Abstract
Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects’ demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine.
Highlights
Since Hans Selye’s definition of stress as “the nonspecific response of the body to any demand”,1 many studies have revealed the negative influence of an overload of stress on health and wellbeing
We show that physiological signals differ significantly according to reported stress levels and identified stress digital phenotypes, characterized by self-reported poor health and high depression, anxiety and stress scores, that are associated with blunted physiological stress responses
We found significant differences between physiological features for ECG, skin conductance (SC), and skin temperature (ST) between different stress levels and nighttime baseline, confirming laboratory findings and indicating the potential of psychophysiological stress detection in daily life on a large-scale population
Summary
Since Hans Selye’s definition of stress as “the nonspecific response of the body to any demand”,1 many studies have revealed the negative influence of an overload of stress on health and wellbeing. Observational data suggest associations between psychological stress and depression, cardiovascular disease, sudden death, and myocardial infarction.[2,3] Early detection and prevention of the adverse consequences of stress are of utmost importance, and require personalized prevention and treatment strategies that take individual variability into account, as is suggested in the Precision Medicine Initiative.[4]. Digital phenotypes are a new paradigm to extend our assessment of human illness beyond traditional examinations.[5] They represent a subject’s interactions with digital technologies such as connected health devices and smartphones to generate longitudinal, individual health profiles. Leveraging data-driven approaches, these data can fundamentally change our understanding of disease prognoses and provide new insights towards disease prevention and early detection.[5]
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