Research and Practice in Couple Therapy

The present study aimed to investigate the effectiveness of Emotionally Focused Couple Therapy (EFCT) on attachment injuries, dyadic trust, and marital forgiveness among couples experiencing distress related to emotional or sexual infidelity. This study employed a quasi-experimental design with pretest, posttest, and three-month follow-up assessments using an experimental and a waitlist control group. The population consisted of couples seeking counseling services for infidelity-related relationship problems in Canada during 2025–2026. Following screening procedures, 60 couples (120 individuals) who met the inclusion criteria were selected and randomly assigned to either the experimental group (30 couples) or the control group (30 couples). Participants in the experimental group received twelve weekly 90-minute sessions of Emotionally Focused Couple Therapy, whereas the control group received no intervention during the study period. Data were collected using the Attachment Injury Resolution Scale, the Dyadic Trust Scale, and the Marital Forgiveness Scale. The data were analyzed using repeated-measures multivariate analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results of repeated-measures multivariate analysis of variance revealed significant effects of time, group, and Time × Group interaction for all dependent variables. Significant interaction effects were found for attachment injuries (F = 103.84, p < .001, η² = .66), dyadic trust (F = 95.76, p < .001, η² = .62), and marital forgiveness (F = 129.86, p < .001, η² = .72), indicating that participants receiving EFCT experienced significantly greater improvements than those in the control group. Bonferroni pairwise comparisons demonstrated significant reductions in attachment injuries and significant increases in dyadic trust and marital forgiveness from pretest to posttest and from pretest to follow-up (p < .001). No significant differences were observed between posttest and follow-up scores, indicating maintenance of treatment gains across the follow-up period. The findings indicate that Emotionally Focused Couple Therapy is an effective intervention for couples experiencing infidelity-related distress. By addressing attachment-related vulnerabilities and promoting emotionally corrective interactions, EFCT significantly reduced attachment injuries while enhancing dyadic trust and marital forgiveness. The sustained improvements observed at follow-up suggest that the intervention facilitates enduring changes in relational functioning and supports long-term recovery following experiences of betrayal.

The present study aimed to investigate the effectiveness of Emotionally Focused Couple Therapy (EFCT) on attachment injuries, dyadic trust, and marital forgiveness among couples experiencing distress related to emotional or sexual infidelity. This study employed a quasi-experimental design with pretest, posttest, and three-month follow-up assessments using an experimental and a waitlist control group. The population consisted of couples seeking counseling services for infidelity-related relationship problems in Canada during 2025–2026. Following screening procedures, 60 couples (120 individuals) who met the inclusion criteria were selected and randomly assigned to either the experimental group (30 couples) or the control group (30 couples). Participants in the experimental group received twelve weekly 90-minute sessions of Emotionally Focused Couple Therapy, whereas the control group received no intervention during the study period. Data were collected using the Attachment Injury Resolution Scale, the Dyadic Trust Scale, and the Marital Forgiveness Scale. The data were analyzed using repeated-measures multivariate analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results of repeated-measures multivariate analysis of variance revealed significant effects of time, group, and Time × Group interaction for all dependent variables. Significant interaction effects were found for attachment injuries (F = 103.84, p < .001, η² = .66), dyadic trust (F = 95.76, p < .001, η² = .62), and marital forgiveness (F = 129.86, p < .001, η² = .72), indicating that participants receiving EFCT experienced significantly greater improvements than those in the control group. Bonferroni pairwise comparisons demonstrated significant reductions in attachment injuries and significant increases in dyadic trust and marital forgiveness from pretest to posttest and from pretest to follow-up (p < .001). No significant differences were observed between posttest and follow-up scores, indicating maintenance of treatment gains across the follow-up period. The findings indicate that Emotionally Focused Couple Therapy is an effective intervention for couples experiencing infidelity-related distress. By addressing attachment-related vulnerabilities and promoting emotionally corrective interactions, EFCT significantly reduced attachment injuries while enhancing dyadic trust and marital forgiveness. The sustained improvements observed at follow-up suggest that the intervention facilitates enduring changes in relational functioning and supports long-term recovery following experiences of betrayal.

