Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms

Aug 5, 2026

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Cognitive training can reduce psychological distress, yet evidence shows inconsistent effects across studies. A pre-registered individual-level meta-analysis uses machine learning and conventional statistics to analyze 22 datasets covering 1,544 participants who completed working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. Baseline depression and anxiety severity most strongly predicts improvement, and additional training sessions further enhance benefits. Attention and interpretation bias modification show the greatest promise.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background and rationale

Heterogeneous effects in cognitive training

Study design and data

Individual-level meta-analysis approach

Results

Predictors of treatment improvement

Training type comparisons

Implications

Personalized cognitive training guidance

npj | digital medicine Article | Published in partnership with Seoul National University Bundang Hospital | | | | --- | --- | --- | | [https://doi.org/10.1038/s41746-025-01449-w](https://doi.org/10.1038/s41746-025-01449-w) | | | | Machine learning meta-analysis identiﬁes individual characteristics moderating cognitive intervention efﬁcacy for anxiety and depression symptoms
Check for updates | | | | Thalia Richter 1,2,3,4,24 , Reut Shani1,2,3,24, Shachaf Tal1, Nazanin Derakshan5, Noga Cohen6,7, Philip M. Enock8, Richard J. McNally8, Nilly Mor9,10, ShimritDaches11, AlishiaD. Williams12, JennyYiend 13, Per Carlbring 14,15, Jennie M. Kuckertz16,17,18, Wenhui Yang19, Andrea Reinecke20,
Christopher G. Beevers21, Brian E. Bunnell22, Ernst H. W. Koster23, Sigal Zilcha-Mano1 &
Hadas Okon-Singer1,2,3 | | | | Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level metaanalysis to identify factors contributing to cognitive training efﬁcacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modiﬁcation, interpretation bias modiﬁcation, or inhibitory control training. Baseline depression and anxiety symptoms were found to be the most inﬂuential factor, with individuals with more severe symptoms showing the greatest improvement. The number of training sessions was also important, with more sessions yielding greater beneﬁts. Cognitive trainings were associated with higher predicted improvement than control conditions, with attention and interpretation bias modiﬁcation showing the most promise. Despite the limitations of heterogeneous datasets, this investigation highlights the value of large-scale comprehensive analyses in guiding the development of personalized training interventions. | | | | Interventions targeting the cognitive mechanisms underlying anxiety and depressive symptoms have gained signiﬁcant research interest in recent decades due to the high prevalence of these symptoms and the potential of online and remote treatment delivery.
Computerized cognitive interventions target the neurobiological mechanisms underlying cognitive and emotional domains by means of a demanding and engaging task that is repeatedly performed on a technological device1,2. Studies that have tested the efﬁcacy of different cognitive training interventions in alleviating maladaptive symptoms of anxiety and depression have yielded contradictory outcomes (for a more thorough review see refs. 3,4).
Evidence suggests that speciﬁc characteristics of computerized cognitive training (such as the number of training sessions5) inﬂuence training efﬁcacy. Moreover, some studies indicate that speciﬁc types of training methods are more effective in certain situations and/or for speciﬁc populations (e.g., greater improvements from attention bias modiﬁcation for | individuals dealing with generalized anxiety or depression, but lower improvements for those dealing with social anxiety and post-traumatic stress disorder6). Comparing the effectiveness of computerized training techniques, as well asthe effect ofindividual differenceson training efﬁcacy, is highly valuable for several reasons: (a) they can provide guidelines to determine the optimal conditions and characteristics of effective training regimes1; (b) they can advance knowledge on central mechanisms underlying successful training7; and (c) they can promote individually tailored training programs8. Anxiety and depression are the most prevalent mental health disorders globally, imposing a substantial individual and economic burden that exceeds many other conditions9, 10. Current treatments often yield only moderate success rates11, 12, partly due to their broad, nonpersonal

FAQs

这项研究的核心目标是什么？

研究旨在通过预注册的个体层面荟萃分析，识别哪些因素会调节认知训练对焦虑与抑郁症状的有效性。

哪些个体因素与改善幅度关系最强？

基线抑郁与焦虑症状水平是最关键的影响因素，症状更严重的个体通常表现出更大的改善。

训练项目类型和训练次数对结果有什么影响？

训练次数越多，获益通常越大；在训练类型中，注意偏差修正与解释偏差修正与对照条件相比更可能带来更高的预测改善，并显示出最有前景的效果。

Aug 5, 2026

Upload by Miles