{"paper_id":"02d4eb0e-3c30-4f77-bd6d-e2bfe86ab199","body_text":"Heterogeneous Pathways of Readiness to Change in Anorexia Nervosa: A Longitudinal Trajectory Analysis | 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 Heterogeneous Pathways of Readiness to Change in Anorexia Nervosa: A Longitudinal Trajectory Analysis Magnus Sjögren¹, Rene Klinkby Støving², Mia Beck Lichtenstein³ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7704217/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Readiness to change constitutes a key determinant of treatment outcome in anorexia nervosa (AN), yet its developmental patterns during inpatient care remain poorly understood. This study aimed to characterize longitudinal readiness trajectories in AN inpatients and explore associations with psychiatric comorbidity. Methods: We conducted a prospective longitudinal study including 97 female inpatients with AN (mean age 26.7 ± 9.4 years) and 29 age-matched healthy controls. We assessed readiness to change at three optimal timepoints: baseline (1st admission), 8 weeks into treatment, and readmission baseline (2nd admission). We applied K-means cluster analysis with silhouette validation to identify distinct readiness trajectories. Complete case analysis (n=24) and multiple imputation sensitivity analyses were performed. Comprehensive statistical analyses included confidence intervals and effect sizes for all major comparisons. Results: K-means cluster analysis with optimal silhouette score (0.427) identified two distinct readiness trajectories. Cluster 1 (Stable-High, n=17, 70.8%) maintained consistently moderate-to-high readiness, while Cluster 2 (Declining, n=7, 29.2%) showed substantial decline. Between-cluster differences were significant at 8 weeks (Cohen's d = 3.34, p < 0.001) and readmission (Cohen's d = 1.67, p = 0.001). Patients with AN demonstrated significantly lower baseline readiness than healthy controls (37.2 ± 13.6 vs 13.4 ± 7.8, Cohen's d = 1.86, p < 0.001). Psychiatric comorbidities showed no significant associations with baseline readiness or trajectory membership. Conclusions: Readiness to change in AN inpatients follows two distinct trajectories, with approximately 30% experiencing severe motivational decline during treatment. The substantial difference from healthy controls confirms readiness as a core treatment target. These findings support trajectory-specific interventions to optimize treatment engagement. Level of evidence: Level III: Prospective observational cohort study with statistical modeling Readiness to change Anorexia Nervosa trajectories K-means clustering inpatient treatment Figures Figure 1 What is already known on this subject? Readiness to change is a key predictor of treatment outcome in anorexia nervosa, with higher baseline readiness associated with better therapeutic response The Transtheoretical Model provides a framework for understanding motivation in eating disorders, but longitudinal patterns of readiness during inpatient treatment remain poorly characterized Previous studies report conflicting findings about whether readiness improves, remains stable, or declines during AN treatment, with limited data on trajectory patterns Psychiatric comorbidities such as depression and anxiety are prevalent in AN and theoretically linked to impaired treatment motivation, but their specific impact on readiness remains unclear No previous studies have systematically compared readiness to change between AN and healthy controls to establish whether low motivation represents AN-specific pathology What this study adds? First study to identify distinct longitudinal trajectories of readiness to change in AN inpatients using robust K-means clustering methodology Demonstrates that approximately 30% of patients experience profound motivational collapse during early treatment (dropping from 30.7 to 7.0 points at 8 weeks), with minimal recovery by readmission Establishes that AN have significantly lower baseline readiness than healthy controls (Cohen's d = 1.86), confirming readiness as intrinsic AN pathology rather than general treatment ambivalence Contradicts theoretical predictions by showing psychiatric comorbidities (depression and anxiety) do not significantly impact baseline readiness or trajectory membership in this sample Provides optimal timepoint selection methodology for trajectory analysis in the challenging context of high attrition rates typical of AN research Offers evidence-based foundation for developing trajectory-specific motivational interventions, particularly targeting the critical 8-week period when high-risk patients experience severe readiness decline. 1. Introduction Anorexia nervosa (AN), the most severe eating disorder, poses exceptional threats to physical and mental well-being and carries the highest mortality rate among psychiatric conditions ( 1 ). Recent epidemiological data indicate a prevalence of 1–4% for eating disorders ( 2 , 3 ), with AN presenting at approximately 0.3% prevalence. The persistence of symptoms in 20–23% of individuals diagnosed with AN represents a significant clinical challenge ( 4 , 5 ). Despite this burden, knowledge about modifiable mechanisms that could influence disorder outcomes remains limited, with effective treatment options largely elusive ( 6 ). AN represents a severe psychiatric illness characterized by restrictive eating behaviors, distorted body image, and intense fear of weight gain. Patients with AN commonly exhibit pronounced ambivalence toward treatment due to the complex interplay of egosyntonic and egodystonic aspects of their disorder ( 7 , 8 ). The egosyntonic nature of AN leads patients to perceive their eating behaviors as aligned with their identity and values, while the egodystonic elements create distress and desire for change. This ambivalence significantly impacts patients' readiness for treatment and their capacity to engage in behavioral changes ( 7 , 8 ), while simultaneously presenting a potential therapeutic target. This ambivalence can be conceptualized within the Transtheoretical Model (TTM) framework ( 9 , 10 ), which provides a comprehensive understanding of the dynamic process of behavioral change in AN. The TTM outlines five stages of change: precontemplation, contemplation, preparation, action, and maintenance, describing change as a non-linear process with potential relapses into previous stages. Understanding a patient's stage of change proves essential for determining optimal treatment timing and explaining potential treatment non-response, particularly when working with individuals in early stages of change ( 9 , 10 ). Recent research has increasingly highlighted the importance of readiness to change in AN treatment outcomes. A systematic review and meta-analysis by Vall and Wade ( 11 ) demonstrated that higher levels of readiness to change at treatment initiation predicted superior outcomes in eating disorders, though several factors may interfere with readiness, including comorbidity, treatment autonomy limitations, and interpersonal relationships ( 12 ). However, the trajectory of readiness to change during treatment remains poorly characterized ( 13 ). While some studies suggest that readiness increases over the course of treatment ( 14 , 15 ), others have documented stability or even decreases in readiness ( 16 ). Additionally, readiness may vary across different aspects of the disorder ( 17 ). Enhancing readiness to change remains essential given its association with treatment outcomes and relapse risk ( 18 ). Despite growing recognition of readiness to change as a critical factor in AN treatment outcomes, key gaps persist in understanding its longitudinal trajectory and modifiable predictors. First, while comorbidities such as depression and anxiety are prevalent in AN and theoretically linked to impaired motivation, their specific impact on readiness to change remains poorly quantified. Second, existing studies yield conflicting evidence about whether readiness improves during treatment, with limited data on how it evolves across multiple admissions—a common feature of AN's chronic course. Third, the absence of comparisons with healthy controls obscures whether low readiness reflects AN-specific pathology or general treatment ambivalence. We aimed to compare baseline readiness to change between patients with anorexia nervosa (AN) and healthy controls (HC), hypothesizing that readiness would be significantly lower among Patients with AN. Additionally, we sought to identify and characterize distinct longitudinal trajectories of readiness to change in AN inpatients using K-means clustering with silhouette validation ( 19 ), anticipating heterogeneous patterns across the treatment and follow-up period. Finally, we explored how psychiatric comorbidities might associate with these readiness trajectories, utilizing robust statistical modeling and multiple imputation ( 20 ) to address missing data and ensure reliable longitudinal analysis. 2. Materials & Methods 2.1. Participants We conducted a prospective longitudinal study including 97 female inpatients with anorexia nervosa (AN) (mean age 26.7 ± 9.4 years, 95% CI: [24.9, 28.6]), all meeting DSM-5 criteria for AN. We classified severity as mild (26.7%), moderate (30.0%), or severe (43.3%) according to DSM-5 specifiers. We recruited a comparison group of 29 age- and gender-matched healthy controls (HC) without any history of eating disorders to contextualize readiness to change in the AN group (mean age 25.9 ± 5.8 years, 95% CI: [23.7, 28.0]). Optimal Timepoint Selection : To optimize statistical power while maintaining clinical relevance, we selected three timepoints that maximized data completeness: baseline (99.0% complete), 8 weeks (57.7% complete), and readmission baseline (30.9% complete), yielding 24 complete cases for trajectory analysis compared to only 5 complete cases when including the 12-week timepoint. This approach ensured adequate statistical power for trajectory modeling ( 21 ). All patients participated in the PROspective Longitudinal all-comer inclusion study on Eating Disorders (PROLED) study, which commenced in 2016 as a clinical, longitudinal study planned to follow patients annually over 10 years. We collected all data in the current study before September 2024. The study operated at the Psychiatric Center Ballerup (PCB) in Denmark and received approval from the local ethics board (id: H-15012537; addendum 77106) and the data processing board. The general inclusion criteria in PROLED are: Adult individuals (age 18–65 years) Admission to the ED unit in Psychiatric Center Ballerup (PCB), Denmark A diagnosis of AN Signed written informed consent We focused the study on changes in readiness to change ED behavior from baseline to follow-up, with additional analyses on comorbidities. The mean duration of illness was 8.2 ± 7.8 years. We did not offer enrollment to any patient undergoing forced care at the time of PROLED study screening. However, once forced care changed to voluntary care, we offered enrollment to such patients. 