RESUMEN
PURPOSE: This study tested the hypothesis that ecological momentary assessment (EMA) of pelvic pain (PP) and urinary urgency (UU) would reveal unique Urologic Chronic Pelvic Pain Syndrome (UCPPS) phenotypes that would be associated with disease specific quality of life (QOL) and illness impact metrics (IIM). MATERIALS AND METHODS: A previously validated smart phone app (M-app) was provided to willing Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) participants. M-app notifications were sent 4-times daily for 14 days inquiring about PP and UU severity. A clustering algorithm that accounted for variance placed participants into PP and UU variability? clusters. Associations between clusters and QOL and IIM were then determined. RESULTS: A total of 204 participants enrolled in the M-app study (64% female). M-app compliance was high (median 63% of surveys). Cluster analysis revealed k = 3 (high, low, none) PP clusters and k = 2 (high, low) UU clusters. When adjusting for baseline pain severity, high PP variability, but not UU variability, was strongly associated with QOL and IIM; specifically worse mood, worse sleep and higher anxiety. UU and PP clusters were associated with each other (p < 0.0001), but a large percentage (33%) of patients with high PP variability had low UU variability. CONCLUSIONS: PP variability is an independent predictor of worse QOL and more severe IIM in UCPPS participants after controlling for baseline pain severity and UU. These findings suggest alternative pain indices, such as pain variability and unpredictability, may be useful adjuncts to traditional measures of worst and average pain when assessing UCPPS treatment responses.
