Personality Traits are Associated with Dietary Behaviors and Psychosocial Outcomes in Adolescents with Celiac Disease: A Cross-Sectional Study

Personality Traits are Associated with Dietary Behaviors and Psychosocial Outcomes in Adolescents with Celiac Disease: A Cross-Sectional Study | 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 Research Article Personality Traits are Associated with Dietary Behaviors and Psychosocial Outcomes in Adolescents with Celiac Disease: A Cross-Sectional Study Zhijun Chen, Anne R. Lee, Pamela Koch, Benjamin Lebwohl, Peter HR Green, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9058390/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose: Celiac disease (CeD) requires strict lifelong adherence to a gluten-free diet (GFD), which can be particularly challenging during adolescence. This cross-sectional study examined associations between personality traits, dietary behaviors, and psychosocial well-being in adolescents with CeD. Methods: Fifty adolescents aged 14-17 years were recruited from a U.S. CeD referral center. Personality traits (Big Five Inventory), gluten free diet adherence (CDAT), maladaptive eating behaviors (CD-FAB), quality of life (CDPQOL), anxiety (STAI-C), and depression (CES-DC) were measured. Results: Participants were mostly female (76%), with a mean age of 15.2 years. Sixty-two percent demonstrated good or excellent GFD adherence (CDAT < 13). Overall QOL was rated as “good” (Mean CDPQOL: 62.8 out of 100). Maladaptive food attitudes and behaviors were common (Mean CD-FAB: 32.0), and about one-third reported anxiety or depression symptoms. Poorer GFD adherence was associated with lower conscientiousness (β = −0.44, p = .02). More maladaptive food attitudes and behaviors were associated with higher openness (β = 0.45, p = .01). Lower QOL was associated with higher openness (β = −6.08, p = .011). Increased trait anxiety was associated with higher neuroticism (β = 6.12, p < .001). Increased depressive symptoms were associated with higher neuroticism (β = 4.21, p = .006) and lower extraversion (β = −2.42, p = .043). Conclusion: Higher openness may make a GFD feel more restrictive, lower conscientiousness may hinder GFD adherence planning, and higher neuroticism may increase anxiety and depression risk.Personality-informed counseling could help clinicians tailor support to improve adherence and well-being. celiac disease personality traits big five gluten-free diet diet adherence quality of life adolescent Figures Figure 1 Practitioner Points Personality traits were associated with dietary behaviors and psychosocial outcomes in adolescents with CeD. An adolescent with higher openness (i.e., more novelty-seeking) may make a GFD feel more restrictive, lower conscientiousness may hinder adherence planning, and higher neuroticism may increase anxiety and depression risk. Clinicians may consider asking questions to gauge personality traits and incorporate personality-informed strategies into nutrition counseling to support GFD adherence and psychological well-being in CeD. Introduction Celiac disease (CeD) is a genetically-mediated autoimmune disease triggered by dietary gluten intake, causing damage to the mucosal lining of the small intestine [ 1 ]. Currently, the only treatment for CeD is a lifelong gluten-free diet (GFD) [ 2 ]. The GFD can be challenging to follow, particularly for adolescents, an age group often concerned with fitting in with peers, experiencing reduced reliance on parents, and navigating increased independence in decision-making [ 3 ]. Among adolescent patients with CeD, GFD adherence rates in the U.S. ranged from 23% to 98%, with a median of 79% [ 4 ]. Challenges of a GFD may be associated with impaired psychosocial well-being, lower quality of life (QOL), and mental health outcomes in adolescents [ 5 ]. Adolescents who are less adherent to a GFD experience greater disease burden, poorer QOL, and more CeD symptoms [ 6 ]. Adolescence is a time of rapid physiological, psychological, and social changes [ 7 ]. It is also a critical period for identity and personality development [ 8 , 9 ]. However, little is known about how personality may shape an adolescent’s approach to a GFD when diagnosed with CeD, or how managing CeD may in turn influence personality development. Personality traits have been defined as the enduring patterns of thinking, feelings, and behaving that distinguish individuals from one another and reflect how a person typically responds in situations [ 10 ]. McCrae and Costa developed the five-factor model to describe the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism [ 11 ]. Big Five personality traits can shape how individuals perceive burden and demands, self-regulate behavior, cope with stressors, and navigate social situations [ 12 , 13 ]. In adults with CeD, personality traits have been associated with GFD and eating behaviors. Lower conscientiousness has been associated with higher nonadherence to the GFD [ 14 ]. And adults with CeD with higher neuroticism were associated with more maladaptive eating behaviors and poorer QOL [ 15 ]. Evidence for associations between personality traits and gluten-free diet adherence in adolescents with CeD has relied primarily on temperament measures, not the Big Five. Wagner and colleagues compared personality traits in adolescents with CeD and those with both CeD and an eating disorder using the Junior Temperament and Character Inventory. Female adolescents with CeD and an eating disorder demonstrated lower self-directedness, indicating lower self-regulatory behaviors, than those with CeD alone [ 16 ]. When comparing the temperament of adolescents with CeD, those who strictly adhered to the diet showed lower novelty seeking, impulsivity, and rule transgression, and higher eagerness for work and perfectionism (persistence), compared with those who had more than two diet transgressions per month [ 17 ]. Little is known about how the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) among adolescents with CeD are associated with eating behaviors, including maladaptive eating and GFD adherence, as well as QOL, anxiety, and depression. This study aimed to examine how Big Five personality traits are associated with dietary behaviors relevant to CeD (gluten-free diet adherence and food attitudes and beliefs) and well-being outcomes (quality of life, anxiety, and depression) in adolescents with CeD. Understanding these associations may provide insights into individual variation in CeD management and inform clinical practice by enabling more tailored nutrition counseling. Methods Study design This cross-sectional study analyzed data from the Celiac Disease Eating Pattern (CD-EAT) study, which examined the relationship between CeD and eating patterns in 50 adults and 50 adolescents-caregiver dyads. Data on adults has been published previously [ 15 , 18 ]. Data on adolescents was collected from February 14, 2020, through March 18, 2025. IRB approval was obtained from the Celiac Disease Center at Columbia University Irving Medical Center (CUIMC) (Rascal IRB-AAAS5501) and from Teachers College (IRB 19–479 in September 2019; IRB 24–460 2024). Participants Participants were caregiver-adolescent dyads recruited from a U.S. CeD Referral Center. Adolescents 14 to 17 years were included if they had a confirmed diagnosis of CeD either by biopsy or serology based on ESPGHAN standards; were currently being treated at the Celiac Disease Center; had been following a gluten-free diet (GFD) for at least one year, and did not have a current or prior diagnosed eating disorder (e.g., anorexia nervosa, bulimia nervosa) as diagnosed by a physician. Adolescent eligibility required their own completion of the BFI and other assessments, as well as one caregiver independently completing the BFI to assess caregivers’ personality traits. Exclusion criteria included self-diagnosed CeD, age less than 14 years old or greater than 17 years old, had never been seen as a patient at the Celiac Disease Center, or had a current or prior diagnosis of an eating disorder based on DSM-5 criteria. Informed consent was obtained from caregivers and adolescents provided assent. Adolescents received a $ 50 gift card for their participation. There were 108 patients prescreened by staff to determine potential eligibility of which 49 were excluded for not meeting eligibility criteria. Among the 59 patients who were approached to participate, 9 declined resulting in 50 participants. The sample size of 50 was chosen so as to obtain preliminary cross-sectional data on personality and CeD outcomes. Study Measures: Anthropometric measures Current weight was measured by a Healthometer brand digital scale. Height was measured using a Tanita stadiometer (model #HR200). Participants were asked to remove their shoes and jackets before their height and weight were measured. Demographic and Health History Age, gender, ethnicity, race, education, household income, and age at CeD diagnosis were collected for the adolescent participant, and the caregiver’s gender and education were collected. The CeD diagnostic method and registered dietitian visits were also collected. Personality traits : Personality traits were measured using the Big Five Inventory (BFI), a 44-item inventory assessing: Openness to Experience (10 items), Conscientiousness (9 items), Extroversion (8 items), Agreeableness (9 items), Neuroticism (8 items), and as established by John, Donahue, and Kentle [ 19 ]. Likert-type responses are used (from 1 = Strongly disagree to 5 = Strongly agree). Items are reverse-coded as appropriate and summed to create subscales, with higher scores indicating greater expression of the measured personality dimension. Openness to Experience (subscale range: 10–50) measures the preference for variety and being creative, and novelty-seeking [ 20 – 22 ]. Conscientiousness (subscale range: 9–45) measures the tendency toward organization and goal-oriented behavior. Extraversion (subscale range: 8–40) reflects sociability and a propensity for positive emotions. Agreeableness (subscale range: 9–45) measures cooperative, trusting, and humble traits. Neuroticism (subscale range: 8–40) measures the tendency to experience negative emotions, including anxiety, nervousness, sadness, and tension, rather than emotional stability. The BFI demonstrated good validity and reliability in adult and adolescent samples [ 23 – 27 ]. Gluten Free Diet Adherence (CDAT) : The Celiac Dietary Adherence Test (CDAT) is a 7-item instrument that assesses four dimensions of GFD adherence in individuals with CeD: CeD Symptoms, Self-efficacy, Reasons for following a GFD, and Level of Adherence [ 28 ]. Response options range from 1 (none of the time) to 5 (all of the time). Total scores range from 7 to 35, with scores below 13 indicating excellent or very good adherence to a GFD, scores between 13 and 17 indicating moderate adherence, and scores above 17 indicating poor adherence. The CDAT has not been validated in adolescent populations; however, it has been used in studies including adolescents [ 29 – 31 ]. Food Attitudes and Behaviors (CD-FAB) : The Celiac Disease-specific Food Attitudes and Behaviors (CD-FAB) is an 11-item tool that assesses food attitudes and behaviors related to avoiding minor gluten contamination and concerns about food safety (e.g., handling of food, trust, risk-taking, and food safety), as described by Satherley, Howard, and Higgs [ 32 ]. CD-FAB used Likert-type responses (from 1 = Strongly disagree to 7 = Strongly agree), with higher scores indicating more maladaptive eating attitudes and behaviors. Total score ranged from 11 to 77, with three subscales: food attitudes (ranging from 3 to 21), fear response (ranging from 4 to 28), and adaptive response (ranging from 4 to 28). Clinical relevance cut-offs have not been established. The tool was originally developed and validated by Satherley, Howard, and Higgs in an adult UK population; it has not yet been validated or used in adolescents [ 32 ]. For the present study, we compared our version with the original adult scale and made minor wording adjustments to ensure appropriateness for adolescents (ages 14–17). For example, we changed “I get concerned being near others when they are eating gluten” to “I get worried being near others when they are eating gluten” since worried is a term more commonly used by adolescents. Celiac Disease Quality of Life (CDPQOL) : The Celiac Disease-Specific Pediatric Quality of Life (CDPQOL) inventory is a 17-item, validated measure for pediatric patients aged 13–18 with CeD to measure QOL [ 33 ]. The inventory had Likert-type responses ranging from 0 = never, 1 = almost never, 2 = sometimes, 3 = often, to 4 = almost always across the first 17 items. It has four subscales: social (7 items), uncertainty (3 items), isolation (4 items), and limitations (3 items). In this study, raw scores (0–68) were converted to scaled scores (0-100), with higher scores indicating better QOL. Clinical relevance cut-offs have not yet been determined. Anxiety (STAI-C) The State-Trait Anxiety Inventory for Children (STAI-C) is a two-part measure with 20 items evaluating temporary anxiety “at the moment” (State Anxiety) and 20 items assessing usual or ongoing anxiety (Trait Anxiety), rated on a 3-point Likert scale, developed for use in children aged 9–12 years [ 34 ]. Item scores are summed to obtain subset total scores; the range for each subtest is 20 to 60, with higher scores indicating greater anxiety. It does not have a set cutoff point. A previous study in adults with CeD used a cutoff point of 39 [ 15 ]. This value is a commonly used STAI cutpoint for identifying clinically significant symptoms [ 35 ]. Therefore, this study used the same cutoff for analysis. The STAI-C was evaluated for validity and reliability in healthy adolescents and adolescents with substance use disorders aged 12 to 18 years [ 36 , 37 ]. Studies in CeD populations have used the STAI-C to assess anxiety, although the instrument has not been formally validated in this group [ 38 ]. Depression (CES-DC) The Center for Epidemiologic Studies Depression Scale for Children (CES-DC) is a validated 20-item screening tool for depression in children and adolescents aged 6 to 17, based on the DSM-V criteria for a major depressive episode [ 39 , 40 ]. The items are rated on a 4-point Likert scale from 0 (not at all), 1 (a little), 2 (some), to 3 (a lot). The total scores range from 0 to 60, with higher scores indicating greater depressive symptoms. A CES-DC score greater than 15 suggests clinical depression [ 40 , 41 ]. CeD Symptoms (CDSD) The Celiac Disease Symptom Diary (CDSD) is a 6-item Patient-Reported Outcome (PRO) daily symptom diary developed in accordance with the US Food and Drug Administration’s PRO Guidance. It assesses the presence or absence of CeD-related symptoms over the past 24 hours, including diarrhea, changes in bowel movements, abdominal pain, bloating, nausea, and tiredness. The instrument is typically administered over a 7-day period; however, this study used a single-day assessment. Total scores range from 0 to 10, with higher scores indicating greater symptom severity [ 42 ]. Statistical analysis Mean ± standard deviation (SD) and range (minimum–maximum) for continuous variables, and as n (%) for categorical variables were reported. BMI percentiles were calculated using the CDC growth charts (R package cdcanthro ). Mean ± standard error (SE), 95% CI, and range (minimum-maximum) were reported for BFI. Internal consistency of BFI was evaluated using Cronbach’s alpha (α > 0.70). Normal distribution of the data was assessed using the Shapiro–Wilk test. Bivariate correlation between BFI, dietary behavior outcomes (CDAT and CD-FAB), and psychosocial outcomes (CDPQOL, STAI, CES-DC) was examined using spearman correlations. Multiple regression models were used to examine associations between personality traits and CDAT, CD-FAB, adjusting for gender and age at diagnosis. We hypothesized that personality traits (BFI) would be associated with adherence to the gluten-free diet (CDAT) and maladaptive eating behaviors (CDFAB), and used the following regression models to test these associations: CDAT Total Score = β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε CD-FAB Overall Score = β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε. The associations between personality traits and QOL, anxiety, and depression was assessed using multiple