Background. 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.

Methods. 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).

Results. 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.

Conclusions. 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.