Unveiling Emotional Eating Heterogeneity in Iranian Young Adults: A Latent Profile Analysis of Executive Functions, Emotion Regulation and Reinforcement Sensitivity

Unveiling Emotional Eating Heterogeneity in Iranian Young Adults: A Latent Profile Analysis of Executive Functions, Emotion Regulation and Reinforcement Sensitivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling Emotional Eating Heterogeneity in Iranian Young Adults: A Latent Profile Analysis of Executive Functions, Emotion Regulation and Reinforcement Sensitivity Sadegh Rahmatiᵃ, Alireza Moradiᵃ, Jafar Hasaniᵃ, Hojjatollah Farahaniᵇ, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7985224/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: While emotion regulation, executive function and reinforcement sensitivity play central roles in emotional eating, few studies have investigated these variables in non-Western societies. Method: This study used latent profile analysis (LPA) to analyze 10 subscales from the Dutch Eating Behavior Questionnaire and Difficulties in Emotion Regulation Scale-16 and the Behavior Rating Inventory of Executive Function-Adult Version to identify emotional eating patterns among 1147 Iranian university students (73.8% female, mean age = 21.44 years). Results: The analysis revealed five distinct eating behavior patterns: 1) weakly balanced, 2) emotionally rewarding-driven, 3) capable and adaptive, 4) vulnerable and high-risk, and 5) balanced with threat sensitivity. These profiles revealed different patterns of emotional regulation and eating behavior. The behavioral inhibition system (BIS) and behavioral activation system (BAS) sensitivities between profiles showed significant differences (p < .001), with the Capable and Adaptive profiles showing the highest BIS score (M = 15.22) and the reward-driven profiles displaying an elevated BAS, whereas the FFFS sensitivities remained stable. Discussion: The findings showed that women and older participants were more likely to fall into vulnerable eating profile categories. The eating habits of Iranians are influenced by their cultural background, fasting customs and practices of eating together. Research evidence supports a total eating disorder model that includes temperament factors but shows that cultural differences in the treatment of eating disorders and obesity need to be recognized. Conclusions: Analysis of the evolution of eating patterns through biological indicators is needed to develop enhanced treatment protocols. Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Emotional Eating Latent Profile Analysis Emotion Regulation Executive Function Reinforcement Sensitivity Theory Dutch Eating Behavior Questionnaire Iranian Culture Figures Figure 1 Figure 2 Figure 3 Plain English Summary People who eat when they feel upset, stressed or sad develop weight gain and multiple health problems through emotional eating. The ways in which people express their emotions through eating differ among individuals, but researchers understand very little about this behavior in Iranian culture, which follows non-Western traditions. The research surveyed 1,100 young Iranian university students about their eating habits and emotional management and their reasons for eating. The researchers applied a grouping approach to identify five distinct eating patterns among participants. People with "weakly balanced" eating habits maintain good control over their food but struggle to recognize their emotions. The "emotionally reward-driven" pattern involves using food for comfort and enjoyment, whereas external factors trigger eating behaviors. People with "capable and adaptive" eating habits possess strong emotional competencies and maintain consistent eating routines. The "vulnerable and high-risk" eating pattern shows poor control, which results in dangerous and unpredictable eating behaviors. The "Balanced with Threat Sensitivity" group maintains stability but shows excessive sensitivity to fear-based fears. Riskier eating patterns appeared more frequently among female students and those who were older. The practice of sharing carbohydrate-rich meals during Ramadan fasting in Iran creates an environment where people use food as a way to handle emotions instead of talking about their feelings openly. The identified patterns demonstrate that emotional eating exists in different forms, so treatment approaches need to be individualized between mindfulness training for stable eaters and skill development therapy for those at risk. The prevention of obesity and eating problems in young people can be enhanced through cultural modifications. Introduction The practice of eating food because of emotional states, especially in response to negative emotions, defines emotional eating (EE) (Barnhart et al., 2021; Bongers & Jansen, 2016(. While the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) does not recognize EE as an official diagnosis, EE appears in various psychological conditions, including depression (Lazarevich et al., 2016; Muha et al., 2024), anxiety (Goossens et al., 2009; Silva et al., 2025), posttraumatic stress disorder (PTSD) (Talbot et al., 2013; Tayhan & Korkmaz, 2025), bipolar disorder (Carmassi et al., 2025; Martin et al., 2016) and eating disorders (Reichenberger et al., 2021). The practice of eating due to emotions leads to multiple health problems, including obesity and being overweight (Jáuregui-Lobera & Montes-Martínez, 2020; Konttinen, 2020; Vasileiou & Abbott, 2023), with a national study showing that EE affects 44.9% of overweight and obese individuals (Chew et al., 2025). Research focused on those seeking treatment indicates that obesity exists in multiple forms, which is influenced by distinct temperament characteristics. For example, research has shown two distinct personality types among patients with obesity: 1) the resilient and high-functioning type, which shows strong effortful control but behavioral inhibition system (BIS)/behavioral activation system (BAS) activity, where the BIS refers to the behavioral inhibition system sensitive to punishment and avoidance, whereas the BAS refers to the behavioral activation system sensitive to reward and approach; this low activity indicates reduced sensitivity to punitive or rewarding stimuli, contributing to emotional stability and high functioning; and 2) the emotionally dysregulated and undercontrolled type, which shows weak effortful control and high BIS/BAS activity, where high activity indicates heightened sensitivity, which can lead to emotional dysregulation and impulsive behaviors in obese individuals (Müller et al., 2014). A recent study using latent profile analysis on prebariatric patients revealed five distinct clusters that combined temperament characteristics with emotion regulation and eating behavior patterns. The research model established relationships between eating disorder psychopathology and depression and quality of life through different levels of eating disorder symptoms (Schäfer et al., 2017). This research demonstrated that EE and obesity are linked to specific temperament characteristics, emotional regulation problems and eating disorders. Additionally, research on binge eating disorder subtypes has shown that negative emotionality, approach behavior and executive function deficits create distinct subgroups that differ in their EF impairment levels and emotional trigger responses (Brucar et al., 2025). The subtyping method demonstrates that negative emotionality acts as an indicator of emotional regulation problems, which intensifies executive function weaknesses to produce binge eating behaviors that resemble EE. The conventional variable-centered method of regression analysis assumes that all individuals possess identical characteristics, which results in a single average parameter value (Howard & Hoffman, 2017; Papola & Patel, 2025). The traditional approach fails to recognize individual differences and behavioral interactions because EE includes both binge eating and restrictive eating patterns (Laursen & Hoff, 2006; Papola & Patel, 2025). Person-centered methods, such as latent profile analysis (LPA), identify separate subgroups of people who demonstrate unique behavioral patterns. Nordgren et al. (2022) identified three eating disorder subgroups and reported that people with severe emotion regulation problems exhibited the most severe binge-eating and vomiting, substance use and self-harm behaviors. A study by He et al. (2020) used LPA to identify four eating patterns in young adults: 1) nonemotional eating, 2) emotional overeating and undereating, 3) emotional overeating and 4) emotionally undereating. The "emotional overeating" and undereating" and "emotional overeating" groups included more women, and the high BMI and “emotional overeating” and undereating” groups presented more eating disorder symptoms and psychological issues. The results demonstrate that LPA detects diverse EE patterns, which include binge and restrictive eating behaviors, that standard variable-centered methods fail to capture (Marsh et al., 2009). EE exists in multiple forms because of intricate neurocognitive mechanisms. The core symptoms of EE according to Gratz and Roemer (2004) involve ER regulation problems, which include nonacceptance, goal-directed behavior difficulties, impulse control problems, inadequate ER strategies and poor emotional awareness (Shriver et al., 2020). Difficulties in emotion regulation predict stress-induced eating (Zhou et al., 2025). Executive functioning has also been found to affect how people respond to food cues (Favieri et al., 2024). A person with weaker behavioral regulation has greater difficulty controlling their desire to eat (Dohle et al., 2018). The relationship between emotion regulation and executive functioning shows a dynamic interaction. Emotional regulation functions as a link between components of executive functioning (e.g., impulsivity and metacognition) and disordered eating; strong metacognitive abilities support adaptive emotion regulation techniques, whereas difficulties with emotion regulation reduce executive functioning capabilities, which worsens EE through unhelpful coping mechanisms (Estévez et al., 2024). For example, the Behavioral Regulation Index (BRI), a subscale of the Behavior Rating Inventory of Executive Function assessing behavioral control in executive functioning, operates at a low level in some individuals, which impairs emotion regulation, making it difficult to suppress reward-based eating when negative emotions appear (Kelly et al., 2020). According to the revised reinforcement sensitivity theory (Gray & McNaughton, 2000), neurobiological systems influence behavioral patterns through the behavioral inhibition system (BIS), which detects punishment or threat signals in the brain, thereby promoting avoidance behaviors and contributing to the obsessive‒compulsive symptoms often observed in restrictive eating patterns (Amani & Keyvanlo, 2022); the behavioral activation system (BAS), which responds to reward cues by increasing approach-oriented actions, leading to impulsive binge eating episodes in response to positive or negative emotional triggers (Sutton et al., 2022); and the fight‒flight‒freezing (FFS) system, which governs immediate threat responses by freezing behavior that can mimic restrictive eating as a form of immobilization, although experiential avoidance strategies—such as the suppression of food-related negative emotions—can help mitigate these acute reactions (Thompson et al., 2014). The combination of high BAS sensitivity with reward-driven eating behavior requires enhanced BRI function, but FFFS freezing responses can produce distinct eating patterns (Smith et al., 2023). Brucar et al. (2025) demonstrated, via neurobehavioral subtyping, that EF deficits paired with high negative emotionality create binge eating risk profiles, which require comprehensive models for EE research. Conceptual Framework Revised reinforcement sensitivity theory posits that behavioral inhibition, behavioral activation and fight-flight-freeze systems create complex interactions that determine how people respond to punishments and reward and threat cues, which leads to EE behaviors. The extent and nature of EE behaviors stem from individual differences in sensitivity levels. Research shows that both punishment/threat sensitivity (BIS/FFFS) and reward sensitivity (BAS) lead to increased eating pathology and EE (Sutton et al., 2022; Wilson et al., 2021). People with low behavioral control and high FFFS sensitivity tend to develop restrictive eating patterns when they have high nonacceptance of emotions (Claes et al., 2021; Matton et al., 2017; Wilson et al. 2019). There is empirical support for this integrative framework through latent class analysis among adults with obesity seeking treatment (Müller et al., 2014). Müller et al. (2014) identified two distinct clusters; the resilient/high-functioning cluster showed high effortful control and low BIS/BAS scores, whereas the emotionally dysregulated/undercontrolled cluster displayed low effortful control and elevated BIS/BAS scores. The emotionally dysregulated/undercontrolled group members displayed more severe eating disorder symptoms, higher depression levels and worse executive functioning abilities. Schäfer et al. (2017) built upon this model by studying prebariatric patients through a combination of emotion dysregulation and disinhibited eating, which resulted in five distinct patient groups on the basis of self-control and psychopathology profiles. More recent studies have further supported these mechanisms. Specifically, emotional regulation impairments stem from executive function deficits, which are connected to eating disorders. Additionally, the severity and nature of disordered eating behaviors depend on executive function and behavioral activation system (BAS)/behavioral inhibition system (BIS) sensitivities (Ramalho et al., 2023; Mohorić et al., 2023). Furthermore, the research of Bazo Perez and Frazier (2024) and Zhou et al. (2025) revealed that people who have both emotion regulation and executive functioning deficits remain at risk for developing EE and associated mental health problems. Taken together, these findings provide further support for a multidimensional model informed by temperament in the etiology and intervention of EE. While research has made significant progress in understanding eating behaviors, several gaps still exist. First, the majority of existing studies have studied emotion regulation, executive functioning and EE independently (Favieri et al., 2024; Shriver et al., 2020) without investigating their combined effects on individual eating patterns. The current research methods fail to study intricate eating patterns, which include both binge eating and restrictive eating, and LPA is needed to detect these complex patterns (Howard & Hoffman, 2017; Papola & Patel, 2025). Second, among LPA studies about eating disorders, most have analyzed binge-eating patterns in clinical populations (Müller et al., 2014; Schäfer et al., 2017), but few have studied restrictive eating or mixed eating patterns in nonclinical populations. Third, there are few LPA studies on EE in non-Western contexts, such as Iran. The collectivist nature of Iranian society leads people to hide their negative feelings because they want to preserve social unity, which might result in individuals expressing their distress through EE as a hidden way to cope (Haghighian Roudsari et al., 2017; Vaziri et al., 2021). People show more intense EE responses to emotional stimuli after Ramadan fasting because of the dietary restrictions of this cultural practice (Amerzadeh et al., 2024; Gilavand & Fatahiasl, 2018; Weingarten & Elston, 1990). Additionally, the practice of dining with others, while following high-carb diets, leads to greater external eating because social gatherings expose people to additional food triggers (Aghayan et al., 2020; Akhoundan et al., 2016; Ebrahimi et al., 2020). These cultural elements may yield unique EE profiles among those in Iran, but no prior LPA studies have been conducted among Iranian young adults. Therefore, there is a need for research on Iranian cultural effects on EE. Fourth, to the best of our knowledge, no study has integrated these 10 subscales—from the Dutch Eating Behavior Questionnaire (DEBQ; e.g., emotional and restraint eating), the 16-item Difficulties in Emotion Regulation Scale (DERS-16; e.g., impulse control and strategies), and the Behavior Rating Inventory of Executive Function-Adult (BRIEF-A; e.g., behavioral inhibition and