Evidence map›Paper›PMID 42079320›Full record

ArticleFrontiers in psychiatry2026

Protocol for a randomized trial to predict the efficacy of cognitive and behavioral interventions for symptoms of depression.

Jialing Ding, Jamie C Chiu, Seohyun Moon, Yongjing Ren, David M Turner, Gal Shoval, Yael Niv, Isabel M Berwian

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06631183 (Behavioral Study to Predict the Efficacy of a Self-Help Tool), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT06631183 naactive not recruitingnot on this map

Behavioral Study to Predict the Efficacy of a Self-Help Tool

TypeinterventionalSponsorTrustees of Princeton UniversityRan2024 to 2025Enrolled1,500ConditionsSelf-reported Symptoms of DepressionArmsSelf-help information based on principles of behavioral activation, Self-help information based on principles of cognitive restructuring
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Jialing Ding *Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Jamie C Chiu *Department of Psychology, Princeton University, Princeton, NJ, United States.
Seohyun MoonPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Yongjing RenPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
David M TurnerPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Gal ShovalPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Yael Niv *Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Isabel M Berwian *Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cognitive behavioral therapy (CBT) is one of the most common interventions for depression and has two key components: Cognitive Restructuring (CR) and Behavioral Activation (BA). However, no evidence-based guidelines exist to help clients and clinicians decide whether CBT would be a good first-line treatment for a given individual based on their personal characteristics, and which CBT intervention would benefit them more. We propose that specific capacities to learn from new information and experiences are prerequisites for response to CBT and that BA and CR require different learning capacities. In this study, we aim to develop predictive models of symptom change based on computationally-derived variables from behavioral tasks, in addition to clinical and demographic self-report data, to identify parameters and variables that can determine which individuals with depressive symptoms would benefit from CBT-based interventions and, ideally, which specific interventions they would benefit from more. Methods and analysis: We plan to recruit at least 1,500 adult participants who report having symptoms of depression and reside in U.S. After completing a series of questionnaires and behavioral tasks to assess their learning propensities, participants will be randomly assigned to a BA or a CR group. Using an online self-help tool, participants will then engage with designated modules according to their assigned group for five weeks. We will assess symptoms 1 week post-intervention (main end point of study) and follow up at 6, 18, and 42 weeks post-intervention. Upon enrolling and consenting into the main study, participants will be randomly assigned to either the training dataset or the held-out test dataset at a ratio of 2:1. This enables a clean separation of training and test datasets and prevent data leakage. We plan to build cross-validated predictive algorithms on the training dataset, and preregister our analysis plan before we validate our models and hypotheses in the held-out, unseen, test dataset. Enrollment of the study started 23rd January, 2024. Study protocol registration: ClinicalTrials.gov, identifier (NCT06631183). The protocol follows the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Numbers in brackets follow subsection numbers in the guidelines.

Indexed as

cognitive behavioral therapy (CBT)depressionprediction modelpsychotherapyrandomized clinical trial

Identifiers

PMID42079320
PMCPMC13128560

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.