Decoding stress and coping ability levels through drawing features in the Person-In-The-Rain test: An exploratory study using a digital drawing device

Current study examined the validity of a digitalized Person-in-the-Rain test (dPITR) in assessing stress and coping abilities using traditional univariate and machine learning methods. By analyzing drawings and comparing results with established self-reported measures (Perceived Stress Scale and Ways of Coping Questionnaire), the study identifies significant relationships between drawing features and stress and coping scores. Machine learning models (K-Nearest Neighbor, Support Vector Machine, Random Forest) revealed complex, non-linear patterns, with stress-related features showing significant predictive power for perceived stress and coping styles. The findings support dPITR as a reliable, non-invasive tool for assessing psychological constructs and highlight the value of integrating advanced analytics in psychological assessments. This digital approach offers promising applications for scalable and precise mental health evaluations. • Digital Person-in-the-Rain (dPITR) test validates stress and coping responses. • dPITR stress scores positively correlate with negative stress perception. • Machine learning reveals complex stress-coping patterns beyond univariate analysis. • Deep learning identifies key drawing features associated with stress and coping levels. • dPITR enables rapid and objective stress pre-screening across various settings.