Trajectories of mHealth-tracked mental health symptoms and their predictors in chronic pelvic pain

Background.Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women worldwide and are characterized by psychosocial comorbidities, including 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 to 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. Method.The study sample included 4,270 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. ResultsMVPA was a significant, curvilinear predictor of GMH-T (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 ({beta}=2.24, {beta}=-1.16, respectively; p<0.05). ConclusionThese 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.