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

2019 ◽  
Author(s):  
Ryan Lee Romero ◽  
Frederick Kates ◽  
Mark Hart ◽  
Amanda Ojeda ◽  
Itai Meirom ◽  
...  
Keyword(s):  
American Sign Language ◽  
Hard Of Hearing ◽  
Rating Scale ◽  
Mobile Apps ◽  
Evaluation Process ◽  
Target Population ◽  
Mobile App ◽  
Exclusion Criteria ◽  
Content Expert ◽  
Mhealth Apps

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.

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

10.2196/14198 ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. e14198 ◽  
Author(s):  
Ryan Lee Romero ◽  
Frederick Kates ◽  
Mark Hart ◽  
Amanda Ojeda ◽  
Itai Meirom ◽  
...  
Keyword(s):  
American Sign Language ◽  
Hard Of Hearing ◽  
Rating Scale ◽  
Mobile Apps ◽  
Evaluation Process ◽  
Target Population ◽  
Mobile App ◽  
Exclusion Criteria ◽  
Content Expert ◽  
Mhealth Apps

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.

scholarly journals Quality of Mobile Apps for Care Partners of People with Alzheimer’s Disease and Related Dementias: Mobile App Rating Scale Evaluation (Preprint)

2021 ◽  
Author(s):  
Nicole E Werner ◽  
Janetta C Brown ◽  
Priya Loganathar ◽  
Richard J Holden
Keyword(s):  
Rating Scale ◽  
Mobile Apps ◽  
Mobile App ◽  
Future Research ◽  
App Development ◽  
Care Partners ◽  
Acceptable Quality ◽  
The Us ◽  
Google Play

BACKGROUND The over 11 million care partners in the US who provide care to people living with Alzheimer’s disease and related dementias (ADRD) cite persistent and pervasive unmet needs related to all aspects of their caregiving role. The proliferation of mobile applications (apps) for care partners has potential to meet the care partners’ needs, but the quality of apps is unknown. OBJECTIVE The present study aimed to 1) evaluate the quality of publicly available apps for care partners of people living with ADRD and 2) identify design features of low- and high-quality apps to guide future research and app development. METHODS We searched the US Apple and Google Play app stores with the criteria that the app needed to be 1) available in US Google play or Apple app stores, 2) directly accessible to users “out of the box”, 3) primarily intended for use by an informal (family, friend) caregiver or caregivers of a person with dementia. The included apps were then evaluated using the Mobile App Rating Scale (MARS), which includes descriptive app classification and rating using 23 items across five dimensions: engagement, functionality, aesthetics, information, and subjective quality. Next, we computed descriptive statistics for each rating. To identify recommendations for future research and app development, we categorized rater comments on the score driving factors for each item and what the app could have done to improve the score for that item. RESULTS We evaluated 17 apps (41% iOS only, 12% Android only, 47% both iOS and Android). We found that on average, the apps are of minimally acceptable quality. Although we identified apps above and below minimally acceptable quality, many apps had broken features and were rated as below acceptable for engagement and information. CONCLUSIONS Minimally acceptable quality is likely insufficient to meet care partner needs. Future research should establish minimum quality standards across dimensions for mobile apps for care partners. The design features of high-quality apps we identified in this research can provide the foundation for benchmarking those standards.

Nick Noguez and Michael Gonzalez Discuss their Research Project: Are Social Features in Mobile Health Apps Effective in Engaging Users? A Content Analysis of Smoking Cessation Mobile Apps

2019 ◽  
Vol 4 ◽  
Author(s):  
Nick Noguez And Michael Gonzalez
Keyword(s):  
Smoking Cessation ◽  
Content Analysis ◽  
Mobile Health ◽  
Social Engagement ◽  
Rating Scale ◽  
Mobile Apps ◽  
Mobile App ◽  
Health Apps ◽  
Social Features ◽  
Mobile Health Apps

