Predictive Identification of Engagement Decline in Digital Therapeutic Interventions

Predictive Identification of Engagement Decline in Digital Therapeutic Interventions presents a computational framework for detecting early indicators of declining user engagement within digital health and digital therapeutic platforms. Sustained engagement is a major challenge for digital therapeutics, particularly in long-term behavioral health, rehabilitation, and preventative care programs. This research explores how behavioral interaction data such as session frequency, response latency, completion patterns, and user interaction trends can be analyzed to identify early warning signals of engagement decline. The proposed framework leverages predictive analytics to enable earlier intervention strategies that can help maintain adherence and improve clinical outcomes. The poster outlines the conceptual architecture of the predictive model, the behavioral features used for engagement analysis, and the potential clinical applications of engagement monitoring systems. Early identification of disengagement may allow healthcare providers and digital health platforms to adapt therapeutic interventions, personalize treatment pathways, and improve long-term patient outcomes. This work contributes to the fields of digital health, behavioral analytics, and machine learning-driven healthcare systems.