linear regression models, adjusting for gender, age at diagnosis, and CeD symptoms. We hypothesized that personality traits (BFI) would be associated with QOL (CDPQOL), anxiety (STAI) and depression (CES-DC), and used the following regression models to test these associations: CDPQOL Total Score = β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε STAI = β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε CES-DC = β₀ + β₁(BFI traits) + β₂(CDAT) + β₃(CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε The selection of covariates was based on prior research [ 29 , 43 , 44 ] and clinical relevance as well as the sample size. Since years since diagnosis and age at diagnosis were highly linearly correlated, age at diagnosis was included in the model. Model assumptions (linearity, normality, homoscedasticity, and independence of residuals) were evaluated. For each model, unstandardized coefficients ( b ), standardized coefficients (β), standard errors (SE), t -statistics, 95% confidence intervals, and p -values were reported where appropriate. Overall model fit was reported with F -statistic, R² , and adjusted R² . A value of p < .05 was considered statistically significant. When data were missing, item-level missing data were replaced using the mean of the remaining items within the corresponding scale. Analyses were conducted in RStudio (Version 1.3.1073; PBC, Boston, MA). Results The demographic and health characteristics of the study sample are summarized in Table 1. Table 1. Demographics and Health Characteristics of Study Sample Adolescents (N=50) N (Mean ± SD or %) Gender Identity (n, %) Female Male 38 (76%) 12 (24%) Age (M ± SD, range) 15.2 ± 1.0 (14 – 17) Age at diagnosis (M ± SD, range) 10.3 ± 3.9 (1.9 - 16.8) Race (n, %) Asian Black or African American Other White 1 (2%) 0 1 (2%) 48 (96%) Ethnicity (n, %) Hispanic Non-Hispanic 3 (6%) 47 (94%) Years Since Diagnosis (M ± SD, range) <5 ≥5 5.2 ± 4.0 (0.3 - 15.6) 29 (58%) 21 (42%) CeD diagnosis method Biopsy only ESPHGAN standard 43 (86%) 7 (14%) BMI Percentile (M ± SD, range) Underweight 95% 55.1 ± 23.4 (0.2 - 99.7) 2 (4%) 39 (78%) 6 (12%) 3 (6%) RDN visit (n, %) RDN currently RDN past only RDN never 36 (72%) 7 (14%) 7 (14%) Caregiver Sex (n, %) Female Male 47 (94%) 3 (6%) Caregiver Education (n, %) < High School or High School graduate Some college College graduate Post-graduate training 2 (4%) 0 20 (40%) 28 (56%) Household income (n, %) $100,000 Did not disclose 2 (4%) 1 (2%) 28 (56%) 19 (38%) Note: CeD = celiac disease; M = mean; SD = standard deviation; RDN = Registered Dietitian Nutritionists. Among the 50 participants, 76% were female. The mean age was 15.2 years (SD = 1.0), and the mean age at diagnosis was 10.3 years (SD = 3.9). The sample was predominantly white (96%). Participants had lived with CeD for an average of 5.2 years (SD = 4.0). Most adolescents were diagnosed by duodenal biopsy (86%) and 14% were diagnosed using serology only (meeting ESPHGAN criteria). Most (78%) were in the normal BMI percentile for age. Seventy-two percent of adolescents were currently seeing a registered dietitian nutritionist (RDN). Caregivers were predominantly female (94%), being the mothers of the participants, highly educated, with more than half reporting postgraduate training. Over half had a household income >$100,000 per year. The big five personality scores for openness (mean ± SE) were 35.4 ± 0.75, conscientiousness 31.2 ± 0.83, extraversion 26.4 ± 0.79, agreeableness 34.7 ± 0.78, and neuroticism 23.6 ± 0.85 (Table 2). Table 2. Descriptive Statistics of Big Five Inventory Scores Traits Instrument Range Mean ± SE 95% CI Participant Range Big Five Openness (O) Conscientiousness (C) Extraversion (E) Agreeableness (A) Neuroticism (N) 10 – 50 9 – 45 8 – 40 9 – 45 8 – 40 35.4 ± 0.75 31.2 ± 0.83 26.4 ± 0.79 34.7 ± 0.78 23.6 ± 0.85 33.9 - 36.9 29.6 - 32.8 24.8 - 27.9 33.2 - 36.2 21.9 - 25.2 25 - 48 15 - 42 15 - 40 15 - 43 8 - 36 Note: Higher scores mean greater levels of BFI traits. Sixty-two percent of participants reported good/excellent adherence to the GFD (CDAT <13). The CD-FAB eating attitudes and behaviors instrument had an average total score of M = 32.0, SD = 11.4 (range = 14–66). The mean CDPQOL score was 62.8 ± 17.7 (14.7–89.7), indicating good overall QOL. Almost all participants (98%), except one, met the clinical cutoff suggesting state anxiety, and sixteen participants (32%) met the clinical cutoff suggesting trait anxiety. Eighteen participants (36%) met the clinical cutoff suggesting depression. The mean frequency of CeD symptoms over the past 24 hours was 1.5 ± 1.1 (range: 0–5). Twelve percent of participants (n = 6) reported no symptoms, 54% (n = 27) reported one symptom, and 34% (n = 17) reported two or more symptoms. Only six participants (12%) did not report experiencing CeD-related symptoms in the preceding 24 hours (Table 3). Table 3. Descriptives of Key Study Measures Study Measures n (%) or Mean ± SD (range) Gluten-free Diet Adherence CDAT Overall 17 (Poor Adherence) 12.1 ± 4.3 (7 - 23) 31 (62%) 13 (26%) 6 (12%) Maladaptive Food Attitudes & Behavior , CD-FAB Total 32.0 ± 11.4 (14 – 66) Celiac-Specific QOL , CDPQOL Total 62.8 ± 17.7 (14.7 – 89.7) % meeting clinical cutoff for anxiety STAI State STAI Trait 49 (98%) 16 (32%) % meeting clinical cutoff for depression CES-DC 18 (36%) # symptoms past 24 hrs, CDSD 0 1 2+ 1.5 ± 1.1 (0 - 5) 6 (12%) 27 (54%) 17 (34%) Note: CDAT = Celiac Dietary Adherence Test; CD-FAB = Celiac Disease Food and Attitudes Behavior Checklist; CDPQOL = Celiac Disease Specific Pediatric Quality of Life Scale; STAI-C cutoff ≥ 39; Depression Cutoff ≥ 15; CDSD = Celiac Disease Symptoms Diary Spearman correlations indicated that Big Five personality traits were significantly associated with multiple outcomes in adolescents with CeD (Table 4). Table 4. Spearman Correlation of BFI and Study Outcomes Big Five Subcomponents of Adolescents (ρ, p, SE, 95% CI) Study Measures O C E A N GFD Adherence CDAT ρ=-0.064 (0.146) [-0.336, 0.218] ρ=-0.414** (0.146) [-0.621, -0.153] ρ=-0.266 (0.146) [-0.507, 0.013] ρ=-0.224 (0.146) [-0.473, 0.058] ρ=0.387** (0.146) [0.121, 0.600] Maladaptive Food Attitudes & Behaviors CD-FAB Total Score ρ=0.347* (0.146) [0.076, 0.570] ρ=-0.009 (0.146) [-0.286, 0.270] ρ=-0.095 (0.146) [-0.363, 0.189] ρ=-0.101 (0.146) [-0.369, 0.182] ρ=0.099 (0.146) [-0.185, 0.367] CeD-Specific QOL CDPQOL Total Score ρ=-0.343* (0.146) [-0.568, -0.072] ρ=0.252 (0.146) [-0.028, 0.496] ρ=0.231 (0.146) [-0.051, 0.478] ρ=0.289* (0.146) [0.012, 0.525] ρ= -0.469*** (0.146) [-0.661, -0.220] Anxiety STAI-C State ρ=0.195 (0.146) [-0.088, 0.449] ρ=-0.127 (0.146) [-0.391, 0.157] ρ=-0.170 (0.146) [-0.428, 0.114] ρ=0.175 (0.146) [-0.109, 0.432] ρ=0.284* (0.146) [0.006, 0.521] Anxiety STAI-C Trait ρ=0.185 (0.146) [-0.099, 0.440] ρ=-0.477*** (0.146) [-0.667, -0.229] ρ=-0.319* (0.146) [-0.548, -0.044] ρ=-0.304* (0.146) [-0.537, -0.028] ρ=0.780*** (0.146) [0.641, 0.870] Depression CES-DC ρ=0.206 (0.146) [-0.077, 0.458] ρ=-0.499*** (0.146) [-0.683, -0.256] ρ=-0.421** (0.146) [-0.626, -0.162] ρ=-0.278 (0.146) [-0.517, 0.000] ρ=0.740*** (0.146) [0.582, 0.845] Note: Values represent Spearman correlation coefficients (ρ) with standard errors in parentheses and 95% confidence intervals in brackets. *p < .05, **p < .01, ***p < .001 O = Openness; C = Conscientiousness; E = Extraversion; A = Agreeableness; N = Neuroticism; CDAT = Celiac Dietary Adherence Test (higher scores indicate poorer adherence). CD-FAB = Celiac Disease Food Attitudes and Behavior Questionnaire (higher score means more maladaptive food attitudes and behavior). CDPQOL = Celiac Disease Pediatric Quality of Life (higher scores means better QOL); STAI-C = State-Trait Anxiety Inventory for Children; CES-DC = Center for Epidemiologic Studies Depression Scale for Children. Higher conscientiousness (ρ = −0.414, p < .01) was associated with better adherence (lower CDAT scores), whereas higher neuroticism (ρ = 0.387, p < .01) was associated with poorer adherence. Higher openness was associated with more maladaptive food attitudes and behaviors (higher CD-FAB scores) (ρ = 0.347, p < .05) and lower quality of life (ρ = −0.343, p < .05). Additionally, lower quality of life was associated with lower agreeableness (ρ = 0.289, p < .05) and higher neuroticism (ρ = −0.469, p < .001). Higher state anxiety was associated with higher neuroticism (ρ = 0.284, p < .05). Higher trait anxiety was associated with lower conscientiousness (ρ = −0.477, p < .001), lower extraversion (ρ = −0.319, p < .05), and lower agreeableness (ρ = −0.304, p < .05), as well as higher neuroticism (ρ = 0.780, p < .001). More depressive symptoms were associated with lower conscientiousness (ρ = −0.499, p < .001), lower extraversion (ρ = −0.421, p < .01), and higher neuroticism (ρ = 0.740, p < .001). In order to examine the association between the Big Five personality traits and adherence to a gluten-free diet, multiple regression analysis was conducted as described in the Methods section. The multiple regression model between personality trait (BFI) and GFD adherence (CDAT) was statistically significant, F (7, 42) = 2.87, p = .02, explaining approximately 32% of the variance in adherence scores (R² = .32, adjusted R² = .21; Table 5). Table 5. Multiple Regression of Personality Trait (BFI) and GFD Adherence (CDAT) Predicting Variable b β SE t p 95% CI Openness 0.105 0.129 0.128 0.820 .42 [-0.153, 0.363] Conscientiousness -0.320 -0.436 0.126 -2.54 . 02* [-0.575, -0.065] Extraversion -0.124 -0.160 0.123 -1.01 .32 [-0.372, 0.124] Agreeableness 0.033 0.042 0.114 0.287 .78 [-0.197, 0.262] Neuroticism -0.007 -0.009 0.149 -0.044 .97 [-0.308, 0.295] Gender -1.50 -0.347 1.47 -1.02 .31 [-4.46, 1.46] Age at Diagnosis 0.214 0.195 0.158 1.36 .18 [-0.104, 0.533] Model Summary R² = 0.32; Adj. R² = 0.21; F (7, 42) = 2.87; p = 0.02* Model: CDAT Total Score = β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε Note: CDAT = Celiac Dietary Adherence Test; Gender coded 0 = male, 1 = female; Age at diagnosis treated as continuous. b = unstandardized regression coefficient; β = standardized coefficient; SE = standard error; t = t -statistic; p = significance level; CI = confidence interval. F = overall model F-statistic; df₁ = numerator degrees of freedom (number of predictors); df₂ = denominator degrees of freedom (residual degrees of freedom); R² = proportion of variance in CDAT explained by the model; Adj. R² = adjusted proportion of variance accounting for model complexity. *p < .05; * *p < .01; ** *p < .001. Higher conscientiousness was associated with better GFD adherence (lower CDAT scores) (β = −0.44, SE = 0.13, p = .02). All other personality traits, gender, and age at diagnosis were not significantly associated with CDAT in the model (p > .05). The multiple regression model between personality trait (BFI) and maladaptive food attitudes and behaviors (CD-FAB) was not statistically significant, F (7, 42) = 1.37, p = .24 (Table 6). Table 6. Multiple Regression of Personality Trait (BFI) and Maladaptive Eating Attitudes and Behaviors (CD-FAB) Predictor b β SE t p 95% CI Openness 0.957 0.445 0.370 2.59 .01* [0.210, 1.70] Conscientiousness -0.086 -0.044 0.365 -0.235 .82 [-0.821, 0.650] Extraversion -0.569 -0.278 0.355 -1.60 .12 [-1.29, 0.148] Agreeableness -0.080 -0.039 0.329 -0.244 .81 [-0.744, 0.584] Neuroticism -0.105 -0.055 0.432 -0.243 .81 [-0.976, 0.767] Gender -0.358 -0.031 4.24 -0.084 .93 [-8.90, 8.19] Age at Diagnosis -0.024 -0.008 0.456 -0.052 .96 [-0.944, 0.897] Model Summary R² = 0.19, Adj. R² = 0.05; F (7, 42) = 1.37, p = 0.24. Model: CD-FAB Total Score = β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε Note: CD-FAB = Celiac Disease Food Attitudes and Behaviors. Gender coded 0 = male, 1 = female; Age at diagnosis treated as continuous. b = unstandardized regression coefficient; β = standardized coefficient; SE = standard error; t = t-statistic; CI = confidence interval. Significance markers: *p < .05; **p < .01; ***p < .001. However, higher openness was positively associated with more maladaptive food-related attitudes and behaviors after adjusting for all other variables (β = 0.45, SE = 0.37, p = .01). No other personality traits, gender, or age at diagnosis were significantly associated with CD-FAB (ps > .05). When assessing the relationship between personality traits (BFI) and CeD-specific QOL (CDPQOL), the multiple regression model between BFI and QOL explained a substantial proportion of variance in QOL (R² = 0.65, adjusted R² = 0.56; F (10, 39) = 7.16, p < 0.001) (Table 7). Table 7. Multiple Regression of Personality Traits (BFI) and Quality of Life (QOL) Model: QOL Total Score = β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε Note: CDAT score lower means better GFD adherence; β = standardized coefficient; SE = standard error; t = t-statistic; β means with every 1 standard deviation increase. Significance markers: *p < .05; **p < .01; ***p < .001. Adolescents with lower QOL were significantly associated with higher openness (β = −6.08, SE = 2.27, p = 0.011), poorer gluten-free diet adherence (higher CDAT scores) (β = −5.56, SE = 2.08, p = 0.011), more maladaptive food attitudes and behaviors (lower CD-FAB scores) (β = −4.53, SE = 1.94, p = 0.025), and greater CeD symptom burden over the past 24 hours (β = −4.46, SE = 2.07, p = 0.038). For the relationship between BFI and trait anxiety, the multiple regression model was significant (R² = 0.68, adjusted R² = 0.59; F (10, 39) = 8.12, p < 0.001) (Table 8). Table 8． Multiple Regression of Personality Traits (BFI) and Trait Anxiety (STAI-C) Model: Trait Anxiety = β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε Note: β = standardized coefficient; SE = standard error; t = t-statistic; β coefficients represent change in anxiety score per 1 SD increase in predictors. Higher scores indicate greater trait anxiety. Significance markers: *p < .05; **p < .01; ***p < .001. Adolescents with higher anxiety were significantly associated with higher neuroticism (β = 6.12, SE = 1.29, p < 0.001) and greater CeD symptom burden (β = 2.09, SE = 0.98, p = 0.039). No other personality traits, gluten-free diet adherence, food attitudes and behaviors, gender, or age at diagnosis were significantly associated with trait anxiety. In the multiple regression model between BFI and depressive symptoms, the model reached statistical significance (R² = 0.68, adjusted R² = 0.59; F(10, 39) = 8.14, p < 0.001) (Table 9). Table 9. Multiple Regression of Personality Traits (BFI) and Depression (CES-DC) Model: Depression = β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β 4 (gender) + β 5 (age at diagnosis) + β 6 (CeD Symptoms) + ε Note: β = standardized coefficient; SE = standard error; t = t-statistic; β coefficients represent change in depressive symptom score per 1 SD increase in predictors. Higher CES-DC scores indicate greater depressive symptoms. Significance markers: *p < .05; **p < .01; ***p < .001. Adolescents with higher depressive symptoms were significantly associated with higher neuroticism (β = 4.21, SE = 1.45, p = 0.006), lower extraversion (β = −2.42, SE = 1.16, p = 0.043), and poorer gluten-free diet adherence (higher CDAT scores) (β = 2.76, SE = 1.10, p = 0.016). No other personality traits, food attitudes and behaviors, gender, age at diagnosis, or CeD symptom burden were significantly associated with depressive symptoms. Overall, adolescents' personality traits were associated with dietary behaviors and psychosocial outcomes among adolescents with CeD, as summarized in Figure 1. Lower conscientiousness was associated with poorer adherence to the gluten-free diet, whereas higher openness was associated with more maladaptive food attitudes and behaviors. Lower quality of life was associated with higher openness, poorer dietary adherence, more maladaptive food attitudes and behaviors, and greater CeD symptom burden. Higher trait anxiety was associated with higher neuroticism and greater CeD symptom burden. More depressive symptoms were associated with higher neuroticism, lower extraversion, and poorer GF dietary adherence. Discussion Our results suggest an association between adolescent personality traits and dietary behaviors, and with psychosocial outcomes related to CeD. Gluten-free diet adherence. This study found the average CDAT score was 12.1 ± 4.3 (range = 7–23), which is considered good adherence to the GFD. This study also found that adolescents with poorer GFD adherence was associated with