GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

We present the first multi-year mobile sensing datasets. Our multi-year data
collection studies span four years (10 weeks each year, from 2018 to 2021).
The four datasets contain data collected from 705 person-years (497 unique
participants) with diverse racial, ability, and immigrant backgrounds. Each
year, participants would install a mobile app on their phones and wear a
fitness tracker. The app and wearable device passively track multiple sensor
streams in the background 24×7, including location, phone usage, calls,
Bluetooth, physical activity, and sleep behavior. In addition, participants
completed weekly short surveys and two comprehensive surveys on health
behaviors and symptoms, social well-being, emotional states, mental health,
and other metrics. Our dataset analysis indicates that our datasets capture a
wide range of daily human routines, and reveal insights between daily
behaviors and important well-being metrics ( _e.g._ , depression status). We
envision our multi-year datasets can support the ML community in developing
generalizable longitudinal behavior modeling algorithms.