  Despite the ubiquity of smartphone ownership and the increasing integration of social engagement features in smoking cessation apps to engage users, thesocial engagement features that exist in current smoking cessation apps and how effective these social features are in engaging users remain unclear. To fill the gap in the literature, a content analysis of free and paid smoking cessation mobile apps isconducted to examine a) the presence of socialengagement features(e.g., social support, social announcement, social referencing) and non-social engagement features (e.g., personal environmental changes, goal setting), and b) their relationship with user ratingsand engagement scores (e.g., Mobile App rating scale [MARS]). The findings will not only extend the mobile health apps engagement typology,but also inform smoking cessation mobile apps design.

scholarly journals Measuring the Quality of Clinical Skills Mobile Apps for Student Learning: Systematic Search, Analysis, and Comparison of Two Measurement Scales (Preprint)

2020 ◽  
Author(s):  
Tehmina Gladman ◽  
Grace Tylee ◽  
Steve Gallagher ◽  
Jonathan Mair ◽  
Rebecca Grainger
Keyword(s):  
Student Learning ◽  
Teaching And Learning ◽  
Rating Scale ◽  
Clinical Skills ◽  
Mobile Apps ◽  
Mobile App ◽  
Systematic Search ◽  
Measurement Scales ◽  
High Quality

BACKGROUND Mobile apps are widely used in health professions, which increases the need for simple methods to determine the quality of apps. In particular, teachers need the ability to curate high-quality mobile apps for student learning. OBJECTIVE This study aims to systematically search for and evaluate the quality of clinical skills mobile apps as learning tools. The quality of apps meeting the specified criteria was evaluated using two measures—the widely used Mobile App Rating Scale (MARS), which measures general app quality, and the Mobile App Rubric for Learning (MARuL), a recently developed instrument that measures the value of apps for student learning—to assess whether MARuL is more effective than MARS in identifying high-quality apps for learning. METHODS Two mobile app stores were systematically searched using clinical skills terms commonly found in medical education and apps meeting the criteria identified using an approach based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 9 apps were identified during the screening process. The apps were rated independently by 2 reviewers using MARS and MARuL. RESULTS The intraclass correlation coefficients (ICCs) for the 2 raters using MARS and MARuL were the same (MARS ICC [two-way]=0.68; <i>P</i>&lt;.001 and MARuL ICC [two-way]=0.68; <i>P</i>&lt;.001). Of the 9 apps, Geeky Medics-OSCE revision (MARS Android=3.74; MARS iOS=3.68; MARuL Android=75; and MARuL iOS=73) and OSCE PASS: Medical Revision (MARS Android=3.79; MARS iOS=3.71; MARuL Android=69; and MARuL iOS=73) scored highly on both measures of app quality and for both Android and iOS. Both measures also showed agreement for the lowest rated app, Patient Education Institute (MARS Android=2.21; MARS iOS=2.11; MARuL Android=18; and MARuL iOS=21.5), which had the lowest scores in all categories except information (MARS) and professional (MARuL) in both operating systems. MARS and MARuL were both able to differentiate between the highest and lowest quality apps; however, MARuL was better able to differentiate apps based on teaching and learning quality. CONCLUSIONS This systematic search and rating of clinical skills apps for learning found that the quality of apps was highly variable. However, 2 apps—Geeky Medics-OSCE revision and OSCE PASS: Medical Revision—rated highly for both versions and with both quality measures. MARS and MARuL showed similar abilities to differentiate the quality of the 9 apps. However, MARuL’s incorporation of teaching and learning elements as part of a multidimensional measure of quality may make it more appropriate for use with apps focused on teaching and learning, whereas MARS’s more general rating of quality may be more appropriate for health apps targeting a general health audience. Ratings of the 9 apps by both measures also highlighted the variable quality of clinical skills mobile apps for learning. CLINICALTRIAL

scholarly journals Vaping-Related Mobile Apps Available in the Google Play Store After the Apple Ban: Content Review

10.2196/20009 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e20009
Author(s):  
Meredith C Meacham ◽  
Erin A Vogel ◽  
Johannes Thrul
Keyword(s):  
Rating Scale ◽  
Mobile Apps ◽  
Mobile App ◽  
Sources Of Information ◽  
App Store ◽  
Quit Smoking ◽  
Quitting Smoking ◽  
Do It Yourself ◽  
Content Review ◽  
Google Play