metacognition) subscales in LPA for nonclinical EE heterogeneity, enabling more nuanced profiling in general populations. However, profile differences in rRST sensitivities, including FFFS for restrictive eating, have not been examined (Siligato et al., 2024). The research introduces LPA to Iranian university students through these subscales while evaluating the BIS, BAS and FFFS as measurement proxies. The research findings will improve EE theoretical models for minority populations and create obesity and eating disorder prevention methods for particular cultural environments that can be used in related Persianate countries (e.g., Afghanistan, Tajikistan; Sahrai et al., 2022; McNamara & Wood, 2019). Current Study The current study aimed to explore latent EE profiles by applying LPA based on eating behavior (using the Dutch Eating Behavior Questionnaire (DEBQ)), emotion dysregulation (using the Difficulties in Emotion Regulation Scale (DERS-16)) and executive functions (using the Behavior Rating Inventory of Executive Function-Adult version (BRIEF-A)) among Iranian university students. Furthermore, it aimed to compare these profiles on the basis of reinforcement sensitivity theory (rRST: BIS, BAS FFFS proxies) to provide insights into the neurobiopsychological mechanisms that might be involved in the heterogeneity of EE. This exploratory methodology contributes to personalized interventions and greater etiologically consideration of EE in a nonclinical, non-Western cultural context. Method Participants The research received approval from Kharazmi University's institutional ethical committee (Ethics Approval Code: IR.KHU.REC.1403.135). The participants signed written consent forms, and the researchers protected their privacy through data anonymization. All methods were performed in accordance with the relevant guidelines and regulations, as outlined in the Declaration of Helsinki. The research involved 1200 university students in Karaj, Iran, aged over 18 years, who were selected through convenience sampling. The research study follows LPA guidelines, as it contains at least 500 participants (Spurk et al., 2020) and is similar to other studies in that it offers adequate power for the detection of distinct profiles on the basis of 10 available subscales (Sultson & Akkermann, 2019). After excluding participants who did not meet the eligibility criteria, 1147 participants were included in the final analysis (refer to Data Collection and Processing). The research involved participants who were not enrolled in psychology or counseling programs and who did not have any severe psychological or neurological conditions. Measures Dutch Eating Behavior Questionnaire (DEBQ) The DEBQ (van Strien et al.,1986) consists of 33 items that evaluate three eating patterns: restrained eating (10 items), emotional eating (13 items) and external eating (10 items). The survey uses a 5-point Likert scale, which ranges from 1 ( never ) to 5 ( very often ), to measure each behavior's frequency. A higher score indicates greater engagement in each behavior. The Persian version showed high internal consistency (Cronbach's α = 0.77–0.83) and test-retest reliability (r = 0.72–0.83) and confirmed factorial validity through confirmatory factor analysis (Nejati et al., 2018). This scale was used with permission from Hogrefe Publishing. Difficulties in Emotion Regulation Scale-16 (DERS-16) The DERS-16 (Bjureberg et al., 2016) represents a shortened version of the DERS (Gratz & Roemer, 2004), which contains 16 items to assess five emotion regulation dimensions: nonacceptance of emotions (3 items), difficulties in goal-directed behavior (3 items), impulse control difficulties (3 items), limited access to strategies (5 items), and lack of emotional clarity (2 items). The assessment tool uses a 5-point Likert scale where participants rate their experiences from 1 ( almost never ) to 5 ( almost always ) to indicate their level of difficulty. The Persian adaptation of the DERS-16 has strong internal consistency (α = 0.80–0.92) and test-retest reliability ( r = 0.88) (Shahabi et al., 2020). The emotional awareness subscale that was present in the original Gratz and Roemer scale (2004) has been removed in this version. BIS/BAS Scales The BIS/BAS scales (Carver & White, 1994) measure reinforcement sensitivity. It contains 20 items that are divided into seven punishment sensitivity items for the BIS and 13 reward sensitivity items that contain three subcategories: reward responsiveness (5 items), drive (4 items) and fun-seeking (4 items). The survey evaluates each item through a 4-point Likert scale ranging from 1 ( strongly disagree ) to 4 ( strongly agree ). Higher scores indicate greater sensitivity. The Persian version of the scale has demonstrated acceptable internal consistency (α = 0.69 for the BIS, 0.65–0.87 for the BAS subscales) and test-retest reliability (r = 0.68–0.71), with a confirmed factor structure and valid convergent/divergent relationships (Mohammadi, 2008). Behavior Rating Inventory of Executive Function Adult Version The BRIEF-A (Roth et al., 2005) contains 75 items that evaluate executive functioning through two separate indices: the Behavioral Regulation Index (BRI), with 30 items (including inhibit, shift, emotional control, and self-monitor), and the metacognitive index (MI), with 45 items (including initiate, working memory, plan/organize, task monitor, and organization of materials). The assessment uses a 3-point Likert scale, and higher scores indicate more severe impairment. The Persian version of the BRIEF-A demonstrated strong internal consistency (α = 0.65–0.96) and reliable test-retest results (Mani et al., 2018). Data collection and analysis With the approval of Kharazmi University’s IRB, researchers attended specific universities in Karaj city (Iran) to recruit students. After providing written informed consent, the participants completed the DEBQ, DERS-16, BIS/BAS, and BRIEF-A in person. A total of 1,200 questionnaires were returned. The information was then entered into SPSS (version 21) for initial analysis. Participants with more than 10% missing data were excluded, leaving a total sample of 1,147 participants. To obtain standardized scores and thus comparability between the indicators, z values were created for the 10 input subscales (DEBQ: restrained, emotional, external eating; DERS-16: nonacceptance, goal-directed behavior difficulties, impulse control, limited strategies, emotional clarity; BRIEF-A: BRI, MI) prior to LPA. This method for LPA is preferred because it is sensitive to scale differences (Spurk et al., 2020). Quartiles of variables related to participant performance were statistically examined prior to LPA. Furthermore, standard statistical checks (e.g., skewness/kurtosis for multivariate normality, variance inflation factor for common multicollinearity, and sample size) were performed via SPSS 21. LPA was performed via the tidylpa package (Rosenberg et al., 2018) in R, and profiles were derived with respect to 10 standardized subscales: DEBQ (restrained eating, emotional eating, EE); DERS-16 (nonacceptance, difficulties in goal-directed behavior, impulse control, limited access to emotion regulation strategies, lack of emotional clarity); and BRIEF-A (behavioral regulation index (metacognition index)). Models with 1–8 profiles were compared, and the best model was chosen according to the Bayesian information criterion (BIC), sample-adjusted BIC (SABIC), entropy (>0.80) and bootstrap likelihood ratio test (BLRT). Missing data were accounted for via full information maximum likelihood (FIML), which maximizes data retention. Covariates (age and sex) were included to control for possible confounding effects. Profiles of the rRST (i.e., proxies for the BIS, BAS and FFFS) were compared via ANOVA or multinomial logistic regression after controlling for covariates. Results Sample Description The sample included 295 (25.7%) men and 847 (73.9%) women, with 5 (0.4%) individuals for whom sex was unknown/not reported. The average age of the participants was 21.44 years ( SD = 2.79). Fifty-seven participants (5.0%) were married, while 1,089 participants (94.9%) were single, and the marital status was not reported for one participant (0.1%). With respect to educational level, 1,070 participants (93.3%) were undergraduate students, 56 participants (4.9%) were master's students, and 19 participants (1.7%) were PhD students; information on education was not available for 2 individuals (0.2%). LPA : Model identification Two alternative models were identified via latent profile analysis. In Model 1, profiles were distinct from one another, and individuals who were in the same profile had homogeneous behavior. In contrast, in Model 2, the profiles were still separate, but the individuals within each profile were heterogeneous. The results of the model comparisons are shown in Table 1, and the results reveal that Model 2 (consisting of five profiles) was favored, as it yielded the lowest BIC value (BIC= 67033.731), along with adequate fit indices, including a significant BLRT test ( p < . 001) and an entropy exceeding 0.80. Thus, the five-profile solution was considered to be the most adequate structure to successfully account for heterogeneity in EE as measured by 10 standardized subscales: DEBQ (restrained, emotional, external eating scores), DERS-16 (nonacceptance, difficulties engaging in goal-directed behaviors, impulse control difficulties, lack of strategies/limited access to strategies and clarity about emotion), and BRIEF-A (behavioral regulation index, metacognitive index). Table 1: Fit Indices for Latent Profile Models (Profiles 1--8) Model Classes AIC BIC SABIC AWE CLC KIC BLRT_p Entropy 1 1 71123.217 71224.115 71160.589 71423.013 71085.217 71146.217 1.000 1 2 68541.515 68697.907 68599.442 69007.609 68481.206 68575.515 0.010 0.845 1 3 67535.871 67747.757 67614.352 68167.919 67453.595 67580.871 0.010 0.862 1 4 67266.313 67533.693 67365.348 68006.417 67161.958 67323.313 0.010 0.828 1 5 67159.173 67482.047 67278.763 68123.348 67032.747 67226.173 0.010 0.787 1 6 67031.367 67409.735 67171.511 68161.543 66882.924 67103.367 0.010 0.779 1 7 67023.361 67457.223 67184.060 68319.612 66852.834 67112.361 0.040 0.736 1 8 66876.149 67365.505 67057.403 68338.277 66683.733 66976.149 0.010 0.792 2 1 71123.217 71224.115 71160.589 71423.013 71085.217 71146.217 1.000 2 2 68109.250 68406.091 68225.826 68816.210 68118.971 68243.250 0.010 0.861 2 3 67003.387 67316.171 67119.240 67937.204 66881.139 67068.387 0.010 0.876 2 4 66668.691 67087.418 66823.784 67919.363 66504.472 66754.691 0.010 0.891 2 5 66509.061 67033.731 66703.395 68076.785 66302.685 66616.061 0.010 0.812 2 6 66413.151 67043.764 66646.726 68297.755 66165.764 66541.151 0.010 0.806 2 7 66321.041 67057.597 66593.855 68292.588 66036.606 66472.041 0.010 0.782 2 8 66221.298 67063.797 66533.353 68379.741 65883.639 66391.298 0.010 0.778 AIC= Akaike information criterion; BIC= Bayesian information criterion; SABIC= sample-size adjusted Bayesian information criterion; AWE= approximate weight of evidence; CLC= classification likelihood criterion; KIC= Kullback information criterion; BLRT= bootstrap likelihood ratio test; Figure 1 also shows that Model 2 has a lower BIC and can be the selected model. The results of Figure 1 and Table 1 both indicate the superiority of Model 2 over Model 1. Characterization of Profiles (Model 2) Figure 2 shows five mutual coefficient plots for the five factor extracted profiles resulting from the Z scores of 10 indicator variables: DEBQ (restrained eating, emotional eating, and external eating); DERS-16 (nonacceptance, difficulties in goal-directed behavior, impulse control difficulties, limited strategies, and emotional clarity); and BRIEF-A (behavioral regulation index metacognition index). The naming of the profiles was based on the consensus of four psychology experts. Profile 1: Consisting of highly regulated emotional strategies and goal-directed behavior, members of this group showed low levels of restrained and external eating behavior and high control over EE. In terms of emotion regulation, this group presented poor emotional clarity, low–moderate levels of emotional avoidance, moderate impulsivity, moderate–high levels of acceptance of negative emotions and high levels of emotional inhibition and behavioral control. They also had high metacognitive ability. We labeled this profile "Weakly Balanced" . Profile 2: This profile was characterized by greater EE and external eating, as well as more restrained eating, than the other profiles. Members in this group showed good inhibitory control and significant goal-directed behavior; their emotional regulation capacity was weak. They also had low emotional inhibition and metacognitive regulation; thus, their emotional instability may be exacerbated by the inability to control EE and external triggers. The profile has been designated "emotionally rewarding-driven". Profile 3: Consisting of high urges for goal-directed behaviors and emotional regulation, members in this group displayed mild emotional clarity and strong emotional inhibition but higher levels of EE and impulsivity. EEs were well regulated, but those in this group presented significant external eating. Restraint eating was also very low, which implied emotion-based reactive eating. Members in this group had moderate levels of metacognitive ability and self-regulation skills but struggled with balancing emotional clarity and EE. We assigned the label "Capable and Adaptive" to this profile. Profile 4: Consisting of high levels of EE and external eating, participants in this profile had a lower capacity to regulate emotions and more difficulties in emotional clarity and impulse control. They had restrained eating, and emotional inhibition was moderate, suggesting that while participants could suppress emotional reactions to some degree, they may struggle with long-term emotional control. This group has poor metacognitive function, which may lead to a lack of effective introspection of their emotional responses. This profile was labeled "vulnerable and high risk ". Profile 5: Consisting of higher scores on EE and acting impulsively, participants in this cluster had moderate emotional regulation strategies but lacked emotional clarity, making emotional management challenging. They displayed high levels of external eating and restrained eating behavior, indicating problems with response to emotional or external cues. The participants in this profile exhibited a relatively low level of emotional inhibition, which could be associated with their high levels of emotional instability and impulsivity. They also had impaired metacognitive function. This profile was named "Balanced with Threat Sensitivity". Demographic characteristics across profiles The demographic characteristics of the five profiles are provided in Table 2. As shown in Table 3, there was no significant difference in education level or marital status among the profiles. However, the gender distribution differed significantly, χ² (4) = 21.040, p <. 001 (minimum expected count = 40.04), Profile 1 had a more equal gender ratio, with 33.7% of the sample being men, whereas Profiles 2–5 were predominantly women (71.6–80.9% women). There was also a statistically significant effect for age, F (4, 1139) = 4.221, p =. 002 (Table 4), with Profile 4 having the highest mean age ( M = 22.07, SD = 3.85) and Profile 3 having the lowest mean age ( M =20.90, SD =2.15). Post hoc tests for between-group comparisons revealed a significant difference between Profiles 3 and 4 (mean difference = -1.17, SE = .295, p =. 