2.2. Healthy Controls We recruited healthy controls (HC; n = 29) from the local community and screened them using the EDE-Q to exclude subclinical eating pathology. All HC completed a comprehensive questionnaire on their psychiatric and somatic health. Exclusion criteria included any lifetime diagnosis of an eating disorder or major psychiatric illness. 2.3. Weight Restoration Treatment All patients with AN diagnosis underwent a weight restoration program, previously described ( 22 – 24 ). In summary, we provided meals five times per day during monitoring by trained nurses to ensure proper renourishment. A dietician held individual weekly meetings with each patient to ensure a meal plan enabling approximately 1 kg weight increase per week up to an ideal body weight (IBW; for clinical treatment at PCB) of BMI 20 for women and BMI 21 for men. We supported weight gain through restrictions in physical activity, monitored meals, and post-meal rest. During these eight weeks, we provided no formal psychotherapy, although we offered individual meetings with psychologists and nurses for supportive purposes. 2.4. Clinical and Psychometric Measures All patients underwent initial complete diagnostic work-up including comprehensive diagnostic interview by a psychologist, and medical and psychiatric examinations conducted by either a specialist psychiatrist or a General Practitioner with special training in EDs. Additionally, we performed the Eating Disorder Examination (diagnostic questions; EDE ( 25 )) and routine clinical and laboratory assessments to maintain high-quality diagnosing of ED and comorbid disorders. We validated all primary diagnoses and any comorbid diagnoses using the ICD-10 checklist ( 26 ). 2.5. Readiness to Change Behavior We assessed changes in the total score of the Danish version of the Eating Disorder Readiness Ruler (ED-RR) ( 24 , 27 ). The ED-RR represents a self-report instrument designed to quickly assess readiness to change problematic eating behaviors in individuals with clinical eating disorders. The ED-RR psychometric properties include two factors explaining 59% of the variance, reflecting restriction and body image preoccupation, and binge-eating and vomiting symptoms, with internal consistency alpha coefficients of 0.77 and 0.84, respectively. The ED-RR has demonstrated validation in ED populations, including AN ( 27 , 28 ). Higher readiness scores on the ED-RR associate with greater autonomous readiness and significant symptom reductions over time. We assessed readiness at three optimal timepoints: baseline of 1st admission (T1), at 8 weeks into 1st admission (T2), and baseline of 2nd admission (T3). The ED-RR had not been previously assessed in HC, and we based the scores from the HC group on eating a wide variety of healthy foods from all nutritional categories. 2.6. Statistical Analysis 2.6.1 Trajectory Analysis We performed K-means cluster analysis to identify distinct longitudinal trajectories of readiness to change across three timepoints among patients with complete data (n = 24). We determined the optimal number of clusters using silhouette analysis ( 19 ), calculating silhouette scores for cluster solutions ranging from k = 2 to k = 5, with the highest silhouette score indicating optimal cluster separation. We performed K-means clustering using Euclidean distance with random seed set for reproducibility (n_init = 10, random_state = 42). We conducted one-way ANOVAs and independent t-tests to assess differences in mean readiness scores between clusters at each timepoint. We calculated effect sizes (Cohen's d) ( 29 ) and 95% confidence intervals for all comparisons. 2.6.2 Missing Data Analysis We performed comprehensive missing data analysis to characterize patterns of missingness across timepoints. We conducted multiple imputation (MICE) ( 30 ) using chained equations to create complete datasets for sensitivity analyses. 2.6.3 Baseline Comparisons We used independent t-tests to compare baseline readiness between AN and healthy controls, calculating effect sizes and 95% confidence intervals. We created composite variables for any depression disorder and any anxiety disorder from individual diagnostic categories. 2.6.4 Exploratory Comorbidity Analysis We explored potential associations between psychiatric comorbidities and readiness to change. We created composite variables for any depression disorder and any anxiety disorder from individual diagnostic categories, then compared baseline readiness scores between comorbid and non-comorbid patients using independent t-tests with effect sizes. We also examined whether comorbidity status was associated with trajectory membership using descriptive analyses. 2.6.5 Statistical Reporting All analyses included 95% confidence intervals, effect sizes, and complete statistical reporting following current guidelines. We set statistical significance at p < 0.05. 3. Results 3.1. Baseline Characteristics Our study included 97 female patients with AN (mean age 26.7 ± 9.4 years, 95% CI: [24.9, 28.6]) and 29 HC (mean age 25.9 ± 5.8 years, 95% CI: [23.7, 28.0]). The mean BMI at baseline for Patients with AN was 15.1 ± 2.5 kg/m² (95% CI: [14.6, 15.6]), significantly lower than healthy population norms. Depression and anxiety disorders showed moderate prevalence in the AN group (17.5% and 16.5%, respectively) compared to 0% in controls. The mean duration of illness for Patients with AN was 8.2 ± 7.8 years. Table 1: Baseline Clinical Characteristics Characteristic AN (n=97) Healthy controls (n=29) Statistical comparison Age (years), mean ± SD (95% CI) 26.7 ± 9.4 (24.9-28.6) 25.9 ± 5.8 (23.7-28.0) p = 0.64 Female, n (%) 93 (95.9) 26 (89.7) p = 0.20 BMI (kg/m²), mean ± SD (95% CI) 15.1 ± 2.5 (14.6-15.6) N/A N/A Duration of illness (years), mean ± SD (95% CI) 8.2 ± 7.8 (6.6-9.8) N/A N/A Any depression disorder, n (%) 17 (17.5) 0 (0) p < 0.05 Any anxiety disorder, n (%) 16 (16.5) 0 (0) p < 0.05 Abbreviations: AN=Anorexia Nervosa, CI=Confidence Intervals, SD=Standard Deviation. 3.2. Baseline Readiness Comparison: AN vs Healthy Controls AN demonstrated significantly lower baseline readiness to change compared to healthy controls (37.2 ± 13.6 vs 13.4 ± 7.8, t(115) = 7.71, p < 0.001, Cohen's d = 1.86, 95% CI of difference: [17.7, 29.9]), indicating a large effect size. This substantial difference confirms that low readiness constitutes a distinctive feature of AN pathology rather than general treatment ambivalence. 3.3. Missing Data Pattern Our missing data analysis revealed significant attrition across timepoints (Table 2). The substantial attrition (75.3% missing data for complete 3-timepoint analysis) reflects the clinical reality of AN treatment, necessitating our robust missing data approaches including multiple imputation sensitivity analyses. Table 2: Missing Data Pattern by Timepoint Timepoint Available n (%) Missing n (%) Cumulative retention (%) Baseline 1st admission 96 (99.0) 1 (1.0) 99.0 8 weeks 1st admission 56 (57.7) 41 (42.3) 57.7 Baseline 2nd admission 30 (30.9) 67 (69.1) 30.9 Complete cases (3 timepoints) 24 (24.7) 73 (75.3) 24.7 3.4. Readiness Trajectories Silhouette analysis identified k=2 as the optimal cluster solution (silhouette score = 0.427), indicating good cluster separation and superior to k=3 (silhouette score = 0.397), k=4 (silhouette score = 0.414), and k=5 (silhouette score = 0.424). K-means cluster analysis identified two distinct longitudinal trajectories of readiness to change among the 24 patients with complete data: Cluster 1 (Stable-High, n=17, 70.8%) : This trajectory demonstrated consistently moderate-to-high readiness scores across all timepoints, with slight increase during treatment followed by maintenance: T1: 38.8 ± 11.8 (95% CI: [33.2, 44.4]) → T2: 42.6 ± 10.7 (95% CI: [37.5, 47.7]) → T3: 38.0 ± 14.5 (95% CI: [31.1, 44.9]). Cluster 2 (Declining, n=7, 29.2%) : This trajectory showed moderate initial readiness followed by profound decline during treatment with minimal recovery by readmission: T1: 30.7 ± 10.8 (95% CI: [22.7, 38.7]) → T2: 7.0 ± 10.6 (95% CI: [-0.9, 14.9]) → T3: 12.4 ± 17.4 (95% CI: [-0.5, 25.3]). Table 3: Trajectory-specific Mean Readiness Scores with Statistical Comparisons Timepoint Cluster 1 (n=17) Mean ± SD (95% CI) Cluster 2 (n=7) Mean ± SD (95% CI) Statistical comparison Cohen's d T1 (Baseline 1 st admission) 38.8 ± 11.8 (33.2-44.4) 30.7 ± 10.8 (22.7-38.7) t(22) = 1.56, p = 0.132 0.70 T2 (8 weeks 1 st admission) 42.6 ± 10.7 (37.5-47.7) 7.0 ± 10.6 (-0.9-14.9) t(22) = 7.43, p < 0.001 3.34 T3 (Baseline 2 nd admission) 38.0 ± 14.5 (31.1-44.9) 12.4 ± 17.4 (-0.5-25.3) t(22) = 3.71, p = 0.001 1.67 3.5. Cluster Characteristics The two trajectory groups did not differ significantly in baseline demographic characteristics. Age, BMI, and comorbidity rates proved comparable between clusters, suggesting that trajectory membership cannot be easily predicted from standard clinical variables at admission. 