Background In response to health concerns about vaping devices (eg, youth nicotine use, lung injury), Apple removed 181 previously approved vaping-related apps from the App Store in November 2019. This policy change may lessen youth exposure to content that glamorizes vaping; however, it may also block important sources of information and vaping device control for adults seeking to use vaping devices safely. Objective Understanding the types of nicotine and cannabis vaping–related apps still available in the competing Google Play Store can shed light on how digital apps may reflect information available to consumers. Methods In December 2019, we searched the Google Play Store for vaping-related apps using the keywords "vape" and "vaping" and reviewed the first 100 apps presented in the results. We reviewed app titles, descriptions, screenshots, and metadata to categorize the intended substance (nicotine or cannabis/tetrahydrocannabinol) and the app’s purpose. The most installed apps in each purpose category were downloaded and evaluated for quality and usability with the Mobile App Rating Scale. Results Of the first 100 apps, 79 were related to vaping. Of these 79 apps, 43 (54%) were specific to nicotine, 3 (4%) were specific to cannabis, 1 (1%) was intended for either, and for the remaining 31 (39%), the intended substance was unclear. The most common purposes of the apps were making do-it-yourself e-liquids (28/79, 35%) or coils (25/79, 32%), games/entertainment (19/79, 24%), social networking (16/79, 20%), and shopping for vaping products (15/79, 19%). Of the 79 apps, at least 4 apps (5%) paired with vaping devices to control temperature or dose settings, 8 apps (10%) claimed to help people quit smoking using vaping, and 2 apps (3%) had the goal of helping people quit vaping. Conclusions The majority of vaping-related apps in the Google Play Store had features either to help users continue vaping, such as information for modifying devices, or to maintain interest in vaping. Few apps were for controlling device settings or assisting with quitting smoking or vaping. Assuming that these Google Play Store apps were similar in content to the Apple App Store apps that were removed, it appears that Apple’s ban would have a minimal effect on people who vape with the intention of quitting smoking or who are seeking information about safer vaping via mobile apps.

scholarly journals Mobile apps for detecting falsified and substandard drugs: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246061
Author(s):  
Agustín Ciapponi ◽  
Manuel Donato ◽  
A. Metin Gülmezoglu ◽  
Tomás Alconada ◽  
Ariel Bardach
Keyword(s):  
Systematic Review ◽  
Rating Scale ◽  
Mobile Apps ◽  
Mobile App ◽  
Economic Consequences ◽  
Google Scholar ◽  
Medical Database ◽  
Bar Code ◽  
Medical Databases

The use of substandard and counterfeit medicines (SCM) leads to significant health and economic consequences, like treatment failure, rise of antimicrobial resistance, extra expenditures of individuals or households and serious adverse drug reactions including death. Our objective was to systematically search, identify and compare relevant available mobile applications (apps) for smartphones and tablets, which use could potentially affect clinical and public health outcomes. We carried out a systematic review of the literature in January 2020, including major medical databases, and app stores. We used the validated Mobile App Rating Scale (MARS) to assess the quality of apps, (1 worst score, 3 acceptable score, and 5 best score). We planned to evaluate the accuracy of the mobile apps to detect SCM. We retrieved 335 references through medical databases and 42 from Apple, Google stores and Google Scholar. We finally included two studies of the medical database, 25 apps (eight from the App Store, eight from Google Play, eight from both stores, and one from Google Scholar), and 16 websites. We only found one report on the accuracy of a mobile apps detecting SCMs. Most apps use the imprint, color or shape for pill identification, and only a few offer pill detection through photographs or bar code. The MARS mean score for the apps was 3.17 (acceptable), with a maximum of 4.9 and a minimum of 1.1. The ‘functionality’ dimension resulted in the highest mean score (3.4), while the ‘engagement’ and ‘information’ dimensions showed the lowest one (3.0). In conclusion, we found a remarkable evidence gap about the accuracy of mobile apps in detecting SCMs. However, mobile apps could potentially be useful to screen for SCM by assessing the physical characteristics of pills, although this should still be assessed in properly designed research studies.

