Adolescents often experience a heightened incidence of depressive symptoms, which can persist without early intervention. However, adolescents often struggle to identify depressive symptoms, and even when they are aware of these symptoms, seeking help is not always their immediate response. This study aimed to explore the relationship between passively collected digital data, specifically keystroke and stylus data collected via mobile devices, and the manifestation of depressive symptoms.

A total of 927 first-year middle school students from schools in Seoul solved Korean language and math problems. Throughout this study, 77 types of keystroke and stylus data were collected, including parameters such as the number of key presses, tap pressure, stroke speed, and stroke acceleration. Depressive symptoms were measured using the self-rated Patient Health Questionnaire-9 (PHQ-9).

Multiple regression analysis highlighted the significance of stroke length, speed, and acceleration, the average y-coordinate, the tap pressure, and the number of incorrect answers in relation to PHQ-9 scores. The keystroke and stylus metadata were able to reflect mood, energy, cognitive abilities, and psychomotor symptoms among adolescents with depressive symptoms.

This study demonstrates the potential of automatically collected data during school exams or classes for the early screening of clinical depressive symptoms in students. This study has the potential to serve as a cornerstone in the development of digital data frameworks for the early detection of depressive symptoms in adolescents.