Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142394
Title: Web-based cognitive bias modification interventions for psychiatric disorders : scoping review
Authors: Zhang, Melvyn
Ying, Jiangbo
Song, Guo
Fung, Daniel S. S.
Smith, Helen
Keywords: Science::Medicine
Issue Date: 2018
Source: Zhang, M., Ying, J., Song, G., Fung, D. S. S., & Smith, H. (2019). Web-based cognitive bias modification interventions for psychiatric disorders : scoping review. JMIR Mental Health, 6(10), e11841-. doi:10.2196/11841
Journal: JMIR Mental Health
Abstract: Background: Cognitive biases refer to automatic attentional or interpretational tendencies, which result in individuals with addictive disorders to automatically attend to substance-related stimuli and those with anxiety disorders to attend to threatening stimuli. To date, several studies have examined the efficacy of cognitive bias modification, and meta-analytical studies have synthesized the evidence for overall efficacy. The clinical utility of cognitive bias modification interventions has previously been limited to the confines of a laboratory, but recent advances in Web technologies can change this. Objective: This scoping review aimed to determine the scope of Web-based cognitive bias interventions and highlight their effectiveness. Methods: Databases (PubMed and MEDLINE, EMBASE, PsycINFO, ScienceDirect, and Cochrane Central) were searched from inception to December 5, 2017. The following search terminologies were used: (“attention bias” OR “cognitive bias” OR “approach bias” OR “avoidance bias” OR “interpretative bias”) AND (“Internet” OR “Web” OR “Online”). The methods for this scoping review are based on the previously published protocol. For the synthesis of the evidence, a narrative synthesis was undertaken, as a meta-analysis was not appropriate, given the lack of reported effect sizes and the heterogeneity in the outcomes reported. Results: Of the 2674 unique articles identified, we identified 22 randomized controlled studies that met our inclusion criteria: alcohol use disorder (n=2), tobacco use disorder (n=2), depressive disorder (n=3), anxiety and depressive symptoms in adolescents (n=3), obsessive-compulsive disorder (OCD; n=2), social anxiety disorder (n=9), and anxiety disorder (n=1). The sample sizes of these studies ranged from 16 to 434 participants. There is preliminary evidence to suggest that Web-based interventions could reduce biases among adolescents with heightened symptoms of anxiety and depression and among individuals with OCD. Conclusions: This is the first scoping review that mapped out the scope of cognitive bias modification interventions for psychiatric disorders. Web-based interventions have been applied predominantly for social anxiety and addictive disorders. Larger cohorts must be used in future studies to better determine the effectiveness of Web-based cognitive bias interventions.
URI: https://hdl.handle.net/10356/142394
ISSN: 2368-7959
DOI: 10.2196/11841
Rights: © 2019 Melvyn Wb Zhang, Jiangbo Ying, Guo Song, Daniel S S Fung, Helen Smith. Originally published in JMIR Mental Health (http://mental.jmir.org), 26.10.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

SCOPUSTM   
Citations 20

8
Updated on Jan 30, 2023

Web of ScienceTM
Citations 20

8
Updated on Feb 2, 2023

Page view(s)

140
Updated on Feb 5, 2023

Download(s) 50

30
Updated on Feb 5, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.