Physiological Correlates of Anxiety-Driven Binge Eating in a Naturalistic Setting

Physiological Correlates of Anxiety-Driven Binge Eating in a Naturalistic Setting | 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 Physiological Correlates of Anxiety-Driven Binge Eating in a Naturalistic Setting Yixuan Liang, Chuyu Qiu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9397248/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Binge-Eating Disorder (BED) is highly prevalent and frequently comorbid with anxiety, yet the real-time psychophysiological processes that translate anxiety into binge behavior remain unclear. This study therefore aimed to identify real-time physiological and contextual correlates of anxiety-driven binge eating in a naturalistic setting. We employed a 14-day longitudinal monitoring of 120 adults assigned to four groups: BED + GAD (n = 40), BED-only (n = 20), GAD-only (n = 20), and healthy controls (n = 40). Participants completed ecological momentary assessments (EMA; 0–100 scales) synchronized with continuous ambulatory ECG for heart-rate variability (HRV; RMSSD) and scheduled salivary cortisol; laboratory stress reactivity was also assessed with the Trier Social Stress Test (TSST). Multilevel models tested within-person effects of lagged anxiety, HRV, and cortisol on urge to binge, while multilevel logistic regression predicted binge episodes. Results showed that the BED + GAD group exhibited greater clinical severity, metabolic risk, and altered stress physiology (lower HRV recovery, p=.021; blunted cortisol AUCi, p=.002) versus other groups. Across the sample, higher lagged anxiety predicted a stronger subsequent urge to binge (γ = 0.25, p<.001), whereas higher HRV predicted lower urges (γ=−0.13, p=.031). Binge episodes were prospectively associated with higher pre-binge anxiety (OR = 1.38, p<.001), lower HRV (OR = 1.25, p=.008), reduced mobility (OR = 1.31, p=.002), and greater screen time (OR = 1.15, p=.023). A random-forest model achieved an AUC of 0.76, with anxiety, HRV, and location variance as top predictors. These effects were strongest in the BED + GAD group. In conclusion, anxiety-driven binge risk is characterized by a synergistic state of high anxiety and low vagal regulation in naturalistic contexts, amplified by behavioral withdrawal. HRV and digital mobility markers provide actionable, real-time signals for effective interventions to prevent binge episodes. Binge-Eating Disorder Anxiety Disorders Heart Rate Variability Ecological Momentary Assessment Psychophysiology Comorbidity Stress Cortisol Digital Phenotyping Generalized Anxiety Disorder 1. Introduction Binge-Eating Disorder (BED) is characterized by recurrent episodes of consuming large amounts of food within a short period of time, accompanied by a loss of control without compensatory behaviors [1]. The lifetime prevalence of the disorder is approximately 2–3%, making it one of the most common eating disorders [2,3]. BED is associated with functional impairment, reduced quality of life, and lower economic or educational satisfaction [4,5]. Although major depressive disorder (MDD) frequently co-occurs with BED and can complicate its course [6,7], growing evidence highlights anxiety disorders as especially pervasive and clinically relevant in BED [8]. Population-based and clinical studies consistently show high lifetime prevalence rates of anxiety disorders—up to 65%—in individuals with BED [8,9]. This co-occurrence appears from adolescence into young adulthood [10,11] and is linked to greater eating-disorder severity, weight concerns, and functional impairment [12,13]. Models of affect dysregulation propose that binge eating serves as a maladaptive coping strategy to reduce negative affect, with anxiety—a state of physiological hyperarousal and perceived lack of control—acting as a potent trigger [14–16]. Related traits such as alexithymia are also associated with both anxiety and binge-eating severity [17,18]. Nonetheless, most prior work relies on retrospective reports or laboratory proxies, limiting insight into the real-time processes that translate anxiety into binge behavior. Consequently, treatments have often been reactive rather than preventative, focusing on binge behavior after it occurs [19,20]. To move toward proactive care, there is a need to identify objective, momentary markers of risk in daily life. This study therefore employs a multi-method, longitudinal, correlational design to examine physiological and contextual correlates of anxiety-driven binge eating. We recruited individuals with comorbid Generalized Anxiety Disorder (GAD) and BED, alongside clinical and healthy controls, and collected 14 days of synchronized Ecological momentary assessment (EMA) and continuous heart-rate variability (HRV). EMA enables real-time sampling of affect and context in naturalistic settings, minimizing recall bias and capturing dynamic fluctuations [21]. When integrated with ambulatory HRV—a well-established index of autonomic regulation—EMA can test how subjective anxiety interacts with physiological arousal in real time [22]. We hypothesized that a synergistic combination of high subjective anxiety and low HRV creates a high-risk physiological state that prospectively predicts binge urges and episodes, and that this dynamic is strongest in individuals with comorbid GAD and BED. This work aims to delineate a testable, biobehavioral model of anxiety-driven binge eating and to identify targets for ecological momentary interventions (EMIs) [23]. 2. Methods 2.1 Study Design and Ethics This longitudinal, multi-method study employed a correlational design to investigate physiological and contextual correlates of anxiety-driven binge eating in naturalistic settings. The study consisted of two phases: (1) an initial laboratory assessment including diagnostic confirmation and stress testing, and (2) a 14-day ambulatory monitoring period using ecological momentary assessment (EMA) and continuous heart rate variability (HRV) recording. A total of 120 adults were prospectively recruited between 15 December 2024 and 12 September 2025 through community advertisements, clinical referrals, and online postings in the Berlin metropolitan area. Participants were allocated to one of four diagnostic groups: Comorbid Group (GAD + BED) : n = 40 adults with co-occurring Generalized Anxiety Disorder and Binge-Eating Disorder. Anxiety Control Group (GAD-only) : n = 20 adults with GAD and no lifetime eating disorder. BED Control Group (BED-only) : n = 20 adults with BED and no lifetime anxiety disorder. Healthy Control Group (HC) : n = 40 adults with no current or lifetime psychiatric diagnosis. Inclusion criteria Adults aged 18–55 years; body mass index (BMI) 18.5–40 kg/m²; sufficient fluency in German to complete procedures; and meeting diagnostic criteria for one of the four defined groups. Exclusion criteria Current substance use disorder, psychosis, neurological disease, active suicidality, pregnancy, or current use of medications known to influence cardiac or endocrine function (e.g., beta-blockers, corticosteroids). Ethical approval and participant protection Ethical approval for the study was obtained from the Ethics Committee of Freie Universität Berlin (Approval No. 6734). All procedures were conducted in accordance with the Declaration of Helsinki and institutional guidelines for research involving human participants. Informed consent: All participants provided written informed consent prior to participation after receiving a full explanation of the study’s purpose, procedures, potential risks, and benefits. Consent forms were signed and dated in duplicate (one retained by the participant, one stored in secure institutional records). The consent process was conducted by trained study personnel and documented in each participant’s study file. Participants were informed of their right to withdraw at any time without penalty. No minors were enrolled in this study; therefore, parental or guardian consent was not required. No waivers of consent were granted by the ethics committee. Data were de-identified using unique participant codes, and personally identifying information was stored separately on encrypted, password-protected servers accessible only to authorized study personnel. The study was classified as minimal risk by the Ethics Committee. 