scholarly journals Win Big Fast! An Evaluation of Mobile Applications Available in Australia for Problem Gambling

2020 ◽  
Vol 45 ◽  
Author(s):  
Kelly Ridley ◽  
Amy Wiltshire ◽  
Mathew Coleman
Keyword(s):  
Problem Gambling ◽  
Nous Avons ◽  
Rating Scale ◽  
Mobile Apps ◽  
Descriptive Analysis ◽  
Mobile App ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Traditional Therapies ◽  
Therapeutic Model

With the increase in availability of gambling applications (apps) for mobile phones, it has never been easier for individuals to access gaming systems. A proportion of these users will be affected by gambling disorder (GD). Traditional therapies for GD can be geographically and financially difficult to access. Mobile health apps can be useful for other addictions and provide another avenue of treatment for GD. Our objective in this study was to review the features, models of treatment, and aims of apps marketed to assist people in addressing their gambling. We searched the three largest app stores in Australia and performed a descriptive analysis based on the Mobile App Rating Scale of the apps purporting to be of assistance in managing GD or problem gambling. The number of apps available for addressing GD in Australia was vastly outnumbered by the number of apps for gambling or gaming. Apps that met the inclusion criteria most often aimed at total cessation of gambling, but did not use a recognizable therapeutic model. A majority of apps featured a single tool, most often a sober time tracker. Few of the apps were affiliated with existing services, and those that were tended to have a broader range of features and tools. Mobile apps present another way for individuals who are struggling with GD or problem gambling to access treatment. For apps to be effective, more attention needs to be paid to their design in order for them to be both useful and noticeable in the milieu of more invitingly designed apps that promote gambling.RésuméÉtant donné le nombre grandissant d’applications de jeux de hasard pour téléphone mobile, il n’a jamais été aussi facile d’accéder à des systèmes de jeu. Un certain nombre des utilisateurs de ces appareils développeront une dépendance au jeu (DJ). Les thérapies conventionnelles en matière de DJ peuvent être difficiles d’accès en raison de la distance géographique et de leur coût. Les applications mobiles dédiées à la santé, parfois pour traiter d’autres formes de dépendance, pourraient offrir des possibilités de traitement du jeu pathologique. Nous avons analysé les caractéristiques, les modèles de traitement et les objectifs des applications qui prétendent aider les individus à dominer leur DP. Nous avons fouillé les trois principales boutiques d’applications d’Australie à la recherche de tels produits, puis les avons soumis à une analyse descriptive fondée sur un Mobile App Rating Scale [échelle d’évaluation des applications mobiles]. Le nombre d’applications destinées au contrôle de la DJ est largement inférieur à celui des produits dédiés à la pratique des jeux de hasard et des jeux vidéo. Les applications retenues visent pour la plupart l’abandon définitif du jeu, sans reposer sur un modèle thérapeutique reconnaissable. La majorité comporte un seul et unique outil, soit un dispositif de minutage du temps passé sans jouer. Quelques-unes sont jumelées à des services existants; elles tendent à offrir un éventail plus grand de caractéristiques et d’outils. Les applications mobiles offrent aux personnes aux prises avec une dépendance au jeu une autre voie d’accès au traitement. Pour améliorer leur efficacité, toutefois, il faudra accorder une plus grande attention à leur conception et faire en sorte qu’elles se démarquent nettement des applications autrement plus attrayantes qui font la promotion du jeu.

scholarly journals Goldilocks and the Three Bears: A Just-Right Hybrid Model to Synthesize the Growing Landscape of Publicly Available Health-Related Mobile Apps (Preprint)

2021 ◽  
Author(s):  
Nancy Lau ◽  
Alison O'Daffer ◽  
Joyce Yi-Frazier ◽  
Abby R Rosenberg
Keyword(s):  
Hybrid Model ◽  
Mobile Apps ◽  
Evidence Base ◽  
Mobile App ◽  
Essential Question ◽  
Health Related ◽  
Rapid Pace ◽  
Research Standards ◽  
State Of The Science ◽  
Mhealth Apps

