KMAN Publication Inc. (KMANPUB) Journal of Adolescent and Youth Psychological Studies (JAYPS) 2981-2526 7 Serial Number 44 2026 04 10 Predicting Adolescent Depressive Symptom Severity from Rumination, Sleep Variability, and Heart-Rate Variability Using Multimodal Deep Learning Predicting Adolescent Depressive Symptom Severity from Rumination, Sleep Variability, and Heart-Rate Variability Using Multimodal Deep Learning 1 11 10.61838/kman.jayps.5214 EN Journal Article 2025 10 16 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The objective of this study was to develop and evaluate a multimodal deep learning architecture capable of predicting adolescent depressive symptom severity by dynamically integrating static cognitive rumination scores with continuous, time-series sequences of sleep variability and heart-rate variability.</p> <p><strong>Methods and Materials: </strong>A two-week prospective observational study was conducted involving &nbsp;adolescents (ages ) from urban and semi-urban districts of Lagos, Nigeria. Baseline cognitive vulnerability and depressive symptoms were assessed via self-report using the Ruminative Responses Scale (RRS) and the Patient Health Questionnaire for Adolescents (PHQ-A). Continuous physiological data, specifically nocturnal sleep variability (standard deviation of total sleep time) and heart-rate variability (HRV; specifically RMSSD), were collected continuously using wrist-worn actigraphy and photoplethysmography (PPG) devices. Data analysis was executed using a hybrid multimodal deep learning architecture featuring late fusion, which utilized Long Short-Term Memory (LSTM) networks to process the physiological time-series data and a Multilayer Perceptron (MLP) for the static cognitive data. The model was trained using the Adam optimizer to minimize Mean Squared Error and evaluated utilizing -fold cross-validation.</p> <p><strong>Findings: </strong>The final sample had a mean age of years ( ), with baseline PHQ-A scores of ( ) and baseline RRS scores of . Bivariate analyses indicated that follow-up depression severity was significantly predicted by rumination ( ), sleep variability ( ), and nocturnal RMSSD ( ). The multimodal late-fusion network demonstrated exceptional predictive accuracy ( , ), substantially outperforming all isolated unimodal models (Static MLP ; Sleep LSTM ; HRV LSTM ). Ablation studies confirmed the necessity of all modalities and temporal dynamics; excluding the rumination feature caused a significant performance drop ( ), and replacing the time-series physiological sequences with aggregated -day averages resulted in an identical loss of predictive power ( ).</p> <p><strong>Conclusion: </strong>Fusing objective, time-series physiological biomarkers with cognitive vulnerability profiles via multimodal deep learning provides a highly accurate and transformative computational framework for the proactive risk stratification and early clinical intervention of adolescent depression.</p> </td> </tr> </tbody> </table> https://journals.kmanpub.com/index.php/jayps/article/download/5214/9468