Longitudinal Changes in Maladaptive Personality Domains and Clinical Outcomes: A Study Protocol

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Hopwood, Giovanni Michelini, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6377656/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 Background : Maladaptive personality traits are transdiagnostic risk factors associated with functional impairment, lower treatment efficacy, and poor mental health outcomes. These traits, encompassing domains such as Negative Affectivity, Detachment, Antagonism, Disinhibition, Psychoticism, and Anankastia, contribute to difficulties in emotional regulation, interpersonal relationships, and occupational functioning. Despite growing evidence that personality traits can change over time, longitudinal research examining within-person fluctuations in maladaptive traits during psychotherapy remains scarce. This study protocol outlines a longitudinal research project aimed at investigating the dynamic interplay between maladaptive personality domains and treatment outcomes in a clinical sample undergoing Cognitive Behavioral Therapy. By examining both within-person fluctuations and between-person differences over multiple time points, this study seeks to clarify how personality change relates to symptom improvement and psychosocial functioning, addressing a critical gap in the literature. Methods : This preregistered longitudinal study will recruit patients from inTHERAPY, an Italian psychotherapy service specializing in Cognitive Behavioral Therapy. A total of 200 participants will be assessed across five time points (baseline, 3, 6, 9, and 12 months). Personality domains and clinical symptoms will be systematically evaluated throughout treatment to track individual trajectories of change. Data will be analyzed using Random Intercept Cross-Lagged Panel Models to investigate reciprocal relationships between personality domains and clinical outcomes, distinguishing between between-person differences and within-person fluctuations and Linear Growth Curve Models to examine mean-level change in maladaptive personality domains over time. An exploratory analysis will also be conducted to assess whether patients tend to show the greatest change in the personality domains most elevated at baseline. Discussion: Understanding the temporal interplay between maladaptive personality domains and clinical outcomes could provide valuable insights for personalized psychotherapy. Identifying which personality domains change most significantly during Cognitive Behavioral Therapy - and whether such changes predict symptom improvement - may inform the development of more targeted interventions. Furthermore, this study’s findings could enhance clinical decision-making by identifying key personality factors influencing therapy trajectories, ultimately improving treatment planning for individuals with impaired maladaptive personality domains. Cognitive Behavioral Therapy Maladaptive Personality Domains Personality Change Longitudinal Study Therapy Outcomes Figures Figure 1 Figure 2 Introduction Psychotherapeutic interventions should prioritize assisting individuals in managing their maladaptive trait expressions rather than attempting to alter their fundamental personality traits (Bach & Presnall-Shvorin, 2020). Maladaptive personality traits are risk factors for social and occupational difficulties, contribute to the onset and chronicity of mental disorders, and reduce treatment efficacy (Haehner, Wright & Bleidorn, 2024; Hengartner, 2015; Fowler et al., 2022). The classical trait perspective posits that personality traits in adulthood are biologically based temperaments that remain stable over time and are not influenced by environmental factors (McCrae et al., 2000). However, more recent research suggests that personality traits can change over time (Hampson & Goldberg, 2020), particularly maladaptive traits (Bleidorn et al., 2022), and that such changes can occur in response to different types of interventions (Roberts et al., 2017). In recent years, the conceptualization of personality pathology has shifted from categorical to dimensional models, as reflected in the Alternative Model for Personality Disorders (AMPD) of the DSM-5-TR (APA, 2022) and the ICD-11 (WHO, 2022). The AMPD framework conceptualizes personality disorders based on psychosocial functioning (Criterion A) and maladaptive personality traits (Criterion B). Within this framework, the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) was developed to assess maladaptive personality traits, which are organized into five broad domains: Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. These domains encompass 25 specific trait facets (APA, 2022). Notably, these five maladaptive trait domains represent dysfunctional variants of the well-established personality dimensions in the Big Five model (Goldberg, 1993) or the Five-Factor Model (FFM; Digman, 1990). Following the development of the original PID-5, which consists of 220 items, several alternative versions have been created to facilitate assessment in different contexts. These include the PID-5-SF with 100 items (Maples et al., 2015), the PID-5-BF with 25 items (Krueger et al., 2013), the PID-5-BF+ with 34 items (Kerber et al., 2020), and the PID-5-BF+M with 36 items (Bach et al., 2020). In addition to serving as efficient screening tools for maladaptive personality domains, the brief versions plus offer the advantage of assessing Anankastia, a domain introduced in the ICD-11, thereby expanding their clinical and research utility. However, maladaptive personality traits are not exclusive to personality disorders (Gonçalves et al., 2022; Von Schrottenberg et al., 2024), different levels of these traits are present in other mental health disorders and subclinical levels in a substantial proportion of the general population (Gleason et al., 2014). Personality traits exist on a continuum, meaning that all individuals can be placed along the spectrum of trait dimensions rather than being categorized as having or lacking specific traits. Given this perspective, personality trait change can be conceptualized differently, with two primary approaches being widely examined: differential stability and absolute change (Hopwood & Bleidorn, 2018). Differential stability refers to the extent to which individuals maintain their relative rank on a given trait over time (Anusic & Schimmack, 2016), typically assessed using test-retest correlations, where effect sizes of 0.10 are considered small, 0.30 moderate, and 0.50 large (Cohen, 1988). In contrast, absolute change reflects systematic increases or decreases in trait expression within a population, regardless of individual rank-order stability (Wagner et al., 2016). This form of change is commonly quantified using Cohen’s d, where values of 0.20 indicate small changes, 0.50 moderate changes, and 0.80 large changes (Cohen, 1988). A growing body of research suggests that personality trait change is not only possible but can be facilitated through structured interventions. A systematic review by Roberts et al. (2017) examined personality trait changes following different types of interventions, with a specific focus on clinical treatments. Their findings indicate that supportive therapy, Cognitive Behavioral Therapy (CBT), and psychodynamic therapy all yielded significant personality trait modifications, with CBT demonstrating slightly larger effect sizes. Notably, patients with anxiety and personality disorders exhibited the most substantial changes, particularly in reductions in neuroticism (Roberts et al., 2017). Further supporting these findings, a randomized controlled trial (RCT) by Rek et al. (2022) comparing Schema Therapy (ST) and CBT found that maladaptive trait domains decreased throughout treatment regardless of the intervention type, indicating that both ST and CBT are effective in promoting pathological personality change. Another RCT (Niemeijer et al., 2023) investigated the association between changes in maladaptive personality domains and anxiety and depression symptoms in a clinical sample undergoing CBT. Their findings showed that decreases in Negative Affectivity predicted lower levels of both depression and anxiety symptoms, while reductions in Detachment were specifically associated with decreases in depression symptoms. Despite numerous studies investigating personality trait change (Hopwood et al., 2009; Kennaira et al., 2020; Norman et al., 2024; Roberts et al., 2017), there remains a significant gap in the systematic monitoring of maladaptive personality domains throughout psychotherapy (Kiel, Hopwood, & Lind, 2024) to evaluate their impact on psychosocial functioning and clinical outcomes. The variability of personality expression across different time points and how these fluctuations may influence treatment trajectories remains underexplored (Roberts et al., 2017). Additionally, longitudinal studies examining personality trait change with larger clinical samples are still scarce (Hopwood & Bleidorn, 2018). Addressing this gap requires research designs that incorporate multiple assessment points and sufficient statistical power to capture within-person changes over time, ultimately contributing to the development of more targeted and effective interventions. Aims Building on these considerations, this preregistered study (Ocera et al., 2025) aims to investigate the relationship between CBT, maladaptive personality domains, and clinical outcomes through a longitudinal design with different time points. Specifically, it seeks to answer the following key research questions: (1) Do patients with higher maladaptive domains also have lower functioning and worse clinical symptoms? (1a) Do maladaptive personality domains decrease over time in the overall sample, indicating a general group-level change? (2) Do patients who exhibit higher maladaptive domains than their average also have worse functioning and clinical symptoms at the same time? (3) Do changes in maladaptive personality domains during therapy lead to improvements in functioning and symptom severity? (4) Do patients tend to change the personality domain they struggle with the most as a function of CBT? Methods Ethics The present study is part of the “ Protocol for accessing and analyzing clinical data gathered at InTherapy for developing experimental research projects, epidemiological studies, and validation of new psychotherapy efficacy monitoring instruments for adults ”, a research project that aims to create an automated data collection protocol for conducting epidemiological studies, treatment efficacy verification, and instrument validation. The project has received ethical approval from the Sigmund Freud University - Wien Ethics Committee (Protocol Number: CD9THAHBC8SG@G91206). Participants Participants will be recruited from inTHERAPY, a CBT-specializing psychotherapy service. Inclusion criteria include being 18 years or older, fluent in Italian, able to provide informed consent, and having a diagnosed mental disorder. Exclusion criteria include the absence of a clinical diagnosis, organic brain