Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions

TL;DR

This paper develops foundation models from behavioral data collected via wearables, demonstrating improved health prediction accuracy and versatility across various real-world health tasks.

Contribution

It introduces a novel approach to modeling behavioral signals from wearables using large-scale data, optimizing architectures specifically for this data type, and showing enhanced health prediction performance.

Findings

Strong performance on diverse health tasks

Behavioral data models outperform sensor-only models

Combining behavioral and sensor data yields further improvements

Abstract

Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.