SynthesisJournal of medical Internet research2021
Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.
Synthesis in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06783907 (Technological-based Personalized Care Intervention for Supporting Older People With Diabetes Mellitus), which is not on this map. Cited by 40 papers, 4 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.
Technological-based Personalized Care Intervention for Supporting Older People With Diabetes Mellitus
Who cites it
40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Toward Participatory Precision Health With Co-Designed Recommendations: Systematic Review of Just-in-Time Adaptive Interventions in Adolescents and Young Adults.Journal of medical Internet research · 2026Pooled it
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.Frontiers in digital health · 2025Pooled it
- Automated, tailored adaptive mobile messaging to reduce alcohol consumption in help-seeking adults: A randomized controlled trial.Addiction (Abingdon, England) · 2024Trial
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Optimizing Usability of Digital Health Interventions for Nondigitally Native Adults: A Framework-Based Approach to Develop Just-in-Time Adaptive Interventions (JITAIs) and Other Adaptations.JMIR human factors · 2026Article
- A clinically actionable framework for personalizing iCBT to improve depression outcomes.NPJ digital medicine · 2026Review
- Understanding the impact of perceived app usability on the efficacy of mobile health intervention for traumatic brain injury caregivers.Rehabilitation psychology · 2026Article
- Impact of Pollution on Mental Health: A Systematic Review of Associations, Methodological Challenges, and Future Directions.Health science reports · 2026Review
- Skepticism and excitement when co-designing just-in-time mental health apps with minoritized youth.Internet interventions · 2026Article
- Article
- Patient and Care Team Perspectives of Barriers to and Facilitators for the Implementation of a Digital Health Program for Depression in Primary Care: Qualitative Study.Journal of medical Internet research · 2026Article
- ChatGPT as therapy: A qualitative and network-based thematic profiling of shared experiences, attitudes, and beliefs on Reddit.Journal of psychiatric research · 2025Article
- Evaluating and Optimizing Just-in-Time Adaptive Interventions in a Digital Mental Health Intervention (Wysa for Chronic Pain) for Middle-Aged and Older Adults With Chronic Pain: Protocol for a Series of Randomized Trials.JMIR research protocols · 2025Article
- A Social Support Just-in-Time Adaptive Intervention for Individuals With Depressive Symptoms: Feasibility Study With a Microrandomized Trial Design.JMIR mental health · 2025Article
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- A Just-in-Time Adaptive Intervention (Shift) to Manage Problem Anger After Trauma: Co-Design and Development Study.JMIR human factors · 2025Article
- Evaluating a mental health support mobile app for adults with type 1 diabetes living in rural and remote communities: The REACHOUT pilot study.Diabetic medicine : a journal of the British Diabetic Association · 2025Article
- Just-In-Time Adaptive Interventions to Promote Behavioral Health: Protocol for a Systematic Review.JMIR research protocols · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundThe number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual's burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively.
objectiveThe aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression.
methodsWe systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app's website, information on the app's website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed.
resultsNone of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms.
conclusionsPromising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.
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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.