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

Thalia Richter, Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, Shimrit Daches, Alishia D. Williams, et al.
(2025) NPJ DIGITAL MEDICINE. 8(1).
Author
Organization
Abstract
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 meta-analysis 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.
Keywords
ATTENTION BIAS MODIFICATION, RANDOMIZED CONTROLLED-TRIAL, WORKING-MEMORY, CAPACITY, LEVEL METAANALYSIS, EXECUTIVE-CONTROL, TRAINING TRANSFER, SOCIAL ANXIETY, DISORDER, INFORMATION, BENEFITS

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MLA
Richter, Thalia, et al. “Machine Learning Meta-Analysis Identifies Individual Characteristics Moderating Cognitive Intervention Efficacy for Anxiety and Depression Symptoms.” NPJ DIGITAL MEDICINE, vol. 8, no. 1, 2025, doi:10.1038/s41746-025-01449-w.
APA
Richter, T., Shani, R., Tal, S., Derakshan, N., Cohen, N., Enock, P. M., … Okon-Singer, H. (2025). Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms. NPJ DIGITAL MEDICINE, 8(1). https://doi.org/10.1038/s41746-025-01449-w
Chicago author-date
Richter, Thalia, Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, et al. 2025. “Machine Learning Meta-Analysis Identifies Individual Characteristics Moderating Cognitive Intervention Efficacy for Anxiety and Depression Symptoms.” NPJ DIGITAL MEDICINE 8 (1). https://doi.org/10.1038/s41746-025-01449-w.
Chicago author-date (all authors)
Richter, Thalia, Reut Shani, Shachaf Tal, Nazanin Derakshan, Noga Cohen, Philip M. Enock, Richard J. McNally, Nilly Mor, Shimrit Daches, Alishia D. Williams, Jenny Yiend, Per Carlbring, Jennie M. Kuckertz, Wenhui Yang, Andrea Reinecke, Christopher G. Beevers, Brian E. Bunnell, Ernst Koster, Sigal Zilcha-Mano, and Hadas Okon-Singer. 2025. “Machine Learning Meta-Analysis Identifies Individual Characteristics Moderating Cognitive Intervention Efficacy for Anxiety and Depression Symptoms.” NPJ DIGITAL MEDICINE 8 (1). doi:10.1038/s41746-025-01449-w.
Vancouver
1.
Richter T, Shani R, Tal S, Derakshan N, Cohen N, Enock PM, et al. Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms. NPJ DIGITAL MEDICINE. 2025;8(1).
IEEE
[1]
T. Richter et al., “Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms,” NPJ DIGITAL MEDICINE, vol. 8, no. 1, 2025.
@article{01JKWSWKKRH1VVVKPDFFM3R0G2,
  abstract     = {{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 meta-analysis 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.}},
  articleno    = {{65}},
  author       = {{Richter, Thalia and Shani, Reut and Tal, Shachaf and Derakshan, Nazanin and Cohen, Noga and Enock, Philip M. and McNally, Richard J. and Mor, Nilly and Daches, Shimrit and Williams, Alishia D. and Yiend, Jenny and Carlbring, Per and Kuckertz, Jennie M. and Yang, Wenhui and Reinecke, Andrea and Beevers, Christopher G. and Bunnell, Brian E. and Koster, Ernst and Zilcha-Mano, Sigal and Okon-Singer, Hadas}},
  issn         = {{2398-6352}},
  journal      = {{NPJ DIGITAL MEDICINE}},
  keywords     = {{ATTENTION BIAS MODIFICATION,RANDOMIZED CONTROLLED-TRIAL,WORKING-MEMORY,CAPACITY,LEVEL METAANALYSIS,EXECUTIVE-CONTROL,TRAINING TRANSFER,SOCIAL ANXIETY,DISORDER,INFORMATION,BENEFITS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{15}},
  title        = {{Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms}},
  url          = {{http://doi.org/10.1038/s41746-025-01449-w}},
  volume       = {{8}},
  year         = {{2025}},
}
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