disease, developmental disorders, or intellectual disabilities. Recruitment will be conducted through inTHERAPY's clinical intake process ( Figure 1 ) starting from June 2025. Measures During the psychodiagnostic assessment phase, patients will also be administered a questionnaire that contains basic medical information in a narrative format, which may be subject to further exploration in the feedback meeting. The following instruments will be administered to participants during the assessment phase and subsequently throughout psychotherapy for monitoring purposes. All assessments will be conducted and completed via the inTHERAPY app. Covariates Relevant covariates will be collected to account for potential confounding factors and better capture individual differences in baseline levels and change trajectories. These covariates include dropout status, therapy modality (in-person vs. online therapy), gender, age, and the presence or absence of a personality disorder diagnosis (as assessed with the SCID-5-PD; Somma et al., 2017). They will be modeled as time-invariant predictors in the statistical analyses, allowing us to assess their influence on both the initial levels and the development of maladaptive personality domains and clinical outcomes over time. Maladaptive personality domains As part of the initial assessment at baseline, the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012; Italian version Fossati et al., 2017) will be administered, as it is included in the standard psychodiagnostic protocol of the inTHERAPY clinical service at Studi Cognitivi. The PID-5 is a comprehensive 220-item instrument designed to assess maladaptive personality traits across five broad domains: Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. Each item is rated on a four-point Likert scale, ranging from 0 (very false or often false) to 3 (very true or often true). However, for longitudinal analyses, only the 36 items corresponding to the Personality Inventory for DSM-5 - Brief Form Plus Modified (PID-5-BF+M; Bach et al., 2020) will be extracted and scored from the baseline data. To monitor changes throughout therapy, the PID-5-BF+M will be directly administered at subsequent time points. The PID-5-BF+M is a 36-item instrument that retains the five core maladaptive personality domains of the original PID-5 while also incorporating the Anankastia domain from the ICD-11 model of personality disorders. This version offers a practical and time-efficient assessment of personality pathology, making it suitable for repeated measurements throughout treatment. Each item is rated on a scale from 1 (very false or often false) to 3 (very true or often true). Clinical outcomes Generalized Anxiety Disorder – 7 (GAD-7; Spitzer et al., 2006; Italian version Ivziku et al., 2018). The GAD-7 consists of 7 items, each assessing symptoms of anxiety (such as worry, difficulty relaxing, irritability) on a scale from 0 (never) to 3 (nearly every day). The total score provides a measure of anxiety severity, aiding in the identification of clinical risk levels. Patient Health Questionnaire – 9 (PHQ-9; Kroenke et al., 1999; Italian version Rizzo et al., 2000). This nine-item tool is designed to screen, diagnose, and monitor depression. Items are on a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day). Work and Social Adjustment Scale (WSAS; Mundt et al., 2002; Italian version Rossi et al., 2005). The WSAS measures the impact of mental or physical health problems on an individual’s work and social life. The scale includes 5 items that assess levels of impairment in areas such as work, home management, social relationships, private life, and leisure activities. Each item is rated on a scale from 0 (not at all) to 8 (very severely), with higher scores indicating greater levels of difficulty or impairment. The total aggregate score will be used as a global index of psychosocial functioning. Statistical analyses Descriptive statistics will be conducted to provide an overview of the sample characteristics and trends over time. Inferential analyses will include Random Intercept Cross-Lagged Panel Models (RI-CLPM; Hamaker et al., 2015) to examine the reciprocal associations between maladaptive personality domains and clinical outcomes across multiple time points (Research Questions 1, 2 and 3). This modeling approach ( Figure 2 ) distinguishes between stable between-person differences and within-person fluctuations over time, providing a more precise examination of dynamic relationships. Specifically: Autoregressive associations (α, δ) assess the extent to which an individual’s deviation from their expected score on maladaptive personality domains (PID-5-BF+M) or clinical outcomes (GAD-7, PHQ-9, WSAS) at a given time point influences their score on the same variable at the subsequent measurement occasion. Within-wave associations (correlation between u and v) capture the concurrent relationship between fluctuations in maladaptive personality domains and clinical outcomes within the same measurement occasion, reflecting short-term interdependencies. Cross-lagged associations (β, γ) estimate the extent to which deviations from an individual's expected score on one variable (e.g., maladaptive personality domains) predict subsequent deviations in another variable (e.g., clinical outcomes) across different time points while controlling for temporal stability. We will estimate Linear Growth Models (Duncan & Duncan, 2009) to examine group-level change in maladaptive personality domains across five time points (Research Question 1a). This model is defined by two latent factors representing the intercept (initial level) and slope (linear rate of change) of each maladaptive personality domain over time. To address Research Question 4, we will conduct an exploratory analysis to examine whether patients tend to show the greatest reduction in the maladaptive personality domain that was most elevated at baseline. For each individual, we will identify the highest-scoring domain at T1 and compare the magnitude of change in that domain to changes in the remaining domains throughout treatment. All analyses will be conducted in R using the lavaan package (Rosseel, 2012) for structural equation modeling and semTools (Jorgensen et al., 2022) for model evaluation. A significance level of α = .05 will be applied to all statistical tests. Sample size and time points A priori power analysis for the RI-CLPM using Monte Carlo simulations (Mulder, 2022) could not be conducted, as no previous studies have examined the relationship between maladaptive personality domains (PID-5) and clinical outcomes (e.g., anxiety, depression, psychosocial functioning) using this modeling approach. However, several studies have applied this model with sample sizes ranging between 100 and 200 participants (Simkin, Hodson & Veale, 2022). Therefore, we plan to recruit a minimum of 200 participants. The following time points will be considered: T1 (baseline assessment), T2 (after 3 months from the start of the treatment), T3 (after 6 months), T4 (after 9 months), and T5 (after 12 months). Location Recruitment will be entirely voluntary. Patients will be recruited from inTHERAPY, the private clinical service of Studi Cognitivi, which specializes in CBT. inTHERAPY operates in multiple cities across Italy and offers both in-person and online psychotherapy services. All therapists at inTHERAPY are CBT-trained psychotherapists who have completed their four-year training at the Studi Cognitivi School of Specialization in Cognitive Behavioral Psychotherapy. The therapists will be responsible for recruiting participants among new patients initiating therapy, ensuring they meet the inclusion and exclusion criteria. They will explain the research project to potential participants and, if they agree to take part, obtain their informed consent. Discussion Maladaptive personality traits predispose individuals to treatment resistance, poor response, or relapse (Hengartner, 2015). Beyond treatment efficacy, maladaptive traits significantly impact quality of life and overall functioning (Hobbs et al., 2023). High scores on pathological personality dimensions are associated with lower quality of life (QoL) and greater disability/functional impairment across various domains. Individuals with elevated maladaptive traits are more likely to experience socio-occupational difficulties, such as unemployment, interpersonal conflicts, and early retirement due to disability. Additionally, they tend to exhibit lower recovery capacity and impaired social functioning, even while receiving treatment for other mental disorders (Hobbs et al., 2023). These findings underscore the clinical importance of assessing patients' personality structures, as severe maladaptive traits can compromise treatment outcomes, contribute to residual symptomatology, and hinder functional recovery in daily life. Maladaptive personality domains demonstrate a transdiagnostic nature, extending beyond personality disorders to a wide range of mental health conditions. For instance, patients with depressive or bipolar disorders tend to score higher on Detachment in the PID-5 compared to individuals with psychotic disorders or substance use disorders (Heath et al., 2018). Conversely, patients with alcohol use disorder exhibit higher Disinhibition and lower Psychoticism relative to other clinical groups (Heath et al., 2018). These variations suggest that pathological personality traits can be measured across different psychiatric diagnoses, delineating distinct personality profiles for each clinical population. Conceptually, this dimensional approach helps explain the high rates of comorbidity and diagnostic overlap observed in clinical practice. Traditional categorical models often struggle to account for the substantial co-occurrence of mental disorders, whereas a trait-based dimensional model more effectively captures underlying personality factors shared across different conditions (Hobbs et al., 2023). Longitudinal monitoring of maladaptive traits and personality domains provides a crucial added value in psychotherapy. Tracking these traits over time allows clinicians to observe not only reductions in clinical symptoms but also the evolution of patients' maladaptive personality profiles. From a clinical perspective, identifying changes in personality traits throughout therapy can help assess treatment progress more comprehensively. For instance, a lack of improvement in key personality traits may indicate the need to adjust therapeutic strategies, whereas reductions in maladaptive traits - beyond symptom reduction - may predict more stable treatment outcomes and a lower risk of relapse. This study aims to investigate the role of maladaptive personality domains in CBT by examining their relationship with clinical outcomes and their trajectory over time. The strengths of this research include the use of a clinical sample in a naturalistic setting and the systematic monitoring of personality domains alongside clinical outcomes throughout therapy. However, certain limitations must be acknowledged. First, controlling for concomitant pharmacotherapy