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

Bryan Tomas Tricoche, Bryan T Tricoche, Billy A Caceres, Leslee J Shaw, Carol Ewing Garber, Stefan Konigorski, Sahiti Kolli, Sai Kolli, Thomas J Fuchs, Ipek Ensari
BMC women's health · 2025 · vol. 26(1) , pp. 29 · doi:10.1186/s12905-025-04087-2 · PMID:41382090 · PMC12802236 · W4417297144
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⚙ AI-generated summary by claude@2026-06, 2026-06-10 ⓘ

This study used functional mixture models to identify four distinct physical activity profiles in adults with endometriosis, finding that higher activity levels correlated with lower pain but not necessarily lower fatigue.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This study analyzed objectively measured daily physical activity in 171 adults with confirmed endometriosis from the NIH All of Us Research Program, using Fitbit wrist-worn trackers over 30 consecutive days (4,795 person-level days), with missing days imputed via multiple imputed chained equations. Using functional mixture models on daily step-count trajectories, the authors identified four latent physical activity phenotype clusters (chosen by Bayesian Information Criterion): “High Active,” “High Moderate,” “Low Moderate,” and “Insufficiently Active,” which differed in both activity volume and variability. Exploratory comparisons using PROMIS pain and fatigue in a subset of 129 participants suggested higher-activity phenotypes generally reported lower pain, while the “High Active” group had the highest proportion of individuals reporting severe-to-moderate fatigue. The paper’s caveats include reliance on Fitbit proprietary intensity algorithms and the exploratory, limited subset nature of symptom questionnaire analyses. This paper is centrally about endometriosis — it identifies distinct physical activity trajectory phenotypes in people with endometriosis using functional mixture modeling of wearable-derived step data.

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Abstract

BACKGROUND: Endometriosis is a chronic condition associated with severe pelvic pain, dysmenorrhea, infertility, and worsening quality of life. Regular physical activity (PA) is effective for pain management and reducing chronic disease symptoms, yet individuals with endometriosis are more likely to be insufficiently active. This study investigated latent profiles of daily PA trajectories in this population via clustering. METHODS: We analyzed 171 adults (4,795 person-level days) with a confirmed diagnosis of endometriosis enrolled in the All of Us Research Program. PA data were collected from participants using Fitbit wrist-worn trackers. We used 30 consecutive days of data from each individual, allowing up to 10 days of missingness, imputed using multiple imputed chained equations. Functional mixture models (FMMs) were used to identify latent PA trajectory clusters using daily step counts as the outcome variable. The optimal number of clusters was selected via Bayesian Information Criterion (BIC). Exploratory analyses of PROMIS pain and fatigue surveys were conducted in a subset of 129 participants who completed the surveys after their PA time windows. RESULTS: FMM-identified profiles differed both with respect to PA volume and variability. Combinatory model fit indices supported a 4-cluster (K = 4) solution. The "High Active" phenotype exhibited the highest volume and variability of daily step counts and moderate-to-vigorous PA (MVPA) minutes over the sampling period (Steps: Mean (SD) = 12918.8 (5606.4); MVPA: Mean (SD) = 75.2 (64.6)). The "High Moderate" phenotype exhibited the second highest activity (Steps = 9283.9 (3661.2); MVPA = 58.2 (59.6)), followed by "Low Moderate" (Steps = 6234.0 (2515.8); MVPA = 18.6 (32.3)), and "Insufficiently Active" (Steps = 4317.1; MVPA = 17.2 (28.9)). Exploratory analyses revealed that higher-activity phenotypes tended to report lower pain scores. However, the "High Active" phenotype had the highest proportion of individuals reporting severe to moderate fatigue. CONCLUSION: 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.

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Condition tags

endometriosisdysmenorrheainfertility

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (44)

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