2026-05-29T21:32:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/308782026-02-03T11:35:27Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Díaz-Mohedo, Esther author Carrillo-León, Antonio Luis author Calvache-Mateo, Andrés author Ptak, Magdalena author Romero-Franco, Natalia author Fernández, Juan Carlos author 2024-03-15 Background Chronic Pelvic Pain (CPP) has been described as a public health priority worldwide, and it is among the most prevalent and costly healthcare problems. Graded motor imagery (GMI) is a therapeutic tool that has been successfully used to improve pain in several chronic conditions. GMI therapy is divided into three stages: laterality training (LRJT, Left Right Judgement Task), imagined movements, and mirror therapy. No tool that allows working with LRJT in pelvic floor has been developed to date. Objective This research aims to describe the process followed for the development of a highly usable, multi-language and multi-platform mobile application using GMI with LRJT to improve the treatment of patients with CPP. In addition, this will require achieving two other goals: firstly, to generate 550 pelvic floor images and, subsequently, to carry out an empirical study to objectively classify them into different difficulty levels of. This will allow the app to properly organize and plan the different therapy sessions to be followed by each patient. Methodology For the design, evaluation and development of the app, an open methodology of user-centered design (MPIu + a) was applied. Furthermore, to classify and establish the pelvic floor images of the app in different difficulty levels, an observational, cross-sectional study was conducted with 132 volunteers through non-probabilistic sampling. Results On one hand, applying MPIu+a, a total of 5 phases were required to generate an easy-to-use mobile application. On the other hand, the 550 pelvic floor images were classified into 3 difficulty levels (based on the percentage of correct answers and response time used by the participants in the classification process of each image): Level 1 (191 images with Accuracy = 100 % and RT = [0–2.5] seconds); Level 2 (208 images with Accuracy = 75–100 % and RT = [2.5–5] seconds); and Level 3 (151 images with Accuracy = 50–75 % and RT > 5 s)... Esther Díaz-Mohedo, Antonio L. Carrillo-León, Andrés Calvache-Mateo, Magdalena Ptak, Natalia Romero-Franco, Juan Carlos-Fernández, App-Mohedo®: A mobile app for the management of chronic pelvic pain. A design and development study, International Journal of Medical Informatics, Volume 186, 2024, 105410, ISSN 1386-5056, https://doi.org/10.1016/j.ijmedinf.2024.105410 https://hdl.handle.net/10630/30878 10.1016/j.ijmedinf.2024.105410 Informática - Aplicaciones Dolor pélvico Pelvis - Tratamiento App-Mohedo®: a mobile app for the management of chronic pelvic pain. A design and development study