The present study aimed to predict work–family conflict and marital satisfaction among married employees using machine learning algorithms based on job stress, emotional exhaustion, dyadic coping, and partner support. This cross-sectional predictive study was conducted among 584 married employees recruited from governmental and private organizations in Tehran, Iran, through multistage cluster sampling. Participants completed the Work–Family Conflict Scale, Revised Dyadic Adjustment Scale, Job Stress Scale, Emotional Exhaustion subscale of the Maslach Burnout Inventory, Dyadic Coping Inventory, and Spousal Support Scale. Following data preprocessing, normalization, and missing-value treatment, the dataset was divided into training (80%) and testing (20%) subsets. Several machine learning algorithms, including Multiple Linear Regression, Support Vector Regression, Random Forest Regression, Gradient Boosting Regression, Artificial Neural Networks, and Extreme Gradient Boosting (XGBoost), were implemented. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Feature importance analyses were conducted using permutation importance and SHAP techniques. Correlation analyses revealed that work–family conflict was positively associated with job stress (r = 0.68, p < .001) and emotional exhaustion (r = 0.64, p < .001), while negatively associated with dyadic coping (r = -0.49, p < .001), partner support (r = -0.56, p < .001), and marital satisfaction (r = -0.62, p < .001). Marital satisfaction demonstrated significant positive relationships with dyadic coping (r = 0.73, p < .001) and partner support (r = 0.76, p < .001). Among all predictive models, XGBoost demonstrated the highest accuracy. The optimal model explained 81% of the variance in work–family conflict (R² = 0.81, RMSE = 5.14) and 84% of the variance in marital satisfaction (R² = 0.84, RMSE = 4.37). Feature importance analyses indicated that job stress and emotional exhaustion were the strongest predictors of work–family conflict, whereas partner support and dyadic coping emerged as the most influential predictors of marital satisfaction. The findings demonstrate that machine learning approaches can accurately predict work–family conflict and marital satisfaction using a combination of occupational and relational variables. Job stress and emotional exhaustion substantially increase vulnerability to work–family conflict, whereas dyadic coping and partner support function as powerful protective factors that enhance marital satisfaction. These results underscore the importance of strengthening both workplace well-being and couple-based coping resources to promote healthier marital relationships and better adaptation to work–family demands.

The objective of this study was to develop and evaluate machine learning models capable of classifying adult attachment styles using multimodal dyadic behavioral, emotional, and physiological indicators derived from real-time couple interactions. A cross-sectional observational design was implemented with adult romantic couples recruited from community settings in Canada. Both partners in each dyad participated in standardized interaction tasks designed to elicit attachment-relevant behaviors, including conflict discussion and support-seeking exchanges. Adult attachment styles were assessed using validated self-report measures and used as supervised learning labels. Multimodal data were collected, including behavioral coding of dyadic interactions, self-reported emotional responses, physiological indices of autonomic regulation, and paralinguistic and facial-expression features extracted from audio–video recordings. Machine learning pipelines incorporated data preprocessing, feature extraction at the dyadic level, dimensionality reduction, and model training using multiple classification algorithms. Stratified dyad-level cross-validation and hyperparameter optimization were applied to ensure robust generalization and prevent data leakage. Non-linear and ensemble-based models significantly outperformed linear classifiers in attachment style prediction, with neural network and gradient boosting models achieving the highest accuracy and area under the receiver operating characteristic curve. Dyadic emotional synchrony and observed behavioral responsiveness emerged as the strongest predictors of attachment style classification, followed by self-reported attachment dimensions. Physiological and paralinguistic indicators provided incremental predictive value when integrated with behavioral features. Cross-validation analyses demonstrated high stability across folds, and misclassification patterns primarily occurred between theoretically adjacent attachment styles, indicating construct-consistent overlap rather than random error. The findings demonstrate that adult attachment styles can be accurately classified using machine learning models trained on multimodal dyadic interaction data, supporting a relational and interaction-based conceptualization of attachment. This approach offers theoretical advances in attachment research and practical implications for objective assessment and intervention planning in couple and relational contexts.

This study aimed to develop and interpret machine learning models capable of identifying high-conflict couples at elevated risk for intimate partner violence by integrating multidimensional dyadic, psychological, and relational data. A cross-sectional predictive design was employed with a sample of 368 high-conflict heterosexual couples recruited from counseling and community support settings in Italy. Partners independently completed validated self-report measures assessing conflict dynamics, attachment orientations, emotional regulation, perceived stress, jealousy, relationship satisfaction, and intimate partner violence risk, alongside demographic information. Dyadic data were preprocessed and structured to preserve partner-level and couple-level information. Multiple supervised machine learning algorithms, including regularized logistic regression, support vector machines, random forest, and gradient boosting, were trained and evaluated using stratified cross-validation. Model interpretability was examined using explainable artificial intelligence techniques based on feature attribution. Ensemble-based models outperformed linear and kernel-based approaches, with the gradient boosting model demonstrating the highest predictive accuracy and discrimination (accuracy = 0.88; AUC = 0.94). Sensitivity to high-risk classifications was robust across ensemble models, indicating effective identification of couples at elevated risk. Feature importance analyses revealed that conflict escalation, anger dysregulation, attachment anxiety, perceived stress, and jealousy intensity were the strongest contributors to risk classification, while lower relationship satisfaction showed a smaller but meaningful effect. The results indicated that nonlinear interactions among relational and emotional variables substantially enhanced predictive performance. The findings demonstrate that explainable machine learning models can reliably identify high-conflict couples at risk for intimate partner violence by capturing complex dyadic interaction patterns. Integrating such models into preventive and clinical contexts may support earlier detection, targeted intervention, and ethically informed decision-making, complementing traditional assessment approaches.

