Journal of Personality and Psychosomatic Research (JPPR)
E-ISSN: 3041-8542 Director in Charge: Dr. Ali Aghaziarati Editor-in-chief: Prof. Dr. Şennur Tutarel Kışlak Owner: KMAN Research Institute Publisher: KMANPUB (KMAN Publication Inc.) Contact Email: jppr@kmanpub.com Open Access: YES The Journal of Personality and Psychosomatic Research, published by KMAN Publication Inc. (KMANPUB), is a reputable scholarly open-access journal that focuses on the intersection of personality psychology and psychosomatic medicine. The journal covers a wide range of topics within these fields, including trait theories, personality development, personality assessment and measurement, psychoneuroimmunology, stress and health, mind-body interactions, psychological factors in chronic illness, health behavior change, quality of life and well-being, psychotherapy and treatment outcomes, diagnostic and assessment tools, mental health interventions, social cognition and behavior, interpersonal relationships, group dynamics and influence, attention and perception, memory and learning, cognitive processes and decision making, brain-behavior relationships, cognitive impairment and dementia, neurorehabilitation and recovery, happiness and well-being, resilience and positive adaptation, positive interventions and flourishing, lifespan development, attachment and social development, cognitive and emotional development, culture and self-concept, cross-cultural differences in psychological processes, and acculturation and cultural adaptation. With its rigorous open peer-review process and diverse range of articles, the Journal of Personality and Psychosomatic Research serves as a valuable resource for researchers, practitioners, and students interested in understanding the complex interplay between personality factors and physical and mental health outcomes. About the Publisher Publisher: KMAN Publication Inc. Publisher Office: Unit 5‑10825 Yonge St, Richmond Hill, Ontario, Canada, L4C 3E3 https://journals.kmanpub.com Email: kmanpu@kmanpub.com Telegram: +1 (647) 656‑4401 | WhatsApp: +1 (437) 429‑3507 Germany Office: Dratelnstrasse, Wohnung A60, 21119 Hamburg, Germany (+4915207720487)
Predicting Somatic Symptom Disorder Using Facets of Neuroticism, Somatosensory Amplification, and Interoceptive Sensitivity: An Explainable Machine Learning Analysis
The objective of this study was to utilize an explainable machine learning approach to predict Somatic Symptom Disorder by delineating the complex, non-linear contributions of specific neuroticism facets, somatosensory amplification, and multidimensional interoceptive sensitivity. A quantitative, cross-sectional design was employed with a sample of Malaysian adults. Data were collected using the Patient Health Questionnaire-15 (PHQ-15), the Neuroticism domain of the NEO-PI-R, the Somatosensory Amplification Scale (SSAS), and the Multidimensional Assessment of Interoceptive Awareness (MAIA). Predictive modeling was conducted using advanced ensemble tree-based algorithms (Random Forest, Gradient Boosting, and XGBoost), and model interpretability was achieved using SHapley Additive exPlanations (SHAP) to evaluate the marginal contribution of each psychological feature. The XGBoost algorithm demonstrated superior predictive performance, achieving an overall accuracy of and an Area Under the Curve (AUC) of . SHAP explainability metrics revealed that Somatosensory Amplification was the most profound positive predictor of somatic distress, followed by the specific Anxiety and Vulnerability facets of neuroticism. Conversely, adaptive interoceptive capacities, specifically the “Not-Worrying” and “Body Trusting” dimensions, emerged as the strongest negative (protective) predictors against Somatic Symptom Disorder classification. Explainable machine learning demonstrates that specific sensory appraisal mechanisms and interoceptive deficits are more critical than broad personality domains in driving somatic distress, providing highly specific targets for precision psychological interventions.
Copyright (c) 2026 Aina Syafiqah Noor (Corresponding Author); Syarifah Maisarah, Sanduni Jayawardena (Author)
Using Machine Learning to Identify Latent Profiles of Somatosensory Amplification and Catastrophic Misinterpretation of Bodily Cues in Psychosomatic Patients
The study aimed to identify distinct latent profiles of psychosomatic patients based on their levels of somatosensory amplification and catastrophic misinterpretation of bodily cues using a person-centered machine learning approach. This cross-sectional study involved a sample of psychosomatic patients recruited from clinical settings in South Africa. Participants were assessed using the Somatosensory Amplification Scale (SSAS) and the Body Sensations Questionnaire (BSQ). Statistical analysis was performed using Gaussian Mixture Modeling (Latent Profile Analysis) to identify unobserved subpopulations. Model selection was determined by fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT). A strong significant positive correlation was observed between somatosensory amplification and the catastrophic misinterpretation of bodily cues ( , ). Latent Profile Analysis identified an optimal three-profile solution: “Low Symptom Burden” ( ; ), “Moderate Symptom Burden” ( ; ), and “Severe Symptom Burden” ( ; ). Comparative analyses revealed that the “Severe Symptom Burden” profile was characterized by a significantly higher proportion of female participants, higher unemployment rates, and longer illness durations ( ), whereas age and educational background did not significantly differentiate the groups. The identification of three distinct latent profiles underscores the clinical heterogeneity of psychosomatic populations and highlights the necessity for tailored, precision-based psychological interventions that address specific levels of cognitive-perceptual distortion.
Copyright (c) 2026 Iheanyi Iwueze (Author); Zanele Mthembu (Corresponding Author)
Modeling the Contribution of Cognitive Fusion, Rumination, and Worry to Psychosomatic Distress: An Explainable Machine Learning Study
The present study aimed to model the unique and interactive contributions of cognitive fusion, rumination, and worry to psychosomatic distress within a Tunisian adult sample by applying and interpreting an explainable machine learning framework. A cross-sectional design was utilized to evaluate 1254 adult participants (M_age=32.4, SD=8.7) from Tunisia, recruited via snowball sampling. Self-report data were collected using the Cognitive Fusion Questionnaire (CFQ), Ruminative Responses Scale (RRS), Penn State Worry Questionnaire (PSWQ), and Patient Health Questionnaire-15 (PHQ-15). The data analysis employed an advanced machine learning pipeline, comparing Random Forest, Support Vector Regression, and eXtreme Gradient Boosting (XGBoost) algorithms. The optimal model was subsequently interpreted using the SHapley Additive exPlanations (SHAP) framework to extract global feature importance, non-linear thresholds, and interaction effects. Bivariate correlations demonstrated significant positive associations between all cognitive variables and psychosomatic distress (r=0.51 to 0.62). The XGBoost algorithm achieved the highest predictive performance (R^2=0.463, RMSE=2.41). SHAP global feature importance analysis ranked Worry as the top predictor of psychosomatic distress (Mean ∣SHAP∣=1.45), followed sequentially by Rumination and Cognitive Fusion. Furthermore, SHAP dependence plots revealed critical non-linear dynamics, identifying a specific threshold where worry scores exceeding 55 exponentially accelerated physical distress, as well as a significant interaction effect indicating that high cognitive fusion severely amplifies the detrimental somatic impact of rumination. Treating severe psychosomatic distress requires targeted interventions that rapidly reduce worry below critical severity thresholds and utilize cognitive defusion strategies to break the compounding cycle of rumination and psychological entanglement.
