Research

Collaborative Research

I am passionate about applying contemporary research designs and advanced statistical methodologies to advance nursing and health sciences, with a focus on tackling the methodological challenges inherent in real-world data.

• Symptom Cluster Research
  My work focuses on disentangling the complex relationships among co-occurring symptoms to identify heterogeneous patient subgroups that share similar symptom patterns. Using advanced statistical approaches, including various latent variable models and factor analysis, I analyze both cross-sectional and longitudinal data to capture how symptom clusters emerge and evolve over time. This line of research provides critical insights into the underlying mechanisms driving symptom co-occurrence and persistence, laying the groundwork for more precise and targeted interventions that address multiple symptoms simultaneously. To date, my research has focused on Metabolic syndrome, Pain, Postpartum symptoms and Traumatic brain injury. Since this analytic framework is broadly applicable, I welcome collaborations in other disease areas where understanding patient and symptom heterogeneity can inform more personalized and effective symptom management strategies.

• Mobile Health
  My research in digital health focuses on harnessing intensive longitudinal data (ILD) from mobile and wearable devices to better understand and predict health behaviors and outcomes. Specifically, I am interested in:

  • Developing prediction models based on ILD to capture dynamic, within-person changes over time
  • Characterizing engagement patterns in digital health interventions and examining how these patterns relate to patient outcomes

Through this work, I aim to translate high-frequency digital data into actionable insights that enhance personalization, sustain engagement, and ultimately improve health outcomes. Currently, I am working on interventions that use mobile health devices to support chronic disease management. I welcome collaborations in other disease areas that leverage intensive data and prediction modeling to advance digital health research and practice.