3.6. Exploratory Analysis of Psychiatric Comorbidity Exploratory analyses revealed no significant associations between psychiatric comorbidities and baseline readiness to change. Patients with depression showed non-significantly higher baseline readiness compared to those without depression (39.8 ± 11.7 vs 36.6 ± 14.0, t(94) = -0.87, p = 0.387, Cohen's d = 0.23). Similarly, patients with anxiety disorders showed non-significantly higher readiness than those without anxiety (40.8 ± 14.0 vs 36.4 ± 13.5, t(94) = -1.16, p = 0.249, Cohen's d = 0.32). Among the 24 patients with complete trajectory data, depression and anxiety showed no clear pattern of association with trajectory membership, with small group counts limiting definitive conclusions. Table 4: Baseline Readiness Differences by Psychiatric Comorbidity Status Comorbidity Status Present (n) Mean ± SD Absent (n) Mean ± SD Statistical comparison Any depression disorder 17 39.8 ± 11.7 79 36.6 ± 14.0 t(94) = -0.87, p = 0.387, d = 0.23 Any anxiety disorder 16 40.8 ± 14.0 80 36.4 ± 13.5 t(94) = -1.16, p = 0.249, d = 0.32 3.7. Sensitivity Analysis Multiple imputation (MICE) analyses confirmed the robustness of our trajectory findings. The two-cluster solution remained stable across imputed datasets, with silhouette scores consistent within 5% of the complete-case analysis. This supports the validity of the trajectory patterns despite substantial missing data. 4. Discussion We identified two distinct trajectories of readiness to change in AN inpatients using robust K-means clustering with optimal timepoint selection, providing novel insights into the heterogeneous nature of treatment motivation during inpatient care. The Stable-High trajectory (70.8% of patients) maintained consistently moderate-to-high readiness scores throughout treatment, while the Declining trajectory (29.2%) experienced severe motivational decline during early treatment with minimal recovery by readmission. The finding that nearly one-third of patients experienced profound motivational collapse during early treatment—dropping from moderate readiness (30.7 points) to near-zero motivation (7.0 points) at 8 weeks—represents a critical clinical phenomenon with an exceptionally large effect size (Cohen's d = 3.34). This pattern aligns with clinical observations of treatment resistance during initial phases of inpatient care and may reflect the challenging nature of weight restoration and the egosyntonic aspects of AN, where treatment confronts patients' core beliefs and behaviors. The minimal recovery observed by readmission (12.4 points) suggests persistent motivational challenges that may contribute to treatment dropout and relapse. The large effect size (Cohen's d = 1.86) for the difference in baseline readiness between Patients with AN and healthy controls provides strong evidence that low readiness constitutes an intrinsic feature of AN pathology rather than a general response to treatment situations. This finding supports the conceptualization of readiness to change as a key therapeutic target in AN treatment (31). 4.1. Clinical Implications These findings have several important clinical implications. First, the identification of distinct readiness trajectories suggests that uniform approaches to motivation enhancement may prove suboptimal. Patients in the Stable-High group may benefit from interventions that maintain and harness their existing motivation, while those in the Declining group require intensive support during the critical 8-week period when readiness to change drops severely. Second, the profound decline pattern provides a predictable timepoint for targeted interventions. Clinicians should anticipate this decline and implement proactive strategies to support high-risk patients. This might include enhanced motivational interviewing (32), family involvement, or other evidence-based approaches to boost treatment engagement. Third, early identification of patients likely to follow the Declining trajectory could enable personalized treatment approaches. While our analysis suggests that standard baseline variables do not reliably predict trajectory membership, future research might identify more sensitive predictors to guide clinical decision-making. 4.2. Psychiatric Comorbidity Considerations Contrary to our initial hypothesis, psychiatric comorbidities did not significantly impact baseline readiness to change in our sample. This finding diverges from some theoretical predictions that depression and anxiety might impair treatment motivation. The small effect sizes and non-significant associations may reflect the complex relationship between comorbidity and motivation in AN, where multiple factors interact to influence readiness. Small group sizes (17.5% depression, 16.5% anxiety) limited the power to detect subtle effects; larger studies are needed to clarify the role of comorbidity in AN treatment motivation. 4.3. Methodological Strengths and Limitations Strengths of our study include the longitudinal design with optimal timepoint selection based on data availability, robust cluster validation using silhouette analysis, comprehensive statistical reporting with confidence intervals and effect sizes, and inclusion of healthy controls for context. The use of multiple imputation for sensitivity analyses strengthens confidence in our findings. Limitations include the focus on female inpatients with AN, limiting generalizability to males or outpatient populations. The substantial attrition reflects the clinical reality of AN treatment but limits our complete case analysis to 24 patients. Our reliance on self-report measures for readiness may introduce bias, as patients with AN often struggle with insight and accurate self-assessment. The relatively small number of complete cases limits power for detecting smaller subgroups or more complex relationships. 4.4. Future Research Directions Future studies should investigate mechanisms underlying different readiness trajectories, particularly factors that predict severe motivational decline. We recommend longitudinal research with larger samples (n≥100 complete cases), enhanced retention strategies (such as intensive case management and technological aids), and longer follow-up periods to establish trajectory stability and relationship to long-term outcomes. Intervention studies targeting trajectory-specific approaches to motivation enhancement would prove valuable. For example, intensive motivational interventions during weeks 4-12 for high-risk patients, or maintenance strategies for stable-high patients. Additionally, exploration of neurobiological or psychological markers that might predict trajectory membership could enable personalized treatment approaches. Replication studies in outpatient settings, different cultural contexts, and male populations would enhance generalizability. Finally, development of real-time trajectory monitoring tools could enable dynamic treatment adjustments based on observed motivational patterns. 5. Conclusions We identified two distinct trajectories of readiness to change in AN inpatients using optimal timepoint selection and robust statistical methods: a majority maintaining stable moderate-high readiness to change (70.8%) and a concerning minority experiencing profound motivational collapse (29.2%). The substantial difference in baseline readiness between AN and healthy controls (Cohen's d = 1.86) confirms that low motivation constitutes an intrinsic feature of AN pathology. These findings support the development of trajectory-specific interventions to optimize treatment engagement, particularly during the vulnerable early treatment period when approximately 30% of patients experience severe decline in readiness to change. Understanding these patterns may help clinicians better support patients through the complex process of recovery from AN, ultimately improving treatment outcomes and reducing relapse risk. Abbreviations AN: Anorexia Nervosa BMI: Body Mass Index ED: Eating Disorder EDE: Eating Disorder Examination EDE-Q: Eating Disorder Examination Questionnaire ED-RR: Eating Disorder Readiness Ruler HC: Healthy Controls TTM: Transtheoretical Model Declarations Author Contributions: Conceptualization, M.S.; methodology, M.S.; validation, M.S., M.L., R.S.; formal analysis, M.S.; investigation, M.S. resources, M.S.; data curation M.S.; writing M.S., M.L., R.S.; writing—review and editing, M.S., M.L., R.S.; project administration, M.S.; funding acquisition, M.S. MS, RS, ML have all read and agreed to the published version of the manuscript. Funding: We are grateful for the funding support from the following external funds: Axel Muusfeldts Fond, the Dagmar Marshalls Fond, the Direktör Emil C. Hert og Hustru Inger Hertz Fond, the Jascha Fonden. 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Sjögren M., Kizilkaya I., Støving R.K.: Inpatient Weight Restoration Treatment Is Associated with Decrease in Post-Meal Anxiety. Journal of Personalized Medicine, 11, 1079, 2021. Seidel M., Brooker H., Lauenborg K., Wesnes K., Sjogren M.: Cognitive Function in Adults with Enduring Anorexia Nervosa. Nutrients, 13, 2021. Fjeldstad M., Kvist T., Sjogren M.: Weight Gain in Adults with Avoidant/Restrictive Food Intake Disorder Compared to Restrictive Anorexia Nervosa-Pilot Findings from a Longitudinal Study. Nutrients, 13, 2021. Luce K.H., Crowther J.H.: The reliability of the Eating Disorder Examination-Self-Report Questionnaire Version (EDE-Q). Int J Eat Disord, 25, 349–351, 1999. Janca A., Ustun T.B., Early T.S., Sartorius N.: The ICD-10 symptom checklist: a companion to the ICD-10 classification of mental and behavioural disorders. Social psychiatry and psychiatric epidemiology, 28, 239–242, 1993. St-Hilaire A., Axelrod K., Geller J., Mazanek Antunes J., Steiger H.: A Readiness Ruler for Assessing Motivation to Change in People with Eating Disorders. Eur Eat Disord Rev, 25, 417–422, 2017. Geller J., Zaitsoff S.L., Srikameswaran S.: Tracking Readiness and Motivation for Change in Individuals with Eating Disorders Over the Course of Treatment. Cognitive Therapy and Research, 29, 611–625, 2005. Cohen J.: Statistical Power Analysis for the Behavioral Sciences. New York, 1988. Little R.J., Rubin D.B.: Statistical Analysis with Missing Data, John Wiley & Sons, 2019. Cowdrey F.A., Park R.J.: The role of experiential avoidance, rumination and mindfulness in eating disorders. Eat Behav, 13, 100–105, 2012. Kaplan A.S., Olmsted M.P.: Partial hospitalization. In: Garner D.M., Garfinkel P.E. (Eds.), Handbook of treatment for eating disorders, The Guilford Press, 1997, pp. 354–360. Additional Declarations No competing interests reported. 