Physical Activity Phenotypes in Endometriosis Using Unsupervised Learning via Functional Mixture Models

IntroductionEndometriosis 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 aims to investigate latent profiles of daily PA trajectories in this population via clustering. MethodsThe study sample included 173 adults (4,895 person-level days) with a confirmed diagnosis of endometriosis enrolled in the All of Us Research Program. PA data was 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 (which were imputed using multiple imputed chained equations). Functional mixture models (FMMs) identified latent PA trajectory clusters using daily step counts as the clustering variable. The optimal number of clusters was determined using the Bayesian information criterion (BIC). ResultsThe best-fitting FMM identified K=4 distinct clusters (BICK=4 = -11884.442, BICK=3 = -11892.236, BICK=5 = -11907.917). The High Active phenotype exhibited the highest volume and variability of step counts over the sample period (Mean = 12777.0, SD=5248.2) as well as the highest volume of moderate-to-vigorous PA (MVPA) minutes (Mean = 70.8). The High Moderate phenotype exhibited the second highest volume and variability of step counts (Mean = 9125.4, SD = 3631.0) and second highest MVPA minutes. The Low Moderate phenotype exhibited the second lowest volume and variability of step counts (Mean = 6067.5, SD =2797.0) and the second lowest MVPA minutes (Mean = 24.3). The Insufficiently Active cluster exhibited the lowest volume and variability of step counts (Mean = 4235.7, SD = 2174.7) and the lowest MVPA (Mean = 16.1). ConclusionThis is the first study to investigate and report distinct PA profiles among a national sample of individuals living with endometriosis, and via objectively estimated PA data. Identifying distinct clusters of PA patterns in endometriosis based on within- and between individual PA variance may help identify those at risk and inform the development of personalized interventions to promote PA and improve overall health outcomes.