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Several mobile devices have multiple sensors on board and interact with smartphones. This allows for a complex online evaluation of physiological data, important for interactive psychophysiological assessments, which targets the triggering of psychological states based on physiological data such as heart rate variability (HRV). However, algorithms designed to trigger meaningful physiological processes are rare. One exception is the concept of additional HRV reduction (AddHRVr), which aims to control for metabolic-related changes in cardiac activity. In this study we present an approach, based on data of a previous study, which allows algorithm settings to be derived that could be used to automatically trigger the assessment of psychosocial states by online-analysis of transient HRV changes in a sample of 38 firefighters. Settings of a static and a dynamic AddHRVr algorithm were systematically manipulated and quantified by binary triggers. These triggers were subjected to multilevel models predicting increases of objective stress during a period of 24 h. Effect estimates (i.e., odds) and bootstrap power simulations were calculated to inform about the most robust algorithm settings. This study delivers evidence that a dynamic AddHRVr algorithm can trigger transitions of stress, which should be further validated in future interactive psychophysiological assessments.

Feelings from the Heart Part II: Simulation and Validation of Static and Dynamic HRV Decrease-Trigger Algorithms to Detect Stress in Firefighters