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Sjögren¹\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDADxgYwZUO6ljTSLTtMWIlu+/Frj3n+MNgzt7df+/Bxx/nEtTMSmD98wKPF7ExOuTFvG0NiY8+Z4pkzz9xO3HYjgU1yBj4tB3LSpHkbGBIYZ+QkM/O2QbQw8+DTcv5NmjTIYWAtf9vOgbQwf/6DT8uN9GPSPGwMjI0z0g8zM7YdAGlhkMbnfbMbb9gk57ZJgPzCzNjblmy87czDNskevA5Lfybx5o+NvWF7+2OGn212stuOJx/+8AOfNQw8BkBCgsGwAcwAAWis4gbsD8CUPIwxCkbBKBgFowAdAAAks1NBBw6EHQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Umeå University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Magnus\",\"middleName\":\"\",\"lastName\":\"Sjögren¹\",\"suffix\":\"\"},{\"id\":522466159,\"identity\":\"c864b91f-60d0-4b6f-a18e-1b2ebb93477a\",\"order_by\":1,\"name\":\"Rene Klinkby Støving²\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Southern 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14:20:52\",\"extension\":\"html\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":101553,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7704217/v1/0caee25255ea283091b20b5a.html\"},{\"id\":93340094,\"identity\":\"09ffb1a1-b904-40ad-a441-4ba3598394f0\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:28:52\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":42688,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eReadiness trajectories in inpatient with AN patients over time of treatment\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7704217/v1/24504c99fe733bfd6e728050.png\"},{\"id\":99545038,\"identity\":\"96803fc4-9826-4d21-b09c-62871cb9adb0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-05 15:54:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1059776,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7704217/v1/bf09b063-f4dd-4459-b4f4-6650a8ebe9fe.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Heterogeneous Pathways of Readiness to Change in Anorexia Nervosa: A Longitudinal Trajectory Analysis\",\"fulltext\":[{\"header\":\"What is already known on this subject?\",\"content\":\"\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003eReadiness to change is a key predictor of treatment outcome in anorexia nervosa, with higher baseline readiness associated with better therapeutic response\\u003c/li\\u003e\\n \\u003cli\\u003eThe Transtheoretical Model provides a framework for understanding motivation in eating disorders, but longitudinal patterns of readiness during inpatient treatment remain poorly characterized\\u003c/li\\u003e\\n \\u003cli\\u003ePrevious studies report conflicting findings about whether readiness improves, remains stable, or declines during AN treatment, with limited data on trajectory patterns\\u003c/li\\u003e\\n \\u003cli\\u003ePsychiatric comorbidities such as depression and anxiety are prevalent in AN and theoretically linked to impaired treatment motivation, but their specific impact on readiness remains unclear\\u003c/li\\u003e\\n \\u003cli\\u003eNo previous studies have systematically compared readiness to change between AN and healthy controls to establish whether low motivation represents AN-specific pathology\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWhat this study adds?\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cul type=\\\"disc\\\"\\u003e\\n \\u003cli\\u003eFirst study to identify distinct longitudinal trajectories of readiness to change in AN inpatients using robust K-means clustering methodology\\u003c/li\\u003e\\n \\u003cli\\u003eDemonstrates that approximately 30% of patients experience profound motivational collapse during early treatment (dropping from 30.7 to 7.0 points at 8 weeks), with minimal recovery by readmission\\u003c/li\\u003e\\n \\u003cli\\u003eEstablishes that AN have significantly lower baseline readiness than healthy controls (Cohen's d = 1.86), confirming readiness as intrinsic AN pathology rather than general treatment ambivalence\\u003c/li\\u003e\\n \\u003cli\\u003eContradicts theoretical predictions by showing psychiatric comorbidities (depression and anxiety) do not significantly impact baseline readiness or trajectory membership in this sample\\u003c/li\\u003e\\n \\u003cli\\u003eProvides optimal timepoint selection methodology for trajectory analysis in the challenging context of high attrition rates typical of AN research\\u003c/li\\u003e\\n \\u003cli\\u003eOffers evidence-based foundation for developing trajectory-specific motivational interventions, particularly targeting the critical 8-week period when high-risk patients experience severe readiness decline.\\u003cstrong\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eAnorexia nervosa (AN), the most severe eating disorder, poses exceptional threats to physical and mental well-being and carries the highest mortality rate among psychiatric conditions (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Recent epidemiological data indicate a prevalence of 1\\u0026ndash;4% for eating disorders (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e), with AN presenting at approximately 0.3% prevalence. The persistence of symptoms in 20\\u0026ndash;23% of individuals diagnosed with AN represents a significant clinical challenge (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). Despite this burden, knowledge about modifiable mechanisms that could influence disorder outcomes remains limited, with effective treatment options largely elusive (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAN represents a severe psychiatric illness characterized by restrictive eating behaviors, distorted body image, and intense fear of weight gain. Patients with AN commonly exhibit pronounced ambivalence toward treatment due to the complex interplay of egosyntonic and egodystonic aspects of their disorder (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). The egosyntonic nature of AN leads patients to perceive their eating behaviors as aligned with their identity and values, while the egodystonic elements create distress and desire for change. This ambivalence significantly impacts patients' readiness for treatment and their capacity to engage in behavioral changes (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e), while simultaneously presenting a potential therapeutic target.\\u003c/p\\u003e\\u003cp\\u003eThis ambivalence can be conceptualized within the Transtheoretical Model (TTM) framework (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e), which provides a comprehensive understanding of the dynamic process of behavioral change in AN. The TTM outlines five stages of change: precontemplation, contemplation, preparation, action, and maintenance, describing change as a non-linear process with potential relapses into previous stages. Understanding a patient's stage of change proves essential for determining optimal treatment timing and explaining potential treatment non-response, particularly when working with individuals in early stages of change (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eRecent research has increasingly highlighted the importance of readiness to change in AN treatment outcomes. A systematic review and meta-analysis by Vall and Wade (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e) demonstrated that higher levels of readiness to change at treatment initiation predicted superior outcomes in eating disorders, though several factors may interfere with readiness, including comorbidity, treatment autonomy limitations, and interpersonal relationships (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). However, the trajectory of readiness to change during treatment remains poorly characterized (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). While some studies suggest that readiness increases over the course of treatment (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), others have documented stability or even decreases in readiness (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Additionally, readiness may vary across different aspects of the disorder (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). Enhancing readiness to change remains essential given its association with treatment outcomes and relapse risk (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDespite growing recognition of readiness to change as a critical factor in AN treatment outcomes, key gaps persist in understanding its longitudinal trajectory and modifiable predictors. First, while comorbidities such as depression and anxiety are prevalent in AN and theoretically linked to impaired motivation, their specific impact on readiness to change remains poorly quantified. Second, existing studies yield conflicting evidence about whether readiness improves during treatment, with limited data on how it evolves across multiple admissions\\u0026mdash;a common feature of AN's chronic course. Third, the absence of comparisons with healthy controls obscures whether low readiness reflects AN-specific pathology or general treatment ambivalence.\\u003c/p\\u003e\\u003cp\\u003eWe aimed to compare baseline readiness to change between patients with anorexia nervosa (AN) and healthy controls (HC), hypothesizing that readiness would be significantly lower among Patients with AN. Additionally, we sought to identify and characterize distinct longitudinal trajectories of readiness to change in AN inpatients using K-means clustering with silhouette validation (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e), anticipating heterogeneous patterns across the treatment and follow-up period. Finally, we explored how psychiatric comorbidities might associate with these readiness trajectories, utilizing robust statistical modeling and multiple imputation (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e) to address missing data and ensure reliable longitudinal analysis.\\u003c/p\\u003e\"},{\"header\":\"2. Materials \\u0026 Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. Participants\\u003c/h2\\u003e\\u003cp\\u003eWe conducted a prospective longitudinal study including 97 female inpatients with anorexia nervosa (AN) (mean age 26.