Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders

Journal of Pain Research (Feb 2025) Trajectories of mHealth-Tracked Mental Health and Their Predictors in Female Chronic Pelvic Pain Disorders Abstract Emily L Leventhal,1,2 Nivedita Nukavarapu,1,2 Noemie Elhadad,3 Suzanne R Bakken,4 Michal A Elovitz,5 Robert P Hirten,1,2,6 Jovita Rodrigues,1,2 Matteo Danieletto,1,2 Kyle Landell,1,2 Ipek Ensari1,2 1Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 2Hasso Plattner Institute for Digital Health Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 3Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 4Columbia University School of Nursing, Columbia University Irving Medical Center, New York, NY, USA; 5Department of Obstetrics, Gynecology and Reproductive Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 6The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USACorrespondence: Ipek Ensari, Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA, Email [email protected]: 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=− 1.16, respectively; p< 0.05).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.Keywords: chronic pelvic pain, digital health, functional data modeling, global mental health