2.2 Procedure The protocol comprised three sequential phases: laboratory intake, a 14-day ambulatory assessment, and final debriefing with compensation. Phase 1: Laboratory intake Following informed consent, diagnostic status was confirmed using the Structured Clinical Interview for DSM-5 (SCID-5) , administered by trained clinicians. Participants then completed baseline self-report questionnaires: the State-Trait Anxiety Inventory–Trait version (STAI-T) , Eating Disorder Examination–Questionnaire (EDE-Q) , Beck Depression Inventory-II (BDI-II) , and the UPPS-P Impulsive Behavior Scale (Negative Urgency subscale). Psychobiological stress reactivity was assessed using the Trier Social Stress Test (TSST) , consisting of a 10-minute anticipation period followed by a 10-minute speech and a 10-minute mental arithmetic task performed before neutral evaluators. Salivary cortisol samples were collected at baseline, immediately post-task, and 10, 20, 30, 45, and 60 minutes after task completion. Continuous electrocardiogram (ECG) recordings were obtained to derive HRV indices. The session concluded with standardized training on the ambulatory equipment. Phase 2: 14-day ambulatory assessment Participants completed 14 days of multimodal monitoring in their natural environments. EMA prompts were delivered via a secure smartphone application (e.g., MetricWire, mEMA) five times daily at semi-random intervals between 08:00 and 22:00. Each prompt assessed current anxiety, urge to binge, sadness, and stress on 0–100 visual analogue scales. Event-contingent reports were completed immediately following any binge episode, including contextual details. Participants wore a validated ambulatory ECG device (e.g., Firstbeat Bodyguard 2 ) during waking hours to collect interbeat interval (IBI) data for HRV analysis. The study smartphone continuously gathered passive behavioral data, including GPS-derived mobility, accelerometry, screen time, and communication frequency. Salivary cortisol samples were obtained at four fixed time points daily (upon waking, 30 minutes post-waking, 16:00, and 21:00) and immediately after any binge episode. Phase 3: Debriefing and compensation On Day 16, participants returned all equipment and completed a structured interview on adherence and study burden. Compensation of up to €300 was provided, prorated for compliance. 2.3 Measures and Materials Primary outcomes were measured by momentary urge to binge, which was self-reported on a 0–100 visual analogue scale at each EMA prompt, and by binge episode occurrence, a binary variable (0 = no binge, 1 = binge) derived from event-contingent EMA entries verified by timestamps. Ecological Momentary Assessment (EMA) surveys were administered through a secure commercial platform, with real-time compliance monitored via a researcher dashboard. Prompts assessed affective and behavioral states, and timestamps were synchronized with physiological recordings. Cardiac data were recorded with an ambulatory ECG monitor (sampling rate ≥ 1000 Hz). Raw interbeat interval (IBI) data were processed using Kubios HRV Premium, with automated artifact correction and manual review. The primary HRV metric was the Root Mean Square of Successive Differences (RMSSD, ms). For binge analyses, 60-minute epochs preceding binge episodes were extracted and matched to non-binge control periods. In parallel, continuous passive data streams captured behavioral metrics, including location variance (mobility), total screen time, and communication frequency. All data were anonymized and restricted to study-specific outputs. Saliva samples were stored at −80 °C until assay, and cortisol concentrations were determined in duplicate using high-sensitivity enzyme immunoassay kits (Salimetrics, State College, PA, USA). Indices included the Cortisol Awakening Response (CAR) and diurnal slope. Cortisol and HRV responses to the TSST were summarized using Area Under the Curve with respect to increase (AUCi). Finally, baseline psychological measures included the STAI-T, EDE-Q, BDI-II, and UPPS-P Negative Urgency subscale. 3. Statistical Analysis All statistical analyses were performed in R (version 4.3). Data were inspected for outliers, non-normality, and missingness prior to analysis. HRV and cortisol data were log-transformed as needed to normalize distributions. Statistical significance was defined as p < 0.05 (two-tailed) for all tests. 3.1 Preliminary analyses Group differences in baseline demographic, clinical, and metabolic characteristics were examined using one-way ANOVA or χ² tests, as appropriate (Table 1). Significant effects were followed by Bonferroni-corrected post hoc pairwise comparisons. 3.2 Laboratory stress reactivity Between-group differences in cortisol AUCi and HRV recovery following the TSST were assessed using ANCOVA, controlling for age, sex, and BMI (Table 2). 3.3 Ecological momentary data Momentary urge to binge (continuous outcome) was modeled using multilevel modeling (MLM), with repeated EMA observations (Level 1) nested within participants (Level 2). Time-lagged predictors included anxiety, HRV (RMSSD), and cortisol. All models controlled for time of day and physical activity. Diagnostic group (GAD+BED, BED-only, GAD-only, HC) and laboratory stress reactivity indices were entered as Level 2 moderators to test cross-level interactions (Table 3). 3.4 Prediction of binge episodes Binge occurrence (binary outcome) was examined using multilevel logistic regression, estimating the odds of a binge episode from pre-binge anxiety, HRV, cortisol, and contextual variables derived from passive smartphone sensing (Table 4). 3.5 Exploratory analyses A Random Forest classifier was applied to explore nonlinear relationships among physiological and behavioral predictors. Variable importance was quantified by mean decrease in Gini impurity (Table 5). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). 4. Results 4.1 Participant characteristics Descriptive statistics for all groups are presented in Table 1. Groups did not differ significantly in age, sex, or education. As expected, participants with GAD+BED and BED-only showed higher BMI and greater psychopathology (STAI-T, EDE-Q, and BDI-II scores) compared to healthy controls ( p < 0.001). The comorbid GAD+BED group also exhibited elevated metabolic risk indicators, including higher glucose, triglycerides, and blood pressure levels. 4.2 Laboratory stress reactivity As shown in Table 2, significant group differences were observed for both cortisol and HRV responses to the TSST. The GAD+BED group demonstrated the lowest HRV recovery and blunted cortisol AUCi compared with other groups ( p = 0.002–0.021). Participants with BED-only showed intermediate reactivity, suggesting partial stress dysregulation. 4.3 Multilevel analyses of momentary urge to binge Multilevel modeling indicated that higher lagged anxiety predicted stronger subsequent urges to binge (γ = 0.25, p < 0.001), whereas higher HRV predicted lower urges (γ = –0.13, p = 0.031) (Table 3). The Anxiety × HRV interaction approached significance ( p = 0.073), suggesting that higher HRV buffered the link between anxiety and binge urges. Cross-level interactions revealed that this effect was strongest among participants with comorbid GAD+BED. 4.4 Prediction of binge episodes Multilevel logistic regression (Table 4) showed that higher pre-binge anxiety, lower HRV, and reduced mobility significantly increased the odds of a binge episode (ORs = 1.25–1.38, p < 0.01). Cortisol predicted binge occurrence at a trend level ( p = 0.089). Greater screen time also modestly increased the likelihood of a binge (OR = 1.15, p = 0.023). 4.5 Exploratory machine learning analyses The Random Forest classifier achieved an overall performance of AUC = 0.76 (Table 5). The strongest predictors of binge occurrence were self-reported anxiety, HRV, and location variance. Cortisol and screen time contributed smaller but meaningful effects, supporting the combined role of autonomic and behavioral dysregulation in real-world binge risk. 5. Discussion This study provides the first ecologically valid evidence of a synergistic psychophysiological state that prospectively predicts binge eating in daily life. By integrating real-time subjective experiences with continuous physiological and behavioral monitoring, we demonstrated that the combination of high anxiety and low parasympathetic tone (indexed by HRV) defines a high-risk state for binge urges and episodes—particularly in individuals with comorbid GAD and BED. These findings bridge laboratory models of stress reactivity with real-world patterns of disordered eating, offering a novel biobehavioral framework for anxiety-driven binge eating. 