Copyright (c) 2026 Ardit Hoxha (Author); Yasmine Ben Salah (Corresponding Author); Nouhayla Hafidi (Author)
Personality‑Based Digital Phenotyping of Psychosomatic Health Using Machine Learning
This study aimed to investigate whether integrating personality traits with smartphone‑derived digital phenotyping indicators can predict psychosomatic health using machine learning models. A cross‑sectional study was conducted with 524 adult participants recruited from Germany. Personality traits were assessed using the Big Five Inventory (BFI‑44), psychosomatic symptoms were measured using the Patient Health Questionnaire (PHQ‑15), and psychological distress was evaluated using the Depression Anxiety Stress Scales (DASS‑21). Participants also installed a custom smartphone application that passively collected behavioral data for four weeks, including daily phone usage duration, nighttime phone inactivity, communication frequency, response latency, and mobility variability. Data analysis involved descriptive statistics, correlation analyses, and machine learning modeling using Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting algorithms implemented in Python with scikit‑learn. Model performance was evaluated using accuracy, precision, recall, F1‑score, and AUC‑ROC metrics. The sample consisted of 524 participants (mean age = 29.4 ± 7.8 years), including 56% females and 44% males. Moderate to severe psychosomatic symptoms were identified in 31.5% of participants based on PHQ‑15 scores. Neuroticism showed significant positive correlations with psychosomatic symptoms (r = 0.46), depression (r = 0.49), anxiety (r = 0.52), and stress (r = 0.47), while conscientiousness and agreeableness showed negative correlations with psychosomatic symptoms (r = −0.28 and r = −0.21, respectively). Participants with higher psychosomatic symptom scores demonstrated longer daily smartphone usage (mean = 5.2 hours vs. 3.7 hours), reduced nighttime inactivity periods (mean = 5.8 hours vs. 7.1 hours), and lower mobility variability. Among the predictive models, Gradient Boosting achieved the best performance with an accuracy of 0.82, F1‑score of 0.80, and AUC‑ROC of 0.87, outperforming Random Forest (accuracy = 0.79), Support Vector Machine (accuracy = 0.76), and Logistic Regression (accuracy = 0.73). The findings suggest that combining personality assessments with smartphone‑based digital phenotyping can effectively predict psychosomatic health and may support early identification of psychosomatic risk through machine learning approaches.
Copyright (c) 2026 Lena Hoffmann (Corresponding Author)
Modeling Personality–Emotion–Somatic Symptom Pathways with Explainable Machine Learning
This study aimed to examine the pathways linking personality traits, emotional distress, and somatic symptoms using explainable machine learning techniques. This cross‑sectional study included 1,024 adults from Mexico (54.3% women; mean age = 34.7 years, SD = 11.2). Participants completed validated psychological instruments, including the Big Five Inventory (BFI‑44) to assess personality traits, the Depression Anxiety Stress Scales (DASS‑21) to measure emotional distress, and the Patient Health Questionnaire Somatic Symptom Scale (PHQ‑15) to assess somatic symptom severity. Descriptive statistics and Pearson correlations were first conducted to examine relationships among variables. Subsequently, machine learning models—Random Forest, Gradient Boosting, and XGBoost—were developed to predict somatic symptoms. Model performance was evaluated using the coefficient of determination (R²), and SHapley Additive exPlanations (SHAP) were used to identify the relative importance of predictors and interpret the models. Correlation analyses indicated significant positive associations between neuroticism, depression, anxiety, stress, and somatic symptoms (r range = .29–.48, p < .001). Neuroticism showed the strongest correlations with anxiety (r = .48) and somatic symptoms (r = .41). Among the machine learning models, XGBoost demonstrated the best predictive performance (R² = 0.42), followed by Gradient Boosting (R² = 0.39) and Random Forest (R² = 0.36). SHAP analyses revealed that anxiety and neuroticism were the most influential predictors of somatic symptoms, followed by stress and depression. Interaction analysis suggested that individuals with high neuroticism combined with high anxiety exhibited nearly twice the predicted level of somatic symptom severity compared to individuals with low scores on these variables. The findings highlight the central role of emotional distress and neuroticism in predicting somatic symptoms and demonstrate the value of explainable machine learning for identifying complex psychosomatic pathways.
Copyright (c) 2026 Mariana Torres (Corresponding Author); Fabricio Peixoto , Jaime Martínez-Pacheco Martínez-Pacheco (Author)
Modeling the Influence of Fear‑Avoidance Beliefs and Cognitive Biases on Psychosomatic Symptom Maintenance Using Machine Learning
This study aimed to model the influence of fear‑avoidance beliefs and cognitive biases on psychosomatic symptom maintenance using machine learning techniques. A cross‑sectional study was conducted among 512 adults recruited from the general population in Romania. Psychosomatic symptom severity was assessed using the Patient Health Questionnaire‑15 (PHQ‑15), fear‑avoidance beliefs were measured with the Fear‑Avoidance Beliefs Questionnaire (FABQ), and health anxiety and cognitive bias related to bodily sensations were assessed using the Health Anxiety Inventory and a cognitive bias measure. Data were analyzed using several machine learning algorithms, including Random Forest, Gradient Boosting, Support Vector Regression, and Linear Regression. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²), and feature importance analysis was conducted to identify the most influential predictors of psychosomatic symptom severity. After data screening, the final sample consisted of 498 participants (mean age = 36.8 ± 11.4 years). The mean psychosomatic symptom score measured by the PHQ‑15 was 10.9 ± 5.7. Significant positive correlations were observed between psychosomatic symptoms and health anxiety (r = 0.61), cognitive bias (r = 0.57), and fear‑avoidance beliefs (r = 0.52). Among the machine learning models, the Random Forest algorithm demonstrated the best predictive performance, explaining 68% of the variance in psychosomatic symptom severity (R² = 0.68). Feature importance analysis indicated that health anxiety, cognitive bias, and fear‑avoidance beliefs were the most influential predictors of psychosomatic symptoms. Fear‑avoidance beliefs and cognitive biases play a significant role in the maintenance of psychosomatic symptoms.
Copyright (c) 2026 Sophie Nakamanya (Author); Andrei Dumitrescu (Corresponding Author); Pascal Reinert (Author)
Predicting Stress-Induced Somatic Symptoms from Personality and Behavioral Indicators Using Machine Learning
This study aimed to predict the severity of stress-induced somatic symptoms using machine learning models applied to personality traits and behavioral data. A cross-sectional study was conducted with 1248 Brazilian adults using digital surveys that included the Patient Health Questionnaire for Somatic Symptoms (PHQ-15), the Big Five Inventory, and a behavioral assessment. Predictive modeling involved training and comparing Multiple Linear Regression, Support Vector Regression (SVR), Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms on an 80/20 train-test split (test set n=250), evaluating performance via R^2, MAE, and RMSE. The XGBoost model demonstrated superior predictive performance (R^2=0.684, MAE=2.15, RMSE=2.61) for somatic symptom severity (M=9.42, SD=4.65). Feature importance analysis ranked Neuroticism (34.5%) as the strongest predictor, followed by Sleep Duration (18.2%), Conscientiousness (14.6%), and Physical Exercise (11.3%), aligning with significant bivariate correlations for Neuroticism (r=0.58), sleep duration (r=-0.41), and physical activity (r=-0.35). Machine learning algorithms effectively predict somatic symptom severity, highlighting the paramount influence of neuroticism and the protective role of modifiable health behaviors.
