Deep Learning Prediction of Emotional Dysregulation in Adolescents Based on Social Media Use Patterns, Sleep Quality, and Parental Attachment Styles - Hovhannisyan, Arman; Restrepo, Daniela; Tsiklauri, Nino (Journal of Adolescent and Youth Psychological Studies (JAYPS), 2026)
Objective: The objective of this study was to develop and validate a deep learning model for predicting emotional dysregulation in Colombian adolescents using social media use patterns, sleep quality, and parental attachment styles. Methods and Materials: This quantitative, cross-sectional predictive study was conducted among 842 adolescents aged 13–18 years from secondary schools in Bogotá, Medellín, and Cali, Colombia. Participants completed standardized measures of emotional dysregulation, sleep quality, and parental attachment, and objective social media usage metadata were collected via smartphone monitoring over a 14-day period. Data were preprocessed and analyzed using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture. Model performance was evaluated using mean absolute error, root mean square error, coefficient of determination, area under the receiver operating characteristic curve, and classification accuracy. Explainable artificial intelligence techniques were applied to determine the relative importance of predictors. Findings: The deep learning model demonstrated high predictive performance (R² = .79; RMSE = 5.37; AUC = .91; classification accuracy = 87.6%). Emotional dysregulation was significantly associated with social media use (r = .58, p < .001), sleep quality (r = .62, p < .001), and parental attachment (r = −.55, p < .001). Feature importance analysis identified sleep quality (32.4%), nocturnal screen exposure (21.8%), emotional dependency on social media (18.6%), parental attachment security (15.3%), and daily social media duration (11.9%) as the most influential predictors. Conclusion: The findings indicate that emotional dysregulation in adolescence is strongly shaped by the interaction of digital behavior, sleep processes, and attachment relationships, and that deep learning models offer a powerful tool for early identification of adolescents at elevated emotional risk.