Feelings from the heart: Developing HRV decrease-trigger algorithms via multilevel hyperplane simulation to detect psychosocially meaningful episodes in everyday life.

Heart rate variability (HRV) has been associated with diverse psychosocial concepts, like stress, anxiety, depression, rumination, social support, and positive affect, among others. Although recent ecological momentary assessment research devoted the analysis of cardiac-psychosocial interactions in daily life, traditional time sampling designs are compromised by a random pairing of cardiac and psychosocial variables across several time points. In this study, we present an approach based on the concept of additional heart rate and additional HRV reductions, which aims to control for metabolic-related changes in cardiac activity. This approach allows derivation of algorithm settings, which can later be used to automatically trigger the assessment of psychosocial states by online-analysis of transient HRV changes. We used an already published data set in order to identify potential triggers offline indexing meaningful HRV decrements as related to low quality social interactions. First, two algorithm settings for a non-metabolic HRV decrease trigger (i.e., the number of HRV decreases in a specified time window) were systematically manipulated and quantified by binary triggers (HRV decrease detected vs. not). Second, triggers were then entered in multilevel models predicting (lower levels of) social support. Effect estimates and bootstrap power simulations were visualized on hyperplanes to determine the most robust algorithm settings. A setting associated with 13 HRV decreases out of 29 min seems to be particularly sensitive to low quality of social interactions. Further algorithm refinements and validation studies are encouraged.