Copyright (c) 2026 María José Ibañez (Author); Becky Lima (Corresponding Author); Daniela Cevallos (Author)
A Network‑Informed Machine Learning Model of How Trait Negative Affectivity and Pain Catastrophizing Predict Somatic Symptom Intensity
The objective of this study was to estimate a psychological network of trait negative affectivity and pain catastrophizing to identify critical bridge nodes, and subsequently utilize these specific structural bridges as features in a machine learning model to predict the continuous intensity of somatic symptoms. This cross-sectional study recruited a community sample of adults from Georgia who completed standardized online self-report questionnaires, including the Positive and Negative Affect Schedule (PANAS-NA), the Pain Catastrophizing Scale (PCS), and the Patient Health Questionnaire (PHQ-15). Data analysis utilized a two-phase methodology: first, a regularized partial correlation network analysis was conducted to map item-level interactions and compute bridge expected influence; second, machine learning algorithms (Random Forest, Support Vector Regression, Elastic Net) were trained using the identified bridge nodes as input features to predict somatic symptom intensity. Macroscopic analyses demonstrated robust positive correlations between negative affectivity, catastrophizing, and somatic burden. Network analysis revealed that the emotional state of feeling “afraid” and the cognitive appraisal of “helplessness” served as the paramount structural bridge nodes connecting the two psychological constructs. The network-informed Random Forest model demonstrated strong predictive performance on unseen test data ( ), with feature importance metrics confirming that “helplessness” was the most critical individual predictor, independently accounting for of the model’s out-of-sample predictive accuracy. Targeted clinical interventions that specifically dismantle the catastrophic cognition of helplessness may prove more efficacious in reducing somatic symptom severity than broad treatments aimed at generalized negative affect.
Copyright (c) 2026 Maia Bolashvili (Author); Nino Beridze (Corresponding Author)
Alexithymia, Trait Avoidance, and Somatic Distress: A Transdiagnostic Model of Emotion–Body Disconnection
This study aimed to test a transdiagnostic model in which alexithymia is associated with somatic distress both directly and indirectly through trait avoidance. A cross-sectional correlational design was employed in a community sample of adults recruited from Sweden. Participants completed validated self-report measures assessing alexithymia, trait avoidance, and somatic distress. Alexithymia was measured using a multidimensional scale capturing difficulties in identifying and describing feelings as well as externally oriented thinking, trait avoidance was assessed via a measure of experiential avoidance and psychological inflexibility, and somatic distress was evaluated using a standardized instrument indexing the severity of common bodily symptoms. Data were analyzed using structural equation modeling. Preliminary analyses included descriptive statistics, reliability estimation, and correlation analyses. Confirmatory factor analysis was conducted to evaluate the adequacy of the measurement model prior to testing structural paths. Mediation was examined using bootstrapped indirect effects while controlling for relevant demographic variables. Structural equation modeling demonstrated good overall model fit. Alexithymia showed a significant positive direct effect on trait avoidance and a smaller but significant direct effect on somatic distress. Trait avoidance exhibited a strong positive direct effect on somatic distress. Mediation analysis revealed a significant indirect effect of alexithymia on somatic distress through trait avoidance, with the indirect pathway accounting for a substantial proportion of the total effect. The mediation was partial, indicating that alexithymia influenced somatic distress through both avoidance-related and direct pathways. The findings support a transdiagnostic model of emotion–body disconnection in which alexithymia functions as a dispositional vulnerability that increases reliance on avoidance strategies, thereby intensifying somatic distress. Trait avoidance emerged as a central mechanism translating deficits in emotional awareness into bodily symptom burden. These results highlight the importance of targeting emotional awareness and avoidance processes in the assessment and treatment of somatic distress across diagnostic categories.
Copyright (c) 2026 Carola Leonardi (Corresponding Author); Ascher Munion (Author)
Trait Emotional Stability as a Protective Factor Against Psychosomatic Symptom Amplification: A Multilevel Mediation Analysis
The objective of this study was to examine whether trait emotional stability functions as a protective factor against psychosomatic symptom amplification and psychosomatic symptom severity through the mediating role of perceived stress. This study employed a quantitative cross-sectional design using data collected from an adult community sample in the United Kingdom. Participants completed standardized self-report measures assessing trait emotional stability, perceived stress, somatosensory amplification, and psychosomatic symptom severity. Validated instruments with established psychometric properties were used to ensure reliability and construct validity. Data were analyzed using multilevel modeling techniques to account for potential clustering effects. A multilevel mediation framework was specified in which trait emotional stability served as the predictor, perceived stress as the mediator, and psychosomatic symptom amplification and symptom severity as outcome variables. Indirect effects were tested using bootstrapping procedures with bias-corrected confidence intervals. Multilevel regression analyses indicated that trait emotional stability was significantly and negatively associated with perceived stress, somatosensory amplification, and psychosomatic symptom severity. Perceived stress was positively associated with both somatosensory amplification and psychosomatic symptom severity. Somatosensory amplification emerged as a strong positive predictor of psychosomatic symptom severity when included alongside emotional stability and perceived stress. Mediation analyses demonstrated significant indirect effects of emotional stability on both psychosomatic symptom amplification and symptom severity via perceived stress, while direct effects remained significant, indicating partial mediation. The findings suggest that trait emotional stability protects against psychosomatic symptom amplification and somatic symptom burden through both direct pathways and indirect stress-related mechanisms, highlighting the importance of personality-informed and stress-focused approaches in psychosomatic research and practice.
Copyright (c) 2026 Madeleine Casad (Corresponding Author); Parichehr Mehdiabadi (Author)
Personality-Driven Adaptive Psychosomatic Treatment Planning via Artificial Intelligence
The objective of this study was to develop and empirically evaluate an artificial intelligence–based framework that integrates personality traits with psychosomatic symptom profiles to generate adaptive, personalized treatment plans in psychosomatic medicine. A mixed-methods, model-development study was conducted using clinical data from adult patients with psychosomatic complaints recruited from outpatient psychosomatic and psychological services in Taiwan. Standardized personality assessments, psychosomatic symptom measures, clinician-rated evaluations, and behavioral indicators were collected and integrated into a secure digital dataset. Machine learning techniques, including unsupervised clustering and supervised predictive modeling, were applied to identify latent personality–symptom patterns and to generate individualized treatment recommendations across multiple psychosomatic intervention modalities. Model performance was evaluated using cross-validation procedures, and explainable AI methods were employed to enhance interpretability and clinical transparency. Unsupervised learning identified four distinct personality–psychosomatic clusters characterized by differential trait configurations and symptom profiles. Predictive modeling demonstrated high classification accuracy and strong discriminative capacity in matching patients to optimal treatment modalities. Inferential analyses indicated that personality traits significantly contributed to treatment recommendation variance beyond symptom severity alone, and adaptive recommendations differed systematically across clusters, supporting the model’s capacity for clinically meaningful personalization. The findings suggest that integrating personality traits into AI-driven psychosomatic treatment planning enables robust patient stratification, improves personalization of intervention strategies, and offers a scalable decision-support approach aligned with contemporary precision medicine principles. This framework represents a promising step toward adaptive, person-centered psychosomatic care that complements clinical expertise and supports iterative treatment optimization.