004, 95% CI [-2.08, -.26]). Thus, the results suggest a possible influence of gender and age on the elaboration of emotions and eating behaviors associated with each profile. Table 2: Crosstab of demographic characteristics (gender, education, marital status) across profiles Profile Gender Education Marital Status Male Female Total Bachelor's degree Master's degree PhD Total Single Married Total 1 Count 112 220 332 308 19 6 333 320 14 334 % within Profile 33.7% 66.3% 100% 92.5% 5.7% 1.8% 100% 95.8% 4.2% 100% % within Characteristic 38.0% 26.0% 29.1% 28.8% 33.9% 31.6% 29.1% 24.6% 29.4% 29.1% % of Total 9.8% 19.3% 29.1% 26.9% 1.7% 0.5% 29.1% 27.9% 1.2% 29.1% 2 Count 41 174 215 199 13 3 215 199 16 215 % within Profile 19.1% 80.9% 100% 92.6% 6.0% 1.4% 100% 92.6% 7.4% 100% % within Characteristic 13.9% 20.5% 18.8% 18.6% 23.2% 15.8% 18.8% 18.3% 28.1% 18.8% % of Total 3.6% 15.2% 18.8% 17.4% 1.1% 0.3% 18.8% 17.4% 1.4% 18.8% 3 Count 40 166 206 196 6 3 206 199 7 206 % within Profile 19.4% 80.6% 100% 95.6% 2.9% 1.5% 100% 96.6% 3.4% 100% % within Characteristic 13.6% 19.6% 18.0% 18.4% 10.7% 18.8% 18.0% 18.3% 12.3% 18% % of Total 3.5% 14.5% 18.0% 17.2% 0.5% 0.3% 18.0% 17.4% 0.6% 18% 4 Count 44 111 155 144 6 6 156 146 10 156 % within Profile 28.4% 71.6% 100% 92.3% 3.8% 3.8% 100% 93.6% 6.4% 100% % within Characteristic 14.9% 13.1% 13.6% 13.5% 10.7% 31.6% 13.6% 13.4% 17.5% 13.6% % of Total 3.9% 9.7% 13.6% 12.6% 0.5% 0.5% 13.6% 12.7% 0.9% 13.6% 5 Count 58 176 234 222 12 1 235 225 10 235 % within Profile 24.8% 75.2% 100% 94.5% 5.1% 0.4% 100% 95.4% 4.3% 100% % within Characteristic 19.7% 20.8% 20.5% 20.7% 21.4% 5.3% 20.5% 20.7% 17.5% 20.5% % of Total 5.1% 15.4% 20.5% 19.4% 1.0% 0.1% 20.5% 19.6% 0.9% 20.5% Total Count 295 847 1142 1070 56 19 1145 1089 57 1146 % within Profile 25.8% 74.2% 100% 93.4% 4.9% 1.7% 100% 95.0% 5.0% 100% % within Characteristic 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% % of Total 25.8% 74.2% 100% 93.4% 4.9% 1.7% 100% 95.0% 5.0% 100% Table 3: Chi-square tests of independence for demographic characteristics across profiles Gender Education Marital Status Value Df Asymp. Sig. (2-sided) Value df Asymp. Sig. (2-sided) Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 21.040 a 4 .000 10.095 b 8 0.258 5.223 c 4 0.265 Likelihood Ratio 21.048 4 .000 10.037 8 0.262 4.984 4 0.289 Linear-by-Linear Association 3.058 1 .080 .704 1 0.401 .012 1 0.912 0 cells (.0%) have an expected count of less than 5. The minimum expected count is 40.04. Four cells (26.7%) had expected counts of less than 5. The minimum expected count is 2.59. 0 cells (.0%) have an expected count of less than 5. The minimum expected count is 7.76. Chi-square tests of independence (Table 3) revealed a significant relationship between sex and profile membership, χ² (4) = 21.040, p <. 001 (minimum expected count = 40.04). In contrast, no notable correlations were found for education, χ² (8) = 10.095, p =. 258 (4 cells had the expected count). Table 4: Descriptive Statistics for Age Across Profiles Profile N Mean SD 1 333 21.56 2.82 2 215 21.47 3 3 205 20.90 2.15 4 156 22.07 3.85 5 235 21.31 2.07 Total 1144 21.44 2.79 The means and standard deviations of age for the five profiling groups are shown in Table 4. The test results revealed that age had a statistically significant effect ( F ( 4, 1139 ) = 4.221 , p = .002), Profile 4 had the highest mean age ( M = 22.07 , SD = 3.85), and Profile 3 had the lowest ( M = 20.90 , SD= 2.15). Post hoc tests for between-group comparisons in Table 5 utilizing Scheffé’s method indicated a significant difference between Profiles 3 and 4 ( MD = -1.17 , p =.004). Thus, the results clarify the possible mediating influence of age on the elaboration of emotions and eating behaviors associated with each profile. Table 5: Multiple comparisons (Scheffe) for age (I) Profile (J) Profile Mean Diff (I-J) SE Sig. 95% Confidence Interval Lower Upper 3 4 -1.17* .295 .004 -2.08 -.26 (Others nonsig, e.g., 1 vs. 3: .66, p=.133) *. The mean difference is significant at the .05 level. Table 5 shows post hoc comparisons for age by profile. The only significant difference was between Profile 3 (young) and Profile 4 (old), with a mean difference of -1.17 (p =. 004). All other pairwise comparisons were not significant. This means that age may also be a differentiating factor between profiles, namely, the ages of older Profile 4 and younger Profile 3. Profile differences in the rRST Figure 3 illustrates the differences between the identified profiles across the dimensions of the rRST. For Profile 1 (weakly balanced), the BIS system scores were near zero or low, indicating reduced threat sensitivity. The BAS system scores were negative values, which indicated weaker responses to rewarding stimuli and drive for positive goals. The fight/flight/freeze system produced low positive scores, suggesting a reduced ability to respond effectively to dangerous situations. For Profile 2 (emotionally reward driven), the BIS system scores were moderate values, which indicated average threat sensitivity. The BAS system scores were positive, indicating strong responses to rewarding stimuli and a drive for positive outcomes. The Fight/Flight/Freeze System scores were slightly negative, suggesting that individuals did not tend to overreact to severe threats. For Profile 3 (capable and adaptive), the BIS scores were strongly negative, which indicated weak threat detection abilities and reduced negative behavior control. The BAS system scores were elevated, suggesting heightened sensitivity to rewards and positive drive. The fight/flight/freeze system produced high negative scores, suggesting minimal threat overreaction. For Profile 4 (vulnerable and high risk), the BIS scores were strongly negative, which indicated a reduced threat detection ability. The BAS scores were positive, indicating a strong drive to pursue rewards and positive stimuli. The fight/flight/freeze system scores were strongly negative, which indicated a weak response to threatening situations. Finally, for Profile 5 (balanced with threat sensitivity), the BIS scores had extremely low negative values, suggesting a severe lack of threat sensitivity. The BAS scores were high and positive, suggesting a strong pursuit of rewarding stimuli. The fight/flight/freeze system (FFFS) produced scores with substantial positive values, which indicated heightened extreme reactions when faced with threats. Table 6: ANOVA for the rRST across Profiles (Tests of Between-Subjects Effects) Dependent Variable Source Type III SS Df Mean Square F Sig. Partial Eta Squared BIS Profile 362.732 4 90.683 18.654 .000 .068 BAS Profile 727.499 4 181.875 8.130 .000 .031 FFFS Profile 9.113 4 2.278 1.360 .246 .005 As shown in Table 6, the results from the profiles demonstrated substantial variations in rRST sensitivity levels. The BIS model explained 6.8% of the total variance, R²= 0.068, adjusted R² =0.064, F (4, 1031) = 4.861. The R² value for the BAS reached 0.031, whereas the FFFS had an R² value of 0.005. As shown in Table 7, Scheffe’s post hoc tests demonstrated important significant differences between the profiles, where the BIS scores were significantly higher in Profile 3 than in Profile 4. The z score patterns in Figure 3 are supported by the ANOVA results. Table 7: Multiple comparisons (Scheffe) for rRST (selected significant differences) Dependent Variable (I) Profile (J) Profile Mean Diff (I-J) SE Sig. 95% Confidence Interval Lower Upper BIS 1 3 -.87* .207 .001 -1.51 -.23 3 4 2.10* .247 .000 1.33 2.86 4 5 -.93* .241 .005 -1.68 -.19 BAS 1 2 -2.06* .431 .000 -3.39 -.73 1 3 -2.07* .444 .000 -3.44 -.70 FFFS (No significant differences; e.g., 1 vs. 3: .17, p=.762) *. The mean difference is significant at the .05 level. Table 8: Homogeneous Subsets (Scheffe) for the rRST Dependent Variable Profile N Subset 1 Subset 2 Subset 3 BIS 4 141 13.13 5 208 14.06 2 215 14.34 1 333 14.35 3 206 15.22 Sig. 1.000 .800 1.000 BAS 1 333 39.51 4 156 40.43 5 208 40.82 2 215 41.57 3 206 41.58 Sig. .116 .219 FFFS All profiles 4.59-4.86 (no sig diff, Sig=.370) Table 8 presents the results of the Scheffe test for the rRST and identifies homogeneous subscales within each of the rRST dimensions. Key findings include the following: For the BIS, Profile 3 had the highest sensitivity to punishment (M = 15.22), whereas Profile 4 had the lowest sensitivity (M = 13.13). For the BAS, Profile 3 and Profile 2 had the highest sensitivity to rewards (M = 41.57– 41.58), whereas Profile 1 recorded a lower score (M = 39.51). For the FFFS, no significant differences were observed between the profiles (values between 4.59 and 4.86). These findings suggest that the BIS and BAS have a significant effect on emotional eating behaviors, whereas the FFFS has a lesser effect on determining emotional eating profiles. Discussion This study aimed to identify various emotional eating patterns (EEs) in Iranian university students through latent profile analysis (LPA) via 10 standardized subscales from the DEBQ, DERS-16 and BRIEF-A. This study analyzed how different rRST proxies, including the BIS, BAS and FFFS, are related to emotional eating patterns in nonclinical Iranian university students, who represent an underrepresented cultural group. The research identified five distinct profiles that showed significant demographic and rRST differences, demonstrating that person-centered approaches outperform variable-centered methods in studying EE patterns (Howard & Hoffman, 2017; Marsh et al., 2009). Profile Heterogeneity and Theoretical Integration The five-profile solution reveals different EE configurations, which range from resilient to vulnerable, through distinct patterns between eating behaviors, emotion regulation deficits and executive function impairments. In Profile 1 (“Weakly Balanced”), participants showed resilience through a combination of strong emotion regulation techniques and metacognitive executive function abilities but also had average impulsivity and unclear emotional responses. The "resilient/high-functioning" subtype described by Müller et al. (2014), in their study focused on treatment-seekers with obesity, matches this profile. This suggests that strong effortful control may help reduce psychopathology, yet their minimal clarity deficit might create subtle problems with long-term adaptability. This, in turn, could worsen EE during extended stress periods (Bjureberg et al., 2016; Zhou et al., 2025). Members in Profile 3 ("Capable and Adaptive") showed a reactive yet manageable eating pattern. It combines high emotional eating and impulsivity with strong emotion regulation and moderate executive functioning abilities. This aligns with He et al.'s (2020) "high emotional eating" class. However, it also highlights the potential protective function of executive functioning against distress-induced overeating (Favieri et al., 2024). The vulnerable profiles demonstrate how different patterns of maladaptive behavior create specific harmful sequences of events. Profile 2 ("Emotionally Reward-Driven") included strong emotional and external eating behaviors, together with weak emotion regulation and executive functioning abilities. This aligns with Schäfer et al.'s (2017) "emotionally dysregulated/undercontrolled" prebariatric subtype. This subtype shows how poor behavioral regulation can lead to increased disinhibited responses (Dohle et al., 2018). Profile 4 ("Vulnerable and high-risk") combined high emotional eating with compensatory restraint and impulsivity. This aligns with Nordgren et al.'s (2022) severe emotion regulation dysfunction group, indicating a pattern whereby executive functioning breakdowns (such as metacognitive failures) create binge-restrict hybrid eating patterns through inadequate self-reflection. Estévez et al. (2024) explained the mechanism through which emotion regulation functions as a mediator between metacognition/impulsivity and eating disorders by showing that emotion regulation problems reduce executive functioning capabilities, which leads to more severe eating disorder symptoms (indirect effect via ER: β = 0.27, 95% CI [2.01, 2.799]). The eating patterns in Profile 5 ("Balanced with Threat Sensitivity") show mixed behaviors and impulsivity while maintaining average emotion regulation levels that could indicate threat-avoidant processes that lead to experiential avoidance (Thompson et al., 2014). The different profiles demonstrate how ER deficits, which cause impulse control problems, reduce executive functioning capabilities to increase the risk of EE problems (Arexis et al., 2023; Brucar et al., 2025; Mohorić et al., 2023Ramalho et al., 2023;). rRST's discriminative role The different profiles of the rRST proxies show that the BIS and BAS function as essential regulators, whereas the FFFS shows minimal variation. The low BIS scores in Profiles 1 and 4 enable threat avoidance, which helps prevent EE in resilient situations but might lead to increased restraint in vulnerable contexts (Claes et al., 2021). The combination of high BIS scores ( M =15.22) and elevated BAS scores ( M =41.58) in Profile 3 indicates dual hypersensitivity that strengthens adaptive impulsivity (Sutton et al.,2022; Wilson et al., 2021). The two profiles, with moderate to high BAS scores (Profiles 2 and 5), show reward-driven eating behaviors, which may lead to poor behavioral control that fails to stop appetitive impulses when experiencing distress (Dohle et al., 2018; Kelly et al., 2020). Brucar et al. (2025) support this finding through subtyping research, which shows that approach behavior (BAS-like) with executive functioning creates high-engagement binge eating clusters that match our reward-driven profiles. The lack of an FFFS effect might result from measurement tools and cultural elements that reduce the visibility of fear reactions (Reuter et al., 2015; Siligato et al., 2024). The slight increase in FFFS scores in Profile 5 indicates hidden threat sensitivity, which requires more precise evaluation methods. The results confirm the rRST model because the BAS intensifies eating episodes in situations with weak executive functioning, but the BIS controls avoidance behaviors, which produces distinct risk patterns (Wilson et al., 2019). Estévez et al. (2024) examined whether emotion regulation mediated the relationship between impulsivity and eating disorders. They reported that rRST sensitivities affected EE through emotion regulation-executive functioning pathways, but neuroimaging studies are needed to confirm these connections. Demographic and Cultural Nuances The study revealed that sex was a distinguishing factor. Men comprised 33.7% of Profile 1 (compared with 19.1–28.4% in other profiles), which is consistent with research indicating greater emotional eating vulnerability among women in high-risk profiles (Konttinen, 2020; Chew et al., 2025). The age difference between Profiles 3 and 4 suggests that age may influence emotional regulation and executive functioning (Zhou et al., 2025). We also found a uniform distribution of education and marital status among the participants. The Iranian cultural emphasis on collectivism, which values group unity and emotional control over personal expression (Hofstede, 2011; Haghighian Roudsari et al., 2017), potentially strengthens the use of EE as a hidden coping strategy. The suppression of negative emotions in collectivist societies leads to internalized EE because these cultures value group harmony more than individual freedom to express emotions does (Markus & Kitayama, 1991, 2010). The social pressure to conform in collectivist societies may lead to greater emotion regulation deficits in Profiles 2 to 5 (Vaziri et al., 2022). The study results show that people in Profiles 2, 4 and 5 tend to eat more outside (M = 3.2–3.6 for external eating; see Table 3), suggesting that participants in these profiles have greater responsiveness to environmental food cues. In the Iranian cultural context, this may be amplified by communal dining settings with abundant high-carbohydrate options, potentially contributing to overeating (Akhoundan et al., 2016; Aghayan et al., 2020; Ebrahimi et al., 2020). Our team observed more than 1,200 students during data collection and reported that they used shared meals as a stress-relief method while academic pressure increased, but they expressed their distress through eating rather than speaking because of their cultural background and gender expectations. The research team observed students using food as a coping mechanism during data collection because their cultural background caused them to hide their emotions through eating instead of talking about their feelings. The traditional practice of Ramadan fasting used to promote self-control but recent cultural changes among Iranian youth because of urbanization, social media and secularization has weakened its impact (Weingarten & Elston, 1990; Amerzadeh et al., 2024). The two profiles show different levels of restraint, but researchers need to exercise caution because they observe changing attitudes among students during their interactions. Brucar et al. (2025) extend their research by showing that Western BED subtyping produces similar results regarding EF-negative emotionality connections, which suggests that collectivist societies might increase these effects through ER suppression, thus requiring future comparative studies. Implications The five identified profiles enable specific intervention approaches. This may include mindfulness training for emotional awareness for those in Profiles 1 and 3 and BAS-focused CBT with inhibitory training for those in Profiles 2 and 5. DBT-EF