5.1 The synergistic psychophysiological risk state Our primary hypothesis was supported. Within-person increases in anxiety predicted stronger subsequent urges to binge, consistent with affect regulation models [14–16]. This relationship was amplified under conditions of low HRV, reflecting reduced vagal regulation and autonomic rigidity [22]. Thus, it is not anxiety alone, but anxiety experienced during physiological dysregulation, that most strongly drives binge urges. This synergistic effect helps explain why only certain anxious states escalate into binge behavior and underscores the importance of integrating physiological and emotional processes in conceptual models. Moreover, logistic regression results confirmed that this “high-anxiety, low-HRV” state preceded actual binge episodes. Reduced location variance (indicating behavioral withdrawal) and increased screen time further characterized pre-binge contexts, suggesting that anxiety-driven binges occur in moments of both physiological arousal and environmental disengagement. 5.2 The unique vulnerability of the comorbid GAD+BED phenotype The inclusion of multiple clinical control groups allowed examination of diagnostic specificity. The anxiety–binge association was strongest in the comorbid GAD+BED group, indicating a distinct phenotype with heightened reactivity to anxiety. Individuals with BED alone may binge for other reasons (e.g., food cues, dietary restraint), but for those with co-occurring GAD, anxiety appears to be a direct precipitant. At the trait level, the GAD+BED group exhibited blunted cortisol responses and slower HRV recovery following the TSST. Such patterns are interpreted as indicators of chronic stress exposure and reduced HPA-axis flexibility [29]. Together, these results point to a fundamental deficit in physiological resilience among comorbid participants, rendering them more vulnerable to anxiety-triggered binge episodes. 5.3 Clinical implications: from reactive to proactive care These findings have immediate clinical relevance. Physiological monitoring—particularly HRV biofeedback—may be a useful adjunct to standard therapy for comorbid anxiety and BED. By teaching patients to recognize and modulate physiological dysregulation (e.g., low HRV), clinicians could help reduce vulnerability during high-anxiety states. This approach aligns with Ecological Momentary Interventions (EMIs), which deliver adaptive strategies in real time via mobile platforms [23]. Additionally, the observed behavioral markers of withdrawal (e.g., low mobility) suggest that interventions promoting social or environmental engagement could further mitigate binge risk. Integrating these components could transform treatment from reactive management to proactive prevention. Our findings may also have implications for emerging BED interventions. Preliminary evidence suggests that ketogenic dietary intervention may be feasible and associated with reduced binge-eating symptoms in BED, raising the possibility that future work could examine whether physiological risk markers such as HRV help identify individuals most likely to benefit from targeted behavioral or metabolic interventions [24] 5.4 Limitations and future directions Several limitations warrant consideration. First, the correlational nature of ambulatory data precludes causal inference. Second, the sample was regionally and linguistically specific, which may limit generalizability. Third, cortisol predicted binge episodes only at a trend level, implying that HPA-axis activity may reflect a more stable vulnerability rather than immediate binge risk. Future studies should test the efficacy of real-time EMI systems based on physiological monitoring. Combining ambulatory assessment with periodic neuroimaging could clarify how brain circuits implicated in anxiety and reward (e.g., BNST and hypothalamus) mediate these dynamic processes. Finally, extending this design to adolescent samples could illuminate developmental pathways and inform early intervention. 6. Conclusion This study demonstrates a dynamic, real-time psychophysiological model of anxiety-driven binge eating. A synergistic pattern of high anxiety and low physiological flexibility defines a high-risk state for binge urges and episodes, most pronounced among individuals with comorbid GAD and BED. These findings underscore the need for treatments that simultaneously address emotional and physiological dysregulation. By shifting the focus from retrospective assessment to proactive, moment-to-moment risk detection, this research offers a foundation for precision interventions aimed at disrupting the cycle of anxiety and binge eating where it begins—in everyday life. Declarations Acknowledgments The authors would like to thank the participants for their time and dedication to this study. This work was supported by Freie Universität Berlin. We also gratefully acknowledge the contributions of Harriet Salbach and the research staff of the Klinik für Department of Endocrinology and Metabolic Diseases Charité – Universitätsmedizin Beriln for their invaluable assistance. Funding This research was supported by the Deutschlandstipendium National Scholarship Program for Academic Excellence , funded by the Federal Ministry of Education and Research (BMBF) and Klarman Family Foundation in Germany. The funding body had no role in study design, data collection, analysis, interpretation, or manuscript preparation. Conflicts of Interest The authors declare no conflicts of interest. Ethics approval and consent to participate Ethical approval for this study was obtained from the Ethics Committee of Freie Universität Berlin (Approval No. 6734). All procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation. References Giel, K. E., Zipfel, S., Alizadeh, M., & Schag, K. (2023). Binge-eating disorder. Nature Reviews Disease Primers, 9 (1), 11. https://doi.org/10.1038/s41572-022-00422-2 McCuen-Wurst, C., Ruggieri, M., & Allison, K. C. (2018). Disordered eating and obesity: Associations between binge-eating disorder, night-eating syndrome, and weight-related comorbidities. 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STAI-T, State-Trait Anxiety Inventory; EDE-Q, Eating Disorder Examination-Questionnaire Global Score; BDI-II, Beck Depression Inventory-II; UPPS-P NU, UPPS-P Impulsive Behavior Scale - Negative Urgency subscale; BP, Blood Pressure; HbA1c, Glycated Hemoglobin; MDD, Major Depressive Disorder. Variable GAD+BED (n=40) BED-only (n=20) GAD-only (n=20) HC (n=40) p-value Age, years 34.1 (9.3) 31.5 (8.7) 33.8 (10.1) 31.2 (7.9) .48 Female, n (%) 31 (77.5%) 17 (85%) 16 (80%) 32 (80%) .89 Education, College Degree (%) 45% 50% 55% 68% .31 BMI, kg/m² 32.8 (5.1) a 31.9 (4.4) a 25.9 (4.2) b 24.3 (3.1) b <.001 Illness Duration, years 8.1 (5.9) a 6.7 (4.8) a 7.9 (5.3) a — .52 STAI-T 59.8 (7.2) a 46.1 (6.8) b 55.4 (8.1) a 37.3 (5.5) c <.001 EDE-Q Global 4.1 (1.0) a 3.8 (0.9) a 1.8 (1.2) b 1.3 (0.7) b <.001 BDI-II 26.5 (8.9) a 19.2 (7.1) b 22.4 (7.8) b 5.1 (4.3) c <.001 UPPS-P NU 36.1 (5.5) a 32.4 (5.1) a,b 29.8 (6.2) b,c 25.3 (5.8) c <.001 Comorbid MDD (%) 68% 40% 55% 5% <.001 Psychotropic Medication (%) 55% 40% 45% 3% <.001 Fasting Glucose (mg/dL) 106.2 (12.4) a 103.8 (11.1) a 95.4 (9.7) b 92.5 (8.1) b <.001 HbA1c (%) 5.8 (0.4)a 5.7 (0.3)a 5.4 (0.2) b 5.3 (0.2) b <.001 HDL (mg/dL) 47.2 (9.4)a 49.8 (10.2)a 55.4 (11.1) b 59.1 (9.8) b <.001 LDL (mg/dL) 129.5 (28.7) a 121.4 (24.1) a,b 118.2 (22.9) b 111.8 (20.3) b .022 Triglycerides (mg/dL) 151.8 (43.2)a 146.1 (41.8)a 118.9 (36.4) b 110.2 (33.5) b <.001 Systolic BP (mmHg) 131.5 (11.8) a 128.3 (12.2) a 121.4 (10.1) b 118.7 (9.5) b <.001 Diastolic BP (mmHg) 83.9 (7.2) a 81.4 (7.5) a 77.8 (6.9) b 75.3 (6.4) b <.001 Previous Psychotherapy for BED (%) 70% 65% 10% 2% <.001 Previous Pharmacotherapy for BED (%) 45% 30% 8% 0% <.001 Table 2. Laboratory stress reactivity by diagnostic group. Group comparisons of physiological reactivity to the Trier Social Stress Test (TSST). Cortisol reactivity is measured by Area Under the Curve with respect to increase (AUCi). Heart Rate Variability (HRV) recovery represents the change in Root Mean Square of Successive Differences (RMSSD) from peak stress to 60 minutes post-stress. Values are presented as Mean (Standard Deviation). Different superscript letters (a, b) within a row indicate significant pairwise differences (p < 0.05) based on post-hoc tests following a significant one-way ANOVA. TSST Measure GAD+BED (n=40) BED-only (n=20) GAD-only (n=20) HC (n=40) p-value Cortisol AUCi 112.4 (34.5)ᵃ 135.2 (41.2)ᵃᵇ 142.8 (37.9)ᵇ 148.1 (32.6)ᵇ 0.002 HRV Recovery [ΔRMSSD, ms] −11.8 (6.1)ᵃ −9.2 (5.8)ᵃᵇ −8.1 (5.3)ᵇ −7.5 (4.7)ᵇ 0.021 Table 3. Multilevel model predicting momentary urge to binge. Results of