Copyright (c) 2026 Shang-Chi Chien (Corresponding Author); Dandan Ma , jing Wei (Author)
Predicting Somatic Complaints from Personality Traits and Smartphone-Derived Behavioral Data Using Machine Learning
This study aimed to predict somatic complaints by integrating Big Five personality traits and passive smartphone-derived behavioral indicators within a machine learning framework. A cross-sectional observational design was employed with an adult community sample recruited in Armenia. Participants completed standardized self-report measures assessing somatic complaints and Big Five personality traits. In parallel, passive smartphone sensing data were collected continuously over a four-week period using a custom Android application that captured behavioral indicators such as screen time, sleep regularity proxies, physical inactivity duration, nighttime phone activity, and app usage patterns, without recording personal content. Data preprocessing included cleaning, aggregation of behavioral features at the participant level, and standardization of predictors. Supervised machine learning models were trained to predict somatic complaint scores, including regularized linear models, support vector regression, random forest, and gradient boosting. Model evaluation used train–test splits with cross-validation, and performance was assessed using error-based indices and explained variance metrics. Nonlinear ensemble models significantly outperformed linear approaches in predicting somatic complaints, with gradient boosting explaining the largest proportion of variance. Models combining personality traits and smartphone-derived behavioral data demonstrated significantly higher predictive accuracy than models using either data source alone. Neuroticism showed the strongest positive contribution to somatic complaints, while conscientiousness and extraversion contributed negatively. Among behavioral indicators, sleep regularity, daily screen time, nighttime phone activity, and physical inactivity emerged as significant predictors. Model performance remained stable across gender and age subgroups, indicating robustness of the predictive relationships. Integrating personality traits with passive smartphone-derived behavioral data using machine learning provides a powerful and ecologically valid approach for predicting somatic complaints, supporting biopsychosocial models of psychosomatic health and highlighting the potential of digital phenotyping for personalized health assessment.
Copyright (c) 2026 Narek Vardanyan , Valerya Pylypenko (Author); Gayane Harutyunyan (Corresponding Author)
Neurobiological Correlates of Personality-Driven Psychosomatic Reactivity: A Psychoneuroimmunological Perspective
This study aimed to examine an integrated psychoneuroimmunological model linking personality traits to psychosomatic reactivity through perceived stress, neuroendocrine activity, and immune activation. A cross-sectional correlational design was employed with a community-based adult sample recruited from urban health centers in Mexico. Participants completed validated self-report measures assessing personality traits, perceived stress, and psychosomatic symptoms. Neuroendocrine activity was indexed using salivary cortisol collected at awakening and 30 minutes post-awakening, while immune activation was assessed through serum inflammatory markers, including interleukin-6 and C-reactive protein. Data were analyzed using correlation analyses, multiple regression models, mediation analyses with bootstrapping, and structural equation modeling to test direct and indirect pathways among psychological and biological variables. Inferential analyses indicated that neuroticism was a strong positive predictor of psychosomatic symptoms, whereas conscientiousness and agreeableness were significant negative predictors. Perceived stress significantly mediated the relationship between neuroticism and psychosomatic symptom severity. Elevated cortisol awakening response was associated with higher perceived stress and greater somatic symptom burden and functioned as an additional mediator. Inflammatory markers were positively associated with cortisol activity and psychosomatic symptoms. Structural equation modeling demonstrated good model fit and supported a sequential pathway from personality traits to stress appraisal, neuroendocrine dysregulation, immune activation, and psychosomatic reactivity, with protective traits exerting indirect buffering effects through reduced stress responses. The findings support a psychoneuroimmunological model in which personality traits function as central regulators of psychosomatic reactivity by shaping psychological stress appraisal and downstream neuroendocrine and immune processes, highlighting the importance of integrative, personality-informed approaches to psychosomatic health.
Copyright (c) 2026 Daymar Avilés-Santa , Przemyslaw Wandzilak (Author); Derek Péloquin (Corresponding Author); Sue J Critchley (Author)
Personality as a Core Organizing Factor in Mind–Body Health: A Computational Modeling Study
This study aimed to examine whether personality traits function as a core organizing system underlying mind–body health by integrating multivariate and computational modeling approaches. A cross-sectional design was employed with an adult sample recruited from Portugal. Participants completed validated self-report instruments assessing personality traits and multiple domains of mind–body health, including physical and mental health, perceived stress, affective balance, and sleep quality. Data were first examined using descriptive statistics and correlational analyses to establish basic relationships among variables. Structural equation modeling was then applied to test a theoretically informed model in which personality traits predicted latent dimensions of mind–body health. To complement explanatory analyses, machine learning techniques, including linear and non-linear predictive models, were used to estimate the extent to which personality traits could predict composite mind–body health outcomes. Model performance was evaluated using explained variance and error indices, and feature importance analyses were conducted to identify the relative contribution of each personality trait. Inferential analyses revealed significant and systematic associations between personality traits and all major mind–body health indicators. Neuroticism emerged as a strong positive predictor of stress, negative affect, and sleep disturbances and a strong negative predictor of physical and mental health, whereas Conscientiousness showed the opposite pattern, functioning as a robust protective factor. Structural equation modeling demonstrated that personality traits exerted simultaneous and differential effects on latent health, stress–affect, and sleep regulation factors, with excellent model fit indices. Machine learning models significantly outperformed traditional linear regression, indicating non-linear and interactive relationships between personality and health, with Neuroticism and Conscientiousness showing the highest predictive importance. The findings support the conceptualization of personality as a central organizing factor in mind–body health, shaping the structure, regulation, and predictability of physical and psychological well-being, and highlight the value of computational approaches for advancing integrative psychosomatic models.
