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Related Experiment Video

Updated: Jan 8, 2026

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility

Published on: April 5, 2024

Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models.

Bryan T Tricoche¹, Billy A Caceres², Leslee J Shaw^3,4

¹Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

BMC Women'S Health

|December 11, 2025

Summary

This summary is machine-generated.

This study identified four distinct physical activity (PA) profiles in individuals with endometriosis. Higher activity levels were linked to lower pain, but also higher fatigue in the most active group.

Keywords:

Endometriosis Fatigue Fitbit Functional data analysis Functional mixture models Pain Physical activity Symptom management Unsupervised learning Wearable devices

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Last Updated: Jan 8, 2026

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility

Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility

Published on: April 5, 2024

A Syngeneic Murine Model of Endometriosis using Naturally Cycling Mice

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Published on: November 24, 2020

Noninvasive Monitoring of Lesion Size in a Heterologous Mouse Model of Endometriosis

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Published on: February 26, 2019

Area of Science:

Endocrinology and Metabolism
Exercise Physiology
Chronic Disease Management

Background:

Endometriosis significantly impacts quality of life, causing pain and infertility.
Despite physical activity (PA) benefits, endometriosis patients are often inactive.
Understanding PA patterns is crucial for targeted interventions.

Purpose of the Study:

To investigate distinct daily physical activity (PA) trajectories in individuals with endometriosis.
To identify latent profiles of PA using clustering techniques.
To explore the relationship between PA profiles and self-reported pain and fatigue.

Main Methods:

Analyzed 30-day PA data from 171 endometriosis patients using Fitbit trackers.
Employed functional mixture models (FMMs) to identify PA trajectory clusters based on daily step counts.
Conducted exploratory analyses of pain and fatigue surveys in a subset of participants.

Main Results:

Four distinct PA profiles were identified: "High Active," "High Moderate," "Low Moderate," and "Insufficiently Active."
Higher PA volume and variability characterized the "High Active" group.
Exploratory analysis showed lower pain scores with higher activity, but the "High Active" group reported more fatigue.

Conclusions:

This study provides the first objective PA profiles for individuals with endometriosis.
Identifying distinct PA phenotypes can aid in risk stratification.
Findings can inform personalized interventions to promote PA and improve health outcomes for endometriosis patients.