Predicting individual response to a web-based positive psychology intervention: a machine learning approach

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is l...

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Bibliographic Details
Published in:The journal of positive psychology 2024-07, Vol.19 (4), p.675-685
Main Authors: Collins, Amanda C., Price, George D., Woodworth, Rosalind J., Jacobson, Nicholas C.
Format: Article
Language:English
Subjects:
depression
digital intervention
Happiness
Intervention
machine learning
Mental depression
Positive psychology
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Summary:Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test  = 0.30 ± 0.09; depressive symptoms: r Test  = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.
ISSN:1743-9760
1743-9779
DOI:10.1080/17439760.2023.2254743