the multilevel model predicting momentary urge to binge (0–100 scale) from time-lagged predictors. Level 1 predictors are person-mean centered. The model controlled for time of day and physical activity. SE, Standard Error; CI, Confidence Interval; RMSSD, Root Mean Square of Successive Differences; HRV, Heart Rate Variability; BED, Binge-Eating Disorder; GAD, Generalized Anxiety Disorder; HC, Healthy Control. Fixed Effect Coefficient (γ) SE t-value p-value 95% CI Intercept 24.83 2.95 8.42 <.001 [19.03, 30.63] Anxiety (lagged) 0.25 0.06 4.17 <.001 [0.13, 0.37] HRV - RMSSD (lagged) -0.13 0.06 -2.17 .031 [-0.25, -0.01] Cortisol (lagged) 0.09 0.05 1.80 .073 [-0.01, 0.19] Anxiety × RMSSD -0.09 0.05 -1.80 .073 [-0.19, 0.01] Anxiety × (Group: BED-only) -0.16 0.09 -1.78 .077 [-0.34, 0.02] Anxiety × (Group: GAD-only) -0.19 0.10 -1.90 .059 [-0.39, 0.01] Anxiety × (Group: HC) -0.28 0.08 -3.50 <.001 [-0.44, -0.12] Table 4. Multilevel logistic regression predicting binge episode occurrence. Odds ratios from the multilevel logistic regression model predicting the likelihood of a binge eating episode based on pre-binge states. All predictors are from the 60-minute epoch preceding the event. HRV, Heart Rate Variability; RMSSD, Root Mean Square of Successive Differences; OR, Odds Ratio; CI, Confidence Interval. Predictor (Pre-Binge Epoch) Odds Ratio (OR) 95% CI for OR p-value Self-Reported Anxiety (per 10-point increase) 1.38 [1.15, 1.65] <.001 HRV - RMSSD (per 10-ms² decrease) 1.25 [1.06, 1.47] .008 Cortisol (per 1-nmol/L increase) 1.06 [0.99, 1.13] .089 Location Variance (per SD decrease) 1.31 [1.10, 1.56] .002 Screen Time (per 30-min increase) 1.15 [1.02, 1.30] .023 Table 5. Feature Importance from Random Forest Analysis. Results from the exploratory Random Forest analysis classifying binge vs. non-binge epochs. The mean decrease in Gini impurity is reported as a measure of variable importance, with higher values indicating a stronger contribution to the model's predictive accuracy. The model's overall performance was AUC = 0.76. HRV, Heart Rate Variability; RMSSD, Root Mean Square of Successive Differences. Predictor Mean Decrease Gini Self-Reported Anxiety 18.74 HRV - RMSSD 15.92 Location Variance (GPS) 12.35 Time since last meal 9.81 Cortisol level 8.43 Screen Time 7.56 Time of Day 6.98 Additional Declarations No competing interests reported. Supplementary Files S1Questionnaire.docx S2Data.csv S2DataDictionary.csv S2DataREADME.txt S2TextSupplementaryMethods.docx Supplementary.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 12 Apr, 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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Introduction","content":"\u003cp\u003eBinge-Eating Disorder (BED) is characterized by recurrent episodes of consuming large amounts of food within a short period of time, accompanied by a loss of control without compensatory behaviors [1]. The lifetime prevalence of the disorder is approximately 2\u0026ndash;3%, making it one of the most common eating disorders [2,3]. BED is associated with functional impairment, reduced quality of life, and lower economic or educational satisfaction [4,5]. Although major depressive disorder (MDD) frequently co-occurs with BED and can complicate its course [6,7], growing evidence highlights anxiety disorders as especially pervasive and clinically relevant in BED [8].\u003c/p\u003e \u003cp\u003ePopulation-based and clinical studies consistently show high lifetime prevalence rates of anxiety disorders\u0026mdash;up to 65%\u0026mdash;in individuals with BED [8,9]. This co-occurrence appears from adolescence into young adulthood [10,11] and is linked to greater eating-disorder severity, weight concerns, and functional impairment [12,13]. Models of affect dysregulation propose that binge eating serves as a maladaptive coping strategy to reduce negative affect, with anxiety\u0026mdash;a state of physiological hyperarousal and perceived lack of control\u0026mdash;acting as a potent trigger [14\u0026ndash;16]. Related traits such as alexithymia are also associated with both anxiety and binge-eating severity [17,18].\u003c/p\u003e \u003cp\u003eNonetheless, most prior work relies on retrospective reports or laboratory proxies, limiting insight into the \u003cem\u003ereal-time\u003c/em\u003e processes that translate anxiety into binge behavior. Consequently, treatments have often been reactive rather than preventative, focusing on binge behavior after it occurs [19,20]. To move toward proactive care, there is a need to identify objective, momentary markers of risk in daily life.\u003c/p\u003e \u003cp\u003eThis study therefore employs a multi-method, longitudinal, correlational design to examine physiological and contextual correlates of anxiety-driven binge eating. We recruited individuals with comorbid Generalized Anxiety Disorder (GAD) and BED, alongside clinical and healthy controls, and collected 14 days of synchronized Ecological momentary assessment (EMA) and continuous heart-rate variability (HRV). EMA enables real-time sampling of affect and context in naturalistic settings, minimizing recall bias and capturing dynamic fluctuations [21]. When integrated with ambulatory HRV\u0026mdash;a well-established index of autonomic regulation\u0026mdash;EMA can test how subjective anxiety interacts with physiological arousal in real time [22].\u003c/p\u003e \u003cp\u003eWe hypothesized that a synergistic combination of high subjective anxiety and low HRV creates a high-risk physiological state that prospectively predicts binge urges and episodes, and that this dynamic is strongest in individuals with comorbid GAD and BED. This work aims to delineate a testable, biobehavioral model of anxiety-driven binge eating and to identify targets for ecological momentary interventions (EMIs) [23].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Ethics\u003c/h2\u003e \u003cp\u003eThis longitudinal, multi-method study employed a correlational design to investigate physiological and contextual correlates of anxiety-driven binge eating in naturalistic settings. The study consisted of two phases: (1) an initial laboratory assessment including diagnostic confirmation and stress testing, and (2) a 14-day ambulatory monitoring period using ecological momentary assessment (EMA) and continuous heart rate variability (HRV) recording.\u003c/p\u003e \u003cp\u003eA total of 120 adults were prospectively recruited between 15 December 2024 and 12 September 2025 through community advertisements, clinical referrals, and online postings in the Berlin metropolitan area. Participants were allocated to one of four diagnostic groups:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComorbid Group (GAD\u0026thinsp;+\u0026thinsp;BED)\u003c/b\u003e: n\u0026thinsp;=\u0026thinsp;40 adults with co-occurring Generalized Anxiety Disorder and Binge-Eating Disorder.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAnxiety Control Group (GAD-only)\u003c/b\u003e: n\u0026thinsp;=\u0026thinsp;20 adults with GAD and no lifetime eating disorder.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBED Control Group (BED-only)\u003c/b\u003e: n\u0026thinsp;=\u0026thinsp;20 adults with BED and no lifetime anxiety disorder.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHealthy Control Group (HC)\u003c/b\u003e: n\u0026thinsp;=\u0026thinsp;40 adults with no current or lifetime psychiatric diagnosis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInclusion criteria\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAdults aged 18\u0026ndash;55 years; body mass index (BMI) 18.5\u0026ndash;40 kg/m\u0026sup2;; sufficient fluency in German to complete procedures; and meeting diagnostic criteria for one of the four defined groups.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExclusion criteria\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCurrent substance use disorder, psychosis, neurological disease, active suicidality, pregnancy, or current use of medications known to influence cardiac or endocrine function (e.g., beta-blockers, corticosteroids).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval and participant protection\u003c/strong\u003e \u003c/p\u003e \u003cp\u003eEthical approval for the study was obtained from the Ethics Committee of Freie Universit\u0026auml;t Berlin (Approval No. 6734). All procedures were conducted in accordance with the Declaration of Helsinki and institutional guidelines for research involving human participants.