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Crowdsourced data collection is a scalable approach to collecting mobile broadband performance data across space. However, existing platforms for crowdsourced mobile broadband measurements are not designed to engage workers over time or space, which can lead to spatial misrepresentation and stale data. With the insight that games and play ofer naturally engaging frameworks for users, we held fve iterative, participatory design sessions with 11 participants to co-design a catalog of 11 game concepts that could be used to create more spatially representative mobile broadband data sets. Importantly, we found that while games varied substantially with respect to theme, all used a few common game mechanics to incorporate mobile broadband data collection into play. This indicates that a designed prototype might focus on offering a customizable gaming structure that would allow communities and individuals to create thematic content that could overlay onto a set of common mechanics that could support more representative geospatial data collection. 

Despite high incidence of depression, anxiety, and post traumatic stress disorder, stigma and lack of access to culturally responsive behavioral health care resources prevents many Native Americans (NA) from seeking care. However, the rise of culturally-responsive in-person and digital behavioral health resources for NA communities provides new opportunities to address these longstanding health equity issues. The major challenge is helping people in NA communities find these meaningful resources and helping anchor institutions understand how resources are being sought and utilized to support more responsive internal programming. In this context, we have partnered with Hopi Behavioral Health Services (HBHS) to design the Resilience Resource Database to digitally disseminate mental and behavioral health resources. This paper presents initial findings that have resulted from the initial stage of an iterative participatory design process with HBHS. 

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.