Evidence map›Paper›PMID 36970284›Full record

ArticleFrontiers in psychiatry2023

Attention bias modification for depression: A systematic review and meta-analysis.

Hai-Sha Xia, Yu-Xi Li, Qing-Yun Zhang, Dong-Ling Zhong, Xiao-Bo Liu, Xin-Yun Gou, Jin Fan, Jing Zhao, Yue Zhang, Shuang-Chun Ai and 3 more

Open access · goldFull text read
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
7.9field-weighted citation impact, top 2% 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.

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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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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

13 authors at 3 institutions in 1 country.

Hai-Sha XiaSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yu-Xi LiSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Qing-Yun ZhangDepartment of Rehabilitation, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
Dong-Ling ZhongSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiao-Bo LiuSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xin-Yun GouSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jin FanSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Jing ZhaoSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yue ZhangSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Shuang-Chun AiDepartment of rehabilitation, Mianyang Hospital of Traditional Chinese Medicine, Mianyang, China.
Jia-Xi HuangMental Health Center, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
Juan LiSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Rong-Jiang JinSchool of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Chengdu University of Traditional Chinese Medicine · CNMianyang Third People's Hospital · CNSichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Depression is a mental health disorder characterized by affective, somatic, and cognitive symptoms. Attention bias modification (ABM) has been widely used to treat depression. However, the results seem inconsistent. We conducted a systematic review and meta-analysis to investigate the efficacy of ABM for depression and to explore the optimal protocol of ABM. Methods: Seven databases were systematically searched from their inceptions to 5 October 2022 to include randomized controlled trials (RCTs) of ABM for depression. Two independent reviewers selected the eligible articles, extracted data, and evaluated the risk of bias using version 2 of the Cochrane risk-of-bias tool (ROB 2.0) for randomized trials. The primary outcome was the evaluation of depressive symptoms using widely accepted and validated scales. The secondary outcomes included rumination and attentional control. Meta-analysis was conducted by using RevMan (version 5.4) and Stata (version 12.0). Subgroup analyses and meta-regressions were performed to identify the source of heterogeneity. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Results: A total of 19 trials involving 20 datasets (1,262 participants) were included. The overall risk of bias in one study was rated as low risk of bias, three studies were considered as high, and the remaining studies were some concerns. Compared with attention control training (ACT), ABM had a greater effect in the improvement of depression (SMD = -0.48, 95% CI -0.80 to -0.17, Conclusion: Due to high heterogeneity and limited studies, not enough current evidence supported that ABM could be an effective intervention to relieve depressive symptoms. More rigorous RCTs are required to verify the benefits and to explore the optimal protocol of ABM training for depression.

Indexed as

attention bias modificationcognitive deficitsdepressionmeta-analysissystematic review

Identifiers

PMID36970284
PMCPMC10036757
OpenAlexW4323859244

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read31
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.