The Functionality of Mobile Apps for Anxiety: Systematic Search and Analysis of Engagement and Tailoring Features

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Abstract

Background

A range of mobile apps for anxiety have been developed in response to the high prevalence of anxiety disorders. Although the number of publicly available apps for anxiety is increasing, attrition rates among mobile apps are high. These apps must be engaging and relevant to end users to be effective; thus, engagement features and the ability to tailor delivery to the needs of individual users are key. However, our understanding of the functionality of these apps concerning engagement and tailoring features is limited.

Objective

The aim of this study is to review how cognitive behavioral elements are delivered by anxiety apps and their functionalities to support user engagement and tailoring based on user needs.

Methods

A systematic search for anxiety apps described as being based on cognitive behavioral therapy (CBT) was conducted on Android and iPhone marketplaces. Apps were included if they mentioned the use of CBT for anxiety-related disorders. We identified 597 apps, of which 36 met the inclusion criteria and were reviewed through direct use.

Results

Cognitive behavioral apps for anxiety incorporate a variety of functionalities, offer several engagement features, and integrate low-intensity CBT exercises. However, the provision of features to support engagement is highly uneven, and support is provided only for low-intensity CBT treatment. Cognitive behavioral elements combine various modalities to deliver intervention content and support the interactive delivery of these elements. Options for personalization are limited and restricted to goal selection upon beginning use or based on self-monitoring entries. Apps do not appear to provide individualized content to users based on their input.

Conclusions

Engagement and tailoring features can be significantly expanded in existing apps, which make limited use of social features and clinical support and do not use sophisticated features such as personalization based on sensor data. To guide the evolution of these interventions, further research is needed to explore the effectiveness of different types of engagement features and approaches to tailoring therapeutic content.

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Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps

Stoyan Stoyanov, Leanne Hides, David J Kavanagh … (2015)

Background The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond “star”-ratings. Objective The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. Methods A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. Results There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). Conclusions The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.

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Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Inbal Nahum-Shani, Shawna Smith, Bonnie J Spring … (2017)

Background The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention We clarify the scientific motivation for the Just-In-Time Adaptive Interventions, define its fundamental components, and discuss key design principles for each component.

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Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials.

Joseph Firth, John Torous, Jennifer Lynn Nicholas … (2017)

Various psychological interventions are effective for reducing symptoms of anxiety when used alone, or as an adjunct to anti-anxiety medications. Recent studies have further indicated that smartphone-supported psychological interventions may also reduce anxiety, although the role of mobile devices in the treatment and management of anxiety disorders has yet to be established.

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Author and article information

Contributors

Gavin Doherty:

ORCID: https://orcid.org/0000-0002-9617-7008

School of Computer Science and StatisticsTrinity College DublinCollege GreenDublinIreland353 01 8963858Gavin.Doherty@tcd.ie

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: October 2021

Publication date (Electronic): 6 October 2021

Volume: 9

Issue: 10

Electronic Location Identifier: e26712

Affiliations

[1 ] School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

[2 ] Department of Psychological Science University of California, Irvine Irvine, CA United States

[3 ] Department of Informatics University of California, Irvine Irvine, CA United States

[4 ] UCL Interaction Centre University College London London United Kingdom

Author notes

Corresponding Author: Gavin Doherty Gavin.Doherty@ 123456tcd.ie

Author information

Andreas Balaskas https://orcid.org/0000-0001-5679-4186

Stephen M Schueller https://orcid.org/0000-0002-1003-0399

Anna L Cox https://orcid.org/0000-0003-2231-2964

Gavin Doherty https://orcid.org/0000-0002-9617-7008

Article

Publisher ID: v9i10e26712

DOI: 10.2196/26712

PMC ID: 8529472

PubMed ID: 34612833

SO-VID: 41f3e2a0-b353-4d52-8f03-3b6088555502

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 23 December 2020

Date revision requested : 12 February 2021

Date revision received : 2 April 2021

Date accepted : 15 July 2021

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