Martinelli Anne, Grüll Jessica, Baum Corinna
Department of Psychology, Fresenius University of Applied Sciences, Frankfurt a.M, Germany.
Behav Res Ther. 2022 Oct;157:104180. doi: 10.1016/j.brat.2022.104180. Epub 2022 Aug 17.
This systematic review and meta-analysis examines the effect of Cognitive Bias Modification for attention (CBM-A) and interpretation (CBM-I) on reducing the targeted biases and investigates moderators of each approach. PsycINFO, PsychArticles, and PubMED databases were searched for randomized-controlled studies published before March 2020 with pre- and post-CBM cognitive bias outcome measures, resulting in 91 CBM-A (n = 5914 individuals) and 70 CBM-I samples (n = 4802 individuals). Random-effects models and Hedge's g calculation showed significant medium overall effects of bias reduction with moderate to high heterogeneity (CBM-A g = 0.49 [0.36, 0.64], I = 85.19%; CBM-I g = 0.58 [0.48, 0.68], I = 70.92%). Effect sizes did not differ between approaches and remained significant after trim-and-fill adjustment for possible publication bias. Moderator variables were investigated with meta-regression and subgroup analyses. Participant age, symptom type, control condition and number of trials moderated CBM-A; student and clinical status moderated CBM-I effect size. Results support attention and interpretation modification in controlled laboratory and variable (online) training settings for non-clinical and clinical samples across various symptom types (anxiety, depression, substance use, eating disorders). Further empirical evidence is necessary to determine optimal sample and methodological combinations most strongly associated with adaptive behavioral outcomes.
本系统评价和荟萃分析考察了注意力认知偏差修正(CBM-A)和解释认知偏差修正(CBM-I)对减少目标偏差的效果,并探究了每种方法的调节因素。在PsycINFO、PsychArticles和PubMed数据库中检索了2020年3月之前发表的随机对照研究,这些研究采用了CBM前后的认知偏差结果测量,最终得到91项CBM-A研究(n = 5914名个体)和70项CBM-I研究样本(n = 4802名个体)。随机效应模型和Hedge's g计算表明,偏差减少具有显著的中等总体效应,异质性为中到高(CBM-A g = 0.49 [0.36, 0.64],I = 85.19%;CBM-I g = 0.58 [0.48, 0.68],I = 70.92%)。两种方法的效应量没有差异,在对可能的发表偏倚进行修剪和填充调整后仍具有显著性。通过元回归和亚组分析对调节变量进行了研究。参与者年龄、症状类型、对照条件和试验次数调节了CBM-A的效应;学生和临床状态调节了CBM-I的效应量。结果支持在受控实验室和可变(在线)训练环境中,对各种症状类型(焦虑、抑郁、物质使用、饮食失调)的非临床和临床样本进行注意力和解释修正。需要进一步的实证证据来确定与适应性行为结果最密切相关的最佳样本和方法组合。