Abstract
Background: Social cognition training (SCT) can improve social cognition deficits in schizophrenia. However, little is known about patterns of response to SCT or individual characteristics that predict response. Methods: 76 adults with schizophrenia randomized to receive 8–12 weeks of remotely-delivered SCT were included in this analysis. Social cognition was measured with a composite of six assessments. Latent class growth analyses identified trajectories of social cognitive response to SCT. Random forest and logistic regression models were trained to predict membership in the trajectory group that showed improvement from baseline measures including symptoms, functioning, motivation, and cognition. Results: Five trajectory groups were identified: Group 1 (29 %) began with slightly above average social cognition, and this ability significantly improved with SCT. Group 2 (9 %) had baseline social cognition approximately one standard deviation above the sample mean and did not improve with training. Groups 3 (18 %) and 4 (36 %) began with average to slightly below-average social cognition and showed non-significant trends toward improvement. Group 5 (8 %) began with social cognition approximately one standard deviation below the sample mean, and experienced significant deterioration in social cognition. The random forest model had the best performance, predicting Group 1 membership with an area under the curve of 0.73 (SD 0.24; 95 % CI [0.51–0.87]). Conclusions: Findings suggest that there are distinct patterns of response to SCT in schizophrenia and that those with slightly above average social cognition at baseline may be most likely to experience gains. Results may inform future research seeking to individualize SCT treatment for schizophrenia.
Original language | English |
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Pages (from-to) | 92-99 |
Number of pages | 8 |
Journal | Schizophrenia Research |
Volume | 266 |
DOIs | |
State | Published - Apr 2024 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2024 Elsevier B.V.
Funding
Research reported in this publication was supported by the National Institute of Mental Health (NIMH) Award R44MH091793 and by the National Center for Advancing Translational Sciences of the National Institutes of Health (Award Number UL1-TR002494 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funders | Funder number |
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National Institutes of Health | UL1-TR002494 |
National Institute of Mental Health | R44MH091793 |
National Center for Advancing Translational Sciences |
Keywords
- Cognitive remediation
- Machine learning
- Precision medicine
- Psychosis
- Treatment response