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Background While there are thousands of behavioral health apps available to consumers, users often quickly discontinue their use, which limits their therapeutic value. By varying the types and number of ways that users can interact with behavioral health mobile health apps, developers may be able to support greater therapeutic engagement and increase app stickiness. Objective The main objective of this analysis was to systematically characterize the types of user interactions that are available in behavioral health apps and then examine if greater interactivity was associated with greater user satisfaction, as measured by app metrics. Methods Using a modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) methodology, we searched several different app clearinghouse websites and identified 76 behavioral health apps that included some type of interactivity. We then filtered the results to ensure we were examining behavioral health apps and further refined our search to include apps that identified one or more of the following terms: peer or therapist forum, discussion, feedback, professional, licensed, buddy, friend, artificial intelligence, chatbot, counselor, therapist, provider, mentor, bot, coach, message, comment, chat room, community, games, care team, connect, share, and support in the app descriptions. In the final group of 34 apps, we examined the presence of 6 types of human-machine interactivities: human-to-human with peers, human-to-human with providers, human-to–artificial intelligence, human-to-algorithms, human-to-data, and novel interactive smartphone modalities. We also downloaded information on app user ratings and visibility, as well as reviewed other key app features. Results We found that on average, the 34 apps reviewed included 2.53 (SD 1.05; range 1-5) features of interactivity. The most common types of interactivities were human-to-data (n=34, 100%), followed by human-to-algorithm (n=15, 44.2%). The least common type of interactivity was human–artificial intelligence (n=7, 20.5%). There were no significant associations between the total number of app interactivity features and user ratings or app visibility. We found that a full range of therapeutic interactivity features were not used in behavioral health apps. Conclusions Ideally, app developers would do well to include more interactivity features in behavioral health apps in order to fully use the capabilities of smartphone technologies and increase app stickiness. Theoretically, increased user engagement would occur by using multiple types of user interactivity, thereby maximizing the benefits that a person would receive when using a mobile health app.
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Epidemiology as a Framework for Large-Scale Mobile Application Accessibility Assessment
Mobile accessibility is often a property considered at the level of a single mobile application (app), but rarely on a larger scale of the entire app "ecosystem," such as all apps in an app store, their companies, developers, and user influences. We present a novel conceptual framework for the accessibility of mobile apps inspired by epidemiology. It considers apps within their ecosystems, over time, and at a population level. Under this metaphor, "inaccessibility" is a set of diseases that can be viewed through an epidemiological lens. Accordingly, our framework puts forth notions like risk and protective factors, prevalence, and health indicators found within a population of apps. This new framing offers terminology, motivation, and techniques to reframe how we approach and measure app accessibility. It establishes how app accessibility can benefit from multi-factor, longitudinal, and population-based analyses. Our epidemiology-inspired conceptual framework is the main contribution of this work, intended to provoke thought and inspire new work enhancing app accessibility at a systemic level. In a preliminary exercising of our framework, we perform an analysis of the prevalence of common determinants or accessibility barriers. We assess the health of a stratified sample of 100 popular Android apps using Google's Accessibility Scanner. We find that 100% of apps have at least one of nine accessibility errors and examine which errors are most common. A preliminary analysis of the frequency of co-occurrences of multiple errors in a single app is also presented. We find 72% of apps have five or six errors, suggesting an interaction among different errors or an underlying influence.
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- PAR ID:
- 10063402
- Date Published:
- Journal Name:
- Proceedings of the ACM Conference on Computers and Accessibility (ASSETS 2017)
- Page Range / eLocation ID:
- 2-11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3132525.3132547
