A smartphone-based virtual coach designed to support people with atrial fibrillation through long-term education, medication adherence, symptom reporting, and heart rhythm monitoring
Designed and evaluated conversational health experiences that supported ongoing patient education, self-management, and longitudinal engagement.
Worked with large-scale interaction, behavioral, and monitoring data including engagement patterns, reporting behaviors, and longitudinal system-use metrics.
Applied SQL and quantitative longitudinal analysis to evaluate sustained engagement, feature utilization, retention, and user behavior over time.
Translated real-world behavioral data into UX and product insights for conversational AI, remote monitoring, and chronic disease management systems.
Atrial fibrillation is a complex chronic heart condition that requires ongoing self-management. Patients need support for understanding the condition, taking medications, recognizing symptoms, and regularly using heart rhythm monitoring tools. Many mobile health systems struggle with sustained engagement, especially beyond the first few weeks.
Voluntary-use health apps often decline in use over time, but chronic disease management requires repeated use and ongoing support.
For AF management, regular ECG readings can help patients and clinicians understand heart rhythm events and symptoms.
The design challenge was not only collecting logs, but interpreting what those logs reveal about engagement, adherence, and patient experience.
The AF virtual coach combined a smartphone-based embodied conversational agent, a portable ECG sensor, a central database, and a clinician monitoring workstation. The agent provided education, encouraged ECG use, supported medication and symptom reporting, and used social conversation and storytelling to promote sustained engagement.
Mapped healthcare workflows, patient behaviors, and engagement objectives to understand how digital interventions support long-term condition management.
Worked with large-scale interaction logs, behavioral data, and monitoring records using SQL and structured analytics workflows.
Analyzed retention, engagement trends, feature adoption, reporting behaviors, and sustained system use across extended user interactions.
Translated behavioral and quantitative findings into design insights for conversational AI, patient engagement, and digital health product strategy.
Used SQL and behavioral analytics to evaluate sustained user engagement, interaction frequency, retention patterns, and feature adoption over time.
Analyzed interaction logs, symptom reporting, medication adherence, and ECG-monitoring behaviors to understand how users engage with chronic disease management technologies.
Integrated longitudinal usage metrics, engagement trends, and patient-reported outcomes to evaluate trust, satisfaction, adherence, and system effectiveness.
Generated insights relevant to conversational AI, remote patient monitoring, adaptive engagement systems, and AI-driven chronic disease management platforms.