Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 9, 2022
Open Peer Review Period: Dec 8, 2022 - Feb 2, 2023
Date Accepted: May 5, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Examining Interactivity in Behavioral Health Apps
ABSTRACT
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. Clinical Trial: N/A
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