scholarly journals Effects of cognitive bias modification on social anxiety: A meta-analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0175107 ◽  
Author(s):  
Haining Liu ◽  
Xianwen Li ◽  
Buxin Han ◽  
Xiaoqian Liu
2019 ◽  
Vol 10 (3) ◽  
pp. 204380871987527 ◽  
Author(s):  
Xiran Sun ◽  
Ranming Yang ◽  
Qin Zhang ◽  
Jing Xiao ◽  
Chieh Li ◽  
...  

To address the unmet need for treatment of social anxiety disorder in China, it is timely and relevant to identify more effective, accessible, economic, and easily disseminated interventions. The present study examined the effect of an eight-session program for cognitive bias modification for interpretation (CBM-I). Smartphones were used in the training of reducing interpretation bias and social anxiety of Chinese undergraduates with high social anxiety. In total, 38 participants were randomly assigned to either a CBM-I training group ( n = 19) or a control group ( n = 19). As a result, the CBM-I training group provided more positive interpretations in ambiguous situations and less social anxiety than the control group. Results indicate that CBM-I training via smartphones can effectively promote positive interpretations of ambiguous situations and relieve social anxiety. CBM-I via smartphones may have clinical utility when applied as a multisession intervention of social anxiety for Chinese undergraduates.


2019 ◽  
Vol 29 (1) ◽  
pp. 52-78 ◽  
Author(s):  
Marilisa Boffo ◽  
Oulmann Zerhouni ◽  
Quentin F. Gronau ◽  
Ruben J. J. van Beek ◽  
Kyriaki Nikolaou ◽  
...  

2018 ◽  
Author(s):  
Melvyn Zhang ◽  
Jiangbo Ying ◽  
Guo Song ◽  
Daniel S S Fung ◽  
Helen Smith

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.


2017 ◽  
Vol 59 (8) ◽  
pp. 831-844 ◽  
Author(s):  
Georgina Krebs ◽  
Victoria Pile ◽  
Sean Grant ◽  
Michelle Degli Esposti ◽  
Paul Montgomery ◽  
...  

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