Wearable Wellness: Depression Screening via Fitbit Data Collected During COVID-19 Pandemic

The COVID-19 pandemic has greatly increased depression among adolescents. The current depression diagnosis process requires significant patient effort and can be costly. Prior research through passively collected data has shown promising depression screening results but is limited by complex data collection and privacy concerns. In this research, we create multiple machine learning models to screen physiological data collected from Fitbit, a wearable biomarker, and depression screening surveys across 166 college students. The highest-scoring model on these physiological modalities achieved an F1-score of 0.92. Our research findings highlight the potential impact of digital technology development in current clinical practices.