Research and Practice in Couple Therapy

This study aimed to examine and classify trauma-related emotional patterns in couples facing PTSD using multimodal deep learning models to predict high-risk emotional episodes during conflict interactions. The study employed a cross-sectional design involving 112 couples (224 individuals) from the United States in which one partner met diagnostic criteria for PTSD. Each couple participated in a 40-minute conflict-discussion task recorded through synchronized audio, video, and physiological sensors. Linguistic transcripts, facial expressions, acoustic features, and autonomic indicators (electrodermal activity, heart-rate variability, and peripheral temperature) were extracted and temporally aligned. These modalities were analyzed using a multimodal transformer architecture integrating text embeddings, CNN-LSTM visual features, physiological time-series data, and acoustic spectral representations. Additional analyses included t-SNE–based latent clustering of emotional patterns and risk-surface modeling of predicted high-risk episodes as a nonlinear function of physiological arousal and negative emotional language. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC metrics with ten-fold stratified cross-validation. The multimodal transformer significantly outperformed baseline and unimodal models in predicting high-risk PTSD-related emotional episodes (AUC = .90; F1 = .83; accuracy = .86; p < .001 vs. clinical baseline). All unimodal models performed above chance but remained significantly weaker than the multimodal approach (p < .01). Latent clustering revealed four statistically distinct emotional interaction patterns (hyperaroused escalation, avoidant disengagement, mixed volatile–repairing, and numbed coexistence), each differing significantly in PTSD severity, relationship quality, and proportion of high-risk segments (all ps < .05). Nonlinear risk-surface analysis demonstrated a strong interaction effect between negative emotional language and physiological arousal in predicting emotional risk (βinteraction = .41, p < .001). Multimodal deep learning provides a highly sensitive and integrative method for identifying trauma-related emotional risk states in couples facing PTSD, revealing clinically meaningful emotional clusters and nonlinear escalation patterns that may inform assessment and intervention.

This study aims to identify the most influential psychological, communicative, and digital-behavioral predictors of relationship burnout in couples using machine learning feature-importance analysis. A cross-sectional predictive design was implemented with a sample of 206 couples (412 individuals) from Turkey, collected through online recruitment. Participants completed validated measures assessing emotional exhaustion, communication avoidance, stress and emotional dysregulation, relational motivation, conflict behaviors, sexual and relational satisfaction, and digital interaction patterns such as shared online activity and response latency during conflictual exchanges. Data preprocessing included normalization, encoding, missing-value correction, and outlier management. Multiple machine learning models—random forests, gradient-boosted trees (XGBoost), multilayer perceptron networks, support vector machines, and logistic regression—were trained on a stratified 80/20 train–test split. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC. Feature importance was assessed using permutation importance, model-specific variable importance scores, and SHAP (SHapley Additive exPlanations) values to identify consistent early warning indicators. XGBoost achieved the highest predictive performance (Accuracy = 0.89, AUC = 0.94), followed by random forests (Accuracy = 0.86, AUC = 0.91). SHAP analysis revealed emotional exhaustion as the strongest predictor, followed by communication avoidance, response latency during conflict, emotional dysregulation, weekly conflict episodes, relational motivation, and digital disengagement ratio. Interaction effects showed that high emotional exhaustion combined with high communication avoidance produced a multiplicative increase in predicted burnout probability, confirming nonlinear relational deterioration patterns captured by the models. Machine learning modeling effectively identified early warning signs of relationship burnout, demonstrating that emotional, communicative, and digital-behavioral variables jointly predict relational decline. These findings highlight the need for integrating computational analytics into clinical screening and preventive relationship interventions.

This study aimed to identify and compare generational differences in the predictors of relationship stability using a large-scale machine-learning framework applied to adults in the United States. This cross-sectional study analyzed data from 4,812 adults representing Baby Boomers, Generation X, Millennials, and Generation Z. Participants completed a multidimensional online assessment capturing psychological, relational, socioeconomic, and digital-behavioral variables. Quantitative scales measured communication clarity, emotional support, attachment anxiety, conflict recovery time, financial stress, and additional relational factors, while open-ended items provided qualitative textual data. Preprocessing included imputation, normalization, categorical encoding, and NLP-based embedding of narrative responses. Machine-learning models—including gradient-boosted trees and random forest algorithms—were used to predict relationship stability, with performance evaluated via accuracy, AUC, precision, recall, and F1-scores. SHAP analysis was conducted to interpret feature importance globally and within generational subgroups. Machine-learning models achieved strong predictive performance across generations (AUC range: 0.84–0.92). SHAP values revealed significant generational differences: communication clarity was the strongest predictor for Baby Boomers and Generation X, financial stress and attachment anxiety were dominant predictors for Millennials and Gen Z, and digital comparison exposure showed sharply increasing influence from older to younger cohorts. Relationship length exhibited high predictive value for older generations but minimal influence among younger adults. Across all models, higher emotional support and shorter conflict recovery time significantly increased predicted relationship stability, while elevated financial stress and attachment anxiety significantly reduced stability probabilities. Generational cohorts differ markedly in the factors that predict relationship stability, with younger adults exhibiting heightened sensitivity to financial strain, emotional insecurity, and digital comparison pressures. Machine-learning modeling reveals that relationship stability is not governed by universal predictors but instead emerges from generationally distinct psychological, socioeconomic, and technological influences.