hybrid approaches may be beneficial for those in Profile 4. These intervention approaches need to be designed to take into account Iran's cultural stigma toward mental health care (Gratz & Roemer, 2004; Dohle et al., 2018). Estévez et al. (2024) support ER-based therapy approaches because of their proven strong mediating effects in young adults with high impulsivity. Brucar et al. (2025) also proposed the need for neuroimaging-based subtyping for individualized treatment approaches. Research evidence now points toward complex EE development patterns that depend on individual temperament characteristics instead of fixed personality traits (Mohorić et al., 2023). The DEBQ-DERS-BRIEF-A may be a screening tool that helps healthcare professionals detect Iranian youth who are at high risk for emotional eating because it identifies 44.9% of overweight youth who exhibit this behavior (Chew et al., 2025; Jáuregui-Lobera & Montes-Martínez, 2020). The identified profiles enable public health programs to deliver early prevention services, whereas educational programs with cultural adaptations can teach people about EE specifically by targeting women who experience social expectations in traditional societies (Vaziri et al., 2022). The method unites diverse cultural perspectives through individualized weight management solutions that combine ER training with neurobehavioral evaluation (Brucar et al., 2025). Limitations and Future Directions The study has limitations because it uses convenience sampling, which creates an urban bias that restricts the ability to generalize findings to rural Iranian populations and diverse Iranian subgroups. The self-reported data from the DERS-16 and BRIEF-A may produce exaggerated deficit scores because the DERS-16 lacks emotional awareness assessment. This study lacks neuroimaging data, which prevents researchers from understanding the neurobiopsychological basis of ER and EF deficits between different profiles. Additionally, there is underrepresentation of men. Additionally, cross-sectional studies cannot infer causality. Future research should use quantitative electroencephalography (qEEG) to identify brain wave patterns in the prefrontal cortex and amygdala regions because these areas are involved in emotional processing and cognitive control according to Wierenga et al. (2014). This research establishes unique neural patterns for each profile, which could guide specific intervention approaches. This study needs to be replicated with diverse clinical and cultural groups while direct rRST measurement techniques are used. Research on LPAs between collectivist Iran and individualist societies needs validation to determine which EE mechanisms operate universally versus those that are specific to particular cultures (Haghighian Roudsari et al., 2017; Sahrai et al., 2022; McNamara & Wood, 2019). The research of Estévez et al. (2024) and Brucar et al. (2025) can be expanded through longitudinal studies with neuroimaging and mediation analysis to study EE-related ER-EF development, which will improve transdiagnostic models. The culturally sensitive LPA method creates individualized EE treatment plans on the basis of ER-EF-rRST relationships to address obesity-related psychological aspects. Conclusion The research used latent profile analysis (LPA) to identify different emotional eating patterns among Iranian university students, which resulted in five distinct profiles: "Weakly Balanced," "Emotionally Reward-Driven," "Capable and Adaptive," "Vulnerable and High-Risk," and "Balanced with Threat Sensitivity." The five identified profiles demonstrated different levels of emotion regulation (ER) and executive functions (EF) and reinforcement sensitivities (BIS/BAS/FFFS), where the BAS and BIS distinguished reward-driven and threat-sensitive patterns, but the FFFS showed little variation. Research has demonstrated that person-centered methods provide a better understanding of EE behavior while showing how Iranian cultural elements strengthen these patterns through stress management through eating. This research provides a new understanding of EE behavior and supports individualized treatment approaches that combine mindfulness training for adaptive cases with BAS-centered CBT and inhibitory exercises for high-risk patients. This study provides essential groundwork for future research, which should include tracking EE development patterns through brain imaging techniques and studying eating disorder prevention methods in collectivist versus individualist societies. The combination of ER and EF with RST in therapeutic approaches will create better prevention methods for eating disorders and obesity in non-Western populations through culturally appropriate interventions that lead to enhanced mental and physical health results for Iranian youth and comparable communities. Declarations Ethics approval and consent to participate The study was approved by the institutional ethical committee of Kharazmi University (Ethics Approval Code: IR.KHU.REC.1403.135). The participants provided written informed consent, and the data were anonymized to protect privacy. Conflict of interest The authors declare that they have no conflicts of interest. Availability of data and materials Data are available upon reasonable request and subject to ethical and privacy restrictions. Funding This work is based upon research funded by the Iran National Science Foundation (INSF) under project No.4036394 Authors' contributions SR performed all stages of the research, including conceptualization and methodology development, data curation, original draft writing and manuscript review and editing. AM participated in the development of the research plan and methodology, supervised the work and wrote the review and editing sections. JH took part in conceptualization and methodology development, formal analysis and supervision, and writing, review, and editing of the manuscript. HF participated in the methodology development, formal analysis, advisory work and manuscript review and editing. LJ offered advisory help and performed critical review and editing tasks. The authors conducted a final review of the manuscript before giving their approval. Consent for publication Not applicable Clinical trial number Not applicable. Acknowledgments Our research team members who collected data receive our heartfelt appreciation for their dedicated work. The team members worked with complete dedication to collect responses from 1,200 participants at different universities throughout Karaj, Iran. The study became possible through the essential contributions of these members. The research team expresses gratitude to Ms. Baghdadi and Ms. Heydari and Ms. Sameei and Mr. Shadmanfar and Mr. Zamiri and Mr. Khaki for their dedicated work and professional conduct during the research process. The team members provided essential support for data integrity while creating a positive collaborative environment throughout the project. References Aghayan, M., Asghari, G., Yuzbashian, E., Mahdavi, M., Mirmiran, P., & Azizi, F. (2020). Secular trend in dietary patterns of Iranian adults from 2006 to 2017: Tehran lipid and glucose study. 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Identifying prebariatric subtypes based on temperament traits, emotion dysregulation, and disinhibited eating: A latent profile analysis. International Journal of Eating Disorders , 50(10), 1172-1182. https://doi.org/10.1002/eat.22760 Shahabi, M., Hasani, J., & Bjureberg, J. (2020). Psychometric properties of the brief Persian version of the Difficulties in Emotion Regulation Scale (The DERS-16). Assessment for Effective Intervention , 45(2), 135-143. https://doi.org/10.1177/1534508418800210 Shriver, L. H., Dollar, J. M., Calkins, S. D., Keane, S. P., Shanahan, L., & Wideman, L. (2020). Emotional eating in adolescence: Effects of emotion regulation, weight status and negative body image. Nutrients , 13(1), 79. https://doi.org/10.3390/nu13010079 Siligato, E., Iuele, G., Barbera, M., Bruno, F., Tordonato, G., Mautone, A., & Rizzo, A. (2024). Freezing effect and bystander effect: Overlaps and differences. Psych , 6(1), 273-287. https://doi.org/10.3390/psych6010017 Silva, I., Meireles, A. 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Journal of Human Nutrition and Dietetics , 36(5), 1922-1930. https://doi.org/10.1111/jhn.13176 Vaziri, A., Selehi, M., Hassani-Abharian, P., Shariatirad, S., Mahjoub, A., Dehjalali, R., & Noroozi, A. (2022). Psychometric properties of the shortened Farsi version of the Food Cravings Questionnaire-Trait. Eastern Mediterranean Health Journal , 28(1), 41-49. https://doi.org/10.26719/emhj.21.066 Weingarten, H. P., & Elston, D. (1990). The phenomenology of food cravings. Appetite , 15(3), 231-246. https://doi.org/10.1016/0195-6663(90)90023-2 Wierenga, C. E., Ely, A., Bischoff-Grethe, A., Bailer, U. F., Simmons, A. N., & Kaye, W. H. (2014). Are extremes of consumption in eating disorders related to an altered balance between reward and inhibition? Frontiers in Behavioral Neuroscience , 8, 410. https://doi.org/10.3389/fnbeh.2014.00410 Wilson, D. R., Loxton, N. J., & O'Donovan, A. (2021). From BIS to binge: The role of negative affect in the pathway between personality and binge eating. Eating behaviors , 41 , 101479. https://doi.org/10.1016/j.eatbeh.2021.101479 Wilson, D. R., Loxton, N. J., O'Shannessy, D., Sheeran, N., & Morgan, A. (2019). Similarities and differences in revised reinforcement sensitivities across eating disorder subtypes. Appetite , 133 , 70-76. https://doi.org/10.1016/j.appet.2018.10.023 Yang, H., Zhou, X., Xie, L., & Sun, J. (2023). The effect of emotion regulation on emotional eating among undergraduate students in China: The chain mediating role of impulsivity and depressive symptoms. Plos one , 18 (6), e0280701. https://doi.org/10.1371/journal.pone.0280701 Zhou, R., Zhang, L., Liu, Z., & Cao, B. (2025). Emotion regulation difficulties and disordered eating in adolescents and young adults: A meta-analysis. Journal of Eating Disorders , 13(1), 25. https://doi.org/10.1186/s40337-025-01197-y Additional Declarations No competing interests reported. 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05:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7985224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7985224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104311312,"identity":"fa59c9a6-eeba-4855-8b81-9e2c3136beb9","added_by":"auto","created_at":"2026-03-10 10:57:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270095,"visible":true,"origin":"","legend":"\u003cp\u003eBIC Rplot for 2 latent profile analysis models\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985224/v1/f2731054219d024d948c5831.jpeg"},{"id":104311256,"identity":"d5d0dc73-d87b-43da-a0a0-9c852583df94","added_by":"auto","created_at":"2026-03-10 10:57:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298867,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the obtained profiles on the basis ofindicator variables\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985224/v1/67196aa1c80d57f9caa321ff.jpeg"},{"id":104311323,"identity":"8a636539-bad4-47bc-9ca1-9471ba3b9228","added_by":"auto","created_at":"2026-03-10 10:57:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144311,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in Emotional Eating Profiles across Reinforcement Sensitivity Theory (rRST) Dimensions\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7985224/v1/f56512aa016292f452c8b9b8.jpeg"},{"id":104405968,"identity":"dbacd1ae-5108-497f-8c4b-d934e92fd0f9","added_by":"auto","created_at":"2026-03-11 12:24:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2075133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7985224/v1/2fd0add4-7916-4f1b-9670-c2a03b1c4b58.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling Emotional Eating Heterogeneity in Iranian Young Adults: A Latent Profile Analysis of Executive Functions, Emotion Regulation and Reinforcement Sensitivity","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003ePeople who eat when they feel upset, stressed or sad develop weight gain and multiple health problems through emotional eating. The ways in which people express their emotions through eating differ among individuals, but researchers understand very little about this behavior in Iranian culture, which follows non-Western traditions.\u003c/p\u003e\n\u003cp\u003eThe research surveyed 1,100 young Iranian university students about their eating habits and emotional management and their reasons for eating. The researchers applied a grouping approach to identify five distinct eating patterns among participants.\u003c/p\u003e\n\u003cp\u003ePeople with \u0026quot;weakly balanced\u0026quot; eating habits maintain good control over their food but struggle to recognize their emotions.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;emotionally reward-driven\u0026quot; pattern involves using food for comfort and enjoyment, whereas external factors trigger eating behaviors.\u003c/p\u003e\n\u003cp\u003ePeople with \u0026quot;capable and adaptive\u0026quot; eating habits possess strong emotional competencies and maintain consistent eating routines.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;vulnerable and high-risk\u0026quot; eating pattern shows poor control, which results in dangerous and unpredictable eating behaviors.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;Balanced with Threat Sensitivity\u0026quot; group maintains stability but shows excessive sensitivity to fear-based fears.\u003c/p\u003e\n\u003cp\u003eRiskier eating patterns appeared more frequently among female students and those who were older. The practice of sharing carbohydrate-rich meals during Ramadan fasting in Iran creates an environment where people use food as a way to handle emotions instead of talking about their feelings openly.\u003c/p\u003e\n\u003cp\u003eThe identified patterns demonstrate that emotional eating exists in different forms, so treatment approaches need to be individualized between mindfulness training for stable eaters and skill development therapy for those at risk. The prevention of obesity and eating problems in young people can be enhanced through cultural modifications.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe practice of eating food because of emotional states, especially in response to negative emotions, defines emotional eating (EE) (Barnhart et al., 2021; Bongers \u0026amp; Jansen, 2016(. While the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) does not recognize EE as an official diagnosis, EE appears in various psychological conditions, including depression (Lazarevich et al., 2016; Muha et al., 2024), anxiety (Goossens et al., 2009; Silva et al., 2025), posttraumatic stress disorder (PTSD) (Talbot et al., 2013; Tayhan \u0026amp; Korkmaz, 2025), bipolar disorder (Carmassi et al., 2025; Martin et al., 2016) and eating disorders (Reichenberger et al., 2021). The practice of eating due to emotions leads to multiple health problems, including obesity and being overweight (J\u0026aacute;uregui-Lobera \u0026amp; Montes-Mart\u0026iacute;nez, 2020; Konttinen, 2020; Vasileiou \u0026amp; Abbott, 2023), with a national study showing that EE affects 44.9% of overweight and obese individuals (Chew et al., 2025).\u003c/p\u003e\n\u003cp\u003eResearch focused on those seeking treatment indicates that obesity exists in multiple forms, which is influenced by distinct temperament characteristics. For example, research has shown two distinct personality types among patients with obesity: 1) the resilient and high-functioning type, which shows strong effortful control but behavioral inhibition system (BIS)/behavioral activation system (BAS)\u0026nbsp;activity, where the BIS refers to the behavioral inhibition system sensitive to punishment and avoidance, whereas the BAS refers to the behavioral activation system sensitive to reward and approach; this low activity indicates reduced sensitivity to punitive or rewarding stimuli, contributing to emotional stability and high functioning; and 2) the emotionally dysregulated and undercontrolled type, which shows weak effortful control and high BIS/BAS activity, where high activity indicates heightened sensitivity, which can lead to emotional dysregulation and impulsive behaviors in obese individuals (M\u0026uuml;ller et al., 2014). A recent study using latent profile analysis on prebariatric patients revealed five distinct clusters that combined temperament characteristics with emotion regulation and eating behavior patterns. The research model established relationships between eating disorder psychopathology and depression and quality of life through different levels of eating disorder symptoms (Sch\u0026auml;fer et al., 2017). This research demonstrated that EE and obesity are linked to specific temperament characteristics, emotional regulation problems and eating disorders. Additionally, research on binge eating disorder subtypes has shown that negative emotionality, approach behavior and executive function deficits create distinct subgroups that differ in their EF impairment levels and emotional trigger responses (Brucar et al., 2025). The subtyping method demonstrates that negative emotionality acts as an indicator of emotional regulation problems, which intensifies executive function weaknesses to produce binge eating behaviors that resemble EE.