Presentation (CTI)

OBJECTIVES: Despite the shift toward primary data as a core study outcome, patient engagement in digital health studies has traditionally focused on app interactions and behavioral metrics. Current engagement metrics in digital health studies primarily rely on app usage frequency, task completion, or login counts, which fail to detect declining data quality driven by cognitive burden or emotional disengagement. This narrow lens overlooks the psychological and emotional drivers of sustained, high-quality participation.
We present a comprehensive Patient Engagement Metrics Framework designed to transition from behavioral interaction tracking to meaningful research participation and data quality assurance. The framework provides actionable intelligence for targeted interventions across heterogeneous patient populations.
METHODS: The framework was developed through synthesis of existing patient engagement literature and applied experience in digital health research. It integrates a four-pillar mental model comprising: (1) Application Adoption & Accessibility, (2) Behavioral Engagement, (3) Cognitive Engagement, and (4) Emotional Engagement. It proposes consistent definitions for core engagement indicators to support standardized measurement across studies
RESULTS: This framework enables: (1) early identification of at-risk participants before data quality deteriorates, (2) quantification of engagement heterogeneity across demographics and recruitment pathways, (3) benchmarking of engagement performance across studies and therapeutic areas, and (4) validation of engagement metrics as surrogate endpoints for research quality.
CONCLUSIONS: This multi-dimensional patient engagement framework operationalizes psychological science into defensible metrics, establishing industry-leading standards for digital primary data quality while advancing participant-centered research design.