Journal of Personality and Psychosomatic Research (JPPR)

A Network‑Informed Machine Learning Model of How Trait Negative Affectivity and Pain Catastrophizing Predict Somatic Symptom Intensity

Maia Bolashvili iD , Nino Beridze ^* iD

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.

Modeling the Influence of Fear‑Avoidance Beliefs and Cognitive Biases on Psychosomatic Symptom Maintenance Using Machine Learning

Sophie Nakamanya iD , Andrei Dumitrescu ^* iD , Pascal Reinert iD

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.

Predicting Somatic Symptom Disorder Using Facets of Neuroticism, Somatosensory Amplification, and Interoceptive Sensitivity: An Explainable Machine Learning Analysis

Aina Syafiqah Noor ^* iD , Syarifah Maisarah iD , Sanduni Jayawardena iD

https://doi.org/10.61838/kman.jppr.5172

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.

Predicting Somatic Complaints from Personality Traits and Smartphone-Derived Behavioral Data Using Machine Learning

Narek Vardanyan iD , Valerya Pylypenko iD , Gayane Harutyunyan ^* iD

https://doi.org/10.61838/kman.jppr.5103

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.

Personality-Driven Adaptive Psychosomatic Treatment Planning via Artificial Intelligence

Shang-Chi Chien ^* iD , Dandan Ma iD , jing Wei iD

https://doi.org/10.61838/kman.jppr.5104

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.

Trait Emotional Stability as a Protective Factor Against Psychosomatic Symptom Amplification: A Multilevel Mediation Analysis

Madeleine Casad ^* iD , Parichehr Mehdiabadi iD

https://doi.org/10.61838/kman.jppr.5106

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.

Alexithymia, Trait Avoidance, and Somatic Distress: A Transdiagnostic Model of Emotion–Body Disconnection

Carola Leonardi iD , Ascher Munion iD

https://doi.org/10.61838/kman.jppr.5150

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.

Predicting Stress-Induced Somatic Symptoms from Personality and Behavioral Indicators Using Machine Learning

María José Ibañez iD , Becky Lima ^* iD , Daniela Cevallos iD

https://doi.org/10.61838/kman.jppr.5157

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.

Journal of Personality and Psychosomatic Research (JPPR)

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Predicting Somatic Complaints from Personality Traits and Smartphone-Derived Behavioral Data Using Machine Learning

Personality-Driven Adaptive Psychosomatic Treatment Planning via Artificial Intelligence

Trait Emotional Stability as a Protective Factor Against Psychosomatic Symptom Amplification: A Multilevel Mediation Analysis

Alexithymia, Trait Avoidance, and Somatic Distress: A Transdiagnostic Model of Emotion–Body Disconnection

Predicting Stress-Induced Somatic Symptoms from Personality and Behavioral Indicators Using Machine Learning

Modeling Personality–Emotion–Somatic Symptom Pathways with Explainable Machine Learning

Personality‑Based Digital Phenotyping of Psychosomatic Health Using Machine Learning

Modeling the Contribution of Cognitive Fusion, Rumination, and Worry to Psychosomatic Distress: An Explainable Machine Learning Study

Using Machine Learning to Identify Latent Profiles of Somatosensory Amplification and Catastrophic Misinterpretation of Bodily Cues in Psychosomatic Patients

Predicting Somatic Symptom Disorder Using Facets of Neuroticism, Somatosensory Amplification, and Interoceptive Sensitivity: An Explainable Machine Learning Analysis

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A Network‑Informed Machine Learning Model of How Trait Negative Affectivity and Pain Catastrophizing Predict Somatic Symptom Intensity

Modeling the Influence of Fear‑Avoidance Beliefs and Cognitive Biases on Psychosomatic Symptom Maintenance Using Machine Learning

Predicting Somatic Symptom Disorder Using Facets of Neuroticism, Somatosensory Amplification, and Interoceptive Sensitivity: An Explainable Machine Learning Analysis

Predicting Somatic Complaints from Personality Traits and Smartphone-Derived Behavioral Data Using Machine Learning

Personality-Driven Adaptive Psychosomatic Treatment Planning via Artificial Intelligence

Trait Emotional Stability as a Protective Factor Against Psychosomatic Symptom Amplification: A Multilevel Mediation Analysis

Alexithymia, Trait Avoidance, and Somatic Distress: A Transdiagnostic Model of Emotion–Body Disconnection

Predicting Stress-Induced Somatic Symptoms from Personality and Behavioral Indicators Using Machine Learning