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理想的盆底肌训练依从性电子健康系统:系统评价。

An ideal e-health system for pelvic floor muscle training adherence: Systematic review.

机构信息

Paraná State University, Inspirar Faculty, Rede Perineo.net, Florianópolis, Santa Catarina, Brazil.

Paraná State University, Curitiba, Paraná, Brazil.

出版信息

Neurourol Urodyn. 2019 Jan;38(1):63-80. doi: 10.1002/nau.23835. Epub 2018 Oct 30.

Abstract

BACKGROUND

Nowadays, Pelvic Floor Muscle Training (PFMT) is a first line, level 1 evidence-based treatment for urinary incontinence (UI), but adherence to PFMT is often problematic. Today, there are several mobile applications (mApps) for PFMT, but many lack specific strategies for enhancing adherence.

AIMS

To review available mApps for improvement of adherence to PFMT, and to introduce a new App so called iPelvis.

METHODS

Review study all available mApps for PFMT and relevant literature regarding adherence by electronic search through the databases Pubmed, Embase, CINAHL, LILACS, PEDro, and Scielo. Based on these results, development of a mApp, called "iPelvis" for Apple™ and Android™ systems, implementing relevant strategies to improve adherence.

RESULTS

Based on the current adherence literature we were able to identify 12 variables helping to create the optimal mApp for PFMT. None of the identified 61 mApps found for Android™ and 16 for Apple™ has all these 12 variables. iPelvis mApp and websites were constructed taking into consideration those 12 variables and its construct is now being subject to ongoing validation studies.

CONCLUSION

MApps for PFMT are an essential part of first-line, efficient interventions of UI and have potentials to improve adherence, in case these respect the principles of PFMT, motor learning and adherence to PFMT. iPelvis has been constructed respecting all essential variables related to adherence to PFMT and may enhance the effects of UI treatment.

摘要

背景

如今,盆底肌训练(PFMT)是治疗尿失禁(UI)的一线一级循证治疗方法,但 PFMT 的依从性往往存在问题。如今,有几种用于 PFMT 的移动应用程序(mApp),但许多应用程序缺乏增强依从性的具体策略。

目的

回顾可用于提高 PFMT 依从性的 mApp,并介绍一种名为 iPelvis 的新 App。

方法

通过电子搜索 Pubmed、Embase、CINAHL、LILACS、PEDro 和 Scielo 等数据库,对所有用于 PFMT 的 mApp 和关于依从性的相关文献进行综述研究。基于这些结果,为 Apple™ 和 Android™ 系统开发了一个名为“iPelvis”的 mApp,该应用程序实施了相关策略以提高依从性。

结果

根据目前的依从性文献,我们能够确定 12 个有助于创建用于 PFMT 的最佳 mApp 的变量。在为 Android™ 找到的 61 个 mApp 和为 Apple™ 找到的 16 个 mApp 中,没有一个具有所有这 12 个变量。考虑到这 12 个变量,构建了 iPelvis mApp 和网站,其构建现在正在进行中,以验证研究。

结论

PFMT 的 mApp 是 UI 一线高效干预的重要组成部分,在这些 mApp 尊重 PFMT、运动学习和 PFMT 依从性的原则的情况下,具有提高依从性的潜力。iPelvis 构建时考虑到了与 PFMT 依从性相关的所有重要变量,并可能增强 UI 治疗的效果。

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