The objective of this study was to develop and evaluate an artificial intelligence–based model capable of predicting intimacy decline in romantic couples using dyadic attachment orientations and individual and shared stress patterns. The study employed a longitudinal, correlational design with a predictive modeling framework. Married and long-term cohabiting couples from Turkey participated as dyads, with both partners independently completing validated self-report measures of attachment anxiety and avoidance, perceived individual stress, dyadic stress, and relational intimacy. Data were collected at baseline and at a six-month follow-up to capture changes in intimacy over time. After data preprocessing and dyadic feature construction, multiple supervised machine learning algorithms were trained and validated using cross-validation procedures to predict intimacy decline as both a categorical and continuous outcome. The predictive models demonstrated strong performance, with ensemble-based algorithms achieving the highest classification accuracy and area under the curve values in distinguishing couples with declining intimacy from those with stable intimacy. Inferential feature analyses indicated that attachment anxiety, attachment avoidance, dyadic stress, and interaction terms between attachment insecurity and stress were the most influential predictors. Models incorporating dyadic discrepancy indicators consistently outperformed those based solely on individual-level features, indicating significant partner interdependence effects. Higher combined levels of attachment insecurity and stress were associated with greater magnitude of intimacy decline over time. The findings indicate that intimacy decline can be accurately predicted using AI-based models that integrate dyadic attachment and stress variables, supporting the view that intimacy erosion emerges from complex, interactive relational processes. These results highlight the potential of artificial intelligence to inform early identification and prevention strategies in couple and family interventions.

The objective of this study was to develop and evaluate a machine learning–based early warning system capable of predicting therapy failure trajectories among high-conflict couples during the early phases of couple therapy. This longitudinal observational study was conducted with high-conflict couples undergoing outpatient couple therapy in Germany. Multimodal data were collected from both partners and therapists across early and mid-treatment sessions, including self-reported relational functioning, emotional regulation indicators, therapeutic alliance ratings, therapist session evaluations, ecological momentary assessments, and automated interactional features derived from session recordings. Therapy failure was operationalized as premature dropout, therapist-rated non-response, or reliable deterioration in relationship satisfaction over time. Multiple machine learning models, including regularized logistic regression, random forest, gradient boosting, and recurrent neural networks, were trained using longitudinal features capturing both static baseline characteristics and dynamic process indicators. Model performance was evaluated using cross-validated inferential metrics emphasizing early detection accuracy. Inferential analyses demonstrated that all machine learning models significantly outperformed chance-level prediction, with recurrent neural network models yielding the highest discriminative accuracy and sensitivity for early therapy failure detection. Dynamic process variables, particularly early-session therapeutic alliance variability, escalating conflict trajectories, emotional spillover volatility, and dyadic interaction asymmetries, showed statistically stronger predictive contributions than baseline relational characteristics. The early warning system successfully identified a substantial proportion of therapy failure cases within the first four therapy sessions, indicating robust temporal predictive validity. The findings indicate that therapy failure in high-conflict couples follows identifiable dynamic patterns that can be detected early using machine learning approaches. Implementing early warning systems in couple therapy may enable proactive, adaptive interventions that reduce dropout and non-response, thereby improving therapeutic outcomes for high-conflict couples.

The objective of this study was to examine whether deep neural network–derived indicators of emotional synchrony predict marital satisfaction among married couples in the United States. This study employed a cross-sectional, observational design with a correlational–predictive framework. A total of 286 married couples residing in the United States participated in the study. Couples completed a standardized measure of marital satisfaction and engaged in a structured dyadic interaction task during which facial expressions and vocal signals were recorded. Multimodal emotional data were extracted from synchronized facial and vocal streams and transformed into dynamic emotional synchrony indices capturing both concurrent and lagged emotional alignment between partners. These indices were used as inputs to deep neural network models specifically designed to analyze dyadic temporal data. Model training, validation, and testing were conducted at the couple level to prevent data leakage, and explainable artificial intelligence techniques were applied to identify the most influential emotional features contributing to prediction accuracy. Deep neural network models demonstrated that overall emotional synchrony significantly predicted marital satisfaction, with multimodal models outperforming unimodal models. Models incorporating both facial and vocal synchrony explained a substantially greater proportion of variance in marital satisfaction than models based on static or single-modality features. Positive affect synchrony emerged as the strongest predictor, while negative affect synchrony showed a significant inverse association with marital satisfaction. Temporal models capturing dynamic emotional alignment significantly outperformed static models, indicating the critical role of time-dependent emotional processes in marital relationships. The findings provide compelling evidence that emotional synchrony, particularly dynamic coordination of positive emotions, is a robust predictor of marital satisfaction and highlight the value of deep neural network approaches for advancing the study of emotional processes in intimate relationships.