\u003c/p\u003e\n\u003cp\u003eThe conventional variable-centered method of regression analysis assumes that all individuals possess identical characteristics, which results in a single average parameter value (Howard \u0026amp; Hoffman, 2017; Papola \u0026amp; Patel, 2025). The traditional approach fails to recognize individual differences and behavioral interactions because EE includes both binge eating and restrictive eating patterns (Laursen \u0026amp; Hoff, 2006; Papola \u0026amp; Patel, 2025). Person-centered methods, such as latent profile analysis (LPA), identify separate subgroups of people who demonstrate unique behavioral patterns. Nordgren et al. (2022) identified three eating disorder subgroups and reported that people with severe emotion regulation problems exhibited the most severe binge-eating and vomiting, substance use and self-harm behaviors. A study by He et al. (2020) used LPA to identify four eating patterns in young adults: 1) nonemotional eating, 2) emotional overeating\u0026nbsp;and\u0026nbsp;undereating, 3) emotional\u0026nbsp;overeating\u0026nbsp;and 4)\u0026nbsp;emotionally undereating. The \u0026quot;emotional\u0026nbsp;overeating\u0026quot;\u0026nbsp;and\u0026nbsp;undereating\u0026quot; and \u0026quot;emotional\u0026nbsp;overeating\u0026quot; groups\u0026nbsp;included\u0026nbsp;more women,\u0026nbsp;and\u0026nbsp;the\u0026nbsp;high BMI and \u0026ldquo;emotional\u0026nbsp;overeating\u0026rdquo; and undereating\u0026rdquo; groups presented\u0026nbsp;more eating disorder symptoms and psychological issues. The results demonstrate that LPA detects diverse EE patterns, which include binge and restrictive eating behaviors, that standard variable-centered methods fail to capture (Marsh et al., 2009).\u003c/p\u003e\n\u003cp\u003eEE exists in multiple forms because of intricate neurocognitive mechanisms. The core symptoms of EE according to Gratz and Roemer (2004) involve ER regulation problems, which include nonacceptance, goal-directed behavior difficulties, impulse control problems, inadequate ER strategies and poor emotional awareness (Shriver et al., 2020). Difficulties in emotion regulation predict stress-induced eating (Zhou et al., 2025). Executive functioning has also been found to affect how people respond to food cues (Favieri et al., 2024). A person with weaker behavioral regulation has greater difficulty controlling their desire to eat (Dohle et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe relationship between emotion regulation and executive functioning shows a dynamic interaction. Emotional regulation functions as a link between components of executive functioning (e.g., impulsivity and metacognition) and disordered eating; strong metacognitive abilities support adaptive emotion regulation techniques, whereas difficulties with emotion regulation reduce executive functioning capabilities, which worsens EE through unhelpful coping mechanisms (Est\u0026eacute;vez et al., 2024). For example, the Behavioral Regulation Index (BRI), a subscale of the Behavior Rating Inventory of Executive Function assessing behavioral control in executive functioning, operates at a low level in some individuals, which impairs emotion regulation, making it difficult to suppress reward-based eating when negative emotions appear (Kelly et al., 2020). According to the revised reinforcement sensitivity theory (Gray \u0026amp; McNaughton, 2000), neurobiological systems influence behavioral patterns through the behavioral inhibition system (BIS), which detects punishment or\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ethreat signals in the brain, thereby promoting avoidance behaviors and contributing to the obsessive‒compulsive symptoms often observed in restrictive eating patterns (Amani \u0026amp; Keyvanlo, 2022); the behavioral activation system (BAS), which responds to reward cues by increasing approach-oriented actions, leading to impulsive binge eating episodes in response to positive or negative emotional triggers (Sutton et al., 2022); and the fight‒flight‒freezing (FFS) system, which governs immediate threat responses by freezing behavior that can mimic restrictive eating as a form of immobilization, although experiential avoidance strategies\u0026mdash;such as the suppression of food-related negative emotions\u0026mdash;can help mitigate these acute reactions (Thompson et al., 2014). The combination of high BAS sensitivity with reward-driven eating behavior requires enhanced BRI function, but FFFS freezing responses can produce distinct eating patterns (Smith et al., 2023). Brucar et al. (2025) demonstrated, via neurobehavioral subtyping, that EF deficits paired with high negative emotionality create binge eating risk profiles, which require comprehensive models for EE research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRevised reinforcement sensitivity theory posits that behavioral inhibition, behavioral activation and fight-flight-freeze systems create complex interactions that determine how people respond to punishments and reward and threat cues, which leads to EE behaviors. The extent and nature of EE behaviors stem from individual differences in sensitivity levels. Research shows that both punishment/threat sensitivity (BIS/FFFS) and reward sensitivity (BAS) lead to increased eating pathology and EE (Sutton et al., 2022; Wilson et al., 2021). People with low behavioral control and high FFFS sensitivity tend to develop restrictive eating patterns when they have high nonacceptance of emotions (Claes et al., 2021; Matton et al., 2017; Wilson et al. 2019). There is empirical support for this integrative framework through latent class analysis among adults with obesity seeking treatment (M\u0026uuml;ller et al., 2014). M\u0026uuml;ller et al. (2014) identified two distinct clusters; the resilient/high-functioning cluster showed high effortful control and low BIS/BAS scores, whereas the emotionally dysregulated/undercontrolled cluster displayed low effortful control and elevated BIS/BAS scores. The emotionally dysregulated/undercontrolled group members displayed more severe eating disorder symptoms, higher depression levels and worse executive functioning abilities. Sch\u0026auml;fer et al. (2017) built upon this model by studying prebariatric patients through a combination of emotion dysregulation and disinhibited eating, which resulted in five distinct patient groups on the basis of self-control and psychopathology profiles.\u003c/p\u003e\n\u003cp\u003eMore recent studies have further supported these mechanisms. Specifically, emotional regulation impairments stem from executive function deficits, which are connected to eating disorders. Additionally, the severity and nature of disordered eating behaviors depend on executive function and behavioral activation system (BAS)/behavioral inhibition system (BIS) sensitivities (Ramalho et al., 2023; Mohorić et al., 2023). Furthermore, the research of Bazo Perez and Frazier (2024) and Zhou et al. (2025) revealed that people who have both emotion regulation and executive functioning deficits remain at risk for developing EE and associated mental health problems. Taken together, these findings provide further support for a multidimensional model informed by temperament in the etiology and intervention of EE.\u003c/p\u003e\n\u003cp\u003eWhile research has made significant progress in understanding eating behaviors, several gaps still exist. First, the majority of existing studies have studied emotion regulation, executive functioning and EE independently (Favieri et al., 2024; Shriver et al., 2020) without investigating their combined effects on individual eating patterns. The current research methods fail to study intricate eating patterns, which include both binge eating and restrictive eating, and LPA is needed to detect these complex patterns (Howard \u0026amp; Hoffman, 2017; Papola \u0026amp; Patel, 2025). Second, among LPA studies about eating disorders, most have analyzed binge-eating patterns in clinical populations (M\u0026uuml;ller et al., 2014; Sch\u0026auml;fer et al., 2017), but few have studied restrictive eating or mixed eating patterns in nonclinical populations.\u003c/p\u003e\n\u003cp\u003eThird, there are few LPA studies on EE in non-Western contexts, such as Iran. The collectivist nature of Iranian society leads people to hide their negative feelings because they want to preserve social unity, which might result in individuals expressing their distress through EE as a hidden way to cope (Haghighian Roudsari et al., 2017; Vaziri et al., 2021). People show more intense EE responses to emotional stimuli after Ramadan fasting because of the dietary restrictions of this cultural practice (Amerzadeh et al., 2024; Gilavand \u0026amp; Fatahiasl, 2018; Weingarten \u0026amp; Elston, 1990). Additionally, the practice of dining with others, while following high-carb diets, leads to greater external eating because social gatherings expose people to additional food triggers (Aghayan et al., 2020; Akhoundan et al., 2016; Ebrahimi et al., 2020). These cultural elements may yield unique EE profiles among those in Iran, but no prior LPA studies have been conducted among Iranian young adults. Therefore, there is a need for research on Iranian cultural effects on EE.\u003c/p\u003e\n\u003cp\u003eFourth, to the best of our knowledge, no study has integrated these 10 subscales\u0026mdash;from the Dutch Eating Behavior Questionnaire (DEBQ; e.g., emotional and restraint eating), the 16-item Difficulties in Emotion Regulation Scale (DERS-16; e.g., impulse control and strategies), and the Behavior Rating Inventory of Executive Function-Adult (BRIEF-A; e.g., behavioral inhibition and metacognition)\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esubscales in LPA for nonclinical EE heterogeneity, enabling more nuanced profiling in general populations. However, profile differences in rRST sensitivities, including FFFS for restrictive eating, have not been examined (Siligato et al., 2024). The research introduces LPA to Iranian university students through these subscales while evaluating the BIS, BAS and FFFS as measurement proxies. The research findings will improve EE theoretical models for minority populations and create obesity and eating disorder prevention methods for particular cultural environments that can be used in related Persianate countries (e.g., Afghanistan, Tajikistan; Sahrai et al.,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e2022; McNamara \u0026amp; Wood, 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study aimed to explore latent EE profiles by applying LPA based on eating behavior (using the Dutch Eating Behavior Questionnaire (DEBQ)), emotion dysregulation (using the Difficulties in Emotion Regulation Scale (DERS-16)) and executive functions (using the Behavior Rating Inventory of Executive Function-Adult version (BRIEF-A)) among Iranian university students. Furthermore, it aimed to compare these profiles on the basis of reinforcement sensitivity theory (rRST: BIS, BAS FFFS proxies) to provide insights into the neurobiopsychological mechanisms that might be involved in the heterogeneity of EE. This exploratory methodology contributes to personalized interventions and greater etiologically consideration of EE in a nonclinical, non-Western cultural context.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research received approval from Kharazmi University\u0026apos;s institutional ethical committee (Ethics Approval Code: IR.KHU.REC.1403.135). The participants signed written consent forms, and the researchers protected their privacy through data anonymization. All methods were performed in accordance with the relevant guidelines and regulations, as outlined in the Declaration of Helsinki. \u0026nbsp; The research involved 1200 university students in Karaj, Iran, aged over 18 years, who were selected through convenience sampling. The research study follows LPA guidelines, as it contains at least 500 participants (Spurk et al., 2020) and is similar to other studies in that it offers adequate power for the detection of distinct profiles on the basis of 10 available subscales (Sultson \u0026amp; Akkermann, 2019). After excluding participants who did not meet the eligibility criteria, 1147 participants were included in the final analysis (refer to Data Collection and Processing). The research involved participants who were not enrolled in psychology or counseling programs and who did not have any severe psychological or neurological conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDutch Eating Behavior Questionnaire (DEBQ)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEBQ (van Strien et al.,1986) consists of 33 items that evaluate three eating patterns: restrained eating (10 items), emotional eating (13 items) and external eating (10 items). The survey uses a 5-point Likert scale, which ranges from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003every often\u003c/em\u003e), to measure each behavior\u0026apos;s frequency. A higher score indicates greater engagement in each behavior. The Persian version showed high internal consistency (Cronbach\u0026apos;s \u0026alpha; = 0.77\u0026ndash;0.83) and test-retest reliability (r = 0.72\u0026ndash;0.83) and confirmed factorial validity through confirmatory factor analysis (Nejati et al., 2018). This scale was used with permission from Hogrefe Publishing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifficulties in Emotion Regulation Scale-16 (DERS-16)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The DERS-16 (Bjureberg et al., 2016) represents a shortened version of the DERS (Gratz \u0026amp; Roemer, 2004), which contains 16 items to assess five emotion regulation dimensions: nonacceptance of emotions (3 items), difficulties in goal-directed behavior (3 items), impulse control difficulties (3 items), limited access to strategies (5 items), and lack of emotional clarity (2 items). The assessment tool uses a 5-point Likert scale where participants rate their experiences from 1 (\u003cem\u003ealmost never\u003c/em\u003e) to 5 (\u003cem\u003ealmost always\u003c/em\u003e) to indicate their level of difficulty. The Persian adaptation of the DERS-16 has strong internal consistency (\u0026alpha; = 0.80\u0026ndash;0.92) and test-retest reliability (\u003cem\u003er\u003c/em\u003e = 0.88) (Shahabi et al., 2020). The emotional awareness subscale that was present in the original Gratz and Roemer scale\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(2004) has been removed in this version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBIS/BAS Scales\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe BIS/BAS scales (Carver \u0026amp; White, 1994) measure reinforcement sensitivity. It contains 20 items that are divided into seven punishment sensitivity items for the BIS and 13 reward sensitivity items that contain three subcategories: reward responsiveness (5 items), drive (4 items) and fun-seeking (4 items). The survey evaluates each item through a 4-point Likert scale ranging from 1 (\u003cem\u003estrongly disagree\u003c/em\u003e) to 4 (\u003cem\u003estrongly agree\u003c/em\u003e). Higher scores indicate greater sensitivity. The Persian version of the scale has demonstrated acceptable internal consistency (\u0026alpha; = 0.69 for the BIS, 0.65\u0026ndash;0.87 for the BAS subscales) and test-retest reliability (r = 0.68\u0026ndash;0.71), with a confirmed factor structure and valid convergent/divergent relationships (Mohammadi, 2008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavior Rating Inventory of Executive Function\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAdult Version\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BRIEF-A (Roth et al., 2005) contains 75 items that evaluate executive functioning through two separate indices: the Behavioral Regulation Index (BRI), with 30 items (including inhibit, shift, emotional control, and self-monitor), and the metacognitive index (MI), with 45 items (including initiate, working memory, plan/organize, task monitor, and organization of materials). The assessment uses a 3-point Likert scale, and higher scores indicate more severe impairment. The Persian version of the BRIEF-A demonstrated strong internal consistency (\u0026alpha; = 0.65\u0026ndash;0.96) and reliable test-retest results (Mani et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecollection\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the approval of Kharazmi University\u0026rsquo;s IRB, researchers attended specific universities in Karaj city (Iran) to recruit students. After providing written informed consent, the participants completed the DEBQ, DERS-16, BIS/BAS, and BRIEF-A in person. A total of 1,200 questionnaires were returned. The information was then entered into SPSS (version 21) for initial analysis. Participants with more than 10% missing data were excluded, leaving a total sample of 1,147 participants. To obtain standardized scores and thus comparability between the indicators, z values were created for the 10 input subscales (DEBQ: restrained, emotional, external eating; DERS-16: nonacceptance, goal-directed behavior difficulties, impulse control, limited strategies, emotional clarity; BRIEF-A: BRI, MI) prior to LPA. This method for LPA is preferred because it is sensitive to scale differences (Spurk et al., 2020).