This study demonstrates digital phenotyping's potential to track NSSI thoughts through active and passive data, indicating personalized interventions may improve outcomes.
Key Points
The model achieved an accuracy of 0.666 using active variables, showing predictive potential for NSSI thoughts.
Key emotional predictors include depression and anger, which were significant in the fixed effects analysis of multilevel logistic regression.
Digital phenotyping effectively utilizes smartphone and wearable technology to gather real-time data relevant to NSSI.
Findings suggest that targeted interventions based on real-time mood and physiological data could enhance NSSI prevention strategies.