Influence of stress on physiological synchrony in a stressful versus non-stressful group setting

Abstract

Physiological synchrony (PS) is defined as the co-occurrence and interdependence of physiological activity between interaction partners. Previous research has uncovered numerous influences on the extent of PS, such as relationship type or individual characteristics. Here, we investigate the influence of acute stress on PS. We do so in a setting in which PS was not promoted, but contact between group members was explicitly minimized. We reanalyzed cortisol, alpha-amylase, and subjective stress data from 138 participants (mean age = [Formula: see text], 47.1% female) who previously underwent the Trier Social Stress Test for groups (TSST-G) or a non-stressful control task together, collected as part of a larger project by Popovic et al. (Sci Rep 10: 7845, 2020). Using a stability and influence model, an established method to test for synchrony, we tested whether individuals' cortisol and alpha-amylase concentrations could be predicted by group members' levels. We found cortisol PS in participants who were in the same group, the extent of which was stronger in the non-stressful control condition. For alpha-amylase, participants were synchronized as well; furthermore, there was an interaction between previous stress levels and PS. This suggests that while synchrony of both stress markers can occur in group settings even with spurious interaction, stressor exposure might attenuate its extent. We argue that if PS occurs in a sample where interaction was minimal, the phenomenon might be more widespread than previously thought. Furthermore, stressor exposure might influence whether a situation allows for PS. We conclude that PS should be investigated within group settings with various degrees of social interaction to further expose mechanisms of and influence on PS.

Keywords: Alpha-amylase; Cortisol; Endocrine synchrony; Physiological synchrony; Stress contagion; Trier Social Stress Test for groups.

Fig. 1

Cortisol (left), subjective stress (middle), and alpha-amylase (right) trajectories, mean, and standard errors for both experimental conditions. The gray rectangle indicates the time span of the intervention. Time = minutes since the first stress assessment.

Fig. 2

Boxplot of AUC${}_I$ for cortisol in each experimental condition. While conditions differ significantly in overall cortisol responses, there is also pronounced variation within each condition

Fig. 3

Scatter plot of receivers’ and senders’ concurrent cortisol values (lag size $k=0$; A), and senders’ lagged cortisol values (lag size $k=1$; B) not differentiated by time point or condition. The entire model explained about 94% of the variance in the dependent variable (other independent variables not shown)

Fig. 4

Receivers’ cortisol as predicted by our model versus measured receivers’ cortisol

Fig. 5

Cross-correlation coefficients (CC coefficients) for pairwise cortisol time series by lag size k. The color indicates whether a given CC coefficient exceeded the conventional limit for significance (CCSL). CC coefficients for negative lag sizes are symmetrical to those for positive lag sizes

Fig. 6

Cross-correlation coefficients (CC coefficients) for pairwise alpha-amylase time series by lag size k. The color indicates whether a given CC coefficient exceeded the conventional limit for significance (CCSL). CC coefficients for negative lag sizes are symmetrical to those for positive lag sizes

Fig. 7

The relationship between senders’ and receivers’ alpha-amylase (physiological influence or synchrony) is moderated by receivers’ own previous stress values