lower conscientiousness. This was not surprising since individuals higher in conscientiousness are generally better at setting goals and engaging in consistent planning [ 45 ]. Those low in conscientiousness may struggle with impulse control, meal planning, consistent label reading, and ongoing GFD monitoring. No prior studies in adolescents with CeD have used the BFI to assess personality traits and GFD adherence. One previous study of 281 children and adolescents with biopsy-confirmed CeD and 95 controls used the Junior Temperament and Character Inventory and found that the adherent group scored higher on persistence, a trait reflecting eagerness, effortful behavior, and perfectionism, compared with the non-adherent group [ 16 ]. Persistence in the Junior Temperament and Character Inventory aligns conceptually with Conscientiousness in the BFI, which is consistent with the present study’s finding. The adherent group scored lower on novelty seeking, a trait related to extravagance, impulsivity, and rule transgression, compared with the non-adherent group, which conceptually overlapped with Openness in the BFI. However, the present study did not observe significant differences in Openness between GFD adherent and non-adherent adolescents. Among adults with CeD, evidence similarly highlights the importance of personality in dietary adherence. In a sample of 143 adults (76.6% female; mean age = 50.35 ± 16.21 years), the NEO Personality Inventory–Revised (NEO PI-R) was used to assess Big Five traits, those who were more conscientious and more open to reexamining social, political, and religious values (similar to Openness domain in BFI) were more likely to adhere to the GFD. Conversely, individuals who tend to accept authority, honor tradition, and prefer conservative viewpoints were less adherent [ 14 ]. The current study extends the existing literature by suggesting that similar mechanisms may operate in adolescents with CeD, in which lower conscientiousness may contribute to challenges in maintaining strict adherence to the GFD. Food attitudes and behaviors. Maladaptive food attitudes and behaviors were common (mean CD-FAB = 32.0). In the current adolescent sample, more maladaptive patterns were associated with higher openness to experience. Maladaptive food attitudes and behaviors have been associated with lower QOL [ 15 , 29 ] and may be associated with higher levels of disordered eating [ 32 ]. Openness is characterized by a tendency to seek novelty and curiosity, which may be associated with being more willing to experiment with new gluten-free foods, recipes, and travel. The tendency toward novelty seeking, combined with real-life challenges in finding suitable food products and anxiety in social situations, may be described as “curiosity-driven tension.” Other studies have shown openness was associated with poor outcomes. For example, in adults with food allergies, those who have high openness predicted behaviors such as difficulty finding suitable foods when grocery shopping, anxiety at social occasions involving food, and feeling embarrassed and poorly understood regarding their food allergy [ 46 ]. Similarly, for adolescents managing a restrictive GFD, this desire to explore can make the limitations of a GFD feel more burdensome. For example, they may feel a greater sense of missing out or disappointment when near gluten-containing foods or when eating foods not prepared at home [ 47 ]. These reactions may heighten perceived dietary risk, increase vigilance, and contribute to more maladaptive food attitudes and behaviors. Quality of life. Overall quality of life was rated as good (mean CDPQOL = 62.8), although lower than in previous studies of children and adolescents with CeD at the same celiac center [ 29 , 48 , 49 ]. In the current study, adolescents with CeD who reported lower QOL tended to show higher Openness, poorer dietary adherence, more maladaptive food attitudes and behaviors, and more CeD symptoms. These findings were somewhat surprising in that we initially thought openness would promote quality of life. However, prior work has shown that social constraints associated with the GFD substantially shape adolescents’ well-being [ 50 ]. Compared to adolescents with CeD who prefer familiarity, those high in openness may find the restrictive and socially limiting nature of the GFD to be more burdensome versus manageable. Such individuals who are curious and value novelty-seeking may be more motivated to travel and explore new recipes and restaurants, which in turn, may feel especially constrained by the GFD and negatively affecting their QOL. Anxiety and depression. 98% of the adolescents in the study reported elevated state anxiety; additionally, sixteen adolescents (32%) met the clinical cutoff for trait anxiety (35.5 ± 8.7). This high level of state (“at the moment”) anxiety warrants further examination but may reflect stress or nervousness that comes with CeD-related medical visit as participants were all attending a GI or dietitian appointment concurrently. This environment could potentially heighten situational stress and elevate state anxiety scores as a previous study of U.S. adolescents (age 11.84 ± 1.28) during the COVID-19 stay-at-home order reported elevated state anxiety (47.37 ± 3.15) [ 51 ]. Among adults with CeD, trait anxiety has been reported at 39.8 ± 10.7, with 44% above the clinical cutoff of anxiety, suggesting that elevated anxiety is common across age groups in CeD [ 15 ]. This study also found higher trait anxiety was associated with greater neuroticism and symptom burden. Adolescents high in Neuroticism may experience worry, rumination, and emotional distress more intensely for concerns about gluten exposure, difficulties navigating food-related social situations, contributing to elevated levels of trait anxiety. Approximately one-third of adolescents reported elevated depressive symptoms. Adolescents with more depressive symptoms tended to exhibit higher neuroticism, lower extraversion, and poorer adherence to the gluten-free diet. A previous study from the same center examined 15 adolescents with celiac disease (CeD) (mean age = 15.5 years, SD = 1.6) and reported a mean CES-DC score of 10.4 (SD = 10.3) [ 48 ]. That study did not report the percentage of participants exceeding the clinical cutoff. The proportion of adolescents with CeD meeting the threshold for depressive symptoms in the present study was notably higher than that observed in normative samples. Given the cross-sectional nature of the study, it remains unclear whether adolescents first experience depressive symptoms that make CeD management more challenging, or whether the daily demands of CeD contribute to the development of depressive symptoms, which in turn may further affect adherence and overall well-being. Evidence in adults with CeD suggests a bidirectional pattern: in a population-based cohort, individuals with pre-existing anxiety or depression were more likely to achieve mucosal healing after diagnosis, with anxiety between diagnosis and follow-up biopsy increasing healing odds nearly nine-fold (OR = 8.94, 95% CI = 2.03–39.27), and prior depression increasing the odds by 47% (OR = 1.47, 95% CI = 1.01–2.15) [ 52 ]. However, individuals without prior anxiety or depression who achieved mucosal healing later showed a higher risk of developing anxiety (HR = 1.49, 95% CI = 1.12–1.96) and, among women, depression (HR = 1.39, 95% CI = 1.05–1.82) [ 52 ]. Implications In clinical practice, the study's findings could inform tailoring nutrition counseling and psychosocial support to adolescents’ personality profiles. The Big Five Inventory (44 items) is primarily used for research, and administering the full version may be impractical in clinical settings. However, during clinic visits, healthcare providers may ask a few targeted, simple questions to approximate clients’ personality traits. A few open-ended questions can be adapted from the Big Five Inventory-10 (BFI-10) for use in nutrition consultation, although this approach has not been formally validated [ 53 ] or evaluated with CeD patients. Examples are presented in Table 10 , including primary questions assessing the Big Five personality traits and behavioral probes. Table 10 Questions for Assessing Personality Traits and Potential Strategies During Nutrition Consultation Big Five Traits Questions for Assessing Personality Traits Interpretation Potential Strategies Openness Primary : Would you describe yourself as open to new experiences (e.g., trying new foods or travelling)? Probe : What does your typical eating routine look like? Higher openness may reflect willingness to try new GF foods, recipes, and dinging experiences; lower openness may reflect preference for routine and repetitive GF food choices. Introduce novel GF foods and recipes, identify new dining options, and support positive social eating experiences with peers. * Conscientiousness Primary : Would you describe yourself as organized and disciplined? Probe : When making diet or health changes, how do you approach them? Lower conscientiousness may be associated with limited meal planning, inconsistent label reading, reduced cross-contact precautions, less structured monitoring, or reliance on spontaneous decisions. Emphasize structured routines, meal-planning, label reading skills, executive-function supports (e.g., task breakdown, shopping lists), and regular feedback. * Extraversion Primary : Would you describe yourself as outgoing and sociable? Probe : How do social gatherings influence what and how you eat? Higher extraversion may increase exposure to social eating pressures. Lower extraversion may be associated with more solitary eating patterns. Facilitate peer support (in-person or online), support groups, and opportunities for structed discussion about navigating social eating situations. * Agreeableness Primary : Would you describe yourself as generally trusting and cooperative? Probe : How comfortable are you in saying no in social situations? Higher agreeableness may be associated with difficulty in setting boundaries or declining potential unsafe foods. Lower agreeableness facilitates firmer boundary-setting. Although this study did not find a direct association between agreeableness and CeD-related outcomes, support may include self-advocacy and boundary-setting skills, including role-play for communicating dietary needs and managing social pressures to minimize GFD transgressions. * Neuroticism Primary : Would you describe yourself as prone to stress or anxiety? Probe : Do you notice changes in your eating when you feel stressed or anxious? Higher neuroticism may manifest as emotional reactivity, stress-related eating, anxiety about cross-contact, or perceived loss of control. Incorporate emotional regulation (e.g., mindfulness), coping skills training, role-play for anxiety-provoking eating situations. * *If concerns related to disordered eating are identified, referral to mental health professionals is recommended. Based on the client's answers, dietitians may be able to gauge the client's personality traits (especially high openness, low conscientiousness, low extraversion, and high neuroticism) and offer tailored nutrition support accordingly (Table 10 ). If dietitians identify adolescents with higher openness, they may find them to be more receptive to new ideas and experiences. These individuals may benefit from dietitians who adopt a creative approach, sharing information about new restaurants and travel opportunities where GF foods can be obtained safely, while encouraging curiosity and engagement in novel social experiences. For those with lower conscientiousness, additional support is needed to promote good GFD adherence, including structured routines, label reading and meal-planning supports such as role-playing, mealtime reminders, shopping lists, and timely feedback. For individuals with higher neuroticism and lower extraversion, providing support for social eating pressures around meals without becoming hypervigilant or access to peer support may be helpful as mindfulness may further improve coping skills and emotional regulation skills. When anxiety, depressive symptoms, or disordered eating behaviors are identified, referral to mental health professionals should be considered. Strength and Limitations This study is among the first to explore the Big Five Personality Inventory (BFI) in adolescents with biopsy- and/or serology-confirmed CeD, offering data on each big five traits in adolescents with CeD, novel insights into how individual psychological traits may shape diet-related disease management behaviors and patients’ QOL as well as anxiety and depression outcomes, addressing a major gap in the pediatric celiac literature. This study has several limitations. First, the sample is predominantly white, highly educated, and primarily female. Non-white individuals are underrepresented in CeD research conducted in the United States and Europe, and since CeD does not affect only Caucasian individuals, expanding diagnostic efforts across diverse communities remains a critical need [ 54 ]. Second, the small sample size (n = 50) limits statistical power. Third, the cross-sectional design prevents causal inferences. Fourth, during recruitment, caregivers sat in the same room as the CeD patients, and some may have instructed the adolescent on how to complete the questionnaire when the adolescent was unsure. This behavior may alter adolescents’ responses when their caregivers are present. Finally, personality disorders and other psychiatric conditions were not controlled for within the sample (except for the exclusion of eating disorders). We did not know whether personality disorders or other psychiatric conditions could have influenced the responses of adolescents. Conclusion Our findings demonstrate that adolescents’ personality traits, particularly low conscientiousness, high openness, and high neuroticism, and low extraversion, are meaningfully associated with lowerdiet adherence, maladaptive dietary behaviors,lower QOL, and higher anxiety and depression, respectively. This underscores the importance of individual personality traits in the management of CeD. Examining the feasibility of incorporating personality traits assessment and tailored dietary interventions in counseling is warranted. Longitudinal studies are needed to clarify the directionality between personality traits, dietary behavior outcomes, and psychosocial well-being. Statements and Declarations Funding: This study received no external funding. Competing Interests: The authors declare no conflicts of interest. AI Tool: During the preparation of this work the author used ChatGPT 5.2 in order to check grammar. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication. Author Contributions Statement Z.C. contributed to the study conception and design, data acquisition, data analysis, and interpretation of the findings, prepared the tables and figures, and wrote the first draft of the manuscript. 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APA PsycTests. https://doi.org/10.1037/t12228-000 Faulstich, M. E., Carey, M. P., Ruggiero, L., Enyart, P., & Gresham, F. (1986). Assessment of depression in childhood and adolescence: an evaluation of the Center for Epidemiological Studies Depression Scale for Children (CES-DC). The American journal of psychiatry , 143 (8), 1024–1027. https://doi.org/10.1176/ajp.143.8.1024 Adelman, D., Leffler, D., Lebwohl, B., Nehra, V., Hansen, J., Minaya, M. T., Van Dyke, C., Marcantonio, A., & Acaster, S. (2012). Celiac disease symptom frequency and severity using a disease-specific patient-reported outcome diary: Observations from A psychometric validation study in 202 patients. American Journal of Gastroenterology , 107 . https://doi.org/10.14309/00000434-201210001-01509 Serin, Y., Andruškienė, J., Verma, A. K., Śmiełowska, M., Dzingelevičius, N., Vilčiauskis, A., Vaičekauskaitė, R., Bradauskienė, V., Buszewski, B., & Dzingelevičienė, R. (2025). Evaluation of Quality of Life in Adult Celiac Patients Living in Lithuania and Their Compliance with a Gluten-Free Diet: A Pilot Study. Medicina , 61 (7), 1278. https://doi.org/10.3390/medicina61071278 Stroebele-Benschop, N., Rau, C. J., Dieze, A., & Bschaden, A. (2025). Life Challenges and Quality of Life of People Living With Coeliac Disease: Time of Diagnosis Matters. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association , 38 (1), 10.1111/jhn.13413. https://doi.org/10.1111/jhn.13413 Roberts, B. W., Jackson, J. J., Fayard, J. V., Edmonds, G., & Meints, J. (2009). Conscientiousness. In M. R. Leary & R. H. Hoyle (Eds.), Handbook of individual differences in social behavior (pp. 369–381). The Guilford Press. Conner, T. S., Mirosa, M., Bremer, P., & Peniamina, R. (2018). The Role of Personality in Daily Food Allergy Experiences. Frontiers in psychology , 9 , 29. https://doi.org/10.3389/fpsyg.2018.00029 Ciacci, C., & Zingone, F. (2015). The Perceived Social Burden in Celiac Disease. Diseases (Basel, Switzerland) , 3 (2), 102–110. https://doi.org/10.3390/diseases3020102 Wolf, R. L., Lebwohl, B., Lee, A. R., Zybert, P., Reilly, N. R., Cadenhead, J., Amengual, C., & Green, P. H. R. (2018). Hypervigilance to a Gluten-Free Diet and Decreased Quality of Life in Teenagers and Adults with Celiac Disease. Digestive diseases and sciences , 63 (6), 1438–1448. https://doi.org/10.1007/s10620-018-4936-4 Cadenhead, J. W., Martínez-Steele, E., Contento, I., Kushi, L. H., Lee, A. R., Nguyen, T. T. T., Lebwohl, B., Green, P. H. R., & Wolf, R. L. (2023). Diet quality, ultra-processed food consumption, and quality of life in a cross-sectional cohort of adults and teens with celiac disease. Journal of human nutrition and dietetics: the official journal of the British Dietetic Association , 36 (4), 1144–1158. https://doi.org/10.1111/jhn.13137 Koç, N., Özün, Ö. İ., Başaran, E. G., Çalişkan, H. A., Ay, B., Özkeçeci, C. F., & Balamtekin, N. (2025). The relationship between diet quality, social participation, quality of life, and occupational balance in adolescents with Celiac disease. Journal of health, population, and nutrition , 44 (1), 364. https://doi.org/10.1186/s41043-025-01024-9 Alves, J. M., Yunker, A. G., DeFendis, A., Xiang, A. H., & Page, K. A. (2020). Associations between Affect, Physical Activity, and Anxiety Among US Children During COVID-19. medRxiv : the preprint server for health sciences , 2020.10.20.20216424. https://doi.org/10.1101/2020.10.20.20216424 Ludvigsson, J. F., Lebwohl, B., Chen, Q., Bröms, G., Wolf, R. L., Green, P. H. R., & Emilsson, L. (2018). Anxiety after coeliac disease diagnosis predicts mucosal healing: a population-based study. Alimentary pharmacology & therapeutics, 48(10), 1091–1098. https://doi.org/10.1111/apt.14991 Rammstedt, B. & John, O. P. (2007). Measuring personality in one minute or less: A 10 item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203‐212 Lebwohl B. (2015). Celiac disease and the forgotten 10%: the "silent minority". Digestive diseases and sciences , 60 (6), 1517–1518. https://doi.org/10.1007/s10620-015-3572-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 10 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 07 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9058390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622852615,"identity":"6e8fecb6-b9bb-45a9-bac1-e22c0afb1d25","order_by":0,"name":"Zhijun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsWrCQBzH8Qt/OJeLWf+hQ17hRHASn+VCIN2EUpBMEhAuS8HVQV/D+SSDi8QHyHLZW4hzl15baLLkdOxw3+k3/D/DnxCX618G51vL579bEyLo92BWQlPALMWfLR4jbEbYpewIuUeiQs10KK/rIIdGx/K4HI+Upz/kMPHexDN/kTWiolMey/qVMgGTg4UAEoWhIZwwiobE0rzz5FsIRS9HX1Y9EujRp40wBIrsonoEBQUbQZbCFLMk3JXmF1GZX7DZhPtqmETF9da0fBEExabR7apeRtvk1L6vhkkX/C0vf+Te5XK5XJa+AFdnSmuKgcdNAAAAAElFTkSuQmCC","orcid":"","institution":"Teachers College, Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Chen","suffix":""},{"id":622852616,"identity":"d5ec538c-82ab-46e4-950f-9f66ae4c79f6","order_by":1,"name":"Anne R. Lee","email":"","orcid":"","institution":"Celiac Disease Center at Columbia University Irving Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"R.","lastName":"Lee","suffix":""},{"id":622852617,"identity":"5c220de0-7473-405e-ba76-f5029b369390","order_by":2,"name":"Pamela Koch","email":"","orcid":"","institution":"Teachers College, Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Koch","suffix":""},{"id":622852618,"identity":"9a25f32b-476d-45f2-8645-424fc5230382","order_by":3,"name":"Benjamin Lebwohl","email":"","orcid":"","institution":"Celiac Disease Center at Columbia University Irving Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Lebwohl","suffix":""},{"id":622852619,"identity":"e54fbc6e-9d18-4fb0-ae77-f82754a7e4af","order_by":4,"name":"Peter HR Green","email":"","orcid":"","institution":"Celiac Disease Center at Columbia University Irving Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"HR","lastName":"Green","suffix":""},{"id":622852620,"identity":"7c376676-071a-4d87-856d-32e4c0170a4b","order_by":5,"name":"Jessica Lebovits","email":"","orcid":"","institution":"Celiac Disease Center at Columbia University Irving Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Lebovits","suffix":""},{"id":622852621,"identity":"80fdcf41-9d2e-49a8-9568-2b646bea263d","order_by":6,"name":"Randi L Wolf","email":"","orcid":"","institution":"Teachers College, Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Randi","middleName":"L","lastName":"Wolf","suffix":""}],"badges":[],"createdAt":"2026-03-07 12:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9058390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9058390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107254561,"identity":"b584cf88-deb2-4552-9656-adada7c1beee","added_by":"auto","created_at":"2026-04-19 12:03:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115741,"visible":true,"origin":"","legend":"\u003cp\u003eSummary Image of Results for A Maladaptive Profile\u003c/p\u003e\n\u003cp\u003eNote: Identical superscripts indicate a significant association (p\u0026lt;0.05) between the BFI traits and outcome measures based on the adjusted multiple regression models.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9058390/v1/cfbcbf1ecc0b9139eb77cda0.png"},{"id":107484435,"identity":"17a595bd-9fdc-4e40-9a59-f64a15fe5be8","added_by":"auto","created_at":"2026-04-22 02:32:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9058390/v1/8b85623b-8994-495d-b026-3a36d7f50a94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personality Traits are Associated with Dietary Behaviors and Psychosocial Outcomes in Adolescents with Celiac Disease: A Cross-Sectional Study","fulltext":[{"header":"Practitioner Points","content":"\u003col\u003e\n \u003cli\u003ePersonality traits were associated with dietary behaviors and psychosocial outcomes in adolescents with CeD. \u003c/li\u003e\n \u003cli\u003eAn adolescent with higher openness (i.e., more novelty-seeking) may make a GFD feel more restrictive, lower conscientiousness may hinder adherence planning, and higher neuroticism may increase anxiety and depression risk.\u003c/li\u003e\n \u003cli\u003eClinicians may consider asking questions to gauge personality traits and incorporate personality-informed strategies into nutrition counseling to support GFD adherence and psychological well-being in CeD.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Introduction","content":"\u003cp\u003eCeliac disease (CeD) is a genetically-mediated autoimmune disease triggered by dietary gluten intake, causing damage to the mucosal lining of the small intestine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, the only treatment for CeD is a lifelong gluten-free diet (GFD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The GFD can be challenging to follow, particularly for adolescents, an age group often concerned with fitting in with peers, experiencing reduced reliance on parents, and navigating increased independence in decision-making [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among adolescent patients with CeD, GFD adherence rates in the U.S. ranged from 23% to 98%, with a median of 79% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Challenges of a GFD may be associated with impaired psychosocial well-being, lower quality of life (QOL), and mental health outcomes in adolescents [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Adolescents who are less adherent to a GFD experience greater disease burden, poorer QOL, and more CeD symptoms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescence is a time of rapid physiological, psychological, and social changes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is also a critical period for identity and personality development [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, little is known about how personality may shape an adolescent\u0026rsquo;s approach to a GFD when diagnosed with CeD, or how managing CeD may in turn influence personality development.\u003c/p\u003e \u003cp\u003ePersonality traits have been defined as the enduring patterns of thinking, feelings, and behaving that distinguish individuals from one another and reflect how a person typically responds in situations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. McCrae and Costa developed the five-factor model to describe the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Big Five personality traits can shape how individuals perceive burden and demands, self-regulate behavior, cope with stressors, and navigate social situations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In adults with CeD, personality traits have been associated with GFD and eating behaviors. Lower conscientiousness has been associated with higher nonadherence to the GFD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. And adults with CeD with higher neuroticism were associated with more maladaptive eating behaviors and poorer QOL [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence for associations between personality traits and gluten-free diet adherence in adolescents with CeD has relied primarily on temperament measures, not the Big Five. Wagner and colleagues compared personality traits in adolescents with CeD and those with both CeD and an eating disorder using the Junior Temperament and Character Inventory. Female adolescents with CeD and an eating disorder demonstrated lower self-directedness, indicating lower self-regulatory behaviors, than those with CeD alone [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. When comparing the temperament of adolescents with CeD, those who strictly adhered to the diet showed lower novelty seeking, impulsivity, and rule transgression, and higher eagerness for work and perfectionism (persistence), compared with those who had more than two diet transgressions per month [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLittle is known about how the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) among adolescents with CeD are associated with eating behaviors, including maladaptive eating and GFD adherence, as well as QOL, anxiety, and depression. This study aimed to examine how Big Five personality traits are associated with dietary behaviors relevant to CeD (gluten-free diet adherence and food attitudes and beliefs) and well-being outcomes (quality of life, anxiety, and depression) in adolescents with CeD. Understanding these associations may provide insights into individual variation in CeD management and inform clinical practice by enabling more tailored nutrition counseling.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eStudy design\u003c/strong\u003e \u003cp\u003eThis cross-sectional study analyzed data from the Celiac Disease Eating Pattern (CD-EAT) study, which examined the relationship between CeD and eating patterns in 50 adults and 50 adolescents-caregiver dyads. Data on adults has been published previously [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Data on adolescents was collected from February 14, 2020, through March 18, 2025. IRB approval was obtained from the Celiac Disease Center at Columbia University Irving Medical Center (CUIMC) (Rascal IRB-AAAS5501) and from Teachers College (IRB 19\u0026ndash;479 in September 2019; IRB 24\u0026ndash;460 2024).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipants\u003c/strong\u003e \u003cp\u003eParticipants were caregiver-adolescent dyads recruited from a U.S. CeD Referral Center. Adolescents 14 to 17 years were included if they had a confirmed diagnosis of CeD either by biopsy or serology based on ESPGHAN standards; were currently being treated at the Celiac Disease Center; had been following a gluten-free diet (GFD) for at least one year, and did not have a current or prior diagnosed eating disorder (e.g., anorexia nervosa, bulimia nervosa) as diagnosed by a physician. Adolescent eligibility required their own completion of the BFI and other assessments, as well as one caregiver independently completing the BFI to assess caregivers\u0026rsquo; personality traits. Exclusion criteria included self-diagnosed CeD, age less than 14 years old or greater than 17 years old, had never been seen as a patient at the Celiac Disease Center, or had a current or prior diagnosis of an eating disorder based on DSM-5 criteria. Informed consent was obtained from caregivers and adolescents provided assent. Adolescents received a \u003cspan\u003e$\u003c/span\u003e50 gift card for their participation. There were 108 patients prescreened by staff to determine potential eligibility of which 49 were excluded for not meeting eligibility criteria. Among the 59 patients who were approached to participate, 9 declined resulting in 50 participants. The sample size of 50 was chosen so as to obtain preliminary cross-sectional data on personality and CeD outcomes.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Measures:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAnthropometric measures\u003c/strong\u003e \u003cp\u003eCurrent weight was measured by a Healthometer brand digital scale. Height was measured using a Tanita stadiometer (model #HR200). Participants were asked to remove their shoes and jackets before their height and weight were measured.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic and Health History\u003c/strong\u003e \u003cp\u003eAge, gender, ethnicity, race, education, household income, and age at CeD diagnosis were collected for the adolescent participant, and the caregiver\u0026rsquo;s gender and education were collected. The CeD diagnostic method and registered dietitian visits were also collected.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003ePersonality traits\u003c/b\u003e: Personality traits were measured using the Big Five Inventory (BFI), a 44-item inventory assessing: Openness to Experience (10 items), Conscientiousness (9 items), Extroversion (8 items), Agreeableness (9 items), Neuroticism (8 items), and as established by John, Donahue, and Kentle [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Likert-type responses are used (from 1\u0026thinsp;=\u0026thinsp;Strongly disagree to 5\u0026thinsp;=\u0026thinsp;Strongly agree). Items are reverse-coded as appropriate and summed to create subscales, with higher scores indicating greater expression of the measured personality dimension. Openness to Experience (subscale range: 10\u0026ndash;50) measures the preference for variety and being creative, and novelty-seeking [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Conscientiousness (subscale range: 9\u0026ndash;45) measures the tendency toward organization and goal-oriented behavior. Extraversion (subscale range: 8\u0026ndash;40) reflects sociability and a propensity for positive emotions. Agreeableness (subscale range: 9\u0026ndash;45) measures cooperative, trusting, and humble traits. Neuroticism (subscale range: 8\u0026ndash;40) measures the tendency to experience negative emotions, including anxiety, nervousness, sadness, and tension, rather than emotional stability. The BFI demonstrated good validity and reliability in adult and adolescent samples [\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eGluten Free Diet Adherence (CDAT)\u003c/b\u003e: The Celiac Dietary Adherence Test (CDAT) is a 7-item instrument that assesses four dimensions of GFD adherence in individuals with CeD: CeD Symptoms, Self-efficacy, Reasons for following a GFD, and Level of Adherence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Response options range from 1 (none of the time) to 5 (all of the time). Total scores range from 7 to 35, with scores below 13 indicating excellent or very good adherence to a GFD, scores between 13 and 17 indicating moderate adherence, and scores above 17 indicating poor adherence. The CDAT has not been validated in adolescent populations; however, it has been used in studies including adolescents [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eFood Attitudes and Behaviors (CD-FAB)\u003c/b\u003e: The Celiac Disease-specific Food Attitudes and Behaviors (CD-FAB) is an 11-item tool that assesses food attitudes and behaviors related to avoiding minor gluten contamination and concerns about food safety (e.g., handling of food, trust, risk-taking, and food safety), as described by Satherley, Howard, and Higgs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. CD-FAB used Likert-type responses (from 1\u0026thinsp;=\u0026thinsp;Strongly disagree to 7\u0026thinsp;=\u0026thinsp;Strongly agree), with higher scores indicating more maladaptive eating attitudes and behaviors. Total score ranged from 11 to 77, with three subscales: food attitudes (ranging from 3 to 21), fear response (ranging from 4 to 28), and adaptive response (ranging from 4 to 28). Clinical relevance cut-offs have not been established. The tool was originally developed and validated by Satherley, Howard, and Higgs in an adult UK population; it has not yet been validated or used in adolescents [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For the present study, we compared our version with the original adult scale and made minor wording adjustments to ensure appropriateness for adolescents (ages 14\u0026ndash;17). For example, we changed \u0026ldquo;I get \u003cem\u003econcerned\u003c/em\u003e being near others when they are eating gluten\u0026rdquo; to \u0026ldquo;I get \u003cem\u003eworried\u003c/em\u003e being near others when they are eating gluten\u0026rdquo; since worried is a term more commonly used by adolescents.