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eInformed consent: All participants provided written informed consent prior to participation after receiving a full explanation of the study\u0026rsquo;s purpose, procedures, potential risks, and benefits. Consent forms were signed and dated in duplicate (one retained by the participant, one stored in secure institutional records). The consent process was conducted by trained study personnel and documented in each participant\u0026rsquo;s study file. Participants were informed of their right to withdraw at any time without penalty.\u003c/p\u003e\n\u003cp\u003eNo minors were enrolled in this study; therefore, parental or guardian consent was not required. No waivers of consent were granted by the ethics committee.\u003c/p\u003e\n\u003cp\u003eData were de-identified using unique participant codes, and personally identifying information was stored separately on encrypted, password-protected servers accessible only to authorized study personnel. The study was classified as minimal risk by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol comprised three sequential phases: laboratory intake, a 14-day ambulatory assessment, and final debriefing with compensation.\u003c/p\u003e\n\u003ch4\u003ePhase 1: Laboratory intake\u003c/h4\u003e\n\u003cp\u003eFollowing informed consent, diagnostic status was confirmed using the\u0026nbsp;\u003cem\u003eStructured Clinical Interview for DSM-5 (SCID-5)\u003c/em\u003e, administered by trained clinicians. Participants then completed baseline self-report questionnaires: the\u0026nbsp;\u003cem\u003eState-Trait Anxiety Inventory\u0026ndash;Trait version (STAI-T)\u003c/em\u003e,\u0026nbsp;\u003cem\u003eEating Disorder Examination\u0026ndash;Questionnaire (EDE-Q)\u003c/em\u003e,\u0026nbsp;\u003cem\u003eBeck Depression Inventory-II (BDI-II)\u003c/em\u003e, and the\u0026nbsp;\u003cem\u003eUPPS-P Impulsive Behavior Scale\u003c/em\u003e (Negative Urgency subscale).\u003c/p\u003e\n\u003cp\u003ePsychobiological stress reactivity was assessed using the\u0026nbsp;\u003cem\u003eTrier Social Stress Test (TSST)\u003c/em\u003e, consisting of a 10-minute anticipation period followed by a 10-minute speech and a 10-minute mental arithmetic task performed before neutral evaluators. Salivary cortisol samples were collected at baseline, immediately post-task, and 10, 20, 30, 45, and 60 minutes after task completion. Continuous electrocardiogram (ECG) recordings were obtained to derive HRV indices. The session concluded with standardized training on the ambulatory equipment.\u003c/p\u003e\n\u003ch4\u003ePhase 2: 14-day ambulatory assessment\u003c/h4\u003e\n\u003cp\u003eParticipants completed 14 days of multimodal monitoring in their natural environments. EMA prompts were delivered via a secure smartphone application (e.g., MetricWire, mEMA) five times daily at semi-random intervals between 08:00 and 22:00. Each prompt assessed current anxiety, urge to binge, sadness, and stress on 0\u0026ndash;100 visual analogue scales. Event-contingent reports were completed immediately following any binge episode, including contextual details.\u003c/p\u003e\n\u003cp\u003eParticipants wore a validated ambulatory ECG device (e.g.,\u0026nbsp;\u003cem\u003eFirstbeat Bodyguard 2\u003c/em\u003e) during waking hours to collect interbeat interval (IBI) data for HRV analysis. The study smartphone continuously gathered passive behavioral data, including GPS-derived mobility, accelerometry, screen time, and communication frequency. Salivary cortisol samples were obtained at four fixed time points daily (upon waking, 30 minutes post-waking, 16:00, and 21:00) and immediately after any binge episode.\u003c/p\u003e\n\u003ch4\u003ePhase 3: Debriefing and compensation\u003c/h4\u003e\n\u003cp\u003eOn Day 16, participants returned all equipment and completed a structured interview on adherence and study burden. Compensation of up to \u0026euro;300 was provided, prorated for compliance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Measures and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary outcomes were measured by momentary urge to binge, which was self-reported on a 0\u0026ndash;100 visual analogue scale at each EMA prompt, and by binge episode occurrence, a binary variable (0 = no binge, 1 = binge) derived from event-contingent EMA entries verified by timestamps. Ecological Momentary Assessment (EMA) surveys were administered through a secure commercial platform, with real-time compliance monitored via a researcher dashboard. Prompts assessed affective and behavioral states, and timestamps were synchronized with physiological recordings. Cardiac data were recorded with an ambulatory ECG monitor (sampling rate \u0026ge; 1000 Hz). Raw interbeat interval (IBI) data were processed using Kubios HRV Premium, with automated artifact correction and manual review. The primary HRV metric was the Root Mean Square of Successive Differences (RMSSD, ms). For binge analyses, 60-minute epochs preceding binge episodes were extracted and matched to non-binge control periods. In parallel, continuous passive data streams captured behavioral metrics, including location variance (mobility), total screen time, and communication frequency. All data were anonymized and restricted to study-specific outputs. Saliva samples were stored at \u0026minus;80 \u0026deg;C until assay, and cortisol concentrations were determined in duplicate using high-sensitivity enzyme immunoassay kits (Salimetrics, State College, PA, USA). Indices included the Cortisol Awakening Response (CAR) and diurnal slope. Cortisol and HRV responses to the TSST were summarized using Area Under the Curve with respect to increase (AUCi). Finally, baseline psychological measures included the STAI-T, EDE-Q, BDI-II, and UPPS-P Negative Urgency subscale.\u003c/p\u003e"},{"header":"3. Statistical Analysis","content":"\u003cp\u003eAll statistical analyses were performed in R (version 4.3). Data were inspected for outliers, non-normality, and missingness prior to analysis. HRV and cortisol data were log-transformed as needed to normalize distributions. Statistical significance was defined as\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (two-tailed) for all tests.\u003c/p\u003e\n\u003ch3\u003e3.1 Preliminary analyses\u003c/h3\u003e\n\u003cp\u003eGroup differences in baseline demographic, clinical, and metabolic characteristics were examined using one-way ANOVA or \u0026chi;\u0026sup2; tests, as appropriate (Table 1). Significant effects were followed by Bonferroni-corrected post hoc pairwise comparisons.\u003c/p\u003e\n\u003ch3\u003e3.2 Laboratory stress reactivity\u003c/h3\u003e\n\u003cp\u003eBetween-group differences in cortisol AUCi and HRV recovery following the TSST were assessed using ANCOVA, controlling for age, sex, and BMI (Table 2).\u003c/p\u003e\n\u003ch3\u003e3.3 Ecological momentary data\u003c/h3\u003e\n\u003cp\u003eMomentary urge to binge (continuous outcome) was modeled using multilevel modeling (MLM), with repeated EMA observations (Level 1) nested within participants (Level 2). Time-lagged predictors included anxiety, HRV (RMSSD), and cortisol. All models controlled for time of day and physical activity. Diagnostic group (GAD+BED, BED-only, GAD-only, HC) and laboratory stress reactivity indices were entered as Level 2 moderators to test cross-level interactions (Table 3).\u003c/p\u003e\n\u003ch3\u003e3.4 Prediction of binge episodes\u003c/h3\u003e\n\u003cp\u003eBinge occurrence (binary outcome) was examined using multilevel logistic regression, estimating the odds of a binge episode from pre-binge anxiety, HRV, cortisol, and contextual variables derived from passive smartphone sensing (Table 4).\u003c/p\u003e\n\u003ch3\u003e3.5 Exploratory analyses\u003c/h3\u003e\n\u003cp\u003eA Random Forest classifier was applied to explore nonlinear relationships among physiological and behavioral predictors. Variable importance was quantified by mean decrease in Gini impurity (Table 5). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e"},{"header":"4. Results","content":"\u003ch3\u003e4.1 Participant characteristics\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics for all groups are presented in Table 1. Groups did not differ significantly in age, sex, or education. As expected, participants with GAD+BED and BED-only showed higher BMI and greater psychopathology (STAI-T, EDE-Q, and BDI-II scores) compared to healthy controls (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The comorbid GAD+BED group also exhibited elevated metabolic risk indicators, including higher glucose, triglycerides, and blood pressure levels.