This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2832) Mobile Health (mhealth) (3164) Obstetrics, Gynecology, and Reproductive Medicine (279)

Published on in Vol 4, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15094, first published .
Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment

Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment

Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment

Authors of this article:

Erika Marie Rodriguez1, 2 Author Orcid Image ;   Daniel Thomas3 Author Orcid Image ;   Anna Druet3 Author Orcid Image ;   Marija Vlajic-Wheeler3 Author Orcid Image ;   Kevin James Lane4 Author Orcid Image ;   Shruthi Mahalingaiah1, 2, 5 Author Orcid Image
Article Authors Cited by (11) Tweetations (4) Metrics

Journals

  1. Zheng C, Yu Y, Bai X. Polycystic ovary syndrome and related inflammation in radiomics; relationship with patient outcome. Seminars in Cell & Developmental Biology 2024;154:328 View
  2. Stujenske T, Mu Q, Pérez Capotosto M, Bouchard T. Survey Analysis of Quantitative and Qualitative Menstrual Cycle Tracking Technologies. Medicina 2023;59(9):1509 View
  3. Barrera F, Brown E, Rojo A, Obeso J, Plata H, Lincango E, Terry N, Rodríguez-Gutiérrez R, Hall J, Shekhar S. Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review. Frontiers in Endocrinology 2023;14 View
  4. Adnan T, Li H, Peer K, Peebles E, James K, Mahalingaiah S. Evaluation of Menstrual Cycle Tracking Behaviors in the Ovulation and Menstruation Health Pilot Study: Cross-Sectional Study. Journal of Medical Internet Research 2023;25:e42164 View
  5. Peven K, Wickham A, Wilks O, Kaplan Y, Marhol A, Ahmed S, Bamford R, Cunningham A, Prentice C, Meczner A, Fenech M, Gilbert S, Klepchukova A, Ponzo S, Zhaunova L. Assessment of a Digital Symptom Checker Tool's Accuracy in Suggesting Reproductive Health Conditions: Clinical Vignettes Study. JMIR mHealth and uHealth 2023;11:e46718 View
  6. Ramaiyer M, El Sabeh M, Zhu J, Shea A, Segev D, Yenokyan G, Borahay M. The association of COVID-19 vaccination and menstrual health: A period-tracking app-based cohort study. Vaccine: X 2024;19:100501 View
  7. Graca S, Alloh F, Lagojda L, Dallaway A, Kyrou I, Randeva H, Kite C. Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review. Healthcare 2024;12(16):1671 View
  8. Sacca L, Lobaina D, Knopf E, Burgoa S, Jimenez S, Okwaraji G, Etzel M, Vardanyan V, Tharp M, Rao M, Jhumkhawala V, Sohmer J, Densley S, Linzer N, Meka P, Diaz D, Knecht M, Hopkins D, Kitsantas P, Mejia M, Wilson C. Assessment of the Validity and Quality of Polycystic Ovarian Syndrome (PCOS) Screening Tools Available for Women Globally: A Systematic Review. Clinics and Practice 2024;14(5):1625 View

Books/Policy Documents

  1. Korte L. Handbuch Digitale Gesundheitswirtschaft. View
  2. Vemulavada S, Karthikvatsan S, Babu A, Kadalmani B, Devi T, Sasipraba T, Manikkam R, Parthasarathy K, Balaji V. Translational Research in Biomedical Sciences: Recent Progress and Future Prospects. View

Conference Proceedings

  1. Vasavi R, Priscilla Prathibha S, Valiveti H, Maringanti S, Parsa A. 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). Polycystic Ovary Syndrome Monitoring using Machine Learning View