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.4 years, 95% CI: [24.9, 28.6]), all meeting DSM-5 criteria for AN. We classified severity as mild (26.7%), moderate (30.0%), or severe (43.3%) according to DSM-5 specifiers. We recruited a comparison group of 29 age- and gender-matched healthy controls (HC) without any history of eating disorders to contextualize readiness to change in the AN group (mean age 25.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.8 years, 95% CI: [23.7, 28.0]).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eOptimal Timepoint Selection\\u003c/b\\u003e: To optimize statistical power while maintaining clinical relevance, we selected three timepoints that maximized data completeness: baseline (99.0% complete), 8 weeks (57.7% complete), and readmission baseline (30.9% complete), yielding 24 complete cases for trajectory analysis compared to only 5 complete cases when including the 12-week timepoint. This approach ensured adequate statistical power for trajectory modeling (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAll patients participated in the PROspective Longitudinal all-comer inclusion study on Eating Disorders (PROLED) study, which commenced in 2016 as a clinical, longitudinal study planned to follow patients annually over 10 years. We collected all data in the current study before September 2024. The study operated at the Psychiatric Center Ballerup (PCB) in Denmark and received approval from the local ethics board (id: H-15012537; addendum 77106) and the data processing board.\\u003c/p\\u003e\\u003cp\\u003eThe general inclusion criteria in PROLED are:\\u003c/p\\u003e\\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eAdult individuals (age 18\\u0026ndash;65 years)\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eAdmission to the ED unit in Psychiatric Center Ballerup (PCB), Denmark\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eA diagnosis of AN\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eSigned written informed consent\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\u003cp\\u003eWe focused the study on changes in readiness to change ED behavior from baseline to follow-up, with additional analyses on comorbidities. The mean duration of illness was 8.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.8 years. We did not offer enrollment to any patient undergoing forced care at the time of PROLED study screening. However, once forced care changed to voluntary care, we offered enrollment to such patients.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Healthy Controls\\u003c/h2\\u003e\\u003cp\\u003eWe recruited healthy controls (HC; n\\u0026thinsp;=\\u0026thinsp;29) from the local community and screened them using the EDE-Q to exclude subclinical eating pathology. All HC completed a comprehensive questionnaire on their psychiatric and somatic health. Exclusion criteria included any lifetime diagnosis of an eating disorder or major psychiatric illness.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Weight Restoration Treatment\\u003c/h2\\u003e\\u003cp\\u003eAll patients with AN diagnosis underwent a weight restoration program, previously described (\\u003cspan additionalcitationids=\\\"CR23\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). In summary, we provided meals five times per day during monitoring by trained nurses to ensure proper renourishment. A dietician held individual weekly meetings with each patient to ensure a meal plan enabling approximately 1 kg weight increase per week up to an ideal body weight (IBW; for clinical treatment at PCB) of BMI 20 for women and BMI 21 for men. We supported weight gain through restrictions in physical activity, monitored meals, and post-meal rest. During these eight weeks, we provided no formal psychotherapy, although we offered individual meetings with psychologists and nurses for supportive purposes.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4. Clinical and Psychometric Measures\\u003c/h2\\u003e\\u003cp\\u003eAll patients underwent initial complete diagnostic work-up including comprehensive diagnostic interview by a psychologist, and medical and psychiatric examinations conducted by either a specialist psychiatrist or a General Practitioner with special training in EDs. Additionally, we performed the Eating Disorder Examination (diagnostic questions; EDE (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e)) and routine clinical and laboratory assessments to maintain high-quality diagnosing of ED and comorbid disorders. We validated all primary diagnoses and any comorbid diagnoses using the ICD-10 checklist (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5. Readiness to Change Behavior\\u003c/h2\\u003e\\u003cp\\u003eWe assessed changes in the total score of the Danish version of the Eating Disorder Readiness Ruler (ED-RR) (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). The ED-RR represents a self-report instrument designed to quickly assess readiness to change problematic eating behaviors in individuals with clinical eating disorders. The ED-RR psychometric properties include two factors explaining 59% of the variance, reflecting restriction and body image preoccupation, and binge-eating and vomiting symptoms, with internal consistency alpha coefficients of 0.77 and 0.84, respectively. The ED-RR has demonstrated validation in ED populations, including AN (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). Higher readiness scores on the ED-RR associate with greater autonomous readiness and significant symptom reductions over time.\\u003c/p\\u003e\\u003cp\\u003eWe assessed readiness at three optimal timepoints: baseline of 1st admission (T1), at 8 weeks into 1st admission (T2), and baseline of 2nd admission (T3). The ED-RR had not been previously assessed in HC, and we based the scores from the HC group on eating a wide variety of healthy foods from all nutritional categories.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.6. Statistical Analysis\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.6.1 Trajectory Analysis\\u003c/h2\\u003e\\u003cp\\u003eWe performed K-means cluster analysis to identify distinct longitudinal trajectories of readiness to change across three timepoints among patients with complete data (n\\u0026thinsp;=\\u0026thinsp;24). We determined the optimal number of clusters using silhouette analysis (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e), calculating silhouette scores for cluster solutions ranging from k\\u0026thinsp;=\\u0026thinsp;2 to k\\u0026thinsp;=\\u0026thinsp;5, with the highest silhouette score indicating optimal cluster separation. We performed K-means clustering using Euclidean distance with random seed set for reproducibility (n_init\\u0026thinsp;=\\u0026thinsp;10, random_state\\u0026thinsp;=\\u0026thinsp;42).\\u003c/p\\u003e\\u003cp\\u003eWe conducted one-way ANOVAs and independent t-tests to assess differences in mean readiness scores between clusters at each timepoint. We calculated effect sizes (Cohen's d) (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e) and 95% confidence intervals for all comparisons.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.6.2 Missing Data Analysis\\u003c/h2\\u003e\\u003cp\\u003eWe performed comprehensive missing data analysis to characterize patterns of missingness across timepoints. We conducted multiple imputation (MICE) (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e) using chained equations to create complete datasets for sensitivity analyses.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.6.3 Baseline Comparisons\\u003c/h2\\u003e\\u003cp\\u003eWe used independent t-tests to compare baseline readiness between AN and healthy controls, calculating effect sizes and 95% confidence intervals. We created composite variables for any depression disorder and any anxiety disorder from individual diagnostic categories.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.6.4 Exploratory Comorbidity Analysis\\u003c/h2\\u003e\\u003cp\\u003eWe explored potential associations between psychiatric comorbidities and readiness to change. We created composite variables for any depression disorder and any anxiety disorder from individual diagnostic categories, then compared baseline readiness scores between comorbid and non-comorbid patients using independent t-tests with effect sizes. We also examined whether comorbidity status was associated with trajectory membership using descriptive analyses.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2.6.5 Statistical Reporting\\u003c/h2\\u003e\\u003cp\\u003e All analyses included 95% confidence intervals, effect sizes, and complete statistical reporting following current guidelines. We set statistical significance at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e3.1. Baseline Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur study included 97 female patients with AN (mean age 26.7 \\u0026plusmn; 9.4 years, 95% CI: [24.9, 28.6]) and 29 HC (mean age 25.9 \\u0026plusmn; 5.8 years, 95% CI: [23.7, 28.0]). The mean BMI at baseline for Patients with AN was 15.1 \\u0026plusmn; 2.5 kg/m\\u0026sup2; (95% CI: [14.6, 15.6]), significantly lower than healthy population norms. Depression and anxiety disorders showed moderate prevalence in the AN group (17.5% and 16.5%, respectively) compared to 0% in controls. The mean duration of illness for Patients with AN was 8.2 \\u0026plusmn; 7.8 years.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1: Baseline Clinical Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCharacteristic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAN (n=97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eHealthy controls (n=29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eStatistical comparison\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAge (years), mean \\u0026plusmn; SD (95% CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e26.7 \\u0026plusmn; 9.4 (24.9-28.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e25.9 \\u0026plusmn; 5.8 (23.7-28.