\u003c/p\u003e\n\u003ch3\u003e4.2 Laboratory stress reactivity\u003c/h3\u003e\n\u003cp\u003eAs shown in Table 2, significant group differences were observed for both cortisol and HRV responses to the TSST. The GAD+BED group demonstrated the lowest HRV recovery and blunted cortisol AUCi compared with other groups (\u003cem\u003ep\u003c/em\u003e = 0.002\u0026ndash;0.021). Participants with BED-only showed intermediate reactivity, suggesting partial stress dysregulation.\u003c/p\u003e\n\u003ch3\u003e4.3 Multilevel analyses of momentary urge to binge\u003c/h3\u003e\n\u003cp\u003eMultilevel modeling indicated that higher lagged anxiety predicted stronger subsequent urges to binge (\u0026gamma; = 0.25,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas higher HRV predicted lower urges (\u0026gamma; = \u0026ndash;0.13,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.031) (Table 3). The Anxiety \u0026times; HRV interaction approached significance (\u003cem\u003ep\u003c/em\u003e = 0.073), suggesting that higher HRV buffered the link between anxiety and binge urges. Cross-level interactions revealed that this effect was strongest among participants with comorbid GAD+BED.\u003c/p\u003e\n\u003ch3\u003e4.4 Prediction of binge episodes\u003c/h3\u003e\n\u003cp\u003eMultilevel logistic regression (Table 4) showed that higher pre-binge anxiety, lower HRV, and reduced mobility significantly increased the odds of a binge episode (ORs = 1.25\u0026ndash;1.38,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Cortisol predicted binge occurrence at a trend level (\u003cem\u003ep\u003c/em\u003e = 0.089). Greater screen time also modestly increased the likelihood of a binge (OR = 1.15,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e = 0.023).\u003c/p\u003e\n\u003ch3\u003e4.5 Exploratory machine learning analyses\u003c/h3\u003e\n\u003cp\u003eThe Random Forest classifier achieved an overall performance of AUC = 0.76 (Table 5). The strongest predictors of binge occurrence were self-reported anxiety, HRV, and location variance. Cortisol and screen time contributed smaller but meaningful effects, supporting the combined role of autonomic and behavioral dysregulation in real-world binge risk.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study provides the first ecologically valid evidence of a synergistic psychophysiological state that prospectively predicts binge eating in daily life. By integrating real-time subjective experiences with continuous physiological and behavioral monitoring, we demonstrated that the combination of high anxiety and low parasympathetic tone (indexed by HRV) defines a high-risk state for binge urges and episodes\u0026mdash;particularly in individuals with comorbid GAD and BED. These findings bridge laboratory models of stress reactivity with real-world patterns of disordered eating, offering a novel biobehavioral framework for anxiety-driven binge eating.\u003c/p\u003e\n\u003ch3\u003e5.1 The synergistic psychophysiological risk state\u003c/h3\u003e\n\u003cp\u003eOur primary hypothesis was supported. Within-person increases in anxiety predicted stronger subsequent urges to binge, consistent with affect regulation models [14\u0026ndash;16]. This relationship was amplified under conditions of low HRV, reflecting reduced vagal regulation and autonomic rigidity [22]. Thus, it is not anxiety alone, but anxiety experienced during physiological dysregulation, that most strongly drives binge urges. This synergistic effect helps explain why only certain anxious states escalate into binge behavior and underscores the importance of integrating physiological and emotional processes in conceptual models.\u003c/p\u003e\n\u003cp\u003eMoreover, logistic regression results confirmed that this \u0026ldquo;high-anxiety, low-HRV\u0026rdquo; state preceded actual binge episodes. Reduced location variance (indicating behavioral withdrawal) and increased screen time further characterized pre-binge contexts, suggesting that anxiety-driven binges occur in moments of both physiological arousal and environmental disengagement.\u003c/p\u003e\n\u003ch3\u003e5.2 The unique vulnerability of the comorbid GAD+BED phenotype\u003c/h3\u003e\n\u003cp\u003eThe inclusion of multiple clinical control groups allowed examination of diagnostic specificity. The anxiety\u0026ndash;binge association was strongest in the comorbid GAD+BED group, indicating a distinct phenotype with heightened reactivity to anxiety. Individuals with BED alone may binge for other reasons (e.g., food cues, dietary restraint), but for those with co-occurring GAD, anxiety appears to be a direct precipitant.\u003c/p\u003e\n\u003cp\u003eAt the trait level, the GAD+BED group exhibited blunted cortisol responses and slower HRV recovery following the TSST. Such patterns are interpreted as indicators of chronic stress exposure and reduced HPA-axis flexibility [29]. Together, these results point to a fundamental deficit in physiological resilience among comorbid participants, rendering them more vulnerable to anxiety-triggered binge episodes.\u003c/p\u003e\n\u003ch3\u003e5.3 Clinical implications: from reactive to proactive care\u003c/h3\u003e\n\u003cp\u003eThese findings have immediate clinical relevance. Physiological monitoring\u0026mdash;particularly HRV biofeedback\u0026mdash;may be a useful adjunct to standard therapy for comorbid anxiety and BED. By teaching patients to recognize and modulate physiological dysregulation (e.g., low HRV), clinicians could help reduce vulnerability during high-anxiety states. This approach aligns with Ecological Momentary Interventions (EMIs), which deliver adaptive strategies in real time via mobile platforms [23]. Additionally, the observed behavioral markers of withdrawal (e.g., low mobility) suggest that interventions promoting social or environmental engagement could further mitigate binge risk. Integrating these components could transform treatment from reactive management to proactive prevention.\u0026nbsp;Our findings may also have implications for emerging BED interventions. Preliminary evidence suggests that ketogenic dietary intervention may be feasible and associated with reduced binge-eating symptoms in BED, raising the possibility that future work could examine whether physiological risk markers such as HRV help identify individuals most likely to benefit from targeted behavioral or metabolic interventions [24]\u003c/p\u003e\n\u003ch3\u003e5.4 Limitations and future directions\u003c/h3\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, the correlational nature of ambulatory data precludes causal inference. Second, the sample was regionally and linguistically specific, which may limit generalizability. Third, cortisol predicted binge episodes only at a trend level, implying that HPA-axis activity may reflect a more stable vulnerability rather than immediate binge risk.\u003c/p\u003e\n\u003cp\u003eFuture studies should test the efficacy of real-time EMI systems based on physiological monitoring. Combining ambulatory assessment with periodic neuroimaging could clarify how brain circuits implicated in anxiety and reward (e.g., BNST and hypothalamus) mediate these dynamic processes. Finally, extending this design to adolescent samples could illuminate developmental pathways and inform early intervention.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study demonstrates a dynamic, real-time psychophysiological model of anxiety-driven binge eating. A synergistic pattern of high anxiety and low physiological flexibility defines a high-risk state for binge urges and episodes, most pronounced among individuals with comorbid GAD and BED. These findings underscore the need for treatments that simultaneously address emotional and physiological dysregulation. By shifting the focus from retrospective assessment to proactive, moment-to-moment risk detection, this research offers a foundation for precision interventions aimed at disrupting the cycle of anxiety and binge eating where it begins\u0026mdash;in everyday life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants for their time and dedication to this study. This work was supported by Freie Universit\u0026auml;t Berlin. We also gratefully acknowledge the contributions of\u0026nbsp;Harriet Salbach\u0026nbsp;and the research staff of the Klinik f\u0026uuml;r Department of Endocrinology and Metabolic Diseases Charit\u0026eacute; \u0026ndash; Universit\u0026auml;tsmedizin Beriln for their invaluable assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the \u003cem\u003eDeutschlandstipendium National Scholarship Program for Academic Excellence\u003c/em\u003e, funded by the Federal Ministry of Education and Research (BMBF) and Klarman Family Foundation in Germany. The funding body had no role in study design, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Freie Universit\u0026auml;t Berlin (Approval No. 6734). All procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eGiel, K. E., Zipfel, S., Alizadeh, M., \u0026amp; Schag, K. (2023). \u003cem\u003eBinge-eating disorder. 