests to disclose.

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