SynthesisFrontiers in digital health2025
Personalization variables in digital mental health interventions for depression and anxiety in adolescents and youth: a scoping review.
Synthesis in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Trial
- Effectiveness of network analysis-driven personalized digital interventions versus standard intervention for depression: a proof-of-concept pilot randomized controlled trial.Molecular psychiatry · 2026Trial
- Effectiveness, Usability, and Satisfaction of a Self-Administered Digital Intervention for Reducing Depression, Anxiety, and Stress in a University Community in the Andean Region of Peru: Randomized Controlled Trial.JMIR formative research · 2025Trial
- Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era.JMIR mental health · 2026Article
- Feasibility and clinical outcomes of tailored digital CBT-E guided self-help for youth with binge-eating symptoms: A pilot randomized controlled trial.Internet interventions · 2026Article
- Working Alliance and Subjective Engagement with a Digital Avatar CBT Platform (RITchBehavioral sciences (Basel, Switzerland) · 2026Article
- GPT-Powered Chatbot-Based Positive Psychology Intervention for Well-Being Among Parents of Children With Autism Spectrum Disorder: Single-Arm Mixed Methods Study.JMIR formative research · 2026Article
- Patient engagement in digital health: a preliminary observation on user-centred and stakeholder feedback.BMJ open · 2026Article
- Money and mental health: a scoping review of financial variables, data sources, and analytical methods.Frontiers in public health · 2026Article
- Navigating self-injury in a digital world: adolescents' perspectives on coping, help-seeking, and technology.Frontiers in digital health · 2026Article
- Adolescents' and youths' perceived barriers and facilitators to engaging with digital mental health interventions for depression and anxiety: A scoping review.Internet interventions · 2025Review
- A literature review of remote mental health screening: barriers, potential solutions, and tools.Frontiers in digital health · 2025Review
- 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
- Designing a Mobile App for Post Traumatic Stress Disorder: Insights From Lived Experience and Clinical Practice.Healthcare technology lettersArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The impact of personalization on user engagement and adherence in digital mental health interventions (DMHIs) has been widely explored. However, there is a lack of clarity regarding the prevalence of its application, as well as the dimensions and mechanisms of personalization within DMHIs for adolescents and youth. Methods: To understand how personalization has been applied in DMHIs for adolescents and young people, a scoping review was conducted. Empirical studies on DMHIs for adolescents and youth with depression and anxiety, published between 2013 and July 2024, were extracted from PubMed and Scopus. A total of 67 studies were included in the review. Additionally, we expanded an existing personalization framework, which originally classified personalization into four dimensions (content, order, guidance, and communication) and four mechanisms (user choice, provider choice, rule-based, and machine learning), by incorporating non-therapeutic elements. Results: The adapted framework includes therapeutic and non-therapeutic content, order, guidance, therapeutic and non-therapeutic communication, interfaces (customization of non-therapeutic visual or interactive components), and interactivity (personalization of user preferences), while retaining the original mechanisms. Half of the interventions studied used only one personalization dimension (51%), and more than two-thirds used only one personalization mechanism. This review found that personalization of therapeutic content (51% of the interventions) and interfaces (25%) were favored. User choice was the most prevalent personalization mechanism, present in 60% of interventions. Additionally, machine learning mechanisms were employed in a substantial number of cases (30%), but there were no instances of generative artificial intelligence (AI) among the included studies. Discussion: The findings of the review suggest that although personalization elements of the interventions are reported in the articles, their impact on younger people's experience with DMHIs and adherence to mental health protocols is not thoroughly addressed. Future interventions may benefit from incorporating generative AI, while adhering to standard clinical research practices, to further personalize user experiences.
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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.