This study aimed to determine whether natural language patterns extracted from early couple therapy sessions can accurately predict premature therapy dropout. The study used a mixed quantitative–qualitative design involving 148 couples from Canada who participated in community, private, and university-based couple therapy settings. High-quality audio recordings of the first two therapy sessions were transcribed and preprocessed using advanced natural language processing techniques, including tokenization, lemmatization, turn-level segmentation, and affective, syntactic, and semantic feature extraction via transformer-based models. Communication variables such as emotional disengagement, interruption frequency, partner-focused pronoun use, and demand–withdraw sequences were quantified. Machine-learning models—logistic regression, random forest, gradient boosting, Bi-LSTM networks, and transformer architectures—were trained to predict dropout, defined as termination prior to session four without therapist-approved discontinuation. Model performance metrics included accuracy, precision, recall, F1 score, and ROC-AUC. SHAP values were used to interpret model-level decision patterns. Reflexive thematic analysis of therapist notes complemented quantitative findings to contextualize communication deficits. Inferential analyses revealed significant differences between dropout and treatment-completion groups across multiple linguistic variables, including higher negative affect, lower partner-focused pronouns, greater interruption frequency, and elevated demand–withdraw cycles among dropout couples (all p < .001). Transformer-based models achieved the strongest predictive accuracy (92%) and highest ROC-AUC (0.96), outperforming all traditional and neural baselines. SHAP interpretability demonstrated that emotional disengagement markers, interruption frequency, topic abruptions, and conversational asymmetry were the most influential predictors of dropout. Communication reciprocity declined over time in dropout couples, whereas it increased in treatment completers. Early-session communication deficits captured through natural language processing serve as powerful predictors of premature dropout in couple therapy. Incorporating automated linguistic assessment tools into routine clinical practice may enable earlier identification of at-risk couples and support targeted intervention strategies to reduce attrition.

The objective of this study was to determine whether deep neural networks can accurately predict couple therapy outcomes using only pre-treatment psychological and relational assessments. This quantitative predictive study analyzed pre-treatment data from 176 Canadian couples seeking therapy for relational distress, communication difficulties, or emotional disconnection. Participants completed standardized intake measures including relationship satisfaction, communication patterns, emotional symptoms, attachment orientations, and dyadic demographic variables. All data were preprocessed, normalized, and transformed into dyadic-level and discrepancy-level features. A deep neural network was developed using TensorFlow/Keras, optimized via hyperparameter tuning, and evaluated against baseline machine-learning models. The dataset was split into training, validation, and test subsets using a couple-level 70/15/15 partition to preserve dyadic independence. The deep neural network demonstrated superior predictive accuracy compared to random forest, support vector regression, and linear regression models, achieving an R² of .71 on the test set. SHAP analyses revealed that relationship satisfaction, demand–withdraw patterns, attachment avoidance, partner stress discrepancy, and constructive communication were the strongest predictors of therapy outcomes. Inferential patterns indicated significant non-linear interactions between emotional symptoms and communication variables, with higher improvement predicted for couples displaying lower avoidance, greater baseline cohesion, and smaller dyadic emotional discrepancies. Predicted-versus-actual outcome comparisons showed strong convergence, with minimal dispersion around the diagonal line of fit. Deep neural networks offer a powerful method for predicting couple therapy outcomes using intake assessments, capturing the complex non-linear dynamics inherent in relational functioning. By identifying key pre-treatment predictors such as satisfaction, communication patterns, attachment profiles, and dyadic discrepancies, these models can support personalized treatment planning and enhance clinical decision-making. The findings underscore the promise of computational approaches in advancing precision-based psychological interventions for couples.

