Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach

Area of Science:

• Psychology
• Computer Science
• Digital Health

Background:

• Positive psychology interventions (PPIs) effectively enhance happiness and reduce depressive symptoms.
• Web-based PPIs are common but do not benefit all individuals equally.
• Identifying individuals likely to benefit from web-based PPIs is crucial for personalized intervention strategies.

Purpose of the Study:

• To utilize machine learning to predict individual responses to web-based positive psychology interventions.
• To identify baseline prognostic indicators that predict an individual's likelihood of benefiting from PPIs.

Main Methods:

• Employed machine learning models to analyze baseline data from 120 participants.
• Assessed the predictive accuracy of baseline features for changes in happiness and depressive symptoms.

Main Results:

• Machine learning models showed moderate correlations in predicting outcomes.
• Predictive accuracy for happiness change was r = 0.30 ± 0.09.
• Predictive accuracy for depressive symptom change was r = 0.39 ± 0.06.

Conclusions:

• Baseline characteristics can predict treatment outcomes for web-based PPIs.
• Machine learning offers a viable approach for predicting individual responses to PPIs.
• These findings have significant clinical implications for tailoring interventions to individual needs.