Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Mar 29, 2019
Date Accepted: Aug 18, 2019
Modifying the Mobile App Rating Scale with a Content Expert to Evaluate Deaf and Hard-of-Hearing Applications
ABSTRACT
Background:
The spread of technology and dissemination of knowledge across the world-wide-web has prompted the development of applications for American Sign Language (ASL) translation, interpretation, and syntax recognition. There is limited literature regarding the quality, effectiveness, and appropriateness of mHealth apps for the deaf and hard-of-hearing (DHOH); these applications 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.
Objective:
To evaluate the quality and effectiveness of DHOH applications using a standardized scale. Secondly, to identify content-specific criteria to improve the evaluation process by using a content expert; to use a content expert to more accurately evaluate applications and features supporting the DHOH.
Methods:
A list of potential apps for evaluation was generated after a preliminary screening for apps related to the DHOH. An inclusion and exclusion criteria was developed to refine the master list of applications. 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.
Results:
The results indicate a clear distinction in Mobile App Rating Scale scores between 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 application turnover rate suggest opportunities for improved app effectiveness and evaluation.
Conclusions:
As more mHealth apps enter the market for the deaf and hard-of-hearing population, more criteria-based evaluation is needed to ensure the safety and appropriateness of apps for intended users. Evaluation of population-specific mHealth apps can benefit from content-specific measurement criteria developed by a content expert in the field.
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