ExSmokerApp: An End-to-End Design Framework for Digital Phenotyping and Privacy-Aware Adaptive Smoking Cessation Support

Background: Smoking cessation remains a major public health challenge, with more than 75% of individuals returning to smoking within six months of a quit attempt. Despite the growing availability of mobile health (mHealth) applications, many existing solutions lack integrated behavioral monitoring, privacy-aware data processing, and adaptive intervention mechanisms. Methods: This study presents ExSmokerApp, an end-to-end design framework for smoking cessation support and relapse prevention based on Digital Phenotyping. The framework combines a structured pre-quit preparation phase with post-quit behavioral monitoring, Ecological Momentary Assessment (EMA), and a crisis-support module. It further defines a privacy-aware edge–cloud architecture and outlines a future on-device machine learning pipeline for relapse-risk estimation and Just-in-Time Adaptive Interventions (JITAIs). Results: The ExSmokerApp design framework has been developed with a functional mobile application and supporting backend infrastructure, incorporating behavioral data collection, local storage, cloud synchronization, and preliminary technical testing. The resulting architecture establishes a methodological foundation for future machine-learning development, while predictive models, automated risk detection, and clinical effectiveness remain to be evaluated. Conclusions: ExSmokerApp establishes a clearly defined and reproducible architectural and methodological framework that other engineers and researchers can adapt and implement for smoking cessation applications. Future beta testing will assess technical feasibility, usability, behavioral data collection, and the potential for subsequent predictive-model development and validation.