Evidence map›Paper›PMID 32012079›Full record

ReviewJMIR mental health2020

Functionality of Top-Rated Mobile Apps for Depression: Systematic Search and Evaluation.

Chengcheng Qu, Corina Sas, Claudia Daudén Roquet, Gavin Doherty

Erratum issued Registry-linked trialAbstract readReview
In one paragraph

Review in JMIR mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05234970 (iWellness Study), which is not on this map. Cited by 36 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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.

NCT05234970 nacompletednot on this mapstarted 2022, after this paper: background citation

iWellness Study: Evaluation of a CBT Chatbot App to Promote Mental Wellness in Students

TypeinterventionalSponsorHarvard UniversityRan2022 to 2022Enrolled289ConditionsDepression, AnxietyArmsWysa, Harvard College App
3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Chengcheng QuSchool of Computing and Communications, Lancaster University, Lancaster, United Kingdom.ORCID https://orcid.org/0000-0001-8285-1594
Corina SasSchool of Computing and Communications, Lancaster University, Lancaster, United Kingdom.ORCID https://orcid.org/0000-0001-9297-9612
Claudia Daudén RoquetSchool of Computing and Communications, Lancaster University, Lancaster, United Kingdom.ORCID https://orcid.org/0000-0001-7490-6049
Gavin DohertySchool of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-9617-7008

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the last decade, there has been a proliferation of mobile apps claiming to support the needs of people living with depression. However, it is unclear what functionality is actually provided by apps for depression, or for whom they are intended.

objectiveThis paper aimed to explore the key features of top-rated apps for depression, including descriptive characteristics, functionality, and ethical concerns, to better inform the design of apps for depression.

methodsWe reviewed top-rated iPhone OS (iOS) and Android mobile apps for depression retrieved from app marketplaces in spring 2019. We applied a systematic analysis to review the selected apps, for which data were gathered from the 2 marketplaces and through direct use of the apps. We report an in-depth analysis of app functionality, namely, screening, tracking, and provision of interventions. Of the initially identified 482 apps, 29 apps met the criteria for inclusion in this review. Apps were included if they remained accessible at the moment of evaluation, were offered in mental health-relevant categories, received a review score greater than 4.0 out of 5.0 by more than 100 reviewers, and had depression as a primary target.

resultsThe analysis revealed that a majority of apps specify the evidence base for their intervention (18/29, 62%), whereas a smaller proportion describes receiving clinical input into their design (12/29, 41%). All the selected apps are rated as suitable for children and adolescents on the marketplace, but 83% (24/29) do not provide a privacy policy consistent with their rating. The findings also show that most apps provide multiple functions. The most commonly implemented functions include provision of interventions (24/29, 83%) either as a digitalized therapeutic intervention or as support for mood expression; tracking (19/29, 66%) of moods, thoughts, or behaviors for supporting the intervention; and screening (9/29, 31%) to inform the decision to use the app and its intervention. Some apps include overtly negative content.

conclusionsCurrently available top-ranked apps for depression on the major marketplaces provide diverse functionality to benefit users across a range of age groups; however, guidelines and frameworks are still needed to ensure users' privacy and safety while using them. Suggestions include clearly defining the age of the target population and explicit disclosure of the sharing of users' sensitive data with third parties. In addition, we found an opportunity for apps to better leverage digital affordances for mitigating harm, for personalizing interventions, and for tracking multimodal content. The study further demonstrated the need to consider potential risks while using depression apps, including the use of nonvalidated screening tools, tracking negative moods or thinking patterns, and exposing users to negative emotional expression content.

Indexed as

depressionethicsmHealthmobile appsreview

Identifiers

PMID32012079
PMCPMC7007593

What OpenQuestion holds

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Registered trials

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.