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ep = 0.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eFemale, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e93 (95.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e26 (89.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ep = 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBMI (kg/m\\u0026sup2;), mean \\u0026plusmn; SD (95% CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e15.1 \\u0026plusmn; 2.5 (14.6-15.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN/A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN/A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eDuration of illness (years), mean \\u0026plusmn; SD (95% CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8.2 \\u0026plusmn; 7.8 (6.6-9.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN/A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN/A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAny depression disorder, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e17 (17.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0 (0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ep \\u0026lt; 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAny anxiety disorder, n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e16 (16.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0 (0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ep \\u0026lt; 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eAbbreviations: AN=Anorexia Nervosa, CI=Confidence Intervals, SD=Standard Deviation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.2. Baseline Readiness Comparison: AN vs Healthy Controls\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAN demonstrated significantly lower baseline readiness to change compared to healthy controls (37.2 \\u0026plusmn; 13.6 vs 13.4 \\u0026plusmn; 7.8, t(115) = 7.71, p \\u0026lt; 0.001, Cohen\\u0026apos;s d = 1.86, 95% CI of difference: [17.7, 29.9]), indicating a large effect size. This substantial difference confirms that low readiness constitutes a distinctive feature of AN pathology rather than general treatment ambivalence.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3. Missing Data Pattern\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur missing data analysis revealed significant attrition across timepoints (Table 2). The substantial attrition (75.3% missing data for complete 3-timepoint analysis) reflects the clinical reality of AN treatment, necessitating our robust missing data approaches including multiple imputation sensitivity analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2: Missing Data Pattern by Timepoint\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTimepoint\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAvailable n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMissing n (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCumulative retention (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBaseline 1st admission\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e96 (99.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1 (1.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e99.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8 weeks 1st admission\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e56 (57.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e41 (42.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e57.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eBaseline 2nd admission\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e30 (30.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e67 (69.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e30.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComplete cases (3 timepoints)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e24 (24.7)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e73 (75.3)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e24.7\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4. Readiness Trajectories\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSilhouette analysis identified k=2 as the optimal cluster solution (silhouette score = 0.427), indicating good cluster separation and superior to k=3 (silhouette score = 0.397), k=4 (silhouette score = 0.414), and k=5 (silhouette score = 0.424).\\u003c/p\\u003e\\n\\u003cp\\u003eK-means cluster analysis identified two distinct longitudinal trajectories of readiness to change among the 24 patients with complete data:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCluster 1 (Stable-High, n=17, 70.8%)\\u003c/strong\\u003e: This trajectory demonstrated consistently moderate-to-high readiness scores across all timepoints, with slight increase during treatment followed by maintenance: T1: 38.8 \\u0026plusmn; 11.8 (95% CI: [33.2, 44.4]) \\u0026rarr; T2: 42.6 \\u0026plusmn; 10.7 (95% CI: [37.5, 47.7]) \\u0026rarr; T3: 38.0 \\u0026plusmn; 14.5 (95% CI: [31.1, 44.9]).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCluster 2 (Declining, n=7, 29.2%)\\u003c/strong\\u003e: This trajectory showed moderate initial readiness followed by profound decline during treatment with minimal recovery by readmission: T1: 30.7 \\u0026plusmn; 10.8 (95% CI: [22.7, 38.7]) \\u0026rarr; T2: 7.0 \\u0026plusmn; 10.6 (95% CI: [-0.9, 14.9]) \\u0026rarr; T3: 12.4 \\u0026plusmn; 17.4 (95% CI: [-0.5, 25.3]).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3: Trajectory-specific Mean Readiness Scores with Statistical Comparisons\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eTimepoint\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCluster 1 (n=17) Mean \\u0026plusmn; SD (95% CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCluster 2 (n=7) Mean \\u0026plusmn; SD (95% CI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eStatistical comparison\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCohen\\u0026apos;s d\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eT1 (Baseline 1\\u003csup\\u003est\\u003c/sup\\u003e admission)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e38.8 \\u0026plusmn; 11.8 (33.2-44.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e30.7 \\u0026plusmn; 10.8 (22.7-38.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003et(22) = 1.56, p = 0.132\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e0.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eT2 (8 weeks 1\\u003csup\\u003est\\u003c/sup\\u003e admission)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e42.6 \\u0026plusmn; 10.7 (37.5-47.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7.0 \\u0026plusmn; 10.6 (-0.9-14.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003et(22) = 7.43, p \\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eT3 (Baseline 2\\u003csup\\u003end\\u003c/sup\\u003e admission)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e38.0 \\u0026plusmn; 14.5 (31.1-44.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e12.4 \\u0026plusmn; 17.4 (-0.5-25.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003et(22) = 3.71, p = 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.5. Cluster Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe two trajectory groups did not differ significantly in baseline demographic characteristics. Age, BMI, and comorbidity rates proved comparable between clusters, suggesting that trajectory membership cannot be easily predicted from standard clinical variables at admission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.6. Exploratory Analysis of Psychiatric Comorbidity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eExploratory analyses revealed no significant associations between psychiatric comorbidities and baseline readiness to change. Patients with depression showed non-significantly higher baseline readiness compared to those without depression (39.8 \\u0026plusmn; 11.7 vs 36.6 \\u0026plusmn; 14.0, t(94) = -0.87, p = 0.387, Cohen\\u0026apos;s d = 0.23). Similarly, patients with anxiety disorders showed non-significantly higher readiness than those without anxiety (40.8 \\u0026plusmn; 14.0 vs 36.4 \\u0026plusmn; 13.5, t(94) = -1.16, p = 0.249, Cohen\\u0026apos;s d = 0.32).\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the 24 patients with complete trajectory data, depression and anxiety showed no clear pattern of association with trajectory membership, with small group counts limiting definitive conclusions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4: Baseline Readiness Differences by Psychiatric Comorbidity Status\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eComorbidity Status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003ePresent (n)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMean \\u0026plusmn; SD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAbsent (n)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMean \\u0026plusmn; SD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eStatistical comparison\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAny depression disorder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e39.8 \\u0026plusmn; 11.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e36.6 \\u0026plusmn; 14.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003et(94) = -0.87, p = 0.387, d = 0.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAny anxiety disorder\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e40.8 \\u0026plusmn; 14.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e36.4 \\u0026plusmn; 13.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003et(94) = -1.16, p = 0.249, d = 0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.7. Sensitivity Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMultiple imputation (MICE) analyses confirmed the robustness of our trajectory findings. The two-cluster solution remained stable across imputed datasets, with silhouette scores consistent within 5% of the complete-case analysis. This supports the validity of the trajectory patterns despite substantial missing data.