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCeliac Disease Quality of Life (CDPQOL)\u003c/b\u003e: The Celiac Disease-Specific Pediatric Quality of Life (CDPQOL) inventory is a 17-item, validated measure for pediatric patients aged 13\u0026ndash;18 with CeD to measure QOL [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The inventory had Likert-type responses ranging from 0\u0026thinsp;=\u0026thinsp;never, 1\u0026thinsp;=\u0026thinsp;almost never, 2\u0026thinsp;=\u0026thinsp;sometimes, 3\u0026thinsp;=\u0026thinsp;often, to 4\u0026thinsp;=\u0026thinsp;almost always across the first 17 items. It has four subscales: social (7 items), uncertainty (3 items), isolation (4 items), and limitations (3 items). In this study, raw scores (0\u0026ndash;68) were converted to scaled scores (0-100), with higher scores indicating better QOL. Clinical relevance cut-offs have not yet been determined.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnxiety (STAI-C)\u003c/strong\u003e \u003cp\u003eThe State-Trait Anxiety Inventory for Children (STAI-C) is a two-part measure with 20 items evaluating temporary anxiety \u0026ldquo;at the moment\u0026rdquo; (State Anxiety) and 20 items assessing usual or ongoing anxiety (Trait Anxiety), rated on a 3-point Likert scale, developed for use in children aged 9\u0026ndash;12 years [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Item scores are summed to obtain subset total scores; the range for each subtest is 20 to 60, with higher scores indicating greater anxiety. It does not have a set cutoff point. A previous study in adults with CeD used a cutoff point of 39 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This value is a commonly used STAI cutpoint for identifying clinically significant symptoms [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, this study used the same cutoff for analysis. The STAI-C was evaluated for validity and reliability in healthy adolescents and adolescents with substance use disorders aged 12 to 18 years [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Studies in CeD populations have used the STAI-C to assess anxiety, although the instrument has not been formally validated in this group [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDepression (CES-DC)\u003c/strong\u003e \u003cp\u003eThe Center for Epidemiologic Studies Depression Scale for Children (CES-DC) is a validated 20-item screening tool for depression in children and adolescents aged 6 to 17, based on the DSM-V criteria for a major depressive episode [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The items are rated on a 4-point Likert scale from 0 (not at all), 1 (a little), 2 (some), to 3 (a lot). The total scores range from 0 to 60, with higher scores indicating greater depressive symptoms. A CES-DC score greater than 15 suggests clinical depression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCeD Symptoms (CDSD)\u003c/strong\u003e \u003cp\u003eThe Celiac Disease Symptom Diary (CDSD) is a 6-item Patient-Reported Outcome (PRO) daily symptom diary developed in accordance with the US Food and Drug Administration\u0026rsquo;s PRO Guidance. It assesses the presence or absence of CeD-related symptoms over the past 24 hours, including diarrhea, changes in bowel movements, abdominal pain, bloating, nausea, and tiredness. The instrument is typically administered over a 7-day period; however, this study used a single-day assessment. Total scores range from 0 to 10, with higher scores indicating greater symptom severity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and range (minimum\u0026ndash;maximum) for continuous variables, and as n (%) for categorical variables were reported. BMI percentiles were calculated using the CDC growth charts (R package \u003cem\u003ecdcanthro\u003c/em\u003e). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE), 95% CI, and range (minimum-maximum) were reported for BFI. Internal consistency of BFI was evaluated using Cronbach\u0026rsquo;s alpha (α\u0026thinsp;\u0026gt;\u0026thinsp;0.70). Normal distribution of the data was assessed using the Shapiro\u0026ndash;Wilk test. Bivariate correlation between BFI, dietary behavior outcomes (CDAT and CD-FAB), and psychosocial outcomes (CDPQOL, STAI, CES-DC) was examined using spearman correlations.\u003c/p\u003e \u003cp\u003eMultiple regression models were used to examine associations between personality traits and CDAT, CD-FAB, adjusting for gender and age at diagnosis. We hypothesized that personality traits (BFI) would be associated with adherence to the gluten-free diet (CDAT) and maladaptive eating behaviors (CDFAB), and used the following regression models to test these associations:\u003c/p\u003e \u003cp\u003eCDAT Total Score\u0026thinsp;=\u0026thinsp;β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε\u003c/p\u003e \u003cp\u003eCD-FAB Overall Score\u0026thinsp;=\u0026thinsp;β₀ + β₁ (BFI traits) + β₂ (gender) + β₃ (age at diagnosis) + ε.\u003c/p\u003e \u003cp\u003eThe associations between personality traits and QOL, anxiety, and depression was assessed using multiple linear regression models, adjusting for gender, age at diagnosis, and CeD symptoms. We hypothesized that personality traits (BFI) would be associated with QOL (CDPQOL), anxiety (STAI) and depression (CES-DC), and used the following regression models to test these associations:\u003c/p\u003e \u003cp\u003eCDPQOL Total Score\u0026thinsp;=\u0026thinsp;β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β\u003csub\u003e4\u003c/sub\u003e (gender) + β\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + β\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + ε\u003c/p\u003e \u003cp\u003eSTAI\u0026thinsp;=\u0026thinsp;β₀ + β₁ (BFI traits) + β₂ (CDAT) + β₃ (CD-FAB) + β\u003csub\u003e4\u003c/sub\u003e (gender) + β\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + β\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + ε\u003c/p\u003e \u003cp\u003eCES-DC\u0026thinsp;=\u0026thinsp;β₀ + β₁(BFI traits) + β₂(CDAT) + β₃(CD-FAB) + β\u003csub\u003e4\u003c/sub\u003e (gender) + β\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + β\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + ε\u003c/p\u003e \u003cp\u003eThe selection of covariates was based on prior research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and clinical relevance as well as the sample size. Since years since diagnosis and age at diagnosis were highly linearly correlated, age at diagnosis was included in the model.\u003c/p\u003e \u003cp\u003eModel assumptions (linearity, normality, homoscedasticity, and independence of residuals) were evaluated. For each model, unstandardized coefficients (\u003cem\u003eb\u003c/em\u003e), standardized coefficients (β), standard errors (SE), \u003cem\u003et\u003c/em\u003e-statistics, 95% confidence intervals, and \u003cem\u003ep\u003c/em\u003e-values were reported where appropriate. Overall model fit was reported with \u003cem\u003eF\u003c/em\u003e-statistic, \u003cem\u003eR\u0026sup2;\u003c/em\u003e, and adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e. A value of \u003cem\u003ep\u003c/em\u003e \u0026lt; .05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eWhen data were missing, item-level missing data were replaced using the mean of the remaining items within the corresponding scale. Analyses were conducted in RStudio (Version 1.3.1073; PBC, Boston, MA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic and health characteristics of the study sample are summarized in Table 1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Demographics and Health Characteristics of Study Sample\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eAdolescents (N=50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eN (Mean \u0026plusmn; SD or %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eGender Identity (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (76%)\u003c/p\u003e\n \u003cp\u003e12 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eAge\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(M \u0026plusmn; SD, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e15.2 \u0026plusmn; 1.0 (14 \u0026ndash; 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eAge at diagnosis (M \u0026plusmn; SD, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e10.3 \u0026plusmn; 3.9 (1.9 - 16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eRace (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBlack or African American\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003cp\u003e48 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eEthnicity (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (6%)\u003c/p\u003e\n \u003cp\u003e47 (94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eYears Since Diagnosis (M \u0026plusmn; SD, range)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 4.0 (0.3 - 15.6)\u003c/p\u003e\n \u003cp\u003e29 (58%)\u003c/p\u003e\n \u003cp\u003e21 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eCeD diagnosis method\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Biopsy only\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ESPHGAN standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43 (86%)\u003c/p\u003e\n \u003cp\u003e7 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eBMI Percentile (M \u0026plusmn; SD, range)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Underweight \u0026lt; 5%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Normal Weight 5% - 85%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Overweight 85% - 95%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Obese \u0026gt; 95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e55.1 \u0026plusmn; 23.4 (0.2 - 99.7)\u003c/p\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003cp\u003e39 (78%)\u003c/p\u003e\n \u003cp\u003e6 (12%)\u003c/p\u003e\n \u003cp\u003e3 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eRDN visit (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;RDN currently\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;RDN past only\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;RDN never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (72%)\u003c/p\u003e\n \u003cp\u003e7 (14%)\u003c/p\u003e\n \u003cp\u003e7 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eCaregiver Sex (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (94%)\u003c/p\u003e\n \u003cp\u003e3 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eCaregiver Education (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt; High School or High School graduate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Some college\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;College graduate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Post-graduate training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e20 (40%)\u003c/p\u003e\n \u003cp\u003e28 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64.775%;\"\u003e\n \u003cp\u003eHousehold income (n, %)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;$50,000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; $50,000-$100,000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;$100,000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Did not disclose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (4%)\u003c/p\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003cp\u003e28 (56%)\u003c/p\u003e\n \u003cp\u003e19 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CeD = celiac disease; M = mean; SD = standard deviation; RDN = Registered Dietitian Nutritionists.\u003c/p\u003e\n\u003cp\u003eAmong the 50 participants, 76% were female. The mean age was 15.2 years (SD = 1.0), and the mean age at diagnosis was 10.3 years (SD = 3.9). The sample was predominantly white (96%). Participants had lived with CeD for an average of 5.2 years (SD = 4.0). Most adolescents were diagnosed by duodenal biopsy (86%) and 14% were diagnosed using serology only (meeting ESPHGAN criteria). Most (78%) were in the normal BMI percentile for age. Seventy-two percent of adolescents were currently seeing a registered dietitian nutritionist (RDN). Caregivers were predominantly female (94%), being the mothers of the participants, highly educated, with more than half reporting postgraduate training. Over half had a household income \u0026gt;$100,000 per year. The big five personality scores for openness (mean \u0026plusmn; SE) were 35.4 \u0026plusmn; 0.75, conscientiousness 31.2 \u0026plusmn; 0.83, extraversion 26.4 \u0026plusmn; 0.79, agreeableness 34.7 \u0026plusmn; 0.78, and neuroticism 23.6 \u0026plusmn; 0.85 (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Descriptive Statistics of Big Five Inventory Scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstrument Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2244%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2436%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBig Five\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOpenness (O)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConscientiousness (C)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExtraversion (E)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAgreeableness (A)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNeuroticism (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 \u0026ndash; 50\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;9 \u0026ndash; 45\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;8 \u0026ndash; 40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;9 \u0026ndash; 45\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;8 \u0026ndash; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35.4 \u0026plusmn; 0.75\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.2 \u0026plusmn; 0.83\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.4 \u0026plusmn; 0.79\u003c/p\u003e\n \u003cp\u003e34.7 \u0026plusmn; 0.78\u003c/p\u003e\n \u003cp\u003e23.6 \u0026plusmn; 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2244%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33.9 - 36.9\u003c/p\u003e\n \u003cp\u003e29.6 - 32.8\u003c/p\u003e\n \u003cp\u003e24.8 - 27.9\u003c/p\u003e\n \u003cp\u003e33.2 - 36.2\u003c/p\u003e\n \u003cp\u003e21.9 - 25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7564%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 - 48\u003c/p\u003e\n \u003cp\u003e15 - 42\u003c/p\u003e\n \u003cp\u003e15 - 40\u003c/p\u003e\n \u003cp\u003e15 - 43\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;8 - 36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Higher scores mean greater levels of BFI traits.