Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models
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. Identifying phenotypes based on within- and between-individual variance may help identify those at risk and inform the development of personalized interventions aimed at promoting PA and improving health outcomes in this population.
Authors
- Stefan Konigorski (ORCID: https://orcid.org/0000-0002-9966-6819)
- Thomas J. Fuchs (ORCID: https://orcid.org/0000-0001-7603-8687)
- Sai Kolli (ORCID: https://orcid.org/0009-0003-8464-3192)
- Ipek Ensari (ORCID: https://orcid.org/0000-0001-7745-8501)
- Leslee J. Shaw (ORCID: https://orcid.org/0000-0003-1268-1491)
- Carol Ewing Garber (ORCID: https://orcid.org/0000-0002-1268-576X)
- Billy A. Caceres (ORCID: https://orcid.org/0000-0001-6865-5546)
- Bryan Tomas Tricoche (ORCID: https://orcid.org/0000-0002-0140-2875)
Institutions
- Mount Sinai Health System (US)
- Hasso Plattner Institute (DE)
- University of Potsdam (DE)
- Columbia University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- BMC Women s Health
- Published
- 2025-12-11
- DOI
- https://doi.org/10.1186/s12905-025-04087-2
- Primary Topic
- Endometriosis Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00