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Changes to Positive Self-Schemas After a Positive Imagery Training are Predicted by Participant Characteristics in a Sample with Elevated Depressive Symptoms.

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Title: Changes to Positive Self-Schemas After a Positive Imagery Training are Predicted by Participant Characteristics in a Sample with Elevated Depressive Symptoms.
Authors: Collins, Amanda C. (AUTHOR), Price, George D. (AUTHOR), Dainer-Best, Justin (AUTHOR), Haddox, Dawson (AUTHOR), Beevers, Christopher G. (AUTHOR), Jacobson, Nicholas C. (AUTHOR)
Source: Cognitive Therapy & Research. Jun2025, Vol. 49 Issue 3, p512-522. 11p.
Subjects: Psychology of learning, Cognitive psychology, Machine learning, Pathological psychology, Mental depression
Abstract: Background: Depressed individuals have both heightened negative self-views and reduced positive self-views. The self-referential encoding task (SRET) can capture depressed individuals' self-schemas by asking them to endorse whether a word describes them or not. Digital interventions that target positive biases in depression can help improve positive self-schemas; however, it is important to determine who may respond best to these interventions. In the current study, we used a machine learning approach to predict changes in positive self-schemas on the SRET after a digital intervention. Methods: Participants were randomized to a digital imagery training that was either positive (n = 39) or neutral (n = 38) and completed the intervention every other day for 2 weeks. Participants also completed the SRET and self-report measures at pre-, mid-, and post-intervention to measure their self-schemas and psychopathology symptoms. Results: Results indicate the models were able to moderately predict changes in the number of self-referential positive words endorsed on the SRET, solely using participants' baseline characteristics (rTest = 0.33). Conclusions: These findings suggest that certain characteristics may predict response to a digital intervention focused on improving positive biases, and current findings emphasize the use of machine learning to improve treatment match and triage persons to treatments that may work best. [ABSTRACT FROM AUTHOR]
Copyright of Cognitive Therapy & Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Changes to Positive Self-Schemas After a Positive Imagery Training are Predicted by Participant Characteristics in a Sample with Elevated Depressive Symptoms.
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  Data: Background: Depressed individuals have both heightened negative self-views and reduced positive self-views. The self-referential encoding task (SRET) can capture depressed individuals' self-schemas by asking them to endorse whether a word describes them or not. Digital interventions that target positive biases in depression can help improve positive self-schemas; however, it is important to determine who may respond best to these interventions. In the current study, we used a machine learning approach to predict changes in positive self-schemas on the SRET after a digital intervention. Methods: Participants were randomized to a digital imagery training that was either positive (n = 39) or neutral (n = 38) and completed the intervention every other day for 2 weeks. Participants also completed the SRET and self-report measures at pre-, mid-, and post-intervention to measure their self-schemas and psychopathology symptoms. Results: Results indicate the models were able to moderately predict changes in the number of self-referential positive words endorsed on the SRET, solely using participants' baseline characteristics (rTest = 0.33). Conclusions: These findings suggest that certain characteristics may predict response to a digital intervention focused on improving positive biases, and current findings emphasize the use of machine learning to improve treatment match and triage persons to treatments that may work best. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognitive Therapy & Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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