\u003c/p\u003e\n\u003cp\u003eSixty-two percent of participants reported good/excellent adherence to the GFD (CDAT \u0026lt;13). The CD-FAB eating attitudes and behaviors instrument had an average total score of \u003cem\u003eM\u003c/em\u003e = 32.0, \u003cem\u003eSD\u003c/em\u003e = 11.4 (range = 14\u0026ndash;66). The mean CDPQOL score was 62.8 \u0026plusmn; 17.7 (14.7\u0026ndash;89.7), indicating good overall QOL. Almost all participants (98%), except one, met the clinical cutoff suggesting state anxiety, and sixteen participants (32%) met the clinical cutoff suggesting trait anxiety. Eighteen participants (36%) met the clinical cutoff suggesting depression. The mean frequency of CeD symptoms over the past 24 hours was 1.5 \u0026plusmn; 1.1 (range: 0\u0026ndash;5). Twelve percent of participants (n = 6) reported no symptoms, 54% (n = 27) reported one symptom, and 34% (n = 17) reported two or more symptoms. Only six participants (12%) did not report experiencing CeD-related symptoms in the preceding 24 hours (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Descriptives of Key Study Measures\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003eStudy Measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003en (%) or\u003c/p\u003e\n \u003cp\u003eMean \u0026plusmn; SD (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003eGluten-free Diet Adherence\u003c/p\u003e\n \u003cp\u003eCDAT Overall\u003c/p\u003e\n \u003cp\u003e\u0026lt;13 (Good/Excellent Adherence)\u003c/p\u003e\n \u003cp\u003e13-17 (Moderate Adherence)\u003c/p\u003e\n \u003cp\u003e\u0026gt;17 (Poor Adherence)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.1 \u0026plusmn; 4.3 (7 - 23)\u003c/p\u003e\n \u003cp\u003e31 (62%)\u003c/p\u003e\n \u003cp\u003e13 (26%)\u003c/p\u003e\n \u003cp\u003e6 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003eMaladaptive Food Attitudes \u0026amp; Behavior\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eCD-FAB Total\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e32.0 \u0026plusmn; 11.4 (14 \u0026ndash; 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003eCeliac-Specific QOL\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eCDPQOL Total\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e62.8 \u0026plusmn; 17.7 (14.7 \u0026ndash; 89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003e% meeting clinical cutoff for anxiety\u003c/p\u003e\n \u003cp\u003eSTAI State\u003c/p\u003e\n \u003cp\u003eSTAI Trait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49 (98%)\u003c/p\u003e\n \u003cp\u003e16 (32%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003e% meeting clinical cutoff for depression\u003c/p\u003e\n \u003cp\u003eCES-DC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65.6863%;\"\u003e\n \u003cp\u003e# symptoms past 24 hrs, CDSD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.3137%;\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 1.1 (0 - 5)\u003c/p\u003e\n \u003cp\u003e6 (12%)\u003c/p\u003e\n \u003cp\u003e27 (54%)\u003c/p\u003e\n \u003cp\u003e17 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CDAT = Celiac Dietary Adherence Test; CD-FAB = Celiac Disease Food and Attitudes Behavior Checklist; CDPQOL = Celiac Disease Specific Pediatric Quality of Life Scale; STAI-C cutoff \u0026nbsp;\u0026ge; 39; Depression Cutoff \u0026nbsp;\u0026ge; 15; CDSD = Celiac Disease Symptoms Diary\u003c/p\u003e\n\u003cp\u003eSpearman correlations indicated that Big Five personality traits were significantly associated with multiple outcomes in adolescents with CeD (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4. Spearman Correlation of BFI and Study Outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBig Five Subcomponents of Adolescents (\u0026rho;, p, SE, 95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Measures\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFD Adherence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCDAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.064\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.336, 0.218]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.414** (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.621,\u003c/p\u003e\n \u003cp\u003e-0.153]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.266 (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.507, 0.013]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.224 (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.473, 0.058]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.387** (0.146)\u003c/p\u003e\n \u003cp\u003e[0.121, 0.600]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaladaptive Food Attitudes \u0026amp; Behaviors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCD-FAB\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=0.347*\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[0.076, 0.570]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.009 (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.286, 0.270]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.095 (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.363, 0.189]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.101 (0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.369, 0.182]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.099\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.185, 0.367]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCeD-Specific QOL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCDPQOL Total Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.343*\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.568, -0.072]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=0.252\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.028, 0.496]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.231\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.051, 0.478]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.289* (0.146)\u003c/p\u003e\n \u003cp\u003e[0.012, 0.525]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=\u003c/p\u003e\n \u003cp\u003e-0.469*** (0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.661,\u003c/p\u003e\n \u003cp\u003e-0.220]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSTAI-C State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=0.195\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.088, 0.449]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.127 (0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.391, 0.157]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.170 (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.428, 0.114]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.175\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.109, 0.432]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.284* (0.146)\u003c/p\u003e\n \u003cp\u003e[0.006, 0.521]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSTAI-C Trait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=0.185\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.099, 0.440]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.477*** (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.667,\u003c/p\u003e\n \u003cp\u003e-0.229]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.319* (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.548,\u003c/p\u003e\n \u003cp\u003e-0.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.304* (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.537,\u003c/p\u003e\n \u003cp\u003e-0.028]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.780*** (0.146)\u003c/p\u003e\n \u003cp\u003e[0.641, 0.870]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCES-DC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026rho;=0.206\u003c/p\u003e\n \u003cp\u003e(0.146)\u003c/p\u003e\n \u003cp\u003e[-0.077, 0.458]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.499*** (0.146)\u003c/p\u003e\n \u003cp\u003e[-0.683,\u003c/p\u003e\n \u003cp\u003e-0.256]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.421** (0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.626,\u003c/p\u003e\n \u003cp\u003e-0.162]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=-0.278 (0.146)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[-0.517, 0.000]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026rho;=0.740*** (0.146)\u003c/p\u003e\n \u003cp\u003e[0.582, 0.845]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eValues represent Spearman correlation coefficients (\u0026rho;) with standard errors in parentheses and 95% confidence intervals in brackets. *p \u0026lt; .05, **p \u0026lt; .01, ***p \u0026lt; .001\u003c/p\u003e\n\u003cp\u003eO = Openness; C = Conscientiousness; E = Extraversion; A = Agreeableness; N = Neuroticism; CDAT = Celiac Dietary Adherence Test (higher scores indicate poorer adherence). CD-FAB = Celiac Disease Food Attitudes and Behavior Questionnaire (higher score means more maladaptive food attitudes and behavior). CDPQOL = Celiac Disease Pediatric Quality of Life (higher scores means better QOL); STAI-C = State-Trait Anxiety Inventory for Children; CES-DC = Center for Epidemiologic Studies Depression Scale for Children.\u003c/p\u003e\n\u003cp\u003eHigher conscientiousness (\u0026rho; = \u0026minus;0.414, p \u0026lt; .01) was associated with better adherence (lower CDAT scores), whereas higher neuroticism (\u0026rho; = 0.387, p \u0026lt; .01) was associated with poorer adherence. Higher openness was associated with more maladaptive food attitudes and behaviors (higher CD-FAB scores) (\u0026rho; = 0.347, p \u0026lt; .05) and lower quality of life (\u0026rho; = \u0026minus;0.343, p \u0026lt; .05). Additionally, lower quality of life was associated with lower agreeableness (\u0026rho; = 0.289, p \u0026lt; .05) and higher neuroticism (\u0026rho; = \u0026minus;0.469, p \u0026lt; .001). Higher state anxiety was associated with higher neuroticism (\u0026rho; = 0.284, p \u0026lt; .05). Higher trait anxiety was associated with lower conscientiousness (\u0026rho; = \u0026minus;0.477, p \u0026lt; .001), lower extraversion (\u0026rho; = \u0026minus;0.319, p \u0026lt; .05), and lower agreeableness (\u0026rho; = \u0026minus;0.304, p \u0026lt; .05), as well as higher neuroticism (\u0026rho; = 0.780, p \u0026lt; .001). More depressive symptoms were associated with lower conscientiousness (\u0026rho; = \u0026minus;0.499, p \u0026lt; .001), lower extraversion (\u0026rho; = \u0026minus;0.421, p \u0026lt; .01), and higher neuroticism (\u0026rho; = 0.740, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIn order to examine the association between the Big Five personality traits and adherence to a gluten-free diet, multiple regression analysis was conducted as described in the Methods section. The multiple regression model between personality trait (BFI) and GFD adherence (CDAT) was statistically significant, F (7, 42) = 2.87, p = .02, explaining approximately 32% of the variance in adherence scores (R\u0026sup2; = .32, adjusted R\u0026sup2; = .21; Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Multiple Regression of Personality Trait (BFI) and GFD Adherence (CDAT)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"514\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicting Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpenness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.153, 0.363]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConscientiousness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.\u003cstrong\u003e02*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.575, -0.065]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtraversion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.372, 0.124]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreeableness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.197, 0.262]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuroticism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.308, 0.295]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-4.46, 1.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e[-0.104, 0.533]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 376px;\"\u003e\n \u003cp\u003eR\u0026sup2; = 0.32; Adj. R\u0026sup2; = 0.21;\u003c/p\u003e\n \u003cp\u003eF (7, 42) = 2.87; p = 0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel: CDAT Total Score = \u0026beta;₀ + \u0026beta;₁ (BFI traits) + \u0026beta;₂ (gender) + \u0026beta;₃ (age at diagnosis) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eNote: CDAT = Celiac Dietary Adherence Test; Gender coded 0 = male, 1 = female; Age at diagnosis treated as continuous. \u003cem\u003eb\u003c/em\u003e = unstandardized regression coefficient; \u0026beta; = standardized coefficient; SE = standard error; \u003cem\u003et\u003c/em\u003e = \u003cem\u003et\u003c/em\u003e-statistic; \u003cem\u003ep\u003c/em\u003e = significance level; CI = confidence interval. \u003cem\u003eF\u003c/em\u003e = overall model F-statistic; \u003cem\u003edf₁\u003c/em\u003e = numerator degrees of freedom (number of predictors); \u003cem\u003edf₂\u003c/em\u003e = denominator degrees of freedom (residual degrees of freedom); \u003cem\u003eR\u0026sup2;\u003c/em\u003e = proportion of variance in CDAT explained by the model; Adj. \u003cem\u003eR\u0026sup2;\u003c/em\u003e = adjusted proportion of variance accounting for model complexity.\u003cem\u003e\u0026nbsp;*p\u003c/em\u003e \u0026lt; .05; *\u003cem\u003e*p\u003c/em\u003e \u0026lt; .01; **\u003cem\u003e*p\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eHigher conscientiousness was associated with better GFD adherence (lower CDAT scores) (\u0026beta; = \u0026minus;0.44, SE = 0.13, p = .02). All other personality traits, gender, and age at diagnosis were not significantly associated with CDAT in the model (p \u0026gt; .05). The multiple regression model between personality trait (BFI) and maladaptive food attitudes and behaviors (CD-FAB) was not statistically significant, F (7, 42) = 1.37, p = .24 (Table 6).\u003c/p\u003e\n\u003cp\u003eTable 6. Multiple Regression of Personality Trait (BFI) and Maladaptive Eating Attitudes and Behaviors (CD-FAB)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"508\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpenness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.01*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[0.210, 1.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConscientiousness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-0.821, 0.650]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtraversion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-1.29, 0.148]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreeableness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-0.744, 0.584]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuroticism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-0.976, 0.767]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-8.90, 8.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e[-0.944, 0.897]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 370px;\"\u003e\n \u003cp\u003eR\u0026sup2; = 0.19, Adj. R\u0026sup2; = 0.05;\u003c/p\u003e\n \u003cp\u003eF (7, 42) = 1.37, p = 0.24.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel: CD-FAB Total Score = \u0026beta;₀ + \u0026beta;₁ (BFI traits) + \u0026beta;₂ (gender) + \u0026beta;₃ (age at diagnosis) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eNote: CD-FAB = Celiac Disease Food Attitudes and Behaviors. Gender coded 0 = male, 1 = female; Age at diagnosis treated as continuous. b = unstandardized regression coefficient; \u0026beta; = standardized coefficient; SE = standard error; t = t-statistic; CI = confidence interval. Significance markers: *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eHowever, higher openness was positively associated with more maladaptive food-related attitudes and behaviors after adjusting for all other variables (\u0026beta; = 0.45, SE = 0.37, p = .01). No other personality traits, gender, or age at diagnosis were significantly associated with CD-FAB (ps \u0026gt; .05).\u003c/p\u003e\n\u003cp\u003eWhen assessing the relationship between personality traits (BFI) and CeD-specific QOL (CDPQOL), the multiple regression model between BFI and QOL explained a substantial proportion of variance in QOL (R\u0026sup2; = 0.65, adjusted R\u0026sup2; = 0.56; F (10, 39) = 7.16, p \u0026lt; 0.001) (Table 7).