is inherently challenging, making it difficult to isolate the effects of psychotherapy from those of medication. Additionally, the variability in therapeutic protocols adopted at inTHERAPY - including different third-wave CBT approaches such as Dialectical Behavior Therapy (DBT), Schema Therapy, and Acceptance and Commitment Therapy (ACT) - may introduce heterogeneity into the findings. Another significant challenge involves recruitment and longitudinal follow-up: patients with severe maladaptive traits may be more prone to dropping out of treatment (Berghuis, Bandell & Krueger, 2021), potentially leading to selection biases. Another limitation concerns the outcome measures selected for monitoring clinical change. Specifically, the PHQ-9 and GAD-7 - while widely used and well-validated - are strongly associated with the Big Five trait of Neuroticism (Yang et al., 2023; Navrady et al., 2017), which is related to the Negative Affectivity domain of the PID-5. Nonetheless, these instruments are embedded in routine clinical assessment protocols, including those adopted by inTHERAPY. Notably, both measures are also integral to the IAPT model (Improving Access to Psychological Therapies) implemented within the UK’s National Health Service, which systematically tracks treatment outcomes for depression and anxiety within stepped-care interventions (Clark, 2011). While the IAPT model does not account for comorbid personality pathology, it provides a scalable framework for outcome monitoring in large-scale clinical contexts. In line with this rationale, PHQ-9 and GAD-7 have been retained as outcome measures in the present study. Finally, future studies would benefit from incorporating an additional follow-up time point after therapy completion to assess the stability of personality trait changes over time. In conclusion, this study represents an important step toward a more nuanced understanding of the relationship between maladaptive personality domains and psychotherapy. It has practical implications for improving clinical interventions. Identifying key personality domains associated with treatment outcomes may facilitate the development of personalized therapeutic approaches, ultimately enhancing the effectiveness of psychotherapy for patients with maladaptive personality domains. Abbreviations CBT: Cognitive Behavioral Therapy GAD-7: Generalized Anxiety Disorder IAPT: Improving Access to Psychological Therapies PHQ-9: Patient Health Questionnaire PID: Personality Inventory for DSM-5 RI-CLPM: Random-Intercept Cross Lagged Panel Model SCID-5-PD: Structured Clinical Interview for DSM-5 – Personality Disorders WSAS: Work and Social Adjustment Scale Declarations Ethical considerations and consent to participate Participants will receive detailed written and oral study information before providing written informed consent. This project was approved by the Ethics Committee of the Faculty of Psychotherapy Science and the Faculty of Psychology of Sigmund Freud University, Wien (approval no. CD9THAHBC8SG@G91206). Availability of data and materials Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. Competing interest The authors report there are no competing interests to declare. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Authors’ Contributions Conceptualization: AO, CJH, and GC; Methodology: AO, CJH, and GM; Writing - original draft: AO; Writing - review & editing: CJH, GM, RP, MF and GC Acknowledgments Not applicable References American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787 Anusic, I., & Schimmack, U. (2016). Stability and change of personality traits, self-esteem, and well-being: Introducing the meta-analytic stability and change model of retest correlations. Journal of Personality and Social Psychology , 110 (5), 766–781. https://doi.org/10.1037/pspp0000066 Bach, B., & Presnall-Shvorin, J. (2020). Using DSM-5 and ICD-11 personality traits in clinical treatment. In Cambridge University Press eBooks (pp. 450–467). https://doi.org/10.1017/9781108333931.079 Bach, B., Kerber, A., Aluja, A., Bastiaens, T., Keeley, J. W., Claes, L., Fossati, A., Gutierrez, F., Oliveira, S. E., Pires, R., Riegel, K. D., Rolland, J., Roskam, I., Sellbom, M., Somma, A., Spanemberg, L., Strus, W., Thimm, J. C., Wright, A. G., & Zimmermann, J. (2020). International Assessment of DSM-5 and ICD-11 Personality Disorder Traits: Toward a Common Nosology in DSM-5.1. Psychopathology , 53 (3–4), 179–188. https://doi.org/10.1159/000507589 Berghuis, H., Bandell, C. C., & Krueger, R. F. (2021). Predicting dropout using DSM–5 Section II personality disorders, and DSM–5 Section III personality traits, in a (day)clinical sample of personality disorders. Personality Disorders Theory Research and Treatment , 12 (4), 331–338. https://doi.org/10.1037/per0000429 Bleidorn, W., Schwaba, T., Zheng, A., Hopwood, C. J., Sosa, S. S., Roberts, B. W., & Briley, D. A. (2022). Personality stability and change: A meta-analysis of longitudinal studies. Psychological Bulletin . https://doi.org/10.1037/bul0000365 Clark, D. M. (2011). Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: the IAPT experience. International review of psychiatry (Abingdon, England), 23(4), 318–327. https://doi.org/10.3109/09540261.2011.606803 Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. Digman, J. M. (1990). Personality Structure: Emergence of the Five-Factor Model. Annual Review of Psychology , 41 (1), 417–440. https://doi.org/10.1146/annurev.ps.41.020190.002221 Duncan, T. E., & Duncan, S. C. (2009). The ABC’s of LGM: An Introductory Guide to Latent Variable Growth Curve Modeling. Social and Personality Psychology Compass , 3 (6), 979–991. https://doi.org/10.1111/j.1751-9004.2009.00224.x Fossati, A., Somma, A., Borroni, S., Markon, K. E., & Krueger, R. F. (2017). The Personality Inventory for DSM-5 Brief Form: Evidence for Reliability and Construct Validity in a sample of Community-Dwelling Italian Adolescents. Assessment , 24 (5), 615–631. https://doi.org/10.1177/1073191115621793 Fowler, J. C., Lamkin, J., Allen, J. G., Madan, A., Oldham, J. M., Petersen, N. J., & Frueh, B. C. (2022). Personality trait domains predict psychiatric symptom and functional outcomes. Psychotherapy , 59 (1), 38–47. https://doi.org/10.1037/pst0000421 Gleason, M. E. J., Weinstein, Y., Balsis, S., & Oltmanns, T. F. (2014). The enduring impact of maladaptive personality traits on relationship quality and health in later life. Journal of Personality , 82 (6), 493–501. https://doi.org/10.1111/jopy.12068 Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist , 48 (1), 26–34. https://doi.org/10.1037/0003-066x.48.1.26 Gonçalves, B., Pires, R., Ferreira, A. S., Marques, J. G., Paulino, M., & Henriques‐Calado, J. (2021). Maladaptive personality traits in depressive and substance use disorders: A study with the PID‐5. Scandinavian Journal of Psychology , 63 (2), 136–143. https://doi.org/10.1111/sjop.12783 Haehner, P., Sleep, C. E., Miller, J. D., Lynam, D. R., & Hopwood, C. J. (2023). The longitudinal (Co)Development of personality traits and the level of personality functioning after negative life events. Clinical Psychological Science , 12 (4), 782–801. https://doi.org/10.1177/21677026231197607 Haehner, P., Wright, A. J., & Bleidorn, W. (2024). A systematic review of volitional personality change research. Communications Psychology , 2 (1). https://doi.org/10.1038/s44271-024-00167-5 Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods , 20 (1), 102–116. https://doi.org/10.1037/a0038889 Hampson, S. E., & Goldberg, L. R. (2020). Personality Stability and Change over Time. The Wiley Encyclopedia of Personality and Individual Differences , 317–321. https://doi.org/10.1002/9781118970843.ch53 Heath, L. M., Drvaric, L., Hendershot, C. S., Quilty, L. C., & Bagby, R. M. (2018). Normative and maladaptive personality trait models of mood, psychotic, and substance use disorders. Journal of Psychopathology and Behavioral Assessment , 40 (4), 606–613. https://doi.org/10.1007/s10862-018-9688-0 Hengartner, M. P. (2015). The detrimental impact of maladaptive personality on public mental health: a challenge for psychiatric practice. Frontiers in Psychiatry , 6 . https://doi.org/10.3389/fpsyt.2015.00087 Hobbs, K. A., Mann, F. D., Latzman, R. D., Zimmermann, J., Jaeger, U., Markon, K., & Krueger, R. F. (2023). Pathological personality in relation to multiple domains of quality of life and impairment: Evidence for the specific relevance of the maladaptive poles of major trait domains. Journal of Psychopathology and Clinical Science , 132 (2), 135–144. https://doi.org/10.1037/abn0000810 Hopwood, C. J., & Bleidorn, W. (2018). Stability and change in personality and personality disorders. Current Opinion in Psychology , 21 , 6–10. https://doi.org/10.1016/j.copsyc.2017.08.034 Hopwood, C. J., Newman, D. A., Donnellan, M. B., Markowitz, J. C., Grilo, C. M., Sanislow, C. A., Ansell, E. B., McGlashan, T. H., Skodol, A. E., Shea, M. T., Gunderson, J. G., Zanarini, M. C., & Morey, L. C. (2009). The stability of personality traits in individuals with borderline personality disorder. Journal of Abnormal Psychology , 118 (4), 806–815. https://doi.org/10.1037/a0016954 Ivziku, D., Clari, M., Piredda, M., De Marinis, M. G., & Matarese, M. (2018). Anxiety, depression and quality of life in chronic obstructive pulmonary disease patients and caregivers: an actor–partner interdependence model analysis. Quality of Life Research, 28(2), 461–472. https://doi.org/10.1007/s11136-018-2024-z Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling. R package version 0.5-6. Retrieved from https://CRAN.R-project.org/package=semTools Kennair, L. E. O., Solem, S., Hagen, R., Havnen, A., Nysæter, T. E., & Hjemdal, O. (2020). Change in personality traits and facets (Revised NEO Personality Inventory) following metacognitive therapy or cognitive behaviour therapy for generalized anxiety disorder: Results from a randomized controlled trial. Clinical Psychology & Psychotherapy , 28 (4), 872–881. https://doi.org/10.1002/cpp.2541 Kerber, A., Schultze, M., Müller, S., Rühling, R. M., Wright, A. G. C., Spitzer, C., Krueger, R. F., Knaevelsrud, C., & Zimmermann, J. (2020). Development of a short and ICD-11 compatible measure for DSM-5 maladaptive personality traits using Ant colony optimization algorithms. Assessment , 29 (3), 467–487. https://doi.org/10.1177/1073191120971848 Kiel, L., Hopwood, C. J., & Lind, M. (2024). Changes in personality functioning and pathological personality traits as a function of treatment: a Feasibility study. Journal of Psychopathology and Behavioral Assessment , 46 (3), 695–704. https://doi.org/10.1007/s10862-024-10138-z Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (1999). Patient Health Questionnaire-9 [Dataset]. In PsycTESTS Dataset . https://doi.org/10.1037/t06165-000 Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A. E. (2012). Personality inventory for DSM-5 [Dataset]. In PsycTESTS Dataset . https://doi.org/10.1037/t30042-000 Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A. E. (2013). The personality inventory for DSM-5—brief form (PID-5-BF)—adult. American Psychiatric Association: Author. Maples, J. L., Carter, N. T., Few, L. R., Crego, C., Gore, W. L., Samuel, D. B., Williamson, R. L., Lynam, D. R., Widiger, T. A., Markon, K. E., Krueger, R. F., & Miller, J. D. (2015). Testing whether the DSM-5 personality disorder trait model can be measured with a reduced set of items: An item response theory investigation of the Personality Inventory for DSM-5. Psychological Assessment , 27 (4), 1195–1210. https://doi.org/10.1037/pas0000120 McCrae, R. R., Costa, P. T., Ostendorf, F., Angleitner, A., Hřebíčková, M., Avia, M. D., Sanz, J., Sánchez-Bernardos, M. L., Kusdil, M. E., Woodfield, R., Saunders, P. R., & Smith, P. B. (2000). Nature over nurture: Temperament, personality, and life span development. Journal of Personality and Social Psychology , 78 (1), 173–186. https://doi.org/10.1037/0022-3514.78.1.173 Mulder, J. D. (2022). Power analysis for the Random Intercept Cross-Lagged Panel model using the POWRICLPM R-Package. Structural Equation Modeling a Multidisciplinary Journal , 30 (4), 645–658. https://doi.org/10.1080/10705511.2022.2122467 Mundt, J. C., Marks, I. M., Shear, M. K., & Greist, J. M. (2002). The Work and Social Adjustment Scale: a simple measure of impairment in functioning. The British Journal of Psychiatry , 180 (5), 461–464. https://doi.org/10.1192/bjp.180.5.461 Navrady, L., Ritchie, S., Chan, S., Kerr, D., Adams, M., Hawkins, E., Porteous, D., Deary, I., Gale, C., Batty, G., & McIntosh, A. (2017). Intelligence and neuroticism in relation to depression and psychological distress: Evidence from two large population cohorts. European Psychiatry , 43 , 58–65. https://doi.org/10.1016/j.eurpsy.2016.12.012 Niemeijer, M., Reinholt, N., Poulsen, S., Bach, B., Christensen, A. B., Eskildsen, A., Hvenegaard, M., Arendt, M., & Arnfred, S. (2023). Trait and symptom change in group cognitive behaviour therapy for anxiety and depression. Clinical Psychology & Psychotherapy , 30 (5), 1058–1070. https://doi.org/10.1002/cpp.2857 Norman, U. A., Truijens, F., Desmet, M., De Smet, M., & Meganck, R. (2024). Impact of personality style changes on CBT and PDT treatment responses in major depression. Acta Psychologica , 246 , 104295. https://doi.org/10.1016/j.actpsy.2024.104295 Ocera, A., Hopwood, C. J., Michelini, G., Piron, R., Fanfoni, M., & Caselli, G. (2025). Longitudinal Changes in Maladaptive Personality Domains and Clinical Outcomes: A Study Protocol. https://doi.org/10.17605/OSF.IO/UCEXP Rek, K., Kappelmann, N., Zimmermann, J., Rein, M., Egli, S., & Kopf-Beck, J. (2022). Evaluating the role of maladaptive personality traits in schema therapy and cognitive behavioural therapy for depression. Psychological Medicine , 53 (10), 4405–4414. https://doi.org/10.1017/s0033291722001209 Rizzo, R., Piccinelli, M., Mazzi, M. A., Bellantuono, C., & Tansella, M. (2000). The Personal Health Questionnaire: a new screening instrument for detection of ICD-10 depressive disorders in primary care. Psychological Medicine , 30 (4), 831–840. https://doi.org/10.1017/s0033291799002512 Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic review of personality trait change through intervention. Psychological Bulletin , 143 (2), 117–141. https://doi.org/10.1037/bul0000088 Rosseel, Y. (2012). “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software , 48(2), 1–36. doi:10.18637/jss.v048.i02 Rossi, A., Rucci, P., Mauri, M., Maina, G., Pieraccini, F., Pallanti, S., & Endicott, J. (2005). Validity and reliability of the Italian version of the Quality of Life, Enjoyment and Satisfaction questionnaire. Quality of Life Research , 14 (10), 2323–2328. https://doi.org/10.1007/s11136-005-7387-2 Simkin, V., Hodsoll, J., & Veale, D. (2022). The relationship between symptoms of obsessive compulsive disorder and depression during therapy: A random intercept cross-lagged panel model. Journal of Behavior Therapy and Experimental Psychiatry , 76 , 101748. https://doi.org/10.1016/j.jbtep.2022.101748 Somma, A., Borroni, S., Maffei, C., Besson, E., Garbini, A., Granozio, S., Limuti, B., Perego, G., Pietrobon, A., Rugi, C., Turano, E., & Fossati, A. (2017). Inter-rater reliability of the Italian Translation of the Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD): A study on consecutively admitted clinical adult participants. Journal of Psychopathology, 23(3), 105–111. Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). Generalized Anxiety Disorder 7 [Dataset]. In PsycTESTS Dataset . https://doi.org/10.1037/t02591-000 Von Schrottenberg, V., Kerber, A., Sterner, P., Teusen, C., Beigel, P., Linde, K., Henningsen, P., Herpertz, S. C., Gensichen, J., & Schneider, A. (2024). Exploring Associations of Somatic Symptom Disorder with Personality Dysfunction and Specific Maladaptive Traits. Psychopathology , 1–12. https://doi.org/10.1159/000540161 Wagner, J., Ram, N., Smith, J., & Gerstorf, D. (2016). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories. Journal of Personality and Social Psychology , 111 (3), 411–429. https://doi.org/10.1037/pspp0000071 World Health Organization. (2022). ICD-11: International classification of diseases (11th revision). https://icd.who.int/ Yang, T., Guo, Z., Zhu, X., Liu, X., & Guo, Y. (2023). The interplay of personality traits, anxiety, and depression in Chinese college students: a network analysis. Frontiers in Public Health , 11 . https://doi.org/10.3389/fpubh.2023.1204285 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6377656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":439162172,"identity":"f72b555a-11bf-465b-9c00-e56a569baaa2","order_by":0,"name":"Alessandro Ocera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie2QsQrCMBCGrwTqUu1qQXyGQBcfJ0FwUwQXBwcnJ3H3LQo+gIFAu9Q9YgdB6KJDQRCHIl6DgghtHR3yDckR8nH/HYDB8McwiOYgjrq28KK/KLEAwXRN8D+tdxgoVpxvpaKN25Bhdodk1NqfuWCzpAuutNswzksVbznor5eQTrzDMBAsTH1o91GpCEaV44MDkgdasSXfKjesUdyrlRfKfofKQ/K5DlbdhRDdRTUDwReoQE0wnMUnHZrydYzB+EoWs5Aeo36pghs7WZdpwlfRbpNlN1lszFJZ3i1VXvHE1wOrEZBvxWAwGAwfPAHfElmsdon8+AAAAABJRU5ErkJggg==","orcid":"","institution":"Sigmund Freud University","correspondingAuthor":true,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Ocera","suffix":""},{"id":439162173,"identity":"44043328-e1d9-4a8d-9dc2-26ac99955f9c","order_by":1,"name":"Christopher J. Hopwood","email":"","orcid":"","institution":"University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"J.","lastName":"Hopwood","suffix":""},{"id":439162174,"identity":"c69b59be-0f0f-4337-8616-1cf4a37cd78d","order_by":2,"name":"Giovanni Michelini","email":"","orcid":"","institution":"Sigmund Freud University","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Michelini","suffix":""},{"id":439162175,"identity":"889e4335-8305-4748-b180-a43e4da30d81","order_by":3,"name":"Rossana Piron","email":"","orcid":"","institution":"Studi Cognitivi","correspondingAuthor":false,"prefix":"","firstName":"Rossana","middleName":"","lastName":"Piron","suffix":""},{"id":439162176,"identity":"614bd635-a062-4bb4-b487-e4cd02380612","order_by":4,"name":"Marta Fanfoni","email":"","orcid":"","institution":"Studi Cognitivi","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Fanfoni","suffix":""},{"id":439162177,"identity":"52eec3fc-fbaa-458b-8283-920611ce222e","order_by":5,"name":"Gabriele Caselli","email":"","orcid":"","institution":"Sigmund Freud University","correspondingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Caselli","suffix":""}],"badges":[],"createdAt":"2025-04-04 16:23:08","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6377656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6377656/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80049950,"identity":"2dd95a68-8513-4086-a767-af482f870ed1","added_by":"auto","created_at":"2025-04-07 10:15:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127184,"visible":true,"origin":"","legend":"\u003cp\u003einTHERAPY patients’ clinical path\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6377656/v1/126a2357f4125468481a4812.png"},{"id":80047959,"identity":"c0000666-a1f2-4f34-8fac-3c54f1629639","added_by":"auto","created_at":"2025-04-07 09:59:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191810,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the Random-Intercept Cross Panel Model (Hamaker et al., 2015) of the relationship between maladaptive personality domains and clinical outcomes during therapy across five waves. \u003cem\u003eNote\u003c/em\u003e. P: personality domains; O: outcomes; α/δ autoregressive associations; u/v: within-wave associations; β/γ: cross-lagged associations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6377656/v1/dee4983891574879a9a2c07f.png"},{"id":80184894,"identity":"79d0382b-5557-41ef-ac89-7d3f048acd30","added_by":"auto","created_at":"2025-04-09 02:01:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822495,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6377656/v1/19e96c56-a257-4f07-b603-cdfc751a23e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal Changes in Maladaptive Personality Domains and Clinical Outcomes: A Study Protocol","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychotherapeutic interventions should prioritize assisting individuals in managing their maladaptive trait expressions rather than attempting to alter their fundamental personality traits (Bach \u0026amp; Presnall-Shvorin, 2020). Maladaptive personality traits are risk factors for social and occupational difficulties, contribute to the onset and chronicity of mental disorders, and reduce treatment efficacy (Haehner, Wright \u0026amp; Bleidorn, 2024; Hengartner, 2015; Fowler et al., 2022). The classical trait perspective posits that personality traits in adulthood are biologically based temperaments that remain stable over time and are not influenced by environmental factors (McCrae et al., 2000). However, more recent research suggests that personality traits can change over time (Hampson \u0026amp; Goldberg, 2020), particularly maladaptive traits (Bleidorn et al., 2022), and that such changes can occur in response to different types of interventions (Roberts et al., 2017). \u003c/p\u003e\n\u003cp\u003eIn recent years, the conceptualization of personality pathology has shifted from categorical to dimensional models, as reflected in the Alternative Model for Personality Disorders (AMPD) of the DSM-5-TR (APA, 2022) and the ICD-11 (WHO, 2022). The AMPD framework conceptualizes personality disorders based on psychosocial functioning (Criterion A) and maladaptive personality traits (Criterion B). Within this framework, the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) was developed to assess maladaptive personality traits, which are organized into five broad domains: Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. These domains encompass 25 specific trait facets (APA, 2022). Notably, these five maladaptive trait domains represent dysfunctional variants of the well-established personality dimensions in the Big Five model (Goldberg, 1993) or the Five-Factor Model (FFM; Digman, 1990). Following the development of the original PID-5, which consists of 220 items, several alternative versions have been created to facilitate assessment in different contexts. These include the PID-5-SF with 100 items (Maples et al., 2015), the PID-5-BF with 25 items (Krueger et al., 2013), the PID-5-BF+ with 34 items (Kerber et al., 2020), and the PID-5-BF+M with 36 items (Bach et al., 2020). In addition to serving as efficient screening tools for maladaptive personality domains, the brief versions plus offer the advantage of assessing Anankastia, a domain introduced in the ICD-11, thereby expanding their clinical and research utility.