Evidence map›Paper›PMID 32743079›Full record

ArticleJournal of psychiatry and brain science2020

Testing the Mechanism of Action of Computerized Cognitive Training in Young Adults with Depression: Protocol for a Blinded, Randomized, Controlled Treatment Trial.

Sara N Rushia, Sophie Schiff, Dakota A Egglefield, Jeffrey N Motter, Alice Grinberg, Daniel G Saldana, Al Amira Safa Shehab, Jin Fan, Joel R Sneed

Registry-linked trialOpen access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of psychiatry and brain science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03869463 (Testing the Mechanism of Action of Computerized Cognitive Training in Young Adults With 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
0.2field-weighted citation impact, top 50% of its field
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.

NCT03869463 naterminatednot on this map

Testing the Mechanism of Action of Computerized Cognitive Training in Young Adults With Depression: Clinical, Cognitive and Neuroimaging Outcomes

TypeinterventionalSponsorQueens College, The City University of New YorkRan2019 to 2023Enrolled28ConditionsDepressive SymptomsArmsComputerized Cognitive Training, Control
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

9 authors at 4 institutions in 1 country.

Sara N RushiaDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Sophie SchiffDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Dakota A EgglefieldDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Jeffrey N MotterDepartment of Psychiatry, Columbia University Medical Center, New York, NY 10032, USA.
Alice GrinbergDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Daniel G SaldanaDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Al Amira Safa ShehabDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Jin FanDepartment of Psychology, Queens College, City University of New York, Flushing, NY 11367, USA.
Joel R SneedDepartment of Psychology, The Graduate Center, City University of New York, New York, NY 10016, USA.
Queens College, CUNY · USThe Graduate Center, CUNY · USCity University of New York · USNew York Psychoanalytic Society and Institute · US

Funding

Testing the mechanism of action of computerized cognitive training in young adults with depression:Clinical, cognitive and neuroimaging outcomesR15MH119626 · NIMH · QUEENS COLLEGE · PI SNEED, JOEL R. · 2019 to 2019
$462k
NIMH NIH HHS R15 MH119626
6 · The paper itself

Abstract

backgroundDepression is associated with a broad range of cognitive deficits, including processing speed (PS) and executive functioning (EF). Cognitive symptoms commonly persist with the resolution of affective symptoms and increase risk of relapse and recurrence. The cognitive control network is comprised of brain areas implicated in EF and mood regulatory functions. Prior research has demonstrated the effectiveness of computerized cognitive training (CCT) focused on PS and EF in mitigating both cognitive and affective symptoms of depression.

methodsNinety participants aged 18-29 with a current diagnosis of major depressive disorder or persistent depressive disorder, or a Hamilton Depression Rating Scale score ≥12, will be randomized to either PS/EF CCT, verbal CCT, or waitlist control. Participants in the active groups will complete 15 min of training 5 days/week for 8 weeks. Clinical and neuropsychological assessments will be completed at baseline, week 4, week 8, and 3-month follow-up. Structural and functional magnetic resonance imaging (fMRI) will be completed at baseline and week 8. We will compare changes in mood, cognition, daily functioning, and fMRI data. We will explore cognitive control network functioning using resting-state and task-based fMRI.

resultsRecruitment began in October 2019; we expect to finish recruitment by April 2022 and subsequently begin data analysis.

conclusionsThis study is innovative in that it will include both active and waitlist control conditions and will explore changes in neural activation. Identifying the neural networks associated with improvements following CCT will allow for the development of more precise and effective interventions.

trial registrationClinicalTrials.gov NCT03869463; https://clinicaltrials.gov/ct2/show/NCT03869463.

Indexed as

clinical trialscomputerized cognitive trainingdepressionmagnetic resonance imaging

Identifiers

PMID32743079
PMCPMC7394311
OpenAlexW3036888713

What OpenQuestion 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 OpenQuestion graph.