Welcome to Citizen Endo

Citizen Endo is a Columbia University research project led by the Department of Biomedical Informatics in partnership with patients to better understand endometriosis.

What is Citizen Endo?

In order to study a disease, we need to know what it looks like. When it comes to endometriosis, there are many different ways in which the disease can present itself in patients. There is a disconnect between how doctors think about the disease and what patients experience on a daily basis. Bridging this gap is critical to further research on endometriosis. Citizen Endo will use a series of studies to better understand the disease from the perspective of patients.

We are academic researchers who make tools (like mobile apps) to collect health data, explore it, and generate meaningful insights. We need patients' help to make this project a success. Success means engaging patients as active participants in our research so that we can gain a comprehensive description of the disease through patient-generated health data. We also seek to help patients make sense of the data they generate.

Ongoing Research Studies


1. Phendo

Phendo is a study to track, manage, and understand endometriosis. Learn more here. Ready to get started? You can participate by downloading the app for iOS or the app for Android!




2. ENDL

ENDL is a study to understand the relationship between the menstrual cycle, pain, and quality of life. Learn more here. Ready to get started? You can participate by downloading the app for iOS!




Read previous and completed studies here.

Contact Us!

Have a question? Want to learn more? Email us at citizenendo@columbia.edu

Follow us on Facebook, Twitter, Instagram, and Medium

Our Team

Noémie Elhadad, PhD

Principal Investigator

Sharon Lipsky Gorman, MA, MS

Software Engineer

Adrienne Pichon, MPH

Research Coordinator

Emma Horan, BS

Research Coordinator

Research Publications

1. Pichon, A., Schiffer, K., Horan, E., et al. Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease. Proc. ACM Hum.-Comput. Interact. 4, 261 (2021). PDF.

2. Ensari, I., Pichon, A., Lipsky-Gorman, S., et al. Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis. Appl Clin Inform 11, 05 (2020). PDF.

3. Li, K., Urteaga, I., Wiggins, C.H. et al. Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. npj Digit. Med. 3, 79 (2020). PDF.

4. Urteaga, I., McKillop, M., Elhadad N. Learning endometriosis phenotypes from patient-generated data. npj Digit. Med. 3, 88 (2020). PDF.

5. Pratap, A., Neto, E.C., Snyder, P. Stepnowsky, C., Elhadad, N. et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. npj Digit. Med. 3, 21 (2020). PDF.

6. Urteaga, I., McKillop, M., Gorman, S., Elhadad N. Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data. in 2018 Machine Learning for Healthcare (2018). PDF.

7. McKillop, M., Mamykina, L., Elhadad, N. Designing in the Dark: Eliciting Self-Tracking Dimensions for Understanding Enigmatic Disease. ACM CHI Conference on Human Factors in Computing Systems (2018). PDF.

8. McKillop, M., Voigt, N., Schnall, R., & Elhadad, N. (2016). Exploring self-tracking as a participatory research activity among women with endometriosis. J. Participatory Med. 8, e17 (2016). HTML.