Department of Physiotherapy. University of Málaga. Spain.
Department of Languages and Computer Science. University of Málaga. Spain.
Int J Med Inform. 2024 Jun;186:105410. doi: 10.1016/j.ijmedinf.2024.105410. Epub 2024 Mar 15.
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.
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.
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.
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).
App-Mohedo® is the first multi-platform, multi-language and easy-to-use mobile application that, through GMI with LRJT, and with an adequate bank of images classified into three levels of difficulty, can be used as a complementary therapeutic tool in the treatment of patients with CPP. This work can also serve as an example, model or guide when applying a user-centered methodology, as MPIu + a, to the development of other apps, especially in the field of health.
慢性盆腔痛（CPP）已被描述为全球公共卫生重点问题，它是最常见和最昂贵的医疗保健问题之一。分级运动想象（GMI）是一种治疗工具，已成功用于改善多种慢性疾病的疼痛。GMI 治疗分为三个阶段：偏侧性训练（LRJT，左右判断任务）、想象运动和镜像治疗。迄今为止，还没有开发出用于骨盆底 LRJT 的工具。
本研究旨在描述使用具有 LRJT 的 GMI 开发高度可用的多语言和多平台移动应用程序的过程，以改善 CPP 患者的治疗效果。此外，这还需要实现另外两个目标：首先，生成 550 张骨盆底图像，随后进行实证研究，客观地将它们分为不同难度水平。这将允许应用程序适当地组织和规划每个患者要遵循的不同治疗课程。
为了设计、评估和开发应用程序，采用了一种以用户为中心的开放设计方法（MPIu+a）。此外，为了对应用程序中的骨盆底图像进行分类并建立不同的难度级别，对 132 名志愿者进行了非概率抽样的观察性、横断面研究。
一方面，通过应用 MPIu+a，总共需要 5 个阶段来生成一个易于使用的移动应用程序。另一方面，550 张骨盆底图像被分为 3 个难度级别（基于参与者在每个图像的分类过程中正确答案的百分比和反应时间）：第 1 级（191 张图像，准确率为 100%，反应时间为[0-2.5]秒）；第 2 级（208 张图像，准确率为 75-100%，反应时间为[2.5-5]秒）；第 3 级（151 张图像，准确率为 50-75%，反应时间>5 秒）。
App-Mohedo®是第一个多平台、多语言且易于使用的移动应用程序，它通过具有 LRJT 的 GMI，并使用适当的三级难度分类图像库，可作为 CPP 患者治疗的补充治疗工具。这项工作还可以作为在开发其他应用程序（尤其是在健康领域）时应用以用户为中心的方法学（如 MPIu+a）的示例、模型或指南。