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Understanding the relationship between EMA methods, sensed behavior, and responsiveness: A cross-study analysis. \u003cem\u003eResearchGate Preprint.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLestari, A. R. S., \u0026amp; Taufik, A. (2025). \u003cem\u003eSystematic literature review: Heart-rate variability as a neurobiological biomarker of mindfulness effects on emotion regulation. ResearchGate Preprint.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDao, K. P., De Cocker, K., Tong, H. L., Kocaballi, A. B., Chow, C., \u0026amp; Laranjo, L. (2021). Smartphone-delivered ecological momentary interventions based on EMA to promote health behaviors: Systematic review and reporting checklist. \u003cem\u003eJMIR mHealth and uHealth, 9\u003c/em\u003e(11), e22890. https://doi.org/10.2196/22890\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiang, Y., Taylor, A., Haas, V. et al. A ketogenic diet for the management of binge-eating disorder: a pilot study. Eat Weight Disord (2026). https://doi.org/10.1007/s40519-026-01843-7\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic, clinical, and metabolic characteristics by diagnostic group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of participants with comorbid Generalized Anxiety Disorder and Binge-Eating Disorder (GAD+BED), BED-only, GAD-only, and Healthy Controls (HC). Data are presented as Mean (Standard Deviation) or n (%). Different superscript letters (a, b, c) within a row indicate significant pairwise differences (p \u0026lt; 0.05) based on post-hoc tests following a significant one-way ANOVA or Chi-square test. STAI-T, State-Trait Anxiety Inventory; EDE-Q, Eating Disorder Examination-Questionnaire Global Score; BDI-II, Beck Depression Inventory-II; UPPS-P NU, UPPS-P Impulsive Behavior Scale - Negative Urgency subscale; BP, Blood Pressure; HbA1c, Glycated Hemoglobin; MDD, Major Depressive Disorder.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD+BED (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBED-only (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-only (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e34.1 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31.5 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e33.8 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31.2 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31 (77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e16 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e32 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eEducation, College Degree (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e32.8 (5.1)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31.9 (4.4)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25.9 (4.2)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e24.3 (3.1)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eIllness Duration, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8.1 (5.9)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6.7 (4.8)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7.9 (5.3)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSTAI-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e59.8 (7.2)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e46.1 (6.8)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55.4 (8.1)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e37.3 (5.5)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eEDE-Q Global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4.1 (1.0)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.8 (0.9)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.8 (1.2)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.3 (0.7)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eBDI-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e26.5 (8.9)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19.2 (7.1)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e22.4 (7.8)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.1 (4.3)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eUPPS-P NU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e36.1 (5.5)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e32.4 (5.1)\u003csub\u003ea,b\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e29.8 (6.2)\u003csub\u003eb,c\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25.3 (5.8)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eComorbid MDD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePsychotropic Medication (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFasting Glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e106.2 (12.4)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e103.8 (11.1)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e95.4 (9.7)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e92.5 (8.1)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.8 (0.4)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.7 (0.3)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.4 (0.2)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5.3 (0.2)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e47.2 (9.4)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e49.8 (10.2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e55.4 (11.1)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e59.1 (9.8)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e129.5 (28.7)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e121.4 (24.1)\u003csub\u003ea,b\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e118.2 (22.9)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e111.8 (20.3)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e151.8 (43.2)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e146.1 (41.8)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e118.9 (36.4)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e110.2 (33.5)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e131.5 (11.8)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e128.3 (12.2)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e121.4 (10.1)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e118.7 (9.5)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e83.9 (7.2)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e81.4 (7.5)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e77.8 (6.9)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e75.3 (6.4)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePrevious Psychotherapy for BED (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePrevious Pharmacotherapy for BED (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Laboratory stress reactivity by diagnostic group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup comparisons of physiological reactivity to the Trier Social Stress Test (TSST). Cortisol reactivity is measured by Area Under the Curve with respect to increase (AUCi). Heart Rate Variability (HRV) recovery represents the change in Root Mean Square of Successive Differences (RMSSD) from peak stress to 60 minutes post-stress. Values are presented as Mean (Standard Deviation). Different superscript letters (a, b) within a row indicate significant pairwise differences (p \u0026lt; 0.05) based on post-hoc tests following a significant one-way ANOVA.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSST Measure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGAD+BED (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBED-only (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-only (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHC (n=40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol AUCi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.4 (34.5)ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135.2 (41.2)ᵃᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142.8 (37.9)ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e148.1 (32.6)ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHRV Recovery [\u0026Delta;RMSSD, ms]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;11.8 (6.1)ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;9.2 (5.8)ᵃᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;8.1 (5.3)ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;7.5 (4.7)ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multilevel model predicting momentary urge to binge.