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. |
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| 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] |
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| Database: | Psychology and Behavioral Sciences Collection |
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