The objective of this study is to develop and validate machine learning models capable of accurately identifying couples at early risk for emotional disengagement based on psychological, behavioral, and communication-related indicators. This predictive cross-sectional study included 156 couples (N = 312 individuals) from across Canada who completed validated measures assessing emotional disengagement, relationship satisfaction, conflict frequency, attachment dimensions, daily supportive behaviors, and communication responsiveness. Data were collected through secure online surveys, and dyadic datasets were prepared using actor–partner structures to preserve relational interdependence. Five machine learning algorithms—logistic regression, random forest, gradient boosting, support vector classifier, and a neural network model—were trained on cleaned and normalized datasets, with performance evaluated through accuracy, precision, recall, F1 score, and area under the ROC curve. Cross-validation procedures ensured the robustness and generalizability of the models. Inferential analyses demonstrated that gradient boosting achieved the highest predictive performance (AUC = 0.94), followed closely by neural networks (AUC = 0.93) and random forests (AUC = 0.91). All models significantly outperformed logistic regression and support vector classifiers, indicating the superiority of non-linear approaches for capturing subtle relational patterns. Emotional withdrawal was the strongest predictor across all models (p < .001), followed by daily supportive behaviors, communication responsiveness, attachment avoidance, and conflict frequency. Demographic variables showed no significant predictive contribution (all p > .05). Confusion matrix analyses revealed that gradient boosting produced the lowest false-negative rate, confirming its utility for early detection. The study demonstrates that machine learning models, particularly gradient boosting and neural networks, can accurately detect early signs of emotional disengagement in couples by integrating relational, emotional, and behavioral indicators. These findings highlight the potential of computational approaches to support preventive interventions and enhance clinical assessment in couple therapy.

This study aimed to examine the predictive roles of narcissistic traits and emotional coldness in determining levels of marital empathy among married adults. A correlational descriptive design was employed using a sample of 393 married individuals from Tunisia, selected based on Morgan and Krejcie’s sample size determination table. Participants completed standardized self-report questionnaires measuring narcissistic traits, emotional coldness, and marital empathy. Data were analyzed using SPSS version 27. Descriptive statistics were calculated, followed by Pearson correlation to examine bivariate associations and multiple linear regression to assess the predictive power of independent variables on marital empathy. Assumptions for regression analysis were tested and confirmed prior to analysis. Pearson correlation results revealed significant negative relationships between marital empathy and both narcissistic traits (r = -0.42, p < .01) and emotional coldness (r = -0.58, p < .01). A moderate positive correlation was also found between narcissistic traits and emotional coldness (r = 0.46, p < .01). The regression model was significant, F(2, 390) = 126.45, p < .001, accounting for 40% of the variance in marital empathy (R² = 0.40). Both narcissistic traits (β = -0.29, p < .001) and emotional coldness (β = -0.45, p < .001) emerged as significant negative predictors, with emotional coldness having a stronger effect. The findings underscore the detrimental effects of narcissistic traits and emotional coldness on marital empathy. Emotional coldness, in particular, is a stronger predictor of empathic deficits in marital relationships. These results highlight the need for interventions that target emotional responsiveness and personality-driven barriers to empathy in couples therapy and relationship counseling.

This study aimed to investigate the predictive roles of spiritual closeness and life goals alignment in marital identity fusion among married individuals in Egypt. A correlational descriptive design was employed with a sample of 400 married participants selected based on the Morgan and Krejcie sampling table. Standardized instruments were used to measure marital identity fusion, spiritual closeness, and life goals alignment. Data were analyzed using Pearson correlation and multiple linear regression via SPSS version 27. All assumptions of normality, linearity, multicollinearity, and homoscedasticity were tested and met prior to conducting inferential analyses. Pearson correlation analysis showed that marital identity fusion was positively and significantly correlated with both spiritual closeness (r = .64, p < .001) and life goals alignment (r = .59, p < .001). The multiple regression model was statistically significant, F(2, 397) = 182.71, p < .001, explaining 48% of the variance in marital identity fusion (R² = .48). Both spiritual closeness (β = .44, t = 7.83, p < .001) and life goals alignment (β = .38, t = 7.14, p < .001) were significant predictors, indicating that higher levels of these relational dimensions are associated with stronger psychological identity integration between spouses. The findings underscore the importance of spiritual closeness and life goals alignment in fostering marital identity fusion. These variables contribute significantly and independently to the psychological merging of partners in marital relationships. The results highlight the value of integrating spiritual and aspirational congruence into marital counseling and educational interventions to enhance marital cohesion and stability.