ArticleJournal of anxiety disorders2024
Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model.
Article in Journal of anxiety disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Self-Guided Internet-Based Mindfulness-Informed Stress Management for Generalized Anxiety Disorder: Randomized Controlled Trial With Longitudinal Network Analysis.Journal of medical Internet research · 2026Trial
- Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.Journal of affective disorders · 2026Trial
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Work-Related Cognitive Behavioral Therapy for racially and economically diverse unemployed persons with social anxiety: A randomized clinical trial.Journal of anxiety disorders · 2024Trial
- Article
- Neuropsychological Mechanisms Associated with the Effectiveness of AI-Delivered Health Promotion Programs: A Comprehensive Meta-Analysis.Brain sciences · 2026Review
- Predicting Efficacy of Virtual Reality-Based Stabilization for Individuals With Posttraumatic Stress Symptoms: A Machine Learning Approach.Psychiatry investigation · 2025Article
- Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.Child psychiatry and human development · 2025Article
- Development of a machine learning-based multivariable prediction model for the naturalistic course of generalized anxiety disorder.Journal of anxiety disorders · 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
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.
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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.