Copyright (c) 2026 José Carvalho (Corresponding Author); Clarice Santos Figueiredo , Antonio Raposo (Author)
Explainable AI Models for Identifying Personality-Driven Risk Factors in Psychosomatic Disorders
The objective of this study was to identify and transparently explain personality-driven risk factors associated with psychosomatic symptom severity using explainable artificial intelligence models. This cross-sectional study was conducted on an adult clinical sample recruited from psychosomatic and general health settings in Chile. Participants completed validated self-report instruments assessing personality traits, stress-related psychological variables, and psychosomatic symptom severity. After data preprocessing and standardization, multiple supervised machine learning models were developed to predict psychosomatic symptom severity based on personality and psychological predictors. Model performance was evaluated using cross-validation procedures. The best-performing model was further analyzed using explainable artificial intelligence techniques to identify global and individual-level contributions of predictors, enabling transparent interpretation of nonlinear effects and interactions among personality traits. Nonlinear ensemble models significantly outperformed linear models in predicting psychosomatic symptom severity, explaining a substantial proportion of variance. Stress reactivity emerged as the strongest predictor, followed by emotional instability, perceived stress, and negative affectivity. Explainable analyses revealed threshold and interaction effects, indicating sharp increases in psychosomatic risk at high levels of stress reactivity and compounded effects when combined with emotional instability. Self-regulation-related traits demonstrated a protective effect, particularly at low to moderate stress levels, although this effect diminished under extreme stress conditions. Individual-level explanations highlighted marked heterogeneity in risk profiles, with distinct personality configurations driving symptom severity across participants. The findings indicate that psychosomatic symptom severity is shaped by dynamic, nonlinear interactions among personality traits and stress-related factors rather than isolated linear effects.
Copyright (c) 2026 Claudia Moreno Ruiz (Corresponding Author); Héctor Jasso-Fuentes , Tiziana Lorenzini (Author)
Predicting Somatic Symptom Disorder Using Ensemble Learning on Personality, Stress, and Emotion Regulation Data
The objective of this study was to develop and evaluate ensemble learning models to predict somatic symptom disorder based on personality traits, perceived stress, and emotion regulation variables. This cross-sectional study was conducted on an adult sample from South Africa using a predictive modeling design. Participants completed standardized self-report measures assessing somatic symptom severity, personality traits, perceived stress, and multiple dimensions of emotion regulation. Data preprocessing included handling missing values, standardization of continuous variables, and feature preparation for machine learning analysis. Several base classifiers were trained and integrated using ensemble learning techniques, including random forest, gradient boosting, and stacking. Model training and evaluation were performed using stratified k-fold cross-validation to ensure robustness and reduce overfitting. Predictive performance was assessed using multiple inferential metrics, including accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Feature importance and incremental modeling analyses were conducted to examine the relative and combined contributions of predictor domains. The ensemble learning models demonstrated strong predictive performance, with the stacking ensemble yielding the highest accuracy and AUC, indicating excellent discrimination between higher and lower somatic symptom severity. Neuroticism and perceived stress emerged as the most influential predictors, followed by key emotion regulation difficulties, particularly impulse control problems and limited access to adaptive regulation strategies. Incremental analyses showed that models incorporating personality traits alone achieved moderate prediction, which significantly improved with the addition of perceived stress and further improved when emotion regulation variables were included.
Copyright (c) 2026 Sinokuthokoza Segun Oyeyiola (Author)
AI-Based Profiling of Personality Disorder Traits Associated with Chronic Somatic Symptoms
This study aimed to identify and model personality disorder–related trait profiles associated with the severity of chronic somatic symptoms using artificial intelligence–based analytical approaches. A cross-sectional observational design was employed with an adult sample recruited in Hungary. Participants completed standardized self-report measures assessing chronic somatic symptom severity, dimensional personality disorder traits, and psychological distress variables. Data were collected electronically following informed consent and ethical approval. Multiple supervised machine learning models, including regularized regression and ensemble-based algorithms, were trained to predict somatic symptom severity from personality trait dimensions while controlling for demographic and psychological covariates. Model performance was evaluated using cross-validation procedures, and explainable AI techniques were applied to determine the relative importance and direction of trait contributions. Ensemble machine learning models demonstrated superior predictive performance compared with linear approaches, indicating non-linear and interactive associations between personality traits and chronic somatic symptom severity. Negative affectivity emerged as the strongest predictor, followed by detachment, with both traits showing robust positive associations with symptom severity. Disinhibition and psychoticism contributed moderately, while antagonism exhibited a smaller but significant effect. Personality disorder traits provided substantial incremental explanatory value beyond anxiety and depressive symptoms, accounting for additional variance in somatic symptom severity. Explainability analyses confirmed the dominance of internalizing and emotion-related traits in the AI-derived personality profiles associated with higher symptom burden. The findings indicate that chronic somatic symptoms are strongly linked to specific configurations of maladaptive personality traits, particularly negative affectivity and detachment, and that AI-based modeling offers a powerful framework for capturing these complex relationships.
Copyright (c) 2026 Orsolya Demetrovics (Author); Norbert Hetényi (Corresponding Author)
Borderline Personality Features, Somatization, and Stress Reactivity: A Biopsychosocial Model
The objective of this study was to examine the relationships between borderline personality features, stress reactivity, and somatization within a biopsychosocial framework and to test the mediating role of stress reactivity in the association between borderline personality features and somatic symptom severity. This study used a cross-sectional correlational design and was conducted in an adult community sample from Colombia. Participants completed validated self-report measures assessing borderline personality features, perceived stress reactivity, and somatic symptoms. Borderline personality features were measured using the Borderline Features scale of the Personality Assessment Inventory, stress reactivity was assessed with the Perceived Stress Scale, and somatization was measured using the Patient Health Questionnaire–15. Data were analyzed using descriptive statistics, Pearson correlation analyses, multiple regression models, and regression-based mediation analyses with bootstrapped confidence intervals to test the proposed biopsychosocial model. Inferential analyses revealed significant positive correlations between borderline personality features and somatization, borderline personality features and stress reactivity, and stress reactivity and somatization. Multiple regression analysis indicated that both borderline personality features and stress reactivity independently and significantly predicted somatic symptom severity. Mediation analysis demonstrated that stress reactivity partially mediated the relationship between borderline personality features and somatization, with a significant indirect effect and a remaining significant direct effect of borderline personality features on somatic symptoms. The findings support an integrated biopsychosocial model in which borderline personality features are associated with increased somatic symptom burden both directly and indirectly through heightened stress reactivity, underscoring the central role of stress-related processes in the embodiment of personality-related emotional vulnerability.
Copyright (c) 2026 Mercedes Narcisa Idrovo (Corresponding Author); Stephanie Flores Carrera (Author)
Identifying Key Psychological Predictors of Mind–Body Health Discrepancy
This study aimed to identify the key psychological predictors that contribute to discrepancies between perceived mental well-being and physical health among adults in Singapore. A qualitative research design with an interpretive phenomenological approach was adopted to explore the psychological mechanisms underlying mind–body health incongruence. A total of 21 adult participants (12 females, 9 males; aged 26–54 years) were recruited through purposeful sampling based on self-reported differences between their psychological and physical health experiences. Data were collected through semi-structured, in-depth interviews lasting 60–90 minutes and analyzed using Braun and Clarke’s thematic analysis framework with the aid of NVivo 14 software. Theoretical saturation was achieved after the 21st interview. Rigor was ensured through member checking, audit trails, and peer debriefing. The analysis revealed four major psychological domains explaining the mind–body health discrepancy: (1) Emotional dysregulation and somatic sensitivity, reflecting the translation of unprocessed emotions into bodily symptoms; (2) Cognitive appraisal and belief systems, indicating that maladaptive health beliefs, perfectionism, and mind–body dualism contribute to misaligned health perceptions; (3) Behavioral and lifestyle misalignment, showing inconsistent self-care and maladaptive coping strategies that sustain perceptual dissonance; and (4) Interpersonal and sociocultural determinants, encompassing social expectations, family communication patterns, and cultural norms that reinforce emotional suppression and health misperception. Collectively, these themes suggest that subjective health perception is shaped more by cognitive–emotional regulation and social context than by biological status. The study underscores that mind–body health discrepancy arises from the convergence of emotional, cognitive, behavioral, and sociocultural factors. Enhancing emotional regulation, cognitive flexibility, and social support systems may reduce such incongruence and promote holistic well-being. These findings provide a conceptual foundation for integrative psychological and medical interventions.
