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从表格到片剂(T2T)言语和语言治疗软件开发路线图。

The Table to Tablet (T2T) Speech and Language Therapy Software Development Roadmap.

作者信息

Jesus Luis Mt, Santos Joaquim, Martinez Joana

机构信息

University of Aveiro, Aveiro, Portugal.

出版信息

JMIR Res Protoc. 2019 Jan 30;8(1):e11596. doi: 10.2196/11596.

DOI:10.2196/11596
PMID:30698534
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6372937/
Abstract

BACKGROUND

Few studies have analyzed gains in using computers in speech and language therapy interventions for children with speech and/or language disorders when compared to a control group, but virtual tutors and computer-based visual feedback have been gaining interest in the literature. Previous systematic reviews mainly focused on development technological details of computer-based speech training systems or the potential of integrating mobile technology into education and rehabilitation, but recent systematic reviews have also evaluated the efficacy of computer-based speech and language therapy for children and how digital technology can support different activities, at school or elsewhere.

OBJECTIVE

This study aimed to analyze a continuous communication and joint team approach to develop solutions focused on the real needs of end users, which digitally emulate reliable and validated physical intervention materials for children with speech sound disorders (SSD).

METHODS

The Table to Tablet (T2T) software was developed using a design-based research methodology, which included four phases: activities development; ethnographic pretesting with a sample from the target population; software development; and beta-testing. The technology used to develop the software, the method used to ensure satisfaction and replay ability of the intervention materials, and results from the ethnographic and beta-testing phases are presented.

RESULTS

Nineteen activities were developed during the first phase, which were then tested, with 7 service users, using a physical prototype. The beta-test approach included extensive testing and reformulation, supported by direct, nonparticipant observation and data collection using a questionnaire designed for children. Feedback was used to improve the software and interaction with users.

CONCLUSIONS

The use of T2T-based intervention programmes by speech and language therapists (SLTs) will allow these professionals to make a better and more effective communication intervention, based on proven methodologies, that coexists in a structured physical and a digital version. These versions provide a full, 6-week intervention program, with minimal effort in preparing the session by the SLTs while delivering a very consistent intervention, with high replay value. A continuous communication and joint team approach was beneficial to the project and to the development of a solution focused on the real needs of SLTs and children with SSD. All problems were approached as a team with different skills and expertise, which minimized errors (eg, the developer making choices that would save him from spending time doing something that would not be used) and time spent. To add to this, the importance of integrating the end users as testers and collecting their opinions and actions per session allowed the production of better-targeted activities.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02490826; https://clinicaltrials.gov/ct2/show/NCT02490826.

摘要

背景

与对照组相比,很少有研究分析在针对言语和/或语言障碍儿童的言语和语言治疗干预中使用计算机的效果,但虚拟导师和基于计算机的视觉反馈在文献中越来越受到关注。以往的系统评价主要关注基于计算机的言语训练系统的技术细节发展,或将移动技术整合到教育与康复中的潜力,但最近的系统评价也评估了基于计算机的言语和语言治疗对儿童的疗效,以及数字技术如何在学校或其他场所支持不同活动。

目的

本研究旨在分析一种持续沟通和联合团队的方法,以开发针对最终用户实际需求的解决方案,该方案以数字方式模拟用于语音障碍(SSD)儿童的可靠且经过验证的物理干预材料。

方法

使用基于设计的研究方法开发了从表格到平板电脑(T2T)软件,该方法包括四个阶段:活动开发;对目标人群样本进行人种志预测试;软件开发;以及测试版测试。介绍了用于开发该软件的技术、用于确保干预材料满意度和可重复性的方法,以及人种志和测试版测试阶段的结果。

结果

在第一阶段开发了19项活动,然后使用物理原型对7名服务用户进行了测试。测试版测试方法包括广泛的测试和重新制定,由直接的非参与观察以及使用专为儿童设计的问卷进行数据收集提供支持。反馈被用于改进软件和与用户的交互。

结论

言语和语言治疗师(SLT)使用基于T2T的干预计划,将使这些专业人员能够基于经过验证的方法进行更好、更有效的沟通干预,该干预以结构化的物理和数字版本共存。这些版本提供了一个完整的为期6周的干预计划,SLT在准备课程时只需付出最小的努力,同时提供非常一致的干预,具有很高的重复价值。持续沟通和联合团队的方法对该项目以及针对SLT和SSD儿童实际需求的解决方案的开发有益。所有问题都由具有不同技能和专业知识的团队共同处理,这最大限度地减少了错误(例如,开发者做出的选择可以避免他花费时间去做不会被使用的事情)和所花费的时间。此外,将最终用户纳入测试人员并收集他们每节课的意见和行为的重要性,使得能够制作出更具针对性的活动。

试验注册

ClinicalTrials.gov NCT02490826;https://clinicaltrials.gov/ct2/show/NCT02490826 。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/2f73d1082016/resprot_v8i1e11596_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/4fd745e448e8/resprot_v8i1e11596_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/cf3b085c5793/resprot_v8i1e11596_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/6e95c09599a1/resprot_v8i1e11596_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/b8fbec092546/resprot_v8i1e11596_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/9203d388ea0e/resprot_v8i1e11596_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/2f73d1082016/resprot_v8i1e11596_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/4fd745e448e8/resprot_v8i1e11596_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/cf3b085c5793/resprot_v8i1e11596_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/6e95c09599a1/resprot_v8i1e11596_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/b8fbec092546/resprot_v8i1e11596_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/9203d388ea0e/resprot_v8i1e11596_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a7/6372937/2f73d1082016/resprot_v8i1e11596_fig6.jpg

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