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eWe identified two distinct trajectories of readiness to change in AN inpatients using robust K-means clustering with optimal timepoint selection, providing novel insights into the heterogeneous nature of treatment motivation during inpatient care. The Stable-High trajectory (70.8% of patients) maintained consistently moderate-to-high readiness scores throughout treatment, while the Declining trajectory (29.2%) experienced severe motivational decline during early treatment with minimal recovery by readmission.\\u003c/p\\u003e\\n\\u003cp\\u003eThe finding that nearly one-third of patients experienced profound motivational collapse during early treatment—dropping from moderate readiness (30.7 points) to near-zero motivation (7.0 points) at 8 weeks—represents a critical clinical phenomenon with an exceptionally large effect size (Cohen's d = 3.34). This pattern aligns with clinical observations of treatment resistance during initial phases of inpatient care and may reflect the challenging nature of weight restoration and the egosyntonic aspects of AN, where treatment confronts patients' core beliefs and behaviors. The minimal recovery observed by readmission (12.4 points) suggests persistent motivational challenges that may contribute to treatment dropout and relapse.\\u003c/p\\u003e\\n\\u003cp\\u003eThe large effect size (Cohen's d = 1.86) for the difference in baseline readiness between Patients with AN and healthy controls provides strong evidence that low readiness constitutes an intrinsic feature of AN pathology rather than a general response to treatment situations. This finding supports the conceptualization of readiness to change as a key therapeutic target in AN treatment (31).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4.1. Clinical Implications\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThese findings have several important clinical implications. First, the identification of distinct readiness trajectories suggests that uniform approaches to motivation enhancement may prove suboptimal. Patients in the Stable-High group may benefit from interventions that maintain and harness their existing motivation, while those in the Declining group require intensive support during the critical 8-week period when readiness to change drops severely.\\u003c/p\\u003e\\n\\u003cp\\u003eSecond, the profound decline pattern provides a predictable timepoint for targeted interventions. Clinicians should anticipate this decline and implement proactive strategies to support high-risk patients. This might include enhanced motivational interviewing (32), family involvement, or other evidence-based approaches to boost treatment engagement.\\u003c/p\\u003e\\n\\u003cp\\u003eThird, early identification of patients likely to follow the Declining trajectory could enable personalized treatment approaches. While our analysis suggests that standard baseline variables do not reliably predict trajectory membership, future research might identify more sensitive predictors to guide clinical decision-making.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4.2. Psychiatric Comorbidity Considerations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eContrary to our initial hypothesis, psychiatric comorbidities did not significantly impact baseline readiness to change in our sample. This finding diverges from some theoretical predictions that depression and anxiety might impair treatment motivation. The small effect sizes and non-significant associations may reflect the complex relationship between comorbidity and motivation in AN, where multiple factors interact to influence readiness. Small group sizes (17.5% depression, 16.5% anxiety) limited the power to detect subtle effects; larger studies are needed to clarify the role of comorbidity in AN treatment motivation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4.3. Methodological Strengths and Limitations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStrengths\\u003c/strong\\u003e of our study include the longitudinal design with optimal timepoint selection based on data availability, robust cluster validation using silhouette analysis, comprehensive statistical reporting with confidence intervals and effect sizes, and inclusion of healthy controls for context. The use of multiple imputation for sensitivity analyses strengthens confidence in our findings.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLimitations\\u003c/strong\\u003e include the focus on female inpatients with AN, limiting generalizability to males or outpatient populations. The substantial attrition reflects the clinical reality of AN treatment but limits our complete case analysis to 24 patients. Our reliance on self-report measures for readiness may introduce bias, as patients with AN often struggle with insight and accurate self-assessment. The relatively small number of complete cases limits power for detecting smaller subgroups or more complex relationships.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4.4. Future Research Directions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFuture studies should investigate mechanisms underlying different readiness trajectories, particularly factors that predict severe motivational decline. We recommend longitudinal research with larger samples (n≥100 complete cases), enhanced retention strategies (such as intensive case management and technological aids), and longer follow-up periods to establish trajectory stability and relationship to long-term outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003eIntervention studies targeting trajectory-specific approaches to motivation enhancement would prove valuable. For example, intensive motivational interventions during weeks 4-12 for high-risk patients, or maintenance strategies for stable-high patients. Additionally, exploration of neurobiological or psychological markers that might predict trajectory membership could enable personalized treatment approaches.\\u003c/p\\u003e\\n\\u003cp\\u003eReplication studies in outpatient settings, different cultural contexts, and male populations would enhance generalizability. Finally, development of real-time trajectory monitoring tools could enable dynamic treatment adjustments based on observed motivational patterns.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eWe identified two distinct trajectories of readiness to change in AN inpatients using optimal timepoint selection and robust statistical methods: a majority maintaining stable moderate-high readiness to change (70.8%) and a concerning minority experiencing profound motivational collapse (29.2%). The substantial difference in baseline readiness between AN and healthy controls (Cohen's d = 1.86) confirms that low motivation constitutes an intrinsic feature of AN pathology. These findings support the development of trajectory-specific interventions to optimize treatment engagement, particularly during the vulnerable early treatment period when approximately 30% of patients experience severe decline in readiness to change. Understanding these patterns may help clinicians better support patients through the complex process of recovery from AN, ultimately improving treatment outcomes and reducing relapse risk.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cul\\u003e\\n \\u003cli\\u003eAN: Anorexia Nervosa\\u003c/li\\u003e\\n \\u003cli\\u003eBMI: Body Mass Index\\u003c/li\\u003e\\n \\u003cli\\u003eED: Eating Disorder\\u003c/li\\u003e\\n \\u003cli\\u003eEDE: Eating Disorder Examination\\u003c/li\\u003e\\n \\u003cli\\u003eEDE-Q: Eating Disorder Examination Questionnaire\\u003c/li\\u003e\\n \\u003cli\\u003eED-RR: Eating Disorder Readiness Ruler\\u003c/li\\u003e\\n \\u003cli\\u003eHC: Healthy Controls\\u003c/li\\u003e\\n \\u003cli\\u003eTTM: Transtheoretical Model\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions:\\u003c/strong\\u003e Conceptualization, M.S.; methodology, M.S.; validation, M.S., M.L., R.S.; formal analysis, M.S.; investigation, M.S. resources, M.S.; data curation M.S.; writing M.S., M.L., R.S.; writing—review and editing, M.S., M.L., R.S.; project administration, M.S.; funding acquisition, M.S. MS, RS, ML have all read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e We are grateful for the funding support from the following external funds: Axel Muusfeldts Fond, the Dagmar Marshalls Fond, the Direktör Emil C. Hert og Hustru Inger Hertz Fond, the Jascha Fonden. In addition, we are grateful for the internal support from the Psychiatric Center Ballerup and the Psychiatry section of the Capitol Region of Denmark.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInstitutional Review Board Statement:\\u003c/strong\\u003e The PROLED study has been reviewed and approved by the National Committee on Health Research Ethics (Ethical Application Ref: DNVK Journal no: H-15012537) and is conducted in compliance with the Declaration of Helsinki and International Conference on Harmonisation and Good Clinical Practice Guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed Consent Statement:\\u003c/strong\\u003e All subjects participating in the PROLED study have provided their written informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement:\\u003c/strong\\u003e The datasets used in this study are currently not available since the prospective study is still ongoing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interest:\\u003c/strong\\u003e The authors declare no conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eVolpe U., Tortorella A., Manchia M., Monteleone A.M., Albert U., Monteleone P.: Eating disorders: What age at onset? Psychiatry Res, 238, 225\\u0026ndash;227, 2016.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSwanson S.A., Crow S.J., Le Grange D., Swendsen J., Merikangas K.R.: Prevalence and correlates of eating disorders in adolescents. Results from the national comorbidity survey replication adolescent supplement. Arch Gen Psychiatry, 68, 714\\u0026ndash;723, 2011.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eErskine H.E., Baxter A.J., Patton G., Moffitt T.E., Patel V., Whiteford H.A., Scott J.G.: The global coverage of prevalence data for mental disorders in children and adolescents. Epidemiol Psychiatr Sci, 26, 395\\u0026ndash;402, 2017.