Quality of Deaf and Hard-of-Hearing Mobile Apps: Evaluation Using the Mobile App Rating Scale (MARS) With Additional Criteria From a Content Expert

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality

“…The aim of this study was to conduct a user-centered design analysis of the usability, engagement, and quality of popular evidence-based apps for mental health self-management utilizing the well-established Mobile Application Rating Scale (MARS) [7]. Previous publications have utilized the MARS to evaluate apps on an eclectic array of health-related topics including blood pressure, mindfulness, nutrition, diet and physical activity, deafness and hard-of-hearing, and drug-drug interactions [8][9][10][11][12][13]. This study evaluated the quality of mental health apps that are successful across both research and commercial sectors.…”

Assessment of the Quality of Mobile Applications (Apps) for Management of Low Back Pain Using the Mobile App Rating Scale (MARS)

“…The Mobile App Rating Scale (MARS) is an objective, valid, easy-to-use assessment tool designed for classifying and assessing the quality of mobile health (mHealth) apps [ 29 ]. This tool has shown to be a reliable [ 29 , 32 ] and widely applied [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ] way to systematise assessment of the quality of mobile apps. However, training in the use of the tool and expertise in mHealth and the specific health field is required to administer it [ 44 ].…”

Identification and Evaluation of Methodologies to Assess the Quality of Mobile Health Apps in High-, Low-, and Middle-Income Countries: Rapid Review

“…The methodologies proposed in category 2 of the papers focused specifically on asthma [ 35 ], pain management [ 36 ], medication adherence [ 37 ], medication-related problems [ 38 ], hard of hearing [ 39 ], diabetes mellitus [ 40 ], infant feeding [ 41 ], and nutritional [ 42 ] mHealth apps. The methodologies proposed within this category of papers were highly specific to one topic of medicine.…”