Passive Measurement of Autonomic Arousal in Real-World Settings
This addresses the lack of validated methods for monitoring stress in everyday life, which is important for health applications, though it is incremental as it builds on existing sensor technology.
They tackled the problem of measuring autonomic arousal in real-world settings by developing the Fitbit Body Response Algorithm, which achieved an accuracy of 0.85 in predicting perceived stress using wrist-based sensors.
The autonomic nervous system (ANS) is activated during stress, which can have negative effects on cardiovascular health, sleep, the immune system, and mental health. While there are ways to quantify ANS activity in laboratories, there is a paucity of methods that have been validated in real-world contexts. We present the Fitbit Body Response Algorithm, an approach to continuous remote measurement of ANS activation through widely available remote wrist-based sensors. The design was validated via two experiments, a Trier Social Stress Test (n = 45) and ecological momentary assessments (EMA) of perceived stress (n=87), providing both controlled and ecologically valid test data. Model performance predicting perceived stress when using all available sensor modalities was consistent with expectations (accuracy=0.85) and outperformed models with access to only a subset of the signals. We discuss and address challenges to sensing that arise in real world settings that do not present in conventional lab environments.