RT Journal Article
T1 App-Mohedo®: a mobile app for the management of chronic pelvic pain. A design and development study
A1 Díaz-Mohedo, Esther
A1 Carrillo-León, Antonio Luis
A1 Calvache-Mateo, Andrés
A1 Ptak, Magdalena
A1 Romero-Franco, Natalia
A1 Fernández, Juan Carlos
K1 Informática - Aplicaciones
K1 Dolor pélvico
K1 Pelvis - Tratamiento
AB BackgroundChronic 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.ObjectiveThis 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.MethodologyFor 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.ResultsOn 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)...
PB Elsevier
YR 2024
FD 2024-03-15
LK https://hdl.handle.net/10630/30878
UL https://hdl.handle.net/10630/30878
LA eng
NO 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
NO Funding for open access charge: Universidad de Malaga/CBUA.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026