Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping-Based Exploration.

OBJECTIVE The application of digital phenotyping in personality research leverages smartphone-generated data to quantify individual differences in personality constructs. It can be conceptualized as an extension of Experience Sampling Methods (ESMs), as it allows for the continuous, in situ collection of behavioral and contextual data. This study expands beyond the FFM/Big5 model to include 59 traits/types from 16 personality constructs, including temperament and personal value theories. METHOD Digital footprints were collected from 104 participants' smartphones over 7-10 days. Both hypothesis-testing (deductive) and machine learning (inductive) methods were applied to analyze the data. RESULTS Four personality constructs of 16 (25%) were successfully predicted (r 0.034-0.53): Adult Attachment, FFM/Big5, Distress Tolerance, and Creativity, given an adopted r ≥ 0.34 threshold for successful predictions. Overall, a total of 22 out of 59 individual traits and types of the 16 constructs were successfully predicted (37.29%). Gradient Boosted Trees emerged as the most effective machine learning predictive model (compared with Decision Tree, Random Forest, and Support Vector Machine), particularly when analyzing communication-related information features. CONCLUSIONS This study demonstrates the capacity of Digital Phenotyping of smartphone data to broaden the possibilities of remote personality psychology research and highlights its potential applicability in People Analytics research and additional cross-disciplinaryscholarly fields.