Evidence map›Paper›PMID 39870867›Full record

ArticleNPJ digital medicine2025

Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms.

Thalia Richter, Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M Enock, Richard J McNally, Nilly Mor, Shimrit Daches, Alishia D Williams and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Digital Cognitive Twins in mental health.Nature. Mental health · 2025
    Article
  4. 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

20 authors.

Thalia Richter *School of Psychological Sciences, University of Haifa, Haifa, Israel. trichter@campus.haifa.ac.il.ORCID http://orcid.org/0000-0002-4302-2125
Reut Shani *School of Psychological Sciences, University of Haifa, Haifa, Israel.
Shachaf TalSchool of Psychological Sciences, University of Haifa, Haifa, Israel.
Nazanin DerakshanCentre for Resilience and Posttraumatic Growth, National Centre for Integrative Oncology (NCIO), Reading, UK.
Noga CohenDepartment of Special Education, University of Haifa, Haifa, Israel.
Philip M EnockDepartment of Psychology, Harvard University, Cambridge, MA, USA.
Richard J McNallyDepartment of Psychology, Harvard University, Cambridge, MA, USA.
Nilly MorDepartment of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel.
Shimrit DachesPsychology Department, Bar Ilan University, Ramat Gan, Israel.
Alishia D WilliamsSchool of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW, Australia.
Jenny YiendKing's College London, London, UK.ORCID http://orcid.org/0000-0002-1967-6292
Per CarlbringDepartment of Psychology, Stockholm University, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-2172-8813
Jennie M KuckertzDepartment of Psychiatry, McLean Hospital, Belmont, MA, USA.
Wenhui YangDepartment of Psychology, Hunan Normal University, Hunan, China.
Andrea ReineckeDepartment of Psychiatry, University of Oxford, Oxford, UK.
Christopher G BeeversInstitute for Mental Health Research and Department of Psychology, University of Texas at Austin, Austin, TX, USA.
Brian E BunnellDepartment of Psychiatry and Behavioral Neurosciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Ernst H W KosterDepartment of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium.
Sigal Zilcha-ManoSchool of Psychological Sciences, University of Haifa, Haifa, Israel.
Hadas Okon-SingerSchool of Psychological Sciences, University of Haifa, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. Baseline depression and anxiety symptoms were found to be the most influential factor, with individuals with more severe symptoms showing the greatest improvement. The number of training sessions was also important, with more sessions yielding greater benefits. Cognitive trainings were associated with higher predicted improvement than control conditions, with attention and interpretation bias modification showing the most promise. Despite the limitations of heterogeneous datasets, this investigation highlights the value of large-scale comprehensive analyses in guiding the development of personalized training interventions.

Identifiers

PMID39870867
PMCPMC11772606

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.