Native American communities are disproportionately affected by a number of behavioral health disparities, including higher rates of depression, substance abuse, and suicide. As mobile health (mHealth) interventions gain traction as methods for addressing these disparities, they continue to lack relevance to Native American youth. In an effort to explore the design of relevant behavioral mHealth intervention for Native American communities, we have developed ARORA (Amplifying Resilience Over Restricted Internet Access), a prototype behavioral mHealth intervention that has been co-designed with Native American youth, a community advisory board, and a clinical psychologist. In this paper, we qualitatively analyze our co-design and focus group sessions using a grounded theory approach and identify the key themes that Native American community members have identified as being critical components of relevant mHealth designs. Notably, we find that the Native American youth who participated in our focus groups desired a greater level of didactic interaction with cultural and behavioral health elements. We conclude with a discussion of the significant challenges we faced in our efforts to co-design software with Native American stakeholders and provide recommendations that might guide other HCI researchers and designers through challenges that arise during the process of cross-cultural design.
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.
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 programs, app developers may be able to support greater therapeutic engagement. OBJECTIVE The main objective for this analysis was to systematically characterize the types of user interactions that are available in behavioral health mHealth apps, and then to examine if interactivity was associated with user satisfaction and app visibility. METHODS We examined 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, AI, 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 six types of human-machine interactivities: human-to-human with peers, human-to-human with providers, humans-to-artificial intelligence (AI), 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 included 2.53 (sd 1.05) (range 1 to 5) features of interactivity. Most common types of interactivities were human-to-data (100%), followed by human-to-algorithm (42.9%). The least common type of interactivity was human-AI (20.0%). 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 utilized in behavioral health apps. CONCLUSIONS Ideally, app developers would do well to include more interactivity features in apps and fully utilize the capability of smartphone technologies. Theoretically, increased user engagement would occur through multiple types of user interactivity, thereby maximizing the benefits that a person could receive when using an mHealth app. CLINICALTRIAL N/A
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