\u003c/p\u003e\n\u003cp\u003eHowever, maladaptive personality traits are not exclusive to personality disorders (Gon\u0026ccedil;alves et al., 2022; Von Schrottenberg et al., 2024), different levels of these traits are present in other mental health disorders and subclinical levels in a substantial proportion of the general population (Gleason et al., 2014). Personality traits exist on a continuum, meaning that all individuals can be placed along the spectrum of trait dimensions rather than being categorized as having or lacking specific traits.\u003c/p\u003e\n\u003cp\u003eGiven this perspective, personality trait change can be conceptualized differently, with two primary approaches being widely examined: differential stability and absolute change (Hopwood \u0026amp; Bleidorn, 2018). Differential stability refers to the extent to which individuals maintain their relative rank on a given trait over time (Anusic \u0026amp; Schimmack, 2016), typically assessed using test-retest correlations, where effect sizes of 0.10 are considered small, 0.30 moderate, and 0.50 large (Cohen, 1988). In contrast, absolute change reflects systematic increases or decreases in trait expression within a population, regardless of individual rank-order stability (Wagner et al., 2016). This form of change is commonly quantified using Cohen\u0026rsquo;s d, where values of 0.20 indicate small changes, 0.50 moderate changes, and 0.80 large changes (Cohen, 1988).\u003c/p\u003e\n\u003cp\u003eA growing body of research suggests that personality trait change is not only possible but can be facilitated through structured interventions. A systematic review by Roberts et al. (2017) examined personality trait changes following different types of interventions, with a specific focus on clinical treatments. Their findings indicate that supportive therapy, Cognitive Behavioral Therapy (CBT), and psychodynamic therapy all yielded significant personality trait modifications, with CBT demonstrating slightly larger effect sizes. Notably, patients with anxiety and personality disorders exhibited the most substantial changes, particularly in reductions in neuroticism (Roberts et al., 2017). Further supporting these findings, a randomized controlled trial (RCT) by Rek et al. (2022) comparing Schema Therapy (ST) and CBT found that maladaptive trait domains decreased throughout treatment regardless of the intervention type, indicating that both ST and CBT are effective in promoting pathological personality change. Another RCT (Niemeijer et al., 2023) investigated the association between changes in maladaptive personality domains and anxiety and depression symptoms in a clinical sample undergoing CBT. Their findings showed that decreases in Negative Affectivity predicted lower levels of both depression and anxiety symptoms, while reductions in Detachment were specifically associated with decreases in depression symptoms.\u003c/p\u003e\n\u003cp\u003eDespite numerous studies investigating personality trait change (Hopwood et al., 2009; Kennaira et al., 2020; Norman et al., 2024; Roberts et al., 2017), there remains a significant gap in the systematic monitoring of maladaptive personality domains throughout psychotherapy (Kiel, Hopwood, \u0026amp; Lind, 2024) to evaluate their impact on psychosocial functioning and clinical outcomes. The variability of personality expression across different time points and how these fluctuations may influence treatment trajectories remains underexplored (Roberts et al., 2017). Additionally, longitudinal studies examining personality trait change with larger clinical samples are still scarce (Hopwood \u0026amp; Bleidorn, 2018). Addressing this gap requires research designs that incorporate multiple assessment points and sufficient statistical power to capture within-person changes over time, ultimately contributing to the development of more targeted and effective interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAims\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on these considerations, this preregistered study (Ocera et al., 2025) aims to investigate the relationship between CBT, maladaptive personality domains, and clinical outcomes through a longitudinal design with different time points. Specifically, it seeks to answer the following key research questions: \u003c/p\u003e\n\u003cp\u003e(1) \u003cem\u003eDo patients with higher maladaptive domains also have lower functioning and worse clinical symptoms?\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003e(1a) \u003cem\u003eDo maladaptive personality domains decrease over time in the overall sample, indicating a general group-level change?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(2) \u003cem\u003eDo patients who exhibit higher maladaptive domains than their average also have worse functioning and clinical symptoms at the same time?\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003e(3) \u003cem\u003eDo changes in maladaptive personality domains during therapy lead to improvements in functioning and symptom severity?\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003e(4) \u003cem\u003eDo patients tend to change the personality domain they struggle with the most as a function of CBT?\u003c/em\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study is part of the \u0026ldquo;\u003cem\u003eProtocol for accessing and analyzing clinical data gathered at InTherapy for developing experimental research projects, epidemiological studies, and validation of new psychotherapy efficacy monitoring instruments for adults\u003c/em\u003e\u0026rdquo;, a research project that aims to create an automated data collection protocol for conducting epidemiological studies, treatment efficacy verification, and instrument validation. The project has received ethical approval from the Sigmund Freud University - Wien Ethics Committee (Protocol Number: CD9THAHBC8SG@G91206). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants will be recruited from inTHERAPY, a CBT-specializing psychotherapy service. Inclusion criteria include being 18 years or older, fluent in Italian, able to provide informed consent, and having a diagnosed mental disorder. Exclusion criteria include the absence of a clinical diagnosis, organic brain disease, developmental disorders, or intellectual disabilities. Recruitment will be conducted through inTHERAPY\u0026apos;s clinical intake process (\u003cstrong\u003eFigure 1\u003c/strong\u003e) starting from June 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the psychodiagnostic assessment phase, patients will also be administered a questionnaire that contains basic medical information in a narrative format, which may be subject to further exploration in the feedback meeting. The following instruments will be administered to participants during the assessment phase and subsequently throughout psychotherapy for monitoring purposes. All assessments will be conducted and completed via the inTHERAPY app. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelevant covariates will be collected to account for potential confounding factors and better capture individual differences in baseline levels and change trajectories. These covariates include dropout status, therapy modality (in-person vs. online therapy), gender, age, and the presence or absence of a personality disorder diagnosis (as assessed with the SCID-5-PD; Somma et al., 2017). They will be modeled as time-invariant predictors in the statistical analyses, allowing us to assess their influence on both the initial levels and the development of maladaptive personality domains and clinical outcomes over time.\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaladaptive personality domains\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of the initial assessment at baseline, the \u003cem\u003ePersonality Inventory for DSM-5\u003c/em\u003e (PID-5; Krueger et al., 2012; Italian version Fossati et al., 2017) will be administered, as it is included in the standard psychodiagnostic protocol of the inTHERAPY clinical service at Studi Cognitivi. The PID-5 is a comprehensive 220-item instrument designed to assess maladaptive personality traits across five broad domains: Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. Each item is rated on a four-point Likert scale, ranging from 0 (very false or often false) to 3 (very true or often true). However, for longitudinal analyses, only the 36 items corresponding to the \u003cem\u003ePersonality Inventory for DSM-5 - Brief Form Plus Modified\u003c/em\u003e (PID-5-BF+M; Bach et al., 2020) will be extracted and scored from the baseline data.\u003c/p\u003e\n\u003cp\u003eTo monitor changes throughout therapy, the PID-5-BF+M will be directly administered at subsequent time points. The PID-5-BF+M is a 36-item instrument that retains the five core maladaptive personality domains of the original PID-5 while also incorporating the Anankastia domain from the ICD-11 model of personality disorders. This version offers a practical and time-efficient assessment of personality pathology, making it suitable for repeated measurements throughout treatment. Each item is rated on a scale from 1 (very false or often false) to 3 (very true or often true).\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical outcomes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGeneralized Anxiety Disorder \u0026ndash; 7 \u003c/em\u003e(GAD-7; Spitzer et al., 2006; Italian version Ivziku et al., 2018). The GAD-7 consists of 7 items, each assessing symptoms of anxiety (such as worry, difficulty relaxing, irritability) on a scale from 0 (never) to 3 (nearly every day). The total score provides a measure of anxiety severity, aiding in the identification of clinical risk levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient Health Questionnaire \u0026ndash; 9 \u003c/em\u003e(PHQ-9; Kroenke et al., 1999; Italian version Rizzo et al., 2000). This nine-item tool is designed to screen, diagnose, and monitor depression. Items are on a 4-point Likert scale ranging from 0 (not at all) to 3 (nearly every day).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWork and Social Adjustment Scale \u003c/em\u003e(WSAS; Mundt et al., 2002; Italian version Rossi et al., 2005). The WSAS measures the impact of mental or physical health problems on an individual\u0026rsquo;s work and social life. The scale includes 5 items that assess levels of impairment in areas such as work, home management, social relationships, private life, and leisure activities. Each item is rated on a scale from 0 (not at all) to 8 (very severely), with higher scores indicating greater levels of difficulty or impairment. The total aggregate score will be used as a global index of psychosocial functioning.