\u003c/p\u003e\n\u003cp\u003eTable 7. Multiple Regression of Personality Traits (BFI) and Quality of Life (QOL)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eModel: QOL Total Score = \u0026beta;₀ + \u0026beta;₁ (BFI traits) + \u0026beta;₂ (CDAT) + \u0026beta;₃ (CD-FAB) + \u0026beta;\u003csub\u003e4\u003c/sub\u003e (gender) + \u0026beta;\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + \u0026beta;\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eNote: CDAT score lower means better GFD adherence; \u0026beta; = standardized coefficient; SE = standard error; t = t-statistic; \u0026beta; means with every 1 standard deviation increase. Significance markers: *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eAdolescents with lower QOL were significantly associated with higher openness (\u0026beta; = \u0026minus;6.08, SE = 2.27, p = 0.011), poorer gluten-free diet adherence (higher CDAT scores) (\u0026beta; = \u0026minus;5.56, SE = 2.08, p = 0.011), more maladaptive food attitudes and behaviors (lower CD-FAB scores) (\u0026beta; = \u0026minus;4.53, SE = 1.94, p = 0.025), and greater CeD symptom burden over the past 24 hours (\u0026beta; = \u0026minus;4.46, SE = 2.07, p = 0.038).\u003c/p\u003e\n\u003cp\u003eFor the relationship between BFI and trait anxiety, the multiple regression model was significant (R\u0026sup2; = 0.68, adjusted R\u0026sup2; = 0.59; F (10, 39) = 8.12, p \u0026lt; 0.001) (Table 8).\u003c/p\u003e\n\u003cp\u003eTable 8．\u003cstrong\u003eMultiple Regression of Personality Traits (BFI) and Trait Anxiety (STAI-C)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eModel: Trait Anxiety = \u0026beta;₀ + \u0026beta;₁ (BFI traits) + \u0026beta;₂ (CDAT) + \u0026beta;₃ (CD-FAB) + \u0026beta;\u003csub\u003e4\u003c/sub\u003e (gender) + \u0026beta;\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + \u0026beta;\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eNote: \u0026beta; = standardized coefficient; SE = standard error; t = t-statistic; \u0026beta; coefficients represent change in anxiety score per 1 SD increase in predictors. Higher scores indicate greater trait anxiety. Significance markers: *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eAdolescents with higher anxiety were significantly associated with higher neuroticism (\u0026beta; = 6.12, SE = 1.29, p \u0026lt; 0.001) and greater CeD symptom burden (\u0026beta; = 2.09, SE = 0.98, p = 0.039). No other personality traits, gluten-free diet adherence, food attitudes and behaviors, gender, or age at diagnosis were significantly associated with trait anxiety. In the multiple regression model between BFI and depressive symptoms, the model reached statistical significance (R\u0026sup2; = 0.68, adjusted R\u0026sup2; = 0.59; F(10, 39) = 8.14, p \u0026lt; 0.001) (Table 9).\u003c/p\u003e\n\u003cp\u003eTable 9. \u003cstrong\u003eMultiple Regression of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Personality Traits (BFI) and Depression (CES-DC)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eModel: Depression = \u0026beta;₀ + \u0026beta;₁ (BFI traits) + \u0026beta;₂ (CDAT) + \u0026beta;₃ (CD-FAB) + \u0026beta;\u003csub\u003e4\u003c/sub\u003e (gender) + \u0026beta;\u003csub\u003e5\u003c/sub\u003e (age at diagnosis) + \u0026beta;\u003csub\u003e6\u003c/sub\u003e (CeD Symptoms) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eNote: \u0026beta; = standardized coefficient; SE = standard error; t = t-statistic; \u0026beta; coefficients represent change in depressive symptom score per 1 SD increase in predictors. Higher CES-DC scores indicate greater depressive symptoms. Significance markers: *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eAdolescents with higher depressive symptoms were significantly associated with higher neuroticism (\u0026beta; = 4.21, SE = 1.45, p = 0.006), lower extraversion (\u0026beta; = \u0026minus;2.42, SE = 1.16, p = 0.043), and poorer gluten-free diet adherence (higher CDAT scores) (\u0026beta; = 2.76, SE = 1.10, p = 0.016). No other personality traits, food attitudes and behaviors, gender, age at diagnosis, or CeD symptom burden were significantly associated with depressive symptoms.\u003c/p\u003e\n\u003cp\u003eOverall, adolescents\u0026apos; personality traits were associated with dietary behaviors and psychosocial outcomes among adolescents with CeD, as summarized in Figure 1.\u003c/p\u003e\n\u003cp\u003eLower conscientiousness was associated with poorer adherence to the gluten-free diet, whereas higher openness was associated with more maladaptive food attitudes and behaviors. Lower quality of life was associated with higher openness, poorer dietary adherence, more maladaptive food attitudes and behaviors, and greater CeD symptom burden. Higher trait anxiety was associated with higher neuroticism and greater CeD symptom burden. More depressive symptoms were associated with higher neuroticism, lower extraversion, and poorer GF dietary adherence.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results suggest an association between adolescent personality traits and dietary behaviors, and with psychosocial outcomes related to CeD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGluten-free diet adherence.\u003c/b\u003e This study found the average CDAT score was 12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 (range\u0026thinsp;=\u0026thinsp;7\u0026ndash;23), which is considered good adherence to the GFD. This study also found that adolescents with poorer GFD adherence was associated with lower conscientiousness. This was not surprising since individuals higher in conscientiousness are generally better at setting goals and engaging in consistent planning [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Those low in conscientiousness may struggle with impulse control, meal planning, consistent label reading, and ongoing GFD monitoring.\u003c/p\u003e \u003cp\u003eNo prior studies in adolescents with CeD have used the BFI to assess personality traits and GFD adherence. One previous study of 281 children and adolescents with biopsy-confirmed CeD and 95 controls used the Junior Temperament and Character Inventory and found that the adherent group scored higher on persistence, a trait reflecting eagerness, effortful behavior, and perfectionism, compared with the non-adherent group [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Persistence in the Junior Temperament and Character Inventory aligns conceptually with Conscientiousness in the BFI, which is consistent with the present study\u0026rsquo;s finding. The adherent group scored lower on novelty seeking, a trait related to extravagance, impulsivity, and rule transgression, compared with the non-adherent group, which conceptually overlapped with Openness in the BFI. However, the present study did not observe significant differences in Openness between GFD adherent and non-adherent adolescents.\u003c/p\u003e \u003cp\u003eAmong adults with CeD, evidence similarly highlights the importance of personality in dietary adherence. In a sample of 143 adults (76.6% female; mean age\u0026thinsp;=\u0026thinsp;50.35\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21 years), the NEO Personality Inventory\u0026ndash;Revised (NEO PI-R) was used to assess Big Five traits, those who were more conscientious and more open to reexamining social, political, and religious values (similar to Openness domain in BFI) were more likely to adhere to the GFD. Conversely, individuals who tend to accept authority, honor tradition, and prefer conservative viewpoints were less adherent [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study extends the existing literature by suggesting that similar mechanisms may operate in adolescents with CeD, in which lower conscientiousness may contribute to challenges in maintaining strict adherence to the GFD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFood attitudes and behaviors.\u003c/b\u003e Maladaptive food attitudes and behaviors were common (mean CD-FAB\u0026thinsp;=\u0026thinsp;32.0). In the current adolescent sample, more maladaptive patterns were associated with higher openness to experience. Maladaptive food attitudes and behaviors have been associated with lower QOL [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and may be associated with higher levels of disordered eating [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOpenness is characterized by a tendency to seek novelty and curiosity, which may be associated with being more willing to experiment with new gluten-free foods, recipes, and travel. The tendency toward novelty seeking, combined with real-life challenges in finding suitable food products and anxiety in social situations, may be described as \u0026ldquo;curiosity-driven tension.\u0026rdquo; Other studies have shown openness was associated with poor outcomes. For example, in adults with food allergies, those who have high openness predicted behaviors such as difficulty finding suitable foods when grocery shopping, anxiety at social occasions involving food, and feeling embarrassed and poorly understood regarding their food allergy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Similarly, for adolescents managing a restrictive GFD, this desire to explore can make the limitations of a GFD feel more burdensome. For example, they may feel a greater sense of missing out or disappointment when near gluten-containing foods or when eating foods not prepared at home [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These reactions may heighten perceived dietary risk, increase vigilance, and contribute to more maladaptive food attitudes and behaviors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality of life.\u003c/b\u003e Overall quality of life was rated as good (mean CDPQOL\u0026thinsp;=\u0026thinsp;62.8), although lower than in previous studies of children and adolescents with CeD at the same celiac center [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the current study, adolescents with CeD who reported lower QOL tended to show higher Openness, poorer dietary adherence, more maladaptive food attitudes and behaviors, and more CeD symptoms. These findings were somewhat surprising in that we initially thought openness would promote quality of life. However, prior work has shown that social constraints associated with the GFD substantially shape adolescents\u0026rsquo; well-being [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Compared to adolescents with CeD who prefer familiarity, those high in openness may find the restrictive and socially limiting nature of the GFD to be more burdensome versus manageable. Such individuals who are curious and value novelty-seeking may be more motivated to travel and explore new recipes and restaurants, which in turn, may feel especially constrained by the GFD and negatively affecting their QOL.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnxiety and depression.\u003c/b\u003e 98% of the adolescents in the study reported elevated state anxiety; additionally, sixteen adolescents (32%) met the clinical cutoff for trait anxiety (35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7). This high level of state (\u0026ldquo;at the moment\u0026rdquo;) anxiety warrants further examination but may reflect stress or nervousness that comes with CeD-related medical visit as participants were all attending a GI or dietitian appointment concurrently. This environment could potentially heighten situational stress and elevate state anxiety scores as a previous study of U.S. adolescents (age 11.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28) during the COVID-19 stay-at-home order reported elevated state anxiety (47.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Among adults with CeD, trait anxiety has been reported at 39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7, with 44% above the clinical cutoff of anxiety, suggesting that elevated anxiety is common across age groups in CeD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study also found higher trait anxiety was associated with greater neuroticism and symptom burden. Adolescents high in Neuroticism may experience worry, rumination, and emotional distress more intensely for concerns about gluten exposure, difficulties navigating food-related social situations, contributing to elevated levels of trait anxiety.\u003c/p\u003e \u003cp\u003eApproximately one-third of adolescents reported elevated depressive symptoms. Adolescents with more depressive symptoms tended to exhibit higher neuroticism, lower extraversion, and poorer adherence to the gluten-free diet. A previous study from the same center examined 15 adolescents with celiac disease (CeD) (mean age\u0026thinsp;=\u0026thinsp;15.5 years, SD\u0026thinsp;=\u0026thinsp;1.6) and reported a mean CES-DC score of 10.4 (SD\u0026thinsp;=\u0026thinsp;10.3) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. That study did not report the percentage of participants exceeding the clinical cutoff. The proportion of adolescents with CeD meeting the threshold for depressive symptoms in the present study was notably higher than that observed in normative samples. Given the cross-sectional nature of the study, it remains unclear whether adolescents first experience depressive symptoms that make CeD management more challenging, or whether the daily demands of CeD contribute to the development of depressive symptoms, which in turn may further affect adherence and overall well-being. Evidence in adults with CeD suggests a bidirectional pattern: in a population-based cohort, individuals with pre-existing anxiety or depression were more likely to achieve mucosal healing after diagnosis, with anxiety between diagnosis and follow-up biopsy increasing healing odds nearly nine-fold (OR\u0026thinsp;=\u0026thinsp;8.94, 95% CI\u0026thinsp;=\u0026thinsp;2.03\u0026ndash;39.27), and prior depression increasing the odds by 47% (OR\u0026thinsp;=\u0026thinsp;1.47, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;2.15) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, individuals without prior anxiety or depression who achieved mucosal healing later showed a higher risk of developing anxiety (HR\u0026thinsp;=\u0026thinsp;1.49, 95% CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;1.96) and, among women, depression (HR\u0026thinsp;=\u0026thinsp;1.39, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.82) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImplications\u003c/h3\u003e\n\u003cp\u003eIn clinical practice, the study's findings could inform tailoring nutrition counseling and psychosocial support to adolescents\u0026rsquo; personality profiles. The Big Five Inventory (44 items) is primarily used for research, and administering the full version may be impractical in clinical settings. However, during clinic visits, healthcare providers may ask a few targeted, simple questions to approximate clients\u0026rsquo; personality traits. A few open-ended questions can be adapted from the Big Five Inventory-10 (BFI-10) for use in nutrition consultation, although this approach has not been formally validated [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] or evaluated with CeD patients. Examples are presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, including primary questions assessing the Big Five personality traits and behavioral probes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuestions for Assessing Personality Traits and Potential Strategies During Nutrition Consultation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig Five Traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions for Assessing Personality Traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotential Strategies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e: Would you describe yourself as open to new experiences (e.g., trying new foods or travelling)?\u003c/p\u003e \u003cp\u003e\u003cb\u003eProbe\u003c/b\u003e: What does your typical eating routine look like?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher openness may reflect willingness to try new GF foods, recipes, and dinging experiences; lower openness may reflect preference for routine and repetitive GF food choices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntroduce novel GF foods and recipes, identify new dining options, and support positive social eating experiences with peers. *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWould you describe yourself as organized and disciplined?\u003c/p\u003e \u003cp\u003e\u003cb\u003eProbe\u003c/b\u003e: When making diet or health changes, how do you approach them?