UNSTRUCTURED Mobile health (mHealth) technologies have provided an innovative platform for the deployment of health care diagnostics, symptom monitoring, and prevention and intervention programs. Such health-related smartphone apps are universally accepted by patients and providers with over 50 million users worldwide. Despite the rise in popularity and accessibility among consumers, the evidence base in support of health-related apps has fallen well behind the rapid pace of industry development. To bridge this evidence gap, researchers are beginning to consider how to best apply evidence-based research standards to the systematic synthesis of the mHealth consumer market. In this viewpoint, we argue for the adoption of a “hybrid model” that combines a traditional systematic review with a systematic search of mobile app download platforms for health sciences researchers interested in synthesizing the state of the science of consumer apps. This approach, which we have successfully executed in a recent review, maximizes the benefits of traditional and novel approaches to address the essential question of whether popular consumer mHealth apps work.

Nutrition-Related Mobile Apps in the French App Store: Assessment of Functionality and Quality (Preprint)

2021 ◽  
Author(s):  
Florence Carrouel ◽  
Prescilla Martinon ◽  
Ina Saliasi ◽  
Denis Bourgeois ◽  
Colette Smenteck ◽  
...  
Keyword(s):  
Rating Scale ◽  
Mobile Apps ◽  
Intraclass Correlation ◽  
Quality Score ◽  
Mobile App ◽  
Subjective Quality ◽  
App Store ◽  
Nutritional Health ◽  
Long Term Impact ◽  
Poor Nutrition

BACKGROUND The global burden of disease attributes 20% of deaths to poor nutrition. Although hundreds of nutrition-related mobile applications have been created to help improve this situation and these have been downloaded by millions of users, the effectiveness of integrating these technologies on the adoption of healthy eating remains mixed. Similarly, no significant evaluation of nutrition applications in French, spoken by approximately 300 million people, has yet been identified in the literature. OBJECTIVE The aim of this study is to review which nutrition mobile apps are currently available on the French market, and to carry out an exhaustive assessment of their quality using the Mobile App Rating Scale (MARS) tool. METHODS A screening of apps related to nutritional health was conducted from March 10 to 17, 2021, on the Google Play Store and the French App Store. A shortlist of 15 apps was identified and assessed using the French version of MARS. Eight dietitian nutritionists assigned to assess seven apps. Remaining apps were randomly allocated to ensure four ratings per app. Intraclass correlation was used to evaluate inter-rater agreement. Mean ± SD scores and their distributions for each section and item were calculated. RESULTS The top scores for quality were obtained by Yazio (mean 3.84 ± standard deviation 0.32), FeelEat (3.71 ± 0.47) and BonneApp (3.65 ± 0.09). The engagement scores (Section A) ranged from 1.95 ± 0.5 for iEatBetter to 3.85 ± 0.44 for Feeleat. The functionality scores (Section B) ranged from 2.25 ± 0.54 for Naor to 4.25 ± 0.46 for Yazio. The Aesthetics scores (Section C) ranged from 2.17 ± 0.34 for Naor to 3.88 ± 0.47 for Yazio. The information scores (Section D) ranged from 2.38 ± 0.60 for iEatBetter:Journal alimentaire to 3.73 ± 0.29 for Yazio. The MARS subjective quality (Section E) varied from 1.13 ± 0.26 for Naor and 1.13 ± 0.25 iEatBetter:Journal alimentaire to 2.28 ± 0.88 for Compteur de calories Fatsecret. The specificity of apps varied from 1.38 ± 0.64 for iEatBetter:Journal alimentaire to 3.50 ± 0.91 for Feeleat. The app-specific score was always lower than the subjective quality score that was always lower than the quality score and that was lower than the rating score from the iOS or Android app stores. CONCLUSIONS Although the prevention and information messages regarding nutritional habits are not scientifically verified before marketing, dieteticians-nutritionists evaluated that the apps quality was quite relevant. The subjective quality and mobile app specificities were associated with lower ratings. Further investigations are needed to assess their alignment with recommendations and their long-term impact on users.

Sign in / Sign up

Export Citation Format

Share Document