\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics will be conducted to provide an overview of the sample characteristics and trends over time. \u003c/p\u003e\n\u003cp\u003eInferential analyses will include Random Intercept Cross-Lagged Panel Models (RI-CLPM; Hamaker et al., 2015) to examine the reciprocal associations between maladaptive personality domains and clinical outcomes across multiple time points (Research Questions 1, 2 and 3). This modeling approach (\u003cstrong\u003eFigure 2\u003c/strong\u003e) distinguishes between stable between-person differences and within-person fluctuations over time, providing a more precise examination of dynamic relationships. Specifically:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAutoregressive associations (\u0026alpha;, \u0026delta;) assess the extent to which an individual\u0026rsquo;s deviation from their expected score on maladaptive personality domains (PID-5-BF+M) or clinical outcomes (GAD-7, PHQ-9, WSAS) at a given time point influences their score on the same variable at the subsequent measurement occasion.\u003c/li\u003e\n\u003cli\u003eWithin-wave associations (correlation between u and v) capture the concurrent relationship between fluctuations in maladaptive personality domains and clinical outcomes within the same measurement occasion, reflecting short-term interdependencies.\u003c/li\u003e\n\u003cli\u003eCross-lagged associations (\u0026beta;, \u0026gamma;) estimate the extent to which deviations from an individual\u0026apos;s expected score on one variable (e.g., maladaptive personality domains) predict subsequent deviations in another variable (e.g., clinical outcomes) across different time points while controlling for temporal stability.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe will estimate Linear Growth Models (Duncan \u0026amp; Duncan, 2009) to examine group-level change in maladaptive personality domains across five time points (Research Question 1a). This model is defined by two latent factors representing the intercept (initial level) and slope (linear rate of change) of each maladaptive personality domain over time.\u003c/p\u003e\n\u003cp\u003eTo address Research Question 4, we will conduct an exploratory analysis to examine whether patients tend to show the greatest reduction in the maladaptive personality domain that was most elevated at baseline. For each individual, we will identify the highest-scoring domain at T1 and compare the magnitude of change in that domain to changes in the remaining domains throughout treatment.\u003c/p\u003e\n\u003cp\u003eAll analyses will be conducted in R using the \u003cem\u003elavaan\u003c/em\u003e package (Rosseel, 2012) for structural equation modeling and \u003cem\u003esemTools\u003c/em\u003e (Jorgensen et al., 2022) for model evaluation. A significance level of \u0026alpha; = .05 will be applied to all statistical tests.\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample size and time points\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA priori power analysis for the RI-CLPM using Monte Carlo simulations (Mulder, 2022) could not be conducted, as no previous studies have examined the relationship between maladaptive personality domains (PID-5) and clinical outcomes (e.g., anxiety, depression, psychosocial functioning) using this modeling approach. However, several studies have applied this model with sample sizes ranging between 100 and 200 participants (Simkin, Hodson \u0026amp; Veale, 2022). Therefore, we plan to recruit a minimum of 200 participants. The following time points will be considered: T1 (baseline assessment), T2 (after 3 months from the start of the treatment), T3 (after 6 months), T4 (after 9 months), and T5 (after 12 months). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLocation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecruitment will be entirely voluntary. Patients will be recruited from inTHERAPY, the private clinical service of Studi Cognitivi, which specializes in CBT. inTHERAPY operates in multiple cities across Italy and offers both in-person and online psychotherapy services. All therapists at inTHERAPY are CBT-trained psychotherapists who have completed their four-year training at the Studi Cognitivi School of Specialization in Cognitive Behavioral Psychotherapy. The therapists will be responsible for recruiting participants among new patients initiating therapy, ensuring they meet the inclusion and exclusion criteria. They will explain the research project to potential participants and, if they agree to take part, obtain their informed consent.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaladaptive personality traits predispose individuals to treatment resistance, poor response, or relapse (Hengartner, 2015). Beyond treatment efficacy, maladaptive traits significantly impact quality of life and overall functioning (Hobbs et al., 2023). High scores on pathological personality dimensions are associated with lower quality of life (QoL) and greater disability/functional impairment across various domains. Individuals with elevated maladaptive traits are more likely to experience socio-occupational difficulties, such as unemployment, interpersonal conflicts, and early retirement due to disability. Additionally, they tend to exhibit lower recovery capacity and impaired social functioning, even while receiving treatment for other mental disorders (Hobbs et al., 2023). These findings underscore the clinical importance of assessing patients\u0026apos; personality structures, as severe maladaptive traits can compromise treatment outcomes, contribute to residual symptomatology, and hinder functional recovery in daily life.\u003c/p\u003e\n\u003cp\u003eMaladaptive personality domains demonstrate a transdiagnostic nature, extending beyond personality disorders to a wide range of mental health conditions. For instance, patients with depressive or bipolar disorders tend to score higher on Detachment in the PID-5 compared to individuals with psychotic disorders or substance use disorders (Heath et al., 2018). Conversely, patients with alcohol use disorder exhibit higher Disinhibition and lower Psychoticism relative to other clinical groups (Heath et al., 2018). These variations suggest that pathological personality traits can be measured across different psychiatric diagnoses, delineating distinct personality profiles for each clinical population. Conceptually, this dimensional approach helps explain the high rates of comorbidity and diagnostic overlap observed in clinical practice. Traditional categorical models often struggle to account for the substantial co-occurrence of mental disorders, whereas a trait-based dimensional model more effectively captures underlying personality factors shared across different conditions (Hobbs et al., 2023).\u003c/p\u003e\n\u003cp\u003eLongitudinal monitoring of maladaptive traits and personality domains provides a crucial added value in psychotherapy. Tracking these traits over time allows clinicians to observe not only reductions in clinical symptoms but also the evolution of patients\u0026apos; maladaptive personality profiles. From a clinical perspective, identifying changes in personality traits throughout therapy can help assess treatment progress more comprehensively. For instance, a lack of improvement in key personality traits may indicate the need to adjust therapeutic strategies, whereas reductions in maladaptive traits - beyond symptom reduction - may predict more stable treatment outcomes and a lower risk of relapse.\u003c/p\u003e\n\u003cp\u003eThis study aims to investigate the role of maladaptive personality domains in CBT by examining their relationship with clinical outcomes and their trajectory over time. The strengths of this research include the use of a clinical sample in a naturalistic setting and the systematic monitoring of personality domains alongside clinical outcomes throughout therapy. However, certain limitations must be acknowledged. First, controlling for concomitant pharmacotherapy is inherently challenging, making it difficult to isolate the effects of psychotherapy from those of medication. Additionally, the variability in therapeutic protocols adopted at inTHERAPY - including different third-wave CBT approaches such as Dialectical Behavior Therapy (DBT), Schema Therapy, and Acceptance and Commitment Therapy (ACT) - may introduce heterogeneity into the findings. Another significant challenge involves recruitment and longitudinal follow-up: patients with severe maladaptive traits may be more prone to dropping out of treatment (Berghuis, Bandell \u0026amp; Krueger, 2021), potentially leading to selection biases. \u003c/p\u003e\n\u003cp\u003eAnother limitation concerns the outcome measures selected for monitoring clinical change. Specifically, the PHQ-9 and GAD-7 - while widely used and well-validated - are strongly associated with the Big Five trait of Neuroticism (Yang et al., 2023; Navrady et al., 2017), which is related to the Negative Affectivity domain of the PID-5. Nonetheless, these instruments are embedded in routine clinical assessment protocols, including those adopted by inTHERAPY. Notably, both measures are also integral to the IAPT model (Improving Access to Psychological Therapies) implemented within the UK\u0026rsquo;s National Health Service, which systematically tracks treatment outcomes for depression and anxiety within stepped-care interventions (Clark, 2011). While the IAPT model does not account for comorbid personality pathology, it provides a scalable framework for outcome monitoring in large-scale clinical contexts. In line with this rationale, PHQ-9 and GAD-7 have been retained as outcome measures in the present study. \u003c/p\u003e\n\u003cp\u003eFinally, future studies would benefit from incorporating an additional follow-up time point after therapy completion to assess the stability of personality trait changes over time.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study represents an important step toward a more nuanced understanding of the relationship between maladaptive personality domains and psychotherapy. It has practical implications for improving clinical interventions. Identifying key personality domains associated with treatment outcomes may facilitate the development of personalized therapeutic approaches, ultimately enhancing the effectiveness of psychotherapy for patients with maladaptive personality domains.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCBT: Cognitive Behavioral Therapy\u003c/p\u003e\n\u003cp\u003eGAD-7: Generalized Anxiety Disorder\u003c/p\u003e\n\u003cp\u003eIAPT: Improving Access to Psychological Therapies\u003c/p\u003e\n\u003cp\u003ePHQ-9: Patient Health Questionnaire\u003c/p\u003e\n\u003cp\u003ePID: Personality Inventory for DSM-5\u003c/p\u003e\n\u003cp\u003eRI-CLPM: Random-Intercept Cross Lagged Panel Model\u003c/p\u003e\n\u003cp\u003eSCID-5-PD: Structured Clinical Interview for DSM-5 \u0026ndash; Personality Disorders\u003c/p\u003e\n\u003cp\u003eWSAS: Work and Social Adjustment Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants will receive detailed written and oral study information before providing written informed consent. This project was approved by the Ethics Committee of the Faculty of Psychotherapy Science and the Faculty of Psychology of Sigmund Freud University, Wien (approval no. CD9THAHBC8SG@G91206).