\u003c/p\u003e\n\u003cp\u003eQuartiles of variables related to participant performance were statistically examined prior to LPA. Furthermore, standard statistical checks (e.g., skewness/kurtosis for multivariate normality, variance inflation factor for common multicollinearity, and sample size) were performed via SPSS 21. LPA was performed via the tidylpa package (Rosenberg et al., 2018) in R, and profiles were derived with respect to 10 standardized subscales: DEBQ (restrained eating, emotional eating, EE); DERS-16 (nonacceptance, difficulties in goal-directed behavior, impulse control, limited access to emotion regulation strategies, lack of emotional clarity); and BRIEF-A (behavioral regulation index (metacognition index)). Models with 1\u0026ndash;8 profiles were compared, and the best model was chosen according to the Bayesian information criterion (BIC), sample-adjusted BIC (SABIC), entropy (\u0026gt;0.80) and bootstrap likelihood ratio test (BLRT). Missing data were accounted for via full information maximum likelihood (FIML), which maximizes data retention. Covariates (age and sex) were included to control for possible confounding effects. Profiles of the rRST (i.e., proxies for the BIS, BAS and FFFS) were compared via ANOVA or multinomial logistic regression after controlling for covariates.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample included 295 (25.7%) men and 847 (73.9%) women, with 5 (0.4%) individuals for whom sex was unknown/not reported. The average age of the participants was 21.44 years (\u003cem\u003eSD\u003c/em\u003e = 2.79). Fifty-seven participants (5.0%) were married, while 1,089 participants (94.9%) were single, and the marital status was not reported for one participant (0.1%). With respect to educational level, 1,070 participants (93.3%) were undergraduate students, 56 participants (4.9%) were master\u0026apos;s students, and 19 participants (1.7%) were PhD students; information on education was not available for 2 individuals (0.2%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLPA\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e:\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eidentification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo alternative models were identified via latent profile analysis. In Model 1, profiles were distinct from one another,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eand individuals who were in the same profile had homogeneous behavior. In contrast, in Model 2, the profiles were still separate, but the individuals within each profile were heterogeneous. The results of the model comparisons are shown in Table 1, and the results reveal that Model 2 (consisting of five profiles) was favored, as it yielded the lowest BIC value (BIC= 67033.731), along with adequate fit indices, including a significant BLRT test (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; . 001) and an entropy exceeding 0.80. Thus, the five-profile solution was considered to be the most adequate structure to successfully account for heterogeneity in EE as measured by 10 standardized subscales: DEBQ (restrained, emotional, external eating scores), DERS-16 (nonacceptance, difficulties engaging in goal-directed behaviors, impulse control difficulties, lack of strategies/limited access to strategies and clarity about emotion), and BRIEF-A (behavioral regulation index, metacognitive index).\u003c/p\u003e\n\u003cp\u003eTable 1: Fit Indices for Latent Profile Models (Profiles 1--8)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"726\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003eClasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003eSABIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003eAWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003eCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003eKIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003eBLRT_p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e71123.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e71224.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e71160.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e71423.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e71085.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e71146.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68541.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68697.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e68599.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e69007.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68481.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e68575.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67535.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67747.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e67614.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68167.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n 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8.26446%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67023.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67457.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e67184.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68319.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66852.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e67112.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66876.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67365.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e67057.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68338.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66683.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n 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10.7438%;\"\u003e\n \u003cp\u003e68225.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68816.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68118.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e68243.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67003.387\u003c/p\u003e\n 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style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66668.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67087.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66823.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67919.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66504.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66754.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n 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style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66413.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67043.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66646.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68297.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66165.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66541.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66321.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67057.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66593.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68292.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66036.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66472.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.26446%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.09091%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e66221.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e67063.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66533.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e68379.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e65883.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7438%;\"\u003e\n \u003cp\u003e66391.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.91736%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5702%;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAIC= Akaike information criterion; BIC= Bayesian information criterion; SABIC= sample-size adjusted Bayesian information criterion; AWE= approximate weight of evidence; CLC= classification likelihood criterion; KIC= Kullback information criterion; BLRT= bootstrap likelihood ratio test;\u003c/p\u003e\n\u003cp\u003eFigure 1 also shows that Model 2 has a lower BIC and can be the selected model. The results of Figure 1 and Table 1 both indicate the superiority of Model 2 over Model 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eProfiles (Model 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows five mutual coefficient plots for the five factor extracted profiles resulting from the Z scores of 10 indicator variables: DEBQ (restrained eating, emotional eating, and external eating); DERS-16 (nonacceptance, difficulties in goal-directed behavior, impulse control difficulties, limited strategies, and emotional clarity); and BRIEF-A (behavioral regulation index metacognition index).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eThe naming of the profiles was based on the consensus of four psychology experts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile 1:\u0026nbsp;\u003c/strong\u003eConsisting of highly regulated emotional strategies and goal-directed behavior, members of this group showed low levels of restrained and external eating behavior and high control over EE. In terms of emotion regulation, this group presented poor emotional clarity, low\u0026ndash;moderate levels of emotional avoidance, moderate impulsivity, moderate\u0026ndash;high levels of acceptance of negative emotions and high levels of emotional inhibition and behavioral control. They also had high metacognitive ability. We labeled this profile \u003cstrong\u003e\u0026quot;Weakly Balanced\u0026quot;\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile 2:\u003c/strong\u003e This profile was characterized by greater EE and external eating, as well as more restrained eating, than the other profiles. Members in this group showed good inhibitory control and significant goal-directed behavior; their emotional regulation capacity was weak. They also had low emotional inhibition and metacognitive regulation; thus, their emotional instability may be exacerbated by the inability to control EE and external triggers. The profile has been designated \u0026quot;emotionally rewarding-driven\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile 3:\u0026nbsp;\u003c/strong\u003eConsisting of high urges for goal-directed behaviors and emotional regulation, members in this group displayed mild emotional clarity and strong emotional inhibition but higher levels of EE and impulsivity. EEs were well regulated, but those in this group presented significant external eating. Restraint eating was also very low, which implied emotion-based reactive eating. Members in this group had moderate levels of metacognitive ability and self-regulation skills but struggled with balancing emotional clarity and EE. We assigned the label \u003cstrong\u003e\u0026quot;Capable and Adaptive\u0026quot;\u003c/strong\u003e to this profile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile 4:\u0026nbsp;\u003c/strong\u003eConsisting of high levels of EE and external eating, participants in this profile had a lower capacity to regulate emotions and more difficulties in emotional clarity and impulse control. They had restrained eating, and emotional inhibition was moderate, suggesting that while participants could suppress emotional reactions to some degree, they may struggle with long-term emotional control. This group has poor metacognitive function, which may lead to a lack of effective introspection of their emotional responses. This profile was \u003cstrong\u003elabeled \u0026quot;vulnerable and high risk\u003c/strong\u003e\u003cstrong\u003e\u0026quot;.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile 5:\u0026nbsp;\u003c/strong\u003eConsisting of higher scores on EE and acting impulsively, participants in this cluster had moderate emotional regulation strategies but lacked emotional clarity, making emotional management challenging. They displayed high levels of external eating and restrained eating behavior, indicating problems with response to emotional or external cues. The participants in this profile exhibited a relatively low level of emotional inhibition, which could be associated with their high levels of emotional instability and impulsivity. They also had impaired metacognitive function. This profile was named \u003cstrong\u003e\u0026quot;Balanced with Threat Sensitivity\u0026quot;.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echaracteristics across profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of the five profiles are provided in Table 2. As shown in Table 3, there was no significant difference in education level or marital status among the profiles. However, the gender distribution differed significantly, \u0026chi;\u0026sup2; (4) = 21.040, p \u0026lt;. 001 (minimum expected count = 40.04), Profile 1 had a more equal gender ratio, with 33.7% of the sample being men, whereas Profiles 2\u0026ndash;5 were predominantly women (71.6\u0026ndash;80.9% women). There was also a statistically significant effect for age, \u003cem\u003eF\u003c/em\u003e (4, 1139) = 4.221,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e =. 002 (Table 4), with Profile 4 having the highest mean age (\u003cem\u003eM\u003c/em\u003e = 22.07, \u003cem\u003eSD\u003c/em\u003e = 3.85) and Profile 3 having the lowest mean age (\u003cem\u003eM\u003c/em\u003e =20.90, \u003cem\u003eSD\u003c/em\u003e=2.15). Post hoc tests for between-group comparisons revealed a significant difference between Profiles 3 and 4 (mean difference = -1.17, \u003cem\u003eSE\u003c/em\u003e= .295, \u003cem\u003ep\u003c/em\u003e =. 004, 95% CI [-2.08, -.26]). Thus, the results suggest a possible influence of gender and age on the elaboration of emotions and eating behaviors associated with each profile.\u003c/p\u003e\n\u003cp\u003eTable 2: Crosstab of demographic characteristics (gender, education, marital status) across profiles\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePhD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e33.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e66.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e95.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e38.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e33.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e24.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e19.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e27.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e80.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e92.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e23.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e15.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e28.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e17.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e17.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e80.