1. Graham, E.J,  Elhadad, N., Albers, D. (2023) Reduced model for female endocrine dynamics: Validation and functional variations. Mathematical Biosciences.

2. Elhadad, N., Urteaga, I., Lipsky-Gorman, S., McKillop, M. (2022) User Engagement Metrics and Patterns in Phendo, an Endometriosis Research Mobile App.

3. Ensari, I., Horan, E., Elhadad, N., Bakken, S. (2022) Evaluation of a disease-specific mHealth-based exercise self-tracking measure. medRxiv.

4. Ensari, I., Lipsky-Gorman, S., Horan, E., Bakken, S., Elhadad, N. (2022) Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open.

5. Pichon, A., Jackman, K., Winkler, I., Bobel, C., Elhadad, N. (2021) The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps. Journal of the American Medical Informatics Association.

6. Pichon, A., Schiffer, K., Horan, E., Massey, B., Bakken, S., Mamykina, L., Elhadad, N. (2021) Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease. CSCW, ACM Conference on Human-Computer Interaction.

7. Ensari, I., Pichon, A., Lipsky-Gorman, S., Bakken, S., Elhadad, N. (2020) Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis. Applied Clinical Informatics.

8. Li, K., Urteaga, I., Wiggins, C.H., Druet, A., Shea, A., Vitzthum, V, Elhadad, N.  (2020) Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. Nature Digital Medicine.

9. Urteaga, I., McKillop, M., Elhadad N. (2020) Learning endometriosis phenotypes from patient-generated data. Nature Digital Medicine.

10. Pratap, A., Neto, E.C., Snyder, P., Stepnowsky, C., Elhadad, N., Grant, D., Mohebbi, M., Mooney, S., Suver, C., Wilbanks, J., Mangravite, L., Heagerty, P., Arean, P., Omberg, L. (2020) Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. Nature Digital Medicine.

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

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

13. McKillop, M., Voigt, N., Schnall, R., Elhadad, N. (2016) Exploring self-tracking as a participatory research activity among women with endometriosis. Journal of Participatory Medicine.