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing does not apply to this article as no datasets were generated or analyzed during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: AO, CJH, and GC; Methodology: AO, CJH, and GM; Writing - original draft: AO; Writing - review \u0026amp; editing: CJH, GM, RP, MF and GC\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. (2022). \u003cem\u003eDiagnostic and statistical manual of mental disorders\u003c/em\u003e (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787\u003c/li\u003e\n\u003cli\u003eAnusic, I., \u0026amp; Schimmack, U. (2016). Stability and change of personality traits, self-esteem, and well-being: Introducing the meta-analytic stability and change model of retest correlations. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(5), 766\u0026ndash;781. https://doi.org/10.1037/pspp0000066\u003c/li\u003e\n\u003cli\u003eBach, B., \u0026amp; Presnall-Shvorin, J. (2020). Using DSM-5 and ICD-11 personality traits in clinical treatment. In \u003cem\u003eCambridge University Press eBooks\u003c/em\u003e (pp. 450\u0026ndash;467). https://doi.org/10.1017/9781108333931.079\u003c/li\u003e\n\u003cli\u003eBach, B., Kerber, A., Aluja, A., Bastiaens, T., Keeley, J. W., Claes, L., Fossati, A., Gutierrez, F., Oliveira, S. E., Pires, R., Riegel, K. D., Rolland, J., Roskam, I., Sellbom, M., Somma, A., Spanemberg, L., Strus, W., Thimm, J. C., Wright, A. G., \u0026amp; Zimmermann, J. (2020). International Assessment of DSM-5 and ICD-11 Personality Disorder Traits: Toward a Common Nosology in DSM-5.1. \u003cem\u003ePsychopathology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3\u0026ndash;4), 179\u0026ndash;188. https://doi.org/10.1159/000507589\u003c/li\u003e\n\u003cli\u003eBerghuis, H., Bandell, C. C., \u0026amp; Krueger, R. F. (2021). Predicting dropout using DSM\u0026ndash;5 Section II personality disorders, and DSM\u0026ndash;5 Section III personality traits, in a (day)clinical sample of personality disorders. \u003cem\u003ePersonality Disorders Theory Research and Treatment\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 331\u0026ndash;338. https://doi.org/10.1037/per0000429\u003c/li\u003e\n\u003cli\u003eBleidorn, W., Schwaba, T., Zheng, A., Hopwood, C. J., Sosa, S. S., Roberts, B. W., \u0026amp; Briley, D. A. (2022). Personality stability and change: A meta-analysis of longitudinal studies. \u003cem\u003ePsychological Bulletin\u003c/em\u003e. https://doi.org/10.1037/bul0000365\u003c/li\u003e\n\u003cli\u003eClark, D. M. (2011). Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: the IAPT experience. International review of psychiatry (Abingdon, England), 23(4), 318\u0026ndash;327. https://doi.org/10.3109/09540261.2011.606803\u003c/li\u003e\n\u003cli\u003eCohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.\u003c/li\u003e\n\u003cli\u003eDigman, J. M. (1990). Personality Structure: Emergence of the Five-Factor Model. \u003cem\u003eAnnual Review of Psychology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1), 417\u0026ndash;440. https://doi.org/10.1146/annurev.ps.41.020190.002221\u003c/li\u003e\n\u003cli\u003eDuncan, T. E., \u0026amp; Duncan, S. C. (2009). The ABC\u0026rsquo;s of LGM: An Introductory Guide to Latent Variable Growth Curve Modeling. \u003cem\u003eSocial and Personality Psychology Compass\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(6), 979\u0026ndash;991. https://doi.org/10.1111/j.1751-9004.2009.00224.x\u003c/li\u003e\n\u003cli\u003eFossati, A., Somma, A., Borroni, S., Markon, K. E., \u0026amp; Krueger, R. F. (2017). The Personality Inventory for DSM-5 Brief Form: Evidence for Reliability and Construct Validity in a sample of Community-Dwelling Italian Adolescents. \u003cem\u003eAssessment\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(5), 615\u0026ndash;631. https://doi.org/10.1177/1073191115621793\u003c/li\u003e\n\u003cli\u003eFowler, J. C., Lamkin, J., Allen, J. G., Madan, A., Oldham, J. M., Petersen, N. J., \u0026amp; Frueh, B. C. (2022). Personality trait domains predict psychiatric symptom and functional outcomes. \u003cem\u003ePsychotherapy\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(1), 38\u0026ndash;47. https://doi.org/10.1037/pst0000421\u003c/li\u003e\n\u003cli\u003eGleason, M. E. J., Weinstein, Y., Balsis, S., \u0026amp; Oltmanns, T. F. (2014). The enduring impact of maladaptive personality traits on relationship quality and health in later life. \u003cem\u003eJournal of Personality\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(6), 493\u0026ndash;501. https://doi.org/10.1111/jopy.12068\u003c/li\u003e\n\u003cli\u003eGoldberg, L. R. (1993). The structure of phenotypic personality traits. \u003cem\u003eAmerican Psychologist\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(1), 26\u0026ndash;34. https://doi.org/10.1037/0003-066x.48.1.26\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves, B., Pires, R., Ferreira, A. S., Marques, J. G., Paulino, M., \u0026amp; Henriques‐Calado, J. (2021). Maladaptive personality traits in depressive and substance use disorders: A study with the PID‐5. \u003cem\u003eScandinavian Journal of Psychology\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(2), 136\u0026ndash;143. https://doi.org/10.1111/sjop.12783\u003c/li\u003e\n\u003cli\u003eHaehner, P., Sleep, C. E., Miller, J. D., Lynam, D. R., \u0026amp; Hopwood, C. J. (2023). The longitudinal (Co)Development of personality traits and the level of personality functioning after negative life events. \u003cem\u003eClinical Psychological Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 782\u0026ndash;801. https://doi.org/10.1177/21677026231197607\u003c/li\u003e\n\u003cli\u003eHaehner, P., Wright, A. J., \u0026amp; Bleidorn, W. (2024). A systematic review of volitional personality change research. \u003cem\u003eCommunications Psychology\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1). https://doi.org/10.1038/s44271-024-00167-5\u003c/li\u003e\n\u003cli\u003eHamaker, E. L., Kuiper, R. M., \u0026amp; Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 102\u0026ndash;116. https://doi.org/10.1037/a0038889\u003c/li\u003e\n\u003cli\u003eHampson, S. E., \u0026amp; Goldberg, L. R. (2020). Personality Stability and Change over Time. \u003cem\u003eThe Wiley Encyclopedia of Personality and Individual Differences\u003c/em\u003e, 317\u0026ndash;321. https://doi.org/10.1002/9781118970843.ch53\u003c/li\u003e\n\u003cli\u003eHeath, L. M., Drvaric, L., Hendershot, C. S., Quilty, L. C., \u0026amp; Bagby, R. M. (2018). Normative and maladaptive personality trait models of mood, psychotic, and substance use disorders. \u003cem\u003eJournal of Psychopathology and Behavioral Assessment\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(4), 606\u0026ndash;613. https://doi.org/10.1007/s10862-018-9688-0\u003c/li\u003e\n\u003cli\u003eHengartner, M. P. (2015). The detrimental impact of maladaptive personality on public mental health: a challenge for psychiatric practice. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e. https://doi.org/10.3389/fpsyt.2015.00087\u003c/li\u003e\n\u003cli\u003eHobbs, K. A., Mann, F. D., Latzman, R. D., Zimmermann, J., Jaeger, U., Markon, K., \u0026amp; Krueger, R. F. (2023). Pathological personality in relation to multiple domains of quality of life and impairment: Evidence for the specific relevance of the maladaptive poles of major trait domains. \u003cem\u003eJournal of Psychopathology and Clinical Science\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(2), 135\u0026ndash;144. https://doi.org/10.1037/abn0000810\u003c/li\u003e\n\u003cli\u003eHopwood, C. J., \u0026amp; Bleidorn, W. (2018). Stability and change in personality and personality disorders. \u003cem\u003eCurrent Opinion in Psychology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 6\u0026ndash;10. https://doi.org/10.1016/j.copsyc.2017.08.034\u003c/li\u003e\n\u003cli\u003eHopwood, C. J., Newman, D. A., Donnellan, M. B., Markowitz, J. C., Grilo, C. M., Sanislow, C. A., Ansell, E. B., McGlashan, T. H., Skodol, A. E., Shea, M. T., Gunderson, J. G., Zanarini, M. C., \u0026amp; Morey, L. C. (2009). The stability of personality traits in individuals with borderline personality disorder. \u003cem\u003eJournal of Abnormal Psychology\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(4), 806\u0026ndash;815. https://doi.org/10.1037/a0016954\u003c/li\u003e\n\u003cli\u003eIvziku, D., Clari, M., Piredda, M., De Marinis, M. G., \u0026amp; Matarese, M. (2018). Anxiety, depression and quality of life in chronic obstructive pulmonary disease patients and caregivers: an actor\u0026ndash;partner interdependence model analysis. Quality of Life Research, 28(2), 461\u0026ndash;472. https://doi.org/10.1007/s11136-018-2024-z\u003c/li\u003e\n\u003cli\u003eJorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., \u0026amp; Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling. R package version 0.5-6. Retrieved from https://CRAN.R-project.org/package=semTools\u003c/li\u003e\n\u003cli\u003eKennair, L. E. O., Solem, S., Hagen, R., Havnen, A., Nys\u0026aelig;ter, T. E., \u0026amp; Hjemdal, O. (2020). Change in personality traits and facets (Revised NEO Personality Inventory) following metacognitive therapy or cognitive behaviour therapy for generalized anxiety disorder: Results from a randomized controlled trial. \u003cem\u003eClinical Psychology \u0026amp; Psychotherapy\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(4), 872\u0026ndash;881. https://doi.org/10.1002/cpp.2541\u003c/li\u003e\n\u003cli\u003eKerber, A., Schultze, M., M\u0026uuml;ller, S., R\u0026uuml;hling, R. M., Wright, A. G. C., Spitzer, C., Krueger, R. F., Knaevelsrud, C., \u0026amp; Zimmermann, J. (2020). Development of a short and ICD-11 compatible measure for DSM-5 maladaptive personality traits using Ant colony optimization algorithms. \u003cem\u003eAssessment\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 467\u0026ndash;487. https://doi.org/10.1177/1073191120971848\u003c/li\u003e\n\u003cli\u003eKiel, L., Hopwood, C. J., \u0026amp; Lind, M. (2024). Changes in personality functioning and pathological personality traits as a function of treatment: a Feasibility study. \u003cem\u003eJournal of Psychopathology and Behavioral Assessment\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 695\u0026ndash;704. https://doi.org/10.1007/s10862-024-10138-z\u003c/li\u003e\n\u003cli\u003eKroenke, K., Spitzer, R. L., \u0026amp; Williams, J. B. W. (1999). Patient Health Questionnaire-9 [Dataset]. In \u003cem\u003ePsycTESTS Dataset\u003c/em\u003e. https://doi.org/10.1037/t06165-000\u003c/li\u003e\n\u003cli\u003eKrueger, R. F., Derringer, J., Markon, K. E., Watson, D., \u0026amp; Skodol, A. E. (2012). Personality inventory for DSM-5 [Dataset]. In \u003cem\u003ePsycTESTS Dataset\u003c/em\u003e. https://doi.org/10.1037/t30042-000\u003c/li\u003e\n\u003cli\u003eKrueger, R. F., Derringer, J., Markon, K. E., Watson, D., \u0026amp; Skodol, A. E. (2013). The personality inventory for DSM-5\u0026mdash;brief form (PID-5-BF)\u0026mdash;adult. American Psychiatric Association: Author.