College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.
JMIR Mhealth Uhealth. 2019 Oct 30;7(10):e14198. doi: 10.2196/14198.
The spread of technology and dissemination of knowledge across the World Wide Web has prompted the development of apps for American Sign Language (ASL) translation, interpretation, and syntax recognition. There is limited literature regarding the quality, effectiveness, and appropriateness of mobile health (mHealth) apps for the deaf and hard-of-hearing (DHOH) that pose to aid the DHOH in their everyday communication and activities. Other than the star-rating system with minimal comments regarding quality, the evaluation metrics used to rate mobile apps are commonly subjective.
This study aimed to evaluate the quality and effectiveness of DHOH apps using a standardized scale. In addition, it also aimed to identify content-specific criteria to improve the evaluation process by using a content expert, and to use the content expert to more accurately evaluate apps and features supporting the DHOH.
A list of potential apps for evaluation was generated after a preliminary screening for apps related to the DHOH. Inclusion and exclusion criteria were developed to refine the master list of apps. The study modified a standardized rating scale with additional content-specific criteria applicable to the DHOH population for app evaluation. This was accomplished by including a DHOH content expert in the design of content-specific criteria.
The results indicate a clear distinction in Mobile App Rating Scale (MARS) scores among apps within the study's three app categories: ASL translators (highest score=3.72), speech-to-text (highest score=3.6), and hard-of-hearing assistants (highest score=3.90). Of the 217 apps obtained from the search criteria, 21 apps met the inclusion and exclusion criteria. Furthermore, the limited consideration for measures specific to the target population along with a high app turnover rate suggests opportunities for improved app effectiveness and evaluation.
As more mHealth apps enter the market for the DHOH population, more criteria-based evaluation is needed to ensure the safety and appropriateness of the apps for the intended users. Evaluation of population-specific mHealth apps can benefit from content-specific measurement criteria developed by a content expert in the field.
随着技术的普及和网络知识的传播,美国手语(ASL)翻译、口译和句法识别应用程序也应运而生。目前,关于为聋人和重听人士(DHOH)提供帮助的移动健康(mHealth)应用程序的质量、效果和适宜性的文献有限,这些应用程序旨在帮助 DHOH 进行日常交流和活动。除了质量评价的星级系统和一些关于质量的简要评论外,用于评价移动应用程序的评价指标通常是主观的。
本研究旨在使用标准化量表评价 DHOH 应用程序的质量和效果。此外,还旨在通过使用内容专家来确定内容特定的标准,以改进评价过程,并使用内容专家更准确地评估支持 DHOH 的应用程序和功能。
在对与 DHOH 相关的应用程序进行初步筛选后,生成了一份潜在应用程序的评估清单。制定了纳入和排除标准,以完善应用程序的主清单。该研究通过包括 DHOH 内容专家来设计内容特定标准,对标准化评分量表进行了修改,以纳入适用于 DHOH 人群的特定内容标准。
研究结果表明,在所研究的三个应用程序类别中,应用程序的移动应用程序评级量表(MARS)评分之间存在明显差异:ASL 翻译器(最高得分为 3.72)、语音转文本(最高得分为 3.6)和听力障碍助手(最高得分为 3.90)。从搜索标准中获得的 217 个应用程序中,有 21 个符合纳入和排除标准。此外,由于目标人群的特定措施考虑有限,以及应用程序的高周转率,这表明需要改进应用程序的效果和评估。
随着更多的 mHealth 应用程序进入 DHOH 人群市场,需要更多基于标准的评估,以确保应用程序对预期用户的安全性和适宜性。对特定人群的 mHealth 应用程序的评估可以从该领域的内容专家开发的特定内容测量标准中受益。