Smartphone sensor-based depression detection in campus environments: a proof-of-concept study with small-sample behavioral analysis.

INTRODUCTION

Depression is a rising global health issue, particularly among adolescents, with university students facing distinct mental health challenges.

METHODS

This proof-of-concept study explores smartphone sensor-based depression detection in Chinese university campus settings using a small sample of 12 participants. We utilized data from accelerometers, gyroscopes, and light sensors to establish associations between smartphone-derived behavioral patterns and PHQ-9 scores, a standard depression measure. A customized data processing scheme tailored to campus life enabled the extraction of 18 feature sequences reflecting depressive symptoms. Feature selection was conducted using Pearson correlation, and model validation was performed using leave-one-out cross-validation with common classification algorithms.

RESULTS

The results yielded accuracy rates between 73.11% and 88.24%. Findings showed negative correlations between PHQ-9 scores and dietary regularity, bedtime, and physical activity levels.

DISCUSSION

This pioneering study highlights smartphone sensors' potential for early depression detection in Chinese higher education, supporting non-invasive mental health interventions.