Featured Media, Interviews, and Research Coverage
Selected public-facing coverage related to my research in women’s health, chronic pelvic pain, digital health, and AI-enabled approaches to patient-generated data.
Exercise and pain in endometriosis
- ENDOUbt / Eureka Health— September 12, 2024
“How can I keep exercising when endometriosis pain makes movement hard?” – Expert-informed public-facing article citing research on exercise and symptom management in endometriosis.
- EndoNews — September 1, 2022
“Endometriosis-related pain symptoms fade with regular exercise” – Patient-facing coverage related to research on exercise and symptom experience in endometriosis.
Published in BMJ Open.
Prolonged sitting, movement breaks, and cardiometabolic health
- SciTech Daily — February 24, 2023
- Columbia University Newsroom — January 12, 2023
Published in Medicine & Science in Sports & Exercise
Selected Publications
My scholarship focuses on women’s reproductive health, chronic pelvic pain, digital health, and AI-enabled approaches to patient-generated data. Below is a curated selection of publications that reflects the central themes of my work. For a complete bibliography, please visit my external publication profiles.
“Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence”
Computer Human Interactions 2026, Barcelona, Spain.
Findings provide actionable levers for: designers of mHealth apps (timing of prompts, onboarding), researchers setting mHealth-based tracking protocols (flexible vs fixed times), and clinicians using self-tracking as part of chronic symptom management.
“Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders.”
Journal of Pain Research, 2025.
These findings suggest that engaging in MVPA is beneficial to the mental health of females with CPPDs. Additionally, this study demonstrates the potential of ambulatory mHealth-based data combined with functional models for delineating inter-individual and temporal variability.
“Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis”
Applied Clinical Informatics, 2021
For enigmatic diseases like endometriosis, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.
“Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation”
BMJ Open, 2022
Regular exercise might influence the association of pain symptoms with exercise. These findings can inform exercise recommendations for endometriosis pain management, especially for those at greater risk of lack of regular exercise.
“Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models”
BMC Women’s Health, 2026
This is the first study to investigate and report distinct PA profiles among a nationally-representative sample of individuals living with endometriosis using objectively-estimated PA. Higher-activity phenotypes report lower pain scores, and higher fatigue scores.
“Phenotyping Adolescent Endometriosis: Characterizing Symptom Heterogeneity Through Note- and Patient-Level Clustering”
medRxiv, 2025
This is the first study to directly compare note-and patient-level clustering of EHR notes in endometriosis and detect less clinically recognizable phenotypes. Our unsupervised learning models identify 3 phenotypes: classic, GI-dominant, “feature-absent”, distinguished by hormonal intervention and pain medication use.
For interviews, speaking invitations, or research inquiries
Please use the Contact page or my Mount Sinai profile for the most current contact information and institutional details.