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.
October 19, 2020
Women Tech Charge
"Endometriosis and How AI Can Help"
January 17, 2020
"Phendo: Understanding Endometriosis"
June 19, 2019
CBS This Morning
"How endometriosis disrupts women's lives"
March 28, 2019
Good Morning America
"Why does it take so long for women to be diagnosed with endometriosis?"
March 21, 2019
"This Endometriosis App Can Change the Way We View Women's Health"
March 14, 2019
"Comment vous pouvez aider la recherche pour l'endométriose"
March 6, 2019
BBC World Service
"Can women's health apps help treat endometriosis?"
March 1, 2019
"This professor suffers from a mystery disease, so she developed an app to track its effects"
August 1, 2018
Nature Medicine News
"Discovery cycle: Period-tracking apps offer new insights on women’s health"
"From Code to Cure"
June 23, 2017
"Citizen Endo’s Goal: 10,000 Women Using Its Mobile App to Track Endometriosis by 2018"
May 5, 2017
Columbia University Medical Center News
"With Columbia App, Women with Endometriosis Become Citizen-Scientists"
Read previous and completed studies here.
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.