Copyright (c) 2026 Angel Lee , Yoko Wong (Author); Xian Seng Neo (Corresponding Author)
Identifying Core Personality Predictors of Psychophysiological Health Outcomes
This study aimed to explore how stable personality traits, particularly conscientiousness and neuroticism, influence psychophysiological health outcomes through emotional regulation, behavioral patterns, and cognitive–affective integration among adults in Canada. This qualitative study employed a descriptive–exploratory design to examine the lived experiences of individuals regarding the interaction between personality and psychophysiological well-being. Twenty-one adult participants (11 women, 10 men) aged 25–55 from various provinces in Canada were selected using purposive sampling. Data were collected through semi-structured interviews, each lasting 60–90 minutes, and analyzed using thematic analysis in NVivo 14 software. Sampling continued until theoretical saturation was achieved. The analytic process followed Braun and Clarke’s six-step framework, including open and axial coding to derive major themes. Credibility was ensured through member checking and peer debriefing. Three major themes emerged from the data: (1) Emotional Regulation and Stress Reactivity, capturing the link between personality traits, emotion control, and physiological stress responses; (2) Personality Dispositions Influencing Health Behavior, emphasizing conscientiousness and neuroticism as primary determinants of health-promoting or risk behaviors; and (3) Cognitive–Affective Integration and Meaning Construction, illustrating how mindfulness, meaning in life, and self-reflection mediate personality’s effect on physiological well-being. Participants high in conscientiousness reported adaptive coping, lower stress reactivity, and greater resilience, while those with high neuroticism experienced more psychosomatic symptoms but sometimes converted anxiety into health vigilance—reflecting the “healthy neuroticism” effect. Personality traits, particularly conscientiousness and neuroticism, are central predictors of psychophysiological health. Their interaction shapes emotional regulation, behavioral consistency, and physiological adaptation.
Copyright (c) 2026 Sayed Hadi Sayed Alitabar (Corresponding Author); Farzad Goli , Ava Goli (Author)
Prioritizing Personality-Based Pathways of Stress Reactivity and Psychosomatic Illness
This study aimed to identify, conceptualize, and prioritize the personality-based pathways that contribute to stress reactivity and psychosomatic illness through an integrated mixed-method approach. The research employed a two-phase sequential mixed-method design. In the qualitative phase, a comprehensive literature review was conducted until theoretical saturation was achieved, and the extracted data were analyzed using NVivo 14 to identify recurring personality-based mechanisms underlying psychosomatic illness. In the quantitative phase, a structured questionnaire developed from the qualitative findings was distributed to 250 adult participants in the United Arab Emirates. Respondents rated the importance of nine personality-based pathways on a five-point Likert scale. Data were analyzed using SPSS-26, employing descriptive and inferential statistics to rank the identified pathways based on mean scores and standard deviations. The analysis revealed that neuroticism and emotional instability (M = 4.62, SD = 0.41), psychophysiological integration pathways (M = 4.56, SD = 0.48), and alexithymia and emotional unawareness (M = 4.47, SD = 0.53) were the top-ranked predictors of psychosomatic vulnerability. Mid-level contributors included perfectionism and self-criticism, stress appraisal and cognitive schemas, and emotion regulation strategies, while extraversion and social connectivity and conscientiousness and coping regulation ranked lowest, indicating a primarily protective rather than causative role. Inferential tests confirmed significant differences in perceived importance across demographic groups (p < .05), reinforcing the robustness of the model. The findings demonstrate that psychosomatic illness arises from a hierarchical interaction of personality-driven emotional, cognitive, and physiological processes, with neuroticism, alexithymia, and physiological dysregulation forming the core vulnerability triad. These results underscore the importance of personality assessment in psychosomatic health management and highlight the need for personality-informed preventive and therapeutic interventions.
Copyright (c) 2026 Azizreza Ghasemzadeh (Corresponding Author); Maryam Saadat (Author)
Ranking Cognitive–Affective Factors in the Development of Psychosomatic Illness
This study aimed to identify and rank the key cognitive–affective factors contributing to the development of psychosomatic illness through an integrated qualitative–quantitative research design. A mixed-methods sequential design was employed. In the qualitative phase, an extensive literature review of 127 scientific sources published between 2000 and 2025 was conducted to identify the principal cognitive–affective mechanisms underlying psychosomatic illness. Using NVivo 14 software, open and axial coding were applied until theoretical saturation was achieved, resulting in seven major thematic categories. In the quantitative phase, a structured questionnaire was developed based on these qualitative findings and administered to 288 adult participants from Portugal. Participants rated the importance of each factor on a 5-point Likert scale. The collected data were analyzed using SPSS version 26, with descriptive and inferential analyses determining the ranking and significance of each factor. The results showed that emotional dysregulation was the most influential factor contributing to psychosomatic illness (M = 4.62, SD = 0.43), followed by stress reactivity and coping deficits (M = 4.48, SD = 0.52) and maladaptive cognitive appraisal (M = 4.36, SD = 0.58). Relational and attachment factors ranked fourth (M = 4.27, SD = 0.61), while body–emotion misattribution and personality traits followed with moderate influence. Health beliefs and mind–body awareness received the lowest mean score (M = 3.89, SD = 0.67). Inferential analyses indicated statistically significant differences across demographic groups, with higher psychosomatic sensitivity observed among participants with greater emotional suppression and lower coping efficacy. The findings highlight emotional dysregulation and maladaptive stress processing as the most critical cognitive–affective determinants of psychosomatic illness. Interventions targeting emotion regulation, adaptive coping, and cognitive restructuring are essential to prevent and manage psychosomatic symptoms.