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e95.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e19.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e14.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e17.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e17.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e71.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e92.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e93.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e13.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e24.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e75.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e94.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e95.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e20.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e19.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e20.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e25.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e93.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% within Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e25.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e93.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Chi-square tests of independence for demographic characteristics across profiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAsymp. Sig. (2-sided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAsymp. Sig. (2-sided)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAsymp. Sig. (2-sided)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003ePearson Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e21.040\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10.095\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.223\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLikelihood Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e21.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLinear-by-Linear Association\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col class=\"decimal_type\" style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e0 cells (.0%) have an expected count of less than 5. The minimum expected count is 40.04.\u003c/li\u003e\n \u003cli\u003eFour cells (26.7%) had expected counts of less than 5. The minimum expected count is 2.59.\u003c/li\u003e\n \u003cli\u003e0 cells (.0%) have an expected count of less than 5. The minimum expected count is 7.76.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eChi-square tests of independence (Table 3) revealed a significant relationship between sex and profile membership, \u0026chi;\u0026sup2; (4) = 21.040, p \u0026lt;. 001 (minimum expected count = 40.04). In contrast, no notable correlations were found for education, \u0026chi;\u0026sup2; (8) = 10.095, \u003cem\u003ep\u003c/em\u003e =. 258 (4 cells had the expected count).\u003c/p\u003e\n\u003cp\u003eTable 4: Descriptive Statistics for Age Across Profiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e20.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe means and standard deviations of age for the five profiling groups are shown in Table 4. The test results revealed that age had a statistically significant effect (\u003cem\u003eF (\u003c/em\u003e4, 1139\u003cem\u003e) =\u0026nbsp;\u003c/em\u003e4.221\u003cem\u003e, p =\u003c/em\u003e.002), Profile 4 had the highest mean age (\u003cem\u003eM =\u0026nbsp;\u003c/em\u003e22.07\u003cem\u003e, SD =\u0026nbsp;\u003c/em\u003e3.85), and Profile 3 had the lowest (\u003cem\u003eM =\u003c/em\u003e20.90\u003cem\u003e, SD=\u003c/em\u003e2.15). Post hoc tests for between-group comparisons in Table 5 utilizing Scheff\u0026eacute;\u0026rsquo;s method indicated a significant difference between Profiles 3 and 4 (\u003cem\u003eMD =\u0026nbsp;\u003c/em\u003e-1.17\u003cem\u003e, p\u0026nbsp;\u003c/em\u003e=.004). Thus, the results clarify the possible mediating influence of age on the elaboration of emotions and eating behaviors associated with each profile.\u003c/p\u003e\n\u003cp\u003eTable 5: Multiple comparisons (Scheffe) for age\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(I) Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003e(J) Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eMean Diff (I-J)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 178px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.17*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 623px;\"\u003e\n \u003cp\u003e(Others nonsig, e.g., 1 vs. 3: .66, p=.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*. The mean difference is significant at the .05 level.\u003c/p\u003e\n\u003cp\u003eTable 5 shows post hoc comparisons for age by profile. The only significant difference was between Profile 3 (young) and Profile 4 (old), with a mean difference of -1.17 (p =. 004). All other pairwise comparisons were not significant. This means that age may also be a differentiating factor between profiles, namely, the ages of older Profile 4 and younger Profile 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edifferences\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003erRST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the differences between the identified profiles across the dimensions of the rRST. For \u003cstrong\u003eProfile 1 (weakly balanced),\u003c/strong\u003e the BIS system scores were near zero or low, indicating reduced threat sensitivity. The BAS system scores were negative values, which indicated weaker responses to rewarding stimuli and drive for positive goals. The fight/flight/freeze system produced low positive scores, suggesting a reduced ability to respond effectively to dangerous situations. For \u003cstrong\u003eProfile 2 (emotionally\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ereward driven),\u003c/strong\u003e the BIS system scores were moderate values, which indicated average threat sensitivity. The BAS system scores were positive, indicating strong responses to rewarding stimuli and a drive for positive outcomes. The Fight/Flight/Freeze System scores were slightly negative, suggesting that individuals did not tend to overreact to severe threats. For \u003cstrong\u003eProfile 3 (capable and adaptive),\u003c/strong\u003e the BIS scores were strongly negative, which indicated weak threat detection abilities and reduced negative behavior control. The BAS system scores were elevated, suggesting heightened sensitivity to rewards and positive drive. The fight/flight/freeze system produced high negative scores, suggesting minimal threat overreaction. For \u003cstrong\u003eProfile 4 (vulnerable and high risk),\u003c/strong\u003e the BIS scores were strongly negative, which indicated a reduced threat detection ability. The BAS scores were positive, indicating a strong drive to pursue rewards and positive stimuli. The fight/flight/freeze system scores were strongly negative, which indicated a weak response to threatening situations. Finally, for \u003cstrong\u003eProfile 5 (balanced with threat sensitivity),\u003c/strong\u003e the BIS scores had extremely low negative values, suggesting a severe lack of threat sensitivity. The BAS scores were high and positive, suggesting a strong pursuit of rewarding stimuli. The fight/flight/freeze system (FFFS) produced scores with substantial positive values, which indicated heightened extreme reactions when faced with threats.\u003c/p\u003e\n\u003cp\u003eTable 6: ANOVA for the rRST across Profiles (Tests of Between-Subjects Effects)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eType III SS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ePartial Eta Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eBIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e362.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e90.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e18.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eBAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e727.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e181.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eFFFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Table 6, the results from the profiles demonstrated substantial variations in rRST sensitivity levels. The BIS model explained 6.8% of the total variance, R\u0026sup2;= 0.068, adjusted R\u0026sup2; =0.064, \u003cem\u003eF\u003c/em\u003e (4, 1031) = 4.861. The R\u0026sup2; value for the BAS reached 0.031, whereas the FFFS had an R\u0026sup2; value of 0.005. As shown in Table 7, Scheffe\u0026rsquo;s post hoc tests demonstrated important significant differences between the profiles, where the BIS scores were significantly higher in Profile 3 than in Profile 4. The z score patterns in Figure 3 are supported by the ANOVA results.\u003c/p\u003e\n\u003cp\u003eTable 7: Multiple comparisons (Scheffe) for rRST (selected significant differences)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e(I) Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e(J) Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eMean Diff (I-J)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 155px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.87*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eBAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-2.06*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-2.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 623px;\"\u003e\n \u003cp\u003eFFFS\u003c/p\u003e\n \u003cp\u003e(No significant differences; e.g., 1 vs. 3: .17, p=.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*. The mean difference is significant at the .05 level.\u003c/p\u003e\n\u003cp\u003eTable 8: Homogeneous Subsets (Scheffe) for the rRST\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eProfile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSubset 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSubset 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSubset 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e39.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e41.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e41.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFFFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAll profiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.59-4.86 (no sig diff, Sig=.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8 presents the results of the Scheffe test for the rRST and identifies homogeneous subscales within each of the rRST dimensions. Key findings include the following:\u003c/p\u003e\n\u003cp\u003eFor the BIS, Profile 3 had the highest sensitivity to punishment (M = 15.22), whereas Profile 4 had the lowest sensitivity (M = 13.13). For the BAS, Profile 3 and Profile 2 had the highest sensitivity to rewards (M = 41.57\u0026ndash; 41.58), whereas Profile 1 recorded a lower score (M = 39.51). For the FFFS, no significant differences were observed between the profiles (values between 4.59 and 4.86).\u003c/p\u003e\n\u003cp\u003eThese findings suggest that the BIS and BAS have a significant effect on emotional eating behaviors, whereas the FFFS has a lesser effect on determining emotional eating profiles.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify various emotional eating patterns (EEs) in Iranian university students through latent profile analysis (LPA) via 10 standardized subscales from the DEBQ, DERS-16 and BRIEF-A. This study analyzed how different rRST proxies, including the BIS, BAS and FFFS, are related to emotional eating patterns in nonclinical Iranian university students, who represent an underrepresented cultural group. The research identified five distinct profiles that showed significant demographic and rRST differences,\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003edemonstrating that person-centered approaches outperform variable-centered methods in studying EE patterns (Howard \u0026amp; Hoffman, 2017; Marsh et al., 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfile Heterogeneity and Theoretical Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe five-profile solution reveals different EE configurations, which range from resilient to vulnerable, through distinct patterns between eating behaviors, emotion regulation deficits and executive function impairments.\u003c/p\u003e\n\u003cp\u003eIn Profile 1 (\u0026ldquo;Weakly Balanced\u0026rdquo;), participants showed resilience through a combination of strong emotion regulation techniques and metacognitive executive function abilities but also had average impulsivity and unclear emotional responses. The \u0026quot;resilient/high-functioning\u0026quot; subtype described by M\u0026uuml;ller et al. (2014), in their study focused on treatment-seekers with obesity, matches this profile. This suggests that strong effortful control may help reduce psychopathology, yet their minimal clarity deficit might create subtle problems with long-term adaptability. This, in turn, could worsen EE during extended stress periods (Bjureberg et al., 2016; Zhou et al., 2025).\u003c/p\u003e\n\u003cp\u003eMembers in Profile 3 (\u0026quot;Capable and Adaptive\u0026quot;) showed a reactive yet manageable eating pattern. It combines high emotional eating and impulsivity with strong emotion regulation and moderate executive functioning abilities. This aligns with He et al.\u0026apos;s (2020) \u0026quot;high emotional eating\u0026quot; class. However, it also highlights the potential protective function of executive functioning against distress-induced overeating (Favieri et al., 2024). The vulnerable profiles demonstrate how different patterns of maladaptive behavior create specific harmful sequences of events.\u003c/p\u003e\n\u003cp\u003eProfile 2 (\u0026quot;Emotionally Reward-Driven\u0026quot;) included strong emotional and external eating behaviors, together with weak emotion regulation and executive functioning abilities. This aligns with Sch\u0026auml;fer et al.\u0026apos;s (2017) \u0026quot;emotionally dysregulated/undercontrolled\u0026quot; prebariatric subtype. This subtype shows how poor behavioral regulation can lead to increased disinhibited responses (Dohle et al., 2018). Profile 4 (\u0026quot;Vulnerable and high-risk\u0026quot;) combined high emotional eating with compensatory restraint and impulsivity. This aligns with Nordgren et al.\u0026apos;s (2022) severe emotion regulation dysfunction group, indicating a pattern whereby executive functioning breakdowns (such as metacognitive failures) create binge-restrict hybrid eating patterns through inadequate self-reflection. Est\u0026eacute;vez et al. (2024) explained the mechanism through which emotion regulation functions as a mediator between metacognition/impulsivity and eating disorders by showing that emotion regulation problems reduce executive functioning capabilities, which leads to more severe eating disorder symptoms (indirect effect via ER: \u0026beta; = 0.27, 95% CI [2.01, 2.799]).\u003c/p\u003e\n\u003cp\u003eThe eating patterns in Profile 5 (\u0026quot;Balanced with Threat Sensitivity\u0026quot;) show mixed behaviors and impulsivity while maintaining average emotion regulation levels that could indicate threat-avoidant processes that lead to experiential avoidance (Thompson et al., 2014). The different profiles demonstrate how ER deficits, which cause impulse control problems, reduce executive functioning capabilities to increase the risk of EE problems (Arexis et al., 2023; Brucar et al., 2025; Mohorić et al., 2023Ramalho et al., 2023;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003erRST\u0026apos;s discriminative role\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe different profiles of the rRST proxies show that the BIS and BAS function as essential regulators, whereas the FFFS shows minimal variation. The low BIS scores in Profiles 1 and 4 enable threat avoidance, which helps prevent EE in resilient situations but might lead to increased restraint in vulnerable contexts (Claes et al., 2021). The combination of high BIS scores (\u003cem\u003eM\u003c/em\u003e=15.22) and elevated BAS scores (\u003cem\u003eM\u003c/em\u003e=41.58) in Profile 3 indicates dual hypersensitivity that strengthens adaptive impulsivity (Sutton et al.,2022; Wilson et al., 2021). The two profiles, with moderate to high BAS scores (Profiles 2 and 5), show reward-driven eating behaviors, which may lead to poor behavioral control that fails to stop appetitive impulses when experiencing distress (Dohle et al., 2018; Kelly et al., 2020). Brucar et al. (2025) support this finding through subtyping research, which shows that approach behavior (BAS-like) with executive functioning creates high-engagement binge eating clusters that match our reward-driven profiles.