ArticleJournal of pain research2025
Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders.
Article in Journal of pain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Associations between anxiety, somatic pain and sleep disturbances in major depressive disorder and chronic pain: A cross-sectional study with gender-specific insights.General psychiatry · 2026Article
- Robust forecasting of sedentary bouts in chronic pelvic pain disorders for on-device learning and real-time deployment.npj women's health · 2026Article
- Physical activity phenotypes in endometriosis using unsupervised learning via functional mixture models.BMC women's health · 2025Article
- Cancer therapy and cachexia.The Journal of clinical investigation · 2025Review
- Insights into endometriosis symptom trajectories and assessment of surgical intervention outcomes using longitudinal actigraphy.NPJ digital medicine · 2025Article
- Self-reported and tracker-estimated physical activity outcomes in women with chronic pelvic pain disorders: A longitudinal evaluation of construct validity.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Background: Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women worldwide and are characterized by psychosocial comorbidities, including a reduced quality of life and 2-10-fold increased risk of depression and anxiety. Despite its prevalence and morbidity, CPPDs are often inadequately managed with few patients experiencing relief from any medical intervention. Characterizing mental health symptom trajectories and lifestyle predictors of mental health is a starting point for enhancing patient self-efficacy in managing symptoms. Here, we investigate the association between mental health, pain, and physical activity (PA) in females with CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data. Methods: The study sample included 4270 person-level days and 799 person-level weeks of data from CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) and physical functioning and pain weekly for 14 weeks using a research mHealth app, and moderate-to-vigorous PA (MVPA) was passively collected via activity trackers. Data Analysis: We used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA and weekly pain outcomes while adjusting for baseline measures, time in study, and the random intercept of the individual. We converted 7-day MVPA data into a single smooth using spline basis functions to model the potential non-linear relationship. Results: MVPA was a significant, curvilinear predictor of GMH-T (F=18.989, p<0.001), independent of pain measures and prior psychiatric diagnosis. Physical functioning was positively associated with GMH-T, while pain was negatively associated with GMH-T (B=2.24, B Conclusion: These findings suggest that engaging in MVPA is beneficial to the mental health of females with CPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data combined with functional models for delineating inter-individual and temporal variability.
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