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This work introduces the CAST framework to connect measurement choices with person-specific models of exposure, behavior, physiology, and experience and proposes four synchronized measurement modules linking mobile and wearable data with self-reports and intervention responses.
An in-the-wild study of task planning using time-stamped logs of 24,265 tasks from 957 users of a widely used daily planner app, observed over six weeks alongside self-reported surveys to support evaluating planning tools across the task lifecycle.
Recursive interaction with time-series data enables TimeRLM to achieve unprecedented accuracy in anomaly localization, outperforming traditional models by a wide margin.
The ABC framework reveals how thoughtful design can transform digital health interventions from theoretical solutions into sustainable real-world applications.
By utilizing autonomous, LLM-driven coaching, modular software design, and cross-platform accessibility, the next-generation My Heart Counts smartphone application-based study will provide a scalable model for inclusive and decentralized preventive care of patients with cardiovascular disease.