Copyright (c) 2026 Giselle Mata , Diana Rodrigues da Silva (Author); Beatriz Peixoto (Corresponding Author)
Dimensions of Sexual Self-Concept in Women with Endometriosis: A Qualitative Exploration
This study aimed to explore and interpret the dimensions of sexual self-concept among women living with endometriosis, focusing on how chronic pelvic pain, emotional distress, and relational factors shape their perceptions of sexuality and identity. A qualitative exploratory design was employed using semi-structured, in-depth interviews with 20 women diagnosed with endometriosis residing in Canada. Participants were selected through purposive sampling, ensuring variation in age, marital status, and duration since diagnosis. Interviews continued until theoretical saturation was achieved. Data were analyzed using thematic analysis following Braun and Clarke’s six-phase framework, supported by NVivo 14 software for coding and data organization. Trustworthiness was ensured through member checking, peer debriefing, and maintaining an audit trail. Analysis revealed three overarching themes: (1) Altered Body and Self-Perception, describing loss of femininity, body shame, and internalized guilt; (2) Emotional and Relational Challenges in Sexual Intimacy, highlighting fear of pain, avoidance of intercourse, communication barriers, and relational insecurity; and (3) Coping, Adaptation, and Sexual Empowerment, encompassing self-acceptance, meaning-making, spiritual coping, and redefinition of sexual identity. Participants demonstrated both vulnerability and resilience, reconstructing their sexual self-concept through self-compassion, supportive relationships, and advocacy. The findings indicate that endometriosis affects not only physical sexuality but also emotional identity and relational dynamics. Sexual self-concept in women with endometriosis is a multidimensional construct shaped by pain, body image, partner understanding, and personal meaning-making. The study underscores the importance of integrating psychosocial and relational support into endometriosis care to promote sexual well-being and identity reconstruction.
Copyright (c) 2025 Mehdi Rostami (Corresponding Author); Leila Saeidtaleshi (Author)
Identifying Work-Related Psychosomatic Stressors in Healthcare Workers: A Qualitative Exploration
This study aimed to explore and identify the organizational, psychosocial, and behavioral factors contributing to psychosomatic stress among healthcare workers in the United States through an in-depth qualitative analysis of their lived experiences. A qualitative exploratory design was employed to capture healthcare workers’ subjective experiences of psychosomatic stress. Twenty-three participants, including nurses, physicians, technicians, and administrative staff, were purposively selected from hospitals and clinics across the United States. Data were collected through semi-structured interviews conducted face-to-face or via secure online platforms. Interviews were transcribed verbatim and analyzed thematically using NVivo 14 software. Thematic analysis followed the Braun and Clarke (2006) framework, allowing the identification of recurrent patterns and relationships among concepts. Data collection continued until theoretical saturation was reached. Three main themes emerged: (1) Organizational and structural stressors—including work overload, administrative pressure, resource scarcity, and unsafe working conditions—were linked to chronic fatigue, headaches, and sleep disturbances; (2) Psychosocial and emotional stressors—such as compassion fatigue, interpersonal strain, and work–family conflict—contributed to emotional exhaustion and physical symptoms; and (3) Psychosomatic manifestations and coping responses—showing how stress materialized as physical pain, anxiety, and maladaptive behaviors, while some workers adopted adaptive coping strategies like mindfulness and peer support. The findings suggest that psychosomatic stress in healthcare settings stems from systemic imbalance rather than individual vulnerability, confirming strong interdependence between emotional and physiological domains. The study highlights that healthcare workers’ psychosomatic distress is rooted in organizational dysfunction and emotional overload.
Copyright (c) 2025 Abbie Wilson , Veronica Longo , Lia Corazza (Author); Seyed Amir Saadati (Corresponding Author)
Components of Body–Self Ambiguity in Dermatology Patients: A Qualitative Study
This study aimed to explore the lived experiences and psychological components of body–self ambiguity among patients with chronic dermatological conditions, focusing on how individuals interpret, negotiate, and reconstruct the meaning of their altered embodiment. This qualitative phenomenological study was conducted with 16 dermatology patients (9 women and 7 men) from clinical centers in Tehran between March and August 2025. Participants were selected through purposive sampling to ensure variation in age, gender, diagnosis, and illness duration. Data were collected using semi-structured, in-depth interviews that invited participants to describe their personal experiences of body–self disconnection and social perception. Interviews were recorded, transcribed verbatim, and analyzed through Braun and Clarke’s six-phase thematic analysis using NVivo 14 software. Theoretical saturation was achieved after the sixteenth interview. Trustworthiness was ensured through triangulation, member checking, and maintaining an audit trail of analytical decisions. Analysis revealed four overarching themes representing the components of body–self ambiguity in dermatology patients: (1) Fragmented Body Awareness, describing disconnection from one’s bodily image and sensory estrangement; (2) Emotional Turbulence in the Self–Body Relationship, involving shame, anxiety, and emotional fatigue in response to visible changes; (3) Social Mirror and External Reflection, capturing stigma, social withdrawal, and the internalization of others’ judgments; and (4) Meaning Reconstruction and Adaptation, denoting acceptance, spirituality, and creative self-expression as pathways toward re-integrating body and self. These findings highlight body–self ambiguity as a fluid and multidimensional experience shaped by emotional, social, and cultural contexts. The study concludes that dermatological illness profoundly alters patients’ bodily self-concept, evoking fragmentation yet also fostering potential for meaning reconstruction.
Copyright (c) 2025 Mansour Abdi (Corresponding Author); Parisa Bagheri, Maryam Ranjbar (Author)
Dimensions of Resilience Among Women with Autoimmune Conditions: A Qualitative Study in Tehran
This study aimed to explore and conceptualize the multidimensional nature of resilience among women living with autoimmune conditions, focusing on their cognitive, emotional, social, and spiritual adaptation processes. A qualitative phenomenological design was employed to gain a deep understanding of women’s lived experiences of resilience while coping with autoimmune diseases. Using purposive sampling, twenty-one women diagnosed with autoimmune conditions—including lupus, rheumatoid arthritis, multiple sclerosis, and thyroid disorders—were recruited from medical centers in Tehran. Data were collected through semi-structured, in-depth interviews lasting 45–75 minutes. Interviews were recorded, transcribed verbatim, and analyzed thematically using Braun and Clarke’s (2006) framework with the assistance of NVivo 14 software. Data collection continued until theoretical saturation was reached. Trustworthiness was ensured through member checking, peer debriefing, and maintaining an audit trail. Thematic analysis revealed four overarching dimensions of resilience: (1) Cognitive Adaptation—involving reframing illness, future orientation, and meaning-making through knowledge; (2) Emotional Regulation—including acceptance, emotional endurance, and positive emotional expression; (3) Social Connectedness—encompassing family support, peer empathy, and identity reconstruction through relationships; and (4) Spiritual and Existential Resilience—defined by faith, gratitude, moral integrity, and spiritual integration with medical care. Resilience among women with autoimmune diseases emerges as a multifaceted and culturally embedded phenomenon, rooted in cognitive flexibility, emotional mastery, social solidarity, and spiritual meaning-making. Interventions should therefore adopt holistic and culturally sensitive approaches that address both psychosocial and existential needs, empowering women to live purposefully despite chronic adversity.
