TY - JOUR
T1 - Personalized cognitive training
T2 - Protocol for individual-level meta-analysis implementing machine learning methods
AU - Shani, Reut
AU - Tal, Shachaf
AU - Derakshan, Nazanin
AU - Cohen, Noga
AU - Enock, Philip M.
AU - McNally, Richard J.
AU - Mor, Nilly
AU - Daches, Shimrit
AU - Williams, Alishia D.
AU - Yiend, Jenny
AU - Carlbring, Per
AU - Kuckertz, Jennie M.
AU - Yang, Wenhui
AU - Reinecke, Andrea
AU - Beevers, Christopher G.
AU - Bunnell, Brian E.
AU - Koster, Ernst H.W.
AU - Zilcha-Mano, Sigal
AU - Okon-Singer, Hadas
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
AB - Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.
KW - Cognitive remediation
KW - Cognitive training
KW - Machine learning
KW - Meta-analysis
KW - Personalized treatment
UR - http://www.scopus.com/inward/record.url?scp=85104575850&partnerID=8YFLogxK
U2 - 10.1016/j.jpsychires.2021.03.043
DO - 10.1016/j.jpsychires.2021.03.043
M3 - Article
C2 - 33901837
AN - SCOPUS:85104575850
SN - 0022-3956
VL - 138
SP - 342
EP - 348
JO - Journal of Psychiatric Research
JF - Journal of Psychiatric Research
ER -