\u003c/p\u003e\n\u003cp\u003eThe lack of an FFFS effect might result from measurement tools and cultural elements that reduce the visibility of fear reactions (Reuter et al., 2015; Siligato et al., 2024). The slight increase in FFFS scores in Profile 5 indicates hidden threat sensitivity, which requires more precise evaluation methods. The results confirm the rRST model because the BAS intensifies eating episodes in situations with weak executive functioning, but the BIS controls avoidance behaviors, which produces distinct risk patterns (Wilson et al., 2019). Est\u0026eacute;vez et al. (2024) examined whether emotion regulation mediated the relationship between impulsivity and eating disorders. They reported that rRST sensitivities affected EE through emotion regulation-executive functioning pathways, but neuroimaging studies are needed to confirm these connections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic and Cultural Nuances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that sex was a distinguishing factor. Men comprised 33.7% of Profile 1 (compared with 19.1\u0026ndash;28.4% in other profiles),\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ewhich is consistent with research indicating greater emotional eating vulnerability among women in high-risk profiles\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(Konttinen, 2020; Chew et al., 2025). The age difference between Profiles 3 and 4 suggests that age may influence emotional regulation and executive functioning (Zhou et al., 2025). We also found a uniform distribution of education and marital status among the participants.\u003c/p\u003e\n\u003cp\u003eThe Iranian cultural emphasis on collectivism, which values group unity and emotional control over personal expression (Hofstede, 2011; Haghighian Roudsari et al., 2017), potentially strengthens the use of EE as a hidden coping strategy. The suppression of negative emotions in collectivist societies leads to internalized EE because these cultures value group harmony more than individual freedom to express emotions does (Markus \u0026amp; Kitayama, 1991, 2010). The social pressure to conform in collectivist societies may lead to greater emotion regulation deficits in Profiles 2 to 5 (Vaziri et al., 2022). The study results show that people in Profiles 2, 4 and 5 tend to eat more outside (M = 3.2\u0026ndash;3.6 for external eating; see Table 3), suggesting that participants in these profiles have greater responsiveness to environmental food cues. In the Iranian cultural context, this may be amplified by communal dining settings with abundant high-carbohydrate options, potentially contributing to overeating\u0026nbsp;(Akhoundan et al., 2016; Aghayan et al., 2020; Ebrahimi et al., 2020). Our team observed more than 1,200 students during data collection and reported that they used shared meals as a stress-relief method while academic pressure increased, but they expressed their distress through eating rather than speaking because of their cultural background and gender expectations. The research team observed students using food as a coping mechanism during data collection because their cultural background caused them to hide their emotions through eating instead of talking about their feelings.\u003c/p\u003e\n\u003cp\u003eThe traditional practice of Ramadan fasting used to promote self-control but recent cultural changes among Iranian youth because of urbanization, social media and secularization has weakened its impact (Weingarten \u0026amp; Elston, 1990; Amerzadeh et al., 2024). The two profiles show different levels of restraint, but researchers need to exercise caution because they observe changing attitudes among students during their interactions. Brucar et al. (2025) extend their research by showing that Western BED subtyping produces similar results regarding EF-negative emotionality connections, which suggests that collectivist societies might increase these effects through ER suppression, thus requiring future comparative studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe five identified profiles enable specific intervention approaches. This may include mindfulness training for emotional awareness for those in Profiles 1 and 3 and BAS-focused CBT with inhibitory training for those in Profiles 2 and 5. DBT-EF hybrid approaches may be beneficial for those in Profile 4. These intervention approaches need to be designed to take into account Iran\u0026apos;s cultural stigma toward mental health care (Gratz \u0026amp; Roemer, 2004; Dohle et al., 2018). Est\u0026eacute;vez et al. (2024) support ER-based therapy approaches because of their proven strong mediating effects in young adults with high impulsivity. Brucar et al. (2025) also proposed the need for neuroimaging-based subtyping for individualized treatment approaches. Research evidence now points toward complex EE development patterns that depend on individual temperament characteristics instead of fixed personality traits (Mohorić et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe DEBQ-DERS-BRIEF-A may be a screening tool that helps healthcare professionals detect Iranian youth who are at high risk for emotional eating because it identifies 44.9% of overweight youth who exhibit this behavior (Chew et al., 2025; J\u0026aacute;uregui-Lobera \u0026amp; Montes-Mart\u0026iacute;nez, 2020). The identified profiles enable public health programs to deliver early prevention services, whereas educational programs with cultural adaptations can teach people about EE specifically by targeting women who experience social expectations in traditional societies (Vaziri et al., 2022). The method unites diverse cultural perspectives through individualized weight management solutions that combine ER training with neurobehavioral evaluation (Brucar et al., 2025).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has limitations because it uses convenience sampling, which creates an urban bias that restricts the ability to generalize findings to rural Iranian populations and diverse Iranian subgroups. The self-reported data from the DERS-16 and BRIEF-A may produce exaggerated deficit scores because the DERS-16 lacks emotional awareness assessment. This study lacks neuroimaging data, which prevents researchers from understanding the neurobiopsychological basis of ER and EF deficits between different profiles. Additionally, there is underrepresentation of men. Additionally, cross-sectional studies cannot infer causality.\u003c/p\u003e\n\u003cp\u003eFuture research should use quantitative electroencephalography (qEEG) to identify brain wave patterns in the prefrontal cortex and amygdala regions because these areas are involved in emotional processing and cognitive control according to Wierenga et al. (2014). This research establishes unique neural patterns for each profile, which could guide specific intervention approaches. This study needs to be replicated with diverse clinical and cultural groups while direct rRST measurement techniques are used. Research on LPAs between collectivist Iran and individualist societies needs validation to determine which EE mechanisms operate universally versus those that are specific to particular cultures (Haghighian Roudsari et al., 2017; Sahrai et al., 2022; McNamara \u0026amp; Wood, 2019). The research of Est\u0026eacute;vez et al. (2024) and Brucar et al. (2025) can be expanded through longitudinal studies with neuroimaging and mediation analysis to study EE-related ER-EF development, which will improve transdiagnostic models. The culturally sensitive LPA method creates individualized EE treatment plans on the basis of ER-EF-rRST relationships to address obesity-related psychological aspects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research used latent profile analysis (LPA) to identify different emotional eating patterns among Iranian university students, which resulted in five distinct profiles: \u0026quot;Weakly Balanced,\u0026quot; \u0026quot;Emotionally Reward-Driven,\u0026quot; \u0026quot;Capable and Adaptive,\u0026quot; \u0026quot;Vulnerable and High-Risk,\u0026quot; and \u0026quot;Balanced with Threat Sensitivity.\u0026quot; The five identified profiles demonstrated different levels of emotion regulation (ER) and executive functions (EF) and reinforcement sensitivities (BIS/BAS/FFFS), where the BAS and BIS distinguished reward-driven and threat-sensitive patterns, but the FFFS showed little variation. Research has demonstrated that person-centered methods provide a better understanding of EE behavior while showing how Iranian cultural elements strengthen these patterns through stress management through eating. This research provides a new understanding of EE behavior and supports individualized treatment approaches that combine mindfulness training for adaptive cases with BAS-centered CBT and inhibitory exercises for high-risk patients.\u003c/p\u003e\n\u003cp\u003eThis study provides essential groundwork for future research, which should include tracking EE development patterns through brain imaging techniques and studying eating disorder prevention methods in collectivist versus individualist societies. The combination of ER and EF with RST in therapeutic approaches will create better prevention methods for eating disorders and obesity in non-Western populations through culturally appropriate interventions that lead to enhanced mental and physical health results for Iranian youth and comparable communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional ethical committee of Kharazmi University\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(Ethics Approval Code: IR.KHU.REC.1403.135). The participants provided written informed consent, and the data were anonymized to protect privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003einterest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request and subject to ethical and privacy restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is based upon research funded by the Iran National Science Foundation (INSF) under project No.4036394\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003econtributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSR performed all stages of the research, including conceptualization and methodology development, data curation, original draft writing and manuscript review and editing. AM participated in the development of the research plan and methodology, supervised the work and wrote the review and editing sections. JH took part in conceptualization and methodology development, formal analysis and supervision, and writing, review, and editing of the manuscript. HF participated in the methodology development, formal analysis, advisory work and manuscript review and editing. LJ offered advisory help and performed critical review and editing tasks. The authors conducted a final review of the manuscript before giving their approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research team members who collected data receive our heartfelt appreciation for their dedicated work. The team members worked with complete dedication to collect responses from 1,200 participants at different universities throughout Karaj, Iran. The study became possible through the essential contributions of these members. The research team expresses gratitude to Ms. Baghdadi and Ms. Heydari and Ms. Sameei and Mr. Shadmanfar and Mr. Zamiri and Mr. Khaki for their dedicated work and professional conduct during the research process. The team members provided essential support for data integrity while creating a positive collaborative environment throughout the project.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAghayan, M., Asghari, G., Yuzbashian, E., Mahdavi, M., Mirmiran, P., \u0026amp; Azizi, F. (2020). Secular trend in dietary patterns of Iranian adults from 2006 to 2017: Tehran lipid and glucose study. \u003cem\u003eNutrition Journal\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 110. https://doi.org/10.1186/s12937-020-00624-x\u003c/p\u003e\n\u003cp\u003eAkhoundan, M., Shadman, Z., Jandaghi, P., Aboeerad, M., Larijani, B., Jamshidi, Z., ... Khoshniat Nikoo, M. (2016). 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The effect of emotion regulation on emotional eating among undergraduate students in China: The chain mediating role of impulsivity and depressive symptoms. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(6), e0280701. https://doi.org/10.1371/journal.pone.0280701\u003c/p\u003e\n\u003cp\u003eZhou, R., Zhang, L., Liu, Z., \u0026amp; Cao, B. (2025). Emotion regulation difficulties and disordered eating in adolescents and young adults: A meta-analysis. \u003cem\u003eJournal of Eating Disorders\u003c/em\u003e, 13(1), 25. https://doi.org/10.1186/s40337-025-01197-y\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emotional Eating, Latent Profile Analysis, Emotion Regulation, Executive Function, Reinforcement Sensitivity Theory, Dutch Eating Behavior Questionnaire, Iranian Culture","lastPublishedDoi":"10.21203/rs.3.rs-7985224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7985224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: While emotion regulation, executive function and reinforcement sensitivity play central roles in emotional eating, few studies have investigated these variables in non-Western societies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: This study used latent profile analysis (LPA) to analyze 10 subscales from the Dutch Eating Behavior Questionnaire and Difficulties in Emotion Regulation Scale-16 and the Behavior Rating Inventory of Executive Function-Adult Version to identify emotional eating patterns among 1147 Iranian university students (73.8% female, mean age = 21.44 years).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The analysis revealed five distinct eating behavior patterns: 1) weakly balanced, 2) emotionally rewarding-driven, 3) capable and adaptive, 4) vulnerable and high-risk, and 5) balanced with threat sensitivity. These profiles revealed different patterns of emotional regulation and eating behavior. The behavioral inhibition system (BIS) and behavioral activation system (BAS) sensitivities between profiles showed significant differences (p \u0026lt; .001), with the Capable and Adaptive profiles showing the highest BIS score (M = 15.22) and the reward-driven profiles displaying an elevated BAS, whereas the FFFS sensitivities remained stable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiscussion: The findings showed that women and older participants were more likely to fall into vulnerable eating profile categories. The eating habits of Iranians are influenced by their cultural background, fasting customs and practices of eating together. Research evidence supports a total eating disorder model that includes temperament factors but shows that cultural differences in the treatment of eating disorders and obesity need to be recognized.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: Analysis of the evolution of eating patterns through biological indicators is needed to develop enhanced treatment protocols.\u003c/p\u003e","manuscriptTitle":"Unveiling Emotional Eating Heterogeneity in Iranian Young Adults: A Latent Profile Analysis of Executive Functions, Emotion Regulation and Reinforcement Sensitivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 10:56:30","doi":"10.21203/rs.3.rs-7985224/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-18T17:47:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143727062151012351582688975876080181828","date":"2026-03-04T07:46:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T07:15:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T07:54:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-18T09:05:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T18:42:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-12T20:29:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"02b36f01-15ac-4264-9e1f-93176a4eea41","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64034423,"name":"Health sciences/Health care"},{"id":64034424,"name":"Biological sciences/Neuroscience"},{"id":64034425,"name":"Biological sciences/Psychology"},{"id":64034426,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-10T10:56:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 10:56:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7985224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7985224","identity":"rs-7985224","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}