ReviewJMIR mental health2020
Functionality of Top-Rated Mobile Apps for Depression: Systematic Search and Evaluation.
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.
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.
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.
iWellness Study: Evaluation of a CBT Chatbot App to Promote Mental Wellness in Students
Who cites it
36 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Mood Monitoring, Mood Tracking, and Ambulatory Assessment Interventions in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR mental health · 2026Pooled it
- Mobile applications in adolescent psychotherapy during the COVID-19 pandemic: a systematic review.Frontiers in public health · 2024Pooled it
- A systematic review of features and content quality of Arabic mental mHealth apps.Frontiers in digital health · 2024Pooled it
- Providing Human Support for the Use of Digital Mental Health Interventions: Systematic Meta-review.Journal of medical Internet research · 2023Pooled it
- Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.Journal of medical Internet research · 2021Pooled it
- Use of a Smartphone-Based Mobile App for Weight Management in Obese Minority Stroke Survivors: Pilot Randomized Controlled Trial With Open Blinded End Point.JMIR mHealth and uHealth · 2020Trial
- Functionality Review of Mobile Apps for the Tracking and Self-Management of Fatigue: Systematic Search in App Stores and Content Analysis.JMIR mHealth and uHealth · 2026Article
- Article
- Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation and Non-Meditation Sessions.Sensors (Basel, Switzerland) · 2025Article
- Requirements and Concerns of Individuals Remitted From Depression for an Early Relapse Detection mHealth App: Focus Group Study.JMIR mHealth and uHealth · 2025Article
- Utilizing Mobile Health Technology to Enhance Brace Compliance: Feasibility and Effectiveness of an App-Based Monitoring System for Adolescents with Idiopathic Scoliosis.Journal of personalized medicine · 2025Article
- Cognitive Training Mobile Apps for Older Adults With Cognitive Impairment: App Store Search and Quality Evaluation.JMIR mHealth and uHealth · 2025Article
- Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.Brain informatics · 2025Article
- Top-funded companies offering digital health interventions for the prevention and treatment of depression: a systematic market analysis.Archives of public health = Archives belges de sante publique · 2024Article
- Development of a Multiplatform Tool for the Prevention of Prevalent Mental Health Pathologies in Adults: Protocol for a Randomized Control Trial.JMIR research protocols · 2024Article
- Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.Brain informatics · 2023Article
- User-Centered Design of a Digitally Enabled Care Pathway in a Large Health System: Qualitative Interview Study.JMIR human factors · 2023Article
- Design and Implementation of a Digitally Enabled Care Pathway to Improve Management of Depression in a Large Health Care System: Protocol for the Implementation of a Patient Care Platform.JMIR research protocols · 2023Article
- Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.JMIR human factors · 2022Article
- Making remote measurement technology work in multiple sclerosis, epilepsy and depression: survey of healthcare professionals.BMC medical informatics and decision making · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.