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSteinhausen H.C.: The outcome of anorexia nervosa in the 20th century. Am J Psychiatry, 159, 1284\\u0026ndash;1293, 2002.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSteinhausen H.C., Weber S.: The outcome of bulimia nervosa: findings from one-quarter century of research. Am J Psychiatry, 166, 1331\\u0026ndash;1341, 2009.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eStriegel Weissman R., Rosselli F.: Reducing the burden of suffering from eating disorders: Unmet treatment needs, cost of illness, and the quest for cost-effectiveness. Behav Res Ther, 88, 49\\u0026ndash;64, 2017.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePrice-Evans K., Treasure J.: The Use of Motivational Interviewing in Anorexia Nervosa. Child Adolesc Ment Health, 16, 65\\u0026ndash;70, 2011.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVitousek K., Watson S., Wilson G.T.: Enhancing motivation for change in treatment-resistant eating disorders. Clinical psychology review, 18, 391\\u0026ndash;420, 1998.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMiller W.R., Rollnick S.: Ten things that motivational interviewing is not. Behav Cogn Psychother, 37, 129\\u0026ndash;140, 2009.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eProchaska J.O., Velicer W.F.: The transtheoretical model of health behavior change. Am J Health Promot, 12, 38\\u0026ndash;48, 1997.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVall E., Wade T.D.: Predictors of treatment outcome in individuals with eating disorders: A systematic review and meta-analysis. Int J Eat Disord, 48, 946\\u0026ndash;971, 2015.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRobinson L., Flynn M., Cooper M.: Individual differences in motivation to change in individuals with eating disorders: A systematic review. Int J Eat Disord, 57, 1069\\u0026ndash;1087, 2024.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHoetzel K., von Brachel R., Schlossmacher L., Vocks S.: Assessing motivation to change in eating disorders: a systematic review. J Eat Disord, 1, 38, 2013.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAlgars M., Ramberg C., Moszny J., Hagman J., Rintala H., Santtila P.: Readiness and motivation for change among young women with broadly defined eating disorders. Eat Disord, 23, 242\\u0026ndash;252, 2015.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLee H., Desai S., Choi Y.N.: Improvements in Quality of Life and Readiness for Change After Participating in an Eating Disorder Psychoeducation Group: A Pilot Study. International Journal of Group Psychotherapy, 74, 268\\u0026ndash;303, 2024.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGregertsen E.C., Mandy W., Kanakam N., Armstrong S., Serpell L.: Pre-treatment patient characteristics as predictors of drop-out and treatment outcome in individual and family therapy for adolescents and adults with anorexia nervosa: A systematic review and meta-analysis. Psychiatry Res, 271, 484\\u0026ndash;501, 2019.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMartini M., Longo P., Toppino F., De Bacco C., Preti A., Abbate-Daga G., Panero M.: The structure of motivation: Assessing readiness to change dimensions and their predictive value with the network validation of the Italian version of the Anorexia Nervosa Stages of Change Questionnarie. Eur Eat Disord Rev, 33, 118\\u0026ndash;132, 2025.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBrockmeyer T., Friederich H.C., Schmidt U.: Advances in the treatment of anorexia nervosa: a review of established and emerging interventions. Psychol Med, 48, 1228\\u0026ndash;1256, 2018.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRousseeuw P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53\\u0026ndash;65, 1987.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLittle J.N., Codd R.T., 3rd: Radically Open Dialectical Behavior Therapy (RO DBT) in the treatment of perfectionism: A case study. Journal of clinical psychology, 76, 2097\\u0026ndash;2108, 2020.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNagin D.S., Odgers C.L.: Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol, 6, 109\\u0026ndash;138, 2010.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSj\\u0026ouml;gren M., Kizilkaya I., St\\u0026oslash;ving R.K.: Inpatient Weight Restoration Treatment Is Associated with Decrease in Post-Meal Anxiety. Journal of Personalized Medicine, 11, 1079, 2021.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSeidel M., Brooker H., Lauenborg K., Wesnes K., Sjogren M.: Cognitive Function in Adults with Enduring Anorexia Nervosa. Nutrients, 13, 2021.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFjeldstad M., Kvist T., Sjogren M.: Weight Gain in Adults with Avoidant/Restrictive Food Intake Disorder Compared to Restrictive Anorexia Nervosa-Pilot Findings from a Longitudinal Study. Nutrients, 13, 2021.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLuce K.H., Crowther J.H.: The reliability of the Eating Disorder Examination-Self-Report Questionnaire Version (EDE-Q). Int J Eat Disord, 25, 349\\u0026ndash;351, 1999.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJanca A., Ustun T.B., Early T.S., Sartorius N.: The ICD-10 symptom checklist: a companion to the ICD-10 classification of mental and behavioural disorders. Social psychiatry and psychiatric epidemiology, 28, 239\\u0026ndash;242, 1993.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSt-Hilaire A., Axelrod K., Geller J., Mazanek Antunes J., Steiger H.: A Readiness Ruler for Assessing Motivation to Change in People with Eating Disorders. Eur Eat Disord Rev, 25, 417\\u0026ndash;422, 2017.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGeller J., Zaitsoff S.L., Srikameswaran S.: Tracking Readiness and Motivation for Change in Individuals with Eating Disorders Over the Course of Treatment. Cognitive Therapy and Research, 29, 611\\u0026ndash;625, 2005.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCohen J.: Statistical Power Analysis for the Behavioral Sciences. New York, 1988.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLittle R.J., Rubin D.B.: Statistical Analysis with Missing Data, John Wiley \\u0026amp; Sons, 2019.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCowdrey F.A., Park R.J.: The role of experiential avoidance, rumination and mindfulness in eating disorders. Eat Behav, 13, 100\\u0026ndash;105, 2012.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKaplan A.S., Olmsted M.P.: Partial hospitalization. In: Garner D.M., Garfinkel P.E. (Eds.), Handbook of treatment for eating disorders, The Guilford Press, 1997, pp. 354\\u0026ndash;360.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Readiness to change, Anorexia Nervosa, trajectories, K-means clustering, inpatient treatment\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7704217/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7704217/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003ePurpose: \\u003c/strong\\u003eReadiness to change constitutes a key determinant of treatment outcome in anorexia nervosa (AN), yet its developmental patterns during inpatient care remain poorly understood. This study aimed to characterize longitudinal readiness trajectories in AN inpatients and explore associations with psychiatric comorbidity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eWe conducted a prospective longitudinal study including 97 female inpatients with AN (mean age 26.7 ± 9.4 years) and 29 age-matched healthy controls. We assessed readiness to change at three optimal timepoints: baseline (1st admission), 8 weeks into treatment, and readmission baseline (2nd admission). We applied K-means cluster analysis with silhouette validation to identify distinct readiness trajectories. Complete case analysis (n=24) and multiple imputation sensitivity analyses were performed. Comprehensive statistical analyses included confidence intervals and effect sizes for all major comparisons.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003eK-means cluster analysis with optimal silhouette score (0.427) identified two distinct readiness trajectories. Cluster 1 (Stable-High, n=17, 70.8%) maintained consistently moderate-to-high readiness, while Cluster 2 (Declining, n=7, 29.2%) showed substantial decline. Between-cluster differences were significant at 8 weeks (Cohen's d = 3.34, p \\u0026lt; 0.001) and readmission (Cohen's d = 1.67, p = 0.001). Patients with AN demonstrated significantly lower baseline readiness than healthy controls (37.2 ± 13.6 vs 13.4 ± 7.8, Cohen's d = 1.86, p \\u0026lt; 0.001). Psychiatric comorbidities showed no significant associations with baseline readiness or trajectory membership.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003e Readiness to change in AN inpatients follows two distinct trajectories, with approximately 30% experiencing severe motivational decline during treatment. The substantial difference from healthy controls confirms readiness as a core treatment target. These findings support trajectory-specific interventions to optimize treatment engagement.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLevel of evidence: \\u0026nbsp;\\u003c/strong\\u003eLevel III: Prospective observational cohort study with statistical modeling\\u003c/p\\u003e\",\"manuscriptTitle\":\"Heterogeneous Pathways of Readiness to Change in Anorexia Nervosa: A Longitudinal Trajectory Analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-12 14:20:47\",\"doi\":\"10.21203/rs.3.rs-7704217/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"79b86a4b-573d-4709-b5f1-35cfb7f429da\",\"owner\":[],\"postedDate\":\"October 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-05T15:54:18+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-12 14:20:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7704217\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7704217\",\"identity\":\"rs-7704217\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}