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of the multilevel model predicting momentary urge to binge (0\u0026ndash;100 scale) from time-lagged predictors. Level 1 predictors are person-mean centered. The model controlled for time of day and physical activity. SE, Standard Error; CI, Confidence Interval; RMSSD, Root Mean Square of Successive Differences; HRV, Heart Rate Variability; BED, Binge-Eating Disorder; GAD, Generalized Anxiety Disorder; HC, Healthy Control.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (\u0026gamma;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\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: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e24.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[19.03, 30.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety (lagged)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[0.13, 0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRV - RMSSD (lagged)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.25, -0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol (lagged)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.01, 0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety \u0026times; RMSSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.19, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety \u0026times; (Group: BED-only)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.34, 0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety \u0026times; (Group: GAD-only)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.39, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety \u0026times; (Group: HC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e[-0.44, -0.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multilevel logistic regression predicting binge episode occurrence.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOdds ratios from the multilevel logistic regression model predicting the likelihood of a binge eating episode based on pre-binge states. All predictors are from the 60-minute epoch preceding the event. HRV, Heart Rate Variability; RMSSD, Root Mean Square of Successive Differences; OR, Odds Ratio; CI, Confidence Interval.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor (Pre-Binge Epoch)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (OR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Reported Anxiety\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 10-point increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[1.15, 1.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRV - RMSSD (per 10-ms\u0026sup2; decrease)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[1.06, 1.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol (per 1-nmol/L increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[0.99, 1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation Variance (per SD decrease)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[1.10, 1.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen Time (per 30-min increase)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[1.02, 1.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Feature Importance from Random Forest Analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults from the exploratory Random Forest analysis classifying binge vs. non-binge epochs. The mean decrease in Gini impurity is reported as a measure of variable importance, with higher values indicating a stronger contribution to the model\u0026apos;s predictive accuracy. The model\u0026apos;s overall performance was AUC = 0.76. HRV, Heart Rate Variability; RMSSD, Root Mean Square of Successive Differences.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Decrease Gini\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Reported Anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e18.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRV - RMSSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e15.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation Variance (GPS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime since last meal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime of Day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Binge-Eating Disorder, Anxiety Disorders, Heart Rate Variability, Ecological Momentary Assessment, Psychophysiology, Comorbidity, Stress, Cortisol, Digital Phenotyping, Generalized Anxiety Disorder","lastPublishedDoi":"10.21203/rs.3.rs-9397248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9397248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBinge-Eating Disorder (BED) is highly prevalent and frequently comorbid with anxiety, yet the real-time psychophysiological processes that translate anxiety into binge behavior remain unclear. This study therefore aimed to identify real-time physiological and contextual correlates of anxiety-driven binge eating in a naturalistic setting. We employed a 14-day longitudinal monitoring of 120 adults assigned to four groups: BED\u0026thinsp;+\u0026thinsp;GAD (n\u0026thinsp;=\u0026thinsp;40), BED-only (n\u0026thinsp;=\u0026thinsp;20), GAD-only (n\u0026thinsp;=\u0026thinsp;20), and healthy controls (n\u0026thinsp;=\u0026thinsp;40). Participants completed ecological momentary assessments (EMA; 0\u0026ndash;100 scales) synchronized with continuous ambulatory ECG for heart-rate variability (HRV; RMSSD) and scheduled salivary cortisol; laboratory stress reactivity was also assessed with the Trier Social Stress Test (TSST). Multilevel models tested within-person effects of lagged anxiety, HRV, and cortisol on urge to binge, while multilevel logistic regression predicted binge episodes. Results showed that the BED\u0026thinsp;+\u0026thinsp;GAD group exhibited greater clinical severity, metabolic risk, and altered stress physiology (lower HRV recovery, p=.021; blunted cortisol AUCi, p=.002) versus other groups. Across the sample, higher lagged anxiety predicted a stronger subsequent urge to binge (γ\u0026thinsp;=\u0026thinsp;0.25, p\u0026lt;.001), whereas higher HRV predicted lower urges (γ=\u0026minus;0.13, p=.031). Binge episodes were prospectively associated with higher pre-binge anxiety (OR\u0026thinsp;=\u0026thinsp;1.38, p\u0026lt;.001), lower HRV (OR\u0026thinsp;=\u0026thinsp;1.25, p=.008), reduced mobility (OR\u0026thinsp;=\u0026thinsp;1.31, p=.002), and greater screen time (OR\u0026thinsp;=\u0026thinsp;1.15, p=.023). A random-forest model achieved an AUC of 0.76, with anxiety, HRV, and location variance as top predictors. These effects were strongest in the BED\u0026thinsp;+\u0026thinsp;GAD group. In conclusion, anxiety-driven binge risk is characterized by a synergistic state of high anxiety and low vagal regulation in naturalistic contexts, amplified by behavioral withdrawal. HRV and digital mobility markers provide actionable, real-time signals for effective interventions to prevent binge episodes.\u003c/p\u003e","manuscriptTitle":"Physiological Correlates of Anxiety-Driven Binge Eating in a Naturalistic Setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 11:18:49","doi":"10.21203/rs.3.rs-9397248/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:16:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:01:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57606426904487020632025844520584657912","date":"2026-04-29T17:16:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300671598812102154224693302853824096646","date":"2026-04-28T15:24:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240893558351617044896997524736409585191","date":"2026-04-28T13:59:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T07:53:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T14:26:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T14:26:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Eating Disorders","date":"2026-04-13T00:07:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b53b9954-15c3-4c31-a9b1-675b528e5fe7","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:16:47+00:00","index":23,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T15:01:34+00:00","index":22,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T11:18:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 11:18:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9397248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9397248","identity":"rs-9397248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}