Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.

Nur Hani Zainal, Michelle G Newman

Registry-linked trialAbstract readRandomized Controlled Trial

In one paragraph

Trial report in Journal of affective disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04846777 (Brief Smartphone Treatment Study for Anxiety and Depression), which is not on this map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from 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.

2 · The registry

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.

NCT04846777 narecruitingnot on this map

Brief Smartphone Treatment Study for Anxiety and Depression

TypeinterventionalSponsorPenn State UniversityRan2018 to 2023Enrolled300ConditionsGeneralized Anxiety DisorderArmsMindfulness ecological momentary intervention, Self-monitoring placebo

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The relationship between trait mindfulness and psychotic-like experiences in a brief AI-generated music listening context: the roles of presence, perceived interactivity, and emotional arousal.Frontiers in psychiatry · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nur Hani ZainalNational University of Singapore, Department of Psychology, Kent Ridge Campus, Singapore. Electronic address: hanizainal@nus.edu.sg.

Michelle G NewmanThe Pennsylvania State University, Department of Psychology, University Park, PA, USA.

Funding

NIMH NIH HHS R01 MH115128

6 · The paper itself

Abstract

backgroundAlthough mindfulness ecological momentary interventions (MEMI) appear effective in alleviating worry symptoms, treatment engagement remains suboptimal. Determining baseline variables of MEMI over self-monitoring placebo (SM) can inform tailored interventions for individuals with generalized anxiety disorder (GAD).

methodMachine meta-learning methods (ML) were applied to predict two-week engagement (log-transformed number of prompts completed) among individuals randomized to MEMI or SM (N = 110). Sixteen baseline variables comprised the predictor set: clinical, demographic, process, and executive functioning (EF) factors. Random forest using a five-fold nested cross-validation approach mitigated overfitting. X-learner meta-algorithms estimated conditional average treatment engagement (CATE). Shapley additive explanations evaluated relative importance.

resultsThe 16-predictor model displayed strong predictive performance (R-squared [R LIMITATIONS: The small sample size, single engagement metric, and brief duration might constrain generalizability. DISCUSSION: Integrating robust ML approaches could optimally identify prescriptive predictors of engagement to brief digital mental health interventions to inform targeted treatments.

trial registrationClinicalTrials.gov ID (NCT04846777).

Indexed as

Generalized Anxiety DisorderMachine LearningMindfulnessAdultEcological Momentary AssessmentExecutive FunctionFemaleHumansMaleMiddle AgedTreatment OutcomeDigital treatment engagementGeneralized anxiety disorderInterpretable machine learningMindfulness ecological momentary interventionPrecision medicineRandomized controlled trial

Identifiers

PMID41422949

DOI10.1016/j.jad.2025.120963

PMCPMC12933389

What Socratic holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

NCT04846777ClinicalTrials.gov

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 Socratic graph.