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.

This study aimed to investigate whether interindividual differences in autonomic inhibitory control predict safety learning and fear extinction in an interoceptive fear conditioning paradigm. Data from a previously reported study (N = 40) were extended (N = 17) and re-analyzed to test whether healthy participants' resting heart rate variability (HRV) - a proxy of cardiac vagal tone - predicts learning performance. The conditioned stimulus (CS) was a slight sensation of breathlessness induced by a flow resistor, the unconditioned stimulus (US) was an aversive short-lasting suffocation experience induced by a complete occlusion of the breathing circuitry. During acquisition, the paired group received 6 paired CS-US presentations; the control group received 6 explicitly unpaired CS-US presentations. In the extinction phase, both groups were exposed to 6 CS-only presentations. Measures included startle blink EMG, skin conductance responses (SCR) and US-expectancy ratings. Resting HRV significantly predicted the startle blink EMG learning curves both during acquisition and extinction. In the unpaired group, higher levels of HRV at rest predicted safety learning to the CS during acquisition. In the paired group, higher levels of HRV were associated with better extinction. Our findings suggest that the strength or integrity of prefrontal inhibitory mechanisms involved in safety- and extinction learning can be indexed by HRV at rest.

The present study aimed to establish a new interoceptive fear conditioning paradigm. The conditioned stimulus (CS) was a flow resistor that slightly obstructs breathing; the unconditional stimulus (US) was a breathing occlusion. The paired group (N = 21) received 6 acquisition trials with paired CS-US presentations. The unpaired group (N = 19) received 6 trials of unpaired CS-US presentations. In the extinction phase, both groups were administered 6 CS-only trials. Measurements included startle eyeblink response, electrodermal responses, and self-reported US expectancy. In the paired group, startle blink responses were larger during CS compared to intertrial interval during acquisition and extinction. Electrodermal and US expectancies were larger for the paired than for the unpaired group during acquisition, but not during extinction. The present paradigm successfully established interoceptive fear conditioning with panicrelevant stimuli.

HighlightsA novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.Significant decreases in quality level were observed for different noise levels.