Currently accepted at: JMIR Mental Health
Date Submitted: Oct 21, 2017
Open Peer Review Period: Oct 22, 2017 - Jun 14, 2018
Date Accepted: Jun 14, 2018
(closed for review but you can still tweet)
Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data
SAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum (â€œthe Social Cloudâ€). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.
The objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation.
We use data mining techniques on aggregate data obtained from 105,380 registered users of the appâ€™s cloud services.
Engagement generally conformed to common mobile participation patterns with an inverted pyramid or â€œfunnelâ€ of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation.
The data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement.
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