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower conscientiousness may be associated with limited meal planning, inconsistent label reading, reduced cross-contact precautions, less structured monitoring, or reliance on spontaneous decisions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmphasize structured routines, meal-planning, label reading skills, executive-function supports (e.g., task breakdown, shopping lists), and regular feedback. *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e: Would you describe yourself as outgoing and sociable?\u003c/p\u003e \u003cp\u003e\u003cb\u003eProbe\u003c/b\u003e: How do social gatherings influence what and how you eat?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher extraversion may increase exposure to social eating pressures. Lower extraversion may be associated with more solitary eating patterns.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitate peer support (in-person or online), support groups, and opportunities for structed discussion about navigating social eating situations. *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e: Would you describe yourself as generally trusting and cooperative?\u003c/p\u003e \u003cp\u003e\u003cb\u003eProbe\u003c/b\u003e: How comfortable are you in saying no in social situations?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher agreeableness may be associated with difficulty in setting boundaries or declining potential unsafe foods. Lower agreeableness facilitates firmer boundary-setting.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlthough this study did not find a direct association between agreeableness and CeD-related outcomes, support may include self-advocacy and boundary-setting skills, including role-play for communicating dietary needs and managing social pressures to minimize GFD transgressions. *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e: Would you describe yourself as prone to stress or anxiety?\u003c/p\u003e \u003cp\u003e\u003cb\u003eProbe\u003c/b\u003e: Do you notice changes in your eating when you feel stressed or anxious?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher neuroticism may manifest as emotional reactivity, stress-related eating, anxiety about cross-contact, or perceived loss of control.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncorporate emotional regulation (e.g., mindfulness), coping skills training, role-play for anxiety-provoking eating situations. *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*If concerns related to disordered eating are identified, referral to mental health professionals is recommended.\u003c/p\u003e \u003cp\u003eBased on the client's answers, dietitians may be able to gauge the client's personality traits (especially high openness, low conscientiousness, low extraversion, and high neuroticism) and offer tailored nutrition support accordingly (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). If dietitians identify adolescents with higher openness, they may find them to be more receptive to new ideas and experiences. These individuals may benefit from dietitians who adopt a creative approach, sharing information about new restaurants and travel opportunities where GF foods can be obtained safely, while encouraging curiosity and engagement in novel social experiences. For those with lower conscientiousness, additional support is needed to promote good GFD adherence, including structured routines, label reading and meal-planning supports such as role-playing, mealtime reminders, shopping lists, and timely feedback. For individuals with higher neuroticism and lower extraversion, providing support for social eating pressures around meals without becoming hypervigilant or access to peer support may be helpful as mindfulness may further improve coping skills and emotional regulation skills. When anxiety, depressive symptoms, or disordered eating behaviors are identified, referral to mental health professionals should be considered.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrength and Limitations\u003c/h2\u003e \u003cp\u003eThis study is among the first to explore the Big Five Personality Inventory (BFI) in adolescents with biopsy- and/or serology-confirmed CeD, offering data on each big five traits in adolescents with CeD, novel insights into how individual psychological traits may shape diet-related disease management behaviors and patients\u0026rsquo; QOL as well as anxiety and depression outcomes, addressing a major gap in the pediatric celiac literature. This study has several limitations. First, the sample is predominantly white, highly educated, and primarily female. Non-white individuals are underrepresented in CeD research conducted in the United States and Europe, and since CeD does not affect only Caucasian individuals, expanding diagnostic efforts across diverse communities remains a critical need [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Second, the small sample size (n\u0026thinsp;=\u0026thinsp;50) limits statistical power. Third, the cross-sectional design prevents causal inferences. Fourth, during recruitment, caregivers sat in the same room as the CeD patients, and some may have instructed the adolescent on how to complete the questionnaire when the adolescent was unsure. This behavior may alter adolescents\u0026rsquo; responses when their caregivers are present. Finally, personality disorders and other psychiatric conditions were not controlled for within the sample (except for the exclusion of eating disorders). We did not know whether personality disorders or other psychiatric conditions could have influenced the responses of adolescents.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings demonstrate that adolescents’ personality traits, particularly low conscientiousness, high openness, and high neuroticism, and low extraversion, are meaningfully associated with lowerdiet adherence, maladaptive dietary behaviors,lower QOL, and higher anxiety and depression, respectively. This underscores the importance of individual personality traits in the management of CeD. Examining the feasibility of incorporating personality traits assessment and tailored dietary interventions in counseling is warranted. Longitudinal studies are needed to clarify the directionality between personality traits, dietary behavior outcomes, and psychosocial well-being.\u003c/p\u003e\n"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study received no external funding.\u003cbr\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Tool:\u0026nbsp;\u003c/strong\u003eDuring the preparation of this work the author used ChatGPT 5.2 in order to check grammar. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.C. contributed to the study conception and design, data acquisition, data analysis, and interpretation of the findings, prepared the tables and figures, and wrote the first draft of the manuscript. A.R.L., B.L., P.H.R.G., and J.L. contributed to data acquisition and interpretation of the findings. P.K. contributed to interpretation of the findings. R.L.W. contributed to the study conception and design and critically revised the manuscript. All authors reviewed the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGreen, P. H. R., Krishnareddy, S., \u0026amp; Lebwohl, B. (2015). 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Comparison of Quality of Life, Anxiety, and Depression Levels in Celiac Patients With Children Without Chronic Illnesses. \u003cem\u003eChildren\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(8), 1080. https://doi.org/10.3390/children12081080\u003c/li\u003e\n \u003cli\u003eFendrich, M., Weissman, M. M., \u0026amp; Warner, V. (1990). Screening for depressive disorder in children and adolescents: validating the Center for Epidemiologic Studies Depression Scale for Children. \u003cem\u003eAmerican journal of epidemiology\u003c/em\u003e, \u003cem\u003e131\u003c/em\u003e(3), 538\u0026ndash;551. https://doi.org/10.1093/oxfordjournals.aje.a115529\u003c/li\u003e\n \u003cli\u003eWeissman, M. M., Orvaschel, H., \u0026amp; Padian, N. (1980). \u003cem\u003eCenter for Epidemiological Studies Depression Scale for Children (CES-DC)\u003c/em\u003e [Database record]. APA PsycTests. https://doi.org/10.1037/t12228-000\u003c/li\u003e\n \u003cli\u003eFaulstich, M. E., Carey, M. P., Ruggiero, L., Enyart, P., \u0026amp; Gresham, F. (1986). Assessment of depression in childhood and adolescence: an evaluation of the Center for Epidemiological Studies Depression Scale for Children (CES-DC). \u003cem\u003eThe American journal of psychiatry\u003c/em\u003e, \u003cem\u003e143\u003c/em\u003e(8), 1024\u0026ndash;1027. https://doi.org/10.1176/ajp.143.8.1024\u003c/li\u003e\n \u003cli\u003eAdelman, D., Leffler, D., Lebwohl, B., Nehra, V., Hansen, J., Minaya, M. T., Van Dyke, C., Marcantonio, A., \u0026amp; Acaster, S. (2012). Celiac disease symptom frequency and severity using a disease-specific patient-reported outcome diary: Observations from A psychometric validation study in 202 patients. \u003cem\u003eAmerican Journal of Gastroenterology\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e.\u003cu\u003e\u0026nbsp;\u003c/u\u003ehttps://doi.org/10.14309/00000434-201210001-01509\u003c/li\u003e\n \u003cli\u003eSerin, Y., Andru\u0026scaron;kienė, J., Verma, A. K., Śmiełowska, M., Dzingelevičius, N., Vilčiauskis, A., Vaičekauskaitė, R., Bradauskienė, V., Buszewski, B., \u0026amp; Dzingelevičienė, R. (2025). Evaluation of Quality of Life in Adult Celiac Patients Living in Lithuania and Their Compliance with a Gluten-Free Diet: A Pilot Study. \u003cem\u003eMedicina\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(7), 1278. https://doi.org/10.3390/medicina61071278\u003c/li\u003e\n \u003cli\u003eStroebele-Benschop, N., Rau, C. J., Dieze, A., \u0026amp; Bschaden, A. (2025). Life Challenges and Quality of Life of People Living With Coeliac Disease: Time of Diagnosis Matters. \u003cem\u003eJournal of human nutrition and dietetics : the official journal of the British Dietetic Association\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 10.1111/jhn.13413. https://doi.org/10.1111/jhn.13413\u003c/li\u003e\n \u003cli\u003eRoberts, B. W., Jackson, J. J., Fayard, J. V., Edmonds, G., \u0026amp; Meints, J. (2009). Conscientiousness. In M. R. Leary \u0026amp; R. H. Hoyle (Eds.), \u003cem\u003eHandbook of individual differences in social behavior\u003c/em\u003e (pp. 369\u0026ndash;381). The Guilford Press.\u003c/li\u003e\n \u003cli\u003eConner, T. S., Mirosa, M., Bremer, P., \u0026amp; Peniamina, R. (2018). The Role of Personality in Daily Food Allergy Experiences. \u003cem\u003eFrontiers in psychology\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e29. https://doi.org/10.3389/fpsyg.2018.00029\u003c/li\u003e\n \u003cli\u003eCiacci, C., \u0026amp; Zingone, F. (2015). The Perceived Social Burden in Celiac Disease. \u003cem\u003eDiseases (Basel, Switzerland)\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 102\u0026ndash;110. https://doi.org/10.3390/diseases3020102\u003c/li\u003e\n \u003cli\u003eWolf, R. L., Lebwohl, B., Lee, A. R., Zybert, P., Reilly, N. R., Cadenhead, J., Amengual, C., \u0026amp; Green, P. H. R. (2018). 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A., Ay, B., \u0026Ouml;zke\u0026ccedil;eci, C. F., \u0026amp; Balamtekin, N. (2025). The relationship between diet quality, social participation, quality of life, and occupational balance in adolescents with Celiac disease. \u003cem\u003eJournal of health, population, and nutrition\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(1), 364. https://doi.org/10.1186/s41043-025-01024-9\u003c/li\u003e\n \u003cli\u003eAlves, J. M., Yunker, A. G., DeFendis, A., Xiang, A. H., \u0026amp; Page, K. A. (2020). Associations between Affect, Physical Activity, and Anxiety Among US Children During COVID-19. \u003cem\u003emedRxiv : the preprint server for health sciences\u003c/em\u003e, 2020.10.20.20216424. https://doi.org/10.1101/2020.10.20.20216424\u003c/li\u003e\n \u003cli\u003eLudvigsson, J. F., Lebwohl, B., Chen, Q., Br\u0026ouml;ms, G., Wolf, R. L., Green, P. H. R., \u0026amp; Emilsson, L. (2018). Anxiety after coeliac disease diagnosis predicts mucosal healing: a population-based study. Alimentary pharmacology \u0026amp; therapeutics, 48(10), 1091\u0026ndash;1098. https://doi.org/10.1111/apt.14991\u003c/li\u003e\n \u003cli\u003eRammstedt, B. \u0026amp; John, O. P. (2007). Measuring personality in one minute or less: A 10 item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203‐212\u003c/li\u003e\n \u003cli\u003eLebwohl B. (2015). Celiac disease and the forgotten 10%: the \u0026quot;silent minority\u0026quot;. \u003cem\u003eDigestive diseases and sciences\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(6), 1517\u0026ndash;1518. https://doi.org/10.1007/s10620-015-3572-5\u003c/li\u003e\n\u003c/ol\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":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"celiac disease, personality traits, big five, gluten-free diet, diet adherence, quality of life, adolescent","lastPublishedDoi":"10.21203/rs.3.rs-9058390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9058390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: Celiac disease (CeD) requires strict lifelong adherence to a gluten-free diet (GFD), which can be particularly challenging during adolescence. This cross-sectional study examined associations between personality traits, dietary behaviors, and psychosocial well-being in adolescents with CeD.\u003c/p\u003e\n\u003cp\u003eMethods: Fifty adolescents aged 14-17 years were recruited from a U.S. CeD referral center. Personality traits (Big Five Inventory), gluten free diet adherence (CDAT), maladaptive eating behaviors (CD-FAB), quality of life (CDPQOL), anxiety (STAI-C), and depression (CES-DC) were measured.\u003c/p\u003e\n\u003cp\u003eResults: Participants were mostly female (76%), with a mean age of 15.2 years. Sixty-two percent demonstrated good or excellent GFD adherence (CDAT \u0026lt; 13). Overall QOL was rated as “good” (Mean CDPQOL: 62.8 out of 100). Maladaptive food attitudes and behaviors were common (Mean CD-FAB: 32.0), and about one-third reported anxiety or depression symptoms. Poorer GFD adherence was associated with lower conscientiousness (β = −0.44, p = .02). More maladaptive food attitudes and behaviors were associated with higher openness (β = 0.45, p = .01). Lower QOL was associated with higher openness (β = −6.08, p = .011). Increased trait anxiety was associated with higher neuroticism (β = 6.12, p \u0026lt; .001). Increased depressive symptoms were associated with higher neuroticism (β = 4.21, p = .006) and lower extraversion (β = −2.42, p = .043).\u003c/p\u003e\n\u003cp\u003eConclusion: Higher openness may make a GFD feel more restrictive, lower conscientiousness may hinder GFD adherence planning, and higher neuroticism may increase anxiety and depression risk.Personality-informed counseling could help clinicians tailor support to improve adherence and well-being.\u003c/p\u003e","manuscriptTitle":"Personality Traits are Associated with Dietary Behaviors and Psychosocial Outcomes in Adolescents with Celiac Disease: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:03:48","doi":"10.21203/rs.3.rs-9058390/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:23:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95494563501455509427006328687864571983","date":"2026-04-13T05:46:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T08:51:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T20:45:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T13:08:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Digestive Diseases and Sciences","date":"2026-03-07T12:25:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"digestive-diseases-and-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ddsj","sideBox":"Learn more about [Digestive Diseases and Sciences](http://link.springer.com/journal/10620)","snPcode":"10620","submissionUrl":"https://submission.nature.com/new-submission/10620/3","title":"Digestive Diseases and Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"80e39750-bccc-46ca-8675-27e8ce282a40","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:23:12+00:00","index":13,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T12:03:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:03:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9058390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9058390","identity":"rs-9058390","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}