Copyright (c) 2025 Sepideh Mahmoodzadeh (Author); Mehdi Ghezelseflo (Corresponding Author); Farnaz Rahsepar Monfared (Author)
Sleep Quality Mediating the Relationship between Workload Stress and Emotional Exhaustion in Employed Females
This study aimed to examine the mediating role of sleep quality in the relationship between workload stress and emotional exhaustion among employed females. A descriptive correlational design was adopted, and data were collected from 390 employed women in Canada using standardized self-report instruments. Workload stress was measured by the Workload Subscale of the Occupational Stress Inventory–Revised (Osipow, 1998), sleep quality by the Pittsburgh Sleep Quality Index (Buysse et al., 1989), and emotional exhaustion by the Maslach Burnout Inventory (Maslach & Jackson, 1981). Sampling was determined using the Morgan and Krejcie (1970) table, ensuring representativeness across occupational groups. Data analysis was conducted using SPSS-27 for descriptive and correlational statistics and AMOS-21 for Structural Equation Modeling (SEM) to test the mediation model. Model fit was evaluated through multiple indices, including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA. The results indicated that workload stress significantly predicted emotional exhaustion both directly (β = 0.41, p < .001) and indirectly through sleep quality (β = 0.16, p < .001). Workload stress was positively associated with poor sleep quality (β = 0.38, p < .001), and poor sleep quality was strongly related to higher emotional exhaustion (β = 0.44, p < .001). The total effect of workload stress on emotional exhaustion was substantial (β = 0.57, p < .001). Model fit indices demonstrated an excellent fit (χ²(54) = 87.43, χ²/df = 1.62, GFI = 0.96, AGFI = 0.93, CFI = 0.98, TLI = 0.97, RMSEA = 0.041), confirming the adequacy of the hypothesized model. The study highlights that workload stress significantly contributes to emotional exhaustion among employed females, with sleep quality serving as a crucial mediating mechanism. These findings emphasize the importance of sleep health as a psychological and physiological buffer in occupational stress management. Organizations should prioritize interventions that regulate workload and promote restorative sleep to mitigate emotional exhaustion and enhance employee well-being.
Copyright (c) 2025 Paulo Castro-Medina, Sabine Kraus (Author); Karina Batthyany (Corresponding Author); Neda Atapour (Author)
Cognitive Flexibility Mediating the Link Between Perfectionism and Impostor Feelings Among Female Undergraduate Students
This study aimed to examine the mediating role of cognitive flexibility in the relationship between perfectionism and impostor feelings among female undergraduate students in Canada. A descriptive correlational research design was employed with a total sample of 400 female undergraduate students selected through stratified random sampling based on the Morgan and Krejcie (1970) table. Standardized self-report instruments were used: the Frost Multidimensional Perfectionism Scale (FMPS; Frost et al., 1990) to assess perfectionism, the Cognitive Flexibility Scale (CFS; Dennis & Vander Wal, 2010) to measure cognitive flexibility, and the Clance Impostor Phenomenon Scale (CIPS; Clance, 1985) to assess impostor feelings. Data were analyzed using SPSS version 27 for descriptive and correlation analyses and AMOS version 21 for structural equation modeling (SEM). Model fit was evaluated through indices including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA, with significance determined at p < .05. Results revealed significant relationships among the study variables. Perfectionism was positively correlated with impostor feelings (r = .57, p < .001), while cognitive flexibility was negatively correlated with both perfectionism (r = −.48, p < .001) and impostor feelings (r = −.53, p < .001). The SEM analysis demonstrated excellent model fit (χ²/df = 1.48, GFI = .95, CFI = .97, RMSEA = .035). Perfectionism exerted a significant direct effect on impostor feelings (β = 0.38, p < .001) and a negative effect on cognitive flexibility (β = −0.41, p < .001). Cognitive flexibility significantly predicted lower impostor feelings (β = −0.33, p < .001) and partially mediated the perfectionism–impostor relationship (indirect β = 0.13, p < .01). The findings suggest that cognitive flexibility functions as a protective cognitive mechanism that mitigates the negative impact of perfectionism on impostor feelings. Enhancing cognitive flexibility may therefore reduce self-doubt and promote adaptive coping among perfectionistic female students.
Copyright (c) 2025 Daniela Gottschlich (Corresponding Author); Seyed Amir Saadati (Author)
Evaluating a Yoga-Based Intervention on Mindful Awareness and Stress Reactivity
This study aimed to evaluate the effectiveness of an eight-session yoga-based intervention in enhancing mindful awareness and reducing stress reactivity among adults in Kenya. A randomized controlled trial design was employed with 30 adult participants randomly assigned to either an experimental group (n = 15), which received the yoga intervention, or a control group (n = 15). The intervention consisted of weekly 60–75-minute sessions over eight weeks, integrating yoga postures, breathing exercises, and mindfulness practices. Standardized measurement tools—the Mindful Attention Awareness Scale (MAAS) and the Perceived Stress Scale (PSS-10)—were administered at three time points: pre-test, post-test, and five-month follow-up. Data were analyzed using repeated measures ANOVA and Bonferroni post-hoc tests in SPSS-27. Results indicated a significant increase in mindful awareness in the experimental group compared to the control group over time (F(2, 56) = 18.52, p < .001, η² = .407), with sustained effects at follow-up. Similarly, stress reactivity significantly decreased in the experimental group relative to controls (F(2, 56) = 16.73, p < .001, η² = .374), and the improvements persisted at five-month follow-up. Bonferroni post-hoc tests confirmed significant differences between pre-test and post-test, as well as pre-test and follow-up, for both variables (all p < .001). The findings suggest that a structured yoga-based intervention can significantly improve mindful awareness and reduce stress reactivity in adults, with effects maintained over time. These results support the integration of yoga and mindfulness practices into mental health and wellness programs, especially in low-resource settings.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
The Effectiveness of Breathing and Relaxation Techniques on Somatic Tension and Trait Anger
This study aimed to evaluate the effectiveness of a structured breathing and relaxation intervention in reducing somatic tension and trait anger among Canadian adults. A randomized controlled trial was conducted with 30 participants (15 intervention, 15 control) recruited from community and educational centers in Isfahan, Iran. The intervention group underwent ten 60-minute sessions over five weeks, incorporating diaphragmatic breathing, progressive muscle relaxation, and guided imagery techniques. Assessments were conducted at pre-test, post-test, and five-month follow-up using the Pennebaker Inventory of Limbic Languidness (Muscle Tension Subscale) and the State-Trait Anger Expression Inventory-2 (STAXI-2). Data were analyzed using repeated measures ANOVA with Bonferroni post-hoc tests via SPSS-27. Significant main effects of time and group, as well as time × group interactions, were found for both somatic tension (F(2,56) = 36.71, p < .001, η² = .58) and trait anger (F(2,56) = 27.43, p < .001, η² = .49). Post-hoc comparisons revealed a significant reduction in somatic tension from pre-test to post-test (MD = 9.62, p < .001) and from pre-test to follow-up (MD = 11.79, p < .001). Trait anger also decreased significantly between pre-test and post-test (MD = 6.12, p < .001), and between pre-test and follow-up (MD = 6.95, p < .001), with sustained effects over time. Breathing and relaxation techniques are effective in reducing both somatic and emotional symptoms, with benefits persisting up to five months post-intervention. These findings support the integration of psychophysiological self-regulation practices into mental health and stress management programs as non-invasive and cost-effective strategies.