MP86-08 USE OF A MOBILE APP FOR ECOLOGICAL MOMENTARY ASSESSMENT OF PAIN AND OTHER SYMPTOMS IN PATIENTS WITH UROLOGIC CHRONIC PELVIC PAIN SYNDROME

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Practice Patterns, Quality of Life and Shared Decision Making V1 Apr 2017MP86-08 USE OF A MOBILE APP FOR ECOLOGICAL MOMENTARY ASSESSMENT OF PAIN AND OTHER SYMPTOMS IN PATIENTS WITH UROLOGIC CHRONIC PELVIC PAIN SYNDROME James Griffith, Ted Herman, Anthony Andrys, Michael Bass, Bayley Taple, Brett Lloyd, and Bradley Erickson James GriffithJames Griffith More articles by this author , Ted HermanTed Herman More articles by this author , Anthony AndrysAnthony Andrys More articles by this author , Michael BassMichael Bass More articles by this author , Bayley TapleBayley Taple More articles by this author , Brett LloydBrett Lloyd More articles by this author , and Bradley EricksonBradley Erickson More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2691AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Pain in patients with chronic pelvic pain varies widely by location, intensity, severity and by its intermittency. Static assessment of pain does not allow for a comprehensive description of this inherent heterogeneity of pain, potentially obscuring phenotypic differences in UCPPS patients that could help predict treatment response and disease course. Ecological Momentary Assessment (EMA) of pain is a way to describe pain more comprehensively. Mobile apps can communicate directly with patients at all times of the day in most locations and are thus, perfectly suited to collect EMA pain data. For the Multidisciplinary Approach to Pelvic Pain (MAPP) Research Network, we developed a mobile app to capture pain and associated symptoms in a time-efficient manner. We present the results from the app beta testing herein. We hypothesized that participants would find the app easy to use and that utilization of the app would reveal significant daily pain variability. METHODS A total of 22 participants completed beta testing for 14 days, locating and rating their pain and other symptoms. Mobile phone notifications that linked to and opened the app were sent at wake up, 4 and 8 hours after wake-up and bedtime. On Day 15 participants completed the NASA task load index to assess app usability and satisfaction. Participants then completed a qualitative exit interview to give us feedback on all aspects of the app and testing. Thus, we obtained both quantitative and qualitative information during the beta-testing period. RESULTS We created a summary score for pain using the maximum pain rating across different areas of the body at a particular time. Using a linear model with time of day, nested with testing day, we found that pain varied significantly during the day, F (37, 257) = 1.75, p = .007. Qualitative interviews and App ratings (see figure) suggested that the app was simple and easy to use (e.g. low mental demand, high app compliance) CONCLUSIONS Our quantitative and qualitative results show that our app is easy to use for participants and is able to capture intra-day variability in pain across multiple areas of the body. Moreover, our participants indicated that they would be willing to use this app going forward both for research and clinical/treatment purposes. Mobile apps appear to be well-suited to study chronic urologic conditions. Utilization of the app for EPA testing is currently underway within the MAPP Research Network. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1159 Advertisement Copyright & Permissions© 2017MetricsAuthor Information James Griffith More articles by this author Ted Herman More articles by this author Anthony Andrys More articles by this author Michael Bass More articles by this author Bayley Taple More articles by this author Brett Lloyd More articles by this author Bradley Erickson More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...