ArticleJournal of the American Medical Informatics Association : JAMIA2018
Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Effectiveness of evidence based mental health apps on user health outcome: A systematic literature review.PloS one · 2025Pooled it
- Health Recommender Systems: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Digital Mental Health Treatment and Symptoms of Depression and Anxiety in Breast Cancer Survivors: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Enhancing Engagement, Practice Integration, and Skill Learning in Mobile Technology-Delivered Interventions Using Human Support: Randomized Controlled Trial With Depressed College Students.JMIR formative research · 2025Trial
- Mobile health-based home rehabilitation education improving early outcomes after anterior cruciate ligament reconstruction: A randomized controlled clinical trial.Frontiers in public health · 2022Trial
- Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial.JMIR mHealth and uHealth · 2020Trial
- Comparison of the Effects of Coaching and Receipt of App Recommendations on Depression, Anxiety, and Engagement in the IntelliCare Platform: Factorial Randomized Controlled Trial.Journal of medical Internet research · 2019Trial
- Increasing engagement with cognitive-behavioral therapy (CBT) using generative AI: a randomized controlled trial (RCT).Communications medicine · 2026Article
- Impact of coping profiles on social identity in a youth chronic pain recommender system: qualitative descriptive study.Frontiers in digital health · 2026Article
- Effectiveness of a guided mobile app intervention for depression and anxiety in university students (PROMES-U study): protocol of a multicenter randomized controlled trial.BMC public health · 2025Article
- A multiagent reinforcement learning algorithm for personalized recommendations in bipolar disorder.PNAS nexus · 2025Article
- Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study.Journal of medical Internet research · 2025Observational
- We don't need more apps, we need connection: recommender systems as under-explored chance to promote students' mental health at universities.Frontiers in psychology · 2025Article
- Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study.JMIR mental health · 2024Article
- Article
- Combining Behavioral Economics-Based Incentives With the Anchoring Strategy: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2023Article
- Monotone response surface of multi-factor condition: estimation and Bayes classifiers.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
- Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study.International journal of environmental research and public health · 2022Article
- Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study.JMIR medical informatics · 2022Article
- What Works Best to Engage Participants in Mobile App Interventions and e-Health: A Scoping Review.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Objective: While depression and anxiety are common mental health issues, only a small segment of the population has access to standard one-on-one treatment. The use of smartphone apps can fill this gap. An app recommender system may help improve user engagement of these apps and eventually symptoms. Methods: IntelliCare was a suite of apps for depression and anxiety, with a Hub app that provided app recommendations aiming to increase user engagement. This study captured the records of 8057 users of 12 apps. We measured overall engagement and app-specific usage longitudinally by the number of weekly app sessions ("loyalty") and the number of days with app usage ("regularity") over 16 weeks. Hub and non-Hub users were compared using zero-inflated Poisson regression for loyalty, linear regression for regularity, and Cox regression for engagement duration. Adjusted analyses were performed in 4561 users for whom we had baseline characteristics. Impact of Hub recommendations was assessed using the same approach. Results: When compared to non-Hub users in adjusted analyses, Hub users had a lower risk of discontinuing IntelliCare (hazard ratio = 0.67, 95% CI, 0.62-0.71), higher loyalty (2- to 5-fold), and higher regularity (0.1-0.4 day/week greater). Among Hub users, Hub recommendations increased app-specific loyalty and regularity in all 12 apps. Discussion/Conclusion: Centralized app recommendations increase overall user engagement of the apps, as well as app-specific usage. Further studies relating app usage to symptoms can validate that such a recommender improves clinical benefits and does so at scale.
Indexed as
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What OpenQuestion holds
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.