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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)

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/mental.9235

The final accepted version (not copyedited yet) is in this tab.

The final, peer-reviewed published version of this preprint can be found here:

Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

Matthews P, Topham P, Caleb-Solly P

Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

JMIR Ment Health 2018;5(4):e58

DOI: 10.2196/mental.9235

PMID: 30287415

PMCID: 6324647

Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

  • Paul Matthews; 
  • Phil Topham; 
  • Praminda Caleb-Solly

ABSTRACT

Background:

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 (“the Social Cloud”). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.

Objective:

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.

Methods:

We use data mining techniques on aggregate data obtained from 105,380 registered users of the app’s cloud services.

Results:

Engagement generally conformed to common mobile participation patterns with an inverted pyramid or “funnel” 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.

Conclusions:

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.


 Citation

Please cite as:

Matthews P, Topham P, Caleb-Solly P

Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

JMIR Mental Health. (forthcoming/in press)

DOI: 10.2196/mental.9235

URL: https://preprints.jmir.org/preprint/9235

PMID: 30287415

PMCID: 6324647

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.