BERD Webinar, "Forecasting and Diagnosing Stress-Induced Physical Activity Decline with Deep Learning"

Major disruptions—like injury or the COVID-19 lockdown—can cause sudden drops in physical activity, but people recover in very different ways. Identifying who is likely to stay disengaged early on is critical for timely intervention, yet difficult when activity patterns are noisy and highly individualized.

This webinar introduces a new, two-step approach that combines modern machine learning with interpretable statistical methods to detect stress-related declines in physical activity. Using wearable device data from an aging study during the COVID-19 lockdown, we show how pre-disruption activity patterns can be used to forecast future behavior and identify distinct recovery profiles.

Through real-world examples, Young Won Cho will highlight how advanced models can improve early detection while delivering practical, transparent, and actionable results for health researchers and intervention designers.