Evidence map›Paper›PMID 34612833›Full record

ArticleJMIR mHealth and uHealth2021

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

Andreas Balaskas, Stephen M Schueller, Anna L Cox, Gavin Doherty

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 26 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Review
  8. Approaches to Tailoring Between-Session Mental Health Therapy Activities.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 3 countries.

Andreas BalaskasSchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.ORCID 0000-0001-5679-4186
Stephen M SchuellerDepartment of Psychological Science, University of California, Irvine, Irvine, CA, United States.ORCID 0000-0002-1003-0399
Anna L CoxUCL Interaction Centre, University College London, London, United Kingdom.ORCID 0000-0003-2231-2964
Gavin DohertySchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.ORCID 0000-0002-9617-7008
Trinity College Dublin · IELondon Centre for Nanotechnology · GBUniversity of California, Irvine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA 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.

objectiveThe 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.

methodsA 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.

resultsCognitive 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.

conclusionsEngagement 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.

Indexed as

Cognitive Behavioral TherapyMobile ApplicationsAnxietyAnxiety DisordersExerciseHumansanxietycognitive behavioral therapymental healthmHealthmobile appsmobile phonestress

Identifiers

PMID34612833
PMCPMC8529472
OpenAlexW3204267868

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.