\u003c/li\u003e\n\u003cli\u003eMaples, J. L., Carter, N. T., Few, L. R., Crego, C., Gore, W. L., Samuel, D. B., Williamson, R. L., Lynam, D. R., Widiger, T. A., Markon, K. E., Krueger, R. F., \u0026amp; Miller, J. D. (2015). Testing whether the DSM-5 personality disorder trait model can be measured with a reduced set of items: An item response theory investigation of the Personality Inventory for DSM-5. \u003cem\u003ePsychological Assessment\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 1195\u0026ndash;1210. https://doi.org/10.1037/pas0000120\u003c/li\u003e\n\u003cli\u003eMcCrae, R. R., Costa, P. T., Ostendorf, F., Angleitner, A., Hřeb\u0026iacute;čkov\u0026aacute;, M., Avia, M. D., Sanz, J., S\u0026aacute;nchez-Bernardos, M. L., Kusdil, M. E., Woodfield, R., Saunders, P. R., \u0026amp; Smith, P. B. (2000). Nature over nurture: Temperament, personality, and life span development. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(1), 173\u0026ndash;186. https://doi.org/10.1037/0022-3514.78.1.173\u003c/li\u003e\n\u003cli\u003eMulder, J. D. (2022). Power analysis for the Random Intercept Cross-Lagged Panel model using the POWRICLPM R-Package. \u003cem\u003eStructural Equation Modeling a Multidisciplinary Journal\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 645\u0026ndash;658. https://doi.org/10.1080/10705511.2022.2122467\u003c/li\u003e\n\u003cli\u003eMundt, J. C., Marks, I. M., Shear, M. K., \u0026amp; Greist, J. M. (2002). The Work and Social Adjustment Scale: a simple measure of impairment in functioning. \u003cem\u003eThe British Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e(5), 461\u0026ndash;464. https://doi.org/10.1192/bjp.180.5.461\u003c/li\u003e\n\u003cli\u003eNavrady, L., Ritchie, S., Chan, S., Kerr, D., Adams, M., Hawkins, E., Porteous, D., Deary, I., Gale, C., Batty, G., \u0026amp; McIntosh, A. (2017). Intelligence and neuroticism in relation to depression and psychological distress: Evidence from two large population cohorts. \u003cem\u003eEuropean Psychiatry\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 58\u0026ndash;65. https://doi.org/10.1016/j.eurpsy.2016.12.012\u003c/li\u003e\n\u003cli\u003eNiemeijer, M., Reinholt, N., Poulsen, S., Bach, B., Christensen, A. B., Eskildsen, A., Hvenegaard, M., Arendt, M., \u0026amp; Arnfred, S. (2023). Trait and symptom change in group cognitive behaviour therapy for anxiety and depression. \u003cem\u003eClinical Psychology \u0026amp; Psychotherapy\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(5), 1058\u0026ndash;1070. https://doi.org/10.1002/cpp.2857\u003c/li\u003e\n\u003cli\u003eNorman, U. A., Truijens, F., Desmet, M., De Smet, M., \u0026amp; Meganck, R. (2024). Impact of personality style changes on CBT and PDT treatment responses in major depression. \u003cem\u003eActa Psychologica\u003c/em\u003e, \u003cem\u003e246\u003c/em\u003e, 104295. https://doi.org/10.1016/j.actpsy.2024.104295\u003c/li\u003e\n\u003cli\u003eOcera, A., Hopwood, C. J., Michelini, G., Piron, R., Fanfoni, M., \u0026amp; Caselli, G. (2025). Longitudinal Changes in Maladaptive Personality Domains and Clinical Outcomes: A Study Protocol. https://doi.org/10.17605/OSF.IO/UCEXP\u003c/li\u003e\n\u003cli\u003eRek, K., Kappelmann, N., Zimmermann, J., Rein, M., Egli, S., \u0026amp; Kopf-Beck, J. (2022). Evaluating the role of maladaptive personality traits in schema therapy and cognitive behavioural therapy for depression. \u003cem\u003ePsychological Medicine\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(10), 4405\u0026ndash;4414. https://doi.org/10.1017/s0033291722001209\u003c/li\u003e\n\u003cli\u003eRizzo, R., Piccinelli, M., Mazzi, M. A., Bellantuono, C., \u0026amp; Tansella, M. (2000). The Personal Health Questionnaire: a new screening instrument for detection of ICD-10 depressive disorders in primary care. \u003cem\u003ePsychological Medicine\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 831\u0026ndash;840. https://doi.org/10.1017/s0033291799002512\u003c/li\u003e\n\u003cli\u003eRoberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., \u0026amp; Hill, P. L. (2017). A systematic review of personality trait change through intervention. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e143\u003c/em\u003e(2), 117\u0026ndash;141. https://doi.org/10.1037/bul0000088\u003c/li\u003e\n\u003cli\u003eRosseel, Y. (2012). \u0026ldquo;lavaan: An R Package for Structural Equation Modeling.\u0026rdquo; \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, 48(2), 1\u0026ndash;36. doi:10.18637/jss.v048.i02\u003c/li\u003e\n\u003cli\u003eRossi, A., Rucci, P., Mauri, M., Maina, G., Pieraccini, F., Pallanti, S., \u0026amp; Endicott, J. (2005). Validity and reliability of the Italian version of the Quality of Life, Enjoyment and Satisfaction questionnaire. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), 2323\u0026ndash;2328. https://doi.org/10.1007/s11136-005-7387-2\u003c/li\u003e\n\u003cli\u003eSimkin, V., Hodsoll, J., \u0026amp; Veale, D. (2022). The relationship between symptoms of obsessive compulsive disorder and depression during therapy: A random intercept cross-lagged panel model. \u003cem\u003eJournal of Behavior Therapy and Experimental Psychiatry\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e, 101748. https://doi.org/10.1016/j.jbtep.2022.101748\u003c/li\u003e\n\u003cli\u003eSomma, A., Borroni, S., Maffei, C., Besson, E., Garbini, A., Granozio, S., Limuti, B., Perego, G., Pietrobon, A., Rugi, C., Turano, E., \u0026amp; Fossati, A. (2017). Inter-rater reliability of the Italian Translation of the Structured Clinical Interview for DSM-5 Personality Disorders (SCID-5-PD): A study on consecutively admitted clinical adult participants. Journal of Psychopathology, 23(3), 105\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eSpitzer, R. L., Kroenke, K., Williams, J. B. W., \u0026amp; L\u0026ouml;we, B. (2006). Generalized Anxiety Disorder 7 [Dataset]. In \u003cem\u003ePsycTESTS Dataset\u003c/em\u003e. https://doi.org/10.1037/t02591-000\u003c/li\u003e\n\u003cli\u003eVon Schrottenberg, V., Kerber, A., Sterner, P., Teusen, C., Beigel, P., Linde, K., Henningsen, P., Herpertz, S. C., Gensichen, J., \u0026amp; Schneider, A. (2024). Exploring Associations of Somatic Symptom Disorder with Personality Dysfunction and Specific Maladaptive Traits. \u003cem\u003ePsychopathology\u003c/em\u003e, 1\u0026ndash;12. https://doi.org/10.1159/000540161\u003c/li\u003e\n\u003cli\u003eWagner, J., Ram, N., Smith, J., \u0026amp; Gerstorf, D. (2016). Personality trait development at the end of life: Antecedents and correlates of mean-level trajectories. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(3), 411\u0026ndash;429. https://doi.org/10.1037/pspp0000071\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2022). \u003cem\u003eICD-11:\u003c/em\u003e \u003cem\u003eInternational classification of diseases \u003c/em\u003e(11th revision). https://icd.who.int/\u003c/li\u003e\n\u003cli\u003eYang, T., Guo, Z., Zhu, X., Liu, X., \u0026amp; Guo, Y. (2023). The interplay of personality traits, anxiety, and depression in Chinese college students: a network analysis. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e. https://doi.org/10.3389/fpubh.2023.1204285\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"Cognitive Behavioral Therapy, Maladaptive Personality Domains, Personality Change, Longitudinal Study, Therapy Outcomes","lastPublishedDoi":"10.21203/rs.3.rs-6377656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6377656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Maladaptive personality traits are transdiagnostic risk factors associated with functional impairment, lower treatment efficacy, and poor mental health outcomes. These traits, encompassing domains such as Negative Affectivity, Detachment, Antagonism, Disinhibition, Psychoticism, and Anankastia, contribute to difficulties in emotional regulation, interpersonal relationships, and occupational functioning. Despite growing evidence that personality traits can change over time, longitudinal research examining within-person fluctuations in maladaptive traits during psychotherapy remains scarce. This study protocol outlines a longitudinal research project aimed at investigating the dynamic interplay between maladaptive personality domains and treatment outcomes in a clinical sample undergoing Cognitive Behavioral Therapy. By examining both within-person fluctuations and between-person differences over multiple time points, this study seeks to clarify how personality change relates to symptom improvement and psychosocial functioning, addressing a critical gap in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This preregistered longitudinal study will recruit patients from inTHERAPY, an Italian psychotherapy service specializing in Cognitive Behavioral Therapy. A total of 200 participants will be assessed across five time points (baseline, 3, 6, 9, and 12 months). Personality domains and clinical symptoms will be systematically evaluated throughout treatment to track individual trajectories of change. Data will be analyzed using Random Intercept Cross-Lagged Panel Models to investigate reciprocal relationships between personality domains and clinical outcomes, distinguishing between between-person differences and within-person fluctuations and Linear Growth Curve Models to examine mean-level change in maladaptive personality domains over time. An exploratory analysis will also be conducted to assess whether patients tend to show the greatest change in the personality domains most elevated at baseline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eUnderstanding the temporal interplay between maladaptive personality domains and clinical outcomes could provide valuable insights for personalized psychotherapy. Identifying which personality domains change most significantly during Cognitive Behavioral Therapy - and whether such changes predict symptom improvement - may inform the development of more targeted interventions. Furthermore, this study’s findings could enhance clinical decision-making by identifying key personality factors influencing therapy trajectories, ultimately improving treatment planning for individuals with impaired maladaptive personality domains.\u003c/p\u003e","manuscriptTitle":"Longitudinal Changes in Maladaptive Personality Domains and Clinical Outcomes: A Study Protocol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-07 09:59:42","doi":"10.21203/rs.3.rs-6377656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"a84b5c42-d3b4-410f-ade4-f756cf5a71cc","owner":[],"postedDate":"April 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-09T01:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-07 09:59:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6377656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6377656","identity":"rs-6377656","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}