# A Gamified AI-Driven System for Depression Monitoring and Management
## 6 Conclusions  
This study presents a gamified, AI-driven system for monitoring and managing depression, integrating therapeutic micro-games, adaptive ecological momentary assessments, environmental context sensing, and machine learning analytics. Evaluated using synthetic data, the platform was rigorously tested for classification accuracy, adaptability, and research potential without immediate real-world data collection.  
Evaluation results demonstrated high classification accuracy across models, with neural networks achieving an ROC-AUC of 0.931. Feature importance analysis confirmed the relevance of cognitive, emotional, and environmental variables, validating the platform's multimodal design. Neural networks and random forest models outperformed logistic regression across all metrics. While logistic regression offered interpretability, it performed less well on both the F1 score and ROC-AUC. Temperature, humidity, and mood score were identified as the most influential features, consistent with established clinical indicators of depression.  
Beyond prediction, the system innovates in adaptive user interaction. Importantly, this system is not intended to replace specialized human intervention but to act as a complementary tool. It offers scalable, real-time support that can bridge gaps in care or assist in self-regulation between professional sessions, particularly in underserved or low-resource contexts. The longitudinal simulation of survey branching and real-time question adaptation validates the logic engine, which is crucial for sustained user engagement and high-quality data collection. Integrating environmental variables like temperature and humidity adds context-awareness to mental health assessment.  
The system's dual functionality, supporting both real-time user feedback and research experimentation, makes it a flexible platform for advancing mental health technologies.  
Future WorkThe next phase will involve real-world pilot deployments to assess system performance with live data, evaluate long-term user engagement, and improve model generalizability. Integration with passive sensing via wearables and smartphones, including objective monitoring of sleep, activity, and physiological signals, will enhance multimodal data collection. The platform's potential to address other conditions, such as anxiety and stress, will also be explored, advancing it from a conceptual prototype to a scalable clinical tool. This will support more precise, continuous monitoring and improve model robustness in real-world settings.  
We plan to conduct empirical benchmarking using established clinical metrics (e.g., PHQ-9 accuracy, engagement retention, symptom tracking sensitivity), comparing our system with tools like Woebot, Wysa, and Moodpath [17].  
Conceptualization, S.Z. and A.R.; methodology, S.Z.; software, A.R.; validation, S.Z. and A.R.; resources, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, M.N., R.S. and S.M.; visualization, S.Z. and A.R.; supervision, M.N., R.S. and S.M.; project administration, M.N. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the code being long and complex, which requires careful handling and documentation to ensure proper usage.  
The authors declare no conflicts of interest.